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This work aims at corroborating the importance and efficacy of mutual learning in motor imagery ( MI ) brain–computer interface ( BCI ) by leveraging the insights obtained through our participation in the BCI race of the Cybathlon event . We hypothesized that , contrary to the popular trend of focusing mostly on the machine learning aspects of MI BCI training , a comprehensive mutual learning methodology that reinstates the three learning pillars ( at the machine , subject , and application level ) as equally significant could lead to a BCI–user symbiotic system able to succeed in real-world scenarios such as the Cybathlon event . Two severely impaired participants with chronic spinal cord injury ( SCI ) , were trained following our mutual learning approach to control their avatar in a virtual BCI race game . The competition outcomes substantiate the effectiveness of this type of training . Most importantly , the present study is one among very few to provide multifaceted evidence on the efficacy of subject learning during BCI training . Learning correlates could be derived at all levels of the interface—application , BCI output , and electroencephalography ( EEG ) neuroimaging—with two end-users , sufficiently longitudinal evaluation , and , importantly , under real-world and even adverse conditions .
Since the first demonstration of the profound clinical potential of brain–computer interfaces ( BCIs ) [1] , the vast majority of studies have pertained to methodological and technical challenges involving experimentation with able-bodied individuals . While these works can be largely credited with the field’s nowadays widely acknowledged versatility and technological maturity , they carry limited evidence regarding its translational impact . Restricting the scope to the case of BCI for communication and control , the number of published works involving end-users in the last 20 years remains to date a modest double-digit figure [2] . As a result , the general concerns about the non-universal usability , robustness , and , especially , the role of training raised by able-bodied user studies [3–7] are even more pressing with regard to end-user populations . In this study , we investigated the hypothesis that mutual learning is a critical factor for the success of motor imagery ( MI ) BCI in translational applications . Contrary to a popular trend of focusing almost exclusively on the machine learning aspects of MI training , our hypothesis propounds that a holistic mutual learning training approach grounded symmetrically on all three learning pillars ( at the machine , subject , and application level ) would be the optimal training apparatus for preparing two end-user participants for the Cybathlon BCI race , the first international BCI competition [8] . Historically , the BCI field has evolved from systems employing simple decoders and relying on the users’ ability to learn to modulate their brain activity ( conventionally requiring long training periods ) [1 , 9 , 10] towards systems deploying elaborate signal processing and pattern recognition algorithms to minimize the user’s training time and to increase information transfer rates [11] . The early approaches exploited classical neurofeedback theories ( a form of operant conditioning ) , tailoring the interface to the needs of assistive scenarios . However , following the artificial intelligence ( AI ) revolution , it is the latter trend that has greatly dominated the field in the last 15 years . This is substantiated by the fact that more than half of published BCI works research signal-processing and machine-learning methods [12] . Beyond riding the wave of the multidisciplinary progress in AI and data analysis , treating BCI as a primarily neural decoding problem has its roots in two reasons . On the one hand , the emergence of interfaces based on evoked responses ( P300 , steady-state visually evoked potentials [SSVEP] ) as the most efficient BCI solution for communication [13–16] has promoted the use of machine learning because the margin for humans to learn to regulate evoked potentials is considered to be narrow . On the other hand , the machine learning trend has also prevailed in sensorimotor rhythm ( SMR ) -based BCIs and invasive BCIs that decode different movement parameters . This is grounded in the possibility to tap directly on natural sensorimotor circuits [17]—i . e . , to exploit the preexisting correlates of imagined and real movements . However , although machine learning has been critical for major achievements in BCI , “zero-training” and universal BCI remains elusive . On the contrary , co-adaptive ( a term we use interchangeably to mutual learning ) interfaces , in which the capacities of both learning agents—the brain and the machine—are accommodated and coordinated , has been very early proposed as a remedy [18] and more recently increasingly adopted and modeled as a training strategy [19–21] . Under this view , successful BCI requires that the user and the embedded decoder engage in a mutual learning process , in which users must learn to generate distinct brain patterns for different mental tasks , while machine learning techniques ought to discover , interpret , and allow a model’s adaptation to the potentially changing individual brain patterns associated to these tasks [22] . Co-adaptation has been studied in depth in the context of invasive and semi-invasive brain–machine interfaces with human and nonhuman primates [19 , 23 , 24] . Although it has also been researched in noninvasive SMR-based BCI [21] , this body of literature is still characterized by a strong focus on the machine learning side and , in particular , the challenges related to online decoder parameter estimation [25–29] . Evidence that co-adaptive MI BCIs might also be able to promote and increase the ability of the users to voluntary modulate their brain signals ( subject learning ) is , in fact , scarce , most often indirect and rather inconclusive . Indeed , mutual learning has been claimed mostly on the grounds of adequate and improved BCI classification accuracy [25 , 27 , 30–35] or application performances [36 , 37] . However , those are indirect measures of improved brain signal modulation . Direct evidence of learned SMR modulation at the BCI feature level is , in fact , rare or incomplete , derived in able-bodied populations and not longitudinal [10 , 23 , 26 , 28 , 38–41] . Notwithstanding a few exceptions of longitudinal and translational studies in which thorough neuroimaging evidence is also provided [9 , 42] , the extent and impact of subject learning effects in noninvasive MI BCI training remain rather disputable . The third level that we believe promotes acquisition of BCI skills is at the application side , an aspect that is not usually studied in BCI . As for any human–computer interface , we conjecture that the design of the interaction can have a strong impact on how suitable the system is for its user and on how the latter learns to purposefully modulate his/her brain rhythms . To our best knowledge , this is the first time that the influence of the application design on subject learning is quantified in BCI . According to our hypothesis , endowing our two end-user participants with mutual learning would facilitate the emergence of SMR modulations—supported and complemented ( but not overshadowed ) by both the use of machine-learning techniques and the refinement of the interaction with the application—that participants can largely sustain even in adverse conditions like the public Cybathlon BCI race . Cybathlon has been the first international para-Olympics for disabled individuals in control of bionic assistive technology ( AT ) [43] , featuring 12 end-users in the BCI race with a level of impairment in the American Spinal Injury Association ( ASIA ) scale of at least C . Two male individuals ( P1 and P2 ) , tetraplegic ( ASIA A ) and wheelchair-bound as a result of accident-inflicted spinal cord injury ( SCI ) have been trained to operate our MI BCI for the Cybathlon BCI race as “pilots” of our “Brain Tweakers” team . Coherently to our hypothesis , training followed a mutual learning approach . The BCI race consisted of four brain-controlled avatars competing in a virtual race game called “Brain Runners , ” where up to three mental commands ( or intentional idling ) should be issued on corresponding color-coded track segments ( “pads” ) to accelerate one's avatar ( Fig 1A and S1 Movie ) . In the absence of BCI input , avatars would walk at medium pace towards the finish line . Timely , correct commands would speed them up and erroneous ones slow them down [8 , 44] . Our results showcase strong and continuous learning effects at all targeted levels—machine , subject , and application—with both end-users over a longitudinal study lasting several months . This study provides direct evidence on the existence , extent , and impact of subject learning in translational , noninvasive MI BCI . Importantly , these learning effects were achieved under uncontrolled circumstances at the pilot’s homes with minimal expert personnel intervention , while the learned outcome was replicated at a demanding international competition—the first of its kind—under adverse circumstances , where our pilots were able to excel . Although the competition demands have imposed the nature of this study as observational and uncontrolled , we believe our work still pinpoints key ingredients of a successful mutual-learning scheme and contributes to the consolidation of the notion that BCI is a “skill to be learned” [45 , 46] in the field of electroencephalography ( EEG ) - and SMR-based interfaces , in which we believe it has been largely neglected .
The BCI race discipline of the Cybathlon has provided an ideal opportunity and a unique testbed for the present study on mutual learning . Eleven international BCI teams participated at the event . Each pilot had to mentally control his own avatar in a virtual race game by forwarding three different commands ( Fig 1A ) . The race completion time was the criterion for winning the game . The competition consisted of two phases: Qualifiers and Finals . The four pilots who marked the best completion times in the ensemble of Qualifiers advanced to Final A , the second-best group of four pilots proceeded to Final B and the remaining competitors were eliminated for the rest of the tournament . The first three pilots in Final A received the gold , silver , and bronze medals , respectively . The official results of the Cybathlon BCI discipline are reported in Table 1 . In order to appreciate the race completion time of the BCI pilots , perfect control would make the avatar finish in 54 s , continuous wrong commands would result in 240 s , and a system not delivering any command would yield 162 s . P1 qualified with 90 . 1 s , a performance that set the competition record , almost 32 s ahead of the second-best time belonging to our second pilot , P2 ( 122 . 5 s ) . In the final , the third-best competition time ( 125 . 3 s ) was made by P2 to win the gold medal . The closest times belonging to the pilots of other competing teams throughout the tournament were 132 , 135 , 136 , and 146 s . P1 experienced a momentary loss of BCI control and had to compromise with the fourth place in the final ( 189 . 8 s ) . The Cybathlon racing application naturally determined the race completion time as the primary outcome of our study . Fig 1B shows that our training procedure reduced the race completion time of P1 from 139 . 2 ± 16 . 1 s ( N = 18 , first four racing sessions ) to 116 . 5 ± 23 . 2 s ( N = 34 , last four racing sessions , including the competition day ) and similarly for P2 from 145 . 9 ± 26 . 1 s ( N = 22 ) to 117 . 9 ± 12 . 5 s ( N = 21 ) . Both these improvements are statistically significant ( p < . 001 , two-sided Wilcoxon ranksum tests ) . The race completion times of our pilots throughout training ( Fig 1C ) averaged 126 . 9 ± 21 . 3 ( N = 182 ) s for P1 and 130 . 3 ± 22 . 9 ( N = 57 ) s for P2 , with all-time records of 83 . 3 and 86 . 3 s , respectively . Significant negative Pearson correlations between race time and ( chronological ) race index establish the existence of a significant training effect on race time ( Fig 1C , P1: r = −0 . 34 , p < . 001 , N = 182; P2: r = −0 . 47 , p < . 001 , N = 57 ) . P1 achieved slightly better average and record performances , while P2 exhibited superior stability , having race time standard deviation of 12 . 9 s in the last 5 sessions ( N = 28 ) , as opposed to 20 . 6 s for P1 ( N = 50 ) . We employ “pad crossing time” as the optimal index to evaluate BCI performance , since it assesses BCI command delivery accuracy and speed in a single metric , while also better reflecting the task at hand [47] . The more widely used metric of BCI command accuracy is also provided below . Fig 2 illustrates that the high-yielding application performances come as a result of our pilots' ability to adequately master all four individual subtasks required by the application: the intentional control ( IC ) ability to deliver the correct command on the action pads ( spin , jump , slide ) and the intentional non-control ( INC ) ability to “rest/idle” on the white pads [48–50] . The illustrated median pad crossing time performances ( for P1/P2 ) across all races ( training and competition ) were 4 . 9/4 . 4 s ( N = 853/205 ) for spin , 4 . 1/4 . 9 s ( N = 766/198 ) for jump , and 6 . 2/7 . 2 s ( N = 463/196 ) for slide , which compare favorably to the lower bound ( 2 s ) while lying far away from this metric's imposed upper bounds ( 11 s if no mental command is forwarded , 19 s for continuously erroneous command delivery ) for all active command types . Remarkably , a similar argument can be made for the INC ability . The median crossing time of white pads was 10 . 7 s and 8 . 4 s for P1 ( N = 510 ) and P2 ( N = 151 ) , respectively—far below the worst-case scenario of 19 s and closer to the optimum of 5 . 5 s . It is also worth noting that the average pad crossing time correlates with the primary outcome of race completion time ( P1: r = 0 . 79 , p < . 001 , N = 162; P2: r = 0 . 92 , p < . 001 , N = 45 ) , showing that improvements in BCI performances have driven the application performance enhancement . Furthermore , average pad crossing time improves with training , as shown by its correlation with the run index ( P1: r = −0 . 40 , p < . 001 , N = 162; P2: r = −0 . 43 , p = . 003 , N = 45 ) . Fig 3A verifies increasing trends of command accuracy for both pilots and all command types . This can be quantified by significant positive correlations of the overall accuracy to the ( chronological ) race index ( P1: r = 0 . 70 , p < . 001 , N = 162 races; P2: r = 0 . 66 , p < . 001 , N = 45 races ) . Fig 3B showcases that the average total accuracy of P1 improved significantly from 53 . 8% ( N = 18 ) to 93 . 8% ( N = 41 ) and that of P2 from 81 . 9% ( N = 24 ) to 96 . 8% ( N = 21 ) ( P1 and P2: p < . 001 with two-sided Wilcoxon ranksum tests ) . Both pilots exhibited significant command accuracy increase in all individual tasks ( the only exception being the spin command for P2 , with stable accuracy ) . In the same sessions , the percentage of pads crossed without a false positive increased from 19 . 2% to 29 . 1% for P1 and slightly deteriorated for P2 ( from 34 . 3% to 31 . 0% ) . Like the pad crossing time , command accuracy correlates with the race completion time ( P1: r = −0 . 62 , p < . 001 , N = 162 races; P2: r = −0 . 57 , p < . 001 , N = 45 races ) . Our training approach targeted sessions twice a week and initially involved “offline , ” open-loop BCI training , in which our pilots performed a number of MI tasks without observing real-time feedback so as to identify the optimal tasks and calibrate the BCI . This was followed by “online , ” closed-loop BCI feedback training allowing the users to gradually optimize the modulation of their brain rhythms [51] . Finally , race training allowed our end-users to familiarize with the actual BCI application demands while further improving their BCI skills . BCI recalibration was performed only twice per pilot ( P1: 30/06/2016 and 14/09/2016; P2: 11/08/2016 and 08/09/2016 ) . Table 2 presents the selected spatiospectral features ( bands and Laplacian channels ) . Fig 4A demonstrates that our incremental mutual learning procedure has been very effective in bringing up an emerging SMR pattern ( high β-band , 22–32 Hz ) for both pilots , coherent with both hands MI ( lateral , electrodes FC3 , C3 , CP3 , FC4 , C4 , CP4 of the 10–20 EEG system ) and both feet MI ( medial , electrodes FCz , Cz , CPz ) locations of the sensorimotor cortex ( see also S1 Fig for discriminancy maps in higher-frequency resolution ) . Fig 4B further substantiates a significant enhancement trend of these patterns' discriminancy over runs ( P1 , N = 214: r = 0 . 47 , p < . 001 for medial and r = 0 . 44 , p < . 001 for lateral locations; P2 , N = 79: r = 0 . 47 , p < . 001 for medial and r = 0 . 64 , p < . 001 for lateral locations ) , accounting for considerable , statistically significant increase for both pilots and locations between the first and last four sessions ( Fig 4C ) . The overall discriminancy of our pilots’ SMRs ( average of medial and lateral locations for P1 , lateral for P2 ) correlates well with the total command accuracy ( P1: r = 0 . 56 , p < . 001 , N = 162; P2: r = 0 . 37 , p = . 013 , N = 45 ) , the average pad crossing time ( P1: r = −0 . 42 , p < . 001 , N = 162; P2: r = −0 . 45 , p = . 002 , N = 45 ) , and the race completion time ( P1: r = −0 . 39 , p < . 001 , N = 162; P2: r = −0 . 29 , p = . 0056 , N = 45 ) . Hence , increased SMR modulation ( discriminancy ) seems to be crucial for enhanced BCI and , through the latter , also application performances . Fig 5 sheds light on the neurophysiological basis of P1’s poor performance in the final . It can be seen that P1’s inability in this particular race to deliver any command associated to the Both Hands MI task ( spin , slide ) has been accompanied by the disappearance of this task’s identified EEG correlates , namely the β-band SMR discriminancy in locations contralateral to the dominant right hand , selected for the classifier used in the competition ( CP3 , Table 2 ) . On the contrary , pilot P2 largely maintained the same brain pattern in both competition races , even increasing the strength of medial modulation in the final ( channels Cz and CPz , both channels were selected for the classifier used in the competition ) . The BCI’s configuration ( choice of appropriate values of some hyperparameters , such as the decision threshold ) and the application control paradigm have substantially benefited from our pilot's input , following a user-centered approach in BCI design . In particular , end-user feedback has largely shaped our BCI’s control paradigm ( see Materials and methods ) . As shown in Fig 1C , early attempts with a 3-class BCI ( paradigm 1 ) severely compromised the total command accuracy ( Fig 3A ) , which is reflected in the high race completion times during this period . Supporting only two commands ( paradigm 2 ) was clearly suboptimal , since the application demands could not be fully met with a binary input . Thus , while the two separable MI tasks ( kinesthetic both hands and feet MI for both our pilots ) were directly mapped to the spin and jump avatar actions , two different solutions were evaluated for the slide command . Paradigm 3 would make the avatar slide after a configurable inactivity period . Paradigm 4 would trigger sliding when two commands of different type were forwarded within a configurable timeout [44] . The latter protocol has been shown to be significantly superior for P1 ( who executed enough races with each control paradigm ) in terms of the median time spent on yellow pads ( Fig 6A ) that reduced significantly ( p < . 001 , two-sided Wilcoxon ranksum test ) from 12 . 4 s ( N = 83 ) with paradigm 3 to only 5 . 1 s ( N = 363 ) with paradigm 4 . Simultaneously , the slide command accuracy increased significantly ( Fig 6B , p = . 0019 , two-sided Wilcoxon ranksum test ) from 67 . 2% ± 37 . 8% ( N = 26 ) to 91 . 2% ± 17 . 0% ( N = 94 ) . This naturally led to important reduction of the race completion time with paradigm 4 ( Fig 6C , 121 . 2 ± 20 . 1 s , N = 114 against 129 . 5 ± 12 . 4 s , N = 26 , p = . 0039 , two-sided Wilcoxon ranksum test ) , which was finally selected for the competition . While , as shown below , this improvement must be confounded with subject learning effects , an immediate effect of the control paradigm on performance can also be established by comparing the last 10 races with paradigm 3 against the 10 first ones with paradigm 4 ( 130 . 1 ± 17 . 2 s to 112 . 4 ± 15 . 1 s , p = . 0312 with two-sided Wilcoxon ranksum test ) . Importantly , during these races , P1 alternated between paradigms 3 and 4 ( Fig 1A ) , and for this reason , we cannot expect strong subject learning effects . The subject and machine learning processes have been always thought to affect each other [18 , 20 , 52] . Interestingly , we can show that the involvement of the application in the learning process also creates bidirectional interactions . Specifically , Fig 6D shows the feature discriminancy of the first and last 10 runs of the training periods with the three control paradigms . Interestingly , the discriminancy significantly increased only in the case of control paradigm 4 ( 0 . 27 ± 0 . 07 to 0 . 34 ± 0 . 05 , p = . 045 , two-sided Wilcoxon ranksum test ) , while no difference ( or even a reduction ) is reported for the other two paradigms . Results suggest that the refinement of the control paradigm might have had a critical role in facilitating subject learning .
It is critical to comment on the reasons why we perceive the indications provided so far in the BCI literature regarding subject learning to be insufficient . To begin with , subject learning in online MI BCI is most often hypothesized to occur “by default” in analogy to neurofeedback training [1 , 46 , 54 , 55] . However , this extrapolation is by no means straightforward , as neurofeedback typically exerts lesser demands , requiring control over predefined brain signals by direct observation [56] , while SMR BCIs are complex pattern-recognition systems feeding back transformations of multivariate brain activity [46] . In fact , as mentioned previously and further developed below , evidence of subject learning in BCI is scarce . It is important to note that we wish not to challenge the theory that BCI and neurofeedback learning share the same underlying plasticity mechanisms [46] but , on the contrary , substantiate it by providing solid experimental evidence . Another similar , overstated extrapolation regards evidence from invasive and semi-invasive BCI , where learning and co-adaptation have been well documented [19 , 23 , 24 , 57–59] . Again , given the significant differences in terms of signal-to-noise ratio ( SNR ) and other basic characteristics of ( semi- ) invasive and noninvasive signals , these studies cannot be said to certainly generalize to noninvasive MI BCI . Interestingly , users have reported reaching a state of proficiency through learning where BCI control becomes “automatic , ” as they no longer need to engage explicitly in MI [23 , 40–42 , 50 , 60 , 61] . This was also reported by our pilot P2 [44] . However , such claims are rather qualitative and do not constitute hard evidence for the existence of subject learning . We argue that this effect must still be accompanied by increasing and consolidated separability of the brain patterns in order to drive BCI performance upwards . It is mainly the lack of quantitative evidence of subject learning in EEG SMR BCI that is problematic . Firstly , works where users acquired BCI control ( able-bodied [3 , 25] and end-users [40 , 41 , 51 , 62 , 63] ) do not report any learning metric over time . Secondly , other training studies claim learnability in BCIs only on the grounds of improved online classification accuracy [9 , 27 , 30 , 31 , 33 , 35 , 51] or application performances [36 , 37] . However , accuracy and application-specific performance metrics do not imply improvements of brain signal modulation . Better performance could be due to decoder recalibration [29] , re-parameterizations of the BCI , and the application and adoption of better mental strategies [9 , 64 , 65] , among other factors . Hence , we consider that deriving some index of neuroimaging-based separability at the feature level in order to quantify the user’s BCI aptitude ( and its evolution over time ) is a sine qua non prerequisite for corroborating the existence of subject learning [46] . Evolution of SMR modulation has been reported , but these studies suffer from certain shortcomings . Some works find no evident learning effects at the neural correlate level [25 , 34] . Other studies have reported emergence of such SMR modulations [26 , 28 , 38 , 39] , but given the short number of experimental sessions they carried out , the observed neurophysiological patterns might only be indicative of transitory effects rather than consolidated subject learning . Our previous work has even reported a short-term decrease in feature discriminancy during adaptive spelling [29] . The most complete evidence of subject learning with obvious translational implications is offered in [9] , [10] , and [42] . These works report on longitudinal training and involve end-users . Furthermore , [10] and [42] substantiate learning effects with event-related desynchronization/synchronization ( ERD/ERS ) maps and SMR topographies , respectively , over 3–4 time points throughout the training period . Nevertheless , these works do not explicitly relate induced brain rhythm changes to BCI performance or show that SMR improvements were consistent and continuous . The present manuscript provides results that address such limitations in the literature on mutual learning with respect to its subject learning component while also offering novel insights on a possible role of the application on subject learning . From the machine learning perspective , our results clearly show a positive correlation of the BCI performances ( BCI decoding accuracy and pad crossing time ) to chronological runs for both users ( Fig 3A ) . This positively influences the application outcomes with a decrease of the race time over the whole training period , as BCI performances correlate significantly with race time improvement ( Fig 1C ) . As already mentioned , BCI was recalibrated only twice for each user ( Table 2 ) , but possible new classifiers were trained after every session with the new recorded data and the simulated performances were evaluated . In such an iterative process , most of the classifiers were discarded during the training period due to similar performances . One might argue that such an infrequent BCI recalibration contradicts the mutual learning hypothesis . However , this approach is substantiated by the fact that BCI decoding achieved high-level accuracy ( Fig 3A ) for both users after the initial recalibrations . Thus , we had assumed that the machine learning model was sufficiently optimized . We have selected feature discriminability as the index to assess the effects of subject learning at the neurophysiological level because it directly measures users’ ability to modulate different SMRs . In this respect , subject learning is substantiated by the gradual increase of feature discriminability ( Fig 4 ) . The reported correlations between discriminancy , BCI performances , and race time establish the impact of subject learning within the mutual learning scheme . Several indications assert that the learning effects observed here correspond to instrumental learning , as traditionally hypothesized [46] . First , SMR discriminancy increase is shown to be gradual and smooth for both users ( Fig 4 ) , as expected for neurofeedback operant conditioning . No apparent “breakthroughs” are evident , which could support the only likely alternative hypothesis , that of the employment of better mental strategies sparking immediate , rather than gradual , improvements [9 , 64] . At the level of mechanisms , our feedback training design has respected the neuropsychological basis of operant conditioning , namely immediacy and contingency of the visual feedback to the targeted brain rhythms . Indeed , during races , BCI commands always coincide with the presence of SMR , which has to be sufficiently large for the BCI to reach the decision threshold . Thus , although the BCI did not deliver a command to the avatar every time the pilot generated an SMR , the opposite holds: whenever the BCI delivered it , the pilot was eliciting an SMR . Another clear manifestation of the instrumental nature of subject learning is the fact that , as shown in Table 2 , the brain features that responded to training were among those selected for classification and feedback provision . According to our hypothesis , the third pillar of mutual learning , the application design , can play a critical role . In this regard , our results show that the subject learning has substantially benefited from the refinement of the control paradigm according to P1’s suggestions . This new control paradigm seems to have directly influenced his ability to learn how to modulate his brain patterns ( Fig 6D ) . In fact , the user not only exhibited a general improvement of the features’ separability from the initial design to the final one ( from control paradigm 1 to 4 , Fig 6D ) but also a significant positive trend only in the case of the last control paradigm . In the other cases , discriminancy remains stable ( or even decreases ) over time . It is interesting to note that , while one might have expected a stabilization of feature discriminancy once BCI command accuracy saturated to high levels ( Fig 3A ) , it continues to increase for both pilots even after the last recalibration ( Fig 4B ) . This might be explained by the fact that the Cybathlon application imposed high demands not only on command accuracy but also on delivery speed , which had further margins of improvement ( Fig 2A ) . Our results are in line with the emerging belief about the need for more stimulating BCI training contexts [47 , 53 , 66] . The present study suffers certain limitations , the main one being that it was conceived as an uncontrolled , observational study . Nevertheless , we can rely on our competitors as a fair control group because they have essentially adopted a training methodology mainly based on machine learning , as per the results of the questionnaire ( S1 Table ) , while we followed a more holistic mutual learning methodology . Indeed , their approach involved frequent classifier recalibration and feature re-selection , as well as training protocols that were relatively short and/or not particularly intense . Of note , the differences in machine-learning methods of all participating teams were too subtle to explain the competition outcomes according to the organizers [8] . A second important limitation regards the fact that we report on only two individuals . Still , the fact that both participants exhibited the same training effects and comparable performances makes us confident that our conclusions should generalize , at least to populations with similar clinical profiles . Due to the logistical constraints of the Cybathlon , the available neuroimaging data was limited to 16 EEG channels . Thus , we have not been able to investigate more deeply the brain plasticity effects induced by subject learning . However , it must be noted that the extracted SMR discriminancy index would be the primary descriptor of learning anyway , since the latter can only be an instance of neurofeedback operant conditioning if learned brain activity modulation happens with respect to the same neural activity that is fed back to the user ( in our case , SMRs on selected channels and bands ) . Unsatisfactory robustness of our BCI , especially for P1 , is another important shortcoming . Lack of robustness is a well-known issue of all BCI paradigms and has been associated to the nonstationarity of brain signals [18 , 25 , 27] . As shown , although P1 showcased better average performance , he also exhibited higher variability than P2 . This effect , also reflected in our pilot's competition outcomes in which P1 set the record time but was unable to replicate it a few hours later , suggests that stability ( robustness ) is at least as crucial as performance ( effectiveness ) for optimal BCI control . We have shown that loss of control for P1 in the final was the result of the disappearance of the SMR modulations normally induced by his Both Hands MI ( Fig 5 ) . Various psychological factors ( such as motivation , attention , and stress ) have been implicated in these negative effects [2 , 47 , 53 , 66–68] , which unfortunately are quite frequent in MI BCI operation . On the other side , P2 seemed to have gained stability along his training . We speculate that , although not the only factor , longitudinal mutual learning could help increase robustness . We believe that the present study pinpoints critical elements of a successful mutual learning methodology , in spite of the aforementioned limitations and although such recommendations are to some extent speculative . We denote that our training apparatus , which certainly falls under the category of “conventional” MI BCI training protocols relying on visual feedback training on top of initial machine calibration with spontaneous SMRs , has been very similar to the one we have applied in our previous work [51] . There , a considerable number of end-users failed to acquire BCI control , especially those without distinct spontaneous SMRs at training onset . We postulate it is mostly the small , but potentially crucial , differences between that and the present study that might explain the different outcomes . First and foremost , our previous study imposed up to 10 training sessions with low intensity ( maximum twice but mostly once per week or even every other week ) before a performance criterion could be reached and allow a user to proceed with application control . Our Cybathlon data , especially those of P1 ( Fig 4A ) , show that this amount of training would be insufficient to develop their full BCI potential , even despite increased training intensity . The experiences shared by our Cybathlon competitors point towards the same direction . Second , training with the BCI application rather than towards it—like in [51] and most other studies—had a profound impact , as shown in Fig 6 , for both application performances and subject learning . This might be related to a need for getting accustomed to the actual application demands [47] but probably also to increased user motivation [68] provided both by the gaming application and the goal of participating in an international competition [8] . Based on this experience , we believe that novel motivational paradigms should consider incorporating the element of “competition” ( for instance , training with multiplayer games ) . Related to this , another contributing factor to successful SMR enhancement might have been that we have implemented an “incremental learning” approach as advocated in [53] and shown in [69] , where open-loop , closed-loop , and application training ( tasks of increasing difficulty ) followed each other throughout training . S2 Fig illustrates how the SMR brain patterns of both participants for these different stages resemble each other but are enhanced in magnitude of discriminancy , suggesting that they both gradually adapted to the increasing task demands . Last but not least , we postulate that , despite current opinion considering this potentially detrimental to BCI accuracy , infrequent recalibration of the BCI has also been beneficial to the subject learning side ( Fig 4 ) while still adequately accommodating the machine learning side of our mutual learning scheme ( Fig 2 and Fig 3 ) . Frequent or continuous recalibration , especially in case it is accompanied by re-selecting the classifier’s features , creates a situation in which the subject’s learning could be hindered by the demand to adapt to a continuously changing decoder [29 , 70] . Since the plasticity/stability dilemma with respect to MI BCI co-adaptation has not been adequately studied so far [21 , 52] , we believe that a parsimonious approach eventually trading off decoding accuracy in the short term in order to better fulfill the subject learning objective in the long term , as done here , is preferable . Comparison with our competitors’ known strategies are in agreement with this assessment . Such fine-tuning of the machine- and subject-learning demands warrant further research and might unlock the full potential of MI BCI co-adaptation . In conclusion , the Cybathlon 2016 provided the ideal framework to implement and evaluate the effects of longitudinal mutual learning , which allowed us to showcase continuous and consolidated learning not only on the machine side ( which is regularly well documented ) but also—and most importantly—on the user side , as well as an effect of application training on subject learning . Furthermore , the Cybathlon motivated the recruitment of two end-user participants and the involvement of a real BCI application operated in real-world circumstances , which advocates translational implications of our findings . Importantly , all learning indices ( the subject’s and the machine’s ) , as well as application performances , can be shown to correlate with the amount of training and with one another , which establishes that the individual subject and machine learning improvements are not irrelevant but actually drive the enhancement of BCI-actuated application control .
This study has been approved by the Cantonal Committee of Vaud ( VD , Switzerland ) for ethics in human research ( CER-VD ) under protocol number PB_2017–00295 ( 20/15 CCVEM ) . The objective of this study was to train two end-users with severe motor impairments following a mutual learning approach so as to control the Brain Runners BCI application and participate in the Cybathlon BCI race . In pursuit of this goal , the Brain Tweakers have applied the ensemble of BCI machine-learning and signal-processing methods , control paradigms , and mutual learning protocols developed in our lab . The competition and logistical constraints have imposed the nature of this study as an uncontrolled ( observational ) and longitudinal two-case study . Our inclusion criteria necessarily coincided with those of the Cybathlon BCI race: minimum age of 18 , sufficient cognitive and communication abilities to understand the discipline’s rules , and tetraplegia or tetraparesia as a result of SCI , amyotrophic lateral sclerosis ( ALS ) , or another lesion , quantified with a score of “C” or above on the ASIA impairment scale . The exclusion criteria consisted of cardiac pacemakers , cyber-sickness , and epilepsy . All EEG and race time data collected have been included into our statistical analysis , and no outliers have been defined . The race completion time is naturally the study’s primary outcome . Each individual training run or race is an evaluation end point . We additionally define a number of essential secondary outcomes evaluating our mutual learning protocol’s machine learning effects ( i . e . , the time spent on each pad type and the BCI command delivery accuracy ) and subject learning effects ( SMR brain pattern discriminancy ) . Both our pilots—48-year-old P1 , injured in December 1989 , and 30-year-old P2 , injured in May 2003—have sustained complete lesions at level C5–C6 and have scored “A” ( Complete injury—No motor or sensory function is preserved in the sacral segments S4 or S5 ) in the ASIA impairment scale . Both end-users were under medication for the treatment of spasms and other symptoms related to their medical condition . Residual motor abilities included , for both pilots , unaffected bilateral control of shoulder and elbow movements and compromised control of wrist movements , while neither of the two maintained control over the fingers . Certified confirmation by their medical doctor of safety to participate in the Cybathlon event was requested and signed for both pilots , and insurance against accidents and injuries was taken , as per Cybathlon’s regulations . A safety and eligibility check was also conducted by the organizers the day before the competition . Both participants maintain no control of the lower limbs and only limited control of the upper limbs . They are both able to stabilize their neck and head , but only P2 can also stabilize his trunk . Neither of our pilots carries pacemakers or other implants , suffers epilepsy or cyber-sickness , or needs respiratory assistance . They both use other advanced AT in their daily lives , like driving aids and speech-to-text software . P1 had several years prior participated in the MI BCI studies reported in [35 , 51 , 71] , while P2 was BCI naive at the onset of his Cybathlon training . Informed consents have been signed in accordance with the Declaration of Helsinki , and their participation in the training sessions as well as in the competition has been approved by the Swiss committees for ethics in human research ( protocol number PB_2017–00295 , 20/15 CCVEM of the Cantonal Committee of Vaud , Switzerland for ethics in human research , CER-VD ) . The Cybathlon competition comprised six different disciplines , each concerning a different type of AT ( functional electrical stimulation , powered arm and leg prostheses , exoskeletons , wheelchairs , and BCI ) [43] . The BCI race [8] consisted of ( up to ) four brain-controlled avatars , each actuated by a disabled pilot by means of mental commands , so as to reach the finishing line of a virtual race game called “Brain Runners” ahead of its opponents . Avatars would by default proceed at slow speed towards the finish line . The BCI pilot should be able to forward three mental commands to his/her avatar ( spin , jump over prickles , slide under electrical rays ) , each of which would accelerate it only when issued while the avatar was traversing the corresponding color-coded track segment called “pad” ( spin on cyan , jump on magenta , and slide on yellow pads ) . The acceleration effect would last until the avatar reached the beginning of the next pad or upon reception of a following erroneous command overriding the user’s correct command ( whichever happened first ) . In addition to these three “action” pads , a fourth type ( white pads ) required “idling” to avoid any command delivery . A misplaced command , including false positives on the white pads , would slow down the pilot's progress towards the finish line of the track for 4 s ( this timer would reset if another erroneous command or false positive was received in the meantime ) , until the beginning of the next pad or a following correct command overriding the erroneous one ( whichever happened first ) . Besides the accelerating/slowing down behavior of the avatar , a thunder of the corresponding color briefly appearing over the avatar’s head would inform the pilot of the command currently sent . Support of at least one mental command was required to participate in the competition . The standard track was composed of 16 pads ( four of each type ) randomly arranged so that the order of pads was not known to the competitors beforehand and was different for every race . The starting and finishing lines were situated on two additional white pads , so that the total distance to be covered by the pilots’ avatars was 500 virtual meters . The lower bound of race completion time on this track ( i . e . , the one achieved with an ideal input ) is 54 s . The corresponding upper bound ( continuous erroneous delivery ) is 327 s , although only times below 240 s were considered valid in the actual competition . Since the avatars would proceed by default forward at a low “baseline” speed , the race completion time in case of no input whatsoever would be 162 s . The equivalent minimum , no-response , and maximum crossing times for the action pads were 2 s , 11 s , and 19 s , respectively . Hence , 11–19 s is the time frame within which a user is required to forward a correct command , with delivery speed being equally important to command accuracy . The minimum and maximum crossing times for the white pads were 5 . 5 s and 19 s , respectively . The corresponding times for the starting white pad were 5 s and 13 s , while for the ending white pad 3 s and 10 s . The exclusion criteria for the technology provider dictated the use of noninvasive interfaces , while visual , tactile , or any kind of BCI feedback other than the one provided directly by the Brain Runners graphical user interface was prohibited , effectively excluding synchronous , stimuli-driven BCI paradigms like P300 and SSVEP . Besides the Brain Tweakers , another 10 international franchises participated in the tournament . From the 13 originally declared teams , two teams withdrew and one participated “out of competition” due to pilot ineligibility . The BCI race tournament format involved , initially , four qualification races ( morning ) . The pilots marking the best four race completion times would qualify to Final A ( afternoon ) and compete for one of the 3 medals ( gold , silver , and bronze ) , while the second-best four times would compete for places 5–8 in Final B . The event took place in a crowded , sold-out arena in front of a loud audience of roughly 4 , 600 spectators . A mock-up “rehearsal” event was held on July 14th , 2015 , to ensure the best possible preparation for both the teams and the organizers . Our mutual ( subject and machine ) learning approach involved three different training modalities aiming to establish , on the one hand , the end-users’ best possible control over spontaneous modulation of their SMRs by means of MI tasks and , on the other hand , their fast and accurate recognition on the part of the trained MI BCI algorithm . MI is defined as the mental rehearsal of a movement without overt motor output [72] . For MI tasks related to completely paralyzed limbs ( legs for both P1 and P2 ) , our pilots were instructed to attempt the corresponding movement , otherwise imagination suppressing any overt motor act was requested . During the competition , judges controlled for violations of the latter prerequisite . Initially , open-loop , “offline” training ( MI without real-time feedback ) was applied , in order to exploit and calibrate the BCI on spontaneous SMR modulations the users could already elicit for the tested MI tasks . In this phase , we have mainly explored the existence of distinct brain patterns corresponding to right hand , left hand , both hands , both feet MI , and rest . Subsequently , offline runs were limited to both hands and both feet MI , which both our pilots were found to optimally modulate , so as to collect “clean” data for updating the BCI algorithm’s parameters . P1 has also unsuccessfully tried imagination of tongue movement , as well as “word” and “mathematical association” mental tasks . Closed-loop , “online” sessions followed , where the pilots proceeded with real-time BCI control of a conventional , continuous visual feedback cursor targeting the enhancement of the patterns' discriminancy in an operant conditioning fashion ( feedback training ) , while the BCI parameters were later recalibrated to better reflect the evolving brain patterns with the derived EEG data . Online runs were mainly conducted using the discriminant ( coincidentally , for both our pilots ) both hands and both feet MI tasks ( 2-class ) . P1 attempted to operate a 3-class online modality ( left hand , right hand , feet MI ) for a few sessions . More details on the visual interface of these two modalities and exactly how the BCI feedback cursor is driven by the BCI algorithm , can be found in section “BCI implementation” in Appendix A of [51] , as well as in S1 Movie . The third and latest stage was dominated by actual racing with the training version of the Brain Runners game delivered to the contestants so that our pilots could get accustomed to the real application's demands , in which one had to rely solely on the discrete feedback embedded into the cluttered graphics of the game itself . Offline , online , and racing runs were often interleaved ( S2 Table ) in order to make these transitions smoother . For the first racing runs only , we allowed our pilots to also observe the visual BCI feedback . During race training , our pilots would generally compete against the “bot” avatar option provided by the game . The “skill level” of this bot competitor was gradually increased to increasingly challenge our pilots . The racing track was randomized for each race , simulating the actual Cybathlon conditions . Prior to and including the competition day , P1 received 35 training sessions within the period April–October 2016 , while P2 underwent 16 sessions from July–October 2016 , both in an individualized and flexible schedule ( approximately twice a week ) , which was intensified as the competition day was approaching . P1 executed in total 40 offline , 12 online , and 182 race runs , while P2 did 15 , 19 , and 57 runs , respectively ( S2 Table ) . All training sessions took place at the pilots’ homes under the supervision of one or two BCI engineers , except for two distinct sessions accommodated in the laboratory , where our two pilots competed against each other in the presence of a crowd of spectators , so as to simulate and get used to the special conditions they would cope with on the competition day . The Brain Tweakers participation in the Cybathlon BCI race relied on the EEG-based MI BCI design previously developed in CNBI , which had already been shown to allow end-users to successfully operate a number of BCI prototypes [51] . For both user training and competitive racing , EEG was acquired with a lightweight , 16-channel g . USBamp amplifier ( g . Tec medical engineering , Schiedelberg , Austria ) . The experimental setup during training additionally consisted of one laptop running the BCI algorithms and another one running the Brain Runners game . In the actual competition , the latter was substituted by the competition’s dedicated monitor displaying the race from each pilot’s individual viewpoint . The EEG signal was recorded at 512 Hz sampling rate , band-pass filtered within 0 . 1 and 100 Hz , and notch-filtered at 50 Hz . The monitored EEG channels were selected so as to adequately cover the sensorimotor cortex ( S3A Fig ) . The signal was spatially filtered with a Laplacian derivation , and the power spectral density ( Welch periodogram ) of each channel was computed with 2 Hz resolution in 1 s-long windows sliding every 62 . 5 ms . Feature selection was performed by ranking the candidate spatiospectral features according to discriminant power , calculated through canonical variate analysis , eventually manually selecting the most discriminant and neurophysiologically relevant ones . A Gaussian classifier outputting a probability distribution over two MI tasks was used to classify the consecutive feature vectors in real time . The Gaussian classifier was trained with a gradient-descent supervised learning approach using the labeled MI datasets resulting from the aforementioned training protocols . The samples with “uncertain” probability distributions ( where the maximum probability does not exceed a certain threshold ) were rejected , while the remaining ones were fed to an evidence accumulation module smoothing the classifier output by means of a leaky integrator ( exponential smoothing ) . A final decision is emitted by the BCI system once the pilot is able to push the integrated probabilities of some mental class to reach a configurable decision threshold by consistently performing the corresponding MI task , thus forwarding the associated command to his/her Brain Runners avatar . Upon delivery of a BCI command , the integrated probabilities are reset to the uniform distribution so as to start an unbiased new trial . A refractory period of 1 s was set in between consecutive commands . An artifact rejection scheme would block the BCI output once ocular and facial muscle artifacts were detected . A more detailed description of all the above methods is provided in Appendix A of [51] and the references therein . Under the Cybathlon BCI race regulations , all teams should embed an artifact removal or rejection framework into their BCI system , ensuring that the pilot’s avatar is actuated by means of brain signals only , without interference from other signals originating from muscle activity or at the level of the peripheral nervous system ( PNS ) . Thus , the Brain Tweakers artifact control scheme targeted the detection of electrooculogram ( EOG ) and facial electromyogram ( EMG ) signals , upon which the BCI output was blocked for a configurable interval preventing any outgoing command towards the pilot’s BrainRunners avatar . Respecting the need for a minimally obtrusive setup , only four electrode/sensor pairs are employed to extract two bipolar EOG channels , by means of a second synced g . USBamp device . One sensor is placed on either eye canthus , a third one on the pilot’s nasion bone , while the last sensor acts as the reference and is placed on the pilot’s forehead ( S3B Fig ) . In sync with the EEG acquisition , EOG signals are acquired at 512 Hz in frames of 62 . 5 ms . Artifact detection is performed separately on each consecutive frame , resulting in very fluid and responsive detection of artifact onset and offset . For each frame , the original channels EOGi , i ∈ [1 , 4] are combined to form a horizontal ( EOGh = EOG1 − EOG3 ) and a vertical ( EOGv = EOG2 − ( EOG1 + EOG3 ) /2 ) channel , specializing in capturing horizontal and vertical eye movements , respectively . The average of all channels was also extracted and monitored , as it is particularly sensitive to eye blinks and intense facial muscle flexions . All channels were band-pass filtered between 1 and 10 Hz with a second-order Butterworth filter and rectified . Finally , the processed channel frames are compared against a common configurable threshold . The individual frame decision was 1 when any of the processed samples within the current frame exceeds the threshold and 0 otherwise . The final artifact detection module would communicate an artifact onset event to the game controller upon a frame decision transition from 0 to 1 , signaling the blocking of the BCI output . An artifact offset event lifting the BCI command blocking was issued after a configurable timeout since the latest artifact onset detection . Since a distinct feature of this study is BCI operation in real-world conditions , where arbitrary artifact contamination is common , we opted for transparency to always report imaging ( in particular , discriminancy ) results directly on raw data . S4 Fig illustrates that three lateral channels of P2 ( of which the only selected channel is CP3 ) exhibit an unidentified high-frequency component late in training . This effect only concerns two of the features selected for P2’s BCI control ( CP3/30 Hz and CP3/32 Hz ) and approximately one-fourth of the executed runs . Applying the artifact removal algorithm FORCe [73] effectively eliminates this component ( S1 Fig ) . Importantly , S5 Fig shows that the discriminancy of these two selected features constitutes a genuine EEG MI correlate , as it is present at different sessions of this pilot’s training in the absence of the potentially artifactual component . Of note , all effects shown in the manuscript regarding discriminancy ( including correlations with the amount of training , race completion time , pad crossing time , and BCI accuracy ) still hold for both pilots after FORCe artifact removal , as shown in S6 Fig for pilot P2 . The game control paradigm defines the way the pilot’s motor imaginations translate into avatar actions through the emitted BCI commands . Several control paradigms have been designed and tested throughout the training period in close cooperation between the Brain Tweakers researchers and pilots . Initially , we explored the straightforward option of a 3-class BCI ( paradigm 1 ) employing right hand , left hand , and both feet MI . Thereby , each BCI command was directly mapped to a certain avatar action ( right hand MI to spin , both feet MI to jump , and left hand MI to slide ) . A 2-class BCI ( paradigm 2 ) preserving the previous mapping but leaving the slide command unsupported was also tested . Given the unsatisfactory outcome of these two approaches , another two paradigms were designed , both investigating well-known human–computer interaction principles for supporting all three avatar commands given only a binary input . Specifically , the two separable MI tasks ( both hands and feet MI for both our pilots ) were again directly mapped to the spin and jump avatar actions . Additionally , paradigm 3 would make the avatar slide after a configurable period of INC . Paradigm 4 , on the other hand , would trigger sliding when two consecutive commands of different types ( i . e . , a spin/jump or jump/spin pair ) were forwarded within a configurable interval . Paradigm 4 was adopted for the competition . In all four tested control paradigms , “idling” is achieved through the “resting” mental task , where the pilot is deliberately not engaging in any MI task ( INC ) . Since the BCI classifier is continuously ( every 62 . 5 ms , i . e . , at 16 Hz ) outputting a probability distribution over the MI mental classes ( not including the resting state ) , INC is achieved through a statistical approach in which , thanks to the evidence accumulation module and the BCI’s optimized parametrization ( decision and sample rejection thresholds , smoothing parameter ) , a BCI command is only forwarded when the user is consistently performing the associated MI . Otherwise , the integrated probabilities will tend to fluctuate below the decision thresholds , avoiding any command forwarding [51] . The race completion and the pad crossing times are measured in seconds ( s ) . BCI performance is quantified through BCI command accuracy , which is the percentage of pads in a race in which the correct command has been delivered within the given time frame ( i . e . , while the pilot’s avatar is on the particular pad ) . Pad crossing times ( “time on pad” metric ) are reported to simultaneously evaluate BCI command accuracy and delivery speed . The total BCI command accuracy in a race is computed as the average per command accuracies ( class-specific true positive rates ) . For the white pads , an equivalent accuracy metric ( true negative rate ) is calculated as the percentage of white pads in the race that the pilot managed to cross without delivering any command . Finally , discriminancy of a given spatiospectral EEG feature ( corresponding to a certain EEG channel and a frequency band ) for two mental classes is quantified through Fisher score as FS=|μ1−μ2|s12+s22 , where μ1 , μ2 are the means and s1 , s2 are the standard deviations of this feature’s sample values for mental class 1 ( Both Hands ) and 2 ( Both Feet ) , respectively . Discriminancy over two large and physiologically relevant to MI topographic ( lateral: FC3 , FC4 , C3 , C4 , CP3 , CP4 and medial: FCz , Cz , CPz ) and spectral ( μ: 8–14 Hz and β: 22-32Hz ) regions is computed as the average Fisher score of all features corresponding to the channels and frequency bands of the regions in question . S2 Table presents the list of sessions executed and the type of data acquired in each . Point estimates are reported using averages or medians and dispersions as standard deviation or 25th and 75th percentiles , when the underlying distribution is normal or skewed , respectively . Training effects are shown by reporting Pearson correlation coefficients and their significance at the 95% confidence interval through Student t test distribution . The same type of correlation is employed to study the relationship of the application , machine , and subject learning evaluation metrics . Additionally , the first and last four sessions are compared and tested for significant differences at the 95% confidence interval using unpaired , two-sided Wilcoxon nonparametric rank-sum tests . A short questionnaire requesting information on essential details of the user and system training methodology adopted has been addressed to all competing teams in an attempt to identify critical elements of successful training strategies . The questionnaire consisted of the following 5 questions: Teams BrainGain [74] , Athena-Minerva [75 , 76] , NeuroCONCISE [77 , 78] , OpenBMI , Mahidol BCI , and MIRAGE91 [37] have provided the requested info . | Noninvasive brain–computer interface ( BCI ) based on imagined movements can restore functions lost to disability by enabling spontaneous , direct brain control of external devices without risks associated with surgical implantation of neural interfaces . We hypothesized that , contrary to the popular trend of focusing on the machine learning aspects of BCI training , a comprehensive mutual learning methodology could strongly promote users’ acquisition of BCI skills and lead to a system able to succeed in real-world scenarios such as the Cybathlon event , the first international competition for disabled pilots in control of assistive technology . Two severely impaired participants with chronic spinal cord injury ( SCI ) were trained following our mutual learning approach to control their avatar in a virtual BCI race game . The evolution of the training process , including competition outcomes ( gold medal , tournament record ) , substantiates the effectiveness of this type of training . Most importantly , the present study provides multifaceted evidence on the efficacy of subject learning during BCI training . Learning correlates could be derived at all levels of the interface—application , BCI output , and electroencephalography—with two end-users , longitudinal evaluation , and , importantly , under real-world and even adverse conditions . | [
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] | 2018 | The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users |
Insulin/IGF-1 signaling ( IIS ) has been well studied for its role in the control of life span extension and resistance to a variety of stresses . The Drosophila melanogaster insulin-like receptor ( InR ) mutant showed extended life span due to reduced juvenile hormone ( JH ) levels . However , little is known about the mechanism of cross talk between IIS and JH in regulation of life span extension and resistance to starvation . In the current study , we investigated the role of IIS and JH signaling in regulation of resistance to starvation . Reduction in JH biosynthesis , JH action , or insulin-like peptide 2 ( ILP2 ) syntheses by RNA interference ( RNAi ) -aided knockdown in the expression of genes coding for juvenile hormone acid methyltransferase ( JHAMT ) , methoprene-tolerant ( Met ) , or ILP2 respectively decreased lipid and carbohydrate metabolism and extended the survival of starved beetles . Interestingly , the extension of life span could be restored by injection of bovine insulin into JHAMT RNAi beetles but not by application of JH III to ILP2 RNAi beetles . These data suggest that JH controls starvation resistance by regulating synthesis of ILP2 . More importantly , JH regulates trehalose homeostasis , including trehalose transport and metabolism , and controls utilization of stored nutrients in starved adults .
Many biological functions of juvenile hormone ( JH ) in regulation of almost every aspect of an insect's life have been reported since its discovery in 1965 [1] , [2] . To maintain the larval state , JH induces the expression of the genes coding for transcription factors such as Kr-h1 to prevent metamorphosis; knockdown in the expression of the gene coding for Kr-h1 by RNAi in larvae leads to precocious metamorphosis that cannot be rescued by exogenous JH application [3] . During the larval stage , JH suppresses imaginal disc growth promoted by nutrition [4] , and the nutritional signals mediated by insulin/IGF signaling ( IIS ) can override JH suppression [5]; but , in the absence of JH , the wing disc grows despite severe starvation . Interestingly , the wing disc growth is well correlated with trehalose levels during the larval stage until the critical weight is reached; starvation causes a decline in hemolymph glucose and trehalose and cessation of wing imaginal disk growth , which can be rescued by injection of trehalose . After reaching the critical weight , the trehalose response to starvation disappears and the action of insulin becomes decoupled from nutrition . The wing disks also lose their sensitivity to repression by JH [6] . To direct reproductive maturation in Drosophila melanogaster and Tribolium castaneum , JH regulates the production of male accessory gland proteins in the male and vitellogenin ( Vg ) in the female [7]–[9] . A basic helix-loop-helix ( bHLH ) per-Arnt-Sim ( PAS ) family transcription factor , methoprene-tolerant ( Met ) interacts with other members of this family including steroid receptor co-activator ( SRC ) and Cycle; binds to both JH and JH response elements ( JHRE ) present in the promoters of JH-response genes [10]–[15] . The function of JH has been well studied in the regulation of molting , metamorphosis , and reproduction . However , mechanisms of JH action in regulation of life span and starvation resistance are still unclear . In the monarch butterfly , migrant adults live longer than summer adults when both are maintained under standard laboratory conditions . Interestingly , the longevity of migrant adults is restored to that of summer adults by treatment with JH I , and the life span of summer adults is increased by 100% when the corpora allata are surgically removed [16] . Similarly , in the InR mutant of D . melanogaster , life span extension is due to reduced JH levels [17] . These studies showed that lower levels of JH could extend the life span under certain conditions . However , the underlying mechanisms of JH action on the longevity and the cross talk between JH and IIS pathway are still not well understood . The IIS function in life span , longevity , and stress resistance has been thoroughly investigated because of evolutionarily conserved function from yeast to mammals [18] . These functions include regulation of cellular adaptation to stress stimuli , such as nutrient-poor conditions [19] and oxidative stress [20] , [21] , promoting autophagy [22] and regulation of metabolism [23] . T . castaneum is a good model for these studies because of efficient functioning of RNAi and rapid JH response . In the previous studies , we showed that JH regulates ILP2 and ILP3 synthesis; ILP2 and ILP3 in turn regulate Vg synthesis [9] . These studies provided a good model to explore the interplay between JH and IIS signaling pathways . Here , we investigated the effects of JH and IIS on survival and carbohydrate metabolism in adults under starvation . RNAi , topical application of JH III , and injection of bovine insulin were used to modify JH and/or insulin levels in the adults of T . castaneum to study the cross-talk between JH and IIS signaling in regulation of resistance to starvation .
To determine the role of JH in the survival of the starved T . castaneum , the newly emerged male adults were injected with malE ( dsRNA prepared using a bacterial gene malE as a control ) , JHAMT ( a key enzyme in JH synthesis ) , and Met ( JH receptor ) . The control-starved beetles began to die on seventh day post-adult emergence ( PAE ) , and all beetles died by the fourteenth day PAE . However , a block in JH synthesis or its action by knockdown in the expression of genes coding for JHAMT ( mean survival 12 . 8 days ) or Met ( mean survival 12 . 7 days ) extended survival of the starved beetles by one day ( control mean survival 11 . 7 days , P = 0 . 00002 in log rank test , Fig . 1A ) . To determine whether or not ILPs are involved in regulation of starvation survival , ILP1 , ILP2 , ILP3 , and ILP4 dsRNA were injected into newly emerged adults , and the survival of RNAi beetles was monitored under starvation conditions . All four dsRNAs caused more than 80% reduction in their target mRNA levels ( Fig . S1 ) . As shown in Figure 1B , only ILP2 knockdown extended life span ( mean survival 12 . 9 days ) similar to that in JHAMT or Met RNAi beetles when compared with the control ( 12 . 1 days mean survival , P = 3 . 39E-06 in log rank test ) . While ILP1 knockdown shortened the survival , ILP3 and ILP4 knockdown did not show any significant effect . In addition , injection of bovine insulin decreased survival of JHAMT ( 12 . 9 days mean survival ) and ILP2 RNAi beetles ( 12 . 6 days mean survival ) to that in control insects ( 12 . 1 days mean survival , P = 0 . 005 , Fig . 1C ) . The application of JH III decreased the survival of JHAMT RNAi beetles ( from 11 . 6 to 11 . 0 days mean survival , P = 0 . 038 ) , but not ILP2 RNAi beetles ( 11 . 5 days mean survival for both , P = 0 . 467 ) . ( Fig . 1D ) . These data suggest that both IIS and JH may work through similar or overlapping mechanisms to regulate survival of starved adults and that JH may work upstream to the IIS pathway . To determine the major energy source for starved beetles , the total lipid , carbohydrate , and protein levels were measured in fed and starved beetles . In the fed beetles , the levels of all three nutrients did not change significantly during days 3–8 PAE ( Fig . 2A ) . In contrast , in the starved animals , the levels of all three nutrients gradually decreased from day 3 to day 8 PAE ( Fig . 2A ) . These data suggest that the beetles use all three sources of nutrients during starvation . To determine whether JH or IIS regulate metabolism of these macromolecules , the levels of these macromolecules were determined in JHAMT or ILP2 RNAi beetles . Strikingly , higher protein , carbohydrate , and lipid levels were detected in JHAMT and ILP2 RNAi beetles when compared to the levels in the control beetles injected with malE dsRNA ( Fig . 2B ) . These data suggest that the life span extension during starvation in either JH or IIS deficient animals could be due to the reduced metabolism . To determine whether the trehalose , a major sugar in most insects , or the glucose , a major sugar in most animals , is utilized during starvation , trehalose or glucose were fed to the starved beetles . When beetles were fed on non-nutritional cellulose diet or cellulose diet supplemented with 10% trehalose or 10% glucose , the beetles fed on a trehalose-supplemented diet lived significantly longer when compared to the other two groups ( P = 0 . 001 , Fig . 3A ) . There was no significant difference in the survival of cellulose-fed or cellulose+10% glucose-fed beetles ( Fig . 3A ) . These data suggest that major insect sugar trehalose is important for survival of starved beetles . Moreover , the ratio of glucose and trehalose in the hemolymph increased in the control beetles upon starvation from day 4 to day 6 , suggesting more glucose is needed during starvation for the energy supply . However , this ratio decreased by 77–81% , 37–93% , and 70–89% in starved ILP2 , JHAMT , or Met RNAi beetles respectively , when compared to the levels in control beetles ( Fig . 3B ) . These data suggest that JH and ILP2 regulate trehalose levels in starved beetles . Trehalose homeostasis is controlled by trehalose-6-phosphate synthase ( TPS ) , the main enzyme involved in the synthesis of trehalose in the fat body [24]; Trehalose transporter ( TRET ) , the direction of transport depends on the concentration gradient of trehalose [25]; and the trahalase , the major enzyme involved in conversion of trehalose to glucose in various insect tissues [26] . To determine the relative contribution of TRET , TPS , and trehalase in extending life span in starved beetles , we identified genes coding for trehalase ( G04791 ) , TRET ( G13653 ) , and TPS ( G07883 ) based on sequence similarity with their homologs in other insects . These genes are highly conserved among insects ( Fig . S2 ) . We injected trehalase , TRET or TPS dsRNA into newly emerged beetles . The dsRNA injected beetles were starved for 13 days and life span changes were monitored . The TRET RNAi beetles showed a slight but significant increase by 0 . 26 day of mean survival in life span when compared to the control beetles ( P = 0 . 042 , Fig . 3C ) . Trehalase RNAi beetles showed no differences from the control , and the TPS RNAi beetles showed a decrease by 0 . 21 day of mean survival in life span when compared to the control beetles ( P = 0 . 05 , Fig . 3C ) . Similarly , the ratio between glucose and trehalose decreased by 44–66% in TRET RNAi beetles and increased by 1 . 2–3 . 9-fold in TPS RNAi beetles during starvation ( Fig . 3B ) . RNAi studies showed that the mRNA levels of TRET and trehalase , but not TPS , decreased in beetles injected with JHAMT dsRNA , suggesting that JH is required for expression of genes coding for TRET and trehalase during starvation ( Fig . 3D ) . Studies on expression of genes coding for TPS , TRET , and trehalase in male adults showed that gene coding for TPS is predominantly expressed in the testis , gene coding for TRET is predominantly expressed in the alimentary canal , and gene coding for trehalase is expressed in the fat body , head , and alimentary canal ( Fig . 4A ) . Comparison of TPS , TRET , and trehalase mRNA levels in starved and fed adults showed that TRET mRNA levels are higher in the starved beetles when compared to their levels in fed beetles . In contrast , the TPS mRNA levels are higher in the fed beetles than in the starved beetles . However , trehalase mRNA levels did not vary between starved and fed beetles ( Fig . 4B ) . TRET mRNA levels decreased in JHAMT and Met RNAi insects when compared to their levels in control insects in the alimentary canal but not in the fat body or head of starved insects , suggesting that JH regulates the expression of this gene in the alimentary canal ( Fig . 5A ) . Moreover , topical application of JH III induced the expression of the gene coding for TRET in the alimentary canal but not in the fat body or head ( Fig . 5B ) . Similarly , the mRNA levels of trehalase in the fat body decreased in JHAMT , Met , and ILP2 RNAi insects ( Fig . 5C ) . A decrease in mRNA levels of trehalase was observed in the alimentary canal isolated from JHAMT and Met RNAi beetles ( Fig . 5C ) . Similarly , head tissue dissected from JHAMT , ILP2 , and Met RNAi insects showed a decrease in trehalase mRNA levels ( Fig . 5C ) . Topical application of JH III induced the gene coding for trehalase in the fat body but not in the alimentary canal or head ( Fig . 5D ) . Injection of insulin into starved males on day 5 induced trehalase gene expression by 2 . 2 and 1 . 9-fold in the fat body and head respectively when compared to the levels in the same tissues dissected from control beetles ( Fig . 5D ) . These data suggest that both JH and insulin regulate expression of the gene coding for trehalase and JH but not insulin regulates expression of the gene coding for TRET .
The first major contribution of the current study is the discovery that JH and ILP2 regulate trehalose homeostasis in starved beetles . RNAi-aided knockdown in the expression of genes coding for JHAMT ( a key enzyme in JH synthesis ) and Met ( JH receptor ) or ILP2 extended the survival of starved beetles ( Fig . 1&S4 ) . Injection of bovine insulin rescued the effects of both JHAMT and ILP2 RNAi on starvation survival . In contrast , topical application of JH III restored starvation resistance to the control level in starved JHAMT RNAi adults , but not in the ILP2 RNAi adults ( Fig . 1C & D ) . RNAi-aided knockdown in the expression of genes coding for JHAMT or Met in male adult beetles caused a decrease in expression of ILP2 suggesting that both JH and its receptor are required for expression of this gene ( Fig . S3 ) . In addition , topical application of JH III induces expression of ILP2 in male beetles ( Fig . S3 ) . Moreover , JH titer could have been higher in starved beetles than the titers in the fed beetles as suggested by both JHAMT and Kr-h1 mRNA levels ( Fig . S5 ) . Taken together , these data suggest that JH regulates starvation resistance at least partially working through ILP2 . Similar results on the role of JH in starvation resistance and extending life span have been reported in the burying beetles including Nicrophorus orbicollis , N . tomentosus , and Ptomascopus morio [27]; in D . melanogaster [17] , [28]; and in the monarch butterfly [16] . In both T . castaneum [9] and Apis mellifera [29] , JH induces expression of ILPs . In T . castaneum JH induces expression of ILP2 and ILP3 in females and regulates expression of Vg genes through insulin pathway . In A . mellifera , JH works through ILP1 and regulates carbohydrate metabolism when worker bees shift from nursing to foraging . The conserved roles of IIS pathway have been well studied in regulation of life span and reproduction from yeast to mammals [18] , [29] , [30] . In D . melanogaster , partial ablation of the insulin producing cells , the median neurosecretory cells in the brain , has extended life span , reduced fecundity , altered lipid and carbohydrate metabolism and increased oxidative stress resistance [31] , [32] . These previous studies and our data reported in this paper suggest that JH regulates metabolic and reproductive processes at least partially through IIS signaling pathway . How does JH regulate carbohydrate metabolism ? In T . castaneum males , JH regulates expression of genes coding for trehalase and TRET , the two proteins critical for trehalose metabolism and transport ( Fig . 5 ) . Knockdown in expression of genes coding for JHAMT , Met , or ILP2 in the starved T . castaneum caused a decrease in trehalase mRNA levels in the fat body , which is the major tissue for storage of nutrients ( Figs . 3 & 5 ) . This would have caused a decrease in metabolism of trehalose to glucose , resulting in an increase in trehalose and decrease in glucose in the hemolymph . In T . castaneum , JH regulates expression of the gene coding for trehalase through the ILP2 and IIS pathway . Interestingly , JH but not ILP2 regulates expression of the gene coding for TRET in T . castaneum , suggesting that JH may recruit Met to bind the promoter region of the gene coding for TRET , which is a different mechanism from that described for trehalase regulation ( Fig . 5 ) . Studies are in progress to test this hypothesis . Our data suggest that IIS is not involved in transcriptional regulation of the gene coding for TRET . However , it is possible that IIS may regulate translocation of TRET protein similar to insulin regulation of glucose transporter 4 ( GLUT4 ) in humans by stimulating translocation of GLUT4 to the plasma membrane [33]–[35] . In type II diabetes patients , expression levels of the gene coding for GLUT4 and its translocation influence glucose transport [36] , [37] . Insulin regulation of trehalose levels has been reported in Caenorhabditis elegans , D . melanogaster , and Bombyx mori [31] , [38]–[41] . Insulin signaling regulates trehalose homeostasis by controlling expression of the gene coding for trehalase in the silkworm B . mori [42] , [43] by a direct molecular interaction with trehalase in Tenebrio molitor [26] and by regulating the trehalose synthesis in C . elegans [39] . However , in the starved male T . castaneum , knockdown in the expression of the gene coding for either ILP2 or JHAMT did not affect TPS mRNA levels , suggesting that TPS is not under the control of JH or IIS in starved male beetles . It is possible that trehalose synthesis , an energy consuming process , is not active during starvation . The second major contribution of the current studies is the discovery that trehalose plays an important role in starvation resistance in T . castaneum . Feeding trehalose but not glucose extended the starvation survival , suggesting that trehalose plays an important role in starvation resistance in addition to being an energy source . Trehalose alters the life span as shown in both IIS-reduced C . elegans and JH-deficient fruit fly [38] , [39] . In addition to the main function as an energy source [24] , [44] , trehalose could be acting as a chemical chaperone or as a metabolism modifier in protection of beetles from death . Insect hemolymph as a “sink” or “reserve” carries a variety of metabolites [45]–[47] . The hemolymph composition of metabolites reflects nutrient intake and serves as a feedback signal to regulate food intake [48] and the rate of energy expenditure [49] . It is also possible that the trehalose distributed to the tissues and organs could help protect cells against heat , cold , desiccation , anoxia , and oxidation and retard age-associated decline in survivorship and extend life span [50] . In addition , trehalose induces autophagy , independent of TOR , clears the aggregate-prone proteins associated with Parkinson's [51] and Huntington's [52] diseases . The trehalose homeostasis including trehalose levels and trehalose distribution play important roles in starvation resistance . Here , we found that both IIS and JH signaling pathways are involved in controlling starvation resistance via regulating trehalose homeostasis . The detailed molecular mechanisms that govern the cross-talk between these two major signaling pathways in regulation of trehalose homeostasis are the focus of intense research in several laboratories around the world .
Strain GA-1 of T . castaneum was reared on organic wheat flour containing 10% yeast at 30±1°C under standard conditions . New adults were separated within 6 hours post-adult eclosion ( PAE ) and staged from then onward . Total RNA was isolated using the TRI reagent ( Molecular Research Center Inc . , Cincinnati , Ohio ) . The DNA was eliminated from the total RNA using DNase I ( Ambion Inc . , Austin , Texas ) and 2 µg of total RNA for each sample was used for cDNA synthesis . Primers used in quantitative reverse transcriptase PCR ( qRT-PCR ) are listed in Table 1 or previously published [9] , [12] . QRT-PCR reactions were performed using a common program as follows: initial incubation of 95°C for 3 min was followed by 40 cycles of 95°C for 10 s , 55°C for 1 min , Relative levels of mRNAs were quantified in triplicates and normalized using an internal control ( ribosomal protein 49 , RP49 mRNA ) . For dsRNA synthesis , genomic DNA was used as a template to amplify fragments of genes in Table 1 , and the PCR products and the MEGA script RNAi Kit ( Ambion Inc . , Austin , Texas ) were employed for dsRNA synthesis . Genomic DNA was extracted from T . castaneum adults and purified using the DNeasy Tissue Kit ( QIAGEN , Valencia , CA ) . All the primers used for dsRNA synthesis and real time PCR are shown in Table 1 . For annealing dsRNA , the reaction mixture was incubated at 75°C for 5 minutes and cooled to room temperature over a period of 60 minutes . After treatment with DNase , dsRNA was purified by phenol/chloroform extraction followed by ethanol precipitation . The dsRNA concentration was determined using a Nano Drop 2000 ( Thermo Scientific , Pittsburgh , PA ) . The dsRNA was prepared using 808 bp PCR fragment of E . coli malE gene amplified from 28iMal vector ( New England Biolabs , Ipswich , MA ) was used as a control . Newly hatched male adults ( within 6 hours after emergence ) were anesthetized with ether vapor for 4–5 minutes and lined on a glass slide covered with two-sided tape . The dsRNA was injected into the dorsal side of the first or second abdominal segment using an injection needle pulled from a glass capillary tube using a needle puller ( Idaho Technology , Salt Lake City , UT ) . About 0 . 8–1 µg ( 0 . 1 µl ) dsRNA was injected into each new male adult . The malE dsRNA was used as a control . The injected beetles were removed from the slide and reared in whole wheat flour at 30±1°C . To restore the starvation survival by topical application of JH III or injection of bovine insulin , 0 . 5 µl of 10 mM JH III in acetone or acetone alone was topically applied to the males injected with malE , ILP2 , or JHAMT dsRNA on day 3 , day 5 , and day 7 . 0 . 2 µl 25 mM HEPES , pH 8 . 2 , or 10 mg/ml bovine insulin solution in 25 mM HEPES ( Sigma Aldrich , St . Louis , MO ) , was injected into malE , ILP2 , or JHAMT RNAi males on day 5 PAE . Total amount of carbohydrates was determined using an Anthrone-based method [53] . A 1 µg/µl solution of glycogen was used as the standard , from which 0–200 µg calibration series were prepared . Three adults were placed in each tube and crushed with a homogenizer in 1 ml of Anthrone reagent . Standards and samples were heated at 92°C for 17 minutes . The samples were allowed to cool to room temperature and optical density ( OD ) was measured at 625 nm . The amount of total lipids was estimated using the vanillin reagent method [54] . A 1 µg/µl solution of commercial vegetable oil was used as a standard by preparing 0–400 µg calibration series . Three male adults were placed in each tube and crushed with a homogenizer in 500 µl mixture of chloroform–methanol . Samples were kept in a heating block to evaporate the chloroform–methanol . After evaporating the solvent , 200 µl of sulfuric acid was added , and samples were heated in a heating block at 99°C for 10 minutes . The samples were cooled to room temperature , and 800 µl of vanillin reagent was added to each tube and mixed well . Standards and samples were incubated for 30 minutes , and ODs of samples were read at 490 nm . Total protein levels were determined using the Bradford reagent ( Sigma Aldrich , St . Louis , MO ) , and a series of dilutions of bovine serum albumin were used to prepare the standard curve . To extract hemolymph from the beetles on days 4 , 5 , and 6 after injection of malE , ILP2 , JHAMT , Met , TRET , or TPS dsRNA on day 0 newly emerged adults , the wings were removed and a few holes were poked into the body with forceps . The wings were placed in a microfuge tube containing 250 µl 0 . 25 M Na2CO3 buffer . The supernatant was collected after centrifugation at a full speed for 10 minutes . Trehalose is a non-reducing sugar resistant to 100°C . The hemolymph in the Na2CO3 buffer was incubated in a 95°C water bath for 2 hours to inactive all enzymes . 150 µl 1 M acetic acid and 600 µl 0 . 25 M Na-acetate ( pH 5 . 2 ) were added , and the solution was centrifuged ( 10 minutes , 12 , 000 rpm , 24°C ) . One hundred microliters of supernatant were incubated overnight at 37°C with 1 µl porcine kidney trehalase ( Sigma Aldrich , St . Louis , MO ) to convert trehalose into glucose . Thirty microliters of this solution were added to 100 microliters of a glucose reagent solution ( Sigma Aldrich , St . Louis , MO ) and incubated 20 minutes at 37°C . Glucose concentration was quantified at 340 nm with a spectrophotometer . The trehalose dihydrate ( Sigma Aldrich , St . Louis , MO ) was used as a control and also used to prepare reference curves . Newly emerged beetles were injected with dsRNA and reared without diet in the 96-well plate individually at 30°C incubator and checked at 5:00 pm every day . Male adults were used in all the experiments . All the data were analyzed using the SPSS 13 . 0 . The Kaplan-Meier program was used to analyze the survival time and the Log rank analysis was performed to compare the effect of JH III and insulin treatment . To compare nutrient levels , mRNA levels or the ratio between glucose and trehalose , the one-way ANOVA was used , for all the data analysis , the P-value for statistical significance is defined as P<0 . 05 . | Both juvenile hormone ( JH ) and Insulin/IGF-1 signaling ( IIS ) regulate life span and starvation resistance in insects . Regulation of longevity and starvation resistance by IIS has been well studied , yet little is known about the underlying mechanisms and cross talk between these two hormones . The red flour beetle , Tribolium castaneum , is a good model to study cross talk between JH and IIS because both of these pathways are important in regulation of life span and starvation resistance . The starved male beetles with either reduced JH or ILP2 levels live longer due to a lower rate in lipid and carbohydrate metabolism when compared with the control beetles . Juvenile hormone regulates starvation survival through regulation of synthesis of ILP2 , trehalose transporter ( TRET ) , and trehalase . Reduction in JH levels or its action or ILP2 expression decreased trehalase levels in the fat body , resulting in a slower rate of conversion of trehalose to glucose . Reduction in JH levels or its action also caused a decrease in TRET levels in the alimentary canal leading to a lower rate of uptake of trehalose into this tissue resulting in more trehalose available in the hemolymph . Trehalose likely regulates various processes to protect beetles from stress . | [
"Abstract",
"Introduction",
"Results",
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] | [
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] | 2013 | Juvenile Hormone and Insulin Regulate Trehalose Homeostasis in the Red Flour Beetle, Tribolium castaneum |
In the past few years , several studies have been directed to understanding the complexity of functional interactions between different brain regions during various human behaviors . Among these , neuroimaging research installed the notion that speech and language require an orchestration of brain regions for comprehension , planning , and integration of a heard sound with a spoken word . However , these studies have been largely limited to mapping the neural correlates of separate speech elements and examining distinct cortical or subcortical circuits involved in different aspects of speech control . As a result , the complexity of the brain network machinery controlling speech and language remained largely unknown . Using graph theoretical analysis of functional MRI ( fMRI ) data in healthy subjects , we quantified the large-scale speech network topology by constructing functional brain networks of increasing hierarchy from the resting state to motor output of meaningless syllables to complex production of real-life speech as well as compared to non-speech-related sequential finger tapping and pure tone discrimination networks . We identified a segregated network of highly connected local neural communities ( hubs ) in the primary sensorimotor and parietal regions , which formed a commonly shared core hub network across the examined conditions , with the left area 4p playing an important role in speech network organization . These sensorimotor core hubs exhibited features of flexible hubs based on their participation in several functional domains across different networks and ability to adaptively switch long-range functional connectivity depending on task content , resulting in a distinct community structure of each examined network . Specifically , compared to other tasks , speech production was characterized by the formation of six distinct neural communities with specialized recruitment of the prefrontal cortex , insula , putamen , and thalamus , which collectively forged the formation of the functional speech connectome . In addition , the observed capacity of the primary sensorimotor cortex to exhibit operational heterogeneity challenged the established concept of unimodality of this region .
Extensive neuroimaging research over the past two decades installed the notion that speech and language require an orchestration between several brain regions for comprehension , planning , and integration of a heard sound with a spoken word [1–6] . However , studies investigating brain networks of speech and language control have been largely limited to the examination of distinct cortical and subcortical circuits involved in a range of speech controlling components , such as speech motor output [7–13] , verbal fluency [14–16] , phonological and semantic processing [17–21] , verbal and tonal working memory [22–26] , speech monitoring and discrimination [27–29] , neural synchronization [30 , 31] , and integration [32–38] . Moreover , the majority of these studies were directed toward mapping the neural correlates of separate speech elements , such as production of meaningless syllable sequences or single words [7 , 13 , 39–46] , with only a handful of studies examining real-life speech production [6 , 9 , 38 , 47 , 48] . As a result , our understanding of the complexity of brain network machinery controlling speech and language is very limited . One particularly significant and outstanding question concerns the large-scale architecture , interactions , and functional specialization of brain regions within the speech network for shaping the production of spoken language . Here , we applied graph theoretical analysis [49–52] to functional MRI ( fMRI ) data of healthy adult individuals during the resting state , production of meaningless syllables as a motor task relevant to speaking but with minimal linguistic meaning , and production of grammatically correct , meaningful real-life English sentences in order to examine functional networks of increasing hierarchy and to quantify the intermediate steps in the formation of the speech production network . To further delineate speech network characteristics and community-based architecture , we conducted a follow up study to investigate the formation of nodal communities across all examined conditions , as well as in comparison with the modular structure of functional networks during the performance of a nonlinguistic task ( i . e . , auditory temporal discrimination of pure tones ) and a simple nonspeech motor task ( i . e . , sequential finger tapping ) . In the first experiment , we hypothesized that the speech production network ( SPN ) , compared to the resting state ( RSN ) and syllable production ( SylPN ) networks , would exhibit enhanced functional segregation with densely interconnected local communities ( hubs ) centered on the sensorimotor cortex . Importantly , functional specialization of these sensorimotor hubs during speech production would ensure the distinct and unique recruitment of the multimodal integrative cortical regions , such as the prefrontal and inferior parietal cortices , into the SPN but not the RSN or SylPN . In the second experiment , we hypothesized that , while the functional network during each condition ( i . e . , resting state , syllable production , speech production , finger tapping , and auditory discrimination ) would be characterized by a distinct community-based structure , the emergence of specialized processing communities and refined modular architecture of SPN would uniquely reflect the complexity of network configuration for speech production .
To identify the global configuration and characteristic features of the SPN , we performed a community-based analysis of functional networks during the RSN , SylPN , and SPN , as well as during the performance of an unrelated motor task of sequential finger tapping ( finger-tapping network; FTN ) and an unrelated auditory task of pure tone temporal discrimination ( auditory discrimination network; ADN ) . To estimate network communication patterns , we classified high-strength hubs into connector ( linking communities ) and provincial hubs ( connecting nodes within the same module ) based on their participation in intermodular versus intramodular links [58] . All functional networks exhibited different patterns of modularity; while the RSN consisted of five distinct modules , the SylPN , FTN , and ADN scaled down to three modules , and the SPN expanded to six modules ( Fig 6 ) . During the resting state , functional network communities spanned ( I ) the frontoparietal cortex , ( II ) the temporooccipital cortex , ( III ) the insula/opercular cortex , basal ganglia , and thalamus , ( IV ) the sensorimotor and parietal cortex , and ( V ) the cerebellum . The RSN was characterized by a large number of connector hubs compared to relatively few provincial hubs ( 22 versus 6 hubs , respectively ) . Connector hubs linking different communities originated from modules I–IV and included sensorimotor and parietal cortical regions , whereas provincial hubs were centered on the primary sensorimotor cortex and exclusively recruited from module IV ( Fig 7 ) . Task performance dramatically changed the RSN modular configuration . The nodal community structure of the SylPN consisted of three modules and was characterized by two large communities ( modules I and III ) , which were located within the left and right hemispheres ( Fig 6 ) , respectively , comprising 70% of all network nodes ( 105 out of a total 150 nodes , Fig 7 ) . These relatively symmetrical modules predominantly included the sensorimotor , inferior frontal , inferior parietal , and superior temporal cortical regions , the basal ganglia , and the thalamus , whereas a smaller module II included mainly cerebellar and occipital regions . Hub classification revealed that all SylPN hubs participated as connector hubs and were situated around the sensorimotor and inferior parietal cortical regions with the left-sided module I containing the majority of connectors ( 10 out of 15 hubs ) . Speech production introduced the most complex network community architecture with the emergence of six distinct modules , which represented the largest number of modules compared across all experimental conditions . While left- and right-hemispheric modules I and IV of the SPN included predominantly sensorimotor , inferior frontal , inferior parietal , insular/opercular , and temporal cortical regions and largely corresponded to modules I and III of the SylPN , the new and extended nodal communities were formed and centered around the frontoparietooccipital cortex ( module II ) , the basal ganglia and thalamus ( module III ) , the hippocampus and thalamus ( module V ) , and the cerebellum ( module VI ) . The SPN was further characterized by a low ratio of connector to provincial hubs ( 7 versus 12 hubs , respectively ) , suggesting a high degree of network segregation with only a few network-wide coordinators . Similar to the SylPN , nonlinguistic ADN and nonspeech motor FTN were comprised of three network communities of a distinct spatial profile ( Fig 7 ) . The FTN showed a community structure comparable to SylPN with symmetrical modules I and III located within the left and right hemispheres ( Fig 6 ) , respectively , and connector-only hubs linking the three nodal communities ( Fig 7 ) . This finding points to a lack of locally controlled information flow within nodal communities in both the FTN and SylPN , which is reflective of the low complexity of these two motor production tasks . In contrast to the SylPN and FTN , which largely lacked the involvement of the frontal cortical regions , the ADN formed an extensive module II , which included the vast majority of frontal and parietal regions , bilaterally . Compared to the ADN , the SPN recruited a smaller subset of the frontoparietal regions , which were distributed across five different modules ( I–IV and VI ) , suggesting intramodular importance of particular frontoparietal regions within the SPN . To quantify the observed differences in network community divisions across all experimental conditions , we assessed the partition distance pd between network community structures by calculating normalized mutual information coefficients between the respective partition vectors across all networks . Comparing the SPN to all other conditions , the highest degree of similarity with respect to their partitions was found with the SylPN ( pd ( SPN , SylPN ) = 0 . 27 ) , followed by the RSN ( pd ( SPN , RSN ) = 0 . 25 ) . The FTN community structure exhibited less correspondence to the SPN ( pd ( SPN , FTN ) = 0 . 23 ) , whereas the ADN showed the most discordance in its modular architecture compared to the SPN ( pd ( SPN , ADN ) = 0 . 15 ) , confirming the distinctly different topologies of the non-speech-related networks . Finally , based on our finding that primary sensorimotor and premotor cortices contributed to the formation of a shared hub network across all experimental conditions ( Figs 4 , 6 , and 7 ) , we examined the existence of adaptive flexible hubs in these sensorimotor regions by comparing their participation coefficients , pci , estimating the uniformity of connections of these regions across all networks [59 , 60] . Compared to the frontoparietal regions , which were recently reported to host flexible hubs rapidly adapting their connectivity patterns depending on task demands [60] , we found that the shared sensorimotor hubs in areas 6 , 4a , 4p , 3b , and 1 across the RSN , SylPN , SPN , FTN , and ADN also show high participation coefficients similar to shared frontoparietal hubs ( sensorimotor versus frontoparietal pci: 0 . 62 ± 0 . 15 versus 0 . 67 ± 0 . 07 , p = 0 . 11 ) .
The formation of hubs and their roles within and across network communities provided detailed insights into the functional specialization of the speech connectome ( Figs 4 and 7 ) . We found that the SPN shared common hubs with the RSN and SylPN , constituting a core hub network , which was centered on the left laryngeal and orofacial regions of the primary motor cortex and its main input regions in the surrounding premotor , somatosensory , and parietal cortices . Notably , the strength of SPN hubs was significantly greater than those of the RSN or SylPN , which may be explained by the task complexity . Our data further highlighted the role of the left area 4p as an important core hub during speaking . The area 4p ( posterior part of the primary motor cortex ) is known to be involved in initiation and execution of motor commands and the modulation of movement-related attention . A recent meta-analysis of speech-related fMRI literature showed that the peak of activity within the laryngeal motor cortex is located in the area 4p [63] . Conversely , the area 4a ( anterior part of the primary motor cortex ) functionally resembles the secondary motor cortex and requires higher order sensory feedback for motor execution [64–67] . Our findings demonstrate that the left area 4p but not either left or right area 4a has distinctive strength within the core hub network during speaking compared to syllable production and the resting state . The importance of area 4p within the SPN is further emphasized by its role as a provincial hub , which established dense intramodular connections and largely influenced its own nodal community comprised of the majority of sensorimotor core hub regions contributing to the final common cortical pathway of the SPN . In contrast , area 4a served as a connector hub , fulfilling its role in network-wide information integration not limited to a particular module . Based on the distinct involvement of area 4p and area 4a , these two primary motor cortical subdivisions may differentially influence the hub network connectivity for formation of long-range neural connections during speech production compared to other conditions . Major differences between the SPN and all other networks were found in their hub network connectivity patterns . Detailed investigation of the connectivity profiles of the SPN compared to the RSN and SylPN showed that , while these networks share the same sensorimotor hubs , the connections of the common hub network with other brain regions largely varied depending on the state of brain activity ( i . e . , resting , syllable production , or speaking ) ( Figs 4 and 7 ) . The same finding held true when comparing the architecture of the SPN with the FTN and ADN . Being centrally embedded within the network , the sensorimotor hubs recruited an exclusive set of high-degree and high-strength nodes in the SPN . Compared to the RSN , the SPN-only brain regions included the parietal operculum , insula , middle/posterior cingulate cortex , putamen , thalamus , and cerebellum , while the parietal and temporal cortex had different regions specifically designated to either the SPN or RSN ( Fig 4A ) . Comparisons between the SylPN and SPN showed that the SPN recruited the prefrontal cortex , insula , putamen , and thalamus , whereas the SylPN involved the parietal , temporal , and occipital cortices , and the cerebellum ( Fig 4B ) . These data demonstrate that the transition from the resting state to meaningless syllable production to meaningful speaking is contingent upon the involvement of the multimodal associative regions , such as the frontoparietal cortices as well as the basal ganglia and cerebellum ( see Discussion in S1 Text for the discussion of subcortical structures ) . In particular , compared to the resting state , the SPN recruited a wide array of brain regions across multiple nodal communities ( Fig 6 ) . The SPN core sensorimotor hub network established connections with the brain regions responsible for sound perception and encoding ( auditory cortex ) , phonological and semantic processing ( parietal cortex ) , lexical decisions and narrative comprehension ( middle/posterior cingulate cortex ) , motor planning and temporal processing of auditory stimuli ( insula ) , control of learned voice production and suppression of unintended responses ( basal ganglia ) , and modulation of vocal motor timing and sequence processing ( cerebellum ) ( reviewed in [1 , 68] ) . In contrast to the SylPN , the SPN further refined its network properties and functional specializations by recruiting brain regions responsible for high-order planning and processing . Among these regions , the involvement of the prefrontal cortex was particularly prominent , as a strong relationship with the core hub network was found only in the SPN but not in the SylPN ( Fig 2II–2IIIC ) . Similar to the SylPN , the speech-unrelated FTN also showed no prefrontal component within its large-scale network , which attests to the similarities between the simple vocal motor task of syllable production and the simple motor task of finger tapping , as well as further underscores the distinct prefrontal involvement in the SPN . However , the question arises as to whether the prefrontal cortical participation is related more to general cognitive processing than speech production . Comparisons between the SPN and the nonlinguistic decision-making ADN showed that while both networks shared common nodes in the prefrontal cortex , the SPN required only a specialized subset of the prefrontal region , which was additionally assigned to different neural communities as opposed to forming one large frontoparietal community in the ADN . Such network topology suggests that the prefrontal cortex may play a specialized role in the formation of cognitive aspects of speech control , such as verbal fluency , semantic context associations and violations , word retrieval and sentence generation , stimulus monitoring , and attention-demanding speech comprehension [69–76] , which are not characteristic of the production of meaningless syllables , other simple motor or speech-unrelated cognitive tasks . Taken together , our study demonstrates that the production of a syllable , as a speech building block , leads to an integrated network configuration dominated by connector hub regions with almost no participation of the prefrontal cortex in the large-scale network , while the ability to produce and monitor meaningful speech requires locally segregated information processing by specialized communities and information transfer through the prefrontal cortical regions within the SPN . To emphasize that the changes in network topology observed from resting to syllable production to speaking truly represent characteristics of speech-relevant motor networks , we contrasted the network architecture of the RSN , SPN , and SylPN with the topology of the FTN representing a nonspeech related motor control task and of the ADN as an example of a nonlinguistic decision-making task . Our findings demonstrated once again that among all examined conditions , the SPN showed a unique functional architecture ( Figs 6 and 7 ) that reflected the high level of complexity of meaningful speech as opposed to repetitive syllable production , finger tapping , and auditory temporal discrimination . While the SylPN exhibited a connectivity profile similar to the SPN on a nodal level ( Figs 2 and 3 ) , the recruitment of nodes into network communities showed a distinctly more complex and segregated network organization in the SPN than in the SylPN ( Fig 6 ) . In contrast , the topology of the SylPN , FTN , and ADN was characterized by the emergence of three nodal communities , which were , however , distinctly configured depending on task demands . The SylPN had two large , relatively symmetrical left and right communities with the left component containing the majority of the connector hubs; the FTN had its connector hubs evenly distributed across both hemispheres , whereas the community partition of the ADN illustrated the distributed functional couplings of frontoparietal areas across the network ( orange module in Fig 6 ) . As all examined networks had commonly shared hubs in the primary sensorimotor and premotor cortical regions , it is plausible to suggest that behaviorally driven organization and functional specialization of the core sensorimotor network ( hubs ) defined the unique topological arrangement of large-scale networks , especially in the SPN and SylPN . Based on the participation of the same sensorimotor hubs in several functional domains across different networks and their ability to adaptively establish connectivity with a large range of brain regions ( Fig 7 ) , the detected sensorimotor hubs may be considered as flexible network hubs [60 , 77] with a potential capacity for operational heterogeneity . This novel finding challenges the previous concept of the sensorimotor cortex to exert only low order unimodal influences on other regions [77–80] . In summary , several new findings emerged from our utilization of multivariate graph theoretical analysis of the SPN . Specifically , combining the analysis of functional interactions at the level of network communities with the assessment of individual nodes provided detailed quantitative evidence suggesting that speech production requires specialized network organization around the core local communities centered on the sensorimotor cortex . Because of their high strength , degree , and heterogeneity of functional connections and participation across various behaviors , these sensorimotor regions may be considered to be multimodal flexible network hubs . Among these , area 4p of the primary motor cortex emerged as a particularly important cortical hub in speech controlling network . Furthermore , the production of real-life speech depended on the proper orchestration of a large-scale network , comprised of specialized cortical and subcortical nodes in the prefrontal cortex , insula , putamen , and thalamus , which were less important for other networks , including the SylPN . Collectively , these individual nodes and their roles within functionally specialized nodal communities determined the reconfiguration of global network architecture from the resting state to syllable production to speaking and clarified the distinct functional specializations of core sensorimotor hub regions within large-scale neural networks .
Written informed consent was obtained from all subjects prior to study participation , which was approved by the Institutional Review Boards of the Icahn School of Medicine at Mount Sinai and National Institute of Neurological Disorders and Stroke , National Institutes of Health . Twenty-seven right-handed monolingual English-speaking healthy subjects ( 18 females , 9 males , age 52 . 2 ± 11 . 3 years [mean ± SD] ) without any history of neurological , psychiatric , or laryngeal disorders participated in the study . Among these , 20 subjects ( 13 females , 7 males , age 55 . 2 ± 9 . 8 years ) participated in the initial resting state and speech production fMRI study . As a follow-up study , 14 subjects ( 5 same and 9 new; total: 7 females , 7 males , age 52 . 0 ± 13 . 1 years ) participated in the other task-production fMRI studies , including syllable production , sequential finger tapping , and pure tone auditory temporal discrimination . To ensure the compatibility of data from different subject groups , we conducted a comparison of time series in all hub regions in the two groups of subjects from the original and follow-up studies and found no statistically significant differences between these groups ( all p > 0 . 07 adjusted for family wise error [FWE] based on the maximal statistic Tmax [81] ) , indicating that these data were coherent and not biased by intersubject differences . Written informed consent was obtained from all subjects prior to study participation , which was approved by the Institutional Review Boards of the Icahn School of Medicine at Mount Sinai and National Institute of Neurological Disorders and Stroke , National Institutes of Health . Brain images were acquired on a 3 . 0 Tesla GE scanner equipped with a quadrature birdcage radio frequency head coil ( Milwaukee , WI ) . Data preprocessing was performed using AFNI software [85] following standard steps . Based on the cytoarchitectonic maximum probability maps and macrolabel atlas [91] , the whole brain was parcellated into 212 regions of interest ( ROIs ) , including 142 cortical , 36 subcortical , and 34 cerebellar regions ( Fig 8A ) . For each ROI , a voxelwise-averaged time series of rs-fMRI and task-production fMRI was computed . Because the main focus of this study was to investigate statistical dependence of neural processing sites distributed throughout the entire brain , zero-lag Pearson’s correlation coefficients were calculated for each pair of regions and each condition , giving rise to 212 x 212 correlation matrices ( Fig 8B ) . Visual inspection of per-subject correlation histograms revealed only a negligible number of negative entries , which were removed from the matrices [92 , 93] . All connectivity matrices are publicly available at http://figshare . com/articles/The_Functional_Connectome_of_Speech_Control/1431873; the codes used to transform the fMRI data to networks can be found at http://research . mssm . edu/simonyanlab/analytical-tools/ . Visual inspection of rs-fMRI data in six subjects showed pronounced atypical variations in average correlation strength , which were suggestive of susceptibility artifacts in the resting-state data . These subjects were excluded from rs-fMRI analysis , and their speech-related fMRI datasets were removed for consistency of data analysis , thus reducing the number of subjects to 14 per group in each condition . None of these six subjects were among the recruited for the follow-up syllable production study . As a next step , the individual datasets during each condition ( i . e . , rest , speech , syllable production ) were used to construct weighted undirected graphs by using the 212 brain regions as nodes vi of a network with the associated correlation coefficients representing the weights of the graph's edges . The density ( or cost ) of each network was computed as the number of actual connections divided by the number of maximal possible connections in the graph [93] , which yielded an average density of 88% ± 4% ( mean ± SD ) for RSN , 92% ± 6% for SPN , and 92% ± 5% for SylPN . Because networks with a density > 50% tend to exhibit random network characteristics [93 , 94] , we reduced the cost of the network by removing edges with a weight less than a given percentage of the maximum weight in the network until the network had a density of 50% . Because our principal goal was to examine the organization of the SPN in comparison to the RSN and SylPN , we first applied this thresholding to the SPN and then adjusted the RSN and SylPN accordingly so that all graphs had the same number of nodes and edges for between-network comparisons [95] . Specifically , the least densely interconnected nodes were first removed from the 212-node SPN , and then the same nodes were excluded from the RSN and SylPN . Such an elimination strategy allowed us to create a refinement of the initial whole-brain parcellation based on the speech production task . We tested the validity and efficacy of the employed nodal elimination strategy by means of random networks . As a first step , synthetic reference networks with the same number of nonzero elements as the original 212-node per-subject networks were constructed . This gave rise to three groups corresponding to the RSN , SPN , and SylPN with 14 random networks per group . Then the randomized SPN group was thresholded down to 50% density , and the same nodes were then removed from the randomized RSN and SylPN graphs . The resulting downsized networks exhibited a connectivity structure comparable to the initial 212-node random graphs , demonstrating that the employed elimination strategy did not diminish the underlying topological structure of the networks ( see details in Methods of S1 Text and S2 Fig ) . As a last step in network construction , we removed sparsely connected , low-degree nodes with fewer connections than 5% of the maximal number of nodal links in the speech graphs . Based on this multistep thresholding , a total of 62 nodes were detached from the SPN , which reduced the number of regions from 212 to 150 ( 73 in the left hemisphere and 77 in the right hemisphere ) at a target density of 50% ( Fig 8B ) . The same 62 nodes were then removed from the RSN and SylPN in order to allow for comparisons with SPN properties . The majority of removed regions were located in the ventral parts of the brain , comprising areas that are especially prone to fMRI susceptibility artifacts [96] . All resultant 42 ( 14 RSN , 14 SPN , and 14 SylPN ) weighted undirected graphs with n = 150 nodes were thresholded again to obtain a common density range of 77%–86% ( 10 values , 1% increments ) . Over this range , group-averaged networks were computed as reported earlier [53] and had a density of 60%–78% ( 10 values , 2% increments ) ( Fig 8B ) . In the follow-up second experiment , we assessed the global reconfiguration of brain networks during speech production in contrast to the resting state , syllable production , sequential finger tapping , and an auditory discrimination task . The same 212 ROI whole-brain parcellation was used to construct weighted , undirected networks for all experimental conditions , where sparsely-occurring negative correlation values were assigned the edge weight zero [92 , 93] . In order to avoid a restriction of nodal community formation to an SPN-based subset of nodes , the above-employed nodal elimination strategy was replaced by individual thresholding of the RSN , SylPN , FTN , and ADN independently from the SPN . To ensure comparability of results between different networks and to the findings from the first experiment , all networks were downsized to 150 nodes . | Speech production is a complex process that requires the orchestration of multiple brain regions . However , our current understanding of the large-scale neural architecture during speaking remains scant , as research has mostly focused on examining distinct brain circuits involved in distinct aspects of speech control . Here , we performed graph theoretical analyses of functional MRI data acquired from healthy subjects in order to reveal how brain regions relate to one another while speaking . We constructed functional brain networks of increasing hierarchy from rest to simple vocal motor output to the production of real-life speech , and compared these to nonspeech control tasks such as finger tapping and pure tone discrimination . We discovered a specialized network of densely connected sensorimotor regions , which formed a common processing core across all conditions . Specifically , the primary sensorimotor cortex participated in multiple functional domains across different networks and modulated long-range connections depending on task content , which challenges the established concept of low-order unimodal function of this region . Compared to other tasks , speech production was characterized by the formation of six distinct neural communities with specialized recruitment of the prefrontal cortex , insula , putamen , and thalamus , which collectively formed the functional speech connectome . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | The Functional Connectome of Speech Control |
Despite progresses in ancestral protein sequence reconstruction , much needs to be unraveled about the nature of the putative last common ancestral proteome that served as the prototype of all extant lifeforms . Here , we present data that indicate a steady decline ( oil escape ) in proteome hydrophobicity over species evolvedness ( node number ) evident in 272 diverse proteomes , which indicates a highly hydrophobic ( oily ) last common ancestor ( LCA ) . This trend , obtained from simple considerations ( free from sequence reconstruction methods ) , was corroborated by regression studies within homologous and orthologous protein clusters as well as phylogenetic estimates of the ancestral oil content . While indicating an inherent irreversibility in molecular evolution , oil escape also serves as a rare and universal reaction-coordinate for evolution ( reinforcing Darwin's principle of Common Descent ) , and may prove important in matters such as ( i ) explaining the emergence of intrinsically disordered proteins , ( ii ) developing composition- and speciation-based “global” molecular clocks , and ( iii ) improving the statistical methods for ancestral sequence reconstruction .
What did the proteome of the first successful lifeform look like ? This question is critical to the understanding of how life as we know it first began on earth . Despite the progresses in ancestral sequence reconstruction methods [1]–[3] , predicting the proteome of the Last Common Ancestor ( LCA ) from today's proteomes is impeded by the inevitable accumulated errors implicit in any extrapolation method [4] . Also , while interesting predictions regarding ancestral genome behavior can be made [5] , [6] , the sequence reconstruction methods utilized to make those predictions are fraught with precarious ( or even potentially “flawed” ) presuppositions [7]–[11] . Here we re-address the question of what the composition of the LCA may have been , and provide an answer sourced from simple considerations that our LCA might have had a highly hydrophobic ( oily ) proteome , which has slowly been equilibrating over evolutionary time . The results are obtained independently of sequence reconstruction methods and previously published statistical techniques [1]–[3] , [5] , [6] , which provides a method-independent snapshot into the molecular nature of the last common ancestor . It is important to first introduce the notion that proteome compositions equilibrate at “glacial” speeds ( over billions of years; Section S1 . 1 in Text S1 , and discussed below ) , which will be important in extrapolating trends obtained from present proteomes to properties of the LCA's proteome . While mutations accumulate within a proteome at a relatively steady rate ( to the order of about 1 substitution per billion years per nucleotide site [12] ) , the oil composition of that proteome–described as the cumulative percent composition within the sequence ( %FILV ) of the four most hydrophobic residues as per the Kyte-Doolittle scale [Phenylalanine ( F ) , Isoleucine ( I ) , Leucine ( L ) and Valine ( V ) ]–is expected to change at a much slower rate due to reasons such as the proteome's massive size ( Section S1 . 1 in Text S1 ) and the biochemical impediments associated with drastically changing a protein's composition ( Section S1 . 2 in Text S1 ) . Given this expected glacial drift/equilibration in proteome composition , in accordance with previous discussions [5] , one can expect one of three general trends in the change of proteome oil content over even large evolutionary time ( negative , neutral or positive correlations which is dependent on the LCA's original proteome oil content; Figure 1 ) . Also important to tracking changes in oil content over evolutionary time is the finding that , although all species have existed in some form for equal amounts of physical time ( an expected outcome of common descent ) , their genomes are not equally deviated from the last common ancestor ( Sections S1 . 3 and S1 . 4 in Text S1 ) ; the number of non-synonymous nucleotide substitutions accumulated since emerging from the LCA appears to be proportional to the number of speciation events that the species has encountered sans phylogenetic errors [13]–[15] . What this indicates is that , while a component of molecular evolution is due to the “constantly ticking” molecular clock caused by neutral or nearly neutral mutations [16]–[19] , another component of genetic deviation from the LCA may be attributed to substitutions associated with speciation events ( roughly proportional to species node number in the tree of life; see Methods ) , even though the exact extent and mechanism of substitutions in this regime is not known [15] . Importantly , it is especially expected that substitutions causing changes in oil content , due to being quite the opposite of neutral in fitness effects ( Section S1 . 2 in Text S1 ) , are expected to dominantly occur not during neutral drift but during the non-neutral component of molecular evolution , i . e . , in events such as speciation ( Section S1 . 4 in Text S1 ) . In this report , we use species node number as a measure of evolutionary age or “deviatedness” from the LCA ( also roughly proportional to organismal complexity ) to study the changes in proteome and protein composition over evolutionary time . While this study may be susceptible to the various expected local inaccuracies involved in building the tree of life ( ToL ) , the global trend–that lower node-number organisms are “older” genomes with less genomic deviation from the LCA–is a notion that is acceptable , and such a precedence has been set [13]–[15] , [20] . Additionally , given our interest in finding potential low-resolution or global trends over evolutionary time , the utility of node number is uniquely warranted .
We obtained and studied all of the proteomes available within the Ensembl genome databases ( 272 diverse proteomes belonging to 152 distinct species sourced from all domains of life; listed in Section S5 in Text S1 ) for a relationship between a species node number and its proteome oil content . Remarkably , the proteomes displayed a striking , universal relationship between the proteome oil content ( %FILV ) , and the species node number in both individual Ensembl databases ( Figure 2A; annotated axes in each panel are different and elaborated in Figure S1A in Text S1 ) and the merged data ( Figure 2B ) , which is unexpected given the high diversity of the proteomes studied and the coarse nature of the ToL . Other metrics for oil content ( hydrophobicity scales ) showed similar results ( Figure S2 in Text S1 ) ; however , %FILV provided the strictest trend and so is kept as the main metric henceforth . Given the highly diverse nature of the organisms in our collection , environmental variables are not expected to contribute strongly to this universal trend ( also discussed later in Other Trends ) ; Oil escape is also reasserted ( albeit loosely ) by paleobiological records , where the relationship between proteome oil content and genus-level First Occurrence ( FO ) records is also negative ( Figure 2C; see Methods ) . The FO records are not necessarily sufficient evidence for oil escape , but rather are referenced for their potential utility of a paleo-geological metric for time ( FO dates ) in replacing the more abstract node number , which , if possible , would make oil composition a “global” molecular clock ( discussed later ) . Our hope is that Figure 2C may inspire the further collection and utilization of more FO data to that aim . Here we show that “oil escape” occurs not only at the proteome level , but also at the individual protein composition level ( which is evidenced by changes in oil content in groups of homologous , and later , orthologous , proteins over organism node space ) . Our “single protein” studies were performed on clusters of protein sequences homologous to “seed” protein domains listed in the SCOP database ( v1 . 75 , redundancy ) [21] . Within a cluster , each proteome was represented at most once , and homology was ascertained by BLAST-P's default values [22] . Interestingly , protein-resolution oil escapes , explicitly shown for three homologous clusters in Figure 3A and indicated by negative Spearman's correlation coefficients , are not chance events as would be naïvely expected , but are the dominantly occurring trends among homologous clusters , with over 92 . 4% of the 5809 homologous protein clusters describing a decline in oil content over node space ( Figure 3B ) . If we discount the statistically insignificant trends ( i . e . , if we omit those clusters showing two-tailed or , see Figure 3C ) , then the clusters displaying oil escape further increases to a compelling 97 . 8% of the 4518 viable clusters ( also , analysis of clusters specific to individual Ensemble databases also resulted in qualitatively identical results; Figure S13 in Text S1 ) . Figure 3B cannot be explained by the addition of more hydrophilic domains to a sequence over time , since 87 . 8% of the 4222 statistically significant ( ) size-homogenized clusters display oil escape ( here , size-homogeneity was ensured by culling all sequences that differ in length from the seed sequence by 20%; data not shown ) . Also , the decrease in oil content over node number is not expected to occur predominantly due to the addition of hydrophilic loops over node number as homologous clusters do not display a bias towards negative relationships between oil content and protein length within homologous clusters ( Figure S9 in Text S1 ) . The homologous clusters of proteins used in Figure 3 contain protein pairs related by both orthologous and paralogous relationships ( i . e . , the proteins can either be related by direct descent , or by duplication and then descent , respectively ) , and the paralogous relatives in the cluster may cause inaccuracies in gene classification at the functional level [23] , although our broad protein-fold resolution of inquiry may not elicit such issues . Still , the same study was carried out among smaller clusters of purely orthologous proteins ( COGs database [24] ) , which also predominantly displayed oil escape among clusters describing significant trends ( Figure S10B in Text S1 ) ; interestingly , including paralogs into the clusters ( Figure S10A in Text S1 ) reduces the extent of oil escape observed , which indicates that Figure 3 may even be underestimating the extent of oil escape actually occurring . These data , when consolidated , indicate that a large majority of proteins ( homologs , orthologs , and individual domains ) display marked oil escape , which reiterates the proteome-level studies in Figure 2 , all of which indicate that more ancient proteomes ( and so , by extrapolation , the LCA ) display oilier residues ( and , incidentally , fewer “specificity incurring” residues; see Figure S3 in Text S1 ) than the newcomers . Despite the rough ( coarse grained ) nature of the utilized tree of life ( ToL ) , the trend evident in Figure 2 is interesting , since a common trend–oil escape–appears to unite the behavior of all tested proteomes spanning the domains of life , which offers a unique glimpse into the LCA's molecular composition . This , however , requires that the character trait–oil content–be dependent on the species' ancestral history rather than on external factors ( such as non-homogeneous changes in environmental temperatures ) , i . e . , species oil content must display strong dependence on the topology of the tree of life , which is akin to phylogenetic dependence ( PD ) in purely phylogenetic trees [25] , [26] . Using comparative methods , we confirm that while oil escape is dependent on the utilized ToL , that attribute is not sufficient to reiterate oil escape , which strengthens the notion of a directed Brownian evolution of oil content from an oily LCA . The dependence of a character state on a particular tree may be estimated using Pagel's metric [25] , [26] , which normally ranges from 0 ( no dependence ) to 1 ( strong dependence ) . We estimated ( see method ) , from established methods [26] , a strong bias of the pruned ToL on species oil content ( ; compared to calculated from 1000 datasets with shuffled oil contents; Figure S14 in Text S1 ) , which indicates that oil content is strongly dependent on a species' evolutionary history , and less likely swayed ( or biased ) by “other” ( phylogenetically independent ) forces not associated with shared history ( e . g . , non-homogeneous , evolutionary pressures ) . It is also important to note that PD , while useful in precluding non-tree-based ( or non systematic ) biases in our trend , is not sufficient to reproduce the oil escape trend . This is easily evidenced by studying a “neutral drift” version of the original tree , where branches are modified to ensure that all species ages are identical ( root to tip lengths are identical ) ; while this tree does not display oil escape ( given that all species ages are identical and not node number dependent ) , just like in the original ToL , high dependence is observed ( ; see Figure S14 in Text S1 ) . Reconstruction of the LCA state . From ancestral state estimation methods [27] ( described in Methods ) , the “neutral drift” tree describes a random diffusion from an estimated ancestral state ( LCA's estimated oil content , rate of change per node , and average error ; notation borrowed from [27] ) that is very close to today's average proteome oil content ( 26 . 1% ) , while the original “speciation” tree describes a steady drift from an oily LCA ( ; per node and ) , given the high and substantially negative . The “neutral tree” model of evolution by random diffusion is disregarded since the expected symmetric distribution of species about the predicted ancestral state is not observed ( Figure S15 in Text S1 ) . This leaves us with the “oil escape” model proposed in this paper ( e . g . , Figures 2 , 3 ) , which may now be described as a “biased” Brownian motion in genome/proteome space at play particularly during speciation events .
We discuss the two major problems that may potentially plague tree-related studies . First , the number of sequenced genomes ( whose species populate the tree ) is minuscule compared to the number of species observed in the biological universe ( the incompleteness problem ) . Second , the genomes that are sequenced may be biased with respect to historic choice of model species , ease of handling new species , et cetera ( the selection bias problem ) . Here we demonstrate how such biases do not disrupt our tree-related inferences . Incompleteness problem . ( i ) It has been shown that the maximum likelihood comparative methodology used above is robust to incomplete phylogenies [26] , which is reflected in our finding that the generalized least squares estimate for ancestral oil content is consistently reiterated ( averaging at ) even when up to 70% of the species collection is randomly culled ( Figure 4 ) . ( ii ) the ToL utilized in our studies is obtained by pruning NCBI's ToL [28] , [29] without collapsing single-children nodes [30] , i . e . , the node number ( or shared path ) of a species ( or pair of species ) within the pruned tree is equal to its corresponding value in the exhaustive NCBI tree ( Section S6 . 1 in Text S1 ) . Given the extensive species coverage in NCBI's taxonomy records ( over species are recorded [29] ) , the addition of newly sequenced species to our study will likely not modify the node number values for the extant species ( or extant values ) , and so , adding new species proteomes to our relatively incomplete set of species will likely have no affect on the tree topology . These points indicate that while adding new proteomes to our analysis may improve the statistical relevance of our findings , data incompleteness is not expected to strongly disrupt our general inferences . Selection bias problem . To ascertain the importance of selection bias ( of sequenced species ) in our results , we reduced the sampling bias by coarse graining our dataset to the genus level ( averaging the node numbers and %FILV's within genera ) , which , while losing some statistical significance , reiterates the oil escape trend with negative %FILV vs . node number Spearman ( ; ; 76 data points ) . Our studies on randomly culled species sets ( Figure 4 , expanded in Figure S16 in Text S1 ) also indicate that bias due to over-sampling of related species is not likely to drastically modify the primary observation of oil escape , since randomized culling of species will likely reduce sampling bias ( as oversampled relatives are more likely to be culled than unique species ) , which does not change our estimated ancestral oil content until very high culling frequencies are approached . Finally , it is important to note that the robustness of our data and analysis to incompleteness and selection bias may be due to the highly diverse dataset sourced from varied environments and node space ( for example , the following node numbers host diverse species: node number 29 hosts the zebra fish , mosquito , and monkey; node number 23 hosts the louse and the platypus ) . In conclusion to the previous sections , oil escape has been evidenced from simple regression analysis of whole proteomes ( Figure 2 ) , protein clusters ( Figure 3 ) , and by comparative methods . Finally , given that the biases due to incompleteness and sampling of the sequenced species and are not expected to negate/diminish the general trend of oil escape ( see text , Figure 4 , and Figures S15 and S16 in Text S1 ) , we expect that oil escape is significant as a trend and merits discussion . It must be noted that while other phylogenetically dependent traits may modulate oil escape , given the highly diverse nature of the tree , the possibility of explaining the entire trend with other niche based relationships/dependences is low . Figures S4 and S9 in Text S1 already indicate that oil escape can not be satisfactorily described by the increase of the protein length or the addition of loops or intrinsically disordered regions within globular proteins . Here , we will discuss trends in amino acid and composition previously reported in the literature to ensure that other potential relationships are also independent of the oil escape seen here , i . e . , oil escape is a previously unreported trend . ( i ) Optimal growth temperatures ( OGT ) . A previous report has established a strong positive correlation ( ) between proteome %IVYWREL ( single-letter amino acid codes used ) and a prokaryote's optimal growth temperature or OGT [31] ( the relationship is reiterated in Figure S5A in Text S1 ) . At first glance , species age ( node number ) and proteome %IVYWREL appears to also correlate well ( , ; see Figure 5A ) , which could have indicated an interesting relationship between change in living conditions ( temperature ) and the progression of complex lifeforms . However , the utility of %IVYWREL as an OGT “thermometer” is contingent upon equal representation ( and positive correlation ) of both high and low hydrophobic amino acid groups within IVYWREL ( i . e . , %ILVW and %ERY are also expected to positively correlate with each other; see Figure S5B–F in Text S1 ) ; given the lack of positive correlation between %ILVW and %ERY ( , ) , the trend in Figure 5A is merely a result of oil escape , which is also indicated by the opposing relationships in Figure 5B , C; the trend in Figure 5A appears to be caused primarily by oil escape . ( ii ) Oil escape is not a spurious relationship . With the advent of genome and proteome databases , a number of additional interrelationships associated with genome/proteome composition have been reported . Of particular interest to the focus of this paper ( oil escape ) are the reported relationships between %GC content and genome size [32] , organismal complexity and genome/proteome length [33] , and %GC and hydrophobicity [34]–[38] . It is important to exclude the possibility that oil escape may be a consequence of potentially more strongly evidenced relationships . For example , could %GC content , which is known to be a correlate with hydrophobicity [39] , [40] , be a stronger correlate with node number ? Or are other sets of correlations transitively inducing the effect of oil escape ? In order to tackle these questions , we calculated all possible relationships between species cDNA %GC ( also obtained from Ensembl genomes [41] ) , proteome oil content ( %FILV ) , species node number ( N ) , and proteome length L ( a proxy for organismal complexity in prokaryotes [33] , [42]–[44] ) , which can be described as a complete correlation network ( Figure 6A ) where labeled edges ( a–f ) indicate Spearman correlation coefficients between properties shown as nodes . It is evident from this graph that oil escape ( d ) displays a Spearman correlation that is highest in magnitude ( and consequently , also lowest in p-value ) . While this may hint at the independence of oil escape from other variables , only statistical tests [45] are able to to strike out the possibility that other variables in Figure 6A are incapable of causing oil escape . This requires the introduction of a statistical criterion that establishes transitivity ( and hence the possibility of causality [46] ) to a triplet of variables [45] , [47] . Given three variables , and , and their Spearman or Pearson correlation coefficients , , and , one can show that if , then 's sign must be commensurate to the two other relationships , i . e . , 's sign must equal that of [45] , [47] . However , when this criterion is not met , the trend between and ( indicated by ) can not be caused by the two other correlations . Therefore , while causality may be incapable of being proven using correlations , the lack of causality or transitivity can be shown in some situations . Given this criterion , we can conclude that none of the observed adjacent relationships in Figure 6A are able to cause oil escape d ( since , from Figure 6A , both and are less than 1 ) . ( iii ) GC content vs node number: a possibly independent trend . So far , we have shown that oil escape ( d ) is likely not caused by any other pair of correlations in Figure 6A . However , we are still left with the question of which of the remaining correlations are caused by others . For example , while a strong relationship is indicated between species %FILV and %GC ( c ) , the relationship is possibly not direct , which we can be shown by a practical implementation of “proof by contradiction” . First , we assume that a non-spurious negative relationship exists between oil content and %GC , i . e . , the expected biases [39] , [40] in the codon table ( or genetic structure ) [48] , [49] are the main causes of the %FILV-%GC relationship . With this assumption , we are able to quantify the expected %FILV-%GC relationship by generating , for each , randomly assembled genes that each code for a hundred amino acid peptide with oil content . From these pairs of gene %GC and corresponding protein %FILV , we created a probability distribution that is represented as a contour plot in Figure 6B ( the additional “limiting” contour line dividing the impossible , , from possible , , was added by analyzing the extremum possibilities from the codon table ) . Importantly , the relationship observed between proteome %FILV and cDNA %GC ( red circles in Figure 6B ) displays no correspondence with the expected probability distribution . Also , when sampling generated sequences taken between the observed range , the expected Spearman correlation between %FILV and %GC is ( ) , which indicates a relatively weak relationship ( as indicated by the broad contour distribution in Figure 6B ) that is significantly distinct from the observed ( ) . These incongruencies between expected and observed relationships indicate that the observed relationship between %FILV ( or hydrophobicity [35] ) and %GC is not directly caused by the codon table , and , therefore , the relationship between %GC and node number observed is independent of oil escape . We leave the exploration of the cause of trend ( e ) to future research . ( iv ) A note on population size . Effective population size is an important factor in population genetics [50] , particularly because deleterious mutations that result in offspring of lower fitness are more likely to be maintained in smaller effective population sizes [51] . It is therefore important to establish whether population size is capable of affecting changes in proteome and genome composition . Given the sparse statistics on effective population sizes , we chose to use proteome length ( ) as an inverse metric for population size , which is strongly indicated in previous reports [33] , [42]–[44] . It is important to recognize that can be utilized to relate population size to %FILV or %GC only if the relationships display transitivity [45] , [47] via , which is a property that still requires verification in future studies ( in the absence of transitivity , all relationships involving population size must be discounted ) . The first correlation that we can verify is the expected positive relationship [33] , [42] , [44] between organismal complexity ( node number ) and proteome length ( b ) , and , assuming transitivity , a negative relationship between complexity ( via ) and population size ( via ) [33] , [42]–[44] . We now direct our attention to the relationship between population size and %GC . Assuming transitivity , %GC content displays only a weak correlation with population size through ( Figure 6A a ) . While the actual trend ( Figure S12B in Text S1 ) does not qualitatively reproduce the non-monotonic relationship between between %GC and population size reported from simulations [51] , the trend ( especially when calculated exclusively for bacteria studies , where ( ) do match the %GC versus studies in bacteria [32] . The reason for the incongruence with simulation [51] may be due to the possible lack of transitivity of population size and %GC through . However , assuming transitivity , our data supports the notion that genomes with high AT content display a fitness advantage compared to genomes with low AT content; this implies a mutational bias towards high AT content , which is universally observed even in high-GC bacteria [52] . Finally , we will evidence that , despite the apparent correlations , %FILV is not likely to be strongly or directly dependent on and therefore ( assuming transitivity ) population size . While %FILV does display a negative correlation with , there is high probability that the result is spurious and caused by relationships b and d , since , making b and d transitive [45] , [47] , while and are both less than 1 . The potentially causal relationship is also indicated by the stronger correlation of with ( Figure 6A b ) and a high phylogenetic dependence of on the tree of life and therefore node number ( , where indicates the strongest dependence and trees with shuffled leaf attributes yield ) . Therefore , our studies are unable to discern a strong relationship between species population size and proteome %FILV . The lack of this relationship is possibly due to the lack of transitivity between the involved proxy variables . Regardless , the measure of fitness of a protein is not likely to be directly related to the thermodynamic stability of the protein , but to the capacity to maintain a particular near-ground-state ensemble of structures , which is not strongly dependent on only hydrophobicity , but dependent on both hydrophobicity and the buried polar interactions that impart greater folding specificity [53]–[55] , and defray , in part , the entropic cost of hydrophobic collapse [56] . To conclude this section , we find that oil escape ( %FILV vs node number ) is indeed a significant relationship that is not expected to be caused by other variables such as %GC content , genome length ( ) , population size ( through ) , and optimal growth temperatures . Besides displaying a strong monotonic trend ( ) , the scatter plot that describes oil escape ( Figure 2B ) is also modeled well ( with correlation coefficient for two instances described below ) by the following asymptotic closed form: ( 1 ) Here , , , , and are the node number , asymptote ( in ) , ordinate-intersect , and “rate constant” of the trend , respectively . Keeping all 's variable , and fitting to the data in Figure 2B , we obtained the asymptote ( with ; Section S4 in Text S1 ) , which , despite the coarse grained quality of node number ( and the tree of life ) , is remarkably close to the expected % of codons ( ) that code for oily residues ( from the universal codon table , ) . Also , constraining still results in a similarly high ( Section S4 in Text S1 ) . These findings strengthen the idea of asymptotic decline in oil content , where the LCA's proteome originated as more oily than expected from sequence entropy considerations ( if one expects equal distributions of codon usage ) , following which later organisms asymptotically approach more moderate values ( ) . It is important to note that , for our data , alternative genetic codon tables do not change the value of [48] , [49] , making this a universal maximum for sequence entropy . As a footnote , while one of the six alternative nuclear codon tables has a lower of , this “outlier table” is only utilized by a subset of the Candida genus of yeasts that is not utilized/represented in our data . Finally , the nearing of later proteomes' oil content towards the “maximal entropy” value ( ) may have interesting implications regarding changes in the rates of molecular evolution . The list of species used in our proteome/protein-level studies are diverse , sourced from all domains of life and displays a large range of cellular makeup , body types , biochemistries , evolutionary niches , etc . Given this diversity , we conjecture that oil escape is not driven by an adaptive pressure , as the niche diversity precludes such a broad spectrum pressure . Additionally , given that previous trends such as dependence of proteome composition on oil content do not explain the oil escape trend , we provide a hypothesis based not on extant adaptive pressures but on the features of the last common ancestor ( LCA ) . It is important to note that oil escape must be a trace ( fossil ) drift from the LCA which would have predominantly begun after the production of the first fit proteome with acceptable but relatively high oil content ( this is especially the case as all observed oil contents today are expected to be within the range of “acceptable” oil content , which is relatively quite broad , ; Figure S4 in Text S1 ) . The subsequent reduction in oil content should be considered to be more of a passive drift driven by the impetus to maximize sequence information entropy through evolutionary time , i . e . , adaptive pressures and neutral drift would not be driving forces in oil escape , although coupling of the passive drift with either phenomenon is possible . Still , oil escape is not likely to be coupled to neutral drift , which can primarily account for substitutions of nearly-neutral fitness . Alternatively , oil escape may easily be coupled to mutations occurring during speciation events , partly due to the putative increase in substitution rates [13]–[15] , [57]–[64] , fixation probabilities [65] , and hitchhiking [66]–[68] of composition-changing mutations with adaptive mutations occurring during speciation events . It is important to reiterate that while oil escape itself might be coupled to adaptive processes , the impetus to shift the oil content of a sequence is driven by the need to maximize information entropy and not adaptive forces . This passive drift is slow in effect , as change in oil content ( and most other compositions , like charge content ) is impeded by the biochemical requirements of a protein ( Sections S1 . 2 and S1 . 4 in Text S1 ) and the proteome's massive size ( Section S1 . 1 in Text S1 ) , and therefore is not expected to be “washed away” by a random mutational walk over billions of years ( Section S1 . 1 in Text S1 ) . This ensures the persistence of oil escape even billions of years after the initiation of the trend , i . e . , one may consider oil escape as a trace fossil . These considerations indicate that the oil escape trend summarized in Figure 2B appears to be a rare , universal “reaction coordinate” for evolution , adhered to ( at least roughly ) by all species tested ranging from bacteria to animals , which may serve as a unique composition-level or “global” molecular clock that , if calibrated , may augment the utility of sequence-based “local” molecular clocks [69] . The calibration itself would require more species-level paleobiological first occurrence data for species whose genomes are sequenced . Figure 2C , while rough and statistically sub-optimal , provides a proof of concept for such a calibration project . It is also important to note that features such as variable substitution rates [70] , [71] may pose inherent problems to the utility of oil escape as a molecular clock . However , the finding of a genome “core” that describes , even for bacteria , genes with conservative substitution rates , lower involvement in HGTs , and higher reliability in reconstructing robust trees [72]–[80] indicates that the selection of appropriate protein collections for utility in the global molecular clock may ameliorate a number of inherent problems associated with a global molecular clock . Finally , oil escape is a function of the number of substitutions associated with node number and therefore speciation , making it distinct from neutral molecular evolution . Whether mutations associated with speciation events can be used as a metric for evolutionary age remains to be seen and warrants further research . The possibility of recombination and horizontal gene transfer ( HGT ) [81]–[84] between species indicates that distinct genes may have distinct evolutionary histories , resulting in the tree of life being rendered , in one extreme formulation [72] , [73] , as a network of life [74] . Such a free exchange of genes between species would mean a much quicker equilibration of any compositional inequities between species and hence a drastic degradation of the oil escape signal . However , the following points indicate that HGTs are not significantly detrimental to the oil escape signal: ( a ) complex organisms , while not completely immune to HGT , are generally not affected by this mode of evolution [85] , ( b ) a large percent ( ) of the genes/proteins that are crucial to the core functioning of an organism are not replaced or affected by HGT [72]–[74] , while “accessory” or niche-associated genes partake most in HGTs [74] . The observation of oil escape in both bacterial and complex organism databases ( hosting fungi , plants , and metazoa ) and orthologous groups spanning all domains of life ( mimicking core genes/proteins ) indicates that , while horizontal gene transfer may contribute to the noise in the oil escape signal , the complete degradation of the signal is not evident . While tree reconstructions based on one or few genes may be prone to errors due to recombination and HGT events , utilization of a high volume of independent reference points ( such as a whole proteins ) results in trees that are robust to recombination and HGT events [78]–[80] . This indicates that the NCBI tree of life , which is expertly compiled from a highly diverse set of phylogenetic and taxonomic data , has a very low chance of being drastically affected by HGT and recombination . Intrinsically disordered proteins ( IDPs ) are low-hydrophobicity proteins that remain unfolded for most of their existence [86] . Recently , a strong connection has been recognized between the rise of complex organisms and the increase in the incidence of IDPs in genomes [87] . The driving forces behind both the increase in organismal complexity and the increase in the incidence of IDPs are still unknown , although they are believed to not be driven by adaptive evolution [88] . It is interesting then that oil escape , when considered as a shift in Gaussian distributions of oil content within a proteome , is able to predict the gradual increase in IDP incidence by the gradual decrease in oil content . Particularly , the shift of the distribution of oil content from prokaryote to eukaryote ( as observed in Figure S11 in Text S1 ) appears to push the left tail/fringe of the distribution beyond a hydrophilicity threshold that allows for IDPs to exist [86] . Also , given that bacterial proteomes alone also exhibit statistically-significant oil escape ( Figure S1 in Text S1 ) , we can conclude that oil escape is not caused by the increase in IDPs ( and hence organismic complexity ) , while the increase in the incidence of IDPs may be driven by oil escape . To our knowledge , oil escape from a relatively oily ancestor is the first non-adaptive explanation of the emergence of IDPs , which fits well with the assertion that no adaptive processes can account for the increase in organismal complexity [88] . Given the enormity of sequence space , the chances of reverting to a distant common ancestor are abysmal , a notion that helps paint a one-directional picture of molecular and hence organismal evolution ( see , e . g . , work by Bridgham et al . [89] ) . To add to this unidirectionality , Jordan et al . have provided an interesting observation [5] indicating that there might even be a directionality or “irreversibility” to the types of mutations incurred within a protein ( e . g . , they reported that residue substitutions occur more often than substitutions ) , which contradicts the general notion of reversibility or symmetry in point mutations ( where and substitutions are equally probable and the substitution matrix is symmetric ) . While this paper has been challenged [7]–[10] primarily due to the reconstruction methods utilized within the paper , we find from a much simpler methodology that such a directionality may exist , albeit in not exactly the same form as reported previously ( see Figure S7 in Text S1 ) . Finally , to underline the dangers involved in extrapolating from present day sequences to a putative common ancestor by statistical methods , it is important to recognize that the LCA's proteome composition may have not been the most “likely” sequence from a sequence entropy standpoint; as indicated in Figure 2B , the ancestral sequence may have high oil content , indicating a less-likely low sequence entropy . This raises an interesting and unforeseen pitfall in the backwards extrapolation of the last common ancestor's sequence using methods involving maximum likelihood methods: while backwards-extrapolation may hold ground when used in less diverged groups , when extending further back in evolutionary time , we might be presently exploring a biophysically meaningful but historically meaningless section of sequence space . Such a pitfall may be circumvented by applying a “field” ( or mutational bias ) to a prediction method . Looking forward , the notion of oil escape has varied implications . Most importantly , oil escape–a universal trend relating oil content to tree node number–evidences the emergence of all known organisms from an oily last common ancestor , and provides a biophysical explanation to the emergence of intrinsically disordered proteins [86] . Additionally , the existence of at least two seemingly independent constraints on genome/proteome composition ( evidenced in Figure 6 ) indicates a robustness of evolving genomes placed under multiple universal constraints . Finally , the universal fossil trend in proteomes today , aside from serving as the first potential global molecular clock , provides glimpses into the earliest proteome , and may provide validity to the possible irreversibility of evolution [5] .
The predicted proteome and cDNA sequences used were both obtained from Ensemble genome databases [41] which , at the time of procurement , hosted 272 diverse proteomes belonging to 152 distinct species sourced from all domains of life ( listed in Section S5 in Text S1 ) . Both proteome and cDNA sequences were obtained from all predicted genes , known or otherwise [41] . Only amino acids that belong to the natural 20 amino acid repertoire were used in our proteome calculations . Similarly , only nucleotides A , T , G and C were used in our cDNA calculations . Expanding on the general idea that species that are less diverged from the last common ancestor ( e . g . , bacteria ) possess older proteins/proteomes than more diverged species ( e . g . , fruit fly ) [13]–[15] , [20] , we define a species/proteome's modernity or newness as the minimum number of nodes– node number–separating the species or organism from the LCA ( root ) in the tree of life ( ToL ) . The pruned tree of life ( Section S6 . 2 in Text S1 ) , containing all studied species ( Section S5 in Text S1 ) , was obtained using NCBI Taxonomy's Common Tree algorithm [28] , [29] ( accessed from the interactive tree of life [30] with the selected option of leaving “internal nodes expanded” ) . This tree classification system is based on expert but heuristic gathering of phylogenetic , taxonomic and other biological information ( excerpt from the NCBI taxonomy website: “…[The] database does not follow a single taxonomic treatise but rather attempts to incorporate phylogenetic and taxonomic knowledge from a variety of sources , including the published literature , web-based databases , and the advice of sequence submitters and outside taxonomy experts”; http://www . ncbi . nlm . nih . gov/Taxonomy/taxonomyhome . html/index . cgi ? chapter=howcite ) , and is therefore not biased towards specific classification methods . For example , while distinct issues are associated with ignoring horizontal gene transfer [74] and recombination [81] in phylogenetic tree reconstruction , a robust and faithful tracing of lineages is possible with the utilization of larger sequence datasets sourced from whole proteomes [78]–[80] . The paleobiological FO records ( listed in parenthesis in Section S5 in Text S1 ) are obtained from the online paleobiology database ( pbdb . org ) . Due to the relative sparseness of the online records , we associated a species in our collection to its genus FO record , which makes this metric only a coarse grained indicator of evolutionary age ( e . g . , the mosquito species Anopheles gambiae , was linked to the genus Anopheles , which has the FO date of 23 . 030 Ma or million years before current date ) . This section summarizes the phylogenetic method introduced by Pagel [25] and expanded by Freckleton et al . [26] . The ToL may be described as a variance-covariance matrix , where elements denote the evolutionary path lengths shared between species and ( so , indicates species 's node number in the original tree and a constant in the “neutral drift” tree ) . Also , we may set a ( column ) vector which contains the character traits of the species in the tree ( in our case , is equal to the oil content of species ) . The extent to which a given character state depends on its position in the phylogeny may be assessed using an off diagonal multiplier , where the variance-covariance matrix is transformed to by multiplying all off diagonal elements ( ) by where normally . The estimated will be that which maximizes the likelihood function ( obtained from Equation 4 in Freckleton et al . [26] ) for a given ( tree ) and ( character set; for examples of such -searches , see Figure S14 in Text S1 ) . A maximum likelihood estimate of would indicate that the character state is evolving according to the Brownian model of evolution on the phylogeny , while ( where only the diagonal elements in remain non-zero ) indicates a character trait that is independent of the given phylogeny and shared histories . For convenience , Equation 4 in Freckleton et al . , which is a joint-normal probability density , is repeated here: ( 2 ) where is the character state at operational time 0 , is the variance of the Brownian noise introduced in the character state per time unit , and is an “design matrix” of ones which describes a Brownian mode of evolution ( setting or , which describes a simplified “biased” Brownian model of evolution , does not significantly change our estimated ) . The maximum likelihood estimate for isand the unbiased ( restricted maximum likelihood ) estimate for variance isBy maximizing Equation 2 , our maximum likelihood for a given tree may be estimated . For both the original and “nearly neutral” trees we obtained of ( Figure S14 in Text S1 ) , indicating strong phylogenetic dependence . Note that the estimated are likely not accurate , since it's value is directly controlled by the “design matrix” , whose model ( ) is over-simplified to an unbiased Brownian diffusion about the ancestral state . We use a previously described generalized mean squares model of evolution [27] , which describes the character state of species by , where is the character state of the ancestor ( LCA ) at operational time 0 , is the estimated rate of change of the character state per operational time unit ( e . g . , node number ) , is the random error , and is an matrix whose first column elements all equal 1 and the second column elements depicts the species operational time/node number ( i . e . , and 's operational time or node number ) [27] . From the generalized least squares method [27] , we can estimate both and by solving for ( 3 ) where is the variance-covariance matrix of the given tree ( as above; i . e . , ) . Also , the error for species may then be obtained from . While the validity of the previous -method of obtaining was predicated by the choice of the design matrix and the statistical inaccuracies of the tree ( caused by it's coarse-grained nature ) , the current reconstruction method does away with the Brownian diffusion model , and so is only dependent on statistical inaccuracies of the tree . However , given that , it is safer to use such estimates as qualitative checks for hypothesis/data validity ( e . g . , in Figure 4 ) rather than an absolute prediction of the ancestral state . | Although of importance to both evolution and protein design , the manner in which the first proteome came to be , and the actual features of the earliest ancestral proteomes are both unknown . Through the analysis of diverse proteomes , we provide glimpses into the composition of the last common ancestor ( LUCA ) of all lifeforms , which indicate that the earliest/last common ancestor had a proteome that was highly hydrophobic/oily . Notably , the evidence presented ( a ) indicates that proteomes of all species ranging from bacteria to mammals appear to adhere to the same universal constraint ( “oil escape” ) set into motion by the last common ancestor more than 3 . 5 billion years ago , ( b ) indicates the presence of a previously untapped global ( composition-level ) molecular clock , and ( c ) strengthens the non-equilibrium/directional view of amino acid substitutions that challenges central dogmas regarding reversibility in molecular evolution . | [
"Abstract",
"Introduction",
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] | [
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] | 2012 | A Universal Trend among Proteomes Indicates an Oily Last Common Ancestor |
The AraC Negative Regulators ( ANR ) comprise a large family of virulence regulators distributed among diverse clinically important Gram-negative pathogens , including Vibrio spp . , Salmonella spp . , Shigella spp . , Yersinia spp . , Citrobacter spp . , and pathogenic E . coli strains . We have previously reported broad effects of the ANR members on regulators of the AraC/XylS family . Here , we interrogate possible broader effects of the ANR members on the bacterial transcriptome . Our studies focused on Aar ( AggR-activated regulator ) , an ANR family archetype in enteroaggregative E . coli ( EAEC ) isolate 042 . Transcriptome analysis of EAEC strain 042 , 042aar and 042aar ( pAar ) identified more than 200 genes that were differentially expressed ( +/- 1 . 5 fold , p<0 . 05 ) . Most of those genes are located on the bacterial chromosome ( 195 genes , 92 . 85% ) , and are associated with regulation , transport , metabolism , and pathogenesis , based on the predicted annotation; a considerable number of Aar-regulated genes encoded for hypothetical proteins ( 46 genes , 21 . 9% ) and regulatory proteins ( 25 , 11 . 9% ) . Notably , the transcriptional expression of three histone-like regulators , H-NS ( orf1292 ) , H-NS homolog ( orf2834 ) and StpA , was down-regulated in the absence of aar and may explain some of the effects of Aar on gene expression . By employing a bacterial two-hybrid system , LacZ reporter assays , pull-down and electrophoretic mobility shift assay ( EMSA ) analysis , we demonstrated that Aar binds directly to H-NS and modulates H-NS-induced gene silencing . Importantly , Aar was highly expressed in the mouse intestinal tract and was found to be necessary for maximal H-NS expression . In conclusion , this work further extends our knowledge of genes under the control of Aar and its biological relevance in vivo .
Pathogenic bacteria utilize elaborate regulatory mechanisms to effect appropriate expression of virulence-associated traits . The availability of genomic data sets and new high-throughput methods have illuminated not only a large number of new virulence loci , but also exposed previously unappreciated regulatory systems . We have recently described the ANR ( AraC Negative Regulators ) family , a large family of bacterial gene regulators expressed by diverse clinically important Gram-negative pathogens . Organisms implicated include Vibrio spp . , Salmonella spp . , Shigella spp . , Yersinia spp . , Citrobacter spp . , pathogenic E . coli strains including enterotoxigenic ( ETEC ) and enteroaggregative E . coli ( EAEC ) , and members of the Pasteurellaceae [1 , 2] . Genes coding for ANR are variously present either on the chromosome or on plasmids [1] . EAEC is a diarrheagenic pathotype linked to traveler’s diarrhea , foodborne outbreaks and sporadic diarrhea in industrialized and developing countries [3–8] . The ability of this pathogen to colonize the mucosa is attributed to the presence of virulence factors regulated by the transcriptional activator AggR [9–14] . Aar ( AggR activated regulator ) was the first characterized ANR protein , identified in EAEC strain 042 by its ability to repress AggR activity [1 , 2 , 13] . Aar is a 63-amino-acid protein with a molecular mass of 7 . 23 kDa . The protein comprises three alpha helical domains required for oligomerization [2] . Aar does not have apparent DNA binding capability , but instead binds to the AggR protein directly , thus inhibiting the latter’s ability to bind to bacterial promoter regions [2] . Disruption of aar leads to increased expression of AggR and its regulon , which comprises at least 44 genes with putative virulence functions [1 , 13] . ANR homologs of Vibrio cholerae , Citrobacter rodentium , Salmonella enterica and ETEC rescued aar mutants in EAEC strain 042 [1 , 2] . H-NS ( Histone-like Nucleoid Structuring ) is a nucleoid-associated protein widely distributed among Gram-negative bacteria [15 , 16] . In E . coli , up to 5 to 10% of the genome is subject to H-NS-dependent regulation [17 , 18] . H-NS binds AT-rich regions thought to be acquired by horizontal gene transfer [19–22] , and such regions are characteristic of many virulence-associated loci . Oligomerization of H-NS is critical for its regulatory activity [23–26] . The H-NS protein is not only capable of interacting directly with DNA but also with other regulatory proteins and itself [23 , 25 , 27] . One of the best-characterized H-NS binding partners is its paralog , StpA [28 , 29] . The main purpose of this study is to characterize the global regulatory effects of the ANR family member Aar . We selected EAEC as a model organism in which to study the biological relevance of the ANR family , and performed a global transcriptome analysis of EAEC strain 042 , its mutant 042aar and complemented in trans strain 042aar ( pAar ) ; 210 genes were differentially expressed . Further functional studies suggested that Aar not only interacts with members of the AraC family [2] , but also with members of the histone-like family ( orf1292 , orf2834 and stpA ) . We assess here the effect of Aar-H-NS interaction in H-NS-mediated regulation .
We evaluated the global effect of Aar in EAEC 042 by using RNA-seq technology . RNA was extracted from wild type ( wt ) 042 , its isogenic 042aar mutant and complemented in trans 042aar ( pAar ) strain grown in DMEM-high glucose , which activates expression of the master transcriptional regulator AggR and AggR-dependent Aar [13] . The samples were converted into cDNA libraries using the Ovation Prokaryotic RNA-Seq System ( NuGen ) and sequenced on the Illumina HiSeq 2000 as indicated in material and methods . We identified 210 genes that were differentially expressed ( +/- 1 . 5 fold , p<0 . 05 ) ( Fig 1 ) . Aar-regulated genes were mainly found on the chromosome ( 195 genes , 92 . 85% ) , and were grouped in eight major categories: hypothetical proteins ( 21 . 9% ) , proteins involved in metabolic functions ( 17 . 14% ) , transporter proteins ( 17 . 61% ) , regulatory proteins ( 11 . 9% ) , putative virulence factors ( 12 . 4% ) , membrane proteins ( 6 . 6% ) , phage proteins ( 6 . 2% ) , and proteins involved in diverse other functions ( 6 . 19% ) ( Fig 1E and S1 to S8 Figs ) . In the pAA plasmid , the Aar-regulated genes were found principally in AT-rich regions ( S9 Fig ) . Putative gene assignments and homologies of the differentially transcribed genes evaluated in this study are listed in Table 1 . Forty-six differentially-regulated genes were assigned as hypothetical proteins ( S1 Fig ) . Seven out of forty-six hypothetical proteins were encoded on the pAA plasmid , including the AggR-regulated pAA047 gene . In agreement with previous findings , AggR-regulated genes ( 23 out of 44 ) were up-regulated in the absence of aar ( depicted in yellow , S1 , S2 , S4 , S5 and S8 Figs ) [13] . Unexpectedly , we observed that the majority of genes regulated by Aar ( 88% ) were not dependent on transcriptional activator AggR ( Fig 1F ) . We validated our transcriptome database by qRT-PCR . Twenty-three genes were selected for this analysis based on the p-value and consistency between the groups in the database , as well as potential biological relevance . These genes included hypothetical proteins orf1228 , orf2823 , orf3192 , orf3205 , orf3334 and orf4746; transporter proteins orf0690 and orf4080; putative virulence factors orf3928 , orf3931 , orf3932 , orf4082 ( lpfC ) ; transcriptional factors aggR , orf3045 , orf3191 , gadEWX; AggR-regulated dispersin ( aap ) ; outer membrane proteins orf0904 ( ompX ) , orf1042 ( ompA ) , orf1904 ( osmE ) and hypothetical orf2223 ( Fig 2 ) . Strains were grown in DMEM-high glucose for 5 h ( late log phase ) as previously standardized [1] , and total RNA was isolated and prepared for qRT-PCR . Our data showed that 20 out of 23 analyzed genes exhibited lower expression in the 042aar mutant ( ~2 to 20 fold ) ( Fig 2 ) . These findings were consistent with the RNA-seq data , which revealed that the majority of genes ( 78 genes out of 131 ) were down regulated in 042aar compared to wild type 042 ( Fig 1A and 1B ) . Only 3 out of 23 genes ( orf3334 , aggR , and AggR-dependent aap ) showed greater levels of expression in the 042aar strain ( Fig 2L , 2S and 2T ) . Complementation in trans with the Aar gene resulted in wild type levels in most of the cases ( Fig 2 ) . We reported that Aar binds with high affinity to the central linker domain of AraC-like members and abolishes their DNA binding activity [2] . In agreement with this report , two members of the AraC family , AggR and GadX , were detected in the transcriptome database ( S5 Fig ) . As expected , we observed that 042aar shows increased expression of aggR in late logarithmic growth phase ( ~2 fold ) ( Fig 2S ) . A similar effect was observed for AggR-regulated genes including aap ( Fig 2T ) ; these included pAA plasmid genes aafA , aafB , aafD , aatA , pAA003 , pAA005 , pAA005A , pAA047 , and chromosomally-encoded genes orf3182 , orf3184 , orf4562 , orf4563 , orf4564 , orf4565 , orf4568 , orf4569 , orf4570 , orf4572 , orf4574 , orf4574A , orf4576 , orf4581 and orf4582 ( depicted in yellow , S1 , S2 , S4 , S5 and S8 Figs ) . In contrast to aggR , transcription of gadX in 042 was reduced after mutation of aar ( Fig 2U ) . We observed not only gadX but also its downstream gene hdeB to be regulated by Aar [2] . To confirm the regulatory effect of Aar on the acid resistance operon in 042 , we evaluated the expression of the transcriptional gadE and gadW genes by qRT-PCR . We observed that the three transcriptional regulators GadEWX of the operon were down-regulated in 042aar ( Fig 2U ) suggesting that the acid resistance operon may be affected by Aar . Transcriptional levels of 25 transcriptional factors were affected by Aar in 042 ( S5 Fig ) . However , only six out of the 25 transcriptional factors were complemented in trans by Aar , including AggR , H-NS ( orf1292 ) , putative H-NS homolog orf2834 and orf3045 , orf3204 and orf4555 ( S5 Fig ) . Given the relevance of the global regulator H-NS in gene regulation , we sought to dissect further features of the Aar regulatory system on H-NS homologs in 042 by qRT-PCR ( Fig 3 ) . Levels of transcription of orf1292 and orf2834 were compared between 042aar and the wild type 042 strain . We observed 2–5 fold higher hns mRNA levels in 042 compared to the 042aar strain in the log phase ( Fig 3A and 3B ) . The presence of multiple H-NS like members in the same bacterium has been reported [15 , 30 , 31] . In E . coli K12 , StpA partially restores hns inactivation . In addition to H-NS and StpA , Shigella flexneri 2a encodes Sfh , a third member of H-NS like family [30] . EAEC 042 has three members of the H-NS-like family; H-NS ( orf1292 ) , H-NS ( h ) ( orf2834 ) and StpA . We applied a variety of prediction algorithms to reveal the presence of conserved secondary structure in the H-NS members . The PROMALS3D algorithm strongly predicted that the H-NS members have high similarity along the entire structure ( 70 to 80% similarity ) ( Fig 4R ) . The structural similarities between members of the H-NS family suggests that Aar may be regulating not only H-NS and H-NS ( h ) but also StpA in EAEC 042 in a four-way interaction circuit . To test this hypothesis , we generated a set of single and double aar-hns mutants in 042 ( 042aar , 042orf1292 , 042orf2834 , 042stpA , 042aarorf1292 , 042aarorf2834 and 042orf1292orf2834 ) ( Fig 4 ) . All the strains generated with the exception of 042orf1292orf2834 showed no growth defect in Aar-inducing conditions ( Fig 4B ) . We observed that not only orf1292 and orf2834 but also stpA were transcriptionally affected by Aar ( Fig 4C , 4G and 4K ) . The most dramatic effect of Aar mutation was observed in the logarithmic phase of growth ( 4h ) , where 042aar strain showed reduced transcriptional levels for orf1292 ( 3 . 4 fold ) , orf2834 ( 2 . 47 fold ) and stpA ( 2 . 35 fold ) ( Fig 4C , 4G and 4K ) . Transcriptomic analysis showed that deletion of either of the H-NS-like members alters the balance of the system . For example , deletion of orf1292 drastically up-regulates the transcriptional expression of orf2834 ( 268-fold compared to WT ) ( Fig 4H ) and stpA ( 96-fold compared to WT ) ( Fig 4L ) in the early phase of the logarithmic growth ( 2–6 h ) . Similar findings were observed in 042aarorf1292 ( orf2834 and stpA were increased 42 fold and 18 fold respectively ) . The strong increase in the expression of StpA and H-NS ( h ) , may compensate for the detrimental effects of hns reduction in the absence of aar . We observed that deletion of orf2834 reduced the transcriptional expression of orf1292 and stpA as previously reported for its homolog shf in Shigella flexnery ( Fig 4E and 4M ) [30] . Our finding supports the hypothesis that some of the effects of Aar on gene expression are likely through the modulation of the H-NS family in a four-way regulatory circuit ( Aar , Orf1292 ( H-NS ) , Orf2834 ( H-NS ( h ) ) and StpA ) ( Fig 4A ) . Therefore , all three H-NS homologs may have the ability to interact and modulate Aar-regulated genes . To test this hypothesize , we evaluate the transcriptional expression of three known Aar regulated genes ( orf2223 , orf3192 and orf3928 ) in our collection of 042 derivatives ( Fig 5 ) . We observed that regulation of each gene was dependent on the bacterial growth phase , and on the concentration of Aar and H-NS-like members . Each analyzed gene showed different regulatory properties . However , we observed that Aar activity was dependent on H-NS members ( Fig 5 ) . Given that Aar does not have DNA binding domains , acts via direct binding to AggR and has structural similarity to the dimerization domain of H-NS ( Fig 6A ) , we hypothesized that Aar might interact directly with H-NS family proteins . To test this hypothesis , we exploited the BACTH bacterial two-hybrid system , which has been used to detect protein-protein interaction of regulatory proteins in bacteria by us and other groups [2 , 32] . To perform this experiment , aar and hns genes ( orf1292 and orf2834 ) were fused to T25 and T18 fragments of the catalytic domain of Bordetella pertussis adenylate cyclase , expressed from plasmids pKNT25 and pUT18 respectively . The resulting plasmids were co-transformed into the reporter strain E . coli BTH101 . We observed protein-protein interaction between Aar and H-NS manifested by the appearance of an intense to moderate green color on agar plates ( Fig 6B ) . These qualitative observations were supported by quantification of β-galactosidase activity ( Fig 6C ) . To verify the specificity of our system , twelve transcriptional factors ( orf0808 , orf1127 , orf1292 , orf2020 , orf2058 , orf2834 , orf2881 , orf2888 , orf3191 , orf3204 , orf4499 , and orf4555 ) were evaluated in the BACTH system ( Fig 6D ) . The transcriptional factors were selected based on the transcriptome sequencing data ( orf1127 , orf1292 , orf2058 , orf2834 , orf3191 , orf3204 , orf4499 , and orf4555 ) . Four unrelated transcriptional factors ( orf0808 , orf2020 , orf2881 , and orf2888 ) were also included in the study . Only 4 out of 12 transcriptional regulators revealed interaction , including orf1292 and orf2834 . As expected , the greatest levels of β-galactosidase activity were observed for hns-related genes orf1292 and orf2834 . Interestingly , we observed that orf1127 ( BssS , biofilm regulator ) and orf2058 ( Transcriptional activator FlhD ) were slightly able to interact with Aar in this assay ( Fig 6D ) . The molecular interaction between H-NS and Aar was also modeled and assembled by TM-Score ( TM-score 0 . 2793 and 0 . 3555 ) [33] , RasMol [34] and the UCSF Chimera package [35] . The second α-helix of Aar was predicted to interact with the oligomerization domain of H-NS ( S10 Fig ) . If interaction with H-NS is an important regulatory feature of the ANR family , then ANR-H-NS protein-protein interaction should also be demonstrated with other members of the ANR family . ANRVibrio and Cnr-2 ( of ETEC ) have been shown to rescue Aar activity in the 042aar strain [2] . Using the BACTH system , we observed protein-protein interaction between Cnr-2 and both orf1292 and orf2834 H-NS regulators ( Fig 6C ) . ANRVibrio showed binding only to orf1292 but not to orf2834 ( Fig 6C ) . To confirm interaction of Aar and H-NS proteins in vivo , we performed a pull-down assay ( Fig 6E ) . E . coli K12 transformed with pAar ( H6 ) , pH-NS ( HA ) or pAar ( H6 ) H-NS ( HA ) plasmids , expressing His-tagged Aar , HA-tagged H-NS or both , respectively , were cultivated overnight at 37°C . As a control of the pull-down assay , E . coli K12 was also transformed with pEspP ( H6 ) [36] and pH-NS ( HA ) ( S11 Fig ) . The samples were sonicated and the supernatants incubated with cobalt resin . The samples were separated in SDS-PAGE gels and analyzed by western blot with specific antibodies against the protein-tags H6 and HA . Our data show that H-NS ( HA ) is pulled-down only in presence of Aar ( H6 ) but not with the EspP ( H6 ) protein ( Fig 6E ) . Protein-protein interaction of Aar-H-NS and dependence of hns expression on aar was unexpected , and provides a potential mechanism for global effects of Aar on gene expression in EAEC . We sought to confirm this relationship using a LacZ reporter system . The regulatory region of H-NS ( orf1292 , region 1 , 377 , 848–1 , 377 , 154 ) was cloned into plasmid pEF-ENTR-lacZ ( Fig 7A ) . E . coli K-12 BW25113 and an E . coli K-12 BW25113 hns mutant were co-transformed with pPH-NSLacZ and pAar plasmids . Higher levels of β-galactosidase activity were detected in the parental E . coli K-12 BW25113 strain in the presence of Aar ( ~3 fold ) and were dependent on the presence of H-NS ( Fig 7B and 7C ) . We hypothesized that Aar-H-NS interaction would modify H-NS binding to H-NS regulated promoters , consequently altering H-NS-mediated negative regulation . To test this hypothesis , we performed EMSA experiments using 5’ biotinylated H-NS orf1292 and orf2834 probes ( GenBank , FN554766 , 1 , 377 , 242–1 , 377 , 539 and 3 , 024 , 825–3 , 024 , 981 regions ) ( Fig 7A ) . Probes were incubated with either MBP-Aar , MBP-H-NS or both , in the presence of factor Xa as described in Materials and Methods . The purified native form of Aar is insoluble and requires the use of MBP for its expression [37] . We observed that Aar and H-NS interacted only when MBP was removed with factor Xa . Only H-NS and H-NS/Aar , but not Aar bound to these probes ( Fig 7D ) . Intriguingly , we found that when both H-NS and Aar were incubated together the probes super-shifted , suggesting binding of Aar to the H-NS-probe complex ( Fig 7D ) . Given that the regulation of gene expression by H-NS is modulated by variations in the number and organization of binding sites [18 , 38] , we hypothesized that Aar may affect binding of H-NS to DNA more avidly in some promoters than others . To test this hypothesis , we performed EMSA experiments using promoters of three different HNS-regulated genes , two of which were previously evaluated in this study ( orf2223 and orf3928 ) ( Fig 5 ) and proV . The latter is a well-characterized H-NS-regulated gene ( H-NS binding regions of the proV DNA are depicted in red in Fig 8A [39] ) . We demonstrated that H-NS binds to orf2223 and orf3928 probes by EMSA as shown in Fig 8B . As expected , Aar does not bind to these probes but alters H-NS DNA binding at different levels as judged by the presence of free probes and probe-shift patterns in EMSAs ( Fig 8C and 8D ) . Aar inhibited H-NS binding to orf2223 and orf3928 DNA probes ( Fig 8C ) , but only modified the H-NS binding pattern to proV probe , shown at various H-NS concentrations ( 1 . 25–5 μM ) ( Fig 8D ) . To further disrupt the H-NS-proV DNA interaction with Aar , we performed EMSA using increasing concentration of Aar ( 0 . 8–12 . 8 μM ) and a constant concentration of H-NS ( 2 . 5 μM ) . In these conditions , we observed that the H-NS-proV probe complex bands become progressively diminished with increasing concentrations of the Aar protein , however we did not observe free-probe species ( Fig 8E ) . We also performed an Aar DNA protection assay . Target DNA was incubated for 30 min with increasing concentrations of H-NS in the presence or absence of Aar . The samples were treated with DNaseI for 30 min and the enzyme was inactivated at 75°C for 10 min , followed by EMSA analysis as indicated in materials and methods . We showed that incubation of the DNA-HNS complex with Aar increased susceptibility of DNA to degradation by DNaseI ( Fig 8F ) . Together , these findings suggest a differential effect of Aar on H-NS binding to HNS-regulated promoters . We sought to dissect further features of the Aar-H-NS regulatory system in the streptomycin-treated mouse model , which has been used previously to study gene regulation in vivo for this pathogen [40] . Groups of 10 mice were inoculated with 0 . 2 X 1010 CFU of 042 , 042aar , and 042aar ( pAar ) . As a control , groups of 5 mice were inoculated with either 042aggR or 042hns . No differences in bacterial shedding were observed between the groups at 24 h post-inoculation . Mice were euthanized 24 h post-bacterial inoculation . aar and hns transcription was ascertained by qRT-PCR in fecal samples from different intestinal compartments . Notably , aar was highly expressed in the intestinal lumen ( 3–50 fold ) in the early stages of infection . aar expression was higher in cecum than in colon ( Fig 9A ) . We observed that the expression of aar was dependent on AggR; aar expression in the 042aggR mutant was reduced in vivo ( 2–4 fold ) ( Fig 9A , triangles ) . H-NS expression was substantially reduced in the colon in 042aar ( ~10 fold ) compared to the parent strain but not in cecum ( Fig 9B ) . Taken together , we provide evidence that Aar is expressed in vivo and is required for maximal expression of H-NS .
The bacterial chromosome is compacted to fit in the bacterium by histone-like proteins [41 , 42] . Spatial and temporal expression of virulence factors depends on the controlled removal of global histone-like regulators from the chromosome . H-NS , a member of this family , forms high molecular weight complexes and silences gene expression through promoter occlusion and shaping of DNA [23–26] . H-NS members are well recognized as negative regulators of 5 to 10% of the bacterial genome [17 , 18] . hns mutation in uropathogenic E . coli resulted in increased expression of H-NS-regulated fimbriae ( SfaA and PrfA ) , iron uptake systems , and genes involved in stress adaptation [43] . H-NS has been shown to silence expression of genes with low GC content; we have reported that genes under AggR control have this feature [31 , 44] . In this study , we demonstrated that aar mutation in EAEC affects the expression of more than 200 genes . Some of the effects of Aar on gene expression are likely through the expression of histone-like family ( Fig 4 ) , including LpfC fimbriae , LPS-related enzymes orf3928 , orf3931 and orf3932 , genes involved in stress adaptation ( gadXW transcriptional factors ) , and porins ( Fig 2 ) . This observation in conjunction with two-hybrid system and pull-down assays ( Fig 6 ) , lacZ fusion assays ( Fig 7 ) , and EMSA studies ( Figs 7 and 8 ) , confirmed that Aar exerts global regulatory activity at least in part via H-NS , and that this effect may be due to direct binding of the three proteins present in EAEC 042 strain . We have shown that Aar binds to H-NS and alters its binding to H-NS regulated promoters in different ways , perhaps depending on the presence of low or high affinity H-NS binding sites . Similar H-NS-DNA shift patterns have been seen with other small regulatory proteins such as the family of naturally occurring truncated H-NS derivatives lacking the DNA-binding domain , termed H-NST family in pathogenic E . coli strains [45] , and the 5 . 5 protein of bacteriophage T7 [46] . Detailed analysis of these proteins shows that , although they do not avoid the binding of H-NS to DNA upon interaction with H-NS , they affect the high order oligomerization of H-NS and exhibit a potent anti-H-NS function with global effects [45 , 46] . It is tempting to hypothesize that the outcomes of Aar/H-NS and Aar/AraC-like regulator interactions assure a concerted expression of fitness and virulence factors that prepare the bacteria for colonization ( Fig 10 ) . During the initial stage of the infection , H-NS ( an abundant protein at 20 , 000 copies per cell ) is intimately associated with DNA , including genes in the AggR regulon . Upon AggR activation , Aar could act to lift H-NS silencing of the regulon , thereby augmenting the effects of AggR . Paradoxically , H-NS acts to repress its own expression , and therefore binding of Aar to H-NS could increase expression of the latter protein , possibly thereby diminishing expression of the AggR regulon . Our data revealed that Aar affects up to 4 . 0% of bacterial genes under the conditions tested ( Fig 1 ) , and therefore paradoxical , nuanced control of such a large regulon could be beneficial to the bacterium . Also remarkable is the fact that Aar modifies the activity of three additional H-NS members . It is tempting to hypothesize that the net effects of Aar may be enhanced by affecting three partners of the same family . Additional studies are underway to dissect the contributions of these related H-NS proteins . H-NS is required for virulence in pathogenic bacteria including uropathogenic E . coli , Salmonella typhimurium , and Vibrio cholerae [19 , 43 , 47 , 48] . Here we report that Aar regulates H-NS activity in EAEC , and that H-NS is highly expressed in the intestinal lumen in early stages of the infection . Expression of H-NS is significantly decreased in an 042aar mutant in the colon , suggesting a role for both ANR and H-NS families in the context of infection . Taken together , our findings suggest that ANR is a common , highly conserved mechanism of regulation of bacterial virulence in vivo .
Bacterial strains , plasmids and primers used in this study are shown in S1 and S2 Tables . The nomenclature used by NCBI ( http://www . ncbi . nlm . nih . gov ) to describe genomic sequences in EAEC042 ( FN554766 ) or genes in pAA plasmid ( FN554767 ) was applied in this study . Putative gene assignments and homologies are listed in Table 1 . Strains EAEC 042 , 042pAA- , 042aggR , and 042aar were previously described [1 , 10 , 49 , 50] . In this study , 042 derivatives were generated by lambda red technology [51] . The locus for orf1292 ( 1 , 376 , 681–1 , 377 , 424 ) , orf2834 ( 3 , 024 , 998–3 , 025 , 605 ) , and stpA ( 3 , 058 , 170–3 , 058 , 930 ) in 042 ( GenBank FN554766 . 1 ) were replaced with the kanamycin ( km ) resistance marker . 042 derivatives were identified by PCR using specific primers for orf1292 , orf2834 and stpA ( S2 Table ) . Bacterial cultures were routinely propagated in Luria Broth ( LB ) and Dulbecco’s modified Eagle’s medium with 0 . 4% glucose ( DMEM high glucose ) ( Gibco ) as previously described [13] . To examine the transcriptome in an unbiased manner , RNA-seq analysis was performed . RNA was extracted from EAEC 042 derivatives grown in DMEM-Glucose . RNA was extracted with TRIzol ( Invitrogen ) and treated with RNase-free DNase set ( Qiagen ) to remove contaminating DNA . The samples were purified in RNeasy Mini kit columns ( Qiagen ) and used for library construction . The RNA samples were converted into cDNA libraries using the Ovation Prokaryotic RNA-Seq System ( NuGen ) and sequenced on the Illumina HiSeq 2000 to generate 100 bp paired-end reads at the Institute for Genome Sciences ( http://www . igs . umaryland . edu/resources/grc/analysis . php ) . Reads were mapped to the EAEC 042 chromosome and pAA plasmid with the BWA aligner [52] . Counts for each annotated genomic feature were determined by htseq-count ( http://htseq . readthedocs . io/en/release_0 . 9 . 0/ ) . Differential expression between counts for each feature was then calculated with DESeq [53] using the false-detection rate-adjusted Benjamini Hochberg P value . The fold change of differentially expressed genes vs . P value was plotted by using the GraphPad Prism 6 ( GraphPad Software , Inc . , CA , USA ) . H-NS was cloned into pMAL-c5x plasmid ( New England Biolabs ) and expressed as fusion proteins with the maltose binding protein ( MBP ) . Aar-MBP protein was purified as published before [2] . H-NS-MBP was expressed in E . coli NEB Express ( New England Biolabs ) at 37°C . Cells were grown in 1 liter of LB to an OD600 of 0 . 6 and induced for 3 h with 0 . 3 mM IPTG . The bacteria were harvested by centrifugation , and bacterial pellets were resuspended in 25 ml column buffer ( 20 mM Tris-HCL , 200 mM NaCl , 1 mM DTT , and 1 mM EDTA , pH 7 . 5 ) , and lysed by sonication on ice . Bacterial preparations were centrifuged and cleared lysates were loaded onto an amylose resin column ( New England Biolabs ) , washed with 5 volumes of column buffer , and eluted with column buffer containing 10 mM maltose . Pure protein preparations of H-NS-MBP were dialyzed overnight in PBS . Direct binding of H-NS and effects of Aar in the DNA binding activity to orf2223 , orf3928 and proV regulatory region were evaluated by EMSA as previously described [39] . Aar and H-NS were expressed as fusion proteins with the maltose binding protein ( MBP ) . The proteins were purified and the MBP tag was cleaved with 1% of Factor Xa as previously reported [2] . Probes ( 1μg ) were amplified by PCR ( Genbank , FN554766 , region 3 , 066 , 656–3 , 067 , 066 ) , purified and incubated with H-NS . The samples were prepared in 20–50 μl reaction mixtures containing 10mM Tris-HCl ( pH 7 . 5 ) , 1 mM Na-EDTA , 80 mM NaCl , 10 mM β-mercaptoethanol , and 4% glycerol . Samples were incubated for 30 minutes a 37°C in either the presence or absence of Aar . In parallel , 5’biotinylated probes for orf1292 and orf2834 were amplified by PCR . EMSAs were performed as previously described [2] , using the reaction mixtures described above . Following electrophoresis , the gel was incubated in a denaturing solution ( 1 . 5 M NaCl , 0 . 5 M NaOH ) for 30 min , washed in water , and washed twice in a neutralizing solution ( 1 . 5 M NaCl , 0 . 5 M Tris-HCl pH 7 . 2 , 1 mM EDTA ) for 15 min . Samples were transferred to a Zeta-probe membrane , and probes were visualized using the Chemiluminescent Nucleic Acid Detection kit ( Thermo Scientific ) . For the DNase I protection assays , after protein-DNA complex formation , the samples were incubated with 25 ηg of DNase I ( RNase-free DNase set ) ( Qiagen ) for 30 min . The DNase I was inactivated at 75°C for 10 min . The samples were resolved on 1 . 2% agarose gels with 0 . 5 x TBE ( Tris-borate-EDTA buffer ) as the running buffer and stained with ethidium bromide . E . coli K-12 transformed with plasmid expressing Aar and H-NS proteins ( pAar ( H6 ) H-NS ( HA ) , pAar ( H6 ) , pH-NS ( HA ) and pEspP ( H6 ) H-NS ( HA ) ) were grown in LB to OD600 nm of 0 . 4 . Expression of Aar and H-NS was induced with 2% of arabinose overnight at 30°C . The bacteria culture was treated with 5% formaldehyde and incubated for 10 min before quenching with PBS-glycine ( 0 . 125 M final glycine concentration ) . The bacterial cultures were pelleted , washed , and resuspended in 6 ml of lysis buffer ( 50 mM of sodium phosphate , 300 mM sodium chloride , 10 mM imidazole , 10 μM β-mercaptoethanol , 5% glycerol ) . The bacterial suspension was sonicated for 2 min at 22 μm amplitude . The procedure was repeated until the solution change color to translucent . Bacterial preparations were centrifuged and 600 μl of cleared lysates were incubated overnight with cobalt resin . The proteins were purified following the manufacture specifications ( Thermo Fisher Scientific ) , analyzed by SDS-PAGE and confirmed by Western blotting assay using specific anti-HA and anti-H6 antibodies ( Thermo Fisher Scientific ) . The genes for orf0808 , orf1127 , orf1292 , orf2020 , orf2058 , orf2834 , orf2881 , orf2888 , orf3191 , orf3204 , orf4499 and orf4555 were amplified by PCR , digested with BamHI/EcoRI and cloned into pKNT25 plasmid . The plasmids were analyzed by PCR and sequenced at the University of Virginia DNA Science Core . Plasmids pKT25/pUT18C and pKT25Zip/pUT18CZip were used as experimental negative and positive controls , respectively . The plasmids and primers used in this work are listed in S1 and S2 Tables . The plasmids were purified and cotransformed into the reporter strain E . coli BTH101 . Colonies were selected on LB agar plates containing carbenicillin ( 100 μg/ml ) , kanamycin ( 50 μg/ml ) , 5-bromo-4-chloro-3indolyl-β-d-galactopyranoside ( X-Gal ) ( 40 μg/ml ) , and isopropyl-β-d-thiogalactopyranoside ( IPTG ) ( 1 mM ) . E . coli BTH101 was cotransformed with pUT18 and pKNT25 derivatives encoding ANR and regulatory proteins . The clones were grown at room temperature for 48–72 h in LB plates with 1 mM IPTG . β-Galactosidase assays were performed accordingly to the method of Miller . Briefly , bacterial samples were suspended in 1 ml of Z buffer ( 60 mM Na2HPO4·7H2O , 40 mM NaH2PO4·H2O , 10 mM KCl , 1 mM MgS04·7H2O , 50 mM β-mercaptoethanol ) , 20 μl of 0 . 1% SDS and 40 μl of chloroform . 100 μl of sample was incubated with 20 μl of ONPG ( 4 mg/ml ) for 2 min at room temperature . The reaction was terminated by the addition of 50 μl of 1 M Na2CO3 . Samples were diluted in 800 μl of Z buffer . Optical densities at 420 ηM , 550 ηM and 600 ηM were determined . β-galactosidase activity was calculated by using the Miller formula ( Miller unit = 1000 x ( Abs420 - ( 1 . 75 x Abs550 ) / T x V x Abs600 ) ; T , reaction time; V , volume of culture assayed in milliliter ) . For the H-NS regulatory region fused to the LacZ reporter system , H-NS region ( GenBank , FN554766 , orf1292 region; 1 , 377 , 848–1 , 377 , 154 ) was amplified by PCR digested with NheI and BamHI and cloned into pEF-ENTR-lacZ plasmid to generate pPH-NSLacZ . The pEF-ENTR-lacZ plasmid was a gift from Eric Campeau ( Addgene Plasmid #17430 ) [54] . E coli K-12 BW25113 ( keio parental strain ) and E . coli K-12 BW25113 hns ( Keio hns knockout ) [55] were cotransformed with pPH-NSLacZ and pAar . As controls , the strains were transformed with empty pKNT15 and pBAD30 plasmids . The strains were grown at 37°C , 1 ml of bacteria was pelleted and suspended in 1 ml of Z buffer and prepared for LacZ assay as indicated above . qRT-PCR analysis was performed to corroborate the microarray data . Briefly , overnight bacterial cultures of EAEC were diluted 1:100 into 13 ml of DMEM high glucose ( aar-inducing conditions ) , and incubated at 37°C with shaking for 5 h . Samples were collected at various time points along the log phase of growth . Extraction of RNA , cDNA synthesis and qRT-PCR assays were performed as previously described [13] . Reactions were run in experimental duplicate using two independent cDNA preparations . Expression levels for each queried gene were normalized to the constitutively expressed cat gene in EAEC 042 . Groups of 5–10 male BALB/c mice , 5 wks old ( Jackson Laboratories ) were provided with drinking water ad libitum containing 5 g/liter streptomycin for 24 h prior to bacterial inoculation . The inoculation strains ( 042 , 042aar , 042aar ( pAar ) , 042hns and 042aggR ) were grown overnight in LB broth , diluted 1:100 and incubated in DMEM-high glucose for 5 h . Bacteria were pelleted and adjusted to 0 . 2 X 1010 cfu/ml . Mice were orogastrically inoculated with 0 . 2 ml of the inoculum and euthanized 24 h post-inoculation . Cecum , proximal and distal colon compartments were excised . The intestinal compartments were kept in 1 ml of RNA later stabilization solution ( Thermo Fisher Scientific ) . The samples were homogenized and filtered on sterile gauze pads . The samples were pelleted and resuspended in 1ml of TRIzol ( Invitrogen ) . Extraction of RNA , cDNA synthesis and qRT-PCR assays were performed as previously described [13] . Reactions were run in experimental duplicate . Expression levels for each queried gene were normalized to the constitutively expressed cat gene in EAEC 042 . Animal experiments were performed in accordance with the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health and with the permission of the American Association for the Assessment and Accreditation of Laboratory Animal Care . The protocol was reviewed and approved by the Institutional Animal Care and Use Committee of the University of Virginia ( Protocol No . 3999 ) . Protein secondary structures were analyzed by using Promals3d algorithms http://prodata . swmed . edu/promals3d/promals3d . php . The sequences for ANR homologs and H-NS homologs were obtained from NCBI . Statistical analysis of the data for β-galactosidase assays , qRT-PCR , and mice experiments was performed by using the GraphPad Prism 6 ( GraphPad Software , Inc . , CA , USA ) . The statistical significance of the differences in the sample means was calculated by using ANOVA with post hoc Tukey’s correction . Results were considered significant at P < 0 . 05 . The CG profile of pAA plasmid was generated by using GC-Profile algorithms ( http://tubic . tju . edu . cn/GC-Profile/ ) . H-NS–Aar interaction was also modeled and assembled by TM-Score [33] , RasMol [34] and the UCSF Chimera package [35] . | The AraC Negative Regulators ( ANR ) is a large family of negative regulators distributed in several clinically relevant Gram-negative pathogens , including Vibrio spp . , Salmonella spp . , Shigella spp . , Yersinia spp . , Citrobacter spp . , pathogenic E . coli , and members of the Pasteurellaceae . Previously , we showed that the ANR family suppresses transcriptional expression of virulence factors such as fimbriae , toxins , and the type VI secretion system by directly down-regulating AraC/XylS master regulators . Transcriptome and biochemical analysis of Aar ( AggR-activated regulator ) , an ANR family archetype in enteroaggregative E . coli ( EAEC ) 042 , demonstrated that Aar binds directly to H-NS and modulates the H-NS-induced gene expression . Accordingly , mutation of aar decreased expression of the H-NS-regulated Lpf fimbriae , LPS-related enzymes , GadXW operon and porins . Importantly , Aar was highly expressed in the mouse intestinal tract and was found to be necessary for maximal H-NS expression . These findings unveil an exquisite regulatory network in pathogenic bacteria , which operates by concomitant control of master transcriptional regulators of the AraC family and global negative H-NS regulators . | [
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] | 2017 | The AraC Negative Regulator family modulates the activity of histone-like proteins in pathogenic bacteria |
Function prediction by homology is widely used to provide preliminary functional annotations for genes for which experimental evidence of function is unavailable or limited . This approach has been shown to be prone to systematic error , including percolation of annotation errors through sequence databases . Phylogenomic analysis avoids these errors in function prediction but has been difficult to automate for high-throughput application . To address this limitation , we present a computationally efficient pipeline for phylogenomic classification of proteins . This pipeline uses the SCI-PHY ( Subfamily Classification in Phylogenomics ) algorithm for automatic subfamily identification , followed by subfamily hidden Markov model ( HMM ) construction . A simple and computationally efficient scoring scheme using family and subfamily HMMs enables classification of novel sequences to protein families and subfamilies . Sequences representing entirely novel subfamilies are differentiated from those that can be classified to subfamilies in the input training set using logistic regression . Subfamily HMM parameters are estimated using an information-sharing protocol , enabling subfamilies containing even a single sequence to benefit from conservation patterns defining the family as a whole or in related subfamilies . SCI-PHY subfamilies correspond closely to functional subtypes defined by experts and to conserved clades found by phylogenetic analysis . Extensive comparisons of subfamily and family HMM performances show that subfamily HMMs dramatically improve the separation between homologous and non-homologous proteins in sequence database searches . Subfamily HMMs also provide extremely high specificity of classification and can be used to predict entirely novel subtypes . The SCI-PHY Web server at http://phylogenomics . berkeley . edu/SCI-PHY/ allows users to upload a multiple sequence alignment for subfamily identification and subfamily HMM construction . Biologists wishing to provide their own subfamily definitions can do so . Source code is available on the Web page . The Berkeley Phylogenomics Group PhyloFacts resource contains pre-calculated subfamily predictions and subfamily HMMs for more than 40 , 000 protein families and domains at http://phylogenomics . berkeley . edu/phylofacts/ .
De novo subfamily identification—partitioning of sequences in a dataset into subtypes—provides two advantages for high-throughput systems of functional classification . First , assuming at least one subfamily member has been experimentally characterized , it becomes possible to infer function for other members of the subfamily . Second , the identification and curation of known subfamilies enables biologists to use sequence-based classification methods ( e . g . , using profiles , HMMs [32] , or support vector machines ( SVMs ) [33] ) to assign novel sequences to existing subtypes . Existing methods for de novo identification of specific subtypes fall into two camps: those that define clusters using pairwise similarity , e . g . , InParanoid [24] , OrthoMCL [21] , Ncut [25] , CD-HIT [22 , 34] , and those that cluster by cutting a phylogenetic or hierarchical tree , e . g . , RIO [29] , Orthostrapper [28] , Secator [35] , SCI-PHY . Both Secator [35] and SCI-PHY identify subfamilies using hierarchical tree construction and analysis . Secator uses a sequence dissimilarity measure to define an optimal cut of the tree . SCI-PHY uses minimum description length principles from information theory to cut the tree into subfamilies [36] . SCI-PHY exploits two powerful tools to construct a hierarchical tree: Dirichlet mixture densities [37] and relative entropy [38] . Dirichlet mixture densities are used to construct profiles for subtrees due to their utility in enhancing sensitivity with no reduction in specificity [39] . Relative entropy is used as a distance function between subtree profiles to determine the join order in the tree . See Methods for details . SCI-PHY is a fast method of subfamily identification which uses only sequence information , in contrast to phylogenetic tree methods that require species information to resolve orthologs from paralogs for functional analysis . Therefore , SCI-PHY is especially advantageous in situations where species information is not known , such as in environmental sequences . Our experiments show that SCI-PHY subfamilies correspond closely to subtypes found by experts and also to conserved clades identified using standard phylogenetic tree analysis . The availability of subfamily classifications enables high-throughput functional annotation: as new sequences are released to the sequence databases , sequence-based classification methods can be used to efficiently assign unknown sequences to pre-defined subtypes . Several classes of methods have been developed for this task . Profiles and HMMs are statistical models that generalize the information in a multiple sequence alignment ( MSA ) [32] , and can be used to develop subfamily profiles or HMMs , as described in this paper . However , most profile/HMM libraries ( e . g . , PFAM [40] , the NCBI CDD [41] , SMART [42] , etc . ) have focused on modeling large diverse clusters of proteins spanning many different functions , enabling high sensitivity , but affording only a fairly coarse level of functional annotation [29] . Methods designed specifically for classification of sequences to predefined subfamilies include the profile-based method of Hannenhalli and Russell [43] and SVMs [44] . Hannenhalli and Russell developed a profile-based subfamily classification system that attempts to determine which alignment positions discriminate between subfamilies . SVMs use both positive and negative training examples to allow classification of sequences to different subtypes ( e . g . , [33] ) . Weston et al . developed a semi-supervised algorithm that incorporates unlabeled proteins into an SVM-based discriminative classifier [45] . Our approach to classification of novel sequences to functional subfamilies uses subfamily hidden Markov models and a computationally efficient scoring system [31] . Note that subfamilies may be either automatically or manually defined; the system is independent of the origin of the classification . Subfamily HMMs are constructed using an information-sharing protocol that enables small subfamilies to benefit from the information contained in the rest of the family ( such as catalytic residues showing universal conservation ) while retaining specificity at subfamily-defining regions or motifs . This improves the sensitivity of the subfamily HMM to detect new members while providing for extremely high specificity of classification . A preliminary study of SHMM performance on a small dataset of nine protein families has been previously published [31] . Here , we present results on a larger , representative dataset of 515 families , and compare SHMMs to other sequence classification systems . The ability to predict novel subtypes in a protein family is extremely valuable in identifying functional shifts in newly sequenced genomes . In addition to classification of novel sequences to predefined subfamilies , we present a method of logistic regression of positive and negative examples for subfamilies . This method enables discrimination between novel sequences that can be reliably classified to an existing subfamily and those that are more likely to represent entirely different subtypes from any previously observed .
We compared SCI-PHY to three other methods for protein subfamily identification that depend only on sequence information: Secator , Ncut , and CD-HIT . CD-HIT takes a user-specified minimum percent identity as a parameter for determining cluster membership; we present results for two identity cutoffs: a comparatively low value ( 40% ) in order to identify fairly general functional groups , and a higher value ( 70% ) that has been identified as the minimal identity required to guarantee functional similarity within subfamilies [51 , 52] . We refer to these as CD-HIT40 and CD-HIT70 . Results for additional percent identity values are available in Dataset S1 . Secator , CD-HIT40 , CD-HIT70 , and SCI-PHY were compared on the EXPERT and EC datasets , spanning a total of 62 distinct protein superfamilies each containing multiple subtypes . Due to Ncut's high computational cost , we analyzed Ncut performance on the EXPERT dataset only . We used three scoring functions—purity , edit , and variation of information—to measure the agreement between the reference subtypes in each benchmark dataset and the subfamilies predicted by the methods tested . The purity score is a simple measure of each method's ability to properly separate reference subtypes , measured by the fraction of predicted subfamilies that contain sequences of only one reference subtype . Since perfect purity can be achieved trivially by placing every sequence in its own class , we exclude singleton subfamilies from the purity calculation . The edit distance between the two dataset partitions measures the number of split or merge operations required to transform one partition into the other . For instance , if one partition contains two clusters whose members are found as one large cluster in the other partition , a single merge or split operation suffices to transform one into the other , producing an edit distance of 1 . On the other hand , if a cluster of k members in the reference ( trusted ) partition is divided into k singletons in the predicted partition , ⌈log2 k⌉ merge operations are required . Thus , the edit distance penalizes over-division of a reference subtype more than it does two or more reference subtypes being merged . The Variation of Information ( VI ) distance [53] calculates the amount of information ( in bits ) within each partition that is not present in the other . A perfect score of zero indicates that the partitions are identical . The purity score and edit and VI distances were chosen to be complementary measures: purity represents the overall precision or specificity in separating functional subtypes , while the edit and VI distance are somewhat analogous to sensitivity or recall . An ideal subfamily classification will produce classes having sequences of only one type ( perfect precision ) and maximize the size of these clusters ( perfect recall ) . The purity function provides a means to measure the first attribute , while the edit and VI distances provide a means to measure the second . Finally , we also assessed agreement between SCI-PHY subfamilies and phylogenetic trees and found that SCI-PHY subfamilies typically correspond to well-supported clades within the family ( Dataset S2 ) .
Our results show that subfamily HMMs provide high specificity of sequence classification to functional subtypes , providing a kind of automated phylogenomic inference that approximates the results achievable from a more compute-intensive phylogenetic reconstruction . The information-sharing protocol we present produces subfamily HMMs that generalize effectively to distant homologs . Information sharing leverages available training data and helps to smooth estimated amino acid distributions to prevent overly specific HMM parameters in small subfamilies . This information-sharing protocol more efficiently separates homologs from non-homologs than subfamily HMMs without information sharing , but at a slight cost in subfamily specificity ( i . e . , the error rate for subfamily classification without information sharing is 0 . 8% , while our standard information-sharing protocol has an error rate of 1 . 5% ) . In these experiments , family and subfamily HMMs showed similar classification error rates , although subfamily HMMs produce much more significant e-values for true positives , in addition to identifying subfamily membership . This suggests a simple way to reduce the computational burden of using SHMMs , which we use in practice . Rather than scoring novel sequences against all SHMMs from all families , we screen sequences for family membership using family HMMs and then identify the appropriate subfamily by scoring the sequence only to the SHMMs of that family . Since most HMM libraries contain thousands of families , the average increase in scoring runs due to the use of SHMMs is then marginal . Logistic regression of subfamily HMM scores enables us to discriminate between sequences representing entirely novel subtypes and sequences that can be assigned to existing subtypes . This confers a unique capability to subfamily classification systems that is critical to prevent overly specific ( incorrect ) predictions of molecular function for novel sequences . All methods of constructing subfamily models as a means of classifying novel sequences will be sensitive to the inclusion of outlier sequences in a family . A single or small number of outlier sequences normally have minimal effect on a profile or HMM constructed for the family as a whole ( since their contribution is typically washed out by the dominant group ) and may remain undetected . However , the use of subfamily models , whether through subfamily HMMs , as outlined here , or by another method , can magnify the power of these outliers to attract and recruit their relatives . This may be desirable when outliers are actual homologs , but is generally not desirable in the case of spurious database hits . However , if non-homologous outliers can be flagged , their corresponding subfamily models can be used as decoys , differentiating true family members from those that only appear to be related .
The input to SCI-PHY is an MSA , from which a hierarchical tree and subfamily decomposition are estimated . SCI-PHY uses agglomerative ( bottom-up ) clustering to construct a hierarchical tree: the input objects form the leaves in the tree; similar objects are joined by edges to form subtrees , and the process is iterated until a rooted tree is obtained . Algorithm Input: MSA Initialization: Each sequence forms a separate class ( leaf in tree ) . For each class , construct a profile , using Dirichlet mixture densities [37] . Compute the pairwise distances between all classes , using relative entropy ( Equation 1 ) between their profiles . Find the closest pair . Agglomeration: While ( #classes >1 ) do: 1 . Join the two closest classes into a new class , represented by a new node in the tree . Add edges from the new node to each daughter node . 2 . Construct a profile for the new class based on the joint MSA . 3 . Compute the distance between this new class and other classes ( Equation 1 ) . 4 . Compute the encoding cost of this partition , under a Dirichlet mixture density ( Equation 2 ) . Output: 1 . Hierarchical tree . 2 . Predicted subfamilies , corresponding to the stage in the agglomeration having the lowest encoding cost . Subtree profile construction: each class ( individual sequence or set of sequences ) is represented by a profile [62] of amino acid distributions . Profiles are estimated using Dirichlet mixture densities [37] , which helps generalize the amino acid distributions to include probabilities for similar amino acids at each position . Distances between profiles: the distance function between profiles is a symmetrized form of relative entropy [38] summed over the alignment length ( the total relative entropy or TRE ) . The TRE for profiles p and q is averaged over all columns c , such that both pc and qc distributions are based on columns with ≥1 amino acid ( i . e . , neither column contains only gap characters ) . where pci is the probability of amino acid i at position c in profile p . Encoding cost determination of subfamily decomposition: subfamily identification is achieved using minimum-description–length principles to determine a cut of the tree into subtrees . There are a very large number of potential tree cuts; we employ a heuristic that examines only those partitions produced during the agglomerative clustering . At each iteration in the tree-building process , we have a forest of distinct subtrees that correspond to a particular cut of the tree . For each of these cuts , we evaluate the cost to encode the current set of subtree alignments under a Dirichlet mixture density [36 , 37] . The encoding cost function , assuming all subfamilies are independent , is defined as where N is the number of sequences in the MSA , S is the current number of subtrees , and P ( ncs | α ) is the probability of ncs , the ordered vector of observed amino acids for subfamily s at column c , under the Dirichlet mixture density α . P ( ncs | α ) is obtained by integrating out the multinomial parameter θ from the model ( see [63] for a review of the relevant mathematics ) . The canonical formulation for this quantity is In this equation , Z is the normalizing constant for the Dirichlet distribution , αj is the jth component of α , and qj is its mixture coefficient . The encoding cost function has two components: the first term is the cost to encode the subfamily labels for each sequence; the second term is the cost to encode each of the subtree alignments for that stage in the agglomeration . The two terms have opposite effects . The first term is large at program commencement when the number of subfamilies is largest , and reduces at each iteration , until it reaches zero at program termination , when there is one subfamily . The second term is minimized when the sequences within each subfamily are very similar to each other . At program commencement , for an input MSA with N sequences , there will be N separate subfamily alignments to be encoded . As the algorithm continues , the number of subfamilies decreases , until at program termination there is a single subfamily . As very similar sequences are joined into subtrees , the encoding cost decreases . For most protein families , the encoding cost curve decreases steadily to a minimum and then increases as subtrees with different amino acid preferences are joined . The stage in the agglomeration for which the encoding cost is minimal is used to determine a cut of the tree into subfamilies . See Figure 4 . Sequence weighting is a standard approach in profile and HMM construction to prevent large subgroups from dominating amino acid distributions [64] . We use sequence weighting in both SCI-PHY tree construction and in SHMM construction . We estimate sequence weights for each subfamily in a two-step process . In the first step , we estimate the total number of independent counts ( NIC ) in the alignment , as follows . We compute for every position in the alignment the frequency of the most frequent amino acid ( ignoring gaps ) to derive the positional conservation propensity . We then find the average of this value over all columns having at least one amino acid to obtain the overall conservation propensity ( Pcons ) . The NIC for the alignment can then be defined as , where N is the number of sequences in the alignment . This has the effect of producing an NIC of 1 when the sequences in the alignment are 100% identical , and having the NIC approach N as the diversity in the alignment increases . In the second step , the relative weights for sequences in each subfamily can then be derived independently ( e . g . , [64] ) , normalizing them to sum to the NIC for that subfamily . The input to subfamily HMM construction is an MSA and a decomposition of the alignment into subfamilies . We construct SHMMs in a multi-step process . Subfamily HMM architecture and transition parameters: first , we construct a family HMM using the entire input MSA as input and the SAM w0 . 5 software , developed by the University of California Santa Cruz ( UCSC ) Computational Biology group to optimize hidden Markov models for remote homolog detection [56] . To construct the subfamily HMM , the overall architecture and transition parameters in the family HMM are copied without modification for each SHMM . Keeping the overall architecture fixed enables sequence alignments to any SHMM to be easily mapped to the family as a whole . Match-state amino acid emission parameter estimation: we first identify all positions that are conserved across the family as a whole , allowing gaps . The probability distribution for these positions is taken from the family HMM and fixed within all subfamilies , representing their common functional or structural role . This heuristic also enables small subfamilies to have conserved distributions at positions that clearly define the family as a whole . Next , we identify columns in subfamily alignments containing only gap characters; the match state distributions for these positions are copied from the corresponding match state of the general HMM . This heuristic compensates for any fragmentary or partially aligning sequences included in the MSA . For all other columns , we estimate the amino acid distributions for each subfamily s using an information-sharing protocol enabled by the use of Dirichlet mixture densities . For notational simplicity , we suppress the c index in the equations that follow . Step 1 . We estimate a posterior Dirichlet mixture density from the prior density α using the weighted amino acid counts in s . The mixture coefficients q are updated by setting each component j to its posterior probability given the observed ( weighted ) counts ns: The component parameters are also updated to include the weighted counts of observed amino acids: where i ranges over the twenty amino acids . Step 2 . We then include counts from other subfamilies s′ in proportion to their probability under , obtaining the vector of total training counts t as Step 3 . Finally , we obtain a posterior estimate of the amino acid distribution from the original α using t rather than n where |x| indicates the magnitude of x . Thus , the generalization capability of subfamily HMMs is enhanced by adding in weighted counts from subfamilies having similar amino acids at corresponding positions . Construction of benchmark datasets . SCOP-PFAM515: protein families from the PFAM [40] resource were selected according to the following criteria: ( 1 ) the PFAM alignment had to match exactly one SCOP superfamily , and ( 2 ) SCI-PHY analysis of the alignment had to detect ≥2 subfamilies . The first criterion was determined based on scoring the Astral PDB90 dataset of structural domains [65] against all the HMMs in PFAM using the HMMER software ( version 2 . 3 . 2 ) [66] , and accepting only those PFAM families matching exactly one SCOP superfamily within the family gathering threshold . The second criterion was chosen to ensure that comparing subfamily and family HMM performance would be informative . Since each SCOP fold can contain numerous superfamilies , PFAM families meeting these criteria were filtered based on the top HMM score to select a single representative of each SCOP fold , to ensure that no fold dominated the results . This produced a set of 515 PFAM families . PFAM full alignments for these families were edited to remove sequences with >95% identity to other sequences in the MSA , columns with >70% gaps , and fragmentary sequences having >30% gap characters in the remaining columns . EC dataset construction: the SCOP-PFAM515 families were examined to find those containing multiple enzymatic functions based on EC numbers obtained from the UniprotKB [67] database for sequences aligning over >75% of their lengths . The requirement of a minimum fractional alignment was included to prevent an assigned enzymatic function being associated with a domain not represented by the PFAM family selected . 57 families having multiple EC numbers remained after this procedure . Since the majority of the sequences in each of these families had no assigned EC numbers , subfamily clustering methods were performed on the full ( edited ) alignments , but accuracy was assessed using only annotated sequences aligning over >75% of their lengths . EXPERT: we selected the enolase and crotonase enzyme families from the SFLD [48] , the amine and secretin families from the GPCRDB [49] , and the NHR family from NucleaRDB [50] . For the enolase and crotonase families , the full-length sequences were aligned to the structural alignment for the families downloaded from the SFLD Web site . The common domain in the aminergic GPCRs was identified by aligning the full-length sequences from GPCRDB to the PFAM 7tm_1 HMM ( PF00001 ) . Similarly , the PFAM 7tm_2 HMM ( PF00002 ) was used for the secretin-like GPCRs . The ligand-binding domain of nuclear hormone receptors was identified using the PFAM Hormone_recep HMM ( PF00104 ) . Next , we constructed an MSA for the identified domains from each family using the MUSCLE software [68] . Finally , we masked columns with ≥70% gap characters and made the alignment non-redundant at 98% identity ( by restricting the alignment to a representative set such that no two sequences had ≥98% identity ) . Evaluating predicted subfamilies relative to expert-defined subtypes . Several scoring functions were developed to enable us to evaluate the performance of predictive methods . Subfamily purity is measured as the fraction of subfamilies that contain only one expert subtype or EC number . The VI is a distance metric on partitions [53] . As such , it obeys the triangle inequality: VI ( A , B ) + VI ( B , C ) ≥ VI ( A , C ) for partitions A , B , C . Given two partitions , the VI index measures the amount of information in each partition that is not shared between them . It is calculated as where H is the entropy of a partition , and I is the mutual information between two partitions: Here , nk is the number of items in cluster k of partition S , nk , k′ is the number of overlapping items between cluster k in partition S and cluster k′ in partition S′ , K and K′ are the total number of clusters in partitions S and S′ , respectively , and N is the total number of items in the set . The Edit Distance is defined as the minimum number of split or merge operations required to transform one partition into the other . A split or merge affecting multiple data points is considered one operation . For instance , two clusters containing five sequences may be merged into one ten-sequence cluster with an edit distance of one . The edit distance between a reference and a predicted partition with clusters k and k′ , respectively , is calculated as where rk , k′ equals 1 if clusters k and k′ have items in common , and zero otherwise , and K and K′ are the number of clusters in each partition . Like the VI distance , the edit distance is a metric on partitions . Identical partitions will have an edit distance of zero . The edit distance is bounded by . SHMM construction and performance: HMM construction and scoring: we used the UCSC Sequence Alignment and Modeling ( SAM ) software system [69 , 70] to score sequences in both fold prediction and classification experiments . All family HMMs were estimated using the SAM w0 . 5 software . Reverse scores for SHMMs and family HMMs were derived using the hmmscore program and local–local scoring ( SAM parameter -sw 2 ) . Family HMM e-values for sequences were calculated using the SAM recommended protocol: family HMMs were calibrated with the hmmscore program using the -calibrate option prior to scoring . Raw SHMM scores were obtained by scoring a sequence against all SHMMs in the family and retaining the top score . SHMM e-values were derived by fitting EVD parameters to raw SHMM scores for randomly generated sequences , using a maximum likelihood approach similar to that implemented in HMMER [71 , 72] . E-values were calculated based on a fixed database size of 105 . Weighted coverage and ROC curves were calculated by normalizing the contribution of each true positive by the size of its superfamily , so that all superfamilies contributed equally to the dataset [73] . Subfamily classification and novel subtype detection: for leave-one-out experiments , we chose ten sequences at random from each family , which were removed and tested separately . Sequences drawn from singleton subfamilies were replaced and not used . The modified alignment was then used to construct a new set of SHMMs , keeping the original SCI-PHY subfamily decomposition . To simulate classification of remotely related sequences , all sequences having identity greater than a specific cutoff to the withheld sequence were also removed from the alignment . We tested cutoffs of 30% , 40% , 50% , 60% , and 70% identity ( see Table 4 ) . In some cases , there were no sequences below the threshold within the subfamily; these were removed from the test . For the BLAST method , test sequences were assigned to the subfamily of the highest-scoring hit . To enable direct comparison , the Hannenhalli and Russell sub-profile method was re-implemented to use HMMs constructed from SAM w0 . 5 software rather than HMMER as in [43] . In the novel subtype detection experiments , we removed up to five complete subfamilies at random from each family , ignoring families with only two subfamilies ( preventing the case where regression curves would have been trained with no negative examples ) . Results were normalized by subfamily size . Logistic regression parameters were fit using the iteratively re-weighted least squares ( IRLS , [74] ) algorithm ( implemented in R [75] ) . | Predicting the function of a gene or protein ( gene product ) from its primary sequence is a major focus of many bioinformatics methods . In this paper , the authors present a three-stage computational pipeline for gene functional annotation in an evolutionary framework to reduce the systematic errors associated with the standard protocol ( annotation transfer from predicted homologs ) . In the first stage , a functional hierarchy is estimated for each protein family and subfamilies are identified . In the second stage , hidden Markov models ( HMMs ) ( a type of statistical model ) are constructed for each subfamily to model both the family-defining and subfamily-specific signatures . In the third stage , subfamily HMMs are used to assign novel sequences to functional subtypes . Extensive experimental validation of these methods shows that predicted subfamilies correspond closely to functional subtypes identified by experts and to conserved clades in phylogenetic trees; that subfamily HMMs increase the separation between homologs and non-homologs in sequence database discrimination tests relative to the use of a single HMM for the family; and that specificity of classification of novel sequences to subfamilies using subfamily HMMs is near perfect ( 1 . 5% error rate when sequences are assigned to the top-scoring subfamily , and <0 . 5% error rate when logistic regression of scores is employed ) . | [
"Abstract",
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"Methods"
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"evolutionary",
"biology",
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] | 2007 | Automated Protein Subfamily Identification and Classification |
Dyshomeostasis of both ceramides and sphingosine-1-phosphate ( S1P ) in the brain has been implicated in aging-associated neurodegenerative disorders in humans . However , mechanisms that maintain the homeostasis of these bioactive sphingolipids in the brain remain unclear . Mouse alkaline ceramidase 3 ( Acer3 ) , which preferentially catalyzes the hydrolysis of C18:1-ceramide , a major unsaturated long-chain ceramide species in the brain , is upregulated with age in the mouse brain . Acer3 knockout causes an age-dependent accumulation of various ceramides and C18:1-monohexosylceramide and abolishes the age-related increase in the levels of sphingosine and S1P in the brain; thereby resulting in Purkinje cell degeneration in the cerebellum and deficits in motor coordination and balance . Our results indicate that Acer3 plays critically protective roles in controlling the homeostasis of various sphingolipids , including ceramides , sphingosine , S1P , and certain complex sphingolipids in the brain and protects Purkinje cells from premature degeneration .
Ceramides consisting of a sphingoid base moiety and an amide-linked acyl chain are the precursors of all complex sphingolipids that are essential for brain development , maturation , and proper functioning in mammals [1 , 2] . Ceramides are also essential precursors of sphingosine-1-phosphate ( S1P ) , a bioactive lipid implicated in survival of neural stem cells and neurons [3 , 4] . On the other hand , an aberrant accumulation of ceramides in the brain has been implicated in various aging-related neurodegenerative diseases [5 , 6] , likely due to their signaling roles in cellular stress responses such as apoptosis [7] and senescence [8] . These observations suggest that the homeostasis of ceramides must be fine-tuned in the brain during normal aging process , else pathological consequences may result . However , how this is accomplished remains unclear; although it is generally believed that cellular or tissue levels of ceramides are determined by a balance between their synthesis and utilization or degradation . Ceramides are generated through multiple metabolic pathways in mammals [9] . They are formed from dihydroceramides [10 , 11] , which in turn are synthesized de novo from dihydrosphingosine and fatty acyl-CoAs through the action of ( dihydro ) ceramide synthases encoded by 6 distinct genes ( CERS1-6 ) [12–17] . Ceramides can also be directly synthesized from sphingosine ( SPH ) and acyl-CoAs by the action of the CERS through a salvage pathway [18 , 19] . Following their synthesis by these two pathways in the endoplasmic reticulum ( ER ) [20 , 21] , ceramides are transported to the Golgi complex [22 , 23] , where they are incorporated into complex sphingolipids [9] , which can be converted back to ceramides either on the plasma membrane [24] or in lysosomes [25] . Once generated , ceramides are then hydrolyzed to SPH and fatty acids by the action of ceramidases encoded by five distinct ceramidase genes , including the acid ceramidase ( ASAH1 ) [26] , neutral ceramidase ( ASAH2 ) [27] , alkaline ceramidase 1 ( ACER1 ) [28] , alkaline ceramidase 2 ( ACER2 ) [29] , and alkaline ceramidase 3 ( ACER3 ) [30] . These ceramidases have distinct cellular localizations , substrate specificities , and tissue distributions [31] . ASAH1 , a lysosomal ceramidase , is ubiquitously expressed and preferentially catalyzes the hydrolysis of C12-14-ceramides , so called medium-chain ceramides [32] . ASAH2 is localized to mitochondria and the plasma membrane in a membrane-bound form [27] or it is secreted from cells after its membrane-anchoring domain is cleaved [33] . ASAH2 , which is expressed in different tissues [27] , has been shown to catalyze the hydrolysis of various ceramides ranging from long-chain ( C16-20 ) ( LCCs ) to very long-chain ( ≥C22 ) ceramides ( VLCCs ) in vitro [34] . ACER1 , an ER ceramidase that is predominantly expressed in the skin [28] , uses VLCCs as substrates [28] . ACER2 , a Golgi ceramidase that is ubiquitously expressed at low levels [29] , has broad substrate specificity [35] . ACER3 , which is localized to both the ER and Golgi complex and is highly expressed in various tissues , was the first mammalian alkaline ceramidase to be identified by our group [30] . Initially we found that ACER3 prefers a synthetic fluorescent phytoceramide ( NBD-C12-phytoceramide ) over fluorescent ceramide ( NBD-C12-ceramide ) or dihydroceramide ( NBD-C12-dihydroceramide ) as a substrate [30] . Later , we discovered that ACER3 catalyzes the hydrolysis of ceramides , dihydroceramides , and phytoceramides carrying an unsaturated long acyl chain ( C18:1 or C20:1 ) with similar efficiency [36] Increasing studies suggest that ceramidases may play a key role in regulating the homeostasis of both ceramides and S1P in cells and/or tissues by controlling the hydrolysis of ceramides and the generation of SPH , the immediate precursor of S1P [29 , 37–39] . Genetic studies of human diseases and knockout mouse models are beginning to shed light on the distinct roles of ceramidases . A genetic deficiency in ASAH1 causes a massive accumulation of ceramides in various tissues including the brain , leading to the lysosomal storage disease called Farber’s disease [32 , 40] . A mouse model of Farber’s disease also exhibits accumulation of ceramides in different tissues including the brain and displays various pathological phenotypes including neuronal degeneration [41] . Asah2 knockout does not alter the brain levels of ceramides or SPH [37] , suggesting a minimal role for this enzyme in the homeostasis of ceramides in the brain . However , the role for the alkaline ceramidases ( Acer1-3 ) in regulating the homeostasis of ceramides and S1P in the brain remains unclear although their activities have been shown to be increased with age in the mouse brain [42] . In this study , we demonstrate that Acer3 , the most abundant member in the alkaline ceramidase family , is upregulated with age in the mouse brain; plays important roles in sustaining the homeostasis of ceramides and their metabolites SPH and S1P in the aging brain; and protects PCs from premature degeneration and thereby cerebellar ataxia .
It has been shown that alkaline ceramidase activity increases with age in the mouse brain [42] . We hypothesized that this ceramidase activity increase may be important in preventing an accumulation of ceramides in the mature brain by catalyzing the hydrolysis of ceramides . To test this possibility , we determined whether inhibiting the increase in ceramidase activity would lead to an aberrant accumulation of ceramides in the brain and consequent neurological disorder . To achieve this goal , we first determined which alkaline ceramidase ( s ) is upregulated with age in mouse brain . With quantitative real-time polymerase chain reaction ( qPCR ) , we demonstrated that the mRNA levels of Acer3 but not Acer1 or Acer2 were increased in both cerebrum and cerebellum of C57BL6/J wild-type ( WT ) mice at 8 months of age compared to mice at 6 weeks of age ( Fig 1A ) . We also found that Acer3 mRNA levels were higher in the cerebellum than in the cerebrum ( Fig 1B ) . To determine if the increase in Acer3 mRNA levels results in an increase in its enzymatic activity , we measured alkaline ceramidase activity on NBD-ribo-C12-NBD-phytoceramide ( NBD-C12-PHC ) , a synthetic substrate specific for the human ACER3 [30] and presumably for the mouse Acer3 as well . Our results showed that alkaline ceramidase activity toward NBD-C12-PHC was increased in both the cerebrum and cerebellum in 8-month-old WT mice compared to 6-week-old WT mice ( Fig 1C ) , suggesting that Acer3 activity is indeed increased with age in the mouse brain . Correlating with Acer3 mRNA levels , we found that Acer3 activity was higher in the cerebellum than in the cerebrum ( Fig 1C ) . These results suggest that Acer3 is upregulated with age in both the cerebrum and cerebellum . To elucidate the physiological function of age-dependent upregulation of Acer3 in the brain , we generated a mouse model deficient in Acer3 as described in Materials and Methods ( Fig 2 ) . Interbreeding of mice heterozygous for an Acer3 null allele produced WT ( Acer3+/+ ) , heterozygous ( Acer3+/- ) , and homozygous ( Acer3-/- ) offspring at a Mendelian ratio ( Acer3+/+: Acer3+/-: Acer3-/-; 151:319:143 ) , suggesting the absence of significant embryonic lethality in Acer3 knockout mice . Intercrossing of Acer3-/- mice produced similar offspring numbers as with the intercrossing of Acer3+/+ mice ( Fig 3A ) , suggesting that both male and female Acer3-/- mice have normal fertility . There was no significant difference in body weight between Acer3+/+ and Acer3-/- mice when measured at 6 weeks , 4 , 6 , 8 , or 12 months of age ( Fig 3B ) . The macroscopic and microscopic analyses found no abnormalities in the anatomy of major organs in young mice . These results suggest that Acer3 knockout does not appear to cause any major defect in mouse development at least prior to middle age . To confirm that the deletion of exon 8 of the Acer3 gene indeed results in a truncated coding sequence of Acer3 , we amplified the coding sequence from cDNAs that are transcribed from RNAs from brain tissues of Acer3+/+ or Acer3-/- mice . With reverse transcription PCR ( RT-PCR ) , we demonstrated that the coding sequence of Acer3 in Acer3-/- mice is shorter in length than that in Acer3+/+ mice ( Fig 4A ) , suggesting that a truncated coding sequence is transcribed in Acer3-/- mice . To confirm that deleting exon 8 of the Acer3 gene inactivates the catalytic function of Acer3 , we compared alkaline ceramidase activity on NBD-C12-PHC in the brain , liver , and lung tissues from Acer3-/- and Acer3+/+ mice . We found that Acer3+/+ mice had much higher alkaline ceramidase activity on NBD-C12-PHC in these tissues than Acer3-/- mice at either 6 weeks of age ( Fig 4B and S1A Fig ) or 8 months of age ( Fig 4B ) although this alkaline ceramidase activity was not totally abolished in some Acer3-/- tissues . There are two possible explanations for these results: 1 ) the catalytic activity of Acer3 is abolished by deleting exon 8 and the residue alkaline ceramidase activity on NBD-C12-PHC comes from another ceramidase ( s ) with a minor activity on NBD-C12-PHC; and 2 ) the catalytic activity of Acer3 is markedly reduced but not abolished by the deletion of exon 8 . These results also confirm that NBD-C12-PHC is a substrate highly specific for Acer3 and that the alkaline ceramidase activity on NBD-C12-PHC correlates with Acer3 expression levels in tissues . The inactivation of the Acer3 gene was further confirmed by performing alkaline activity assays using regular ceramides carrying various acyl chains as substrates . The results showed that Acer3 knockout markedly decreased alkaline ceramidase activity on C18:1-ceramide , but not C16:0 , C18:0 , C24:1 , or C24:0-ceramide in brain ( Fig 4C ) , liver , or lung tissues ( S1B Fig ) , further confirming that the deletion of exon 8 markedly reduces or inactivate the catalytic activity of Acer3 . These results also suggest that , similar to its human counterpart ACER3 , the mouse Acer3 also prefers ULCCs as substrates . To investigate if Acer3 plays a role in regulating the homeostasis of ceramides and their sphingolipid derivatives in the brain during aging , we first measured the levels of ceramides in cerebral and cerebellar tissues from Acer3+/+ and Acer3-/- mice at 6 weeks and 8 months of age using LC-MS/MS . In mice at 6 weeks of age , Acer3 knockout increased not only the levels of ULCCs ( C18:1 and C20:1-ceramides ) but also the levels of saturated LCCs ( SLCCs; C16:0 , C18:0 , and/or C20:0-ceramides ) in the cerebrum ( Fig 5A ) , and to a greater extent in the cerebellum ( Fig 5B ) . Acer3 deficiency increased the total levels of ceramides in the cerebellum ( Fig 5B ) and to a lesser extent in the cerebrum ( Fig 5A ) in 6-week-old mice . In mice at 8 months of age , Acer3 knockout further accumulated ULCCs and SLCCs in both the cerebrum and the cerebellum , while there was an accumulation of VLCCs ( C22 and C22:1-ceramide ) only in the cerebellum . The total levels of ceramides were therefore elevated in both brain regions , but were greater in the cerebellum ( Fig 5A and 5B ) . It is worth noting that parallel to the upregulation of Acer3 in the brain , the levels of C18:1-ceramide were decreased in both the cerebrum and cerebellum in 8-month-old mice compared to 6-week-old wild-type mice . Importantly , this decrease was inhibited by Acer3 knockout , suggesting that Acer3 upregulation is important in prohibiting the aberrant accumulation of C18:1-ceramide in the aging brain . Because ceramidases , including Acer3 , catalyze the hydrolysis of ceramides into SPH , which in turn is phosphorylated to form S1P , Acer3 upregulation in the brain may increase the levels of both SPH and S1P in this tissue . To test this possibility , the levels of SPH and S1P were determined in the cerebrum or cerebellum in Acer3+/+ or Acer3-/- mice at 6 weeks or 8-months of age using LC-MS/MS . The results showed that in mice at 6 weeks of age , Acer3 knockout significantly decreased the levels of SPH ( by 14 . 10% ) but not S1P in the cerebrum , whereas , in mice at 8 months of age , Acer3 knockout significantly decreased the levels of SPH ( by 21 . 74% and 23 . 13% in the cerebrum and cerebellum , respectively ) and S1P ( by 28 . 77% and 28 . 27% in the cerebrum and cerebellum , respectively ) ( Fig 5C ) . It is worth noting that in line with the age-dependent upregulation of Acer3 , the levels of both SPH and S1P were increased in either the cerebrum or cerebellum in 8-month-old Acer3+/+ mice compared to 6-week-old Acer3+/+ mice . These age-dependent effects were abolished by Acer3 knockout ( Fig 5C ) , suggesting that age-related Acer3 upregulation is responsible for the increases in the levels of both SPH and S1P in the aging mouse brain . Ceramides can be incorporated into various complex sphingolipids . Since we observed an increase in the levels of brain ceramides in Acer3-/- mice , we postulated that Acer3 deficiency may increase the levels of complex sphingolipids in the brain . To investigate this possibility , the levels of monohexosylceramides ( HexCers ) and sphingomyelins were determined in the cerebrum and cerebellum in Acer3+/+ and Acer3-/- mice using LC-MS/MS . Acer3 knockout increased the levels of C18:1-HexCer , a very minor HexCer species , in the cerebrum ( Fig 6A ) and cerebellum ( Fig 6B ) in 6-week-old mice and to a greater extent in 8-month-old mice without affecting the levels of other HexCer species or total levels of HexCers ( Fig 6A and 6B ) . These results suggest that Acer3 regulates brain C18:1-HexCer specifically and that its deficiency does not affect the total levels of this type of sphingolipids . As for sphingomyelins , the results showed that Acer3 knockout slightly but significantly increased the levels of C18:1 and C20:1-sphingomyelin in the cerebellum in mice at either 6 weeks or 8 months of age without affecting the total levels of sphingomyelins in this tissue and this effect was not observed in the cerebrum ( Fig 6C and 6D ) . These results suggest that Acer3 regulates unsaturated long-chain sphingomyelins specially and that its deficiency does not affect the total levels of sphingomyelins . Ceramides have been shown to mediate various stress responses including apoptosis [43] . Since Acer3 deficiency caused an aberrant accumulation of LCCs in the brain , especially in the cerebellum , we hypothesized that Acer3 deficiency may lead to neuronal dysfunction . To assess the neurological consequences of Acer3 deficiency , Acer3-/- and their littermate Acer3+/+ mice at different ages were subjected to a battery of behavioral tests . General locomotor activity and movement pathways were evaluated by open field tests . Total walking distance , average velocity , corner area latency , and rearing activity were quantified . Movement pathways of 6-week-old , 8-month-old , and 12-month-old mice are illustrated in S2A Fig . After being placed in the center of an open field , both Acer3+/+ and Acer3-/- mice ran to the edge , then explored the whole area starting from the edge to the central area . During the 5 min period , no significant difference was observed in total walking distance ( S2B Fig ) , average velocity ( S2C Fig ) , or corner area latency ( S2D Fig ) between Acer3+/+ and Acer3-/- mice at 6 weeks , 8 months , or 12 months of age . However , rearing activity was significantly decreased in Acer3-/- mice at 8 months and 12 months of age compared to their wild-type littermates , although such a difference was not observed in mice at 6 weeks of age ( S2E Fig ) . These results suggest that Acer3 deficiency decreases open field rearing activity without affecting general locomotor activity in aging mice . Motor coordination and synchrony were assessed by analyzing the footprint patterns as mice walked along a narrow corridor . The footprint patterns of Acer3+/+ and Acer3-/- mice at 6 weeks , 8 months , and 12 months of age are illustrated in S3A Fig . At 6 weeks or 8 months of age , both Acer3+/+ and Acer3-/- mice walked in a straight line with an even alternating gait and placed the hindpaw closed to the position where the ipsilateral forepaw had been in the previous step ( S3B Fig ) . However , at 12 months of age , although both Acer3+/+ and Acer3-/- mice walked in a straight line with an even alternating gait , their footprints of the hindpaw and ipsilateral forepaw did not overlap as perfectly as the other age groups ( S3A Fig ) . The resulting footprint patterns were analyzed quantitatively by measuring stride length , hindpaw step width , forepaw step width , and the extent of overlap between the forepaws and hindpaw placement measured by the distance between the footprints . As shown in S3A Fig , Acer3+/+ and Acer3-/- mice exhibited similar stride lengths and step widths at each age . There was no significant difference in the front/hind footprint overlapping between Acer3-/- mice and their WT littermates at each age ( S3B Fig ) , indicating that Acer3 knockout does not disrupt the uniformity of step alternation in mice at these ages . To assess motor coordination and muscle strength , we first performed inverted wire hanging tests . The tests demonstrated that Acer3-/- mice fell off the mesh upon inverting the wire mesh and did not attempt to walk on the inverted grid after they reached certain age ( starting at 6 months of age ) . In contrast , their wild-type littermates continued grasping the mesh and also attempted to change foot placements on the inverted mesh ( S1 Video ) . These results suggest that Acer3 deficiency may reduce grip strength and/or impair motor coordination . Deficits in muscle strength were quantified by grip strength measurements . We found no significant difference in forepaw or hindpaw grip strength measurements between Acer3+/+ mice and Acer3-/- mice at 4 , 6 , 8 , or 12 months of age ( S4A and S4B Fig ) , suggesting that Acer3 knockout does not compromise the muscle strength of mice up to 12 months of age . We then performed rotarod tests to assess general motor coordination and balance . These tests were carried out with three task difficulties by varying the speed , and the latency to fall off the rod was recorded . We found that Acer3-/- mice did not exhibit any significant difference in the latency to fall prior to 6 months of age when compared to its age-matched WT littermates ( Fig 7A–7C ) . However , starting from 8 months of age , Acer3-/- mice had difficulty staying on the rotarod at all testing speeds—walking on the rod was characterized by frequent bilateral foot-slips , decline in bilateral alternating movement patterns between the forelimb and hindlimbs and lack of balance , all of which led the Acer3-/- mice to fall off the rod within a very short period . This was in striking contrast to the age-matched WT littermates who demonstrated significantly longer durations of walking times on the rotarod at all speed settings ( Fig 7A–7D , S2 Video ) . Next , beam walking tests were performed to compare the fine skill movement of the hindlimbs ( paw placement on the beam ) and balance capabilities of Acer3+/+ and Acer3-/- mice . Two task difficulties were achieved by varying the diameter of cross-section of the beams . Traversing latency , walking distance , and foot-slip frequency were recorded during the beam test . Since rotarod tests revealed an obvious deficit in motor coordination and balance at 8 months of age in Acer3-/- mice but not in younger mice , we chose 6-week-old and 8-month-old mice for these tests . At 6 weeks of age , both Acer3+/+ and Acer3-/- mice traversed both types of beams with no difficulty and showed no difference in traversing latency ( Fig 7E ) , walking distance ( Fig 7F ) , or foot-slips ( Fig 7G and 7H ) . At 8 months of age , Acer3 knockout mice made more foot-slips than their WT littermates on the 17-mm beam although they could still traverse the beam ( Fig 7H ) . However , at 8 months of age , Acer3-/- mice failed to complete all walking trials on the 10-mm beam , showed a significant increase in time to traverse the beam ( Fig 7E ) , a marked decrease in average walking distance ( Fig 7F ) , and exhibited more foot-slips ( Fig 7G and 7H ) compared to their WT littermates ( S3 Video ) . Finally , the hindlimb clasping reflex was tested to determine if Acer3-/- mice exhibit any neuropathology . Acer3+/+ and Acer3-/- mice were suspended by the base of the tail for 10 s , and the duration of hindlimb clasping was scored . At 6 weeks to 6 months of age , both Acer3+/+ and Acer3-/- knockout mice splayed their hindlimbs outward and away from the abdomen with no clasping reflex ( Fig 8A ) . Starting at 8 months of age , while Acer3+/+ mice exhibited the same hindlimb action with no clasping reflex as the 6-week-old mice , Acer3-/- mice retracted their hindlimbs toward the abdomen with a significantly higher hindlimb clasping reflex score than Acer3+/+ mice ( Fig 8A and 8B ) . Collectively , the behavioral tests suggest that Acer3 knockout in mice at 8 months of age drastically impairs overall motor coordination , skilled hindlimb function and balance capabilities without affecting general locomotor activity . The impairment of motor coordination and balance capacity reflects a dysfunction of the cerebellum [44] . PCs are the exclusive output neurons in the cerebellar cortex [45] and are shown to be sensitive to age-related damage in the cerebellum [45 , 46] . These observations prompted us to test if Acer3 deficiency caused pathological effects on PCs in this brain region . We examined PCs in Acer3+/+ and Acer3-/- mice at 6 weeks or 8 months of age by immunohistochemical ( IHC ) staining with an antibody against calbindin D-28K , a PC marker . As shown in Fig 9A , calbindin D staining revealed well-arranged PCs between the molecular layer and granular layer of the cerebellum in both Acer3+/+ and Acer3-/- mice at 6 weeks of age with a similar cell number . At 8 months of age , while Acer3+/+ mice still maintained a similar number and pattern of PCs to 6-week-old mice , Acer3-/- mice exhibited a significant decrease in the number of PCs compared to their WT littermates and young animals ( Fig 9A and 9B ) . Since Acer3 deficiency results in the accumulation of brain ceramides , which have been suggested to be pro-death bioactive lipids [47] , we investigated if Acer3 deficiency induces apoptosis in PCs by performing terminal deoxynucleotidyl transferase dUTP nick-end labeling ( TUNEL ) assays . Interestingly , no apoptotic PCs were seen in the cerebellum of either 8-month-old Acer3-/- mice or their WT littermates ( Fig 9C ) . These results suggest that Acer3 knockout induces premature degeneration of PCs through apoptosis that escapes from detection by TUNEL assays or through a mechanism other than apoptosis . Because dyshomeostasis of sphingolipids could lead to a defect in myelination , which has been linked to various neurological disorders including cerebellar ataxia [48 , 49] , we tested if Acer3 deficiency impaired myelination in the cerebellum in mice at 8 months of age . Luxol fast blue staining showed that the width of myelinated tracts was not altered in Acer3-/- mice as compared to Acer3+/+ mice ( Fig 10A and 10B ) . Consistently , Western blot analyses found no difference in the levels of myelin basic protein ( MBP ) in the cerebellum between Acer3+/+ and Acer3-/- mice ( Fig 10C and 10D ) . Electron microscopy revealed that Acer3 deficiency did not alter the ultrastructure of myelin sheaths in the cerebellum ( Fig 10E ) . These results suggest that Acer3 deficiency does not affect myelination .
Brain ceramide levels are increased in aging-associated conditions , such as Alzheimer’s disease ( AD ) [5 , 50] and during normal rodent aging [5]; thereby exerting pathological effects on brain functions [41 , 51] . On the other hand , S1P levels are decreased in the brain with AD [6 , 52] . Recent animal studies have demonstrated that a reduction in the levels of brain S1P is partly responsible for degeneration of PCs in a mouse model of Niemann–Pick disease type C ( NPC ) [4] . However , much remains unclear about how the homeostasis of ceramides and S1P in the brain is sustained during the normal aging process . In this study , we demonstrate for the first time that age-dependent upregulation of Acer3 plays a key role in sustaining the homeostasis of both ceramides and S1P in the aging mouse brain; thus protecting PCs from premature degeneration and hence from cerebellar ataxia . Several studies have demonstrated that the levels of ceramides are elevated in tissues of aged rodents [53–55] , including the brain [5] , but it remains unclear whether this is also true in the aging brain of middle-aged mice . Middle age has been largely understudied in the context of aging , but investigating this life stage may be especially important for our understanding of aging . To this end , we measured the levels of ceramides in the brain in middle-aged mice versus young adult mice through a lipidomic approach . We demonstrate that the total levels of ceramides are moderately lower in the cerebrum but slightly higher in the cerebellum in middle-aged mice compared to young mice ( Fig 5A and 5B ) . Interestingly , we observed that the levels of SPH and S1P were much higher in both brain regions in middle-aged mice than in young mice ( Fig 5C ) . These results suggest that an aberrant accumulation of ceramides does not occur in the mouse brain at least up to the middle age ( 8 months ) although there are age-associated increases in the levels of both SPH and S1P . Another novel finding of our study is that Acer3 upregulation prevents abnormal accumulation of ceramides and increases the levels of their metabolites SPH and S1P in the aging brain . It has been reported previously that alkaline ceramidase activity is increased with age in the mouse brain [42]; here we identify the alkaline ceramidase isozyme that encodes the increased activity . We demonstrate that the mRNA levels of Acer3 , but not other alkaline ceramidases , are upregulated with age in the mouse brain ( Fig 1A ) . We confirmed that alkaline ceramidase activity on NBD-C12-PHC , a preferred substrate for Acer3 but a poor substrate for the other ceramidases , was also increased with age in both the cerebrum and cerebellum ( Fig 1C ) . The underlying mechanism for such age-dependent Acer3 upregulation however needs further investigation . Consistent with Acer3 upregulation , there is a decrease in levels of the brain C18:1-ceramide , a preferred substrate of Acer3 , along with concurrent increases in SPH and S1P levels in middle-aged mice compared to young adult mice ( Fig 5A and 5B ) ; indicating that Acer3 upregulation has a role in preventing C18:1-ceramide accumulation in the aging brain . This is supported by our finding that Acer3 deficiency increased the levels of C18:1-ceramide in the brain . C18:1-ceramide is abundant in the brain whereas it is scarce in the non-central nervous system ( CNS ) tissue such as liver and lungs under basal conditions ( S1 Table ) . Ceramides with different acyl-chains are synthesized by the action of different CerSs [56] . Ginkel et al . have demonstrated that knocking out CerS1 markedly lowered the levels of C18:1-ceramide in the mouse brain , thereby implying that CerS1 is a CerS isozyme responsible for the biosynthesis of this particular ceramide species in addition to SLCCs [48] . CerS1 is mainly and highly expressed in the brain [16] , explaining why the brain has the highest content of C18:1-ceramide among other tissues ( Fig 5A and 5B , and S1 Table ) . This tissue-specific ceramide composition explains why Acer3 knockout dramatically increased the levels of C18:1-ceramide and total levels of ceramides in the brain ( Fig 5A and 5B ) , but did so only modestly in other tissues ( S1 Fig and S1 Table ) . Unexpectedly , Acer3 knockout also caused a marked increase in the levels of other long-chain ceramides in the brain ( C16 , C18:0 , C20:0 , C22:0 , and C22:1-ceramide ) , although these are not the most preferred substrates of Acer3 ( Fig 5A and 5B ) . We propose that some of ULCCs accumulated due to Acer3 deficiency were converted by other ceramidases to SPH , which was in turn re-acylated to form other ceramides by the action of different CerS isozymes . Our study demonstrates that Acer3 deficiency also led to an increase in the levels of C18:1- and/or C20:1-HexCers and sphingomyelins in the brain , most likely due to an accumulation of common precursors such as the ULCCs . Acer3 deficiency also increased the levels of these complex sphingolipid species in non-CNS tissues ( S1 Table ) . However , Acer3 deficiency did not increase the total levels of these complex sphingolipids in all the tissues examined , probably because these ULC complex sphingolipids are minor species in these tissues ( S1 Table ) [36] . Similarly , total levels of more complex glycosphingolipids , such as gangliosides and sulfatides , which are derived from HexCers , are not expected to be significantly affected by Acer3 deficiency although C18:1 or C20:1-gangliosides or sulfatides may be increased . To our knowledge , our study is the first to discover an age-dependent increase in the levels of both SPH and S1P in the mouse brain ( Fig 5C ) . Acer3 knockout nearly abolished the age-dependent increase in the levels of both brain SPH and S1P ( Fig 5C ) , suggesting that Acer3 upregulation is solely responsible for the increased generation of these bioactive lipids in the aging brain although other ceramidase activities were also found to be increased with age in the brain [42] . Acer3 deficiency also led to a slight decrease in the levels of both SPH and S1P in the lungs but not in the liver ( S1 Table ) , suggesting that Acer3 has a minimum role in regulating SPH and S1P levels in the non-CNS tissue likely due to the scarcity of its substrates , ULCCs , in these tissues . Acer3 deficiency mainly affects brain function without inducing any major pathological effect on other tissues that we examined ( S5 and S6 Figs ) . In the brain , Acer3 deficiency also causes a region-specific effect on the homeostasis of sphingolipids , altering the levels of ceramides , SPH , and S1P to a much greater extent in the cerebellum than in the cerebrum . This is consistent with the fact that Acer3 expression is much higher in the cerebellum than in the cerebrum ( Fig 1B and 1C ) . We showed that Acer3 knockout impaired motor coordination in mice ( Fig 7 ) without affecting general locomotor activity ( S2 Fig ) or muscle strength ( S4 Fig ) . Mechanistic studies suggest that Acer3 deficiency impairs motor coordination at least in part due to the degeneration of PCs ( Fig 9 ) although dysfunction of other types of neurons in different brain regions may also contribute to this phenotype . Interestingly , the degeneration of PC and impairment in motor coordination were observed in middle-aged knockout , but not younger mice . These data coincide with the finding that Acer3 deficiency altered the homeostasis of ceramides and their metabolites in the cerebellum to a greater extent in middle-aged mice than in young mice . It is worth noting that Acer3+/- mice at 8 months of age behaved similarly to Acer3+/+ mice at the same age in the rotarod ( S7A Fig ) or hindlimb clasping test ( S7B Fig ) , suggesting that a single copy of the Acer3 gene is sufficient to maintain motor coordination function . This is consistent with the finding that there is no difference in the content of any aforementioned brain ceramides between Acer3+/+ and Acer3+/- mice ( S7D and S7E Fig ) , suggesting that Acer3 is a haplosufficient gene in maintaining the homeostasis of sphingolipids in the brain . The close correlation between the dyshomeostasis of sphingolipids and pathology strongly suggests that Acer3 deficiency causes PC degeneration by altering the levels of sphingolipids in PCs and/or other brain cell types that interact with PCs . Ceramides , especially SLCCs , have been shown to be pro-death bioactive lipids [57] , whereas S1P functions as a pro-survival bioactive lipid [58] . The PC degeneration by Acer3 deficiency is probably due to both an aberrant rise in ceramides and a significant decline in S1P in the cerebellum . Because there was no difference in the number of PCs between Acer3+/+ and Acer3-/- mice at a young age , it appears that Acer3 deficiency does not affect the development of PCs . We surmise therefore that the PC loss in middle-aged mice is likely due to cell death . Since we did not observe apoptosis in all PCs in middle-aged Acer3-/- mice ( Fig 9C ) , the PC loss may be through a non-apoptotic cell death mechanism or that the neuronal death pathway operates gradually in the Acer3-/- mice , thus eluding detection . Interestingly , recent studies have also demonstrated that inhibiting biosynthesis of LCCs in the mouse brain due to mutation [59] or deficiency [48] in CerS1 leads to progressive cerebellar ataxia due to the degeneration of PCs; thereby suggesting that LCCs are essential for the survival of PCs , although their aberrant increase is also detrimental to these cells . Whether the PC degeneration resulting from Acer3 deficiency is a cell-autonomous or nonautonomous trait remains unclear because the global gene knockout approach cannot define a cell-type-specific effect of Acer3 deficiency on the dyshomeostasis of sphingolipids . Because ULCCs are mainly synthesized by CerS1 , which is expressed in neurons rather than glia cells in the brain [48] , we postulate that PC degeneration due to Acer3 deficiency may be a direct pathological effect of the dyshomeostasis of sphingolipids in neurons rather than in glial cells . However , to resolve this uncertainty requires a genetic approach by which Acer3 is ablated specifically in PCs . This approach is currently being explored in our laboratory . Although SPH , similar to ceramides , has been implicated in apoptosis in various cell types [47] , recent findings demonstrated that SPH can also regulate synaptic vesicle fusion and exocytosis [60] . Whether Acer3 deficiency adversely affects the neuronal synapse function by inhibiting SPH generation requires further investigations . An aberrant accumulation of glycosphingolipids or sphingomyelins has been shown to impair neuronal function in patients and in the mice with deficiency or mutations in enzymes responsible for their breakdown [61 , 62] . As discussed earlier , Acer3 deficiency only slightly increased the levels of some minor species of monohexosylceramides and sphingomyelins without affecting their total levels , so the pathological effects observed in Acer3 null mice may not be due to the minor alterations in these complex sphingolipids . Recently , alterations in gangliosides and/or sulfatides have been suggested to be detrimental to cerebral and cerebellar myelination , which is key to neuronal function [48] . Due to an insignificant effect on these complex sphingolipids , Acer3 deficiency is unlikely to impair myelination and myelination-associated neuronal function . Consistent with this view , we showed that Acer3 deficiency does not affect myelination and general locomotion activity . However , there is a possibility that an increase in C18:1 or C20:1-sphingomyelin and complex glycosphingolipids may adversely affect other neuronal functions , thereby contributing to the impairment in motor coordination and balance . This study and previous studies [37 , 41] are beginning to establish the proposition that different ceramidases have distinct roles in regulating the homeostasis of ceramides in vivo . In addition to Acer3 knockout mice , mouse models deficient in Asah1 [41] and Asah2 [37] , respectively , have been established . Different from Acer3 deficiency , Asah2 deficiency mainly alters the homeostasis of ceramides and SPH in intestines but not in the mouse brain [37] . Consistently , the deficiency of this ceramidase has not yet been reported to have any neurological phenotype in mice . We do not yet know the role for Acer1 or Acer2 in regulating the homeostasis of ceramides in the CNS , but a mouse model deficient in Acer1 or Acer2 is being developed in our laboratory . Compared to Acer3 knockout mice , Asah1 knockout mice have shown more profound phenotypic changes . Li et al have demonstrated that global knockout of Asah1 leads to embryonic lethality [63] . The same group has shown that knocking out Asah1 specifically in ovary caused apoptosis of oocytes , resulting in female infertility [64] . Recently , Alayoubi et al revealed that mice carrying a mutated Asah1 gene , which encodes an acid ceramidase mutant with minimal enzymatic activity , had necrosis in various tissues and died at a young age [41] . Asah1 deficiency markedly increased the total levels of ceramides in various tissues whereas Acer3 deficiency did so mainly in the brain . The distinct pathological consequences resulting from Acer3 and Asah1 deficiency are likely due to their distinct roles in regulating the hydrolysis of different ceramide species in tissues and in different subcellular compartments . In conclusion , the present study demonstrates that the age-associated upregulation of Acer3 plays a crucial role in the survival and/or function of PC in aging mice via maintaining the homeostasis of ceramides and their metabolites in the cerebellum . The Acer3 knockout mouse model can serve as an invaluable tool for elucidating the pathological role for dysregulation of ceramides in neurodegenerative diseases in humans .
All mice were housed under a constant room temperature ( 22°C ) , humidity level ( 55% ) , and a 12-h light:12-h dark cycle with food ( W . F . Fisher & Son; Somerville , NJ ) and water available ad libitum . The procedures for the generation of Acer3 null mice were approved under the number AR2123 by the Institutional Animal Care and Use Committee ( IACUC ) of the Medical University of South Carolina , SC , USA , and other animal studies were approved under the number 580863 by the IACUC of Stony Brook University , Stony Brook , NY , USA . All the animal studies reported in this study were conducted according to the Animal Research: Reporting In Vivo Experiments ( ARRIVE ) guidelines , developed by the National Centre for the Replacement , Refinement & Reduction of Animals in Research ( NC3Rs ) . The mouse Acer3 gene consists of 11 exons ( Fig 2A ) . An Acer3 targeting vector was constructed so that upon homologous recombination between the targeting vector and the WT Acer3 allele , exon 8 of the Acer3 gene was replaced by the neomycin resistant gene ( Neo ) cassette ( Fig 2A ) . To construct the Acer3 targeting vector , with PCR , we amplified a 1 . 3 kb fragment ( the short arm ) and 5 . 8 kb fragment ( the long arm ) upstream and downstream , respectively , of exon 8 from the genomic DNA of 129 SvEv embryonic stem ( ES ) cells . The short and long arms were inserted upstream and downstream , respectively , of the Neo gene in the vector OSDUPDEL ( Fig 2A ) , a gift from Dr . Oliver Smithies at the University of North Carolina at Chapel Hill , Chapel Hill , North Carolina . The resulting Acer3 targeting construct was linearized by the restriction enzyme Not1 and transfected into 129SvEv ES cells by electroporation as described [65] . Transfected ES cells were cultured in medium containing G418 ( 200 μg/ml ) , and the resulting G418-resistant ES clones in which one copy of the Acer3 alleles was correctly targeted by Neo were screened by Southern blot analyses ( Fig 2B ) , expanded , and injected into mouse C57BL/6J blastocysts using standard procedures as described [65] . The injected blastocysts were transplanted into the uteri of pseudopregnant C57BL/6J mice , and the resulting chimeric mice were crossed to WT C57BL/6J mice to generate Acer3+/- mice . Acer3+/- mice with a mixed genetic background were backcrossed to WT C57BL/6J mice for 16 generations to obtain Acer3+/- mice with the sole C57BL/6J genetic background . These heterozygous mice were inbred to generate Acer3-/- mice and their Acer3+/+ littermates , which were used for further studies . DNA was isolated from mouse tail clips as described [66] and subjected to PCR-based genotyping using two different primer pairs ( F1/B1 and F1/B2 ) as shown in Fig 2A . Acer3+/+ , Acer3+/- , and Acer3-/- mice were identified according to the PCR product patterns as shown in Fig 2C . Protein concentrations were determined with bovine serum albumin ( BSA ) as a standard using bicinchoninic acid ( BCA ) protein determination kits ( Thermo Scientific; Waltham , MA ) according to the manufacturer’s instructions . Tissues were collected from mice euthanized by CO2 suffocation followed by cervical dislocation , rinsed with phosphate buffered saline ( PBS ) , snap-frozen in liquid nitrogen , and stored at -80°C . The tissues were homogenized on ice with an electric tissue tearor ( Biospec Products; Bartlesville , OK ) in Buffer A ( 25 mM Tris-HCl , pH 7 . 4 , 150 mM NaCl and 0 . 25 M sucrose ) supplemented with a protease inhibitor mixture ( Roche; Indianapolis , IN ) . After brief sonication , the tissue homogenates were centrifuged at 1 , 000 g at 4°C for 5 min to sediment nuclei and tissue debris , and the resulting supernatants were centrifuged at 100 , 000 g at 4°C for 45 min to pellet all cell membranes , which were resuspended by brief sonication in Buffer B ( 25 mM Tris , pH7 . 4 , 5mM CaCl2 and 150 mM NaCl ) . Membrane homogenates ( 20 μg or 40 μg of protein per tissue ) were measured for alkaline ceramidase activity using NBD-C12-PHC as a substrate by a thin layer chromatography ( TLC ) method [30] or using regular ceramides as substrates by liquid chromatography tandem-mass spectrometric analysis ( LC-MS/MS ) method [35] . Briefly , NBD-C12-PHC or a regular ceramide was dispersed by water bath sonication in Buffer C ( 25 mM glycine-NaOH , pH 9 . 4 , 5 mM CaCl2 , 150 mM NaCl , and 0 . 3% Triton X-100 ) . The lipid-detergent mixtures were boiled for 30 s and chilled on ice immediately to form homogeneous lipid-detergent micelles , which were mixed with an equal volume of membrane homogenates suspended in Buffer B . Enzymatic reaction was carried out at 37°C for 40 min , and stopped by heating at 100°C on a heating block . The reaction mixtures were completely dried , cooled down to room temperature , and dissolved in chloroform/methanol ( 2:1 ) . For the TLC method , 20 μl of each reaction mixture was spotted onto TLC plates , which were developed in a solvent system consisting of chloroform , methanol , and 25% ammonium hydroxide ( 90:30:0 . 5 ) . The TLC plates were dried and scanned by an imaging system ( Typhoon FLA 7000 , GE Healthcare Life Sciences; Pittsburgh , PA ) set at the fluorescence mode . The fluorescent band of NBD-C12-fatty acid ( NBD-C12-FA ) released from NBD-C12-PHC was identified according to the standard NBD-C12-FA spotted on the same TLC plate . The content of NBD-C12-FA in each reaction was determined according to a standard curve generated from known concentrations of the standard NBD-C12-FA . For the LC-MS/MS method , lipids were extracted from dried reaction mixtures with chloroform and methanol . Sphingoid bases in the lipid extracts were determined by LC-MS/MS with D-e-C17-SPH as an internal standard as described [35] . Tissues collected from Acer3+/+ or Acer3-/- mice were homogenized on ice as previously described in buffer E ( 25 mM Tris–HCl , pH 7 . 4 , 150 mM NaCl , 1mM EDTA , and 1mM EGTA ) . Lipids from tissue homogenates ( 2 mg protein per sample ) were extracted with ethyl acetate/isopropanol/water ( 60/30/10 , v/v ) . The lipid extracts were dried under N2 gas stream and reconstituted in methanol , and sphingolipids were determined by LC-MS/MS performed on a TSQ 7000 triple quadrupole mass spectrometer ( Thermo Finnigan; Ringoes , NJ ) as described [35] . Amounts of sphingolipids in different samples were normalized to protein contents . Mice were deeply anesthetized with urethane ( 1 . 5 g/kg ) and perfused transcardially with PBS followed by 4% paraformaldehyde ( PFA ) as described [59] . Brains , left lateral lobes of liver , and left lungs were removed and post-fixed in 4% PFA before dehydration and embedding in paraffin . Sagittal sections from one individual mouse brain were stained with an antibody against the PC marker calbindin-D-28K ( Sigma-aldrich , St . Louis , MO ) using a Histostain-Plus IHC staining kit ( Invitrogen; Grand Island , NY ) . Cells stained positive for calbindin D in the cerebellum were enumerated from 3 random 10× fields of view under an Imager M2 microscope ( Zeiss; Thornwood , NY ) in a blind manner , and three serial sections were scored and the average score of each mouse cerebellum was calculated . Lung and liver tissue sections were stained with anti-Ki67 antibody ( Biocare Medical; Concord , CA ) using a Histostain-Plus IHC staining kit ( Invitrogen; Grand Island , NY ) . Ki67-positive cells were scored under an Imager M2 microscope ( Zeiss; Thornwood , NY ) in a blind manner . In lungs , Ki67-positive cells in the bronchial epithelium and alveoli were scored from 4 random 10× and 4 random 20× microscopic fields of view , respectively , and the percentage of Ki67-positive cells was calculated . In liver , Ki67 positive cells were enumerated from 4 random 10× fields of view . After mice were perfused with 4% PFA under deep anesthesia , brains were removed and cut into sagittal slices on a cryostat . Cryosections were subjected to TUNEL assays using an in situ cell death detection kit ( Roche; Indianapolis , IN ) according to the manufacturer’s instructions . After TUNEL staining , sections were subjected to IFS with calbindin D-28K antibody ( Sigma-aldrich , St . Louis , MO ) . TUNEL and IFS were analyzed under an Imager M2 microscope ( Zeiss; Thornwood , NY ) in a blind manner . Lung and liver paraffin-embedded sections were prepared as described above and subjected to TUNEL assays using TACS® 2 TdT diaminobenzidine kit ( Trevigen; Gaithersburg , MD ) according to the manufacturer’s instructions . TUNEL-positive cells were scored in a blind manner as described earlier . In the lung sections , positive cells in bronchial epithelium and in alveoli were counted from 4 random 10× and 4 random 20× microscopic fields of view , respectively . In the liver sections , positive cells were enumerated from 4 random 10× fields of view . RNAs were extracted from fresh brain tissues using RNeasy mini kits ( Qiagen; Valencia , CA ) according to the manufacturer’s instructions . The RNAs were reversely transcribed into cDNAs , which were subjected to qPCR analyses as described [67] using primer pairs specific to each of the following genes: Acer1 ( 5’-ATGCTCATAGGTCTGTTCTC-3’ and 5'-AGTGGTTATAGTTACCAGGC-3’ ) , Acer2 ( 5’-GTGTGGCATATTCTCATCTG-3’ and 5’-TAAGGGACACCAATAAAAGC-3’ ) , Acer3 ( 5’-GTGTGGCATATTCTCATCTG-3’ and 5’-TAAGGGACACCAA TAAAAGC ) , and β-Actin ( 5’- GATGTATGAAGGCTTTGGTC-3’ and 5’-TGTGCACTTTTATTGGTCTC-3’ ) . qPCR was performed on an ABI Prism 7000 sequence detection system and mRNA levels for each gene were analyzed with the ABI Prism 7000 software ( Applied Biosystems ) . Relative mRNA levels of Acer1 , Acer2 , or Acer3 were estimated using ΔΔCt method as described [68] with β-Actin as internal control . Paraffin cerebellar sections from 8-month-old mice were subjected to Luxol fast blue staining and myelin sheaths were assessed as described [59 , 69] . Briefly , images were taken using a black and white camera then pseudo-colored using Imagepro software . The width of myelinated tracts was measured as an indicator for alteration of myelin . Mouse tissues were minced with a razor blade and homogenized as previously described in Buffer F ( 50 mM Tris-HCl pH 7 . 5 , 250 mM NaCl , 1mM EDTA , 1mM EGTA , 1% Triton X-100 , and 1% SDS ) supplemented with protease inhibitor mixture ( Roche; Indianapolis , IN ) and phosphatase inhibitor cocktail ( Thermo Scientific; Waltham , MA ) . Proteins were separated on SDS-polyacrylamide gels and transferred onto nitrocellulose membranes , which were probed with the primary antibody against Ki67 ( Biocare Medical; Concord , CA ) , proliferating cell nuclear antigen ( PCNA ) , caspase 3 ( Cell Signaling; Beverly , MA ) , cleaved caspase 3 ( Cell Signaling; Beverly , MA ) , MBP ( Sigma-aldrich; St . Louis , MO ) , or β-actin ( Santa Cruz; Dallas , Texas ) . Band density was quantified by densitometry using NCBI ImageJ . Open field tests were performed as described [70] with slight modification . The open field consists of a square arena ( 40×40 cm ) , with a floor enclosed by continuously 25-cm-high walls . The animals were tested in this field during the light phase of their light/dark cycle . The test was initiated by placing a single mouse in the center of the arena and letting it move freely for 5 min . Mouse behavior was continuously videotaped by a video camera placed over the structure and then encoded using a continuous sampling method by Ethovision XT 7 . 0 ( Noldus; Attleboro , MA ) . The arena was cleaned with ethanol after every test . A number of parameters were collected during the session . They comprised: ( 1 ) Velocity: the distance of the mouse moved per second ( cm/s ) ; ( 2 ) Distance: the distance ( cm ) the mouse moved in the five min of the test; ( 3 ) Latency to corner: the time ( s ) from when the mouse was first placed in the arena to when the mouse faced into a corner; ( 4 ) Rearing: the number of times the mouse reared on its hindpaws . Mice were placed in the center of a wooden-framed wire mesh , and the frame was inverted at a height of 35 cm above soft padding . Mice were observed for movement while hanging from the wire mesh , and the hang time was measured from the time the frame was inverted to the time the mouse fell . The maximum length of time the frame was inverted was one minute [71] . The muscular strength of mouse forepaws or hindpaws was measured using a grip strength meter ( TSE Instruments International; Chesterfield , MO ) . Two attachments were used—a bar specific for mouse forepaws and a wire grid that allowed for simultaneous grip with all four paws . In both testing scenarios , the mouse was held by the base of its tail and allowed to firmly grab the attachment with either the forepaws , or with forepaws and hindpaws combined . A gentle and consistent tug backwards on the tail was applied to ensure it was firmly gripping the attachment before being quickly pulled off perpendicular to the force gauge in one motion [72 , 73] . The software for the meter displayed the peak force achieved during this motion . Five successive measurements were taken and the three highest measurements were averaged to give the strength score . Each mouse was given fifteen seconds of rest between each trial . The peak force ( N ) of each trial for the hindpaws was calculated by subtracting the peak force for the forepaws from the peak force of all four paws . Rotarod tests were performed using a rotarod device ( Coulbourn Instruments; Whitehall , Pennsylvania ) . Mice were trained for 10 min , 6 days a week , for a total of 9 sessions . Each session included 3 trials . The first two trials ( 3 min each ) consisted of assisted training where the mice were gently supported by the trainer’s fingers or hand while walking on the rod . The third trial was consisted of independent walking ( for 4 min ) . For each trial , animals were trained at their maximum tolerable speed . Training was truncated at 25 rpm for both assisted and independent stepping . Mice that achieved independent stepping at 25 rpm before 9 sessions were still given the same training duration until 9 sessions were met . Note that this baseline training was crucial to ensure that mice were falling off the rod not because they were unfamiliar with the task , but because they were not able to meet the demands of the rotarod task . Mice were tested for two consecutive days immediately following their final training day . Each testing day consisted of three constant speeds; the latency to fall at each constant running speed was used as measures for rotarod tests [74 , 75] and the highest score of both days was recorded . Footprint tests were performed as described [76] . Briefly , the hindfeet and forefeet of the mice were coated with blue and red nontoxic paints , respectively . Mice were then allowed to walk along a 40-cm-long , 10-cm-wide runway ( with 15-cm-high walls ) into an enclosed box . All mice had three training runs and were then given one testing run . A fresh sheet of white paper was placed on the floor of the runway for each run . The footprint patterns were analyzed for four parameters ( all measured in centimeters ) : ( 1 ) stride length measured as the average distance of forward movement between each stride , ( 2 ) hindpaw step width measured as the average distance between left and right hind footprints , ( 3 ) forepaw step width measured as the average distance between and left and right front footprints , and ( 4 ) distance between ipsilateral forepaw and hindpaw footprints measured to indicate the extent of overlap between ipsilateral forepaws and hindpaws ( paw overlap ) . For each parameter , three values were measured , excluding where the animal was initiating and finishing movement , respectively . The mean value of each set of three values was used in subsequent datum analyses . Beam walking tests were performed as described [76] with slight modification . Two customized round beams with diameters of 17 and 10 mm were used . The beams were placed horizontally , 50 cm above the bench surface , with one end mounted on a narrow support and the other end attached to an enclosed box ( 20×10×10 cm ) into which mice could escape . During training , mice were placed at the start of the beam and trained over 3 days ( 4 trials per day ) to traverse the beam to the enclosed box . Once the mice were trained , they received 3 consecutive trials on each beam , progressing from the 17-mm to 10-mm beam . Mice were allowed up to 60 s to traverse each beam . The time to traverse each beam was recorded . If a mouse fell off the beam , its latency was recorded as 60 s . The walking distance was measured for each trial . Mouse hindlimb movement was continuously monitored by a video camera , and the recorded videos were used for enumerating the times that the hindfeet of mice slip off each beam in each trial . For each parameter , the mean value of the three trials for each beam was used in subsequent datum analyses . Hindlimb-clasping tests were carried out as described [77] . Mice were individually lifted by grasping their tails near the base and their hindlimb positions were observed for 10 s . If the hindlimbs were consistently splayed outward and away from the abdomen , it was scored as 0 . If one hindlimb was retracted toward the abdomen for more than 5 s , it was scored as 1 . If both hindlimbs were partially retracted toward the abdomen for more than 5 s , it was scored as 2 . If its hindlimbs were entirely retracted and touching the abdomen for more than 5 s , it was scored as 3 . Cerebella were dissected from mice perfused with 3 . 4% PFA/1 . 25% glutaraldehyde under deep anesthesia and sliced into 60 μm sagittal sections under a microtome [78] . The tissue sections were then processed for TEM as describe[79] . Briefly , the tissue sections were washed with 0 . 1 M sodium cacodylate buffer ( pH 7 . 4 ) containing 2 mM CaCl2 and post-fixed for 1 h in a reduced osmium solution containing 2% osmium tetroxide , 1 . 5% potassium ferrocyanide 2 mM CaCl2 in 0 . 15 mM sodium cacodylate buffer ( pH 7 . 4 ) . After being washed with double-distilled water ( ddH2O ) , the tissue sections were incubated with a 1% thiocarbohydrazide ( TCH ) solution in ddH2O for 20 minutes at room temperature ( RT ) . After being washed with ddH2O , the tissue sections were fixed with 2% osmium tetroxide in ddH2O for 30 min at Rt , followed by 1% uranyl acetate ( aqueous ) overnight at 4°C . After being washed with ddH2O , the tissue sections were subjected to en bloc Walton’s lead aspartate staining in a 60°C oven for 30 min as described [80] After five 3-min rinses , the tissue sections were dehydrated sequentially in 20% , 50% , 70% , 90% , 100% , and 100% ice-cold ethanol ( anhydrous ) , 5 min each , followed by anhydrous ice-cold acetone at RT for 10 min . The tissue sections were infiltrated with 30% Epon-Aradite for 2 h , 50% Epon-Aradite for another 2 h , 75% Epon-Aradite overnight , and 100% Epon-Aradite for a day with one change before being embedded in two permanox slides by flat-embedding procedure in a 60°C oven for 48 h . The polymerized tissue blocks were cut into ultrathin sections ( 80 nm ) , which were collected on 150 mesh grids . and examined under an FEI CM120 transmission electron microscope ( equipped with a Gatan GIF100 image filter ) operating at a beam energy of 120 keV . Images were acquired using a Gatan 1 k × 1 k cooled charge-coupled device ( CCD ) camera . Statistical analyses were performed using the Student’s t-test or two-way Anova using Graphpad Prism 5 . 0 . Post-hoc Bonferroni’s correction was used for multiple comparisons . p-values<0 . 05 were considered statistically significant . | Bioactive sphingolipids , such as ceramides and sphingosine-1-phosphates , have been implicated in neurodegenerative diseases . However , it remains unclear as to how the homeostasis of these bioactive lipids is sustained . Alkaline ceramidase 3 ( ACER3 ) catalyzes the hydrolysis of saturated long-chain ceramides ( C18:1-ceramide and C20:1-ceramide ) to generate sphingosine ( SPH ) , which is phosphorylated to form sphingosine-1-phosphate ( S1P ) . In this study we found that Acer3 is upregulated with age in the mouse brain and blocking Acer3 upregulation elevates the levels of ceramides while reducing S1P levels in the brain , thereby resulting in Purkinje cell loss and cerebellar ataxia . This study not only offers novel insights into the role for the homeostasis of ceramides and their metabolites in regulating normal aging of the cerebellum , but also provides a useful genetic tool to dissect the mechanism by which an aberrant accumulation of ceramides results in age-related neurological disorders . | [
"Abstract",
"Introduction",
"Results",
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"Methods"
] | [] | 2015 | Alkaline Ceramidase 3 Deficiency Results in Purkinje Cell Degeneration and Cerebellar Ataxia Due to Dyshomeostasis of Sphingolipids in the Brain |
Program decision-making for trachoma elimination currently relies on conjunctival clinical signs . Antibody tests may provide additional information on the epidemiology of trachoma , particularly in regions where it is disappearing or elimination targets have been met . A cluster-randomized trial of mass azithromycin distribution strategies for trachoma elimination was conducted over three years in a mesoendemic region of Niger . Dried blood spots were collected from a random sample of children aged 1–5 years in each of 24 study communities at 36 months after initiation of the intervention . A multiplex bead assay was used to test for antibodies to two Chlamydia trachomatis antigens , Pgp3 and CT694 . We compared seropositivity to either antigen to clinical signs of active trachoma ( trachomatous inflammation—follicular [TF] and trachomatous inflammation—intense [TI] ) at the individual and cluster level , and to ocular chlamydia prevalence at the community level . Of 988 children with antibody data , TF prevalence was 7 . 8% ( 95% CI 6 . 1 to 9 . 5 ) and TI prevalence was 1 . 6% ( 95% CI 0 . 9 to 2 . 6 ) . The overall prevalence of antibody positivity to Pgp3 was 27 . 2% ( 95% CI 24 . 5 to 30 ) , and to CT694 was 23 . 7% ( 95% CI 21 to 26 . 2 ) . Ocular chlamydia infection prevalence was 5 . 2% ( 95% CI 2 . 8 to 7 . 6 ) . Seropositivity to Pgp3 and/or CT694 was significantly associated with TF at the individual and community level and with ocular chlamydia infection and TI at the community level . Older children were more likely to be seropositive than younger children . Seropositivity to Pgp3 and CT694 correlates with clinical signs and ocular chlamydia infection in a mesoendemic region of Niger . ClinicalTrials . gov NCT00792922 .
Trachoma , caused by repeated ocular infection with Chlamydia trachomatis ( Ct ) , has been targeted by the World Health Organization ( WHO ) for elimination as a public health problem by the year 2020 . As part of the strategy to achieve elimination , WHO recommends annual mass drug administration ( MDA ) of azithromycin in endemic districts [1] . Program targets related to MDA focus on the district-level prevalence of trachomatous inflammation—follicular ( TF ) amongst children aged 1–9 years . To monitor progress towards elimination , population-based impact surveys are recommended to evaluate whether a district has reached the threshold of less than 5% TF prevalence in 1–9-year-olds and can cease azithromycin distribution . Two years after cessation of MDA , a surveillance survey to ensure that district-wide TF prevalence in 1–9-year-olds remains below 5% is conducted prior to the validation process . Currently , there are no guidelines for post-validation surveillance . These surveys rely on a clinical grading scheme that is relatively inexpensive and simple to perform , but is poorly correlated with ocular Ct infection in low-prevalence settings [2] . Following MDA , the clinical sign trachomatous inflammation—intense ( TI ) has been shown to correlate better with infection than TF does [3] . However the measurement of clinical signs is subject to inter-grader variability and lack of real-time auditing since grading is performed in the field and thus can only later be validated or audited if images are taken . As trachoma elimination programs stand to benefit from an accurate , reproducible assessment of trachoma prevalence , other testing methods may be useful to help guide program decisions . These include tests of infection ( polymerase chain reaction [PCR] testing of ocular swabs ) and antibody-based testing [4–7] . Antibodies to Ct antigens may act as markers of cumulative exposure to Ct . Two previously described Ct antigens , Pgp3 and CT694 , have been shown to be reactive against sera in young children living in trachoma-endemic communities [4 , 7 , 8] . At the individual level , antibodies to these proteins demonstrate high sensitivity to ocular infection and high specificity against non-endemic control specimens [8–10] . However , individual associations may not always hold at the community level , and trachoma elimination programs treat ocular Ct infection on a population level . Additionally , as antibody markers are not yet widely used to assess for Ct prevalence , better characterization of how seropositivity compares to other methods of assessing trachoma prevalence is necessary . Here , we evaluate the association between seropositivity , PCR positivity , and clinical signs of active trachoma ( TF and TI ) at the individual and community level in a region of Niger where some trachoma transmission is occurring ( TF prevalence approximately 25% at baseline ) . Data were collected during the final follow-up visit of the Partnership for the Rapid Elimination of Trachoma ( PRET ) -Niger trial , in which communities were randomized to receive annual or biannual oral azithromycin for 3 years in order to assess the impact of treatment frequency on ocular chlamydia infection [11] .
The study methods have been previously reported in detail elsewhere [11–13] . Briefly , a cluster randomized trial of annual versus biannual mass azithromycin distribution for trachoma control was conducted in the Matameye district of the Zinder region of Niger from May 2010 until August 2013 [4–6] . Data on active trachoma and ocular infection were collected biannually on children aged 0–5 years; dried blood spots for serological analysis were collected only at the 36-month time point and only from children aged 1–5 years . Dried blood spots were shipped to CDC at ambient temperature and tested for antibodies from July to August 2014 . Communities were chosen from among six different catchment areas for primary health care facilities and were eligible for inclusion if they met the following criteria: ( 1 ) contained a population between 250 to 600 persons , ( 2 ) were located more than 4 kilometers from the center of any semi-urban area , and ( 3 ) had a prevalence of active trachoma more than 10% in children aged 0–5 years [11] . 235 communities in the 6 health centers were deemed eligible , of which 48 were randomly selected for inclusion in the trial . Children aged 1–5 years were included in this analysis , due to the inability of antibody tests to differentiate between maternal-child antibodies in <1–year-olds . 48 communities were randomly divided into 4 treatment arms in a 2x2 factorial design ( 12 communities per arm ) , comparing two azithromycin coverage targets ( standard versus enhanced coverage ) and annual versus biannual treatment . Randomization of communities to treatment arms was done using RANDOM and SORT functions in Microsoft Excel ( Version 2003 ) . Only communities from the enhanced coverage arms were included in testing for antibodies ( N = 24 communities ) for logistical reasons . Trained study health workers conducted a full household census in all communities prior to the initial survey visit . During the baseline visit , adults in the household consented to census data collection , and study personnel recorded the name , sex , and age or date of birth for all individuals in the household . After consent was obtained , study participants were examined for the presence of TF and TI . Clinical grading of each everted superior tarsal conjunctiva was performed using a 2 . 5x binocular loupe and a torch light , if necessary , per the WHO grading system . Clinical grading was performed according to the WHO simplified grading system of TF being the presence 5 or more follicles >0 . 5 mm in diameter and TI as inflammation severe enough to obscure 50% of deep tarsal vessels in one or both eyes [14] . Prior to swabbing , a trained photographer took at least 2 photographs of the right eyelid of all participants using a Nikon D-series camera and a Micro Nikon 105 mm; f/2 . 8 lens ( Nikon , Tokyo , Japan ) . After conjunctival examination , a Dacron swab was passed 3 times over the right upper tarsal conjunctiva , rotating the swab approximately 120 degrees between each pass . All samples were placed immediately on cold packs in the field and transferred to -20°C within 10 hours , then shipped on cold packs to University of California , San Francisco , CA , USA where they were stored in -80°C freezers until processing . PCR testing was performed for children aged 0–5 years . Samples from the same village , age in years , and visit were randomly pooled into groups of five for group testing , with a possible remainder pool of one to four samples [15] . Pooled samples were tested for the presence of Ct DNA using the Roche Amplicor qualitative PCR assay ( Roche Molecular Systems , Indianapolis , IN , USA ) . Community prevalence was estimated from the pools as previously described [11 , 15] . In communities randomized to annual treatment , study participants age 6 months of age and older received a directly observed dose of oral azithromycin ( 20 mg/kg up to a maximum dose of 1 g in adults ) . In biannually treated communities , only children up to 12 years of age were offered treatment . Children under 6 months of age in all communities were offered topical tetracycline ointment ( 1% ) to be applied to both eyes twice a day for six weeks . Pregnant women in the annual arm and individuals allergic to macrolides were offered topical tetracycline . All communities were visited up to four days in order to achieve 90% treatment coverage [12 , 13] . Children under 5 years of age were selected randomly in each village for blood sample collection via finger stick or heel stick , with a goal of 50 children per village . Blood spots were analyzed for antibody to Ct antigens Pgp3 and CT694 using a multiplex bead array assay on a Luminex 200 platform , as previously described [7] . Results were reported as median fluorescence intensity minus background ( MFI-BG ) where background is the signal from beads run with buffer only . Positivity cut-off for Pgp3 was greater than or equal to 1083 , and CT694 cutoff was greater than or equal to 496 as determined by receiver operator characteristic ( ROC ) curve analysis from a pediatric U . S . -based negative panel ( N = 117 ) and Tanzania based positive panel from children with ocular Ct infection ( N = 40 ) [7] . Data were entered into a customized database ( Microsoft Access v2007 ) developed at the Dana Center , Johns Hopkins University . To estimate associations between seropositivity , clinical trachoma , and age at the individual level , we used generalized linear models with a binomial distribution and log link to estimate prevalence ratios ( PR ) . All standard errors were clustered at the community level , which was the randomization unit of the study . As individual-level PCR data were not available , associations between seropositivity to the Ct antigen and ocular chlamydia infection were conducted only at the community level . We additionally analyzed the association between seropositivity and clinical trachoma at the cluster level . We used linear regression models to evaluate relationships between trachoma indicators at the community level . All analyses were conducted in Stata 14 . 1 ( StataCorp , College Station , TX ) . All procedures and protocols for this study were approved by the Committee for Human Research of UCSF , and le Comité Consultatif National d’Ethique du Minstère de la Santé Publique , Niger ( Ethical Committee , Niger Ministry of Health ) . The study’s Data Safety and Monitoring Committee observed the study implementation during annual reviews of quality assurance , as appointed by the PRET study Executive Committee . All village leaders of communities within the study agreed to participate in the trial with written ( thumbprint ) consent . For children under the age of 16 , consent was given by a parent or a guardian . All persons participating in the trial were given the opportunity to be treated according to their community’s random treatment assignment . Communities not included in the study were offered treatment through the national treatment program . CDC personnel did not have access to personal identifying information and were determined to be non-engaged in the study .
Children with antibodies to either Pgp3 or CT694 were more likely to have TF ( PR , 1 . 90 , 95% CI 1 . 49 to 2 . 43 , P<0 . 001 ) and also TI , although the latter relationship was not statistically significant ( PR 1 . 65 , 95% CI 0 . 99 to 2 . 75 , P = 0 . 06 ) . Fig 1 shows the community-level prevalence of TF and ocular chlamydia compared to seropositivity to Pgp3 and/or CT694 . At the community level , Pgp3 and/or CT694 seroprevalence was significantly correlated with ocular chlamydia infection ( linear regression coefficient 0 . 19 , 95% CI 0 . 08 to 0 . 29 , P = 0 . 001 ) , TF prevalence ( linear regression coefficient 0 . 25 , 95% CI 0 . 13 to 0 . 36 , P<0 . 001 ) , and TI prevalence ( linear regression coefficient 0 . 07 , 95% CI 0 . 004 to 0 . 14 , P = 0 . 04 ) . The probability of having an antibody response to Pgp3 or CT694 increased with increasing age ( PR 1 . 25 per one-year increase in age , 95% CI 1 . 17 to 1 . 35 , P-trend<0 . 001; Fig 2 ) . Older age was not significantly associated with a diagnosis of TF ( PR 1 . 02 per year , 95% CI 0 . 89 to 1 . 16 , P-trend = 0 . 82 ) or TI ( PR 0 . 87 , 95% CI 0 . 60 to 1 . 25 , P-trend = 0 . 45 ) . There was no significant difference between study arms in the percentage of children antibody-positive to Pgp3 ( PR 0 . 72 biannual versus annual , 95% CI 0 . 39 to 1 . 33 , P = 0 . 29 ) or CT694 ( PR 0 . 81 biannual versus annual , 95% CI 0 . 51 to 1 . 28 , P = 0 . 36; Table 2 ) .
The presence of antibodies to Ct antigens was correlated with both TF and ocular Ct infection following a 36-month annual or biannual mass azithromycin distribution program in a trachoma-endemic region of Niger . However , there was no difference in serologic outcomes by study arm , consistent with clinical data suggesting that biannual treatment did not significantly alter transmission of ocular chlamydia compared to annual treatment [11] . That serologic outcomes were consistent with other trachoma indicators supports the finding that antibodies to Ct antigens are correlated with TF and ocular Ct infection and provide complementary information . Future work evaluating serologic outcomes in a trial with a significant effect on ocular Ct infection or TF would provide additional evidence about the relationship between these indicators . Currently , decision-making for MDA in trachoma elimination programs relies solely on clinical grading of TF . Grading of clinical trachoma is subjective , and prevalence surveys have demonstrated that there is poor agreement between clinical disease and ocular Ct infection , particularly after multiple rounds of antibiotic treatment [2 , 3 , 14 , 16] . Additionally , TF may be observed in the absence of infection , either as residual inflammation from the etiologic agent Ct[17 , 18] , or due to other non-chlamydial bacteria [19] . Point prevalence of TF , or a test of infection , reflects disease or infection state at a current point in time , whereas serologic patterns may allow for identification of longer-term patterns in Ct transmission [7 , 8] . Anti-Ct antibody responses increased with age in this study , whereas TF prevalence was not significantly different across age groups , suggesting that antibody positivity rates represent the pool of exposed individuals rather than currently or recently-infected ones . While PCR assessment of ocular Ct infection and clinical grading for TF and TI represent cross-sectional prevalence of trachoma , age-seroprevalence curves may provide additional insight to changes in transmission of ocular Ct over time . PCR or NAAT testing has historically been too costly for use in program settings[20] but cost-effective PCR tests are now being evaluated in program contexts [6] . In ocular swab specimens , PCR tests for Ct infection are a more specific indicator for the causative agent of trachoma than antibody testing in sera , as antibody responses to the antigens we studied cannot differentiate between exposure to ocular Ct and other chlamydial infections . Perinatal transmission of Ct from mothers with urogenital chlamydia could potentially lead to seropositivity among young children , as could sexual exposure in individuals after the age of sexual debut . However , focusing on the younger children and on the age-seroprevalence curve rather than absolute rates of seropositivity may allow for distinction between ongoing ocular Ct transmission and a single exposure . For example , in the Solomon Islands , a lack of increase in seropositivity to Pgp3 in 1–9-year-olds correlated with low infection rates , despite the 26% prevalence of TF , contrasting to the steep increase in seropositivity with age observed amongst 1–9-year-olds in Kiribati where TF prevalence was 28% and infection prevalence was 24% [21 , 22] . Antibody testing for chlamydial antigens has been conducted in a number of trachoma program settings . In treatment-naïve communities , the slope of the age seroprevalence curve increased with increasing community TF prevalence [4 , 21] . A significant decline in antibody responses has been shown following mass azithromycin distribution compared to pre-treatment levels in a cross-sectional study [8] . In this mesoendemic region of Niger , we noted more than 30% prevalence of antibodies to Pgp3 and CT694 , consistent with results from mesoendemic communities in Tanzania [4] . In settings where surveillance surveys for trachoma elimination have been conducted , age seroprevalence curves corresponded to decreases in trachoma transmission [23] . Seroprevalence of antibodies to Pgp3 in children ages 1 to 9 years in these surveys ranged from less than 2% in some surveys[24 , 25] to as high as 7 . 5%[26] but without a steep increase in age seroprevalence curves seen in settings with ongoing transmission . The current data add to this body of knowledge by evaluating antibody responses at impact surveys after 3 rounds of MDA , a program setting for which limited data exist . TF prevalence in this study was 7 . 5% after 3 years of MDA and thus antibody data do not come from a setting in which elimination thresholds have been achieved . Furthermore , the communities included in this analysis were treated with an enhanced coverage target , with up to four days of treatment . Antibiotic coverage may have been higher than is seen in programmatic or trial settings with lower coverage targets . The antibody responses and curves therefore may not be representative of what would be seen in a previously-endemic setting that has reached the elimination threshold . Trachoma programs typically include children up to age 9 in monitoring . Other studies have shown further increases in antibody responses in children aged 6–9 years[21 , 26] , and inclusion of those ages here may have improved our ability to draw inferences from the shape of the curve . Since ocular swabs were pooled for PCR analysis , we were unable to obtain individual-level correlations between PCR and antibody positivity . WHO currently recommends use of TF for deciding programmatic endpoints . Laboratory testing , including Ct serology , could be used as a supplement or replacement for TF when conducting surveillance after validation of the elimination of trachoma as a public health problem , given serology is generally inexpensive , objective , and provides estimates of exposure over time . [26] The elimination thresholds do not require a complete absence of ocular Ct infection , and therefore infection may still be present in communities that have reached the district-wide elimination threshold , as was seen in Tanzania where infection was present but did not lead to re-emergence . [27] Having a test of exposure , or of repeated infection , would allow more complete evaluation of the history of exposure to ocular Ct in children . The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention . | Trachoma programs currently use the clinical sign of trachomatous inflammation-follicular ( TF ) to guide community treatment decisions and evaluate response to mass drug administration with azithromycin . These programs rely on clinical grading that poorly correlates with infection with the causative agent of trachoma , Chlamydia trachomatis ( Ct ) , in low prevalence areas . Serologic measures of Ct may provide additional information about exposure and transmission patterns . Here , we evaluated the relationship between serologic markers of Ct , infection , and TF at the individual and community levels to evaluate the utility of serology for measuring trachoma in a mesoendemic region of Niger . We found that serologic markers correlated with both infection and TF , indicating that inclusion of serologic markers may be useful to guide trachoma decision making . | [
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] | 2019 | Community-level chlamydial serology for assessing trachoma elimination in trachoma-endemic Niger |
Bloodstream form trypanosomes avoid the host immune response by switching the expression of their surface proteins between Variant Surface Glycoproteins ( VSG ) , only one of which is expressed at any given time . Monoallelic transcription of the telomeric VSG Expression Site ( ES ) by RNA polymerase I ( RNA pol I ) localizes to a unique nuclear body named the ESB . Most work has focused on silencing mechanisms of inactive VSG-ESs , but the mechanisms involved in transcriptional activation of a single VSG-ES remain largely unknown . Here , we identify a highly SUMOylated focus ( HSF ) in the nucleus of the bloodstream form that partially colocalizes with the ESB and the active VSG-ES locus . SUMOylation of chromatin-associated proteins was enriched along the active VSG-ES transcriptional unit , in contrast to silent VSG-ES or rDNA , suggesting that it is a distinct feature of VSG-ES monoallelic expression . In addition , sequences upstream of the active VSG-ES promoter were highly enriched in SUMOylated proteins . We identified TbSIZ1/PIAS1 as the SUMO E3 ligase responsible for SUMOylation in the active VSG-ES chromatin . Reduction of SUMO-conjugated proteins by TbSIZ1 knockdown decreased the recruitment of RNA pol I to the VSG-ES and the VSG-ES-derived transcripts . Furthermore , cells depleted of SUMO conjugated proteins by TbUBC9 and TbSUMO knockdown confirmed the positive function of SUMO for VSG-ES expression . In addition , the largest subunit of RNA pol I TbRPA1 was SUMOylated in a TbSIZ-dependent manner . Our results show a positive mechanism associated with active VSG-ES expression via post-translational modification , and indicate that chromatin SUMOylation plays an important role in the regulation of VSG-ES . Thus , protein SUMOylation is linked to active gene expression in this protozoan parasite that diverged early in evolution .
Trypanosoma brucei displays a sophisticated mechanism of antigenic variation of the Variant Surface Glycoprotein ( VSG ) that allows the parasite to elude the host immune antibody response , ensuring a persistent infection [1] , [2] . Antigenic variation is achieved by mutually exclusive expression of only one out of approximately 1000 VSG genes . The monoallelic expressed VSG gene is located at the end of a telomeric Expression Site ( ES ) locus . There are about 15 different VSG expression sites ( VSG-ESs ) , which share highly homologous sequences at the promoter region [3] . The identification of a single extra-nucleolar RNA polymerase I-containing nuclear body , named the expression site body ( ESB ) , which is associated with the GFP-tagged active VSG-ES promoter suggests a model whereby ESB-dependent VSG-ES recruitment leads to the expression of a single VSG on the surface of the parasite [4]–[6] . Transcription of the VSG-ES and maintaining monoallelic expression seem be controlled at multiple levels . Several proteins have been involved in silencing of inactive VSG-ESs , such as telomeric protein RAP1 , DOT1 histone methyltransferase , the factor ISWI and chromatin remodeler complex FACT [7]–[10] . Recently , it has been reported that the active VSG-ES promoter is depleted of histones [11] , [12] . Whilst most studies have focused on regulation of VSG-ES silencing , there must be specific factors required to guarantee high levels of transcription of the active VSG-ES . The architectural protein TDP1 , a high mobility group ( HMG ) containing protein , facilitates RNA pol I activity , however is required for both VSG-ES and rDNA transcription [13] . In T . brucei , the VSG-ES is transcribed by RNA polymerase I ( RNA pol I ) , an exceptional feature among eukaryotes since RNA pol I does not usually transcribe protein-coding genes . However , TbRPB7 , a dissociable subunit of the RNA pol II complex , is also required for in vivo RNA pol I transcription of the VSG gene [14] . This is a controversial issue in the field since TbRPB7 does not seem to be required for in vitro transcription [15] . These discrepancies maybe explained by a possible function of TbRPB7 in vivo , as we discussed previously [16] . Based in our previous results we sought for TbRPB7-interacting proteins in search for possible factors involved in VSG-ES regulation . To do so , we directed a yeast two-hybrid screen ( Hybrigenics ) and identified several proteins , including a protein with a conserved SUMO E3 ligase domain ( MIZ/SP-RING ) , that we named TbSIZ1 . SUMO ( Small Ubiquitin-like MOdifier ) is a reversible post-translational protein modification involved in many cellular processes , including the regulation of nuclear bodies . The first SUMO gene was identified in S . cerevisiae ( SMT3 ) ; the peptide was found covalently attached to the Ran GTPase-activating protein , modifying the localization of this protein in the cell [17] , [18] . SUMO are ∼12 kDa proteins with a 3D structure similar to ubiquitin , whilst sharing just 20% sequence identity . Invertebrates such as yeast , C . elegans , and D . melanogaster contain a single SUMO gene , whereas plants and vertebrates have several SUMO genes [19] . SUMOylation , like ubiquitylation , involves a pathway that requires three enzymatic steps . First , the SUMO protein is activated at its C terminus by the E1 activating enzyme [20] . The activated SUMO is then transferred to the E2 conjugating enzyme UBC9 and to the substrate forming an isopeptide bond . This last step is mediated by SUMO E3 ligases , which determine substrate specificity and catalyse the transfer of SUMO from UBC9 [21] , [22] . Three protein families have been identified to date as SUMO E3 ligases . The main group is characterized by a conserved SP-RING motif , which is essential for their function . This group includes the PIAS family ( Protein inhibitor of activated STAT ) PIAS1-3 in mammals [23] , and Siz1 , Siz2 and Mms21 in budding yeast [21] , [24] . One of their mechanisms consists in re-localization of transcriptional regulators to different subnuclear compartments [25] . The second type of SUMO E3 ligases is represented by the nuclear import factor RanBP2 , which mediates nucleo-cytoplasmic transport [26] . The third group was discovered with the polycomb protein Pc2 , which forms PcG nuclear bodies involved in gene silencing [27] . SUMO modification regulates protein activity in diverse ways . SUMO can modulate the ability of proteins to interact with their partners , alter their patterns of sub-cellular localization and control their stability . The most common group of SUMO substrates are transcription factors , whose transcriptional activity can be modulated positively or negatively as a result of SUMOylation [28] . In T . brucei , there is a single SUMO protein which has been shown to be essential in procyclic [29] and bloodstream forms [30] of the parasite . Recently , proteomic analysis of SUMO substrates in T . cruzi showed at least 236 proteins involved in several cellular processes [31] . Together these data suggest that SUMO is essential and SUMOylation is a conserved process in trypanosomatids . The lack of an anti-SUMO antibody specific for TbSUMO hampered a proper analysis of the SUMO conjugated proteins [32] . Thus , a possible SUMO function in gene expression and subcellular localization of SUMO-conjugated proteins in the infective form of this protozoan parasite are totally unknown . We here show the presence of a single site in the nucleus highly enriched in SUMOylated proteins , which associates with the VSG-ES chromatin and the nuclear body ESB . Importantly , we identify the SUMO E3 ligase , named TbSIZ1 , responsible for the VSG-ES chromatin SUMOylation . Our data indicate that SUMOylation of chromatin-associated proteins at the active VSG-ES promoter is highly enriched in a TbSIZ1-dependent manner . SUMOylation of chromatin-associated proteins contributes to efficient recruitment of RNA polymerase I to the VSG-ES promoter and is important for VSG-ES expression . In addition , RNA pol I largest subunit TbRPA1 is SUMOylated in a TbSIZ1-depending manner . However , additional chromatin-associated proteins are SUMOylated in the active VSG-ES since SUMO was detected upstream of the promoter . This epigenetic mark in chromatin was not detected in silent VSG-ESs nor in rDNA or EP transcribed also by RNA pol I , suggesting that SUMOylation is involved in VSG-ES monoallelic active expression rather than in silencing .
To investigate SUMO-conjugated protein expression we first developed a monoclonal antibody ( mAb 1C9H8 ) against Trypanosoma brucei SUMO expressed as recombinant protein . Western blot analysis showed that the most abundant SUMO-conjugated proteins are larger than 70 kDa in bloodstream form trypanosome total extracts ( Figure 1A ) , similar to the pattern described in other eukaryotes [21] , [33] . The mAb 1C9H8 recognized free SUMO and SUMO-conjugated proteins since SUMO depletion by RNAi of the coding region showed a significant decreased signal after 48 h of depletion by Western blot analysis ( Figure 1A ) . RNAi-induced lines were compared to the parental cell line since uninduced cell lines generally showed some depletion of the target protein due to leaky RNAi expression . Additional RNAi experiments using the TbSUMO 5′ UTR showed a similar depletion of SUMO by Western blot analysis ( Figure S1A ) . Importantly , the use of N-Ethylmaleimide ( NEM ) , a well-known inhibitor of de-sumoylases , reduced the signal of free SUMO in protein extracts and stabilized SUMO-conjugated proteins ( Figure S1B ) , suggesting NEM inhibits trypanosome de-sumoylation . The previous use of the anti-Trypanosoma cruzi SUMO antiserum against T . brucei SUMO conjugated proteins [30] is controversial [32] . We compared the anti-TcSUMO rabbit antiserum on TbSUMO-depleted extracts by RNAi with the signal obtained using the anti-TbSUMO mAb on the same Western blot ( Figure S1C ) . While the signal generated by the anti-TbSUMO mAb was abolished upon depletion , anti-TcSUMO signal was not significantly reduced . Altogether , these data suggest that the anti-TbSUMO mAb 1C9H8 showed specificity to recognize SUMO-conjugated proteins in T . brucei extracts . Comparative analysis of T . brucei total extracts in bloodstream and procyclic ( insect form ) developmental stages of the parasite showed differential expression pattern of several SUMO-conjugated proteins ( Figure 1B ) . Next , we analyzed the subcellular localization of SUMOylated proteins by three-dimensional immunofluorescence ( 3D-IF ) microscopy using the mAb anti-TbSUMO 1C9H8 . SUMO modified proteins localized mainly to the nucleus , excluding the nucleolus , in a diffuse pattern with one Highly SUMOylated Focus ( HSF ) ( Figure 1C ) . Statistical IF analysis for the detection of this single HSF revealed a significant visualization in 74 . 9% of the nuclei , irrespective of cell cycle stage ( Figures S2A and S2B ) . Conversely , in the procyclic insect form , where no VSG is expressed , SUMO-conjugated nuclear proteins are located in numerous small foci dispersed in the nucleus ( Figure 1D ) . We carried out a series of double 3D-IF experiments to investigate a possible association of SUMO with trypanosome sub-nuclear compartments in bloodstream form nuclei . Anti-TbRPA1 ( RNA pol I largest subunit ) affinity-purified antiserum is known to recognize not only the nucleolus but also the extra-nucleolar body named ESB [4] . Double IF analysis by 3D-deconvolution microscopy using the mAb anti-TbSUMO and the anti-TbRPA1 antiserum showed that the HSF partially colocalized with the nuclear body ESB ( Figure 2A ) . To further investigate the association between RNA pol I and SUMOylated proteins in the nucleus , 3D-IF analysis was performed in a cell line expressing a YFP-tagged TbRPB5z [34] , a subunit specific of RNA pol I complex in trypanosomes . Consistent with the nuclear localization of TbRPA1 , TbRPB5z was associated with the HSF in the nucleus ( Figure S2C ) , suggesting that the RNA pol I complex located in the extra-nucleolar ESB is associated with the HSF . Next , we wished to investigate whether the HSF was associated with the VSG-ES chromosome position in the nucleus . To do so , we performed indirect 3D-IF analysis utilizing a cell line tagged with the GFP-Lac upstream of the active VSG-ES promoter [4] . Double 3D-IF analysis using anti-TbSUMO and anti-GFP antibodies showed that the GFP-tagged active VSG-ES partially colocalized with the HSF in a large percentage of cells ( 76 . 1% ) ( Figures 2B and S2D ) . As control , we investigated a possible association of SUMOylated proteins with the ribosomal DNA ( rDNA ) in the nucleus , another locus transcribed by RNA pol I . Nuclear position analysis of the rDNA locus , marked with the GFP-Lac [35] , showed a lack of significant colocalization with SUMOylated proteins ( Figures 2C and S2D ) . The monoallelic VSG-ES transcriptional state is maintained over many generations and during S-phase , G2-phase and early mitosis the active VSG-ES locus remains associated with the single ESB [34] . Thus , we decided to investigate the dynamic of the HSF throughout the cell cycle . Trypanosome cell cycle phases are clearly distinguishable because kinetoplast mitochondrial DNA ( K ) segregation occurs prior to the onset of mitosis and nuclear ( N ) division . Thus , DAPI staining identifies a population with 1K1N cells ( G1 and G1-S ) and 2K1N cells ( G2 ) . To analyze HSF dynamics throughout the cell cycle we performed double indirect 3D-IF in bloodstream form trypanosomes using antiserum against TbRPA1 and the mAb anti-TbSUMO . This analysis revealed that the HSF and the ESB partially colocalized in every stage of the cell cycle ( Figure S3 ) . Anti-TbSUMO labeling allowed us now to distinguish in the nucleus the ESB when is located closed of the nucelolus ( Figure S3 , G1 cell ) . In pre-mitotic cells a single ESB remained associated to the VSG-ES sister chromatids during segregation [34] . Thus , we investigated a possible association of the HSF with the active VSG-ES chromatids during the cell cycle using the GFP-tagged VSG-ES cell line . Double 3D-IF analysis using anti-SUMO and anti-GFP antibodies showed a single HSF in the nucleus , which was associated with the active VSG-ES locus throughout the cell cycle . Interestingly , the two sister chromatids of the active VSG-ES in pre-mitotic cells were associated with a single HSF . Once cells enter into mitosis and sister chromatids are clearly separated , two HSFs associated with each chromatid were detected ( Figure S4 ) . Nuclear localization analysis by 3D-IF analysis suggested that SUMO-conjugated proteins associate with the active VSG-ES telomeric locus in the bloodstream form ( Figure 2B ) . Next , we decided to investigate in detail the occupancy of SUMOylated proteins along the VSG-ES locus by chromatin immunoprecipitation ( ChIP ) analysis and quantitative PCR ( qPCR ) . To overcome the problem of highly homologous sequences among different VSG-ES promoter regions [3] , ChIP experiments were performed using two cell lines containing the Firefly-luciferase ( FLuc ) reporter gene inserted 400 bp downstream of the VSG-ES promoter in an active ( SALR ) [14] , or inactive ( SILR ) transcriptional state . The SILR cell line contains the same Fluc cassette than the SARL cell line but downstream of a silent VSG-ES promoter ( BES5 , VSG800 ) , as revealed by reporter activities and sequence analysis ( see Supporting Information Text S1 ) . In addition , to monitor a RNA pol II transcribed locus , the Renilla-luciferase ( RLuc ) reporter gene was inserted within the tubulin locus in both cell lines ( Figure 3A ) . ChIP analysis using anti-TbRPA1 showed that the VSG-ES chromatin is highly enriched in RNA pol I in the active transcriptional state , in contrast to the inactive VSG-ES with immunoprecipitation levels close to the background ( Figures 3B and 3C ) . The high enrichment of TbRPA1 at the active VSG-ES compared to inactive was demonstrated using the unique sequences ( FLuc ) inserted downstream of the promoter . TbRPA1 immunoprecipitated 42-fold higher at the FLuc the active VSG-ES ( 2 . 52% input ) compared to FLuc in the inactive VSG-ES ( 0 . 06% input ) . TbRPA1 levels at additional unique sequences such as the pseudo-VSG and the telomeric VSG221 showed that the active VSG-ES chromatin is highly occupied by the TbRPA1 ( Figures 3B and 3C ) . The differences of the TbRPA1 occupancy between the active and inactive VSG-ES sequences support a transcription initiation control as one of the mechanisms involved in VSG-ES monoallelic expression . Next , we investigated the presence of chromatin-associated SUMOylated proteins within the VSG-ES locus by ChIP using the anti-TbSUMO mAb . Interestingly , we detected SUMOylated proteins enriched at the entire active VSG-ES transcription unit , from sequences downstream of the promoter to the telomeric VSG gene ( Figures 3D and 3E ) . SUMOylated proteins were immunoprecipitated more efficiently at the reporter inserted downstream of the active VSG-ES promoter ( FLuc SALR: 0 . 43% input-background ) than at the inactive ( FLuc SILR: 0 . 05% input-background ) and the difference was statistically significant ( p value<0 . 01 ) . Similarly , the active VSG221 was significantly immunoprecitated while other telomeric VSG genes such as VSG121 , VSGVO2 and VSGJS1 , which include also basic copies , were very low , near to background levels ( Figures 3D and 3E ) . Furthermore , significant SUMOylation level was not detected at other RNA pol I-transcribed loci ( rDNA or EP procyclin ) , nor at other RNA pol II or RNA pol III loci analyzed ( Figures 3D and 3E ) . In other eukaryotes , SUMOylated proteins were detected at RNA pol II promoters and play important roles in their activity [36] , [37] . Thus , we investigated the presence of SUMOylated proteins in the chromatin upstream of the VSG-ES promoters ( Figure 4A ) . ChIP-qPCR analysis revealed a high enrichment of SUMOylated chromatin-associated proteins upstream of the promoter region , which was notably higher in the fragments 6 and 5 ( 1 . 5% and 1 . 6% input-background ) ( Figure 4B ) . As a negative control , we compared with fragment 7 upstream of the 50 bp repeats , which showed no significant enrichment ( 0 , 01% input ) ( Figure 4B ) . The trypanosome genome contains at least 15 different VSG-ESs with highly conserved sequences at the promoter region , suggesting that the primers used for ChIP qPCR may anneal on many different VSG-ESs . Relative quantification revealed that sequences 4 , 3 and 1 were highly conserved in many VSG-ESs ( Figure S5A ) , suggesting that SUMO ChIP values upstream of the promoter ( fragments 2 , 3 and 4 in Figure 4B ) , represented as percentage of input , were in fact underestimated . To confirm this hypothesis , we cloned and sequenced PCR fragments from the region 4 using ChIPed and genomic DNA as templates . Sequences obtained from genomic DNA yielded 14 different sequences including one from the VSG221-ES , indicating these PCR primers amplify most of the VSG-ESs in the genome . However , analysis of the anti-TbSUMO ChIPed fragments identified 11 sequences identical to the active VSG221-ES promoter region , and 7 sequences 99% homologous ( Figure S5 B ) . These results together indicate that the chromatin upstream of the active VSG-ES promoter is highly enriched in SUMOylated proteins . We also analyzed in detail chromatin SUMOylation along the rDNA promoter region , however no significant levels were detected in any of the positions analyzed , including the non-transcribed upstream spacer and the coding region for the 18S rRNA ( Figures 4C and 4D ) . We previously proposed that TbRPB7 functions in trypanosome RNA pol I transcription by recruiting transcription or RNA processing factors to the VSG-ES chromatin [14] . Thus , we searched for TbRPB7-interacting proteins by a yeast two-hybrid screen ( Hybrigenics ) . This approach detected several putative interacting proteins , including a topoisomerase , a ubiquitin ligase and a protein with a SP-RING conserved domain characteristic of SUMO E3 ligases [38] , which we named TbSIZ1 ( Tb927 . 9 . 11070 ) , an ortholog of yeast SIZ and mammalian PIAS . Sequence alignment of SP-RING domains of previously characterized SUMO E3 ligases revealed a significant conservation with TbSIZ1 ( Figure 5A ) . We developed a mouse monoclonal antibody ( 7G9D4 ) anti-TbSIZ1 that allowed us to identify TbSIZ1 as a 72 kDa protein highly expressed in the infective bloodstream form of the parasite ( Figure 5B ) . Subcellular localization analysis detected TbSIZ1 mainly in numerous nuclear foci ( Figure S6A ) , similar to the pattern described for other SUMO E3 ligases in other eukaryotes [39] , [40] . Although TbSIZ1 was not enriched in a single nuclear area as the HSF , we investigate a possible colocalization with the active VSG-ES . Statistical analysis using the cell line with the GFP-tagged active VSG-ES showed that TbSIZ1 was associated with this locus in 87% of G1 cells ( 1K1N cells ) , while in G2 and pre-mitotic cells this percentage was reduced to 40% ( 2K1N cells ) ( Figure S6B ) . The lack of significant colocalization of TbSIZ1 with the rDNA locus suggests the interaction of TbSIZ1 and the active VSG-ES is not accidental ( Figure S6B ) . SUMO E3 ligases are important for the efficient transfer of a SUMO group from the conjugating enzyme E2 to specific substrates [22] . To characterize the function of TbSIZ1 we generated bloodstream form cell lines where depletion of TbSIZ1 was performed by RNA interference ( RNAi ) . Western blot analysis confirmed TbSIZ1 depletion after 48 h of RNAi induction ( Figure 5C ) , while only a minor effect in cell growth or cell cycle progression was detected ( Figure S7A ) . However , at 72 h after induction of the TbSIZ1 RNAi the protein level increased suggesting TbSIZ1 depletion is partial and transitory ( Figure 5C ) . Importantly , TbSIZ1 partial depletion reduced the signal of SUMO-conjugated proteins by Western blot analysis ( Figure 5D ) , indicating that TbSIZ1 is a functional SUMO E3 ligase . Consistently with Western analysis ( Figure 5D ) , depletion of TbSIZ1 also reduced the nuclear signal of SUMO conjugates analyzed by IF using the anti-TbSUMO mAb ( Figure S7B ) . Unfortunately , while the nuclear signal of TbSUMO was significantly reduced by TbSIZ1 depletion , we failed to completely eliminate the HSF signal in the nucleus . TbSIZ1 depletion functioned with variable penetrance in each cell , since the SUMO conjugated protein signal was reduced with different efficiency . SUMOylation is essential in T . brucei since SUMO depletion by RNAi induced deregulation of the cell cycle [29] . Notwithstanding , we decided to analyze the stability of the HSF signal in the nucleus upon TbSUMO RNAi . Similar to TbSIZ1 depletion , TbSUMO RNAi clearly reduced SUMOylation in the nucleus , however the HSF was weaker but still detected ( Figure S7B ) . TbRPB7 is required for VSG-ES transcription in vivo [14] , and TbSIZ1 was identified as a TbRPB7-interacting protein , thus we decided to investigate a possible function of TbSIZ1 on VSG-ES chromatin SUMOylation , To do so , we performed a series of anti-TbSUMO ChIP experiments after 48 h of TbSIZ1 depletion . Upon TbSIZ1 knockdown , reduced levels of SUMO were detected at all positions along the active VSG-ES compared with the parental cell line ( Figure 6A ) . The reduction of SUMOylated chromatin after TbSIZ1 partial depletion was particularly significant at the region upstream of the VSG-ES promoter , where the highest enrichment of SUMO conjugated proteins was detected ( Figure 4B ) . As expected , TbSIZ1 knockdown induced no changes in chromatin SUMOylation in loci where SUMO was undetectable , such as the silent telomeric VSGs ( Figure 6A ) . Altogether , these data suggest that the SUMO E3 ligase TbSIZ1 is responsible for the SUMOylation of chromatin-associated proteins detected in the active VSG-ES . The detection of SUMOylated proteins associated specifically to the active VSG-ES chromatin , contrary to inactive VSG-ES , suggests a positive role of chromatin SUMOylation in transcription driven by RNA pol I in trypanosomes . To test this hypothesis , we analyzed the effect of reduced chromatin SUMOylation induced by TbSIZ1 depletion on TbRPA1 occupancy at the VSG-ES chromatin . ChIP values obtained using anti-TbRPA1 and chromatin isolated from TbSIZ1 depleted cells were compared with the values from the original cell line ( Figure 6B ) . These experiments detected lower levels of TbRPA1 recruited to the active VSG-ES after TbSIZ1 depletion . The reduction of TbRPA1 occupancy extended from the promoter region to the telomeric VSG221 gene . We did not detect TbRPA1 changes in the silent telomeric VSGs . The RNA pol I recruited to the active VSG-ES after TbSIZ1 depletion was about 50% less in single copy genes ( FLuc , Pseudo VSG & VSG221 ) , suggesting SUMOylation of chromatin associated proteins is important to achieve full transcription of this locus . To determine whether reduced levels of RNA pol I occupancy affect expression levels we performed RT-qPCR analysis in cells after 48 h of TbSIZ1 depletion . We detected reduced levels of the FLuc and VSG221 mRNAs , without a significant effect in RNA pol II-transcribed RLuc or myosin genes or in U2 transcribed by RNA pol III ( Figure 6C ) . Importantly , we also analyzed rDNA transcription driven by RNA pol I , but no significant changes were detected in either the mature 18S or the pre-spliced rRNA780 RNAs ( Figure 6C ) . These results suggest that TbSIZ1-mediated SUMOylation of chromatin-associated proteins positively regulates VSG-ES transcription . To further investigate SUMOylation in VSG-ES expression and to rule out the possibility of a SUMO-ligase independent function for TbSIZ1 , we decided to analyze the effect of inducing a global reduction in SUMO levels . Thus , we generated cell lines where depletion of either TbUBC9 ( E2 conjugase ) or TbSUMO was performed by RNAi . TbUBC9 depletion reduced the SUMO-conjugated proteins very efficiently as detected by Western blot analysis ( Figure S8 ) . Depletion of TbUBC9 protein levels was confirmed using a mouse antiserum we developed against recombinant TbUBC9 ( Figure S8 ) . Cells depleted of SUMO-conjugated proteins by either TbSUMO or TbUBC9 knockdown analyzed by ChIP using anti-TbRPA1 showed a significant reduction in RNA pol I occupancy in the VSG-ES ( Figure 7A ) . TbRPA1 recruitment was decreased along the entire VSG-ES locus without increasing the occupancy in the silent telomeric VSGs . The reduction of the RNA pol I occupancy upon TbSUMO or TbUBC9 depletion correlated well with a decrease of mRNA derived from the active VSG-ES , while ribosomal RNA levels were not reduced ( Figure 7B ) . TbSUMO or TbUBC9 are essential genes , however their depletion has a greater effect on the VSG-ES mRNA levels as compared to RNA pol II-derived mRNAs ( Figure 7B ) . These results altogether suggest that SUMOylation of chromatin-associated proteins is important for active VSG-ES expression . Next , we performed quantitative Western blot analysis of VSG expression after SUMO depletion to investigate whether VSG protein levels were affected . The VSG221 expression level was analyzed using anti-BiP antibody as loading control from three independent TbSUMO RNAi clones ( Figures 7C and 7D ) . Quantification of VSG221 expression relative to the parental cell line extracts suggested that VSG protein level was significantly downregulated upon SUMO depletion . The extent of reduction of VSG expression after SUMO RNAi was variable but consistent , suggesting SUMO functions positively in VSG expression . The detection of the HSF in the nucleus ( Figure 2 ) and the high occupancy of SUMOylated proteins at the VSG-ES chromatin ( Figures 3 and 4 ) suggest that a large number of SUMOylated proteins occur at this site , similar to Protein Group SUMOylation described previously [41] . Identification of the SUMO-conjugated proteins in the HSF is beyond the scope of this work , however the largest subunit of the RNA polymerase I is an obvious candidate since it is SUMOylated in other eukaryotes [42] . To investigate a possible TbRPA1-SUMO conjugation , we performed IP assays utilizing anti-TbSUMO mAb and affinity-purified TbRPA1 antiserum under denaturing conditions , which preserve SUMO conjugation ( see Supplementary Information Text S1 ) . IP experiments revealed that TbRPA1 is SUMOylated as shown by Western analysis using anti-TbRPA1 on a SUMO IPed extract ( Figure 8A ) . The reciprocal experiment using anti-TbSUMO antibody on anti-TbRPA1 IPed extract reproducibly detected TbRPA1-TbSUMO conjugates ( Figure 8B ) . The low detection of SUMO-conjugated TbRPA1 is probably due to the large number of SUMOylated proteins and the small percentage of TbRPA1 that is SUMOylated , as occurs with other SUMO targets in eukaryotes [33] . This result suggests that under normal growth conditions a fraction of TbRPA1 is SUMOylated . While this may contribute to the SUMOylation we detected by ChIP on the VSG-ES chromatin , SUMOylation is also detected upstream of the promoter ( Figure 4B ) , suggesting additional SUMOylated chromatin proteins occur at this region . We addressed whether the fraction of SUMO-conjugated TbRPA1 is the one that resides in the extra-nucleolar body ESB . To do so , we used the Proximity Ligation Assay ( PLA ) ( O-link Bioscience ) . This technique exploits the distance requirements of a PCR reaction by linking two primers to the two secondary antibodies . The PLA assays showed that the fraction of TbRPA1 that is SUMOylated resides at an extra-nucleolar site ( Figure S9 ) . This result , together with the IF colocalization analysis of the HSF with both the active VSG-ES locus and the TbRPA1 ( Figures 2A and 2B ) suggest that SUMOylated TbRPA1 occurs at the nuclear body ESB . Finally , we wished to investigate whether SUMOylation of TbRPA1 is mediated by TbSIZ1 . To characterize a possible function of TbSIZ1 in TbRPA1 SUMOylation we performed a series of co-IP experiments using protein extracts isolated from TbSIZ1 depleted cells and compared to the parental cell line . Figure 8C shows that TbSIZ1 depletion significantly reduced the amount of TbRPA1 IPed using anti-TbSUMO antibody . This result suggests that TbSIZ1 mediates SUMO targeting of TbRPA1 .
The importance of nuclear bodies and the three-dimensional organization of chromosomes in the regulation of gene expression is becoming evident in eukaryotes [43] . In trypanosomes , it was suggested that the recruitment of a single Variant Surface Glycoprotein Expression Site ( VSG-ES ) telomeric locus to a discrete , RNA pol I-containing nuclear body ( ESB ) underlies the mechanism responsible for VSG monoallelic expression [4] , [5] , [44] . Here , by nuclear localization analysis using 3D microscopy , we describe a highly SUMOylated focus ( HSF ) ( Figure 1C ) . The nuclear position of the HSF partially colocalizes with the active VSG-ES locus and the nuclear body ESB ( Figure 2 ) . Unfortunately , our attempts to completely eliminate the HSF in the nucleus by TbSUMO or TbSIZ1 RNAi were unsuccessful ( Figure S7B ) . Previous evidence whereby SUMO modifies the interaction properties of conjugated proteins and affects their subnuclear localization [45] suggests that SUMOylation of nuclear proteins at the HSF might be involved in the nuclear body ESB regulation in trypanosomes . SUMO-conjugated proteins are localized to the active VSG-ES chromatin , in contrast to any other loci examined ( Figure 3 ) . We have investigated the possibility that SUMOylated proteins associate with other loci transcribed by RNA pol I . SUMOylated chromatin was not detected at the rDNA or EP procyclin loci . These results suggest that the association of SUMO-conjugated proteins to chromatin is a distinct feature of the VSG-ES regulation . SUMOylation of chromatin-associated proteins at the active VSG-ES extends from ∼1 Kb upstream of the promoter down to the telomeric VSG , while SUMO was not detected at silent VSG-ESs or VSG basic copies chromatin ( Figures 3 and 4 ) . Whilst SUMOylation has been classically associated with transcriptional repression [46]–[48] , there is some evidence that SUMOylation can also function as a transcriptional activator , particularly to modify gene-specific transcription factors or co-regulators [28] , [49] . In HeLa cells , SUMO-1 was found at the chromatin just upstream of the transcription start site on many of the most active genes [36] . Depletion of SUMO-1 resulted in down regulation of transcription supporting the idea that marking of promoters by SUMO-1 is associated with transcriptional activation [36] . PIAS E3 ligases function as enhancers of c-Myb activity in active nuclear RNA pol II foci [39] . In trypanosomes , the transcriptionally active VSG-ES promoter and the nuclear body ESB are identified here as being highly SUMOylated ( Figures 1C and 2 ) . TbSIZ1 is the first SUMO E3 ligase functionally analyzed in T . brucei . It contains a conserved SP-RING domain essential for the ligase activity described previously in other eukaryotes [38] . TbSIZ1 depletion has a mild effect on cell growth and cell cycle progression ( Figure S7A ) . This result is similar to other SUMO ligases , such as S . pombe Pli1 and S . cerevisiae Siz1 and Siz2 , for which deletion does not affect cell growth [20] , [40] . Interestingly , TbSIZ1 depletion reduced some SUMO-conjugated protein bands more efficiently than others analyzed by Western blot ( Figure 5D ) . This supports the idea of specificity of TbSIZ1 substrates , similarly to the role of previously described SIZ/PIAS E3 ligases [21] . In the present work , we show that TbSIZ1 functions in vivo as a SUMO E3 ligase of chromatin-associated proteins detected at the active VSG-ES chromatin by ChIP . Depletion of TbSIZ1 causes reduction in SUMOylation of the active VSG-ES with a concomitant reduction in RNA pol I occupancy and transcriptional activity ( Figure 6B and C ) . We ruled out the possibility of a SUMO ligase independent function of TbSIZ1 by TbSUMO or TbUCB9 RNAi experiments , which also reduced both RNA pol I recruitment and VSG-ES expression ( Figure 7 ) . This finding is similar to observations in yeast , where SUMOylation of chromatin-associated proteins in actively transcribed genes is dependent on the E2 conjugating enzyme Ubc9 [37] . Interestingly , we did not detect significant levels of SUMOylated chromatin in the other RNA pol I-driven control loci as rDNA , EP or silent VSG-ESs loci , suggesting that SUMO plays a distinct function in VSG-ES positive regulation . We showed that the constitutive rDNA promoters have no detectable levels of SUMOylated chromatin ( Figure 4D ) , contrary to the switchable VSG-ES promoter , which is highly SUMOylated only in the active transcriptional state . Activation of inducible promoters has been shown to result in chromatin SUMOylation , suggesting that gene activation involves SUMOylation of promoter-bound factors [37] . Our results suggest a function of SUMO in VSG-ES active transcription , since upon SUMO depletion by TbSIZ1 , TbUBC9 or TbSUMO RNAi , both recruitment of the RNA pol I at the VSG-ES promoter and VSG-ES derived transcripts are reduced . The finding that SUMOylation is important for VSG-ES expression suggests that factors previously implicated in VSG regulation maybe modified by SUMOylation . An obvious candidate as SUMO substrate is the RNA pol I complex , responsible for VSG transcription . In other eukaryotes , several subunits of the RNA pol I , including RPA1 , were described to be SUMOylated in large scale proteomics analyses [42] , [50] . Indeed , we find by IP experiments that TbRPA1 is SUMOylated ( Figure 8 ) . However , the high SUMO enrichment detected 1 Kb upstream of the VSG-ES promoter cannot be accounted for TbRPA1 . Thus , SUMOylated proteins detected upstream of the VSG-ES promoter may include transcription factors or structural components of chromatin , similar to what has been described in other eukaryotes [47] . Simultaneous SUMOylation of Protein Groups by modification of multiple targets providing synergy in a specific process has been recently described for DNA repair [41] , [51] . Proteomic studies have shown that several proteins in the same complexes or biochemical pathways are SUMOylated [42] , [50] . Protein group SUMOylation may also be associated with a specific subnuclear localization of SUMOylated proteins [52] . The HSF is frequently larger than the ESB detected using anti-TbRPA1 , suggesting that additional factors involved in processes previously associated with VSG expression , such as transcription elongation and mRNA maturation , may be present in the HSF [2] . Our data showed that RPA1 immunoprecipitated 42-fold higher at the active VSG-ES as compared to a single inactive VSG-ES , however we still detect some TbRPA1 at the inactive VSG-ES site ( FLuc SILR: 0 . 061% input , before removing background ) . Possibly , this polymerase in the inactive VSG-ESs promoter region is enough to produce detectable mRNA described recently [53] . It seems likely that the HSF described in this work represents a group of post-transcriptionally modified proteins , as Protein Group SUMOylation , functionally associated with VSG-ES transcription initiation , elongation and mRNA maturation . Some of the proteins involved in the regulation of antigenic variation and VSG-ES expression in trypanosomes have been previously described as SUMO targets in other eukaryotes . Among the possible chromatin-associated factors that could be SUMOylated at the active VSG-ES is the architectural chromatin protein TDP1 , which was reported to be enriched at the active VSG-ES and rDNA , facilitating RNA pol I transcription [13] . The yeast ortholog Hmo1 has been recently identified by proteomic analysis as SUMO-conjugated protein [50] . Thus , it seems possible that the fraction of TDP1 at the ESB is SUMOylated in the HSF . Recent data showed that Cohesin subunits Smc1/3 and Scc1/3 are SUMOylated in yeast [54] . In Trypanosoma cruzi SMC3 was also identified as a SUMOylated protein by proteomic approaches [31] . We have recently identified Cohesin complex as a factor involved in VSG-ES switching [34] . Preliminary results suggest that SMC3 is SUMOylated in T . brucei , however and contrary to TbRPA1 , SUMO is not targeted to TbSMC3 by TbSIZ1 ( manuscript in preparation ) . ChIP experiments showed highly-enriched SUMOylated chromatin upstream of the VSG-ES promoter ( Figure 4 ) , suggesting structural components of chromatin might be also targets for SUMO at this particular location . In trypanosomes , post-translational histone modifications are being associated with repression of silent VSG-ESs ( see for review [55] ) . Histone SUMOylation is associated with transcriptional repression in S . cerevisiae , where all four core histones are SUMOylated [47] . However we describe the lack of SUMOylated chromatin at silent VSG-ESs , while the active VSG-ES chromatin is highly enriched in SUMO . Our results show SUMO as a post-translational modification of proteins associated with the active transcriptional state of the VSG-ES . Ever since the finding that the ESB is associated with VSG-ES monoallelic expression [4] , we and others have searched for a particular factor located exclusively at this unique nuclear body . However , specific post-translational modifications of common factors may also account for this body . Here we report that TbRPA1 , the largest subunit of RNA pol I , is SUMOylated by TbSIZ1 ( Figure 8C ) , in addition IF colocalization analysis and PLA ( Figures 2B and S8 ) , strongly suggest that the fraction of SUMOylated TbRPA1 resides at the ESB rather than in the nucleolus . SUMO modification has been involved in the re-localization of transcriptional regulators to different subnuclear compartments [56] and stabilizes interactions between the functionally related proteins [41] . Taken together , our results suggest a model whereby SUMOylation of chromatin-associated proteins mediated by TbSIZ1 at the active VSG-ES locus may function to nucleate factors to the ESB . The complex regulation of antigenic variation involves monoallelic transcription of a single VSG-ES out of a multiallelic gene family at any given time . Our results show a positive mechanism via SUMOylation that marks the active VSG-ES chromatin . In other eukaryotes , SUMOylation of transcription factors and chromatin proteins is a negative mark that represses gene expression in most cases . The surprising observation about the specificity of chromatin SUMOylation for the active transcription state in an early-branched eukaryote suggests that the post-translational modification of proteins by SUMO play a basic role in the positive regulation of transcription in eukaryotes . Chromatin SUMOylation as an epigenetic mark for the monoallelically expressed VSG-ES could apply more widely to the regulation of antigenic variation in other protozoan parasites [57] , [58] .
T . brucei bloodstream form ( Lister 427 , antigenic type MiTat 1 . 2 , clone 221a ) and 427 procyclic form were used in this study . The dual-reporter SALR cell line in the bloodstream single-marker cell line was previously described [14] , [59] . SALR dual reporter cell line contains a Firefly Luciferase ( FLuc ) -reporter integrated 405 bp downstream of the active ES promoter , and Renilla-Luciferase reporter integrated in the tubulin locus . The generation of dual-reporter SILR cell line was similar to the SALR cell line , but the same construct containing the Fluc gene was integrated downstream of an inactive ES promoter ( see Supporting Information Text S1 ) . The insertion site was identified by PCR and sequencing of the flanking luciferase region from SALR and SILR genomic DNA confirming FLuc is inserted in the active VSG221-ES in SALR , and in SILR downstream of the inactive VSG-ES promoter BES5/TAR98 VSG800/427-18 [3] . The VSG221-ES and rDNA GFP-LacI tagged cell lines have been previously described [4] , [35] . The cell line expressing a YFP-TbRPB5z fusion was described before [35] . N-terminal fragment of TbSIZ1 ( Tb927 . 9 . 11070 ) , full-length of TbSUMO ( Tb927 . 5 . 3210 ) and TbUBC9 ( Tb927 . 2 . 2460 ) were amplified by PCR ( See primers in Table S1 ) . PCR products were cloned into BamHI and HindIII sites of pET28a vector ( Novagen ) , and expressed as an N-terminal His tag . Purification of recombinant proteins was performed using NI Sepharose Fast Flow 6 ( GE Healthcare ) . Purified recombinant proteins were inoculated in mice and used to generate anti-TbSIZ1 ( 7G9B4 ) and anti-TbSUMO ( 1C9H8 ) monoclonal antibodies ( mAb ) , using standard procedures . Hybridomas were first screened against the recombinant proteins by ELISA and later confirmed by western blot analysis using trypanosome protein extracts since recognized a single protein of the expected size . Hybridomas 7G9B4 and 1C9H8 cell lines were grown as ascites . Anti-TbUBC9 mouse antiserum was generated using purified recombinant his-tagged TbUBC9 as antigen using standard procedures . Mouse anti-TbSUMO ( 1C9H8 ) mAb ( 1∶2000 ) , rabbit anti-TbRPA1 affinity-purified antiserum ( 1∶600 ) [4] and rabbit anti-GFP polyclonal ( 1∶5000; Invitrogen ) were used as primary antibodies . Goat anti-mouse and anti-rabbit Alexa 488 or 594 conjugated antibodies ( Invitrogen ) were used as secondary antibodies . Detailed subcellular localization and colocalization analysis was performed by deconvolution 3D microscopy as described previously [35] ( see Supporting Information Text S1 ) . ChIP was performed as described previously [60] with some modifications . In brief , T . brucei bloodstream cultures were fixed in 1% formaldehyde at 37° for 15 min . Pellets were resuspended in 1 ml of lysis buffer per 108 cells and sonicated to shear the chromatin to ∼300pb in length . Sheared chromatin was diluted 1∶5 in ChIP dilution buffer and pre-cleared with Sepharose 4B beads ( Sigma ) . An aliquot of the input DNA ( 10% ) was saved . 2 . 5 ml of pre-cleared chromatin ( 5×107 cells per ChIP ) was incubated overnight at 4°C with each antibody ( 6 µg of anti-TbRPA1 , 60 µg of anti-TbSUMO 1C9H8 , 60 µg of unspecific antiserum ) . Next , protein G Sepharose ( Sigma ) was added and incubated for 1 hr at 4°C; Immunoprecipitates were washed and eluted from the beads . Crosslinks were reversed at 65°C for 15 h . After RNase and Proteinase K treatment , DNA was extracted with phenol∶chloroform and ethanol precipitated . DNA was resuspended in 50 µl of miliQ water and analyzed by quantitative PCR ( qPCR ) . To compare the amount of DNA immunoprecipitated to the total input DNA , 10% of the pre-cleared chromatin saved as input was processed with the eluted immunoprecipitates beginning at the crosslink reversal step . Quantitative PCR ( qPCR ) was performed using the SYBR green supermix ( Quanta Biosciences ) in a CFX96 cycler ( BioRad ) , as described below for RT-qPCR . qPCR mixtures contained 2 µl of a 1∶5 dilution of the ChIPed DNA or a 1∶50 , 1∶100 , 1∶200 dilution of the input sample and 500 nM of each primer in a final reaction volume of 10 µl . All reactions were performed in duplicate and each product was verified by melting curve analysis . The PCR primers used to analyze target fragments were designed by using the Primer3 software and synthetized by Sigma , targets and sequences are listed in Table S1 . Standard curves with serial dilutions of input DNA were made to determine PCR efficiency and to determine IP percentages . The relative amount of each specific PCR fragment in the ChIPed DNA and in the input DNA was calculated against the standard curve equation , next the percentage of input immunoprecipitaded was calculated . Finally the background values from unspecific antiserum ( pre-bleed rabbit antiserum ChIP ) were subtracted from the values obtained with the specific antibodies . Fold values were determined using the percentage of input immunoprecipitated before the background correction , since the background values were very similar between the loci to compare . Independent ChIP experiments were performed at least three times and statistical analysis ( Student's t-test ) was applied to compare data sets . See supplementary information ( Text S1 ) for more details and primer sequences are provided in the Table S1 . RNAi constructs were made using the p2T7Bla vector [14] , which allows Tet-inducible expression of dsRNA from opposite T7 promoters [61] . Since most of the RNAi constructs using this vector were leaky , comparative analyses always included in addition of the dox induced ( + ) and uninduced ( − ) RNAi , the parental cell line ( SALR ) [14] . Fragments corresponding to 642-pb of TbSIZ1 gene , full length of TbSUMO ORF gene ( 345-pb ) and 5′UTR TbSUMO fragment ( 121 bp ) and full length TbUBC9 gene ( 594-pb ) were amplified by PCR using primers described in Table S1 and cloned into BamHI and HindIII sites of p2T7Bla . The constructs were linearized and stably transfected into the dual-reporter cell line SALR [14] . dsRNA synthesis was induced by the addition of µg ml−1 of doxycycline . At least three independent clones from each construction were analyzed and depletion of the proteins was confirmed by Western blot using specific antibodies . Total RNA samples were extracted from 40 ml parasite cultures ( 4×107 cells ) using the High Pure RNA isolation Kit ( Roche ) and treated with integrated DNA digestion and DNase removal following manufacturer's instructions . RNA quality was verified by gel analysis , nanodrop quantification and A260/A280 ratio . cDNA was synthesized from 2 µg of RNA with the SuperScript IIII Reverse Transcriptase ( Invitrogene ) and random primers ( Invitrogene ) following manufacturer's instructions . RNA samples not treated with reverse transcriptase were used as a negative RT control and analyzed by quantitative PCR for DNA contamination assessment . Quantitative PCR was performed using the SYBR green supermix ( Quanta Biosciences ) in a CFX96 cycler ( BioRad ) , using 96-well clear low profile plates , sealed with clear optical adhesive covers . PCR mixtures contained 5 µl of 2× SYBR green supermix , 500 nM of each primer and 1 µl of cDNA for single copy genes or 1 µl of a 1∶100 dilution for multicopy genes in a final reaction volume of 10 µl . All reactions were performed in duplicate and each product was verified by melting curve analysis . The PCR protocol used was 95°C for 3 min followed by 32 cycles of 95°C for 30 sec , 60°C for 30 sec , 72°C for 30 sec , then 72°C for 1 min and final melting curve from 55 to 90°C , increment 0 . 5°C/5 sec . Fluorescence readings were taken during the extension step . PCR primers were designed by using the Primer3 software and synthetized by Sigma . Primer targets and sequences are listed in Table S1 . Standard curves for each primer pair were generated with serial dilutions of cDNA to determine PCR efficiency . The relative levels of gene expression between a given sample and the control sample ( Parental cell line ) were calculated using the ΔΔCT method with the Bio-Rad CFX Manager software . The U2 gene transcribed by RNA pol III was used as reference gene to normalize RNA starting quantity since it was stably expressed , invariant expression was confirmed using Myosin B or Renilla-luciferase ( RLuc ) as reference genes . Three RNAi independent clones were analyzed and statistical analysis ( Student's t-Test ) using SigmaPlot software was performed . See the Supplemental Material and Methods ( Text S1 ) in Supporting Information for additional protocols . | African trypanosomes have evolved one of the most complex strategies of immune evasion by routinely switching the expression of surface proteins called Variant Surface Glycoproteins ( VSG ) , only one of which is expressed at any given time . Previous work has suggested that the recruitment of a single VSG telomeric locus to a discrete nuclear body ( ESB ) underlies the mechanism responsible for VSG monoallelic expression . Our findings establish unexpected roles for SUMOylation as a specific post-translational modification that marks the ESB and the VSG-ES chromatin . We describe a highly SUMOylated focus ( HSF ) as a novel nuclear structure that partially colocalizes with the VSG-ES locus and the nuclear body ESB . Furthermore , chromatin SUMOylation is a distinct feature of the active VSG-ES locus , in contrast to other loci investigated . SUMOylation of chromatin-associated proteins is required for efficient recruitment of the polymerase to the VSG-ES promoter and for VSG-ES expression . Altogether , these data suggest the presence of a large number of SUMOylated proteins associated with monoallelic expression as Protein Group SUMOylation . In contrast to the wealth of literature focused on VSG regulation by silencing , our results indicate a positive mechanism via SUMOylation to regulate VSG expression in the infectious form of this protozoan parasite . | [
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] | 2014 | SUMOylation by the E3 Ligase TbSIZ1/PIAS1 Positively Regulates VSG Expression in Trypanosoma brucei |
Cerebral malaria is a major , life-threatening complication of Plasmodium falciparum malaria , and has very high mortality rate . In murine malaria models , natural killer ( NK ) cell responses have been shown to play a crucial role in the pathogenesis of cerebral malaria . To investigate the role of NK cells in the developmental process of human cerebral malaria , we conducted a case-control study examining genotypes for killer immunoglobulin-like receptors ( KIR ) and their human leukocyte antigen ( HLA ) class I ligands in 477 malaria patients . We found that the combination of KIR2DL3 and its cognate HLA-C1 ligand was significantly associated with the development of cerebral malaria when compared with non-cerebral malaria ( odds ratio 3 . 14 , 95% confidence interval 1 . 52–6 . 48 , P = 0 . 00079 , corrected P = 0 . 02 ) . In contrast , no other KIR-HLA pairs showed a significant association with cerebral malaria , suggesting that the NK cell repertoire shaped by the KIR2DL3-HLA-C1 interaction shows certain functional responses that facilitate development of cerebral malaria . Furthermore , the frequency of the KIR2DL3-HLA-C1 combination was found to be significantly lower in malaria high-endemic populations . These results suggest that natural selection has reduced the frequency of the KIR2DL3-HLA-C1 combination in malaria high-endemic populations because of the propensity of interaction between KIR2DL3 and C1 to favor development of cerebral malaria . Our findings provide one possible explanation for KIR-HLA co-evolution driven by a microbial pathogen , and its effect on the global distribution of malaria , KIR and HLA .
Malaria is a serious infectious disease , affecting over 300 million people and causing more than 1 million deaths annually worldwide [1] . Cerebral malaria is a major , life-threatening complication of Plasmodium falciparum malaria , and has a high mortality rate [2] . Host immune system and genetic factors have been considered to play a crucial role in the pathogenesis of cerebral malaria . In experimental models , the polymorphic loci responsible for susceptibility to cerebral malaria were shown to map to the natural killer ( NK ) complex region on mouse chromosome 6 , which contains clustered genes encoding NK cell receptors [3] . Furthermore , NK cell depletion resulted in significant protection against cerebral malaria , suggesting the involvement of NK activity in its pathogenesis [4] . In humans , NK cells are an early source of IFN-γ in response to malaria infection [5] , [6] , and this cytokine is known to be potentially involved in the pathogenesis of cerebral malaria [7]–[9] . NK cells also show differences in responsiveness to P . falciparum-infected erythrocytes among malaria-naive donors [6] , [10] , [11] , suggesting the presence of a genetic determinant for heterogeneous NK responsiveness . These observations have suggested that the genes encoding NK receptors and their ligands have critical roles in the development of cerebral malaria in humans . Killer immunoglobulin-like receptors ( KIR ) are a diverse family of activating and inhibitory receptors expressed on human NK cells , and a subset of T cells . Seventeen different KIR genes have been identified to date [12] . The KIR loci exhibit high levels of genetic polymorphism in terms of gene content ( e . g . , presence or absence of a gene ) and allelic diversity , which are considered to have been shaped by natural selection [13]–[15] . Some inhibitory KIRs recognize human leukocyte antigen ( HLA ) class I molecules as their ligands . KIR2DL1 recognizes HLA-C group 2 ( HLA-C2 ) allotypes having asparagine at amino acid position 80 , whereas KIR2DL2 and KIR2DL3 recognize HLA-C group 1 ( HLA-C1 ) allotypes having lysine at amino acid position 80 [16] . KIR2DL2 and KIR2DL3 also recognize HLA-B*4601 , which acquired the C1 epitope by gene conversion [17] . KIR3DL1 recognizes HLA-A and HLA-B allotypes having the Bw4 epitope determined by amino acid positions 77–83 [18] , [19] . Because both HLA and KIR genes are located on different chromosomes and segregate independently , some individuals lack particular KIR-HLA receptor-ligand pairs . Numerous studies have shown that certain KIR-HLA receptor-ligand combinations are associated with susceptibility to infectious and autoimmune diseases , such as clearance of HCV , microscopic polyangiitis , type 1 diabetes and HIV disease progression [20]–[23] . Based on the above observations , we hypothesized that KIR-HLA receptor-ligand combinations are associated with cerebral malaria , and KIR-HLA receptor-ligand diversity has been shaped by fatal malaria as a selective pressure in malaria-high endemic regions . To test this hypothesis , we first examined the possible association between KIR-HLA receptor-ligand combinations and cerebral malaria in Thailand . We show herein that the KIR2DL3-HLAC1 receptor-ligand pair is significantly associated with the development of cerebral malaria . In addition , comparison of both the KIR2DL3 and HLA-C1 gene frequencies between malaria high-endemic and low-endemic populations suggest that natural selection has acted on both KIR2DL3 and HLA-C1 . To our knowledge , this is the first genetic association study to suggest an influence of NK cells in the pathogenesis of cerebral malaria on KIR and HLA frequencies in human populations where malaria is endemic .
To test our hypothesis that shaping of KIR-HLA receptor-ligand diversity in human populations has been affected by cerebral malaria , a life-threatening complication of malaria , we first searched for specific KIR-HLA receptor-ligand combinations associated with cerebral malaria . To this end , we analyzed 477 malaria patients living in Northwest Thailand . Because other ethnic groups such as Karen and Burmese also resided in this area , we recruited only those patients who self-identified as Thai , excluding others from the analyses . Since the primary interest of this study was the development of cerebral malaria after infection , we selected mild ( n = 203 ) and non-cerebral severe ( n = 165 ) malaria patient groups as controls to compare with the study group of cerebral malaria patients ( n = 109 ) . Figure 1 and Table 1 show the KIR genotypes and the frequency of KIR-HLA receptor-ligand combinations in the three groups . Among the six inhibitory KIR-HLA receptor-ligand pairs , the KIR2DL3-HLA-C1 pair was significantly more frequent in the cerebral malaria group when compared with non-cerebral severe ( odds ratio ( OR ) 3 . 44 , 95% confidence interval ( 95%CI ) 1 . 59–7 . 43 , P = 0 . 0010 , corrected P ( Pc ) = 0 . 03 ) and mild malaria ( OR 2 . 90 , 95%CI 1 . 35–6 . 21 , P = 0 . 004 , Pc = 0 . 12 ) groups . In contrast , other KIR-HLA pairs showed no significant associations with cerebral malaria . When non-cerebral severe and mild malaria were merged into one group as non-cerebral malaria , the most significant association was obtained with the combination of KIR2DL3 and HLA-C1 and cerebral malaria , as compared to the non-cerebral malaria group ( OR 3 . 14 , 95%CI 1 . 52–6 . 48 , P = 0 . 00079 , Pc = 0 . 02 ) . For comparison , we also examined the HLA-C1 , C2 and Bw4 frequencies within our Thai malaria patient groups with those of other populations in Thailand that were HLA typed to four digit resolution , and obtained from the Allele Frequency Net Database ( population: Thailand ) [24] . Although there were no significant differences in the frequencies of HLA-C1 , C2 , Bw4 and each individual HLA-C allele between these populations ( Table 2 and supplementary table S1 ) , the genotype frequencies of HLA-C1 and C2 showed statistically significant difference between cerebral and non-cerebral severe malaria ( P = 0 . 008 ) , and between cerebral and mild malaria groups ( P = 0 . 002 ) . These associations are presumably due to HLA-C1 , because the carrier frequencies of HLA-C1 were significantly higher in the cerebral malaria group , compared with the non-cerebral malaria group ( OR 7 . 08 , 95%CI 1 . 69–29 . 7 , P = 0 . 001 , Table 2 ) , but those of HLA-C2 were not significantly low ( Table 2 ) . In addition , a significant association of the HLA-C1 positivity with cerebral malaria might be secondary resulting from the combinatory effect of KIR2DL3-HLA-C1 , because the combination of KIR2DL3 and HLA-C1 was more significantly associated with cerebral malaria than HLA-C1 alone ( P = 0 . 00079 vs . P = 0 . 001 ) . This is also supported by the observations that HLA-C1 in combination with KIR2DL2 , another HLA-C1 receptor , showed no significant association with cerebral malaria , and in HLA-C1 positive individuals , KIR2DL3 positivity showed a trend for association with cerebral malaria ( Table 2 ) , although this failed to reach statistical significance ( OR = 1 . 89 , 95%CI = 0 . 82–4 . 37 , P = 0 . 15 ) . In this regard , however , the result should be interpreted with caution because we could not evaluate the independent effect of HLA-C1 alone and in combination with KIR2DL3 by logistic regression analysis ( data not shown ) , because most cerebral malaria patients ( 100 of 109 patients ) had both HLA-C1 and KIR2DL3 . Therefore , we cannot rule out the possibility of the independent effect of HLA-C1 on the development of cerebral malaria . To further investigate potentially functional combinations of KIR2DL3 and each HLA-C1 allele , we compared the frequencies of the combinations of KIR2DL3 and each HLA-C1 allele ( C*01 , C*03 , C*07 , C*08 , C*12 , and C*14 ) between malaria patient groups . However , there were no significant differences ( supplementary table S2 ) , suggesting that the present association does not come from a specific HLA-C1 allele . Some activating KIRs are also reported to bind to particular HLA class I molecules , although some of these claims are controversial . The combination of KIR3DS1 and HLA-Bw4 is associated with slower progression of HIV infection [25] , and under some condition , KIR3DS1 is thought to recognize HLA-Bw4 . KIR2DS1 recognizes HLA-C2 , depending on the presented peptide [26] . KIR2DS4 binds to HLA-A11 , and some HLA-C alleles ( C*16:01 , C*01:02 , C*14:02 , C*05:01 , C*02:02 , and C*04:01 ) [27] . When we examined the possible association of these combinations between three malaria patient groups , we observed no significant association except for KIR2DS1-HLA-C2 , which was significantly associated with non-cerebral severe malaria ( Table 1 ) . Since strong linkage disequilibrium ( LD ) is a prominent feature of KIR region , KIR gene profiles were classified based on the centromeric and telomeric regions of the KIR A and B haplotypes ( Cen-A/B and Tel-A/B ) as described previously ( Figure 1 ) [28]–[30] . When we compared the Cen-A/B and Tel-A/B frequencies between our malaria patient groups , there were no significant differences ( Table 3 ) . Since KIR2DL3 is on the centromeric KIR A haplotype , the presence of both centromeric KIR A haplotype and HLA-C1 was also significantly associated with cerebral malaria when compared with non-cerebral malaria ( OR 3 . 14 , 95%CI 1 . 52–6 . 48 , P = 0 . 00079 ) . Because the presence or absence of KIR2DL1 is a simple distinction of two centromeric KIR B regions and KIR2DL1 is in positive linkage disequilibrium with KIR2DL3 , we also examined whether the presence or absence of KIR2DL1 in combination with HLA-C1 was associated with cerebral malaria . However , this did not reach statistical significance ( P>0 . 01 ) . We also compared our malaria patient groups with a Thai population from Bangkok , which was genotyped for all of the KIR loci , and available from the Allele Frequency Net Database ( population: Thailand Bangkok KIR pop 2 ) [24] . KIR genotype frequencies showed significant difference between the Thai population ( Bangkok ) and malaria patient groups ( Table 3 ) . Because there is no information available about exposure to P . falciparum in these data on Bangkok Thais , we cannot distinguish at present whether the significant difference between the Bangkok population and our malaria patient groups results from different genetic backgrounds or susceptibility to infection . These observations suggest that KIR2DL3 in combination with HLA-C1 is primarily associated with the development of cerebral malaria . For comparison , we also analyzed KIR carrier , profiles , the KIR AA and Bx genotype frequencies , between the pairs of malaria patient groups in this study . However , none of them showed significant differences ( Figure 1 and Table 3 ) . In order to rule out the possibility of spurious associations resulting from population stratification , association analyses between cerebral and non-cerebral malaria were applied to random combinations of two single nucleotide polymorphisms ( SNPs ) , which exhibit no LD and are independent of KIR and HLA . To this end , we used a total of 18 SNPs , which were previously genotyped [31]–[36] , or additionally genotyped in this study as candidate SNPs for susceptibility to cerebral malaria . Those 18 SNPs were divided into 11 neutral and 7 non-neutral SNPs , as evaluated by heterozygosity , FST and iHS statistics using the Human Evolution Database ( supplementary table S3 and supplementary table S4 ) [37] . We defined the SNPs as non-neutral when one of the three statistics reached statistical significance . We analyzed neutral and non-neutral SNPs separately . A total of 220 and 84 random combinations of two SNPs from neutral and non-neutral SNPs were obtained , respectively . When we performed association analyses using those SNPs on cerebral and non-cerebral malaria patients , neither distribution of p values was biased toward false positive association ( supplementary Figure S1 , P>0 . 01 , df = 19 ) , indicating that cerebral and non-cerebral malaria groups have no significant population structure . Therefore , these data suggest that the significant association of the KIR2DL3-HLA-C1 combination with cerebral malaria observed in this study is not due to the population stratification . Malaria is one of the strongest selective pressures acting on the human genome . This is evident from the similarity in the global distributions of endemic malaria and the red blood cell disorders that confer protection against malaria [38] . Thus variants providing resistance to malaria were driven to high frequency in malaria-endemic areas . As cerebral malaria is a life-threatening disease and a potential selective force , it is possible to detect the signature of natural selection acting on the genes associated with cerebral malaria by comparing the allele frequencies of genes between malaria high-endemic and low-endemic populations . Thus , we tested the hypothesis that the frequency of the KIR2DL3-HLA-C1 combination is lower in malaria high-endemic populations due to natural selection by fatal malaria . To this end , a total of 29 worldwide populations , for which both KIR2DL3 and HLA-C1 gene frequencies were available from an earlier study [15] , were analyzed . Table 4 and Figure 2 show the gene frequencies of KIR and HLA , and the location of the 29 populations plotted on a world map of estimated percentage of malaria cases due to P . falciparum , respectively . We used the product of KIR2DL3 and HLA-C1 gene frequencies in a population as a measure , which we call the GF*GF index , of the frequency of KIR2DL3-HLA-C1 combination in that population . The 29 populations were classified into either P . falciparum malaria high-endemic or low-endemic populations as described in the methods section , and their GF*GF indices for KIR2DL3 and HLA-C1 were compared using the Wilcoxon rank sum test ( Figure 3 ) . The GF*GF index was significantly lower in malaria high-endemic than in malaria low-endemic populations ( P = 0 . 00045 , and supplementary Figure S2 ) . The GF*GF index for the mild malaria group also showed a lower value of 0 . 52 than that of malaria low-endemic Northeast Asians . In contrast , the GF*GF indices for the combinations of KIR2DL1 with its HLA-C2 ligand and KIR2DL2 with its HLA-C1 ligand showed no significant differences between malaria high-endemic and low-endemic populations . Although the GF*GF index for the combination of KIR3DL1 with HLA-Bw4 was significantly higher in malaria high-endemic than in malaria low-endemic populations ( P = 0 . 0042 ) , this is most likely due to a significant negative correlation between HLA-C1 and HLA-Bw4 gene frequencies in these 29 populations ( r = −0 . 42 , P = 0 . 023 , Figure 4 ) , which is explained by strong linkage disequilibrium between HLA-C and HLA-B [39] . These results suggest that either or both of the KIR2DL3 and HLA-C1 gene frequencies have decreased due to the potentially fatal interaction of KIR2DL3-HLA-C1 in cerebral malaria . In this regard , however , it cannot be ruled out that this observation was confounded by population demographic history of malaria high-endemic and low-endemic regions . Thus , to distinguish between natural selection and the confounding effects of population demographic history , an empirical distribution of the Wilcoxon rank sum test statistics were used to compare the GF*GF indices for 1 , 051 genome-wide SNPs between malaria high-endemic and low-endemic populations as described in the methods section . On empirical distribution , the GF*GF index of KIR2DL3 and HLA-C1 also showed significant difference between malaria high-endemic and low-endemic populations ( above the 98th percentile , Figure 5 ) . Taken together , these results suggest that natural selection by cerebral malaria has operated to avoid KIR2DL3-HLA-C1 interaction in malaria high-endemic populations .
Although HLA class I is not expressed on erythrocytes , KIR2DL3-expressing NK cells can respond to inflamed tissues during blood-stage malaria . Artavanis-Tsakonas et al found a significant association between a KIR3DL2 allele expressed by individual donors and the likelihood of making a strong NK response to P . falciparum-infected red blood cells [40] . However , the presence or absence of HLA ligand was unknown , and the KIR3DL2 allele could not explain all of the NK responses in their study . In addition , NK activation induced by P . falciparum-infected red blood cells required myeloid accessory cells [11] , [41] , interactions described in a “ménage à trois model” [42] . Because myeloid accessory cells express HLA class I , KIR2DL3-expressing NK cells might be able to respond in these conditions . KIR2DL3 binds to HLA-C1 with weaker affinity than does KIR2DL2 , an allelic form of KIR2DL3 [20] , [43] , [44] , and therefore HLA-C1-mediated inhibition of NK cells might be weaker in KIR2DL3-expressing NK cells . Alternatively , the association observed in this study might also be explained by a process , described as “licensing” , ”disarming” or “education” , in which the presence of the particular KIR-HLA receptor-ligand pair confers functional competence on NK cells and influences differences in NK cell functional responses among individuals [45]–[50] . Given this observation , the NK cell repertoire shaped by the KIR2DL3-HLA-C1 interaction could exhibit unhelpful responses that increase susceptibility to cerebral malaria . In addition , NK cells were shown to stimulate recruitment of T cells to the brain during Plasmodium berghei-mediated cerebral malaria [4] . Thus , NK cells might not directly engage in the pathogenesis of cerebral malaria , but regulate the activation of other immune system cells , which then cause the pathology . Taniguchi et al reported that Plasmodium-positive individuals showed a higher frequency of KIR3DL1/KIR3DS1 heterozygosity than Plasmodium-negative individuals [51] . However , KIR3DL1/KIR3DS1 heterozygosity showed no significant association with cerebral malaria in our study ( Figure 1 ) . The two studies are different in terms of the outcomes analyzed ( infection vs . cerebral malaria ) , and the Plasmodium species analyzed ( all four human Plasmodium spp . vs . P . falciparum ) . In our study design , which focused on the analysis of cerebral malaria after infection , the effect of the HLA-C1 and KIR2DL3 combination on susceptibility to infection could not be analyzed . To make that possible , it would have been necessary to collect information on the exposure of the subjects to P . falciparum . We previously reported that the allele frequencies of HLA-B46 were statistically different between non-cerebral severe malaria and cerebral malaria using the same cohort [52] . All the individuals carrying HLA-B46 at the HLA-B locus also had HLA-C1 bearing HLA-C owing to the strong linkage disequilibrium in our study population between HLA-B46 and HLA-C1 ( D′ = 1 ) , suggesting HLA-B46 as a potential confounder . We also reported that the frequency of some TNF alleles were significantly greater in patients with cerebral malaria than in patients with non-cerebral malaria [35] . Although the TNF gene is located near HLA region , the associated TNF alleles were not in linkage disequilibrium with HLA-C1 ( D′ = 0 . 07 ) . The combination of HLA-C1 and KIR2DL3 remained significantly associated with cerebral malaria after adjustment for the HLA-B46 and TNF alleles ( OR = 2 . 94 , 95%CI 1 . 47–6 . 58 , P = 0 . 004 ) by logistic regression analysis , indicating that the combination of HLA-C1 with KIR2DL3 is an independent risk factor for cerebral malaria . Unexpectedly , the combination of KIR2DS1 and HLA-C2 showed significant association with non-cerebral severe malaria . KIR2DS1 was reported to interact with up-regulated peptide-HLA-C2 complexes on Epstein-Barr virus-infected cells [26] . Therefore , KIR2DS1 might recognize the peptide-HLA-C2 complexes up-regulated by inflammatory conditions during blood-stage malaria . The Thai patient cohort in this study consisted of residents of Suan Phung . Because other ethnic groups such as Karen and Burmese also resided in this area , we recruited only the patients who were self-identified as Thai . In addition , to exclude a possible spurious association by the population stratification owing to mixture of different ethnic populations , we selected 11 neutral and 7 non-neutral SNPs , which are independent of KIR and HLA , and not in LD with each other , and then , performed association analyses using those SNPs on cerebral and non-cerebral malaria patients . Those analyses suggested that the significant association of KIR2DL3-HLA-C1 combination with cerebral malaria observed in this study is not due to population stratification . The mean ages for the malaria patients in this study were higher than the mean age of 4 . 3 years reported for Gambian patients with cerebral malaria [53] . This might be explained by the intensity of malaria transmission . In low and medium transmission settings , cerebral malaria occurs both in adults and children , whereas in high malaria transmission settings , cerebral malaria occurs almost exclusively in infants and young children [54] . The combination of HLA-C1 and KIR2DL3 remained significant even after adjustment for age ( OR 3 . 46 , 95%CI 1 . 63–8 . 08 , P = 0 . 0021 ) . Malaria was historically common in the Mediterranean littoral , and could therefore have affected the KIR and HLA-C genotype distributions in Europe . However , since malaria endemicity is thought to have been lower in the Mediterranean littoral than in Africa , we classified Europe as “low-endemic region” , assuming that the current relative malaria endemicity is similar to that in the past . A number of studies have suggested that KIR has co-evolved with HLA [14] , [15] , [55] , [56] . However , little is known about candidate pathogens acting as strong selective pressures on KIR and HLA . Our data indicate how selection by malaria could have contributed to the relative frequencies of KIR2DL3 and HLA-C1 in human populations . A recent study showed that natural selection to reduce the frequency and avidity of the KIR2DL3-HLA-C1 interaction has operated in the Yucpa Amerindian tribe living at the border between Venezuela and Colombia [56] . This observation might be partly explained by the selective pressure of malaria , as Colombia is a malaria high-endemic region . Previous study showed that the KIR2DL3-HLA-C1 conferred a protection against HCV [20] . In contrast , KIR2DL3-HLA-C1 was significantly associated with susceptibility to cerebral malaria in this study . An opposite effect was reported for HIV and HPV , where specific KIR-HLA combinations giving strong NK responses were implicated in resistance to AIDS progression [25] and in susceptibility to HPV-related cervical carcinoma [57] . These observations suggest that a stronger activating KIR-HLA combination is advantageous for clearance of pathogen , but is more likely to cause the severity of disease owing to excessive response . Taken together , the present results show a significant association between the KIR2DL3-HLA-C1 receptor-ligand pair and cerebral malaria , and the signature of natural selection acting on both KIR2DL3 and HLA-C1 due to cerebral malaria . It has been reported that NK cells are required for vaccine-induced protective immunity [58] , indicating that understanding the roles of NK cells in malaria is useful for a new vaccine design and a new therapy focused on NK cells . Therefore , our results could have implications for malaria control strategies .
This study was approved by the institutional review board of the Faculty of Tropical Medicine , Mahidol University ( Approval reference number: TM-IRB 39 ) , and the Research Ethics Committee of the Graduate School of Comprehensive Human Sciences , University of Tsukuba ( Approval reference number: 148-1 ) . Written informed consent was obtained from all patients . A case-control study was conducted of 477 malaria patients ( 203 mild malaria , 165 non-cerebral severe malaria , and 109 cerebral malaria patients ) living in Suan Phung , Ratchaburi-Province , Northwest Thailand . This cohort was designed for the analysis of genetic factors associated with cerebral or severe malaria after infection and DNA samples of these patients were previously collected [59] . We recruited only the patients who were self-identified Thai , and excluded Karen and Burmese . A normal healthy control population from the same area was not included in this study because the primary interest of this study was the development of cerebral malaria after infection . All patients underwent treatment at the Hospital for Tropical Diseases , Faculty of Tropical Medicine , Mahidol University . Clinical manifestations of malaria were classified according to the definitions and associated criteria published by world health organization ( WHO ) 2000 . Cerebral malaria was defined as unrousable coma ( Glasgow coma scale of 9 or less ) , positive blood smear for the asexual form of P . falciparum and exclusion of other causes of coma . Non-cerebral severe malaria was defined as having a positive blood smear and fever in addition to one of the following signs: high parasitemia ( >100 , 000 parasites/µL ) , hypoglycemia ( glucose level <2 . 2 mmol/L ) , severe anemia ( hematocrit <20% or hemoglobin level <7 . 0 g/dL ) , and increased serum levels of creatinine ( >3 . 0 mg/dL ) . Mild malaria was characterized by a positive blood smear and fever without other causes of infections and had no manifestations of severe malaria as described above . Patients aged 13 years or older were analyzed in this study , and the mean ages for patients with mild , non-cerebral severe , and cerebral malaria were 25 . 5 , 23 . 7 and 28 . 6 years , respectively . Genomic DNA was extracted from peripheral blood leukocytes using a QIAamp blood kit ( Qiagen ) . Alleles at the HLA-A , HLA-B and -C loci were determined using a Luminex Multi-Analyte Profiling system ( xMAP ) with a WAKFlow HLA typing kit ( Wakunaga , Hiroshima , Japan ) , which is based on polymerase chain reaction-reverse sequence-specific oligonucleotide probes ( PCR-rSSOP ) , according to the manufacturer's instructions . The number of probes for HLA-A , HLA-B and HLA-C was 72 , 92 and 48 , respectively . HLA-Bw4 , HLA-C1 and HLA-C2 KIR ligands were assigned based on the amino acid residues of the HLA-A , HLA-B and HLA-C alleles , as described previously [25] , [60] . KIR genotyping was performed using xMAP with KIR SSO Genotyping Test , lot #002 ( One Lambda , Canoga Park , CA ) , according to the manufacturer's instructions . The presence or absence of the following 16 KIR genes was identified: KIR2DL1 , KIR2DL2 , KIR2DL3 , KIR2DL4 , KIR2DL5 , KIR2DS1 , KIR2DS2 , KIR2DS3 , KIR2DS4 , KIR2DS5 , KIR3DL1 , KIR3DL2 , KIR3DL3 , KIR3DS1 , KIR2DP1 and KIR3DP1 . KIR2DL5A and KIR2DL5B genes could not be distinguished using this typing system . KIR gene profiles were determined by the presence or absence of each KIR gene in a given individual . The genotypes of KIR AA or Bx , centromeric ( Cen-A/B ) or telomeric ( Tel-A/B ) parts of the KIR genes were deduced from the KIR profiles as defined previously [28] , [29] , [30] . Cen-B1 and Cen-B2 were grouped together as Cen-B in this study . KIR gene profiles are shown in Figure 1 . One individual in the non-cerebral severe malaria group was negative for the KIR2DL4 gene , which is considered to be present in virtually all individuals and is termed a framework KIR gene . KIR profile #38 of this individual was identical to that of one Bubi individual in an earlier study [61] . Carrier frequencies for each KIR-HLA receptor-ligand pair were compared between the cerebral malaria patient group and the group of non-cerebral malaria or mild malaria patients using the Fisher's exact test based on a 2×2 contingency table . To further assess the effects of the KIR-HLA receptor-ligand pair of interest on cerebral malaria , a logistic regression analysis was performed after adjustment for age , in which the presence or absence of the specific KIR-HLA receptor-ligand pair , and age ( linear ) were independent variables . OR and 95%CI were estimated in order to examine the effect size of the association . P values of <0 . 01 were regarded as statistically significant . Bonferroni correction for multiple testing was applied to our data of KIR-HLA combinations using the number of comparisons performed by our primary factors of interest in Table 1 ( i . e . 30 tests = 10 combinations ×3 comparisons between two groups ) . Bonferroni correction for multiple testing was also applied to the additional analysis of HLA using the additional number of comparisons in Table 2 ( i . e . 30 tests +7 additional factors ×3 comparisons between two groups = 51 tests ) . In order to exclude the possibility of population stratification , distribution of P values obtained from the SNPs , which are independent of KIR and HLA , was constructed to examine whether P values were not biased toward false positive association . A total of 18 SNPs , some of which were genotyped previously , or additionally genotyped in this study , were used . These 18 SNPs were divided into 11 neutral and 7 non-neutral SNPs , as evaluated by heterozygosity , FST and iHS statistics using Human Evolution Database ( http://124 . 16 . 129 . 22/db/table . php ) [37] . The procedure was performed as follows; two SNPs were randomly selected from 11 neutral or 7 non-neutral SNPs , resulting in a total of 220 or 84 combinations , respectively , and then , carrier frequencies for one allele in one SNP and one allele in another SNP were compared between cerebral and non-cerebral malaria groups by chi-square test with one degree of freedom . We tested for uniformity of P-value distributions using the chi-square test of goodness-of-fit . In order to test our hypothesis that the KIR2DL3 and HLA-C1 pair , which showed a significant association with cerebral malaria in this study , has been under natural selection in malaria high-endemic regions , we compared the frequency of the KIR2DL3-HLA-C1 combination between malaria high-endemic and low-endemic populations using KIR2DL3 and HLA-C1 gene frequencies . The KIR carrier frequency ( CF ) data for 29 populations were obtained from an earlier report [15] . Gene frequencies ( GF ) of HLA-C1 , HLA-C2 , KIR2DL2 , KIR2DL3 , and KIR3DL1 were calculated from the carrier frequencies , assuming that HLA-C1 , KIR2DL3 , and KIR3DL1 are allelic to HLA-C2 , KIR2DL2 , and KIR3DS1 , respectively . Gene frequencies of HLA-Bw4 and KIR2DL1 were estimated according to the formula , , as described previously [15] , since information about another allele of the same locus were not available for these two loci . We used the product of KIR2DL3 and HLA-C1 gene frequencies in a population as a measure , which we call the GF*GF index , of the frequency of the KIR2DL3-HLA-C1 combination in that population . The GF*GF index is defined as the product of two different gene frequencies . To compare the frequencies of KIR2DL3-HLA-C1 combination between malaria high-endemic and low-endemic groups , the GF*GF index of KIR2DL3 and HLA-C1 was calculated for 29 populations that were classified as either malaria high-endemic or low-endemic groups , and assessed using Wilcoxon rank sum test . Malaria high-endemic areas in this study were defined as areas where more than 25% of malaria cases are due to P . falciparum as described in the WHO report [62] ( Figure 2 ) . This analysis is based on the relative malaria endemicity , assuming that current relative malaria endemicity is similar to the past . In order to distinguish between natural selection and the confounding effects of population demographic history , an empirical genome-wide distribution of Wilcoxon rank sum test statistics was constructed using the following procedures . First , a total of 1 , 051 genome-wide SNPs in 29 populations were obtained from the Allele Frequency Database ( ALFRED ) [63] , a web-based freely accessible compilation of allele frequency data on DNA sequence polymorphisms in anthropologically defined human populations ( http://alfred . med . yale . edu ) . Second , as both KIR2DL3 and HLA-C1 are ancestral alleles [64] , ancestral allele frequencies were selected from these 1 , 051 SNPs . The ancestral allele of a SNP was determined by the comparison of human DNA to chimpanzee DNA , and available at dbSNP FTP site ( ftp://ftp . ncbi . nih . gov/snp ) . Third , as both KIR2DL3 and HLA-C1 are located on different chromosomes , the GF*GF index of two ancestral alleles on different chromosomes was calculated for each population . Consequently , a total of 429 , 281 GF*GF indices per population were obtained and compared between malaria high-endemic and low-endemic groups using the Wilcoxon rank sum test . Values beyond the 95th percentile were regarded as significantly lower . To assess the significance of the correlation between HLA-C1 and HLA-Bw4 gene frequencies in the 29 populations , Pearson's product-moment correlation coefficient was used . | NK cells play an important role in early defense against pathogens . Killer immunoglobulin-like receptors ( KIR ) are a diverse family of activating and inhibitory receptors expressed on human NK cells . Some inhibitory KIRs recognize human leukocyte antigen ( HLA ) class I molecules as their ligands . The KIR loci exhibit presence or absence polymorphism , and thus , some individuals lack particular KIR-HLA receptor-ligand pairs , which affects their NK cell responses . We herein show that presence of both KIR2DL3 and its cognate HLA-C1 ligand in malaria patients was strongly associated with the development of human cerebral malaria . This result suggests that NK cells from the patients carrying both KIR2DL3 and HLA-C1 exhibit functional responses that facilitate development of cerebral malaria . In addition , the gene frequency of the KIR2DL3 and HLA-C1 combination was found to be significantly lower in populations with high-endemic malaria . These observations suggest that the combination of KIR2DL3 and HLA-C1 has decreased in malaria high-endemic populations under selection from cerebral malaria , a major life-threatening complication of Plasmodium falciparum malaria . | [
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] | 2012 | Significant Association of KIR2DL3-HLA-C1 Combination with Cerebral Malaria and Implications for Co-evolution of KIR and HLA |
A characteristic signature of adaptive radiation is a slowing of the rate of speciation toward the present . On the basis of molecular phylogenies , studies of single clades have frequently found evidence for a slowdown in diversification rate and have interpreted this as evidence for density dependent speciation . However , we demonstrated via simulation that large clades are expected to show stronger slowdowns than small clades , even if the probability of speciation and extinction remains constant through time . This is a consequence of exponential growth: clades , which , by chance , diversify at above the average rate early in their history , will tend to be large . They will also tend to regress back to the average diversification rate later on , and therefore show a slowdown . We conducted a meta-analysis of the distribution of speciation events through time , focusing on sequence-based phylogenies for 45 clades of birds . Thirteen of the 23 clades ( 57% ) that include more than 20 species show significant slowdowns . The high frequency of slowdowns observed in large clades is even more extreme than expected under a purely stochastic constant-rate model , but is consistent with the adaptive radiation model . Taken together , our data strongly support a model of density-dependent speciation in birds , whereby speciation slows as ecological opportunities and geographical space place limits on clade growth .
Patterns of speciation vary across both time and space . The changing time course of speciation has been emphasized in the context of adaptive radiations , where it is proposed that a diversity of unexploited resources stimulates a burst of speciation , with speciation slowing down as niches become filled [1–3] . This is for two complementary reasons . First , Mayr [4] noted that speciation may be more difficult in an environment full of competitors , because populations find it more difficult to persist in new locations , which is an essential requirement for populations to differentiate to the level of full species [5] . Second , Rice and Hostert [6] and Schluter [5 , 7] suggested that reproductive isolation between populations should evolve more quickly early in an adaptive radiation rather than later , because divergent selection pressures are stronger early on . In the fossil record , rates of morphological evolution are clearly episodic , and this is some of the strongest evidence for the process of adaptive radiation [8] . However , because speciation and lineage splitting can occur with little morphological evolution [8 , 9] , the question of whether speciation rates vary through time is best assessed using reconstructed phylogenies [10] . A large number of molecular phylogenies are now available and , when they are calibrated in terms of time , they often show a signature of a decrease in speciation rates toward the present . The evidence comes from a graph of the logarithm of the number of lineages present in the phylogeny against time , referred to as a lineage-through-time plot [10–12] . In a pure birth ( or Yule ) model [13] , with a constant probability of speciation through time and no extinction , the expectation of the lineage-through-time plot is a straight line . In fact , in many studies , as one nears the present , fewer lineages than expected accumulate [11 , 12 , 14–22] . Such slowdowns have often been interpreted in terms of adaptive radiation ( e . g . , [11 , 17 , 19 , 21] ) . In such cases , the combination of geographical boundaries limiting clade distributions and restricted availability of ecological niches leads to a slowing of speciation rates as species accumulate [23–25] . Although adaptive radiation models predict a slowdown of speciation rate as clades grow large , such a pattern can emerge from simple stochastic models of constant speciation and extinction probabilities . This is because large clades are produced when , by chance , multiple speciation events have happened early during diversification , and small clades are produced when , by chance , few speciation events have happened early . As time proceeds and lineages accumulate , both large and small clades are likely to regress back to the universal average speciation rate , thus generating a slowdown for large clades and a speedup for small clades . We simulated tree growth under pure birth [13] and birth–death [26] models to assess the magnitude of this effect , and showed that the result is generally an inflated type 1 error , which is further exacerbated because researchers tend to study large clades . We conducted a meta-analysis of 45 phylogenetic reconstructions for clades of birds ( at or around the genus level ) totaling approximately 1 , 350 species ( depending on how species are defined ) . We adopt the widely used γ test statistic to test for slowdowns in speciation rate [12] . This statistic relates to the distribution of internode distances through time , and under the pure birth model follows a standard normal distribution with a mean of 0 . A γ value less than −1 . 645 rejects the pure-birth model under a one-tailed test and provides support ( α = 0 . 05 ) for the alternative hypothesis of slowdown . In practice , this is likely to be a conservative test , because the effect of constant extinction rates is to produce the appearance of a speedup in diversification rates on the reconstructed phylogeny ( i . e . , with constant birth and death rates , the expected value of γ is positive [12] ) . Across 45 phylogenies , we estimate an average γ value that is significantly less than zero , and this is consistent with a decline in speciation rates through time to half ( or less ) of the initial rates . Although the clades we focus on may be a nonrandom selection with a probable bias toward large young clades , the frequency of significant slowdowns that we observe in large clades is significantly greater than expected under constant rate models . Results , therefore , provide general support for density-dependent speciation .
Our simulations showed that even under a pure birth model , a negative correlation of γ with clade size is expected ( Figure 1A and Table S1 ) . This is because small clades ( e . g . , clades containing four species ) are biased toward those that began speciating at a slow ( below average ) rate . Conversely , large clades ( e . g . , clades of size > 100 ) are biased toward those that began speciating at a fast ( above average ) rate . Intermediate sized clades consist of a mixture of lineages that showed rapid and slow rates of early diversification and therefore possess a symmetrical distribution of γ values centered on 0 . The average γ value estimated for each set of constant rate parameters tended to be less than 0 , due to the smallest clades ( i . e . , those that should show a strong speed-up ) having too few species to permit calculation of γ ) ( Table S1 ) . The negative correlation between γ and clade size held when the birth rate was allowed to vary between different simulations . When extinction was added , so long as the death rate , d , remained low ( <0 . 25 × birth rate , b ) , the negative association between clade size and γ remained , albeit with a reduced correlation coefficient , when compared to the pure birth model . When d = b there was no correlation between clade size and γ ( Table S1 ) . In the d = b scenario only those lineages that diversified rapidly initially had surviving members at the end of the simulations [25] , with the result that extinction eroded variation in γ . We conducted two further sets of simulations . First , when we studied clades of the same size , we found that clade age was positively correlated with γ , r = 0 . 52 . Thus , younger lineages showed more evidence for slowdowns ( Figure 1B ) . Second , under a constant rate birth-death scenario the strength of the correlation increased to r = 0 . 58 when b = 0 . 25d , and r = 0 . 82 when d = b . The explanation for this positive correlation is similar to that underpinning the negative relationship between clade size and γ . Young clades are typically those that diversified fastest initially before slowing down to the average rate . Older clades are typically those that diversified slowly at first before accelerating up to the average rate . In the dataset of 45 bird clades ( Table 1 ) , the mean γ = −0 . 98 ± 0 . 20 standard error was significantly different from 0 ( t = 4 . 89 , p < 0 . 001 ) . A third of all clades had a γ ≤ −1 . 645 , which suggests that slowdowns are widespread . However , such a conclusion may be biased by the over-representation of large clades in our dataset . Fifty-seven percent ( 12/21 ) of clades with more than 15 lineages at 2 Mya had a γ < −1 . 645 . Although we have shown that under constant rate models , larger clades tend to show a slowdown , the high frequency of significant slowdowns that we observed in such large clades exceeded the null expectation ( based on χ2 tests across a range of birth and death values , p always less than 0 . 01 , see Table S1 ) . Thus , we have evidence that at least some clades are showing a deterministic decline in speciation rate toward the present . The correlation of clade size with γ was highly significant , r = −0 . 58 , p < 0 . 001 ( Figure 2A ) . A strong negative correlation is expected whenever there are true slowdowns in the data , because the associated larger sample size of larger clades results in a greater power ( more-negative γ ) and thus it is difficult to estimate the magnitude of the slowdown . However , simulations described in Protocol S1 suggest that a statistically significant γ value in clades of size 15 requires an average slowing of speciation rate later in the radiation to 10%–50% ( or less ) of the initial rate . γ was not correlated with clade age ( Figure 1B; r = −0 . 02 , p = 0 . 88 ) . When variation in clade size was accounted for using multiple regression , there was a marginally nonsignificant positive correlation between clade age and γ ( Table 2 ) . Together , clade age and clade size explained 39% of the total variance in γ . Ten of the 45 trees were found to be significantly more imbalanced than expected under the equal rates Markov model of tree growth ( Table 1 ) . There was some evidence for a general departure from the degree of balance expected under a pure birth model ( median β = −0 . 56 ) , although this departure was less than that reported for a large sample of published trees [27] . After controlling for clade size and age using multiple regression , tree imbalance ( estimated using the tree-splitting parameter β [28] ) showed a weak positive correlation with γ , b = 0 . 40 ± 0 . 16 , partial r2 = 0 . 08 , p < 0 . 05 . This implies that more imbalanced trees have a slightly greater tendency to show speciation slowdowns .
The empirical results provide strong evidence for slowdown in speciation rate in large clades . The magnitude of slowdown seems to be quite large . For example , among the 22 clades with 15 or more lineages at 2 Mya , the median value for γ = −1 . 77 , and the median clade size is 29 , consistent with a slowing of speciation rate in the later stages of a radiation to 10%–50% of the initial rate ( Protocol S1 ) . Our conclusions are predicated on the assumption that the molecular phylogenies accurately reconstruct the timing of speciation events . In particular , if saturation is present in the molecular data , deep branch lengths will be consistently underestimated , leading to a bias toward a negative γ [29] . Over the time scales of this investigation—and given our use of the complex GTR + I + G model—this seems unlikely to be a problem . Further , if nucleotide saturation is driving patterns , we expect to see greater slowdown in older clades , the opposite of what is observed . Nor is the negative correlation of γ with clade size ( Figure 2A ) expected to arise as a consequence of nucleotide saturation . Estimates of speciation patterns based on gene trees add error to the estimate of speciation times ( e . g . , [30] ) , but this source of error should make slowdowns more difficult to detect , rather than introducing a bias . The observed negative relationship between clade size and γ is not limited to this study . Indeed , two earlier studies based partly on subsets of the data analyzed here reported a similar trend [18 , 19] . Two predictions regarding temporal patterns of speciation arise from adaptive radiation models [2] . First , clades should show a slowdown in speciation toward the present as niche space is filled . This prediction rests on the assumption that members of a clade , in our case usually a genus , experience competition over niche space . Second , given density-dependent speciation , slowdowns should be particularly evident in large clades [18] . We found strong support for both these predictions . However , the same predictions also arise under a simple model where speciation rates are constant across time ( and this also applies to models which allow for low levels of extinction , see Table S1 ) . This is because small clades are likely to have diversified , by chance , slowly early on , and large clades are likely to have diversified quickly early on , both followed by regression to the mean . Thus a bias on the part of researchers toward studying large clades leads to the expectation that those clades that are studied will show the pattern of slowdown . This bias probably affects tests of other questions [25 , 31] . For example , very few phylogenetic studies have identified an extinction rate greater than zero , which is paradoxical given estimates of extinction derived from fossils [32] . A signal of extinction is identified from reconstructed phylogenies , because , under a constant rate model , extinction leads to an increase in the observed branching rate toward the tips of the reconstructed tree for extant species [10] . Given that we expect to find a decrease in diversification rate toward the present in large young phylogenies ( if b and d are relatively constant through time and d/b is low ) , then a bias toward testing for extinction in such large young clades introduces a strong bias against detecting extinction . Under the adaptive radiation model , a negative correlation between clade age and γ is expected , because rapid initial diversification followed by a slowdown should result in more-pronounced slowdowns in older clades . The reverse is expected under constant rate models: after controlling for clade size , a positive correlation between clade age and γ ( i . e . , younger clades tend to show the strongest slowdowns ) is predicted ( Figure 1B ) . This is because clades that quickly attain a given size are likely to have experienced above-average rates of initial diversification . As lineages accumulate , the overall diversification rate will approach the underlying mean , resulting in slowdowns . Thus our finding of a tendency toward a positive association ( albeit marginally nonsignificant ) between clade age and γ ( Table 2 ) is more in accord with the constant speciation model than the adaptive radiation model . Both the adaptive radiation model and constant-rate , stochastic model predict negative γ in large clades , whereas the marginally nonsignificant positive correlation between clade age and γ is more consistent with the constant rate null model . However , constant rate models cannot explain the very high prevalence of significant slowdowns observed in large clades . In particular , our simulations show that if the actual extinction rate across bird lineages over the past 20 million years has approached the speciation rate , then the probability that the strong slowdowns observed in large clades could have arisen under a constant-rate birth–death model becomes vanishingly small . We thus conclude there is strong evidence for density-dependent cladogenesis in large clades . Speciation rates across a whole clade may slow through time because a few ecologically unusual and/or geographically restricted lineages persist for a long period of time without speciating , even as the rest of the clade continues to follow a constant birth–death model [23] . This should create strong tree imbalance . However , the tree-imbalance parameter we used explains only a small proportion of the variance in γ ( partial r2 = 0 . 08 , n = 45 clades ) , and it is likely that slowdowns are the result of more general ecological interactions . For example , in the Old World Leaf Warblers ( Phyllscopus and Seicercus ) , related sympatric species in the Himalayas are old and occupy different habitats , which are presumed to have arisen in association with mountain building or climate change 8–10 Mya [33] . Even Leaf Warbler allospecies , with abutting geographical ranges , are typically separated by millions of years [33] . We suggest that limited ecological space in this and other groups has restricted the ease of range expansions , and consequently further allopatric speciation . Similarly , Ricklefs [24] found a negative correlation between number of species in a clade and age of the clade across passerine birds , and he interpreted this finding in terms of niche-filling , as we do here . Some alternative explanations for slowdowns have been suggested , including nonrandom extinction [14 , 34] and episodic appearances of multiple barriers [14 , 15 , 20] , but these seem less likely to produce such a general pattern . In conclusion , we find that two factors contribute to the prevalence of slowdowns reported in large phylogenies . First , the strong signal of slowdown supports an adaptive radiation model , where speciation is accelerated in empty environments and slows as niches get filled . Second , speciation events happening randomly within clades through time may also result in the presence of a slowdown in large young clades . Randomness does not mean that speciation is completely unpredictable , but rather that multiple independent causes are likely to contribute [35] . Speciation may be promoted by factors such as occasional extinctions creating new ecological opportunities , appearance of habitat that can be exploited by multiple lineages ( rather than a single lineage that rapidly diversifies ) , the strength of barriers , chance dispersal events , and the occasional evolution of traits within lineages that affect speciation probability . The overall importance of random processes as causes of slowdowns depends on the true extinction rate . If extinction rates are low , the importance of stochastic factors in generating slowdowns may have been underestimated . If , as seems likely , extinction rates approach the speciation rate [36 , 37] , then constant birth–death models on their own cannot explain slowdowns . Instead , our findings of strong slowdowns provide support for nonrandom processes of species diversification through time .
We used the γ statistic ( Equation 1 ) to assess slowdown [12] . This is based on the cumulative frequency of internode distances ( g2–gn ) as one counts from the root to the tips of the tree , where n equals the number of taxa . Under the pure birth model , this statistic follows a normal distribution with mean = 0 and standard deviation = 1 . A negative value of γ indicates that nodes are distributed more toward the root of the phylogeny than expected under the null , implying a slowdown toward the present . One-tailed significance for testing slowdowns at α = 0 . 05 is γ < −1 . 645 [12] . Simulations have demonstrated that a decrease in speciation through time can generate significantly negative γ values , but that an increase in extinction through time does not [18] . We simulated tree growth under the pure birth model ( birth rate b = 0 . 2 ) for a fixed time duration and calculated the relationship between γ and clade size across 10 , 000 γ replicates using R code [38] kindly provided by L . Harmon . We repeated this for different durations ( 19 separate runs , with an arbitrary time duration , assigned integer values between 2 to 20 ) . We also examined variants of this model where extinction ( death rates d of 0 . 05 and 0 . 2 ) was included and where the birth parameter was allowed to vary among clades ( each b was obtained from a normal distribution with mean = 0 . 2 and standard deviation = 0 . 04 ) . We then examined the relationship between clade age and γ under these constant models , using Phylogen v1 . 1 ( available from http://evolve . zoo . ox . ac . uk/ ) to simulate 10 , 000 trees of a set size ( 50 species ) under pure birth and birth-death models ( b = 0 . 2 , d = 0 , 0 . 05 , and 0 . 2 ) . We selected bird clades at or about the genus level , for which more than 70% of the species ( usually more than 80% ) have been sequenced for mitochondrial protein coding genes ( Table 1 and Table S2 ) . Typically the amount of sequence available was between 1 , 000 and 2 , 000 base pairs , although for a few clades , we only had 600 base pairs . We downloaded sequences from GenBank using Geneious [39] and aligned them using ClustalW [40] and by eye in the program MEGA v3 . 1 [41] . We reconstructed phylogenetic trees using a relaxed clock Bayesian method [42] implemented in BEAST v1 . 4 . 4 [43] . We set the mean rate of molecular evolution to be 1% per lineage per million years [36] , a GTR + I + G model of substitution , and assumed that rate variation among adjacent branches in a tree was uncorrelated and drawn from a log-normal distribution . We used a neighbor-joining model to obtain a prior distribution for the tree , and a pure birth prior on branching rates . We conducted two runs of 5 million generations each and used Tracer [44] to assess convergence , that the two runs were sampling from the same posterior distribution , and that the estimated sample size for each parameter was of sufficient size to obtain good parameter estimates ( i . e . , > 200 ) . There were few cases where the estimated sample size fell below 200 for a single parameter . We used Tracer to determine how many burn-in generations to discard , which was always 1 million , except in the Estrildidae ( 2 million ) . Using this approach , we were able to obtain a posterior distribution of rooted and dated trees . By sampling every 4 , 000 generations ( 6 , 000 in the Estrildidae ) , we obtained 2 , 000 trees from the posterior distribution of the Bayesian runs ( 1 , 000 in the Estrildidae ) . The Bayesian posterior distribution of trees for each of the phylogenies reconstructed from sequences stored in GenBank is available on request from the authors . We calculated γ across all of the sampled posterior distribution of trees , as follows . First , we counted lineages through time only up to the last bifurcation event prior to 2 Mya . We did this because lineage-splitting events that occur after 2 Mya are not often recorded as different species ( especially under the biological species concept [45] ) , or alternatively over-recorded ( as a result of excessive splitting of distinctive populations following a strict application of the phylogenetic species concept , [46 , 47] ) . We obtained the median γ across the trees sampled from the Bayesian posterior distribution . All γ estimates were obtained using the LASER R library [48 , 49] . Incomplete sampling can bias estimates of γ [12] . Thus for all phylogenies in which taxon sampling was incomplete , we simulated 2 , 500 trees of the same size and same number of missing taxa using PhyloGen [50] . We obtained a γ value for each simulated tree and adjusted the median γ estimated from our data by subtracting the simulated median and dividing by the standard deviation of the simulated values [17] . This approach assumes that missing taxa are randomly distributed on the tree and also that all missing taxa insert before 2 Mya . The latter assumption may slightly bias the results toward estimating a more-positive γ . However , an alternative approach where we did not correct for missing taxa gave very similar results . We compared the frequency of significant ( γ < −1 . 645 ) versus nonsignificant slowdowns observed in large clades ( defined as those with more than 15 lineages at 2 Mya ) to the null expectation generated under 10 , 000 constant rate simulations , using a χ2 goodness-of-fit test . This comparison was repeated across all of the birth and death parameter space described above ( i . e . , b = 0 . 2 , d = 0 , 0 . 05 , and 0 . 2 and simulation duration = 2–20 time units ) . Using multiple regression , we tested whether clade size and age were significant predictors of γ . Clade size was calculated as the number of extant lineages in a clade at 2 Mya . Clade age was estimated as the median root age across the posterior distribution of trees . Pybus and Harvey [12] cautioned that the behavior of the γ statistic on unbalanced phylogenetic trees was unknown . To evaluate the extent to which this was likely to be a problem , we examined whether trees depart from the pure birth/equal rates Markov expectation using Colless' [51] imbalance statistic , Ic . The null expectation for the imbalance statistic across each tree was generated via Monte Carlo simulations of trees of the same size under a Yule ( or equal rates Markov ) model using the apTreeshape R library [52] . We also calculated the median of the maximum likelihood estimate of the β tree-splitting parameter ( examined in the range −2 to 100 ) for each tree [28] using R code kindly provided by M . Blum [27] . We examined whether the degree of tree imbalance affected γ in a multiple regression in which clade size , clade age , and β were predictors . The β parameter was preferred to the Ic statistic as its expectation under a Yule model is independent of clade size . A β of zero is expected under the Yule null , while β < 0 and > 0 correspond to trees that are more imbalanced or balanced than the Yule expectation . | It is probable that the number of species that a given region can support is limited; however , it is unclear whether the limit is approached sufficiently in nature such that the rate at which new species form slows down . Using the pattern of phylogenetic branching , a technique that estimates evolutionary relationships based on molecular data , we demonstrate that in large clades of birds , there is a decrease in the per-lineage probability of speciation as the number of species in the clade increase . We also show that this pattern can arise even if speciation and extinction occur randomly through time . This is because large clades are likely , by chance , to have rapidly speciated early in their history , and will relax back to the average speciation rate later on . We account for this effect , and we still find evidence that , as a clade grows to large size , the per-lineage probability of speciation declines . These results strongly suggest that speciation rates are slowed as environments fill up with competitors . | [
"Abstract",
"Introduction",
"Results",
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"Materials",
"and",
"Methods"
] | [
"evolutionary",
"biology",
"ecology"
] | 2008 | Density-Dependent Cladogenesis in Birds |
Morphological dynamics of mitochondria is associated with key cellular processes related to aging and neuronal degenerative diseases , but the lack of standard quantification of mitochondrial morphology impedes systematic investigation . This paper presents an automated system for the quantification and classification of mitochondrial morphology . We discovered six morphological subtypes of mitochondria for objective quantification of mitochondrial morphology . These six subtypes are small globules , swollen globules , straight tubules , twisted tubules , branched tubules and loops . The subtyping was derived by applying consensus clustering to a huge collection of more than 200 thousand mitochondrial images extracted from 1422 micrographs of Chinese hamster ovary ( CHO ) cells treated with different drugs , and was validated by evidence of functional similarity reported in the literature . Quantitative statistics of subtype compositions in cells is useful for correlating drug response and mitochondrial dynamics . Combining the quantitative results with our biochemical studies about the effects of squamocin on CHO cells reveals new roles of Caspases in the regulatory mechanisms of mitochondrial dynamics . This system is not only of value to the mitochondrial field , but also applicable to the investigation of other subcellular organelle morphology .
Recent studies have shown that the fusion-fission dynamics of mitochondria are essential to many cellular processes , including ATP-level maintenance , redox signaling , oxidative stress generation , and cell death [1]–[4] . Meanwhile , it is also known that dysfunctional mitochondrial dynamics ushers the aging process and neuronal degenerative diseases [5]–[11] . Since mitochondrial morphology reveals physiological and pathological status , tracking mitochondrial morphological differences becomes important . Previous studies roughly classified mitochondrial morphology into various subtypes , such as , fragmented globules , tubular threads , networks , clumps or swollen granules , and usually the classification was performed by human inspection [2] , [8] , [9] , [12] , which inevitably introduces biases and inconsistency and lowers replicability of the results . Previous attempts of automatic quantification include measuring length , width , area and other primitive parameters of mitochondrial objects [13] and also skeleton length [14] , but these measures are insufficient to fully distinguish the morphological diversity of mitochondria . They [13] , [14] investigated only the average of these feature values within each cell , while in this paper , we present a computational approach that allows us to identify representative morphological subtypes and quantify the morphological diversity of mitochondria . Our approach consists of successive steps of image segmentation , consensus clustering and classifier learning designed to identify subtypes as well as construct a subtype classifier . A large set of fluorescent microscopic images of Chinese Hamster Ovary ( CHO ) cells were used as the sample to derive the subtypes . A subset of these CHO cells was treated with squamocin , a compound known to induce apoptosis [15]–[18] . Squamocin treatment results in mitochondrial fragmentation , which can then be suppressed by inhibitors of Caspases 8 and 9 ( z-IETD and z-LEHD , respectively ) , but cells are still killed by squamocin even with the presence of these inhibitors [19] . One possible reason is that Caspase inhibitors may not have fully restored mitochondrial structures . With the developed computational approach , we were able to quantify the difference of morphological changes of mitochondria in cells under different treatments . We used a previously developed image segmentation method [20] to accurately extract each individual mitochondrial object from cell micrographs . This method applies adaptive local normalization [20] and Otsu's image thresholding method [21] to deal with noisy background and variant object intensity that are constantly present in fluorescent micrographs , a challenging issue for existing image binarization methods . We then applied this method to extract a large number of mitochondrial objects . Each object was then represented by a set of image features , including morphological , skeletal , and binary Haralick texture features . According to result of consensus clustering of the image features , we merged clusters that are functionally similar to define six morphological subtypes of mitochondria . This new subtyping covers mitochondrial morphology reported in the literature and provides an unambiguous observable indicator of the status of a mitochondrion in a cell . We then applied supervised learning algorithms to train an automatic classification system to classify mitochondrial objects in images into one of the six subtypes . The resulting system combined with object extraction was named “MicroP , ” which allows us to accurately measure mitochondrial subtype compositions in individual cells to profile the outcome of different drug treatments . We used MicroP to study the effect of squamocin , a compound known to induce cell apoptosis by triggering mitochondrial fragmentation . Inhibitors of Caspases 8 and 9 can partially rescue the fragmentation but the mechanism was not previously characterized . Analyzing subtype composition of cells treated by squamocin and Caspase inhibitors reveals a more comprehensive picture of how Caspases 8 and 9 interact with mitochondrial fusion-fission regulatory proteins to influence morphology and functions of mitochondria .
We extracted 225 , 556 mitochondrial objects from the whole dataset of 1422 cells , and a total of 19 distinct morphologies were identified by consensus clustering with manual validation as shown in Figure 1 . Inspecting these morphologies reveals that these automatically derived morphological clusters indeed have distinctive shapes . To develop representative morphological subtypes for automatic classification , we further regrouped these 19 clusters according to morphological similarity and molecular functional characteristics , supported by evidence reported in the literature . The result is six subtypes: small globules , swollen globules , straight tubules , twisted tubules , branched tubules and loops . These subtypes provide useful indicators of specific cell conditions , and will be used as a basis for generating manually labeled mitochondria as training sets . Small globules ( Figure 1A ) comprise the largest clusters in the clustering results . Swollen globules ( Figure 1B ) are in fact found as outliers in the clusters of small globules , since their number is too small to be distinguished by unsupervised clustering . But they are an important morphological subtype of mitochondria , suggested to represent dysfunctional mitochondria undergoing mitophagy by previous investigations [9] . Therefore , we separated swollen globules manually from the clusters of small globules using the mean plus 3 standard deviation of the area as a reference threshold to form another subtype . Straight , twisted , and branched tubules were separated by unsupervised clustering into clusters of distinct lengths . However , we assumed that the difference in length only reflects different frequencies of fusion events but not different biological mechanisms of formation . Also , to the best of our knowledge , there is no report in the literature that explicitly distinguishes mitochondrial morphology by length . Thus length was excluded as a criterion to differentiate morphological subtypes . We merged five straight tubule clusters of distinct lengths ( Figure 1D ) into a single subtype , and similarly merged 3 clusters of twisted tubules ( Figure 1E ) and 5 clusters of branched tubules ( Figure 1F ) . Twisted tubules have only three instead of five distinct lengths probably because longer twisted tubules have a higher chance to form branched tubules , and that tubules need to reach a certain length before they can become “twisted . ” Mitochondria become twisted when they are not fully associated with microtubules , and portions of mitochondria not associated with microtubule motors will collide with water molecules and become twisted . It was observed from our time-lapse images ( data not shown ) that these twisted mitochondria are constantly moving . We consider only those mitochondria that exhibit these characteristics as “twisted tubules” but not those mildly curved ones . Finally , we grouped “horseshoe” mitochondria with “donuts” into a single “loops” subtype ( Figure 1C ) , since both of them maintain a much higher degree of stable curvature than twisted tubules . We observed that “horseshoe” mitochondria stably maintain a high degree of curvature , which would require extra force , e . g . , inter-mitochondrial end-to-end fusion , to stabilize . In contrast , twisted mitochondria dynamically vibrate their ends due to collision with other molecules , e . g . , water , which makes them appear twisted . We trained an ensemble of three classifiers on a training set manually labeled with the six morphological subtypes defined in the last section . Performance of the three classifiers were individually assessed both by holdout testing accuracy and visual inspection of results for unlabeled mitochondrial micrographs . Table 1 reports the performance assessment . The figures are averages of 20 runs of holdout testing , where each run used a random partition of training and holdout sets . Table 2 shows the aggregate confusion matrix over all holdout testing runs for all classifiers . Performance for most subtypes are above 80% in accuracy , except for twisted tubules ( ) , which are often confused with straight tubules by the classifiers . This is expected because it is also difficult for a human to judge whether a short mitochondrial tubule is twisted or straight . Next , the entire set of labeled mitochondria samples were used as the training examples for the three classifiers . Table 3 shows the cross validation accuracy results . We also report the optimal parameters used for each classifier . Visual inspection of classification results of unlabeled mitochondria in micrographs shows that all three classifiers performed adequately , with complementary types of errors for each morphology subtype . Hence we used an ensemble classifier , which combines the decisions of the three classifiers by majority vote , as the final classifier for unlabeled cells . The ensemble classifier outperforms individual classifiers in holdout testing . Figures S5–7 in Text S1 show the histograms of basic morphological features of mitochondria for different subtypes . These features are strongly correlated with the subjective criteria used by human inspectors in manual labeling of the training samples . Morphological subtyping of mitochondria can help quantify how mitochondrial morphology are affected by drug treatments . Following up on our previous biochemical studies [19] , [22] , we investigate whether squamocin-induced reduction of mitochondrial biomass and mitochondrial fragmentation are fully restored by z-IETD and z-LEHD . Identifying morphological subtypes may provide new clues for the design of further experiments to determine how these features are correlated with apoptosis . Figure 2 shows the effects of different treatments on the total number and area of mitochondria in cells . Squamocin induces large numbers of mitochondria but reduces total mitochondria area compared to control . z-IETD and z-LEHD fully restore the number of mitochondria to the control level but only partially restore the area . Among them , z-LEHD shows slightly better restoration ability than z-IETD . These results suggest that squamocin increases the mitochondria number by inducing mitochondrial fission and possibly also reduces the sizes of individual mitochondria . Squamocin may also reduce biogenesis or enhance degradation of mitochondria , resulting in reduction of total mitochondrial area in cells . In some images of squamocin-treated cells , the intensity of a few mitochondria was lower than the threshold of the segmentation algorithm and thus those mitochondria were omitted and may contribute to reduction of the total area . However , their number is too small to affect the main conclusion here . Figure 3 shows the average ratio of mitochondrial subtypes in cells treated by different drugs . Small globules and straight tubules are predominant in all treatments , followed by branched tubules . The other subtypes are relatively rare . As expected , squamocin induces more small globules and notably decreases the number of branched , twisted tubules and loops . Straight tubules , however , are not affected as much . z-IETD and z-LEHD restore squamocin-reduced straight and twisted tubules completely , but only partially restore branched tubules . Comparing the two Caspase inhibitors , it can be seen that z-LEHD restores more branched tubules than z-IETD , but the difference is not significant . Long error bars ( representing standard deviation ) in the figure implies that responses of cells to these treatments were quite heterogeneous . To determine whether the shape of each subtype is affected by different drug treatments , differences in the distributions of mitochondrial morphological features for each subtype was investigated . No significant difference was found for average morphological feature values for cells given different treatments , either over all subtypes or within individual subtypes ( data not shown ) . However , it was observed that tubules were slightly shorter in squamocin-treated cells and that loops were slightly shorter in z-IETD cells . In summary , drug treatments affect the distribution of subtypes in cells but not the shape of the subtypes , although this is expected partly due to the use of the same classifier for all treatment populations . The correlations between the ratios of mitochondrial subtypes in each cell may hint at the biological mechanism of their formation . Positively correlated subtypes may be formed by the same mechanism while negative correlation of subtypes implies transition between subtypes . Figure 4 shows the correlation heat map of different subtypes in cells treated by different drugs . The patterns for DMSO ( control ) and squamocin are quite different whereas those for control and z-LEHD are similar . The correlation pattern of z-LEHD is closer to control than z-IETD , while z-IETD is closer to control than squamocin . This provides further evidence of better restoration capability of z-LEHD . Notable pairwise correlations include: One of the challenges in cell-based analysis is that cells may be at different phases of the cell cycle and have different timing to respond to treatments . The target of this study , mitochondrial morphology , is especially sensitive to bioenergetics , cell cycle , aging , regulatory proteins and various stresses [9] . Therefore , although general trends of squamocin and the restorative effects of Caspase inhibitors can be verified visually , it is challenging to rigorously validate the differences by statistical analysis . For cell response profiling , we used a set of cell features designed to characterize the composition of mitochondrial subtypes in cells . The difference between cells in terms of their mitochondrial morphology can be easily measured as the Euclidean distance in cell feature space . Figure 5A shows the result of multidimensional scaling ( MDS ) that maps the cells from multidimensional space of cell features to a two dimensional space so that we can visualize their difference . Each point in the figure is a cell that is colored differently , representing the treatment received . The MDS plot shows that cells receiving the same treatment respond heterogeneously and those treated differently overlap . Diamonds in the plot mark the mean responses for each treatment . The distance between the mean responses of Squamocin and DMSO ( control ) is the largest , implying that responses to these treatments are the most differentiated . Between Squamocin and DMSO lies z-IETD and z-LEHD successively . This result suggests a trend of differing restoration ability of Caspase inhibitors , similar to the results in Figures 2 and 3 . Since both MDS and subtype ratio results show that the responses of cells to the same treatment are heterogeneous and many cells in different treatments have similar responses in terms of mitochondrial morphology , these overlapped responses may be considered as transitional responses or no response . Here , we used the silhouette coefficient analysis [23] to identify cells with representative responses for each treatment . By concentrating on representative cells , a more reliable profiling of these treatments can be established . We removed cells with silhouette coefficient from the analysis , since they are on average closer to cells in another treatment population than to cells in its own treatment population . In the end , the percentages of cells remaining with silhouette coefficient are 82 . 6% , 45 . 4% , 46 . 7% and 15 . 0% for treatment populations DMSO , Squamocin , z-IETD and z-LEHD , respectively . Figure 5B shows the MDS plot of only these representative cells with the mean responses re-computed . The plot shows that the relative distances between the mean responses stays the same as in Figure 5A , but the relative positions are changed . The subtyping results of representative cells from each population ( Figure 6 ) suggest that in Figure 5B , the x-axis is highly correlated with the degree of fragmentation while the y-axis is highly correlated with the prevalence of twisted tubules and loops . The equivalents of Figures 2 , 3 , and 4 calculated using only representative cells are shown in Figures S11–13 in Text S1 , respectively . These additional results strengthen the support of our main conclusions . Figure 6 shows subtyping results of select representative cells in different treatment populations . The distributions of the cell features of representative cells ( those with ) reveal unique characteristics of the effect of the different drugs on the composition of morphological subtypes of mitochondria: For cells with silhouette coefficient , we also keep track of which other treatment population it is closest to , and the percentages of cells in each treatment that are closer to every other treatment populations is shown in Table 4 . It can be seen that except for the z-LEHD population , the majority of cells in each treatment is closer to its own treatment population .
The six representative subtypes of mitochondrial morphology: small globules , swollen globules , straight tubules , twisted tubules , branched tubules and loops , are identified based on the unsupervised consensus clustering results and evidence of functional similarity reported in the literature . Based on previous literature and hints from subtype ratio correlations ( Figure 4 ) , we propose a model for the formation and transition of the six subtypes ( Figure 7 ) . Previous studies identified three types of mitochondrial morphologies , fragmented , tubular , and network-like mitochondria , and showed that regulatory proteins of mitochondrial dynamics determine which type of mitochondria is predominant [1] . It is also known that bioenergetic states and oxidative stress will affect the distribution of mitochondrial morphology [24] . In our subtyping , fragmented mitochondria are represented by “small globules , ” and tubular mitochondria are subdivided into four different subtypes: straight , twisted , branched tubules and loops . Formation of straight or twisted mitochondria are mainly dependent on the assembly of microtubules , but independent of other factors affecting mitochondrial morphology [25]–[27] . Branched tubules result from inter-mitochondrial end-to-end and side-to-side fusion , and has a unique molecular mechanism of formation and distribution specific to physiological conditions [28]–[30] . Donut-shaped mitochondria are induced by moderate mitochondrial membrane potential and low cellular respiration [2] , [7] , [31] . Network-like mitochondria were not observed in our dataset . Finally , swollen granules are a new unique structure characterized recently . Mitochondria can become swollen due to dysfunction or various conditions ( e . g . loss of / exchange activity ) , and are subsequently targeted for “mitophagy . ” Another formation mechanism involves fragmented mitochondria that are packed into autophagosomes [9] . Recently , Yoshii et al . [32] also showed that the outer membrane of mitochondria can be ruptured by mitophagy , resulting in swollen mitochondria . The quantitative analysis in this study demonstrates for the first time that compositions of mitochondrial morphological subtypes may be heterogeneous within a treatment population , with representative morphologies specific to drug treatments . We consider here the biological significance of these representative mitochondrial morphologies for each treatment condition . The control ( DMSO ) cells are characterized by tubular mitochondria , especially branched ones ( Figure 6A ) , while small globules are the representative mitochondrial morphology of squamocin-treated cells that are responsive to squamocin ( Figure 6B ) . Table 4 shows that a majority of control cells ( 83% ) are closer to the control population . Hence these cells can be considered to be representative mitochondrial morphology in the control condition . For squamocin-treated cells , 45% are closer to their own population , while 30% are closer to the control population . This indicates that the CHO cells in this research responded heterogeneously when treated by squamocin . Our unpublished data show that longer treatment duration will not change the percentage of cells affected by squamocin and that the effect of squamocin examined in this study is its terminal effect . Cells treated with z-IETD after squamocin are characterized by straight tubules rescued from squamocin-induced mitochondrial fission , as can be observed from the representative cells in the z-IETD population in Figure 6C . These cells comprise about 47% of the z-IETD population , much larger than the percentage of cells that are closer to other populations . The morphological composition appears to be restored partially to that of the control . In contrast , treatment with z-LEHD after squamocin is more effective for rescuing tubular mitochondria from squamocin-induced fission , as 54% of the z-LEHD population are closer to the control population , while only about 15% are representative . Therefore , the relatively large number of twisted tubules and loops observed in representative cells in the z-LEHD population ( shown in Figure 6D ) may represent only a transitional morphology during tubule restoration by z-LEHD . In summary , the mitochondrial morphologies of representative cells in the control , squamocin and z-IETD populations are all unique and specific to their respective treatment conditions . While in the z-LEHD population , a majority of cells are restored by z-LEHD with the mitochondrial morphology similar to control condition . Representative cells in z-LEHD population are the minority and may be special cases of intermediate responses . Squamocin is a potent inhibitor which blocks mitochondrial functions and causes oxidative stress [15]– . Our results in general show that cells treated with squamocin contain more small globules or fragmented mitochondria and total mitochondrial area is reduced . The squamocin-induced mitochondrial morphological change can be partially rescued by inhibiting Caspases 8 and 9 with z-IETD and z-LEHD treatments , respectively , with z-LEHD being more effective . These results provide the first evidence that Caspases 8 and 9 are directly involved in mitochondrial dynamics . Another interesting finding is that after cells are treated by squamocin , applying inhibitors of Caspases 8 and 9 can rescue most of the tubular mitochondria with the exception of branched ones . According to our analysis and previous investigations , we proposed a hypothetical model of squamocin-induced mitochondrial morphological changes , as shown in Figure 8 . We discuss these results in more detail in the following sections . In this study , we developed MicroP for automatic classification and quantification of mitochondrial morphology in cell micrographs , which helped us confirm the number and range of representative morphological subtypes , the effects of treatments on the ratio of subtypes in cells , and make sense of the subpopulations within heterogeneous cell responses to different drug treatments . The main contributions of our automated system in this study are summarized as follows: First , our computational method allows objective subtyping and automatic quantification of mitochondrial morphology in cell micrographs , which enables profiling of cell responses to drug treatments . Second , using multidimensional scaling and silhouette coefficients to characterize cell response profiles , we discovered that Caspases suppress elongation fusion of mitochondria but not branching . Our quantification analysis also differentiates the effects of Caspases 8 and 9 inhibitors on squamocin-treated cells . For example , Caspase 9 inhibitor ( z-LEHD ) rescues longer mitochondria and results in larger numbers of twisted and looped mitochondria than Caspase 8 inhibitor ( z-IETD ) . Given the heterogeneity of cell responses to drug treatments , it would be challenging to reach these findings if not for the subtyping and quantitative analysis . Finally , our correlation analysis of subtype ratios within individual cells reveals unexpected trends which provide directions for further investigations . For example , the stronger negative correlations of branched tubule mitochondria with small globules than those of other tubule subtypes suggests a higher fission rate of branched mitochondria than other subtypes of tubular mitochondria . Our future work is to simultaneously measure cell viability and morphological subtypes of mitochondria to reveal their correlation and to validate the proposed pathway model with further biochemical and cell biological experiments . We will utilize 3D time-lapsed imaging to provide solid evidence to study the transition of mitochondrial subtypes during different treatments , and to verify our interpretations of the correlations of mitochondrial morphological subtypes . Our long-term goal is to investigate whether morphological features of mitochondria are specific to neurodegenerative diseases or aging and evaluate the use of mitochondrial morphological features as “high content” biomarkers .
CHO-K1 cells were obtained from the Food Industry Research and Development Institute ( Hsinchu , Taiwan ) . CHO-K1 cells were cultured in McCoy' 5A containing 10% fetal bovine serum and incubated in an incubator containing 5% at . For cell imaging , CHO-K1 cells expressing DsRed-mito ( cells ) were seeded on a 24mm round coverslip ( thickness: 0 . 17mm ) . After 24h culture , cells were pre-treated with z-IETD ( Caspase 8 inhibitor; Sigma , U . S . A . ) , z-LEHD ( Caspase 9 inhibitor; Sigma , U . S . A . ) , or a control medium without any drugs for 2h . Cells pre-treated with control medium were further incubated with either 0 . 05% DMSO or squamocin for 24h . Cells pre-treated with Caspase inhibitors were further treated with squamocin for 24h . The following labels are used to refer to the different treatments in subsequent sections: DMSO ( DMSO + control ) , Squamocin ( squamocin + control ) , z-IETD ( Squamocin + z-IETD ) and z-LEHD ( squamocin + z-LEHD ) . The coverslip attached with cells was put on a chamber and examined with a fluorescence microscope ( IX-71 , Olympus ) with a objective ( PlanApo , NA1 . 45 , Olympus ) . Monochromator equipped with Xe-lamp ( polychrome II , Till-photonics , Gräfelfing , Germany ) was driven by TILLVision 4 . 0 ( Till-Photonic , Gräfelfing , Germany ) to excite DsRed-mito ( 550 nm ) . Fluorescence was filtered by a filter cube ( Mitotracker orange: 565DCLP ( BS ) , D605/55m ( Em ) ; Chroma , Rockingham , Vermont , USA ) , and 2D fluorescent cell images were acquired by a CCD camera ( IMAGO , Till-Photonics Germany; exposure: 500ms; resolution: 12-bit , pixels , pixel size: ) . The images are scaled down to 8-bit for image analysis , and pixel width is about 165nm . We have compared 2D ( epi-fluorescence microscopy ) and 3D ( confocal fluorescence microscopy ) CHO micrographs , and found that CHO cells are flat and most of the mitochondria in 2D micrographs are in focus and clear enough for high content image analysis ( see Section S2 . 1 in Text S1 ) . Therefore , 2D micrographs are the major data source used in this study . Segmentation of individual cells within each micrograph field was done semi-automatically . First the centroid of each cell nucleus was specified manually , which indicated the position of each individual cell object . Then the Delaunay triangulation was calculated using all cell centroids and their dual Voronoi diagram [44] was used as the final cell segmentation . The resulting single-cell images were manually validated and grouped by drug treatments . In the resulting set , the DMSO group contains 178 cell images , the Squamocin group 357 cell images , the z-IETD group 454 cell images , and z-LEHD group 433 cell images , for a total of 1422 single cell images . Mitochondria extraction involved segmenting each single-cell image into mitochondria and background . Cell micrographs of mitochondria exhibit varying background brightness and contrast levels , hence proper segmentation needs to take into account the statistical properties associated with each locality in the image . Here , we used our segmentation algorithm described in [20] . In this algorithm , adaptive local normalization is used to preprocess the images ( with parameter value ) and then Otsu's thresholding [21] was applied to the normalized image to obtain the final segmentation . Adaptive local normalization applies dynamic window sizes determined by the intensity structure of each pixel region to effectively deal with local contrast and background variation , and at the same time enhance detailed subcellular structures . This segmentation algorithm was quantitatively validated using manually generated gold standard segmentations from the same dataset used in this work with approximately equal amount of images from each treatment [20] . All the CHO cells from the four treatment conditions were quite flat and easily focused on a 2D focal plane . Seriously out-of-focus and blurred mitochondria generally only constituted a small fraction of all mitochondria in individual cells . Cells treated by squamocin were a little bit rounder , but their mitochondria were still focused well on a single plane ( as shown in Figures S14-16 in Text S1 ) . Visual examination showed that cells from four treatment conditions were roughly equally focused on average , and there were no seriously out-of-focus cells in this dataset . To further test the robustness of our segmentation method , we analyzed representative subsets of cells from each treatment condition with perturbed threshold values ( data not shown ) . For most cell features , such as total mitochondrial area and relative ratios of morphological subtypes , the results are consistent across different threshold values . After segmentation , binary object images representing mitochondria were extracted by standard object labeling with 4-neighbor connectivity . Postprocessing was performed to remove objects with low intensity ( both in the original or normalized images ) and small area , as well as any objects touching the image boundary . Figure S17a-c in Text S1 illustrate the results of these processing steps on an example micrograph . A total of 225 , 556 objects/mitochondria were obtained from segmenting the single-cell images , of which 27 , 752 are in the DMSO group , 66 , 438 in the Squamocin group , 67 , 288 in the z-IETD group , and 64 , 078 in z-LEHD group . From each segmented binary mitochondrion object , a set of image features was extracted to represent its morphology . These image features can be divided into three categories: morphological features based on the object binary mask , skeleton features based on one-pixel wide homotopic skeleton , and binary texture features based on the object bounding convex hull . Table S1 in Text S1 contains a summary of these features and their notations , for more details please refer to Section S2 . 3 in Text S1 . These mitochondrial features are used for both consensus clustering and classification , described in subsequent sections . To identify meaningful morphological subtypes of mitochondria , the Gaussian mixture model ( GMM ) clustering was applied to cluster mitochondrial objects with the optimal number of clusters determined by Bayesian information criterion ( BIC ) . We used the GMM implementation in the MATLAB Statistics Toolbox with default values for all parameters . We performed multiple clustering runs on sampling-with-replacement random subsets of objects to ensure the robustness of the clustering . Processing small subsets also helped us save computational cost due to both large data size and slow convergence . For each run , a random subset of 10% of all objects was chosen , then GMM clustering was performed with to , and the value with a minimum BIC was chosen as the optimal number of clusters for the subset . Feature transformation ( see Section S2 . 4 in Text S1 ) was performed for each sampled subset before clustering . Clustering results from multiple runs were compared and combined by matching up clusters with similar morphology so that well-represented morphologies of sufficient diversity would be discovered . Matching clusters from different clusterings are done by calculating the average distance between objects in the clusters . The distance between two clusters and from two different clusterings and are defined as the average distance over all pairs of objects , , where are the Euclidean distance of the mitochondrial feature vectors of and . Two clusters and are considered a match if both , and . Eventually , we obtained a final set of 19 clusters . These matched clusters were visually verified by human experts as reasonable and labeled with biological significance . Using the set of morphology clusters of mitochondria identified by the above process , we defined a set of six distinct and meaningful mitochondria subtypes . We then developed an automated classification system to classify a mitochondria object into one of the six subtypes . The training data set for the classification system was generated by manually labeling the morphological subtypes . Example images in the subtype clusters found by consensus clustering were used as visual references for labeling . A total of 10662 mitochondrial objects were labeled as the gold standard for training , validation and evaluation of the classification system . The gold standard contains 5183 small globule examples , 190 swollen globules , 2923 straight tubules , 961 twisted tubules , 693 branched tubules , and 712 loops . The proportion was made to reflect that of the observed population of mitochondria , and adjusted to have sufficient training samples for each subtype . We used an ensemble of three classifiers trained by three supervised learning methods , respectively . These methods include decision trees , support vector machines ( SVM ) with a radial basis function kernel , and SVM with a linear kernel . The final decision of the ensemble classifier is determined by majority vote . We applied a coarse-to-fine grid search to optimize hyperparameters for the SVM with stratified 10-fold cross validation accuracy as the criterion . We extended binary SVM to multi-class classification using the “one-against-one” approach [45] , in which classifiers are constructed to classify an input into classes . Each classifier is trained to distinguish two of the classes , as implemented in LIBSVM [46] . For the decision tree method [47] , the MATLAB Statistics Toolbox implementation was used . A full tree was first constructed from the training data and optimal pruning level determined by stratified 10-fold cross validation ( via a complexity measure as implemented in MATLAB ) . Misclassification cost was defined as one for each error and zero for a correct classification . The feature set used was the same as the one for the consensus clustering . No feature selection was performed since there are only about 20 features and both SVM and decision trees are relatively robust against redundant features . Also most feature subsets obtained using forward selection failed to improve classifier performance ( data not shown ) . Performance of the classifiers were assessed by holdout testing containing 50% of the gold standard selected by stratified sampling . The remaining 50% were then used as training and validation sets for both optimization and training of the classifiers . This process was repeated for 20 times to obtain an aggregate final accuracy rate for each classifier and the ensemble . Figure S17 ( d ) in Text S1 shows a typical example of the mitochondria classification . We used the trained ensemble classifier to automatically classify mitochondria in each cell into one of the six subtypes , and calculated a set of cell-level features based on the distribution of mitochondrial subtypes in each cell . These features include total number and area of mitochondria , and the ratio of each subtype in a cell ( see Table S2 in Text S1 for the complete list of cell features ) . These cell features represent the mitochondrial morphology characteristics within a cell . To characterize the distribution of cells in the feature space , we used multidimensional scaling ( MDS ) to map the cells onto a 2D space to visualize their distribution . We used the MDS implementation in the MATLAB Statistics Toolbox and performed non-metric MDS using the Euclidean distance of transformed feature values as cell distance , and the stress loss function normalized by the sum of squares of the inter-point distances as the goodness-of-fit criterion . To find representative cell responses , we first divided the cells into four clusters according to treatment received , and the silhouette coefficient of each cell was computed to estimate the representativeness of its responses among all cells receiving the same treatment . The silhouette coefficient is defined for an object in cluster by ( 1 ) where is the average distance from object to every other objects in the same cluster , and is the average distance from object to every object in cluster . So when , , the average distance of object is the shortest to objects in its own cluster , hence it can be viewed as a “representative” object in its cluster . Conversely , when , average distance to another cluster is the smallest for object than to objects in its own cluster , hence it cannot represent the cluster that it belongs to . Here we chose as the threshold for a cell to be representative in its treatment population . | Mitochondria are “cellular power plants” that synthesize adenosine triphosphate ( ATP ) from degradation of nutrients , providing chemical energy for cellular activities . In addition , mitochondria are involved in a range of other cellular processes , such as signaling , cell differentiation , cell death , cell cycle and cell growth . Dysfunctional mitochondrial dynamics have been linked to several neurodegenerative diseases , and may play a role in the aging process . Previous studies on the correlation between mitochondrial morphological changes and pathological processes involve mostly manual or semi-automated classification and quantification of morphological features , which introduces biases and inconsistency , and are labor intensive . In this work we have developed an automated quantification system for mitochondrial morphology , which is able to extract and distinguish six representative morphological subtypes within cells . Using this system , we have analyzed 1422 cells and extracted more than 200 thousand individual mitochondrion , and calculated morphological statistics for each cell . From the numerical results we were able to derive new biological conclusions about mitochondrial morphological dynamics . With this new system , investigations of mitochondrial morphology can be scaled up and objectively quantified , allowing standardization of morphological distinctions and replicability between experiments . This system will facilitate future research on the relation between subcellular morphology and various physiological processes . | [
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] | 2011 | Automatic Morphological Subtyping Reveals New Roles of Caspases in Mitochondrial Dynamics |
Dog-bites and rabies are under-reported in developing countries such as Pakistan and there is a poor understanding of the disease burden . We prospectively collected data utilizing mobile phones for dog-bite and rabies surveillance across nine emergency rooms ( ER ) in Pakistan , recording patient health-seeking behaviors , access to care and analyzed spatial distribution of cases from Karachi . A total of 6212 dog-bite cases were identified over two years starting in February 2009 with largest number reported from Karachi ( 59 . 7% ) , followed by Peshawar ( 13 . 1% ) and Hyderabad ( 11 . 4% ) . Severity of dog-bites was assessed using the WHO classification . Forty percent of patients had Category I ( least severe ) bites , 28 . 1% had Category II bites and 31 . 9% had Category III ( most severe bites ) . Patients visiting a large public hospital ER in Karachi were least likely to seek immediate healthcare at non-medical facilities ( Odds Ratio = 0 . 20 , 95% CI 0 . 17–0 . 23 , p-value<0 . 01 ) , and had shorter mean travel time to emergency rooms , adjusted for age and gender ( 32 . 78 min , 95% CI 31 . 82–33 . 78 , p-value<0 . 01 ) than patients visiting hospitals in smaller cities . Spatial analysis of dog-bites in Karachi suggested clustering of cases ( Moran's I = 0 . 02 , p value<0 . 01 ) , and increased risk of exposure in particular around Korangi and Malir that are adjacent to the city's largest abattoir in Landhi . The direct cost of operating the mHealth surveillance system was USD 7 . 15 per dog-bite case reported , or approximately USD 44 , 408 over two years . Our findings suggest significant differences in access to care and health-seeking behaviors in Pakistan following dog-bites . The distribution of cases in Karachi was suggestive of clustering of cases that could guide targeted disease-control efforts in the city . Mobile phone technologies for health ( mHealth ) allowed for the operation of a national-level disease reporting and surveillance system at a low cost .
Infectious disease surveillance continues to remain challenging in developing countries with resource constraints , weak health systems and poor reporting mechanisms [1] , [2] . Existing limitations in achieving these core capacities of the International Health Regulations ( IHR ) have been further compounded in Pakistan by the closure of the Ministry of Health in 2011 and devolution of some of its roles to the provinces , which has disrupted central information collection and dissemination processes [3] , [4] . Donor resources for surveillance are currently dedicated towards certain high priority programs such as active surveillance for acute flaccid paralysis under the polio eradication initiative , while surveillance for other endemic or emerging infectious diseases has been given far less attention . Determining a more accurate burden of these less-studied illnesses is necessary to design appropriate preventative measures and to establish best clinical practice . Recent innovations in mobile phone technologies and the rapid growth of the telecommunications sector in developing countries like Pakistan provide possible solutions to filling this knowledge gap . Rabies is a notifiable disease in most developed countries; however , cases are generally underreported in countries like Pakistan and there is a poor understanding of the disease burden [5] . South Asia is one of the few regions of the world where the epidemiology of rabies is driven through the urban cycle ( primary transmission of the virus occurs through dog-bites rather than wildlife ) , even though effective control and preventative measures for the disease have long been established [6] . In resource-constrained settings , high-risk areas need to be identified to target interventions for effective rabies control and elimination . In addition , gaps need to be identified in clinical and public health practice where appropriate preventative treatment is either delayed or is inadequate following dog-bites . Routine surveillance of dog-bites and rabies in Pakistan is currently conducted under the government's HMIS ( Health Management Information System ) reporting program but poor quality of collected data prevents evidence-based disease control efforts . Published journal articles on the other hand have relied upon retrospectively collected data from hospital records [7]–[9] and may not adequately capture information regarding key rabies prevention measures recommended by the WHO or to guide efforts towards local canine rabies elimination [10] . We present an analysis of prospectively collected data utilizing mobile phone based health technologies , or mHealth , for dog-bite and rabies surveillance across nine sites in Pakistan , with technical and financial support from the World Health Organization ( WHO ) .
The aims of this study were to: 1 ) estimate the burden of dog-bites in Pakistan; 2 ) describe the frequency , age and gender distributions and severity of dog-bite cases across the reporting sites; 3 ) assess differences in patient health-seeking behavior and patient access to care amongst the reporting sites; 4 ) identify high-risk neighborhoods for dog-bite exposure in Karachi , Pakistan's most populous city; 5 ) describe implementation costs of the mHealth surveillance system from a health services perspective . Surveillance was carried out from February 2009 to February 2011 . Figure 1 shows the locations of participating Emergency Rooms ( ER ) in Hyderabad , Thatta , Bahawalpur , Abbottabad , Peshawar , Mansehra , Quetta and two sites in Karachi ( one public and one private ) . ERs were based in high volume tertiary care hospitals that were well known as major referral hospitals for dog-bites and provided rabies vaccination , either the sheep-brain tissue vaccine from the government or cell-culture vaccine from philanthropic donors . The hospital ERs served as reporting centers for the study . Several other public and private facilities providing emergency medical care exist in these cities , particularly in Karachi . For the remaining cities , the selected hospitals were the primary facilities for emergency care serving a mix of urban and rural populations . Reporting from two centers in Lahore and Rawalpindi ceased during the course of the study due to deteriorating security at the time and poor institutional support for surveillance . Limited data was collected from these centers and is not included in the present analysis . Cases were defined as all patients presenting with dog-bites to emergency rooms at enlisted centers and patients with dog-bites referred from other facilities . If dog-bite victims did not seek healthcare , or sought care with traditional healthcare providers and alternative medical centers from where referrals were not made to an enlisted center , those cases would not be reported through our system . In addition , information on some cases was not captured because of refusals to take part in the questionnaire and could not be included . Lay persons with at least 12 years of education were recruited on recommendation of local hospital administrators , and were provided training for the role of “screener” to implement the study protocols regarding informed consent procedures and phone-based screening . The training emphasized basic research ethics and the necessity of voluntary informed consent . Training was provided onsite for data entry and storage using the mobile phone and included role-play to enable screeners to familiarize themselves with the study tools . The administrative heads of ERs were involved in supervision of screeners and in providing local logistical support . Screeners were provided a monthly stipend of approximately USD 80 per month . Dog-bite cases were approached immediately after initial treatment , introduced to the study and its objectives and consent was requested for an interview . A standardized questionnaire using a mobile phone based application was completed for all consenting patients . The questionnaire was created using the openXdata software package for low-cost mobile devices . OpenXdata is an open source software for service providers and researchers to design and manage forms for data collection using mobile devices and remotely monitor data collection activity in the field . OpenXdata has two components , a web-based server application and openXdata mobile which is installed on low cost phones . We provided a GPS-enabled Nokia 6220 mobile phone ( costing USD 250 each ) to screeners at each site programmed with forms to collect patient demographics , details about the nature of the bite and geographic location of the incident . A patient's attendants provided history of exposure and treatment in cases where rabies was suspected and the patient was incapable of responding . The use of openXdata allowed for predefined validation checks as data was being entered into the phone , and eliminated the use of paper forms other than for consent . Date and time stamps were automatically recorded along with GPS coordinates each morning immediately outside the site to monitor the screeners attendance at the hospital . All data entered via mobile phones was uploaded on to the centralized openXdata server , using a GPRS connection . Ethical approval for the study was obtained from the Institutional Review Board ( IRB ) at Interactive Research & Development ( IRD ) which is registered with the U . S . Department of Health and Human Services ( DHHS ) , Office for Human Research Protections ( IRB # 00005148 ) . Informed consent was obtained from patients or from guardians in the case of children , through signatures or thumbprints ( for illiterate patients ) onto paper consent forms , each of which was bar-coded with the patient ID to enable linking with electronic forms . Approval for the use of thumb ink prints in patients unable to provide signed consent as well as for guardians in the case of children was provided by the IRB . An IRB exemption was subsequently obtained for retrospective analysis of surveillance data following closure of the study and data collection activities . Statistical analysis was carried out using Stata version 12 . 1 ( College Station , TX: StataCorp LP ) . We calculated summary statistics by center for total number of cases , patient demographics and severity of bites based on the WHO classification [10] . Type of care immediately sought after the bite , included visits to hospitals , general practitioners , homeopathic facilities , spiritual healers or self-treatment at home . These were categorized as a binary variable to compare treatment at a medical facility versus treatment at a non-medical facility and administering self-treatment . Summary statistics by center were calculated for type of immediate care sought after dog-bites and patient travel time to emergency room . We subsequently fitted a multiple logistic regression to the binary variable as a measure of assessing differences in health-seeking behavior with reporting center , age ( centered at the mean ) and gender as explanatory variables . Reporting center was a categorical variable with nine levels and we set the public hospital in Karachi as the reference level . Due to its more central urban location we hypothesized that the victims presenting to this center would have the greatest health access and shortest travel times . In addition , it reported the largest number of cases therefore was a suitable center to select as a reference category for fitting the model . Pearson's chi-squared was used to test for goodness of fit . The test was appropriate as the model consisted mostly of non-unique covariate patterns [11] . Multiple regression analyses were performed with travel time to ER as the response variable , in order to assess differences in health access of patients following dog-bites with reporting center , age ( centered at the mean ) and gender as explanatory variables . During model validation , time data ( recorded in minutes ) were log-transformed for the residuals to approximate a normal distribution . Robust regression using bootstrapping for 95% confidence intervals was utilized in order to take into account the effect of outliers , but these were not excluded from the analysis as they may represent cases that were travelling to centers from distant locations and provide valuable information with regards to medical access . Spatial analyses were limited to the city of Karachi as it had the highest number of cases and administrative boundaries for the city ( as distinct town units ) were easily available to permit geocoding of addresses . In addition , an insufficient number of addresses in other cities were deemed appropriately detailed for inclusion in spatial analysis , or were outside city limits . Due to the presence of a large stray dog population and wide variations in social determinants of health such as poverty and homelessness we hypothesized a non-uniform distribution of dog-bite incidence in the city [12] . As a predominantly Muslim country , dog-ownership is quite low in Pakistan . Stray-dogs are frequently encountered in Karachi , particularly in low-income areas and are therefore less likely to be vaccinated [12] . Each patient's address was manually geocoded to an administrative town in Karachi as most respondents did not know the zip ( postal ) codes for their house address for automated matching to administrative towns . Counts were aggregated up to area-levels for each of the administrative towns and were utilized to generate choropleth maps using ArcGIS ( ESRI 2011 . ArcGIS Desktop: Release 10 . Redlands , CA: Environmental Systems Research Institute ) . Population estimates were obtained from the last available census carried out in the city in 1998 using a uniform annual population growth rate of 3 . 5% for each town up to the year of the study [13] . This growth rate accounts for both natural growth of population as well as increases due to migration into the city . Distribution of dog-bites was examined for spatial dependence and clustering of cases using the Moran's I test statistic , a global index for spatial autocorrelation that compares differences in counts amongst contiguous area-units after adjusting for population in the overall dataset . We used a Poisson regression , with counts of bites as the response variable and town as the explanatory variable , to indicate whether there was spatial dependence between towns . Population estimates were log transformed and used as an offset for the two years to assess differences in incidence rate ratios between individual towns . Risk estimates of dog-bites for each of Karachi's 18 towns were calculated using the ratio of observed cases to the expected ( based on total population ) to identify towns with increased risk of exposure . A spatial cluster detection analysis for increased rates was subsequently carried out using a discrete Poisson model , with the maximum spatial cluster size as <50% of the population ( on SaTScan version 8 . 0 ) to identify primary and secondary clusters with increased rates [14] , [15] . The costs of the mHealth surveillance system have been described in US Dollars and International US Dollars ( I$ ) , a conceptual currency which adjusts costs for purchasing power parities between countries . The costs have been calculated from a health services perspective and do not include patient related variables such as lost productivity .
A total of 6 , 470 cases of animal-bites were reported during the 24 months of surveillance , of which 96% ( 6 , 212 ) were due to dog-bites . The largest number of cases was reported at the public tertiary care hospital in Karachi ( 48 . 26% ) , as shown in Table 1 . Females accounted for only 20 . 6% of the total and their proportions remained low after adjusting for centers . The median age for cases was 20 years ( inter-quartile range , 10–35 ) and 38 . 9% of cases were under 15 years of age . Using the WHO classification system for severity of dog bites with Category III being the most severe , 40% of patients had Category I ( least severe ) bites , 28 . 1% had Category II bites and 31 . 9% had Category III bites . Following adjustment for center , two public hospitals in Bahawalpur and Karachi reported a greater proportion of Category I bites , where as the other centers reported greater numbers of Category III bites . Less than 1% of cases reported receiving pre-exposure vaccination across all study sites . A previous study from a major public-sector tertiary care hospital in Karachi estimated that 25–35% of cases in the city visited its facility for management of dog-bites , [7] . The estimation was based on interviews with hospital staff and the hospital's proportion of total vaccines consumed in the city . The ERs included in this study were tertiary care referral centers of similar size therefore we used the total number of cases notified and the same estimated range of the proportion of dog-bites reported to extrapolate the incidence of dog-bites in the catchment areas of these centers . For the two centers in Karachi this equates to 8 , 565 to 11 , 992 ( 2 , 998/0 . 25 to 2 , 998/0 . 35 ) dog-bite cases for the public hospital and 2 , 031 to 2 , 844 ( 711/0 . 25 to 711/0 . 35 ) for the private hospital . For the centers outside of Karachi this equates to a total of 7 , 151 to 10 , 012 ( 2 , 503/0 . 25 to 2 , 503/0 . 35 ) cases of dog-bites . The estimated range of dog-bites over the two-year period in the catchment area of these centers is therefore , 17 , 774 to 24 , 848 . Based on the total populations where these centers are located the estimated incidence of dog-bites is 38 to 53 per 100 , 000 per year . Overall , a greater proportion of cases sought immediate healthcare at a non-medical facility or administered self-treatment ( 52 . 29% ) ( Table 2 ) . The median time to ER was 30 minutes ( IQR 5–1 , 800 minutes ) and ranged from 5 to 7 , 800 minutes . A proportionately higher number of cases from the Karachi public hospital sought immediate care at medical facility ( 80 . 92% ) whereas non-medical or self-treatment was more commonly reported for the remaining centers . Cases reporting from Hyderabad had the shortest median travel time to ER ( 20 minutes ) where as those reporting from Mansehra had the highest ( 120 minutes ) . Table 3 describes results of the regression analyses of patient health-seeking behavior and travel time to ER . Patients from all other centers , relative to those visiting the public hospital in Karachi ( reference level ) were more likely to seek immediate healthcare at a non-medical facility , adjusted for age and gender ( p<0 . 01 ) . Peshawar and Bahawalpur were most likely to seek immediate health care at a non-medical facility or administer self-treatment compared to visiting a medical facility ( adjusted odds ratio 144 . 45 and 131 . 36 respectively ) . These odds ratios imply that cases from Peshawar and Bahawalpur were over a hundred times more likely to seek immediate care at a non-medical facility as compared to the public hospital in Karachi . Excluding Karachi , cases from Abbottabad ( adjusted odds ratio 5 . 12 ) and Hyderabad ( adjusted odds ratio 6 . 87 ) were relatively less likely to seek immediate care at a non-medical facility or administer self-treatment as compared to the remaining centers . Cases reporting to the Hyderabad center had the shortest travel time to the ER compared to the rest of the centers , while those at Mansehra and Abbottabad had the longest travel times . Age and gender were not significantly associated with health-seeking behaviors or travel times to ER . Overall 13 cases died due to rabies at an Emergency Room , 8 of which took place in Mansehra . Only one case was confirmed using the Fluorescent Antibody Test ( FAT ) on post-mortem examination , with the remaining diagnoses based on clinical symptoms . Figure 2 displays aggregated counts of dog-bite cases for the city of Karachi . Korangi , Jamshed , Landhi and Malir had the highest number of cases of dog-bites . Figure 3 displays incidence rates of dog-bites in the city . The incidence rates are higher in the central towns of Korangi , Malir and Jamshed , although high rates were also observed in the more peripheral towns ( Bin Qasim and Gadap ) . The Moran's I index for spatial autocorrelation was 0 . 20 , statistically significant for spatial clustering of cases ( p-value<0 . 01 ) . There were significant differences in incidence rates between administrative units ( towns ) based upon Poisson regression analyses , further suggestive of spatial dependence . Figure 4 shows the highest risk estimates for dog-bites in Korangi , Malir and Karachi Cantonment , although Landhi , Jamshed , Gadap and Bin Qasim also had risk estimates of greater than 1 . Cluster analysis ( Figure 5 ) identified Korangi as the primary cluster with a relative risk of 7 . 14 ( log likelihood ratio 942 . 16 , p-value<0 . 01 ) . Karachi Cantonment ( log likelihood ratio 91 . 95 , p-value<0 . 01 ) and Jamshed ( log likelihood ratio 35 . 08 , p-value<0 . 01 ) were identified as secondary clusters , each with a relative risk of 2 . 10 . These three towns of Karachi represent contiguous area-units where a clustering of high dog-bite rates was observed . As described in Table 4 , our experience highlights low deployment and operations costs for our mHealth system across a wide geographic region . The total direct costs of equipment and staffing for two years of surveillance at 8 sites was USD 44 , 408 with approximately USD 19 , 385 for capital costs and USD 25 , 023 for salary support and other operational costs . We estimated the cost per case detected was USD 7 . 15 ( I$ 17 . 26 ) . The average cost per center was USD 4 , 934 per center enrolled . Costs did not vary significantly between centers and differences were largely attributable to travel expenses for management and program staff for on-site trainings .
We identified a high burden of severe ( World Health Organization category III ) dog-bites and substantial variation in patient health-seeking behavior and access to care across different regions of Pakistan . Spatial analyses were suggestive of clustering of dog-bite cases in Karachi and can facilitate preventive efforts . Low-cost mobile phone technologies have the potential to address gaps in surveillance systems in low-resource settings . Further cost-effectiveness studies are required of large-scale implementations of mHealth based infectious diseases surveillance systems in developing countries to generate evidence for necessary resources for scale-up and sustainability . | Resource constraints prevent adequate surveillance of neglected infectious diseases such as rabies in developing countries leading to a poor understanding of the disease burden and limited evidence with which to design effective control measures . We utilized a low cost mobile-phone based system to carry out the first prospective surveillance of dog-bites and rabies in Pakistan by screening all patients presenting to nine emergency rooms in eight cities over a two-year period . We found a large number of dog-bite cases ( nearly a third of which were severe based on a World Health Organization classification ) with substantial geographical variability in time to presentation as well as health-seeking behavior following dog-bites across the reporting sites . Spatial analyses of collected data from Karachi , Pakistan's largest city identified areas with increased risk of dog-bite exposure , which has implications for the design of necessary control measures such as dog vaccination . While mobile phone based technologies have the potential to address limitations in disease surveillance in developing countries , the cost-effectiveness of large scale implementations of such strategies need to be explored and further evaluated where appropriate . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [] | 2013 | Geographic Variation in Access to Dog-Bite Care in Pakistan and Risk of Dog-Bite Exposure in Karachi: Prospective Surveillance Using a Low-Cost Mobile Phone System |
Bacteriophages are major genetic factors promoting horizontal gene transfer ( HGT ) between bacteria . Their roles in dynamic bacterial genome evolution have been increasingly highlighted by the fact that many sequenced bacterial genomes contain multiple prophages carrying a wide range of genes . Enterohemorrhagic Escherichia coli O157 is the most striking case . A sequenced strain ( O157 Sakai ) possesses 18 prophages ( Sp1–Sp18 ) that encode numerous genes related to O157 virulence , including those for two potent cytotoxins , Shiga toxins ( Stx ) 1 and 2 . However , most of these prophages appeared to contain multiple genetic defects . To understand whether these defective prophages have the potential to act as mobile genetic elements to spread virulence determinants , we looked closely at the Sp1–Sp18 sequences , defined the genetic defects of each Sp , and then systematically analyzed all Sps for their biological activities . We show that many of the defective prophages , including the Stx1 phage , are inducible and released from O157 cells as particulate DNA . In fact , some prophages can even be transferred to other E . coli strains . We also show that new Stx1 phages are generated by recombination between the Stx1 and Stx2 phage genomes . The results indicate that these defective prophages are not simply genetic remnants generated in the course of O157 evolution , but rather genetic elements with a high potential for disseminating virulence-related genes and other genetic traits to other bacteria . We speculate that recombination and various other types of inter-prophage interactions in the O157 prophage pool potentiate such activities . Our data provide new insights into the potential activities of the defective prophages embedded in bacterial genomes and lead to the formulation of a novel concept of inter-prophage interactions in defective prophage communities .
Horizontal gene transfer ( HGT ) is a major mechanism involved in bacterial evolution . In HGT between bacteria , viruses known as bacteriophages ( or phages ) play particularly important roles as gene transfer vehicles [1] , [2] . Incoming temperate bacteriophages parasitize their hosts by integrating their genomes into the host genetic material . The additional genetic information that they provide to the host bacterium encodes various novel abilities , such as niche adaptation and the production of new virulence factors [2] , [3] . Although phage-mediated HGT was first described in the 1950s in the conversion of Corynebacterium diphtheriae strains that did not produce a toxin to strains that did [4] , studies in recent decades have identified a number of virulence determinants carried by phages [1]–[3] , [5]–[7] . Furthermore , because numerous bacterial genomes have been sequenced , it has become increasingly clear that many bacterial genomes contain multiple prophages carrying a variety of genes [8] . However , the prophages identified from the genome sequences often contain genetic defects , such as deletions or disruptions of genes required for phage induction and propagation . Thus , such prophages are regarded simply as genetic remnants , and investigators tend to ignore the possibility that they might function as mobile genetic elements or participate in HGT . Enterohemorrhagic Escherichia coli ( EHEC ) comprise a distinct class of E . coli strains that cause diarrhea , hemorrhagic colitis , and hemolytic uremic syndromes [9] . Among the various EHEC strains , the most dominant are the strains of serotype O157:H7 [10] . The genome of EHEC O157:H7 strain Sakai ( referred to as O157 Sakai ) contains 18 prophages ( Sp1 to Sp18 ) and 6 prophage-like elements ( SpLE1 to SpLE6 ) , amounting to 16% of the total genome [11] , [12] . These Sps and SpLEs have carried many virulence-related genes into the O157 Sakai genome , including the Shiga toxin genes ( stx1 and stx2 ) , a set of genes for a type III secretion system ( T3SS ) , numerous T3SS effector proteins , and transcriptional regulators for T3SS gene expression [11] , [13] . A recent genomic comparison of O157 strains has further revealed that variation in prophage regions is a major factor generating the genomic diversity among O157 strains [14] , [15] . An initial analysis indicated that , among the 18 Sps , 11 ( Sp3–Sp6 , Sp8–Sp12 , Sp14 and Sp15 ) retain features of lambdoid phages , one ( Sp13 ) has features similar to those of P2 , one ( Sp1 ) contains P4 features , and one ( Sp18 ) retains Mu features . The other four Sps ( Sp1 Sp7 , Sp16 , and Sp17 ) were unable to be assigned to particular phage families due to their chimeric or highly disrupted genomic backbones . Most of the lambdoid prophages resemble one another and contain various genetic defects ranging from frame-shift mutations to deletions and insertions of so-called insertion sequence ( IS ) elements [12] . Thus , a functional analysis of the prophage pool of O157 Sakai could reveal whether defective prophages have any biological activity and , perhaps more importantly , whether they have the potential to disseminate virulence factors among bacteria . In the present study , we used bioinformatic analyses to re-evaluate the genomic structures of each Sp and to define their genetic defects by comparing them with their respective well-characterized prototype phages . We then systematically analyzed each Sp for its ability to excise itself from the host genome , replicate , and package its phage DNA . Our results indicate that many of the apparently defective prophages can excise themselves , replicate , and be released from O157 cells as particulate DNA . Furthermore , using Sp derivatives carrying a chloramphenicol-resistance ( CmR ) gene , we demonstrated the transferability of apparently defective prophages to other E . coli strains . Our data indicate that defective prophages in the O157 prophage pool are not simply genetic remnants but have significant potential to act as mobile genetic elements that can mediate the spread of virulence-related genes from O157 to other bacteria . The results further suggest that various inter-prophage interactions in the prophage pool potentiate the biological activities of the defective prophages .
The results of a genomic comparison of 18 Sps with their corresponding prototype phages are summarized below ( Figure 1 and Table 1; see Figure S1 for more detail ) . i ) Lambdoid prophages: Among the 11 prophages with well-conserved lambdoid features , Sp3 and Sp8 lack repressor and anti-repressor functions ( CI and Cro ) , which are at the center of the regulation of lysogenization and induction of lambdoid phages [16]–[19] . In the other lambdoid prophages , the repressors of Sp11 and Sp12 have been disrupted and those of Sp4 , Sp6 , Sp9 , and Sp10 lack a peptidase domain that is required for SOS-induction ( Figure 1A and Figure S2 ) . Integrase ( Int ) , which mediates a bidirectional process of phage genome integration and excision [20] , has been disrupted in Sp3 , Sp11 , and Sp12 ( Figure 1A ) . Although Sp4 and Sp14 appear to encode intact integrases , their putative excisionase ( Xis ) proteins that regulate the directionality of the Int function [21] lack DNA binding motifs ( data not shown ) and are probably non-functional . Replication initiator protein O and elongation protein P [22]–[24] are apparently functional in all lambdoid prophages except for Sp3 ( Figure 1A ) . Whereas six ( Sp6 , Sp8 , Sp9 , Sp11 , Sp14 and Sp15 ) have λ-type helicase loaders , three ( Sp4 , Sp10 and Sp12 ) have DnaC-type helicase loaders and Sp5 has an elongation protein almost identical to that of phage HK022 , which belongs to the DnaB family ( Figure S3 ) . Three Sps ( Sp3 , Sp5 , and Sp15 ) have a general recombination system similar to that of phage λ , consisting of Exo , Bet , and Gam proteins ( Figure 1A ) , but those of Sp3 and Sp15 have been disrupted . Seven Sps ( Sp4 , Sp6 , Sp9–Sp12 , and Sp14 ) possess a different type of recombination system that contains enterobacterial exodeoxyribonuclease VIII ( Exo VIII ) -type proteins instead of the λ-type exonuclease , but this system is intact only in Sp10 . All lambdoid Sps , except for Sp4 , encode intact terminase , the key enzyme for DNA packaging [25] , [26] . In Sp4 , the nu1 gene for the terminase small subunit protein has been disrupted by an IS insertion ( Figure 1A ) . The morphogenesis regions of Sp3 , Sp4 , Sp8 , Sp11 , Sp14 , and Sp15 follow the gene organization of λ [18] , [26] , but Sp6 , Sp9 , Sp10 , and Sp12 exhibit slightly different gene organizations in the head formation region ( Figure 1A ) . Of these 10 Sps , all genes for morphogenetic function are conserved only in three ( Sp8 , Sp10 , and Sp11 ) . Sp15 ( Stx1 prophage ) also contains multiple defects in the morphogenic functions . Most of the putative morphogenic genes of Sp5 ( Stx2 prophage ) differ from those of λ and remain uncharacterized . However , another O157 Stx2-converting phage , called 933W , contains a set of genes nearly identical to that of Sp5 and has been shown to be fully active [27] . All 11 lambdoid Sps encode the Q protein , a regulator of late transcription . A full set of genes for cell lysis is also present in all lambdoid Sps , although some variation in gene organization is observed ( Figure 1A ) . In Sp5 , an IS-insertion has occurred upstream of the lysis region , but it has not disrupted any protein-coding genes; indeed , an Sp5 derivative has been shown to transfer from O157 Sakai to K-12 [28] . Based on our in silico analysis of the 11 lambdoid Sps , we predicted that ( 1 ) three ( Sp3 , Sp11 , and Sp12 ) would no longer be able to excise themselves from the host chromosome and ( 2 ) the other eight prophages would be excisable , but seven ( Sp4 , Sp6 , Sp8–Sp10 , Sp14 , and Sp15 ) would have some defects in morphogenesis or other functions , and only Sp5 would be capable of the full complement of viral functions ( Table 1 ) . ii ) P2 and P4-like prophages: Phage P2 functions as a helper for the satellite phage P4 [29]–[33] . The P2–P4 couple in O157 Sakai is atypical because homologues of the P2 ogr gene and the P4 ε gene [30] , [31] are present in Sp2 ( P4-like phage ) and Sp13 ( P2-like phage ) , respectively ( Figure 1B and 1C ) . In addition , Sp13 lacks most phage functions , including most of the morphogenetic genes ( Figure 1B and Table 1 ) ; thus , it may no longer propagate by itself or work as a helper for Sp2 . iii ) Mu-like prophage: Sp18 is predicted to be intact and spontaneously inducible like the prototype Mu phage because most features for Mu-like phages [34] , [35] are conserved ( Figure 1D ) . Sp18 also contains an invertible host-specificity region , but the encoded genes are distinct from those of Mu [35] . iv ) Others: The other four prophage genomes have been severely degraded , but all may have been derived from lambdoid phages because many of their residual genes are homologous to λ genes ( Figure 1E ) . Reassessment of the Sp17 region revealed that two prophages ( referred to as Sp17a and Sp17b ) have been integrated in tandem in this region . Sp7 shows interesting chimeric features of lambdoid and P4-like phages . Sp7 encodes a P4-like Int , as well as a P4-like Xis ( Vis-homologue ) [36] . In addition , Sp7 contains a gene similar to the P4 α gene , but the gene has been disrupted by multiple frame-shift mutations . To experimentally evaluate the inducibility of each Sp , we first examined the amplification of prophage DNA upon mitomycin C ( MMC ) treatment of O157 Sakai cells using an oligo DNA microarray . Cell lysis started 2 to 3 hr after the addition of MMC ( 1 µg/ml ) to the early log-phase culture , and the optical density ( OD ) returned to basal levels within 6 to 8 hr as a result of cell lysis ( Figure S4 ) . We isolated total cellular DNA from aliquots of cultures at 1-hr intervals from 0 hr to 4 hr after the addition of MMC . We then analyzed the total DNA using the microarray ( Figure 2A ) . We observed selective amplification of Sp5 , Sp13 and Sp15 regions , although amplification of the Sp13 regions was delayed relative to that of the other two regions . Interestingly , the Sp5-flanking regions exhibited significant amplification ( R1 and R2 in Figure 2A ) . The Sp15-flanking regions also showed substantial amplification ( R3 and R4 in Figure 2A ) , but the amplification of the R3 and R4 regions was asymmetric and smaller than that for the Sp5-flanking regions . A similar phenomenon in phage λ is known as the “regional replication” of prophage-flanking regions . In “regional replication , ” upon induction , the chromosomal regions flanking the λ prophage genome replicate together with the λ genome that remains to be excised from the chromosome [37] . Similar phenomena have also been reported for other lambdoid phages [38] . Importantly , the amplified prophage-flanking regions of Sp5 and Sp15 included prophages Sp4 and Sp14 , respectively ( Figure 2A ) . Our preliminary microarray analysis of a spontaneous Sp5 deletion mutant confirmed that the amplification of the Sp5-flanking region in response to MMC treatment requires the presence of Sp5 ( data not shown ) . We also analyzed transcriptional changes of the prophage genes upon MMC treatment using the microarray ( Figure 2B ) . A large number of chromosomal genes were up- or down-regulated by MMC treatment ( data not shown ) , as described in K-12 [39] , [40] . Among the prophage genes , we observed a marked increase in the transcript levels of the Sp5 , Sp13 , and Sp15 genes , especially in their early regions , which is in agreement with their selective DNA amplification upon MMC treatment ( Figure 2A ) . No significant transcriptional changes in response to MMC treatment were detected for most genes of other prophages , except for those of Sp1 . Although this highly degraded lambdoid prophage showed no DNA amplification upon MMC treatment ( Figure 2A ) , many of its residual early genes exhibited clear induction . The biological significance of this phenomenon is unknown . To determine whether the Sps amplified by MMC treatment are excised from the host chromosome into a circular form and whether other Sps are excisable but to a much lesser extent , we looked for the presence of circular forms of all Sps using PCR amplification of the attachment site ( attP ) -flanking regions that are generated by excision and circularization ( Figure 3 ) . In MMC-treated O157 Sakai cells , we detected circularized DNA not only from Sp5 , Sp13 , and Sp15 but also from Sp6 , Sp7 , Sp9 , and Sp10 ( Figure 3B ) . In addition , the circular forms of Sp4 and Sp14 genomes were detected , although the amount of circularized Sp14 was significantly lower than those of the other prophages . Furthermore , we also detected the circular form of DNA for all of these prophages , except for Sp4 and Sp14 , in O157 Sakai cells that had not been treated with MMC . These results indicate that the nine prophages can be excised into a circular form by MMC-mediated or spontaneous induction and that the Sp4 and Sp14 genomes , which were amplified by regional replication , can also be excised and cyclized . Circularized Sp18 DNA was not detected , which is consistent with previous results for the prototype Mu phage [34] , [35] . By sequencing the PCR products obtained in this analysis , we confirmed that the circularized prophage genomes arose by site-specific recombination between the left and right phage attachment sites ( attL and attR ) . This analysis also allowed us to precisely determine the core attachment sequences of these nine prophages ( Figure S5 ) . The results largely agreed with our previous predictions , except for the results with Sp9 [12] . The attL and attR sites of Sp9 are each located 121 bp upstream of the predicted positions , and we identified the true core sequence of 28 bp . We also used the same strategy to analyze six prophage-like elements ( SpLEs ) of O157 Sakai . However , we detected no excised and circularized DNA from any of these elements , either in untreated cells or in cells treated with MMC ( data not shown ) . This suggests that these elements have no ( or very low ) mobility or require other types of stimuli to be mobilized . Because we found that many Sps are excised into circular forms , we quantified the circularization and/or replication of these Sps using quantitative PCR ( qPCR ) ( Figure 3 ) . In untreated cells , we detected similar amounts of circularized DNA of Sp5 , Sp6 , Sp7 , Sp13 , and Sp15 and a slightly lower level of circularized Sp9 DNA . In cells treated with MMC , the relative amounts of circularized DNA of Sp5 , Sp13 , and Sp15 increased to approximately 300 , 30 , and 40 times higher than their levels in untreated cells , respectively ( Figure 3C ) . In contrast , the levels of Sp6 , Sp9 and Sp10 were lower than in the untreated cells . It is noteworthy that , in complete agreement with the results of the qualitative PCR analysis ( Figure 3B ) , considerable amounts of circularized Sp4 and Sp14 were generated in the MMC-treated cells , whereas hardly any was detected in the untreated cells . Sp18 also proved to be non-inducible by MMC , as described for phage Mu [35] . DNA microarrays were used to monitor prophage induction [38] , [41] . Microarray analysis can provide a gross image of the replication pattern of prophages augmented by MMC treatment , as seen in Figure 2 , but it cannot detect spontaneous induction of prophages . Thus , as our present data show ( Figure 3 , see also Figure S6 ) , qPCR analysis is required to obtain the true picture of prophage induction . This may also be true in transcriptome analysis of prophage genes ( Figure 2B ) . In general , the stability of a prophage is tightly coupled with the physiology of the host cell . Under conditions that generate DNA injury—in the present study through MMC treatment—prophages are de-repressed by a RecA-mediated mechanism ( the SOS response ) to enter the lytic pathway [42] . The RecA protein stimulates self-cleavage of the repressor protein , which leads to the expression of genes required for the lytic pathway [43] , [44] . The non-inducible nature of Sp6 , Sp9 and Sp10 by MMC treatment is consistent with the fact that the peptidase motif is missing in the repressors of these lambdoid Sps ( Figure S2 ) . Phage P2 is insensitive to the SOS response and is thus non-inducible by MMC treatment , because its repressor intrinsically lacks the peptidase motif [30] . The repressor of Sp13 , a P2-like prophage , also lacks the peptidase motif . Thus , the MMC-mediated induction of Sp13 observed in this analysis is remarkable ( Figures 2 , Figure 3 , and Figure S6 ) . Although the mechanism is yet to be elucidated , a P4 ε-like gene encoded on Sp13 ( Figure 1 and Figure S2 ) may be involved in this unique behavior because the P4 ε gene product de-represses the P2 genome by binding to the P2 repressor [30] , [31] . To investigate whether the Sps that were circularized and replicated by spontaneous or MMC-mediated induction could be packaged into phage particles , we first attempted field inversion gel electrophoresis ( FIGE ) analysis of DNA isolated from phage particles . Particles were taken from supernatants of bacterial cultures that were either treated with MMC or left untreated ( Figure 4A ) . In the untreated sample , we detected packaged DNA of Sp5 , Sp10 , and Sp18 . However , upon MMC treatment , a large amount of Sp5 DNA accumulated and generated extensive smearing that prevented the visualization of minor species of packaged phage DNA . We therefore quantified particulate DNA of each Sp by qPCR using the same set of PCR primers used for the quantification of intracellular phage DNA ( Figure 4B ) . In the untreated sample , we detected DNase-resistant forms of DNA for at least five prophages ( Sp5 , Sp7 , Sp9 , Sp10 , and Sp15 ) . They included two Sps ( Sp9 and Sp15 ) that contain genetic defects in head formation ( Figure 1 and Table 1 ) . This result suggests that the defects of these two prophages were complemented , probably by other prophages that provided all or some of the gene products required for head formation . The amount of Sp6 DNA was marginal compared with the control chromosomal DNA ( CB1 and CB2 in Figure 4B ) . The Sp13 DNA appears to be inefficiently packaged or unstable . As expected from the data on phage Mu , the Sp18 DNA was efficiently packaged . In the MMC-treated sample , a large amount of packaged Sp5 DNA was detected , reaching levels of ≥1010 molecules per milliliter of culture . Although at much lower levels , we detected considerable amounts of packaged DNA from at least six other Sps ( Sp4 , Sp7 , Sp10 , Sp13 , Sp15 , and Sp18 ) . Interestingly , this group includes Sp13 , which also lacks the genes for head formation and DNA packaging ( Figure 1 and Table 1 ) . Of the two Sps ( Sp4 and Sp14 ) whose genomes are amplified only by the regional replication of Sp5 and Sp15 , respectively , we detected packaged Sp4 genomic DNA , although it also contains defects in head formation and DNA packaging . Thus , these defects of Sp13 and Sp4 must have also been complemented by other prophages . To examine the transferability of packaged Sp genomes , we marked the eight Sps ( Sp4–Sp7 , Sp9 , Sp10 , Sp13 , and Sp15 ) by replacing “moron” genes of each phage genome , which are not required for phage propagation , with a CmR gene cassette ( Figures 5A and 5B ) . The stx2 gene in Sp5 and stx1 gene in Sp15 were replaced with the cassette . Incorporation of the CmR cassette into the prophage genomes did not affect DNA packaging because the levels of particulate phage DNA detected for each CmR-derivative were similar to those observed for the wild-type O157 Sakai ( data not shown ) . To examine whether the CmR marker can be transferred to two K-12 derivatives ( strains MG1655 and MC1061 ) , we analyzed the culture supernatants prepared from O157 Sakai containing each CmR-marked Sp derivative with or without MMC treatment . We found that the CmR gene on four Sps ( Sp5 , Sp6 , Sp10 , and Sp15 ) is transferable to K-12 and stably maintained , although the efficiency of transfer was low in all cases except that of Sp5 ( Table 2 ) . Among the four Sps , three ( Sp5 , Sp6 , and Sp10 ) were integrated at the same chromosomal loci in K-12 , as in O157 Sakai ( Figure 5C ) . The integration site of Sp10 was already occupied by the Rac prophage in K-12 , but the Sp10 derivative was integrated in tandem with Rac using the attR sequence of Rac as the attB site ( Figure 5D ) . In contrast , the Sp15 derivative was not integrated into the yehV locus in K-12 , the chromosomal locus where Sp15 is present in O157 Sakai ( Figure 5C ) . This suggests that recombination occurred between Sp15 derivatives and other Sps that allowed the transfer of the CmR gene ( by which the stx1 gene was replaced ) to K-12 ( see the next section ) . Among the three Sps that were successfully transferred to K-12 , the Sp5 derivative produced infective phage particles in K-12 ( Table 2 ) . In contrast , we could not detect the production of infective Sp6 or Sp10 derivatives in K-12 . This result suggests that these two Sps may require the support of other Sps , available only in the O157 cell , to produce infective phage particles efficiently . To analyze the CmR-marked Sp15 ( Sp15Δstx1::CmR ) transductants , we first performed PCR scanning analysis of the stx1-flanking region of an Sp15Δstx1::CmR-transductant of K12 MG1655 ( Figure 6A , see Figure S7 for more details ) . The results indicated that the transductant contains an Sp15Δstx1::CmR-derived DNA segment covering the stx1 region , but also that some recombination had occurred between the P and stx1 ( replaced by the CmR cassette ) genes and between the stx1 and nu1 genes in the Sp15Δstx1::CmR genome . By DNA sequence homology analysis between Sp15 and other Sps , we found that , although many lambdoid Sps contain one or more genomic segments that are highly homologous to the stx1-flanking region of Sp15 , only Sp5 contains both segments homologous to the upstream and downstream regions of the stx1 gene ( Figure S7 ) . These Sp5 segments are also present in the upstream and downstream regions of the stx2 gene . This suggested that the CmR Sp15 derivative transferred to K-12 may have been generated by recombination between Sp15Δstx1::CmR and Sp5 . We therefore analyzed the genome of the CmR Sp15 derivative by PCR using two primer pairs: those specific to the CmR cassette and the Sp5 P gene and those specific to the CmR cassette and the Sp5 Nu1 gene ( Figure 6B and Figure S7 ) . The two primer pairs yielded 4 . 9-kb and 6 . 4 kb amplicons , respectively , both of which were absent in the donor O157 Sakai derivative containing Sp15Δstx1::CmR . Furthermore , we confirmed that the Sp15Δstx1::CmR transductant contains an Sp5-like phage in the wrbA locus , the integration site of Sp5 ( Figure 6C and Figure S7 ) . All these data indicated that the CmR cassette-carrying phage is a chimeric phage that was generated by replacing the stx2 regions of Sp5 with the stx1 region of Sp15Δstx1::CmR . We analyzed 21 additional Sp15Δstx1::CmR transductants using the same methods . The results indicated that all of the transductants contain chimeric phages of Sp15Δstx1::CmR and Sp5 ( data not shown ) . However , the data from our preliminary sequence analysis of the PCR products covering recombination points suggested that several ( at least four ) types of chimeric phages had been generated ( more details of these chimeric phages will be described elsewhere ) . It may also be worth noting that this phenomenon was observed only in MMC-treated O157 cells ( Table 2 ) . Finally , we performed an electron microscopic examination of phage particles that were present in the culture supernatants of MMC-treated and untreated O157 Sakai cells ( Figure 7 ) . The MMC-treated sample contained numerous phage particles with a short tail attached to a head approximately 56 nm in diameter ( Figure 7A ) . The dominant induction of Sp5 by MMC treatment ( Figure 4 ) suggests that these phage particles originated from Sp5 . In fact , K-12 strains lysogenized by CmR-marked Sp5 produced phage particles with the identical morphology . The morphology of Sp5 ( Figure 7A ) is highly similar to that of the previously reported Stx2 phage of O157 EDL933 [27] . We were unable to detect other phage types in the MMC-treated sample . However , in addition to Sp5 , at least two other types of phage particles were detected in the untreated sample . The second type had a head with a hexagonal outline approximately 49 nm in diameter , which was connected by a neck to a contractile and non-flexible tail ( the uncontracted sheath is approximately 100 nm long and the contracted one is 55 nm ) ( Figure 7B ) . The similarity to the morphology of Mu phage particles indicates that this second type most likely originated from Sp18 . The third type had a head with an elongated hexagonal outline ( 44 wide and 95 nm long ) and a 147 nm long flexible tail ( Figure 7C ) . This phage probably derived from some of the lambdoid prophages , but its origin is difficult to pinpoint because lambdoid prophages other than Sp5 ( including Sp10 and Sp6 ) contain very similar morphogenetic genes ( Figure 1 ) . We also examined the culture supernatants of K-12 strains lysogenized with CmR derivatives of Sp6 and Sp10 for the production of phage particles , but no phage particle was detected in the culture supernatants of Sp6 and Sp10 lysogens ( data not shown ) .
The results of our in silico analysis of the potential activities of 18 prophages on the O157 Sakai genome indicate that all but Sp5 contain one or more genetic defects ( Figure 1 , Figure S1 , and Table 1 ) . This suggests that the present-day O157 prophage pool may have low potential activity as mobile genetic elements to spread virulence genes , although their mobility in evolutionary history has played an essential role in the emergence of this highly virulent E . coli lineage . Nevertheless , our systematic experimental evaluation of the Sps revealed that many have unexpectedly high potential activity to function as mobile genetic elements . First , nine Sps could excise themselves from the chromosome and replicate in the O157 cells in response to spontaneous or MMC-mediated induction ( Figure 3 , Figure 4 , and Table 2 ) . They can be divided into three groups according to their induction patterns: ( i ) spontaneously inducible ( Sp6 , Sp7 , Sp9 , Sp10 , and Sp18 ) , ( ii ) spontaneously inducible and further enhanced by MMC-mediated induction ( Sp5 , Sp13 , and Sp15 ) , and ( iii ) inducible only by the regional replication of other prophages ( Sp4 by Sp5 and Sp14 by Sp15 ) . Second , most of these Sps , except Sp6 and Sp14 , were packaged into phage particles ( Figure 4 ) , although half of them ( Sp4 , Sp9 , Sp13 , and Sp15 ) contain defects in head formation or DNA packaging . Third , we found that the CmR gene cassette on four Sps is transferable to other E . coli strains , although we used only two K-12 derivatives as recipients ( Table 2 ) . Three ( Sp5 , Sp6 , and Sp10 ) were transferred to K-12 and stably lysogenized in the chromosome ( Figure 5 ) . This result indicates that Sp6 , which is also defective in head and tail formation , can be packaged , although this was not clear from the particulate DNA quantification by qPCR ( Figure 4 ) . It is also important that these three phages carry several important virulence determinants , including the stx2 genes and multiple non-LEE effector genes ( Figure 1 and Figure S1 ) . Fourth , the CmR gene cassette inserted into the Sp15 genome by replacing the stx1 gene was transferred to K-12 , and this transfer was achieved through the generation of a chimera between Sp15 and Sp5 ( Figure 6 and Figure S7 ) . In addition , three types of phage with distinct morphologies were detected in the culture supernatant of O157 Sakai ( Figure 7 ) . Two derive from Sp5 and Sp18 , respectively , but the origin of the third remains undetermined . These results indicate that many apparently defective prophages of O157 Sakai should not be regarded as simple phage remnants , but rather as active genetic elements that can potentially mediate or assist HGT of various virulence determinants encoded on the O157 genome . The results further suggest that various types of inter-prophage interactions occur in the O157 prophage pool , and these interactions induce the biological activities of the defective prophages . Inter-prophage interactions that most likely occurred in the O157 prophage pool complemented various defects in morphogenetic functions by providing the proteins for phage particle formation . Although the details of this complementation remain to be elucidated , the lambdoid phages on the O157 genome share nearly identical morphogenetic genes [11] , [12] in various combinations ( Figure 1 ) . They therefore appear capable of supplying virion proteins compatible with those of other lambdoid phages . In fact , we identified one type of phage particle that differs from that of Sp5 but has lambdoid features in the culture supernatant of O157 Sakai ( Figure 7C ) . In some cases , whole virion proteins may be provided by other prophages; this may be the situation with Sp7 and Sp13 . Both have severe defects in morphogenetic functions , but are nevertheless packaged . Furthermore , because Sp13 is the only member of the P2-like phage family in the O157 prophage pool , this type of inter-prophage interaction may occur between very different types of bacteriophages . In the case of Sp7 , another type of interaction may complement its defect in replication function . This highly degraded prophage lacks most morphogenesis genes , as well as repressor and antirepressor genes . Furthermore , the replication gene , which resembles the P4 α gene , has been disrupted into three fragments ( Figure 1E and Figure S1 ) . Nevertheless , Sp7 is spontaneously inducible and a significant amount of circularized DNA was observed to accumulate in the O157 cells ( Figure 4 ) . Thus , the replication of Sp7 may be mediated by the replication proteins of Sp13 ( P2-like phage ) or Sp2 ( P4-like phage ) , although we cannot exclude the possibility that some ( or all ) of the fragmented polypeptides of Sp7 may still contain some replication initiation activity . Replication or amplification of the Sp4 and Sp14 genomes is another type of inter-prophage interaction . Their genomic DNA can be amplified only by the regional replication of Sp5 and Sp15 , respectively . Although both lack the genes for excisionase , integrases alone appear capable of mediating their excision from the chromosome . Thus , the two prophage genomes amplified by regional replication are excised into a circularized form ( Figure 3 and Figure 4 ) . More interestingly , although Sp4 does not encode an intact packaging enzyme ( terminase ) by itself ( Figure 1 and Figure S1 ) , its amplified genome was found to be packaged ( Figure 4 ) . Most likely , this packaging was carried out by the terminase of Sp14 , because the putative cos sequence of Sp4 , which needs to be digested by terminase for packaging , is nearly identical to that of Sp14 ( Figure S8 ) . Finally , the recombination between Sp15 and Sp5 can also be regarded as an inter-prophage interaction because it occurred in the O157 prophage pool and generated new Stx1-tranducing phages ( Figure 5 and Figure S7 ) . This type of inter-prophage interaction can occur between other lambdoid prophages of O157 as well , because they share nearly identical sequences , which can therefore recombine [11] , [12] . Similar recombination may also occur between the resident prophages and newly incoming phages . In this way , high levels of excision and replication of defective prophage genomes in O157 cells may provide significant opportunities for such recombination . This may explain why a surprisingly high level of structural variation is observed in the prophage regions among O157 genomes [14] , [45] , which , in turn , supports the hypothesis that O157 cells function as “phage factories” that produce a wide variety of bacteriophages in nature [12] . In conclusion , many of the prophages of O157 Sakai that contain a wide range of genetic defects show unexpectedly high potential activity as mobile genetic elements , and this mobility is probably achieved through various types of inter-prophage interactions that occur in the O157 prophage pool . Thus , these apparently defective prophages are not simply remnants generated in the course of O157 evolution , but instead should be regarded as genetic elements that are potentially capable of spreading virulence determinants and other genetic traits to other bacterial strains . Similarly to E . coli , many other bacteria contain multiple prophages with genetic defects , and the potential of these sequence elements to function as mobile elements has been largely ignored . Our findings suggest that more attention should be paid to their potential roles in HGT between bacteria and in the evolution of bacterial pathogens .
O157 Sakai ( RIMD 050995 ) was isolated in a large outbreak that occurred in Sakai city , Japan , in 1996 [46] , and the complete genome sequence has been determined [11] . Cells were grown overnight to stationary phase at 37°C in Luria-Bertani ( LB ) medium . For prophage induction with MMC , cells were grown to early log phase ( OD600 0 . 2–0 . 4 ) , and MMC was added to the culture to a final concentration of 1 µg/ml . At 1-hr intervals , we isolated aliquots of the culture and collected the cells by centrifugation at 4°C . Total cellular DNA was isolated from the cells using the Genomic-tip 100/G and the Genomic DNA buffer set ( Qiagen , CA , USA ) according to the manufacturer's instructions . Phage particles were isolated from the culture supernatants 10 hr after the addition of MMC . The culture was first treated with chloroform , and bacterial cell debris was removed by centrifugation . The supernatant was filtered through 0 . 22-µm pore-size filters ( Millipore Corp . , MA , USA ) and incubated with 200 U/ml DNase I ( Invitrogen , CA , USA ) at 37°C for 1 hour . After incubation , 0 . 25 volumes of a solution containing 20% polyethylene glycol 8000 ( PEG ) and 10% NaCl was added to the sample . The mixture was kept at 4°C overnight and then centrifuged at 12 , 000×g for 1 hour to precipitate phage particles . The phage particles were suspended in SM buffer ( 0 . 58% NaCl , 0 . 2% MgSO4⋅7H2O , 1 M Tris-Cl ( pH 7 . 5 ) , 0 . 01% gelatin ) and incubated with DNase I ( final concentration , 1000 U/ml ) and RNase A ( 50 µg/ml ) ( Stratagene , CA , USA ) at 37°C for 1 hour . After DNase and RNase treatment , the sample was treated with proteinase K ( 100 µg/ml; Wako , Osaka , Japan ) at 50°C for 1 hour , and phage DNA was isolated using the Genomic-tip 20/G . Total cellar DNA and phage DNA from untreated samples were prepared by the same protocol , except that no MMC was added to the culture . Comparative analysis of O157 Sakai prophage genomes was performed using the “in silico MolecularCloning ( R ) ( IMC ) ” software ( Genomics edition , version 1 . 4 . 71 , In Silico Biology , Inc . , Kanagawa , Japan ) . Homology searches were performed using BLAST2 [47] , and functional motifs were searched using InterProScan ( http://www . ebi . ac . uk/Tools/InterProScan/ ) . Multiple protein sequence alignment was carried out using CLUSTALW [48] and analyzed by the multiple alignment editor Jalview ( http://www . ebi . ac . uk/clustalw/ ) . A neighbor-joining tree for replication elongation proteins was generated using MEGA3 [49] . An overnight culture in LB medium was diluted to an OD600 of 0 . 2 and grown for 1 hour . At this point ( 0 min ) , MMC was added to the culture at a concentration of 1 µg/ml . Samples were collected at 0-min , 45-min and 90-min intervals from the cultures treated with MMC or those left untreated . The RNAprotect Bacteria Reagent ( QIAGEN , Valencia , CA ) was immediately added to the samples , and total RNA was isolated using the RNeasy Plus Mini kit ( QIAGEN , Valencia , CA ) according to the manufacturer's instructions . RNA quality was assessed by spectrophotometry using the NanoDrop instrument ( NanoDrop Technologies , Inc . , USA ) and by agarose gel electrophoresis . Probes ( 60 mer ) were designed for the 5 , 447 protein-coding genes of the O157 Sakai genome . The O157 Sakai genome contains many multi-copy genes that are derived from IS elements and lambdoid prophages sharing nearly identical sequences . Thus , to avoid effects due to cross hybridization , all data for the probes that showed >80% DNA sequence identity to any other genomic regions of the O157 Sakai genome were removed from the data set . Finally , we used 4 , 507 probes representing 4 , 507 genes . Among the 4 , 507 probes , 452 were for the genes on 18 prophage regions . Arrays were produced by Agilent Technologies ( Palo Alto , CA , USA ) by the in situ oligonucleotide synthesis method . For DNA microarray analyses , test and reference DNA ( 250 ng ) were chemically labeled with ULS-Cy3 and ULS-Cy5 , respectively , using the Agilent Oligo CGH Microarray Kit ( Agilent Technologies ) . The fluorescently labeled DNA was purified by the Agilent KREApure column . The Cy5-labeled and Cy3-labeled DNA were mixed and used for hybridization . For RNA analysis , total RNA ( 10 µg ) was reverse transcribed and labeled with amino-allyl dUTP using MMLV-RT ( Agilent Technologies ) and random hexamers ( Invitrogen ) . The cDNA from test and control samples was labeled with Cy3 and Cy5 dye , respectively ( Monofunctional NHS-ester Dye , Amersham ) . The Cy3-labeled and Cy5-labeled cDNAs were purified , combined , and used for hybridization . The arrays were scanned using an Agilent scanner ( Agilent Technologies ) , and data extraction , filtering and normalization were conducted using Feature Extraction software ( Agilent Technologies ) according to the manufacturer's instructions . Each sample was examined twice using the labeled DNAs and cDNAs independently prepared for each hybridization . Data analysis and visualization were done using Microsoft Excel and MultiExperiment Viewer ( The Institute for Genome Research ) [50] . PCR was carried out using an Ex-taq PCR amplification kit ( Takara Bio , Kyoto , Japan ) . The PCR cycling program consisted of 29 cycles of 45 sec at 95°C , 45 sec at 60°C , and 1 min at 72°C , with an additional step of 2 min at 72°C . Sequencing of the PCR products was carried out using an ABI PRISM 3100 automated sequencer ( PE Biosystems , CA , USA ) . Primers designed for PCR amplification were used as sequencing primers . Sequencher™ software ( version 4 . 2 . 2 , Gene Codes Corporation , MI , USA ) was used for sequence data analyses . All primers used are listed in Table S1 . TaqMan probes and PCR primers for real-time qPCR ( Table S1 ) were designed using Primer Express software ( Primer Express™ , PE BioSystems ) according to the manufacturer's instructions . All analyses were performed using the ABI PRISM 7000 Sequence Detection System ( PE BioSystems ) . To analyze the intracellular prophage DNA , 10 ng of total cellular DNA was used as template DNA in a 50 µl reaction volume . To analyze the phage particle DNA , phage DNA isolated from 1 ml ( MMC-treated samples ) or 50 ml ( untreated samples ) of culture supernatants was used as the template . Primers and TaqMan probes were used at a concentration of 400 nM and 250 nM , respectively . FAM ( 5′ ) and TAMRA ( 3′ ) were used as reporter and quencher dyes for TaqMan probes , respectively . The PCR cycling program consisted of 45 cycles of 15 sec at 95°C and 1 min at 60°C . For quantification of DNA , the standard curve method was employed . Standard curves were constructed over the range from 103 copies/µl to 107 copies/µl for each amplicon . Quantities of two chromosomal backbone regions ( CB1 and CB2 in Figure 3 and Figure 4 ) were monitored as controls in qPCR analyses . The concentrations of chromosomal backbone DNA measured in the total cellular DNA preparations were comparable with those estimated from the numbers of cells used for DNA preparation ( Figure 3 and Figure 4 ) , indicating the validity of the real-time qPCR assays . FIGE analysis of packaged phage DNA was performed using phage particles collected from culture supernatants by PEG/NaCl precipitation . Precipitated phage particles were embedded in plugs of 1% Certified Low Melt Agarose ( Bio-Rad Laboratories , Inc . , CA , USA ) and treated at 37°C for 2 hr with DNase I ( 1000 U/ml ) and RNase A ( 50 µg/ml ) in DNase I buffer ( 10 mM Tris ( pH 7 . 5 ) , 2 . 5 mM MgCl2 , and 0 . 5 mM CaCl2 ) , followed by overnight incubation with proteinase K ( 100 µg/ml ) at 50°C in TE containing 1% sodium dodecyl sulfate . After the plugs were washed three times with TE buffer at 15-minute intervals , the plugs were sliced into appropriate sizes and subjected to FIGE analysis . FIGE was performed using a CHEF MAPPER ( Bio-Rad Laboratories ) with a 1% agarose gel , and initial and final switch times of 0 . 11 and 0 . 92 sec , respectively . Total run time was 20 . 3 h with 9 . 0 V/cm ( forward ) and 6 . 0 V/cm ( reverse ) voltage at a constant temperature of 14°C . The gel was stained with ethidium bromide to visualize DNA bands . The CmR cassette was inserted into eight Sps ( Figure 5A ) by replacing moron genes on each prophage genome using the method described by Datsenko and Wanner [51] . Primer sequences utilized for gene replacement and confirmation of replacement are listed in Table S2 . In Sp5 ( Stx2 phage ) and Sp15 ( Stx1 phage ) , the entire stx genes were replaced by the CmR cassette . In other Sps , genes for T3SS effectors or other morons were selected as targets for gene replacement . Therefore , the O157 Sakai derivatives generated in this study are predicted to have reduced potential virulence . CmR-O157 Sakai derivatives ( Table S3 ) were grown at 37°C overnight in LB containing 40 µg/ml of Cm . The cells were subcultured to an OD600 of 0 . 2–0 . 4 in LB without antibiotic and then cultivated at 37°C for 10 hrs with vigorous shaking in the presence or absence of MMC ( 10 µg/ml ) . Phage particles in the culture supernatants were recovered by PEG/NaCl precipitation and suspended in SM buffer . The precipitated phage preparations were gently treated with 0 . 1 volumes of chloroform . After brief centrifugation , aqueous phases of each sample were collected and incubated at 37°C for 15 min to remove residual chloroform . Phage solution prepared from a 20-ml culture supernatant of each O157 Sakai derivative was incubated with 108 recipient cells suspended in 100 µl of the SM buffer at 28°C for 1 hour . Finally , the recipient cells were plated on LB agar plates containing Cm ( 50 µg/ml ) and incubated overnight at 37°C . Randomly selected colonies were checked by the agglutination test using anti-O157 serum to ensure that all were derived from K-12 strains; colonies were subsequently used in further analyses . The CmR transductants obtained were verified for the lysogeny of CmR-marked prophages using PCR ( Figure 5 ) . Primers used are listed in Table S4 . To analyze the chimeric phages of Sp15Δstx1::CmR and Sp5 , PCR scanning analysis of the stx1-flanking region was performed using eight primer pairs ( Figure 6A , Figure S7 , and Table S5 ) . To confirm the chimeric structure of the recombinant phage , we performed two types of PCR analyses , one using the forward ( P_F ) and reverse ( 4R ) primers specific to the P gene of Sp5 and the CmR gene cassette , respectively , and another using the forward ( 6F ) and reverse ( T_R ) primers specific to the CmR gene cassette and the Sp5 nu1 gene , respectively . These primer sequences are also listed in Table S6 . Integration of an Sp5-like phage into the wrbA locus in Sp15Δstx1::CmR-transductants was confirmed by PCR using two primer pairs ( Table S4 ) to amplify the left ( primers LbF and LbR ) and right ( RbF and RbR ) attachment sites of Sp5 ( Figure 6C and Figure S7 ) . Phage particles were collected by PEG/NaCl precipitation from the culture supernatants of MMC-treated or untreated O157 Sakai and suspended in SM buffer . A 10-µl drop of the suspension was placed on copper grids with carbon-coated Formvar films and negatively contrasted with 2% uranyl acetate dihydrate . Samples were examined using a transmission electron microscope ( 1200EX , JEOL , Tokyo ) operated at 80 kV . Average dimensions were measured with the image processing and analysis software ImageJ ( http://rsb . info . nih . gov/ij/ ) . All sequence information for prophages and genes of the O157 Sakai genome is available at the DDBJ/EMBL/NCBI database ( accession no . BA000007 ) | Bacterial viruses , known as bacteriophages or phages , are major factors promoting horizontal gene transfer ( HGT ) between bacteria , and this activity has sparked new interest in light of the discovery that many sequenced bacterial genomes harbor multiple prophages carrying a wide range of genes , including those related to virulence . However , prophages identified from genome sequences often contain various genetic defects , and they have therefore been regarded as merely genetic vestiges , with no attention paid to their potential activities as mobile genetic elements . Enterohemorraghic Escherichia coli O157 , which harbors as many as 18 prophages , is the most striking such example . The O157 prophages carry numerous genes related to O157 virulence , but most possess multiple genetic defects . In this study , we analyze the functionalities of O157 prophages and report that many of the apparently defective prophages are inducible and released from the O157 cells as particulate DNA and that some can be transferred to other E . coli strains . We should therefore regard these prophages as having high potential to disseminate virulence determinants . Our results further suggest that their activities as mobile genetic elements are potentiated by various types of interactions among the prophages , formulating a novel concept of inter-prophage interactions in defective prophage communities . | [
"Abstract",
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] | [
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] | 2009 | The Defective Prophage Pool of Escherichia coli O157: Prophage–Prophage Interactions Potentiate Horizontal Transfer of Virulence Determinants |
Genome-wide maps of DNase I hypersensitive sites ( DHSs ) reveal that most human promoters contain perpetually active cis-regulatory elements between −150 bp and +50 bp ( −150/+50 bp ) relative to the transcription start site ( TSS ) . Transcription factors ( TFs ) recruit cofactors ( chromatin remodelers , histone/protein-modifying enzymes , and scaffold proteins ) to these elements in order to organize the local chromatin structure and coordinate the balance of post-translational modifications nearby , contributing to the overall regulation of transcription . However , the rules of TF-mediated cofactor recruitment to the −150/+50 bp promoter regions remain poorly understood . Here , we provide evidence for a general model in which a series of cis-regulatory elements ( here termed ‘cardinal’ motifs ) prefer acting individually , rather than in fixed combinations , within the −150/+50 bp regions to recruit TFs that dictate cofactor signatures distinctive of specific promoter subsets . Subsequently , human promoters can be subclassified based on the presence of cardinal elements and their associated cofactor signatures . In this study , furthermore , we have focused on promoters containing the nuclear respiratory factor 1 ( NRF1 ) motif as the cardinal cis-regulatory element and have identified the pervasive association of NRF1 with the cofactor lysine-specific demethylase 1 ( LSD1/KDM1A ) . This signature might be distinctive of promoters regulating nuclear-encoded mitochondrial and other particular genes in at least some cells . Together , we propose that decoding a signature-based , expanded model of control at proximal promoter regions should lead to a better understanding of coordinated regulation of gene transcription .
DNase I hypersensitive sites ( DHSs ) mark ‘open’ chromatin regions in the human genome [1] . When profiled at genome-wide scale in many different tissues and cell types , DHS profiles reveal that most human promoters ( at the transcriptional start site , TSS ) remain in an ‘open’ chromatin state [2]–[4] . These ‘open’ chromatin areas center between −150 bp and +50 bp relative to the TSS ( +1 ) , although they could be larger depending on the mode of transcription initiation and identity of the specific promoter [2] , [5]–[8] . They are also flanked by nucleosomes heavily modified with histone H3 lysine 4 dimethylation ( H3K4me2 ) and trimethylation ( H3K4me3 ) , which also remain largely invariant across different cell and tissue types [2] , [9] . Together , therefore , promoters ( at the TSS ) show a rather persistent chromatin organization that is likely associated with control of basal transcription [2] , [9]–[13] . In fact , −40 bp to +40 bp regions ( also known as ‘core’ promoters ) generally act as entry sites for the pre-initiation complex ( PIC ) [5] , [6] , [14] , and −150 bp to −40 bp regions ( also known as ‘proximal’ promoters ) contain abundant and conserved cis-acting regulatory elements that contribute to basal transcription [15]–[18] . The genome-wide profiling of transcription factors ( TFs ) and cofactors ( i . e . TF-associated factors that do not bind to DNA and that often act as chromatin remodeling activities , histone/protein-modifying enzymes , or scaffold proteins ) has recently provided valuable information that may change our understanding of how chromatin organization is established in promoters . Proximal promoters have been traditionally viewed as the main targets of TFs in the human genome; however , most TF binding profiles consistently reveal preferential binding to distal , rather than proximal , genomic sites ( e . g . [19]–[23] ) . In an apparent paradox , many cofactors ( such as histone/protein-modifying activities ) often show preferential binding to promoter , rather than distal , genomic sites , which is more consistent with the traditional view that promoters are the major recruiters of transcriptional regulators in the genome [24]–[32] . In some cases , it has been proposed that histone/protein-modifying activities may directly recognize promoter-specific cis-regulatory elements , such as in the case of JARID2 and KDM2A , which are two lysine demethylase activities ( KDMs ) that remove methyl groups from lysine residues and directly recognize GC-rich sites [24] , [33] , [34] . GC-rich sites are common at proximal promoters [35] , [36] . However , it is unclear how a highly abundant TF at promoters , such as Sp1 , which binds with high affinity to GC-rich sites , functions to facilitate or compete with the binding of these cofactors at these regions . The same question stands for other abundant proximal promoter TFs ( e . g . [12] , [37]–[41] ) . It is also unclear the recruitment in most of other cases in which histone/protein-modifying activities do not recognize DNA . Together , preventing a clear picture of how TF binding patterns relate to those of histone/protein-modifying enzymes at promoter regions . Here , we have analyzed 21 , 000 human promoters from −150 bp to +50 bp relative to the TSS to investigate the role of cis-regulatory elements and their cognate TFs in recruiting histone/protein-modifying activities , particularly KDMs , to these sites . Co-occurrence analysis of the most highly enriched of these elements ( here termed ‘cardinal’ motifs ) confirms that they tend to occupy these regions in patterns that are independent from one another , thus suggesting the existence of promoter subclasses based on the independent presence of these motifs . To validate this model , we profiled NRF1 and subunit B of NFY ( NFYB ) , which constitutively recognize two of the most abundant cardinal elements , NRF1 and NFY/CCAAT . Our data confirmed that both ‘cardinal TFs’ ( for recognizing cardinal motifs ) occupy two largely independent promoter subsets . Furthermore , we screened for KDM activities that may selectively act via one TF but not the other , finding that LSD1 acts as a specific and pervasive cofactor of NRF1 . We further explored the binding profiles of approximately 60 other cofactors reported in the literature , which resulted in the identification of other strong cardinal motif-cofactor signatures . Together , we propose that an important function of cardinal cis-regulatory elements at promoter DHSs is to dictate a selective regulatory code of histone/protein-modifying activities and other cofactors that distinguishes promoter subclasses . Intriguingly , each subclass shows qualitative and quantitative differences with regard to the type and number of cofactors recruited , thus suggesting that there is a complex regulatory layer depending on the presence of cardinal elements that might contribute to the chromatin organization and regulation of DHS promoters .
To guide the discovery of new regulatory mechanisms acting via core/proximal promoter regions , we analyzed −150/+50 bp regions ( relative to the TSS ) based on previous studies showing that these genomic coordinates overlap with the center of DNase I hypersensitivity in active human promoters [2] , and accumulate promoter-specific motifs [37] , [38] , [42]–[45] . We extracted ‘all’ −150/+50 bp regions in the human genome ( n = 21 , 000; independently of their chromatin state in a particular cell or condition ) and performed de novo motif discovery analysis . This analysis resulted in the identification of nine highly enriched motifs , which we defined as ‘cardinal’ cis-regulatory elements . As expected , these nine elements included the TATA-box , as well as sequences recognized by well know ubiquitous TFs common in promoters: Sp1/GC-rich , NFY/CCAAT-box , ETS/GABP/NRF2 , NRF1 , CREB/CRE-MYC/E-box , and YY1 ( Figure 1A ) . We also identified two cardinal motifs whose recognition by specific TFs has been poorly established: Clus1 [37] , which may act as binding site for the zinc finger TF Kaiso/ZBTB33 [46]; and a sequence that we named GFY ( for general factor Y ) , which may act as a binding site for the TFs Ronin/Hcf-1 and Zfp143/Rbp-J [46] , [47] ( Figure 1A ) . Co-occurrence analysis of these nine elements showed differential patterns of co-enrichment . For example , each of these sequences ( with the only exception of YY1 ) showed a higher tendency to co-exist with copies of itself than with copies of the other eight cardinal elements within the same −150/+50 bp region ( see the dark blue squares mostly in the diagonal in Figure 1B ) . Therefore , our analysis indicates that −150/+50 bp regions are more likely occupied by a single type of element rather than by fixed combinations of different cardinal elements . Only in the case of the NFY/CCAAT-box and the Sp1/GC-rich motifs did we observe high tendencies to co-occur ( Figure 1B; summarized in Figure S1A ) . In addition , we performed co-occurrence analysis using experimentally defined promoter DHSs in breast cancer MCF7 cells and derived essentially the same conclusion , although positive co-occurrences between different cardinal motifs were even less significant ( Figure S1B ) . To test the predictive value of these analyses we focused on motifs other than TATA-box and Sp1/GC-rich because these two are well documented in the literature . Thus we performed chromatin immunoprecipitation followed by massively parallel sequencing ( ChIP-seq ) using antibodies that recognize nuclear respiratory factor 1 ( NRF1 , or alpha-PAL ) , which binds as a homodimer to the NRF1 site [48] and the nuclear transcription factor Y ( NFY , or CBF ) , which binds as an obligatory heterotrimer of NFYA , NFYB , and NFYC to the NFY/CCAAT site [49] . Based on the computational analysis , we predicted that NRF1 and NFY would only occasionally coincide at −150/+50 bp regions ( Figures 1B and S1B ) . ChIP-seq analysis in MCF7 cells revealed 1 , 264 and 1 , 522 high confidence NRF1 and NFYB peaks , respectively ( Supplementary Tables S1 and S2 ) , and as expected , these peaks were found preferentially at promoter regions ( Figure S1C ) , particularly within −150/+50 bp regions ( 43–45% , Figure 1C ) , also at the center of DHS and at the nucleosome-free or -depleted region ( NFR/NDRs ) ( Figure 1D ) , and being surrounded by nucleosomes containing H3K4me2 and H3K4me3 ( Figures 1D and S1D ) . We also performed analyses of ChIP-seq datasets available in the literature ( although in some cases in other cell lines ) and established a similar relationship between the set of predicted motifs , the actual TF peaks , experimentally defined DHSs , and the profiles of histone marks H3K4me2 and H3K4me3 ( Figure S1E ) . Importantly , -returning to the case of NRF1 and NFYB- both TFs rarely co-localized at −150/+50 bp ( ∼4% , Figure 1E ) , consistent with our prediction . This low rate of co-binding of NRF1 and NFYB did not substantially increase upon examining wider promoter regions ( 7–8% between −800 bp and +200 bp , Figure S2A ) , thus confirming their apparent binding antagonism in promoters across the human genome . Since NRF1 and NFYB occupy only 25% and 18% of their respective predicted sites at −150/+50 bp regions ( based on the comparison of ChIP-seq data and computational prediction of NRF1 and NFY sites ) , we could not exclude the possibility that their poor co-localization may be a result of technical limitations associated with the ChIP-seq assay . To assess this possibility , we alternatively assessed genomic binding of NRF1 and NFYB using the highly sensitive ChIP-DSL assay [50] . This assay , in contrast to ChIP-seq , is a targeted approach that lacks the direct amplification of ChIP'ed DNA ( see Methods for more details ) . Using the Hu20K array ( which allows for the targeted testing of ∼20 , 000 human promoters between −800 bp and +200 bp relative to the TSS ) , we re-identified 63–73% of the NRF1 and 72–81% of the NFYB ChIP-seq-positive promoters ( depending on how stringently we defined a ChIP-DSL-positive hit: p<0 . 0001-p<0 . 01; Figure S2B ) . Using the most stringent analysis ( p<0 . 0001 ) , the ChIP-DSL assay identified large subsets of NRF1 and NFYB positive promoters that were not identified by ChIP-seq ( 1 , 320 and 1 , 525 , respectively; Figure S2B ) , which were also highly enriched in NRF1 or NFY/CCAAT motifs . However , consistent with our ChIP-seq analyses , they showed relatively poor NRF1 and NFYB co-localization ( Figure S2C ) . The only exception to this observation was a small subset of NRF1 and NFYB co-occupied promoters ( n = 332 , Figure S2C ) , in which NRF1 and NFY/CCAAT motifs also co-occurred with abnormal high significance ( Figure S2D ) . Overall , therefore , our ChIP-seq and ChIP-DSL results with NRF1 and NFYB confirm the predictive value of our computational analysis , which suggests that cardinal motifs ( and their cognate TFs ) tend to be present independently rather than in fixed pairs within these regions . In fact , combined analysis of the NRF1 and NFYB ChIP-DSL datasets showed that almost 35% of all human promoters tested on the Hu20K array contain one or the other TF , although they still poorly coincide . We also performed gene ontology ( GO ) analysis of genes associated with −150/+50 bp NRF1 and NFYB ChIP-seq peaks to associate them with biological functions . As expected , based on the known functions of NRF1 , genes associated with this TF were linked to RNA processing and metabolism , translation , mitochondria , and intracellular transport of proteins ( Figure 1F , top panel ) , whereas those associated with NFYB were linked to cell cycle , regulation of transcription , and response to DNA damage , among others ( Figure 1F , bottom panel ) . Similar results were obtained using the ChIP-DSL data ( Figure S2E ) . Therefore , the tendency to occupy different promoters may also be associated with their specific biological functions . If many −150/+50 bp regions could be distinguished by a single cardinal cis-regulatory element and its cognate TF , then we hypothesized that this element could also lead to selective ( distinguishable ) recruitment of cofactors via TFs . To test this hypothesis , we focused on KDMs because these histone-modifying enzymes have been repeatedly shown to bind preferentially to promoters in genome-wide tests [24] , [26]–[31] , although their rules of TF-mediated recruitment to promoters are poorly understood , especially on a genome-wide scale [51] , [52] . In order to elucidate the role of KDMs on NRF1- and NFYB-mediated transcription , we tested the effects of short interfering RNA ( siRNA ) -mediated depletion of KDMs on luciferase transcription under the control of three canonical NRF1 sites ( 3×NRF1-Luc ) or three canonical NFY sites ( 3×NFY-Luc ) in HEK293T cells ( Figure 2A ) . Although these sites are not in the context of endogenous promoters ( thus results should be taken with caution ) , this minimalist strategy ensures that the only difference between these two promoters is the cardinal motif . We tested 27 siRNAs that corresponded to the 27 KDMs that are expressed in these cells ( based on gene expression profiles ) , out of around 30 encoded in the human genome . KDM specificity for NRF1 or NFY sites was established by comparing the relative effects of the same KDM siRNA treatment on NRF1- and NFY-regulated luciferase transcription with respect to control siRNA ( Figure S3A ) . By performing these comparisons , we identified 13 siRNA treatments that had selective influences ( p-value<0 . 05 ) on luciferase transcription depending on whether the motifs were NRF1 or NFY ( summarized in Figure 2B ) . Six of these siRNAs specifically altered 3×NRF1-dependent transcription ( Figure 2B , top ) , while 8 siRNAs specifically affected 3×NFY-dependent transcription ( Figure 2B , bottom ) . One siRNA treatment ( KDM5C siRNA ) had significant effects on both 3×NRF1- and 3×NFY-dependent transcriptional units when compared to siRNA control , but with opposite effect on each reporter . Three other siRNAs ( red/blue circles in Figure 2B ) also induced changes in the expressions of both reporters when compared to siRNA control , however , these effects were in the same direction and clearly more significant for one site over the other ( p-value<0 . 05 between them ) . Overall , 8 siRNA treatments induced down-regulation of gene expression ( Figure 2B , left ) , while 6 induced up-regulation compared to control siRNA ( Figure 2B , right ) , which may interestingly suggest that these cardinal motifs impose a balance of positive and negative activities on the same promoter , rather than an exclusive effect of a single activity . The results of this analysis also suggest that 3× copies of the same cardinal motif at −150/+50 bp regions are sufficient to dictate a rich and selective regulatory pattern of KDMs . Because the results just described could be either the result of direct or indirect effects , we capitalized on our specific result revealing that LSD1 siRNA alters NRF1- but not NFY-motif mediated transcription ( Figure 2B , top and left ) by using the case of LSD1 to study cardinal motif-induced KDM regulatory patterns in more detail . For our study , we performed ChIP-seq analysis of lysine specific demethylase 1 ( LSD1 ) in MCF7 cells , identifying 3 , 690 high confidence peaks that showed high preference for annotated promoter regions ( Figure 2C , pie chart ) . A full list of genomic locations can be found in Supplementary Table S3 . LSD1 shows its strongest binding preference for the −150/+50 bp region ( 42% , Figure 2C ) , at the center of the promoter DHS and NFR/NDR ( Figure S3B ) . Surprisingly , this reveals that LSD1 is a genuine promoter DHS-specific factor , similar to NRF1 and NFYB ( Figure 1C ) . When comparing the binding patterns of LSD1 , NRF1 , and NFYB , we observed that virtually all NRF1-positive regions were occupied by LSD1 ( 99% ) , with only a few NFYB-positive regions being occupied by LSD1 if NRF1 was not nearby ( 4% , Figure 2D ) . We also observed similar results for LSD1 promoter occupation using the highly sensitive ChIP-DSL assay ( Figure S3C ) , as well as a high correlation of LSD1 binding with NRF1 on a genome-wide scale ( Figure S3D ) . In the context of a third ‘cardinal’ TF , Sp1 , we also observed a strong associative preference for LSD1 contingent on co-localization with NRF1 in a limited analysis of ∼2 , 000 human promoters ( Figure S3E ) . Not surprisingly , de novo motif discovery analysis of LSD1 ChIP-seq peaks revealed overwhelming enrichment of NRF1 sites ( Figure S4A ) . We also observed enrichment of the estrogen responsive element ( ERE ) in agreement with our own previous studies showing that estrogen receptor alpha ( ERα ) recruits LSD1 to ERα-regulated regions via EREs [53] . However , the co-localization of ERα and LSD1 was mostly found at distal ( non-promoter or H3K4me3-negative ) sites ( Figure S4B and S4C ) . In contrast , the co-association between NRF1 and LSD1 was characteristic of promoter ( or H3K4me3-positive ) regions ( Figure S4C ) . Next , we examined whether strong NRF1 and LSD1 co-association is a cell type-specific feature . We performed LSD1 ChIP-DSL analyses in human mammary epithelial ( HMEC ) , prostate cancer ( LNCaP ) , osteosarcoma ( U2OS ) , and neuroblastoma ( SH-SY5Y ) cells . Our results showed significant NRF1 motif enrichment in LSD1 peaks in all four examined cell lines , although the levels of enrichment were slightly different among them ( Figure S5A ) . U2OS cells showed almost identical motif enrichment to that observed in MCF7 cells ( Figure S5A ) , and more than 80% of the LSD1-positive promoters in these cells were also LSD1-positive in MCF7 cells . Based on this finding , we included U2OS cells in some of the experiments reported later in this study . To confirm the high binding coincidence of LSD1 in U2OS and MCF7 cells , we performed standard ChIP analysis on random targets ( Figure S5B ) , and on a few classic NRF1-regulated promoters ( Figure S5C ) , and observed almost identical LSD1 binding patterns in both cell lines . The LSD1 binding program has also been recently reported in mouse embryonic stem cells ( mESCs ) [54] , and although no particular connection between LSD1 and NRF1 was highlighted in this study , we analyzed the available data and observed that the NRF1 site is also significantly enriched between -150 bp and +50 bp relative to the TSS in these cells ( p = 1e-42 ) . In fact , the LSD1 binding map in human MCF7 and mouse ESCs differs at many sites ( which is expected , especially at distal regions , since these two lines derive from different organisms and are completely different in many aspects [55] ) , but they show remarkably similar binding profiles at many TSSs , including at those of classic NRF1 target promoters ( Figures S5D and S6 ) . Taken together , our results show that the strong co-association of NRF1 motifs , NRF1 TF , and the LSD1 cofactor at −150/+50 bp regions can be observed in different cell lines and organisms , which supports a model in which cis-regulatory elements in these regions dictate strong and common cofactor signatures . Careful examination of LSD1 and NRF1 ChIP-seq peaks revealed their very close alignment at specific loci ( Figures 3A and S7 ) and on a genome-wide scale ( Figure 3B ) . To test whether NRF1 ( indirectly , the NRF1 motif ) could be responsible for recruiting LSD1 to a promoter region , we took advantage of the same luciferase expression system that we previously employed to test the involvement of KDMs in cardinal motif dependent transcription ( Figure 2A ) . For these experiments , we engineered the construct containing the luciferase gene under control of 3×NRF1 sites ( 3×NRF1 ) to contain a sequence variant with point mutations expected to disrupt binding of NRF1 ( scheme in Figure 3C , and Methods ) . Using the wild-type and mutated reporter constructs , we observed that both the levels of luciferase expression ( measured by the luciferase assay ) and NRF1/LSD1 binding ( measured by standard ChIP ) were completely dependent on the presence of wild-type NRF1 motifs , suggesting that LSD1 acts via NRF1 sites as a consequence of direct recruitment by NRF1 ( Figure 3C ) . Also in support of this model , endogenous LSD1 co-immunoprecipitated with endogenous NRF1 and vice versa , whereas LSD1 did not co-immunoprecipitate with endogenous NFYB in the same cell extracts ( Figure 3D ) . Size exclusion chromatography of nuclear extracts also suggested that NRF1 and LSD1 interact ( directly or indirectly ) , since a pool of NRF1 co-fractionates with a pool of LSD1 as part of what could be a ‘super’-multiprotein complex of a molecular size larger than 2MDa ( Figure 3E ) . We also observed an additional pool of NRF1 and LSD1 that co-fractionated in very slow elution fractions ( Figure 3E ) , but these fractions likely corresponded to elution as individual molecules , rather than as physically associated partners . Taken together , these results suggest that NRF1 , via NRF1 sites , could mediate the recruitment of LSD1 to promoter regions , which is consistent with their strong co-association on a genome-wide scale and in different cell types . The almost pervasive association of NRF1 with LSD1 at −150/+50 bp regions does not directly imply a functional relationship between them or , if such relationship exists , that it is functionally universal at every single promoter . In fact , any functional relationship between these two factors may be complex because LSD1 may act as either a coactivator or corepressor of transcription , depending on the context of its binding [56]–[60] . Our data obtained in the context of the 3×NRF1 sites suggested that LSD1 can act as coactivator of NRF1 , at least under this ‘artificial’ condition ( Figure 2B ) . To test this hypothesis on endogenous NRF1 sites , we tested two classic NRF1 targets ( TFAM and FXR2 ) and three new NRF1/LSD1 targets uncovered in this study ( CDC42 , CDC2 , and SAP18 ) . For these five genes , both LSD1 and NRF1 knockdown resulted in decreased expression when compared to control siRNA , suggesting that LSD1 is in fact a coactivator of NRF1-mediated transcription ( Figure 4A ) . To test this possibility on a genome-wide scale , we performed whole-genome expression profiling analysis following NRF1 , LSD1 , or control siRNA treatment . We identified 2 , 351 genes as significantly altered by NRF1 knockdown , and 1 , 091 genes as significantly altered by LSD1 knockdown , both compared to control siRNA . Of these genes , a very significant number of them were altered by both LSD1 and NRF1 siRNA treatments ( n = 518 , p-value<1 . 0E-10 ) , or 22% and 47% of all NRF1 and LSD1 siRNA-affected genes , respectively ( Figure 4B ) . About 90% of these 518 genes were affected in the same direction by both treatments ( either up or down-regulated ) . Additionally , motif analysis of the −150/+50 bp regions associated with the NRF1 and LSD1 siRNA-altered genes showed high enrichment of NRF1 motifs , thus supporting the idea that many LSD1-functionally regulated genes ( and obviously many NRF1-regulated genes ) are bona fide NRF1 motif-containing promoters ( Figure 4C ) . We were initially surprised that NFY/CCAAT motifs were also significantly enriched in the NRF1 siRNA-altered genes ( Figure 4C , left panel ) , however , we suspect that this enrichment may derive from the unexpected NRF1 siRNA-mediated up-regulation of NFYA and NFYB genes ( as determined by microarray ) , which are two components of the trimer that constitutes NFY , thus potentially affecting NFY/CCAAT motif-containing promoters indirectly . Our genome-wide analysis supports the idea that NRF1 and LSD1 co-regulate gene transcription , but to establish the homogeneity or heterogeneity of this functional partnership we classified the full set of genes altered by both siRNA treatments into four classes: Class I included genes down-regulated by both siRNA treatments ( n = 193 ) ; Class II included genes up-regulated by both siRNAs ( n = 272 ) ; Class III included genes down-regulated by NRF1 siRNA , but up-regulated by LSD1 siRNA treatment ( n = 35 ) ; and Class IV included genes up-regulated by NRF1 siRNA , but down-regulated by LSD1 siRNA ( n = 18; Figure 4D ) . We also organized those genes only affected by one siRNA treatment into four classes ( Classes V–VIII; Figure 4D ) . Next , to establish which transcriptional output is more likely to be associated with direct versus indirect NRF1/LSD1-dependent effects , we calculated the enrichment of NRF1- or LSD1-occupied promoters for genes in each class ( based on ChIP-DSL data ) and compared this value to the enrichment of NRF1- or LSD1-occupied promoters for genes not affected by NRF1/LSD1 siRNA ( which we defined as ‘background’ ) . A ratio greater than one ( >1 ) with respect to background might be associated with a higher frequency of direct effects mediated by the siRNA treatment , while a ratio lower than one ( <1 ) might be associated with a higher frequency of indirect effects ( since the rate of promoter binding in this case is lower than that observed in background , i . e . in siRNA-unaffected genes ) . Following our analysis , we observed that genes down-regulated by both NRF1 and LSD1 siRNAs tend to show a higher frequency of NRF1 and LSD1 binding at their promoters than background ( Figure 4E , Class I ) . In contrast , genes up-regulated by both siRNA treatments tend to show a higher frequency of LSD1 , but not NRF1 , binding at their promoters ( Figure 4E , Class II ) . The ratio obtained for genes down-regulated by NRF1 siRNA and up-regulated by LSD1 siRNA suggests a general enrichment in direct NRF1 , but indirect LSD1 effects ( Figure 4E , Class III ) , while the ratio obtained for genes up-regulated by NRF1 siRNA and down-regulated by LSD1 siRNA suggests that both NRF1 and LSD1 affect these genes indirectly ( Figure 4E , Class IV ) . These results are consistent with the current view that NRF1 binds to promoters to activate gene transcription ( Classes I , III , and VII ) , and that LSD1 either acts as a co-activator ( Classes I and V ) or a co-repressor of transcription ( Classes II and VI ) . The only classes in which both NRF1 and LSD1 show a tendency to co-bind are I , V , VI , and VII , thus suggesting that in the context of the whole human genome , LSD1 may act as a NRF1 coactivator in some cases ( Class I ) , or remain inactive in others ( Class VII ) , at least under the experimental conditions that we tested . Class VI represents an interesting case in which LSD1 may inhibit the NRF1 activity , thus LSD1 knockdown impairs the negative effect on NRF1-mediated activation , but NRF1 knockdown per se has no effect on gene expression under already the condition of LSD1-mediated NRF1 inhibition . More difficult to explain are the Class V promoters . In this case , it is possible that other TFs ( besides NRF1 ) may recruit LSD1 to these promoters , even if NRF1 is present , thus resulting in LSD1-dependent genes that are associated with , yet functionally independent of NRF1 . Overall , this functional analysis suggests that even if a motif/TF/cofactor signature is pervasive in promoters across the genome , the functional relevance could be rather complex , thus emphasizing that these signatures should not be interpreted as representative of universal functional outcomes . Finally , we explored the existence of additional strong partnerships associated with cardinal motifs . We analyzed a number of ENCODE ChIP-seq experiments and data from multiple sources to generate a heatmap of motif/cofactor preferences at promoter regions ( Figure 5A ) . As predicted by our model , the set of TFs binding to the most highly enriched cardinal elements show no significant preference for motifs other than their cognate sites , thus suggesting that they bind with preference independently and define subsets of promoters ( Figure 5A: Sp1 as cognate of GC-rich , GABPA as cognate of ETS , and ZBTB33 as cognate of Clus1 ) . Also as predicted by our model , we observed that some cofactors show strong preferences for single or only a few motifs , thus supporting the idea of cardinal motif-associated cofactor signatures ( Figure 5A and 5B ) . For example , NRF1 motif-enriched promoters were distinctly associated with LSD1 , but also with JARID1C/KDM5C , which is consistent with the result that KDM5C siRNA treatment altered the expression of the 3×NRF1-luciferase construct in our screen ( Figure 2B ) . We also observed evidence for the preferential binding to NRF1 sites by the histone/protein-methyltranferase ESET , which has been reported to add methyl marks that can later be specifically removed by LSD1 [53] , [61] , thus suggesting a particular signature of NRF1-LSD1-ESET . Other potential partnerships with NRF1 are: the histone acetyltransferase , PCAF , the ATP-chromatin remodeler , CHD7 , the methyl-DNA binding protein , MBD4 , the histone/protein deacetylase ( HDAC ) , HDAC8 , the E3 ubiquitin-protein ligase , RING2 , corepressors SUZ12 and NCoR , and the dimethyl arginine binding protein , TDRD3 ( Figure 5B ) . Some of these cofactors were distinctly associated with NRF1 sites ( for example , LSD1 and ESET ) , while others were associated with other motifs ( for example , CHD7 and NCoR; Figure 5A ) . Perhaps , it was initially expected that those motifs with higher number of associated cofactors were Sp1/GC-rich and ETS motifs , because these are two of the top-most enriched cardinal sequences ( Figure 1A ) . However , using the same argument , it was surprising to observe that few number of cofactors were associated with NRF1 , NFY , and CREB/E-box/MYC motifs ( Figure 5A ) , considering that their enrichment in human promoters is comparable at least to that of ETS motifs ( Figure 1A ) . Overall , these data reveal that cardinal elements may define strong cofactor signatures . To also test the model of cardinal motif-cofactor regulatory signatures experimentally , we engineered nine constructs to contain NRF1 and/or NFYB motifs in the context of the sequence of their ‘natural’ proximal promoter ( −150/+50 bp ) , but upstream the luciferase gene , and determined the effect on this gene of depleting the same 27 KDMs that we tested in our original screen with multimerized sites ( Figure 2B ) . Three of these engineered regions are targets of NRF1 , but not NFYB; three are targets of NFYB , but not NRF1; and three are targets of both ( based on ChIP-seq data , Figure S8A ) . According to our model , they should show at least two main ( or perhaps three , adding the case of mixed NRF1-NFY promoters ) basic patterns of KDM siRNA-mediated effects as result of their cardinal NRF1 or NFY motif composition . In fact , we observed that the global effects of the KDM siRNA treatments on the three NRF1-driven promoters clustered together , while the global effects of the same treatments on the three NFY-driven promoters did the same ( Figure S8B ) . Analyses of the mixed promoters ( whose alignment to the proposed model was initially harder to predict ) resulted in two of the promoters clearly clustering with NRF1-driven promoters , while the third promoter still clustered with them , but less clearly ( Figure S8C ) . Thus these results ( based on a limited test ) suggest that cofactor signatures at promoters that contain a combination of cardinal motifs resemble those of promoters with single cardinal motifs , instead of representing a mix of effects induced by the individual motifs , or showing a completely new regulatory signature . More in general , this test with nine constructs reinforces our proposed model that cardinal motifs are strong determinants of cofactor signatures at proximal promoter regions .
For decades , promoters have been known to be critically important in gene transcription regulation [5] , [6] , [14] . Recently , new approaches that allow analyses of chromatin structure at genome-wide scale are adding new and more global perspectives to our understanding of these regions . For example , these analyses reveal that most human promoters are ‘open’ chromatin regions that center between −150 and +50 bp relative to the TSS , although spread less intensively in slightly wider regions [2] , [9] . This finding suggests that the bulk of TF binding and cofactor activities occurring at promoters might be limited to these short genomic regions . Consistent with this possibility , a recent functional analysis of 46 promoters indicated that regions immediately next to the TSS are necessary but also sufficient to control basal transcription [62] . In an effort to understand how these short regions operate on a genome-wide scale , and more particularly to understand their mechanisms of cofactor recruitment , we have explored the role of cis-regulatory elements that are highly enriched at −150/+50 bp in dictating the recruitment of cofactors , in order to establish some basic rules . We have termed these elements as cardinal motifs . Our studies suggest that cardinal motifs tend to occupy −150/+50 bp regions independently rather than in fixed combinations , and that they direct signatures of cofactor recruitment that allow us to classify human promoters into subgroups based on these motifs and their cofactor signatures ( Figure 5C ) . The set of cardinal elements that we have identified in our analysis of n = 21 , 000 human promoters is not substantially different than that uncovered in previous computational analyses [37] , [38] , [42]–[45] . Similarly , the poor co-occurrence among these motifs in proximal promoter regions was suggested in the past [37] ( we show a direct comparison between our analysis and this previous study in Figure S9A ) . Perhaps the major differences between this report and this study are quantitative in terms of motif co-occurrences ( Figure S9A ) . With respect to the few qualitative differences , this previous study identified an additional motif , USF , which we did not find in our analysis; in contrast , this previous study did not include the YY1 and GFY elements , which we observe as highly enriched in our dataset ( Figure S9A ) . These differences might be explained in part by the facts that we used a different algorithm and that analyzed a different number of promoters and length of sequences in our tests ( we studied n = 21 , 000 human promoters between −150 bp and +50 bp relative to TSS , compared to n = 13 , 010 human promoters between −1 , 000 bp and +500 bp in the previous study ) . In any case , the model and its functional implications were not experimentally tested in this previous study , which is what we attempt here . In a way , the model that cardinal motifs ( or TFs ) prefer acting independently than in fixed combinations contrasts with the observation that motifs ( or TFs ) often operate as combinations at distal ‘open’ chromatin regions ( i . e . enhancers and other genomic elements; e . g . [20]–[23] , [63] ) . Therefore , the dominance of a single cardinal motif at a single −150/+50 bp region might be a feature rather ‘exclusive’ of ‘open’ chromatin regions at promoters . Our model , however , does not exclude the possibility that cardinal motifs ( via the TFs that recognize them ) may act combinatorially via short- or long-range interactions in the nuclear space , or in trans , with other motifs ( or TFs ) . It does not contradict either previous reports indicating that cardinal motifs act synergistically or cooperatively in many proximal promoter regions ( e . g . [64]–[68] ) . In fact , we may see this phenomenon in a significant number of cases ( e . g . see examples in Figures 3A and S7 , and especially Figure S2C and S2D ) . But we propose that these cases do not represent the most general rule ( Figures 1E and S2C ) . Furthermore , we tested whether the preference to occupy different promoter subsets is maintained if the margins of the promoter sequence for analysis are wider ( Figure S2A ) or are defined experimentally based on DHSs ( Figure S1B ) , achieving the same conclusion . We also tested whether this preference is maintained when proximal promoters are subclassified based on the mode of transcription initiation ( ‘focused’ or ‘dispersed’ [5] , [6] ) . In particular , we took advantage of available 5′ RNA-seq data and identified 2 , 838 focused and 5 , 220 dispersed promoters in MCF7 cells ( see Methods for details ) . As expected , focused promoters show higher preference to contain TATA and YY1 motifs ( blue , Figure S9B ) . Dispersed promoters show higher preference to contain ETS and NRF1 motifs ( red , Figure S9B ) . Despite these preferences , the observed co-occurrences between TATA and YY1 or ETS and NRF1 are not enriched within each group ( Figure S9C ) . What is the role of cardinal elements at proximal promoters ? Since −150/+50 bp regions are at the center of promoter DHSs [2] , it is an interesting possibility that TFs recognizing these motifs are responsible of the typical chromatin features of these regions ( ‘open’ chromatin surrounded by nucleosomes that are heavily modified ) . At enhancers , a special class of TFs termed ‘pioneer factors’ have been suggested to contribute to their chromatin organization [69] . Pioneer factors can be distinguished by their ability to bind first in a temporal sequence of additional binding events , and to bind independently rather than cooperatively to chromatin [69] . Pioneer factors can also be distinguished since they establish competence for gene expression , rather than activate transcription [69] . Examples of pioneer factors include: lineage-specific transcription factors , FoxA1/3 and GATA3/4 . The fact that cardinal TFs are often constitutively bound to the genome , as pioneer factors FoxA1/3 and GATA3/4 at enhancers , and that some of them remodel chromatin [70] , makes them qualify as candidate pioneer factors . If , furthermore , we consider our model that cardinal TFs prefer binding independently to many promoters , it would be a third property of pioneer factors . Thus TFs recognizing cardinal motifs might be in fact ‘promoter-specific pioneer factors’ . However , we can only speculate on this possibility , since other typical features of a pioneer factor , such as be permissive for transcription rather than directly activate transcription , and bind first in a sequence of binding events , are not obvious in the case of cardinal TFs . Based on: 1 ) our analysis of >60 ChIP-seq experiments revealing that each cardinal motif might be associated with the selective recruitment of a subset of cofactors ( Figure 5A ) ; 2 ) the observation that knockdown analysis of a large panel of KDMs shows that proximal promoters regulated by the same motif exhibit relatively similar patterns of regulation ( Figure S8 ) ; 3 ) that three copies of a single motif could dictate a complex regulatory pattern of KDMs ( Figure 2B ) ; and , 4 ) that some cardinal TFs strongly associate ( biochemically and functionally ) with specific cofactors ( e . g . NRF1 and LSD1 ) ; we propose here that a main role of cardinal elements at proximal promoters is to dictate a signature of cofactors . What is the functional relevance of dictating these signatures ? These cofactors may potentially be involved in dictating the particular chromatin structure of these regions , and/or control of transcription initiation and RNA PolII pause-release . If that is the case , it is intriguing that two of these elements ( Sp1/GC-rich and ETS ) account for most of the cofactor binding preferences that we have identified in our analysis ( see Figure 5B ) . Initially , this observation may suggest that there are a series of promoter subclasses of which we might not know much yet about their regulation . However , it is also possible that our analysis of cofactors was too restricted ( limited to around 60 ChIP-seq datasets ) , thus we may have specifically missed cofactors that are associated with other cardinal motifs . It is also possible that ChIP-seq data for those cofactors that are specifically associated with cardinal motifs other than Sp1/GC-rich and ETS are not yet available in the literature . For example , PGC1α is a cell type-specific cofactor that is well known to regulate NRF1-dependent transcription , but we did not find available ChIP-seq data for this specific cofactor . But it is also possible that there is no bias in our analyses or in the list of cofactors profiled up-to-date by ChIP-seq , and that in fact there are two basic regulatory strategies for the way cardinal motifs regulate transcription . For example , some elements ( such as Sp1/GC-rich and ETS ) might be associated with heavier promoter-dependent regulation , while other elements ( such as NRF1 , NFY/CCAAT , CREB/MYC , and YY1 ) might be subject to heavier enhancer/distal-dependent regulation , thus revealing a genuine and essential regulatory difference between these two groups . In fact , CBP is a well-known cofactor of CREB , but CBP preferentially binds to distal sites , in contrast to CREB , which preferentially binds to promoter regions [39] , perhaps sin agreement with this possibility . Further studies will be necessary to elucidate a broader and clearer picture of the role of cardinal elements in selective recruitment of cofactors . Based on the relatively low number of cofactors we currently know that act via cardinal motifs other than Sp1/GC-rich or ETS ( Figure 5B and 5C ) , our finding that the subset of promoters dominated by NRF1 elements is strongly associated with LSD1 is of special importance . LSD1 ( also known as KDM1A , AOF2 , and BHC110 ) was the first KDM discovered [71] . LSD1 is a flavin adenine dinucleotide ( FAD ) -dependent amine oxidase , which requires FAD , an intermediate metabolite of the mitochondrial respiratory chain , to remove methyl marks from histone and non-histone proteins [71]–[73] . Interestingly , we have observed that the ‘NRF1/LSD1’ signature is associated with the control of nuclear-encoded mitochondrial genes , which are also well-known NRF1 targets ( Figures S5C , S5D and S6 ) . Furthermore , genes in which LSD1-acts as a coactivator of NRF1 ( Class I in Figures 4D and 4E ) show “mitochondrial” and “RNA processing” functions as the most significantly associated GO terms ( Figure S10A ) . Although similar functions were also associated by GO analysis of genes regulated ( functionally ) independently by NRF1 and LSD1 ( Class V and VII; Figures 4D , 4F , S10C , and S10E ) . In fact , it is remarkable that LSD1 occupies an impressive 1/3 of all active , nuclear-encoded mitochondrial genes in MCF7 cells ( considering only those promoters that are H3K4me3-positive or active in these cells; Figure S10G ) . Similarly , an analysis of protein-protein interacting networks of LSD1 peaks suggests that LSD1 regulates “mitochondrial metabolism” , as well as “RNA metabolism/translation” and “cell cycle” ( Figure S11 ) . Previous studies already associated LSD1 to mitochondrial functions in fission yeast ( S . pombe ) [74] . Moreover , it has been reported that the levels of FAD modulate the switch to lipid storage in mouse adipocytes via repressing PGC1β/PPARGC1B , PDK4 , FATP1/SLC27A1 , and ATGL/PNPLA2 genes in an LSD1-dependent manner [75] . However , we did not find LSD1 at the promoters of these four specific targets in MCF7 cells , and these genes were not expressed in these cells ( data not shown ) , perhaps suggesting an adipocyte-specific , LSD1-dependent regulatory mechanism . In fact , mitochondrial regulation is not identical in every cell type , and the content and morphology of mitochondria are largely determined by nuclear-encoded genes of variable expression across tissues [76] , [77] . In our analyses , LSD1 may also act as NRF1 negative modulator ( Class VI; Figures 4D and 4F ) , but this repressive activity is associated with cell motility , signaling , and cell adhesion , among other GO terms ( Figures S10D ) . In summary , the pervasive association between NRF1 and LSD1 cannot be interpreted as associated with a single or universal functional outcome , although when acting as NRF1 coactivator seems strongly associated with control of mitochondrial metabolism and biogenesis ( Figures S12 ) . In MCF7 cells , we found that 76% of LSD1 peaks occur at or near H3K4me3-marked regions , which corresponded to epigenetically defined promoter regions ( Figure S4C ) . This result is in agreement with the finding that LSD1 is a component of the >2MDa MLL1 complex [78] , since MLL1 can also be found associated to thousands of promoters in the human genome [79] . However , LSD1 does not seem to act dominantly via promoters in every cell , which might be expected since LSD1 interacts with many TFs in a cell-type-specific manner [53] . For example , the binding map of LSD1 in erythromyeloblastoid leukemia K562 cells [3] , [30] only marginally overlaps with that in MCF7 cells ( 3% at −150/+50 bp ) , since most LSD1 binding in K562 cells is distal . It is unclear to us why LSD1 binds strongly to promoters in MCF7 cells and other cell lines [53] , [80]–[82] ( this can also be observed using the datasets of others [54] , [83] ) , while in K562 cells it shows poor association with promoter regions . We suspect that some cells represent special cases , but this demands further exploration . In conclusion , we propose a general model in which cardinal cis-regulatory elements acting via promoter DHSs dictate the selective recruitment of cofactors to specific promoters , thus establishing a co-regulatory code that would be largely distinctive of each cardinal motif and promoter subset defined by these elements . We have started to decode this signature-based model , but it will be necessary to identify the full repertoire of ‘regulatory logic operations’ ( as first defined in [84] ) to have a complete understanding of how these motifs function .
Human breast cancer MCF7 cells were cultured in DMEM ( 1× ) +GlutaMAX-I medium ( Life Technologies ) supplemented with 10% fetal bovine serum ( FBS , Omega Scientific ) . Human prostate cancer LNCaP cells were cultured in Advanced DMEM/F12 ( 1× ) medium ( Life Technologies ) supplemented with 10% FBS . Human osteosarcoma U2OS cells , human embryonic kidney ( HEK ) 293T cells , and human neuroblastoma SH-SY5Y cells were cultured according to The American Type Culture Collection ( ATCC ) protocols . Primary normal human epithelial cells ( HMEC , CC-2651 ) were cultured using media and protocols provided by Lonza Bioproducts , commercial supplier of these cells . Cell lines were maintained in cell incubators at 37°C and 5% CO2 . MCF7 , LNCaP , and HMEC cells were hormone-deprived for 4 days in phenol-free plus charcoal-depleted FBS before each experiment , and then treated 1 hr with 100 nM 17β-estradiol ( E2 , Sigma-Aldrich ) in the case of MCF7 and HMEC cells , or with 100 nM dihydrotestosterone ( DHT , Sigma-Aldrich ) in the case of LNCaP cells , as previously reported [22] , [50] , [53] . Anti-LSD1 antibodies were previously described [53] . Anti-NRF1 ( PAC102 ) antibodies were a generous gift from Dr . Danny Reines [64] . Anti-NFYB ( H-209 , sc-10779 ) , anti-FoxA1 ( C-20 , sc-6553 ) , anti-RNA PolII ( N-20 , sc-899 ) , and anti-ERα ( HC-20 , sc-543 ) antibodies were purchased from Santa Cruz Biotechnology . Anti-H3K4me3 ( 07-473 ) antibodies were purchased from Upstate/Millipore . Anti-actin ( MAB1501 ) antibodies were purchased from Chemicon . Core promoter sequences from −150 to +50 bp relative to TSS were extracted from the UCSC genome browser by using genome assembly hg16 and mm3 . 5′ RNA-seq data from MCF7 cells is available at the Database of Transcription Start Sites ( DBTSS ) . The following were the sources of DNaseI-seq , MNase-seq , ChIP-seq , and ChIP-DSL data generated in previous studies and used here: RNA PolII ChIP-seq in E2-treated MCF7 cells reported in [85]; RNA PolII ChIP-DSL in E2-treated MCF7 cells reported in [50]; ERα ChIP-seq in E2-treated MCF7 cells reported in [86]; DNaseI-seq and H3K4me2-MNase-seq in MCF7 cells reported in [87]; LSD1 ChIP-DSL in E2-treated MCF7 cells reported in [53]; and LSD1 ChIP-seq in ESCs reported in [54] . Multiple datasets were also generated by The ENCODE Project ( as indicated in figure legends ) . The rest of ChIP-seq and ChIP-DSL experiments are reported in this study . De novo motif discovery analysis was performed by HOMER ( http://biowhat . ucsd . edu/homer/ ) , as described in previous studies [22] , [23] , [27] , [50] . Motif enrichment at these regions was determined in comparison to background regions randomly selected from the genome matched for GC% . Sequence logos were generated using the web-based application , WebLOGO ( http://weblogo . berkeley . edu ) . Motif enrichment heatmaps and dendrograms were created by clustering Hypergeometric log P-values using open source software Cluster ( http://bonsai . ims . u-tokyo . ac . jp/~mdehoon/software/cluster/software . htm#ctv ) . For enrichment analysis of cardinal motifs in publically available cofactor ChIP-Seq datasets , we first identified promoter regions containing ChIP-Seq-identified cofactor peaks within 500 bp of the annotated TSS ( we used peak lists provided by the authors reporting each ChIP-seq dataset ) . We determined the enrichment of each cardinal motif by HOMER in each set of promoters ( negative log of the hypergeometric p-value ) . The vector of motif enrichment for each experiment was then normalized and centered on the mean value to reveal the preferences of each experiment for cardinal motifs . To determine motif enrichment preferences of cardinal motifs in promoters subclassified based on their mode of transcription initiation , we took advantage of publically available 5′ RNA-Seq data obtained in MCF7 cells deposited in DBTSS . We defined focused and dispersed promoters as having , respectively , ≥90% and <90% 5′ RNA reads within ±5 bp of the primary TSS relative to the surrounding 100 bp promoter . This criteria identified n = 2 , 838 ( 35% ) focused and n = 5 , 220 ( 65% ) dispersed promoters in these cells . We then analyzed the preference of each group to contain a specific cardinal cis-regulatory element by HOMER . Chromatin immunoprecipitations ( ChIPs ) for standard RT-qPCR analysis and ChIP-seq/DSL experiments were performed as previously described [22] , [50] , [53] . RT-qPCRs for standard ChIPs were conducted in an Mx3000P Real-Time PCR Instrument ( Agilent ) with 2×Brilliant qPCR Master mix ( Stratagene ) . PCR settings were the following: 10 min at 95°C followed by 40 cycles of 95°C for 15 sec , 58°C for 15 sec , and 25 sec for 72°C . Primer sequences were the following: FMR1-forward 5′-CCAGGCCACTTGAAGAGAGA-3′ and FMR1-reverse 5′-TGCGGGTGTAAACACTGAAAC-3′; TFAM-forward 5′-ACCGGATGTTAGCAGATTTCC-3′ and TFAM-reverse 5′-CCTCCTGGCAATACACAACTC-3′; FXR2-forward 5′-CAAGGTTAGAGCCCCAGCTA-3′ and FXR2-reverse 5′-GCGGTGAAGAAAGAAGGCTA-3′; GAPD-forward 5′-TCCTCCTGTTTCATCCAAGC-3′ and GAPD-reverse 5′-TAGTAGCCGGGCCCTACTTT-3′; and , UMODL1-forward 5′-CCTTCAGTTCCCGGGAGTA-3′ and UMODL1-reverse 5′-CTGGAAGGAAATACGTCCACA-3′ . For ChIPs in plasmids , we engineered a construct with 3×NRF1 sites upstream the minimal promoter in the pGL2 plasmid ( see ‘siRNA screen’ for more details about this construct , below ) and a mutant 3×NRF1 version that contains the following fragment: 5′-TGTTTATTTTCAGacgtacgtTGTTTATTTTCAGacgtacgtTGTTTATTTTCAG-3′ . Chromatin for ChIP to test plasmids was prepared as for standard ChIPs . Analysis of ChIP-seq experiments was previously described [22] , [23] , [27] . ChIP-seq experiments were performed in an Illumina GA2 sequencing instrument . DNA libraries were performed as previously described [22] , [27] . Biological triplicates ( n = 3 ) were pooled before DNA library preparation . A full list of NRF1 , NFYB , and LSD1 ChIP-seq peaks can be found in Supplementary Table S1 , S2 , S3 . ChIPs followed by DSL ( DNA , Selection , and Ligation , or ChIP-DSL ) were performed and analyzed as previously described [50] . Briefly , the ChIP-DSL assay uses ChIP DNA as a template for oligonucleotide ligation , not for direct amplification . ChIP-DSL is based on DNA-mediated isolation ( or selection ) of a pool of pre-designed 40-mer oligonucleotides ( plus T3 and T7 5′ ends ) that are synthesized as pairs ( T3+20-mer and T7+20-mer ) but that can be easily ligated once associated to their correct DNA template in the genome , since both should anneal adjacently . Ligated oligonucleotides can be then amplified after release from the template based on the presence of T3 and T7 sequences . These oligonucleotides are carefully pre-designed to anneal at similar temperature and having similar GC content . In a typical ChIP-DSL experiment , ChIP'ed DNA is purified and then randomly biotinylated . Biotinylated DNA is incubated with the pool of DSL ( T3+20-mer and T7+20-mer ) oligonucleotides before isolation with streptavidine-coated beads . After extensive washing of excess ( not annealed ) oligonucleotides , those annealed in to their right DNA template can be ligated based on their close proximity . After amplification , PCR fragments ( all the same size , with similar annealing temperature , and similar GC content ) are hybridized to an array of complementary sequences . We performed ChIP-DSL experiments in triplicate ( n = 3 ) . For LSD1 , NRF1 and NFYB ChIP-DSL analyses , we hybridized the samples onto the Hu20K array , which contains 40-mer sequences for n≈20 , 000 human proximal promoters between −800 bp and +200 bp ( one sequence per promoter ) . The experimentally calculated false positive rate for a PolII ChIP-DSL experiment in MCF7 cells is 3% , and the false negative rate is 33% in the Hu20K array [50] . For Sp1 ChIP-DSL analysis , we hybridized the samples onto the Hu2K array , which is a small version of the Hu20K array with mostly promoters of cell cycle-regulated genes . As an additional note , the numbers and percentages provided in the different panels/figures with ChIP-DSL experiments were calculated based on the actual number of spots on the array providing reliable signal ( e . g . n = 17 , 288 in the Hu20K array ) , not for the total number of promoters actually spotted on the array ( n = 20 , 000 in the Hu20K array ) . A full summary of ChIP-DSL positive hits can be found in Supplementary Table S4 . All transient transfections ( siRNAs and plasmids ) were performed with Lipofectamine 2000 following the manufacturer's protocol ( Invitrogen ) . Transfections were performed one day after seeding of cells , and the effects induced by the siRNA treatments were measured after 2/3 days . Transient transfections in HEK293T and U2OS cells were performed at 70–90% confluency . Transient transfections in MCF7 cells were performed at 10–20% confluency . Specifically for the siRNA screen with multimerized 3×NRF1 or 3×NFY sites and for the gene expression analyses based on RT-qPCR and microarrays , transient transfections were performed in 6-well plates . For the siRNA screen with natural promoters and for transient transfections , experiments were performed in 96-well plates . In 96-well plates , we transfected 8 . 67 pmols of siRNA ( purchased from Sigma-Aldrich ) , 0 . 1 µg of pGL2-Luciferase constructs ( see below ) , and 0 . 01 µg pRL ( Renilla ) -TK . We measured dual Luciferase/Renilla expression with the Dual-Glo Luciferase Kit ( Promega ) using a Veritas Microplate Luminometer ( Turner Biosystems/Promega ) and following standard procedures . Collected data were normalized ( Firefly signal/Renilla signal ) and referred to the control ( scrambled siRNA ) value . We engineered two constructs for the siRNA screen with multimerized sites containing 3×NRF1 or 3×NFY/CCAAT sites upstream the luciferase gene in the pGL2 plasmid harboring the TK minimal promoter . The two cloned fragments were the following: 5′-TGCGCATGCGCAGacgtacgtTGCGCATGCGCAGacgtacgtTGCGCATGCGCAG-3′ , which contains three copies of the NRF1 site found in the FMR1 promoter ( in uppercase ) [64]; and tctgATTGGctggttaaggcatctgcttaacttctgATTGGctggttaaggcatctgcttaactacgATTGGcta , which contains three copies of a consensus NFY/CCAAT-box . For the siRNA screen with sites in the context of their natural promoters , we engineered the following regions upstream the luciferase gene in the pGL2-basic plasmid: −153/+29 bp from the GDPD1 promoter , −158/+30 bp from the ASNSD1 promoter , −150/+50 bp from the ZBTB17 promoter , −150/+50 bp from the ZNF695 promoter , −150/+50 bp from the STIP1 promoter , −150/+50 bp from the AKAP8L promoter , −150/+37 bp from the ZWINT promoter , −400/+50 bp ) from the RFX1 promoter , and −150/+100 bp from the CCT3 promoter . These regions and their margins were selected based on our NRF1 and NFYB ChIP-seq datasets , and contain NRF1 peaks and recognizable NRF1 motifs ( for GDPD1 , ASNSD1 , ZBTB17 ) , NFYB peaks and recognizable NFY/CCAAT motifs ( for ZWINT , RFX1 , and CCT3 ) , or NRF1 and NFYB peaks and recognizable NRF1 and NFY/CCAAT motifs ( for ZNF695 , STIP1 , and AKAP8L ) . The position of the TSS was based on the gene annotation in the UCSC browser . For data visualization of the siRNA screen results with natural promoters , we represented normalized luciferase signal in heatmaps created by clustering Hypergeometric log values using Euclidean distance and average linkage with TMEV 4 . 9 Software ( Dana-Farber Cancer Institute ) . Total RNA was extracted with the RNeasy Kit ( Qiagen ) and DNA was eliminated with on-colum DNase treatment ( Qiagen ) following the manufacturer's protocols . Total RNA was converted into cDNA with the SuperScript First-Strand Synthesis Kit ( Invitrogen ) following the manufacturer's protocol . Real-time PCR was conducted in a Mx3000P Real-Time PCR Instrument ( Agilent ) . Brilliant qPCR Master mix ( Stratagene ) . PCR settings were the following: 10 min at 95°C followed by 40 cycles of 95°C for 15 sec , 58°C for 15 sec , and 25 sec for 72°C . Primer sequences were the following: TFAM-forward 5′-GTGATTCACCGCAGGAAAAG-3′ and TFAM-reverse 5′-CTGGTTTCCTGTGCCTATCC-3′ , FXR2-forward 5′-AACCGTGGTAATCGGACTGA-3′ and FXR2-reverse 5′-GGTGCAGGTTGGAGGTTTTA-3′ , for CDC42-forward 5′-TACTGCAGGGCAAGAGGATT-3′ and CDC42-reverse 5′-CCCAACAAGCAAGAAAGGAG-3′ , CDC2-forward 5′-CCATGGGGATTCAGAAATTG-3′ and CDC2-reverse 5′-CCATTTTGCCAGAAATTCGT-3′ , SAP18-forward 5′-TGGATGCAACCTTGAAAGAA-3′ and SAP18-reverse 5′-TGGAATCATCAGTCCCCTTT-3′ , and ACTB-forward 5′-GTGGGCATGGGTCAGAAG-3′ and ACTB-reverse 5′-TCCATCACGATGCCAGTG-3′ . Cycle threshold ( Ct ) values were extracted with MxPro qPCR Software ( Agilent ) to calculate difference Ct ( ΔCt ) values with respect to control . For microarray analysis , cDNA quality was assessed by the Agilent Bioanalyzer ( Agilent ) . Gene expression profiling was performed using the Human Illumina Sentrix Expression BeadChips system , as previously described [50] in the Biogem core ( UCSD ) . Genome expression data are available in the GEO database . Co-immunoprecipitation ( co-IP ) experiments were carried out as previously described [88] . Briefly , three 10 cm plates of confluent MCF7 cells were washed with ice-cold PBS . Cells were then disrupted and homogenized with IPH buffer ( 50 mM Tris-HCl , pH 8 . 0 , 150 mM NaCl , 5 mM EDTA , 0 . 5% NP-40 , with Roche's cocktail protease inhibitors ) . Samples were incubated on ice for 20 min and cleared by maximum centrifugation ( 14 , 000×g ) for 10 minutes at 4°C . The supernatant volume was divided into four aliquots for overnight incubation with specific antibodies . Immuno-complexes were isolated after 2 hrs incubation with protein A beads ( Sigma-Aldrich ) . Protein A beads were then pelleted and washed 3 times in IPH buffer . Protein A beads were then resuspended in loading buffer and the solution was analyzed by Western blot . Nuclear extracts from MCF7 cells were prepared fresh similarly as in [89] , with the following modification: nuclear pellet ( after discarding cytoplasmic and membranous fractions ) was carefully resuspended in nuclear extraction buffer ( 20 mM HEPES-KOH pH 7 . 9 , 25% glycerol , 400 mM NaCl , 1 . 5 mM MgCl2 , 0 . 2 mM EDTA , 0 . 5 dithiothreitol , 0 . 05% NP-40 , and Roche's cocktail protease inhibitors ) and maintained on ice for 40 min . The soluble fraction was obtained after centrifugation for 10 min at 14 , 000×g and 4°C . This fraction was then loaded onto a Superose 6 column ( Pharmacia ) previously equilibrated with nuclear extraction buffer without glycerol . The column was applied into the FPLC system and ran following manufacturer's recommendations ( Pharmacia ) , as previously reported [90] . Elution fractions of 0 . 5 mL were collected and analyzed by Western blot . Previously , we ran an independent sample of proteins of known molecular sizes ( HMW or high-molecular weight Calibration kit , Pharmacia ) to determine elution volumes based on protein size and the size exclusion ( void ) of the column . This set of proteins markers contained the following: blue dextran ( >2MDa ) , thyroglobulin ( 669KDa ) , ferritin ( 440KDa ) , and catalase ( 232KDa ) . Proteins were loaded and run on 4–12% Bis-Tris gels with MES running buffer ( Life Technologies ) . After transfer onto 0 . 2 µm-pore PVDF ( BioRad ) or nitrocellulose ( Whatman ) membranes , membranes were blocked with 5% milk/TBST for 30 min and probed with antibodies diluted in 5% BSA/TBST overnight at 4°C . Immunodetection was achieved after incubation with HRP-conjugated ( Invitrogen ) goat anti-mouse or goat anti-rabbit diluted 1∶5 , 000 in blocking solution . HRP signal was detected by ECL ( Amersham-GE ) and autoradiography film . The analysis of functional gene annotations was performed using the Database for Annotation , Visualization , and Integrated Discovery ( DAVID ) 2 . 1 website [91] . We queried functional annotations using official gene symbols . We selected genes for analysis based on three different criteria: genes in which we detected NRF1 or NFYB peaks at their −150/+50 bp regions relative to TSS based on ChIP-seq data; genes in which we detected NRF1 or NFYB binding at their −800/+200 bp regions ( p<0 . 0001 ) based on ChIP-DSL data; and genes affected by different siRNA treatments ( specified in figure panels ) based on gene expression microarrays . A representative selection of the most enriched functional terms and the p-value of their enrichment were extracted for visualization using Excel . Networks of annotated biological functions associated with LSD1 targets were constructed with open source Cytoscape version 2 . 8 . 3 [92] , and the following plugins: MiMI , which integrates data from multiple databases [93]; MCODE , which finds highly interconnected regions in a ( sub ) network [94]; NetworkAnalyzer , which computes and displays networks [95]; and Random Network , which generates random networks and compare them to existing networks . We searched the following databases: BIND , CCCB , DIP , GRID , HPRD , IntAct , KEGG , MDC , MINT , PubMed , and Reactome . For visualization , we show only interactions between query genes . List of network attributes: gene name ( closest gene to a LSD1 ChIP-seq peak ) , Entrez gene ID , absolute distance from TSS ( bases ) , peak genomic localization annotation ( promoter , intron , exon , TTS , intergenic ) , and ChIP-seq signal ( Tag counts ) . List of visualization parameters: ‘node color’ represents proximity to TSS ( green gradient , <400 bp; white , at 400 bp; red gradient , >400 bp ) ; ‘node size’ represents ChIP-seq signal intensity; ‘node shape’ represents genomic location annotation ( promoter = circle; exon = rectangle; intron = diamond; TTS = triangle; and intergenic = hexagon ) . Sub-networks were generated with MCODE and those with the highest score were selected . Network statistics were calculated using NetworkAnalyzer plugin , which provides clustering coefficients for ( sub ) networks , and Random Network plugin , which provides clustering coefficients for random ( sub ) networks . We applied paired two-tailed t-test to compare ( sub ) network with identical ( same number of nodes ) randomly generated ( sub ) networks . Non-sequencing experiments were performed in triplicate ( including ChIP-DSL experiments ) , while sequencing experiments were performed in triplicate but pooled before DNA library preparation . Data is presented as mean ± standard error of the mean ( s . e . m . ) of replicates . Comparisons between two groups were performed using paired two-tailed t-test . P-values<0 . 05 were considered statistically significant . Additional information about statistical analyses is provided in independent subsection ( see above ) . | Human cells exploit different mechanisms to coordinate the expression of both protein-coding and non-coding RNAs . Elucidating these mechanisms is essential to understanding normal physiology and disease . In our attempt to identify new regulatory layers acting particularly at proximal promoters , we have computationally analyzed the genomic sequences located from −150 bp to +50 bp relative to the transcriptional start site ( TSS ) , which are often at the center of ‘open’ chromatin regions in human promoters . We have confirmed the presence of a series of cis-regulatory elements ( here referred to as ‘cardinal’ motifs ) that show a strong preference for these short regions . Interestingly , these elements tend to act independently rather than in fixed combinations . Therefore , we propose that they confer unique regulatory features to the human promoter subsets that contain each of these particular elements . In agreement with this model , we have identified a large repertoire of preferential partnerships between transcription factors recognizing cardinal motifs and their associated proteins ( cofactors ) , thus decoding a signature-based model that distinguishes distinctive regulatory types of promoters based on cardinal motifs . These signatures may underlie a new layer of transcriptional regulation to orchestrate coordinated gene expression in human promoters . | [
"Abstract",
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"Methods"
] | [] | 2013 | Decoding a Signature-Based Model of Transcription Cofactor Recruitment Dictated by Cardinal Cis-Regulatory Elements in Proximal Promoter Regions |
Trypanosoma cruzi is the causal agent of Chagas Disease . Recently , the genomes of representative strains from two major evolutionary lineages were sequenced , allowing the construction of a detailed genetic diversity map for this important parasite . However this map is focused on coding regions of the genome , leaving a vast space of regulatory regions uncharacterized in terms of their evolutionary conservation and/or divergence . Using data from the hybrid CL Brener and Sylvio X10 genomes ( from the TcVI and TcI Discrete Typing Units , respectively ) , we identified intergenic regions that share a common evolutionary ancestry , and are present in both CL Brener haplotypes ( TcII-like and TcIII-like ) and in the TcI genome; as well as intergenic regions that were conserved in only two of the three genomes/haplotypes analyzed . The genetic diversity in these regions was characterized in terms of the accumulation of indels and nucleotide changes . Based on this analysis we have identified i ) a core of highly conserved intergenic regions , which remained essentially unchanged in independently evolving lineages; ii ) intergenic regions that show high diversity in spite of still retaining their corresponding upstream and downstream coding sequences; iii ) a number of defined sequence motifs that are shared by a number of unrelated intergenic regions . A fraction of indels explains the diversification of some intergenic regions by the expansion/contraction of microsatellite-like repeats .
Understanding the functional significance of noncoding DNA is one of the major challenges of current genomics research . Although a number of early studies suggested that noncoding DNA evolve largely free from selective constraints , recent genome-wide comparative studies in higher eukaryotes showed that some noncoding DNA , particularly introns , are subjected to significant evolutionary pressures [1]–[3] . Kinetoplastid protozoa's genes are essentially introns-free and predominantly arranged in long constitutively transcribed polycistronic units [4] , [5] that are subsequently processed to produce mature monocistronic mRNAs by coupled reactions of trans-splicing of a small 39 nt spliced leader ( SL ) sequence to the 5′- end , and polyadenylation of the 3′ end [6] . Therefore , the intergenic region contains intron-like features such as polypyrimidine tracts and ( trans- ) splicing acceptor sites that drive the maturation of mRNAs . Evenmore , noticeable regulatory DNA sequence motifs are absent as well , suggesting a major role of post-transcriptional mechanisms in the regulation of mRNA abundance [7] . Trans-acting factors ( RNA-binding proteins and other cofactors ) selectively destabilize mRNAs [5] and regulate their abundance , by binding to structural RNA motifs in untranslated regions [8] , [9] . In other organisms , it has also been shown that functionally related mRNAs are co-expressed by the action of RNA-binding proteins [10] . In trypanosomes , gene expression could be explained by post-transcriptional RNA regulons , composed of sets of mRNAs sharing regulatory motifs [11]–[15] . Alternative RNA processing sites also suggest the existence of other dynamic mechanisms affecting the presence of regulatory elements in transcripts [16] , [17] . Given their importance in the regulation of gene expression in trypanosomes , we were interested in analyzing the genetic diversity and apparent selection in intergenic and untranslated regions of T . cruzi . Previous studies have described their composition and sequence features [18] , [19] , but none have focused on their diversity or degree of conservation . Recently , we and others have analyzed a number of complete T . cruzi genomes and characterized the genetic diversity of their protein coding regions [20] , [21] . However , an important part of the genome was left uncharacterized in terms of their diversity and the apparent selective pressure affecting these noncoding regions . T . cruzi , the aetiological agent of Chagas disease , is a vector-borne infection with a high prevalence in Central and South America [22]–[24] . Its reproduction is mostly clonal , with almost no genetic recombination [25] , [26] . During its evolutionary history a highly structured population , currently composed of six major lineages , or Discrete Typing Units ( DTUs ) TcI to TcVI [27]–[29] , was produced . Despite this predominant clonality , there is evidence of composite ( TcIII and TcIV ) and hybrid ( TcV and TCVI ) genomes [30]–[33] . Hybrid lineages have alleles from ancestral TcII and TcIII haplotypes , whereas the ancestry of composite lineages is not yet fully understood . The remarkable genetic heterogeneity of these divergent evolving groups could partially account for their wide range of biological features , eco-epidemiological traits [34] , [35] , and the large spectrum of clinical manifestations of Chagas disease [36] , [37] . During its complex life cycle , alternating between an insect and a mammalian vertebrate host , T . cruzi must quickly adapt to diverse environments , while undergoing dramatic morphological and functional changes [38] . This feat is dependent on drastic changes on gene expression [39] , [40] . The genome sequence of the T . cruzi hybrid CL Brener ( TcVI ) strain [41] evinced two divergent haplotypes ( TcII-like and TcIII-like ) . Each haploid genome was comprised of Mb that harbors protein-coding genes located in 40 chromosomes , with at least 30–50% of repetitive sequences [42] , [43] . Recently , the non-hybrid Sylvio X10 strain ( TcI ) was sequenced and it revealed a conserved core of genes and a significant reduction of large gene families , which explains the smaller genome size ( 44 Mb ) [20] . This comparative genome analysis was focused on protein coding regions , but no further inquiry was done on intergenic and/or putative untranslated regions . In this paper , we have extended our analysis of genetic diversity to intergenic regions ( IGRs ) in order to provide a detailed comparative characterization of allelic variants of extant genomes . As a result we found a core of highly conserved IGRs , a considerable number of divergent IGRs , and a number of IGR segments of variable length that are shared between unrelated genomic loci .
We retrieved the analyzed sequence data from TriTrypDB ( http://tritrypdb . org/tritrypdb/ , version 4 . 0 ) , belonging to homologous chromosome-sized scaffolds [44] from the hybrid CL Brener ( TcII-like and TcIII-like haplotypes ) and to contigs from the non-hybrid Sylvio X10 ( TcI ) T . cruzi strains . We defined the IGR unit , which was the minimum comparable genomic region in our analysis , as the sequence between two consecutive annotated coding sequences ( CDSs , see Figure 1 ) . Coding sequences upstream and downstream of a given IGR must be both colinear and orthologous in the hybrid CL Brener ( TcII-like and TcIII-like alleles ) and non-hybrid Sylvio X10 ( TcI ) genomes . These non-coding IGR regions were extracted according to the coordinates of its adjacent consecutive CDSs , first from from CL Brener contigs and subsequently searched in the Sylvio X10 genome using BLAT [45] . Best reciprocal hits between homologous regions were selected if sequences were % identical . Sequences producing multiple-hits CDSs and pseudogenes were removed in order to obtain pairs of syntenic orthologous single-copy genes where the shared ancestry of IGR blocks could be safely assume . However , it is possible that near-identical copies of the same gene could have collapsed during genome assembly , therefore passing this filter . The final curation of our dataset was achieved by filtering IGRs that match the following criteria: i ) absence of ambiguous DNA bases; ii ) absence of long gaps at the end of aligned flanking CDSs ( presence of such gaps may suggest an inaccurate or incomplete annotation of the CDS translational start , and may induce us to include unnanotated parts of a CDS within the IGR ) ; and iii ) presence of corresponding start and stop codons at the ends of all CDS . A summary of the data analyzed is shown in Table S1 . This constitutes the complete dataset of coding and non-coding DNA that was examined . All downstream analyses were performed by a series of custom scripts written in Perl and R languages . We determined the nucleotide composition and length for each IGR unit in all haplotypes using standard bioinformatics tools . Sequences were aligned using different algorithms for coding ( CDS ) or non-coding ( IGR ) regions , as described below . Alignment of coding sequences was done using MUSCLE [46] , [47] . However , to align IGR regions , which contain more gaps and polymorphims , we used the MCALIGN algorithm [48] , [49] , that explicitly models indels based on prior information . To generate this prior information , we first aligned IGR regions using SIGMA [50] , [51] , which is designed specifically for noncoding DNA . From these data we determined the indel length frequency distribution , which was used as input to MCALIGN to obtain the final alignments of IGRs . Polymorphisms were labeled as transitions/transversions , and for each IGR we calculated the number of single nucleotide polymorphisms ( SNPs ) per site as a measure of sequence divergence , using the total length of the alignment as the number of sites . In the case of indels , for each IGR we produced a list of indels of different sizes and report the number of indels per site as a measure of sequence divergence . To detect blocks of sequence similarity among unrelated ( non-orthologous ) IGRs , we performed all-vs-all BLASTN searches , using a local database of IGR regions extracted from all haplotypes . Using a custom Perl script we skipped orthologous IGRs in the list of BLASTN hits , and then manually analyzed the remaining hits to find significant alignments against non-orthologous IGRs .
Using the selection strategy described in Methods ( Figure 1 ) , we were able to retrieve 4 , 312 , 4 , 642 and 2 , 644 IGRs that were flanked by 5 , 284 , 5 , 637 and 3 , 886 CDSs , belonging to TcII-like , TcIII-like and TcI , respectively . These genomic regions comprise approximately 43% of the haploid megabase-sized scaffolds of the CL Brener genome , 29 . 3% of the contigs of the Sylvio X10 genome , and correspond to % of the annotated protein-coding genes in the reference CL Brener genome . This coverage is in agreement with current estimations of the repetitive content of the T . cruzi genome , estimated to be 30–50% . Another additional factor that could be responsible for filtering out bona fide IGRs is the quality of the assembly of current draft genomes . Notwithstanding these caveats , the fraction of the genome analyzed corresponds to a “core” of genes and their associated IGRs which have been maintained together over the evolution of the species . All this information is available as Supplementary Material ( Table S3 ) . After identification of these IGRs in a 3-way comparison between the two CL Brener haplotypes and the Sylvio X10 contigs , we grouped these regions according to their shared ancestry , and discarded problematic IGRs ( see below ) . As depicted in the Venn diagram in Figure 2 , we identified a total of 6 , 036 IGRs from the 3 haplotypes analyzed . From these , only 1 , 719 were strictly shared between the three haplotypes . In agreement with previous comparative analyzes of TcI vs CL Brener sequences [20] , [33] , [59] , IGRs from the TcI genome were most often conserved when compared with the TcIII-like haplotype of CL Brener . Interestingly , the TcII-like and TcIII-like haplotypes shared 2 , 935 IGRs , which is close to the 2 , 601 IGRs shared by TcI and TcIII-like haplotypes . For some of the characterizations reported below , we also discarded a number of IGRs from the analysis . Problematic IGRs were those that contained stretches of Ns introduced by the sequencing center/author ( indicating gaps in the assembly ) . As such , they break our requirement of strict colinearity ( Figure 1 ) and were not considered further . These can be observed in the Venn diagram as sets of IGRs that are apparently unique to one genome/lineage . In the case of the Sylvio X10 genome ( which is not scaffolded ) , there were only 17 IGRs with gaps in the assembly , whereas this figure raises to and IGRs in the case of the TcII-like and TcIII-like haplotypes of CL Brener , respectively . Although these numbers may seem large , in comparison with the Sylvio X10 genome , they do not reflect an intrinsic diversity of IGR regions between these haplotypes but rather the high degree of scaffolding of the reference CL Brener genome[44] ( joining two contigs together in a scaffold required the introduction of Ns to indicate an assembly gap ) . Based on this observation we decided to perform our analysis of genetic diversity on the set of 3 , 843 “good” IGRs ( those present at the intersections of the Venn diagram ) . These represent a core set of IGRs which were maintained together as a block with their corresponding flanking coding sequences , over large periods of time . The method described in the previous section for the identification of orthologous IGR regions allowed us to obtain groups of IGRs with shared ancestry between T . cruzi lineages . However , in spite of being maintained together over large evolutionary spans , further characterization of the genetic diversity present in these 1 , 719 IGRs showed both highly conserved IGRs , with little or no accumulation of indels and/or SNPs , and highly divergent IGRs . In this section , we describe an analysis of this diversity , decomposed into i ) length differences ( indels ) and ii ) nucleotide differences ( SNPs ) . Because IGR regions contain a number of essential regulatory regions , this diversity could have important functional implications . In the case of polypyrimidine ( PPY ) tracts ( which are essential for trans-splicing in trypanosomes[60]–[62] ) , it has been demonstrated that both the base composition [63] , and the length [64] of a PPY tract are important for its efficiency . Therefore changes affecting the length , composition and/or sequence of PPY tracts in allelic IGRs may be affecting the location and/or efficiency of polyadenylation and trans-splicing of the flanking genes . The conservation of the length of IGR and CDS sequences , which serves as a measure of accumulation of indels , were broadly comparable in all T . cruzi haplotypes . As shown in Figure 3 , apart from a few outliers , there was a good conservation of the length of IGRs across genomes/haplotypes . Again , we found a higher correlation between TcI and TcIII-like haplotypes in both coding and non-coding genomic regions ( : 0 . 8957 , ( : 0 . 997 , see Supplementary File S1 ) , followed by the remaining pairs , with lower correlation coefficients ( TcI vs TcII-like , : 0 . 8456; TcII-like vs TcIII-like , : 0 . 8729 ) . In all these cases the same trend is observed in CDS regions but of course with higher correlation coefficient values ( Figure S1 ) . The base composition of these IGRs revealed a marked T bias ( 34% ) , and a significant underrepresentation of C ( 17 . 5% ) , when compared with coding sequences ( 24 . 1% of T and the 23 . 4% of C ) ( Figure 4 ) . These two bases are essential components of regulatory motifs within IGRs ( PPY tracts ) . In the case of short IGRs , this functional constraint alone may explain the compositional bias of IGRs . Also , the lower proportion of GC in IGR regions ( 40% ) when compared to CDS regions ( 51% ) could be a possible consequence of mutational and selective forces that could be favouring AT-rich regulatory motifs [65]–[67] and establish a stable molecular structure that might modulate the transcription of genes [11] and enhance the affinity for RNA-binding proteins [68]–[70] . In order to relate compositional and length features , we also surveyed the occurrence of non-overlapping homopolymer tracts in both CDS and IGR regions . We detected a higher frequency of each type of polynucleotide ( dA , dC , dG and dT ) in non-coding DNA , in strong contrast with coding DNA ( Figure 4 ) . Although the virtual absence of long homopolymeric tracts could be expected for coding regions , it was intriguing to see that IGR regions have a marked preference for dA and dT . This preference could have functional consequences either in the transcriptional and/or post-transcriptional regulation of gene expression . If these homopolymeric tracts are present in a mature transcript , they could bind regulatory RNA binding proteins , as described previously for T . cruzi [66] , [68] . However , these tracts could also regulate the expression of genes by producing long nucleosome-free regions ( LNFRs ) . In a genome-wide analysis of nucleosome occupancy and nucleosome DNA sequence preference in yeast , Kaplan et al . showed that AAAAA and ATATA are the most prominent patterns in LNFRs [71] , and more generally , that poly ( dA-dT ) are good LNFR indicators , in agreement with previous observations [72]–[76] . Although in trypanosomes there are apparently no promoters regulating transcription initiation by RNA polymerases , it has been shown that there could be regulation of transcription through nucleosome and/or chromatin remodeling [77] , [78] . The previous comparative analysis of Sylvio X10/1 and CL Brener genomes [20] showed that the highly conserved “core” of coding regions had a lower mean nucleotide diversity between TcI and TcIII-like than TcI and TcII-like haplotypes . In our study we further expanded this analysis to non-coding IGRs . We found less nucleotide differences between TcI and TcIII-like haplotypes in both regions ( : , : ) than those observed between TcI and TcII-like haplotypes ( : 0 , 022 , : 0 , 052 ) or between TcII-like and TcIII-like haplotypes ( : , : ) ( data in Supplementary Figure S2 ) . We also performed additional nonparametric statistical tests which evinced significant differences among possible pairwise comparisons in both IGRs and CDSs regions . The Kruskal-Wallis test ( , a posteriori test ) revealed disparities between the medians of these groups , while the Mann-Whitney U test ( 2-tailed , ) enabled testing significant differences of unique specific pairs of data . These results confirmed the greater genetic diversity between TcI and TcII-like haplotypes in a curated and representative sample of 1 , 719 IGRs , and 2 , 648 CDS . This finding is in agreement with the current proposed evolutionary relationships among DTUs [30] , [31] , [33] , [59] and the longer estimated divergence time between TcI and TcII haplotypes . To visualize the dynamics of constraints on genetic diversity along large genomic regions , we next dissected the diversity into 3 main components ( compositional bias , sequence divergence ( SNPs , fixed changes ) , and indels ) , and observed their fluctuations over relatively large spans . For this analysis we selected a couple of genomic regions that had blocks of IGRs and CDSs that meet the criteria of Figure 1 of common evolutionary ancestry in the three haplotypes . The analysis shows fluctuations in the 3 components analyzed ( see Figures 5 , and S3 ) , which follow the expected pattern based on CDS vs IGR comparison . However other trends are also apparent . Although the fluctuation of thymidine composition is large when comparing CDS vs IGR regions , it does not change significantly across IGR regions . In contrast , indels and sequence divergence ( i . e . SNPs ) show more variability across IGRs , with some IGRs accumulating more changes than others . This analysis also showed that the measured components ( SNPs , %T and %indels ) peak abruptly within IGRs . However , when averaged across all IGRs they do not show a particular bias towards the 5′ end of the downstream CDS , the 3′ end of the upstream CDS , or the middle of the IGR ( see Figures 5 and S3 ) ( Kruskal-Wallis test , a posteriori test ) . Despite the strict requirements for allelic and orthologous genomic regions in our analysis ( see Figure 1 ) , we were able to identify significant diversity in these IGRs . The plot in Figure 6 shows the genetic diversity , decomposed in i ) number of indels , measured as the ocurrence of indel events per total available sites , and ii ) sequence divergence ( SNPs ) , which essentially measures the proportion of base changes . Average values show , as expected , a higher accumulation of changes in IGR regions , with fold higher sequence divergence ( SNPs per site , 0 . 051 vs 0 . 0196 in IGR vs CDS regions , respectively ) and fold higher number of indels ( 0 . 0096 vs 0 . 0003 in IGR vs CDS regions , respectively ) . As can be observed in Figure 6 , within the shared 1 , 719 IGRs there is a core of highly conserved IGRs showing medium to low values of indels and SNPs per site . We defined them as placed within the 5th and 95th percentiles ( see shaded rectangle in Figure 6 ) of both distributions ( number of indels total sites , and sequence divergence ) . These IGRs constitute 90 . 6% of all IGRs . Outside this core , we found pairs of orthologous IGRs which are deviating either through accumulation of indels or SNPs , but rarely both . We only detected 0 . 7% of IGRs above the 95th percentile of both distributions ( indels and SNPs per site ) . Whereas 4 . 3% of the IGRs display a higher proportion of either indels or SNPs per site . Indels are usually caused by polymerase slippage due to mispairing of a template strand during DNA replication and/or repair . Because IGRs accumulate more and longer homopolymer tracts ( see Figure 4B ) and/or are enriched in low-complexity nucleotide tracts , which are known to be prone to polymerase slippage [79] . However there are other mechanisms that could generate relatively large indels between orthologous IGRs , such as the insertion of a large transposon-like element , the expansion of microsatellite-like repeats , or large deletions . We next analyzed in more detail the types of indel accumulation , that contribute to the diversity between IGRs . To assess this , we searched a database of repetitive elements ( RepBase , see Methods ) , and also looked for microsatellite-like repeats within IGRs . As can be observed ( see Figure 7 ) , most of the cases cannot be explained by these processes and are probably caused by other types of indel mutations . Within a set of 721 IGR regions ( those with a size difference bp between alleles ) , only in 55 IGRs the main cause of length difference was the insertion of a transposon-like repetitive element ( SIRE/Viper/LINE1 ) . And only in 14 of these the transposon-like element was essentially complete ( % of the element was contained within the IGR ) . Similarly , only in 97 cases the expansion/contraction of microsatellite-like repeats could be invoked as the cause of the length difference between IGRs . One interesting case is a putative protein kinase ( TcCLB . 510565 . 70 ) that has a complete LTR/VIPER element in the IGR of the TcII-like haplotype , which is absent from the TcIII-like haplotype . Further inspection of other genomic data reveals that this element is apparently missing from TcI lineages ( neither the SylvioX10 , nor the JRcl4 IGR have this element ) , but present in the TcII lineage ( Esmeraldo cl3 ) . This suggests that the insertion pre-dated the hybridization event leading to the current extant TcVI lineage . A complete list of these sort of insertions is provided in Table S4 . We also observed large size differences that could not be attributed to transposons . The IGR downstream of an acidocalcisomal exopolyphosphatase ( TcCLB . 511577 . 110 ) is large ( 2 . 2 Kbp ) in the TcI ( Sylvio X10 ) and TcIII-like haplotypes , but shorter ( only 600 bp ) in the TcII-like and TcII ( Esmeraldo cl3 ) haplotypes ( See Figure S4 ) . Since there are no repetitive elements within this IGR , it is likely that a single large deletion event took place , while preserving the 3′ UTR of the exopolyphosphatase and a short stretch of the 5′ UTR of the downstream hypothetical protein . Finally , an example of a large size difference that could be driven by expansion/contraction of microsatellite-like repeats is the IGR downstream of a putative sphingosine kinase ( TcCLB . 508211 . 30/TcCLB . 507515 . 120 ) . Figure S5 shows an alignment of this IGR , which displays a number of ( CATA/TATG ) n , ( TA ) n , ( TAA/TTA ) n repeats , along with other imperfect repeats , and homopolymers ( mostly ( T ) n ) . In this case the contraction/expansion of these repeats could have worked together with an ancestral indel event in shaping the current form of this IGR in the TcII-like lineage . It is tempting to speculate that in any of these cases the observed variation between IGRs could differentially affect the regulation of upstream and/or downstream transcripts in one or more lineages . This is because in trypanosomes , the co-transcriptional processing of polycistronic transcripts , display a tight coupling of polyadenylation of the upstream CDS and trans-splicing of the downstream CDS . A large insertion or deletion could therefore alter this coupling or effectively decouple these processes , potentially altering the expression pattern of one or both flanking CDSs . Unfortunately there are currently no genome-wide studies of gene expression for different lineages or under different conditions that could be compared . While analyzing intergenic regions we noticed a number of such regions that produced a higher than expected number of BLASTN hits when doing an all vs all comparison . These additional BLAST hits were produced by the presence of blocks of significant sequence similarity between non-allelic regions . Based on this BLAST analysis , we were able to identify 21 elements of defined sequence , which were shared by 2–4 different IGRs . These sequence elements are embedded within larger IGR regions , but the sequence similarity shared by the non-allelic IGRs in each group is limited to a portion of the IGR , and there is no other sequence similarity between the flanking coding sequences . Information about these IGRs is provided as supplementary information ( Dataset S1 ) , including the sequences themselves , as well as multiple sequence alignments and a schematic figure showing the relative location of each element . The mean length of these blocks was 427 bp with an average identity of 90 . 71% . The relative location of these sequence elements within each group of IGRs varies . In some cases the shared element is at the 5′ end of the IGRs ( e . g . case #12 ) , or at a similar short distance to the 5′ end ( case #13 ) . In these cases , one could assume that these shared regions could be part of the corresponding 3′-UTRs of the upstream genes . In other cases ( case #10 ) , the shared region appears closer to the 3′ end of the IGR ( e . g . could form part of the 5′-UTR of the downstream gene ) . Whereas in other cases the shared element is located either closer to the 3′ or 5′ end in different IGRs . To further address the functional relevance of these conserved blocks of sequence we first considered the possibility that they were non-coding RNAs , or small ORFs ( sORFs ) . However , a search of the Rfam database revealed only one case assignable to ncRNA in these IGRs ( IGR 1869 , Case 4 , Dataset S1 ) . This IGR contains a Gln tRNA but in a region that is not part of the block of similarity shared with other IGRs . We also failed to reveal similarity between these conserved stretches and recently described ncRNAs in T . cruzi [80] . And a BLASTP search against a list of validated sORFs from yeast [81] , [82] revealed only partial matches of dubious significance . Finally , to see if these elements were transcribed in T . cruzi we took advantage of the recent availability of RNAseq ( transcriptomic ) data from the TcAdriana strain ( TcI , obtained by 454 sequencing of an epimastigote cDNA library , Westeergard G and Vazquez MP , unpublished [83] ) . After mapping reads to these IGRs , we were able to confirm that nearly all of them ( the exception is case #19 ) showed evidence of expression at different levels . A careful analysis of the mapped reads ( looking for specific SNPs and indels ) revealed that all the IGRs in these groups were transcribed . We further examined these reads to look for evidence of trans-splicing , to see if these could be independent , short transcripts , or if , alternatively , they were part of the UTRs of the upstream genes . However , we did not find any matches to the T . cruzi miniexon ( spliced leader ) sequence in these mapped reads . Therefore , the most plausible explanation is that the transcript that include these shared IGR blocks originate from the 3′UTR of the upstream CDSs ( or are part of dicistronic transcripts [84] ) . To explore the diversity of IGR lengths , and to validate the observed IGR sizes , we selected a number of interesting cases and performed PCR experiments followed by gel electrophoresis . This analysis was done using a complete panel of strains representative of all major lineages/DTUs . As can be seen in Figure 8 , except for the lack of amplification of long PCR products , the amplification produced the expected results in all cases . The IGR in case 1 , which was selected based on its size conservation in analyzed genomes , also showed no apparent size differences in other examined strains . In cases 2–6 , there was a unique IGR length associated with one DTU/lineage in our bioinformatics analysis ( see Table S2 ) . In case 2 , the unique IGR length observed in Sylvio X10 ( TcI ) was validated in 2 other strains from the same DTU . In this case , the IGR length observed for the TcII-like and TcIII-like alleles in our bioinformatics analysis is the same observed in all other strains/DTUs analyzed . In case 3 , we validated the predicted IGR size , and discovered that all strains analyzed from the TcIV DTU carried a longer IGR region . In case 4 strains from the TcI , TcII and TcIV DTUs showed a similar length that was different from TcIII , whereas the hybrid lineages showed two bands with sizes that agree with the proposed ancestral hybridization of TcII and TcIII parental DTUs . Finally , the fifth and sixth cases correspond to two cases ( IGRs 333 and 2320 ) both of which were already mentioned in the text . IGR 333 corresponds to a small IGR in all lineages , except in the TcIII-like haplotype of CL Brener , where there is a 4 . 4 Kbp insertion of an LTR/Viper element . Failure of amplification in TcIII strains is expected given the large size of the PCR product and the thermal cycling protocol employed . Similarly , IGR 2320 displayed a large deletion in TcI and in the TcIII-like haplotype ( see Fig S5 ) , which was validated in other TcI and TcIII strains . Failure to amplify this IGR in TcII strains is consistent with the prediction of a large IGR size . In this validation study the failure to also amplify this IGR from TcIV strains suggests that this IGR is also large in these strains , or that a chromosomal rearrangement has occurred , which rewired the corresponding upstream and downstream coding sequences . These results show that the IGR sizes observed in silico could be validated experimentally , and in many independent strains from each DTU . Overall we have observed a high degree of IGR size conservation within DTUs , at least in these selected cases . It is important to observe that in all cases the TcII and TcIII strains produced amplification bands of the expected size based on the analysis of the CL Brener ( TcVI ) genome . This further validates the idea that by analyzing an hybrid genome we can get an insight into the genetic diversity of IGR ( this work ) and CDS regions [21] of T . cruzi . Finally , the expanded strain panel used allowed us to also inspect the size conservation in other DTUs ( TcIII , TcIV ) , for which there is currently no genome sequence available . The genome of T . cruzi is highly repetitive [41] , [42] , [85] as well as complex , due to the chromosomal rearrangements observed in different strains , and the hybrid nature of strains from lineages TcV and TcVI [86] , [87] . However , both recent work focused on coding sequences [20] , [21] , as well as this work focused on noncoding IGRs show that in spite of this complexity there is a highly conserved core of genes that have maintained their IGRs , with a significant fraction of them under apparent purifying selection , as suggested by the paucity of accumulation of mutations and/or indels . At least in the subset of genes analyzed , it is clear that in those cases where IGRs differ between the analyzed genomes , indels outnumber other types of mutations ( SNPs/base changes due to polymerase read errors or DNA repair and insertions of transposable elements ) . Although mostly focused on single-copy genes/IGRs , this work represents the first global analysis of genetic diversity of T . cruzi non-coding DNA . | Chagas disease is caused by the protozoan parasite Trypanosoma cruzi , and poses a serious public health problem in the America , with approximately 8 million people infected and 200 , 000 new cases reported annually . The disease has different clinical manifestations . The fact that infections by the same species cause different clinical outcomes is believed to be determined , at least in part , by the genetic background of the parasite ( infection by different strains ) . Previous characterizations of the genetic diversity in Trypanosoma cruzi were carried out on the protein-coding portions of the genome . However , the genetic diversity of non-coding intergenic regions remained unexplored . These regions are particularly important in trypanosomes because they contain essential regulatory sequences that drive the process of mRNA maturation and that ultimately govern the expression of genes . In this study , we analyzed the genetic diversity present in non-coding regions of the genome , and provide a broad picture of the selective forces acting on this subset of the genome . Based on this analysis we identified a highly conserved core of intergenic regions , that were maintained essentially unchanged over large evolutionary periods of time , as well as a highly divergent set of intergenic regions . | [
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] | 2014 | A Genome-Wide Analysis of Genetic Diversity in Trypanosoma cruzi Intergenic Regions |
The subcellular localization of the epidermal growth factor receptor ( EGFR ) in polarized epithelial cells profoundly affects the activity of the intracellular signaling pathways activated after EGF ligand binding . Therefore , changes in EGFR localization and signaling are implicated in various human diseases , including different types of cancer . We have performed the first in vivo EGFR localization screen in an animal model by observing the expression of the EGFR ortholog LET-23 in the vulval epithelium of live C . elegans larvae . After systematically testing all genes known to produce an aberrant vulval phenotype , we have identified 81 genes regulating various aspects of EGFR localization and expression . In particular , we have found that ERM-1 , the sole C . elegans Ezrin/Radixin/Moesin homolog , regulates EGFR localization and signaling in the vulval cells . ERM-1 interacts with the EGFR at the basolateral plasma membrane in a complex distinct from the previously identified LIN-2/LIN-7/LIN-10 receptor localization complex . We propose that ERM-1 binds to and sequesters basolateral LET-23 EGFR in an actin-rich inactive membrane compartment to restrict receptor mobility and signaling . In this manner , ERM-1 prevents the immediate activation of the entire pool of LET-23 EGFR and permits the generation of a long-lasting inductive signal . The regulation of receptor localization thus serves to fine-tune the temporal activation of intracellular signaling pathways .
The formation of epithelial tissues involves the polarized distribution of growth factor receptors that determine cell proliferation and differentiation . Notably , changes in EGFR localization have a major impact on signaling and organogenesis [1]–[3] . In C . elegans , the let-23 gene encodes the sole member of the EGFR/ErbB family of receptor tyrosine kinases . let-23 is involved in a variety of developmental processes including the induction of the hermaphrodite vulva [4] . In early second stage ( L2 ) larvae , LET-23 is expressed at equal levels in the six equivalent vulval precursor cells ( VPCs ) ( P3 . p through P8 . p ) ( Figure 1A ) [5] , [6] . Beginning in the L2 stage , the gonadal anchor cell ( AC ) secretes the EGF ortholog LIN-3 , which binds to LET-23 on the basolateral plasma membrane of the VPCs to activate the LET-60 RAS/MPK-1 MAPK signaling pathway [4] ( Figure 1B ) . In order to reach high levels of receptor activity , LET-23 must be retained on the basolateral membrane of the VPCs by a ternary protein complex consisting of the PDZ-domain proteins LIN-2 CASK , LIN-10 MINT and LIN-7 VELIS . LIN-7 directly binds to the C-terminal PDZ binding motif of LET-23 [5] . The VPC that is nearest to the AC , P6 . p , receives most of the inductive LIN-3 signal and hence adopts the primary ( 1° ) cell fate . P6 . p then produces several DELTA ligands , which induce via the NOTCH pathway the secondary ( 2° ) cell fate in the neighboring VPCs P5 . p and P7 . p [7] , [8] ( Figure 1B ) . NOTCH signaling blocks RAS/MAPK signaling and results in the endocytosis and degradation of LET-23 in the 2° VPCs [9]–[11] . The distal VPCs P3 . p , P4 . p and P8 . p , which receive only little inductive signal , down-regulate LET-23 expression and adopt the tertiary ( 3° ) , uninduced cell fate . As the pathway components are conserved , the study of vulval induction of the worm can be used to find new core components or those required for fine-tune the signaling output . For this purpose , we performed the first systematic in vivo screen for regulators of LET-23 EGFR localization and expression in live C . elegans larvae . Through this approach , we have identified 81 genes causing a variety of LET-23::GFP mislocalization phenotypes upon RNAi knock-down . A subset of these genes also controls the strength of the LET-23 EGFR signal produced in the VPCs . We have identified ERM-1 , the homologue of mammalian Ezrin , Radixin and Moesin proteins , as a temporal regulator of LET-23 EGFR signaling . Based on our genetic and biochemical data , we propose that ERM-1 binds to and sequesters the LET-23 EGFR in an inactive compartment at or close to the basolateral membrane of the VPCs . In this manner , ERM-1 competes with the activating LET-23/LIN-2/LIN-7/LIN-10 complex [5] . ERM-1 may act as a buffer that prevents the immediate activation of the entire pool of basolateral LET-23 EGFR at vulval induction and thus allows the generation of a prolonged signal .
We performed RNAi knock-down of all genes ( 705 clones ) reported to exhibit a protruding vulva ( Pvl ) phenotype , which is indicative of a defect in vulval fate specification or execution ( Table S1 ) and examined LET-23 localization and expression in the vulval epithelium of live L3 larvae expressing a functional LET-23::GFP reporter ( Figure 1C–E ) . The LET-23::GFP reporter used showed the same vulval expression pattern as endogenous LET-23 detected by antibody staining [5] , and LET-23::GFP protein levels in total extracts were comparable to endogenous LET-23 levels ( Figure S1A ) . Moreover , let-23::gfp efficiently rescued the let-23 ( sy1 ) vulvaless ( Vul ) phenotype ( Figure S1B ) , and RNAi against lin-7 or a mutation in lin-2 caused an apical mislocalization of LET-23::GFP , as shown previously for endogenous LET-23 by antibody staining [5] ( Figure 1I , and Figure S1D ) . In total , we identified 81 candidates that change different aspects of LET-23::GFP expression or localization ( Table 1 ) . We further classified these genes according to the specific mislocalization phenotypes observed ( Figure 1F ) : Apical enrichment ( 24 genes , Figure 1I , J ) , accumulation in intracellular punctae or uniform cytoplasmic distribution ( 23 genes , Figure 1M and Figure S2 , C , D ) , persisting expression in the 2° cells ( 31 genes , Figure 1K and Figure S2E ) , enrichment on the lateral membrane ( 2 genes , Figure S2F ) and complex mislocalization phenotypes ( 10 genes , Figure 1L ) . Grouping the 81 genes into Clusters of Orthologous Groups ( KOGs ) indicated that a variety of processes are involved in regulating LET-23 localization ( Figure 1G ) [12] . In particular , genes involved in transcription , intracellular trafficking , signal transduction and protein stability and posttranslational modification were slightly overrepresented , while genes involved in chromatin modification , DNA replication and repair were underrepresented when compared to the distribution of the KOGs among the genes causing a Pvl phenotype that were screened . For a subset of the candidates with predicted roles in signaling or trafficking , we examined whether inhibition of these genes altered the activity of the RAS/MAPK pathway . This was tested by performing RNAi in the sensitized let-60 ras ( n1046 ) gain-of-function background in which more than 3 VPCs are induced [13] and scoring the average number of induced VPCs per animal ( Figure 1Q ) . It should be noted that the VPCs in the let-60 ras ( n1046 ) gain-of-function background are still sensitive to the AC signal [13] . In those cases where RNAi caused a penetrant embryonic or larval lethal phenotype , we performed Pn . p cell-specific RNAi using an rde-1 ( ne219lf ) ; let-60 ( n1046gf ) RNAi resistant background expressing rde-1 ( wt ) from the Pn . p cell-specific lin-31 promoter [14] . For example , RNAi against sft-4 or against the small GTPase aex-6 caused persistent LET-23::GFP expression in 2° VPCs ( Figure 1K and Table 1 ) . Moreover , Pn . p cell-specific sft-4 RNAi significantly enhanced vulval induction in the let-60 ( gf ) background ( Figure 1Q ) . The yeast sft-4 homolog ERV29 encodes a SURF protein with a putative di-lysine endoplasmic reticulum ( ER ) localization signal that sorts secretory cargo proteins in the ER into COPII vesicles [15] . An sft-4::gfp translational reporter showed expression in the VPCs in perinuclear structures that resemble the ER ( Figure 1N ) . Thus , ER to Golgi transport might be involved in controlling LET-23 turnover in the VPCs . RNAi of C11H1 . 3 caused a complex mislocalization phenotype with a moderate apical enrichment , punctate LET-23::GFP accumulation at or close to the apical membrane ( Figure 1L ) and an increase in vulval induction in the let-60 ( n1046gf ) background ( Figure 1Q ) . C11H1 . 3 encodes a predicted E3 ubiquitin ligase that is expressed in the VPCs in intracellular vesicles ( Figure 1O ) . Therefore , C11H1 . 3 may control LET-23 localization and stability through ubiquitination of the receptor itself or of an associated factor . A penetrant mislocalization phenotype with punctate cytoplasmic accumulation of LET-23::GFP was observed in ego-2 RNAi treated animals ( Figure 1M ) , and a translational ego-2::gfp reporter was expressed in the cytoplasm and nuclei of all the VPCs ( Figure 1P ) . ego-2 encodes a BRO1 domain protein that is related to mammalian PTPN23 , which regulates the transport of ubiquitinated EGFR through the ESCRT III complex to the intralumenal vesicles of multivesicular bodies [16] . Interestingly , ego-2 has also been reported to regulate GLP-1 NOTCH signaling during germ cell development and embryogenesis as well as LIN-12 NOTCH signaling during somatic gonad development [17] . Therefore , ego-2 might be a general regulator of LET-23 EGFR and LIN-12/GLP-1 NOTCH via control of their endocytic transport . One of the strongest apical enrichment mislocalization phenotypes was observed in erm-1 RNAi treated animals ( Figure 1J ) , prompting us to analyze the role of ERM-1 in LET-23 localization and signaling in more detail . erm-1 encodes the sole C . elegans member of the Ezrin , Radixin and Moesin ( ERM ) protein family . ERM proteins link the cortical actin cytoskeleton and the plasma membrane and recruit transmembrane proteins to specific membrane compartment [18] . In addition , C . elegans ERM-1 is required for apical lumen morphogenesis in the intestine [19] [20] . In contrast to the apical localization observed in the intestine , an ERM-1::mCherry reporter showed basolateral and junctional localization and a partial overlap with LET-23::GFP in the VPCs and their descendants ( Figure 2A-A″ ) . Only after vulval invagination ( at the Pn . pxxx stage ) , ERM-1 relocalized to the apical , luminal plasma membrane of the vulval toroids ( data not shown ) . To confirm the RNAi phenotype , we examined LET-23::GFP expression in erm-1 ( tm677 ) null mutants . Homozygous erm-1 ( tm677 ) larvae showed decreased basolateral and increased apical membrane localization of LET-23::GFP in the VPCs and their descendants , resulting in a significantly increased ratio of apical to basolateral LET-23::GFP signal intensity when compared to heterozygous erm-1 ( tm677 ) /+ controls ( Figure 2B–D ) . The localization of the apical junction marker DLG-1::RFP [21] or the plasma membrane marker CED-10::GFP [22] were not changed , indicating that overall polarity of the VPCs was not altered in erm-1 ( tm677 ) mutants ( data not shown and Figure S3 ) . However , we detected a reduced basolateral staining of the F-actin reporter lifeAct::GFP [23] in the VPCs of erm-1 ( tm677 ) mutants , which is consistent with the role of ERM proteins as membrane linkers for cortical F-actin ( Figure 2D–F′ ) . To test whether the reduced basolateral expression of LET-23::GFP is due to decreased basolateral secretion or to an increased membrane mobility and recycling rate of LET-23 , we performed Fluorescence Recovery After Photobleaching ( FRAP ) experiments on the basal and lateral membranes of the vulval cells at the Pn . pxx stage and calculated the mobile fraction and half time of recovery ( t1/2 ) of LET-23::GFP ( Figure 2G–L ) ( see materials and methods ) . In erm-1 ( tm677 ) larvae , the total mobile fraction of LET-23::GFP was significantly higher than in heterozygous controls in both the basal and lateral compartments , while the t1/2 was not significantly changed ( Figure 2L , t1/2 = 76 sec in heterozygous erm-1 ( tm677 ) /+ vs . 81 sec in homozygous erm-1 ( tm677 ) mutants ) . Thus , erm-1 ( tm677 ) mutants exhibit an increased mobility of LET-23::GFP on the basolateral plasma membrane , rather than a decreased rate of basolateral secretion or retention . Changes in the ligand concentrations could alter the steady-state levels of LET-23 EGFR on the basolateral membrane . For example , reducing the dose of LIN-3 EGF may decrease receptor endocytosis and thus diminish the ratio of apical to basal EGFR , while increasing the dose of LIN-3 may promote receptor endocytosis on the basolateral membrane and therefore increase the apical to basal ratio . On the other hand , mutations in components of the LIN-2/LIN-7/LIN-10 complex that is necessary to retain the EGFR on the basolateral membrane also cause a strong reduction in basolateral EGFR localization , yet they result in reduced receptor activation [5] . To distinguish between these different scenarios , we tested if the increased apical LET-23::GFP localization in erm-1 ( tm677 ) mutants could be due to a higher rate of LET-23 endocytosis after binding to LIN-3 EGF secreted from the AC . In lin-3 ( e1417 ) mutants , in which LIN-3 activity in the AC is strongly reduced [24] , apical LET-23::GFP localization was nearly two-fold reduced ( Figure 3A , C ) . However , in erm-1 ( tm677 ) ; lin-3 ( e1417 ) double mutants the apical to basal LET-23::GFP ratio was lower than in erm-1 ( tm677 ) but higher than in lin-3 ( e1417 ) single mutants ( Figure 3B , C ) . Since the viable lin-3 ( e1417 ) allele used does not eliminate all LIN-3 activity , we conclude that the apical accumulation of LET-23::GFP in the absence of ERM-1 is at least in part ligand-dependent . On the other hand , a pulse of ectopic LIN-3 ubiquitously expressed under control of the heat-shock promoter hs::lin-3 [25] caused the almost complete disappearance of LET-23::GFP from the basolateral membrane and accumulation on the apical membrane within 230 minutes ( Figure 3D , F ) . In homozygous erm-1 ( tm677 ) mutants , however , a LIN-3 pulse caused a smaller increase in the apical LET-23::GFP pool and persisting receptor expression on the basolateral membrane ( Figure 3E , F ) . Thus , not only LET-23 endocytosis but also basolateral recycling are increased in erm-1 ( tm677 ) mutants , which is consistent with the increased mobile fraction of LET-23::GFP observed in the FRAP experiments ( Figure 2L ) . By contrast , activation of the EGFR signaling pathway downstream of the receptor using for example the let-60 ( gf ) mutation did not change LET-23::GFP localization ( data not shown ) . Thus , the LIN-3 ligand stimulates and ERM-1 inhibits internalization and recycling of LET-23 on the basolateral membrane . Enhanced receptor endocytosis could result in the attenuation of LET-23 signaling , while faster recycling to the plasma membrane could promote signaling [1] . To determine how the altered LET-23 dynamics in erm-1 mutants affects signaling , we performed epistasis analysis by combining erm-1 ( tm677 ) with mutations in different components of the EGFR/RAS/MAPK pathway [4] and quantifying vulval induction . In erm-1 ( tm677 ) single mutants , the three proximal VPCs were always induced as in the wild-type ( Figure 4A ) . However , in let-60 ( gf ) ; erm-1 ( tm677 ) double mutants , the average number of induced VPCs was significantly increased compared to let-60 ( gf ) ; erm-1 ( tm677 ) /+ controls , resulting in an enhanced Multivulva ( Muv ) phenotype ( Figure 4A ) . Thus , ERM-1 negatively regulates RAS/MAPK signaling during vulval induction . Mutations in the lin-2/lin-7/lin-10 receptor localization complex or in the PDZ binding motif in let-23 ( sy1 ) cause a penetrant Vulvaless ( Vul ) phenotype because LET-23 mislocalized to the apical membrane cannot bind to LIN-3 [5] . Interestingly , erm-1 ( tm677 ) partially suppressed the lin-2 ( n397 ) , lin-7 ( e1413 ) , lin-10 ( e1439 ) and let-23 ( sy1 ) Vul phenotypes ( Figure 4A ) , indicating that ERM-1 inhibits vulval induction independently of the LIN-2/LIN-7/LIN-10 receptor localization complex . However , the suppression of the lin-2 ( n397 ) Vul phenotype was not accompanied by a visible re-localization of LET-23::GFP to the basolateral membrane ( data not shown ) . In contrast , erm-1 ( tm677 ) did not suppress the lin-3 ( e1417 ) Vul phenotype , suggesting that vulval induction in erm-1 ( tm677 ) mutants still depends on the AC signal ( Figure 4A ) . ERM proteins are composed of an N-terminal FERM domain and a C-terminal actin-binding domain [26] . They can switch from a closed , inactive conformation in the cytoplasm to an open , active conformation at the plasma membrane [27] . The FERM domain in the open conformation interacts with plasma membrane proteins either directly or indirectly through adaptor proteins [26] , while binding of the actin cytoskeleton to the C-terminus of ERM proteins regulates the activity of the entire complex [28] . Since our genetic analysis indicated that ERM-1 controls LET-23 signaling independently of the LIN-2/LIN-7/LIN-10 complex , we tested if ERM-1 and LET-23 might exist in an alternate complex . For this purpose , different portions of purified recombinant ERM-1 tagged at the N-terminus with glutathione S-transferase ( GST ) were incubated with total worm lysates , and bound LET-23 was detected on Western-blots . LET-23 from wild-type worm extracts bound to N-terminal fragments containing the entire FERM domain ( GST::ERM-11–240 and GST::ERM-11–311 ) , while a C-terminal fragment ( GST::ERM-1312–564 ) or truncated FERM domains ( GST::ERM-11–100 or GST::ERM-11–167 ) did not bind LET-23 ( Figure 4B , C ) . Moreover , LET-23 extracted from sy1 mutants , in which LET-23 lacks the PDZ binding motif , or from lin-7 ( e1413 ) mutants still bound to the ERM-1 FERM domain ( Figure 4D , E ) . Thus , LET-23 and ERM-1 exist in a complex that is distinct from the LIN-2/LIN-7/LIN-10 localization complex . The increased basolateral LET-23 mobility in erm-1 ( tm677 ) mutants may result in an overall elevated activity of the RAS/MAPK pathway , as more LET-23 molecules are available to interact with LIN-3 . The co-localization of LET-23 and ERM-1 together with the in vitro protein interaction experiments suggested that both proteins form a complex at the basolateral membrane of the VPCs . We thus hypothesized that ERM-1 may prevent the instant activation of the entire basolateral pool of LET-23 once the AC begins to secrete LIN-3 at the mid L2 stage , allowing the cells to maintain a high LET-23 activity after vulval induction . To test this model , we quantified the expression levels of the RAS/MAPK target EGL-17::CFP [29] in the descendants of the VPCs . In wild-type mid L3 larvae , we observed a peak of EGL-17::CFP expression after vulval induction in the 1° descendants of P6 . p ( Figure 5A–C′ , G ) . By contrast , erm-1 ( tm677 ) mutants showed a gradual decrease rather than an increase in EGL-17::CFP expression after vulval induction ( Figure 5D–G ) . Thus , ERM-1 is required for the generation of a long-lasting RAS/MAPK signal in the 1° vulval cells after fate specification has occurred .
In order to systematically search for regulators of LET-23 EGFR trafficking and signaling , we performed an in vivo receptor localization screen in C . elegans larvae . There do exist certain limitations of this system , such as the inability to isolate individual cell for biochemical studies . However , an important advantage of our approach over previous studies performed with cultured epithelial cells [1] is the ability to observe the dynamics of receptor trafficking under normal conditions , in epithelial cells embedded in their natural environment and receiving physiological concentrations of various extracellular signals . The different regulators of LET-23 EGFR localization and signaling identified in our screen point at a complex network controlling LET-23 EGFR trafficking and signaling in different sub-cellular compartments . In a system , such as the VPCs , where ligand availability is limiting [8] , [30] , these additional control mechanisms at the level of the receptor are necessary to prevent too many cells from engaging in signaling at the same time and to focus the inductive signal on a single cell ( P6 . p ) . A perturbation of LET-23 EGFR trafficking can lead to a multivulva phenotype because decreased ligand sequestering by the 1° VPC P6 . p results in increased LET-23 EGFR activation in the distal VPCs [30] . The down regulation of the LET-23 EGFR in all but the 1° VPC is therefore an important mechanism to break the symmetry of the initially equivalent VPCs and select a single cell for the 1° fate . The most frequent phenotype we observed in our screen ( 31 genes ) was persisting LET-23 EGFR expression in 2° VPCs , and for those genes that had a significant effect on signaling we found increased rather than decreased vulval induction in the let-60 background . This suggests that a relatively large number of negative regulators of EGFR signaling is required to generate the invariant pattern of vulval cell fates with a single 1° cell flanked by two 2° cells . A recent study has indicated that around 38% of all predicted protein coding genes in C . elegans possess at least one human homolog [31] . However , we found for 91% of the genes identified in our screen ( 74 of 81 ) at least one human homolog in the OrthoList , suggesting that the mechanisms regulating EGFR trafficking are strongly conserved . Further studies of these mammalian homologs may provide new means of interfering with deregulated EGFR signaling in human cells . We describe a new function of the C . elegans Ezrin homolog ERM-1 in regulating EGFR signaling on the basolateral membrane of the vulval cells . Based on the subcellular localization and dynamics and on genetic and biochemical data , we propose that ERM-1 forms a complex with the LET-23 EGFR at the basolateral plasma membrane to recruit the receptor into an actin-rich inactive membrane compartment and limit receptor activation ( Figure 5H , I ) . A similar function has been proposed for mammalian NF2 Merlin , which shares similarity to Ezrin/Radixin/Moesin proteins . In confluent cultured epithelial cells , Merlin coordinates adherens junction stabilization with EGFR signaling by recruiting the EGFR into an apical membrane compartment , where the receptor cannot be activated [32] . In analogy , ERM-1 may link a fraction of the LET-23 EGFR pool at the basolateral membrane to cortical F-actin and restrict the access of the receptor to the activating LIN-2/LIN-7/LIN-10 complex [5] . In the absence of the tripartite LIN-2/LIN-7/LIN-10 complex , most of the residual basolateral LET-23 EGFR is probably bound and inactivated by ERM-1 . The inhibitory ERM-1 complex thus antagonizes the activating LIN-2/LIN-7/LIN-10 complex to prevent the instant activation and subsequent degradation of the entire basolateral pool of LET-23 EGFR once the AC begins to secrete LIN-3 in the mid-L2 stage . This mechanism allows the vulval cells to maintain high LET-23 EGFR activity at later time points after vulval induction . LET-23 EGFR may be released from the ERM-1 complex when the vulval lumen is formed and ERM-1 relocalizes to the apical plasma membrane of the toroids . Such a buffering mechanism may be important , as sustained RAS/MAPK signaling is required during the subsequent phase of vulval morphogenesis when RAS/MAPK activity induces the expression of genes required for the execution of the vulval fates [4] , [33] . Thus , the strength and duration of EGFR activation during development must be precisely controlled to achieve the correct levels of RAS/MAPK activity required for organogenesis .
C . elegans strains were maintained at 20°C on standard nematode growth media [34] and the reference wild-type strain of C . elegans used was Bristol N2 . Mutant strains used: LGI: erm-1 ( tm677 ) /hT2[bli-4 ( e937 ) let ( q782 ) qIs48] ( I;III ) , rde1 ( ne219 ) , lin-10 ( e1339 ) . LGII: let-23 ( sy1 ) , lin-7 ( e1413 ) , syIs12[hs::lin-3EGF][25] . LGIII: unc-119 ( ed3 ) , unc-119 ( e2498 ) . LGIV: lin-3 ( e1417 ) , let-60 ( n1046 ) . LGX: lin-2 ( n397 ) . Integrated and extra-chromosomal arrays: qyIs23[Pcdh-3::mCherry::plcδPH; unc-119 ( + ) ] II [35] , zhIs038[let-23::gfp , unc-119 ( + ) ] IV , zhEx484[C11H1 . 3::gfp; Pmyo-2::mCherry] , zhIs396[Pdlg-1::lifeact::gfp::unc-54 3′utr , Plin-48::gfp] [23] , zhEx486[sft-4::gfp; Pmyo-2::mCherry] , zhEx487[ego-2::gfp; Pmyo-2::mCherry] , zhEx519 [erm-1::mCherry; unc-119 ( + ) ;Pmyo-2::mCherry]; zhEx418[Plin-31::rde-1; Pmyo-2::mcherry] . The construction of the translational reporter constructs used in this study is described in the Text S1 and Table S2 ( see also Figure S4 ) . Extra-chromosomal arrays were obtained by microinjection of plasmids at 20 to 50 ng/µl along with the coinjection marker Pmyo2::mCherry or unc-119 ( + ) at 2 to 10 ng/µl and pBluescript to a final concentration of 150 to 200 ng/µl as described [36] . zhIs038 was obtained by bombardment of unc-119 mutants with plasmid coated gold particles as described [37] . Primers used and details on the construction of plasmids can be found in the Supplementary Material . GST fusion proteins were purified from E . coli BL-21 with glutathione-sepharose beads , incubated with 500 µg total protein worm extracts , and bound LET-23 was detected on Western blots with affinity-purified rabbit anti-LET-23 antibodies ( 1∶1000 dilution ) raised against the C-terminal 196 amino acids [5] . RNAi was performed by bacterial feeding as described [38] . LET-23::GFP localization was scored in L3 larvae of the F1 generation mounted on 3% agarose pads supplemented with 5 mM tetramisol . For each RNAi clone , the vulval cells in 30 to 50 animals were observed at 40 to 63-fold magnification with a Leica DMRA wide-field microscope . Positive RNAi clones from the rescreen were verified by DNA sequencing . Images were recorded with a Hamamatsu ORCA-ER CCD camera controlled by the Openlab 5 software package ( Improvision ) . Confocal images were recorded with a Olympus FV-1000 or a Zeiss LSM710 confocal microscope and analyzed with ImageJ [39] . Apical to basolateral intensity ratios were determined in mid-sagittal frames taken with the same illumination and same exposure settings by manually selecting the basal and apical membrane compartments and measuring total fluorescence intensities . Larvae at the Pn . pxx stage were imaged at 20°C using a 63×/1 . 4 NA oil lens on an Zeiss LSM710 confocal microscope equipped with 458/488/514 nm argon and 405 nm diode lasers . A selected area of the basal or lateral membrane was bleached using the 488 nm argon laser at 85% power setting for 886 . 6 µsec to bleach around 70% of the signal , and fluorescence recovery was monitored over the following 296 seconds taking a frame every 8 seconds with a 488 nm laser excitation at 20% power intensity , a pinhole equivalent to 2 . 12 Airey units , a frame size of 256×256 dpi , and a pixel size of 0 . 53 µm . Data were analyzed in ImageJ by first registering the images with the StackReg plugin and then using the FRAP Norm plugin by Joris Meys [40] to measure recovery . Normalized curves were fitted to the formula I ( t ) = A· ( 1-e−kt ) +C using the solver function in MS Excel to calculate the total mobile fraction A and half time as t1/2 = 1/k . Synchronized L3 larvae were heat-treated at 33°C for 30 minutes in a water bath , transferred to 20°C , and imaged at 120 , 180 , 230 minute after induction under the same illumination and exposure conditions . Quantification of the apical to basal LET-23::GFP intensity ratio with and without heat-shock treatment as described in the results . | Abnormal signaling by the epidermal growth factor receptor ( EGFR ) contributes to the development of various human diseases , including different cancer types . One important mechanism that controls intracellular signal transduction is by regulation of the subcellular receptor localization in the signal-receiving cell . We are investigating the regulation of the EGFR homolog LET-23 in the Nematode C . elegans by observing the localization of the EGFR in the epithelial cells of live animals . This approach has allowed us to study the dynamics of receptor trafficking in cells embedded in their natural environment and receiving physiological concentrations of various extracellular signals . In a systematic RNA interference screen , we have identified 81 genes controlling EGFR localization and signaling in different subcellular compartments . One new regulator of EGFR signaling identified in this screen encodes the Ezrin Homolog ERM-1 . We show genetic and biochemical evidence indicating that ERM-1 is part of a buffering mechanism to maintain a pool of immobile EGFR in the basolateral membrane compartment of the epithelial cells . This mechanism permits the generation of a long-lasting EGFR signal during multiple rounds of cell divisions . The control of receptor localization is thus necessary for the precise temporal regulation of signal transduction during animal development . | [
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] | 2014 | An In Vivo EGF Receptor Localization Screen in C. elegans Identifies the Ezrin Homolog ERM-1 as a Temporal Regulator of Signaling |
Gene expression in individual cells is highly variable and sporadic , often resulting in the synthesis of mRNAs and proteins in bursts . Such bursting has important consequences for cell-fate decisions in diverse processes ranging from HIV-1 viral infections to stem-cell differentiation . It is generally assumed that bursts are geometrically distributed and that they arrive according to a Poisson process . On the other hand , recent single-cell experiments provide evidence for complex burst arrival processes , highlighting the need for analysis of more general stochastic models . To address this issue , we invoke a mapping between general stochastic models of gene expression and systems studied in queueing theory to derive exact analytical expressions for the moments associated with mRNA/protein steady-state distributions . These results are then used to derive noise signatures , i . e . explicit conditions based entirely on experimentally measurable quantities , that determine if the burst distributions deviate from the geometric distribution or if burst arrival deviates from a Poisson process . For non-Poisson arrivals , we develop approaches for accurate estimation of burst parameters . The proposed approaches can lead to new insights into transcriptional bursting based on measurements of steady-state mRNA/protein distributions .
The cellular response to fluctuating environments requires adjustments to cellular phenotypes driven by underlying changes in gene expression . Given the inherent stochasticity of cellular reactions , biological circuits controlling gene expression have to operate in the presence of significant noise [1–15] . While noise reduction and filtering is essential for several cellular processes [16] , cells can also amplify and utilize intrinsic noise to generate phenotypic diversity that enables survival under stressful conditions [17] . Recent studies have demonstrated the importance of such bet-hedging survival strategies in diverse processes ranging from viral infections to bacterial competence [17] . Quantifying the kinetic mechanisms of gene expression that drive variations in a population of cells will thus contribute towards a fundamental understanding of cellular functions with important applications to human health . Recent experiments focusing on gene expression at the single-cell level have revealed striking differences from the corresponding population-averaged behavior . In particular , it has been demonstrated that transcription in single cells is sporadic , with mRNA synthesis often occurring in bursts followed by variable periods of inactivity [7 , 18–28] . Such transcriptional bursting can give rise to high variability in gene expression products and to phenotypic variations in a population of genetically identical cells [29–32] . Furthermore , dynamical parameters that characterize transcriptional bursting of key genes can significantly influence cell-fate decisions in diverse processes ranging from HIV-1 viral infections to stem-cell differentiation [17] . Correspondingly , there is significant interest in developing approaches for quantifying parameters related to transcriptional bursting such as frequency and mean burst size . In recent years , multiple studies have provided evidence for bursty synthesis of mRNAs [20–25 , 33 , 34] and proteins [35 , 36] . Experimental approaches in such studies include both steady-state measurements and time-dependent measurements of the mean and variance of gene expression products at the single-cell level . While obtaining time-lapse measurements of bursts at the single-cell level can be challenging , steady-state measurements at the single-cell level are now carried out routinely . It would thus be desirable to develop approaches for making inferences about burst parameters in gene expression using steady-state measurements at the single-cell level . As noted in [37] , steady-state measurements of the mean and variance alone cannot be used for estimating burst parameters for general models of gene expression , e . g . when burst arrival is governed by complex promoter-based regulation [38] . Additional insights into processes leading to transcriptional bursting can potentially be obtained using measurements of higher moments . However , analytical results for higher moments of steady-state mRNA and protein distributions in general models of expression have not been obtained so far . The derivation of the corresponding analytical expressions will elucidate how measurement of higher moments can potentially lead to quantification of burst parameters . To address these issues , it is essential to develop and analyze a general class of stochastic models of gene expression . A simple stochastic model that is widely used in analyzing bursting in gene expression is the random telegraph model that takes into account the switching of promoter between transcriptionally active ( ON ) and inactive ( OFF ) states [39–41] . This model has been used as the basis for several studies focusing on inferring gene expression parameters based on observations of the mean and variance of mRNA/protein distributions [13 , 27 , 42] . In this model , in the limit that we have transcriptional bursting , the arrival of bursts is a Poisson process . Correspondingly , the waiting-time distribution between arrival of mRNA bursts is assumed to be exponential . In general , this assumption is not valid as there are multiple kinetic steps involved in promoter activation [37 , 43 , 44] . Recent experiments on mammalian genes [7 , 45 , 46] have demonstrated that the waiting-time for arrival of bursts does not have an exponential distribution . In view of these experimental observations , it is natural to ask: Using steady-state measurements , can we infer if the burst arrival process is not a Poisson process ? If so , how can we estimate the corresponding burst parameters ? Furthermore , in estimating burst size it is commonly assumed that mRNA/protein bursts are geometrically distributed . This assumption , which has been validated by experimental observations for some genes , is derived from the corresponding distribution of bursts in the random telegraph model . However , given the complexity and diversity of gene expression mechanisms , it is possible that several promoters involve multiple rate-limiting steps in the transition from the ON state to the OFF state . In such cases , the transcriptional burst size distribution will not be a geometric distribution . This observation leads to the following question: Can we use steady-state measurements of moments to determine if the burst distribution deviates from a geometric distribution ? The aim of this paper is to address the above questions by considering models with general arrival processes for mRNA creation . The paper is organized as follows . First , we introduce a class of gene expression models with general arrival processes leading to mRNA/protein bursts with arbitrary burst distribution . Then we review the mapping from gene expression models to systems studied in queuing theory [43 , 47 , 48] and use this mapping to derive steady-state moments for mRNA/protein distributions . The analytical expressions obtained for the steady-state moments are used to develop approaches for estimating burst parameters for general arrival processes . Finally , we use the results obtained to derive conditions relating experimentally measurable quantities that determine if the arrival of mRNA bursts deviates from a Poisson process and if the distribution of mRNA bursts deviates from a geometric distribution .
We consider a general model of gene expression [43] as outlined in Fig 1 . In the model , mRNAs are produced in bursts , with f ( t ) representing a general arrival time distribution for mRNA bursts . The mRNA burst distribution can be arbitrary . Each mRNA then produces proteins with rate kp , and finally , both mRNAs and proteins decay with rates μm and μp , respectively . Note that the model also allows for post-transcriptional regulation since the protein burst distribution from each mRNA can be arbitrary; the only assumption is that each mRNA produces proteins independently . In the limit μp ≪ μm , we can use the bursty synthesis approximation [40] for analyzing protein dynamics . This approximation consists of two steps: 1 ) obtaining the distribution of proteins produced from each mRNA and 2 ) assuming that the proteins are produced in instantaneous bursts . The corresponding distribution for the number of proteins created is referred to as the protein burst distribution . A detailed justification of the validity of this approximation has been provided in previous work [40 , 49] . Let a p ( z ) = ∑ n = 0 ∞ z n p ( n ) denote the generating function of the protein burst distribution p ( n ) produced by a single mRNA , and let A p ( z ) = ∑ n = 0 ∞ z n P ( n ) denote the generating function of the protein burst distribution P ( n ) produced by all the mRNAs in a burst . If we denote by Am ( z ) the generating function of the mRNA burst distribution , then we have the following relation between the generating functions A p ( z ) = A m [ a p ( z ) ] . ( 1 ) The above relation follows from the observation that the number of proteins produced in a burst is a compound random variable: the sum of m independent identical random variables , each of which corresponds to the number of proteins produced from a single mRNA in the burst and m itself is a random variable denoting the number of mRNAs produced in the burst . While the analytical results that we derive are valid for general mRNA and protein burst distributions , we will primarily focus on a specific class of burst distributions . Simple kinetic models and the results from multiple experiments indicate that mRNA burst distributions are geometric [35] . Similarly , the burst distribution of proteins produced from a single mRNA is a geometric distribution with mean ⟨pb⟩ = kp/μm . For a geometric distribution with mean ⟨pb⟩ , the generating function is given by ap ( z ) =1[ 1+〈pb〉 ( 1−z ) ] . If we condition the geometric distribution on the production of at least 1 mRNA , then the generating function for the corresponding conditional geometric distribution is given by Am ( z ) =z[ 1+〈mb〉 ( 1−z ) ] with ( 1 + ⟨mb⟩ ) as the mean mRNA burst size . Note that in the limit ⟨mb⟩ → 0 , this distribution reduces to exactly 1 mRNA produced in each burst . Thus the conditional geometric distribution provides a unified representation of both Poisson arrival process for mRNAs ( ⟨mb⟩ → 0 ) and processes leading to transcriptional bursting ( ⟨mb⟩ > 0 ) . Consider now the protein burst distribution produced by an underlying conditional geometric mRNA burst distribution with mean ( 1 + ⟨mb⟩ ) . Using Eq ( 1 ) , we see that the corresponding generating function of the protein burst distribution is given by A p ( z ) = 1 1 + ⟨ m b ⟩ ⟨ p b ⟩ ( 1 - z ) . This is the generating function for a geometric distribution with mean b = ( 1 + ⟨mb⟩ ) ⟨pb⟩ ) , where ⟨pb⟩ = kp/μp represents the mean protein burst size from a single mRNA . Single-cell experiments have demonstrated that the protein burst mean b can be directly measured in some cases [35] . However , if the protein production rate kp is not known , the preceding analysis implies that measurements of protein burst distributions ( which determine b ) cannot be used to determine the degree of transcriptional bursting ( 1 + ⟨mb⟩ ) . Since the mean transcriptional burst size is an important parameter characterizing bursting , it is of interest to develop approaches for estimating it based on available experiments . Previous work [50] has argued that the mean transcriptional burst size cannot be determined using measurements of protein burst distributions alone or by using only protein steady-state distributions . It was suggested that combining such measurements can potentially provide a way of determining the mean transcriptional burst size . To explore this possibility , it is necessary to derive analytical results connecting moments of burst and steady-state distributions for general kinetic schemes . To obtain steady-state moments for the model outlined in Fig 1 , we invoke the mapping of this gene expression model to systems studied in queueing theory [43 , 48 , 51 , 52] . Broadly speaking , queueing theory is the mathematical theory of waiting lines formed by customers who , arriving according to some random protocol , stay in the system until they receive service from a group of servers . Such queues are typically characterized by specifying a ) the stochastic process governing the arrival of customers , b ) distribution of number of customers in each arrival , c ) the stochastic process governing departure of customers , and d ) the number of servers . When the gene expression model in Fig 1 is expressed in the language of queueing theory , individual mRNAs/proteins are the analogs of customers in queueing models . The production of mRNAs/proteins in bursts corresponds to the arrival of customers in batches . Just as the customers leave the queue after receiving service , mRNAs/proteins exit the system upon degradation . Thus the waiting-time distribution for mRNA/protein decay is the analog of service time distribution for customers in queueing models . For the model in Fig 1 , their decay time distribution is the exponential distribution . Also , since mRNAs/proteins are degraded independently of each other , the corresponding number of servers in queueing models is ∞ ( which ensures that presence of a customer in the system does not affect the service time of any other customer in the system ) . Based on the above mapping , the queueing system corresponding to the model outlined in Fig 1 is the GIX/M/∞ system [43 , 48] . In this model , the symbol G refers to a general waiting-time distribution for the arrival process , IX denotes customers arriving in batches of independently distributed random sizes X , M stands for Markovian ( i . e . exponential ) service-time distribution for customers and ‘∞’ stands for infinite servers . For the GIX/M/∞ model , exact results for iteratively obtaining the moments of the steady-state distribution of the number of customers have been derived [48] . Using these results , explicit expressions for the first four moments of the steady-state distribution are provided in the Supplementary S1 Text . Applying the mapping discussed above , these results can be translated into exact expressions for the moments of mRNA/protein steady-state distributions , as discussed below . Let us first examine the expressions for steady-state means of mRNAs , ⟨ms⟩ , and proteins , ⟨ps⟩ , which are given by ⟨ m s ⟩ = k b μ m ⟨ m b ⟩ , ⟨ p s ⟩ = k b μ p b , ( 2 ) where kb stands for the mean arrival rate of mRNA bursts and b = ⟨mb⟩⟨pb⟩ is the mean of the protein burst distribution ( including contributions from all the mRNAs ) . Although Eq ( 2 ) has been derived by assuming that the arrival of mRNAs/proteins is a renewal process , it is valid for arbitrary arrival processes . This is because Eq ( 2 ) is a direct consequence of Little’s Law [47 , 53] which is valid for general arrival processes . The above equations , Eq ( 2 ) , can be used to determine the mean transcriptional burst size , provided the protein burst distribution can be measured experimentally . To see this , we note that dividing the expressions for the mean mRNA and protein levels leads to b ⟨ m b ⟩ = μ p μ m ⟨ p s ⟩ ⟨ m s ⟩ . ( 3 ) Since the steady-state means ⟨ms⟩ and ⟨ps⟩ as well as the degradation rates μm and μp are parameters that can be measured experimentally , the above equation implies that the ratio b/⟨mb⟩ can be determined experimentally . Given b/⟨mb⟩ = kp/μm , this implies that the mean protein production rate kp can also be determined experimentally . This is an important result since it provides an approach for determining the mean protein production rate kp that is valid for arbitrary arrival processes for mRNAs . Furthermore , the above equation implies that , if the mean of protein burst distribution b can be measured [36] , then the mean transcriptional burst size ⟨mb⟩ can also be determined . Thus , if we have measurements for mean mRNA and protein numbers and also the mean of protein burst distribution , then these measurements can be used to determine the degree of transcriptional bursting ⟨mb⟩ as well as the parameters ⟨pb⟩ and kp . It is noteworthy that this procedure for estimating the burst parameters is valid for arbitrary stochastic processes corresponding to mRNA transcription . We next turn to expressions for higher moments of mRNA and protein steady-state distributions . The noise in mRNA steady-state distributions is given by σ m s 2 ⟨ m s ⟩ 2 = 1 ⟨ m s ⟩ + μ m k b + μ m 2 k b [ K g ( μ m ) - 1 + σ m b 2 ⟨ m b ⟩ 2 - ( 1 + 1 ⟨ m b ⟩ ) ] , ( 4 ) where σ m b 2 is the variance of mRNA burst distribution and Kg ( μm ) is the so-called gestation factor , K g ( μ m ) = 1 + 2 [ f L ( μ m ) 1 - f L ( μ m ) - k b μ m ] , ( 5 ) with fL ( s ) denoting the Laplace transform of arrival time distribution of mRNA bursts . The function Kg ( μm ) encodes information about the arrival process . Specifically , we note that for Poisson arrivals , we have Kg ( μm ) = 1 . For proteins ( in the burst limit μm ≫ μp ) , we obtain [43] σps2〈ps〉2=1〈ps〉+μpkb+μp2kb[ Kg ( μp ) −1+σmb2〈mb〉2 − ( 1−1〈mb〉 ) + ( σpb2〈pb〉2− ( 1+1〈pb〉 ) ) 1〈mb〉 ] ( 6 ) where Kg ( μp ) is given by Eq ( 5 ) and σ p b 2 is the variance of protein burst distribution produced by a single mRNA . The expression for protein noise is composed of the noise term for the basic two-stage model of gene expression [40] and additive noise contributions due to: a ) deviations from exponential waiting-time distribution for the arrival process , b ) deviations from conditional geometric distributions for mRNA burst distributions and c ) deviations from geometric distributions for protein burst distributions . For both mRNAs and proteins , the noise in steady-state distributions depends on all the moments of the burst arrival time distribution through the term Kg . Therefore , arrival processes corresponding to different kinetic schemes for transcription will make different contributions to the overall noise , even if they have identical means and variances for the the burst arrival time distribution . We note from Eq ( 4 ) that , for Poisson arrivals , i . e . Kg = 1 , and geometrically distributed burst , i . e . σ m b 2 = 〈 m b 〉 ( 〈 m b 〉 - 1 ) , the equations for the noise and mean have only two unknown burst parameters , kb and ⟨mb⟩ . In this case , experimental measurements of the first two moments of the steady-state distribution are sufficient to estimate the burst parameters , as has been done in multiple studies . However , when the arrival process is non-Poisson or if the burst distribution deviates from a geometric distribution , measurements of the first two steady-state moments are not sufficient for estimating the burst parameters . This observation motivates the need for analytical expressions for the higher moments which we turn to next . We now derive analytical expressions for the third moment , specifically the skewness parameter . For mRNAs , the exact expression for skewness γms is given by γ m s σ m s 3 m s = 1 + ⟨ m s ⟩ ⟨ m b ⟩ K 1 ( μ m ) + 2 ⟨ m b ⟩ 2 K 2 ( μ m , ⟨ m b ⟩ ) + ( σ m b 2 + ⟨ m b ⟩ 2 - ⟨ m b ⟩ ) K 3 ( μ m , ⟨ m b ⟩ ) + ⟨ m b ( m b - 1 ) ( m b - 2 ) ⟩ 3 ⟨ m b ⟩ , ( 7 ) where we have defined K 1 ( μ m ) = K g ( 2 μ m ) - K g ( μ m ) , K 2 ( μ m , ⟨ m b ⟩ ) = K g ( μ m ) - 1 4 ( 3 ⟨ m b ⟩ + K g ( 2 μ m ) - 1 ) , K 3 ( μ m , ⟨ m b ⟩ ) = 3 2 ⟨ m b ⟩ + K g ( μ m ) + K g ( 2 μ m ) 2 - 1 . ( 8 ) For proteins , we obtain in the burst-limit ( μm ≫ μp ) , γ p s σ p s 3 p s = 1 + ( A 1 p ) 2 [ ⟨ p s ⟩ b K 1 ( μ p ) + 2 K 2 ( μ p , A 1 p ) ] + A 2 p K 3 ( μ p , A 1 p ) + A 3 p 3 A 1 p , ( 9 ) where the functions K 1 , K 2 , K 2 are given by Eq ( 8 ) , A k p is defined by A k p = d k A p ( z ) / d z k | z = 1 and , using Eq ( 1 ) [54] , we obtain the parameters A k p as: A1p=〈mb〉〈pb〉 , A2p=〈mb〉 ( σpb2−〈pb〉 ) + ( σmb2+〈mb〉2 ) 〈pb〉2 , A3p=〈pb〉3〈mb ( mb−1 ) ( mb−2 ) 〉+3〈mb ( mb−1 ) 〉〈pb〉 〈pb ( pb−1 ) 〉+〈mb〉 〈pb ( pb−1 ) ( pb−2 ) 〉 . ( 10 ) Similarly , expressions for higher order moments of protein and mRNA steady-state distributions can be obtained iteratively . The corresponding expressions for the kurtosis are provided in the S1 Text . The analytical results derived above for proteins are exact in the burst limit , which assumes that proteins are produced instantaneously from all the mRNAs in a burst . Going beyond the burst limit ( i . e . not limited to μm ≫ μp ) , exact results for the higher moments of the protein steady-state distribution will , in general , depend on the details of the kinetic scheme for gene expression . However , we can derive approximate analytical expressions for general schemes by requiring that: a ) the results reduce to the exact results in the burst limit and b ) they match the exact results for the two-stage model of gene expression . For the two-stage model , exact results for the first four moments have been derived by Bokes et . al [55] . Comparing these exact results with our results derived in the burst limit , we observe that results of [55] can be reproduced by a suitable scaling of the burst-size parameters A k p . For example , the exact expression for the noise is obtained by the following scaling [43]: ( σ p s 2 ⟨ p s ⟩ 2 - 1 ⟨ p s ⟩ ) → ( σ p s 2 ⟨ p s ⟩ 2 - 1 ⟨ p s ⟩ ) 1 1 + μ p μ m . ( 11 ) Similarly , for the expression for skewness , the parameters A 2 p and A 3 p are scaled as: A 2 p → A 2 p 1 1 + μ p μ m and A 3 p → A 3 p 1 ( 1 + μ p μ m ) ( 1 + 2 μ p μ m ) . ( 12 ) As shown in Fig 2 ( for the random telegraph model ) the analytical expressions using this approach are in good agreement with results from simulations [56] . It is noteworthy that the results derived are valid for a general class of kinetic schemes of gene expression . For a specific kinetic scheme , we can determine the corresponding waiting-time distribution for the arrival process and the burst distributions for mRNA and proteins . Substituting these results in the equations derived leads to the corresponding expressions for the moments of the steady-state distribution . The results obtained can thus provide insight into how specific kinetic schemes of gene expression ( e . g . combining promoter-based regulation and post-transcriptional regulation ) can be used to impact the noise and higher moments of steady-state distributions . The results derived for the steady-state moments indicate that , if the burst arrival process is not a Poisson process , then it is no longer accurate to estimate burst parameters based on measurements of mean and variance only , as has been done in previous studies [13] . In the following , we present approaches for estimating burst parameters in the general case . We begin by considering the general kinetic scheme shown in Fig 3 . This form for the kinetic scheme is supported by recent experiments in mammalian cells which suggest the presence of multiple rate-limiting steps between transition of the promoter from OFF to ON state [45 , 57] . However , as observed in these experiments , a promoter in the ON state switches to the OFF state by a single rate-limiting step . We model the promoter switching from OFF to ON state by a general waiting-time distribution , g ( t ) . The switching rate from ON to OFF state is given by β . The analytical expressions derived for the steady-state moments for mRNAs and proteins can also be used to make inferences about the burst arrival process based on steady-state measurements . Since multiple studies assume that the burst arrival process is characterized by an exponential waiting-time distribution , it would be useful to determine if this assumption is invalid using measurements of steady-state distributions . As shown below , we can obtain conditions for the same using the results derived for higher moments . In the following , we will first focus on the cases that the mRNA burst distribution is conditional geometric and the protein burst distribution is geometric , which is consistent with multiple experimental observations . As discussed , choosing the conditional geometric distribution for mRNAs allows us to consider both single mRNA arrivals and geometric mRNA bursts in one framework . Since experiments can provide measurements of both mRNA and protein steady-state distributions , it is useful to have conditions for the arrival process using either mRNA data or protein data or both mRNA and protein data . Based on these three possibilities , we present three different conditions in the following . As discussed in the previous section , it is widely assumed that the mRNA burst distribution can be represented by a conditional geometric distribution ( i . e . including both single mRNA arrivals and geometrically distributed burst arrivals ) . While this assumption is consistent with multiple experimental observations , for general kinetic schemes the possibility of non-geometric mRNA burst distributions has to be considered . Thus , it is of interest to examine if the results obtained can be used to determine if the mRNA burst distribution deviates from a conditional geometric distribution . To address the possibility of non-geometric mRNA burst distributions , let us first consider that the random variable corresponding to the mRNA burst distribution ( mb ) has a conditional geometric distribution . That is , the probability that a burst produces n mRNA molecules is given by P ( m b = n ) = ( 1 - p ) n - 1 p , ( 29 ) where 0 < p ≤ 1 , and n = 1 , 2 , 3…∞ . This distribution leads to σ m b 2 = ⟨ m b ⟩ ( ⟨ m b ⟩ - 1 ) . ( 30 ) Using Eqs ( 2 ) and ( 30 ) in Eq ( 4 ) , and denoting F m = σ m s 2 / 〈 m s 〉 as the Fano factor of mRNA copy number , Eq ( 4 ) can be rewritten as F m ( μ m ) = ⟨ m b ⟩ 2 [ 1 + K g ( μ m ) ] . ( 31 ) Similarly , using the burst size distribution from Eq ( 29 ) , the skewness in Eq ( 7 ) is given by γ m s σ m s 3 ⟨ m s ⟩ = 1 + ⟨ m s ⟩ ⟨ m b ⟩ K 1 ( μ m ) + 2 ⟨ m b ⟩ 2 K 2 ( μ m , ⟨ m b ⟩ ) + 2 ( ⟨ m b ⟩ - 1 ) [ ( 1 + K 3 ( μ m , ⟨ m b ⟩ ) ) ⟨ m b ⟩ - 1 ] . ( 32 ) We note that Eq ( 32 ) connects experimentally measurable moments of the steady-state distribution to the parameters Kg ( μm ) , Kg ( 2μm ) and ⟨mb⟩ . Furthermore , note that Eq ( 31 ) can be recast as Kg ( μm ) = ( 2Fm ( μm ) /⟨mb⟩ ) −1 . Now , considering a change in the degradation rate from μm to 2μm ( keeping the mean burst size , ⟨mb⟩ invariant ) , we obtain K g ( 2 μ m ) = ( 2 F m ( 2 μ m ) / ⟨ m b ⟩ ) - 1 . ( 33 ) Using the above in Eq ( 32 ) , we get an expression connecting experimentally measurable quantities associated with moments of the mRNA steady-state distribution . The resulting expression is: G m ≡ γ m s σ m s 3 ⟨ m s ⟩ - 1 2 F m ( 2 μ m ) ( ⟨ m s ⟩ - 1 ) + F m ( 1 - 2 ⟨ m s ⟩ + 2 F m ( 2 μ m ) ) = 1 . ( 34 ) We note that the above expression has been derived by making just one assumption , namely , the mRNA burst distribution is a conditional geometric distribution . The derived expression thus indicates that a combination of experimentally measurable quantities has to deviate from 1 if the mRNA burst distribution deviates from a conditional geometric distribution . Thus the analytical results derived provide a signature for deviation from conditional geometric mRNA bursts using measurements of the first three moments of the mRNA steady-state distribution . The main requirement for using the above relation is that measurements of mRNA steady-state distribution can be carried out at two different rates of the mRNAs μm and 2μm . Given that mRNA degradation rates can be tuned experimentally , a straightforward strategy to ensure that the degradation rate is tuned to twice the original value ( 2μm ) is to compare the mean mRNA levels at μm and 2μm . Given these measurements , a value of G m ≠ 1 implies that bursts are not distributed geometrically . The strength of this result lies in the fact that it holds for general arrival processes for mRNA bursts with arbitrary waiting-time distributions . Let us consider a specific simple model to illustrate the condition derived above . First , let the arrival process for mRNA bursts be a Poisson process . For this , arrival time distributions of mRNA bursts in the time domain , t , and in the Laplace domain , s , are given by f ( t ) = k b e - k b t , f L ( s ) = k b / ( k b + s ) , ( 35 ) where kb is the rate of arrival of mRNA bursts . For the mRNA burst distribution , let us assume that it is given by the negative binomial distribution , i . e . P ( m b = n ) = ( n + r - 1 ) ! n ! ( r - 1 ) ! p n ( 1 - p ) r , ( 36 ) where 0 < p ≤ 1 , r ≥ 1 , and n = 0 , 1 , 2 , 3…∞ . For r = 1 , the above reduces to the geometric distribution and therefore we expect Gm = 1 in this limit . Using the expressions for the moments derived in the previous section , we obtain an explicit expression for Gm ( Supplementary S3 Text ) : G m = 1 3 ( - p + 1 p r + 1 + 4 p ( r - 1 ) + 2 + 2 ) . ( 37 ) Notice that for the geometric bursts ( r = 1 ) we get G m = 1 , as expected . However , for non-geometric bursts , deviations of G m from 1 are observed ( also see Fig S3–1 in Supplementary S3 Text ) . Two additional examples of microscopic models for non-geometric bursts ( the two state random telegraph model and a model with three promoter states where mRNAs are produced from two states ) are discussed in the . The preceding analysis can be extended to protein steady-state distributions to derive a similar condition for deviations from geometric burst distributions in terms of steady state moments associated with proteins ( see Supplementary S4 Text ) . In this paper we study stochastic gene expression models with a general renewal-type arrival process for mRNAs . By mapping such a generic model of gene expression to systems studied in queueing theory , we derive analytical expressions for the moments for mRNA and protein steady-state distributions . While the focus of this work is on using approaches drawn from queueing theory , it is noteworthy that the kinetic scheme defined in Fig 1 can also be analyzed using the general theory of branching processes with immigration [58] . In future work , it would be of interest to explore potential connections between complementary approaches to such models based on branching processes and queueing theory . While previous studies [37 , 43] have focused on protein noise , in the present work we derive analytic expressions for higher order moments of both mRNA and protein steady-state distributions . For arbitrary kinetic schemes , the results obtained determine how the moments of steady-state distributions depend on model parameters . They elucidate how different sources ( promoter-based regulation , transcriptional bursting , post-transcriptional regulation ) combine to determine the overall noise and higher moments . Furthermore , the results derived show how parameters of interest ( such as mean protein production rate kp ) can be estimated for general models ( i . e . without making any assumptions about specific features of the models ) . The expressions derived for the moments can also be used to infer if the arrival process for mRNAs is non-Poisson or if the mRNA burst distribution deviates from the geometric distribution . Correspondingly , we obtain analytic conditions that provide signatures for non-Poisson arrivals of mRNA bursts and for non-geometric mRNA burst distributions . These conditions involve relations between combinations of of experimentally measurable quantities and can thus be tested by using measurements of either mRNA steady-state distributions or protein steady-state distributions or both . Apart from obtaining insights into the statistics of the arrival process , we can use the results derived for steady-state moments for accurately estimating burst parameters using an iterative approach . Notably , the results and the approaches developed in this work are valid for general models of gene expression i . e . , given the general assumptions made , they do not depend on the specifics of the kinetic schemes . It is important to note that the burst parameter estimation approaches presented in this paper rely on the accurate measurements of higher order moments , such as skewness or kurtosis . This , in turn requires that we have relatively large sample sizes . For example , simulations of two state random telegraph model ( see Supplementary S5 Text ) indicate that for the standard error in skewness to be below 10% , the sample size should be ∼ 1000 . Current experimental limitations on measurements of mRNA distributions ( e . g . using RNA FISH ) do not allow for such large sample sizes and thus do not lead to accurate computation of skewness or kurtosis . While accurate measurements of higher moments are not readily available in the existing data , it is hoped that our results will provide motivation for carrying out the corresponding experiments in future . The combination of these experimental results with our theoretical approaches can be used in obtaining accurate representations of the arrival process and burst parameters for a wide range of cellular systems . | One of the fundamental problems in biology is understanding how phenotypic variations arise among individuals in a population . Recent research has shown that phenotypic variations can arise due to probabilistic cell-fate decisions driven by inherent randomness ( noise ) in the process of gene expression . One of the manifestations of such stochasticity in gene expression is the production of mRNAs and proteins in bursts . Bursting in gene expression is known to impact cell-fate in diverse systems ranging from latency in HIV-1 viral infections to cellular differentiation . Recent single-cell experiments provide evidence for complex arrival processes leading to bursting , however an analytical framework connecting such burst arrival processes with the corresponding higher moments of mRNA/protein distributions is currently lacking . We address this issue by invoking a mapping between general models of gene expression and systems studied in queueing theory . The framework developed and the results derived lead to new approaches for testing commonly used assumptions in modeling gene expression and for accurate estimation of burst parameters . Notably , the phenomenon of stochastic bursting has been observed in a wide range of disciplines ranging from neuroscience and finance to cell biology . The approaches developed and results obtained in this work will thus contribute towards quantitative characterization of burst processes in diverse systems of current interest . | [
"Abstract",
"Introduction",
"Results"
] | [] | 2015 | Transcriptional Bursting in Gene Expression: Analytical Results for General Stochastic Models |
In this study we develop a modeling framework for predicting baroreceptor firing rate as a function of blood pressure . We test models within this framework both quantitatively and qualitatively using data from rats . The models describe three components: arterial wall deformation , stimulation of mechanoreceptors located in the BR nerve-endings , and modulation of the action potential frequency . The three sub-systems are modeled individually following well-established biological principles . The first submodel , predicting arterial wall deformation , uses blood pressure as an input and outputs circumferential strain . The mechanoreceptor stimulation model , uses circumferential strain as an input , predicting receptor deformation as an output . Finally , the neural model takes receptor deformation as an input predicting the BR firing rate as an output . Our results show that nonlinear dependence of firing rate on pressure can be accounted for by taking into account the nonlinear elastic properties of the artery wall . This was observed when testing the models using multiple experiments with a single set of parameters . We find that to model the response to a square pressure stimulus , giving rise to post-excitatory depression , it is necessary to include an integrate-and-fire model , which allows the firing rate to cease when the stimulus falls below a given threshold . We show that our modeling framework in combination with sensitivity analysis and parameter estimation can be used to test and compare models . Finally , we demonstrate that our preferred model can exhibit all known dynamics and that it is advantageous to combine qualitative and quantitative analysis methods .
The main role of the cardiovascular ( CV ) system is to provide adequate oxygenation of all tissues , a function which is achieved by maintaining homeostasis of blood flow and pressure . When a mammal is subjected to an orthostatic maneuver ( e . g . , running , jumping , etc . ) , its blood volume is redistributed , moving the system state away from homeostasis [1] . To re-establish homeostasis a number of control mechanisms are activated regulating vascular resistance and compliance , and cardiac pumping efficiency and frequency . An important contributor to this control system is the baroreflex , which uses specialized neurons called baroreceptors ( BR ) for signaling [2] . The BR neurons originate in the arterial wall and terminate in the nucleus solitary tract ( NTS ) , where sensory information is integrated . These neurons are continuously stimulated via activation/inhibition of mechanosensitive receptors responding to changes in arterial wall stretch imposed by pulsating blood pressure [3] . This stimulus modulates the formation of action potentials propagating along the BR nerves terminating in the NTS , where efferent signals are generated to regulate heart rate , cardiac contractility , as well as vascular resistance and compliance . It is known that the baroreflex system contributes to short-term blood pressure regulation , operating on a time-scale of seconds to minutes [4] . For example , upon head-up tilt , blood is pooled in the lower extremities , increasing blood pressure in the lower body , while decreasing it in the upper body , causing an imbalance , which persists until the baroreflex system is activated . Figure 1 shows a schematic representation of the baroreflex pathways . While the BR pathways are generally well established , analysis of the complete control system , including afferent and efferent signaling , is hindered by the difficulty of measuring the activity of each component without disrupting the feedback loop . For example , in vivo , only macroscopic quantities can be measured non-invasively including heart rate and blood pressure . From such measurements it is difficult to examine how the individual components of the system interact and consequently it is difficult to determine which sub-systems are compromised in subjects experiencing baroreflex failure [5] or decreased arterial baroreflex sensitivity [6] . These difficulties limit the development of targeted diagnosis procedures and treatment plans aiming to alleviate symptoms for patients . Mathematical modeling is an eminent tool for gaining more insight into this complex feedback loop , offering a stringent and systematic way to identify underlying mechanisms of the system . For example , the only way to estimate model parameters and thereby suggest essential biomarkers , which may not be directly measurable , is by using models in combination with direct measurements . Modeling also offers a way to understand complex systems , as it makes the inaccessible accessible , a concept denoted the “mathematical microscope” [7] . This paper focuses solely on the afferent part of the baroreflex system , while future studies will address efferent signaling and integration of the two parts within a system level model . Since the 1950s researchers have put forward numerous mathematical models [8]–[19] , which tried to integrate known dynamics with hypothesized mechanisms in order to provide more understanding of the system as a whole . Many insights have been gained , however , most of these models were developed to describe BR response to a particular stimulus , rather than to a range of stimuli eliciting all known responses . Therefore they all lead to different hypotheses explaining the system mechanisms . Inspired by shortcomings of previous studies , we developed a modeling framework containing model components reflecting physiological pathways . This framework splits the afferent signaling into three parts describing vessel wall deformation , mechanoreceptor stimulation , and the frequency of action potential generation . For each component we propose multiple models , which we test both qualitatively and quantitatively . This new approach allows us to understand the contribution of each component model to the overall signal . For example , if the objective is to build a BR model that can reflect the response to a sinusoidal pressure stimulus observed experimentally , the modeling framework can be used to identify which combinations of components are sufficient to describe the experimental outcome , and which component models may be excluded from possible explanations of observed features . Moreover , we show how our framework may be used to inform hypotheses , by suggesting a particular component mechanism responsible for generating a given pressure-response feature of BR firing .
In this section we describe the main qualitative characteristics of BR firing rate as well as the data used for quantitative model tests . To model the dynamics , which produce the BR firing rate in response to given blood pressure stimuli , we include three components separating distinct physiological pathways , and for each component we develop a number of linear and nonlinear models . The three components ( Figure 3 ) include: arterial wall deformation , mechanoreceptor stimulation , and action potential generation . As a driving force for the models we use arterial pressure , which determines arterial wall deformation quantified by the wall strain . The wall deformation stimulates the stretch sensitive mechanoreceptors found in the BR nerve endings within the arterial wall . Thus changes in blood pressure modulate the opening of these channels , and thereby the current flowing through them , which determine the rate at which action potentials are formed . The time between subsequent action potentials determines the firing rate , and thus our models relate the receptor strain to the frequency of action potentials , thereby allowing us to predict the BR firing rate . For each model component , described below , we review previous modeling methodologies and use these to inform the design of the new component models , collectively used to describe the firing rate of afferent BR neurons in response to an applied blood pressure stimulus .
Models will be tested quantitatively using three types of pressure stimuli: sinusoidal at a fixed frequency , a step-increase , and a step-increase followed by a step decrease ( Figure 2A–C ) . We investigated six linear and nonlinear models summarized in Table 3 . For the wall strain three models were investigated , the simplest assumes the wall strain has a spring-like response ( denoted ) . The second model ( denoted ) accounts sigmoidally for increased stiffening with increased pressure , and finally we investigate a viscoelastic model ( ) . The mechanoreceptor strain , is modeled using one , two , and three Voigt bodies , respectively , in series with the spring ( ) . Finally , two models were used for determining the BR firing rate , a linear model ( ) and an integrate-and-fire model ( ) . As mentioned above , these models can all be described as a system of algebraic and differential equations . For all models the model input is pressure and the model output is BR firing rate , initial conditions were computed to ensure that model solutions start at steady state . The objective was to estimate model parameters minimizing the least squares error between the model and data . This is calculated from the point wise residual error between model and datawhere is the average firing rate of the specific data set considered and denotes the parameter vector . To estimate the parameters we minimize the sum of squares cost function ( referred to as RMSE in Tables 4 , 5 , and 6 ) Since data is only available for the BR firing rate and the pressure stimuli , for most models not all parameters are identifiable . We denote as identifiable parameters , those that are sensitive and not correlated , given the model output and the associated available data [65] . In this study , identifiability of parameters was determined using sensitivity based methods [66] . Subsequently , for models completely characterized by smooth functions , the Levenberg-Marquardt method was used to estimate model parameters , while for models not fulfilling this requirement ( the integrate-and-fire models ) , parameters were estimated using the Nelder-Mead method . Both used optimization algorithms from Kelley [67] . Below we first describe the methodology used for sensitivity analysis and parameter identification and subsequently we discuss results obtained using nonlinear optimization , the latter is separated according to the input stimulus . Sensitivity analysis: For any smooth model of the form ( 23 ) , the sensitivities [68]–[70] can be computed asFollowing Pope et al . [71] , we use a finite difference approximation to compute where is the unit vector in the component direction and is a small number . The BR firing rate is obtained computationally , with an integration tolerance of imposed on solution of the differential equations , thus is bounded by . To satisfy this requirement we let . Sensitivities are ranked by averaging time-varying functions using the two-norm . For each model , this ranking was used to separate parameters into two groups: one group consisted of parameters to which the model output was sensitive , and the other group consisted of parameters to which the model output was insensitive . Estimating only sensitive parameters allows more reliable estimation of parameters [72] . Not all sensitive parameters are practically identifiable [65] , [66] . To identify parameter correlations , we used the QR-SVD subset selection method [71] , [73] , [74] . We also used a method based on covariance analysis to identify pairs of correlated parameters [66] . For each pair of correlated parameters the least sensitive parameter was kept fixed at its nominal value while the other was included in the subset . Parameter correlations were computed fromwhere is the variance of the assumed noise in the data , is the covariance matrix , and is the correlation coefficient . Parameters for which are labeled as correlated . For the models studied in this work we let . Once a set of uncorrelated sensitive parameters were identified , we used either the Levenberg-Marquardt or the Nelder-Mead method to estimate the subset of practically identifiable model parameters [67] . The Levenberg-Marquardt method was used for models that can be described using smooth functions , while the Nelder-Mead method was used for models including the leaky integrate-and-fire component . Since this model contains a discontinuity the gradient based Levenberg-Marquardt method is not applicable . Sinusoidal stimulus: Now we present results obtained using sinusoidal forcing allowing us investigate asymmetry of the model response . Results ( Figure 6 ) show BR firing rate as a function of time and BR firing rate as a function of stimulus . For both graphs model results are marked with red lines and data with black . The associated pressure stimulus is depicted in Figure 2A . For this stimulus we analyzed five models . We first describe results obtained with the three linear models , analyzing the impact of including one , two , or three Voigt bodies . Second we discuss results obtained with the nonlinear models analyzing the impact of including more advanced description of the wall strain . For this stimulus we did not analyze the integrate-and-fire model , since we did not anticipate any added effect of this model because of the input rage of the pressure stimulus . The three linear models include a component determining the wall strain , described using a linear elastic function of pressure , a component representing mechanoreceptor stimulation , described using one , two , and three Voigt bodies , and a component predicting the BR firing rate . The three models have , , and parameters , respectively , as well as two additional parameters and associated with the sinusoidal stimulus . In [20 , p . 695] the authors indicated that phase measurements are less accurate than amplitude measurements due to the inaccuracies associated with assigning interspike intervals to bins . Thus , the parameters and were added to the parameter set . Sensitivity analysis together with subset selection allowed us to identify four uncorrelated parameters including , and , which were estimated for all three models . The nominal values for the model parameters ( listed in Table 4 ) were computed as follows . The parameter ( ) , where is Young's modulus ( mmHg ) , ( mm ) is the wall thickness , and is the zero pressure radius as described in the Methods section ( see also Table 2 ) . We use approximating a lower bound to values observed in a previous study [75] . In [76 , Figure 1] Feng et al . provide detailed measurements of the external diameter and thickness for the rat aortic arch , measured in adult male Sprague-Dawley rats . They found that in the region with aortic BR endings the average values of and . Using these values we compute ( ) . We note this parameter and were highly correlated indicating equivalent fits could be achieved through adjustment of either parameter . No direct experiments exist allowing estimation of nominal values for the elastic and viscous constants associated with mechanoreceptor strain . These parameters appear only in the dynamic part of the model and determine the adaptation time-scales . To ensure that the three models are distinct , it is essential that parameters representing time-scales are separated , otherwise the models would essentially reduce to one . This knowledge , along with values chosen in the study by Bugenhagen et al . [56] motivated our choice for nominal parameter values . To avoid the problem of structural nonidentifiability [65] we rescaled the parameters as follows and for . The full list of the model parameters together with their initial conditions , units and literature reference is provided in Table 2 . As for the stimulus , the average pressure ( 127 mmHg ) and the amplitude ( 5 mmHg ) was provided in [20] . To compute the frequency and the shift of the pressure , we digitized the stimulus provided in [20] , Figure 2A , and then fitted to a sinusoidal function , obtaining and . As noted in Figure 6 , results of parameter estimation with each of the three models were indistinguishable , though estimated parameter values varied significantly , the latter is due to added complexity associated with adding more Voigt bodies . The fact that graphs were almost identical was also reflected by the least squares cost RMSE ( and the coefficient of determination ) for models , and we obtained 2 . 522 ( 0 . 949 ) , 2 . 507 ( 0 . 950 ) , and 2 . 495 ( 0 . 951 ) , respectively , see Table 4 . Next , we investigated the impact of including more complex wall models . Additionally , we incorporated a nonlinear response wall model , and a viscous wall model . To be more precise we compare the BR response of the following three models , and described using 7 , 8 , 9 parameters plus the two parameters associated with the stimulus . We examined the ability of each of these models to fit the sinusoidal stimulus . Sensitivity analysis and subset selection allowed us to estimate 4–6 parameters . All models allowed us to estimate , , , and . In addition , for the nonlinear elastic model was added to the subset and for the viscoelastic model and were added to the subset . Given that the more complex nonlinear models allows estimation of more parameters , one should anticipate better results . But due to the limited dynamics embedded within the pressure stimulus , adding more complex wall models did not improve results as reflected by the least squares cost RMSE ( and the coefficient of determination ) , which for , and gave 2 . 507 ( 0 . 950 ) , 2 . 517 ( 0 . 950 ) , and 2 . 458 ( 0 . 952 ) , respectively; see Table 5 . Step-increase stimulus: This section presents results with the same five models previously used for prediction of the BR response with the sinusoidal pressure stimulus . As with the sinusoidal stimulus we do not test the integrate-and-fire model , due to the nature of the input stimulus . Again , we first discuss results obtained with the three linear models , and followed by results obtained using the more complex nonlinear and viscoelastic wall models . Studies were done to capture the effect of overshoot and adaptation in response to four input stimuli varying in the magnitude of the pressure step . All stimuli start at the same baseline pressure , and the step-increase was imposed at the same time . As before the three models have , , and parameters , respectively , but functions describing the “smooth” step pressure increase ( 2 ) only involve one additional parameter , representing the onset of the step-increase . This parameter was not provided in [20] . Subset selection together with efforts to make model comparison possible resulted in . As reported in [20 , Figure 5] the baseline pressure associated with the step-increase stimulus was set to 115 mmHg , and the step-increases ( from the baseline ) to 128 , 134 , 137 , 143 mmHg , respectively . Figure 7 ( A–D ) shows the ability of the three linear BR models to reflect observed overshoot and adaptation . Each panel shows the optimized firing rate . The least squares cost RMSE ( and the coefficient of determination ) of model for the optimized values of its parameters with respect to the four step-increases 128 , 134 , 137 , and 143 mmHg were: 1 . 860 ( 0 . 899 ) , 2 . 677 ( 0 . 919 ) , 2 . 420 ( 0 . 969 ) , and 1 . 832 ( 0 . 983 ) . Marginal improvements were obtained with , which gave: 1 . 800 ( 0 . 905 ) , 2 . 702 ( 0 . 917 ) , 2 . 390 ( 0 . 970 ) , and 1 . 823 ( 0 . 983 ) , and finally , for the values were: 1 . 764 ( 0 . 909 ) , 2 . 700 ( 0 . 918 ) , 2 . 390 ( 0 . 970 ) , and 1 . 809 ( 0 . 983 ) , see Table 4 . Similar to the sigmoidal stimulus , no improvements ( results not shown ) were obtained with the more advanced nonlinear and viscoelsatic wall models . Square stimulus: The square stimulus is characterized by a constant pressure input followed by a step-increase after which the pressure is decreased to its baseline value . This type of stimulus primarily tested the models' ability to reflect PED followed by recovery , although other features including adaptation and overshoot are also shown . Similar to previous studies we first investigated the simpler linear models including one , two and three Voigt bodies . For the square input stimulus , in Figure 8A , we plot BR firing rate data extracted from Saum et al . [32] ( circles ) and the corresponding optimized fit using ( solid line ) , changing the number of Voigt bodies did not improve the model response . This model has 7 parameters and additional two and related with the input stimulus ( 3 ) . Subset selection together with our effort to make model comparisons possible made us estimate the parameters . The least squares cost RMSE ( and the coefficient ) with optimized parameters was 7 . 384 ( 0 . 862 for ) , see Table 6 . While the model , as anticipated , was able to produce overshoot and adaptation , this model was not able to capture PED accurately . We hypothesize that the inability to show PED is due to the simple linear firing rate model , which does not allow the BR firing rate to cease for sub-threshold stimuli . Thus , we first investigated the impact of exchanging the linear BR firing rate model with the integrate-and-fire model . Including the integrate-and-fire model clearly improved results ( not shown ) though with the linear wall model it was difficult to accurately fit the data both during adaptation and recovery . Subsequently , we analyzed the impact of exchanging the linear wall model with the nonlinear wall model , keeping the integrate-and-fire model . Results with this model ( ) is shown in Figure 8B . This figure shows the recorded BR firing rate ( circles ) and the model fit ( solid line ) in response to the square pulse stimulus . Model parameters estimated include . Optimized parameter values and units are given in Table 6 together with the and RMSE errors . Finally , we investigated the impact of adding a viscoelastic wall model , which did not provide any additional improvements . Simultaneous fits: Figure 7 showed that linear models can exhibit overshoot , adaptation , and can fit the firing rate data for all four step-increases , though as reported in Table 4 , each step-increase resulted in significantly different parameter estimates . However , data are extracted from experiments done within the same fiber , thus we expected only small variation in parameter values . We performed additional optimizations to investigate if the observed differences in the parameter estimates , were simply a result of performing optimizations for one stimulus at the time . To remedy this problem , we estimated one set of parameters for all four step-increases . Results of this simulation are shown in Figure 9A ( computed with the model ) . This simulation confirms that the simple linear model cannot estimate one set of parameters that allows simultaneous fit of the response to all four pressure stimuli . Similar results were obtained with the other models . In particular , it should be noted that the overshoot is diminished for the smaller step-increases , and that the model was unable to capture the correct baseline firing rate . In contrast , when including a nonlinear elastic wall we were able to estimate one set of parameters that allowed us to simultaneous fit the response to all four pressure stimuli . This model accurately reproduced the baseline firing rate as well as the overshoot and adaptation observed in response to the step-increase ( Figure 9B ) . We hypothesize that this difference is due to larger range of pressure within the applied stimuli , where the known nonlinear behavior of the arterial wall deformation plays an important role . It is known that arteries appear stiffer at higher pressures than at lower pressure . Thus the nonlinear wall model significantly improves the fit . In the previous section we showed the ability of our proposed linear and nonlinear BR models to fit the firing rate data measured from rats . It is well known ( see section Methods ) that the BR firing rate can exhibit a number of qualitative characteristics including saturation , threshold , adaptation , overshoot , PED and rectification . The quantitative data used to test the model in the previous section showed adaptation , overshoot , and PED , in response to a sinusoidal ( with fixed amplitude ) and step changes ( increase/decrease ) in blood pressure . However , these stimuli did not test saturation , threshold , or rectification . Although the models show adaptation , no clear conclusion could be drawn to determine how many Voigt elements ( time-scales ) were needed to reflect known BR firing rate observations . Now we show our preferred model with estimated parameters , including nonlinear deformation of the elastic wall , two Voigt bodies for computing nerve ending stimulation , and a leaky integrate-and-fire model for predicting firing rate , exhibits the features not yet studied experimentally . This was done using ramp and sinusoidal ( with varied amplitude and frequency ) pressure stimuli . Rectification: Figure 10A presents the model's response to a sinusoidal wave pressure stimulus with various amplitude . This simulation is motivated by the observation of Brown et al . [20 , Figure 6] that a 2 . 5 increase in amplitude of the sinusoidal stimulus resulted in an increased amplitude of the firing rate , with a lower mean firing rate . Moreover , it was noted that for large amplitude stimulation the firing rate ceases during the trough of the pressure wave . These two observations are referred to as rectification . One could question if the simpler linear model is able to display this phenomena . The linear wall model would certainly be able to reproduce the increased amplitude for a single stimulus , but again , if multiple stimuli were tested , correct predictions require the nonlinear wall model . Moreover , the ability of the firing rate to cease requires the threshold built into the integrate-and-fire model . With the simple linear neuron model , the firing rate would become negative , which does not represent what happens physiologically . Threshold and saturation: Two other prominent firing characteristics are threshold and saturation . In [27 , Figure 5] Seagard et al . noted that BRs with a higher threshold pressure were less sensitive , had lower discharge rates , and had higher values for saturation . Receptors with higher discharge rates were also more sensitive and were found to have afferent fibers with greater conduction velocities . In Figure 10 B we show that our model is able to reproduce qualitatively similar saturation features . Adaptation: Even though our quantitative models were able to capture adaptation , it was noted that results with one , two , or three Voigt bodies were similar , in other words , the models could not clearly distinguish if the adaptation process included one or three time-scales . Yet , several authors ( e . g . , [11] , [12] , [30] , [77] ) have hypothesized that adaptation occurs with more then one time constant . It is also known that the muscle spindle can produce a response of this kind to a clipped-off ramp stretch [78] . Figure 10 C shows that the studied model admits the fast adaptation and the slow adaptation in agreement with experiments . We also plot an exponential fit and show that a similar adaptation is not possible by only one exponential function . This qualitative feature made us include two Voigt bodies in our preferred model , a conclusion that could not have been made strictly from quantitative simulations presented in the previous section . Asymmetry: In Figure 10 D we show that our preferred model clearly exhibits asymmetry when exposed to a ramp-up followed by a ramp-down pressure stimulus , which agrees with experiments ( see e . g . , [23] ) .
The objective of this study was to develop a mathematical framework for constructing computationally efficient and accurate BR models , which in contrast to the existent models , are able to reflect all known qualitative BR firing features as well as fit quantitative data . Our overall aim was not to focus on a concrete experimental species but rather to formulate a family of BR models , which could potentially be included in a more comprehensive model of CV system . Quantitative computations were done comparing our models to experimental measurements by Brown et al . [20] and Saum et al . [32]; while qualitative studies were performed to show that our preferred generic model is able to exhibit all known firing rate responses . All models used blood pressure as an input and computed the BR firing rate as an output . Although our procedure was designed to be generically applicable to various species and multiple types of baroreceptors , we tested our models using only quantitative data from experiments preformed using aortic baroreceptors from rats . We believe that this is the first work that offers a systematic approach to building and evaluating BR models with the objective to provide the simplest possible family of generic models . Our modeling framework first analyzed the known physiology and common features of the firing rate observed in the BRs of various species . Second we generated submodels describing each stage of the physiological response: arterial wall deformation , stimulation of mechanosensitive channels found in the BR nerve endings , and generation of action potentials . Finally we modeled the BR system by combining the submodels in various configurations ( summarized in Table 3 ) . Each of these configurations was tested in order to determine the contributions of each component to the transduction of the BR signal . This process allowed identification of the importance of nonlinear effects of two critical sub-systems in the BR response , the arterial wall and the neuron itself . This framework advanced the state of BR modeling by first evaluating models comparatively with respect to the same data and features , second by generating a model which fits all known characteristics of BR firing qualitatively , and third by developing a model which is capable of fitting multiple data sets of BR firing rates quantitatively . A particular insight was revealed by consideration of BR models with various descriptions of the arterial wall . Applying our framework demonstrated the insufficiency of linear wall models' representations of the response of a single BR neuron to multiple step-pressure inputs ( see Figure 9A ) . A nonlinear elastic wall model was required to implement a model capable of accurately fitting the BR response to multiple pressure levels with one set of parameter values ( see Figure 9B ) . The choice of this model is further motivated by the well known fact that arteries exhibit nonlinear deformation with saturation at both high and low pressures [23] , [27] . Additionally by applying our framework and considering the effects of including the viscoelastic wall model , we found that the additional complexity did not contribute to better definition of BR dynamics , despite previous studies having shown wall deformation does have viscous components [45] , [79] . This is likely due to our modeling choice for nerve ending stimulation . This portion was modeled using two Voigt bodies in series to allow adaptation at multiple time-scales . Data is not available to separate the viscoelastic part of the wall-deformation with the viscoelastic deformation associated with stimulation of the mechanosensitive channels , thus indirectly our model exhibits both features . One explanation would consider the first Voigt body to be associated with wall deformation while the second is associated with nerve ending deformation . Moreover , it should be emphasized qualitative simulations were needed to show that the two Voigt bodies allow multiple time-scales , a feature we were not able to extract from simulations alone . These considerations , and our studies , affirm the importance of viscoelastic effects; however , in terms of simplicity it is advantageous to isolate the viscoelastic components within the model , and further we note linear viscoelastic effects are sufficient to capture the dynamics of BR firing when coupled with a nonlinear elastic total deformation of the arterial wall . To our knowledge , this study provides the first direct measure of the importance of incorporating various time-scales in BR models . It is believed that various time-scales in the adaptation process are due to the viscoelastic coupling of the nerve ending to the arterial wall . We chose to emphasize this in our modeling process by considering different numbers of Voigt bodies in series with a spring . In Table 4 we show the results of testing three models , , and differing only with respect to their nerve ending models , , and , respectively . Our findings indicate that no more then two timescales in the adaptation process are needed in order to achieve a very precise fit to the data . This conclusion is closely related to the fact that we tested our models using rat data with fairly limited pressure-stimulus response as only this type of experiments are currently available . To test this component more carefully , it is essential to analyze data recorded over longer time-scales . Another insight afforded by this investigation highlights the importance of nonlinearities in the neural response to mechanoreceptor strain . As hypothesized previously [30] , our study affirms the nonlinearities of action potential generation , even for the leaky integrate-and-fire model are sufficient to produce the hysteretic phenomenon of PED . In contrast the simple linear model of firing in response to mechanoreceptor strain does not allow for the asymmetric responses seen in PED as well as in the response to sinusoidal stimulus with high amplitude . The nonlinear-elastic wall in combination with two Voigt bodies modeling mechanoreceptor stimulation responds in an equal but opposite manner to rising and falling pressure , thus the change in firing rate with the linear model is symmetric to step-increase and step-decrease , which is not reflective of the data . We affirm the hypothesis that the neuron itself is responsible for generating PED , as this feature was robustly represented by the leaky integrate-and-fire model regardless of the mathematical description for arterial wall strain . This would provide a good explanation for the observation of PED in multiple species , many of which have a high degree of variability in the viscoelasticity in their respective arterial walls . The results and insights generated through application of our proposed modeling framework are not limited to those presented in this study . In addition it provides a means to identify which features and what level of detail of the underlying physiological systems are of greatest significance in generating BR dynamics . This ability is useful in developing experiments which may be able to isolate physiology responsible for a given phenomenon , such as the responsibility of the neuron in generating PED . Further this approach provides evaluative power to make design decisions when developing a model for a specific data interpretation or simulation task . An example of this follows from our insights into the role of the arterial wall in BR signal transduction . Although the arterial wall may best be modeled using viscoelastic theory , our framework allows a modeling decision to be made in favor of simplicity if only the output dynamics are of interested . This investigation further suggests a methodology for integrating a model generated in this manner into a model of larger scope . Suppose a mathematical representation of an overall baroreflex system ( see Figure 1 ) is desired to reflect only normal physiological conditions , then it may be sufficient to use only simplified description of the BR signal . For example a simple linear firing rate model may be adequate for simulations operating in the range above the firing rate threshold . However , to reflect heart rate at various abnormal physiological conditions a more complex model combining nonlinear deformation with the leaky integrate-and-fire model may be necessary . Additionally , application of our modeling approach to a larger CV model might reveal features of the BR subsystem with importance in maintaining homeostasis . We hypothesize that overshoot , adaptation and recovery , features of the BR firing in response the extremes of pressure waves , are critical for regulation of blood pressure during stressful situations , such as a head-up-tilt experiment . | Many people have experienced lightheadedness when standing up , yet the exact cause of this phenomenon remains unknown . For some people , lightheadedness occurs because of anomalies in the blood pressure control system , which keeps blood flow and pressure at homeostasis . One way to explore this system is via mathematical modeling , which can offer valuable insights into the complex dynamic processes . This study develops a framework for modeling activity of the baroreceptor neurons . The models consist of three components reflecting three physiological mechanisms relating blood pressure to the baroreceptor firing rate: modulation of arterial blood pressure causes dilation of the arterial wall , stimulating mechanoreceptors within the baroreceptor nerve endings , emanating from the aortic arch and carotid sinus , which in turn modulates the firing rate of the baroreceptor neurons . This signal is integrated in the brain stem , stimulating baroreflex efferents to counteract the pressure increase . In this study , we review the main static and dynamic features of the baroreceptor firing activity , and show , using a combination of modeling techniques and rat aortic baroreceptor data , how to build a computationally efficient , yet biologically correct model . These models are important components for describing efferent responses , such as: heart rate , contractility or stroke volume . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [] | 2013 | Modeling the Afferent Dynamics of the Baroreflex Control System |
The distribution and intensity of transmission of vector-borne pathogens can be strongly influenced by the competence of vectors . Vector competence , in turn , can be influenced by temperature and viral genetics . West Nile virus ( WNV ) was introduced into the United States of America in 1999 and subsequently spread throughout much of the Americas . Previously , we have shown that a novel genotype of WNV , WN02 , first detected in 2001 , spread across the US and was more efficient than the introduced genotype , NY99 , at infecting , disseminating , and being transmitted by Culex mosquitoes . In the current study , we determined the relationship between temperature and time since feeding on the probability of transmitting each genotype of WNV . We found that the advantage of the WN02 genotype increases with the product of time and temperature . Thus , warmer temperatures would have facilitated the invasion of the WN02 genotype . In addition , we found that transmission of WNV accelerated sharply with increasing temperature , T , ( best fit by a function of T4 ) showing that traditional degree-day models underestimate the impact of temperature on WNV transmission . This laboratory study suggests that both viral evolution and temperature help shape the distribution and intensity of transmission of WNV , and provides a model for predicting the impact of temperature and global warming on WNV transmission .
The interaction between pathogens , their vectors , and vertebrate hosts is a dynamic one , and evolution in any one of the three can significantly alter transmission dynamics . Theory suggests that pathogens will evolve to maximize their fitness , which is a function of transmissibility and virulence to the host [1] , [2] . Pathogens that infect and replicate well in their vertebrate hosts and vectors may decrease the survival of both which may reduce their lifespan for transmission . At the same time , the distribution and intensity of transmission of vector-borne pathogens is strongly influenced by the interaction of temperature , vectors , hosts , and pathogen genetics . Temperature can determine both the latitudinal boundary and upper elevational limit of pathogen transmission if the extrinsic incubation period ( EIP ) is greater than the longevity of the vector [3] . Temperature also has been linked to changes in the intensity of transmission of pathogens [4] , [5] , which may be linked to temperature-induced changes in the EIP , the longevity , and the feeding rate of vectors [6] , [7] . West Nile virus ( WNV; Flaviviridae , Flavivirus ) , is a single-stranded positive-sense RNA virus that was introduced into the western hemisphere in 1999 and has subsequently spread throughout much of North , Central , and South America [8] , [9] . It is primarily transmitted between birds ( especially American robins , Turdus migratorius , in many areas [10]–[14] ) and Culex mosquitoes [8] , [15] and has caused at least 2 , 500 reported cases each year since 2002 for a total of 32 , 135 total reported cases , 11 , 243 cases of encephalitis , and 1 , 125 deaths , with an estimated 1 . 56 million infections and 310 , 000 illnesses from 1999–2007 [9] , [16]–[18] . In addition , WNV has evolved over the past 7 years , and a genotype that was first isolated in 2001 ( termed WN02 ) has displaced the introduced genotype ( termed NY99 ) [19] , [20] . WNV strains in the WN02 or North American dominant genotype have three consensus changes in the full length genome compared to NY99 [19] , [20] . The rapid expansion of the WN02 genotype has been linked to a shorter extrinsic incubation period in Culex mosquitoes [21] , [22] , but the full mechanisms of displacement are not yet known . In particular , in previous studies WN02 genotypes were transmitted more efficiently than NY99 by Cx pipiens on 5 and 7 days post feeding , but not day 9 and by Cx . tarsalis from 5 to 14 days post feeding [21] , [22] . The vector competence of mosquitoes characterizes their ability to transmit a pathogen after taking an infected blood meal . The fraction of vectors transmitting the pathogen is known to vary between populations of a species [23]–[25] , and increase with time [26] , [27] and temperature for WNV [28]–[30] and many other pathogens , including western equine encephalomyelitis virus and St . Louis encephalitis virus in Cx . tarsalis [28] , [31] , Rift Valley Fever virus in Aedes fowleri [32] , Ockelbo virus in Culex spp [33] and Aedes spp [34] , and African horse sickness virus , bluetongue virus , and epizootic hemorrhagic disease in Culicoides sonorensis [35] . However , the exact relationship between vector competence , temperature and the time since feeding on an infected host is not clear , and in other studies the influence of temperature on vector competence varies , sometimes depending on the mosquito species infected [33] , [36] , [37] . A degree day model developed for Cx . tarsalis has been used to model the effect of temperature on WNV transmission across North America [28] , [38] . In this approach , a mosquito ( or a fraction of a population of mosquitoes ) feeding on an infected host becomes infectious after a time period at a certain temperature , termed the number of degree days . Degree days are often measured as the number of days since feeding multiplied by the temperature in degrees Celsius above a minimum temperature threshold ( Tthr ) below which no transmission is assumed to occur . However , the exponential increase with temperature in chemical and molecular processes contributing to viral replication would suggest that the relationship between transmission and the number of degree days should be accelerating and would not be well described by a simple degree day model . Transmission would be expected to be higher at higher temperatures given the same number of degree days . For example , if Tthr = 14°C , transmission would be expected to be higher after seven days at 30°C ( 16° above the threshold temperature of zero transmission ) than after 16 days at 21°C ( 7° above the Tthr ) , even though the same number of degree days , 112 , is the same in both cases . Here we explore the relationship between temperature and transmission of two genotypes of WNV ( NY99 and WN02 ) and test the adequacy of a simple degree day model for WNV transmission by Culex pipiens , a key enzootic and bridge vector for WNV in the northern USA [15] , [39] .
Two strains of WNV were used , one belonging to genotype NY99 and one to WN02 ( strain designations are NY99-3356 and WN02-1956 , Genbank accession number , AF404756 and AY590210 , respectively ) . Previous work suggested that there was little phenotypic variation between strains within genotype [21] , [22] . NY99-3356 was passed twice in Vero ( African Green Monkey kidney ) cells , and WN02-1956 was passed once in Vero cells followed by one passage in C6/36 ( Aedes albopictus ) cells prior to use in these studies . Colonized Cx . pipiens were reared and maintained in the Wadsworth Center Arbovirus Laboratory BSL-2 insectary . The colony was established in 2002 from egg rafts collected in Pennsylvania ( courtesy of Michael Hutchinson ) and has been maintained continuously using defibrinated goose blood ( Hema Resourse and Supply , OR ) for egg production and 10% sucrose ad lib for maintenance at 27°C with 16∶8 L∶D light cycle and 85% humidity . All experiments with infectious virus were performed in the BSL-3 laboratories or insectaries at the Wadsworth Center Arbovirus Laboratories . Seven day-old mosquitoes were deprived of sucrose and water for 48 h and then fed on a suspension of defibrinated goose blood ( Hema Resourse and Supply , OR ) plus a final concentration of 2 . 5% sucrose and either a NY99 or a WN02 virus , using a Hemotek feeding system ( Discovery Workshops , UK ) . The WNV titer in the bloodmeals was 1 . 2–1 . 4×108 plaque-forming units ( PFU ) /ml . Mosquitoes were allowed to feed for up to 1 . 5 h at which time engorged mosquitoes were separated from unfed mosquitoes under CO2 anesthesia . Fully engorged mosquitoes were placed into 0 . 6L cardboard cartons , supplied with 10% sucrose ad lib , and held at the prescribed temperatures under 85% RH , photoperiod of 16∶8 ( L∶D ) . Groups of 25 mosquitoes were removed at several different intervals post-feeding and anesthetized with triethylamine ( Sigma , St . Louis , MO ) . The days sampled included days 4 , 7 , 10 , 14 , 18 , 21 , 24 , 28 , 31 , 34 , and 40 for 15°C , 18°C , and 22°C and additional early sampling at days 0 . 5 , 1 , 1 . 5 , 2 , 2 . 5 , 3 for experiments at 32°C . Legs were removed and placed in 1 . 0 ml of mosquito diluent ( MD; 20% heat-inactivated fetal bovine serum [FBS] in Dulbecco's phosphate-buffered saline plus 50 ug/ml penicillin/streptomycin , 50 ug/ml gentamicin , and 2 . 5 ug/ml Fungizone ) and frozen at −80°C for subsequent assay . Salivary secretions were collected using a modified in vitro capillary transmission assay [40] . Briefly , mosquito mouthparts were inserted into a capillary tube containing approximately 10 µL of a mixture of 50% sucrose and FBS ( 1∶1 ) for 30 minutes , at which time the contents were placed into 0 . 3 ml MD in a microfuge tube . Bodies were placed in 1 . 0 ml MD and all samples were frozen at −80°C for subsequent assay . Bodies and legs were homogenized separately using a mixer mill ( Qiagen , Valencia , CA ) at 24 cycles/s for 30 s and then clarified by centrifugation . Samples were analyzed for the presence of infectious virus by plaque assay on Vero cells as previously described [41] . We treated each group of 25 individual mosquitoes tested after a fixed time at a temperature as an experimental unit ( data point ) and the fraction of mosquitoes that were infected ( # with virus in the body/# fed ) , had disseminated infections ( # with virus in their legs/# fed ) , or transmitted ( # expectorating virus/# fed ) as dependent variables . We built regression models ( using SPSS v 15 . 0 ) including degree days ( DD = tT , where t = time or days since feeding , and T = temperature in degrees Celsius ) and a genotype by DD interaction ( to test for a temperature and time varying advantage of WN02 ) as independent variables . We note that this statistical model ( and the one described below ) assumes that infection and transmission are increasing functions of temperature and time since feeding , and statistical effects model differences between genotypes as differences in the rates of increase ( slopes ) , rather than fixed differences ( intercept or main effects ) . We believe intercept differences are less biologically realistic because infection , dissemination , and transmission all start at zero and increase with time and temperature . In essence , the statistical effect of viral genotype is assumed to influence the rate at which the probability of a group of mosquitoes transmitting and becoming infected increases with temperature and time . We also note that our degree model implicitly assumes the minimum temperature threshold is 0°C ( since it uses the raw temperature ) , which is likely too low , as no transmission was observed at 10°C in Cx . tarsalis held for 110 days [28] . However , the fit of the data were much better using raw temperature than either ( Temperature −10°C ) or ( Temperature −14 . 3°C ) ( the residual error from models in Table 2 with a threshold of 14 . 3°C and 10°C were 3 . 02 and 1 . 87 , respectively , compared to 0 . 99 with a threshold of 0°C; all regressions had the same number of predictors ) . We arc-sin square-root transformed the three dependent variables to normalize the residuals . We omitted an intercept from the model because we assumed that , except for residual virus in the blood meal , infection and transmission would be 0 at degree-day 0 . The qualitative conclusions presented below were identical using an intercept . We then tested the hypothesis that infection , disseminated infection , and transmission should accelerate with increasing temperature faster than the DD model , by regressing the residuals of the previous models against temperature . A priori , we hypothesized that the increase in transmission and infection with temperature would be a balance between chemical and kinetic processes that increase exponentially ( i . e . as eT where T is temperature , and e is the base of the natural logarithm ) and rate-limiting processes that would constrain viral replication . Since the residuals were significantly correlated with temperature for all three dependent variables ( see Results ) we attempted to determine if DD with a higher order temperature term would provide a better fit to the data . To facilitate model comparison we replaced the DD model term ( tT ) with a term that was the product of the days since feeding ( t ) and temperature ( T ) , raised to the power n ( tTn ) , and compared models with increasing n . For the DD by genotype interaction we also used DD with temperature also raised to the nth power . Finally , the qualitative results of both our analyses were unaffected by using actual temperature to calculate degree days , or degrees above a previously reported [28] threshold for zero transmission of 14 . 3°C . Our own data , and those in ref . [28] , show that low-level transmission occurs at 14–15°C ( but after very long periods that may exceed mosquito lifespan in the field ) .
We examined a total of 2075 Cx . pipiens mosquitoes in 83 groups of 25 individuals and examined midgut infection , disseminated infection , and transmission from 12 hours to 40 days post-feeding ( Figure 1 ) . At 32°C , we detected transmission at 12 , 36 , and 60 hours for the WN02 genotype , and on day 3 ( 72 hours ) for the NY99 genotype of WNV ( Figure 1C ) . Virus was also present in the legs ( and abdomens ) of these mosquitoes at these time points ( Figure 1B ) and was not present in the saliva of any of the mosquitoes that were not infected , so it is unlikely that mechanical transmission or regurgitation accounted for the virus detected in the transmission assays ( see also Discussion , below ) . The initial regressions indicated that the fraction of mosquitoes infected and the fraction with disseminated infections increased with degree days ( DD = tT ) since feeding ( Figures 1 , 2; Table 1 ) . However , neither was significantly different between genotypes ( Figures 1 , 2; Table 1 ) . In contrast , transmission of WNV by Cx . pipiens mosquitoes was significantly influenced by both DD and a genotype by DD interaction ( Figures 1 , 2; Table 1 ) . The coefficient of this last term indicated that the fraction of mosquitoes transmitting WNV increased faster for the WN02 genotype than the NY99 genotype ( and the fitted function for WN02 was greater at all times and temperatures since both lines intersect the origin ) . In fact , the fraction of mosquitoes transmitting the WN02 genotype was greater than or equal to the fraction transmitting the NY99 genotype for all but two of the 42 time-temperature samplings ( the two exceptions were at 22°C on days 7 and 10 where 1/25 mosquitoes transmitted the NY99 genotype but 0/25 transmitted the WN02 genotype ) . Thus , the WN02 genotype appeared to have an advantage at both high and low temperatures , and this advantage increased with time and temperature . However , the residuals of regressions for all three dependent variables was significantly correlated with temperature ( all p<0 . 001 ) , suggesting that a degree day predictor using a linear product of temperature and incubation period ( tT ) was not fully capturing the temperature-dependent acceleration in infection and transmission . In the second statistical analysis , we found that both transmission and disseminated infection was best predicted by a model including DD with temperature raised to the 4th power ( tT4 ) , and a DD by genotype interaction ( Table 2 ) . The results were the same if the fraction transmitting was expressed as the fraction of infected mosquitoes transmitting ( arc-sin square root transformed fraction of infected transmitting , DD: 9 . 09×10−8 tT4; p <0 . 0005; DD-genotype interaction: −3 . 30×10−8; p<0 . 0005 ) . Infection was also best predicted by a model including DD with temperature raised to the 4th power ( tT4 ) , but was not significantly influenced by the DD by genotype interaction ( Table 2 ) . The residual error in these second set of regressions was substantially lower compared to the first statistical analysis , with the same number of independent variables ( Tables 1 , 2 ) . The significant negative coefficient for DD by genotype interaction term for transmission and disseminated infection again indicates that the fraction of Cx . pipiens infected with and transmitting genotype WN02 increased faster than mosquitoes transmitting NY99 , as illustrated by the raw data ( Figure 1 ) , and the fitted relationships ( Figure 2 ) . Thus , WN02 would have a significant advantage over NY99 under warmer conditions after the same incubation period .
The relationship between temperature and the transmission of pathogens has gained substantial attention recently , because projected changes in global temperature may increase the health burden of some diseases [42] . We have shown that , in the laboratory , increases in temperature have a two-fold impact on WNV transmission . First , as has been shown previously , increasing temperatures significantly increased viral infection , dissemination , and transmission , most likely through increased viral replication . Our study used the plaque assay which measured the presence of infectious virus and not the presence of unpackaged viral RNA , to test for infection , dissemination , and transmission in the mosquito . As a result , since the replication cycle is completed more quickly at higher temperatures , this will lead to greater concentration of infectious virus above the limit of detection in each mosquito . This is the case for replication in all tissues , and as such , increased temperature would affect not only infection kinetics , but dissemination and transmission kinetics as well . Second , warmer temperatures increased the advantage of the WN02 genotype over the NY99 genotype virus , and this advantage accelerated with temperature . Thus , the WN02 genotype appears to be better adapted to warmer temperatures than NY99 , and NY99 was better adapted to warm conditions than a South African strain of WNV in Cx . tarsalis [28] . This result highlights the importance of understanding vector-pathogen-environment interactions and the role of pathogen evolution in influencing transmission . We also have shown that the advantage of WN02 over the NY99 genotype extends beyond day 7 post infection in Cx . pipiens , as we had observed in Cx . tarsalis [22] . The disparate results between our study and previous research that indicated no difference on day 9 [21] is likely due to extending the experiments past day 9 ( up to day 40 at some temperatures ) and including additional experiments resulting in much larger sample sizes . Nonetheless , our results support the earlier assertion that the WN02 genotype has an advantage over the NY99 genotype in the laboratory . Our results refine the WNV temperature-transmission relationship and show that WNV transmission in mosquitoes accelerates nonlinearly with the extrinsic incubation temperature , suggesting that even a small increase in temperatures can have a significant impact . They show that traditional degree day models for WNV may not accurately describe the impact of temperature on transmission . Instead , transmission may be more accurately modeled using degree day functions that include a temperature term raised to a power greater than 1 . For WNV , we found that a degree day term with temperature raised to the fourth power , tT4 , was most accurate in explaining variation in transmission in our data . The implications of this difference are that even relatively small changes in temperature ( e . g . the 2°C projected change in global temperatures [43] ) have the potential to substantially increase transmission , and traditional degree day models used to investigate the potential impact of global warming will thus underestimate the effects of warming on transmission of WNV by mosquitoes . For example , if we fit a linear degree day model , tT , to our data , an increase from 28°C to 30°C would be predicted to increase temperature only 0 . 9% ( from 12 . 1% to 13 . 0% ) , whereas the fitted model with tT4 this increase from 28°C to 30°C would actually increase transmission 7 . 8% ( from 11 . 4% to 19 . 2% ) . In our study , we occasionally detected infectious virus in both salivary secretions and the legs of mosquitoes only 12 hours after feeding on an infected blood meal . Although under normal conditions the WNV replication cycle requires 10–12 hours [44] , it is known that the virus replicates more quickly at higher temperatures [45] . Thus it may be possible that sufficient levels of replication took place in some mosquitoes held at 32°C to result in dissemination and transmission very quickly after feeding . It is equally possible that at high temperatures the cell junctions of the epithelium of the midgut were disrupted or increasingly permeable creating a rapid mechanism for midgut escape , possibly via leakage of virus , as has been observed with other virus-mosquito pairs [46]–[48] . This would have facilitated early escape of the virus to the legs , and subsequent infection of the salivary glands . Since the only mosquitoes that had virus in their salivary secretions were those that had virus in their legs , this argues against either mechanical transmission due to residual virus on the proboscis or regurgitation of virus from the midgut during the capillary transmission assay . Nonetheless , we cannot entirely rule out these other explanations , and furthermore , it is unlikely that mosquitoes would feed again 12 or 24 hours after the initial blood meal unless they had only obtained a partial or interrupted blood meal . It should be noted that our study did not evaluate the impact of mosquito rearing temperature , as all immature stages were maintained at 27°C . Previous work showed that vector competence for several flaviviruses , including Murray Valley [49] , Japanese encephalitis [50] , and St . Louis [51] encephalitis viruses , and dengue [52] and yellow fever [53] viruses , was depressed by maintaining adults at temperatures lower than those they experienced during larval development . In contrast , transmission of two alphaviruses , eastern equine encephalitis [54] and western equine encephalomyelitis [55] , were not observed to decrease when adults were maintained at temperatures lower than the rearing temperature , and early season populations were considerably more susceptible to infection that those collected during midsummer . These results contribute to our broader understanding of how factors can generate spatial and temporal variation in transmission of pathogens . The transmission of WNV by Cx . pipiens has been shown to be influenced by host availability [12] , mosquito genetic ancestry [56] , and now the interaction of temperature and viral genotype . A key goal of future research will be to link the temperature-transmission patterns observed in the laboratory to patterns of transmission in the field . This should enable more accurate predictions of the impact of climate and climate change on the transmission of WNV and other vector borne pathogens . | West Nile virus ( WNV ) was introduced into New York in 1999 and subsequently expanded its range to include much of North , Central , and South America . Previously , we have shown that a new strain of WNV ( referred to as WN02 ) that was first detected in 2001 and subsequently spread across North America was more efficient at infecting and being transmitted by Culex mosquitoes than the strain that was originally introduced ( referred to as NY99 ) . In the current study , we determined how temperature and time since feeding on infected blood affected the probability that mosquitoes would transmit these two strains of WNV . We found that the advantage of the WN02 strain over the NY99 strain increased with both temperature and time . Thus , warmer temperatures would have facilitated the invasion of the WN02 strain . In addition , we found that transmission of both strains of WNV accelerated sharply with increasing temperature , such that small increases in temperature had relatively large effects on transmission . This laboratory study suggests that both viral evolution and temperature influence the distribution and intensity of transmission of WNV , and provides a model for predicting the impact of temperature and global warming on virus transmission . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"ecology/global",
"change",
"ecology",
"virology/virus",
"evolution",
"and",
"symbiosis",
"virology/emerging",
"viral",
"diseases"
] | 2008 | Temperature, Viral Genetics, and the Transmission of West Nile Virus by Culex pipiens Mosquitoes |
Plant pathogens , such as bacteria , fungi , oomycetes and nematodes , rely on wide range of virulent effectors delivered into host cells to suppress plant immunity . Although phytobacterial effectors have been intensively investigated , little is known about the function of effectors of plant-parasitic nematodes , such as Globodera pallida , a cyst nematode responsible for vast losses in the potato and tomato industries . Here , we demonstrate using in vivo and in vitro ubiquitination assays the potato cyst nematode ( Globodera pallida ) effector RHA1B is an E3 ubiquitin ligase that employs multiple host plant E2 ubiquitin conjugation enzymes to catalyze ubiquitination . RHA1B was able to suppress effector-triggered immunity ( ETI ) , as manifested by suppression of hypersensitive response ( HR ) mediated by a broad range of nucleotide-binding leucine-rich repeat ( NB-LRR ) immune receptors , presumably via E3-dependent degradation of the NB-LRR receptors . RHA1B also blocked the flg22-triggered expression of Acre31 and WRKY22 , marker genes of pathogen‐associated molecular pattern ( PAMP ) ‐triggered immunity ( PTI ) , but this did not require the E3 activity of RHA1B . Moreover , transgenic potato overexpressing the RHA1B transgene exhibited enhanced susceptibility to G . pallida . Thus , our data suggest RHA1B facilitates nematode parasitism not only by triggering degradation of NB-LRR immune receptors to block ETI signaling but also by suppressing PTI signaling via an as yet unknown E3-independent mechanism .
Globodera pallida , a plant-parasitic cyst nematode , is a global threat to agronomically important crops such as potato and tomato . This sedentary plant endoparasite penetrates plant root systems to reach the inner cortex where it establishes a permanent feeding site , a multi-nucleate structure termed syncytium [1] . The transformation of plant root cells into syncytia by cyst nematodes is mediated by effectors produced in the nematode pharyngeal glands . It is generally believed that nematode effectors must manipulate various host physiological processes , particularly suppressing the plant defense responses , via largely unknown mechanisms , in order to facilitate the formation and maintenance of syncytia [1] . The first layer of plant defense is governed by membrane-associated pattern recognition receptors ( PRRs ) that recognize pathogen associated molecular patterns ( PAMPs ) and/or endogenous damage-associated molecular patterns ( DAMPs ) . This is termed PAMP-triggered immunity ( PTI ) and , to overcome PTI in host plants , pathogens use the secreted effectors to suppress PTI [2] . In turn , to counteract the interference of PTI by pathogen effectors , resistant plants have evolved a second layer of defense termed effector-triggered immunity ( ETI ) , which is mediated by recognition of specific effectors by cytoplasmic NB-LRR-type immune receptors . A characteristic hallmark of ETI is the hypersensitive response ( HR ) , which is a form of rapid , localized cell death at the infection site . Based on genome and transcriptome analysis , large numbers of putative effectors have been predicted in the cyst nematodes , Globodera spp . and Heterodera spp . [3–5] and several of them have been demonstrated to be able to suppress ETI and/or PTI signaling [6–11] . For example , the G . rostochiensis effector , GrUBCEP12 is processed into a free ubiquitin ( Ub ) and a short carboxyl extension peptide ( CEP12 ) in plant cells [6] . CEP12 , in turn , interferes with PTI signaling , as manifested by blockage of flg22-induced ROS production and PTI marker gene expression [7] , and disruption of ETI signaling , as shown by suppression of HR mediated by the NB-LRR receptor Gpa2 [6] . The ubiquitin proteasome system plays a significant role in many plant physiological processes by removal of intracellular proteins . The ubiquitin pathway involves sequential action of E1 ( ubiquitin activating enzyme ) , E2 ( ubiquitin conjugating enzyme ) and E3 ( ubiquitin ligase ) enzymes to covalently link ubiquitin to E3-specified substrate proteins which are then transported to the proteasome for degradation [12] . A growing body of evidence has suggested that pathogens may use effectors to manipulate host ubiquitin pathway to promote pathogenesis , during which effectors may either hijack the host ubiquitin system or themselves possess E3 activity to ubiquitinate host defense-related factors for degradation [13 , 14] . Significantly , few phytobacterial effectors have been demonstrated to act an E3 ubiquitin ligase for such virulent activity , including Pseudomonas effector AvrPtoB [15 , 16] , Xanthomonas effector XopL [17] , and Ralstonia effectors RipAW and RipAR [18] . However , no effectors have been identified as ubiquitin ligase from eukaryotic ( plant or animal ) pathogens , including fungi , oomycetes , or nematodes . Although several plant-parasitic nematode effectors have been demonstrated to suppress PTI and ETI signaling , the mechanistic basis by which effectors manipulate host defense is unknown , mainly due to lack of understanding of the biochemical characteristics of the effectors . In this study , we identified a novel G . pallida RHA1B effector that is an E3 ubiquitin ligase , which resembles a typical Really Interesting New Gene ( RING ) -type E3 and can utilize multiple host E2 ubiquitin conjugation enzymes to catalyze ubiquitination . We demonstrated that RHA1B not only manipulates the host ubiquitin system as a functional E3 ubiquitin ligase to suppress ETI signaling , but also utilizes an E3 activity-independent mechanism to block PTI signaling .
The analysis of a G . pallida life stage-specific transcriptome revealed a long list of genes encoding potential effector proteins of unknown function [5] . The RHA1B ( GPLIN_000167300 ) , which was strongly induced ( about six hundred eighty-fold ) during the early parasitic stage [5] , possessed a unique RING-H2 finger domain ( C4H2C3-type ) predicted by PROSITE [19] ( Fig 1A ) and contained an N-terminal signal peptide ( SP ) of 22 amino acids ( as predicted by SignalP [20] ) . Since plant-parasitic nematode effectors are predominantly expressed in esophageal glands that are connected to a hollow protrusible stylet [21] , the spatial expression of the RHA1B gene was determined by in situ hybridization . Consistent with the prediction of the dorsal gland-specific promoter element motif ( DOG box ) in the promoter of RHA1B gene [22] , the digoxigenin ( DIG ) -labeled anti-sense cDNA probe of RHA1B exclusively hybridized to the dorsal esophageal gland cells in parasitic J2 ( para-J2 ) nematodes ( Fig 1B ) , whereas no signal was detected in pre-parasitic J2 ( pre-J2 ) nematodes , suggesting that the expression of RHA1B gene is induced upon entrance of G . pallida into host roots . Next we sought to test where RHA1B localizes in plant cells . Due to lack of RHA1B antibody that could be used to determine the localization of RHA1B in plant cells , we generated a N-terminal GFP-RHA1B fusion construct to examine the localization of RHA1B in plant cells as a GFP-RHA1B fusion protein . The GFP-fused RHA1B effector was transiently expressed in Nicotiana benthamiana leaves and visualized in epidermal leaf tissues by fluorescence microscopy . It is believed that the mature effector proteins lack the N-terminal SP , thus we used the RHA1B construct without SP in our study ( referred as ΔSPRHA1B hereafter ) . As shown in Fig 1C , the fluorescence signal of the CaMV 35S promoter-driven GFP-ΔSPRHA1B fusion protein was found exclusively in the cytoplasm , whereas the free GFP signal was present in both cytoplasm and nucleus , suggesting RHA1B does not localizes to any specific subcellular compartment when ectopically expressed as a GFP-fusion protein in N . benthamiana leaves . The amino acid sequence of RHA1B indicates that it could be a RING-type ubiquitin ligase possessing a typical C4H2C3 amino acid motif . To determine whether RHA1B has in vitro ubiquitination capability in the presence of ubiquitin enzymes E1 and E2 together with free ubiquitin [12] , we incubated the recombinant MBP-tagged ΔSPRHA1B ( MBP-ΔSPRHA1B ) protein with HA-tagged ubiquitin ( HA-Ub ) in the presence of ubiquitin E1 ( AtUbA1 ) and E2 ( AtUbC8 ) , followed by Western blotting ( WB ) analysis using an anti-HA antibody . As shown in Fig 2A , when all components were present , a multi-banding smear began at the molecular weight position of MBP-ΔSPRHA1B and progressed upwards . This result , characteristic of self-ubiquitination with poly-ubiquitin , was abolished in various controls lacking either one or more components , suggesting RHA1B possesses E3 ligase activity . This enzymatic activity was further verified by the detection of RHA1B-promoted in vivo ubiquitination in plant cells . Although the substrates of RHA1B remain unidentified , transient co-expression of ΔSPRHA1B and ubiquitin in N . benthamiana leaves resulted in a dramatically increased poly-ubiquitination signal in plant cells ( Fig 2B lane 3 ) , indicating the cellular event of ubiquitination was enhanced in the presence of ΔSPRHA1B . Thus , we concluded that the G . pallida RHA1B effector is a functional E3 ubiquitin ligase . The RING domain , typically consisting of two Zn-finger motifs stabilized by conserved Cys residues , is indispensable for the E3 ubiquitin ligase activity since it is involved in binding to the E2 conjugating enzyme in order to facilitate transfer of ubiquitin to a substrate protein . To determine whether the RING domain of RHA1B is required for its E3 activity , we generated a ΔSPRHA1BC135S mutant , in which the conserved Cys in the RING domain was substituted with a Ser . The in vitro ubiquitination assay indicated that the ΔSPRHA1BC135S mutant no longer has E3 activity ( Fig 2C lane 3 ) , suggesting that the Zn-finger motif is essential for the E3 activity . Beside the RING domain , Lys residue ( s ) , which may serve as self-ubiquitination sites of E3 , could also be important for the activity of an E3 ligase . Thus , we next determined whether replacing the Lys residue of RHA1B has an effect on its E3 activity . There is only one Lys residue ( K146 ) in the mature ΔSPRHA1B protein and we substituted it with Arg . When the ΔSPRHA1BK146R mutant was tested in the in vitro ubiquitination assay , only marginal poly-ubiquitination was observed ( Fig 2C lane 4 ) . This result suggested that this Lys is required either for the E3 activity of RHA1B or , as a ubiquitination site , for self-ubiquitination . To further clarify these possibilities , we conducted an in vivo ubiquitination investigation on the ΔSPRHA1BK146R mutant , with the wild type ( WT ) ΔSPRHA1B and ΔSPRHA1BC135S mutant serving as positive and negative controls , respectively . As shown in Fig 2D , neither the ΔSPRHA1BK146R nor the ΔSPRHA1BC135S mutant could promote in vivo ubiquitination , suggesting that the E3 activity is abolished in the ΔSPRHA1BK146R mutant . Taken together , our results indicated that both the conserved Zn-finger motif and the Lys146 residue are indispensable for the intrinsic E3 activity of RHA1B . The ubiquitination reaction mediated by the RING-type E3 ubiquitin ligase is accomplished by transferring ubiquitin to a substrate protein from an intermediate formed by the E2 ubiquitin-conjugating enzyme [12] . Many studies have shown the appropriate combination of E2 with E3 is critical for the potential activity of a given E3 [12] . Significantly , there are dozens of E2 ubiquitin conjugating enzymes in plants and each one may be involved in different cellular processes , thus determination of the E2 specificity of RHA1B would not only indicate which plant E2 ( s ) could be exploited by RHA1B but also imply what host signaling pathways might be altered by RHA1B . Since tomato is a natural host of G . pallida and all tomato E2s have been characterized recently [23] , we sought to determine the E2-E3 specificity between tomato E2 enzymes and RHA1B to define the E2 protein ( s ) that cooperates with RHA1B . To this end , one representative E2 ( SlUBC1/4/6/7/12/13/17/20/22/27/32 ) was randomly selected from 10 ( II , VI , V , III , IX , X , VIII , XII , I , IV , respectively ) tomato ubiquitin E2 families . Our in vitro ubiquitination assay indicated that , among these 10 E2s , SlUBC12 , 13 and 20 were able to facilitate the ubiquitination mediated by RHA1B ( Fig 3 ) , suggesting that RHA1B can exploit multiple E2 conjugating enzymes to potentially alter manipulate various cellular processes in host cells . Next , we sought to determine the role of RHA1B in manipulating host defense to promote G . pallida parasitism . PTI activates the initial plant defense response to pathogen invasion and appears to be conserved across the plant kingdom . We adopted a well-established PTI signaling system triggered by the bacterial PAMP flg22 in N . benthamiana to determine whether RHA1B interferes with generic basal defense [24] . ΔSPRHA1B was transiently expressed via Agrobacterium in N . benthamiana leaves for 24 hours . Parallel expression of vector or Pseudomonas effector AvrPtoB provided negative and positive control for PTI suppression , respectively . Duplicate agro-infiltrated areas were injected with 100nM flg22 to trigger PTI signaling . Tissue samples were collected one hour after flg22 treatment and assayed by quantitative Real-Time PCR ( qRT-PCR ) for the induction of two PTI marker genes , NbAcre31 and NbWRKY22 [25] . Our results indicated that , like the phytobacterial effector AvrPtoB , RHA1B blocked flg22-induced PTI signaling ( Fig 4A ) , suggesting that RHA1B is capable of interfering with host basal defenses during nematode infection . In addition , we determined the role of the E3 activity in RHA1B-mediated suppression on PTI by examining the effect of the E3-deficient ΔSPRHA1BC135S mutant on PTI signaling . ΔSPRHA1BC135S was included in a similar Agrobacterium-mediated transient assay on flg22-triggered NbAcre31 and NbWRKY22 expression . Our results indicated that ΔSPRHA1BC135S still blocks NbAcre31 and NbWRKY22 induction by flg22 ( Fig 4A ) , suggesting that the E3 activity of RHA1B is dispensable for interference with PTI signaling . The HR cell death developed on N . benthamiana leaves , resulting from Agrobacterium-mediated transient co-expression of a plant NB-LRR protein and its cognate pathogen avirulence effector or expression of an auto-active NB-LRR mutant , has been widely considered as a diagnostic indicator of ETI . Thus , we used this fast and reliable system to determine whether RHA1B can interfere with the ETI signaling . We first tested the NB-LRR receptor Gpa2 that confers resistance to G . pallida in potato and our result showed that RHA1B can suppress cell death triggered by co-expression of Gpa2 and RBP-1 [26] ( Fig 4B ) . Since Gpa2 belongs to the CC-NB-LRR subgroup ( containing a coiled-coil ( CC ) domain at its N terminus ) of NB-LRR receptor and confers resistance to nematode , we asked whether or not the cell death suppression activity of RHA1B is specific to Gpa2-mediated ETI signaling . We included in our cell death suppression assay other NB-LRR receptors , which confer resistance to bacterium , virus or oomycete and belong to CC-NB-LRR or NTR-NB-LRR ( containing a Toll-interleukin 1-like receptor ( TIR ) domain at its N terminus ) subgroup . The tested NB-LRR receptors were Rx1 ( conferring resistance to virus , CC-NB-LRR subgroup ) [27] , Prf ( conferring resistance to the bacterium , CC-NB-LRR subgroup ) [28] , Rpi-blb1 ( conferring resistance to the oomycete , CC-NB-LRR subgroup ) [29] , Bs4 ( conferring resistance to bacterium; NTR-NB-LRR subgroup ) [30] . As shown in Fig 4B , RHA1B was able to suppress cell death mediated by these NB-LRR immune receptors , where cell death was initiated by the auto-active NB-LRR mutant alone or the WT NB-LRR with its cognate avirulent effector . However , RHA1B was unable to block cell death induced by the pro-apoptotic mouse protein Bax [31] or MAPKKKα that is a downstream component of ETI signaling [32] . Taken together , our results not only indicated RHA1B can suppress ETI signaling mediated by a broad range of NB-LRR receptors , but also suggested that RHA1B is not a general cell death suppressor and it may target the early step ( s ) of ETI signaling . We next sought to investigate molecular mechanism by which RHA1B suppresses ETI signaling . Given that RHA1B effector is an E3 ubiquitin ligase whose main function is to ubiquitinate proteins for proteasome-mediated degradation , it was logical to hypothesize that RHA1B relies on its E3 activity to specifically promote degradation of NB-LRR receptors , thereby suppressing ETI signaling . To test this notion , we first examined the cell death suppression capability of the E3-deficient ΔSPRHA1BC135S mutant . Our result showed that the ΔSPRHA1BC135S mutant cannot suppress cell death mediated by any of the tested NB-LRR receptors ( Fig 5A ) . Next , we determined whether RHA1B does indeed trigger degradation of NB-LRR receptors in plant cells . It is notable that the auto-active Rx1D461V and Rpi-blb1D475V trigger extremely strong cell death in N . benthamiana accompanied by non-specific protein degradation that makes Rx1D461V and Rpi-blb1D475V proteins undetectable by Western blotting [33] , thus we used the WT Rx1 and Rpi-blb1 in our assay . We co-expressed the epitope-tagged WT or auto-active NB-LRR receptors Myc-Gpa2 , PrfD1416V-HA , Myc-Rx1 , Rpi-blb1-HA , or Bs4-Myc with HA-ΔSPRHA1B , HA-ΔSPRHA1BC135S mutant , or empty vector in N . benthamiana leaves and monitored the protein levels of those NB-LRR receptors . Our Western blotting analysis indicated that all tested NB-LRR receptors fail to accumulate in the presence of RHA1B . Significantly , such RHA1B-promoted degradation of NB-LRR receptors was completely dependent on the E3 activity of RHA1B as all tested NB-LRR receptors accumulated normally when co-expressed with the E3-deficient HA-ΔSPRHA1BC135S mutant ( Fig 5B ) . Since the E3 activity of RHA1B was dispensable for interference with PTI signaling ( Fig 4A ) , our results also suggest that RHA1B possesses two distinct activities to suppress ETI and PTI , one E3-dependent and one E3-independent , respectively . To verify the indispensability of E3 activity on PTI suppression , we examined whether ΔSPRHA1B is able to promote degradation of FLS2 , the PRR receptor for the PAMP flg22 [34] . ΔSPRHA1B did not trigger degradation of FLS2 ( Fig 5B ) , further indicating that RHA1B targets unknown component ( s ) other than PRR receptor of the PTI signaling pathway . To obtain genetic evidence for an involvement of the RHA1B in nematode parasitism , we sought to generate transgenic potato plants overexpressing RHA1B under the constitutive cauliflower mosaic virus ( CaMV ) 35S promoter and determine the altered susceptibility to G . pallida . Given the fact that RHA1B is a strong ubiquitin ligase that might target many host proteins for ubiquitination and degradation , which could affect various physiological processes of the host cells , it is possible that overexpression of RHA1B results in development and/or growth defects in potato plants . Even though much effort has been spent to generate transgenic potato plants expressing 35S::ΔSPRHA1B construct , only three transgenic lines ( designated as 35S::ΔSPRHA1B-1 , -4 , -5 , respectively ) have been obtained . Significantly , all transgenic potato plants exhibited different levels of inhibition on root growth ( Fig 6A ) . qRT-PCR assay on root tissues indicated that 35S::ΔSPRHA1B-4 expressed ΔSPRHA1B at extreme high level ( about 15 fold higher than line 35S::ΔSPRHA1B-5 ) , whereas 35S::ΔSPRHA1B-1 and -5 exhibited moderate levels of ΔSPRHA1B expression ( Fig 6B ) . In consistence with the qRT- PCR results , the root growth of 35S::ΔSPRHA1B-4 plants was almost completely arrested ( Fig 6A ) , therefore , this line was not suitable for nematode infection assay . Nevertheless , 35S::ΔSPRHA1B-1 and -5 transgenic lines were examined for their susceptibility to G . pallida infection . Although we cannot completely rule out the possibility that the enhanced number of nematodes in the transgenic potato roots compared to that in the WT potato roots ( Fig 6C ) is an artifact of the abnormal root development , our data strongly suggest that RHA1B plays a significant role in G . pallida parasitism .
Cyst nematodes move along host roots intracellularly penetrating root cells where they likely trigger DAMP-induced defenses . In order to mount a successful infection , they need to manipulate host PTI signaling . Indeed , the cyst nematode effector HaANNEXIN and the effector-derived CEP12 peptide have been shown to compromise flg22-induced ROS production and expression of PTI marker genes Pti5 and Acre31 [7 , 8] . Based on the flg22-triggered PTI marker gene expression , we concluded that RHA1B also blocks flg22-induced PTI signaling in our experimental system ( Fig 4A ) . Significantly , this PTI suppression is not dependent on the E3 activity of RHA1B , as the E3-deficient ΔSPRHA1BC135S mutant was still able to suppress the induction of NbAcre31 or NbWRKY22 by flg22 ( Fig 4A ) , further supporting that RHA1B possesses a distinct E3-independent virulence activity . Similar E3-independent PTI-suppression activity has been found in the phytobacterial effector AvrPtoB . The N-terminus of ArvPtoB lacking E3 activity is able to suppress PTI signaling , whereas the C-terminus of AvroPtoB possesses and E3 activity responsible for ETI suppression [35 , 36] . Thus , we speculate RHA1B , like the phytobacterial effector AvrPtoB , possesses two distinct virulence potentials ( one is responsible for PTI suppression and the other one interferes with ETI ( discussed below ) ) that involve different virulence‐promoting mechanisms . After its mobile phase , a cyst nematode commits itself to form a feeding site ( syncytium ) in host roots and becomes sessile for the rest of its life . Thus , protection of the feeding site , which is the only source of food available for nematode to achieve its reproductive stage , is essential . The potato NB-LRR receptor Gpa2 specifically limits G . pallida infection by recognizing the nematode GpRBP1 effector to trigger HR cell death . These dead cells can form a barrier to separate the syncytium from cells in the vascular bundle that provide the nutrients for nematode [1] . Therefore , G . pallida must suppress such HR cell death-dependent host immunity for survival . Recently a few cyst nematode effectors have been shown to suppress the HR cell death mediated by NB-LRR immune receptors . These effectors include GrUBCEP12[6] , HaANNEXIN [8] , GrEXPB2 [9] , and SPRYSEC family members unique to the genus Globodera ( GrSPRYSEC-19 [11] , GrSPRYSEC-4 , GrSPRYSEC-5 and GrSPRYSEC-8 , GrSPRYSEC-18 [10] ) . Interestingly , GrSPRYSEC19 and GrUBCEP12 can suppress cell death mediated by Gpa2 that provides resistance to some isolates of G . pallida [6 , 11] . We found G . pallida effector RHA1B can suppress HR cell death mediated by a broad range of NB-LRR immune receptors , including the CC-NB-LRR subgroup Gpa2 , Prf , Rpi-blb1 , Rx1 , and the TIR-NB-LRR subgroup Bs4 ( Fig 4B ) . Although only Gpa2 is related to nematode resistance , our data suggest G . pallida has evolved RHA1B as a powerful effector with potential to subvert host immunity mediated by a broad range of NB-LRR receptors . However , RHA1B was unable to block cell death triggered by the pro-apoptotic mouse protein Bax or the HR cell death signaling component MAPKKKα ( Fig 4B ) , suggesting that RHA1B does not act as a general cell death suppressor . This phenomenon is consistent with our further analysis showing that RHA1B-dependent cell death suppression is correlated with RHA1B-triggered specific degradation of the NB-LRR receptors . We have also examined possible in planta interaction between RHA1B with Gpa2 by co-immunoprecipitation assay . Our results indicated that RHA1B does not interact with Gpa2 in vivo ( S1 Fig ) , suggesting that RHA1B does not directly ubiquitinate Gpa2 for degradation . Nevertheless , given that the intrinsic E3 activity of RHA1B is required for both degradation of NB-LRR receptors and HR cell death suppression , we hypothesize that RHA1B indirectly targets NB-LRR receptors for degradation . For example , RHA1B might ubiquitinate and promote proteolysis of as yet unidentified factor ( s ) , such as molecular chaperones and/or co-chaperones that help fold or stabilize NB-LRR proteins . A growing body of evidence has shown that the ubiquitin pathway plays important role in the plant-nematode interactions . Several nematode effectors have been described as potential components of the ubiquitin pathway . For example , GrUBCEP12 is a ubiquitin carboxyl extension protein that is processed into a free ubiquitin and CEP12 in plant cells [6]; GrSKP1 is an E3 adaptor-like protein [9 , 37] . However , these nematode effectors differ from RHA1B in that they likely rely on exploiting host E3 ligases , rather than embodying such enzymatic activity . RHA1B is a nematode effector equipped with E3 ligase activity , by which it promotes degradation of NB-LRR receptors to interfere with ETI signaling . To our knowledge , this is the first effector with E3 ubiquitin ligase identified from eukaryotic ( plant or animal ) pathogens , including fungi , oomycetes and nematodes . Although the E3 ligase confers the substrate specificity of ubiquitination , the E2-E3 combination cooperatively determines the topology of the polyubiquitin chain , which determines the fate of the ubiquitinated protein [38] . We found that RHA1B can function in concert with three host E2s ( SlUBC12 , 13 and 20 ) when ubiquitinating in vitro ( Fig 3 ) . Among these three E2s , SlUBC13 exclusively catalyzes the Lys-63-linked ubiquitination [39] , which is generally involved in non-proteolytic processes , such as protein trafficking , translation and DNA repair [40] . In contrast , SlUBC12 belongs to group III of the tomato E2s that are also used by the phytobacterial effector AvrPtoB to ubiquitinate host defense-related proteins for degradation [23] . It is also notable that three E2s possess distinct ubiquitination potential when catalyzing ubiquitination with RHA1B in vitro , as indicated by difference in the size range of ubiquitination-associated smears observed in the in vitro ubiquitination assay ( Fig 3 ) . The ability of RHA1B to manipulate host plant physiological processes is further supported by the phenotypic changes observed in transgenic potato plants over-expressing RHA1B . All transgenic potato plants exhibited different levels of arrest on root development and growth , which was correlated with the expression levels of the RHA1B transgene ( Fig 6A & 6B ) . Moreover , the tested 35S::ΔSPRHA1B-1 and -5 transgenic lines exhibited enhanced susceptibility to G . pallida infection ( Fig 6C ) , despite the fact that we cannot completely rule out the possibility that the enhanced numbers of nematodes in the transgenic potato roots could be an artifact of the abnormal root development caused by over-expression of the RHA1B effector . Thus , we hypothesize that the ability of RHA1B to exploit multiple host E2s could arm G . pallida with a unique advantage in parasitism in which the RHA1B-E2 combinations provide the parasite with strategy to affect a wider range of physiological processes of host plants via manipulation of both proteolytic and non-proteolytic protein process in infected hosts .
The RHA1B gene without signal peptide-coding region was PCR-amplified from the G . pallida J3 cDNA using primers listed in S1 Table . RHA1B was cloned into pBIN-ARS vector [41] carrying 5’-terminal sequences encoding GFP or HA epitope tags for Agrobacterium-mediated transient expression or generation of transgenic potato plants , respectively . The C135S or K146R substitution in RHA1B was introduced using PfuUltra ( Agilent , Santa Clara , CA , USA ) polymerase-driven PCR , the resulting ΔSPRHA1BC135S and ΔSPRHA1BK146R mutants were verified by DNA sequencing and cloned into the pMAL-c2 vector ( NEB , Ipswich , MA , USA ) to generate recombinant protein . In situ hybridizations were performed using preparasitic J2s ( pre-J2s ) and parasitic J2s ( para-J2s ) of G . pallida nematodes isolated from inoculated Desiree potato plants following the protocol described in a previous publication [42] . The DNA probes specific for the digoxigenin ( DIG ) -labelled sense ( negative control ) and antisense single-stranded cDNA were synthesized using a PCR DIG probe Synthesis Kit ( Roche Applied Science , Indianapolis , USA ) . Hybridization signals within the nematodes were detected using alkaline phosphatase conjugated antidigoxigenin antibody ( diluted 1:100 ) and substrate , and the nematode sections were observed using a stereo-microscope ( Leica Microsystems , Wetzlar , Germany ) to detect the hybridized probes in the nematodes tissue . Primers used for in situ hybridization are listed in S1 Table . Agrobacterium-mediated transient expression on N . benthamiana leaves was carried out as described previously [35] . Agrobacterium tumefaciens GV2260 strains expressing differentially tagged proteins were syringe-infiltrated into N . benthamiana leaves . The concentrations of agrobacterial inoculum varied from OD600 = 0 . 05 to OD600 = 0 . 4 . For the subcellular localization assay , A . tumefaciens GV2260 strain containing the appropriate GFP chimera construct was injected into N . benthamiana leaves . After 36 hours , the epidermal cell layers were examined using confocal microscope ( Olympus ) to capture the GFP signal . For Western blotting assay , Agrobacterium-infected N . benthamiana leaf tissues were collected at 28–36 hours after infiltration and ground with liquid nitrogen . The fine tissue powder was resuspended with 300 μl of protein extraction buffer ( 50mM Tris-HCl pH 7 . 5 , 150mM NaCl , 5mM EDTA , 2mM DTT , 10% glycerol , 1% polyvinylpolypyrolidone , 1mM PMSF , plant protease inhibitor cocktail ( Sigma-Aldrich , Saint Louis , USA ) ) and centrifuged at 13 , 000g/4°C for 15 minutes . Protein samples were separated on 10% SDS-PAGE gels , transferred onto PVDF membrane and probed with anti-HA ( Sigma-Aldrich , Saint Louis , USA , Cat# H3663; RRID:AB_262051 ) , anti-FLAG ( Sigma-Aldrich , Saint Louis , USA , Cat# F3165; RRID:AB_259529 ) , or anti-Myc primary antibody ( Sigma-Aldrich , Saint Louis , USA , Cat# M4439; RRID:AB_439694 ) , followed by anti-mouse secondary antibody ( Sigma-Aldrich , Saint Louis , USA , Cat# A9044; RRID:AB_258431 ) . Protein signal was detected with ECL Prime ( GE Healthcare , Chicago , USA ) . The pMAL-c2 plasmid harboring MBP-RHA1B , MBP-RHA1BC135S or MBP-RHA1BK146R construct was expressed in E . coli BL21 using 0 . 5μM IPTG for induction . Recombinant proteins were purified using the Amylose Resin ( NEB , Ipswich , USA ) following the manufacturer’s instructions . The in vitro ubiquitination assay was performed as described previously [16] with few adjustments . 40ng His-E1 ( AtUBA1 ) , 100ng His-E2 ( AtUBC8 ) , 1μg MBP-RHA1B ( or MBP-RHA1BC135S or MBP-RHA1BC135S ) , 2μg HA-Ub ( Boston Biochem , Boston , USA ) were incubated in the ubiquitination buffer ( 50mM Tris HCl , pH7 . 5 , 2mM ATP , 5mM MgCl2 , 30mM creatine phosphate ( Sigma-Aldrich , St . Louis , USA ) containing 50ng/μl creatine phosphokinase ( Sigma-Aldrich , St . Louis , USA ) ) . The 30μl reaction mixture was incubated for 2 hours at 30°C . Proteins were separated on 7 . 5% SDS-PAGE gels and identified by Western blotting using anti-HA antibody ( Sigma-Aldrich , St . Louis , USA ) . N . benthamiana leaves were agroinfiltrated with desired constructs for transient protein expression . 36 hours after infiltration , either water control or 100nM flg22 ( PhytoTechnology Laboratories , Shawnee Mission , USA ) were injected to induce NbAcer31 and NbWRKY22 expression . Total RNA from agroinfiltrated leaf discs treated with flg22 for 1 hour was isolated with TRIzol reagent ( Invitrogen , Carlsbad , USA ) . One microgram of total RNA was treated with DNase I ( Invitrogen , Carlsbad , USA ) , followed by reverse transcription using a Super Script II reverse transcriptase ( Invitrogen , Carlsbad , USA ) . qRT-PCR analysis was performed on an ABI Prism 7100 sequence detection system using Power SYBR Green reagents ( Life Technologies , Carlsbad , USA ) . The N . benthamiana EF1 gene was used as an internal control for normalization [43] . Relative expression ratios were determined based on the comparative CT method ( ΔΔCT ) using the StepOne Software . Primers used in qRT-PCR are listed in S1 Table . Transgenic potato plants over-expressing RHA1B were generated via Agrobacterium-mediated transformation [44] . Propagated plants of the transgenic lines were used for nematode inoculation . Transgenic and non-transgenic control plant seedlings were inoculated with 10 G . pallida cysts in the root zone for six weeks . All life stages of nematode were counted from individual plants using acid fuchsin assay [45] . | Globodera pallida is a plant-parasitic cyst nematode that causes vast losses in economically important crops such as potato and tomato . To successfully parasitize host plants , G . pallida produces proteins called effectors to overcome plant defenses . Here , we report identification of a novel G . pallida effector RHA1B as an E3 ubiquitin ligase , which is responsible for ubiquitin-proteasome-mediated protein degradation in general . We found that RHA1B can suppress plant defense signaling via both E3-dependent and -independent manners . In particular , it promotes degradation of a broad range of NB-LRR immune receptors . In addition , expression of RHA1B in potato plants made the plants more susceptible to G . pallida infection , indicating that RHA1B acts as an effector that aids parasitism . Overall , we found RHA1B as the first effector with ubiquitin ligase activity identified from eukaryotic pathogen infecting plants or animals . Our data suggest nematode uses RHA1B as a powerful weapon to manipulate host cellular signaling pathways , thereby interfering with plant immunity for successful parasitism . | [
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] | 2019 | The potato cyst nematode effector RHA1B is a ubiquitin ligase and uses two distinct mechanisms to suppress plant immune signaling |
Chikungunya virus ( CHIKV ) , a reemerging pathogen causes a self limited illness characterized by fever , headache , myalgia and arthralgia . However , 10–20% affected individuals develop persistent arthralgia which contributes to considerable morbidity . The exact molecular mechanisms underlying these manifestations are not well understood . The present study investigated the possible occurrence of molecular mimicry between CHIKV E1 glycoprotein and host human components . Bioinformatic tools were used to identify peptides of CHIKV E1 exhibiting similarity to host components . Two peptides ( A&B ) were identified using several bioinformatic tools , synthesised and used to validate the results obtained in silico . An ELISA was designed to assess the immunoreactivity of serum samples from CHIKV patients to these peptides . Further , experiments were conducted in a C57BL/6J experimental mouse model to investigate if peptide A and peptide B were indeed capable of inducing pathology . The serum samples showed reactivity of varying degrees , indicating that these peptides are indeed being recognized by the host immune system during CHIKV infection . Further , these peptides when injected into C57BL/6J mice were able to induce significant inflammation in the muscles of C57BL/6J mice , similar to that observed in animals that were injected with CHIKV alone . Additionally , animals that were primed initially with CHIKV followed by a subsequent injection of the CHIKV peptides exhibited enhanced inflammatory pathology in the skeletal muscles as compared to animals that were injected with peptides or virus alone . Collectively these observations validate the hypothesis that molecular mimicry between CHIKV E1 protein and host proteins does contribute to pathology in CHIKV infection .
Chikungunya fever is caused by a arbovirus belonging to Family Togaviridae and Genus Alphavirus . CHIKV is positive sense RNA virus with about 11 . 8 kb long genome . The prevalence of CHIKV has increased globally . It caused massive outbreaks when it re- emerged in 2005 in French Reunion islands where it affected about 33% of the total population . CHIKV outbreaks also occurred in India during the same period , southern states in India recorded a total of 1 . 3 million cases [1 , 2] . Chikungunya fever is characterized by fever , headache , myalgia and arthralgia . Though Chikungunya is a self limiting illness [3] , a small proportion of 10–20% of affected individuals develop persistent arthralgia . The risk factors associated with the development of persistent arthralgia include older age of patients ( >40 years ) and pre existing rheumatic problems . However , the precise molecular mechanisms of pathogenesis that lead to the development of these complications are poorly understood . Experimental evidence of CHIKV persistence in macrophages of Macaca species has been demonstrated [4] and it has been suggested as one of the factors contributing to residual arthralgia . Although , molecular mimicry as the cause of prolonged joint manifestations had not been proved conclusively in Chikungunya infection , there are reports which suggest that such a phenomenon might be operational . Therefore , in this study we investigated the possible occurrence of molecular mimicry between CHIKV E1 and host components using a three pronged strategy: ( i ) identification of homologous regions between CHIKV proteins and host tissue components using bioinformatics tools , ( ii ) establishing cross reactivity between serum samples obtained from CHIKV infected patients and peptides exhibiting molecular mimicry and ( iii ) validating the ability of the cross reactive peptides in inducing joint and muscle pathology in a mouse model . We demonstrate the occurrence of molecular mimicry between CHIKV envelope glycoprotein ( E1 ) and the host components .
A clinical isolate of CHIKV ( Chikungunya virus strain DRDE-06; GenBank accession number: EF210157 . 2 ) was used for all the in vivo experiments in this study . The bioinformatics related work was carried out using the CHIKV E1 protein sequence from the prototype strain CHIKV S27 available in the SWISS PROT ( ID:Q8JUX5 ) . Further , a multiple sequence alignment of the E1 glycoprotein of DRDE-06 sequences and CHIKV S27 revealed a 98% homology between the two strains . CHIKV peptides were custom synthesised from commercial sources ( Hysel Pvt Ltd . , India ) and obtained as a lyophilised powder . The non-specific peptide was a gift from XCyton diagnostics private Ltd , Bangalore , India . Rabbit anti-human polyclonal-HRP conjugate was procured from Dako , Denmark , while Goat anti-mouse IgG-HRP was obtained from Genei , Bangalore . All work related to animals was conducted with good animal practice defined by committee for the purpose of control and supervision of experiments of animals . The use of animals was approved by the institutional animal ethics committee ( IAEC ) of NIMHANS ( Approval reference no: AEC/41/222 ( B ) /NV dated 05 . 10 . 2010 ) . The animals were housed in cages maintained in hygienic conditions with good ventilation , in a room maintaining the usual day and night cycle . The animals used for the experiments were euthanized by cervical dislocation and animal ethics were strictly adhered to at all times , while bleeding and sacrificing the animals . The use of human samples for the study was approved by was approved by institute ethics committee at NIMHANS ( Approval reference no: NIMHANS/68th IEC/2010 ) which adheres to the ethical guidelines for biomedical research on human participants developed by the Indian Council for Medical research ( ICMR ) . Written informed consent was obtained from all the subjects themselves in the study . C57BL/6J strain of mice were obtained from NIMHANS Central animal research facility and used in the study . Eight day old mouse pups were procured from the animal facility along with the mother and the mouse pups were used for the experiments . The human samples used in this study were received at the Department of Neurovirology , National Institute of Mental Health and Neurosciences ( NIMHANS ) , which is one of the twelve designated national apex laboratories for the diagnosis of Chikungunya in India . All the subjects enrolled in the study presented to the hospital/clinics with fever , joint pain , rash , myalgia , conjunctival redness , and headache . Additionally , the prevalence and local outbreaks in the region aided in making a clinical diagnosis of Chikungunya fever . Blood samples ( 3–5 ml clotted blood ) were collected from thirty six subjects , serum separated and stored in aliquots at -70°C until all the tests were performed . The CHIKV infection was confirmed by detection of CHIKV specific IgM antibodies using an ELISA ( National Institute Virology , Pune ) and/or CHIKV RNA by TaqMan real time PCR targeting the NSP4 region [5] . Serum samples collected from 31 healthy individuals served as controls . CHIKV was grown in C6/36 cell line and infectious fluid was harvested . CHIKV infected C6/36 fluid was centrifuged at 10 , 000 rpm for 20minutes to remove debris and NaCl was added to the supernatant to obtain a final concentration of 0 . 5 molar . Subsequently , polyethylene glycol was added to the mixture to obtain a final concentration of 10% ( w/v ) and the suspension stirred on ice bath for 20 minutes . The mixture was incubated overnight at 4°C , and centrifuged at 3000 rpm for 30 minutes to obtain the virus rich precipitate . The precipitate was dissolved in 1/100th of original infected cell culture fluid volume using GTNE buffer . CHIKV was purified using a discontinuous sucrose gradient method . Briefly , 5ml of 20% sucrose ( w/v ) in GTNE buffer was carefully overlaid onto 2 . 5ml of 50% ( w/v ) sucrose . Subsequently , 2 . 5ml of CHIKV obtained after PEG concentration was overlaid onto the discontinuous sucrose gradient and centrifuged at 28 , 000 rpm for 2 hours at 4°C using a ultracentrifuge ( Beckman Coulter , USA ) . The band at the inter-phase was collected and re-suspended in 10–12 volumes of PBS ( pH7 . 2 ) and centrifuged at 28 , 000 rpm for 2 hours to obtain a purified virus pellet . The pellet was dissolved in 1ml of fresh PBS and frozen in small aliquots at -70°C . The complete genome sequence of a prototype CHIKV S27 belonging to African genotype was obtained from the Gen Bank . The sequence of CHIKV E1 glycoprotein was obtained from SWISSPROT ( Q8JUX5 ) . This sequence was uploaded into Immune Epitope Database and Analysis Resource ( IEDB ) server available at http://www . immuneepitope . org/ . The server predicts the antigenic determinants using five different algorithms—Chou and Fasman beta turn prediction , Emini surface accessibility prediction , Karplus and Schulz flexibility prediction , Kolaskar and Tongoankar antigenicity prediction , Parker hydrophilicity prediction . The antigenic peptides from E1 region were deduced after considering hydrophilicity , surface probability , chain flexibility and secondary structure antigenic index both as text and graphs . The results obtained from the server were combined to construct a graph using MS- EXCEL , which in turn yielded putative epitopic regions of CHIKV E1 glycoprotein . The peaks with antigenic propensity , surface accessibility , flexibility , hydrophilicity and beta turns were considered . The results obtained through IEDB were further confirmed using additional server- European Molecular Biology Open Software Suite ( EMBOSS ) available at http://liv . bmc . uu . se/cgi-bin/emboss/antigenic . The results obtained using the Chou and Fasman criteria for beta turn prediction was also verified using COUDES server . The existence of sequence similarity between CHIKV E1 glycoprotein and Human HLA-B27 was investigated using BLAST . The existence of structural similarity between the CHIKV E1 glycoprotein and human host components were determined by using BioXGEM server and number of hits obtained in the non-redundant protein database ( nrPDB ) were limited to first 100 in the output . Multiple sequence alignment of E1 glycoprotein sequences of Alphaviruses- CHIKV , ONNV , RRV , SFV , and EEEV was done using CLUSTALW available at http://www . ebi . ac . uk/Tools/clustalw2/index . html . The optimal concentration of the peptide to be coated onto the ELISA plate was predetermined in an initial experiment and was found to be 25μg/well . The peptides were coated onto the ELISA microwells using carbonate buffer and incubated overnight at 4°C . The plate was washed three times with phosphate buffered saline with tween ( PBST ) and quenched using 1% skimmed milk powder in PBST for one hour at 37°C . The plates were washed with PBST and reacted with 100μl of serum samples ( 1:100 dilution in PBS containing 0 . 25% triton-X 100 ) obtained from patients infected with CHIKV ( n = 36 ) as well as serum samples obtained from control subjects ( n = 31 ) . The samples were incubated for 90 minutes at 37°C , followed by five washes with 1X PBST . Subsequently , rabbit polyclonal anti-human antibodies tagged with HRP ( Dako , Denmark ) was diluted 1:1000 and 100μl was added to each well and the plate was incubated at room temperature for 90 minutes . The plate was washed five times with PBST and 100μl of the TMB solution was added and incubated in the dark for 10 minutes . The reaction was stopped by the addition 4N sulphuric acid and the OD values were read at 492nm using ELISA reader ( Thermo scientific , USA ) . Eight day old C57BL/6J pups were procured along with the mother and the pups were used for the in vivo experiments . They were assigned to nine different groups with each group comprising of 6 pups as shown in Table 1 . Prior to determining the role of peptides in the possible enhancement of pathology related to CHIKV infection , the pathological changes induced by the CHIKV in C57BL/6J mice were studied ( Group 1 ) . For this purpose , CHIKV ( 105 PFU/50μl ) was inoculated into the foot pad of 8 day old mice . The control group of mice ( Group 2 ) received equal volume ( 50 μl ) of Eagles Minimum Essential Medium ( EMEM ) . The mice were kept under observation for 12 days only post infection . At the end of the observation period , blood was collected from the mice through retro orbital plexus bleeding , and the mice euthanized to obtain the following organs- brain , thymus , heart , lungs , spleen , liver , kidneys , upper limbs and lower limbs . For histopathological studies the tissues were fixed in 4% paraformaldehyde , while for PCR the tissues were collected in EMEM . CHIKV infection was confirmed by two methods- presence of CHIKV specific IgG antibodies in the serum and detection of CHIKV nucleic acid in the serum and harvested tissues using TaqMan real- time PCR [5] . Eight day old pups in Group 3 , 4 and 5 were injected with two doses ( 50μg /dose ) of CHIKV Peptide A , CHIKV Peptide B and Non-specific peptide respectively on day 0 and day 5 . The peptides were reconstituted in sterile 1X PBS ( pH 7 . 2 ) . Equal volumes of peptide solution and Freund’s incomplete adjuvant were mixed and emulsified to obtain water in oil emulsion . The emulsion was stored at -70°C until use . In all these three groups blood was collected 12th day post inoculation by retro orbital bleeding and fresh tissues were harvested and processed for PCR and paraformaldehyde fixed tissues for histopathology . Mice in Group 6 were injected with EMEM alone followed by PBS emulsified in Freund’s incomplete adjuvant 5 days post infection with CHIKV . C57BL/6J mice in Group 7 , 8 and 9 were injected with CHIKV ( 105 PFU/50 μl ) followed by 50μg of the CHIKV Peptide A , CHIKV Peptide B and Non-specific peptide respectively , on 5th day post CHIKV inoculation through the same route at the same site . In these groups of mice the blood was collected and tissues harvested on 12th day post infection , and processed for PCR and histopathological examination . Serum was separated from the blood and stored at -70°C . Polystyrene ELISA microwells ( Nunc , Denmark ) were coated with purified CHIKV in carbonate buffer at a concentration of 1μg/well diluted in carbonate buffer ( Appendix I ) . The plate was incubated overnight at 4°C , washed thrice with PBST and quenched using 1X PBST containing 1% skimmed milk powder . Serum samples were diluted 1:100 in PBST and 100μl added to the wells and incubated at 37°C for 1 hour followed by washing for five times with PBST . Subsequently , 100 μl of a 1:5000 dilution of Goat anti-mouse IgG conjugated with HRP ( Genei , Bangalore ) was added to the wells , incubated at 37°C for 1 hour followed by washing for 5 times with PBST . Finally , 100μl of the substrate solution ( TMB ) was added to all the wells and incubated in the dark for 10 minutes . The reaction was stopped by the addition 4N sulphuric acid . The OD values were read at 450nm using an ELISA reader ( Thermo scientific , China ) . The tissues collected in EMEM were homogenised using a motorised hand held homogeniser . The homogenates were spun at 8 , 000 rpm for 10 minutes at 4°C . The supernatants were collected , 200 μl of the supernatant was used for RNA extraction using QIAmp viral RNA extraction kit ( Qiagen , Germany ) . The eluted RNA was converted to cDNA using high capacity reverse transcription kit ( ABI , USA ) . The cDNA was stored at -20°C until tested . Similarly whole blood RNA extraction kit was used to extract RNA from blood and converted to cDNA which was used in the TaqMan real time PCR as described earlier [5] . All tissues were fixed in paraformaldehyde and embedded in paraffin for processing and 4 μm thick sections obtained were mounted on selin coated glass slides . Subsequently , the tissue sections were de-paraffinised with two changes of xylene and rehydrated in absolute alcohol followed by washing briefly in tap water . Staining of the sections was carried out using Harris haemotoxylin for 5–8 minutes , followed by washing under running tap water for 5 minutes and differentiated in 1% acid alcohol for 30 seconds . The sections were subsequently washed under running tap water for 1 minute , treated with bluing saturated lithium carbonate solution for 30–60 seconds and washed under tap water for 1 minute . Counter staining of sections was carried out using eosin-phloxine solution for 1 minute followed by dehydration in 95% alcohol and absolute alcohol for 5 minutes each . The sections were finally immersed in xylene twice for 2 minutes each for clearing and then mounted with DPX .
As described in the materials and methods section , the sequence of the African prototype of CHIKV S27 EI glycoprotein ( Q8JUX5 ) was obtained from the SWISSPROT database and subjected to immune epitope analysis using five algorithms in IEDB and EMBOSS programs . The results are depicted in Fig 1 . The scores obtained for the five algorithms were uploaded into an MS Excel sheet to generate a combined graph which yielded the putative epitopic regions of E1 glycoprotein of CHIKV . Analysis of the peaks in the graph with respect to antigenic propensity , surface accessibility , flexibility , hydrophilicity and B turns enabled prediction of the following epitopic regions: These regions of CHIKV E1 glycoprotein satisfy all the criteria necessary for a given peptide to be considered immunogenic and capable of eliciting an immune response in the host system . Subsequently multiple sequence alignment of E1 glycoprotein of Alphaviruses- CHIKV , Onyong Onyong Virus ( ONNV ) , Ross River Virus ( RRV ) , Semiliki Forest Virus ( SFV ) , and Eastern Equine Encephalitis Virus ( EEEV ) was done through CLUSTALW . The results of the alignment are depicted in Fig 2 . As evident from the figure , the alignment revealed two motifs SKD and KCA present only in arthritogenic Alphaviruses ( CHIKV , ONNV , RRV ) and not in encephalitogenic Alphaviruses ( SFV , EEEV ) . Furthermore , the amino acid sequences SKD and KCA were present in the immunodominant peptides 1 and 2 deduced from E1 glycoprotein respectively . All further experiments using human serum samples and mouse models were therefore restricted only to these two peptides . The sequence similarity between the CHIKV E1 glycoprotein and HLA- B27 was determined using BLAST . The alpha chain of HLA-B27 molecule shared a partial homology from the stretch ranging from 216–220 of CHIKV E1 glycoprotein as well as the immunodominant region of Peptide A ( Fig 3 ) . The output obtained through the BioXGEM 3D BLAST performed on the CHIKV E1 was limited to first 100 hits . The output of the BLAST was further analyzed and limited only to human proteins ( Table 2 ) . Further analysis of these human proteins was limited to those that are known to contribute to the inflammation and arthritic pathology . Amongst these , six human proteins- Human complement component 3 , complement component 5 [6] , fibronectin [7] , kelch like protein [8] , mast/stem cell growth receptor [9] and beta arrestin 1 [10] which exhibited maximum similarity to CHIKV EI protein were only considered for further analysis . Prominent among these six were complement proteins C3 and C5 . When the structural similarity between the E1 glycoprotein and complement component C3 was analyzed , the sequence of amino acids in the region of CHIKV E1 glycoprotein which shared homology with complement component C3 were also present in Peptide A and Peptide B ( Fig 4 ) . All the patients in the study were from the state of Karnataka , South India . Among the 36 patients with confirmed CHIKV infection , 17 were females and 19 were males . The mean age of the patients was 40 . 64 years . All the patients whose samples were used in the study presented with fever , while 28 ( 78% ) had arthralgia , 30 ( 83% ) had myalgia , 20 ( 56% ) had complained of headache . Rash and gastrointestinal symptoms were seen in 14 ( 39% ) and 10 ( 28% ) of the patients each , and conjunctival redness was seen in 1 ( 3% ) of the patients . Amongst 3/36 patients ( 8% ) persistent arthralgia was reported at 12 weeks after onset of initial symptoms . Hence follow up samples blood samples could be collected from these three patients at 12 weeks . In all other patients no follow up samples could be collected . A sample was considered to be positive in the peptide ELISA if it had an OD value equal to or greater than that of the cut-off value . The cut off value for each of the peptides was calculated by using OD values obtained with sera of healthy control subjects ( n = 31 ) using the formula Mean OD of control samples + 2SD . The cut off OD value for Peptide A was 0 . 373 and for Peptide B it was 0 . 408 . Amongst the 36 samples obtained from confirmed CHIKV patients , 24 ( 66 . 66% ) showed reactivity to the peptide A and 27 ( 75% ) to peptide B ( Fig 5 ) . The OD values of CHIKV positive samples towards these peptides ranged from 1 . 501 to 0 . 11 for Peptide A , while it varied from 1 . 378 to 0 . 203 for Peptide B . These experiments were carried out to determine the possible synergistic role of peptides in the enhancement of pathology in CHIKV infection: All the mice were confirmed to have CHIKV infection by the detection of anti CHIKV antibodies in the serum by ELISA . The cut-off in the ELISA was determined using the mean + 2SD OD values of serum samples obtained from uninfected control mice and it was 0 . 253 . The OD values of serum samples from all the infected animals were found to be > 0 . 253 and hence considered positive for CHIKV-specific antibodies . In addition , the presence of CHIKV nucleic acids was demonstrable in the muscle tissue by TaqMan real time PCR while , it was not detected in the blood and other tissues of the infected group of mice . All the control group of animals were negative for CHIKV nucleic acids . In order to have an objective assessment of the pathological features noted in all groups of animals , a semi quantitative scale for scoring the degree of inflammation centred mainly around the muscles was evolved and the slides were evaluated by a pathologist blinded to the groups and the same is depicted in Fig 6 . The inflammation was graded as minimal ( 1+ ) , mild ( 2+ ) , moderate ( 3+ ) and severe ( 4+ ) . The salient histopathological features noted in CHIKV infected mice were as follows: ( i ) the soft tissue around the elbow and knee joints were relatively normal with no evidence of synovitis or arthritis , while tissues close to the shoulder and the hip joint revealed moderate degree of lymphohistiocytic infiltration virus , ( ii ) the major muscles of the hip and the shoulder had multifocal lymphocytic infiltrate in the endomysium with myonecrosis , ( iii ) random and occasion muscle fibres close to inflammation revealed cytoplasmic basophilia and central nucleation with prominent nucleolus indicative of regenerative activity , similar to polymyositis noted in human subjects ( iv ) the synovium and periarticular soft tissue had sparse inflammation while the articular cartilage and the articular cavity were free of inflammation , ( v ) the epimysium around the muscle and tendinous portion close to the insertion had variable lympho-histiocytic inflammation indicating tenosynovitis , ( vi ) the striking pathology was mineralization of the necrosed muscle belly especially the lateral group of muscles close to the hip and shoulder joints similar to some cases of chronic polymyositis in human subjects . Other than these features noted in the limbs and joints all the other organs did not reveal any significant pathological changes . Some of the salient features noted in CHIKV infected mice ( Group 1 ) are depicted in Fig 7 . The degree of pathological damage centred on the muscles in the various groups of mice was graded and a comparative chart was prepared ( Table 3 ) . As evident from the table , the group of mice that were mock infected ( Group 2 ) and subsequently did not receive any peptides revealed sparse ( 1+ ) inflammation in the muscles probably related to the procedure . Similar findings were also noted in the mock infected animals that received a single dose of Freund’s incomplete adjuvant ( Group 6 ) . In mice that received two doses of non-specific peptide but no virus ( Group 5 ) hyperplasia of the bone marrow was noted with sparse inflammation ( 1+ ) . On the other hand , mice that received two doses of CHIKV specific peptides but no virus ( Groups 3 & 4 ) exhibited myositis , muscle necrosis , vasculitis and hyperplasia of the marrow ( immune mediated inflammatory muscle and marrow reactive changes ) and an overall inflammation score of 3+ ( Fig 8 ) . In the three groups of mice that received an initial inoculum of virus followed by a single dose of either CHIKV specific peptides ( Groups 7&8 ) or non-specific peptide ( Group 9 ) the pathological features were more florid ( Fig 9 ) . Amongst these three groups of animals , the mice that received virus followed by non-specific peptide exhibited features similar to those observed in mice that received virus alone ( Group 1 ) . The animals in Groups 7 and 8 had the highest overall inflammatory score ( 4+ ) and showed multiple features including myositis , muscle necrosis , focal regeneration , mineralization and calcification of the necrotic muscles , hyperplasia of marrow in the long bones .
Molecular mimicry represents shared immunologic epitopes between a microbe and the host . In a viral system , viruses have been shown to have cross reactive epitopes with host self proteins [11] . Molecular mimicry can be either in the form of sequence homology wherein the host and the infectious agent share the sequence of similar or identical amino acids or it might be due to the conformational similarity between the host and the infectious agent in question [11] . Molecular mimicry is one of the major mechanisms for the induction of autoimmune diseases through the activation of autoreactive T cells in the host immune system . Several elegant examples of molecular mimicry leading to autoimmune manifestations have been described following bacterial and viral infections [11] . Chikungunya fever is a self limiting illness , however in 20–30% of the patients arthralgia persists for a period of two years and above [12] . More than half of all CHIKV infected patients in La Reunion Island during the 2005–2006 epidemics had complaints of persistent joint pain / recurring stiffness [13] . The arthritis attributed to CHIKV infection indeed mimics rheumatoid arthritis , as discussed by Bouquillard et al [14] wherein 21 patients infected with CHIKV in Reunion islands developed RA . Further , Malvy et al [15] suggested that molecular mimicry may be responsible for chronic manifestations as symptoms continue to persist despite CHIKV not being detectable in the synovial tissue . We investigated molecular mimicry in this study by using a combined approach of identifying homologous regions between CHIKV glycoprotein E1 protein and host tissue components using bioinformatics tools , the ability of these designed peptides to cross react with serum samples from CHIKV infected patients and inducing immune mediated joint and muscle pathology in a mouse model . In order to determine if there are any “arthritogenic” motifs within the E 1 protein , a multiple sequence alignment of amino acid sequences of E1 glycoprotein of CHIKV was carried out with other alphaviruses such as ONNV , RRV , SFV and EEEV using CLUSTALW ( Fig 2 ) . The alignment revealed that two common motif ( s ) SKD and KCA were present only in arthritogenic alphaviruses such as CHIKV , RRV and ONNV but not in the ‘encephalitogenic’ alphaviruses such as SFV and VEEV ( Fig 2 ) . The presence of these two motifs seen only in arthritogenic alphaviruses lead us to postulate that these two motifs may have a role in the development of arthralgia which is a hallmark in the disease produced by these group of viruses . Simultaneously , the immunodominant epitopes in the CHIKV E1 glycoprotein were deduced using IEDB and EMBOSS programs which use criteria like presence of beta turn , surface accessibility , flexibility , antigenicity and hydrophilicity of the regions to be studied . On combining the common results obtained with these two programs , four immunodominant regions were obtained in CHIKV E1 glycoprotein ( Peptide A , B , C and D ) . Interestingly , it was observed that the “arthritogenic” motifs SKD and KCA were also present in the immunodominant Peptides A and B respectively . Sequence homology comparisons between CHIKV E1 glycoprotein and various human proteins using BLAST revealed that a homology of four consecutive amino acids TQLV/TELV exist between the CHIKV E1 glycoprotein and HLA-B27 molecule ( Fig 3 ) . It is a well established that HLA B27 has been implicated in the pathogenesis of autoimmune arthritis and ankylosing spondylitis [16] . Interestingly this consecutive sequence of amino acids was also present in one of the immunodominant peptides ( Peptide A ) of CHIKV E1 glycoprotein ( Fig 3 ) . Having ascertained that there is amino acid homology between CHIKV E1 glycoprotein and HLA B27 molecule , the occurrence of structural homology was also explored using the BioXGEM program . This program searches for the longest common substructures existing between the query structure and every structure in the database . The output of the query while executing the program was however limited to the first 100 hits . Amongst these 100 proteins , further analysis was restricted only to human proteins present in the output . Amongst the 20 human proteins in the list ( Table 2 ) , six proteins were shortlisted as they are known to contribute to either inflammation or arthritic pathology . The most prominent amongst them was complement components C3and C5 . Indeed , C3 complement component has been implicated in inflammation and tissue injury in other related alphaviral infections [17] and therefore further analysis was confined to the homology between the CHIKV E1 glycoprotein and the C3 complement component . The analysis showed that the homology occurred between Von Willebrand Factor ( VWF ) domain of C3 and CHIKV E1 glycoprotein ( Panel A , Fig 5 ) . Interestingly , the amino acid sequences in the region of CHIKV E1 glycoprotein exhibiting homology were also present in the immunodominant Peptides A and B ( Panel B , Fig 5 ) . In summary , combining the data from the various bioinformatics approaches and employing a logical algorithm relevant to the pathogenesis of arthritis the choice narrowed down to two immunodominant peptides of CHIKV E protein ( Peptides A & B ) . Therefore all further experiments were carried out using only these two peptides . The Peptides A and B were used in an ELISA to assess the immunoreactivity of serum samples obtained from CHIKV confirmed patients as well as healthy control subjects . The serum samples obtained from CHIKV confirmed patients ( n = 36 ) showed reactivity to both the peptides to varying degrees . Antibodies to Peptide A was noted in 24/36 ( 66 . 66% ) of samples while 27/36 ( 75% ) of serum samples showed reactivity to Peptide B ( Fig 5 ) . These results indicate that the two peptides are indeed being recognized by the host immune system during CHIKV infection . As evident form Fig 5 , it was interesting to note that the sera from three patients who had persistent arthralgia 12 weeks after the onset of initial symptoms indeed exhibited much higher OD values ( 0 . 9 to 1 . 3 for peptide A and 1 . 2 for peptide B ) to the two peptides A & B as compared to other patients ( OD values were close to cut off and between 0 . 4 and 0 . 6 ) . Although , a quantitative ELISA would have delineated these differences better our ELISA was designed only as qualitative assay . Further , experiments were conducted to investigate if peptide A and peptide B were capable of inducing pathology in an experimental mouse model . The results indicated that these two peptides on their own were able to induce significant inflammation in the muscles of C57BL/6J mice ( Fig 8 and Table 3 ) similar to that observed in animals ( 3+ ) that were injected with CHIKV . Further , animals that were primed initially with CHIKV followed by a subsequent injection of the two CHIKV peptides exhibited enhanced inflammatory pathology ( 4+ ) as compared to animals that were injected with peptides or virus alone ( Fig 9 and Table 3 ) . On the contrary , animals that received an unrelated peptide ( i . e . not containing the arthritogenic motifs or exhibiting homology to host proteins ) either before or after priming with CHIKV exhibited minimal muscle inflammation ( 1+ ) . Collectively these observations validate the hypothesis that molecular mimicry between CHIKV E1 protein and host proteins does contribute to pathology in CHIKV infection . Such observations have not been reported hitherto in CHIKV infection although molecular mimicry as a mechanism leading to autoimmune phenomena has been demonstrated in several microbial infections including viruses and bacteria [18] . Among the viruses , molecular mimicry has been noted in Theiler murine encephalitis virus ( TMEV ) , Hepatitis B virus ( HBV ) , and SFV and Coxsackie viral infections [11] . In SFV infection of C57BL/6J mice a similar approach using bioinformatic tools derived peptides and validation of these peptides invivo for their ability to induce autoimmune demyelination was undertaken [19] . An algorithmic approach was used to demonstrate amino acid homology between immunogenic epitopes of SFV and various myelin proteins . The criterion used for the occurrence of molecular mimicry was the presence of three similar consecutive amino acids . It was observed that myelin oligodendrocyte protein ( MOG ) shared homology with SFV E2 glycoprotein . The injection of a peptide containing the sequence of shared amino acids into mice caused demyelination with presence of multifocal vacuolation in the CNS white matter [19] . The phenomenon of molecular mimicry has also been studied in SARS infection [20] . Eleven peptides derived from SARS spike ( S ) protein which shared homology with various human proteins were synthesised and their reactivity was assessed using serum samples obtained from SARS patients . Serum samples recognised only 2/11 peptides and the authors concluded that these two peptides may contribute to viral pathogenesis through the phenomena of molecular mimicry . The limitations of the present study are twofold . Firstly , HLA-B27 typing was not performed in the CHIKV confirmed patients and controls of this study . However , serum reactivity of Peptide A which shared homology with HLA B27 molecule suggests that this molecule may have an important role to play in persistent arthralgia as 3/36 patients exhibited high reactivity to it . The geographical and ethnic variation in the prevalence of HLA-B27 globally is well documented [21] and further its occurrence in CHIKV related complications is also reported to be very low [15 , 22] . Therefore it may be argued that the observance of molecular mimicry between CHIV E1 glycoprotein and HLA-B27 may not be the major factor contributing to complications that ensue following acute infection . Secondly , the time interval between the injection of CHIKV and the peptides into animal the model ( Groups 7& 8 ) is rather short rendering it difficult to conclude whether the inflammatory response noted was due to the immunopathologic process of molecular mimicry or a direct inflammatory effect of both the virus and the peptide . However , the muscle fibrosis and calcification in the muscles noted in the experimental animals recapitulates immune mediated polymyositis in humans during the evolution and progression . The other limitation with all studies conducted using animal models to demonstrate molecular mimicry as a mechanism of pathogenesis is that an adjuvant such as CFA/FICA is invariably required to elicit immune mediated damage . This suggests that in addition to having cross reacting disease inducing epitopes , sufficient activation of antigen presenting cells is required . Consequently , the most difficult part in the studies investigating molecular mimicry is to correlate/extrapolate the findings obtained in vitro and/or animal studies to the disease occurring in the natural host . Despite this limitation , the concept of molecular mimicry remains a viable hypothesis for framing questions and approaches to understanding the pathogenetic mechanisms involved during the disease process . Therefore , it would be definitely worthwhile to pursue future studies with the two CHIKV peptides described in this study using transgenic animal models and human T and B cell responses to the peptides . | The outcome of Chikungunya virus infection is usually benign but persistent arthritis has been reported in 10–20% of patients after Chikungunya fever . However , some reports have suggested that similarity between host proteins and viral proteins ( molecular mimicry ) leads to immune mediated damage . However , this has not been proved conclusively . Therefore , this study was undertaken to identify if molecular mimicry exists between CHIKV and host components . Using various bioinformatics tools we identified common sequences and structural homology between glycoprotein of the virus and two human host tissue proteins- HLA-B27 molecule and a domain of complement C3 . Two peptides having homology to these human tissue components were synthesized . These peptides were recognized by antibodies present in serum of CHIKV patients . Experiments were conducted to investigate if the peptides were capable of inducing pathology in an experimental C57BL/6J mouse model . Both the peptides on their own were able to induce significant inflammation in the muscles of C57BL/6J mice similar to that observed in animals that were injected with CHIKV alone . Additionally , animals that were injected initially with CHIKV followed by a subsequent injection of the two CHIKV peptides exhibited increased pathology as compared to animals that were injected with peptides or virus alone . | [
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] | 2017 | Molecular Mimicry between Chikungunya Virus and Host Components: A Possible Mechanism for the Arthritic Manifestations |
Proteome balance is safeguarded by the proteostasis network ( PN ) , an intricately regulated network of conserved processes that evolved to maintain native function of the diverse ensemble of protein species , ensuring cellular and organismal health . Proteostasis imbalances and collapse are implicated in a spectrum of human diseases , from neurodegeneration to cancer . The characteristics of PN disease alterations however have not been assessed in a systematic way . Since the chaperome is among the central components of the PN , we focused on the chaperome in our study by utilizing a curated functional ontology of the human chaperome that we connect in a high-confidence physical protein-protein interaction network . Challenged by the lack of a systems-level understanding of proteostasis alterations in the heterogeneous spectrum of human cancers , we assessed gene expression across more than 10 , 000 patient biopsies covering 22 solid cancers . We derived a novel customized Meta-PCA dimension reduction approach yielding M-scores as quantitative indicators of disease expression changes to condense the complexity of cancer transcriptomics datasets into quantitative functional network topographies . We confirm upregulation of the HSP90 family and also highlight HSP60s , Prefoldins , HSP100s , ER- and mitochondria-specific chaperones as pan-cancer enriched . Our analysis also reveals a surprisingly consistent strong downregulation of small heat shock proteins ( sHSPs ) and we stratify two cancer groups based on the preferential upregulation of ATP-dependent chaperones . Strikingly , our analyses highlight similarities between stem cell and cancer proteostasis , and diametrically opposed chaperome deregulation between cancers and neurodegenerative diseases . We developed a web-based Proteostasis Profiler tool ( Pro2 ) enabling intuitive analysis and visual exploration of proteostasis disease alterations using gene expression data . Our study showcases a comprehensive profiling of chaperome shifts in human cancers and sets the stage for a systematic global analysis of PN alterations across the human diseasome towards novel hypotheses for therapeutic network re-adjustment in proteostasis disorders .
Eukaryotic proteomes comprise a complex repertoire of diverse protein species that are organized in a modular interactome network in order to execute native function in support of proteostasis and a healthy cellular phenotype . Proteome balance is safeguarded by the proteostasis network ( PN ) , an intricately regulated network of conserved processes that have evolved to safeguard the healthy folded proteome [1] . Cellular proteostasis capacity is limited within the constraints of each cell’s proteostasis boundary [2] . Proteostasis imbalances , deficiency and functional collapse are implicated in a broad and increasing spectrum of protein conformational diseases with loss of native function or gain of toxic function , ranging from metabolic and neurodegenerative diseases to cancer [3 , 4] . Increasing awareness of the fundamental role of the PN in cellular health , its relevance in diseases and potential as a therapeutic target of proteostasis regulator ( PR ) drugs call for a systematic and systems-level assessment of PN deregulation throughout the human diseasome , towards improved understanding of diseases of proteostasis deficiency and rationalized network-informed approaches to therapeutic proteostasis re-adjustment . Important progress has been made in our understanding of proteostasis biology , building on fundamental insights on conserved proteostasis processes and their role in disease , such as chaperone-assisted protein folding and quality control [5–8] , clearance through autophagy [9–13] and the ubiquitin-proteasome system ( UPS ) [14–16] , followed by the appreciation of their concerted action within a conserved tightly regulated PN [1 , 17] . The identification , development and first clinical evaluations of small molecule PR drugs for therapeutic re-adjustment of proteostasis diseases such as cystic fibrosis represents a novel and powerful therapeutic paradigm [1 , 2 , 18–23] . First investigations have started to explore systems-level quantitative and functional approaches to assess the implications of PN functional arms such as the chaperome in human tissue aging and disease [4 , 24–26] . A precise understanding of the molecular mechanisms by which PN alterations contribute to disease could open novel therapeutic intervention strategies in a wide spectrum of proteostasis-related diseases . Still , to date , there has been no systematic study addressing the characteristics and extent of PN alterations in human diseases at a systems-level . The folding functional arm , the human chaperome , is highly conserved and of central importance in the PN , responsible for maintaining the native folded proteome . In cancers , mutations and genomic instability inevitably entail alterations of proteome composition and balance that are far less well explored than the consequences of nucleic acid sequence alterations . Post-translational alterations at the proteomic level are beyond the reach of DNA repair mechanisms and cancer cells are constantly challenged by the need to accommodate large amounts of proteotoxic stress in consequence of increased translational flux and proliferation as well as proteotoxic stressors . Proteome instability and pathological alterations in the abundance of key signalling or housekeeping molecules such as kinases , metabolic enzymes or molecular transporters have to be buffered by the PN to ensure cellular survival . The cancerous state poses characteristic requirements on the PN , such as high chaperone levels and elevated proteasome activity in order to ensure for sufficient correction or elimination of aberrant protein species in light of increased translational flux and metabolic stress [27] . This chronic challenge ultimately drives cancer cells into a dependency on quality control and stress response mechanisms , a phenomenon previously described as non-oncogene addiction [28 , 29] . Several individual chaperones and heat shock proteins such as HSP90 have consistently been found upregulated in cancers [27] . However , the profile and extent of chaperome differential expression has not been assessed systematically across the human cancer landscape . Challenged by the genetic complexity and heterogeneity , collective prevalence and unmet medical need of the wide spectrum of human cancers as well as the lack of a systems-level understanding of proteostasis alterations during carcinogenic transformation , we developed a novel integrated analytical pipeline and software toolkit for the quantitative profiling of chaperome changes across the human cancer landscape ( Fig 1 ) . We utilized an expert-curated functional chaperome ontology comprising the ensemble of 332 human chaperone and co-chaperone genes [4] ( Fig 1A ) . In order to apply our analytical workflow on a recent and comprehensive cancer gene expression dataset with clinical relevance , we turned to The Cancer Genome Atlas ( TCGA ) compendium [30] . We started with a customized genomic analysis pipeline in order to map chaperome functional family expression changes across TCGA solid cancers compared to matching normal tissue ( Fig 1B ) . The resulting top-level view on cancer chaperome deregulation revealed a broad chaperome upregulation throughout the majority of cancers . This consistent and high overall chaperome upregulation prompted us to zoom in on functional sub-families . This analysis surfaced clusters of chaperome functional family up- and downregulation signatures that enabled further stratification of cancers . In summary , our analysis of the 10 major chaperome functional families reveals pronounced tissue differences of cancer chaperome deregulation . The preferential upregulation of ATP-dependent chaperone families such as HSP90s and HSP60s , while ATP-independent chaperones , co-chaperones , and small heat shock proteins ( sHSPs ) are consistently downregulated , is opposed to chaperome alteration patterns observed in brain tissues during aging and in neurodegenerative diseases [4] . These characteristic chaperome-wide differences further justify our approach and need for systematic maps of PN deregulation across the human diseasome . In order to enable comprehensive , contextual , and quantitative representations of the complexity of chaperome alterations across a large number of patient biopsy disease datasets , we developed a new custom data dimensionality reduction and visualisation approach . Combining Meta-PCA , a novel principal component analysis ( PCA ) based two-step dimension reduction algorithm and its resulting quantitative M-scores of chaperome functional family disease alteration of gene expression with contextual polar plot visualisations , we provide intuitive quantitative maps of cancer chaperome gene expression changes ( Fig 1B ) . The mechanistic understanding of genotype-phenotype relationships in complex genetically heterogeneous diseases such as cancers requires the consideration of the cellular interactome network [31] . To reduce complexity and to highlight contextual changes of chaperome functional families , we first generated a custom curated high-confidence physical protein-protein interaction ( PPI ) chaperome network . We then collapsed proteins ( nodes ) and physical PPIs ( edges ) into a meta-network , where meta-nodes represent respective functional family members and meta-edges bundle the interactions between families ( Fig 1C ) . This curated high-quality chaperome meta-network base-grid enabled the contextual projection of cancer-specific chaperome functional family differential gene expression ( M-scores ) onto the underlying interactome . We integrated these dimensions into interactome-guided , three-dimensional topographic maps visualising chaperome functional family cancer differential gene expression changes in the context of interactome network proximity , intuitively providing quantitative views of cancer chaperome deregulation ( Fig 1D ) . To make these resources easily available to the community , we developed Proteostasis Profiler ( Pro2 ) , an integrated web-based suite of applications enabling intuitive quantitative analyses and comparative visualisation of differential expression of complex PN alterations across large disease dataset compendia such as the TCGA . Visualisation and analysis features include heat map clustering and polar plot display . Integrated meta-networks and interactome-guided 3D topographic maps ease comparative exploration of cancer chaperome deregulation in the context of interactome network wiring . Pro2 is designed to serve the scientific community as a user-friendly application for systems-level exploration of PN disease alterations , at reduced complexity . Overall , this study represents a systematically derived systems-level atlas of chaperome deregulation maps in cancers and neurodegenerative diseases , with a detailed focus on chaperome functional family alterations . The integrated genomic analysis workflow , built into the Pro2 suite of visualisation tools , provides a resource and analytical platform for future characterisation and exploration of PN deregulation patterns across the human diseasome , and as a readout interface for network shifts induced by therapeutic regulation .
Homeostasis of the cellular proteome , or proteostasis , is fine-tuned by the proteostasis network ( PN ) , an intricately regulated network of conserved processes that have evolved to safeguard the native functional proteome and cellular health . The human chaperome , an ensemble of 332 chaperones and co-chaperones , represents a central functional arm within the PN in charge of maintaining the cellular folding landscape ( S1A Table ) [4] . Motivated by the genetic heterogeneity of cancers , their prevalence and associated medical need as well as the lack of a systems-level understanding of the role of proteostasis genomic alterations during carcinogenesis , we systematically assessed chaperome gene expression changes across the diverse spectrum of human cancers . We focused on an established resource of human cancer patient biopsy RNA-seq datasets provided through The Cancer Genome Atlas ( TCGA ) [30 , 32] . We considered 22 human solid cancers with available corresponding healthy counterpart tissue biopsy data . To obtain global views on chaperome commonalities or differences between cancers , we applied Gene Set Analysis ( GSA ) in order to quantify gene expression changes of the chaperome and its functional families . GSA is an advanced derivative of Gene Set Enrichment Analysis ( GSEA ) that methodologically differs primarily through its use of the maxmean statistic , the mean of the positive or negative gene scores in each gene set , whichever is larger in absolute value , that has proven superior to the modified Kolmogorov-Smirnov statistic used in GSEA [33] . Secondly , GSA uses a different null distribution for false discovery rate ( FDR ) estimations , through a restandardization of genes in addition to sample permutation in GSEA . This step is crucial , as it allows assessing statistical robustness of the expert-curated chaperome functional ontology gene family groups . We obtained the GSA derived probability ( p values ) for each functional gene group to be significantly up- or downregulated in cancer as ∆GSA values in the interval [-1 , +1] according to ( ( 1—upregulation p value ) — ( 1—downregulation p value ) ) . Notably , the human chaperome is predominantly upregulated across the majority of TCGA solid cancers with a ∆GSA group mean change of +0 . 50 as compared to 100 random sets of non-chaperome genes ( Fig 2A ) . This overall chaperome upregulation highlights cellular non-oncogene addiction to chaperone-assisted folding and protein quality control mechanisms in consequence of increased client load , further challenging cellular proteostasis and driving “proteostasis addiction” in cancers [34] . Despite the diverse established knowledge about the role of chaperone upregulation in cancer , the deregulation of the human chaperome has not been assessed at a systems-level throughout the human cancer landscape . To functionally resolve the general chaperome upregulation across cancers , we zoomed in on functional family gene expression alterations . GSA followed by Euclidean clustering of chaperome functional families revealed characteristic cancer differences . We found the key ATP-dependent HSP90 and HSP60 families , of which selected members have previously been shown to be upregulated in cancers , amongst the most highly upregulated functional families with ∆GSA group mean changes of +0 . 55 and +0 . 51 , respectively , alongside ER-specific chaperone factors ( +0 . 53 ) , followed by Prefoldins ( PFDs , +0 . 40 ) , HSP100 AAA+ ATPases ( +0 . 32 ) , and mitochondria-specific chaperones ( MITOs , +0 . 09 ) ( Fig 2B ) . These six functional families of predominantly ATP-dependent chaperones represent an upregulation cluster with an overall group mean change of +0 . 40 . Intriguingly , the HSP70-HSP40 system and the large family of TPR-domain containing co-chaperones are overall repressed , with less consistent and largely cancer-specific alterations . HSP40 co-chaperones ( -0 . 09 ) cluster closest with HSP70s ( -0 . 10 ) , indicative of the functional relationship they engage in during the HSP70 chaperone cycle . While HSP40 co-chaperones are overall weakly downregulated ( -0 . 09 ) , also the second group of co-chaperones , the TPR-domain containing proteins , clustered with the HSP70-HSP40 system and were overall downregulated ( -0 . 18 ) . Strikingly , sHSPs ( -0 . 74 ) were overall very consistently and strongly downregulated . Overall , sHSPs , TPRs , and the HSP70-HSP40 system clustered in a downregulation cluster with an overall group mean change of -0 . 28 across cancers . Besides marked differences in the pattern of cancer functional family changes , Euclidean clustering of cancer groups ( rows ) revealed two major clusters ( Fig 2B ) ( with a p-value equal to 0 . 016 via multiscale bootstrap resampling ) . The vast majority of cancers is characterised by the consistent upregulation of HSP90s , ER-specific chaperones , HSP60s , PFDs , HSP100s and MITOs , opposed by a very consistent downregulation of sHSPs and a more cancer-specific overall downregulation of the HSP70-HSP40 system an TPR-domain co-chaperones . This group comprises Cluster I , representing ~91% of cancers , while Cluster II comprises ~9% of cancers with largely opposed chaperome deregulation signatures , in this set namely skin cutaneous melanoma ( SKCM ) , and pheochromocytoma and paraganglioma ( PCPG ) . In summary , systematically assessing gene expression data derived from a total of 10 , 456 patient samples uncovers broad differences in chaperome-scale deregulation across the variety of human solid cancers . While the vast majority of cancers shows consistent and strong upregulation of chaperome genes , this analysis reveals marked clusters of chaperome functional family expression signatures that further stratify cancers by differential chaperome expression . The major ATP-dependent chaperone functional families are consistently upregulated across a majority of cancers , while co-chaperones and sHSPs are consistently repressed ( Fig 2B ) . In order to quantify this trend , we assessed the 22 differentially regulated cancer chaperomes for functional characteristics . First , we compared expression of 88 chaperones against 244 co-chaperones represented in the human chaperome [4] . Projecting TCGA cancer groups by their chaperone and co-chaperone differential expression highlights a significant preponderance of cancer chaperome upregulation , including both chaperones and co-chaperones , while only a minor fraction of each is downregulated ( S2A and S2B Fig ) . Overall , chaperones tend to be more upregulated than co-chaperones ( S2B Fig ) . Consistently , within the group of chaperones , we find an overall preponderance of upregulation of both ATP-dependent and ATP-independent chaperones , while only small fractions each are downregulated ( S2C and S2D Fig ) . The 50 ATP-dependent chaperones are more upregulated than the 38 ATP-independent chaperones , while ATP-independent chaperones are more downregulated than ATP-dependent chaperones ( S2D Fig ) . This analysis exposes a sub-group of cancers as notable exceptions to these trends , suggesting fundamental differences in chaperome deregulation . Projection of TCGA cancer groups by chaperone and co-chaperone up- and downregulation lends support for two groups of cancers , Group 1 and Group 2 ( Fig 3A ) . These groups are recapitulated when projecting cancers by up- and downregulation of ATP-dependent versus ATP-independent chaperones ( Fig 3C ) . K-means clustering confirms the significant separation of the Group 2 cancers pheochromocytoma and paraganglioma ( PCPG ) , thyroid carcinoma ( THCA ) , and the three kidney cancers kidney chromophobe ( KICH ) , kidney renal papillary cell carcinoma ( KIRP ) , and kidney renal clear cell carcinoma ( KIRC ) from Group 1 cancers , with a median silhouette width of s = 0 . 63 ( Fig 3A ) and s = 0 . 68 ( Fig 3C ) . Group 1 cancers ( red ) represent the majority of cancers , characterized by strong overall chaperome upregulation , with low chaperone and co-chaperone repression , and a trend for upregulation of ATP-dependent chaperones ( Fig 3B and 3D ) . Five Group 2 cancers however partition more distantly , with a lack of chaperome upregulation ( Fig 3A and 3C ) . These cancers include three different kidney cancers , KICH , KIRP , and KIRC , which consistently lack chaperome upregulation ( Fig 3A and 3B ) . Also , ATP-dependent chaperones are not preferentially upregulated in kidney cancers . Rather , an inverse trend is observed with increased downregulation of ATP-dependent chaperones ( Fig 3C and 3D ) . Notably also , pheochromocytoma and paraganglioma ( PCPG ) , rare related tumors of orthosympathetic origin , similarly show an even more prominent inverse alteration , with a preponderance of overall chaperome downregulation and preferential downregulation of ATP-dependent chaperones . Pheochromocytomas originate in the adrenal medulla , with close spatial association to the kidney , whose cancers are also Group 2 cancers . THCA is similar to PCPG , with a preferential downregulation of the chaperome and both PCPG and THCA represent tumors forming from cells of neuroendocrine origin . Collectively , the data point to a preferential upregulation of ATP-dependent chaperones in the majority of cancers , which we refer to as Group 1 cancers , with general differences in chaperome deregulation in Group 2 cancers , comprising kidney cancers and cancers of neuroendocrine origin , such as PCPG and THCA . Human embryonic stem cells ( hESCs ) are characterized by their capacity to replicate infinitely in culture , while maintaining a pluripotent state [35] . This immortal , undifferentiated phenotype resembles hallmark features of cancer cells such as an elevated global translational rate [36] and is expected to demand increased PN capacity capable of buffering imbalances to maintain proteostasis . Given the “stemness” phenotype of cancer cells and their resemblances with pluripotent stem cells we hypothesized that the consistent chaperome upregulation in cancers acts to mimic an enhanced stem cell PN setup . Increased proteasome activity [37] and elevated overall levels of the TRiC/CCT complex [38] , representatives of the clearance and folding functional arms of the PN , respectively , have recently been associated with the intrinsic PN of pluripotent stem cells that acts to support their identity and immortality . It can be hypothesized that increased levels of central PN processes in stem cells exemplify characteristics of an enhanced PN setup . We thus assessed to which extent this stem cell PN setup is recapitulated in cancers . First , we assessed differential changes of the proteasome across TCGA cancers and observed an overall consistent upregulation of the 43 proteasomal genes ( HGNC Family ID 690 ) in > 70% of cancers ( Fig 4A , S1B Table ) [39] , matching the role of increased proteasomal activity for proteome maintenance in stem cells [37] . Notably , Group 1 and Group 2 cancers , which are specifically defined based on chaperome differential expression signatures ( Fig 3 ) , do not co-partition with cancer clusters obtained by proteasome differential expression ( Fig 4A ) . Next , we assessed cancer differential expression of the eukaryotic chaperonin TRiC/CCT , a hetero-oligomeric complex of two stacked rings with each eight paralogous subunits representing the cytoplasmic ATP-driven HSP60 chaperones in charge of folding approximately 10% of the proteome [40] . TRiC/CCT is highly conserved and essential for cell viability [40] . Loss of complex subunits induces cell death and a decline of pluripotency of hESCs and induced pluripotent stem cells ( iPSCs ) [38] . Within the PN , TRiC/CCT mediated folding and autophagic clearance act in concert to prevent aggregation [41] . TRiC/CCT levels decline during stem cell differentiation , and CCT8 acts as complex assembly factor [38] . Intrigued by the finding that CCT8 is the most highly elevated subunit in stem cells and likely acting as assembly factor [38] , we assessed differential expression of individual TRiC/CCT subunits across cancers . Hierarchical clustering of subunit expression across solid cancers highlighted CCT8 as highly consistently upregulated across all cancers ( mean change = 0 . 76 , t test ) , and as overall second most highly upregulated subunit besides CCT6A ( mean change = 0 . 786 , t test ) ( Fig 4B ) . Consistent with the overall preferential downregulation of ATP-dependent chaperones observed in Group 2 cancers ( Fig 3 ) , we found these cancers to cluster together with overall lowest TRiC/CCT expression , where PCPG stands out with a consistent downregulation of all subunits ( Fig 4B ) . Together these findings suggest that proteostasis shifts in cancer cells add to an altered , enhanced PN state that mimics the immortal and resilient stem cell phenotype , buffering genome instability and ensuing proteomic imbalances in support of sustained and increased cellular proliferation throughout cancerogenesis . Overall chaperome upregulation in cancers , with preferential enrichment for upregulation of ATP-dependent chaperones , alongside consistent downregulation of sHSPs , is diametrically opposed to chaperome deregulation trends previously observed in a study of chaperome alterations in human aging brains and in patient brains with age-onset neurodegenerative diseases [4] . While sHSPs were the only chaperome family found significantly induced in brain aging and the age-onset neurodegenerative diseases Alzheimer’s ( AD ) , Huntington’s ( HD ) , and Parkinson’s ( PD ) disease , this family is consistently downregulated across cancers ( Fig 2B ) . This opposed chaperome deregulation points towards characteristic and fundamental differences in PN deregulation between disease families . To investigate this disease group difference further , we applied the analysis outlined for cancers above also on the gene expression datasets that had earlier revealed global chaperome repression in AD , HD , and PD [4] . Our analysis reproduced the human chaperome as overall downregulated across AD , HD , and PD as compared to random permutations of non-chaperome genes ( -0 . 20 ∆GSA group mean change , Fig 5A ) . Delving deeper into chaperome functional subfamilies , we reproduce earlier findings reporting broad repression of the major chaperome functional families except for sHSPs , the only family found strongly upregulated ( +0 . 76 ) accompanied by slight upregulation of ER-specific chaperones ( +0 . 21 ) and TPRs ( +0 . 13 ) ( Fig 5B ) . Thereby , our analytical workflow reproduces previously observed trends obtained in independent analyses , with different methods . With strong sHSP repression and upregulation of the HSP90 , ER , HSP60 , PFD , HSP100 and MITO chaperone families , cancers and neurodegenerative diseases display markedly diametrically opposed chaperome deregulation , not only at the overall chaperome-level ( Fig 5C ) , but also with respect to alteration trends of chaperome functional families , where 70% of functional groups are altered in opposite directions ( Fig 5D ) . These opposing chaperome deregulation signatures are in line with differing implications of proteostasis alterations in these diseases . While broad chaperome repression and proteostasis functional collapse is associated with aggregation and cytotoxicity of chronically expressed misfolding-prone proteins in neurodegenerative diseases [4] , enhanced proteostasis buffering capacity is associated with “stemness” , immortality and proliferative potential of both stem and cancer cells [27] . Indeed , epidemiological evidence suggests an inverse correlation between cancers and neurodegenerative diseases [45–48] , supportive of a potential mechanistic link between opposed chaperome deregulation and the molecular underpinnings of the two disease groups . These global differences in chaperome deregulation call for a systematic and quantitative assessment of PN deregulation dynamics in human diseases . In light of the diverse signatures of differential chaperome deregulation observed across cancers ( Fig 2 ) , and motivated by the increasing amount of genomics datasets available for cancers and other human diseases , we aimed at reducing data complexity by extracting quantitative indicators of chaperome differential cancer gene expression alterations , in order to gain insights through reduced complexity while retaining maximum information content . We devised Meta-PCA , a novel principal component analysis ( PCA ) based semi-supervised two-step dimension reduction approach that facilitates stratification of cancer patient and normal control samples within heterogeneous gene expression datasets ( Fig 1B ) . Based on previous work on dimensionality reduction of heterogeneous gene expression datasets [49 , 50] , we hypothesized that the underlying information contained in each chaperome functional group has a low dimensionality ( meaning the functionality of each chaperome group can be quantified using only few , possibly one , variable ) and could be surfaced using PCA , if there were sufficient samples available to represent the complete heterogeneity of chaperome alterations in cancers . Compared to conventional PCA , our method can deal with the effect of different group sizes , which as a confounder would negatively affect PCA results . Compared to simply calculating mean expression values of each functional group’s genes , Meta-PCA considers a wider range of gene expression information inherent to each functional group , resulting in scores with higher resolution . This could also be achieved by fully supervised methods such as Linear Discriminant Analysis . However , this case requires use of a method , which is blind to sample annotations so that these can later be used for validation , such as the unsupervised classification of cancer tissue type . Meta-PCA first uses tissue-wise PCA analyses to separate cancerous from control samples for individual tissues and maps functional family group gene expression changes to the global , or “meta” , mean expression change across cancers in order to obtain M-scores as quantitative indices of relative disease gene expression change ( Eq 1 ) . Inherent to the Meta-PCA method , patient-specific genomic variability is averaged out through the use of Meta-PCs derived from the TCGA collection of cancer biopsy samples , yielding mean reference boundaries . Assessing quantitative M-scores obtained through Meta-PCA against differential gene expression ( ∆GSA ) obtained via GSA ( Fig 2 ) , we observed an overall significant Pearson correlation of 0 . 61 ( S1 Fig ) . In addition to general comparability between results obtained through both methods , Meta-PCA analysis reduces complexity while retaining genomic information . Therefore , we focus on Meta-PCA for quantitative representation of proteostasis alterations in human diseases . We plot differential chaperome changes as M-scores for all cancer samples and chaperome functional families simultaneously using polar plots , such that axes represent functional families or sub-groups ( Fig 2B ) . We obtain the mean of all biopsy samples as reference boundaries for healthy ( blue line ) and cancer groups ( red line ) and include the 90% confidence interval ( CI ) ( red and blue halos ) ( Fig 6 ) . This quantitative visualisation reduces complexity and highlights relativity of disease gene expression changes at a chaperome-scale . The polar maps recapitulate characteristic chaperome deregulation signatures in GSA-derived clusters of functional family upregulation and downregulation signatures , for instance in Cluster I cancers such as lung adenocarcinoma ( LUAD ) ( Figs 2 and 6A ) , or the inverse trend with overall chaperome downregulation in Cluster II cancers such as pheochromocytoma and paraganglioma ( PCPG ) ( Figs 2 and 6B ) . Concordantly , chaperome polar maps reveal characteristic patterns of cancer groups stratified based on differential expression of ATP-dependent chaperones versus ATP-independent co-chaperones , Group 1 versus Group 2 cancers ( Figs 3 and 6 ) . In LUAD , representative of Group 1 cancers , most functional chaperome families , with a preferential enrichment of ATP-dependent chaperones , are upregulated , while sHSPs are reduced ( Fig 6A ) . On the contrary , in Group 2 cancers such as PCPG , gene expression of most chaperome functional families is downregulated ( Fig 6B ) . Inconsistencies between these broad clusters exist , suggesting differences in tissue of origin and molecular underpinnings of respective cancers . However , broad commonalities between distinct cancers originating from the same organ are revealed . For instance , lung adenocarcinoma ( LUAD ) and lung squamous cell carcinoma ( LUSC ) share overall similarity , revealing only subtle differences , for instance in HSP40 expression ( Figs 2 and S3 ) . The kidney cancers KICH , KIRP , and KIRC also show similar patterns . As Group 2 cancers , they share and stand out against other cancers with a lack of preferential upregulation of ATP-dependent chaperones ( Fig 3 ) , and overall reduced upregulation , or downregulation , of HSP60s ( Figs 2 , 3 , 4B and S3 ) . A recent study indeed implicated HSP60 downregulation in tumorigenesis and progression of clear cell renal cell carcinoma ( KIRC ) by disrupting mitochondrial proteostasis [51] . Overall , these contextual quantitative representations enable an appreciation of the complex chaperome shifts in different cancer tissues derived from > 10 , 000 patient biopsy samples . The resulting compendium of differential cancer chaperome polar plots ( S3 Fig ) is also available online through the Proteostasis Profiler ( Pro2 ) tool associated to this study . The integration of disease-related differential transcriptomic changes with the cellular protein interactome network , or the edgotype , is instrumental to our understanding of genotype—phenotype relationships [31] . Towards integrated quantitative views of cancer chaperome deregulation , we curated a high-confidence physical chaperome protein-protein interactome network ( CHAP-PPI ) to serve as coordinate base grid layout for the analysis of differential chaperome topographies ( Fig 1C ) . We started with 328 , 244 unique human PPIs ( edges ) between 16 , 995 proteins ( nodes ) downloaded from the BioGRID , IntAct , DIP , and MINT databases [52] , [53] , [54] , [55] . Zooming in on cancer chaperome alterations in the context of physical interactome wiring , we extracted the CHAP-PPI considering the 332 human chaperome genes as previously described [4] . Considering edges with the PSI-MI annotation 'physical association' we obtained 272 , 367 unique physical edges , of which 666 unique edges connect 220 chaperome nodes . We developed a custom script to curate the high-confidence physical CHAP-PPI , considering edges with multiple pieces of evidence , either experimental methods or publications ( PMIDs ) , as more reliable than those supported by only a single piece of evidence . The curation script resolves ambiguous database annotation of methods terms through up-propagation within the PSI-MI ontology tree , only accepting uniquely different or rejecting identical experimental evidence . Automated interactome curation results in eight curation levels ( L1—L8 ) , through which we obtain three interactomes of increasing confidence level ( see Methods ) . All 666 unique physical edges between 220 chaperome nodes , without curation for type or number of evidence , represent the single evidence chaperome interactome ( SE-CHAP ) . Curating for high-confidence interactions , we obtained a multiple evidence chaperome ( ME-CHAP ) comprised of 222 unique physical chaperome edges between 128 chaperome nodes , of which a subset of 132 interactions between 96 nodes is supported by multiple different experimental methods ( MM-CHAP ) ( S2 Table ) . In order to enable focussed views on transcriptomic alterations of top-level chaperome functional families , we collapsed individual nodes onto functional family meta-nodes , and edges shared between families were collapsed as meta-edges such that meta-node sizes correspond to the number of family members and meta-edge thickness represents the number of shared interactions between families . We considered the meta-interactome derived from the curated high-confidence ME-CHAP interactome ( S2 Table ) , where all meta-nodes corresponding to the 10 functional chaperome families are fully inter-connected in a single network component . We set node colour to visualize cancer gene expression changes ( M-scores ) and applied a force-directed spring layout algorithm to optimize graph layout [56] . The resulting integrated cancer chaperome meta-interactomes visualize relative chaperome differential changes at reduced complexity across diverse human cancers in the context of physical interactome connectivity ( Figs 7A and S4 ) . Next , we extract x-y coordinates of the chaperome meta-nodes in the optimized meta-network graph to serve as a 2-dimensional base grid ( x-y coordinates ) guiding the spatial layout of 3-dimensional chaperome topographic maps of differential chaperome gene expression changes ( M-scores ) between cancerous and healthy biopsies ( z coordinate ) ( Figs 7B and S5 ) . This interactome-guided topographic display of differential chaperome alterations enables dimensionality and complexity reduction for the coherent display and comparative analysis of functional network shifts that can serve to compare differential changes i ) in disease versus controls , ii ) between diseases and disease classes , and iii ) between perturbed or unperturbed states across large numbers of heterogeneous genomic datasets . Furthermore , this visualization lends itself for a systems-level assessment of PN deregulation topologies and their readjustment in human disease and therapeutic intervention . We implemented topographic map visualisations into the Proteostasis Profiler ( Pro2 ) suite of tools , to improve accessibility and applicability by the scientific community . Here we exemplify a systematic analysis of differential chaperome gene expression alterations in cancers and neurodegenerative diseases . We reduce complexity through the focus on top-level chaperome functional families . The challenge in this analysis is in the complexity and heterogeneity of available samples for disease groups such as cancers , combined with the multitude of diverse biological processes interconnected within the PN and within its functional processes , as highlighted here at hands of the human chaperome . To date , there has been no systematic interactome-guided analysis of the implications and alterations of cellular proteostasis biology at a systems-level , in a comprehensive set of diseases , such as cancers . Here , we showcase an integrated analytical workflow for the dimension reduction , analysis and visualization of chaperome differential alterations in a representative set of human solid cancers . Our approach focuses on the visualisation of a confined set of Meta-PCA derived quantitative M-scores as descriptors of top-level chaperome functional families . We have developed “Proteostasis Profiler” ( Pro2 ) as an integrated web-based resource and suite of tools , for interactive dimensionality-reduction , analysis and visualisation of disease-specific alterations of proteostasis functional arms , such as the chaperome , in the context of the interactome network . In this study we highlight Pro2 use-cases for the human chaperome across TCGA solid cancers in comparison to the three major neurodegenerative diseases using differential gene expression heat maps ( ∆GSA ) ( Figs 2 and 5 ) , Meta-PCA derived quantitative polar plots ( M-scores ) ( Figs 6 and S3 ) , meta-interactomes and interactome-guided 3-D topographic maps ( Figs 7 , S4 and S5 ) . Pro2 provides an integrated online suite for the application of the underlying algorithms . Pro2 is accessible directly at http://www . proteostasys . org .
Cancer prevalence , genetic complexity and heterogeneity represent unmet medical need and a significant challenge to personalized medicine , calling for genome-informed therapeutic intervention strategies [57] . While important progress has been made in the elucidation of proteostasis alterations in human diseases , revealing numerous alterations of PN functional processes not only in neurodegenerative or metabolic diseases but also in cancers , paradoxically the characteristics and extent of PN alterations in cancers are largely unexplored and not understood at a systems-level . Cancer cell line global transcriptional characteristics have been extensively studied [58] and numerous individual studies have assessed alterations of various chaperone and co-chaperone expression levels in specific cancers [27 , 59] . In light of limitations in the clinical translation of hypotheses derived from cell lines and the lack of a systems-level understanding of proteostasis alterations in human disease , we argue that precise quantitative maps of proteostasis deregulation in human disease derived directly from clinical biopsy data will enable precise understanding of the role of PN alterations in pathogenesis towards testable hypotheses and rationalised approaches of PR therapy [1 , 60] . Here , we focused on the human chaperome , a central PN component , and highly conserved facilitator and safeguard of the healthy folded proteome using an expert-curated human chaperome functional gene ontology comprising an ensemble of 332 chaperone and co-chaperone genes [4] to systematically characterize chaperome alterations in a representative clinically relevant dataset of 22 human solid cancers with matching healthy tissue , corresponding to over 10 , 000 patient biopsy samples provided through the TCGA consortium [30] . We found the human chaperome to be consistently highly upregulated across the vast majority of cancers assessed . While numerous individual chaperones and co-chaperones have previously been found upregulated in individual cancers [27 , 59] , this knowledge has not been coherently derived from consistent data resources or systematic genome-wide analyses in biopsy tissue before . Here , we provide systematic quantitative maps of chaperome deregulation in cancers that highlight the relevance , characteristics and extent of chaperome upregulation in cancers . Our analysis revealed chaperome deregulation signatures that not only feature broad upregulation of ATP-dependent chaperones but also consistent repression of ER-specific chaperones and the ATP-independent sHSPs . The data also suggest two cancer groups that can be stratified specifically by their chaperome deregulation patterns . Overall chaperome upregulation across cancers is in agreement with existing evidence on individual chaperones that has been previously reviewed [59 , 61 , 62] . For instance , elevated heat shock protein expression levels have been reported for HSP90 in breast and lung cancers [63 , 64] , HSP70 was found increased in breast , oral , cervical and renal cancers [65–68] , and HSP60 showed increased expression in Hodgkin’s disease [69] . The cellular safeguarding functions of chaperones are subverted during oncogenesis to facilitate malignant transformation in light of increased translational flux and aberrant protein species in cancer cells [27] . Increased chaperone levels have previously been correlated with poor prognosis and cancer survival [59 , 63 , 70] . Chronic dependency on stress response and quality control mechanisms drives cancer cells into a phenotype of non-oncogene addiction [28] . The observed extent of chaperome alterations suggests a broader state of cancer “chaperome addiction” , beyond the dependency on individual chaperones . Evidence points towards functional associations between increased proteostasis buffering capacity and maintenance of “stemness” , immortality and proliferative potential in both cancer cells and pluripotent stem cells [27] . For instance , autophagy was found to maintain “stemness” by preventing senescence through sustained proteostasis [71] . Increased proteasomal activity and elevated levels of the HSP60 chaperonin complex TRiC/CCT have recently been linked to stem cell identity by conferring proteostasis robustness [37 , 38] . Fundamental similarities between stem cells and cancer raise the question to the extent of similarity between cancer and stem cell PN states and capacity . Our data suggest that cancers consistently display signatures of elevated proteostasis functional processes such as the chaperome and proteasome-mediated clearance , and are in agreement with the hypothesis that upregulated clearance mechanisms such as the proteasome and increased chaperome topologies , particularly increases in ATP-driven chaperones such as the HSP60 chaperonin complex TRiC/CCT , confer increased proteostasis capacity and survival benefits to cancer cells just like they are essential to stem cell biology . Precise knowledge of systems-level network deregulation therefore sheds light on fundamental processes at play from stem cell biology to cancerogenesis . Chaperone upregulation is largely regulated through heat shock factor 1 ( HSF1 ) [29] . Overexpression of the TRiC/CCT subunit CCT8 protects against hsf-1 knockdown in C . elegans [38] , consistent with a regulatory connection between TRiC/CCT and HSF1 [72] . Connecting processes at the PN level , this evidence suggests a connection between TRiC/CCT and HSF1 stress response signalling also in cancers [38 , 73] . While increased expression of TRiC/CCT subunits has been observed in cancer cell lines [74] , and increases in CCT8 expression are linked to individual cancers [75 , 76] , we describe consistent TRiC/CCT upregulation within global cancer chaperome signatures throughout the majority of TCGA solid cancers , or Group 1 cancers , whereas Group 2 cancers lack chaperome and , to large extent , TRiC/CCT upregulation . Contrary to stem cell proteostasis , which is set up to maintain pluripotency and proliferative capacity , neurodegenerative diseases such as Alzheimer’s ( AD ) , Huntington’s ( HD ) , and Parkinson’s disease ( PD ) display signs of proteostasis functional collapse . Misfolding diseases feature overexpression of aggregation—prone proteins such as Aβ in AD , α-synuclein in PD , or huntingtin in HD that entail a “toxic-gain-of-function” resulting in chaperome overload , gradually exceeding proteostasis capacity [77] , while “loss-of-function” misfolding diseases feature specific perturbations such as dysfunctional ∆F508-CFTR in cystic fibrosis [78] . Functionally deficient steady-state dynamics of the folding environment affect cellular protein repair capacity and proteome maintenance [79] . Most cancer cells however harbour manifold genetic aberrations even at the karyotype level that likely entail dramatic effects on proteome balance [80] . The collective damage caused by oncoprotein expression , compromised DNA repair , genomic instability , reactive oxygen species ( ROS ) , elevated global translation and chaperome overload triggers stress response mechanisms in light of a challenged cellular proteostasis capacity [81] . Chaperome deregulation dynamics observed in cancers indeed display concordantly opposed trends as compared to alterations in the major neurodegenerative diseases . A recent study linked repression of ~30% of the human chaperome in aging brains and in neurodegenerative diseases to proteostasis functional collapse and pointed to the role of a chaperome sub-network as a conserved proteostasis safeguard [4] . Intriguingly , while only ~8% of the human orthologous chaperome had protective phenotypes upon functional perturbation in C . elegans models of amyloid β ( Aβ ) and polyQ proteotoxicity , chaperones and co-chaperones far less well studied than HSP90 had equally strong protective effects [4] . Similarly , an overlap between the chaperome and the “essentialome” set of 1 , 658 core fitness genes in K562 leukemia cells [82] found only 55 overlapping with the 332 chaperome genes [83] . Interestingly , HSP60s showed the highest fraction of essential chaperones in agreement with their function as a highly conserved folding complex that hosts ~10% of the proteome’s clients [40] . Collectively , these findings suggest a highly functionally redundant and robust role of the central conserved chaperome within the PN [84] . In summary , our study showcases a systematic profiling of the extent of chaperome deregulation , as a central PN functional arm , in a panel of human cancers and three major neurodegenerative disorders , accompanied by a resource of quantitative multi-dimensional maps with reduced complexity . Therapeutic PN regulation for increased or restored proteostasis capacity may be beneficial in both loss-of-function and gain-of-toxic-function diseases of protein misfolding [2] . Attenuating the PN on the other hand , such as inhibiting chaperones like HSP70 and HSP90 or the UPS clearance machinery , are widely acknowledged as promising therapeutic avenues in cancers [27 , 83 , 85 , 86] . While this manuscript was in preparation , Rodina and co-workers reported findings on a highly integrated chaperome subnetwork , or ‘epichaperome’ , as a classifier of cancers with high sensitivity to HSP90 inhibition , while cancers with a less interconnected chaperome are less vulnerable by HSP90 inhibition [26] . Several HSP90 inhibitors have shown encouraging results in clinical trials [87] . Our study further supports the central role of the chaperome in PN biology , justifying particular focus on understanding chaperome alterations in human diseases at a systems level . The characteristic signatures of cancer chaperome alterations revealed in this study suggest broad commonalities and differences that could serve as testable hypotheses for therapeutic chaperome targeting strategies in cancer . Our results underline the value of charting quantitative systems-level maps and provide a resource towards an improved functional understanding of proteostasis biology in health and disease . A systems-level understanding of contextual PN alterations throughout the human diseasome will be instrumental for charting a clearer picture of the PN as a therapeutic target space , and as a resource for clinical biomarkers , including the chaperome . In face of increasing amounts of genome-scale disease data we are confronted with tremendous challenges of data complexity . Therefore , our study provides Proteostasis Profiler ( Pro2 ) , an integrated web-based suite of tools enabling processing , analysis and visualisation of proteostasis alterations in human diseases at reduced dimensionality , towards hypotheses-building for mechanistic understanding and clinical translation .
Focussing on pan-cancer analysis of the human chaperome , we chose The Cancer Genome Atlas ( TCGA ) as the main source for our analyses , as an established dataset that is widely used and adopted by the scientific community . The Broad Institute TCGA GDAC Firehose was accessed to download TCGA RNAseqv2 raw counts data followed by application of the voom method for the transformation of count data to normalized log2-counts per million ( logCPM ) [88] . Each of these logCPM values were centered gene-wise for sample normalization and comparability and used for all analyses . Considering TCGA clinical data annotation , we extracted those 22 tissue biopsy group datasets that provide both “primary solid tumor” and “solid tissue normal” sample type annotations . We applied Gene Set Analysis ( GSA ) [33] , an advanced derivative of Gene Set Enrichment Analysis ( GSEA ) [89] , in order to assess chaperome gene family expression changes between cancerous and corresponding healthy tissue samples . When applying GSA , we implemented 100 permutations of chaperome genes contained in each functional family in order to allow for statistical assessment of differential expression upon re-standardization of gene groups for more accurate inferences . When applying GSA to the chaperome as one set in comparison to the whole genome ( non-chap set ) , we randomly sampled 332 genes from the whole genome , excluding chaperome genes , and compared them to the 332 chaperome genes in order to exclude bias on group sizes in the comparisons . We applied this random sampling process 100 times in addition to 100 permutations we had on each GSA calculation . We calculated the mean value of all results as a robust measure of chaperome changes with respect to the genome . Results are displayed as heatmaps indicating significance of up or down-regulation of gene expression as ∆GSA values derived from the difference of ( 1—upregulation p value ) — ( 1—downregulation p value ) in disease compared to matching healthy tissue for TCGA cancer datasets , or control patient biopsies for neurodegenerative disease datasets ( AD , HD , PD ) . ∆GSA values are normalized within the interval [-1 , +1] , where ‘+1’ indicates significant upregulation ( upregulation p value = 0 ) , while ‘-1’ indicates significant down-regulation ( downregulation p value = 0 ) , accordingly . Bar graphs represent group mean changes of each chaperome functional family gene group over all diseases . We subdivided the human chaperome into functional subsets of chaperones and co-chaperones , and further divided chaperones into two sets of ATP-dependent and ATP-independent chaperones according to the annotations provided by Brehme et al . 2014 [4] . We performed linear modelling using the Limma package in R . Genes with p values < 0 . 05 following Benjamini-Hochberg correction are considered in the fraction of differentially expressed genes corresponding to each functional subset . Gene Set Analysis ( GSA ) is a statistical hypothesis testing method that is by definition limited to confirmatory data analysis with respect to pre-existing hypotheses . In order to serve the goal of quantitative exploratory pan-cancer chaperome analysis , while retaining a maximum information content during model reduction , we devised Meta-PCA , a novel quantitative multi-step dimension reduction model fitting strategy based on principal component analysis ( PCA ) . Principal component analysis ( PCA ) uses orthogonal transformation to convert a set of variables to linearly uncorrelated variables , such that they are ordered by their information content , which allows for removal of dimensions with lowest information content for dimensionality reduction in complex heterogeneous datasets . In order to stratify cancer from healthy biopsy gene expression samples based on chaperome functional family gene expression in highly convoluted datasets comprising multiple different cancer types , we designed Meta-PCA as a novel two-step method capable of handling this type of heterogeneous data . We hypothesized that each chaperome functional family or process can be described by a low number of variable dimensions , considering that genes within each group are either related or act together in molecular complexes . Therefore , we used a PCA-based approach for quantitative assessment and dimensionality reduction of functional chaperome alterations based on disease gene expression data . Challenged by highly varying sample counts in the different TCGA cancer group datasets , where datasets ( tissues ) with high sample numbers are at risk of dominating PCA results as compared to cancer groups with low sample numbers , we developed a custom approach that is not limited by a lack of underlying models for interpolation or undesirable loss of information , such as in up- or down-sampling , respectively , allowing us to consider all samples in the included TCGA cancer groups . Assuming distinct roles for each chaperome functional group we define MCHAPx=Fx ( Gx ) ( 1 ) where M denotes the M-score of chaperome ( CHAP ) family x , Gx is the vector of gene expression values corresponding to genes in CHAP family x , and Fx is the function we want to fit . For simplicity , we considered a linear first degree model as follows: Fx is a vector of weights Wx with identical length as the vector Gx , and we aim to find Wx for all x using PCA . Assuming equivalent biological function of each CHAPx among all tissues , we first calculate Fx for each tissue in order to separate disease from healthy samples for each tissue , and then combine all “relevant” PCs in order to obtain the main underlying PC , or ‘Meta-PC’ , of the corresponding CHAP group . We outline the ‘Meta-PCA’ algorithm as follows: Step 0: For each CHAP group and tissue we assume a model MCHAPxt=Fxt ( Gxt ) ( 2 ) Where MCHAPxt is the M-score of CHAP group x in tissue t , Fxt is the unknown function mapping gene expression values for CHAP group x in tissue t to an M-score value , and Gxt is the gene expression vector of all genes in CHAP group x in tissue t . Step 1: Assuming MCHAPxt can be approximated using PC1 , we assume Fxt is equal to W tx , which is the vector of weights for CHAP group x and tissue t . Then we calculate PCA on the gene expression matrix ( GEX ) comprising all genes in CHAP group x , and all samples of tissue t , including ‘solid tissue normal’ and ‘primary solid tumors’ . So in this step we have Fxt≃Wxt as loadings of PC1 . Step 2: The Fxt≃Wxt assumption in Step 1 is not necessarily true; PCA extracts the most variable direction in GEX , but in case CHAP group x does not change drastically between healthy and cancer , PC1 will represent an unwanted variable or even noise . So we have to filter out the Fxt that did not fit well to the data . For this we use Student’s t-test . For each tissue , we test the separation of MCHAPxt between ‘solid tissue normal’ and ‘primary solid tumor’ samples , and discard all Fxt with p values > 10−4 . Step 3: We combine all Wxt to obtain Wx , which is the universal mapping of gene expressions in CHAP group x to its corresponding M-score , regardless of tissue type . Therefore , we calculate Wx as Wx≃M¯xt ( 3 ) where the loading of each gene in the universal mapping is the mean value of all the loadings of the same gene on different tissues . Importantly , prior to calculating mean loadings , we set all Wxt to be uni-directed in order preserve directionality of change from healthy to cancer , yielding final Meta-PCs . Wx can be used as the universal function Fx ( Eq 1 ) in order to map a query sample to the corresponding M-score of CHAP group x . Step 3’: In order to validate Fx and resulting M-scores we performed random forest regression using 80% of M-scores and their annotation labels as training set and 20% as test set . In order to visually represent quantifications of chaperome functional family differential cancer gene expression , we used Meta-PCA fitted functions in order to calculate disease-specific M-scores for each chaperome functional gene group as described . We then plotted relevant M-scores using polar plots , such that radial axes represent functional processes . Human physical protein—protein interactions ( PPIs ) , hereafter referred to as ‘edges’ , were downloaded on 23 Dec 2016 from the BioGRID [52] , IntAct [53] , DIP [54] , and MINT [55] databases . In order to obtain a high confidence chaperome physical protein—protein interactome network , we developed a custom Python script to curate raw interactome pairs , or edges , as downloaded from the above databases , considering edges detected by multiple experimental methods as more reliable than those detected by only a single method . Similarly , edges supported by multiple publications are considered at higher confidence than edges supported by only one study . Edges supported by multiple methods and / or multiple studies are collectively referred to as ‘multiple evidence’ ( ME ) , of which those identified by multiple different methodologies represent a subset of highest confidence ( MM ) . The Python script processes the interactome raw data as follows: UniProt IDs are mapped to NCBI Entrez Gene IDs and for each human PPI between any two chaperome members ( nodes ) , interacting partners are mapped to Gene IDs . Only edges annotated with PSI-MI term 'physical association' type are considered . Eight different curation levels exist: Considering these curation levels , three physical chaperome ( CHAP ) interactomes of increasing confidence level are obtained ( S2 Table ) : Different PPI source databases may annotate an identical reported PPI to different PSI-MI terms situated at different depth of the same branch within the PSI-MI ontology tree . In these cases , PPIs that are actually only supported by one piece of evidence can unintentionally be mislabelled as multiple evidence PPIs . Our automated quality curation script resolves this problem through up-propagation within the PSI-MI—ontology tree . Assume one PPI is annotated with two different interaction detection methods , A and B , then 1 ) if PSI-MI ontology tree levels of method A and method B are identical but their PSI-MI terms ( IDs ) are different , then the methods are considered as different , otherwise A and B are considered the same and the interaction is eliminated from the MM-CHAP interactome , 2 ) if the level of method A is higher ( deeper in the ontology tree ) than the level of method B , then the code searches for its parent situated at the same level as method B and compares the parent method ID with B to determine if the methods are identical or different . We considered 2-dimensional physical interactome information to guide the spatial layout ( x-y coordinates ) of human chaperome functional ontology families in a 3-dimensional ( x-y-z coordinates ) topographic representation of chaperome M-score changes between disease and healthy tissue ( z coordinate ) . Physical chaperome protein-protein interactome network data ( PPIs ) was obtained and curated as described above . We considered a network involving only high quality curated interactions supported by multiple pieces of evidence ( ME-CHAP ) . We used the R package iGraph in order to collapse nodes corresponding to each level 1 functional ontology family into meta-nodes , and edges shared between all members of any two different level 1 functional families into meta-edges , such that meta-node size corresponds to the number of family members and meta-edge thickness represents the number of shared interactions between two families . Meta-node colour is set to reflect gene expression changes of each respective functional family in disease . We then applied a force-directed network graph layout algorithm to the meta-network according to Kamada and Kawai [56] and extracted resulting x-y coordinates of each family meta-node in the network . We used Python to draw the meta-network according to the parameters obtained in iGraph to serve as interactome-guided base grid for disease-specific quantitative 3-dimensional topographic network representations . To this end we expanded the 2-dimensional network landscape with Meta-PCA derived chaperome M-score values ( z coordinate ) . We designed a web-based Proteostasis Profiler ( Pro2 ) in order to enable visual exploration of the data and results described in this manuscript , obtained through our algorithms and visualisation tools . Pro2 is accessible directly at http://www . proteostasys . org . Pro2 is implemented using Django ( https://www . djangoproject . com/ ) ) ) , a web framework written in Python language ( https://www . python . org ) . All the charts in the tool are generated using the plotly platform ( https://plot . ly ) . The Pro2 tool itself is hosted on the Heroku platform ( https://www . heroku . com ) . All R and Python scripts and code related to this manuscript are accessible through the Proteostasis Profiler ( Pro2 ) Github repository at https://github . com/brehmelab/Pro2 . | Protein homeostasis , or proteostasis , is maintained by the proteostasis network ( PN ) , an intricately regulated modular network of interacting processes that evolved to balance the native proteome , supporting cellular and organismal health throughout lifespan . Imbalances and collapse of cellular proteostasis capacity , the capacity to buffer against cytotoxic damage and stress , is increasingly implicated in some of the most challenging diseases of our time , including neurodegeneration and cancers . The systems-level PN alterations in these diseases are not understood to date . Here , we address this challenge , focussing on the human chaperome , the ensemble of chaperones and co-chaperones , which represents a central conserved PN functional arm . We devised a novel data dimensionality reduction approach enabling quantitative contextual visualization of chaperome alterations in the heterogeneous spectrum of cancers based on gene expression data from thousands of patient biopsies . We developed Proteostasis Profiler ( Pro2 ) , a new web-tool enabling intuitive visualisation of cancer chaperome deregulation maps . We stratify two cancer groups based on diverging chaperome deregulation and highlight similarities between cancer and stem cell proteostasis . Our study also exposes drastically opposed shifts between cancers and neurodegenerative diseases . Collectively , this study sets the stage for a systematic global analysis of PN alterations across the human diseasome . | [
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] | 2018 | A systematic atlas of chaperome deregulation topologies across the human cancer landscape |
KSHV is endemic in Uganda and the HIV epidemic has dramatically increased the incidence of Kaposi sarcoma ( KS ) . To investigate the role of KSHV in the development of KS , we obtained KS biopsies from ART-naïve , HIV-positive individuals in Uganda and analyzed the tumors using RNAseq to globally characterize the KSHV transcriptome . Phylogenetic analysis of ORF75 sequences from 23 tumors revealed 6 distinct genetic clusters with KSHV strains exhibiting M , N or P alleles . RNA reads mapping to specific unique coding sequence ( UCDS ) features were quantitated using a gene feature file previously developed to globally analyze and quantitate KSHV transcription in infected endothelial cells . A pattern of high level expression was detected in the KSHV latency region that was common to all KS tumors . The clear majority of transcription was derived from the downstream latency transcript promoter P3 ( LTd ) flanking ORF72 , with little evidence of transcription from the P1 ( LTc ) latency promoter , which is constitutive in KSHV-infected lymphomas and tissue-culture cells . RNAseq data provided evidence of alternate P3 ( LTd ) transcript editing , splicing and termination resulting in multiple gene products , with 90% of the P3 ( LTd ) transcripts spliced to release the intronic source of the microRNAs K1-9 and 11 . The spliced transcripts encode a regulatory uORF upstream of Kaposin A with alterations in intervening repeat sequences yielding novel or deleted Kaposin B/C-like sequences . Hierarchical clustering and PCA analysis of KSHV transcripts revealed three clusters of tumors with different latent and lytic gene expression profiles . Paradoxically , tumors with a latent phenotype had high levels of total KSHV transcription , while tumors with a lytic phenotype had low levels of total KSHV transcription . Morphologically distinct KS tumors from the same individual showed similar KSHV gene expression profiles suggesting that the tumor microenvironment and host response play important roles in the activation level of KSHV within the infected tumor cells .
Since its discovery in 1994 , the Kaposi sarcoma-associated herpesvirus ( KSHV ) , also known as human herpesvirus-8 ( HHV-8 ) , has been identified as the etiologic cause of all types of Kaposi sarcoma ( KS ) , and is etiologically associated with primary effusion lymphoma ( PEL ) and multicentric Castleman Disease ( MCD ) [1] . The KSHV genome encodes more than 90 genes , including a core of genes highly conserved among the different herpesviruses [2] . In addition , a number of novel genes exhibiting sequence homology to cellular genes implicated in mitosis , cell cycle regulation and immunity have been identified [3] . In vitro , KSHV infects a variety of cell types including endothelial , epithelial , fibroblast and lymphocyte lineages [4] and establishes a latent infection in which only a subset of genes are detected , including LANA ( ORF73 ) —the latency-associated nuclear antigen , vCYC ( ORF72 ) –a cyclin D homolog , and vFLIP ( ORF71 ) –a homolog of the Fas-associated protein with death domain-like interleukin 1beta-converting enzyme/caspase-8-inhibitory protein [5–7] . Early studies with cultured PEL cells determined that these genes were present on a tricistronic mRNA originating from the constitutive latency transcript ( LTc ) promoter upstream of ORF73 [6 , 8] . Latent KSHV infections , first characterized in endothelial cells in vitro , resulted in the majority of cells expressing LANA as punctate dots in the nucleus , with a small population of cells ( ~1% ) expressing ORF59 , a DNA polymerase processivity factor [9–11] . In most cell types , the widespread lytic reactivation necessary for production of infectious virus was achieved only by using chemical inducers such as the phorbol ester TPA or the HDAC inhibitor sodium butyrate or by overexpression of exogenous recombinant ORF50 , the replication transactivator ( RTA ) [4 , 11 , 12] . The complement of KSHV genes has been divided into functional groups based on their initial expression during establishment of viral latency and their response to artificial induction , with latency , immediate-early , early and late gene designations . While ORF50 RTA and ORF59 represent immediate-early and early genes , respectively , the genes encoding the major capsid protein ( MCP; ORF25 ) , and the virion envelope glycoproteins , gB ( ORF8 ) and K8 . 1 are examples of KSHV late genes . Early attempts to determine the expression profile of KSHV in KS tumors examined the RNA transcripts in KS lesions by Northern analysis . Two small RNA transcripts were detected , including T0 . 7 encoding the K12 Kaposin membrane-associated protein and T1 . 1 , a polyadenylated nuclear RNA ( PAN ) [13] . Using in situ hybridization , the T0 . 7 RNA was detected in all KS tumor cells , while the T1 . 1 RNA was present in only 10% of the T0 . 7 positive cells [14] . In addition , RNA encoding the MCP late gene was detected in the same cells containing the T1 . 1 transcript . Once antibodies were available , the expression and localization of viral proteins was examined by immunohistochemical methods . The major latency-associated protein LANA was consistently detected in the nuclei of the vast majority of spindeloid tumor cells in the KS lesion [5 , 15 , 16] . In contrast , markers of lytic replication , including ORF50 RTA , ORF59 , and the vIL-6 homolog K2 were detected very rarely ( <1% ) in the tumor cells , while no expression of late genes , including ORF26 and ORFK8 . 1 was observed [17–22] . Using an array of real-time PCR assays targeting the majority of known KSHV genes , the expression of mRNA transcripts from the latency locus , including ORFs 71 ( vFLIP ) , 72 ( vCYC ) and 73 ( LANA ) , was detected in 21 KS biopsies [23] . Subsequently , using the same PCR array , two types of transcriptional signatures were detected in a panel of KS tumors [24] . In half of these tumors , KSHV transcription was limited to the latency-associated genes . In the other half of the KS tumors , variable and incomplete expression of viral lytic mRNAs was observed . We have utilized RNA deep sequencing ( RNAseq ) to globally examine the KSHV transcriptome in latently-infected tissue culture cells in vitro [25] . Due to the highly complex nature of the KSHV transcriptome , we developed a novel approach to more accurately quantitate specific viral transcripts using unique coding sequence ( UCDS ) features targeting non-overlapping regions of KSHV transcripts . We sequenced the RNA transcripts from in vitro infected cells to a great depth , with more than a million reads mapping to the KSHV genome . High levels of transcripts were observed across the complete KSHV genome in the absence of artificial induction with chemicals or recombinant proteins . This allowed us to develop a detailed map of KSHV transcription , which informed the development of the UCDS features in a new gene feature file ( KSHV NC_009333 UCDS ver 020116 . GFF ) to globally analyze and quantitate KSHV gene expression [25] . Recently , RNAseq has been used to characterize the viral and cellular transcriptome in KS tumor and non-cancer biopsies of African epidemic HIV+ KS patients undergoing anti-retroviral therapy ( ART ) [26] . Lesions from four individuals were analyzed yielding 718–17 , 202 reads that mapped to known KSHV ORFs in the NC_009333 KSHV reference genome . High level expression of the latency region was reported but no obvious pattern was observed between the tumor biopsies . In the current study , we have used RNAseq to analyze and quantitate KSHV gene expression in a large collection of 41 KS tumor biopsies from HIV-infected individuals in Uganda who were naïve to ART . The RNAseq libraries were sequenced to an average depth of 100 million reads yielding up to 159 , 000 KSHV-mapped reads . Using the new gene feature file , we quantitated the RNA reads mapping to non-overlapping UCDS features and identified a set of transcripts from the latency region that was highly and consistently expressed in all the KS tumors .
Thirty Ugandan participants contributed 41 cutaneous KS tumors for RNA-Seq analysis , with 11 participants contributing 2 samples ( S1 Table ) . The majority of participants were men ( 21/26; 80% ) with a median age of 34 years ( range , 23–56 years ) ( Table 1 ) . At the time of KS diagnosis , all had advanced T1 tumor stage per AIDS Clinical Trials Group staging criteria , which includes those with extensive oral KS , visceral KS or tumor-associated edema [27] . All patients were naïve to antiretroviral treatment . Median CD4 T-cell count was 183 cells/mm3 ( IQR , 58 , 331 cells/mm3 ) and median HIV plasma RNA was 5 . 5 log10 copies/mL ( IQR , 5 . 1 , 5 . 6 log10 copies/mL ) . Tumor samples represented a range of morphotypes , including 24 macular ( 58% ) , 13 nodular ( 32% ) , and 4 fungating ( 10% ) lesions . Total nucleic acids were extracted from the 41 KS biopsies and cDNA libraries were prepared from poly-A-selected RNA and subjected to RNA deep-sequencing on the Illumina platform ( S1 Table ) . 37 independent KS samples were sequenced for 50 bp from paired-end non-stranded libraries . Four additional KS samples were sequenced from stranded libraries to distinguish sense and anti-sense RNA transcripts . Total reads ranged from 81–124 million for the paired-end libraries and 35–52 million for the stranded libraries , which were analyzed at a lower depth of sequencing . RNA reads mapping to the human genome HG19 were subtracted from the libraries and the remaining reads were mapped onto the KSHV NCBI reference sequence NC_009333 , strain GK18 , with mapped KSHV reads ranging from 13 to 158 , 924 , with a median of 10 , 232 ( Fig 1A ) . Five of the KS biopsies had greater than 100 , 000 KSHV-mapped reads . While seven of the KS tumors had less than 1 , 000 total KSHV-mapped reads , the level of reads mapping to the human genome in these samples was comparable to the other KS samples ( S1 Table ) . A comparison of KSHV mRNA expression in the different tumor morphotypes showed no significant differences in total KSHV-mapped reads ( Fig 1B ) . The KSHV genome copy number per cell was determined for four of the KS samples ( 001_C , 006_B , 026_B , and 029_B ) . A comparison of the Ct values obtained from qPCR assays targeting KSHV and the cellular gene oncostatin M , essentially as described in [28] , revealed similar KSHV genome copy numbers ranging from 0 . 5–0 . 9 KSHV genomes/cell ( mean = 0 . 6 ) . A comparison of the number of mapped KSHV reads/KSHV genome copies showed a variance of 8 . 6% across the four KS biopsies . The RNA-seq reads mapping to the complete KSHV genome were visualized with the Integrated Genome Viewer ( IGV ) , using a linear scale to provide an overview of the highly expressed regions of the genome . Representative data from KS tumors with 2 , 432 to 158 , 924 total KSHV-mapped reads are shown ( Fig 2 ) , while data from the seven KS tumors with total KSHV-mapped reads less than 1 , 000 are provided in S1 Fig The highest level of RNA reads mapped to the region of the T0 . 7 RNA transcript within the latency locus at the right end of the KSHV genome ( Fig 2 ) . T0 . 7 encodes ORF K12 Kaposin A , a small transmembrane protein implicated in cell adhesion and transformation [29 , 30] . High levels of reads mapping to the T0 . 7/K12 region were consistently detected in all the KS tumor samples , regardless of the level of total KSHV-mapped reads in each sample . High levels of RNA reads also mapped to a small region of the KSHV genome located immediately to the right of ORF72 , observable as a single vertical line of mapped reads ( Fig 2; indicated with an asterisk in the bottom graphic ) . A lower but consistent level of reads mapped to the adjacent region containing ORFs 71 and 72 . ORFs 71 and 72 encode vFLIP , which functions to promote cell survival and inhibit KSHV lytic replication , and vCYC , which regulates cell-cycle progression , respectively [31] . Downstream of the latency locus at the right end of the genome , a moderate level of reads mapped consistently to the region encoding ORF75 , a large tegument protein essential for viral lytic replication [32] , and K15 , a multiple-pass membrane protein that modulates cellular signaling pathways associated with KSHV-induced angiogenesis [33–35] ( Fig 2 ) . At the left end of the genome , a moderate level of reads mapped inconsistently to the multifunctional regulatory polyadenylated nuclear ( PAN; T1 . 1 ) RNA transcript , with higher levels in the tumors with lower total KSHV-mapped reads shown in the upper part of Fig 2 . One fungating tumor 023_B showed high levels of both PAN and total KSHV-mapped reads . Some tumor samples contained moderate levels of reads mapping to ORFK2 , the viral interleukin-6 ( vIL-6 ) homolog , or ORFK5 , the ubiquitin ligase modulator of immune response ( MIR2 ) . One tumor , 008_B , showed high level expression of a sharply delineated region of the KSHV genome extending from ORFK3 to ORF19 , with very high level of reads mapping to ORFK5 ( Fig 2 , bracketed ) . This transcript pattern was unique to this tumor and was not seen in the paired tumor from the same individual ( 008_C ) or any other KS tumor . This sample is further described below . To analyze the complete range of read depths across the KSHV genome , the mapped reads were also visualized using a log-based scale ( Fig 3 ) . This analysis revealed the presence of RNA reads mapping across the complete KSHV genome with concentrations in regions encoding specific genes associated with lytic replication ( ORFs 6 , 59 , 60 ) , gene regulation ( ORFs K8 , 57 ) , virion structure ( ORFs 11 , 17 . 5 , 27 , 33 , 38 , K8 . 1 , 65 , 66 ) and immune modulation ( ORFs 4 , K2 , K3 , K4 , K5 , 45 , K10 ) ( Fig 3 ) . Similar patterns of transcription were visually observed in the different KS tumors regardless of the total number of KSHV-mapped reads in each sample ( see for example , tumors 11_D and 013_C , with 5 , 928 and 158 , 924 total KSHV-mapped reads , respectively ) . This similarity was also observed in the 7 KS tumors containing less than 1 , 000 total KSHV-mapped reads ( S1 Fig ) , suggesting that overall the majority of KSHV-infected cells in the KS lesions expressed the same basic transcriptome pattern . Of note , occasional KS samples , such as 030_B , had minimal levels of reads mapping to ORFK15 , even though high levels of reads mapped to the adjacent ORF75 ( Fig 3 , arrow ) . Different alleles of ORFK15 have large sequence variation extending into the ORF75 sequence [36 , 37] , which could have affected the ability of ORFK15 reads in the Ugandan tumors to map to the NC_009333 reference sequence . The RNA-seq mapping studies were performed by aligning the RNA reads from the different Ugandan KS tumors to the GK18 NCBI Reference sequence ( NC_009333 ) present in a patient with a case of the classic ( HIV-negative ) KS . The IGV view of the aligned reads from the Ugandan KS tumors revealed numerous mismatches indicating the presence of different KSHV strains in the KS tumor samples . To perform an initial phylogenetic analysis of these strains , we compared the RNA sequences of the large open reading frame encoding ORF75 , which was highly expressed in nearly every KS tumor sample . Although most previous studies of KSHV phylogeny have used the widely divergent K1 and K15 sequences [36 , 38] , these genes , especially K1 , were too minimally and inconsistently expressed in the KS tumors to obtain sufficient sequence for phylogenetic analysis . The complete coding sequences of ORF75 ( 3891 bp ) were assembled from the 50 bp reads for 23 of the Ugandan KS samples . These sequences were aligned with published OR75 sequences from 16 unique KSHV genomes from Zambia [39] and ORF75 sequences in the NCBI database from KSHV strains in several KS biopsies and PEL cell lines obtained in Western countries . Maximum likelihood analysis revealed two major ( A , C ) and four minor ORF75 clusters ( B , D-F ) ( Fig 4 ) . A BLAST alignment with ORF75 partial sequences that have been previously typed [40] indicated that Cluster A corresponded to subtype [B] , Clusters B and C corresponded to subtype A/[B] , Cluster D corresponded to subtype R/A , Cluster E corresponded with subtype R/M and Cluster F corresponded with subtype N . The vast majority of Zambian sequences were distributed in the major clusters A and C . Previous studies have shown that ORF75 sequences are linked to the adjacent K15 subtypes , of which 3 distinct alleles P , M and N have been detected [36] . The majority of the ORF75 sequences , including those in Clusters A-D were linked to the K15 P-allele ( Fig 4 ) , which was present in the GK18 reference sequence . In contrast , the U030 and ZM123 sequences , like the BC1 sequence , were linked to the M-allele , while U012 , ZM095 and ZM128 [39] , were linked to the K15 N-allele . The sequence differences between the K15 P-allele of GK18 and the more distantly related M and N alleles resulted in essentially no RNA reads from the U012 and U030 tumor samples aligning with the K15 region of GK18 ( see Fig 3 ) . Although our phylogenetic analysis was based on a single gene , it is clear that the KSHV strains infecting the different Ugandan KS tumors show high variability , with strong similarity to KSHV strains identified previously in the Zambian KS samples [39] . Overall , the RNAseq analysis indicated the presence of a single KSHV strain in each tumor . In all cases , but subject 008 ( discussed below ) , independent tumors from the same patient contained the same KSHV strain . To accurately correlate the RNA reads to specific mRNA transcripts in the latency region , we previously developed a map of transcripts that had been identified in the literature [25] . Since these transcripts were initially characterized in different KSHV strains with different sizes , their positions were mapped onto the sequence of the NCBI reference sequence for KSHV , strain GK18 ( NC_009333 ) , and the sizes of the GK18 transcripts were predicted from the transcription start and polyadenylation ( poly-A ) termination sites ( S2 Table ) . A map of the spliced and unspliced latency region transcripts is shown in Fig 5B , with the transcripts grouped according to their use of the poly-A termination sites at bp 117 , 553 ( Group A ) , bp 122 , 342 ( Group B ) , and bp 123 , 015 ( Group C ) . Due to heterogeneity in the DR5 , DR6 and DR7 repeat regions , the sizes of the transcripts from the GK18 strain vary from the published transcript sizes determined for other KSHV strains , such as BCBL-1 , as indicated in the corresponding references ( summarized in S2 Table ) . To analyze the read depth and splicing events in the latency region , Sashimi plots were produced by IGV from the TopHat2 analysis of the RNA reads for the KS tumor samples . Sashimi plots graphically present the read depth across a selected region and show the location and quantity of split RNA reads that define the presence of a splicing event within an mRNA transcript . Sashimi plots of four representative KS tumor samples ( Fig 5A ) were aligned with the different spliced and unspliced transcripts generated from the latency region ( ORFs 69-K14 ) ( Fig 5B ) . This alignment clearly showed a high level of RNA reads mapping to the T0 . 7 RNA ( herein designated as T0 . 7A ) from the P5 latency promoter , which encodes Kaposin A . In addition , a high level of reads mapped to the adjacent DR5 and DR6 repeat regions , even though the TopHat analysis limited reads to a single map position . A number of spliced and unspliced transcripts that terminate at a common poly-A site ( bp 117 , 553 ) downstream ORF K12 contain the repeat regions . These transcripts are derived from different latency transcript ( LT ) promoters and include the spliced T1 . 6A and T1 . 8A transcripts from the P1/LTc ( constitutive ) promoter , the spliced and unspliced T1 . 7A and T6 . 5A transcripts from the P3/LTd , ( downstream ) promoter , and the unspliced T1 . 5A transcript from the P4 promoter ( Fig 5B ) . Analysis of the Sashimi plots across the latency region revealed a high level of split reads mapping between the highly expressed DR6 repeat region and the highly expressed genomic region downstream of the P3 ( LTd ) promoter , herein designated as P3-exon1 ( Fig 5A and 5B ) , which was indicated in Fig 2 with an asterisk . Examination of the corresponding sequences in the KSHV genome revealed classical splice donor ( AG|gt ) and acceptor ( ttacgcccccttcgcag|G ) sites at bp 123 , 843 and bp 119 , 047 , respectively . These sites define the presence of a 4 , 796 bp intron ( Fig 5B; labeled “a” ) , which is spliced from the pre-mRNA for transcripts T1 . 7A and T1 . 8A , and encompasses the sequences encoding ORFs 72 and 71 , the right origin of replication and flanking long inverted repeat ( LIR2 ) and the microRNAs miR K1-9 and 11 . A high level of reads split across intron “a” were detected in the vast majority of KS tumors ( Table 2 ) . The depth of the split reads across intron “a” ( for ex . see Fig 5A , ranging from 100 reads ( 030_C ) to 1 , 307 reads ( 013_C ) ; Table 2 ) was similar to the depth of the reads in the flanking exons suggesting that the majority of the reads mapping to the K12/DR5/DR6 region were derived from the T1 . 7A spliced transcript ( Fig 5B , double asterisk ) . This conclusion was confirmed by RT-PCR amplification of the spliced T1 . 7A transcript from four KS biopsies using primers derived from the K12 region and the exon junction spanning the spliced “a” intron ( S2 Fig ) . While there was evidence for the splicing of intron “b” in some tumors ( see for example Fig 5A: 013_C- “b” = 32 reads; Table 2 ) , compatible with the processing of transcript T1 . 8A from the upstream P1 ( LTc ) latency promoter , there was no corresponding accumulation of RNA reads mapping to the 5’ exon of this transcript ( Figs 3 and 5A ) . Thus , the splicing data indicates that the high level of split reads across the K12/DR5/DR6 region and P3-Exon1 are derived from the spliced T1 . 7A transcript initiating from the P3 ( LTd ) promoter , which is highly expressed in all the KS tumors . Quantitation of transcripts from complex genomes , such as KSHV , has been difficult due to the compact nature of the genome and the presence of numerous overlapping transcripts that are differentially expressed . We developed a novel approach to quantitate RNAseq reads mapping to specific genes and gene features in the highly complex KSHV genome using unique coding sequence ( UCDS ) features specific for all known KSHV ORFs and transcriptional regions [25] . Published information and transcript data from RNAseq analysis of primary latent KSHV infections of several endothelial cell types and long-term latent infections in PEL cells were used to identify transcription start and termination sites and globally map mRNA transcripts . The UCDS features were devised to be non-overlapping , separated by the length of a read ( 50 bp ) so that algorithms , such as HTSEQ , using the “intersection_nonempty” setting could specifically identify and distinguish reads mapping to the different targeted gene features on both DNA strands . RNA read depth was determined by quantitating the reads aligning to the UCDS features in a simplified gene feature file ( KSHV NC_009333 UCDS ver 020116 . gff; S1 File ) based on the KSHV reference genome NC_009333 [25] . The read count was normalized to the size of the feature and number of total KSHV-mapped reads in each tumor sample , yielding a relative transcript level , in transcripts per million KSHV reads ( TPM ) , as described in Materials and Methods . Read data and normalization for each KS sample is provided in S3 Table This normalization allowed the expression of each KSHV gene to be compared as a proportion of the total number of KSHV-mapped reads in each sample . In this and subsequent quantitative analyses of read depths across the KSHV genome , the analyses were limited to 34 of the 41 KS tumor samples , which had sufficient levels of RNA reads mapping to the KSHV genome ( >1000 ) for objective comparisons ( see Fig 1 ) . The median normalized level of transcripts targeted by our set of UCDS features across the entire KSHV genome for the 34 samples is provided in S4 Table . To more accurately reflect the protein coding potential for genes in regions with overlapping polycistronic transcripts , we determined the level of the primary transcripts for each ORF derived from its associated promoter , in which the ORF would be the first in the transcript to be translated through 5’ CAP-dependent initiation . The contribution of overlapping transcripts derived from distal promoters was subtracted , yielding an estimation of the primary transcript levels for each ORF ( S4 Table ) , as discussed previously Bruce et al [25] . In the latency and flanking regions , UCDS features were identified targeting the coding sequences for the ORFs K12A ( T0 . 7 ) , 72 , 71 , 73 , K14 , 74 , 75 and K15 , as well as additional areas of interest , including the direct repeats DR5 and DR6 , the region encoding microRNAs miR-K1-9 , 11 ( miR-region ) , and the short P3-Exon1 downstream of the ORF72 P3 ( LTd ) promoter ( K12Aa ) ( Fig 5C ) . Quantitation of the reads mapping to the latency region from the 34 KS tumor samples revealed very high transcript levels for the K12/T0 . 7 region ( UCDS K12A ) and flanking DR5 ( UCDS DR5 ) and DR6 ( UCDS DR6 ) repeat regions ( median = 162 , 467 , 310 , 389 and 65 , 018 TPM , respectively ) ( Fig 5D , boxed in red; S4 Table ) . Although the known transcripts in this region span both the DR5 and DR6 region , transcribed right to left ( Fig 5B ) , significantly less reads mapped to the DR6 UCDS feature , which would detect the 5’ region of such transcripts . The DR6 repeat and upstream flanking regions are known to have significant sequence heterogeneity between different KSHV strains [41] , suggesting that the relatively lower level of reads mapping to the DR6 UCDS feature could be due to mismatches between the reads from the Ugandan KSHV strains and the NC_009333 KSHV reference sequence used for the mapping . Very high levels of transcripts ( median = 123 , 915 TPM ) were detected using the K12Aa UCDS feature ( Fig 5D , boxed in red ) , which targets the small P3-Exon1 downstream of the P3 ( LTd ) promoter ( Fig 5B and 5C ) . The similarity in the transcript levels detected across the K12A , DR5 , DR6 and K12Aa UCDS features ( Fig 5D , boxed in red ) correlates with the presence of the common spliced transcript ( T1 . 7A ) derived from the P3 ( LTd ) promoter , which encodes the Kaposin A/B/C complex ( Fig 5A , double asterisk ) , as described above . This was confirmed by RT-PCR of RNA from 4 KS tumor biopsies ( S2 Fig ) . Moderate levels of transcripts ( median = 15 , 794 and 14 , 430 TPM ) were detected using UCDS features targeting ORF71 and ORF72 , respectively ( Fig 5D; S4 Table ) . Previous studies have determined that the major transcripts encoding ORFs 71 and 72 are either bicistronic ( 5’ ORF72/ORF71 3’ ) initiating from the P3 ( LTd ) promoter or tricistronic ( 5’ ORF73 , ORF72 , ORF71 3’ ) initiating from either the P1 ( LTc ) or P2 ( LTi ) promoters ( Fig 5B; S2 Table ) . The similarity in the read depths detected using the ORF71 and ORF72 UCDS features suggests that the majority of these reads are derived from the major unspliced bicistronic transcript ( T1 . 7B ) from the P3 ( LTd ) promoter ( Fig 5A , single asterisk ) . While these reads could also be derived from the common spliced bicistronic transcript ( T1 . 8B ) from the P1 ( LTc ) promoter ( see Fig 5B ) , only a small number of reads mapped to the 5’ exon of this transcript near the P1 ( LTc ) promoter ( see Fig 5A ) . Only a low level of transcripts ( 3 , 300 TPM ) were detected with the miR UCDS feature , which targets transcripts containing the miR region flanking the LIR2 ( Fig 5C ) , suggesting that the majority of the transcripts encoding ORF71 terminated at the poly-A site ( bp 122 , 342 ) downstream of ORF71 ( Fig 5B ) . Quantitation of the reads mapping to the non-repetitive regions of ORF73 using the ORF73A and ORF73B UCDS features revealed low levels of the tricistronic transcripts T5 . 2B , T5 . 4B , T5 . 5B and T5 . 7B encoding ORFs 73 , 72 and 71 ( Fig 5D; S4 Table ) . This supports the conclusion that the majority of the ORF71 and ORF72 reads map to the unspliced bicistronic T1 . 7B transcript from the P3 ( LTd ) promoter and not from transcripts from the P1 ( LTc ) promoter . To further analyze transcription across the ORF72/71 locus , we determined the ratio of transcripts detected using the ORF72 and ORF71 UCDS features . The ORF71/ORF72 ratios in the different tumors ranged from 0 . 7 to 2 . 1 , suggesting differential expression of the two ORFs that was not strictly compatible with the single bicistronic T1 . 7B transcript ( S3 Fig ) . Seven tumors showed higher levels of transcripts encoding ORF72 ( ORF71/72 ratios from . 7 to 0 . 9 ) ( S3A Fig ) indicating the presence of a ORF72 monocistronic transcript T1 . 0C ( Fig 5B ) , identified previously by Sarid et al [42] . Twenty-one tumors showed higher levels of transcripts encoding ORF71 ( ORF71/72 ratios from 1 . 1 to 2 . 1 ) ( S3A Fig ) , indicating the presence of a ORF71 monocistronic transcript T0 . 9B ( Fig 5B ) , identified previously by Grundhoff and Ganem [43] . Kaposin A is part of a complex translational program that generates multiple novel proteins from the K12 locus . The Kaposin A sequence is downstream of the DR5 and DR6 repeat sequences , which encode variant translation products initiating at alternate CUG initiation codons that have been detected in cultured PEL cells [41 , 44] . The CUG initiation codons occur in the sequence region upstream of the DR6 repeat region , which is highly variable in different KSHV strains [41] . Previous studies identified a non-spliced transcript from a promoter , herein referred to as P4 , corresponding to T1 . 5A ( see Fig 5B ) , whose major translation product in the KSHV strains present in BCBL-1 and JSC-1 cells was a CUG-initiated open reading frame encoding a highly repetitive protein sequence , with an “LAH” N-terminal sequence ( Kaposin B ) [44] ( Fig 6B ) . A minor translation product , Kaposin C , initiated from an alternate downstream CUG initiation codon with a “LQY” N-terminal sequence . Kaposin C contained a similar repetitive protein sequence as Kaposin B but was fused to Kaposin A [44] ( Fig 6B ) . Functional studies have shown that the BCBL-1 Kaposin B can modulate mRNA turnover by stabilizing cytokine mRNAs [45] . However , the functions of Kaposins A and C are as yet unclear , as functions attributed to Kaposin A , such as transforming potential [30] , have been further attributed to the microRNA miR-K10 , which is embedded within the Kaposin A coding sequence [46] . Comparison of the K12 locus of the BCBL-1/JSC KSHV strain with the GK18 reference strain revealed a 2 bp deletion in the BCBL-1/JSC-1 sequence between the DR6 and DR5 repeat regions ( Fig 6A and 6B ) . This insertion alters the open reading frames such that the “LAH” initiated open reading frame from the upstream CUG codon in GK18 is now fused with Kaposin A , suggesting that the major translational product of the GK18 K12 locus would be a novel protein , herein termed “Kaposin D” ( Fig 6A ) . This indicates that the minor translation product of the GK18 K12 locus from the downstream CUG initiation codon would be a novel protein “Kaposin E” , with the N-terminal sequence “LQY” . Unlike Kaposin C in the BCBL-1 strain , Kaposin E would lack the downstream Kaposin A fusion . The putative DNA and encoded protein sequences in this region are provided in S4 Fig . The RNA sequence data in our study revealed several nucleotide differences between the Ugandan KSHV strains and both the GK18 and BCBL-1/JSC-1 strains . A single nucleotide deletion was detected downstream of the P4 promoter before the CUG initiation codons in all the Ugandan KS samples ( Fig 6C and S4 Fig ) . All Ugandan KSHV strains also had an additional nucleotide deletion between the DR5 repeat and the Kaposin A reading frame ( “A” bp 118 , 228 , NC_009333 ) . The deletion of this base , which is also detected in all the published Zambian KSHV strains [39] , changes the open reading frame through the repeat region that is contiguous with the Kaposin A sequence downstream . In a subset of Ugandan KSHV strains , from KS tumors 001 , 005 , 006 , 015 , 018 , 020 , 023 , 026 , 028 , 029 and 034 , this deletion results in neither open reading frame from the two CUG initiation codons creating a fusion with Kaposin A , as exemplified by the Zambian KSHV strain , ZM114 ( Fig 6C ) . Thus , these KSHV strains would encode Kaposin B as the major CUG-initiated translation product and Kaposin E , as the minor-CUG initiated product , with Kaposin A as a downstream AUG-initiated product . The remaining Ugandan KSHV strains , from KS tumors 003 , 004 , 007 , 008 , 009 , 010 , 012 , 013 , 022 , 024 , 030 , 037 , 088 , 099 , and 101 had additional sequence differences that altered the possible protein products expressed from this locus . In these strains , the major CUG-initiated ORF , Kaposin B , was eliminated by a change in the putative “CUG” initiation codon to “CGC” ( Fig 6D and S4 Fig ) . In addition , the minor CUG-initiated ORF , Kaposin C , was eliminated by a change in the codon flanking the “CUG” initiator from “CGA” to the stop codon “TGA” , thus immediately terminating translation . A similar situation was seen in the Zambian KSHV strain , ZM004 ( Fig 6D ) , and was confirmed by PCR amplification and sequencing . Small 5’ upstream ORFs ( uORFs ) are known to regulate expression of downstream ORFs by capturing or slowing the progression of ribosomes scanning the transcript for favorable initiation codons [47] . Previous studies have shown that a small 24 aa uORF is encoded upstream of ORF72 in the initial P3-exon1 of the T1 . 7B bicistronic ORF72/71 transcript from the P3 ( LTd ) promoter ( Fig 7A ) . This uORF attenuates the expression of the ORF72 10–20 fold in the T1 . 7B bicistronic transcript [43] , and would be expected to attenuate ORF72 expression in the T1 . 0C monocistronic transcript and ORF71 expression in the T0 . 9B monocistronic transcript ( Fig 7A ) . This uORF is also encoded in the initial 5’ exon of the highly expressed T1 . 7A spliced transcript from the same promoter and is positioned upstream of the ORFs encoding the Kaposin A/B/C complex ( Fig 7A ) . Thus , this uORF would be expected to also attenuate translation of the Kaposin B/C complex proteins , especially since these proteins initiate from CUG codons . The processing of the pre-mRNA for the T1 . 7A spliced latency transcript generates an RNA intron that is believed to be the source of the major KSHV microRNA species , miRs-K1-9 and 11 [48–50] ( Figs 5B and 7A ) . Therefore , the high levels of the T1 . 7A spliced transcript could generate elevated levels of these microRNAs in the KS tumors . Since the other KSHV microRNAs , miR-K10 and miR-K12 , are located within the retained exon in the T1 . 7A spliced transcript , such transcripts could also be processed to produce these microRNAs . Processed microRNAs are not detected in our RNAseq protocol , since they do not contain the 3’ poly-A region used for RNA purification . Thus , purification of small RNA species in the KS tumor samples would be needed to confirm the presence of KSHV microRNAs in the KS tumors . RNA editing of bp 118 , 096 ( NC_009333 ) ( Fig 7A ) has been shown to convert the transformation-associated miR-K10a containing an adenine in its seed sequence into the non-transforming miR-K10b containing an inosine [46] . We examined the RNA reads from the KS tumors for the presence of an edited base in the RNA reads . The fraction of ORFK12 transcripts containing an edited base ranged from 0 to 59% across the 34 KS tumors with a median of 13% ( Fig 7C ) , suggesting that the transforming miR-K10a would be the major microRNA form produced in most of the KS tumors . Since miR-K10 is embedded within the mRNA transcripts encoding Kaposin A , RNA editing also affects the encoded Kaposin A protein sequence , changing a serine to glycine [51] ( Fig 7A and 7B ) . Our analysis revealed that only a fraction of the ORFK12 transcripts in the majority of KS tumors would encode the altered Kaposin A . The spliced and non-spliced Group B latency transcripts transcribed from the P1 ( LTc ) promoter and P2 ( LTi ) promoter terminate at the poly-A site downstream of ORF71 ( Fig 5B , S2 Table ) . Although previous in vitro studies indicated that the P1 and P2 transcripts were the major latency-associated transcripts in PEL cells , there was evidence for only minimal expression in the KS tumors ( Figs 3 and 5A ) . Only two tumors had evidence for splicing of intron “e” within the major latency-associated spliced ORF73/72/71 tricistronic transcript ( T5 . 2B ) , with only 3 reads detected ( Table 2 ) . No evidence was detected for splicing of intron “f” ( T5 . 4B ) , which is similar to intron “e” but from an adjacent splice acceptor site ( “f” acceptor: bp 127 , 626 [6] compared to “e” acceptor: bp 127 , 462 [8] ) . Transcripts initiating from the P1 ( LTc ) promoter upstream of ORF73 LANA were quantitated using the ORF73A and ORF73B UCDS features ( Fig 5C ) . These features flank the large DR7 repeat region within the ORF73 coding sequence , which was not used for quantitation due to its repetitive nature . Consistent low levels of LANA reads ( median = 2 , 195 TPM ) were detected in the KS tumors ( Fig 5D; S4 Table ) , indicating low levels of the T5 . 2B , T5 . 4B , T5 . 5B , and T5 . 7B ORF73/72/71 tricistronic spliced and unspliced transcripts derived from the P1 ( LTc ) or P2 ( LTi ) promoter . The low level of transcription from the P1 ( LTc ) promoter correlates with the very low levels of split reads detected for introns “e” and “f” in the Group B transcripts ( Fig 5A and Table 2 ) . RT-PCR analysis of RNA from four of the KS tumor samples with the highest levels of KSHV-mapped reads failed to detect any of the tricistronic transcripts from the P1 ( LTc ) or P2 ( LTi ) promoters , confirming the RNAseq data ( S2 Fig ) . Additional genes adjacent to the latency locus showed consistent and high-level expression in the KS tumors . These included ORF75 and K15 ( Figs 2 and 3 ) , which are expressed in the same orientation as the latency genes described above . While the vast majority of the KS samples showed comparable levels of reads mapping to both ORF75 and K15 ( Fig 3 ) , tumors from two individuals , 012 and 030 , had high levels of reads mapping to ORF75 with no reads mapping to K15 ( Fig 3 , arrow ) . The different alleles of K15 have significant sequence variation extending into the ORF75 sequence . The phylogenetic analysis of the ORF75 sequences indicated that the KSHV strains in tumors 012 and 030 contain the K15 alleles N and M , respectively , thus reads from these strains do not map to the P-allele containing GK18 reference sequence . UCDS features were developed targeting the large ORF75 open reading frame ( 3 , 891 bp ) ( ORF75 UCDS ) and the largest exon coding the C-terminal domain of K15 ( 464 bp ) ( K15a UCDS ) from the heavily spliced K15 gene , which appears to be present in all K15 transcripts . Quantitation of the reads mapping to the ORF75 and K15a UCDS features revealed moderately high transcript levels across 32 KS samples ( median = 21 , 490 and 15 , 866 TPM respectively ) ( Fig 5D; S4 Table ) . The KS samples from 012 and 030 were excluded , as their true read counts were not captured in this analysis . A single poly-A transcription termination signal has been identified for these transcripts downstream of ORF75 [52] , suggesting that all of the transcripts detected by the K15a UCDS feature would be bicistronic , encoding K15 at the 5’ end and ORF75 at the 3’ end . Thirty of thirty-two tumor samples showed higher levels of ORF75 transcripts compared to K15 ( Fig 5D; S4 Table; S3B Fig ) , indicating the presence of an ORF75 monocistronic transcript initiating upstream of the AUG initiator of ORF75 . Quantitative analysis revealed that approximately two-thirds of the transcripts encoding ORF75 would be bicistronic , with K15 as the primary CAP-dependent translation product . The remaining transcripts would be monocistronic , with ORF75 as the primary translation product . It is not known whether ORF75 translation could occur through IRES-mediated initiation in the bicistronic transcripts , as was observed for ORF71 [43] . One KS tumor , 008_B , exhibited an unusual high level of reads mapping to the genomic region from ORFK3 to ORF19 , as indicated above ( Figs 2 and 3; bracketed in black ) and in more detail ( Fig 8A ) . The RNA reads at the borders of this region abruptly stopped in the middle of ORFK3 at one end and ORF19 at the other end , with no correspondence to possible RNA termination sites . High level expression of this region was not detected in KS tumor 008_C ( Figs 2 , 3 and 8A ) , which was isolated from a different location in the same patient , suggesting the presence of a genomic rearrangement in the KSHV strain in the 008_B tumor . By decreasing the alignment threshold in the initial Bowtie2 alignments , we identified reads containing partial sequences aligning to the ORFK3 and ORF19 regions flanking this highly expressed region . Sequence analysis of these reads revealed that a 14 , 813 bp ORFK3-ORF19 genomic region had been translocated from its original position at the left end of the genome ( bp 19 , 168 to 33 , 980 ) to the right end of the genome within the long-inverted repeat ( LIR2 ) ( bp 119 , 504 , numbering from the NC_009333 sequence ) ( Fig 8 ) . A model genomic sequence of NC_009333 with this translocation was created and used to map the 008_B reads ( as indicated in Fig 8 ) . This analysis revealed high levels of spliced transcripts derived from the highly expressed latency promoter P3 ( LTd ) upstream containing the 5’ P3-Exon1 ( Fig 8D and 8E ) . Novel transcripts were detected in which the P3-exon1 was spliced to various acceptor sites in downstream genes within the translocated genome fragment . A spliced transcript encoding ORFK5 was expressed at the highest level . The majority of other genes contained in the translocated region were also highly expressed with non-spliced transcripts driven by their original promoters . Since high level expression of these genes was not observed in the paired 008_C tumor , it appears that the translocation of this genomic region to the highly expressed latency region was responsible for the increased gene expression . At the left end of the KSHV genome , high levels of RNA reads mapped to the PAN ( T1 . 1 ) mRNA transcript in some KS tumors ( Figs 2 and 3; ex . KS tumors 011_D and 023_B ) . A UCDS feature targeting the PAN RNA was used to quantitate the RNA reads mapping to PAN . While transcript levels reached nearly 300 , 000 TPM in several KS samples , with a median of 37 , 413 TPM , significant variation was observed across the KS tumors ( Fig 9A and 9D; S4 Table ) . The PAN transcript overlaps the longer T6 . 1 transcript encoding ORFK7 ( Fig 9B ) . A UCDS feature was developed to target the ORFK7 transcript upstream of PAN ( Fig 9C ) to avoid overlap issues in which high levels of ORFK7 expression were observed in many previous studies that were actually due to PAN . While minimal expression of ORFK7 was detected , with a median of 267 TPM , a strong correlation was observed between the expression levels of ORFK7 and PAN in the KS tumors ( R = 0 . 6401 , P<0 . 0001 ) . Using UCDS features , moderate levels of reads mapping to ORFK2 and ORFK5 were consistently detected in the vast majority of the KS tumors ( median = 10 , 138 and 4 , 373 TPM , respectively ) ( Fig 9D; bracketed in red ) . Quantitative analysis of RNA reads mapping to the other KSHV genes in this region revealed 10 to 100-fold lower levels of transcripts with high variability across the 34 KS tumors ( Fig 9D and S4 Table ) . The transcript levels corresponding to each UCDS feature across the KSHV genome ( described previously [25] ) were compared for 34 of the KS tumor samples using a hierarchical clustering algorithm implemented in CIMminer [53] . This analysis was limited to the KS tumor samples with more than 1 , 000 total KSHV-mapped reads . The initial analysis showed expression of the genes in their order within the KSHV genome using the “equal width” binning method to map the TPM expression . This method divides the weightrange of data values into equal width intervals and each interval is mapped to one color for display in the clustered image map ( Fig 10; left end genome-top , right end genome-bottom ) . Obvious high levels of transcripts were detected at the right end of the genome in the latency region , mapping to UCDS features for ORFK12 ( UCDS-K12A ) , the adjacent direct repeat regions DR5 ( UCDS-DR5 ) and DR6 ( UCDS-DR6 ) , the P3-Exon1 flanking ORF72 ( UCDS-K12Aa ) , and the terminal ORF75 ( UCDS-75 ) and ORFK15 ( UCDS-K15a ) ( Fig 10 ) . Elevated levels of PAN and ORFK2 transcripts were detected in some of the tumors at the left end of the genome . The hierarchical clustering analysis identified three clusters of KS tumors . Cluster I tumors displayed high level expression of transcripts containing the DR6 repeat region and minimal expression of PAN , while Cluster III tumors displayed high level expression of PAN and lower levels of expression of DR6 ( Fig 10 ) . Cluster II tumors were intermediate . A second analysis was performed using the “quantile” binning method which divides the weightrange of expression data values into intervals each with approximately the same number of data points , spreading out the color differences in the image map . The KS tumors in Cluster I displayed low levels of gene expression throughout the KSHV genome outside of the latency region ( Fig 11 ) . In contrast , tumors in Cluster III showed moderate levels of lytic gene expression throughout the KSHV genome . Abnormally high expression of the genomic region ORFK3 to ORF19 was clearly observed in the KS tumor 008_B in this cluster . This is the region that was translocated downstream of the P3 ( LTd ) latency promoter in this tumor . An obvious block of expressed genes was observed in the Cluster III tumors between the long-inverted repeat ( LIR1 ) and PAN at the left end of the genome ( Fig 11 , top ) . The Cluster II tumors were intermediate with elevated gene expression across the majority of the KSHV genome . Both of the KS tumors from patient 030 showed unusually high expression of the genomic region from ORF39 through the right end of the genome . This patient was the only one to carry a KSHV strain with a K15 M-allele ( Fig 4 ) , as shown by the lack of reads mapping to the K15 UCDS . The hierarchical clustering analysis with quantile binning showed an obvious pattern of transcription including expression of transcripts detected by UCDS features for ORFs K11 , K2 , K4 , LIR1 , K5 , PAN , 38 , 45 , K8 , K8 . 1 , 57 , 58 , 59 , and 65 , in addition to latency transcripts K12A , DR5 , DR6 , miR , 71 , 72 and K12Aa ( Fig 11 , labeled with red text ) . Other genes , such as ORFs 17 . 5 , 27 , 33 and 69 were expressed in a subset of tumors . Higher levels of reads mapped to genes that flanked poly-A transcription termination sites ( Fig 11 , indicated at right ) . Most of these genes are present in loci with bicistronic or polycistronic transcripts that terminate at the same site . Thus , the high level of reads mapping to the ORFs adjacent to the poly-A site , in most cases , is due to overlapping polycistronic transcripts that contain the sequences encoding the poly-A-flanking ORF , as we have shown previously [25] . Thus , the high levels of reads mapping to these poly-A-flanking ORFs would not correlate directly with their CAP-dependent protein expression . The cutaneous KS tumor biopsies had been collected to represent different morphologies , sometimes from the same patient , including macular tumors with a flat appearance , nodular tumors with a distinct 3-dimensional structure and fungating tumors showing ulcerations and necrosis [54] . No correlations were observed between the morphological state and the level of total KSHV-mapped reads ( Fig 1B ) , and the hierarchical clustering analysis revealed no obvious correlations between the morphological state and the patterns of gene expression ( Fig 11 ) . Cluster I was composed of 7 different KS tumors ( 5 macular; 1 nodular; 1 fungating ) from 4 different individuals with a median read count of 38 , 605 ( range 15 , 978–134 , 856 ) . Cluster II was composed of 18 KS tumors ( 10 macular; 8 nodular ) from 15 different individuals with a median read count of 25 , 538 ( range 2 , 303–158 , 924 ) . Cluster III was composed of 9 KS tumors ( 5 macular; 3 nodular; 1 fungating ) from 8 different individuals with a median read count of 3 , 136 ( range 2 , 548–146 , 773 ) . Surprisingly , we observed a strong correlation between the gene expression patterns of KS tumor samples from the same patient , regardless of morphotype ( Fig 11 , bottom; red dot = macular , blue dot = nodular ) . Seven of 8 paired samples from the same individuals showed clustered gene expression profiles . The only paired sample that did not cluster was from individual 008 . The 008_B sample in this pair ( Fig 11 , indicated with an asterisk at the bottom ) exhibited the unusual high-level expression of the region between ORFK3 and ORF19 , which was due to the translocation not present in the paired 008_C sample . Gene expression in the translocated region was mainly driven by the P3 ( LTd ) promoter in the latency region , rather than by the normal gene specific promoters . Principal component analysis ( PCA ) was used to reduce the complexity of the expression data from the 34 KS tumors . Principal components 1 ( PC1 ) and 2 ( PC2 ) captured 88 . 4% and 6 . 2% of the variation in the gene expression data , respectively . This analysis confirmed the grouping of gene expression detected by hierarchical clustering , where the tumor samples are indicated in blue text ( Cluster I ) , black text ( Cluster II ) and orange text ( Cluster III ) ( Fig 12A ) . As seen in the hierarchical clustering analysis , no similarities were detected between the patterns of gene expression and tumor morphotype in the PCA analysis ( Fig 12A: morphotype of each tumor is color-coded ) . The PCA analysis revealed a gradient of KSHV gene expression from the more latent phenotype in Cluster I ( upper right- hand quadrant ) to the more lytic phenotype in Cluster III ( lower left-hand quadrant ) . The similarity in the pattern of gene expression between the paired tumor samples from the same individual was confirmed , with the exception of sample 008_B ( labeled with * ) , as discussed above ( Fig 12A ) . Since PAN expression is considered a marker of lytic activation , we compared PAN expression with the expression of the major T1 . 7A latency transcript . The RNA reads mapping to the PAN UCDS feature ( size = 1 . 126 Kb ) were compared to those mapping to the K12A UCDS feature ( size = 0 . 183 Kb ) , which detects the Kaposin region in the major T1 . 7A latency transcript and overlapping transcripts . The reads were normalized to compare transcript levels using reads per kilobase ( RPK ) , plotted as circular dots using the color code for morphotypes ( Fig 12B ) . Because read data for these two features were present in all 41 of the KS tumor samples , we also compared the PAN/K12A ratio of the 7 KS tumor samples with less than 1 , 000 total KSHV mapped reads that were excluded in earlier expression analysis ( plotted as triangles ) . Although the total number of KSHV-mapped reads was low in these samples , they showed an expression pattern that mirrored those seen in the lytic Cluster III ( S1 Fig ) . The tumor sample 008_B with the altered genome was excluded . The three expression clusters showed statistically significant differences in the PAN/K12A ratio with median ratios of 0 . 046 ( Cluster I; IQR = 0 . 009:0 . 084 ) , 0 . 1545 ( Cluster II; IQR = 0 . 0138:0 . 297 ) and 1 . 635 ( Cluster III; IQR = 1 . 026:2 . 092 ) ( Fig 12B ) , indicating that the PAN/K12A ratio could be used to distinguish the different tumor clusters . Notably , three of the four fungating samples showed high PAN/K12A ratios , compatible with a lytic phenotype . The number of KSHV-mapped RNA reads present in each tumor sample was compared between tumors in the different expression clusters . KSHV-mapped reads were normalized to the total read depth of each tumor library , indicated as KSHV-mapped reads per 100 million library reads . The 7 KS tumors with less than 1 , 000 total KSHV-mapped reds were included in lytic Cluster III ( triangles ) due to the similarities in the PAN/K12A RPK ratios with the Cluster III tumors and the presence of lytic gene expression ( S1 Fig ) . Both the latency Cluster I and intermediate Cluster II tumors showed high levels of KSHV-mapped reads in the tumor libraries ( Cluster I median = 38 , 995 , IQR = 18 , 393:139 , 027; Cluster II median = 25 , 059 , IQR = 5 , 307:92 , 593 ) . The lytic Cluster III tumors showed 23 and 10-fold lower levels of KSHV-mapped reads , respectively ( Cluster III median = 1 , 694 , IQR = 99:3 , 490 ) ( Fig 12C ) . While the extremely high level of KSHV-mapped reads for the fungating tumor 023B ( red dot ) is shown in parentheses , it was excluded from the column analysis . The presence of high levels of KSHV-mapped reads in the KS tumors with latent phenotypes and the absence of KSHV-mapped reads in the KS tumors with lytic phenotypes is summarized graphically in Fig 12A . A set of genes that were highly expressed across the set of 34 KS tumors was identified . Median transcript levels above 5 , 000 TPM were observed for 11 UCDS features targeting the latency region T1 . 7A ( Kaposin A/B/C ) spliced transcript and the ORF72/71 and ORF-K15/75 bicistronic transcripts , as well as the lytic region transcripts for PAN , K2 and K5 ( Fig 13A ) . Median transcript levels of 1–5 , 000 TPM were observed for 14 other UCDS features targeting ORFs 11 , OLAP , K4 , 38 , 45 , 50 , K8 , K81 , 57 , 58 , 59 , 63 , miR and 73 ( Fig 13A ) . To identify biologically relevant KSHV gene regulation modules , the Pearson correlation was determined for each pair of highly expressed genes . The analysis was limited to the highly expressed genes as the read level across most of the other genes was low and variable . The 008_B tumor , which showed the unusual genomic translocation , was not included . Hierarchical clustering revealed groups of co-expressed genes that showed similar expression correlation profiles across the 33 KS tumor , indicated by cohesive purple squares along the diagonal ( Fig 13B ) . These clusters of genes represent modules undergoing similar regulation of gene expression in the tumors . The orange areas showed clusters of genes that were negatively correlated . A strong correlated expression of the different transcripts derived from the latency P3 ( LTd ) promoter was observed in Cluster A , including the spliced Kaposin A/B/C T1 . 7A transcript ( detected with the K12A and K12Aa UCDS features ) , the unspliced bicistronic ORF71/72 T1 . 7B transcript ( detected with the ORF 71 and 72 UCDS features ) , and the unspliced T6 . 5A transcript ( detected with the miR UCDS feature ( see Fig 5 for transcript details ) . The large Cluster C contains a number of co-regulated genes implicated in the lytic replication cycle , including genes involved in regulation ( ORFs 45 , 50 , K8 , 57 , and PAN ) , immune modulation ( ORFs K4 , K5 ) , replication ( ORF59 ) and virion structure ( ORFs 38 , K8 . 1 , 65 ) . Other genes in Cluster C with unknown function include ORF58 and OLAP [55] . The expression of these genes is positively correlated with the expression of other genes in Cluster C , but negatively correlated with the expression of the other highly expressed genes . Cluster B contained a number of genes showing variable co-regulation , including K2 ( vIL-6 ) , ORF11 , K15 , ORF73 and ORF75 . The expression of these genes negatively correlated with the expression of the P3 ( LTd ) transcripts in Cluster A and the lytic replication-associated transcripts in Cluster C .
Our study is the first to use RNAseq analysis to quantitatively evaluate KSHV transcription at a detailed individual gene level in in vivo KS tumors . Our analysis showed that the most highly and consistently expressed transcripts in the Ugandan KS tumors were derived from the downstream latency transcript promoter LTd flanking ORF72 [41 , 49] , herein designated as the third latency promoter—P3 ( LTd ) . Very high levels of reads mapped to each of the UCDS features for K12A ( Kaposin ) , K12Aa ( P3-Exon1 ) and the direct repeats DR5 and DR6 within the latency region of the genome . These features target different regions of a 1 . 7 Kb spliced transcript from the P3 ( LTd ) promoter ( herein referred to as T1 . 7A ) [41 , 49] . The read depths of the K12Aa ( P3-Exon1 ) and K12A ( Kaposin ) features , which target the 5’ and 3’ ends of the T1 . 7A transcript , respectively , are quite similar , while the read depths of the DR5 and DR6 features were more variable , due to length of the UCDS feature , GC-content , sequence repeats in the DR5 region , sequence variability in the DR6 region and mismatches between RNA reads of the KS tumors and the KSHV GK18 reference sequence used for mapping . The presence of a single set of closely linked poly-A transcription termination signals in the region downstream of ORFK12 [52 , 56] , the strong correlation between the expression levels of the different regions of the T1 . 7A transcript in the different KS tumors , and the correspondingly high level of split reads mapping across the splice junction between upstream P3-exon1 splice donor and the downstream DR6 splice acceptor together support the conclusion that the P3 ( LTd ) spliced T1 . 7A transcript is the most highly expressed transcript in the Ugandan KS tumors and was confirmed by RT-PCR analysis . Early in situ studies of KS tumor lesions detected hybridization of a probe derived from the 0 . 7 Kb transcript encoding ORFK12 in every KS tumor examined [13 , 14 , 57–59] . Subsequent PCR and Northern analyses in cultured PEL cells indicated that transcripts hybridizing to the 0 . 7 Kb probe were almost always larger than 0 . 7 Kb , initiating from a promoter ( P4 ) upstream of the GC-rich DR6 repeat region [44] ( herein referred to as transcript T1 . 5A ) or from the P3 ( LTd ) promoter upstream of ORF72 ( Transcript T1 . 7A ) [41 , 49] . The T0 . 7 , T1 . 5A and T1 . 7A transcripts all terminate within a set of closely spaced poly-A termination signals ( Group A ) . Our quantitative RNAseq and RT-PCR data provided strong evidence that the vast majority of transcripts hybridizing to the 0 . 7 Kb probe in KS tumors correspond to the spliced T1 . 7A transcript from the P3 ( LTd ) promoter . Previous In situ hybridization studies also detected strong signals with probes specific for the ORF71 and ORF72 latency genes in the majority of spindle cells in KS tumors [59 , 60] . Subsequent analysis of latently-infected PEL cells identified three transcripts encoding ORFs 71 and 72 , which are derived from the constitutive P1 ( LTc ) promoter upstream of ORF73 and co-terminate at the poly-A site downstream of ORF71 ( Group B transcripts ) [6] . These transcripts include a full length 5 . 7 Kb unspliced tricistronic transcript ( T5 . 7B in this study ) encoding ORFs 73 , 72 and 71 , and three spliced transcripts , each containing the same short 5’ exon downstream of the P1 ( LTc ) promoter that is alternatively spliced to either ORF73 producing large 5 . 2 and 5 . 4 Kb tricistronic ORF73/72/71 transcripts ( T5 . 2B and T5 . 4B in this study ) or ORF72 producing a 1 . 8 Kb bicistronic ORF72/71 transcript ( T1 . 8B in this study ) . Although the tricistronic ORF73/72/71 transcripts have been detected in PEL cells in vitro using an ORF73 probe , in situ hybridization signals have not been observed in KS lesions in vivo , indicating the low abundance of tricistronic ORF73/72/71 transcripts encoding ORF73 LANA [61] . Our RNAseq analysis confirmed this observation as very few RNAseq reads from the KS tumors mapped to any of the tricistronic ORF73/72/71 transcripts and very few split RNA reads identified the splicing events downstream of the P1 ( LTc ) promoter . Furthermore , no triscistronic transcripts were detected by RT-PCR analysis of the KS tumor RNAs . As the tricistronic ORF73/72/71 transcripts are the only transcripts known to encode the latency associated nuclear antigen ORF73/LANA , which is ubiquitously present in KSHV-infected KS spindle cells , the LANA protein appears to be more stable than its mRNA transcript . Thus , while the LTc promoter is constitutive in KSHV-infected PEL cells , its activity in the KS tumors appears to be quite low , regardless of morphotype or activation state . We detected moderate and consistent levels of reads mapping to the ORF71 and ORF72 UCDS features in all the KS tumors , confirming the previous in situ hybridization studies . The RNA mapping and RT-PCR data indicate that the majority of these reads were derived from an unspliced bicistronic transcript encoding ORF72 at the 5’ end and ORF71 at the 3’ end ( herein referred to as T1 . 7B ) , which has been previously characterized in cultured PEL cells [49 , 50] . Like the spliced T1 . 7A transcript described above , the unspliced T1 . 7B transcript is transcribed from the P3 ( LTd ) promoter but terminates at the poly-A site downstream of ORF71 . A small upstream uORF in the initial 5’ region of this transcript has been shown to downregulate ORF72 expression , and a downstream IRES site facilitates expression of ORF71 , resulting in coordinated expression of both ORF71 and ORF72 in PEL cells [43 , 62] . As the unspliced T1 . 7B and spliced T1 . 7A transcripts are produced from the same P3 ( LTd ) promoter , they both contain the same 5’ region encoding the small uORF upstream of ORF72 , herein called P3exon1 . A very high level of RNA reads mapped to the K12Aa UCDS feature that targets the P3exon1 region of both transcripts . The expression level of the bicistronic unspliced T1 . 7B transcript detected with the ORF71 and ORF72 UCDS features was only 10% of the expression level of the shared 5’ P3exon1 , indicating that 90% of the P3 ( LTd ) transcripts bypass the termination signal after ORF71 producing long initial pre-mRNA species , which terminate downstream of ORFK12 ( the unspliced transcript is referred to herein as T6 . 5A ) . The majority of this mRNA is processed to remove the intron containing ORFs 72 , 71 and microRNAs by splicing , generating the spliced T1 . 7A transcript described above . Read data mapping to the miR UCDS feature targeting the intronic region indicated that < 5% of the P3 ( LTd ) transcripts correspond to the unspliced T6 . 5A precursor transcript . A previous study suggested that the transcripts hybridizing to ORF71 and ORF72 probes in KS lesions were bicistronic spliced transcripts derived from the upstream P1 ( LTc ) promoter [6] , as has been observed in PEL cells . TopHat2 analysis of RNA reads in our study detected little evidence for splicing of transcripts from the P1 ( LTc ) promoter , and essentially no expression of the 5’ exon downstream of the P1 ( LTc ) promoter . Thus , our RNA-seq data indicates that the transcripts encoding ORF72 and ORF71 in KS lesions are derived from the P3 ( LTd ) promoter , not the P1 ( LTc ) promoter . Note that the spliced T1 . 8B bicistronic ORF72/71 transcript from the P1 ( LTc ) promoter and the unspliced T1 . 7B bicistronic ORF72/71 transcripts from the downstream P3 ( LTd ) promoter are of similar size and could have been mistaken for each other by Northern analysis in previous studies . While the majority of transcripts detected using the ORF71 and ORF72 UCDS features appeared to be bicistronic , confirming previous data from PEL cells , our analysis provided evidence for expression in some tumors of monocistronic ORF71 ( T0 . 9B ) and ORF72 ( T1 . 0C ) transcripts from the P3 ( LTd ) promoter that have been detected previously [42 , 43] . Variable numbers of repeats and sequence heterogeneity in DR5/DR6 repeat region have been detected previously in other KSHV strains , which alter protein translation from the T1 . 7A mRNA [41] . Our RNAseq analysis revealed two nucleotide deletions in the overlapping T1 . 7A and T1 . 5 transcripts that are conserved in the Ugandan KSHV strains , both of which would alter the open reading frames in these transcripts from those described previously in BCBL-1 and other KSHV strains . In eleven Uganda KSHV strains typified by the Zambian KSHV strain ZM114 , the putative protein products of the T1 . 5/T1 . 7A transcripts would be a BCBL-1 Kaposin B homolog and a novel protein , herein called Kaposin E , that would initiate with the same CUG codon as BCBL-1 Kaposin C but lack the fusion with Kaposin A . In fifteen other Uganda KSHV strains , typified by the Zambian KSHV strain ZM004 , additional nucleotide changes in the T1 . 5A/T1 . 7A transcripts eliminate the CUG-initiated Kaposin B and C ORFs leaving Kaposin A as the only predicted translational product of this transcript . While other non-AUG translation initiation could be utilized in these KSHV strains , it is not clear what protein products would be translated . Due to the limited amount of RNA template in our biopsies and the repetitive nature of the sequence and the high GC content ( in some regions >95% ) , we were unable to PCR amplify T1 . 7A transcripts from these or other KSHV strains to determine the exact nucleotide sequence . Since translation products from this mRNA could be the major KSHV-encoded proteins in the KS tumors , identification and functional characterization of the T1 . 7A-encoded proteins will be critical for understanding the role of KSHV in the KS tumors . While the exact coding potential for the T1 . 7A transcripts in the Ugandan KSHV strains awaits functional analysis , it is clear that splicing of this transcript liberates a 4 . 8 Kb intron , which is the substrate for processing of the majority of the KSHV microRNAs [49] . Thus , a major outcome of the expression of the T1 . 7A transcript would be the production of this set of KSHV microRNAs [49] . Additional transcripts from the P1 ( LTc ) promoter upstream of ORF73 , including T1 . 8A and T1 . 6A , also generate the region encoding these microRNAs as an intron during splicing . However , we observed very low and variable expression of the P1 ( LTc ) transcripts in the KS tumors , indicating that splicing of the T1 . 7A transcript from the P3 ( LTd ) would generate the vast majority of processed microRNAs in the tumors . In addition to encoding the Kaposin A ORF downstream of the DR5/DR6 repetitive sequences , the T1 . 7A transcript is a precursor of the miR-K10a and miR-K12 microRNAs , which are embedded within the 3’ end of the transcript [46] . Thus , the RNAseq data indicates that a major functional role of P3 ( LTd ) promoter in the establishment and progression of KS tumors would be to generate the precursor substrates for all the KSHV microRNAs . The P3 ( LTd ) promoter has been shown to be constitutively active in PEL cells , and it is not clear whether its activity is increased by reactivation using RTA [41 , 49 , 50] . Unexpectedly , we identified one KS tumor in a sampling of paired tumors from individual 008 , which contained a translocation of a 14 Kb region of the left end of the KSHV genome flanking the long-inverted repeat LIR1 into the LIR2 repeat within the latency region at the right end of the genome . This translocation is located downstream of the highly active P3 ( LTd ) latency promoter , resulting in numerous novel transcripts from the P3 ( LTd ) promoter containing the initial P3-Exon1 spliced to acceptor sites in the translocated K3 , K5 , and ORF17 genes . This translocation did not appear to inhibit the splicing of the highly expressed T1 . 7A transcript encoding the Kaposin A/B/C complex , even though the excised intron was 14 Kb longer . A spliced transcript containing the P3-Exon1 spliced to ORFK5 was the most highly expressed transcript in the 008_B tumor . Since the P3-Exon1 encodes the regulatory uORF , described above , the translation of protein products from the highly expressed novel spliced transcripts in this tumor could be downregulated . The majority of highly expressed genes in the translocated region were transcribed right to left in the same orientation as the transcripts from the P3 ( LTd ) promoter from the same DNA strand . While elevated levels of reads also mapped to other genes in the translocated region , such as PAN , OLAP , and ORF18 , these genes would have been transcribed from their proper promoters on the other strand in the opposite direction . As this tumor was not analyzed using a stranded RNA library , it was unclear whether the RNA reads mapped to PAN , OLAP and ORF18 transcripts from the opposite strand . No other KS tumor exhibited this translocation suggesting that the translocation was a unique event in the 008_B tumor . However , the existence of this translocation and the resulting altered transcription pattern indicates the plasticity of the KSHV genome and the strong role of the P3 ( LTd ) promoter in the development of KS . Using a PAN-specific UCDS gene feature , we detected expression of the PAN RNA in most of the 34 Ugandan KS tumors . Although the median expression level for PAN was high , it was 5-fold less than the expression level of the K12A ( T0 . 7 ) region . The range of PAN expression levels was large , ranging from barely detectable in some tumors to extremely high in other tumors , with expression levels higher than K12A ( T0 . 7 ) in 7/34 KS tumors . While previous in situ hybridization studies identified specific cells expressing PAN , our RNAseq analysis only detected the average expression level within the biopsied lesion . The expression of PAN correlated positively with the expression of a large number of lytic cycle genes , including K6 ( vMIP-1 ) , ORF59 , K8 ( bZIP ) , ORF25 ( MCP ) , ORF26 ( VP23 ) , and ORF65 ( SCP ) , which were previously detected by in situ hybridization in limited numbers of KS spindle cells . Only a weak correlation was detected between the expression of these lytic cycle genes and ORF50 ( RTA ) . There was no correlation between the expression of these lytic genes and genes , including K2 ( vIL-6 ) , ORF16 ( vBCL-2 ) , K10 ( vIRF-4 ) , or K11 ( vIRF-2 ) , whose expression has also been observed infrequently in KS spindle cells . Surprisingly , the RNA-seq data showed a strong correlation between the expression of K2 ( vIL-6 ) and the genes for the ORF11 tegument and the ORF4 complement control ( KCP ) proteins , indicating a common regulation of gene expression that was not shared with PAN or the other lytic genes . This data suggests that the expression of discrete subgroups of genes in the lytic pathway can be differentially regulated in the tumors . A recent study by Tso et al . used RNAseq to analyze KS lesions from four HIV-infected individuals from Zambia and Tanzania that were undergoing anti-retroviral therapy [26] . These RNA samples were sequenced to a depth of 10–13 million total reads obtaining 718 , 1 , 650 , 3 , 441 and 17 , 202 reads ( median = 2 , 545 ) mapping to the NC_009333 KSHV reference sequence . No obvious pattern of KSHV expression was reported across the four samples , presumably due to the limited sample size and variable KSHV read depths of the different biopsies . High levels of latency gene transcripts were reported , including ORF73 LANA , as well as an increased expression of viral immune modulation genes , including ORF-K2 ( viral interleukin-6 ) , ORF-K5 ( modulator of immune recognition ) , ORF-K7 ( viral inhibitor of apoptosis ) and ORF75 ( degradation of ND10 protein ) . In contrast , we analyzed RNA libraries from 41 KS biopsies at a 10-fold greater depth with a 5-fold greater depth of KSHV-mapped reads ( median = 10 , 232; maximum = 158 , 924 ) . The increased sample size and depth of sequencing allowed for clear patterns of KSHV expression to be detected in the panel of KS tumors from anti-retroviral therapy-naïve Ugandans in our study . We detected high levels of ORF75 and ORFK15 transcripts in all the KS biopsies , but only variable levels of ORFK2 and ORFK5 transcripts . We determined that 76% of the RNA reads mapping to ORF75 are derived from K15/ORF75 bicistronic transcripts , as K15 transcripts terminate downstream of ORF75 [52] . Since it is not known whether ORF75 protein is translated from the bicistronic transcripts through alternate initiation pathways , the biological relevance of the bicistronic transcripts to ORF75 function is unclear . While ORFK15 protein is abundantly expressed in KS tumor biopsies where it is believed contribute to the invasiveness and angiogenic properties of the tumor cells [34 , 35] , we have identified no reports of ORF75 tumor expression . Even though transcripts of both ORFK15 and ORF75 are highly expressed in the most latent KS tumors , both genes are required for viral lytic replication in vitro [32 , 63] , suggesting important pleiotrophic effects in the virus life cycle . As we have indicated previously , mapping of RNAseq reads to the regions of the KSHV genome encoding entire ORFs , as performed in the Tso et al study , is problematic due to the compact size of the KSHV genome and overlapping nature of the KSHV transcripts [25] . While Tso et al detected high-level expression of ORF73 LANA by mapping reads to the entire ORF , we detected only minimal expression of ORF73 using UCDS features that avoided the large repetitive sequence within LANA . This raises the question whether the ORF73 expression in the Tso study could have been due to multiple mapping of reads to repetitive sequences . Our mapping protocol limited reads to a single mapping event to avoid this problem . As discussed above , a large T6 . 1 transcript derived from the positive strand of the KSHV genome encoding ORFK7 overlaps with the PAN RNA derived from the same DNA strand and ORFK5 transcripts from the opposite strand ( see Fig 9B ) . Using the UCDS approach with non-overlapping PAN and ORFK7 UCDS features , we determined that the high-level expression of ORFK7 detected using the typical mapping to the NC_009333 ORFs is due to high levels of PAN transcripts , not ORFK7 . With the UCDS approach , we detected high levels of PAN in some tumors with very low levels of ORFK7 transcripts in all tumors . Tso et al did not report PAN transcript levels as PAN is not an ORF in the NC_009333 accession record and therefore was not quantitated . Using deep sequencing of RNA transcripts , our study quantitated the expression level of genes across the entire KSHV genome using UCDS features , providing a complete global analysis of the KSHV transcriptome in the KS lesions from HIV-infected Ugandans that were anti-retroviral therapy naive . Unsupervised hierarchical clustering and PCA analysis of gene expression revealed three distinct tumor clusters . Cluster I consisted of 7 tumors from four individuals with a mixture of morphotypes . These tumors showed a predominant latency phenotype with low levels of gene expression outside of the latency locus with 22 ORFK12 transcripts per PAN transcript . Cluster III consisted of seventeen tumors from fourteen individuals with a mixture of morphotypes , which showed a predominant lytic phenotype with gene expression detected across the entire KSHV genome . In contrast , these tumors expressed 1 . 6 PAN transcripts per ORFK12 transcript . Cluster II consisted of eighteen tumors from fifteen individuals with a mixture of morphotypes , which showed elevated gene expression across the right end of the genome and limited gene expression across the left end of the genome with 6 . 0 ORFK12 transcripts per PAN transcript . Surprisingly , the Cluster III KS tumors with the most lytic phenotypes contained the fewest number of RNA reads mapping to the KSHV genome , while the Cluster I tumors with the most latent phenotypes contained the highest number of KSHV-mapped RNA reads . This is in direct contrast to what was expected from previous studies on KSHV infection in vitro , where reactivation of latent virus was shown to initiate new lytic gene transcription but not alter constitutive latent transcription [64] . Of the 41 KS tumor samples sequenced , 7 had fewer than 1 , 000 KSHV-mapped reads , and 3 had fewer than 100 KSHV reads although all the tumors were advanced T1 tumor stage . We found that KS tumors with 15 , 000 to 150 , 000 total KSHV-mapped reads had similar viral loads of ~0 . 5 KSHV genomes/cell , suggesting that KSHV gene expression did not correlate with the level of KSHV infection in cells in the lesion . While the KS samples with less than 1 , 000 KSHV-mapped reads were excluded from hierarchical cluster analysis of the RNAseq data due to problematic normalization and clustering , we found that these tumors had a high PAN/K12 read ratio , consistent with a lytic phenotype . Thus , all the KS tumors except for one fungating sample ( 23B ) showed a significant correlation between lytic gene expression across the entire KSHV genome and low levels of total KSHV transcription in the KS lesion . Interestingly , previous studies using double staining of KS lesions showed that vIL6- or vGPCR-positive KS spindle cells lacked punctate nuclear LANA spots indicating a decreased expression of LANA and possibly other latency proteins during lytic reactivation [21] . In a previous PCR profiling study of KSHV expression in KS tumors from Malawi , 10 of 61 tumors were excluded from analysis due to the lack of detectable mRNA for KSHV latent genes and three additional samples were excluded because they had low levels of KSHV latent mRNA [24] . Since these samples were excluded from analysis in this study , it is not known whether they displayed a lytic phenotype , as was seen for comparable tumor types using RNAseq analysis in our study . It was previously reported that qPCR profiling of Malawian KS tumors detected abundant transcription within the viral latency locus including ORF73 ( LANA ) , ORF72 ( vCyc ) , ORF71 ( vFLIP ) , ORFK12 ( Kaposin ) and microRNAs [24] . This study detected a poly-A site position effect in which the PCR signal for ORF71 , which is proximal to the poly-A site , was higher than the distal ORF72 or ORF73 . Since these signals were believed to originate from the same tricistronic mRNA transcript , it was concluded that poly-A purification of the mRNA templates had artifactually increased the transcript levels for the genes adjacent to the poly-A site . Using our RNAseq approach , we were able to characterize the latency region transcription in KS tumors in a more granular detail . We detected minimal numbers of RNA reads mapping to LANA on the tricistronic ORF73/72/71 latency transcript of the P1 promoter and showed that the vast majority of reads mapping to ORFs 71 and 72 were actually derived from the T1 . 7B bicistronic transcript of the P3/LTd promoter . These findings explain the results of the Hosseinipour study and indicate that the increased expression of ORFs 71 and 72 is not an artifact of poly-A purification . An important consideration in our study was the use of the KSHV-GK18 reference sequence ( NC_009333 ) to map the RNA reads from the different KSHV strains in the Ugandan KS tumors . We previously developed a detailed map of the KSHV transcriptome annotated to the NC_009333 reference sequence . We also developed a detailed gene feature file ( GFF ) for the NC_009333 sequence with UCDS features allowing quantitation of overlapping genes in the KSHV genome [25] . Since our phylogenetic analysis revealed the presence of multiple KSHV strains in the 34 Ugandan KS tumors , we decided to use the NC_009333 sequence as a common alignment target for consistency . We observed instances of sequence mismatches between KS tumor RNA reads and the aligned NC_009333 sequence throughout the KSHV genome , however , there were only a few obvious regions where the variations in read sequence affected transcript quantitation , as seen for K15 , as described above . We observed some mapping issues within the K1 gene , which is known to exhibit high levels of variation in specific regions of the gene . However , there was sufficient homology between the reads and the GK18 sequence outside of the K1 variant regions to determine K1 expression . We also observed problems mapping reads to the DR5 and DR6 repeat regions , which have shown high levels of sequence variation . To compare sequences across problematic regions , we performed additional mapping studies using published KSHV strains with variant sequences as references . In summary , quantitative RNAseq analysis using a unique set of UCDS gene features has provided an in-depth analysis of the KSHV transcriptome in 41 T1 stage KS lesions from 30 HIV-infected ART-naïve Ugandans , yielding a unique resource for subsequent analysis of specific transcripts by other approaches . Hierarchical clustering and PCA analysis of KSHV transcripts revealed three clusters of tumors displaying a gradient of KSHV gene expression ranging from minimal gene expression outside of the latency locus ( latent expression ) to wide-spread gene expression across the viral genome ( lytic expression ) . Paradoxically , the tumors with the latent phenotype had high levels of total KSHV transcription while the tumors with the lytic phenotype had low levels of total KSHV transcription . Morphologically distinct KS tumors from the same individual showed similar KSHV gene expression profiles suggesting that the tumor microenvironment and host response played a determining role in the activation level of KSHV within the infected tumor cells .
HIV infected adults with KS were recruited from the Uganda Cancer Institute ( UCI ) /Hutchinson Center Cancer Alliance in Kampala , Uganda . Eligible participants had to be at least 18 years of age and have late stage ( T1 ) KS by AIDS Clinical Trials Group staging criteria and be naïve for antiretroviral therapy ( ART ) . All KS samples were obtained with written informed consent . Total nucleic acids were extracted using RLT buffer ( Qiagen ) and RNA was isolated using the RNeasy mini kit with DNAse treatment step . Total RNA integrity was checked using an Agilent 2200 TapeStation ( Agilent Technologies , Inc . , Santa Clara , CA ) and quantified using a Trinean DropSense96 spectrophotometer ( Caliper Life Sciences , Hopkinton , MA ) . Unstranded RNA-seq libraries were prepared from 300 ng of total RNA using the TruSeq RNA Sample Prep Kit v2 ( Illumina , Inc . , San Diego , CA , USA ) . Four KS tumors libraries were prepared using the TruSeq Stranded mRNA Library Kit ( Illumina ) from 100 ng of total RNA . Library size distributions were validated using an Agilent 2200 TapeStation ( Agilent Technologies , Santa Clara , CA , USA ) . Additional library QC , pooling of indexed libraries , and cluster optimization was performed using Life Technologies’ Invitrogen Qubit 2 . 0 Fluorometer ( Life Technologies-Invitrogen , Carlsbad , CA , USA ) . The unstranded RNA-seq libraries were pooled ( 5-plex ) and the stranded libraries were pooled ( 4-plex ) and each pool was clustered onto a flow cell lane . Sequencing was performed using an Illumina HiSeq 2500 in “High Output” mode with a paired-end , 50 base reads ( PE50 ) sequencing strategy for the unstranded libraries and non-paired end for the stranded libraries . Image analysis and base calling was performed using Illumina’s Real Time Analysis v1 . 18 software , followed by ‘demultiplexing’ of indexed reads and generation of FASTQ files , using Illumina’s bcl2fastq Conversion Software v1 . 8 . 2 . RNA from four KS tumors was analyzed by RT-PCR to detect different spliced and unspliced transcripts from the latency region of KSHV ( S2 Fig ) . Forward PCR primers from the 3’ end of the ORF73 sequence ( P1/2F , bp124121 5’ CCCTGCCATTAACCCAGCCAG 3’ bp124101 ) and the P3-exon1 sequence downstream of the P3 promoter ( P3F , bp123950 5’ ACCCATCTACCTCAACTGAAC 3’ bp123930 ) were develop to use in conjunction with a reverse primer from the 3’ end of the ORF72 sequence ( 72R , bp123757 5’ CGATCCTCACATAGCGTGGGA 3’ bp123777 ) to amplify a 406 bp fragment of the tricistronic ORF73/72/71 ( T5 . 2–5 . 7B ) transcript and a 194 bp fragment of the bicistronic ORF72/71 ( T1 . 7B ) transcript , respectively . Reverse PCR primers from the 3’ end of the K12 sequence ( KapR1 , bp118037 5’ CGTTGCAACTCGTGTCCTGAATG 3’ bp118059 ) and the sequence spanning the exon junction of transcript T1 . 7A ( P4R , bp119036 5’ TTATAGCGTTTC 3’ bp119047: bp123843 5’ CTGTAGAGCCTG 3’ bp123854 ) , were developed to amplify 1 , 042 and 120 bp fragments of the T1 . 7A spliced transcript encoding the K12 Kaposin A/B/C region , respectively , using the P3F forward primer . A forward PCR primer from the sequence downstream of the P4 promoter flanking the DR6 repeat region ( P4F , bp 118973 5’ TTGGATTTACACGTATCGAGG 3’ bp 118953 ) was developed to amplify a 186 bp fragment of K12 using the KapR1 reverse primer . PCR reaction conditions were developed using BCBL-1 DNA as template . cDNA was prepared from KS tumor RNA using Tetro reverse transcriptase ( Bioline ) and random decamers at 45 °C in the presence of RiboSafe ( Bioline ) , and PCR amplification was performed using MyTaq HS polymerase ( Bioline ) and forward and reverse primers ( 5 . 0 μM ) , after denaturing at 95° 2 . 5 minutes with 45 cycles of 95° 15 sec/60° 30 sec/ 72° 60 sec ) . The PCR products were visualized by electrophoresis using ethidium bromide . Reactions without reverse transcriptase were performed to control for DNA contamination . RNA reads mapping to the human genome ( hg19 ) were removed using the Bowtie2 program ( version 2 . 2 . 6 ) [65] . The remaining RNA reads were aligned to the KSHV reference sequence NC_009333 for the KSHV GK18 strain using TopHat2 ( version 2 . 0 . 14 ) [66] in a local instance of Galaxy [67] . Mapped reads were visualized using the Integrated Genome Viewer ( IGV; version 2 . 3 . 75 ) [68] . For quantitation purposes , the reads from paired-end libraries ( non-stranded ) were analyzed as unpaired ( single-end data ) to allow each read of the pair to map unambiguously to a single gene feature . The reads from both strands of the stranded libraries ( non-paired end ) were either concatenated and analyzed together ( librarytype = unstranded ) for comparison to the non-stranded paired end libraries or were analyzed separately ( librarytype = FR ) to show strand specificity . TopHat2 was used to detect splicing events ab initio . The default presets were used except that the maximum intron length was decreased to 10 , 000 and the maximum number of alignments allowed was decreased from 20 to 1 , to avoid overcounting reads to repetitive regions . HTSeq ( version 0 . 6 ) [69] was used to quantitate the reads mapping to the unique set of UCDS gene features within the novel revised gene feature file “KSHV NC_009333 UCDS ver 020116 . GFF“ ( S1 File ) [25] . The “intersection ( non-empty ) ” setting in HTSeq was used to count all reads mapping completely or partially to a UCDS feature to maximize read count ( featuretype = UCDS; IDattribute = gene ) . No reads were eliminated by ambiguity since the UCDS features were 50 bp apart , the length of a read . The read count was expressed as transcripts per million ( TPM ) by first normalizing the read count to reads per kilobase ( RPK ) by dividing the read counts by the length of the UCDS gene feature , in kilobases . The “per million” scaling factor was determined by summing all of the RPK values in a sample and dividing by 1 , 000 , 000 . Each RPK value was then divided by the “per million” scaling factor to give TPM of mapped KSHV reads . Hierarchical clustering of TPM normalized expression levels was performed using the algorithm implemented in CIMminer [53] . Hierarchical clustering of the gene correlation matrix was performed by calculating the Pearson correlation between the normalized transcript levels ( TPM ) associated with each pair of UCDS gene features , using a script in R to create and output the correlation matrix . Shiny web applications were developed for R-based principal component analysis in Fig 12A ( available at https://efg-ds . shinyapps . io/pcaApp/ ) and the boxplot analysis of gene expression levels in Fig 5C ( available at https://efg-ds . shinyapps . io/boxplotApp/ ) . Phylogenetic analysis of the KSHV strains in the KS tumors was performed on the complete coding sequences of ORF75 ( 3 , 891 bp ) , which were assembled from the 50 bp RNA reads for 23 of the KS tumors with IGV , using maximum likelihood analysis . The sequences have been deposited in Genbank . The biological samples in the study were obtained specifically for the study . All participants provided written informed consent . The protocol was approved by the institutional review boards at the Fred Hutchinson Cancer Research Center , the Makerere University School of Medicine , and the Uganda National Council for Science and Technology . | Kaposi’s sarcoma ( KS ) is among the world’s most common AIDS-associated malignancies . The Kaposi sarcoma-associated herpesvirus ( KSHV ) was first identified in KS tumors and is now known to be the causative agent of all forms of KS , including classical , endemic , iatrogenic and HIV-associated . KSHV is endemic to sub-Saharan Africa with high infection rates in children and adults . Compounded with the high rate of HIV and AIDS in this area , pediatric and adult KS are some of the most common malignancies with the highest fatality rates . We used RNA deep sequencing to characterize KSHV expression in a large collection of KS biopsies from HIV-infected Ugandans . Using a novel approach to quantitate expression in complex genomes like KSHV , we found that RNA from a single KSHV promoter within the latency region constituted the majority of KSHV transcripts in the KS tumors . Alternate RNA processing produced different spliced and un-spliced transcripts with different coding potentials . Differential expression of other KSHV genes was detected which segregated the tumors into three different types depending on their expression of lytic or latency genes . Quantitative analysis of KSHV expression in KS tumors provides an important basis for future studies on the role of KSHV in the development of KS . | [
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] | 2018 | Quantitative RNAseq analysis of Ugandan KS tumors reveals KSHV gene expression dominated by transcription from the LTd downstream latency promoter |
Histone acetylation has been linked to developmental changes in gene expression and is a validated drug target of apicomplexan parasites , but little is known about the roles of individual histone modifying enzymes and how they are recruited to target genes . The protozoan parasite Toxoplasma gondii ( phylum Apicomplexa ) is unusual among invertebrates in possessing two GCN5-family lysine acetyltransferases ( KATs ) . While GCN5a is required for gene expression in response to alkaline stress , this KAT is dispensable for parasite proliferation in normal culture conditions . In contrast , GCN5b cannot be disrupted , suggesting it is essential for Toxoplasma viability . To further explore the function of GCN5b , we generated clonal parasites expressing an inducible HA-tagged dominant-negative form of GCN5b containing a point mutation that ablates enzymatic activity ( E703G ) . Stabilization of this dominant-negative GCN5b was mediated through ligand-binding to a destabilization domain ( dd ) fused to the protein . Induced accumulation of the ddHAGCN5b ( E703G ) protein led to a rapid arrest in parasite replication . Growth arrest was accompanied by a decrease in histone H3 acetylation at specific lysine residues as well as reduced expression of GCN5b target genes in GCN5b ( E703G ) parasites , which were identified using chromatin immunoprecipitation coupled with microarray hybridization ( ChIP-chip ) . Proteomics studies revealed that GCN5b interacts with AP2-domain proteins , apicomplexan plant-like transcription factors , as well as a “core complex” that includes the co-activator ADA2-A , TFIID subunits , LEO1 polymerase-associated factor ( Paf1 ) subunit , and RRM proteins . The dominant-negative phenotype of ddHAGCN5b ( E703G ) parasites , considered with the proteomics and ChIP-chip data , indicate that GCN5b plays a central role in transcriptional and chromatin remodeling complexes . We conclude that GCN5b has a non-redundant and indispensable role in regulating gene expression required during the Toxoplasma lytic cycle .
Lysine acetylation of histones is a well-characterized post-translational modification linked to the activation of gene expression . Initially identified in the free-living protozoan Tetrahymena thermophila , the first histone acetyltransferase ( HAT ) was homologous to a transcriptional adaptor protein in yeast known as GCN5 [1] . It has since been elucidated that GCN5 is a highly conserved catalytic component present in multiple protein complexes linked to the regulation of gene expression [2] . When GCN5 HATs were found to have non-histone substrates as well , they became referred to as lysine ( K ) acetyltransferases ( KATs ) [3] . The number of GCN5 KATs and their impact on cells or organisms depends on the species . GCN5 generally appears to be required for stress responses [4]–[6] . Consistent with this idea , the single GCN5 is dispensable in Saccharomyces cerevisiae , but required for growth on minimal media [7] . In contrast , mammals possess two GCN5 family members , one of which is required for mouse embryogenesis [8] . Null mutants of the other GCN5 ( also known as PCAF , or p300/CBP-associating factor ) have no discernible phenotype in mice [8] , [9] . Together , these reports suggest that GCN5-mediated acetylation is an important facet of cellular biology , particularly during stress or adaptive responses . The relevance of lysine acetylation in pathogenic protozoa is underscored by potent antiprotozoal activity of lysine deacetylation inhibitors like apicidin and FR235222 [10] , [11] . Histone acetylation has also been linked to a number of processes that underlie pathogenesis of apicomplexan parasites , including antigenic variation in Plasmodium ( malaria ) and developmental transitions in Toxoplasma gondii [5] , [12] , [13] . An extensive repertoire of histone modification machinery is present in these parasites , suggesting that epigenetic-based regulation contributes to gene expression control [14] . A related oddity of the Apicomplexa is that these early-branching eukaryotes appear to use an expanded lineage of so-called Apetela-2 ( AP2 ) proteins as transcription factors rather than the basic leucine zipper ( bZIP ) factors that are conserved throughout most of the eukaryotic kingdom [15] , [16] . ApiAP2 proteins harbor a plant-like DNA-binding domain and emerging evidence supports that at least some function as bona fide transcriptional regulators [17] , [18] . Toxoplasma has a number of unusual features with respect to its GCN5 KATs . First , there are two GCN5-family members in Toxoplasma ( GCN5a and b ) whereas other invertebrates , including Plasmodium , possess only one [19] , [20] . Second , both TgGCN5s have long N-terminal extensions devoid of known protein domains . These N-terminal extensions are not homologous to those seen in higher eukaryotes , nor are they homologous to each other or other apicomplexan GCN5s [20] . One function of the TgGCN5 N-terminal extensions is to translocate the KAT into the parasite nucleus via a basic-rich nuclear localization signal [21] , [22] . Yeast two-hybrid studies have suggested that the N-terminal extension of Plasmodium GCN5 plays a major role in mediating protein-protein interactions [23] . We previously generated a gene knockout of GCN5a , but similar methods have not produced viable GCN5b knockouts . GCN5a was found to be dispensable for parasite proliferation in vitro , but required for the parasite to respond properly to alkaline stress [5] . These findings are consistent with the well-documented role of GCN5 KATs in the cellular stress response . The inability to knockout GCN5b suggested it is essential for parasite viability . To gain a better understanding of its function in parasite physiology , we expressed a dominant-negative form of GCN5b that lowered histone acetylation , altered gene expression , and arrested parasite proliferation . We also biochemically purified the multi-subunit GCN5b complex to identify interacting proteins and performed a genome-wide ChIP-chip analysis . Collectively , these findings establish that the KAT GCN5b interacts with AP2 factors to regulate the expression of a wide variety of genes that are essential for parasite replication .
To define the role of GCN5b in Toxoplasma , we attempted to generate a gene knockout . Repeated attempts to disrupt or replace GCN5b using homologous recombination in haploid type I RH strain tachyzoites have not been successful , in contrast to our ability to knockout GCN5a using the same approach [5] . More recent attempts to knockout the GCN5b locus in a Δku80 background also failed to generate viable parasites , further suggesting that GCN5b is essential in tachyzoites . We then pursued an inducible dominant-negative strategy to ascertain the importance of GCN5b in Toxoplasma . As GCN5 KATs function in multi-subunit complexes , GCN5b is a good candidate for a dominant-negative strategy whereby an ectopically expressed enzymatically dead version would compete for essential interacting proteins from the endogenous protein . Consequently , the activities of the endogenous GCN5b complex would be attenuated . We generated clonal parasites in an RH background expressing a catalytically inactive form of GCN5b ( mutated glutamic acid 703 to glycine , E703G [24] ) fused to a destabilization domain and HA tag ( ddHA ) at the N-terminus . In vitro HAT assays using purified ddHAGCN5b proteins confirm the E703G mutation ablates enzymatic activity ( Supplemental Figure S1 ) . We also generated a clone expressing wild-type ( WT ) GCN5b in the same fashion to serve as a control in phenotypic analyses . The dd domain directs its fusion partner to the proteasome for rapid degradation , but this can be averted by adding Shield ligand to the culture medium [25] . Fusion of ddHA to the N-terminus of GCN5b or GCN5b ( E703G ) allowed their ectopic expression to be regulated via Shield , as assessed in immunofluorescence assays ( IFAs ) and immunoblots using anti-HA ( Fig . 1 ) . Fusion of ddHA did not disrupt nuclear localization of WT or mutant GCN5b ( Fig . 1 ) . No difference in parasite replication was observed between parental wild-type parasites and those expressing ectopic ddHAGCN5b protein at any concentration of Shield ( Fig . 2A and 2B ) . In contrast , parasites induced to express ddHAGCN5b ( E703G ) underwent rapid growth arrest in 48 hours with as little as 10 nM Shield ( Fig . 2C ) . At 500 nM Shield , over 80% of the parasite vacuoles contained only 16 parasites , compared to the control in which most vacuoles contained 64 parasites . Similar results were obtained when we used a PCR-based assay for the B1 gene to measure parasite replication [5] ( data not shown ) . The growth arrest observed for the Shield-treated ddHAGCN5b ( E703G ) parasites is reversible , as parasite plaques were evident in monolayers 48 hours after removal of Shield ( Supplemental Figure S2 ) . No plaques were present in monolayers infected with ddHAGCN5b ( E703G ) parasites that were maintained on Shield . As an additional control , ddHAGCN5b ( E703G ) parasites were treated with 1 . 0 µM pyrimethamine for 48 hours , which irreversibly kills the parasites as indicated by no plaque formation after removal of the drug ( Supplemental Figure S2 ) . These studies indicate that induction of the dominant-negative GCN5b attenuates the activity of the endogenous GCN5b complex , which results in stalled replication . However , as suggested by the genetic studies , complete ablation of GCN5b is not tolerated by tachyzoites . We considered that the replication arrest observed in ddHAGCN5b ( E703G ) parasites may be due to a reduction in histone acetylation , which in turn would lead to dysregulation of the transcriptome . To assess this possibility , we analyzed the acetylation level of individual lysine residues in histone H3 , the preferred substrate of GCN5 family KATs , purified from Shield- versus vehicle-treated ddHAGCN5b or ddHAGCN5b ( E703G ) expressing parasites . While total levels of H3 protein remain unaltered , acetylation of lysines K9 and K14 was specifically reduced in parasites expressing ddHAGCN5b ( E703G ) ( Fig . 3 ) . Interestingly , the acetylation status of H3K18 was not affected , which may be explained by the fact that GCN5a , which would not be attenuated by the expression of ddHAGCN5b ( E703G ) , has an exquisite affinity for this particular lysine residue on H3 [20] . These data indicate that the expression of ddHAGCN5b ( E703G ) protein diminishes acetylation on histone H3 . We reasoned that if the growth arrest of Shield-treated ddHAGCN5b ( E703G ) parasites was due to hypoacetylation of histones , then inhibition of lysine deacetylases ( KDACs ) should help restore replication . We therefore incubated ddHAGCN5b ( E703G ) parasites with 500 nM Shield in combination with increasing sublethal concentrations of either apicidin or TSA , two independent , broad-spectrum KDAC inhibitors . Parasite plaque assays were performed one week post-infection . Consistent with results seen in Fig . 1 , virtually no plaques were evident in infected monolayers that contained Shield with no KDAC inhibitor ( Fig . 4 ) . However , Shield-treated ddHAGCN5b ( E703G ) parasites were able to resume proliferation with the inclusion of either KDAC inhibitor in dose-dependent fashion ( Fig . 4 ) . To identify genes regulated by GCN5b , we performed a genome-wide ChIP-chip analysis on HA-tagged GCN5b expressing parasites . Immunoprecipitated DNA associated with GCN5b was identified following hybridization to custom Nimblegen microarrays that tile the entire Toxoplasma genome . Three replicates were performed . GCN5b was detected at 195 tachyzoite genes in all 3 replicates and 1090 genes in two ChIP-chip replicates , when 0 . 05 was used as the FDR for significant peaks ( Supplemental Dataset S1 ) . Although we observe variability in the location of GCN5b peaks , we detect a statistically significant overlap between the three ChIP-chip replicates that is not present when we randomize the peak positions for statistically significant peaks ( FDR<0 . 05; see methods for details ) . It was expected that GCN5b would localize to gene promoters , but GCN5b was also detected in gene bodies , with no detectable preference for gene bodies or intergenic regions ( Fig . 5 ) when significant peaks ( FDR<0 . 05 ) were statistically compared . Analyses that compared “promoters” ( H3K9ac; H3K4me3 ) , “active genes” ( H3K4me1 ) , and “centromeres” ( CenH3 ) using genome-wide epigenome mapping yielded similar results [26] , [27] . The distance between each GCN5b-associating site ( FDR<0 . 05 ) and the nearest transcription start site ( TSS ) was also calculated and plotted as a histogram ( Supplemental Figure S3 ) . Results show that enrichment of GCN5b occurs up- and down-stream , rather than at the TSSs . These data are consistent with recent studies showing a role for GCN5 in transcriptional elongation by promoting nucleosome eviction [28]–[30] . Overall , the ChIP-chip results establish that GCN5b is present within or near the loci of tachyzoite genes involved in a wide variety of cellular functions ( see Supplemental dataset S1 for breakdown of KEGG and GO classifications of the 195 genes identified in all 3 replicates ) . Many of the genes are annotated as hypothetical genes of unknown function in the ToxoDB . The high confidence genes coinciding with GCN5b localization are associated with gene expression and RNA processing , as well as metabolic genes , rather than genes linked to virulence . Although GCN5b did not show a detectable preference for functional regions of the genome , there was a significant association with genes containing introns versus those that do not . By hypergeometric test , each of the three ChIP-chip replicates has a statistically significant ( p<0 . 05 ) preference for intron-containing genes ( 5 , 989 ) versus those that do not ( 2 , 083 ) . While associated with intron-containing genes , GCN5b did not show a statistical association with introns versus exons . We then performed a more targeted approach to verify if select genes detected by the ChIP-chip study were modulated by GCN5b activity . Primers were designed to amplify selected mRNAs in ddHAGCN5b or ddHAGCN5b ( E703G ) parasites in the presence or absence of Shield . Six primer pairs were designed to amplify mRNAs from GCN5b-associated genes and another 6 primer pairs were designed to mRNAs of genes that were not detected in the GCN5b ChIP-chip ( Supplemental Table S1 ) . Virtually no changes in mRNA levels were detected in ddHAGCN5b parasites regardless of whether GCN5b was detected at the gene locus ( Table 1 ) . However , expression levels of mRNAs in ddHAGCN5b ( E703G ) parasites were clearly altered , suggesting a role for GCN5b in gene activation . All 6 genes detected in the GCN5b ChIP-chip had lowered mRNA levels in the ddHAGCN5b ( E703G ) parasites following Shield treatment ( Table 1 ) . Genes that were not detected in the GCN5b ChIP-chip experiments generally exhibited no significant difference in mRNA levels in the ddHAGCN5b ( E703G ) parasites following Shield treatment , however one gene showed higher mRNA levels and another showed lower mRNA levels ( Table 1 ) . The altered expression of these two genes may be due to sensitivity of the ChIP-chip or indirect effects impacting their mRNA levels . Nevertheless , the results show a general trend that is consistent with the decreased acetylation observed in ddHAGCN5b ( E703G ) parasites leading to decreased transcription of GCN5b-associated genes . This independent qRT-PCR analysis not only highlights the fidelity of the ChIP-chip dataset , but supports the idea that the dysregulation of gene expression induced by the accumulation of ddHAGCN5b ( E703G ) protein contributes to the arrest in parasite replication . As GCN5 KATs lack DNA-binding domains , the complex must be recruited to target genes by virtue of a DNA-bound transcription factor , e . g . GCN4 in Saccharomyces cerevisiae or its human counterpart , ATF4 [31] . However , this well-conserved class of transcription factor is not present in Apicomplexa . We therefore performed biochemical purifications of the GCN5b complex from nuclear fractions of intracellular tachyzoites to define the KAT's interactome . Two independent co-immunoprecipitations were performed using RH parasites stably expressing HA-tagged GCN5b . Supplemental dataset S2 is a complete list of proteins identified in each pull down experiment . As expected , GCN5b itself as well as the known interacting co-activator protein , ADA2-A [20] , were identified in each purification . Supporting the idea that DNA-binding proteins would recruit GCN5b to specific gene sites , four AP2 factors were identified in association with GCN5b ( AP2IX-7 , AP2X-8 , AP2XI-2 , and AP2XII-4 ) . Another GCN5b-interacting protein with a probable DNA-binding domain is the AT-hook protein ( TGME49_109250 ) . Consistent with its frequent localization on gene bodies , GCN5b was associated with RTF1 , LEO1 , and CTR9 , three components of the PAF ( polymerase associated factor ) complex associated with mRNA elongation [32] . GCN5 activities are typically coordinated with those of SWI/SNF complexes [33] , and we detected two distinct SWI/SNF ATPases ( TGME49_120300 and TGME49_078440 ) associated with GCN5b . The finding that plant-like AP2 factors may partner with KAT complexes to alter transcription is of particular relevance . Associations between GCN5 and AP2 proteins have yet to be demonstrated for any species , including plants . To validate that GCN5b and these AP2 factors reside in the same complex , we endogenously tagged AP2IX-7 and AP2X-8 with a C-terminal 3×HA tag ( Fig . 6A ) . Reciprocal co-immunoprecipitation of each AP2 factor pulled down GCN5b and many of the other proteins seen in the previous GCN5b IPs ( Supplemental dataset S2 ) . We refer to proteins that were pulled down consistently in all three purifications as the GCN5b/AP2 “core complex” ( Table 2 ) . To further confirm the association of GCN5b with these AP2 factors , we performed Western blots for GCN5b in AP2IX-7HA and AP2X-8HA immunoprecipitates . We also endogenously tagged AP2X-5 , an AP2 factor that was not seen in the GCN5b IPs , to serve as a control . As shown in Fig . 6B , GCN5b was detected in a Western blot of HA-immunoprecipitated AP2IX-7HA and AP2X-8HA , but not AP2X-5HA . Collectively , these results demonstrate specific interactions between GCN5b , AP2IX-7 , and AP2X-8 . Whether these three proteins interact directly , or through contact with other proteins in the complex , remains to be elucidated . The GCN5b complex associates with components of the TFIID transcription complex ( TAF1/TAF250 and TAF5 ) , affirming its role in facilitating transcriptional activation . Intriguingly , a surprising number of proteins associated with pre-mRNA splicing were also identified in the GCN5b complex ( Table 2 ) . In keeping with prior observations that Toxoplasma lacks homologues of most proteins found in the GCN5 complexes in other species [20] , the majority of proteins in the GCN5b complex are novel interacting partners , with four being hypothetical proteins with unknown function ( Table 2 ) . Twelve of the 20 subunits compromising the GCN5b core complex were detected as acetylated in a previous study ( shaded gray in Table 2 ) . Up to four proteins in the GCN5b complex , including GCN5b itself as well as TAF1/TAF250 [34] , contain bromodomains that recognize acetylated lysine residues [35] ( Supplemental dataset S2 ) . The high degree of acetylated subunits and the presence of multiple bromodomain modules in the complex supports the idea that they participate in intracomplex protein-protein interactions through the binding of acetylated lysines [36] .
The objective of this study was to gain a better understanding of the role played by the second GCN5 KAT in Toxoplasma parasites through biochemical purification of associating proteins , ChIP-chip analyses , and the generation of mutants . Our findings reveal that GCN5b interacts with a large number of novel proteins and is enriched at genes involved in transcription , translation , and metabolism . Consistent with previous failures to knockout GCN5b , inducible expression of a catalytically dead version acted like a dominant-negative mutant and displayed replicative arrest , supporting that GCN5b is essential in tachyzoites . The GCN5b interactome is unique and includes components possessing plant-like AP2 DNA-binding domains , thereby providing a probable mechanism by which the complex can be recruited to target promoters . A high-throughput yeast two-hybrid approach previously identified the single Plasmodium falciparum GCN5 to be the most interconnected protein in the parasite integrating chromatin modification , transcriptional regulation , mRNA stability , and ubiquitination [23] . The discovery of AP2 factors in the GCN5b interactome prompted us to re-examine the PfGCN5 data , since this analysis was done prior to the identification of AP2 domains . PfGCN5 interacts with two predicted AP2 factors , PF3D7_1007700 and PF3D7_0802100 , but they do not have similar DNA-binding domains or other conserved domains that might suggest orthology to the TgAP2s interacting with GCN5b . Interestingly , no other PfGCN5-interacting proteins cross-reference to the GCN5b interacting proteins . This may be due to the different techniques that were used ( yeast two-hybrid using only the N-terminal extension of PfGCN5 as bait versus biochemical purification of full-length GCN5b ) , or could indicate that GCN5 complexes between apicomplexan species have significantly diverged . In support of this , the lengthy N-terminal extensions between PfGCN5 and GCN5b share no obvious sequence homology . Another possibility is that the PfGCN5 complex may be more analogous to that of GCN5a , whose interactome has yet to be resolved . It is well-established that histone acetylation complexes generally aggregate at gene promoters , but the considerable proportion of GCN5b located within gene bodies is not without precedent . Govind et al . reported that yeast GCN5 plays a role in transcriptional elongation by promoting histone eviction [28] . GCN5 was also found to be predominantly localized to coding regions of highly transcribed genes in fission yeast , where it interplays with an HDAC to modulate H3K14-Ac levels and transcriptional elongation [30] . Interestingly , an Spt6 homologue was purified with the GCN5b complex , a protein that has been implicated in transcription elongation through binding of RNA polymerase II [37] . In other species , GCN5 has also been shown to play a role in co-transcriptional splicing [38] . The disproportionate number of pre-mRNA splicing components that we identified in the GCN5b complex might be suggestive of additional roles for GCN5b in splicing . Although it was not preferentially associated with introns versus exons , GCN5b preferentially associated with genes containing introns in our ChIP-chip analysis , providing further evidence that GCN5b macromolecular complexes may be involved in the modulating splicing . Recently , a novel Toxoplasma G1 cell cycle mutant was found to map to an RRM protein that interacts with the splicesome [39] . Further studies are required to delineate the interaction of GCN5b with the splicesome and whether the dominant negative effects of ddHAGCN5b ( E703G ) affect Toxoplasma splicing . It is probable that GCN5b forms multiple complexes and contributes to an assortment of cell biological functions , as seen in other species [40] . The GCN5b-interacting proteins described in this report were isolated from intracellular , replicating tachyzoites . It is possible that GCN5b partners with different components in extracellular tachyzoites or when parasites are subjected to different stress conditions . While our data clearly shows that histone acetylation is decreased in the dominant-negative clone , we cannot conclude that the arrest in parasite replication is solely due to dysregulation of gene expression . Complicating matters is the recent observation that lysine acetylation is widespread on hundreds of non-histone proteins , many of which reside in the parasite nucleus [36] . It is conceivable that GCN5b has non-histone substrates and decreased efficiency in acetylation of those substrates contributes to the replication arrest in the dominant-negative parasites . Toxoplasma is unique as a lower eukaryote to possess a pair of GCN5 KATs . Studies to date suggest that these two GCN5 KATs have non-redundant functions in tachyzoites . GCN5b was not sufficient to compensate for a lack of GCN5a , which is required for adequate responses to alkaline stress [5] . Similarly , GCN5a is not able to compensate when the function of GCN5b is attenuated through expression of a dominant-negative version . It has been previously reported that inhibition of parasite histone modifying enzymes is deleterious to protozoan pathogens [41] . Our findings suggest that pharmacological inhibition of GCN5b or disruption of the GCN5b complex may be novel avenues for therapy against toxoplasmosis .
All Toxoplasma lines ( RH strain ) were propagated in monolayers of human foreskin fibroblasts ( HFFs ) in Dulbecco modified Eagle's medium ( DMEM ) supplemented with 1% heat-inactivated fetal bovine serum ( Gibco/Invitrogen ) . Cultures were maintained in a humidified , 37°C incubator with 5% CO2 . To isolate parasites for experiments , intracellular tachyzoites were harvested through syringe passage of infected host cells followed by filtration through a 3 micron filter [42] . Where designated , Shield-1 ( CheminPharma ) , dissolved in ethanol , was added to culture medium . For some experiments , KDAC inhibitors were added to the culture: TSA ( Sigma #T8552 ) or apicidin ( Calbiochem #178276 ) . Plasmids were introduced into Toxoplasma via electroporation , subjected to drug selection ( 20 µM chloramphenicol or 1 µM pyrimethamine ) , and cloned by limiting dilution as previously described [42] . Parasite replication assays were performed as previously described [5] , [43] . Immunofluorescence assays ( IFA ) were performed as previously described [21] . Briefly , HFF monolayers grown on coverslips were inoculated with the designated parasite line , sometimes containing Shield-1 or EtOH vehicle . After removal of culture medium , infected HFFs were fixed in 3% paraformaldehyde for 10 min and then were permeabilized with 0 . 3% Triton X-100 for 10 min . For visualization of HA-fusion proteins , rat monoclonal anti-HA primary antibody ( Roche #11867423001 ) was applied at 1∶2 , 000 followed by goat anti-rat Alexa Fluor 488 secondary antibody at 1∶2 , 000 ( Invitrogen #A-11006 ) . Nuclei were co-stained with 4′ , 6-diamidino-2-phenylindole ( DAPI ) . Samples were visualized using a Leica DMLB fluorescent microscope . Western blots to monitor Shield-based protein stabilization were performed by resolving 50 µg parasite lysate on a 4–12% Tris-acetate polyacrylamide gradient gel ( Invitrogen ) and probing with 1∶2 , 000 rat anti-HA monoclonal antibody as the primary antibody ( Roche #11867423001 ) . Analysis of histones and tubulin used the following primary antibodies: rabbit polyclonal anti-H3 antibody ( Abcam #ab1791 , 1∶2 , 000 ) , rabbit polyclonal antibodies against acetyl H3K9 ( Millipore #06-942 , 1∶2 , 000 ) , acetyl H3K14 ( Millipore #06-911 , 1∶2 , 000 ) , acetyl H3K18 ( Abcam #ab1191 , 1∶2 , 000 ) , and rabbit polyclonal antibody against Toxoplasma β-tubulin ( kindly provided by Dr . David Sibley , 1∶5 , 000 ) . Anti-rat or anti-rabbit antibodies conjugated with horseradish peroxidase ( GE Healthcare ) were used as secondary antibodies at 1∶5 , 000 . The blots were visualized using Chemiluminescence Western Blot Substrate ( Pierce ) . The GCN5b complex was purified from RHΔhxgprt parasites stably transfected to ectopically express an Nt HAmyc-tagged form of full-length GCN5b driven by the Toxoplasma tubulin ( TUB1 ) promoter ( described in [21] ) . Two experiments were performed following an initial co-IP done by Dr . Ali Hakimi . AP2 factor complexes were independently purified from parasites expressing AP2X-8 and AP2IX-7 endogenously tagged with HA ( see above ) . Parental RHΔhxgprt ( for GCN5b line ) and RHΔku80 ( for AP2 lines ) were run in parallel as negative controls . Large-scale tachyzoite cultures were grown in monolayers of HFF cells at 37°C for 42 hours post-infection . Prior to egress , culture medium was removed and the cell monolayers were washed once with PBS , scraped into cold PBS and then collected by centrifugation at 4°C for 10 min at 700×g . The cell pellets were resuspended in 25 ml cold PBS , and sequentially passed through 20/23/25-gauge needles in a 30 ml syringe to release intracellular parasites from the host cells . To prepare parasite nuclear extracts , 3×109 parasites were incubated 5 min on ice in lysis buffer A ( 0 . 1% [v/v] NP-40 , 10 mM HEPES pH 7 . 4 , 10 mM KCl , 10% [v/v] glycerol , 20 mM sodium butyrate , plus protease inhibitors ) , and the nuclei were pelleted by centrifugation 6 , 000×g for 8 min at 4°C . The parasite nuclei were then incubated 30 min at 4°C in lysis buffer B ( 0 . 1% [v/v] NP-40 , 10 mM HEPES pH 7 . 4 , 400 mM KCl , 10% [v/v] glycerol , 20 mM sodium butyrate , plus protease inhibitors ) with rotation , and subjected to five freeze-thaw cycles followed by vortexing for 1 min at 4°C before freezing . The nuclear extracts were clarified by centrifugation at 12 , 000×g for 30 min at 4°C . The mixture of the clarified nuclear extracts ( 1 part ) with lysis buffer A ( 2 parts ) was used for co-immunoprecipitation . Nuclear extracts were incubated with mouse monoclonal anti-HA-tag magnetic beads ( μMACS Anti-HA Microbeads; Miltenyi Biotec ) overnight at 4°C with rotation . After the beads were washed 4 times with cold wash buffer 1 ( 150 mM NaCl , 1% NP-40 , 0 . 5% sodium deoxycholate , 0 . 1% SDS , 50 mM Tris-HCl pH 8 . 0 ) and once with wash buffer 2 ( 20 mM Tris-HCl pH 7 . 5 ) by using μ Column ( pre-washed with buffer containing 0 . 1% [v/v] NP-40 , 10 mM HEPES pH 7 . 4 , 150 mM KCl , plus protease inhibitors ) in the magnetic field of the separator , the bound proteins were eluted from the magnetic beads by applying 50 µl of Laemmli's sample buffer ( pre-heated to 95°C ) to the column . Eluted proteins were separated by SDS-PAGE ( Any kD™ precast polyacrylamide gel; Bio-Rad ) , and stained with Coomassie blue ( GelCode Blue Stain Reagent; Pierce ) . The entire length of each sample lane was systematically cut into 24 slices and the gel slices were maintained in MilliQ water until trypsin digestion . Proteins from a Coomassie-stained gel were reduced then alkylated with TCEP and iodoacetamide prior to digestion with trypsin . Trypsin ( Sequencing grade , Promega ) digestion was carried out for 1 hour at 50°C using 10 ng/ul solution in 25 mM ammonium bicarbonate/0 . 1%ProteaseMax ( Promega ) . The resulting digest was then diluted with 2%Acetonitrile/2%TFA prior to LC-MS/MS analysis . Nanospray LC-MS/MS was performed on a LTQ linear ion trap mass spectrometer ( LTQ , Thermo , San Jose , CA ) interfaced with a Rapid Separation LC3000 system ( Dionex Corporation , Sunnyvale , CA ) . Thirty-five µL of the sample was loaded on an Acclaim PepMap C18 Nanotrap column ( 5 µm , 100 Å , /100 µm i . d . ×2 cm ) from the autosampler with a 50 µl sample loop with the loading buffer ( 2% Acetonitrile/water+0 . 1% trifluoroacetic acid ) at a flow rate of 8 µl/min . After 15 minutes , the trap column was switched in line with the Acclaim PepMap RSLC C18 column ( 2 µm , 100 Å , 75 µm i . d . ×25 cm ) ( Dionex Corp ) . The peptides are eluted with gradient separation using mobile phase A ( 2% Acetonitrile/water +0 . 1% formic acid ) and mobile phase B ( 80% acetonitrile/water+0 . 1% formic acid ) . Solvent B was increased from 2 to 35% over 40 min , increased to 90% over a 5-min period and held at 90% for 10 min at a flow rate of 300 nL/min . The 10 most intense ions with charge state from +2 to +4 determined from an initial survey scan from 300–1600 m/z , were selected for fragmentation ( MS/MS ) . MS/MS was performed using an isolation width of 2 m/z; normalized collision energy of 35%; activation time of 30 ms and a minimum signal intensity of 10 , 000 counts . The dynamic exclusion option is enabled . Once a certain ion is selected once for MS/MS in 7 sec , this ion is excluded from being selected again for a period of 15 sec . Mgf files were created from the raw LTQ mass spectrometer LC-MS/MS data using Proteome Discoverer 1 . 2 ( ThermoScientific ) . The created mgf files were used to search the Toxo_Human Combined database [45] using the in-house Mascot Protein Search engine ( Matrix Science ) with the following parameters: trypsin 2 missed cleavages; fixed modification of carbamidomethylation ( Cys ) ; variable modifications of deamidation ( Asn and Gln ) , pyro-glu ( Glu and Gln ) and oxidation ( Met ) ; monoisotopic masses; peptide mass tolerance of 2 Da; product ion mass tolerance of 0 . 6 Da . The final list of identified proteins was generated by Scaffold 3 . 5 . 1 ( Proteome Software ) with following filters: 99% minimum protein probability , minimum number peptides of 2 and 95% peptide probability . All searches were performed against a decoy database and yielded no hits ( i . e . a FDR of 0% ) . For final presentation of data , proteins appearing in the negative control parental lines and human proteins were treated as non-specific contaminants . Parasites from AP2IX-7HA , AP2X-5HA , AP2X-8HA , and parental RHΔku80 lines were harvested in lysis buffer ( 150 mM NaCl , 50 mM TrisCl pH 7 . 4 , 0 . 1% NP-40 ) with 1× protein inhibitor cocktail ( Sigma ) and 1 mM PMSF . The lysates were then sonicated and centrifuged to remove the insoluble fraction . Immunoprecipitations were performed using anti-HA high affinity matrix ( Roche ) and 300 µg total parasite protein . After overnight incubation at 4°C , the beads were washed 3× in lysis buffer and treated at 95°C for 10 minutes to elute proteins . Eluted proteins were resolved by SDS-PAGE and analyzed by Western blot with HA ( 1∶2 , 000 ) , GCN5b ( 1∶500 ) , or β-tubulin ( 1∶1 , 000 ) antibodies . ChIP was performed as described [26] with some modifications . Briefly , intracellular tachyzoites grown in HFF cell monolayers for 42 hours were cross-linked for 10 min with 1% formaldehyde in PBS and quenched with 125 mM glycine for 5 min at room temperature . The cell monolayers were washed with PBS , scraped into PBS and then collected by centrifugation at 4°C for 10 min at 700×g . The cell pellets were resuspended in cold PBS , and sequentially passed through 20/23/25-gauge needles in a syringe to release intracellular parasites from the host cells . The parasites were then centrifuged at 4°C for 15 min at 700×g , resuspended in lysis buffer ( 50 mM HEPES , pH 7 . 5 , 150 mM NaCl , 1% NP-40 , 0 . 1% SDS , 0 . 1% sodium deoxycholate , 1 mM EDTA , plus protease inhibitors ) , and the chromatin was sheared by sonication yielding DNA fragments of 500–1 , 000 bp . The chromatin was clarified by centrifugation at 12 , 000×g for 10 min at 4°C; 10% of the clarified chromatin was saved as the input sample and the remaining 90% was used for immunoprecipitation . Immunoprecipitations were performed with mouse monoclonal anti-HA-tag magnetic beads ( μMACS Anti-HA Microbeads; Miltenyi Biotec ) overnight at 4°C with rotation , washed extensively , and the GCN5b chromatin was eluted with 1% SDS in TE buffer . Both input and GCN5b chromatin were reverse cross-linked by incubation overnight at 65°C and purified using the Qiagen MinElute PCR purification kit . Purified DNA was amplified by using GenomePlex Complete Whole Genome Amplification kit ( WGA2; Sigma ) and amplified DNA was further purified using the Qiagen MinElute PCR purification kit . The genome-wide chip was designed using the Nimblegen isothermal protocol design based upon Release 4 . 1 of the Toxoplasma ME49 genome with a total of 732 , 672 toxo-specific probes with average spacing of probes every 86 bp using an estimated genome size of 63 Mb . Chip design is available under GEO identifier GPL15563 and GPL15564 and the data is supplied in supplemental dataset S1 . H3K4me3 , H3K9ac , H3K4me1 , H3K9me2 , and CenH3 data are also accessible at www . toxodb . org . Hybridization to arrays was performed using standard Nimblegen protocols at Nimblegen or in the Albert Einstein College of Medicine Epigenomics Facility as described in [26] with an input DNA and experimental DNA hybridization performed simultaneously for each experiment on the same chip . ChIP-chip analysis was performed three times . Microarrays were scanned once with excitation at 635 nm for the immunoprecipitated DNA and 532 nm for total genomic DNA . The ratio of probe intensities is calculated and then log-base 2 transformed . After this , the bi-weight mean is calculated for all of the probe ratios . This mean is then subtracted from each of the probe ratios in order to scale the data . GCN5b localization within the genome was determined using NimbleScan's peak calling algorithm . The algorithm employs a sliding window to look for consecutive probes with high ratios . The threshold for high ratio is percentage of the theoretical maximum ratio value ( mean plus six standard deviations ) . The threshold steps down from 90% to 15% to calculate the significance of the peak . The false discovery rate ( FDR ) was estimated by randomly permuting probe ratio values . Positions of peaks with a false-discovery rate ( FDR ) <0 . 05 within each chromosome were compared between biological replicates using custom Perl scripts . Reproducibility of GCN5b peaks was judged using the “makeVennDiagram” function from the ChIPpeakAnno package of the Bioconductor project [46] . We first selected those peaks that have an FDR below 0 . 05 . We then identified peaks from different replicates that overlap by 50 nucleotides or more . Finally , we used the hypergeometric distribution to calculate the significance of the overlap . The probability of finding the number of overlapping peaks by chance was calculated as: 1 vs 2 7 . 33 E-105; 2 vs 3 2 . 56 E-19; 1 vs 3 0 . 0005 . Association between GCN5b and specific genes was established by using the “findOverlappingPeaks” function from the ChIPpeakAnno package of the Bioconductor project . We used the gene annotations from ToxoDB , release 6 . 1 , ME49 strain . Gene associations for each replicate were determined independently . We used two different randomization strategies to test the reproducibility of our GCN5b ChIP . First , we reassigned the starting position of each of our significant peaks ( FDR< = 0 . 05 ) to a random probe from the microarray while keeping the peak widths the same . We used the hypergeometric distribution to calculate the significance of the overlap with the randomly selected peaks in each replicate . The probability of chance overlap in each of these cases was close to one . In a second test , we randomly selected an equal number of peaks from each replicate , regardless of FDR . These peaks were reanalyzed using the hypergeometric distribution to calculate the significance of the overlap with the peaks in each replicate . These randomizations also resulted in a probability of close to one that the overlap was due to chance . Both randomization tests were performed 1 , 000 times . For examination of GCN5b peaks with introns or genes with introns , gene models and annotations from www . toxodb . org V6 were used . Significance of overlaps was determined by hypergeometric test for intron-containing genes ( 5 , 989 ) versus intronless genes ( 2 , 083 ) . To further test this , we selected random peaks from each experiment rather than only the significant peaks ( FDR<0 . 5% ) . We repeated the same test with the randomized peaks . Using the same methods , we also tested for an association between GCN5b binding with introns or exons . We also tested for an association with active promoters ( as defined by dual marking with H3K9ac and H3K4me3 [26] ) , active coding regions ( defined by the H3K4me1 mark [26] ) , or centromeres [27] and measured the distribution of GCN5b peaks from the inferred transcription start site using data from Yamagishi et al . [47] . 1 . 0 µg of total RNA purified from intracellular parasites was transcribed into cDNA using Omniscript reverse transcriptase with oligo-dT primers according to the manufacturer's protocol ( Qiagen ) . qRT-PCR was performed in 25 µl volume reactions containing SYBR Green PCR Master Mix ( Applied Biosystems ) , 0 . 5 mM of each forward and reverse primer ( Supplemental Table S1 ) , and 1 . 0 µl of a 1∶10 dilution of cDNA . Target genes were amplified using the 7500 Real-time PCR system and analyzed with relative quantification software ( 7500 software v2 . 0 . 1 , Applied Biosystems ) . The ratio of mRNA levels in Shield-treated parasites versus EtOH-treated parasites was calculated using Toxoplasma β-tubulin as an internal control for normalization ( GCN5b was not detected at β–tubulin in any of the three ChIP-chip experiments ) . Reactions were performed in triplicate and Student's t-test was applied to RT-PCR data . | Toxoplasma gondii is a protozoan parasite that causes significant opportunistic infection in AIDS and other immunocompromised patients . Acute episodes of toxoplasmosis stem from tissue destruction caused by the rapidly growing form of the parasite , the tachyzoite . In this study , we identify a lysine acetyltransferase ( KAT ) enzyme called GCN5b that is an essential driver of tachyzoite proliferation . Our studies show that GCN5b is present at a wide variety of parasite genes and that expression of defective GCN5b compromises gene expression through its diminished ability to acetylate histone proteins . We also identified the likely mechanism by which GCN5b is recruited to target genes by co-purifying this KAT with plant-like AP2-domain proteins , a subset of which function as DNA-binding transcription factors in Apicomplexa . Our findings demonstrate that KATs play a critical role in parasite replication , which leads to tissue destruction and acute disease in the host . Parasite KAT enzyme complexes may therefore serve as attractive targets for future drug development . | [
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] | [] | 2014 | Lysine Acetyltransferase GCN5b Interacts with AP2 Factors and Is Required for Toxoplasma gondii Proliferation |
The currently used anthelmintic drugs , in single oral application , have low efficacy against Trichuris trichiura infection , and hence novel anthelmintic drugs are needed . Nitazoxanide has been suggested as potential drug candidate . The efficacy and safety of a single oral dose of nitazoxanide ( 1 , 000 mg ) , or albendazole ( 400 mg ) , and a nitazoxanide-albendazole combination ( 1 , 000 mg–400 mg ) , with each drug administered separately on two consecutive days , were assessed in a double-blind , randomized , placebo-controlled trial in two schools on Pemba , Tanzania . Cure and egg reduction rates were calculated by per-protocol analysis and by available case analysis . Adverse events were assessed and graded before treatment and four times after treatment . Complete data for the per-protocol analysis were available from 533 T . trichiura-positive children . Cure rates against T . trichiura were low regardless of the treatment ( nitazoxanide-albendazole , 16 . 0%; albendazole , 14 . 5%; and nitazoxanide , 6 . 6% ) . Egg reduction rates were 54 . 9% for the nitazoxanide-albendazole combination , 45 . 6% for single albendazole , and 13 . 4% for single nitazoxanide . Similar cure and egg reduction rates were calculated using the available case analysis . Children receiving nitazoxanide had significantly more adverse events compared to placebo recipients . Most of the adverse events were mild and had resolved within 24 hours posttreatment . Nitazoxanide shows no effect on T . trichiura infection . The low efficacy of albendazole against T . trichiura in the current setting characterized by high anthelmintic drug pressure is confirmed . There is a pressing need to develop new anthelmintics against trichuriasis . Controlled-Trials . com ISRCTN08336605
Infections with the soil-transmitted helminths , Ascaris lumbricoides , Trichuris trichiura , and the two hookworm species Ancylostoma duodenale and Necator americanus , are the most common infections of humans , causing an estimated global burden of 39 million disability-adjusted life years lost ( DALYs ) [1]–[3] . Globally more than 5 billion people are at risk and at least 1 billion people are currently infected with one or several of these nematodes [1] , [3] , [4] . On Pemba , soil-transmitted helminth infections remain of considerable public health importance with particularly high prevalences observed for T . trichiura and hookworm [5] . Preventive chemotherapy targeting at-risk communities ( i . e . , school-aged children ) is in place in many countries . These programs aim at morbidity control , and hence intensity of infection is kept below a threshold of disease [6] . The two benzimidazoles , albendazole and mebendazole , as well as pyrantel pamoate and levamisole are recommended drugs against infections with soil-transmitted helminths [7] , [8] . These drugs have been widely used as they were developed and put on the market between 1966 ( pyrantel pamoate ) and 1980 ( albendazole ) [9] . The four drugs exhibit distinct differences in their therapeutic profile , with the exception of considerably high efficacy of all drugs against A . lumbricoides . For the treatment of hookworm infection , only albendazole achieves satisfactory cure rates when administered as a single oral dose . Of particular concern is the situation for T . trichiura; single doses of mebendazole show the highest cure rates , but these are usually low or only moderate ( <50% ) [10] . The pressing need for developing new anthelmintic drugs , particularly for T . trichiura , cannot be emphasized enough . In addition , the therapeutically useful life span of these drugs is endangered should resistance develop and start to spread [11]–[13] . The antiprotozoal drug nitazoxanide , which is marketed for the treatment of intestinal parasitic infections ( i . e . , Cryptosporidium parvum and Giardia intestinalis ) has been reported to also have trichuricidal properties [14] , [15] . Silbereisen and colleagues recently showed that nitazoxanide is highly active against Trichuris muris in vitro [16] . In clinical trials high cure rates were reported against T . trichiura as well as A . lumbricoides and hookworm , following multiple doses ( 200 mg for children aged below 12 years and 500 mg for patients aged 12 years and above , twice daily for 3 days ) of nitazoxanide [17]–[19] . Hence , nitazoxanide has been listed as a potential drug candidate for human-soil transmitted helminthiasis and further research was suggested [20] . We report the findings of a randomized , double-blind , placebo-controlled trial , which specifically assessed the efficacy and safety of nitazoxanide given as a single dose ( 1 , 000 mg ) in the treatment of T . trichiura in school-aged children on Pemba , Tanzania . The standard treatment with albendazole ( 400 mg ) served as a benchmark . In addition , one group of children was given nitazoxanide ( 1 , 000 mg ) on the first day and albendazole ( 400 mg ) on the following day , in order to evaluate if an enhanced efficacy would be observed following this combination chemotherapy . A fourth group of children received placebo ( the treatment groups are summarized in Figure 1 ) . Since A . lumbricoides and hookworm coexist in the current setting , outcomes on these nematodes are also reported .
Ethical clearance was obtained from the ethics committee of Basel , Switzerland ( EKBB , reference no . 225/10 ) and from the Ministry of Health and Social Welfare in Zanzibar ( ZAMREC , reference no . 0001/010 ) . The study is registered at Current Controlled Trials ( ISRCTN08336605 ) . Written informed consent was acquired from the children's parents or legal guardians to participate in this trial . Children assented orally . It was emphasized that study participation would be voluntary and withdrawal possible at any time . Our trial was carried out in June and July 2011 on Pemba , one of the major islands in the Zanzibar archipelago , which belongs to Tanzania . The schools of Wawi ( geographical coordinates; 5°15′22″S latitude , 39°47′28″E longitude ) and Al-Sadik ( 5°15′42″S , 39°48′25″E ) , both located less than 10 km from Chake Chake , the main town of Pemba ( 5°14′45″S , 39°45′00″E , ∼22 , 000 inhabitants ) , were selected . In the school year 2010/2011 , a total of 1 , 100 children were registered in Wawi and 437 in Al-Sadik . Both schools were easily accessible by car from the Public Health Laboratory–Ivo de Carneri ( PHL–IdC ) in Chake Chake . To determine the sample size , we assumed a cure rate of 28% for single oral albendazole against T . trichiura based on data from a meta-analysis [21] . The efficacy of single oral nitazoxanide against T . trichiura is not known . Therefore we assumed that it would be at least similar to that of albendazole ( 20–30% ) . Moreover , we hypothesized that an albendazole-nitazoxanide combination would achieve a considerably higher cure rate ( 50% ) . Monte Carlo simulations ( imperfect test with a sensitivity of 90% ) computed a sample size of 95 T . trichiura-infected individuals in each arm to detect a difference of single medication treatments versus both , the placebo group and the albendazole-nitazoxanide group at a significance level of 5% with 80% power . Allowing for drop outs and assuming an overall T . trichiura prevalence of 80% , we targeted approximately 125 children per treatment arm for the baseline screening . Before the onset of the study , the headmasters of Wawi and Al-Sadik were asked for permission to carry out the trial at their schools . The parents of the children were invited to the schools , so that they could be explained the purpose and procedures of the study , including potential risks and benefits . Questions could be asked in a discussion round and clarification was given to parents . At the first day of enrolment , all children attending standards one to five received an empty stool container together with a consent form; both labeled with unique identification numbers ( ID ) . After recording their name , sex , age , and school grade , children were invited to return the container with a fresh sample of the next morning stool together with the signed consent form . Filled stool containers and signed consent forms were collected from the children and a new empty container was handed out for collection of a second stool sample on the following day . All children who returned a signed informed consent and had two stool samples were assigned to one of the four treatment groups , regardless of their helminths infection status . Before drug administration , all children were examined by a physician . Exclusion criteria were: ( i ) presence of any abnormal medical condition , judged by the study physician; ( ii ) history of acute or severe chronic disease ( cancer , diabetes , chronic heart , liver , or renal disease ) ; and ( iii ) recent use of anthelmintic drugs ( within past 4 weeks ) . Additionally , the weight and height of all children were measured and if there was any indication of fever , axillary temperature was taken using a digital thermometer . Three weeks posttreatment , children were asked again for two stool samples collected over consecutive days to determine the efficacy of the different treatments . At the end of the study , all children who were still infected with soil-transmitted helminths were offered a dose of albendazole ( 400 mg ) following national guidelines [7] , [8] . An independent statistician created a randomization code assigning to each ID a number from 1–4 , representing the four treatment arms: ( i ) nitazoxanide ( 1 , 000 mg ) plus albendazole ( 400 mg ) ; ( ii ) nitazoxanide-matching placebo plus albendazole ( 400 mg ) ; ( iii ) nitazoxanide ( 1 , 000 mg ) plus albendazole-matching placebo; and ( iv ) two placebos . For blinding purposes , the tablets were packed before treatment into small plastic bags , labeled with the unique IDs . Nitazoxanide ( Alinia® ) and albendazole ( Zentel® ) tablets were the products of Romark and GlaxoSmithKline , respectively . Placebos were produced at the Department of Pharmaceutical Sciences , University of Basel by one of the authors ( R . A . ) . During the trial all drugs were stored at room temperature , not exceeding 25°C . Since interactions between nitazoxanide and albendazole have not been studied before , the two drugs were administered on two consecutive days . Hence , on the first day of treatment , children received two tablets of nitazoxanide ( 500 mg each ) or two nitazoxanide-matching placebos . On the second day , children received one tablet of albendazole ( 400 mg ) or an albendazole-matching placebo . Since the trial was double-blinded , neither the child , nor the person giving the treatment knew to which treatment arm the child was allocated to . Before treatment , children were asked if they suffer from any adverse events . Drugs were then administered with a cup of water and each child received a small snack . Three hours after treatment , adverse events were actively assessed by interviewing each child . On the following day , before receiving the second treatment , adverse events were investigated again . The same procedures were repeated 3 and 24 hours after the second treatment . Stool samples were transferred to PHL–IdC . Duplicate Kato-Katz thick smears , using 41 . 7 mg templates , were prepared from each stool sample [22] . Kato-Katz thick smears were examined under a microscope by experienced laboratory technicians within 20–40 min after preparation , as recommended to avoid over clearing of hookworm eggs [23] . All hookworm eggs were counted . Subsequently , the slides were re-examined for T . trichiura and A . lumbricoides , with parasite eggs counted and recorded separately . To ensure high quality of the diagnosis , 10% of the slides , selected at random , were re-examined . In cases of discordant results , slides were read a third time and results discussed until agreement was reached . Parasitological data and reported adverse events were double entered into an Excel spreadsheet and cross-checked . In case of discrepancy , the original files were consulted to correct the data entry . All statistical analyses were performed with Stata 10 . 1 software ( StataCorp ) . Cure rates were calculated as the proportion of egg-positive children at baseline , who became egg-negative after treatment . Egg counts from the four Kato-Katz thick smears were added up for each species and multiplied by a factor 6 and expressed as eggs per gram of stool ( EPG ) . Infection intensity was classified using pre-defined cut-offs by the World Health Organization ( WHO; T . trichiura light , 1–999 EPG; moderate , 1 , 000–9 , 999; and heavy , ≥10 , 000 ) [24] . Differences among treatment arms concerning cure rates and observed adverse events were analyzed with logistic regressions . Geometric mean egg counts were calculated for the different treatment arms before and after drug intervention to calculate the respective egg reduction rates ( ERR ) . We also calculated the average dose ( mg/kg ) of each drug that the children received per treatment arm and analyzed with a logistic regression if the dose of treatment had an influence on the cure rates . We used two different types of analyses: ( i ) per-protocol analysis , including only those children who had complete data records ( quadruplicate Kato-Katz results before and after treatment and being treated ) ; and ( ii ) available case analysis ( which is sometimes erroneously referred to as an intention-to-treat analysis [25] ) based on the treatment intent , hence analyzing data from all individuals who were assigned to one of the four treatment arms and had primary outcome data . A greater emphasis is given in our manuscript on the per-protocol analysis ( available case results summarized in Table S1 ) since a bias might have been introduced in the available case analysis given that children absent on the first treatment day ( nitazoxanide ) were shifted to a later starting treatment period , while this was not possible for children missing the second treatment day ( albendazole ) .
From the 928 children invited to participate in the study , 172 refused to participate or did not return a signed consent form . Another 47 children failed to provide two stool samples and from seven children the IDs on the stool samples were lost and hence they had to be excluded from the trial . One child received anthelmintic treatment less than 4 weeks before the onset of our trial and was therefore excluded . The remaining 701 children ( 549 from Wawi and 152 from Al-Sadik ) were randomly assigned to one of the four treatment arms independently of their parasitological status . Of these , 124 were T . trichiura-negative and therefore excluded from the final analysis ( Figure 1 ) . Fifteen children missed the first treatment , and 14 children were absent on the second day of treatment . At follow-up , 13 children provided no or only a single stool sample and two children were excluded due to other reasons . In total 44 T . trichiura-infected children were lost during treatment and follow-up . Hence , 533 children were included in the per-protocol analysis . The loss of participants was distributed equally over the different treatment arms; the double placebo group was characterized by loss of the most participants ( n = 14 ) . Of the 29 participants not receiving treatment , all 14 who missed the second treatment provided primary end point data and could therefore be followed up; hence 547 children were included in the available case analysis . Most of the 701 children subjected to multiple Kato-Katz thick smears readings were diagnosed positive for T . trichiura ( n = 577 , 82% ) . Infections were mainly of light intensity ( 94% ) and only one heavy T . trichiura infection was identified . Prevalence of hookworm and A . lumbricoides were 7% and 5% , respectively , and most of these infections were of light intensity . An infection with all three helminth species was diagnosed in 11 children ( 1 . 5% ) . The mean age of the 701 children was 10 years ( range 7–15 years ) . Mean age , weight , and height were comparable in all four treatment arms ( Table 1 ) . There was a similar number of boys ( n = 348 ) and girls ( n = 353 ) participating in the study . Only very low cure rates were observed regardless of whether the sequentially administered nitazoxanide-albendazole combination , albendazole , or nitazoxanide were administered . In more detail , using the per-protocol analysis , nitazoxanide combined with albendazole achieved a cure rate of 16 . 0% ( 95% confidence interval ( CI ) , 9 . 7–22 . 4% ) , whereas single doses of albendazole or nitazoxanide resulted in cure rates of 14 . 5% ( 95% CI , 8 . 4–20 . 6% ) , and 6 . 6% ( 95% CI , 2 . 4–10 . 8% ) , respectively . Children receiving placebo showed an apparent cure rate of 8 . 9% ( 95% CI , 4 . 0–13 . 8% ) ( Table 2 ) . Similar results were observed using the available case analysis ( Table S1 ) . Comparing the treatment outcomes using a logistic regression revealed that albendazole had a significant effect on infections with T . trichiura ( odds ratio ( OR ) , 0 . 47; 95% CI , 0 . 27–0 . 81; p = 0 . 007 ) while nitazoxanide showed no effect ( OR , 1 . 04; 95% CI , 0 . 61–1 . 78; p = 0 . 89 ) . There was some indication of an interaction between the two drugs ( OR , 0 . 64; 95% CI , 0 . 21–1 . 98 ) , however , this result was not significant ( p = 0 . 44 ) . The ERR for T . trichiura was 54 . 9% ( bootstrap 95% CI , 37 . 7–67 . 9% ) for the nitazoxanide-albendazole combination , 45 . 6% ( 95% CI , 25 . 9–61 . 0% ) for albendazole alone , 13 . 4% ( 95% CI , 0 . 0–33 . 7% ) for nitazoxanide alone , and 17 . 6% ( 95% CI , 0 . 0–36 . 7% ) for the placebo-controlled treatment arm ( Table 2 ) . Large confidence intervals indicate a high variance in ERRs . Therefore the only significant difference between the treatment arms ( defined by non-overlapping bootstrap CI ) , was found between nitazoxanide-albendazole compared to the nitazoxanide monotherapy treatment group and the placebo-controlled treatment arm . However , even though CIs are overlapping , there seems to be a trend that albendazole alone resulted in higher ERRs . The amount of milligrams of drug administered per kilogram of body weight ranged from 22 . 7 to 64 . 9 mg/kg for nitazoxanide and from 7 . 7 to 27 . 2 mg/kg for albendazole ( Table 2 ) . Logistic regression revealed no statistically significant association between body weight and cure rates between the different treatment arms which received an active drug . Less than 10% of the children were infected with hookworm ( n = 48; 7% ) and A . lumbricoides ( n = 31; 4% ) . Albendazole was highly efficacious against both parasites with cure rates of 100% against A . lumbricoides and 81 . 8% against hookworm ( 95% CI , 54 . 6–100% ) . Nitazoxanide showed moderate efficacy against those two nematode species with cure rates of 66 . 7% ( 95% CI , 35 . 4–98 . 0% ) against hookworm and 62 . 5% ( 95% CI 19 . 2–100 . 0% ) against A . lumbricoides ( Table 2 ) . These data , however , lacked statistical significance . Of note , placebo had an apparent cure rate against hookworm infection of 55 . 6% . In total , 678 of the treated children answered a standardized questionnaire pertaining to adverse events . However , not all of these children responded at each of the five follow-up time points ( Table 3 ) . Before treatment , 28 ( 4 . 1% ) children reported minor symptoms ( e . g . , headache and abdominal pain ) . After treatment ( both first and second round of treatment ) a total of 244 children complained at least once about minor adverse events at one of the four follow-up examinations ( 3 and 24 hours after each treatment ) . Only one child had moderate adverse events , namely headache 24 hours after receiving nitazoxanide . This child was treated with paracetamol and the headache resolved within 3 hours . In total 307 adverse events were reported after starting the treatment regimen . Abdominal cramps and headache were the most frequent ones ( 165 times ( 53 . 7% ) and 69 times ( 22 . 5% ) , respectively ) ( Table S2 ) . Other reported adverse events were nausea ( 6 . 8% ) , vertigo ( 5 . 5% ) , diarrhea ( 4 . 6% ) , fever ( 3 . 6% ) , allergic reaction ( 1 . 6% ) , vomiting ( 1 . 3% ) , and fatigue ( 0 . 3% ) . Three hours after the first treatment , minor adverse events were reported by 101 ( 14 . 9% ) participants . Children who received placebo reported significantly more about minor adverse events compared to the pretreatment situation ( OR , 2 . 97; 95% CI , 1 . 50–6 . 21 ) ( Table 3 ) . Children who were given single nitazoxanide had significantly more adverse events 3 hours after treatment compared to the placebo recipients at the same time point ( OR , 2 . 02; 95% CI , 1 . 28–3 . 24 ) . On the next day ( 24 hours after the first treatment ) 56 ( 8 . 3% ) children reported adverse events . Participants receiving nitazoxanide still had significantly higher odds of reporting adverse events ( OR , 2 . 49; 95% CI , 1 . 38–4 . 50 ) . Three hours after the second treatment , children treated with albendazole did not report significantly more adverse events than placebo recipients ( OR , 1 . 47; 95% CI , 0 . 59–3 . 70 ) . At this time point , nitazoxanide was no longer associated with significantly more adverse events than placebo recipients ( OR , 2 . 04; 95% CI , 0 . 85–4 . 91 ) and both drugs combined ( treatment group 1 ) showed no cumulative effect regarding adverse events ( OR , 0 . 67; 95% CI , 0 . 21–2 . 18 ) . Twenty-four hours after the second treatment adverse events were resolved . Logistic regression revealed ORs of 0 . 77 ( 95% CI 0 . 35–1 . 71 ) for albendazole , 0 . 76 ( 95% CI 0 . 35–1 . 69 ) for nitazoxanide , and 1 . 40 ( 95% CI 0 . 44–4 . 41 ) for the sequentially administered nitazoxanide-albendazole combination .
Following up on the promising trichuricidal properties observed in in vitro studies [16] , we carried out the first randomized , double-blind , placebo-controlled trial administering nitazoxanide as a single dose of 1 , 000 mg to T . trichiura-infected school-aged children in a highly endemic area in Pemba . In addition , since combination chemotherapy is being advocated in many therapeutic areas , as it enhances efficacy and lowers the risk of resistance development [26] , one group of children was treated with a nitazoxanide-albendazole combination which was administered over two consecutive days . A high single dose of nitazoxanide ( 1 , 000 mg ) showed no therapeutic effect against T . trichiura . This result is in contrast to previous studies , which reported high cure rates when the drug was administered as multiple dose treatment regimen ( 6 times 200 mg or 500 mg ) to T . trichiura-infected patients [17]–[19] . It is plausible that pharmacokinetic properties of “multiple doses a day” nitazoxanide are superior to “once a day” nitazoxanide in that it achieves a longer half life . However , since the global strategy targeting neglected tropical diseases advocates preventive chemotherapy ( i . e . , regular deworming with single oral doses ) , multiple dosing is currently not recommended , as it poses operational and financial challenges [27] . A rigorous diagnostic approach as performed in this study ( two times duplicate Kato-Katz thick smears before and after treatment ) can lead to lower observed cure rates , since , especially light infections , are more likely to be detected [28] . Nevertheless , two of the above mentioned studies which found high cure rates and ERRs for nitazoxanide against T . trichiura infections also collected several stool samples after treatment , and hence pursued a thorough diagnostic approach . Diagnosis , therefore , does not seem to be the main reason for the contradictory results . The standard treatment albendazole revealed a very low cure rate against T . trichiura when given at a single dose of 400 mg , which is in agreement with the results of several previous studies [21] , [29] , [30] . It is interesting to note that the ERR following albendazole treatment was still moderate ( 73% ) in the same setting 12 years ago , but was similarly low as the ERR obtained in this trial , in a study conducted by Knopp et al . in 2009 in neighboring Unguja , Zanzibar [29] , [30] . This might be an indicator of tolerance or resistance development to albendazole against T . trichiura . Of note , Levecke et al . [31] recently showed that ERRs can differ strongly between individual settings and that albendazole has higher ERRs in settings with low infection intensities . Our trial could not confirm this hypothesis since albendazole achieved only a low ERR in children characterized by low infection intensities . The combination of albendazole and nitazoxanide had slightly , though not significantly higher cure rates and ERRs than albendazole alone . Since drug interactions have not been evaluated for this combination , drugs were administered on subsequent days . One disadvantage of spacing the drug is that synergistic effects might be missed . On the other hand , a recent study which examined the effect of a simultaneous nitazoxanide-albendazole combination against adult T . muris in vitro detected an antagonistic effect [32] . Only a few children were ( co ) -infected with hookworm and/or A . lumbricoides . Nonetheless , these data illustrate that the standard treatment albendazole has a high efficacy against these parasites . The good activity against these two helminths and low efficacy against T . trichiura is also supported by the baseline prevalences ( T . trichiura ( 82 . 2% ) , hookworm ( 6 . 8% ) , and A . lumbricoides ( 4 . 6% ) ) observed in the current trial . The settings where the study was conducted ( Wawi and Al-Sadik schools ) are part of the annual deworming in Zanzibar and are therefore regularly treated with albendazole . The placebo-controlled treatment group had a cure rate of 55 . 6% against hookworms . This finding might indicate that the diagnostic tool used to detect hookworm eggs in this study was not sufficiently sensitive . One reason might be that subjects with light infections may only shed very few or sometimes no eggs , resulting in a negative Kato-Katz test result [33] , [34] . To overcome this problem in future studies it might be advisable to use an additional diagnostic technique with higher sensitivity such as the FLOTAC technique , which allows using a larger amount of stool , or indirect diagnostic technique such as multiplex real-time PCR [33] , [35]–[38] . We observed a high frequency of adverse events , but these were mostly mild , were , at times , already reported before treatment , and also by placebo recipients [39]; and hence were not consistently treatment related . Nevertheless , 3 and 24 hours after the first day of treatment ( administration of nitazoxanide or a nitazoxanide-matching placebo ) children treated with nitazoxanide reported significantly more adverse events compared to those who had received placebo . This increase in adverse events was not observed after the second day of treatment ( children treated with albendazole suffered from a similar number of adverse events episodes than children who had obtained placebo ) , suggesting that the standard treatment albendazole triggers less adverse events than nitazoxanide . Adverse events related to treatment were resolved 24 hours after the second treatment , and the highest number of adverse events was reported from placebo-treated children . In conclusion , a single oral dose of nitazoxanide cannot be recommended for the treatment of infection with T . trichiura since we observed low cure rate and ERR as well as significantly more adverse events than the standard drug albendazole . Note that , nitazoxanide is also much more expensive than the benzimidazoles , pyrantel pamoate , or levamisole . Moreover , also albendazole showed a low efficacy against T . trichiura in our study setting which did also not significantly improve by adding nitazoxanide on the next treatment day . Therefore the discovery and development of novel anthelmintic drugs , in particular against infections with T . trichiura , has a high priority . | More than 5 billion people are at risk of infection with one of the three most common intestinal worms , the roundworm Ascaris lumbricoides , the whipworm Trichuris trichiura , and two different kinds of hookworms . The global strategy to control these intestinal worm infections is through the regular administration of deworming drugs to school-aged children ( albendazole , 400 mg; mebendazole , 500 mg ) . However , especially against T . trichiura , a low treatment response is observed with single doses of both drugs . We tested the antiprotozoal drug nitazoxanide , which had shown promising trichuricidal properties in in vitro experiments . A randomized controlled trial was carried out on the island of Pemba in Tanzania . Four treatment arms were included: ( i ) single albendazole ( 400 mg ) , ( ii ) single nitazoxanide ( 1 , 000 mg ) , ( iii ) nitazoxanide-albendazole combination ( 1 , 000 mg–400 mg ) with each drug given separately on two consecutive days , and ( iv ) placebo . Children were asked for adverse events at several time points after treatment . Nitazoxanide showed no ability to cure T . trichiura-infected children and caused significantly more mild adverse events than placebo . Albendazole and the nitazoxanide-albendazole combination showed only a minimal effect against T . trichiura . Our results emphasize the urgent need to develop new , safe , and effective anthelmintic drugs against T . trichiura . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"medicine",
"infectious",
"diseases",
"neglected",
"tropical",
"diseases",
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] | 2012 | Efficacy and Safety of Nitazoxanide, Albendazole, and Nitazoxanide-Albendazole against Trichuris trichiura Infection: A Randomized Controlled Trial |
Patterns of somatic mutations in cancer genes provide information about their functional role in tumourigenesis , and thus indicate their potential for therapeutic exploitation . Yet , the classical distinction between oncogene and tumour suppressor may not always apply . For instance , TP53 has been simultaneously associated with tumour suppressing and promoting activities . Here , we uncover a similar phenomenon for GATA3 , a frequently mutated , yet poorly understood , breast cancer gene . We identify two functional classes of frameshift mutations that are associated with distinct expression profiles in tumours , differential disease-free patient survival and gain- and loss-of-function activities in a cell line model . Furthermore , we find an estrogen receptor-independent synthetic lethal interaction between a GATA3 frameshift mutant with an extended C-terminus and the histone methyltransferases G9A and GLP , indicating perturbed epigenetic regulation . Our findings reveal important insights into mutant GATA3 function and breast cancer , provide the first potential therapeutic strategy and suggest that dual tumour suppressive and oncogenic activities are more widespread than previously appreciated .
High-throughput genome sequencing has allowed the systematic analysis of the complex mutational landscape of tumours and has provided key insights into tumour evolution and cancer etiology [1–3] . Mutation patterns in individual genes also reveal important insights into their role in tumourigenesis and can assist in distinguishing driver from passenger mutations [1–4] . Mutation rates are elevated in protein domains or regulatory sites , indicating their functional importance for cancer development [5 , 6] . It is typically assumed that all mutations within an individual gene have the same downstream consequences for tumourigenesis . However , at least one notable example challenges this paradigm . Distinct mutations in the TP53 gene ( encoding p53 ) lead to both loss-of-function and gain-of-function , impinging on multiple different pathways [7–10] . Yet , it is unclear if this type of dual activity of mutant p53 represents an exceptional case or is more common . We hypothesised that mutations in different positions in a cancer gene may result in different downstream consequences . To investigate this , we developed an unbiased computational approach and applied it to breast cancer , as large publicly available data sets are available for this cancer type . Breast cancer has been studied extensively in terms of its molecular and genetic markers . Its classification into subtypes according to expression of receptors and gene expression profiles is used for diagnostic and prognostic purposes and forms the basis for treatment decisions [11–17] . Breast cancer is genetically heterogeneous and only four driver genes are mutated in more than 10% of patients [18–25]: PIK3CA ( encoding the catalytic subunit of PI3K ) , CDH1 ( encoding E-cadherin ) , TP53 , and GATA3 ( encoding GATA-binding protein 3 ) . While the roles of the pro-survival PI3K pathway , cell adhesion , and p53 as the guardian of the genome in tumourigenesis are well studied , comparatively little is known about the role of the equally commonly mutated gene GATA3 . To some extent this is due to the relatively recent discovery of the high prevalence of GATA3 mutations [19–22 , 26] . In addition , model systems ( e . g . , cell lines , animal models ) to study GATA3 in breast cancer are lacking , hampering functional studies . GATA3 is a member of the GATA family of transcription factors and forms homodimers that bind conserved hexanucleotide sequences containing the central GATA motif [27–29] . It is a master regulator of helper T cell specification [30] and plays a critical role in development and differentiation of various tissues , including the mammary gland [31–33] . During normal mammary development , GATA3 , together with the estrogen receptor ( ER ) [34–37] , controls differentiation of the luminal epithelium in the terminal end buds in the breast . In adult tissues , GATA3 helps to maintain the luminal identity [38–41] . The contribution of GATA3 to cancer is , in contrast , poorly understood . Most of our current knowledge regarding GATA3’s potential function in breast cancer has been revealed from genomic studies highlighting an ER/FOXA1/GATA3 co-operating network of transcription factors in luminal tumours [14] and ER-positive cell line models [34 , 35 , 37 , 42 , 43] . Yet , the observation of GATA3 downregulation during tumour progression and predominant frameshift mutations have led to the view that GATA3 acts primarily as a tumour suppressor [44 , 45] . In this study , we identify differential functional consequences of mutation types in GATA3 . We present evidence that the most common mutation type results in a protein with elongated C-terminus that displays effects consistent with gain-of-function activity in a cell line model . This is highly surprising , as frameshift mutations are generally believed to yield inactive proteins due to premature termination of translation . In addition , we describe a synthetic lethal interaction between this GATA3 mutant and drugs targeting the histone methyltransferases G9A and GLP , providing a first putative therapeutic opportunity for patients carrying GATA3 mutations . Together , our findings demonstrate that different mutations in the same gene can result in differential drug sensitivities and contest the view that GATA3 acts only as a tumour suppressor .
To study mutation patterns in breast cancer , we used publicly available data from The Cancer Genome Atlas ( TCGA ) [23] and from the Molecular Taxonomy of Breast Cancer International Consortium ( METABRIC ) [25] . Fig 1A shows the most commonly mutated genes in breast cancer . Somatic mutations in these recurrently mutated breast cancer genes are often mutually exclusive [46 , 47] ( Fig 1A , S1 Table ) and distributed in a non-uniform fashion along the gene body ( Fig 1B ) . The observed patterns are largely consistent between the TCGA and METABRIC datasets . For instance , PIK3CA mutations chiefly occur at just two positions corresponding to different protein domains: E545 in a helical regulatory domain and H1047 in the kinase domain [48] . Clear hotspot mutations at single amino acid residues or within narrow regions are also present in TP53 , and to some extent in GATA3 [49] . Mutations in GATA3 ( Entrez Gene ID: 2625 ) have not yet been extensively characterised , but the non-uniform distribution and mutual exclusivity with mutations in other cancer genes are strong indicators that GATA3 is a cancer driver gene [25 , 50] ( Fig 1B , S1 Table ) . In order to assess potential functional consequences of regional mutation patterns , we devised an unbiased , systematic approach for linking the position of a mutation within a cancer gene with gene expression data . We reasoned that such an analysis could highlight domains in cancer genes that–when mutated–would result in differential downstream effects . First , we extracted from TCGA the genomic position for each mutation found in a patient in the seven selected driver genes , and the non-driver control gene TTN [51] , along with the gene expression profiles from the same patients . Next , we used a segmentation approach to identify regions within a driver gene that led to a change in expression levels of another gene ( see Materials and Methods ) . The identification of such patterns would suggest that the mutations in a particular region of the gene are functionally distinct . We termed genes that displayed altered expression along distinguishable segments of driver mutations “response genes” and refer to the border between two segments as a “segmentation breakpoint” ( Fig 1C ) . Strikingly , we found that the highest number of response genes was associated with GATA3 mutations ( Fig 1D , S2 Table ) , where more than 900 genes displayed a segmented pattern . In comparison , around 200 response genes were linked with PIK3CA mutations , and fewer than 100 response genes were identified for the non-driver control gene TTN . The observation that the majority of response genes displayed a single breakpoint ( Fig 1D ) suggests that patient-derived GATA3 mutations can be divided into two functionally distinct regions and that mutations in these regions are associated with differential gene expression in tumours . Most GATA3 mutations ( 66/99; 67% ) in the TCGA dataset are heterozygous frameshift mutations in exon 5 and exon 6 ( Fig 2A ) . Frameshifts in general lead to premature stop codons , which can substantially disrupt protein function . Indeed , approximately 41% ( 27/66 ) of the frameshift mutations in GATA3 are predicted to result in an early stop codon ( Fig 2B ) . These truncated proteins ( hereafter referred to as GATA3-trunc ) are stable and expressed in tumours [52] , and , as GATA3 likely forms a homodimer [29] , it is probable that they may act in a dominant negative manner [53 , 54] . These mutations would thus be consistent with a haplo-insufficient tumour suppressor function of GATA3 . Interestingly , most ( 39/66 , 59% ) frameshift mutations in GATA3 result in a protein with an extended C-terminus . These extension mutations occur predominantly ( 33/39 , 85% ) in exon 6 and affect the resulting mutant proteins starting from different residues between alanine 395 and glycine 444 , with a hotspot ( 11/39 ) at proline 409 ( Fig 2A and 2B ) [49] . The mutations are strongly biased toward the +1 frame ( Fig 2A , bottom ) . This is surprising , as -1 frameshifts in this position would result in a shortened and aberrant C-terminus . The alternative +1 frame alters up to 49 amino acids of the original C-terminus and extends the protein by 63 novel amino acids ( hereafter GATA3-ext , Fig 2B ) . Because frameshift mutations in the TCGA dataset as a whole do not display a frame preference , the bias toward the +1 frame in GATA3 is suggestive of positive selection . One potential explanation for this could be that the GATA3-ext mutation is functionally distinct from other ( truncating ) mutations , for instance by providing a gain-of-function . Together , this demonstrates that our analysis can identify functional distributions of mutations as well as novel candidate tumourigenic mechanisms . We next revisited our segmentation analysis to investigate the positional distributions of mutations and segmentation breakpoints ( S1A–S1H Fig ) . In GATA3 , the distribution of breakpoints present in single-breakpoint response genes was distinct from the distribution of all mutations ( Fig 2C ) . It sharply peaked at a position that separated GATA3-ext from GATA3-trunc mutations . This is illustrated with BCAS1 , the strongest GATA3 response gene ( Fig 2D , S3 Table ) . This indicates that genes like BCAS1 are differentially expressed in tumours that contain the GATA3-ext mutation but not in tumours harbouring the GATA3-trunc mutation . Other response genes such as SYT17 displayed more complex patterns , but one of the breakpoints often tended to separate the extension and truncation mutants ( Fig 2D ) . Thus , the differential expression of response genes can functionally define the type of mutation . Together with the observation that the +1 frameshift mutations are under positive selection , this suggests that these GATA3-ext mutations are mechanistically distinct . To investigate what functional aspects of cancer cells specifically correlate with GATA3-ext mutations , we calculated the association of this mutation class with gene expression levels without performing segmentation . We divided patients carrying GATA3 mutations into two groups: those with GATA3-ext mutations and those with all other types . Corroborating the segmentation analysis , we obtained differentially expressed genes between these groups ( S4 Table ) , which matched many of the previously identified response genes . This confirms that cellular processes are indeed differentially affected in GATA3-ext tumours in comparison to the other GATA3 mutant tumours . Differential gene expression in GATA3-ext tumours may indicate distinct tumour characteristics and this could affect disease progression and therapeutic response . To address this , we used the previously defined patient groups and performed survival analysis of the TCGA patients . Only GATA3-ext patients progressed during the follow-up period of the cohort but no patients with an alternative GATA3 mutation did . Accordingly , patients with GATA3-ext mutations displayed significantly ( p = 0 . 0029 ) shortened disease-free survival in the TCGA cohort ( Fig 2E ) . This indicates that GATA3-ext is a putative biomarker for disease progression and is consistent with the notion that extension mutants have important mechanistic properties that are distinct from other GATA3 mutations . To assess whether GATA3-ext mutations are associated with similar expression changes in an independent patient cohort , we analysed the METABRIC dataset . The METABRIC cohort carries relatively fewer GATA3-ext mutations and displays a moderately different mutation distribution in GATA3 ( compare lower panels of S1D with S1I Fig ) . Interestingly , substantial differences in GATA3 mutation patterns have also been noted in a Chinese cohort [46] ( see Discussion ) , suggesting that genetic background and environmental factors play an important role in GATA3-driven breast cancer . We further noted that in the METABRIC cohort disease-free survival was similar for patients harbouring GATA3-ext or other GATA mutations , further indicating considerable differences in cohort composition ( S1J Fig ) . Despite these dissimilarities , we repeated the segmentation analysis in both the METABRIC and TCGA datasets using the GATA3-ext gene signature derived from TCGA ( S4 Table ) . The analysis was limited to 46/50 genes , as expression data for four genes were not present in the METABRIC cohort . As expected , all 46 genes showed segmentation for GATA3 in the TCGA dataset ( S1K Fig ) . Notably , 34/46 signature genes qualified as GATA3-specific response genes in the independent METABRIC dataset ( Fig 2F , S1I Fig ) . This implies that these genes are differentially expressed in GATA3-ext tumours . Next , we calculated the fold change of the 46 genes in GATA3-ext samples relative to all other GATA3 mutant tumours . Strikingly , the changes in both datasets occurred in consistent directions for all 46 genes ( Fig 2G ) , indicating qualitative agreement between TCGA and METABRIC for the GATA3-ext gene signature . Hence , the distinctive effects of GATA3-ext are recapitulated in an independent breast cancer patient cohort . Following investigations of GATA3 mutation types in human patient data , we wished to study these mutations types in vitro in order to understand their functional implications in more detail . An endogenous heterozygous truncating mutation in exon 5 ( cDNA:1006insG ) in the commonly used MCF7 breast cancer cell line has been previously reported and was shown to decrease DNA binding and increase protein half-life [53 , 55] . However , we did not find cancer cell lines with GATA3-ext mutations by mining the Cancer Cell Line Encyclopaedia ( CCLE ) [56] and the Catalogue of Somatic Mutations in Cancer ( COSMIC ) [57] databases or by analysing 45 breast cancer cell lines by Sanger sequencing and Western blot . The high frequency of GATA3 mutations in breast tumours is thus not well represented in cell lines . We also tested tumour tissue from 10 luminal patient-derived xenograft ( PDX ) mouse models and did not detect any GATA3-ext mutations either . Although the small sample numbers preclude a strong conclusion , together these results suggest that GATA3-ext mutant cells may not adapt well to ex vivo culture conditions . The lack of cell lines with naturally occurring GATA3-ext mutations impelled us to search for an alternative model system . To distinguish putative gain-of-function from dominant negative effects , we wished to study GATA3-ext in the absence of wild-type GATA3 . We attempted to inactivate the endogenous locus by CRISPR/Cas9 gene editing in several ER-positive breast cancer cell lines ( MCF7 , T47D and CAMA1 ) , but this did not yield viable homozygous null clones ( ~150 clones analysed ) . CRISPR/Cas9-directed replacement of endogenous GATA3 by GATA3-ext was equally unsuccessful ( >100 clones analysed ) . This suggests that at least one copy of wild-type GATA3 is required for viability in these cell lines , which is in accordance with the findings from human cancer samples but complicates the introduction of a mutated version for in vitro models . To establish an alternative model , we used the non-tumourigenic MCF10A breast epithelial cell line that naturally expresses very low protein levels of endogenous GATA3 ( Fig 3A and 3B ) . We stably expressed wild-type GATA3 ( GATA3-wt ) , GATA3-ext ( cDNA:1224insG; p:P409fs ) and GATA3-trunc ( cDNA:1006insG; p:G336fs ) through retroviral transduction and puromycin selection ( Fig 3A ) . The GATA3-ext protein was stable , albeit expressed at slightly lower levels than GATA3-wt . Importantly , the expression levels of the GATA3 proteins were in the physiological range of endogenous GATA3 observed in various other breast cancer cell lines ( Fig 3B ) . Furthermore , confocal microscopy showed nuclear localisation for both mutants as well as for the wild-type protein ( S2A Fig ) . We noted a slight but significant decrease in growth rate of MCF10A GATA3-ext cells as compared to GATA3-wt ( Fig 3C ) . This was consistent between independent infections of the parental MCF10A cells with titrated virus , excluding an effect of the viral transduction itself . The specific effect of GATA3-ext shows that this mutation affects MCF10A cells’ ability to proliferate in standard tissue culture medium conditions . We next performed RNA sequencing on MCF10A GATA3-wt , GATA3-ext , GATA3-trunc or control vector expressing cells to characterise the effects of GATA3 mutations at the cellular level . The expression of GATA3-wt and GATA3-ext resulted in up- or downregulation of 725 and 853 genes , respectively , with respect to the control ( p <0 . 05 , FC >1 . 5 ) , indicating widespread transcriptional changes . In contrast , expression of GATA3-trunc yielded a considerably smaller signature ( 134 genes ) , which could be indicative of loss-of-function ( Fig 3D , S5 Table ) . The majority of the GATA3-wt and GATA3-ext signatures consisted of uniquely regulated , non-overlapping genes ( Fig 3D , S2B Fig ) . Accordingly , gene ontology ( GO ) analysis revealed significant enrichment of gene sets relating to unique terms for each of the GATA3 constructs ( S5 Table ) . For instance , the GATA3-wt gene set is enriched for cytokine-linked processes , whereas the GATA3-ext signature shows a significant enrichment for peptidyl-tyrosine modification processes . These results indicate that expression of GATA3-ext and GATA3-trunc invoke starkly distinct changes in gene expression , and the large number of uniquely regulated genes in GATA3-ext cells supports a gain-of-function of this mutant . We found a small 4-gene overlap between the TCGA and MCF10A GATA3-ext signatures ( S2B and S2C Fig ) . We validated one of these , the triglyceride metabolism gene PNPLA3 , in an independent set of experiments by qRT-PCR and observed consistent downregulation in cells expressing GATA3-ext . ( S2D and S2E Fig ) . This is in agreement with patient tumour data ( S2F Fig ) . Yet , the observation that most signature genes derived from the ER-negative MCF10A cell line model do not overlap with the patient data reflects the well-known biological differences between patient tumour samples and cell culture model systems . Together , these data indicate that GATA3-ext is functionally active upon overexpression and that GATA3-ext and GATA3-trunc mutants are mechanistically different from each other and from the wild-type protein . Chemical genetic interactions can reveal therapeutic vulnerabilities and pinpoint cellular processes that are affected by mutant proteins [58 , 59] . Therefore , we performed a chemical genetic screen to identify compounds that specifically affect GATA3-ext cells . We assembled a small-molecule library containing ~100 approved and experimental anti-cancer drugs , and a number of tool compounds . We used MCF10A cells expressing the GATA3-ext protein or a control vector and exposed them to compounds for 6 days before measuring viability . To mimic limited nutrient supply in a tumour and to render the cells more responsive to drugs , cells were treated under reduced media supplement conditions ( S3A and S3B Fig , S6 Table ) . The compound that most strongly reduced GATA3-ext viability was BIX101294 [60 , 61] , a specific inhibitor of the histone 3 lysine 9 methyltransferases G9A ( EHMT2 ) and GLP ( G9A-like protein; EHMT1 ) ( Fig 3E , S6 Table ) . As expected , BIX01294 reduced global histone 3 lysine 9 dimethylation ( H3K9me2 ) in MCF10A-GATA3-ext and control cells over time ( Fig 3F ) . We validated this unexpected interaction with short- and long-term treatment and in both full and reduced media supplement conditions ( Fig 3G and 3H and S3C Fig ) . The effective concentration resulting in a 50% inhibition of viability ( EC50 ) for cells expressing the GATA3-ext mutant was consistently 5- to 10-fold lower than in control cells . To determine if this sensitivity was specific for GATA3-ext , we tested the compound on MCF10A cells expressing GATA3-wt or GATA3-trunc . The sensitivity of these cells to the compound was identical to control cells infected with an empty vector ( Fig 3I and 3J ) . Accordingly , MCF7 cells , which heterozygously express GATA3-trunc , display average sensitivity to BIX101294 when compared to a panel of 25 other breast ( cancer ) cell lines ( S3D Fig ) . Next , we wished to rule out that the observed effects of GATA3-ext overexpression were due to a dominant negative effect . To address this , we depleted endogenous GATA3 by shRNAs and tested if this could phenocopy GATA3-ext expression . The knockdown did not result in enhanced sensitivity to BIX101294 ( S4A Fig ) . We thus conclude that the sensitivity arises from a specific interaction between the drug and the extended GATA3 protein . GATA3-ext mutations in patients are predominantly heterozygous , and as endogenous GATA3 protein levels in MCF10A cells are very low , we co-expressed GATA3-ext and GATA3-wt to assess whether the presence of GATA3-wt alters the differential toxicity of BIX0124 . GATA3-ext+wt cells were equally sensitive as GATA3-ext cells ( S4B Fig ) . This experiment suggests that the GATA3-ext-induced BIX01294 sensitivity is independent of the presence of a wild-type GATA3 allele . Together , these data further highlight functional differences between GATA3 truncation and extension mutants and imply that extension mutants act by a mechanism that is different from typical loss-of-function or dominant negative effects . G9A and GLP are histone methyltransferases ( HMTs ) that form a heterodimer and catalyse specific mono- and di-methylation at histone 3 lysine 9 ( H3K9 ) [62] . Di-methylation of this residue is associated with transcriptional repression and has been demonstrated to occur aberrantly at tumour suppressor genes , often coinciding with upregulation of G9A [63] . In the TCGA dataset , EHMT1 and EHMT2 are not differentially expressed in GATA3-ext tumours and do not show a segmentation pattern ( S5 Fig ) . To assess the specificity of the synthetic interaction between GATA3-ext and G9A/GLP , we tested a second G9A/GLP inhibitor ( UNC0638 [64] ) . Although this compound did not score as a hit in the screen , possibly due to a suboptimal screening concentration , repeated validation showed a similar degree of hypersensitivity of GATA3-ext cells ( Fig 4A and 4B and S3C Fig , S4 Fig ) . Next , we tested a set of inhibitors of various other HMTs and did not detect differential sensitivity ( Fig 4C–4F ) , suggesting that the interaction with GATA3-ext does not occur with histone methyltransferase activity in general . Further , GATA3-ext and control cells were equally responsive to other structurally similar quinazoline compounds not targeting G9A/GLP , consistent with a specific and on-target effect of BIX01294 and UNC0638 ( Fig 4G , S6A Fig ) . In order to verify the involvement of G9A and GLP more directly , we depleted them by shRNA in GATA3-wt , GATA3-ext and control cells . Only the viability of GATA3-ext cells was significantly affected ( Fig 4H , S6B Fig ) , suggesting that both enzymes contribute to the sensitivity to BIX01294 and UNC0638 . To characterise the mechanisms underlying the sensitivity of GATA3-ext cells to G9A/GLP inhibition , we first analysed potential cell cycle effects upon BIX01294 treatment . We did not observe a difference in cell cycle progression between GATA3-ext and control cells as assessed by BrdU incorporation or DNA content ( S7 Fig ) . However , GATA3-ext cells were more prone to undergo apoptosis upon drug treatment than control cells ( Fig 5A ) . As GATA3 is functionally linked with ER expression and activity [34–37] , we also assessed the impact of ER signalling on sensitivity to G9A/GLP inhibition in GATA3-ext cells . We expressed ERα in our MCF10A model and confirmed that ER target genes were induced upon ER expression and/or treatment with the ER agonist β-estradiol ( E2 ) ( Fig 5B and 5C ) . The sensitivity of GATA3-ext cells to G9A/GLP inhibition was not significantly influenced by the level of ER signalling ( Fig 5D ) , suggesting a mechanism that is independent from previously described functional interactions of GATA3 .
Recent studies have begun to address the role of GATA3 in breast cancer . GATA3 has been suggested as a negative regulator of epithelial-to-mesenchymal transition and metastasis but putative tumour promoting effects have also been reported [26 , 45 , 53–55 , 65–76] . Critically , these studies have almost exclusively focussed on wild-type GATA3 and only a few have studied GATA3 truncating mutations . To our knowledge , our study is the first that highlights and addresses the most frequent GATA3 mutation type , i . e . mutations resulting in an extended C-terminal protein . Protein-extending mutations in cancer genes are unusual but not unprecedented . Recently , frameshift extension mutations in CALR ( encoding calreticulin ) were identified in myeloproliferative neoplasms [77] and WT1 extension mutants have been described in Wilms kidney tumours [78] . Cancer driver mutations are often divided into gain-of-function and loss-of-function mutations . Loss-of-function mutations result in an inactive or less active protein , whereas gain-of-function mutations lead to a more active protein or acquisition of a different function . Several observations in our study indicate that GATA3-ext proteins are mechanistically distinct from other GATA3 mutants and GATA3 wild-type , hinting toward a gain-of-function: First , GATA3-trunc mutants lack a larger part of the normal GATA3 protein sequence than GATA3-ext . This makes it rather unlikely that GATA3-ext is more perturbed in its normal physiological function than other GATA3 mutants . Second , in patients , GATA3-ext is associated with the differential expression of a distinct group of response genes that is not affected by other GATA3 mutants . Differential effects on gene expression were also observed in the MCF10A cell line model expressing GATA3-ext and GATA3-trunc . Third , we have found differences in outcome for patients harbouring GATA3-ext mutations , at least in the TCGA cohort . There , GATA3-ext is associated with reduced disease-free survival compared to other GATA3 mutations , suggesting that these tumours display a different pathology with respect to recurrence . Of note , all GATA3 mutations together correlated with improved disease-free and overall survival in a Chinese patient cohort [46] . GATA3 mutations as a whole displayed a marginally significant trend to improved overall survival only in ER-positive patients in the TCGA and METABRIC cohorts [25 , 46] but not in a smaller Dutch study [76] . Interestingly , GATA3 frameshift mutations were strongly underrepresented in the Chinese cohort ( 22% vs . 78% missense mutations ) as compared to TCGA ( 93% vs . 7% ) . The authors suggest different mutational evolution of luminal breast cancer in different populations as an explanation for these discrepancies , with few Asian patients being included in the TCGA cohort [46] . However , these studies do not discriminate between GATA3-ext , GATA3-trunc or other GATA3 mutations . Our survival analysis indicates that indeed this separation is important , as only GATA3-ext mutations are associated with reduced disease-free survival . Fourth , we observe strong genetic selection for +1 frameshift mutations , leading to one specific C-terminal extension . Fifth , GATA3-ext is stable in cells and displays functional characteristics ( e . g . , drug sensitivities ) that are not observed in cells expressing other GATA3 proteins or cells in which GATA3 is depleted . Taken together , these lines of evidence provide substantial support for the hypothesis that GATA3-ext adopts certain neomorphic functions that might replace or act in addition to its wild-type properties . Importantly , our findings challenge the view that GATA3 only acts as a tumour suppressor that is downregulated or inactivated in breast cancer [14–16 , 19–26 , 79] . This GATA3-ext gain-of-function hypothesis parallels TP53 mutations in certain aspects , including gain- and loss-of-function in the same gene . For this reason , we have adopted the gain-of-function terminology in analogy to p53 and propose to label GATA3-truncation mutations as primarily loss-of-function and GATA3-extension mutations as gain-of-function . Like GATA3 , p53 is a transcription factor that acts as a homo-oligomer , and hence , gain-of-function mutations do not necessarily imply a constitutively active form of the protein , as it is observed for many kinase gain-of-function mutants . Instead , a plethora of different functions for oncogenic p53 have been described , including altered subcellular localization , changed DNA-binding affinities and a different spectrum of binding partners and target genes . Ultimately , these activities can lead to enhanced proliferation , inhibition of apoptosis , chemoresistance , or increased invasiveness [8–10] . It remains unclear how GATA3-ext exerts its specific activity . It has been postulated [80] that the GATA3 C-terminus is essential for maintaining protein stability but we did not observe strong differences upon ectopic expression in MCF10A cells . Therefore , an alternative mechanism is likely to underpin GATA3-ext function . For instance , GATA3-ext may display differential binding partners or altered DNA binding sites . The GATA3-ext protein rendered cells sensitive to inhibition of the G9A and GLP histone methyltransferases . G9A and GLP are upregulated in a number of cancers , correlating with higher H3K9me2 levels and silencing of tumour suppressor genes [63] . Intriguingly , wild-type GATA3 and G9A have been recently found to physically interact [65] . The biochemical and functional interaction of GATA3 with histone methyltransferases may explain the changes of active histone modifications and altered enhancer accessibility in breast cancer cells depleted of GATA3 [37] . Yet , if and how this relates to drug sensitivity specifically in GATA3-ext expressing cells remains unclear . Our MCF10A cell line model does not fully recapitulate the context of GATA3 mutations in tumours in several ways , among them the heterozygous mutation state and the ER status . Due to lack of a more appropriate model system , we addressed these concerns separately by co-expression and knock-down experiments of mutant and wild-type GATA3 and ESR1 ( encoding ER ) . Even though RNA sequencing data from the MCF10A model only show a marginal overlap with the TCGA patient-derived GATA3-ext signature on an individual gene level , we believe that the MCF10A cell line model provides a valid context to study basic mechanistic differences between GATA3-wt , GATA3-ext and GATA3-trunc . The patient data and MCF10A model agree in that GATA3-ext and GATA3-trunc mutants act in a mechanistically different manner from each other and from the wild-type protein . We believe that this finding is biologically and potentially clinically relevant despite the exact mechanisms not yet being understood . In this regard , the identified synthetic lethal interaction between GATA3-ext and G9A/GLP inhibition provides the first clinically testable hypothesis for application of these drugs and the first lead for a treatment of this major subgroup of breast cancer patients . Thus , further pre-clinical study of the uncovered gene-drug interaction is warranted . Together , our study provides important insights into the function and potential druggability of one of the most frequent breast cancer mutants and a striking example of how different mutations in the same cancer driver can result in distinct downstream consequences .
The GATA3-ext cDNA sequence was synthesised by Epoch Life Science and shuttled into Gateway-compatible pBABE-puro or -neo vectors . GATA3-wt and GATA3-trunc were generated using the QuikChange II Site-Directed Mutagenesis kit in the same plasmid . The ESR1 ( ER ) sequence was obtained from pEGFP-C1-ER ( gift from Michael Mancini , Addgene plasmid #28230 ) and Gateway-cloned into pBABE-neo . Most shRNA sequences were obtained from The RNAi Consortium ( TRC ) ; shGAT3_1 was in pSicoR; shGATA3_2 and shGATA3_3 were cloned into pLKO . 1-puro; shG9A_1 , shG9A_2 , shGLP_1 , and shGLP_2 were cloned into pLKO . 1-hygro , which was derived from pLKO . 1-puro by replacing the puromycin with a hygromycin cassette ( BamHI/NsiI ) . If not indicated otherwise , corresponding empty vectors were used as controls . The control cDNA for the small-molecule screen ( pBABE-puro TBX3 ) was a gift from Thijn Brummelkamp . shRNA sequences are provided in S6 Table . MCF10A cells were purchased from ATCC and grown in DMEM/F12 medium ( Gibco ) +5% horse serum ( Gibco ) +0 . 02μg/ml EGF ( Sigma ) +0 . 5μg/ml hydrocortisone ( Sigma ) +0 . 1μg/ml cholera toxin ( Sigma ) +10μg/ml insulin ( Gibco ) +1% penicillin/streptomycin ( Gibco ) ( = full supplements ) . In “reduced supplement conditions” , all ingredients except antibiotics were used at 20% of their original concentration . MCF7 cells were obtained from ATCC and grown in DMEM medium +10% FCS ( Gibco or Sigma ) +1% penicillin/streptomycin ( Gibco ) . All other cell lines ( except HMEC , which were a gift from Christoph Gebeshuber ) were obtained from ATCC and cultured in recommended conditions ( S6 Table ) . Cells were cultured at 37°C and 5% CO2 ( except for cells in Leibovitz’s L-15 medium , which were cultured without CO2 ) and regularly tested for mycoplasma infection . Stable cell lines were generated by retro- or lentiviral infection and subsequent selection with puromycin ( 2μg/ml , Sigma ) or neomycin ( geneticin , 500μg/ml , Gibco ) for at least 3 days . Viral particles were produced by transfecting the retro- or lentiviral vector and corresponding packaging plasmids ( encoding polymerase and envelope proteins ) into HEK293-T cells . The supernatant was harvested 48–72 hours post transfection and its virus titer was determined on MCF10A cells . Target cells were then infected with an approximate MOI of 1 in presence of 7–10μg/ml polybrene ( Sigma , Millipore ) . MCF10A cells were seeded in reduced supplement conditions ( unless indicated otherwise ) and treated with drugs or DMSO for 4 days ( dose response curves ) or 10 days ( colony formation assays ) in triplicates . All other cell lines were seeded in their respective full media and treated for 3–11 days until reaching 90% confluency . Alternatively , cells were infected with lentivirus ( shGLP/shG9A ) on the next day and subsequently selected with hygromycin ( 50μg/ml , Sigma ) for 4 days . Cell viability was measured by luminescent ATP read-out ( CellTiterGlo , Promega ) and normalised to the control ( plotted as lowest concentration point to allow log-calculation ) , or cells were fixed using 3 . 7% paraformaldehyde and stained with 0 . 1% crystal violet in 5% ethanol . Data analysis and Area under curve ( AUC ) calculations were performed in GraphPad Prism . For measuring doubling time , cells were seeded at defined numbers ( CASY Cell Counter , OMNI Life Science; BioRad TC20 Automated Cell Counter ) , counted every 4 days and re-seeded . Cumulative cell numbers and doubling times were calculated in Microsoft Excel and GraphPad Prism . The majority of small-molecules used in this study were purchased from SYNthesis med chem , Selleck and Sigma . Erastin , imatinib and erlotinib were gifts from Georg Winter and Giulio Superti-Furga , TH588 from Ulrika Warpman Berglund and Thomas Helleday , and DBZ JQ-1 , IC092605 . 1 and IC040751 . 1 from James Bradner . Further information on vendors/sources and concentrations is provided in S6 Table . For the small-molecule screen , cells were seeded in 384-well plates in reduced supplement conditions and compounds were added the next day in quadruplicates at concentrations equalling a previously determined EC20 . After 6 days , viability was measured as described . Drawings of chemical structures were generated using Avogadro [81] . Cells were treated with BIX01294 or DMSO for 5 days and then stained with propidium iodide ( 0 . 02μg/μl; Sigma ) and AnnexinV AlexaFlour 647 ( BioLegend ) in Binding Buffer ( BioLegend ) according to manufacturer’s instructions . 20 , 000–30 , 000 cells were analysed by FACS ( BD FACSCalibur ) . Data analysis was performed with BD CellQuest Pro , FlowJo and GraphPad Prism . Camptothecin ( 3μM for 16 hours; Sigma ) was used as a positive control . Total RNA was isolated using the QIAGEN RNeasy Mini kit , DNase digested ( Ambion TURBO DNA-free ) , normalised by concentration and 0 . 2–1μg were reverse transcribed with random hexamer primers ( Fermentas RevertAid Reverse Transcriptase kit ) . qRT-PCR was performed in triplicates with 1μl cDNA using KAPA SYBR FAST Master Mix on an AppliedBiosystems StepOne Plus or 7900HT Fast Real-Time PCR System . mRNA levels were normalised to GAPDH and displayed relative to the control cell line ( ΔΔCt method ) . Primer sequences are provided in S6 Table . For the analysis of ER target genes , cells were starved for 16 hours and treated with 10nM β-estradiol ( E2 ) for 6 hours in absence of serum and growth factors before total RNA was isolated . Cells were counted and lysed in reducing sample buffer by boiling . Proteins were separated by SDS-PAGE on 4–12% Bis-Tris-Gels ( Invitrogen ) and then transferred to Amersham Hybond PVDF membranes ( GE Healthcare ) . Blocking and antibody incubations were carried out in 0 . 2% I-Block ( Tropix ) in PBST and membranes were washed with PBS +0 . 1% Tween-20 . HRP-coupled secondary antibodies ( goat anti-rabbit or anti-mouse , BioRad , 1:10 , 000 ) were detected with Western Lightning ECL Plus ( Perkin Elmer ) and visualised using a BioRad ChemiDoc or MF-ChemiBIS 3 . 2 ( DNR Bio-Imaging Systems ) imaging system . Antibody details are provided in S6 Table . Total RNA was isolated from MCF10A cells ( 2 independent infections per condition ) using the QIAGEN RNeasy Mini kit and DNase digested ( Ambion TURBO DNA-free ) . 1μg was used as input for library preparation with the Illumina TruSeq RNA Sample Kit v2 according to manufacturer’s instructions . cDNA concentrations were measured using Qubit dsDNA HS assay on a Qubit 2 . 0 Fluorometric Quantitation System ( Life Technologies ) . 15ng of amplified libraries were pooled , analysed for size distribution using an Agilent Tapestation 2200 D1000 and quantified using Picogreen . Sequencing was performed on a single lane as 75bp paired end reads on a HiSeq4000 according to Illumina specifications . Approximately 30 million reads were generated per sample . The sequences were demultiplexed , quality controlled with FastQC v0 . 10 . 1 and aligned to hg19 using STAR v2 . 4 . 2a [82] . Gene counts were obtained with featureCounts ( Subread , v1 . 4 . 5-p1 ) [83] . Differential gene expression analysis was performed in R using the packages edgeR v3 . 12 . 1 [84] , GeneOverlap v1 . 6 . 0 ( Shen and Sinai , 2013 ) and topGO v2 . 22 . 0 ( org . Hs . eg . db v3 . 2 . 3 and GO . db v3 . 2 . 2 ) ( Alexa and Rahnenfuhrer , 2016 ) . Venn diagrams were produced using BioVenn [85] . All raw sequencing data have been deposited in the European Nucleotide Archive under project PRJEB14813 . Cells were treated with 1μM BIX01294 for 3 days , followed by a 15-minute BrdU pulse . As control , an S phase arrest was induced with 1μM Camptothecin ( Sigma ) for 4 hours prior to BrdU incorporation . Cells were fixed in ice-cold 70% ethanol for 24 hours and resuspended in PBS containing 0 . 5% Tween-20 , 10μg/ml propidium iodide and 500μg/ml RNaseA . Incorporated BrdU was detected using a FITC-conjugated anti-BrdU antibody ( Becton-Dickson ) according to manufacturer’s instructions . 20 , 000 cells were analysed by FACS ( BD FACSCalibur ) . Data analysis was performed with BD CellQuest Pro , FlowJo and GraphPad Prism . For immunofluorescence microscopy , cells were plated onto coverslips ( VWR ) in a 24-well plate . Next day , cells were washed twice with ice-cold PBS and fixed with 4% PFA +0 . 1% Triton X-100 in PBS for 20 minutes on ice . Cells were permeabilised with 0 . 5% Triton X-100 in PBS for 20 minutes and blocked with 10% FCS +0 . 1% Triton X-100 in PBS for 1 hour with three washes between individual steps . Primary ( GATA3 D13C9 , Cell Signaling #5852 , 1:100 ) and secondary ( AlexaFluor 546 goat anti-rabbit , Invitrogen , 1:500 ) antibodies were diluted in blocking solution and incubated for 1 hour at RT . Finally , cells were stained with DAPI ( 10μg/ml , Sigma ) for 10 minutes at RT in the dark . Slides were mounted in 85% glycerol and images were acquired on a Zeiss LSM 710 confocal imaging system . Data for Figs 1A , 1B , 2A , 2B and 2E and S1J and S1 Table were downloaded and/or visualised through the cBioPortal for Cancer Genomics [86 , 87] ( http://www . cbioportal . org; datasets “Breast Invasive Carcinoma , TCGA , Cell 2015 , 1105 samples” and “METABRIC Breast Cancer , 1980 samples” ) . Survival analysis was performed in GraphPad Prism and R . For the initial segmentation analysis based on TCGA , expression and mutation data of the BRCA cohort were downloaded from the TCGA data portal ( https://tcga-data . nci . nih . gov/tcga/ ) . Mutation data were subset to eliminate all mutations marked as “Silent” . For each candidate gene with high mutation load , the data were further trimmed to eliminate patients with more than one mutation in that gene . The mutation positions in the gene of interest were ranked and then compared to ranked , normalised expression levels of every expressed gene in the transcriptome . Segmentation was carried out with the R package DNAcopy [88] using a strict cut-off for definition of breakpoints ( alpha = 0 . 005 ) , a corresponding large number of permutations ( nperm = 20000 ) , and a strict minimum for segment length ( min . width = 5 ) . Genes whose expression across the cohort showed at least one breakpoint along the target gene were termed response genes . Breakpoints found through the segmentation analysis were visualised along the body of the gene using density plots . Densities were computed using breakpoint positions for genes showing one breakpoint and for those showing two breakpoints or more . For GATA3 , details for mutation start site and mutation sequence were jointly used to classify patients into groups: protein truncations in the +1 and -1 frames , protein extensions in the +1 and -1 frame , and all other mutations; patients with multiple mutations were excluded . Gene expression comparisons were then performed between the dominant group ( protein extensions in the +1 frame ) and all other mutant groups combined . In each comparison , we evaluated fold changes by comparing median expression in each group and statistical significance through Wilcoxon tests . For the validation analysis using the METABRIC dataset , mutation profiles on driver genes and expression data for the GATA3-ext signature genes were obtained from the METABRIC consortium ( see Acknowledgments ) . Mutation profiles were subset to remove mutations marked as “Silent” as before; mutations marked as “RNA” ( noncoding substitution variants in untranslated regions of the genes ) were also removed . All other calculations were carried out using the same procedures as for the TCGA dataset . To assess consistency between TCGA and METABRIC on the GATA3-ext signature , we compared fold changes between GATA3-ext and other GATA3-mutant patients in the two datasets . The comparison was targeted on the small gene signature obtained from the TCGA analysis . | Cancer is a disease caused by genetic mutations . Mutation patterns are often indicative of a gene’s function as either tumour promoting or tumour suppressive . Here we describe the frequently mutated , but poorly studied , breast cancer gene GATA3 as a rare exception: We discover that two different functional classes of mutations in this gene can lead to either gain- or loss-of-function activities . The most common type of mutations , resulting in an unusually extended protein , is associated with differential gene expression and decreased disease-free survival . This mutant , in contrast to other mutations or the non-mutated protein , renders cells specifically vulnerable to inhibitors of two chromatin-modifying enzymes , the histone methyltransferases G9A and GLP . Our findings shed light on the functional consequences of frequent GATA3 mutations in breast cancer and represent a first lead toward personalised therapy for a large subgroup of breast cancer patients . | [
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] | 2016 | Gain- and Loss-of-Function Mutations in the Breast Cancer Gene GATA3 Result in Differential Drug Sensitivity |
The possibility that a multi-host wildlife reservoir is responsible for maintaining transmission of Leishmania ( Viannia ) braziliensis causing human cutaneous and mucocutaneous leishmaniasis is tested by comparative analysis of infection progression and infectiousness to sandflies in rodent host species previously shown to have high natural infection prevalences in both sylvatic or/and peridomestic habitats in close proximity to humans in northeast Brazil . The clinical and parasitological outcomes , and infectiousness to sandflies , were observed in 54 colonized animals of three species ( 18 Necromys lasiurus , 18 Nectomys squamipes and 18 Rattus rattus ) experimentally infected with high ( 5 . 5×106/ml ) or low ( 2 . 8×105/ml ) dose L . ( V . ) braziliensis ( MBOL/BR/2000/CPqAM95 ) inoculum . Clinical signs of infection were monitored daily . Whole animal xenodiagnoses were performed 6 months post inoculation using Lutzomyia longipalpis originating from flies caught in Passira , Pernambuco , after this parasite evaluation was performed at necropsy . Heterogeneities in Leishmania parasite loads were measured by quantitative PCR in ear skin , liver and spleen tissues . All three rodent species proved to establish infection characterized by short-term self-resolving skin lesions , located on ears and tail but not on footpads ( one site of inoculation ) , and variable parasite loads detected in all three tissues with maximum burdens of 8 . 1×103 ( skin ) , 2 . 8×103 ( spleen ) , and 8 . 9×102 ( liver ) . All three host species , 18/18 N . lasiurus , 10/18 N . squamipes and 6/18 R . rattus , also proved infectious to sandflies in cross-sectional study . R . rattus supported significantly lower tissue parasite loads compared to those in N . lasiurus and N . squamipes , and N . lasiurus appeared to be more infectious , on average , than either N . squamipes or R . rattus . A multi-host reservoir of cutaneous leishmaniasis is indicated in this region of Brazil , though with apparent differences in the competence between the rodent species . The results provide preliminary insights into links between sylvatic and peri-domestic transmission cycles associated with overlaps in the rodent species’ ecological niches .
Transmission of zoonotic pathogens may involve one , or typically more than one , reservoir host . Compared to pathogens with single reservoir hosts , those involving multi-host communities usually show reduced transmission rates through a process of zooprophylaxis or “dilution effect” due to heterogeneities in their competence to support pathogen replication and in their infectious duration , resulting in reduced pathogen-host contact , or vector-infectious host contact in the case of vector-borne pathogens [1 , 2 , 3] . The less common case in nature is that multi-host communities are more homogeneous as competent reservoirs , such that transmission is amplified , otherwise known as zoopotentiation; complexities in these scenarios are discussed elsewhere [2 , 4] . Quantification of host heterogeneity has led to a better understanding of transmission dynamics [1 , 5 , 6] , and improved mathematical predictions of transmission hotspots towards development of disease surveillance and control strategies [7 , 8] . Zoonotic cutaneous leishmaniasis ( ZCL ) is a prime example where infection has been detected in multiple host species in different habitats , but where the competence of hosts and sand fly vectors in putative transmission cycles , are not well defined . Across the Americas , the predominant aetiological agent of ZCL is L . ( Viannia . ) braziliensis causing , in humans , small simple self-healing cutaneous lesions to disfiguring and destructive lesions known as espundia or mucosal leishmaniasis that can result in irreversible disfigurement of the upper nasal tract . In Brazil the dominant parasite causing cutaneous leishmaniasis is L . ( V . ) braziliensis and there are approximately 26 , 000 reported new human cases per year but estimates of annual incidences range from 72 , 800 to 119 , 600 [9] . L . ( V . ) braziliensis infections have been identified in sylvatic vectors and small mammals in the Atlantic rainforest biome [10 , 11] , however transmission has expanded into anthropogenic habitats where infection is observed in more synanthropic and peridomestic species including rodents , marsupials , domestic dogs and equids [11 , 12 , 13] that may or may not be epidemiologically significant for transmission to humans . Human transmission is predominantly peridomestic as indicated by case age distributions e . g . not limited to adults , forest or plantation workers [14] , and the known vector Lu . whitmani , is captured in large numbers in animals sheds [11 , 15] . Control of human ZCL currently relies on human case detection and treatment , however since humans are not thought to be particularly infectious , interrupting transmission necessarily relies on reservoir or/and vector control . There are no comparative transmission studies of L . ( V . ) braziliensis in small mammal that are indicated as being natural hosts . By experimental infection , this study aims to compare the reservoir competence of wild and synanthropic rodents previously implicated as reservoirs of L . ( V . ) braziliensis in northeast Brazil 25 . These experiments provide the initial data towards defining their individual vs collective susceptibility to infection , ability to support parasite replication , and their infectiousness to phlebotomine sand flies for onward transmission .
After experimental infection , animals were monitored daily to detect any clinical changes including lesions on the inoculation site , hair loss , or splenomegaly . Xenodiagnosis was performed on 18 of each rodent species six months after inoculation using 7-day old sand flies , from the first generation Lu . longipalpis captured in a well studied foci , in the Municipality of Passira , Pernambuco , northeast Brazil ( 7° 56’S , 35° 35’W ) . The mating song of this population has been determined as a burst type , being very similar to Camara and Bacarena populations of Pará State [22] . Burst song populations are principally coastal and all have the cembrene-1 pheromone[23] . The animals were anesthetized with ketamine hydrochloride at 10% and placed in cages into which female sand flies were released and allowed to feed for about 40 minutes in the presence of a similar number of male sand flies in order to induce feeding and copulation . Blood-fed females were then transferred to plastic pots that were stored in boxes with light filter protection and kept under controlled laboratory conditions until the seventh day when they were dissected to detect promastigote forms under optical microscopy . After conclusion of xenodiagnosis , the animals were euthanized with a CO2 inhalation process . Fragments of approximately 50mg of ear skin , spleen and liver were collected from each euthanized animals , and Leishmania parasite DNA quantified by quantitative PCR ( qPCR ) . DNA was extracted from tissues using DNeasy Blood & Tissue kit ( Qiagen ) according to the manufacturer’s protocol . The initial molecular detection protocol consisted of a nested PCR assay using two pairs of SSU rDNA ( Small Subunit Ribosomal gene ) derived oligonucleotides . The first PCR used SSU rDNA primers [24] that amplify a conserved region of all trypanosomatids ( S12: 5’-GGTTGATTCCGTCAACGGAC-3’ and S4: 5’-GATCCAGCTGCAGGTTCACC-3’ ) ; internal oligonucleotides PCR products were analyzed by electrophoresis in agarose gel . The second reaction was a real time PCR ( qPCR ) to quantify the parasite load [25] using primers that amplify a common region of the Leishmania ( Viannia ) subgenus ( S17: 5’-CCAAGCTGCCCAGTAGAAT-3’ and S18: 5’-TCGGGCGGATAAAACACC-3’ ) . The quantification protocol consisted of a real time SYBR-Green PCR; tissue parasite loads were standardized as number of SSU rDNA copies per host glyceraldehyde-3-phosphate dehydrogenase ( GAPDH ) copy number . The PCR conditions were optimized to generate a single melting curve of the product . Established experimental infection was defined as the presence of one or more condition: development of skin lesions associated with symptomatic rodent ZCL , detection of splenomegaly at necropsy , qPCR detection of Leishmania in tissue samples ( ear skin , liver , spleen ) , or infectiousness to sand flies . For statistical analyses , Leishmania loads were log10+1 transformed and tested using general linearised Poisson models ( negative binomial over-dispersion coefficient α<0 . 088 , χ2<0 . 94 , P>0 . 281 in each case ) . The relationships between infectiousness ( proportion of sandflies infected ) or presence/absence of skin lesions against independent variables were analysed using logistic regression weighted by sample sizes . Depending on the outcome of interest , multivariate analysis adjusted for covariates including inoculum size ( high dose or low dose ) , skin tissue log10 parasite load , inoculum size × skin log10 load interaction term , times to lesion onset and lesion recovery , and rodent species . All analyses were carried out using Stata v . 13 . 1 software ( Stata Corporation , College Station , Texas , USA ) . Approvals to conduct this study and to capture wild animals to establish laboratory colonies were obtained from the Animal Research Ethics Committee of Oswaldo Cruz Foundation , Rio de Janeiro ( Protocol No . L-056/05 ) , and endorsed by the Brazilian Institute of Environment ( IBAMA License No . 12 . 749–1 ) . All the experimental animals were handled in accordance with the recommended guidelines and safety measures; . captured animals , experimental animals and the colonies were all kept in quarantine that involved microbiological testing , safety barriers with micro- and macro-isolators , and under strict hygiene conditions [26 , 27] following security standards ( International Organization for Standardization—IS0/15189 ) .
Within 3 months of being inoculated and before sampling four high dose rodents ( 2 N . lasiurus , 2 N . squamipes ) and one low dose R . rattus died , thus final follow-up sample sizes were therefore 18 N . lasiurus , 18 N . squamipes and 19 R . rattus ( 55 animals in total ) . Infections were confirmed in 18/18 N . lasiurus , 18/18 N . squamipes and 9/19 R . rattus by molecular methods and xenodiagnosis ( Tables 1 & 2 ) . The two experimental inoculum doses appeared similar in successfully establishing rodent infection ( 26/29 high dose vs 19/26 low dose animals ) ( χ2 = 2 . 55 P = 0 . 614 ) , though some specific differences were observed as described below . All control hamsters developed infection , that included lesions at the inoculation site and all tissues were positive by nested PCR that confirmed the infectiousness of the high and low dose innoculum . One or more skin lesions associated with infection were observed in 14/55 animals , where 9 and 4 of the high dose animals respectively developed 1 and 2 lesions located on the ear ( 1 N . lasiurus , 3 N . squamipes , 2 R . rattus ) , ear and tail base ( 4 N . lasiurus ) tail base ( 3 N . lasiurus ) and one low dose animal developed a tail base lesion ( 1 R . rattus ) ; no lesions were observed on the footpads at the site of experimental inoculation in any animal . The higher experimental dose induced a higher proportion of animals to present skin lesions ( 13/26 50% ) compared to the low dose group ( 1/29 3 . 5% ) ( χ2 = 9 . 44 P = 0 . 002 ) ( Table 1 and Fig 1 ) . Splenomegaly at necropsy was rare for both doses ( Table 1 ) . At the time that the xenodiagnoses were performed no animals had visible lesions . In high dose animals , average times to lesion development post inoculation were 38 ( 95% CI: 33 . 9–42 . 6 ) , 48 ( 33 . 3–62 . 0 ) and 51 ( 51 . 0–51 . 0 ) days respectively for N . lasiurus , N . squamipes and R . rattus . Time to lesion onset was statistically shorter in N . lasiurus than N . squamipes or R . rattus ( z>2 . 88 , P< = 0 . 004 ) , but not dissimilar between N . squamipes and R . rattus ( z = 0 . 85 , P = 0 . 398 ) . All lesions spontaneously recovered within one month of onset , after an average 14 ( 95% CI: 10 . 9–17 . 1 ) , 21 ( 17 . 8–23 . 5 ) and 19 ( 6 . 3–31 . 7 ) days for the three species , respectively . Lesion duration was shorter ( i . e . faster recovery ) in N . lasiurus compared to N . squamipes or to R . rattus ( z>2 . 01 , P< = 0 . 044 ) , but not statistically different between N . squamipes and R . rattus ( z = -0 . 58 , P = 0 . 561 ) . The results of tissue parasite loads were quantified by qPCR in single skin , spleen , and liver tissue samples from all follow-up animals at necropsy are show in Table 3 . Maximum tissue burdens were 8 . 1×103 in skin , 2 . 8×103 in spleen , and 8 . 9×102 in liver samples . Substantial variation in Leishmania loads were observed between individual tissues , animals , and inocula dose ( Table 3 and Fig 2 ) . Log10 parasite loads in the three tissues were only moderately correlated ( Spearman’s r = 0 . 64–0 . 67 , P<0 . 001 ) . Average log10 skin tissue loads were lower in low dose vs high dose animals ( z = -2 . 87 , P = 0 . 004 ) , whereas the variation in liver and spleen tissues loads did not significantly differ between dose groups when adjusting for inter-species variation ( z<0 . 84 , P>0 . 05 ) . Leishmania loads in R . rattus tissues were generally lower compared to those in N . lasiurus ( all tissue comparisons: z>-4 . 1 , P<0 . 0001 ) or in N . squamipes ( z>-4 . 9 , P<0 . 0001 ) , whereas those of N . lasiurus and N . squamipes were not significantly different from one another ( z<2 . 4 , NS ) . Six months after being infected a total of 299 and 448 female Lu . longipalpis fed respectively on 25 high and 29 low dose animals . No lesions were present on any animals at this time . Flagellates were detected in sand flies fed on 18/18 N . lasiurus , 10/18 N . squamipes and 6/18 R . rattus . The low dose inoculum tended to induce a higher proportion of animals to be infectiousness to sandflies ( 25/29 86 . 2% ) compared to the high dose group ( 9/25 36% ) ( χ2 = 3 . 46 P = 0 . 063 ) ( Table 2 ) . A median 15 ( 95% C . I . : 6 . 9–24 . 1 ) , 12 ( 4 . 3–14 . 7 ) , and 17 ( 6 . 6–21 . 0 ) engorged females sand flies fed on N . lasiurus , N . squamipes and R . rattus , survived to dissection . Mutlivariate logistic regression of the proportion of flies infected ( for N = 34 animals ) , adjusting for skin tissue Leishmania load × inoculum group interactions , indicated that N . lasiurus tended to be more infectious on average than either N . squamipes ( z = -2 . 91 , P = 0 . 004 ) or R . rattus ( z = -1 . 73 , P = 0 . 084 ) . The infectiousness of N . squamipes and R . rattus was not statstically different from each other ( z = 0 . 43 , P = 0 . 670 ) . Despite the general low tissue parasite loads in R . rattus , a significant proportion of low dose animals were infectious to sandflies ( Table 2 ) . Notwithstanding , the proportion of exposed sand flies infected was generally positively associated with the log10 parasite loads in skin tissue when accounting for differences between rodent species ( z = 4 . 69 , P<0 . 001 ) , but was not associated with either time to lesion onset post inoculation ( z-1 . 56 , NS ) or to lesion duration ( z = 0 . 51 , NS ) .
This study investigated the comparative development of experimental infection in three putative rodent reservoir species , and their relative ability to transmit L . braziliensis to blood-feeding sandflies . We show that all three rodent species established infections , supported persistent Leishmania burdens in multiple tissue , and presented transient clinical lesions which developed within an average 38–51 days post inoculation , that spontaneously resolved within an average 14–19 days . All three rodent species were also able to infect sandflies as demonstrated by xenodiagnosis performed at c . 6 months post inoculation , by which time all skin lesions had visually healed . We found an association between infectiousness to sand flies and Leishmania loads in ear skin tissue , but not to lesion presence/absence , onset or duration time . Comparing rodent species , N . lasiurus tended to have a greater likelihood of being infectious ( 18/18 animals ) , compared to the other two species , and for comparable skin log10 Leishmania loads , both N . lasiurus and N . squamipes infected a greater average proportion of sand flies than did R . rattus . R . rattus also appeared less likely to establish experimental infection at either inoculum dose , evidenced by less clinical signs and lower parasite loads . Despite these observations , the low dose group of R . rattus still proved to be infectious to a small proportion of sand flies , at least at 6 months post inoculation . These collective results suggest that the investigated rodent species represent a multi-host reservoir , though with variable reservoir competence by cross-sectional comparison . Whether any single rodent species can maintain a transmission cycle independently ( R0>1 ) requires further study and parameter estimation [28] . For example , there is likely to be a trade-off between duration and degree of infectiousness relative to the host’s life expectancy: low-level infectiousness over sustained periods could be more significant than high-level infectiousness over a shortened life expectancy resulting from acute infection; data on their comparative longitudinal profile to indicate life-long transmission potential would be informative ( e . g . [29] ) . Our observations of R . rattus may indicate a greater innate resistance of this species to L . ( V . ) braziliensis than the other rodent species . It is also possible that this host resolved higher parasitological infections within a shorter time frame than our sampling regime . R . rattus experimentally infected with another cutaneous causing species , L . tropica , presented asymptomatic infections despite ear tissue parasite loads of 4×103−106 with no significant decline over 24m follow-up , and were infectious to a low proportion of sandflies ( 0–7% ) even when fed on the site of experimental inoculation [30] . In contrast to current results , a threshold of infectiousness is positively associated with high parasite loads in ear skin of dogs naturally infected with L . infantum [31] . Unexpectedly , we observed infectiousness to sandflies to be higher in animals inoculated with the low dose compared to the high dose , the latter was associated with skin lesions and higher parasitaemia . In nature Leishmania inoculum sizes from a single infected sandfly have been found to be in the order of 4–40 , 000 metacyclic promastigotes [32 , 33 , 34] . No such figures are available for L . ( V . ) braziliensis . Ideally in such experiments the infecting organisms should come from the bite of a sandfly but at the moment this is technically impossible for L . ( V . ) braziliensis . In the absence of this possibility the inoculum should contain a similar number of organisms to those delivered by the sandfly . We calculate that our lower dose contained a maximum of approximately 22 , 000 metacyclic promastigotes which is within the higher range of parasites delivered by a sandfly infected with a leishmania of the subgenus L . ( Leishmania ) , but our higher dose did not fall within the above mentioned range . As we have already said lesions are not at all typical of natural L . ( V . ) braziliensis infections and because the higher dose produced lesions in many animals we decided to use an inoculum containing fewer organisms in our second experiment . Other factors influence an infection and besides the number of metacyclic promastigotes such sandfly and parasite antigens accompanying the bite and previous exposure to sand fly saliva . The latter may mount a protective response against lesion development , after subsequent challenge [35] . None of our rodents exposed to sand fly bites before the xenodiagnosis nor was any sand fly saliva associated with the inoculation . Significantly more animals , which received the higher dose , had lesions compared to only a single animal that received the lower dose developed a lesion , which mirrored more closely what we have seen in wild infections . The fact that fewer animals that received the higher dose were infectious may reflect a forced immunity produced by more parasites . Natural infections of L . ( V . ) braziliensis in free-ranging small mammals are occult [11 , 36 , 37 , 38] in contrast to rodents naturally infected with some Leishmania belonging to the subgenus L . ( Leishmania ) such as L . ( L . ) amazonensis and L . ( L . ) major [39] that often present parasite rich lesions . Since 1996 we have examined over 1 , 000 small silvatic mammals for infections of L . ( V . ) braziliensis and periodically isolated this parasite from blood , spleen and liver [11] . The skin from wild animals was positive in PCR tests but we have never managed to isolate the parasite from this tissue nor have we seen any leishmanial skin lesions . It’s quite feasible that flies become infected from parasites present in the skin as well as parasites liberated into the blood from the liver and spleen . L . ( V . ) braziliensis has also been detected molecularly in apparently normal skin of asymptomatic wild mammals captured in other endemic areas [37 , 38] but no isolations were obtained . It is possible that cross-sectional field studies fail to detect short-lived clinical signs in naturally infected captured rodents , a question that can only be resolved by longitudinal follow-up studies . Indeed little is known of the tissue tropism of Leishmania species in their natural hosts , and in laboratory animals some strains of L . ( V . ) braziliensis are predominantly cutaneous while others also visceralize [40] . The appearance of metastatic lesions at the base of the tail in 13 of our high dose animals and 1 of the low dose animals and the complete absence of lesions at the site of inoculation on the foot pad is extremely interesting . The base of the tail is one preferential feeding site for sand flies and leishmanial lesions have been frequently observed at the base of the tail for L . ( L . ) mexicana and L . ( L . ) amazonensis [41 , 42] . So why did the lesions appear at this site ? A possible reason is a tissue tropism which favors the parasite being in a place where it is readily available to the vector and may be present in the absence of visible lesions . This indicates that future studies on reservoirs need to concentrate on material from the tail base irrespective of the presence of a lesion . We detected a poor correlation between the log10 parasite loads in rodent skin , liver and spleen tissues . In longitudinal studies of Leishmania loads in dog tissues naturally infected with L . infantum , we similarly observed a poor correlation between tissue loads , however , this was explained by the observed proportional shift in parasite loads in the skin relative to in bone marrow which increased during the time course of infection [31] . Many of the clinical forms of L . ( V . ) braziliensis in man , such as disseminated cutaneous and mucocutaneous presentations , are considered due to metastatic spread from an initial active or cured lesion or some internal tissue . L . tropica loads of 7 . 5×103–6×104/cm2 were reported in cutaneous sites ( tail tissue , but not in liver , spleen , blood , or bone marrow ) disseminated from the experimental inoculation site in R . rattus [30] . It appears that L . ( V . ) braziliensis , and other Leishmania species , have adopted a strategy in the host to become persistently available to sandflies in the skin and peripheral blood following parasites dissemination from the site of inoculation or/and multiplication in liver and spleen tissues . One caveat of the current study is that xenodiagnosis was performed using Lu . longipalpis rather than Lu . whitmani , a confirmed vector of L . ( V . ) braziliensis . Lu longipalpis is highly susceptible to many Leishmania species , including a number of L . ( Viannia ) species [43] thus being classified as a permissive vector [44] , and has been considered a potential vector of L . ( L . ) amazonensis and L . ( V . ) braziliensis in Brazil [45] . Here we necessarily treat the xenodiagnosis results as comparative values , assuming that any bias associated with relevant vectorial capacity components is uniform across rodent species . Lu . whitmani and Lu . intermedia as well as other sand fly species are considered to be competent vectors of L . ( V . ) braziliensis , based on epidemiological and parasitological observations of wild caught infected female flies . The absence of suitable models to assess vector competence for L . ( V . ) braziliensis is reflected by the fact that there is only one published account of the successful experimental transmission and this was with a naturally infected fly [46] . Lu . longipalpis is considered to be a permissive vector [44] because it supports the development and adherence of different Leishmania including species of subgenus L . ( Viannia ) [47] [48] as well as other promastigote producing heteroxenous parasites , such as Endotrypanum [49] . However , its capacity as a vector involving colonization of the cardial region and the production of metacyclic promastigotes has yet to be assessed for this group of parasites . Within this frame work we consider that it is perfectly valid to use Lu . longipalpis to assess the comparative infectiousness of these rodent hosts . Whether its sensitivity in detecting infection is equal to that of the natural vectors can only be determined by comparative experiments . Lu . longipalpis is a complex of sibling species[22] so another question is are there differences in their susceptibility to infection ? So far there is no evidence to suggest such differences exist . Our flies belong to the burst song group which is the same as flies that have been widely used by other workers under the name Marajó . The rodent species evaluated in the current study were selected on the basis of consistent high infection rates or/and high abundance in multiple field studies in northeast Brazil [10 , 11] . N . squamipes is the largest rodent of the three species ( c . 240gm vs R . rattus 50gm and N . lasiurus 160gm ) which may attract relatively more sand flies [15 , 50 , 51] . The comparative roles of domesticated hosts , such as dogs and equids , have yet to be quantified: it is known that infection prevalences in dogs are comparable to those in rodents [11 , 52] , and that dogs can infect Lu . whitmani when fed on their skin lesions [53 , 54] . However , there are few xenodiagnosis studies on naturally infected hosts of Leishmania , and detection of infection does not necessarily equate to transmission potential ( e . g . [55] ) . This study provides some preliminary insights into the likely transition from the assumed original transmission cycle of L . ( V . ) braziliensis involving Atlantic forest small mammals and sand fly vectors , to a more peridomestic cycle involving , not least , the infectious rodents described here , that are associated with overlapping sylvatic and peridomestic habitats ( N . lasiurus and N . squamipes ) and domestic habitats ( R . rattus ) respectively [11 , 15] . The expansion of this apparent “bridge” between sylvatic and peridomestic transmission habitats are facilitated by the widespread deforestation and conversion of remaining forest to sugarcane and banana plantations . The consequence of environmental shifts on multi-host identity and diversity e . g . proportion of opportunistic and/or competent host species in anthropogenic habitats , may prove to be positive or negative for human transmission [2] . Potential changes in zoopotentiation or zooprophylaxis may be offset by the sand fly vector’s restricted feeding behaviour: Lu . whitmani demonstrates a degree of domesticity , feeding site and host choice loyalty , potentially limiting vector-host contact to more predominant competent species [15 , 51 , 56] . This focus lies at the southern edge of the geographical range of N . squamipes [57] , with the possibility that other species inhabit it’s ecological niche elsewhere [10] . Research is now needed to place the current results in context of longitudinal field studies of natural infection and transmission and including in domesticated animal hosts . | Across the Americas , Leishmania ( V . ) braziliensis is the predominant Leishmania species causing cutaneous and mucocutaneous leishmaniasis in humans . Transmitted by Phlebotomine sandflies , questions remain about the epidemiological contributions of the numerous zoonotic and more domestic host species . Domestication of the principal vector and human infection patterns suggest that human infection risk is predominantly peridomestic , whereas control strategies will be more complex if there is a link to a wildlife transmission cycle . Almost no studies have been conducted on the transmission potential of natural hosts of L . ( V . ) braziliensis . This study evaluates the infectiousness of experimentally infected natural rodent host species , that in different ecological habitats are proposed to act as a single or a multi-host reservoir . Clinical and parasitological development , and the ability to transmit Leishmania to sandflies , was observed under experimental conditions using a single strain of L . ( V . ) braziliensis isolated from the wild rat , Necromys lasiurus . Xenodiagnoses were performed with laboratory bred sand fly females established from a local population of Lutzomyia longipalpis . All three rodent species developed disseminated subclinical parasitological infections , but clinical signs ( lesions ) were transient and self-resolving . N . squamipes , N . lasiurus and R . rattus were all infectious when asymptomatic , though their competence in transmission potential appears to differ with R . rattus showing signs of lower susceptibility . These results provide further evidence that a multi-host reservoir is responsible for maintaining transmission with a bridge between infectious sylvatic and peridomestic rodent populations . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [] | 2015 | Infectiousness of Sylvatic and Synanthropic Small Rodents Implicates a Multi-host Reservoir of Leishmania (Viannia) braziliensis |
The automated comparison of protein-ligand binding sites provides useful insights into yet unexplored site similarities . Various stages of computational and chemical biology research can benefit from this knowledge . The search for putative off-targets and the establishment of polypharmacological effects by comparing binding sites led to promising results for numerous projects . Although many cavity comparison methods are available , a comprehensive analysis to guide the choice of a tool for a specific application is wanting . Moreover , the broad variety of binding site modeling approaches , comparison algorithms , and scoring metrics impedes this choice . Herein , we aim to elucidate strengths and weaknesses of binding site comparison methodologies . A detailed benchmark study is the only possibility to rationalize the selection of appropriate tools for different scenarios . Specific evaluation data sets were developed to shed light on multiple aspects of binding site comparison . An assembly of all applied benchmark sets ( ProSPECCTs–Protein Site Pairs for the Evaluation of Cavity Comparison Tools ) is made available for the evaluation and optimization of further and still emerging methods . The results indicate the importance of such analyses to facilitate the choice of a methodology that complies with the requirements of a specific scientific challenge .
Due to the huge number of available binding site comparison algorithms[5] , a large-scale analysis of all methods is beyond the scope of this article . We have therefore restricted the evaluation to a small , but still diverse subset of promising algorithms . These were derived from an analysis of successful applications within medicinal chemistry projects[5] . A comparison of web server-based tools became infeasible due to the number and size of analyzed data sets , so we restricted our evaluation to standalone tools . The methods analyzed herein and their fields of utilization are summarized in Table 1 . Intriguingly , the success of nearly all of those studies resulted from the use of binding site comparison as part of a workflow combining different computational methods , e . g . MD simulations or molecular docking studies . A recent , impressive example shows that the combination of various tools of structure-based modeling and binding site comparison delivers insight into putative mechanisms of drug action[17] . Herein , we analyzed binding site comparison methods which are based on fingerprints ( e . g . , SiteAlign[18] and TIFP ( Interaction Fingerprint Triplets ) [19] ) , graphs ( e . g . , Cavbase[20 , 21] and IsoMIF[22] ) , grids ( VolSite/Shaper[23] ) , and those that make use of alternative approaches ( PocketMatch[24] and SiteHopper[25] ) . KRIPO ( Key Representation of Interaction in Pockets ) , that was originally designed for sub-pocket matching to facilitate bioisosteric replacement , can also be applied for ligand binding site comparison[26] and was included in our analysis . Additionally , TM-align[27] , which was developed to compare protein structures based on their overall structure , was evaluated as it was successfully applied in various medicinal chemistry scenarios . The freely available tools CMASA[28] , COFACTOR[29] , and PocketFEATURE[30] could not be analyzed . Both CMASA and COFACTOR can only be applied with pre-prepared data sets; they enable the user to compare the binding site of interest against sets of precomputed binding sites . PocketFEATURE is not publicly available . Grim ( Graph Interaction Matching ) [19] and RAPMAD ( Rapid Pocket Matching using Distances ) [31] were included as they make use of binding site representations highly similar to TIFP and Cavbase . The impact of the underlying data structures was evaluated ( fingerprints vs . graph models for TIFP and Grim , histograms vs . graph models for RAPMAD and Cavbase , respectively ) . While fingerprint- and histogram-based methods are usually characterized by low computational demands , graph models enable a more accurate binding site characterization accompanied by higher run times . The representation of the binding site for the subsequent comparison algorithm has a large impact on the outcome of the investigations . We therefore classified all approaches as depicted in Fig 1 . While most tools encode the binding site features based on the underlying ligand-interacting residues , other approaches use surface-based binding site representations , e . g . through a projection of physicochemical site features onto the respective surface patches . Probably the highest level of abstraction is achieved by programs that encode protein-ligand interactions , i . e . they do not depend on a distinct set of residue classes or functional groups , but on different types of interactions . While the residue type is crucial for most residue- and surface-based methods , interaction-based methods often rely on the nature of the bound ligand . However , this classification only holds true for binding site modeling . The final scoring scheme applied to a binding site pair match might also include other properties , for example , surface similarity . Moreover , the data structure for comparison differs for methods within a category . While residue-based comparisons are achieved using versatile approaches , trends can be derived for surface- and interaction-based methods . Interaction-based methods make use of graph and fingerprint representations . Graph and 3D point approaches are often used for surface-based comparisons . Brief descriptions of the methods and explanations of the scoring schemes are given in the SI ( S1 Text ) to outline the general concept underlying each method . This information on different scoring measures and comparison approaches is included to support the understanding of the outcomes presented herein . The explanations are by no means complete , but are focused on the most essential points . Although these descriptions are sufficient for the scope of this study , we encourage readers to refer to the methods’ publications to gain further insight into usage and parameter details . In most cases , default settings were applied in our work , hence parameter optimization might lead to improved performance .
As for many other computational methods , the success of binding site comparison methods has to be evaluated using benchmark data sets . Some evaluation sets previously created to test the tools analyzed herein are summarized in the SI ( S1 Table ) . Although we do not claim that our collection is comprehensive , there are no commonly used , state-of-the-art evaluation data sets available . Standardized benchmark sets are accessible for broadly applied modeling approaches such as pharmacophore searches[61] and molecular docking[16 , 62] . In contrast , the high diversity of the applied benchmark sets for binding site comparison makes it difficult to draw definitive conclusions in comparing the different tools . There is a need for a common evaluation scheme to assess the applicability of highly diverse methodologies . Even knowledge of the underlying concept is not sufficient to make a confident choice of an appropriate tool . For example , Cavbase and SiteAlign make use of similar binding site representations , but the underlying comparison algorithms and scoring schemes differ . It is not obvious whether a certain method should be used preferentially . The evaluation of SiteAlign ( S1 Table ) would suggest that the method is able to detect evolutionary relationships by identifying similarities between binding sites with similar biological functions . However , it has been successfully applied for the investigation of protein-ligand interactions ( Table 1 ) . TM-align was never evaluated as a cavity comparison tool , but it has given hints on interesting binding site relationships ( Table 1 ) . The high number of application domains encouraged us to develop novel , objective data sets with respect to specific aspects of binding site comparison . Pairs of similar and dissimilar binding sites enable an objective and detailed analysis of available tools . Various benchmark studies were performed for the methods mentioned above . Based on the results , we attempted to discern whether a suitable method , both in general and for specific application domains , can be selected . The first data set ( structures with identical sequences ) was designed to evaluate the tools’ sensitivity to the binding site definition . This definition often depends on the size and location of bound ligands . Different ligands can address various regions of the binding site of interest ( sub-pockets ) . Because of these different site definitions , similar sub-pockets are more difficult to match . Although binding sites can interact with a broad variety of ligands , they share common properties and distinct similarities . Thus , the scoring scheme has to be optimized for this scenario to enrich similar binding site pairs with different cavity definitions in a ranked list . Data set 1 contains structures with identical sequences , which bind to chemically different ligands located at identical sites , leading to diverse binding site definitions used for the comparisons . The second data set assesses the tools’ performance with respect to the binding site flexibility . This is an important factor when comparing two similar pockets . The data set is based on protein models extracted from ligand-bound solution NMR structures with more than one model in the structure ensemble . The next two data sets were used to elucidate the scoring metrics’ discrimination performance on nearly identical binding sites differing by a single substitution or by multiple mutations . Artificial protein structures were created by randomly picking binding site residues and substituting them with physicochemically different residues ( decoy set 1 ) and residues that lead to a change in the binding sites’ size and physicochemical properties ( decoy set 2 ) . A comparison of binding site pairs of proteins on a data set containing both the original sequences and their artificially generated counterparts should lead to an enrichment of binding site pairs of proteins with the original sequences . Pairs of original and modified binding sites should obtain lower similarity scores . Two pre-existing data sets were used to evaluate whether tools are able to differentiate between binding sites that bind different ligands and to identify similarities between binding sites occupied by identical or highly similar ligands . One such data set was described by Kahraman et al . [63] . This was originally designed to evaluate whether binding site shape and ligand shape are related . The authors state that the variability of binding site shape cannot solely be explained by the conformational variability of the ligand . Although the data set structures are derived from unrelated proteins , it was not investigated whether there are local similarities between the binding sites with similar ligands . In contrast , the data set of Barelier et al . [64] comprises 62 pairs of unrelated proteins binding to similar ligands . It includes 19 pairs of binding sites that show local similarities ( as “observed” by the ligand atoms ) whereas the remaining pairs do not display any obvious resemblance . Finally , we established a data set of binding site pairs whose similarity was correctly identified by at least one binding site comparison tool as described in a recent perspective[5] . We combined those similar binding site pairs with a diverse data set of sc-PDB[65] derived binding sites . A comparison of the query structures ( binding sites with known similarities ) to the complete data set ( data set of successful applications ) can be performed . This analysis allows the assessment of whether the tools are able to enrich similar site pairs within the high-scoring hits . This benchmark set contains the most interesting pairs as their similarity proved useful in various medicinal chemistry projects . Table 2 gives an overview of the benchmark data sets , their main goals , composition , and experimental quality ( if applicable ) . For some data sets , we generated two versions ( e . g . data set 1 and data set 1 . 2 ) to focus on specific aspects of binding site comparison . Details regarding the individual data sets can be found in the Methods section and S1 Fig and S2 , S3 and S5–S11 Tables . As all data sets include active ( i . e . similar ) and inactive ( i . e . dissimilar ) pairs , the ROC ( receiver operating characteristics ) curves as well as EFs ( enrichment factors ) at different percentages of the screened data set for all analyses can be calculated . A detailed summary of all AUC ( area under the ROC curve ) values and EFs obtained for the applied tools is given in S14 , S16 , S18 , S20 , S22 , S24 , S26 , S28 , S30 and S32 Tables . The significance of the AUC values and the differences between the methods’ AUC values for the different data sets are provided in S15 , S17 , S19 , S21 , S23 , S25 , S27 , S29 , S31 and S33 Tables . A final aspect for choosing an appropriate tool is the computational cost required by the different methods . The interplay of cavity preparation , number of modeled binding site properties , the implementation of the comparison algorithm , and filtering steps determines the final CPU time . Therefore , the run time of binding site preparation as well as comparison per method was analyzed . Table 6 summarizes the different algorithms with respect to the run time for binding site pre-processing of all 100 structures of data set 5 and for 10 , 000 comparisons ( all-against-all ) . As PocketMatch , SiteAlign , and TM-align rely on user-defined cavities , which were prepared in an automated manner using a Python script , i . e . exemplary run times are given that might change for a different site extraction method . Interestingly , the tools were not as robust as expected and failed at different steps of preparation and comparison . The failure rates of all methods for the analyzed data sets are given in S10 Fig . The most interesting finding was the importance of PDB to MOL2 conversion to decrease the failure rates of Grim and TIFP . Both methods should be preferentially applied with MOL2 files of the protein-ligand complexes . The calculation of the average run times per comparison takes the number of omitted comparisons into consideration . For Cavbase and RAPMAD , all structures could be processed , but the ligand-defined binding site was not identified for nine structures . Therefore , the comparison run time is given for 8 , 281 comparisons . For FuzCav and Shaper , only 96 out of 100 binding sites could be prepared . A pre-processing of the prepared pockets ( with the PDB file parser pdbconv of IChem ) with VolSite for a subsequent Shaper comparison ( VolSite/Shaper ) led to only 57 pockets derived from PDB files . In contrast , 76 cavities were extracted with MOL2 files as input for pdbconv . TIFP calculations led to 47 fingerprints for PDB files and 77 for MOL2 files . In Table 6 , the numbers in brackets summarize the numbers of prepared structures and comparisons for each tool . A clear correlation between the comparison method used and the run time could not be observed . Nevertheless , it is possible to differentiate between very fast methods ( several microseconds per comparison ) , moderately fast methods ( several milliseconds per comparison ) , and comparably slow methods ( several seconds per comparison ) . Depending on the desired outcome and the size of the data set , the computational cost might be a limiting factor and the use of some methods becomes infeasible . For a comparison of a single binding site of interest against all known pockets as stored in the sc-PDB[65] ( 9 , 283 entries ) on a single CPU , three days will be necessary using SiteAlign while PocketMatch will perform these comparisons within 0 . 28 seconds . For an all-against-all comparison , RAPMAD is approximately 9 , 200 times faster than the graph-based method Cavbase . In contrast , TIFP is only twice as fast as the graph-based method Grim . Such minor differences suggest the use of the better-performing method . The run time also depends on the type of comparisons . Pairwise comparisons were performed for Grim , IsoMIF , VolSite/Shaper , SiteAlign , SMAP , TIFP , and TM-align , i . e . the tool was invoked for each comparison separately . This might become the time-limiting step for some methods . Cavbase , FuzCav , PocketMatch , ProBiS , RAPMAD , SiteEngine , and SiteHopper allow the comparison of one query against a list of targets in one run . This causes a speedup as the tool has to be invoked only once . For ProBiS , the conversion of PDB files to a surface file format is necessary . Thus the cavity preparation time reflects this preliminary step . Additionally , KRIPO and RAPMAD enable even faster comparisons by providing SQLite databases of the modeled sites and the automated calculation of similarity matrices . These factors illustrate the difficulty in analyzing and comparing run times . In this analysis , we chose the fastest option available for each tool . The computational cost will therefore differ depending on the tool’s implementation and the type of approach employed ( comparing distinct binding site pairs , query-based analyses , generation of distance matrices , etc . ) . Finally , the nature of the data set has to be taken into consideration . Changing the binding site size might have a huge impact on the run time per comparison . While the run times of fingerprint-based methods are often not affected by this factor , graph-based methods might become significantly slower with an increasing graph size . Cavbase , for example , fails to compare very large binding sites . ProBiS uses pre-filtering steps to speed up comparisons .
Plenty of factors influence the decision for a suitable binding site comparison tool . S13 Table summarizes the tools’ characteristics with respect to the most important criteria and provides a detailed assessment of the tools’ performance . First of all , the necessary pre-processing of structures might have an impact on the choice . The preparation of large data sets , e . g . for the prediction of potential off-targets using all known binding sites , requires tools that enable an automated and flawless binding site processing and annotation . We divided this criterion into two parts . Firstly , it is important that binding sites can be prepared in an automated fashion . For example , the KRIPO developers generated scripts to automatically process the binding sites of interest . The same holds true for FuzCav , Grim , TIFP , Shaper , and VolSite/Shaper ( although for some PDB structures ligand MOL2 templates have to be provided ) . Cavbase and RAPMAD require XML-formatted cavity descriptions as input which can be generated with the help of the CSD Python API[90] in an automated fashion . Secondly , the inclusion of all available binding sites might be crucial ( S10 Fig ) . The prediction of potential off-targets and identification of novel binding sites require comprehensive cavity data sets . It is also vital that the chosen tools are reasonably fast with respect to such applications ( run time ) . Sometimes , it is of interest to investigate the similarity between predicted binding sites and already known ones , e . g . for the identification of druggable binding sites of novel targets . Many interaction-based methods will not be able to compare predicted pockets ( Grim , KRIPO , TIFP ) . SiteEngine will also fail as the tool relies on protein- and ligand-surface construction to compare the resulting sites’ surfaces . Furthermore , SMAP does not provide the possibility to process detected binding sites . The only possibility is the generation of putative ligand binding modes via docking or pharmacophore searches to obtain structures with bound ligands for these methods . The tools Cavbase , FuzCav , RAPMAD , Shaper , and IsoMIF come along with implemented binding site identification approaches . Additionally , PocketMatch , ProBiS , SiteAlign , and TM-align process user-defined binding sites based on residues . Therefore , it is possible to apply them to externally predicted binding sites . SiteHopper was shown to compare predicted pockets as long as fpocket[91]-derived cavities are utilized as a pseudo-ligand[53] . Based on the analyzed data sets we can also draw conclusions with respect to a suitable input for binding site comparison . Tools that showed a poor performance for data set 1 should not be used if only a small number of protein-ligand complex structures are known . These tools are very sensitive toward the nature of the ligand . The problem may possibly be circumvented by using docking-derived poses of ligands in the binding site of interest . Some methods’ scoring schemes suffer from a small similarity score difference window for similar and dissimilar binding sites . In many cases , it is advisable to use all available crystal structures or multiple NMR models to retrieve promising results . Although the accuracy of NMR structures is poorly validated[92] , they seem to be an acceptable choice for different structural biology and computational modeling challenges[93 , 94] . The G-factors calculated with PROCHECK-NMR ( Table 2 ) indicate a lower quality as compared to the X-ray data sets . Nevertheless , the average of -0 . 28 point toward usual dihedral angles and main-chain covalent forces . Several tools were developed to facilitate the selection of a diverse set of representative protein structures[95 , 96] . In the absence of experimental structural ensembles , the use of MD-derived binding site conformations as input for a comparison might be beneficial . With respect to the decoy data sets , there are only a few tools that are able to distinguish between similar pockets with minor dissimilarities and highly dissimilar sites . SMAP , SiteHopper , and KRIPO are best suited to convincingly penalize differences between protein structures and their artificially created decoy variants with different numbers of substitutions . Other tools failed to score dissimilarities appropriately . This can be partially attributed to the fact that the binding site’s shape was retained in data set 3; yet , the introduction of major shape differences ( data set 4 ) did not cause substantial improvements . Tools which do not reliably score minor differences in structures might provide better results if they are applied to elucidate similarities between unrelated cavities . In such cases , residue changes and geometric deviations have to be tolerated to identify such partial similarities within the best-ranked binding site pairs . The evaluation of the tools with regard to the data set of Barelier et al . [64] shows that a final visual inspection is unavoidable if the user is interested in a drug repurposing strategy or the establishment of polypharmacology . Therefore , we have to differentiate between the methods depending on the possibility to visually analyze the resulting binding site alignment . The data set of similar binding site pairs extracted from lifelike analyses gives some insight into the basic applicability of the tools for different scenarios . The observation that all tools showed a very high early enrichment underlines the fact that all tools perform well for relevant examples . The individual strengths and weaknesses of all tools finally level out to considerably high early enrichment of similar binding site pairs for all methods . Whenever possible , it is advisable to use more than one tool as they might complement one another . The comparison methods Grim and TIFP are characterized by a comparably low applicability toward the identification of binding site similarities . The original application domain both tools were designed for was the rescoring of docking poses and pose selection , e . g . in a structure-based virtual screening campaign . For these approaches they perform well as shown in the original publication[19] and quite recently for the D3R grand challenge 2015[97] . We could not use the pre-processed structures as stored in the sc-PDB[65] as our data sets make use of all available PDB structures . Restricting the binding site comparison with TIFP and Grim to sc-PDB derived structures might provide a different perspective for both tools as their performance depends highly on the pre-processing of the binding sites . Both tools assign interaction patterns depending on the nature of the ligand which impairs their success for some of our chosen data sets . Nevertheless , their performance on the data set of lifelike examples was good . Finally , the application of score thresholds to discriminate between similar and dissimilar binding sites can be discussed . Similarity scoring for binding sites is challenging . Often , score cut-off values have to be optimized for specific studies[11] . Our investigations also show the strong context dependency of such thresholds ( S12 Table ) . If a clear classification of similar and dissimilar pairs is necessary , the appropriate cut-off has to be evaluated with a data set that reflects the main purpose of the analysis . Score thresholds for the decoy data sets ( data set 3 and 4 ) should be taken into account if binding sites with minor dissimilarities are compared . In contrast to the elucidation of local binding site similarities or similarities of pockets of unrelated proteins , the less restrictive cut-offs determined for the data set of Kahraman and colleagues[63] or data set 7 are more appropriate . The methods SMAP and ProBiS offer additional statistical measures to estimate the significance of a binding site match . Nevertheless , we want to emphasize that the application of thresholds often means disregarding potentially interesting and yet unexplored binding site similarities . The Venn diagram in Fig 15A summarizes six potentially meaningful categories which influence the decision on a useful tool . Depending on the problem to be addressed , other criteria out of a multitude as given in S13 Table might also facilitate the choice . We strongly recommend this comprehensive table for further details . Nevertheless , we wanted to give an example for guiding the way to the most appropriate tool according to the criteria selected . FuzCav seems to fulfil all criteria in the Venn diagram and can be safely applied for evolutionary analyses . However , the binding site matches cannot be visualized . Hence , the method is not suited for query-based drug repurposing projects which require a detailed examination of the identified similarities . The use of KRIPO enables the generation of site alignments , but the method is not applicable for predicted sites and consequently not suited for function prediction . ProBiS failed to rank the similarity according to the number of residue substitutions and will probably fail to accurately score minor cavity dissimilarities . As this is decisive in the analysis of evolutionary binding site relationships , such scenarios should be analyzed with SiteAlign , SiteEngine , or SMAP . A potential user can thereby iteratively exclude tools to arrive at a final choice . Fig 15B puts all results into context and can be used to discuss possible application areas for the binding site analysis tools . The applicability suggestions for the different types of tools are based on previous studies illustrating different uses in drug discovery[5] . However , the diagram also hints at possible fields of applications yet unexploited , for example , the use of interaction-based methods for selectivity prediction or surface-based methods for target elucidation . Of course , the general aim of a study , the basis of data , and the number of comparisons necessary will influence the choice of a method . The embedding of a tool in an elaborate in silico workflow might serve to diminish certain weaknesses . Methods never applied to date for a certain field of research might provide useful results within specialized projects . Some obvious new applications are the use of interaction-based methods for the analysis of protein-protein and protein-ligand interactions or drug repurposing approaches exploiting the outcome of surface-based site comparisons . The prediction of off-targets with comparably fast residue-based approaches is another unexplored application . Furthermore , some tools have never been applied in independent studies . Several projects which made use of Grim[97] , RAPMAD[98] , and SiteHopper[99] demonstrate their unique capabilities . Their strengths as highlighted herein might encourage researchers to actively use these tools . Although it is not possible to identify any single tool which fits all needs and performs well for all data sets , we can provide some guidance regarding different aspects of binding site comparison . The impact of the ease and completeness of binding site pre-processing steps increases with an increasing number of proteins used for comparison . While both factors are crucial for elaborate projects , e . g . off-target prediction or the identification of novel binding sites , their importance decreases for relatively small data sets used in selectivity profiling or polypharmacology prediction . An application to predicted binding sites is relevant for off-target prediction , drug repurposing , or the identification of potential targets . For polypharmacology elucidation , one generally refers to known druggable binding sites . The analysis of similar binding sites to elucidate minor structural differences is highly influenced by the binding site’s definition and flexibility , as is the ranking with respect to binding site properties . In contrast , the elucidation of similarities between the cavities of unrelated proteins does not necessarily depend on accurate scoring . MD simulations or the use of NMR ensembles might help to circumvent a potential failure of binding site comparison due to insufficient consideration of protein flexibility . It is necessary to unravel the similarity between functionally unrelated binding sites which bind similar ligands when understanding polypharmacology . Visualization of identified similarities , which is essential when dealing with non-obvious similarities , is not necessarily crucial for selectivity profiling or analyzing evolutionary relationships . These criteria should become the focus of future benchmark analyses of other promising site comparison approaches as such analyses can guide the rational choice of a method . Ultimately , the choice of a comparison method depends on the focus of the study . These investigations can help to ease the choice of a suitable tool , though restricted to a limited subset of available approaches . The publication of the generated data sets and the benchmark results can assist in the assessment of tools and the establishment of reliable workflows that consider individual strengths and weaknesses . We hope that the assembly of benchmark sets ( ProSPECCTs ) and the conclusions drawn from the evaluation encourage researchers to objectively assess the advantages and drawbacks of individual approaches . Finally , this guide could facilitate the final choice of a suitable method and enable researchers to derive an advantage from these–as far as our experience goes—widely underemployed binding site comparison approaches .
The structures of all data sets were prepared in the same way to ensure an equivalent basis for all binding site comparison tools . First , modified residues were identified and the respective HETATM record names in the PDB files were changed to ATOM . This modification did not affect the tools’ performance , but was essential for the site processing with SiteEngine which detects ligands based on the HETATM record names . The binding site’s defining ligand was identified and renamed to LIG for further processing steps . Other HETATM entries were deleted to ensure the exclusion of buffer ions , cofactors , and prosthetic groups . The final steps were realized with the help of the pdbcur tool of the CCP4[101] software package ( version 6 . 5 ) . Alternative locations with the highest occupancy were retained , while for locations with identical occupancy values the first one was retained . Finally , ANISOU entries were removed . The resolution and R-factors ( R work ) for all structures were downloaded as a report from the PDB[1] . The mean values , standard deviations , minima , and maxima of these parameters were calculated for all data sets including X-ray structures . G-factors were calculated using PROCHECK[66] and PROCHECK-NMR[67] . These values measure the degree of unusual dihedral angles and covalent forces of the main chain . G-factors below -0 . 5 hint at unusual structure properties . For the groups of structures with identical sequences and the NMR ensembles , all residues were renumbered according to the sequence alignment calculated with default settings in MOE2013[102] . Binding site residues were assigned based on a 5 Å radius of all ligand atoms . Subsequently , the Cα atoms and all atoms of the binding site-defining residues were aligned using the “match” command of UCSF Chimera[60] . The mean RMSD values , standard deviations , minima and maxima of all pairwise comparison were calculated to characterize the binding site flexibility . For the data set of identical structures , Tanimoto coefficients based on the ECFP4 fingerprints were calculated in a pairwise manner for all groups using KNIME[103] . Binding site descriptors were calculated using DoGSite . The binding site ligand was chosen as the reference ligand and the pocket was defined by the ligand . Apart from these changes , default settings were used . The resulting pocket descriptors were analyzed for each NMR ensemble of data set 2 , the sequence-culled set of the sc-PDB and the structures of the data set of Barelier and co-workers[64] . Unless stated differently , default parameters of the analyzed tools were used . The exception that holds true for all methods is that the scoring measure used for ranking was selected based on the early enrichment for the data sets of structures with identical sequences , NMR , and decoy structures with descending priority ( Table 9 ) . Regardless , all scores were calculated for further evaluation . Additional information for the different scoring schemes used is given in the SI ( S1 Text ) . Most binding site comparison tools applied in this study have some major limitations that have to be taken into account when applying them to different types of data sets . They will be discussed in the following . PocketMatch ( version 2 . 0 ) , SiteAlign ( version 4 . 0 ) , and TM-align ( version 20170708 ) rely on pre-processed ligand binding sites . The respective binding sites were extracted by means of a Python script which creates an output PDB file that contains all protein atoms within a given radius of the ligand atoms . This excised binding site was used for the comparison . A 5 Å radius was applied for the creation of input cavities for PocketMatch and SiteAlign . Residues within 10 Å of the ligand were used as the binding site definition for TM-align to guarantee a sufficient number of residues to yield a reliable alignment . The programs Cavbase and RAPMAD require XML-formatted pockets as input for binding site comparison . The extraction of the pockets was achieved using the CSD Python API 1 . 3[90] from the CCDC . Residues with missing backbone atoms which were not part of the binding site were excluded to obtain pre-processed cavities as these residues were not properly processed . The cavity extraction is based on pockets detected by LIGSITE[76] . Thus , the binding site of interest could not be found for some PDB entries . We used only cavities including ligand atoms whereas other detected pockets were excluded from the analysis . If more than one pocket for the ligand of interest was identified , the pocket with the largest cavity volume was used . Some very large cavities could not be processed using Cavbase . The use of RAPMAD is restricted to similarity scoring of binding sites as it does not generate a binding site alignment . FuzCav comparisons can be performed for binding sites extracted with the pdbconv tool of IChem , i . e . the complete site or residues within 4 , 6 , 8 , 10 , or 12 Å of the ligand can be used for the comparison . A 6Å radius was used in our analyses . IsoMIF is a molecular interaction-based method that generally relies on bound ligands . A cut-off radius can be used to restrict the binding site dimensions depending on the ligand . The use of the additional tool GetCleft[108] allows for the inclusion of predicted cavities . KRIPO fingerprint databases can be individually created by users for their own data sets . A ligand database is provided for all PDB entries . For proprietary or modified structures , a ligand database has to be generated . Fingerprints can be prepared for ligand fragments as well as for complete ligands with the command line tool “kripo” . Subsequently , the KRIPO DB package ( “kripodb” ) was used to perform all-against-all comparisons for the derived fingerprint databases for complete ligands . The final similarity matrix did not include similarities between identical structures . The identity scores were automatically set to 1 . PocketMatch does not take modified residues into account . The tool is suitable for similarity ranking but the alignments are not available as output . The PDB convention of 80 characters per line has to be fulfilled for all input pockets . Binding sites are defined by the ligand structures for ProBiS comparisons . A distance threshold can be applied to modify the cavity definition . By default , ProBiS does not provide scores for all binding site pairs of interest . Cliques with poor scores and/or low z-scores are deleted . The “noprune” and “z-score” options offer the output of insignificant matches together with the significant ones . The use of Shaper relies on cavity definitions by VolSite . The program predicts the druggability of binding sites and excludes those that are denoted non-druggable . Not all cavities can therefore be processed with Shaper . We additionally applied Shaper to binding sites extracted by the pdbconv tool alone to allow for a more complete processing of binding sites although this is not recommended by the developers . The complete detected cavity as well as cavities defined within a 4 , 6 , 8 , 10 , or 12 Å radius of the ligand can be used . According to the recommendations , we used a 6 Å radius . Modified residues cannot be processed by SiteAlign . A binding site definition based on residue names and numbers is necessary . Insertion numbers are not supported requiring a preliminary renumbering of residues with insertion codes in the PDB files . The comparison of one query against a list of targets frequently fails with “Segmentation Error” . It is unavoidable to do comparisons of one query against one target each to avoid high failure rates . Alternate atom locations are not supported by SiteEngine . A single conformation has to be retained in the PDB file . Moreover , the HETATM entries for modified residues have to be changed to ATOM entries for correct surface construction . The tool is highly sensitive toward PDB files with more than 80 characters per line . Some large protein structures were not properly processed and had to be excluded . SiteEngine comparisons depend on ligand-defined cavities and its use is restricted to protein binding sites with bound ligands . The distance cut-off can be manually adjusted to define the binding site . SiteHopper initially creates binding site patches . This step failed for some protein-ligand complexes . Furthermore , the tool was not able to process residues with missing backbone atoms . These residues were therefore excluded for the comparisons after ensuring that they are not part of the binding site of interest . SiteHopper relies on ligand defined binding sites , but the cut-off radius can be adjusted . For small ligands , an additional flag had to be set during protein-ligand splitting ( -min_atoms ) . This flag was set to 0 to process phosphate binding sites and to 1 for the remaining data sets . SMAP ( version 2 . 0 ) comparisons worked for nearly all structures after providing the PDB files in an appropriate manner . In some cases , the tool was highly sensitive with respect to the provided structures ( including REMARK lines ) . The binding site definition can be modified using a distance threshold . The tools TIFP and Grim ( as tools of IChem version 5 . 2 . 6 ) are based on interaction fingerprints . Only ligand-occupied cavities can therefore be compared . The tool pdbconv of the IChem toolbox must be applied to extract proper binding sites and interaction fingerprints . Problems also arose when the ligand of interest giben in the PDB format was not in the predefined template files . Input structure preparation therefore included a PDB to MOL2 conversion with the CSD Python API 1 . 3[90] to obtain more reliable fingerprints . Consequently , we applied two analyses ( with PDB and MOL2 files ) . The same holds true for FuzCav , Shaper , and VolSite/Shaper . The algorithm underlying TM-align relies on a given residue sequence alignment . Binding site atoms within 10 Å of the bound ligand were used to ensure a meaningful comparison . As the tool relies on an initial sequence alignment of cavity residues , a small number of residues prevents the alignment of the residues of interest . The ranking lists of the binding site comparison tools were used to investigate their performance with respect to AUC and EF . The ROC curves were plotted with the help of the KNIME[103] ROC Curve node to analyze sensitivity ( true positive rate , Eq 1 ) and specificity ( true negative rate , Eq 2 ) of the tools . The AUC values for the resulting ROC curves were also calculated using KNIME . Statistical analyses of the AUC differences were performed according to DeLong et al . [109] as implemented in the R[110] package pROC[111] . The EF describes the enrichment of similar ( active ) binding site pairs opposed to the number of similar pairs identified in a random screening ( Eq 3 ) . The EF for x% of the screened data set is calculated based on the number of true actives at this percentage ( N ( actives ) x% ) and the number of all pairs at this percentage ( Nx% ) in the list of pairs with ranked similarity/distance score , the number of true active pairs ( N ( actives ) 100% ) , and the number of all pairs in the complete data set ( N100% ) . For tools with more than one scoring scheme , we analyzed the early EFs . The score that led to the highest early enrichment for the identical structures was taken into account . If no distinction was possible , the results for the data set of NMR structures were used in a similar manner . Finally , the data set of decoy structures was taken into account . The applied scores can be found in Table 9 . Notched box plots of scores for active and inactive pairs in the data set of structures with identical sequences were generated using the software package R[110] . The Welch’s two-sample t-test[112] for the similar and dissimilar pair score distributions was performed using the software package R[110] The Spearman’s rank correlation coefficient ( Spearman’s Rho , rS ) was calculated for the decoy binding site data sets according to Eq 4 . The raw scores ( Xi ) and the number of mutations ( Yi ) are converted to ranks ( rank X , rank Y ) . The covariance of the rank variables divided by the product of the standard deviations of both rank variables gives the final correlation coefficient . In this study , the correlation between binding site similarity score and number of binding site mutations was calculated . The general expectation is that the higher the number of binding site mutations the lower the score . Thus , if the Spearman’s Rho equals -1 , it denotes a perfect correlation . Optimum similarity score cut-off values for each method were determined using the R[110] package pROC[111] . Youden’s J statistic[85] was applied without weights to derive a score threshold that optimizes both sensitivity and specificity for the corresponding data set . The thresholds based on data set 1 and data set 7 were used to determine the methods’ sensitivity and specificity for data set 7 . All-against-all comparisons with the structures of data set 5 were performed on an Intel Xeon workstation ( E5-2690 with 2 . 90GHz and 32 GB RAM ) in a serial manner ( single core ) . The time for the pocket preparation was disregarded for SiteAlign , PocketMatch , and TM-align as this was realized separately . It has to be considered that for the remaining binding site comparison software , this is achieved within the given run time for preparation . For ProBiS , preparation and comparison are performed on the fly . Additionally , the time for summarizing the results is not included . The run time was assessed with the Linux “time” command ( user time ) . | Binding site similarities are useful in the context of promiscuity prediction , drug repurposing , the analysis of protein-ligand and protein-protein complexes , function prediction , and further fields of general interest in chemical biology and biochemistry . Many years of research have led to the development of a multitude of methods for binding site analysis and comparison . On the one hand , their availability supports research . On the other hand , the huge number of methods hampers the efficient selection of a specific tool . Our research is dedicated to the analysis of different cavity comparison tools . We use several binding site data sets to establish guidelines which can be applied to ensure a successful application of comparison methods by circumventing potential pitfalls . | [
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] | 2018 | A benchmark driven guide to binding site comparison: An exhaustive evaluation using tailor-made data sets (ProSPECCTs) |
The threshold firing frequency of a neuron is a characterizing feature of its dynamical behaviour , in turn determining its role in the oscillatory activity of the brain . Two main types of dynamics have been identified in brain neurons . Type 1 dynamics ( regular spiking ) shows a continuous relationship between frequency and stimulation current ( f-Istim ) and , thus , an arbitrarily low frequency at threshold current; Type 2 ( fast spiking ) shows a discontinuous f-Istim relationship and a minimum threshold frequency . In a previous study of a hippocampal neuron model , we demonstrated that its dynamics could be of both Type 1 and Type 2 , depending on ion channel density . In the present study we analyse the effect of varying channel density on threshold firing frequency on two well-studied axon membranes , namely the frog myelinated axon and the squid giant axon . Moreover , we analyse the hippocampal neuron model in more detail . The models are all based on voltage-clamp studies , thus comprising experimentally measurable parameters . The choice of analysing effects of channel density modifications is due to their physiological and pharmacological relevance . We show , using bifurcation analysis , that both axon models display exclusively Type 2 dynamics , independently of ion channel density . Nevertheless , both models have a region in the channel-density plane characterized by an N-shaped steady-state current-voltage relationship ( a prerequisite for Type 1 dynamics and associated with this type of dynamics in the hippocampal model ) . In summary , our results suggest that the hippocampal soma and the two axon membranes represent two distinct kinds of membranes; membranes with a channel-density dependent switching between Type 1 and 2 dynamics , and membranes with a channel-density independent dynamics . The difference between the two membrane types suggests functional differences , compatible with a more flexible role of the soma membrane than that of the axon membrane .
It is now more than 60 years since Alan Hodgkin categorized the firing behaviour in his classical study of isolated axons from the crab Carcinus maenas [1] . In many respects his experiments still form the basis for analysis of firing patterns in nervous systems . Using threshold dynamics and maximum frequency as parameters , he identified two major classes of repetitively firing axons ( he also defined a class of axons which only fired with difficulty , Class 3 ) : Class 1 axons start firing with very low frequency at threshold stimulation , yielding a continuous f-Istim relationship , whereas Class 2 axons start firing abruptly with a relatively high frequency ( typically 75 Hz ) at threshold , yielding a discontinuous f-Istim relationship . On the basis of a similar categorization mammalian cortical neurons have also been separated into main classes [2] , [3] , one exhibiting Class 1 characteristics ( regular spiking neurons ) and another Class 2 characteristics ( fast spiking neurons ) . The former class consists primarily of pyramidal neurons and the latter primarily of interneurons . This differential classification of excitability has been shown to correlate with a differential bifurcation behaviour of corresponding dynamical models [4]–[6] and successfully been used in analysing the coding properties of neurons [2]–[7] . To avoid confusion , and in accordance with the notation of Tateno and Robinson [7] , we in the following use the terms Type 1 and Type 2 dynamics when referring to continuous and discontinuous f-Istim relationships , respectively . This classification takes the threshold dynamics of the regular and fast spiking neurons , and that of the Class 1 and 2 axons , into account , but not all behavioural aspects of these classes [8] . The intricate interactions between the many factors involved in the dynamical regulation of neuronal firing are poorly understood [8] . The dominant idea is that different combinations of ion channel types explain the different patterns [9] . In a previous study we proposed a complementary explanation [10] , [11] . We showed that both Type 1 and Type 2 behaviour can be simulated in a dynamical model of a hippocampal neuron [12] by varying the membrane density of voltage-gated Na and K channels ( i . e . the number of channels per unit of membrane area , reflected in the Na and K permeabilities when all channels are open; see Figure 1 and Methods ) . The model used was four-dimensional and based on a detailed experimental voltage-clamp study , thus comprising experimentally estimated parameters . The choice of ion channel densities as bifurcation parameters was due to their physiological and pharmacological relevance . Many drugs act by specifically blocking channels and thereby reducing ion channel density both at a somatic and at an axonal level . Perhaps the most used local anaesthetic drug , lidocaine , acts by blocking sodium channels in axons and sensory nerve endings [13] . An increasing number of studies suggest a role for physiological regulation of channel densities , even at a relatively short time scale [14]–[19] . Each type of dynamics , i . e . , Type 1 and 2 , was found to be associated with distinct regions in the channel density plane ( − ) or with corresponding surface areas of an oscillation volume in the −−Istim space ( Figure 2 ) . In regions with high and low values ( region C1 ) the model exhibits Type 1 dynamics , whereas in regions with higher values ( regions A2 and B ) the model generates Type 2 dynamics . A bifurcation analysis ( see Methods ) showed that the Type 1 dynamics of the model is due to saddle-node on invariant circle ( SNIC ) bifurcations [10] , [11] . Figure 3A portrays such a bifurcation in a V-Istim plot , calculated for the model using region C1 values . The Type 2 dynamics was found to be due to either local Andronov-Hopf bifurcations and/or global double limit cycle bifurcations [10] , [11] . The dynamics of the model associated with region B values is due to double limit cycle and subcritical Andronov-Hopf bifurcations ( Figure 3B ) , while the dynamics associated with region A2 is exclusively due to double limit cycle bifurcations ( Figure 3C ) . The double limit cycle bifurcation implies an unstable limit cycle , which is part of a separating structure ( sometimes referred to as a separatrix [9] , [20] ) which separates trajectories turning to a central stable point and those approaching a stable limit cycle . However , preliminary calculations suggested that the bifurcation structure at the border between regions B ( Andronov-Hopf ) and C1 ( saddle node ) is more complex than previously described . When more bifurcation parameters are changed ( in our case channel densities and stimulation current ) a more intricate loss of stability occurs ( e . g . bifurcations with a co-dimension 2 ) [21] . Thus , to obtain a better understanding of the processes we reanalysed the hippocampal neuron model in more detail . Furthermore , we extended the analysis to two other well-described excitable membranes , i . e . , the myelinated axon of Xenopus laevis [22] and the giant axon of Loligo forbesi [23] . We found that oscillations associated with a subregion of region C1 of the hippocampal model show Type 2 dynamics , and that the oscillations of both axon models exclusively show Type 2 dynamics . We investigated the mathematical background to these findings , using techniques from bifurcation theory . The results suggest that the hippocampal soma and the two studied axon membranes represent two distinct types of membrane with respect to the excitability pattern; membranes with a channel-density dependent switching between Type 1 and 2 dynamics , and membranes with a channel-density independent dynamics . The difference between the two membrane types suggests functional differences , compatible with a more flexible role of the soma membrane than that of the axon membrane .
The three membrane models analysed here are all based on voltage-clamp data , and on the formalism originally developed by Hodgkin and Huxley [23] to describe the dynamics of the squid giant axon . The hippocampal neuron model used is that developed by Johansson and Århem [12] to describe small-sized interneurons in hippocampal slices of the rat Rattus norvegicus . The myelinated axon model used is basically the same as that developed by Frankenhaeuser and Huxley [22] to describe sciatic nerve fibres from the African clawed frog ( Xenopus laevis ) [24] , [25] . The giant axon model used is that of Hodgkin and Huxley [23] , describing the dynamics of the giant axon of the squid Loligo forbesi . All the models assume that the membrane current consists of a capacitive current ( IC ) and a three-component ionic current ( Iionic ) , consisting of a Na current ( INa ) , a delayed rectifier K current ( IK ) , and a leak current ( Ileak ) . It should be noted that in all three models the description of the K currents are based on experimentally measured currents , which cannot be regarded as homogeneous , but are most likely the sum of currents passing through several types of voltage-gated K channels . The descriptions of the currents differ slightly between the three membrane types . For instance , the Na and K currents of the squid axon are described using the conductance concept , while the corresponding currents of the myelinated axon and the hippocampal somatic membrane use the permeability concept , developed by Goldman [26] and Hodgkin and Katz [27] ( see Methods ) . Istim is equal to the sum of the capacitive current ( IC ) , charging the capacitor , and the ionic current Iionic . Thus , ( 1 ) where CM is the membrane capacitance . To obtain the time-course of V ( t ) , we solve this differential equation numerically , using the expressions for Iionic presented in the section of Methods . For the analysis of the mathematical nature of the oscillatory activity we determine the stationary potentials ( Vs ) , i . e . the potentials at which the system is in a stationary state and , consequently , the time derivatives of all variables are zero . This was done by solving the following equation for different values of Istim , and ( or and values depending on model; see Methods ) : ( 2 ) where Iss is Iionic at steady state . The stability of the system in the neighbourhood of the stationary potentials was examined by a linearization procedure as described in Methods . Graphical solutions to Equation 2 are presented in Figure 4 . A more detailed version of Equation 2 is given by Equation 17 in Methods . As shown previously [4] , [5] , [10] , [11] , some region in the − ( or − ) plane must be associated with a non-monotonic Iss-V curve for the model to produce Type 1 dynamics when entering the oscillatory regime . This is due to the nature of a saddle-node bifurcation on an invariant circle ( SNIC ) , requiring an Istim interval at which Equation 2 yields three solutions ( i . e . Vs1 , Vs2 and Vs3; see Methods ) . Thus , Type 1 threshold dynamics occurs only when ( or ) is of a relatively large magnitude , giving the Iss-V curve a non-monotonic , N-like shape ( Figure 4 ) . Hence , a switch from Type 1 to Type 2 firing dynamics takes place when ( or ) is reduced ( and or remains intact ) , corresponding to Na channels being blocked . It should here noted that the existence of three stationary solutions of Equation 2 does not guarantee Type 1 dynamics , as has been pointed out previously [5] and will be seen in the following . In our previous examination of the hippocampal model ( see Figure 2 ) , we defined the C area as the region where the model shows a non-monotonic Iss-V relationship ( and Equation 2 yields three stationary potentials at some Istim ) , with area C1 representing the subregion associated with oscillations . As mentioned above , most of this region is associated with Type 1 threshold dynamics . A more detailed analysis reveals , however , that for density values close to the border of the B area the model demonstrates Type 2 dynamics ( Figure 5 ) . This can be shown to be due to an Andronov-Hopf bifurcation when the most negative stationary potential ( Vs1 ) becomes unstable as indicated in the bifurcation diagram of Figure 6 ( cf . Figure 3A ) . Figure 7A depicts an oscillation map in the channel density plane , on which minimum frequencies are indicated . As seen on Figure 7B , the line delineating zero frequency deviates from the C1 border at = 14 µm/s and forms a separate , narrow region below this border at higher densities of the Na channel . Below we will denote this subregion C1b and the remaining , larger , region of C1 , associated with Type 1 behaviour , C1a . The oscillation map revised accordingly is shown in Figure 7B , where the border between C1a and C1b represents a curve in the ion channel density plane at which Bogdanov-Takens bifurcations occur [28] , [29] ( see Methods and Table 1 ) . ( For the role of Bogdanov-Takens bifurcations in the Hodgkin-Huxley model , integrate-and-fire models and the Morris-Lecar model , see [30]–[33] . ) It should be noted that in addition to the C1b region , there is another C1 subregion , a narrow strip along the borders to the B , A1 and C2 regions for values below 14 µm/s , associated with a saddle-node bifurcation that causes Type 2 dynamics ( non-SNIC ) , CIc . However , for reasons that will become clear from the analysis of the behaviour of axonal membranes , we will here focus on the dynamics associated with the C1b region . A summary of the regions in the density plane is given in Table 1 . The explanation for the deviant ( i . e . Type 2 ) threshold dynamics in the region associated with non-monotonic Iss-V curves ( region C1b ) becomes evident when the corresponding Istim− diagrams are considered ( similar bifurcation diagrams have been successfully used to analyse comparable models [34] ) . Figure 8 illustrates two such diagrams for = 40 µm/s , one overview and another highlighting the structure at the cusp of the three-root region ( which is part of the non-monotonic Iss-V region ) , with thick continuous lines . The thin continuous line marks points associated with Andronov-Hopf bifurcation dynamics and the hatched line depicts the double-limit cycle bifurcation . The Andronov-Hopf bifurcation line intersects the three-solution region and collides with the saddle-node bifurcation line in a Bodganov-Takens bifurcation point [29] . Hence , at the cusp of the three-root region , the Andronov-Hopf line forms a small subregion , characterised by unstable Vs1 and Vs3 . Thus , at these permeabilities and stimulation currents the threshold dynamics is due to subcritical Andronov-Hopf bifurcations and not to saddle-node bifurcations , and the model will show typical Type 2 behaviour with a minimum non-zero threshold frequency . How general is our present description of neuronal models ? And how does the density of channels influence the threshold dynamics and firing patterns in other models ? To address these issues , two well-described excitable membranes , i . e . , the node in myelinated axons of Xenopus leavis [22] and the giant axon of the squid Loligo forbesi [23] were examined . Both these membranes are similar to the hippocampal membrane with reference to channel composition and kinetics ( see Table 2 ) , as can be inferred from the similar mathematical formalism used ( see Methods ) . Nevertheless , the dynamics of both axon membranes were found to show principal differences from that of the hippocampal neuron membrane .
What is the functional reason for the difference in the flexibility of threshold dynamics between the two membrane types defined above ( M1/2 and M2 , represented by membranes of the hippocampal neuron and the axons ) ? Type 1 and 2 dynamics per se most likely have different functional roles; Type 1 dynamics being required for low frequency firing and Type 2 being essential for doublet spiking ( in hippocampal interneurons , [40]; in dorsal horn neurons , [41] ) . and for synchronization of firing in coupled neurons ( in the synchronization case due to the fact that both a phase advance and a phase delay are possible; the phase response curve being predominantly positive when the oscillations appear via a saddle-node on invariant cycle bifurcation , but both negative and positive in the case of the Andronov-Hopf bifurcation; see [42] ) . But what about the difference in flexibility between the two membrane types presently discussed ? The membranes of the soma and the proximal portion of the axon , which most likely determine the dynamics of the hippocampal neurons analysed here , can be assumed to show a considerable flexibility in their roles as integrative summing points , requiring ( transmitter- or trafficking- ) induced switchability between Type 1 and Type 2 dynamics ( see e . g . [43] , [44] ) . Such flexibility has also been shown in cortical fast spiking ( Type 2 ) interneurons [45]; Type 2 dynamics being changed to Type 1 dynamics when the K channel density ( in soma ) is reduced in dynamic clamp experiments . Similarly , fast spiking mesenchaplic V neurons have been shown to belong to the M1/2 class [37] . In contrast to the flexible or plastic soma membrane , the axon membranes form passive information transport chains , requiring reliable triggering mechanisms ( i . e . high current thresholds leading to rejection of low stimulus noise , and temporal all-or-none responses , meaning that the first spike always occurs early at threshold stimulation ) and , therefore , Type 2 dynamics . It should be pointed out , however , that the main value of the axon type dynamics most likely relates to its action when associated with a trigger zone , which likely is the case of the distal process of the dorsal root ganglion . Features associated with Type 2 dynamics , such as subthreshold oscillations and doublet spiking have been postulated to play an important role in pain modulation [46] , [47] . Thus , the Type 2 nature of the axon plays a role both in the information propagation and modulation . Clearly , this discussion , based on an analysis of axons from one amphibian ( Xenopus laevis ) and one cephalopod ( Loligo forbesi ) , cannot be generalized to axons from all animal phyla . As mentioned above , certain axons from the arthropod Carcinus maenas display Type 1 dynamics [1] , [23] , suggesting that that their cell membranes are of M1/2 type ( for a computational analysis of modifying the dynamics of squid axons , see [48] ) . What about vertebrate axons in general ? The phylogenetic bifurcation between the vertebrate and the arthropod lines occurred more than 500 million years ago , allowing a considerable time for specialization of axon membranes . To get information on this issue , we used the data from a voltage-clamp analysis of myelinated rat axons by Brismar [35] to construct and evaluate a dynamical model . The computations suggest that the rat myelinated axon membrane is of M2 type , exclusively displaying Type 2 dynamics ( Figure S1 and Text S1 ) . In conclusion , the present analysis shows that axon membranes of two vertebrate and one mollusc species are of M2 type , and axon membranes from one arthropod species are either of M1/2 or of M2 type . More studies are needed to determine whether vertebrate axons mainly are of M2 type or not . It should here be noted that the phylogenetic distance between present day molluscs and arthropods is considerably shorter than that between present day vertebrates and molluscs or arthropods . Mammalian cortical pyramidal cells have been shown to display both Type 1 ( regular spiking ) and Type 2 dynamics ( fast spiking ) , with Type 1 in majority [7] , [44] . Assuming that the trigger zone dynamics is of critical importance for the dynamics of the neuron in toto , the present analysis suggests that the membrane of the trigger zone of the majority of pyramidal cells is of M1/2 type . This also suggests that the assumed trigger zone of pyramidal cells , the initial segment of the axon [49] , [50] , is not formed by a M2 membrane , contrary to the presently studied axons . A way to experimentally test the hypothesis of a M1/2 membrane as trigger zone in pyramidal cells could be to analyse the results of introducing K channels with the dynamic clamp technique . Such a test is under way . Contrary to the majority of pyramidal cells , mammalian cortical interneurons mainly display Type 2 dynamics ( fast spiking ) . This suggests that the membranes of their trigger zones are either of M1/2 or of M2 type . In the latter case the trigger zone could be assumed to be located in the axon proper; i . e . in the first node of Ranvier or in an initial segment that is more functionally ( and structurally ? ) axon-like than that of the pyramidal cells . A way to experimentally separate between these two hypotheses ( whether the trigger zone in interneurons is of M1/2 or M2 type ) could be to analyse the dynamics after blocking K channels . Such a test is also under way . As pointed out previously [10] , the possibility to modify the threshold dynamics of neurons suggests novel scenarios for the action of channel active drugs such as general anaesthetics; implying mechanisms where selective blocking ion channels in critical neurons induces a switch from one brain state ( e . g . associated with consciousness ) characterized by certain frequency patterns to another state ( e . g . associated with general anaesthesia ) characterized by other frequency patterns . Network modelling has shown that such ideas are feasible . Thus , selectively blocking K channels in critical inhibitory neurons ( assuming M1/2 membrane trigger zones ) in a network of excitatory and inhibitory neurons , distance-dependently connected , can lead to switches from unsynchronised high frequency to synchronised low frequency mean network oscillations [39] . The mechanisms of synchronisation at the network level are still not well understood , but the mechanisms at a cellular level have been extensively studied and a tight connection between the bifurcational structure and the phase-response curve has been established [44] , [51]–[53] . Interneurons with Type 2 dynamics have recently been shown to account for the cortical γ-oscillations ( 20–80 Hz ) [54] , which are considered to provide a temporal structure for information processing in the brain [55] . Since two of the eigenvalues always are real and negative in the models here discussed , it suggests that the systems essentially are of a two-dimensional character . The decisive variation of the four variables ( i . e . V , m , h , n ) may then take place on a two-dimensional surface in the four-dimensional variable space . Hence , a model with reduction of variables ( as is done in e . g . Fitzhugh-Nagumo and Morris-Lecar models [20] , [56] ) can give a relatively good description of an excitable membrane . The emergence of a limit cycle following a stability loss can under these circumstances be understood by the Poincaré-Bendixson theorem [57] , since the system remains in a finite domain on a curved plane in the phase space . That the system remains in a finite domain is obvious from analysing the variables; the membrane potential V is limited by the reversal potential of Na+ and K+ , as well as by the capacitive properties , and the gating parameters are limited by the values 0 and 1 . Particularly , the two-dimensional character of the models eliminates more complex types of solutions , such as irregular , “chaotic” solutions or oscillations with two separate frequencies . Nevertheless , local and highly unstable chaos has been reported in a Hodgkin-Huxley system [36] , why the models are unlikely to be two-dimensional in the whole parameter space . A more stable chaos seems to require that more voltage-dependent ion channels are added to the model . We thus added two artificial ion channels to the hippocampal model and found chaotic firing ( Figure S2 ) . A rather extensive search for chaotic firing in models with just one added ion channel gave no positive results .
The time evolution of the membrane potential ( V ) was calculated by solving the following equation ( derived from Equation 1 ) numerically: ( 3 ) where INa and IK are functions of the activation parameters m and n , and the inactivation parameter h . Ileak is given by ( 4 ) INa and IK for the hippocampal and the myelinated axon model are described by the following expressions , based on the permeability concept of Goldman [26] and Hodgkin and Katz [27]: ( 5 ) and ( 6 ) where and denote the Na and K permeabilities when all Na and K channels are open , and thus represent the Na and K channel densities . R , T and F denote the gas constant , the thermodynamic temperature and the Faraday constant , respectively , and define . [Na]o , [Na]i , [K]o and [K]i are the external and internal concentrations of Na and K ions . The parameter values for the three models are listed in Table 3 . For the squid axon model we use the original expressions by Hodgkin and Huxley [23] based on the conductance concept: ( 7 ) and ( 8 ) where and denote the Na and K conductances when all Na and K channels are open , thus representing Na and K channel densities . The activation and inactivation parameters ( m , h and n ) are in all three models described by their time derivatives: ( 9 ) ( 10 ) ( 11 ) where αi and βi denote rate functions . For the hippocampal neuron model they are defined as follows: ( 12 ) For the myelinated axon model the rate functions are defined as: ( 13 ) For the squid axon model the rate functions are defined as: ( 14 ) The stability analysis of the differential equations was performed as briefly described by Århem et al . [11] . The stationary potentials can be calculated with the expression for the gating parameters ( m , n and h ) at steady state together with Equation 3 . The time derivates of the gating parameters are zero at stationary potentials , and hence the stationary values of the parameters ( denoted m∞ etc ) become: ( 15 ) ( 16 ) ( 17 ) Introducing these expressions into Equation 3 , we obtain the following equation , the roots of which yield the stationary potentials ( Vs ) : ( 18 ) This equation can be solved numerically and always yields at least one Vs . However , if the Na channel density ( or ) is large enough , the equation can for a defined stimulation interval give three equilibrium points , a requisite for the system to provide a saddle-node bifurcation ( see Figure 3 ) . We investigated the character of the equilibrium points , i . e . r* ( V , m∞ ( V ) , h∞ ( V ) , n∞ ( V ) ) , when the stimulation current ( Istim ) and the permeability or conductance parameters ( and or and ) representing the density of Na and K channels , were varied . This was done by linearizing the differential equations close to r* and by solving the characteristic equation ( 19 ) where denotes the identity matrix , and JM the Jacobian matrix ( 20 ) where , , and denote the time derivatives of the parameters . The solution to Equation 18 are the four eigenvalues λi ( i = 1 , 2 , 3 or 4 ) yielding an approximate time evolution of the system . Hence any perturbation δr around the equilibrium point r* can be written as ( 21 ) where ci ( i = 1 , 2 , 3 or 4 ) depends on initial conditions and ri ( i = 1 , 2 , 3 or 4 ) is the associated eigenvector . Two of the eigenvalues are in the present system ( here called λ3 and λ4 ) always real and negative . Consequently the remaining two eigenvalues determine the character of the Vs ( see Table 4 ) ; the two negative eigenvalues will cause its associated terms in Equation 20 to decay to zero . Hence , Equation 20 can be approximated as ( 22 ) If λ1 and λ2 are a complex conjugated pair ( λ1 , 2 = a±bi ) , one can rewrite the equation , using Euler's formula , as ( 23 ) why b/2π correlates with the firing frequency . All computations were done in custom software written in Mathematica 6 . 0 . 2 ( Wolfram Research , Inc . ) on a 64-bit IBM compatible computer . All values are given in SI-units . | All activity of the brain is manifested in electrical oscillatory patterns , shaped by the firing dynamics of the many neurons forming the brain networks . The underlying mechanisms of the firing pattern in the single neurons are still not fully understood . The distribution and identity of different channel types have been suggested as critical factors . We have suggested that the density of channels in the membrane is a fundamental complementary mechanism . In a hippocampal soma membrane model study we have shown that altering the ion channel densities can cause the membrane to switch between two qualitatively different firing patterns . Here we extend the analysis to two axon membranes . Unexpectedly , both show that channel density alterations do not cause switches between different firing behaviours . We believe that this is an important property of axon membranes , explaining their limited flexibility . | [
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] | 2010 | Ion Channel Density Regulates Switches between Regular and Fast Spiking in Soma but Not in Axons |
The retinoid X receptors ( RXRs ) are ligand-activated transcription factors which heterodimerize with a number of nuclear hormone receptors , thereby controlling a variety of ( patho ) -physiological processes . Although synthetic RXR ligands are developed for the treatment of various diseases , endogenous ligand ( s ) for these receptors have not been conclusively identified . We show here that mice lacking cellular retinol binding protein ( Rbp1-/- ) display memory deficits reflecting compromised RXR signaling . Using HPLC-MS and chemical synthesis we identified in Rbp1-/- mice reduced levels of 9-cis-13 , 14-dihydroretinoic acid ( 9CDHRA ) , which acts as an RXR ligand since it binds and transactivates RXR in various assays . 9CDHRA rescues the Rbp1-/- phenotype similarly to a synthetic RXR ligand and displays similar transcriptional activity in cultured human dendritic cells . High endogenous levels of 9CDHRA in mice indicate physiological relevance of these data and that 9CDHRA acts as an endogenous RXR ligand .
Micronutrients such as vitamin A and polyunsaturated fatty acids are essential ingredients of mammalian diet and can act as bioactive molecules . Nuclear hormone receptors sense such molecular signals and accordingly regulate gene expression , thus functioning as ligand-controlled transcription factors . Retinoid X receptors ( RXRs ) occupy a central place in nuclear receptor signaling as obligatory heterodimerization partners for several of those receptors . RXR ligands can regulate the activity of only some heterodimers including for example LXR-RXR or PPAR-RXR , collectively called permissive heterodimers in opposition to non-permissive heterodimers , like RAR-RXR , which cannot be activated by RXR ligands alone [1 , 2] . Ligand-dependent modulation might be particularly relevant for the control of a wide range of physiological events . For example , among complex functions , working memory was shown sensitive to RXR ligand activities in mice [3] , whereas at the cellular and molecular level , differentiation of monocyte-derived dendritic cells is one of the well characterized experimental models used to study the activities of RXR ligands [4 , 5] . Such ligands are also known to act as powerful inducers of apoptosis in cancer cells [6 , 7] or as modulators of lipid and glucose metabolism , which has stimulated their clinical development for the treatment of cancer and metabolic diseases [8] . Recent studies on antidepressant or neuro-regenerative activities of RXR specific agonists suggest also their utility for the treatment of some neuropsychiatric or neurodegenerative disorders [3 , 9 , 10] . In parallel to development and use of synthetic RXR ligands , several endogenous agonists have been proposed [11 , 12 , 13] , but their physiological relevance remains questionable for different reasons . For example , 9-cis-retinoic acid ( 9CRA ) , an isomer of all-trans-retinoic acid ( ATRA ) , was proposed and largely accepted as an endogenous RXR ligand [14 , 15] . However , although 9CRA can bind and activate RXRs at low concentrations , it was either undetectable [16 , 17 , 18 , 19 , 20 , 21] or was not present in sufficient concentrations [22] to enable RXR-mediated signaling in mammalian organisms . Docosahexaenoic acid ( DHA ) , an alternative RXR ligand , was shown to bind and transactivate RXRs under pharmacological conditions [11 , 23 , 24] , but in the physiological setting it can be detected in the brain mainly in esterified form contributing to e . g . structural components of the cell , while the pool of this fatty acid available for RXR activation remains too low [25 , 26] . Finally , phytanic acid [27 , 28] also suggested to bind RXR was not conclusively proven to be physiologically relevant . In this study , we addressed the nature of known and novel endogenous retinoids and their role in RXR signaling in vivo by chemical , molecular and functional studies in distinct models .
In order to search for endogenous retinoids which may act as RXR ligand ( s ) , we first employed behavioral and pharmacological analyses sensitive to RXR signaling as a tool to identify animal models with reduced RXR signaling . In particular , we focused on spatial working memory previously reported as dependent on RXR and not RAR functions and more importantly also dependent on RXR ligand activities [3] . Using delayed non-match to place ( DNMTP ) task , we found that mice carrying a null mutation of cellular retinol binding protein I ( RBP1 ) , known for its role in retinoid metabolism [29] , display memory deficits which phenocopy the effect of the loss of function of Rxrγ , a functionally predominant RXR in control of working memory ( Fig 1A and ref . [3] ) . In particular , Rbp1-/- and Rxrγ -/- mice performed significantly worse when compared to wild type ( WT ) mice at 3 or 6 min inter-trial intervals ( ITI ) in DNMTP task , attaining chance level ( complete forgetting ) already at 6 min , whereas WT mice performed at chance level only at 12 or 18 min depending on individual ( Fig 1A , see grey part of the left panel ) . These data suggest that RXR signaling is compromised in Rbp1-/- mice . To challenge this hypothesis functionally , we took advantage of the sensitivity of working memory performance in delayed task to treatment with RXR agonists [3] . Activation of RXR signaling by the synthetic RXR agonist , UVI2108 ( also known as SR11217 or BMS649 ) or by ATRA , which under pharmacological conditions is rapidly transformed in vivo to RXR agonist 9CRA[12 , 30] , reversed memory deficits in Rbp1-/- mice , but remained ineffective in mice lacking RXRγ ( Fig 1B ) . Working memory deficits were also observed in a distinct , rodent specific memory test of spontaneous alternation in the Y-maze ( S1 Fig ) . Treatments with ATRA , UVI2108 and other RXR agonists , including DHA and methoprene acid , but not pan-RAR agonist TTNPB led to pro-mnemonic effects in Rbp1-/- , but not Rxrγ -/- mice ( S1 Fig ) , supporting the possibility of compromised RXR signaling due to reduced availability of RXR ligand ( s ) in Rbp1-/- mice . Accordingly , reduced expression of RXRγ could not explain behavioral deficits in Rbp1-/- mice . On the contrary , expression of RXRγ was clearly increased in Rbp1-/- striatum attaining level of 3 . 2 ± 0 . 6 in Rbp1-/- mice as compared to 1 . 2 ± 0 . 3 arbitrary RNA units ( qRT-PCR ) . To evaluate RXR ligand availability in Rbp1-/- mice , we first addressed concentration of 9CRA in mouse brain and serum . Using a sensitive method of retinoic acid detection based on HPLC separation followed by highly specific DAD detection and destructive MS-MS [20] , we clearly identified ATRA in serum ( 0 . 3 ± 0 . 1 ng/ml ) and brain ( 0 . 6 ± 0 . 1 ng/g ) samples from WT mice , whereas in the range of 9CRA elution no conclusive peak was identified indicating that 9CRA is absent or its levels were under our detection limit of 0 . 1 ng/g and thereby too low for sufficient RXR-activation in WT ( Fig 2A ) and Rbp1-/- animals . We then focused on dihydroretinoids described as novel endogenous retinoids [31 , 32] . Using stereo- and enantiocontrolled organic synthesis we obtained a series of dihydroretinoids , including all-trans-13 , 14-dihydroretinoic acid ( ATDHRA ) and its stereoisomer 9-cis-13 , 14-dihydroretinoic acid ( 9CDHRA; see Materials and methods for details of its synthesis ) , which we next used as reference molecules in HPLC-MS-MS analyses . Such analyses were focused on liver , as major site of Rbp1 expression , serum , through which retinoids are distributed to target organs , and brain , with discrete areas expressing Rbp1 [33] . We identified two major peaks , which co-eluted with standards of ATDHRA and 9CDHRA at UV specific absorption of 290 nm ( Fig 2B , left panel ) . Such co-elution was also observed at dihydroretinoid-specific MS-MS settings ( Fig 2B , right panel ) . Concentrations of 9CDHRA were high in serum samples attaining 118 ± 15 ng/ml ( corresponding to ~4 x 10-7M ) , 135 ± 12 ng/g in mouse liver ( corresponding to a concentration of ~7x10-7M ) and relatively low ( 7 ± 1 ng/g , corresponding to ~2 x 10-8M ) in brain . A direct comparison of these retinol metabolites in WT and littermate Rbp1-/- mice ( Fig 2C ) showed comparable concentration of ATDHRA in contrast to significantly decreased 9CDHRA levels in serum , liver and brain of Rbp1-/- mice . Importantly , whereas such decrease in serum may be at the origin of systemic reduction of RXR signaling , almost complete loss of 9CDHRA availability in brains of Rbp1-/- mice suggest more significant reduction of local RXR signalling in this organ . Furthermore , whole brain measures reflect most probably more dramatic changes of 9CDHRA levels in discrete brain areas expressing Rbp1 [33] . Unfortunately it is technically impossible to identify retinoid concentrations in these small areas , which can be only prompted by whole brain measures . Direct evidence for 9CDHRA binding to RXR was given by electrospray ionisation mass spectrometry ( ESI-MS ) performed in non-denaturing conditions with purified RXR ligand binding domains ( LBD ) . In order to evaluate relative affinities of R- and S-enantiomers and 9CDHRA for RXR LBD , titration experiments were monitored by ESI-MS . As shown in Fig 3A and 3B , all retinoids bind to hRXRα LBD used in these studies as model RXR LBD due to high conservation of LBD structure among all RXRs . Analyses of peak amplitude revealed that R-9CDHRA has approximately 30% lower affinity than 9CRA , but about 65% higher affinity than S-9CDHRA . Quantitative binding affinities to RXRa LBD obtained by fluorescence quenching assay ( S2 Fig ) are equal to 90 ± 20 nM for R-9CDHRA and 20 ± 10 nM for 9CRA , and fall in the range of published Kd [34] indicating that 9CDHRA binds RXRs with high affinity at concentrations which are physiologically relevant . Since 9CRA can bind to RARs we also tested affinity of 9CDHRA for all three RAR isotypes . ESI-MS experiments performed with hRARα , β and γ LBDs ( S3A and S3B Fig ) revealed that 9CDHRA ( R and S ) bind to all RAR LBD isotypes . To provide structural evidence of the binding of R-9CDHRA to RXR , the hRXRα LBD was crystallized in complex with R-9CDHRA and a 13-residue peptide comprising the nuclear receptor-binding surface NR2 of NCoA2 . Note that the residues lining the ligand binding pocket are strictly conserved between RXRα and RXRγ and that the conclusions drawn for RXRα will also be valid for RXRγ . The structure refined at 1 . 8 Å resolution ( S1 Table ) revealed the canonical active agonist conformation common to all previously reported agonist-bound nuclear receptor LBDs with 12 or 13 α-helices organized in a three-layered sandwich ( S3C and S3D Fig ) . R-9CDHRA adopts a similar binding mode as 9CRA [35 , 36] including interactions of the carboxylate group of the ligand with Arg316 ( H5 ) , and hydrogen bonds with the amide group of Ala327 in the beta turn ( Fig 3C and 3D ) . The number of contacts is similar between the two ligands although some interactions are weaker in the case of R-9CDHRA compared to 9CRA as for example the interactions with Leu436 ( 4 . 0 Å instead of 3 . 6 Å for 9CRA ) , Arg316 ( 2 . 7 Å instead of 2 . 3 Å ) or Trp305 ( 4 . 3 Å instead of 3 . 5 Å ) that account for the weaker binding of R-9CDHRA . In silico comparison of S- and R-9CDHRA binding mode in RXRα LBP revealed that the opposite configuration at C13 leading to slightly different side chain conformation of S-9CDHRA may underlay its lower affinity to RXR ( S3E Fig ) , further supported by the lower relative binding affinity measured by ESI-MS . The relevance of R-9CDHRA and S-9CDHRA receptor binding for transcriptional activities of RXRs was tested in COS1 reporter cell lines transfected with a RXRα expression vector ( Fig 3E–3G ) . In agreement with previous reports , 9CRA induced transcription of reporter gene at concentrations starting from 10-9M , whereas R-9CDHRA or S-9CDHRA displayed similar activity to 9CRA , although at concentrations higher than 10-7M . Importantly , the activities of R- and S-9CDHRA at 10-5M were prevented by co-treatment with an RXR-antagonist LG101208 at 10-6M ( Fig 3F ) . 9CDHRAs also activated RAR-RXR signaling in COS1 model reporter cells ( transfected with RARα and RXRα expression vectors ) starting at 10-6M ( Fig 3G ) . Considering that all RXR isotypes share the same structure of their ligand binding pockets , the present data obtained with the RXRα isotype indicate that 9CDHRA may efficiently bind to all RXRs and induce their transcriptional activities at concentrations found in physiological conditions . Behavioral analyses revealed that the 9CDHRA modulation of RXR functions is also relevant in vivo . Accordingly , acute treatment with R-9CDHRA improved memory performance of Rbp1-/- mice as compared to vehicle treatment or chance level of 50% when tested in DNMTP task at ITI of 6 min ( Fig 3H ) . R-9CDHRA treatments also raised performance of WT mice when tested at long ITIs of 12 or 18 min , at which time the corresponding WT mice treated with vehicle performed at chance level . Such treatment did not improve performance of Rxrγ-/- mice ( 57 ± 7% of correct choices ) indicating RXR specificity of 9CDHRA effects , which is further supported by similar effects of 9CDHRA and UVI2108 treatments ( compare Fig 3H and 3L ) . In order to identify 9CDHRA specificity for induction of RXR-dependent transcriptional activity at the transcriptomic level and its capacity to activate permissive heterodimers , we took advantage of human differentiating monocyte-derived dendritic cell cultures , a well characterized in vitro model for studies of signaling through RXR and its heterodimers [4 , 5] . The gene expression changes induced by R-9CDHRA , S-9CDHRA , other RXR ligands or ligands for RXR partners , revealed that R-9CDHRA and 9CRA regulate approximately the same number of transcripts ( 518 and 450 , respectively; Fig 4 ) . Importantly , 384 transcripts were similarly regulated by both agonists ( Fig 4A and 4B ) , which corresponded to 85% of all transcripts regulated by 9CRA . Within this set , a group of 61 transcripts was also regulated by LG268 , a synthetic RXR specific ligand used in our analysis as a reference in previous studies of this model [4 , 5] . Remarkably , none of the transcripts were regulated solely by 9CRA and LG268 , and not by 9CDHRA , indicating that 9CDHRA induces similar gene expression changes as 9CRA . We also investigated the capacity of 9CDHRA for activating permissive heterodimers , e . g . LXRα/γ-RXR , PPARγ -RXR , and PPARδ-RXR . As expected , we found that R-9CDHRA , similarly to 9CRA and LG268 , could induce the expression of many genes , which are known as direct targets of RXR permissive heterodimers . Accordingly these genes were also regulated by LXR or PPAR specific ligands ( Fig 4C ) . To address whether 9CDHRA also activates RAR-RXR target genes we compared the effect of RXR ligands and AM580 , a synthetic RARα selective ligand . Typically genes induced by AM580 were not induced by any other agonist of permissive partners ( Fig 4C ) , but were also induced by 9CDHRA or 9CRA . Collectively , gene expression profiling indicated that R- and S-9CDHRAs display RXR agonist activity , but can also activate RARs , acting thus with similar selectivity to 9CRA .
Although RXRs occupy central position in signaling of several nuclear hormone receptors acting as their heterodimerisation partner , endogenous ligand ( s ) of RXRs , its metabolic pathway and physiological functions were not conclusively determined . We found that Rbp1-/- displayed a phenotype suggestive of reduced RXR signaling , which could not be attributed to the reduced levels of RXR expression or of 9CRA , the potential endogenous RXR ligand which we and others failed to detect [16 , 17 , 18 , 19 , 20] in wild type animals . Using chemical approaches of HPLC-MS and organic synthesis we identified 9CDHRA , a novel endogenous retinoid , the concentrations of which were significantly reduced in serum , liver and brain of Rbp1-/- mice . Several lines of evidence indicate that 9CDHRA treatment activates RXRs in vitro and in vivo at physiologically relevant concentrations , suggesting that it acts as an endogenous RXR agonist . We report herein that RBP1 modulates animal behavior by control of the availability of an RXR ligand . Accordingly , mice carrying null mutation of RBP1 display working memory deficits , the hallmark of deficient signalling through RXRγ , a functionally predominant RXR in the control of working memory in adult mice [3 , 37] . Reduced expression of RXRγ do not account for these changes , suggesting compromised availability of RXR ligand . This unique model enabled us to search for the endogenous RXR ligand ( s ) . As our initial analyses failed to detect 9CRA in wild type and Rbp1-/- mice , we turned our attention to dihydroretinoids proposed recently as a novel group of bioactive , endogenous retinoids [31] . Using HPLC-MS-MS conditions specific for detection of dihydroretinoic acids , including 13 , 14-dihydroretinoic acids , and aided by organic synthesis we detected the presence of ATDHRA and 9CDHRA in mouse serum , liver and brain in WT mice and Rbp1-/- mice . Serum and liver concentration of 9CDHRA were particularly high , ranging from 4 to 7x10-7M , and much lower in whole-brain extracts in WT animals . Nevertheless they were significantly reduced in all corresponding samples of Rbp1-/- mice . Reduced serum availability of 9CDHRA in Rbp1-/- mice may result from reduced synthesis of 9CDHRA in the liver , the main site of RBP1 expression [29] . Because levels of ATDHRA were comparable in the serum , liver and brain of WT and Rbp1-/- mice , reduced levels of 9CDHRA in Rbp1-/- mice indicate that RBP1 plays an important role specifically in generation of different forms of 9-cis-retinoids as previously suggested [38] . Thus , 9CDHRA , similarly to ATRA and 1 , 25-dihydroxy-vitamin D3 could act in endocrine and paracrine manner as a lipid hormone of nutritional origin being distributed in the serum but also synthetized locally in specific organs [39 , 40] . In consequence , reduced systemic levels of 9CDHRA may synergize with local reduction of its synthesis in specific brain areas of Rbp1-/- mice leading to compromised RXR activities and mnemonic deficits . In favor of this hypothesis , systemic administration of R-9CDHRA , a 9CDHRA enantiomer obtained by stereoselective chemical synthesis , normalized working memory deficits in Rbp1-/- mice . That such effects are mediated by RXRs may be suggested by absence of promnemonic effects of 9CDHRA and other RXR ligands in mice carrying null mutation of RXRγ , a functionally predominant RXR in the control of these brain functions [3] . Direct evidence of 9CDHRA binding to RXRs is provided by electrospray ionisation mass spectrometry ( ESI-MS ) performed in non-denaturing conditions using purified RXR LBD and fluorescence quenching assay . In particular , R-9CDHRA binds RXR LBD with affinity close to that of 9CRA as indicated by respective Kd values of 90 ± 20 nM for 9CDHRA and 20 ± 10 nM for 9CRA . Such data are supported by the crystal structure of the complex of R-9CDHRA with RXR LBD , in which R-9CDHRA adopts the canonical active agonist conformation and the carboxylate interacts with Arg316 . Importantly , the R-9CDHRA enantiomer efficiently induces RXR transcriptional activity in reporter cell assays at physiologically relevant concentrations below 10-6M , which can be prevented by co-administration of RXR pan-antagonist LG101208 . Although S-9CDHRA displays lower affinity to bind RXR LBD in ESI-MS , most probably due to the inverse positioning of the C20-carbon atom , it is also active in the transactivation of RXR in vitro with threshold concentration between 10–7 and 10-6M . Further relevance of 9CDHRA for the activation of RXRs in vitro was indicated by the regulation of transcriptional targets of LXR-RXR or PPAR-RXR permissive heterodimers , which are known to be sensitive to pharmacological activation of RXR as well as its nuclear receptor partners . Such activation was demonstrated in human dendritic cells cultured in vitro , a well-established model for analyses of RXR signalling [4 , 5] . Importantly , almost all ( 68 out of 72 ) transcripts regulated by LG268 ( RXR-specific agonist ) were also regulated by 9CDHRA . Such a result obtained in a course of transcriptomics study was very similar to the data obtained for 9CRA , which controlled 61 out 72 LG268 transcriptional targets , indicating the extremely high capacity of 9CDHRA and 9CRA to control RXR transcriptional targets . Such genes correspond most probably to permissive heterodimers , and indirect targets of liganded RXR and their regulation provide evidence that 9CDHRA can control RXR signalling also in human cells . High degree of overlap between transcriptional activities of 9CRA and 9CDHRA , which goes beyond the activation of RXR-specific transcripts , reflects their capacity to bind and transactivate also RARs . That 9CRA and 9CDHRA act as a mixed RXR and RAR agonists is supported by about 80% overlap in transcriptional changes induced by R-9CDHRA ( or S-9CDHRA ) and 9CRA . Whereas 13 , 14-dihydroretinol was detected by Moise and colleagues [31] as a hepatic retinol saturase ( RETSAT ) metabolite in the mammalian organism , other dihydroretinoids or their precursors were also identified in other vertebrates [41 , 42] and also non-vertebrates [43] . Besides RETSAT-mediated retinol metabolism as the major potential pathway for endogenous 9CDHRA synthesis , also apo-carotenoids and carotenoids may serve as substrates for dehydrogenation via RETSAT or other saturases , followed by the synthesis of dihydroretinoic acids [44 , 45] . This metabolic pathway may be phylogenetically ancient , as RXR orthologs from several non-vertebrate species including mollusk [46] , primitive chordate like amphioxus [47] and some primitive insects like Tribolium [48] or Locusta migratoria [49 , 50] have also a potential to bind RXR ligands . In addition , Locusta migratora ultraspiracle ( a fly RXR ortholog ) , displayed higher affinity to bind 9CRA than human RXR [49] , raising the possibility that 9CDHRA could also activate the USP pathway and be an ancestral RXR ligand . Based on our data , it is tempting to suggest that in addition to the active ligands originating from vitamin A1 and vitamin A2 , 9CDHRA and its nutritional precursors may represent a third novel distinct pathway of vitamin A metabolism and signaling , which evolved specifically to control RXR activity . In summary we have characterized 9CDHRA as the first endogenous and physiologically relevant retinoid that acts as RXR ligand in mammals . Reduced memory functions due to compromised RXR-mediated signaling in Rbp1-/- mice result from lower brain levels of 9CDHRA , reflecting reduced serum transport and local brain synthesis of 9CDHRA in Rbp1-/- mice . Future determination of the metabolic pathways involved in 9CDHRA synthesis and signaling and its tissue specificity will be important for further understanding of the functional relevance of 9CDHRA to animal physiology , pathology and evolution .
Rbp1-/- and Rxrγ -/- mutants as well as wild type ( WT ) control mice were raised on a mixed genetic background ( 50% C57BL/6J and 50% 129SvEms/j; bred for more than 10 generations ) from heterozygous crosses as described [29 , 51] , and tested at the age of 3 months for chemical analyses and 3–6 months for behavioral analyses . All mice were housed in groups of 4–5 mice per cage in a 7am-7pm light/dark cycle in individually ventilated cages ( Techniplast , Italy ) . Food ( standard chow diet , D04 from SAFE , France ) and water were freely available . All experiments were carried out in accordance with the European Community Council Directives of 24 November 1986 ( 86/609/EEC ) and in compliance with the guidelines of CNRS and the French Agricultural and Forestry Ministry ( decree 87848 ) . All behavioral tests were carried out in the Institute Clinique de la Souris ( http://www . ics-mci . fr/ ) according to standard operating procedures . To study working memory , behaviorally naïve groups of mice were tested in the DNMTP in the T-maze according to a protocol previously described [52] with modifications to facilitate pharmacological tests [3] . Spontaneous alternation was evaluated in the Y-maze apparatus according to the protocol described in detail in the S1 Text . Serum and tissue samples for chemical analyses were collected during the light phase of the light/dark cycle between 3-4pm , around 9–10 hours after the last major food intake , which in our lighting conditions takes place around 6-7am . High performance liquid chromatography mass spectrometry–mass spectrometry ( HPLC-MS-MS ) analyses were performed under dark yellow/amber light using previously validated protocol [20] . For the detection of 13 , 14-dihydroretinoic acid MS-MS settings were 303 -> 207 m/z using the same dwell time and collision energy comparable to the MS-MS specific settings of retinoic acids . Quantification was performed as previously described [20] . For details of sample preparation see S1 Text . COS1 cells were maintained in DMEM medium with 10% FBS , 5% L-glutamine , 1% penicillin streptomycin in 24-well plates and transfections were carried out in triplicates . Cells were transfected with equal amounts of relevant plasmids including Gal-RXRα-LBD for RXR-reporter line or Gal-RARα-LBD and Gal-RXRα-LBD for RAR-RXR reporter line , a reporter plasmid ( luciferase MH100-TKLuc reporter construct with GAL-binding site [53] and beta-galactosidase ( for transfection efficiency calculation ) . For details of transfection and measurements see the S1 Text . cDNAs encoding hRXRα LBD ( 223–462 ) , hRARα LBD ( 153–421 ) , hRARβ LBD ( 169–414 ) and hRARγ LBD ( 178–423 ) were cloned into the pET28b vector to generate N-terminal His-tag fusion proteins . Purification was carried out as previously described [54 , 55] , including a metal affinity chromatography and a gel filtration . For details of sample preparation and ESI-MS and fluorescence quenching analyses see the S1 Text . Details on crystallization , X-ray data collection and structure determination can be found in the S1 Text , S3 Fig and S1 Table . The coordinates and structure factors are deposited in the Protein Data Bank under the accession codes 4ZSH . R-9CDHRA , UVI2108 , ATRA ( Sigma ) , DHA ( Sigma ) , MA ( Sigma ) and TTNPB ( Sigma ) , were dissolved in ethanol and DMSO , and then mixed with sunflower oil , so that the final solution contained 3% ethanol and 3% DMSO . Vehicle treatments consisted of 3% ethanol and 3% DMSO solution in sunflower oil . Treatments were administered by intraperitoneal injections at volume/weight ratio 3 ml/kg between 8-10am and 5–6 h before the test as previously validated [3] . The generation and transcriptional analysis of differentiating DCs were performed as described previously [5] and are detailed in the S1 Text . Microarray data were deposited into the Gene Expression Omnibus database under accession no . GSE48573 . The comparisons of behavioral performance in Rbp1-/- and Rxrγ-/- mice were carried out using the protected least significant difference ( PLSD ) Fischer test . The pharmacological data for the treatments in WT and Rbp1-/- or Rxrγ-/- mice were analysed using 2-way analysis of variance ( ANOVA ) —with treatment and genotype as two independent factors and behavioral responses as dependent variables . The evolution of learning curves in WT , Rbp1-/- and Rxrγ-/- mice were done using ANOVA on repeated measures . Global and post-hoc statistical analyses were performed using student t-test for two-group comparisons . Significant differences are indicated in the corresponding figures . To confirm whether relative MS-MS signal detected at 303>207 m/z corresponds to 9CDHRA , the stereoselective synthesis of both enantiomers of 9-cis-13 , 14-dihydroretinoic acid was carried out following the previously described strategy based on a palladium-catalyzed Csp2-Csp2 Suzuki coupling [56] . Details of the stereocontrolled synthesis , purification and characterization of the ( R ) - and ( S ) -enantiomers of 9-cis-13 , 14-dihydroretinoic acid are provided in the S1 Text . | Daily nutrition , in addition to being a source of energy , contains micronutrients , a class of nutrients including vitamins which are essential for life and which act by orchestrating a vast number of developmental and physiological processes . During metabolism , micronutrients are frequently transformed into their bioactive forms . Nuclear hormone receptors are a family of proteins functioning as ligand-regulated transcription factors which can sense such bioactive molecules and translate those signals into transcriptional , adaptive responses . Retinoid X receptors occupy a central place in this signaling as they directly interact , and thereby control , activities of several nuclear hormone receptors . We report here the identification of a novel bioactive form of vitamin A , which is the first endogenous form of this vitamin capable to bind and activate retinoid X receptors . Accordingly , we show that this single molecule displays biological activity similar to synthetic agonists of retinoid X receptors and coordinates transcriptional activities of several nuclear receptor signaling pathways . Those findings may have immediate biomedical implications , as retinoid X receptors are implicated in the control of a number of physiological functions and their pathology . | [
"Abstract",
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] | [] | 2015 | 9-cis-13,14-Dihydroretinoic Acid Is an Endogenous Retinoid Acting as RXR Ligand in Mice |
The acrofacial dysostoses ( AFD ) are a genetically heterogeneous group of inherited disorders with craniofacial and limb abnormalities . Rodriguez syndrome is a severe , usually perinatal lethal AFD , characterized by severe retrognathia , oligodactyly and lower limb abnormalities . Rodriguez syndrome has been proposed to be a severe form of Nager syndrome , a non-lethal AFD that results from mutations in SF3B4 , a component of the U2 small nuclear ribonucleoprotein particle ( U2 snRNP ) . Furthermore , a case with a phenotype intermediate between Rodriguez and Nager syndromes has been shown to have an SF3B4 mutation . We identified heterozygosity for SF3B4 mutations in Rodriguez syndrome , confirming that the phenotype is a dominant disorder that is allelic with Nager syndrome . The mutations led to reduced SF3B4 synthesis and defects in mRNA splicing , primarily exon skipping . The mutations also led to reduced expression in growth plate chondrocytes of target genes , including the DLX5 , DLX6 , SOX9 , and SOX6 transcription factor genes , which are known to be important for skeletal development . These data provide mechanistic insight toward understanding how SF3B4 mutations lead to the skeletal abnormalities observed in the acrofacial dysostoses .
The acrofacial dysostoses ( AFD ) are a genetically heterogeneous group of inherited disorders unified by craniofacial and limb abnormalities . At least 18 types of AFD have been characterized and , depending on the specific patterns of limb abnormalities , they have been further classified into those with preaxial limb abnormalities , those with postaxial defects and those which cannot be classified into the first two groups [1] . Miller syndrome ( OMIM 263750 ) is a recessively inherited AFD , which results from pyrimidine biosynthesis defects due to mutations in DHODH [2] . Weyers AFD ( OMIM 193530 ) is dominantly inherited phenotype caused by mutations in either EVC1 or EVC2 , leading to defective cilia-mediated hedgehog signaling [3 , 4] . Dominant mutations affecting the RNA polymerase subunit POLR1A and ribosomal biogenesis have been found in AFD , Cincinnati type ( OMIM 616462 ) [5] . Related mandibulofacial dysostosis phenotypes , including Treacher-Collins syndrome , can also result from defects in ribosomal biogenesis [6] . EFTUD2 encodes a GTPase which is a component of the U5 snRNP . Identification of EFTUD2 mutations in mandibulofacial dysostosis of the Guion-Almeida type ( OMIM 610536 ) demonstrated that pre-mRNA splicing abnormalities could produce an AFD [7] . Also demonstrating a link with pre-mRNA splicing , mutations in EIF4A3 which affect the activity of the exon junction complex have been characterized in Richieri-Costa-Pereira syndrome ( OMIM 268305 ) [8] . Similarly , mutations in the gene encoding the SF3B4 protein ( also known as SAP49 ) , a component of the U2 snRNP , have been found to produce Nager syndrome ( OMIM 154400 ) [9–12] . While the genes involved in the characterized AFDs demonstrate that defects in a variety of basic biochemical processes can lead to these phenotypes , the underlying reasons for the distinct distribution of abnormalities in the craniofacies and distal limbs are not well understood . Rodriguez syndrome ( OMIM 201170 ) is a severe , usually perinatal lethal AFD characterized by severe retrognathia , hypertelorism with deep set eyes and deficient supra-orbital ridges , low set and posteriorly rotated ears , and oligodactyly with additional preaxial or postaxial abnormalities . Lower limb abnormalities , including fibular hypoplasia and equinovarus , are common and the spine , ribs and pelvis can also be involved . The initial description of the phenotype was a sibship of three affected offspring , suggesting recessive inheritance , but the seven cases reported subsequently were all sporadic in their families and did not recur , a pattern more consistent with de novo dominant mutations [13–19] . Recently , McPherson et al . described a case of a surviving child with a phenotype intermediate between Rodriguez syndrome and the non-lethal Nager syndrome form of AFD [14] . The child was heterozygous for a mutation in SF3B4 , supporting dominant mutations as underlying the phenotype and raising the possibility that these two AFDs are allelic . To test this hypothesis , we determined the sequence of SF3B4 in one case of classic Rodriguez syndrome and in a set of monozygotic twins with an intermediate phenotype , identifying heterozygosity for SF3B4 mutations in both families . At the biochemical level , the mutations led to reduced SF3B4 synthesis . Cartilage RNA-seq analysis identified reduced expression and/or altered splicing of target genes , including transcription factors known to be important in skeletal development , consistent with differential effects on pre-mRNA splicing and gene expression as underlying mechanisms of disease .
As SF3B4 is frequently mutated in patients with Nager syndrome and it had been suggested that Rodriguez syndrome might be an allelic disorder [9–12 , 14 , 20] , we determined the sequences of SF3B4 in the two cases . Case R14-123A , with Rodriguez syndrome , was heterozygous for a de novo single base insertion ( c . 614_615insG; p . Asp205Glufs*281 ) in exon 3 ( Fig 2A ) . Case R08-269B , with the intermediate phenotype , was heterozygous for a single base insertion ( c . 1060dupC; p . Arg354Profs*132 ) in exon 5 ( Fig 2B ) . The latter mutation was previously found in two cases of Nager syndrome [9 , 12] . Both mutations led to frameshifts which started at different positions within the coding region but resulted in stop codons that terminated at the same position , which was beyond the normal stop codon ( Fig 2D ) . Such mutations are referred to as a type of nonstop mutation and can lead to nonsense mediated decay of the transcripts [21 , 22] . Sequence analysis of amplified cDNA from R14-123A fibroblasts showed presence of some transcript derived from the mutant allele but always at a reduced level relative to the normal allele sequence ( Fig 2C ) , consistent with instability of the mutant transcript . Based on these findings , we concluded that SF3B4 mutations cause Rodriguez syndrome . To characterize the effect of the mutations on overall SF3B4 expression , qRT-PCR was used to measure SF3B4 mRNA levels in cultured fibroblasts from case R14-123A and in chondrocytes from case R08-269A . There was about a 30% reduction in SF3B4 transcripts in both cases compared with controls ( Fig 3A and 3B ) . As shown in Fig 3C , for R14-123A there was a corresponding decrease in SF3B4 protein relative to the control . Because there was a low level of the mutant transcript , which is predicted to encode a protein with an altered carboxyl-terminus that extends the length of the protein by 60 amino acids , we attempted to detect the mutant protein . The predicted molecular weight of the elongated protein is 52 . 8 kDa , which is 8 . 4 kDa larger than wild-type ( WT ) SF3B4 . Western blotting was performed using an antibody to the amino-terminal end of the protein , which should be unaltered in both normal and mutant SF3B4 , and with an antibody directed against the carboxyl-terminal end , which should only recognize WT SF3B4 . As shown in Fig 3D , with a long exposure , the antibody against the amino-terminal end of the protein detected a faint protein migrating just above the SF3B4 WT protein , in a position corresponding to the predicted molecular weight of the mutant protein . This protein was not detected by the carboxyl-terminal antibody , as would be expected based on the mutation . The data were thus consistent with synthesis of a small amount of the mutant protein by the low level of mutant transcript present in the cultured cells ( Fig 2C ) . Whether the abnormal protein is functional and contributes to the U2 snRNP and the Rodriguez syndrome phenotype is unknown . Immunofluorescence staining of cultured fibroblasts revealed nuclear localization of SF3B4 , in agreement with its spliceosomal function in nucleus , with the fluorescence intensity appropriately reduced in cultured cells from the affected individual ( Fig 3E ) . The affected individuals exhibited shortening or absence of humeri , radii and ulnae , brachydactyly , and delayed ossification of the talus ( Fig 1 and Table 1 ) , which all indicate defective endochondral ossification . We therefore performed histopathological examination of the distal femur growth plates . As shown in Fig 4 , both cases showed a dramatically reduced hypertrophic zone with disorganization of the normal columnar arrangement of chondrocytes . Mutant hypertrophic chondrocytes were also smaller than the corresponding control cells . Case R14-123A had the most severely disrupted morphology , in agreement with the more severe clinical and radiographic phenotype in this case . Cartilage growth plate development is regulated by highly orchestrated cell signaling and transcriptional networks . Profound disorganization of the hypertrophic zone suggested impaired chondrocyte gene expression as a possible result of mutations in SF3B4 . We therefore measured growth plate chondrocyte gene expression by RNA-seq using RNA derived from distal femur cartilage from case R08-269A . This analysis identified 2025 differentially expressed transcripts . Among them , 652 transcripts ( representing 622 genes ) were down-regulated and 1373 transcripts ( representing 1215 genes ) were up-regulated ( S2 Table ) . Gene ontology ( GO ) analysis of the down-regulated transcripts primarily revealed altered expression related to skeletal development , transcriptional regulation , chromosome organization and cell cycle control ( Fig 5A ) . Among the down-regulated genes , there were 15 genes , including DLX5 and SOX6 , that are known key regulators of skeletal development ( Table 2 , Fig 5B and 5C ) . Since SOX9 is is an upstream regulator of SOX6 and plays a pivitol role in chondrocyte differentiation , we examined its expression in the RNA-seq data . As shown in Fig 5D , SOX9 is highly expressed in growth plate chondrocytes and its expression was reduced by 33% in the mutant growth plate . Thus while SOX9 did not meet the screening criteria used to identify differentially expressed genes , its reduced expression was consistent with the secondary reduction of SOX6 transcript levels observed in the RNA-seq data . The SF3B4 transcript level was reduced by 32% in the mutant growth plate ( Fig 5E ) , a result concordant with the quantitative RT-PCR data ( Fig 3B ) . To validate the RNA-seq data , we performed quantitative RT-PCR using the same mutant chondrocyte-derived RNA . The data confirmed the observed reduction in DLX5 , SOX6 , and SOX9 transcript levels ( Fig 5F ) . The expression levels of other U2 snRNP components , SF3B1 and SF3B2 , did not show significant differences relative to control chondrocyte RNA . Because it has been shown that DLX6 coordinates with DLX5 in the regulation of craniofacial and limb development , we also examined DLX6 expression using quantitative PCR . While DLX6 had a low expression level in chondrocytes , and therefore did not meet inclusion criteria for determining whether it was differentially expressed in the RNA-seq analysis , by qRT-PCR its expression was significantly decreased in mutant growth plate derived cartilage RNA ( Fig 5F ) . DLX5/6 proteins and SOX5/6/9 are transcription factors known to regulate chondrocyte gene expression and skeletogenesis [23 , 24] . SOX5 and SOX6 form dimers and recognize sequences with SOX and SOX-like binding sites [25 , 26] . We therefore asked whether their targets were enriched among the 652 down-regulated transcripts in SF3B4 mutant growth plate chondrocytes . As shown in Fig 5G , DLX5 , DLX6 , SOX5 and SOX9 binding motifs were found to be significantly enriched among the promoter regions of the downregulated transcripts ( S2 Table ) , consistent with reduced target gene expression as a regulatory consequence of reduced expression of DLX5/6 and SOX6/9 proteins . Immunohistochemical staining demonstrated co-localization of expression of SF3B4 and DLX5 in normal human fetal growth plate , with expression in all growth plate chondrocytes and overlapping regions of the periosteum , consistent with their roles in growth plate chondrocyte differentiation and cell signaling ( Fig 6 ) . Concordant with their respective functions in splicing and transcription , SF3B4 and DLX5 were specifically localized in chondrocyte nuclei in both wildtype ( Fig 6 ) and mutant growth plates . Because of the role SF3B4 plays in the U2 snRNP complex , the rMATS algorithm was applied to the RNAseq data to determine the effect of reduced SF3B4 on splicing [27] . A total of 1541 significant differential alternative splicing events were identified , which included both exon exclusion ( ΔPSI<0 ) and exon inclusion ( ΔPSI>0 ) scenarios in case R08-269A compared with the controls . These events were divided among five different alternative splicing outcomes . The major effect was exon skipping , with 765 skipped exon ( SE ) events , including 412 exon exclusions and 353 exon inclusions ( Fig 7A and S3 Table ) . The other four outcomes were 124 mutually exclusive exon ( MXE ) , 134 alternative 5’ splice site ( A5SS ) , 269 alternative 3’ splice site ( A3SS ) and 249 retained intron ( RI ) events , with a higher fraction of exon exclusions relative to inclusions ( Fig 7A ) . RPBJ , HIF1A , SMAD3 , and IGF2R are known upstream regulators of SOX9 expression . For all four genes , a minor proportion of their transcripts showed altered splicing , primarily exon skipping or activation of cryptic splice sites ( S2 Fig ) . Among these events , we focused on the abnormal splicing pattern for SMAD3 , as its altered splicing was consistent with the observed reduced expression of SOX9 . SMAD3 is a component of TGF-β signaling pathway and induces primary chondrogenesis when overexpressed in human mesenchymal stem cells ( MSCs ) . SMAD3 also has been shown to associate with SOX9 on the COL2A1 enhancer to promote SOX9-mediated transcriptional activity , and depletion of SMAD3 in MSCs by siRNA reduces SOX9 expression [28 , 29] . In case R08-269A , a proportion of SMAD3 transcripts had either inclusion of exon 3 or increased transcription that started from exon 4 . Exon 3 encodes a linker region that connects the MH1 and MH2 domains of SMAD3 . The SMAD3 splicing variant lacking exon 3 ( SMAD3-Δ3 ) is expressed in multiple human tissues and has positive transcriptional activity [30] . The truncated Smad3 isoform transcribed from an ATG site within exon 4 consists of 7 exons and encodes half of the linker region and the MH2 region . This isoform inhibits activin-induced activation of the FSHβ promoter [31] . Thus together the R08-269A transcripts had reduced expression of the SMAD3-Δ3 transcription activator and increased expression of 5’ truncated transcriptional repressor isoforms , consistent with reduced SMAD3 activity which could lead to reduced expression of SOX9 . Beyond these specific splicing changes , GO analysis of the overall differential alternative splicing events demonstrated enrichment of biological processes relevant to the SF3B4 mutant phenotype , including extracellular matrix organization , skeletal system development , and RNA processing ( Fig 7B ) . These data support a complex effect of altered RNA splicing on downstream gene expression that included signaling molecules , transcription factors and chromatin modifiers that are known to regulate genes essential for skeletal development . Our findings imply that these factors could contribute to the mechanism of disease , but how each molecule individually contributes to this complex phenotype will require development of tissue specific cellular and animal models .
Haploinsufficiency for SF3B4 is considered to be the major cause of Nager syndrome . In an analysis of 35 independent families with Nager syndrome , Bernier et al . identified 18 cases heterozygous for SF3B4 mutations , including 14 frameshift and two nonsense mutations , indicating that dominant , loss-of-function mutations commonly produce the phenotype [9] . Similarly , in the data presented here , two SF3B4 mutations that were both predicted to result in SF3B4 molecules with altered and extended carboxyl-termini , produced similar but more severe acrofacial dysostosis phenotypes , including one case with the lethal Rodriguez syndrome phenotype . Such mutations are predicted to lead to nonsense-mediated decay of the transcript and in the one case examined the mutant transcript was largely degraded . Whether the small amount of remaining mutant mRNA and the small amount of the abnormal protein ( Fig 3D ) synthesized by the cells could exert a dominant-negative or gain-of-function effect on the SF3B complex and contribute to the phenotype by altering the splicing function of the complex is unknown . However , it is noteworthy that a case of Nager syndrome with a de novo deletion at chromosome 1q21 . 2 that covers SF3B4 has been described [33] , supporting SF3B4 haploinsufficiency as the primary driver of the craniofacial and limb defects observed in these acrofacial dysostosis phenotypes . The clinical variability in expression across Nager syndrome cases with defects in SF3B4 does suggest that modifiers can influence the phenotype , so it may be that small differences in splicing activity could contribute measureably to phenotypic expression . Interestingly , even the monozygotic twins described here showed some variability in expression , with twin A exhibiting a more severe clinical and radiographic phenotype , suggesting that either epigenetic phenomena or small temporal developmental differences in gene expression can alter the clinical consequences of SF3B4 mutations . The RNA-seq data demonstrate that reduced SF3B4 activity can alter gene expression , as in growth plate chondrocytes there were 622 genes that showed reduced expression . Among these are genes known to regulate skeletal system development , consistent with the altered growth plate morphology observed in SF3B4 phenotypes and the ongoing growth deficiency in these disorders . Particularly noteworthy were DLX5 and SOX9 , genes with reduced expression and for which mechanistic hypotheses can be advanced to explain some of the skeletal phenotypic features observed in the SF3B4 disorders . DLX5 and its cis-linked paralogue DLX6 are pivotal regulators of craniofacial and limb development . Dlx5 knockout mice showed craniofacial abnormalities [34] and Dlx5/6−/− mutant embryos exhibited severe limb , craniofacial , and axial skeletal defects [35 , 36] . Dlx5−/− mice showed a modest defect in chondrocyte hypertrophy in the long bones of the limbs [37] , while targeted deletion of both Dlx5 and Dlx6 resulted in more pronounced deficiencies in chondrocyte hypertrophy [36] , underscoring their co-regulatory roles in growth plate chondrocyte maturation and endochondral ossification . Reciprocally , forced overexpression of Dlx5 in chondrocytes promoted hypertrophy and induced precocious ossification in the endochondral skeleton [38 , 39] . In humans , chromosomal rearrangements of the 7q21 . 3-q22 . 1 region harboring DLX5 , DLX6 and their regulatory elements ( including enhancers ) have been found in Split-hand/split-foot malformation 1 ( SHFM1 , OMIM 183600 ) , indicating that structural abnormalities affecting both genes can produce SHFM1 [40 , 41] . Monoallelic deletion of only the DLX5/6 locus in some cases indicated that DLX5/6 haploinsufficiency can lead to SHFM1 [42] . Point mutations in three SHFM1 families suggest that mutations in DLX5 alone may be sufficient to produce the phenotype [43–45] . These data support the idea that the approximately half normal DLX5/6 expression resulting from SF3B4 haploinsufficiency could produce the distal limb abnormalities observed in Nager and Rodriguez syndromes . In addition to the split-hand/split foot phenotype , SHFM1 cases can have craniofacial abnormalities , mesomelic limb malformations , hearing loss , and developmental delay . Since many of these features are shared by Rodriguez/Nager syndrome cases , including cleft palate , micrognathia , and hypoplasia/absence of the radii and fibulae , the reduction of DLX5/6 expression that resulted from reduced SF3B4 expression likely contributes to these aspects of the phenotype as well . Because DLX5/6 transcripts were spliced normally , reduced expression of DLX5/6 due to SF3B4 mutation is likely to be indirect . Multiple mechanisms are known to regulate DLX5/6 expression , including regulation by other transcription factors ( e . g . p63 , MEF2C ) [35 , 46] , by distal regulatory elements ( enhancer and noncoding RNA ) [47–51] and by epigenetic mechanisms [52 , 53] . SF3B4 mutations may interfere with one or more of these mechanisms and lead to dysregulation of DLX5/6 gene expression . SOX9 is a transcription factor that regulates skeletal development [54–57] . Heterozygosity for mutations in SOX9 produces campomelic dysplasia [54 , 55] , a generally lethal skeletal dysplasia [58] . Most mutations in campomelic dysplasia result in haploinsufficiency for SOX9 and the phenotypic consequences include micrognathia and abnormal ears , as well as distinct skeletal abnormalities . The characteristic skeletal defects include a bell shaped thorax , eleven pairs of ribs , a lateral clavicular hook , hypoplastic scapulae , poor ossification of the pubis , hip dislocations , hypoplastic fibulae , poor ossification of the talus and club feet . The cases with SF3B4 mutations studied here exhibited many of these findings , supporting the inference that the diminished expression of SOX9 transcript due to SF3B4 haploinsufficiency contributes to their skeletal abnormalities . Furthermore , it is known that the skeleton is exquisitively sensitive to SOX9 dosage . Mouse studies show that haploinsuffiency produces a skeletal phenotype similar to campomelic dysplasia in that Sox9+/- mice show hypoplastic scapulae and thin pubic bones [59] . Similar to the consequences of diminished DLX5/6 expression , because SOX9 transcripts appear to be spliced normally , the mechanism for diminished SOX9 transcript may be indirect , but the remarkable phenotypic overlap supports the hypothesis that reduced SOX9 expression has the predictable biological consequences in the SF3B4 disorder phenotypes . The disrupted hypertrophic zones of the patient growth plates ( Fig 4 ) raise the question of whether downregulation of SOX9 and DLX5 are the cause or consequence of the morphological abnormalities . According to previous studies , Sox9 is expressed in reserve and proliferating chondrocytes but not in the hypertrophic zone [26 , 60] , consistent with an effect on hypertrophic chondrocyte differentiation as a consequence of reduced SOX9 . For Dlx5 , the gene is expressed throughout the growth plate , including in differentiating proliferative chondrocytes as well as the prehypertrophic and hypertrophic zones [37 , 39 , 61] . However , because the hypertrophic zone is much less cellular , loss of hypertrophic chondrocytes could not account for the approximately 50% reduction in DLX5 transcripts . Thus the data again suggest that the morphological alterations are secondary to the reduced expression and not the other way around . The altered splicing and gene expression resulting from SF3B4 mutations echo the spliceosomal defects that have been found to underlie other craniofacial disorders [6] . Haploinsufficiency for EFTUD2 , which encodes a GTPase in the U5 snRNP and has a regulatory role in catalytic splicing and post-splicing-complex disassembly , causes mandibulofacial dysostosis of the Guion-Almeida type ( OMIM 610536 ) , a disorder that shares many clinical features with Rodriguez/Nager syndromes [7] . EFTUD2 has been shown to interact with SF3B4 by yeast two-hybrid and co-immunoprecipitation studies and it has been suggested that interactions between SF3B and U5 proteins may recruit the tri-snRNP ( U4/U6-U5 ) complex to the spliceosome via the U2 complex [62] . In this study , we identified 1541 alternative splicing events , predominantly exon skipping . As shown here , altered splicing has complex consequences , likely contributing both directly and indirectly to the AFD phenotype , a complexity underscored by alterations in expression of multiple transcription factors known to be essential for normal skeletal development . For the craniofacial dysmorphology in particular , knockdown of Sf3b4 in Xenopus has been shown to result in loss of cranial neural crest cells , demonstrating the importance of normal splicing in maintaining craniofacial skeletal precursors during development [63] . The limb deficiencies and craniofacial dysmophologies found in AFDs with spliceosomal defects indicate that altered splicing/expression of key regulators may lead to developmental abnormalities including early digit patterning and neural crest cell formation and proliferation defects [64] , neither of which would be revealed by studying growth plate chondrocytes . For example , p63 is a master trancriptional regulator that controls DLX5/6 expression and skeletogenesis [35 , 65 , 66] . p63−/− mice show severely limb and craniofacial defects , and P63 mutations lead to human disorders affecting the facial and limb structures [65–69] . Multiple p63 isoforms generated by alternative splicing and promoter usage have been identified , which show distinct roles during endochondral ossification [70] . Therefore , mis-spliced p63 may account for the down-regulation of DLX5/6 in Rodriguez/Nager syndrome cases . However , p63 expression was undetectable in growth plate chondroctyes , suggesting the possibility that earlier developmental events may have led to some of the observed effects on gene expression . Analysis of Sf3b4 deficient animal models will help to identify novel regulators and clarify the role of Sf3b4 in skeletogenesis at early developmental stages . In addition to the links between splicing and AFD , other mechanisms may also contribute to the phenotype . In this context , SF3B4 has been shown to bind the bone morphogenetic protein ( BMP ) receptor BMPR-IA and specifically inhibit BMP-mediated osteochondral cell differentiation [71 , 72] . Thus reduced SF3B4 expression could alter more than one developmental pathway to elicit the observed phenotypes . However , Xenopus Sf3b4 knockdown studies did not confirm a link between SF3B4 expression and BMP signaling alterations [63] , indicative of unresolved certainty connecting the SF3B4 disorders and this important skeletal signaling pathway . In summary , the current study identified heterozygosity for SF3B4 mutations in one case of Rodriguez syndrome and second case with a phenotype intermediate between Rodriguez and Nager syndromes . These data demonstrate that Rodriguez syndrome is a dominant disorder that is allelic with Nager syndrome , and show that the two disorders belong to a spectrum unified by mutations in SF3B4 . SF3B4 mutations have also been found in an independent set of Rodriguez syndrome cases ( New dominant mutations in SF3B4 encoding an mRNA spliceosomal protein important in embryonic limb patterning underlie Rodriguez acrofacial dysostosis . MD Irving , B Dimitrov , D Chitayat , JI Rodriguez , MW Wessels , MA Simpson . American Society of Human Genetics Meeting , 2014 ) , further supporting this conclusion . Through RNA-seq analysis , mutant growth plate chondrocytes showed altered splicing and reduced expression of transcription factors and additional genes essential for skeletogenesis , suggesting specific regulatory mechanisms that are disrupted in the SF3B4 disorders . Elucidating the direct and indirect effects on the regulatory gene expression pathways affected by SF3B4 mutations is expected to reveal the developmental hierarchy that defines when and how skeletogenesis is altered to produce the SF3B4 phenotypes .
Cases were recruited and informed consent obtained under a UCLA-approved human subjects protocol , through which clinical evaluation and imaging studies , as well as tissue procurement , were carried out . Chondrocytes were isolated from distal femur growth plate cartilage from case R08-269A and four independent 14–18 week normal fetal growth plates as described previously [73] . Skin fibroblast cultures were established from explanted skin biopsies from case R14-123A and an unaffected 26-week fetus . Fibroblasts were grown at 37°C in Dulbecco's modified Eagle’s medium plus 10% FBS , penicillin , and streptomycin in 5% CO2 . Genomic DNA was isolated from cultured fibroblasts using the QIAamp DNA Mini Kit ( Qiagen ) according to the manufacturer’s protocol . PCR products were generated with primers flanking each coding exon of SF3B4 ( S1 Table ) and their sequences determined by bidirectional Sanger sequencing . Electropherograms were analyzed using the CLC Main Workbench ( CLC Bio ) . DNA sequences were compared with the SF3B4 reference genomic sequence ( NCBI accession number NG_032777 . 1 ) . RNA was isolated from fibroblasts and chondrocytes with TRIZol ( Thermo Fisher Scientific ) and DNA contamination removed with DNase I ( Thermo Fisher Scientific ) . RNA was reverse transcribed using the SuperScript III First-Strand Synthesis System ( Thermo Fisher Scientific ) . Quantitative real-time PCR was performed using SYBR Green Real-Time PCR Master Mix ( Thermo Fisher Scientific ) . Primer sequences for the target genes are listed in the S1 Table . GAPDH was used as a reference gene for normalization . Gene expression was quantified with the ΔΔCT method [74] . Statistical significance was assessed using two-sample Student’s t-test . Sequences of PCR products were determined by Sanger sequence analysis and compared with the NCBI SF3B4 reference sequence ( NCBI accession number NM_005850 . 4 ) . Cells were lysed in RIPA buffer and proteins separated by electrophoresis on SDS-PAGE gels ( Bio-Rad ) . Proteins were transferred to membranes and probed with anti-SF3B1 ( Abcam , ab172634 , 1:1000 dilution ) , anti-SF3B4 N-terminal ( Proteintech , 10482-1-AP , 1:1000 dilution ) , anti-SF3B4 C-terminal ( Bethyl Laboratories , A303-950A , 1:1000 dilution ) or anti-GAPDH ( Cell Signaling Technology , 2118 , 1:10000 dilution ) antibodies . The ECL kit ( Thermo Fisher Scientific ) was used to visualize immunoreactive proteins . The level of SF3B4 was quantified with ImageJ software and normalized by comparison with the expression of GAPDH . Cultured fibroblasts were fixed in 4% PFA , followed by three washes with PBS and one wash with PBST ( 1% Triton X-100 in PBS ) . The samples were then blocked in 10% goat serum for 1h and incubated overnight with anti-SF3B4 polyclonal antibody ( Proteintech , 10482-1-AP 1:50 dilution ) . Cell images were captured using confocal fluorescence microscopy ( Zeiss ) . For Toluidine Blue staining , fetal cartilage samples were fixed in 10% buffered formalin overnight , then washed and transferred to 70% ethanol , dehydrated and embedded without decalcification in methyl methacrylate . The cartilage sections were stained with 0 . 1% Toluidine Blue . For immunohistochemistry , the paraffin embedded sections were deparaffinized , rehydrated in an ethanol series and permeablilized in 0 . 5% Triton X-100 before blocking in 10% goat serum and incubating with the primary antibody . The anti-SF3B4 ( Proteintech , 10482-1-AP , 1:50 dilution ) and anti-DLX5 ( Sigma-Aldrich , HPA005670 , 1:150 dilution ) antibodies were used for staining . The sections were developed with 3 , 3'-diaminobenzidine ( DAB ) solution . Total RNA was isolated with TRIZol ( Thermo Fisher Scientific ) from distal femur growth plate chondrocytes from case R08-269A and four independent 14–18 week fetuses . DNA contamination was removed by DNase I ( Thermo Fisher Scientific ) digestion at 37°C for 30 minutes . The RNA was further purified with the RNeasy Mini Kit ( Qiagen ) . Total RNA samples with RIN ( RNA integrity ) numbers ≥ 8 ( measured with the Agilent 2100 BioAnalyzer ) were used for mRNA library preparation using the TruSeq RNA Preparation Kit ( Illumina ) according to the manufacturer's instructions . 75bp paired-end reads were generated on the HiSeq 2000 or NextSeq 500 sequencer ( Illumina ) . RNA-seq reads were mapped to the human genome ( hg19 ) using TopHat ( v2 . 0 . 9 ) , allowing up to 2-bp mismatches per read [75] . Cufflinks ( v2 . 2 . 1 ) was used to calculate RNA-seq based gene expression levels using the FPKM value ( fragments per kilobase of exon per million fragments mapped ) [76] . Low abundance genes with FPKM values less than 1 were filtered before further analysis . The differentially expressed genes in the case were defined as those genes with more than a 2-fold change in FPKM value compared with the average FPKM value of the normal control samples and more than a 1 . 5-fold change of FPKM value compared with the maximum or minimum FPKM value of the controls . rMATS ( v3 . 2 . 1 ) was used to identify differential alternative splicing events between the case and controls [27] . The change in Percent Spliced In ( ΔPSI or Δψ ) and false discovery rate ( FDR ) were used to identify the most significant altered splicing events . The alternative splicing events were filtered using |ΔPSI| > 0 . 05 , FDR < 0 . 05 and at least 10 reads covering the exon-exon junctions in both case and control samples . Genes with differential expression or alternative splicing were submitted to DAVID for Gene Ontology ( GO ) analysis [77] . Enrichment analysis of known transcription factor binding motifs was performed with CentriMo v4 . 11 . 1 in the MEME suite [78] using the DNA sequences from gene promoter regions ( ±1Kb from transcription start sites ) . Known motifs were obtained from the HOmo sapiens COmprehensive MOdel COllection ( HOCOMOCO ) v10 [79] . Match score ≥ 5 and E-value ≤ 10 were selected as the report threshold . | The acrofacial dysostoses ( AFD ) are inherited disorders with abnormalities of the facial and limb bones . Rodriguez syndrome is a severe type of AFD that is usually lethal in the immediate perinatal period . Rodriguez syndrome has been proposed to be a severe form of Nager syndrome , a non-lethal AFD that results from mutations in SF3B4 , a component of mRNA splicing machinery needed for proper maturation of primary transcripts . Furthermore , a case with a phenotype intermediate between Rodriguez and Nager syndromes has been shown to have an SF3B4 mutation . We found that mutations in SF3B4 produce Rodriguez syndrome , further demonstrating that it is allelic with Nager syndrome . The consequences of the mutations include abnormal splicing and reduced expression in growth plate chondrocytes of genes that are important for proper development of the skeleton , providing mechanistic insight toward understanding how SF3B4 mutations lead to the skeletal abnormalities observed in the acrofacial dysostoses . | [
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] | 2016 | Altered mRNA Splicing, Chondrocyte Gene Expression and Abnormal Skeletal Development due to SF3B4 Mutations in Rodriguez Acrofacial Dysostosis |
Ribonucleotides ( rNMPs ) are frequently incorporated during replication or repair by DNA polymerases and failure to remove them leads to instability of nuclear DNA ( nDNA ) . Conversely , rNMPs appear to be relatively well-tolerated in mitochondrial DNA ( mtDNA ) , although the mechanisms behind the tolerance remain unclear . We here show that the human mitochondrial DNA polymerase gamma ( Pol γ ) bypasses single rNMPs with an unprecedentedly high fidelity and efficiency . In addition , Pol γ exhibits a strikingly low frequency of rNMP incorporation , a property , which we find is independent of its exonuclease activity . However , the physiological levels of free rNTPs partially inhibit DNA synthesis by Pol γ and render the polymerase more sensitive to imbalanced dNTP pools . The characteristics of Pol γ reported here could have implications for forms of mtDNA depletion syndrome ( MDS ) that are associated with imbalanced cellular dNTP pools . Our results show that at the rNTP/dNTP ratios that are expected to prevail in such disease states , Pol γ enters a polymerase/exonuclease idling mode that leads to mtDNA replication stalling . This could ultimately lead to mtDNA depletion and , consequently , to mitochondrial disease phenotypes such as those observed in MDS .
The replication of DNA is a highly accurate process where free deoxyribonucleoside triphosphates ( dNTPs ) are incorporated opposite their complementary base . In general , DNA polymerases are good at discriminating between ribonucleoside triphosphates ( rNTPs; the units that constitute RNA ) and dNTPs by virtue of a steric gate residue that clashes with the 2′-OH group of rNTPs [1] . However , because the rNTP concentration in the cell is several orders of magnitude higher than that of dNTPs , DNA polymerases will occasionally erroneously incorporate rNTPs instead of dNTPs [2] . The presence of embedded ribonucleoside monophosphates ( rNMPs ) in DNA induces structural and chemical changes [3 , 4] that contribute to unwanted effects such as genome instability and replication stress [5 , 6] . Due to their negative influence on DNA stability , rNMPs are actively removed from nuclear DNA ( nDNA ) by the ribonucleotide excision repair ( RER ) pathway that is initiated by cleavage at the incorporated rNMP by the enzyme RNase H2 [6 , 7] . Mutations in RNase H2 lead to an increased rNMP frequency in nDNA and can in humans give rise to rare autoinflammatory disorders [8 , 9] . However , RER-mediated rNMP removal is absent in mitochondria [10 , 11] . Accordingly , mammalian mitochondrial DNA ( mtDNA ) has for decades been known to be rich in rNMPs [12–14] . Recent studies using fibroblast cell lines show that rNMPs are present at a frequency of approximately 54 rNMPs per 16 kb mammalian mtDNA molecule [10] . It is currently unclear whether the rNMPs embedded in mtDNA have a functional significance or if they merely are relatively well-tolerated in the mitochondria and therefore do not undergo the prompt removal observed in nDNA . Nonetheless , mutations in the gene coding for RNase H1 , an endonuclease implicated in the removal of longer stretches of rNMPs , cause adult-onset mitochondrial encephalomyopathy marked by multiple mtDNA deletions [15] , underscoring the importance of at least a certain level of rNMP removal from mtDNA . MtDNA is a 16 . 5 kb circular , double-stranded DNA molecule that encodes for key subunits of the oxidative phosphorylation ( OXPHOS ) system . OXPHOS is responsible for the majority of the ATP production in eukaryotic cells , and malfunctions in this process can lead to neuromuscular disorders , emphasizing the importance of mtDNA integrity [16] . The duplication of mtDNA is performed by a set of dedicated replication proteins that are nuclear-encoded and post-translationally imported into mitochondria . These include the replicative mtDNA polymerase Pol γ , the replicative helicase Twinkle , mitochondrial single-stranded DNA-binding protein ( mtSSB ) and the mitochondrial RNA polymerase that primes mtDNA replication [17] . Pol γ discriminates rigorously between dNTPs and rNTPs [18] , but the frequency of rNMP incorporation into the genome also depends on the ratio between the free dNTPs and rNTPs available in the cell . The mtDNA is especially vulnerable in this respect , since it is replicated independently of cell cycle phase [19] . Outside S phase , dNTP levels are low , and since rNTP levels show little fluctuation over the cell cycle , the rNTP/dNTP ratio is expected to be high [20 , 21] . The rNTP/dNTP ratio might be particularly high in certain tissues of patients that suffer from defects in the mitochondrial dNTP supply . Mutations in e . g . thymidine kinase 2 [22] or deoxyguanosine kinase [23] are expected to lead to decreased mitochondrial pools of certain dNTPs , which in turn leads to mtDNA instability by a still uncertain mechanism . A recent study by Berglund et al . suggested that an increased rNTP/dNTP ratio could cause the elevated levels of mtDNA rNMPs observed in fibroblasts derived from patients with defects in mitochondrial dNTP metabolism [10] . It was further proposed that increased rNMP accumulation in mtDNA might impair consecutive rounds of replication and could therefore contribute to the mtDNA instability and disease phenotypes observed in these patients . However , no studies have so far addressed how the fidelity and efficiency of Pol γ are affected by incorporated rNMPs at physiologically relevant dNTP and rNTP levels . Using highly purified recombinant proteins we show that human Pol γ bypasses single rNMPs with an unprecedentedly high efficiency and fidelity . Additionally , our data indicate that human Pol γ—alone or in the context of the reconstituted mitochondrial replisome—has a striking ability of discriminating against rNMPs during incorporation , and displays several-fold lower frequency of rNMP incorporation compared to nuclear replicative polymerases . However , we show that free rNTPs can negatively impact mtDNA replication , likely by competing with the dNTPs for binding to the active site during DNA synthesis by Pol γ , and can lead to Pol γ stalling . In certain diseases , dNTP pool disturbances lead to compromised mtDNA stability without any major effect on the nDNA . We propose that this differential outcome is at least partly due to the sensitivity of the mitochondrial replication fork to high rNTP/dNTP ratios .
Mammalian mtDNA is known to contain embedded rNMPs [10 , 13 , 24] . We confirmed the uniform distribution of rNMPs in mouse liver mtDNA by Southern blot analysis and found rNMPs to be embedded on average every 500 nucleotides on either strand ( S1 Fig ) . Due to the relative frequency of rNMPs in mature mtDNA , Pol γ is expected to encounter several rNMPs in the template strand during replication of the ~16 kb mtDNA molecule . If these rNMPs impair replication , they could have pathological consequences for patients that , as a result of a shortage of specific dNTPs , have increased levels of rNMPs in mtDNA [10] . The exonuclease deficient variant of Pol γ has previously been reported to be able to bypass rNMPs [18] , however , we wanted to examine the bypass of wild type Pol γ as well as the efficiency of bypass at a range of physiologically relevant dNTP concentrations . These in vitro polymerization reactions were carried out on synthetic DNA templates where Pol γ encountered the ribonucleotide at the 5th position after the initiation of DNA synthesis ( Fig 1A ) ; the control templates contained the corresponding deoxyribonucleoside monophosphate ( dNMP ) in place of rNMP in an identical sequence context . We found that bypass of a single embedded rNMP by Pol γ was not obviously reduced relative to an all-dNMP template , even at the lowest dNTP concentration tested ( 0 . 01 μM; Fig 1B , compare dNMP/rNMP pairs ) . This result indicates that DNA synthesis by the Pol γ holoenzyme is not strongly inhibited by a single rNMP in the DNA template at the range of dNTP concentrations present in vivo . Similar results were observed with the exonuclease-deficient D274A Pol γ variant ( S2 Fig ) . The above reactions were performed with a 2 . 5-fold excess of polymerase over DNA template , whereby re-initiation of DNA synthesis could mask a moderate reduction in bypass efficiency . We therefore performed primer extension assays in reaction mixtures containing a large excess of template-primer over DNA polymerase , so that once a primer is extended , the probability that it will be used a second time is negligible , and the products therefore derive from a single cycle of synthesis . Band intensities were used to calculate termination probabilities at specific nucleotide positions surrounding the rNMP , as previously described ( [25] , also see Materials and Methods ) . Under these conditions , the presence of rNMP in the template led to a relatively moderate increase in termination probability , especially at positions -2 and -1 relative to the embedded rNMP ( Fig 1C ) . In addition , the rUMP-containing template showed an increase in termination probability at position +1 . Taken together , the results of Fig 1 show that even at low dNTP concentrations the efficiency of Pol γ is only slightly affected by single rNMPs present in the DNA template , which is in agreement with the reverse transcriptase activity of Pol γ [26 , 27] . Notably , the observed effect of single rNMPs on the termination probability of Pol γ is considerably lower than that reported for yeast and human nuclear replicative DNA polymerases [28–30] . We next addressed the fidelity of rNMP bypass by sequencing the DNA products of a primer extension assay in order to determine the base inserted by Pol γ opposite a single template rNMP present at position +5 relative to the primer terminus . Sequencing analysis revealed that Pol γ incorporates the correct base opposite a rNMP with similar fidelity as opposite a dNMP ( 98 . 3% versus 98 . 2% , which are expected values for this PCR based assay ) . This shows that a single rNMP in the template has no detectable adverse effects on insertion fidelity . In contrast , Pol γ incorporated the correct base , dC , opposite 8-oxo-7 , 8-dihydroguanine ( 8-oxo-G ) in only 68% of the sequenced products , which is comparable to the 73% found in an earlier report [31] , validating the experimental set-up . The fidelity of the proofreading-deficient D274A mutant of Pol γ was found to be slightly lower opposite an rNMP ( correct base in 96 . 4% of products ) , but a similar drop in fidelity was observed on an all-dNMP template ( 96 . 0% ) . These data suggest that unrepaired rNMPs in the mitochondrial genome are well-tolerated by Pol γ , not only when it comes to bypass efficiency ( Fig 1 ) , but also in terms of replication fidelity . Pol γ , the replicative mitochondrial polymerase , has been suggested to be the main source of the rNMPs embedded in the mitochondrial genome , and it has been shown to incorporate one rNMP for every 2 . 0 ± 0 . 6 × 103 bases on a short 70 nt template in vitro [10] . However , earlier work on nuclear DNA polymerases has shown that in vitro analysis of rNMP insertion frequency is greatly affected by the sequence context of the short , defined oligonucleotides used in these studies [32] . To circumvent this problem , we used the 7 . 3 kb M13 ssDNA as a template to estimate the propensity of Pol γ to incorporate rNMPs ( Fig 2A ) . Use of a long DNA template allows many sequence contexts to be tested simultaneously and the results should therefore give a closer estimate of the rNMP incorporation frequency occurring in vivo . To further increase the relevance of our data , we aimed to perform the reactions at physiologically relevant rNTP and dNTP concentrations . In an attempt to simulate the conditions that prevail during different stages of the cell cycle , two different sets of dNTP concentrations were tested: “normal” that mimic the concentrations present during S phase , and “low” that represent concentrations found during the rest of the cell cycle [33–35] . The latter dNTP concentrations are also expected to be similar to those found in non-dividing cells where nuclear DNA is not being replicated , but mtDNA replication still occurs . As a reference , we carried out reactions using the S . cerevisiae lagging strand DNA polymerase , Pol δ , of which the rNMP incorporation frequency has been reported using an identical assay set-up [7] . Unfortunately , comparison to human nuclear replicative polymerases is prevented by the lack of data on their rNMP incorporation frequencies using a similar long-template assay , and the general discrepancy between incorporation frequencies determined using long vs . short templates [7] . To enable comparison to literature values , some reactions with Pol γ and yeast Pol δ were performed using the nucleotide concentrations measured from logarithmically growing S . cerevisiae cells [2] . In all reactions , the DNA template was coated with the relevant single-stranded DNA binding protein ( Fig 2B , lanes 1–6 with RPA; lanes 7–14 with mtSSB ) to avoid stalling of DNA synthesis due to the formation of secondary DNA structures . Radioactively labelled products of the primer extension reactions on M13 ssDNA were treated with NaOH to hydrolyse the phosphodiester bond on the 3′ side of incorporated rNMPs and analysed by agarose gel electrophoresis under mildly denaturing conditions ( Fig 2B ) . In the absence of rNTPs , Pol γ synthesized long products that were only moderately affected by alkaline treatment ( Fig 2B , compare lanes 7–8 and 11–12 ) . In contrast , the DNA products synthesized in the presence of rNMPs were alkali-sensitive and a range of smaller DNA products was observed ( Fig 2B , compare lanes 9–10 and 13–14 ) indicating rNMP incorporation . The distribution of radioactive signal in individual lanes was quantified and transformed to a size distribution as previously described [7] in order to determine the median length of alkali-stable DNA fragments . The median length values were further used to determine the frequency of rNMP incorporation ( see Materials and Methods for details ) . Please note that because the signal in Fig 2B derives from incorporation of α-32P dCTP , longer products that contain a larger number of radioactive nucleotides appear stronger in intensity than shorter products . This causes the apparent product length on the gel to appear longer than the actual median length determined from the size distribution plot ( in which the signal has been corrected for length ) . At “normal” dNTP levels , the median length of untreated DNA products synthesized by Pol γ was 5 . 2 kb ( Fig 2B , lane 9 ) and this value dropped to 1 . 6 kb after NaOH treatment ( Fig 2B , lane 13 ) . These median lengths correspond to an average rNMP incorporation frequency of 1 rNMP per 2300 nt at “normal” dNTPs levels , while at “low” dNTP concentrations , the average rNMP incorporation frequency was 1 rNMP per 1400 nt ( Fig 2B , lane 10 vs . lane 14 ) . The calculations take into account that Pol γ was unable to fully replicate the 7 . 3 kb template in the presence of “low” dNTPs ( Fig 2B , lane 10 ) due to a strong reduction in replication rate in the presence of rNTPs . In comparison , the rNMP incorporation frequencies obtained for Pol δ were 1 rNMP per 770 , 650 and 400 nt at “S . cerevisiae” , “normal” and “low” dNTP concentrations , respectively ( Fig 2B , lanes 1–6 ) . The rNMP incorporation frequency at “S . cerevisiae” dNTP concentrations is in excellent agreement with the previously reported value of 1 rNMP per 720 nts obtained using an identical approach [7] , thus validating our experimental set-up . In conclusion , the results of Fig 2B show that Pol γ incorporates rNMPs far less frequently than its yeast homolog Mip1 ( 1 rNMP per 600 nts; [11] ) or the yeast nuclear replicative polymerases Pol δ or Pol ε ( 1 rNMP per 720 or 640 nts , respectively [7] ) . For processive replication of double-stranded mtDNA , Pol γ requires the activity of the mitochondrial DNA helicase Twinkle . To examine the impact of Twinkle on rNMP incorporation by Pol γ , we constructed a DNA substrate consisting of a primed single-stranded mini-circle with a 40 nt 5′-overhang to allow Twinkle loading ( Fig 2C ) . Once initiated , leading-strand DNA synthesis , coupled to continuous unwinding of the DNA template , can in theory continue indefinitely ( rolling circle replication ) . This replication system requires ATP , since DNA unwinding by Twinkle is an ATP-dependent process . As the concentration of ATP normally employed in our in vitro assays ( 4 mM ) would interfere with the rNMP incorporation studies , we made use of a creatine phosphokinase-based ATP regeneration system that maintained the ATP concentration at a lower concentration of approximately 300 μM . This way , the influence of free ATP in the assays without rNTPs was minimized while ensuring that efficient rolling circle replication could occur ( S3 Fig ) . In the absence of rNTPs , the reconstituted human mitochondrial replisome consisting of Pol γ , Twinkle and mtSSB synthesized long DNA fragments at all dNTP concentrations tested ( Fig 2D , lanes 1–3 ) . Alkali treatment of the reactions lacking rNTPs resulted in a small drop in DNA fragment size , which was ascribed to the presence of the 300 μM ATP required for Twinkle function ( Fig 2D , compare lanes 1–3 with lanes 7–9 ) . In contrast , the DNA products synthesized in the presence of rNTPs showed a substantial reduction in length upon alkali treatment , consistent with rNTP incorporation ( Fig 2D , compare lanes 4–6 with lanes 10–12 ) . The median length of DNA products synthesized in the presence of rNTPs was 5 . 2 kb using “normal” and 1 . 9 kb using “low” dNTP concentrations ( Fig 2D , lane 4 and 5 ) . NaOH treatment reduced the median length of the DNA products to 1 . 5 kb and 0 . 86 kb under “normal” and “low” dNTPs concentrations , respectively ( Fig 2D , lane 10 and 11 ) . Based on these median length values , the average rNTP insertion frequency of Pol γ holoenzyme in the presence of the helicase Twinkle and mtSSB is 1 rNMP per 2200 and 1600 nts at “normal” and “low” nucleotide concentrations , respectively . As expected , at the somewhat higher dNTP concentrations prevailing in logarithmical-growing S . cerevisiae cells , the rNMP incorporation frequency was the lowest , 1 rNMP per 2400 nts . These incorporation frequencies are similar to the values obtained in the reactions without Twinkle ( Fig 2B ) , showing that the rNMP incorporation propensity of Pol γ is not affected by the presence of other core mitochondrial replication factors . Numerous studies have shown that nuclear replicative DNA polymerases proofread incorporated rNMPs poorly , if at all [29 , 30 , 36] . Because proofreading of rNMPs could explain the relatively low rNMP incorporation frequency by Pol γ observed in Fig 2B , we tested the contribution of its exonuclease activity to rNMP incorporation both in vitro and in vivo . First , we carried out in vitro primer extension assays on M13mp18 ssDNA with either wild type ( WT ) or exonuclease-deficient ( exo- ) Pol γ ( Fig 3A ) . An extended reaction time ( 120 min ) and addition of extra polymerase after half the time ensured that the majority of DNA products synthesized were full-length ( 7 . 3 kb ) ( Fig 3B , lanes 1–4 ) . Analysis of NaOH-treated products synthesized in the presence of rNTPs revealed a comparable distribution of DNA fragment sizes from reactions catalysed by wild type or exo- Pol γ ( Fig 3B , lane 7 vs . 8; and Fig 3C , “WT + rNTPs” vs “exo- + rNTPs” ) . This observation demonstrates that the proofreading activity of Pol γ does not influence the frequency with which it incorporates rNMPs in vitro . To confirm our in vitro findings , we next isolated mtDNA from mice homozygous for an exonuclease-deficient mutant of the catalytic subunit of Pol γ ( PolgAD257A /D257A , hereafter referred to as PolgAD275A ) . Due to the lack of proofreading activity by the PolgAD275A variant , these mice accumulate mtDNA mutations and show signs of premature ageing [37] . Linearized mtDNA isolated from the liver of WT PolgA+/+ and the proofreading-deficient PolgAD275A mutant mice migrated as expected as a 16 kb mtDNA molecule ( Fig 3D , lanes denoted with “C” ) . Consistent with the results shown in S1C Fig , alkali treatment resulted in shorter DNA fragments ( Fig 3D , lanes denoted with “A” ) . The size distribution plots of alkali-treated wild type and PolgAD275A mtDNA were indistinguishable from each other , indicative of a comparable rNMP incorporation profile ( Fig 3E ) . The results of the in vivo and in vitro experiments therefore both indicate that the exonuclease activity of Pol γ does not influence the amount of rNMPs embedded in the mtDNA . Our findings presented so far indicate that rNMPs incorporated in the DNA template do not constitute a considerable impediment to replication by Pol γ . On the other hand , the lower amount of synthesis products observed in experiment in Fig 2B ( lanes 7–10 ) suggested that the presence of free rNTPs in the reaction mix in addition to dNTPs may have an adverse effect on DNA synthesis by Pol γ in vitro , especially when dNTP concentrations are low . To better study the effect rNTPs on the rate of replication by Pol γ , we performed DNA replication experiments with the WT and exo- Pol γ variants in the presence and absence of rNTPs using the 3 kb pBluescript DNA template ( Fig 4A ) . As the reaction progressed , both the WT and exo- Pol γ variants were able to efficiently produce a full-length 3 kb DNA product when only dNTPs were present in the reaction mix ( Fig 4B , lanes 2–5 and lanes 11–14 ) . However , the addition of rNTPs into the reaction substantially reduced the efficiency of replication catalysed by WT Pol γ , and only a faint full-length band was visible even after a 90-min reaction ( Fig 4B , lanes 6–9 ) . In contrast , the length of DNA products synthesized by the exo- Pol γ variant was unaffected by the presence of rNTPs in the reaction ( Fig 4B , compare lanes 11–14 with lanes 15–18 ) . Quantification of full-length reaction products confirmed that the processivity of Pol γ was only affected by the presence of free rNTPs when its proofreading ability was intact ( Fig 4C ) . This suggests that the presence of rNTPs forces the wild type enzyme to slow down and/or stall in a manner that depends on a functional exonuclease domain . However , as shown in Fig 3 , the proofreading activity of Pol γ is unable to selectively remove incorporated rNMPs . Taken together , these findings are consistent with a scenario where the presence of free rNTPs forces WT Pol γ to idle between polymerase and exonuclease modes , leading to slower replication . The above reactions were performed in the presence of 10 μM equimolar dNTPs . However , the rNTP-dependent decrease in Pol γ replication speed was even more pronounced at physiologically relevant ( “normal” ) dNTP concentrations ( S4A Fig , lanes 6–9 ) . Similar reactions with the S . cerevisiae homolog of Pol γ , Mip1 , showed that also the replication rate of this polymerase was affected by the presence of the rNTPs , albeit to a lesser extent than its human counterpart ( S4B Fig ) . Taken together , these results indicate that free ribonucleotides can impair the activity of mitochondrial polymerases , with human Pol γ being more affected than its yeast homolog Mip1 . We next compared the effect of different dNTP concentrations on synthesis by Pol γ when rNTPs were present at constant , physiological concentrations . At high dNTP concentrations ( 25 and 50 μM ) , there was no striking difference in the size of the products from reactions carried out in the absence or presence of rNTPs ( Fig 4D , compare lanes 5–6 with lanes 11–12 ) . However , as dNTP levels decreased , the adverse effect of rNTPs on replication became increasingly evident . At 10 μM dNTPs , approximately half of the products of the dNTP-only reaction were full-length , while the rNTP-containing reactions yielded no full-length product ( Fig 4D , lanes 4 and 10 ) . Therefore , the apparent inhibition of synthesis by Pol γ in the presence of rNTPs was especially pronounced at low dNTP concentrations . Finally , we simulated conditions of imbalanced dNTP pools by lowering one dNTP at a time to 1 μM , while the other dNTPs were kept at “normal” concentrations . As expected , limiting the concentration of one dNTP led to a visible reduction in the size of reaction products ( Fig 4E , compare lane 1 to lanes 2–5 and lane 6 to lanes 7–10 ) . However , while the reactions containing only dNTPs still yielded fairly long products ( Fig 4E , lanes 1–5 ) , the size of the rNTP-containing reaction products was far below full-length ( Fig 4E , lanes 6–10 ) . Decreasing the concentrations of dCTP or dTTP had a more striking effect than limiting dATP or dGTP . The reason that limiting pyrimidines had a greater effect than limiting purines is unclear , and could not be explained by sequence bias in the DNA template where all four bases are represented at equal frequencies . Taken together , the data in Fig 4 suggest that the combined effect of a limiting dNTP together with the presence of rNTPs can lead to severe stalling of replication by Pol γ .
The observation that mtDNA is rich in rNMPs has intrigued researchers for over four decades [10 , 13 , 18 , 24 , 38] . Although rNMPs are erroneously incorporated during DNA synthesis both in the nucleus and in mitochondria , RER efficiently removes the incorporated rNMPs from nuclear DNA . Unlike the nucleus , mitochondria appear to lack rNMP removal pathways [5 , 10] , meaning that incorporated rNMPs persist and could therefore potentially interfere with mtDNA replication , although this phenomenon has not been studied in detail . Our analysis of mouse mtDNA fragmentation by alkaline and RNase H2-treatment confirmed the relative abundance of embedded rNMPs in the mammalian mitochondrial genome , as well as their uniform distribution ( S1 Fig ) . However , we show that the presence of rNMPs in the replication template does not negatively impact the efficiency or fidelity of replication by Pol γ , even at the lower dNTP concentrations that mimic the conditions in cycling or non-dividing cells ( Fig 1 ) . Therefore , the 3- to 4-fold increase in mtDNA rNMPs that was recently reported in fibroblasts derived from patients with disturbed mitochondrial dNTPs pools [10] is , based on our experiments , unlikely to be problematic for the mtDNA replication fork thanks to the high fidelity and efficiency of Pol γ when bypassing single embedded rNMPs . Interestingly , we found that the frequency of rNMP incorporation by Pol γ was at least three-fold lower than that of the yeast nuclear Pol δ and Pol ε ( Fig 2 and [7] ) . Based on our in vitro data , Pol γ is expected to incorporate about 14 rNMPs during the synthesis of one 16 . 5 kb dsDNA molecule of mtDNA in cycling cells ( Fig 2B lane 13 ) . This value is likely to be higher ( 24 rNMPs ) in post-mitotic cells in which the dNTP levels are thought to be substantially lower ( Fig 2B lane 14 ) . However , our Southern blot analysis of mouse liver mtDNA indicated an in vivo rNMP frequency of approximately 1 rNMP per 500 nucleotides ( S1 Fig; corresponds to 65 rNMPs per ds mtDNA ) , which is in good agreement with the values recently reported using a genome-wide next-generation sequencing approach ( 54 rNMPs per mtDNA molecule in human fibroblasts and 36 in HeLa; i . e . a frequency of 1:613 and 1:920 , respectively [10] ) . The in vivo rNMP frequency of mtDNA is therefore 2- to 5-fold higher than expected from the rNMP incorporation rates observed in our in vitro experiments ( Fig 2B and 2D ) . This difference may partly be explained by lower than estimated dNTP concentrations or higher than estimated rNTP concentrations inside mitochondria . We found that decreasing dNTP concentrations to as low as 0 . 5–1 μM in the in vitro replication assay gave rise to an rNMP incorporation frequency that is comparable to the in vivo frequency ( 1:620; S5B Fig ) using exo- Pol γ . However , such low dNTP concentrations in the presence of rNTPs do not support DNA synthesis by the WT Pol γ enzyme ( S4A Fig lane 6–9 ) and are therefore not likely to be the sole explanation to the higher in vivo rNMP frequencies observed by us and others . An alternative , or contributing , explanation for the discrepancy between in vivo and in vitro rNMP levels is that additional polymerases in addition to Pol γ could contribute to incorporation of rNMPs into mtDNA , which then persist due to the absence of ribonucleotide excision repair inside mitochondria [10 , 11] . For instance , Pol β was recently detected in mitochondria [39] . Interestingly , Pol β levels can greatly affect rNMP incorporation in nDNA opposite oxidative DNA lesions [40] . Given the presumably high levels of oxidatively damaged nucleotides in mtDNA [41] , incorporation by Pol β could significantly contribute also to rNMP incorporation frequencies in mtDNA . Similarly , the primase/polymerase PrimPol can synthesize nucleic acids using both rNTPs and dNTPs , and has been shown to be involved in mtDNA maintenance [42–44] . Together , these and possibly other polymerases could influence the rNMP levels in mtDNA [45] . We conclusively show that the comparably low frequency of rNMP incorporation by Pol γ is not due to efficient proofreading of rNMPs , as liver mtDNA from WT and proofreading-deficient Pol γ mice exhibited an indistinguishable profile and frequency of incorporated rNMPs ( Fig 3D and 3E ) . Furthermore , in vitro incorporation frequencies were similar for WT and exonuclease-deficient Pol γ ( Fig 3B and 3C ) . Therefore , the low rNMP frequency of Pol γ is expected to be due to efficient discrimination against rNTPs during the insertion step of DNA synthesis . Finally , we find that the presence of free rNTPs significantly decreases the replication speed by Pol γ ( Fig 4 ) . A similar phenomenon has been described for family B polymerases [7 , 46] , but to our knowledge , this is the first report of such inhibition in a family A polymerase . The negative effect of rNTPs on replication by Pol γ is dependent on the proofreading-activity of the polymerase and is therefore likely due to idling between the polymerase/exonuclease modes in the presence of rNTPs . The inhibition of Pol γ by rNTPs was most pronounced at low dNTP concentrations; at 10 μM dNTPs , a concentration that is comparable to that found in cycling cells , the negative effect of rNTPs impeded synthesis of a full-length product in our assay ( Fig 4D ) . It is likely that at high rNTP/dNTP ratios , Pol γ requires more time to find the correct dNTP as the abundant corresponding rNTP acts as a competitive inhibitor of the enzyme . We show that especially in combination with the decreased level of a single dNTP , the presence of rNTPs in the reaction causes severe replication stalling ( Fig 4E ) . These findings lead us to speculate that the high rNTP/dNTP ratio normally found in cells can be especially challenging in combination with a dNTP pool imbalance such as the ones found in patients suffering from mutations in thymidine kinase 2 ( TK2 ) or deoxyguanosine kinase ( DGUOK ) . Frequent replication stalling under such conditions might prevent the mtDNA replisome from completing replication of the entire mitochondrial genome , potentially leading to mtDNA depletion as it has been reported in patients affected by mtDNA depletion syndromes [47] .
MtDNA mutator mouse samples were obtained from the Stewart lab at the Max Planck Institute for Biology of Ageing , Cologne Germany , where the mice are raised and handled in strict accordance to the guidelines of the Federation of European Laboratory Animal Science Associations ( FELASA ) . Breeding and sacrifice protocols were approved by the “Landesamt für Natur , Umwelt und Verbraucherschutz Nordrhein-Westfalen" ( 84–02 . 04 . 2015 & 84–02 . 05 . 50 . 15 . 004 ) . " Wild type C57BL/6J mice were euthanized at the age of 2 months and livers were frozen in liquid nitrogen . Liver samples from mtDNA mutator mice were gifted from J . B . Stewart of the Max Planck Institute for Biology of Ageing , Cologne , Germany . These mice carried the PolgAD257A allele [37] , but were backcrossed onto the C57Bl/6J nuclear genetic background , and used to generate the mice for this study . Three homozygous mtDNA mutator mice ( 1 male , 2 females ) , and two wild type sibling controls ( 1 male , 1 female ) at 50–53 weeks of age were used . Mice were bred to limit female-transmitted mtDNA mutations [48] . All animal procedures were conducted in accordance with European , national and institutional guidelines and protocols , and were approved by local government authorities . Mouse liver was minced into pieces and homogenized using a glass teflon Dounce homogenizer in homogenization buffer ( 10 mM HEPES pH 7 . 8 with 225 mM mannitol , 75 mM sucrose and 10 mM EDTA ) followed by centrifugation at 800 × g for 10 min at 4°C . The supernatant was then centrifuged at 12 000 × g for 10 min at 4°C to pellet mitochondria that were resuspended in homogenization buffer and overlaid on a 1 . 5 M/1M sucrose gradient in 10 mM HEPES pH 7 . 4 and 10 mM EDTA . After ultracentrifugation at 40 000 × g for 1 h at 4°C in a Beckman SW60Ti rotor , the mitochondrial layer was recovered and diluted in 4 volumes of 10 mM HEPES pH 7 . 4 , 10 mM EDTA . The mitochondria were pelleted at 12 000 × g for 10 min at 4°C in a JA25 . 5 rotor , resuspended in homogenization buffer , treated with Proteinase K , lysed in 20 mM HEPES pH 7 . 8 , 75 mM NaCl , 50 mM EDTA , 1% SDS and treated again with Proteinase K . The mtDNA was extracted once with ( 25:24:1 ) phenol:chloroform:isoamyl alcohol and twice with chloroform . The DNA was precipitated and resuspended in 20 mM HEPES pH 7 . 2 . 1 μg of isolated mtDNA was linearized with SacI , precipitated and dissolved in 10 mM Tris HCl pH 7 . 5 . The DNA was hydrolyzed with 0 . 3 M NaOH at 55°C or digested with RNase H2 ( New England Biolabs ) at 37°C for 2 h . Samples were run on a 0 . 8% agarose alkaline gel ( 30 mM NaOH , 1 mM EDTA ) at 25 V , 4°C for 20 h and blotted onto Hybond-N+ membrane ( Amersham , GE Healthcare ) . Single-stranded probes were end-labelled with γ-32P ATP using T4 polynucleotide kinase ( Thermo Scientific ) following the manufacturer’s protocol . Double-stranded probes were generated by labelling an approximately 500 bp PCR product with α-32P dCTP using Prime-It II Random Primer Labeling kit ( Agilent Technologies ) . Hybridization was for 16 h at 42°C for ssDNA probes and at 65°C for dsDNA probes . The membrane was exposed to a PhosphoImager screen and scanned in a Typhoon laser scanner ( GE Healthcare ) . The radioactive intensity was quantified using ImageJ software and plotted on a distribution plot . The median size of alkali-treated products was determined from the distribution of the radioactivity intensity and related to the size marker that was run in parallel [7] . Human mitochondrial DNA polymerase γ catalytic subunit A ( Pol γ A ) , processivity subunit B ( Pol γ B ) , helicase Twinkle and mitochondrial single-stranded binding protein ( mtSSB ) were expressed and purified as previously described [49 , 50] . For the exonuclease-deficient Pol γ A , a D274A mutant was prepared as previously described [51] . Saccharomyces cerevisiae RPA [52] , PCNA [53] and RFC [54] were purified from Escherichia coli overexpression systems , while S . cerevisiae Pol δ [55] was purified from a yeast overexpression system . Wild type S . cerevisiae mitochondrial DNA polymerase Mip1 was purified as previously described [11] . Single-stranded M13mp18 DNA ( 7 . 3 kb ) was purchased from New England Biolabs . Single-stranded pBlueScript SK+ was prepared as previously described [56] . Primers were either 5′-end labelled with γ-32P using T4 polynucleotide kinase ( Thermo Scientific ) or purchased with 5′-TET fluorescence label . A 36-mer primer 6330 ( Figs 2B and 3B ) or primer 682 ( Fig 4 and S4 Fig ) were annealed in a 1:1 ratio to M13mp18 or pBluescript SK+ , respectively . For the linear 70 nt templates , a 25 nt primer ( Fig 1B ) was annealed to the template containing either a dNTP or a rNTP at position 30 ( S1 Table ) by heating to 80°C and slowly cooling to room temperature . The 70 nt mini-circle template ( Fig 2C ) was prepared as described in [51] . All oligonucleotides used in this study are listed in S1 Table . Primer extension was performed using 2 . 5–10 nM primed circular ssDNA with 12 . 5 nM of WT or exo- Pol γ A and 18 . 75 nM of Pol γ B ( as dimer ) . Additional protein was added after half the incubation time when indicated . MtSSB was added to a final concentration of 750 nM . The following reaction conditions were used: 25 mM Tris-HCl pH 7 . 6 , 10 mM MgCl2 , 1 mM DTT and 100 μg/ml BSA; dNTPs and/or rNTPs were added at indicated concentrations and run at 37°C . The reactions with Pol δ were performed essentially as described [7] at 30°C with the following protein concentrations: 3 nM Pol δ , 375 nM RPA , 15 nM PCNA ( as trimer ) , and 3 nM of RFC . Mip1 reactions were performed with 5 nM of Mip1 in the same buffer conditions as Pol γ , but at 30°C . The reactions were performed in the presence of what we termed”normal” ( 5 μM dATP , 5 μM dCTP , 3 μM dGTP and 10 μM dTTP ) , “low” ( 2 μM dATP , 1 μM dCTP , 1 μM dGTP and 2 μM dTTP ) or “very low” ( 1 μM dATP , 0 . 5 μM dCTP , 0 . 5 μM dGTP and 1 μM dTTP ) dNTP concentrations . rNTP concentrations were kept constant ( 3 000 μM ATP and 500 μM of CTP , GTP and UTP ) . As the relatively high concentration of rNTPs could lead to sequestering of divalent cations , additional magnesium ( 4 . 5 mM ) was included in the reactions containing rNTPs in order to maintain a constant concentration of magnesium throughout the experiment . As a reference , some reactions were performed with the concentrations of nucleotides found in logarithmically-growing S . cerevisiae cells: 16 μM dATP , 14 μM dCTP , 12 μM dGTP , 30 μM dTTP , 3 mM ATP , 0 . 5 mM CTP , 0 . 7 mM GTP and 1 . 7 mM UTP [2] . To follow the reaction , [α-32P]-dCTP ( Perkin Elmer ) was added . The reactions were incubated at 37°C for 1–120 min , stopped with 0 . 5% SDS , 25 mM EDTA and cleaned over G-25 columns to remove excess [α-32P]-dCTP . For incorporation assays , the sample was divided in two; one control was treated with 0 . 3 M NaCl and one sample was treated with 0 . 3 M NaOH . Before gel loading both samples were incubated for 2 h at 55°C . For use as a size reference , 1 kb GeneRuler ( ThermoScientific ) was end-labeled with γ-P32 ATP using T4 polynucleotide kinase ( ThermoScientific ) . Samples and size marker were separated on a 1 . 5% alkaline agarose gel in buffer with 30 mM NaOH and 1 mM EDTA at 17 V for 16 h in 4°C . Visualization and quantification was performed by phosphoimaging of the dried gel on a Typhoon 9400 system ( GE Healthcare ) . The mini-circle substrate ( 5 nM ) was added to a 10 μl reaction mixture containing 25 mM Tris HCl pH 7 . 5 , 75 mM NaCl , 10 mM magnesium acetate , 1 mM DTT , 100 μg/ml BSA , 4 mM ATP , 12 . 5 nM Pol γ A , 18 . 75 nM Pol γ B ( as dimer ) , 250 nM mtSSB and 12 . 5 nM Twinkle . To keep the ATP concentration as low as possible in the reactions without rNTPs , we used an ATP regeneration system consisting of 400 ng creatine kinase and 5 mM creatine-phosphate-Tris . The reactions included the addition of “low” , “normal” or “S . cerevisiae" dNTPs ( concentrations listed above ) and rNTPs where indicated . The reactions were performed at 37°C and started by addition of polymerase . At the indicated time points , the reactions were stopped by the addition of 1 . 1 μl of termination mixture ( 5% SDS , 250 mM EDTA ) and analyzed on an 8% polyacrylamide gel containing 8 M urea . Quantification was performed by phosphoimaging of the dried gel on a Typhoon 9400 system ( GE Healthcare ) . The average rNMP incorporation rate of Pol γ in vitro ( Fig 2B and 2D ) was determined as previously described [7] . Briefly , we determined the median size of DNA fragments in the reaction with only dNTPs ( a ) and in the reaction with both dNTPs and rNTPs ( b ) . These values were used in the following formula to calculate the incorporation frequency: rNTP incorporation frequency = a/ ( a/b—1 ) . 5–10 nM of template was used in primer extension reactions with 25 mM Tris HCl pH 7 . 5 , 10 mM MgCl2 , 1 mM DTT , 100 μg/ml BSA , 12 . 5 nM Pol γ A and 18 . 75 nM Pol γ B ( calculated as dimer ) , with an increasing amount of dNTPs . For single hit conditions , reactions contained 50 nM template and 1 nM protein . Reactions were stopped after 2–20 min with 0 . 5% SDS , 25 mM EDTA and incubated at 50°C for 10 min . The samples were run on a 10–12% polyacrylamide urea gel , dried and exposed to a phosphoimager screen and scanned in a Typhoon 9400 system . Termination probability was calculated as previously described [25] . Briefly , at template position N , the termination probability was determined by the intensity of the band at N and divided by the intensity at position N plus the intensity at bands for longer products , at N , = [N] / ≥ [N] . The sequencing of in vitro synthesized DNA was performed as described [51] except that the primer extension reactions were performed on a 70 nt template containing either a dNMP or rNMP at position 30 . As control template , a 70 nt oligo with 8-oxo-G at position 30 was used . The template was primed with a biotinylated oligonucleotide with a HindIII site ( S1 Table ) . Reactions were stopped by incubation at 70°C for 1 h . Following the manufacturer’s instructions , the bypass products were immobilized on Dynabeads M-280 Streptavidin for 15 min at room temperature . The two strands were denaturated two times in 0 . 1 M NaOH for 5 min . The single-stranded product was washed according to the manufacturer’s instructions and amplified by high fidelity PCR using Phusion High-fidelity DNA polymerase ( NEB ) to generate a double-stranded 104 bp product . The fragment was cleaved using BfaI and HindIII restriction enzymes and cloned into pUC19 . The ligation was transformed into E . coli TOP10 and colonies were sequenced with the M13 ( -49 ) primer . | Mitochondria are essential for energy production , and defects in the maintenance of mitochondrial DNA ( mtDNA ) lead to a variety of human diseases including mtDNA depletion syndrome ( MDS ) . Certain forms of MDS are caused by imbalances in the mitochondrial deoxyribonucleoside triphosphate ( dNTP ) pool , which have also been shown to lead to altered levels of the ribonucleotides ( rNMPs ) that are embedded in mtDNA . In this study , we address the impact of these rNMPs on the mitochondrial DNA polymerase Pol γ at nucleotide concentrations that resemble those found inside a cell . We demonstrate that embedded rNMPs do not impair DNA synthesis by Pol γ even at the lowest concentrations of dNTPs tested . Based on these results , an increase in mtDNA rNMPs is unlikely to explain the mitochondrial defects in MDS . However , we find that Pol γ is inhibited by physiological levels of free ribonucleoside triphosphates ( rNTPs ) . When combined with a dNTP pool imbalance , the presence of rNTPs leads to DNA replication stalling by Pol γ . These characteristics of Pol γ may help to explain the mtDNA depletion in forms of MDS . | [
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] | 2018 | The presence of rNTPs decreases the speed of mitochondrial DNA replication |
The family Flaviviridae , genus Flavivirus , holds many of the world’s most prevalent arboviral diseases that are also considered the most important travel related arboviral infections . In most cases , flavivirus diagnosis in travelers is primarily based on serology as viremia is often low and typically has already been reduced to undetectable levels when symptoms set in and patients seek medical attention . Serological differentiation between flaviviruses and the false-positive results caused by vaccination and cross-reactivity among the different species , are problematic for surveillance and diagnostics of flaviviruses . Their partially overlapping geographic distribution and symptoms , combined with increase in travel , and preexisting antibodies due to flavivirus vaccinations , expand the need for rapid and reliable multiplex diagnostic tests to supplement currently used methods . We describe the development of a multiplex serological protein microarray using recombinant NS1 proteins for detection of medically important viruses within the genus Flavivirus . Sera from clinical flavivirus patients were used for primary development of the protein microarray . Results show a high IgG and IgM sensitivity and specificity for individual NS1 antigens , and limited cross reactivity , even within serocomplexes . In addition , the serology based on this array allows for discrimination between infection and vaccination response for JEV vaccine , and no cross-reactivity with TBEV and YFV vaccine induced antibodies when testing for antibodies to other flaviviruses . Based on these data , multiplex NS1-based protein microarray is a promising tool for surveillance and diagnosis of flaviviruses .
The family Flaviviridae , genus Flavivirus , holds many of the world’s most prevalent arboviral diseases that are also considered the most important travel related arboviral infections . [1] As the geographic distribution and symptoms caused by these viruses overlap , detection requires differential diagnostic algorithms that include multiple flaviviruses . [2] Increase in travel expands the need for rapid and reliable multiplex diagnostic tests in non-endemic countries to supplement currently used methods . [3 , 4] Flaviviruses are single stranded enveloped viruses with an RNA genome of about 11 kb length . The genome is composed of three structural ( Envelope , Capsid and Precursor-membrane ) and seven non-structural proteins ( NS1 , NS2A , NS2B , NS3 , NS4A , NS4B and NS5 ) . [5] Diagnosis is primarily based on serology through detection of IgM and IgG antibodies , as viremia typically has been reduced to undetectable levels when symptoms set in and patients seek medical attention . [5–7] The genus is divided into serocomplexes that are distinguished based on neutralizing antibody reactivity ( Fig . 1 ) . The amino acid homology of the envelope ( E ) protein ( which is the immunodominant antigen for neutralizing antibody assays ) ranges from 40–50% between serocomplexes and 70–80% for virus species within a serocomplex . [5 , 8] Antibodies to flaviviruses are known to cross-react extensively within , and to a certain extent between , serocomplexes when using traditional antibody assays . [9–12] Cross-reactivity occurs also if patients have been vaccinated against flaviviruses such as yellow fever virus ( YFV ) , tick-borne encephalitis virus ( TBEV ) and/or Japanese encephalitis virus ( JEV ) or after secondary infection with a different flavivirus . [9 , 10 , 13] To overcome flavivirus cross-reactivity in diagnostics the use of recombinant antigens in ELISA is to be preferred over whole virus as it increases specificity . [14–16] Envelope , pre M and NS1 recombinant proteins are the most commonly used . [14–16] Of these , the NS1 has shown to be highly immunogenic and important in the development of non-neutralizing protective antibodies . [17 , 18] NS1 is thought to contain more species specific epitopes than the envelope protein , although some cross-reactivity is seen between NS1 proteins . [19–21] NS1 in its natural conformation is thought to elicit a more specific immune response . [22 , 23] The absence of NS1 proteins in inactivated JEV vaccines offers further potential for serological diagnosis through allowing differentiation between vaccinated and infected patients . [24] Thus , NS1 protein shows potential to use in serological differentiation between flavivirus infections . [25 , 26] To enable fast , syndrome based laboratory testing that focuses on multiple rather than individual viruses , we developed a protein microarray , using recombinant NS1 proteins , as a serological test for medically important viruses within the Flavivirus genus .
Sera from anonymized patients were used for primary development of the protein microarray . Patients were diagnosed according to international accepted criteria combining clinical symptoms , epidemiological data , and standard serological methods ( ELISA , IFA ) and laboratory confirmed by either VNT or PCR with the exception of 10 patients suspected of JEV . Information on each patient group used is presented in Table 1 . Custom-made NS1 proteins produced in human embryonic kidney 293 ( HEK293 ) cells to ensure proper folding , glycosylation and dimerization were used ( Immune Technology Inc . , New York , NY , USA ) . A V5-epitope and Histag were added to the C-terminus for protein quantification and filtration . Proteins were expressed for Dengue virus 1 ( genbank:FJ687432 . 1 ) , Dengue virus 2 ( genbank:FJ744720 . 1 ) , Dengue virus 3 , ( genbank:FJ744738 . 1 ) , Dengue virus 4 ( genbank:EU854300 . 1 ) , Japanese encephalitis virus ( genbank:NC_001437 . 1 ) , St . Louis encephalitis virus ( genbank:ACB58159 . 1 ) , Yellow fever virus ( genbank:JN620362 . 1 ) and West Nile virus ( genbank:EU081844 . 1 ) Usutu virus NS1 ( genbank:NC006551 . 1 ) was produced in-house in a HEK293 cell-line . The NS1 gene was produced by Genscript ( NJ , USA ) with an additional V5-epitope and Histag on the C-terminus and cloned into a pcDNA-DEST40 vector ( Invitrogen , Thermo Fisher Scientific , MA , USA ) that contained a neomycine resistance gene . The vector which contained the NS1 gene was transfected into HEK293 cells . Neomycine resistant clones were selected and tested for protein expression by immune fluorescent assay using anti-V5 monoclonal antibody . Selected clones were grown in flasks and secreted NS1 protein into the medium ( Opti-MEM , Thermo Fisher Scientific , MA , USA ) . The secreted protein was purified from the medium by FPLC using a Ni-NTA column ( Qiagen , CA , USA ) according to the manufacturer’s instructions . NS1 antigens at concentrations of around 2mg/ml were mixed with protein arraying buffer ( Maine manufacturing , GVS Group , Italy ) and spotted in triplicate as a within-test control per pad . Antigens were spotted onto a nitrocellulose pad coated glass slide ( Maine manufacturing , GVS Group , Italy ) using a non-contact protein array spotter ( PerkinElmer , Waltham , MA , USA ) as previously described . [27] Per spot two drops of 333 pL of diluted protein were used . After printing , slides were placed in a drying chamber overnight and stored at room temperature until use . Patient sera were tested on dried slides as previously described . [27] In short , slides were incubated in Blotto blocking-buffer ( Thermo Fisher Scientific , MA , USA ) for one hour at 37°C in an incubation chamber to reduce non-specific binding of serum . Serum was diluted in eight two-fold dilution steps ( 1:10 to 1:2560 ) in blotto supplemented with 0 . 1% Surfact-Amp ( Thermo Fisher Scientific , MA , USA ) and incubated for 1 hour at 37°C in a moist chamber . Incubation followed with an Fc-fragment specific IgG or Fc5μ-fragment specific IgM specific conjugate with a Cy5-fluorescent dye ( Invitrogen , CA , USA ) for one hour at 37°C . For IgM detection , serum was first depleted of IgG antibodies using Gullsorb ( Meridian Bioscience , OH , USA ) according to the manufacturer’s instructions . Between each incubation step , slides were washed three times with a protein array washing buffer ( Thermo Fisher Scientifc , MA , USA ) . After final wash , slides were scanned with a Tecan scanner ( Tecan Trading AG , Männedorf , Switzerland ) . A median fluorescence signal ( measured at 647nm ) for each of the triplet spots per antigen was determined by ScanArray Express 4 . 0 . 0 . 0001 supporting program ( PerkinElmer , MA , USA ) using an adaptive circle ( diameter 80–200 μm ) . The fluorescent signal ranged from 0 to a maximum of 65 , 536 units . Results were imported in R for analysis . [28] Virus antigens were spotted in serial two-fold dilutions ranging from 1:2 to 1:16 for initial checkerboard titrations to determine optimum protein concentration as previously described . [27] Antigens were tested using serially diluted anti-V5-epitope monoclonal antibodies ( Invitrogen , Thermo Fisher Scientific , MA , USA ) . Optimum protein concentrations were defined as those at which maximum fluorescent signal and overlapping s-curves were achieved for anti-V5-epitope monoclonal antibodies and were found to be around 0 . 5 mg/ml . To minimize batch-to-batch variations each batch was tested with a serial dilution of anti-V5 monoclonal antibodies . If a variation of more than 10% was found in reference to the initial test batch the slides were excluded . Day-to-day variations were monitored by including a positive and negative WHO DENV1–4 reference serum during each test round . If a dilution-step difference of more than one titer was detected results were excluded . [29] A script was written in R[28] using additional package ‘drc’ version 2 . 3–7[30] , as previously described . [27] The median fluorescent signals were converted into fitted dilution-s-curves per protein for each serum sample . Additional script was written that allowed titers to be calculated on the estimated s-curve at a given ROC calculated cut-off . Optimal signal cut-offs were determined by a log2 transformation of signals to further reduce variance caused by day-to-day and slide-to-slide variations . Optimal signal cut-offs were achieved by selecting the highest possible combination of sensitivity and specificity through ROC optimal curve calculations performed in GraphPad Prism . [31] Titers were defined as the highest serum dilution with a signal above the cut-off determined by ROC analysis . Heat maps were generated using an additional R package ‘stats’[28] and based on pairwise correlation between rows and columns . Mann-Whitney tests were employed to establish the significance of differences between groups . Dengue virus 1 ( genbank:FJ687432 . 1 ) , Dengue virus 2 ( genbank:FJ744720 . 1 ) , Dengue virus 3 , ( genbank:FJ744738 . 1 ) , Dengue virus 4 ( genbank:EU854300 . 1 ) , Japanese encephalitis virus ( genbank:NC_001437 . 1 ) , St . Louis encephalitis virus ( genbank:ACB58159 . 1 ) , Yellow fever virus ( genbank:JN620362 . 1 ) and West Nile virus ( genbank:EU081844 . 1 ) , Usutu virus NS1 ( genbank:NC006551 . 1 ) .
The mean antigen reactivity by NS1 proteins in 1:10 to 1:80 start dilutions was high in homologous DENV , WNV , JEV , SLEV , YFV and Usutu virus ( USUV ) positive control sera and low in negative control sera and in sera from individuals vaccinated for JEV , TBEV or YFV ( p<0 . 01 ) with the exception of YFV NS1 antigen with YFV vaccinees ( Fig . 2 , Fig . 3 , and Table 2 ) . Only some NS1 reactivity was observed in samples from blood donors for other antigens ( 1% ) . At low serum dilutions , some patients showed antibody IgG reactivity to multiple antigens , and therefore ROC curves were calculated in multiple dilutions and the signals for the 1:20 dilutions were used for signal cut-off calculations . The 1:10 and 1:20 serum dilutions produced comparable results in sensitivity and specificity , but with significantly lower background for the 1:20 dilutions . At 1:40 serum dilutions , the sensitivity started to decrease . Only 13 DENV positive patients ( travelers ) had known primary DENV infections with a PCR confirmed serotype ( DENV1–3 ) . All other patients with PCR confirmed DENV ( serotype 1–4 ) were from DENV endemic countries and could not be confirmed as primary infections . As not all DENV infections were known to be primary , the highest signal to DENV1–4 NS1 was used for calculation of the DENV cut-offs . The optimal cut-off for all proteins was around a fluorescent signal of 15 , 000 for IgG and 4 , 000 for IgM , producing sensitivity and specificity of 86% to 100% ( Table 3 ) . For USUV , SLEV and YFV only one or two positive patient samples were available so that proper ROC curves could not be calculated , but background signals were in the same range as for the other antigens ( Table 2 ) . Serum samples from YFV-vaccinees were strongly positive for YFV . Some blood donors had YFV signals above the cut-off , probably reflecting vaccination history ( Fig . 2 ) . In order to study cross-reactivity within and between serocomplexes , serum samples were serially diluted and titers were calculated in R . Typical individual patient profiles are shown in Fig . 4 . To quantify the degree of cross-reactivity , the ratio of the signal for each antigen to the maximum signal measured for that serum ( typically the homologous antigen ) was calculated ( Fig . 5 ) . With one exception for IgG ( serum sample #4 ) , all patients had the highest IgG and IgM reactivity with the homologous NS1 antigen . High level IgG reactivity to a second antigen was observed for two of the DENV patients ( against WNV and JEV , respectively ) and for 2 JEV patients ( against DENV ) ( Fig . 5a ) . One serum sample from a JEV patient ( serum sample 4 ) had a higher titer DENV NS1 in comparison to JEV NS1 . For IgM , only homologous reactivity was observed . IgG profiles from individual patients were combined into a heatmap ( Fig . 6 ) to confirm grouping according to exposure history . One group of patients ( indicated by a star in the heatmap ) showed high titers to multiple DENV serotypes . A larger group had highest titers to a single DENV serotype , suggesting serotype specificity of the antibody array results . As most patients were from different regions , the data were stratified for non-endemic ( travelers ) and multiple DENV endemic countries . This showed a significant difference in titers between groups ( p<0 . 01 ) for IgG but not for IgM ( p = 0 . 25 ) titers . For 13 known primary DENV cases , the serotype had been determined by RT-PCR . All but one serum had highest IgG antibody levels to the infecting serotype , but IgM antibody reactivity was lower and less discriminatory .
Serological differentiation between flaviviruses and the false-positive results caused by vaccination are a serious problem for surveillance and diagnostics of flaviviruses . Analysis of our NS1-based protein microarray results showed a high IgG and IgM sensitivity and specificity for individual antigens even within the same serocomplex , and limited cross reactivity . In addition , the serology based on this array allowed discrimination between infection and vaccination response for JEV vaccine , and no cross-reactivity with TBEV and YFV vaccine induced antibodies when testing for antibodies to other flaviviruses . Based on this data , our multiplex NS1-based protein microarray is a promising tool for surveillance and diagnosis of flaviviruses . | The number of international travelers has increased dramatically in recent decades . This has contributed to the increase in infectious diseases in travelers which are not present in their countries of origin and so may cause a threat to the public health . Viruses transmitted by biting insects ( vector-borne viruses ) are an important group within these travel-related diseases . They are found across the world and can cause debilitating and life-threatening symptoms , like inflammation of the brain or excessive bleeding . Many of these diseases are difficult to distinguish from each other . They cause comparable symptoms and are genetically closely related . Testing for long lists of diseases is time consuming and expensive . Here we develop a novel testing tool that allows doctors and researchers to test for multiple viruses with just one test . The method , which uses a specific part of the virus that makes distinguishing between infections with these closely related viruses possible , requires only one drop of blood . This allows us to test for multiple viruses simultaneously with the same amount of blood previously used to test for only one virus , while distinguishing between genetically closely related viruses . | [
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] | [] | 2015 | Spot the Difference—Development of a Syndrome Based Protein Microarray for Specific Serological Detection of Multiple Flavivirus Infections in Travelers |
The autophagic pathway acts as part of the immune response against a variety of pathogens . However , several pathogens subvert autophagic signaling to promote their own replication . In many cases it has been demonstrated that these pathogens inhibit or delay the degradative aspect of autophagy . Here , using poliovirus as a model virus , we report for the first time bona fide autophagic degradation occurring during infection with a virus whose replication is promoted by autophagy . We found that this degradation is not required to promote poliovirus replication . However , vesicular acidification , which in the case of autophagy precedes delivery of cargo to lysosomes , is required for normal levels of virus production . We show that blocking autophagosome formation inhibits viral RNA synthesis and subsequent steps in the virus cycle , while inhibiting vesicle acidification only inhibits the final maturation cleavage of virus particles . We suggest that particle assembly , genome encapsidation , and virion maturation may occur in a cellular compartment , and we propose the acidic mature autophagosome as a candidate vesicle . We discuss the implications of our findings in understanding the late stages of poliovirus replication , including the formation and maturation of virions and egress of infectious virus from cells .
The Picornaviridae , a family of non-enveloped viruses with a small positive strand RNA genome , includes numerous known and emerging pathogens of medical , veterinary and agricultural importance [1] . Poliovirus ( PV ) is the most extensively studied virus in this family in terms of our collective understanding of its molecular and cellular biology , biochemistry , structure , life cycle , and pathogenesis and is therefore an important model system . Infection with PV results in numerous changes to the host cell , and perhaps one of the most notable is the massive accumulation of cytosolic double-membraned vesicles [2] , [3] . These vesicles are the hallmark of autophagy , a degradative pathway of homeostasis and stress response [4] . Autophagy begins with generation of novel membrane crescents which , as they expand and self-fuse , sequester cytoplasmic contents in double-membraned vesicles , referred to as autophagosomes [5] . As autophagy is induced , increasing amounts of the cellular autophagy protein LC3 become conjugated to the lipid phosphatidylethanolamine . This conjugation confers membrane association and is required for autophagosome formation and membrane expansion [6] , [7] . Autophagosomes fuse with endosomes to form amphisomes . This fusion event provides amphisomes with vacuolar ATPases , and results in their acidification [8] , [9] . Subsequent to acidification , amphisomes fuse with lysosomes to form single-membraned autolysosomes and cargo is degraded [9]–[11] . Poliovirus specifically induces autophagic signaling , and virus production correlates with the level of autophagic activity in cells . LC3 lipidation is evident as early as 3 hours post-infection ( h . p . i . ) , and by 5 h . p . i . double-membraned vesicles are found throughout the cytoplasm [12] , [13] . These vesicles are positive for both LC3 and the endosomal marker LAMP1 , indicating that these vesicles have likely fused with late endosomes . PV-induced vesicles also stain with monodansylcadaverine ( MDC ) , a lysosomotropic agent is concentrated in acidic compartments by an ion-trapping mechanism [12] , [14] . MDC staining co-localizes with LC3 and LAMP1 , indicating that the induced vesicles are likely to be acidic amphisomes . Components of the PV replication complex are located on these vesicles , leading to speculation that they may be the sites of genome replication [12] , [15] . Although autophagic signaling is specifically induced by the action of PV proteins , the fate of the induced autophagosomes has not been investigated [12] , [13] , [15] . The regulation of autophagy can be broadly placed into two classes . One class controls the initiation of autophagosome formation by regulating mamalian target of rapamycin ( mTOR ) inhibition and subsequent LC3 lipid conjugation [16] . The second class controls the stepwise maturation of autophagosomes into degradative autolysosomes . Inhibitors of vesicle acidification , including the weak base ammonium chloride and the vacuolar ATPase inhibitor bafilomycin A1 , have been shown to inhibit amphisome fusion with the lysosome and subsequent degradation of cargo [17]–[19] . The presence of late regulatory mechanisms mean that the formation of autophagosomes can occur without leading to active degradation . During infection with the picornavirus Coxsackievirus B3 ( CVB3 ) double-membraned autophagosomes are observed , but autophagic degradation does not occur [20] , [21] . Similarly , the bacteria Legionella pneumophila induces autophagosomes for use as replicative vesicles , but the bacterium secretes factors that delay maturation and fusion with lysosomes [22]–[24] . By inhibiting the degradative portion of the pathway , these pathogens are thought to maximize the benefits of autophagosome formation . The production of the flavivirus Dengue Virus 2 also correlates with the level of autophagic activity in the cell . Unlike the previous examples , Dengue virus does not appear to replicate its RNA on or within autophagosomes [25] . A series of elegant experiments demonstrated that the virus benefits from the selective autophagic degradation of lipid droplets , known as lipophagy [26] . When lipophagy is inhibited , virus production is reduced . This effect is reversed when cells are supplemented with the products of lipophagy . These data highlight the remarkable diversity in the ways that viruses subvert the autophagic pathway , and raise the possibility that autophagic degradation could itself promote virus production . Inhibitors of vesicle acidification , which would be expected to inhibit autophagic degradation , have been shown to inhibit infection with several viruses including Semliki Forest virus and human rhinovirus 2 [27] , [28] . However , these effects are thought to be primarily associated with elevated pH of the endocytic entry vesicles and not related to autophagy . Previous studies have shown that PV entry , translation , and polyprotein processing are unaffected by these inhibitors [29] . These studies did not investigate overall infectious virus production . Here we show that PV induces bona fide autophagic degradation , although the degradation is not required for normal virus production . We go on to show that formation of autophagosomes promotes viral RNA replication while acidification of cellular vesicles promotes a post-RNA replication step of infectious virus production . Specifically , we find that maturation of assembled particles into infectious virions is promoted by acidic compartments . We suggest that particles which assemble within , or those captured by , autophagosome-like vesicles are exposed to a low-pH environment , facilitating maturation of infectious virus .
Poliovirus Mahoney type 1 was isolated following transfection with an infectious cDNA [30] and propagated as previously described [31] . Poliovirus stocks were titered on H1-Hela cells . H1-Hela cells were maintained in MEM+10% calf serum ( CS ) . 293T cells were maintained in DMEM+10% fetal bovine serum . For collection of intracellular virus cells were washed with PBS , then collected in 1 mL PBS+ 100 µg/mL MgCl2 and 100 µg/mL CaCl2 . Cells were lysed by three cycles of freeze/thawing . Virus was added to monolayers of H1-Hela cells for a 30 minute absorption , after which cells were overlaid with 1% agar in MEM . Plaques were allowed to develop for 48 h , agar overlay was removed and cells stained with crystal violet . MG132 , ammonium chloride ( NH4Cl ) , E64d , pepstatin A , 3-methyladenine ( 3-MA ) , and guanidine HCl were purchased from Sigma . Bafilomycin A1 and a polyclonal antibody against p62 were obtained from Santa Cruz Biotechnology . Polyclonal antibodies against GAPDH and LC3 were purchased from Cell Signaling and MBL respectively . LC3A and LC3B as well as irrelevant siRNA was purchased from Dharmacon and were transfected using Lipofectamine 2000 ( Invitrogen ) according to the manufacturers' instructions . Total RNA was harvested from infected cells using Trizol ( Invitrogen ) according to the manufacturers instructions . Four micrograms of RNA were treated with DNA-free DNase treatment ( Ambion ) and split into two reactions . One half was subjected to reverse transcription using RevertAid First Strand cDNA Synthesis Kit ( Fermentas ) and oligo-dT primers . The second half of DNase treated RNA was subjected to mock reverse transcription in the absence of the enzyme ( “No RT” control ) . cDNA was serially diluted ( 8-fold ) , including “No RT” controls , and measured , in triplicate for each dilution , by real time PCR using iCycler ( Bio-Rad ) . Virus-specific primers were designed by PrimerQuestSM ( Integrated DNA Technologies ) and used to amplify cDNA . Primers were to the PV genome ( TATGATGCATCTAGCCCTGCT and ACAGGTGGTGTGAGTGGT TTAGGT ) and GAPDH ( TGTGATGGGTGTGAACCACGAGAA and GAGCCCTTCCACAATGCCAAAGTT ) . The delta-Ct method was used to quantify relative abundance of viral cDNA . Ct values of “No RT” controls did not exceed background levels . Cells were harvested with phosphate-buffered saline ( PBS ) containing 55 mM EDTA ( pH 8 . 0 ) and collected by centrifugation at 1500 rpm for 5 min . Cell pellets were washed in PBS and pelleted by centrifugation at 7500 rpm . Cell pellets were resuspended in RSB-NP-40 ( 10 mM Tris-HCl [pH 7 . 5] , 10 mM NaCl , 1 . 5 mM MgCl2 , 1% NP-40 ) supplemented with Mini-complete EDTA-free protease inhibitor cocktail ( Roche Applied Science , Indianapolis , IN ) , and incubated on ice for 15 min . Nuclei and insoluble debris were pelleted by centrifugation at 7500 rpm for 5 min . Cell extracts were then stored at −20°C or subjected immediately to sodium dodecyl sulfate-polyacrylamide gel electrophoresis ( SDS-PAGE ) . Immediately following separation proteins were transferred to PVDF membranes ( Thermo Scientific ) . Membranes were blocked for 1 h with 5% nonfat dry milk solution in Tris-buffered saline containing 1 . 0% Tween 20 ( TBST ) . Blots were then incubated with the primary antibody , washed with TBST , and incubated for 1 hr with secondary antibody . Immunoreactive bands were visualized by enhanced chemiluminescence ( HyBlot Cl , Deville Scientific ) . Cells were infected at an MOI of 50 pfu/cell . Protein labeling was performed with 20 µCi of [35S] methionine per mL in methionine-free medium . The radiolabeled cell monolayers were collected in 1 mL PBS and centrifuged at 4500 rpm for 5 minutes . Cells were then resuspended in 30 µL RSB-NP-40 ( 10 mM Tris-HCl [pH 7 . 5] , 10 mM NaCl , 1 . 5 mM MgCl2 , 1% NP-40 ) supplemented with Mini-complete EDTA-free protease inhibitor cocktail ( Roche Applied Science , Indianapolis , IN ) , and incubated on ice for 15 min . Nuclei and insoluble debris were pelleted by centrifugation at 7500 rpm for 5 min . Radiolabeled cell lysates were then stored at −20°C . Proteins were separated on a 13% polyacrylamide gel . Images obtained with the Typhoon FLA 9500 ( GE Healthcare Life Sciences ) and ImageQuant software ( Amersham Biosciences ) . After 3 h of incubation , the cells were washed twice with DME lacking methionine ( GIBCO ) , 3 . 0 ml of DME lacking methionine , containing 100 µCi of [35S]methionine ( ICN ) per ml was added to each plate . Cells were incubated for either 2 or 3 hours at 37 . 0°C . Cells were washed and harvested by scraping into 1 ml of a solution containing 10 mM Tris ( pH 7 . 4 ) , 10 mM NaCl , 1 . 5 mM MgCl2 , 1% Nonidet P-40 , 1 uM phenylmethylsulfonyl fluoride ( Sigma ) , and 40 U of placental RNasin ( Promega ) per ml . Nuclei were pelleted by centrifugation at 1 , 600×g for 10 min at 4°C , and 0 . 5 ml of the resulting cytoplasmic extract was loaded directly on 11-ml gradients containing 15 to 30% sucrose in 10 mM Tris ( pH 7 . 4 ) -10 mM NaCl-1 . 5 mM MgCl2 . The particles were sedimented through the sucrose gradients by centrifugation for 3 h at 27 , 500 rpm at 15°C , with a Beckman SW41 rotor . Viral proteins were separated on a 15% polyacrylamide gel . Images obtained with the Typhoon FLA 9500 ( GE Healthcare Life Sciences ) and ImageQuant software ( Amersham Biosciences ) .
Poliovirus induces autophagic signaling during infection [12] , [32] . Several other pathogens induce autophagic signaling during infection , but subsequently inhibit the degradation of autophagic cargo [21] , [33]–[35] . To ask if poliovirus infection results in autophagic degradation , we monitored the steady-state level of a cellular protein , p62 , over the course of infection . p62 is incorporated into autophagosomes though direct interaction with LC3 on the autophagosome membrane [36] . p62 is degraded following fusion of the autophagosome with the lysosome; therefore , a decrease in cellular levels of p62 reflects increased autophagic degradation [37] . We found that p62 levels begin to decrease by 3 h . p . i . The signal continues to decline over the course of infection , with over 90% of p62 signal gone by 6 h . p . i . ( Figure 1A ) Since poliovirus inhibits cellular translation , we considered that the loss of p62 signal could be the result of normal protein turnover . To test this , mock-infected cells were incubated with cycloheximide for 4 hours to inhibit cellular translation . We observed approximately 47% reduction in p62 levels , likely due to background autophagy , which is high in HeLa cells [38] . However , this background autophagy is insufficient to account for the p62 reduction observed during poliovirus infection . To further elucidate the mechanism of poliovirus-induced p62 degradation , infections were performed following inhibition of either the ubiquitin-proteasome or the lysosomal degradation pathway . Treatment with MG132 , a specific inhibitor of the 26S proteasome , resulted in only a 13% rescue of p62 degradation following PV infection . Treatment of cells with bafilomycin A1 , an inhibitor of vacuolar ATPases , inhibits fusion of amphisomes with lysosomes [18] . In the presence of bafilomycin A1 , PV infection did not reduce p62 levels ( Figure 1B ) . To determine if p62 degradation is specific to autophagy , we transfected cells with siRNA specific to LC3A and LC3B , the predominant splice variants of LC3 used in cellular autophagy ( Figure 2A ) . As a control , we transfected with scrambled , irrelevant siRNA . We achieved a 78% knockdown of LC3 protein , as estimated by Western blot in mock-infected cells . In PV-infected cells , the levels of membrane-bound LC3-II increase due to induction of autophagic signaling [32] . LC3 siRNA treatment reduced LC3-II to levels comparable to that of uninfected cells , likely due to a reduction in the overall amount of LC3 available for modification . This level of knockdown resulted in restoration of 55% of wild-type p62 during infection . To confirm these results , we used the autophagy inhibitor 3-methyladenine ( 3-MA ) , which reduces autophagy signaling by inhibiting type-III PI-3 kinase activity [39] . As seen in Figure 2B , treatment with 3-MA restored over 50% of p62 levels during infection . These data show that PV , which benefits from autophagosome formation , also allows active autophagic degradation during infection . We next wanted to investigate if the virus benefits from active autophagic degradation . PV infections were performed at low ( Figure 3A , C ) or high ( Figure 3B , D ) multiplicity of infection ( MOI ) in the presence of lysosomal protease inhibitors . No effect on virus yield was observed when cells were pre-treated with leupeptin , a thiol protease inhibitor , and infected at an MOI of 0 . 1 pfu/cell [40] . To ensure that leupeptin was inhibiting autophagic degradation , lysates from parallel samples were probed for the autophagosome marker LC3 . ( Figure 3A ) . Lipidated LC3 ( LC3-II ) is incorporated into the autophagosome membrane and is specifically degraded by the autolysosome [41]–[43] . A decrease in background autophagic turnover results in increased LC3-II levels , so , as expected , cells treated with leupeptin showed an increase in LC3-II [38] . We wanted to test the effect of inhibiting autophagic degradation at a high MOI , so , as shown in Figure 3B , we repeated the leupeptin experiment at an MOI of 50 pfu/cell . For high MOI infections , it is possible to directly assay autophagic degradation by turnover of p62 . Our Western blot demonstrates inhibition of PV-induced p62 degradation . To confirm that these results were not limited to leupeptin treatment , infections were performed in the presence of a combination of protease inhibitors E64d , a calpain and cathepsin B inhibitor , and pepstatin A , an inhibitor of aspartic proteases [40] , [44] . As shown in Figure 3C , cells were infected at MOI of 0 . 1 pfu/cell and treated with both E64d and pepstatin A , and no difference in virus yield was observed . LC3-II levels significantly increased following treatment with the protease inhibitors , indicating that lysosomal degradation had effectively been restricted . Figure 3D demonstrates treatment with E64d and pepstatin A during high MOI infections , with no effect of viral yield . As in Figure 3B , a high MOI infection allows us to assay p62 levels . As expected , E64d/Pep . A abrogates PV-induced degradation of p62 . These results show that inhibiting lysosomal degradation does not inhibit virus replication regardless of the nature of the inhibitors or the MOI of the infection . The lumen of the amphisome reaches an acidic pH of approximately 5 . 7 [9] . To investigate whether this acidification is important for virus production , cells were treated with inhibitors of vesicle acidification during infection . Ammonium chloride ( NH4Cl ) is a weak base that is taken up by intracellular vesicles [45] . The acidic environment results in protonation of the base , which raises the lumenal pH as it diffuses out of the vesicle [46] . Cells were infected with PV , then treated with NH4Cl immediately after absorption of the virus . NH4Cl treatment reduced the production of infectious virus progeny by approximately one log at later time points . ( Figure 4A ) Since these are low MOI infections , we wanted to determine if the effect of NH4Cl treatment is due to a delay of the infectious cycle or an overall reduction in PV production . In Figure 4B , we carry the infection out to 16 hours and find that the reduction of PV production in the presence of NH4Cl is maintained throughout . We next wanted to determine if the effect we were seeing was specific to low MOI infections . H1-HeLa cells were infected at an MOI of 50 pfu/cell and cell-associated virus was collected at 6 h . p . i . ( Figure 4C ) The results were strikingly similar to our low MOI infection experiments , with an approximately one-log reduction in viral titer , indicating that the effect is independent of MOI . We also wanted to investigate if the effects of acidification inhibitors would be diminished if they were added following the peaks of both viral translation and transcription . NH4Cl was added to cells at 3 . 5 h . p . i . to limit any possible effects on virus entry or the initiation of virus transcription or translation [47] . Cells were then lysed at 7 h . p . i . , and plaque assays revealed an 8-fold reduction of infectious virus production . ( Figure 4D ) These data are similar to the results in Figure 4A , indicating the effect of NH4Cl on the virus life cycle primarily takes place later than 3 . 5 h . p . i . To ensure that the effect observed was not specific to treatment with NH4Cl , cells were pre-treated with bafilomycin A1 , a specific inhibitor of the vacuolar ATPase responsible for vesicle acidification [48] . Infection with poliovirus following bafilomycin A1 treatment resulted in a 6-fold reduction in infectious virus at 6 h . p . i . ( Figure 4E ) We carried this time course out to 12 h . p . i . , beyond a single viral replication cycle , and found that the effect on viral titer is even more pronounced than in the 6 hour infection , although this may be due to compounding effects that result from the extended pre-treatment with bafilomycin A1 prior to infection . We conclude that inhibiting vesicle acidification reduces infectious poliovirus production significantly . We wanted to understand the reason for the decrease in viral yield when vesicle acidification is inhibited . Poliovirus does not require a pH change in the entry vesicle to enter cells and release its genome , nor to translate and process the viral polyprotein [29] . However , we wanted to test this using our inhibitors and protocols to ensure that acidification of the entry vesicle was not affecting these early steps in the virus replication cycle . To do this , we used pulse labeling to detect translation of viral proteins . Because PV efficiently inhibits protein synthesis , 35S-Methionine labeling at discrete time points during infection should reveal that viral proteins make up the vast majority of protein production within a few hours of infection [49] , [50] . This indicates that virus was able to enter cells , release its genome , and initiate translation . By comparing the pattern of proteins produced to untreated infected cells , we can identify changes in polyprotein processing as well . We observed no significant differences in the pattern of protein labeling from infections done in the absence or presence of NH4Cl . ( Figure 5A ) We conclude that virus entry , translation and polyprotein processing are not affected by NH4Cl treatment . To ask if defects in viral RNA replication can explain the decrease in virus yield following NH4Cl treatment , qRT-PCR was performed using poliovirus specific primers to detect changes in viral genome replication . To ensure that our assay was able to detect changes in viral RNA levels , infections were also done in the presence of guanidine HCl , a specific inhibitor of poliovirus RNA replication [51] . Treatment with guanidine HCl resulted in significantly decreased viral RNA levels at 6 h . p . i . No change in viral RNA levels was observed at 6 h . p . i . when infections were carried out in the presence of NH4Cl . ( Figure 5B ) The reduced titer of intracellular virus from each replicate shows that NH4Cl reduces the yield of intracellular virus at 6 h . p . i . , despite normal levels of viral genome replication . Although tools do not exist to specifically inhibit acidification of the autophagic subset of vesicles , we can inhibit their formation using 3-MA . H1-HeLa cells have high levels of autophagy , and it is difficult to achieve significant reduction of autophagic levels . To examine cells in which autophagy can be efficiently inhibited , we turned to the 293T cell line , in which baseline autophagy levels are much lower [52] . ( Figure S1A ) We examined LC3 modification following PV infection and found that 3-MA was significantly inhibiting viral induction of the autophagic pathway in 293T cells but not in H1-HeLa cells . ( Figure S1B ) It is worth noting that although we could consistently detect slightly lower amounts of virus when H1-HeLa cells were infected in the presence of 3-MA , we were unable to identify changes in RNA levels or any other step in virus production . ( Figure S1C–E ) In 293T cells , 3-MA treatment led to a two-log reduction in viral genomic RNA levels at 6 hours post infection and a one-log reduction in virus titer . ( Figure 5C ) Our data indicate that inhibiting autophagosome formation and inhibiting vesicle acidification have different effects on the virus life cycle . There are , essentially , three events which must occur to produce infectious virus from genomic RNA and capsid proteins . The first , assembly of pentamers from capsid protein , is thought to occur spontaneously [53] . The second step , association of the genomic RNA with pentamers , is thought to nucleate assembly of 150S provirions , named after their sedimentation coefficient on a sucrose gradient [54] . After virions assemble , the final step is a cleavage maturation of one of the capsid proteins , VP0 , which is cleaved into VP4 and VP2 resulting in the infectious 150S virion . This is believed to be an autocatalytic reaction and there is no published evidence of its pH-dependence [55] , [56] . The specific mechanism of virus particle maturation is unclear . We wanted to determine which of these post-RNA replication steps is promoted by vesicle acidification . To analyze virions , we infected cells at an MOI of 50 pfu/cell and labeled viral proteins with 35S-Methionine beginning at 3 h . p . i . At 5 or 6 h . p . i . , cells were collected and gently lysed , and lysates were separated on a continuous 15–30% sucrose gradient . Fractions were collected and analyzed for radioactive content . Figure 6A shows the 35S counts per minute ( CPM ) of fractions from cultures infected in the presence or absence of NH4Cl . In each case , we observe the two expected peaks , 150S and 75S . The 75S peak consists of empty capsids , while the 150S peak fractions contain encapsidated genomes , only some of which are mature and infectious . We found no significant change in the size or magnitude of the 150S peak , although there was a small but consistent shift in the NH4Cl-treated samples , with both peaks having slightly higher mobility in the gradient . At 5 h . p . i . we observe essentially no change in the peaks resulting from NH4Cl treatment . At 6 h . p . i . , we observe an increase in the magnitude of the 75S peak , but little change in the 150S peak , which contains the infectious virions . We conclude that the loss of pfu resulting from NH4Cl treatment is likely not due to defects in particle assembly genome incorporation , as such defects would likely result in a reduction in the amount of 150S particles . To confirm that NH4Cl was having the expected effect on viral titer , we measured pfu from the pooled 150S peak fractions . ( Figure 6B ) As in previous experiments , pfu was reduced by about one log in the presence of NH4Cl . Since the size of the peak was not altered by NH4Cl treatment , we hypothesized that the maturation of 150S particles is promoted by vesicle acidification . We pooled the three 150S and three 75S peak fractions and ran them on an SDS-PAGE . The 75S peak should contain exclusively VP0 , VP1 , and VP3 , whereas in the 150S peak VP0 levels will be depleted and replaced with the cleavage products VP2 and VP4 as provirions mature to infectious virions . For reasons that are unclear , labeled VP4 is difficult to detect by SDS-PAGE [56]–[59] . In Figure 6C we identify three labeled bands in 75S fractions in both treated and untreated cells , which we have labeled as VP0 , VP1 , and VP3 based on classical relative molecular weight observations [55] , [57] . In the 150S fraction , we identify the VP2 band at its expected migration between VP1 and VP3 . In the presence of NH4Cl , the VP2 band is reduced and the VP0 band is more easily detected . We have quantitated these bands from four independent experiments and graphed the ratio of VP0 protein to the levels of VP3 , which is not cleaved during maturation . We find that NH4Cl treatment significantly increases the amount of VP0 present , indicating that vesicle acidification promotes the maturation of encapsidated virus particles into mature , infectious virions . To confirm that the results were not specific to NH4Cl treatment , we repeated these sucrose gradient purification experiments using bafilomycin A1 to inhibit vesicle acidification . We again see no change in the 150S peak containing encapsidated genomes , although the 75S peak appeared to increase in size . ( Figure 7A ) As with NH4Cl treatment , we found an increase in VP0 abundance when vesicle acidification is inhibited , indicating that VP0 cleavage is inhibited . ( Figure 7B ) These data are consistent with the data in Figure 6 , and indicate that NH4Cl is having the expected effect on vesicle acidification . Taken together , our data show that autophagosome formation promotes viral RNA replication , while vesicle acidification promotes maturation of the assembled , encapsidated virus particles into infectious virions .
Many pathogens that induce autophagic signaling to promote their own replication also inhibit autophagic degradation , presumably to prevent their own destruction by the autophagy machinery . This idea is based on experiments using autophagy-subverting pathogens such as Coxsackievirus and Legionella pneumophila [20] , [21] , [60] . In contrast , we have shown that poliovirus , a pathogen whose replication is promoted by autophagic signaling , induces bona fide autophagic protein degradation . However , autophagic degradation does not itself promote poliovirus production . Instead , we find that acidification of cellular compartments is required for normal virus production . Specifically , we have shown that inhibitors of acidification inhibit maturation of virus particles into infectious virions . However , we do not know which cellular compartment is acidifying to promote PV maturation . Since cellular autophagosomes promote PV replication and become acidic prior to their fusion with the lysosome , autophagosomes are an attractive candidate for an acidic compartment to promote virion maturation [18] . During poliovirus infection , autophagic signaling can be identified by 3 h . p . i . , and by 5 h . p . i . the cytoplasm is filled with double-membraned vesicles [15] , [32] , [47] . These autophagosome-like objects , abundant late in infection , appear to be full of viral particles , a phenomenon which has never been explained [2] . Virus-induced autophagosome-like vesicles are marked with proteins from the viral RNA replication complex , and poliovirus , like all positive-strand RNA viruses , replicates its RNA in association with cellular membranes [12] , [15] . These data have lead to the hypothesis that autophagic vesicles are the sites of viral RNA replication . An alternative hypothesis for the site of RNA replication suggests that vesicles derived from the secretory pathway provide a substrate for replication complexes [61] , [62] . For Coxsackievirus B3 , the reorganization of secretory vesicles generates a novel membrane structure to act as a substrate for viral RNA replication complexes [63] . However , since CVB3 does not induce autophagic degradation , there may be important differences between the PV and CVB3 structures [20] , [21] . Recently , EM tomography carried out on PV-infected cells fixed at different h . p . i . suggested that single-membraned vesicles , seen early in infected cells , may be connected to the double-membraned vesicles seen later in infected cells [64] . Research in the autophagy field suggests that vesicles of the secretory pathway are used in the generation of autophagosomes [65] , [66] . Therefore , it is possible that the two hypotheses for the origin of viral replication vesicles are both correct . We propose such a “unified model” , in which single-membraned secretory-derived vesicles predominate at the peak of RNA replication ( 2 . 5 h . p . i . –3 . 5 h . p . i . ) and act as the substrate for RNA replication complexes . ( Figure 8 ) Then , using the cell's autophagic machinery , these vesicles morph into the double-membraned vesicles observed later during infection ( 4 h . p . i . –6 h . p . i . ) . If this model is correct , then it is likely that during the change from single-membraned vesicles to double-membraned vesicles , a significant amount of newly synthesized viral RNA and viral proteins would be taken up into the lumen of the autophagosome-like structures . This would explain the observation of virus particles inside autophagosomes . If the virus only needs the cytosolic face of membranes as a substrate for RNA replication , then the effect of acidification inhibitors would not be significant . However , if characteristics unique to the environment within the vesicle , such as low pH , are important for virus production , then acidification inhibitors would be expected to reduce viral yield . We have provided the first experimental evidence to support this second scenario . Our data demonstrate that inhibition of autophagosome formation by 3-MA affects poliovirus RNA replication . In the context of our model , there are two possible roles for 3-MA . ( Figure 8 ) One , it could inhibit production of single-membraned secretory pathway-derived vesicles , which represent an early step in formation of the autophagosome . This makes sense in context of recent data showing single-membraned vesicles physically associating with double-membraned vesicles as infection progresses [64] . Alternatively , 3-MA could allow single-membraned vesicles to form , but inhibit the formation of double-membraned vesicles , which then act as primary sites of RNA replication . Although this is a formal possibility , we think it less likely in the context of the data indicating an important role for secretory-derived vesicles in viral RNA replication [61] , [63] . In either case , 3-MA inhibits viral RNA replication and all downstream steps in virus production . NH4Cl , however , allows production of early autophagic vesicles , inhibiting only their acidification and maturation , which specifically reduces VP0 cleavage . Inhibiting acidification has no effect on viral genomic RNA replication . These data make it clear that vesicle acidification is important for maturation of newly-assembled poliovirus provirions into infectious particles . There are several possibilities to explain why vesicle acidification promotes virus maturation . It is important to note that because we do not have the technical ability to inhibit acidification of specific compartments , it is possible that acidification of another cellular compartment , not amphisomes , is key to virus production . It is also possible that treating cells with bafilomycin A1 or NH4Cl alters organelle structure or membrane trafficking in a way that inhibits virus production . We think these explanations are unlikely due to the important role autophagosomes play in virus production and the observed presence of virus particles inside autophagosomes [2] . A second possibility is that autophagosomes act as “sponges” for ions , increasing the pH of the cytosol to promote virion maturation [67] . We believe this is unlikely because PV is an enteric virus , capable of remaining infectious in the low pH environment of the human gut , and should not be adversely affected by low pH . Our favored hypothesis is that virions taken up inside acidic amphisomes have a greater likelihood of maturing into infectious virions . ( Figure 8 ) Several pieces of data lead us to believe that the amphisome interior a likely site of virus maturation . First , autophagosome formation is required for normal viral RNA replication , indicating that newly synthesized viral genomes are likely to be associated with , or in close proximity to , autophagosomes . Second , recent published evidence showed that PV-induced double-membraned vesicles are derived from RNA replication-associated vesicles . Finally , PV-induced amphisomes , which stain with the viral RNA replication protein 3A , are clearly acidic due to their co-staining with the lysosomotropic agent monodansylcadaverine [12] . The majority of virus particles observed during infection are cytosolic , which appears to pose a problem . How can an important function of virus maturation occur in a compartment containing a small percentage of particles ? First , the particle-to-pfu ratio of PV has been measured as anywhere from 30 to 1000 [68] , [69] . This means that even at the lowest estimate , the vast majority of particles produced during an replication cycle are not infectious . We find that inhibiting acidification eliminates about 90% of pfu . ( Figure 4 ) Therefore , it is not unreasonable to consider that the particles inside amphisomes may have a much better chance of maturing into infectious virus than those outside the amphisome . In our hands , 10% of pfu are resistant to treatment with acidification inhibitors , and we see evidence of residual VP0 cleavage in the presence of NH4Cl or bafilomycin A1 . ( Figures 6 , 7 ) This could be for multiple reasons . First , our treatments do not completely block either autophagy or vesicle acidification . Second , there may certainly be residual cleavage of VP0 in the cytosol or at neutral pH . In any case , our data do not show that only particles inside acidic compartments will mature . Our model is that poliovirus particle maturation is more efficient for particles within amphisomes . This study therefore has important implications for our understanding of the PV life cycle , because all steps in virus production have long been thought to occur in the cytoplasm . There are few reports in the literature showing that any stage in positive-strand virus production is not cytoplasmic . Picornavirus particles have been observed in autophagosomes , providing a precedent for our hypothesis [2] , [70] . In addition , the alphavirus Brome Mosaic Virus ( BMV ) replicates its RNA within invaginations of the endoplasmic reticulum , and the passage of macromolecules through the neck of these compartments is believed to be tightly regulated by the virus [71] . However , it is clear from the data presented here that the compartmental requirement for PV is very different from BMV . Normal levels of poliovirus production require vesicle acidification for a post-RNA replication step or steps . ( Figures 4–7 ) Virus particles are not truly infectious until the mature particles exit the cell . Viral egress is a poorly understood process , often proposed to be the result of the cytopathic effect at the end of an infectious cycle [72]–[74] Previously it was found that the effect of modulating autophagy is greater on pre-lytic exit of infectious virus than on cell-associated virus [12] , [75] Our data are consistent with the model that a higher percentage of packaged , infectious virus assembled and released as a result of increased autophagy . Vesicles can be induced by the transfection of viral replication proteins into cells , but when those cells are superinfected with PV , the pre-formed vesicles are not used for viral RNA synthesis [76] . These data point to a coupling between RNA replication and vesicle generation and may indicate a viral strategy to ensure that at least some newly replicated RNA reaches the luminal side of the vesicles . If our model is correct and PV particles inside autophagosomes are more likely to be mature , infectious virus , then the viruses inside these double-membraned structures need a mechanism to exit the cell . It is also possible that the steps of virus assembly and encapsidation may occur in the cytosol , but maturation occurs inside acidic vesicles . However , we find this unlikely , as immature virions would face a topology problem , having to cross two membranes to enter the amphisome for efficient maturation cleavage . Therefore , we propose that it is most likely that virions are being assembled and encapsidated inside vesicles . As a non-enveloped virus , poliovirus is not thought to be found in the wild surrounded by a lipid envelope . Therefore , if poliovirus genome encapsidation occurs in autophagosomes , then there must be some mechanism by which virus can exit from these double-membraned vesicles . We describe some possibilities in Figure 8 . The simplest model is that the autophagosome fuses with the plasma membrane , releasing virus into the extracellular space . There are data for such a model in poliovirus , and more recent data has confirmed the existence of a minor secretory pathway linked to autophagy [77]–[80] . However , this fusion would likely have to occur after the double-membraned amphisome is converted to a single-membraned autolysosome , because fusion of a double-membraned vesicle with the plasma membrane would release a cytosol-filled single-membraned vesicle into the extracellular space . In the case of infection , this would release a vesicle presumably filled with multiple virions . Poliovirus is not found in extracellular vesicles , so any such structures would presumably be very short-lived and difficult to detect . Alternatively , there could be a mechanism by which virions re-enter the cytosol after the pH-dependent step . These cytoplasmic viruses would then be released by cell lysis . Our data indicate that acidic amphisomes promote the late , post-RNA replication step of poliovirus particle maturation . The idea that the interior of these vesicles may be the site of virion assembly , genome packaging , maturation , and cell egress has the potential to alter the models in the field describing the latter part of picornavirus infection . | The autophagic degradation pathway is a well-known agent of innate immunity . Several pathogens , including poliovirus ( PV ) , a model for several medically important RNA viruses , subvert this pathway for their own benefit . In doing so , pathogens often inhibit the degradative portion of the pathway , presumably to prevent their own destruction . We show here that , surprisingly , PV infection results in high levels of degradative autophagy . However , we find that autophagic degradation is dispensable for PV replication . Inhibiting the formation of autophagosomes inhibits virus RNA replication and subsequent steps in virus production . Inhibiting the acidification of vesicles , which in the case of autophagosomes precedes fusion with lysosomes and autophagic degradation , inhibits a much later step in virus production . Our data suggest an important role for an acidic compartment of the cell in the final maturation step , cleaving a capsid protein to generate infectious virus . Importantly , these data also call into question the long-standing hypothesis that all steps in the production of infectious poliovirus are cytosolic . | [
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] | 2012 | Intracellular Vesicle Acidification Promotes Maturation of Infectious Poliovirus Particles |
To investigate whether alterations in mitochondrial metabolism affect longevity in Drosophila melanogaster , we studied lifespan in various single gene mutants , using inbred and outbred genetic backgrounds . As positive controls we included the two most intensively studied mutants of Indy , which encodes a Drosophila Krebs cycle intermediate transporter . It has been reported that flies heterozygous for these Indy mutations , which lie outside the coding region , show almost a doubling of lifespan . We report that only one of the two mutants lowers mRNA levels , implying that the lifespan extension observed is not attributable to the Indy mutations themselves . Moreover , neither Indy mutation extended lifespan in female flies in any genetic background tested . In the original genetic background , only the Indy mutation associated with altered RNA expression extended lifespan in male flies . However , this effect was abolished by backcrossing into standard outbred genetic backgrounds , and was associated with an unidentified locus on the X chromosome . The original Indy line with long-lived males is infected by the cytoplasmic symbiont Wolbachia , and the longevity of Indy males disappeared after tetracycline clearance of this endosymbiont . These findings underscore the critical importance of standardisation of genetic background and of cytoplasm in genetic studies of lifespan , and show that the lifespan extension previously claimed for Indy mutants was entirely attributable to confounding variation from these two sources . In addition , we saw no effects on lifespan of expression knockdown of the Indy orthologues nac-2 and nac-3 in the nematode Caenorhabditis elegans .
Mutations in single genes in invertebrate model organisms have been used with great success to discover developmental mechanisms that are evolutionarily conserved in mammals . More recently , it has become apparent that the aging process , too , can be investigated by analysis of single gene mutations that extend lifespan . Thanks in particular to their short lifespans , yeast , nematode worms ( C . elegans ) and fruit flies ( D . melanogaster ) have revealed signalling pathways that modulate aging in multiple species . These include the insulin/IGF-like signalling pathway [1–5] , the amino-acid-sensing target of rapamycin ( TOR ) pathway [6–8] , and the stress-responsive JNK pathway [9–11] . Typically , the mutations used to study developmental mechanisms cause robust phenotypes that are expressed in a range of genetic backgrounds . Moreover , they are not greatly affected by environmental variation , at least not within the range normally encountered during laboratory studies . By contrast , lifespan is highly sensitive to genetic background and environment , necessitating careful precautions when trying to attribute an increase in lifespan to the effects of a single gene mutation . Natural and laboratory populations of outbred , diploid organisms , such as Drosophila and mice , can harbor substantial quantitative genetic variation for lifespan [12–16] , and different wild-type strains can therefore differ considerably in longevity . In addition , as is often the case for fitness-related traits , longevity is shortened by inbreeding depression , and increased by heterosis when separate inbred strains are crossed with each other [17] . Use of inbred laboratory strains in aging research is risky , because fixation of deleterious alleles in such stocks can result in identification of alleles that extend lifespan merely by suppressing shortened lifespan in a strain-specific manner [18 , 19] . For these reasons , when examining the effects of single gene mutations on lifespan it is preferable to backcross into an outbred genetic background with a full , healthy lifespan , similar to that of wild-caught Drosophila . Mutations in single genes can also interact epistatically with the genetic background used and such interactions can be complex and sometimes sex-specific [19–21] . Furthermore , laboratory culture , with its abundant and accessible food supply and pressure for rapid and copious reproduction , can lead to the evolution of accelerated sexual maturation , elevated fecundity , and shorter lifespan [18 , 22–24] . As in inbred strains , a mutation may , potentially , increase lifespan by reversing the lifespan-shortening effects of adaptation to laboratory conditions . Thus , it is important to analyse putative aging genes in several genetic backgrounds with healthy lifespans . An additional confounding factor , almost routinely ignored in aging studies , is maternally inherited Wolbachia , an intracellular symbiotic bacterium that can have unpredictable effects on host fitness–related traits , including lifespan [25–29] . Widespread infection of Wolbachia within laboratory stocks has been shown in a recent survey , indicating its presence in approximately 30% of stocks currently housed at the Bloomington Drosophila Stock Center [30] . We tested the effects on lifespan of heterozygous , single gene mutations affecting the mitochondrial translation machinery and nucleotide metabolism . We were encouraged to pursue this direction by our preliminary finding that flies heterozygous for a mutation in a mitochondrial ribosomal protein S12 ( encoded by technical knockout , tko ) were longer-lived than wild-type flies , without obvious defects in growth or developmental time . As a positive control for these experiments , two mutants for Indy ( I'm not dead yet ) were used . Both Indy206 and Indy302 alleles have been reported to result in very long-lived flies in the heterozygous state , and to a lesser extent in homozygotes [31] . Indy encodes a plasma membrane Krebs cycle intermediate transporter [32] and Indy mutants are reported to cause decreased expression of the gene product [31 , 33 , and references therein] . This strong heterozygous phenotype suggests that mild reduction in expression of Indy has a large impact on lifespan without reduction in the rate of development or growth . Thus , the Indy mutants were potentially similar to heterozygous mutations affecting mitochondrial translation machinery in terms of their lack of developmental or physiological phenotypes coupled with extended adult lifespan . Instead , we discovered that in an outbred genetic background tko and other mitochondrial mutations studied had no effect on lifespan and , surprisingly , neither did either Indy allele in most backgrounds tested . More specifically , we found that Indy302 did not extend lifespan in either sex in any genetic background , while Indy206 was associated with increased lifespan only in one of three genetic backgrounds studied , and even then the effect was male-specific . This genetic-background-specific extension of lifespan in males was largely abolished by tetracycline ( TET ) treatment , which also removed the intracellular symbiont Wolbachia from this mutant stock . The apparent effect of Indy206 on lifespan was thus in large part attributable to the presence of a TET-sensitive modifier . Furthermore , the residual lifespan extension observed was fully reproduced by introducing Chromosome X ( but not the Indy206 mutation on Chromosome 3 ) from the long-lived line into a new genetic background . The Indy206 mutation itself thus played no role in the extension of lifespan . Additionally , three independent RNAi-knockdown experiments targeting worm orthologues of Indy , nac-2 and nac-3 , also implicated in extended lifespan by previous studies [34 , 35] , did not extend lifespan in our hands .
In C . elegans , mutation or knockdown of several genes encoding proteins in the mitochondrial respiratory chain leads to reduced lifespan [36 , 37] but of many others instead increases lifespan [38–40] , by mechanisms that remain uncertain . We examined heterozygous , single gene mutations in flies , to test whether mild impairment of mitochondrial function can lead to extended lifespan . In a pilot experiment , heterozygosity for tko25t , a hypomorphic allele of mitochondrial ribosomal protein S12 [41–43] increased median lifespan by 18% ( unpublished data ) . To verify our finding in a standard genetic background , the tko25t and sesB1 alleles ( encoding mitochondrial adenine nucleotide translocase ) , together with a further candidate mutant , bonsai1 , affecting mitochondrial ribosomal protein S15 [44] , were backcrossed into the white Dahomey ( wDah ) background and lifespan of heterozygous virgin females was then measured . Females were tested , because both tko and sesB are located on the X chromosome and hence adversely affect hemizygous mutant males . Virgins were used to avoid potential confounding effects of the mutations on female reproduction , which could affect lifespan . As a positive control we used Indy206/+ and Indy302/+ females , both reported to be long-lived [31] . Both alleles were backcrossed into our laboratory background wDah , as for the mitochondrial mutants . When tested after six generations of backcrossing , the longevity phenotype of tko25t/+ flies had almost disappeared and there was also no significant difference between tko25t/+ and sesB1/+ lifespans ( Figure 1A ) . Thus , the increased lifespan seen in the pilot experiment was not attributable to the tko25t mutation itself , but most likely reflected heterosis ( hybrid vigour ) between the mutant and the control strain . bonsai/+ females ( Figure 1B ) did show a small but significant increase in median lifespan relative to wDah ( +/+ ) and tko25t/+ . However , the effect was so small that we chose not to study this further . To our surprise , the backcrossed Indy206/+ and Indy302/+ females were not long-lived either . Instead , their lifespan was intermediate between wDah and bonsai/+ ( Figure 1B ) . We were concerned that long-term maintenance of Indy alleles as homozygotes might have dissipated the phenotype , for example , by selection of suppressor mutations . We also wondered whether the discrepancy between our results and earlier reports might reflect differences in the food conditions , or our use of virgin females in the experiment . Recently , strong condition dependency has been reported for another long-lived mutant , methuselah [45] . We therefore backcrossed our mutant lines further and investigated the effects of Indy206 and Indy302 on lifespan in more detail , comparing inbred and outbred laboratory genetic backgrounds . In subsequent experiments , we also included the original lines , which had not been further backcrossed , for comparison . In the following text prefixes CS- , wDah- , and w1118- stand for the original Canton S , white Dahomey and w1118 backgrounds , respectively . The original CS-Indy206 , CS-Indy302 , and the control strain CS-1085 ( from the same mutagenesis but with the insert located outside the Indy region ) were backcrossed for a further 6–10 generations to the outbred wDah stock , ensuring that cytoplasmic constituents , such as mitochondria , were derived from the wDah strain ( see Materials and Methods ) . These mutations were also backcrossed into an inbred w1118 stock for five generations , to determine the effects of a different , inbred genetic background . We first performed further tests to try to reproduce the reported lifespan extension in the original , heterozygous CS-Indy lines [31] . To be as faithful as possible to the original methods [31] , we used a similar cornmeal-based food medium and we also housed experimental flies in both mixed-sex and once-mated , single-sex conditions . However , similar to our earlier findings with wDah-backcrossed virgin females , we did not see lifespan-extension in the original CS-Indy206/+ and CS-Indy302/+ females ( Figure 2A ) . Although we did see a moderate , 16% increase in the median lifespan of CS-Indy206/+ females compared with CS ( +/+ ) , this was not significantly different to the control strain CS-1085/+ . Lifespan in CS-Indy302/+ females was not significantly different from that of CS ( +/+ ) , and these flies were shorter lived than both control CS-1085/+ and CS-Indy206/+ females . By contrast , we did confirm that CS-Indy206/+ males are long-lived , and measured a mean lifespan similar to that observed in [31] ( Figure 2B; 14% and 40% increase in the median lifespan of CS-Indy206/+ males relative to CS ( +/+ ) and CS-1085/+ males , respectively ) . The original CS-Indy302/+ males were not long-lived compared with CS ( +/+ ) , but showed 21% increase in median lifespan compared with CS-1085/+ males . It should be noted also that CS ( +/+ ) males were 23% longer lived than CS-1085/+ males , suggesting that these two control lines are not in a comparable genetic background , or that heterozygosity for the 1085 insertion has an adverse effect on male longevity . The latter is unlikely because , after five generations of backcrossing to the inbred w1118 strain , the w1118-1085/+ control males behaved identically to the parental w1118 ( +/+ ) line ( Figure 2C ) , median lifespan for both being 55 days . Backcrossed w1118-Indy302/+ males also behaved as the controls , showing median lifespan of 56 days . The w1118-Indy206/+ mutant males , however , still showed a small 7% median lifespan-extension compared with both controls , the median being 59 days . The results were similar in both once-mated females kept as single sex and females kept in mixed sex groups with males , although mixed sex conditions drastically reduced lifespans of females , regardless of their genotype ( unpublished data ) . These data show that , on cornmeal-based food , using either mixed or separate sex conditions , only one of the mutant alleles under study resulted in increased lifespan , and only in males . The lack of phenotype in Indy flies was surprising , and we therefore confirmed that the Indy mutations were still present in our stocks . The mutations were as published [31] and were identical in the three genetic backgrounds ( see Figure S1 ) . We were particularly interested in why , even in the original genetic background , we could confirm the reported lifespan extension in Indy206 males but not in Indy302 males . The effect of the different mutant alleles on Indy expression has not been shown previously , and we therefore examined the consequences of the two mutations for Indy mRNA levels . Based on annotation in FlyBase [46] , Indy ( annotation symbol CG3979 ) encodes three putative transcripts ( Indy-RA , Indy-RB , and Indy-RC; Figure 3A ) that differ only in their 5′-exons . To determine how the Indy206 and Indy302 alleles affect the expression of these alternative Indy transcripts , we performed PCR with splice variant–specific primers and template cDNA obtained from homozygous Indy206 and Indy302 mutants ( Figure 3B ) . Catalase ( Cat ) was amplified as a control for cDNA quality , and also to confirm that its expression is not affected in Indy mutants ( the Cat gene is located proximal to Indy in the third chromosome ) . In tests of wild-type flies , cDNA for variants RA and RB was seen , but not for variant RC . Long-range PCR using genomic DNA as a template confirmed that this was not due to a problem with the function of the primers ( unpublished data ) . All three variants were absent from the Indy206 cDNA sample , consistent with the decreased expression of protein reported in [33] . However , the Indy302 mutants showed a similar expression pattern to the wild type . Because PCR methods in general are only semi-quantitative , we performed a northern hybridization using RNA samples from homozygous Indy206 and Indy302 males ( Figure 3C ) . The result confirmed our finding from the PCR assay , namely , that , whereas the Indy206 mutation had a strong effect on gene expression , Indy302 had no detectable effect . A phosphorimager quantification showed that , whereas expression in the Indy206 lines was less than 10% of the wild-type levels , the expression in Indy302 was typically 85% to 110% compared to the corresponding wild-type strains ( Figure 3D ) . As shown in Figures 2B and 2C , the increase in lifespan of w1118-Indy206/+ males after five generations of backcrossing was clearly diminished compared with the same mutation in the original genetic background . We therefore investigated whether thorough backcrossing of Indy206 into the outbred wDah stock would completely abolish the phenotype . The extent to which lifespan is affected by Indy mutations might also depend on food type and we therefore repeated the experiment using sugar-and-yeast-based food ( SY ) . We first measured lifespan of wDah-Indy206/+ males , backcrossed for eight generations , using SY food ( Figure 4A ) . The backcrossed wDah-Indy206/+ males were not long-lived , and behaved as the wDah control ( median 55 and 53 days , respectively ) . As a positive control for the effect of the backcrossing we exposed the original , non-backcrossed mutant lines to the same SY food medium . Again , a robust 48% extension was seen in median lifespan in the original CS-Indy206/+ males compared with CS ( +/+ ) control ( median lifespan 68 and 46 days , respectively ) . The mean lifespan of CS-Indy206/+ males was again very similar to the published data ( mean 66 . 4 days compared with 71 days in [31] ) . We repeated the experiment after ten generations of backcrossing and tested wDah-Indy206 males and females , in homozygous and heterozygous condition ( Figures 4B and 4C ) . No lifespan extension was observed in either genotype , in males ( Figure 4B ) or in females ( Figure 4C ) . The same experiment was conducted using wDah-Indy302 with similar results , except that the females homozygous for the Indy302 insertion were clearly short lived ( Figure S2A and S2B ) . Together , these data confirm that , with SY food as well as with corn meal-based medium ( Figure 2B ) , one may observe the substantial lifespan-extension in the original , non-backcrossed males heterozygous for Indy206 , but that this increase in lifespan is not present in thoroughly backcrossed males carrying the same mutation . Having established that mutations in Indy alleles are not themselves causal for longevity , we explored alternative explanations for the male-specific longevity observed in the original Indy206 line . Wolbachia , an intracellular symbiont found frequently in Drosophila stocks [30] , is a maternally derived factor that can potentially modulate longevity . We investigated the Wolbachia status of these lines by PCR detection of the gene for Wolbachia surface protein ( wsp ) [47 , 48] . All the original mutant lines , including the Canton S control , were infected by these α-proteobacteria ( Figure 5A , upper left panel ) . We also analysed the mutants ( and wild-type controls ) in the two other genetic backgrounds used , and found no signs of infection in either the wDah ( Figure 5A ) or w1118 ( unpublished data ) backgrounds . To test the possibility that the longevity phenotype in the original CS-Indy206 heterozygotes was Wolbachia dependent , we used TET treatment , which removes Wolbachia infection . Canton S and CS-Indy206 lines were cured by adding 25 μg/ml TET to the food medium for three generations . Wolbachia-negative wDah and wDah-Indy206 lines were also treated with TET to provide drug treatment controls . After treatment , the fly stocks were cultured for several generations in TET-free medium , and the removal of Wolbachia from treated lines was confirmed by PCR ( Figure 5A , upper right panel ) . When both parents were TET treated , the resulting CS-Indy206 male progeny showed only a small increase in lifespan relative to Canton S control flies , ( Figure 5B , median lifespan 50 days and 46 days , respectively ) , although this increase was statistically significant . Treatment of one or the other parent only resulted in intermediate lifespans compared with the situation where both parents were nontreated or treated ( Figure 5B , open triangles and open circles ) . Canton S controls were not affected by the treatment ( Figure 5C , all median lifespans between 46 and 48 days ) , implying that there was no adverse effect of treatment on other aspects of metabolism in these flies , such as mitochondrial function . It also showed that Wolbachia removal per se does not affect lifespan of Canton S flies . We performed similar crosses using treated and nontreated Indy206 mutants in the wDah background ( Figure 5D ) and did not in general see a significant effect of TET treatment , median lifespan being between 55 and 60 days for all groups . We conclude that at least part of the lifespan extension observed in original Indy206 males is the result of a TET-sensitive modifier , possibly Wolbachia . However , because a small effect was seen also when only fathers were treated , we cannot exclude a possibility of another bacterial associate . Although the long lifespan of CS-Indy206 males was largely dissipated by TET treatment , it did not completely abolish the phenotype ( Figure 5B ) . We therefore determined the source of this residual effect . Logical possibilities included the mitochondria , and the X chromosome , which in males is maternally derived . We therefore transferred either cytoplasmic constituents or the X chromosome from the long-lived CS-Indy206 strain to the otherwise wDah genetic background ( details in Protocol S1 ) . We took particular care that chromosomes in which recombination between the Canton S and the wDah chromosomes had potentially occurred were eliminated during the procedure . Importantly , these lines were now wild type with respect to the Indy locus . Transfer of cytoplasmic constituents from the long-lived CS-Indy206 to the otherwise wDah background did not affect longevity ( Figure 6 , solid line ) . By contrast , transfer of X chromosome alone from the long-lived CS-Indy206 was enough to extend lifespan of the males in an otherwise wDah genetic background to match that of the long-lived CS-Indy206 males ( Figure 6 , open and black diamonds , respectively ) . This finding demonstrates that the Indy206 mutation itself did not produce the lifespan extension associated with the nuclear genotype of the original CS-Indy206 line . The lifespan extension was due to a combination of a TET-responsive factor together with an X-chromosomal modifier of lifespan in the stock . In the nematode C . elegans , there are three proteins with homology to Drosophila INDY . These are NAC-1 ( F31F6 . 6 , previously known as ceNAC-1 and ceNaDC1 ) , NAC-2 ( R107 . 1 , previously known as ceNAC-2 and ceNaDC3 ) , and NAC-3 ( K08E5 . 2 , previously known as ceNAC-3 and ceNaDC2 ) [34 , 35] . Previously , the reported influence of Indy on lifespan in Drosophila [31] motivated tests for similar effects on lifespan of nac-1 , −2 , and -3 on lifespan in C . elegans . RNA-mediated interference ( RNAi ) knockdown of nac-2 [35] and nac-3 [34] were reported to extend mean lifespan by 19% and 15% , respectively . Our negative results regarding the influence of Indy on Drosophila lifespan motivated us to verify the effects of RNAi knockdown of nac-2 and nac-3 on C . elegans lifespan , employing the previously used nac-2 and nac-3 RNAi feeding plasmids , kindly provided by Dr You-Yun Fei . Using experimental conditions similar , but not identical , to those in the previous studies ( see Materials and Methods ) we saw no effect of nac-2 or nac-3 RNAi on lifespan in two separate experiments ( Figure 7A and 7B ) . These results could imply that any effects on lifespan of RNAi knockdown of nac-2 and nac-3 are sensitive to small differences in experimental conditions . Therefore , we repeated the experiment a third time using conditions more closely replicating the original studies by Fei et al . [34 , 35] , in that RNAi feeding bacteria were preinduced using IPTG before being added to IPTG-containing agar plates . Again , no increases in lifespan were seen ( Figure 7C ) . We verified the efficiency of the RNAi procedure in three ways . First , we used semi-quantitative RT-PCR to check that nac-2 and nac-3 mRNA levels were reduced , and they were ( Figure 7D ) . Second , we performed positive control tests in each trial using daf-2 RNAi . This resulted in a large increase in lifespan in all repeats of the experiment , demonstrating that our RNAi methodology was working normally ( Figure 7A–C ) . Third , we verified by DNA sequencing the identity of the inserts in the nac-2 and nac-3 feeding vectors ( unpublished data ) . The results of the three lifespan experiments are summarized in Table 1 .
We have shown that two Indy mutations , Indy206 and Indy302 , previously reported to extend lifespan to a similar extent , do not decrease expression of Indy mRNA to the same extent , and that Indy302 does not decrease it at all ( Figure 3 ) . In all three genetic backgrounds tested , the expression of all Indy transcripts was severely affected in the Indy206 , but not in the Indy302 mutant . A decrease in transcript levels was reported in both Indy206 and Indy302 mutants ( [33] referred therein as unpublished data ) . Our stocks were verified to be authentic by two independent methods ( see Figure S1 ) and , therefore , we are unable to explain the discrepancy in the results . The data also suggest that only two of the three transcript variants annotated in FlyBase [46] are expressed in adult flies . However , we cannot exclude the possibility of tissue-specific or conditional regulation for the third alternative transcript . When the expression data and lifespan experiments are taken together , inhibition of Indy transcription lacks correlation with lifespan extension . Small , absent , or inconsistent effects of Indy alleles on lifespan were reported earlier . When freshly isogenised mutants were tested , only a small lifespan extension was observed in heterozygous Indy females in short-lived lines with a genetic background expressing a lethal toxin coupled to an age-dependent molecular biomarker [49] . Indy206 and Indy302 insertions that contain a lacZ reporter gene were used as markers to study temporal patterns of gene expression , and their lifespan was reported to be similar to the controls [50 , 51] . A recent study by Khazaeli et al . [52] could not confirm longevity in males homozygous for Indy206 and Indy302 mutations , although even the homozygous Indy mutants were reported to outlive the controls by 10%–20% [31] . Aging-related decline in performance , measured as negative geotaxis , progressed much more rapidly in Indy mutants when compared with chico1 , a long-lived mutant of the insulin/IGF-like signalling pathway [53] . When measured as absolute rate of functional decline , Indy206 mutants were not statistically different from wild-type controls [54] . Unlike many other single gene mutations found to extend lifespan , longevity of Indy mutants has not been studied in multiple genetic backgrounds before and , even in the original backgrounds , the published results proved difficult to repeat in another laboratory [52] . The lack of longevity that we observed in flies carrying Indy mutations was unexpected , because lifespan extensions of 40%–80% were reported in three genetic backgrounds in addition to Canton S [31] . It is not clear , however , whether these findings are derived from thoroughly backcrossed flies or whether F1 hybrids were studied . Based on our results , it seems likely that heterosis between the experimental strains and modifier loci elsewhere in the genome ( such as the one described here ) account for the lifespan extension seen . The fact that excision of the P-elements from the Indy locus apparently rescued longevity [31] might in fact reflect segregation of undefined lifespan-extending modifier ( s ) in the mutant genetic background , or perhaps loss of Wolbachia . Unfortunately the original P-element excision lines are not available for further analysis . Genetic bottlenecks that accompany P-element excisions , or isogenization procedures that result in the introduction of extraneous genetic material , could result in alterations in lifespan . As reported here , the original data on Indy-related longevity can be explained by lifespan-modifying elements that are unconnected to the Indy mutations themselves . Our results imply that a large part of the lifespan-extending effect is due to an X-chromosomal modifier ( s ) . The fact that longevity determinant ( s ) transferred with the X chromosome can increase lifespan in an otherwise wDah genetic background also implies that lack of longevity is not due to “insensitivity” of this background to the levels of Indy , which could potentially result from strain-specific polymorphisms . We have clearly established that wDah can exhibit similar longevity compared with the original mutant line ( see Figure 6 ) , provided that right modifiers are present . Variation in the nuclear background can strongly influence the extent of longevity resulting from single gene interventions , the best studied examples being manipulations of Cu/Zn-superoxide dismutase expression in adult flies [19 , 20] . These studies provided evidence that the impact of Cu/Zn-superoxide dismutase overexpression on longevity is generally stronger in short-lived laboratory lines , and that alleles at other loci interact epistatically with the Cu/Zn-superoxide dismutase transgene to modify its ability to extend longevity . Any particular genetic background is not only defined by its nuclear genome , but also contains a maternally inherited cytoplasmic genome , the mitochondrial DNA . Experiments that combined mitochondrial and nuclear genomes of separate origin have shown that substantial variation in longevity can be attributable to nuclear–mitochondrial interactions [55] . The cytoplasmic endosymbiont Wolbachia , like other bacteria , is sensitive to the TET class of antibiotics , and the presence or absence of Wolbachia can contribute substantially to variation in longevity [28] . However , as mentioned above , not all Wolbachia-positive lines show altered longevity in response to TET treatment ( [26 , 28] , see also Figure 5C ) . We have shown here a decrease in lifespan by TET treatment . This effect was specific for the original long-lived CS-Indy206 line and hence , in this line , the presence of Wolbachia was positively associated with longevity . Transfer of cytoplasmic constituents ( including mitochondria and Wolbachia ) to another genetic background , however , did not result in extended lifespan ( Figure 6 ) . Similarly , TET treatment of fathers also had a significant effect on lifespan of the male progeny . This implies that the effect of Wolbachia is dependent on , and interacts with , other factors in the host genome . We cannot exclude the possibility that the phenotype is dependent on some other bacterial associate in the CS-Indy206 line , which would be similarly eliminated by the drug treatment . However , the fact that Wolbachia frequently infects tissues implicated in determination of longevity , such as nerves , fat body , and the ovary [30] , is a confounding factor in the genetic analysis of longevity , and deserves more attention in the experimental design . Variation in environmental conditions in which lifespan experiments are conducted can result in problems with reproducibility of published data from different laboratories . For example , differences in mating status due to different housing conditions ( mixed sex or single sex ) can strongly affect lifespan . One major source of variation that could be especially important with respect to Indy is diet , given the role of this gene in nutrient transport . We reproduced , in two very different food types , a robust lifespan extension for the original Indy206 line that had not been further backcrossed . This implies that the effects on lifespan in this line are not highly condition dependent with respect to food type . The best-studied environmental intervention that leads to extended lifespan is dietary restriction ( reviewed in [56 , 57] ) . Mutations reducing the levels of Indy have been suggested to alter the metabolism of the fly in a way that favours lifespan extension , possibly by inducing a state similar to dietary restriction [31 , 33 , 34] . To date , however , no reports have addressed the question of how Indy mutations affect survival when dietary conditions are altered . It is also not clear whether long-lived Indy mutants impinge upon any downstream effects on other molecules possibly involved in the dietary restriction pathway , such as Sir2 or Rpd3 [58 , 59] . In our hands , the lifespan of backcrossed Indy mutants proved to be the same as wild type over a wide range of food dilutions ( PM , unpublished data ) , implying that Indy plays no role in the response to dietary restriction in Drosophila . In C . elegans , three gene products showing significant amino-acid sequence homology with Drosophila INDY can be found . RNAi knockdown of two of these genes , nac-2 [35] and nac-3 [34] , has been reported to result in moderate increases in lifespan . By contrast , we saw no effects of RNAi of nac-2 or nac-3 RNAi on lifespan , using similar conditions . This could reflect small differences in the RNAi conditions used: for some genes , the effects of RNAi on lifespan are sensitive to small differences in conditions . In this context , it is worth noting that we did not see a marked decrease in body size in animals subjected to nac-2 RNAi , in contrast to an earlier study [35] . This suggests that RNAi conditions might have been milder in our study , although it is worth emphasizing that daf-2 RNAi increased lifespan to a degree that is comparable to other studies . We also showed that the conditions that we used were sufficient to substantially reduce nac-2 and nac-3 mRNA levels . The basis of the apparent condition dependency of effects of nac-2 and nac-3 RNAi C . elegans lifespan will require further elucidation . Studies of the genetics of aging in Drosophila are highly vulnerable to confounding effects , especially due to heterogeneity between mutant and control populations . Here , we have shown a case in point , based on the analysis of our own initially promising results together with a prominent case from the literature . The data presented here show that mutations in the Indy gene do not extend lifespan , and highlight the importance of carefully controlling genetic background in studies of longevity . Standardisation of genetic background can be achieved by successive backcrossing of a putative aging gene , preferably into several healthy , outbred genetic backgrounds with relatively long-lived wild types . The backcrossing must be conducted in a way that ensures passage of cytoplasmic factors to the progeny , and checks should be made for the presence of intracellular endosymbionts such as Wolbachia .
tko25t and sesB1 mutant flies were supplied by K . M . C . O'Dell and C . -F . Wu . bonsai1 stock was a kind gift from Mireille Galloni . The wild-type stock Dahomey was collected in 1970 in Dahomey ( now Benin ) and has since been maintained in large population cages with overlapping generations on a 12L:12D cycle at 25 °C . This method of husbandry maintains lifespan and fecundity at levels similar to freshly caught stocks [24] . The white Dahomey ( wDah ) stock was derived by incorporation of w1118 deletion into the outbred Dahomey background by successive backcrossing . The inbred w1118 background , obtained from the Drosophila Stock Center ( http://flystocks . bio . indiana . edu ) , was used in some experiments in parallel with wDah . Indy mutant alleles are originally derived from the same mutagenesis , where an effort was made to standardise the genetic background to that of Canton S containing the w1118 deletion [31 , 60] . The original materials ( Indy206 and Indy302 and the control line 1085 ) were provided by Stephen Helfand to the Institute of Medical Technology in Finland in May 2002 , where they were backcrossed for further studies . To backcross these mutants into other genetic backgrounds , females from wDah or w1118 stocks were first mated with Indy206 , Indy302 , or 1085 males , to ensure the transfer of cytoplasmic constituents from wDah or w1118 to the progeny . Heterozygous mutant females were then backcrossed to males with these genetic backgrounds five ( w1118 ) or ten ( wDah ) times . The original and backcrossed stocks were maintained in large numbers in culture bottles at 18 °C on a 12L:12D cycle . Ingredients of different food media are described in Protocol S1 . Unless otherwise stated , to obtain heterozygous experimental flies , homozygous mutant fe-males were crossed to corresponding wild-type ( Canton S , wDah , or w1118 ) males . In one experiment ( data in Figure 4B and 4C ) , heterozygous mutants thoroughly backcrossed to wDah were mated to each other , and wild-type , heterozygous mutant , and homozygous mutant progeny were collected from the same bottles based on intensity of the transgenic eye colour marker . For details of rearing conditions and pre-experimental treatment , see Protocol S1 . All lifespan studies were conducted in vials at 25 °C on a 12L:12D cycle at constant humidity . The flies were transferred to new vials three times per week and deaths were scored every day or every other day . Log-rank tests of survivorship curves were performed by using JMP IN statistical software ( SAS Institute , http://www . sas . com ) . Authenticity of the P{lacW}Indy206 , P{lacW}Indy302 , and P{lacW}1085 insertions was confirmed in all genetic backgrounds by inverse PCR from genomic DNA followed by sequencing ( unpublished data ) . Additionally , PCR reactions with P element–specific primer and primers specific to each insertion site in the genomic DNA were used ( Figure S1 ) . PCR for detection of Wolbachia was performed using primers wsp81F and wsp691R ( kind gift from G . D . D . Hurst ) as described before [47] , and control reactions for DNA quality ( dFoxo ) were performed using primers FoxoEcoRIF ( 5′-GGGGAATTCGTTCAGTGCCGCCTCGGGACTTCC-3′ ) and FoxoNotI R ( 5′-GATCGCGGCCGCGTCCTATCAAAGTAGAGGCGCAGT-3′ ) . For expression analysis , RNA was extracted from 20 males per genotype and cDNA was prepared using standard Trizol methods ( Invitrogen , http://www . invitrogen . com ) . Splice variant–specific PCR was performed from various cDNAs using the following 5′ primers in combination with common region primer IndyR-31 ( 5′-GTTTAGCAGCATAACAGGCAGACATA-3′ ) : IndyRA-51 ( 5′-ATCGGACGAACCGGGCGTG-3′ ) , IndyRB-51 ( 5′-GCAACATATTCATAAAAAGTGGTCTAGCC-3′ ) , and IndyRC-51 ( 5′-CACTCGTTTTCATTCCAATTTTTGCGC-3′ ) . The control primers for Catalase ( Cat ) were Cat-51 ( 5′-CGGCTTCCAATCAGTTGATTGACTAC-3′ ) and Cat-31 ( 5′-TCACATCCTGCAGCAGGATAGG-3′ ) . Catalase was used as a control because it is the gene proximal to Indy , and we wanted to exclude the effect of Indy mutations of Cat expression . Northern hybridization was repeated twice using a probe specific to the common region of Indy ( Figure 3A , grey box ) . The primers used to create the probe were IndyR-51 ( 5′-CGCCACTGGACATCAAAATGGAAAT-3′ ) and IndyR-31 ( above ) . Loading was controlled by ribosomal protein rp49 probe that was amplified as above using primers rp49F ( 5′-AGCATACAGGCCCAAGATCG-3′ ) and rp49R ( 5′-CACCAGGAACTTCTTGAATCCGG-3′ ) . Signals from northern blots were quantified by measuring the 32P-stimulated luminescence ( PSL ) using the FLA-2000 radioisotopic imaging system with Multi Gauge image analysis software ( Fujifilm , http://www . fujifilm . com ) . Lifespan studies: Bacteria-mediated RNA interference ( RNAi ) was used to inhibit gene function [61] . For the nonpreinduced method ( Figure 7A and 7B ) , bacteria ( E . coli ) were grown for 14 h in liquid culture without IPTG , then seeded onto nematode growth medium plates containing 1mM IPTG and 50 μg/ml ampicillin . Seeded plates were allowed to dry for 48 h at room temperature . In the preinduced experiment ( Figure 7C ) , preinduction with 0 . 4 mM IPTG was performed in the liquid culture 4 h before plating . The empty vector L4440 ( pPD129 ) was used as a negative control . As a positive control for the efficacy of the RNAi treatment , we used a daf-2 RNAi feeding strain previously shown to extend lifespan by ~80% [62] . The RNAi clones for nac-2 , nac-3 , and the control vector pPD129 were kindly provided by Y . -Y . Fei [34 , 35] . The daf-2 RNAi clone was kindly provided by A . Dillin [62] . The presence of the correct inserts in each feeding vector was confirmed by DNA sequencing . A wild-type C . elegans strain N2 ( Bristol ) was provided by the Caenorhabditis Genetics Center ( http://www . cbs . umn . edu/CGC ) . Lifespan measurements were performed at 22 °C on age-synchronous populations of nematodes as described previously [34] . RT-PCR methods: Eggs prepared from hypochlorite treatment were plated out onto the respective RNAi feeding bacteria , grown to the L4 stage , and harvested for RNA extraction . Four washes with M9 were used to remove residual bacteria . Total RNA was isolated using the Trizol reagent ( Invitrogen ) . First-strand cDNA was generated from 2 μg of total RNA for each condition using reverse transcriptase priming with Oligo ( dT ) 12–18 primer . cDNA was amplified using two pairs of PCR primers , one pair specific to either ce-nac-2 or ce-nac-3 and a second set specific to ama-1 , the internal control . Oligonucleotides were designed to cover exon/intron boundaries such that only cDNA would be amplified . Cycle numbers were optimised for each primer set to ensure the reaction was within the linear range and each reaction was terminated before reagents became limiting . The intensity of the RT-PCR bands were determined from the agarose gel using the Syngene imaging system with Genesnap and Genetools software ( http://www . syngene . com ) . Levels of ce-nac-2 and ce-nac-3 were calculated as a relative intensity to the intensity of the ama-1 RT-PCR product . The oligonucleotides used were: ama-1 ( 5′-ATCTCGCAGGTTATCGCGTG-3′ and 5′-CGGTGAGGTCCATTCTGAAATC-3′ ) , ce-nac-2 ( 5′-TATTCACAAGAGATACCCCGAG-3′ and 5′-TCCCGATTTATCAACTCCTTCTG-3′ ) , and ce-nac-3 ( 5′-CAAATGGAGAACGTGGCCGTC-3′ and 5′-CGGAGCATCTCTCAAGAAGAAG-3′ ) .
National Center for Biotechnology information ( NCBI ) Entrez Gene ID numbers ( http://www . ncbi . nlm . nih . gov/entrez ) and UniProtKB/Swiss-Prot accession numbers ( http://www . ebi . uniprot . org ) for genes and proteins , respectively: bonsai ( 37587/Q8WTC1 ) , Cat ( 40048/P17336 ) , daf-2 ( 175410/Q968Y9 ) , Indy ( 40049/Q9VVT2 ) , nac-1 ( 181585/Q93655 ) , nac-2 ( 187898/P32739 ) , nac-3 ( 176429/Q21339 ) , sesB ( 32007/Q26365 ) , tko ( 31228/P10735 ) , w ( 31271/P10090 ) , and wsp ( 2738559/Q09TN6 ) . Allele-specific FlyBase ID numbers ( http://flybase . bio . indiana . edu ) : bonsai1 ( FBal0097167 ) , P{lacW}1085 ( FBti0003775 ) , P{lacW}Indy206 ( FBti0004258 ) , P{lacW}Indy302 ( FBti0003781 ) , sesB1 ( FBal0015434 ) , tko25t ( FBal0016812 ) , and w1118 ( FBal0018186 ) . | Human life expectancy is increasing in many populations . Research on aging has gained great attention recently by discoveries of mutations that slow down aging in relatively short-lived models . Studies carried out in yeast , worms , and flies have revealed evolutionarily conserved mechanisms of aging , which are therefore likely to be relevant to mammals , including humans . Therefore , they can provide an important stepping stone for more time-consuming and expensive experiments on mammals . Lifespan studies can be complicated by interactions of genes under study with the environment and with other genes . These effects can be substantially larger than the effects of some mutations with a bona fide effect on lifespan . Here , the authors studied aging in fruit flies using previously described long-lived mutants in the gene Indy , as positive controls for other experiments . Surprisingly , they discovered that Indy mutations do not increase lifespan when the genetic background effects are removed . Similarly , knockdown of genes with a similar function in worms do not increase lifespan in this study . The work presented provides an illustration of how genetic background , and possibly the presence of endosymbionts , can confound studies of the genetics of aging and lead to the spurious appearance of single gene effects on aging where none in fact exist . | [
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] | 2007 | No Influence of Indy on Lifespan in Drosophila after Correction for Genetic and Cytoplasmic Background Effects |
Escherichia coli and Salmonella enterica are models for many experiments in molecular biology including chemotaxis , and most of the results obtained with one organism have been generalized to another . While most components of the chemotaxis pathway are strongly conserved between the two species , Salmonella genomes contain some chemoreceptors and an additional protein , CheV , that are not found in E . coli . The role of CheV was examined in distantly related species Bacillus subtilis and Helicobacter pylori , but its role in bacterial chemotaxis is still not well understood . We tested a hypothesis that in enterobacteria CheV functions as an additional adaptor linking the CheA kinase to certain types of chemoreceptors that cannot be effectively accommodated by the universal adaptor CheW . Phylogenetic profiling , genomic context and comparative protein sequence analyses suggested that CheV interacts with specific domains of CheA and chemoreceptors from an orthologous group exemplified by the Salmonella McpC protein . Structural consideration of the conservation patterns suggests that CheV and CheW share the same binding spot on the chemoreceptor structure , but have some affinity bias towards chemoreceptors from different orthologous groups . Finally , published experimental results and data newly obtained via comparative genomics support the idea that CheV functions as a “phosphate sink” possibly to off-set the over-stimulation of the kinase by certain types of chemoreceptors . Overall , our results strongly suggest that CheV is an additional adaptor for accommodating specific chemoreceptors within the chemotaxis signaling complex .
Bacteria navigate in chemical gradients by regulating their flagellar motility . This behavior , known as chemotaxis , is characterized by high sensitivity and precise adaptation that are attributed to the underlying molecular machinery , which is best understood in the model organism Escherichia coli [1 , 2] . Dedicated chemoreceptors ( methyl-accepting chemotaxis proteins or MCPs ) , the CheW adaptor protein and the CheA histidine kinase form a self-organized protein complex [3–5] . Upon changes in concentrations of specific chemical cues , chemoreceptors modulate the kinase activity which in turn controls the flagella rotation via phosphorylation of the response regulator CheY [6] . Thus , MCPs , CheW , CheA , and CheY comprise an excitation pathway in chemotaxis which delivers the signal from a stimulus to the flagellar motor . The CheR methyltransferase and the CheB methylesterase that covalently modify MCPs encompass an adaptation pathway . Methylation enhances CheA activity , whereas demethylation reduces it [6] . The system also has the CheZ phosphatase , which dephosphorylates CheY leading to signal termination . E . coli has five chemoreceptors . Tar mediates attractant responses to aspartate and maltose [7 , 8] and negative chemotaxis to metals [9] . Tsr governs attractant responses to serine [7] and quorum sensing autoinducer AI-2 [10] , as well as chemotaxis to oxygen , redox , and oxidizable substrates [11 , 12] . Trg mediates attractant responses to ribose and galactose [13] . Tap initiates attractant responses to dipeptides [14] and pyrimidines [15] . Aer mediates responses to oxygen and energy taxis [11 , 12 , 16] Because of its close relatedness to E . coli , Salmonella enterica serovar Typhimurium has been a model for many experiments in chemotaxis and most of the results obtained with one organism have been generalized to another ( reviewed in [1 , 2 , 17 , 18] . The functional similarity among components of the chemotaxis system in the two species is remarkable [19] . However , there are also some noticeable differences . S . enterica has the CheV protein , which is not found in E . coli , and it also has a larger number of chemoreceptor genes than E . coli does . CheV is a fusion of the CheW domain with a response regulator domain similar to CheY . It is postulated to interact with chemoreceptors and CheA as a docking protein similarly to CheW and might play a role in signaling adaptation , as shown in another model organism , Bacillus subtilis [20 , 21]; however , the precise role of CheV is not understood [22] despite of being present in approximately 60% of all sequenced genomes with chemotaxis systems . In fact , all chemotaxis systems identified in prokaryotes contain either CheW or CheV or both [23] and experimental evidence established their role as coupling proteins ( also referred to as adaptors or scaffold proteins ) in several model organisms including E . coli [24] , S . enterica [25] , B . subtilis [20] , and Helicobacter pylori [26] . The CheW domain is topologically similar to SH3 domains [27] from eukaryotic scaffold proteins that also play a key role in signal transduction [28] . S . enterica lacks Tap , but has five chemoreceptors that are not present in E . coli . Tcp mediates attractant responses to citrate and repellent responses to phenol [29] . McpB and McpC mediate repellent responses to cysteine [30] . Function of two other chemoreceptors , Tip [31] and McpA [32] remains unknown . Why does E . coli have one adaptor and S . enterica has two ? Is there a connection between having an extra adaptor ( CheV ) and extra MCPs that are present in Salmonella ? We hypothesized that the function of CheV might be in accommodating certain types of MCPs that cannot be effectively accommodated by CheW . Here , we set up a series of comparative genomics studies to explore this hypothesis and to gain new insights about evolution and the biological function of the CheV protein in the chemotaxis protein complex .
In order to understand the differences that are observed in E . coli and S . enterica , we have analyzed the set of chemotaxis machinery components in all of their close relatives for which genome information was available . The 213 complete genomes of Enterobacteriales available in the MiST2 . 2 database [33] were collected and analyzed for the presence of chemotaxis genes ( S1 Table ) . Essentially all the genomes contain one copy of each of the key chemotaxis proteins: CheA , CheW , CheB , CheR and CheZ . The only exception was a subset of eight closely related Erwinia and Enterobacter species , where an apparent duplication of the nearly entire chemotaxis operon took place ( S1 Table ) . Consequently , these genomes were excluded from analysis . A non-redundant set of 43 genomes ( one representative of each species , randomly chosen , except for E . coli and S . enterica strains used as models in chemotaxis studies ) was analyzed further ( S1 Table ) . The only two variances among the chemotaxis systems of enterobacteria mirror those seen in E . coli and S . enterica: ( i ) the presence of CheV in some genomes and ( ii ) the number of MCP genes per genome ( S1 Table ) . On average , the analyzed genomes of Enterobacteriales contain 15 chemoreceptor genes per genome ( ranging from 2 in Enterobacter aerogenes and few other species to 42 in Pantoea ananatis ) . However , there was a major difference between genomes encoding CheV and genomes without CheV . Genomes without CheV contain on average only 5 chemoreceptor genes ( ranging from 2 to 9 ) ; whereas genomes with CheV contain on average 23 chemoreceptor genes ( ranging from 3 to 42 ) ( Fig 1 ) . The direct relationship between the large number of chemoreceptors and the presence of CheV suggests the hypothesis that the CheV adaptor might be necessary to accommodate certain chemoreceptors . This hypothesis is in line with the previous report that CheV might be a preferential adaptor for the aspartate chemoreceptor in Campylobacter jejuni [34] . To further investigate this hypothesis , we employed a series of comparative genomic approaches . Interpretation of results obtained by these methodologies strongly depends on the evolutionary history of the analyzed genes and the suitability of the dataset . For example , phylogenetic profiling would strongly benefit from independent events of gene loss in an analyzed dataset , because if the products of two genes interact , then the loss of one gene should coincide with the loss of another . Consequently , we analyzed the evolutionary history of the chemotaxis pathway in Enterobacteriales to ensure the dataset is suitable for this type of analysis . We have compared topologies of the maximum-likelihood phylogenetic trees built from ribosomal 16S gene and CheA protein sequences . The nearly identical overall tree topologies and consistency within corresponding clades on both trees strongly suggest that the chemotaxis system in Enterobacteriales evolved vertically without any instances of a horizontal transfer of the cheA gene ( S1 Fig ) . To understand the CheV evolution within Enterobacteriales , we have constructed a maximum-likelihood tree from aligned CheV protein sequences and compared its topology with that generated from CheA sequences ( S2 Fig ) . The nearly identical topology and consistency within clades indicate the ancestral origins and vertical evolution of CheV in Enterobacteriales suggesting that CheV was present in their last common ancestor . This means that enterobacterial genomes without the cheV gene lost it during the course of evolution . We took advantage of this relatively balanced sample of closely related genomes to perform comparative analysis of sequence profiles in order to gain insights into CheV biological function and to identify its potential interacting partners within the chemotaxis pathway . CheV has a response regulator domain ( CheVRR ) , which is homologous to CheY protein [20 , 22] . CheY can bind to P1 and P2 domains of CheA ( here called CheAP1 and CheAP2 respectively ) . The P1 domain ( also known as the histidine phosphotransfer or Hpt domain ) contains a conserved histidine , from which a phosphate group is transferred to CheY; the P2 domain was proposed to be a docking module for CheY [35] . Consequently , we considered the hypothesis that CheVRR can potentially bind to the same domains . The absence of CheV in the genome should change the conservation pattern in its interaction partners , CheA and MCPs , due to relaxing evolutionary pressure on residues that are involved in interaction with CheV . Analysis of multiple sequence alignment of CheAP2 domains of CheA ( S3 Fig ) shows that there is no significant difference in conservation pattern between sequences from genomes with and without CheV ( S4 Fig ) . This suggests that CheV does not interact with CheAP2 . Furthermore , CheAP2 is absent from many CheA proteins . We have analyzed more than 3000 bacterial and archaeal genomes for the presence and absence of CheV and the CheAP2 domain . We found no correlation between the presence of CheV and CheAP2 . There are 2252 genomes with at least one CheAP2 domain in the CheA sequences and 1772 genomes with at least one CheV . Only 729 of these genomes contain both CheAP2 and CheV , which provides evidence that CheV and CheAP2 do not co-evolve . Because interacting proteins and domains are likely to co-evolve ( 36 ) , observed distribution suggests that CheV does not interact with the CheAP2 domain . On the other hand , the analysis of conservation patterns in multiple sequence alignment of the CheAP1 domain ( S5 Fig ) in genomes with and without CheV shows a nearly absolute conservation between the two groups with only one position significantly different ( Fig 2A ) . The position 55 ( numbers for CheA protein in E . coli ) is occupied by a glycine in organisms with CheV , which is changed to an alanine in organisms without CheV . This observation indicated that the CheVRR domain might interact with CheAP1 . To explore this possibility further , we aligned the CheY proteins ( known to interact with CheAP1 ) from the genomes with CheV protein and compared with the alignment of the CheVRR domain from the same organisms ( S6 Fig ) . The conservation within each group ( CheY and CheVRR ) is very high , however , only 21 out of 127 positions ( less than 20% identity ) are shared by both groups and only 11 of these positions are accessible to solvent and thus may participate in the interaction ( Fig 2B ) . We mapped the relevant residues into the proposed interaction model between CheY and CheAP1 for E . coli [35] ( PDB code: 2LP4 ) as a model interaction between CheVRR and CheAP1 ( Fig 2C ) . The only significantly different position in CheAP1 domains from genomes with and without CheV , the Gly55 , lays on the C-terminal part of the second α-helix of the structure of CheAP1 close to the active site for CheY , His48 , within the known binding region of CheY in E . coli . Moreover , mapping the solvent exposed residues that are common to both CheY and CheVRR onto the CheY structure shows that they are localized primarily around the CheAP1 binding region ( Fig 2 ) . Taken together , these results support the hypothesis that CheVRR interacts with CheA via its P1 , but not P2 , domain . In addition to the response regulator domain , CheV also contains an adaptor domain CheW ( CheVW ) . Interestingly , the P5 domain of the histidine kinase ( CheAP5 ) , also known as the regulatory domain , is a CheW domain as well [17 , 27] . The current model for the arrangement of the chemotaxis protein complex encompassing CheA-CheW-MCP proposes two distinct interaction surfaces between CheAP5 domain and the CheW protein forming a CheW domain hexagonal ring with three CheA proteins and three CheW proteins [36 , 37] . As postulated above , we assume that CheV is an adaptor protein similarly to CheW . Then , it is reasonable to assume that CheVW would be a part of the same CheW domain network in the chemotaxis complex patch . Surprisingly , using the computational approach described above , we did not identify any significant difference in conservation pattern between the sequences of CheW proteins from genomes with and without CheV ( S7 Fig ) . The same result was obtained for the CheAP5 protein domain ( S7 Fig ) . Thus , these results do not support the idea that CheV participates in the complex array as a part of the CheW–CheAP5 hexagonal ring . On the other hand , it has been shown previously that CheW from evolutionarily distant species can rescue a system with a cheW knockout , despite the low level of identity between the homologs [38] Thus , an alternative explanation , which opens the possibility for CheVW to be a part of the array , is that the CheW fold evolved to maintain interactions between the adaptor domains CheVW , CheW and CheAP5 despite the low level of conservation at the residue level . This scenario is further supported by the facts that CheW is evolutionarily the most recent fold in the chemotaxis pathway [23] and that the CheW protein is highly dynamic [39]: both properties correlate with high evolvability and robustness–the molecule’s ability to evolve neutrally [40 , 41] . Similarity of the CheVW domain with CheW and CheAP5 suggests that CheV also interacts with chemoreceptors . In Enterobacteriales , chemoreceptors are the only genes of the chemotaxis pathway that are present as multiple homologs in a single genome . This may be a result of both ancestral and recent gene duplications as well as horizontal gene transfer . Therefore , in order to perform a meaningful phylogenetic profile analysis , it is necessary to classify all 644 chemoreceptor sequences in the analyzed enterobacterial pan-genome into orthologous groups . By matching all 644 chemoreceptor sequences in the non-redundant genome set to hidden Markov models designed for various length-classes of the chemoreceptor signaling domain [42] we determined that 599 chemoreceptor sequences belong to the 36H class ( the signaling domain consists of 36 helical heptads ) while 19 sequences belong to the 24H class ( the signaling domain consists of 24 helical heptads ) and 26 sequences remained unclassified . There was no correlation between the presence of CheV and chemoreceptors of a specific length-class . We then employed a principle of clusters of orthologous groups of proteins ( COGs ) [43] to obtain a higher resolution classification of chemoreceptors in enterobacteria ( see Materials and Methods for details ) . Resulting chemoreceptor COGs in enterobacteria are visualized in Fig 3 and COG assignments of E . coli K12 and S . enterica LT2 chemoreceptors are specified in Table 1 . The largest cluster of chemoreceptors ( COG1 ) contains Tsr , Tar and Tap , whereas the other two E . coli chemoreceptors belong to separate groups: Trg in COG6 and Aer in COG3 , which is consistent with recent phylogenetic studies [44] . The citrate sensor Tcp in S . enterica was found in COG1 ( Fig 3 , Table 1 ) , which is also consistent with previous findings showing its relatedness to Tsr and Tar [45] . As a final result , all 644 chemoreceptor sequences in the pan-genome of analyzed enterobacteria were assigned to 99 GOGs that contained from 161 member sequences ( COG1 ) to a single member sequence ( COG44 to COG99 ) ( S1 Dataset ) . We employed a principle of phylogenetic profiling to test a hypothesis that specific chemoreceptor COGs are linked to CheV . This method is based on the assumption that proteins that function together in a pathway or structural complex are likely to co-evolve [46] . We mapped instances of the presence and absence of CheV and all 99 chemoreceptor COGs onto the CheA phylogenetic tree in order to determine whether the presence of genes from any of the COGs correlate with presence of CheV in the genomes of Enterobacteriales ( S8 Fig ) . As a result , we have found the strongest correlation ( r = 0 . 77 ) between CheV and the second largest orthologous group–COG2 , exemplified by the S . enterica McpC chemoreceptor ( Fig 4 ) , which suggests that chemoreceptors of COG2 need CheV to function optimally . We have further tested this hypothesis by using genomic context methods postulating that if two proteins interact , then in some genomes their genes can be fused or located adjacent to each other on the chromosome [47] . While we detected no fusion events between cheV and mcp genes in Enterobacteriales , the gene neighborhood analysis revealed that in two Pantoea genomes the cheV gene was adjacent to the mcp gene ( locus tags Pat9b_0852/Pat_9b_0851 and Pvag_0292/Pvag_0291 ) . Both mcp gene products belong to COG2 ( S1 Dataset , S9 Fig ) , which further strengthens our hypothesis . No other cases of cheV and mcp gene neighborhood were found in the analyzed dataset . We also mined a rich transcriptomic compendium for S . enterica serovar Typhimurium [48] in search for co-expression patterns between cheV and any of the mcp genes . We found no correlation between expression levels of a specific adaptor ( CheW or CheV ) and any MCP; however , interestingly , McpC appears to be a high-abundance chemoreceptor in Salmonella , similarly to Tar and Tsr ( S2 Dataset ) . If our hypothesis is correct , we expect that the COG2 group of receptors has unique features detectable as specific conservation patterns in chemoreceptor sequences from this group relative to other groups . Comparing chemoreceptors from COG2 and those from other COGs known to work with CheW might suggest which of these unique features are related to the interaction with CheV . We can assume with confidence that receptors from COG1 utilize CheW as an adaptor—E . coli has three out of five receptors from COG1 and does not have CheV . Thus , if COG1 chemoreceptors utilize CheW and not CheV , but COG2 chemoreceptors utilize CheV instead of or in addition to CheW , then COG1 and COG2 chemoreceptors should have group-specific conserved positions in their signaling domains responsible for the interaction with different adaptors . We constructed multiple sequence alignment of the signaling domains from COG1 and COG2 sequences , as well as from COG6 sequences ( S9 Fig ) . We used COG6 , the group containing the product of the trg gene from E . coli and S . enterica , as a control , because Trg is known to only utilize CheW and it has the same membrane topology as COG1 and COG2 , in contrast to COG3 ( exemplified by the E . coli Aer chemoreceptor ) , which is also known to interact with CheW but has a different membrane topology . In order to avoid evolutionary bias , we selected sequences only from organisms that have chemoreceptors from COG1 , COG2 and COG6 as well as CheV proteins , ( see Materials and Methods ) . Positions that are highly conserved ( >90% identity ) in COG1 and COG6 , but differently highly conserved ( >90% identity ) in COG2 are likely to be important for the interaction between COG2 receptors and CheV . Surprisingly , there is only one position in the alignment that has the aforementioned characteristics: position 278 ( numbers are given for the E . coli Tar chemoreceptor ) is conserved in COG1 and COG6 as a glycine , and is also conserved in COG2 but as an alanine ( Fig 5A , S2 Table ) . The position Gly278 lays away from the postulated adaptor binding site in the chemoreceptor structure: approximately from Asp365 to Leu415 [49 , 50 , 51] and is unlikely to be the CheV binding site on the chemoreceptor . Interestingly , this position has been a target of intense mutagenesis and is known to dramatically increase the kinase activity upon mutation to any other amino acid . In fact , mutations at the Gly278 site , including the alanine substitution , show the highest activation of the kinase in E . coli/Samonella chemotaxis system to date [52] . In addition , our recent molecular dynamic simulation study showed Gly278 as the site of the chemoreceptor with highest propensity for bending [53] . The bending mechanism of the chemoreceptor has been proposed to influence and even control the kinase activity in several studies [54 , 55] . Thus , we predict that McpC and other chemoreceptors from COG2 that have Ala instead of Gly in position 278 tend to naturally increase the level of kinase activity in comparison to other chemoreceptors . Within the proposed adaptor binding region , which shows overall extreme conservation not only among enterobacteria , but across prokaryotes [42] , only one position , 406 , has a unique type of distribution–conserved glutamine in COG2 and a glutamine/serine mix in COG1 and COG6 ( Fig 5B ) –which contrast to the norm that overall , COG6 is more conserved than COG1 , which is more conserved than COG2 . It is striking that among 50 amino acid positions in this highly evolutionarily constrained region , 49 positions had higher information content in COG1 and only 1 position had higher information content in COG2 ( S10 Fig ) . We hypothesize that having a serine in the position 406 might increase the binding affinity between CheVW and the chemoreceptor . This single difference among the highly conserved region of protein interaction suggests that CheVW must have a mix of highly conserved residues in common with CheW protein and some that must be different and yet conserved among CheV proteins in the vicinity of the adaptor binding region for chemoreceptors due to some specificity towards receptors from COG2 . We aligned sequences of CheW proteins and CheVW domains from the non-redundant set of Enterobacteriales genomes ( S11 Fig ) . Only sequences from organisms with CheV and CheW genes were selected to build sequence logos used to identify conservation patterns between these two groups ( Fig 6A ) . We then mapped positions that are 100% conserved between and within CheW and CheVW sequences onto the CheW NMR model ( PDB code: 2HO9 ) [57] ( Fig 6B ) . Both types of residues are located in the solvent exposed central groove between the two β-barrel subdomains , which has been implicated in the interaction of CheW with chemoreceptors [24 , 50 , 58] . Residues forming the Arg62-Glu38 salt bridge , which was suggested to maintain a specific geometry between chemoreceptor and kinase binding sites on CheW [39] , were universally conserved in CheW and CheVW ( Fig 6 ) . These results suggest that the predicted chemoreceptor interaction region of the adaptor structure is conserved in both CheW and CheVW domains and contains a set of residues conserved in both adaptors and a set of residues uniquely conserved in each adaptor family . This is line with the previous findings [22 , 26] and supports the hypothesis that CheW and CheVW share the same binding spot on chemoreceptors , but have some affinity bias towards chemoreceptors from different orthologous groups . It is known that mixed teams of chemoreceptors come together to form a single cluster in organisms with a single chemotaxis array [59] . Based on our findings we suggest that CheV is necessary to accommodate chemoreceptors from COG2 in the chemotaxis array . Because of the uniquely conserved alanine in the position 278 in COG2 chemoreceptors , we propose that as these receptors are incorporated into the chemotaxis protein cluster , the base level of kinase activity increases , because position 278 in these receptors is occupied exclusively by alanine ( a change from a uniformly conserved glycine to alanine in this position in COG1 chemoreceptors elevates the kinase activity ) . As previously shown , the presence of CheV in other chemotaxis systems influences the levels of phosphorylated CheY ( CheY-P ) [22] and our results suggest that in enterobacteria , CheVRR specifically interacts with CheAP1 , a known CheY-interacting domain . Thus , we propose that CheV might work as a phosphate sink [60] “stealing” the extra phosphor groups from CheAP1 ( resulting from over-stimulation of the kinase by COG2 chemoreceptors ) before they can reach CheY and consequently normalizing the overall CheY-P concentration downstream of the system . Interestingly , based on experimental evidence the role of a phosphate sink for CheV was previously suggested in H . pylori [61] and mentioned as a possibility in B . subtilis [20] . In order for this mechanism to work , we anticipate that precise positioning of CheV relative to CheA and CheW might not be essential given the stochastic nature of the chemotaxis system and that only the overall concentration of CheY-P needs to be controlled . Our lack of support for a hypothetical CheVW−CheW/CheAP5 interaction appears to be in contrast with our findings strongly suggesting that CheV interacts with chemoreceptors in the same binding region as CheW and CheAP5 . However , the latest model for chemotaxis array assembly predicts an “empty” chemoreceptor hexagonal ring neighboring a CheA-CheW filled hexagonal ring with three kinases and three CheWs [36 , 37] . In line with this model and our findings , we propose two competing models that differ solely on whether the CheVW−CheW/CheAP5 interaction takes place or not . We propose that CheV is incorporated in the chemotaxis array , by either ( i ) fully occupying one of the “empty” rings ( Fig 7A ) or ( ii ) mixing with the hexagonal ring made of CheW and CheAP5 ( Fig 7B ) . In fact , the conservation of position 406 in COG2 chemoreceptors suggests that this position might determine whether the chemoreceptor will be facing the kinase/CheW or CheV . Clearly , only experimental verification can provide support for or against this hypothesis and help distinguishing between the two competing models for CheV positioning with the signaling array . In summary , we tested a hypothesis that in enterobacteria CheV functions as an additional adaptor linking the CheA kinase to certain types of chemoreceptors that cannot be effectively accommodated by the universal adaptor CheW . Phylogenetic profiling , genomic context and comparative protein sequence analyses suggested that CheV interacts with chemoreceptors from an orthologous group COG2 exemplified by the Salmonella McpC protein . The biological function for CheV proposed here should be taken with caution when extrapolated to organisms outside enterobacteria . The chemotaxis system of F7 class ( classification according to [23] ) in enterobacteria differs dramatically from the F1 system in B . subtilis or the F3 system in H . pylori , both are model organisms to study CheV [20–22 , 26 , 61] . While we observed the direct relationship between the large number of chemoreceptors and the presence of CheV in enterobacteria , outliers are present both in and outside this group of organisms , For example , the model organism H . pylori has only four chemoreceptors and three CheV proteins [26] . Nevertheless , while the model for CheV interaction with the signaling array proposed here might not be generally applicable to other systems , the postulate that an additional adaptor , such as CheV , is necessary to incorporate certain types of chemoreceptors into the signaling array is likely to be broadly relevant .
The primary source of data in this study is the MiST2 . 2 database [33] including pre-computed domain counts , classification of chemotaxis genes , protein and ribosomal 16S sequences . CheA and CheV proteins were assigned to chemotaxis classes [23] using previously described hidden Markov models [62] and the HMMER v3 . 0 software package [63] . Chemoreceptors were assigned to heptad classes using previously described hidden Markov models [42] using HMMER v2 . 0 [64] . Sequence alignments were built using L-INSI-I algorithm from MAFFT v6 . 864b package [65] . Phylogenetic trees were constructed using PhyML v3 . 0 [66] . Figures and calculations were produced by custom made scripts using ggplot2 [67] package for R language and NetworkX v1 . 8 . 1[68] and Numpy [69] modules for Python . Information content logos were built using Weblogo 3 . 0 [70] . Maximum likelihood phylogenetic trees of protein sequences were built using PhyML with the following options: JTT model , empirical amino acid frequencies , 4 substitution categories , estimated gamma distribution parameter and subtree pruning and regrafting ( SPR ) topology search . Maximum likelihood phylogenetic tree of the ribosomal 16S DNA sequences was built using PhyML with the following options: GTR model , 20 substitution categories , estimated gamma distribution parameter and subtree pruning and regrafting ( SPR ) topology search . Potential gene fusion events and gene neighborhoods of cheV genes were visualized and analyzed using the MiST database [33] . Expression data for chemotaxis genes was compiled from the Salmonella gene expression compendium [48] . To obtain clusters of orthologous groups of MCPs , all chemoreceptor sequences were compared to each other using all-versus-all BLAST [71] . Two sequences were merged into a cluster if the E-value of the reciprocal best BLAST hit was below selected threshold of 10E-30 with 95% length coverage . Any given sequence with a reciprocal best BLAST hit to a sequence from a cluster became a member of this cluster . If a sequence had BLAST hits to sequences from two clusters , the clusters were merged . In a graphical representation of clustering , each cluster ( COG ) is represented independently of each other using the algorithm Neato from the NetworkX module for Python , where distances between nodes ( sequences ) are calculated based on connectivity within the cluster ( number of reciprocal best BLAST hits with the other members of the cluster ) . The edges connecting the nodes are all equivalent , reflecting the binary ( reciprocal best BLAST hit or not ) nature of the graph . Thus , nodes with high connectivity are central while nodes with less connectivity tend to be placed in peripheral regions of the graph . | Due to the overwhelming complexity and diversity of biological systems , the functional roles of the majority of proteins encoded in sequenced genomes remain unknown or poorly understood . The multi-protein pathway controlling chemotaxis in bacteria and archaea is an example of such complexity and diversity . Chemotaxis pathway in E . coli is one of the best understood signal transduction networks in nature; however , this model organism lacks some of the system components , such as CheV , that are found in many other species . The biological role of CheV is still under avid debate . CheV is an auxiliary component of many chemotaxis systems and is present in important human pathogens , such as Salmonella and Helicobacter , where chemotaxis is being studied as an important virulence trait . Here we established the evolutionary history of the chemotaxis pathway in enterobacteria and combined a computational genomics approach with available structural information to propose a role for CheV . Our results show that CheV in enterics evolved as an adaptor for a specific type of chemoreceptors . Furthermore , we propose that some CheV-associated chemoreceptors might increase the kinase activity above the base level , and in these cases CheV acts as an attenuator . | [
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] | 2016 | Evolutionary Genomics Suggests That CheV Is an Additional Adaptor for Accommodating Specific Chemoreceptors within the Chemotaxis Signaling Complex |
The hyaluronidase enzyme is generally known as a spreading factor in animal venoms . Although its activity has been demonstrated in several organisms , a deeper knowledge about hyaluronidase and the venom spreading process from the bite/sting site until its elimination from the victim's body is still in need . Herein , we further pursued the goal of demonstrating the effects of inhibition of T . serrulatus venom ( TsV ) hyaluronidase on venom biodistribution . We used technetium-99m radiolabeled Tityus serrulatus venom ( 99mTc-TsV ) to evaluate the venom distribution kinetics in mice . To understand the hyaluronidase’s role in the venom’s biodistribution , 99mTc-TsV was immunoneutralized with specific anti-T . serrulatus hyaluronidase serum . Venom biodistribution was monitored by scintigraphic images of treated animals and by measuring radioactivity levels in tissues as heart , liver , lungs , spleen , thyroid , and kidneys . In general , results revealed that hyaluronidase inhibition delays venom components distribution , when compared to the non-neutralized 99mTc-TsV control group . Scintigraphic images showed that the majority of the immunoneutralized venom is retained at the injection site , whereas non-treated venom is quickly biodistributed throughout the animal’s body . At the first 30 min , concentration peaks are observed in the heart , liver , lungs , spleen , and thyroid , which gradually decreases over time . On the other hand , immunoneutralized 99mTc-TsV takes 240 min to reach high concentrations in the organs . A higher concentration of immunoneutralized 99mTc-TsV was observed in the kidneys in comparison with the non-treated venom . Further , in situ neutralization of 99mTc-TsV by anti-T . serrulatus hyaluronidase serum at zero , ten , and 30 min post venom injection showed that late inhibition of hyaluronidase can still affect venom biodistribution . In this assay , immunoneutralized 99mTc-TsV was accumulated in the bloodstream until 120 or 240 min after TsV injection , depending on anti-hyaluronidase administration time . Altogether , our data show that immunoneutralization of hyaluronidase prevents venom spreading from the injection site . By comparing TsV biodistribution in the absence or presence of anti-hyaluronidase serum , the results obtained in the present work show that hyaluronidase has a key role not only in the venom spreading from the inoculation point to the bloodstream , but also in venom biodistribution from the bloodstream to target organs . Our findings demonstrate that hyaluronidase is indeed an important spreading factor of TsV and its inhibition can be used as a novel first-aid strategy in envenoming .
Scorpionism is considered a serious public health threat and was officially recognized as a neglected tropical disease by the Brazilian Academy of Sciences [1] . In Brazil , scorpion sting reports have been increasing over the years , reaching 90 , 000 accidents in 2016 , and outnumbering the accidents caused by other venomous animals such as spiders and snakes [2] . The yellow scorpion Tityus serrulatus ( Ts ) ( Lutz and Mello Campos , 1922 ) is considered the most venomous scorpion in South America [3–5] , causing serious envenomation accidents mainly in southeast Brazil [6] and representing the species of greatest medical-scientific importance in the country . The symptomatology of scorpionism involves local pain , which can be associated with nausea , sweating , tachycardia , fever , and stirring . Moderate complications may include epigastric pain , cramps , vomiting , hypotension , diarrhea , bradycardia , and dyspnea . Severe envenoming may present several potentially lethal complications , such as cardio-respiratory failure [7–11] . These symptoms are related to the synergic action of a variety of toxic components present in the venom . Ts venom ( TsV ) consists of a complex mixture of components such as mucus , lipids , amines , nucleotides , inorganic salts , hyaluronidases , serine proteases , metalloproteases , natriuretic peptides , bradykinin potentiating peptides , antimicrobial peptides , high molecular weight ( Mw ) proteins , and ion channel active neurotoxins , which are the major toxic components [12–24] . Hyaluronidases are extensively found in the venoms of various animals such as snakes , scorpions , spiders , and others [25] . Venom hyaluronidases are always referred to as "spreading factors" [26 , 27] , as they hydrolyze the hyaluronic acid ( HA ) present in the interstitial matrix , thus helping the venom toxins to reach the victim’s bloodstream and invade its organism . Hyaluronidase’s enzymatic action increases membrane absorbency , reduces viscosity , and makes tissues more permeable to injected fluids ( spreading effect ) . Therefore , hyaluronidase acts as a catalyst for systemic envenoming [25] . TsV hyaluronidase activity was first demonstrated by Possani’s group [28] , and the enzyme was later isolated and partially characterized by Pessini and collaborators [14] . Horta et al . [16] further expanded these studies by performing extensive molecular , biological , and immunological characterization of TsV hyaluronidase . The authors described the sequence of two enzyme isoforms showing 83% identity , Ts-Hyal-1 and Ts-Hyal-2 , by cDNA analysis of the venom gland . A purified native Ts hyaluronidase was used to produce anti-hyaluronidase serum in rabbits . Epitopes common to both isoforms were mapped , and it was shown that they include active site residues . Most importantly , it was demonstrated for the first time that in vivo neutralization assays with anti-hyaluronidase serum inhibited and delayed mouse death after injection of a lethal dose of TsV , thus confirming the influence of hyaluronidase in TsV lethality [16] . The active recombinant hyaluronidase Ts-Hyal-1 from TsV was produced and characterized . It is an important biotechnological tool for the attainment of sufficient amounts of the enzyme for structural and functional studies [29] . Herein , we further pursued the goal of demonstrating the effects of inhibition of TsV hyaluronidase on venom biodistribution . Our results show that inhibition of the hyaluronidase activity of TsV in mice hinders venom spreading from the injection site as well as its biodistribution to the tissues .
T . serrulatus scorpions were collected in Belo Horizonte , Minas Gerais , Brazil , with proper licensing from the competent authorities ( IBAMA , Instituto Brasileiro do Meio Ambiente e dos Recursos Naturais Renováveis , protocol number 31800–1 ) . Venom was obtained from female scorpions regularly milked twice a month by electrical stimulation of telson . After extraction , venom was solubilized in ultrapure water and centrifuged at 16 , 000g at 4°C for 10 min . The supernatant was quantified using Bio-Rad “Protein DC assay” kit [30] , and stored at -20°C until use . Female Swiss CF1 mice ( 6–8 weeks old , 24–28 g ) were obtained from the animal care facilities ( CEBIO ) of the Federal University of Minas Gerais ( UFMG ) . Animals had free access to water and food and were kept under controlled environmental conditions . The Ethics Committee ( Comissão de Ética no Uso de Animais , CEUA ) of UFMG certifies that the procedures using animals in this work are in agreement with the Ethical Principals established by the Brazilian Council for the Control of Animal Experimentation ( CONCEA ) . Protocol number 05/2016 . Approved: March 8 , 2016 . Rabbit anti-hyaluronidase and pre-immune sera used in this work were produced by Horta et al . [16] . Hyaluronidase activity was measured according to the turbidimetric method described by Pukrittayakamee et al . [31] with modifications [16] . The assay mixture contained 12 . 5 μg of HA ( Sigma-Aldrich ) , acetate buffer ( 0 . 2 M sodium acetate-acetic acid pH 6 . 0 , 0 . 15 M NaCl ) , and test ( or control ) sample in a final volume of 250 μl . Commercial hyaluronidase from bovine testis ( 12 . 5 μg; Apsen ) was used as a positive control , and ultrapure water was used as a negative control . Assay mixtures were incubated for 15 min at 37°C , and reactions were stopped by adding 500 μl of stop solution containing 2 . 5% ( w/v ) cetyltrimethylammonium bromide ( CTAB ) dissolved in 2% ( w/v ) NaOH . Assays were monitored by absorbance at 400 nm against a blank of acetate buffer ( 250 μl ) and stop solution ( 500 μl ) . Turbidity of the samples decreased proportionally to the enzymatic activity of hyaluronidase . Values were expressed as percentages of hyaluronidase activity relative to the negative ( no addition of enzyme , 0% activity ) and positive ( addition of commercial enzyme , 100% activity ) controls . The tested samples were native hyaluronidase purified from TsV ( 0 . 5 μg , produced by Horta et al . [16] ) , TsV ( 2 μg ) , TsV neutralized with rabbit pre-immune serum ( 2 μg of TsV incubated for 1 h at 37°C with 10 μl of pre-immune serum ) , TsV neutralized with anti-hyaluronidase serum ( 2 μg of TsV incubated for 1 h at 37°C with 10 μl of anti-hyaluronidase serum ) . To label TsV with technetium-99m ( 99mTc; IPEN São Paulo ) , a sealed vial containing 200 μg of SnCl2 . H2O solution in 0 . 25 mol/l HCl ( 2 mg/ml ) and 50 μg of NaBH4 solution in 0 . 1 mol/l NaOH ( 1 mg/ml ) was prepared . The pH was adjusted to 7 . 4 using 1 mol/l NaOH . Next , 25 μl of TsV ( 5 g/l in saline 0 . 9% w/v ) was added , and vacuum was applied to the vial , followed by addition of 0 . 1 ml of Na99mTcO4 ( 3 . 7 MBq ) . The solution was kept at room temperature for 15 min . Radiochemical purity was determined by thin layer chromatography ( TLC-SG , Merck ) using acetone as the mobile phase to quantify 99mTcO4- . Strips radioactivity was determined by a gamma counter ( Wallac Wizard 1470–020 Gamma Counter , PerkinElmer Inc . ) . 99mTcO2 was removed from the preparation using a 0 . 45 μm syringe filter [32] . Tests in saline 0 . 9% ( w/v ) and in mice plasma were performed to evaluate the stability of the radiolabeled complex 99mTc-TsV . TLC-SG was used to evaluate the stability of the radiolabeled complex diluted in saline . The labeled solution was kept at room temperature , and aliquots were taken at 1 , 2 , 4 , 6 and 24 h for determination of radiochemical purity . A volume of 90μl 99mTc-TsV solution was incubated with 1 ml of fresh mouse plasma at 37°C under agitation . Radiochemical stability was determined by TLC-SG from samples taken at 1 , 2 , 4 , 6 and 24 h after incubation . An amount equivalent to 3 . 7 MBq of 99mTc-TsV was diluted ( 10% v/v ) in phosphate-buffered saline ( PBS; control ) or anti-hyaluronidase serum and incubated for 1 h at 37°C . Then , 50 μl of the samples were intramuscularly injected into the right tight of healthy Swiss mice ( 6–8 weeks old , 24–28 g; n = 6 per group ) . Mice were anesthetized with a mixture of xylazine ( 15 mg/kg ) and ketamine ( 80 mg/kg ) , and an incision was made in the animals’ tails for blood collection in pre-weighed tubes at 1 , 5 , 10 , 15 , 20 , 30 , 45 , 60 , 90 , 120 , 240 , and 1440 min after administration of the samples . The tubes were weighted , and their radioactivity determined by a gamma counter . These data were used to plot the percentage of dose injected per gram tissue ( % ID/g ) versus time . Aliquots of 18 MBq of 99mTc-TsV in 10% ( v/v ) PBS ( control ) or anti-hyaluronidase serum ( pre-incubated for 1 h at 37°C ) were intramuscularly injected ( 50 μl ) into the right tight of healthy Swiss mice ( 6–8 weeks old , 24–28 g; n = 3 per group ) . Animals were anesthetized at 30 , 60 , and 120 min after sample administration with a mixture of ketamine ( 80 mg/kg ) and xylazine ( 15 mg/kg ) and placed horizontally under a gamma camera ( Nuclide TM TH 22 , Mediso ) . Images were collected with a Low Energy High Resolution ( LEHR ) collimator and 256x256x16 dimension matrices with a 300 s acquisition time , using a 20% symmetrical window with a fixed energy peak at 140 KeV . Aliquots of 3 . 7 MBq of 99mTc-TsV in 10% ( v/v ) PBS ( control ) or anti-hyaluronidase serum ( pre-incubated for 1 h at 37°C ) were intramuscularly injected ( 50 μl ) into the right tight of healthy Swiss mice ( 6–8 weeks old , 24–28 g; n = 6 per group ) . Mice were euthanized at 30 , 60 , 240 , and 1440 min post-injection , and heart , liver , lungs , spleen , thyroid , and kidneys were dissected , dried with filter paper , and weighed . The radioactivity in each tissue was determined by a gamma counter . A standard dose containing the same injected amount of 99mTc-TsV was counted simultaneously in a separate tube , which was defined as 100% radioactivity . The results were expressed as the percentage of injected dose per gram of tissue ( %ID/g ) . An amount equivalent to 3 . 7 MBq of 99mTc-TsV diluted in PBS was intramuscularly injected ( 25 μl ) into the right tight of healthy Swiss mice ( 6–8 weeks old , 24–28 g; n = 6 per group ) . Next , anti-hyaluronidase serum was inoculated ( 25 μl , intramuscularly ) into the same site of 99mTc-TsV injection at different time-points ( 0 , 10 , and 30 min post-injection of 99mTc-TsV ) . Mice were anesthetized with a mixture of xylazine ( 15 mg/kg ) and ketamine ( 80 mg/kg ) , and an incision was made in the animals’ tails for blood collection in pre-weighed tubes at 1 , 5 , 10 , 15 , 20 , 30 , 45 , 60 , 90 , 120 , and 240 min after administration of 99mTc-TsV . The tubes were weighted , and their radioactivity determined by a gamma counter . Data were used to plot the percentage of dose injected per gram tissue ( % ID/g ) versus time . Sample sizes were calculated using G Power version 3 . 1 . To compare multiple means , the sample size was calculated considering alpha ( α ) , power effect , effect size ( f ) , and population size ( n ) . To estimate number of mice needed in the 99mTc-TsV biodistribution assays , parameters were set at f = 0 . 7 , α = 0 . 05 , power = 0 . 8 , and groups = 4 . Data were expressed as mean ± S . E . M . Graphs were plotted using the software GraphPad PRISM version 5 . 00 ( La Jolla , CA , USA ) . All statistical tests were carried out on R version 3 . 4 . 4 . Significance level was set at 0 . 05 , and tests were performed two-sided . Effect of serum administration and time on the mean 99mTc-TsV biodistribution was evaluated using two-way ANOVA . Normality and equal variance suppositions were assessed using Shapiro-Wilk and Levene’s tests , respectively . Effects of serum administration and time on 99mTc-TsV mean blood clearance were analyzed using a linear mixed model ( lme function on nlme package ) .
In the turbidimetric assays , commercial hyaluronidase from bovine testis exhibited high hyaluronidase activity ( 99 , 98 ± 0 , 01% activity ) , which was referred to as 100% activity ( positive control ) . Ultrapure water had no enzyme activity ( -0 , 003 ± 0 , 002% activity ) , which was referred to as 0% activity ( negative control ) . TsV ( 99 , 57 ± 0 , 16% activity ) and native hyaluronidase purified from TsV ( 99 , 73 ± 0 , 11% activity ) presented high enzymatic activity , similar to that observed for the positive control ( Fig 1 ) . In the in vitro neutralization assay , pre-incubation of TsV with anti-hyaluronidase serum completely neutralized hyaluronidase activity ( 0 , 005 ± 0 , 003% activity ) . Rabbit pre-immune serum was used to test unspecific neutralization of the venom content and did not neutralize TsV enzymatic activity ( 99 , 39 ± 0 , 24% activity ) ( Fig 1 ) . Western blot results for anti-hyaluronidase and pre-immune sera to TsV are shown in supporting information ( S1 Methods , S1 Fig ) . The radiochemical efficiency of the TsV labeling with technetium-99m was determined by TLC . The results indicated high radiochemical yield ( 95 . 2 ± 2 . 4% ) . Radiochemical yields higher than 90% are recommended for in vivo application of radiopharmaceuticals [33] . Therefore , our 99mTc-TsV complex presented suitable radiochemical characteristics , which encouraged further in vivo studies . The radiolabeling stability curve for 99mTc-TsV is shown in Fig 2 . Stability tests were performed after 1 , 2 , 4 , 6 and 24 h of incubation of 99mTc-TsV in saline 0 . 9% ( w/v ) at room temperature or in fresh mouse plasma at 37°C . High stability was observed over long periods of time ( >95% ) , thus indicating suitability for further biodistribution assays . Blood clearance of 99mTc-TsV diluted in PBS or pre-neutralized with anti-hyaluronidase serum is shown in Fig 3 . Following the injection into healthy Swiss mice , the 99mTc-TsV complex showed quick absorption , reaching the highest bloodstream levels after 30 min . After this time point , 99mTc-TsV concentration in the bloodstream decreases , which indicates biodistribution to the tissues . In contrast , the 99mTc-TsV complex pre-neutralized with anti-hyaluronidase serum reaches lower levels in the bloodstream compared to the 99mTc-TsV in PBS . This result shows that neutralization of TsV hyaluronidase activity significantly reduces TsV spreading from the injection site to the blood circulation . Scintigraphic images of the mice corroborated the blood clearance results and showed that 99mTc-TsV diluted in PBS quickly spreads from the injection site in the right tight muscle to the whole body between 30 and 120 min after TsV injection ( Fig 4A ) . On the other hand , when 99mTc-TsV was neutralized with anti-hyaluronidase serum , TsV spreading from the injection site was visibly reduced at all times evaluated . Noteworthy , the labeled neutralized TsV remained at the site of injection ( right tight muscle ) ( Fig 4B ) . Regarding the kinetics of TsV spreading , high levels of 99mTc-TsV diluted in PBS were absorbed by the organs , particularly the kidneys and bladder ( Fig 4A ) , while lower levels of 99mTc-TsV neutralized with anti-hyaluronidase serum reached these tissues over time ( Fig 4B ) . Tissues such as heart , liver , lungs , spleen , and thyroid displayed different uptake levels of 99mTc-TsV diluted in PBS and 99mTc-TsV neutralized with anti-hyaluronidase serum ( Fig 5 ) . Higher concentration of 99mTc-TsV diluted in PBS was initially detected in these tissues ( 30 min ) , indicating a quick biodistribution of TsV from the injection site to the bloodstream and subsequently to the organs . After reaching the organs , labeled TsV concentration decreased over time , thus suggesting the elimination of the radiolabeled complex from the mice’s body . In agreement , 99mTc-TsV levels increased in the kidneys , pointing towards renal elimination . In contrast , the neutralized 99mTc-TsV was initially detected at lower concentrations in the heart , liver , lungs , spleen , and thyroid , only reaching high levels at 240 min post-injection , after which time levels begin to decrease again ( Fig 5 ) . Thus , hyaluronidase inhibition delays TsV incorporation into the bloodstream and organs . Moreover , lower concentrations of neutralized TsV reached the kidneys , when compared with 99mTc-TsV diluted in PBS . A blood clearance test was performed to evaluate the efficiency of anti-hyaluronidase serum to neutralize previously injected TsV in situ , thus simulating a first-aid treatment for scorpion sting . Animals injected with 99mTc-TsV diluted in PBS ( control group ) presented a quick absorption of TsV from the injection site into the bloodstream , followed by a decrease in blood concentration ( Fig 6 ) , corroborating the results previously observed in the blood clearance assay ( Fig 3 ) . On the other hand , animals treated with anti-hyaluronidase serum injected in the right tight muscle at 0 , 10 , and 30 min post-injection of 99mTc-TsV diluted in PBS showed higher concentrations of labeled TsV in the bloodstream over time at all times tested ( Fig 6A–6C ) . The increased concentration of neutralized TsV in the bloodstream indicates hindered biodistribution to the tissues ( Fig 6 ) . Altogether , our data reveal the potential use of hyaluronidase inhibition as a novel first-aid strategy in envenoming .
Hyaluronidases are involved in different processes such as inflammation , angiogenesis , embryogenesis , wound healing , tumor growth and progression , and systemic diffusion of venom toxins [34–38] . The role of hyaluronidases in venom spreading has been investigated , as this enzyme is a component vastly described both in vertebrate and invertebrate animal venoms . Specifically , for scorpions , a search in NCBI protein database reveals at least 20 hyaluronidase protein sequences already described for 15 different species [39] . However , no study so far has shown how hyaluronidase activity interferes with venom distribution [16 , 17 , 40–44] . Previous studies have demonstrated the role of hyaluronidase in venom dissemination [36 , 44] , in enhancing the effects of hemorrhagic toxins from snake venoms [27 , 37] , in triggering allergic reactions to bee and wasp venoms [45–47] , and in increasing the effects of other toxins from spider and scorpion venoms [14 , 48] . Moreover , Horta and collaborators [16] have greatly advanced the understanding of hyaluronidase in T . serrulatus through characterization studies showing the role of this enzyme in venom lethality . However , evidence demonstrating the role of hyaluronidases in venom spreading and describing the steps from scorpion sting to venom elimination was still lacking . In the present study , we demonstrated how the inhibition of TsV hyaluronidase activity using anti-hyaluronidase serum affects venom biodistribution . Our data show that TsV distribution kinetics is fast and efficient . TsV is distributed from the injection site to the bloodstream and organs in the first 30 min post-injection ( Figs 3 , 4A and 5 ) . The biodistribution is not uniform for all tissues . After that time , the level of TsV decreases in the organs ( Fig 5 ) and increases in the kidneys ( Figs 4A and 5 ) , thus indicating a renal route of elimination of the radiolabeled complex 99mTc-TsV . This shows that the venom is quickly biodistributed from the injection site to target organs such as heart , liver , lungs , and spleen , where it activates receptors and other biological targets . The binding triggers signaling cascades that culminate in all the symptomatology of scorpion sting , including the potentially lethal cardiogenic shock and pulmonary oedema . Following biodistribution , renal excretion is an important route of elimination of TsV from the organism [49 , 50] . Previous studies have used a toxic fraction isolated from T . serrulatus venom radiolabeled with 99m-Tc ( 99m-TcTsTx ) for biodistribution assays in young rats [49] . In that study , it was observed that the isolated fraction is not regularly distributed , it is first detected at low levels in the organs and reaches its maximum concentration in the brain , heart , thyroid , lungs , spleen , liver , and blood after 60 to 180 min . In the kidneys , the highest concentration of 99m-TcTsTx is detected 360 min after injection [49] . The biodistribution and elimination of TsTx are slower compared to that of the total venom observed in the present study and may be explained by the low molecular weight of the TsTx fraction ( ~7 kDa ) that lacks hyaluronidase ( ~ 50 kDa ) in its composition . Studies of venom biodistribution and neutralization of circulating venom allow a better understanding of the pathophysiology of envenomation , especially through the determination of venom levels in tissues [49 , 51–55] . TsV is composed mainly by low molecular weight neurotoxic peptides which modulate Na+ , K+ , Ca2+ and Cl- channels in excitable membranes , thereby causing a massive release of neurotransmitters and stimulation of the autonomic nervous system . The synergism of various toxins from TsV is responsible for its deleterious effects [18 , 50] . In the present work , we observed that the inhibition of hyaluronidase in TsV caused by anti-hyaluronidase serum delays the process of venom distribution . Higher levels of TsV are detected at the injection site , and reduced levels are detected in the organs at early times ( 30 min ) when compared to the control group ( Figs 4B and 5 ) . The immunoneutralized venom is retained in the right tight muscle , and its spread from the injection site to the bloodstream is reduced ( Fig 4B ) . Over time , the levels of TsV in the organs show delayed increase when compared to control . The highest concentrations of immunoneutralized venom are observed in the tissues 240 min after injection , which represents a 190-min delay in comparison with the untreated venom . Lower levels of immunoneutralized TsV were also observed in the kidneys , in relation to the control , indicating reduced renal clearance ( Figs 4 and 5 ) . The delay in the biodistribution of TsV caused by inhibition of hyaluronidase may compromise the synergistic action of the venom’s components , which are relevant in the envenoming process , and may culminate with the reduction of TsV lethality observed by Horta et al . [16] . Revelo et al . [51] demonstrated the effect of T . serrulatus antivenom on the biodistribution of TsV . High levels of venom were detected in mice serum and organs up to 8 h after subcutaneous injection of TsV . In contrast , when antivenom was applied intravenously at times 0 or 1 h after venom injection , venom levels detected in blood and tissues were significantly reduced [51] . These data show the effectiveness of antivenom in blocking venom biodistribution and indicate that anti-hyaluronidase antibodies may exist in total antivenom . Due to the hyaluronidase action facilitating initial venom dissemination , we hypothesized that anti-hyaluronidase serum could complement anti-serum therapy as a first-aid treatment for envenomation . Some studies point to the use of hyaluronidase activity inhibition in envenoming processes as a first-aid action . As inhibitors of viper venom hyaluronidase have long been used for this purpose , neutralization of scorpion hyaluronidase could be a similar therapeutic approach to arrest the main effects of envenomation [56 , 57] . Here we proceeded with inhibition of hyaluronidase after venom injection at times 0 , 10 , and 30 min , and observed a higher concentration of labeled TsV in the bloodstream until 120 or 240 min after TsV injection , depending on anti-hyaluronidase administration time , when compared to the control group ( Fig 6 ) . These results corroborated the 99mTc-TsV biodistribution assays previously neutralized with anti-hyaluronidase serum , which showed that the maximum concentration of TsV detected in tissues occurs 240 min after injection ( Fig 5 ) . Thus , aiming at using hyaluronidase neutralization as a first-aid treatment , our results were effective in showing a delay in the biodistribution of TsV to target organs and its accumulation in the bloodstream . In a real envenoming situation , delaying venom biodistribution may compensate for the time required for the sting victims to seek medical attention and treatment with antivenom serum , especially in remote locations with poor access to hospitals . This would represent a breakthrough in the treatment of systemic envenoming by venomous animals , which are considered neglected issues by the World Health Organization ( WHO ) due to the lack of adequate access to antivenom therapy where they are needed [58] . In addition , the accumulation of TsV in the bloodstream as a result of hyaluronidase activity neutralization ( Fig 6 ) suggests a correlation between hyaluronidase activity and venom flow from the bloodstream to the tissues . These results indicate that TsV hyaluronidase is important not only to allow venom access from the sting/bite site to the bloodstream ( Figs 3 and 4 ) but is also involved in the biodistribution of TsV from the blood to the organs ( Figs 5 and 6 ) . In endothelial cells , hyaluronic acid ( HA ) stimulates cell proliferation , migration , and neovascularization , and regulates endothelial barrier function [59] . As a key component of the glycocalyx in the vascular wall , HA is crucial for vascular integrity and maintenance of blood vessel continuity [60] . Especially in the glomerulus , HA is pivotal to the integrity of protein permeability barrier [61 , 62] . Our results suggest that TsV hyaluronidase is relevant to the cleavage of the HA present in the endothelial barrier and , therefore , promotes the biodistribution of TsV from the blood to the tissues . Studies of this nature can contribute to the development of more effective envenomation therapies and help clarify the mechanisms of action of components from the TsV . Herein , hyaluronidase was shown as a key enzyme for the biodistribution of TsV from the venom injection site to the bloodstream and subsequently to the target tissues . This enzyme promotes the rapid distribution of TsV toxins through the victim's body and is pivotal in the envenoming process . Since we have proved the critical role of hyaluronidase in scorpionism , our findings lead the way for new therapeutic strategies . | Hyaluronidases are known as the venom components responsible for disseminating toxins from the injection site to the victim’s organism . Therefore , understanding how the venom distribution occurs and the role of hyaluronidases in this process is crucial in the field of toxinology . In this study , we inhibited Tityus serrulatus venom ( TsV ) hyaluronidase’s action using specific anti-Ts-hyaluronidase antibodies . Labeling TsV with a radioactive compound enabled monitoring of its biodistribution in mice . Our results show that , upon hyaluronidase inhibition , TsV remains at the injection site for longer , and only a reduced amount of the venom reaches the bloodstream . Consequently , the venom arrives later at target organs like the heart , liver , lungs , spleen , and thyroid . Considering the possible application of hyaluronidase inhibition as a therapeutic resource in envenoming first-aid treatment , we performed the administration of hyaluronidase neutralizing antibodies at different times after TsV injection . We observed that TsV remains in the bloodstream and its arrival at tissues is delayed by 120 or 240 min after TsV injection , depending on anti-hyaluronidase administration times . Our data show that hyaluronidase plays a crucial role in TsV spreading from the injection site to the bloodstream and from the bloodstream to the organs , thus suggesting that its inhibition can help to improve envenoming’s treatment . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [] | 2019 | Inhibition of Tityus serrulatus venom hyaluronidase affects venom biodistribution |
The origin of avian flight is one of the most controversial debates in Paleontology . This paper investigates the wing performance of Caudipteryx , the most basal non-volant dinosaur with pennaceous feathered forelimbs by using modal effective mass theory . From a mechanical standpoint , the forced vibrations excited by hindlimb locomotion stimulate the movement of wings , creating a flapping-like motion in response . This shows that the origin of the avian flight stroke should lie in a completely natural process of active locomotion on the ground . In this regard , flapping in the history of evolution of avian flight should have already occurred when the dinosaurs were equipped with pennaceous remiges and rectrices . The forced vibrations provided the initial training for flapping the feathered wings of theropods similar to Caudipteryx .
The origin of avian flight has been debated for over 150 years , ever since the discovery of the first fossil of Archaeopteryx in 1861 [1–35] . Being widely considered as the oldest and most basal-known avian taxon , Archaeopteryx is characterized by a long boney tail , three clawed digits forming the manus , teeth throughout the upper and lower jaws , a furcula , a non-ossified sternum , and perhaps most importantly , forelimbs with elongate asymmetrical feathers forming large wings . It is widely accepted that birds are nestled within the derived lineage of theropod dinosaurs , the Maniraptora . However , it is still subject to heavy debate how flight evolved within the Dinosauria , and multiple origins of flight appear increasingly probable [1–12 , 24 , 31] . Many researchers consider that avian flight evolved through a number of stages from a ground-dwelling quadrupedal reptile [14–18] , cursorial bipedal ground-dweller [13–17 , 19] , and arboreal life [14 , 20] including parachuting [14 , 21 , 22] , gliding [14 , 16 , 20 , 22 , 23] , and eventually achieving active powered flapping flight [14 , 16 , 20–23] . However , there is increasing support from studies of juvenile birds for a ground up hypothesis in which flight evolved in a terrestrial animal and the flight stroke evolved directly without an intervening gliding phase [36–43] . Among non-avian dinosaurs [20] , Caudipteryx represents the most basal taxon with almost completely preserved feathered forelimbs that could be considered ‘proto-wings’ making this taxon important to the study [21] of flight origins [15 , 22 , 26] . Some other non-volant theropods from the Cretaceous period have been reported with feathered forelimbs [27 , 28] . Caudipteryx is a basal member of the Pennaraptora [1] , a derived group of maniraptoran dinosaurs , sometimes closely allied with birds and the most primitive group with pennaceous feathers . Caudipteryx has short forelimbs with distally located symmetrical pennaceous feathers and long hindlimbs . The feathering of both fore-and hindlimbs indicates that Caudipteryx was not a volant theropod [26] . Caudipteryx further differs from modern birds which have abbreviated tails and forward centered mass locating near the wings [29] . However , the most primitive winged dinosaur , Caudipteryx , is clearly terrestrial , investigating the aerodynamic properties of the proto-wings of Caudipteryx has the potential to shed light on the origin of avian flight [30] . We estimated the maximum running speed of Caudipteryx to be about 8 m/s . This value was based on the skeletal hindlimb proportions of BPM 00001 and on adopting the assumptions [44 , 45] with respect to the limb posture of small theropods and the range of Froude numbers ( up to 17 ) they might have utilized in running ( see S1 Speed for detailed calculation about speed ) [44 , 45] . We also focused our analysis on a literally generic Caudipteryx with a body mass of 5 kg , a realistic value given that an empirical equation for estimating theropod body masses on the basis of femoral length [46] produces results ranging from 4 . 74 kg to 5 . 18 kg ( mean value = 4 . 96 kg ) for a total of five described specimens ( see S1 Mass for detailed calculation about mass ) [24 , 47 , 48] . Any part , mechanism or system has its particular natural frequencies and corresponding mode shapes [46–51] . Mathematically we can compute which natural frequency and related mode shape is significant and effective to take them into account [49 , 52] . The theory of modal effective mass is based on natural frequency , modal analysis and effective masses associated with different directions [49] . The modal effective mass is a measure to classify the importance of a mode shape when a structure is excited by the enforced acceleration from base . A high effective mass in a certain direction will lead to a high reaction force at the base and will be easily excited . Resonance phenomenon occurs on the Caudipteryx when the frequency of the forced vibrations excited by running legs is matched with any natural frequency of Caudipteryx . Hence , by detecting effective natural frequencies of the whole body and analysis of corresponding mode shapes , the velocities of the Caudipteryx that stimulate the wings to flap can be obtained ( 50 cm is measured for the step length of Caudipteryx ) . To this end , a simplified mathematical model , a Finite Element Model , a reconstructed physical model of Caudipteryx , and experiment on young ostrich have been utilized . The simplified mathematics model helps us to understand how to face with the kinematics of Caudipteryx . Finite Element ( FE ) model gives a precise and acceptable result to compare with the reconstructed model on the test rig and experiment on running juvenile ostrich proves the mathematical analyses and simulations .
All experiments using juvenile ostriches , data collection and data analysis procedures in this research were carried out in full accordance with ethical rules for animal welfare and according to the requirements of the Ethics Committee of Tsinghua University . Effective mass categorizes the significance of a mode shape while a structure is excited by forced vibrations from the base . A higher effective mass will certainly lead to a higher reaction force from the basis , while mode shapes with lower related modal effective masses are hardly excited by base vibration and will provide lower reaction forces at the basis [49–55] ( see S1 Text for detailed explanations about modal effective mass method ) . The analyses using the theory of modal effective masses represent that at which velocities , Caudipteryx could most obviously sense flapping on its wings and shoulder joints . This phenomenon is purely governed by the natural biophysics . Finite Element Model of Caudipteryx provided a precise analysis as the number of elements were sufficient enough and non-structural masses to cover the whole body mass to reach to 5 Kg were also taken into account . Also , except those elements which have boundary conditions , all elements have full DOF in any direction ( Fig 2 and S1 Fig ) . We reconstructed the real-sized robot of Caudipteryx on the test rig in accordance with the existing fossils ( BPM0001 ) ( S2 Fig ) . The robot is composed of body , tail , neck , wings and legs , and the skeleton is fabricated from ABS plastic . There is no definite evidence for tertiary feathers of Caudipteryx . We therefore only reconstructed the primary and secondary remiges from the feathers of extant birds . Using metal pins , we attached them to the antebrachial to generate artificial articulated wings . In the reconstructed wings , we imbedded force sensors ( S3A Fig ) to collect the data of lift and thrust/drag ( S3C , S3D , S3E and S3F Fig ) . In order to verify experiment by Caudipteryx robot and the forced vibrations phenomenon induced by legs , we also implemented the experiment on a half-adulted ostrich whose mass is 6 . 7 kilograms ( Fig 3A ) as a similar living bird to Caudipteryx . This process was performed through observations on a running juvenile ostrich ( S2 Video ) and experiments on running ostriches ( S3 Video ) . A device was fixed on the ostrich’s back ( Fig 3B ) to measure the velocity , acceleration ( S3 Fig ) , rolling angles of body , and wings ( S4 Fig ) . To investigate the responses of the body and the wings in running and the advantage of aerodynamic effects of flapping wings of feathered dinosaurs , we fabricated four different sizes of feathered forearms with the simplest plate wings ( Fig 3C ) and executed experiments on the ostrich . Therefore , lift and thrust/drag forces produced by artificial wings during running were also measured by the force sensors ( S3A and S3B Fig ) . The connections of the shoulder joints were particularly designed in order to avoid the effect of frictions and inertial forces during locomotion .
Mathematical model shows the first mode of the forced vibrations to flap the wings when Caudipteryx ran on the ground at the speed of about 2 m/s , the mode shape of which is expressed with a vector of n1 = ( 0 . 393 0 . 62 0 . 62 0 . 158 0 . 158 0 . 432 0 . 45 ) T . The FE model analysis results of the modal effective mass of the Caudipteryx ( Table E in S1 Table ) indicate that the effective natural modes occur only in vertical direction ( Y-axis ) and they are almost zero in lateral motions ( X and Z axes ) . It expresses that the first natural frequency of about 1 . 99 Hz is not effective , but the second one of about 2 . 58 Hz and the third mode of about 5 . 79 Hz considering the maximum speed of Caudipteryx ( the forecasted velocity is about 8 m/s for Caudipteryx ) are effective and important . In other words , the oscillation about the torso axis is the first mode ( S1 Fig ) . Therefore , the Caudipteryx should roll its whole body about the torso direction when they ran at a low speed ( around 2 m/s ) near the first primary frequency . The second primary mode ( the most effective mode ) occurred as the running speed approached to 2 . 5 m/s . It means flapping modes were easily excited at low frequency while Caudipteryx ran on the ground at the velocity from around 2 . 5 m/s to a little faster than 5 . 8 m/s ( S1 Fig ) . We fabricated four simplest plate wings with different sizes and did experiments on the ostrich to compare the lift forces obtained from the flapping wings passively applied by forced vibrations during running . At the same running speed , the wings with filament feathers ( 1st wing ) provided the smallest lift , the largest value of which is less than 0 . 13 N , while the ones with longer feathers could provide larger lift ( 2nd and 3rd wings ) , and the longest feather ( 4th wing ) could provide the largest lift which exceeds 0 . 42 N ( S3 Fig ) .
In the simplified rigid body system of seven degrees of freedom of Caudipteryx , the whole system can be excited by the displacements of feet , x4 and x5 during running . After this excitation , the whole body masses move along their individual vertical directions in this model ( Fig 1 ) . It illustrates the kinematics of Caudipteryx mathematically . In order to obtain the precise results using computer simulation , Finite Element Method reveals the phenomenon that the maximum effective mass occurs in the second mode which is a flapping mode . Only in the most effective mode , could the wings of Caudipteryx be excited to flap evidently and then sense lift . Therefore , the results of the FEM model ( second model ) through Finite Element Method have been considered because of having the highest accuracy . On the other hand , in the FE model simulated by FEM , computer calculations represent that the first natural frequency which had been roughly calculated in the first mathematical model ( first model ) is almost equal to that of the FEM model; and the other natural frequencies ( from the second to the seventh ) in comparison with the FEM model ( second model ) have some deviations but still acceptable . Also in the first model the modal effective masses of each natural mode might not be equal to the accurate FEM model , but the summation of which in simplified seven-degree-of-freedom model must be 5 kilograms . The reason is the limitation on the number of elements/masses ( solely seven masses ) and having only one DOF in the vertical direction . The effective mass analysis discovers that the first mode has never been effective ( Table E in S1 Table ) . As the speed approached to the second primary frequency , the Caudipteryx output the second oscillation mode . It is the flapping of the wings up and down with the same amplitudes and same directions . The simulation has been extended by either increasing or decreasing the mass of each part of the Caudipteryx ( Table D in S1 Table ) and assumed eight excessive masses except the actual one ( S5 Fig ) ( by measuring ) from 2 kg to 10 kg in a similar geometrical model . Hence , the frequencies and corresponding effective masses in Y-axis have been studied ( Table F in S1 Table ) . The analyses reveal that the performance of effective modes of any model ( models A , B , … , I ) are identical but at different frequencies . It means that in all mass distribution models , effective mode mainly depends on the creature’s velocity . When the forced vibration frequency is near the second natural frequency , the flapping mode will be occurred . The natural frequency decreases from 4 . 0 Hz in mass model A to 1 . 8 Hz in mass model I ( S5 Fig ) in the second mode . Therefore , as the weight of the creature increases , the velocity necessary to reach flapping mode might be decreased . With the observation of the experiments , we realized that when the speed of the reconstructed Caudipteryx robot on the test rig ( S2 Fig ) reached 2 . 31 m/s ( near the value of what has been simulated by FEM model ) , the robot’s wings started to output most obvious flapping motions which is the resonance of forced vibrations in physics ( Fig 4 ) . Using theory of modal effective mass and reconstruction of Caudipteryx zoui ( BPM0001 ) ( S6 Fig and Table A in S1 Table ) , we infer that flapping flight could be developed earlier than gliding in the evolution of avian flight . When the running speed was near the second primary speed of about 2 . 5 m/s , both wings of the Caudipteryx generated oscillations similar to flapping wings . Step length in running animals varies with speed and gait and animals do not just have one step length . Any given velocity in this research such as 2 , 2 . 5 and 5 . 79 m/s dedicated to first , second and third modes was obtained by measuring and assuming some parameters from the fossil such as step length , stiffness and mass ( see S1 Text for detailed explanations about Caudipteryx velocity and step length ) . To eliminate these uncertain values , we used interval analysis which is a powerful mathematical tool in engineering ( see S1 Text for detailed explanations about Interval Analysis method ) . Modal effective mass and Interval Analysis represent that flapping motion occurred at lower velocity . It means , if step length was between 30 cm to 70 cm and if mass was between 3 to 7 kg , Caudipteryx had flapping motion and it occurred at lower velocities ( there must be a value that will render the second mode although we do not know the exact number which is in a certain Interval ) . Hence , the velocities of 2 . 5 m/s and 5 . 86 m/s are only two cases among all possibilities . Therefore , the conclusion that the second and the third modes must occur at a certain value is an objective conclusion . Further , the physical phenomenon of flapping motion ( induced by forced harmonious vibrations ) always be generated in running , but we cannot obtain the precise value of running speed since it might be expressed with an interval of velocity . Hence , the role of body oscillation during a run should be taken into account in order to understand the origin and evolution of avian flapping wings . Experiment results on ostrich indicated that the vibrations of the feathered wings were easily induced when ostrich ran on the ground . Under the assumption of the same length of forearms for the feathered dinosaurs , the wing with the shortest feathers generated the flapping motions with the largest amplitude while the ones with longer feathers produced the flapping motions with smaller amplitudes ( S4 Fig ) . This is interpreted by the air resistance . The larger the wing area , the larger the resistance , and the smaller the amplitude for the passive vibrations . This experiment suggests that the flapping motion might be developed by the forced vibrations during terrestrial locomotion when the winged dinosaur appeared on the earth . However , the lift obtained from the running-foot forced vibrations shows that the longer and larger the wing was , the larger the lift would be ( S3 Fig ) . Therefore , forced vibrations may represent the earliest stages in the evolution of forelimb flapping in winged theropods . This suggests that flapping behavior evolved in non-volant theropods long time ago before they could actively fly . Experiments on the Caudipteryx robot based on the fossil ( Caudipteryx sp . IVPP V12430 ) and the experiments on artificial wings placed on the back of a juvenile ostrich indicated that the forced vibrations of plumage forearms during walking and running taught the winged theropods to flap their wings . These analyses suggest that the impetus of the evolution of powered flight in the theropod lineage that lead to Aves may have been an entirely natural phenomenon produced by bipedal motion in the presence of feathered forelimbs . | The origin of avian flight in the perspective of mechanics has been investigated for the first time . We reported the first evidence for flapping hypothesis based on principle of physical modeling . This is significant because using modal effective mass method and reconstructed Caudipteryx , the most basal non-volant winged dinosaur , we captured significant and negligible modes and realized that resonance oscillation of Caudipteryx wings could occur as the running speed approached to the primary frequencies . Such forced vibrations induced by legs' motions during running trained the Caudipteryx and the other feathered dinosaurs to flap their wings . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [] | 2019 | Identification of avian flapping motion from non-volant winged dinosaurs based on modal effective mass analysis |
Control and prevention of dengue relies heavily on the application of insecticides to control dengue vector mosquitoes . In Colombia , application of the larvicide temephos to the aquatic breeding sites of Aedes aegypti is a key part of the dengue control strategy . Resistance to temephos was recently detected in the dengue-endemic city of Cucuta , leading to questions about its efficacy as a control tool . Here , we characterize the underlying mechanisms and estimate the operational impact of this resistance . Larval bioassays of Ae . aegypti larvae from Cucuta determined the temephos LC50 to be 0 . 066 ppm ( 95% CI 0 . 06–0 . 074 ) , approximately 15× higher than the value obtained from a susceptible laboratory colony . The efficacy of the field dose of temephos at killing this resistant Cucuta population was greatly reduced , with mortality rates <80% two weeks after application and <50% after 4 weeks . Neither biochemical assays nor partial sequencing of the ace-1 gene implicated target site resistance as the primary resistance mechanism . Synergism assays and microarray analysis suggested that metabolic mechanisms were most likely responsible for the temephos resistance . Interestingly , although the greatest synergism was observed with the carboxylesterase inhibitor , DEF , the primary candidate genes from the microarray analysis , and confirmed by quantitative PCR , were cytochrome P450 oxidases , notably CYP6N12 , CYP6F3 and CYP6M11 . In Colombia , resistance to temephos in Ae . aegypti compromises the duration of its effect as a vector control tool . Several candidate genes potentially responsible for metabolic resistance to temephos were identified . Given the limited number of insecticides that are approved for vector control , future chemical-based control strategies should take into account the mechanisms underlying the resistance to discern which insecticides would likely lead to the greatest control efficacy while minimizing further selection of resistant phenotypes .
Dengue fever is the most rapidly expanding arboviral disease in the world . Approximately 50 million infections occur in 100 countries annually [1] , [2] , and 60% of those are estimated to occur in the Americas [3] . In Colombia , dengue is considered a major public health problem , with approximately 25 million people at risk of infection . The primary vector of dengue , the Aedes aegypti mosquito , is found in more than 90% of the national territory [4] . Ae . aegypti is highly anthropophilic , with markedly endophilic and endophagic behaviors; these characteristics are directly related to its high efficiency as a disease vector [5] , [6] . In the absence of a vaccine or effective therapeutic medications , vector control remains the only available strategy to control and prevent dengue transmission [6] . Many dengue vector control interventions target the immature stages of the mosquito , which breed in artificial containers in close proximity to human dwellings . The most widely used method for controlling immature Ae . aegypti is the periodic treatment of actual and potential breeding sites with chemical larvicides . The organophosphate ( OP ) insecticide temephos is commonly used to control immature dengue vectors due to its cost-effectiveness and community acceptance [5] , [7] , [8] . As a consequence of its widespread use , resistance to temephos in Ae . aegypti has been reported in many Latin American countries , including Brazil [9] , Cuba [10] , El Salvador [11] , Argentina [12] , Bolivia [13] , Venezuela [14] , Peru [15] and Colombia [16] . It is believed that the extent of temephos resistance is underestimated due to under-reporting and lack of surveillance [8] . Despite increasing reports of temephos resistance in Ae . aegypti , the molecular mechanisms underpinning it are not well-characterized . In several mosquito species of medical importance such as Anopheles gambiae , Culex pipiens and Culex tritaeniorhynchus , mutations on the acetylcholinesterase gene ( ace-1 ) have been associated with OP resistance [16] , [17] , [18] . However , no mutations at this target site have been found related to OP resistance in Ae . aegypti . The three main enzyme families involved in xenobiotic detoxification in mosquitoes , glutathione S-transferases ( GST ) , cytochrome P450 monooxygenases ( CYP450 ) and carboxylesterases ( CE ) have been associated with temephos resistance in Ae . aegypti [19] , with elevated CE activity most widely implicated . Recently , increased activity of the esterase “A4” in Ae . aegypti was partially characterized and strongly correlated with temephos resistance [20]; however , its genomic identity remains unknown . Temephos is currently one of the most commonly used insecticides in Colombia [21] . In the densely populated , dengue endemic city of San Jose de Cucuta ( ‘Cucuta’ ) , temephos was used for nearly 40 years as a routine Ae . aegypti control measure but applications ceased when resistance was recently detected . Despite the potential implications of this resistance for the efficacy of dengue vector control , neither the operational impact nor the mechanisms of temephos resistance have been characterized . In this study , we explore the mechanisms of temephos resistance in Ae . aegypti from Cucuta and estimate the impact of this resistance on the efficacy of temephos-based vector control operations .
Cucuta is a city located in the eastern range of the Andes mountains of Colombia ( 7°54′0″N , 72°30′0″W ) , at 320 meters above sea level and with an average temperature of 28°C . Since the municipal water supply is frequently interrupted , people typically store water in large ground level cement tanks , or in some cases , in plastic tanks on the roof . These containers provide abundant breeding sites for Ae . aegypti . Verbal permission was obtained from householders to conduct entomological collections on their premises in March 2010 . Oviposition traps ( ‘ovitraps’ ) were placed in 500 houses , while approximately 200 houses were visited for larval collections . The houses were located in five different areas of the city which were selected due to historically high levels of dengue transmission . Larval collections were made directly by removing larvae from household water storage tanks and other breeding sites , such as cans , bottles , tires , and miscellaneous discarded items , generally located in the patio area . They were taken to the insectaries at the Biologia y Control de Enfermedades Infecciosas group at the Universidad de Antioquia in Medellin , and reared under standard conditions ( temperature: 28+/−1°C; relative humidity: 75+/−5%; photoperiod: 12 hours day/night ) . To increase larval numbers , approximately 500 ovitraps [22] were placed inside houses and in backyard/patio locations . After four days , the ovitraps were retrieved and checked for eggs . Positive traps were taken to the insectary where the eggs were hatched and the offspring were reared . All field samples were pooled to create the Cucuta strain . Three insecticide-susceptible strains of Ae . aegypti were used as controls in this study . The New Orleans ( NO ) strain was originally collected in the namesake city located in Louisiana , United States . The Rockefeller ( RCK ) strain originated in Cuba nearly a century ago , while the Bora Bora ( BB ) strain was collected on its namesake island in French Polynesia in the 1960s [23] . Standard WHO larval bioassays were conducted to detect the level of susceptibility to temephos [24] . Each bioassay consisted of four replicates per insecticide concentration; each replicate used twenty late 3rd/early 4th instar larvae . Eight doses of temephos ( Pestanal , analytic standard ) ranging from 0 . 01 to 0 . 15 ppm were tested with both the Cucuta strain and a susceptible reference strain ( NO ) . Mortality was recorded after 24 hours of exposure . LC50 values and confidence intervals were calculated using XLSTAT software ( Addinsoft , Paris , France ) . Given that permethrin resistance had previously been reported in this population , standard WHO larval bioassays were also conducted using 6 concentrations of permethrin: 0 . 0075 , 0 . 01 , 0 . 02 , 0 . 03 , 0 . 04 and 0 . 05 ppm . To assess the role of the three main detoxification enzyme families in temephos resistance , larvae were exposed to either diethyl-maleate ( DEM ) , piperonyl-butoxide ( PBO ) or S , S , S-tributyl phosphorotrithioate ( DEF ) ( Sigma-Aldrich ) as inhibitors of GSTs , CYP450s and CEs , respectively . Standard temephos larval bioassays with three doses ranging from 0 . 05 ppm to 0 . 15 ppm were carried out with the addition of a specified concentration of synergist: either DEM at 1 ppm , PBO at 0 . 3 ppm or DEF at 0 . 5 ppm [25] . Each bioassay consisted of three replicates per insecticide concentration; each replicate used twenty late 3rd/early 4th instar larvae . The same assay was carried out using the NO strain as a control . Resistance ratios were calculated by dividing Cucuta values by NO values at LC50 , and synergism ratios were calculated as the ratio between the LC50 obtained with each synergist and the LC50 obtained without synergists . This assay was conducted in a semi-field environment at the University of Antioquia in Medellin , Colombia . Based on the methodologies proposed by Montella et al . and Lima et al . [26] , [27] , white plastic buckets were filled with 15 liters of tap water , and were kept outdoors , protected from direct rain and sunlight exposure . Mesh lids secured with elastic bands were used to prevent the introduction of wild mosquitoes or detritus into the buckets during the course of the experiment . A dose of 1 ppm of temephos ( Abate , Fitogranos , Bogotá , Colombia ) , was used to treat each container , which is equivalent to the dose applied to breeding sites by vector control personnel . There were 2 experimental groups , each with three replicates: in Group 1 , 3 liters of the temephos-treated water were replaced with fresh , untreated tap water twice a week , while in Group 2 , the original temephos-treated water remained for the duration of the experiment without replacement . Twice a week over a 2-month period , 20 third instar larvae from the temephos-resistant Cucuta strain ( F4 generation ) were introduced into each container and mortality was recorded after 24 hours . Both dead and surviving larvae were removed from the containers after counting . Simultaneously , the same methodology was carried out using larvae from the RCK susceptible reference strain . Control containers without temephos were maintained under the same conditions for both experimental groups . Water temperature and pH were recorded twice a week , before each mortality recording . Activity levels of insensitive acetylcholinesterase ( iAChE ) , glutathione S-transferases ( GST ) , mixed function oxidases ( MFO ) , α-esterases and β-esterases were tested in Cucuta larvae , with larvae from the NO strain used as a negative control . Procedures were based on mosquito-specific biochemical assay protocols reported elsewhere [28] , [29] , [30] . Briefly , 30 individual larvae were homogenized in 100 µL of 0 . 01 M potassium phosphate buffer ( KPO4 ) , ph 7 . 2 , and then the volume was diluted to 2 ml , and 100 µl of each sample were transferred by triplicate to a 96-well microtiter plate . For the iAChE assay , 100 µl of acetylcholine iodide ( ATCH ) with propoxur and 100 µl of dithio-bis2-nitrobenzoic acid were added to each well; absorbance was recorded immediately ( T0 ) and after 10 minutes ( T10 ) in a Varioskan Flash Multimode Reader ( Thermo Scientific , Delaware , USA ) at a wavelength of 414 nm . As a positive control for elevated iAChE activity , the Anopheles gambiae AKRON strain ( supplied by MR4 , Manassas , Virginia , USA ) was used . For the GST assay , 100 µl of reduced glutathione and 100 µl of 1-chloro-2 , 4 – dinitrobenzene ( CDNB ) were added to each well . Absorbance readings were taken at T0 and T10 at a wavelength of 340 nm . For the MFO assay , 200 µl of tetramethyl-benzidine dihydrochloride ( TMBZ ) prepared in methanol and 0 . 25 M sodium acetate buffer were added to each well , followed by 25 µl of 3% hydrogen peroxide ( H2O2 ) . The microplate was incubated at room temperature for 10 minutes before reading at a wavelength of 620 nm . To detect α- and β-esterase activity , 100 µl of α-/β-naphthyl acetate were added to each well , followed by a 20 minute incubation at room temperature . 100 µl of dianizidine were then added , followed by a 4 minute incubation , and then absorbance was read at a wavelength of 540 nm . To avoid bias due to natural variations in the size of the larvae , the total protein content of each sample was estimated . In triplicate , 200 µl of Bradford® reagent was added to 20 µl of homogenate ( diluted to 100 µL by adding KPO4 buffer ) and the microplate was read at 620 nm . A standard bovine serum albumin ( Sigma ) calibration curve was done for comparison . All replicates showing a coefficient of variation >0 . 20 were discarded .
The LC50 of temephos for the Cucuta strain of Ae . aegypti was 0 . 066 ppm ( 95% CI 0 . 06–0 . 074 ) , approximately 15× higher than the value for the susceptible NO strain ( 0 . 0043; 95% CI 0 . 004–0 . 005 ) . The LC95 of the Cucuta strain was 0 . 18 ppm ( 95% CI 0 . 15–0 . 23 ) , with a resistance ratio ( RR ) of 14 relative to the NO strain . Addition of the synergist DEF increased temephos susceptibility by approximately 36× in the Cucuta strain and by 7× in the New Orleans strain ( Table 1 ) . DEM resulted in a small decrease in the LC50 in the NO strain only . No effect of PBO was observed in either strain ( Table 1 ) . Larval bioassays using permethrin indicated that the Cucuta strain was also resistant to this insecticide , with an LC50 of 0 . 017 ppm ( 95% CI 0 . 015–0 . 019 ) and a RR50 of 16 relative to NO . Temephos applied at the concentration used in routine vector control activities remained effective against both the RCK reference strain and the Cucuta strain for 8 weeks , provided the water was not replaced . With water replacement , over 20% of the Cucuta mosquitoes were surviving in treated containers by 4 weeks post-treatment , and nearly 80% were surviving after 2 months ( Figure 1 ) . In contrast , 100% mortality of the susceptible RCK larvae was maintained up to week six with water replacement . Water temperature for all containers ranged between 21°C and 25°C , and pH ranged between 8 . 0 and 8 . 6 ( except during the second week when it briefly decreased to 7 . 4 ) . None of the enzyme families in the Cucuta strain showed enhanced activity with the model substrates used , when compared with the NO strain . Similarly , there was no evidence of AChE insensitivity , with both the Cucuta and NO strains being equally inhibited by propoxur ( Supplementary information , Figure S1 ) . A 1614 bp fragment of the ace-1 gene was sequenced from two pools of 10 larvae from the Cucuta strain . No amino acid polymorphisms were identified within the strain and only a single synonymous mutation was detected ( position 1423 , CTA to TTA ) when compared to the reference sequence in Vectorbase ( AAEL000511 ) . To select candidate genes related to temephos resistance , genes significantly differentially expressed in each experiment were filtered as shown in Figure 2 . Firstly , only genes that were differentially transcribed in both CucR-NO and CucR-BB microarrays were selected ( Table S3a ) . This resulted in a list of genes differentially transcribed between Cucuta and both geographically distinct reference strains , thus reducing the bias introduced by differences in geographic origin of the reference strains . Then , this list was cross-referenced with the differentially transcribed genes resulting from CucR-CucU microarray . This step removed any genes that did not show differential expression between the unexposed Cucuta population and those surviving the temephos LC50 in an attempt to select for genes contributing to the temephos-resistant phenotype . The resulting gene list ( Table S3b ) contained 124 probes , 63 of which were upregulated in the CucR population in all 3 comparisons . This list was then further reduced by filtering out any probes that were more highly expressed in Cucuta mosquitoes surviving permethrin exposure than those surviving temephos exposure , resulting in a final list of 41 upregulated candidate genes associated with temephos resistance ( Figure 2; Table 2 ) . The most over-transcribed gene in the CucR population was a putative chymotrypsin ( AAEL011230-RA ) , with a 30-fold positive change when compared with CucU larvae . An UDP-glucosyl/glucuronosyl transferase ( AAEL003076-RA ) was also highly over-transcribed in this population . Three detoxification genes belonging to the CYP6 subfamily were also present on the temephos resistance candidate list: CYP6N12 , CYP6F3 and CYP6M11 ( Table 2 ) . Microarray data were submitted to ArrayExpress ( accession number E-MTAB-1682 ) . Three of the four genes selected for validation , CYP6N12 , CYP6F3 and the acetyl coA synthetase , showed similar fold changes by qPCR and microarray ( Table 3 ) . However , the over expression of CYP6M11 was not confirmed by qPCR . A correlation analysis between the qPCR and microarray data yielded a R2 value of 0 . 31 .
In Colombia , dengue transmission is a major public health problem which has led to ongoing efforts to prevent and control dengue epidemics . As part of this effort , the National Network for Surveillance of Insecticide Resistance was created , and widespread screening of Ae . aegypti susceptibility was carried out in 2005–2008 across dengue endemic regions . Moderate to high levels of resistance were reported for all four major insecticide classes across the country [21] , [33] . The principal intervention to control Ae . aegypti in Colombia is the application of temephos ( Abate sand-core granules ) at a concentration of 1 ppm to domestic and peridomestic water storage containers [34] , as recommended by the WHO [5] . Resistance to temephos in Ae . aegypti has been previously reported in Colombia [21] , [35] . In contrast with the findings presented here , one of these studies [21] detected elevated MFO and esterase activity in temephos resistant populations . The present study determined that Ae . aegypti from Cucuta were able to survive 15× higher doses of temephos than a standard susceptible strain . Insecticide resistance can potentially compromise vector control measures . It has been reported that temephos resistance can affect the efficiency of this insecticide under both field and semi-field conditions [36] , [37] . Insecticide bioassays with water renewal emulate the routine water replacement carried out in zones where Ae . aegypti breeding sites are intra- or peri-domestic water storage containers , offering a better approximation of the impact insecticide resistance may have on vector control measures [38] . The finding that , after approximately one month , the majority of Cucuta larvae survived this simulated field trial , suggests that the residual effect of routine control measures is compromised by the high level of resistance . Similar efficacy losses have been reported elsewhere in temephos resistant Ae . aegypti [26] , [27] , [38] , [39] . Semi-field or laboratory bioassays can only detect resistance when it is already present in high frequencies in a vector population . Detecting resistance at an early stage could improve vector control efficacy by triggering the implementation of alternative control strategies pre-emptively , before resistance is present at high frequencies . In order to design appropriate diagnostic tools that can detect incipient resistance , the molecular mechanisms underlying resistant phenotypes must be characterized . The most recognized OP target site resistance mechanism is insensitive acetylcholinesterase ( iAChE ) . Although mutations on the gene encoding this enzyme ( ace-1 ) have been associated with OP resistance in Culex pipiens , Culex tritaeniorhynchus , Anopheles gambiae and Anopheles albimanus [16] , [17] , [18] , [40] , this has not yet been observed in Ae . aegypti . It has been hypothesized that the absence of these mutations in this species is because some of the most common mutations , such as G119S , are unlikely to occur spontaneously [41] . The Cucuta strain did not exhibit iAChE and no amino acid changes on the ace-1 gene were detected in temephos resistant mosquitoes . This is consistent with other findings that suggest that target-site resistance plays only a minor role in temephos resistance for Ae . aegypti [30] . Synergist bioassays suggested that carboxylesterases were potentially responsible for temephos resistance in the Cucuta strain . However , the biochemical assays did not detect any elevation in α- or β-esterase activity in the resistant population . There are several possible explanations for this apparent contradiction . The biochemical assays used model substrates which may not be recognized by all members of the CE family . In other insects , increased esterase activity has been associated with an amino acid alteration in a particular α-esterase [42] , [43] , [44] which is actually associated with a decrease in activity against a model substrate . Alternatively , the synergistic activity of DEF may be unrelated to its role as a CE inhibitor . To obtain a more comprehensive picture of specific genes involved in insecticide resistance , a transcriptional analysis was performed . This approach makes no assumption about the mechanisms involved but does rely on detecting changes in gene expression , and hence would not detect resistance mechanisms that resulted from an increased affinity of an enzyme for the insecticide , for example . Two different susceptible reference strains , NO and BB , were used to minimize the genetic variation due to biological differences between strains . After a stringent analytical pipeline ( Figure 2 ) , 41 genes were identified that met the following criteria: 1 . expressed at higher levels in the Cucuta population than in both the susceptible lab strains , 2 . expressed at higher levels in Cucuta mosquitoes that had survived temephos exposure than in those not exposed to temephos and 3 . expressed at higher levels in Cucuta mosquitoes surviving temephos exposure than in Cucuta mosquitoes surviving permethrin exposure . This final step was included as the Cucuta population was found to be resistant to both insecticides , but in this study we were particularly interested in those genes responsible for temephos resistance . It is recognized , however , that this step will have filtered out any genes that may be involved in cross resistance to both insecticides . The final candidate list did not contain any CEs , which is in contrast with the extensive body of literature that closely relates OP resistance with this class of detoxifying enzymes [45] , but in agreement with the results from the biochemical assays which did not support a role for elevated esterase activity in conferring the resistant phenotype . However , the possibility that temephos resistance is related to amino-acid substitutions on specific esterase genes , as has been previously reported for several insecticides in other dipteran species [43] , [46] , [47] , [48] , cannot be discounted by the microarray results . Three gene members of the CYP6 P450 enzyme sub-family were found related to temephos resistance in this study . CYP6M11 has been reported previously as induced in Ae . aegypti in response to xenobiotics [49] , permethrin selection [50] and in larvae [51] and adults [19] of temephos-resistant strains . Although this gene was found to be upregulated in the microarray analysis , the over expression of this P450 could not be confirmed by qPCR . The genes CYP6N12 and CYP6F3 , identified as temephos resistance candidates in the current study , have previously been associated with resistance to the neonicotinoid imidacloprid and permethrin [50] , [52] , [53]; CYP6N12 was also associated with tolerance to the polycyclic aromatic hydrocarbon ( PAH ) fluoranthene [53] and temephos [54] . It has previously been suggested that the conjugation of xenobiotics with glucose is an important detoxification pathway in insects [55] . Although UDPGTs have been described in some medically and agriculturally important insects as allelochemical detoxifiers [56] , [57] , they have only been demonstrated to be involved in insecticide resistance once [56] . Recently , high levels of UDPGT over-expression in the metabolic response of Ae . aegypti larvae to permethrin have been reported [50] . In the present study , one UDPGT ( AAEL003076-RA ) was associated with temephos resistance , suggesting that further studies are warranted on this transferase gene family to confirm its role in insecticide detoxification . Serine proteases are a group of well-studied enzymes responsible for a variety of functions such as digestion , oogenesis , immune response , blood coagulation and metamorphosis [58] , [59] , [60] . In the present study , a chymotrypsin ( AAEL011230-RA ) was the most over transcribed gene in the temephos resistant population ( 30 . 2-fold difference between temephos survivors and non-exposed Cucuta larvae , and 87 . 1-fold difference between temephos survivors and NO ) . Although it has been reported previously that trypsins and chymotrypsins from Culex pipiens pallens are able to metabolize the pyrethroid deltamethrin [61] , [62] , there is no evidence so far to confirm that these enzymes can metabolize temephos . Organophosphate insecticides are also known to inhibit certain serine proteases , including chymotrypsins [63] . Functional characterization is needed to clarify the role of the chymotrypsin reported here in temephos resistance or in temephos-permethrin cross resistance . The application of the organophosphate temephos to breeding sites is a pillar of Ae . aegypti immature control worldwide . However , its widespread , long-term use has led to the emergence of resistance in different parts of the world . Our findings demonstrate that a high level of temephos resistance significantly impacts the performance of this insecticide by reducing its residual efficacy by more than half , which in turn impacts vector control efficiency . As such , it is critical to develop tools that can detect resistance at its earliest stages of development , before resistance reaches levels at which control efficacy is compromised . The development of such tools requires a detailed understanding of the molecular basis and mechanisms underpinning resistance to insecticides . The results of the present study provide a comprehensive analysis of temephos resistance in Ae . aegypti from Cucuta , Colombia , and provide novel insights into the mechanisms underlying temephos resistance in this important disease vector . Through deeper understandings of the interactions between genes responsible for resistance to temephos and other insecticide groups , vector control programs can design control strategies that minimize the selection of resistant phenotypes and maintain vector control efficacy in the long term . In the case of Cucuta , the public health authorities have begun implementing alternative larval control strategies , including biological control ( using small fish ) and the application of pyriproxyfen ( a juvenile hormone analogue ) to breeding sites . Ongoing monitoring of temephos resistance will yield useful information about how the large scale deployment of these alternative strategies affects temephos resistance levels and its underlying mechanisms over time . | Dengue fever , caused by viruses transmitted by the Aedes aegypti mosquito , is an important threat to public health in many tropical and subtropical countries . In the absence of a vaccine or specific drug treatment , prevention and control of dengue transmission relies on interventions targeting vector mosquito populations . In the city of Cucuta , Colombia , the insecticide temephos was used for several decades to control Ae . aegypti larvae , until resistance was recently reported . In this study , the resistance to temephos in this population was quantified , and its impact on control activities estimated using simulated field trials . The mechanisms underlying the resistance were determined to be metabolic , with several key detoxification enzymes identified as potential candidates . This should be taken into account when devising future vector control and insecticide resistance management strategies in this region of Colombia . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [] | 2013 | Temephos Resistance in Aedes aegypti in Colombia Compromises Dengue Vector Control |
Nutrient availability has profound influence on development . In the nematode C . elegans , nutrient availability governs post-embryonic development . L1-stage larvae remain in a state of developmental arrest after hatching until they feed . This “L1 arrest” ( or "L1 diapause" ) is associated with increased stress resistance , supporting starvation survival . Loss of the transcription factor daf-16/FOXO , an effector of insulin/IGF signaling , results in arrest-defective and starvation-sensitive phenotypes . We show that daf-16/FOXO regulates L1 arrest cell-nonautonomously , suggesting that insulin/IGF signaling regulates at least one additional signaling pathway . We used mRNA-seq to identify candidate signaling molecules affected by daf-16/FOXO during L1 arrest . dbl-1/TGF-β , a ligand for the Sma/Mab pathway , daf-12/NHR and daf-36/oxygenase , an upstream component of the daf-12 steroid hormone signaling pathway , were up-regulated during L1 arrest in a daf-16/FOXO mutant . Using genetic epistasis analysis , we show that dbl-1/TGF-β and daf-12/NHR steroid hormone signaling pathways are required for the daf-16/FOXO arrest-defective phenotype , suggesting that daf-16/FOXO represses dbl-1/TGF-β , daf-12/NHR and daf-36/oxygenase . The dbl-1/TGF-β and daf-12/NHR pathways have not previously been shown to affect L1 development , but we found that disruption of these pathways delayed L1 development in fed larvae , consistent with these pathways promoting development in starved daf-16/FOXO mutants . Though the dbl-1/TGF-β and daf-12/NHR pathways are epistatic to daf-16/FOXO for the arrest-defective phenotype , disruption of these pathways does not suppress starvation sensitivity of daf-16/FOXO mutants . This observation uncouples starvation survival from developmental arrest , indicating that DAF-16/FOXO targets distinct effectors for each phenotype and revealing that inappropriate development during starvation does not cause the early demise of daf-16/FOXO mutants . Overall , this study shows that daf-16/FOXO promotes developmental arrest cell-nonautonomously by repressing pathways that promote larval development .
C . elegans L1-stage larvae must feed upon hatching in order to exit developmental arrest . Larvae in L1 arrest have increased stress resistance and can survive for weeks , initiating postembryonic development when food is available [1] . C . elegans larvae can also arrest development during the dauer stage , an alternative to the third larval stage that forms in response to crowding , nutrient stress or high temperature [2] . Unlike dauer formation , L1 arrest is an acute starvation response without an alternative developmental program , making it an excellent model for nutritional control of development . The insulin/insulin-like growth factor ( IGF ) signaling pathway is a key regulator of L1 arrest , mediating the systemic response to nutrient availability [3 , 4] . In fed larvae , insulin-like peptides act through the insulin/IGF receptor DAF-2/InsR , activating a conserved PI3K cascade to repress function of the forkhead-type transcription factor DAF-16/FOXO [5–8] . In starved larvae , DAF-16/FOXO is active and promotes L1 arrest [3] . A variety of genome-wide gene expression analyses have been published for daf-16/FOXO , and a meta-analysis of them identified thousands of genes whose expression is positively or negatively affected by daf-16/FOXO activity [9] . This study also identified the transcription factor PQM-1 as a mediator of daf-16-dependent effects on gene expression . However , these experiments were done in young adult animals and with a daf-2/InsR mutant background to activate DAF-16/FOXO as opposed to starvation . DAF-16/FOXO target genes that promote L1 arrest are largely unknown . daf-16/FOXO activates the cyclin-dependent kinase inhibitor cki-1/p27 and represses the developmental timing microRNA lin-4 [3] , but whether such regulation is direct is unclear . The insulin/IGF pathway is pleiotropic , serving as a key regulator in dauer formation , lifespan , associative learning , and stress resistance [5 , 10–15] . daf-2/InsR and daf-16/FOXO affect lifespan and dauer formation cell-nonautonomously [16–20]; that is , daf-16/FOXO activity in limited tissues affects the phenotype of the entire organism . The insulin/IGF pathway is highly conserved , and it also affects lifespan cell-nonautonomously in Drosophila and mice [21–24] . daf-16/FOXO activity in one tissue has been shown to affect daf-16/FOXO activity in other tissues through feedback regulation , termed FOXO-to-FOXO signaling [25–27] . Insulin/IGF receptor cell-nonautonomy could result from FOXO-to-FOXO signaling , since FOXO is still present in the affected tissues . However , FOXO cell-nonautonomy is inconsistent with FOXO-to-FOXO signaling because in this experimental scenario FOXO is not present in the affected tissues . Instead , FOXO cell-nonautonomy suggests FOXO regulates an additional signaling pathway to affect cells where it is not present . The daf-12/nuclear hormone receptor ( NHR ) signaling pathway is an attractive candidate for a signaling pathway mediating daf-16/FOXO cell-nonautonomy . daf-12/NHR signaling is known to play a key role in coordinating a variety of systemic effects , including dauer formation , aging , and developmental timing [28] . The Rieske oxygenase DAF-36 and the cytochrome P450 DAF-9 are necessary for the production of dafachronic acid , which is a ligand for the nuclear hormone receptor DAF-12 [29–33] . Ligand-bound DAF-12 promotes dauer bypass and reproductive development , and ligand-free DAF-12 , together with its co-repressor DIN-1/SHARP , promotes dauer formation [34 , 35] . Another potential candidate for a signaling pathway mediating the effects of FOXO cell-nonautonomy is the Transforming Growth Factor-β ( TGF-β ) Sma/Mab pathway . This pathway was first identified by mutations causing small body size and male tail abnormalities [36–38] . Core pathway components include the TGF-β ligand DBL-1 , the TGF-β receptor subunits DAF-4 and SMA-6 , the SMADs SMA-2 , SMA-3 , and SMA-4 , and the SMAD co-factor SMA-9 [38–43] . The dbl-1/TGF-β pathway has also been shown to regulate reproductive aging , aversive olfactory learning , and mesodermal patterning [44–46] . Here we show that daf-16/FOXO promotes L1 arrest cell-nonautonomously . mRNA-seq reveals that daf-16/FOXO has overlapping but distinct effects on gene expression in starved L1 larvae compared to young adults with reduced insulin/IGF signaling . Our mRNA-seq analysis identified Sma/Mab TGF-β and daf-12/NHR pathways as candidate mediators of FOXO cell-nonautonomy . We show that daf-16/FOXO promotes developmental arrest throughout the animal by inhibiting expression of Sma/Mab TGF-β and daf-12/NHR steroid hormone signaling pathway components . These pathways promote development in fed L1 larvae and in starved daf-16/FOXO mutants , but their activation does not cause the early demise of daf-16/FOXO mutants during starvation .
Loss of daf-16/FOXO reduces starvation survival during L1 arrest [3 , 47] . To determine if daf-16/FOXO expression in a specific tissue is sufficient to rescue starvation survival , tissue-specific promoters were used to express GFP-tagged DAF-16 in a daf-16null background [18 , 48] . Visible DAF-16::GFP expression patterns were restricted to the tissues targeted by each promoter . Expression from the native daf-16 promoter resulted in complete rescue of the starvation survival defect while expression from neuronal ( Punc-119 ) , intestinal ( Pges-1 ) , and epidermal ( Pcol-12 ) promoters resulted in significant partial rescue ( Fig 1A , S1 Table ) . Additional transgenic lines with the same promoters provided comparable results ( S1 Fig ) . These data show that DAF-16::GFP expression in various individual tissues is sufficient to affect a whole-animal phenotype . Though it is plausible that individual tissues could have an additive effect on survival , we were unable to detect differences between single and double promoter rescues ( S1 Fig ) . These results suggest that daf-16 functions cell-nonautonomously during L1 arrest as it does for adult lifespan [16 , 18–20] . daf-16 mutants inappropriately initiate somatic postembryonic development during starvation [3] . To determine if daf-16/FOXO regulates developmental arrest cell-nonautonomously , a variety of markers were examined in tissue-specific DAF-16::GFP rescue strains . The M cell is a mesoblast that undergoes a series of divisions to produce 18 cells during L1 development [49] . The M cell lineage was visualized using a Phlh-8::GFP reporter [50] . The M cell of wild-type larvae did not divide during L1 arrest , though a significant proportion of daf-16null larvae had at least one M lineage division ( Fig 1B ) . Expression of DAF-16::GFP from the native promoter ( Pdaf-16 ) resulted in complete rescue while expression from the intestinal ( Pges-1 ) , neuronal ( Punc-119 ) and epidermal ( Pcol-12 ) promoters resulted in significant partial rescue of the daf-16null phenotype . The muscle promoter ( Pmyo-3 ) had no effect . Pairs of tissue-specific promoters also provided significant rescue but were not significantly different from rescue with single promoters , failing to provide evidence of additive effects ( S2 Table ) . There appeared to be variation between independent transgenic lines for each promoter and promoter pair , suggesting allele-specific effects can confound effects of promoter/site of expression ( S2 Fig ) . Rescue of the M cell lineage division defect by expression of DAF-16::GFP in other cells implies that daf-16/FOXO can function cell-nonautonomously during L1 arrest . We investigated cells from different lineages with different developmental fates and behaviors to determine if the cell-nonautonomous effect of daf-16/FOXO extends to development of the entire animal . The intestinal rescue line had the strongest effect of the integrated lines tested , so we used it for these assays . P cells migrate ventrally and divide , and some of their descendants differentiate into the VB motor neurons near the L1 molt [49] . The Pdel-1::GFP reporter is expressed specifically in differentiated VB motor neurons in late L1 larvae [51] . Expression was not detectable in wild type during L1 arrest , but the reporter was active in a significant proportion of daf-16null larvae , reflecting inappropriate VB differentiation ( Fig 1D and 1E ) . Expression of DAF-16::GFP from the intestinal promoter suppressed inappropriate differentiation ( Fig 1D and 1E ) . The lateral epidermal seam cells divide about five hours after hatching in fed larvae [49 , 52] . AJM-1::GFP marks the adherens junctions of the seam cells , enabling progression of seam cell development to be visualized during L1 development ( S3 Fig ) AJM-1::GFP was used to determine if seam cells v1–6 divide in starved larvae [53] . daf-16null larvae had significantly more divisions than wild type when starved ( Fig 1F and 1G ) , as expected [3] . Intestinal expression of DAF-16::GFP suppressed the division of seam cells in otherwise daf-16null animals ( Fig 1F and 1G ) . These results show that daf-16 can function cell-nonautonomously to promote developmental arrest in a variety of tissues throughout the body , consistent with systemic regulation . Cell-nonautonomous function of daf-16/FOXO suggests that it regulates at least one additional signaling pathway . FOXO-to-FOXO signaling cannot account for daf-16/FOXO cell-nonautonomy , since daf-16/FOXO is not available as an effector of insulin/IGF signaling in the affected cells . That is , when expressing daf-16/FOXO in specific tissues in a daf-16null background daf-16 is absent from the cells being assayed for division . However , feedback regulation of insulin/IGF signaling could nonetheless be responsible if an alternative effector were involved ( i . e . "FOXO-to-effector X" signaling ) . Double mutant analysis of daf-2/InsR and daf-16/FOXO showed that daf-2/InsR was not epistatic to daf-16/FOXO for M cell division ( S4 Fig ) , arguing against this possibility . Insulin-like peptides ins-4 , ins-6 , and daf-28 function redundantly to promote L1 development [54] . Simultaneous disruption of ins-4 , ins-5 , ins-6 , and daf-28 along with daf-16/FOXO showed that these insulin-like peptides were also not epistatic to daf-16/FOXO ( S4 Fig ) . The daf-2 allele used is not null ( null is lethal ) and other insulin-like peptides could be involved , but these results suggest that the cell-nonautonomous effects of daf-16/FOXO on M cell division are not mediated through insulin/IGF signaling . We used mRNA-seq for expression analysis during L1 arrest to identify candidate signaling molecules regulated by daf-16/FOXO activity . A variety of studies have investigated the effects of daf-16 on gene expression in the context of aging . These studies used fed daf-2/InsR adults as a background within which the effect of daf-16 mutation was examined . We suspected that the effects of daf-16/FOXO on gene expression during L1 arrest were sufficiently different from its effects in young adult daf-2/InsR mutants to warrant analysis of L1 arrest in a wild-type background . mRNA-seq analysis of wild type and daf-16null worms on the first day of L1 starvation identified 1 , 353 genes with reduced expression and 558 genes with increased expression in the mutant with a false-discovery rate ( FDR ) of 5% ( S1 Dataset ) . This analysis revealed widespread effects of DAF-16/FOXO on gene expression in starved L1 larvae , consistent with the phenotypic consequences of its disruption . Gene Ontology ( GO ) term enrichment analysis revealed a variety of effects on metabolic and immune system gene expression , with distinct patterns of enrichment for genes up- and down-regulated in the mutant and no overlapping terms between the two gene sets ( Fig 2A , S1 Dataset ) . Notably , the term "determination of adult lifespan" was prominently enriched among genes down-regulated in the mutant , consistent with the known role of daf-16/FOXO in promoting lifespan . These results suggest that we effectively captured the effects of daf-16/FOXO on gene expression underlying the phenotypic consequences of its disruption during L1 arrest . Our mRNA-seq analysis revealed similar but different effects of daf-16/FOXO on gene expression during L1 arrest compared to other contexts . A meta-analysis of multiple different genome-wide analyses of daf-16/FOXO-dependent gene expression in young adult animals with a daf-2/InsR mutant background has been published [9] . We compared the genes identified in our analysis to the "Class I" and "Class II" genes defined in the meta-analysis ( Class I genes have reduced expression in the daf-16/FOXO mutant , as if activated by daf-16 , and Class II genes have increased expression , as if repressed ) to validate our mRNA-seq results . We found significant overlap between Class I genes and those with decreased expression in the daf-16/FOXO mutant during L1 arrest ( "Down"; Fig 2B; hypergeometric p-value = 2 . 8e-88 ) . We also found significant overlap between Class II genes and those with increased expression in the daf-16/FOXO mutant during L1 arrest ( "Up"; hypergeometric p-value = 5 . 3e-87 ) . These observations corroborate our results in that there is significant overlap with the effects of activating daf-16/FOXO via starvation in L1 larvae compared to activating daf-16/FOXO with reduction of daf-2/InsR function in adults . However , because of differences in experimental design , this comparison also allowed us to determine the extent to which the genes affected by daf-16/FOXO activity depends on stage ( L1 larvae vs . young adults ) and condition ( wild type vs . daf-2 mutant and starved vs . fed ) . Despite the significance of the overlap , the majority of the Up and Down genes we identified were not identified as Class II or Class I genes , respectively ( Fig 2B ) . This result shows that daf-16/FOXO-dependent effects on gene expression are sensitive to stage and/or condition . We examined the Up and Down genes for candidate signaling molecules that could mediate cell-nonautonomous effects of daf-16/FOXO . dbl-1/TGF-β expression was increased 2 . 1-fold in the mutant ( FDR = 0 . 8%; Fig 2C; S1 Dataset ) , though other components of the Sma/Mab pathway were not significantly affected . In addition , expression of the Rieske oxygenase daf-36 was increased 3 . 6-fold ( FDR = 3% ) . DAF-36 is necessary to produce the ligand for DAF-12/NHR , and daf-12/NHR expression was increased 1 . 7-fold ( FDR = 11% ) , though only marginally significant . Other components of the daf-12/NHR pathway were not significantly affected . The NanoString nCounter platform was used to measure transcript abundance and validate the mRNA-seq results for dbl-1 , daf-36 , and daf-12 . dbl-1 and daf-12 were expressed significantly higher in daf-16null mutants ( p<0 . 005; S5 Fig ) . Although daf-36 showed a marginally significant increase in expression ( p = 0 . 04 ) , it was below the limits of reliable detection for this assay so we performed qRT-PCR [55] . qRT-PCR of daf-36 showed an increase of 1 . 7-fold in the mutant ( SEM = 0 . 32; p = 0 . 01 ) . These results suggest daf-16/FOXO activity during L1 arrest leads to repression of dbl-1/TGF-β , daf-36 and daf-12/NHR . Given the effect of daf-16/FOXO on the dbl-1/TGF-β and daf-36/oxygenase pathways , we hypothesized that the transcription factors SMA-9/co-SMAD , the downstream effector of dbl-1 signaling , and DAF-12/NHR may contribute to the differential expression of genes in the daf-16null mutant . We performed transcription factor binding motif enrichment analysis for DAF-16 ( DBE motif; positive-control ) , SMA-9/co-SMAD ( DBD-1 and DBD-2 motifs of the homolog Schnurri ) , and DAF-12/NHR ( M-2 motif ) [56–58] . We also performed motif enrichment analysis for PQM-1 ( DAE motif ) , a transcription factor known to be associated with both Class I and Class II genes but not implicated in young larvae [9] . As expected , we found enrichment for the DAF-16 motif ( DBE ) in the Down genes ( p = 9 . 4x10-6 ) . We also found enrichment for the PQM-1 motif ( DAE ) in both Up and Down genes ( p = 3 . 1x10-47 and p = 1 . 0x10-7 , respectively ) , suggesting a functional role of pqm-1 in L1 larvae . We failed to detect enrichment of the DAF-12/NHR motif in either set of genes . However , DAF-12/NHR may act on a small but functionally important set of genes , and DAF-16/FOXO is likely to regulate other genes that affect expression , both of which could limit our ability to detect enrichment of the DAF-12/NHR motif . We found enrichment of the SMA-9/co-SMAD motif ( DBD-1 ) in the Down genes ( p = 0 . 023 ) . SMA-9/SMAD has been shown to act as both a transcriptional activator and repressor [59] , and this result suggests it actively represses a significant number of the Down genes . This result suggests that a subset of DAF-16-regulated genes are direct targets of the dbl-1/TGF-β signaling pathway . We used genetic epistasis analysis to determine if the effects of daf-16/FOXO on gene expression identified by mRNA-seq are functionally significant in vivo . Specifically , we hypothesized that up-regulation of daf-12/NHR signaling in the daf-16null mutant contributes to its arrest-defective phenotype . Components of the pathway daf-36/oxygenase , daf-9/CYP450 , and daf-12/NHR were epistatic to daf-16/FOXO with respect to M cell division ( Fig 3A and 3D ) . daf-12 ( m20 ) is a null allele for all but the B isoform , which is a short isoform containing the ligand-binding domain ( LBD ) but not the DNA-binding domain ( DBD ) . rh61rh411 is a null allele for all daf-12 isoforms . daf-12 ( rh273 ) contains a substitution in the LBD , which is hypothesized to interrupt ligand binding [34] . All three alleles suppressed the daf-16null arrest-defective phenotype . din-1/SHARP collaborates with ligand-free daf-12 as a co-repressor in promoting dauer formation [35] . din-1 was dispensable for the daf-16null arrest-defective phenotype ( Fig 3A ) , suggesting that ligand-bound daf-12/NHR promotes M cell division and that it is normally inhibited by daf-16/FOXO activity during starvation . Pharmacological manipulation of the daf-12/NHR pathway corroborated genetic analysis . Dafadine is a small molecule inhibitor of daf-9 [60] . Wild-type and daf-16null worms were exposed to dafadine or dafachronic acid during starvation . Dafachronic acid did not cause an arrest-defective phenotype , suggesting that the steroid hormone pathway is not sufficient to promote development . Dafadine suppressed M cell lineage divisions in daf-16null worms ( Fig 3B ) , phenocopying daf-9 ( m540 ) . These results further support the conclusion that daf-16/FOXO activity inhibits daf-12/NHR signaling during L1 arrest , and that this inhibition is functionally significant . Given the mRNA-seq results , we also hypothesized that up-regulation of dbl-1/TGF-β signaling in the daf-16null mutant contributes to its arrest-defective phenotype . Mutations affecting dbl-1/TGF-β and its downstream effector sma-9/co-SMAD suppressed the daf-16null arrest-defective phenotype ( Fig 3C and 3D ) . These results suggest that daf-16/FOXO also inhibits dbl-1/TGF-β signaling during L1 arrest , and that this inhibition has physiological consequences . daf-12/NHR and dbl-1/TGF-β signaling were also required for inappropriate seam cell divisions in starved daf-16null worms . Both daf-36 and dbl-1/TGF-β were epistatic to daf-16/FOXO for seam cell division ( Fig 3E and 3F ) . Overall , these data suggest that daf-16/FOXO activity leads to repression of both Sma/Mab TGF-β and daf-12/NHR signaling during starvation to promote developmental arrest . We hypothesized that daf-12/NHR and dbl-1/TGF-β signaling promote L1 development in fed larvae , since they are required for the daf-16null arrest-defective phenotype , though such early larval function has not been shown . We used Phlh-8::GFP , AJM-1::GFP and molting to monitor the rate of L1 development in fed larvae with mutations in these pathways . dbl-1/TGF-β and sma-9/co-SMAD mutants clearly had delayed M cell lineage divisions ( Fig 4A ) , consistent with our hypothesis . The daf-36 mutant also had significantly fewer M cell divisions on average than wild type ( Fig 4B ) , suggesting that ligand-bound DAF-12 promotes L1 development . daf-12 ( rh273 ) and daf-12 ( rh274 ) , which have a constitutive dauer-formation phenotype due to mutations in the LBD , also caused developmental delay ( Fig 4B ) . These results suggest that ligand-free DAF-12 represses L1 development , analogous to its repression of reproductive development during dauer formation . However , the null alleles of daf-12 had no effect ( Fig 4C ) , which we believe is due to the opposing effects of ligand-bound and -free forms of DAF-12 . This interpretation is supported by the fact that these same alleles do not cause a constitutive dauer-formation phenotype [34] . The null allele for din-1 , the co-repressor for ligand-free DAF-12 , also had no effect on M cell lineage division rate , as expected based on our model and its role in regulation of dauer formation . daf-9 ( m540 ) is a suspected hypomorph [30] , and residual activity appeared to be sufficient for normal developmental rate ( Fig 4C ) . These results support the conclusion that daf-12/NHR and dbl-1/TGF-β signaling promote M cell lineage division in fed L1 larvae . We assessed seam cell divisions and molting progression in fed larvae to broaden our developmental analysis , examining developmental events that occur before and after M cell division , respectively . daf-36 and dbl-1/TGF-β mutants had significantly fewer seam cell divisions than wild type ( Fig 4D ) . L1 larvae have a stage-specific cuticular ridge ( alae ) on their lateral midline that is absent from L2 larvae . The presence or absence of alae was scored , along with detached cuticles , to identify L1 or L2 larvae , and larvae undergoing the L1 molt , respectively . The L1 molt was significantly delayed in mutants of the daf-36 and dbl-1 pathways compared to wild type ( Fig 4E ) . These data further support the conclusion that Sma/Mab TGF-β and daf-12/NHR signaling promote L1 development in fed larvae . Does initiation of post-embryonic development in daf-16null cause reduced starvation survival ? dbl-1/TGF-β and sma-9/co-SMAD were not epistatic to daf-16/FOXO for starvation survival ( Fig 5A; S3 Table ) . Mutations affecting steroid hormone pathway components daf-36 , daf-9 , and daf-12/NHR also did not affect starvation survival in a wild-type or daf-16null background ( Fig 5B; S3 Table ) . din-1/SHARP and daf-12/NHR null alleles also had no effect on starvation survival in either background ( Fig 5C; S3 Table ) . Together these results suggest that dbl-1/TGF-β and daf-12/NHR signaling do not affect starvation survival , implying that development during starvation is not the cause of daf-16null starvation sensitivity .
This work shows that daf-16/FOXO functions cell-nonautonomously to regulate C . elegans L1 arrest . A recent study corroborates this finding , highlighting the need to determine the molecular basis of cell-nonautonomous function [61] . The dbl-1/TGF-β and daf-12/NHR signaling pathways have not been reported to affect early larval development , but our results reveal that these pathways promote L1 development in fed larvae . mRNA expression analysis together with genetic epistasis analysis suggest that daf-16/FOXO inhibits these pathways to promote developmental arrest . Taken together , this work shows that daf-16/FOXO promotes L1 arrest by inhibiting pathways that promote development ( Fig 6 ) . Our results suggest that daf-16/FOXO can function in the intestine , nervous system or epidermis to promote starvation survival and developmental arrest . We analyzed four independent transgenic lines per tissue-specific rescue construct . Apparent allele-specific effects confounded our ability to distinguish tissue-specific effects on rescue . Variation in promoter strength may also obscure tissue-specific effects . These technical complications limit our ability to conclude whether daf-16/FOXO activity in one tissue is functionally more important than another . Though we could not statistically distinguish the effects of rescue in intestine , nervous system or epidermis , we clearly found that muscle rescue had no effect on starvation survival or developmental arrest . Expression in the intestine , nervous system or epidermis was sufficient to partially rescue daf-16null , but rescue was not complete , though it was complete when the native daf-16 promoter was used . We considered that additive effects of multiple tissues might explain this finding , but we did not observe a significant change in penetrance when pairs of promoters were used for rescue . Our starvation survival assay may have lacked sufficient dynamic range to observe this . Alternatively , and particularly with respect to developmental arrest , different components of a single pathway may be regulated by DAF-16/FOXO in different tissues such that regulation in any single tissue is sufficient to disrupt the pathway . Consistent with this interpretation , dbl-1/TGF-β is expressed in neurons while its receptor and downstream SMADs are expressed broadly [40 , 41 , 43 , 62 , 63] . Likewise , daf-36 is expressed in the intestine , daf-9/CYP450 is expressed in the epidermis and XXX cells , and daf-12/NHR is expressed broadly [29 , 32 , 34 , 64] . We speculate that DAF-16/FOXO functions in several of these tissues to inhibit these genes directly or indirectly . Anatomical sites of action for the insulin/IGF signaling pathway have been characterized for regulation of dauer formation , aging , reproductive aging , developmental arrest and starvation survival . These analyses have produced largely overlapping results , implicating primarily the intestine , nervous system and epidermis . Rescue of daf-2/InsR or age-1/PI3K suggested the nervous system as a key site of action in regulation of aging , but rescue of daf-16/FOXO in a daf-2/InsR mutant background suggested the intestine [18 , 19] . However , because daf-2 and age-1 antagonize daf-16 , these two experimental designs should not be expected to produce the same results . Nonetheless , both studies actually found some effect in each site . In addition , a subsequent study found that epidermal rescue of daf-16/FOXO in a daf-2/InsR mutant background can also rescue lifespan [20] . Curiously , daf-16/FOXO expression in the intestine was originally found not to affect dauer formation while neural expression did [18] , but more recently it was reported that daf-16/FOXO expression in the intestine determines dauer formation without contribution from neurons [17] . It is unclear why there are such discrepancies among these results , though specific experimental conditions , promoters , and in some cases limited numbers of transgenic alleles could contribute . Muscle expression of daf-16/FOXO was not found to affect lifespan or dauer formation in any of these studies , but it does affect reproductive aging [65] . Overlapping but discordant results are also evident with respect to regulation of developmental arrest . For example , the daf-16/FOXO target mir-235 , which robustly phenocopies daf-16null , was found to function in the epidermis and nervous system to promote developmental arrest in starved L1 larvae [66] . In contrast , transgenic rescue of daf-18/PTEN , which is thought to act through daf-16/FOXO for arrest of somatic cells , highlighted the epidermis as the primary site of action but also showed that it can function cell-autonomously as well [61] . Likewise , rescue of daf-16/FOXO in the context of starvation-induced post-dauer arrest also implicated the epidermis [48] . Though we found the epidermis to be an important site of daf-16/FOXO action in regulation of L1 arrest , our results also suggest that the nervous system and intestine are important . Neurons and intestine also appear to be important sites of action upstream of daf-2/InsR and daf-16/FOXO . That is , the insulin-like peptides ins-4 , ins-6 , and daf-28 have been shown to be regulators of L1 arrest and dauer formation with expression in chemosensory and motor neurons as well as intestine [17 , 54] . As the organismal signaling network mediating nutritional control of dauer formation , aging , and developmental arrest is characterized it is increasingly clear that it is complex , being distributed over several different tissues and likely involving feedback and crosstalk at a variety of levels . Our results reveal that dbl-1/TGF-β and daf-12/NHR signaling pathways are each required for the daf-16null arrest-defective phenotype . However , the dbl-1/TGF-β and daf-12/NHR signaling pathways do not affect starvation survival , uncoupling control of development and starvation resistance , as seen with mir-235 [66] . This finding is also consistent with previous work showing no effect of daf-12/NHR on starvation survival [67] . Such uncoupling suggests that daf-16/FOXO acts through different target genes to promote starvation resistance and developmental arrest , and that daf-16/FOXO mutants do not die rapidly during starvation as a result of inappropriate development . It has been suggested that DAF-16 functions as a transcriptional activator [9 , 68 , 69] , implying that repression of dbl-1/TGF-β and daf-12/NHR signaling is indirect . Our mRNA-seq analysis presumably captures direct and indirect effects , but it identified 2 . 4 times more genes with decreased expression in daf-16null than with increased expression , consistent with daf-16/FOXO having more effect as an activator than a repressor . However , DAF-16/FOXO directly binds the promoter of daf-12/NHR as well as components of the dbl-1/TGF-β pathway , including the SMADs sma-2 and sma-3 and sma-9/co-SMAD , based on DamID analysis in adults [69] . Likewise , modENCODE data for DAF-16 ChIP-seq in L3-stage larvae suggests direct binding to daf-12/NHR and the upstream cytochrome P450 daf-9 as well as the TGF-β Sma/Mab receptor subunits sma-6 and daf-4 , and sma-2 and sma-3 [9 , 70] . In Drosophila , dFOXO directly binds the promoter of the TGF-β ligand dawdle and the daf-12/NHR homolog dHR96 [71 , 72] . These observations suggest that daf-16/FOXO regulation could actually be direct . daf-16/FOXO activates the microRNA mir-235 during L1 arrest [66] , which could function as an intermediate if regulation is indirect . However , mir-235 is not expressed in and does not function in the intestine , a site of daf-16/FOXO action based on our results . DAF-16/FOXO antagonizes the transcription factor PQM-1 , which promotes expression of genes up-regulated in daf-16/FOXO mutants [9] , suggesting it too could function as an intermediate . Consistent with this possibility , we found significant enrichment of the PQM-1 binding site motif ( DAE ) among genes up- and down-regulated in daf-16null , suggesting PQM-1 contributes to gene regulation during L1 arrest . In addition , insulin/IGF signaling and the Rag-TORC1 pathway crosstalk in regulation of L1 arrest [61] , providing an additional possibility for indirect effects . It is also important to note that the changes in expression observed for daf-36 , daf-12 , and dbl-1 in daf-16null were relatively small , suggesting that post-transcriptional mechanisms could also contribute to regulation . FOXO regulates TGF-β and NHR signaling in other contexts . daf-16/FOXO functions upstream of daf-36/oxygenase , daf-9/CYP450 , and daf-12/NHR in dauer formation [14 , 73] . daf-9 also functions downstream of daf-16/FOXO in progression through post-dauer developmental checkpoints , though daf-12/NHR was not implicated [48] . In addition , dbl-1/TGF-β and insulin/IGF signaling both participate in pathogen-associated olfactory learning [10 , 46] . Regulation of TGF-β signaling by insulin/IGF signaling has not been reported in C . elegans or mammals , but dFOXO represses the TGF-β ligand dawdle in Drosophila , affecting lifespan [71] . These observations suggest that FOXO regulation of TGF-β and steroid hormone signaling pathways is broadly significant and conserved among metazoa .
Strains were maintained on agar plates containing standard nematode growth media ( NGM ) seeded with E . coli OP50 at 20°C . Strains containing the alleles daf-12 ( rh273 ) and daf-12 ( rh274 ) were maintained at 15°C . The wild-type strain N2 ( Bristol ) and the following mutants and transgenes were used: daf-2 ( e979 ) , daf-2 ( e1370 ) , daf-16 ( mgDf47 ) , daf-16 ( mgDf50 ) , daf-16 ( mu86 ) , ins-4 ins-5 ins-6 ( hpDf761 ) , daf-28 ( tm2308 ) , daf-36 ( k114 ) , daf-9 ( m540 ) , daf-9 ( dh6 ) , daf-12 ( m20 ) , daf-12 ( rh273 ) , daf-12 ( rh274 ) , daf-12 ( rh61rh411 ) , din-1 ( dh127 ) , dbl-1 ( wk70 ) , sma-9 ( wk55 ) , ayIs7[Phlh-8::GFP] , syIs78[Pajm-1::AJM-1::GFP] , wdIs3[Pdel-1::GFP] , qyEx264[Pmyo-3::GFP::DAF-16] , qyIs288[Pdaf-16::GFP::DAF-16] , qyIs290[Pcol-12::GFP::DAF-16] , qyIs292[Pges-1::GFP::DAF-16] , qyIs294[Punc-119::GFP::DAF-16] , dukEx88–90[Phlh-8::GFP +unc-119 ( + ) + Pcol-12::DAF-16::GFP] , dukEx91 , 92 , 103[Phlh-8::GFP +unc-119 ( + ) + Pges-1::DAF-16::GFP] , dukEx93–95[Phlh-8::GFP +unc-119 ( + ) + Pcol-12::DAF-16::GFP + Pges-1::DAF-16::GFP] , dukEx96–98[Phlh-8::GFP +unc-119 ( + ) + Punc-119::DAF-16::GFP + Pges-1::DAF-16::GFP] , dukEx99[Phlh-8::GFP +unc-119 ( + ) ] , dukEx100–102[Phlh-8::GFP +unc-119 ( + ) + Pcol-12::DAF-16::GFP + Punc-119::DAF-16::GFP] , dukEx104–106[Phlh-8::GFP +unc-119 ( + ) + Punc-119::DAF-16::GFP] , dukEx107–109[Phlh-8::GFP +unc-119 ( + ) + Pdaf-16::DAF-16::GFP] , and dukEx110–112[Phlh-8::GFP +unc-119 ( + ) + Pmyo-3::DAF-16::GFP] . Standard genetic techniques were used to create different combinations of alleles . Mixed-stage cultures on 10 cm NGM plates were washed from the plates using S-basal and centrifuged . A hypochlorite solution ( 7:2:1 ddH2O , sodium hypochlorite ( Sigma ) , 5 M KOH ) was added to dissolve the animals . Worms were centrifuged after 1 . 5–2 minutes in the hypochlorite solution and fresh solution was added . Total time in the hypochlorite solution was 8–10 minutes . Embryos were washed three times in S-basal buffer ( including 0 . 1% ethanol and 5 ng/μL cholesterol ) before final suspension in 5 mL S-basal at a density of 1 . 5 worms/μL . Embryos were cultured in a 16 mm glass tube on a tissue culture roller drum at approximately 25 rpm and 21–22°C . For the M cell division and VB motor neuron differentiation assays during starvation , the larvae were starved for 7 days before 200 larvae per replicate were examined on a slide on a compound fluorescent microscope . For the dafadine and dafachronic acid experiments , 25 μM DMSO , 25 μM dafadine , or 50 nM Δ7-dafachronic acid was added to the final suspension in S-basal . For the seam cell division assay during starvation , the larvae were starved for 3 days and the v1–6 cells on one side of the animal were scored for 60–70 larvae per replicate . The data for wild type ( N2 ) was published previously ( GEO accession number GSE33023; "0 hr recovery" ) [74] , and daf-16null data are first reported here . Two biological replicates were performed . Worms were cultured , RNA was prepared and sequencing and analysis were done as described [74] . The only exceptions to this pertain to the software used to count reads aligning to genes . Briefly , liquid culture was used and total RNA was prepared with TRIzol ( Invitrogen ) . Strain GR1307 [daf-16 ( mgDf50 ) ] was used . mRNA was isolated by polyA-selection . Sequencing libraries were prepared using the Solid Total RNA-seq Kit using the Whole Transcriptome Protocol ( Applied Biosystems ) . Twelve PCR cycles were used to amplify the libraries . Fifty base pair single-end reads were sequenced on the Solid 4 system according to the manufacturer's protocols in the Genome Sequencing Shared Resource at Duke University . Sequencing reads were mapped using Bowtie v0 . 12 . 7 allowing two mismatches and requiring unique alignments [75] and genome coordinates for WS210 . The script htseq-count [76] was used to count reads mapping to genes defined using WS220 annotation that had been mapped to WS210 coordinates . Reads were counted using the options “-m union–s yes” . DESeq v1 . 20 . 0 was used to analyze differential gene expression [77] . Cufflinks v2 . 1 . 1 was used to determine fragments per kilobase per million ( FPKM; [78] ) . GO term enrichments were identified using GOrilla [79] . Enrichments were plotted in semantic space using REVIGO [80] . Specific parameters used with GOrilla and REVIGO are documented in S1 Dataset . GEO accession number for the daf-16null dataset is GSE69329 . Enrichment for DBE ( DAF-16 ) and DAE ( PQM-1 ) [9] , SMAD DBD-1/DBD-2 ( SMA-9 ) [56] and M-2 ( DAF-12 ) [57] motifs were calculated using the AME application of the MEME software package [58] . The primary sequences scanned were the 700 bp upstream of the most upstream translation start sites of the Up and Down genes ( Fig 2B ) from version WS220 of the C . elegans genome . The background control promoter sequences ( N = 2350 ) was comprised of all genes well detected in the mRNA data ( expression in both WT and DAF-16 being in the top 75% percentile ) and a having a strong lack of evidence of differential expression between wild-type and the DAF-16 mutant ( adjusted p-value > 0 . 9 ) . For each motif the background model was set to uniform , and the remaining AME parameters were default . NanoString expression analysis was conducted as described with the following exceptions [54] . All five strains were cultured at 15°C since daf-2 mutants are temperature sensitive . Worms were cultured in liquid at 180 rpm and 4–5 worms/μL with 40 mg/ml HB101 as food . N2 , daf-16 ( mgDf47 ) , and daf-16 ( mgDf47 ) ; daf-2 ( e1370 ) were bleached after 5 d culture as young gravid adults . daf-2 ( e1370 ) and daf-2 ( e979 ) were bleached at 6 d and 7 d , respectively , as young gravid adults . For each strain , embryos were divided into three flasks of S-basal at 5 worms/μL and cultured at 15°C , 20°C or 25°C at 180 rpm . Arrested L1 larvae were collected 24 hr after bleaching for 20°C and 25°C , and after 48 hr for 15°C . Larvae were washed , pelleted and flash frozen . Two or three biological replicates were included for each strain . Total RNA was prepared with TRIzol , and 3 μg was used for each hybridization . Data were first normalized by spike-in controls and then normalized by three internal control genes included as targets in the codeset ( grld-1 , rnf-5 and T16G12 . 6 ) . Internal controls were identified from genome-wide time-series analysis of fed and starved L1 larvae by virtue of moderate expression levels and invariant expression over time and between conditions [74 , 81] . RNA was collected from N2 and daf-16 ( mgDf50 ) after 1 day of starvation in S-basal . cDNA was synthesized from 1 μg of total RNA using oligonucleotide ( dT ) primers and SuperScript III Reverse Transcriptase ( Thermo Fisher ) . qPCR was performed with Brilliant II QPCR Master Mix ( Agilent ) according to manufacturer’s protocol . The genes T16G12 . 6 , rnf-5 , and grld-1 were used as internal controls ( also used as internal controls for NanoString analysis ) . Primers were PrimeTime qPCR primers from IDT as follows ( in the order of forward , probe , reverse ) : daf-36 , ATCACAGACTCATATTGCCCG , TGTCACGTACTACCCGTCCTCCAA , ACACATTTTCCAGTTTCTGCAC; T16G12 . 6 , CACCACAGACACAAGAACACTA , AACCATACGGGACATCAGCCCTTG , CGGCCAAATTGAAGCGAATC; rnf-5 , AACCACCACCGCAATCAT , ATGCACATTTGGTCCGCCGC , TCAACGGGAACAGACCATTC; grld-1 , AAGCTGCAGGCGTTGTAA , TGGGAAGATGTAGAGAATGCCGCC , AAGAGCTCCGAGCAAGAATG . Three technical replicates were performed for each of three biological replicates . Standard curves were analyzed to determine reaction efficiency . daf-36 was normalized based on the internal controls and reaction efficiencies to calculate a fold-change between N2 and daf-16 null . An unpaired t-test was performed to determine significance . Following hypochlorite treatment , cultures were synchronized by overnight passage in L1 arrest at a density of 1 . 5 worms/μL in 5 mL S-basal . For recovery and development , 2 , 000 arrested L1 larvae were plated per NGM OP50 plate and placed at 20°C for 6 hours ( AJM-1 marker assay ) , 12 hours ( M cell marker assay ) , or 18 hours ( molting progression assay ) . Larvae were then washed off the plate with S-basal , centrifuged , and mounted on an agarose pad . A compound fluorescent microscope was used to score cell divisions or cuticle structure in 200 worms ( M cell assay ) or 75 worms ( seam cell and molting progression assays ) per replicate . Animals were treated in hypochlorite solution and suspended in S-basal as described above . 100 μL aliquots were sampled on different days and placed around the edge of an OP50 lawn on NGM plates . Number of plated worms ( Tp ) was counted and the plates were incubated at 20°C . After two days the number of animals that survived ( Ts ) was counted . Survival was calculated as Ts/Tp . The daf-16 tissue-specific starvation survival experiments were done with an alternative protocol . Instead of S-basal , virgin S-basal ( no ethanol or cholesterol ) was used for both hypochlorite treatment and final suspension . Worms were suspended at 1 worm/μL . For the integrated strains , 100 μL aliquots were sampled every two days and spontaneous movement in liquid was used to score survival . For the strains with an extrachromosomal array , the culture was centrifuged after seven days and the pelleted worms were placed on NGM plates . Worms expressing GFP were then scored as alive or dead based on spontaneous movement . Data were handled in R and Excel . Graphs were plotted in the R package ggplot2 or Excel . Statistical tests were performed in R or Excel . Starvation survival analysis was performed on 50% survival times ( thalf ) , which were obtained by fitting survival data for each trial with the function S=100−1001+e ( thalf−t ) /rate which we have modified slightly from [82] . Goodness of fit is reported as R2 in S1 and S3 Tables . | Animals must cope with feast and famine in the wild . Environmental fluctuations require a balancing act between development in favorable conditions and survival during starvation . Disruption of the pathways that govern this balance can lead to cancer , where cells proliferate when they should not , and metabolic diseases , where nutrient sensing is impaired . In the roundworm Caenorhabditis elegans , larval development is controlled by nutrient availability . Larvae are able to survive starvation by stopping development and starting again after feeding . Stopping and starting development in this multicellular animal requires signaling to coordinate development across tissues and organs . How such coordination is accomplished is poorly understood . Insulin/insulin-like growth factor ( IGF ) signaling governs larval development in response to nutrient availability . Here we show that insulin/IGF signaling activity in one tissue can affect the development of other tissues , suggesting regulation of additional signaling pathways . We identified two pathways that promote development in fed larvae and are repressed by lack of insulin/IGF signaling in starved larvae . Repression of these pathways is crucial to stopping development throughout the animal during starvation . These three pathways are widely conserved and associated with disease , suggesting the nutrient-dependent regulatory network they comprise is important to human health . | [
"Abstract",
"Introduction",
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"Methods"
] | [] | 2015 | dbl-1/TGF-β and daf-12/NHR Signaling Mediate Cell-Nonautonomous Effects of daf-16/FOXO on Starvation-Induced Developmental Arrest |
The Gram-negative bacterium Proteus mirabilis is a leading cause of catheter-associated urinary tract infections ( CAUTIs ) , which are often polymicrobial . Numerous prior studies have uncovered virulence factors for P . mirabilis pathogenicity in a murine model of ascending UTI , but little is known concerning pathogenesis during CAUTI or polymicrobial infection . In this study , we utilized five pools of 10 , 000 transposon mutants each and transposon insertion-site sequencing ( Tn-Seq ) to identify the full arsenal of P . mirabilis HI4320 fitness factors for single-species versus polymicrobial CAUTI with Providencia stuartii BE2467 . 436 genes in the input pools lacked transposon insertions and were therefore concluded to be essential for P . mirabilis growth in rich medium . 629 genes were identified as P . mirabilis fitness factors during single-species CAUTI . Tn-Seq from coinfection with P . stuartii revealed 217/629 ( 35% ) of the same genes as identified by single-species Tn-Seq , and 1353 additional factors that specifically contribute to colonization during coinfection . Mutants were constructed in eight genes of interest to validate the initial screen: 7/8 ( 88% ) mutants exhibited the expected phenotypes for single-species CAUTI , and 3/3 ( 100% ) validated the expected phenotypes for polymicrobial CAUTI . This approach provided validation of numerous previously described P . mirabilis fitness determinants from an ascending model of UTI , the discovery of novel fitness determinants specifically for CAUTI , and a stringent assessment of how polymicrobial infection influences fitness requirements . For instance , we describe a requirement for branched-chain amino acid biosynthesis by P . mirabilis during coinfection due to high-affinity import of leucine by P . stuartii . Further investigation of genes and pathways that provide a competitive advantage during both single-species and polymicrobial CAUTI will likely provide robust targets for therapeutic intervention to reduce P . mirabilis CAUTI incidence and severity .
The Gram-negative bacterium Proteus mirabilis thrives in a wide variety of environments , including soil , water sources , sewage , and as a commensal in the intestinal tract of humans and animals [1 , 2] . P . mirabilis has also been identified as the causative agent of numerous human illnesses including cystitis , pyelonephritis , prostatitis , as well as intra-abdominal , wound , eye , and burn infections [2] . While it is capable of causing uncomplicated urinary tract infections ( UTIs ) , this organism is a much more common cause of catheter-associated UTI ( CAUTI ) [3–5] . Indeed , we recently identified P . mirabilis as the most common cause of CAUTIs in twelve Michigan nursing homes [6] . CAUTIs are frequently polymicrobial [7 , 8] , and P . mirabilis is one of the most common organisms present during polymicrobial urine colonization and infection [3 , 6] . UTIs and CAUTIs involving P . mirabilis are typically complicated by the formation of bladder and kidney stones ( urolithiasis ) and permanent renal damage [9–11] , and may progress to bacteremia [12 , 13] . Despite these potentially severe complications of P . mirabilis infection , there are no currently licensed vaccines available for this organism and multidrug-resistant isolates are increasingly common [14 , 15] . No previous studies have explored P . mirabilis genes essential for growth in vitro , but numerous fitness and virulence factors have been examined in a murine model of ascending UTI , including but not limited to urease , fimbriae , and hemolysin ( see [2 , 16] for review ) . Fitness factors for colonization of the urinary tract have also been explored in three non-saturating signature-tagged mutagenesis ( STM ) studies in the ascending UTI model using P . mirabilis strain HI4320 , which achieved a combined 70% theoretical coverage of the genome and revealed traditional virulence factors , metabolic pathways important for infection , and fitness factors with no prior links to P . mirabilis pathogenicity [17–19] . For P . mirabilis infection studies , the murine model of ascending UTI is generally considered to represent complicated UTI due to the occurrence of urolithiasis in this infection model [20] . However , we recently adapted a murine model of CAUTI for investigation of P . mirabilis pathogenicity , and verified that maintenance of a catheter segment within the bladder dramatically increases inflammation and infection severity [21] . Thus , different genes and pathways are likely required for fitness in the catheterized bladder environment than those identified in the ascending UTI model . An additional consideration in identifying fitness and virulence factors of P . mirabilis is the frequent involvement of this organism in polymicrobial infections . We recently determined that other uropathogens , such as Escherichia coli , Enterococcus species , and Providencia stuartii , are capable of influencing P . mirabilis urease activity [21] . Co-culture with P . stuartii also enhanced P . mirabilis cytotoxicity independent of urease activity , altered the immune response to infection , and resulted in greater tissue damage , indicating that other virulence factors are affected by polymicrobial interactions [21] . All prior studies of P . mirabilis fitness factors have been conducted with pure cultures of P . mirabilis in isolation , yet the potential clearly exists for other organisms to influence expression of P . mirabilis virulence factors , metabolic requirements , and factors required for adaptation to changes in the bladder environment in response to the presence of a second pathogen . In this study , we generated a genome-saturating library of transposon mutants and utilized transposon insertion-site sequencing ( Tn-Seq ) to identify P . mirabilis genes essential for growth in rich medium and the full arsenal of fitness factors for single-species infection in a CAUTI model , while concurrently determining how fitness requirements changed during polymicrobial infection with P . stuartii , a common co-colonizing partner of P . mirabilis [3 , 4 , 22–25] .
Based on the P . mirabilis HI4320 genome size ( 4 . 063 Mbp , with approximately 3747 genes [26] ) , it is estimated that 34 , 249 transposon mutants are required for 99 . 99% probability of full genome coverage [27] . A similar strategy as detailed by Crimmins et al . [28] for maximum colonization density was used to determine the appropriate transposon library pool density . From our recent investigation of experimental CAUTI in CBA/J mice , the minimum colonization density achieved by P . mirabilis in the urine , bladder , and kidneys of mice 4-days post inoculation is expected to be ~1x104 CFU [21] . Preliminary experiments confirmed a minimum bladder colonization of 1x104 CFU/gram of tissue at 4-h and 4-days post-inoculation , and indicated lack of a significant bottleneck in the CAUTI model ( S1 Fig ) . We therefore concluded that generation of transposon pools containing 1x104 transposon mutants would be ideal . Approximately 50 , 000 transposon mutants from three independent matings were collected and pooled in groups of 1x104 mutants to generate five transposon mutant libraries . Randomness of insertions was verified by Southern blot , and the majority of mutants harbored only one transposon insertion as expected ( S2 Fig ) . Raw Illumina reads were filtered and transposon insertion-sites were uniquely mapped to genomic coordinates using a method adapted from the previously published work by Goodman et al . [29] . Saturation was achieved for the P . mirabilis HI4320 chromosome and 36 Kb plasmid ( pHI4320 ) , and the majority of insertion-sites were represented in three or more input pools ( Fig 1 ) . However , there were a few noticeable gaps in both the chromosome and plasmid maps . To determine if these gaps represented genes essential for growth of P . mirabilis in LB medium , a Bayesian mixture model was used to estimate essentiality based on absence or underrepresentation of transposon insertions in these genes within the input pools ( see Materials and methods ) . The model identified 436 genes ( 11 . 6% of the 3747 genes encoded by P . mirabilis HI4320 ) as potentially essential for P . mirabilis growth ( S1 Table ) , 279 ( 64 . 0% ) of which were present in the bacterial section of the Database of Essential Genes ( DEG , http://www . essentialgene . org/ ) . Three hundred thirty-three of the estimated essential genes had a Cluster of Orthologous Groups of proteins ( COG ) assignment ( Fig 2 ) . Among the list of P . mirabilis estimated essential genes are numerous genes listed as essential in the majority of bacterial species present in DEG , including genes pertaining to cell cycle control and division ( such as the fts cell division proteins ) , cell wall biogenesis , replication ( including DNA polymerase I , DNA polymerase III , and DNA gyrase ) , RNA polymerase σ70 ( rpoD ) , σ32 ( rpoH ) , and σ24 ( rpoE ) , nucleoid protein H-NS ( hns ) , numerous ribosomal proteins and tRNA synthetases , and ATP synthase . There are five ATP-dependent proteases in bacteria that comprise the majority of energy-dependent protein degradation: HflB ( FtsH ) , the Clp proteases ( ClpAP , ClpXP , and ClpYQ ) , and the Lon protease [30] . Insertions in three of these proteases ( HflB , ClpAP , and ClpXP ) were absent from the input pools and therefore identified as essential for growth of P . mirabilis in rich medium , underscoring the importance of degrading misfolded and unstable proteins for P . mirabilis growth in rich medium . The replication initiation protein PMIP01 on the P . mirabilis plasmid ( pHI4320 ) was also identified as essential . Infection studies to screen for P . mirabilis fitness factors during single-species and polymicrobial CAUTI were conducted in parallel to utilize the same inocula as the input samples for each infection type . A conceptual model of the infection and sequencing scheme is shown in Fig 3 . For each of the five transposon library pools , 5–10 CBA/J mice were transurethrally inoculated with 1x105 CFU ( 10X coverage of each mutant within the pool ) , and 5–10 CBA/J mice were inoculated with 1x105 CFU of a 1:1 mixture of the transposon library and wild-type P . stuartii BE2467 ( 5x104 CFU of the P . mirabilis transposon mutant library for 5X coverage of each mutant within the pool ) . In all cases , a 4 mm segment of sterile silicone catheter tubing was carefully advanced into the bladder during inoculation and retained for the duration of the study as described previously [21] . Due to the level of encrustation in mice infected with urease-positive organisms , the catheter segments were generally embedded in the bladder tissue and therefore were not removed prior to homogenization of bladder samples . Thus , all CFUs recovered from bladder samples actually represent colonization of the catheter segment as well as the bladder . Single-species infections and coinfections with the P . mirabilis transposon mutant pools resulted in comparable bladder and kidney colonization in all mice ( Fig 4A and 4B ) . Differential plating revealed that the majority of coinfected mice were highly colonized by both P . mirabilis and P . stuartii , as expected ( Fig 4C ) . In cases where an individual mouse exhibited low colonization by P . mirabilis in either the bladder or kidneys ( <1x104 CFU ) , the samples from that mouse were excluded from further study . Bladder and kidney output samples for sequencing were chosen from the most highly colonized mice as follows: four mice from the single-species infection group and four from the coinfection group , per transposon pool , resulting in 20 bladder samples and 20 kidney samples from each infection type . A fitness index was calculated for each gene by taking into account the number of unique insertion-sites within that gene and the depth of reads at each insertion-site for the combined output samples recovered from the organs of infected mice compared to the combined input pools ( see Materials and methods ) . Tn-Seq from single-species CAUTI identified 629 genes ( 16 . 8% of the total genes encoded by P . mirabilis HI4320 ) as candidate fitness factors . Due to the stringent cutoffs that were used for estimation of fitness factors ( see Materials and methods section ) , the range of fitness defects was compressed , spanning a 2- to 40-fold reduction in recovery from output samples compared to the input samples . The top candidate fitness factors for colonization of the catheterized bladder ( excluding tRNAs , pseudogenes , and genes not present in COG ) are shown in Table 1 . Four of these genes were previously identified by signature-tagged mutagenesis ( STM ) in the ascending UTI model [18 , 19] , and eight were previously shown to be upregulated by P . mirabilis in the ascending UTI model [31] . Importantly , many of the top hits for bladder colonization represent multiple genes in an operon , including the phosphate ABC transporter ( pstSCAB ) and the cytochrome bo3 quinol oxidase ( cyoABCDE ) . Top candidate fitness factors for kidney colonization ( again excluding tRNAs , pseudogenes , and genes not present in COG ) are shown in Table 2 . These include the same four genes identified by STM that were important for bladder colonization , and fourteen genes that were upregulated by P . mirabilis in the ascending UTI model [31] . Upon further analysis , fitness factors for single-species CAUTI fell into three categories: 93 genes for colonization of the catheterized bladder that were not significant for kidney colonization ( S2 Table ) , 209 genes for kidney colonization that were not significant for bladder colonization ( S3 Table ) , and 286 genes that were important for colonization of both organs ( S4 Table ) . Fitness factors for colonization of each organ were randomly distributed across the chromosome and plasmid ( S3 Fig ) . Of the 286 genes important for both bladder and kidney colonization , 203 were present in COG: 24 pertained to transcription ( 10 . 9% of functional category assignments ) ; 21 ( 9 . 5% ) translation , ribosomal structure , and biogenesis; 19 ( 8 . 6% ) amino acid transport and metabolism; 19 ( 8 . 6% ) post-translational modification , protein turnover , and chaperones; 18 ( 8 . 2% ) energy production and conversion; and 16 ( 7 . 3% ) cell wall and envelope biogenesis ( Fig 5A ) . Regarding fitness factors for bladder colonization alone , 64 of the 93 candidate factors were present in COG and revealed a greater proportion of genes pertaining to amino acid transport , carbohydrate transport and metabolism , and inorganic ion transport and metabolism than the genes important for colonization of both organs , and fewer candidate genes pertaining to translation and cell wall biogenesis ( Fig 5B ) . Interestingly , the candidate fitness factors for kidney colonization alone followed a similar distribution as the genes important for colonization of both the bladder and kidneys with a few notable exceptions , including fewer genes pertaining to translation or carbohydrate transport and metabolism , and more pertaining to replication , recombination , and repair and extracellular structures . ( Fig 5C ) . Thus , functional categories important for kidney colonization are likely to be involved in fitness during bladder colonization as well . Overall , of the fifty-four P . mirabilis fitness factors previously identified by signature-tagged mutagenesis ( STM ) in the ascending UTI model [17–19] , 31 ( 57% ) were significant for CAUTI ( denoted with a “*” in the supplemental tables ) . These include the phosphate ABC transporter ( pstSCAB ) , pyruvate dehydrogenase ( aceE and aceF ) , an AsnC transcriptional regulator ( PMI1431 ) , a d-methionine ABC transporter protein ( metN ) , inosine-5’-monophosphate dehydrogenase ( guaB ) , a bifunctional polymyxin resistance protein ( arnA ) , a polysaccharide deacetylase next to the Arn operon ( PMI1046 ) , a dihydrouridine synthase ( dusB ) , and a hypothetical protein ( PMI3700 ) . Urease is another well-known fitness factor for P . mirabilis UTI and prior STM studies that we previously verified as important for colonization in the CAUTI model [21] , and a urease accessory protein ( ureG ) required for the catalytically-active enzyme was also identified in the present CAUTI Tn-Seq . Tn-Seq from coinfection with P . stuartii revealed 1570 candidate P . mirabilis fitness factors , ranging from 2- to 30-fold reduction of transposon insertions in these genes from the combined output samples compared to the input samples . Candidate fitness factors represented 217/629 ( 34 . 5% ) of the genes identified above for single-species CAUTI , indicating substantial overlap between the two infection models , as well as 1353 additional factors that may specifically contribute to colonization during coinfection . Sixty-two genes were candidate fitness factors for bladder colonization alone ( S5 Table ) , 10 of which were also important for bladder colonization during single-species infection . These bladder-specific fitness factors , regardless of infection type , include two members of a carbohydrate ABC transporter ( ugpC and ugpE ) , a nucleoid-associated protein ( ndpA ) , the anti σE protein ( rseA ) , an HxlR-family transcriptional regulator , and a siderophore receptor ( ireA ) . 213 genes were candidate fitness factors for kidney colonization alone ( S6 Table ) , 61 of which were also important for kidney colonization during single-species infection . The majority of the kidney-specific fitness factors , regardless of infection type , pertained to amino acid transport and metabolism ( including the d-serine ammonia-lyase dsdA and the l-serine ammonia lyase sdaA ) , cell wall and envelope biogenesis , energy production and conversion ( including succinate dehydrogenase and formate acetyltransferase ) , posttranslational modification and protein turnover ( sufD , an iron-regulated ABC transporter permease protein , and surA , a periplasmic chaperone for outer membrane proteins ) , transcriptional regulation ( including the phoP two-component response regulator , and an RpiR-family transcriptional regulator encoded by PMI2974 ) , and four plasmid-encoded genes ( PMIP09 , which encodes the pilX4 conjugal transfer protein , PMIP10 , PMIP28 , and PMIP30 ) . The Sec-independent protein translocase TatC ( twin arginine transporter ) was also identified as important for kidney colonization but not bladder colonization in both infection types . 909 genes were important for both bladder and kidney colonization during coinfection ( S7 Table ) , 717 of which were present in COG and primarily pertained to transport and metabolism of amino acids , inorganic ions , carbohydrates , as well as energy production and conversion ( Fig 10A ) . The COG functional categories represented by P . mirabilis fitness factors during coinfection followed a similar trend as single-species infection: fitness factors for bladder colonization alone revealed a greater proportion of genes pertaining to translation and transcription compared to the genes important for colonization of both organs ( Fig 10B ) , and fitness factors for kidney colonization alone followed a similar distribution as genes important for colonization of the bladder and kidneys , albeit with an increase in factors with unknown function and a decrease in factors pertaining to cell motility ( Fig 10C ) . Fifteen of the genes important for colonization of both the bladder and kidneys during coinfection were also important for colonization of both organs during single-species infection . Seven of these genes have unknown functions or are not in COG; the other genes pertain to translation , ribosomal structure , and biogenesis ( dusC , PMI3283 , and rimI , an alanine acetyltransferase ) , cell wall/envelop biogenesis and defense ( mdtA , a multidrug resistance efflux transporter ) , motility ( PMI2642 ) , and inorganic ion transport and metabolism ( ppaA ) . The factors encoded by these genes appear to represent a putative core set of fitness requirements for P . mirabilis colonization of the murine urinary tract . Intriguingly , there were also 109 genes for which transposon insertions resulted in a fitness defect during single-species infection but provided a competitive advantage during polymicrobial CAUTI ( S8 Table ) . Seventy-nine of these genes were present in COG , and included factors involved in energy production and conversion , transcription , carbohydrate transport and metabolism , inorganic ion transport ( particularly iron and phosphate ) , amino acid transport and metabolism , and nucleotide transport and metabolism .
The majority of the large regions of the chromosome that lacked transposon insertions and were visible as gaps in Fig 1 did indeed correspond to genes identified as essential . For instance , the largest region that lacked transposon insertions was 12 , 423 bp encompassing 18 genes involved in cell division and cell wall biogenesis . The next largest regions that lacked transposon insertions were a 7 , 543 bp region encompassing genes necessary for translation , and a 6 , 650 bp region encompassing an intergenic region and murI , a glutamate racemase that is essential for cell wall biosynthesis . However , this was not always the case for gaps in transposon insertions on plasmid pHI4320 . The largest region lacking insertions on the plasmid was 1 , 252 bp encompassing an intergenic region and a portion of PMIP30 , which encodes a putative colicin . This gene was not identified as essential as there were 81 insertion sites after the gap which were well-represented in the input pools . Another gap on the plasmid of 826 bp corresponds to a portion of PMIP42 , a putative RelB antitoxin . Similar to PMIP30 , PMIP42 contained two insertion sites after the gap that were highly represented in the input pools , which likely caused it to fall below the threshold for being considered essential . Thus , the model used to identify putative essential genes provided very conservative estimates , and is likely an underrepresentation of the genes required for optimal growth of P . mirabilis in LB . It is therefore notable that the other ~500 bp gaps in transposon insertions on the plasmid were within a conjugal transfer protein ( PMIP09 ) , a colicin immunity protein ( PMIP31 ) , a plasmid stability/partitioning protein ( PMIP34 ) , a type IA DNA topoisomerase ( PMIP25 ) , and HN-S family protein ( PMIP26 ) . These findings are in agreement with the replication initiation protein ( PMIP01 ) being essential , and underscore the importance of plasmid maintenance and replication to P . mirabilis growth . In addition to the numerous genes previously identified as essential for growth in other bacterial species , the list of P . mirabilis estimated essential genes contained a few unusual items . For instance , all but one of the genes in the non-oxidative pentose phosphate pathway were identified as putative essential genes ( rpiA , rpe , tktA , and talB ) , indicating a central role for this pathway during growth of P . mirabilis in rich medium . Phosphoglycerate kinase ( pgk ) , which is involved in glycolysis , gluconeogenesis , and glycerol degradation , was also identified as a possible essential gene . Factors involved in inorganic ion transport and metabolism were a surprising find , as these genes are not commonly identified as essential . These included genes involved in potassium uptake ( trkA and trkH ) , intracellular sulfur oxidation ( PMI2797 and PMI2798 ) , tellurite resistance ( terC ) , a sulfite reductase ( PMI0794 ) , and magnesium and cobalt efflux ( corC ) . TrkA and TrkH comprise a high-rate low-affinity potassium-translocating system that requires ATP via the Sap system [37] , and all members of the Sap dipeptide transport system transcriptional unit ( sapBCDF ) were also identified as putative essential genes . Six fimbrial genes were also identified as potential essentials , including three homologs of the mrpJ fimbrial operon regulator [38] . Little is known concerning the identified fimbrial genes ( pmpB , fim3J , fim5G , fim7D , fim8J , fim10D , and fim10J ) , or why disruption of these genes would impact growth in rich medium . Three of these fimbrial genes encode homologs of mrpJ , a transcriptional regulator that represses motility but also influences expression of other adhesins , virulence factors , and metabolic pathways [39] . Homologs of mrpJ are also capable of regulating motility and adherence [38] , so it is possible that these mrpJ homologs similarly play a role in a broad regulatory network that could contribute to growth , or that tight regulation of the expression of these operons is important during growth in rich medium . Two homologs of the type VI secretion system ( T6SS ) secreted protein Hcp ( PMI0750 and PMI1117 ) were also identified as putative essential genes , as well as 17 transposases and 1 phage repressor protein . In each of these cases , the identified genes contained >15 TA sites for transposon insertion , but <3 total insertions were recovered from the five combined input pools . While these genes may be important for growth in rich medium , it is also possible that the mariner transposon wasn’t able to efficiently target these chromosomal locations . Thus , the importance of the unique genes estimated to be essential for P . mirabilis should be interpreted with caution . As mentioned above , the genes involved in putrescine uptake and biosynthesis were only identified as fitness requirements for P . mirabilis single-species CAUTI and not polymicrobial CAUTI , indicating that the presence of P . stuartii alleviates the polyamine requirement of P . mirabilis . The same appears to be true of the high-affinity zinc transport system encoded by znuABC . Stress responses also appear to be more important during single-species CAUTI than coinfection , as the majority of the stress-related genes identified as P . mirabilis fitness factors for single-species CAUTI were not significant during coinfection . Similarly , glutamine synthetase ( glnA ) was important for single-species fitness but not coinfection , further underscoring the differences in metabolic pathways favored by P . mirabilis during these two infection types . Further research is needed to determine which of these shifts in fitness requirements are specifically due to P . stuartii and which are due to the altered host environment and response to coinfection compared to P . mirabilis single-species infection . The combination of genome-saturating transposon mutant libraries and Tn-Seq has allowed for the first global estimation of P . mirabilis essential genes , validation of numerous P . mirabilis virulence factors and fitness determinants from decades of studies using the ascending model of UTI , the discovery of novel fitness determinants specifically for CAUTI , and a stringent assessment of how polymicrobial infection influences fitness requirements . For instance , proteobactin , a yersiniabactin-related siderophore ( nrp ) , and heme receptors were promising targets for perturbing P . mirabilis ascending UTI [56] , but these factors do not appear to be important for fitness during single-species infection in the CAUTI model . BCAA biosynthesis is an intriguing target for reducing bacterial colonization , but our results indicate that this pathway is only important for P . mirabilis during polymicrobial infection and therefore would not be a suitable target for single-species infection by P . mirabilis . In contrast , our data indicate that polyamine uptake and biosynthesis may be promising targets for perturbing P . mirabilis single-species CAUTI , but these pathways were not important during coinfection , which may limit their potential as therapeutic targets given the high frequency of P . mirabilis polymicrobial colonization and infection . Further research is needed concerning the numerous genes and pathways that provided a competitive advantage to P . mirabilis during both single-species and polymicrobial CAUTI , as the underlying mechanisms of these fitness requirements are the most likely to provide conserved targets for therapeutic intervention aimed at reducing P . mirabilis colonization or minimizing risk of progression to severe infection and urosepsis . We have clearly demonstrated that the CAUTI coinfection model can be used to examine the interplay between fitness requirements for both species during coinfection . It is therefore likely that investigation of the fitness requirements of other common CAUTI pathogens for single-species and polymicrobial CAUTI will further elucidate complex bacterial interactions that contribute to disease severity , and may even uncover conserved bacterial targets for therapeutic intervention . | Proteus mirabilis is a common cause of single-species and polymicrobial catheter-associated urinary tract infections ( CAUTIs ) . Prior studies have uncovered P . mirabilis virulence factors for single-species ascending UTI , but little is known concerning pathogenesis during CAUTI or polymicrobial infection . Using transposon insertion-site sequencing ( Tn-Seq ) , we performed a global assessment of P . mirabilis fitness factors for CAUTI while simultaneously determining how coinfection with another CAUTI pathogen , Providencia stuartii , alters P . mirabilis fitness requirements . This approach provides six important contributions to the field: 1 ) the first global estimation of P . mirabilis genes essential for growth , 2 ) validation of a role for known P . mirabilis fitness factors during CAUTI , 3 ) identification of novel fitness factors , 4 ) identification of core fitness factors for both single-species and polymicrobial CAUTI , 5 ) identification of single-species fitness factors that are complemented during polymicrobial infection , and 6 ) identification of factors that only provide a competitive advantage during polymicrobial infection . We further demonstrate that the CAUTI model can be used to examine the interplay between fitness requirements of both species during coinfection . Investigation of fitness requirements for other pathogens during single-species and polymicrobial CAUTI will elucidate complex interactions that contribute to disease severity and uncover conserved targets for therapeutic intervention . | [
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] | 2017 | Genome-wide transposon mutagenesis of Proteus mirabilis: Essential genes, fitness factors for catheter-associated urinary tract infection, and the impact of polymicrobial infection on fitness requirements |
ABC transporters are a large family of membrane proteins involved in a variety of cellular processes , including multidrug and tumor resistance and ion channel regulation . Advances in the structural and functional understanding of ABC transporters have revealed that hydrolysis at the two canonical nucleotide-binding sites ( NBSs ) is co-operative and non-simultaneous . A conserved core architecture of bacterial and eukaryotic ABC exporters has been established , as exemplified by the crystal structure of the homodimeric multidrug exporter Sav1866 . Currently , it is unclear how sequential ATP hydrolysis arises in a symmetric homodimeric transporter , since it implies at least transient asymmetry at the NBSs . We show by molecular dynamics simulation that the initially symmetric structure of Sav1866 readily undergoes asymmetric transitions at its NBSs in a pre-hydrolytic nucleotide configuration . MgATP-binding residues and a network of charged residues at the dimer interface are shown to form a sequence of putative molecular switches that allow ATP hydrolysis only at one NBS . We extend our findings to eukaryotic ABC exporters which often consist of two non-identical half-transporters , frequently with degeneracy substitutions at one of their two NBSs . Interestingly , many residues involved in asymmetric conformational switching in Sav1866 are substituted in degenerate eukaryotic NBS . This finding strengthens recent suggestions that the interplay of a consensus and a degenerate NBS in eukaroytic ABC proteins pre-determines the sequence of hydrolysis at the two NBSs .
ATP-binding cassette ( ABC ) transporters are a large family of membrane proteins that use MgATP hydrolysis to drive the import or export of solutes to or from the cytoplasm . They undertake a number of physiological roles , for example bacterial nutrient uptake , bacterial drug resistance , tumor drug resistance , and peptide secretion [1] . Other ABC proteins act as nucleotide-gated Cl− channels ( CFTR ) [2] , [3] or ion channel regulators ( SUR ) [4] , [5] . Most ABC transporters consist of two transmembrane domains ( TMDs ) that provide a pathway across the membrane for the transported substrate , and two nucleotide-binding domains ( NBDs ) which form two nucleotide-binding sites ( NBSs ) at their dimer interface ( see Figure 1 ) [6] , [7] . In bacterial ABC importers the four constituent domains are distinct polypeptides . Bacterial exporters are typically formed by dimers of TMD-NBD half-transporters . Thus , most bacterial ABC transporters are formed of two identical TMDs and two identical NBDs , and feature consensus nucleotide binding/hydrolysis motifs at both NBSs . Such motifs are the Walker-A , Walker-B , Q-loop , signature and switch motifs , as illustrated in Figure 1B [8] , [9] . ABC transporters are thought to hydrolyze the two MgATP molecules sequentially , as opposed to simultaneously , possibly involving a mechanism of alternating catalytic sites [9] , [10] . This model implies that the NBD homodimer must adopt an asymmetric conformation when in a pre-hydrolytic nucleotide-bound configuration , at least transiently [11] . Such conformational switching to allow nucleotide hydrolysis at one or the other of the NBSs might be expected to occur stochastically . Indeed , asymmetric homodimers have been observed in crystal structures of the MgATP-bound NBDs of the ABC transporter HlyB [11] . Specifically , an intra-NBD salt bridge between a basic residue near the signature sequence ( R611 ) and an acidic residue of the Q-loop ( D551 ) was found to exist at only one of the two NBDs . An inter-NBD salt bridge involving the corresponding residues has also been shown to be functionally important in the eukaryotic ABC transporter Tap1/2 [12] . In contrast to homodimeric bacterial ABC proteins , many eukaryotic ABC transporters consist of two asymmetric halves , often resulting in one consensus and one degenerate NBS . The degenerate NBS typically displays markedly reduced ATPase activity compared to the consensus NBS [13]–[15] . Recent experiments suggest that such asymmetry can lead to the consensus NBS being first to hydrolyze ATP , followed by the degenerate site ( e . g . in MDR3 , Tap1/2 ) [16] , [17] . Such a preferential order of ATP hydrolysis at the two NBSs has been proposed to help to control the transport of particularly complex substrates [16] , [18] . Degenerate NBSs have been identified in several eukaryotic ABC transporters , including MDR3 , TAP1/2 , MRP1 , CFTR , and SUR [14]–[17] , [19] . In this work we use molecular dynamics ( MD ) simulations to explore the transient asymmetry that a homodimeric ABC exporter may adopt when in a pre-hydrolytic , MgATP-bound configuration . We simulate the bacterial multidrug exporter Sav1866 , which is thought to represent the core architecture of ABC exporters [20] , [6] , [21] , and has been used widely as a template for modeling of eukaryotic exporter structures [22] , [23] , [18] , [24] . A number of other ABC transporters ( e . g . BtuCD [25] , [26] ) and their NBDs ( e . g . [27]–[32] have been subjects of MD simulation before , as such simulations can provide a valuable tool for exploring the conformational dynamics of channel and transport proteins in relation to function [33] , [34] . It is now well established that MgATP binding/unbinding to the NBS closes/opens the NBD dimer , and that the helical subdomain of a NBD can rotate relative to its catalytic core domain [27] , [28] , [30] , [32] , [35] . However , it is unclear how a closed symmetric NBD dimer undergoes asymmetric transitions , and how these are affected by and linked to the TMDs . Our multiple MD simulations show that an initially symmetrical MgATP-bound state of Sav1866 exhibits rapid ( initial steps on ∼10 ns timescale ) and stochastic switching into asymmetric NBD conformations . The NBDs seemingly adopt hydrolytically favorable conformations in only one NBS . We further show how the switching at the NBS is reflected in a network of charged residues at the TMD-NBD interface . We extend our observations of stochastic switching in symmetric NBDs to suggest how degeneracy in eukaryotic ABC exporters may lead to preferential switching . This provides a rationale for understanding residue substitutions in ABC proteins , such as CFTR , MRP1 , Tap1/2 and SUR .
Polar hydrogens were added to the ADP-bound crystal structure of Sav1866 ( pdb id 2HYD ) using GROMACS [36] , [37] . A doubly protonated state was chosen for the side chain of H534 . Default protonation states were assumed for all other residues . MgATP was modeled on the bound ADP by positioning the γ-phosphate in line with the α- and β-phosphate moieties followed by careful energy minimization . The Mg2+ ion was positioned next to the β- and γ-phosphates towards the Walker-B motif . In the default ATP force field the γ-phosphate is singly protonated . We modified this by removing the hydrogen from the γ-phosphate and redistributing the partial charges evenly over the phosphate oxygen atoms such that the overall charge of the MgATP is -2e . The protein was positioned manually in a preformed DPPC bilayer containing 512 lipid molecules . A shell of lipid molecules around the protein was removed , and the remaining 376 lipids were relaxed around the protein by a 0 . 5 ns simulation with position restraints on all non-hydrogen protein and MgATP atoms ( harmonic restraints , force constant 1000 kJ mol−1 nm−2 ) . In a similar fashion , we inserted the Sav1866 structure into a POPC bilayer . A snapshot of the bilayer-inserted Sav1866 structure is shown in Figure 1A . The position restrained simulations should allow for water to enter the nucleotide-binding sites . The Sav1866 crystal structure itself contains no waters near the ADP phosphate moieties . MD simulations were performed using the GROMACS 3 . 3 . 1 software [36] , [37] and the ff53a6 version of the GROMOS96 force field [38] , [39] . The simulation box adopted dimensions of 118×118×185 Å , and was solvated by 60750 water molecules , using the single point charge ( SPC ) water model [40] . The system was energy minimized by 1000 steps of steepest descent . After the position-restrained run ( see above ) , we performed a warm-up simulation across a temperature interval of 100 K–310 K in steps of 50 K and 50 ps simulation . Production runs were performed with a time step of 2 fs . The particle mesh Ewald summation method [41] with a real space cut-off of 12 Å was used to calculate long-range electrostatic interactions . Hydrogen bonds were analyzed using a geometric definition , namely a maximum donor-acceptor distance of 3 . 5 Å , and a maximum acceptor-donor-hydrogen angle of 30° . Atomic contacts between two residues were defined individually for each simulation snapshot as the number of atom pairs between the two residues whose positions are closer than 4 Å . Images were prepared using the programs VMD [42] , ClustalX [43] , xmgrace , and MATLAB ( The MathWorks , Inc . ) . We also included snapshots of the simulations as a PDB file ( see supplementary Text S1 ) containing the protein , MgATP , and 40 water molecules that are closest to the MgATP molecules . The snapshots are from one simulation at 0 , 5 , 10 , 20 , 30 and 40 ns . We first performed six repeat simulations of Sav1866 with MgATP nucleotide in a DPPC lipid bilayer , each with different starting velocities and a duration of 40 ns . In order to probe the effects of the lipid environment in our simulations , we also performed a 30 ns simulation of Sav1866 in a POPC bilayer . In addition , we performed two further control simulations with bound ADP and DPPC lipid , both to a duration of 30 ns .
Root-mean-square deviations ( rmsd ) of Cα atoms in our six simulations with DPPC lipid and one simulation with POPC lipid are within typical values observed for protein simulations: 2 . 5–4 Å for the whole protein , and 2–2 . 5 Å for individual NBD subunits ( see Table 1 ) . Given the relatively large size of dimeric Sav1866 ( 1156 aa ) , rmsd values in this range are indicative of structural stability . The variation seen between graphs of rmsd vs . time of individual repeats ( Figure 2 ) might be indicative of distinct , stochastic conformational changes . This would suggest that different repeat simulations may cover different regions of the accessible conformational space . However it should be kept in mind that the variation in rmsds is within experimental limits for a protein of this size . The domain-specific rmsd values for the NBDs in the 30 ns simulation performed with POPC lipid are within the range observed in the 40 ns simulations with DPPC lipid , but those of the TMDs are lower in the POPC simulation ( Table 1 ) . This indicates that the different lipid environments may have an effect on the TMD conformation . However , the rmsd of the whole protein vs . time for the POPC simulation falls within the range defined by the other simulations . Further , considering the high overall stability of the protein and the identical rmsd values seen for the NBDs , it seems very unlikely that the lipid environment would have any effect on the cytosolic regions of Sav1866 during the time covered in our simulations . Therefore , we chose to treat the POPC simulation as a seventh repeat , and collated data derived from it with that from the six DPPC simulations . To identify possible binding modes for MgATP at the two NBSs we determined atomic contacts between MgATP and the most important nucleotide-binding residues . In addition to inspecting various contacts as a function of time for each simulation ( e . g . Figure 3A , SI Figure S1 ) , we analyzed contacts over an ensemble of seven simulations . In particular , we compared patterns of atomic contacts ( using a 4 Å cut-off ) at the two NBSs of the protein via scatter plots with contact numbers at the two NBS on the two axes ( Figure 3B–E ) . Such a representation allows for visualization of possible asymmetry between the two NBSs , seen as off-diagonal elements in the scatter plot . It should be noted that , in our definition , closely interacting residues will yield more than one atomic contact . Therefore , the number of contacts is not only an indication of the existence of an interaction , but also a measure of its strength . From this analysis , MgATP appears to form several distinct binding modes ( Figure 3 ) . For example , MgATP and the Q-loop Gln residue ( Q422 ) show three levels of interaction , exhibiting either ∼5 , 10–15 , or >15 contacts ( Figure 3B ) . These levels arise from distinct interaction patterns , corresponding to coordination of the Mg2+ ion by the Gln oxygen atom , H-bonding of the Gln amide to ATP phosphate oxygen atoms , or both interactions together . An orientation of the Q-loop Gln that encompassed this range of Gln-MgATP interactions was observed in the MgAMP-PNP-bound crystal structure of the Rad50 ATPase domain , which is homologous to ABC transporter NBDs [44] . The Q-loop-MgATP association in our simulations supports the idea that the Q-loop Gln senses the presence of MgATP [45] . In comparison , Q422 forms no contacts to nucleotide in the ADP-bound crystal structure . Distinct levels of contact are also seen between MgATP and the Walker-B Glu ( E503; Figure 3C ) , resulting from involvement of E503 in Mg2+ coordination . The possibility of direct E503-MgATP interactions seen in our simulations adds to the conformational variability available to this crucial catalytic residue ( see also below ) . It should be noted that we also observe direct interactions of the Walker-B aspartate D502 with Mg2+ ( data not shown ) . Our simulations also feature contacts of the switch motif H534 to MgATP ( Figure 3D ) , arising through H-bonding of its ε-NH to the ATP γ-phosphate and electrostatic attractions . The switch motif His ( H534 ) and the Walker-B motif Glu ( E503 ) are known to form a functionally important “catalytic dyad” [46] . As can be seen ( Figure 3E ) this dyad forms stably in all simulations of the pre-hydrolytic state , and associates more tightly than in the ( ADP-bound ) crystal structure . The relatively wide distribution of contacts suggests a degree of conformational flexibility with low energetic barriers for the catalytic dyad ( see also Figures 3F , G ) . In accordance with this idea , the catalytic dyad adopts various different conformations in existing ABC NBD crystal structures . We also find that the switch motif H534 can H-bond directly to the D509 side chain of the D-loop ( SALD motif ) of the opposite monomer ( see Figure 3G ) . This is particularly interesting since the D-loop has been implicated in inter-NBD cross-talk in other ABC proteins [27] , [47] . As noted above , the off-diagonal elements in the scatter plots of contact numbers ( Figure 3B–E ) indicate that asymmetric interactions are present in all examined residue and nucleotide pairs . That is , in individual simulations ( or segments of simulations ) one NBS can form more contacts than the other , indicative of stochastic switching in symmetric NBDs . However , the off-diagonal elements are not always equally distributed either side of the diagonal . This is to be expected of intrinsically stochastic switching , combined with incomplete sampling of the space of possible interactions in our simulations . The ADP-bound simulations show hardly any interactions of the studied residues with the nucleotide ( see SI Figure S7 ) . This is not surprising , since the studied residues are mostly thought to interact with the gamma-phosphate moiety of ATP . The conserved Q-loop motif has been implicated in distinguishing between ATP and ADP at the NBS [45] . Because of its location at the NBD-TMD interface , the Q-loop is also well positioned to relay conformational changes to the TMDs [20] . In Sav1866 , D423 ( of the Q-loop ) and K483 ( adjacent to the LSGGQ signature sequence ) form inter-NBD charge pairs in the ADP-bound crystal structure . Likewise , the crystal structures of HlyB revealed a conserved aspartate of the Q-loop to form a charge pair with a basic residue next to the signature sequence . This intra-NBD charge pair was seen only at one NBS in MgATP-bound HlyB dimers [11] . Mutagenesis studies of the ABC transporter Tap1/2 suggested that the equivalent charge pair forms in an inter-NBD manner [12] . In our simulations , inter-NBD D423-K483 interactions were frequent ( Figure 4A ) whereas only very few intra-NBD charge pairs formed ( data not shown ) . We also observed contacts between D423 and the TMDs ( Figure 4B ) . It is evident from Figure 4A that the D423-K483 interaction does not occur simultaneously at both NBDs . Rather , there appears to be competition between K483 and the TMDs for interaction with D423 . Thus configurations are frequent in which D423 of one monomer pairs with K483 of the other monomer , and D423 of the other monomer interacts with the TMDs ( see time-resolved plots in SI Figure S2 , and also SI Figure S6 ) . In effect , dissociation from K483 frees D423 to interact with the TMDs . Furthermore , a scatter plot of the D423-K483 contacts versus the Q-loop Gln ( Q422 ) contacts to MgATP indicates that a strong D423-K483 interaction coincides with the neighboring Q422 being either in a high-contact binding mode to MgATP , or being completely dissociated from MgATP ( Figure 4D ) . This suggests that the conformation of the ATP-sensing Q422 may influence the interactions of its neighboring D423 with K483 , and vice versa . In the Sav1866 crystal structures , the D423 residues of each of the two Q-loops interact with the R474 residues of the so-called x-loops of the opposite NBDs ( Figure 4C , see also below ) . These charge pairs dissociate in almost all of our simulations ( Figure 4C , SI Figure S6 ) and D423 alternates between interacting with K483 , R474 and the TMDs ( Figure 4E , SI Figure S6 ) . On most occasions , the D423 residues of the two monomers interact asymmetrically with these different residues . Interactions formed by the Q-loop D423 in the ADP-bound simulations also show remarkable drift away from their contact numbers in the starting crystal structure , as shown in a scatter plot similar to that in Figure 4 ( see SI Figure S8 ) . Some of the D423 interactions seem to occur more readily at both monomers in the ADP-simulations than the MgATP-simulations . Sav1866 contains a short sequence motif ( the x-loop ) at the NBD-TMD interface that is specific to ABC exporters [20] . It has been suggested that the GERG motif of the Sav1866 x-loop connects the TMD cytosolic loops ( CLs ) in a nucleotide-dependent manner . To study the role of the GERG motif , we analyzed contacts of E473 and R474 to the four CLs in a similar fashion to the analyses presented above ( see SI Figure S3 ) . This analysis confirms the proposed link between the GERG motif and the CLs: R474 can bind to the CL2 of both TMDs , whilst E473 binds both to the CL1 of the TMD on the opposite monomer and , frequently , to CL2 of the TMD of its own monomer . Most of these interactions form asymmetrically , indicative of stochastic switching , but as already seen for the MgATP binding modes the conformational space is not completely sampled . Instead , the GERG motif of NBD B forms tight interactions to the TMDs more often than that of NBD A . The asymmetric GERG motif interactions with the NBDs arise from a stacking of the R474 pair on the Sav1866 symmetry axis . As indicated by the trajectory of the projection of R474 onto the lipid bilayer normal from one simulation ( Figure 5 , see also SI Figure S4 ) , the arginine residues start next to each other but in the simulations repack to stack on top of one another , breaking the symmetry ( see also Figure 6 ) . As a consequence , the GERG motif whose R474 sits closer to the TMDs forms more overall interactions with the TMDs ( see also SI Figure S5 ) . The cytosolic ends of the TMDs , which form the interface to the x-loops , contain a series of residues along the dimer symmetry axis: Q208 , H204 , Q200 ( see Figure 5 ) . These pairs of residues start the simulations in asymmetric patterns: the pairs of glutamine residues face each other with the amine nitrogen atoms next to the oxygen atoms , while the pair of H204 is stacked along the symmetry axis . As indicated by time-resolved plots ( Figure 5 , SI Figure S4 ) , the pairs of H204 remain stacked throughout the simulations .
The simulations described above reveal a pronounced asymmetry between the dynamic behavior of two initially identical Sav1866 monomers , such that several key interactions are found to exist only at one monomer at a time . These interactions include a salt bridge ( D423-K483 ) that has also been reported to form in only one monomer in crystal structures of the homologous MgATP-bound NBD homodimer HlyB [11] . Interestingly , while the HlyB study reported this salt bridge to be formed by residues within the same NBD monomer , it forms between the two NBDs in Sav1866 . Similar inter-subunit charge pairing has also been reported for the Tap1/2 transporter [12] . The Tap1/2 study further suggested that the inter-NBD charge pairs are structurally coupled and thus tied to conformational changes anticipated during ATP hydrolysis [12] . Our simulations support this idea of a structural coupling of the inter-NBD Q-loop D423-K483 charge pair . This pairing may be dependent on the association of the Q-loop Q422 with MgATP . Furthermore , when D423 is unbound from K483 , it can flip towards the TMDs and interact with the coupling helix on CL2 of the opposite monomer ( Figure 4B ) . Interaction of D423 with CL2 can induce further tightening of NBD-TMD interactions , partly through clamping of the coupling helix by the β-strand on which D423 is located ( Figure 4E ) . The formation of D423-K483 and D423-TMD interactions is preceded by breaking of the D423 charge pairs with R474 of the x-loop . Another region in which we observe asymmetric switching is the GERG motif of the x-loop . Our simulations support its proposed role in linking the CLs together [20] . However , in simulations the GERG motifs of the two monomers can do so asymmetrically . This is a consequence of the pair of R474 rearranging along the Sav1866 central axis , with one R474 interacting tighter with the TMDs than the other . The complex interplay of charged and polar residues at the NBDs and across the NBD-TMD interface is summarized in Figure 6 . Interestingly , Dawson and Locher [48] reported small conformational differences in the x-loop between the ADP-bound crystal form of Sav1866 and a structure crystallized with the non-hydrolyzable ATP-analog AMP-PNP . However , they were uncertain about the significance of these differences because of the limited resolution of the AMP-PNP crystal structure [48] . Our results indicate that the x-loop rearranges readily between the ADP-bound and MgATP-bound forms . However , while the x-loops were modeled symmetrically into the ADP- and AMP-PNP-bound electron densities , our simulations indicate the possibility of an asymmetric arrangement along the dimer axis . The x-loop and K483 lie very close ( in the primary sequence ) to the LSGGQ signature sequence , which is important in MgATP binding and hydrolysis in ABC transporters . Therefore , asymmetric behavior at these regions may be linked to non-simultaneous hydrolysis activity at the two NBS . Our observation of asymmetric switching in Sav1866 is in line with the asymmetric function of its NBDs . As a comparison to this finding , we sought for a protein that requires perfect symmetry for proper function . Selectivity filters of potassium channels are likely to be dependent on their tetrameric symmetry to ensure K+ conduction and selectivity . Using a simulation trajectory generated in our laboratory for another study , we examined an inter-residue interaction at a crucial position near the selectivity filter: that between ( protonated ) E71 and D80 . The E71-D80 interaction has been shown to be involved in slow inactivation of KcsA , underlining its functional importance [49] . We found this interaction to be near constant at all four monomers ( SI Figure S9 ) . Albeit anecdotal , this finding provides a control example in which functional requirements seem to result in preservation of symmetry during the course of a simulation . In the structure determination of ADP-bound Sav1866 , two-fold non-crystallographic symmetry was applied to large parts of the dimer , although some pairs of residues were refined in non-symmetric conformations [20] . Therefore , the starting structure in our simulations was not perfectly symmetric . While most asymmetric residue pairs are located on the outside of the Sav1866 dimer and presumably contribute to crystal packing , a cluster of initially asymmetric residues is found at the cytosolic end of the TMDs , at the dimer interface along the symmetry axis ( see Figure 5 ) . This cluster includes the two H204 residues , which are positioned on the symmetry axis one above the other ( SI Figures S4 , S5 ) . As a consequence of their stacking rearrangement , one of the two R474 residues can interact with H204 of the opposite monomer . Therefore , the starting asymmetry at H204 ( H204 of TMD A closer to the NBDs ) might have biased the simulations towards cases in which R474 of NBD B ends up closer to the TMDs and interacts with H204 of TMD A . The asymmetric arrangement of H204 , as well as Q200 and Q208 , along the symmetry axis may effectively constitute another molecular switch in Sav1866 . It should also be noted that our simulations were performed in the absence of a transported substrate . The binding of substrate is known to promote ATPase activity in ABC transporters including Sav1866 [21] . It is possible that , in the physiological transport cycle , substrate binding to Sav1866 induces conformational changes at the x-loop that render MgATP binding and hydrolysis more favorable at the NBDs . The homodimeric Sav1866 presents two symmetric binding sites to substrates . Therefore , the conformational changes induced by the substrate may also carry information ( possibly via Q200-H204-Q208 and the x-loop ) about which NBS should hydrolyze first in order to optimize translocation of the substrate in its current binding mode . Our simulations were set up by changing the nucleotides in the pseudo-symmetric post-hydrolytic Sav1866 structure into a model of the pre-hydrolytic nucleotide configuration . This allowed us to study possible first steps of a putative pre-hydrolytic switching . We observe asymmetric structural rearrangements relative to the ADP-bound crystal structure , suggesting that the substitution of MgATP into the binding sites initiated a switching process . However , since our simulations are relatively short ( and do not allow for formation or breaking of covalent bonds ) , we cannot expect to see beyond the initial steps of this process . We observe several regions of asymmetric switching . Firstly , the MgATP-sensing Q-loop Q422 forms a high-contact MgATP binding mode typically only at one NBS . Secondly , the neighboring Q-loop residue D423 charge pairs with a basic residue ( K483 ) near the signature sequence only at one NBS at a time , as has been reported in the ABC transporters HlyB and Tap1/2 [11] , [12] . D423 that is not bound to K483 is free to interact with the TMD coupling helices and with R474 of the x-loop GERG motif . Thirdly , the x-loop GERG motif can undergo asymmetric stacking along the Sav1866 symmetry axis , such that R474 of one monomer interacts much more tightly with the TMDs than R474 of the other monomer . Finally , the catalytic dyad E503-H534 frequently associates in different ways at the two NBSs . A schematic diagram of these asymmetric interactions in the simulations is shown in supplementary Figure S6 . Conceivably , the asymmetric interactions in the Sav1866 NBDs effectively constitute a series of molecular switches ( Figures 6 , S6 ) . To complete the asymmetric switching that leads to hydrolysis at one of the NBSs , each of these switches may have to adopt a certain state . Such a series of switches may thereby create a complex , multi-state energy landscape for the conformational transition in pre-hydrolytic Sav1866 . It should be remembered that our simulations sample only about 270 ns of Sav1866 dynamics in this energy landscape , while full transport events probably occur on time scales of ≥10−3 s . Therefore it would be statistically unlikely to observe large conformational transitions of Sav1866 . Even the probable first step of such transitions may be too unlikely to observe , ie , all of the molecular switches to adopt a state needed to complete the initial asymmetric switching . Ultimately , the behaviour of Sav1866 appears stochastic at the level of these molecular switches . A picture emerges in which , at the atomic level , ABC transporters do not function as molecular motors with perfectly defined steps . Rather , coincidental stochastic steps underpin the larger-scale motions required for function . Therefore , our sequence of simulation snapshots ( SI Text S1 ) should not be interpreted as a strictly temporal sequence of conformational change ( see also Figure S6 ) . It should be noted , though , that this picture may be subject to change in the presence of transported substrate , which is missing in our simulations . Many interactions in our system showed considerable variability in contact numbers and possible binding modes between individual runs within the ensemble of seven simulations . It seems likely that substitution of ADP for MgATP at both NBSs left the initial system in an energetically activated ( i . e . non-equilibrium ) state , thus enhancing the extent of conformational space accessible in relatively short ( 40 ns ) simulations . Thus , our approach of repeated MD simulations helps to extend the range of conformations accessed . However , our simulations provide far from exhaustive coverage of the conformational space . This is apparent in the lack of diagonal symmetry in many of the scatter plots of contact numbers . Asymmetric coverage of conformational space could also be indicative of an inherent bias in the starting structure , such as the asymmetric starting orientations of residues along the symmetry axis ( see above ) . In NBD crystal structures with Mg-nucleotides , interactions of the Walker-B aspartate with Mg2+ are water-mediated . This suggests that our protocol of solvating the MgATP may have been insufficient ( see Methods ) , and that our simulations may be missing a water molecule between the Walker-B motif and the Mg . Effectively , our hydration protocol may have resulted in an interchange of parts of the first and second hydration shells of Mg2+ ( namely , the Walker-B aspartate and water ) . Otherwise , our simulations feature quick rearrangements of the NBD residues to accommodate for the MgATP , for example in case of the “ATP-sensing” Q-loop Q422 . However , it should be noted that it remains beyond this study to establish a single physiologically correct MgATP binding mode in Sav1866 and ABC exporters in general . Binding modes of ATP and ATP analogs vary between crystal structures of ABC NBDs ( e . g . Tap1 NBD [17] and FbpC [35] ) and may thus be expected to vary in simulations as well . Finally , we wish to note that our method of visualizing atomic interactions in scatter plots provides a compact and visually intuitive picture of the distinct interaction patterns and the arising asymmetry accessed by our repeat simulations . Similar methods of visualization may be of help in future MD simulation studies , especially as it looks to become more common to extend phase space coverage by repeat simulations . Although our simulations have been performed on a homodimeric bacterial ABC transporter , the results have possible implications for its ( more complex ) eukaryotic counterparts . Eukaryotic ABC transporters are often encoded with the two half-transporters in one protein , thus allowing for sequence and ( potential ) functional differences between the two halves . Indeed , eukaryotic ABC proteins often contain one “consensus” NBS and one “degenerate” NBS [17] with substitutions at the functional motifs ( Figure 7 ) . This holds in particular for the ABCC subfamily [19] , [17] , [12] . Strikingly , residues that seem to act as molecular switches in our simulations are frequently degenerate . Thus , the Q-loop Asp ( D423 in Sav1866 ) is degenerate in NBD1 of the ABCC-type proteins MRP1 , SUR1 , and CFTR . The arginine of the x-loop GERG motif ( R474 in Sav1866 ) is degenerate in NBD2 of all ABCC transporters , the Tap1 half of the Tap1/2 transporter , and NBD1 of CFTR . Because of the domain-swapped NBD interface , the substitution of the x-loop in NBD2 of ABCC transporters adds to their degeneracy at NBS1 . The Walker-B glutamate ( E503 in Sav1866 ) , which is intimately involved in the intricate catalysis chemistry , is degenerate at NBD1 of the ABCC transporters . Further degeneracy substitutions are found at the signature sequence , and the switch motif of NBD1 ( Figure 7 ) . It has been suggested before that the combination of one consensus and one degenerate NBS may lead to directed switching in the pre-hydrolytic conformation [16] , [17] . Eukaryotic ABC transporters may need a certain NBS to always hydrolyze first in order to optimize the transport of complex substrates [17] , [16] . Here we show , for the first time , that many residue positions that contribute to degeneracy in eukaryotic transporters may act as molecular switches , in order to control which NBS will hydrolyze MgATP . Therefore , the identification of these switches in our simulations of a bacterial ABC exporter may provide a novel basis to rationalize degeneracy in eukaryotic transporters . This proposal may be explored further by extended MD simulations of e . g . recent eukaryotic ABC transporter structures [50] . | ABC transporters are a large family of membrane proteins present in all organisms . Typically , they utilize ATP hydrolysis , the most prominent biological energy source , to translocate substrates into cells ( e . g . , bacterial nutritient uptake ) or out of cells ( e . g . , multidrug exporters that contribute to antimicrobial resistance in bacteria and resistance to chemotherapeutic drugs in cancer ) . Also clinically relevant non-transport roles have been identified among ABC proteins . ABC transporters bind two molecules of ATP but do not hydrolyze them simultaneously . Therefore , an ABC transporter that consists of two symmetric halves must temporarily adopt asymmetric conformations at the two ATP-binding sites . Such transient conformational changes are difficult to address biochemically , but may be amenable to study by simulation methods , leading to future experiments . We employ molecular dynamics simulations to study how asymmetric switching might occur in the homodimeric bacterial ABC multidrug exporter Sav1866 . The simulations suggest a mechanism of conformational switching that encompasses the ATP-binding sites and their interface towards the substrate-binding site . We extend our findings to show how asymmetric residue substitutions may render the switching process non-stochastic in mammalian Sav1866-like ABC exporters . This contributes to ongoing discussions about the role of two dissimilar ATP-binding sites in clinically relevant ABC proteins . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"computational",
"biology/molecular",
"dynamics",
"biophysics/theory",
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"simulation",
"biophysics/membrane",
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"transduction"
] | 2010 | Asymmetric Switching in a Homodimeric ABC Transporter: A Simulation Study |
Leptospirosis is a global and re-emerging zoonotic disease caused by Leptospira spirochetes that are shed into the environment by infected animals . Humans can get infected via contact with animal hosts or contaminated environment . In Argentina , the highest annual incidences were reported in the province of Santa Fe , where epidemic outbreaks occurred during flooding events . This study examined the knowledge , attitudes and practices ( KAP ) regarding leptospirosis among residents of riverside slum settlements from Santa Fe after a major flood . A cross-sectional questionnaire was administered to 113 residents of 3 riverside settlements from Santa Fe . The influence of knowledge and attitudes regarding leptospirosis on the likelihood that an individual will use preventive practices were evaluated using linear mixed-effects models . The majority of respondents ( 83 . 2% ) had previously heard about leptospirosis; however specific knowledge about leptospirosis was limited . The results of the modeling efforts , show that the likelihood of using preventive practices was associated with having greater knowledge score , but not with more positive attitudes . We also found that females were more likely to use safer practices than males . Even though the majority of respondents had heard about leptospirosis , a high percentage of them had limited knowledge regarding the severity of the disease and its prevalence in the region . Our results suggest that public health interventions in these riverside communities should focus on educating the public on the multiple dimensions of leptospirosis in order to attain greater adherence to preventive practices instead of intending to change the perceptions or attitudes towards the disease , which did not have a significant influence . The key challenge lies in identifying effective strategies to reach the high risk group for leptospirosis here that is male fishermen , who spend most of the time in precarious campsites on the river islands .
Leptospirosis is a zoonotic disease caused by Leptospira spirochetes . Pathogenic leptospires are excreted in the urine of mammalian hosts such as rodents , dogs and cattle and can persist in the environment for weeks or months [1–4] . Humans serve as incidental hosts , exposure may occur through direct contact with infected animal urine and tissues , or indirect contact with contaminated soil and water [1 , 4 , 5] . The contact with environmental sources of leptospires in urban and rural slum settlements can be increased by lack of basic sanitation , poor housing , crowding and extended time outdoors , together with heavy rainfall and flooding [6–12] . Furthermore , slum residents often engage in informal work such as small-scale construction , subsistence hunting or fishing , and food preparation for vending in the same areas where they reside [1 , 2 , 7 , 10] or maintain subsistence livestock and chickens in their backyards [2 , 4 , 7 , 8 , 11] , increasing the risk of environmental exposure . Every year , between 500 , 000 and 1 . 03 million cases of leptospirosis are reported in the world , with a mortality rate over 10% [1 , 13] . However , the global burden of leptospirosis is thought to be underestimated by several factors , including the broad clinical spectrum of the disease that mimics many other endemic infectious diseases such as dengue and malaria [1 , 13–15] . Aditionally , many countries lack a case notification system or notification is not mandatory [1 , 16 , 17] . Latin America is one of the regions with the highest number of cases of leptospirosis in the world [14 , 16] , having reported 10 , 088 cases in 2014 of which 40 . 2% belonged to Brasil , followed by Perú , Colombia and Ecuador [16] . Even though Argentina reported 217 laboratory confirmed cases in 2014 [16] , between 2005 and 2017 , 14 , 319 suspected cases were reported to the National Health Surveillance System ( SNVS ) [18–23] , being one of the leading countries in alerts of cases in Latin America [16] . The main risk factor for leptospirosis in Argentina is the persistent contact with flooded environments [8 , 23–26] . Floodings may lead to disruption of health services and damage to households and water and sanitation networks , displacing populations and increasing the risk of exposure to rats and pathogens [2 , 15] . The first case of leptospirosis in Argentina , was reported in the province of Santa Fe in 1915 [27] . Leptospirosis is considered an emerging public health problem in the country , and notification of suspected cases is mandatory [8 , 23 , 26] . The highest annual incidence rates of leptospirosis in Argentina occur in the province of Santa Fe [23 , 26 , 28] , representing 46 . 4% of reported cases and 38% of confirmed cases in the country for the period 2012-2017 [18–22] , with outbreaks registered following flooding events [23–26 , 29 , 30] . The number of severe cases of leptospirosis associated with pulmonary hemorrhage has increased in recent years [8 , 26 , 28] . Assessments of people’s knowledge of leptospirosis and health behavior provide critical information for disease prevention [9 , 31–35] . In particular , surveys of knowledge , attitudes and practices ( KAP ) are useful public health tools to identify effective strategies for behavior change towards safer practices [32 , 33 , 36 , 37] . Despite this , leptospirosis remains a neglected disease in Argentina and no studies have been conducted to assess the level of public awareness about the disease . The objectives of this study were to describe the knowledge , attitudes and practices regarding leptospirosis in riverside slum settlements from Santa Fe affected by a flood event and to evaluate the factors influencing preventive practices .
The city of Santa Fe ( 31°38′0″S , 60°42′0″W ) with a population of 391 , 231 in 2010 [38] , is the capital of Santa Fe province , located in north-eastern Argentina in the junction of the Paraná and Salado rivers . Study sites comprised two riverside neighborhoods of Santa Fe and a settlement 30 km NE from the city . All three sites were located in the flood valley of the Paraná river , an area with high susceptibility to floods and different levels of deficiency in sanitary infrastructure . A map was constructed to show how the landscape changed during the flood event ( Fig 1 ) using QGIS 3 . 0 Girona [39] with the Semiautomatic Classification Plugin [40] . The base map and the raster layers of water bodies , before and during the flood event , were created from Landsat8 OLI/TIRS satellite imagery acquired from U . S . Geological Survey ( https://ers . cr . usgs . gov ) . Vector layers were acquired from Natural Earth ( http://www . naturalearthdata . com ) . Site 1 corresponds to the neighborhood called La Vuelta del Paraguayo , located on the banks of the Santa Fe stream ( Fig 1 ) with about 408 residents distributed in 64 households [41] . This site has water supply services and electricity but has no sewers , health centers , paved streets or public transportation [42] . Site 2 corresponds to Colastiné Sur , a riverside neighborhood located on the banks of the Colastiné river ( Fig 1 ) with approximately 1018 residents distributed in 308 households [41] . This site has electricity , a health center , refuse recollection and public transportation but has no sewers , water supply services or paved streets [43] . Site 3 corresponds to a sector of the locality of Los Zapallos , this sector is located at the banks of the Leyes stream , 30 km NE of the city of Santa Fe ( Fig 1 ) . It consists of approximately 564 residents from 92 households . This site has electricity , water supply services , refuse recollection and a nearby health center ( approximately at 1 . 5 km ) but it does not have sewers or paved roads ( Data obtained from the commune of Santa Rosa de Calchines ) . Between March and May of 2016 we conducted a cross-sectional study assessing leptospirosis related KAP . Data was collected after a major flood event of the Paraná river that affected all study sites ( Figs 1 and 2 ) . Questionnaire participants were selected using a census sweep technique which allowed the sampling of both evacuated and non-evacuated households . Census sweeping was chosen as the sampling method to cover these small and clumped resident areas . We tried to minimize the potential influence of our presence on future questionnaire responses by conducting the census in the minimum number of days possible and following a particular direction for the sweeping design . At each visit to the study sites , interviewer-administered questionnaires were used to gather necessary information from one resident per household . Similar to other published KAP studies , the questionnaire was conducted on residents who were at least 12 years old [9 , 34 , 35 , 44 , 45] . Residents were informed about the aspects of the research and a verbal consent was obtained from those willing to answer the questionnaire . Anonymity of the respondents and confidentiality of the data obtained were respected . Approval to conduct the survey was obtained from the local government units of Santa Fe and the Ethics committee of the Universidad Nacional del Litoral ( CAID orientado 2013: “Socioecología de Leptospirosis” ) . The questionnaire had been pre-tested in communities neighboring the study areas . The questionnaire consisted in 36 questions which included demographic factors such as age , sex , level of education attained , occupation and evacuation status . The questionnaire also included questions to asses the respondent’s KAP regarding leptospirosis ( S1 Table ) . After the completion of the survey , an informative flier with most common symptoms of leptospirosis , modes of transmission and preventive actions was given and explained to each respondent ( S1 Fig ) . Computation of practice scores was based on 8 items from the questionnaire ( S1 Table: 2-7 , 9 , 11 ) ranging from a minimum of 0 points to a maximum of 14 points . A low score indicated risky behaviors or habits , while a high score was indicative of safer practices . Frequency of activities such as fishing , hunting and handling livestock , gathering firewood and gardening were categorized in: frequently ( at least once a week ) , rarely ( less than once a month ) or never and were given a score from 0 to 2 respectively . Performing the above activities barefooted subtracted 2 points to the practice scores , while using footwear that is not water-proofed subtracted 1 point and using boots or waders did not subtract points . An extra point was added to the practice scores when the person indicated avoiding situations that are thought to increase transmission risk , such as going to the river islands , spending the night on the islands , walking barefooted through flood water or using river water for consumption or to clean or swim . The computation of knowledge and attitude scores was restricted to those respondents who reported having heard about leptospirosis . Knowledge score increased as the person knew more about the disease . It was based on 7 items from the questionnaire ( S1 Table: 14 , 23-28 ) covering general aspects of leptospirosis , including knowledge on symptoms , transmission and preventive actions with a minimum of 0 and a maximum of 22 points . We included open-ended questions that allowed multiple answers and were scored as the sum of correct minus the incorrect answers . We also included closed-ended questions that were scored as 1 if the answer was “Yes” and 0 if the answer was “No/Does not know” being “Yes” the correct answer . Attitude scores increased as the person responding the questionnaire communicated greater awareness of the risk and/or a greater tendency to act if symptoms appear or during an outbreak . Attitude scores were based on 7 items from the questionnaire ( S1 Table: 16-17 , 29-33 ) with a minimum of 0 and a maximum of 13 points , and included questions regarding perceptions of leptospirosis prevalence in the area and reactions to a potential leptospirosis outbreak that were scored from 0 to 2; questions regarding the perceived risk of leptospirosis in comparison to dengue , and the propensity of seeking medical attention in case of febrile symptoms were scored from 0 to 1 . The crude scores for knowledge , attitudes , and practices were expressed as percentages dividing by the maximum score possible for each category and multiplying by 100 . Data was entered using Microsoft Access , then cleaned and analyzed with R software version 3 . 4 . 1 [46] . Results were presented as frequency ( % ) for categorical variables and as mean ± standard deviation ( SD ) or median ( IQR ) for KAP scores . To compare KAP scores between sites we used analysis of variance ( ANOVA ) followed by Tukey’s post comparison tests or Kruskal-Wallis ANOVA followed by Dunn’s posts comparison tests to asses if there were differences between sites . The level of statistical significance was set at P≤0 . 05 . The factors associated with practice scores were evaluated using linear mixed-effects models ( LMM ) with site as random intercept , using the lme4 package [47] . Respondents who reported hearing about leptospirosis and had no missing data on socio-demographic characteristics were included in the analysis . A list of candidate models was obtained using a manual step-backward procedure from a full model based on the Kenward-Roger approach , using the pbkrtest package [48] . The full model included the respondent’s sex , age , education attained , a dummy variable coded as 1 if the respondent worked outside of his/her home and 0 if not , a dummy variable coded as 1 if the respondent used media ( television , radio , internet , newspaper ) as source of health information or 0 if not , a dummy variable coded as 1 if the respondent used health services as source of health information and 0 if not , a dummy variable coded as 1 if the respondent knew someone that had leptospirosis and 0 if not , plus the respondent’s knowledge score and attitudes score . Models were then compared using second-order Akaike Information Criteria ( AICc ) [49] with the MuMIn package [50] . Inferences were derived from the most parsimonious model among the candidate models with a ΔAICc <4 , and was refitted by Restricted Maximum Likelihood ( REML ) in order to obtain coefficient estimates for the random and fixed effects parameters .
A total of 113 persons from the three study sites responded the survey , representing both evacuated ( 62 . 8% ) and non-evacuated ( 37 . 2% ) households ( Table 1 ) . The majority of the respondents ( 61 . 1% ) were female and had primary school as the highest level of education attained ( 65 . 5%; Table 1 ) . The ages of the respondents ranged from 12 to 77 years old with a median of 37 years old ( IQR: 27-52 ) . Housewives , unemployed , retired and students represented 58 . 4% of the respondents . Of the respondents who worked outside of their home , 15 . 9% were subsistence fishermen , 5 . 3% worked in small-scale construction and 20 . 4% were engaged in other activities such as working at small retail business , domestic services and municipal employment ( Table 1 ) . Ninety four ( 83 . 2% ) respondents reported having previously heard of leptospirosis , and almost half of them ( 47 . 9% ) knew at least one person who had the disease . The majority of respondents who had heard about leptospirosis identified it as a disease associated with rats ( 71 . 3% ) and were aware that leptospirosis has a cure ( 72 . 3% ) but it can be fatal ( 80 . 9% ) . The symptoms of leptospirosis that were frequently identified included fever ( 55 . 3% ) and headache ( 26 . 6% ) ( Fig 3A ) . Almost a third of the respondents ( 29 . 8% ) were not able to describe a mode of transmission . Of those who responded the question about transmission , the majority identified rats and mice as the main animal hosts ( 79 . 8% ) and the urine of these animals as the main mode of transmission ( 46 . 8% ) ( Fig 3B and 3C ) . When asked about preventive actions , 36 . 2% of the respondents were unable to mention a preventive action and only 5 . 3% mentioned avoiding contact with flood water ( Fig 3D ) . Overall mean knowledge score was 33 . 9% ( SD±15 . 9% ) , ANOVA test yielded significant differences among site ( P<0 . 001 ) , with Site 1 ( 42 . 6% , SD ±14 . 6 ) and Site 2 ( 37 . 5% , ±13 . 3% ) having significantly higher scores than Site 3 ( 22 . 3% , ±13 . 5% ) . When asked about where they have heard about leptospirosis , almost half of the respondents ( 48 . 9% ) reported using the media ( television , radio , newspapers , internet ) as their source of information , followed by health services ( 36 . 2% ) , relatives and neighbors ( 29 . 8% ) and schools ( 14 . 9% ) . Regarding the attitudes about leptospirosis , 52 ( 55 . 3% ) respondents believed that there are few cases per year but 57 ( 60 . 6% ) assumed that there could be an epidemic outbreak ( Table 2 ) . When asked how they would act in the face of a possible outbreak , 53 ( 56 . 4% ) respondents said they would be afraid of becoming infected and 80 ( 85 . 1% ) said they would be able to take preventive measures . The majority of respondents considered that dengue is more prevalent than leptospirosis in the area ( 59 . 6% ) , yet there was not a notable distinction on how they perceived their risk to these diseases , approximately a third of the respondents felt more at risk of leptospirosis , a third more at risk of contracting dengue , and a third felt equally at risk to both diseases ( Table 2 ) . While 55 . 3% of the respondents considered dengue to be a more severe disease than leptospirosis , 17% of the respondents considered them equally severe ( Table 2 ) . Of the 94 respondents , 16% were not able to respond the question regarding how prevalent they think these diseases are in the area , 12 . 8% were not able to respond to which of the two they felt more at risk and 7 . 4% were not able to respond the question about the severity of leptospirosis and dengue ( Table 2 ) . On the other hand , we found that the majority of the respondents usually seek medical care ( 77% ) , and when asked if they would seek it in case of persistent fever 96 . 4% gave an affirmative answer . Overall median attitudes score was 76 . 9% ( IQR 30 . 8-100% ) and Kruskal-Wallis ANOVA yielded no significant differences between sites ( P = 0 . 26 ) . For preventive practices , 54 ( 48 . 7% ) out of 113 respondents reported never going fishing , 94 ( 83 . 2% ) reported never going hunting or handling livestock , 61 ( 64% ) reported never doing gardening and 62 ( 54 . 9% ) reported never collecting firewood . Differences between genders were observed in the frequencies of fishing and hunting ( S2 Table ) . Of those respondents that reported performing one or more of those activities ( n = 85 ) , 44 ( 51 . 8% ) wore inappropriate footwear , 36 ( 42 . 4% ) wore boots or wading suits and 5 ( 5 . 9% ) went barefooted . With regard of avoidance of risk practices , 58 ( 51 . 3% ) respondents reported not going to the river islands , 73 ( 64 . 6% ) reported not spending the night at the island , 26 ( 23% ) reported avoiding to walk through flood water , 45 ( 39 . 8% ) reported avoiding to get their feet wet on flood water , 78 ( 69% ) reported to avoid the use of water from the river or flood water to drink or clean and 66 ( 58 . 4% ) avoided to swim in the river or flood water ( Fig 4 ) . Differences between genders were observed in the avoidance of some risk situations ( Fig 4; S2 Table ) . Overall median practices score was 57 . 1% ( IQR 0-100% ) and Kruskal-Wallis ANOVA yielded no significant differences between sites ( P = 0 . 08 ) . The adherence to leptospirosis preventive practices was assessed by modeling practices score as a function of 9 independent variables: sex , age , education and occupational status of the respondent , whether the respondent uses media as source of leptospirosis information , whether the respondent uses health services as source of leptospirosis information , whether the respondent knows someone that has had leptospirosis , knowledge score and attitudes score . Manual step-backward procedure yielded a list of 10 models , from which the most parsimonious model was selected ( Table 3 ) . In the final model , practices score was negatively associated with male sex and positively associated with knowledge score ( Table 4 ) . In this model , fixed effects alone explained 28 . 43% of the variation while both fixed and random effects explained 29 . 87% of the variation ( Table 4 ) , residuals were normally distributed with a mild outlier .
To our knowledge , this is the first study that aimed at describing the knowledge , attitudes and preventive practices associated with leptospirosis in Argentina . Our study , conducted on riverside settlements of the province of Santa Fe , provides relevant information on the risk of leptospirosis among communities from an endemic area highly vulnerable to floods [23–25 , 28–30] . The majority of the respondents in this study had heard about leptospirosis ( 83 . 2% ) , however , many of them were not able to describe leptospirosis symptoms ( 33% ) , nor modes of transmission ( 29 . 8% ) or preventive actions ( 36 . 2% ) . In order for people in these communities to be better prepared to avoid infection or disease progression it is critical to increase the knowledge on these key aspects as previously suggested in studies from Chile [51] , Philippines [32] , Trinidad [35] , Malaysia [33 , 37] and Sri Lanka [44] . In terms of recognizing leptospirosis risk factors , only 25 . 5% of the respondents mentioned contact with flood water as a mode of transmission . Given that this is considered the main mode of transmission in the region [23–26] , it is concerning to find that only a small proportion of the population is aware of this risk . These communities do not have paved roads and most of them have open sewers , commonly known as “zanjas” , that contain stagnant water most of the time . After large rainfall events , dirt roads are filled with puddles and rainfall is mixed with water from the open sewers and/or ponds . In the three study communities it is a common practice to cross these puddles barefooted ( 60 . 2% ) which increases transmission risk . Many respondents work in the informal sector , mainly in outdoor activities such as fishing , hunting and gardening , half of them did not use adequate footwear . Other studies have found that protective gear is often not used due to difficulty in acquiring it or because people feel that it is uncomfortable [9 , 34 , 44] . In our study communities , the use of adequate footwear may increase with a greater awareness that leptospirosis can be acquired through contact with contaminated water . In this region , leptospirosis is also called “the disease of the rats” as most people consider that rats play an important role in disease transmission . In our study , 79 . 8% of the respondents mentioned rats as the source of leptospirosis to humans . In agreement with other studies [33 , 44 , 52] , only a low percentage of respondents recognized dogs ( 12 . 8% ) and cattle/pigs ( 5 . 4% ) as animal sources of leptospirosis . This is of special concern considering that most households have several domestic animals , which typically includes several dogs and some individuals of livestock raised in the vicinity of the household . Additionally , L . interrogans serovar Canicola was isolated from cases of severe to lethal leptospirosis in the area [28 , 53] . Furthermore , only a small percentage of the dog population on these communities is vaccinated against leptospirosis [54 , 55] . Most respondents stated that only a few cases of leptospirosis occur yearly in the province of Santa Fe but believed that it was possible for an epidemic outbreak to occur in the area . This perception is in agreement with the data published by the National Health Surveillance System ( SIVILA ) , that shows a total of 27 confirmed cases of leptospirosis in the province of Santa Fe between the epidemiological weeks 1 and 20 of 2016 , of which 12 belonged to the department La Capital , where the study area is located [56–58] . However , if we consider the diversity of clinical manifestations of this disease and the limitations of laboratory diagnosis in the area , it is probable that leptospirosis is under-reported here [14 , 28 , 53] . Similarities between dengue and leptospirosis symptoms and environmental settings are likely contributing to the underestimation of the number of leptospirosis cases [33 , 35 , 59–61] . Dengue outbreaks during floods such as the 2016 flooding event of the Paraná river [56] , may lead to leptospirosis cases being misdiagnosed as dengue . Between the epidemiological weeks 1 and 20 of 2016 , 1324 dengue cases were reported for the province of Santa Fe [56] . We conducted the questionnaire during this dengue fever outbreak , when a massive campaign was in place to warn the public about the risk of dengue fever . In this scenario , is was surprising to find that approximately 30% of the respondents considered that they were are a greater risk of contracting leptospirosis and 25 . 5% considered that they were equally exposed to both pathogens . Finally , another factor that may contribute to the underestimation of the number of leptospirosis cases is the fact that the environmental setting of these riverside communities restricts the accessibility to health care services and laboratory diagnosis . Overall , knowledge about leptospirosis appeared to decrease as the distance to the city of Santa Fe increases . This could be attributed to a greater distance to the hospitals and to a more limited access to information by the inhabitants of rural areas [62–64] , even though all of them are settlements with apparently similar living conditions . Site differences were not observed in people’s attitudes and practices . Our modeling efforts for explaining the variation in using preventive practices suggest that men have higher risk of contracting leptospirosis than females . In contrast to males , most of the female surveyed said they never went fishing ( 63 . 8% ) , never went hunting ( 94 . 2% ) , never go to the island ( 63 . 8% ) or spent the night at the island ( 76 . 8% ) and do not swim in the river ( 72 . 5% ) ( S2 Table ) . While in other activities such as collecting firewood , gardening and crossing puddles , no significant differences were observed between men and women ( S2 Table ) . We also found that many of the men are engaged in fishing ( 31 . 8% ) and most of the women are either housewives or unemployed ( 63 . 8% ) . This is in agreement with other studies that found a greater probability of infection in men than in females and have provided as a possible explanation to this gender differences the greater engagement of males in outdoor recreational and/or labor activities [1 , 7 , 11 , 14 , 25 , 28 , 32 , 33 , 44] . We also found that the likelihood of using preventive practices increased with knowledge . Most studies on leptospirosis KAP were descriptive and did not tried to identify factors that may influence the predisposition to use preventive practices [9 , 33 , 35 , 37 , 44 , 65] . Our results are consistent with those of Arbiol et al . [32] , Lau et al . [7] and other studies regarding zoonotic disease prevention practices that show that greater knowledge about the disease results in a greater adoption of preventive practices [31 , 45] . The findings should be considered within the limitations of the study . The relatively homogeneous socio-demographic composition of the sample may have impacted on the significance of socio-demographic parameters on preventive practices . Another limitation of this study was the small sample size that may have precluded us from detecting a meaningful effect of other socio-demographic factors . In regards to generalization , our conclusions can only be extrapolated to similar riverside settlements in the region , where subsistence fishing is a common occupation among residents . The results of this study suggest that increasing knowledge about leptospirosis is key for promoting desired , positive behaviors in the community , rather than changing the attitudes towards the disease . Thus , our study underscores the importance of implementing a diverse array of information , education and communication activities to achieve a better understanding of the symptoms , treatment and prevention of leptospirosis by the various actors involved . Health education should reach healthcare providers and the general public , in particular high-risk groups . In these riverside communities , a key challenge lies in identifying effective strategies to reach the principal high-risk group for leptospirosis: males who work or perform recreational activities outdoors and spend time in precarious campsites in the river islands . In this respect , one promising and innovative approach is the development and implementation of social marketing campaigns that use concepts and tools from marketing to reach the public and try to “promote socially beneficial behavior change” [66] . This strategy has been applied successfully to increase global awareness of the Chagas disease issues in a campaign that involved the participation of popular artists and athletes [67] . These campaigns should be designed for specific communities , given that the barriers to engaging in leptospirosis preventive practices are known to differ among communities [51] . For the present study , we developed a flier tailored to the riverside communities ( see S1 Fig that was explained after conducting the questionnaire , and we offered outreach workshops for the local schools . These instances provided opportunities to exchange ideas about preventive practices that seemed more attainable . To identify means to improve public access to information on prevention practices and to adequate health care for early diagnosis and treatment , it is necessary to conform teams composed by health workers , researchers and policy makers . The interdisciplinary approaches to public health issues , promote a better understanding of the problems and provide comprehensive solutions for different scenarios [68 , 69] . In order for these teams to develop more effective public health policies and programs , they need access not only to high-quality epidemiological data [13] , but also to relevant sociocultural information [70] . | Leptospirosis is a zoonotic bacterial disease that has been recognized as a growing public health problem affecting mainly residents from slum settlements located in floodable areas . As such , it is considered a neglected disease that needs greater attention to reduce its global burden . A key step towards this purpose is to identify factors that influence the adherence to preventive practices regarding leptospirosis in endemic areas . We conducted a survey on residents of riverside settlements of the province of Santa Fe , an endemic area in Argentina , in order evaluate the knowledge , attitudes and practices regarding leptospirosis . Our results suggest that risky practices were performed mainly by men and that , contrary to our expectations , having a positive attitude towards leptospirosis does not appear to influence the likelihood of performing preventive practices , while greater knowledge about the disease does lead to safer practices . Public health officials should develop a comprehensive plan with diverse information , education and communication activities to promote a better understanding of the symptoms , treatment and prevention of leptospirosis by the various actors involved . | [
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] | 2018 | Knowledge, attitudes and practices (KAP) regarding leptospirosis among residents of riverside settlements of Santa Fe, Argentina |
Recent Hi-C measurements have revealed numerous intra- and inter-chromosomal interactions in various eukaryotic cells . To what extent these interactions regulate gene expression is not clear . This question is particularly intriguing in budding yeast because it has extensive long-distance chromosomal interactions but few cases of gene regulation over-a-distance . Here , we developed a medium-throughput assay to screen for functional long-distance interactions that affect the average expression level of a reporter gene as well as its cell-to-cell variability ( noise ) . We ectopically inserted an insulated MET3 promoter ( MET3pr ) flanked by ~1kb invariable sequences into thousands of genomic loci , allowing it to make contacts with different parts of the genome , and assayed the MET3pr activity in single cells . Changes of MET3pr activity in this case necessarily involve mechanisms that function over a distance . MET3pr has similar activities at most locations . However , at some locations , they deviate from the norm and exhibit three distinct patterns including low expression / high noise , low expression / low noise , and high expression / low noise . We provided evidence that these three patterns of MET3pr expression are caused by Sir2-mediated silencing , transcriptional interference , and 3D clustering . The clustering also occurs in the native genome and enhances the transcription of endogenous Met4-targeted genes . Overall , our results demonstrate that a small fraction of long-distance chromosomal interactions can affect gene expression in yeast .
Cell proliferation and differentiation depend on rigorously controlled gene activities . Gene regulation is best understood at the level of linear organization of the genome , including the primary DNA sequences and arrays of closely associated regulatory proteins . The three-dimensional ( 3D ) organization of chromosomes also plays an important role in gene regulation [1–3] . Elucidating the regulatory functions of higher order chromatin configuration is a critical component towards the fundamental understanding of eukaryotic gene regulation . Long-distance gene regulation is best elucidated in some specific genomic loci in multi-cellular organisms , such as the locus control region of the murine β-globin genes [4–7] . With the recent development of Chromosome Conformation Capture technique ( 3C ) and its derivatives ( 4C , Hi-C , etc . ) , numerous intra- and inter-chromosomal interactions have been detected in different model organisms [8–11] . These interaction patterns can change with cell types , developmental stages , and environmental stress [12–15] . Some of the long-distance interactions were confirmed to have functional roles . However , in general , to what extent these interactions regulate gene expression is not clear . Traditionally , budding yeast has not been considered as a good model for gene regulation over long distance because the upstream activating sequences ( UASs ) tend to be adjacent to the target genes . Consistent with this idea , artificial displacement of GAL1 UAS away from the TATA box eliminated its activity [16] . There are only several cases in which long-distance interactions have been proposed to regulate gene expression in yeast , including promoter-terminator looping [17 , 18] and inter-allelic interactions between homologous genes [19 , 20] . Nevertheless , the Hi-C experiment in haploid yeast cells revealed extensive long-distance interactions among the chromosomes [11 , 14] . Statistical analysis of the Hi-C data showed that co-regulated yeast loci tend to cluster [21 , 22] , and physically proximal genes tend to co-express [23 , 24] . These studies suggest a role of long-distance chromosomal interactions in gene regulation , although direct evidence is lacking . Other than affecting the average level of gene expression among a population , long-distance interactions may also affect its cell-to-cell variability ( noise ) . The amplitude of gene expression noise is determined by the underlying regulatory mechanism [25–27] . Intuitively , since chromosome organization can be highly dynamic among single cells [28] , it may increase variation in gene expression . In one example , an artificial long-distance activation system consisting of a mouse β-globin gene and a human Locus Control Region showed high expression noise [29] . The generality of this conclusion is unclear . In this work , we set up an experimental scheme to screen for long-distance chromosomal interactions that affect the average level and noise of gene expression . A promoter flanked by invariable sequences of more than 1kb in length is inserted ectopically at thousands of genomic loci , allowing it to make contacts with different parts of the genome . A change in promoter activity in this case would necessarily involve mechanisms that function over a distance . In a small fraction of genomic locations ( 30 out of 1327 ) , we observed modulations of the promoter activity with three distinct patterns . We then thoroughly investigated the regulatory mechanisms that cause these changes . We showed that Sir-dependent silencing and transcriptional interference can repress gene expression to a comparable extent , but cause different levels of noise . We also found evidence that the reporter gene can acquire higher activity by clustering with a subset of co-regulated genes . The latter mechanism is also used in the wild-type yeast to enhance the transcription of native genes .
We reasoned that we want a reporter whose activity is high enough to allow accurate measurement , but not too high to potentially mask the long-distance effects . We therefore chose MET3 promoter ( MET3pr ) driving GFP as our reporter ( Fig 1A ) . MET3pr is an inducible promoter that has mild activity when induced by the depletion of methionine in the media [30] . To enable PCR-based analyses of the promoter without the interference from the endogenous MET3pr , we used the MET3pr from S . kudriavzevii in our construct . These two MET3prs have similar induction kinetics and steady-state expression levels ( S1A and S1B Fig ) . They share ~50% of sequence homology and can be clearly differentiated in PCR with certain primers ( S1C Fig ) . To selectively detect long-distance effect , we embedded the MET3pr in the middle of a ~3kb cassette , so that it is more than 1kb away from the variable chromosomal context at the integration site . Both edges of the cassette are flanked by terminator sequences , which should prevent ( or at least reduce ) invasive transcription elongation . These designs distinguish our study from previous position-dependent gene expression studies in yeast [31 , 32] , where the promoter of the reporter gene was placed immediately downstream an endogenous promoter , and the change in the reporter activity is more likely to reflect local instead of long-distance regulation . To increase the chance of detecting long-distance chromosomal interactions that play a role in gene regulation , we put the reporter gene at highly dispersed genomic locations to explore different chromosomal interactions . We took advantage of the commercially available yeast insertion library , which contains more than 2000 heterozygous diploid strains each with an mTn sequence at a unique genomic locus [33] ( S2 Fig ) . The initial mTn insertion was carried out by a transposition reaction in E . coli [34] , and therefore the insertion sites are not influenced by the chromatin structure in yeast . We added the mTn sequences to the reporter cassette so that it can be integrated at the mTn loci through homologous recombination . Because the integrated reporter disrupts many open reading frames ( ORFs ) , we integrated a MET3pr–mCherry control into a fixed locus to distinguish the regulatory effect specific to GFP from the global effect due to the loss of a resident gene ( Fig 1B; Methods ) . In total , we have constructed 1327 strains , each with a GFP reporter inserted at a unique genomic locus and mCherry at a fixed locus ( see S1 and S2 Tables for strain list and expression data ) . We induced the strains in methionine-free synthetic media for 5–6 hours to reach the steady-state of MET3pr activation ( S1A Fig ) and measured the fluorescent intensity with flow cytometry ( FACS ) , which allows us to evaluate the reporter gene expression in single cells ( Fig 1B; Methods ) . We used the coefficient of variation ( CV; standard deviation divided by the mean ) to quantify the gene expression noise and plotted the noise versus mean for both GFP and mCherry in all our library strains ( Fig 2A ) . We identified the strains with expression levels more than three standard deviation from the mean as “outliers” . For each outlier found in the initial screen , we confirmed its expression using three or more colonies and verified the integration site of the reporter with inverse PCR ( Methods ) . As expected , there are more outliers in GFP expression than in mCherry . In total , 30 strains showed unusual expression in GFP but not in mCherry ( “GFP outliers” ) , 1 in mCherry only , and 2 in both ( S3 Table ) . The following investigation focused on the GFP outliers . The GFP data in Fig 2A fall into four distinct regions ( “profiles” ) . Most strains belong to profile 1 with close-to-average expression level and noise , while the rest can be divided into profile 2 , 3 , and 4 with “low expression / high noise” , “low expression / low noise” , and “high expression / low noise” , respectively . The profile 2–4 strains have close-to-average mCherry level ( Fig 2A ) , showing that the unusual GFP expression is not due to global changes in the MET3pr activity . We selected one strain from each profile ( strain 1–4 correspond to profile 1–4 ) and plotted their FACS data ( Fig 2B ) . Note that the GFP expression in strain 2 and 3 are repressed to similar levels ( 0 . 59 vs 0 . 61 ) , but the noise is significantly higher in strain 2 ( p-value < 0 . 0001 ) . This difference can be clearly visualized in fluorescent images of the two strains ( Fig 2C ) . Since there is strong connection between noise and the underlying gene regulatory mechanism [26 , 27] , these data suggest that the MET3pr repression in profile 2 and 3 is caused by different mechanisms . Consistent with this idea , all of the profile 2 strains have MET3pr-GFP inserted in the silenced regions ( HML , rDNA and telomere ) , and all of the profile 3 and 4 strains have the reporter in non-silencing “euchromatin” ( Fig 2D ) . Eleven strains in profile 1 have the insertion sites in the sub-telomeric regions , which is consistent with a previous finding that some locations at the chromosome ends do not have the silencing effect [35] . GFP intensity reflects an integrated rate of transcription , translation , and post-transcriptional regulations . To test if the GFP outliers originate from altered transcriptional rates , we selected two strains from each profile and analyzed the RNA polymerase II ( Pol II ) distribution on the GFP and mCherry ORFs using chromatin immunoprecipitation ( ChIP ) ( Methods ) . Comparing with profile 1 , the pol II density over the GFP ORF is reduced in profile 2 and 3 ( p-value < 0 . 0001 and 0 . 0027 ) and increased in profile 4 ( p-value = 0 . 03 ) , whereas it remains constant on the mCherry ORF ( Fig 2E ) . Therefore , GFP expression in the outliers is modulated at the transcriptional level . The transcriptional repression in profile 2 is likely due to Sir-dependent silencing that is known to spread over a few thousand bases [36] . Indeed , deletion of Sir2 leads to significant increase of the GFP expression in profile 2 strains ( S3 Fig ) . We next investigated the regulatory mechanisms of profile 3 and 4 . We examined various properties of the reporter insertion sites in profile 3 . We noticed that in these strains , the GFP reporters tend to be inserted into highly expressed ORFs ( Fig 3A ) : according to the database in [37] , 5 out of the 10 genes at the profile 3 insertion sites produce >40 mRNAs per cell , which is significantly higher in comparison to the rest of the genome ( p-value < 2×10−5 ) . This observation raised the possibility that the high-level transcription of the flanking genes may leak onto the MET3pr and interfere with its expression , as shown in some other cases [38 , 39] . To test this idea , we selected two profile 3 strains containing MET3pr-GFP in highly expressed genes ( PDC1 and CIS3 ) and measured the pol II density over the GFP cassette . We probed the ChIP signals over the KanMX promoter , MET3pr , and GFP ORF ( Fig 3B ) . We also included a profile 1 and a profile 2 strain as controls . Strains containing GFP but not mCherry were used here to maintain the S . kud MET3pr as a single copy . Consistent with Fig 2E , the profile 2 and 3 strains have less pol II over the GFP ORF . However , the two profile 3 strains have higher pol II density in the KanMX and MET3 promoters ( Fig 3B ) , supporting the idea that polymerases are “invading” from the upstream gene onto to the MET3pr . To test the causal relation between the invading transcription and GFP repression , we used CRISPR/Cas9 to delete the TATA elements of PDC1 and CIS3 and measured the resulting pol II density and GFP expression ( Methods ) . Deletion of these TATA boxes restored pol II density and GFP expression to the profile 1 level ( Fig 3C and 3D ) . These data show that GFP repression in these profile 3 strains are due to transcriptional interference . There are also some profile 3 strains with GFP not in highly expressed genes ( Fig 3A ) . We selected two of these strains ( SAM2 and SEG2 ) and performed pol II ChIP . Interestingly , the pol II density over the MET3pr is the same as in the profile 1 control , but the density over the GFP ORF is lower ( S4 Fig ) . This result is different from the profile 2 strain where pol II density is lower in both regions ( S4 Fig ) . These data indicate that the transcription of MET3pr in these profile 3 strains initiates at a normal level , but is curtailed in a subsequent step ( e . g . transition from initiation to elongation or during elongation ) . The detailed mechanism of this repression is still unclear . We next studied the mechanism of MET3pr-GFP overexpression in profile 4 strains . Unlike profile 3 strains , the reporter insertion sites in profile 4 strains involve genes with mild expressions ( mRNAs per cell from 0 . 5 to 4 ) ( S4 Table ) . Some profile 4 insertion sites are close to each other in the genome . For example , GFP reporters inserted into five consecutive genes , RSM23 , CWC23 , SOH1 , SCS3 , and MET13 , all showed higher-than-average expression ( p-values = 0 . 0004 , 0 . 008 , 0 . 0033 , 0 . 0048 , and 0 . 005 respectively; Fig 4A and 4B ) . This observation indicates that profile 4 overexpression is regulated by certain property of chromosomal regions , rather than that of individual genes . The profile 4 sites in Fig 4B are adjacent to MET13 , which , similar to MET3 , is also in the methionine metabolic pathway activated by transcription factor Met4 [40 , 41] . It turns out that all profile 4 insertion sites are close to Met4-targeted genes ( average distance 1 . 4 kb ) , which is highly significant comparing with random sites ( p-value < 10−4; Fig 4A , S5 Fig; Methods ) . The reverse is not true: not all GFPs landed close to Met4-targeted genes are overexpressed . Next , we investigated if the overexpression in profile 4 is specific to MET3pr . We carried out a “promoter swapping” experiment , in which we replaced MET3pr in the reporter cassette with GAL1Spr [42] , an attenuated GAL1 promoter with similar firing strength as the MET3pr . We integrated the new reporter into eight loci , two from each profile , induced the GAL1Spr-GFP expression with galactose , and measured the steady-state gene expression under the microscope . When inserted in the profile 2 and 3 sites , GAL1Spr-GFP continues to have lowered expression , as expected from the silencing and transcriptional interference mechanisms ( Fig 4C ) . In contrast , GAL1Spr-GFP in profile 4 loci no longer shows overexpression ( Fig 4C ) , indicating that the hyper-activity in profile 4 is MET3pr-specific . A straightforward explanation for the data above is that some Met4-targeted genes ( like MET13 ) may increase the local concentration of Met4 and/or other co-activators , and thus enhance the MET3pr activity in its vicinity . To test this idea , we deleted the entire MET13 gene ( including the promoter and the transcribed region ) and measured the reporter expression . Since we conducted our experiments in diploids , we deleted the MET13 either in cis or in trans relative to the reporter , or both ( Methods ) . These deletions do not affect MET3pr activity globally because mCherry expression remains unchanged ( Fig 4D ) . Importantly , the GFP expression is not reduced in any of the MET13 deletion strains ( Fig 4D ) , indicating that the overexpression is not due to the presence of a nearby MET13 gene . To test if the overexpression is due to the presence of other local genes , we took the reporter cassette in CWC23 ( site “c” in Fig 4B ) , together with the neighboring genomic sequences ( 3 . 8kb on one side and 5 . 5kb on the other ) , and inserted it into a profile 1 locus on Chr15 ( Methods ) . This translocation reduces the GFP expression to the normal ( profile 1 ) level without affecting the mCherry expression ( Fig 4E ) . This result confirms that the GFP overexpression is not due to the neighboring MET13 gene , nor any other genes within a few kb range . Based on the above evidence , we suspected that the overexpression is related to pathway-specific long-distance chromosomal organization . In particular , the profile 4 sites may be physically located in transcriptional “hotspot ( s ) ” with high local concentration of Met4 and/or related factors that promote MET3pr firing ( Fig 4F ) . If this model is correct , we may be able to detect interactions between at least some of the profile 4 sites . Therefore , we carried out 3C experiments to probe the interactions between a hyper-active MET3pr-GFP and other endogenous profile 4 loci ( Fig 5A; Methods ) . Since previous reports indicate that chromosomal interactions in yeast may change with transcriptional status [19 , 20 , 43] , the 3C assays were performed in either the presence or absence of methionine . We also performed the same measurement between MET3pr-GFP and a cis region ~13kb away as a positive control to ensure the success execution of the 3C assay ( Methods ) . As shown in Fig 5B , a 3C signal was detected between the CWC23-localized GFP and ADK1 . As expected from the 3C procedure , this signal is absent without ligation and/or crosslinking ( Fig 5B ) . This GFP reporter also makes contacts with most of the other profile 4 loci ( Fig 5C ) . Although all of the 3C signals are visible in the presence or absence of methionine , the strength of the interactions increase by 1 . 5–4 fold in the activating condition ( p-values = 0 . 0016 , 0 . 0057 , 0 . 049 , 0 . 0001 for ADK1 , GTO1 , SER33 , PDR12; Fig 5D ) . These results indicate that the overexpressed MET3pr-GFP is in physical proximity to many profile 4 sites before the induction , and they come closer after the induction . To understand how specific the interactions occur at the profile 4 sites , we also included two negative controls in the 3C measurements ( N1 and N2 ) . N1 is a profile 1 site on Chr16 that has similar distance to the centromere as ADK1 . Previous Hi-C experiments indicate that long-distance chromosomal interactions in yeast are partially determined by the Rabl configuration , in which sites with similar distance to the centromere tend to interact [11 , 14] . We do not think the interactions seen in Fig 5C are based on this mechanism because the interacting loci have variable distances to centromeres ( differing by >300kbp; S5 Table ) . Consistent with this idea , ADK1 but not the N1 site shows interaction with GFP . N2 probes the interaction between the same MET3pr-GFP with MET28 , a Met4-targeted gene that is a profile 1 site . No interaction was observed in this case ( Fig 5C and 5E ) . It is important to point out that we have found profile 1 sites at other Met4-targeted genes that make contacts with the overexpressed MET3pr-GFP ( S6A Fig ) . Interestingly , in comparison to Fig 5D , the strengths of these interactions show less changes in the activating condition ( p-value < 0 . 005; S6B Fig ) , indicating that these sites do not cluster further with the profile 4 sites upon induction . When we moved a profile 4 MET3pr-GFP together with 9 . 3kb of neighboring sequences to Chr15 , the GFP no longer shows overexpression ( Fig 4E ) . At this translocated site , MET3pr-GFP loses its interaction with all the profile 4 sites ( Fig 5E ) , and the corresponding 3C signals were undetectable in qPCR . Overall , the correlation between GFP overexpression and its interaction with other profile 4 sites support the model in Fig 4F that profile 4 sites cluster to enhance MET gene expression . In Figs 4 and 5 , we focused on the chromosomal interaction and expression of GFP reporters . We suspect that the endogenous Met4-targeted genes at the profile 4 sites can also benefit from the same long-distance interactions to gain higher expression . To test this idea , we measured pair-wise interactions between the endogenous profile 4 sites ( Fig 6A; Methods ) . Out of the 15 pairs , we detected 10 interactions ( Fig 6B ) . In particular , MET13 makes contacts with all the other profile 4 sites . We then moved the MET13 gene from its endogenous location to a new location on Chr15 ( same location as in Fig 4E; Methods ) . At this ectopic location , MET13 loses its interactions with all of the other profile 4 sites ( Fig 6C ) . Using RT-PCR to measure the average mRNA level of MET13 before and after the translocation , we found that the MET13 expression is significantly reduced at the new location ( p-value = 0 . 0009; Fig 6D ) . The magnitude of the drop ( ~30% ) is consistent with the difference in MET3pr-GFP expression at these two locations . In contrast , mCherry expression driven by the MET13pr at CDC20 locus in these two strains are the same ( Fig 6D ) . Therefore , similar to the MET3pr-GFP reporter , the endogenous MET13 gene shows higher expression at its native genomic locus , which correlates with its long-distance interactions with other profile 4 loci .
Numerous intra- and inter-chromosomal interactions have been discovered in budding yeast , yet their role in gene regulation is far from clear . In this paper , we probed the effect of these interactions on gene expression by ectopically inserting an insulated reporter into thousands of genomic loci and characterizing the reporter activity at the single cell level . At most locations , the expression has similar average level and noise , indicating that the majority of long-distance chromosomal interactions detected by Hi-C do not play a significant role in gene regulation ( at least for MET3pr ) . However , in a small fraction of locations , gene expression deviates from the norm and exhibits three distinct patterns including low expression / high noise , low expression / low noise , and high expression / low noise ( profile 2 , 3 , and 4 ) . Our follow-up studies indicate that profile 2 expression is due to the Sir2-mediated silencing , profile 3 is partially due to transcriptional interference , and profile 4 is due to 3D clustering of Met4-targeted genes . This assay may be used as a general platform to screen for functional long-distance chromosomal interactions that affect gene expression . Silencing and transcriptional interference are well-characterized mechanisms of gene repression . Interestingly , our data revealed that these two mechanisms can repress the average gene expression to a similar extent , but generate different levels of cell-to-cell variability . The detailed mechanism underlying this phenomenon requires further elucidation . Based on previous results [28 , 30] , we hypothesize that the two repression mechanisms have different time scales of action . Sir2-dependent silencing may be maintained at different levels from cell to cell for relatively long period of time ( epigenetic memory ) , resulting in variable “silencing states” . In contrast , transcriptional interference is likely to occur multiple times during the MET3pr activation in each single cell , averaging out the stochasticity of this process and resulting in uniformly reduced transcription . For practical purposes , these two mechanisms can be used by synthetic biologists to engineer different gene expression noise with similar average level of expression . For the overexpression mechanism in the profile 4 strains , here is the evidence that we found: 1 ) overexpressed MET3pr-GFP tends to locate inside or close to a Met4-targeted gene , although the presence of this gene or other neighboring genes are not responsible for the overexpression ( Fig 4A , 4D and 4E ) . 2 ) Overexpression is specific to the MET3pr ( Fig 4C ) . 3 ) Overexpressed MET3pr-GFP contacts many other profile 4 loci , and the intensities of these interactions increase upon induction ( Fig 5C and 5D ) . A large fraction of the profile 4 loci in the native genome also interact with each other ( Fig 6B ) . 4 ) When either the overexpressed GFP reporter or the endogenous MET13 gene is translocated to a different genomic locus , they lose the interactions with other profile 4 loci and show reduced expression ( Figs 4E , 5E , 6C and 6D ) . We interpret the interactions among the profile 4 sites as “clustering” of a subset of Met4-targeted genes . This interpretation is supported by previous findings that co-activated genes tend to cluster in 3D space [21 , 22 , 24 , 44] . Interestingly , there seems to be a hierarchy among the profile 4 loci interactions: when we removed MET13 from the cluster , not only the interactions between MET13 and other profile 4 sites disappear , but also many interactions between ADK1/XKS1 , SER33/GTO1 , and PDR12 ( S7A Fig ) . In contrast , the interactions between ADK1-XKS1 and GTO1-SER33 remain present . These data indicate that ADK1-XKS1 and GTO1-SER33 may form “sub-clusters” , which are brought together by MET13 ( S7B Fig ) . Similar phenomenon has been observed in mammalian cells [23] . The translocation of MET13 also results in mild but significant reduction in the expression of ADK1 , GTO1 , and PDR12 ( p-value < 0 . 034 , 0 . 028 , 0 . 022 , respectively; S7C Fig ) . Overall , these data suggest that MET13 is important for clustering and overexpression among profile 4 genes . The detailed molecular mechanism underlying these clustering is not clear . Our data indicate that some degree of clustering occurs prior to induction , which is consistent with previous analysis showing that Met4-targets form significant 3D contacts even in rich media [22] . These interactions are either a passive consequence of chromosome folding , or actively mediated by some DNA-binding proteins or RNAs constitutively associated with these loci . The strengths of the interactions are quantitatively increased by transcriptional activation ( Fig 5D ) , indicating that the transcription factors , transcription machinery , or RNAs may further enhance or stabilize the clustering . In Figs 5 and 6 , we show that clustering and overexpression are positively correlated . In particular , when translocated to a different genomic location , MET3pr-GFP and the endogenous MET13 gene lose both the long-distance interaction and overexpression . Some Met4-targets show interaction with profile 4 sites but do not support MET3pr-GFP overexpression . The intensity of these interactions do not increase significantly after induction . Taken together , these results paint the following picture . A fraction of Met4-targeted genes are in physical proximity in the nucleus before induction , and a subset of these genes cluster more upon induction . Such clustering may directly lead to enhanced gene expression by creating a sub nuclear compartment with elevated local concentrations of transcription activators , GTFs , and/or pol II . A similar mechanism has been proposed to enhance transcription for other co-regulated gene clusters [23 , 24 , 45] . Future effort is needed to unambiguously dissect out the causal relation between clustering and overexpression .
Standard methods were used to construct the strains and plasmids . If not mentioned specifically , plasmids used in the study are derived from pRS yeast shuttle vectors . For the reporter cassette , the S . Kud MET3pr was flanked by an upstream KanMX gene that serves as a selective marker . The cassette contains TEF1 ( from Ashbya Gossypii ) and ADH1 terminator at the 3’ end of the KanMX and GFP gene , respectively . Homologous sequence of the mTn transposon was added on each side of the cassette so that the reporter system can be integrated into the yeast insertion library ( Open Biosystems ) through homologous recombination . The library strain was derived from y800 diploid strain ( MATa leu2-D98cry1R/MATalpha leu2-D98CRY1 ade2-101 HIS3/ade2-101 his3-D200 ura3-52 caniR/ura3-52CAN1 lys2-801/lys2-801 CYH2/cyh2R trp1-1/TRP1 Cir0 carrying pGAL-cre ( amp , ori , CEN , LEU2 ) ) , with an mTn transposon sequence inserted as a single copy into thousands of different genomic loci [46] . The mCherry control was similarly constructed with the URA3 marker . To avoid mCherry double integration , the integration site was chosen within an essential gene , CDC20 , and only the strains with a single mCherry integration can survive . GFP and mCherry transformations were done consecutively in 96 plate format . MET13 deletion was carried out by replacing the entire MET13 gene ( from -237 to +2165 relative to the start of ORF ) with ADE2 gene . We chose this region starting from the end of transcription termination site of the upstream tandem gene SCS3 to the termination site of the convergent downstream gene MON1 [47] . Transformants were tested through tetrad dissection to determine which MET13 allele was deleted . Strains with both alleles deleted were mated from two haploids both lacking MET13 . Since MET13 is essential for viability in–Met media , one copy of the MET13 gene was integrated into a Chr15 locus ( LDS2 , 243695 ) in the double deletion strain ( Figs 4D and 6C ) . To have a fair comparison of the 3C signal in Fig 6B and 6C , we deleted one MET13 gene from the endogenous locus in Fig 6B , so that both strains contain single-copied MET13 . The inserted MET13 region in Fig 4E includes a ~9 . 3 kb fragment from the endogenous genome ( ChrVII: 262709–274780 ) and the MET3pr-GFP reporter inserted in CWC23 . Strains containing the reporter genes were grown overnight in SCD + 10X Methionine ( 0 . 2 g/L ) in a deep 96-well plate , spun down , washed , and diluted into SCD—Met media to OD660 ~ 0 . 05 for induction . After 6 h , samples were sonicated in Branson 5800 water bath for 20mins to break cell cluster into single cells and were then transferred into a shallow 96-well plate for flow cytometry measurement with BD LSR-Fortessa . The GFP is excited by the 488nm laser and filtered by a 525/50 PMT . The mCherry is excited by the 532nm laser and filtered by the 610/20 PMT . Data were quantified through Flowjo and Matlab program . We first gated the data based on the FSC ( forward scattering ) and SSC ( side scattering ) to select cells with regular size and shape , and gated these cells again based on the presence of both GFP and mCherry signal ( a small fraction of cells , usually less than 2% , lose one fluorescence through loss of heterozygosity ) . We used these GFP-only and mCherry-only strains to calculate the crosstalk between the two fluorescence channels , and eliminated the crosstalk for the cells containing both GFP and mCherry . The final fluorescent signals were normalized based on the average expression of the profile 1 strains . “Outliers” are the strains with expression more than 3 standard deviation away from the mean . Protocol was adapted from a previously described method [48] . Cells were grown in 5 ml YEPD liquid media overnight to OD660 ~ 0 . 2 , and genomic DNA was extracted through standard method . 5 ug of genomic DNA was used for AluI ( 4bp-cutter ) digestion in a final volume of 50 ul overnight . 10 ul of digested DNA was added to 190 ul ligation mix containing 20 ul of 10x T4 DNA ligation Buffer , 0 . 2 μl of T4 DNA Ligase ( NEB , 400U/μl ) and 169 . 8 ul of water for intramolecular ligation at 16°C for > 4 h . The ligation products were ethanol precipitated and resuspended in 20 ul TE buffer . A pair of primers facing outwards in the GFP cassette were used to amplify the nearby unknown genomic sequences . PCR products were purified for Sanger sequencing . We confirmed the GFP reporter cassette insertion sites by mapping the sequencing results to the yeast genome . We used the instrumentation and data acquisition platform as described in a previous study [49] . Cells were grown in SCD + 10X Met liquid media at 30°C to OD660 ~ 0 . 2 , washed , and then transferred onto a SCD - Met agarose pad for induction . After 6 h , the agarose pad was put under the fluorescent microscope for imaging . The GFP and mCherry fluorescent intensity within each cell boundary were quantified . The crosstalk between GFP and mCherry fluorescence is negligible in this case . ChIP protocol was modified from a previously described method [19] . Cells were grown in 100 ml SCD - Met to reach OD660 ~ 0 . 4 and then crosslinked by formaldehyde ( final concentration 1% ) . After quenched with 6 ml of 2 . 5 M Glycine , these cells were harvested by centrifugation and disrupted by glass beads for 30 min at 4°C . The cell extract was then sonicated ( Qsonica ) to fragment chromatin to an average length of 350 bp . The whole cell extract was subjected to Rpb3 antibody ( Biolegend ) incubation followed by Protein A/G PLUS-Agarose ( Santa Cruz Biotechnology , sc-2003 ) incubation . An aliquot of the whole cell extract was saved for input control . We extracted DNA from the input and immunoprecipitated samples and quantified them by qPCR analysis . See S6 Table for the primer sequences used in the qPCR . TATA consensus regions of two highly transcribed genes PDC1 and CIS3 were identified based on previous ChIP-exo study [50] . We used the one-vector CRISPR-Cas9 system [51] to delete these TATA elements . We inserted the 20mer guide DNA sequences ( see S6 Table ) into pML104 , which also contains TDH3pr-driven Cas9 protein [51] . We transformed the modified pML104 plasmid into yeast together with a ~ 100 bp double stranded DNA fragment carrying the desired TATA-element deletion and a mutated PAM sequence ( AGG to ACG ) . Transformants were selected on D-URA plates , confirmed with Sanger sequencing , and then transferred to D + FOA plates to pop-out the modified pML104 plasmid to avoid any potential side-effect of Cas9 . Protocols are adapted from Singh and Hampsey [8 , 18] . Strains were incubated overnight at 30°C in SCD + 10 X Met and were then inoculated in a 50 ml SCD ± Met to an OD ~ 0 . 6–0 . 8 . Cells were collected and resuspended in 10 ml of spheroplasting buffer ( 0 . 4 M sorbitol , 0 . 4 M KCl , 40 mM sodium phosphate buffer pH 7 . 2 , and 0 . 5 mM MgCl2 ) . 25 ul of Zymolyase 100T solution ( 20 mg/ml zymolyase 100-T , 2% glucose and 50 mM Tris-HCl , pH 7 . 5 ) were added at 30°C for 40 min to convert cells to spheroplasts . After washing twice in 10 ml of MES buffer ( 0 . 1 M MES , 1 . 2 M sorbitol , 1 mM EDTA pH8 . 0 , and 0 . 5 M MgCl2 , adjust to pH 6 . 4 ) , the spheroplasts were crosslinked by formaldehyde ( final concentration 1% ) for 15 min and quenched by 2 . 5 M glycine for 5 min . The crosslinked spheroplasts were washed twice and resuspended with 1X cutsmart Buffer ( NEB ) in 36 . 5 ul aliquots . Note that reactions should not be pooled as it will compromise the quality of the reaction . In one tube , we added 3 . 8 ul of 1% SDS ( incubated for 10 min at 65°C ) , 4 . 4 ul 10% Triton X-100 , and 60 U of HindIII to digest overnight with gentle rotation at 37°C . 8 . 6 ul of 10% SDS was added to inactivate HindIII by incubating at 65°C for 20 min . Digested chromatin was diluted in ligation mix to allow intramolecular ligation . For each tube , we added 74 . 5 ul of 10% Triton X-100 , 74 . 5 ul of 10X ligation buffer , 8 ul of 10 mg/ml BSA , 8 ul of 100 mM ATP , 596 ul of ddH2O and 800 units of T4 DNA ligase , and incubated at 16°C for 4 hours . After the overnight treatment with proteinase K , we extracted the DNA with phenol/chloroform . Typically we get 5 ug of DNA at this step , and we use 100 ng for each 3C PCR amplification . See S6 Table for the primer sequences . We used a list of Met4 targets from Yeastract database for the bioinformatic analysis [52–55] . The list contains 405 documented Met4-activated genes from literature based on ChIP and microarray data . We calculated the average distance of all the profile 4 insertion sites to the closest Met4 targets ( relative to the start of ORF ) . As a control , we selected 5000 random locations in the yeast genome and calculated their distances to the nearest Met4 target ( see S5 Fig for histogram ) . The comparison between the two distances above show that profile 4 sites tend to locate near Met4-activated genes . | Eukaryotic transcription occurs within the nucleus where DNA is packaged into high order chromosome structures . Some long-distance chromosomal interactions play an important role in gene regulation in higher eukaryotic species , such as mouse and human . In budding yeast , gene expression is traditionally thought to be regulated over short distances because the upstream regulatory sequences ( URSs ) are usually located close to the core promoters . However , recent chromosome conformation capture experiments have detected numerous long-distance chromosomal interactions in the yeast genome . The function of these interactions in gene regulation remains unclear . Here , we developed a new assay to screen for long-distance interactions that affect the activity of a reporter gene . We found three regulatory mechanisms that act from a distance: silencing , transcriptional interference , and 3D clustering , which alter expression level of the reporter gene as well as its cell-to-cell variability . Our results demonstrate that transcription in budding yeast , similar to transcription in higher eukaryotes , can be regulated over long distances . We anticipate our assay can be used as a general platform to screen for functional long-distance chromosomal interactions that affect gene expression . | [
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] | 2017 | Three distinct mechanisms of long-distance modulation of gene expression in yeast |
The deep history and early diversification of retroviruses remains elusive , largely because few retroviruses have been characterized in vertebrates other than mammals and birds . Endogenous retroviruses ( ERVs ) documented past retroviral infections and thus provide ‘molecular fossils’ for studying the deep history of retroviruses . Here we perform a comprehensive phylogenomic analysis of ERVs within the genomes of 92 non-avian/mammalian vertebrates , including 72 fishes , 4 amphibians , and 16 reptiles . We find that ERVs are present in all the genomes of jawed vertebrates , revealing the ubiquitous presence of ERVs in jawed vertebrates . We identify a total of >8 , 000 ERVs and reconstruct ~450 complete or partial ERV genomes , which dramatically expands the phylogenetic diversity of retroviruses and suggests that the diversity of exogenous retroviruses might be much underestimated in non-avian/mammalian vertebrates . Phylogenetic analyses show that retroviruses cluster into five major groups with different host distributions , providing important insights into the classification and diversification of retroviruses . Moreover , we find retroviruses mainly underwent frequent host switches in non-avian/mammalian vertebrates , with exception of spumavirus-related viruses that codiverged with their ray-finned fish hosts . Interestingly , ray-finned fishes and turtles appear to serve as unappreciated hubs for the transmission of retroviruses . Finally , we find retroviruses underwent many independent water-land transmissions , indicating the water-land interface is not a strict barrier for retrovirus transmission . Our analyses provide unprecedented insights into and valuable resources for studying the diversification , key evolutionary transitions , and macroevolution of retroviruses .
Retroviruses ( family Retroviridae ) exclusively infect vertebrates and cause a wide variety of diseases , such as AIDS and cancers [1 , 2] . Different from other RNA viruses , the replication of retroviruses requires reverse transcription of viral RNA into DNA and integration of the newly synthesized DNA into host chromosomes [1–3] . Retroviral infection primarily occurs in host somatic cells . On occasion , retroviruses infect germline cells , and the integrated retroviruses in germline cells ( known as endogenous retroviruses [ERVs] ) begin to be vertically inherited [1–3] . ERVs are thought to be highly abundant in the vertebrate genomes; for example , ERVs make up ~8% of the human genome [2] . Once embedded in host genomes , ERVs accumulate substitutions at a rate several orders of magnitude lower than exogenous retroviruses [3] . ERVs recorded past retroviral infections over time , sampling ancient extinct retroviral diversity . ERVs could thus provide ‘molecular fossils’ for studying the deep history and macroevolution of retroviruses as well as the host-retrovirus relationship [3–5] . Exogenous retroviruses are traditionally classified into seven genera , i . e . Alpharetrovirus , Betaretrovirus , Gammaretrovirus , Deltaretrovirus , Epsilonretrovirus , Lentivirus , and Spumavirus ( also known as foamy virus ) , whereas ERVs do not follow the classification of exogenous retroviruses [3 , 6] . Based on their relationships with exogenous retroviruses , ERVs are roughly classified into three classes: class I ERVs are closely related to gammaretroviruses and epsilonretroviruses , class II ERVs are closely related to betaretroviruses , and class III ERVs are closely related to foamy viruses [3 , 7] . However , the ERV classification system has not been well designed and has many practical problems: i ) the term “Class” ranks above the term “Family” in traditional taxonomy [3 , 7]; ii ) the classification systems for exogenous retroviruses and ERVs were developed separately and have been poorly incorporated; iii ) some ERVs arose from recent endogenization events and nest within the diversity of exogenous retroviruses , such as endogenous lentiviruses identified recently in mammals [8–11] , thus those ERVs cannot be readily classified into a certain ERV class . The recent explosion of genome-scale data provides great opportunities to systemically analyze the diversity and evolution of ERVs within the vertebrate genomes . Multi-species genome-wide ERV studies have placed much emphasis on mammals and birds and unmasked many novel aspects of the distribution , diversity , and evolution of retroviruses [12–16] . However , many important issues related to early diversification , key evolutionary transitions , and macroevolutionary patterns of retroviruses remain to be clarified . Retroviral fossils within non-avian/mammalian vertebrates appear to hold the key to understanding the deep history and early diversification of retroviruses [17] . For example , the identification of endogenous foamy virus in fishes reveals an ancient marine origin of this retroviral group , and possibly the whole retroviruses [10 , 18 , 19] . Several attempts to mine ERVs in some non-avian/mammalian vertebrate genomes have been made [16 , 20–22] , but these genome-scale surveys exploited only very limited number of species ( one to around ten ) . Here we performed genome-wide mining of ERVs within the genomes of 92 non-avian/mammalian vertebrate species ( 72 fishes , 4 amphibians , and 16 reptiles ) , which include all the currently available genomes of non-avian/mammalian vertebrates . Analyses of ERVs within non-avian/mammalian vertebrates reveal unexpected retroviral diversity and clarify many issues in the classification , early diversification , key evolutionary transitions , and macroevolution of retroviruses .
To explore the diversity of ERVs in non-avian/mammalian vertebrates , we used a combined similarity search and phylogenetic analysis approach to identify ERVs in the genomes of non-avian/mammalian vertebrates . Briefly , we first performed similarity search to identify retrovirus-like sequences . Because retroviruses share detectable sequence similarity with other retrotransposons , we then performed phylogenetic analyses to identify authentic ERVs ( see Materials and Methods for details ) . Our study includes a total of 92 non-avian/mammalian vertebrate species , including 2 jawless fishes , 3 cartilaginous fishes , 66 ray-finned fishes , 1 lobe-finned fish , 4 amphibians , and 16 reptiles ( S1 Fig and S1 Table ) . These species include all the non-avian/mammalian vertebrates whose genomes have been sequenced to date and cover a broad range of non-avian/mammalian vertebrate diversity . Our ERV detection approach does not rely on identification of long terminal repeats ( LTRs ) first and is thus more sensitive for the detection of degraded or fragmented ERVs . We found the presence of ERVs in the genomes of all the jawed vertebrates , revealing the ubiquitous presence of ERVs in the genomes of jawed vertebrates [23] . Taken together , a total of 8 , 075 ERVs were identified ( S1 Table; S1 Data ) . For jawless fishes , our genome-scale mining identified the presence of ERVs in the sea lamprey ( Petromyzon marinus ) but not in the Arctic lamprey ( Lethenteron camtschaticum ) . ERVs were estimated to invade into lamprey genomes around 27–34 million years ago , which appears to occur after the divergence of the sea lamprey and the Arctic lamprey around 30–38 million years ago [24] and is compatible with the identification of ERVs in the sea lamprey but not the Arctic lamprey . The ERVs we identified in the sea lamprey are phylogenetically close to ERVs of ray-finned and lobe-finned fishes , indicating that the sea lamprey retrovirus might not represent an ancient retroviral lineage but might arise from a more recent cross-species transmission ( Fig 1 and S2 Fig ) . We did not find any ERV within the genome of lancelet ( Branchiostoma floridae ) , which belongs to subphylum Cephalochordata and is closely related to the subphylum Vertebrata . The distribution of ERVs in vertebrates implies that retroviruses originated within the vertebrate lineages , likely before the origin of jawed vertebrates >450 million years ago [25] . However , the possibility that retroviruses arose before the emergence of vertebrates and failed to colonize the germline of earlier-branching animals cannot be fully excluded . However , no jawed vertebrate species escaped the activity of ERVs , suggesting the high capability of endogenization of retroviruses and making the possibility of failing to colonize earlier-branching animal genomes highly unlikely . We reconstructed 452 consensus sequences of partial or complete ERV genomes ( S2 Data ) , because many ERVs identified here were highly degraded/fragmented and might confound phylogenetic and evolutionary analyses . To clarify the relationship among and evolutionary history of retroviruses , we performed phylogenetic analysis of the reconstructed non-avian/mammalian vertebrate ERVs , representative bird and mammal ERVs , and representative exogenous retroviruses ( S2 Table ) . Our phylogenetic analysis recapitulates the conventional groupings of seven exogenous retroviral genera ( Fig 1 and S2 Fig ) . Interestingly , exogenous retroviruses appear to only represent a small fraction of retroviral diversity ( Fig 1 and S2 Fig ) . There are an enormous number of lineages dispersed outside the diversity of exogenous retroviral groups and ERVs of mammals and birds ( Fig 1 and S2 Fig ) , suggesting the existence of extraordinary hidden diversity of retroviral diversity in non-avian/mammalian vertebrates . It is highly likely that there might be many uncharacterized exogenous retroviruses circulating among non-avian/mammalian vertebrates . Indeed , recent virus discovery studies based on meta-genomics and meta-transcriptomic approaches found many novel RNA viruses in non-avian/mammalian vertebrates [26 , 27] . Our phylogenetic analysis shows that retroviruses group into five major clades with strong supports ( Fig 1 and S2 Fig ) , which are designated clades Jin , Mu , Shui , Huo , and Tu , following Wu Xing ( Five Elements ) that traditional Chinese culture used to explain myriad of phenomena , from nature to medicine to politics . Clade Jin includes gammaretroviruses and exclusively infects amniotes . Clade Mu includes epsilonretroviruses and their hosts include nearly all the jawed vertebrates ( except birds ) . Clade Shui is closely related to alpha- , beta- , delta-retroviruses and lentiviruses and infects amniotes . Clade Huo is related to snakehead retrovirus and has the widest host distribution , infecting all the major vertebrate lineages . Clade Tu is related to foamy viruses and has patchy host distributions; it infects jawed vertebrates but no Tu retrovirus has been found in reptiles and birds . It follows that different viral clades appear to have different host distributions ( Fig 2 ) . In terms of the relationship between major retroviral clades and ERV classes , Jin and Mu clades include class I ERVs , Clade Shui includes class II ERVs , and Clade Huo includes class III ERVs [17] . While the current classification systems of ERVs and exogenous retroviruses consider ERVs and exogenous retroviruses independently , our provisional nomenclature takes both exogenous and endogenous retroviruses into account . Nevertheless , the non-avian/mammalian vertebrate ERVs will provide a useful resource for further development of evolutionary history-based classification and nomenclature system of retroviruses . All the major jawed vertebrate groups are infected by viruses of at least three clades , whereas jawless fishes contain a single viral lineage within clade Huo . Among all the vertebrate groups , mammals are infected by all the five major lineages and have the widest viral spectrum ( Fig 2 ) . Fish retroviruses branch earlier than others within clades Mu , Huo , and Tu , indicating that these clades might have an aquatic origin . However , mammal retroviruses appear to occupy phylogenetically basal positions within clades Jin and Shui , and other amniote retroviruses fall within the diversity of mammal retroviruses . This phylogenetic pattern suggests that the current Jin and Shui retroviral diversity have a mammalian origin . However , it remains unclear how mammals were infected by Jin and Shui retroviruses . It is likely that there are Jin and Shui ERVs in non-avian/mammalian vertebrates and further genome-mining of non-avian/mammalian vertebrates might help solve these mysteries . Our phylogenetic analysis has important implications in clarifying the origin and host distribution of many specific retroviral lineages: i ) Fish epsilon-like retroviruses ( clade Mu ) were proposed to arise from multiple cross-species transmission events , possibly from amphibians [15] . However , this conclusion is based on screening limited number of non-avian/mammalian vertebrates . Our phylogenetic analysis shows that epsilon-like retroviruses originated in fish species and amphibian viruses arose multiple times through cross-species transmissions from fishes ( Fig 1 ) . Mammalian epsilon-like retroviruses fall into the diversity of reptile retroviruses and thus might originate from cross-species transmission from reptiles . ii ) Mammalian gammaretroviruses ( clade Jin ) nest within reptile retroviruses , suggesting they arose from host-switching from reptiles to mammals . iii ) ERVs related to betaretroviruses ( within clade Shui ) were found to be present in pythons . We also identified similar ERVs within the genomes of other Squamata species as well as Testudines , suggesting this retroviral lineage might be more widely distributed in reptiles . Our phylogenetic analysis shows that retroviruses ( except clade Tu ) generally do not reflect the phylogenetic relationships of their hosts and retroviruses from distinct vertebrate groups are often closely related ( Fig 1 ) . For example , retroviruses of cartilaginous fishes do not occupy basal positions within any major retroviral clades , but were distributed throughout the phylogenetic tree . The phylogenetic pattern indicates retroviruses underwent complex and frequent host switches . To estimate the relative importance of host switch and co-speciation in the evolution of non-avian/mammalian vertebrate retroviruses , we performed a global assessment of the correspondence between retrovirus and non-avian/mammalian vertebrate phylogenies using an event-based approach . Sampling bias might have important effects on the interpretation of host-virus relationship , as exemplified by primates and lentiviruses [28] . Because sampling of fishes ( 72 species ) and reptiles ( 16 species ) are relatively good in our study , we examined host-virus relationship for two fish retrovirus groups within clade Mu , one fish retrovirus group within clade Tu , and one reptile retrovirus group within clade Huo . Our analyses show that all these three retroviral lineages within clades Mu and Huo mainly underwent cross-species transmission ( p > 0 . 05 ) ( Fig 3A–3C and Table 1 ) . However , the fish retroviruses within clade Tu ( related to foamy virus ) mainly co-diverged with their hosts ( p < 0 . 01 ) ( Fig 3D and Table 1 ) . Indeed , foamy virus , which belongs to clade Tu , has been proposed to co-diverge with their hosts [10 , 19 , 29] . The reasons why the pattern of cross-species transmission for these retroviral groups are different remain largely unknown . Interclass transmission was thought to occur infrequently during the evolution of retroviruses , with only a few cases documented [14 , 30 , 31]; for example , avian reticuloendotheliosis viruses derived directly from mammalian retroviruses [31] . To further explore the transmission among major lineages of vertebrates , we reconstructed an undirected network in which edges represent transmission events between hosts without known direction ( see Materials and Methods for details ) . Because the host states for most internal nodes cannot be reconstructed unambiguously , we only examine transmission events at terminal nodes , which might only reflect recent transmission events . We found that ray-finned fishes and turtles represent transmission “hubs” , which have high connectivity ( 12 and 13 transmission events , respectively ) with other lineages ( Fig 4 ) . Transmission is more likely to occur between lineages with overlapping ecological niches; all the transmission partners of the ray-finned fishes live at least partially in aquatic environments . The number of interclass transmission events should be much underestimated , because the transmissions at terminal nodes might only reflect recent transmissions and these interclass transmissions might occur through other intermediate hosts . It follows that interclass transmission might be more frequent than previously thought . It should be noted that our analysis might be confounded by different frequencies with which different retroviral lineages invaded host germ lines and rate of fixation in host populations . Nevertheless , our analyses do suggest that retroviruses ( except the clade Tu retroviruses ) underwent complex and frequent host switches . It still remains unclear how retroviruses that infect tetrapods originated . There are two possible evolutionary scenarios: i ) The retroviruses underwent water-to-land transition simultaneously with the conquest of land by their tetrapod hosts ( Fig 5A ) ; ii ) The tetrapod retroviruses independently originated by cross-species transmissions from fishes to tetrapods after the origin of tetrapods ( Fig 5B ) . Through the comprehensive phylogenetic analysis of retroviruses , we found retroviruses of aquatic and terrestrial origins are frequently interconnected with each other especially in clades Mu and Huo ( S3 Fig ) , indicating many independent transfers between water and land . These transfers usually occurred among different vertebrate groups and do not have a common pattern , suggesting tetrapod retroviruses have multiple aquatic origins ( Fig 1 and S3 Fig ) . For example , amphibian retroviruses within clade Mu nest within ray-finned fish viruses , which can be explained by recent cross-species transmission ( Fig 1 ) . Together with recent identification of several instances of cross-species transmission from aquatic to terrestrial vertebrates , such as hepadnaviruses [32 , 33] , our results suggest that the water-land interface might be not a strict barrier for the transmission of retroviruses .
Previous multispecies studies have placed much emphasis on ERVs within the genomes of mammals [14 , 16] . The studies on ERVs in non-avian/mammalian vertebrates , which account for >75% vertebrate diversity [34] , involved only limited number of species ( from one to about ten ) [16 , 20–22] . Here we perform a phylogenomic analysis of ERVs in 92 non-avian/mammalian vertebrates , representing the most comprehensive analysis of ERVs in non-avian/mammalian vertebrates . We provide a more sensitive workflow for identifying fragmented and degraded ERVs . Our analyses reveal the unappreciated diversity of retroviruses in non-avian/mammalian vertebrates and provide novel insights into the macroevolution and evolution of retroviruses in vertebrates . However , the non-avian/mammalian vertebrates we used in this study only represent a small proportion ( ~0 . 2% ) of their extant diversity . Therefore , there are more endogenous retroviruses waiting for discovery , which might improve our understanding of the diversity and evolution of retroviruses . Understanding the diversity and evolution of retroviruses has important implications in helping predict further retroviral outbreaks and design control measures .
All the genome sequences of non-avian/mammalian vertebrates were retrieved from NCBI genome resource ( https://www . ncbi . nlm . nih . gov/genome/ ) . Given retroviruses have coexisted with their vertebrate hosts for millions of years , some ancient ERVs might be fragmented and highly degraded . However , most of the automatic ERV detection software , such as RetroTector , are not tailored for the absence of LTRs and fail to detect the evolutionarily old ERVs [35] . Therefore , we used a combined similarity search and phylogenetic analysis approach to mine ERVs . First , we performed similarity search against the genomes of non-avian/mammalian vertebrates using tblastn algorithm with the Pol protein sequences of representative retroviruses as queries . Because there are many frameshift mutations within ERVs , many significant hits only correspond to partial regions of ERVs . We retrieved and concatenated the significant hits from tblastn results , if they are adjacent to each other in both ERV genome and host genome sequences . Next , because retroviruses share detectable sequence similarity with other retrotransposons , we performed phylogenetic analyses of concatenated sequences and sequences of representative retroviruses and retrotransposons [36] . The concatenated sequences that cluster with retroviruses are ERV sequences . The phylogenetic analyses were performed by an approximately maximum likelihood method implemented in FastTree 2 . 0 [37] . Given some recovered ERVs are fragmented , we reconstructed consensus sequences for ERVs . For the ERV cluster that contains sequences from one species in the phylogenetic tree based on the Pol proteins , we retrieved the longest ERV sequence within the ERV cluster . Then the ERV sequence was further used as a query to search its paralogous sequences within the same genome through the blastn algorithm with an e cutoff value of 10−10 . Only the resulting significant hits within the 5 , 000 bp before/after the Pol proteins that belong to the ERV cluster were used to reconstruct consensus sequences of each retroviral cluster using Geneious 10 [38] . For the ERV cluster that contains sequences from two species , we reconstructed consensus sequence for each species . Conserved domains were identified by Conserved Domain Database ( CDD ) search [39] . All protein sequences were aligned using MAFFT version 7 with the E-INS-i strategy , an accurate method [40] . The alignment was then manually edited to remove ambiguous regions . We reconstructed phylogenetic tree based on the RT protein of the reconstructed consensus sequences of non-avian/mammalian vertebrate ERV and representative exogenous retroviruses and endogenous retroviruses ( S2 Table ) . We used the RT protein sequences of Cer1-6 as outgroups , because Cer1-6 belong to the Metaviridae family and Metaviridae is the retrotransposon group most closely related to retroviruses [41] . The phylogenetic analysis was performed using a maximum-likelihood based algorithm implemented in PhyML 3 . 1 [42] . The RtRev substitution model which is specific for RT-containing genes [43] was used , with four gamma-distributed rate categories . The NNI tree topology search algorithm was used . The tree branch supports were evaluated by the aBayes algorithm [42] . The ERVs within the genome of the sea lamprey cluster together , suggesting they arose from a single invasion event . The divergence among the ERVs in the sea lamprey reflects the invasion time . We retrieved the sea lamprey ERVs and aligned them using MAFFT version 7 [40] . Pairwise genetic distance among the sea lamprey ERVs was calculated with Kimura two-parameter substitution model . The invasion time t = d/2μ , where d is the largest pairwise distance among ERVs , and μ is the neutral evolutionary rate of hosts and is about 1 . 9–2 . 4 × 10−9 substitutions per site per year [24] . To investigate the major macroevolutionary mode of retroviruses , we used an event-based method through Jane 4 [44] to assess the relationships between host and retrovirus phylogenies . Jane mapped five events of virus phylogeny ( cospeciation , duplication , duplication & host switching , loss and failure to diverge ) onto the host tree and each event was assigned to a cost . A best mapping was sought by minimizing the total cost . Inferred from previous documents , we assigned three cost schemes ( cospeciation-duplication-duplication & host switching-loss-failure to diverge ) shown as follows , 0-1-2-1-1 ( Jane’s default setting ) , -1-0-0-0-0 [19 , 45] , and 0-1-1-2-0 [44] . Then Jane performed statistical analyses to assess the host-virus phylogeny congruence by generating random parasite trees , with the sample size of 500 . We first collapsed all the ERVs that are from species that belong to the same class ( from the same order for reptiles , given reptiles are paraphyletic ) and clustered together into a group . Because the host states for internal nodes cannot be reconstructed unambiguously and assigned to a specific state with 100% certainty , we identified two groups that share common ancestry at terminal nodes and assigned one undirected interclass transmission event for each such node . This method is more likely to identify recent transmission events . | Retroviruses infect a wide range of vertebrates and cause many diseases , such as AIDS and cancers . To date , retroviruses have been rarely characterized in vertebrates other than mammals and birds , impeding our understanding of the diversity and early evolution of retroviruses . Retroviruses can occasionally integrate into host genomes and become endogenous retroviruses ( ERVs ) , which provide molecular fossils for studying the long-term evolution of retroviruses . Here we performed comparative genomic and evolutionary analyses of ERVs within 92 non-avian/mammalian vertebrates ( fishes , amphibians , and reptiles ) and uncovered extraordinary diversity of retroviruses in non-avian/mammalian vertebrates . Our analyses reveal an ancient aquatic origin of retroviruses and retroviruses underwent frequent host-switching . Our findings have important implications in understanding the deep history and evolutionary mode of retroviruses . | [
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] | 2018 | Endogenous retroviruses of non-avian/mammalian vertebrates illuminate diversity and deep history of retroviruses |
Neglected tropical diseases are co-endemic in many areas of the world , including sub Saharan Africa . Currently lymphatic filariasis ( albendazole/ivermectin ) and trachoma ( azithromycin ) are treated separately . Consequently , financial and logistical benefit can be gained from integration of preventive chemotherapy programs in such areas . 4 villages in two co-endemic districts ( Kolondièba and Bougouni ) of Sikasso , Mali , were randomly assigned to coadministered treatment ( ivermectin/albendazole/azithromycin ) or standard therapy ( ivermectin/albendazole with azithromycin 1 week later ) . These villages had previously undergone 4 annual MDA campaigns with ivermectin/albendazole and 2 with azithromycin . One village was randomly assigned to each treatment arm in each district . There were 7515 eligible individuals in the 4 villages , 3011 ( 40 . 1% ) of whom participated in the study . No serious adverse events occurred , and the majority of adverse events were mild in intensity ( mainly headache , abdominal pain , diarrhoea and “other signs/symptoms” ) . The median time to the onset of the first event , of any type , was later ( 8 days ) in the two standard treatment villages than in the co-administration villages . Overall the number of subjects reporting any event was similar in the co-administration group compared to the standard treatment group [18 . 7% ( 281/1501 ) vs . 15 . 8% ( 239/1510 ) ] . However , the event frequency was higher in the coadministration group ( 30 . 4% ) than in the standard treatment group ( 11 . 0% ) in Kolondièba , while the opposite was observed in Bougouni ( 7 . 1% and 20 . 9% respectively ) . Additionally , the overall frequency of adverse events in the co-administration group ( 18 . 7% ) was comparable to or lower than published frequencies for ivermectin+albendazole alone . These data suggest that co-administration of ivermectin+albendazole and azithromycin is safe; however the small number of villages studied and the large differences between them resulted in an inability to calculate a meaningful overall estimate of the difference in adverse event rates between the regimens . Further work is therefore needed before co-administration can be definitively recommended . ClinicalTrials . gov; NCT01586169
Lymphatic filariasis ( LF ) , a leading cause of permanent and long-term disability globally , affects over 120 million people in more than 80 countries in tropical and subtropical areas [1] . Trachoma is the main cause of infectious blindness , responsible for around 5% of the world's blind people [2] . These two infections represent important public health problems in West Africa . Mali alone , with a population of 14 . 5 million ( 2009 ) , has approximately 300 , 000 people who are at risk of disability from these two diseases [3] . While the safety and efficacy of separate treatments for trachoma and LF are well documented , their integrated treatment still represents a global challenge . Azithromycin , an antibiotic , has been used safely for over 10 years in trachoma treatment programs [4] , [5] . The coadministration of single doses of two anti-parasitic drugs , ivermectin and albendazole , is a standard and safe treatment of LF in African countries where onchocerciasis is co-endemic and has been shown to reduce transmission following several rounds of MDA [6] . Ivermectin is indicated on its own for the treatment of onchocerciasis and strongyloidiasis , and for LF in combination with albendazole [7] . Albendazole is indicated for soil transmitted nematode infections and for systemic helminth infections such as neurocysticercosis and echinococcosis . It is used to treat LF in combination with ivermectin in areas co-endemic for onchocerciasis and in combination with diethylcarbamazine in all other LF-endemic areas . Both ivermectin and albendazole have an excellent safety record when used alone and also , for the last 10 years , in combination [8] , [9] . Currently it is recommended that the administration of azithromycin and ivermectin/albendazole be separated by 7 to 14 days in co-endemic areas [10] , [11] , thus increasing the number of subject contacts and therefore overall cost of the interventions . It is therefore desirable to consider coadministration of drugs for the two diseases if this can be shown to be safe . Integrating the treatment of LF and trachoma would present logistical , health ( greater compliance through integration ) and economic advantages , helping reduce the burden on an already strained healthcare system . Given the disabling consequences of untreated LF and blinding trachoma , the large populations at risk for both diseases and the resource constraints of the endemic countries , the potential to improve the lives of the patients , optimize available healthcare resources and maximize the number of people reached make the minimal risks of initiating this new three-drug treatment administration acceptable [12] . It is recognized that the current approach is effective , safe , and free from SAE [8] , [9] , [13] , [14] , [15] . However , before integration can occur , the safety of coadministration of the three drugs ( azithromycin , ivermectin and albendazole ) has to be examined . The data to support such an approach is limited to a pharmacokinetic study [16] on the interaction of the three drugs ( azithromycin , ivermectin and albendazole ) conducted in the United States in 18 healthy volunteers in 2004 . Coadministration resulted in an increase in systemic exposure ( Cmax and AUC 0-t ) of azithromycin by 13% and 20% and in ivermectin exposure by 31% and 27% respectively . At the same time , albendazole sulfoxide exposure was decreased by 16% and 14% respectively . The changes in exposure to azithromycin and albendazole were considered not to be of clinical importance , while the increase in ivermectin exposure is of potential concern . Increased exposure to ivermectin could potentially lead to high levels in the brain , although a study in normal volunteers receiving 10 times the current dose did not show significant CNS toxicity [17] , [18] . In this small population of normal volunteers , increased ivermectin exposure was not associated with an increased incidence of adverse events , such as dizziness , typically associated with ivermectin . One subject reported mild indigestion following administration of the ivermectin/albendazole combination . The other subject reported mild disequilibrium eight days after taking the three drugs together . Neither of these events required treatment . While these results are encouraging , they do not eliminate the risk of pharmacodynamic interactions or effects in infected patients . Therefore given the outcome of the pharmacokinetic study in healthy volunteers and the expected increased cost-effectiveness of co-administration [12] , the current pharmacovigilance study was undertaken under the auspices of the International Trachoma Initiative ( ITI ) with support from the Bill & Melinda Gates Foundation and conducted by the Center for Disease Control of Mali , to study the safety of coadministration of azithromycin , ivermectin and albendazole for the mass treatment of trachoma and LF in adults and children aged 5 and over in the Sikasso region of Mali .
The protocol was approved by the Ethics Committee of the Faculty of Medicine , Pharmacy and Odontostomatology ( FMPOS ) of the University of Bamako , and Malian Ministry of Health through the National Directorate of Health and the Directorate of Pharmacy and Drugs . Once the study sites had been identified , the aim of the study and details of the procedures to be undertaken were presented through the different levels of administration to the level of the individual villages where meetings were held to provide community understanding of the protocol . Once community agreement had been obtained , a census of each village was undertaken to identify households , families and individuals , each of whom received a unique number . Subsequently , potential subjects in the study provided their own written consent to information provided on audiotape in Bambara ( the local language ) . Parents or guardians provided written consent for all children participating in the study . Mali was chosen because trachoma and LF were co-endemic and the baseline prevalence for both diseases from prior surveys was relatively high . In addition , the infrastructure existed to allow a study of this nature to be conducted . In this study , the treatments were allocated to entire villages ( clusters ) . The decision to use a cluster design was taken for logistical reasons . With the number of subjects involved , it would be very difficult to implement individual randomisation within a village . Furthermore , the study needed to assess the effects of mass treatment in the community as that is the target setting for the treatments under study . The villages chosen for the study needed to be accessible , for logistical reasons , with access to a health facility , but sufficiently far apart to avoid “contamination” between them . They had to be within 5 km of a serviceable road and within 15 km of a Community Health Centre ( CSCOM ) at the very peripheral level of the health system organization in Mali or a district Reference Health Center ( CSREF ) that is responsible for providing care to complicated cases referred from the different CSCOMs of the district . Villages also needed to be separated by at least 15 km and be on an East-West axis since the endemicity/epidemiology of LF is more homogeneous than on a North-South axis . They also needed to have populations of about 1200 people of local ethnic groups with similar socio-cultural habits . The anticipated exclusion rate was 25% so village populations needed to be large enough to allow for 750 subjects to be recruited to the study . Two districts , Bougouni and Kolondièba , in the Sikasso region of southern Mali ( Figure S1 ) , were selected for the study . Prior to study initiation , these districts had both received four annual standard treatments with albendazole and ivermectin for LF and two mass treatments with azithromycin for trachoma . It is likely that these interventions had considerably reduced the trachoma infection rate and filarial parasite load compared to historical data for the area . For logistical reasons , the infection rates were not re-evaluated immediately prior to the study Trachoma is highly endemic in the Sikasso region and preliminary results of surveys conducted by ITI in June 2008 ( ITI unpublished data , 2008 ) , overall prevalence of trachoma was 14 . 65% in Bougouni and 24 . 4% in Kolondièba , while the prevalence in 1 to 9 year old children of the main stages of trachoma , Trachoma Inflammation-Follicular ( TF ) and Trachomatous Inflammation-Intense ( TI ) in Bougouni and Kolondièba were 5 . 8% ( 84/1456 ) and 7 . 5% ( 109/1453 ) respectively , a substantial reduction from earlier surveys [19] . The prevalence of LF in 2004 at before MDA was started was 19 . 8% in Bougouni and 22 . 4% in Kolondièba ( National LF Elimination Programme , unpublished report ) . LF prevalence was not assessed in the study villages prior to the study initiation but data from neighbouring villages reported 14% and 24 . 4% respectively in the districts of Bougouni ( Mena , Tienkoungoba ) and Kolondièba ( Bougoula , Kebila ) ( unpublished data , National LF Elimination Program , Mali ) . Based on the selection criteria above , two villages in each district were chosen , Tienkoungoba ( longitude −6 . 581690 , latitude 11 . 456120 ) [standard treatment] and Ména ( longitude −6 . 812560 , latitude 11 . 527540 ) [co-administered treatment] in the District of Bougouni and the villages of Bougoula ( longitude −7 . 094090 , latitude 11 . 253810 ) [standard treatment] and Kebila ( longitude −7 . 041090 , latitude 11 . 276370 ) [co-administered treatment] in the District of Kolondièba . Allocation of treatment in each district was decided by the toss of a coin by the investigators following the investigators' training session in Bamako before the treatment phase field trip . Prior to initiation of the field based phases of the study ( census and treatment/follow-up ) , the whole team was recruited and underwent intensive training to ensure that every member knew and understood their role . Since the treatment phase of the study was to be conducted simultaneously in the 4 villages , the data collection teams were all trained together on study methodology and especially the use of the predetermined adverse event questionaires . The details of the processes and the materials used may be found in the Supplimentary file ‘Study Protocol’ Section 9 pages 32–35 , and Appendices D through K . Following a complete census of the participating villages , all residents were invited to attend for screening . Male and female consenting/assenting subjects resident in the village for at least the previous 3 months and aged between 5 and 65 years were screened against inclusion criteria with a history and medical examination , which included documentation of signs/symptoms of trachoma ( according to the WHO classification , and identified by health technicians specialised in ophthalmology ) and LF ( elephantiasis , lymphoedema , hydrocoele ) , prior to formal enrolment . Enrolment continued until the target number of subjects , approximately 40% of the total popualtion in each village , was reached . Blood tests for microfilaraemia or antigenaemia were not taken . All children over 5 years of age but under 90 cm in height and all pregnant and lactating women were excluded . In addition , all those who had significant acute or chronic illnesses or had a history of allergy to the drugs to be given were excluded . Subjects excluded after consent and non-enroled village residents were offered treatment after the study using the standard regimens for trachoma and LF according to national guidelines . Following consent and screening , all enrolled subjects within a village received the drugs appropriate to the randomization . Patients were identified after randomization since the whole village was alocated to the same treatment . Subjects were aware of the treatment they would receive during the consent process . Appropriate doses for azithromycin and ivermectin were determined using a height pole marked with the doses on either side of the pole ( Table 1 ) . In the standard treatment villages , all subjects were treated on day 0 , receiving ivermectin based on height ( up to 4×3 mg tablets ) and albendazole irrespective of height ( 1×400 mg tablet ) . They then received azithromycin based on height ( up to 4×250 mg tablets ) 7 days later . In the coadministration villages , all subjects received ivermectin based on height ( up to 4×3 mg tablets ) , albendazole irrespective of height ( 1×400 mg tablet ) and azithromycin based on height ( up to 4×250 mg tablets ) on day 1 . No treatment was given 7 days later . All treatment administration was witnessed by study staff to ensure the medications were taken . Study staff were not blinded to the treatment administration . Subjects were seen and assessed on Days 1 , 8 and 15 . On Day 0 , in addition to a general examination , they were asked about any complaints from a pre-specified list ( e . g . abdominal pain , headaches ) or others ( recorded verbatim ) that existed prior to treatment . On Days 8 and 15 , the subjects underwent a further clinical examination and were asked whether they had experienced any AE since the administration of the drug , or whether any complaints present prior to treatment had changed in any way . Subjects were also able to present themselves at the health centres , which were manned throughout the study , if they were concerned about any events between the planned assessment visits . All AEs were classified either as ‘minor’ ( not interfering with daily function ) or ‘major’ ( interfering with daily function ) . If AEs required treatment , this was based on the presenting signs and symptoms . Study investigators were located in village health centers or at other appropriate sites recommended by villagers . Subjects went to the agreed site to be assessed for eligibility for the study . Households were grouped by sector and assigned investigators with the aid of volunteers chosen by the villagers . These volunteers also helped check that all households had been surveyed and assisted in finding those who did not return for study assessments on Days 8 and 15 . Any subjects who had not returned for the Day 8 assessment by Day 14 were excluded from the study . Similarly , any subjects not returning for the Day 15 assessment by Day 17 were excluded . The study was designed to show that there was no difference in the overall incidence of AE between the standard and coadministered treatment . Previous data on azithromycin suggested an AE rate of 5% [20] , [21] . The incidence of AE with coadministered treatment was estimated as 8% . With power of 80% and a two-sided 95% confidence interval for the difference between rates , the sample size chosen was 1125 subjects per treatment . The sample size per group was increased to 1328 to allow for an 18% refusal rate . This was in turn rounded up to 1500 per group to increase the power . This required approximately 750 subjects per village in order to produce clusters of equal size . The original sample size calculations did not take into account that the study was based on clusters ( i . e . , villages ) , where all subjects within the same cluster received the same treatment and was thus grossly underpowered . Analysis was therefore conducted at an individual , rather than a cluster level . The objective of the study was to assess safety of the three drugs when coadministered . The primary outcome was therefore the overall incidence of any AE . Any complaint , either one itemized in a pre-prepared list in the case record form ( CRF ) , or any other untoward event occurring after treatment administration , or an existing complaint which worsened after drug administration , was considered as an AE . Each event was classified as minor ( not interfering with daily function ) , moderate ( interfering to some extent with daily function ) or major ( significant effect on daily function ) . The timing of each event was derived as the first day on which the event was recorded , calculated from the day of first drug administration . The duration was also calculated as the number of days from first appearance to last appearance ( inclusive ) . The incidence of each event itemized on the list in the CRF was calculated by counting the number of subjects who reported it at any time during the study . The incidence of any AE was derived in the same way , by counting the number of subjects reporting at least one event , whether on the list or not , at some stage during the study . Subjects reporting several events were only counted once . The time to first onset for each subject was calculated as the day on which the first AE of any kind occurred . The maximal severity for each subject was calculated as the maximal severity of any event reported during the study . Since the study design did not fully account for the cluster design , and since there were only two clusters for each treatment , the scope for statistical analysis was limited . The overall incidence of AE in each village was calculated as a simple percentage of the total number of subjects who received treatment . A 95% confidence interval was calculated using exact methods if necessary ( Wilson method , [22] ) . Note that even if there are no occurrences of a specific event in the study , the prevalence can still be estimated with its 95% confidence interval . The time to first onset of events in each village was investigated using survival analysis methods . Data from any subject reporting no AE by the end of the surveillance period was considered to be censored at the time of last assessment . The analysis was repeated for those subjects who experienced any AE and the median time to first onset was estimated with its 95% confidence interval . The severities of events were summarized in a table . The frequency of occurrence of each event itemized in the CRF was summarized for each treatment within each district in tables and bar charts showing the percentage of the study population within the village who reported the event . The incidence of each type of event for each village was also shown with associated 95% confidence intervals graphically , with a separate plot for each type of event . The time to onset of the most frequently occurring events was also investigated using survival analysis methods . The age-sex distribution of the study population within each village was compared to the overall village population using graphs . The intra-class correlation coefficient and associated 95% confidence interval was calculated using the ANOVA estimator [23] . Due to the small number of clusters , the resulting estimate should be treated with caution .
There were 9109 persons in the villages in the two districts of whom 7515 were eligible for inclusion ( aged between 5 and 65 years , inclusive ) . 3011 subjects were chosen at random from this pool to be included in the study population ( Table 2 ) , selection continuing until the target number in a village was reached , with 1510 receiving the standard treatment ( 755 in each district ) and 1501 receiving the coadministered treatment ( 750 in Bougouni , 751 in Kolondièba ) ( Figure 1 ) . Recruitment of subjects started on February 7 , 2010 with the first treatment being administered the same day while the final subject assessment was done on February 26 , 2010 . The recruitment took 3 to 4 days per village and all the villages began the recruitment on the same day The male to female ratio was approximately equal in all villages ( Table 2 ) . In all villages , the proportion of males included in the study was slightly higher than the overall proportion of males in the village , with the proportion of females being correspondingly lower . The village allocated the coadministered treatment in the Kolondièba district ( Kebila ) was larger than the rest , hence the study population formed a smaller proportion of the total village population . The age distribution of the study population in each village was similar ( Table 2 ) , with approximately 70% aged under 19 years and 10 . 8% aged over 50 years which represents the age structure within the villages as a whole . The age ranged from 5 to 65 years in all villages , with a mean of 20 years and a median of 12 years ( Bougouni ) or 13 years ( Kolondièba ) . The majority of study subjects were from the treatment villages ( Table 2 ) . The proportion of outsiders recruited to the study in Kolondièba was slightly higher ( 3 . 9% ) than in Bougouni ( 0 . 3% ) , although the majority came from neighbouring villages and had been domiciled in the treatment villages for at least 3 months . None came from non-endemic areas . The height and weight distributions were similar for the four villages . Slightly less than 5% of the total study population were found to have signs of trachoma . Two of the villages ( Bougouni standard and Kolondièba coadministered treatment ) had low rates of trachoma ( 0 . 9% and 1 . 9% ) while the other two ( Bougouni coadministered and Kolondièba standard treatment ) had higher rates ( 6 . 0% and 8 . 7% ) . For these latter two villages the most frequent types of trachoma were follicular and scarring trachoma ( eyelid scarring ) . 12 of the 14 subjects in Kebila ( Kolondièba , coadministered treatment ) had follicular trachoma . No subject in any of the villages had corneal opacity . Other findings from the eye examination were seen for between 0 . 8% ( Kolondièba , coadministered treatment ) and 5 . 9% ( Bougouni , coadministered treatment ) . The pattern was similar as seen for the overall incidence of trachoma , with a lower level of other findings for Bougouni standard and Kolondièba coadministered treatment ( less than 3% of subjects ) and higher levels for Bougouni coadministered and Kolondièba standard treatment ( above 4% ) . The most frequently occurring findings were chronic tropical endemic limboconjunctivitis ( TELC ) , cataract , pterygium and conjunctivitis . The number of abnormal findings from the LF examination was very low: 12 subjects with hydrocoele ( 6 on each treatment ) . One subject ( Kolondièba , coadministered treatment ) had both hydrocoele and lymphoedema: this was the only subject to have lymphoedema . The number of tablets administered was almost always correct according to height . There were more dosing errors for azithromycin than for ivermectin . There was a slightly higher error rate for the administration of the coadministered drugs than for the standard treatment: 13 subjects ( 0 . 9% ) and 27 subjects ( 1 . 8% ) had incorrect numbers of ivermectin and azithromycin tablets respectively in the coadministered groups compared to 3 ( 0 . 2% ) and 12 ( 0 . 8% ) for the standard treatments . There were two instances of regurgitation of drugs on Day 0 , both with the coadministered treatment . There were also two cases on Day 8 ( azithromycin only ) . The drugs were not taken again in only one case . The majority , at least 95% , of subjects in all villages except Kebila ( Kolondièba , coadministered treatment ) took their drugs after a meal: in Kebila , only 577 subjects ( 76 . 8% ) took their drugs after a meal . On Day 8 , all the subjects with data recorded took their medication ( azithromycin only ) after a meal; 2 subjects had no data on time of drug intake recorded . There were no losses to follow-up , all subjects allocated to treatment being evaluated within the timelines established for the protocol . Table 3 summarizes the prevalence of complaints reported prior to treatment administration , with the most frequently occurring shown in Figure 2 . The prevalence ranged from 26 . 2% ( Kolondièba , standard treatment ) to 46 . 9% ( Bougouni , standard treatment ) . The most frequently reported of the complaints named in the CRF were headaches ( 348 subjects , 162 on standard treatment , 186 on coadministered treatment ) with prevalence ranging from 8 . 3% ( Kolondièba , standard treatment ) to 15 . 3% ( Kolondièba , coadministered treatment ) , and abdominal pain ( 239 subjects , 118 on standard treatment , 121 on coadministered treatment ) , with prevalences ranging from 4 . 9% ( Bougouni , coadministered treatment ) to 11 . 2% ( Kolondièba , coadministered treatment ) . However , there were more subjects with pre-existing complaints classified as “Other”: 496 subjects ( 202 on standard treatment , 294 on coadministered treatment ) . The frequency of these varied widely between the villages: 57 subjects ( 7 . 5% ) in Bougoula ( Kolondièba , standard treatment ) , 100 subjects ( 13 . 3% ) in Ména ( Bougouni , coadministered treatment ) , 145 subjects ( 19 . 2% ) in Tienkoungoba ( Bougouni , standard treatment ) and 194 subjects ( 25 . 8% ) in Kebila ( Kolondièba , coadministered treatment ) . The most commonly reported were cough ( all villages except for Bougoula ) , lower back pain ( Kolondièba , both villages ) , epigastralgia ( Bougouni , both villages ) and various other types of pain . Table 4 summarizes the overall incidence of AE . The incidence of events in each village with its 95% confidence interval is also summarized in Figure 3 . The confidence intervals are all narrow due to the large sample size in each village , and there is no overlap between the four . The difference between treatment regimens differs between the districts , with a higher incidence for the coadministered treatment than the standard treatment in Kolondièba ( 30 . 4% vs . 11 . 0% ) , but a lower incidence in Bougouni ( 7 . 1% vs . 20 . 9% ) . Due to the clear differences between the incidences of AE in each village , formal statistical comparison between the two treatments is not appropriate . The intra-class correlation coefficient ( ICC ) was estimated to be 0 . 069 ( 95% CI: 0 to 0 . 174 ) . No serious adverse events were reported following treatment . Twelve subjects ( 8 out of 241 with any AE on standard treatment and 4 out of 281 on coadministered treatment ) reported events of major severity . Where necessary , subjects were treated symptomatically , most requiring paracetamol for pain or headache . Most subjects only experienced one type of AE: 66 . 4% ( 160/241 ) of subjects reported an AE with the standard treatment and 70 . 1% ( 197/281 ) with the coadministered treatment . Fewer than 2% of all subjects in each village reported 3 or more events . Of those subjects who did report an AE , 7 . 6% and 12 . 0% in the control villages and 9 . 4% and 5 . 7% in the coadministration villages reported 3 or more . One subject ( Bougouni , standard treatment ) had seven types of AE recorded . Overall , there was no difference in AE rates according to whether there were pre-existing complaints reported . In most cases , pre-existing complaints had either disappeared or improved by Day 8 . Some were ongoing , and a small number still continued up to Day 15 . At the Day 8 assessment , a small number of complaints were reported as worse after treatment: these were mainly headaches ( 7 subjects ) and abdominal pain ( 6 subjects ) . There was also one case each of fever , joint pain and deafness , which were exacerbated by treatment . At the final ( Day 15 ) assessment , only one subject reported an exacerbation of a pre-existing complaint . This was a case of abdominal pain in the standard treatment ( Kolondièba ) . All but one of the headaches exacerbated by treatment were reported as AEs . Of the six subjects with abdominal pain , four were reported as an AE , two were not . The case of worsened fever was not reported as an AE , nor was the exacerbated deafness . There was one case of exacerbated joint pain that was reported as an AE . Table 5 shows the frequency of each individual type of event as listed in the CRF . The data is summarized graphically for the most frequent events in Figure 4 . Abdominal pain , headaches , diarrhea and events other than those itemized in the CRF were the most common types of event in all villages . The incidence of each of the main events with 95% confidence intervals is summarized for each village in Figure 5 . Overall , and in three of the four villages , the most frequently reported event was abdominal pain , with rates ranging from 2 . 1% ( Bougouni , coadministered treatment ) to 17 . 2% ( Kolondièba , coadministered treatment ) which was considerably higher than in any of the other three villages ( Figures 4 and 5 ) . The exception was Tienkoungoba ( Bougouni , standard treatment ) , where more people experienced headaches ( 55 subjects , 7 . 3% ) or other types of event ( 56 subjects , 7 . 4% ) . The incidence of diarrhea was slightly higher in the coadministered treatment village in Kolondièba than in the other three villages ( 6 . 7% compared to rates below 3% ) . The incidence of joint or muscular pain was similar in all four villages . The only other events reported by more than 1% of the study population in any of the villages were vomiting ( 2 . 8% Kolondièba , coadministered treatment ) , nausea ( 2 . 0% Kolondièba , coadministered treatment ) and fever ( 1 . 2% , Bougouni , standard treatment ) . Of these , the only event for which the upper limit of the confidence interval exceeded 4% was vomiting ( Kolondièba , coadministered treatment , upper 95% confidence limit = 4 . 2% ) . The upper 95% confidence limit for the prevalence of events specified in the CRF but which were not experienced by any subjects in the study is 0 . 5% . With the exception of nausea and vomiting , the incidences of all other AE types specified in the CRF are comparable between the villages , with overlapping confidence intervals . Of the five subjects who had pre-existing complaints exacerbated which were not recorded as AE , two reported worsened abdominal pain , and one each reported heightened fever , headache and exacerbated deafness . Of the 12 subjects with AE classified as major , 8 received standard treatment ( 6 in Bougouni district , 2 in Kolondièba ) and 4 received coadministered treatment ( 1 in Bougouni district , 3 in Kolondièba ) . The distribution of events and their nature is shown in Table 6 . There was no clear pattern to the types of event reported as “Other” . In Tienkoungoba ( standard treatment , Bougouni ) , the most common events were respiratory in nature ( 18 cases , comprising coughs and rhinitis ) , and vertigo ( 15 cases ) . In Bougoula ( standard treatment , Kolondièba ) where the event rate was lower , vertigo was the most commonly reported “Other” event ( 6 cases ) . In Kebila ( coadministered treatment , Kolondièba ) , the most common events were somnolence ( 26 cases ) and vertigo ( 9 cases ) . The most frequent type of “Other” event in Mena ( coadministered treatment , Bougouni ) was epigastralgia , reported by 3 subjects . There is a clear difference between the villages in time to onset of events , with most of the events occurring immediately after treatment in Kebila ( Kolondièba , coadministered treatment , Figure 6 ) . There is also evidence that the event rate increased on day 8 with the administration of azithromycin in the standard treatment villages . The median time to the onset of the first event , of any type , calculated from all those with any event , is 8 days for the two standard treatment villages compared to 2 days and 0 days for the coadministered treatment villages in Bougouni and Kolondièba respectively . For the five most common types of event , namely , headaches , abdominal pain , diarrhea , joint/muscular pain and other non-specified events , a similar pattern in terms of time to onset was seen . This was most marked for abdominal pain: in Kebila ( Kolondièba , coadministered treatment ) , almost all cases of abdominal pain first occurred on the day of drug administration . For diarrhea and events not specified in CRF , however , the time to onset was most rapid in the Kolondièba coadministered treatment village . The median time to onset of events with the standard treatment was always Day 8 , coinciding with the administration of azithromycin . It is clear from the graphs that onset was quickest with the coadministered treatment in Kolondièba , with most events being reported as starting on the day of treatment administration . Duration of adverse events ( number of days from first to last reporting of an event ) is summarized in Figure S2
The design of the study with a small number of large clusters means that comparisons of the overall AE rates for the two treatment regimens are difficult to make since there were only two villages receiving each treatment , and the AE rates differed markedly between villages . Nevertheless , there is no clear evidence to suggest that the coadministered treatment is associated with a higher rate of AE than the standard treatment . The types of event occurring most frequently were similar for all four villages , and the events reported , especially abdominal pain and diarrhoea were temporally accociated with azithomycin administration as expected . The study demonstrated that it is feasible to administer the three drugs together , which would significantly reduce the logistical demands on conducting mass treatment of LF and trachoma in this setting . However , given the limitations of the current study , further investigation would be desirable . Any new study will need to consider the design issues raised here , and especially whether a cluster randomised approach is appropriate . Furthermore , the studies need to be conducted in areas where there is an appreciable prevalence of LF to determine whether coadministered treatment increases the incidence of AEs associated with microfilaraemia . | Neglected tropical diseases are co-endemic in many areas of the world . Currently lymphatic filariasis ( albendazole+ivermectin ) and trachoma ( azithromycin ) are treated separately . Benefits can be gained from integration of preventive chemotherapy programs in such areas . To assess the safety of this approach , 4 villages in two co-endemic districts in Mali were randomly assigned to coadministered treatment ( ivermectin/albendazole/azithromycin ) or standard therapy ( ivermectin/albendazole with azithromycin 1 week later ) . One village was randomly assigned to each treatment in each district . 3011 ( 40 . 1% ) of 7515 eligible individuals in the 4 villages participated in the study . No serious adverse events occurred , and most events were mild in intensity ( mainly headache , abdominal pain , and diarrhoea ) . Overall the number of subjects reporting any event was similar with co-administration compared to standard treatment [18 . 7% ( 281/1501 ) vs . 15 . 8% ( 239/1510 ) ] . The overall frequency of adverse events with co-administration was comparable to or lower than published frequencies for ivermectin/albendazole alone . These data suggest that co-administration is safe; however the small number of villages studied and the large differences between them meant that a meaningful estimate of the differences could not be calculated , and further work will be needed before a recommendation can be made . | [
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] | 2013 | A Cluster Randomized Study of The Safety of Integrated Treatment of Trachoma and Lymphatic Filariasis in Children and Adults in Sikasso, Mali |
Tetanus neurotoxin causes the disease tetanus , which is characterized by rigid paralysis . The toxin acts by inhibiting the release of neurotransmitters from inhibitory neurons in the spinal cord that innervate motor neurons and is unique among the clostridial neurotoxins due to its ability to shuttle from the periphery to the central nervous system . Tetanus neurotoxin is thought to interact with a high affinity receptor complex that is composed of lipid and protein components; however , the identity of the protein receptor remains elusive . In the current study , we demonstrate that toxin binding , to dissociated hippocampal and spinal cord neurons , is greatly enhanced by driving synaptic vesicle exocytosis . Moreover , tetanus neurotoxin entry and subsequent cleavage of synaptobrevin II , the substrate for this toxin , was also dependent on synaptic vesicle recycling . Next , we identified the potential synaptic vesicle binding protein for the toxin and found that it corresponded to SV2; tetanus neurotoxin was unable to cleave synaptobrevin II in SV2 knockout neurons . Toxin entry into knockout neurons was rescued by infecting with viruses that express SV2A or SV2B . Tetanus toxin elicited the hyper excitability in dissociated spinal cord neurons - due to preferential loss of inhibitory transmission - that is characteristic of the disease . Surprisingly , in dissociated cortical cultures , low concentrations of the toxin preferentially acted on excitatory neurons . Further examination of the distribution of SV2A and SV2B in both spinal cord and cortical neurons revealed that SV2B is to a large extent localized to excitatory terminals , while SV2A is localized to inhibitory terminals . Therefore , the distinct effects of tetanus toxin on cortical and spinal cord neurons are not due to differential expression of SV2 isoforms . In summary , the findings reported here indicate that SV2A and SV2B mediate binding and entry of tetanus neurotoxin into central neurons .
The Clostridium genus of bacteria are responsible for the production of the clostridial neurotoxins ( CNTs ) , which include both tetanus neurotoxin ( TeNT ) and seven botulinum neurotoxins ( BoNT/A–G ) [1] . TeNT is synthesized by Clostridium tetani , and is one of the most toxic substances known to humans; it causes the disease tetanus [2] , [3] . Spores enter via deep wounds where they germinate in the anaerobic environment , releasing TeNT via autolysis [1] . Upon exposure to fatal levels of the toxin , patients eventually die of respiratory or heart failure , thereby generating a rich anaerobic environment in which the bacteria can proliferate [4] . Tetanus kills hundreds of thousands of people each year in countries in which regular tetanus vaccinations are not carried out [1] . Structurally , the CNTs are 150 kDa proteins composed of a heavy chain ( HC ) and a light chain ( LC ) that are linked through a disulfide bond . The 100 kDa HC , which has two functional domains , mediates binding to neuronal receptors and also creates a pore that mediates the translocation of the 50 kDa LC , a zinc-dependent endoprotease , into the cytosol [1] , [5] . The LC then cleaves one or more of three soluble N-ethylmaleimide-sensitive fusion protein receptor ( SNARE ) proteins: BoNT/A and E cleave the plasma membrane protein SNAP-25 ( synaptosomal-associated protein of 25 kDa ) ; BoNT/B , D , F , G and TeNT cleave the vesicle protein synaptobrevin ( syb ) ; and BoNT/C cleaves both SNAP-25 and syntaxin-1 [6] , [7] , [8] , [9] , [10] . Assembly of syb•syntaxin•SNAP-25 into parallel four-helix bundles is thought to pull the vesicle and plasma membranes together to drive membrane fusion [11] . Cleavage of these SNAREs by the CNTs either severs them from the membrane or disrupts their ability to assemble into stable/functional fusion complexes , thereby blocking synaptic vesicle ( SVs ) exocytosis and neurotransmitter release [1] , [12] . While TeNT causes rigid paralysis , the BoNTs cause flaccid paralysis [13] . These opposite symptoms are the result of different sites of action . The BoNTs exert their effects at the neuromuscular junction ( NMJ ) by cleaving one or more of the three synaptic SNARE proteins . While TeNT also enters the nervous system via presynaptic terminals of the α-motor neuron ( MN ) at the NMJ , it does not act at this site but rather undergoes retrograde transport into the spinal cord . To achieve this , TeNT localizes to lipid rafts that contain high local concentrations of cholesterol , polysialogangliosides ( PSGs ) , and glycophospoinositol ( GPI ) -anchored proteins in the terminals of MNs [14] , [15] . Once bound , TeNT is internalized into non-acidified vesicles that harbor growth factor receptors [16] . The TeNT-harboring vesicle is sorted via a Rab 5/7 dependent pathway and transported back to the cell body of the MN [17] . TeNT then undergoes transcytosis by being released from the MNs such that it enters upstream inhibitory neurons to cleave SNAREs and inhibit transmitter release [18] , [19] . The pathway by which TeNT acts on inhibitory neurons occurs via four steps . 1 ) TeNT binds to presynaptic terminals through interactions with a “dual receptor” composed of lipid and protein components that , together , form high-affinity receptors [20] . As opposed to the other CNTs , which harbor one PSG binding site ( which is the lipid component of the dual receptor ) , it was discovered that TeNT contains two binding sites for PSGs . Among the PSGs , TeNT exhibits stronger interactions with GT1b , GD1b , and GQ1b [1] . Furthermore , mice lacking PSGs were resistant to TeNT as compared to wild-type ( WT ) mice [21] , [22] . Experiments performed with spinal cord neurons and rat brain membranes also indicated the presence of a protease-sensitive protein receptor; however , the identity of this protein remains unknown [23] , [24] , [25] . 2 ) Once bound to the membrane , TeNT is internalized via endocytosis . 3 ) Following endocytosis , acidification of the vesicle lumen triggers conformational changes in the HC which cause it to form a translocation channel or pore in the vesicular membrane . The LC translocates through the HC pore and the disulfide bond connecting the HC and the LC becomes reduced in the cytosol . 4 ) The LC cleaves syb to inhibit SV exocytosis . The resultant loss of inhibitory neurotransmission results in hyper-excitability of the MN , thereby enhancing release of acetylcholine and producing rigid paralysis [26] . The protein receptors for BoNT/A , B , E and G have recently been identified [27] , [28] , [29] , [30] , [31] , [32] , [33] . The unique ability of TeNT to shuttle from the periphery to the central nervous system has made determining the receptor ( s ) for this toxin a greater challenge . At present , the route of entry of TeNT into central neurons is unclear , with some reports indicating that , in hippocampal cultures , binding and entry was dependent on SV recycling [34] . However , other studies indicate that entry of TeNT into spinal cord neurons was mediated by non-SV carriers [35] , [36] , and thus this question remains an open issue . Furthermore , the receptor-binding domain of TeNT was reported to bind to a GPI-anchored protein that was sensitive to phosphoinositol specific phosholipase C ( PI-PLC ) treatment in MNs , spinal cord neurons , and PC12 cells ( rat pheochromocytoma cell line ) . This putative receptor was identified as Thy-1 [35] , [37] , but whether Thy-1 is required for binding and uptake of TeNT has yet to be tested . As previously hypothesized , the protein receptors for TeNT in inhibitory neurons and MNs are most likely to be distinct proteins [1] . Namely , the receptor that is present in MNs directs TeNT into a non-acidified compartment to circumvent translocation , whereas the receptor that is expressed by inhibitory neurons is targeted to vesicular structures that undergo acidification , thus allowing for translocation [16] . Secondly , we note that dogs and cats are orders of magnitude more resistant to TeNT when injected into the periphery as compared to into the spinal cord [1] , [38] , [39] . It is therefore possible that dogs and cats have substantial sequence variation in the MN receptor that prevents proper interactions with TeNT , resulting in reduced uptake and retrograde transport . The identification of the somatic and central neuron protein receptors for TeNT will aid in developing antagonists to combat the disease , especially in nations without a tetanus vaccination regimen [1] . Due to the toxin's unique ability to be transported from MNs into inhibitory interneurons in the central nervous system , the findings described here will also aid in the development of novel drug delivery methods for the treatment of central nervous system diseases [40] .
As alluded to before , there are conflicting reports regarding the mode of entry for TeNT into central neurons . It was first suggested that in hippocampal neurons , TeNT enters through recycling SVs [34] . However , a later paper reported that the sensitivity of mouse cerebellar slices to TeNT was reduced upon treatment with PI-PLC , indicating the receptor had a GPI-anchor moiety [35] . Furthermore in another study , gold-labeled TeNT was not commonly found in SVs in cultured spinal cord neurons [36] . These results argued against a SV uptake pathway because these vesicles are devoid of GPI-linked proteins [41] . To address these apparent disparities , we utilized a recombinant fragment of TeNT , corresponding to its receptor-binding domain , to carry out binding assays using cultured neurons . This fragment harbored a 3x-FLAG epitope tag that was used for our imaging studies [37] , [42] , [43] . This C-terminal subdomain of the heavy chain ( HCR/T ) has been shown to have the same binding , uptake , and retrograde transport properties as the full length and enzymatically active holotoxin [44] , [45] , [46] , [47] Extracellular exposure of neurons to solutions that contain high concentrations of potassium results in membrane depolarization; calcium then enters pre-synaptic terminals through voltage-gated calcium channels thereby triggering SV exocytosis . We applied this technique to investigate the binding of HCR/T to hippocampal neurons under depolarizing and non-depolarizing conditions . We utilized hippocampal neurons as they have proven to be a useful model system that has been pivotal in the identification of CNT receptors [27] , [28] , [30] , [34] . Incubation of HCR/T with these neurons in a buffer containing tetrodotoxin ( TTX ) , which blocks action potentials and as a result active SV recycling , resulted in little binding of HCR/T . However , when experiments were performed in the presence of high potassium buffer , a large increase in HCR/T fluorescence was observed ( Figure 1A ) . Quantitative analysis revealed a 3-fold increase in the fluorescence intensity of HCR/T at excitatory terminals ( vGLUT1 ) and a 10-fold increase at inhibitory terminals ( vGAT ) upon stimulation with high potassium ( Figure 1B ) . These results suggest that the binding partner for TeNT is localized to the lumen of SVs . TeNT causes disease by exerting its effects at inhibitory neurons in the spinal cord , so we extended our experiments to cultured dissociated spinal cord neurons obtained from embryonic rats . Again , HCR/T was incubated in TTX ( non-depolarizing ) and high potassium ( depolarizing ) conditions , and we observed a 4-fold increase of HCR/T binding upon depolarization of inhibitory boutons ( Figure 1C–D ) . The increase in TeNT binding to spinal cord terminals under high potassium versus TTX conditions strongly suggests that the binding partner for TeNT is localized to SVs . The previous experiments strongly suggest that TeNT binds to a receptor that is a resident of SVs , so we next determined whether this interaction results in functional entry of the toxin . To test this idea , we determined whether the ability of TeNT holotoxin to enter neurons and cleave syb II also depended on SV recycling . We employed a monoclonal antibody raised against syb II ( Cl . 69 . 1 ) that cannot recognize the enzymatically cleaved form of the protein . We began by comparing the ability of TeNT to cleave syb II under TTX ( which blocks action potentials to inhibit SV recycling ) or high potassium ( which depolarizes neurons to drive SV recycling ) conditions . We observed that syb II fluorescence was markedly reduced when hippocampal neurons were depolarized with potassium to drive SV recycling ( Figure 2A ) ; this occurred at both excitatory and inhibitory nerve terminals ( Figure 2B ) . Syb II immunofluorescence was reduced in excitatory terminals by 39% in the TTX condition and was further reduced by 65% of under depolarizing conditions , as compared to control . Inhibitory terminals exhibited no significant reduction of syb II immunofluorescence under TTX conditions , but a 53% reduction was observed in neurons that had been depolarized ( Figure 2B ) . To test the physiological relevance of these observations , we monitored syb II levels in spinal cord neurons ( Figure 2C ) and observed that under high potassium conditions syb II immunofluorescence was reduced by 50% as compared to control and non-stimulating ( i . e . TTX ) conditions ( Figure 2D ) . These data further reinforce the idea that TeNT entry into inhibitory spinal cord neurons occurs through recycling SVs , relying on a receptor that is localized to SVs . To further confirm the requirement of SVs for the entry of TeNT into neurons , we carried out experiments in which we inhibited steps in the SV cycle and assayed for protection from the toxin . For these experiments we took advantage of a dynamin point mutant that interferes with the GTPase function of the protein . This mutant ( K44A ) acts as a dominant negative that inhibits endocytosis [48] . As shown in Figure 3A , neurons that expressed the K44A mutant were protected from the entry of TeNT , as evidenced by the lack of cleavage of syb II , as compared to neurons that expressed WT dynamin . We next determined whether we could prevent TeNT entry by inhibiting the exocytosis ( and thereby blocking compensatory endocytosis ) of SVs . As BoNT/A and BoNT/E cleave SNAP-25 instead of syb II , we were able to inhibit exocytosis in neurons while still monitor the entry of TeNT through cleavage of syb II . Hence , we first pre-treated spinal cord neurons with BoNT/A to cleave SNAP-25 and inhibit exocytosis [1] , and then we subsequently assayed for entry of TeNT by monitoring cleavage of syb II . We observed that syb II was largely protected from the effects of TeNT at inhibitory terminals when exocytosis had been inhibited by prior treatment with BoNT/A ( Figure 3B ) . Similar results were observed using hippocampal neurons that had been pretreated with BoNT/E , which also cleaves SNAP-25 ( Figure 3C ) . Together , the results reported thus far establish the notion that the primary route for TeNT-induced toxicity is through recycling SVs and not through an alternative pathway . To identify the potential SV binding partner for TeNT , we biotinylated this toxin , as well as BoNT/B , BoNT/E ( as controls ) , and bound them to neutravidin beads , through biotin-avidin interactions . After incubating the toxin-linked beads with brain detergent extracts ( BDE ) , we screened for bound SV proteins . Previously it has been shown that the receptor for BoNT/B is synaptotagmin ( syt ) I/II and the receptor for BoNT/E is SV2A/B [29] , [30] . Consistent with previous reports , we detected that BoNT/B associated with syt I and BoNT/E with SV2 , but surprisingly , we observed that TeNT also strongly associated with SV2 ( Figure 4A ) . To further confirm that TeNT associated with SV2 , we used HCR/T to see if it could compete with BoNT/E for binding to its native receptor , SV2 , on hippocampal neurons . A 100-fold molar excess of HCR/T , compared to BoNT/E , markedly reduced BoNT/E binding to nerve terminals in this preparation ( Figure 4B ) . Moreover , competition of HCR/T with BoNT/E in hippocampal neurons reduced the extent of cleavage of SNAP-25 ( Figure 4C ) . These data are in agreement with previous studies on the NMJ that also demonstrate HCR/T can antagonize BoNT/E entry [49] , [50] . The ability of the receptor-binding domain of TeNT to reduce BoNT/E binding and entry into hippocampal neurons - through competition for binding SV2 - further supports the idea that TeNT utilizes SV2 as its receptor protein . SV2 is a 12 transmembrane protein that is heavily glycosylated and is homologous to transmembrane transporters . SV2 exists in 3 isoforms - A , B , and C - and SV2 A and B , but not C , knock-out ( KO ) mice have been generated [51] , [52] . We utilized dissociated spinal cord neurons from SV2 KO mice in order to directly determine whether SV2 was critical for the action of TeNT at a functional level . We observed significant reductions of HCR/T binding to inhibitory terminals in KO mice ( Figure 5A ) . Compared to WT , there was a 30% reduction in HCR/T binding to SV2B KO spinal cord neurons and a 55% reduction of HCR/T binding to inhibitory terminals of SV2A/B KO neurons ( Figure 5B ) . These results further demonstrate that SV2 plays a critical role in the binding of TeNT to inhibitory terminals in the spinal cord . To investigate whether SV2 was necessary for entry of TeNT , we cultured SV2A/B KO hippocampal neurons which express little SV2C [27] and examined whether TeNT was able to cleave syb II . In Figure 5C , SV2A/B KO neurons were largely protected from TeNT and these neurons could be re-sensitized with the infection of SV2A or B lentivirus ( which infects greater than 90% of the cells ) [28] , [30] . In addition , we cultured SV2B and SV2A/B KO spinal cord neurons and observed that while SV2B KO mice were still sensitive to the addition of TeNT , SV2A/B double KO spinal neurons were protected from TeNT; again the neurons were re-sensitized to the toxin after infection with SV2A virus ( Figure 5D ) . Together , these data indicate that SV2A and SV2B mediate entry of TeNT . SV2 has three putative N-linked glycosylation sites at amino acids 498 , 548 , and 573 [53] , [54] , [55] , [56] , [57] . It has been shown that BoNT/E requires the third N-linked glycosylation site of SV2 to enter neurons [30] , so we investigated whether the glycosylation of SV2 was also critical for TeNT entry . To address this , each of the three SV2A N-linked glycosylation sites were mutated to generate individual site mutants and were expressed in hippocampal neurons using lentivirus . None of the individual glycosylation mutations affected TeNT entry ( Figure 5E ) . We note that PNGase F treatment cannot access all the N-glycosylation sites of SV2 in cultured neurons and triple glycosylation mutants of SV2 do not target properly to SVs , so we are unable to completely rule out whether the glycosylation of multiple sites might play a role in toxin binding or entry ( [30];data not shown ) . However , the experiments reported here clearly demonstrate that – in contrast to BoNT/E [30] - loss of glycosylation at each of the individual glycosylation sites does not impact the entry of TeNT . Next , we turned to an in vivo mouse model to investigate whether SV2B KO mice are resistant to TeNT intoxication . We injected WT and SV2B KO littermates with 5 µg/mouse of TeNT and determined the length of time required for the mice to expire . WT mice survived ∼190 minutes post-injection , while SV2B KO mice were resistant to TeNT and survived ∼400 minutes post-injection . The average survival time of KO mice ( ∼400 minutes ) injected with 5 µg TeNT was longer than that of WT mice injected with 1 µg of TeNT ( ∼300 minutes ) indicating the effective concentration of TeNT was reduced by at least five-fold in SV2B KO mice . ( Figure 5F ) . In order to determine whether the uptake of other toxins was altered in SV2A/B double KO neurons , we used BoNT/F , which also utilizes recycling SVs [58] , as a control . We titrated BoNT/F from 0 . 3 to 10 nM on WT and knockout neurons and observed no significant difference in binding and entry , as evidenced by cleavage of syb II , between these two conditions ( Figure 5G ) . These data indicate that loss of SV2 does not affect normal uptake of toxins that target SVs and furthermore , in contrast to previous suggestions , SV2A/B is not required for normal uptake of BoNT/F [50] , [58] . To further understand how TeNT targets inhibitory neurons when released from MNs in the spinal cord , we first tested cortical neurons at low concentrations of TeNT to determine which population of neurons TeNT would affect first . Surprisingly in Figure 6A , at 0 . 5 pM toxin , miniature excitatory postsynaptic currents ( mEPSCs ) were reduced to 20% of control as compared to 60% for miniature inhibitory postsynaptic currents ( mIPSCs ) . This is counter-intuitive because during the normal course of tetanus pathology , TeNT affects inhibitory neurons rather than excitatory neurons [1] . However , when spinal cord neurons were treated with 50 pM TeNT , typical hyper excitability of the culture – due to preferential loss of inhibitory transmission - was evident 3 hours after treatment ( Figure 6B ) . However , extended incubation resulted in the inhibition of all action potentials ( data not shown ) . Since we observed distinct effects in cortical and spinal cord neurons , we wondered whether different expression patterns of SV2 isoforms could be responsible for the opposite phenotypes observed in the two types of cultures . We found that among cortical neurons , SV2A is largely colocalized with inhibitory neurons , while SV2B is colocalized with excitatory neurons ( Figure 6C–D ) . In adult spinal cord slices , we focused our studies on the ventral horn , where the cell bodies of MN are located . In this area , SV2A was more colocalized to inhibitory neurons and SV2B was largely colocalized with excitatory terminals . Since the SV2 expression patterns for spinal cord versus cortical neurons are similar , but TeNT has differential effects on excitatory versus inhibitory synaptic transmission in these two preparations , other yet to be defined factors must underlie the selective action of TeNT on inhibitory synaptic transmission observed in vivo .
Functionally , TeNT causes rigid paralysis , through a reduction of the strength of inhibitory inputs on MNs; however , the pathway through which TeNT enters neurons is unresolved . The majority of studies indicate that TeNT enters neurons through a non-SV pathway [35] , [36] , although one report concluded that the toxin enters via recycling SVs [34] . Among the CNTs , TeNT has the most unique intoxication pathway , yet little is understood the precise mechanism by which it reaches its target . In the current study , we demonstrate that in cultured hippocampal and spinal cord neurons , a receptor-binding fragment of TeNT exhibits robust binding to nerve terminals only after we stimulated robust SV exocytosis to deposit SV proteins into the plasma membrane . This result indicates that SVs harbor a receptor for the TeNT . We then extended these observations by determining whether full-length TeNT achieves functional entry by being internalized via recycling SVs . Indeed , in hippocampal neurons , as well as in inhibitory spinal cord neurons that are the physiological targets of TeNT , we observed markedly enhanced cleavage of TeNT's substrate , syb II , under conditions that stimulate SV exo- and endocytosis . In addition , dominant negative dynamin was also effective at inhibiting TeNT entry into hippocampal neurons . Finally , to inhibit SV exocytosis , spinal cord neurons or hippocampal neurons were pretreated with BoNT/A or E , respectively , and were protected from the effects of TeNT . Collectively , these findings firmly establish that the predominant pathway by which TeNT enters central neurons is through recycling SVs . Most of the early efforts to identify the receptor for TeNT were focused on MNs where it was suggested that the TeNT receptor contained a PI-PLC sensitive GPI-anchor and was localized to lipid rafts [14] , [35] , [37] . This GPI-anchored protein was postulated to be the receptor important for retrograde transport of TeNT from the periphery of the MN to the soma , which is located in the spinal cord . Much of the literature has been dedicated to studying this non-SV receptor , but surprisingly , little work has been done to investigate the receptor in spinal cord neurons in the central nervous system . We provide the first direct evidence that a distinct vesicular compartment was required for entry into spinal cord neurons versus MNs [14] , [35] , [37] , [59] , [60] . Because neuronal activity and SV recycling were required for the cleavage of syb II , indicating that the toxin receptor resided on SVs , we began to screen SV proteins for TeNT binding activity . In order to achieve this , we used biotinylated toxins incubated with detergent solubilized brain extracts and identified the primary protein associated with TeNT to be SV2 . To further confirm that TeNT uses SV2 as a receptor , we assayed for competition between TeNT and a toxin known to use SV2A/B as its receptor , BoNT/E [30] . Notably , we found that an excess amount of TeNT efficiently occluded the binding and entry of BoNT/E , consistent with competitive binding for sites on SV2 . To determine , definitively , whether TeNT relies on SV2 to bind and enter neurons , we took advantage of SV2A/B KO mice [51] , [52] . We first cultured spinal cord neurons that lacked SV2A/B and monitored binding of a receptor-binding fragment of TeNT; WT neurons served as controls . It was observed that SV2B KO neurons had reduced binding and SV2A/B double KO neurons exhibited a further decrease in binding of HCR/T . Thus , both SV2A and B are important for TeNT to recognize and associate with the surface of central neurons . Furthermore , double KO neurons were largely protected from TeNT , as evidenced by the lack of cleavage of syb II; moreover , these neurons could be re-sensitized through infection with viruses that expressed SV2A or B . Using viruses that encode mutant forms of SV2 , we found that glycosylation at any one of the three N-linked glycosylation sites was not required for TeNT to bind and enter neurons . In contrast , the third glycosylation site plays a critical role for binding and entry of BoNT/E and glycosylation appears to enhance the ability of BoNT/A to enter neurons via SV2 [30] . Given this result , in conjunction with the competition studies described above , TeNT and BoNT/E are unlikely to bind to the exact same sites on SV2 , since BoNT/E requires glycosylation of the third N-linked glycosylation site in intra-lumenal loop 4 whereas TeNT does not . However , it seems likely that these two toxins compete for binding ( Figure 4B–C ) due to a steric hindrance; two different toxin molecules are unlikely to be able to simultaneously bind to the relatively small intra-lumenal loops of SV2 that are exposed to the extracellular milieu during exocytosis . Finally , intravenous injections revealed that SV2B KO mice are at least five-times more resistant to TeNT as compared to WT littermates . The finding that SV2A/B double KO neurons exhibit decreased binding and entry of TeNT , in conjunction with the ability of TeNT to compete with BoNT/E , strongly implicates SV2 as the receptor for TeNT . However , it remains possible that loss of SV2 might prevent the proper expression or targeting of yet-to-be identified protein receptors for TeNT . Our experiments showing normal entry of BoNT/F , another toxin that requires SV recycling for entry [58] , into SV2A/B double KO neurons argues against the notion that SV2 plays a general permissive role in toxin entry . Interestingly , when we exposed cortical neurons to low concentrations of TeNT we discovered an unexpected preferential action on excitatory versus inhibitory neurotransmission . In contrast , in spinal cord cultures , TeNT preferentially acted on inhibitory neurons resulting in the expected pathological symptoms of hyper excitability . To further investigate how TeNT is directed to specific populations of neurons , we examined the distribution of SV2A and SV2B in both preparations . We found that SV2B expression is largely localized to excitatory terminals while SV2A is preferentially localized to inhibitory neurons in both cortical cultures and spinal cord slices; this differential distribution was more striking with cortical neurons . Thus , SV2A/B expression patterns do not seem to determine or dictate the specificity of TeNT for inhibitory neurons . It was previously suggested that structural features - especially the organization of neurons that is lost in dissociated culture - can be important for the selective action of TeNT on inhibitory neurons in the spinal cord [1] . Since inhibitory neurons typically synapse directly onto the cell body , while excitatory neurons form synapses on dendritic spines , it is possible that the retrograde carrier for TeNT predominately undergoes transcytosis at the cell body , thus allowing TeNT to preferentially target inhibitory neurons . The retrograde transport of TeNT along axons of MNs , followed by transcytosis and final entry - through SVs - into inhibitory neurons is reminiscent of the transport and transcytosis of BoNT/A through the epithelial lining of the intestine and its subsequent selective entry into MNs . As TeNT and the BoNTs are related members of the CNT family , it is perhaps not surprising that these two toxins would both make use of transcytosis pathways , but that these pathways diverged during evolution to allow for distinct points of entry . TeNT is typically introduced through deep wounds , so it utilizes the MN as a mechanism of transport back to the spinal cord , where its enzymatic activity is focused on inhibitory neurons . BoNTs are typically ingested and need to enter the bloodstream to access the NMJ; this is achieved via transport across the epithelial layer in the gastrointestinal tract . It is important to note that MNs also express SV2 [27] . This raises the question as to how TeNT selectively targets inhibitory interneurons , without affecting MNs; that is , why is the toxin not taken up into SV2-harboring SVs that acidify in MNs , thereby triggering translocation and resulting in flaccid paralysis ( note: at extremely high concentrations , TeNT can in fact inhibit neurotransmitter release from MNs , probably via interactions with SV2 [61] ) . As alluded to above , previous studies indicated that SVs are not the major mode of entry as TeNT in MNs; e . g . TeNT shows little colocalization with the SV protein , syb II [59] . Another issue raised by the results presented here is the question of how TeNT is released from its receptor in MNs such that it can bind to SV2 on inhibitory interneurons following transcytosis . In our first model , we envision that once TeNT reaches the NMJ , the protein receptor ( s ) responsible for retrograde transport ( peripheral receptor ) has a lower affinity for TeNT as compared to SV2 . To prevent the entry of TeNT into SVs , the peripheral receptor would have to be present in large excess as compared to SV2 , resulting in the sequestration of TeNT into the retrograde pathway . Next , TeNT undergoes retrograde transport back to the soma of the MN and the lack of acidification during transport prevents toxin translocation; hence the toxin does not gain access to the cytosol to cleave syb II [16] . During transcytosis , the peripheral receptor is no longer in abundance as compared to SV2 . In addition , the higher affinity of SV2 for TeNT would allow for efficient capture of the toxin once it has dissociated from the MN , thus favoring binding and entry into the upstream inhibitory neuron . Alternatively , recent data suggesting that PSGs are not internalized along with TeNT in MNs provide a second hypothesis [59] . As opposed to the previous model , the peripheral receptor , rather than SV2 , has a higher affinity for TeNT . Therefore , once TeNT reaches a MN , it targets the peripheral receptor rather than SV2 . As mentioned above , PSGs are important co-receptors for the CNTs . Once bound to PSGs and the peripheral receptor , TeNT is subsequently internalized into a vesicle bound for retrograde transport; however , PSGs are not internalized and remain on the plasma membrane [59] , [62] . The loss of PSGs might dramatically reduce the affinity of TeNT for the peripheral receptor , relative to SV2 , so TeNT can be released from the MN and target inhibitory neurons after transcytosis . Restated: the lack of internalization of PSGs into retrograde carriers might permit the release of the toxin from the MN . Further studies to identify the peripheral receptor will help address these questions . In conclusion , we demonstrate that the recycling SVs are the primary mode of entry for TeNT into hippocampal and spinal cord neurons . Furthermore , SV2 is critical for the binding and entry of TeNT into neurons . To our knowledge , this is the first definitive identification of a protein receptor that is critical for the entry of TeNT into central neurons . This discovery identifies a new target that can be exploited to prevent tetanus . In addition , our greater understanding of the mechanism of TeNT entry should facilitate the development of a new class of therapeutics that allow for the delivery of drugs and genes to the central nervous system .
All animal care and experiment protocols in this study were conducted under the guidelines set by the NIH Guide for the Care and Use of Laboratory Animals handbook . The protocols were reviewed and approved by the Animal Care and Use Committee ( ACUC ) at the University of Wisconsin - Madison ( assurance number: A3368-01 ) . Monoclonal antibodies directed against syb II ( Cl . 69 . 1 ) , SV2 ( pan-SV2 ) , syp ( Cl . 7 . 2 ) , and SNAP-25 ( Cl . 71 . 1 ) were generously provided by R . Jahn ( Max-Planck-Institute for Biophysical Chemistry , Gottingen , Germany ) . Rabbit polyclonal antibodies against BoNT/B and BoNT/E were described previously [29] . Guinea pig anti-vesicular glutamate transporter 1 and 2 ( vGLUT1/2 ) antibodies were purchased from Chemicon ( Temecula , CA ) . Mouse anti-FLAG antibody was purchased from Sigma-Aldrich ( St . Louis , MO ) . Rabbit and mouse anti-vesicular GABA transporter ( vGAT ) and rabbit anti-SV2A and B antibodies were purchased from Synaptic Systems ( Gottingen , Germany ) . Rabbit anti-HA tag and mouse anti-actin antibodies were purchased from Abcam ( Cambridge , MA ) . TeNT was purchased from List Biological Laboratories ( Campbell , CA ) . BoNT/B and BoNT/E were purified as previously described [63] , [64] . Tetrodotoxin was purchased from Sigma-Aldrich ( St . Louis , MO ) . HCR/T , purified as previously described [65] , was generously provided by J . Barbieri ( Medical College of Wisconsin , Milwaukee , WI ) . SV2A , SV2B , and SV2A/B knockout mouse lines were previously described [52] . Rat and mouse hippocampal neurons were cultured as described previously [30] . Cultured rat spinal cord neurons were prepared from embryonic ( E ) 14–15 day pups . SV2 knockout spinal cord neurons were prepared from E12 . 5∼14 timed pregnant mice . Spinal cord neurons were dissected in Hybernate E medium ( Brain Bits , Springfield , IL ) , Spinal cords were cut into 12 pieces , incubated with 0 . 025% trypsin ( Invitrogen , Carlsbad , California ) for 15 minutes at 37°C , dissociated with DNase ( 20 µg/ml ) , washed with DMEM supplemented with 10% fetal bovine serum ( FBS ) , and then triturated . Neurons were plated on 12 mm glass coverslips coated with poly-D-lysine and rat-tail collagen . Neurons were grown in DMEM with 10% FBS overnight . Afterwards , the media was replaced with Neurobasal medium supplemented with B-27 ( 2% ) and Glutamax ( 2 mM ) . Neurons were used between 14–24 days in vitro ( DIV ) . Transient transfection of neurons and lentiviral infections were performed as described previously [30] . Spinal cords were dissected from adult mice ( 4–6 months ) and embedded in agarose . 300 µm serial sections were taken from the lumbar and thoracic sections with a vibratome . Neurons were incubated in the following buffers: TTX ( 150 mM NaCl , 4 mM KCl , 4 mM MgCl2 , 10 mM D-glucose , 10 mM HEPES , 1 µM tetrodotoxin ) and high K+ ( same as TTX buffer but adjusted to 55 mM KCl , 99 mM NaCl , 2 mM CaCl2 , 2 mM MgCl2 and without the addition of tetrodotoxin ) at pH 7 . 4 with an osmolarity adjusted to 310 mOsm . Unless otherwise noted , neurons were incubated with toxins in high K+ buffer for 5 min . Images were collected with an Olympus FV1000 confocal microscope under a 60× water immersion lens ( Melville , NY ) , Neuronal lysates were collected with 100 µl lysis buffer ( 20 mM Tris , 150 mM NaCl , 1% Triton X-100 , 0 . 05% SDS , 0 . 5% PMSF , 0 . 5 µg/ml leupeptin , 0 . 7 µg/ml pepstatin , 1 µg/ml aprotinin , pH 7 . 4 ) per well ( 24-well plate ) . Lysates were subjected to SDS-PAGE and immunoblot analysis . 0 . 4 mg of BoNT/B , E , and TeNT were dialyzed overnight at 4°C against 0 . 1M Na-MES buffer ( pH 6 . 0 ) . BoNT/A , B and TeNT were incubated with 0 . 025 mg EDC and 0 . 067 mg EZ-link biotin PEO4-amine at RT for 2 hr ( Thermo Fisher , Waltham , MA ) . The reaction mixture was then dialyzed against PBS ( pH 7 . 4 ) overnight at 4°C . 2 . 5 µg of biotinylated toxin was bound to neutravidin beads ( Thermo Fisher , Waltham , MA ) that were pre-blocked with 2% BSA and 0 . 1% cold water fish gelatin ( Sigma-Aldrich , St . Louis , MO ) . Beads were incubated with rat brain detergent extracts and 250 µg/ml mixed PSGs for 1 hr at 4°C . Bound material ( 20% ) was subjected to SDS-PAGE and immunoblot analysis . SV2B WT and KO littermates were injected intravenously with 100 µl of the indicated amount of TeNT resuspended in GelPhos ( 30 mM sodium phosphate , 0 . 2% gelatin , pH 6 . 3 , autoclaved ) . Four mice were used in each experimental condition . Mice that survived longer than 420 minutes were euthanized . Whole-cell recordings were performed using a MultiClamp 700B amplifier ( Molecular Devices ) . The bath solution consists of ( in mM ) 128 NaCl , 30 glucose , 5 KCl , 5 CaCl2 , 1 MgCl2 , 25 HEPES; pH 7 . 3 . For recording action potentials or mEPSCs , the pipette solution contained ( in mM ) 125 K-gluconate , 10 KCl , 5 EGTA , 10 Tris-phosphocreatine , 4 magnesium ATP , 0 . 5 sodium GTP , 10 HEPES , pH 7 . 3 ( 305 mOsm ) . For recording mIPSCs , the pipette solution contained ( in mM ) 147 CsCl2 , 2 EGTA , 5 Tris-phosphocreatine , 2 magnesium ATP , 0 . 5 sodium GTP , 10 HEPES , pH 7 . 3 ( 305 mOsm ) . To isolate AMPA receptor-mediated mEPSCs , 0 . 5 µM TTX ( sodium channel blocker , Tocris ) , 50 µM D-AP5 ( NMDA receptor antagonist; Tocris ) and 20 µM bicuculline ( GABAA receptor antagonist; Tocris ) were added . To isolate GABAA receptor-mediated mIPSCs , bicuculline was replaced with 20 µM CNQX ( AMPA receptor antagonist , Tocris ) . Recordings of mEPSCs and mIPSCs were performed in voltage-clamp mode with membrane potential held at −70 mV . Recordings of action potentials were performed in current-clamp mode with current held at 0 pA . Data were acquired using pClamp ( Molecular Devices ) software , sampled at 10 kHz , and filtered at 2 kHz . Off-line data analysis was performed using Clampfit ( Molecular Devices ) or MiniAnalysis ( Synaptosoft ) software . All experiments were carried out at room temperature . Statistical significance was evaluated by two-tailed unpaired Student's t-test: *p<0 . 05 , **p<0 . 01 , ***p<0 . 001 . | Tetanus neurotoxin is one of the most deadly bacterial toxins known and is the causative agent for the disease tetanus , also known as lockjaw . Tetanus neurotoxin utilizes motor neurons as a means of transport in order to enter the spinal cord . Once in the spinal cord , the toxin leaves motor neurons and enters inhibitory neurons through a “Trojan-horse” strategy , thereby preventing the release of inhibitory neurotransmitters onto motor neurons . This causes hyper-excitability of the motor neuron and excessive release of acetylcholine at the neuromuscular junction , resulting in rigid paralysis . There is a major gap in our understanding of the mechanism by which tetanus neurotoxin enters neurons . In the current study we discovered that the “Trojan-horse” , utilized by tetanus neurotoxin to enter central neurons , corresponds to recycling synaptic vesicles . Furthermore , we discovered that SV2 is critical for the binding and entry of tetanus neurotoxin into these neurons . These findings will enable further development of drugs that antagonize the action of the toxin and will also aid in the development of drug delivery systems that target spinal cord neurons . | [
"Abstract",
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"Results",
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] | [
"biochemistry",
"infectious",
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] | 2010 | SV2 Mediates Entry of Tetanus Neurotoxin into Central Neurons |
“Nanobacteria” are nanometer-scale spherical and ovoid particles which have spurred one of the biggest controversies in modern microbiology . Their biological nature has been severely challenged by both geologists and microbiologists , with opinions ranging from considering them crystal structures to new life forms . Although the nature of these autonomously replicating particles is still under debate , their role in several calcification-related diseases has been reported . In order to gain better insights on this calciferous agent , we performed a large-scale project , including the analysis of “nanobacteria” susceptibility to physical and chemical compounds as well as the comprehensive nucleotide , biochemical , proteomic , and antigenic analysis of these particles . Our results definitively ruled out the existence of “nanobacteria” as living organisms and pointed out the paradoxical role of fetuin ( an anti-mineralization protein ) in the formation of these self-propagating mineral complexes which we propose to call “nanons . ” The presence of fetuin within renal calculi was also evidenced , suggesting its role as a hydroxyapatite nucleating factor .
“Nanobacteria” are mysterious particles that have spurred one of the biggest controversies in modern microbiology [1–3] . First discovered by geologists as 100 nm coccoid particles present on mineral surfaces [4] , such structures were later found in human and cow blood as well as in commercial cell culture serum [5] . The culturability of “nanobacteria” was then reported by Kajander's team [6] who established a link between these particles and kidney stone formation [7] . The data described by Cisar's group reached completely opposite conclusions as Kajander's original assertion considering nanobacteria as living microorganisms [2] . In contrast to what would be expected from growth of a living entity , Cisar et al . failed to detect nucleic acids and suggested that observed biomineralization may be initiated by non living macromolecules generating self propagating microcrystalline apatite . In the last few years , these calcifying nanoparticles have been associated with several human diseases including polycystic kidney disease , renal calculi , and chronic prostatitis [8] . However , despite the various pathological disorders they cause , whether nanobacteria are living or nonliving cells is still under debate [9] . Here , a comprehensive analysis was undertaken with the Nanobacterium sp . strain Seralab 901045 provided by O . Kajander ( Nanobac Oy , Kuopio , Finland ) , in order to gain better insights on such a propagating calcifying agent putatively endowed with pathogenic properties . To address this question , several features of the “nanobacteria” previously reported were examined in detail including their propagation conditions , susceptibility to various chemical and physical treatments and their effect on eukaryotic cell viability . Their nucleic acid and proteomic content was also carefully analyzed . The antigenic properties of “nanobacteria” were investigated by immunization of mice with such particles and the specificity of the response was determined against bacteria from Rickettsia , Coxiella and Bartonella genera . Our data provided evidence that the particles previously attributed to “nanobacteria” are self-propagating mineral-fetuin complexes that we propose to call “nanons . ”
After a 10-d incubation in DMEM supplemented with 10% heat-decomplemented fetal calf serum , nanons formed a clearly visible film at the bottom of culture flask . Subculturing was done every 14 d at a 1:10 dilution . Also , serum-free DMEM sustained both growth and subculture of nanons following the same procedure . Under these conditions , the ability to form particles was transferable for 3 to 5 passages . In contrast , nanons did not grow in vitamin-free DMEM . However , addition of any one of the 11 vitamins comprised in this medium restored growth . Growth on Loeffler medium was not successful . Hoechst 33342 did not stain nanons . In contrast , small fluorescent particles were visible after acridine orange and DAPI staining . Among the 22 PCR assays targeting 16S rRNA gene , 8 yielded amplicons . Their DNA sequence was found to be 99% similar to that of Arthrobacter luteolus in 2 cases and 97% similar to that of Bacillus sp . in other ones . Two amplicons exhibited similarities with Stenotrophomonas maltophila ( 99% ) and Pseudomonas sp . ( 99% ) , respectively . Mixed sequences were obtained in the remaining 2 cases . All attempts to extract RNA failed ( not shown ) . Exposure of nanons to DNAse ( 10 μg/mL ) or RNAse ( 16 μg/mL ) did not alter particle formation . In contrast , we observed that nanon propagation was suppressed following UV or 32 Ggy gamma ray irradiation . The nanon monolayer was also destroyed after a 3- to 5-s contact with either trypsin ( 0 . 5% ) or EDTA ( 50 mM ) as well as under acidic conditions ( DMEM pH 5 ) , as shown in real-time movies ( http://ifr48 . timone . univ-mrs . fr/files/Nanons_Videos/Nanons_films_14 . 06 . 07 . zip ) . In contrast , alkalization of the buffer in the 8 to 10 range and exposure to proteinase K ( 100 μg/mL ) failed to alter the growth . We also investigated the effect of some antibiotics including gentamicine , cotrimoxazole , doxycycline and oxytetracycline hydrochloride . While a subtle decrease in the number of particles counted at day 10 was noted for the last two compounds , we failed to observe a significant inhibition of nanon propagation as compared to the control . Nanons did not stain with Gimenez or Gram stains . When observed by electron microscopy , they were visualized by a central core , a first ring and a second electron-dense ring ( Figure 1A ) . Scanning microscopy indicates a double population , including a first population with Gaussian repartition and a 431 ± 10 nm size with larger nanons appearing twice as large as budding nanons . These particles exhibited antigenic properties . Thus , sera of mice immunized with nanons exhibited a positive signal when tested by immunofluorescence while any recognition was detected with pre-immune sera ( Figure 1B ) . These antibodies were specific since they failed to recognize microorganisms from the genus Rickettsia , Bartonella and Coxiella when tested at the same dilution ( not shown ) . Nanons had a deleterious effect on amoebae . Thus , when these eukaryotic cells were cultivated in the presence of nanons , we observed less living trophozoïtes ( p = 0 . 008 , Kruskal-Wallis test ) and cysts ( p = 0 . 001 , Anova test ) . The number of dead trophozoïtes increased ( p = 0 . 05 , Kruskal-Wallis test ) in comparison with nanon-free amoebae . However , the major difference was the larger number of dead cysts observed in amoebae cultivated with nanons ( p < 0 . 0001 ) . Nanons also had a deleterious effect on THP-1 and HeLa cells . In the presence of 10% fetal calf serum , nanons did not affect the viability of each cell type . In the presence of 2% fetal calf serum , nanons increased cell death after 24 h in both THP-1 cells ( 4% ± 1% vs 12% ± 2% , p < 0 . 002 ) and HeLa cells ( 15% ± 3% vs 30% ± 5% , p < 0 . 001 ) . Beyond 24 h , nanons did not modify the survival pattern of THP-1 cells but they amplified cell death in HeLa cells ( 22% ± 3% vs 58% ± 9% , p < 0 . 0001 after 3 d ) . Analysis of nanons by SDS-PAGE after mineral phase dissolution with EDTA revealed that proteins were present in our sample ( Figure 2A ) . Three major bands with an apparent MW of 70 , 65 and 30 kDa were observed . This profile was close to that reported for biofilms-associated macromolecules generated from saliva samples [2] . Here , the DMEM medium used for propagation was not supplemented with bovine serum and was certified free of proteic contaminants ( Gibco-BRL ) . Accordingly , when the same experiment was performed using DMEM concentrated by the lyophilization process , we failed to observe these bands ( not shown ) . A Western blot analysis was then performed using sera of immunized mice . Results obtained ( Figure 2B ) showed that both the 70- and 65-kDa bands were antigenically positive when probed with anti-nanon antibodies in contrast with the 30-kDa product . These results suggested that corresponding proteins were specific from the nanon particles . In order to identify the proteins present in nanon samples , different strategies were applied . First , a MALDI-TOF mass spectrometry analysis was carried out on the bands excised from an SDS-PAGE silver-stained gel . The mass spectrum shown in Figure 3A corresponds to the trypsin digest of the protein with an observed MW of 65 kDa . Only peptide masses obtained from this spectrum and differing by less than 0 . 1 Da than those determined by a virtual tryptic digestion of the candidate proteins were considered . Using such parameters , the highest identification score corresponded to the bovine fetuin precursor ( gi_A35714 ) . As illustrated in Figure 3B , 27% coverage of this theoretical protein sequence was obtained . This result was confirmed by direct N-terminal sequencing which was attempted in parallel and allowed identification of 12 residues of the primary protein sequence which are QPLDPVAGYKET . With the exception of the first and the last amino acid residue , this peptide sequence shares 100% homology with the previously identified fetuin . This proteomic approach also led to the identification of the 30-kDa protein as the apolipoprotein A-1 precursor ( coverage 53% ) which has a predicted molecular weight of 30 . 2 kDa . Interestingly , we observed a close pattern for both nanon extracts and commercially available fetuin following silver staining ( Figure 4A ) . On a 10% SDS-PAGE , the latter has an apparent molecular weight higher than that expected and several bands ranging from 64 to 70 kDa are observed ( line 2 , Figure 4A ) . The presence of fetuin in nanons was next examined by Western blotting . As shown in Figure 4B both nanons and purified fetuin were recognized by anti-fetuin antibodies . A similar profile was observed with anti-nanon antibodies . In contrast , the monoclonal anti-nanobacteria antibodies ( NanoVision 8D10 , Nanobac , Kuopio , Finland ) gave a less specific pattern ( Protocol S1 and Figure S1 ) . Post- and pre-embedding immunogold staining of nanons indicated that fetuin was located both inside the particles and at their surface ( Figure 5 ) . The amount of alpha-fetuin , estimated through an ELISA-based approach , ranged from 31–227 pg in the whole nanon particules to 44–117 pg in the corresponding serum-free DMEM culture supernatants . Similarly , the amount of total proteins , glucose and of several minerals including sodium , potassium , chlorine and calcium were found identical in both fractions . In contrast , other biochemical analyses indicated that nanon particles were depleted in phosphorus ( 0 . 7 mmol/L versus 3 . 6 mmol/L ) and CO2 ( 6 mmol/L versus 23 mmol/L ) in comparison with supernatants . The soluble material obtained from human kidney stone extracts showed well-defined bands when analyzed by SDS-PAGE ( Figure 6 ) . Western blot analysis demonstrated that the major soluble component present in such samples is recognized by the anti-nanon antibodies . A strong pattern of recognition was also obtained with anti-human fetuin antibodies . Accordingly , the fetuin concentration measured in 5 specimens ranged from <0 . 1 pg/ml to 0 . 84 pg/ml .
Consistent with data published by Cisar et al . [2] , we failed to clearly demonstrate the presence of nucleic acids in nanons . Indeed , we observed discrepant results using various nucleic acid stains , such as nanons being easily stained by orange acridine but poorly stained by DAPI and Hoechst 33342 . Also , the growth of nanons was not altered in presence of either DNAse or RNAse . Finally , 16S rRNA gene amplification and sequencing most often identified α-proteobacteria and γ-proteobacteria , both known to be waterborne contaminants in PCR-based experiments [10] . 16S rRNA gene sequence of Nanobacterium sanguineum ( GenBank accession number X98418 ) and Nanobacterium sp . ( GenBank accession number X98419 ) have been previously found to be indistinguishable from those of Phyllobacterium mysinecearum , a microorganism identified as a source of contaminating 16S rDNA in PCR studies [2] . It is thought that previously reported 16S DNA amplifications by PCR using “nanobacteria” as template result from PCR artifacts [2 , 11] . These data led us to hypothesize that nanons might have the ability to trap any contaminant 16S rDNA fragment present in the medium or environment rather than displaying original sequences from an emerging microorganism . All together , the data suggest that the nanon is a nucleic-acid free , transferable biological entity . The spectacular dissolution of nanons exposed to the calcium chelator EDTA or to acidic medium confirmed that the formation of these particles is related to a calcification process [1] . As previously mentioned [2] , such a demineralization could explain the inhibition of nanon growth observed with some antibiotics [12] . When tested at concentrations comparable with the prescription in human beings [13] , we failed to detect a significant inhibitory effect of antibiotics against nanon growth . Apatite is the major constituent of nanons but these particles are also composed of other unidentified compounds . When injected into mice , nanons induced a specific immune response . This is consistent with a recent report about a scientist who was accidentally exposed to a splash of nanobacteria in the eye in 1993 and who still exhibits a high IgG titer against these particles [14] . Such antigenic feature cannot be related to apatite . Indeed , the non-immunogenic nature of this biomineral supports its use for mineralizing applications in preventive and restorative dentistry . The existence of apatite-protein complexes within nanons was related a few years ago but the nanon-associated proteins were not characterized [15] . The putative role of one or several proteins in nanon propagation is also supported by the inhibitory effects of gamma or UV irradiation . Indeed , exposure of proteic molecules to these treatments can provoke secondary conformational changes of proteins which alter their functional properties in turn [16] . This hypothesis was emphasized by the inhibitory activity of trypsin evidenced here for the first time . SDS-PAGE analysis of nanons was achieved from samples grown in DMEM without serum for at least 2 passages . This procedure allowed discarding of any adsorption of seric proteins to apatite . The profile of silver-stained proteins was found similar to that reported by Cisar et al . , who worked on “nanobacteria” originating from saliva [2] . Based on the discrepancy between the low number of proteins detected and that expected from a putative microbial proteome , these proteins were considered as salivary contaminants . We then found that the 65-kDa protein was recognized by anti-nanon antibodies . Interestingly , recent experiments aimed to measure 35S-methionine incorporation in nanon cultures showed that one protein with similar size was primarily labeled during the course of replication [17] . By using two different approaches used here , this protein was identified as fetuin . When commercially available purified fetuin was analysed by SDS-PAGE , several bands were visualized . This migration profile appeared closely related to that in nanons . The observed MW of the largest protein was higher than the theoretical fetuin MW of 48 kDa . Such a shift in migration was previously described and most probably results from glycosylations [18] . Fetuin ( α2-Heremans Schmid glycoprotein ) is indeed a plasma protein exhibiting several secondary modifications including N- and O-glycosylations . Such post-translational modifications could also afford the recognition of nanons by chlamydial LPS antibodies [19] . The presence of fetuin within nanons was further confirmed by a Western blot and immunogold staining . In addition , we noticed that purified fetuin was recognized by anti-nanon antibodies . The recovery of nanons from human calculi and the observation of nanoforms in calcified arterial tissues [8] are compatible with the presence of fetuin in such samples . Accordingly , this protein was observed in calcified vascular smooth muscle cells [20] . Several studies aimed at identifying renal stone proteins hypothesized to play a role in stone formation were achieved [21] . Here , we provided evidence for the first time for fetuin association with renal stones . As recently reviewed [9] , while associated with several diseases , nothing was done to identify or characterize the novel form of life known as “nanobacteria . ” Here , we demonstrated these particles are self-propagated mineral protein complex containing fetuin as the major biological component which we propose to call nanons . The serum protein fetuin was described as a potent inhibitor of apatite formation and of calcium phosphate mineral precipitation [22] . Its inhibitory activity was shown to be mediated by the transient formation of a fetuin-mineral complex [23] also described as colloidal calciprotein particles containing fetuin , calcium and phosphate [24] . In fact , the comparison of the electron microscopy structure of these 30- to 150-nm particles [24] to that of nanons indicated that these particles are closely related . This relationship was never pointed out before . Recently , a strong correlation between the serum levels of the fetuin-mineral complex and arterial calcifications induced by vitamin D in a rat was demonstrated , supporting the blood-borne theory of artery calcification [25] . However , the biochemical basis of these findings , which are somewhere in contradiction with the inhibitory known effect of fetuin against mineralization , were not clarified . The propagation of nanons in vitro suggests that fetuin should promote hydroxyapatite nucleation . Accordingly , it was demonstrated that polyanionic proteins can nucleate crystal formation when adsorbed onto a rigid substrate while they exert an inhibitory effect when free in solution [26] . We can also hypothesize that the conformational change of the fetuin protein , equivalent to that observed in prions , can occur [27] . This highly speculative hypothesis leads to a new “pathogenic” fetuin isoform able to induce hydroxyapatite crystallization and to promote calcification . In this respect , we noticed that the prion preparations are often contaminated by nucleic acids [28] as observed for nanons . It will be highly interesting in future studies to gain a greater understanding of the mechanisms by which fetuin promote mineralization . This should be helpful to design future therapeutic strategies for the treatment of kidney stones and other pathological states to which “nanobacteria” have been associated [8] .
Nanobacterium sp . strain Seralab 901045 ( herein designed as “nanons” ) provided by O . Kajander ( Nanobac Oy , Kuopio , Finland ) was propagated in DMEM ( Invitrogen , Paisley , UK ) with 10% heat-decomplemented fetal calf serum ( Gibco-BRL , Invitrogen , Paisley , UK ) in sterile plastic culture flasks ( Becton Dickinson , Le Pont de Claix , France ) at 37°C under room atmosphere . Subculture was done every 14 d by doing a 1:10 dilution in this complete culture medium . Growth was then attempted in Loeffler medium ( BioMérieux , Marcy-l'Etoile , France ) and in fetal calf serum-free DMEM . Determination of a minimal culture medium was assayed by subculturing nanons into a chemically defined medium obtained by stepwise addition of every one of the DMEM constituents to sterile distilled water up to obtaining the whole medium reconstitution . The 16S rRNA gene was amplified by PCR using nanon-extracted DNA as a template ( QIAamp tissue kit , QIAGEN , Hilden , Germany ) and with universal primers fD1 and rp2 [29] ( Eurogentec , Seraing , Belgium ) . Sequencing of amplified products was carried out as previously described [30] . The resulting sequences were compared with those available in the GenBank database using the gapped BLASTN 2 . 0 . 5 program through the National Center for Biotechnology Information server . The GenBank accession numbers for the sequences determined in this work were EF585587 to EF585591 . The growth of nanons was studied in serum-free DMEM after UV irradiation and 35 Ggy gamma-irradiation ( Isotron , Marseille , France ) . In these experiments , nanon growth was detected by weekly observation of a white film at the bottom of the flask and by a smear preparation of acridine orange staining . Every experiment was done three times . The growth of nanons was studied in serum-free DMEM after a 60 min incubation at 37°C in the presence of either 50 μg/mL DNAse ( Invitrogen ) , 50 μg/mL RNAse ( Roche Diagnostics , Mannheim , Germany ) , 50 mM EDTA ( Sigma-Aldrich , Saint-Quentin Fallavier , France ) , 0 . 5% trypsin ( Gibco-BRL ) or 100 μg/mL proteinase K ( Eurobio , Les Ulis , France ) . Growth was studied in parallel after heat-inactivation ( 10 min at 70°C ) of the above-mentioned enzymes . Enzyme-treated nanons were plated onto a 150 cm2 culture flask and incubated for 4 wk at 37°C in the presence of serum-free DMEM . Growth of nanons was detected by the weekly observation of a white film on the bottom of the flask and by a smear preparation of acridine orange staining . Growth of nanons at various pH was determined in sterile 96-well microtitration plates . The wells were filled with 100 μl of nanons resuspended in serum-free DMEM ( 106 particles/mL ) and the microtitration plate was maintained at 37°C for 10 d , i . e . , up to the formation of a visible white film at the bottom of each well . The pH of both the serum-free DMEM and of the nanon culture supernatant was measured . This supernatant was replaced by fresh DMEM supplemented with the appropriate volume of sterile HCl or NaOH in order to yield DMEM ranging from pH 4 to 10 . Wells were observed daily for the presence of a white film and nanons were observed by acridine orange staining of a cytospin smear and counted by using the Microcyte cell flow cytometer ( CellD , Roquemaure , France ) after a 5-d incubation at 37°C . Each condition was tested three times . Antibiotics used in this study were doxycycline ( Pfizer , Neuilly , France ) at 10 μg/ml , oxytetracycline hydrochloride ( Sigma-Aldrich ) at 10 μg/ml , gentamicin ( Dakota Pharm , Creteil , France ) at 10 μg/ml and cotrimoxazole ( Roche Diagnostics ) at 80 μg/ml of sulfamethoxazole . Growth of nanons was performed for 10 d as described above before being harvested and diluted 1:20 in DMEM . Diluted nanons were subcultured in a 25 cm2 culture flask supplemented or not with antibiotics . For each condition , nanons were numerated by using the Microcyte apparatus at day 0 and after 3 , 7 and 10 d of incubation at 37°C to monitor the kinetic of growth . Experiments were carried out twice . Cells: THP-1 monocytic cells and HeLa cells were cultured in RPMI 1640 containing 2% or 10% fetal calf serum , 25 mM HEPES , 2 mM L-glutamine , 100 U/ml penicillin , and 100 μg/ml streptomycin ( Invitrogen ) . THP1 cells and HeLa cells ( 5 × 105 cells/ml ) were grown in presence of nanons for different periods . After washing to remove unbound nanons , HeLa cells were incubated with 500 μL trypsin/EDTA for 2 min . The viability of suspended THP1 and Hela cells was determined by trypan blue dye test exclusion . Results are given as the mean ± SE . Statistical analysis was conducted with analysis of variance and regression analysis . Differences were considered significant when p < 0 . 05 . Amoeba: In amoeba co-culture experiments , 2 wells containing 2 mL of a suspension of Acanthamoeba polyphaga amoebae ( 105 cell/ml ) in Page's Amoebal Saline ( PAS ) , prepared as described [31] , were inoculated with 100 μL of a suspension of nanons in the same medium . Four control wells inoculated with 100 μl of PAS were used as controls . Viability of trophozoites and kysts were determined by trypan blue exclusion for 10 d . Nanons were tentatively stained by using Gimenez , Gram and orange acridine staining , as well as with two DNA dyes , namely Hoechst 33342 and DAPI ( Molecular Probes , Eugene , Oregon ) . For transmission electron microscopy , nanons were pelleted by centrifugation and prefixed for 1 h at room temperature in 2 . 5% glutaraldehyde ( Electron Microscopy Sciences , Hatfield , PA , USA ) in phosphate buffer saline ( PBS ) , pH 7 . 2 . After a 1-h washing in PBS , they were fixed for 1 h at room temperature with 1% osmium tetroxide , dehydrated through increasing concentrations ( 25% to 100% ) of ethanol , and embedded in Epon 812 resin ( Electron Microscopy Sciences ) . Thin sections were cut and poststained with a saturated solution of methanol-uranyl acetate and lead citrate in water before examination on a Philips Morgagni 268 D electron microscope ( FEI Compagny France , Limeil-Brevannes , France ) . Polyclonal anti-nanon antibodies: Two 6-wk-old female Balb/C mice were inoculated intraperitoneally with 20 μg of nanons emulsified in Freund's complete adjuvant ( 1:1 , v/v ) . Two booster doses were given in Freund's incomplete adjuvant at 14-d intervals . Bleeding was performed 2 wk after the last immunisation under Forene ( Abbott , Rungis , France ) anaesthesia and serum separated by centrifugation was stored at 4°C until use . Polyclonal anti-bovine fetuin antibodies: One 6-wk-old female Balb/C mouse was inoculated intraperitoneally with 10 μg bovin fetuin ( Sigma-Aldrich ) mixed with 400 μg aluminium hydroxide and 10 μg CpG as previously described [32] . Following two booster doses at 14-d intervals , bleeding was performed as described above . For immunofluorescence assays , nanons were deposited on slides with a pen nib then air dried and fixed in acetone for 2 min . Wells were saturated by a 30-min incubation with PBS supplemented with 5% bovine serum albumin ( BSA ) and overlaid with 30 μl serum dilutions in PBS-BSA 3% . Bound antibodies were detected with a goat F ( ab′ ) 2 fragment anti-mouse IgG-FITC conjugated ( Beckman-Coulter , Marseille , France ) diluted 1:400 in PBS-BSA 3% containing 0 . 2% Evans blue ( Bio-Mérieux , Marcy l'Etoile , France ) . All incubations were performed for 30 min in a moist chamber at 37°C and were followed by a washing in PBS two times of 10 min each , 5 min rinsing in demineralised water and drying in air . Slides were mounted with fluoprep ( Bio Mérieux ) then examined under an Olympus BX51 epifluorescence microscope at ×400 magnification . Sera of healthy mice were used as negative controls . Two flasks ( 175 cm2 ) containing nanons grown in DMEM without serum supplementation were scraped and the resulting suspension was centrifuged ( 10 , 000g , 30 min , 4°C ) . The pellet was resuspended in 300 μl EDTA 1 M , pH 8 . 9 and dialysed for 5 h against 2 l Tris-HCl 50 mM , pH 8 . 0 , EDTA 2 mM and using Spectra/Por 2 Dialysis Membranes ( 12–14 kDa molecular weight cutoff , Spectrum Laboratories , CA , USA ) . Final protein concentration of the sample was determined using a BioRad Protein Assay ( Hercules , CA , USA ) and estimated to be around 50 μg/flask . Nanons or bovine fetuin were fractionated on 10% polyacrylamide gels before silver staining [33] . Alternatively , separated proteins were transferred onto a nitrocellulose membrane ( Trans-blot Transfer Medium , Biorad , Hercules , CA , USA ) at 100 V for 1 h . Following a 1-h incubation with 5 % non-fat dry milk in PBS-Tween 20 ( 0 . 2% ) as the blocking reagent , membranes were probed for 1 h with mice polyclonal anti-nanon antibodies or anti-bovine fetuin antibodies . Membranes were then washed 3 times for 10 min in PBS-Tween and incubated for an additional hour with horseradish peroxidase-conjugated goat anti-mouse secondary antibodies ( Amersham Biosciences , 1:1 , 000 ) . After washings , blots were revealed by chemiluminescence assays ( ECL , Amersham Biosciences ) . The resulting signal was detected on Hyperfilm ECL ( Amersham Biosciences ) by using an automated film processor ( Hyperprocessor , Amersham Biosciences ) . Major bands observed on silver-stained SDS-PAGE were excised from the gels and subjected to in-gel digestion with trypsin ( Sequencing grade modified porcine trypsine; Promega , Madison , WI , USA ) as described [33] . Resulting tryptic peptides were then analysed by a matrix-assisted laser desorption ionization-time of flight ( MALDI-TOF ) mass spectrometer ( Bruker Ultraflex spectrometer , Bruker Daltonics , Wissembourg , France ) previously calibrated using autolytic peptides from trypsin . The peptide mass data obtained were analysed using Mascot software available online ( http://www . expasy . org/tools/peptident . html ) . In some experiments , separated proteins were transferred onto an Immobilon-P PVDF-membrane ( Millipore , Bedford , MA , USA ) in semi-dry blotter equipment ( Hoefer TE 77 , Amersham Biosciences ) for 2 h at a constant current of 400 mA , 80 W . Major bands visualised by staining with 0 . 02% Ponceau S red in 1% acetic acid were excised and stored in methanol for subsequent N amino acid sequence analysis by automated Edman degradation carried out using a Model 476A pulsed liquid protein sequencer ( Applied Biosystems ) . The concentrations of sodium , potassium , chlorine , CO2 , total proteins , glucose , calcium , and phosphorus were measured in parallel into serum-free DMEM nanon supernatant and nanon particles by using the Beckman LX apparatus for biochemical analyses in clinical biochemistry . Before assays , culture supernatants and nanons were lyophilized and resuspended into 1 ml of DMEM or distilled water . The concentration of alpha-fetuin was determined from pellets and supernatants of 5 distinct nanon cultures by using a home-developed enzyme-linked immunosorbent assay ( ELISA ) technique . The concentration of alpha-fetuin was also estimated from the renal calculi weighed and then ground using ultrasonic sounds and mixed with 500 μL of Tween 20 . The ELISA test was carried out on the supernatant obtained after centrifugation ( 800g , 10 min ) . Alpha-fetuin polyclonal antibodies , streptavidine-coupled antibodies and recombinant alpha-fetuin used in this assay were from R&D Systems Europe ( Lille , France ) . The optical density was measured at 450 nm ( EMS reader ) procedure . The limit of detection was of 0 . 1 pg . The post-embedding immunogold labeling of nanons was done following a method previously described [34] with some modifications . Briefly , nanon pellets were fixed overnight in 2% glutaraldehyde and were then treated with 1% ( v/v ) osmium tetroxyde before dehydratation through an ethanol series and infiltration with EPON 812 resin . Polymerization was for 72 h at 60°C . Ultrathin sections ( 70 nm ) were cut using an Ultramicrotome ( ULTRACUT E Leica ) , collected on grids without formvar ( Electron Microscopy Sciences ) and then incubated in PBS buffer supplemented with 0 . 2% BSA ( Roche Diagnostics ) . The sections were then treated with 0 . 05 M lysine ( Sigma-Aldrich ) in order to neutralize putative residual aldehyde groups . After repeated washings with PBS , the sections were then incubated for 3 h at 37°C in a humidified atmosphere with mouse anti-nanon antibodies ( 1:200 ) or anti-fetuin antibodies ( 1:100 ) . The secondary reaction used goat anti-mouse antibodies conjugated to 25 nm colloidal gold particles ( Aurion EM Reagents , the Netherlands ) followed by washings with PBS and H2O . Gold particles were visualized using the R-GENT SE-EM silver enhancement kit ( Aurion ) , following the instructions of the manufacturer . Labeled sections were viewed with a Philips electron microscope ( MORGAGNI 268D ) at 80 kV . Whole-mount immunogold staining was carried out on nanons deposited on 400 mesh formvar coated Cu-grids ( Electron Microscopy Sciences ) , fixed with 1% glutaraldehyde and treated with 50 mM NH4Cl . Following a preincubation step in a solution containing 1% normal goat serum ( NGS , Aurion ) , 1% BSA and 0 . 2% Tween-20 , nanons were labeled by sequential incubations with the mouse anti-fetuin antibodies ( 1:400 ) followed by reaction with a secondary goat-anti-mouse antibody conjugated with 10 nm colloidal gold particles ( Aurion ) . After washings and an aldehyde-fixation , a treatment with 1% phosphotungstic acid pH 7 . 2 ( ICN Biomedicals ) was done to produce an intense electron-opaque stain and samples were viewed as described above . Five human kidney stones were suspended in 100 μl EGTA 0 . 5 M , pH 9 . 0 and mechanically broken by sonification . Each sample was then diluted in half in Laemmli buffer and heated 5 min at 95°C . The unbroken stone pieces were removed by centrifugation ( 10 , 000g , 10 min ) and corresponding supernatants were processed for immunoblot analyses using either anti-nanon or anti-fetuin antibodies produced in the laboratory . | In the last decade , the exact nature of nanobacteria was one of the most controversial of scientific questions . An audacious theory proposed the existence of nanobacteria , initially discovered in Italian hot spring deposits , as a new life form responsible for a wide range of diseases in humans , thus qualifying them as new agents of emerging infectious diseases . The community of microbiologists remained therefore skeptical about the fact that such structures , 100 times smaller than bacteria and highly resistant to heat and other treatments that would normally kill the latter , could be living entities fully capable of self-replication . Other scientists wondered if they might be an unusual form of crystal rather than micro-organisms . The comprehensive characterization of nanobacteria was the focus of our study . Our results definitively ruled out the existence of nanobacteria as living entities and revealed that they correspond to self-propagating mineral-fetuin complexes that we called “nanons . ” | [
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"none",
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] | 2008 | Nanobacteria Are Mineralo Fetuin Complexes |
The stress-activated protein kinase Gcn2 regulates protein synthesis by phosphorylation of translation initiation factor eIF2α , from yeast to mammals . The Gcn2 kinase domain ( KD ) is inherently inactive and requires allosteric stimulation by adjoining regulatory domains . Gcn2 contains a pseudokinase domain ( YKD ) required for high-level eIF2α phosphorylation in amino acid starved yeast cells; however , the role of the YKD in KD activation was unknown . We isolated substitutions of evolutionarily conserved YKD amino acids that impair Gcn2 activation without reducing binding of the activating ligand , uncharged tRNA , to the histidyl-tRNA synthetase-related domain of Gcn2 . Several such Gcn− substitutions cluster in predicted helices E and I ( αE and αI ) of the YKD . We also identified Gcd− substitutions , evoking constitutive activation of Gcn2 , mapping in αI of the YKD . Interestingly , αI Gcd− substitutions enhance YKD-KD interactions in vitro , whereas Gcn− substitutions in αE and αI suppress both this effect and the constitutive activation of Gcn2 conferred by YKD Gcd− substitutions . These findings indicate that the YKD interacts directly with the KD for activation of kinase function and identify likely sites of direct YKD-KD contact . We propose that tRNA binding to the HisRS domain evokes a conformational change that increases access of the YKD to sites of allosteric activation in the adjoining KD .
Eukaryotic cells harbor stress-activated protein kinases that down-regulate protein synthesis and simultaneously up-regulate transcriptional activators at the translational level . This dual response allows cells to reduce bulk protein synthesis while re-programming transcription to favor expression of gene products with functions in stress management . The key target of these kinases is Ser-51 of the α-subunit of translation initiation factor 2 ( eIF2α ) . The eIF2 bound to GTP transfers methionyl-initiator tRNA to the 40S ribosomal subunit to produce the 43S preinitiation complex at the beginning of the translation initiation pathway . On subsequent recognition of the AUG codon in mRNA by initiator tRNA , the GTP is hydrolyzed and eIF2-GDP is released from the 40S subunit for recycling to eIF2-GTP by the guanine nucleotide exchange factor eIF2B . Ser-51 phosphorylation converts eIF2 into an inhibitor of eIF2B , reducing the concentration of eIF2-GTP and delaying new rounds of translation initiation . The reduced eIF2-GTP level stimulates translation of GCN4 mRNA in yeast and ATF4 mRNA in mammals , both encoding transcriptional activators of stress genes , by allowing 43S complexes to circumvent small open reading frames present in their mRNA leaders that would normally block initiation at the protein coding sequences for Gcn4/Atf4 [1] , [2] ( reviewed in [3] ) . The four mammalian eIF2α kinases , PKR , HRI , PERK , and Gcn2 , have conserved kinase domains ( KDs ) but unique regulatory regions that mediate activation by distinct stress signals . PKR is activated by dsRNA generated during virus infection , and represents a key component of the antiviral defense mechanism , whereas Gcn2 is activated by uncharged tRNA that accumulates in amino acid-starved cells and most likely other stress conditions . The ensuing induction of Gcn4 in yeast evokes transcriptional activation of nearly all amino acid biosynthetic enzymes subject to the general amino acid control with attendant up-regulation of amino acid biosynthesis ( reviewed in [3] ) . Translational control by mammalian Gcn2 is important for lipid homeostasis under starvation conditions [4] , in behavioral aversion to amino acid-deficient diets [5] , and in learning and memory [6] . It has also been implicated in tumor cell survival , both innate and T-cell mediated immune responses , and DNA repair upon UV irradiation ( reviewed in [7] ) . Because eIF2α kinases act by inhibiting translation , their functions must be tightly regulated to allow high-level kinase activity only under appropriate stress conditions . We showed previously that the Gcn2 KD is intrinsically inert and depends on stimulatory interactions with adjacent domains in the protein to achieve an active conformation [8] . This latency of Gcn2 depends on a rigid hinge connecting the N- and C-lobes , which promotes a partially closed active site cleft and occluded ATP-binding pocket , and a non-productive orientation of helix αC in the N-lobe that impedes proper disposition of a critical Lys reside that positions the ATP phosphates for catalysis [9] , [10] . Binding of uncharged tRNA to a region C-terminal to the KD , related in sequence to the enzyme histidyl-tRNA synthetase ( HisRS ) , which aminoacylates tRNAHis , is required to activate Gcn2 in amino acid-starved cells [11] , [12] , [13] , [14] . An N-terminal segment in the HisRS domain that interacts with a portion of the KD containing the hinge is required for kinase activation [15] , suggesting that tRNA binding alters the HisRS-KD interface to evoke an active conformation of the KD . As in other kinases , autophosphorylation of the activation loop of the KD is additionally required to activate Gcn2 [15] , [16] , as is dimerization of the KD [17] in a back-to-back orientation described for the active KD dimer of PKR [18] . The KD , HisRS region , and extreme C-terminal domain of Gcn2 ( CTD ) are capable of self-interaction as isolated domains; however , only the CTD is essential for dimerization and attendant activation of full-length Gcn2 [19] , [20] . Since the HisRS-related domain and attendant tRNA-binding by Gcn2 are dispensable for dimerization , Gcn2 likely dimerizes constitutively through CTD self-interaction [19] . It is possible that the mode of KD dimerization switches from the antiparallel orientation seen in the crystal structure of the Gcn2 KD in an inactive conformation [9] to the parallel , PKR-like mode of dimerization deduced from genetic experiments [17] to represent the active conformation for Gcn2 [18] . In addition to dimerization , the CTD mediates ribosome association of Gcn2 [21] , which is critical for activation of Gcn2 by uncharged tRNA in vivo [22] . The CTD also appears to interact with the KD in a manner that impedes kinase activation [14] , [15] , suggesting that dissociation of the CTD from the KD is a key step in the kinase activation pathway . The CTD further mediates an interaction with translation elongation factor eEF1A that appears to inhibit Gcn2 function in nonstarved cells and can be overcome by uncharged tRNA [23] . Activation of Gcn2 by uncharged tRNA additionally requires the functions of 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 to stimulate Gcn2 kinase function in yeast cells [24] , [25] , [26] , [27] , [28] . These findings , plus the fact that overexpression of translation elongation factor 3 impedes Gcn2 activation in vivo [29] , support a model in which Gcn2 is activated by uncharged tRNA that binds first to the decoding center of a translating ribosome and is then transferred to the HisRS domain in Gcn2 , and that Gcn1/Gcn20 stimulate one or both of these binding reactions involving uncharged tRNA [28] . Gcn2 contains a region N-terminal to the KD ( aa291-538 ) that displays strong sequence similarity to authentic kinases , but lacks critical residues required for binding ATP and catalysis , and this “pseudokinase” domain ( YKD ) in mouse Gcn2 was found incapable of binding ATP or Mg+2 in vitro [30] . Studies of YKDs in other systems have indicated functions in regulating authentic KDs , as allosteric modulators of active KD conformation or as scaffolding molecules that promote assembly of higher-order kinase signaling complexes . Gcn2 and the Janus tyrosine kinase ( JAK ) family provide the only known instances where a YKD and KD reside in the same polypeptide . The YKD in the JAKs appears to maintain latency of the KD in the absence of cytokines; however , the molecular mechanism of YKD regulatory function is not well understood ( reviewed in [30] , [31] ) . Elimination of the YKD from yeast Gcn2 abolishes activation of Gcn2 in amino acid starved cells and impairs the kinase activity of Gcn2 in vitro [13] , [32] without affecting ribosome-binding [21] , dimerization by full-length Gcn2 [19] or Gcn2 interaction with positive effectors Gcn1/Gcn20 [26] . Thus , the YKD seems to be required primarily for activation of the latent KD in Gcn2 by uncharged tRNA . The isolated yeast Gcn2 YKD can interact directly in vitro with the Gcn2 KD and CTD , and the YKD was shown to be required for high-level association of full-length Gcn2 with the isolated Gcn2 KD fused to LexA in vivo , presumably via YKD·LexA-KD interactions [19] . Hence , we hypothesized that the YKD allosterically activates Gcn2 via direct interaction with the KD . To test this hypothesis rigorously , we have produced a structural model of the Gcn2 YKD based on its homology to authentic kinases , and made substitutions of residues predicted to be both surface-exposed and conserved among the YKDs of Gcn2 from different fungi . In this way , we identified ( Gcn− ) substitutions that impair Gcn2 activation under amino acid starvation conditions that , interestingly , appear to cluster on one face of the predicted tertiary structure of the YKD . We also conducted random mutagenesis of the YKD and identified ( Gcd− ) substitutions that confer constitutive activation of Gcn2 function and derepression of Gcn4 target genes involved in amino acid biosynthesis in vivo . Biochemical analysis of exemplar Gcn− and Gcd− substitutions provide strong evidence that the YKD directly interacts with the KD within the Gcn2 dimer to evoke allosteric activation of eIF2α kinase function in amino acid-starved cells , and the Gcn−/Gcd− substitutions identify likely points of KD-YKD association in the YKD . Our results have important implications for the mechanism of Gcn2 activation by uncharged tRNA , and for the molecular functions of pseudokinases .
To identify evolutionarily conserved residues and amino acids that are potentially critical for the regulatory function of the Gcn2 YKD , we constructed multiple sequence alignments of this domain using Gcn2 sequences from diverse fungi as well as the sequences of authentic KDs from Gcn2 and 11 other kinases ( Fig . S1A–G ) . In accordance with previous alignments [30] , [31] , our analysis indicated that fungal Gcn2 YKDs lack critical features of authentic KDs , including the glycine-rich P-loop between the β1 and β2 strands , the “VAIK” motif ( containing the critical Lys residue in β3 that positions ATP ) , and the “HRD” motif ( containing the catalytic Asp ) ( Fig . S1A–G ) . They also lack the “DFG” motif in the activation loop , whose Asp residue promotes Mg+2 and ATP binding . As noted above , the mouse Gcn2 YKD was found to be incapable of binding ATP or Mg+2 , supporting the conclusion that the Gcn2 YKD lacks kinase activity [30] . However , as shown in Fig . 1 , there are numerous YKD segments highly conserved among fungal Gcn2 homologs , which likely include residues with important regulatory functions . We focused our mutagenesis experiments on conserved residues that , in most cases , are predicted to reside on the surface of the YKD and , hence , might contribute to its putative regulatory interactions with the KD in Gcn2 . Because the structure of the YKD is unknown , we used our sequence alignment containing Gcn2 YKDs and authentic KDs ( Fig . S1A–G ) and projected sequence conservation for each YKD residue that could be aligned with a corresponding residue in authentic KDs onto the crystal structure of the authentic KD of S . cerevisiae Gcn2 [9] . The results ( Fig . 2 ) predict that most highly conserved , surface-exposed residues occur in β3 and helix αC in the N-terminal lobe ( N-lobe ) , in the hinge connecting the N- and C-terminal lobes , and in the activation loop and helices E and I in the C-terminal lobe . Except for β3 and αC , these conserved segments appear to comprise a largely contiguous surface on the “back-side” of the predicted C-lobe facing away from the “active site” cleft ( Fig . 2 , view II ) . To probe the regulatory functions of conserved YKD residues predicted to be surface-exposed in the structural model ( Fig . 2 ) , we used site-directed mutagenesis of GCN2 on a single-copy ( sc ) plasmid to alter 43 such residues , generally making alanine substitutions or introducing a charged residue in place of a bulky hydrophobic residue or one of opposite charge . The resulting mutant GCN2 alleles were tested for complementation of the 3-aminotriazole ( 3-AT ) sensitivity of a ( gcn2Δ ) strain lacking chromosomal GCN2 . 3-AT is an inhibitor of histidine biosynthesis that activates Gcn2 , with attendant induction of GCN4 translation , and the induced Gcn4 stimulates transcription of histidine ( and other amino acid ) biosynthetic enzymes in a manner required for growth in the presence of 3-AT . Thus , mutations that reduce Gcn2 activation confer 3-AT sensitivity ( 3-ATS ) , as illustrated in Fig . 3A ( row 2 ) for the double substitution in the HisRS-like domain ( Y1119L/R1120L ) encoded by the gcn2-m2 allele , which impairs tRNA binding [12] , [14] . By contrast , the GCN2c-M788V allele , conferring constitutive activation of Gcn2 [33] , supports strong growth on 3-AT comparable to that of wild-type ( WT ) GCN2 ( Fig . 3A , row 3 ) . Most mutations we examined did not detectably affect Gcn2 function , conferring no reduction in growth on 3-AT medium ( Fig . S2A–B and data not shown; summarized in Fig . S2C ) . However , we identified several substitutions that conferred 3-ATS phenotypes comparable to that of m2 , indicating strong Gcn− phenotypes ( Fig . 3A and data not shown; summarized as red substitutions in Fig . 1 and listed in Fig . S2C ) . These Gcn− mutations include substitutions of a residue at the beginning of helix αC ( E307P ) , substitutions of 5 residues in predicted αE ( R371A , L377K , L378K , E379K and H385A ) , a double substitution at the C-terminal end of the predicted activation loop ( P448L/E449L ) , and 3 substitutions in the predicted C-terminal helix αI ( L521K , F526K , and R528A ) . ( Henceforth , for simplicity , we will refer to secondary structure elements of the YKD without stipulating in every instance that they are hypothetical predictions of the model in Fig . 2 . ) Consistent with their strong 3-ATS phenotypes , the Gcn− YKD substitutions impaired eIF2α phosphorylation by Gcn2 in vivo . Western analysis of whole cell extracts ( WCEs ) from WT cells revealed that 3-AT evokes the expected increase in eIF2α phosphorylated on Ser-51 ( eIF2α-P ) relative to total eIF2α , whereas m2 cells have no detectable eIF2α-P; and M788V cells display high-level eIF2α-P with or without 3-AT treatment ( Fig . 3B , lanes 1–6 & 17–22; and data not shown ) . Importantly , except for R371A and H385A , all of the Gcn− YKD substitutions greatly reduce or abolish eIF2α-P both in non-starvation conditions and in 3AT-treated cells , without producing a noticeable reduction in Gcn2 abundance ( Fig . 3B , lanes 7–16 & 23–30 ) . Consistent with its leaky 3-ATS growth phenotype , the R371A mutation confers only a moderate reduction in eIF2α-P in 3AT-treated cells ( lanes 1–2 vs . 9–10 ) . The H385A allele was eliminated from consideration because it produced no detectable Gcn2 ( data not shown; Fig . S2C ) . Thus , as summarized in Fig . 3C , conserved surface residues in predicted helices C , E , and I , and in the activation loop of the YKD are required for WT activation of Gcn2 in vivo . These residues might mediate an important regulatory interaction between the YKD and the KD that overcomes the latency of KD function in response to amino acid starvation . ( Two of the Gcn− substitutions in helix αE , L377K and L378K , alter residues predicted to be buried in the YKD and thus might disrupt αE rather than eliminating a specific contact involving the YKD; hence , E379K was chosen as the exemplar αE substitution for subsequent analyses below . ) We reasoned that if the positive regulatory function of the YKD is modulated by amino acid availability , it should be possible to obtain GCN2c mutations mapping in the YKD that constitutively activate Gcn2 function . To test this prediction , we randomly mutagenized the YKD coding sequences in GCN2 , introduced a library of mutant plasmids into the gcn2Δ strain and selected for clones growing on medium containing tryptophan analog 5-fluorotryptophan ( 5-FT ) and histidine analog triazolealanine ( TRA ) . Resistance to both 5-FT and TRA ( 5FTR/TRAR ) results from Gcn4-mediated derepression of tryptophan and histidine biosynthetic enzymes in nonstarvation conditions , diminishing the toxic effects of 5-FT/TRA on protein synthesis , and is a sensitive indicator of constitutive activation of Gcn2 [33] . Accordingly , GCN2c mutations , such as M788V , confer growth on 5-FT/TRA medium , whereas GCN2+ cells ( and Gcn− strains like gcn2-m2 ) are sensitive to the analogs ( 5-FTS/TRAS ) ( Fig . 4A , rows 1–3 ) . By screening the mutagenized plasmid library , we identified three mutations conferring growth on 5-FT/TRA medium that alter residues located within , or just C-terminal to , helix αI ( Fig . 1 , green substitutions ) . T518A , mapping in the N-terminus of αI , confers a mild 5-FTR/TRAR phenotype , whereas L527I and N530K , mapping within or just C-terminal to αI , confer stronger analog-resistance phenotypes , which for L527I and the double substitution T518A/L527I are equivalent to that of GCN2c-M788V ( Fig . 4A ) . We also identified a mutation just N-terminal to predicted αH , D497Y , with a mild 5-FTR/TRAR phenotype similar to that of T518A . Importantly , these mutations elevate eIF2α-P under nonstarvation conditions to an extent commensurate with their 5-FTR/TRAR phenotypes , as the mutations conferring the strongest 5-FTR/TRAR phenotypes , L527I and T518A/L527I , also evoke the largest eIF2α-P/eIF2α ratios in cells grown without 3-AT ( Fig . 4B ) . These YKD mutations also derepress expression of a Gcn4-dependent HIS4-lacZ reporter in nonstarvation conditions , thus confirming their Gcd− phenotypes . As expected , WT cells express this reporter at low levels in non-starvation conditions , whereas GCN2c-M788V cells display ∼5-fold higher levels of reporter expression . The YKD mutations elevate HIS4-lacZ expression to an extent that parallels their 5-FTR/TRAR phenotypes , with D497Y and T518A conferring only ∼150% increases , N530K and L527I conferring ∼2-fold and ∼5-fold increases , respectively , relative to WT , and T518A/L527I exceeding the effect of GCN2c-M788V ( Fig . 4C ) . Hence , these four YKD mutations are bona fide GCN2c alleles that activate Gcn2 in non-starvation conditions . Interestingly , they alter surface-exposed residues , with the two mutations with strongest Gcd− phenotypes , N530K and L527I , altering residues predicted to have the greatest exposure ( among the Gcd− substitutions ) and to reside in proximity to one another ( Fig . 4D ) . It is also intriguing that both Gcn− and Gcd− substitutions were identified in helix αI , in one case ( F526K and L527I ) substituting adjacent residues with opposite outcomes for Gcn2 function ( Fig . 4D ) , thus underscoring the importance of αI in regulating Gcn2 function . Three other GCN2c mutations were identified in our screen that alter residues located near the predicted hinge connecting the N- and C-lobes of the YKD . These include Y353F , mapping between β5 and αD in the hinge itself , G363F mapping between αD and αE , and D406A mapping between β7 and β8 ( Fig . 1 ) . Although individually they confer only slight increases in growth on 5-FT/TRA medium , stronger 5-FTR/TRAR phenotypes were produced by the combination of G363F and D406A , or of all three mutations , in the same allele , which was achieved by site-directed mutagenesis ( Fig . 5A ) . Moreover , whereas the single mutations evoked relatively small increases in basal eIF2α-P , the double and triple mutants conferred relatively larger increases in eIF2α-P under nonstarvation conditions compared to WT cells ( Fig . 5B ) . The double and triple YKD mutations also derepressed the HIS4-lacZ fusion in non-starvation conditions , conferring Gcd− phenotypes ( Fig . 5C ) . Thus , G363F/D406A and Y353F/G363F/D406A are additional GCN2c alleles that activate Gcn2 in non-starvation conditions . It is intriguing that an alignment of the YKD and authentic KD of yeast Gcn2 reveals that the YKD residues substituted by Y353F , G363F , and D406A align closely with KD residues , R794 , E803 and R847 , respectively ( Fig . 5D ) , which interact with one another and rigidify the hinge of the KD [9] ( Fig . 5E ) . Two of these KD residues ( R794 and E803 ) are altered by GCN2c mutations [8] , [33] , leading to the model that hinge rigidity contributes to latency of the Gcn2 KD by impeding inter-lobe mobility [9] . It is intriguing to consider the possibility that the Y353F and D406A substitutions could eliminate hydrogen bonding and salt-bridge interactions , respectively , and that G363F could perturb the orientation of a nearby residue in αD , which all could normally promote hinge rigidity of the YKD . In this event , increasing the flexibility of the predicted hinge and interlobe mobility in the YKD could be responsible for the ability of these substitutions to activate the KD in the absence of high-level binding of uncharged tRNA to the HisRS domain and thereby confer the Gcd− phenotype . It is noteworthy that all of the Gcd− variants harboring single substitutions in the YKD confer higher levels of eIF2α-P in histidine starved , 3-AT-treated cells compared to non-starved cells ( eg . , + and − lanes for D497Y and T518A in Fig . 4B ) . This observation suggests that the Gcd− mutants retain the ability to bind uncharged tRNA and can be activated to a greater extent than WT Gcn2 by basal levels of uncharged tRNA in non-starved cells . Uncharged tRNA is the activating ligand for Gcn2 , and there are Gcn− and Gcd− mutations known that impair or enhance tRNA binding , respectively , by purified Gcn2 in vitro [14] , [34] . While there is no evidence that the YKD affects tRNA binding to the HisRS-like domain in Gcn2 , this possibility could not be dismissed a priori . Accordingly , we examined whether exemplar Gcn− and Gcd− mutations in the YKD have the expected effects on Gcn2 kinase function in vitro , and whether these alterations in kinase activity are associated with corresponding changes in tRNA binding . Mutant and WT Gcn2 proteins were purified from yeast and tested for kinase activity using [32P]-labeled ATP and a recombinant yeast eIF2α peptide as substrates , and SDS-PAGE/autoradiography to detect the reaction products . It was shown previously that WT Gcn2 displays similar kinase activity whether purified from starved or non-starved cells and that the Gcn− m2 mutation , which impairs tRNA binding by Gcn2 , reduces the kinase activity of purified Gcn2 . This effect of the m2 mutation was attributed to reduced activation of Gcn2 in vitro by deacylated tRNA present in cell lysates prior to Gcn2 purification [13] . Consistent with previous findings [13] , we observed that a Gcn2 mutant harboring a substitution in the HisRS domain ( R1325E ) that impairs tRNA binding in vitro ( see below ) reduces both the autophosphorylation and eIF2α kinase activity of purified Gcn2 ( Fig . 6A–B ) . The YKD Gcn− mutants E379K ( αE substitution ) and R528A ( αI substitution ) exhibit comparable ( E379K ) or even larger ( R528A ) reductions in both autophosphorylation and eIF2α phosphorylation compared to the R1327K variant . These findings are consistent with the conclusion that the YKD Gcn− substitutions impair activation of Gcn2 by uncharged tRNA . By contrast , the Gcd− mutants L527I ( αI substitution ) and Y353F/G363F/D406A ( hinge-related substitutions ) display substantially higher than WT observed rates of autophosphorylation and substrate phosphorylation ( Fig . 6A–B ) , consistent with the idea that these substitutions increase the ability of the KD to be activated by uncharged tRNA . To determine if the changes in kinase activity evoked by these mutations result from alterations in tRNA binding affinity , we tested the purified Gcn2 variants for binding of [32P]-labeled total tRNA using a gel mobility shift assay to detect Gcn2-tRNA complexes . The R1325E substitution in the HisRS domain eliminated detectable binding of uncharged tRNA by Gcn2 in vitro ( Fig . 6C ) , consistent with its strong Gcn− phenotype ( S . L . and A . G . H . , unpublished observations ) . By contrast , the Gcn− mutants E379K and R528A exhibit tRNA binding indistinguishable from WT Gcn2 ( Fig . 6C ) , implying that their inability to be activated in starved cells does not result from reduced binding of uncharged tRNA to the HisRS-like domain . Interestingly , the Gcd− mutants L527I and Y353F/G363F/D406A show reduced tRNA binding activity ( Fig . 6C ) , at odds with the possibility that the constitutive activation of kinase function displayed by these variants results from increased affinity for uncharged tRNA . One explanation for this last result could be that the reduced tRNA binding by the Gcd− variants results from a putative negative autoregulation of tRNA binding in response to hyperactivation of kinase function . Another possibility would be that it reflects a greater than WT level of co-purification of the Gcd− variants with endogenous tRNA . The latter explanation is very improbable in view of our finding that the purified Gcn2 preparations contain only small amounts of tRNA , estimated to be <5% on a molar basis , which do not vary between the WT , Gcd− and Gcn− proteins analyzed in Fig . 6 ( data not shown ) . We next considered the possibility that the YKD mutations alter a regulatory interaction between the YKD and KD that evokes allosteric activation of Gcn2 kinase function . To address this possibility , we first employed an assay described previously wherein full-length Gcn2 ( WT or YKD mutants ) is coimmunoprecipitated from cell extracts with an HA epitope-tagged LexA fusion to a Gcn2 KD segment ( residues 720–999 ) that harbors a portion of the large Gcn2-specific insert between strands β4 and β5 , strand β5 from the N-lobe , the hinge , and entire C-lobe . While this represents an incomplete KD , the C-lobe can be expected to fold independently of the N-lobe [9]; and this KD segment was shown previously to interact specifically with the CTD in a manner dependent on the C-terminal portion of the large KD insert and impaired by the GCN2c-E803V Gcd− substitution of a key residue in the KD region [15] . Moreover , in agreement with previous results , we found that deletion of the entire YKD reduces interaction of otherwise full-length Gcn2 with this HA-LexA-KD fusion . Thus , the proportion of Gcn2-ΔYKD present in the input ( I ) sample that co-immunoprecipitates with the HA-LexA-KD fusion in the pellet ( P ) fraction was decreased to ∼1/3rd of the level seen with WT Gcn2 ( Fig . 7A–B , WT vs . ΔYKD ) . This reduction has been attributed to loss of interaction between the YKD in full-length Gcn2 and the KD segment in HA-LexA-KD [19] . We found here that YKD Gcn− substitutions in αC ( E307P ) , αE ( E379K ) , the activation loop ( P448L/P449L ) and αI ( R528A ) , as well as the Gcd− hinge-related triple substitutions Y353F/G363F/D406A , had little or no effect on the percentage of Gcn2 coimmunoprecipitated with HA-LexA-KD . By contrast , the Gcd− substitutions in αI , T518A , L527I , and T518A/L527I , all reproducibly increased the proportion of the input Gcn2 recovered with HA-LexA-KD in the pellet ( Fig . 7A–B ) . ( Note that results in Fig . 7B were obtained by calculating the ratio of P to I signals for each variant and normalizing the P∶I ratios by the intensity of HA-LexA-KD in the P fraction for that variant . ) It is noteworthy that the Gcn2-T518A/L527I double mutant showed a reproducibly higher level of coimmunoprecipitation with HA-LexA-KD compared to the corresponding two single mutants ( Fig . 7B ) , commensurate with the relatively stronger Gcd− phenotype of the double mutant ( Fig . 4C ) . Considering that the increases in Gcn2:HA-LexA-KD association provoked by the Gcd− substitutions T518A and L527I were ≤33% ( Fig . 7B ) , we sought additional evidence that these results are physiologically relevant to the increased kinase function evoked by the Gcd− substitutions . To this end , we evaluated the effects of double substitutions combining these two Gcd− substitutions with Gcn− substitutions E379K and/or R528A . Interestingly , all three double mutants we examined exhibit 3-ATS and 5-FTS/TRAS phenotypes indistinguishable from the Gcn− single mutants ( Fig . 7C , rows 15–17 vs . 9–10 , 4 & 6; and data not shown ) , indicating that the Gcn− substitutions fully suppress the activating effects of the Gcd− substitutions . We reasoned that if the tighter association between the YKD and KD contributes to the Gcd− phenotypes of T518A and L527I , then the Gcn− substitutions should also suppress this aspect of the Gcd− substitutions . Supporting this prediction , combining αE Gcn− substitution E379K with each of the αI Gcd− substitutions reduced the proportions of Gcn2 that coimmunoprecipitated with HA-LexA-KD ( normalized P∶I ratios ) from the elevated values seen for the Gcd− single mutants to the lower values given by the Gcn− single mutants ( Fig . 7A–B ) . Similar results were observed on combining Gcn− ( R528A ) and Gcd− substitutions ( T518A ) in αI ( Fig . 7A–B ) , supporting the idea that tighter association between the YKD and authentic KD contributes to the activating effect of Gcd− substitutions in the αI segment of Gcn2 . We sought next to provide evidence that Gcd− substitutions in αI increase a direct interaction between the YKD and KD . To this end , we incubated [35S]-methionine-labeled WT or mutant YKD fragments , synthesized by in vitro transcription/translation , with yeast extracts from a gcn2Δ strain containing the HA-LexA-KD fusion described above and measured the amounts of labeled YKD fragments that coimmunoprecipitated from the extracts with HA-LexA-KD . Interestingly , the L527I and T518A/L527I YKD fragments , harboring Gcd− substitutions in αI , were co-immunoprecipitated with HA-LexA-KD at levels ∼4-fold higher than that observed for WT YKD ( Fig . 8A ) . By contrast , the R528A YKD fragment , harboring a Gcn− substitution in αI coimmunoprecipitated with HA-LexA-KD at only 60% of the WT value ( Fig . 8A ) . Importantly , Gcn− substitution E379K abolished the effect of the double Gcd− substitutions T518A/L527I in the relevant triple mutant to render a level of YKD fragment binding only slightly higher than that given by E379K alone ( Fig . 8B ) . These findings strongly support the notion that Gcd− substitutions in αI increase the affinity of the YKD for the KD of Gcn2 , whereas Gcn− substitutions in αE or αI suppress this tighter interaction .
In this report , we provide strong evidence that the Gcn2 YKD is a positive regulatory domain required to overcome the latency of the adjoining KD under amino acid starvation conditions , and we identified specific residues located in a discrete region of the YKD that are likely situated at the YKD-KD interface and modulate , positively or negatively , this regulatory interaction . Mutation of certain residues at this predicted interface greatly impairs or eliminates phosphorylation of eIF2α on Ser-51 and confers the expected strong sensitivity to an inhibitor of histidine biosynthesis ( 3-AT ) that signifies decreased induction of GCN4 and its target amino acid biosynthetic genes . These Gcn− mutations were obtained by targeting a subset of residues found to be evolutionarily conserved among YKDs of fungal Gcn2 homologs and that were predicted to reside on the surface of the YKD . These latter predictions were based on a sequence alignment of fungal Gcn2 YKDs with a group of authentic KDs , which allowed us to project onto the crystal structure of the yeast Gcn2 KD the sequence conservation of all YKD residues that can be aligned with residues in authentic KDs . According to the predicted tertiary structure of the YKD ( Fig . 2 ) , the substitutions found to have Gcn− phenotypes alter residues that are visible on one face of the predicted YKD ( Fig . 3C , middle image ) and , hence , could define a single , continuous regulatory surface comprised of residues near helix αC , within the activation loop , and belonging to predicted helices αE and αI . As discussed below , however , substitutions in αC and the activation loop might identify an interaction site ( s ) distinct from that defined by Gcn− substitutions in αE and αI of the YKD . We also identified Gcd− substitutions in the YKD , which increase eIF2α phosphorylation and derepress expression of the Gcn4-dependent HIS4-lacZ reporter in nonstarvation conditions . It is intriguing that the most potent of these substitutions also map in predicted αI , and that substitutions of adjacent residues in αI were identified that either impair ( Gcn− ) or constitutively activate ( Gcd− ) Gcn2 kinase function . These results identify αI as a key regulatory element of the YKD . Another group of Gcd− substitutions alter residues predicted to reside in the hinge region of the YKD , which align closely with residues in the authentic Gcn2 KD that interact with one another and promote hinge rigidity in a manner believed to promote latency of the KD by restricting inter-lobe mobility [9] . Thus , inter-lobe mobility might also be important for the regulatory interactions of the YKD . Of course , knowledge of the true locations of the YKD residues altered to produce Gcn− or Gcd− phenotypes will require structural analysis of this domain . However , the fact that the YKD structural model was instrumental in identifying functionally important conserved residues , at least some of which alter physical interaction between the YKD and KD , supports the idea that the affected amino acids define an important regulatory interface between the YKD and KD of Gcn2 . Previous work from our laboratory on Gcn2 derivatives lacking the entire YKD indicated that the YKD is not required for ribosome-binding [21] , dimerization by full-length Gcn2 [19] , or interactions with positive effectors Gcn1/Gcn20 [26] . Given that the YKD also is not required for kinase function per se [32] , it appears to mediate the stimulatory effect of uncharged tRNA that elevates eIF2α phosphorylation in starved cells . In addition , we showed here that neither Gcn− nor Gcd− substitutions in the YKD alter tRNA binding by purified Gcn2 in a manner that would explain the alterations in kinase activity conferred by these substitutions . Accordingly , we hypothesized that the YKD substitutions alter a physical interaction between the YKD and KD that allosterically activates kinase function in starved cells in response to increased occupancy of the HisRS-like domain by uncharged tRNA . Supporting this model , we found that the Gcd− substitutions T518A and L527I in predicted αI of the YKD produce additive increases in association of full-length Gcn2 with the isolated Gcn2 KD in a LexA fusion in yeast WCEs . We showed previously , and confirmed here , that eliminating the YKD reduces the ability of otherwise WT Gcn2 to coimmunoprecipitate with the LexA-KD fusion . Because eliminating the authentic KD from Gcn2 abolishes , and not merely reduces , its interaction with LexA-KD in this assay [19] , we presume that LexA-KD dimerizes with the KD in full-length Gcn2 and that additional interaction of the YKD with the KD moiety of LexA-KD increases the stability of the Gcn2·LexA-KD complex . In this view , the Gcd− substitutions T518A and L527I increase the yield of Gcn2·LexA-KD complexes by strengthening YKD-KD interaction , which suggests in turn that the ability of these Gcd− substitutions to activate kinase function results from tighter YKD-KD association within Gcn2 . Additional evidence supporting this conclusion came from our finding that combining the Gcd− substitutions T518A and L527I with the Gcn− substitutions E379K and R528A completely suppressed the Gcd− phenotype of the former mutations , with the strong 3ATS/Gcn− phenotype of the latter mutations being expressed in the double mutants . Importantly , the Gcn− substitutions E379K/R528A also abolished the increased coimmunoprecipitation of Gcn2 with the LexA-KD conferred by the Gcd− substitutions . The co-suppression of both phenotypes strongly supports a mechanistic linkage between increased interaction between Gcn2 and LexA-KD ( signifying increased YKD-KD association ) and elevated Gcn2 kinase function . Further bolstering this conclusion , we provided direct evidence that the YKD Gcd− substitutions T518A and L527I enhance interaction of recombinant YKD with LexA-KD in complexes reconstituted in vitro , whereas the Gcn− substitutions E379K and R528A masked the stabilizing effect of the Gcd− substitutions on YKD-KD association in the E379K/T518A/L527I triple mutant . It might seem puzzling that ( i ) the YKD Gcd− substitutions had greater effects on YKD-KD interactions than did the Gcn− substitutions , even though both categories of substitutions evoke strong changes in Gcn2 kinase function in vivo; whereas the Gcn− substitutions did substantially affect YKD-KD interactions when examined in the presence of YKD Gcd− substitutions ( Figs . 7–8 ) . One way to explain these findings is to propose that the Gcn− substitutions weaken a tighter YKD-KD association that is normally established only during activation of full-length Gcn2 by uncharged tRNA in starved cells . The normal activation process could be disrupted in the artificial Gcn2·LexA-KD complexes formed in the assays of Fig . 7; and they cannot occur in the assays of direct YKD-KD interactions shown in Fig . 8 because the tRNA-binding HisRS domain is absent in those constructs . By contrast , because the Gcd− substitutions bypass the normal activation mechanism and strengthen YKD-KD interactions constitutively , their effects can be observed in either assay; and they can be reversed by Gcn− substitutions that weaken direct YKD-KD contacts in the Gcd− Gcn− YKD double mutants . The fact that the Gcd− triple substitution of YKD hinge residues Y353F/G363F/D406A had only a small effect on direct interaction between the YKD and KD ( Fig . 7A–B ) might indicate that these residues are not present at the YKD-KD interface and act indirectly to alter the conformation of full-length Gcn2 in a way that increases access of the YKD to the KD . However , as this triple substitution has a weaker Gcd− phenotype compared to those conferred by the αI Gcd− substitutions ( cf . Figs . 4C &5C ) , it might simply have a correspondingly smaller effect on direct YKD-KD association . Together , our results suggest that the YKD interacts directly with the KD dependent on residues in helix αI to mediate allosteric activation of Gcn2 kinase function . If this interaction is restricted to starvation conditions , as suggested above , then it presumably depends on other conformational changes within Gcn2 triggered by binding uncharged tRNA to the HisRS-like domain . One interesting possibility is prompted by our previous evidence that the CTD interacts with the KD to inhibit kinase function in a manner overcome by tRNA binding to the HisRS domain [15] . The CTD could inhibit the KD , at least partially , by the indirect mechanism of blocking the proposed stimulatory YKD-KD interaction . Binding of uncharged tRNA to the HisRS-like domain would partly dissociate the HisRS/CTD module from the KD segment and provide the YKD with access to its binding site ( s ) in the KD for allosteric stimulation of kinase function ( Fig . 8C ) . Our identification of Gcn− mutations in the predicted activation loop of the YKD , and of Gcd− substitutions in the YKD hinge region , raise the possibility that the conformation of the YKD is altered during the activation process in a manner that stabilizes the stimulatory YKD-KD interaction . It is unclear , however , how tRNA binding to the HisRS region would trigger this hypothetical alteration of YKD conformation . Accordingly , the predicted activation loop and hinge region , being exposed on the YKD surface , might simply provide additional contact points for the KD or CTD rather than mediating a conformational rearrangement of the YKD . In this view , the αI-αE surface of the C-lobe and one or more YKD segments including residues in αC , the hinge and activation loop would make independent stimulatory contacts with the KD ( Fig . 8C ) . A precedent for this idea of a multivalent YKD-KD interaction surface is provided by activation of kinase LKB1 by the YKD STRAD in a manner facilitated by the scaffold protein MO25 . Substrate binding determinants and the activation loop of STRAD contact the N- and C- lobe of LKB1 , while STRAD's αC anchors it to MO25 , and MO25 stabilizes the active conformation of the LKB1 ( nonphosphorylated ) activation loop [35] .
Multiple sequence alignments were generated using MUSCLE at http://www . ebi . ac . uk/Tools/msa/muscle/ . ConSurf [36] and PyMOL [37] were used to obtain sequence conservation scores and generate the surface representation of sequence conservation on the crystal structure of the yeast Gcn2 KD ( pdb: 1ZYC ) . Plasmids employed are listed in Table 1 . QuikChange site-directed mutagenesis ( Stratagene ) was used to generate the novel derivatives of plasmid p722 ( pSL201-pSL242 ) and p630 ( pSL301-pSL311 ) and GCN2 was sequenced in its entirety for those alleles exhibiting significant Gcn− or Gcd− phenotypes . For Gcd− mutations identified by random mutagenesis , p722 was subjected to error-prone PCR mutagenesis using the GeneMorph II kit ( Stratagene ) by using primer pairs PS-1 ( 5′-ATAGCAAATTTAGAGAAAGAGTTAG-3′ ) and PS-2 ( 5′-CTTAACAGCAGTCATCGGTTTTAC-3′ ) . The BlpI -XhoI 1 . 4-kb GCN2 fragment encoding the YKD was isolated from plasmid DNA prepared from a pool of E . coli transformants harboring mutagenized plasmids and subcloned into p722 . Plasmid DNA prepared from a pool of the resulting E . coli transformants was introduced into yeast strain H1149 and transformants were selected on minimal ( SD ) medium containing 0 . 5 mM 5-FT . Resident plasmids were isolated from colony-purified transformants and subjected to DNA sequence analysis to identify the mutations . As multiple mutations generally occurred , site-directed mutagenesis was used to produce plasmids pSL233 , pSL234 , pSL235 , pSL237 , pSL238 , pSL239 and pSL240 , containing only single mutations in GCN2 . pSL102-pSL106 were generated by replacing the 1 . 2-kb BlpI-BspEI fragment encoding the YKD in pSL101 with the corresponding fragment from p722 derivatives harboring the appropriate GCN2 mutations . The same strategy was used to construct pSL401-pSL405 from pHQ539 . Yeast strains employed included H1149 ( MATα gcn2Δ::LEU2 ino1 ura3-52 leu2-3 leu2-112 <HIS4-lacZ> ) [11] , HQY132 ( MATα trp1 ura3 his3 lexAop-LEU2 gcn2Δ::hisG ) [19] , and H2684 ( MATa ino1 ura3-52 gcn1Δ gcn2Δ gcn20Δ ) ( M . Marton and A . G . H . , unpublished observations ) . Transformants of H2684 bearing plasmids pSL101 , pSL102 , pSL103 , pSL104 , pSL105 , or pSL106 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 ∼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 1× TEV buffer ( 50 mM Tris pH 8 , 0 . 5 mM EDTA , 1 mM 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 −80°C . The eIF2α−ΔC protein was purified from E . coli as previously described [13] . Assays of β-galactosidase activity in WCEs were performed as described previously [38] . For Western analysis , WCEs were prepared by trichloroacetic acid extraction , as described previously [39] , and immunoblot analysis was conducted as described [19] using phosphospecific antibodies against eIF2α-P ( Biosource International ) and polyclonal antibodies against eIF2α [40] or Gcn2 [16] . Assaying autophosphorylation and eIF2α phosphorylation by purified Gcn2 was conducted as described previously [8] . Binding of tRNA was measured with a gel mobility shift assay as follows . Total yeast tRNA was purchased from Roche . tRNA was first dephosphorylated using calf intestine alkaline phosphatase ( New England BioLabs ) for 1 h at 37°C in 1× Dephosphorylation buffer provided with the enzyme and followed by phenol/chloroform extraction and ethanol precipitation . Three micrograms of the dephosphorylated tRNA was phosphorylated using 25 pmol of [γ-32P]ATP and 10 units polynucleotide kinase for 1 h in 1× kinase buffer provided with the enzyme . The reaction was stopped by adding EDTA to 1 mM and heating for 2 min at 95°C . tRNA was purified from free nucleotides using MicroSpin G-25 Columns ( GE Healthcare ) . Fifty nanograms of [32P]-labelled tRNA were mixed with purified Gcn2 ( 1–4 µM ) and 10 U RNasin Ribonuclease Inhibitor ( N2511; Promega ) , in 1× GMSA buffer ( 2 mM HEPES [pH 7 . 4] , 15 mM NaCl , 15 mM MgCl2 , 10% glycerol ) in a total volume of 20 µl . After incubating for 30 min at 30°C , 4 µl of 6× nucleic acid loading buffer ( 30% ( v/v ) glycerol , 0 . 25% ( w/v ) bromophenol blue ) were added and the mixture was resolved by electrophoresis on a 1% agarose gel cast in 1× MOPS buffer ( Quality Biological , Inc ) at 100 V for 1 . 5 h . RNA and protein molecules were transferred from the gel to a nitrocellulose membrane ( 162-0097; Bio-Rad , Hercules , CA , USA ) by capillary action in 10× SSC for 16 h . [32P]tRNA–Gcn2 complexes were quantified with a phosphorimager ( Molecular Dynamics ) using the Image Quant software provided by the vendor . Coimmunoprecipitations of Gcn2 with LexA-HA-KD fusion protein were conducted as described previously [19] using HA-probe Antibody agarose conjugate ( sc-7392 AC ) and LexA antibodies ( sc-7544 , 1∶2000 dilution ) purchased from Santa Cruz . Coimmunoprecipitation of [35S]-methionine-labeled Gcn2 YKD polypeptides with LexA-HA-KD fusion protein was executed 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 . In vitro-translated proteins were partially purified by ammonium sulfate precipitation as described previously [41] and resuspended in 50 µl of buffer A ( 20 mM Tris/HCl pH 7 . 5 , 100 mM NaCl , 0 . 2 mM EDTA , 1 mM DTT ) containing 12 . 5% glycerol . Fifty µg of WCE prepared from p2825 transformants of HQY132 and 10 µL of in vitro-translated proteins were diluted to a final volume of 200 µL with breaking buffer ( 50 mM Tris/HCl , ph 7 . 5 , 50 mM NaCl , 0 . 1% Triton-100 , 1 mM DTT ) containing protease inhibitors ( Aprotinin 10 µg/mL , Leupeptin 10 µg/mL , Pepstatin 10 µg/mL and 1 mM PMSF ) and pre-incubated with 20 µL of protein A-agarose beads ( Santa Cruz , sc-2001 ) suspended in breaking buffer for 1 h at 4°C with rocking . The beads were removed by centrifugation and the supernatant added to HA-probe Antibody agarose conjugate ( Santa Cruz , sc-7392 ) and incubated at 4°C for 2 h with rocking . The beads were collected by centrifugation , washed three times with 500 µl of breaking buffer , and resuspended in 40 µl of Tris-Glycine SDS Sample Buffer ( Novex ) . Proteins in the immune complexes were resolved by sodium dodecyl sulfate ( SDS ) -polyacrylamide gel electrophoresis ( PAGE ) . For detecting [35S]-labeled proteins , gels were fixed with 25% ethanol/10% acetic acid , treated with Amplify ( Amersham ) , dried , and subjected to fluorography . | 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 down-regulate general protein synthesis by phosphorylation of eukaryotic translation initiation factor 2 alpha ( eIF2α ) . The yeast Saccharomyces cerevisiae has a single eIF2α kinase , Gcn2 , activated by uncharged tRNAs accumulating in amino acid starved cells , which bind to a regulatory domain homologous to histidyl-tRNA synthetase . Gcn2 also contains a degenerate , pseudokinase domain ( YKD ) of largely unknown function , juxtaposed to the authentic , functional kinase domain ( KD ) . Our study demonstrates that direct interaction between the YKD and KD is essential for activation of Gcn2 , and identifies likely KD-contact sites in the YKD that can be altered to either impair or constitutively activate kinase function . Our results provide the first functional insights into the regulatory role of the enigmatic YKD of Gcn2 . | [
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] | 2014 | Enhanced Interaction between Pseudokinase and Kinase Domains in Gcn2 stimulates eIF2α Phosphorylation in Starved Cells |
Drugs currently used for the treatment of Chagas’ disease , nifurtimox and benznidazole , have a limited effectiveness and toxic side effects . With the aim of finding new therapeutic approaches , in vitro and in vivo anti-Trypanosoma cruzi activity of vitamin C alone and combined with benznidazole were investigated . The trypanocidal activity on epimastigote and trypomastigote forms was evaluated by counting parasites in a Neubauer chamber after treatment with the compounds . For the amastigote stage , transgenic parasites expressing β-galactosidase were used and quantified by measuring the β-galactosidase activity . The cytotoxicity of compounds was tested on Vero cells . The redox state of the parasite was evaluated by determining the reduced thiol levels ( spectrophotometric assay ) and the intracellular oxidative state ( by flow cytometry ) . The in vivo trypanocidal activity was evaluated on a murine model of Chagas’ disease . The trypanocidal activity of vitamin C and benznidazole was similar for the three parasite forms . When combining both drugs , vitamin C did not induce any change in the antiparasitic activity of benznidazole on trypomastigotes; however , on mammal cells , vitamin C diminished the cytotoxicity degree of benznidazole . Two mechanisms of action may be postulated for vitamin C: a lethal pro-oxidant effect on the parasite when used alone , and an antioxidant effect , when combined with benznidazole . A similar behavior was observed on infected mice; i . e . , parasite counts in infected mice treated with vitamin C were lower than that of the control group . Animals treated with benznidazole presented lower parasitemia levels , as compared with those treated with vitamin C alone . Again , vitamin C did not cause any effect on the antiparasitic profile of benznidazole . Even though a combined treatment was employed , the antioxidant effect of vitamin C on the host was evidenced; a 100% survival was observed and the weight loss occurring during the acute phase of the infection was reduced . Based on these results , the combination of vitamin C with benznidazole could be considered as an alternative treatment for Chagas’ disease . These preliminary results encourage further research to improve the treatment of Chagas’ disease .
Trypanosoma cruzi is the causative agent of Chagas’ disease , which was declared of worldwide interest by the World Health Organization ( WHO ) [1] . To fight this illness that originated in Latin America ( American trypanosomiasis ) , nifurtimox and benznidazole ( Bnz ) are used . These drugs are known to cause considerable secondary effects and have limited effectiveness . For this reason , over the past years , studies have been undertaken to find novel therapeutic alternatives to treat this disease . Many drugs have been reported to exert their anti-T . cruzi effect through the generation of reactive oxygen species ( ROS ) [2–4] . The infection with T . cruzi is known to cause and inflammatory response together with the generation of oxidative stress , which plays a central role in the pathophysiology of the disease [5–8] . Both events suggest that the production of ROS could be an efficient strategy against the parasite . It is known that ascorbic acid or vitamin C ( Vit C ) exerts a dual effect , acting as antioxidant at physiological concentrations ( 40–80 μM ) , and as a pro-oxidant at pharmacological high concentrations ( 1 to 5 mM , achieved only by intravenous administration ) [9 , 10] . Ascorbate acts as an antioxidant neutralizing potentially harmful free radicals , while as pro-oxidant , in the presence of catalytic metallic ions , ascorbate induces the formation of H2O2 and ROS . The pro-oxidant effect is the main mechanism by which high doses of Vit C cause cell death . Thus , Vit C can manifest its known selective cytotoxic effect on tumor cells , which are particularly vulnerable to the oxidative stress induced by H2O2 [10–12] . Such pro-oxidant effect could also account for the antiparasitic activity previously demonstrated in our laboratory when T . cruzi parasites were treated with a combination of Vitamin B12 and Vit C [3] . In this case , the induction of the toxic effect would be mediated by cobalt ions contained in the corrinic ring of the vitamin B12 . T . cruzi can either synthesize ascorbic acid or obtain it from the culture medium [13] . Our hypothesis is that Vit C has the capacity to kill the parasite acting as a pro-oxidant compound by reacting with traces of heavy metal ions present inside the parasite´s cytoplasm and/or protect the host from the oxidative stress generated by the T . cruzi infection . Thus , the antiparasitic activity of Vit C was studied in vitro on the three stages of T . cruzi , as well as in vivo using an acute Chagas’ disease murine model . The effect caused by the combined administration of Vit C and benznidazole ( Bnz ) was also assessed .
Hemin , EDTA , NADH , RPMI-1640 culture medium , dithionitrobenzoate ( DTNB ) and L ( + ) ascorbic acid GR ( Vit C ) were obtained from Sigma Chem . Co . ( Saint Louis , MO , USA ) . Yeast extract , tryptose and brain heart infusion ( BHI ) were from Difco Laboratories ( Sparks , MD , USA ) . Bnz was kindly provided by Roche ( Argentina ) . All other chemicals were of the highest purity commercially available . Trypanosoma cruzi epimastigotes ( Tulahuén strain ) were grown at 28 ºC in a liquid medium containing 3 . 3% BHI ( Difco ) , 0 . 3% tryptose ( Difco ) , 0 . 3% disodium phosphate , 0 . 04% potassium chloride , 0 . 03% dextrose , and hemin ( 20 μg mL-1 ) . After sterilization , penicillin ( 100 IU mL-1 ) , streptomycin ( 100 μg mL-1 ) and 20% v/v heat-inactivated fetal calf serum ( Natocor ) were added . T . cruzi bloodstream trypomastigotes were obtained from infected CF1 mice by cardiac puncture at the peak of parasitemia on day 15 post-infection . Trypomastigotes were routinely maintained by infecting 21 day-old CF1 mice . T . cruzi parasites from the Tulahuén strain expressing the β-gal gene ( Tul-β-Gal ) were kindly provided by Dr . Buckner [14] . Outbred CF1 male and inbred C3H/HeN female mice were nursed at the Departamento de Microbiología , Facultad de Medicina , Universidad de Buenos Aires . To evaluate growth inhibition on epimastigotes , Vit C was tested at concentrations ranging from 1 to 10 μM . Bnz was used as reference drug at concentrations ranging from 2 . 5 to 10 μM . The percentage of inhibition ( I% ) and 50% inhibitory concentration ( IC50 ) values were estimated by counting the parasites in a Neubauer chamber , as previously described [3] . The trypanocidal activity was also evaluated on bloodstream trypomastigotes . Parasites were cultured in the presence of 5 to 100 μM of Vit C or 0 . 38 to 380 μM of Bnz , under the conditions stated elsewhere [3] . Living parasites were then counted in a Neubauer chamber and the percentage of lysis ( % L ) and the concentration that causes 50% lysis ( IC50 ) were calculated as previously reported [3] . The growth inhibition of amastigotes was determined in J774 murine macrophages cultures infected with transgenic trypomastigotes expressing β-galactosidase ( Tul- β-Gal ) . Vit C and Bnz were assayed from 0 . 5 to 40 μM . Parasite counts were then determined spectrophotometrically by measuring the product of the enzymatic reaction . I% and IC50 values were then calculated as previously described [3] . The cytotoxicity assay was carried out in vitro , on Vero cells ( from the Departamento de Química Biológica , Facultad de Ciencias Exactas y Naturales , Universidad de Buenos Aires ) . Briefly , cells were seeded on a 24-well plate . Each well contained 500 μL of a cell suspension at 9x105 cells mL-1 . The plate was then incubated for 24 h at 37°C . After this period , Vit C ( from 5 to 5000 μM ) and Bnz ( 3 to 3000 μM ) was added . The plate was incubated for 48 h at 37°C . After incubation , the culture medium was replaced with phosphate buffered saline PBS and MTT was added at a final concentration of 0 . 5 mg/ml . Cells were incubated for 1 h at 37°C and then resuspended in 500 μL of dimethyl sulfoxide ( DMSO ) . The absorbance was read at 570 nm using an ELISA plate reader . Absorbance values of wells containing only medium and reagents were used as blank reaction . MTT assays were done in triplicate . The 50% cytotoxic concentration ( CC50 ) was defined as that causing a 50% of death . The selectivity index ( SI ) was calculated as CC50/IC50 . The intracellular redox state was measured on 4-day-growth epimastigotes treated with different concentrations of Vit C ( 5 , 15 and 30 μM ) during 2 , 5 , 8 and 10 h . The following assays were then carried out . The content of thiol groups was determined by measuring the change in light absorbance ( 410 nm ) occurring when SH-groups reduce DTNB . The quantification was carried out in cell-free extract , following the methodology described previously [15] . The intracellular oxidative stress was evaluated by flow cytometry using 2' , 7'-dichlorodihydrofluorescein diacetate ( H2DCFDA ) as fluorescent probe , following the methodology previously described [4] . The fluorescence intensity of the oxidized probe was measured in a Becton Dickinson FACScalibur flow cytometer . Results were analyzed using the FlowJo V10 software and the ratio Gmt/Gmc was determined , where Gmt and Gmc correspond to the geometric mean of histograms obtained with treated and untreated ( control ) cells , respectively . Six to eight week-old C3H/HeN mice were infected with 5x103 trypomastigotes through the intraperitoneal route . Seven days after the infection , mice were treated with either Vit C ( 1 . 5 mg/kg/day ) or Bnz ( 0 . 75 mg/kg/day ) , or the combination of both . Bnz concentration used arise from S1 Fig . These drugs were resuspended in 0 . 05M PBS ( pH 7 . 2 ) . The control group was treated only with the vehicle . Drugs were administered from Monday to Friday during 2 weeks ( days 7 to 12 and 15 to 20 post-infection ) through the intraperitoneal route . Parasitemia levels were monitored every other day by counting parasites in a Neubauer chamber , and using 5 μl of blood diluted 1:5 in lysis buffer ( 0 . 75% NH4Cl , 0 . 2% Tris , pH 7 . 2 ) . The number of dead mice was registered every day . The weight of each animal was registered between days 9 and 16 post-infection . Results were expressed as weight loss percentage considering 100% the body weight corresponding to day 9 post-infection . Animal experiments were approved by the Institutional Committee for the Care and Use of Laboratory Animals ( CICUAL ) of Universidad de Buenos Aires , Facultad de Medicina , Argentina ( identification no . 2943/2013 ) . Animals received human care and were treated in accordance with guidelines established by the Animal Care and Use Committee of the Argentine Association of Specialists in Laboratory Animals ( AADEALC ) , which were in accordance with the Guide for the Care and Use of the Laboratory Animals of the National Research Council of the National Academies [16] . All data are expressed as means ± SEM . To calculate the IC50 , I% or L% values were plotted against the log of drug concentration ( μM ) and fitted to a sigmoidal curve determined by a non-linear regression ( Sigma Plot 12 software ) . The significance of differences was evaluated with either the Student´s t test or one-way analysis of variance ( ANOVA ) with post-hoc analysis using the Tukey’s test . A p< 0 . 05 was considered significant . Parasitemia and weight loss were analyzed using the non-parametric Mann-Whitney’s U test . Parasitemia was also expressed as Area Under the Curve ( AUC ) . Survival curves were compared with a log-rank test . Results presented are representative of three to four independent experiments .
The in vitro antiparasitic activity of Vit C was evaluated on the three parasite stages ( Fig 1 ) . On epimastigotes , Vit C was found to be 1 . 3–1 . 8 times more active than Bnz , whereas Bnz was 1 . 4 to 1 . 8 times more active than Vit C on bloodstream trypomastigotes . In amastigotes , similar IC50 values were obtained for Vit C and Bnz . In order to carry out an in vivo evaluation of the combined treatment with Vit C + Bnz , the effect of Vit C ( 13 . 6 to 81 . 8 μM ) in combination with Bnz ( 33 . 3 μM ) on trypomastigote stage was evaluated ( Table 1 ) . The addition of different concentrations of Vit C ( which in the absence of Bnz caused a 4–60% of lysis ) to a fixed concentration of Bnz did not increase the trypanocidal activity exerted by Bnz alone . Vero cells were treated with different concentrations of Vit C and Bnz ( Fig 2A ) . Vit C did not cause any cytotoxic effect at concentrations of up to 2500 μM . Nevertheless , higher concentrations ( 5000 μM ) proved to exert a cytotoxic effect of 35% . A CC50 = 90 . 9 ± 2 . 50 μM was obtained for Bnz . The SI is a parameter representing the cytotoxic effects of a drug in mammalian cells , and its antiparasitic activity . SI values were calculated for Vit C and Bnz on all T . cruzi forms: for epimastigotes , the SI for Bnz was 14 . 12 , whereas for Vit C this value was > 684 . 93 . For trypomastigotes , SI values were 2 . 74 and > 49 . 40 for Bnz and Vit C , respectively . For amastigotes , the SI values were 20 . 10 for Bnz and > 537 . 63 for Vit C . In order to perform an in vivo assessment of the combined treatment with Vit C + Bnz , the effect of Vit C ( 5 to 5000 μM ) on the cytotoxicity exerted by Bnz ( 100 μM ) was assayed ( Fig 2B ) . A significant decrease in the cytotoxicity caused by Bnz was observed in the presence of Vit C at concentrations ranging from 5 to 500 μM . However , this tendency was not observed when 5000 μM of Vit C were added , since cell viability values were the lowest . Due to its pro-oxidant and/or antioxidant effect , it is postulated that Vit C would induce a redox imbalance leading to changes in both the level of free low molecular weight thiols and the oxidative state inside the parasite . Such changes were evaluated on T . cruzi epimastigotes treated with different concentrations of Vit C ( 5 , 15 and 30 μM ) during short times ( 2 , 5 , 8 and 10 h ) . As shown in Fig 3A , a significant Vit C concentration-independent decrease in the thiol content ( ~50% ) was observed between 2 h and 5 h post-treatment . From 5 h until 8 h , the thiol content increased in a Vit C concentration-dependent manner , reaching maximum values of 191% and 133% ( values that were above the control ) , for 30 μM and 15 μM Vit C , respectively . After reaching this maximum value , the concentration of SH-groups decreased to return to control values after 10 h post-treatment . This behavior is modified when , to the different Vit C concentrations , a fixed concentration of Bnz ( 15 μM ) is added ( Fig 3B ) . An increase in the content of thiols was observed when the concentrations of Vit C were increased ( 2 h post-treatment ) . At 5 h , minimum values of 40 , 60 and 85% corresponding to 30 , 15 and 5 μM Vit C , respectively were observed . Thiol levels returned to the initial values after 8 h post-treatment and almost no variations were observed between 8 h and 10 h . It is noteworthy that the thiol content in controls ( corresponding to different times ) observed in presence of Bnz ( Fig 3B ) is 3 to 5 times lower than those measured in its absence ( Fig 3A ) , which was at all timepoints ~153 . 52 nmol/mg of protein . For the treatments described above , no changes in the parasites’ intracellular oxidative stress ( evaluated with H2DCFDA fluorescent probe ) were observed , as compared to controls ( S1 Table ) . The antiparasitic activity of Vit C was measured using a murine model of acute Chagas’ disease . For this experiment , 4 groups of 6 animals each were used , which received Vit C alone or Bnz alone or Vit C plus Bnz or PBS . At the peak of parasitemia ( Fig 4A ) , on day 15 , treated groups had lower parasite counts , as compared to the control group ( p< 0 . 01 ) . While a parasitemia value of ( 60 . 5 ± 1 . 5 ) x 106 parasites/ml was obtained for the control group , the group treated with Vit C alone presented a 55% decrease in such values . Mice treated with Bnz alone or combined with Vit C presented similar parasitemia levels [ ( 18 . 65 ± 1 . 5 ) x 106 and ( 16 . 66 ± 1 . 5 ) x 106 parasites/ml , respectively] . In terms of area under the parasitemia curves , decreases of 40 . 45%; 58 . 65% and 63 . 79% were observed in mice treated only with Vit C , only with Bnz , or the combination of both , respectively . While in the control group a 100% of mortality was observed between days 14 and 29 post-infection , the group treated with Bnz + Vit C showed 100% survival until the end of the treatment period ( Fig 4B ) . Mice treated with the combination of compounds presented a reduction of weight loss ( Fig 4C ) , which accounts for an improved health status .
Chagas’ disease or American trypanosomiasis , currently treated with nifurtimox and Bnz , requires new and more efficient therapeutic alternatives . Employing the Tulahuén strain , we observed in vitro anti-T . cruzi activity for Vit C on the three parasite forms . This activity is similar that obtained with Bnz ( Table 1 ) . In previous studies employing the Y strain of T . cruzi , the IC50 values obtained for Vit C , in the epimastigotes , trypomastigotes and amastigotes , were 550; 9 and 138 times higher the ones obtained herein , respectively [17] . These variations might be caused by two reasons: the different strain of T . cruzi that was used ( Y strain instead of Tulahuén strain ) and the range of high concentrations of Vit . C that were tested ( 0 . 35 to 2 . 84 mM instead of 2 . 5 to 10 μM for epimastigotes , 0 . 09 to 1 . 42 mM instead of 0 . 38 to 380 μM for trypomastigotes and 0 . 18 to 2 . 84 mM instead of 0 . 5 to 40 μM for amastigotes ) . It is known that , depending on the concentration , Vit C can act as an antioxidant ( at physiological concentrations ) or as a pro-oxidant agent ( at pharmacological concentrations ) [9 , 10] . As an antioxidant , ascorbate can donate an electron to potentially harmful radicals such as the hydroxyl , alkoxyl , peroxyl , thiol and tocopheroxyl radicals; while being oxidized to the ascorbate radical . To act as a pro-oxidant , the presence of catalytic metal ions ( such as Fe , Cu ) is required . In the presence of iron , ascorbate is oxidized to the ascorbate radical while it reduces ferric ( Fe3+ ) to ferrous ( Fe2+ ) iron; Fe2+ can rapidly react with O2 , producing the superoxide radical and Fe3+; then superoxide radicals dismutate to H2O2 and O2 . Finally , H2O2 reacts with Fe2+ to generate , through the Fenton reaction , Fe3+ and the highly oxidant hydroxyl radical . The presence of ascorbate allows the recycling of Fe3+ to Fe2+ , which again catalyzes the formation of highly reactive oxidants from H2O2 [9 , 10] . NADH- and NADPH-dependent reductases are responsible for reducing the ascorbate radical back to ascorbate [9 , 10] . The harmful pro-oxidant effect of Vit C is responsible for cell death and would account for the antiparasitic activity observed on T . cruzi . The antiparasitic effect of Vit C is also corroborating that , in addition to being synthesized , this compound can be captured from the environment [13] to cause parasite death . Unlike Bnz , which is cytotoxic , ( CC50: 90 . 9 ± 2 . 50 μM ) , Vit C showed did not exert a cytotoxic effect , at least at concentrations ≤2 , 500 μM ( Fig 2A ) . Consequently , the SI value for Vit C is >50 , which is considered adequate for trypanocidal drugs [18] . Upon testing Vit C at 5 , 000 μM , a cytotoxic effect of ~60% was manifested . On the other hand , the cytotoxicity exerted by 100 μM Bnz decreased significantly ( from 50 to 20% ) in the presence of 5 μM Vit C ( Fig 2B ) . According to other researchers , the treatment with Vit C alone was not efficient at reducing the parasitemia levels in both the acute and chronic murine model of Chagas’ disease [19 , 20] . Considering these results and assuming that the secondary effects could be in part related to Bnz cytotoxicity , which significantly decreased in the presence of Vit C , it would be suitable , and taking into account previous works , to implement a combined treatment with Vit C + Bnz . Therefore , the effect of such combination on the viability of trypomastigotes was evaluated in in vitro assays ( Table 1 ) . Neither additive , synergistic , nor antagonistic effects were observed . The percentage of lysis obtained with the combination of both drugs ( Vit C and Bnz ) was not different from that obtained with Bnz only . This behavior indicates that the presence of Bnz would abrogate the pro-oxidant effect of Vit C . Assuming that the level of reduced thiols is as an indicator of the cell redox state; a decrease or an increase in reduced thiol levels could be associated with the pro-oxidant and antioxidant effect of Vit C , respectively . Upon treating epimastigotes with high concentrations of Vit C for short periods of time , ( conditions that were previously determined as the most suitable to detect the mode of action [3 , 4 , 21] , we observed a different behavior as regards thiol levels obtained either in the absence ( Fig 3A ) or the presence of Bnz ( Fig 3B ) . In the absence of Bnz , the pro-oxidant effect is independent of the concentration of Vit C , and precedes the antioxidant effect; which is Vit . C concentration-dependent and returns the thiol levels to baseline values at the end of the treatment ( 10 h ) . When thiol levels are determined after treatment with both Vit C and Bnz ( 15 μM ) , first the antioxidant and then the pro-oxidant effect is observed; being both dependent on the vitamin concentration . Metabolomics analyses have identified that the covalent binding of Bnz , or its reduction products , to low molecular weight thiols , as well as to protein thiols , are the main mechanism of action of Bnz on T . cruzi [22] . This fall in the thiol content caused by Bnz was considered in the control values ( 0 μM Vit C ) , therefore it does not affect the results showed in Fig 3B , Considering the depletion of thiol levels produced by Bnz , an antioxidant effect of Vit C , manifested after 2 h after the addition of Bnz , would be expected . The latter effect would contribute to the restoration of baseline thiol levels within the parasite . Results obtained during the first hours of treatment demonstrated a pro-oxidant effect of Vit C in the absence of Bnz , while in the presence of this drug , the antioxidant effect prevails . This finding would explain the lack of pro-oxidant or antitrypanocidal effect of Vit C when added in combination with Bnz on bloodstream trypomastigotes ( Table 1 ) . The treatment with different Vit C concentrations ( 5 μM to 30 μM ) for short periods of time ( 2 h to 10 h ) did not affect the intracellular oxidative stage ( S1 Table ) , thus evidencing the efficacy of the parasite’s defense system to counteract a redox imbalance . When evaluating the in vivo effect of Vit C alone or combined with Bnz , on a murine model of acute Chagas’ disease , a good correlation between the parasitemia levels and the results obtained in vitro was observed . Due to the pro-oxidant capacity , mice treated with Vit C had parasitemia levels that were significantly lower than those of control mice , but higher than those obtained with Bnz alone . The combined treatment did not cause a significant decrease in the parasitemia levels , as compared to the levels obtained in animals treated with Bnz alone ( in accordance with Table 1 ) . When Vit C is combined with Bnz , its antiparasitic/pro-oxidant effect is not manifested; however , it could be hypothesized that an antioxidant effect would be operating in both parasite and host; protecting the mice from the oxidative stress induced by the infection The latter phenomenon would account for both , the 100% survival and the reduction of weight loss of mice , which accounts for an improved health status . It must be born in mind that the antioxidant effect of Vit C has a stimulatory effect acting on several cell functions of both the innate and adaptive immune system [23] . Therefore , the role of the immune system in animal survival cannot be ruled out ( studies currently in progress ) . In this paper , the dual effect of Vit C on the parasite was demonstrated . Vit C was proved to have a pro-oxidant effect when administered alone , and an antioxidant effect when used in combination with Bnz . On the host , only the antioxidant effect was evident . Based on these results , the combined therapy employing Vit C together with Bnz arises as a promising alternative therapy for the treatment of Chagas’ disease . Vit C would also contribute to counteract the serious secondary effects caused by Bnz . | The huge worldwide expansion of Chagas’ disease ( American trypanosomiasis ) that has occurred as a result of the population mobility from Latin America , has caused this parasitic disease to become an important topic for the World Health Organization . Cases of Chagas’ disease have been reported in the United States and Canada , as well as in Europe and the Western Pacific . The efficacy of current drugs ( nifurtimox and benznidazole ) to treat this disease is limited . Besides , these drugs cause adverse effects . For these reasons , a continuous research to find therapeutic alternatives is required . In our laboratory , we evaluate the anti-Trypanosoma cruzi effect of both synthetic and natural products . Promising results have been obtained in our laboratory when the effect of vitamin B12 was tested on infected mice . As a continuation of that work , herein we evaluated the effects of the combined treatment of vitamin C and benznidazole as a therapeutic alternative . Vitamin C was found to diminish the cytotoxicity of the antichagasic drug . | [
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] | 2018 | Anti-parasitic effect of vitamin C alone and in combination with benznidazole against Trypanosoma cruzi |
Trombiculid mites are the vectors of scrub typhus , with infected larval mites ( chiggers ) transmitting the causative agent , Orientia tsutsugamushi , during feeding . Co-existence of multiple O . tsutsugamushi strains within infected mites has previously been reported in naturally infected , laboratory-reared mite lines using molecular methods to characterize the 56-kDa type-specific antigen ( TSA ) gene . In the current study , more advanced next-generation sequencing technology was used to reveal the heterogeneity of O . tsutsugamushi genotypes in field-collected trombiculid mites from rodents and small mammals in scrub typhus-endemic areas of Thailand . Twenty-eight trombiculid mites collected from 10 small mammals were positive for O . tsutsugamushi , corresponding to a prevalence rate of 0 . 7% within the mite population . Twenty-four of the infected mites were Leptotrombidium spp . , indicating that this genus is the main vector for O . tsutsugamushi transmission in Thailand . In addition , O . tsutsugamushi was detected in the mite genera Ascoschoengastia , Blankaartia , Gahrliepia , and Lorillatum . Of the 10 infested small animal hosts , six had 2–10 infected mites feeding at the time of collection . Deep sequencing was used to characterize mixed infections ( two to three O . tsutsugamushi genotypes within an individual mite ) , and 5 of the 28 infected mites ( 17 . 9% ) contained mixed infections . Additionally , 56-kDa TSA gene sequence analysis revealed identical bacterial genotypes among co-feeding mites with single or mixed infections . These results suggest that co-feeding transmission may occur during the feeding process , and could explain the occurrence of mixed infections in individual mites , as well as the recovery of multiple infected mites from the same host . This study also revealed highly diverse within-host O . tsutsugamushi genotypes . The occurrence of multiple O . tsutsugamushi genotypes within individual mites has important implications , and could provide a mechanism for pathogen evolution/diversification in the mite vector .
Scrub typhus is caused by intracellular Gram-negative bacteria belonging to the genus Orientia , although O . tsutsugamushi was thought to be the only causative agent until recently . Orientia species have been separated from the genus Rickettsia on the basis of differences in genome arrangement and cell wall structure , as well as the substantial genetic distance between the two genera [1 , 2] . Recently , the incidence of scrub typhus has drastically increased in several tropical and sub-tropical countries , including Bhutan , Nepal , and Thailand [3 , 4] . Environmental changes such as deforestation , urbanization , and even natural disasters have been suggested to play crucial roles in this reemergence [5–7] . In addition , new species of Orientia and the presence of the pathogen in locations outside of the previously described endemic region of the Tsutsugamushi triangle have been documented [8] . Recent evidence suggests the emergence of new Orientia species in both the Middle East and South America , with infected patients presenting with symptoms similar to those of scrub typhus [9 , 10] . Serology testing of patient-paired serum ( acute and convalescent ) revealed four-fold increases in antibody titers against O . tsutsugamushi antigens in these patients . Comparison of the 16S rRNA , 47-kDa HtrA , and 56-kDa type-specific antigen ( TSA ) gene sequences against the GenBank database showed that the causative agents were most closely related to , but clearly separated from , O . tsutsugamushi . Based on these findings , one of the isolates was proposed as a new species of Orientia , and named Candidatis Orientia chuto [10] . Currently , there is no vaccine available for scrub typhus infection . The prophylactic treatment with antibiotic as a prevention method is recommended by World Health Organization ( WHO ) under special circumstances in endemic areas . Other general protective measures include avoiding exposure conditions , wearing appropriate clothing , and using insect and spatial repellents to prevent the chigger bites [11 , 12] . Patients initially present with non-specific flu-like symptoms such as fever , rash , headache , myalgia , cough , generalized lymphadenopathy , nausea , vomiting , and abdominal pain approximately 5–14 days after being bitten by an infected mite [13 , 14] . The fatality rate can be as high as 50% among these patients if left untreated . An eschar at the bite site is a feature of scrub typhus disease; however , the prevalence of eschar formation varies from 1–97% depending on the geographical area [15–17] . Trombiculid mites are the primarily vectors of O . tsutsugamushi , especially species belonging to the genus Leptotrombidium , including L . arenicola , L . deliense , L . pallidum , L . fletcheri , L . scutellare , L . chiangraiensis , L . imphalum , and L . akamushi [18–20] . In South Korea and Japan , L . pallidum and L . scutellare are the predominant vector species , and are distributed throughout the islands [21–23] , while in Taiwan and Thailand , L . deliense is the main vector [24 , 25] . Other mite genera are also recognized as potential vectors of scrub typhus , including Blankaartia and Ascoschoengastia; however , O . tsutsugamushi carriage has not been as deeply investigated in these genera [26 , 27] . In general , mite species known to be important or potential vectors of O . tsutsugamushi are widely distributed throughout Asia , northern Australia , and the western Pacific Islands , placing an estimated one billion people at risk of scrub typhus [28] . To assess the diversity of O . tsutsugamushi isolates serotyping was developed utilizing serum raised in laboratory animals to individual orientia isolates . The produced antiserum or antisera reactive to new orientia isolates by IFA indicated how closely related the new isolate was to known isolates used to produce the antisera and consequently the lack of reactivity indicated the new isolate was antigenically distinct from the isolate ( s ) that produced the sera . Early on three prototypes ( antigenically distinct isolates ) strains were identified , Karp , Kato and Gilliam . Subsequently other antigenically distinct isolates were identified . Utilizing the serotyping technique it was found that Karp was the most prevalent O . tsutsugamushi serotype of isolates found in rodents and vectors in Thailand , with other serotypes appearing to be less common [29] . With advances in genetic characterization of bacteria the relationship between serotyping and genotyping of orientia isolates was assessed . Early studies showed that serotyped O . tsutsugamushi isolates using hyperimmune serum raised against prototype strains of the genotypes ( genetically distinct ) Karp , Gilliam , Kato , and TA716 , resulted in cross-reactivity against genetically diverse isolates suggesting that many serotypes existed [30 , 31] . Interestingly , the Gilliam and Japanese Gilliam serotypes suggested that these orientia isolates were closely related antigenically , however with genotyping based on the 56-kDa TSA gene they were shown to be genetically disparate ( 25 ) . Thus , the genotyping method based on the 56-kDa TSA gene sequence and subsequent phylogenetic analysis , revealed substantial genotypic diversity among O . tsutsugamushi isolates from patients and rodent hosts in many parts of Thailand contrary to the serotyping study just mentioned above and that many of these new isolates were found to have much less similarity to the prototype strains than previously thought using serotyping techniques [25 , 32 , 33] . O . tsutsugamushi is maintained through both transovarial and transstadial transmission processes in the mite population , with these mechanisms thought to be the main ways in which the bacterium is maintained in the wild [20 , 34–36] . Nevertheless , a small number of studies have reported the acquisition of O . tsutsugamushi from an infected host to naïve mites , although the bacteria failed to transmit transovarially [37–39] . Moreover , co-feeding transmission of O . tsutsugamushi from infected mite ( s ) to co-feeding naïve mites of the same or different species has been demonstrated in a laboratory setting; however , the study did not confirm whether this type of infection resulted in subsequent transstadial and transovarial transmission [40] . In ticks , co-feeding transmission of pathogens such as viruses occurs when infected individuals co-fed with uninfected individuals [41–44] . This direct tick-to-tick mode of transmission is thought to be an important process in maintaining viruses in nature . In addition , the bacterial pathogens responsible for Lyme disease , Borrelia burgdorferi and Borrelia afzelii , can be transmitted from infected nymphal Ixodes ricinus L . ticks to co-fed uninfected larval ticks [45 , 46] . Although co-feeding transmission of these bacterial spirochetes is thought to occur in nature , albeit to a lesser extent than was observed in the laboratory , the perpetuation of the bacteria in nature largely depends on acquisition from systemically-infected hosts . Co-feeding transmission of Rickettsia has been observed among ticks , with uninfected Rhipicephalus sanguineus ticks being much more likely to acquire a R . conorii infection when feeding in close proximity to infected ticks , compared to feeding on an infected animal host [47] . A recent study has also demonstrated the co-feeding transmission of Rickettsia felis from infected cat fleas ( Ctenocephalides felis ) to naïve cat fleas or rat fleas ( Xenopsylla cheopis ) on a vertebrate host , and this mode of transmission could be crucial for the maintenance of R . felis within the vector population [48] . Although the occurrence of horizontal transmission and co-feeding transmission of O . tsutsugamishi has not been well documented , this mode of transmission might play an important role in maintaining O . tsutsugamushi in nature . Our previous study showed the co-existence of two O . tsutsugamushi genotypes in single laboratory-reared mite lines [35] . Twelve colonies from three species of Leptotrombidium mites ( L . chiangraiensis ( Lc ) , L . imphalum ( Li ) , and L . deliense ( Ld ) ) were studied . An O . tsutsugamushi Karp-like genotype was found as a single genotype in the L . chiangraiensis line , but as part of a co-infection with an UT302-like genotype in L . imphalum lines or with a Gilliam-like genotype in the L . deliense line . Moreover , the co-existing genotypes were well maintained through transovarial and transstadial transmission in L . imphalum mites [36] . Co-infection with multiple O . tsutsugamushi sequence types was observed in 25% of patients in a study conducted by Sonthayanon et al . [49] . The study , along with several others , also identified a high degree of genetic diversity within the O . tsutsugamushi isolates recovered from patients [49–51] . Indeed , the genome of O . tsutsugamushi shows a high level of plasticity , with 50% of the genome containing repetitive sequences derived from integrative and conjugative elements . Amongst these , several hundred transposases , phage integrases , and transposable elements have been identified , as well as a massive duplication of components of the conjugative type IV secretion system ( 359 tra genes ) , which is distributed throughout the genome [52 , 53] . These findings indicate that genetic recombination , duplication , and rearrangement are major mechanisms driving O . tsutsugamushi genomic diversity and complexity . As O . tsutsugamushi is an obligate intracellular parasite , recombination would occur during coinfection of different bacterial strains within the same mammalian host or vector , resulting in the exchange of genetic material between the bacterial strains . The co-existence of O . tsutsugamushi strains of different genotypes in a single trombiculid mite is confirmation that these mechanisms are likely to contribute to the genomic diversity/complexity of the bacterial pathogen . In this study , we aimed to examine the prevalence of mixed infection of O . tsutsugamushi genotypes in individual field-collected mites in high and low scrub typhus-endemic areas of Thailand . Next generation sequencing ( deep sequencing ) of the 56-kDa TSA gene was used to accurately determine the presence and abundance of each O . tsutsugamushi genotype in individual infected mites . Additionally , the evidence for horizontal transmission of O . tsutsugamushi between co-feeding mites was discussed based on the study data . The results presented in this study represent an epidemiological assessment of O . tsutsugamushi carriage in a wild vector population in an area with active scrub typhus transmission .
Rodents and mites were collected during the wet season ( Jun–Aug ) in the northeastern ( Sisaket and Loei provinces ) , western ( Tak Province ) , and southern ( Pang Nga and Chumphon provinces ) regions of Thailand in 2015 ( Table 1 ) . All study sites were on private land , and permission was obtained from each of the owners to conduct research on their land . None of the field studies involved endangered or protected species . Rodents were captured using live traps baited with bananas , palm fruit , or dried fish , and were collected from orchards , palm and rubber plantations , cultivated rice-fields , grassland areas , edges of dense forest , stream margins , and around dwellings . Traps were set for 3–5 nights and were checked early in the morning . Captured rodents were removed from the traps , euthanized using carbon dioxide , and processed immediately at the site of collection . Blood , serum , and tissue samples ( liver , spleen , kidney , and lung ) were collected and stored on dry ice . Ears were removed and stored in 70% ethanol for mite collection . All tissues were then transported to the AFRIMS laboratory for further processing . All rodents were later identified to the species level as described previously [54] . A recent genetic analysis of rodents in Southeast Asia showed that the black rat , Rattus rattus sensu stricto , is not found in Thailand . However , three morphologically similar species are present; Rattus tanezumi , Rattus sakeratensis , and an additional mitochondrial lineage of unclear taxonomic status referred to as ‘Rattus R3’ [55] . These three species were not separated in this study using molecular methods and are referred to collectively as Rattus rattus complex . Meanwhile , all other members of the genus Rattus are identified to species . Genomic DNA was extracted from rodent spleen samples using a Wizard Genomic DNA Purification Kit ( Promega , Madison , WI ) according to the manufacturer’s instructions , with some modifications as per a previously published protocol [56] . Briefly , spleen tissue was cut into pieces ( ~3 mm in diameter ) and added to 600 μl of Nuclei Lysis Solution ( Promega , Madison , WI ) . The mixture was homogenized with beads using a TissueLyser II apparatus ( Qiagen , Hilden , Germany ) at 25 Hz for two rounds of 5 min each . The homogenized solution was then incubated with 20 μl of Proteinase K solution ( 20 mg/ml ) at 55°C for 1 h , and then with 3 μl of RNase A ( 10 mg/ml ) at 37°C for 15 min . A 200-μl volume of protein precipitation solution was then added , and the sample was mixed vigorously by vortex then incubated on ice for 5 min . Insoluble materials were removed by centrifugation at 20 , 000 x g for 4 min , and the supernatant was transferred to a new tube . DNA was precipitated by adding 600 μl of isopropanol , followed by centrifugation at 20 , 000 x g for 1 min . The resulting DNA pellet was washed with 70% ethanol and then air dried . Dried DNA was resuspended in 200 μl of EB buffer ( 10 mM Tris Cl , pH 8 . 5 ) and stored at −20°C until further analysis . Mites in their larval stage ( chigger ) were collected from rodent ears by paintbrush under the stereomicroscope and stored separately . Each mite was individually identified to the genus level under a high resolution microscope ( 400× , Nikon ECLIPSE Ni-U microscope , Tokyo , Japan ) using a taxonomic key [57] . Mites were not identified to the species level because this would have required the mites to be cleared and slide-mounted , which would prevent molecular testing . The genus of each mite was recorded , along with its host species and other field site information . Mites collected from wild-caught rodents were individually subjected to genomic DNA extraction using a modified tissue protocol from the QIAamp DNA Mini Kit ( Qiagen ) with a previously published protocol [35] . Mites were punctured with a fine needle under stereomicroscope to release the tissue from the hard chitin exoskeleton prior to DNA extraction . Eluted DNA solution was stored at −20°C until further use in O . tsutsugamushi quantitative polymerase chain reaction ( qPCR ) screening assays . Chigger cytochrome oxidase subunit 1 ( host COI gene ) was examined by conventional PCR on all mite samples before being subjected to Otsu47 qPCR assay as describe previously [58] . Field-collected mites and animal tissue ( spleen ) samples were screened for the presence of O . tsutsugamushi by qPCR analysis . The primers and probe were designed to detect a portion of the 47-kDa HtrA gene [59] . The reaction mixtures ( 25 μl ) contained 12 . 5 μl of 2× Platinum Quantitative PCR SuperMix-UDG ( Invitrogen , Foster City , CA ) , 0 . 2 mM probe , and 0 . 2 mM each primer . The qPCR assay , referred to as Otsu47 , was performed by incubating samples at 95°C for 2 min , followed by 45 cycles of 95°C for 15 s and 60°C for 1 min . Reactions were carried out using the ABI 7500 Fast Real-time PCR System ( Life Technologies , Carlsbad , CA ) . Trombiculid mites or animal tissue found to be positive for the presence of O . tsutsugamushi was then used as the basis for amplification and next-generation sequencing ( NGS ) of the 56-kDa TSA gene from O . tsutsugamushi . A fragment of the 56-kDa TSA gene ( variable domains I–III , 600–700 bp ) was amplified in a first-round PCR using primers RTS-8 and RTS-9 [60] . The reaction was performed in a 20-μl volume containing 5 μl of DNA template , 300 nM each primer , 200 μM dNTPs , 1 . 5 mM MgCl2 , 1× PCR buffer , and 0 . 4 U of iProof High-Fidelity DNA Polymerase ( Bio-Rad , Hercules , CA ) . Amplification was performed using a DNA thermal cycler under the following conditions: initial denaturation at 98°C for 2 min; 30 cycles of 98°C for 10 s , 45°C for 20 s , and 72°C for 45s; and a final extension at 72°C for 10 min . The second amplification was performed using fusion primers for NGS . The fusion primers comprised three parts: ( i ) 21-bp A and B sequencing adapters plus 4 bp of key sequence ( bold letters ) ( 5′-CGTATCGCCTCCCTCGCGCCATCAG-3′ and 5′-CTATGCGCCTTGCCAGCCCGCTCAG-3′ , respectively ) , ( ii ) 10-bp multiplex identifier ( MID ) sequences ( with a unique MID for each sample ) , and ( iii ) 56-kDa TSA gene-specific sequences RTS-6 and RTS-7 [60] . PCR assays were carried out in 50-μl reaction volumes containing 1 . 5 mM MgCl2 , 200 μM dNTPs , 0 . 3 μM each primer , and 0 . 5 U of iProof High-Fidelity DNA Polymerase . Five microliter of diluted product from the first amplification ( 1:50 ) was used as template . The thermal cycler parameters consisted of 98°C for 2 min , followed by 30 cycles of 98°C for 10 s , 55°C for 30 s , and 72°C for 30 s . The resulting amplicons were purified using AMPure beads ( Agencourt Bioscience Corporation , Beverly , MA ) , and amplicon concentrations were measured using the Quant-iT PicoGreen dsDNA Assay ( Invitrogen ) according to the manufacturer’s protocol . Each PCR amplicon was then diluted to 109 copies/μl and pooled ( 10–15 amplicons per pool ) . Sample pools were amplified by emulsion PCR using a GS Junior+ emPCR Kit ( Lib-A ) with a ratio of 0 . 5 copies per bead . Amplicons were then sequenced in both forward and reverse directions at 5000× coverage per sample on a 454 GS Junior Genome Sequencer Instrument using the GS Junior+ Sequencing Kit XL+ chemistry ( Roche 454 Life Sciences , Branford , CT ) . The average read length obtained from the NGS runs was 633 nucleotides , with a median number of reads per sample of 10 , 198 , ( range 3 , 749–28 , 943 ) . Raw reads were quality filtered and de-multiplexed with standard quality filtering parameters using CLC Genomics Workbench v 9 . 0 software ( Qiagen ) . After quality trimming , reads < 500 bp and > 800 bp in length were discarded . All trimmed reads were then de-multiplexed using the MID barcodes . All reads from each sample were mapped to a full reference set ( S1 Table ) using the Map Reads to Reference function in the CLC Genomics Workbench software . Each read was mapped to the reference sequences for which it showed the best match using the parameters length fraction = 0 . 5 and similarity fraction = 0 . 8 ( at least 50% of the total alignment and 80% identity ) , then the reads were counted and extracted from each reference . In order to be assigned to a O . tsutsugamushi genotype , the number of reads aligned to each reference had to be at least 5% of the total reads . The extracted reads per reference were assembled using de novo assembly , and the consensus sequence was extracted . The GenBank accession numbers for consensus sequences reported in this study were provided at the end of this article . The consensus sequences were aligned with reference sequences retrieved from the GenBank database using the MUSCLE codon alignment algorithm [61] . A maximum likelihood phylogenetic tree was then constructed based on the 56-kDa TSA gene variable domain I–III sequences using the GTR+G model of nucleotide substitution with bootstrapping ( 1000 replicates ) in MEGA 6 [62] ( S1 Fig ) . The 56-kDa TSA gene was amplified by nested polymerase chain reaction using our previously designed primer [35] for the first-round PCR , and then nested PCR was carried out as previously described [63] . The resulting 615-678-bp amplicon was purified using a Qiagen DNA Purification Kit to remove salts and primer dimers . The purified fragment was then cloned into pCR2 . 1-TOPO and transformed into Escherichia coli DH5a-T1R as per the manufacturer’s instructions ( Invitrogen ) . Transformants were randomly selected and screened by nested PCR . Between 6 and 28 clones from individual chiggers appeared to contain the correct 56-kDa TSA gene insert fragment . Plasmids containing the correct 56-kDa TSA gene insert were then purified from the E . coli host using a QIAprep Spin Miniprep Kit ( Qiagen ) , and sent for DNA sequencing ( Sanger method ) by AITBiotech ( Singapore ) . All statistical analyses ( Chi-square tests ) and graphical illustrations presented in this study were performed in the R environment for statistical computing [64–66] . A nucleotide distance matrix was generated using “DNADist DNA Distance Matrix” in BioEdit [67] . A heatmap dendrogram was generated in R using the heatmap . 2 function from the package Gplots and applying complete linkage clustering and Euclidean distances computed among 36 genotypes from 28 infected mites [68] . Rodents were trapped according to the institutional animal collection protocol titled “Field Sampling of Small Mammal ( Orders: Erinaceomorpha , Soricomorpha , Scandentia , Macroscelidea , and Rodentia ) Populations to Support Zoonotic Diseases Surveillance and Ectoparasite Collection” ( PN# 12–06 ) , reviewed and approved by the USAMC-AFRIMS Institutional Animal Care and Use Committee ( IACUC ) . All sampling procedures and experimental manipulations were reviewed and approved as part of obtaining the animal collection protocol ( PN# 12–06 ) . Research was conducted in compliance with the Animal Welfare Act and other federal statutes and regulations relating to animals and experiments involving animals , and adhered to principles outlined in the Guide for the Care and Use of Laboratory Animals , NRC Publication , 2011 edition . The GenBank ( http://www . ncbi . nlm . nih . gov/Genbank/ ) accession numbers for the 56-kDa TSA gene described in this paper are: MH290189 ( Lep . DS092 . 9c ) , MH290190 ( Lep . DS092 . 8c ) , MH290191 ( Lep . DS021 . b ) , MH290192 ( Lep . DS016 . b ) , MH290193 ( Lep . DS092 . 6b ) , MH290194 ( Lep . DS020 . 2a ) , MH290195 ( Lep . DS092 . 8a ) , MH290196 ( Lep . DS021 . 1h ) , MH290197 ( Lep . DS016 . e ) , MH290198 ( Lep . DS092 . 8e ) , MH290199 ( Lep . DS092 . 9e ) , MH290200 ( Lep . DS092 . 10e ) , MH290201 ( Lep . DS123 . 1e ) , MH290202 ( Lep . DS078 . d ) , MH290203 ( Lep . DS021 . 3f ) , MH290204 ( Lep . DS092 . 7g ) , MH290205 ( Lep . DS092 . 5g ) , MH290206 ( Lep . DS092 . 4g ) , MH290207 ( Lep . DS092 . 2g ) , MH290208 ( Lep . DS092 . 1g ) , MH290209 ( Lep . DS020 . 1g ) , MH290210 ( Lep . DS021 . 4g ) , MH290211 ( Lep . DS123 . 2g ) , MH290212 ( Lep . DS027 . g ) , MH290213 ( Lep . DS030 . g ) , MH290214 ( Asc . MS651 . g ) , MH290215 ( Lep . DS024 . b ) , MH290216 ( Bla . DS123 . a ) , MH290217 ( Lep . DS092 . 3e ) , MH290218 ( Bla . DS123 . e ) , MH290219 ( Lep . DS024 . d ) , MH290220 ( Lep . DS024 . g ) , MH290221 ( Bla . DS123 . g ) , MH290222 ( Gah . DS024 . g ) , MH290223 ( Lep . DS021 . 2g ) , and MH290224 ( Lor . MS651 . g ) .
Collection sites were selected based on our annual surveillance data ( 2012–2016 ) on scrub typhus prevalence in small mammals and trombiculid mites in Thailand ( S2 Fig ) . The data revealed a high prevalence of O . tsutsugamushi in Chumphon and Pang Nga provinces ( southern Thailand ) , while low prevalence rates were observed in Sisaket and Loei ( northeastern Thailand ) and Tak ( western Thailand ) provinces . Therefore , we decided to survey both high and low prevalence areas for comparison . Small mammals and rodents were collected during the rainy season ( Jun–Aug ) of 2015 . In total , 275 small mammals and rodents were collected from five provinces in three regions of Thailand ( Table 1 ) . The collected animals belonged to eight genera and 15 species ( Table 2 ) . The greatest rodent species diversity was observed in Pang Nga Province , followed by Tak Province . R . rattus complex ( n = 113 ) , Bandicota indica ( greater bandicoot rat , n = 54 ) , and Mus caroli ( ryukyu mouse , n = 42 ) were the most abundant species sampled in this study , accounting for 41 . 1% , 19 . 6% , and 15 . 3% of total rodents collected , respectively . R . rattus complex were the most abundant species in all provinces ( 34 . 5–65 . 3% ) except Tak Province , in which M . caroli ( 46 . 2% ) was the most abundant species collected . A total of 4 , 281 trombiculid mites were collected from 176 of the 275 ( 64 . 0% ) small mammals and rodents examined in this study ( Table 2 ) . The infestation rate by host species was determined for the most abundant species , with the highest infestation rates observed in R . rattus complex ( 101/113 , 89 . 4% ) , B . indica ( 45/54 , 83 . 3% ) , and Tupaia glis ( common tree shrew; 9/13 , 69 . 2% ) . Overall , mite infestation rates among animals collected from each of the provinces ranged from 33–86 . 7% . Of the infested animals , those from Sisaket , Chumphon , and Pang Nga provinces were heavily infested , with a chigger index ( average number of chiggers per animal ) ranging from 19 . 1–21 . 4 ( Table 2 ) . However , when present , the number of mites on individual hosts was highly variable ( 1–181 ) . The most abundant mite genus collected from animals in this study was Leptotrombidium ( 49 . 1% ) , followed by Gahrliepia ( 24 . 4% ) and Ascoschoengastia ( 20 . 4% ) . Overall , a significantly greater proportion of Leptotrombidium mites were collected relative to other mite genera ( P < 0 . 001 , Chi-square test ) , with the abundance of Leptotrombidium spp . clearly observed in Pang Nga and Chumphon provinces ( southern Thailand ) ( Table 2 ) . The greatest numbers of mites were collected from R . rattus complex ( 2 , 831 , 66 . 1% ) , B . indica ( 987 , 23 . 1% ) , and T . glis ( 223 , 5 . 2% ) , with mean chigger index scores of 25 . 1 ( 2 , 831/113 ) , 18 . 3 ( 987/54 ) , and 12 . 2 ( 223/13 ) , respectively ( Fig 1A ) . However , when considering the proportion of Leptotrombidium spp . amongst all mite genera collected from these hosts , T . glis individuals showed the highest proportion of Leptotrombidium spp . mites ( Chi-square test , P < 0 . 001 ) . The majority of animals were infested with mites belonging to one ( n = 74 , 42% ) or two ( n = 73 , 41 . 5% ) different genera , while only 27 ( 15 . 3% ) and two ( 1 . 1% ) animals were infested with three or four different mite genera , respectively , with R . rattus complex accounting for the majority of these animals ( 19/29 , 65 . 5% ) ( Fig 1B ) . The overall prevalence of O . tsutsugamushi in the animal populations was very low ( 1 . 1% , 3/275 ) . The three infected rodents were identified as R . rattus complex ( n = 2 ) and B . indica ( n = 1 ) , and were collected from Pang Nga Province ( southern Thailand ) ( Table 1 ) . However , trombiculid mites collected from the infected animals were all negative for O . tsutsugamushi . Genotyping of O . tsutsugamushi from these three rodents revealed single infections with Karp A-genotype strains ( 26–30 clones , S2 Table ) . Screening of the 4 , 281 individual mites for the presence of O . tsutsugamushi using the Otsu47 qPCR assay revealed positive results for 28 mites ( prevalence = 0 . 7% ) . Twenty-four of the 28 infected mites were Leptotrombidium spp . ( 85 . 7% ) , while the remaining four mites were Ascoschoengastia spp . , Lorillatum spp . , Gahrliepia spp . , and Blankaartia spp . ( Table 3 ) . The 28 trombiculid mites were collected from 10 animals: two mites were from one R . rattus complex individual collected in Srisaket Province , while the remaining 26 infected mites were from nine animals collected in Pang Nga Province ( four R . rattus complex , three B . indica , and two T . glis ) . However , tissue samples from all 10 animals were negative for O . tsutsugamushi infection using the same assay . The overall infection rate among mites collected from these 10 animals was 8 . 4% ( 28/333 ) , whereas the O . tsutsugamushi prevalence among mites per host varied from 2 . 2–21 . 3% . Prevalence rates for infection among the same mite species per host ranged from 0–21 . 3% ( Table 3 ) . The number of infected mites per host varied from 1–10 , with four hosts having one infected mite and six hosts with more than two infected mites ( 2–10 infected mites ) . Genotyping of O . tsutsugamushi was conducted for the 28 positive mites by NGS . Of these , the 56-kDa TSA gene was successfully amplified and sequenced from 21 mites using the NGS method . The target gene from the remaining seven mites was genotyped by cloning , as shown in Table 4 . The genotypic characterization was based on 56-kDa TSA gene sequence identity to reference sequences in the database , as well as phylogenetic analysis ( S1 Table and S1 Fig ) . Overall , eight O . tsutsugamushi genotypes were detected in the mite population . The majority of infected mites showed single infection ( 23/28 , 82 . 1% ) , with only five mites ( 5/28 , 17 . 9% ) showing mixed infection with 2–3 different O . tsutsugamushi genotypes . The most prevalent O . tsutsugamushi genotype found in the mites was TA763 B ( 44 . 4% ) , followed by Kato B ( 19 . 4% ) ( Table 4 ) . The majority of single infections ( n = 23 ) were identified as genotype TA763 B strains ( 14/23 , 60 . 9% ) , followed by Kato B ( 3/23 , 13 . 0% ) and Karp A ( 2/23 , 8 . 7% ) . The remaining four single infections were identified as genotypes TA763 A , Kato A , and Gilliam JG-C , as well as an unknown genotype . Twenty of the 23 infected mites ( 86 . 9% ) with single infection were Leptotrombidium spp . , while the remaining mites were Gahrliepia spp . , Ascoschoengastia spp . , and Lorillatum spp . All mixed O . tsutsugamushi genotype infections ( n = 5 ) were identified in animals from Pang Nga Province . Four infected mites were Leptotrombidium spp . , and one was Blankaartia spp . Two Leptotrombidium mites had a mixed infection with two O . tsutsugamushi genotypes , whereas the rest of the mites ( two Leptotrombidium spp . and one Blankaartia spp . ) showed mixed infection with three genotypes . The genotype composition and the relative abundance of each of the genotypes were determined by the number of NGS reads , or by the number of clones for those sequences amplified using the cloning technique ( Table 4 ) . Of the 10 small mammal and rodent hosts infested with Orientia-infected mites , six were found to have 2–10 infected mites feeding on them at the time of collection ( Table 3 ) . Three of these animals had two Orientia-infected mites , while the remaining three hosts had 3 , 5 , and 10 Orientia-infected mites , respectively . The relationship between the O . tsutsugamushi genotypes and the mite genera collected from each host was then examined . The majority of infected mites were Leptotrombidium spp . , and were collected from hosts where this genus was predominant ( 86 . 2–100% ) . However , when a host was infested with several mite genera , the number of infected mites proportionately reflected the relative abundance of each of the mite genera . For example , 90 mites were collected from R . rattus complex individual MS0651 , and belonged to three different genera: 71 Ascoschoengastia spp . , 16 Lorillatum spp . , and 3 Gahrliepia spp . Of these 90 mites , only three were infected with O . tsutsugamushi , two of which were Ascoschoengastia spp . and one was Lorillatum spp . No infection was detected in Gahrliepia spp . mites , which were present on the host in the lowest numbers . The same situation occurred with mites collected from R . rattus complex individual DS024 ( n = 19 ) , which showed an almost equal prevalence of Leptotrombidium spp . ( n = 10 ) and Gahrliepia spp . ( n = 9 ) . In this case , one infected mite was identified from each genus . Also , among mites collected from B . indica individual DS123 ( n = 36 ) , two of the infected mites were Leptotrombidium spp . ( n = 26 ) , while the remaining infected mite was Blankaartia spp . ( n = 10 ) . A network graph was generated to determine the relationships between O . tsutsugamushi genotypes detected in mites collected from the same animal host ( Fig 2A ) . From hosts infested with 2–10 infected mites , identical O . tsutsugamushi genotypes were detected among infected mites collected from the same host ( Table 3 ) . For example , two R . rattus complex individuals ( MS0651 , black line; DS024 , purple line in Fig 2A ) each had two infected mites ( MS0651: Ascoschoengastia and Lorillatum mites; DS024: Gahrliepia and Leptotrombidium mites ) , each of which carried the TA763 B genotype . An interesting case was observed in a B . indica rat ( DS123 , red line in Fig 2A ) infested with three infected mites . Two of the mites ( Leptotrombidium spp . ) had the Kato B and TA763 B genotypes , respectively , while the third infected mite ( Blankaartia spp . , white circle ) contained both genotypes . A similar situation was observed in another B . indica rat ( DS092 , green line ) infested with 10 infected Leptotrombidium mites . Eight mites had a single infection , while two had mixed infection . Among the mites with a single infection , five shared the same TA763 B genotype and two had the Kato B genotype . The latter genotype was also detected in two infected mites with mixed infection , and both of these mites also shared the Karp C genotype . In contrast , in a common tree shrew ( DS021 , T . glis , blue line in Fig 2A ) , only two out of five infected mites ( Leptotrombidium spp . ) shared the same genotype ( TA763 B ) . Likewise , two infected Leptotrombidum spp . infested on another common tree shrew ( DS020 , T . glis , pink line in Fig 2A ) carried different O . tsutsugamushi genotypes ( TA763 B and Gilliam ) . The heatmap shown in Fig 2B outlines the nucleotide sequence distance matrix of the 36 O . tsutsugamushi 56-kDa TSA gene genotypes detected in 28 infected mites . Alignment and identity analysis of the 56-kDa TSA gene sequences belonging to the same genotypes revealed a high level of similarity for each of the genotypes , with percent identities ranging from 98–100% , except for the Kato A genotype , where only 89% identity was observed .
This study is the first to provide evidence of the co-existence of multiple O . tsutsugamushi genotypes in field-collected trombiculid mites using NGS based on the 56-kDa TSA gene ( variable domains I–III ) . These findings build on our previous research showing that different O . tsutsugamushi strains can co-exist in laboratory-reared Leptotrombidium mites maintained in an ABSL-3 facility [35 , 36] . Co-existing genotypes , representative of mixed infections , occurred in five out of 28 infected mites ( 17 . 9% ) examined in the current study . In addition , identical O . tsutsugamushi genotypes were detected among co-feeding trombiculid mites , which may indicate co-feeding transmission . Co-feeding transmission of O . tsutsugamushi occurs among laboratory-reared infected and uninfected mites ( Leptotrombidium spp . and Blankaartia spp . ) that had co-fed on ICR mice [40] . The current study suggests that co-feeding transmission may also occur naturally among field-collected trombiculid mites feeding on the same animal host , leading to mixed infections or multiple infected mites carrying identical O . tsutsugamushi genotypes . This conclusion is supported by two main observations . First , a single O . tsutsugamushi genotype was detected in multiple mites belonging to different genera . Second , one O . tsutsugamushi genotype was detected in a mite with mixed infection , with the same genotype also found in a co-feeding mite with single infection ( Table 4; Fig 2A ) . For example , B . indica hosts DS092 and DS123 were infested with 10 and 3 infected mites , respectively , all of which shared similar/identical O . tsutsugamushi genotypes in both single and mixed infections . In addition , the majority of mites with mixed infection ( three Leptotrombidium spp . , one Blankaartia spp . ) were found on hosts infested with more than two infected mites . Thus , a higher number of infected mites per host increases the likelihood of an uninfected mite acquiring O . tsutsugamushi during co-feeding [40] . Among the rodents collected in the current study , mites were mostly collected from the inner earlobe , and some hosts were heavily infested ( as many as 181 mites/host ) . Only a small number of mites were found on the soft tissues of the ventral and genital areas . From previous field experience , clusters of mites are rarely found on the ventral surfaces of rodents , possibly because rodents are able to groom these areas; whereas , they cannot groom inside their ears and clusters of mites are able to form . In contrast , mites on the common tree shrew ( T . glis ) were predominantly found on the ventral and genital areas , with few mites in the inner earlobe . This is probably because the skin in the inner ear of shrews is very thick compared to rodents , and is not conducive to chigger feeing . The observation that mites were more widely dispersed on shrews and did not feed in tight clusters within the inner earlobe could be one possible hypothesis to explain why infected mites on shrews did not have identical O . tsutsugamushi genotypes , and showed no evidence of co-feeding transmission . Larval mites usually feed once on a rodent host for a period of 2–5 days [40 , 69] . Mites on rodents were most often observed feeding in tight clusters in the inner earlobe , supporting the likelihood that if there was an infected mite , the close proximity and duration of feeding would provide the opportunity for co-feeding transmission of O . tsutsugamushi from infected to naïve mites . Similar observations have been made for Rhipicephalus sanguines ticks , where co-feeding transmission efficiency of R . conorii is greatly increased by close proximity of infected and naïve ticks [47] . Therefore , we speculate that co-feeding transmission could be another mode of O . tsutsugamushi transmission , in addition to transstadial and transovarial transmission processes , and play a significant role in maintaining O . tsutsugamushi in mite populations in nature . In the high-prevalence area ( Pang Nga Province ) , Leptotrombidium species were the most abundant mites , and were mainly associated with R . rattus complex and B . indica rats . Moreover , Leptotrombidium was the most commonly infected mite genus in this area , which is consistent with previous studies showing that Leptotrombidium spp . are still the main vectors for scrub typhus transmission [23 , 70–73] . Interestingly , our findings confirm a previous study that used direct immunofluorescence assays ( DFA ) to show that O . tsutsugamushi was present in Blankaartia acuscutellaris mites collected from rodents in central Thailand [27] . However , further investigation by the same group failed to isolate O . tsutsugamushi in laboratory mice from field-collected B . acuscutellaris [26] . The authors suggested that the detection of O . tsutsugamushi by DFA in B . acuscutellaris , which has been reported previously by Tanskul et al . [27] , might be a result of co-feeding transmission or acquisition of the bacterium from a systemically infected host , as these modes of transmission have been shown to occur in an experimental mice and laboratory-reared mites [40] . However , our current study used molecular detection methods to identify O . tsutsugamushi infection not only in Bankaartia spp . , but also in Ascoschoengastia spp . , Gahrliepia spp . , and Lorillatum spp . Additionally , some of these genera were found feeding in the earlobe in close proximity to Leptotrombidium spp . Acquisition from a systemically infected host could be excluded , as all hosts infested with O . tsutsugamushi-positive mites were negative for O . tsutsugamushi infection by molecular assay . Therefore , the presence of O . tsutsugamushi in these mites could be a result of co-feeding transmission from infected Leptotrombidium mite ( s ) feeding in the close proximity on the same host . This could explain the finding of an identical O . tsutsugamushi genotype in Gahrliepia spp . and Leptotrombidium spp . mites on R . rattus complex individual DS024 , and in a Blankaartia spp . mite on B . indica individual DS123 . However , the Blankaartia mite collected from DS123 also carried the Gilliam genotype , which was not detected in its co-feeding Leptotrombidium mites . So , the question remains of how the Blankaartia mite acquired the Gilliam genotype . A similar observation was made among Ascoschoengastia and Lorillatum mites co-feeding on R . rattus complex MS651 , where both mites carried TA763 B genotype bacteria . However , because all of these conclusions are based solely on data from field-collected mites , where transmission could not be monitored , other unknown factors could have influenced the results . For example , Gilliam genotype detected in Blankaartia mite infested on host DS123 may have been present in the mite prior to attachment to this host ( i . e . it has been maintained in the mite via transovarial transmission ) . Further study is therefore needed to investigate whether other genera are capable of transmitting O . tsutsugamushi in nature , and to confirm whether co-feeding transmission results in mixed infection of O . tsutsugamushi genotypes . Such co-feeding transmission studies can be conducted using laboratory-reared infected mites and animal models , allowing for more controlled experimentation . It is worth noting that an abundance of Leptotrombidium mites or a higher chigger index does not necessarily directly correspond to an increased prevalence of O . tsutsugamushi in mite populations . This was the case in Chumphon Province , which had the second highest abundance of mites , and where the majority of mites were Leptotrombidium spp . However , no infected mites were detected in this area . In addition , two infected mites ( Ascoschoengastia spp . and Lorillatum spp . ) were collected from a R . rattus complex in Sisaket Province; however , no Leptotrombidium spp . mites were collected from animals captured in this area . Based on our study data , Pang Nga Province could be considered a hotspot for scrub typhus transmission within mite populations , as multiple infected mites were usually found on each animal host . Interestingly , during the study period , 211 human cases of scrub typhus were reported in Pang Nga Province by the Thai Ministry of Public Health . The current study also revealed highly diverse within-host O . tsutsugamushi genotypes , with as many as three different O . tsutsugamushi genotypes found in individual mites . The ecological dynamics of multiple O . tsutsugamushi genotypes in one host could drive pathogen evolution/diversification in the vector . This may be consistent with the hypothesis that genetic recombination among different O . tsutsugamushi genotypes can occur in the mite vector [74] . | Scrub typhus is a leading cause of undifferentiated febrile illness , putting 1 billion people at risk of infection . Trombiculid mites are the major vectors of the causative agent of scrub typhus , Orientia tsutsugamushi ( OT ) , which is transferred through the bite of an infected mite . Previously , we reported the co-existence of multiple OT strains within laboratory-reared mites . Here , we extended our investigation into wild trombiculid mite populations collected from areas of Thailand where scrub typhus is endemic . More advanced next-generation sequencing ( NGS ) technology was used to examine the presence and abundance of mixed infection in individual mites based on sequence analysis of the 56-kDa type-specific antigen gene . NGS data revealed heterogeneity of OT genotypes in field-collected mites , and mixed infection with two to three OT genotypes was consistently observed ( prevalence rate = 17 . 9% ) . Moreover , the identical OT genotype detected among co-feeding mites with single or mixed infections suggesting that co-feeding transmission may occur during the feeding process . This result could explain the occurrence of mixed infections in individual mites , as well as the recovery of multiple infected mites from the same host . | [
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] | 2018 | Heterogeneity of Orientia tsutsugamushi genotypes in field-collected trombiculid mites from wild-caught small mammals in Thailand |
The rapidly increasing amount of public data in chemistry and biology provides new opportunities for large-scale data mining for drug discovery . Systematic integration of these heterogeneous sets and provision of algorithms to data mine the integrated sets would permit investigation of complex mechanisms of action of drugs . In this work we integrated and annotated data from public datasets relating to drugs , chemical compounds , protein targets , diseases , side effects and pathways , building a semantic linked network consisting of over 290 , 000 nodes and 720 , 000 edges . We developed a statistical model to assess the association of drug target pairs based on their relation with other linked objects . Validation experiments demonstrate the model can correctly identify known direct drug target pairs with high precision . Indirect drug target pairs ( for example drugs which change gene expression level ) are also identified but not as strongly as direct pairs . We further calculated the association scores for 157 drugs from 10 disease areas against 1683 human targets , and measured their similarity using a score matrix . The similarity network indicates that drugs from the same disease area tend to cluster together in ways that are not captured by structural similarity , with several potential new drug pairings being identified . This work thus provides a novel , validated alternative to existing drug target prediction algorithms . The web service is freely available at: http://chem2bio2rdf . org/slap .
Understanding the interaction of drugs with multiple targets can identify potential side effects and toxicities [1]–[3] , as well as possible new applications of existing drugs [4]–[8] . Many efforts have been made to integrate drug-target interactions in a large scale [9]–[12] . A variety of computational approaches have been previously explored for predicting drug-target interactions , including molecular docking [3] , [13] , [14] , ligand-based predictive models [15] , [16] , phenotype similarity ( side effect similarity [17] or gene expression profile similarity [18] ) and chemical ontology similarity [19] . Some similarity measurements have been combined to elucidate drug targets [20] . Network analysis based on the topology of known drug target network has also been utilized for drug target prediction , but is currently limited to small data sets [21] , [22] . Recent advances in the Semantic Web [23] have enabled the creation of large heterogeneous networks of experimental and other data in life sciences ( for example: Chem2Bio2RDF [24] , LODD [25] , Bio2RDF [26] , OpenPHACTS ( http://openphacts . org ) , linked life data ( http://linkedlifedata . com ) and Linked Open Data ( http://linkeddata . org ) ) , where the nodes can include physical and abstract entities ( compounds , protein targets , substructures , side effects , diseases , pathways , tissues , gene ontology terms and so on ) , and the edges ( or links ) represent various relations between objects such as drug-drug interactions , and drug target interactions , protein-protein interactions and so on . The ability to easily integrate heterogeneous datasets in a meaningful fashion makes semantic technologies attractive , although it is only recently that supporting technologies have adequately matured to make them useful in the biological sciences: in particular the advent of fast triple stores for data storage , the SPARQL query language ( http://www . w3 . org/TR/rdf-sparql-query/ ) for searching , and the OWL ontology language ( http://www . w3 . org/TR/owl-features/ ) for the description of ontologies . Despite remaining deficiencies which are being addressed in the Semantic Web community ( including difficulty weighting edges and maintaining provenance information ) there are now many examples of successful use of semantics in the life sciences [27] . In contrast to hyperlinked data , semantic linked data encodes explicit meanings of nodes and links , allowing traversing from one node to another via particular kinds of relationship . Prediction of links not in the dataset , based on the existing links , is widely used in social networking , in which it is assumed that two nodes are similar if they share similar topology ( e . g . , a certain number of neighbors , and similar shortest paths ) [28]–[30] . For example , in a coauthorship network , two authors are similar in terms of research interests if they coauthor lots of papers , hence their potential collaboration could be predicted ( it should be noted that social networks generally only deal with positive relationships; drug discovery data is different in that negative relationships such as inactivity are important ) . In this work , we sought to use such semantic methods to integrate and annotate the data in relation to drug target interaction , constructing a heterogeneous network composed by over 290 k nodes and 720 k edges . We further developed a statistical model called Semantic Link Association Prediction ( SLAP ) to assess the association of drug target pairs and to predict missing links . An association score is calculated based on the topology and semantics of the neighborhood . We demonstrate that SLAP can correctly identify known drug target pairs from random pairs with high accuracy and can also identify indirect drug target relations ( e . g . , the change of gene expression level ) . The association scores of a drug against a set of targets constitute a biological signature that allows assessing the similarity of drugs in the context of the whole system . The resulting drug similarity network clusters drugs from the same therapeutic indication in ways not observed using chemical structure similarity , and can also be used to identify potential new indications for existing drugs .
The SLAP pipeline is shown in Figure 1 . A heterogeneous network consisting of 295 , 897 nodes and 727 , 997 edges was constructed from 17 public data sources pertaining to drug target interaction . Every node and edge was semantically annotated using a systems chemical biology/chemogenomics ontology previously developed in our labs [31] . The nodes were grouped into 10 classes which are linked by 12 types ( Figure 1b ) . A single node is an instance of a corresponding class , for example: a node for the drug Troglitazone ( labeled as 5591 in Figure 2 ) is an instance of class Chemical Compound . We term paths of nodes and edges that share the same semantics ( but different data ) path patterns - each path is an instance of a path pattern . Table 1 shows 6 path pattern examples between Drugs and Targets . In Figure 2 , the path from node 5591 ( Troglitazone ) to node PPARG ( Glitazone receptor ) via ACSL4 ( Long-chain-fatty-acid CoA ligase 4 ) and 446284 ( Eicosapentaenoic acid ) is an instance of the path pattern 1 in Table 1 . We can interpret this path as indicating Troglitazone could bind to ACSL4 which shares compound Eicosapentaenoic acid with target PPARG . With the assumption that two nodes are associated if they link to at least one other node , or their linked nodes are linked , their relations can be assessed by the analysis of the links ( or paths ) between the two nodes [32] . The strength of their relation in the network can be measured by the distance , the number of shortest paths and other topological properties between the two nodes . In our example of the relationship between Troglitazone and target PPARG , several paths provide “evidence” of a relationship: Troglitazone and Rosiglitazone both are hypoglycemic drugs and the latter is the ligand of PPARG; Troglitazone binds to ACSL4 which shares pathway ( PPAR signaling pathway ) , ligand ( Eicosapentaenoic acid ) and GO term ( response to nutrient ) with PPARG . A total of 1684 paths ( length ) belonging to 10 path patterns contribute to their relation . Each path between two nodes may contribute to the relation between them , but the degree of contribution varies depending on path distance and the weight of the edges involved in the path . For example , a gene ontology molecular function term ( GO:0005515 ) shared by proteins is not as informative as a binding term ( GO:0005488 ) in assessing the similarity of two proteins . Thus the weight of the edge linking one protein node to the molecular function node is lower than that linking to the binding node . According to this observation , we developed a statistical model to measure the weight of edges as well as the significance of paths ( see methods ) . The model takes into account the distance and the weight of each edge , and renders a raw score indicating the strength of each path . We found that the raw scores within the same path pattern are normally distributed , while the mean and standard deviation of patterns are different ( Figure S1 ) . Z scores converted from raw scores based on pattern score distribution are used to measure the contribution to the association: the higher the z score , the more contribution the path has . The sum of z scores of all paths is defined as association score indicating the association strength of the drug target pair . The logarithm of association scores of random drug target pairs fit to a normal distribution ( Figure S2 ) , that enables calculation of the significance of a given association score . For our Troglitazone & PPARG example , the p-value is 9 . 06E-6 , indicating a strong association . A low p-value between a drug-target pair indicates a strong probability of association between the drug and target , but it does not necessarily mean the drug and target would interact biologically . Some patterns may be uninformative . We therefore considered each pattern as a feature and assessed each feature alone for its ability to identify drug-target pairs from random pairs across the set . Table 1 lists three informative patterns and three uninformative patterns along with ROC scores . The first two patterns illustrate the drug likely interacts with a protein that shares commonalities in terms of GO or ligand binding profile with an existing target that the drug already is known to interact with . The third pattern indicates that the drug likely interacts with a protein with which another structural similar drug could interact . As a result of this analysis , 12 “uninformative” patterns were removed . The sum of z score of a given pair is the sum of z scores of the paths belonging to the informative patterns . We randomly selected 1000 known drug target pairs from DrugBank and compared their association scores with 1000 random pairs of drugs and targets sampled from DrugBank . For each drug target pair , their direct link was removed in the score calculation so that their association is only determined by their neighborhood properties . We thus aimed to test the ability of SLAP to correctly identify “missing links” in the data , with the assumption that this might be used , for instance , to profile a group of compounds against an identified set of targets . As Figure 3 shows , random pairs have a broad range of scores , but most of them are close to zero . Overall , real drug-target pairs have much higher scores than random pairs ( using paired t test ) . We also took all drug target pairs from DrugBank ( in total 5607 pairs in which 4508 pairs have at least one path with length ) . We sampled the same number of random drug target pairs as decoys to check the capability of identifying real drug target pairs by SLAP . We compared SLAP with other link prediction methods adopted in social network analysis [32] . The AUROC of SLAP is 0 . 92 , outperforming other methods ( i . e . , the number of shortest paths , and the number of valid paths ) ( Figure 4 ) . As the ratio between true drug target pairs versus random pairs decreases ( e . g . , ratio = 1/12 ) , the ROC scores do not vary very much ( ) and SLAP still performs much better than others , although the precision goes down considerably ( Figure S5 ) . Even when random pairs are 12 times more than positive pairs , the precision still can reach 0 . 6 while recall is 0 . 7 . In addition , we noticed using the sum ( or max or mean ) of raw score of the shortest path ( without converting into z scores ) performs as a random choice , indicating the importance of introducing random samples . Since several drug target prediction approaches reported that the performances may vary among different target classes [33] , we grouped the drug target pairs into 5 classes ( Enzyme , Membrane Receptor , Ion Channel , Transporter and Transcription Factor ) , and found that the score does not have any preference to a particular target class , indicating SLAP is capable of treating different classes of protein targets ( Figure S4 ) . As far as we are aware , SLAP is the only large predictive network model that has been applied to drug discovery data . However other drug-target prediction methods have been the subject of recent publications [7] , [17] , [34] , and we thus sought to consider how the effectiveness of SLAP compares with these methods . We ran SLAP against 23 drug target pairs ( including 15 aminergic G-protein-coupled receptors and 8 cross-boundary targets ) predicted and confirmed in using the SEA method [7] , a novel drug prediction method based on similarity analysis . 9 pairs of aminergic GPCRs were identified by SLAP ( ) ; 1 pair was not decided ( ) ; the rest of GPCRs have no mappings in the network ( the drug was not found in the network ) , while only one of eight cross-boundary targets was identified by SLAP ( see Table S4 ) , indicating that , SLAP is not capable of finding surprising pairs ( cross-boundary targets ) . For example , Vadilex , an ion channel drug was predicted in SEA as a ligand of a transporter , a totally different target , but was not identified by SLAP . Nevertheless , SLAP performs considerably well among GPCRs in this case . In addition , we examined drug target pairs from MATADOR [35] which serves as an external dataset for validation . 1065 direct pairs were collected , of which 444 pairings are not represented in our network . 560 out of 621 known pairs and 170 out of 444 unknown drug target pairs were identified by SLAP ( ) . By calculating association scores across multiple targets , SLAP can be used to build a polypharmacology profile of a drug even when a full data matrix is not available from drug-target experiments . We took all the 164 small molecules from the Connectivity Map ( CMap ) , an online dataset mapping relationships of disease profiles to known drugs [18] , and 113 molecules that were mapped to our network were used to build a library . The association scores of these compounds against 1683 targets were calculated , yielding a score matrix . The targets of which max score is smaller than 113 ( ) were eliminated so that each remaining protein is a target of at least one drug . After this filtering , a matrix composed by 113 compounds and 679 targets was built . We used the signature of a given drug to compare it with all the compounds in the library to find the most similar drugs according to Pearson correlation coefficient . Following the CMap approach , 8 queries including 2 HDAC inhibitors , 1 estrogen and 5 Phenothiazines were created and the similar pairs are listed in Table S5 . We set 0 . 75 as threshold . 21 pairs were identified by SLAP , 19 out of 21 pairs were actually the pairs identified by CMap . SLAP recovered all HDAC inhibitors , but missed two hits ( Genistein and Tamoxifen ) for estrogen , however , both hits rank very high . Two Phenothiazines were not recovered using this similarity threshold , but they are quite similar to other three Phenothiazines compared to the remaining compounds in the library . The results show that most of hits identified by SLAP are true positive , indicating that the profiles derived from SLAP resemble gene expression profiles being used for target identification . We took 157 drugs from 10 disease areas to determine whether SLAP is able to distinguish drugs from different therapeutic areas . For each drug , we ran SLAP against 1683 human targets and got an association score for each drug target pair , creating a score matrix . We only kept the drugs and targets in which the max score is at least larger than 113 ( ) to make sure each drug has at least one valid target and each target has at least one valid drug . The matrix was then reduced to , followed by the correlation calculation of every drug pairs . Only pairs with coefficient were taken to build a network ( see methods ) .
In this paper we demonstrate the SLAP method of association prediction and the utility of predicting associations based on semantic networks . The method performs extremely well in correctly identifying known drug-target pairs in the data , has been shown to outperform similar link prediction methods used in social networking , and compares favorably with the established SEA method for predicting new drug-target interactions , as well as with the CMap method for associating drugs with changes in gene expression levels . We introduce the use of a drug-similarity network based on association profiles of drugs across targets , and use these to propose potential new drug indications , although these indications have not yet been validated experimentally . The use of large semantically annotated datasets to identify potential relationships from the linked data is a very new area , and we consider this an initial work in this field . There are several limitations to our current version . First , adding more data pertaining to drugs and targets would help identify more pairs . The side effect , disease and chemical ontology data are only linked to a limited number of drugs at present , and protein-protein interaction and protein pathway mapping data should greatly enhance its utility . In particular , the ability to embed compounds into the network for which there is no public information using chemical structure similarity , or new targets into the network using sequence similarity , would enable predictions to be made ( albeit more indirectly ) for newly synthesized or resolved compounds and targets . Second , as the complexity of path finding increases dramatically with increasing path length , only shortest paths with length was considered , thus potentially missing important path patterns that have a greater path length . Third , edge weights are defined with the assumption that the probability from one node to its neighbors with same semantic type ( e . g . , from one drug to its targets ) is equal . An important limitation of our current algorithm is that it does not enable differentiation of relationships other than categorical ones defined in the ontology . For instance , binding affinity could be used to weight the edge between drug and target , the edge with lower affinity is expected to have higher probability than that with higher affinity ( or inactive interaction ) . Using such data brings up the issue of comparability between datasets: some chemogenomics datasets such as DrugBank currently do not provide sufficient binding affinities , but the weighting schema can be modified straightforwardly in SLAP once the data is provided . In addition , binding types ( agonist/antagonist , activator/inhibitor ) can be incorporated to classify and weight edges . Fourth , it should be pointed out that using large public integrated datasets means there is often a fuzziness between “no data” and “inactive data”: i . e . we cannot assume that because two items do not have a relationship in the dataset , that they are not related - for instance that a drug cannot inhibit a target . A key question in employing any drug-target prediction method is the extent to which it requires data completeness - in the extreme a full experimental matrix - to work properly ( i . e . if it needs to be trained with consistent known active/inactive information for all compounds against all targets ) . Our methods does not require such training , indeed its purpose is to suggest potential “missing links” in incomplete data . However , it should be pointed out that the level of data completeness in a set will affect the path lengths , z-scores and associations scores produced . We believe that overall SLAP should be considered a useful tool for predicting that a relationship exists between drugs and targets , and thus as a tool primarily for ideas generation and for suggesting relationships to be probed experimentally: its purpose is to predict a relationship , not necessarily indicating a strong physical interaction . We believe it is also useful , as demonstrated in our drug network , for profiling compounds by their target associations ( and vice versa ) and we plan to explore other types of network that can be derived from SLAP . Many drug target prediction methods only employ single kinds of information or relationship ( e . g . , substructure , side effect , etc . ) , these methods are limited due to incompleteness of the data , for instance drug target relation are far from complete [37] . The employment of various data information can compensate for the lack of completeness of individual information . SLAP shows a direction to leverage such information for drug target prediction . Several sample pairs along with their key information are listed in Table S3 . For instance , the association between pyridoxal phosphate ( CID: 1051 ) and cysteine conjugate-beta lyase 2 ( CCBL2 ) is very strong ( p-value = 1 . 9E-3 ) , but if we removed gene ontology information , their association would become very weak ( p-value = 0 . 02 ) ; the association between Dexamethasone ( CID:5743 ) and annexin A1 ( ANXA1 ) would hardly be captured if substructure information were not considered . The most compelling advantage of SLAP is its consideration of relations from a system level rather than just by known binding affinity data . Other than direct drug target interactions , SLAP is also capable of recognizing indirect interactions ( e . g . , the change of gene expression level ) from random pairs , although the association scores are often smaller than direct interactions ( Figure S3 ) . It thus allows us to evaluate drug similarity based on the biological function . The network demonstrates that such similarity measurements not only is able to identify the drug action modes but also could suggest the new use of drugs .
We extracted drug-target interactions and the data contributing to either the similarity of compounds , the similarity of targets or chemical target interaction from the Chem2Bio2RDF set [24] , and added semantic annotations using the Chem2Bio2OWL ontology [31] , to create a semantic drug-target network . For example , two compounds are similar if they share same side effects , same substructures or same chemical ontology terms; two targets are similar if they share the same gene ontology terms or ligands , or they function in the same pathway . Ten classes of entities and 12 link types were defined in Table S1 and Table S2 respectively . A link between a drug and a target via bind type is established if there is a binding affinity smaller than 30 um if exists . Each node in the network is an instance of one of the classes . The detailed information on the collection of individual nodes and edges are in the supporting Text S1 . Drug target pairs from DrugBank were used to build the network . We took only the pairs in which drugs were small molecules ( by mapping to PubChem ) and targets are Homo sapiens ( by mapping to HGNC ) . A total of 5607 pairs were extracted from the network as one benchmark dataset for model evaluation . The drug target pairs were grouped into 6 classes according to ChEMBL [38] target classification ( i . e . , enzyme ( 2393 pairs ) , membrane receptor ( 862 pairs ) , ion channel ( 392 pairs ) , transporter ( 209 pairs ) , transcription factor ( 208 pairs ) and others ( 1543 pairs ) ) . Another benchmark dataset was created from MATADOR [35] which was not used for network building . We took drug target pairs with direct interaction types and confidence score from MATADOR . 1176 direct pairs in MATADOR were used , in which 1065 pairs have at least one path with length . 3665 indirect pairs in MATADOR were also extracted for evaluating indirect drug target interaction . Indirect interactions are caused by many different mechanisms , such as binding with drug metabolites or changing gene expressions [35] . A heap-based Dijkstra algorithm was employed to quickly find the paths between two nodes [39] , [40] . It can achieve a complexity of O ( nlogn ) . Each path is represented as: . The length of a path is the number of edges between two nodes . We only took the paths of length . Only significant paths ( assessed by statistical models ) are visualized in Cytoscape [41] . Let graph as , as the th shortest path from node to . as the edge from node to node . as the link ( relation ) type of . It is assumed that it has an equal probability traversing node to its neighbor node within the same type , thus:where is the degree of node . As the probability of each edge is independent , the probability traversing from to via a path is:where m is the number of nodes in the path . Since p is very small , the logarithm is applied , Accordingly , the probability traversing from to via a path is: We consider the graph as undirected , then we take the average as the raw score of path between and : We randomly sampled 100 , 000 drug target pairs from DrugBank covering 1355 approved small molecular drugs and 1683 human targets , 54 , 414 pairs have at least one shortest path with length . The sampling yielded 2 , 344 , 026 paths , which were categorized into 34 path patterns . The scores of each pattern were fitted to a normal distribution ( Figure S1 ) and the expected mean and standard deviation were estimated , followed by calculation of the z score of every path . Only the paths with z score greater than 0 were considered as the valid paths contributing to the association . The z scores of all the valid paths from to were summed up to get its association score , which was later used to measure the strength of the association . where ; n is the number of shortest paths between the nodes and ; and are expected mean , expected standard deviation of the pattern to which belongs . Some patterns may be not helpful or even noisy for assessing drug target association . We built a test set consisting of drug target pairs from DrugBank and the same number of random drug target pairs sampled from the set of drugs and targets composing the real drug target pairs . For one pair , raw scores of all the paths within a path pattern were calculated and summed up as a score for that path pattern . The scores were then used to rank the pairs in the test set . The evaluation of each pattern was performed using the area under ROC . We also applied the same procedure to the direct pairs from MATADOR . The patterns with low ROC ( ) were considered as uninformative . The uninformative patterns agreed by both test sets taken from DrugBank and MATADOR were removed . The logarithmic association scores of random pairs conforms to a normal distribution ( Figure S2 ) ; p-value is estimated to show the probability of observing a given score by random chance alone . Lower p-value indicates stronger relation between two objects . A test set was composed of a set of drug target pairs from DrugBank and the same number of random pairs as decoys . Three another test sets were created by increasing the number of random pairs such that the sizes of random pairs are 4 , 8 and 12 times more than true drug target pairs . For each pair , the paths including the direct link if exists were removed , and the z scores of all valid paths were summed up as association score . The scores were ranked to generate ROC curves [42] , which are widely adopted to measure drug target prediction methods [20] , [22] , [33] , [43] . We also considered Precision and Recall ( PR ) curve , which shows the ratio of true positives among all the predicted positives under a given recall rate [44] . PR curve is more informative and biologically meaningful while the dataset is imbalanced . The same procedure was also applied to another dataset collected from MATADOR . Other than using SLAP scores , we considered the number of shortest paths ( maximum length 3 ) , the number of valid paths ( significant path defined in the model ) , the sum of raw score of all paths , the max raw score among all paths , and the average raw score of all paths . In addition , we took the pairs validated in experiments in a recent published paper [7] as novel pairs , after manually mapping their drugs and targets to PubChem CIDs and gene symbols , we ran SLAP to get p-values of all the valid pairs . We identified drug-disease pairs from Yildirim et al . [45] , then mapped the drugs to PubChem CIDs ( the default compound identifier in the network ) . Many drugs have multiple indications , so in order to visualize drugs by therapeutic indications , only drugs with one indication were kept . We also only kept the top 10 diseases ordered by the number of related drugs . The association scores of all mapped drugs against a set of human targets construct biological signatures which were later used for measuring drug similarity using Pearson correlation coefficient . The pairs with coefficient constitute the network . Drug structural similarity was measured by Tanimoto coefficient using MACCS fingerprint . | Modern drug discovery requires the understanding of chemogenomics , the complex interaction of chemical compounds and drugs with a wide variety of protein target and genes in the body . A large amount of data pertaining to such relationships exists in publicly-accessible datasets but it is siloed and thus impossible to use in an integrated fashion . In this work we have integrated and semantically annotated a large amount of public data from a wide range of databases , including compound-gene , drug-drug , protein-protein , drug-side effects and so on , to create a complex network of interactions relating to compounds and protein targets . We developed a statistical algorithm called Semantic Link Association Prediction ( SLAP ) for predicting “missing links” in this data network: i . e . compound-target interactions for which there is no experimental data but which are statistically probable given the other relationships that exist in this set . We present validation experiments which show this method works with a high degree of accuracy , and also demonstrate how it can be used to create a drug similarity network to make predictions of new indications for existing drugs . | [
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] | 2012 | Assessing Drug Target Association Using Semantic Linked Data |
The chemotaxis sensory system allows bacteria such as Escherichia coli to swim towards nutrients and away from repellents . The underlying pathway is remarkably sensitive in detecting chemical gradients over a wide range of ambient concentrations . Interactions among receptors , which are predominantly clustered at the cell poles , are crucial to this sensitivity . Although it has been suggested that the kinase CheA and the adapter protein CheW are integral for receptor connectivity , the exact coupling mechanism remains unclear . Here , we present a statistical-mechanics approach to model the receptor linkage mechanism itself , building on nanodisc and electron cryotomography experiments . Specifically , we investigate how the sensing behavior of mixed receptor clusters is affected by variations in the expression levels of CheA and CheW at a constant receptor density in the membrane . Our model compares favorably with dose-response curves from in vivo Förster resonance energy transfer ( FRET ) measurements , demonstrating that the receptor-methylation level has only minor effects on receptor cooperativity . Importantly , our model provides an explanation for the non-intuitive conclusion that the receptor cooperativity decreases with increasing levels of CheA , a core signaling protein associated with the receptors , whereas the receptor cooperativity increases with increasing levels of CheW , a key adapter protein . Finally , we propose an evolutionary advantage as explanation for the recently suggested CheW-only linker structures .
Escherichia coli cells are able to sense changes in the chemical environment , allowing the bacteria to move towards higher concentrations of attractants and lower concentrations of repellents . The chemotaxis system is remarkable for its high sensitivity , wide dynamic range , and precise adaptation while only involving a small number of molecular components [1–3] . Despite the importance of receptor clustering in accounting for these signaling properties [4–7] , there are still unresolved issues with the clusters , in particular with respect to the nature of the coupling mechanism between receptors [8] . It has been proposed that receptors assemble into larger arrays via the connection of the kinase CheA and the adapter protein CheW [9 , 10] , with potentially complementary effects of membrane-mediated interactions [11] . Unexpectedly , in vivo Förster resonance energy transfer ( FRET ) shows that increasing the expression level of CheA of engineered non-adapting receptors decreases the cooperativity among receptors . In contrast , expressing more CheW increases the cooperativity , albeit in different ranges of expression levels [12] . This raises the question of how these different observations can be reconciled . In E . coli , there are four types of methyl-accepting chemoreceptors: the high-abundance Tar and Tsr receptors that sense serine and aspartate , respectively , and the low-abundance Trg and Tap receptors [13 , 14] . In addition , Aer is a chemoreceptor-like sensor of redox potential [15] . The chemoreceptors form homodimers , which assemble into trimers of dimers ( TDs ) [16 , 17] . On a larger scale , these TDs cluster at cell poles [18–20] . CheW and CheA , which interact with the cytoplasmic domain of the receptors [21] , are involved in the stabilization of these clusters [22] , which in turn consist of smaller complexes ( signaling teams ) [6 , 23 , 24] . Signal transduction is triggered by ligand-receptor binding , which leads to a conformational change in the cytoplasmic domains of the receptors [25–27] . The removal of attractant ( or addition of repellent ) activates autophosphorylation of the kinase CheA , which is associated with the receptors via the adapter protein CheW ( Fig 1A ) . The phosphoryl group is then transferred to the response regulator protein CheY , which diffuses through the cytoplasm . CheY-P binds to the flagellar motors to induce clockwise rotation and tumbling of the cell . In contrast , addition of attractant ( or removal of repellent ) inhibits autophosphorylation of CheA . CheY-P dephosphorylation by phosphatase CheZ leads to counterclockwise rotation and straight swimming [1] . To avoid saturation of the sensory system , adaptation is implemented via covalent receptor modification . This is achieved through changing the receptor-methylation level by the activities of the methyltransferase CheR and the methylesterase CheB , which antagonistically add and remove , respectively , methyl groups at four or five , depending on the receptor , specific glutamate residues on each receptor monomer [33] , respectively . Methylation by CheR increases the activity of CheA , i . e . , its autophosphorylation rate , thus counteracting the effect of attractant binding . In contrast , CheB activation by phosphorylation by CheA-P decreases CheA activity [12] . Through genetic engineering , the glutamate residues ( E ) can be replaced by one to four glutamine residues ( Q ) to mimic increasing receptor-methylation levels in the absence of CheR and CheB [2] . The E . coli chemotaxis pathway is exceptionally well characterized and is thus amenable to modeling at a high quantitative level . To explain the receptor cooperativity , which generates the high sensitivity of the system , the mechanism of receptor-receptor coupling has attracted much interest [8 , 34–37] . Electron cryotomography ( EC ) images of the TDs in quick-frozen cells led to the idea that TDs form densely packed hexagonal ‘honeycomb’ arrays ( Fig 1B ) [28 , 32 , 38] . These and other in vitro experiments using nanodiscs and nanoscale plugs to imitate cellular membranes suggest that –CheW–CheA2–CheW– is the structural core unit linking two TDs ( see Fig 1C for a simplified depiction ) [9] . An approach to indirectly study the cooperative behavior of the specific receptors inside the cells is to monitor the signaling activity of CheY-P/CheZ pairs via FRET , with the FRET signal being proportional to the overall CheA activity [39] . An increase in the concentration of CheW was observed to enhance the cooperativity of the FRET response mechanism , whereas , unexpectedly , an increase in CheA concentration led to the opposite effect [12] . It is well known that multimeric protein complexes can be inhibited by high concentrations of one of their components , similar to the prozone phenomenon in precipitin tests [40] . However , it is unclear how the FRET results relate to other experimental observations , including the proposed linker and lattice structures . Here , we use statistical-mechanics modeling within the framework of the Monod-Wyman-Changeux ( MWC ) model [41] for cooperative receptor complexes to unify the assumed linker and lattice structures with the seemingly contradictory FRET results . By implementing the linker structure we initially fit our model of receptor complexes of up to four TDs to FRET data obtained with cells that express only the Tar receptor in different non-adapting modification states . Next , we apply our model to Tar–Tsr–Tap and Tsr–only cells in the non-adapting QEQE modification state , which mimics half-methylated receptors . As a result we recover the experimentally observed decrease in cooperativity of the response to serine with increasing CheA concentration , whereas increasing CheW yields the observed enhanced cooperativity . Note , other higher order effects of protein overexpression , such as membrane invaginations or interference of CheA/ CheW with clustering , are not included . Our results surmise that the observed opposing trends in cooperativity are based on a critical combination of the correct linker architecture and a constant average complex size .
At the heart of our approach lies the MWC model [5 , 6 , 12] . Chemoreceptors are regarded as two-state systems being either active ( on ) or inactive ( off ) , with conformation-dependent dissociation constants K D on and K D off for a specific ligand . As the attractant affinity of inactive receptors is higher than for active receptors ( K D on ≫ K D off ) , the state ratio tips towards inactive receptors with increasing ligand concentration c . In contrast , receptor modification m favors the active state in the absence of ligands represented by an energy offset Δϵ ( m ) . The resulting single-dimer free energies in the active and inactive states are given by f on = Δ ϵ ( m ) - ln ( 1 + c K D on ) + μ f off = - ln ( 1 + c K D off ) + μ , ( 1 ) with μ the chemical potential of the receptors in the membrane . All energies are expressed in units of the thermal energy , kB T . In our approach , we allow for an ensemble of different complexes with varying complex size x ( i . e . number of connected TDs ) and partially developed linkers as rest groups R ( Fig 1D ) . All receptors within a complex are assumed to share the same conformational state because of tight coupling . For simplicity , we consider the –CheW–CheA2–CheW– linker structure [9] , which we incorporate by assigning energies μW and μA2 for each CheW and CheA2 molecule integrated in a specific receptor-complex type ( see Discussion section for an alternative linker structure ) . These energies are of the forms μ W = ln ( ( K D W · K D A ) 1 / 2 [ W ] ) μ A 2 = ln ( K D A [ A ] ) , ( 2 ) where [W] and [A] indicate monomer concentrations and K D W and K D A are dissociation constants for CheW–receptor and CheW–CheA2 binding , respectively . In particular [W] and [A] are expressed as fractional changes i and j of wild-type expression levels [W]0 and [A]0 , respectively: [ W ] ( i ) = i · [ W ] 0 [ A ] ( j ) = j · [ A ] 0 . ( 3 ) The TD is assumed to be the smallest receptor unit [9 , 42] , and the maximal number of connected TDs is restricted to four , in line with observed Hill coefficients from FRET [12 , 23] . ( Including larger complex sizes does not alter the model predictions , but increases the computational complexity significantly; see Materials and Methods . ) Each dimer can maximally bind to one molecule of CheW , whereas CheA is assumed to not interact with receptor dimers directly . In order to restrain the combinatorial complexity partially developed linkers are only considered in a symmetric manner , i . e . all rest groups are assumed to be identical in a complex . Furthermore , we attribute an attractant energy J to each linker within an active complex , a treatment in line with the previously proposed enhanced coupling among active receptor dimers [24] , albeit independent of receptor-modification level . The resulting free energies for a complex of size x and rest group R are given by ( cf . Fig 1D ) F on ( x , R ) = 3 x f on + ( x - 1 ) ( μ A 2 + 2 μ W + J ) + R ( μ A 2 , μ W ) F off ( x , R ) = 3 x f off + ( x - 1 ) ( μ A 2 + 2 μ W ) + R ( μ A 2 , μ W ) , ( 4 ) with 3x receptor dimers per complex of size x and x − 1 linkers . Such a complex has x + 2 rest groups with R ( μA2 , μW ) given by R 1 = 0 R 2 ( μ W ) = ( x + 2 ) μ W R 3 ( μ A 2 , μ W ) = ( x + 2 ) ( μ W + μ A 2 ) , ( 5 ) for Eq ( 1 ) no rest group , Eq ( 2 ) a CheW and Eq ( 3 ) a CheW and a CheA dimer , respectively . The probability PS for a certain complex type S ( x , R ) and its probability P S on of being active follow from standard combinatorial reasoning and the partition function Z Z ≡ 1 + ∑ S ( e - F on ( S ) + e - F off ( S ) ) ( 6 ) P S = e - F on ( S ) + e - F off ( S ) Z ( 7 ) P S on = ( 1 + e F on ( S ) - F off ( S ) ) - 1 , ( 8 ) where the number 1 in the partition function Z reflects the possibility of an empty membrane site . Assuming the FRET signal to report the number nA2 ( S ) of CheA2 dimers within an active complex , we define the receptor activity as A = ∑ S P ( S , on ) · n A 2 ( S ) = ∑ S P S · P S on · n A 2 ( S ) . ( 9 ) In contrast , the classical MWC model for coupled receptors describes the response of a single complex of N TDs to a change in ligand concentration . Without incorporating the receptor coupling explicitly , the corresponding activity A ˇ reads [23] A ˇ = ( 1 + exp [ N { Δ ϵ ( m ) + log ( 1 + c / K D off 1 + c / K D on ) } ] ) - 1 . ( 10 ) In the past , the Hill coefficient nH and complex size N have broadly been treated as equivalent to quantify the cooperative behavior of receptor complexes , and in [23] , an increase in N with receptor-modification level was equated with an increase in receptor cooperativity . However , both quantities are not necessarily the same as approximating Eq ( 10 ) by a Hill function with nH = N requires c ⪡ K D on [6] . We found that , in the classical MWC model , the response of differently modified Tar receptors to MeAsp , a non-metabolizable analog of aspartate , can also be described with a fixed N for all modification levels . This treatment results in a similar quality of fit when relating the reduced number of parameters to the new χ2 goodness-of-fit value ( see S1 Fig ) . As our model incorporates an ensemble of complexes of varying sizes , the finding of a constant complex size N in the classical MWC model is naturally generalized by a constant average complex size 〈N〉 with respect to ligand concentration and receptor-modification state . The average complex size , which we term receptor density ρ , is given by ρ = ∑ S 3 · x · P S = 3 ⟨ x ⟩ ≡ constant , ( 11 ) with x being the number of dimers of a given complex type S . The chemical potential μ in Eq ( 1 ) is adjusted throughout the simulation to fulfill this condition , reflecting anticipated regulation of the receptor-expression level by the cell . Biologically , a constant receptor density can be achieved by random receptor insertion into a growing membrane at constant rate [35] . Since wild-type cells express and insert receptors in the QEQE modification state [2] , we do not expect a modification-dependent insertion rate . Although allowing for a modification-dependent ρ would increase the quality of fit because of an increased number of fitting parameters , our minimal model with constant ρ can describe the data very well .
In order to test our model , we firstly applied it to FRET data of Tar-only receptors in different non-adapting receptor-modification states from Ref . [23] i . e . Tar{QEQE} , Tar{QEQQ} and Tar{QQQQ} . The dose-response curves of the chemoreceptors match closely the statistical-mechanics model with fixed receptor density , and hence fixed average complex size ( Fig 2A ) . Fig 2B displays the fitted receptor density ρ next to the Hill coefficients nH of the experimental curves ( see Materials and Methods ) and the complex size N of the classical MWC model , taken from [23] . Although the classical MWC model predicts a rise in complex size with modification level [23] , including its implementation based on a dynamic Ising model [24] , this is not true for the Hill coefficients ( see also S1 Fig ) . This finding shows that receptor modification is not the main determinant of receptor cooperativity . In our model , the chemical potential μ can be regarded as the cost function for the cell to provide a constant complex size in the membrane . By definition , the chemical potential μ ≡ ∂F/∂N reflects the amount of energy required for adding a particle to a system with free energy F . Although the value of the parameter μ , introduced to ensure constant receptor density ρ , is gained by solving a highly nonlinear equation , its behavior with respect to ligand concentration is very homogeneous and characterized by two regimes , as shown in Fig 2C . While this cost is approximately constant for c < cH , with cH being the half-maximum concentration obtained from Hill fits , the cost necessary to maintain a constant density increases rapidly for ligand concentrations beyond cH . In this second regime , the curves for all modification levels m are of the form f ( c ) = f0 + ln c , which is the functional description of an ideal chemical potential . Although the slope in the second regime is the same for all values of m , the different offsets f0 ( m ) reflect the modification-dependent energy Δϵ ( m ) . Note , if we were instead to keep μ constant ( and not ρ ) , then bumps would appear in the dose-response curves as a result of the receptor density increasing with ligand concentration ( see S2 Fig ) . In summary , our model is capable of quantitatively describing dose-response curves from in vivo FRET , in particular the receptor-receptor cooperativity . Although in spirit similar to other recent statistical-mechanics models , most noticeably by Hansen et al . [24] and Lan et al . [43] , only our model addresses the protein connectivity in receptor complexes . While the receptor density ρ is assumed to be constant on a short time scale , the rate of receptor expression and insertion into the membrane can be regulated by the cell on a longer time scale . Hence , as a further test of our statistical-mechanics model , we investigated how a change in receptor density ρ affects CheA activity at wild-type expression levels for CheA and CheW . Fig 3A shows modeled dose-response curves for different ρ values of 1 . 5 ⋅ ρ0 , ρ0 and 0 . 5 ⋅ ρ0 with ρ0 = 7 . 5 the wild-type receptor density and otherwise using the same parameter set as in Fig 2 . An increase in receptor density is directly associated with an enhanced signal amplitude because more CheA molecules are incorporated into the complexes . Fig 3B reflects the associated trend in cooperativity by comparing density ρ and Hill coefficient nH . In qualitative agreement with experimental observations [12] and in line with previous modeling [6] , larger complex sizes lead to higher sensitivities and hence steeper dose-response curves given a certain receptor-modification state . Since the expression level of receptors ( and other chemotaxis proteins ) is highest under nutrient-poor conditions , the resulting increase in receptor density and cooperativity leads to enhanced sensitivity when it is most crucial for cell survival [44] . To gain insight into the role of CheA and CheW in forming receptor complexes , we varied the expression levels [A] and [W] to study the effect on receptor activity . According to the experimental observations in [12] , we set the CheW concentrations to 0 . 7 , 0 . 1 and 0 . 01 and the CheA concentrations to 8 , 0 . 3 and 0 . 25 times the wild-type values [W]0 and [A]0 , respectively . This allowed us to make the comparison with experimental dose-response curves from FRET of Tsr–only cells ( for varying CheW ) and Tar–Tsr–Tap cells ( for varying CheA ) , both in the non-adapting QEQE modification state . To keep the overall number of parameters small , the data for changes in [A] and [W] was fitted with the same parameter set ( Δϵ , ρ , K D , Tsr on , K D , Tsr off , J , μ W 0 and μ A 2 0 ) . Multiplication of the calculated activities with scaling parameters sA and sW , respectively allows for comparison with the FRET signal amplitudes . Subsequently , a Hill function was fitted to the model curves and the model Hill parameters were compared with the experimental values . Note that our minimal model does not account for alternative forms of signaling disruption upon over- or underexpression of CheA/CheW , such as zipper-like invaginations of the cell membrane [45] or interference with trimer formation [16] . Fig 4A and 4B show the model data next to the experimentally determined Hill curves for variations in [W] . Enhanced CheW expression results in raised activity amplitudes and Hill coefficients ( Fig 4C and 4D ) . Although the nH values from the model change significantly with expression level [W] at a 95% confidence level , which is in qualitative agreement with the experimental data , especially with respect to the highest CheW expression level , the change in nH is less pronounced for the model than the experimental data . The positive correlation between kinase activity and amount of available CheW becomes evident in the distribution of complex species at half-maximum concentration ( Fig 4E ) . Whereas low levels of [W] favor independent , single TDs , larger complexes are more likely to form for larger [W] . As the probability for an empty membrane site also increases , the receptor density remains constant . Changing [A] in our model has the opposite effect on the Hill coefficient as changing [W] . This result is in line with experimental data ( Fig 5A , 5B and 5C ) . The activity amplitude reflecting the amount of active CheA molecules benefits from higher CheA levels , as one would expect ( Fig 5D ) . In contrast , Hill coefficients are higher for smaller [A] , recovering the naively unexpected experimental observations ( Fig 5C ) . Looking at the distribution of complexes at half-maximum ligand concentration ( Fig 5E ) , we note that although high CheA concentrations favor rest groups including CheA , complex sizes of 3 and 4 TDs are more likely at lower concentrations of CheA . The opposing trends in nH concerning variations in [A] and [W] are a direct result of the linker stoichiometry and fixed average complex size . For complexes with rest groups , the ratio of CheW molecules per TD is independent of the complex size ( Fig 6A ) . However , for species without rest groups , this ratio increases with the number of coupled TDs . As a result , an enhancement in [W] yields larger complexes that directly incorporate more CheA molecules . Furthermore , empty sites ensure a constant receptor density even when expression levels of CheW and CheA are extremely low . In this case , the receptor density still remains constant as empty sites can be occupied by individual TDs . This requires a dilute membrane , i . e . , a receptor density not much larger than 〈ρ〉 = 3〈x〉 = 9 ( see Fig 2B ) . In contrast , the corresponding ratio of CheA dimers per TD is highest for single TDs with full rest groups and decreases with increasing complex size ( Fig 6B ) . The CheA molecules within the rest groups contribute to the FRET amplitude but not to the receptor cooperativity . An accompanying rise in the number of occupied membrane sites ensures a constant receptor density . Our model qualitatively reproduces the experimental results obtained when the expression levels of CheW and CheA were changed . However , there are quantitative differences , especially with respect to the change in cooperativity as a function of the expression level of CheW . This change is less pronounced in the model than in the experiment . Recent findings from electron cryotomography may shed light on the reasons for these discrepancies . Although both studies stressed the importance of one dimeric CheA and two CheWs as the minimal unit needed for kinase activation , Briegel et al . [30] and Liu et al . [31] proposed additional CheW-only linkers , underlining the role of CheW in the cooperative behavior of TDs . Such structures could explain how increased levels of CheW contribute to the cooperativity of TDs . In order to quantify this effect , we allowed for additional CheW-only linkers in our model ( Fig 7 ) . The dimeric appearance of CheW in the linker is accounted for by a new parameter μW2; we keep the previously introduced rest groups for simplicity . Fig 8 shows the results for varying expression levels of CheW and CheA . The dose-response curves of the new model exhibit the same trends in Hill coefficient and amplitude for variation in [W] ( Fig 8A ) and [A] ( Fig 8B ) as before , in agreement with experimental results ( see also S3 Fig ) . However , the difference in behavior is manifested in the comparison panels below . The previously obtained minor changes in receptor cooperativity as a function of [W] are now much more pronounced ( Fig 8C ) , although the modeled Hill coefficients for [A] variation are larger than the experimental ones ( Fig 8D ) . The excess CheW leads to formation of CheW-only linkers and hence larger complex sizes when the amount of available CheA is held constant . In order to make predictions beyond the data used to fit the model , we created surface plots of amplitudes and Hill coefficients covering several orders of magnitude for expression levels of CheW and CheA ( Fig 9 ) . The receptor activity and hence amplitude increases monotonically with the level of CheA , whereas the increase in amplitude with respect to the level of CheW is only pronounced in a subspace around the experimental data ( Fig 9A ) . In the case of high CheA levels , CheW-only linkers exclude CheA from signaling . This also occurs at the wild-type CheA level , although the extent of the effect strongly relies on model parameters . The surface plot showing the Hill coefficients as a function of the expression levels of CheW and CheA has a saddle-like form ( Fig 9B ) . Although the right flank is consistent with the FRET data at high levels of CheA ( small Hill coefficients ) , the Hill coefficient also decreases at very low levels of CheA as the receptor activity diminishes . To test to what extent the model predictions depend on the actual values of parameters μ W 0 , μ A 2 0 and μ W 2 0 , we varied these parameters and found that the general shape of the surface plot was preserved . Taken together , these observations suggest the need for regulation of both CheW and CheA by the cell to balance signaling amplitude and sensitivity . Indeed , as CheW and CheA are required in comparable amounts [13] , both are expressed from the same operon [46] .
Receptor coupling plays a key role in the remarkable sensing and signaling properties of bacterial chemotaxis . These networks can explain the high sensitivity , wide dynamic range and precise adaptation . In this work we present a statistical-mechanics model of different complex sizes , modeling for the first time a molecular linker architecture consistent with ( i ) FRET dose-response curves , ( ii ) cryotomography data and ( iii ) nanodisc experiments . The linker –CheW–CheA2–CheW– proposed by Li and Hazelbauer [9] is incorporated by assigning expression level-dependent energies μW and μA2 respectively for each CheW and CheA2 molecule within a complex as part of a fully or partially developed linker . A coupling energy J < 0 attributed to linkers between active TDs indicates that the coupling between active trimers is stronger than between inactive trimers , in agreement with previous modeling [24] . Although the actual distribution of complex sizes is influenced by expression levels [W] and [A] , a readily adapted chemical potential μ ensures a fixed average complex size ρ with respect to ligand concentration c . Our model was first applied to describe the dose-response of Tar receptors in different modification states to MeAsp , a non-metabolizable analog of aspartate . We mainly considered a constant , modification-independent ρ , a constraint that not only reduces the number of parameters but also calls into question that the complex size increases with receptor-modification level [23] . In our work we discovered the discrepancies between the number of connected TDs N and the curves’ Hill coefficients nH within the classical MWC model . An increase in N is not directly associated with an increase in nH . In our statistical-mechanics model , the approximately constant nH is explained by a constant average complex size across all receptor-modification levels . Indeed , experiments show that both the level of expression of receptors and the insertion of newly synthesized receptors into the inner membrane by the Sec-machinery are highly regulated [47 , 48] . Hansen et al . [24] previously presented a dynamic-signaling-team approach to describe the data obtained with Tar-only cells in which the allosteric coupling among trimers is represented by a modification-dependent trimer-trimer interaction energy J ^ ( m ) without modeling the actual protein connectivity . Limited conformational spread and hence a finite complex size is achieved by using a long-range repulsion energy U between all trimers within a complex . In contrast , our model is simpler while providing valuable insights . Neither μW and μA2 nor J in our ensemble model depend on the modification state of the receptor , and μ ensures constant average complex size without introducing a repulsive term . Furthermore , the chemical potential μ ( c ) provides insights into the energetic cost of insertion of receptors into the membrane and its dependence on ligand concentration c , albeit based on an equilibrium mechanism . For constant J and ρ , we conclude that receptor modification mainly governs the ‘turn off’-ligand concentration , whereas its influence on receptor clustering is limited . This finding is supported by Briegel et al . [49] , who found that the receptor array order and the spacing of receptors in different modification states were indistinguishable . This is in stark contrast to Hansen et al . [24] , who predict a strong increase in average complex size with increasing receptor-modification level . High-resolution imaging of equilibrated receptors in artificial membranes by electron or total internal reflection fluorescence ( TIRF ) microscopy may allow direct determination of receptor-complex distributions and their dependence on receptor-modification level and ligand concentration . Using photoactivated localization microscopy ( PALM ) [35] or quantitative immunoblotting [13] , such an investigation could also be performed on intact cells . Although CheA and CheW have long been known to mediate receptor interactions [12 , 22] , an increase in the expression level of CheA leads to a reduction in receptor cooperativity [12] . Varying expression levels of CheA and CheW in our model produced results in agreement with experimental data of Sourjik and Berg [12] , thereby supporting the linker architecture we employed . The striking observation that increased CheA levels lead to higher kinase activities but lower cooperativity is based on the fact that the number of CheA dimers per TD is highest for single trimers with almost fully developed linker rest groups ( Fig 6B ) . Hence , overexpression of CheA , a bridging molecule at the center of the linker , promotes smaller complex sizes . CheA molecules within the rest groups do not contribute to TD coupling and curve steepness , but nevertheless add to the activity of the FRET signal . In contrast to what is observed with CheA , raising the level of CheW leads to larger complex sizes and an increased number of empty membrane sites . Again , this behavior becomes comprehensible when the number of CheW molecules per TD ( Fig 6A ) is taken into account . While this ratio is constant for complexes with rest groups , it increases with complex size in the absence of partially developed linkers . Larger complexes directly incorporate more CheA to enhance cooperativity as well as the amplitudes of FRET signals observed both in the model and experimentally . In light of our model the experimental observations are produced by a combination of constant receptor density and ( partial ) linkers . Although partial linkers play a crucial role in the mechanism of our model , their inclusion might appear arbitrary at first . Interestingly , Briegel et al . [30] recently observed a range of assembly intermediates and partial receptor hexagons forming when [W] and [A] were varied . Our surface plots of amplitudes and Hill coefficients also make testable predictions for wide-ranging CheA and CheW expression levels ( Fig 9 ) . Is there any evidence to suggest that ρ remains constant when CheA and CheW expression levels change ? First , CheA and CheW binding to the receptors occurs after insertion of the receptors into the membrane . Second , increasing the expression of a protein , e . g . , of CheW , should remove ribosomes from translating receptor mRNA [50 , 51] . Although expected to be a minor perturbation , this may lead to a reduced receptor density and hence cooperativity . However , the opposite trend is observed in FRET experiments [12] . Although our assumed linear linker structure –CheW–CheA2–CheW– matches observed stoichiometries [9 , 13] , electron cryotomography images suggest that reality is more complicated [30 , 52] . Modeling of the electron density and spin-labeling studies suggest that CheW and the P5 domain of CheA form alternating CheW/CheA rings connecting the trimers , with P5 occupying positions approximately equivalent to CheW ( see Fig 10 ) . This arrangement is consistent with the strong structural homology between P5 and CheW . However , to describe the FRET data obtained with cells with overexpressed CheA and CheW [12] , our model predicts that CheA2 has the role of a bridging molecule and connects trimers via a CheW associated with each trimer . Indeed , an alternative linker with direct receptor-CheA binding and hence symmetric roles of CheA and CheW upon clustering does not match the FRET data ( see panel D in S4 Fig ) . This view is supported by binding assays , which show that CheW binds much firmer to receptor trimers than CheA to trimers ( see Fig . 5A , B in [9] and also discussion in [29] ) . Although our model qualitatively reproduces the experimental FRET data , the change in cooperativity with variation in [W] is less pronounced in the simulation than in experiments . Recent findings based on electron cryotomography offer a possible explanation for this shortcoming . Briegel et al . [30] and Liu et al . [31] stress the importance of the implemented core unit stoichiometry , but they propose a second type of linker that only involves CheW , with P5/CheW interactions replaced by CheW/CheW interactions [31] . To investigate the consequences of these findings for signaling behavior , we allowed for an additional –CheW–CheW2–CheW– linker in our model . The simulated dose-response curves show a greatly enhanced change of cooperativity with variation in [W] ( Figs 8C and 11A ) . The generally increased Hill coefficients , and hence sensitivity , may reveal an evolutionary advantage that is not apparent in the tomography images but is detected by FRET . However , whereas CheW-only linkers fit the FRET observations , their incorporation into complexes needs to be tightly regulated . Moreover , in addition to excluding CheA from signaling ( Fig 11B ) , high levels of CheW were also claimed to disrupt receptor clustering [53] . Taken together these observations suggest that an optimal level of CheW is required for cooperative signaling by receptors ( Fig 11C ) . In conclusion , our work integrates functional ( FRET ) and structural ( nanodisc and electron cryotomography ) data , explains the paradoxes that increased levels of CheA lead to less cooperativity , and provides a functional role for CheW-only linkers . Our proposed linker –CheW–CheA2–CheW– is consistent both with the data from experiments with nanodiscs [9] and with images from electron cryotomography [29–31] , if the P5 domain of CheA binds more weakly to the receptor than does CheW . We predict that the observed tetrameric CheW linker , if incorporated at an optimal level , increases the cooperativity while keeping the receptor activity at a sufficiently high level . An increased understanding of the protein connectivity in receptor clusters may aid not only in describing the fundamental biology of receptor signaling , including the role of cytoplasmic receptor clusters in Rhodobacter sphaeroides and Vibrio cholerae [52] , but may also contribute to the design of novel biosensors [54] .
Keeping ρ constant requires nonlinear optimization of μ at every ligand concentration . For performance reasons we therefore chose to implement the model in C# and used a custom-written toolbox to connect to MATLAB 2014a for parameter optimization and plotting . The value for μ is determined based on Brent’s method for root-finding [55] . Fitting of model parameters employs Global Search from MATLAB Global Optimization Toolbox . Multiple start points are generated using scatter-search options ( 5000 trial points ) . For the different start points square deviations from experimental data are minimized using the function fmincon with interior point optimization . Note while the number of molecular species in the model increases linearly with the maximal complex size , the computational time is determined by the root finding . The latter becomes considerably harder with additional exponentials of increasing arguments in Eqs ( 6 ) , ( 7 ) and ( 11 ) . In order to quantify the cooperative behavior of the complexes , Hill functions A ( c ) Eq ( 12 ) with amplitude A0 , half-maximum concentration cH and Hill coefficient nH are fitted to the model evaluated at 50 logarithmically spaced concentrations between c = 0 . 001mM and c = 1mM . The Hill coefficients in the comparative plot Fig 2B result from direct fitting to the experimental data . A ( c ) = A 0 1 + ( c c H ) n H ( 12 ) Though parameter confidence intervals can be calculated based on robust regression and the resulting covariance matrix , especially for highly nonlinear models as ours their validity is questionable given the underlying linear theory [56] . We therefore decided against including confidence intervals except for the fitted Hill curves . We note that for all simulations with variations in expression of CheA and CheW the Hill amplitudes match quantitatively much better their experimental counterparts than do the Hill coefficients . This observation is partly owed to the fitting routine . With logarithmically spaced concentrations , a difference in amplitude between model and experimental curve directly impacts the corresponding χ2 goodness-of-fit value . In contrast , a small variation in the Hill coefficient only influences the slope of the curve within a relatively narrow range of ligand concentrations and hence is less reflected in the optimization function value . | Receptor clusters of the bacterial chemotaxis sensory system act as antennae to amplify tiny changes in concentrations in the chemical environment of the cell , ultimately steering the cell towards nutrients and away from toxins . Despite bacterial chemotaxis being the most widely studied sensory pathway , the exact architecture of the receptor clusters remains speculative , with understanding suffering from a number of paradoxical observations . To address these issues with respect to the protein arrangement in the linkers connecting receptors , we present a statistical-mechanics model that combines insights from electron cryotomography on the linker architecture with results from fluorescence imaging of signaling in living cells . Although the signaling data for different expression levels of key molecular components in the linkers seems contradictory at first , our model reconciles these predictions with structural and biochemical data . Finally , we provide an evolutionary explanation for the observation that some of the incorporated linkers do not seem to transmit signals from the receptors . | [
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] | [] | 2015 | Protein Connectivity in Chemotaxis Receptor Complexes |
A variety of pathologies are associated with exposure to supraphysiological concentrations of essential metals and to non-essential metals and metalloids . The molecular mechanisms linking metal exposure to human pathologies have not been clearly defined . To address these gaps in our understanding of the molecular biology of transition metals , the genomic effects of exposure to Group IB ( copper , silver ) , IIB ( zinc , cadmium , mercury ) , VIA ( chromium ) , and VB ( arsenic ) elements on the yeast Saccharomyces cerevisiae were examined . Two comprehensive sets of metal-responsive genomic profiles were generated following exposure to equi-toxic concentrations of metal: one that provides information on the transcriptional changes associated with metal exposure ( transcriptome ) , and a second that provides information on the relationship between the expression of ∼4 , 700 non-essential genes and sensitivity to metal exposure ( deletome ) . Approximately 22% of the genome was affected by exposure to at least one metal . Principal component and cluster analyses suggest that the chemical properties of the metal are major determinants in defining the expression profile . Furthermore , cells may have developed common or convergent regulatory mechanisms to accommodate metal exposure . The transcriptome and deletome had 22 genes in common , however , comparison between Gene Ontology biological processes for the two gene sets revealed that metal stress adaptation and detoxification categories were commonly enriched . Analysis of the transcriptome and deletome identified several evolutionarily conserved , signal transduction pathways that may be involved in regulating the responses to metal exposure . In this study , we identified genes and cognate signaling pathways that respond to exposure to essential and non-essential metals . In addition , genes that are essential for survival in the presence of these metals were identified . This information will contribute to our understanding of the molecular mechanism by which organisms respond to metal stress , and could lead to an understanding of the connection between environmental stress and signal transduction pathways .
Throughout the world , environmental and human health threats are posed by contamination from transition metals . Metals can be introduced into the environment through both natural and anthropogenic routes [1] . Human exposure routes include absorption through the skin , ingestion , and inhalation [2] . A variety of pathologies are associated with exposure to supraphysiological concentrations of essential metals ( copper , chromium , zinc ) and to non-essential metals and metalloids ( cadmium , mercury , silver , arsenic ) including cancer , organ damage , central nervous system disorders , cognitive dysfunction and psychological disorders , and birth defects [3] . The molecular mechanisms linking metal exposure to human pathologies have not been clearly defined . Exposure to metals is however , associated with increased levels of intracellular oxidative damage , including lipid peroxidation , protein denaturation , and DNA strand breaks [4] . Increased oxidative stress may be caused by metal-catalyzed redox reactions , depletion of glutathione , or inhibition of the enzymes that remove reactive oxygen species [4] . Transition metals can also alter the function of proteins by directly binding to sulfhydryl groups or by substituting for metal cofactors [5] . In addition to cellular damage , exposure to metals is associated with the activation of a variety of intracellular signal transduction pathways including those regulated by mitogen activated protein kinases ( MAPKs ) , NF-κB , and calcium-dependant kinases [4] . The inappropriate activation of these pathways may contribute to the etiology of metal-induced cancer and developmental abnormalities ( reviewed in: [3] , [5]–[9] ) . To defend against metal toxicity , sophisticated defense mechanisms have evolved . These include regulating intracellular metal concentrations via chelation and ion pumps , removal of reactive oxygen species , and repair of metal-induced damage [8] . Although many of the genes and cognate regulatory pathways have been identified , the consequence of metal exposure on a systematic level has not been examined . Several questions remain including: ( a ) what are the global effects of metal exposure on the eukaryotic transcriptome; ( b ) what are the similarities/differences in the transcriptional response among transition metals and metalloids; ( c ) what are the roles of differentially expressed genes in the defense against metal toxicity; and ( d ) are there additional metal-responsive genes and regulatory pathways ? To address these gaps in our understanding of the molecular biology of transition metals , the genomic effects of exposure to Group IB ( copper , silver ) , IIB ( zinc , cadmium , mercury ) , VIA ( chromium ) and VB ( arsenic ) elements on the yeast Saccharomyces cerevisiae were examined . Mechanisms of metal homeostasis and tolerance have been examined in Saccharomyces cerevisiae [8] . Like other eukaryotes , yeast respond to metals by decreasing metal accumulation , increasing metal chelation , and compartmentalizing metal-ligand complexes 10–12 . In this report , two comprehensive sets of metal-responsive genomic profiles are presented: one that provides information on the transcriptional changes associated with metal exposure ( transcriptome ) , and a second that provides information on the relationship between the expression of ∼4 , 700 non-essential genes and sensitivity to metal exposure ( deletome ) . These global genomic profiles were integrated to identify cellular pathways that regulate gene expression and are required for cell survival under toxic conditions . Integrating genomic profiles for gene expression and growth phenotype is a powerful tool for understanding the mechanisms involved in global responses and adaptation [13] , [14] . To properly integrate the responses , transcriptomes , after exposure to equi-toxic concentrations of different metals/metalloids , were examined using a single strain of wild type , diploid yeast cultured under consistent growth conditions , DNA microarray platforms , data extraction , and analysis protocols . Thus , differences in the genomic responses can be attributed to the effects of different chemical exposures .
A total of 1 , 341 unique genes were differentially expressed among the fourteen exposure conditions ( seven metals at two concentrations ) . Arsenic ( 1 . 25 mM ) affected the expression of the largest number of genes , 762; while cadmium ( 25 µM ) affected the fewest , 174 . The majority of the treatments changed the expression of 230 to 470 genes ( Table 1 ) . Principal Component Analysis ( PCA ) showed tight clustering of the three biological replicates for each treatment , indicating the reproducibility of the experiments ( Figure 1 ) . Expression patterns associated with the two concentrations of the same metal were sufficiently different to distribute the samples into separate but adjacent clusters , suggesting similarities in their transcription profiles . PCA also suggested that the type of metal or its chemical/toxicological profile was a major contributing factor in the transcriptional profile . For example , cadmium and mercury , which are mammalian toxicants and Group IIB metals , formed a separate , tight cluster . Chromium is unique in that it is a Group VIB metal , while the other metals are Groups IB or IIB . Both concentrations of chromium clustered together , but were separate from the other metals . Copper was also separate from the other IB and IIB metals . Copper is unique in that it is the only IB metal that is redox active in vivo . Arsenic is also redox active in vivo and clustered close to copper . The highest concentration of arsenic ( 1 . 25 mM ) was separate from the other treatments . Hierarchical clustering by treatment showed a similar grouping of metals: the redox active elements copper and arsenic clustered together separate from the other metals . Likewise , chromium was separate from the other metals ( Figure 1 ) . K-means clustering identified six distinct groups of genes ( Figure 2 , Table S1 ) . The two largest clusters , Cluster I ( induced genes ) and Cluster V ( repressed genes ) contained 388 and 447 genes , respectively . The expression of these genes was similarly affected by all metals; therefore they are referred to as common metal responsive ( CMR ) genes . Significantly enriched biological processes for the induced-CMR genes included metal ion transport and homeostasis , detoxification of reactive oxygen species , carbohydrate metabolism , fatty acid metabolism , polyamine transport , and RNA polymerase II transcription ( Figure 2 , Table S2 ) . Several of these gene products are involved in glycolysis , oxidative phosphorylation and alcohol metabolism , and are necessary for energy production of ATP-dependent molecular chaperons and other cellular stress responses [15] . Genes involved in response to reactive oxygen species were also induced , which was expected since transition metal exposure is associated with increased oxidative stress [4] . The expression of polyamine transporter genes TPO3 , TPO1 , TPO2 , and PTK1 was induced . Polyamines also protect yeast from reactive oxygen species [16] . The expression of genes involved in maintaining ion homeostasis ( NFS1 , SOD1 , ATX1 , FRE8 , SMF3 , CCC2 , ATM1 ) and iron transport ( ARN1 , ARN2 , SIT1 , FTR1 , FET3 ) were also induced . Increased expression of these genes may serve a protective role by removing excessive metals . It may also reflect a disruption in normal metal metabolism . For example , ARN1 , ARN2 , SIT1 , FTR1 , and FET3 are normally regulated by the iron transcription factors , Aft1 and Aft2 , which are activated when iron is scarce [17] , [18] . Thus , exposure to elevated levels of one metal may disrupt the metal sensing systems of others . The significantly enriched biological processes of repressed CMR genes included polysaccharide biosynthesis , G-protein signaling , protein targeting , and transport ( Figure 2 , Table S2 ) . Genes encoding subunits of protein kinase CK2 ( CKB2 , CKA1 , CKA2 ) were repressed , suggesting that metal exposure could lead to the inhibition of protein kinase CK2 responsive signaling . The expression of transition metal transporters , CTR1 , FET4 , LPE10 , BSD2 , COX17 , ZRT1 and ZRT2 was repressed , which could result in a decrease in metal uptake . Interestingly , genes encoding carbohydrate transporters ( HXT2 , HXT4 , HXT6 , HXT7 , MTH1 , MAL11 , MAL31 ) were also repressed . It is known that hexose transporters transport arsenite [8] , [19] . Thus , the suppression of these transporters may lead to a decrease in intracellular metal concentrations . The differences in Clusters II , III , IV and VI can be partially attributed to the expression patterns associated with copper and arsenic ( Figure 2 ) . Cluster III shows increased gene expression in cells exposed to the highest concentration of arsenic and both concentrations of copper , and repression with other metals . The biological processes enriched in this cluster include energy generation , response to stress , and trehalose biosynthesis ( Figure 2 , Table S2 ) . These processes are similar to those identified in Cluster I . Cluster IV showed an opposite pattern of expression compared to Cluster III . Enriched biological processes in Cluster IV were related to ribosome biogenesis and assembly . Some of the genes in this cluster encode ribosomal proteins; however , many are components of large or small ribosomal subunit processomes . Processomes are ribonucleoproteins required for the processing of 35S primary transcripts , 20S pre-rRNAs and 27S pre-RNAs [20] . Genes involved in transcription termination ( RTT103 , SYC1 , RAI1 SWD2 ) were also repressed . These proteins are responsible for the generation of the free 3′ end of mRNA and are involved in the transcription termination of RNA polymerase II after passing the poly ( A ) site [21] . Cluster II contains genes involved in the biological processes of sulfur metabolism and response to oxidative stress . Arsenic , mercury , chromium and cadmium induced the expression of genes that function in sulphate assimilation and glutathione biosynthesis pathways [14] , [22]–[24] . Enriched biological processes in Cluster VI included protein folding , telomere maintenance , and proteolysis . Cytoscape was used to identify the metal-responsive protein-protein and protein-DNA interacting networks [25] , [26] . The ten most significant sub-networks are presented in Table 2 . The majority of the biological processes associated with the genes at the center of the sub-networks for the Group IB and IIB transition metals were involved in small and large ribosomal subunit processomes [27] , [28]: silver ( 10/10 ) , cadmium ( 10/10 ) , copper ( 9/10 ) , mercury ( 7/10 ) and zinc ( 6/10 ) . The sub-networks for chromium were unique and centered on transcription factors , proteasomes and kinases . The sub-networks for the metalloid arsenic were also unique with nine of the sub-networks centered on transcription factors involved in stress tolerance . Both arsenic and copper contain the sub-network centered around the stress activated transcription factor MSN2 [29] . To identify genes whose deletion renders yeast most sensitive to metal toxicity , a GIF≥5 ( ≥80% growth inhibition ) was applied to the deletome of yeast exposed to metals at EC10 ( Table S3 ) . Forty two genes were identified whose deletion caused a significant growth inhibition at the EC10 . Genes involved in vacuole organization and biogenesis were essential for viability at low chromium and zinc concentrations ( Table 3 ) . Genes involved in the maintenance of cell wall integrity and those needed for bud neck formation mediated resistance to cadmium , chromium or copper toxicity . Protein kinase mutants ( slt2Δ , bck1Δ , ypk1Δ ) are also very sensitive to cadmium and chromium treatments . SLT2 and BCK1 encode MAPK and MAPKKK , respectively , which are regulated by PKC1-mediated signaling pathways [30] . Mutants cys3Δ , cys4Δ , ( cysteine biosynthesis ) sam1Δ ( S-adenosylmethionine synthesis ) , and sod1Δ ( reactive oxygen scavenging ) were sensitive to chromium . These results suggest that maintaining cell wall integrity , chelating metals , sequestering metals in vacuoles , and reducing oxidative stress are fundamental processes for mediating resistance to metal toxicity . Using a GIF≥2 , 90 and 540 strains were identified that showed growth inhibition at the EC10 and EC50 , respectively ( Figure 3 , Table S3 ) . 98% of the mutants that were sensitive at the EC10 were also sensitive at the EC50 . Silver and mercury affected the growth of the fewest number of deletion strains , while cadmium and chromium affected the highest number of strains ( Table 1 ) . Hierarchical cluster analysis revealed that the type of metal defined the genes contained within each cluster in the deletome ( Figure 3 ) . This is in contrast to the transcriptome results in which clusters contained genes responding to all ( e . g . , CMR ) or several metals . This suggests that resistance to metal toxicity involves metal specific responses , while the effect on transcription may be partially metal-independent . Gene Ontology analysis of the genes in the five major clusters for the deletome indicates that Cluster I , which is dominated by mutants sensitive to arsenic , was significantly enriched in the biological processes of stress-related transcription regulation , tubulin folding , signal transduction , secretory pathway , and response to stimulus ( Figure 3 , Table S4 ) . Cluster II , which is dominated by mutants sensitive to copper , was enriched in biological processes of vesicle mediated transport mechanism . Mutants for cellular morphogenesis ( sac6Δ , end3Δ , cln3Δ , bni1Δ , tpm1Δ ) and cation homeostasis ( trk1Δ , hal5Δ , csg2Δ , nhx1Δ , sat4Δ , spf1Δ ) were also sensitive to copper . Enriched biological processes that map to Cluster III , which is dominated by mutants sensitive to cadmium , contained cell surface receptor-linked signal transduction , morphogenesis , chromatin modification , glutathione biosynthesis , and response to stress; including DNA damage . Sulfur amino acid metabolism , ubiquitin-dependent protein sorting , and trehalose biosynthesis were enriched in Cluster IV , which is dominated by mutants sensitive to chromium . Cluster V , which is dominated by mutants sensitive to zinc , was enriched with vesicle-mediated transport processes , as well as poly ( A ) tail shortening and negative regulation of DNA recombination . When similar treatment conditions were compared , less than 2% of the genes identified in the metal-responsive transcriptome were found to affect resistance to metal toxicity ( Figure 4 ) . At a GIF≥2 , eight genes were required for resistance to silver and mercury toxicity . However , exposure to identical concentrations of silver and mercury resulted in the differential expression of 551 and 533 genes respectively ( Figure 4 , Table 1 ) . Cadmium at EC50 affected the transcription of the lowest number of genes; however , it caused growth inhibition in the highest number of mutant yeast stains . Overlapping regions in the transcriptome/deletome Venn diagrams identified CYS3 as both transcriptionally responsive and essential for viability in the presence of arsenic , cadmium and copper . Similarly , ADH1 was required for resistance to arsenic and copper toxicity , and its expression was up-regulated by these metals ( Table S5 ) . The results from the Gene Ontology analyses of transcriptome and deletome profiles were combined and a matrix was constructed . Z-scores for each GO term were combined , clustered and visualized ( Figure 5 , Table S6 ) . Hierarchical clustering by experiment resulted in three distinctive clusters: growth inhibition screening ( i . e . , deletome ) , down-regulated genes , and up-regulated genes . The biological processes of cation and transition metal transports were common among the three experimental groups . Sulfur amino acid transport and biosynthesis , enriched in the up-regulated gene cluster , was also required for cell growth under arsenic , cadmium , chromium and copper treatments ( Figure 5 ) .
Global transcriptome and deletome profiles for yeast exposed to Group IB ( copper , silver ) , IIB ( zinc , cadmium , mercury ) , VIA ( chromium ) and VB ( arsenic ) elements were generated and compared . Genomic data sets were generated using an experimental design to minimize variations associated with differences in yeast strain backgrounds , microarray platforms , and data extraction and analysis techniques . In addition , equi-toxic concentrations of metals were used to minimize differences due to variations in the level of toxicity . Several studies have shown that minimizing experimental variation is essential for decreasing statistical variability in microarray data [31] , [32] . Thus , by minimizing experimental variations , transcriptional responses associated primarily with the different metals were identified . Several studies have compared the effects of exposure to multiple metals on the transcriptome in yeast and mammalian cells . There was , however , little overlap between the responsive genes reported in these studies and those presented in the current report . When enriched GO categories were compared however , genes involved in detoxification of oxidative stress commonly respond to stress conditions [33]–[35] . Transcriptome analysis revealed that each of the seven metals affected the level of expression of 175–760 genes ( Table 1 ) . PCA and hierarchical clustering by experimental conditions grouped the expression patterns by physiological/chemical characteristics of the metals . PCA and clustering placed cadmium and mercury , two non-essential metals , close together and separate from the essential metals; zinc , chromium , and copper . Silver closely grouped with zinc . This may be because silver can substitute for other metals in many biological activities in yeast and may be sensed as an essential metal [36] , [37] . Although chromium is essential , it is a Group VIB transition metal with different chemical properties compared to the others . These results suggest that the chemical properties of the metal/metalloid contribute to the pattern of the genomic response . K-means clustering illustrated that the expression of ∼60% of the genes was similarly affected by all seven metals . In addition , two sub-sets of metals: ( A ) zinc , chromium , mercury , silver and cadmium; and ( B ) arsenic and copper , affected the expression of similar genes . This suggests that cells may have developed common or convergent transcription regulatory mechanisms to accommodate metal exposure . Copper and arsenic , which undergo redox reactions in vivo , affected the expression of similar genes . This suggests that the ability to participate in redox reactions defines a unique set of metal-responsive genes . Although chromium is redox active in vivo , it clustered distinctly from copper and arsenic ( Figure 2 ) . Several chromium responsive genes in Clusters IV and VI did , however , show expression levels similar to those of copper and arsenic . These results suggest that the ability to undergo redox reactions in vivo contributes to the global transcriptional response , but other chemical properties of the metal may dominate the response . Generating precursor metabolites and energy , synthesizing proteins and amino acids , transporting chemicals and proteins , and responding to oxidative stress were common responses to metal exposure . Furthermore , there were increases in detoxifying processes such as metal chelation , sequestration of metals into endosomal vacuoles , and exocytosis . These responses may reduce the intracellular levels of the metal by decreasing the number of metal-importing transporters and increasing metal efflux through major facilitators . The processes identified in this study are similar to adaptations in yeast exposed to other environmental stressors , as well as in human fibroblasts exposed to arsenic [15] , [38]–[40] . The expression of genes associated with the production of ribosomes was also affected by metal exposure . Other environmental stresses have also been shown to repress ribosomal protein expression and the translation apparatus [38] , [41] , [42] . Ribosome production may utilize more than 50% of the synthetic effort of rapidly growing eukaryotic cells [41] , [43] . Therefore , by inhibiting ribosome synthesis , cells may be able to redirect these resources towards the defense against metal toxicity [44] . Analyses of the transcriptome and deletome identified several evolutionarily conserved signal transduction pathways that may be involved in regulating the responses to metal exposure . These include those mediated by cAMP-dependent protein kinase A ( PKA ) , protein kinase CK2 , and MAPK . The PKA pathway coordinates post-translational regulation of a variety of proteins such as key enzymes of glycolysis and gluconeogenesis , and the transcriptional control of ribosomal protein and stress response proteins [45]–[47] . Of the 1 , 341 metal responsive genes ∼10% are stress response genes whose expression is controlled by the PKA-regulated transcription factors Msn2p/Msn4p [29] , [48] , [49] . When PKA is activated , Msn2p/Msn4p activity is repressed . Non-activation of PKA due to low cAMP levels will ultimately lead to the derepression of Msn2p/Msn4p [50] . The mechanism by which metals inactivate PKA has not been fully defined; however , our results suggest that metal exposure may reduce the levels of cytoplasmic glucose . In yeast , low glucose causes a decrease in the level of cAMP with concomitant low PKA activity [51]–[55] . Changes in glucose levels may be the result of a combination of changes in yeast metabolism . There were decreases in the expression of high affinity hexose plasma membrane transporters ( HXT2 , HXT4 , HXT6 , HXT7 ) and increases in the expression of genes associated with glycolysis and hexose metabolism ( PGK1 ENO2 FBA1 TYE7/SGC1 CDC19 ) ( Figure 2; Table S1 ) . Furthermore , there was a decrease in the expression of the maltase genes MAL12 and MAL32 , which metabolize maltose into glucose [56] , [57] . The combination of decreased sugar production and transport , and increased metabolism may lead to a level of glucose that would limit PKA activity . This will ultimately lead to increased stress response gene expression via Msn2p/Msn4p . Protein kinase CK2 functions in diverse cellular processes [58] , [59] . Protein kinase CK2 , along with Utp22 , Rrp7 and Ifh1 comprises the CURI protein complex . It has been suggested that the CURI complex is involved in ribosome synthesis , mediating transcription and processing of pre-rRNAs , and the transcription of ribosomal protein genes [60] . Metal treatment caused a decrease in CKA1 , CKA2 and CKB2 expression . This is similar to the results obtained in arsenic exposed JB6 mouse epidermal cells [61] . Thus , decreased ribosome biosynthesis associated with metal exposure may be caused by the repression of protein kinase CK2 , as well as decreased PKA activity [14] , [55] , [62] . MAPK cascades participate in various cellular processes including apoptosis , differentiation and the stress response . MAPKs have been shown to mediate inducible transcription in response to exposure of the seven metals examined in this study [63]–[65] . In yeast , five MAPKs have been identified [66] . Among them , HOG1 , a mammalian p38 homolog , regulates the high osmolarity glycerol response , which is activated through two independent upstream pathways that converge at MAPKK PBS2 [67]–[69] . The deletome analysis revealed that proteins in the high osmolarity glycerol response ( Sho1 , Ste20 , Ssk1 , Ssk2 , Pbs2 , Hog1 ) are required for arsenic , cadmium and zinc tolerance . Another MAPK cascade that regulates the cell wall construction pathway ( Bck1 , Mkk1 ) was required for cadmium tolerance . Cytoscape analysis of the transcriptome revealed that a group of proteins that interact with the MAPKKK Pkh2 , a serine/threonine kinase involved in maintenance of cell wall integrity , significantly changed in their levels of expression ( Table 2 ) . These results are consistent with other studies that demonstrated a role of MAPK cascades in controlling metal-responsive transcription . Furthermore , they indicate that proper functioning of the MAPK pathways is essential for survival to environmental stresses . The deletome was created by measuring the growth characteristics of each mutant strain in the presence of metal . Hierarchical clustering showed limited overlap in the genes comprising the different clusters of mutants exposed to the metals ( Figure 3 ) . This suggests that resistance to metal toxicity may have divergent mechanisms . Mercury and silver had the lowest number of genes in the deletome . This may be a consequence in how yeast responds to exposure of these metals . When yeast was grown in increasing concentrations of these metals , there was a concentration-dependent increase in the length of the stationary growth phases . However , exponential growth rates and maximal cell densities were similar at almost all of the mercury and silver concentrations ( Figure S1 ) . In contrast , growth in the presence of the other metals caused concentration-dependent decreases in growth rates and maximal cell densities . Thus , yeast may better adapt to silver and mercury exposure . Exposure to metals at concentrations beyond which yeast can adapt may reveal additional genes in the deletome . Alternatively , monitoring deletion strains for metal-induced changes in the stationary phase identify other essential genes . A comparison of genes in the transcriptome and deletome did not identify many common genes ( Figure 4 ) . This lack of correlation has been observed in other studies . It may be attributed to genetic redundancy or the inability of microarrays to measure non-transcriptionally regulated changes in activity [13] , [14] , [70] , [71] . The lack of overlap may be due to differences between the measured end points . For the transcriptome , RNA was purified from the cells exposed to metals for 2 hours , during lag phase of growth . Yeast begin to respond to new environments and adapt for growth during this period of time . Thus , the genes identified in the transcriptome may contribute to the early adaptation of metal-induced stress . In contrast , GIF's were calculated from yeast in stationary growth phase and may be related to cell proliferation . Many of the other yeast deletomes also examined cells that are in the stationary phase [70] , [72] , [73] . In the future , a greater overlap between the transcriptome and deletome may be achieved if GIF's are calculated from metal-induced differences in lag times , initial growth rates , and maximum cell density . By combining Gene Ontology results from the transcriptome and deletome , several common processes were identified ( Figure 5 ) . These include cation and transition metal transport , and sulfur amino acid transport and biosynthesis , which were required for cell growth in the presence of arsenic , cadmium , chromium and copper . Gene Ontology results suggest that genes in the deletome may represent convergent or central upstream points in pathways , which ultimately protect cells against metal toxicity . Furthermore , the transcriptome may identify the downstream genes in the cognate pathways . For example , the deletome contained genes involved in serine and threonine metabolism; glutamate , aspartate and arginine metabolism; and shikimate metabolism . These genes are located upstream in the sulfur , methionine and homocysteine metabolic pathways . The downstream genes in these pathways were identified in the transcriptome . Changes in sulfur amino acids is consistent with results obtained in previous studies of arsenic exposure , where processes involved in glutathione synthesis overlapped in the transcriptome and deletome [14] . The capacity to respond to environmental stresses is critical to the survival and propagation of all organisms . Genomic studies provide important information on molecular mechanisms of environmental stress responses . In this study , we have identified genes that respond to exposure of essential and non-essential metals . In addition , genes that are essential for survival in the presence of these metals were identified . This information will contribute to our understanding of the molecular mechanisms by which organisms respond to metal stress , and could lead to an understanding of the connection between environmental stress and signal transduction pathways .
The homozygous diploid Saccharomyces cerevisiae strain BY4743 , the result of mating of two haploid strains BY4741 ( MATa his3Δ1 leu2Δ0 met15Δ0 ura3Δ0 ) and BY4742 ( MATα his3Δ1 leu2Δ0 lys2Δ0 ura3Δ0 ) [74] , was used for all transcriptome and deletome studies . Yeast was grown in YPD ( 1% yeast extract , 2% bactopeptone , 2% glucose ) at 30°C . To define equi-toxic concentrations of the metals , the effects of different concentrations of AgNO3 , NaAsO2 , CdCl2 , CrO3 , CuSO4 , HgCl2 , and ZnSO4 on cell growth and viability were determined . Cell growth was measured by first diluting an overnight culture of yeast to an optical density of 0 . 3 , at 600 nm , in YPD medium , and then incubating for 3 h . This culture was then diluted to an optical density of 0 . 01 with YPD containing various concentrations of metal . Yeast were incubated at 30°C , and growth was monitored by observing changes in OD600 as a function of time ( Figure S1 ) . The EC10 and EC50 , concentration of added metal that inhibits yeast growth by 10% and 50% , respectively , for each metal were then calculated using Toxstat ver . 3 . 4 ( WEST , Inc . , Cheyenne , WY ) . The effect of metal exposure on cell viability was assessed by counting the number of viable yeast cells remaining following 1 , 2 and 3 h exposures of log phase cells to different concentrations of metal ( Figure S1 ) . The EC10 and EC50 concentrations that were used in subsequent transcriptome and deletome studies did not have a significant effect on cell viability ( Figure S1 ) . To prepare RNA for microarray studies , three independent colonies of yeast were grown overnight in YPD . These cultures were then diluted 1∶2 . 3 with YPD , and incubated for an additional 3 h at 30°C . Cells were then diluted to optical density of 0 . 1 ( ∼106 cells/ml ) in YPD containing metals at either the EC10 or EC50 ( Table 4 ) , or no metal added YPD . Yeast were harvested following a 2 h incubation at 30°C , and total RNA was purified as previously described [75] . The quality of the purified RNA was determined with a BioAnalyzer ( Agilent Technologies , Palo Alto , CA ) , and the RNA was then stored at −70°C . For microarray hybridizations , 100 ng of total RNA from metal-treated yeast was amplified and labeled with Cy3 fluorescent dye , and a common reference pool ( no metal added control ) was amplified and labeled with Cy5 using Agilent Technologies Low RNA Input Linear Amplification labeling kit following the manufacturer's protocol . Equal amounts of Cy3- and Cy5-lableled cRNA were then hybridized to an Agilent Yeast Oligonucleotide microarray ( Cat . No . G4140B ) for 17 h at 65°C . The hybridized microarrays were then washed and scanned using an Agilent G2565BA scanner . Data were extracted using Agilent's Feature Extraction software . A total of 84 microarrays were analyzed in this study: 7 metals×2 concentrations×3 biological replicates×2 ( dye-swap ) . This results in six measurements for each treatment condition . To define the deletome , the effect of each metal on the growth of individual yeast strains in a homozygous diploid gene deletion library ( Open Biosystems Huntsville , AL , Cat . No . YSC1056 ) was determined . For growth measurements , a 5 µl sample of each strain was transferred into 195 µl of fresh YPD , in each well of a 96-well plate . For a normalization control , about 103 cells of wild type BY4743 were inoculated into each plate . After 2 days of standing incubation at 30°C , the plate was replicated into thirty 96-well plates containing YPD . Each replicate contained one of the seven metals at one of two concentrations ( Table 4 ) , as well as one replicate with no added metal . There were two replicates for each of the fifteen different experimental conditions to yield a total of thirty plates from each original library plate . Cells were maintained as standing cultures at room temperature , and the optical density at 620 nm was measured for each replicate plate every 90 min for 42 h using a Biomek® FX Laboratory Automation Workstation equipped with a DTX800 Multimode Detector ( Beckman Coulter , Fullerton , CA ) . This process was repeated for each of the 56 plates in the original library , resulting in two replicate growth curves for each of the 4 , 739 genes at 15 different metal exposure conditions . For each deletion strain , treatment and replicate , the minimum OD620 was subtracted from the maximum OD620 over the 42 h period to estimate cell growth . These growth estimates were used to identify strains with metal-responsive genes as follows: Let gs , m be the estimated change in OD for a strain with gene deletion s ( s = 1 , … , 4739 ) and metal treatment m ( m = 1 , … , 15 ) , where s = 1 indicates the strain without a deleted gene , and m = 1 indicates the absence of metal . Then for each strain with a deleted gene , a growth inhibition factor ( GIF ) was calculated as the ratio of the growth of the strain with deleted gene s in the absence of metal ( gs , 1 ) as a fraction of the growth of the wild type strain in the absence of metal ( g1 , 1 ) to the growth of strain with deleted gene s for metal treatment m ( gs , m ) as a fraction of the growth of the wild type strain for the same metal treatment m ( g1 , m ) . That is , Larger values of the GIF indicate greater growth inhibition . Deletions that significantly affect growth in the presence of metal are defined as those that had an average GIF , for both replicates of at least 2 , which corresponds to 50% growth inhibition . An overview of the data analysis stream for both the transcriptome and the deletome data is shown in Figure S2 . Briefly , GeneSpring v . 7 ( Agilent Technologies ) and Rosetta Resolver v . 3 . 2 ( Rosetta Biosoftware , Seattle , WA ) were used to identify genes that showed significant changes in gene expression with any metal treatment . The intensity ratio values from dye-swap hybridizations ( individual metal concentration exposures and biological replicates separately ) were combined using Rosetta Resolver error-weighted averaging and then assessed for significance of differential expression by computing p-values for the combined intensity ratio value for each gene [76] , [77] . GeneSpring was used for global normalization of the raw microarray data using per feature and per chip and intensity dependent ( LOWESS ) normalization . Kruskal-Wallis test ( p<0 . 05 with Bonferroni correction for multiple testing ) of the genes under all experimental conditions ( 87 , 304 gene-treatment combinations; 6 , 236 genes×7 metals×2 concentrations ) identified 4 , 296 significantly changed gene-treatments . A total of 1 , 341 genes were identified in the combined datasets ( from the GeneSpring and the Rosetta Resolver analyses ) that have at least a 2-fold change in the level of expression in at least 1 out of the 14 different metal treatment conditions . Principal component analysis of the three biological replicates for each sample from all metal treatment conditions was performed using Partek Genomics Suite ( Partek Incorporated , St . Louis , MO ) and the intensity ratio data from the 1 , 341 genes . The first three principal components captured over 75% of the variability in the data . Hierarchical and K-means clustering of the transcriptome and deletome were performed using Cluster 3 . 0 and visualized with Java TreeView 1 . 0 . 7 [78] , [79] . K-means clustering on the average fold-change in gene expression values was performed with K = 6 for the genes , K = 3 for the samples Euclidean distance as the similarity metric . Gene Ontology ( GO ) analysis was performed using GO Term Finder ( Saccharomyces Genome Database , Stanford , CA ) . GO categories in the transcriptome and deletome were also compared . Significant GO categories ( Z-score≥2 ) for both profiles were obtained using GenMAPP 2 . 1 ( Gladstone Institutes , San Francisco , CA ) . Z-scores for each GO term from this analysis were combined into a data matrix and subjected to hierarchical cluster analysis . Cytoscape and the jActiveModule plug-in were used to identify neighborhoods in the regulatory networks associated with differentially expressed genes [25] , [26] . In this analysis , the fold change and p-values of 6 , 236 genes under all treatment conditions from Rosetta Resolver were uploaded into Cytoscape . The p-values were processed as previously described [14] , [25] . The yeast interaction network used in this analysis contained 5 , 604 protein modules with 22 , 574 protein-protein or protein-DNA interactions [80] , [81] . | Environmental and human health threats are posed by contamination from transition metals . A variety of pathologies are associated with exposure to supraphysiological concentrations of essential metals and to non-essential metals and metalloids . To defend against metal toxicity , sophisticated defense mechanisms have evolved . Although many of the genes and regulatory pathways have been identified , the consequence of metal exposure on a systematic level has not been examined . To better define the mechanism involved in the metal response , we examined the effects of zinc , cadmium , mercury , copper , silver , chromium , and arsenic on gene expression in the yeast Saccharomyces cerevisiae . In addition , the roles of ∼4 , 500 non-essential genes in protecting yeast from metal toxicity were determined . Data analyses suggest that the chemical properties of the metal are major determinants in defining its biological effect on cells . Furthermore , cells may have developed common or convergent regulatory mechanisms to accommodate metal exposure . Several evolutionarily conserved regulatory pathways were identified that link metal exposure , disruption of normal metabolism and gene expression . These results provide a global understanding of the biological responses to metal exposure and the stress response . | [
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] | 2008 | Global Transcriptome and Deletome Profiles of Yeast Exposed to Transition Metals |
The prolonged survival of Mycobacterium tuberculosis ( M . tb ) in the host fundamentally depends on scavenging essential nutrients from host sources . M . tb scavenges non-heme iron using mycobactin and carboxymycobactin siderophores , synthesized by mycobactin synthases ( Mbt ) . Although a general mechanism for mycobactin biosynthesis has been proposed , the biological functions of individual mbt genes remain largely untested . Through targeted gene deletion and global lipidomic profiling of intact bacteria , we identify the essential biochemical functions of two mycobactin synthases , MbtK and MbtN , in siderophore biosynthesis and their effects on bacterial growth in vitro and in vivo . The deletion mutant , ΔmbtN , produces only saturated mycobactin and carboxymycobactin , demonstrating an essential function of MbtN as the mycobactin dehydrogenase , which affects antigenicity but not iron uptake or M . tb growth . In contrast , deletion of mbtK ablated all known forms of mycobactin and its deoxy precursors , defining MbtK as the essential acyl transferase . The mbtK mutant showed markedly reduced iron scavenging and growth in vitro . Further , ΔmbtK was attenuated for growth in mice , demonstrating a non-redundant role of hydroxamate siderophores in virulence , even when other M . tb iron scavenging mechanisms are operative . The unbiased lipidomic approach also revealed unexpected consequences of perturbing mycobactin biosynthesis , including extreme depletion of mycobacterial phospholipids . Thus , lipidomic profiling highlights connections among iron acquisition , phospholipid homeostasis , and virulence , and identifies MbtK as a lynchpin at the crossroads of these phenotypes .
Mycobacterium tuberculosis ( M . tb ) , an obligate human pathogen , causes tuberculosis , typically through a decades-long infection . Long-term survival in the host necessitated the evolution of mechanisms for scavenging essential nutrients . Iron plays crucial roles in bacterial respiration and DNA synthesis , and its availability affects the outcome of natural tuberculosis infection in humans . Thus , targeting of iron status has been considered as a host-directed approach to treating tuberculosis [1–3] , which increases the need to understand non-redundant pathways of iron acquisition by this pathogen . M . tb acquires heme-bound or non-heme iron from the host using two separate pathways . A recently identified hemophore uptake system imports iron bound to its native host substrate , heme [4 , 5] . Non-heme iron present in host stores is typically bound to proteins , such as transferrin and ferritin . These proteins chelate free iron by binding it with high affinity , thereby sequestering iron from infectious organisms [6] . To acquire non-heme iron , M . tb synthesizes two structurally related , hydroxamate polyketide-polypeptide siderophores: mycobactin and carboxymycobactin ( also known as exochelin ) [7 , 8] . The importance of mycobactin and carboxymycobactin to M . tb virulence has been suggested by impaired growth of a mycobactin mutant in cultured macrophages [9 , 10] and impaired growth in vitro and in vivo of M . tb mutants in the siderophore export apparatus [10–12] . Further , mycobactin biosynthesis genes are up-regulated during infection of macrophages , and three genes ( mbtB , mbtE , and mbtG ) are required for in vitro growth [10 , 13–15] . The necessity of iron scavenging is unquestioned; however , the relative importance during infection of iron acquisition by mycobactin versus the hemophore system remains unknown . Mycobactin is biosynthesized by mycobactin synthases ( Mbt ) , a family of non-ribosomal peptide synth ( et ) ases . Their proposed functions in mycobactin synthesis were assigned based on sequence homology to enzymes of known function , and transcriptional repression in iron-replete conditions by the iron-responsive repressor , IdeR [16–18] . Bioinformatics approaches predict a theoretical biosynthetic mechanism whereby the polyketide-polypeptide backbone is acylated . Of the 14 proposed mycobactin synthases , only MbtB , MbtD , MbtE and MbtG have been directly tested for their non-redundant functions in live M . tb [4 , 9 , 10 , 15] , while others have been evaluated in vitro with recombinant enzymes and artificial substrates or in other mycobacterial species [16 , 19–21] . These studies suggest that two mycobactin synthases , MbtK and MbtN , may function as a mycobactin acyl transferase and dehydrogenase , respectively . These two enzymes are thought to transfer an acyl chain to the ε-amino group of a lysine-containing polyketide-polypeptide , and introduce an unsaturation into fatty acids destined for mycobactin incorporation [16 , 19 , 22] . However , the non-redundant functions of MbtK and MbtN and their roles in mycobacterial growth in vitro or in vivo have not been investigated . Mycobactin’s long , monocarboxyl tail increases its hydrophobicity , promoting adherence to M . tb’s lipidic surface , and likely plays a role in transport across lipid membranes . In contrast , the short , dicarboxyl tail of carboxymycobactin increases its water-solubility , allowing it to be released from the cell surface into aqueous biological solutions . Iron acquisition is thought to occur through initial interaction of iron with carboxymycobactin , followed by transfer of iron to form mycobactin-iron complexes , which convert cationic free iron into a neutral , lipid-linked complex that can pass through two mycobacterial membranes to the cytosol [2 , 17] . Therefore , we sought to determine the roles of lipid-modifying enzymes , MbtN and MbtK , in iron uptake . Hydroxamate siderophores carry a cis unsaturation at C2-3 in the fatty acyl unit [23 , 24] . The unsaturation with this particular location and stereochemistry is highly characteristic of mycobactins and absent from unsaturated fatty acids found in other acylated mycobacterial macromolecules . This unique modification renders unsaturated dideoxymycobactin , a mycobactin precursor , 40 times more antigenic for human T cells , as compared to saturated dideoxymycobactin [25] . Evolutionary conservation of this unsaturation suggests a possible biological role in iron scavenging , but this has not been tested experimentally . We investigated the function of MbtN and MbtK in intact M . tb , where we could observe their biological roles without prior knowledge of their natural substrates or products . These studies rely on new methods of comparative lipidomics , whereby high performance liquid chromatography-mass spectrometry ( HPLC-MS ) detects many thousands of metabolites in one experiment , including both named molecules and unnamed molecules with a unique mass or m/z value [15 , 26–28] . Because the lipidomics platform simultaneously analyzes both expected lipid products and nearly all lipids in the cell , this unbiased approach can measure downstream effects on expected substrates and lipids with no known relationship to the enzyme . After generating mutants with single gene deletions at mbtK or mbtN , both enzymes were found to be essential for modification of siderophore lipids . However , only MbtK is essential for growth in vitro and in vivo . Using the broader detection enabled by the lipidomic approach , we discovered unexpected contributions of mycobactins and iron starvation to membrane phospholipid maintenance .
The genes mbtK and mbtN are likely part of the natural iron scavenging response of M . tb , based on prior studies showing their transcriptional de-repression in iron-depleted medium [16] . Like other enzymes predicted to synthesize the lipid tails of mycobactin and carboxymycobactin , MbtK and MbtN are encoded by the M . tb mbt-2 locus ( Fig . 1A ) . In vitro , recombinant MbtK transfers fatty acids to lysine acceptors on synthetic mycobactin-like peptides , and MbtN reduces fatty acids , suggesting that these enzymes might catalyze similar reactions during mycobactin biosynthesis [16 , 21] . However , the necessity or sufficiency of these enzymes to mycobactin biosynthesis in intact M . tb is unknown . Further , the functions of MbtK and MbtN in biosynthesis of carboxymycobactin and deoxymycobactins , including the dideoxymycobactin antigen for human T cells [25] , have not been investigated . To generate mutants in M . tb H37Rv , regions of approximately 1 kilobase flanking mbtN or mbtK were amplified , fused by polymerase chain reaction ( PCR ) and cloned into the suicide plasmid , pJM1 ( Fig . 1B ) [29 , 30] . Transformants were selected with hygromycin and counterselected with sucrose . Mycobactin J ( 2 μg/ml ) was added in trans during cloning to ensure the initial survival and recovery of mycobactin-deficient mutants . After confirming mutations ( S1 Fig ) , the loci were complemented with integrating plasmid pGH1000A expressing mbtN or mbtK constitutively under a mycobacterial groEL promoter [31] . Mycobactin binds to the cell surface , but carboxymycobactin is released into aqueous media , so the two molecules were isolated from bacteria grown on solid or liquid media , respectively . To stimulate mycobactin production with iron starvation , we cultivated M . tb on iron-depleted agar medium [18] . Colonies grown on triplicate plates were removed by scraping , treated with chloroform:methanol to extract total lipids and then treated with cold ( 4°C ) acetone to precipitate phospholipids and allow enrichment of mycobactin-like molecules in supernatants . Acetone soluble lipids were analyzed by reversed phase high performance liquid chromatography-mass spectrometry ( HPLC-MS ) in the positive mode . For carboxymycobactin , triplicate cultures were grown in iron-depleted liquid medium supplemented with ferric chloride to late log phase , washed and inoculated into unsupplemented iron-depleted medium to induce an iron-starved state [32] . After two weeks of conditioning , supernatants were filtered and extracted with ethyl acetate prior to reversed phase HPLC-MS analysis [15] . Analysis took advantage of a metabolomics platform in which ion monitoring over a large dynamic range of intensity allows nearly simultaneous tracking of compounds present at low and high concentrations within complex lipid mixtures . Chromatographic pre-separation prior to MS analysis allowed separate detection of compounds ranging in polarity from highly hydrophobic neutral lipids to highly polar glycolipids under conditions that minimize cross-suppression [28] . Injection of acetone-soluble lipids from one bacterial culture generated over 10 , 000 distinct ion intensity measurements . Each ion detected in at least two of three replicates corresponds to one “molecular event , ” which is comprised of three linked values: accurate mass , retention time and intensity . We analyzed the expected precursors and products of MbtN and MbtK , as well as performed unbiased analysis of all 15 , 272 molecular events detected . For targeted analysis , we used the MycoMap to deduce the accurate masses corresponding to the molecular variants in four classes: mycobactins , monodeoxymycobactins , dideoxymycobactins , and carboxymycobactins [28] . Based on known patterns of molecular variation in acyl chain length and saturation , we deduced the accurate mass of 14 lipids within each class ( Fig . 1C ) , then tracked the intensity of these seventy targets after gene deletion and iron depletion . Untargeted analysis is based on alignment of all events in the wild type and mutant datasets , a process whereby all events with matching masses and retention times are aligned using XCMS software [28 , 33] . Then , mean intensity ratios measured with or without genes and iron supplementation are measured , providing an organism-wide screen describing the percentage of all detected events that change by at least two-fold after deletion of mbtN ( Fig . 2 ) or mbtK ( Fig . 3 ) . Prior analyses validate that the false positive rate for detecting compounds that are significantly changed within the cell wall is less than 1 percent [28] . Because MbtN is predicted to have dehydrogenase function , a selective loss of unsaturated mycobactin-like metabolites would be expected in ΔmbtN . Therefore , we interrogated molecular events with the expected mass of mycobactins and deoxymycobactins carrying either saturated or unsaturated fatty acyl units in the parental strain and ΔmbtN ( Fig . 2A ) . The assignment of unsaturated mycobactins is preliminarily made on masses that match the predicted m/z and retention time of intact unsaturated mycobactins ( Fig . 1C ) . These assignments were confirmed through CID-MS analysis of products that show equivalent mass of the peptide unit ( m/z 355 . 065 ) and multiple cleavage products corresponding to peptide fragments carrying either saturated ( m/z 713 . 370 , 695 . 360 , 667 . 365 ) or unsaturated ( m/z 711 . 255 , 693 . 343 , 665 . 349 ) fatty acyl units ( Fig . 2C ) . Ruling in this model , targeted analysis of ΔmbtN found these lipid classes were produced with masses matching saturated , but not unsaturated , fatty acids ( Fig . 2A , white shapes ) . In contrast , events corresponding to unsaturated and saturated forms of mycobactin-like molecules were produced in the wild type parent ( Fig . 2A , black shapes ) . Whole organism-lipidomics is a relatively new method that uses computer-assisted methods to measure mycobacterial response . Therefore , we completed validation experiments using conventional or manual biochemical methods to assess key ions with regard to chromatogram shape , intensity and retention time , as well as collision-induced dissociation mass spectrometry ( CID-MS ) to confirm the identities of lipids initially established based on m/z values . Also , manual inspection of chromatograms shows all background signals , which helps to determine whether high fold-change events result from a complete or partial loss of unsaturated forms ( Fig . 2B ) . For example , events corresponding to unsaturated mycobactins showed high fold-change in scatterplots ( Fig . 2A , black diamonds ) , but it was unknown if all unsaturated mycobactins were lost after mbtN deletion . Manual generation of ion chromatograms for a representative unsaturated mycobactin ( Fig . 2B , m/z 923 . 4701 ) showed baseline response in ΔmbtN , suggesting complete ablation . Considering manual analysis of all targets , ion chromatograms corresponding to saturated ( m/z 925 . 4858 ) and unsaturated ( m/z 923 . 4701 ) mycobactin-iron holocomplexes ( [M+Fe-2H]+ ) , saturated ( m/z 909 . 4909 ) and unsaturated ( m/z 907 . 4754 ) monodeoxymycobactin-iron holocomplexes , saturated ( m/z 840 . 5845 ) and unsaturated ( m/z 838 . 5688 ) apoforms ( [M+H]+ ) of dideoxymycobactins , and saturated ( m/z 801 . 2878 ) and unsaturated ( m/z 799 . 2722 ) carboxymycobactin-iron holocomplexes , corroborated the automated lipidomics results ( Fig . 2B and S2 Fig ) . Specifically , all traces in wild type M . tb showed the expected sawtooth chromatogram shape , resulting from nearly co-eluting isobaric variants of mycobactins . Retention times were similar ( ±30 sec ) to the known retention time for mycobactins of 5 to 6 min while carboxymycobactins eluted at 8 to 11 min ( Fig . 2B and S2 Fig ) . Chromatograms from ΔmbtN of unsaturated mycobactin , monodeoxymycobactin , dideoxymycobactin and carboxymycobactin were at baseline , indicating a complete absence , while saturated forms were abundantly produced . Complementation of ΔmbtN restored production of unsaturated mycobactin and its deoxy forms ( Fig . 2B ) . CID-MS in comparison to authentic standards provides a basis for assigning each ion to a chemical structure and localizing the site of the altered saturation ( Fig . 2C ) . Specifically , CID-MS detected characteristic fragments of unsaturated mycobactin ( m/z 665 . 3506 , m/z 355 . 0651 ) in wild type cultures and saturated mycobactin ( m/z 667 . 3605 , m/z 355 . 0634 ) in ΔmbtN cultures , consistent with the identity of compounds . Further , the collision patterns localize the site of altered mass to the fatty acyl unit and not to an unexpected portion of the polyketide-polypeptide unit ( Fig . 2C ) , ruling in the action of the enzyme on the fatty acyl unit . These manual analyses further confirm certain aspects of the new lipidomics methods , and they prove that MbtN is the fatty acyl dehydrogenase that carries out an essential function in biosynthesis of unsaturated mycobactin and its deoxy forms . Next we carried out targeted and untargeted analyses of the 12 , 803 molecular events detected in ΔmbtK and its wild type parent . For all evaluable events , automated ( Fig . 3A ) and manual ( Fig . 3B ) analysis showed acylforms of mycobactins and their deoxy variants with signal intensity at or near zero in ΔmbtK . Signals were substantially restored with mbtK complementation . The corresponding substrate accumulation of mycobactin peptide was undetectable in lipid extracts; peptides are predicted to remain covalently attached to MbtE/F in the absence of MbtK [17 , 34 , 35] . Thus , despite the presence of many known or predicted acyl transferases in the M . tb genome , we conclude that MbtK acts non-redundantly in the transfer of saturated and unsaturated monocarboxylic and fatty acids to the lysine moieties of hydroxamate siderophores . Turning to untargeted analysis of all detected molecular events , a strikingly large percentage , 74 . 2% of 12 , 803 measurements , met the criteria of two-fold signal intensity change ( Fig . 3A ) . This degree of change is higher than that observed in the comparison of replicate , iron-depleted wild type cultures ( S3 Fig ) or ΔmbtN ( Fig . 2A ) . Thus , MbtK deletion also causes an unexpectedly broad remodeling of M . tb’s lipid constituents , involving many types of lipids with masses and retention times that are unrelated to mycobactins . Although MbtN is necessary for synthesis of unsaturated siderophores , the role , if any , of the unsaturation in iron scavenging and growth is unknown . For M . tb H37Rv ΔmbtK , we expected the absence of mycobactin to attenuate growth in vitro when heme iron was unavailable; however , production of biologically active unacylated mycobactin peptides or passive iron uptake might allow some iron scavenging . We grew ΔmbtK and ΔmbtN starter cultures in iron-depleted medium supplemented with 50 μM ferric chloride , washed them in iron-depleted medium , divided cultures in half , and inoculated each half into medium that was supplemented or not with iron . Whereas wild type and ΔmbtN grew well in iron-depleted medium , ΔmbtK was entirely unable to grow over eight days ( Fig . 4A ) . Supplementation with 50 μM ferric chloride substantially rescued ΔmbtK growth , although a growth lag of two days was observed . Thus , ΔmbtK is essential for growth in vitro and the defect relates specifically to impaired iron scavenging . To confirm these results on solid medium , we plated the strains in triplicate on iron-depleted or iron-supplemented agar plates , and again observed scant growth of ΔmbtK on iron-depleted medium . On iron-supplemented agar medium , the relative growth defect of ΔmbtK was partially reversed ( Fig . 4B ) . Thus , MbtK , but not MbtN , is required for non-heme iron acquisition and iron-dependent growth in vitro . These in vitro assays provide iron in a non-heme form . However , in vivo iron can be derived from heme via the hemaphore pathway [4 , 5] , or via scavenging of iron bound to host molecules . Understanding the relative roles of these mechanisms is important to guide development of pharmacological agents that might target these potentially overlapping pathways [6 , 36] . Therefore , mouse infection with ΔmbtK could provide insight into the necessity of mycobactins in vivo , and the relative importance of the mycobactin and hemophore pathways during infection . Fifteen C57/B6 mice were infected by aerosol with ~1 , 000 colony-forming units ( CFU ) of an equal mixture of ΔmbtK and complemented ΔmbtK . Prior to infection , each strain was chromosomally marked with a unique identifier ( q-tag ) to facilitate quantitative PCR ( qPCR ) assessment of survival in vivo [37] . Five mice were sacrificed at 24 hours , 1 week , and 6 weeks post-infection . Lung homogenates were plated and colonies collected for qPCR amplification specific to each q-tag , resulting in a log ratio of chromosomal equivalents ( CEQ ) for each strain compared to the total M . tb burden ( Fig . 4C ) . Compared to the initial inoculum , there was a large ( 10 to 5 , 000-fold ) decrease in ΔmbtK CEQs at all time points post-infection that met significance criteria at 1 ( p< 0 . 0001 ) and 6 ( p< 0 . 0023 ) weeks after infection . However , the pattern of response suggested that between 1 and 6 weeks post-infection , ΔmbtK CEQs began to recover . These results suggest that ΔmbtK has an early virulence attenuation for which other iron-acquisition systems are unable to compensate in vivo , but that MbtK is not absolutely required for growth in vivo . Next , we sought to understand the unexpectedly broad alterations in the lipid profile accompanying mbtK deletion ( Fig . 3A ) . That 74 percent of all events would meet change criteria was surprising , because MbtK is thought to function only in the mycobactin/carboxymycobactin biosynthetic pathway [16 , 22] . Further , variations in culture conditions could not account for such global changes , as replicates of wild type M . tb cultures grown in parallel in iron-depleted medium generated a background change of 10 . 1% ( S2 Fig ) . Our initial analysis of ΔmbtK showed that only a small fraction of the changed events corresponded to masses and retention times of mycobactin-like molecules ( Fig . 3A ) . Therefore , we sought to take advantage of the full breadth of detection available in the lipidomic platform to investigate new mechanisms by which defective MbtK function could impact growth and virulence . One explanation is that MbtK acts on substrates other than mycobactin peptides to generate currently unknown lipopeptides with functions unrelated to the iron scavenging effects of mycobactins . If true , these substrates would accumulate and products disappear in ΔmbtK . Such changes in the intensity of these events would be expected regardless of the presence of iron , and they would be rescued by mbtK complementation , similar to the pattern observed with mycobactin ( Fig . 3B ) . A second general type of ion that could accumulate is unacylated mycobactin peptide . To determine if substrate accumulation could account for these types of lipid changes , we calculated the m/z of mycobactin peptide substrates of MbtK and determined their approximate retention time . Molecular events with masses and retention times corresponding to unknown upstream substrates of MbtK were absent from both ΔmbtK and wild type cells , suggesting substrate accumulation was not the sole cause of intensity changes . The absence of mycobactin peptides in lipid extracts was expected , as they are predicted to remain covalently bound to MbtE or MbtF , the enzymes preceding MbtK in the mycobactin biosynthetic pathway [17 , 34 , 35] . Further , intensities of many molecular events decreased in ΔmbtK could be rescued simply by supplementing cultures with iron . Thus , we could not rule in that altered ions in ΔmbtK were unknown products/substrates of MbtK , but more likely are downstream products appearing due to iron starvation . To our knowledge , iron depletion is not directly linked to altered lipid metabolism in M . tb . However , iron is necessary for oxidative phosphorylation and other core metabolic pathways that might broadly influence downstream events in lipid metabolism . Therefore , we next considered the hypothesis that extreme iron deficiency , due to the absence of mycobactin in ΔmbtK , combined with growth in iron-depleted medium , creates stress that alters lipids that are structurally unrelated to mycobactins , but whose abundance is regulation by mycobactin and iron . We did not have the experimental resources to investigate 12 , 803 targets ( Fig . 3A ) , so we used bioinformatic strategies to prioritize targets . The molecular events we focused on correspond to known molecules with high fold-changes of intensity between iron-starved ΔmbtK and iron-starved wild type bacteria . To generate this list of targets , we first excluded events corresponding to unknown lipids , focusing on molecular events matching known families of lipids in the MycoMap database [28] . We next extended the two-way analysis that compared wild type and ΔmbtK in iron-depleted medium ( Fig . 3A ) , to a four-way analysis that also considered iron supplementation and genetic complementation . This analysis sought to identify those events that were altered by mbtK deletion , but rescued by chemical complementation with iron and by genetic mbtK complementation . Next , we used mass interval criteria to group molecular events corresponding to a lipid family . This approach is based on the fact that most lipid families ( i . e . , polyketides , mycolic acids , fatty acids ) occur as chain length variants , which differ by an integer number of CH2 units . Therefore , ions from one lipid family can be readily recognized as alkane series members when they differ by an integer number of m/z 14 . 0156 . As illustrated in a study of global lipid changes occurring in response to hypoxia [38] , biological regulation by an upstream stimulus can be distinguished from random variation in intensity signals because most or all members of a family are typically regulated in parallel . Finally , rather than compiling all events meeting a minimal 2-fold change in intensity , we ranked molecular events and analyzed those with the highest fold-changes , often exceeding 100-fold . Thus , the focusing strategy applied to 12 , 083 events sought to identify the particular subset of events corresponding to families of lipids that are strongly regulated by MbtK expression and iron availability . This organism-wide lipidomic search returned a remarkably consistent pattern of significantly decreased signals corresponding to cytoplasmic membrane phospholipids in ΔmbtK , as compared to similarly grown wild type or genetically or chemically complemented mutants . Analysis of molecular feature , m/z 853 . 5802 , illustrates the discovery process . This event matches the known mass of the phosphatidylinositol ( PI ) ion [M+H]+ , C44H85O13P , containing 35 carbon atoms and no unsaturations ( C35:0 ) in its two alkyl chains ( Fig . 5A ) . This event has average intensity counts of 2 . 1 x 106 in iron-starved wild type , 1 . 9 x 106 in iron-supplemented wild type , 0 . 3 x 106 in iron-starved ΔmbtK , 2 . 2 x 106 in iron-supplemented ΔmbtK , and 2 . 3 x 106 in iron-starved ΔmbtK complement . Therefore , it meets the criteria for high fold-change and intensity rescue by iron supplementation and complementation . The changes in intensity of PI C35:0 across the five conditions were compared to events corresponding to other PI acyl forms and there were no nearly co-eluting molecules that matched alternatively acylated PI forms . The intensities of these events showed similar patterns among the five conditions that track in parallel with one another and with PI C35:0 . The pattern of relative abundance of PI acylforms in wild type and ΔmbtK was similar , suggesting that absence of MbtK did not simply induce a shift in abundance from one acylform to another ( S1 Table ) . Further , key aspects of this computerized analysis were validated in manual analysis of ion chromatograms , which were consistent with a single molecule at the expected retention time for phosphatidylinositol C35:0 ( Fig . 5B ) . CID-MS of m/z 851 . 5655 was consistent with the [M-H]- ion of phosphatidylinositol C35:0 ( Fig . 5C ) . Although phospholipid concentration is not measured directly in this high throughput method , the intensity count values for time-of-flight MS detection reliably correlate with mass input over a broad concentration range of mycobacterial lipids [28] . Collectively , these data show that the approximately ten-fold change in PI intensity results from mbtK deletion in the setting of iron starvation . By repeating this process across the five conditions , and for events corresponding to known molecules , we observed highly similar patterns of MbtK regulation of every major phospholipid class in the cytoplasmic membrane of M . tb , including phosphatidylethanolamine ( PE ) , cardiolipin ( CL ) , phosphatidylglycerol ( PG ) , and triacylglyceride ( TAG ) ( Fig . 6 and S4 Fig ) . As measured by average ion intensity , the degree of change is high in iron-starved ΔmbtK , with loss of 50 to 90 percent for phospholipids . Finally , in all cases lipid intensity is rescued by mbtK complementation or iron supplementation . This analytic method measures steady state phospholipid pools and does not distinguish between reduced phospholipid synthesis or increased catabolism . However , phospholipid catabolism in response to hypoxic , redox and other stresses has been described previously and is known to occur through the action of phospholipases , which act on abundant membrane phospholipids , generating free fatty acid [39 , 40] . Consistent with this scenario , we observed signal increases for triacylglyceride and free fatty acids in iron-starved ΔmbtK , which were rescued with iron supplementation or complementation ( Fig . 6 ) . To determine whether redox stress occurred in the ΔmbtK mutant , we scanned lipidomics datasets for ions annotated as menaquinone-9 ( C56H80O2 , neutral mass 784 . 6158 ) , a key component of the M . tb electron transport chain ( S4 Fig ) . Menaquinone is the primary quinone in mycobacteria , converting between oxidized menaquinone and reduced menaquinone-H2 [41 , 42] . Annotation of the existing lipidomic dataset identified ions matching the calculated mass of the [M+Na]+ adducts of oxidized and reduced menaquinone-9 in lipid extracts at their expected retention time range of 4–8 mins [28] . Wild type M . tb grown in iron-depleted medium generated slightly more of the putative oxidized ( m/z 807 . 6050 ) than reduced menaquinone-H2 ( m/z 809 . 6207 ) ; however , reduced menaquinone-H2 was substantially more abundant than oxidized menaquinone in iron-depleted ΔmbtK . Complementation of ΔmbtK rescued this phenotype , suggesting mbtK deletion causes an imbalance of putative oxidized and reduced menaquinone . These results show how post facto analysis of organism-wide datasets can be interrogated to understand the downstream events controlling lipid metabolism , pointing toward an unexpected but massive iron-induced depletion of membrane phospholipids and an increased pool of free fatty acids .
When the substrates , cofactors and products of enzymes can be predicted , the reactions they catalyze can be reliably and quantitatively assessed in vitro . Such classical enzymology approaches determine the reactions for which an enzyme is sufficient . These methods predicted that MbtN and MbtK function as a mycobactin dehydrogenase and acyl transferase , respectively [16 , 22] . By combining gene deletion with new profiling platforms , it is possible to determine the diverse biochemical reactions for which an enzyme is necessary in its biological context , based on the natural substrates to which it is exposed [15 , 26 , 28] . Further , this approach can measure the relative contribution of intact pathways that operate in parallel , providing useful benefits to drug discovery , which relies on identification of non-redundant pathways . For example , M . tb expresses over 250 genes predicted to function in lipid metabolism [43] . In many cases , acyl transferases have highly overlapping predicted functions , making it difficult to identify the acyl transferase required for production of one particular lipid [44 , 45] . However , the strategy of lipidomic profiling shows that MbtK is absolutely required for its biochemical role in mycobactin biosynthesis as well its biological role in iron capture in vitro and in vivo . Given the essentiality of iron to infection , it is logical that multiple iron acquisition pathways would be active during M . tb infection . The discovery of the hemophore system [4 , 5] raises questions about its redundancy in vivo with the mycobactin-carboxymycobactin pathway , and the role each plays in virulence . A mutant early in the mycobactin biosynthesis pathway ( mbtE ) is unable to grow in vivo [10]; accordingly , our infection study indicates strong growth attenuation of ΔmbtK in mice at early time points , with some restoration of growth later in infection . Growth attenuation is not as extreme as ΔmmpS4/5 , which is expected because that mutant lacks the siderophore export apparatus and accumulates toxic siderophore intermediates , while ΔmbtK does not [46] . Our data suggest that mycobactin supports growth during M . tb’s paucibacillary stage , but that its necessity during later time points of in vivo growth lessens . The emergence of adaptive immunity after two weeks could affect ΔmbtK growth . A second , more favored scenario to explain the differing roles of MbtK in these settings is that iron is absolutely required for growth in general , and that mycobactin and heme transporters are both somewhat important in vivo , but have distinct roles during the early and late stages of infection , respectively . Other known aspects of the pathophysiology of tuberculosis are consistent with this hypothesis . During early stages of paucibacillary growth , M . tb would be expected to acquire iron from stores in undamaged tissue and invading macrophages . Later stages of tuberculosis cause hemorrhagic lung disease , which could potentially provide heme-bound iron [5] . Increasing evidence supports the hypothesis that paucibacillary growth at the earliest stages of infection can be decisive for transmission and medical intervention [47–50] , so inhibitors of mycobactin function might be considered as therapeutic agents in this setting . These data show that MbtN plays an essential biochemical role in creating unsaturated mycobactins and carboxymycobactins . However , the unsaturation is not required for normal growth in vitro . Prior studies have determined the unusual location and nature of the cis C2-3 unsaturation , present in the carboxyl unit of mycobactin and dicarboxyl unit of carboxymycobactin , which is thought to be unique among acylated lipids in M . tb [23 , 24] . Even if unnecessary for iron uptake in vitro , evolutionary conservation of this unusual unsaturation suggests that it has some biological function . This unsaturation might affect the cellular handling of mycobactin-like molecules , or the known effect of the unsaturation in increasing immunogenicity of dideoxymycobactin , a CD1a-presented T cell antigen , may be a natural but non-nutritional function [25] . Another unexpected conclusion derived from the lipidomics datasets is the broad and marked reduction in pools of membrane phospholipids in iron-starved ΔmbtK . These changes were accompanied by increased signals for free fatty acids , triacylglyceride and menaquinone-H2 , suggesting the bacteria are metabolically responding to redox stress . To our knowledge , direct connections of iron depletion to mycobacterial phospholipid biosynthesis are not documented , but our findings are consistent with published reports that iron-starved M . tb induce a cascade of genes involved in stress responses , which may secondarily affect phospholipid pools [18 , 51] . However , the pool size of phospholipids and fatty acids changed in opposite ways , which might be explained by release of fatty acids in response to stress [27 , 51] . Rescue of the ΔmbtK lipid phenotype by iron suggests a connection between iron starvation stress and lipid metabolism , but does not describe a specific mechanism . Consistent with the increased triacylglyceride signal in iron-starved ΔmbtK , previous studies have demonstrated M . tb accumulation of triacylglyceride during redox stress via DosR or WhiB3 signal transduction [12 , 52 , 53] . Further , redox stress from vitamin C treatment decreased phospholipids in M . tb [40] . Iron can play several roles in maintaining redox balance . For example , iron is an essential component of iron-sulfur clusters in electron transport chain cytochromes and dehydrogenases , which regenerate reducing equivalents such as NAD+/NADH [54] . The inability to oxidize reducing equivalents may cause “reductive stress , ” leading to build up of electrons in the menaquinone pool and excess menaquinone-H2 . Further , the extreme iron starvation that occurs in the absence of MbtK may cause phospholipid catabolism to continue unabated , resulting in decreased plasma membrane integrity and death of some cells . However , the low phospholipid state is not merely a death phenotype , as live bacteria can be grown from affected cultures and infected mice . Our results are consistent with upregulated phospholipid catabolism and glycerol lipid accumulation to ensure a ready supply of carbon . Our data place MbtK at the crossroads of three M . tb phenotypes required for pathogenesis: the ability to acquire iron in vivo , maintenance of phospholipids , and growth in the lung . Affecting all three phenotypes by removing a single enzyme supports targeting of MbtK or other steps in mycobactin synthesis as a therapy for tuberculosis [55] . Further , the unexpected lipid response of M . tb to mbtK deletion was extrapolated using lipidomics as a new approach to describe enzyme function in biological context . Thus , lipidomic profiling provides broad and unexpected insight into mechanisms by which the primary enzymatic mechanism of MbtK—lipid transfer to a peptide—controls a cascade of downstream effects involving mycobactin synthesis , iron depletion and lipid catabolism .
To construct M . tb H37Rv deletion mutants in mbtK or mbtN , 1 kilobase regions of the genome flanking each gene were amplified and cloned into pJM1 , a counterselectable mycobacterial suicide vector containing genes for chloramphenicol resistance , hygromycin resistance ( hyg ) , and sucrose sensitivity ( sacB ) . Transformants having undergone single crossover events were selected on hygromycin . PCR with primers within hyg ( 5′-GAATCCCTGTTACTTCTCGACCGT-3′ and 5′-AGGTCCACG AAGATGTTGGTCC-3′ ) confirmed single crossover events . Transformants were plated on mycobactin-containing sucrose medium to select for double crossover events; PCR with internal primers ( mbtK: 5′-TCATGCTCACCGAGGAACTTGCA T-3′ and 5′-AGA TGTTGGCGGAGTGGATGAA-3′; mbtN: 5′-TCGGTAAACTCGTCGAACTCGCTT-3′ and 5′-CGAAGATGTGCATGCATTCGGAGA-3′ ) and external primers ( mbtK: 5′-ATGATGAGTCGACGTCAGTTCGGT-3′ and 5′-AGGGACTCGAACCCTCAAAC TCTT-3′; mbtN: 5′-ATGCAAGTTCCTCGGTGAGCATGA-3′ and 5′-AGCCGTGAA ATTGGCGAAATCGAG-3′ ) confirmed deletions . To complement , mbtK or mbtN were expressed under the mycobacterial groEL promoter on integrating plasmid pGH1000A [31] and confirmed by PCR . Restoration of mycobactin production was confirmed by HPLC-MS . For comparative lipidomics , three 5 ml cultures of M . tb H37Rv parent and mutant strains were grown to log phase in a defined iron-depleted medium containing 0 . 5% ( w/v ) KH2PO4 , 2% ( v/v ) glycerol , 0 . 5% ( w/v ) L-asparagine and 10% albumin-dextrose-sodium chloride complex ( ADN ) [18 , 56] . Medium containing 0 . 5% KH2PO4 , 2% glycerol and 0 . 5% L-asparagine was incubated overnight with 5% ( w/v ) Chelex-100 ( Bio Rad ) to lower the trace metal concentration . After removing Chelex by filtration , the pH was adjusted to 6 . 8 and medium was supplemented with 10% ADN , 0 . 5 mg ZnCl2 , 0 . 1 mg MnSO4 , and 40 mg MgSO4 per liter . For assaying growth on plates , 50 μl from each initial culture was spread onto an iron-depleted or iron-supplemented plate , containing 1 . 5 g agar ( Bacto ) and 100 mg cyclohexamide per liter . Iron-supplemented plates contained 50 μM FeCl3 . To extract cell lipids , bacteria from triplicate plates were treated with 60 ml 2:1 ( V:V ) , then 1:1 , then 1:2 chloroform:methanol for 1 h each . Extractable lipids were separated by centrifugation , collected and dried . Acetone insoluble cell lipids were made by collecting precipitates from 30 mg total lipids per 0 . 21 ml 4°C acetone on ice for 1 h , followed by washing with 4°C acetone . Acetone precipitates were washed 3 times with 4°C acetone; supernatants and washes were collected and dried to yield acetone soluble cell lipids , which were mass-normalized and analyzed in triplicate by a reversed phase method [15] on an Agilent 6520 Accurate Mass QToF mass spectrometer . Acetone insoluble lipids were analyzed by normal phase HPLC-MS as previously described [28] . Supernatant lipids from triplicate liquid M . tb cultures , used to measure carboxymycobactin production , were extracted with an equal volume of ethyl actetate , dried , mass-normalized , and analyzed by reversed phase HPLC-MS as previously described [15] . Fifteen mice received ~1 , 000 colony-forming units ( CFU ) of an estimated 50:50 mixture of q-tagged ΔmbtK and complemented ΔmbtK via aerosol . Five mice were sacrificed at 1 day , 1 week , and 6 weeks post-infection . Lung homogenates were plated for CFU , colonies were counted and then scraped from plates to prepare genomic DNA . Quantitative PCR was performed in duplicate with individual primers and probes designed to recognize each strain’s q-tag [37] . Log ratios were evaluated by unpaired T-tests . This study was performed in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health . The protocol was approved on June 12th , 2013 by the Harvard Medical Area Standing Committee on Animal Care and Use ( Accreditation #000009 ) . | M . tuberculosis ( M . tb ) survives only if it can acquire iron from its human host , so new therapies for tuberculosis might be discovered if the pathways necessary for iron acquisition are identified . M . tb scavenges iron in two ways: from free iron , or from the blood in the form of heme . To bind free iron , M . tb uses mycobactin , a lipopeptide that tightly binds iron and transports it to the bacterial cytosol . Mycobactin is thought to be required for M . tb virulence , but its biosynthesis is incompletely understood . To investigate mycobactin biosynthesis , we deleted the mbtN and mbtK genes potentially required for generating the mycobactin lipid tail . Then , an organism-wide screen of lipids identified changed molecules that are the direct targets of these genes or have broader downstream functions . MbtK deletion specifically changed the lipid component of mycobactin and created extreme iron-deprivation that prevented growth of M . tb in mice . Unexpectedly , the combination of MbtK loss and iron starvation triggered a global depletion of phospholipids , a key constituent of the bacterial membrane . These studies establish that mycobactins , acting independently of the heme acquisition pathway , impact lipid homeostasis and M . tb survival , supporting efforts to develop host-directed therapies for tuberculosis . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | Lipidomic Analysis Links Mycobactin Synthase K to Iron Uptake and Virulence in M. tuberculosis |
With remarkable spatial and temporal specificities , peripheral membrane proteins bind to biological membranes . They do this without compromising solubility of the protein , and their binding sites are not easily distinguished . Prototypical peripheral membrane binding sites display a combination of patches of basic and hydrophobic amino acids that are also frequently present on other protein surfaces . The purpose of this contribution is to identify simple but essential components for membrane binding , through structural criteria that distinguish exposed hydrophobes at membrane binding sites from those that are frequently found on any protein surface . We formulate the concepts of protruding hydrophobes and co-insertability and have analysed more than 300 families of proteins that are classified as peripheral membrane binders . We find that this structural motif strongly discriminates the surfaces of membrane-binding and non-binding proteins . Our model constitutes a novel formulation of a structural pattern for membrane recognition and emphasizes the importance of subtle structural properties of hydrophobic membrane binding sites .
Biological membranes are ancient and crucial components in the organisation of life . Not only do they define the boundaries of cells and organelles , but they are central to a myriad protein-protein and protein-lipid interactions instrumental in numerous pathways [1–5] . Besides the embedded transmembrane proteins and receptors , a number of soluble proteins interact transiently with the surface of cellular and organellar membranes achieving remarkable spatial and temporal specificities . These proteins ( or domains ) are referred to as peripheral proteins ( or domains ) and their membrane-binding site as interfacial binding site or IBS . Peripheral proteins may bind membranes via lipid-binding domains which are independently folded modules forming an integral part of the overall protein; C2-domains and FYVE-domains are examples of such domains [6 , 7] . Many lipid-processing enzymes , endogenous or secreted by pathogens are also included in the definition of peripheral proteins . Unlike protein-protein or protein-ligand interactions , interfacial binding sites of peripheral proteins are poorly characterized in terms of amino acid composition and structural patterns . Embedded and transmembrane proteins contain well defined regions of hydrophobic surface , clearly identifying their membrane interacting segments . This is seldom the case for peripheral membrane proteins . Currently the prototypical peripheral membrane binding site is described as displaying a combination of basic and hydrophobic amino acids [7 , 8] . Attempts to characterize the energetics of membrane binding has mostly focused on electrostatic complementarity of peripheral proteins with the charged surfaces of membrane [9] , rather than on the desolvation of hydrophobes which is more difficult to isolate in theoretical treatments . Nevertheless the predictive power of implicit membrane models in the prediction of membrane binding sites is a strong indication of the importance of the hydrophobic effect [10] in peripheral membrane binding . For example , Lomize et al . could correctly identify the experimentally known IBS of 53 peripheral peptides and proteins using a model that includes only hydrophobic , desolvation and ionization energy terms [11] . Yet in order to assert the generality of a protein-membrane binding mechanism , it is not enough to demonstrate its validity for a selected set of true positives , but it is also important to evaluate it on a control dataset . As both small hydrophobic patches and charged residues are frequently present on protein surfaces it is challenging to distinguish membrane binding sites from the rest of the peripheral membrane proteins surface solely relying on amino acid composition . There are indications that structural considerations may allow signatures of membrane interacting hydrophobes to be defined . Terms like hydrophobic spikes [12 , 13] and protruding loops [11] have been used to describe membrane binding sites , prompting the idea of hydrophobes protruding from the protein globule . A close look at amphipathic helices , also motivates the concept of protruding hydrophobes . Amphipathic helices are characteristic of membrane-binding peptides and proteins . When such membrane binding helices exist , they are often found lining a protein , forming a cylindrical protrusion from the globule ( e . g . ENTH domain of Epsin , PDBID: 1H0A [14] , shown in Fig 1C and 1D ) . Yet , no generalization of protruding membrane binding sites has been proposed for peripheral membrane proteins . The purpose of this contribution is to identify structural characteristics that distinguish exposed hydrophobes at membrane binding sites from those that are frequently found on any protein surface . We propose a simple definition that formalizes the concept of protruding hydrophobes , and which can be easily computed from the protein structure . This definition allows us to systematically investigate to what extent protruding hydrophobes are found on both binding and non-membrane-binding surfaces , and to identify structural criteria for recognizing exposed hydrophobes that are likely to be important for membrane binding . A major obstacle in developing general association models for peripheral membrane proteins is the scarcity of experimentally verified binding sites , and detailed descriptions of binding orientations . Computational studies on the role of hydrophobes on membrane binding sites have been based so far on relatively small sets of proteins with known binding sites [10 , 11 , 15] . To get around this problem and to leverage the large number of proteins for which membrane binding has been identified without a detailed characterisation of the IBS , we perform a comparative statistical analysis of protein surfaces . Given classifications of proteins that identifies membrane binders , we compare peripheral membrane proteins with protein surfaces that are not membrane-binding and with more general reference proteins . With this we can extend our analysis to hundreds of protein families rather than the few dozens for which binding sites have been partially identified by experiments . With our simple definition of structural protrusions , we perform a statistical analysis of protruding hydrophobes in a large protein structure dataset and our results support their general role in membrane association . We find that protruding hydrophobes can be used to strongly discriminate protein surfaces involved in membrane binding from those that are not . Hydrophobes are much more frequent on protruding sites of peripheral membrane proteins than in the reference dataset , and they have a strong tendency to cluster on positions that can simultaneously interact with the membrane .
First we calculated the frequency of hydrophobes on protrusions in peripheral protein families and compared it to the reference datasets . In Fig 2 , we observe a stark contrast between the set of peripheral proteins and the non-binding surfaces ( compare Fig 2A and 2C ) . Hydrophobes occur with high frequency and in almost all families on protrusions of peripheral proteins . In the reference set on the other hand , hydrophobes on protrusions are much less tolerated , reflected by a histogram mode of zero . While less pronounced , the distinction is also clear for the comparison with reference proteins ( compare Fig 2E and 2G ) . Qualitatively , the frequency of hydrophobes on protrusions is similar in the two reference sets ( Fig 2C and 2G ) but the sets of peripheral proteins differ somewhat suggesting some sensitivity to quaternary structure modeling . For both comparisons however , this trend is specific for protruding positions and does not reflect a general difference in composition of surface exposed amino-acids between the data sets as shown by plots in Fig 2B , 2D , 2F and 2H . Indeed , if we consider the frequency of hydrophobes on all solvent exposed residues , the distributions look quite similar with both sets having histogram modes close to 0 . 2 . This value is in agreement with the fraction of the surface of globular proteins typically reported to be hydrophobic ( for instance 0 . 19 in Ref . [17] ) . The ‘Non-binding surfaces’ are in some cases very small , due to the way we ensure that these surfaces are not interacting with the membrane ( see ‘Materials and methods’ ) . While these small surfaces are relevant samples for calculating average frequencies , the fraction of hydrophobes on such surfaces can take more extreme values ( close to zero or 1 ) . For this reason the tails of the histograms for this reference set are somewhat fatter than those for the peripheral membrane proteins . Given the nature of our model the differences presented in Fig 2 are naturally ascribed to two factors; the accessibility of amino acids compared to other regions of the protein ( they are vertices of the convex hull ) and their low local protein density d defined as the number of neighboring Cα- or Cβ-atoms ( Cf . definition in ‘Materials and methods’ ) . We here explore the dependence of this difference on d . In Fig 3 we show the difference between frequencies of hydrophobes in peripherals and the non-binding surfaces for different ranges of the local protein density d . The leftmost bar ( 0 ≤ d ≤ 6 ) corresponds to chain terminals . The other bars corresponding to ranges covered by our definition of protruding residues ( 7 ≤ d < 22 ) show that hydrophobic residues are more frequently found at vertex residues with low local protein density in the peripheral proteins . This also serves as an a posteriori justification for constricting our definition of protrusions to amino-acids with d < 22 . Assuming that the over-representation of hydrophobes on protrusions in peripheral membrane proteins stems from actual membrane binding sites , we expect those proteins to have more than one hydrophobic protrusion . We estimated the tendency of hydrophobic protrusions to be ‘co-insertable’ by calculating the weighted frequency of co-insertion ( Eq 9 ) ( Cf ‘Materials and methods’ ) for all datasets ( Fig 4 ) . We note that peripheral membrane proteins do indeed tend to have hydrophobes on co-insertable protrusions to a significantly larger extent than what would be expected from randomly scattering hydrophobes among protruding positions . This tendency is much lower for the ‘Non-binding surfaces’ even when considering the extremities of the error bars , which are wide precisely because there are very few protruding hydrophobes in this set . In the ‘Reference Proteins’ the analysis indicates that co-insertability is more common than in the null model , but far less so than in the Peripheral proteins . We further explore the degree of co-insertability of the hydrophobic protrusions present in our datasets . We seek to evaluate to what extent co-insertable hydrophobic protrusions can be used to discriminate likely peripheral membrane binders from other proteins . Fig 5 shows the fraction of proteins in each dataset that have at least one pair of co-insertable hydrophobic protrusions ( labelled ‘Co-ins . ’ ) and the fraction of proteins that have at least one isolated hydrophobic protrusion ( i . e . a protrusion that does not satisfy the criteria that define ‘co-insertability’ ) . While we do see some discrimination between the data sets in the case of isolated protruding hydrophobes , the co-insertable ones prove to be very strong indicators of which proteins surfaces are membrane binding . As the coincidental occurrence of such properties increase with the size of the protein surface , we have grouped the proteins by total number of surface protrusions ( regardless of hydropathic properties ) . We do however see no appreciable difference between the proteins of size 0–25 and those of size 25–50 . We consider the fraction in the reference sets to be a reasonable estimate of a false positive rate for predicting membrane binding function based on the presence of co-instertable protruding hydrophobes . The reference proteins ( Fig 5D–5F ) , indicate a false positive rate in the range of 20%–30% . The lack of membrane interaction is not asserted for this set , and we do expect it to contain some proteins with undetected or unclassified membrane binding . The false positive rate is around 12% for the non-binding surfaces ( Fig 5A–5C ) but with a smaller sample size this estimate comes with somewhat higher error bars . Around 64% and 75% of the peripheral membrane proteins in the respective size-groups have co-insertable protruding hydrophobes . In line with the previous analyses ( Figs 2 and 4 ) the predictive power is somewhat weaker for the ‘Peripheral-P’ dataset compared to ‘Peripheral’ . We interpret this as a dependence on quaternary-structure modeling , which is corroborated by a dedicated analysis presented in the Material and methods section ( Fig 11 ) . We consider the manually curated oligomeric states to be more reliable and therefore expect the peripheral proteins presented in Fig 5A–5C ( Peripheral dataset ) to better represent actual proteins . In order to evaluate how common co-insertable protruding hydrophobes are as membrane-interacting motifs we will assume the rate of occurrence in the set ‘Peripheral’ , and conservatively assume a frequency of occurrence on non-membrane interacting sites around 20% . This is consistent with both extremes of the 95%-confidence intervals in the non-binding surfaces ( Fig 5A–5C ) and the estimate from the reference proteins ( Fig 5D–5F ) . Even when considering that as much as 20% of co-insertable protruding hydrophobes might not be membrane interacting we still expect a rough estimate of around half of the analysed membrane binders to have this motif at their membrane-interacting sites . The analysis presented in Figs 3 and 5 suggests that the concepts of protruding hydrophobes and co-insertability can be used to identify membrane binding residues . Based on these results we seek to define a predictor of membrane binding sites . We define ‘the Likely Inserted Hydrophobe’ as the protruding hydrophobe with the highest number of co-insertable protruding hydrophobes and lowest local protein density , as defined in ‘Materials and methods’ . Fig 6 illustrates that this simple definition is able to identify binding sites on modular membrane-binding domains: C1 , C2 , PX , ENTH , PLA2 and FYVE . For most of these cases , the Likely Inserted Hydrophobe has in fact been experimentally indicated to contribute to membrane binding . For the other examples , it is clearly positioned close to the experimentally identified binding site . A more quantitative comparison between predicted and verified membrane interacting residues is complicated by the sparsity of negative assertions from either methods . Experiments aiming at identifying membrane-binding sites will usually only target some of the amino acids suspected to belong to the membrane binding residues , and usually not conclude on other amino acids . To the extent non-binding amino-acids are investigated or revealed by the mutation of putative membrane binding residues , interpretation of results in this context is also less straightforward as the absence of interaction of an amino-acid with the membrane does not strictly preclude it from being located close to a binding site . Similarly the Likely Inserted Hydrophobe is by definition only one residue and provides no negative prediction of which amino acids do not bind the membrane . We can however make a rough , but well defined , comparison by computing the angle between the vectors connecting the protein center with respectively the mean position of the membrane interacting residues identified in experiments ( t I e ) , and the Likely Inserted Hydrophobe ( t I p , See Eq 11 ) . While this comparison does not provide a quantitative evaluation of whether experimentally determined IBS and predicted residues match exactly , it allows us to separate proteins where the predicted and verified residues are “on the same side” of the protein ( ∠ t I e t I p < 90° ) from those where they are not . We show on Fig 7 such a comparison for proteins whose binding sites are experimentally determined . This is a coarse approximation to the protein orientation , which is sensitive to both protein shape , the selection of residues included in the partial biding sites , and any difference in backbone conformation between bound and unbound protein . Even so , we do expect that wrong binding site predictions should provide angles in the entire range from 0° to 180° with roughly uniform probability . But , we observe that almost all angles are sharper than 90° , indicating a reasonable agreement with experimental data . We also observe a similar range of angles for cases where the membrane interaction of the Likely Inserted Hydrophobe has been experimentally verified ( marked with asterisks ( * ) in Fig 7 ) and the cases where it has not . We would like to emphasise at this point that the Likely Inserted Hydrophobes that are not yet found to be membrane interacting might very well never have been tested . We also calculated all angles between the set of experimentally identified residues and protruding amino acids of all kinds . These results are displayed as box-plots in Fig 7 . While they vary a bit between families we note that all medians are close to 90° , confirming that the statistical expectation for protrusions in general is to have roughly equally many observations larger than and smaller than 90° . Interestingly , the Bovine α-lactalbumin , for which we find no protruding hydrophobes , is analysed in its crystallised form while it is known to bind membranes in a molten globule state [23] . We provide as Supporting Information the complete list of amino acids experimentally identified as being part of membrane binding sites ( Table B in S1 Text ) . It overlaps with the list provided by Lomize et al . [11] , but sometimes differ in exactly which amino acids are included , as we include indicated membrane interacting residues even when they are not inserted in the hydrophobic core of the membrane . The continuum-model presented by Lomize et al . [24] forms the basis for a systematic effort to predict binding orientations for peripheral membrane proteins . The OPM database [25] provides prediction of spatial arrangements of membrane proteins with respect to the lipid bilayer for a selection of peripheral membrane proteins . We here investigate to what extent protruding hydrophobes are captured by the model proposed by Lomize et al . We identify The Likely Inserted Hydrophobe for each of the proteins in our dataset and extract the OPM predicted insertion coordinate of its Cα-atom . The ‘insertion coordinate’ of an atom measures its depth of insertion into the hydrocarbon region of the membrane model and is thus positive for atoms located in the hydrocarbon core and negative for atoms located on either side of the membrane including the interfacial region ( Cf . ‘Materials and methods’ ) . Fig 8 shows histograms of the median insertion coordinate of the Likely Inserted Hydrophobes identified in each family . A clear majority of those residues are located close to the interface of the membrane model in the OPM-predictions ( Fig 8A ) and 75% of the families in the set of peripheral membrane proteins have the median insertion coordinate for the Likely Inserted Hydrophobe within a margin of 0 . 5 nm from the membrane . This fraction is similar to the estimated fraction of proteins that have co-insertable protruding hydrophobes ( Fig 5A and 5B ) . We allow this margin of 0 . 5 nm to compensate for the assumptions of rigid protein , flat membrane , and the distance between Cα-atoms and side-chain atoms . Fractions for other margins can be read from the cumulative histogram shown in Fig 8C . By representing position with the insertion coordinate we effectively project residue coordinates onto the membrane normal . We therefore do not expect surface amino acids to be uniformly distributed along the insertion coordinate axis and present control statistics for randomly chosen protruding amino acids of all hydropathic properties ( Fig 8B and 8D ) . It appears clearly that the high number of Likely Inserted Hydrophobes close to the membrane model is not an effect of having more protein at that location . The analysis presented in Fig 3 indicates that the ability to discriminate the data sets based on the frequency of hydrophobes on protrusions gets lower as the local protein density gets higher . Local protein density of a protrusion is dependent on secondary structure elements with loops , turns and bends being those that intuitively favor low local protein density . These secondary structures typically mark a clear change in direction of the backbone trace , where the neighbouring residues ‘make way’ for the protruding hydrophobe . Fig 9A shows which secondary structure elements the protruding hydrophobes are associated with in the set of peripheral proteins . We note that loops , turns and bends are indeed abundant but so are also helices and not beta-strands . Fig 9B shows a comparison with the reference data set ( ‘Non-binding surfaces’ ) . We see that protruding hydrophobes on turns and bends are not only common in the peripheral membrane proteins as we saw in Fig 9A , but that they are also significantly more frequent than in the reference set . Interestingly , this is not the case for loops . Turns and bends are by definition structural elements with restricted flexibility [26] compared to loops , which are here defined as the absence of any of the other secondary structure definitions ( equivalent to ‘coil’ ) . We expect the latter category to contain less regular , more flexible structures . We speculate that turns and bends provide rigid scaffolds for exposing hydrophobic side chains , which might otherwise rearrange to desolvate when exposed to solvent . We also expect a similar property of rigid scaffolding from amphipathic helices , which is an established motif for membrane association . Fig 9 illustrates however that protrusions are not dominantly helices , confirming that the concept of protruding hydrophobes provides a useful generalisation for the shapes of membrane-binding sites . For purposes of isolating the structural component of hydrophobic membrane association we have until now used a dichotomous definition of hydrophobicity based on signs of free energy of transfer determined by Wimley and White [27] ( leucine , isoleucine , phenylalanine , tyrosine , tryptophan , cysteine and methionine have been considered to be hydrophobic ) . Yet , we do expect different amino acids to have varying contributions to the free energy of binding . We have therefore also assessed the relative importance of different amino acids for discriminating between our sets . Fig 10B shows the comparison of the frequencies of different hydrophobic amino acids on protrusions in the set ‘Peripheral’ and the set ‘Non-binding surfaces’ . Analysis of the other two sets can be found as Supporting Information ( S1 Text ) . As expected we find non-polar residues with large aliphatic or aromatic side chains to be much more frequent at the protrusions of peripheral proteins than on the non-binding surfaces . While the error bars in Fig 10B are not corrected for multiple testing , the signal for the hydrophobes as a group is quite clear . They all occur as over-represented in the set ‘Peripheral’ and the odds-ratio is much larger for phenylalanine , leucine and tryptophan than for any of the amino-acids that are over-represented in the set ‘Non-binding surfaces’ . Analysis of the other two sets can be found as Supporting Information ( S1 Text ) . Recall that ln R ( Eq 10 ) is symmetric around 0 , so the magnitude of the bar representing phenylalanine on one end , can be directly compared to that of the bar representing threonine in the negative direction . Tyrosine on the other hand discriminates the sets poorly compared to its high hydrophobicity score in the Wimley-White scale . We consider this a possible consequence of the orientational restrictions on the binding sites of peripheral membrane proteins . The typical orientations consistent with shallow binding has the residue anchored above the membrane . This probably allows less freedom for the polar hydroxyl group of tyrosine to orient towards regions of higher water density , than it has in the peptides used for the Wimley-White experiments or in transmembrane proteins . We also note with interest that proline is among the residues that are somewhat over-represented in the set of peripheral proteins . In general prolines are conformationally important protein components that restricts the backbone with respect to its immediate neighbours along the peptide chain . They are therefore likely to promote local rigidity . They also serve to induce sharp changes in the backbone direction . We speculate that this would facilitate solvent exposure of neighbouring side-chains as discussed above . Specifically they are in general frequently found on turns [28] . The convex hull representation presents a useful abstraction of proteins for investigating surface properties of approximately rigid protein conformers interacting shallowly with an approximately flat membrane . The model enables statistical analysis of protein structures , which is prohibited by high-resolution models where model parameters and quality controls typically have to be made subjectively for individual protein-membrane systems . We have employed this abstraction specifically to quantify and understand aspects of hydrophobes in peripheral membrane binding . In order to isolate components contributing to membrane binding we have purposefully avoided complicating the interpretation with other known important factors such as electrostatics , conformational flexibility and even relative hydrophobicity . For the purpose of understanding the balance and complementarity between different contributions to membrane-binding and making more generic models it will be necessary to take these other factors into account in ways that allows decomposition of their contribution . In the framework of a non-energetic structural analysis as the one we present in this manuscript , it is natural to do that in terms of comparing presence -or absence- and location of predicted binding sites between protein models . Particularly , models of electrostatic binding are well developed and readily applicable to surface representations of rigid protein conformers . While complex energetic models or machine learning approaches can be expected to yield high performance in predicting membrane-binding properties of proteins , the kind of model presented here provides a clear interpretation of the resulting prediction ( membrane-binding or not ) and mechanistic information . This connection to expert knowledge is invaluable for interpreting automated classifications where the models can not be reliably parameterised against negative data , that is definitely non-binding proteins . The combined use of various binding-site indicators based on different generic binding models such as hydrophobic and electrostatic models can provide a much improved performance in such prediction while maintaining interpretability . Such an approach would also be useful for inference or interpretation of protein specificity towards particular lipid compositions of the interacting membrane . Protein-membrane interactions are typically studied in vitro or in silico and inference to their biological context have to carry over from greatly simplified membrane models . To make sense of such experiments and simulations , it is essential to formulate general models that explain protein association in terms of factors that are present in both model systems and the relevant in vivo counterpart . In pursuit of such general models for membrane recognition , we have formulated the concepts of protruding hydrophobes and co-insertability . We have analysed more than 300 families of proteins that are classified as peripheral membrane binders and identified this model to be a good fit for at least half of them , after cautiously correcting for conservative false positive rates estimated from the reference sets ( Fig 5 ) . The generality of the model is corroborated by three important points . Hydrophobes are clearly over-represented on the protrusions of peripheral membrane proteins ( compare Fig 2A and 2C , and see Fig 3 ) , they tend to locate on co-insertable protrusions ( see Figs 4 and 5 ) , and protruding hydrophobes are generally positioned consistently with experimentally identified binding sites ( Figs 6 and 7 ) . Amphipathic helices are already well known membrane binding motifs which our definition of protrusion is well suited to capture , whenever these are stably folded and exposed . We do however find that the majority of identified protruding hydrophobes are not helices ( Fig 9A ) and that hydrophobes are also highly over-represented on protruding turns and bends ( Fig 9B ) . We therefore propose the concept of protruding hydrophobes as a useful generalisation upon binding motifs that are identified in terms of secondary structure . Investigation of the interfacial binding sites of numerous peripheral membrane proteins has revealed the presence of hydrophobic amino acids and of basic amino acids such as arginines and lysines . This reflects the two universal traits of biological membranes; their hydrophobic core and anionic surface . Yet the focus on the electrostatic component of the free energy of transfer from water to membrane—often referred to as being long-range—has overshadowed the importance of hydrophobic contribution which is sometimes referred to as being short-range . The focus on electrostatic interaction is at least in part to be attributed to the difficulties in evaluating the hydrophobic contribution as opposed to for example , the computational tractability of continuum electrostatic models . In principle the contribution of hydrophobes to membrane binding can only be determined with a rigorous treatment of the hydrophobic effect , which requires very accurate treatment of large systems involving both protein , membrane and solvent . The mere presence of hydrophobes on the protein surface is to a large extent tolerated by non-membrane-binding proteins as well . For both hydrophobes and basic amino acids , it is challenging to determine when their presence on protein surfaces are coincidental , and when they are important for membrane binding . Moreover , amino acids on membrane binding sites are not typically strongly conserved [29] so modeling their generic binding modes is important both for relating binding sites between homologs and for understanding how additional factors determine differences in membrane specificities . Fortunately , as evident from the results presented in this contribution , the role of hydrophobes can often be understood in much simpler terms than what is required for an exact estimate of the energetics of the hydrophobic effect and their importance for membrane-binding can be inferred from comparative statistical analyses . The subtle considerations of protein structure encoded in our definition of protrusions , strongly distinguishes the small hydrophobic patches on peripheral membrane proteins from those on other protein surfaces . This provides reliable evidence to assume their importance for binding .
We have compiled four data sets , two versions of a set of peripheral proteins , and two different reference sets: In our analysis ‘Peripheral’ is always compared to ‘Non-binding surfaces’ , and ‘Peripheral-P’ to ‘Reference Proteins’ . ‘Peripheral’ are all the proteins in OPM classified as type ‘Monotopic/peripheral’ . While the OPM has strict criteria for inclusion , membrane binding is not asserted by experiment in all cases and the set might contain false positives . This data set is provided as Supporting Information ( S1 Dataset ) . The set ‘Non-binding surfaces’ consists of fragments of transmembrane complexes . We obtained these protein fragments from all proteins classified as type ‘Transmembrane’ in OPM . The fragments analysed are composed of all amino acids whose Cα-coordinates are at least 1 . 5 nm from the hydrocarbon region of the membrane model ( parameter ZHDC in the OPM model [32] ) . We rely here on membrane models positioned by the OPM , which we deem reliable for transmembrane proteins . While the entire protein complex was considered when calculating structural properties , only the fragments meeting this distance criteria were considered in the statistical analyses . When these proteins interact with secondary membranes or interact with membranes of extremely high curvature , it is not captured by the OPM model and the assumption that these surfaces are not interacting with membrane may be violated . We have assumed that such issues are exceptional . This data set is provided as Supporting Information ( S2 Dataset ) . We do consider the assumptions mentioned above to be conservative . Inclusion of non-membrane-binding proteins in our set of peripheral membrane proteins would likely weaken any general signal from membrane binding proteins and inclusion of secondary membrane interactions sites in the reference set would probably inflate the number of hydrophobes on protrusions in that set . All protein structures in these two sets are obtained by X-ray crystallography and NMR spectroscopy and we have assumed that at least the backbone coordinates are representative of the solvated state of the proteins . As the source of structural information for this database is the Protein Data Bank ( PDB ) [33] the relevant oligomeric state is not always determined . The curators of the OPM-database have decided on oligomer models , upon which we have relied for the sets ‘Peripheral’ and ‘Non-binding surfaces’ . These are taken from PDBe [34] and generated by PISA [31] or obtained from literature as described by Lomize et al . [25] . Even if the solvent exposed regions of the proteins in the set ‘Non-binding surfaces’ are extracted after relevant properties for potential membrane interaction was calculated , we cannot exclude totally that the surface constructed reflect artifacts of the extraction of fragments from complete protein models . In addition we expect our analysis to be sensitive to quaternary structure modeling as oligomeric protein-protein interfaces may also contain exposed hydrophobic patches [35 , 36] . As a quality control we therefore also performed an analysis ourselves relying solely on computationally predicted quaternary structures and complete protein structures . This is achieved by the comparison of ‘Peripheral-P’ and ‘Reference Proteins’ . The set ‘Reference Proteins’ is constructed from SCOPe [30] and is a subset of all PDB IDs determined by X-ray crystallography , with at least a domain classified in SCOPe [30] in the classes ‘All alpha proteins’ ( sunid: 46456 ) , ‘All beta proteins’ ( sunid: 48724 ) , ‘Alpha and beta proteins ( a+b ) ’ ( sunid: 51349 ) , ‘Alpha and beta proteins ( a/b ) ’ ( sunid: 53931 ) or ‘Multi domain proteins’ ( sunid: 56572 ) . The exclusion of structures not determined by X-ray crystallography ensures the consistency of quaternary structure predictions . All PDB IDs that have one or more domains classified in the same SCOPe-family as any domain in the OPM-database [25] were excluded from the set . This excludes not only the peripheral membrane binders , but also any transmembrane protein found in the reference set used for our primary analysis . In order to avoid redundancy , we iteratively removed proteins with domains that share SCOPe-family classification with any other domain in the set , until there were no such shared classifications left . This process ensures that there is at most one representative for each SCOPe family in the set . We generated quaternary structure models using PISA [31] for all members of this set . While this data set consists of more complete protein surfaces than the dataset of ‘Non-binding surfaces’ , it is intended to be a reference for typical protein surfaces and we do expect it to be a mix of both membrane interacting and non-interacting proteins . This data set is provided as Supporting Information ( S4 Dataset ) . The set ‘Peripheral-P’ was derived from ‘Peripheral’ for comparability with ‘Reference Proteins’ . All structures not determined by X-ray crystallography were excluded and proteins with domains that share SCOPe-family classification with any other domain in the set were iteratively removed to avoid redundancy . Quaternary structure models were predicted using PISA . This data set is provided as Supporting Information ( S3 Dataset ) . A few structures meeting the criteria above were not included in the analysis for technical reasons including issues with formats of PDB files . After exclusion of these cases the final ‘Peripheral’ dataset contains 1012 protein structures classified into 326 families . The final set of ‘Non-binding surfaces’ contains 495 protein structures classified into 158 families . The final set of ‘Peripheral-P’ binders contained 170 proteins ( or families ) and the set ‘Reference Proteins’ contained 2250 proteins ( or 2250 families ) . The two sets of peripheral proteins are both derived from OPM but ‘Peripheral-P’ is organized in a different classification than ‘Peripheral’ and retains fewer structures . In addition their quaternary structures , which are not completely determined by X-ray crystallography , are modeled differently . In Fig 11 , we illustrate this difference in quarternary structure by showing the difference in the number of polypeptide chains present in the models belonging to each of the two sets . Based on experiments reported in available literature [12 , 23 , 37 , 38 , 38–41 , 41 , 42 , 42–70] , we built a dataset of partially identified membrane binding sites on proteins with resolved structures . This set contains membrane interacting residues of 34 protein structures classified into 22 families . A detailed description is provided in the Supporting Information ( Table B in S1 Text ) . The solvent accessible area was calculated with MMTK [74] ( version 2 . 9 . 0 ) , and the convex hull was calculated with Qhull [75] via scipy [76] ( version 0 . 13 . 3 ) . Proportion test confidence intervals were calculated with R [77] ( Version 2 . 12 . 0 ) , odds ratios and corresponding confidence intervals were calculated with the R-package epitools [78] ( version 0 . 5-6 ) . Secondary structure annotations were computed with the CMBI DSSP implementation [79] ( version 2 . 0 . 4 ) . For construction of the set ‘Peripheral-P’ and ‘Reference Proteins’ SCOPe version 2 . 06 was used . PISA predictions were obtained through the “Protein interfaces , surfaces and assemblies” service PISA at the European Bioinformatics Institute . ( http://www . ebi . ac . uk/pdbe/prot_int/pistart . html ) . Where PISA predicted that the asymmetric unit represents the most stable quaternary structure in solution , we obtained structures from the Protein Data Bank ( http://www . rcsb . org/ ) [33] . Otherwise the analyses were implemented by us , using Python and R . Plots were produced with R , and other visualisations using VMD ( Visual Molecular Dynamics ) [80] . Data sets of peripheral membrane proteins were generated on a snapshot of the OPM-database extracted the 23 . Dec . 2013 . | Peripheral membrane proteins bind cellular membranes transiently , and are otherwise soluble proteins . As the interaction between proteins and membranes happens at cellular interfaces they are naturally involved in important interfacial processes such as recognition , signaling and trafficking . Commonly their binding sites are also soluble , and their binding mechanisms poorly understood . This complicates the elaboration of conceptual and quantitative models for peripheral membrane binding and makes binding site prediction difficult . It is therefore of great interest to discover traits that are common between these binding sites and that distinguishes them from other protein surfaces . In this work we identify simple and general structural features that facilitate membrane recognition by soluble proteins . We show that these motifs are highly over-represented on peripheral membrane proteins . | [
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] | 2018 | A model for hydrophobic protrusions on peripheral membrane proteins |
Systemic lupus erythematosus ( SLE ) is a complex trait characterised by the production of a range of auto-antibodies and a diverse set of clinical phenotypes . Currently , ∼8% of the genetic contribution to SLE in Europeans is known , following publication of several moderate-sized genome-wide ( GW ) association studies , which identified loci with a strong effect ( OR>1 . 3 ) . In order to identify additional genes contributing to SLE susceptibility , we conducted a replication study in a UK dataset ( 870 cases , 5 , 551 controls ) of 23 variants that showed moderate-risk for lupus in previous studies . Association analysis in the UK dataset and subsequent meta-analysis with the published data identified five SLE susceptibility genes reaching genome-wide levels of significance ( Pcomb<5×10−8 ) : NCF2 ( Pcomb = 2 . 87×10−11 ) , IKZF1 ( Pcomb = 2 . 33×10−9 ) , IRF8 ( Pcomb = 1 . 24×10−8 ) , IFIH1 ( Pcomb = 1 . 63×10−8 ) , and TYK2 ( Pcomb = 3 . 88×10−8 ) . Each of the five new loci identified here can be mapped into interferon signalling pathways , which are known to play a key role in the pathogenesis of SLE . These results increase the number of established susceptibility genes for lupus to ∼30 and validate the importance of using large datasets to confirm associations of loci which moderately increase the risk for disease .
Systemic lupus erythematosus ( SLE ) is a relapsing-remitting complex trait which most commonly affects women of child-bearing age , with a ratio of 9∶1 in female to males . The disease prevalence varies with ethnicity , being more prevalent in non-European populations ( approximately 1∶500 in populations with African ancestry and 1∶2500 in Northern Europeans ) [1] . The condition is characterised by the production of a diverse range of auto-antibodies against serological , intra-cellular , nucleic acid and cell surface antigens [2] . The wide-ranging clinical phenotypes include skin rash , neuropsychiatric and musculosketal symptoms and lupus nephritis , which may be partially mediated by the extensive deposition of immune complexes . Today , thanks to improved treatments , the 10-year survival rate after diagnosis has increased to 90% , with lower survival rates being related to disease severity or complications from treatment [3] . Increased understanding of the underlying genetic basis for lupus is of key importance in improving the prognosis for lupus patients . Until recently , the genetic basis of lupus remained largely undetermined , with only about ∼8% of the genetic contribution known [4] . However , within the last three years , tremendous progress has been made in defining novel loci , through three moderate-sized genome-wide association studies in European American cohorts and a replication study in a US-Swedish cohort [5]–[7] . The loci previously identified for SLE include genes involved in the innate immune response ( eg . IRF5 ) , T and B cell signalling ( eg . STAT4 , TNFSF4 and BLK ) , autophagy/apoptosis ( eg . ATG5 ) , ubiquitinylation ( UBE2L3 , TNAIP3 , TNIP1 ) and phagocytosis ( ITGAM , FCGR3A and FCGR3B ) . All of these pathways are of potential importance in lupus pathogenesis [8]–[10] . To date , a total of 1729 independent SLE cases have been subjected to genome-wide association genotyping using three genotyping platforms: Illumina 317 K BeadChip [5] , Illumina 550 K BeadChip [6] and Affymetrix 500 K array [7] . There is currently no published meta-analysis of these datasets . The aim of the current work was to perform a replication study using our UK SLE cohort on loci that showed some evidence for association in previous studies in order to extend the list of confirmed susceptibility genes for lupus .
To identify additional susceptibility loci for SLE , we first identified the independent genetic variants that showed moderate risk ( 5×10−3<P>5×10−8 ) in a combined US-Swedish dataset comprising 3273 SLE cases and 12188 controls [4] . We then genotyped 27 independent SNPs in a replication cohort of 905 UK SLE cases and 5551 UK control samples ( Table 1 ) , that included both British 1958 Birth Cohort samples and additional controls from the WTCCC2 project . For the 27 genotyped SNPs , 10 variants which had not been genotyped by the WTCCC2 project , were imputed using IMPUTE2 [11] . This imputation was performed using CEPH HapMap samples as the phased reference sequence and the boundary of the surrounding haplotype blocks used to demarcate the imputation interval . The subsequent association analysis excluded two of these ten imputed SNPs because they had less than 95% certainty for the imputation ( Table S2 ) . In the US/SWE dataset , imputation of selected SNPs not genotyped previously [4] was performed using IMPUTE1 for HapMap . Phase II CEU sample haplotypes were used as reference with subsequent association analysis performed using SNPTEST and a genomic control factor ( lambda-GC ) values of: 1 . 05 ( US dataset ) and 1 . 10 ( SWE dataset ) after correction for population stratification . In the UK replication sample by performing allelic association analysis using PLINK for the 23 SNPs passing QC ( Tables S2 and S3 ) , we demonstrated moderate association ( P≤0 . 05 ) for twelve variants - with a lambda-GC of 1 . 01 following ancestry correction ( see Table 2 and Table 3 ) . Under the null hypothesis , only 1 of the 23 loci would be expected to have P≤0 . 05 . The observed enrichment of associated SLE genes in the UK dataset suggested that many of these loci were likely to be true-positive associations . We confirmed the similarity of odds-ratios ( Het P value ) and direction of the effect between the UK and US-SWE datasets ( Table S4 ) and then performed a meta-analysis using Fisher's combined P-value ( see Materials and Methods ) . This meta-analysis revealed five novel associated loci with P<5×10−8 ( Table 2 ) : NCF2 ( neutrophil cytosolic factor 2 ) ( rs10911363 , Pcomb = 2 . 87×10−11 , ORcomb = 1 . 19 ) ; IKZF1 ( Ikaros family zinc-finger 1 ) ( rs2366293 , Pcomb = 2 . 33×10−9 , ORcomb = 1 . 24 ) ; IRF8 ( interferon regulatory factor 8 ) ( rs2280381 , Pcomb = 1 . 24×10−8 , ORcomb = 1 . 16 ) ; IFIH1 ( interferon-induced helicase C domain-containing protein 1 ) ( rs1990760 , Pcomb = 1 . 63×10−8 , ORcomb = 1 . 15 ) and TYK2 ( tyrosine kinase 2 ) ( rs280519 , Pcomb = 3 . 88×10−8 , ORcomb = 1 . 17 ) ( Table 1 ) . The strength of these associations was similar to those found from a weighted meta-analysis , using the METAL programme ( Table S4 ) . A case-only analysis using PLINK in the combined UK/US/SWE dataset revealed no non-additive interactions between the five newly associated variants ( P>0 . 05 ) . These new SLE loci are discussed in more detail below and with additional information in Text S1 . Three of the SNPs tested were for loci that had shown genome-wide levels of significance in other SLE GWAS studies ( Table S5 ) . In the UK cohort we found further support for the association at JAZF1 ( rs849142 PUK = 0 . 0243 , ORUK = 1 . 13 ) and identified a third associated variant in the first intron of TNIP1 ( rs6889239 PUK = 9 . 06×10−6 , ORUK = 1 . 30 ) , which is in strong LD ( r2 = 0 . 895 ) with both the previous report in Europeans [4] and in perfect LD with a third SNP ( rs10036748 ) , first reported in a Chinese GWAS [12] . All three variants in TNIP1 are located within a 661 bp region of intron 1 . We did not replicate the previous association with IL10 ( rs3024505 , PUK = 0 . 209 ORUK = 1 . 09 ) ( Table S5 ) . These analyses increased the evidence of association for a number of additional loci that had shown borderline significance in the original US/SWE GWAS ( Table 3 ) , including CFB , C12ORF30 , SH2B3 , and IL12B . Genotyping of additional samples will be required to determine if the association signals shown in Table 3 represent confirmed genetic loci for SLE .
The work presented here confirms five new susceptibility loci for SLE at the level of genome-wide significance ( P<5×10−8 ) . Each of the associated variants lie within , or close to , the coding sequence for genes with known roles in immune regulation: NCF2 , IKZF1 , IRF8 , IFIH1 and TYK2 . Interestingly , each of these genes has been implicated in interferon signalling . While the interferons have classically been defined as anti-viral cytokines , recent studies have suggested an important role for interferon in the pathophysiology of SLE [13] . While most evidence points to the role of type I interferon in SLE [14] there is substantial data suggesting that type II interferon ( IFNγ ) is also involved in SLE pathogenesis [15] . NCF2 ( neutrophil cytosolic factor 2 ) ( 1q25 ) , is induced by IFNγ and specifically expressed in a number of immune-cell types , including B-cells . Our data suggest that the NCF2 association is independent from the previously reported signal in the neighbouring locus NMNAT2 , [5] because we found no evidence of strong LD between the genotyped SNP within NMNAT2 ( rs2022013 ) and that in NCF2 ( rs10911363 ) ( r2 = 0 . 136 ) . Logistic regression in the UK replication cohort confirmed that NMNAT2 did not contribute to the association at NCF2 ( P = 0 . 777 ) . NCF2 , as a cytosolic subunit of NADPH-oxidase , may have a role in the increased production of the free radicals characterising B-cell activation [16] ( Figure 1 ) which increases auto-antibody levels and may suggest a mechanism for the involvement of NCF2 as a susceptibility gene for SLE . There are allele-specific significant expression differences for rs10911363 , following a recessive model of basal expression for the risk T allele of rs10911363 in CEPH individuals but not in YRI and ASN ( CHB+JPT ) HapMap cohorts ( PCEPH = 0 . 03 ) ( Figure 2A ) . There is also a significant difference in gene expression for a variant ( rs3845466 ) located 2 kb away from rs10911363 in intron 2 of NCF2 ( Figure S2A ) , using lymphoblastoid cell lines ( LCLs ) from umbilical cords of 75 individuals which were taken from the GENEVAR collection ( P = 0 . 0228 ) . The population-specific nature of this correlation could be because of local differences in the pattern of LD within NCF2 between the CEU , YRI and ASN ( CHB+JPT ) HapMap cohorts . These population specific differences in LD may be between the genotyped SNP and an unknown causal allele ( s ) responsible for an expression difference seen in multiple ethnic backgrounds or between the genotyped marker and an unknown causal allele ( s ) exhibiting population-specific differences in gene expression itself . However , it will be necessary to confirm these findings in primary cells and tissues , because the EBV-transformed B cells model system may not entirely reflect the physiological conditions in peripheral B cells . Indeed a recent report showed that there may be systematic changes in gene expression within EBV-transformed B cells [17] . Nevertheless , with this caveat in mind , and taking each locus on a case-by-case basis , the model-based approach can provide important insights into measurement of transcript levels in ex vivo cells . For example , the increases in transcript levels that we initially observed in EBV-LCLs for OX40L , were also confirmed in peripheral blood B cells [18] . IKZF1 ( Ikaros family zinc-finger 1 ) ( 17p14 . 3 ) is a transcription factor essential for dendritic cell and lymphocyte development . The association with rs2366293 is supported by a report of a second associated variant , rs921916 ( Pcomb = 2 . 0×10−6 ) [4] , found 860 bp away from rs2362293 , which is in strong LD with rs2366293 ( r2 = −0 . 746 , D′ = 0 . 925 ) ( Figure S2B ) . A third SNP , rs4917014 , located ∼200 kb upstream of IKZF1 , showed association with SLE in a Chinese GWAS ( PGWAS = 2 . 93×10−06 ) , but it was a separate signal from the European SNPs ( r2<0 . 0002 ) [9] , [12] . IKZF1 has a role in the production of IFNγ , by blocking the production of the Th1 master-regulator T-bet ( Figure 1 ) . The shifted Th1/Th2 equilibrium ( in favour of Th1 cells ) increases the levels of IFNγ directly [19] rather than indirectly as a result of cross-talk between the type-I and type-II IFN signalling pathways eg ) via type-I interferon mediated activation of STAT1 homodimers , which are the primary means of signalling from IFNγ [20] and have recently been shown to be associated with SLE in a Swedish cohort [21] . The transcription factor IRF8 ( interferon regulatory factor 8 ) ( 16q24 . 1 ) , shows immune-cell restricted expression . rs2280381 is found 64 kb downstream of IRF8 , and is in LD with the coding region ( Figure S2C ) , but independent from a susceptibility allele for multiple sclerosis ( rs17445836 ) , 1 kb away [22] . The lupus variant influences IRF8 gene expression , since LCLs from three HapMap cohorts , showed a significant increase in IRF8 transcript levels in homozygotes for the risk allele ( TT ) compared to homozygotes for the non-risk allele ( CC ) ( P = 0 . 045 ) ( Figure 2A ) . IRF8 also has a key role in regulating the differentiation of myeloid and B-cells and in mice , IRF8 restricts myeloid cell differentiation but promotes B-cell differentiation [23] ( Figure 1 ) . IFIH1 ( interferon-induced helicase C domain-containing protein 1 ) ( 2q24 . 3 ) is an ubiquitiously expressed , cytoplasmic sensor of dsRNA . The SLE risk allele for rs1990760 ( Table 1 ) is identical to that previously reported in two organ-specific autoimmune diseases: T1D [24] and Graves' Disease [25] . Regression analysis using publically available genotype data from HapMap and expression data from GEO dataset GSE12526 revealed that individuals who were homozygous for the common risk T allele of rs1990760 had significantly higher IFIH1 transcript levels compared to individuals who were homozygous for the non-risk allele ( P = 0 . 8 . 19×10−5 ) ( Figure S3B ) . Furthermore , a recent paper showed that the presence of the risk T allele of rs1990760 was correlated with increased levels of IFN-induced gene expression , in lupus patients who were positive for anti-dsDNA antibodies [26] . Another report demonstrated that IFIHI was rapidly up-regulated by type-I IFNs ( Figure 1 ) , and that IFIH1 signalled downstream through NF-κB , to further increase IFN-α production [27] . TYK2 ( tyrosine kinase 2 ) ( 19p13 . 2 ) phosphorylates the receptor subunits of cytokine receptors , including type-I IFN receptors which are found on all nucleated cells , leading to increased production of type I interferon responsive genes ( Figure 1 ) . The significant association in intron 11 TYK2 for rs280519 in our UK cohort ( P = 5 . 24×10−4 ) crossed the threshold for genome-wide significance when combined with the US/Swedish cohort . The association for rs280519 increases the genetic evidence for the involvement of TYK2 reported in a smaller UK family-based SLE cohort [28] . There was an earlier report , using a Swedish/Finnish population , of association in TYK2 . This Swedish/Finnish study showed association for a missense mutation in exon 8 ( rs2304256 ) ( Pcomb = 5 . 60×10−5 , PSwe = 9 . 60×10−5 ) [29] . The Swedish individuals used in the earlier analysis are a subset of the Swedish individuals analysed for this current manuscript and rs2304526 is in moderate LD with the TYK2 SNP that we typed in this current study - rs280519 ( r2CEPH-HapMap = 0 . 373 ) . The association for rs2304256 was replicated in a second moderate sized European study [30] , but not in the GWAS from the SLEGEN consortium [5] . In preliminary analysis in UK cases and controls , there are data to support the fact that rs280519 is enriched in SLE cases ( n = 345 ) with renal disease compared to healthy controls ( n = 5551 ) ( P = 0 . 033 ) . There were variants in several loci for which we have found evidence of association ( P<0 . 05 ) in our UK cohort , but which did not reach genome-wide significance in the combined analysis . One of these variants was rs17696736 , located in intron 15 of C12ORF30 ( MDM20 ) . This protein is a subunit of N-acetyltransferase complex B ( NatB ) , and may promote apoptosis by reducing cell cycle progression [31] . In the joint cohort , rs17696736 was in LD ( r2 = 0 . 625 ) with a second variant on chromosome 12q24 , a missense W262R allele ( rs3184504 ) in the lymphocyte adaptor protein SH2B3 . SH2B3 facilitates T-cell activation by mediating the interaction between the T-cell receptor and T cell signalling molecules [32] . Both MDM20 and SH2B3 are also associated with T1D [33] , and SH2B3 is additionally associated with celiac disease [34] and both myocardial infarction and asthma [35] . The associated variant within IL12B , rs3212227 , is located in the 3′ UTR region , and the SLE risk allele is the same as previously reported for psoriasis [36] . IL12B encodes for the larger subunit ( p40 ) of two cytokines , IL12 and IL23 , and thereby contributes to both Th1 [37] and Th17 [38] immune responses . In summary , we have identified five new genes contributing to SLE risk: NCF2 , IKZF1 , IRF8 , IFIH1 and TYK2 . Dense fine-mapping and/or genomic re-sequencing of each locus will be required to reveal the functional alleles for each gene with respect to immune dysregulation in lupus . Taken together , these findings further support an important role of interferon pathway dysregulation in lupus pathogenesis .
The ethical approval for the study was obtained from the London Multi-Centre Research Ethics Committee ( London MREC ) . All of the 905 UK SLE cases conformed to the ACR criteria for SLE [39] with a diagnosis of SLE being established by telephone interview , health questionnaire and details from clinical notes . Written consent was obtained from all participants . Genomic DNA from the UK samples was isolated from anti-coagulated whole blood by a standard phenol-chloroform extraction . Each of the 27 SNPs were genotyped on a custom Illumina chip , using the BeadXpress platform at the Oklahoma Medical Research Foundation ( OMRF ) , Oklahoma . The panel of ancestry informative markers was typed independently on an Illumina platform at Gen-Probe , Livingstone . Power calculations were performed in the UK case-control dataset for each of the markers tested , using the algorithm described by Purcell et al [40] . Taking into account varying minor allele frequencies for the risk alleles and the differences in effect size ( OR ) , and by employing a population prevalence of 0 . 002 and D′ of 1 , with an type I error rates , alpha = 0 . 05 , each of the SNPs showing novel genome-wide significance in the meta-analysis showed a power of >48% ( 2 ) to detect an association in our cohort . Markers were excluded from the analysis if they showed a genotyping success rate of less than 95% or had a Hardy-Weinberg P value in the B58BCC control samples of less than P = 0 . 001 . A total of 21 cases were removed from the final analysis due to low percentage genotyping ( <95% ) . All samples were filtered for cryptic relatedness and duplication using an identity by state test in PLINK ( PI_HAT score >0 . 1 ) . The full list of genotyped variants and the results of the QC analysis are shown in ( Table S3 ) . A total of 35887 markers , distributed across each autosome , were selected for ancestry correction in the UK case-control cohort , these markers had all been typed as part of the HapMap project and on the WTCCC2 samples . The 35887 SNPs were chosen from a set of Illumina 317 K markers pruned for LD ( r2<0 . 25 ) after removing regions of known extended LD , including the extended MHC and the region covering the inverted repeat on chromosome 8 ( pers commun . David Morris , King's College and Kim Taylor , UCSF ) . This list of AIMs is available directly from the corresponding author , Professor Timothy Vyse . The EIGENSTRAT PCA analysis was performed on the UK cases and also the control samples , both from the genotyped B58BCC and the WTCCC2 out-of-study controls . The eleven populations from HapMap3 were used as external references . Each SNP included in the PCA analysis showed >95% genotyping in the each dataset . Following EIGENSTRAT analysis , a graph was plotted of PC1 against PC2 for all the cases and controls in the UK study cohort ( Figure S1 ) . Individuals were only retained for association analysis if the values for their first two principal components fell within 6 SD of the mean for the CEPH HapMap samples . The genomic inflation factor ( lambda-GC ) for each population was calculated using PLINK . All sample genotype and phenotype data was managed by , and analysis files generated with BC/SNPmax and BC/CLIN software ( Biocomputing Platforms Ltd , Finland ) . The imputation intervals for each imputed variant , defined as the bounds of the haplotype blocks , calculated using the Gabriel algorithm in Haploview , ( for details of the intervals see Table S2 ) . For SNPs which were not genotyped as part of the WTCCC2 project , we performed imputation using a method described by Marchini et al [11] to generate the missing genotypes for case-control association analysis . Each un-typed variant from our list of tested SNPs , was imputed in the WTCCC2 samples , using HAPMAP as the phased reference sequence . The LD pattern around each un-typed variant was examined using the CEPH cohort from HapMap . The boundaries of the haplotype blocks were determined using the default settings for the Gabriel et al algorithm in Haploview . For each imputed variant , these haplotype boundaries were used to define the boundaries of the imputation interval ( Table S2 ) . Only SNPs with greater than a 95% certainty in imputation , assessed using the quality score from the IMPUTE2 output file , were used for subsequent analysis . Allelic association testing , using UK SLE cases with either genotyped control samples or imputed genotypes , was carried out using PLINK ( http://pngu . mgh . harvard . edu/~purcell/plink/ ) . Prior to performing the meta-analysis , the heterogeneity of odds ratios was tested using METAL and the Cochran-Mantel-Haenszel test ( Table S4 ) . SNPs with P value<0 . 001 between the two studies were discarded . Combined analysis of P values generated in the UK samples with those from the US/SWE cohort in published data [4] was conducted using Fisher's combined P value and with a meta-analysis using the programme METAL , which weighted the effect size , based on the inverse of the standard error . To determine whether there was any allele-specific effect on the level of gene expression , we used publically available genotype data on unrelated EBV-transformed B cells ( CEU , YRI and CHB/JPT individuals which were part of the HapMap project ) and expression data from the same individuals ( GSE12526 , GEO database ) [41] . For each locus , which reached genome-wide significance by meta-analysis , we categorised the expression data based on the SNP genotype for the respective associated variant ( homozygote risk allele , heterozygote and homozygous non-risk allele ) . The significance of the correlation between genotype and expression level was then calculated using logistic regression analysis in SNPTEST , using gender as a covariate . Interactions between the five SNPs reaching genome-wide significance following meta-analysis , were assessed using the epistatic option in PLINK . To maximize the power of this test , we restricted our analysis to the SLE affected individuals from the combined US/SWE/UK cohort . | Genome-wide association studies have revolutionised our ability to identify common susceptibility alleles for systemic lupus erythematosus ( SLE ) . In complex diseases such as SLE , where many different genes make a modest contribution to disease susceptibility , it is necessary to perform large-scale association studies to combine results from several datasets , to have sufficient power to identify highly significant novel loci ( P<5×10−8 ) . Using a large SLE collection of 870 UK SLE cases and 5 , 551 UK unaffected individuals , we firstly replicated ten moderate-risk alleles ( P<0 . 05 ) from a US–Swedish study of 3 , 273 SLE cases and 12 , 188 healthy controls . Combining our results with the US-Swedish data identified five new loci , which crossed the level for genome-wide significance: NCF2 ( neutrophil cytosolic factor 2 ) , IKZF1 ( Ikaros family zinc-finger 1 ) , IRF8 ( interferon regulatory factor 8 ) , IFIH1 ( interferon-induced helicase C domain-containing protein 1 ) , and TYK2 ( tyrosine kinase 2 ) . Each of these five genes regulates a different aspect of the immune response and contributes to the production of type-I and type-II interferons . Although further studies will be required to identify the causal alleles within these loci , the confirmation of five new susceptibility genes for lupus makes a significant step forward in our understanding of the genetic contribution to SLE . | [
"Abstract",
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] | 2011 | Association of NCF2, IKZF1, IRF8, IFIH1, and TYK2 with Systemic Lupus Erythematosus |
The monitoring and evaluation of lymphatic filariasis ( LF ) has largely relied on the detection of antigenemia and antibodies in human populations . Molecular xenomonitoring ( MX ) , the detection of parasite DNA/RNA in mosquitoes , may be an effective complementary method , particularly for detecting signals in low-level prevalence areas where Culex is the primary mosquito vector . This paper investigated the application of a household-based sampling method for MX in Tamil Nadu , India . MX surveys were conducted in 2010 in two evaluation units ( EUs ) : 1 ) a hotspot area , defined as sites with community microfilaria prevalence ≥1% , and 2 ) a larger area that also encompassed the hotspots . Households were systematically selected using a sampling interval proportional to the number of households in the EU . Mosquito pools were collected and analyzed by real-time polymerase chain reaction ( qPCR ) . Two independent samples were taken in each EU to assess reproducibility of results . Follow-up surveys were conducted in 2012 . In 2010 , the proportion of positive pools in the hotspot EU was 49 . 3% compared to 23 . 4% in the overall EU . In 2012 , pool positivity was significantly reduced to 24 . 3% and 6 . 5% , respectively ( p<0 . 0001 ) . Pool positivity based on independent samples taken from each EU in 2010 and 2012 were not significantly different except for the hotspot EU in 2012 ( p = 0 . 009 ) . The estimated prevalence of infection in mosquitoes , measured by PoolScreen , declined from 2 . 2–2 . 7% in 2010 to 0 . 6–1 . 2% in 2012 in the hotspot area and from 0 . 9–1 . 1% to 0 . 2–0 . 3% in the larger area . The household-based sampling strategy for MX led to mostly reproducible results and supported the observed LF infection trends found in humans . MX has the potential to be a cost-effective , non-invasive monitoring and evaluation tool with sensitive detection of infection signals in low prevalence settings . Further investigation and application of this sampling strategy for MX are recommended to support its adoption as a standardized method for global LF elimination programs .
Lymphatic filariasis ( LF ) is a mosquito-borne parasitic disease caused by the filarial worm species Wuchereria bancrofti , Brugia malayi and Brugia timori . LF is a major public health problem with nearly 800 million individuals at risk of infection in 73 tropical and subtropical countries worldwide [1] . The burden in India alone comprises nearly one-third of the global total . In response , the country’s LF elimination program has scaled up nationally to reach all 255 endemic districts over 20 states and union territories [2 , 3] . Since 2000 , several of these districts have undergone 10–12 annual rounds of mass drug administration ( MDA ) . As of May 2016 , 72 districts have successfully passed the first transmission assessment survey ( TAS ) and qualified for stopping MDA as per World Health Organization ( WHO ) guidelines [4] . Of the remaining districts , 1 has passed the second TAS , 35 are eligible for conducting the first TAS , and MDA is ongoing in the other 147 districts . Successful elimination of LF requires close monitoring and evaluation of transmission potential in the endemic area to prevent recrudescence . Various diagnostic tools are available for detecting LF antigen and antibody in the infected population [5 , 6] . LF infection in mosquito vectors has been largely determined by dissection , staining , and microscopy [7] , as well as assays by polymerase chain reaction ( PCR ) to detect filarial DNA and/or RNA in mosquitoes [8–14] . Molecular xenomonitoring ( MX ) is the detection of parasite DNA/RNA in mosquitoes and can serve as an alternative method for estimating the infection prevalence in human populations [15 , 16] . However , implementing MX to evaluate the impact and progress of LF national elimination programs has not yet been adopted as a standard monitoring and evaluation tool , in contrast to its wider success in onchocerciasis control and elimination programs [17] . Progress in the application of MX has been most rapid where Culex , common in south and southeast Asia , is the primary vector . Conversely , MX has been constrained in areas where the predominant vectors are Anopheles , as in West Africa , or Aedes , which prevails in the South Pacific . This is largely due to difficulties in collecting these species . Culex is more easily obtained , usually by placing traps in locations thought to be attractive to ovipositing mosquitoes . Although replicable results may result from repeat sampling at the same sites , the arbitrary nature of the selection of sites makes comparisons from different geographic locations problematic . It is preferable for monitoring vector infection that the mosquito collection is done by placing traps in randomly selected sites , but using a systematic sample that can be repeated at different times and at different locations . The WHO convened meetings in 2002 and 2006 to discuss the application of MX for LF elimination programs [18 , 19] . In 2009 , Pedersen et al . provided a comprehensive review of the field and drew attention to the need for careful considerations of mosquito collection methods in addition to other factors [16] . Also in 2009 , an international workshop on MX for LF was hosted by the Vector Control and Research Center ( VCRC ) in Pondicherry , India . A random sampling method for Culex collection was presented that entails selecting a cluster sample of households ( HHs ) at which gravid traps are placed and a pre-determined number of mosquito pools are collected . This paper summarizes the results of studies by the VCRC utilizing this HH-based sampling strategy in 2010 and 2012 in Thanjavur , a semi-rural district in the state of Tamil Nadu , India .
This study involved collection of mosquitoes using gravid traps placed outside the households such that it does not interfere with any domestic activities within or around the households . Therefore , there were no ethical issues and all heads of households consented to the placement of the traps . The study was conducted in the Primary Health Center ( PHC ) of Ammapettai in Thanjavur district , Tamil Nadu , India comprising an area of approximately 40 km2 with a population of 19 , 147 residing in 5 , 910 households . Culex quinquefasciatus is the LF transmitting vector in this PHC . Ammapettai has 18 villages and 15 wards under six health sub-centres and has undergone eight annual rounds of MDA since 1997 –four rounds with diethylcarbamazine ( DEC ) alone and four rounds with DEC plus albendazole ( ALB ) . MDA was not carried out in 1998 , 2005 and 2006 and had been stopped in 2008 after a 2008–2009 mass screening had shown microfilaria ( Mf ) prevalence was less than 1% and antigenemia ( Ag ) prevalence was less than 2% in children 2–10 years old , thus meeting the WHO criteria for stopping MDA [2] . Some wards in Ammapettai , however , were identified as residual hotspots where the Mf prevalence was greater than or equal to 1% [2] , hereafter called ‘hotspots’ in this study . MX surveys were initially carried out between September 2009 and February 2010 in two evaluation units ( EUs ) , which were district subunits and not equivalent to the EUs used for the TAS . The first EU comprised all the hotspot areas where microfilaria prevalence was greater than 1% , as identified in the 2008–2009 mass screening . This hotspot EU consisted of 17 streets under 4 wards in Ammapettai . The second EU consisted of the entire PHC area of Ammapettai , which included the 4 hotspot wards for a total of 33 wards/villages ( sites ) . Fig 1 illustrates the location of Ammapettai within India and distinguishes between hotspot and PHC EU sites in the study . The 2010 hotspot and PHC surveys were repeated between October 2012 and Jan 2013 . All surveys followed the same household selection and mosquito collection procedures outlined further below . Following the 2010 MX surveys , each resident who was found Mf- or Ag-positive in the 2008–2009 mass screening was to be treated with a 12-day course of DEC ( 6 mg/kg body weight ) following the national program guidelines . Of the 369 persons that were positive for Mf or Ag , 303 ( 82 . 1% ) received treatment between June 10–25 , 2010 . Replicating the MX study in 2012 was , therefore , intended to demonstrate whether the treatment also reduced the parasite infection load and if the HH-based sampling strategy would detect such change in the EUs . Two independent samples were collected for each PHC and hotspot survey to assess the sampling method’s reproducibility of results . Therefore , a total of 4 samples were collected each year ( two per EU ) and labeled in this study as: 2010 Hotspot ( sample 1 ) , 2010 Hotspot ( sample 2 ) , 2010 PHC ( sample 1 ) , 2010 PHC ( sample 2 ) , 2012 Hotspot ( sample 1 ) , 2012 Hotspot ( sample 2 ) , 2012 PHC ( sample 1 ) , and 2012 PHC ( sample 2 ) . Independent samples within the same survey ( e . g . 2010 Hotspot Survey–samples 1 and 2 ) were taken no more than 1 month apart in the hotspot area and approximately two months ( 2010 ) and one month apart ( 2012 ) in the PHC areas . All independent samples were collected during the peak biting season to best control the impact of environmental variables . Each sample took a median of two nights in the hotspot EU and three nights in the PHC EU to complete a collection of 2 pools . For each independent sample , the aim was to collect pools of 25 mosquitoes from 200 HH trap locations for a total sample size of 5 , 000 mosquitoes . These parameters were based on a target infection prevalence rate of 0 . 5% , which has been previously recommended for Culex mosquitoes [20] . Other sources have suggested a target rate of 0 . 25% [12 , 19] . Our study was , therefore , powered to correctly detect at least 75% of the time if the true prevalence is less than 0 . 25% , while failing only 5% of the time to detect if the true prevalence is greater than 0 . 5% ( i . e . alpha error ) . Given the low target prevalence rate , pool sizes of 25 mosquitoes were estimated to have negligible measurement bias and deemed appropriate for this study [21] . HHs were randomly selected in each EU as trap location sites . Random selection was done by first calculating a fixed sampling interval proportional to the total number of HHs in the EU to meet the 200 HH target . After enumerating each HH in each village/ward , a random HH was chosen as the first HH in the first village/ward . Every subsequent HH was then selected by adding the fixed sampling interval to the enumerated HHs . Separate sampling intervals were calculated for the PHC and hotspot EUs . The sampling intervals were not proportional to the size of each individual village/ward nor were they reset at the start of each village/ward . On average , 7 HHs were selected per village/ward in the PHC EU and 12 HHs per street in the 4 hotspot EU . As a result , a total of 231 HHs ( 33 villages/wards x 7 HHs ) were selected in the PHC EU and 204 households ( 17 streets x 12 HHs ) in the hotspot EU for placing the mosquito traps . This HH sampling strategy may also employ a two-staged cluster design , where the first stage involves systematically selecting only certain clusters from the EU [22] . Due to the smaller geographic area in this study , all the clusters ( i . e . villages/wards ) from the hotspot and PHC EUs were included for HH selection . A modified version of the CDC Gravid trap ( Model 1712 , John W . Hock Co . USA ) was placed each evening close to the selected HHs [23 , 24] . Mosquito traps and nets containing the catch were collected each morning and returned to a central laboratory where the mosquitoes were killed by freezing before being sorted for Culex quinquefasciatus that were either gravid , semi-gravid , or showed evidence of having recently ingested a blood meal . Mosquitoes that met these criteria were then stored together in pools of 25 mosquitoes ( fewer if the trap yield was insufficient with a minimum of 5 mosquitoes per pool ) after drying them at 95°C for a minimum of 15 minutes for later qPCR analysis . A single trap at the selected HH was used to collect all pools required at that site . If on any given night the trap yield was insufficient to complete a full pool of 25 , mosquitoes from the subsequent night’s yield were added to complete the pool . If more than a full pool was collected , extra mosquitoes were allocated to the next pool . All excess mosquitoes beyond completion of the required number of pools were discarded . Traps were set each day at the same HH locations until the required number of pools had been obtained , or for a maximum of three nights . The oviposition bait was also replaced daily with a fresh batch prior to fixing the trap . An adult HH resident was asked for permission to set the traps outside the HH and field teams placed them in areas less prone to thievery or obstruction . No denials were experienced despite the unpleasant odors of the bait , presumably because residents welcomed the fact that the traps removed mosquitoes from the surrounding area . Batteries , which ran the trap fans , were recharged each day and no instances of trap disruption were encountered ( e . g . vandals stealing the batteries ) . DNA extraction of mosquito pools and real-time PCR analysis for detecting W . bancrofti DNA in individual pools was performed at the VCRC using the BB-grinding method to macerate the mosquito pools [13] , the optimized Qiagen DNA extraction method [25] , and the qPCR assay [11] described in previously published reports . Comparisons of pool positivity ( number of pools positive for filarial parasite DNA over total number of pools screened ) and 95% confidence intervals were conducted with chi-square tests for equality of proportions ( without Yates continuity correction ) using R ( version 3 . 3 . 2 ) . The maximum likelihood estimate and its 95% confidence intervals of W . bancrofti infection prevalence in mosquitoes were made using the PoolScreen software ( version 2 . 0 . 3 ) [21 , 26] . GPS coordinates for trap locations were collected using the Dell Axim X51 personal digital assistant and mapped using ArcGIS ( version 10 . 2 . 1 ) ( ESRI , Redlands , CA ) .
In the hotspot EU ( Table 1 ) , an average of 5 , 012 total mosquitoes per sample ( range: 4 , 867–5 , 175 ) was collected from 207 HHs ( trap locations ) . The mean number of Culex ( gravid , semi-gravid , and bloodfed ) per pool varied between 24 . 5–25 . 0 for the samples in 2010 , and 23 . 7–24 . 0 in 2012 . More than 90% of the pools had 21–25 mosquitoes per pool . The qPCR result for one pool in three of the four independent samples was indeterminate and excluded in the analysis . Fig 2 maps the HH locations of all positive and negative pools for each hotspot EU sample in 2010 and 2012 . In 2010 , the proportion of positive pools was 49 . 3% ( 102/207 ) in the first sample and 42 . 7% ( 88/206 ) in the second sample . The PoolScreen estimated prevalence of infection was 2 . 7% and 2 . 2% , respectively . In 2012 , the proportion of positive pools was 24 . 3% ( 50/206 ) in the first sample and 14 . 1% ( 29/206 ) in the second sample . The PoolScreen estimated prevalence of infection was 1 . 2% and 0 . 6% , respectively . For the PHC EU ( Table 2 ) , the average number of mosquitoes collected per sample was 5 , 311 ( range: 5 , 094–5 , 437 ) from 231 trap locations . The mean pool size varied between 23 . 2 and 23 . 5 for the samples in 2010 , and 22 . 0–23 . 3 in 2012 . Between 81–84% of the pools had 21–25 mosquitoes per pool . Although 231 pools were collected in the second 2010 sample , the qPCR result for one mosquito pool was indeterminate and , therefore , excluded in the analysis . Results for all pools in the other samples were valid and analyzed . Fig 3 maps the HH locations of all positive and negative pools for each PHC EU sample in 2010 and 2012 . Pool positivity in 2010 was 23 . 4% ( 54/231 ) in the first sample and 17 . 8% ( 41/230 ) in the second sample . PoolScreen results were 1 . 1% and 0 . 9% , respectively . In 2012 , pool positivity was 6 . 5% ( 15/231 ) in the first sample and 5 . 2% ( 12/231 ) in the second sample . PoolScreen results were 0 . 3% and 0 . 2% , respectively . Table 3 compares each pair of independent samples taken from the same EU . In testing the equality of proportions , no statistically significant differences ( p>0 . 05 ) in pool positivity were detected between the 2010 hotspot samples or either of the 2010 and 2012 PHC samples . However , a significant difference was observed between the samples in the 2012 hotspot EU ( p = 0 . 009 ) . The results also show that the pool positivity of the second samples were lower than that in the first samples for each survey . The upper 95% confidence interval limits suggest this variability is greater in the hotspot samples than the PHC ones . Pool positivity in 2012 was significantly lower than in 2010 for all hotspot and PHC samples ( Table 4 ) . The PoolScreen estimated prevalence of infection in mosquitoes was also reduced by more than half in each sample for both surveys over the two years . The exact significance of this decline , however , could not be calculated given the current configuration of the PoolScreen program .
MX surveys , using a systematic sampling of HHs for placing gravid traps , provided an efficient method to collect approximately 5 , 000 Culex mosquitoes in pools of 25 mosquitoes from over 200 HHs . This HH-based sampling strategy was successfully implemented in two EUs and independent samples within each survey largely showed reproducible ( i . e . no statistically significant difference ) results in terms of pool positivity . The one exception was the 2012 hotspot survey and in general , the hotspot surveys had more sample variability than the PHC surveys . The exact reason behind this trend remains uncertain as the samples were independent and randomly selected . It is also unclear why the second sample of each survey pair had lower pool positivity but perhaps the one- to two-month time gap between sample collections was a contributing and limiting factor . Our study also provided early evidence that a HH-based sampling method can obtain consistent estimates of the prevalence of filarial DNA measured by the PoolScreen technique . From these initial results , it appears that determining the parasite load in the vector population has great potential for the monitoring and evaluation of LF elimination programs where Culex is the primary vector . This HH-based sampling method for MX also produced results consistent with the previously observed LF infection trends found in humans in Ammapettai . In all the 2010 and 2012 MX samples , the hotspot EU had higher pool positivity and estimated prevalence of filarial infection than the overall PHC area . This finding corroborates the human infection rates determined in the 2008–2009 mass screening survey where Mf prevalence was 1 . 4% ( 0 . 9–1 . 96% ) in the hotspot areas and 0 . 4% ( 0 . 30–0 . 52% ) in the overall PHC area . The human infection rate in the screening survey was significantly higher in the hotspots than in the overall PHC ( P<0 . 001 ) . Since Mf- or Ag-positive individuals were also treated with DEC following the 2010 MX surveys , the decline in pool positivity rates and filarial DNA in the 2012 MX surveys most likely reflects the impact of this treatment . The MX results , therefore , supplied an indirect indicator of LF infection in humans , which can be invaluable for transmission assessment and implementing follow-up interventions . HH-based MX surveys would be particularly helpful in conjunction with the TAS , the method currently recommended by WHO for stopping MDA and post-MDA surveillance [4] . The WHO recommends that an area passes the TAS when filarial antigen prevalence among first and second grade children is less than 1% by ICT ( with a 95% CI of less than 2% ) [4] , a threshold below which transmission is thought to be no longer sustainable in W . bancrofti areas . In Sri Lanka , however , Rao et al . have shown that the TAS did not identify areas shown by MX to have persisting low levels of transmission as evidenced by the continuing prevalence of filarial DNA in mosquitoes over time [27] . Other studies concluded that MX surveys were more sensitive than Mf testing in humans [12 , 22] . As such , MX surveys can be a strong complement to the TAS for both stopping MDA and post-MDA surveillance , particularly where low levels of infection persist and are less detectable through human-based surveys by ICT or Mf testing . This application is relevant to Ammapettai and other areas in India where similar MX studies have begun in districts which either passed the TAS once , passed the TAS twice , or passed the first TAS but the number of Ag-positives was very close to the critical cut-off threshold . MX surveys may also become a more attractive option for post-MDA surveillance as programmatic resources for LF erode , if not disappear , after MDA is discontinued . There may be minimal capacity and little incentive to continually repeat TAS multiple years after drug distribution has stopped . Conversely , there will be plenty of work remaining for entomology staff in assessing threats from other mosquito-borne diseases . Integrating LF to such pre-established monitoring responsibilities may be a more feasible surveillance approach in the long-run than trying to repeat an LF-specific survey such as TAS . With many countries transitioning into post-MDA surveillance mode , it is critical that MX sampling strategies and baseline measures are quickly established . This includes the validation and possible revision of MX thresholds for LF transmission measured through filarial DNA [19] . Selecting adequate EU boundaries is a critical decision for MX surveys and subject to the general limitations of other cluster sample surveys . Larger EUs provide significant cost and resource efficiencies , particularly in a country like India where many EUs need to be evaluated to cover the entire LF transmission area . However , this increases the risk of missing pockets of transmission given that infected areas may be highly focal following MDA . The MX study here used a relatively small EU but was successful in detecting ongoing hotspots of LF transmission . Larger EUs in other MX studies have also succeeded with general results and findings similar to the ones discovered here [22] . It is , therefore , recommended that the epidemiological characteristics and infection risks within the EU are consistent and boundaries are not solely determined by population or geographic size . Further research , however , is required to understand the EU limits for which MX using this HH-based sampling strategy is appropriate , as well as an examination of the logistics , feasibility and cost implications . Implementation costs are another crucial element to consider for LF monitoring and evaluation tools . Given the proper training and resources , MX surveys may be arguably more cost-effective and less onerous to execute than the TAS or other population-based surveys . The cost advantages of MX also include indirect costs such as the efforts needed for permissions and consent . In this study , permissions to place the traps were relatively easy . Permissions for human studies can be more difficult where the Ministry of Education , school principals and parents may all need to consent and wider community sensitization is required . Collecting mosquitoes is also less intrusive than collecting blood from children and does not generate resistance when repeated sampling is done . In fact , collecting and essentially removing mosquitoes was often perceived positively by households where the traps were placed . The current study used a total mosquito samples of approximately 5 , 000 , collecting one pool of 25 mosquitoes from each of 200 HHs . The MX work in Sri Lanka used samples of approximately 7 , 500 mosquitoes , collecting two pools of 25 mosquitoes from each of 150 HHs or 4 pools from 75 HHs [22] . Larger samples may be required for assessing really low prevalence of infection in mosquitoes ( on the order of 0 . 3% or below ) at which Culex transmission in many environments appears to be difficult . Collecting even larger numbers of mosquitoes would further improve precision , but Culex is not always abundant and increasing the number of pools per site will be challenging in some areas . On the other hand , the Sri Lanka work also confirmed that sampling from 75 or 150 HHs is not statistically inferior to sampling from 300 HHs . Reducing the number of mosquito collection sites would vastly improve costs , feasibility , and the overall efficiency of a HH-based MX sampling strategy . MX is admittedly not easy to introduce into programs without PCR or entomological expertise . Given the present capacity in many areas , MX may be better used to assess special situations in which doubt remains about LF infection levels . Nevertheless , simplifications of PCR analysis are being rapidly introduced and the entomological skills required to identify gravid or semi-gravid mosquitoes and to place traps at suitable locations near the selected HHs can be trained or externally provided . Training programs to build MX capacity and resources would also certainly expand should MX progress into a more standardized LF monitoring and evaluation tool . Utilizing regional reference centers with MX resources and expertise offers another option if developing local capacity proves unfeasible . Extending a HH-based MX sampling approach for other vector species poses difficulties primarily due to challenges in trapping . Anopheles mosquitoes require invasive indoor HH trapping procedures and even these procedures have quite low yields . They are not as efficient as other vectors , however , and a transmission threshold of 1% has been suggested as opposed to 0 . 25% or 0 . 50% for Culex [19 , 20] , although any threshold is highly dependent on corresponding biting rates and annual transmission potentials . Regardless , this difference potentially provides the opportunity to reduce the number of Anopheles mosquitoes needed to around 2 , 500 depending on the magnitude of the prevalence to be detected . Despite the limitations of MX with Anopheles , a recent study in northern Nigeria dissected mosquitoes from knock-down collections and clearly showed that the distribution of long lasting insecticide-treated bednets reduced overall W . bancrofti DNA detection prevalence in mosquitoes from 0 . 32% , measured when MDA alone was used , to zero when bednets were added [28] . Although these results were obtained through dissection , which admittedly is not as sensitive as qPCR , they still provide convincing evidence for the significant reduction of infection prevalence in an Anopheles area . Aedes mosquitoes present a further challenge . Since they are highly efficient vectors , LF transmission can be sustained at quite low prevalence . Therefore , a threshold of less than 0 . 1% has been suggested for assessing infection in Aedes mosquitoes [19] . This , however , implies the need for very large mosquito samples . An MX study conducted in American Samoa , collected over 22 , 000 female mosquitoes using BG Sentinel traps ( Biogents AG , Regensburg , Germany ) , most of which were the area’s primary LF vector , Ae . Polynesiensis [29] . While Schmaedick et al . concede that MX for programmatic purposes in Aedes areas will require more efficient collection methods and further research , the results suggest that monitoring LF in Aedes could still prove useful as a supplement or alternative to monitoring in humans to identify areas where infections may exist . A follow-up study in American Samoa confirmed a statistically significant association between MX and human seroprevalence data , which further demonstrates the potential of MX as a long term surveillance strategy to locate transmission hotspots [30] . Our study included PoolScreen results to estimate LF infection prevalence in mosquitoes . PoolScreen has only recently been used to analyze mosquito samples for the prevalence of LF , which is a more focal disease than onchocerciasis for which PoolScreen has been more frequently applied . Given the low prevalence levels being assessed in this study and presumably other post-MDA settings , PoolScreen appears quite practical and reasonable for LF surveillance efforts according to its statistical parameters [21] . Extending PoolScreen’s capability to definitively compare changes in infection rates across independent random samples and calculate design effects for cluster surveys will further validate its program effectiveness and help mitigate remaining statistical concerns .
A method for sampling Culex mosquitoes for the analysis of W . bancrofti infection using gravid traps placed at systematically selected HH sites was successfully implemented across multiple surveys in two separate EUs . The results mostly showed no significant difference in repeat samples and were consistent with the estimated trends for human LF infection in the same area . The overall sampling strategy and results were also in agreement with a larger HH-based MX study in Sri Lanka [22] . As mosquito trapping and qPCR methodologies improve , so will the prospect of utilizing MX for other vector species . Additional work is needed to compare this MX approach to other survey methodologies for assessing LF prevalence in communities , particularly the TAS which is currently recommended by WHO for stopping MDA and post-MDA surveillance decision-making . Statistically , further research will help extend the approach in terms of assessing minimal sample sizes , clustering effects , and epidemiological constraints in different ecological areas . Finally , from an operational standpoint , it will be useful to examine modifications to the HH sampling method that might improve cost-effectiveness and reduce labor requirements . Addressing all these factors while continuing the application of MX in programmatic settings will undoubtedly speed up its adoption into a more standardized and robust LF evaluation tool . | Lymphatic filariasis ( LF ) is one of the world’s foremost debilitating infectious diseases with nearly 800 million people at risk of infection . Given that LF is a mosquito-borne disease , the use of molecular xenomonitoring ( MX ) to detect parasite DNA/RNA in mosquitoes can serve as a valuable tool for LF monitoring and evaluation , particularly in Culex vector areas . We investigated using MX in a low-level prevalence district of Tamil Nadu , India by applying a household-based sampling strategy to determine trap location sites . Two independent mosquito samples were collected in each of a higher human infection hotspot area ( sites with community microfilaria prevalence ≥1% ) and across a larger evaluation area that also encompassed the hotspots . Pooled results showed mostly reproducible outcomes in both settings and a significant higher pool positivity in the hotspot area . A follow-up survey conducted two years later reconfirmed these findings while also showing a reduction in pool positivity and estimated prevalence of infection in mosquitoes in both settings . The utilization of a household-based sampling strategy for MX proved effective and should be further validated in wider epidemiological settings . | [
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"reaction"
] | 2017 | Application of a household-based molecular xenomonitoring strategy to evaluate the lymphatic filariasis elimination program in Tamil Nadu, India |
Enterotoxigenic Escherichia coli ( ETEC ) , defined by their elaboration of heat-labile ( LT ) and/or heat-stable ( ST ) enterotoxins , are a common cause of diarrheal illness in developing countries . Efficient delivery of these toxins requires ETEC to engage target host enterocytes . This engagement is accomplished using a variety of pathovar-specific and conserved E . coli adhesin molecules as well as plasmid encoded colonization factors . Some of these adhesins undergo significant transcriptional modulation as ETEC encounter intestinal epithelia , perhaps suggesting that they cooperatively facilitate interaction with the host . Among genes significantly upregulated on cell contact are those encoding type 1 pili . We therefore investigated the role played by these pili in facilitating ETEC adhesion , and toxin delivery to model intestinal epithelia . We demonstrate that type 1 pili , encoded in the E . coli core genome , play an essential role in ETEC virulence , acting in concert with plasmid-encoded pathovar specific colonization factor ( CF ) fimbriae to promote optimal bacterial adhesion to cultured intestinal epithelium ( CIE ) and to epithelial monolayers differentiated from human small intestinal stem cells . Type 1 pili are tipped with the FimH adhesin which recognizes mannose with stereochemical specificity . Thus , enhanced production of highly mannosylated proteins on intestinal epithelia promoted FimH-mediated ETEC adhesion , while conversely , interruption of FimH lectin-epithelial interactions with soluble mannose , anti-FimH antibodies or mutagenesis of fimH effectively blocked ETEC adhesion . Moreover , fimH mutants were significantly impaired in delivery of both heat-stable and heat-labile toxins to the target epithelial cells in vitro , and these mutants were substantially less virulent in rabbit ileal loop assays , a classical model of ETEC pathogenesis . Collectively , our data suggest that these highly conserved pili play an essential role in virulence of these diverse pathogens .
Among young children under five years of age in developing countries , diarrhea is a leading cause of morbidity and mortality . Enterotoxigenic E . coli ( ETEC ) is one of the most common causes of moderate to severe diarrheal illness and deaths due to diarrhea in young children and incidentally is also the leading bacterial cause of diarrhea [1] . These bacteria are also a leading cause of hospitalization due to severe diarrhea in adults in developing countries [2] and are perennially the predominant cause of diarrheal illness among travelers to the endemic regions [3 , 4] . Additionally , ETEC infections contribute substantially to the burden of diarrheal illness associated with sequelae of malnutrition [5 , 6] , stunted growth [7] and impaired cognitive development [8] . The effects of ETEC infections also appear to be more critical in malnourished children [5] . Thus these pathogens contribute to a complex pattern of poverty , repeated enteric infections , environmental enteropathy [9] , and developmental impairment . ETEC are defined by the production of heat-labile ( LT ) and/or heat-stable ( ST ) enterotoxins [10] , and virulence requires successful delivery of these toxins to cognate receptors on target intestinal epithelial cells . LT binds to cell surface GM1 gangliosides , and following cellular entry this toxin activates production of host cAMP; while ST peptides bind guanylate cyclase C , stimulating production of cGMP [11] . Resulting increases in intracellular concentrations of these cyclic nucleotides modulate ion channels on the surface of intestinal cells leading to net losses of sodium chloride and water into the intestinal lumen and ensuing acute watery diarrhea [11 , 12] . In the classical paradigm of ETEC pathogenesis , these bacteria utilize pathovar-specific fimbrial or non-fimbrial adhesins , known as colonization factors or CFs [13] which allow them to adhere and colonize the small intestine where toxin delivery occurs . However , emerging evidence would suggest that this paradigm is perhaps overly simplistic , and that there are several potential adhesins which effectively act in concert to promote ETEC engagement of the host [14] . E . coli encode a multitude of pili assembled by the chaperone/usher pathway , termed CUP pili , which are important in virulence . CUP pili are tipped with specialized adhesins that recognize specific receptors with stereochemical specificity . CUP adhesins can determine both tissue tropism and the course of disease . For example , type 1 pili are encoded by the fim operon , and chomosomally encoded as part of the core E . coli genome [15 , 16] . Type 1 pili are composite fibers comprised of a pilus rod , made up of FimA subunits arranged in a right handed helical cylinder [17] . The pilus rod is joined to a fibrillum structure tipped with the FimH adhesin that binds mannose with stereochemical specificity [16 , 18–20] . FimH is critical for virulence in extraintestinal E . coli , as it has been well-established that FimH mediated adhesion enables uropathogenic E . coli ( UPEC ) colonization and invasion into bladder epithelial cells [21–23] , as well as the formation of intracellular bacterial communities [24] . ETEC also encode pathovar-specific CUP pili ( fimbriae ) like CFA/I [25] that are encoded on virulence plasmids [26] . More than four decades ago , these ETEC-specific colonization factors were shown to contribute to development of diarrheal illness in humans [27] , and consequently they have been the subject of intensive investigation and a major focus of ETEC vaccine development . Conversely , although early studies described possible type 1 pili expression by ETEC [28 , 29] , relatively little is known about the contribution of these highly conserved structures to virulence . Our more recent observation that the expression of the fim operon is enhanced by pathogen-host cell contact [14] , prompted a thorough investigation of the potential role of type 1 pili in ETEC pathogenesis reported here .
Although a number of earlier studies of ETEC suggested that these pathogens make type 1 pili ( also previously referred to as type 1 somatic pili or type 1 fimbriae ) [29 , 30] , to date there has been no systematic examination of their involvement in ETEC virulence . We therefore first performed studies to confirm the production of type 1 pili by the prototypical ETEC H10407 strain . Type 1 pili tipped with the FimH adhesin were identified on the surface of strain H10407 by transmission electron microscopy after immunogold labeling using anti-FimH antibodies ( Fig 1A ) . Similarly , using flow cytometry we verified production of type 1 pili in H10407 , but not in the corresponding fimH mutant strain ( Fig 1B ) . It has previously been shown that FimH is required to initiate the assembly of type 1 pili and thus fimH mutants are nonpiliated [31] . The expression of type 1 pili is under the transcriptional control of an invertible promoter element that governs phase OFF and phase ON populations [32 , 33] . Interestingly , mutations that inactivate FimH such as the Q133K mutation , bias the fim promoter towards the phase OFF state [34] . Thus , compared to the wild type H10407 strain , or pfimH complemented mutants , isogenic fimH mutants or those complemented with pQ133K , which encodes FimH with a mutation in the mannose binding site [35] , were nonpiliated and incapable of yeast agglutination , a phenotypic assay for expression of type 1 pili [36] ( Fig 1C ) and only the wild type and fimH complemented mutants exhibited demonstrable type 1 pili expression detected by anti-type 1 pili antibodies in immunoblots ( Fig 1D ) . Next , using polarized cultured intestinal epithelia ( CIE ) derived from the C2BBe1 clone of Caco-2 cells which produce apical brush borders with defined microvilli similar to human intestinal enterocytes [37] , we demonstrated that production of FimH was required for effective adhesion . Mutants lacking fimH were significantly less adherent than wild type ETEC ( p<0 . 0001 ) ( Fig 1E and 1F ) , while episomal expression of the fimH gene ( pfimH ) , but not the mutant fimH allele ( pQ133K ) , restored adhesion . Additionally , H10407-fimH:Q133K , which contains the Q133K mutant allele of fimH in the chromosome , abrogated yeast agglutination activity and demonstrated significant decrease in adhesion to intestinal cells ( S1A Fig ) . Similarly , mutants lacking the fimA gene encoding the major type 1 pili pilin subunit exhibited loss of functional type 1 pili in yeast agglutination assays , resulting in significant reduction of adhesion to the CIE ( S1B Fig ) . We found that methyl-α-D-mannose but not the methyl-α-D-galactose control sugar , inhibited FimH mediated yeast agglutination by ETEC ( Fig 2A ) and ETEC adhesion to epithelial cells ( Fig 2C ) . Similarly , antibodies generated against the lectin domain of FimH ( α-FimH ) , but not control antibodies , separated from pre-immune sera , inhibited FimH mediated yeast agglutination with wild type bacteria expressing type 1 pili ( Fig 2B ) and significantly inhibited ETEC adhesion to intestinal cells ( Fig 2D ) . Collectively , these data support the idea that ETEC utilize type 1 pili to engage intestinal epithelia . We next examined the ability of the FimH tip adhesin to directly engage the intestinal epithelial surface . The purified lectin domain of FimH ( FimHLD , 17 kD ) , representing amino acid residues 1–154 of mature FimH , bound to the apical surface of the CIE . In contrast , binding of FimHLD:Q133K , which lacks mannose binding activity , was markedly diminished ( Fig 3A and 3B ) as was binding of the wild type FimHLD protein in the presence of exogenous mannose ( Fig 3B ) . ETEC are noninvasive luminal pathogens thought to engage the microvilli at the apical surface of intestinal epithelial cells . Presumably , the FimH lectin can promote this engagement by interacting with mannosylated glycoconjugates on the glycocalyx covering the microvilli . Indeed , using transmission electron microscopy , we identified FimH by immunogold labeling at the ETEC-microvillus interface ( Fig 3C ) . Because FimH interacts with mannosylated receptors on the epithelial surface , we examined the impact of enhanced glycoprotein mannosylation on ETEC pathogen host interactions . Following CIE treatment with kifunensine , an α-mannosidase class 1 enzyme inhibitor which enhances the display of high-mannose glycoproteins [38] , we observed a significant increase in FimHLD binding ( Fig 4A and 4B ) . Likewise , WT ETEC adhesion to the kifunensine treated CIE was enhanced relative to the untreated control CIE ( p<0 . 0001 ) ( Fig 4C and 4D ) . However , kifunensine treatment had no impact on adhesion of the fimH mutant . These data further suggested that availability of mannosylated glycoproteins on the host cell surface promotes ETEC adhesion through FimH . Because ETEC H10407 expresses both type 1 pili and plasmid encoded CFA/I fimbriae specific to the ETEC pathovar , we investigated whether these CUP structures acted cooperatively in facilitating adhesion to intestinal epithelia . The CFA/I fimbriae are encoded by the cfaABCE operon in which cfaE encodes the CfaE tip adhesin [26 , 39 , 40] . Interestingly , mutations in either fimH or cfaE significantly reduced adherence of ETEC compared to wild type H10407 , suggesting that both pili participate in ETEC adhesion ( S2 Fig ) . Mutants lacking both the type 1 pili and the CFA/I tip adhesin genes ( fimH-cfaE ) demonstrated further reduction in adhesion compared to either of the single mutants ( S2 Fig ) . Although additional data are needed to define the precise respective contributions of the chromosomally encoded type 1 pili and the plasmid encoded pathovar specific CFA/I , these initial data support our hypothesis that the structures act in concert to facilitate adhesion . While the CIE used in this study possess some features of normal intestinal epithelial cells , they are derived from metastatic colon cancer cells which fail to represent the diversity of cell types present in intestinal epithelium . Enteroids , derived from human intestinal stem cells collected from healthy volunteers [41–43] , can recapitulate many aspects of normal physiology and preserve features of human intestinal epithelium . These include presentation of different cell types including enterocytes , goblet cells , Paneth cells , and endocrine cells [44–46] . Therefore , to further assess the contribution of type 1 pili in ETEC adhesion to intestinal epithelia we established polarized intestinal epithelial monolayers derived from ileal specimens obtained from normal adult human subjects . In these enteroid-derived monolayers we were able to identify enterocytes with a defined brush border and distinct microvilli on the apical surface as well as goblet cells , and chromogranin A positive cells suggesting that they faithfully reproduce many features of surfaces normally presented to bacteria within the intestine ( Fig 5A–5E ) . Similar to our studies of CIE , we found that wild type ETEC adhered to the surface of stem cell derived polarized small intestinal ( ileal ) monolayers ( Fig 6A and 6B ) in close approximation with the microvillus surface ( Fig 6C ) , and that mutation of fimH significantly attenuated adherence relative to the wild type parent strain ( Fig 6D ) . Wild type bacteria adhered well to polarized monolayers of enteroid collected from multiple individuals , while the fimH mutant was persistently deficient in its ability to adhere to all target epithelia relative to the parent strain ( Fig 6E ) . These studies suggested that type 1 pili of ETEC potentially play an important role in specifically directing bacterial interaction with the small intestine where release of toxins is thought to provoke the efflux of water and salt that lead to diarrhea . Close contact of ETEC with target epithelial cells is essential for efficient delivery of its enterotoxins [47 , 48] . To investigate the impact of type 1 pili mediated ETEC-host interactions on toxin delivery , we measured the intracellular production of cAMP and cGMP , cyclic nucleotide second messenger markers for delivery of LT and ST , respectively , in infected cells . cAMP and cGMP was significantly increased in target cells infected with WT H10407 relative to fimH or fimA mutants ( Fig 7A and 7B ) . Methyl-α-D-mannose significantly reduced levels of intracellular cGMP and inhibited cAMP activation in target cells infected with WT H10407 ( Fig 7A and 7B ) . These effects were observed despite wild type levels of heat-labile toxin being produced by the mutants ( Fig 7C ) . Together , these data suggested that type 1 pili are required for optimal delivery of both heat-labile and heat-stable enterotoxins to the target epithelial cells . We next examined the contribution of type 1 pili to pathogenesis in the rabbit ileal loop model , a classical model of virulence for V . cholerae and ETEC [49] . Both fimH and fimA mutants were significantly less adherent than the WT H10407 to rabbit ileal intestinal epithelium ( Fig 8A and 8B ) . Likewise , while considerable fluid accumulation , a hallmark of ETEC virulence , was observed in loops infected with WT H10407 ( Fig 8C ) no demonstrable fluid accumulation was observed in control loops infected with eltAB mutants [47] which do not make LT toxin ( S1 Table ) , or those containing only PBS , and we detected significantly less fluid accumulation in the loops infected with either the fimH or fimA mutants ( Fig 8C ) , suggesting that type 1 pili mediated pathogen-host interactions contribute to ETEC virulence in this model . To investigate the prevalence of functional type 1 pili expression in clinical ETEC isolates , we tested 174 geographically and phylogenetically disparate clinical isolates including recently sequenced [50] strains using yeast agglutination assays . Overall , the majority ( 76% ) of the clinical isolates demonstrated yeast agglutination activity indicative of preserved functional type 1 pili expression among ETEC . Importantly , we observed type 1 pili expression in isolates possessing each of the major colonization factors ( CFs ) , and in 82% of isolates without any recognizable CFs ( Table 1 ) . Overall , these data suggest that functional type 1 pili are highly conserved in diverse ETEC clinical isolates . Collectively data presented here provide evidence that type 1 pili play an essential role in the pathogenesis of these highly diverse pathogens . Similar to their well-established role in the pathogenesis of uropathogenic E . coli ( UPEC ) [51] where type 1 pili are required for interaction with bladder epithelia , these structures appear to be highly conserved in clinical isolates and appear to be critical for ETEC adhesion and effective engagement of host intestinal epithelia that are ultimately required for efficient delivery of effector molecules including the known toxins .
Enterotoxigenic E . coli are a remarkably diverse group of pathogens that share plasmid-encoded effector molecules , namely heat-labile toxin ( LT ) and/or heat-stable toxins ( ST ) . In effect , ETEC pathogenesis can be summarized by the virulence features that collectively facilitate the delivery of these toxins [52] . Successful engagement of the complex landscape presented by the intestinal mucosae that includes a secreted mucus layer as well as the glycocalyx , glycoconjugates on the apical surface of the epithelium [53 , 54] , represents an essential step in ETEC virulence . Like other enteric pathogens , ETEC appear to employ a number of different adhesins ( lectins ) that recognize specific carbohydrate moieties on intestinal epithelia [55–58] . While most studies of ETEC adhesion , and consequently vaccine development , had previously focused on plasmid-encoded colonization factors , recent studies have suggested that bacterial adhesion , intestinal colonization , and toxin delivery ultimately represent very complex phenotypes involving the orchestrated deployment of a variety of pathovar specific plasmid encoded adhesins [14 , 59 , 60] as well as highly conserved chromosomally-encoded molecules [61] and other virulence factors including mucinases [62 , 63] . Although a number of early studies had suggested that ETEC have the capacity to make type 1 pili [30 , 64] , their contribution to pathogenesis had not been comprehensively investigated . Here , we demonstrate convincingly that most ETEC make type 1 pili , that these organisms utilize these highly conserved pili to engage model intestinal epithelia , where these interactions are critical for effective delivery of both heat-labile and heat-stable toxins . Additionally , our data demonstrate that type 1 pili act in concert with the plasmid-encoded pathovar specific colonization factors to promote optimal interaction of H10407 with host intestinal epithelial surfaces . The precise interactions between type 1 pili and the host cell surface have not been thoroughly delineated , however , the data included here support the involvement of the minor pilin tip adhesin subunit ( FimH ) in mannose-dependent engagement of one or more host cell receptors [65–68] . Before the advent of recombinant techniques to construct isogenic deletion mutants , earlier investigations of ETEC H10407-P , a plasmid-cured strain of H10407 which lacks the large virulence plasmid encoding CFA/I colonization factor , Knutton et . al . suggested that type 1 pili mediated ETEC adhesion to human intestinal biopsies in a fashion that was inhibited by exogenous mannose [28 , 29] . However , it was suggested that these pili were mediating adhesion to the basolateral surface of enterocytes rather than the apical side [28] . The data presented here overcome many of the technological limitations inherent in these earlier studies and demonstrate convincingly that type 1 pili mediate adhesion of ETEC to apical surface of small intestinal enterocytes , where LT and ST bind to surface GM-1 and guanylate cyclase C receptors , respectively . The present studies raise important questions regarding the nature of type 1 pilus production by ETEC including how these structures might work cooperatively with canonical colonization factor fimbriae , and whether ETEC might encode FimH variants[69 , 70] that favor colonization of the small intestine . Addressing these issues could be relevant to understanding how type 1 pili might be targeted in vaccines . While ETEC cause a tremendous burden of disease in low-middle income countries , and among travelers to these regions , at present , there is no suitable broadly protective vaccine to prevent infections caused by ETEC . This in part relates to substantial genetic and antigenic heterogeneity within the enterotoxigenic Escherichia coli pathovar . Most vaccines to date , in some form , have targeted the plasmid encoded colonization factor antigens . The remarkable heterogeneity of these antigens [71] and the fact that many ETEC [72 , 73] , more than half of isolates in some studies [74] , do not make a recognizable colonization factor have prompted further investigation of these pathogens to define additional vaccine target antigens [75] . Our observation that ETEC isolates from a phylogenetically and geographically diverse collection of strains express functional type 1 pili can potentially inform alternative approaches to design of broadly protective immunogens . In summary , the data presented here demonstrate that ETEC utilize type 1 pili for optimal engagement of the host intestinal epithelium and that these interactions facilitate toxin delivery to the target enterocytes essential for virulence . These studies provide an expanded view of ETEC molecular pathogenesis beyond the canonical paradigm envisioned more than 40 years ago , and potentially afford new avenues for the rational design of strategies to prevent the global burden of disease associated with these important pathogens .
Isogenic fimH and fimA mutants ( S1 Table ) were constructed using lambda red mediated recombination as previously described [76] . To construct the fimH mutant primers jf101413 . 7 and jf101413 . 8 ( S2 Table ) were used to amplify the kanamycin resistance cassette from pKD4 plasmid with 60-bp tails corresponding to the DNA sequence immediately upstream and downstream of fimH . The resulting amplicon was then introduced into H10407 carrying the pKD46 helper plasmid for lambda red-mediated homologous recombination and mutants were selected on 50 μg/ml Kanamycin containing LB-agar plate , and tested for loss of fimH gene by PCR using primers jf120913 . 9 and jf120913 . 10 . A complementation plasmid ( pfimH ) was constructed by amplifying the fimH gene with its native stop codon using primers jf120814 . 1 and jf120814 . 2 and cloning into pFLAG-CTC plasmid using infusion cloning kit ( Clontech , Takara Bio , USA ) . A fimA complementation plasmid was constructed by cloning the fimA gene from H10407 into the EcoR1 and BamH1 sites of pTrc99A[77] . Site-directed mutagenesis of pfimH with primers jf031814 . 1 and jf031814 . 2 was used to change the CAA codon corresponding to the glutamine residue at position 133 of FimH to AAA codon corresponding to the lysine residue resulting in pQ133K ( QuikChange , Stratagene , USA ) . Plasmids pfimH and pQ133K ( S3 Table ) were then introduced into the fimH mutants for complementation . All mutants were checked for motility , growth and secretion of known effector molecules including LT , EtpA and EatA . To construct an expression plasmid for polyhistidine tagged FimH lectin domain ( FimHLD ) , we amplified the N-terminal lectin region of fimH gene using primers jf042314 . 1 and jf042314 . 2 . The amplicon was then cloned into pETDUET1 ( In-Fusion , Clontech ) . Site directed mutagenesis was used to generate the mannose binding deficient FimHLD:Q133K as described above . The resulting plasmids , pfimHLD and pfimHLD:Q133K , were then introduced into BL21 ( DE3 ) pLys strain for expression and purification of polyhistidine-tagged FimHLD and FimHLD:Q133K . Overnight cultures of BL21 ( DE3 ) pLys containing pfimHLD or pfimHLD:Q133K were diluted 1:100 into 2 liters of terrific broth supplemented with 100 μg/ml ampicillin and grown for 3 h at 37°C to optical density of ~0 . 7 at a wavelength of 600 nm ( OD600 ) then induced with 1 mM IPTG for 3 h at 30°C . Cells were harvested , lysed and his-tagged proteins were purified from the bacterial lysates by nickel affinity chromatography using HisTrap HP column ( GE healthcare bioscience , PA , USA ) . His-tagged FimHLD and FimHLD:Q133K were further purified by size exclusion column chromatography using HiLoad16/600 Superdex 200 pg column ( GE ) . Anti-FimH polyclonal rabbit antiserum was produced against the lectin domain of FimH as previously described [61] . Briefly , two New Zealand White rabbits were immunized ( Rockland , USA ) with recombinant polyhistidine-tagged FimHLD . Antibodies were separated from serum components using HiTrap columns prepacked with protein G Sepharose ( GE ) . The resulting polyclonal antibodies were then pre-absorbed using lyophilized strain AAEC191-A [15] , and affinity purification of antibody against FimHLD immobilized on nitrocellulose was performed as previously described [61 , 78] . All experiments were carried out using prototypical ETEC strain H10407[79] or the isogenic mutants ( S2 Table ) . Bacteria were grown at type 1 pili inducing conditions by following static incubation for 24 h at 37°C followed by subculturing at 1:100 for additional 24 h statically at 37°C [34] in LB media supplemented with or without antibiotic , as appropriate , unless otherwise stated . C2BBe1 cells ( ATCC Accession Number CRL-2102 ) , a subclone of the Caco-2 cell colonic adenocarcinoma line , was used for generation of the cultured intestinal epithelium ( CIE ) . In order to generate polarized monolayers which form an apical brush border , with microvilli morphologically comparable to that of the human intestinal epithelium [37] , we seeded ~2 x105 C2BBe1 cells onto Transwell filters ( 0 . 4μM polystyrene membrane , 6 . 5mm diameter insert ) in DMEM media supplemented with 10% FBS and 10 μg/ml human transferrin ( Lonza , MD , USA ) and grew at 37°C with 5% CO2 for three weeks for CIE ( polarized monolayer culture ) . Media were changed every 2–3 days . Formation of microvilli was verified by transmission electron microscopy . Caco-2 cells ( ATCC ) were cultured in MEM media supplemented with 20% FBS . T-84 ( ATCC ) cells were cultured in DMEM/F12 ( 1:1 ) media supplemented with 5% FBS . Strains grown under type 1 pili inducing conditions were processed for flow cytometric analysis . For surface staining , bacterial pellets were washed once with PBS , fixed in 2% paraformaldehyde for 15 min and then incubated with 1%BSA in PBS for 30 min at RT . Bacteria were then incubated with primary antibody ( α-FimH ) at RT for 45 min . After washing with PBS , secondary antibody staining was performed at RT in the dark with species-specific antibodies conjugated with AlexaFluor 546 for an additional 45 min . Bacteria were then washed with PBS and resuspended in 100 μl of PBS for acquisition by flow cytometry ( FACSCalibur , BD Biosciences ) . A minimum of 50 , 000 organism counts were acquired ( CellQuest software , Becton Dickinson ) , and subjected to subsequent analysis ( FlowJo , v7 . 6 . 3 ) . Type 1 pili were extracted by following a previously described method with some modifications [80] . Briefly , bacteria grown in type 1 pili inducing conditions were harvested , re-suspended in 1 ml of 1 mM Tris-HCl ( pH 8 . 0 ) and incubated at 65°C for 1 h with occasional vortexing and pelleted by centrifugation ( 15 , 000 × g for 5 min ) . The supernatant was then transferred to another tube , and an aliquot was precipitated in salt ( 300 mM NaCl and 100 mM MgCl2 ) by incubating overnight at 4°C , followed by centrifugation at 20 , 000 × g for 10 min at 4°C . The protein pellet was re-suspended in 1 mM Tris-HCl ( pH 8 . 0 ) . Fimbrial extracts were separated by sodium dodecyl sulfate ( SDS ) -15% polyacrylamide gel electrophoresis minigels ( Bio-Rad ) . Proteins were either stained with Coomassie brilliant blue or transferred to nitrocellulose membranes ( Bio-Rad ) using a Mini Trans-Blot electrophoretic cell ( Bio-Rad ) for 60 min at 100 V . The membrane was blocked with 5% milk-PBS supplemented with 0 . 05% Tween 20 ( Pierce ) . Incubations with primary ( 1:5 , 000 ) and secondary ( 1:5 , 000 ) antibodies were carried out for 1 h at RT . Chemiluminescent substrate ( Clarity Western ECL substrate , Bio-Rad ) was used for detection . Yeast agglutination as a phenotypic test for the production of type 1 pili [36] , was performed using the following conditions: strains to be tested were grown in 1 ml Luria broth at type 1 pili inducing conditions , harvested and adjusted to OD600 of 1 . 0 in PBS . Agglutination was performed on glass slides by mixing 20 μl bacteria with an equal volume of bakers’ yeast suspension adjusted to OD600 of 1 in PBS . For adhesion assays , bacteria to be tested were grown at type 1 pili inducing conditions and diluted at 1:10 in pre-warmed cell culture media prior to infection . Serial dilutions of each inoculum were plated on Luria agar for CFU count . Infected CIE were incubated at 37°C and 5% CO2 for 1 h , washed with pre-warmed tissue culture medium 3 times with gentle shaking ( 100 rpm ) for 1 min each . The CIE were then either lysed in 0 . 1% Triton X-100 for 5 min , and the cell associated bacteria were recovered by plating lysates onto Luria agar , or processed for fluorescence microscopy for enumeration of bacteria attached to the cells . To investigate the binding of FimH in the context of intestinal epithelial cells , 100 μl of 50 μg/ml biotinylated FimHLD or FimHLD:Q133K were incubated with CIE . After an hour of incubation at 37°C unbound FimHLD were washed off with tissue culture media , the cells were fixed with 2% paraformaldehyde for 30 min at room temperature ( RT ) , washed twice with PBS , and blocked with 1% BSA-PBS for another 30 min at RT . Streptavidin coated Qdot 594 was then used at 1:100 dilution in 1% BSA-PBS to detect biotinylated FimHLD bound to the cell surface . For detection of ETEC adhesion , infected CIEs were incubated with anti-O78 antibody followed by fluorescent labeled secondary . Cell membranes and nuclei were stained as previously described ( CellMask , red for membrane and DAPI for nuclei; Invitrogen ) [61] . Images were acquired on a Zeiss LSM510 confocal microscope , and files were converted to TIFF image using ImageJ ( v1 . 45 ) . Signals were quantified using Volocity software ( version 6 . 2; PerkinElmer , Inc . ) . For localization of FimH on intestinal epithelium , ETEC infected CIE were fixed in 2% paraformaldehyde/0 . 02% glutaraldehyde ( Polysciences Inc . , Warrington , PA ) in 100 mM PIPES/0 . 5 mM MgCl2 , pH 7 . 2 for 1 h at 4°C . Samples were washed with PIPES buffer , blocked with 5% Fetal Bovine Serum /5% Normal Goat Serum for 20 min and subsequently incubated with rabbit anti-FimH antibody for 1 h , followed by secondary goat anti-rabbit antibody conjugated to 18 nm colloidal gold for 1 h . Samples were washed in buffer and postfixed in 1% osmium tetroxide ( Polysciences Inc . ) for 1 h . Samples were then rinsed extensively in dH2O prior to en bloc staining with 1% aqueous uranyl acetate ( Ted Pella Inc . , Redding , CA ) for 1 h . Following several rinses in dH2O , samples were dehydrated in a graded series of ethanol and embedded in Eponate 12 resin ( Ted Pella Inc . ) . Sections of 95 nm were cut with a Leica Ultracut UCT ultramicrotome ( Leica Microsystems Inc . , Bannockburn , IL ) , stained with uranyl acetate and lead citrate , and viewed on a JEOL 1200 EX transmission electron microscope ( JEOL USA Inc . , Peabody , MA ) equipped with an AMT 8 megapixel digital camera ( Advanced Microscopy Techniques , Woburn , MA ) . Enteroids were grown as previously described in detail [45] from purified intestinal cell lines , maintained at the Washington University Digestive Diseases Research Core Center BioSpecimens Core under a protocol approved by the Institutional Review Board . Briefly , cells were thawed and re-suspended in Matrigel ( BD Biosciences , San Jose , CA , 15 μL/well in 24 well plates ) , and incubated at 37°C with a 1:1 mixture of L-WRN conditioned media ( CM ) and primary culture media ( Advanced DEM/F12 , Invitrogen ) supplemented with 20% fetal bovine serum ( FBS ) , 2 mM L-glutamine , 100 units/mL penicillin , 0 . 1 mg/mL streptomycin , 10 μM Y-27632 ( ROCK inhibitor; Tocris Bioscience , R and D Systems , Minneapolis , MN ) , and 10 μM SB 431542 ( transforming growth factor-ß type 1 receptor inhibitor; Tocris Bioscience , R and D Systems ) . For polarization , cells were added to Transwell filters ( Corning ) and incubated in differentiation media ( 1:20 mixture of L-WRN CM and primary culture media ) lacking SB 431542 . Cells were grown to confluence for 3 days in differentiation media before use . For histological analysis ( Day 3 ) , enteroids grown on transwell were fixed in 3 . 7% paraformaldehyde ( Electron Microscopy Sciences , Hatfield , PA ) for 15 min , washed once with PBS and processed for paraffin embedding . Transverse sections ( 5 μm ) were stained with hematoxylin and eosin ( visualized with a Zeiss Axioskop 2 MOT microscope fitted with a CRI Nuance FX multispectral imaging system , Cambridge Research and Instrumentation ) , or used for immunostaining . For immunostaining , sections were de-paraffinized and hydrated , boiled in Unmasking Solution ( Vector Laboratories , Burlingame , CA ) for 25 min , rinsed in PBS , blocked in 1% bovine serum albumin/0 . 1% Triton-X100 for 30 min and incubated with primary antibody at 4°C overnight . Primary antibodies included rabbit anti- ChgA ( 1:100 , Abcam , Cambridge , MA ) , rabbit anti-Muc2 ( 1:100 , Santa Cruz Biotechnology , Inc . , Dallas , Texas ) , mouse monoclonal anti-Villin 1 ( 1:100; Santa Cruz Biotechnology , Inc . ) . Slides were rinsed 3 times with PBS and incubated for 60 min at RT with species specific secondary antibodies ( 1:200; Invitrogen ) conjugated to AlexaFluor488 or AlexaFluor546 . For detection of bacteria , enteroids ( grown for 3 days ) were infected for 1 h , washed 3 times with pre-warmed media , fixed in 3 . 7% paraformaldehyde and either processed for fluorescent microscopy or embedded in paraffin . For the detection of tight junction formation , antibody against zonula occludens-1 ( anti-ZO-1 , mouse monoclonal , Invitrogen ) was used at 1:100 dilutions followed by anti-mouse secondary antibodies conjugated to AlexaFluor488 ( 1:200 , Invitrogen ) . Paraffin embedded sections were deparaffinized and processed for immunostaining with anti-O78 antibody ( 1:200 ) followed by secondary antibodies ( 1:200; Invitrogen ) conjugated to AlexaFluor 546 for the detection of cell associated bacteria . UEA-1 lectin conjugated to FITC ( 1:100 , Sigma ) was applied during secondary antibody incubation . Slides were washed 3 times in PBS and stained with DAPI ( Molecular Probe ) to visualize nuclei and cellmask red ( 1:2000 , Invitrogen ) to visualize plasma membrane and mounted with ProlongGold antifade reagent ( Molecular Probes ) for confocal microscopy ( Nikon ECLIPSE Ti confocal microscope equipped with NIS-Elements imaging software ) . To examine ETEC association with microvilli of polarized primary human small intestinal epithelial cells , infected monolayers were processed for transmission electron microscopy following protocol mentioned above . LT toxin secretion by different strains was measured using previously established GM1-ELISA method [81] . Briefly , ELISA plates ( Costar , Corning , NY ) were coated with 100 μl/well of 1 μg/ml monosialoganglioside GM1 ( Sigma , G-7641 ) , and incubated overnight at RT . After blocking with 2% BSA in PBS-Tween , 100 μl of clarified culture supernatant from overnight Luria broth cultures of bacteria was added to wells in triplicate and incubated for 1 h at 37°C . The wells were then washed and incubated with 100 μl of a 1:5000 dilution of rabbit anti-LTB polyclonal antisera for 1 h . Plates were again washed and then incubated with 100 μl of a 1:5000 dilution of anti-rabbit secondary conjugated with HRP for another hour . Finally plates were developed with 100 μL per well of TMB-H2O2 ( KPL ) substrates and read immediately at 630nm for kinetic measurement ( Eon , BioTek instruments , VT , USA ) . Caco-2 cell monolayers grown in 96-well plates were infected with WT or mutant strains and used to determine alterations in cAMP . After 2 h of infection wells were washed with pre-warmed tissue culture media , incubated for an additional 2 . 5 h , cells lysed in sample buffer and intracellular cAMP concentrations were then determined by ELISA ( cAMP Direct EIA , Arbor Assays , MI , USA ) . Similarly , T-84 cell monolayers grown in 96-well plates were used to determine cGMP concentrations following infection . 1 . 5 h after infection , wells were washed 3 times with pre-warmed tissue culture media , incubated for an additional 3 h , lysed in sample buffer , and intracellular cGMP determined ( cGMP Direct EIA , Arbor Assays , MI , USA ) . The rabbit ileal loop ( RIL ) assay was performed as previously described [49 , 82] . Briefly , bacteria were grown under static conditions in Luria broth at 37°C , then washed with PBS , and diluted yield to a target inoculum of ~1X106 CFU/ml/loop . Adult albino rabbits ( New Zealand strain ) weighing 1 . 5–2 . 0 kg were fasted for 24 h and allowed only water . Following induction of anesthesia with intravenous sodium pentobarbital ( 0 . 5 mL/kg body mass ) , the abdominal cavity was entered , the intestine exposed and the ileum localized . Here , loops of 5 cm in length were created with surgical ligatures , with 2 cm intervals between each loop to yield 5–6 loops per rabbit . Loops were then inoculated with either wild type H10407 as a positive control or mutant strains . Animals were euthanized ~18 h post infection with sodium pentobarbital , the intestine exposed and the ileocaecal region was then removed . The volume of fluid accumulation determined per unit length of gut within each segment , after which tissue specimens from the corresponding segment were collected , fixed in formalin ( 10% ) , paraffin-embedded and subsequently processed for examination of villous associated bacteria following protocol mentioned above . Rabbit ileal loop protocol conducted at icddr , b was reviewed and approved by the institution’s Animal Experimentation Ethics Committee ( AEEC ) . Experimental procedures were carried out by veterinarians at the animal facility in accordance with relevant guidelines . The rules and guidelines followed at icddr , b for animal care and use adhere to the recommendations , with some modifications , stated in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health ( NIH ) . These guidelines and the subsequent modifications were approved by the icddr , b Board of Trustees . All work involving cell lines derived from human biopsy specimens with the Washington University Digestive Diseases Research Core Center BioSpecimens Core was performed under a protocol ( 201406083 ) approved by the Institutional Review Board at Washington University School Medicine . | Enterotoxigenic Escherichia coli ( ETEC ) infections contribute substantially to death and morbidity due to diarrheal illness and are associated with serious sequelae including malnutrition , stunted growth , and intellectual impairment among young children in developing countries . Effective engagement of intestinal epithelial cells is essential for ETEC pathogenicity . Consequently , pathovar specific plasmid-encoded adhesin structures known as colonization factors ( CFs ) have been a principal target for vaccines . However , tremendous inter-strain variation in the carriage of gene clusters encoding different CFs and significant antigenic diversity of the CF adhesins has posed a challenge to vaccine development . In contrast , type 1 pili are encoded by the fim operon located in the chromosome of most ETEC strains and are highly conserved . While type 1 pili are known to play a critical role in virulence of extraintestinal pathogenic E . coli , the present studies represent the first detailed examination of the contribution of these pili in ETEC pathogenesis . Here we demonstrate that ETEC type 1 pili are essential for optimal interactions with intestinal epithelia and that they play a critical role in virulence . These data may inform additional approaches toward development of broadly protective vaccines for these pathogens of global importance . | [
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] | 2017 | Highly conserved type 1 pili promote enterotoxigenic E. coli pathogen-host interactions |
Adipose tissue has emerged as an important regulator of whole-body metabolism , and its capacity to dissipate energy in the form of heat has acquired a special relevance in recent years as potential treatment for obesity . In this context , the p38MAPK pathway has arisen as a key player in the thermogenic program because it is required for the activation of brown adipose tissue ( BAT ) thermogenesis and participates also in the transformation of white adipose tissue ( WAT ) into BAT-like depot called beige/brite tissue . Here , using mice that are deficient in p38α specifically in adipose tissue ( p38αFab-KO ) , we unexpectedly found that lack of p38α protected against high-fat diet ( HFD ) -induced obesity . We also showed that p38αFab-KO mice presented higher energy expenditure due to increased BAT thermogenesis . Mechanistically , we found that lack of p38α resulted in the activation of the related protein kinase family member p38δ . Our results showed that p38δ is activated in BAT by cold exposure , and lack of this kinase specifically in adipose tissue ( p38δ Fab-KO ) resulted in overweight together with reduced energy expenditure and lower body and skin surface temperature in the BAT region . These observations indicate that p38α probably blocks BAT thermogenesis through p38δ inhibition . Consistent with the results obtained in animals , p38α was reduced in visceral and subcutaneous adipose tissue of subjects with obesity and was inversely correlated with body mass index ( BMI ) . Altogether , we have elucidated a mechanism implicated in physiological BAT activation that has potential clinical implications for the treatment of obesity and related diseases such as diabetes .
p38α has emerged as one of the main player that could activate the thermogenic capacity of adipose tissue . Because the thermogenesis of adipose tissue is reduced in obesity [6 , 7 , 21] , we wondered whether expression of this kinase changes in human WAT during obesity . Using 2 cohorts for visceral fat and subcutaneous fat ( sWAT ) of adult patients with 80 and 170 samples , respectively , we found that the expression of p38α ( Mapk14 ) in visceral fat and sWAT from individuals with obesity was reduced compared with those without obesity ( Fig 1A and 1D ) . In fact , mRNA levels of Mapk14 in visceral fat inversely correlated with body mass index ( BMI ) ( Fig 1B ) . It has been suggested that p38α in WAT activates browning by triggering the expression of UCP1 [18] , the main protein responsible for adipose tissue thermogenic capacity [22] . In visceral fat and sWAT from individuals with obesity and those without obesity , we found that expression of Mapk14 correlated positively with the levels of Ucp1 ( Fig 1C and 1E ) . This correlation reinforced the idea that p38α in visceral fat and sWAT controls the levels of UCP1 and could regulate browning in humans . Then , we evaluated the function of p38α in adipose tissue using conditional mice ( p38αFab-KO ) , which lacked p38α in WAT and BAT ( S1 Fig ) . Under normal-chow diet ( ND ) , p38αFab-KO mice had the same weight gain as the control Fab-Cre mice ( S2A Fig ) . However , they presented reduced fat mass , in concordance with lower eWAT , perirenal WAT ( pWAT ) , and BAT weight ( S2B and S2C Fig ) . This reduction in fat accumulation was associated with higher energy expenditure and slight increase of body temperature ( S2G and S2H Fig ) . In fact , these mice presented lower blood glucose levels in fasted and fed conditions ( S2D Fig ) and increased glucose tolerance ( S2E Fig ) , with no differences in insulin sensitivity or insulin-stimulated glucose transporter type 4 ( GLUT4 ) translocation in adipose tissue ( S2E and S2F Fig ) . These data suggest that lack of p38α might protect against type 2 diabetes . Moreover , we evaluated whether lack of p38α affects adipogenesis , browning , and metabolism in eWAT and BAT . BAT from p38αFab-KO mice presented an increase of Cidea , a marker of browning , together with higher expression of glycolytic and β oxidation genes ( S3 Fig ) . To further evaluate the role of p38α in adipose tissue , mice were fed an HFD , and we observed that p38αFab-KO mice were completely protected from diet-induced obesity because their weight was identical to the weight of the control animals in ND ( Fig 2A ) . This reduced weight gain was in line with lower fat mass ( Fig 2B ) and reduced weight of the different fat depots , including eWAT , sWAT , iWAT , pWAT , and BAT ( S4A Fig ) . Moreover , liver weight was also reduced in agreement with protection against HFD-induced liver steatosis in p38αFab-KO mice ( Fig 2C and S4A Fig ) . The protection against HFD-induced obesity was associated with reduced fasted and fed hyperglycaemia in p38αFab-KO mice , with no differences in triglyceridemia ( Fig 2D and S4E Fig ) . In addition , p38αFab-KO mice were protected against HFD-induced glucose intolerance even when glucose dose was adjusted to lean mass ( Fig 2E , S4B Fig . ) and insulin resistance as shown by the reduced glucose levels during the insulin tolerance test ( ITT ) ( Fig 2E ) . HFD-induced obesity was associated with liver insulin resistance and reduced insulin-stimulated Akt phosphorylation in livers from HFD-fed Fab-Cre mice ( S4C Fig ) . Evaluation of insulin sensitivity in several tissues indicated that HFD-fed p38αFab-KO mice presented higher insulin-induced phosphorylation of Akt at Thr308 and Ser473 than HFD-fed Fab-Cre mice in liver and muscle but not in eWAT nor BAT ( S4D Fig ) . Furthermore , we observed a slight increase of insulin-stimulated GLUT4 translocation in eWAT ( Fig 2F ) . Together , these results demonstrate that p38αFab-KO mice are protected against diet-induced obesity and diabetes . Histological analysis showed that interscapular BAT depot from HFD-fed p38αFab-KO mice had small multilocular adipocytes ( Fig 2G ) , whereas in eWAT , we observed a slight decrease of adipocyte size ( Fig 2G ) , which correlates to reduced cell size in BAT and WAT adipocytes from HFD-fed p38αFab-KO with respect to HFD-fed Fab-Cre ( S5A and S5C Fig ) . Then , we evaluated HFD-induced WAT adipocyte expansion by bromodeoxyuridine ( BrdU ) staining [23] , observing reduced expansion in p38αFab-KO ( Fig 3A ) . However , no differences in Ki67 staining were observed after HFD in WAT or BAT adipocytes ( S5A and S5C Fig ) . To further investigate the mechanism by which lack of p38α in adipose tissue could protect against HFD-induced obesity , we evaluated whole-body metabolism using metabolic cages . HFD-fed p38αFab-KO mice showed a significant increase in whole-body energy expenditure analysed by ANCOVA , with no changes in food intake or respiratory exchange ratio ( Fig 3B ) . These data are consistent with the observation that HFD-fed p38αFab-KO mice have higher skin temperature in the region of BAT compared with Fab-Cre mice ( Fig 3C ) . Western blot analysis of BAT indicated that HFD-fed p38αFab-KO mice presented a slight increase of UCP1 expression associated with higher AMPK and Creb phosphorylation ( Fig 3D and 3E ) . In addition , higher expression of UCP1 levels was observed in iWAT from HFD-fed p38αFab-KO mice ( S5B and S7A Figs ) , suggesting an increased browning of this adipose depot . In contrast with the up-regulated UCP1 levels in iWAT , analysis of eWAT by western blot and immunohistochemistry showed that HFD-fed p38αFab-KO mice have reduced UCP1 levels in this tissue ( S6 and S7B Figs ) . These results are in agreement with the results found in human visceral fat ( Fig 1C ) suggesting that , in visceral fat , p38α directly correlates with UCP1 . In vitro–differentiated brown adipocytes from p38αFab-KO mice confirmed a key role of this kinase inhibiting browning in a cell-autonomous manner because several browning markers ( UCP1 , PGC1b , Cidea , Cox7a1 , Cox7a2 , and Cox8b ) were up-regulated in p38αFab-KO brown adipocytes ( S8A Fig ) . In concordance with the results observed in the BAT tissue , glycolytic genes were also up-regulated , while many lipogenic genes that correlated with the lower triglyceride content in p38αFab-KO brown adipocytes were down-regulated ( S8B , S8C , S8D and S8E Fig ) . In addition , p38αFab-KO brown adipocytes have increased expression of perilipin with no changes in adiponectin , suggesting same differentiation capacity but smaller and more abundant lipid droplets ( S8B Fig ) . On the other hand , p38αFab-KO white adipocytes presented the same in vitro differentiation rate judging by red-oil staining and the expression levels of adipocyte markers such as adiponectin and perilipin ( S8F and S8G Fig ) . However , p38αFab-KO white adipocytes have increased expression of leptin ( S8F Fig ) . To further confirm the autonomous role of p38α in BAT , we crossed p38α loxP mice with UCP1-Cre mice [24] , which express Cre recombinase specifically in the interscapular brown fat at room temperature , generating p38αUCP1-KO mice . In agreement with our previous results , these mice were protected against HFD-induced obesity and presented lower fat mass and increased temperature . Furthermore , they had lower blood glucose levels and partial glucose tolerance , indicating that they were protected against HFD-induced diabetes ( Fig 4A–4F ) . Our data at 23 °C demonstrated that lack of p38α resulted in increased whole-body energy expenditure due to the activation of BAT and iWAT thermogenesis . At this temperature , BAT is already fully differentiated; because it is complicated to detect an even higher level of UCP1 , genetic modifications that up-regulate UCP1 levels cannot be easily detected [25] . For this reason , we therefore evaluated p38αFab-KO phenotype in thermoneutrality ( 30 °C ) because it has been suggested to be more similar to the human situation [25] . At 30 °C , p38αFab-KO mice were also protected against HFD-induced obesity ( Fig 5A ) and presented lower body fat mass and increased BAT thermogenesis ( Fig 5B and 5C ) , indicating that , even at temperatures at which BAT is impeded , these mice maintain BAT activation . In fact , UCP1 expression was much higher in BAT from p38αFab-KO than in the control Fab-Cre mice at 30 °C ( Fig 5D ) . In addition , p38αFab-KO were also protected from HFD-induced diabetes at thermoneutrality ( Fig 5E and 5F ) . Together , these data confirm that lack of p38α protects against HFD-induced obesity and diabetes due to an activation of BAT thermogenesis . To gain insight into the molecular mechanism that might account for increased UCP1 levels and thermogenic capacity , we studied the signalling in the different adipose tissue depots . The p38MAPK pathway has been shown to trigger BAT activation in several models [18 , 26–28] . Additionally , it has been found that p38α can inhibit the other p38 isoforms by a negative feedback loop that blocks the activation of the upstream kinases of this pathway [29] . Therefore , we evaluated the expression and phosphorylation state of the other p38s , with a phospho-p38 antibody that recognises all p38 isoforms [30] . Using adipocytes lacking p38γ/δ , we confirmed that p38α/β run around 38 kDa , while p38γ/δ run higher—around 41 kDa—allowing us to distinguish the phosphorylation of these kinases ( S9A Fig ) . Under ND condition , p38δ and p38γ were hyperactivated in eWAT and iWAT from p38αFab-KO ( S9B Fig ) . In agreement , p38δ/γ were activated more when cells were treated with sorbitol and p38α inhibitor SB203580 ( S9C Fig ) . HFD resulted in reduced RNA expression of all the p38 isoforms in BAT , while in eWAT , only p38δ and p38γ decreased ( S9D Fig ) . p38δ and p38γ were hyperactivated in iWAT and BAT from HFD-fed p38αFab-KO , whereas elevated p38δ ( Mapk13 ) RNA levels were also found in BAT and eWAT from HFD-fed p38αFab-KO animals ( Fig 6A , 6B and S7 , S9E and S9F Figs ) . Activation of p38δ in BAT was diminished when mice were maintained at 30 °C ( Fig 6A ) , suggesting that this p38 isoform might activate BAT thermogenesis . To further evaluate this hypothesis , mice lacking p38δ in adipose tissue ( p38δFab-KO ) were generated . In agreement with the importance of this kinase in BAT activation , p38δFab-KO mice fed with ND presented higher body weight , associated with increased fat mass and weight of all fat depots ( Fig 6C and 6D and S10A Fig ) . In concordance , p38δFab-KO presented reduced energy expenditure , whole-body temperature , and decreased BAT thermogenesis ( Fig 6E and 6F ) as well as lower expression levels of Ucp1 and Ppargc1β in BAT ( S10B Fig ) with no differences in protein kinase A ( PKA ) phosphorylation ( S10C Fig ) . p38δ is activated in BAT upon cold exposure and in adipocytes after stimulation with the thyroid hormone T3 or norepinephrine ( NE ) ( Fig 6G and 6H ) , suggesting that this p38 isoform might activate BAT thermogenesis . In fact , at 4 °C , p38δFab-KO mice have lower body and skin temperature in the BAT region ( Fig 6I ) . Moreover , HFD-fed p38δFab-KO mice were more obese with higher fat mass and weight of all fat depots ( S11A–S11C Fig ) . This increased adiposity correlated with lower BAT thermogenesis and lower UCP1 , Ppargc1a , and Cidea levels in BAT ( S11D–S11F Fig ) . Our data indicated that p38δ was triggering thermogenesis because in vitro–differentiated brown adipocytes lacking p38δ have reduced expression of important genes implicated in BAT thermogenesis ( Ppargc1b , Ppargc1a , Cidea , and Cox8b ) and a slight decrease of Ucp1 and Cox7a1 supporting the cell-autonomous effect of p38δ in BAT thermogenesis ( Fig 7A ) , with no differences in amount of mitochondrial DNA ( Fig 7B and 7C ) . Therefore , we evaluated respiration profiles in brown adipocytes lacking p38α and p38δ . Brown adipocytes lacking p38α presented higher leak respiration after isoproterenol ( ISO ) or NE treatment ( Fig 7D ) . However , this augmented respiration capacity induced by NE or ISO was diminished when p38δ was chemically inhibited by BIRB796 , a known inhibitor p38δ [31] , as well as in p38δ-deficient brown adipocytes ( Fig 7E and 7F ) , supporting the important role of this kinase in brown adipocyte activation . In conclusion , we demonstrated that p38α in BAT inhibits p38δ activation , which in turn regulates BAT thermogenesis , energy expenditure , and body weight . We demonstrated that p38α and p38δ have opposite roles in BAT: whereas p38α inhibits BAT thermogenesis , p38δ induces it upon several physiological stimuli ( Fig 8 ) .
Adipose tissue has become an important target for the treatment of obesity , not only because its dysfunction could be responsible for diabetes development but also because increasing BAT thermogenesis and/or browning of WAT could lead to new therapeutic approaches against obesity [32 , 33] . In this scenario , p38MAPK signalling has been proposed to be a key activator of these processes . Consequently , there is an increasing interest to understand the function of this pathway in the regulation of adipose tissue metabolism , remodelling , and browning . A growing number of studies have defined p38MAPK as one of the main pathways that stimulates browning and BAT thermogenesis [18 , 21–23] . However , using genetically modified mice lacking specific p38 family members in adipose tissue , we have shown that lack of p38α in adipose tissue protects against HFD-induced obesity by increasing energy expenditure through the activation of BAT thermogenesis . Mechanistically , lack of p38α results in hyperactivation of p38δ in BAT together with increased UCP1 expression and higher Creb and AMPK phosphorylation . Negative feedback controls by p38α through the regulation of upstream activators of the pathway—such as TAB1 phosphorylation or MKK6 expression—have been previously reported [29 , 34] . Here , we show that negative control of the pathway by p38α has biological and pathological implications . However , it would be interesting to examine the epistatic relationship between p38α and p38δ genetically in future studies . We also demonstrated that p38δ is activated in BAT by 3 stimuli widely known to activate this tissue: cold exposure , NE , and thyroid hormone treatment [35 , 36] , whereas its phosphorylation is reduced under thermoneutrality conditions . In addition , p38δ expression in BAT was reduced in obese mice , while this down-regulation was ablated in p38αFab-KO mice , suggesting that activation of p38δ in p38αFab-KO mice is responsible for the protection against diet-induced obesity observed in these mice . Indeed , inhibition of p38δ in p38αFab-KO brown adipocytes abolished the increased respiratory capacity induced by β3-adrenergic stimuli . In agreement with the role of p38δ-promoting thermogenesis , mice lacking this kinase in adipose tissue developed overweight , even in ND , and showed decreased whole-body energy expenditure associated with lower temperature and reduced BAT activation . Moreover , we confirmed the cell-autonomous role of p38δ inducing browning using differentiated adipocytes . Our results were completely unexpected because the p38MAPK pathway has been shown to trigger BAT activation in several models [18 , 26–28] , and—until now—it was thought that the only implicated family member was p38α . Moreover , we have recently found that hyperactivation of p38α in MKK6-deficient animals induces browning of eWAT [37] . These finding might indicate opposite effects of p38α in eWAT versus iWAT or BAT . While p38α would activate browning in eWAT—increasing energy expenditure—it would prevent it in iWAT , and it would block thermogenesis through the negative regulation of p38δ in BAT . In agreement with this hypothesis , we observed reduced levels of UCP1 in epididymal fat lacking p38α . In fact , our data from human samples indicated that the p38α mRNA levels in visceral fat directly correlates with UCP1 expression and inversely correlates with the BMI , suggesting that p38α triggers visceral fat browning . We also found that p38α in sWAT inversely correlates with UCP1 . This is in accordance with results observed in mouse models , in which we found a decrease of all p38s after HFD in all fat depots . However , the levels of UCP1 expression in these human fat depots is quite low judging by the low Ct obtained ( higher than 29 ) , and evaluation of UCP1 protein expression in human fat depots would be necessary . Moreover , further studies to determinate the expression of p38 family members and upstream kinases in other human fat depots would help us to understand the role of these kinases in human adipocytes . It has been proposed that p38α induces adipogenesis [38–40] . However , using genetically modified animals , we showed here that lack of p38α in preadipocytes did not affect their differentiation to adipocytes , nor did it affect changes in the differentiation markers evaluated in the major fat depots . This capacity of cells lacking p38α to still differentiate to adipocytes could be due to the hyperactivation of the other members of the family: p38γ and p38δ . In fact , it has been shown that p38 isoforms can compensate for each other [30] . Here , we demonstrated the cell-autonomous and opposite effects of 2 p38 isoforms in adipocytes , p38α and p38δ . The cell-specific actions of p38α in each fat depot could be explained by the specific expression pattern of p38 family members—p38α being the main isoform expressed in eWAT , whereas p38δ or p38γ are abundant in BAT or iWAT . Furthermore , our results suggest a different regulation of p38s expression in adipose tissue during obesity , with only decrease of p38δ and p38γ in eWAT and no effects in p38α or p38β . More studies would be necessary to elucidate the function of p38γ in adipose tissue . We also evaluated the controversial role of p38α in GLUT4 translocation [41–43] . Under ND , insulin-induced GLUT4 translocation was the same in both control and p38αFab-KO mice . However , p38αFab-KO mice maintained the insulin-induced translocation after the HFD , perhaps due to the fact that these animals did not gain weight and were protected against diet-induced insulin resistance . In fact , our data suggest that these mice are more glucose tolerant using a dose of glucose based on their total body weight . Due to the potential clinical implications of these results , it would be necessary to further evaluate the function of each p38 family member in browning to better understand how this pathway controls adipose tissue metabolism . In summary , we have demonstrated that p38α and p38δ in adipose tissue have opposite roles: p38α negatively regulates BAT thermogenesis , energy expenditure , and body weight , while p38δ induces thermogenesis in BAT in response to several physiological stimuli . These results have potential clinical implications because inhibition of p38α or activation of p38δ might be of therapeutic interest against obesity .
This population study was approved by the Ethics Committee of the University Hospital of Salamanca and the Carlos III ( CEI PI 09_2017-v3 ) with the all subjects providing written informed consent to undergo visceral fat biopsy under direct vision during surgery . Data were collected on demographic information ( age , sex , and ethnicity ) , anthropomorphic measurements ( BMI ) , smoking and alcohol history , coexisting medical conditions , and medication use . All animal procedures conformed to EU Directive 86/609/EEC and Recommendation 2007/526/EC regarding the protection of animals used for experimental and other scientific purposes , enacted under Spanish law 1201/2005 . The protocols are CNIC 08/13 and PROEX 49/13 . For the analysis of visceral fat , the study population included 71 patients ( 58 adult patients with BMI ≥35 ) , while for the analysis of sWAT , the study population included 170 patients ( 140 adult patients with BMI ≥35 ) , recruited from patients who underwent elective bariatric surgery at the University Hospital of Salamanca . Patients were excluded if they had a history of alcohol use disorders or excessive alcohol consumption ( >30 g/day in men and >20 g/day in women ) or had chronic hepatitis C or B . Control subjects ( n = 13 for visceral fat study; n = 30 for sWAT study ) were recruited among patients who underwent laparoscopic cholecystectomy for gallstone disease . Before surgery , fasting venous blood samples were collected for measuring complete cell blood count , total bilirubin , aspartate aminotransferase ( AST ) , alanine aminotransferase ( ALT ) , total cholesterol , high-density lipoprotein , low-density lipoprotein , triglycerides , creatinine , glucose , and albumin ( S1 and S2 Tables ) . Mice with a germ-line mutation in Mapk14 ( p38α ) and Mapk13 ( p38δ ) have been reported before [44 , 45] . These animals were crossed with Tg ( Fabp4-cre ) 1Rev/J [46] line or B6 . FVB-Tg ( Ucp1-cre ) 1Evdr/J [24] on the C57BL/6J background ( Jackson Laboratory ) to generate the mice lacking p38α or p38δ in adipose tissue ( both WAT and BAT or just in BAT , respectively ) . All mice were maintained on a C57BL/6J background ( back-crossed 10 generations ) . Genotype was confirmed by PCR analysis of genomic DNA . Mice were fed with an ND or an HFD , Research Diets Inc . ) for 8 weeks ad libitum . For fat expansion measurement , mice were treated with BrdU ( 0 . 4 mg/ml; Sigma ) in the drinking water ( water was refreshed every 3 days ) during the first week of a 6-week HFD . For temperature experiments , mice were housed at 30 °C for 8 weeks while feeding an HFD in case of thermoneutrality analysis . Mice were exposed to 4 °C for 1 hour , 1 day , or 1 week in case of cold adaptation studies . Immortalised and primary brown preadipocytes from WT , Fab-Cre , p38αFab-KO , and p38δ-KO mice were differentiated to brown adipocytes in 10% FCS medium supplemented with 20 nM insulin , 1 nM T3 , 125 μM indomethacin , 2 μg/ml dexamethasone , and 50 mM IBMX for 48 hours and maintained with 20 nM of insulin and 1 nM of T3 for 8 days . For some experiments , cultures were incubated with 100 nM T3 for 48 hours before extraction . Immortalised white preadipocytes from Fab-Cre and p38αFab-KO mice were differentiated to adipocytes for 9 days in 8% FCS medium supplemented with 5 μg/ml insulin , 25 μg/ml IBMX , 1 μg/ml dexamethasone , and 1 μM troglitazone . For some experiments , cultures were incubated with 1 μM NE for 1 hour before extraction . Primary brown preadipocytes were plated and differentiated in gelatin-coated ( 0 . 1% ) 96 seahorse plates . MitoStress oxygen consumption rate ( OCR ) was assessed in XF medium containing 25 mM glucose , 2 mM L-glutamine , and 1 mM sodium pyruvate using a XF-96 Extracellular Flux Analyzers ( Seahorse Bioscience , Agilent Technologies ) . Cells were stimulated with following drugs: NE or ISO , oligomycin , FCCP , and antimycin A plus rotenone ( 1 μM finally; all from Sigma Aldrich ) . The protocol for the all drugs followed a 3-minute mix , 2-minute wait , and 3-minute measure cycle that was repeated 3 times . After the analysis , data were normalised to protein level assessed by Bradford quantification . Basal Respiration Capacity ( OCR basal − OCR nonmitochondrial ) and oxygen consumption in response to NE ( OCR NE − OCR basal ) or ISO ( OCR ISO–OCR basal ) were calculated . For some experiments , cultures were pretreated with 10 μM BIRB796 for 1 hour . Samples were lysed with RIPA buffer containing protease and phosphatase inhibitors ( Tris-Hcl 50 mM [pH 7 . 5]; Triton X-100 1%; EDTA 1 mM [pH 8]; EGTA 1 mM; NaF 50 mM; β-glycerophosphate-Na 1 mM; sodium pirophosphate 5 mM; orthovanadate-Na 1 mM; sucrose 0 . 27 M; PMSF 0 . 1 mM; β-mercaptoethanol 1 mM; aprotinin 10 μg/ml; leupeptin 5 μg/ml ) . Lysates were separated by SDS-PAGE and incubated with antibodies diluted 1/1 , 000 against P-Akt308 ( Cell Signaling , 9275s ) , P-Akt473 ( Cell Signaling , 9271s ) , Akt ( Cell Signaling , 9272s ) , UCP1 ( Abcam , AB10983 ) , P-ATF2 ( Cell Signaling , 9225s ) , ATF2 ( Cell Signaling , 9226s ) , P-CREB ( Cell Signaling , 9198 ) , CREB ( Cell Signaling , 4820s ) , P-p38 ( Cell Signaling , 9211s ) —which recognises the phosphorylation in the activation sites of all the p38 isoforms—p38α ( Santa Cruz , sc-535 ) , P-AMPKα ( Cell Signaling , 2531s ) , AMPKα ( Cell Signaling , 2603s ) , P-ACC ( Cell Signaling , 3661s ) , ACC ( Cell Signaling , 3676s ) , PGC1α ( Santa Cruz , sc13067 ) , GAPDH ( Santa Cruz , sc25778 ) , tubulin ( Sigma , T6199 ) , and vinculin ( Sigma , V9131 ) , followed by an incubation with a secondary antibody conjugated with HRP . Reactive bands were detected by chemiluminescence and quantified by Image J software . Specificity of UCP1 antibody was evaluated using brown and eWAT from UCP1 KO animals [47] . For the immunoprecipitation assay , cell extracts were incubated with 4 μg of anti-p38 delta coupled with protein-G-Sepharose . After an overnight incubation at 4°C , the captured proteins were centrifuged at 10 , 000 g , the supernatants discarded , and the beads washed 4 times in lysis buffer . Beads were boiled for 5 minutes at 95 °C in 10 μl sample buffer . The antibodies employed were anti-phospho p38 and anti-p38δ ( Santa Cruz , sc7585 ) . Immune complexes were detected by enhanced chemiluminescence ( NEN ) . Mouse bone marrow ( BM ) and spleens were collected , and single-cell suspension was obtained . Erythrocytes were lysed with a red cell lysis buffer incubation for 3 minutes on ice . Spleen samples were enriched using CD3 ( BioLegend 79751 clone 145-2C11 ) and B220 ( BioLegend 79752 clone RA3-6B2 ) biotinylated antibodies and magnetic Dynabeads Myone streptavidin T1 ( invitrogen ) . Myeloid cells from spleen were labelled by surface staining with FITC-conjugated CD11b ( BioLegend 79749 clone M1/70 ) , PE-conjugated Gr1 ( Ly6G/Ly6C ) ( BDBioscience 79750 clone RB6-8C5 ) , and APC-conjugated F4/80 ( eBiosciences 25-4801-82 clone BM8 ) antibodies , and myeloid cells from BM were labelled by FITC-conjugated Gr1 ( Ly6G/Ly6C ) ( Invitrogen 11-5931-82 clone RB6-8C5 ) , PE-conjugated CD115 ( eBioscience 12-1152-82 clone AFS98 ) , and APC-conjugated F4/80 ( eBiosciences 25-4801-82 clone BM8 ) antibodies . Nuclei were stained with DAPI . Cells were sorted with a fluorescence-assisted cell sorting ( FACS ) Aria ( BD ) as follows: spleen macrophages ( Gr1− Cd11bmedium F4/80+ ) , spleen neutrophils ( Gr1high Cd11b+ ) , and BM monocytes ( CD115+ F4/80− ) . Isolated myeloid cells were lysed and analysed by western blot . Overnight-starved mice were injected intraperitoneally with 1 g/kg of body weight of glucose , and blood glucose levels were quantified with an Ascensia Breeze 2 glucose meter at 0 , 15 , 30 , 60 , 90 , and 120 minutes post injection . Alternatively , GTT was performed injecting intraperitoneally 1 g/kg of lean mass of glucose . ITT was performed by injecting intraperitoneally 0 . 75 IU/kg of insulin at mice starved for 1 hour and detecting blood glucose levels with a glucometer at 0 , 15 , 30 , 60 , 90 , and 120 minutes post injection . Energy expenditure , respiratory exchange , and food intake were quantified using the indirect calorimetry system ( TSE LabMaster , TSE Systems , Germany ) for 3 days . Body temperature was detected by a rectal thermometer ( AZ 8851 K/J/T Handheld Digital Thermometer-Single , AZ Instruments Corp . , Taiwan ) . BAT-adjacent interscapular temperature was quantified by thermographic images using a FLIR T430sc Infrared Camera ( FLIR Systems , Inc . , Wilsonville , OR ) and analysed through FlirIR software . Body , fat , and lean mass were quantified by nuclear magnetic resonance ( Whole Body Composition Analyzer; EchoMRI , Houston , TX ) and analysed by ImageJ software . Blood triglyceride content was quantified using a Dimension RxL Max analyser ( Siemens ) . For triglyceride analysis in cells , brown adipocyte cultures were lysed in isopropanol , centrifuged at 10 , 000 g for 15 minutes at 4 °C , and triglycerides were detected in the supernatant with a commercial kit ( Sigma ) . Brown adipocyte cells were scraped in PBS and pellet lysed in TNES buffer supplemented with Proteinase K ( 20 mg/ml ) overnight at 55 °C . Reaction was stopped with sodium chloride 6 M and samples centrifuged 5 minutes at 13 , 000 g . DNA was precipitated in supernatants with 100% ethanol and washed with 70% ethanol . After drying , DNA was resuspended in DNase free water , quantified , and analysed by RT-PCR . Mitochondrial DNA was detected using primers for COII and nuclear DNA , using primers for Sdh1 ( S3 Table ) . RNA 500ng—extracted with RNeasy Plus Mini kit ( Quiagen ) following manufacturer instructions—was transcribed to cDNA , and qRT-PCR was performed using Fast Sybr Green probe ( Applied Biosystems ) and the appropriated primers in the 7900 Fast Real Time thermocycler ( Applied Biosystems ) . Relative mRNA expression was normalised to Gapdh mRNA measured in each sample . Primers used are listed in S3 Table . Fresh livers , brown , and epididymal white fat were fixed with formalin 10% , included in paraffin , and cut in 5 μm slides followed by a haematoxylin–eosin staining . Fat droplets were detected by oil red staining ( 0 . 7% in propylenglycol ) in 8 mm slides included in OCT compound ( Tissue-Tek ) and in differentiated brown and white adipocytes . Brown adipocytes were stained with Mito Tracker Deep Red ( Invitrogen ) and Bodipy ( Invitrogen ) . Images were captured using Leica SPE confocal microcope ( Leica Microsystems , Wetzlar , Germany ) . For UCP1 immunostaining , brown and epididymal white fat were fixed with formalin 10% , included in paraffin , cut in 5 μm slides , and sequentially stained with a UCP1 antibody ( 1/500 , Abcam , AB10983 ) , a biotinylated goat anti-rabbit secondary antibody ( 1/500 , Jackson Immuno Research Laboratories ) , a streptavidin-conjugated ABC complex ( Vector Laboratories ) , and the substrate 3 , 3’-diaminobenzidene conjugated with horseradish peroxidase ( Vector Laboratories ) , followed by brief counterstaining with Nuclear Fast Red haematoxylin ( Sigma ) . For immunofluorescence analysis , the 5 μm tissue sections were deparaffinised and rehydrated , followed by antigen retrieval in 10 mM sodium citrate ( pH 6 . 0 ) under pressure in a CertoClav EL ( CertoClav Sterilizer GmbH ) . For BrdU staining , sections were treated with DNase 30 minutes at 37 °C . Blocking and staining was performed in 5% BSA in PBS . Sections were incubated in primary antibodies including rat-anti-Ki67 ( eBioscience , 14-5698-82; clone:SolA15 ) ( 1:100 ) , rabbit-anti-GLUT4 ( Abcam , ab654 ) ( 1:1000 ) , mouse-anti-Caveolin-1 ( Sigma , SAB4200216 ) ( 1:500 ) , rat anti-BrdU ( Abcam , Ab6326; clone: BU1/75 [ICR1] ) ( 1:200 ) , and rabbit anti-Perilipin ( Cell Signaling , 9349; clone: D1D8 ) ( 1:400 ) overnight at 4 °C . Secondary antibodies including goat anti-rabbit-A488 , goat anti-rat-A647 , and chicken anti-mouse-A647—all used at 1:500—were purchased from Molecular Probes and incubated with tissue for 1 hour at room temperature . Nuclei were stained with DAPI , and slides were mounted with Vectashield mounting medium ( Vector Laboratories ) and examined using SP5 multi-line inverted confocal microscope . Several confocal images of each tissue section were acquired and analysed for the translocation of GLUT4 or the presence of Ki67 or BrdU in adipocyte nuclei . BAT and WAT cellularity were quantified using Fiji software . Adipocyte nuclei were identified by their location inside adipocyte membranes as described [23] . Results are expressed as mean ± SEM . Statistical analysis was evaluated by student t test and 2-way ANOVA coupled with Bonferroni’s post-tests with values of p < 0 . 05 considered significant . When variances were different , Welch’s test was used . For human studies , variables were compared by means of Mann-Whitney U test or χ2 test . | Accumulation of fat in adipose tissue is essential to store energy and insulate the body; however , excessive body fat leads to obesity . Of the 2 existing types of adipose tissue , white adipose tissue ( WAT ) stores energy , whereas brown adipose tissue ( BAT ) can produce heat . Activation of BAT and transformation of WAT into brown-like ‘brite/beige’ adipocytes have recently emerged as novel strategies against obesity . The uncoupling protein 1 ( UCP1 ) is a hallmark of BAT and is responsible for triggering these 2 processes under the regulation of the p38 MAP kinase ( p38MAPK ) pathway , but the underlying mechanisms remain unknown . Here , we have analysed this process in detail and demonstrate that a protein kinase called p38α directly correlates with UCP1 levels in human adipose tissue , while it inversely correlates with body mass index ( BMI ) . We find that mice lacking p38α in adipose tissue are protected against diet-induced obesity due to increased body temperature . In addition , another p38 family member , p38δ , is activated in these adipocytes lacking p38α and reduces their thermogenic capacity . Our results suggest that these 2 members of the p38 family have opposite roles in controlling thermogenesis . | [
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] | 2018 | p38α blocks brown adipose tissue thermogenesis through p38δ inhibition |
Here , we have investigated the possible effect of B-1 cells on the activity of peritoneal macrophages in E . cuniculi infection . In the presence of B-1 cells , peritoneal macrophages had an M1 profile with showed increased phagocytic capacity and index , associated with the intense microbicidal activity and a higher percentage of apoptotic death . The absence of B-1 cells was associated with a predominance of the M2 macrophages , reduced phagocytic capacity and index and microbicidal activity , increased pro-inflammatory and anti-inflammatory cytokines production , and higher percentual of necrosis death . In addition , in the M2 macrophages , spore of phagocytic E . cuniculi with polar tubular extrusion was observed , which is an important mechanism of evasion of the immune response . The results showed the importance of B-1 cells in the modulation of macrophage function against E . cuniculi infection , increasing microbicidal activity , and reducing the fungal mechanisms involved in the evasion of the immune response .
Microsporidia are obligate intracellular spore-forming microorganisms that can infect a wide range of vertebrate and invertebrate species . These fungi have been recognized as human pathogens and are particularly harmful to immunodeficient patients infected with HIV . Since then , interest among researchers of in vitro culture techniques has increased , with more people studying their biology and immune response against them [1] . Encephalitozoon cuniculi is one of the most common microsporidian species , in humans or animals . It is considered to be an emerging zoonotic and opportunistic pathogen in immunocompromised as well as immunocompetent individuals [2] . Spores of E . cuniculi can survive in macrophages , spread throughout the host , and cause lesions in organs of the urinary , digestive , respiratory , and nervous systems [3] . The adaptive immune response is critical for the elimination of E . cuniculi , but the innate immune response forms the first line of defense against these pathogens . The infection by E . cuniculi induces CD8+ cytotoxic T lymphocyte ( CTL ) response , which lyses the infected cells by perforin-dependent mechanisms [1] . Although antibody response during E . cuniculi infection has been recorded , it is clearly not sufficient to prevent mortality or cure the infection , making cell-mediated immunity critical for the survival of host infected by E . cuniculi [4] . The survival and replication of certain species of microsporidia within macrophages may be associated with the absence of phagosome-lysosome fusion [5] . Internalized microsporidium spores are normally destroyed within macrophages by the toxic activity of reactive oxygen and nitrogen species produced by the respiratory burst , and cytokines released by macrophages may be important in the protection against microsporidia [6] . B-1 cells are a subtype of B cells that account for 35%-70% of the B cells in the peritoneal cavity of mice [7] . They differ from B-2 cells in the expression of surface markers and function [8] . B-1 cells act as antigen-presenting cells , phagocytes , expressing myeloid ( CD11b ) and lymphoid markers ( CD45/B220 , CD5 , CD19 and IgM ) , but not CD23 , unlike B-2 cells [9] . The main function of B-1 cells in the innate immune system is the spontaneous secretion of natural antibodies , thereby maintaining immunoglobulin levels in the body without any stimulus or immunization [10] . In addition , B-1 cells also spontaneously secrete IL-10 , while GM-CSF and IL-3 are secreted after lipopolysaccharide stimulation [10] . B-1 cells also regulate acute and chronic inflammatory diseases through the production of several immunomodulatory molecules , such as interleukin-10 ( IL-10 ) , adenosine , granulocyte-macrophage colony-stimulating factor ( GM-CSF ) , IL-13 , and IL-35 , in the presence or absence of stimulus [11] . The possible role of B-1 cells in the dynamics of the inflammatory process of various etiologies is unknown and researchers have demonstrated the role of these cells in the functional regulation of macrophages . Also , B-1 cells are able to differentiate into phagocytes ( B-1CDP ) , characterized by the expression of F4/80 and increased phagocytic activity [12 , 13] . Furthermore , Popi et al . [14] demonstrated that BALB/c mice were more susceptible to Paracoccidioides brasiliensis infection compared to XID ( B-1 cell deficient ) mice and attributed the down-regulation of macrophage function to IL-10 secreted by B-1 cells . In recent studies from our group , we demonstrated that B-1 cell deficient XID mice were more susceptible to experimental encephalitozoonosis than BALB/c mice , evidenced histologically with more prominent inflammatory lesions and fungal burden [15 , 16] . Although the mechanism of action of B-1 cells in the resistance of BALB/c mice to encephalitozoonosis is not fully understood , a significant increase in the population of peritoneal macrophages was reported in BALB/c mice infected with E . cuniculi [15] . We hypothesized that B-1 cells from the peritoneal cavity ( PerC ) could differentiate in infected animals into B-1 cell-derived phagocytes ( B-1 CDP ) , which could then promote the phagocytosis of E . cuniculi spores and also influence the macrophage function in this context . Herein , we tested this hypothesis using the ultrastructure and phenotypic analysis of adherent peritoneal cells ( APerC ) to evaluate their in vitro behavior in BALB/c and B-1 cell-deficient XID mice against the fungus E . cuniculi . The presence of B-1 cells facilitated the phagocytic and microbicidal activity and increased apoptosis in the APerC cultures . These cells were associated with the presence of M1 macrophages with increased proinflammatory cytokine production . Electron micrographs showed an intimate physical relationship between B-1 cells and macrophages , indicating communication and modulation of activity , demonstrating that the presence of B-1 cells drives the behavior of macrophages and consequently the innate immune response in encephalitozoonosis .
To assess the influence of B-1 cells on the activity of macrophages in E . cuniculi infection , APerC obtained from PerC of B-1 cell-containing BALB/c and B-1 cell-deficient XID mice were co-infected with E . cuniculi for ultrastructural analyses at 1 , 48 , and 144 h . The phagocytosis of spores occurred in cell cultures of both BALB/c and XID APerC groups , characterized by projections of the cellular membrane of macrophages near or around the spores ( Fig 1A and 1B ) , and remained within phagosome vacuoles that were dispersed throughout the cytoplasm . XID macrophages showed large numbers of vacuoles in the cytoplasm and several membrane projections ( pseudopods ) , indicating widespread and strong phagocytic activity ( Fig 1A ) . Intact and degenerating internalized spores surrounded by a vacuolar membrane , which is a typical phagosome vacuole , were observed ( Fig 1C ) . Under normal circumstances , the interaction of phagosomes with each other and with other organelles is tightly regulated , and it is well documented that phagosomes containing inert particles are not subjected to homotypic fusion [17] . The important finding of this study is the formation of megasomes by the fusion of homotypic phagosomes containing a single E . cuniculi spore ( Fig 1D ) . Therefore , the ability of E . cuniculi to induce the fusion of phagosomes resembles that of Helicobacter pylori [18] and Chlamydia trachomatis [19] . In AperC cultures of both BALB/c and XID mice , the microbicidal activity was identified by the presence of a large amount of amorphous and electron-dense material either inside the phagocytic vacuoles and megasome in the cytoplasm of macrophages or undergoing exocytosis ( Fig 1B and 1D ) . The same findings were seen at 48 h ( Fig 2A and 2D ) with an increase in the ratio of degenerated spores , and at 144 h with only a few intact spores inside the phagocytic cells in case of XID APerC ( S1A and S1B Fig ) . Myelin figures indicating spore degeneration were recorded ( Fig 2B ) . Another important finding was the presence of degenerating macrophages ( Fig 2D ) . At 144 h , the APerC of BALB/c showed an absence of macrophages and mature spores of E . cuniculi outside the cells ( S1C and S1D Fig ) and degenerated lymphocytes ( S1D Fig ) . The intracellular proliferative stages of E . cuniculi ( meronts , sporonts , or sporoblast ) were not observed . The B-1 cells were identified in BALB/c APerC by their ultrastructural morphological characteristics such as a lobed nucleus with bridges of the nuclear membrane joining the lobules and a well-developed endoplasmic reticulum with few mitochondria [20] ( Fig 2E ) . We also observed the presence of cytoplasmic projections of pre-B-1 CDP cells and B-1 lymphocytes adhered to or near extracellular E . cuniculi spores in BALB/c APerC ( Fig 2E and insert ) . Intercellular communication involving other lymphocytes and mast cells and macrophages in BALB/c ( Fig 1B ) and XID APerC ( Fig 1D ) . Using Calcofluor stain , we observed increased phagocytic capacity and index in BALB/c APerC compared to XID APerC ( Fig 3 ) , suggesting the involvement of B-1 cells in the increase in phagocytic activity . After 1 h , large numbers of preserved spores were observed within the macrophages from BALB/c and XID , but after 48 h , there were fewer intact spores within the phagocytic vacuoles from BALB/c APerC , while the spores remained preserved in XID APerC . As the ultrastructural analysis revealed that the BALB/c APerC phagocytes were no longer viable after 144 h , we evaluated the level of apoptosis and necrosis after 48 and 96 h for all experimental groups in order to examine the possible influence of E . cuniculi in the process of cell death ( Fig 4 ) . Given the abundance of cell death accompanying intracellular infection , two possibilities have been described as evasion mechanisms: 1 ) the pathogen can be destroyed along with the engulfed apoptotic cell ( host antimicrobial activity ) or 2 ) the pathogen can use the process of efferocytosis to disperse into new cellular hosts ( “Trojan Horse” model ) [21] . About 50–60% of cell death was observed in BALB/c AperC , which was higher than that in XID APerC ( 30–40% ) after 48 h ( Fig 4A ) . At 96 h , the percentage of the death of uninfected BALB/c and XID APerC was similar ( about 70% ) , but a 40 to 50% reduction in the rate of cell death in the infected groups was evident ( Fig 4A ) . In addition , after the infection , the BALB/c and XID APerC behaviors were antagonistic and the rate of apoptosis identified in the infected BALB/c APerC was higher than that observed in XID APerC at both the time intervals ( Fig 4B ) , and infected XID APerC had more necrosis than the other groups ( Fig 4C ) . Similar to our findings , Martin et al . [21] reported that M . tuberculosis-infected macrophages that die by apoptosis are rapidly engulfed by uninfected macrophages via efferocytosis and this process is responsible for the bactericidal effect associated with apoptosis . The spores that were internalized by macrophages in XID APerC cultures had a contact area between the phagocytic vacuolar membrane of the cell and the spore wall ( Fig 5A and insert ) , suggesting intimate contact with the phagocytic cells . Furthermore , we observed rarely extrusion of the polar filament by an internalized spore from the phagosome vacuole at 1 h ( Fig 5B and insert ) . The ejection of the polar filament from the phagosome into the cell has been reported in the literature [1] , but has not been demonstrated by ultrastructure analysis . This finding suggests an evasion of the microbicidal activity . Therefore , we hypothesize that the absence of B-1 cells could direct the macrophages to M2 profile and favor the dissemination of E . cuniculi . To test the influence of B-1 cells in trigger macrophage profile , we evaluated the expression of CD40 and CD206 in macrophages from BALB/c and XID APerC cultures , which may suggest the classical activation—M1 profile ( CD40+high CD206+low ) or the alternative activation—M2 profile ( CD206+high CD40+low ) process . We also analyzed the mean fluorescence intensity of CD80 and CD86 co-stimulatory molecules to measure the activation of these macrophages and aid in the characterization of M1/M2 . The proportion of macrophages in XID APerC ( approximately 60% ) was significantly higher than that in BALB/c APerC ( approximately 20% ) , regardless of microsporidial infection ( Fig 6A ) . Since the BALB/c culture also contains B-1 cells that adhere together with the macrophages in the first incubation [15 , 16] , this result was expected . After infection , the percentage of macrophages in XID APerC was 80% , which is higher than that observed in uninfected cultures , both at 48 and 96 h ( Fig 6A ) . In BALB/c APerC , no difference was observed in this parameter . At 48 h , approximately 80% of the BALB/c APerC macrophages expressed CD40+ while approximately 40% of XID APerC macrophages expressed CD40+ ( Fig 6B ) . At 96 h , the percentage of these cells decreased to about 60% of CD40+ cells in infected BALB/c AperC and about 40% in infected XID APerC . At 48 h , the CD206+ expression on macrophages was higher in XID APerC than in BALB/c APerC ( Fig 6C ) . At 96 h , there was no difference between the groups . We also analyzed the mean fluorescence intensity ( MFI ) ratio of CD40 to CD206 molecules and observed a higher ratio in the infected BALB/c than in XID ( Fig 6D ) , confirming the predominance of CD40 expression in BALB/c APerC . In addition , we observed that infected BALB/c APerC macrophages expressed more CD80 and CD86 costimulatory molecules ( Fig 6F ) than macrophages from the other groups . These molecules are expressed on the surface of activated macrophages , especially M1 macrophages , which present a higher level of expression . These results together indicate a higher proportion of M1 profile macrophages in BALB/c APerC and M2 profile in XID APerC . The prevalence of E . cuniculi infection reinforces these profiles . In general , both the pro-inflammatory cytokines ( TNF-α and MCP1 ) and the anti-inflammatory cytokines IL-6 and IL-10 increased 1 hour of infection in the XID infect APerC ( Fig 7 ) . While the pro-inflammatory cytokines TNF-α and MCP1 showed no difference in relation to the BALB/c groups , the levels of IL-6 and IL-10 in XID APerC infected were about 4-fold higher than that observed in BALB/c APerC infected . At 30 min , only IL-10 levels were significantly higher in XID than BALB/c , in infected groups . At 48 h , XID APerC infected still had higher levels of cytokines ( except TNF-α ) than BALB/c APerC infected . At 96 hours , low or undetectable levels of cytokines were observed in all groups . It was previously demonstrated that B-1 cells differentiate to acquire a mononuclear phagocyte phenotype following attachment to a substrate in vitro , which are named B-1 cell-derived phagocytes ( B-1CDPs ) [22 , 23] . It has already been demonstrated that these cells are able to phagocytose various pathogens in vivo and in vitro [23 , 24 , 25 , 26] . We used B-1 CDP cultures to evaluate the participation of B-1-derived phagocytes in encephalitozoonosis . In B-1 CDP cultures , we observed B-1 CDPs and B-1 cells with preserved characteristics after 1 h and 48 h ( Fig 8 ) , indicating that a part of the B-1 cells had become phagocytes . Mussalem et al . [27] demonstrated that infection with Propionibacterium acnes induced the commitment of B-1 cells to the myeloid lineage and their differentiation into phagocytes . We observed the contact of spores with B-1 cells and pre-B-1-CDPs ( Fig 8A ) . B-1 CDPs with spores of E . cuniculi in the process of lysis within phagocytic vacuoles , amorphous material in megasomes ( Fig 8B ) , and intact spores were also visualized in the B-1 CDP cultures ( Fig 8C ) . Other authors also demonstrated the phagocytic and microbicidal ability of peritoneal B-1 cells [28 , 29] . The ultrastructure of E . cuniculi spores was typical of non-germinated mature spores and showed of a thick wall composed of an electron-dense outer layer ( exospore ) , an electron-lucent inner layer ( endospore ) , and a plasma membrane enclosing the cytoplasm ( Fig 8C ) . B-1 cells and pre-B-1 CDP have abundant microvesicles in their membranes ( Fig 8D and 8E ) , indicating exocytosis . Exosomes are nanosized membrane microvesicles that have the capability to communicate intercellularly and to transport cell components [30] . Thus , here we identified two forms of intercellular communication through contact and release of extracellular vesicles ( Fig 8A and 8E , respectively ) . In B-1 CDP cultures , the percentage of dead cells increased at 96 h , as a result of necrosis and apoptosis , regardless of the infection ( Fig 9A ) . Spores outside the cells were identified under light microscopy and low index of phagocytosis was determined after one hour with an increasing trend across the time intervals ( Fig 9B ) . We observed a considerable increase in the pro-inflammatory cytokine TNF-α at 30 min and 1 h in the infected cultures and an increase in MCP1 at 48 and 96 hours ( Fig 9C ) .
The role of B-1 cells in immunity against fungal , protozoan , bacterial , and helminthic infections has been described by us as well as by other researchers . Popi et al . [14] demonstrated that BALB/c mice were more susceptible to experimental infection by Paracoccidioides brasiliensis than XID mice , suggesting that B-1 cells may favor infection . This was consistent with other experimentally induced infections , such as Mycobacterium bovis bacillus Calmette-Guerin ( BCG ) [31] and Trypanosoma cruzi [32] . B-1 cells secrete IL-10 and use it as an autocrine growth factor . Popi et al . [12] demonstrated in vitro that this cytokine decreases the production of nitric oxide and hydrogen peroxide by macrophages , which decreases their phagocytic capacity , compromising the innate immune response and antigenic presentation . Nevertheless , B-1 cells are critical for the early control of infections with encapsulated bacteria such as Streptococcus pneumoniae [33] , viruses such as the Influenza virus [34] , or fungi [35 , 36] . In line with these studies , our group demonstrated that XID mice were more susceptible to encephalitozoonosis than BALB/c mice , suggesting that the pathogenicity caused by E . cuniculi depends on the relationship between the multiplication of parasites and the host immune response [15 , 16] . In addition , da Costa et al . [15] showed that BALB/c mice infected with E . cuniculi showed an increase in the number of macrophages and plasma levels of IFN-γ , which was responsible for the activation of macrophages and elimination of the pathogens . Confirming this , here we demonstrated in vitro that B-1 cells upregulated the macrophage activity against E . cuniculi , characterized by higher phagocytic index and microbicidal capacity , as well as increased macrophage death by apoptosis . The presence of intimate contact between B-1 cells and macrophages suggested communication between these cells and modulation of activity . In addition , B-1 CDP also showed intense phagocytic and microbicidal activities , a fact that may explain the numerical increase of macrophages in BALB/c mice , as previously described by our group [15] . B-1 cells from the peritoneal cavity of mice or cultures of adherent peritoneal cells can be clearly identified on the basis of their distinct morphology and cell surface phenotype . The main morphological characteristic of these cells is the formation of bridges of the nuclear membrane , suggesting a lobular organization of the nucleus . In addition , B-1 cells are characterized by small membrane projections and a large number of ribosomes , a predominance of euchromatin in B-1 cell nuclei , and more condensed chromatin in the nuclear periphery [20] . In this study , we identified B-1 cells by TEM in BALB/c APerC and B-1 CDP cultures . The B-1 CDPs decrease the expression of immunoglobulin M ( IgM ) but retain the expression of heavy-chain gene-variable VH11 or VH12 , an immunoglobulin gene rearrangement that is predominantly expressed by B-1 cells [23] . The maintenance of lymphoid characteristics in B-1 CDPs is the characteristic of a unique type of phagocyte that is not related to monocyte-derived macrophages . Cell-to-cell communication is required to guarantee proper coordination among different cell types within tissues . Studies have suggested that cells may also communicate by circular membrane fragments called extracellular vesicle that are released from the endosomal compartment as exosomes or shed off from the surface membranes of most cell types [37] . In this study , the identified phagocytic cells of B-1 CDP cultures had abundant extracellular vesicles in their membranes , indicating cell communication . In addition , we observed cells in the process of communicating by cell membrane projections ( pseudopodia ) or adhered cell-to-cell membrane between different types of cells in BALB/c APerC . The plasticity of macrophages has been demonstrated over the last few years . Depending on the stimulus received by macrophages ( pathogens or injured tissue ) , their receptors trigger a decision to kill ( M1 ) or repair ( M2 ) [38] . The data from transcriptome analysis demonstrated the existence of distinct polarization phenotypes of macrophages associated with specific pathological conditions [39 , 40] . It is widely accepted that the phenotype of macrophages reflects the immediate microenvironment , wherein other cells can participate in this process . In addition , the role of B-1 cells in the polarization of peritoneal macrophages to an M2 profile in tumor condition was proposed by Wong et al . [41] . In contrast , we demonstrated that macrophages from BALB/c APerC present M1 profile ( CD40 high CD206 lowCD80/86 high ) while macrophages from XID APerC present M2 profile ( CD206 high CD40lowCD80/86low ) . These results may partly explain the susceptibility of XID mice to encephalitozoonosis when compared to BALB/c mice , as previously demonstrated by our group [15] . As demonstrated in this study , M1 macrophages possess intense phagocytic activity . The presence of megasomes , amorphous material , degenerated spores , and myelin figures within phagocytes in BALB/c APerC confirmed the high microbicidal potential of these macrophages . Furthermore , the expression of CD80 and CD86 produces a second signal necessary for the proliferation and activation of T lymphocytes , a fact that demonstrates the importance of innate response in the production of acquired effective response against microsporidia . APerC infected with E . cuniculi showed higher expression of these molecules . M2 macrophages produce ornithine that promotes proliferation and tissue repair , increasing levels of TGF-β , IL-10 , IFN , chitinases , matrix metalloproteinases , scavenger receptors , and have a poor microbicidal function [38 , 42] . Herein , apart from the M2 profile phenotypically associated with XID APerC macrophages , we observed a lower rate of phagocytosis and delayed microbicidal activity associated with a balance in the production of pro- and anti-inflammatory cytokines , a phenomenon linked with the absence of B-1 cells and E . cuniculi infection . Th2 cytokines have been demonstrated in E . cuniculi infection [15 , 43 , 44] and an increase in the mRNA for IL-10 was observed in the splenocytes of infected animals [43] . This cytokine has been reported to be involved in the regulation of Th1 immune response against Toxoplasma gondii [45] and possibly has a similar role in E . cuniculi infection . In an in vitro study on cell cultures of human macrophages incubated with E . cuniculi spores , an increase in the levels of IL-10 was observed in supernatants of infected cultures . Our findings demonstrated an increase in levels of IL-10 in XID group after 30 minutes and 1 hour of E . cuniculi infection , indicating an anti-inflammatory profile . IL-10 is an anti-inflammatory cytokine . During infection it inhibits the activity of Th1 cells , NK cells , and macrophages , all of which are required for optimal pathogen clearance but also contribute to tissue damage [46] . Macrophages are potent antimicrobial effector cells that can participate in both pro-inflammatory ( classical M1 ) and fibrotic ( alternatively activated M2 ) responses [38 , 47] . Consequently , it is not surprising that pathogens like E . cuniculi have evolved mechanisms to subvert macrophage function or that macrophages are a major source of IL-10 during infection , but in this case it is emphasized that the same occurred in the absence of the B-1 cell in XID group . IL-6 is a pleiotropic cytokine that mediates several biological functions , including regulation of the immune system by anti-inflammatory and pro-inflammatory production [48] . In a previous study , we demonstrated that diabetes mellitus ( DM ) increased the susceptibility of C57BL/6 mice to encephalitozoonosis and DM mice infected with E . cuniculi showed higher levels of IL-6 than DM-uninfected mice , suggesting that DM may also modulate a pro-inflammatory state of the organism [49] . In the current study , we observed an increase in IL-6- in XID APerC after 1 h and 48 h of cultures infection , associated with an increase in the production of IL-10 , a fact that suggests an anti-inflammatory effect of IL-6 corroborating the descriptions referring to its pleiotropic behavior , sometimes as a pro-inflammatory cytokine and sometimes as an anti-inflammatory cytokine . In this study , pro-inflammatory cytokine productions also showed an upward trend in Infected XID , suggesting a balance between the production of anti-inflammatory ( IL-10 and IL-6 ) and pro-inflammatory cytokines ( TNF and MCP1 ) , however with predominance of the anti-inflammatory profile characteristic of M2 macrophages . Chemokines are a group of small molecules that regulate cell trafficking of leukocytes . They mainly act on monocytes , lymphocytes , neutrophils , and eosinophils , and play an important role in host defense mechanisms [50] . MCP-1 , also known as CCL2 , was the first human chemokine to be characterized [51] . This molecule attracts cells of the monocyte lineage , including macrophages , monocytes , and microglia [52] . Mice deficient in MCP-1 were reported to be incapable of effectively recruiting monocytes in response to an inflammatory stimulus , despite the presence of normal numbers of circulating leukocytes [53] . Chemokine production has been documented to be induced by microsporidian infections in human macrophages . A primary human macrophage culture from peripheral blood mononuclear cells was infected with E . cuniculi , revealing the involvement of several chemokines , including MCP-1 , in the inflammatory responses [54] . The results showed that in B-1 CDP cultures an increase in MCP1 production occurs at 48 and 96 hours , indicating that the presence of B-1 cells may favor the recruitment of macrophages and the targeting for an M1 phenotype . The infection process of E . cuniculi involves the forced eversion of a coiled hollow polar filament that pierces the host cell membrane , allowing the passage of infectious sporoplasm into the host cell cytoplasm . If a spore is phagocytosed by a host cell , germination will occur and the polar tube can pierce the phagocytic vacuole , delivering the sporoplasm into the host cell cytoplasm [1] . In XID APerC , we observed the extrusion of the polar filaments of intracellular mature spores from the phagolysosomes at 1 h , suggesting that spores may germinate after phagocytosis and escape . This extrusion of the polar tubule has been described in the literature by immunofluorescence [55] . Herein , we showed ultra-micrographic of this process . These findings indicate an intimate relationship between spores and the phagocytic vacuole , suggesting that some XID APerC macrophages have less microbicidal activity . We hypothesize that these macrophages can be polarized to M2 profile in the absence of B-1 cells and promote the maintenance of E . cuniculi . With the results obtained herein , we have demonstrated that B-1 cells modulate the activity of peritoneal macrophages infected with E . cuniculi to an M1 profile . Furthermore , part of these cells become B-1 CDPs having microbicidal activity against the pathogen , which explains the lower susceptibility of BALB/c mice to encephalitozoonosis associated with innate immune response .
Inbred specific pathogen free ( SPF ) BALB/c and BALB/c XID female mice at 6–8 weeks of age were obtained from the animal facility at Centro de Desenvolvimento de Modelos Experimentais ( CEDEME ) , UNIFESP , Brazil . The animals were housed in polypropylene microisolator cages with a 12-hour light-dark cycle , maintained at 21 ±2°C and >40% humidity , and fed on standard chow and water ad libitum . All the experimental procedures were performed in accordance with guidelines of Conselho Nacional de Controle de Experimentação Animal ( CONCEA ) and were approved by the Ethics Committee for Animal Research at Paulista University , under protocol number 385/15 . Spores of E . cuniculi ( genotype I ) ( from Waterborne Inc . , New Orleans , LA , USA ) that were used in this experiment were previously cultivated in a rabbit kidney cell lineage ( RK-13 , ATCC CCL-37 ) in Eagle medium supplemented with 10% of fetal calf serum ( FCS ) ( Cultilab , Campinas , SP , Brazil ) , pyruvate , nonessential amino acids , and gentamicin at 37°C in 5% CO2 . The spores were purified by centrifugation and cellular debris was excluded by 50% Percoll ( Pharmacia ) as described previously [56] . The APerC were obtained from the peritoneal cavities ( PerC ) of BALB/c and B-1 cell-deficient XID mice . PerC were washed using RPMI-1640 medium and 0 . 5×106 cells were dispensed in each well of 24-well plates and incubated at 37°C in 5% CO2 for 40 min . Non-adherent cells were discarded and RPMI-1640 supplemented with 10% FCS ( R10 ) was added to the adherent fraction . To obtain B-1 CDP , APerC from BALB/c mice were cultured and the enriched B-1 cells in the floating medium were collected from the third day [57] . Cultures of 0 . 5 × 106 cells/well were re-suspended in R10 and re-cultured under the same conditions as described above . The culture of BALB/c APerC , XID APerC , and B-1 CDP were infected simultaneously with E . cuniculi ( 1×106 spores/mL ) in the proportion of two spores per cell ( 2:1 ) . The cultures were incubated at 37°C in 5% CO2 for 30 min , 1h , 48 h , 96 h , and 144 h after infection , following which the supernatants were collected and stored at –80°C to measure the level of NO and cytokines . The cultures uninfected with E . cuniculi were incubated for the same time intervals and used as a group control . The production of NO was measured using a colorimetric indirect method based on the detection of nitrite ( nitrate was initially converted to nitrite by nitrate reductase ) as a product of the Griess reaction ( R&D Systems ) in the supernatants of cell cultures . Briefly , supernatants were mixed with the Griess reagent [equal volumes of 0 . 2% ( w/v ) naphthylethylenediamine in 60% acetic acid and 2% ( w/v ) sulfanilamide in 30% ( v/v ) acetic acid] and incubated at room temperature for 10 min . A spectrophotometer was used to measure the absorbance at 540 nm and fresh culture medium was used as a blank in all the experiments . The amount of nitrite in the test samples was calculated from a sodium nitrite standard curve ( 0 . 78–100 μM ) . Cytokines were measured in culture supernatants using Cytometric Bead Array ( CBA ) Mouse Inflammation Kit ( BD Bioscience , San Jose , CA , USA ) . The kit was used for the simultaneous detection of mouse monocyte chemoattractant protein-1 ( MCP-1 ) , interleukin-4 ( IL-4 ) , IL-6 , IFN-γ , tumor necrosis factor ( TNF-α ) , IL-10 , and IL-12p70 , according to the manufacturer’s instructions . Briefly , the supernatant samples were added to bind to allophycocyanin ( APC ) -conjugated beads specific for the cytokines listed above and phycoerythrin ( PE ) -conjugated antibodies . The samples were incubated for 2 h at room temperature in the dark , then measured using FACS Canto II Flow Cytometer , and analyzed by FCAP ArrayTM Software ( BD Bioscience ) , version 3 . 0 . Individual cytokine concentrations ( pg/mL ) were indicated by the intensity of PE fluorescence and cytokine standard curves . Infected and uninfected APerC were detached using a cell scraper and washed with PBS . The cell suspensions were centrifuged and subsequently washed with PBS and re-suspended in 100 μL PBS supplemented with 1% bovine serum albumin ( BSA ) ( PBS-BSA 1% ) . Each sample was incubated at 4°C for 20 min with anti-CD16/CD32 to block the Fc II and III receptors . After incubation , the cells were washed , divided into two aliquots , and re-suspended in PBS-BSA 1% . Each sample was then incubated with the following monoclonal antibodies: 1 ) fluorescein-isothiocyanate ( FITC ) -conjugated rat anti-mouse CD80/CD86 , 2 ) PE-Cyanine 5 ( PE-Cy5 ) -conjugated anti-mouse CD40 , 3 ) APC-Cy7-conjugated rat anti-mouse CD11b , and 4 ) Alexa Fluor 647 rat anti-mouse CD206 ( BD-Pharmingen , San Diego , CA , USA ) for analysis of the surface markers . The cell pellet was incubated with fluorochrome-conjugated antibodies for 20 min at 4°C , washed with PBS-BSA 1% , re-suspended in 500 μL of PBS , and analyzed with BD Accuri C6 flow cytometer . To investigate the phagocytic capacity and index , 10 μL Calcofluor ( Sigma-Aldrich , St . Louis , USA ) was added per milliliter of the cell cultures to visualize the spores inside the phagocytic cells . Phagocytic capacity and index were calculated according to the formula: FC = number of phagocytes containing at least one ingested spore/100 phagocytes and FI = total number of phagocytic spores/100 phagocytes containing spores . Cell cultures were washed twice with cold PBS and then resuspended in 1x annexin-binding buffer ( BD Biosciences ) at a concentration of 1 × 106 cells/mL . Thereafter , 100 μL of the solution ( 1 × 105 cells ) was transferred to a 5-mL culture tube , to which 1 μL of PE Annexin V and 1 μL of 7-AAD were added and followed by incubation for 15 min on ice in dark conditions . Subsequently , 400 μL of 1x annexin binding buffer was added to the samples , incubated for 30 min , and all the resultant cell suspensions were analyzed using the BD Accuri C6 flow cytometer . The cell volume of BALB/c APerC , XID APerC , and B-1 CDP cells , cultured as described above , was adjusted to 1×107 cells . Each culture was transferred to 25 cm2 bottles and incubated in the same medium containing 10% FCS ( R10 ) at 37°C with 5% CO2 for 40 min . The culture medium was then removed and fresh R10 medium containing E . cuniculi spores ( 2:1 ) was introduced into the bottles . The cultures were collected after 1 h , 48 h , and 144 h of incubation and fixed using 2% glutaraldehyde in 0 . 2 M cacodylate buffer ( pH 7 . 2 ) at 4°C for 10 h . They were then post-fixed in 1% OsO4 buffer for 2 h . Semi-thin sections stained with toluidine blue were made for visualization by light microscope , and ultrathin sections were made for TEM analysis . The groups were compared using the two-way analysis of variance ( ANOVA ) and the significance of the mean difference within and between the groups was evaluated by multiple comparisons using the Bonferroni or Tukey's post-tests . All the experimental data were expressed as mean ± standard error mean , indicated by bars in the figures . P values ≤0 . 05 were considered statistically significant . All the graphs and statistical analyses were made using GraphPad Prism software version 6 . 0 for Windows ( GraphPad Software , San Diego , California , USA ) . | The adaptive immune response plays a key role against Encephalitozoon cuniculi , an opportunistic fungus for T cells immunodeficient patients . The role of B cells and antibody play in natural resistance to Encephalitozoon cuniculi remains unknown . Previously , we demonstrated that B-1 deficient mice ( XID ) , an important component of innate immunity , were more susceptible to encephalitozoonosis , despite the increase in the number of CD4+ and CD8+ T lymphocytes . Here we observed that the absence of B-1 cells was associated with a larger population of M2 macrophages , a balance between anti-inflammatory and pro-inflammatory cytokines profile , which had lower microbicidal activity against E . cuniculi infection . However , in the presence of B-1 cells , peritoneal macrophages had a M1 profile with showed increased microbicidal activity and a higher percentage of apoptotic death . | [
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] | 2019 | B-1 cell-mediated modulation of M1 macrophage profile can ameliorate microbicidal functions and disrupt the evasion mechanisms of Encephalitozoon cuniculi |
This study examines the use of topical pharmacological agents as a snakebite first aid where slowing venom reaching the circulation prevents systemic toxicity . It is based on the fact that toxin molecules in most snake venoms are large molecules and generally first enter and traverse the lymphatic system before accessing the circulation . It follows on from a previous study where it was shown that topical application of a nitric oxide donor slowed lymph flow to a similar extent in humans and rats as well as increased the time to respiratory arrest for subcutaneous injection of an elapid venom ( Pseudonaja textilis , Ptx; Eastern brown snake ) into the hind feet of anaesthetized rats . The effects of topical application of the L-type Ca2+ channel antagonist nifedipine and the local anesthetic lignocaine in inhibiting lymph flow and protecting against envenomation was examined in an anaesthetized rat model . The agents significantly increased dye-measured lymph transit times by 500% and 390% compared to controls and increased the time to respiratory arrest to foot injection of a lethal dose of Ptx venom by 60% and 40% respectively . The study also examined the effect of Ptx venom dose over the lethal range of 0 . 4 to 1 . 5 mg/kg finding a negative linear relationship between increase in venom dose and time to respiratory arrest . The findings suggest that a range of agents that inhibit lymphatic flow could potentially be used as an adjunct treatment to pressure bandaging with immobilization ( PBI ) in snakebite first aid . This is important given that PBI ( a snakebite first aid recommended by the Australian National Health and Medical research Council ) is often incorrectly applied . The use of a local anesthetic would have the added advantage of reducing pain .
Snake envenoming worldwide remains a major health problem with 20 , 000–94 , 000 deaths [1] and a morbidity of several 100 , 000 people per year [2] . Snake envenoming has been identified as a neglected tropical disease and there is a desperate need for improved treatment . First aid procedures are a major part of treating snake envenomings because envenomings nearly always occur away from hospitals leading to delays before antivenom can be administered . In Australia , the only formally accepted snakebite first aid is pressure bandaging with immobilization ( PBI ) [3] , [4] . Local pressure pad with compression , another mechanical method , is also seen as a potentially useful first aid [5] , [6] , [7] . PBI , which has long been endorsed by the National Health and Medical research Council of Australia against bites from Australian snakes , aims to limit venom entry into the circulation by preventing lymphatic transport without inhibiting arterial or venous blood flow . Both mechanical methods are based on the principle that snake toxin molecules are generally large and can't directly enter the circulation , but are readily taken up by the lymphatics [8] . To date these first aid procedures have been primarily applied for elapid snake bites of Australia and New Guinea as these venoms have limited local cytotoxicity with the main concern being death by actions once venoms enter the circulation [9] . It has been shown that PBI is often incorrectly applied particularly by untrained personnel , with reported success rates of 15% in untrained and 50% in trained personnel [10] . Thus , while PBI is highly effective at limiting toxin entry into the circulation in animal studies and mock venom studies [11] , [12] , its application in the clinical setting may be more tenuous . Therefore , co-application of adjunct methods that impede venom transport through the lymphatics may be beneficial . Recently , we reported that topical application of a nitric oxide ( NO ) donor that is known to inhibit lymphatic pumping [13] , as a potential first aid against snakebite . However , there are various other agents that are also known to inhibit spontaneous contractions of lymphatic smooth muscle and hence the intrinsic propulsion of lymph . It is therefore important to investigate whether such agents act in the same way as NO donors with the hope that they may be even more effective or provide a cheaper or more advantageous first aid . The present study compares the efficacy of the NO donor to several other topical agents that have known inhibitory actions on lymphatic function by directly or indirectly targeting the intrinsic lymphatic pump .
All experiments were approved by the University of Newcastle Animal Care and Ethics Committee for ethics approval A-2009-153 according to the Australian Code of Practice for the care and use of animals for scientific purposes as released by the National Health and Medical Research Council of Australia in 2004 . Studies were performed on urethane ( 1 . 5–1 . 75 g/kg i . p . ) -anaesthetized male and female Wistar rats ( weight range 200–550 g ) at 37°C with animals euthanized without recovery at the end of the experiments . In some experiments groin lymphatic vessels were surgically exposed to facilitate measurement of foot to groin lymph transit times [13] . Envenoming was simulated by injection of snake venom ( Pseudonaja textilis , Ptx; Eastern brown snake ) into the feet of deeply anaesthetized rats . Our use of this venom in rats was not intended to mimic P . textilis venom action on humans , but was chosen because of its clear end point in the rat model of our studies . In the present studies we used three separate batches of freeze-dried Ptx venom ( each obtained from pooled venom collected from 10–20 snakes ) . While two donated by the Australian Reptile Park ( Somersby , NSW , Australia ) showed very similar potency , the third batch from Venom Supplies ( Adelaide ) was less potent . Therefore the third batch was applied at proportionally higher concentrations to equate to the toxicity of the other two batches , as tested by trialing different doses until a dose was obtained that produced a similar time to respiratory arrest as for the first two batches for a dose of 1 mg/kg . Experiments involved examining the effects of topical application of test pharmacological agents ( nifedipine – Sigma N7634 , lignocaine - Sigma L1026 , sodium nitroprusside – Sigma S0501 ) dissolved in control solution ( i . e . saline with 1% dimethyl sulphoxide ( DMSO – Sigma 276855 ) added to improve skin wetting ) . Comparison was also made to a commercially available NO donor ointment ( Care Pharmaceuticals , Sydney ) containing 0 . 2% wt/wt of the NO donor glyceryl trinitrate ( GTNO ) . The test agents were applied over the rat hind limb within 1 minute after dye or venom injection with agents reapplied at ∼15 min intervals . The limb was wrapped with a thin layer of tissue to ensure the leg remained exposed to the agent for all experiments and maintained at a temperature of 35±1°C . Two types of experiments were performed . The first group of experiments tested the agent's effectiveness in slowing lymph transport by measuring the arrival time of a marker dye ( India ink ) in surgically exposed groin lymphatics consequent to injection of 50 µl of the dye into the corresponding hind foot of anaesthetized rats . The second group of experiments tested the agent's effectiveness in increasing the time to respiratory arrest of hind foot injection of Ptx venom . Respiration frequency was measured visually or by recording chest movement through a strain gauge connected externally to the rat chest at the level of the diaphragm . Some experiments were made while simultaneously recording blood pressure and heart rate sampling data at 1 kHz ( AD Instruments; Australia ) . Analysis of the rate of venom-induced decline of respiration frequency was generally made from the time of venom injection . In a minority of animals there was an initial increase in respiration rate and in these cases the respiration frequency was analyzed relative to its initial plateau . Statistical significance confirmed by one-way ANOVA followed by Holm-Sidak's multiple comparison test . Data are presented as means ± s . e . m . with n referring to the number of animals .
The effectiveness of two topical pharmacological agents , the Ca2+ channel antagonist nifedipine and the local anesthetic lignocaine at slowing the transit of lymph was tested . Nifedipine ( 0 . 1 mM ) or lignocaine ( 10% ) applied topically within 1 minute after rat hind foot dye injection , significantly ( P<0 . 0001 ) slowed the transit of lymph , increasing the hind limb transit time of lymph by 500 and 390% respectively ( Fig . 1 ) . Simulation of snakebite by injection of Ptx venom into the feet of deeply anaesthetized rats caused a linear reduction in respiration frequency . The rate of decline was reasonably constant over duration of the experiment and was dependent on venom dose with lower venom doses resulting in a slower/shallower decline in respiration frequency . Linear regression of the mean respiratory frequency plotted as a function of time for venom doses of 1 . 0 and 0 . 4 mg/kg provided respective slopes and intercepts of −0 . 82±0 . 05 & −0 . 26±0 . 05 bpm/min and 120±1 & 116±2 min ( Fig . 2 ) . Consistent with this , reducing the venom dose increased the time to respiratory arrest ( Fig . 3 ) . Both parameters showed an approximately linear dependence with venom dose over the range 1 . 5 to 0 . 4 mg/kg with respective slopes of −53±7 min/ ( mg/kg ) and −0 . 6±0 . 1 bpm/min/ ( mg/kg ) and intercepts of 115±7 min and −0 . 3±0 . 1 bpm/min for the time to respiratory arrest and the rate of venom-induced decline of the respiration frequency ( Fig . 4 ) . Rats survived at lower venom doses ( range studied 0 . 1–0 . 2 mg/kg , n = 8 ) for the 2–4 h periods measured . At such doses , rats exhibited a small decrease in respiration rate which then recovered . The effects of topical application of nifedipine , lignocaine or control solution ( 1% DMSO in saline ) to hind limbs were examined by measuring the time to respiratory arrest and the rate of decline of the respiration frequency in anaesthetized rats consequent to foot injection of Ptx venom . Topical application of nifedipine ( 1 mM ) and lignocaine ( 10% ) significantly increased the time to respiratory arrest by 61% and 50% ( Fig . 5 ) . The NO donor sodium nitroprusside when applied at 10 mM or 100 mM caused a similar increase in time to respiratory arrest ( 75±7 min; n = 7 and 80±6 min , n = 5 ) . In preliminary studies we observed that lower doses of nifedipine and lignocaine were similarly effective as those shown at higher doses in figure 5 . Specifically , the time to respiratory arrest for nifedipine at 0 . 1 mM as studied on 2 animals was 87 and 95 min compared to 99±10 min ( n = 8 ) for 1 mM nifedipine . The time to respiratory arrest for lignocaine at 5% as also studied on 2 animals was 90 and 80 min compared to 91±4 ( n = 5 ) for 10% lignocaine . Studies comparing blood pressure or heart rate during application of Ptx venom ( equivalent concentration - 1 mg/kg ) under control ( n = 4 ) and one of the experimental conditions ( topical 1 mM nifedipine; n = 8 ) indicate there was no significant difference in heart rate and systolic or diastolic blood pressures between test and control data or during the recording period before respiratory arrest . ” The proportional effect of lymphatic inhibition by the topical agent was not dependent on the venom concentration and hence time of venom action . For example , while the time period for respiratory arrest was longer being 75±7 min ( n = 6 ) compared to 61±4 min ( n = 11 ) for rats injected with a lower Ptx venom concentration ( near 0 . 65 mg/kg compared to 1 mg/kg respectively ) , the degree of slowing afforded by topical hind limb application of GTNO ointment occurred at a proportionally longer time of 109±9 min ( n = 6 ) compared to 88±7 min ( n = 7 ) , with corresponding ratios ( i . e . +GTNO/−GTNO ) which were very similar ( 1 . 44 vs . 1 . 45 ) .
The key outcome of these studies is that a range of pharmacological agents known to directly or indirectly inhibit lymphatic pumping may be of use as topical treatments in first aid for bites from snakes whose venoms are not highly cytotoxic and where death by central action is the primary concern . It is suggested that they be considered as adjunct first aid to mechanical methods such as PBI ( a snakebite first aid recommended by the Australian National Health and Medical Research Council ) , which while highly effective for limb bites are often incorrectly applied [10] . The topical agents might also be useful for bites to the torso and hence might be used as adjuncts to the local pressure pad with compression approach , which as indicated from animal studies is effective even for such bites [7] . Our previously reported findings for NO releasing ointment [13] indicate that a reasonable approach would be to apply an ointment formulation of the inhibitor just above the bite site . PBI or a local pressure pad with compression would then be applied . Finally , while all the compounds used in this study are used on humans for other purposes , the use of a local anesthetic is probably the most compelling , as it has the added advantage of reducing pain and hence may be readily adopted . However if used then the formulation should be rapidly acting , which was not the case for the commercially available formulations we tested ( unpublished ) , but is the case for a commercially available NO releasing ointment [13] . | Snakebite remains a major problem worldwide causing death or serious illness in many tens of thousands of victims annually . An approach to reduce the burden of envenoming is to provide optimum first aid procedures . We have previously shown that topical application of a nitric oxide ( NO ) donor slowed lymph flow to similar extent in humans and rats as well as increased the time to respiratory arrest by ∼50% for subcutaneous injection of eastern brown snake venom into the hind feet of anaesthetized rats . The present study examines the use of several other topical pharmacological agents that aim to slow venom toxins reaching the circulation through the lymphatic system . The study found that the agents examined were similarly effective to that previously found for the NO donor . The fact that one of these is a commonly used topical local anesthetic may be an ideal adjunct first aid , as it provides first aid while reducing pain . | [
"Abstract",
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] | 2014 | Pharmacological Approaches That Slow Lymphatic Flow As a Snakebite First Aid |
Epigenetic mechanisms are essential for the regulation of all genes in mammalian cells but transcriptional repression including DNA methylation are also major epigenetic mechanisms of defense inactivating potentially harmful pathogens . Epstein-Barr Virus ( EBV ) , however , has evolved to take advantage of CpG methylated DNA to regulate its own biphasic life cycle . We show here that latent EBV DNA has an extreme composition of methylated CpG dinucleotides with a bimodal distribution of unmethylated or fully methylated DNA at active latent genes or completely repressed lytic promoters , respectively . We find this scenario confirmed in primary EBV-infected memory B cells in vivo . Extensive CpG methylation of EBV's DNA argues for a very restricted gene expression during latency . Above-average nucleosomal occupancy , repressive histone marks , and Polycomb-mediated epigenetic silencing further shield early lytic promoters from activation during latency . The very tight repression of viral lytic genes must be overcome when latent EBV enters its lytic phase and supports de novo virus synthesis in infected cells . The EBV-encoded and AP-1 related transcription factor BZLF1 overturns latency and initiates virus synthesis in latently infected cells . Paradoxically , BZLF1 preferentially binds to CpG-methylated motifs in key viral promoters for their activation . Upon BZLF1 binding , we find nucleosomes removed , Polycomb repression lost , and RNA polymerase II recruited to the activated early promoters promoting efficient lytic viral gene expression . Surprisingly , DNA methylation is maintained throughout this phase of viral reactivation and is no hindrance to active transcription of extensively CpG methylated viral genes as thought previously . Thus , we identify BZLF1 as a pioneer factor that reverses epigenetic silencing of viral DNA to allow escape from latency and report on a new paradigm of gene regulation .
Activity and repression of eukaryotic genes correlate with the level of DNA methylation of promoter regions . Prominent models are ß-globin genes . Their sequential developmental activation and silencing in embryonic , fetal , and adult erythroid cells depends on the methylation status of DNA sequences near promoters of globin genes [1] , [2 and references therein] . It appeared that CpG methylation is a stable epigenetic mark transmitting the repressed state of chromatin through mitosis to daughter cells . Little was known about dynamic demethylation ( and methylation ) events at promoters although demethylation is considered to be a prerequisite for gene activation at highly CpG-methylated promoter elements . It is now clear that gene activation can involve the rapid gain or loss of 5′-methylcytosine ( 5mC ) residues in estrogen-responsive promoters [3] , [4] . The methylation status of CpGs close to the transcription start site of the pS2 promoter gene changes upon estrogen induction within minutes indicating that methylation of DNA is dynamic but also involves processes of reactive demethylation [5] . Erasure of DNA methylation and derepression of silenced chromatin has been observed in zygotes and primordial germ cells during fertilization and embryonic development , respectively . Recently , the responsible enzyme ( s ) were identified as members of the Tet ( ten eleven translocation ) family of proteins capable of catalyzing the conversion of 5mC to 5′-formylcytosine followed by the excision by thymine-DNA glycosylase and base excision repair [6]–[10] . Therefore , Tet proteins may drive the process of active CpG-demethylation , which is thought to be crucial to overcome transcriptionally repressed chromatin . Epigenetic information like positioned nucleosomes or posttranslational modifications of N-terminal histone tails provides more flexibility to react to environmental cues . In inducible promoters nucleosome positions change depending on the activation state of the gene [11 for a recent review] . N-terminal modifications of histone tails can be highly dynamic as a distinct epigenetic state can be enzymatically reverted by particular histone-modifying enzymes erasing the previous modifications [12 for a recent review] . Certain histone modifications are flexible in principle but can be stable and heritable through many cell generations . For example , Polycomb group ( PcG ) proteins are regulators that repress genes by keeping a transcriptionally inactive state , which is mediated by H3K27 trimethylation . The common view is that the Polycomb repressive complex 2 ( PRC2 ) acts as the “writer” of the repressed state . It establishes H3K27 trimethylation with its histone methyltransferases EZH1 or EZH2 . A second Polycomb repressive complex , PRC1 , is regarded as the “reader” of the epigenetic state . It recognizes histone H3K27me3 and acts as a silencing complex by ubiquitinating histone H2A [13] or by chromatin compaction of defined nucleosome arrays [14] leading to stably repressed chromatin loci . During specific stages of embryogenesis and stem-cell differentiation certain members of the trithorax group of proteins can remove the methyl groups at lysine 27 of histone 3 to re-install transcriptionally active genes [15] , [16] . The balance between epigenetic stability and flexibility underlies the adaption of EBV to its human host . In infected cells this herpesvirus can adopt two different states , which depend on the epigenetic regulation of its genes . Upon infection of primary human B cells , the virus does not promote de novo virus synthesis but establishes a latent phase , characterized by the expression of a small set of viral genes , which promote cellular proliferation and contribute to viral oncogenesis [17] . Extensive DNA methylation of viral DNA [18] , [19] is thought to contribute to overall gene silencing [20] whereas histone modifications such as H3K4me3 mark the few active promoters of latent genes together with the chromatin insulator protein CTCF and the viral factor EBNA1 [21] , [22] . EBV's EBNA genes can be alternatively expressed from the latent viral Cp- , Wp- or Qp-promoters . Their states of DNA methylation were studied in vivo by several groups [23] , [24] and in vitro in cell lines of different origins [25] , [26] . They found that the methylation status of these promoters determines the expression profile of latent viral genes . In contrast to latent viral promoters , little is known about epigenetic marks at lytic promoters during latency when epigenetic silencing of most viral genes might guarantee the co-existence of EBV with its cellular host in the absence of viral de novo synthesis . The eventual switch to EBV's lytic phase in latently infected cells is initiated by the expression of the viral BZLF1 gene encoding the transcription factor BZLF1 ( also called EB1 , ZEBRA , Z , or Zta ) [27] , [28] . BZLF1 binds sequence-specifically to one class of DNA motifs , termed ZREs , but prefers a second class that contains methylated 5′-cytosine residues ( 5mC ) , termed meZREs [18] , [29]–[31] . meZREs predominate in so-called early viral promoters [32] . Paradoxically , CpG methylation of meZREs is instrumental for the expression of essential lytic genes [33] and indispensable for virus synthesis [18] , [32] , changing the conventional view of DNA methylation solely as a repressive epigenetic feature . EBV's closest relative , Kaposi's sarcoma-associated herpes virus ( KSHV ) , relies on bivalent chromatin in its latent phase to reactivate the lytic phase of its life cycle [34] , [35] . In contrast to this option , we report here on a novel mechanism that deliberately relies on methylated EBV DNA to promote induced transcription of a distinct class of viral genes . Our data suggests that , in combination with a very high concentration of highly methylated CpG dinucleotides , Polycomb-group ( PcG ) proteins introduce repressive modification of histones , which form densely arranged nucleosomes in order to shield the binding sites of BZLF1 in certain promoters of Epstein-Barr virus ( EBV ) to maintain latency . Upon lytic reactivation BZLF1 is expressed and gains access to compacted , highly repressive chromatin , where it binds to its CpG-methylated DNA motifs . BZLF1 binding induces nucleosomal eviction at BZLF1's cognate sites , erases repressive histone marks , recruits RNA polymerase II , and activates transcription of viral genes to trigger escape from latency . Surprisingly , CpG methylation is invariably maintained during this early lytic phase of EBV's life cycle and no barrier to active transcription . These attributes provide a novel paradigm for gene regulation in metazoan cells , which depend on stable chromatin repression to maintain their differentiation state and cellular identity but require the plasticity needed to respond immediately to developmental and environmental cues . EBV exploits these mechanisms in order to sustain its lifestyle .
In the EBV genome a number of meZREs were identified , which presumably contain methylated CpGs [32] . We examined selected regions of the latent EBV genome with deep bisulfite sequencing to assess the state of cytosine methylation in the cell line Raji , which is our prototype of a B cell latently infected with EBV . After bisulfite modification genomic DNA from Raji cells was amplified by PCR with EBV-specific primer pairs . The analysis encompassed 26 regions of EBV with latent , early lytic , and late lytic gene promoters covering 27 , 869 bp of EBV's genome ( Fig . S1A ) . The PCR products were pooled , sequenced , and the percentage of 5mCs was assessed [36] . The coverage of each CpG dinucleotide was 840 reads on average . A list with all CpGs analyzed is available upon request , a graphical view of selected promoters is shown in Fig . 1D and Fig . S1B . Cytosines within CpG dinucleotides were methylated to 66% on average in Raji EBV DNA . We analyzed the distribution of EBV's DNA methylation in this latently infected B cell line . The analysis revealed that the methylation state of EBV DNA is bimodal with 23% hypomethylated ( <20% methylation ) and 59% hypermethylated CpGs ( >80% methylation; Fig . 1A ) . Interestingly , the distribution peaks at the very left ( <2 . 5% methylation ) and the very right ( >97 . 5% methylation ) as shown in Fig . 1A , indicating that many viral CpG dinucleotides are either completely methylated or unmethylated . The CpG-rich BBLF4 promoter is a model of a BZLF1-regulated early lytic promoter [32] and its methylation state in Raji cells is shown in Fig . 1D . As predicted [32] , the majority of CpG dinucleotides in this promoter carries 5mCs , which include the previously identified meZRE motifs . Our laboratory strain of Raji cells contains 16 copies of EBV genomes per cellular genome equivalent as determined by quantitative realtime PCR ( Fig . 1B ) suggesting that cytosine methylation in the BBLF4 promoter is prevalent in all copies of EBV genomes present in Raji cells . Our data so far did not exclude that unmethylated copies of EBV genomes might exist that could bind BZLF1 and e . g . exclusively support transcriptional reactivation of the BBLF4 promoter upon induction of EBV's lytic phase ( see below ) . To address this point we arranged single reads of the deep sequencing analysis of the BBLF4 promoter according to their average degree of CpG methylation ( Fig . 1C ) . We observed that ( i ) the majority of all 31 CpG dinucleotides present in the BBLF4 promoter carries 5mC residues and not a single DNA molecule is entirely unmethylated , ( ii ) CpG methylation is variable at two hotspots while ( iii ) unmethylated meZREs are infrequent and rarely cooccur on the same DNA molecule . Our analysis excludes the existence of EBV genomes that escape CpG methylation in this model ( Fig . 1C ) but highlights the need to override CpG methylation and reverse epigenetic repression upon induction of EBV's lytic phase . The BBLF4 promoter is no exception as other early lytic promoters showed an equally high and homogeneous methylation at meZREs with a median methylation rate of 95 . 3% ( Fig . S1B and Table S1 ) . In contrast , active latent viral genes and their promoters like the Qp promoter ( Fig . 1D , lower panel ) were virtually free of methylated CpG dinucleotides in Raji cells resembling cellular CpG islands with a very high density of CpGs spared from DNA methylation . Next , we wanted to assess the methylation profile of EBV DNA in cells , which are the latent reservoir of EBV in its human host . We sorted memory B cells from PBMCs of healthy donors by multiparameter FACS enriching CD19 positive , CD38 negative , IgD negative , and CD27 positive cells . Cellular DNA was extracted and parts of EBV's genome were analyzed by conventional bisulfite sequencing . The frequency of EBV-infected memory B cells was reported to be very low with only one EBV-positive cell out of 104–106 cells [37] , but we succeeded in obtaining information of the BBLF4 and BZLF1 promoters from DNA samples of two healthy individuals . The BBLF4 promoter was highly CpG-methylated in vivo ( Fig . 1E ) but the pattern was not entirely identical to that of Raji DNA . The four CpGs , which constitute the meZREs of BBLF4 , were fully methylated in memory B cells in vivo . In contrast , CpG dinucleotides in the BZLF1 promoter are rare and were unmethylated in Raji DNA and weakly or variably methylated in memory B cells ( Fig . 1E ) indicating that this locus is probably not controlled by CpG methylation in vivo or in vitro . In addition to cytosine methylation , the density and the position of nucleosomes might also contribute to the transcriptionally repressed state of EBV DNA during latent infection . We therefore investigated the nucleosomal occupancy of EBV's genomic DNA with a particular focus on BZLF1-regulated promoters in Raji cells . This cell line also provides a model for viral reactivation because ectopic expression of BZLF1 efficiently induces EBV's lytic phase in Raji cells ( see below ) . Lytic induction does not lead to de novo synthesis of virus progeny because the EBV genome in Raji cells has a deletion of the BALF2 gene abrogating viral DNA amplification in the lytic phase . As a consequence , the late lytic phase is blocked , which is advantageous for the exact , unequivocal analysis of viral DNA , viral chromatin , and transcripts of early lytic genes shortly after induction . We introduced a conditional allele of BZLF1 into Raji cells to analyze possible changes of epigenetic modifications of viral chromatin at the onset of EBV's lytic phase . The coding sequences of BZLF1 and green fluorescent protein ( GFP ) were placed under the control of the bidirectional conditional promoter ( Fig . S2A ) . Addition of doxycycline led to about 90% GFP-positive cells ( Fig . S2B ) , a rapid induction of BZLF1 ( Fig . S2C ) , and the expression of the BZLF1 target gene BRLF1 in more than 50% of GFP positive cells ( Fig . S2D ) . In a first approach , chromatin of different Raji cell derivates was studied with an MND-on-Chip analysis ( mono-nucleosomal DNA-on-Chip ) . MND-on-Chip experiments rely on the different accessibility of DNA to Micrococcal Nuclease ( MNase ) cleavage in the context of chromatin . Digestion of chromatin with MNase leads to the degradation of free DNA , whereas nucleosomal DNA is protected and can be isolated and identified in e . g . microarray hybridization . Mononucleosomal DNA and sonicated input DNA were labeled with fluorochromes and hybridized to a high-resolution custom-made EBV microarray . The experiments were performed with three different cell lines . Parental Raji cells were analyzed as a model of the latent state . Chromatin of lytically induced and sorted Raji-BZLF1 cells addressed nucleosomal occupancy during EBV's lytic phase . Raji-BZLF1ΔTAD cells constitutively expressed a truncated BZLF1 protein lacking its N-terminal transactivation domain [32] to study the effect of BZLF1's DNA binding domain ( DBD ) on nucleosomal occupancy at BZLF1-regulated promoters . 32 ZREs were selected , which fulfilled certain criteria to ensure their being informative ( Table S2 ) . The “enriched versus input” log2-ratios indicated the occupancy of DNA with nucleosomes . The log2-ratios of the selected ZREs were averaged and the average nucleosome occupancy profiles of the three Raji derivates were overlaid in a window ±2000 bp , centered at the start of the ZRE ( Fig . 2A ) . The average nucleosome occupancy profile displayed increased nucleosome occupancy at ZREs during latency ( parental Raji cells , black line ) , indicated by a high log2-ratio . The elevated log2-ratio dropped after lytic induction ( Raji-BZLF1 cells , red line ) and after binding of truncated BZLF1 ( Raji-BZLF1ΔTAD cells , green line ) . This observation indicated that binding of full length BZLF1 but also the C-terminal half of BZLF1 with its DBD caused a general loss of nucleosomes at ZREs . The log2-ratios of the 32 individual ZREs in lytically induced Raji-BZLF1 cells and parental Raji cells were subtracted and ranked in a heat map shown in the left panel of Fig . 2B . A cluster analysis was performed to probe for possible functional groups among all ZREs . The resulting dendrogram shown on the left side of the heat map identified three functional groups of ZREs . For the subsequent analysis the two most divergent groups , group 1 and group 2 were selected ( Fig . 2B ) . The average nucleosome occupancy profile of group 1 ZREs in parental Raji cells during latency ( Fig . 2B , right panel , black line ) did not display elevated log2-ratios at the ZRE sites in contrast to group 2 ZREs . Their average log2-ratios dropped after induction of the lytic phase ( red line ) , and constitutive binding of BZLF1 caused a similar reduction of average nucleosome occupancies at group 2 ZREs ( green line ) . Next , we analyzed the differences between Raji-BZLF1ΔTAD cells that express the truncated BZLF1 protein constitutively and lytically induced Raji-BZLF1 cells . This analysis addressed the role of BZLF1's N-terminal transactivation domain on nucleosomal displacement . Subtraction of the log2-ratios of the eleven ZREs of group 2 in lytically induced Raji-BZLF1 cells and Raji-BZLF1ΔTAD cells are visualized in a second heat map ( Fig . 2C ) . A cluster analysis and the resulting dendrogram distinguished two subgroups . Subgroup 2a showed similar average nucleosome occupancies between the two datasets , indicating a similar displacement of nucleosomes , as compared to parental Raji cells ( Fig . 2C , upper right panel ) . Average nucleosome occupancy profiles of subgroup 2b showed high nucleosome occupancy in latent parental Raji cells ( black line ) as expected . Binding of truncated BZLF1 ( green line ) caused a small drop in the log2-ratios , suggesting lower nucleosome occupancy on average . The induction of the lytic phase in Raji-BZLF1 cells resulted in a collapse of the log2-ratios ( red line ) , indicating the formation of hypersensitive sites . Control experiments with 32 randomly chosen positions of the EBV genome lacking any ZRE did not reveal differences between the datasets ( Fig . S3 ) . Our findings implied a functional classification of BZLF1-regulated genes . Seven genes are essential for EBV's DNA replication in the lytic phase: BMLF1 , BRLF1 , BMRF1 , BBLF2/3 , BBLF4 , BALF2 , and BALF5 [38] , [39] ( BALF2 is deleted in Raji EBV DNA and excluded from this analysis ) . Five of the remaining six promoters of these genes make up subgroup 2b or belong to subgroup 2a , indicating that theses promoters are repressed very tightly during latency . The promoters of BMLF1 , BRLF1 , and BMRF1 ( subgroup 2b ) probably require the local binding of wild-type BZLF1 for their chromatin remodeling upon lytic induction . The promoters of BBLF2/3 and BBLF4 ( subgroup 2a ) also appeared tightly repressed during latency , but binding of truncated BZLF1 was sufficient to rearrange the promoter nucleosomes . The seventh gene important for lytic replication , DNA polymerase BALF5 , contains a single ZRE in its promoter [32] , which did not fall into any analyzed group . Promoters of group 1 are not regulated by BZLF1 at the level of nucleosomal rearrangements . None of them belongs to the seven genes , which are required for EBV's lytic DNA replication . Table 1 lists the ZREs hierarchically and according to their functional groups , Fig . S4 provides the graphical representation of examples of two members of each group . MND-on-Chip experiments indicated a loss of nucleosomes at ZREs , which was validated in two additional , independent experiments with parental Raji cells ( latent ) and doxycycline induced Raji-BZLF1 cells ( lytic ) . Chromatin immunoprecipitations with a histone H3-specific antibody resulted in the enrichment of nucleosomal DNA containing histone H3 at two different promoters that contained ZREs ( BRLF1 and BMRF1 ) in comparison to the ZRE-free W promoter ( Wp ) during latency in parental Raji cells ( Fig . 3A; 2–3% of input DNA ) . The ZRE-containing promoters of BRLF1 and BMRF1 showed a clear reduction of the histone H3 signal ( 1% of input DNA ) in lytically induced Raji-BZLF1 cells but the ZRE-free Wp was not affected by lytic induction ( Fig . 3A ) . These three promoters were re-analyzed in indirect endlabeling experiments . Chromatin was treated with limited amounts of MNase and cleaved with appropriate restriction endonucleases at sites close to the regions of interest . DNA was purified , separated on agarose gels , and transferred to nylon membranes by Southern blotting . The membranes were hybridized to radioactively labeled probes that were complementary to regions downstream and close to the cleavage sites of the restriction endonucleases . The bands that are visible in the autoradiograms show the boundaries of nucleosomes and hypersensitive sites . The migration of the bands indicates the distance of the boundaries from the restriction endonuclease cleavage sites in the individual loci as shown graphically on the right side of the autoradiograms in Fig . 3B . Top panels in Figure 3B show the results in parental Raji cells . Raji chromatin was partially digested with increasing amounts of MNase as indicated and analyzed for nucleosome occupancies in the promoter regions of BRLF1 , BMRF1 , and Wp . The restriction endonuclease cleavage sites , the probe location , and selected features of EBV's genome are depicted on the right side of the autoradiograms . The lower panels in Figure 3B show the same promoter sites in Raji-BZLF1 cells , without or after addition of doxycycline and overnight induction of the lytic phase of EBV . The uninduced cells displayed a pattern similar to the latent parental Raji cells ( Fig . 3B , upper panels ) as expected . The induced expression of BZLF1 caused major rearrangements in the promoters of BRLF1 and BMRF1 , but not in the ZRE-free W promoter . The BRLF1 promoter acquired a pattern that was typical for a hypersensitive site , which is situated in the same location as the BRLF1 ZRE sites , confirming the microarray and ChIP experiments . The BMRF1 promoter showed overall reduced signal intensities in bands that were more than 1000 bp apart from the restriction endonuclease cleavage site after lytic induction , indicating a loss of nucleosomes in this BZLF1-regulated promoter as well . We considered that the induced expression of BZLF1 and subsequent eviction of nucleosomes lead to an active demethylation of EBV DNA fostering chromatin reactivation and transcriptional activation . The methylation state of four selected regions of the viral genome covering the latent Fp/Qp promoter and three lytic promoters representing an immediate early , an early , and a late gene were determined after induction of the lytic cycle ( Fig . 4 ) . Raji cells with the conditional BZLF1 allele ( ‘Raji-BZLF1’ ) were induced with doxycycline for 15 hours and sorted for GFP-positive cells to obtain a pure population of lytically induced cells . Cellular DNAs of lytically induced Raji-BZLF1 and parental Raji cells were isolated , bisulfite-treated , amplified with suitable primers spanning the Fp/Qp promoter , the BZLF1 promoter , the BBLF4 promoter , and the BDRF1 promoter , and directly sequenced . There was no discernable difference between the DNA methylation pattern of parental Raji cells and lytically induced Raji-BZLF1 cells at any locus ( Fig . 4 ) indicating that active demethylation of the viral genome is not part of EBV's lytic phase . We wanted to challenge the possibility that BZLF1 might induce transcription from partially unmethylated templates of EBV DNA . BZLF1 could bind to unmethylated meZRE-sites , which , nevertheless , are infrequent in Raji DNA during latency ( see above , Fig . 1 ) and only weakly bound by BZLF1 [32] . Towards this end we employed a BZLF1-specific antibody and performed chromatin-immunoprecipitation experiments followed by direct bisulfite sequencing termed ChIP-BS-seq ( Fig . S5 ) . If BZLF1 bound to non-methylated meZREs and/or preferentially supported transcription from partially methylated templates , we would expect an enrichment of DNA with a reduced frequency of methylated CpGs as compared to viral DNA present in latently infected Raji cells . Our results demonstrated that BZLF1-bound DNA was indistinguishable from EBV DNA prevalent in Raji cells during latency ( Fig . S5 ) . This finding further supported our working hypothesis that ( i ) lytic reactivation relies on methylated meZREs and ( ii ) BZLF1-controlled transcription originates from methylated DNA templates . We analyzed promoters representing all different classes of EBV genes and included cellular control loci with chromatin immunoprecipitations ( ChIPs ) using antibodies against various histone modifications or certain chromatin-associated proteins ( Fig . 5 ) . panH3-specific ChIP experiments confirmed the results of the microarray analysis: early lytic promoters and late lytic promoters were enriched in histone H3 during latency but induction of the lytic phase caused a selective loss of histone H3 at early lytic promoters , only ( Fig . 5A ) . The BZLF1 promoter is an exception; lytic induction does not lead to a displacement of histone H3 , which is in accordance with our microarray analysis ( Table 1 ) . We also determined whether lytic promoters carry specific chromatin marks during latency . ChIP experiments with an anti-H3K9me3 antibody indicated that this repressive mark is present at some EBV-loci but that it is not important for the regulation of lytic promoters , as induction of the lytic phase did not alter their occupancy with this modification ( Fig . 5D ) . In sharp contrast , ChIP experiments with an anti-H3K27me3 antibody revealed that all lytic promoters are characterized by this histone modification , which presumably is involved in their efficient repression during latency ( Fig . 5B ) . Induction of the lytic phase selectively erased or reduced this modification at early lytic promoters , consistent with their reactivation . High levels of H3K27me3 are a hallmark of Polycomb repression; therefore we assessed the occupancy of EBV promoters with the H3K27me3 methyltransferase EZH2 , which is a protein component of the Polycomb repressive complex 2 , PRC2 . ChIPs with an EZH2-specific antibody perfectly matched the results of H3K27me3 suggesting that this methyltransferase is responsible for trimethylation of H3K27 in repressed EBV promoters ( Fig . 5E ) . Interestingly , late lytic promoters , which do not support transcription in this model also showed a reduction in EZH2 levels , but to a lower extent . The loss of EZH2 at EBV promoters did not result from a reduction of cellular EZH2 steady-state levels after the induction of EBV's lytic phase ( Fig . 5G ) but was a locus-specific phenomenon . These results demonstrated that ( i ) Polycomb repression is important to maintain EBV's lytic genes in a silent state during latency , and ( ii ) induction of EBV's lytic cycle eliminates this mark relieving the tight repression . We also wanted to assess the mode of activation of early lytic promoters on the chromatin level . In Fig . 5C , the activation mark H3K4me3 appeared enriched at the transcriptionally active Q promoter ( Qp ) during latency , but H3K4me3 was undetectable at early lytic and late lytic promoters . Induction of the lytic phase increased the levels of H3K4me3 at Qp as well as early lytic promoters . Late lytic promoters also showed a minor enrichment of H3K4me3 marks upon lytic induction . ChIP experiments with an anti-PolII antibody showed no significant levels in the latent state ( Fig . 5F ) but in lytically induced cells , PolII was recruited to the latent Q promoter as well as early lytic promoters . In contrast , PolII was not detectable at late lytic promoters after lytic induction . To address the kinetics of lytic gene activation quantitative RT-PCR analyses of Raji-BZLF1 cells documented the induction of PolII-mediated transcription of selected viral genes in a time course experiment ( Fig . 6A ) . Raji-BZLF1 cells were induced with 100 ng/ml doxycycline for 46 h . RNA was prepared every four hours . Expression of a set of early and late lytic genes was tested together with the latent gene EBNA1 . Absolute transcript levels were calculated on the basis of a single cell . The kinetics of induction differed among the early lytic genes indicating that some genes are direct targets of BZLF1 , while other genes are probably induced by a combination of transcription factors or are secondary targets of BZLF1 . The expression levels of the transgene BZLF1 peaked after four hours of doxycycline induction . Expression of BMRF1 and BMLF1 was equally fast . The BBLF4 , BBLF2 , and BALF5 genes were maximally expressed eight hours post induction . BSLF1 and EBNA1 levels continuously increased for a time period of 28 h of induction . Late lytic genes were not expressed or at very low levels , only . We also followed the changes on EBV's chromatin over time to analyze the order of events contributing to the activation of early lytic promoters . ChIP experiments were conducted similar to the experiments described above with two additional , early time points after doxycycline induction of BZLF1 . Polycomb repression of EBV promoters was substantially reduced as early as three hours post induction with signals decreasing further ( Fig . 6B and D ) . Surprisingly , H3K27me3 initially also decreased at late lytic promoters three hours after BZLF1 induction but quickly recovered thereafter . Compared to the rapid loss of repressive chromatin marks , activation marks and recruitment of PolII were slow processes . A significant increase in H3K4me3 signals became apparent 15 hours post induction similar to PolII ( Fig . 6C and E ) . Only the BMRF1 and the Q promoter showed a significant enrichment for PolII 7 hours after adding doxycycline followed by H3K4me3 modifications indicating that these promoters respond very early to lytic induction . As a consequence , BMRF1 together with BZLF1 and BMLF1 was one of the first genes reaching high steady-state levels of transcripts ( Fig . 6A ) . Our time course studies indicated a sequential order of events at viral chromatin: Polycomb repression is relieved before PolII is recruited to the promoter sites inducing viral gene expression . Figure 7 summarizes all epigenetic and functional data obtained in this study with the BBLF4 promoter as a representative example . The promoter comprises several highly methylated meZREs [32] . During latency its nucleosomal occupancy was high and the complete region displayed high levels of H3K27me3 and EZH2 . Activation marks and PolII were not detectable during latency . The situation changed dramatically upon lytic induction as the promoter was completely remodeled: nucleosomes and repressive modifications were evicted and erased , respectively . Instead , the H3K4me3 levels and PolII occupancy rose , inducing the transcription of BBLF4 . It is worth noting that all these dramatic changes occur at chromatin encompassing densely arranged CpGs with highly methylated cytosine residues , which remain unaltered throughout the early lytic phase of EBV's life cycle .
Herpes viruses establish a life-long infection in their hosts . Success of infection relies on two principles: ( i ) in latently infected cells the promoters of lytic genes must be tightly repressed to evade immune recognition of their products by the infected host and ( ii ) induction of the lytic phase has to proceed rapidly and synchronously to escape from latency and virus-specific effector T cells . We found that repression and dynamic reactivation of viral genes are both governed by epigenetic mechanisms . Repressed lytic promoters of EBV are associated with extensive DNA methylation , high nucleosome occupancy , and H3K27me3 histone marks , which are repressive modifications transmitted by Polycomb group ( PcG ) proteins leading to compaction of chromatin . Repressive H3K9me3 histone marks were detectable at low levels as reported recently [40] , but very much in contrast to H3K27me3 , H3K9me3 modifications are not removed upon viral reactivation suggesting that the low level of these modifications are not central to maintaining a repressed chromatin in EBV . The activation mark H3K4me3 and PolII were undetectable at lytic promoters during latency , indicating that EBV's latent DNA is not associated with bivalent chromatin in contrast to latently KSHV-infected cells . DNA methylation is a prerequisite for the activation of EBV's early lytic promoters that rely on meZREs [18] , [32] . Upon lytic reactivation , CpG-methylation of viral DNA was unaltered but repressive histone marks were erased and nucleosomes were evicted in a subset of ZREs , which led to a local opening of promoters and loading of the transcription machinery . Only a subset of genes required the N-terminal transactivation domain of BZLF1 for an efficient nucleosome eviction , presumably by attracting energy-consuming chromatin remodelers . This subset includes genes that initiate EBV's lytic phase and are essential for lytic viral DNA replication ( BMLF1 , BRLF1 , BMRF1 ) . Other early lytic promoters underwent sufficient changes of the nucleosome occupancy also when BZLF1's transactivation domain was missing . The truncated BZLF1 protein has an additional C-terminal domain of debated function , which could also contribute to the recruitment of remodeling factors [41]–[44] . In the moment it is uncertain whether BZLF1's DNA binding domain alone is sufficient to evict nucleosomes . BZLF1 provides a mechanism bypassing epigenetic repression and governing chromatin transitions in EBV . Our data indicate that the rapid reversal of Polycomb silencing , nucleosome remodeling , establishment of the activation mark H3K4me3 , binding of PolII , and transcriptional initiation are controlled temporally . The activation of EBV's early lytic promoters first requires a loss of repressive modifications , before PolII is able to bind to and activate the promoter regions . The activation mark H3K4me3 is established upon polymerase binding , labeling actively transcribed genes . Surprisingly , these chromatin transitions occur in the presence of CpGs that maintain their fully methylated state throughout lytic gene expression . Very much in contrast to EBV , KSHV relies on bivalent chromatin for maintaining its latent state , which is poised for transcriptional activation and readily supports escape from latency [34] , [35] . It seems that KSHV counts on bivalent chromatin as one option of viral reactivation because it lacks a functional analogue of BZLF1 . KSHV encodes a BZLF1 homologue , which does not share the characteristics of EBV's BZLF1 to bind to CpG-methylated DNA sequence motifs ( unpublished data ) . Bivalent chromatin is advantageous when it comes to reactivating from latency but it also comes at a cost because it will occasionally and stochastically trigger low-level viral gene expression , which will not go unnoticed by the immune system of the KSHV-infected host , eradicating the virus-infected cell . In contrast , EBV adopts a fully silenced state of gene expression in memory B cells in vivo [45] , [46] , hiding from the host's educated immune system . Our findings provide a molecular explanation why EBV can afford to adopt an almost perfect state of viral latency during which all but two viral genes , EBER1 and -2 , are silenced . In this situation , which is also termed latency 0 [47] , the exceptional characteristics of BZLF1 alone can reactivate the fully repressed viral chromatin to enable escape from latency . Upon B-cell receptor-mediated activation , BZLF1 is expressed in the latently infected cell capable of binding to meZRE sites [32] . We show in this report that it acts as a pioneer factor reverting repressed , inactive , and completely CpG-methylated viral chromatin . It is interesting to note that CpG dinucleotides in the BZLF1 promoter are unusually rare and unmethylated in Raji cells but also in memory B cells in vivo . Our findings also indicate that the meZRE class of binding sites are not involved in the regulation of BZLF1 itself , which relies on positive feedback loops [48] , [49] . Other mechanisms control and repress the BZLF1 promoter during latency [50 and references therein] and presumably contribute to its reactivation . In essence , EBV's hiding silently during latency is beneficial for a stable and successful virus-host relationship and seems to be superior to KSHV's strategy relying on bivalent chromatin , which might suffer from occasional leakage . It is well known that KSHV is less prevalent in the human population presumably because in an immunocompetent host , it gets eradicated whereas EBV does not . In the immunocompromised patient , KSHV can give rise to B cell lymphomas as well as to Castleman's disease and Kaposi's sarcoma , which all express latent genes together with a subset of lytic viral genes , very much in contrast to most EBV-associated malignancies , in which only latent genes are expressed . The epigenetic regulation of the viral life cycle is the key to EBV's success infecting and persisting in its host , but might also apply to cellular promoters as a response to stress , e . g . UV-irradiation or inflammatory cytokines . It seems unlikely that BZLF1 is the only pioneer factor that can overcome transcriptional repression of highly CpG-methylated genes . Cellular factors of the AP-1 family to which BZLF1 belongs are likely candidates as they can reactivate repressed genes in response to exogenous signals in quiescent cells similar to BZLF1 .
PBMCs were prepared from buffy coats of anonymous donors ( Institute for Transfusion Medicine , Ulm , Germany ) and purified by multi-parameter cell sorting on a FACS Aria III instrument ( Becton-Dickinson ) . Cells were stained with anti-human CD19 ( coupled to eFluor 450; eBioscience ) , anti-human CD38 ( clone HIT2 , coupled to PE; eBioscience ) , anti-human IgD ( coupled to FITC; BD Pharmingen ) and anti-human CD27 ( APC conjugated; BD Bioscience ) . The stained cells were sorted for CD19 positive , CD38 and IgD negative , and CD27 positive cells as described previously [18] . The plasmid pRTS-2 comprises the constitutively active , bicistronic coding sequence of the Tet-repressor-KRAB fusion gene and the tetracycline controlled transactivator rtTA2s-M2 [51] . The coding sequences of BZLF1 and the green fluorescent protein ( GFP ) were placed under the control of the tet promoter to obtain an inducible BZLF1 expression that can be monitored by GFP positive cells with , for example , flow cytometry . Bisulfite modification of DNA was carried out using the EZ Methylation Gold Kit ( Zymo ) . 100 nanogram each of 60 PCR products of bisulfite-modified DNA with an average product size of 463 bps were pooled for deep sequencing on a Roche Genome Sequencer FLX ( Microsynth GmbH , Switzerland ) . Alignment of sequences was done using the CLC genomics workbench software with parameters for high throughput sequencing in the menu “reference assembly” . The local alignment was performed with parameters for long run reads ( mismatch cost = 2 , insertion cost = 3 , deletion cost = 3 , length fraction = 0 . 5 and similarity of 0 . 9 ) . Sequence reads were aligned to a modified version of the B95 . 8 wildtype sequence , in which we converted all cytosines in a non-CpG context to thymines . Cytosines of CpG dinucleotides were kept unconverted . The parameters of the sequence alignment were set to tolerate RY-mismatches in the context of CpG dinucleotides , allowing mismatches of purines and pyrimidines , which are expected if cytosines are unmethylated . Non-specific matches were placed randomly . Methylation analysis was carried out using the BiQ analyzer HT 0 . 9/beta-test version [52] . Data were graphically analyzed and visualized with the Prism software package . 1×107 cells were harvested , washed once with PBS , and resuspended in 250 µl permeabilizing buffer ( 150 mM sucrose , 50 mM Tris-HCl , pH 7 . 9 , 50 mM NaCl , 2 mM CaCl2 ) . Cells were incubated for two minutes at 37°C in the presence 0 . 1% lysolecithine . MNase digestion was carried out using 50 U MNase for five minutes at 37°C . After MNase treatment , cells were lysed in TNESK buffer ( 20 mM Tris-HCl , pH 7 . 9 , 200 mM NaCl , 2 mM EDTA , 2% SDS , 20 µg/ml Proteinase K ) and incubated at 50°C for five hours . DNA was phenol/chloroform purified , precipitated with ethanol and sodium acetate , and loaded quantitatively on an agarose gel to purify the mononucleosomal DNA . Gel purification was carried out using the NucleoSpin Extract II Kit ( Macherey-Nagel ) . For the preparation of input DNA , DNA was extracted from whole cells using the QIAamp DNA Mini Kit . DNA was sheared 15 times for five minutes in a biorupter device ( Diagenode ) to obtain DNA fragment sizes similar to MNase digested DNA . Mononucleosomal DNA and sonicated input DNA with an average length of 150 bp were labeled with the fluorochromes Cy5 and Cy3 , respectively , and hybridized to a high-resolution EBV microarray . The microarray contained the whole EBV B95 . 8 sequence in tiling oligonucleotides of 50 nucleotides in a step size of ten and an offset of five nucleotides between upper and lower DNA strand to provide a final resolution of five nucleotides . DNA was chemically labeled with the Universal Linkage System ( ULS ) of Kreatech diagnostic . Labeling and hybridization was done in cooperation with Imagenes according to their standard protocols . Raw data from the custom Nimblegen microarrays ( . PAIR format ) were analyzed with the software “R” ( http://www . r-project . org ) and the software “Bioconductor” ( http://www . bioconductor . org ) . All functions were called using default parameters if not indicated otherwise . Raw data signals of all replicates were “scale normalized” to compensate for potential biases introduced during the manufacturing process [53] . The log2-ratios ( enriched/input ) of the normalized signals were determined and averaged for each replicate . The ZREs that had been identified in the EBV genome [32] were reviewed for the following criteria for a subsequent bioinformatical analysis: Clusters of ZREs and meZREs or single BZLF1 binding sites had to be at least 1500 bp apart from each other in the EBV genome . In regions that encompass two or more closely spaced ZREs in a cluster , the position with the strongest binding of BZLF1 was selected and included in the analysis . 32 ZREs and meZREs fulfilled the criteria and were selected for the analysis ( see Table S2 ) Average nucleosome occupancies were determined separately for the three different conditions: Raji cells , Raji-BZLF1ΔTAD cells , and lytically induced Raji-BZLF1 cells . A window of ±2000 bp , centered at the start position of each ZRE , was chosen . The log2-ratios ( enriched/input ) were averaged in sliding sub-windows of 150 bp , which is approximately the length of one nucleosome , and a step size of 10 bp . To identify differences between the latent and lytic phase , subtractions of log2-ratios ( enriched/input ) of lytically induced Raji-BZLF1 cells and parental Raji cells were calculated for each individual ZRE and visualized in a heat map . A hierarchical cluster analysis using the Ward's minimum variance method was performed on the same heat map matrix with a window of ±350 bp , centered at the start position of each ZRE to probe for functional groups among ZREs . Average nucleosome occupancy profiles of each group identified by the cluster analysis were presented separately . As a second step , subtraction of log2-ratios ( enriched/input ) and cluster analysis was performed on ZREs that comprised a difference between the latent and lytic states with the datasets Raji-BZLF1 and Raji-BZLF1ΔTAD to determine the role of BZLF1's activation domain on nucleosome occupancies . 32 randomly chosen positions that lack ZREs were chosen and analyzed similar to the ZREs as a control . ChIP experiments were conducted following standard protocols . Chromatin was cross-linked for seven minutes at room temperature and incubated with anti-H3 ( Abcam , #1791-100 ) , anti-H3K4me3 ( Active Motif , #39159 ) , anti-H3K27me3 ( Upstate , #17-622 ) , anti-RNA polymerase II ( N20 , Santa Cruz , #39097 ) , anti-EZH2 ( Active Motif , #39875 ) and anti-H3K9me3 ( Active Motif #39161 ) antibodies . Protocol details are available upon request . ChIP and input samples were analyzed by quantitative real time PCR with the Roche LightCycler 480 . Primer pair sequences are available on request . P values were determined using a non-paired homoscedastic t-test . MNase digestion was performed similarly to the sample preparation for microarray hybridization but with minor changes . Buffer volumes were increased to 1 ml to ensure proper isolation of DNA after MNase treatment and MNase digestion was performed with 0 . 1–1 . 5 U MNase . Chromatin was Proteinase K treated and purified by phenol/chloroform extraction and ethanol precipitation . Cleavage of MNase treated DNA was carried out using 30 µg of DNA and 100 U of the specific restriction endonuclease at 37°C for two hours . Samples were purified and analyzed in Southern blot hybridizations according to standard protocols . After polyacrylamid gel electrophoresis and membrane transfer an anti-EZH2 antibody ( Active Motif , #39933 ) or an anti-α-tubulin antibody ( Santa Cruz , SC-23948 ) were used for protein detection . Isolation of RNA from 1×107 cells per sample was carried out with the RNase Mini Kit ( Qiagen ) . The lysate was homogenized with QiaShredder columns ( Qiagen ) . All subsequent steps were performed according to the instructions of the manufacturer . RNA was eluted in 80 µl of RNase-free water . Prior to cDNA synthesis , contaminating DNA was removed from the RNA preparation with the enzyme DNase I ( Invitrogen ) . 2 µg of extracted RNA were incubated for 90 min at 37°C in the presence of 2 U DNase , 40 U RNase inhibitor and 1× DNase buffer in a 20 µl approach . Remaining DNase was heat inactivated by incubating at 65°C for ten minutes . The efficiency of DNase treatment was controlled by PCR . Reverse transcription of RNA was performed with the SuperScript III First Strand Synthesis SuperMix Kit ( Invitrogen ) according to the manufacturer's instructions . Quantification of cDNA was done with quantitative real time PCR using the Roche LightCycler 480 system . Primer sequences are available on request . Cells were harvested , washed with PBS and fixed with 1% paraformaldehyd for 15 min at room temperature . Fixed cells were washed with PBS +2% FCS and permeabilized with 80% methanol for 30 min at −20°C . The cells were washed with PBS +2% FCS and incubated with a 1∶20 dilution of a BRLF1 antibody ( 8C12 ) [54] for 30 min at room temperature . Cells were washed five times with PBS +2% FCS and subsequently stained with a 1∶5 dilution of an APC-coupled goat-anti mouse IgG antibody ( Biolegend ) for 30 min at 4°C . Cells were washed and analyzed by flow cytometry . | Latency is a fundamental molecular mechanism that is observed in many viruses . We reveal that the human herpes virus Epstein-Barr virus ( EBV ) uses cellular functions of epigenetic repression to establish latency in infected B cells and a previously unknown mechanism to escape from it . We show that the herpesviral DNA genome is transcriptionally silenced by cellular mechanisms during viral latency , which includes excessive methylation of EBV DNA in vitro and in its human host in vivo . Epigenetic modifications like high nucleosome density and repressive histone marks shield and inactivate lytic viral genes during latency . EBV's genuinely repressed chromatin poses the problem of efficient reactivation to support virus synthesis . BZLF1 is the viral switch gene that induces the lytic phase of EBV's life cycle . We show here that this viral transcription factor erases static , repressive chromatin marks reversing epigenetic silencing . DNA methylation is preserved but no hindrance to lytic gene activation because BZLF1 directly binds to methylated viral DNA and overcomes heavily repressed chromatin without the need for active DNA demethylation . DNA demethylation has been thought to be a prerequisite for gene transcription but this virus falsifies this hypothesis and provides a new model for epigenetic gene regulation . | [
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] | 2012 | BZLF1 Governs CpG-Methylated Chromatin of Epstein-Barr Virus Reversing Epigenetic Repression |
Cognitive abilities and disorders unique to humans are thought to result from adaptively driven changes in brain transcriptomes , but little is known about the role of cis-regulatory changes affecting transcription start sites ( TSS ) . Here , we mapped in human , chimpanzee , and macaque prefrontal cortex the genome-wide distribution of histone H3 trimethylated at lysine 4 ( H3K4me3 ) , an epigenetic mark sharply regulated at TSS , and identified 471 sequences with human-specific enrichment or depletion . Among these were 33 loci selectively methylated in neuronal but not non-neuronal chromatin from children and adults , including TSS at DPP10 ( 2q14 . 1 ) , CNTN4 and CHL1 ( 3p26 . 3 ) , and other neuropsychiatric susceptibility genes . Regulatory sequences at DPP10 and additional loci carried a strong footprint of hominid adaptation , including elevated nucleotide substitution rates and regulatory motifs absent in other primates ( including archaic hominins ) , with evidence for selective pressures during more recent evolution and adaptive fixations in modern populations . Chromosome conformation capture at two neurodevelopmental disease loci , 2q14 . 1 and 16p11 . 2 , revealed higher order chromatin structures resulting in physical contact of multiple human-specific H3K4me3 peaks spaced 0 . 5–1 Mb apart , in conjunction with a novel cis-bound antisense RNA linked to Polycomb repressor proteins and downregulated DPP10 expression . Therefore , coordinated epigenetic regulation via newly derived TSS chromatin could play an important role in the emergence of human-specific gene expression networks in brain that contribute to cognitive functions and neurological disease susceptibility in modern day humans .
Cognitive abilities and psychiatric diseases unique to modern humans could be based on genomic features distinguishing our brain cells , including neurons , from those of other primates . Because protein coding sequences for synaptic and other neuron-specific genes are highly conserved across the primate tree [1] , [2] , a significant portion of hominid evolution could be due to DNA sequence changes involving regulatory and non-coding regions at the 5′ end of genes [3] , [4] . Quantifying these differences , however , is ultimately a daunting task , considering that , for example , the chimpanzee–human genome comparison alone reveals close to 35×106 single bp and 5×106 multi-bp substitutions and insertion/deletion events [3] . While a large majority of these are likely to reflect genetic drift and are deemed “non-consequential” with respect to fitness , the challenge is to identify the small subset of regulatory sequence alterations impacting brain function and behavior . Here , we combine comparative genomics and population genetics with genome-scale comparisons for histone H3-trimethyl-lysine 4 ( H3K4me3 ) , an epigenetic mark sharply regulated at transcription start sites ( TSS ) and the 5′ end of transcriptional units in brain and other tissues [5]–[8] that is stably maintained in brain specimens collected postmortem [7] , [9] . Our rationale to focus on TSS chromatin was also guided by the observation that the human brain , and in particular the cerebral cortex , shows distinct changes in gene expression , in comparison to other primates [10] . While there is emerging evidence for an important role of small RNAs shaping human-specific brain transcriptomes via posttranscriptional mechanisms [11] and increased recruitment of recently evolved genes during early brain development [12] , the role of TSS and other cis-regulatory mechanisms remains unclear . Here , we report that cell type-specific epigenome mapping in prefrontal cortex ( PFC , a type of higher order cortex closely associated with the evolution of the primate brain ) revealed hundreds of sequences with human-specific H3K4me3 enrichment in neuronal chromatin , as compared to two other anthropoid primates , the chimpanzee and the macaque . These included multiple sites carrying a strong footprint of hominid evolution , including accelerated nucleotide substitution rates specifically in the human branch of the primate tree , regulatory motifs absent in non-human primates and archaic hominins including Homo neanderthalensis and H . denisova , and evidence for adaptive fixations in modern day humans . The findings presented here provide the first insights into human-specific modifications of the neuronal epigenome , including evidence for coordinated epigenetic regulation of sites separated by megabases of interspersed sequence , which points to a significant intersect between evolutionary changes in TSS function , species-specific chromatin landscapes , and epigenetic inheritance .
The present study focused on the rostral dorsolateral PFC , including cytoarchitectonic Brodmann Area BA10 and the immediately surrounding areas . These brain regions represent a higher association cortex subject to disproportionate morphological expansion during primate evolution [13] , and are involved in cognitive operations important for informed choice and creativity [14] , [15] , among other executive functions . Given that histone methylation in neuronal and non-neuronal chromatin is differentially regulated at thousands of sites genome-wide [7] , we avoided chromatin studies in tissue homogenates because glia-to-neuron ratios are 1 . 4- to 2-fold higher in mature human PFC as compared to chimpanzee and macaque [16] . Instead , we performed cell type-specific epigenome profiling for each of the three primate species , based on NeuN ( “neuron nucleus” ) antigen-based immunotagging and fluorescence-activated sorting , followed by deep sequencing of H3K4me3-tagged neuronal nucleosomes . Prefrontal H3K4me3 epigenomes from NeuN+ nuclei of 11 humans , including seven children and four adults [7] , were compared to four chimpanzees and three macaques of mature age ( Table S1 ) . Sample-to-sample comparison , based on a subset of highly conserved Refseq TSS with one mismatch maximum/36bp , consistently revealed the highest correlations between neuronal epigenomes from the same species ( Table S2 ) . Strikingly , however , the H3K4me3 landscape in human neurons was much more similar to chimpanzee and macaque neurons , when compared to non-neuronal ( NeuN− ) cells [7] from the same specimen/donor or to blood ( Figure 1A ) . Therefore , PFC neuronal epigenomes , including their histone methylation landscapes at TSS , carry a species-specific signature , but show an even larger difference when compared to their surrounding glial and other NeuN− cells . To identify loci with human-specific H3K4me3 enrichment in PFC neurons , we screened 34 , 639 H3K4me3 peaks that were at least 500 bp long and showed a consistent >2-fold H3K4me3 increase for the 11 humans as compared to the average of the seven chimps and macaques and ( ii ) minimum length of 500 bp . We identified 410 peaks in the human genome ( HG19 ) with significant enrichment compared to the two non-human primate species ( with reads also mapped to HG19 ) after correcting for false discovery ( FDR ) , and we call these peaks “HP” hereafter for “human-specific peaks” ( Figure 1D; Table S3 ) . We had previously reported that infant and child PFC neurons tend to have stronger peaks at numerous loci , compared to the adult [7] . To better age-match the human and non-human primate cohorts , we therefore repeated the analysis with our entire , recently published cohort of nine adult humans without known neurological or psychiatric disease [7] , [8] . Using the same set of filter criteria ( >2-fold increase in humans compared to chimpanzees and macaques ) , we identified 425 peaks and 296 of them overlapped with the original 410 HP ( Table S3 ) . Furthermore , 345 of the 410 peaks overlapped with the overlapped with the peaks with >1 . 5-fold increase for nine adult humans ( compared to non-human primates; with correction for FDR ) ( Table S4 ) , indicating that HPs can be detected reliably . To obtain human depleted peaks we used a reciprocal approach where initial peaks were detected in chimpanzee and macaque . For the original cohort of 11 children and adult humans , this resulted in 61 peaks with a significant , at least 2-fold depletion in human PFC neurons ( Table S5 ) . 50 peaks defined by human-specific depletion in the mixed cohort of 11 children and adults were part of the total of 177 peaks with >1 . 5-fold decrease in the cohort of nine adults ( compared to each of the two non-human primate species; Table S6 ) . From this , we conclude that at least 471 loci in the genome of PFC neurons show robust human-specific changes ( gain , 410; loss , 61 ) in histone methylation across a very wide postnatal age range . We further explored chimpanzee-specific changes in the H3K4me3 landscape of PFC neurons by comparing human and chimpanzee peaks within the chimpanzee genome . To this end , we constructed a mono-nucleosomal DNA library from chimpanzee PFC to control for input , and mapped the neuronal H3K4me3 datasets from four chimpanzee PFC specimens , and their 11 human counterparts , to the chimpanzee genome ( PT2 ) . We identified 551 peaks in the PT2 genome that were subject to >2-fold gain and 337 peaks subject to >2-fold depletion , compared to human regardless of the H3K4me3 level in macaque ( Tables S7 and S8 ) . A substantial portion of these PT2-annotated peaks ( 133 and 40 peaks , respectively ) with gain or loss in chimpanzee PFC neurons matched loci with the corresponding , reciprocal changes specific to human PFC neurons in HG19 ( 410 and 61 peaks as described above ) . Genetic differences among these genomes and additional , locus-specific differences in nucleosomal organization ( leading to differences in background signal in the input libraries ) are potential factors that would lead to only partial matching of peaks when species-specific H3K4me3 signals are mapped within the human , or chimpanzee genome , respectively . These findings , taken together , confirm that genome sequence differences in cis are one important factor for the species-specific histone methylation landscapes in PFC neurons . Both catalytic and non-catalytic subunits of H3K4 methyltransferase complex are associated with transgenerational epigenetic inheritance in the worm , Caenorhabditis elegans , and other simple model organisms [17] , and furthermore , H3K4me3 and other epigenetic markings such as DNA cytosine methylation are readily detectable in non-somatic ( “germline”-related ) cells such as sperm , potentially passing on heritable information to human offspring [18] . Therefore , we wanted to explore whether a subset of the 410 loci with at least 2-fold H3K4me3 enrichment in human neurons are subject to species-specific epigenetic regulation in germ tissue . To this end , we screened a human and chimpanzee sperm database on DNA methylation [19] , in order to find out which , if any of the 410 sequences with human-specific H3K4me3 gain in brain overlap with a set of >70 , 000 sequences defined by very low , or non-detectable DNA methylation in human and chimpanzee sperm ( termed ( DNA ) “hypomethylated regions” in [19] ) . Of note , the genome-wide distribution of H3K4me3 and DNA cytosine methylation is mutually exclusive in germ and embryonic stem cells , and gains in DNA methylation generally are associated with loss of H3K4me3 in differentiated tissues [20] , [21] . Unsurprisingly therefore , 300/410 HP peaks in brain matched a DNA hypomethylated sequence in sperm of both species . Strikingly , however , 90/410 , or approximately 22% of HP were selectively ( DNA ) hypomethylated in human but not in chimpanzee sperm ( Table S3 ) , a ratio that is approximately 4-fold higher than the expected 5 . 7% based on 10 , 000 simulations ( p<0 . 00001; see also Text S1 ) ( Figure 1B ) . Conversely , the portion of HP lacking DNA hypomethylation in male germ cells of either species altogether ( 18/410 or 4% ) , or with selective hypomethylation in chimpanzee sperm ( 2/410 or 0 . 5% ) , showed a significant , 5-fold underrepresentation in our dataset ( Figure 1B ) . Thus , approximately one-quarter of the 410 loci with human-specific gain in histone methylation in PFC neurons also carry species-specific DNA methylation signatures in sperm , with extremely strong bias towards human ( DNA ) hypomethylated regions ( 22% ) compared to chimpanzee-specific ( DNA ) hypomethylated regions ( 0 . 5% ) . In striking contrast , fewer than ten of the 61 loci with human-specific H3K4me3 depletion in PFC neurons showed species-specific differences in sperm DNA methylation between species ( six human- and three chimpanzee-specific DNA hypomethylated regions; Table S5 ) . We noticed that , at numerous chromosomal loci , HP tended to group in pairs or clusters ( Table S3 ) . There were more than 245 ( 163 ) from the total of 410 HP spaced less than 1 ( or 0 . 5 ) Mb apart , which is a highly significant , 2- ( or 3- ) fold enrichment compared to random distribution within the total pool of 34 , 639 peaks ( Figure 1C; Text S1 ) . Therefore , sequences with human-specific gain in H3K4me3 in PFC neurons appear to be co-regulated with neighboring sequences on the same chromosome that are decorated with the same type of histone modification . Likewise , the actual number of human-depleted peaks within one 1 Mb ( n = 6 ) was higher than what is expected from random distribution ( n = 2 . 6 ) , ( p = 0 . 051 ) , albeit no firm conclusions can be drawn due to the smaller sample size ( n = 61 ) . This type of non-random distribution due to pairing or clustering of the majority of human-enriched sequences broadly resonates with the recently introduced concept of Mb-sized topological domains as a pervasive feature of genome organization , including increased physical interactions of sequences carrying the same set of epigenetic decorations within a domain [22] . Of note , H3K4 trimethylation of nucleosomes is linked to the RNA polymerase II transcriptional initiation complex , and sharply increased around TSS and broadly correlated with “open chromatin” and gene expression activity [5] , [6] . Therefore , we reasoned that a subset of human-enriched “paired” H3K4me3 peaks could engage in chromatin loopings associated with transcriptional regulation . This is a very plausible hypothesis given that promoters and other regulatory sequences involved in transcriptional regulation are often tethered together in loopings and other higher order chromatin [23] , [24] . To explore this , we screened a database obtained on chromatin interaction analysis by paired-end tag sequencing ( ChIA-PET ) for RNA polymerase II , a technique designed to detect chromosomal loopings bound by the Pol II complex [25] . Indeed , we identified at least three interactions that matched to our H3K4me3 peaks with human-specific gain in PFC neurons ( Table S9 ) , including a loop interspersed by approximately 2 . 5 Mb of sequence in chromosome 16p11 . 2–12 . 2 . This is a risk locus for microdeletions that are linked to a wide spectrum of neurodevelopmental disease including autism spectrum disorder ( ASD ) , intellectual disability ( ID ) , attention deficit hyperactivity disorder ( ADHD ) , seizures , and schizophrenia [26]–[31] . We were able to validate this interaction by chromosome conformation capture ( 3C ) , a technique for mapping long range physical interactions between chromatin segments [32] , in 2/2 human PFC specimens and also in a human embryonic kidney ( HEK ) cell line ( Figure 2 ) . We conclude that human-specific H3K4me3 peaks spaced as far apart as 1 Mb are potentially co-regulated and physically interact via chromatin loopings and other higher order chromatin structures . Next , we wanted to explore whether sequences with human-specific gain in histone methylation , including those that show evidence for pairing and physical interactions , could affect the regulation of gene expression specifically in PFC neurons . To this end , we first identified which portion from the total of 410 human-specific peaks showed much higher H3K4me3 levels selectively in PFC neurons , when compared to their surrounding non-neuronal cells in the PFC . Thus , in addition to the aforementioned filter criteria ( 2-fold increase in human PFC neurons compared to non-human primate PFC neurons ) , we searched for peaks with differential regulation among PFC neurons and non-neurons ( see Text S1 ) . We found 33 HP with selective enrichment in neuronal PFC chromatin ( termed neuHP in the following ) ( Figure S1; Table S10 ) . Among these were two HP spaced less than 0 . 5 Mb apart within the same gene , DPP10 ( chr2q14 . 1 ) , encoding a dipeptidyl peptidase-related protein regulating potassium channels and neuronal excitability ( Figure 3A–3B ) [33] . Interestingly , rare structural variants of DPP10 confer strong genetic susceptibility to autism , while some of the gene's more common variants contribute to a significant risk for bipolar disorder , schizophrenia , and asthma [34]–[36] . Histone methylation at DPP10 was highly regulated in species- and cell type-specific manner , with both DPP10-1 and DPP10-2 peaks defined by a very strong H3K4me3 signal in human PFC neurons ( Figure 3A ) , but only weak or non-detectable peaks in their surrounding NeuN− ( non-neuronal ) nuclei ( Figure S1; Table S10 ) or blood-derived epigenomes [7] . We then employed 3C assays across 1 . 5 Mb of the DPP10 ( chr2q14 . 1 ) in PFC of four humans . To increase the specificity in each 3C PCR assay , we positioned both the forward and reverse primer in the same orientation on the sense strand , and samples processed for 3C while omitting the critical DNA ligation step from the protocol served as negative control ( Figure 3A–3B ) . Indeed , 3C assays on four of four human PFC specimens demonstrated direct contacts between the DPP10-1 and -2 peaks ( Figure 3A ) . As expected for neighboring fragments [32] , DPP10-1 also interacted with portions of the interspersed sequence ( CR2 in Figure 3A ) . These interactions were specific , because several other chromatin segments within the same portion of chr2q14 . 1 did not show longer range interactions with DPP10-1 ( CR1 , CR3 in Figure 3A ) . We further verified one of the DPP10-1/2 physical interactions ( the sequences captured by primers 6 and 17 in Figure 3A ) in four of five brains using 3C-qPCR with a TaqMan probe positioned in fragment 6 . Furthermore , DPP10-2 interacted with a region ( “CR3” in Figure 3A ) 400 kb further downstream positioned in close proximity to a blood-specific H3K4me3 peak . No interactions at the DPP10 locus were observed in cultured cells derived from the H9 embryonic stem cell line ( H9ESC in Figure 3A ) , suggesting that these chromatin architectures are specific for differentiated brain tissue . Of note , similar types of DPP10 physical interactions were found in 3C assays conducted on PFC tissue of three of three macaques ( Figure 3B ) . Because macaque PFC , in comparison to human , shows much weaker H3K4 methylation at these DPP10 sequences , we conclude that the corresponding chromatin tetherings are not critically dependent on human-specific H3K4me3 dosage . Next , we wanted to explore whether human-specific H3K4 methylation at the DPP10 locus is associated with a corresponding change in gene expression at that locus . Notably , H3K4me3 is on a genome-wide scale broadly correlated with transcriptional activity , including negative regulation of RNA expression by generating very short ( ∼50–200 nt ) promoter-associated RNAs . These short transcripts originate at sites of H4K4me3-tagged nucleosomes and act as cis-repressors in conjunction with polycomb and other chromatin remodeling complexes [37] , [38] . Therefore , transcriptional activities due to the emergence of novel H3K4me3 markings in human PFC is likely to be complex , with unique functional implications specific to each genomic locus . To explore the transcriptome at the DPP10 locus in an unbiased manner , we performed RNA-seq on a separate cohort of three adult human PFC ( not part of the aforementioned ChIP-seq studies ) and compared their transcriptional landscapes to similar datasets from chimpanzee and macaque [39] , [40] . Indeed , we found an antisense RNA , LOC389023 , emerging from the second DPP10 peak , DPP10-2 ( chr2q14 . 1 ) ( Figures 3A and 4A ) . In an additional independent analyses ( using a set of human postmortem brains different from the ones used for RNAseq ) quantitative reverse transcriptase ( RT ) -PCR assays further validated the much higher expression of DPP10 antisense transcript in human ( Figure 4B ) , which occurred in conjunction with decreased expression of DPP10 exons downstream of the DPP10-2 promoter ( compared to chimp/macaque ) ( Figure 4A ) . Consistent with the H3K4me3 enrichment specifically in neuronal chromatin , the cellular expression of LOC389023 in adult PFC was confined to a subset of the neuronal layers ( II–IV ) , but absent in neuron-poor compartments such as layer I and subcortical white matter ( Figure 5A and unpublished data ) . Furthermore , the transcript was expressed in fetal and adult PFC but not in cerebellar cortex ( Figure 5B ) . We noticed that LOC389023 harbored a GC-rich stem loop motif that is known to associate with cis-regulatory mechanisms involved in transcriptional repression , including binding to TSS chromatin and components of Polycomb 2 ( PRC2 ) complex ( Figure 5C ) [37] , [41] . Consistent with a possible function inside the nucleus , LOC389023 was highly enriched in nuclear RNA fractions from extracted prenatal and normal ( non-degenerative ) adult human PFC , but not cerebellar cortex ( Figure 5B ) . Indeed , in transiently transfected ( human ) SK-N-MC neuroblastoma cells , LOC389023 showed a specific association with H3K4-trimethylated nucleosomes and SUZ12 ( Figure 5D ) , a zinc finger protein and core component of PRC2 previously shown to interact with stem loop motifs similar to the one shown in Figure 5C [37] . In contrast , EZH2 , a ( H3K27 ) methyltransferase and catalytic component of PRC-2 , did not interact with LOC389023 ( Figure 5D ) , consistent with previous reports on other RNA species carrying a similar stem loop motif [37] . These observations , taken together , are entirely consistent with the aforementioned findings that levels of DPP10 transcript , including exons positioned downstream of the DPP10-2 peak from which LOC389023 originates , are significantly decreased in human PFC as compared to macaque and chimpanzee . Conversely , these two primates show non-detectable ( RNAseq ) or much lower quantitative RT-PCR ( qRT-PCR ) LOC389023 levels in the PFC , as compared to human ( Figure 4A–4B ) . Taken together then , these findings strongly suggest that LOC389023 emerged de novo in human PFC neurons and interacts with localized chromatin templates to mediate transcriptional repression at the DPP10 locus ( Figure 6 ) . The aforementioned human-specific gains in histone methylation at DPP10 and the emergence of human RNA de novo at this locus could reflect a phylogenetically driven reorganization of neuronal functions that may have contributed not only to the emergence of human-specific executive and social-emotional functions , but also for increased susceptibility for developmental brain disease [42] . In this context , we noticed that the 33 neuHP ( which are defined by two criteria which are ( i ) human-specific gain compared to non-human primates and ( ii ) high H3K4me3 in PFC neurons but not their surrounding non-neuronal cells ) included multiple genes conferring susceptibility to neurological disease . Three loci , including DPP10 on chromosome 2q14 . 1 and two genes in close proximity on chromosome 3p26 . 3 , CNTN4 and CHL1 , both encoding cell adhesion molecules [34] , [43]–[45] , confer very strong susceptibility to autism , schizophrenia , and related disease . Other disease-associated loci with human-specific gain selectively in PFC neurons include ADCYAP1 , a schizophrenia [46] , [47] and movement disorder gene [48] that is part of a cAMP-activating pathway also implicated in posttraumatic stress [49] . PDE4DIP ( MYOMEGALIN ) ( Figure 1D ) encodes a centrosomal regulator of brain size and neurogenesis [50] that in some studies was 9-fold higher expressed in human as compared to chimpanzee cortex [51] , [52] . SORCS1 is implicated in beta amyloid processing and Alzheimer disease [53] , [54] and attention deficit hyperactivity disorder [55] , which again are considered human-specific neurological conditions [10] . Because four of 33 , or 12% of neuHP overlapped with neurodevelopmental susceptibility genes ( CNTN4 , CHL1 , DPP10 , SORCS1 ) , we then checked whether the entire set of 410 human-specific peaks is enriched for genes and loci conferring genetic risk for autism , intellectual disability , and related neurological disease with onset in early childhood . However , there was only minimal overlap with the Simons Foundation Autism Research Initiative database ( SFARI ) [56] , and Human unidentified Gene Encoded protein database ( HuGE ) for pervasive developmental disorder ( including autism ) associated polymorphism [57] , and recent reference lists for mental retardation and/or autism-related genes ( each of these databases five or fewer of the human-enriched peaks ) [58] . Likewise , there was minimal , and non-significant overlap with the set of 61 human- and 337 chimpanzee-depleted peaks , or the 551 chimpanzee-enriched in PFC neurons ( five or fewer of peaks/database ) . None of the lists of peaks with human- or chimpanzee-specific gain or loss of H3K4me3 revealed statistical significance for any associations with the Gene Ontology ( GO ) database . We conclude that DNA sequences subject to differential histone methylation in human or chimpanzee PFC neurons are , as a group , not clustered together into specific cellular signaling pathways or functions . Table 1 presents examples of disease-associated genes associated with human-specific gain , or loss of H3K4-trimethylation . We then asked whether the subset of DNA sequences with species- and cell type-specific epigenetic regulation , including the neuHP peaks mentioned above carry a strong footprint of hominid evolution . Indeed , nucleotide substitution analysis revealed that both DPP10 peaks DPP10 -1/2 , as well as ADCYAP1 , CHL1 , CNTN4 , NRSN2 , and SIRPA show a significantly elevated rate , with 2- to 5-fold increase specifically in the human branch of the primate tree , when compared to four other anthropoid primate species ( Pan troglodytes , Gorilla gorilla , Pongo abelii , Macaca mulatta ) ( Table S11 ) . The finding that both DPP10 peaks , DPP10-1 and -2 showed a significant , >4-fold increase in nucleotide substitution rates in the human branch of the primate tree—indicating “co-evolution” ( or coordinated loss of constraint ) —is very plausible given that chromatin structures surrounding these DNA sequences are in direct physical contact ( discussed above ) , reflecting a potential functional interaction and shared regulatory mechanisms between peaks . To further confirm the role of phylogenetic factors in the emergence of human-specific H3K4me3 peaks , we focused on the set of 33 neuHP and calculated the total number of human-specific sequence alterations ( HSAs ) , in a comparative genome analyses across five primates ( H . sapiens , P . troglodytes , G . gorilla , P . abelii , M . mulatta ) . We recorded altogether 1 , 519 HSAs , with >90% as single nucleotide substitutions , five >100 bp INDELs , one ( Alu ) retrotransposon-like element at TRIB3 pseudokinase consistent with a role of mobile elements in primate evolution [3] , and gain or loss of hundreds of regulatory motifs ( Table S12 ) . When compared to a group of ( neuronal ) H3K4me3 peaks showing minimal changes between the three primate species ( Table S13 ) , the neuHP , as a group , showed a significant , 2 . 5-fold increase in the number of HSA ( 20 . 08±5 . 52 HSAs versus 8 . 36±2 . 44 HSAs per 1-kb sequence , p = 2 . 4e−06 , Wilcoxon rank sum test; Figure S3 ) . The findings further confirm that genetic differences related to speciation indeed could play a major role for changes in the brain's histone methylation landscape , particularly for H3K4me3 peaks that are highly specific for human neurons ( neuHP ) . Interestingly , none of the above loci showed evidence for accelerated evolution of neighboring protein coding sequences ( Table S11 ) , reaffirming the view that protein coding sequences for synaptic and other neuron-specific genes are extremely conserved across the primate tree [1] , [2] . These DNA sequence alterations at sites of neuron-restricted H3K4me3 peaks ( with human-specific gain ) point , at least for this subset of loci , to a strong evolutionary footprint before the split of human–chimpanzee lineage several million years ago [3] . Next , we wanted to find out whether there is also evidence for more recent selective pressures at these loci . Indeed , a subset of neuHP contain H . sapiens-specific sequences not only absent in rodents , anthropoid primates , but even in extinct members of the genus homo , including H . neanderthalensis and H . denisova [59] . Some of the ancestral alleles ( including MIAT , SIRPA , NRSN ) shared with archaic hominins exhibit very low frequencies at 0%–3% in all modern populations , and therefore it remains possible that positive selection for newly derived alleles contributed to their high population frequencies in modern humans ( Table S14 ) . However , for the entire set of neuHP that are defined by high H3K4me3 levels in PFC neurons ( but not non-neurons ) , the number of HSAs that emerged after the human lineage was split from H . denisova or H . neanderthalensis were 3 . 31% and 1 . 75% , respectively , which is approximately 2-fold lower as compared to 32 control H3K4me3 peaks with minimal differences among the three primate species ( 5 . 03% and 3 . 77% ) . The 2-fold difference in the number of H . sapiens-specific alleles ( neuHP compared to control peaks ) showed a strong trend toward significant ( p = 0 . 067 ) for the Denisova , and reached the level of significance ( p = 0 . 034 ) for the Neanderthal genome ( based on permutation test with 10 , 000 simulations [60] ) . Taken together , these results suggest that at least a subset of the TSS regions with H3K4me3 enrichment in human ( compared to non-human primates ) were exposed to evolutionary driven DNA sequence changes on a lineage of the common ancestor of H . sapiens and the archaic hominins , but subsequently were stabilized in more recent human evolution , after splitting from other hominins . To further test whether or not there were recent , perhaps even ongoing selective pressures at loci defined by human-specific gain in H3K4me3 peaks of PFC neurons , we searched for overlap among the peaks in our study with hundreds of candidate regions in the human genome showing evidence of selection during the past 10–100 , 000 years from other studies . These loci typically extend over several kb , and were identified in several recent studies on the basis of criteria associated with a “selective sweep , ” which describes the elimination of genetic variation in sequences surrounding an advantageous mutation while it becomes fixed [61]–[64] . However , screening of the entire set of 410 human gain and 61 human depleted H3K4me3 sequences against nine datasets for putative selection in humans [65] revealed only five loci with evidence for recent sweeps ( Table S15 ) . One of these matched to the neuHP on chromosome 2q14 . 1 , corresponding to the second DPP10 ( DPP10-2 ) peak ( see above ) . In independent analyses , using the 1 , 000 genome database , we further confirmed recent adaptive fixations around DPP10-2 ( Table S16 ) , as well as two other loci , POLL and TSPAN4 . While it is presently extremely difficult to determine how much of the genome has been affected by positive selection ( of note , a recent metanalysis of 21 recent studies using total genomic scans for positive selection using human polymorphism data revealed unexpectedly minimal overlap between studies [65] ) , we conclude that the overwhelming majority of loci associated with human-specific H3K4me3 gain or loss in PFC neurons ( compared to non-human primates ) indeed does not show evidence for more recent selective pressures . To provide an example on altered chromatin function due to an alteration in a regulatory DNA sequence that occurred after the human lineage split from the common ancestor with non-human primates , we focused on a change in a GATA-1 motif ( A/TGATTAG ) within a portion of DPP10-2 found in human , within an otherwise deeply conserved sequence across many mammalian lineages ( Table S17 ) . Gel shift assays demonstrate that the human-specific sequence harboring the novel GATA-1 site showed much higher affinity to HeLa nuclear protein extracts , compared to the chimpanzee/other mammal sequence ( Figure 4C ) . The emergence of a novel GATA-1 motif at DPP10 is unlikely to reflect a systemic trend because the motif overall was lost , rather than gained in neuHP ( 10/355 versus 4/375 , χ2 p = 0 . 053 ) . Therefore , evolutionary and highly specific changes in a small subset of regulatory motifs at DPP10 and other loci could potentially result in profound changes in nuclear protein binding at TSS and other regulatory sequences , thereby affecting histone methylation and epigenetic control of gene expression in humans , compared to other mammals including monkeys and great apes . Of note , potentially important changes in chromatin structure and function due to human-specific sequence alterations at a single nucleotide within an otherwise highly conserved mammalian sequence will be difficult to “capture” by comparative genome analyses alone . For example , when the total set of 410 HP was crosschecked against a database of 202 sequences with evidence for human-specific accelerated evolution in loci that are highly conserved between rodent and primate lineages [66] , only one of 410 HP matched ( Table S15 ) . H3K4me3 is a transcriptional mark that on a genome-wide scale is broadly associated with RNA polymerase II occupancies and RNA expression [67] . However , it is also associated with repressive chromatin remodeling complexes and at some loci the mark is linked to short antisense RNAs originating from bidirectional promoters , in conjunction with negative regulation of the ( sense ) gene transcript [37] , [38] . Indeed , this is what we observed for the DPP10 locus ( Figure 6 ) . Therefore , a comprehensive assessment of all transcriptional changes associated with the evolutionary alterations in H3K4me3 landscape of PFC neurons would require deep sequencing of intra- and extranuclear RNA , to ensure full capture of short RNAs and all other transcripts that lack polyadenylation and/or export into cytoplasm . While this is beyond the scope of the present study , we found several additional examples for altered RNA expression at the site of human-specific H3K4me3 change . There were four of 33 neuHP loci associated with novel RNA expression specific for human PFC , including the aforementioned DPP10 locus . The remaining three human-specific transcripts included two additional putative non-coding RNAs , LOC421321 ( chr7p14 . 3 ) and AX746692 ( chr17p11 . 2 ) . There was also a novel transcript for ASPARATE DEHYDROGENASE ISOFORM 2 ( ASPDH ) ( chr19q13 . 33 ) ( Figure S2 ) . Furthermore , a fifth neuHP , positioned within an intronic portion of the tetraspanin gene TSPAN4 ( chr11p15 . 5 ) , was associated with a dramatic , human-specific decrease of local transcript , including the surrounding exons ( Figure S2 ) . Comparative analyses of prefrontal RNA-seq signals for the entire set of the 410 HP included at least 18 loci showing a highly consistent , at least 2-fold increase or decrease in RNA levels of human PFC , compared to the other two primate species ( Table S18 ) .
In the present study , we report that on a genome-wide scale , 471 loci show a robust , human-specific change in H3K4me3 levels at TSS and related regulatory sequences in neuronal chromatin from PFC , in comparison to the chimpanzee and macaque . Among the 410 sequences with human-specific gain in histone methylation , there was a 4-fold overrepresentation of loci subject to species-specific DNA methylation in sperm [19] . This would suggest that there is already considerable “epigenetic distance” between the germline of H . sapiens and non-human primates ( including the great apes ) , which during embryonic development and tissue differentiation is then “carried over” into the brain's epigenome . The fact that many loci show species-specific epigenetic signatures both in sperm [19] and PFC neurons ( Figure 1B ) raises questions about the role of epigenetic inheritance [68] during hominid evolution . However , to further clarify this issue , additional comparative analysis of epigenetic markings in brain and germline will be necessary , including histone methylation maps from oocytes , which currently do not exist . However , the majority of species-specific epigenetic decorations , including those that could be vertically transmitted through the germline , could ultimately be driven by genetic differences . On the basis of DNA methylation analyses in three-generation pedigrees , more than 92% of the differences in methylcytosine load between alleles are explained by haplotype , suggesting a dominant role of genetic variation in the establishment of epigenetic markings , as opposed to environmental influences [69] . A broad overall correlation between genetic and epigenetic differences was also reported in a recent human–chimpanzee sperm DNA methylation study [19] , and there is general consensus that the inherent mutability of methylated cytosine residues due to their spontaneous deamination to thymine is one factor contributing to sequence divergence at CpG rich promoters with differential DNA methylation between species [19] , [70] . Furthermore , human-specific sequences in the DNA binding domains of PRDM9 , which encodes a rapidly evolving methyltransferase regulating H3K4me3 in germ cells , were recently identified as a major driver for human–chimpanzee differences in meiotic recombination and genome organization [71] . It will be interesting to explore whether PRDM9-dependent histone methyltransferase activity was involved in the epigenetic regulation of the human-enriched H3K4me3 peaks that were identified in the present study . Another interesting finding that arose from the present study concerns the non-random distribution of histone methylation peaks with human-specific gain , due to a significant , 2- to 3-fold overrepresentation of peak-pairing or -clustering on a 500 kb to 1 Mb scale . This result fits well with the emerging insights into the spatial organization of interphase chromosomes , including the “loopings , ” “‘tetherings” and “globules” that bring DNA sequences that are spatially separated on the linear genome into close physical contact with each other [72] . Specifically , many chromosomal areas are partitioned into Mb-scale “topological domains” , which are defined by robust physical interaction of intra-domain sequences carrying the same set of epigenetic decorations [22] . These mechanisms could indeed have set the stage for coordinated genetic and epigenetic changes during the course of hominid brain evolution . The DPP10 ( 2q14 . 1 ) neurodevelopmental susceptibility locus provides a particularly illustrative example: here , two H3K4me3 peak sequences with strong human-specific gain were separated by hundreds of kilobases of interspersed sequence , yet showed a strikingly similar , 4-fold acceleration of nucleotide substitution rates specifically in the human branch of the primate tree . Importantly , the two H3K4me3 peaks , DPP10-1 and -2 , as shown here , are bundled together in a loop or other types of higher order chromatin . Therefore , our findings lead to a complex picture of the human-specific shapings of the neuronal epigenome , including a mutual interrelation of DNA sequence alterations and epigenetic adaptations involving histone methylation and higher order chromatin structures . The confluence of these factors could then , in a subset of PFC neurons ( Figure 5A ) , result in the expression of a novel antisense RNA , which associates with transcriptional repressors to regulate the target transcript in cis , DPP10 ( Figures 5D and 6 ) . While the present study identified a few loci , including the aforementioned DPP10 ( chromosome 2q14 . 1 ) , in which DNA sequences associated with a human-specific gain in neuronal histone methylation showed signs for positive selection in the human population , it must be emphasized that the overwhelming majority of sites with human-specific H3K4me3 changes did not show evidence for recent adaptive fixations in the surrounding DNA . Therefore , and perhaps not unsurprisingly , neuronal histone methylation mapping in human , chimpanzee , and macaque primarily reveals information about changes in epigenetic decoration of regulatory sequences in the hominid genome after our lineage split from the common ancestor shared with present-day non-human primates . Moreover , according to the present study , the subset of 33 sequences with human-specific H3K4me3 gain and selective enrichment in neuronal ( as opposed to non-neuronal ) PFC chromatin show a significant , 3-fold increase in human-specific ( DNA sequence ) alterations in comparison to non-human primate genomes . This finding speaks to the importance of evolutionary changes in regulatory sequences important for neuronal functions . Strikingly , however , the same set of sequences show a significant , approximately 1 . 5- to 2-fold decrease in sequence alterations when compared to the two archaic hominin ( H . denisova , H . neanderthalensis ) genomes . This finding further reaffirms that sequences defined by differential epigenetic regulation in human and non-human primate brain , as a group , are unlikely to be of major importance for more recent evolution , including any ( yet elusive ) genetic alterations that may underlie the suspected differences in human and neanderthal brain development [73] . However , these general conclusions by no means rule out a critical role for a subset of human-specific sequence alterations on the single nucleotide level within any of the HPs described here , including the DPP10 locus . Such types of single nucleotide alterations and polymorphisms may be of particular importance at the small number of loci with human-specific H3K4me3 gain that contribute to susceptibility of neurological and psychiatric disorders that are unique to human ( though it should be noticed that as a group , the entire set of sequences subject to human-specific gain , or loss , of H3K4me3 are not significantly enriched for neurodevelopmental disease genes ) . The list would not only include the already discussed ADYCAP1 , CHL1 , CNTN4 , and DPP10 , which were among the narrow list of 33 human-specific peaks highly enriched in neuronal but not non-neuronal PFC chromatin ) , but also DGCR6 , an autism and schizophrenia susceptibility gene [74] , [75] within the DiGeorge/Velocardiofacial syndrome/22q11 risk locus , NOTCH4 and CACNA1C encoding transmembrane signaling proteins linked to schizophrenia and bipolar disorder in multiple genome-wide association studies [76] , [77] , SLC2A3 encoding a neuronal glucose transporter linked to dyslexia and attention-deficit hyperactivity disorder [78] , [79] and the neuronal migration gene TUBB2B that has been linked to polymicrogria and defective neurodevelopment [80] . Furthermore , among the 61 peaks with human-specific loss of H3K4me3 is a 700-bp sequence upstream of the TSS of FOXP2 , encoding a forkhead transcription factor essential for proper human speech and language capabilities [81] and that has been subject to accelerated evolution with amino acid changes leading to partially different molecular functions in human compared to great apes [82] , [83] . The homebox gene LMX1B is another interesting disease-associated gene that is subject to human-specific H3K4me3 depletion ( Table 1 ) . While expression of many of these disease-associated genes is readily detectable even in mouse cerebral cortex [84] , the neuropsychiatric conditions associated with them lack a correlate in anthropoid primates and other animals . This could speak to the functional significance of H3K4 methylation as an additional layer for transcriptional regulation , with adaptive H3K4me3 changes at select loci and TSS potentially resulting in improved cognition while at the same time in the context of genetic or environmental risk factors contribute to neuropsychiatric disease . More generally , our findings are in line with a potential role for epigenetic ( dys ) regulation in the pathophysiology of a wide range of neurological and psychiatric disorders [85]–[88] . Our study also faces important limitations . While we used child and adult brains for cross-species comparisons , human-specific signatures in the cortical transcriptome are thought to be even more pronounced during pre- and perinatal development [89] . Therefore , younger brains could show changes at additional loci , or more pronounced alterations at the TSS of some genes identified in the present study , including the above mentioned susceptibility genes CNTN4 and myelomegalin/PDE4DIP , which are expressed at very high levels in the human frontal lobe at midgestation [90] . In this context , our finding that a large majority , or 345 of 410 H3K4me3 peaks showed a human-specific gain both in children and adults , resonates with Somel and colleagues [11] who suggested that some of the age-sensitive differences in cortical gene expression among primate species are due to trans-acting factors such as microRNA , s while cis-regulatory changes ( which were the focus of the present study ) primarily affect genes that are subject to a lesser regulation by developmental processes . More broadly , our studies supports the general view that transcriptional regulation of both of coding and non-coding ( including antisense ) RNAs could play a role in the evolution of the primate brain [91] . Furthermore , the cell type-specific , neuronal versus non-neuronal chromatin studies as presented here provide a significant advancement over conventional approaches utilizing tissue homogenate . However , pending further technological advances , it will be interesting to explore genome organization in select subsets of nerve cells that bear particularly strong footprints of adaptation , such as the Von Economo neurons , a type of cortical projection neuron highly specific for the hominid lineage of the primate tree and other mammals with complex social and cognitive-emotional skill sets [92] . Furthermore , our focus on PFC does not exclude the possibility that other cortical regions [93] , or specialized sublayers such as within the fourth layer of visual cortex that shows a complex transcriptional architecture [94] , show human-specific histone methylation gains at additional TSS that were missed by the present study . More broadly , the approach provided here , which is region- and cell type-specific epigenome mapping in multiple primate species , highlights the potential of epigenetic markings to identify regulatory non-coding sequences with a potential role in the context of hominid brain evolution and the shaping of human-specific brain functions . Remarkably , a small subset of loci , including the aforementioned DPP10 ( chromosome 2q14 . 1 ) , shows evidence for ongoing selective pressures in humans , resulting in DNA sequence alterations and the remodeling of local histone methylation landscapes , after the last common ancestor of human and non-human primates .
Text S1 contains detailed description for sample preparation for ChIP-seq and RNA-seq , qRT-PCR , gel shift , and 3C assays including primer sequences , RNA immunoprecipitation and in situ hybridization , bioinformatics and analyses of deep sequencing data , exploration of regulatory motifs , calculation of nucleotide substitution rates in the primate tree , and sweep analyses for polymorphic regions . | Primate and human genomes comprise billions of base pairs , but we are unlikely to gain a deeper understanding of brain functions unique to human ( including cognitive abilities and psychiatric diseases ) merely by comparing linear DNA sequences . Such determinants of species-specific function might instead be found in the so-called “epigenetic” characteristics of genomic regions; differences in the protein-packaged chromatin state in which genomic DNA exists in the cell . Here , we examine neurons from the prefrontal cortex , a brain region closely associated with the evolution of the primate brain , and identify hundreds of short DNA sequences defined by human-specific changes in chromatin structure and function when compared to non-human primates . These changes included species-specific regulation of methylation marks on the histone proteins around which genomic DNA is wrapped . Sequences subject to human-specific epigenetic regulation showed significant spatial clustering , and despite being separated by hundreds of thousands of base pairs on the linear genome , were in direct physical contact with each other through chromosomal looping and other higher order chromatin features . This observation raises the intriguing possibility that coordinated epigenetic regulation via newly derived chromatin features at gene transcription start sites could play an important role in the emergence of human-specific gene expression networks in the brain . Finally , we identified a strong genetic footprint of hominid evolution in a small subset of transcription start sites defined by human-specific gains in histone methylation , with particularly strong enrichment in prefrontal cortex neurons . For example , the base pair sequence of DPP10 ( a gene critically important for normal human brain development ) not only showed distinct human-specific changes , but also evidence for more recent selective pressures within the human population . | [
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] | 2012 | Human-Specific Histone Methylation Signatures at Transcription Start Sites in Prefrontal Neurons |
In honey bees ( Apis mellifera ) the behaviorally and reproductively distinct queen and worker female castes derive from the same genome as a result of differential intake of royal jelly and are implemented in concert with DNA methylation . To determine if these very different diet-controlled phenotypes correlate with unique brain methylomes , we conducted a study to determine the methyl cytosine ( mC ) distribution in the brains of queens and workers at single-base-pair resolution using shotgun bisulfite sequencing technology . The whole-genome sequencing was validated by deep 454 sequencing of selected amplicons representing eight methylated genes . We found that nearly all mCs are located in CpG dinucleotides in the exons of 5 , 854 genes showing greater sequence conservation than non-methylated genes . Over 550 genes show significant methylation differences between queens and workers , revealing the intricate dynamics of methylation patterns . The distinctiveness of the differentially methylated genes is underscored by their intermediate CpG densities relative to drastically CpG-depleted methylated genes and to CpG-richer non-methylated genes . We find a strong correlation between methylation patterns and splicing sites including those that have the potential to generate alternative exons . We validate our genome-wide analyses by a detailed examination of two transcript variants encoded by one of the differentially methylated genes . The link between methylation and splicing is further supported by the differential methylation of genes belonging to the histone gene family . We propose that modulation of alternative splicing is one mechanism by which DNA methylation could be linked to gene regulation in the honey bee . Our study describes a level of molecular diversity previously unknown in honey bees that might be important for generating phenotypic flexibility not only during development but also in the adult post-mitotic brain .
Many animal species have evolved the capacity to generate organisms with contrasting morphological , reproductive , and behavioral phenotypes from the same genome . However , the question of how such strikingly different organismal outputs occur with no standard genetic changes remains one of the key unresolved issues in biology . The nutritionally controlled queen/worker developmental divide in the social honey bee Apis mellifera is one of the best known examples of developmental flexibility in any phylum . Despite their identical nature at the DNA level , the queen bee and her workers are strongly differentiated by their anatomical and physiological characteristics and the longevity of the queen [1] . Furthermore , the behaviors of queens and workers are remarkably divergent , varying from the navigational proficiency of foragers to the colony-bound omnipresent chemical influences of the queen which control many aspects of the colony's existence . A diet of royal jelly during larval development clearly influences the epigenetic status of the queen's cells without altering any of the hardwired characteristics of her genome . As a result , two contrasting organismal outputs , fertile queens and non-reproductive workers , are generated from the same genome . Recently , we have shown that diet is not the only modulator of developmental trajectories in honey bees . By silencing the activity of DNA methyltransferase 3 ( DNMT3 ) , a key component of epigenetic machinery controlling global gene reprogramming , we were able to generate adult bees with queen characteristics [2] . This relatively simple perturbation of the DNA methylation system not only mimicked the dietary effect of royal jelly on phenotype but also changed the cytosine methylation pattern of an illustrator gene . Furthermore , analysis of gene expression in both queens and workers suggested that their alternative developmental pathways are associated with subtle transcriptional changes in a particular group of genes encoding conserved physio-metabolic proteins [2] , [3] . These findings prompted us to examine the hypothesis that significant behavioral differences between queens and workers are partly underpinned by differences between their brain epigenomes that have arisen from basically identical genomes during development . The choice of brain tissue is critical because it is a non-dividing , largely diploid tissue and is thus free of any complications that arise from differential genomic replication that may characterize polytene and endopolyploid tissues ( nearly all adult tissues of insects are non-diploid ) . In the context of methylomes , the use of whole bodies , or abdomens , creates an unacceptable mixture of methylomic signatures that simply cannot be deconvoluted in regards to function in any biologically meaningful manner . We used bisulfite converted brain DNA of both castes together with Solexa ( Illumina GA ) sequencing technology [4] to generate a DNA methylation map at single-nucleotide resolution across the Apis genome . This powerful approach has recently been used to compare DNA methylation profiles across a group of selected species , including DNA from a worker honey bee whole body [5] . The results confirm the antiquity of DNA methylation in eukaryotes [6] , [7] and provide more experimental evidence that this epigenomic modification is utilized in a lineage-specific manner [8]–[10] . Here we confirm that in contrast to heavily methylated mammalian genomes [11] , only a small and specific fraction of the honey bee genome is methylated [5] , [10] , [12] , [13] . Furthermore , the methylated cytosines occur in a group of genes showing a higher level of conservation than non-methylated genes . Nearly 600 of those genes show significant methylation differences in the brains of queens and workers , suggesting that their transcription might be epigenetically modulated in a context-dependent manner . Additional deep sequencing of selected genes in all three castes—queens , workers , and drones ( haploid males ) —suggests that brain methylation patterns are unique to each behavioral system . We discuss our findings in the context of epigenetic influences on global regulatory networks and their ability to generate contrasting phenotypic and behavioral outcomes from the same genome .
The sequencing of bisulfite converted Apis DNA yielded a dataset of 131 million reads after filtration and quality checks , 68 . 5% of which were mapped to unique genomic regions . The total sequence output was 18 . 8 giga bases ( 10 . 2 Gb for the queen and 8 . 6 Gb for the worker ) yielding a combined 20× coverage of the 260 Mb genome . Our reads also contained multiple coverage of thousands of unmethylated repeated elements ( ALUs and mariners ) giving false-positive rates of only 0 . 1% for the queen DNA and 0 . 2% for the worker DNA . Figure S1A shows the distribution of the coverage depth for all cytosines on both strands , whereas distribution of the CpG nucleotides is shown in Figure S1B . More than 90% of the 10 , 030 , 209 CpGs in the Apis genome were covered by at least two sequencing reads , allowing for the methylation status of individual sites to be determined with confidence . The characteristics of the brain methylomes of queens and workers are shown in Tables 1 and 2 . Three firm conclusions can be drawn . First , of the over 60 million cytosines that exist in the Apis genome , only approximately 70 , 000 are methylated . Second , nearly all the methylated cytosines occur in CpG dinucleotides . Third , the overriding majority of these methylated sites are in exons . Finally , the number of methylated cytosines in Apis is nearly three orders of magnitude lower than in the human genome [11] . This relatively small number of mCs overcomes the large technical hurdles that exist in both mammalian and plant genomes where the number of methylated sites that need to be examined in terms of their importance to biological phenomena is in the hundreds of millions . As shown in Table 1 the quantities of methylated CpGs ( mCpGs ) in queen and worker brain DNA are very similar , 69 , 064 and 68 , 222 , respectively , with 54 , 312 mCpGs in common . Similarly , the methylation levels of mCpG are almost identical in both castes ( Figure S2 ) . Methylation in honey bees appears to be restricted to cytosines associated with CpG dinucleotides , with no significant non-CpG or asymmetric methylation detected in either genomic or mitochondrial DNA ( Table 1 ) . Therefore , we conclude that methylation at non-CpG sites is either extremely rare or non-existent in the honey bee genome . In accord with previous analyses [2] , [5] , [12] , [13] , methylated sites in Apis appear to be exclusively located in exons with only infrequent mCpGs detected in intronic regions ( Table 2 ) . Most importantly , the methylated exons reside in genomic regions with low CpG observed/expected ( o/e ) ratios ( Figure 1 ) , whereas non-methylated exons fall into the category with high CpG o/e ratios . This bimodal profile is consistent with previous predictions based on bioinformatics analyses [10] , [12] , [13] and reflects the propensity of methylated Cs to be converted over time to thymines , resulting in a lower than expected density of the CpGs in methylated genes . However , the total number of methylated genes in Apis revealed by genome-wide bisulfite sequencing is 5 , 854 instead of the 4 , 000 predicted to be methylated on the basis of local CpG bias . One reason for this difference might be that some genes do not display significant CpG depletion as a result of evolutionary pressure to maintain a particular protein coding sequence . The genome-wide profiling of mCpGs confirms that methylated genes in Apis encode proteins showing a higher degree of conservation than proteins encoded by non-methylated genes [10] . Figures S3 , S4 , S5 and Table S1 show the results of our cross-species comparisons for methylated and non-methylated genes ( Figure S3 ) , for high-CpG and low-CpG genes ( Figure S4 ) , and high-CpG methylated and non-methylated genes ( Figure S5 ) . Most of the highly conserved genes are expected to be utilized by most tissues . In contrast , less conserved genes expressed in specialized tissues , such as those encoding odorant-binding proteins or odorant receptors , are not methylated ( not shown ) . The repeated elements , ALUs , and mariners that harbor most of the DNA methylation content in humans and plants are not methylated in the bee genome , certainly not in the brain ( Figures S6 ) . Similarly , the multi-gene families encoding rRNAs and tRNAs , mitochondrial DNA , and CpG islands show no evidence of methylation in the brain ( Figure S6 ) . Lastly , while methylation of sub-telomeric regions has been shown to be important for the control of telomere length and recombination [14] , the honey bee telomeres are also not methylated ( not shown ) . The lack of methylation in ALUs and transposons has also been reported in a recent study performed on DNA extracted from a worker's whole body [5] . Given the proposed role of cytosine methylation in defense against genomic parasites in plants and vertebrates [7] , the lack of methylation in ALU repeats and mariner transposons suggests that these mobile elements do not significantly impact on genome stability in honey bees . Indeed the bee genome contains an unusually small percentage of common types of transposons and retrotransposons found in other insects , possibly as a result of a strong selective pressure against mobile elements in male bees ( drones ) that develop from unfertilized eggs and carry a haploid set of chromosomes [15] . As in the human and Arabidopsis genomes [4] , [11] , methylation in Apis shows evidence of periodicity , although due to a much lower density of modified CpGs in this species the periodicity of 10 nucleotides ( one helical DNA turn ) is not obvious . However , a 3-base periodic pattern is clearly detectable , reflecting a preferential methylation of CpGs occupying the first and second position of the arginine codons ( autocorrelation data in Figure S7 ) . To validate our Solexa-based methylation results , we designed primers for selected regions of eight nuclear and four mitochondrial genes and re-sequenced the PCR-generated amplicons using 454 technology . As illustrated in Figure 2 , the 454 sequencing profiles are essentially identical with the Solexa-based results . All nuclear genes show differential methylation in the brains of queens and workers , including those cases where the methylation is almost absent , such as GB18602 in queen brains ( Figure 2 ) . No methylation was detected by this approach in the four selected mitochondrial amplicons ( not shown ) . To further expand our analysis , we increased the 454 bisulfite sequencing coverage of the eight nuclear genes selected for validation and also included DNA from drone brains . We obtained several thousand high-quality reads for 24 amplicons ( eight genes in three castes ) , with the total coverage ranging from 48 to 2 , 427× . The results shown in Figure 3 reveal both the dynamics and uniqueness of the methylation patterns in each cast . Out of the eight genes with differential worker/queen methylation , three show similar methylation patterns in workers and drones , but a distinct methylation pattern in queens ( Figure 3A ) . Three additional genes show similar methylation patterns in queens and drones , but a distinct pattern in workers ( Figure 3B ) . Two out of eight analyzed genes ( GB11061 - seryl-tRNA synthetase and GB15356 - syd , chromosome segregation; Figure 3C ) show distinct methylation patterns in all three castes . The latter finding was also confirmed by the analysis of full methylation heatmaps of GB15356 ( Figure 3D ) . GB15356 is strongly methylated in workers , with many reads showing complete methylation in the 5′-half of the amplicon ( Figure 3D ) . In queens , GB15356 methylation is strongly reduced and many reads show no methylation at all . Intriguingly , drones show a bimodal methylation pattern with approximately half of the reads methylated and the other half unmethylated ( Figure 3D ) . These results further illustrate caste-specific differences in methylation patterns and suggest a complex role of DNA methylation in the regulation of caste-specific epigenomic differences in the brain . To determine if there is a link between DNA methylation patterns and the striking morphological and behavioral polymorphisms of queen bees and workers , we examined the levels of CpG methylation in all annotated transcription units in both brains using high stringency criteria ( Supporting Information ) . This approach generated a list of 561 differentially methylated genes ( DMGs , Tables 3 and S2 ) showing significant methylation differences between the two castes . With the exception of highly expressed genes encoding ribosomal proteins , DMGs in Apis are expressed at low or moderate levels across all analyzed tissues ( Tables 3 and S2 ) . In several cases their transcriptional activities were found to be significantly up-regulated in some tissues relative to others . For example , the expression of 3-hydroxyl-CoA dehydrogenase ( GB13368 ) is much higher in the larva than in the adult brain , and RNA-binding protein ( GB12560 ) is significantly up-regulated in the ovaries relative to other tissues ( Table 3 ) . Almost all DMGs encode highly conserved , well-characterized proteins that have been implicated in core processes such as metabolism , RNA synthesis , nucleic acids binding , and signal transduction ( Table S2 ) . While a number of genes could not be clearly assigned to functional categories , their high level of conservation across phyla indicates that they are nevertheless likely to be involved in essential cellular processes ( e . g . GB18943 , GB13480 , and GB18037 ) . Several differently methylated genes encode proteins previously shown to be involved in either brain development or activity-dependent neural functions in both vertebrates and invertebrates . These include the Ephrin receptor GB1258516 [16] , a nicotinic acetylcholine receptor GB19703 , “no extended memory” GB16408 that is encoded by cytochrome B561 in Drosophila , two NMDA receptors GB19334 and GB15722 , and a membrane channel GB12287 that mediates cell adhesion . When defective , GB12287 results in the “big brain” phenotype ( Table S2 ) . We note that Dynactin , used in our previous study [2] to illustrate the methylation differences between the two castes during larval growth in both royal jelly-fed and RNAi-treated individuals , does not show differential methylation in the brain . However , two genes , GB11197 and GB13866 , encoding proteins associated with the large Dynein complex to which Dynactin also belongs are differentially methylated in the brain . Thus , the multi-protein Dynein complex appears to be epigenetically modulated during larval growth and in adult brains . Recently , Elango et al . [13] on the basis of bioinformatic analyses of a dataset of differentially expressed genes in brains of queens and workers proposed that “high-CpG genes in A . mellifera generally are more prone to epigenetic modulation than low-CpG genes . ” We have tested this hypothesis using our new caste-specific brain methylome data . The results summarized in Table S3 suggest that ( a ) the methylation of a gene is a decreasing function of its CpG richness ( Figure S8 ) , ( b ) the “caste-specific genes” [13] that are methylated have a lower CpG content than the non-methylated genes ( Table S3 ) , and ( c ) DMGs are over-represented in the low CpG genes ( Table S3 ) . Therefore , our results do not support the hypothesis of Elango et al . [13] . However , it is noteworthy that although the DMGs are generally CpG-depleted , they tend to be less CpG-depleted than those genes that are not differentially methylated ( Table S3 ) . This intermediate CpG density observed in DMGs underscores the uniqueness of this class of genes and suggests that they might be methylated in a distinct manner from the rest of methylated genes . This class of genes showing differential patterns of methylation associated with phenotypic polymorphism is thus of special importance in the study of complex context-dependent phenotypes . To explore the relationship between differential methylation and expression patterns in queens and workers , we examined in more detail the first gene on the DMG list ( GB18602 ) encoding a putative transmembrane protein with the YhhN domain conserved from bacteria to mammals . Figure 4 shows the distribution of mCpGs against the GB18602 gene model ( Figure 4A and 4B ) and the relative expression of two spliced variants in both castes ( Figure 4C ) . The L variant ( L ) encoding a long protein shows identical expression levels in both queens and workers , whereas the S variant ( S ) encoding a short protein is significantly up-regulated in queen relative to worker brain ( Figure 4C ) . The majority of the differentially methylated sites in the GB18602 locus map to the region spanning the additional cassette-exon that contains a Stop codon for the short protein encoded by the S transcript , suggesting a correlation between methylation and the outcome of alternative splicing of this gene in Apis . The increased level of methylation spanning the conditional splicing event ( insertion or skipping of the cassette-exon ) in the worker brain may impede the inclusion frequency of this exon into the mature transcript . Since the L variant is expressed at the same levels in both castes , the increased methylation in workers appears to be specifically affecting splicing , but not transcription . The observed differential pattern of expression of both transcripts in the brains of queens and workers ( Figure 4C ) supports this idea . Although the function of this gene is not known , the expression profiles of the Drosophila melanogaster ortholog CG7582 suggest that it encodes a protein involved in fat and sugar metabolism [17] . In the fly , which has no CpG methylation , this gene is not alternatively spliced and shows the highest levels of expression in the nervous system ( FlyAtlas . org ) . In contrast , the human ortholog of GB18602 , designated TMEM86A , produces alternatively spliced variants , including one encoding a truncated protein similar to the honey bee variant S . In addition to GB18602 we found numerous other examples of methylated genes in Apis in which most or even all clusters of mCpGs show a non-random , highly significant tendency to be near differentially spliced exons ( Figure S9 ) . Another salient finding relevant to methylation of intron-containing genes is the differential methylation of the multi-gene histone family in Apis . As illustrated in Table 4 and Figure S10 , all intron-containing histone genes are methylated , whereas intronless histone genes show no evidence of methylation . It is noteworthy that the methylated histone genes in Apis belong to a distinct class of histone variants . Unlike the canonical histones these variants are expressed constitutively and independently of replication and act as multifunctional regulators in a range of processes including DNA repair , transcription initiation and termination , meiotic recombination , etc . [18] . It is believed that they represent lineage specific innovation that is important for each organism's evolutionary specialization [18] .
The discovery of a functional DNA methylation system in honey bees and other invertebrates [1] , [7]–[10] , [19] has brought a fresh perspective to the study of epigenetic regulation of development and behavior . It reinforced the view that this covalent modification of DNA is an ancient and widely utilized evolutionary mechanism that was present in the basal Metazoa and has been recruited to serve diverse functions in modern organisms , including regulation of gene expression , cell differentiation , and silencing of transposons [20]–[22] . However , the trajectories from methylation changes to complex phenotypes are indirect , multi-level , and virtually unknown . For example , the hundreds of millions of methylated cytosines in the human genome and their large variation in different cell types in vivo pose a major challenge to uncovering those changes causative to phenotype . By contrast , the honey bee Apis mellifera shares its basic methylation enzymology with humans , yet as shown in this and other studies [5] , [10] , [12] , [13] only a small and specific fraction of its genome is methylated . The present results show that honey bees utilize methyl tags to mark a core of mostly conserved and ubiquitously expressed critical genes whose activities cannot be switched off in most tissues . Recent data suggest that in spite of their permanent expression these genes might not be required at the same level throughout development , or under changing environmental conditions [23]–[25] . In honey bees , feeding of newly hatched larvae destined to become queens with royal jelly leads to metabolic acceleration and increased growth driven by global but relatively subtle changes in the expressional levels of a large number of ubiquitous genes [2] , [3] , [10] . These initial stages of larval development are later followed by the activation of more specific pathways to lay down caste-specific structures [3] , [10] . Interestingly , adult queen bees continue to be fed royal jelly , suggesting that this highly specialized diet is important for maintaining their reproductive as well as behavioral status . One possibility is that adult queens adjust their brain methylomes according to external instructions from their diet . One of the ingredients of royal jelly , phenyl butyrate [26] , is a known histone deacetylase inhibitor and growth regulator that has been implicated in improving cognitive deficits in mice [27] and in life extension of Drosophila [28] . Although the significance of phenyl butyrate in royal jelly is not yet understood , it is conceivable that this complex diet evolved to provide two important functions for honey bees . It primarily serves as the source of nutrients for queen development but also as the regulator of epigenetic networks controlling gene expression in the brain . In addition to having different morphologies , reproductive capacities , and distinct behaviors , the genetically identical queen and worker honey bees also have different synaptic densities in their brains . In a recent study , Groh and Rossler [29] proposed that such developmental , diet-induced heterochrony results in fewer synapses in olfactory centers in queens , which may result in poorer performance on olfactory learning tasks compared to workers . Recent studies using rodent models provided strong support for an idea that the nervous system has co-opted epigenetic mechanisms utilized during development for activity-dependent brain functions , including the generation and maintenance of long-term behavioral memories in adulthood [30] , [31] . Not surprisingly , DNA methylation has also been found to be involved in memory processing in honey bees [32] , highlighting the significance of this epigenomic setting in conserved brain functions . These findings also provided evidence that DNA methylation , once believed to be an inert process after cellular differentiation , is dynamically regulated in the adult brain . Although both DNA methylation and chromatin remodeling have been implicated in these processes , the specific biological mechanisms underlying such adaptations remain largely unknown . Our study provides experimental evidence that at least 560 differentially methylated ubiquitously expressed genes are involved in generating molecular brain diversity in female honey bees . Although it is still unclear how methylation might be linked to the gene regulatory networks , it has been proposed that DNA methylation together with changes in the histone profiles has the capacity to adjust DNA accessibility to cellular machinery by changing chromatin density [33]–[35] . Our findings support this notion and suggest that this mechanism provides an additional level of transcriptional control to fine tune the levels of messenger RNAs , including differentially spliced variants , encoded by the conserved genes . The association of mCpG clusters with alternatively spliced exons and genes containing introns in Apis is reminiscent of the distribution of mCpGs around the exon/intron junctions in human genes [36] . Epigenetic control of both splicing and mRNA levels might be utilized in different lineages , suggesting that a direct relationship between gene methylation and transcription is a widely spread phenomenon in both the animal and plant kingdoms [8] , [37] . Cytosine methylation may interact with other epigenetic features , such as distinctive histone modification signatures that have been shown to correlate with the splicing outcome in a set of human genes [33]–[35] . The correlation between methylation and splicing is further highlighted by the differential methylation of two classes of histone genes in Apis . We find that only intron-containing histone variants are methylated , whereas intronless canonical histone genes are not methylated . Interestingly , histone variants have been implicated in multiple conserved roles in eukaryotes [18] and therefore are part of the cellular maintenance systems together with other ubiquitously expressed genes . In a broader context , methylated cytosines may specify information to set up , proliferate , and regulate splicing patterns during cellular processes such as development and differentiation . Thus , rather than switching the genes on and off by promoter methylation , the intragenic methylation in Apis operates as a modulator of gene activities . As a result the entire topology of a complex brain network can be reprogrammed by subtle adjustments of many genes that act additively to produce a given phenotype [38] . Such adjustable DNA methylation levels generating variability in the transcriptional output of methylated genes could underlie genetically inherited propensity to phenotypic variability in accord with the recently proposed model of stochastic epigenetic variations as a heritable force of evolutionary change [39] . The technical advantages of the low number of methylated cytosines in the genome , together with diet-controlled phenotypes arising from the same genome , make the honey bee an extremely tractable , simplified in vivo system in which to examine fundamental principles underpinning transitions from methylomes to organismal plasticity . In particular , the absence of promoter methylation in honey bees brings into focus gene body methylation as an important mechanism controlling various aspects of transcription . The utility of honey bees for understanding the intricacies of this process in the behavioral context can now be experimentally tested .
Total DNA was extracted from dissected gland-free brains of 50 age-matched egg-laying queens ( 2 . 5 wk old ) and from fifty 8-d-old workers . These individuals represent early stages of the reproductive life of queen bees and mature young workers capable of performing foraging tasks [19] . 5 µg of high molecular weight DNA were used for fragmentation using the Covaris S2 AFA System in a total volume of 100 µl . Fragmentation-run parameters: Duty cycle 10%; Intensity: 5; Cycles/burst: 200; Time: 3 min; number of cycles: 3 , resulting in a total fragmentation-time of 180 s . Fragmentation was confirmed with a 2100 Bioanalyzer ( Agilent Technologies ) using a DNA1000 chip . Fragment sizes were 140 bp on average for queen and worker DNAs , respectively . The fragmented DNAs were concentrated to a final volume of 75 µl using a DNA Speed Vac . End repair of fragmented DNA was carried out in a total volume of 100 µl using the Paired End DNA Sample Prep Kit ( Illumina ) as recommended by the manufacturer . For the ligation of the adaptors , the Illumina Early Access Methylation Adaptor Oligo Kit and the Paired End DNA Sample Prep Kit ( Illumina ) were used , as recommended by the manufacturer . For the size selection of the adaptor-ligated fragments , we used the E-Gel Electrophoresis System ( Invitrogen ) and a Size Select 2% precast agarose gel ( Invitrogen ) . Each fragmented DNA was loaded on two lanes of the E-gel . Electrophoresis was carried out using the “Size Select” program for 16 min . According to the standard loaded ( 50 bp DNA Ladder , Invitrogen ) , 240 bp fragments were extracted from the gel , pooled , and directly transferred to bisulfite treatment without further purification . For the bisulfite treatment we used the EZ-DNA Methylation Kit ( Zymo ) as recommended by the manufacturer with the exception of a modified thermal profile for the bisulfite conversion reaction . The conversion was carried out in a thermal cycler using the following thermal profile: 95°C for 15 s , 50°C for 1 h , repeat from step 1 , 15× , 4°C for at least 10 min . The libraries were subsequently amplified , using the Fast Start High Fidelity PCR System ( Roche ) with buffer 2 , and Illuminas PE1 . 1 and PE2 . 1 amplification primers . PCR thermal profile: 95°C for 2 min , 95°C for 30 s , 65°C for 20 s , 72°C for 30 s , then repeat from step 2 , 11× , 72°C for 7min , hold at 4°C . PCR reactions were purified on PCR purification columns ( MinElute , Qiagen ) and eluted in 20 µl elution buffer ( Qiagen ) . 1 µl of the libraries were analyzed on a 2100 Bioanalyzer ( Agilent Technologies ) using a DNA1000 chip . The fragment sizes were 240 bp and 243 bp for the queen and worker libraries , respectively . The estimated concentrations of the libraries were 0 . 8 ng/µl for the queen library and 5 . 8 ng/µl for the worker library . We used 8 pM of single stranded DNA per lane for Solexa sequencing . In total we sequenced 6 lanes . Worker: 1 . single end - 36 bp - 10 , 187 , 567 reads ( ×2 ) ; 2 . paired end - 76 bp - 7 , 960 , 842 reads ( ×2 ) ; 3 . paired end - 76 bp - 7 , 444 , 938 reads ( ×2 ) ; 4 . paired end - 76 bp - 11 , 642 , 135 reads ( ×2 ) . Queen: 1 . paired end - 76 bp - 16 , 752 , 247 reads ( ×2 ) ; 2 . paired end - 76 bp - 16 , 778 , 784 reads ( ×2 ) . For sequencing we used a Solexa Genoma Analyzer GAIIx with a v2 Paired End Cluster Generation Kit - GA II ( Illumina ) and v3 36 bp Cycle Sequencing Kits ( Illumina ) . Extraction of sequences was done using Illumina Pipeline v1 . 4 software . Image analysis and basecalling was done using Illumina SCS v2 . 5 software . Reads were mapped using BSMAP-1 . 0240 with minor modifications [40] . A number of trimming and mapping options were assessed , and the conditions yielding the highest genome coverage depth was used for further processing ( -s 12 -v 5 -k 6 , for word size , number of mismatches , and number of words ) . Only the reads mapping uniquely were used . Mapping was carried out on a Linux cluster running Debian 5 . 0 ( lenny ) . To increase the accuracy of methylation calls , only those cytosines fulfilling neighborhood quality standards NQS41 were counted [41]; namely , we only took into account bases of quality 20 or more , flanked by at least three perfectly matching bases of quality 15 or more . Deamination efficiency was assessed using the observation that the genomic repeats are not methylated in the honeybee ( Figure S3 ) . The deep coverage of these repeated sequences allowed us to estimate that the deamination rate is 99 . 76% for the queens and 99 . 71% for workers . The methylation status of each cytosine was then assessed by comparing the number of methylated and non-methylated reads to a binomial distribution with a probability of success equal to the deamination rate and a number of trials equal to the number of reads mapping to that cytosine and adjusting the resulting p values for multiple testing with the method of Benjamini and Hochberg [42] . An adjusted p value of 0 . 05 was used as a threshold for methylation calls . All statistical computations were carried out using the R language ( www . r-project . org ) . Honeybee ESTs and predicted genes were loaded into a Mysql database and visualized with Gbrowse ( www . gmod . org ) , where CpG methylation levels in queens and workers were added as separate tracks . Base-wise differences between queen and workers were estimated using Fisher exact tests . Gene-wise differences were assessed by generalized linear models of the binomial family , where methylation levels were modeled as functions of two categorical variables: caste and CpG position . p values were adjusted for multiple testing with the method of Benjamini and Hochberg [42] . Illumina sequencing and BSMAP mapping results were confirmed by 454 sequencing of a set of bisulfite amplicons . Amplicon sequences were selected using raw methylome data and the following criteria: minimum coverage - 5 mapped reads for each queen and worker sample; minimum 2 mCpGs within a maximum of ∼600 bp of sequence showing at least 50% difference in methylation levels between the two samples . In addition , four regions of mtDNA were selected . All primers and other details are listed in Table S4 . All molecular protocols are described elsewhere [2] , [9] , [10] , [43] . | The queen honey bee and her worker sisters do not seem to have much in common . Workers are active and intelligent , skillfully navigating the outside world in search of food for the colony . They never reproduce; that task is left entirely to the much larger and longer-lived queen , who is permanently ensconced within the colony and uses a powerful chemical influence to exert control . Remarkably , these two female castes are generated from identical genomes . The key to each female's developmental destiny is her diet as a larva: future queens are raised on royal jelly . This specialized diet is thought to affect a particular chemical modification , methylation , of the bee's DNA , causing the same genome to be deployed differently . To document differences in this epigenomic setting and hypothesize about its effects on behavior , we performed high-resolution bisulphite sequencing of whole genomes from the brains of queen and worker honey bees . In contrast to the heavily methylated human genome , we found that only a small and specific fraction of the honey bee genome is methylated . Most methylation occurred within conserved genes that provide critical cellular functions . Over 550 genes showed significant methylation differences between the queen and the worker , which may contribute to the profound divergence in behavior . How DNA methylation works on these genes remains unclear , but it may change their accessibility to the cellular machinery that controls their expression . We found a tantalizing clue to a mechanism in the clustering of methylation within parts of genes where splicing occurs , suggesting that methylation could control which of several versions of a gene is expressed . Our study provides the first documentation of extensive molecular differences that may allow honey bees to generate different phenotypes from the same genome . | [
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] | 2010 | The Honey Bee Epigenomes: Differential Methylation of Brain DNA in Queens and Workers |
It is well-known that weakly electric fish can exhibit extreme temporal acuity at the behavioral level , discriminating time intervals in the submicrosecond range . However , relatively little is known about the spatial acuity of the electrosense . Here we use a recently developed model of the electric field generated by Apteronotus leptorhynchus to study spatial acuity and small signal extraction . We show that the quality of sensory information available on the lateral body surface is highest for objects close to the fish's midbody , suggesting that spatial acuity should be highest at this location . Overall , however , this information is relatively blurry and the electrosense exhibits relatively poor acuity . Despite this apparent limitation , weakly electric fish are able to extract the minute signals generated by small prey , even in the presence of large background signals . In fact , we show that the fish's poor spatial acuity may actually enhance prey detection under some conditions . This occurs because the electric image produced by a spatially dense background is relatively “blurred” or spatially uniform . Hence , the small spatially localized prey signal “pops out” when fish motion is simulated . This shows explicitly how the back-and-forth swimming , characteristic of these fish , can be used to generate motion cues that , as in other animals , assist in the extraction of sensory information when signal-to-noise ratios are low . Our study also reveals the importance of the structure of complex electrosensory backgrounds . Whereas large-object spacing is favorable for discriminating the individual elements of a scene , small spacing can increase the fish's ability to resolve a single target object against this background .
Weakly electric fish are commonly found in the freshwater systems of South America and Africa [1 , 2] . These nocturnal fish use a unique sensory modality , called the “electrosense , ” to help them navigate , communicate , and find prey in the absence of strong visual cues [3] . The electrosense involves a specialized electric organ that emits an electric discharge resulting in a dipole-like electric field in the surrounding water [4] . The transdermal potential ( the so-called “electric image” ) is continuously monitored via electroreceptors found in the skin layer . Changes in the spatial properties of the electric image can provide cues that help the fish determine the location , size , and electrical properties of nearby objects [5–10] . Recent studies have shed new light on the weakly electric fish's perceptual world . In the context of distance perception , the amplitude and width of an electric image were shown to be analogous to visual contrast and blur [11] . The electric image produced by an object can also be distorted by nearby objects; consequently , conductive objects can act as electrosensory “mirrors” [12] . In contrast with the visual sense , however , the electrosense has no focusing mechanism and is limited to the near-field , so it is generally considered a “rough” sensory modality [13–16] . In fact , the range of active electrolocation in weakly electric fish is likely only about one body length [7] , and considerably less for small prey-like objects [17] . Within this range , much is known about the fish's temporal acuity [18 , 19] , but relatively little is known about the fish's ability to resolve multiple nearby objects . Here , we consider the notion of “electro-acuity , ” analogous to the notion of visual acuity found in the visuo–sensory lexicon , to investigate the quality of electrosensory information in the spatial domain . A common measure of acuity in other sensory systems is the just-noticeable difference , or the minimum difference between two stimuli such that they are perceptually distinct [20] . In the present context , we consider an analogous measure to describe the quality of electrosensory input available for a discrimination task . We define this measure as the minimum spatial separation of two objects ( Smin ) , such that two distinct peaks remain in the electric image on the fish's skin ( Figure 1 ) . Using a 2-D finite element method model of A . leptorhynchus' electric field [9] , we show that Smin is smallest in the fish's midbody and decreases for objects placed farther away from the fish . This suggests an interesting contrast with the “electrosensory fovea” in the head region [10 , 17] , where the highest density of electroreceptors is found [21] . Overall , we found that electroacuity is poor relative to visual acuity in humans , but is comparable with that of the human somatosensory system . Despite the apparent low quality of electrosensory signals , weakly electric fish are able to detect small prey [7 , 17] . Although there is no direct evidence , it is reasonable to assume that they do so even in the presence of noisy background signals [7] . In a related task ( object tracking ) , background noise has been shown to degrade performance [22 , 23] . Single-cell recordings in midbrain neurons have further revealed that some low-frequency background signals can interfere with directional selectivity [24] . It is thus believed that some of the natural behaviors exhibited by the fish play a central role in signal extraction . In particular , simulations have suggested that tail-bending could improve object detection by increasing the electric image's amplitude [13 , 14] . It has also been suggested that the back-and-forth swimming , or scanning motion , observed in these fish could be used to generate specific electrolocation cues [25–28] , although this has not yet been demonstrated . Indeed , to elucidate the nature of these motion-related cues , we have simulated this scanning motion and show that , under some conditions , this behavior could assist in extracting small prey-like signals from large background ones . We show that the component of the electric image produced by a sufficiently dense background does not change during scanning , whereas the one produced by the prey object , albeit miniscule in comparison , does . This process is similar to motion-related cues and active sensing techniques seen in other contexts [28 , 29] .
In the following analyses , we use our previously described finite-element model of the electric field produced by A . leptorhynchus ( see Materials and Methods and [9 , 30] ) . Figure 1A shows the simulated dipole-like potential map for this fish in the presence of two prey-like objects . Such objects do not greatly perturb the fish's natural field due to their small size and conductivity ( which is similar to that of the water ) . Figure 1B shows overlays of electric images due to single objects at different locations ( i . e . , each image is computed separately ) . Such images show characteristic shapes but vary systematically in amplitude and width with rostral–caudal and lateral location [5 , 9 , 10] . Figure 1C shows images produced by object pairs for three different interobject distances ( shown in inset ) . Prey-like objects that are located too close together ( green trace ) produce a single peak in the electric image ( similar to the images in Figure 1B ) , while objects separated by a larger distance produce two distinct peaks ( red trace ) . The blue trace illustrates the electric image in which two peaks are just barely distinguishable; we define the associated interobject distance as Smin . Thus , Smin , measured in these noiseless conditions , delineates a limit to electroacuity . A smaller Smin suggests better electroacuity ( i . e . , increased spatial resolution ) . For this specific prey-like object and rostro–caudal location , the Smin is 14 mm . This suggests that , at this lateral distance , these two objects must be separated by at least 14 mm , a distance approximately five times their diameter , to be distinguished . Electroacuity varies for different lateral and rostro–caudal object locations ( Figure 2 , see insets ) . Figure 2A and 2C shows the effects of object size and conductivity , respectively , on electroacuity for different lateral positions ( rostro–caudal position fixed near the fish's midpoint , 0 . 11 m ) . Smin increases ( electroacuity decreases ) for objects that are placed farther away from the fish , regardless of object size or conductivity . When objects are far from the fish , Smin is roughly independent of object size ( Figure 2A ) . At the closest location possible for the largest object ( blue curve ) , Smin is smaller than for the other object sizes . This is a consequence of the relative sharpening of the image for close large objects ( see Figure 1B ) . The sharpness of an image can be quantified by the reciprocal of its normalized width ( width divided by amplitude ) . Image sharpness decreases ( normalized width increases ) with lateral distance and , in general , is independent of object size [5] . However , object size becomes a factor for locations close to the skin ( see largest object in Figure 2A and 2B ) , as larger objects produce relatively sharper images in these cases [9] . Note also that there is a slight inflection at a lateral distance of 0 . 016 m ( Figure 2A and 2C ) due to the spatial heterogeneity of the electric field ( higher density of field lines near the zero potential line , which curves rostrally as seen in Figure 1A ) . Figure 2B and 2D shows the effects of object size and conductivity , respectively , on electroacuity for different rostro–caudal positions ( lateral object center-to-skin distance fixed at 0 . 012 m ) . In general , Smin is smaller for larger objects , all along the length of the fish . The largest objects ( 2 cm ) can actually be distinguished in the artificial condition of overlapping ( i . e . , the two objects are fused into a single composite peanut-shaped object ) , suggesting a mechanism for shape discrimination under some conditions . The position x = 0 . 11 m suggests a point of optimal acuity along the side of the fish . The two peaks in the image can be distinguished more easily for objects in this region because this is the rostro–caudal location where electric images are sharpest [9 , 10] , so that there is minimal interaction between the individual images produced by each object . Object conductivity has comparatively little effect on the Smin in both lateral and rostro–caudal directions ( Figure 2C and 2D ) . Overall , Smin varies much more in the lateral direction than in the rostro–caudal direction ( compare Figure 2A–2C and 2B–2D ) due to the relatively large changes in image sharpness as lateral object distance increases [5 , 8] . The effect of water conductivity on electroacuity was also studied for a specific location ( x = 0 . 11 m , y = 0 . 015 m ) . For the range of water conductivity values found in the rivers in which A . leptorhynchus live ( between 0 . 00085 and 0 . 01135 S/m [2] ) , Smin changes only slightly . As an overall trend , Smin decreased as water conductivity diminished ( from 15 . 5 to 12 . 5 mm as water conductivity decreased from 0 . 05 to 0 . 0005 S/m ) . As a first step toward understanding electroacuity in a more natural context , the electric images produced by differently sized arrays of background objects ( with “plant-like” conductivity ) were studied systematically . In Figure 3A , the red trace shows the electric image produced by a single such object located 0 . 11 m caudally from the tip of the fish's head ( red object in inset located close to the fish's midpoint ) . The orange trace shows the electric image produced by three objects: the central one ( red ) plus one ( orange ) added 0 . 03 m on each side . In a similar progression , electric images are shown for up to 11 objects . With larger numbers of aligned objects , the electric images converge . Thus , for an array of seven objects ( approximately a fish body length ) , the image is almost the same as with 11 objects . The electric images are each marked by a singular peak because the interobject distance is too small ( at this lateral distance of 0 . 05 m ) to resolve different peaks , i . e . , object separation is less than Smin . The small bumps at approximately 0 . 03 m and 0 . 2 m are due to abrupt changes in fish geometry near the head and tail , respectively , and are not due to individual objects within the background array . Similar results were also observed for object arrays positioned closer to the fish , where different peaks were observed in the electric image , as well as for solid bars of increasing widths ( unpublished data ) . Figure 3B shows the effect of changing the object spacing in similar arrays . At the largest spacing ( red ) , the image is dominated by the contribution from the central object . For arrays that are more spatially dense ( green , blue ) , the contributions of individual objects are blurred , resulting in an image with a broad peak . These object arrays provide a simplified model of the background signals comprising a natural electrosensory landscape . To better understand how weakly electric fish are able to detect miniscule prey in the presence of large-background signals , we calculated the electric image produced by a small Daphnia-like prey object against a large-background array of objects ( Figure 4 ) . Even though the prey is located just 0 . 008 m from the fish's skin ( compared with the 0 . 05 m lateral position of the background ) , the electric image with the prey and background is not much different than the one obtained with the background alone ( largest deviation between the two images is about 4%; compare Figure 4A and 4B ) . The interesting feature , however , is that the overall image shape is similar regardless of the fish's position during a simulated scanning movement ( even though the background was simulated as a discrete set of objects ) . This can be understood in terms of electroacuity: the background objects are too close together to be distinguished and thus form a blurred image . It is critical to note that during the scan , however , the small blip created by the prey does change location within the electric image ( Figure 4B; note that the images do not overlap perfectly ) . Next , we demonstrate this point explicitly by considering the time-varying image during a simulated scanning movement . The consequence of the relative differences between background and prey during a scanning movement is that the small prey signals can be extracted by looking at the time-varying transdermal potential at specific locations along the fish's body . Figure 5 illustrates the temporal profile of the transdermal potential at two distinct body locations under different conditions . The signal measured at Location A ( see inset ) reveals a clear prey-dependent component ( Figure 5A , compare green and blue traces ) . Note also that this prey signal ( in the presence of the background ) is very similar to that for the prey-alone condition ( Figure 5A , compare blue and red traces ) . When the interobject distance in the background becomes too large , as in Figure 5B , the background image is no longer blurred and individual object characteristics appear , thereby masking the prey-specific signal . This effect can be even more pronounced when the objects are randomly spaced over the same area ( Figure 5C ) . Figure 5D–5F shows a similar result for a different body location ( note that the prey-specific signal occurs slightly later in time at this location , due to the scanning direction ) . Figure 5A and 5B suggests that as the objects within the background are increasingly separated , the prey will be less distinguishable . We confirm these observations in terms of a signal-to-noise ratio ( SNR ) of prey signal versus background ( see Materials and Methods ) . The SNR decreases with increasing interobject separation in the background ( Figure 6; left axis , blue trace ) . For reference , we can compare this situation with the expected discriminability of two individual objects ( see Materials and Methods ) , where the electric image components due to each object become increasingly distinct as the objects are moved apart ( Figure 1B; Figure 5C: right axis , green trace ) . This applies to the case of two prey-like objects in the absence of background , as in Figure 1A and 1C and Figure 2 , as well as to the case of two background-like objects . In a more natural context , the blurriness of the electrosense interestingly has the effect of enhancing sensory performance . And indeed , this should apply to a wide range of electrosensory landscapes , as blurriness will be unaffected by small changes in object conductivity ( Figure 2C and 2D ) .
Many recent studies have contributed to our understanding of electrosensory scene analysis [9 , 26 , 27 , 33 , 34] . In particular , Rother et al . [12] have shown that the electric image due to two objects is the result of complex interactions between the effects of each object . To extend these studies in the context of object discrimination , we have introduced the notion of electroacuity . This measure , comparable to the notion of visual acuity , has helped us quantify the “sharpness” of the electrosense in the spatial domain . Studies have suggested that this was a rather “rough” sensory modality [7 , 14] , and our findings , in terms of the sensory input , confirm this quantitatively . For example , we found that two prey-like objects located within the range of natural prey detection ( which is typically less than 20 mm , [17] ) , must be separated by 9 mm for the electric image to show features of both objects ( Figure 2 ) . We characterize this limit by the quantity Smin , analogous to the psychophysical notion of the just noticeable difference and the Rayleigh criterion in optics ( see Materials and Methods ) . Electroacuity is much lower than human visual acuity [35] . In contrast , the electrosense fares much better when compared with tactile two-point discrimination in humans , where thresholds are as high as 50 mm in some body locations [36 , 37] . The magnitude of Smin will increase with the disparity in both the image amplitudes and widths for the two objects . It will also be influenced by nonlinear effects between image amplitude and image width for close pairs of objects ( which our simulations implicitly capture ) , but we have not systematically investigated them here ( but see [12] ) . That said , to a reasonable approximation , Smin is proportional to the normalized width of the image due to each of the objects ( see Materials and Methods ) . Figure 2B shows that for locations in the rostral half of the fish , Smin changes relatively little . This interesting feature is primarily due to the uniformity of the field in this range: the current lines are nearly perpendicular to the fish body axis . The field uniformity is a result of the spatial filtering effects ( smoothing ) due to the tapered body shape [9 , 10 , 38] . This means that the spatial extent of an object's influence on this field ( image sharpness ) will be relatively constant . For locations closer to the midbody , the field lines are more concentrated ( i . e . , the field is not as uniform as for more rostral locations ) , so the influence of the object is more focused . The image amplitude also increases in this range of body locations ( Figure 1B; Figure 5 of [9] ) , further contributing to a sharper image . However , as outlined in detail in Materials and Methods , although the image amplitude increases , then decreases , in the rostro-to-caudal direction [9] , Smin is determined by image sharpness ( normalized image width ) and is much less sensitive to absolute amplitude ( Figure 2B , compare red and green traces ) . In terms of the quality of sensory input , our results reveal a point of optimal electroacuity located in the fish's midbody . This is in contrast to the notion that optimal discrimination should occur near the fish's head region , the electrosensory fovea , which has the highest density of electroreceptors [21] . However , determining acuity in the head region is a complex task due to a number of factors . For example , some enclosed environments can interact with this geometry and produce an electric “funneling” effect that increases the local field amplitude and enhances object discrimination [39 , 40] . Although these studies were performed on a different species of electric fish ( pulse-type discharge ) with a different electric organ morphology , a detailed investigation of the head region in A . leptorhynchus ( the species we consider here ) is still warranted . This will , however , require a more complicated 3-D model , so determining how the electric field , body geometry , and receptor density combine to determine electroacuity in the electrosensory fovea is not possible at this time . Nevertheless , on the lateral body surface , the combination of body geometry and current density are such that electric images are sharpest in the midbody [9] , thus allowing the objects to be closer in that region before their electric images blur and form a single peak . This apparent tradeoff between more receptors rostrally and higher-quality images caudally may explain why prey detection occurs at approximately equal rates over all rostro–caudal locations [17] . An additional consideration , which again points to interesting future research , is that our current model does not account for the electric field dynamics that could in principle cause midbody acuity to vary over the electric organ discharge cycle . It is possible , for example , that the lowest Smin seen here in the midbody region may shift to other locations for other phases of the cycle , due to the spatial variation of the field in time [38] . In a strict sense , the values we obtain for Smin can be considered as an upper-bound limit on spatial acuity , since various noise sources would undoubtedly result in lower acuity at the behavioural level . However , there are additional cues available from the electric image , and potentially from other sensory modalities , which could help distinguish adjacent objects , and hence increase acuity . Specifically , the electric image produced by two objects is still wider than the image of one of the objects alone , even when their individual peaks are not discernable ( see Figure 1C ) . Moreover , we have only considered two adjacent objects located in parallel with the fish's contour . Indeed , different criteria are required to measure the discrimination of objects that are situated one-behind-the-other ( i . e . , perpendicular to the fish's contour ) . Rother et al . [12] have studied such object locations , but not in the context of spatial acuity . We have shown that electroacuity did not vary with object conductivity . This implies that the fish's ability to resolve two equally sized , equally conductive objects is the same , regardless of whether these objects are animate or inanimate . However , it is possible that the addition of environmental noise to the electric images would make one of these types of objects more “resolvable , ” as the SNR would be greater for high-conductivity objects . Water conductivity , on the other hand , does ( slightly ) affect Smin . Our results are in accord with other findings , which state that object detection is best-achieved in low-conductivity water [17 , 41 , 42] , confirming the notion that increased water conductivity acts as a type of electrosensory “fog . ” To resolve all of these issues , further behavioral experiments are required . Our current studies using a 2-D electric field model [9] have generated many hypotheses to test in such experiments . Despite the fact that the 2-D model very accurately reproduces many spatial aspects of the electric field [9] , ultimately a more detailed 3-D model of the time-varying electric field will be necessary . Measuring electroacuity ( behaviorally ) in these fish could be accomplished by using a forced-choice experimental paradigm . In this task , the fish could be trained to choose between a single object and a pair of objects , with a reward given for the choice of the latter . An estimate of electroacuity could be obtained by tracking the accuracy of the choices as the interobject distance was decreased ( see [33 , 43 , 44] for similar protocols ) . Weakly electric fish are subject to a wide range of stimuli in natural electrosensory landscapes . Large conducting boundaries , such as rocks or the river bottom , constitute extensive background clutter [27] . The fish therefore has the challenging task of extracting small prey signals from enormous background ones . To investigate this scenario , we have modeled a plant-like background . We have shown that , as this background increases in width , the electric images produced on the fish's skin converge ( i . e . , the images are blurred ) . In fact , the image is not much different for background arrays ranging from 0 . 18 m to 0 . 3 m wide . In the presence of such a large-background image , the Smin for prey objects would be much larger than for the conditions we have considered thus far , and may in fact be defined only for much larger objects . In other words , as discussed in the following , the electric image component due to the background obscured that due to the two small prey-like objects . Figure 4 clearly indicates that the effect of a prey is miniscule in the presence of a relatively large-background array . Even at different times during a simulated scanning behavior , the prey only affected the image due to the background by a few percent at most . This suggests that for any static “snapshot” the fish would not be able to extract the prey signal from the large-background signal . On the other hand , weakly electric fish are known to detect minuscule signals under some laboratory conditions [17 , 45] , and presumably can do so in the wild while hunting . We suggest that movement is required in these situations . In fact , due to the blurring effect , the background component of the electric image does not change with fish scanning , whereas the prey component does ( see Figure 4B ) . As a consequence , the small-prey signal is revealed during the scanning motion by looking at the transdermal potential at individual locations on the fish's body ( Figure 5A and 5D ) . In contrast , when background objects are more separated , the prey signal remains confounded by the background ( Figure 5B , 5C , 5E , and 5F ) . The separation of small signals from background is a universal problem in sensory processing . In vision , the so-called figure-from-ground separation has been extensively studied; luminance and contrast differences between figure and ground provide information-rich cues for this task . In the absence of such cues , however , relative motion ( due to figure , background , or observer motion ) can provide information that is critical for effective figure-ground separation [29 , 46] . Motion of an auditory stimulus can also provide cues for sound-source localization in a noisy background [47 , 48] . Though the particular mechanisms involved in each sense may differ [47] , both rely on coherent changes in stimulus parameters ( spatial correlation in vision , systematic sweep of interaural time delays in audition ) . Similarly , we have shown that motion can also lead to small-signal detection in an electrosensory landscape under certain conditions . When the constituent objects of a complex scene are close enough to each other to result in a blurred ( spatially uniform ) image , a small spatially localized prey signal will pop out due to motion cues ( and without motion the prey signal is masked by the large background ) . On the other hand , to evaluate the specific features of a scene , a greater spacing among constituent objects is required ( see Figure 6 ) . It is important to note that we have only considered the information available to the electrosensory system and have not considered the potential for extracting this information . Information encoded by individual electroreceptor afferents will be pooled in the hindbrain electrosensory lateral line lobe ( ELL ) . Here , the principle neurons , ELL pyramidal neurons , have receptive fields that vary systematically in size across three somatotopic maps . The largest of these receptive fields ( lateral segment map ) are about 2 cm in width along the body axis of the fish; the smallest receptive fields ( centromedial segment map ) are about 0 . 5 cm in width [26 , 49] . As previous studies have shown , the different maps may take on different roles depending on the type of information available [26 , 50] . In the context of this paper , the most focused images due to nearby prey objects may be preferentially encoded using pyramidal neurons of the centromedial segment ( smaller receptive fields ) , and the more blurred images due to background objects may be encoded by neurons of the lateral segment ( larger receptive fields ) . In addition , there are mechanisms in the ELL ( via feedback pathways ) that can cancel out predictable or redundant stimuli [51 , 52] . In principle , when the background is spatially uniform ( blurred ) , such feedback mechanisms could cancel out the large-image component due to the background and further enhance small signal extraction during scanning . Recent studies on the signal processing features of ELL neurons have shown that coherence to spatially global time-varying input is high-pass [53] , suggesting again that responses to spatially dense backgrounds can be filtered out . Information encoded by ELL neurons is transmitted to higher-order neurons of the midbrain . Recent studies have described plasticity and motion sensitivity in these neurons [24 , 54] , but further studies will be required to determine how these neurons contribute to the computations involved with prey detection and discrimination in complex landscapes . It has been widely hypothesized that the stereotypical back-and-forth scanning behavior exhibited by weakly electric fish could be used to generate electrolocation cues [25 , 55 , 56] . In fact , cues obtained by self-motion are used by many different animals to extract relevant sensory features [28] . For example , primates move their fingers laterally to detect fine features in textured surfaces , which would otherwise go unnoticed [57]; rodents perform whisking behaviors [58]; and insects , such as mantids , can obtain information about an object's depth using a side-to-side “peering” movement ( by means of motion parallax cues; [59] ) . Such examples have led to the reasonable notion that the exploratory behaviors exhibited by weakly electric fish , such as the aforementioned scanning , act similarly to provide relevant information from complex electrosensory scenes . Our study describes the nature of these motion-generated cues for the first time , and indeed shows that their effectiveness depends on context . In particular , our results predict that weakly electric fish should exhibit the specific search behavior that is most suitable for signal extraction in a given context . The scanning behavior would be best suited for spatially dense or uniform backgrounds , whereas the fish might preferentially use tail-bending in cases where the background is sparse ( as in Figure 5B , 5C , 5D , and 5F where the prey component is confounded with the background signal ) . In future studies , we aim to determine which behaviors are used most frequently by the fish to explore electrosensory landscapes with varying spatial characteristics .
The 2-D electric field of a 21-cm A . leptorhynchus was simulated using a finite-element–method model described previously in [9] . Briefly , the model reproduces the field measured at one phase of the quasisinusoidal electric organ discharge . It consists of three components: an electric organ ( EO ) , a body compartment , and a thin skin layer . The EO current density and the conductivities of the three components were optimized using raw data provided by Christopher Assad [38] . The optimized EO current density is spatially structured; as compared with a simple dipole , it is skewed toward the tail . Such a profile in the EO current density , as well as the spatial filtering due to the tapered body shape , reproduces the asymmetric “multipole” electric field [9 , 10 , 27] . To distinguish this situation from that of a simple dipole , we sometimes refer to the fish's electric field as “dipole-like . ” This model is a 2-D simplification that is static in time , and so , in principle , any results derived from it are qualitative . It is important to note , however , that the model provides a quantitatively accurate representation of the data measured in the horizontal plane [9] , and thus should be very reliable . Of course , as we note in the Results and Discussion sections , there are some questions that will require a detailed time-varying 3-D model . Electric images were calculated in one of two ways using custom MATLAB subroutines . In Figures 1–4 , images are defined as the differences in transdermal potential , with and without objects present ( this has become the standard definition of an electric image , [5] ) . In Figure 5 , images are displayed as the raw transdermal potential , the natural electrosensory input . All images are shown only for the side of the fish body closest to the objects . Water conductivity was set to 0 . 023 S/m , as in [38] . The prey chosen , Daphnia magna , was modeled as a 3 mm–diameter disc with a conductivity of 0 . 0303 S/m , as in [15 , 17] . The background objects ( 2-cm discs ) simulated throughout this paper were based on the conductivity of the aquatic plant Hygrophilia [22] ( 0 . 0005 S/m ) . The goal was not to mimic the plant's geometry accurately , but rather to get a general idea of the effects caused by varying backgrounds with plant-like conductivity and size . To estimate the fish's ability to resolve two distinct objects ( electroacuity ) , the minimal distance Smin was calculated . This measure is the interobject distance , which delimits an electric image with one peak from one with two peaks ( for example , see Figure 1C ) . This quantity depends on a number of parameters such as the object's size , its rostro–caudal and lateral location , and the water conductivity . We can develop more intuition for how Smin behaves assuming that images of objects are idealized Gaussians . Consider two Gaussians along the x-axis , of similar standard deviation σ and amplitudes , but centered on μ1 and ( −μ1 ) , respectively . Assuming linear superposition , their sum along the x-axis will have one or two maxima , depending on the relation between the standard deviation and the mean , i . e . , on the relative width . It can be shown that Smin in this case corresponds to ( 2σ ) . If the amplitudes of the Gaussians change in the same way , as they do when the object is closer to the fish , Smin remains the same; it will increase , however , if there is disparity in the amplitudes . Smin will also increase with increasing image width . Although this provides some insight on the behavior of Smin , it is important to note that linear superposition is not valid in general ( for example , see Rother et al . [12] ) . Also , all of the images we show are computed using our model , which can accommodate arbitrary object combinations . In no cases do we assume linear superposition of images due to individual objects . For a given pair of objects , the rostral object's center coordinates were chosen as the spatial location for which the Smin was determined . Therefore , this object was held stationary during a given Smin measurement . The caudal object was moved systematically in the caudal direction until two distinct peaks appeared in the electric image ( object center-to-skin distance was kept constant ) . Using this technique , Smin measurements were accurate to within 0 . 5 or 1 mm , representing the chosen sampling ( see error bars in Figure 2 ) . In the last part of the paper , where fish motion is simulated , a scanning speed of 0 . 1 m/s was chosen , which is in the range of measured weakly electric fish scanning velocities [45 , 56] . For quantifying the SNR between the two different transdermal potential time series ( Figure 5 , green and blue curves ) , i . e . , the ones produced by the background alone ( Φback ) and by the background and prey ( Φback+prey ) , a root-mean-squared difference measure was used ( Equation 1 ) : where n represents the number of different fish locations that were simulated , i . e . , samples of the transdermal potential at a given body location during a 1-s scan ( we chose n = 21 ) . A large SNR value means that the two time series are very distinct . We have also quantified the discriminability of two objects as they are separated ( Equation 2 ) . Here , we assumed that the separate ( simulated ) electric images generated by each object is a spatial Gaussian function ( along one dimension; each of mean μi and width σi ) and have computed the discriminability d′ [60 , 61]: | Extracting and characterizing small signals in a noisy background is a universal problem in sensory processing . In human audition , this is referred to as the cocktail party problem . Weakly electric knifefish face a similar difficulty . Objects in their environment produce distortions in a self-generated electric field that are used for navigation and prey capture in the dark . While we know prey signals are small ( microvolt range ) , and other environmental signals can be many times larger , we know very little about prey detection in a natural electrosensory landscape . To better understand this problem , we present an analysis of small object discrimination and detection using a recently developed model of the fish's electric field . We show that the electric sense is extremely blurry: two prey must be about five diameters apart to produce distinct signals . But this blurriness can be an asset when prey must be detected in a background of large distracters . We show that the commonly observed “knife-like” scanning behaviour of these fish causes a prey signal to “pop-out” from the blurry background signal . Our study is the first to our knowledge to describe specific motion-generated electrosensory cues , and it provides a novel example of how self-motion can be used to enhance sensory processing . | [
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] | 2007 | Spatial Acuity and Prey Detection in Weakly Electric Fish |
It is estimated that 190 million individuals are at risk of blindness from trachoma , and that control by mass drug administration ( MDA ) is reducing this risk in many populations . Programs are monitored using prevalence of follicular trachoma disease ( TF ) in children . However , as programs progress to low prevalence there are challenges interpreting this indirect measure of infection . PCR and sero-surveillance are being considered as complementary tools to monitor low-level transmission , but there are questions on how they can be most effectively used . We use a previously-published , mathematical model to explore the dynamic relationship between TF and PCR throughout a control program and a sero-catalytic model to evaluate the utility of two cross-sectional sero-surveys for estimating sero-conversion rates . The simulations show that whilst PCR is more sensitive than TF at detecting infection , the probability of detecting at least one positive individual declines during an MDA program more quickly for PCR than for TF ( for the same sample size ) . Towards the end of a program there is a moderate chance of a random sample showing both low PCR prevalence and higher TF prevalence , which may contribute to the lack of correlation observed in epidemiological studies . We also show that conducting two cross-sectional sero-surveys 10 years apart can provide more precise and accurate estimation of epidemiological parameters than a single survey , supporting previous findings that whilst serology holds great promise , multiple cross-sections from the same community are needed to generate the most valuable information about transmission . These results highlight that the quantitative dynamics of infection and disease should be included alongside the many logistical and practical factors to be considered in designing a monitoring and evaluation strategy at the operational research level , in order to help subsequently inform data collection for individual country programs . Whilst our simulations provide some insight , they also highlight that some level of longitudinal , individual-level data on reinfection and disease may be needed to monitor elimination progress .
Trachoma is targeted for elimination as a public health problem by 2020 by the World Health Organization . At the global level there has been a high degree of programmatic success in terms of control [1] , as the established intervention strategies have been highly effective in a large proportion of endemic districts . There do , however , remain a number of districts , primarily in Ethiopia , where disease and infection remain persistent and endemic , despite long-term intervention programmes [2 , 3] . Irrespective of a district or region’s current elimination status robust surveillance systems must be able to effectively monitor overall programmatic success , confirm elimination as well as re-emergence [4] , however the appropriate choice of diagnostic and sampling strategy is unlikely to be uniform when trying to address each of the three aforementioned surveillance questions . Currently polymerase chain-reaction ( PCR ) testing of eye-swabs and clinical examination for inflammation are the most established diagnostic tools for monitoring trachoma surveillance within the key indicator group of 1-9 year olds [5] , although the vast majority of programmatic decisions are currently made based only on TF prevalence . However , an increasing number of studies are looking to assess the value of ‘alternative indicators’ ( serology and PCR for trachoma surveillance ) , as it has been suggested that other factors may cause TF-like symptoms making it difficult to ascertain at low TF prevalence levels whether what is being observed is truly TF . Current epidemiological data suggests that following a period of intervention within a community the relationship between PCR and TF prevalence within the community becomes non-linear [6] and the results from the two diagnostics no longer correspond well with one another . Therefore it can be challenging and unclear how to interpret and explain such data in a programmatic setting [6] . As global prevalence of trachoma continues to decline it becomes increasingly challenging to identify and confirm TF cases and the cost of training graders becomes more expensive [7] , therefore sero-surveillance for trachoma is also currently being evaluated as more long-term tool to monitor low-level transmission and re-emergence ( in addition to PCR ) [8 , 9] . For sero-surveillance to be informative for understanding re-emergence it is first important to understand how serology relates to transmission intensity , and the duration of time which individuals in the population remain sero-positive , in order for us to understand what future sero-prevalence in the community will be post-elimination . As programs approach the elimination phase and non-linearity in diagnostic outcomes become apparent or the utility of new surveillance tools needs to be evaluated , well-designed operational research is required before country specific programme surveillance recommendations can be provided . In this study , we provide two suggestions on how future data for trachoma surveillance could be collected in order to help provide insights into the dynamics of disease as population prevalence declines to help guide monitoring and evaluation . Here we evaluate how the proportion of TF and PCR positive individuals changes over the course of an intervention period and during re-emergence to assess if , or how , this impacts our probability of detecting infection or disease within a community . We assess whether these variations can be explained by the differences in the proportion of people in each state that would test positive with each of the different diagnostic tools . With our findings we suggest the types of data that could be collected to fully elucidate and understand the differences in prevalence patterns observed in these data . We then use simulated serological data to assess the identifiability of key epidemiological parameters from single and multiple cross-sections sampling a range of different age groups . Through this we advise on the optimal range of age groups to sample from in order to estimate the sero-conversion and sero-reversion rates for the population and for the key indicator group of 1-9 year olds .
We simulated prevalence data within a single community of 3 , 000 individuals ( 1/3rd of which were assumed to be aged 1-9 years , denoted N1 ) [10 , 11] to assess the probability of identifying TF and PCR positive individuals . To simulate data we used an age-structured ordinary differential equation ( ODE ) transmission model . We used a previously validated model structure that was identified as the most parsimonious and appropriate model when fitting to a single cross-section of age-specific PCR and TF prevalence data [12] . We used the framework of the classic SEIR model structure , with slightly different notation to indicate the different infection states for trachoma Fig 1 . Individuals were susceptible to infection in the ( S ) state , exposed and incubating in the ( E ) state , who would test PCR positive , infected and infectious ( ID ) with detectable TF and who would also test PCR positive and those who remained diseased but were no longer infectious to others ( D ) ( TF positive only ) , individuals in the D state were susceptible to re-infection with a reduced probability . Those who were re-infected then returned to the AI state ( both PCR and TF positive ) [12] . For each endemicity we simulated 3 annual rounds of MDA with azithromycin distributed to the whole community , assuming 80% coverage and a treatment efficacy of 85% [13] . The baseline values of the model parameters are presented in Table 1 . The code for the model is available as a supplementary file . We used the transmission model to generate prevalence data at different sampling intervals to obtain the proportion of individuals PCR and TF positive at any point in time . The first scenario considered that the sampling was conducted at 6 monthly intervals over the course of 3 annual treatment rounds and we evaluated the probability ( Pi ) of detecting at least 1 TF and/or PCR positive individual . The sample size used at each sampling time point was fixed across the 3 year period . The probability of identifying a PCR positive individual in a given sample collected at time i was the proportion of the population who we would expect to be PCR positive: P i P C R = E i + A I i N 1 ϕ ( 1 ) Where ϕ is the sensitivity of the assay . The probability of detecting at least one PCR positive individual was given by: 1 - ( 1 - P i P C R ) N s a m p l e ( 2 ) where Nsample was the sample size , which was used , unless otherwise stated , 50 children [11] . Only AI and D state individuals test positive for TF therefore the probability of detecting a TF positive individual was: P i T F = A i + D i N 1 ψ ( 3 ) where ψ is the sensitivity of the diagnostic test for TF [17] . We note that sensitivity is a difficult parameter to quantify , particularly for TF , additionally it may reduce as local and global prevalence declines . The probability of detecting a single positive individual was similar to the expression for PCR above ( Eq 2 ) . For the second scenario we simulated the model to endemic equilibrium for a range of TF prevalence levels ( between 6% and 50% ) and assessed after 3 rounds of annual MDA what the probability of detecting at least 1 PCR and TF positive individual was at the end of the intervention period only . For the final time point we also simulated sampling Nsample individuals from a population of individuals with this prevalence of PCR or TF , to demonstrate the range of possible outcomes which one would expect if the dynamics followed the transmission model ( i . e . some correlation between PCR and TF positivity ) to evaluate the range of outcomes that occur by chance . Lastly , we assessed the probability of detecting at least 1 positive individual in a situation where infection and disease were re-emerging within the community two years post-intervention . It has been reported that when only one sero-prevalence cross-section is available it can be challenging to estimates key parameters such as the sero-conversion rate ( λ ) and the sero-reversion rate ( ρ ) simultaneously [18] . This is because with only one cross-section is not always possible to distinguish between a scenario where people sero-convert and sero-revert quickly vs one where they sero-convert and sero-revert slowly , as both scenarios can provide comparable fits to a single cross-sectional dataset . As such , it is typically more preferable to have more than one cross-section from the sample population in order to distinguish between these two competing hypotheses . We simulated sero-prevalence data for individuals aged 1-60 years within a community exposed to trachoma . We simulated 2 cross-sectional surveys , one pre and one post-intervention where in the post intervention data we assumed an 80% reduction in transmission occurred 10 years ago . We assumed that no individuals in the population were sero-positive as a result of exposure to any other pathogens , only trachoma . We fitted sero-catalytic models to data from both cross-sections simultaneously and also to each cross-section individually to assess how the precision and accuracy of the estimates was impacted by fitting to 1 vs 2 cross-sections . When fitting the 2 cross-sections together and the post-intervention only cross-section we estimated 4 parameters the: sero-conversion rate ( λ ) , sero-reversion rate ( ρ ) , the proportional drop in transmission ( γ ) and the time at which the drop in transmission occurred ( Tc ) . Sero-negative individuals become sero-positive at a rate λ and sero-positive individuals become sero-negative at a rate ρ [19] . Thus the proportion of sero-positive individuals within the cross-section collected is determined by the following: d P d t = λ ( t ) ( 1 - P ) ρ P ( 4 ) Where in a model that assumes a change in transmission at an instantaneous point in time λ is defined as follows: λ ( t ) = { λ 0 t < T c λ c ≥ T c ( 5 ) For the pre-intervention dataset we only estimated 2 parameters λ and ρ . We also estimated epidemiological parameters from data collected from only 1-9 year olds ( the key indicator group for surveillance ) , from both cross-sections simultaneously and individually . We then assessed how sampling an additional age group as well the current indicator group impacted the accuracy and precision of parameter estimation . We henceforth define accuracy in terms of parameter estimation as how close the paramater estimate was to the true simulated value , and precision as the narrowness of the credible intervals ( CrI ) for the estimate of any given parameter .
We considered a community with a true endemic disease prevalence of 20% ( 16% infection prevalence ) . Following a single round of treatment , the prevalence of PCR detectable infection dropped much more quickly than the prevalence of TF ( Fig 2a ) . Thus , declines in TF prevalence lagged behind the changes observed for PCR . This was consistent for all three rounds of MDA ( Fig 2a ) . Consequently , true TF prevalence was consistently higher than true PCR prevalence within the period evaluated . The proportion of individuals that were prevalent by any diagnostic test ( Fig 2b ) prior to MDA commencing showed that 6% of exposed individuals would have tested PCR-only positive , 67% would have tested PCR and TF positive , while 27% would have tested TF-only positive ( Fig 2b ) . Once the intervention had begun the ratio of individuals in different infection and disease states altered ( Fig 2b ) . As the intervention period progressed the proportion of individuals that tested PCR-only positive fell from 6% to 3 . 5% , while the proportion of individuals that tested both PCR and TF positive declined substantially from 67% to 41% . By contrast the proportion of TF-only positive people increased markedly from 27% to 55% ( Fig 2b ) . The large decline in the overall proportion of people PCR positive helps to explain the marked reduction in the probability of detection for PCR positive individuals ( Fig 2c ) and the slower decline in the probability of detection of TF positive individuals ( Fig 2c ) . As such , the opportunity to identify individuals who were both TF and PCR positive was most likely to occur when the ratio of PCR to TF positive individuals were similar in the population , which was most likely to occur at endemic equilibrium . Once sampling time point 5 was reached we saw a slight indication that re-emergence may be occurring ( Fig 2a ) . For sampling point S5 in comparison to sampling point S4 there was a marked increase in the proportion of individuals that tested PCR and TF positive ( 41% to 63% , Fig 2b ) and a decrease in the proportion of individuals that tested TF-only positive ( 55% to 30% , Fig 2b ) —these differences were reflected in the increase in probability of detection for PCR , but only a minor increase in the probability of detection of TF positives ( Fig 2c ) . When sampling 50 individuals at each time point , we found that with the exception of time point S4 the expected median prevalence was consistently higher for TF than PCR ( Fig 2c ) , however the variance in the expected TF prevalence was larger than for PCR . Additionally , the lack of overlap in the prevalence estimates over the intervention period suggested that at multiple times during the intervention period it is possible that people will test positive with one diagnostic but not the other ( Fig 2c ) . This coupled with a marked reduction in the probability of detection as prevalence declines suggests non-linearity in the results from different diagnostics is not unexpected . Following 2 years of MDA cessation in the community we considered the dynamics of detection during a potential resurgence ( Fig 3a ) . As re-emergence continued the rate at which TF prevalence increased was faster than that of PCR prevalence , this was because only few people test PCR-only positive , but there was an increase in the number of people who test positive PCR and TF as well as TF positive only . This was likely to be because when prevalence begins to increase gradually the rate of re-infection in the TF only state is low , due to an initial low force of infection in the community . Assessing the proportion of individuals by diagnostic state when re-emergence first began , 5 . 5% of exposed individuals would have only tested PCR positive , 67% would have tested PCR and TF positive , while 27% would have tested TF positive only ( Fig 3b ) . As re-emergence continued to occur across the first 4 sampling time intervals the proportion of TF only positive individuals was consistently higher than PCR and TF positive individuals , at sampling time point 4 , 41% of individuals tested PCR and TF positive , while 55% of individuals were TF positive only . In contrast , at sampling time point 5 the ratio of individuals in different diagnostic states was more comparable to that seen in the community prior to MDA being implemented ( Fig 2b ) where 63% of individuals tested PCR and TF positive and 30% of individuals were only TF positive ( Fig 2b ) . As re-emergence continued the probability of detection with both diagnostics increased , the probability of detection increased at a similar rate as time progressed for both tests ( Fig 3c ) , in contrast to the results seen when prevalence was declining during the MDA programme where the probability of detecting PCR positive individuals declined much more rapidly than TF positive individuals ( Fig 2c ) . The variance in the estimated PCR and TF prevalences were slightly higher for PCR detectable infection , whilst the variance in the estimated prevalence of TF and PCR overlapped at some sampling time points , this was not consistent for all sampling points . Highlighting that in a low prevalence re-emergence setting it’s possible we would find individuals PCR and or TF positive ( Fig 3c ) . In the pre-MDA setting at higher levels of TF prevalence the overall proportion of PCR and TF positive individuals was much higher than at lower levels of endemic prevalence . When TF prevalence was 50% , 74 . 5% of infected individuals were PCR and TF positive , but when TF prevalence was 10% , 64% of individuals were TF and PCR positive ( Fig 4a ) . Here the proportion of TF-only positive individuals increased from 20% , to 32% when endemic prevalence was 50% , in comparison to 10% ( Fig 4a ) . Whilst when TF prevalence was 30% the ratio of individuals in each diagnostic state was more comparable to when TF prevalence was 50%: ( 6 . 8% , 70% , 22% ) ( Fig 4a ) . At high levels of endemic prevalence we would expect the greatest proportion of individuals in the population to be both TF and PCR positive because individuals in the TF only state will be continuously re-infected . However , at lower levels of prevalence the rates of re-infection are not as high , resulting in a higher proportion of TF-only positive individuals . Across all TF prevalence levels post-MDA a comparable ratio of individuals in each diagnostic state to the pre-MDA levels was observed , although for a number of initial prevalence levels the proportion of PCR only positive individuals was slightly higher than at endemic equilibrium . For example , the proportion of PCR positives only increased from 7 . 6% to 9 . 7% ( Fig 4b ) . For lower levels of endemic prevalence post-MDA we observed a slight decrease in the proportion of individuals PCR and TF positive , and a small increase in the proportion of individuals who would test only TF positive—for an endemic TF prevalence of 10% the proportion of individuals TF and PCR positive dropped from 64% to 54 . 5% and the proportion of TF only positive individuals increased from 31% to 38% ( Fig 4b ) . Our simulations have suggested that the expected proportion of individuals detectable as both PCR and TF positive declines as the overall prevalence in the community declines , typically as prevalence declines a higher proportion of individuals become TF-only positive . Additionally , the probability of detecting an individual as PCR positive during an intervention period declines much more quickly than for TF , this difference in the probability of detection may also help to explain disparities in the reported prevalence of infection and disease as transmission declines when surveys are conducted . To understand more clearly what is happening when we observe non-linearity in prevalence in 1-9 year olds by PCR and TF surveillance we would need individual level data on PCR and TF prevalence , this would enable us to see whether the proportion of PCR and TF positives in the data is comparable to the ratios that the model predicts . At both high and low levels of transmission the simulations above suggest that the true underlying PCR and TF prevalence levels do correlate with one another . At high levels of infection and transmission PCR and TF prevalence correlate with one another due to rapid rates of reinfection occurring , ensuring that PCR and TF prevalence correlate well with one another . As prevalence declines although the true underlying prevalence’s may correlate at low prevalence sampling noise can play an important role , leading to some samples being collected in which TF prevalence is much higher than PCR prevalence ( Fig 5 ) . For these simulations , both TF and PCR sensitivity mean that prevalence is usually underestimated ( the coloured dots are down and to the left of the black dot indicating true prevalence ) . If sensitivity declines as prevalence continues to fall , then this discrepancy will be larger . Fitting a 2 parameter model to pre-intervention cross-sectional data the median estimates of λ and ρ were lower than the true values of the simulated data: 0 . 04 and 0 . 02 vs 0 . 10 and 0 . 05 , however the credible intervals included the true value ( Fig 6 ) . Fitting the post-intervention dataset in isolation the estimate of λ was close to the true value ( 0 . 13 vs 0 . 10 ) , however the credible intervals were much wider than when the two cross-sections were fitted simultaneously . The median estimate of γ was lower than the true value , with much wider credible intervals in comparison to when 2 cross-sections were fitted together ( Fig 6 ) . Estimates of the ρ and Tc were similar to the true values , however the precision of the estimates were less than when 2-cross sections were fitted simultaneously ( Fig 6 ) . Fitting 2 cross-sections to data from only 1-9 year olds ( 300 samples ) the median estimated λ was similar to the true estimate ( 0 . 12 vs 0 . 10 ) , Tc was also estimated relatively accurately . The estimate of γ was much lower than the true value 0 . 09 ( CrI: 0 . 01-0 . 28 ) , but the credible intervals did include the true value . The estimate of ρ was higher than the true value ( 0 . 09 vs 0 . 05 ) , but the wider credible intervals still included the true value . Fitting to pre-intervention period data from 1-9 year olds , estimates of λ and ρ were much lower than the true values and the credible intervals did not include the true values , estimates were also much lower than when a single cross section for the full dataset was fitted to , suggesting that fitting to a small cross-section of the population is not sufficient to accurately estimate these parameters . For the post-intervention data in 1-9 year olds λ was over-estimated and the credible interval range was large , much wider than when the full single cross section was evaluated . Estimates of ρ were similar when 1-9s were evaluated as when the full cross-section was , however this is likely to have traded off with the estimate of ρ . The median estimate of gamma was below the true value but similar to when all data was fitted to for the single post-intervention dataset , whilst Tc was above the true value . Therefore overall for the pre-intervention data estimates of λ and ρ were markedly different to the full dataset and when the two cross-section were fitted together . For the post-intervention data estimates of ρ and Tc were similar to when the single full cross section was fitted to and not too dissimilar from when 2 cross-sections were fitted together . However the estimate of λ was much higher in 1-9s in comparison to the full single cross section and gamma was similar to when both cross-sections were fitted together , but lower than when all the data was evaluated . When we fitted only 1-9 year olds the precision and accuracy of the estimated parameters was lower than when the all-age data was fitted to , therefore we evaluated whether sampling an additional age group outside of the current indicator group containing the same total number of samples could help improve the precision and accuracy of the parameter estimates . When including an additional age group outside of the current indicator group , for λ the precision and accuracy of the estimate when 20-30 year olds were also sampled was much improved , and the median estimate of 0 . 095 was very close to the true value of 0 . 10 . The precision of the estimated value of γ was generally poorer than when only 1-9s were evaluated , however when a second group was also fitted to the accuracy of the estimate to the true value was much better than when only 1-9s were fitted to . Including age ranges above 30 years slightly reduced the precision of the estimate in comparison to when 10-20 or 20-30 year olds were included . Incorporating an additional age group up to 50 years of age helped improve the precision and accuracy of the estimated value of ρ , highlighting the value of sampling outside of the current indicator group for more precise parameter estimates . The most precise and accurate estimates of ρ were obtained when individuals 20-30 years were in the sample as well . Tc was also most accurately and precisely estimated when 20-30 year olds were included in the sample .
Inconsistencies in the observations from PCR and TF samples can make interpretation of trachoma surveillance data challenging [6] . The similarity observed between PCR and TF prevalence that breaks down as prevalence declines is currently not well understood or fully explained . In this article we have presented a possible explanation as to how these observations in surveillance data may be occurring . Through evaluating the proportion of individuals that would be present in each diagnostic state in the community with a dynamic model we have shown that as prevalence declines within a community the proportion of individuals PCR only or PCR and TF positive declines and a higher proportion of the PCR or TF positive population are only TF positive . These changes in the proportion of people that would test positive in each diagnostic state impact the diagnostic test results , making the proportion of TF and PCR positives less similar . The dynamics of transmission also mean that as prevalence declines the probability of detecting at least 1 positive individual by PCR with a fixed sample size declines much more rapidly than with TF ( assuming a fixed sensitivity of the diagnostic over time ) . We note that an individual-based modelling approach would be needed to fully explain the observations seen in surveillance data . Importantly , individual-level diagnostic data from low prevalence settings would help us to understand whether the proportions of PCR and TF positives align with those predicted by the model . Individual level data is essential for testing the assumptions in this model and providing guidance on sampling strategies for PCR use in routine surveillance . For sero-surveillance we have shown that much more accurate and precise parameter estimates can be inferred when 2 cross-sections are fitted to in comparison to 1 . Particularly for the pre-intervention cross-section , we clearly saw how estimates of λ and ρ could be traded off with one another causing imprecise estimation [18] . When only fitting to data from 1-9 year olds the accuracy and precision of the parameter estimation was reduced in comparison to fitting to the all-age data . However , through the inclusion of one additional age-group we were able to improve the precision and accuracy of all parameter estimates when fitting 2 cross-sections simultaneously . In this situation it appeared that the inclusion of 20-30 year olds as well as 1-9 year olds had the most substantial impact on improving parameter estimation precision and accuracy . Therefore in terms of helping to quantify epidemiological parameters more accurately in the future we would suggest that at least 2 cross-sections be collected from the same community and that an age-group outside of the 1-9 year old group also be sampled . This will ensure that both ρ and the force of infection ( determined by λ ) are estimated more accurately and with less uncertainty . For a number of NTDs the issue of systematic non-compliance/adherence to treatment has been reported and the potential issues it may present to elimination evaluated , ie . Treatment coverage within the community is not random . However , for the purposes of this study when modelling treatment we have assumed coverage is random . If individuals in the community systematically miss treatment then they may remain a reservoir source of infection , helping to ensure on-going transmission . However , for trachoma in particular little to no epidemiological data has been presented to suggest that systematic non-compliance is occurring during MDA rounds , and generally the coverage level is reported to be at least at the target level of 80% , as such , currently no data are available to indicate to what extent systematic non-compliance may be occurring . Despite our assumption of random coverage we do not expect a large impact at these coverage levels for the qualitative conclusions , unless it is quite extreme . However , if those being treated are the same as those being tested , and there were a group who were consistently not treated or measured , that would be more of a problem for the discussion posed here . Also , at these coverage levels , systematic non-compliance becomes a particular issue when non-adherence to treatment is correlated with infection risk , ie if those more at risk of infection continually miss treatment then they are more likely to remain a reservoir source of infection in the community . If this is the case in the communities we have evaluated , we would be more likely to see faster rates of re-emergence of infection but the qualitative observations and diagnostic outcomes reported in the study would be unlikely to change . The are a number of limitations to the study . Firstly , for both diagnostic tests we assumed 100% specificity [17] , if this assumption were relaxed we would expect an increase in the proportion of overall positives , leading to a possible over-estimate of the prevalence , and thus increasing the probability of detection with each diagnostic . However , despite modelling a slightly higher proportion of positives in the population in comparison to what may be true we would not expect the qualitative form of the relationships observed to be altered . Secondly it is possible that the sensitivity and specificity of the PCR and TF diagnostics may alter over time as prevalence declines [6] , whereas we have only considered a single fixed value . Again , it is likely that this assumption would not alter the qualitative relationships observed here , but potentially the magnitude . As prevalence declines it becomes more challenging to detect both infection and disease , therefore we would expect the true probability of detection to potentially be even lower . Furthermore we would expect the noise around the low prevalence estimates to increase substantially [20] . Thirdly , in the sero-surveillance work , we chose to illustrate the importance of a second cross-section over only one , with an assumed reduction in transmission compared with 10 years previously , as we felt this was a case which would illustrate the point most effectively . However , as we approach an era of elimination for trachoma it is becoming increasingly unlikely that the opportunity will arise to conduct 2 surveys 10 years apart in time , therefore the question becomes how frequently should surveys be conducted in order to help accurately estimate the sero-reversion rate . This crucially depends on the rate of antibody decay , which is not yet known . However with exploratory simulation it may be possible to get a better idea on how frequently surveys should be conducted in order to estimate this . Lastly , in settings where urogenital infection is high such as the South Pacific [21] , individuals may also test sero-positive to anti-trachoma antigens as a result of exposure to urogenital chlamydia . This can complicate the estimation of the sero-reversion and conversion rates for exposure due to trachoma , and would potentially need to be accounted for if sero-surveillance data was being collected in individuals past the age of sexual debut in populations with a high incidence of urogenital chlamydia infection . PCR and sero-surveillance are important potential tools for trachoma surveillance , which may offer additional opportunities for understanding transmission dynamics as incidence declines . This paper highlights some of the links between these dynamics and potential survey design . However , there are , of course , many logistical constraints which would need to be considered before they were implemented widely in routine surveillance . From this study we highlight 2 key recommendations for future data collection for trachoma surveillance , in order to understand low-level transmission dynamics in greater detail as population prevalence declines . Firstly , individual-level diagnostic data from low prevalence settings would help us to understand whether the proportions of PCR and TF positives align with those predicted by the model . Secondly , we clearly highlight that for sero-surveillance more accurate and precise parameter estimates can be inferred when 2 cross-sections are fitted to in comparison to 1 . We would therefore recommend at least 2 cross-sectional serological surveys being conducted several years apart in order to improve the estimation of epidemiological parameters from serological data . | Trachoma is a bacterial infection , which , with repeated infections over time , can lead to blindness . The WHO is aiming to eliminate trachoma as a public health problem by 2020 , however at low prevalence levels the relationship between infection and disease prevalence is non-linear , making the interpretation of data from the two diagnostic tests challenging . However , it is hard to know if this is an expected outcome or a biological inconsistency . Sero-surveillance is being considered as an additional tool to understand transmission when infection and disease prevalence data provide different information . We highlight , through mathematical modelling , that a lack of strong correlation between infection and disease prevalence data at low levels of transmission seen in epidemiological data is not unexpected and demonstrate that multiple sero-surveillance surveys should be conducted from at least 2 different age groups in order to accurately estimate epidemiological parameters that will help to monitor low-level transmission . | [
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] | 2018 | Optimising sampling regimes and data collection to inform surveillance for trachoma control |
Morphogens are secreted signalling molecules that act in a graded manner to control the pattern of cellular differentiation in developing tissues . An example is Sonic hedgehog ( Shh ) , which acts in several developing vertebrate tissues , including the central nervous system , to provide positional information during embryonic patterning . Here we address how Shh signalling assigns the positional identities of distinct neuronal subtype progenitors throughout the ventral neural tube . Assays of intracellular signal transduction and gene expression indicate that the duration as well as level of signalling is critical for morphogen interpretation . Progenitors of the ventral neuronal subtypes are established sequentially , with progressively more ventral identities requiring correspondingly higher levels and longer periods of Shh signalling . Moreover , cells remain sensitive to changes in Shh signalling for an extended time , reverting to antecedent identities if signalling levels fall below a threshold . Thus , the duration of signalling is important not only for the assignment but also for the refinement and maintenance of positional identity . Together the data suggest a dynamic model for ventral neural tube patterning in which positional information corresponds to the time integral of Shh signalling . This suggests an alternative to conventional models of morphogen action that rely solely on the level of signalling .
A defining feature of embryogenesis is the specification of a large variety of cell types in stereotypical spatial and temporal patterns . A common mechanism by which this is achieved involves the deployment of morphogens [1] , [2] , [3] . These secreted molecules are proposed to establish signalling gradients within developing tissue that provide the positional information that guides the pattern of gene expression and cellular differentiation . Of central importance , therefore , is to understand the nature of the positional information produced by the morphogen . Several families of secreted proteins , including members of the Hedgehog ( Hh ) , Transforming Growth Factor beta ( TGF-β ) , and Wingless ( Wnt ) families , operate as morphogens during embryonic development [2] , [4] . One example where progress has been made in understanding the mechanism of morphogen action is Sonic Hedgehog ( Shh ) signalling in the vertebrate central nervous system [5] , [6] , [7] . Shh is produced from the ventral midline of the neural tube and underlying notochord [8] , [9] , [10] , [11] , [12] , [13] and spreads to form a ventral-to-dorsal gradient [14] . Within responding cells , Shh signalling regulates the activity of Gli transcription factors ( Gli1 , 2 , and 3 ) to produce a net increase in their transcriptional activator function [15] , [16] , [17] , [18] , [19] . This , in turn , controls the expression of a set of transcription factors in ventral progenitor cells that subdivide the neuroepithelium into five molecularly distinct domains arrayed along the dorsal-ventral ( DV ) axis [5] , [20] . Each progenitor domain generates one of five different neuronal subtypes; from dorsal to ventral , the domains are termed p0 , p1 , p2 , pMN , and p3 and generate V0 , V1 , V2 neurons , motor neurons ( MNs ) , and V3 neurons , respectively [5] , [20] , [21] . The two most ventral neural progenitor domains , p3 and pMN , are defined by the expression of the transcription factors Nkx2 . 2 and Olig2 , respectively [22] , [23] . In response to Shh signalling , Olig2 is expressed first , in a small group of ventral neural progenitors [14] , [24] . Its expression then gradually expands dorsally . As this happens , the ventral cells that originally expressed Olig2 induce Nkx2 . 2 expression and downregulate Olig2 . Nkx2 . 2 expression then also expands dorsally , downregulating Olig2 in its wake [22] , [23] , [24] , however the dorsal expansion of Nkx2 . 2 is more limited than Olig2 . The end result of the sequential induction and expansion of these two factors is two adjacent but spatially distinct progenitor domains: Olig2 expressing pMN progenitors located dorsal to Nkx2 . 2 expressing p3 cells . Importantly , the order of appearance and the final position of these progenitor domains correspond to their requirement for increasing concentrations of Shh [21] , [24] , [25]—the more ventrally located progenitor domain emerges later and requires higher Shh concentrations . One explanation for this , which would accord with the conventional model for morphogen interpretation [1] , [3] , is that Shh concentration increases over time [14] . As a consequence of this , the threshold concentration for Nkx2 . 2 activation would be reached later than that of Olig2 [14] . However , our previous work suggested a more complex mechanism of morphogen gradient interpretation in which the signalling pathway does not linearly transduce Shh concentrations required for Olig2 and Nkx2 . 2 induction [24] . In this non-canonical model of morphogen interpretation , not only the level of intracellular signalling but also its duration plays a crucial role . In vitro experiments indicate that signal transduction is not linearly proportional to Shh concentration at the concentrations of Shh that induce Nkx2 . 2 and Olig2 [24] . In fact , initially in cells exposed to these concentrations of Shh , signal transduction is saturated resulting in similarly high levels of intracellular Gli activity . However , cells gradually adapt their response to Shh so that they become increasingly less sensitive to the ligand . This means that the time for which intracellular signalling is maintained above a particular threshold is proportional to the extracellular concentration of Shh . Consequently , the concentration of Shh that induces Nkx2 . 2 maintains high levels of intracellular signalling for a longer period of time than the lower , Olig2-inducing concentration . The gradual adaptation of cells to Shh is controlled , at least in part , by the Shh mediated upregulation of Ptc1 , the receptor for Shh that is also a negative regulator of the pathway [26] , [27] , [28] , [29] , [30] , [31] . High Shh concentrations bind to and suppress more Ptc1 than lower Shh concentrations , thereby sustaining intracellular signal transduction for longer [32] , [33] . Importantly , a longer period of signalling is required for the induction of Nkx2 . 2 than for Olig2 . Together , therefore , the data indicate that the differential response to the concentrations of Shh that induce Nkx2 . 2 or Olig2 is a function of the duration of intracellular signalling rather than the levels of signalling . Hence , at these concentrations , increasing the extracellular concentration of Shh results in an increase in the duration of intracellular signalling rather than a change in the level of signalling . We termed this mechanism “temporal adaptation” [24] . Whether this mechanism is relevant for the provision of positional information throughout the concentration range of Shh in the ventral neural tube remains an open question . Moreover , the kinetics of Shh signalling that assign positional identity are unclear , as is the length of time that Shh signalling is required to maintain positional identity once established . To address these issues we first focused on the specification of V0 , V1 , and V2 neurons , which arise from progenitors located dorsal to the pMN and require lower concentrations of Shh than MNs and V3 neurons [34] . We show that these concentrations of Shh do not saturate the signal transduction pathway and Gli activity levels are proportional to Shh concentration . Nevertheless , we provide evidence that the duration of Shh signalling plays an important role in the specification of V0–V2 neurons , such that increasing durations of signalling promote the generation of more ventral identities . Moreover , we provide in vivo evidence that the assignment of progenitor identity in this region of the neural tube is progressive . Together these data indicate that the duration of Shh signalling is important for all ventral progenitors to acquire their correct positional identity . We further show that sustained Shh signalling is required to maintain appropriate progenitor pattern in the ventral neural tube even after positional identity has been induced . Progenitors revert to antecedent identities if signalling is interrupted . Thus the allocation of cell identity in the ventral neural tube appears more plastic than for other well-described morphogen patterned tissues and we discuss the implications for models of tissue patterning by morphogen gradients . Together , the data suggest a model for ventral neural tube patterning in which positional identity of progenitors is dynamic and determined by the level and duration—the time integral—of Shh signalling .
Intracellular signal transduction by Shh results in increased transcriptional activity of the Gli family of zinc finger transcription factors [19] . Our previous studies demonstrated that exposure of neural cells to concentrations of Shh in excess of 1 nM , which induce p3 and pMN progenitors , saturate intracellular signalling and generate similar high levels of Gli activity during the first 12 h [24] . Lower concentrations of Shh are associated with the production of several subtypes of interneurons , dorsal to MNs , including V0 , V1 , and V2 neurons [34] . We therefore assayed the profile of Gli activity induced by lower concentrations of Shh . For this , intermediate neural plate ( [i] ) explants from Hamburger and Hamilton ( HH ) stage 10 chick embryos in ovo electroporated with a Gli reporter ( GBS-luc; Figure 1A; see Materials and Methods; [24] ) and normalization plasmids were cultured in the presence of 0 . 1–2 nM Shh . The level of Gli activity in these explants was measured 6 h after the start of culture and compared to the levels of Gli activity in explants cultured in the absence of Shh ( Figure 1B ) . These data indicated that saturating levels of signal transduction , corresponding to Gli activity ∼25-fold higher than basal levels , were reached at ∼1 nM Shh ( Figure 1B ) . This is consistent with our previous findings [24] . At concentrations less than 1 nM Shh , however , the level of Gli activity was a function of Shh concentration and half maximal levels of Gli activity ( 10–15-fold induction ) were obtained between 0 . 25 nM and 0 . 5 nM Shh ( Figure 1B ) . We next compared the dynamics of Gli activity in explants exposed to 0 . 25 nM and 2 nM Shh for 6–24 h ( Figure 1C ) . For both concentrations , maximum levels of Gli activity were recorded at ∼6 h and subsequently decreased over time . At 2 nM Shh , Gli activity level at 6–12 h was similar to that generated by 1 nM Shh . In contrast to 1 nM Shh , however , cells exposed to 2 nM Shh contained higher levels of Gli activity at 18–24 h , indicating that the rate of signalling decline is inversely correlated with morphogen concentration . This is consistent with the “temporal adaptation” mechanism [24] . At 0 . 25 nM , Shh signalling pathway was not saturated . Thus levels of Gli activity reached in this condition were lower; nevertheless the level of signalling declined over time and was indistinguishable from pre-stimulus levels by 18–24 h . Consequently , lower concentrations of Shh sustain signal transduction above basal levels for shorter periods of time ( Figure 1C , inset ) . Together these data suggest that exposure of neural cells to Shh generates distinctive temporal profiles of intracellular signalling . Gli activity reaches a peak within ∼6 h of exposure to Shh , and these peak levels correspond linearly with Shh concentration for low concentrations and are saturated at high concentrations . Subsequently , for all concentrations , intracellular signalling declines from the peak as cells adapt and the length of time it takes for Gli activity to return to pre-stimulus levels increases with Shh concentration . We next assayed how the observed dynamics of Shh signalling regulate the specification of different neuronal subtypes . We focused our attention on Shh concentrations <1 nM and the generation of V0 , V1 , and V2 neurons within the intermediate region of the neural tube ( Figure 2A ) [34] . Explants were exposed to 0 . 05–0 . 5 nM Shh for 48 h , then assayed for Evx1 , En1 , Chx10 , and MNR2 expression , markers of V0 , V1 , and V2 neurons and MNs , respectively ( Figure 2B , S1A , S1B , S1C , S1D , S1E , S1F; [35] , [36] , [37] , [38] ) . In the absence of Shh , most cells express Pax7 ( Figure S2; [39] ) and few , if any , cells expressed V0–V2 or MN markers ( Figure 2B ) . Addition of 0 . 05–0 . 3 nM Shh resulted in a reduction in Pax7 expression , induction of the intermediate progenitor marker Dbx1 ( Figure S2 ) , and the production of cells expressing Evx1 , En1 , or Chx10 ( Figure 2B ) . There was a trend for the induction of increasing numbers of more ventral cell types as Shh concentration was increased . For instance , the production of V0 neurons peaked at ∼0 . 1 nM while the maximum numbers of V1 neurons were observed at ∼0 . 25 nM Shh and MNs were only induced in significant numbers by concentrations of Shh >0 . 4 nM Shh ( Figure 2B ) . Nevertheless , a mixture of neuronal subtypes was generated in response to all Shh concentrations tested in the range of 0 . 05–0 . 4 nM . Thus , these data indicate that , although low concentrations promote the generation of V0–V2 neurons , incremental differences in concentration are not sufficient to generate distinct neuronal subtypes . Since the Shh concentrations used in these experiments produced distinct levels of Gli activity ( Figure 1B ) , the data suggest that the level of signalling is not sufficient to separate the specification of V0 , V1 , and V2 neurons . As different Shh concentrations could not delineate clear transitions in the production of different neuronal subtypes , we asked whether the length of time cells are exposed to Shh could distinguish the generation of ventral neurons . For this , explants were exposed to 0 . 5 nM Shh for fixed times ranging from 6 h to 48 h . After the indicated period of Shh exposure , Shh was removed and replaced with media lacking Shh; neuronal subtype identity was then assayed in all explants at 48 h ( Figure 2C , 2D ) . Exposure to Shh for only the first 6 h of the 48 h culture period was sufficient to induce V0 and V1 neurons , but few , if any , V2 neurons were generated ( Figure 2D ) . Increasing the duration of Shh signalling resulted in the progressive generation of more ventral cell types: 12 h exposure resulted in V2 generation and a marked reduction in V0 generation , while 18 h exposure was required for the appearance of MNs ( Figure 2D ) . Thus different durations of signalling influenced the neuronal subtype generated in response to a fixed Shh concentration such that more ventral neuronal subtypes were generated in explants exposed for longer periods of time . Notably , different durations of Shh signalling generated well-separated peaks of V0 and V2 neurons , in contrast to the overlapping peaks of neuron production following exposure to different Shh concentrations ( compare Figure 2B with 2D ) . Nevertheless there was still significant overlap in the generation of V0 and V1 neurons ( Figure 2D ) . This might reflect limitations in the resolution of the explant assay or the action of additional signals in the generation of these cell types [40] . To confirm the involvement of signal duration in the assignment of positional identity , we assayed the expression of transcription factor markers of p0–p2 progenitors , which generate V0–V2 neurons . Explants were assayed at 48 h after exposure to 0 . 5 nM Shh for different periods of time ( Figures 2C , 2E , S1G , S1H , S1I , S1J ) . V0 neurons are generated from progenitors that express Dbx1 ventral to the Pax7 boundary; progenitors of V1 neurons express Pax6 but lack Nkx6 . 1 and Dbx1 expression; V2 progenitors express Nkx6 . 1 but not Olig2 ( Figure 2A; [41] ) . Consistent with the profile of neuronal subtype generation , increasing the duration of Shh signalling resulted in a gradual ventralization of progenitor cells . In the absence of Shh , progenitor cells expressed Pax7 . Exposure to Shh for 6 h induced Dbx1 expression and decreased the number of Pax7 expressing cells by ∼40% ( Figure 2E ) . Longer periods of exposure resulted in a gradual decrease in Dbx1 and Pax7 expression and an increase in the expression of Nkx6 . 1; finally Olig2 expression , the pMN marker , was detected ( Figure 2E ) . Thus , similar to neuronal subtype identity , the response of the transcriptional markers of progenitor domains to different durations of Shh exposure corresponds to their DV position in the neural tube . We sought to rule out the possibility that the positional identities induced by different times of Shh exposure were the result of temporal changes in the competence of progenitors . If this were the case , the time at which cells received Shh signalling , rather than duration for which they were exposed to Shh , would determine positional identity . We therefore assessed the effect of adding Shh to explants at different time points . Explants were cultured in the absence of Shh for 12–24 h and then exposed to 0 . 5 nM or 4 nM Shh for an additional 6–24 h ( Figure S3 ) . Assaying the expression of Dbx1 , Nkx6 . 1 , Olig2 , and Nkx2 . 2 revealed that the response of explants was offset by the same amount of time that the addition of Shh was delayed . For example , Dbx1 and Nkx6 . 1 were induced by exposure to 0 . 5 nM Shh for 6 h and 18 h , respectively , regardless of whether Shh exposure was initiated at 0 h or 24 h after the start of the culture period ( Figure S3A ) . Similarly , 12 h of exposure to 4 nM Shh induced Olig2 , while longer times were required for Nkx2 . 2 induction , whether Shh was added immediately after explanting or following 12–24 h ex vivo ( Figure S3B ) . These data argue against an intrinsic timing mechanism that over time changes the competence of progenitors to generate different neuronal subtypes . Instead the data provide strong support for the idea that the duration of Shh signalling plays a central role in determining positional identity . Thus , throughout the ventral neural tube , progressively more ventral fates are generated as the concentration and time of exposure to Shh are increased . To test directly whether progenitors progressively adopt more ventral identities as the duration of Shh exposure is increased , we assayed the dynamics of Dbx1 , Nkx6 . 1 , and Olig2 expression in [i] explants exposed to 0 . 5 nM Shh for 6–36 h ( Figure 3A ) . At 18 h , explants expressed the three markers ( Figure 3B ) . Exposure to 0 . 5 nM Shh for longer periods of time resulted in the gradual increase in the number of Olig2 and Nkx6 . 1 expressing cells and a reduction in Dbx1 expression and only a small number of V0–V2 neurons were present in explants exposed for 36 h to 0 . 5 nM Shh ( Figures 3B , S4A , S4B , S4C ) . These data suggest that continued Shh signalling after 18 h promotes the acquisition of more ventral positional identities at the expense of intermediate fates . Notably , by 18 h the level of Shh signalling in cells exposed to 0 . 5 nM Shh was significantly reduced from the peaks attained at 6–12 h ( Figure 1C; [24] ) . Nevertheless , this level of signalling appears sufficient for the progression of progenitor identity from intermediate to pMN . A consequence of the sequential establishment of positional identity is that the ventral limit of expression of a progenitor marker such as Dbx1 , which identifies p0 progenitors , should be displaced dorsally as development proceeds . In this view , cells that have expressed Dbx1 will contribute to progenitor domains ventral to p0 . To test this , we took advantage of a Dbx1-Cre mouse line [42] and the inducible reporter allele ROSA26floxSTOPfloxYFP ( Figure 3C; Materials and Methods; [43] ) . In these embryos the progeny of any cell that has expressed Dbx1 is indelibly marked by the expression of YFP . In embryos assayed at E11 . 5 , the ventral limit of YFP expression was more ventral than the extant p0 domain , identified by Dbx1 ( unpublished data ) and Cre recombinase expression ( Figure 3D ) , and some Dbx1 progeny were detectable within Olig2 positive progenitors ( Figure 3E ) . Thus , Dbx1 is initially expressed in more ventral progenitors and its expression is refined , eventually becoming restricted to the p0 domain . These data provide evidence that ventral neural tube patterning proceeds via the progressive assignment of ventral identities . The progressive assignment of positional identity by Shh signalling prompted us to question whether continued signalling is required to maintain specific ventral identities once they are induced . To test this , we blocked Shh signalling in explants at different time points using the inhibitor cyclopamine [32] , [44] , an antagonist of Smoothened , the essential intracellular transducer of the pathway [45] , [46] . After explants had been exposed to 0 . 5 nM Shh for 18 h , they were cultured for an additional 6 h , 12 h , or 18 h in fresh media containing 500 nM cyclopamine ( Figures 4A–4C , S3D ) . Explants that had been exposed to 0 . 5 nM Shh for 18 h contained significant numbers of Dbx1 , Nkx6 . 1 , and Olig2 expressing cells ( Figure 3B ) . However , when Shh signalling was inhibited from 18 h , a marked reduction in the expression of ventral progenitor markers Nkx6 . 1 and Olig2 was apparent at 36 h ( Figure 4B , 4C ) , compared to explants exposed to 0 . 5 nM Shh for 18 h ( Figure 3B ) or for 36 h ( Figure 4B , 4C ) . Consistent with this , MN production was reduced when signalling was blocked at 18 h ( Figure S4B , S4C ) . Notably , Olig2 expression was more severely affected than Nkx6 . 1 ( Figure 4C ) . This suggests that Olig2 is more sensitive than Nkx6 . 1 to removal of signalling and that the remaining Nkx6 . 1 expressing cells in these explants represent p2 progenitors . Concomitant with the decrease in ventral progenitor markers , the numbers of Dbx1 expressing progenitors and V0–V2 neurons increased ( Figure 4C , S4B , S4C ) . This change in progenitor identity inversely correlated with the concentration of Shh required for induction and indicates that cells gradually revert to a more dorsal identity when Shh signalling is blocked . These data suggest , therefore , that Shh signalling is required not only to induce ventral identity but also to maintain the identity after it has been assigned . We asked whether the requirement for Shh signalling to maintain progenitor identity is a general property of the response of neural progenitors and is therefore also observed in cells exposed to higher Shh concentrations . For these experiments , explants were exposed to 4 nM Shh for 18 h , and then placed into fresh media containing either 4 nM Shh , or lacking Shh , or containing 4 nM Shh and 500 nM cyclopamine , to block signalling . Incubation of explants was continued for either an additional 6 h , at which time point the level of Gli activity assayed , or an additional 18 h in order to assay Olig2 , Pax6 , and Nkx2 . 2 expression ( Figure 4D ) . Six hours after the addition of 500 nM cyclopamine , Gli activity levels had returned to basal levels ( Figure 4E , iv ) . By contrast , 6 h after removal of Shh from the medium , the levels of Gli activity were significantly lower but remained 2–3-fold above basal levels ( Figure 4E , iii ) . After 18 h exposure to 4 nM Shh , explants expressed a mixture of Olig2 and Nkx2 . 2 ( Figure 4F , i; see also [24] ) ; a few cells expressed both Nkx2 . 2 and Olig2 but the majority of cells expressed one or the other marker . In explants in which exposure to 4 nM Shh was maintained for 36 h , Nkx2 . 2 expression was consolidated and Olig2 was downregulated ( Figure 4F , ii ) . By contrast , in explants in which Shh was removed and incubation continued for an additional 18 h in the absence of Shh , most cells reverted to expressing Olig2 alone and Nkx2 . 2 had largely been downregulated ( Figure 4F , iii ) . This indicates that Gli activity levels 2–3-fold above basal are sufficient to maintain Olig2 , but not Nkx2 . 2 expression . The complete inhibition of Shh signalling with 500 nM cyclopamine resulted in the downregulation of both Nkx2 . 2 and Olig2 ( Figure 4F , iv ) . These explants expressed high levels of Pax6 , a marker of the intermediate neural tube ( Figure 4F , iv; [25] ) . These data demonstrate that continuous Shh signalling is required for maintaining the identity of all ventral neural progenitors . In addition , it suggests that the maintenance of different target genes requires different thresholds of Gli activity . These data provide an explanation for the discrepancy in the identity of progenitor and post-mitotic cells observed in the data from initial experiments using [i] explants ( Figure 2D , 2E ) . In these experiments , the subtype identity of neurons generated by a specific duration of signalling did not always correspond to the progenitor markers expressed in the same conditions . For example , 18–24 h exposure to Shh was sufficient to induce large numbers of MNs in explants assayed at 48 h , however there was no detectable Olig2 expression in explants assayed in the same conditions . This is consistent with the idea that pMN progenitors were present in these explants at an earlier time point , but by the time the assay was performed , these progenitors had disappeared , leaving behind the apparently anomalous post-mitotic neurons they had generated . Together , these data identify a function for Shh signalling in maintaining progenitor identity and suggests a dual role for Shh signalling in the neural tube: first to assign progenitor identity and then to maintain the established positional identity of ventral progenitor cells . The ex vivo data prompted us to ask whether an extended period of Shh signalling is required in vivo to maintain appropriate patterns of gene expression . We first used cyclopamine to block Shh signalling by administering the drug in ovo to HH st . 18 embryos . At this developmental stage , corresponding to ∼36 h after the time at which intermediate regions are explanted from embryos , progenitor domains are well established ( Figure 5A ) and neurogenesis is under way . Expression of progenitor markers of the ventral neural tube was then analyzed 24 h later , after administration of cyclopamine or vehicle alone; this corresponded to ∼HH st . 22 . At thoracic levels , altered ventral patterning was observed in 80% of the embryos exposed to cyclopamine ( Figure 5B ) . In these embryos , the number of Nkx2 . 2+ cells was markedly reduced and the domain of Olig2 shifted ventrally ( Figure 5B , ii ) . Furthermore , the pMN domain was affected in 60% of the samples , with fewer cells expressing Olig2 ( n = 12; Figure 5B , iii ) . Thus , similar to the ex vivo results , continued Shh signalling appears to be required to maintain the appropriate pattern of progenitor identities in the ventral neural tube , and if Shh signalling is interrupted cells transform into a more dorsal identity . To corroborate these data , we blocked Shh signalling , in ovo , by electroporation of Ptc1Δloop2 , a dominant active version of the Shh receptor that cell autonomously blocks intracellular Shh signal transduction ( Figure 5C; [47] ) . Embryos transfected with Ptc1Δloop2 at HH st . 18 were assayed for the expression of progenitor markers of the ventral neural tube 24 h later , at ∼HH st . 22 . Similar to the results of cyclopamine exposure , the inhibition of Shh signalling with Ptc1Δloop2 resulted in the inhibition of Nkx2 . 2 and Olig2 expression ( Figure 5C ) . The effects of Ptc1Δloop2 appeared cell autonomous , as only transfected cells displayed obvious changes in gene expression , consistent with the cell autonomous inhibition of Shh signalling imposed by Ptc1Δloop2 [47] . To test whether an extended period of Shh signalling is also necessary in the mouse neural tube , we deleted Shh from the floor plate at ∼e9 . 5 . Mice containing a conditional null allele for Shh ( Shhflox/flox; [48] ) and a Brn4cre transgene ( Bcre32 [Tg ( Pou3f4-cre ) 32Cre]; [49] ) , which directs cre expression to neural progenitors from ∼e9 . 0 , were used . We confirmed that Shh is lost from the cervical neural tube of Shhflox/flox;Brn4cre embryos by e10 . 5 but left notochord expression of Shh unaffected ( Figure S5A , S5B ) . Moreover , in contrast to embryos lacking Gli2 [50] , in which the notochord remains abutting the neural tube , in Shhflox/flox;Brn4cre embryos the notochord separated normally from the overlaying neural tissue . Assaying expression of Nkx2 . 2 and Olig2 at e9 . 5 revealed that the induction and extent of expression of both proteins were similar in Brn4cre;Shhflox/flox embryos and littermate controls ( Figure 5D , 5E ) . By contrast , analysis of e10 . 5 Brn4cre;Shhflox/flox embryos revealed a marked decrease in the number of cells expressing Nkx2 . 2 and Olig2 and a ventral retraction in the domains of expression ( Figure 5F , 5G ) . Notably , the expression of Olig2 expression was more affected than Nkx2 . 2 . In addition the number of cells expressing Nkx6 . 1 was decreased; concomitantly there was a ventral shift in the expression of Dbx1 and Pax6 ( Figure S5C , S5D; unpublished data ) . By e12 . 5 embryos , the expression of both Nkx2 . 2 and Olig2 was severely affected in Brn4cre;Shhflox/flox and only a few cells expressing either marker could be detected ( Figure 5H , 5I ) . Thus , similar to chick , prolonged exposure to Shh is required to maintain normal ventral pattern in the neural tube . Moreover , these data identify a crucial role for floor plate derived Shh in the patterning of the neural tube . Together , these results indicate that , even after progenitor identity has been assigned , continued Shh signalling is necessary to maintain progenitor identity in vivo in the ventral neural tube . The continuous requirement for Shh signalling in vivo , coupled with the plasticity of progenitor identity in explants , raised the possibility that during normal embryonic development some progenitor cells may transiently express a transcriptional code characteristic of a more ventral progenitor population before reverting to a more dorsal identity . To test this , we used Olig2Cre mice to mark the progeny of cells that have expressed Olig2 [24] . Examining E10 . 5 mice embryos harbouring the Olig2cre allele and the ROSA26-flox-STOP-flox-lacZ or the ROSA26-flox-STOP-flox-GFP lineage tracers [51] revealed cells expressing the lineage marker within the pMN domain and their MN progeny ( Figure 5J , 5K ) . In addition , however , a small number of cells positive for the reporter were observed dorsal to the pMN domain . These were located adjacent to the dorsal limit of the pMN domain , defined by Olig2 expression , but did not express Olig2 protein ( Figure 5J , j ) . This suggests that some cells residing in the p2 progenitor domain transiently expressed Olig2 and activated Cre expression . This was confirmed by examining e11 . 5 embryos . In these embryos a few Chx10 and GATA3 expressing V2 neurons , which are generated from Olig2 negative , p2 progenitors , contained the reporter , indicating that they had been generated from progenitors that had previously expressed Olig2 ( Figure 5K , k and unpublished data ) . Together these data confirm the plasticity of progenitor identity in vivo and emphasize the dynamic way in which positional information is supplied in the neural tube .
In this study we provide evidence that the interpretation of Shh morphogen gradient in the neural tube is dynamic and entails a prolonged period of intracellular signalling to elaborate and maintain gene expression in progenitor cells . Shh induced intracellular Gli activity is required for the patterning of progenitors [22] , [52] . We show that the Gli activity in neural cells reaches a peak ∼6 h after exposure to Shh . Below 1 nM Shh , the intensity of this peak of Gli activity correlates with the concentration of Shh; above 1 nM Shh intracellular signalling appears saturated ( Figure 1B ) . For all Shh concentrations , however , cells progressively adapt their response to Shh , resulting in a gradual decline in Gli activity levels from 6 h onwards . Thus , the time it takes for Gli activity to return to basal levels ( i . e . “the duration of signalling” ) is proportional to Shh concentration for all concentrations . Hence , different concentrations of Shh generate distinct temporal profiles of Gli activity ( Figure 1C ) . The features of this profile—the duration and the level of Gli activity—are used to allocate positional identity to neural progenitors ( Figure 6 ) . Progressively higher levels and longer durations of intracellular Gli activity specify more ventral identities ( Figures 2 , 3 ) and , even for the interneurons that are generated by below saturating concentrations of Shh , the duration of signalling plays a key role . These data indicate , therefore , that the temporal adaptation mechanism of morphogen interpretation allocates positional identity throughout the ventral neural tube . This offers a unifying view of ventral neural tube patterning that differs significantly from conventional models of morphogen action [3] , [24] . Consistent with the dynamic aspect of this model , Shh target genes initially occupy territories that are ventral to their final positions and are progressively refined dorsally over time ( Figure 3D , 3E ) . Moreover , even after a positional identity has been assigned to a cell , it remains susceptible to change . For the more ventral progenitor domains , Gli activity above basal levels is required to maintain an identity ( Figures 4 , 5 ) . Thus , blockade of signalling results in ventral progenitors losing their identity and acquiring an antecedent , more dorsal identity . This emphasizes a second important difference between the mechanisms of Shh interpretation and standard morphogen models , which assert that positional identity of cells can ascend but not descend a gradient [3] , [53] . Together , the data suggest a model in which the positional information provided by Shh can be represented by the time integral—the cumulative level and duration—of signalling and the identity of progenitors is assigned in a progressive and dynamic manner . The use of signal duration in the assignment of positional identity contrasts with other models of morphogen action that contend that the level of intracellular signalling is the main , or sole , factor that determines the response [3] . For example , different levels of nuclear Smad activity , the transcriptional mediator of TGFβ signalling , have been proposed to be sufficient to mediate the graded response necessary for mesoderm patterning [54] , [55] , [56] . Similarly , the graded response of cells to Dpp in Drosophila appears to correlate with the amount of activated Mad transcription factor [57] , [58] , [59] and the anterior-posterior patterning of Drosophila embryos depends on the amount of Bcd protein in each nucleus [60] , [61] . In the case of Shh signalling , the use of duration as well as level of signalling to provide positional information introduces an additional dimension to the process that could be relevant to other responding tissues . Notably , anterior-posterior patterning of the developing limb appears to depend on both a concentration and temporal gradient of Shh signalling [62] , [63] , [64] and , analogous to the neural tube , the specification of digit identity is progressive with the sequential induction of more posterior identities [65] . Likewise , Hh signalling in the wing disc of Drosophila embryos results in the induction of higher response genes later than lower response genes [66] . Thus , the mechanism of morphogen interpretation operating in the neural tube might be common to other tissues patterned by graded Hh signalling . Furthermore , since negative feedback is a general feature of most signal transduction pathways [67] , the graded response of cells to other morphogens might result in similar temporal dynamics and exploit analogous mechanisms . What determines the temporal dynamics of signalling in responding cells ? The Shh dependent upregulation of Ptc1 , and possibly other inhibitors of Shh signalling , results in the cell autonomous desensitization of cells to Shh over time [23] , [24] . This negative feedback is likely to play a significant part in defining the signalling kinetics in responding cells . It would account for the temporal profile and in particular the rate of decline , and hence the duration of signalling , in cells exposed to a fixed concentration of Shh . However , in addition to negative feedback , the period of time a cell is exposed to Shh will also determine the duration of signalling . Progenitors in vivo are subject to a changing gradient of Shh and as a result the time that Shh is available might limit the duration of signalling [14] . This could be particularly relevant in intermediate regions of the neural tube , which generate V0–V2 neurons . It takes a longer time for the Shh gradient to extend to these positions [14] and then the growth of the neural tube and the sequestration of Shh ventrally might limit the period of time for which significant concentrations of Shh are maintained at this distance [23] , [26] , [47] . In agreement with this idea , significant numbers of V0 and V1 neurons are only generated in vitro when explants are exposed to Shh for less than 18 h ( Figure 2D ) . Longer exposure results in cells switching identity towards more ventral fates . A similar model has been proposed for the temporal changes in Hh distribution in the anterior compartment of the Drosophila wing disc [68] . In this view , the duration of Shh exposure is decisive for specifying the intermediate identities , p0–p2 , while the more ventral pMN and p3 identities are chiefly dependent on maintaining appropriate high concentrations of Shh , which are transformed into corresponding periods of intracellular signal transduction by negative feedback . However , the critical test of this model awaits the capability to assay directly Shh protein distribution and intracellular signalling in vivo in the developing neural tube . The central role of Shh in providing positional information to neural progenitors does not exclude the possibility that other extrinsic signals contribute to ventral neural tube patterning . Members of the BMP and Wnt families as well as Notch and retinoic acid signalling have been implicated in aspects of neural tube development and cell fate allocation [6] , [40] . It is possible that these interact , either directly or indirectly , with Shh signalling to refine and modify the positional identity provided by Shh signalling . This could increase the precision or reliability of patterning in vivo . In this context , it is notable that the in vitro generation of V0 and V1 neurons overlap in explants exposed to Shh ( Figure 2D ) . Both retinoic acid and Notch signalling have been implicated in the generation of these neuronal subtypes and it is possible that one or both of these signals are involved in the spatially segregated generation of these interneurons in vivo [40] , [69] . The dynamic nature of Shh mediated pattern formation is emphasized by the requirement for continued Shh signalling to maintain the more ventral progenitor identities even after the gene expression profiles that define these progenitor domains have been induced . Thus , progenitors of MNs and V3 neurons revert to more dorsal identities if signalling is interrupted . This provides an explanation for the requirement for Shh signalling up to the last cell division in order to specify MN subtype identity [39] . Importantly , this also identifies a role for floor plate produced Shh for DV patterning of neural progenitors despite the initial induction of ventral gene expression by Shh secreted from the notochord [14] . Moreover , reversibility of the expression of Hh target genes has also been observed in other tissues , such as the Drosophila wing disc [70] , suggesting it is a common feature of the pathway . It remains to be determined whether the expression of Nkx2 . 2 and Olig2 eventually becomes independent of continued Shh signalling in the neural tube and if so at what time point . Nevertheless , the extended requirement for Shh signalling to maintain correct tissue pattern highlights a difference with other morphogens . In the case of mesoderm induction by Activin [53] and BMP signalling in the telencephalon [71] cells maintain the gene expression profile associated with the highest concentration of morphogen to which they have been exposed , even if the ligand exposure is limited to as little as 20 min [53] . This has been dubbed “the ratchet effect” because cells appear to respond to increases but not decreases in morphogen concentration [3] , [53] . As a result , prolonged exposure to TGF-β morphogens is unnecessary for patterning [53] . One consequence of a self-sustaining memory for the highest morphogen response is that it obviates the need to preserve a stable gradient for a long period time . This might be important in tissues that develop quickly and/or undergo movements that preclude the establishment of a stable gradient . However , cells would be vulnerable to transient fluctuations in signalling and could adopt an identity inappropriate for their position in response to a brief exposure to an anomalously high level of morphogen . Thus , the requirement for an extended period of Shh signalling to maintain positional identity in the neural tube emphasizes the ongoing refinement of progenitor boundaries during development and suggests a way to buffer fluctuations in ligand concentration and enhance the precision of the response . The delayed induction of some Shh target genes in progenitor cells suggests that the transcriptional state of cells is important for progenitor identity specification . This points to a model where the transcriptional network downstream of Shh signalling together with the temporal profile of Gli activity in a cell accounts for differential gene expression . Hence , Gli dependent induction of some genes might take place only after changes in the transcriptional state of a responding cell has been changed by an early period of Gli activity . This would explain the delayed induction of some progenitor markers and a requirement for ongoing signalling for their expression . A well-described example of this strategy is found in the Dpp response of cells of the dorsal ectoderm of Drosophila embryos [72] . In this case , Dpp induces expression of the transcription factor Zen . Then , Zen together with continued Dpp signalling activates a second gene , Race . Thus , the expression of Race takes longer than Zen and requires Dpp signalling to be sustained after Zen is induced . This strategy for morphogen dependent gene regulation has been referred to as “sequential cell context” [73] and represents an example of a feed-forward loop [74] . In the neural tube , it is well established that the transcriptional cross-repression between target genes downstream of Shh plays a key role in defining the spatial extent of progenitor domains [21] . It seems likely that these same regulatory interactions will also be involved in the dynamics of gene expression . For example , the repression of Nkx2 . 2 by Pax6 determines the dorsal limit of Nkx2 . 2 expression [25]; moreover , Nkx2 . 2 expression appears to shift increasingly dorsal in mouse embryos lacking Pax6 ( N . B . et al . , unpublished observation ) . Thus both the temporal and spatial pattern of Nkx2 . 2 appears to be influenced by Pax6 . The reciprocity of many of the cross-regulatory interactions within the transcriptional network might also account for the prolonged requirement for Shh signalling to sustain progenitor identity . In this view , continued Gli activity would be required to ensure that the extant transcriptional state of a responding cell does not revert to a previous state . This strategy of morphogen interpretation suggests a dynamic mechanism in which the positional identity of a cell is determined by the combined action of the ligand gradient and the state of the transcriptional network in the responding cell itself . More generally , the mechanism of Shh morphogen interpretation is reminiscent of “integral feedback control , ” a common control strategy in systems engineering in which the time integral of the difference between the actual output and a target output is fed back into the system to correct the output [75] . In the case of Shh signalling , the Gli dependent induction and gradual accumulation of Ptc1 could be viewed as the “time integral” that corrects the sensitivity of cells to Shh; hence increasing amounts of Ptc1 result in the increasing desensitization of cells to Shh signalling ( Figure 6A , 6B ) . The use of integral feedback control has been noted in several biological systems where it can act as a “gain” control to allow sensing over large concentration ranges or as a means to re-establish homeostasis after a system is disturbed [75] , [76] , [77] . An additional characteristic of this mechanism is that it provides a way to transform different levels of input into corresponding durations of signal output and it is this feature that appears to be exploited by cells to interpret graded Shh signalling . This would reconcile the competing models of pattern formation that have emphasized either concentration or time-dependent mechanisms [73] . Furthermore , in contrast to concentration-based mechanisms of morphogen interpretation where concentrations above the saturation limit elicit the same response , a temporal adaptation mechanism allows cells to discriminate between saturating concentrations of ligand ( Figure 6B ) . This would therefore extend the range of concentrations that can be discerned by a cell and shift the functional range to higher concentrations . The effect would be to increase the potential set of responses that can be elicited by a single signal and render the process less susceptible to the higher levels of noise associated with low concentrations of an extrinsic ligand [78] . A trade off with this mechanism , however , is that it requires patterning to occur over a comparatively long time period in which cell position remains stable relative to the gradient . Nevertheless , despite this limitation , it is possible that the interpretation of other morphogens may use similar mechanisms . For example , a correspondence between the activation of target genes and duration of signalling has been noted for Nodal in zebrafish embryos [79] and Dpp in the Drosophila wing disc [80] . In addition , brief exposure to Wg signalling is sufficient to induce low but not high responses in the wing disc [81] . Thus , the elucidation of the mechanism by which Shh provides positional information to neural cells suggests strategies of morphogen interpretation that may be generally applicable in developing tissues .
In ovo electroporation , fixation , sectioning of embryos , and immunocytochemistry were performed as described ( [21] ) . Antibodies used were rabbit anti-Arx ( a gift from J . Chelly ) , goat anti-β-galactosidase ( Biogenesis ) , rabbit anti-Chx10 ( a gift from T . Jessell ) , rabbit anti-Cre ( Covance ) , Rabbit anti-Dbx1 [34] , mouse anti-En1 ( DSHB ) , mouse anti-Evx1 ( DSHB ) , sheep anti-GFP ( Biogenesis ) , mouse anti-MNR2 ( DSHB ) , rabbit anti-Nkx2 . 2 ( a gift from T . Jessell ) , mouse anti-Nkx2 . 2 ( DSHB ) , mouse anti-Nkx6 . 1 ( DSHB ) , guinea-pig anti-Olig2 [82] , mouse anti-Pax6 ( DSHB ) , mouse anti-Pax7 ( DSHB ) , and mouse anti-Shh ( DSHB ) . The open neural plate of HH stage 10 chick embryos was isolated in L-15 ( Gibco ) media and the intermediate region dissected following Dispase treatment ( Gibco ) . These [i] explants were embedded in Collagen Type I containing DMEM ( Gibco ) . Culture medium contained F-12/Ham ( Gibco ) supplemented with 2 mM of Glutamine , 50 U/ml of Penicillin , 50 µg/ml of Streptomycin and Mito Serum ( BD ) . Recombinant Shh protein was produced as described [39] . Explants were fixed in 0 . 1 M phosphate buffer pH7 . 2 containing 4% PFA prior to immunostaining . Two regions containing approximately 200 cells were chosen from each of 4 or 5 explants for quantitations . For luciferase assays in explants , GBS-luc [83] was electroporated with normalization plasmid ( pRL-TK; Promega ) 2 h prior to the dissection of the explants [24] . Gli activity was measured using the Dual Luciferase Assay ( Promega ) and compared to levels of Gli activity in transfected untreated explants . Eggs were incubated to HH st . 18 and windowed . Embryos were treated with 25 µl of 1 mg/mL solution of cyclopamine ( Toronto Research Chemicals ) in 45% 2-hydropropyl-β-cyclodextrin ( HBC; Sigma ) [44] . The embryos were re-incubated for a further 24 h and then processed for immunocytochemistry . The Dbx1-Cre , Olig2-Cre , ShhFlox , and Bcre32 ( Tg ( Pou3f4-cre ) 32Cre ) mouse lines have been described previously [24] , [42] , [48] , [49] and the conditional lineage tracing alleles engineered in the ROSA26 locus have been described [43] , [51] . All studies in mice were carried out with appropriate permissions and in accordance with the Institutional Animal Care and Use Subcommittee of the National Institute for Medical Research . | In many developing tissues , the pattern in which cell types are generated depends on secreted factors called morphogens . These signalling molecules are produced in specific locations and at specific concentrations , thereby forming concentration gradients . Different target genes are induced at specific distances from the source of the morphogen , and therefore the spatial pattern of gene expression correlates with this concentration gradient . In this study , we examined how cells respond to a morphogen gradient to produce the appropriate pattern of cellular differentiation . We focused on the morphogen Sonic Hedgehog ( Shh ) , which specifies the pattern of different types of neurons in the ventral regions of the neural tube ( the embryo's precursor to the central nervous system ) . We show that in addition to the concentration of Shh , the duration of Shh signalling also contributes to the patterning of the ventral neural tube . A consequence of this is that the genes defining different cellular identities are expressed in a characteristic temporal progression . In addition , sustained Shh signalling is required for more ventral cell types; otherwise they revert to their previous cellular identity . Together these results indicate that dynamic and sustained signalling by Shh is required for the patterning of the ventral neural tube , challenging conventional models of morphogen action that rely solely on the concentration of signal perceived by cells at specific positions in the morphogen gradient . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"developmental",
"biology/pattern",
"formation",
"developmental",
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"biology"
] | 2010 | Dynamic Assignment and Maintenance of Positional Identity in the Ventral Neural Tube by the Morphogen Sonic Hedgehog |
Anaplasma phagocytophilum , the causative agent of Human Granulocytic Anaplasmosis ( HGA ) , is an obligately intracellular α-proteobacterium that is transmitted by Ixodes spp ticks . However , the pathogen is not transovarially transmitted between tick generations and therefore needs to survive in both a mammalian host and the arthropod vector to complete its life cycle . To adapt to different environments , pathogens rely on differential gene expression as well as the modification of proteins and other molecules . Random transposon mutagenesis of A . phagocytophilum resulted in an insertion within the coding region of an o-methyltransferase ( omt ) family 3 gene . In wild-type bacteria , expression of omt was up-regulated during binding to tick cells ( ISE6 ) at 2 hr post-inoculation , but nearly absent by 4 hr p . i . Gene disruption reduced bacterial binding to ISE6 cells , and the mutant bacteria that were able to enter the cells were arrested in their replication and development . Analyses of the proteomes of wild-type versus mutant bacteria during binding to ISE6 cells identified Major Surface Protein 4 ( Msp4 ) , but also hypothetical protein APH_0406 , as the most differentially methylated . Importantly , two glutamic acid residues ( the targets of the OMT ) were methyl-modified in wild-type Msp4 , whereas a single asparagine ( not a target of the OMT ) was methylated in APH_0406 . In vitro methylation assays demonstrated that recombinant OMT specifically methylated Msp4 . Towards a greater understanding of the overall structure and catalytic activity of the OMT , we solved the apo ( PDB_ID:4OA8 ) , the S-adenosine homocystein-bound ( PDB_ID:4OA5 ) , the SAH-Mn2+ bound ( PDB_ID:4PCA ) , and SAM- Mn2+ bound ( PDB_ID:4PCL ) X-ray crystal structures of the enzyme . Here , we characterized a mutation in A . phagocytophilum that affected the ability of the bacteria to productively infect cells from its natural vector . Nevertheless , due to the lack of complementation , we cannot rule out secondary mutations .
Anaplasma phagocytophilum is an obligately intracellular bacterium classified in the order Rickettsiales , and is the causative agent of Human Granulocytic Anaplasmosis ( HGA ) [1] . HGA is characterized by high fevers , rigors , generalized myalgias , and severe headache . It is a potentially life-threatening disease , with 36% of patients diagnosed with HGA requiring hospitalization , 7% needing urgent care , and mortality of ~1% [2] . The incidence of HGA has been increasing steadily , from 348 identified cases in 2000 when it first became reportable to the CDC , to 1761 cases in 2010 [3] and 2 , 782 reported cases in 2013 [4] . Similar trends are evident in other countries in Europe and Asia [reviewed in [5]] . In addition , A . phagocytophilum infects domestic animals such as dogs , cats , and horses , as well as wild mammals from deer and wolves to various rodents [6] . A . phagocytophilum is transmitted by ticks of the Ixodes ricinus complex , with Ixodes scapularis and Ixodes pacificus being the most important vectors in the USA [7] . Transovarial transmission does not occur in these ticks , and has only been reported in tick species and A . phagocytophilum strains that are not implicated in human disease [8] . The natural transmission cycle involves acquisition of the pathogen from small wild rodents by tick larvae , and transstadial transmission to nymphs and adults that may infect a new mammalian host during a subsequent bloodmeal . Therefore , the ability of A . phagocytophilum to cycle between ticks and mammalian hosts is imperative for bacterial survival in nature [8] . The development of A . phagocytophilum in Ixodes sp . vector ticks remains unknown but has been described in tick cell culture where it is biphasic [9] . The time required for A . phagocytophilum to complete development in ISE6 cells differs from that observed in HL-60 and endothelial cells [9] . Adhesion to ISE6 cells started at 30 min p . i . , and by 1 hr p . i the bacteria were attached to the tick cell membrane , initiating the process of endocytosis , which was probably driven by receptor-mediated interactions . By comparison , in HL-60 cell culture , >70% of bacteria were observed binding to host cells in the first 40 min p . i . , and at that time , 26% of the bacteria had been internalized [10] . Internalization in tick cells began at 2 hr p . i . and was complete by 4 hr p . i . , whereas in HL-60 cell culture , only 45 . 5% of the bacteria had entered the cells at this time point [10] . Replication by binary fission started by 8 hr p . i . in tick cells [9] whereas in HL-60 cells only a few bacteria had turned into the reticulate form by 12 hr p . i . and initiated replication [10] . Nevertheless , many of the molecular events involved in the infection of mammalian cells are known [11] , but much less is known in the tick counterpart [12] . Studies to understand vector-pathogen interactions have focused on tick responses and tick factors important for successful establishment of the pathogen in ticks [13–15] , but A . phagocytophilum genes and proteins that are important for development in tick cells and ticks remain largely unidentified . Some studies have examined gene expression of A . phagocytophilum during infection of I . scapularis ticks or I . scapularis ISE6 cells , but have focused on certain periods , such as transmission feeding or late phases of replication [16 , 17] . As a result , little is known about the proteins , and their modifications , necessary for the early phases of infection of tick vector cells by A . phagocytophilum . Survival in dissimilar hosts such as the arthropod vector and the mammal that present important biological differences requires rapid adaptation of bacteria , and involves proteins and other molecules that are differentially expressed or produced in response to host-specific cues [17] . Thus , the identification of such factors is crucial to our understanding of the biology of this important pathogen . Analyses of A . phagocytophilum gene expression and proteomics [16] may fail to identify proteins that are not abundant or not directly involved in infection but still play an important role . The intracellular nature of A . phagocytophilum has made it difficult to study the function of genes involved in intracellular invasion and replication using genetic techniques such as homologous recombination . Nevertheless , random mutagenesis of A . phagocytophilum using the Himar1 transposase system [18] has become an important tool to probe gene function in these and related bacteria [19–21] . Here , we analyzed a mutant , referred to as ΔOMT , with a transposition into genomic locus APH_0584 that contains a gene encoding a member of family 3 S-adenosyl methionine ( AdoMet or SAM ) -dependent o-methyltransferases . Transcription of genomic locus APH_0584 was barely detected in HL-60 , HMEC-1 or ISE6 cells during late phases of infection [17] . However , the mutation of this gene rendered the bacteria unable to efficiently colonize I . scapularis ( ISE6 ) cells . Methyltransferases are involved in important bacterial activities such as cell signaling , cell invasion , and gene expression , as well as in metabolic pathways and pathogenesis [22–24] . They participate in the modification of membrane components , cofactors , signaling and defense compounds [23] , and have been linked to virulence in several bacteria [25–27] , fungi [28] , and viruses [29] . OmpB proteins from several rickettsial pathogens are methylated at multiple residues by lysine methyltransferases [30] , although recombinant OmpB produced by E . coli in the absence of a lysine methyltransferase has been shown to mediate adhesion and invasion of HeLa cells in a Ku70-dependent manner [31] . Methylation of glutamic acid residues in the outer membrane protein OmpL32 of Leptospira interrogans is thought to be involved in its virulence and ability to colonize liver and kidney cells in hamsters [32] . To gain insights into its overall structure and the interaction of the A . phagocytophilum o-methyltransferase with cofactors , we solved the crystal structure of the apo-enzyme , the enzyme bound to S-adenosine homocysteine ( SAH ) , to SAH and manganese , and to SAM and manganese . This revealed large differences with the nearest homolog in the PDB ( o-methyltransferase from the cyanobacterium Synechocystis sp . ; PDB ID: 3CBG ) . Here , we analyzed the phenotypic and proteomic changes that characterized ΔOMT , and present evidence that the o-methyltransferase is involved in adherence to and necessary for replication of A . phagocytophilum in tick cells .
The ΔOMT , selected and maintained in HL-60 cells , was unable to grow in ISE6 cells . The mutant expressed the Green Fluorescent Protein ( GFPuv ) from a Himar1 transposon [18] and Southern blot analysis identified a single insertion site ( Fig 1A ) . Digestion of ΔOMT DNA with BglII yielded a single band hybridizing to the probe , suggesting a clonal population ( Fig 1A ) , although EcoRV yielded several smaller bands that were most likely due to incomplete digestion . Recovery of the transposon along with flanking sequences from ΔOMT DNA by restriction enzyme digestion and cloning indicated transposition into aph_0584 ( Gene ID: 3930223; o-methyltransferase 3 family member ) between nucleotide positions 612707–612706 of the A . phagocytophilum strain HZ genome sequence ( [33]; Fig 1B ) . The single insertion event suggested that the changes in phenotype were due to the disruption of that particular gene . To determine the mechanism whereby the mutation affected the phenotype of A . phagocytophilum , we compared wild-type and mutant bacteria with respect to their ability to invade and replicate in tick and mammalian host cells , determined the timing of wild-type OMT expression and its localization , identified the protein methylated by the enzyme as well as cofactors , and solved the crystal structure of the OMT . First , we analyzed the growth of the mutant in HL-60 and ISE6 cells and compared it to wild-type bacteria under the same conditions . The ΔOMT was not able to replicate in ISE6 cells and qPCR showed that msp5 ( a single copy gene used as a proxy for bacterial numbers ) copy numbers decreased significantly over a 12-day period ( P = 0 . 008 ) ( Fig 2 ) . This was in contrast to the behavior of ΔOMT bacteria in HL-60 cells , in which they were able to multiply in a manner comparable to wild-type bacteria ( Fig 2 , P = 0 . 504 ) . Only the datasets for the 1:16 dilution in ISE6 and 1:100 in HL-60 are shown , but other dilutions presented the same tendency . Because of the rapid decline of ΔOMT numbers noticeable already during the first 24 hr of incubation with ISE6 cells , we tested the ability of the mutant to bind to ISE6 . There was a significant reduction ( >50% ) in binding of ΔOMT ( t-value = -4 . 1011; P = 0 . 0001 ) to ISE6 cells from 0 . 3 bacteria per cell in the wild type to 0 . 12 mutant bacteria per cell ( Fig 3A ) . To support these results , an inhibitor of SAM-dependent methyltransferases was used to reproduce the effects of the lack of methylation brought about by the disruption of omt on binding of A . phagocytophilum to ISE6 cells . Wild-type bacteria were pre-incubated with 20 nM , 30 nM , and 40 nM of adenosine periodate ( AdOx ) for 1 hr before addition to ISE6 cells and incubated for another hr; untreated bacteria served as controls . All concentrations of AdOx affected the ability of A . phagocytophilum to bind to ISE6 cells significantly ( P<0 . 001 ) ( Fig 3B ) . In controls , an average of 0 . 529 bacteria bound per cell , compared to ΔOMT with only 0 . 156 bacteria per cell . 20 nM AdOx decreased binding to 0 . 307 bacteria per cell , which was less than the reduction in attachment observed in ΔOMT ( Fig 3B ) . However , as the concentration of AdOx increased to 30 nM and 40 nM , the effects were stronger than in ΔOMT with only 0 . 093 and 0 . 086 bacteria bound per cell , respectively , and these differences were statistically significant when compared to the ΔOMT and the 20 nM AdOx concentration ( Fig 3B ) . These results corroborated the effects of the inhibition of methylation due to the mutation of omt on A . phagocytophilum binding . The greater inhibition of bacterial binding when using 30 nM and 40 nM of AdOx was probably due to inhibition of other methyltransferases . To test this hypothesis , we pre-incubated ΔOMT bacteria with AdOx before addition to ISE6 cells , as described for wild-type bacteria . Incubation of the ΔOMT with 20 nM and 40 nM did not significantly decrease binding to ISE6 cells ( S1 Fig ) , suggesting that no other methyltransferases were involved in tick cell invasion . Because of the effects on binding to tick cells seen in ΔOMT , we tested the expression of the omt gene by qRT-PCR during early stages of wild-type A . phagocytophilum interaction with and development in ISE6 cells , using rpoB and msp5 genes as normalizers . In our discussion , we focused on the fold change of omt normalized to msp5 , but normalization against either gene showed the same trend ( Fig 4 ) . Up-regulation of the omt gene started at 30 min post-inoculation ( p . i . ) , continued to increase from 3-fold at 30 min to 5-fold by 1 hr p . i . , and by 2 hr , the gene reached its maximum expression , showing 34-fold up-regulation compared to bacteria entering HL-60 cells ( Fig 4 ) . At 4 hr , omt expression decreased to 0 . 97-fold change ( Fig 4 ) , similar to that seen in bacteria infecting HL-60 cells . Our results are congruent with electron microscopy based studies that showed A . phagocytophilum bound to tick cells between 30 min and 1 hr p . i . and cell entry at 2 hr p . i . , the time when we saw maximum omt gene expression [9] . Because omt expression correlated with binding and entry of the bacteria to ISE6 cells , and the mutation of this gene affected the ability of the bacteria to bind to these cells , we investigated the localization of the protein during this step in cell infection . Mouse antiserum against recombinant OMT ( rOMT ) was produced to label the protein during binding of wild-type A . phagocytophilum to ISE6 ( 2 hr p . i . ) , using an immunofluorescence assay ( IFA ) . OMT was detected with mouse anti-rOMT serum followed by incubation with anti-mouse IgG conjugated to AlexaFluor647 ( red fluorescence ) . All bacteria were labeled with dog anti-anaplasma serum followed by incubation with fluorescein isothiocyanate ( FITC ) -conjugated anti-dog IgG ( green fluorescence ) . Bacteria interacting with ISE6 showed strong OMT expression while bacteria interacting with HL-60 showed only slight expression ( Fig 5 ) . This was in agreement with the 34-fold up-regulation of the gene seen by qRT-PCR during adhesion to ISE6 cells ( Fig 4 ) . Bacteria incubated with pre-immune serum did not fluoresce red nor did uninfected ISE6 cells incubated with anti-rOMT antibodies followed by TRITC-conjugated anti-mouse IgG , demonstrating that the serum specifically labeled OMT . We conducted a time course comparison of wild-type versus ΔOMT bacteria to identify the stage at which the infection process failed . To increase the chances of detecting differences between the intracellular development of the ΔOMT and wild-type bacteria , we performed an optimized binding assay in which a sparse monolayer of adherent ISE6 cells growing in MatTek dishes was exposed to numerous bacteria ( 100–300 bacteria/cell ) and washed gently to maximize retention of bacteria bound to the cells . This is in contrast with our previous assay that used suspended ISE6 cells , a lower multiplicity of infection ( MOI ) , and vigorous washes so that only strongly bound bacteria remained , which increased the sensitivity of the assay but made it difficult to track intracellular development of ΔOMT . The development of the ΔOMT was compared to wild-type bacteria using confocal microscopy of fixed and immunofluorescently labeled ISE6 cells in MatTek dishes after 1 hr of exposure to bacteria , and subsequently on days 1 , 2 , 3 , 4 , 5 , 7 , and 10 . With this method there were no differences observed in binding or internalization between the ΔOMT and wild-type bacteria ( Fig 6A and 6B ) . However , by 44 hr , wild-type bacteria started to form morulae , whereas ΔOMT bacteria remained singly within the cells ( Fig 6C ) . By days 3 and 4 , the wild-type bacteria had formed large morulae ( Fig 6D and 6E ) and on days 5–10 , wild-type infections had become asynchronous , with bacteria from lysed cells infecting new cells while other cells harbored large morulae ( Fig 6F–6H ) . ΔOMT bacteria , however , never developed morulae during the 10 days of observation . Only single bacteria were observed within the infected cells throughout ( Fig 6D–6H ) , suggesting that the mutant bacteria were unable to replicate and form morulae within the infected cells even though they were successfully internalized . Observation of ΔOMT bacteria by confocal microscopy identified their location as intracellular . To confirm that in fact the ΔOMT bacteria resided inside the tick cells , we performed a trypsin-protection assay , similar to those used to remove uninternalized bacteria and/or beads from cells to examine binding proteins in Ehrlichia chaffeensis , A . phagocytophilum , and Helicobacter pylori [34–36] . Cultures were used four days after inoculation with either mutant or wild-type bacteria , when wild-type bacteria had formed large intracellular morulae ( S2A–S2C , S2B and S2C Fig ) , whereas mutants persisted as individual intracellular bacteria ( S2D , S2E and S2F Fig ) . Cultures were treated with trypsin once ( wild-type and mutant ) or twice ( mutant only ) to remove any extracellular bacteria , and untrypsinized cells scraped off the growth substrate were used for comparison . As expected , neither mechanical scraping ( S2A and S2B Fig ) nor trypsinization affected the wild-type morulae ( arrow heads ) already formed within ISE6 cells ( S2C Fig ) . Similarly , when ISE6 cultures that had been exposed to ΔOMT bacteria for four days were scraped off the flask ( S2D Fig ) , trypsinized once ( S2E Fig ) or twice ( S2F Fig ) , there was no effect on the bacteria , confirming that they were located intracellularly as also indicated by confocal microscopy . As observed in the previous experiment , mutant bacteria remained as individuals ( arrows ) within the infected cells and were unable to develop to morulae . Additional controls demonstrated that wild-type bacteria adherent to ISE6 cells for 1 hr were removed from the cells by trypsinization ( S2G and S2H Fig ) , another indication that single ΔOMT bacteria visualized four days p . i . had been internalized . Note that host cell nuclei ( asterisks ) were recognized by the dog anti-A . phagocytophilum serum and subsequently labeled by the secondary FITC-conjugated anti-dog antibody ( S2D , S2E and S2F Fig ) . They were not detected in panels A , B , and C because the brightness of the large wild-type morulae required a shorter exposure during image acquisition than that used to image ΔOMT . Anti-nuclear antibodies have been detected in dogs infected with vector-borne pathogens ( Smith et al . 2004 ) , explaining the reactivity of the dog’s antiserum with host cell nuclei . To globally identify proteins that were differentially represented in the ΔOMT compared with the wild-type bacteria , we used a proteomic approach based on iTRAQ ( isobaric tag for relative and absolute quantitation ) technology . Peptides in each sample were labeled with different isotopic tags of known mass to quantify the relative abundance of the proteins in each sample . Both ΔOMT and wild-type bacteria were incubated with ISE6 cells for 4 hr at 34°C . Proteins were extracted from bacteria released from host cells , and triplicate samples were analyzed by tandem mass spectrometry ( MS/MS ) [37] . In each replicate , multiple A . phagocytophilum proteins were identified that appeared to be differentially abundant in the mutant ( S1 Text ) . Of these , 23 A . phagocytophilum proteins ( Table 1 ) were identified as differentially abundant in all replicates: five proteins were less abundant ( hypothetical protein APH_0406 , major surface protein 4 , anti-oxidant AhpCTSA family protein , and ankyrin ( GI88607707 ) ) , and 19 appeared more abundant ( Table 1 ) . Hypothetical protein APH_0406 , and major surface protein 4 ( Msp4 ) presented the lowest relative expression ratios ( both <0 . 2 ) , indicating that they were highly expressed in wild-type bacteria during binding to ISE6 cells compared to the mutant ( Table 1 ) . Several proteins known to be involved in infection of mammalian cells [38 , 39] , or highly expressed in A . phagocytophilum replicating in human cells [17] , were more abundant in the mutant ( Table 1 ) . These proteins included several membrane proteins ( P44-18ES , an OmpA family protein , P44-1 Outer membrane protein , an OMP85 family outer membrane protein , and hypothetical protein APH_0405 ) as well as stress response proteins ( co-chaperone GrpE , chaperonin GroEL , and chaperone DnaK ) ( Table 1 ) . This suggested that , unlike wild-type A . phagocytophilum , the ΔOMT failed to respond to interaction with ISE6 cells in a host cell specific manner , and as a result , the repertoire of proteins in its outer membrane remained unchanged . It is also possible that lack of OMT activity disrupted an environmentally responsive regulatory mechanism or sensor that prepares A . phagocytophilum for changes in hosts . Correct quantification of proteins by iTRAQ is problematic ( Shirran and Blotting 2010 ) , and to confirm these results , we examined transcription of several genes that were more abundant in ΔOMT than wild-type during bacterial adhesion to tick cells ( based on the iTRAQ data ) using qRT-PCR . RNA was isolated from ΔOMT and wild-type bacteria purified from HL-60 cells during late stages of infection to investigate transcription before exposure to ISE6 cells , and after a 2 hr incubation with ISE6 cells for comparison . In HL-60 cells , genes encoding OmpA , p44-18ES , and APH_0404 were strongly up-regulated 19- , 267- , and 5-fold , respectively compared to values obtained after 2 hr in ISE6 cells ( S3 Fig ) . Genes encoding APH_0405 and cytochrome C oxidase subunit II were not regulated ( 1 . 5 and 1 . 3 fold average difference , respectively ( S3 Fig ) ) , whereas msp4 expression was down-regulated ( 0 . 0033 fold average down-regulation in both the ΔOMT and wild-type bacteria ) compared to wild-type bacteria in ISE6 cells ( S3 Fig ) . Thus , the transcript levels mirrored the protein expression detected using iTRAQ , and suggested that the ΔOMT was not able to change gene expression to adapt to conditions in ISE6 . In HL-60 , the ΔOMT and wild-type bacteria had similar transcript levels , indicating that the mutation did not affect the expression of these genes . An analysis of the pathways affected in the ΔOMT during incubation in ISE6 based on iTRAQ data showed that several of the more abundant proteins were involved in transcription and protein metabolism , indicating that the mutant was metabolically active ( Table 2 ) . In our analysis , we only included proteins with a known role in specific pathways , according to information available at the KEGG ( http://www . genome . jp/kegg/ ) pathways website . Hypothetical and porin proteins were not analyzed within specific pathways , since their roles have not been established . Because we thought it possible that the OMT might modify either bacterial or host cell proteins , Anaplasma and host cell peptides identified by iTRAQ as having a methyl modification were analyzed to identify those that were less abundant in ΔOMT bacteria , and in whole ΔOMT inoculated cell cultures compared to control wild-type samples . Among peptides with a <0 . 7 ratio of abundance between the wild-type and mutant ( S2 , S3 and S4 Texts ) , we identified eight A . phagocytophilum proteins with reduced methylation of eight corresponding peptides ( Table 3 ) . Two of the proteins , Msp4 and APH_0406 , were less abundant in the mutant , by a ratio of 0 . 239 for Msp4 and 0 . 7484 for Aph_0406 , and lacked methyl-modifications of specific residues . The affected amino acids were glutamic acid residues ( E ) in the Msp4 peptide VEVEVGYK ( S2 Text ) , and an asparagine residue ( N ) in the APH_0406 peptide NVVLGGMLK ( S2 Text ) . Fifteen tick host cell proteins displayed reduced methylation when inoculated with the ΔOMT as compared to wild-type infected cells , but prolyl 4-hydroxylase alpha subunit ( GI:240974259 ) and flavonol reductase/cinnamoyl-CoA reductase ( GI:241703753 ) were the only two I . scapularis proteins to be both down regulated as well as to present peptides with reduced methylation in all replicates ( S3 Table ) . Since no OMT was detected in ISE6 cells by IFA during infection with wild-type bacteria ( Fig 5 ) , these changes are unlikely to be due to a direct effect of the mutation , but probably reflect an absence of replicating A . phagocytophilum . To test if the A . phagocytophilum proteins identified by iTRAQ as potential substrates were in fact methylated by the OMT , rOMT was produced in E . coli using the complete coding sequence of the gene ( aph_0584 ) cloned into the vector pET29a . The purity of rOMT was verified by gel electrophoresis ( SDS-PAGE ) and Coomassie blue staining ( Fig 7A ) , and its molecular weight ( MW ) corresponded to the predicted MW of ~24 kDa for OMT ( Fig 7A ) . We used the SAM-fluoro:SAM methyltransferase Assay to measure the activity of purified rOMT in in vitro methylation assays with potential substrates ( http://www . gbiosciences . com/ResearchProducts/samfluoro . aspx ) . In this assay , the production of highly fluorescent resorufin ( expressed as resorufin units , RU ) resulting from oxidization of 10-acetyl-3 , 7 , - dihydroxyphenoxazine ( ADHP ) by hydrogen peroxide generated during the reaction and monitored at an excitation wavelength of 540 nm and an emission wavelength of 595 nm . Two higher molecular weight proteins present in the un-induced E . coli lysate co-eluted with rOMT . Methylation assays using only the rOMT along with all reagents except for the substrate ( negative control ) did not demonstrate any detectable increase in fluorescence in the presence of these contaminant proteins , indicating that they did not affect the results of the assay ( Fig 7B ) . Recombinant versions of proteins identified by iTRAQ as differentially methylated between the mutant and the wild-type bacteria were also produced in E . coli , purified as done for rOMT , and tested in the in vitro methylation assay . rOMT ( 40 ng ) and four recombinant A . phagocytophilum protein substrates ( Msp4 , APH_0406 , TypA , and P44-16b; 50 ng each ) were used in methylation reactions for 4 hr at 34°C . We selected these proteins from eight candidates that yielded the strongest reduction in abundance ratios of <0 . 60 ( Table 3 ) . Production of recombinant preprotein translocase subunit SecA was unsuccessful in One Shot BL21[DE3] chemically competent E . coli ( Invitrogen , New York ) , BL21[DE3] ( New England Biolabs , Massachusetts ) , and in Rosetta 2[DE3] E . coli pLysS ( Novagen , Germany ) , thus it was not pursued further . The number of RU ( fluorescence ) from known concentrations of resorufin ( 0 μM , 5 μM , 10 μM , 25 μM , and 50 μM ) was determined to produce a standard curve , and the concentration of resorufin produced in each reaction was calculated from the standard curve values . Of the four proteins tested , only rMsp4 resulted in a significant and rapid increase in resorufin production ( expressed as resorufin units , RU ) when incubated with rOMT ( Fig 7B ) . rAPH_0406 , rTypA , and rp44-16b produced high background fluorescence that resulted in high initial readings ( ~1 , 500–2 , 000 RU; 1 . 6–2 . 1 μM ) , but did not continue to accumulate a significant number of RU , and only reached values of ~2 , 200 RU ( 2 . 3 μM ) ( Fig 7B ) . By contrast , when rMsp4 was used as the substrate , the fluorescence started at a lower reading ( 1 , 200 RU; 1 . 3 μM ) but climbed to higher values ( ~3 , 400 RU; 3 . 6 μM ) , and reached a plateau at around 210 min after the reaction was initiated ( Fig 7B ) . Kinetics of the enzyme reaction were tested with 60 , 80 , and 100 ng of enzyme with a constant concentration of 50 ng rMsp4 , and in the presence of 80 , 100 , and 150 ng of rMsp4 with a constant concentration of rOMT at 40 ng . We expected that if Msp4 was the substrate , the velocity of the reaction would increase with increasing concentrations of the enzyme and substrate , which would result in a shorter time for the reaction to reach Vmax ( the maximum initial velocity when all enzyme molecules present in the reaction are in complex with the substrate ) . As predicted , the reaction reached Vmax in less time with higher concentrations of rOMT or rMsp4 ( S4 Fig ) . At the time required for the enzyme to reach Vmax , the rOMT had an activity of 0 . 13 μM/min with a Km of 5 . 57x105 M and the reaction reached Vmax after 46 min of initiation ( Table 4 ) . Because of the slow reaction kinetics , we suspected that A . phagocytophilum OMT required the addition of specific metal ions to catalyze the reaction , similar other o-methyltransferases [40] . Several concentrations ( 0 . 5 mM , 2 mM , 8 mM , and 16 mM ) of MnCl2 were added to the methylation reaction containing 100 ng ( 17 . 89 ρmoles ) of rOMT and 100 ng ( 16 . 61 ρmoles ) of rMsp4 . This was in addition to the 10 mM Mn2+ already included in the kit ( GBiosciences , pers . comm . ) . Addition of Mn2+ resulted in greater fluorescence ( higher RU ) ( Fig 7C ) and faster reaction times , reaching peak levels of RU by ~100 min after initiation . With the addition of 16 mM of MnCl2 ( 17 mM total Mn2+ ) , 98 , 000 RU ( 53 . 8 μM ) were reached compared to 20 , 000 RU ( 11 μM ) when the enzyme and substrate were used alone with the 10 mM Mn2+ supplied in the kit ( Fig 7C ) . Similar decreases in reaction time ( 130 min ) were observed with the addition of 8 mM of MnCl2 ( Fig 7C ) . The higher activity of the enzyme was also evident from the changes in enzyme activity ( Km ) , and in the time to reach Vmax ( 1 . 67x104 , 1 . 77 μM/min , and 9:00 min , respectively ) ( Table 4 ) . In preliminary tests , MgCl2 did not accelerate the activity of the enzyme reaction significantly . Protein for crystallization experiments was produced and purified by Seattle Biomed , a collaborator within the Seattle Structural Genomics Center for Infectious Disease ( SSGCID ) , and was crystallized as described in the Materials and Methods section . Although this target has 33% sequence identity to its closest neighbor in the Protein Data Base ( PDB ) , phases for the initial X-ray data from the synchrotron could not be determined by molecular replacement ( MR ) . We initially hypothesized that binding of a substrate or co-factor would alter the conformation of the protein to something more amenable to MR . However , even after co-crystallizing the protein with SAH , phases for the X-ray data still remained recalcitrant to being solved by MR . Therefore , we chose to pursue single wavelength anomalous diffraction ( SAD ) phasing by using high concentration soaks ( 0 . 5 M ) with sodium iodide solution , as it has previously yielded de novo phases for many other targets from the SSGCID [41] . Iodide-SAD data were collected on our in-house X-ray generator ( Table 5 ) and PHENIX HySS was able to find 72 iodide ion sites during its search , but we were able to identify 118 in the final structure using anomalous difference map peaks with a contour level of 3 . 5 σ . Phases for the Apo , SAM-Mn2+ , and SAH-Mn2+ datasets ( Table 5 ) were then determined by MR , using the SAH-bound structure as a search model . AnphA . 01233 . a has a canonical o-methyltransferase fold which consists of a central 7-stranded β-sheet that is flanked on both sides by three α-helices ( Fig 8 ) . Since the structure was not solvable by MR , we assayed the PDB for structural homologues using the full-PDB SSM search on the PDBeFold website . The nearest homologue was 3CBG , another o-methyltransferase from Cyanobacterium synechocystis , which had a Cα RMSD of 1 . 69 Å2 . With this much of a difference in structural similarity in the PDB , it is not surprising that MR failed to provide phases . AnphA . 01233 . a crystallizes as a dimer in both the Apo and SAH-bound crystal forms—the Apo form has one dimer per asymmetric unit , while the SAH-bound form has three dimers per asymmetric unit ( Fig 8 ) . The SAH molecule binds at the apex of the β-sheet , and the binding pocket is completely solvent exposed . When aligning a monomer of the Apo- and SAH-bound structures , the RMSD for all Cα carbons is only 0 . 273 Å2 , so no large conformational changes occur due to ligand-binding . However , there is a small movement in a helix , composed of residues 31–40 , that moves towards that substrate in the SAH-bound form as compared to the Apo form ( S5 Fig ) . Enzymatic assays showed that the catalytic activity of the OMT was greatly increased in the presence of the divalent metal ion Mn2+ at >10 mM concentration . Therefore , we chose to attempt co-crystallization experiments with Mn2+ in the presence of both SAM and SAH . Crystals formed readily in multiple initial sparse matrix screen conditions within a week and produced higher resolution data than either of the previous datasets collected in the absence of Mn2+ ( Table 5 ) . After molecular replacement and initial refinement of these structures , a positive Fo-Fc map peak at a contour level of 25 σ was observed in both the 4PCA and 4PCL structures , indicating that manganese was bound to the protein in close proximity to the SAH/SAM binding site ( Fig 9 ) . The Mn2+ ion is coordinated by the side-chains of D136 , D162 , and N163 and waters from the solvent ( Fig 9 ) . This places the Mn2+ ion within 4 . 5 Å of the CE methyl group to be transferred from the SAM co-factor to the hypothesized glutamate substrate of Msp4 . Interestingly , a glutamic acid residue from a neighboring asymmetric unit , E177 , inserts into a catalytic site in the SAM- Mn2+ bound structure . It appears to adopt a slightly different conformation for either chain A or chain B , which contains 2 molecules of OMT per asymmetric unit . In chain A , E177 interacts directly with the manganese ion at a distance of 2 . 5 Å ( Fig 9A ) , whereas in chain B , interaction of E177 with the Mn2+ ion is mediated by two water molecules ( Fig 9B ) . Since the natural substrate of this enzyme is a glutamic acid residue ( s ) from Msp4 , it is likely that the glutamic acid from Msp4 interacts with the OMT enzyme similarly to this . In order to understand the possible relationship of the A . phagocytophilum OMT with other members of this family of enzymes , PSI-BLAST was used to search for homologous OMTs in other organisms . Within the order Rickettsiales , only members of the families Anaplasmataceae and Candidatus Midichloria mitochondrii ( from the new family “Candidatus Midichloriaceae” ) encoded OMTs related to A . phagocytophilum OMT ( S6A Fig ) . However , Δ-proteobacteria encoded OMTs that had even closer homology to A . phagocytophilum OMT , including OMTs from Bdellovibrio bacteriovorus , Gloeocapsa sp . , Anaeromyxobacter dehalogenans , and Haliangium ochraceum ( S6A Fig ) . A PSI-BLAST search assigned a better e-value ( 4e-40 ) to an OMT from B . bacteriovorus than to the C . M . mitochondrii OMT ( 2e-36 ) , suggesting that the former enzyme more closely resembled A . phagocytophilum OMT . Furthermore , when the three motif sites detected by MEME ( S6B Fig ) from the four OMT enzymes were compared , the Δ-proteobacteria OMTs appeared to be more similar to A . phagocytophilum OMT than the C . M . mitochondrii OMT ( S6B Fig ) . Motif 1 of the A . phagocytophilum OMT had 48% identity and 66% similarity to H . ochraceum OMT motif 1 , respectively , whereas C . M . mitochondrii OMT motif 1 only showed 33% identity and 53% similarity . A . phagocytophilum OMT motif 2 exhibited 60% identity and 74% similarity with the corresponding motif in B . bacteriovorus OMT compared to values of 52% identity and 67% similarity to the motif regions of C . M . mitochondrii OMT motif 2 . A . phagocytophilum motif 3 showed 44% identity and 72% similarity to B . bacteriovorus OMT motif 3 compared to 33% identity and 67% similarity with that motif in C . M . mitochondrii OMT ( S6B Fig ) . The tertiary structure of Msp4 was predicted using Phyre2 [42] that compares conserved residues of a query protein to the sequence of proteins with known crystallized structures . The predicted tertiary and secondary structures were used to predict the probable positions of the methylated residues . Msp4 was predicted to form a β-barrel typical of porins ( S7B Fig ) , and the glutamic acid residues that are modified by the OMT are predicted to be located at the start of one of the β-strands forming the beta-barrel ( S7A and S7B Fig ) . Furthermore , transmembrane and signal peptide prediction software suggested that the first ~30 aa residues represented a signal peptide to direct transport of the protein from the cytoplasm to the outer membrane ( S7C Fig ) . These residues corresponded to the α-helix at the N-terminus ( dark blue ) that is probably cleaved before the protein is positioned in the outer membrane ( S7B Fig ) . The protein does not contain predicted transmembrane domains , but it is very likely that its positioning in the outer membrane is similar to that reported for other porins in that the β-barrel spans the membrane , and the portion of the protein with the longest loops is exposed on the outside of the bacteria .
Genetic manipulation of A . phagocytophilum and other members of the Anaplasmataceae is difficult due to the intracellular nature of these organisms , and currently relies on random mutagenesis to study the role of specific genes during pathogenesis in the mammal and development in the tick [18 , 37 , 43] . Targeted mutagenesis in the related organism , Ehrlichia chaffeensis , proved ultimately unsuccessful as the transformants obtained were not able to persist in vitro for more than six days [43] . The recent success of targeted mutagenesis in Rickettsia rickettsii resulting in the disruption of a major surface protein gene ( ompA ) [44] presumed to be a virulence factor without producing a detectable defect provides impetus to develop this method for other Rickettsiales , and serves as a reminder that gene function ultimately must be confirmed by mutational analysis . In this manuscript , we report the effects of the mutation of a specific gene of A . phagocytophilum that we suspect abolished its ability to infect tick cells . However , due to the lack of a complementation system in the Anaplasmataceae , we cannot completely rule out that this change in phenotype was due to secondary mutations . Nevertheless , our conclusions are supported by the effect of the methylation inhibitor AdOx , which mimicked the mutation at a concentration of 30 nM ( Fig 3 ) . Previously , Chen et al . [45] described an A . phagocytophilum mutant with an insertion in the dihydrolipoamide dehydrogenase 1 ( lpda1 ) gene at the APH_0065 locus , which altered the inflammatory response during infection of mice by increasing the production of reactive oxygen species [45] , but had no effect on in vitro growth . The mutant ( ΔOMT ) described here was selected in the human cell line HL-60 in which it replicated in a manner comparable to wild-type bacteria ( Fig 2 ) . The mutant had an insertion in aph_0584 encoding an o-methyltransferase ( OMT ) family 3 ( GI: 88598384; E . C . 2 . 1 . 1 . 24 ) . The inability of ΔOMT to replicate within tick cells highlighted the distinct mechanisms used by A . phagocytophilum for colonization of mammal and tick hosts . It is interesting that the search for OMTs similar to that encoded by aph_0584 only identified an OMT in one Rickettsiales organism , i . e . , in C . M . mitochondrii , which is outside the family Anaplasmataceae . This intracellular organism develops in the mitochondria of I . ricinus ticks and is a member of a new family thought to be closely related to , but distinct from , the Anaplasmataceae [46] . Phylogenetic analysis showed highest similarity with enzymes from members of the Δ-proteobacteria ( S6A Fig ) and analysis of the different motifs present in the OMT indicated that A . phagocytophilum OMT is more similar to the OMT from the Δ-proteobacterium B . bacteriovorus than to that from C . M . mitochondrii ( S6B Fig ) . B . bacteriovorus is a predatory bacterium that attacks gram-negative bacteria and , like A . phagocytophilum , presents a biphasic life cycle with an “attack form” that attaches to the host cell and a “dividing form” that occurs only in the periplasm of its host within a vacuole formed by its own proteins as well as host proteins [47] . Like A . phagocytophilum , B . bacteriovorus differentially expresses genes depending on the phase of development during infection [48] . Two OMTs are differentially regulated depending on the phase of infection; one OMT is up-regulated upon entry to Bdelloplast ( Bd2861 ) and another extracellularly ( Bd0381 ) [48] . Whether or not the up-regulation of the OMT in B . bacteriovorus during cell invasion is involved in the methylation of proteins important for entry is not known , however its up-regulation indicates that it may play such a role . It is possible that an ancestor of the families Anaplasmataceae and “C . Midichloriaceae” obtained their OMTs from members of the Δ-proteobacteria by lateral gene transfer . This possibility is supported by the absence of members of this enzyme family in the Rickettsiales , in which the only enzyme that showed slight similarities with the OMT was a bifunctional methyltransferase ( m7G46 ) present in some Rickettsia species , albeit with high e-values of e0 . 28 –e1 . 2 . These family 3 OMTs existing in Anaplasmataceae and the new family “C . Midichloriaceae” are evidently not required by other members of the Rickettsiales for infection of ticks , which seem to utilize different methyltransferases to carry out similar functions [23] . OMTs of the type encoded by aph_0584 methylate free carboxyl groups on glutamic acid residues of bacterial chemoreceptors [49–51] . Thus , they are involved in environmental sensing , which could also be the case in A . phagocytophilum since a sensor-histidine kinase CckA ( aph_0582 ) is predicted to localize to the membrane and to be involved in signal transduction and regulation of transcription ( http://www . uniprot . org/uniprot/Q2GKC9 ) . Notably , CckA is part of the A . phagocytophilum two component system , and is paired with response/regulator transcription factor CtrA , allowing A . phagocytophilum to respond to environmental changes [52] . A lack in the ability of the ΔOMT to respond when transferred from mammalian to tick cells could explain the continued expression of a set of proteins known to be important for infection of mammalian cells , and to be down-regulated in tick cell culture ( Table 1; S3 Fig ) [17 , 38] . However , it is unlikely that these phenotypic changes are due to a polar effect on the expression of the sensor-histidine kinase ( aph_0582 ) since the transposon promoter drives transcription in the opposite direction from aph_0582 and the insert is located at a distance of around 1600 bp from that gene ( Fig 1B ) . Furthermore , transcription of the flanking genes is independent of the expression of the omt gene , and they do not appear to be part of an operon since no bands were amplified from the intergenic regions between these genes ( S8 Fig ) . Although methyltransferases modifying glutamic acid residues have recently been identified as being widely conserved in eukaryotes , their targets , poly ( A ) -binding proteins , are methylated at additional amino acid residues , placing these enzymes in a different class from A . phagocytophilum OMT [53] . Interestingly , the OMT from B . bacteriovorus ( Bd0381 ) which is homologous to A . phagocytophilum OMT ( e-value 1e-42 ) was shown to be up-regulated when the bacteria were extracellular along with several genes involved in chemotaxis and sensing , including a methyl chemotaxis protein ( Bd2503 ) , pilS sensor protein ( Bd1512 ) , two-component response regulator ( Bd0299 ) , and a sensory box histidine kinase ( Bd1657 ) [48] . Whether the B . bacteriovorus OMT , Bd0381 , is involved in the methylation of the methyl chemotaxis protein is not known . However , it is possible that this OMT plays a role in environmental sensing , and that acquisition of a gene encoding such an enzyme enabled members of the Anaplasmataceae family to adapt to environmental changes more efficiently . Analysis of the behavior of ΔOMT in ISE6 cells demonstrated reduced binding of A . phagocytophilum to tick cells ( Fig 3A ) , partly explaining the decrease in bacterial numbers seen as early as 1 day p . i . ( Fig 2 ) . However , binding was not completely abolished by either the mutation or treatment with Adox ( Fig 3 ) . Therefore , we investigated differences in bacterial internalization and intracellular development . Although the ΔOMT that did bind to tick cells were readily internalized between 1–20 hr ( Fig 6A and 6B ) , the bacteria were not able to form morulae and replicate intracellularly , but persisted as individual bacteria within ISE6 cells for at least 10 days p . i . ( Fig 6C–6H ) . Furthermore , we verified that the ΔOMT bacteria were internalized within ISE6 cells by confocal microscopy and a trypsin-protection assay ( S2 Fig ) . Methylation of outer membrane proteins and virulence factors is increasingly recognized as an essential process during host invasion and infection by several obligately and facultatively intracellular bacteria [22 , 28 , 30 , 32 , 54–58] . Therefore , we considered that the lack of methylation of glutamic acid residues in Msp4 may have played a role in the reduced adhesion to tick cells and was probably responsible for preventing replication of ΔOMT in tick cell culture . Methylation of proteins that mediate adhesion to host cells has been reported in other members of the Rickettsiales [54 , 59] . Methylation of R . prowazekii OmpB by lysine methyltransferases was shown to play a role in adhesion to and infection of endothelial cells , and to be important for virulence [59] . Nevertheless , E . coli expressing recombinant OmpB were able to bind to endothelial cells in the absence of methylation [31] . Likewise , E . coli transformed to express recombinant Msp4 were able to adhere to ISE6 cells in the absence of the omt gene ( S9A Fig and S5 Text ) . Furthermore , E . coli transfected only with msp4 construct bound more readily than those harboring both msp4 and omt ( S9A Fig ) . Analysis of the OMT protein sequence using Phobius predicted a non-cytoplasmic location of the enzyme , suggesting that methylation of Msp4 occurred in the periplasm of A . phagocytophilum ( S9B Fig ) . It is likely that OMT is not transported to the periplasm in E . coli and methylation is thus not carried out efficiently . Because E . coli transfected with only msp4 were able to bind to tick cells , methylation of the protein was not essential for adhesion , explaining why disruption of omt only reduced but did not abolish A . phagocytophilum binding to tick cells ( Fig 3 ) . Productive infection of cells by A . phagocytophilum requires completion of a multi-step process for efficient invasion and replication to occur . Increased expression of OMT in bacteria bound to ISE6 cells compared to those adhering to HL-60 cells ( Fig 5 ) suggested that physical contact with the tick cell outer membrane induced OMT expression . Induction of OMT expression happened rapidly , and waned as bacteria passed into the cytoplasm . iTRAQ identified several potential substrates of the enzyme in A . phagocytophilum and I . scapularis cells , two of which included A . phagocytophilum proteins previously shown to be highly expressed during infection of ISE6 cells , i . e . , Msp4 and APH_0406 [17] . However , in vitro methylation assays ( Fig 7B and 7C ) using recombinant versions of potential substrates only confirmed Msp4 ( Fig 7B ) , and identified Mn2+ as the most effective cofactor , indicating that A . phagocytophilum OMT is a metal dependent methyltransferase . The kinetics of the reaction were comparable to those reported for methyltransferases from R . prowazekii and R . typhi in which the linear portion of the reaction curve occupied 50–300 min [30 , 54] . It is possible that the substrate protein , Msp4 , is methylated by OMT as a linear molecule prior to translocation rather than as a folded protein , as used here , and this may further explain the slow in vitro assay kinetics . These results confirmed Msp4 as a substrate of the OMT , but whether the enzyme methylates other proteins awaits further investigation . The other proteins that showed differential methylation in the proteomic analysis , but were not methylated in vitro , are possibly methylated by other methyltransferases present in A . phagocytophilum or in the host cell . Msp4 is an antigenic protein encoded by a single copy gene that is highly conserved between different strains of A . phagocytophilum [60] , as well as in other members of the genus Anaplasma [61] . As a member of the Msp2 superfamily of proteins [62] , Msp4 is homologous to A . phagocytophilum Msp2 ( P44 ) , which has been shown to facilitate binding to mammalian cells , to be a porin and to be post-translationally modified . It is likely that Msp4 and Msp2 ( P44 ) are structurally and functionally similar but that they have evolved to function in the tick vector and mammal , respectively . The most common non-specific bacterial porins form 16-strand β-barrels configd as trimeric peptide subunits [63 , 64] . More substrate specific bacterial porins are comprised of 18- , 14- , 12- , or 8-strand β-barrels and in some cases are present as monomers ( e . g . , the 14 beta-stranded porins OmpG and CymA in Escherichia coli ) [63] . Since Msp4 is predicted to contain a 14-stranded β-barrel ( S7 Fig ) , and by homology with the A . marginale Msp4 is likely monomeric [65] , we conclude that it is probably substrate specific , which is supported by its activity exclusively in tick cells . It is interesting to note that the glutamic acid residues ( E ) of Msp4 that appear to be important for A . phagocytophilum development inside tick cells are close to one of the loops on the outside of the channel ( S7 Fig ) . Similarly , L . interrogans OmpL32 contains methylated glutamic acid residues that are important for infection and colonization of kidney and liver cells [32] . However , more research is needed to determine the exact function of these methyl modifications of Msp4 . We realize that structure predictions can be unreliable , and ideally the crystal structure of Msp4 in association with that of OMT should be resolved . We also expect that an A . phagocytophilum msp4 mutant would display a similar or even more severely compromised phenotype than the omt mutant , since it is possible that other enzymes participate in methylation of the Msp4 . Until such a mutant is available , the predictions serve as a starting point to infer potential implications of this modification for the biology of this bacterium . The change in phenotype and our proteomic analysis support the conclusion that methylation of Msp4 may be necessary for efficient and productive infection of tick cells . Some porins have been shown to display double functionality , acting also as adhesins and being expressed under specific environmental conditions [64] , characteristics that could fit Msp4 [63] . Partial inhibition of adhesion due to the mutation of omt is not surprising , as it is likely that more than one adhesin is involved in binding to tick cells . This has been shown for invasion of mammalian cells by A . phagocytophilum , where three adhesins have been identified , OmpA , Asp14 and AipA [66–68] . Rickettsiaceae possess two additional adhesins besides OmpB and OmpA , named Adr1 and Adr2 . These were recently identified by proteomic approaches and also presented putative β-barrel structures [69] . Adhesins may also serve to protect the bacteria from mammalian complement that is abundantly present in the blood , and consequently ingested with the tick blood meal . Rickettsia conorii OmpB β-peptide has been shown to interact with mammalian complement regulatory factor H via the exposed loops extending from the transmembrane β-barrel structure , and a number of bacterial factor H-binding proteins have been identified as adhesins [63 , 64] . Anaplasma phagocytophilum also evades complement-mediated killing , but it is not known whether this capability is mediated by binding of complement regulatory factors , or by direct interaction with complement [65] . In the cell-culture system used here , such factors would not be relevant due to the absence of active complement . The most significant phenotypic change due to the mutation in the omt gene was the inability of A . phagocytophilum to replicate and form normal morulae within ISE6 cells ( Fig 6C–6H ) . In ISE6 cells , ΔOMT persisted as individual bacteria , while wild-type bacteria formed large morulae that are distinguishable on day 3 p . i . ( Fig 6D ) . Up-regulation of OMT expression during interaction of A . phagocytophilum wild-type with ISE6 cells ( Figs 4 and 5 ) , as compared to the inability of the ΔOMT bacteria to change protein expression ( Table 1 and S3 Fig ) , indicated that this was necessary for normal morphogenesis of A . phagocytophilum in tick cells ( Fig 6 ) .
The A . phagocytophilum isolate HZ , which was originally cultured from a New York patient [70] , was cultivated in HL-60 cells maintained in RPMI 1640 medium ( Lifetechnologies , New York ) supplemented with 10% FBS ( BenchMark , Gemini Bioproducts , California ) , and 2mM glutamine at 37°C with 5% CO2 in humidified air [71] . Several transformants were generated to express Green Fluorescent Protein ( GFPuv ) and were selected and maintained in HL-60 cells as described [18 , 71] . The ability of the transformants to grow in ISE6 , an I . scapularis embryonic cell line , was tested by inoculating purified cell-free bacteria or whole infected HL-60 cells into 25-cm2 flasks containing confluent cell layers of ISE6 cells . Cultures were maintained in L-15C300 supplemented as described , and the pH was adjusted to 7 . 5–7 . 7 with sterile 1N NaOH [18 , 72] . Growth and development of transformants was evaluated by fluorescence microscopy using an inverted Nikon Diaphot microscope ( Nikon , New York ) to detect A . phagocytophilum expressing GFPuv [73] and by examination of Giemsa stained cell samples spun onto slides . A transposon mutant ( ΔOMT ) deficient for growth in ISE6 cells was cultivated in HL-60 cells as described above by passing 3 x 103 infected HL-60 cells into a new flask containing 3–4 x 105 uninfected cells and 20 ml of fresh medium every 5 days . Spectinomycin and streptomycin ( 100 μg/ml each ) were added to the cultures for selection of mutants carrying the aadA resistance gene encoded on the transposon . The number of insertion sites in the mutant population was determined by Southern blots of DNA purified from a 25-cm2 flask of infected HL-60 cells , using the Puregene Core Kit A ( Qiagen , Maryland ) with an additional phenol-chloroform extraction step , and Phase Lock Gel Heavy ( 5 Prime , Maryland ) to separate phases . DNA concentration was measured with a BioPhotometer ( Eppendorf , New York ) , and DNA extracted from HZ wild-type bacteria served as control . DNA ( 100 ng ) from the mutant and wild-type HZ was digested with BglII and EcoRV and samples were electrophoresed in 1% agarose gels . DNA was transferred and probed as described [73] , using digoxigenin-labeled probes specific for gfpuv ( PCR DIG Probe Synthesis kit; Roche , Indiana ) . A plasmid construct , pHIMAR1-UV-SS , encoding the transposon , served as positive control [18] . To determine genomic insertion sites in the mutant population , 5 μg of ΔOMT DNA was digested with BglII , treated with DNA clean & concentrator ( Zymo Research , California ) and ligated into the pMOD plasmid for electroporation into ElectroMAX DH5α cells ( Invitrogen , New York ) . ElectroMAX DH5α cells containing the transposon were selected on YT plates with 50 μg/ml of spectinomycin and streptomycin . DNA was purified by phenol/chloroform extraction and then sequenced at the BioMedical Genomics Center ( University of Minnesota ) . The ΔOMT and wild-type strains were grown in 25-cm2 flasks containing HL-60 cells as described above . Bacteria were purified from four flasks containing 25 ml of a >90% infected cell suspension by passing the cell suspension through a bent 27 G needle and filtration of the lysate through a 2 μm pore size filter . Purified bacteria were transferred to two 15 ml tubes , centrifuged at 10 , 000 x g for 5 min and then resuspended in 3 ml of RPMI medium supplemented as described above . Bacteria were diluted 1:40 , 1:100 , and 1:400 in 20 ml of uninfected HL-60 cultures , and incubated at 37°C as described above for infected HL-60 cells . Samples of 1 . 5 ml were taken from each culture every day for a 5-day period , and DNA was extracted as described for DNA samples used in Southern blots . The experiment was repeated in triplicate . To generate growth curves of ΔOMT and wild-type bacteria in ISE6 cell cultures , bacteria were purified as described above , and centrifuged at 10 , 000 x g for 5 min at 4°C . Supernatant was discarded , and cell free bacteria were diluted in supplemented L15C300 at ratios of 1:6 , 1:12 , and 1:24 . The experiment was done in triplicate . To assess bacterial growth , DNA was extracted from 1 . 5 ml of mutant and wild-type cultures of bacteria grown in ISE6 cells every 3 days for 12 days as described before . The number of bacteria per sample was determined by qPCR using the primers msp5 fwd and msp5 rev ( S1 Table ) that amplify a fragment of the single copy number msp5 gene . qPCR reactions were performed in an Mx3005p ( Agilent , California ) cycler , using Brilliant II SYBR Green Low ROX QPCR Master Mix ( Agilent , California ) under the following conditions: an initial cycle of 10 min at 95°C , 40 cycles of 30 sec at 95°C , 1 min at 50°C , and 1 min at 72°C , and a final cycle of 1 min at 95°C , 30 sec at 50°C , and 30 sec at 95°C . A standard curve was generated using the msp5 fragment cloned into the pCR4-TOPO vector ( Invitrogen , New York ) . To examine binding of ΔOMT and wild-type A . phagocytophilum to tick cells , we used two different assays to evaluate adhesion to tick cells and subsequent intracellular growth and development . The first assay was carried out under stringent conditions with a low MOI that permitted sensitive assessment of the effect of the mutated omt gene . The second assay ( described further below , under “ISE6 infection time point experiment to evaluate intracellular development of the ΔOMT” ) was designed to allow maximal , saturating binding so that mutant bacteria could be readily observed inside tick cells . For the first assay , bacteria were purified from 20 ml of one fully infected HL-60 culture and were added to about 2 . 5x105 ISE6 cells in 50 μl of supplemented L15C300 medium in a 0 . 5 mL centrifuge tube . To ensure that only activity induced during binding and not cell entry was measured , bacteria were incubated with host cells for 30 min at room temperature , flicking the tube every 5 min to enhance contact between bacteria and cells . The cells were washed twice in unsupplemented L15C300 and centrifuged at 300 x g for 5 min to remove unbound bacteria . The cell pellet was resuspended in phosphate buffered saline ( PBS ) and spun onto microscope slides for 5 min at 60 x g , using a Cytospin 4 centrifuge ( Thermo Shandon , Pennsylvania ) . Slides were fixed in absolute methanol for 5 min and dried at 50°C for 10 min . Bound bacteria were labeled using an IFA with dog polyclonal antibody against A . phagocytophilum and FITC-labeled anti-dog secondary antibodies . DAPI was used to stain the host cell nuclei , and aid in host cell visualization . The number of bacteria bound to 300 cells was counted for each sample . This was repeated in triplicate , and differences were evaluated using Student’s t-test to assess significance with SigmaStats ( Systat Software , California ) . To verify that lack of methylation of substrate brought about by the disruption of the omt gene was the cause of reduced binding of the mutant to ISE6 cells , we added adenosine dialdehyde ( Adenosine periodate Oxidized , or AdOx ( Sigma Aldrich , Missouri ) ) , which inhibits SAM-dependent methyltransferases by increasing the concentration of S-adenosyl-L-homocysteine [29] , to wild-type cultures . Wild-type and ΔOMT bacteria were purified from 50 ml infected HL-60 cells as described . Purified wild-type bacteria were incubated with AdOx at 20 nM , 30 nM , and 40 nM final concentrations for 1 hr at 34°C before adding them to 2x105 uninfected ISE6 cells . Controls consisted of wild-type and ΔOMT bacteria purified the same way , but incubated at 34°C for 1 hr without addition AdOx . The bacteria and ISE6 cells were incubated in 200 μl of supplemented L15C300 medium at 34°C for an additional hr to allow binding . Cells were washed 3 times in supplemented L15C300 medium by centrifugation at 600 xg to remove unbound bacteria . After the final wash , cells were resuspended in 1 ml of supplemented L15C300 medium and 50 μl of the suspension was spun onto microscope slides as described above and fixed in methanol for 10 min . The samples were incubated with dog anti A . phagocytophilum serum and labeled with FITC conjugated anti-dog antibodies . Samples were mounted in Vectashield Mounting medium containing DAPI to aid in host cell visualization ( Vector Laboratories , California ) , and observed using a 100 x oil immersion objective on a Nikon Eclipse E400 microscope . Bacteria were counted as described above for regular binding assays , and differences in the number of bacteria per cell were evaluated using the Student-Newman-Keuls one-way ANOVA on ranks , using SigmaStat . Two thousand LifeAct-mCherry expressing ISE6 cells [74]were seeded onto the glass portion of MatTek dishes ( Ashland , Massachusetts ) in 250 μl of medium the day before bacterial challenge . ΔOMT and HZ wild-type bacteria were cultured in five million HL-60 cells until > 95% infected , and bacteria were beginning to be released from cells . Cell-free bacteria were prepared by passing the infected cells through a 25 G needle 5 times , and intact cells were removed by centrifugation at 1 , 110 x g for 5 min . The supernatant was passed through a 2 μm pore size syringe filter to remove cell debris , and the bacteria were collected by centrifugation at 11 , 000 x g for 5 min . Bacteria were resuspended in 100 μl fresh culture medium and 20 μl of the suspension was inoculated onto ISE6 cell layers after 170 μL of culture medium had been removed . This resulted in a multiplicity of infection of 100–300 bacteria/cell . Dishes were incubated at 34°C in a water saturated atmosphere of 3% CO2 in air for 1 hr with agitation at 10-min intervals to ensure uniform exposure of cells to bacteria . Unbound bacteria were removed by washing the cells once with 2 ml of medium , and 2 mL fresh medium was added and the cultures returned to the incubator . At each of eight time points ( 0 , 1 , 2 , 3 , 4 , 5 , 7 , and 10 days ) , culture medium was removed from MatTek dishes and cells were immediately fixed by flooding with 2 mL methanol ( 1 min ) followed by two additional rinses ( 1 min ) with fresh methanol . Cells were then air dried and stored at room temperature until the bacteria were labeled by IFA . Cells were blocked with 50% FBS in culture medium for 1 hr at room temperature . Bacteria were labeled with dog anti-A . phagocytophilum serum for 1 hr followed by FITC-labeled anti-dog antibodies for 1 hr ( each diluted 1:1 , 000 ) . After each antibody exposure , cells were washed three times in PBS . Cells were mounted in VectaShield ( Vector Laboratories ) medium with 4' , 6-diamidino-2-phenylindole ( DAPI ) , and examined and photographed under a cover slip using confocal microscopy as described above . LifeAct-mCherry-expressing ISE6 cells cultured in MatTek dishes were challenged with ΔOMT or HZ wild-type bacteria at a high MOI as described above , and then exposed to trypsin to confirm the confocal microscopy finding that the ΔOMT bacteria were located inside the ISE6 cells . Four days following challenge , cells were mechanically dislodged using a cell scraper , spun onto slides , air-dried and fixed in methanol . These samples represented non-trypsin controls . Cells in remaining dishes were suspended in 1 ml 0 . 25% trypsin-EDTA ( Gibco ) for 3 min , collected by centrifugation ( 350 x g , 2 min ) , resuspended in 1 ml culture medium , and 100 μl volumes were spun onto microscope slides . Remaining cells were trypsinized a second time for 2 min , washed in medium and spun onto slides . Samples ( mechanically scraped cells , trypsinized 1x , trypsinized 2x ) were immunofluorescence-labeled as described , mounted in VectaShield without DAPI and imaged as above ( S2A–S2F Fig ) . As a positive control to demonstrate that trypsin removed wild-type HZ adherent to ISE6 cells , the same trypsinization procedure was followed after exposing ISE6 cells ( not expressing LifeAct-mCherry ) to HL60-grown HZ bacteria for one hour , and mounting slides with DAPI ( S2G and S2H Fig ) . Relative expression of omt was measured at different times to determine when it was upregulated . Wild-type A . phagocytophilum HZ was purified from HL-60 cells and inoculated into four 25-cm2 flasks containing either 5 ml of uninfected ISE6 or 2 ml of HL-60 cultures . Bacteria inoculated into HL-60 cell cultures or ISE6 cells were incubated for 30 , 60 , 120 , or 240 min at 37°C or 34°C , respectively . Total RNA was extracted from whole infected cell cultures , using the Absolutely RNA Miniprep Kit ( Agilent , California ) according to the manufacturer’s specifications . RNA was DNAse treated with 0 . 5 units TURBO DNAse ( Ambion , New York ) at 37°C for 30 min . The DNAse treatment was repeated twice and RNA concentrations were measured using a Biophotometer . Omt expression was normalized against expression of the rpoB and msp5 genes that had been found to be consistently expressed in both cell types during tiling array analysis [17] . qRT-PCR reactions were carried out using Brilliant II QRT-PCR SYBR Green Low ROX Master Mix ( Agilent , California ) using primers listed in S1 Table . Reaction parameters were as follow: one cycle at 50°C for 30 min , one denaturing cycle at 95°C for 10 min , 40 cycles that consisted of 30 sec at 95°C , 1 min at 50°C , and 1 min at 72°C , and a final cycle of 1 min at 95°C and 30 sec at 50°C . Ct values were established during amplification and the dissociation curve was determined during the final denaturation cycle . Expression of the omt gene was analyzed using the 2-ΔΔct method , and significant differences were determined using Student’s t-test with SigmaStat . The relative expression of the genes that resulted in a greater abundance of encoded proteins in ΔOMT ( Table 1 ) , as well as the expression of msp4 was determined by qRT-PCR , using the primers listed in S1 Table . Total RNA was purified from 8x105 HL-60 cells fully infected with wild-type or mutant bacteria , and from 8 . 4x105 ISE6 cells fully infected with wild-type bacteria , using the Absolutely RNA Miniprep Kit . qRT-PCR reactions were carried out as described above with msp5 and 23s rRNA used as normalizer genes . Gene expression was determined as described above . Recombinant OMT ( rOMT ) protein for antibody production was produced using the pET29a expression vector ( Novagen , Germany ) by amplifying the entire coding region with the primers rOMT fw and rOMT rv ( S2 Table ) and pfu DNA polymerase ( Promega , Wisconsin ) . Conditions were as follows: one denaturing cycle at 94°C for 3 min , 10 cycles with a denaturing step at 94°C for 1 min , 40°C for 1 min for annealing , and extension at 72°C for 2 min , then 20 additional cycles with a denaturing step at 94°C for 1 min , 47°C for 1 min for annealing , and extension at 72°C for 2 min , and a final extension step of 5 min at 72°C . The amplified product was digested with the restriction enzymes SalI and EcoRV , followed by ligation into the vector at 15°C overnight . The plasmid was cloned into One Shot TOP10 competent cells ( Invitrogen , New York ) for replication and purified using the High Pure plasmid isolation kit ( Roche , Indiana ) . Integrity of the plasmid was checked by sequencing with the T7 promoter ( 5’- TAA TAC GAC TCA CTA TAG GG– 3’ ) and the T7 terminator ( 5’- GCT AGT TAT TGC TCA GCG G– 3’ ) primers at the Biomedical Genomics Center of the University of Minnesota . Plasmids were transfected into BL21 ( D3 ) E . coli ( New England Biolabs , Massachussets ) for expression . BL21 ( D3 ) E . coli were inoculated into 100 ml of Superior Broth ( AthenaES , Maryland ) , induced with 200 μM IPTG , and incubated at 37°C overnight with constant shaking . Protein was purified using Ni-NTA Fast Start Kit columns ( Qiagen , Maryland ) . Protein concentrations were measured using the BCA protein assay kit ( Pierce , Illinois ) . Functional rOMT for enzyme activity assays was produced using the expression vector pET29a as described above but amplifying the entire open reading frame of the gene with the primers rOMTns Fw and rOMTns Rv ( S2 Table ) , using the following conditions: one denaturing cycle at 94°C for 3 min , 10 cycles with a denaturing step at 94°C for 1 min , 45°C for 1 min for annealing , and extension at 72°C for 2 min , then 20 additional cycles with a denaturing step at 94°C for 1 min , 54°C for 1 min for annealing , and extension at 72°C for 2 min , and a final extension step of 5 min at 72°C . The amplified product was digested with NdeI and EcoR1 , and ligated into pET29a to produce rOMTns lacking the S-tag present in the plasmid , and cloned into One Shot TOP10 competent cells . After confirming integrity , the plasmid was cloned into Rosetta 2 ( DE3 ) pLysS E . coli ( Novagen , Germany ) . Transformed E . coli were incubated in 100 ml of Superior Broth and induced with 1 mM IPTG at 37°C for 5 hr . Proteins were purified with Ni-NTA Fast Start Kit columns , and protein concentration was measured as described above . Recombinant non-functional rOMT was purified by dialysis in a 3 ml Slide-A-Lyzer Dialysis Cassette 10K MWCO ( Thermo Scientific , Illinois ) against tris-buffered saline ( TBS ) , over night with one buffer change after 2 hr . Four 6–8 weeks old , female C57BL/6J mice ( Jackson Laboratories , Maine ) were immunized by subcutaneous ( s . c . ) injection of 100 μg of recombinant rOMT in TiterMax Research adjuvant ( CytRx Co . , Georgia ) . Mice were boosted twice with 100 μg of rOMT without adjuvant at 14 days and 24 days later . To obtain antiserum , blood was collected 10 days after the second booster , and unimmunized mice from the same cohort were used as controls . Serum was frozen at -20°C for future use . Wild-type bacteria were grown in 50 ml of RPMI1640 containing HL-60 cells until > 90% of the cells were infected . Around 5 x 107 infected cells were used to obtain cell free bacteria by vortexing the infected cells with 60/90 grit silicon carbide ( Lortone , Inc . , Mukilteo , Washington ) for 30 sec followed by filtration through a 2 . 0 μm pore size filter and centrifugation at 700 x g for 5 min to remove remaining cell debris . The percent of infected cells in the culture was calculated by counting the number of infected and uninfected cells in duplicates Giemsa-stained preparations from the same flask and the total number of cells was determined using a hemocytometer . Cell-free bacteria were incubated for 2 hr at 34°C with 2 . 5 x 105 ISE6 cells in MatTek chambers ( MatTek Corp . , Massachusetts ) to allow binding and expression of OMT . Unbound bacteria were removed by rinsing cells twice with culture medium . Likewise , bacteria were incubated with 2 . 5 x 105 HL-60 cells suspended in 500 μl of culture medium for 2 hr at 37°C . Unbound bacteria were removed by washing cells twice and expression of OMT in bound bacteria was analyzed by IFA . Cell samples were fixed for 10 min in methanol , and incubated with anti-rOMT serum diluted 1:200 in PBS containing 3% bovine serum albumin ( BSA ) for 2 . 5 hr at room temperature . Bacteria were labeled with anti-A . phagocytophilum dog serum ( diluted 1:1 , 000 ) . The slides were washed 3 times in PBS and blocked in PBS with 3% BSA for 10 min at room temperature . OMT expressing bacteria were then labeled with anti-mouse antibodies conjugated with AlexaFluor647 ( 1:500 dilution ) ( Jackson ImmunoResearch Laboratories , Inc , Pennsylvania ) for 1 hr at room temperature . All A . phagocytophilum were labeled with anti-A . phagocytophilum dog serum diluted 1:500 followed by incubation with anti-dog IgG conjugated to FITC , using the same procedure . Tick and HL-60 cell nuclei were labeled using DAPI present in the VectaShield mounting medium . Microscopic images were obtained using an Olympus BX61 disk-scanning unit confocal microscope ( Olympus America , Pennsylvania ) utilizing a DSU-D2 confocal disk . Confocal images were acquired with a Photometrics Quantem:512SC EMCCD camera ( Photometrics , Arizona ) , and high resolution images were acquired with a QFire color camera ( Qimaging , California ) . Image capture software was Metamorph ( Molecular Devices , California ) . ImageJ ( US National Institutes of Health ) was used to compile z-projections and Photoshop ( Adobe Systems , California ) was used for cropping . HZ wild-type and ΔOMT bacteria were grown in two 50 ml volumes of RPMI1640 medium with HL-60 cells each and the number of infected cells was determined . Host cell free mutant and wild-type bacteria were purified from 2 . 5 x109 infected cells that were ruptured by repeated passage through a 27 G needle , centrifuged at 600 x g to remove cell debris , and inoculated into a 25-cm2 flask containing 6 x 107 ISE6 cells . This procedure was replicated three times in three independent biological replicates . Cells and bacteria were incubated for 4 hr at 34°C without agitation to allow methylation of possible substrates to be completed , since the time between up-regulation of the gene and completion of the enzymatic reaction was not known . Bacteria were then purified from ISE6 cells as described above . Bacteria were washed in unsupplemented L15C300 medium three times by centrifugation at 16 , 000 xg for 5 min at 4°C to remove FBS , and the final bacteria pellet was extracted for mass spectrometry . Protein concentrations were determined by Bradford assay using two aliquots for each sample . All samples were prepared as follows at the Center for Mass Spectrometry and Proteomics at the University of Minnesota: cell pellets were reconstituted with 120 μl of protein extraction buffer [7 M urea , 2 M thiourea , 0 . 4 M triethylammonium bicarbonate ( TEAB ) pH 8 . 5 , 20% methanol and 4 mM tris ( 2-carboxyethyl ) phosphine ( TCEP ) ] while on ice . Samples were sonicated at 30% amplitude for 7 sec with a Branson Digital Sonifier 250 ( Emerson , Connecticut ) . The samples were homogenized in a Barocycler NEP2320 ( Pressure Biosciences , Inc . , Massachusetts ) by cycling between 35 k psi for 30 sec and 0 k psi for 15 sec for 40 cycles at 37°C . Samples were alkylated for 15 min at room temperature in 8 mM methyl methanethiosulfonate ( MMTS ) . In-solution proteolytic digestions were performed as follow: a 200 μg aliquot of each sample was transferred to a new 1 . 5 ml microfuge tube and brought to the same volume with protein extraction buffer plus 8 mM MMTS . All samples were diluted 4-fold with ultra-pure water , and trypsin ( Promega , Wisconsin ) was added at a 1:35 ratio of trypsin to total protein . Samples were incubated for 16 hr at 37°C after which they were frozen at -80°C for 30 min and dried in a vacuum centrifuge . Each sample was then cleaned using a 4 ml Extract Clean C18 SPE cartridge ( Grace-Davidson , Illinois ) , and eluates were vacuum dried and resuspended in dissolution buffer ( 0 . 5M triethylammonium bicarbonate , pH 8 . 5 ) to a final 2 μg/μl concentration . For each iTRAQ 4-plex ( AB Sciex , California ) , two 50 μg replicates for each sample were labeled with iTRAQ reagent ( AB Sciex , California ) . After labeling , the samples were multiplexed together and vacuum-dried . The multiplexed sample was cleaned with a 4 mL Extract Clean C18 SPE cartridge ( Mandel Scientific Company Inc . , Guelph , Canada ) and the eluate was dried in vacuo . The iTRAQ labeled samples were resuspended in Buffer A ( 10 mM ammonium formate pH 10 in 98:2 water:acetonitrile ) and fractionated offline by high pH C18 reversed-phase ( RP ) chromatography [75] . A MAGIC 2002 HPLC ( Michrom BioResources , Inc . , California ) was used with a C18 Gemini-NX column , 150 mm x 2 mm internal diameter , 5 μm particle , 110 Å pore size ( Phenomenex , California ) . The flow rate was 150 μl/min with a gradient from 5–35% Buffer B ( 10 mM ammonium formate , pH 10 in 10:90 water:acetonitrile ) over 60 min , followed by 35–60% over 5 min . Fractions were collected every 2 min and uv absorbances were monitored at 215 nm and 280 nm . Peptide containing fractions were divided into two equal numbered groups , labeled “early” and “late” . The first “early” fraction was concatenated with the first “late” fraction by 10 mAU volume equivalents of each fraction from uv = 215 nm and repeated until all fractions were concatenated . Concatenated samples were dried in vacuo , resuspended in 98:2O , H2O:acetonitrile , 0 . 1% formic acid and 1–1 . 5 μg aliquots were run on a Velos Orbitrap mass spectrometer ( Thermo Fisher Scientific , Inc . , Massachussets ) as described previously [76] with the exception that the Higher-energy Collisional Dissociation ( HCD ) activation energy was 20 msec . The mass spectrometer RAW data ( Proteowizard files ) were converted as described previously [76] . ProteinPilot 4 . 5 ( AB Sciex , California ) searches were performed against the NCBI reference sequence for the I . scapularis ( taxon 6945; November 14 , 2011 ) protein FASTA database with ( 20468 proteins ) , to which the NCBI reference sequence A . phagocytophilum , HZ ( taxon 212042; November 14 , 2011; 1267 proteins ) and a contaminant database ( thegpm . org/crap/index , 109 proteins ) was appended . Search parameters were: cysteine MMTS; iTRAQ 8plex ( Peptide Labeled ) ; trypsin; instrument Orbi MS ( 1–3ppm ) Orbi MS/MS; biological modifications ID focus; thorough search effort; and False Discovery Rate analysis ( with reversed database ) . The putative function of differentially expressed proteins identified by iTRAQ was explored using the databases in NCBI ( www . ncbi . nlm . nih . gov ) to identify conserved domains , Uniprot ( http://www . uniprot . org/uniprot/ ) , EMBL-EBI ( http://www . ebi . ac . uk/interpro/IEntry ? ac=IPR000866 ) , and OMA ( http://omabrowser . org ) . Pathways which involved proteins differentially expressed during infection with the ΔOMT compared to wildtype were identified using the KEGG pathway tool ( http://www . genome . jp/kegg/tool/map_pathway1 . html ) . To identify the peptides that differed in methylation , a text file of data for all peptides was imported into Excel ( Microsoft , Washington ) , and entered into TextWrangler ( Bare Bones Software , Massachusetts ) , which is a text editing tool . Peptides that were less abundant in the mutant when compared to the wild-type , based on the intensities of the reporter ion signals , were selected and the intensities of spectra were visually inspected in the ProteinPilot viewer software ( AB SCIEX , Massachusetts ) to confirm differences . A functional version of the OMT without the S-tag ( rOMTns ) was produced and purified . Recombinant proteins of potential substrates identified by iTRAQ were produced using the pET29a vector . Primers to amplify partial or complete coding sequences were designed with restrictions sites for NdeI and XhoI enzymes ( S2 Table ) , and DNA amplified using pfu enzyme under conditions listed in Table 2 . The sequence integrity of purified products was confirmed by Sanger sequencing , and DNA cloned into BL21 ( D3 ) E . coli ( New England Biolabs ) for expression . Proteins were produced in 150 ml of Superior Broth after induction with 200 μM IPTG and used in methylation assays described below . Protein concentrations were measured using the BCA micro protein assay kit ( Pierce ) . The SAM-fluoro: SAM Methyltransferase Assay ( G-Biosciences , Missouri ) was used to determine the activity of the enzyme . In this assay , methylation by SAM-dependent methyltransferases is correlated with the production of H2O2 which can be assessed through the production of fluorescent resorufin from 10-acetyl-3 , 7 , -dihydroxyphenoxazine ( ADHP ) . Resorufin production was monitored using an excitation wavelength of 530 nm and an emission wavelength of 595 nm in a Synergy H1 Hybrid microplate reader ( Biotek , Vermont ) during a kinetic run measuring fluorescence every two min for 4 hr at 34°C . Reactions were carried out in a 96 well EIA/RIA plate flat bottom plate ( Costar , New York ) covered with a MicroAmp optical adhesive film ( Applied Biosystems , New York ) to protect samples from evaporation . Assays were performed with 40 ng ( 7 . 14 ρmole ) of the enzyme and 50 ng ( 8 . 31 ρmole ) of substrates . The moles of the substrates were calculated from the theoretical molecular weight in kDa , using Zbionet ( http://www . molbiol . ru/eng/scripts/01_04 . html ) . Positive controls included the addition of AdoHcy alone or in combination with rOMTns to assay reagents provided by the manufacturer . To determine enzyme kinetics , the concentrations of the enzyme or the substrate were increased separately . For the first set of reactions , the amount of enzyme was increased to 60 ng ( 10 . 71 ρmole ) , 80 ng ( 14 . 29 ρmole ) , and 100 ng ( 17 . 89 ρmole ) , while the substrate was left at 50 ng . In the second set , the enzyme concentration was left at 40 ng , while the substrate was used at 80 ng ( 13 . 29 ρmole ) , 100 ng ( 16 . 61 ρmole ) , and 150 ng ( 24 . 92 ρmole ) . Each reaction was done in triplicate and the average of the reactions was analyzed . To determine whether the 10 mM Mn2+ included in the kit was a limiting factor for rOMT activity , additional MnCl2 was included at 0 . 5 mM , 1 mM , 2 mM , 4 mM , 8 mM , and 16 mM concentrations in reactions containing 100 ng of the OMT and 100 ng of the substrate . All reactions were carried out in triplicate using the manufacturer’s recommendations , and the kinetics of the enzyme were analyzed using the Michaelis-Menten equation . Crystals of OMT from A . phagocytophilum were obtained via the sitting drop vapor diffusion method , where 400 nl of protein solution was mixed with 400 nl of precipitant solution in the sample well and then equilibrated against 80 μl of precipitant in the reservoir well of 96-well Compact 300 crystallization plates ( Rigaku Reagents , Washington ) . For Apo OMT , the protein concentration used was 20 mg/ml and the precipitant solution was 0 . 2 M MgCl2 , 0 . 1 M TRIS at pH 8 . 50 , and 20% PEG 8000 . For SAM-Mn and SAH-Mn bound structures protein at 20 mg/ml was pre-incubated with 2 mM of either SAM or SAH and 10 mM MnCl2 for 1 hr before setting up trays . Final crystallization conditions for SAM-Mn+2 and SAH-Mn+2 were 0 . 2 M ammonium chloride , 20% PEG 3350 or 0 . 1 M succinic acid pH 7 . 0 , 15% PEG3350 , respectively . All crystallization experiments took place at 16°C . Crystals were harvested using mounted CryoLoops ( Hampton Research , California ) and then flash-frozen in liquid nitrogen until data collection . Data for SAH-bound OMT were collected on an in-house FR-E+ Superbright ( Rigaku , Washington ) rotating anode X-ray generator at a wavelength of 1 . 54 Å and data for Apo OMT was collected at the LS-CAT 21ID-G beam line at the Advanced Photon Source at a wavelength of 0 . 9786 Å . Data for the SAM-Mn and SAH-Mn complexes were collect at the LS-CAT 21ID-F beam line at the Advanced Photon Source at a wavelength of 0 . 9787 Å . All data were indexed , integrated , and scaled using the programs XDS and XSCALE [77] . Data statistics for both datasets are available in Table 5 . Phases for structure determination of the SAH-bound OMT were obtained via iodide-SAD using the method previously described by Abendroth et al . [41] . Heavy atom searches , phasing , and density modification were performed using the programs PHENIX HySS [78] , Phaser [79] , and SOLVE/RESOLVE [80] . Initial model building into density-modified electron density maps was performed using the program ARP/wARP [81] . Phases for Apo OMT were obtained by molecular replacement using the program Phaser , where the SAH-bound OMT structure was used as the search model . Both structures were refined against the reflection data using the programs PHENIX [82] and REFMAC [83] interspersed with rounds of model building using the program Coot [84] . Figs containing molecular graphics were prepared using the program PyMOL ( https://www . pymol . org ) . To identify the possible origin of the A . phagocytophilum OMT , the corresponding protein sequence available in GenBank for A . phagocytophilum isolate HZ ( GI:88607321 ) was used for a PSI-BLAST . The OMTs with the lowest E-values and highest similarity to the A . phagocytophilum OMT were aligned using ClustalW from MacVector 12 . 0 ( MacVector , Inc , North Carolina ) . A Minimum Evolution phylogenetic tree of all the OMTs was generated using MEGA 4 . 0 . Conserved motifs within the most closely related non-Anaplasmataceae OMTs , along with the OMT from A . phagocytophilum , were identified using MEME ( http://meme . nbcr . net ) [85] . Phyre2 [42] was used to determine the putative tertiary structure of the protein ( Msp4 ) that was identified as being methylated by iTRAQ analysis as well as the in vitro methylation assay . The putative localization of the modified residues was determined from the protein sequence and the structure generated from Phyre2 . Phobius ( http://phobius . sbc . su . se/cgi-bin/predict . pl ) was used to determine where the modified residues were located within the membrane of the bacteria since Msp4 is a surface protein [61] . This study was performed in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health . None of the procedures used caused more than momentary pain , and infection with A . phagocytophilum does not cause illness in mice . Animals were euthanized with CO2 before collecting blood for production of serum following current AVMA guidelines . The protocol was approved by the institutional Animal Care and Use Committee of the University of Minnesota ( Protocol ID: 1303-30435A ) . The NCBI accession numbers for A . phagocytophilum proteins mentioned in the body of the article are the following: O-methyltransferase ( GI:88598384 ) ; Major Surface Protein 4 ( GI:88607879 ) ; Hypothetical protein APH_0406 ( GI:88607117 ) ; OmpA family member ( GI:88607566 ) ; P44_18ES ( GI:88607256 ) ; Major Surface Protein 5 ( GI:88607263 ) ; DNA-dependent RNA polymerase subunit B ( GI:88607105 ) ; Antioxidant AhpCTSA family protein ( GI:88607183 ) ; Ankyrin ( GI:88607707 ) ; P44-1 outer membrane protein ( GI:88607426 ) ; OMP85 ( GI:88607567 ) ; Hypothetical protein APH_0405 ( GI:88607654 ) ; Chaperonine GrpE ( GI:88607566 ) ; Chaperonin GroEL ( GI:88606723 ) ; DnaK ( GI:88607549 ) ; Cytochrome C oxidase subunit II ( GI:88607721 ) ; TypA ( GI:88607727 ) ; Outer membrane protein P44-16b ( GI:88607043 ) ; Preprotein translocase subunit SecA ( GI:886078849 ) . The NCBI accession numbers for proteins from other organism are the following: B . bacteriovorus OMT ( GI:426402377 ) ; H . ochraceum OMT ( GI:262196431 ) ; Anaeromixobacter sp . OMT ( GI:197124171 ) ; C . M . mitochondrii OMT ( GI:339319550 ) ; and Gloeocapsa sp . ( GI:434391334 ) . The PDB accession numbers for the crystal structures mentioned in the article are the following: A . phagocytophilum OMT + SAH ( 4OA5 ) ; A . phagocytophilum OMT Apo ( 4OA8 ) ; A . phagocytophilum OMT + SAM + Mn+2 ( 4PCL ) ; A . phagocytophilum OMT + SAH + Mn+2 ( 4PCA ) ; and OMT C . synechocystis ( 3CBG ) . | Since its discovery in 1994 , Human Granulocytic Anaplasmosis ( HGA ) has become the second most commonly diagnosed tick-borne disease in the US , and it is gaining importance in several countries in Europe . HGA is caused by Anaplasma phagocytophilum , a bacterium transmitted by black-legged ticks and their relatives . Whereas several of the molecules and processes leading to infection of human cells have been identified , little is known about their counterparts in the tick . We analyzed the effects of a mutation in a gene encoding an o-methyltransferase that is involved in methylation of an outer membrane protein . The mutation of the OMT appears to be important for the ability of A . phagocytophilum to adhere to , invade , and replicate in tick cells . Several tests including binding assays , microscopic analysis of the infection cycle within tick cells , gene expression assays , and biochemical assays using recombinant OMT strongly suggested that the mutation of the o-methyltransferase gene arrested the growth and development of this bacterium within tick cells . Proteomic analyses identified several possible OMT substrates , and in vitro methylation assays using recombinant o-methyltransferase identified an outer membrane protein , Msp4 , as a specifically methyl-modified target . Our results indicated that methylation was important for infection of tick cells by A . phagocytophilum , and suggested possible strategies to block transmission of this emerging pathogen . The solved crystal structure of the o-methyltransferase will further stimulate the search for small molecule inhibitors that could break the tick transmission cycle of A . phagocytophilum in nature . | [
"Abstract",
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] | [] | 2015 | An O-Methyltransferase Is Required for Infection of Tick Cells by Anaplasma phagocytophilum |
RIG-I triggers antiviral responses by recognizing viral RNA ( vRNA ) in the cytoplasm . However , the spatio-temporal dynamics of vRNA sensing and signal transduction remain elusive . We investigated the time course of events in cells infected with Newcastle disease virus ( NDV ) , a non-segmented negative-strand RNA virus . RIG-I was recruited to viral replication complexes ( vRC ) and triggered minimal primary type I interferon ( IFN ) production . RIG-I subsequently localized to antiviral stress granules ( avSG ) induced after vRC formation . The inhibition of avSG attenuated secondary IFN production , suggesting avSG as a platform for efficient vRNA detection . avSG selectively captured positive-strand vRNA , and poly ( A ) + RNA induced IFN production . Further investigations suggested that uncapped vRNA derived from read-through transcription was sensed by RIG-I in avSG . These results highlight how viral infections stimulate host stress responses , thereby selectively recruiting uncapped vRNA to avSG , in which RIG-I and other components cooperate in an efficient antiviral program .
Retinoic acid-inducible gene I ( RIG-I ) , a DExD/H-box RNA helicase family protein , is a crucial cytosolic viral RNA ( vRNA ) sensor that initiates signal transduction to produce antiviral cytokines , namely , type I and III interferons ( IFN-α/β and IFN-λ ) [1–4] . RIG-I selectively recognizes relatively short double-stranded RNA ( <100 nt ) , and this recognition is markedly enhanced by the presence of 5’-triphosphate [5–8] . Once RIG-I is activated , it physically associates with downstream IFN-β promoter stimulator 1 ( IPS-1 , also termed MAVS , VISA , or Cardif ) , which is anchored to the mitochondrial outer membrane [9–12] . The RIG-I/IPS-1 interaction leads to the recruitment of a variety of signaling adaptors in order to activate transcription factors including IFN regulatory factors ( IRF ) 3 and 7 as well as NF-κB . These transcription factors activate the genes coding for cytokines and IFNs in addition to IFN-stimulated genes ( ISG ) . Viral infection is a pivotal stressor that stimulates cells to execute intrinsic anti-stress strategies . As a part of the stress response , cells provisionally interrupt translational machinery in order to avoid excrescent proteins and shelter accumulated mRNA by sequestering them in cytoplasmic protein complexes , termed stress granules ( SG ) [13] . SG are typically composed of 40S ribosomal subunits , a subset of translation initiation factors ( eIF2 , 2B , 3 , 4A , 4B , 4E and 4G ) , and host RNA-binding proteins such as Ras GTPase-activating protein-binding protein ( G3BP ) , poly ( A ) -binding protein ( PABP ) , human antigen R ( HuR ) , T-cell intracellular antigen-1 ( TIA-1 ) , and its related protein TIAR [13–17] . A variety of viruses have been shown to induce the formation of SG in infected cells [18] , which is strictly regulated in a steady state; however , once cells are exposed to viral infections , double-stranded RNA ( dsRNA ) -inducible protein kinase R ( PKR ) is activated by viral dsRNA ( vdsRNA ) , an intermediate product generated within the viral replicative life cycle [19] . Activated PKR further phosphorylates the eIF2 α-subunit ( eIF2α ) and then recruits SG components to form the complex [13] . We recently reported that SG played a positive role in antiviral IFN signaling against influenza A virus ( IAV ) lacking an IFN-inhibitory NS1 protein ( IAVΔNS1 ) by recruiting RIG-I and a set of antiviral host proteins to detect the viral infection [20] . Moreover , another DExD/H-box RNA helicase protein DHX36 acts with PKR to induce SG , hence facilitates detection of vRNA by RIG-I [21] . These findings shed light on the function of SG as antiviral SG ( avSG ) , a substantial platform for IFN-inducing signaling triggered by RIG-I . However , vRNA species in avSG have not yet been characterized . Few studies have interpreted the spatial and temporal behaviors of RIG-I in virus-infected cells in relation to the induction of IFN . Newcastle disease virus ( NDV ) of Paramyxoviridae , a negative-single strand RNA virus , has been shown to efficiently trigger the production of IFN in human and rodent cells . NDV encodes a nucleocapsid protein ( N ) , phosphoprotein ( P ) , matrix protein ( M ) , fusion protein ( F ) , hemagglutinin-neuraminidase protein ( HN ) , and large RNA-dependent RNA polymerase protein ( L ) . These genes are arranged on non-segmented viral genomic RNA ( vgRNA ) with extracistronic sequences , known as leader ( Le ) and trailer ( Tr ) , in an order corresponding to 3’-Le-N-P-M-F-HN-L-Tr-5’ [22] . The viral polymerase first transcribes Le sequence into positive-strand RNA without terminal modification [23] . After Le transcription , the polymerase re-initiates transcription for N gene to yield N mRNA with 5’-m7G cap and 3’-poly ( A ) tail . Similarly , the polymerase synthesizes other viral mRNAs ( vmRNAs ) by transcribing downstream genes without dissociation from the template . In later stages of the infection , polymerase switches to the replication mode in order to synthesize the entire length of viral antigenomic RNA as a template for the synthesis of vgRNA . The general strategies of transcription and replication are similar across negative-single strand RNA viruses [24] , and the vRNA species produced during the replication cycle are sensed by RIG-I-like receptors [25 , 26] . In the present study , we analyzed stress responses in NDV-infected cells in relation to the induction of IFN gene activation . We found that NDV infection induced viral replication complexes ( vRC ) to which RIG-I was recruited . The appearance of vRC was followed by the formation of avSG , which also recruited RIG-I . The inhibition of avSG diminished signaling for the IFN induction upon NDV infection , suggesting that avSG played an important role in signal amplification . Our results also indicated that poly ( A ) -containing vmRNAs migrated from vRC to avSG , and that Le-N fusion RNA activated RIG-I in avSG . The results of the present study demonstrated the spatio-temporal dynamics of vRNA detection in triggering the induction of IFN .
We examined viral replication and cellular stress responses by immunostaining NDV-infected HeLa cells . Viral replication was detected by the accumulation of viral N and L proteins ( Fig 1A ) . These viral proteins localized in granules 6 hr post-infection ( hpi ) and persisted to 12 hpi . We detected vRNA by RNA-FISH , which allowed us to specifically detect vRNA with undetectable background signal in mock-infected cells ( S1 Fig ) . The results revealed that these granules also contained positive-strand and negative-strand vRNA ( vRNA ( + ) and vRNA ( - ) ) ( Fig 1B ) . Therefore , we refer these as viral replication complexes ( vRC ) . In order to monitor host stress responses , we detected avSG using antibody to TIAR ( Fig 1A and 1C ) . We also tested other avSG markers , TIA-1 , G3BP1 , and eIF3η ( S2 and S3 Figs ) and confirmed that all of these markers detect avSG . avSG were not detected at 6 hpi , by which time vRC had already been identified; however , avSG were clearly observed as distinct granules from vRC at 12 hpi ( Fig 1A and S3 Fig ) . avSG and vRC did not completely merge , except for occasional contact between vRC and avSG ( below ) . avSG coincided with vRNA ( + ) , but not with vRNA ( - ) ( Fig 1C ) . These results suggested that vRNA ( + ) was generated in vRC and then translocated into avSG . Certain types of RNA viruses including NDV have been shown to produce vdsRNA [27–31] . Therefore , we examined subcellular localization of vdsRNA by immunostaining with an anti-dsRNA antibody , which detected >40 bp dsRNA [32] . We demonstrated that vdsRNA was not detectable within vRC at 6 hpi , but was clearly present at 12 hpi ( S4 Fig ) . The detection of vdsRNA was restricted to vRC which contains L and vRNA ( - ) . avSG contains vRNA ( + ) and TIAR but apparently devoid of vdsRNA . We confirmed specificity of anti dsRNA antibody through the loss of reactivity after ribonuclease digestion ( RNase A and III ) ( S5 Fig ) . In order to elucidate the biological significance of vRC and avSG in antiviral innate immunity , we examined the kinetics of the appearance of these granules along with IFNB gene expression at every 1 . 5 hours up to 12 hpi ( Fig 2A ) . Our RNA-FISH detection of IFNB transcript correlated with the nuclear translocation of IRF-3 and NF-κB ( S6 Fig ) . vRC ( N ) were initially detected as small granules ( 4 . 5 hpi ) , the size of which subsequently increased ( 7 . 5 hpi ) . On the other hand , avSG ( TIAR ) were detected as late as 7 . 5 hpi and persisted thereafter . Quantification ( Fig 2B ) revealed the temporal appearance of these granules: vRC-positive cells ( light gray ) were first detected at 1 . 5 hpi , and reached >90% at 6 hpi; avSG-positive cells ( black ) were detected at 7 . 5 hpi and reached > 90% at 12 hpi . No cells without vRC exhibited avSG , except for those treated with arsenite ( dark gray ) , which was used as a positive control for the formation of SG . Quantification of IFNB FISH ( Fig 2C ) revealed that IFNB mRNA-positive cells ( red + green ) were first detected at 6 hpi and their population subsequently increased . At 6 hpi , IFNB mRNA-positive cells were all vRC-positive ( red ) . Cells triple positive for vRC , avSG , and IFNB mRNA ( green ) were detected after 7 . 5 hpi , and this population increased to 66 . 1% at 12 hpi , suggesting that this type of cell was the main cell producing IFN-β . Cells positive for vRC and IFNB mRNA , but negative for avSG ( red ) remained at approximately 3–5% . We compared RNA-FISH results with RT-qPCR ( Fig 2D ) . The results obtained by two distinct methods are closely correlated and confirmed that gene expression was first detected at 6 hpi and markedly increased up to 12 hpi . Since IFNB mRNA was detected in cells exhibiting vRC at 6 hpi , we speculated that the formation of vRC was responsible for IFNB gene expression at 6 hpi ( Fig 2B ) . However , a population of cells triple positive for vRC , avSG , and IFNB mRNA ( green ) markedly increased , suggesting that avSG contributed to enhancing IFNB gene expression . Since NDV infection is detected by RIG-I , we examined its localization in NDV-infected cells ( Fig 3A ) . RIG-I co-localized with vRC at 6 hpi when avSG was not induced . At 12 hpi , RIG-I co-localized with vRC and avSG . These results were consistent with the formation of vRC and avSG coinciding with the accumulation of IFNB mRNA ( Fig 2 ) and nuclear translocation of IRF-3 ( Fig 3B ) . RIG-I transduces a signal to an adaptor , IPS-1 , which is expressed on mitochondria . We monitored the localization of IPS-1 using HeLa cells stably expressing FLAG-tagged IPS-1 [33] . At 6 hpi , IPS-1 localized in the proximity of vRC , showing a yellow color in the periphery of vRC ( as probed by anti-L ) , suggesting their close interaction ( Fig 3C ) . At 12 hpi , avSG were clearly detected and partially co-localized with IPS-1 ( Fig 3C , bottom right ) . We confirmed that endogenous IPS-1 re-localized upon NDV infection ( S7 Fig ) and interacted with vRC and avSG ( S8 Fig ) . We blocked the formation of SG in order to elucidate the significance of avSG in IFNB gene expression . We knocked down the expression of PKR , which has been shown to act as a sensor for viral infection in order to trigger the formation of avSG [20] . We also targeted G3BP proteins ( G3BP1 and G3BP2: G3BPs ) , which are known to be critical for the assembly and maintenance of SG [17 , 34 , 35] . We knocked down RIG-I for comparison . A western blotting analysis confirmed the efficient knockdown of the targets ( Fig 4A ) . The knockdown did not interfere with IFNAR-mediated STAT1 phosphorylation ( S9 Fig ) . These cells were infected with NDV and IFNB gene expression was examined by RT-qPCR ( Fig 4B ) . As expected , the knockdown of RIG-I impaired IFNB gene expression and the treatment with siPKR or siG3BPs also inhibited IFNB gene induction . The kinetics of vRC , avSG , and IFNB mRNA induction were subsequently examined in these cells ( Fig 4C ) . These results were quantified as in Fig 2 ( Fig 5 ) . In control cells at 12 hpi , vRC were observed in almost all cells ( light gray + black , 97–99% ) . Knockdown of RIG-I , PKR or G3BPs did not decrease the number of vRC-positive cells ( light gray + black , Fig 5C , 5E and 5G ) . The knockdown of RIG-I markedly blocked IFNB mRNA ( Fig 5D , red + green ) expression ( 56 . 1 to 8 . 2% at 12 hpi ) ; however , the number of avSG-positive cells ( Fig 5C , black ) remained constant ( 91 . 4 to 83 . 8% ) . The knockdown of PKR strongly inhibited the formation of avSG ( Fig 5E , black ) ( 91 . 4 to 17 . 9% ) and decreased the number of IFNB mRNA-positive cells ( Fig 5F , red + green ) ( 56 . 1 to 18 . 4% ) . The knockdown of G3BPs also inhibited avSG ( Fig 5G , black ) ( 91 . 4 to 21 . 7% ) and decreased the number of IFNB mRNA-positive cells ( Fig 5H , red + green ) ( 56 . 1 to 21 . 8% ) . Concomitant with the inhibition of avSG , the number of cells exhibiting vRC and IFNB mRNA increased , suggesting that these cells failed to develop into triple positive , high IFNB gene-expressing cells ( Figs 5F and 6H ) . We also examined the expression of ISG20 , ISG56 , and CXCL10 genes by RT-qPCR at 12 hpi ( S10 Fig ) . The expression of these genes was dependent on RIG-I , PKR , and G3BPs . These results strongly suggested that avSG contributed to the robust IFNB gene expression observed at approximately 12 hpi . We found that avSG contained vRNA ( + ) , but not vRNA ( - ) ( Fig 1C ) or vdsRNA ( S4 Fig ) . We suspected that vRNA ( + ) activated RIG-I in avSG , culminating in IFNB gene expression at 12 hpi . Since poly ( A ) + vmRNA is major vRNA ( + ) , we isolated poly ( A ) + RNA from mock and NDV infected cells . These RNA fractions were tested for the induction of Ifnb mRNA in mouse embryonic fibroblasts ( MEFs ) . RNA fractions extracted from uninfected cells did not activate the Ifnb gene ( Fig 6A ) , whereas both poly ( A ) + and poly ( A ) - RNA extracted from NDV-infected cells exhibited strong Ifnb-inducing activity . In addition to Ifnb , Isg56 and Cxcl10 genes were also activated by these RNA fractions ( S11A and S11B Fig ) . It was unexpected that the poly ( A ) + RNA fraction , corresponding to the mRNA fraction , exhibited stimulatory activity . We further tested other viruses belonging to Mononegavirales , RSV ( Fig 6B ) and VSV ( Fig 6C ) . The results revealed that poly ( A ) + RNA from RSV and VSV also exhibit strong stimulatory activity , particularly , majority of the stimulatory activity resides in poly ( A ) + RNA in RSV-infected cells . This activity was sensitive to RNase III or CIAP , but not DNase I , suggesting that this activity resides in poly ( A ) + RNA species with a secondary structure and end phosphate moieties , such as 5’-triphosphate ( Fig 6D and S11C and S11D Fig ) . To examine the presence of 5'-triphosphate in the stimulatory RNA , the RNA fraction was subjected to reaction with capping enzyme of Vaccinia virus , which adds cap to 5'-triphosphate end . Capping reaction diminished stimulatory activity of the RNA ( Fig 6E ) , therefore we speculate that the stimulatory activity is from remaining 5'-triphosphate -containing RNA . As described in the Introduction , transcription of NDV occurs in the order of 3’-Le-N-P-M-F-HN-L-Tr-5’ . The first transcript Le possesses 5'-triphosphate and is devoid of 3’-poly ( A ) . However , transcripts for N , P , M , F , HN and L possess 5'-cap and 3’-poly ( A ) . We analyzed viral N mRNA by strand-specific northern blotting ( Fig 6F ) . RNA extracted from NDV-infected HeLa cells was fractionated into poly ( A ) + and poly ( A ) - fractions . Ethidium bromide staining revealed the virtual absence of ribosomal RNA in poly ( A ) + RNA fraction , demonstrating successful fractionation ( Fig 6F , bottom ) . As expected , N mRNA ( expected size 1 . 7 kilonucleotides , knt ) is enriched in poly ( A ) + RNA fraction ( Fig 6F , top ) . This probe detected additional slow migrating RNA of 3 . 8 knt . We suspected that this larger RNA was read-through N mRNA , an extended transcript covering the N and P genes [36] . Many types of viruses belonging to Mononegavirales have been shown to produce read-through transcripts [37–39] . The result of Fig 6F inspired us to examine the presence of Le-N read-through transcript , because such an RNA possesses both 5'-triphosphate and 3'-poly ( A ) [38] . To detect Le-N read-through RNA , we performed strand-specific northern analysis using Le-specific RNA probe ( Fig 6G ) . Poly ( A ) + RNA from NDV-infected cells exhibited 1 . 7 knt signal by this probe . Le RNA ( 55 nt ) was undetectable in this gel due to its small size . To quantify Le-N read-through RNA , we used a protocol for strand-specific RT-qPCR [40] . This method provides specific detection of the target RNA ( S12 Fig ) . First , we quantified positive strand Le-N sequence ( Fig 6H ) . As expected , Le-N was enriched in poly ( A ) + fraction . Next , we quantified negative strand Le-N sequence to exclude a possibility that the stimulatory activity is from vmRNA partially hybridized with negative-sense strand ( Fig 6I ) . The result revealed that negative strand Le-N sequence was hardly detectable in poly ( A ) + fraction , excluding the possibility mentioned above . Interestingly , this technique allowed us to detect similar read-through products ( Le-NS1 , RSV; Le-N , VSV ) in cells infected with RSV and VSV ( S13 Fig ) . Finally , we synthesized RNA corresponding to Le and the Le-N read-through RNA and examined their IFN-inducing activity ( Fig 7A ) . In vitro product of Le-N read-through RNA with 3’-poly ( A ) + as well as Le RNA were capable of inducing IFNB gene activation . We monitored Cy3-labeled Le-N read-through RNA after transfection . The transfection induced formation of SG containing G3BP1 and Cy3 signal partially co-localized with the SG ( Fig 7B ) . Here , Cy3 signal did not completely coincided with SG , presumably because most Cy3-RNA resided within endosomes and the portion of the RNA escaped into cytoplasm induced SG . The results suggest that Le-N read-through RNA is capable of inducing avSG and the following induction of IFNB gene .
The present study revealed that NDV-derived vRNA species were sensed by two distinct mechanisms . NDV infection resulted in the generation of vRC as early as 1 . 5 hpi and their number subsequently increased ( Fig 2B ) . At 6 hpi , IFNB mRNA was detected in cells exhibiting vRC . vRC contained vRNAs ( - , + ) , vdsRNA , and RIG-I ( Figs 1B and 3A and S4 Fig ) . These results suggested that vRC are a locale in which vRNA is sensed by RIG-I and triggers IFN-inducing signal in the early stages of the infection ( before 7 . 5 hpi ) . At 7 . 5 hpi , avSG were detected with concomitant increases in IFNB mRNA levels ( Fig 2C ) . Since RIG-I also co-localized with avSG ( Fig 3A ) and avSG were strongly correlated with the culmination of IFNB gene induction at >9 hpi , these results suggested that avSG were a second locale for vRNA detection by RIG-I . This is consistent with the result that most ( 77 to 92% ) cells expressing IFNB mRNA were positive for vRC and avSG after 7 . 5 hpi ( Fig 2C ) and that the inhibition of avSG markedly attenuated IFNB gene expression ( Figs 4B and 5 ) . Immunostaining results clearly indicated that vRC and avSG made contact with IPS-1 ( Fig 3C and S8 Fig ) , suggesting signal transduction to IPS-1 from two distinct types of granules . The transfection of RNA extracted from control and NDV-infected cells showed that NDV-derived vRNA activated the IFNB gene by transfection ( Fig 6A ) . We detected vRNAs including vdsRNA in vRC ( Fig 1B and S4 Fig ) , consistent with its function for vgRNA synthesis by viral replicase . However NDV-induced avSG selectively contained vRNA ( + ) , and , hence , vmRNA or viral complementary RNA ( vcRNA ) , a full-length template for the viral genome . This is in contrast to influenza A virus ( IAV ) -infected cells , in which vgRNA was shown to be localized within avSG [20] . avSG induced by infection with encephalomyocarditis virus were previously reported to contained vdsRNA [35] . These findings indicated that the mechanism underlying the induction of avSG and the vRNA content of avSG depended on virus types . We herein revealed that NDV , as well as other viruses belonging to Mononegavirales including RSV and VSV , produced Le-NS1/N fusion RNA with 3’-poly ( A ) ( Fig 6G and 6H and S13C and S13F Fig ) . The Le transcript has been shown to contain 5’-triphosphate [23 , 41] , and synthetic RNA corresponding to the Le-N strongly activated the IFNB gene ( Fig 7A ) . On the other hand , we were unable to detect vcRNA , presumably because of its low abundance in addition to the low sensitivity of our detection . These results prompted us to speculate that viral Le-N RNA synthesized in vRC was transported to avSG and detected by RIG-I . Host mRNA generally conforms to a certain format; therefore , it is not sensed as non-self . Since mRNA with m7G-cap is not sensed by RIG-I , the acquisition of a cap is a major strategy of viruses to escape immune detection . The importance of additional methylation adjacent to the cap has recently been reported [42 , 43] . In the present study , we demonstrated that RIG-I specifically recognized the uncapped vRNA of NDV generated by Le-N read-through transcription . The generation of uncapped vRNA through transcriptional read-through is not limited to NDV , it is commonly found in other negative-strand RNA viruses such as Sendai virus , measles virus ( MeV ) , and vesicular stomatitis virus [37–39] , suggesting that sensing such uncapped vRNA is an important strategy of antiviral innate immunity . In the case of MeV , 5’-triphosphate-ended Le RNA is considered to be a signature of activating RIG-I for IFN induction , and the association of RIG-I with the 5’ end of transcript covers the Le to N regions , corresponding to the Le-N read-through transcript [41 , 44] . In future studies , it will be interesting to explore the general impact of an abortive Le-N read-through transcript , which is exclusively produced by viruses belonging to Mononegavirales , on the RIG-I-driven IFN pathway . Our results suggested that RIG-I detected vRNA within vRC in the early stages of infection and triggered the induction of IFN . However , this early response was more limited than the late response involving avSG , thereby suggesting the more efficient sensing of vRNA in avSG . PKR is known to be essential for the formation of avSG induced by NDV ( Fig 4 ) and other viruses [18] . In MeV infection , PKR was shown to correlate positively with both avSG formation and IFN expression [45–47] . DHX36 , another DExD/H-box helicase , has been shown to cooperate with PKR for the formation of avSG and subsequent viral RNA sensing by RIG-I [21] . Pumilio proteins ( PUM1 and 2 ) are known to cooperate with LGP2 to sense vRNA in order to trigger IFN-inducing signaling [48] . PKR , DHX36 , and PUM1 and 2 are specifically recruited to avSG . A critical signaling molecule for IFN induction , TRIM25 , was shown to be specifically recruited to avSG [21] . Taken together , these findings and the results of the present study demonstrated that avSG serves as a platform for the efficient sensing of vRNA through the recruitment of critical signaling molecules . In summary , the virus sensing in the cytoplasm involves more than a simple interaction between sensor molecules and vRNAs; a more complex mechanism including various RNA binding proteins and stress response machinery is responsible for detecting various vRNA structures generated by the replication of different viruses .
HeLa ( #CCL-2 . 2 , ATCC ) , FLAG-RIG-I/HeLa ( derived from HeLa; #CCL-2 . 2 , ATCC ) , FLAG-IPS-1/HeLa ( derived from HeLa; #CCL-2 . 2 , ATCC ) [33] , EGFP-G3BP1/HeLa ( derived from HeLa; #CCL-2 . 2 , ATCC ) [35] , HEp-2 ( #CCL-23 , ATCC ) , BHK21 ( #CCL-10 , ATCC ) cells , and MEFs ( isolated from embryos under C57BL/6 background , Japan SLC , Inc . ) were maintained in Dulbecco’s Modified Eagle’s Medium ( DMEM ) ( Nacalai Tesque ) supplemented with 10% Fetal Bovine Serum ( FBS ) ( BioWest ) and 1% Penicillin-Streptomycin Mixed Solution ( 100 U/ml and 100 μg/ml respectively ) ( Nacalai Tesque ) . NDV ( strain Miyadera/51 ) was inoculated into 9-day embryonated chicken eggs and incubated for 2 days at 37°C , followed by overnight incubation at 4°C . Allantoic fluid containing NDV was collected from dead eggs . RSV ( strain Long , ATCC VR-26 ) and VSV ( strain Indiana , M mutant ) were propagated in HEp-2 cells and BHK21 cells respectively , and culture supernatant was collected . The virus titer was determined by a plaque assay using HEp-2 cells . Virus was added to cells at a multiplicity of infection ( MOI ) of 1 . After 1-hour incubation , the medium was replaced with fresh DMEM and incubated for the indicated hours of infection . Sodium arsenite , and ribonuclease ( RNase ) A were purchased from SIGMA-ALDRICH . ShortCut RNase III was purchased from New England Biolabs . MitoTracker Red CMXRos was purchased from Thermo Fisher Scientific . Cells were treated as described in the figure legends . Cells were fixed with 4% paraformaldehyde solution for 10 minutes at room temperature . A 0 . 5% Triton X-100 solution was added to the cells for permeabilization and incubated for 5 minutes at room temperate . Regarding blocking , 0 . 5 mg/ml BSA solution in PBST ( PBS containing 0 . 04% Tween-20 ) was added to the cells and incubated for 30 minutes at room temperature . Primary antibodies were diluted in 0 . 5 mg/ml BSA/PBST and added to the cells , then incubated overnight at 4°C . After washing with PBST , secondary antibodies were added to the cells at a 1:1 , 000 dilution in 0 . 5 mg/ml BSA/PBST , and incubated for 1 hour at room temperature . After washing with PBST , 1 μg/ml DAPI solution in PBS was added to the cells to stain the nucleus . Cells were briefly rinsed with PBS , and then mounted with Fluromount-G ( SouthernBiotech ) . Images were taken by the confocal laser scanning microscope , TCS-SP8 ( Leica Microsystems ) . The primary antibodies used were; anti-NDV-N mouse mAb ( provided by Dr . T . Sakaguchi , Hiroshima University in Japan ) , anti-TIA-1 goat pAb ( #sc-1751 , Santa Cruz Biotechnology ) , anti-TIAR rabbit mAb ( #8509 , Cell Signaling Technology ) , anti-TIAR goat pAb ( #sc-1749 , Santa Cruz Biotechnology ) , anti-G3BP1 mouse mAb ( #sc-365338 , Santa Cruz Biotechnology ) , anti-eIF3η ( #sc-16377 , Santa Cruz Biotechnology ) , anti-FLAG M2 mouse mAb ( #F1804 , SIGMA-ALDRICH ) , and anti-dsRNA/J2 mouse mAb ( English & Scientific Consulting Kft . ) . Anti-IPS-1 guinea pig pAb was provided by Dr . I . Julkunen . Anti-RIG-I and anti-NDV-L antibodies were originally generated by immunizing rabbits with synthetic peptides corresponding to amino acids 793–807 of human RIG-I and 1160–1183 of NDV-L , respectively . The secondary antibodies used were; Alexa Fluor 488 donkey anti-rabbit IgG H+L ( #A-21206 ) , Alexa Fluor 488 donkey anti-mouse IgG H+L ( #A-21202 ) , Alexa Fluor 488 Donkey anti-Goat IgG H+L ( #A-11055 ) , Alexa Fluor 594 Donkey anti-Rabbit IgG H+L ( #A-21207 ) , Alexa Fluor 594 Donkey anti-Mouse IgG H+L ( #A-21203 ) , Alexa Fluor 633 Goat anti-Mouse IgG H+L ( #A-21050 ) , Alexa Fluor 633 Donkey anti-Goat IgG H+L ( #A-21082 ) , and Alexa Fluor 647 Donkey anti-Goat IgG H+L ( #A-21447 ) , all purchased from Life Technologies . RNA-FISH assay was performed using the QuantiGene ViewRNA ISH Cell Assay Kit ( Affymetrix ) according to manufacturer’s instructions as below . Cells were fixed in 4% paraformaldehyde solution for 30 minutes and permeabilized for 5 minutes with detergent solution . Protease solution was added to the cells at a 1:4 , 000 dilution in PBS and incubated for 10 minutes . After washing with PBS , the cells were incubated with a probe set at a 1:25 dilution for 3 hours at 40°C . The cells were further and independently incubated with a pre-amplifier , amplifier , and label probe ( all at a 1:25 dilution ) for 30 minutes at 40°C . After washing with PBS , the cells were subjected to an immunofluorescence assay . The probe sets used were; NDV-F ( - ) ( #VF1-15407 ) , NDV-N ( + ) ( #VF4-15408 ) , and human IFNB1 ( #VA1-11281 ) , all purchased from Affymetrix . Confocal micrographs of NDV-infected or arsenite-treated cells were subjected to the automatic analysis module ( Multi Wavelength Cell Scoring ) of MetaMorph Software v7 . 7 ( Molecular Devices ) in order to count vRC and SG speckles and IFNB mRNA dots . Briefly , the total cell number was first determined by counting the number of nuclei in the DAPI channel . A single cell area was segmented from the TIAR or eIF3η channel in reference to the intercellular boundary of the cytoplasmic staining area . The numbers of vRC ( N ) , SG ( TIAR or eIF3η ) , and IFNB mRNA were counted from each channel . siRNAs for RIG-I/DDX58 ( HSS119008 ) , PKR ( HSS108571 ) , G3BP1 ( HSS115444 ) , G3BP2 ( HSS114988 ) , and a negative control ( #12935–300 ) , purchased form Life Technologies , were transfected into 1×105 HeLa cells at a final concentration of 10 nM using Lipofectamine RNAiMAX Reagents ( Life technologies ) . 24 hours after transfection , the cells were transferred to new culture plates with fresh DMEM . After being incubated for a further 24 hours , cells were subjected to the following experiments . Cells were lysed with ice-cold NP-40 lysis buffer ( 50 mM Tris-HCl [pH 8 . 0] , 150 mM NaCl , 1% NP-40 , 1 mM sodium orthovanadate , 1 mM PMSF , and 0 . 1 mg/ml leupeptin ) . After centrifugation , the supernatant was collected , mixed with an equal volume of 2× SDS sample buffer ( 125 mM Tris-HCl [pH 6 . 8] , 4% SDS , 20% glycerol , 0 . 01% BPB , and 10% 2-mercaptoethanol ) , and boiled for 5 minutes . The sample corresponding to a protein amount of 30 μg was applied to 5–20% gradient e-PAGEL ( ATTO ) , separated by a standard SDS-PAGE method , and then transferred onto an Immobilon-P PVDF membrane ( MILLIPORE ) . The membrane was incubated in Tris-buffered saline with 0 . 1% Tween-20 ( TBST ) containing 5% skimmed milk for 30 minutes at room temperature for blocking . The membrane was incubated with a primary antibody diluted in the blocking buffer overnight at 4°C . After washing with TBST , the membrane was incubated with an AP-conjugated secondary antibody diluted in the blocking buffer for 1 hour at room temperature . After washing with TBST , protein bands were visualized using the BCIP-NBT Solution Kit for Alkaline Phosphate Stain ( Nacalai Tesque ) or ECL Prime Western Blotting Detection Reagent ( GE Healthcare ) . The primary antibodies used were; anti-PKR mouse mAb ( #sc-6282 , Santa Cruz Biotechnology ) , anti-phospho-PKR rabbit pAb ( #ab13447 , Abcam ) , anti-G3BP2 goat pAb ( #sc-161612 ) , anti-STAT1 rabbit pAb ( #9172 , Cell Signaling Technology ) , anti-phospho-STAT1rabbit pAb ( #9172 , Cell Signaling Technology ) , and anti-β-Actin mouse mAb ( #A2228 , SIGMA ALDRICH ) . The secondary antibodies used were; goat anti-rabbit IgG-AP ( #sc-2007 , Santa Cruz Biotechnology ) , goat anti-mouse IgG-AP ( #sc-2008 , Santa Cruz Biotechnology ) , anti-rabbit IgG , HRP-linked ( #7074 , Cell Signaling Technology ) , and anti-mouse IgG , HRP-linked ( #7076 , Cell Signaling Technology ) . Total RNA was isolated from cells using TRIzol Reagent ( Ambion ) , and treated with RNase-Free Recombinant DNase I ( Roche Diagnostics ) . After phenol-chloroform extraction and ethanol precipitation , purified total RNA was subjected to cDNA synthesis using a High-Capacity cDNA Reverse Transcription Kit ( Applied Biosystems ) . Gene expression levels were measured by the StepOnePlus Real-Time PCR system ( Applied Biosystems ) using the TaqMan Fast Universal PCR Master Mix ( Applied Biosystems ) , and determined by the 2-ΔΔCt relative quantitative method . The TaqMan probes used for measurements were; IFNB1 ( #Hs01077958_s1 ) , ISG20 ( #Hs00158122_m1 ) , ISG56/IFIT1 ( #Hs01911452_s1 ) , CXCL10 ( #Hs01124251_g1 ) , Ifnb1 ( #Mm00439552_s1 ) , Isg56/Ifit1 ( #Mm00515153_m1 ) , Cxcl10 ( #Mm00445235_m1 ) , and eukaryotic 18S rRNA ( #4333760F ) , all purchased from Applied Biosystems . The probe for NDV-N was designed as below: 5'-GTCCGTATTTGACGAATACGAG-3' ( forward primer ) , 5'-CAAGGGCAACATGGTTCCTC-3' ( reverse primer ) , and 5'-TCAGGCAAGGTGCTC-3' ( probe ) . Poly ( A ) + mRNA was isolated from the total RNA of mock/NDV-infected ( 12 hpi , MOI = 1 ) HeLa cells using the Oligotex-dT30 <Super> mRNA Purification Kit ( TaKaRa ) according to manufacturer’s instructions . Purification was repeated twice to yield a pure poly ( A ) + mRNA fraction . The supernatant after centrifugation was subjected to ethanol precipitation in order to obtain a concentrated poly ( A ) - RNA fraction . NDV gRNA was isolated from virus particles propagated in the embryonated chicken eggs , as described above . Allantoic fluid was centrifuged overnight at 15 , 000 rpm at 4°C , and the pellet was lysed using TRIZOL Reagent ( Ambion ) followed by isopropanol precipitation . 5’-triphosphate RNA was synthesized in vitro as reported previously [49] . RNA samples were treated with 1 U of ShortCut RNase III ( New England Biolabs ) , 1 U of RNase-free DNase I recombinant ( Roche ) , and 15 U of Calf Intestine Alkaline Phosphatase ( Takara ) at 37°C for 30 minutes , or 10 U of Vaccinia Capping Enzyme ( New England Biolabs ) according to the manufacturer’s instruction . After this treatment , RNA samples were purified by phenol-chloroform extraction and ethanol precipitation . Regarding RNA transfection , 200 ng of each RNA sample was transfected into 1×105 MEFs or 2×105 HeLa cells using Lipofectamine 2000 ( Invitrogen ) according to the manufacturer’s instruction . NDV gRNA isolated from virus particles by TRIZOL Reagent was subjected to reverse transcription , as described above . Synthesized cDNA was further subjected to PCR using primer sets including the T7 RNA polymerase promoter sequence ( Table in S1 Table ) . PCR products were in vitro transcribed by the T7 RiboMAX Express Large Scale RNA Production System ( Promega ) according to manufacturer’s instructions . Ribo m7G cap analog ( Promega ) and Cy3-UTP ( GE Healthcare ) were included in the reaction for 5’-m7G capping and Cy3 labeling respectively . Poly ( A ) Tailing Kit ( Ambion ) was used for 3’-poly ( A ) modifications . Unincorporated nucleotides within the samples were removed by NucAway Spin Columns ( Ambion ) . In vitro transcribed RNA samples were transfected , as described above . A denaturing agarose gel was prepared at a final concentration of 1% ( w/v ) Agarose ME ( Nacalai Tesque ) , 1× MESA ( Dojindo ) , 2% formaldehyde ( Nacalai Tesque ) , and 0 . 5 μg/ml ethidium bromide . A total of 250 ng of each RNA sample was mixed with an equal volume of Gel Loading Buffer II ( Ambion ) and incubated at 65°C for 15 minutes , followed by quick cooling on ice , and then electrophoresed in 1× MESA . The gel was transferred onto a nylon membrane Hybond-N ( GE Healthcare ) by a capillary blotting method using 10× SSC Buffer ( Nacalai Tesque ) . After UV cross-linking , the membrane was pre-incubated in PerfectHyb Hybridization Solution ( TOYOBO ) at 65°C for 20 minutes . An RNA probe was added to the solution and incubated at 65°C overnight . The membrane was washed with 2× SSC ( +0 . 1% SDS ) and 0 . 2× SSC ( +0 . 1% SDS ) at 65°C for 15 minutes , and then irradiated onto Storage Phosphor Screen BAS-IP ( GE Healthcare ) . Images were scanned using BAS-5000 Image Analyzer ( Fujifilm ) . In order to prepare RNA probes ( N ( - ) and Le ( - ) ) , NDV cDNA was subjected to PCR using primer sets including the T7 RNA polymerase promoter sequence ( Table in S1 Table ) . PCR products were in vitro transcribed into [α-32P]-CTP-radiolabeled RNA probes by Riboprobe System-T7 ( Promega ) . Unincorporated nucleotides within the samples were removed by NucAway Spin Columns ( Ambion ) . Strand-specific RT-qPCR targeting vRNA was performed as introduced elsewhere [40] . In the RT step , cDNA complementary to the target vRNA was synthesized with the primer including “5’-tag” , of which sequence is unrelated to NDV , RSV , and VSV ( Tables in S2 , S3 and S4 Tables ) . After the reaction , RT sample was treated with 10 U of Exonuclease I ( New England Biolabs ) at 37°C for 1 hour to remove unincorporated primer , and then the reaction was inactivated at 60°C for 30 minutes . The tagged-cDNA was subjected to qPCR analysis with Fast SYBR Green Mater Mix ( Thermo Fisher Scientific ) , using a specific primer set; primer corresponding to tag sequence and vRNA-specific primer ( Tables in S1 , S2 and S3 Tables ) . Standard curve was generated from ten-fold serial dilutions ( 1010 , 109 , 108 , 107 , 106 , 105 , 104 , 103 copies/μl ) of vgRNA isolated from viral particles or in vitro synthesized vRNA . | RIG-I plays a critical role in sensing cytoplasmic vRNA and triggering a downstream signaling cascade to produce the antiviral cytokine IFN . Over the past decade , a number of in vitro studies have been undertaken to address the nature of the ligands for RIG-I , and it was demonstrated that RNA species forming a 5’-triphosphate and short double-stranded motif preferentially activates RIG-I . The next challenge to address would be understanding the physiological behavior of RIG-I in the context of authentic virus infection . In this study , by monitoring the time-course events in NDV-infected cells , we demonstrated that RIG-I senses vRNAs that accumulate in vRC and subsequently in host avSG . We discovered that avSG contains vRNA derived from read-through transcription , which acts as a novel RIG-I ligand . Our findings illustrated how RIG-I encounters its natural ligands , and initiates and integrates IFN signaling through the course of virus infection . | [
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] | 2016 | Leader-Containing Uncapped Viral Transcript Activates RIG-I in Antiviral Stress Granules |
To implement effective control measures , timely outbreak detection is essential . Shigella is the most common cause of bacterial diarrhea in Argentina . Highly resistant clones of Shigella have emerged , and outbreaks have been recognized in closed settings and in whole communities . We hereby report our experience with an evolving , integrated , laboratory-based , near real-time surveillance system operating in six contiguous provinces of Argentina during April 2009 to March 2012 . To detect localized shigellosis outbreaks timely , we used the prospective space-time permutation scan statistic algorithm of SaTScan , embedded in WHONET software . Twenty three laboratories sent updated Shigella data on a weekly basis to the National Reference Laboratory . Cluster detection analysis was performed at several taxonomic levels: for all Shigella spp . , for serotypes within species and for antimicrobial resistance phenotypes within species . Shigella isolates associated with statistically significant signals ( clusters in time/space with recurrence interval ≥365 days ) were subtyped by pulsed field gel electrophoresis ( PFGE ) using PulseNet protocols . In three years of active surveillance , our system detected 32 statistically significant events , 26 of them identified before hospital staff was aware of any unexpected increase in the number of Shigella isolates . Twenty-six signals were investigated by PFGE , which confirmed a close relationship among the isolates for 22 events ( 84 . 6% ) . Seven events were investigated epidemiologically , which revealed links among the patients . Seventeen events were found at the resistance profile level . The system detected events of public health importance: infrequent resistance profiles , long-lasting and/or re-emergent clusters and events important for their duration or size , which were reported to local public health authorities . The WHONET-SaTScan system may serve as a model for surveillance and can be applied to other pathogens , implemented by other networks , and scaled up to national and international levels for early detection and control of outbreaks .
In view of the increasing movement of people , animals , and food products around the globe , new strategies and collaborations are urgently needed to detect the emergence of microbial threats and implement effective control measures . National and regional electronic laboratory-based surveillance collaborations based on routine clinical laboratory test results , as recommended by the WHO-Global Foodborne Infections Network [1] and the WHO-Advisory Group on Integrated Surveillance of Antimicrobial Resistance [2] , offer the potential for real-time monitoring of evolving microbial populations . Sophisticated technologies for differentiating among strains and for processing information have proliferated and been incorporated into surveillance [3] . However , advances in organizational aspects such as timeliness of data entry and analysis and integration of local site-of-care laboratories into national and international surveillance networks have developed more slowly [4] . Statistical analysis of laboratory data for the detection of disease outbreaks in the community or in hospitals has also lagged , and existing statistical approaches have tended to focus on temporal trends [5] , [6] , [7] , [8] , largely ignoring the geographic component of pathogen population dynamics . To be most useful for public health purposes , laboratory-based surveillance should 1 ) be specific , i . e . be capable of distinguishing ( a ) among species and preferably variants within species and ( b ) among antimicrobial resistance profiles within those taxonomic groups; 2 ) have timely electronic data entry; 3 ) integrate multiple laboratories using uniform protocols and a uniform database; 4 ) be linked to and used by the agencies responsible for disease control; and 5 ) implement statistical methods for detecting departures from background levels in both time and space , rather than relying solely on visual inspection of data . Of the bacterial pathogens causing diarrhea , Shigella spp . is one of the most prevalent and most consistently associated with dysentery and persistent diarrhea [9] . Shigellosis kills an estimated 1 . 1 million people per year worldwide , 60% of them children under the age of 5 [10] , and can result in reduced growth in children who survive . Shigella species appear highly adaptable to selective pressure and have developed resistance to a number of antimicrobials with patterns of resistance varying temporally and geographically with antimicrobial usage patterns [11] , [12] , [13] , [14] . Highly resistant clones of Shigella have emerged in Argentina [15] , [16] , [17] . Recently , the unique Shigella flexneri serotype X variant , which emerged in China in 2001 , has rapidly spread , including through Argentina [18] , undergoing frequent serotype switching and acquiring resistance to multiple antimicrobials in the process [19] . We report here our experience with an evolving , integrated , laboratory-based , near real-time surveillance system now operating in six contiguous provinces of Argentina , building on a prior retrospective study [20] . It represents real-world surveillance rather than a static system pre-defined in a formal protocol . This system includes all of the desired laboratory-based surveillance system elements listed above and may serve as a model for surveillance not only of Shigella spp . but of other community-acquired pathogens in Argentina and elsewhere .
The microbiology data used for shigellosis prospective surveillance came from a subset of the national Argentine network for monitoring antimicrobial resistance , WHONET- Argentina , which was established in 1986 by the Ministry of Health through the Dr . Carlos G . Malbrán National Institute of Infectious Diseases ( INEI ) . The group is named after WHONET , a free software for the management of microbiology laboratory data developed by the WHO Collaborating Centre for Surveillance of Antimicrobial Resistance promoted by the World Health Organization . Led by the National Reference Laboratory at INEI , WHONET-Argentina currently includes 89 clinical laboratories representing all geographic jurisdictions and captures detailed data on human pathogens and their susceptibility profiles from routine diagnostic specimens . WHONET-Argentina hospitals were selected to participate in this surveillance initiative on the basis of the completeness and timeliness of data entered into the national WHONET database in previous years , a commitment to send updated data on a weekly basis , and Shigella serotyping ability . An additional criterion was the strength of the respective local public health systems in integrating laboratory , epidemiology , food science , and environmental health efforts for the investigation of outbreaks and sources of infection . On the other hand , surveillance of Shigella infections at the national level is currently conducted by provincial hospitals that report the number of cases weekly to the Ministry of Health . The period covered by this report was from April 1 , 2009 through March 31 , 2012 . The number of laboratories and provinces participating increased during this period ( Table 1 ) , with growth in laboratories' historical data , capacity for complete and timely data entry , and ability to meet the other inclusion criteria . For the first year , seven hospitals in the three contiguous provinces of La Pampa ( LP ) , Neuquén ( NQ ) , and Río Negro ( RN ) participated . In March 2010 , four satellite clinics in the same three provinces were added . In January 2012 , 12 additional laboratories from the three additional contiguous provinces of Córdoba ( CBA ) , Mendoza ( MZA ) , and San Luís ( SL ) were incorporated ( Table 1 ) . Thus , by the end of the evaluation period , 23 laboratories in six provinces were included ( Figure 1 ) . To detect localized shigellosis outbreaks in near real-time , we used the non-parametric space-time permutation scan statistic [28] , as was previously used in a pilot study of historical data in the WHONET-Argentina database . The method searches for statistically significant clusters of Shigella cases in space and time , using cylindrical scanning windows with a circular base of variable location and radius representing geographical space and a variable height representing the number of days in a potential cluster ( ending on the day for which the analysis is being done ) . It does not require population-at-risk data and makes minimal assumptions about the time , location , or size of outbreaks . The method adjusts for any purely geographical variation in disease incidence , whether due to urban vs . rural , north vs . south , dry vs . humid conditions , etc . There is no need to adjust for any of these variables explicitly . Rather , the adjustment is done non-parametrically , using a permutation-based approach that is conditional on the total area counts summed over all days . Similarly , the method adjusts for any purely temporal trends in the data , such as seasonal variation or day-of-week effects . This is done non-parametrically , by day , by using a permutation-based approach that is conditional on the total daily counts summed over all geographic areas . With a seasonal infection such as shigellosis , this ability to automatically adjust for seasonal variation is critical; without such an adjustment it would be difficult to distinguish between epidemiologically important outbreaks and the expected annual increase in shigellosis cases observed each summer . In contrast to other cluster detection methods that adjust for seasonal variation using a parametric model , the space-time permutation scan statistic does not require multiple years of historical data . The paper by Kulldorff et al . [28] describes the method in more detail . In looking for clusters , we set the maximum temporal cluster length at 30 days , meaning clusters of 1 , 2 , 3 , and up to 30 days' duration could be detected . Because data were updated on a weekly rather than daily basis and because of potential delays in data entry , data transmission , or data availability ( e . g . in the ascertainment of organism serotype ) , prospective analyses were run not only for the last date in the dataset but also for each day in the prior several weeks to ensure that recent clusters were not missed . In each of these day-specific analyses , the prior 365 days of data were used as a historical baseline . From April 2009 to August 2011 we used SaTScan 7 . 0 . 1 to search for single hospital clusters; from September 2011 through March 2012 , we used SaTScan 9 . 1 . 1 to search for single or multi-hospital clusters with a maximum radius of 200 kilometers ( Table 1 ) ( Clusters of that apparent or real size could occur if a contaminated food were distributed regionally or if an area of transmission were located between two participating hospitals , leading some patients to go to one hospital and others to the other ) . Per analysis run , the statistical inference is adjusted for the multiple testing inherent in the many potential cluster locations , sizes , and time lengths , and is expressed in terms of a recurrence interval [29] . If a detected group of cases is determined to have a recurrence interval of 400 days , for example , this could be an event of epidemiologic significance . But it could be simply due to chance -during any 400-day period , the expected number of signals of that strength or stronger is one , when the null hypothesis of no clusters is true . Thus , the higher the recurrence interval , the less likely that the observed clustering could be attributed to random variation . In this study , we considered a grouping of cases with a recurrence interval of 365 days or longer to be a statistical “signal” and worth communicating ( after basic data quality checking ) to relevant local or provincial authorities for possible epidemiologic and molecular investigation . Using 365 days as our recurrence interval threshold for notifying public health authorities means that , on average , we could have expected to see one false positive signal per year within each analysis level ( genus , species , serotype , and resistance profile ) . Since statistically significant space-time groupings of cases may be caused by chance or by organizational or procedural factors such as changes in hospital participation , specimen collection practices , or laboratory testing procedures , it is important that statistical signals be evaluated through traditional epidemiologic means before concluding that they are indications of true disease outbreaks . Calculations for the space-time permutation scan statistic were done using the free SaTScan™ software [30] , as imbedded in the free WHONET software . In the rest of this paper , we use the term “signal” to refer to the detection by SaTScan of a group of cases clustered in space and time with a recurrence interval of 365 days or more . We use the term “event” to refer to a group of signals overlapping in space and time , which may represent a single potential disease outbreak . The term “cluster” is used generically , referring to a group of cases regardless of whether or not these cases are truly related to each other epidemiologically . When a signal was detected in weekly analyses , a report including data on each patient was sent by INEI analysts to laboratory and epidemiology personnel in the affected hospital and province . These messages were usually sent on Fridays , but later if there were delays in analysis . The decision by local authorities of whether to investigate a signal depended in part on the recurrence interval and in part on other criteria such as number of patients , location details , and timing and specificity of the signal ( homogeneous clusters detected by resistance phenotype or serotype were considered to be more reliable than heterogeneous clusters detected at the Shigella genus level ) . Since the purpose of this real-world surveillance system was to support local public health , no uniform signal investigation protocol was imposed; investigations variously included patient or clinician surveys , additional sampling of either contacts or suspected sources ( food or water ) for Shigella spp . , and trace-back of suspected food products . The events identified by SaTScan confirmed as outbreaks with investigation by local authorities and the confirmation of strains' relatedness by PFGE were analyzed and informed to National Surveillance System .
There were 32 statistically significant events: 2 , 12 , 3 and 2 of S . flexneri serotypes 1 , 2 , 3 , and AA 479 respectively; 11 of S . sonnei; and 2 of S . boydii serotype 2 ( Table 2 and Table 3 ) . Seventeen of the 32 events were found at the resistance profile level ( and some at higher levels also ) . All 6 participating provinces had events . During the study period , 21 of the events occurred primarily during the Argentine summer months of October–March , while 11 occurred primarily in April–September . The median event duration was 48 . 5 days ( minimum: 3 , maximum: 94 ) . The median number of patients in a signal was 21 . 5 ( minimum: 2 , maximum: 41 ) . Twenty-six of the 32 events were investigated further by PFGE analysis , which confirmed a close relationship among the isolates ( with the first and second enzyme ) for 22 ( 84 . 6% ) of the 26 . In contrast , in Events 10 , 11 , 12 , and 28 ( all involving S . sonnei , PFGE showed diverse genetic subtypes , and we considered these events to be largely chance concentrations of cases , not potential diseases outbreaks . Seven of the events , Events 5 , 7 , 14 , 16 , 17 , 31 , and 32 , were investigated epidemiologically , which revealed links among the patients ( Table 4 ) , consistent with the PFGE findings for these events . Twenty-six of the 28 events considered to represent or possibly represent disease outbreaks were detected before hospital staff was aware of any increase in the number of Shigella isolates . The others were Events 14 and 17 . In Event 14 , S . flexneri 2 in Rio Negro , the hospital bacteriologist suspected the outbreak before the appearance of the first WHONET- SaTScan signal , because the majority of isolates were from patients from the same family . Event 17 was a cluster of S . flexneri 3 associated with a wedding in Neuquén province , which was attended by 150 people , including some from Chile . More than half the participants became ill , and this outbreak was reported immediately to public health authorities at both national and international levels before the laboratory results could be incorporated into the WHONET database and analyzed . The system detected events of public health importance . For example , Events 16 and 21 , both in Rio Negro , involved S . sonnei resistant to both AMP and SXT , an infrequent resistance profile . A number of long-lasting and/or re-emergent clusters were also detected , represented by 4 pairs of related events: PFGE patterns were very similar for Events 1 and 6 , S . flexneri 2 in La Pampa ( Figure 2 ) , and for Events 13 and 14 , S . flexneri 2 in Rio Negro . Events in each pair were separated from each other by at least 3 months . Events 10 and 11 of S . sonnei in La Pampa were separated by one month and included two persistent patterns and single subtypes circulating simultaneously . Events 16 and 21 , S . sonnei with the unusual AMP-SXT resistance phenotype , were centered in two cities ( Viedma and Bariloche ) in Rio Negro province . The predominant PFGE patterns in the latter two events differed by only one band , which is not considered a significant difference in PFGE analysis for Shigella when person-to-person transmission is a prominent feature and the outbreak persists in time [26] Event 16 lasted from December 2010 to March 2011 , while Event 21 occurred in April to May 2011 . Two other events are worthy of mention due to their duration or size . Event 7 , caused by SXT-nonsusceptible S . sonnei in La Pampa province , lasted from December 2009 to March 2010 , with 34 isolates . Among 23 cases of diarrhoea studied trough clinician surveys , 14 ( 60 . 9% ) were epidemiologically linked . Seven of 14 SXT-non-susceptible S . sonnei isolates analyzed by PFGE shared a new pattern in the national database and the other 7 were closely related to this pattern with similarities between 91 . 4% to 97 . 4% , 1 to 3 bands of difference . ( Figure 3 ) . No common source was identified , but most of the cases were associated with two neighboring households found to be epidemiologically linked and to have deficient sanitary conditions . Figure 4 shows the time series of cases , for the period from December 2009 to March 2012 , to highlight the detection and evolution of event 7 . Event 17 , the wedding outbreak in Neuquen province , was noteworthy in that it was detected by our surveillance system on the basis of only 7 isolates when in fact the total number ill , according to public health investigation , was closer to 75 . The 7 S . flexneri 3 isolates from the patients showed indistinguishable PFGE patterns . In general , no common source could be confirmed in the events , even though food and water samples were analyzed in several instances; this may be due to the difficulty for the isolation of Shigella from this kind of sample . Nevertheless , the epidemiologic studies could determine sanitary deficient conditions and probable routes of transmission , mainly from person to person . Furthermore , deficiencies in the conditions for food conservation and elaboration were identified in some events . On this basis , control and prevention measures included recommendations on hygiene and food handling , as well as a notification to the International Health Regulation for an event that affected patients from Chile and Argentina ( Table 4 ) .
In three years of active , near real-time surveillance , building on an earlier , purely retrospective pilot study [28] , our system detected 32 shigellosis events ( Table 2 and 3 ) . Independent suspicion or discovery of only 2 of the 28 events considered suspect of outbreak occurred prior to detection of the first signal by WHONET-SaTScan . Of the 26 events for which we have PFGE evidence , 22 appeared to represent groups of truly genetically interrelated cases , including 9 new patterns ( 1 of them closely related to pattern identified previously ) and 13 subtypes identified before in the NDB , with supporting evidence of epidemiologic linkage for 7 . The 32 detected events represented a broad range of the Shigella variants circulating in Argentina and were distributed among the six participating provinces . Some were of particular public health importance because of long duration or number of patients , e . g . Events 7 and 16 , or because of a distinct resistance profile , e . g . Events 16 and 21 , both S . sonnei AMP-SXT . Four pairs of events that were related according to PFGE patterns may have represented additional long-lasting outbreaks . The two known outbreaks in these six provinces that were not detected by WHONET-SaTScan could not have been found by the system . One , a plasmid-conferred cefpodoxime-resistant cluster of S . sonnei in February-March 2011 , could not have been detected because cefpodoxime resistance was not one of the phenotypes analyzed at the time . However , it did show up as a non-statistically significant cluster of SXT-AMP-nonsusceptible resistant S . sonnei , with RI = 116 . The other , of Shigella flexneri AA479 , appeared in one of the new provinces in August–September 2011 , but this was before the new laboratories' data were incorporated and analyzed and before the variant was given a specific code in WHONET . In Argentina , under the auspices of the international laboratory network PulseNet , it has been possible to maintain active surveillance using PFGE to detect circulating clones [16] , [31] . When PFGE results are communicated to local public health agencies , they inform investigation into possible sources of contamination and their persistence over time [32] . PFGE results of the type we often saw one predominant pattern with other closely related genetic subtypes in the same event are common in Shigella spp . , particularly in long-lasting events with person-to-person transmission , such as Events 7 and 16 . Others have reported widespread outbreaks in which this mode of transmission was confirmed by molecular typing results and epidemiologic data [33] , [34] . Where person-to-person spread is a prominent feature of the outbreak , more variability is expected [26] , [27] . In each of two outbreaks with point-source exposures , event 17 and the outbreak of S . flexneri AA479 in the provinces that had not yet been incorporated into the surveillance system , the PFGE patterns were indistinguishable . There are several limitations to this study . As is often the case in evaluations of surveillance systems , there was no known set of outbreaks that could serve as a gold standard against which to compare all the events . Some events were studied by public health local authorities , while all the cases were reported to the national surveillance system . Therefore , it was not possible to calculate such measures as sensitivity , specificity , or negative predictive value . PFGE testing did stand in as a strong validation method , and 22 ( 84 . 6% ) of 26 events for which PFGE was done showed evidence of close genetic relatedness . However , close relatedness by itself does not prove that isolates belong to a single outbreak , and accompanying epidemiologic studies were not carried out for every event , so we cannot claim to know the positive predictive value of our system . Also , the system was not pre-specified in a static protocol , having changed in important ways over the three years: the inclusion of additional laboratories , changes in parameter settings , and changes in the kinds/phenotypes of Shigella for which clusters could be detected ( Table 1 ) . The results may have looked somewhat different had we included all 23 laboratories and the final parameter settings and Shigella variants from the start . Finally , we did not compare results of different cluster detection methods applied to the same data . We selected the space-time permutation test because it does not require population-at-risk ( denominator ) data and it adjusts for purely spatial and purely temporal variation and can do so without multiple years of historical data . It would be worthwhile in a future methods-oriented endeavour to compare the performance of this method with other cluster detection methods . In large measure , this still-evolving , real-world , laboratory-based surveillance system satisfies criteria for public health utility , including that it 1 ) be specific , 2 ) have timely electronic data entry , 3 ) integrate multiple laboratories using uniform protocols and databases , 4 ) be used by the agencies responsible for disease control , and 5 ) implement statistical methods for detecting departures from background levels in both time and space . We have detected clusters of shigellosis of public health importance , which have been confirmed by PFGE as consisting of closely related clones , and informed local public health efforts . This WHONET-SaTScan system of data organization and analysis could represent a good complementary tool for national surveillance system , for early outbreak detection in real time , signalling the importance to investigate some events , and could be applied to other pathogens , implemented by other networks of laboratories , and scaled up to national and international levels . | Shigellosis causes dysentery and kills an estimated 1 . 1 million people per year worldwide , 60% of them children under the age of 5 . The infectious agent is Shigella spp , transmitted from person to person by fecal-oral route or via ingestion of contaminated food or water . Having a system for early detection of outbreaks would be very useful for implementing control measures that help reduce the number of affected patients , economic losses and prevent the dissemination of antimicrobial resistance . We present the application of a space-time permutation scan statistic implemented within the free SaTScan software for laboratory based surveillance of Shigella cases in six provinces from Argentina . SaTScan was applied on the data loaded into WHONET databases ( an electronic laboratory data system used world-wide ) in the six provinces from April 2009 to March 2012 . The project allowed the identification of 32 events , including several of particular public health importance for their duration or number of affected patients . It also strengthened the relationship between the laboratory and epidemiology staff . In conclusion , the combination of WHONET laboratory data and SaTScan analysis can detect important community outbreaks of antimicrobial-resistant shigellosis in a timely manner , to make a difference to public health . | [
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Meiotic recombination ensures proper chromosome segregation in many sexually reproducing organisms . Despite this crucial function , rates of recombination are highly variable within and between taxa , and the genetic basis of this variation remains poorly understood . Here , we exploit natural variation in the inbred , sequenced lines of the Drosophila melanogaster Genetic Reference Panel ( DGRP ) to map genetic variants affecting recombination rate . We used a two-step crossing scheme and visible markers to measure rates of recombination in a 33 cM interval on the X chromosome and in a 20 . 4 cM interval on chromosome 3R for 205 DGRP lines . Though we cannot exclude that some biases exist due to viability effects associated with the visible markers used in this study , we find ~2-fold variation in recombination rate among lines . Interestingly , we further find that recombination rates are uncorrelated between the two chromosomal intervals . We performed a genome-wide association study to identify genetic variants associated with recombination rate in each of the two intervals surveyed . We refined our list of candidate variants and genes associated with recombination rate variation and selected twenty genes for functional assessment . We present strong evidence that five genes are likely to contribute to natural variation in recombination rate in D . melanogaster; these genes lie outside the canonical meiotic recombination pathway . We also find a weak effect of Wolbachia infection on recombination rate and we confirm the interchromosomal effect . Our results highlight the magnitude of population variation in recombination rate present in D . melanogaster and implicate new genetic factors mediating natural variation in this quantitative trait .
Meiotic recombination , the reciprocal exchange of genetic information between homologous chromosomes during meiosis , is necessary for proper chromosome segregation in many organisms [1] . Interestingly , the distribution of meiotic recombination events , or crossovers , varies dramatically in almost all taxa studied to date [2–12] . In addition , crossover frequency varies within and between species and populations in a huge diversity of organisms including humans , chimpanzees , flies , mice , worms , yeast , and many others [3 , 4 , 6 , 8 , 12–26] . In addition to its role in preserving genomic integrity between generations , recombination is a pivotal force in evolution . Recombination can reduce interference between a genetic variant and the genetic background in which it resides , thereby increasing the efficacy of natural selection [27–29] . Moreover , the exchange of genetic material between homologs creates new allelic combinations and thus contributes to the raw material for the process of evolution . Further highlighting its importance for evolution in general and genome evolution in particular , rates of recombination correlate with numerous genomic features such as the level of DNA polymorphism [30–32] , rates of protein evolution [33 , 34] , density of transposable elements [35–38] , density of satellite DNA [39 , 40] , and codon bias [41 , 42] . Given the importance of recombination and the pervasive natural variation in recombination rate , it is perhaps unsurprising that the genetic basis of this variation has been an active area of research for the last decade . With respect to the genetic basis of the distribution of crossover events , the first known determinant of recombination distribution in metazoans was discovered recently [43–45] . This remarkable discovery implicates PRDM9 in determining the locations of meiotic recombination hotspots in both humans and mice . Sequence variation within Prdm9 also modulates hotspot activity in humans [46] . PRDM9 is a histone methyltransferase that catalyzes histone H3 lysine 4 trimethylation [47] . This rapidly evolving protein [48] was first associated with hybrid sterility in rodents [49] , and evidence continues to accumulate that it is a major component of recombination hotspot determination in mammalian systems [46 , 50–55] . Comparatively less is known in other systems such as Drosophila . Several studies have identified sequence motifs associated with recombination events [7 , 11 , 12 , 56–59] , but none have been functionally validated to date . Drosophila lacks PRDM9 [48 , 58] , and perhaps relatedly , also lacks the highly punctate recombination landscape seen in mammals . While in humans up to 80% of recombination events fall in 10–20% of sequence [6] , crossover distribution in Drosophila is far less heterogeneous [12 , 60] . Recent work in mammals has also provided insight into the genetic architecture of global recombination rate . RNF212 has been repeatedly associated with natural variation in recombination rate in several systems including humans [61 , 62] , cattle [63] , and Soay sheep [64] . Consistent with a role of this protein in modulating recombination rate , RNF212 is essential for meiotic recombination and has a key role in stabilizing meiosis-specific recombination factors in mice [65] . PRDM9 has also been associated with heritable variation in recombination rate in humans and mice [52 , 66] . Other mediators of recombination rate include REC8 [63] , which is a cohesin that is required for proper chromosome segregation in many organisms [67–69] . In humans , inversion 17q21 . 31 , a 900 kb inversion , is associated with increased recombination and reproductive output in European females [70] . The genetic architecture of recombination rate variation outside of mammals remains poorly understood , even in the model organism Drosophila melanogaster . However , it is well-documented that recombination rate is a variable and heritable trait in Drosophila . For instance , classical genetic experiments indicate that the amount of crossing-over as well as the distribution of crossover events can vary among lines of D . melanogaster [12 , 13 , 71 , 72] , suggesting population-level variation in this trait . Additionally , genetic control for crossover rate has been suggested by laboratory selection experiments in which recombination rate itself was successfully subject to artificial selection [73–85] . Finally , changes in recombination rate have been shown to evolve as a correlated response to artificial selection on other characteristics , such as sternopleural bristle number [86] , DDT resistance [87] , geotaxis [88] , and resistance to temperature fluctuations [89] , which is again consistent with segregating natural variation in recombination rate . Additionally , the observation that modifiers of recombination rate are commonly associated with variants controlling completely unrelated traits suggests that these modifiers are pervasive in the genome and/or may have pleiotropic effects . To gain the first insight into the genetic basis of population-level variation in recombination rate in D . melanogaster , we used an association mapping approach . We favored an unbiased approach in part because D . melanogaster lacks homologs of the three known determinants of recombination rate in mammals noted above: RNF212 , REC8 , and PRDM9 . We measured recombination rates on both the 3R and X chromosomes in the 205 fully-sequenced inbred lines of the Drosophila melanogaster Genetic Reference Panel ( DGRP ) [90 , 91] using a two-step crossing scheme . We find nearly 2-fold variation in recombination rate among lines with a standard karyotype . Unexpectedly , we find that recombination rates are uncorrelated between the X and 3rd chromosomes . We leveraged this pervasive population-level variation in recombination rate for genome-wide association ( GWA ) mapping to identify dominant or semi-dominant variants associated with phenotypic variation in recombination rate on each chromosome . We selected the top 20 most promising candidate genes associated with recombination rate and subjected these candidates to both gene-level and allele-level functional assessment . Our functional assays implicate five highly promising candidates as novel mediators of recombination rate variation in D . melanogaster: CG10864 , CG33970 , Eip75B , lola , and Ptp61F . Our results provide new insight into the scale and scope of population level variation in rates of recombination and more importantly implicate new determinants of natural variation in recombination rate in Drosophila .
To assay recombination rate variation in the DGRP , we used a classic two-step crossing scheme ( Fig 1 ) . We measured recombination rates in two different genomic intervals: the 20 . 4 cM interval between ebony ( e ) and rough ( ro ) on chromosome 3R and the 33 cM interval between yellow ( y ) and vermilion ( v ) on the X chromosome . In total , 506 , 045 progeny were scored for recombinant phenotypes ( 217 , 525 for the e ro interval and 288 , 520 for the y v interval ) . On average , each replicate ( there were three replicates per DGRP line per chromosome assay ) contained ~368 progeny ( for the e ro interval ) and ~499 progeny ( for the y v interval ) . We first verified that our data conformed to expectations under Mendelian inheritance . Deviations from these expectations would be consistent with viability defects associated with the visible markers used in this study . To do so , for each line we compared the number of wild-type progeny to the number of progeny possessing both markers ( either e ro or y v ) , summing across all three replicates ( S1 Table ) . We also compared the number of recombinant progeny possessing only one marker to the number of recombinant progeny containing only the other marker ( either e + versus + ro or y + versus + v ) ( S1 Table ) . The null expectation is a 1:1 ratio for the aforementioned pairs of phenotype classes . We used a Bonferroni correction [92 , 93] with α = 0 . 05 to correct for multiple tests . When comparing the ratios of the two non-recombinant haplotypes , we find 15 lines that deviate from the expected 1:1 wild-type: e ro ratio ( Bonferroni-corrected P < 0 . 03 , all comparisons , G-test ) and 8 lines that deviate from the expected 1:1 wild-type: y v ratio ( Bonferroni-corrected P < 0 . 03 , all comparisons , G-test ) . In all but one case , the deviation is in the direction of a relative excess of wild type flies . Only one line deviated significantly in both intervals ( DGRP_819 ) , with more wild-type progeny in both intervals . When comparing the ratios of the two recombinant haplotypes , we find that DGRP_31 deviates significantly from the expected 1:1 e +/ + ro ratio ( Bonferroni-corrected P < 0 . 0001 , G-test ) and that DGRP_819 deviates significantly from the expected 1:1 y +/ + v ratio ( Bonferroni-corrected P < 0 . 0001 , G-test ) . Similarly , we tested for sex ratio unity by comparing the numbers of female and male progeny . There are no deviations from the expected 1:1 male:female ratio in the 205 lines for the e ro interval ( Bonferroni-corrected P > 0 . 10 , all comparisons , G-test ) . For the y v interval , only two lines significantly deviate from expectation ( DGRP_41 AND DGRP_801 ) ( Bonferroni-corrected P < 0 . 0002 , both comparisons , G-test ) , both in the direction of a relative excess of females . To assess the consequences of possible viability defects associated with our visible markers on recombination rate estimation , we analyzed correlations between viability defects and recombination . That is , to address whether epistatic interactions between our visible markers and DGRP genotype yield viability defects , we analyzed whether the ratios of the number of males vs . females , + + individuals versus m1 m2 individuals , or m1 + individuals versus + m2 individuals are correlated with our estimates of recombination within the DGRP ( S2 Table ) . Again , each of these ratios should be 1 , but could be skewed by viability defects associated with the markers . Our analysis demonstrates that in the y v interval , none of these ratios are correlated with our estimates of recombination rate . For the e ro interval , we observe a weak but statistically significant correlation between the ratio of wild type progeny to e ro progeny and recombination rate . However , no significant correlation is seen between the sex ratio and recombination rate or the ratio of the two classes of recombinants and recombination rate for the e ro interval . These data are consistent with weak epistatic interactions between the e ro genetic background and wild-type genetic backgrounds that yield viability defects . Overall , however , our data indicate that our assays for measuring recombination rate largely conform to expectations given Mendelian inheritance . There does not appear to be a large systematic bias towards wild-type chromosomes , indicating that there are no major viability defects associated with any of these mutations alone or in the pairs in which they were used for the current experiment . This confirms previous descriptions of these mutants and their lack of viability defects [94 , 95] . Our analysis does indicate weak viability effects of the e ro background as revealed by epistatic interactions with wild-type genetic backgrounds . As a consequence , the scale and scope of the reported variation in recombination rate may be mis-estimated . Given how weak the viability defects appear to be , we believe any mis-estimation is likely to be small in magnitude . Following the crossing scheme detailed in the Materials and Methods and in Fig 1 , we estimated crossover rate for each DGRP line in the e ro and the y v intervals ( S2 Table; S1A and S1B Fig ) for three replicates . These replicates are largely consistent with one another ( S3 Table; S2A–S2L Fig ) . Analyzing only lines with a standard karyotype on all chromosomes ( n = 112 ) , the average crossover rate for e ro is 20 . 9 ± 0 . 2 cM ( ranging from 14 . 2 cM to 26 . 12 cM ) ( Fig 2A ) . This agrees well with the published map distance of 20 . 4 cM [95] . Among these lines , we observe 1 . 84-fold variation in mean crossover rate . Analyzing only lines with a standard karyotype on all chromosomes , the average crossover rate for y v is 31 . 2 ± 0 . 3 cM ( ranging from 23 . 6 cM to 39 . 30 cM ) ( Fig 2B ) , compared with the published map distance of 33 cM [94] . Similar to the magnitude of population-level variation in recombination rate on 3R , here we observe 1 . 67-fold variation among these lines in mean crossover rate for the y v interval . There is significant genetic variation for crossover rate among lines for both intervals ( Fe ro = 1 . 34 , Pe ro = 0 . 038 and Fy v = 3 . 00 , Py v < 0 . 0001 , ANOVA ) . Using only lines with a standard karyotype ( 112 lines ) , we estimated broad-sense heritability ( H2 ) of recombination rate for the e ro interval as 0 . 12 and for y v interval as 0 . 41 ( Table 1 ) . These results confirm that recombination rate is a heritable trait and has a genetic component . Interestingly , there is no significant correlation between recombination rates in these two intervals ( Spearman’s ρ = 0 . 09 , P = 0 . 36; Fig 2C ) . Consistent with this , a model fitting effects of line , genomic interval , and line-by-interval interaction effects reveals significant interaction effects ( P < 0 . 0001 , ANOVA , S4 Table ) , indicating that the magnitude of the difference in recombination frequency between the two loci surveyed varies significantly among lines . These analyses illustrate that recombination rate on chromosome 3R and chromosome X , at least in the way they have been assayed here , are independent traits in this panel of flies . As a widely-used community resource , the DGRP offers a unique opportunity to examine the relationship between recombination rate and other phenotypes because a variety of phenotypes have been surveyed in this panel . We tested whether crossover rates in the e ro or y v interval ( of lines with standard karyotypes ) were correlated with various traits including organismal fitness . While the majority of correlations were weak and not statistically significant , we elaborate on several interesting significant correlations ( S23 Table ) in S1 Text . Recombination is suppressed within inverted regions , and recombination elsewhere in the genome increases through what is known as the interchromosomal effect [96 , 97] . A large number of the DGRP lines are either homozygous or polymorphic for a chromosomal inversion . To test for the interchromosomal effect , we separated lines with inversions from lines with standard karyotypes and tested whether lines that possessed an inversion somewhere in the genome had higher rates of recombination in our surveyed intervals . Lines with inversions have significantly increased rates of recombination in the y v interval relative to lines with standard karyotypes ( 35 . 1 cM vs . 31 . 0 cM , P < 0 . 0001 , t-test ) . This trend is echoed in the e ro interval ( 20 . 9 cM vs . 20 . 7 cM ) but the difference in recombination frequency between standard and inverted karyotypes is not statistically significant ( P = 0 . 66 , t-test ) . These results are discussed in the context of previous work in S1 Text . In the DGRP , 108 lines are infected with Wolbachia pipientis [91] . To test for an effect of Wolbachia infection on recombination frequency , we used a linear model ( see Materials and Methods ) and fit effects of line and Wolbachia infection status for each interval surveyed . Analyzing only lines with standard karyotype , we find there is a significant effect of Wolbachia infection in the y v interval ( P = 0 . 0003 , ANOVA ) , such that Wolbachia-infected lines have a higher crossover frequency ( 31 . 8 cM ) than uninfected lines ( 30 . 0 cM ) . No effect of Wolbachia infection was found for the e ro interval ( P = 0 . 35 , ANOVA ) . Importantly , estimates of heritability are not driven by Wolbachia infection in either interval ( S5 Table ) . The continuous variation for recombination rate among lines described above ( Fig 2A and 2B ) suggests that the genetic architecture of this trait is likely complex and regulated by many independent genetic factors . The observed variation in recombination rate in the DGRP motivates our association mapping approach to more finely define the genetic basis of this trait . To identify genetic variants contributing to variation in recombination rate , we performed genome wide association mapping on the mean crossover rates from the DGRP in the e ro and y v intervals . Note that given the experimental design of our study ( Fig 1 ) , we are only able to identify variants that are at least partially dominant in their effects on recombination frequency . Recessive modifiers are not captured in this study , likely yielding underestimates of the scope of natural variation in recombination rate in this system . We did the association mapping in three different ways for each interval because of the inversions segregating in the DGRP and the known effect of inversions on recombination frequency ( see [97] for review ) . Of the inversions segregating in the DGRP , none are on the X chromosome . However , 49 lines contained at least one copy of the C , K , Mo or P inversion on chromosome arm 3R; all four of these inversions span at least part of the e ro interval used to assay recombination rate [99] . We thus exclude these lines when analyzing recombination rate data for the 3R interval . The three datasets used for the 3R analyses were: 1 ) lines with no inversion on 3R ( n = 156 ) , 2 ) lines with neither 3R inversions nor inversion polymorphisms elsewhere in the genome ( n = 130 ) , and 3 ) lines with the standard karyotype ( lines lacking inversions; n = 112 ) . The three datasets used for the X chromosome analyses were: 1 ) all lines ( n = 205 ) , 2 ) lines without inversion polymorphisms ( n = 152 ) and 3 ) lines with a standard karyotype ( n = 112 ) . The statistical model used to infer associations assesses and adjusts for significant associations of both Wolbachia status and inversions . For the e ro interval , there is a significant effect of the NS inversion ( P = 0 . 003 , ANOVA; Table 2 ) on crossover rate in the restricted data set that removes lines with inversions on 3R and lines with inversion polymorphisms . For the y v interval , Wolbachia infection is significantly associated with crossover rate in all three of our data sets ( P < 0 . 01 , all cases , ANOVA; Table 2 ) . Additionally , inversions t , NS , K , and Mo are all significantly associated with crossover rate in the y v interval ( P < 0 . 05 , all cases , ANOVA Table 2 ) . These data are summarized in Table 2 . The full results for all six GWA analyses are presented as supplementary tables ( S6–S11 Tables ) . To generate a list of candidate genes and alleles , we combined the results from the different GWAS for each chromosome interval , using a significance threshold of P < 10−5 . For a Venn diagram displaying overlap among the different data sets , see S3 Fig . We tested whether the distribution of these associated variants was significantly different from the null expectation of a uniform distribution across chromosomes ( as a function of the number of polymorphisms on each chromosome ) . Using lines with standard karyotypes , we find that the distribution of associated variants is significantly different from the distribution of variants in the genome for both intervals ( P < 0 . 02 , both comparisons , G-tests ) . It appears that in both intervals , there is an enrichment of associated variants on chromosome 2R ( e ro: 63 versus 33; y v: 29 versus 16; observed versus expected ) . For the e ro interval , the three GWAS yielded a combined total of 688 unique variants at a nominal significance threshold of P < 10−5 . For the y v interval , combining results from all three GWA analyses , we identified 160 unique variants at a nominal significance threshold of P < 10−5 . A description of types and locations of these variants is included in S12 Table . There were no variants that overlapped between the two intervals , consistent with the lack of correlation between the two traits . However different variants in the same gene ( see below ) were shared between the associations found in the two intervals . Variants in 359 genes were implicated as potential candidates from the three e ro GWAS , and variants in 111 genes were associated with recombination rate variation in the y v GWAS . There is very little overlap between these gene lists; a total of fifteen genes showed overlapping ( gene-level ) associations between the e ro and y v GWAS ( bab1 , bun , CG4440 , CG5953 , CG31817 , CG32521 , CR44199 , dnr1 , dpr6 , Eip63E , Eip75B , Ptp61F , Sec16 , Shroom , and SNF4Agamma ) . The effect sizes for these variants were moderate , averaging ~2 . 32 cM for both intervals ( S4A and S4B Fig ) . Fig 3A and 3B displays the Manhattan plots and linkage disequilibrium plots for both intervals for the lines with standard karyotypes while S5 and S6 Figs display the same information for the other data sets analyzed . We sought to functionally assess a subset of the genes identified by our association mapping . We used several criteria to refine our list of candidate associations to a tractable set of 20 candidate genes . First , we restricted our focus to protein-coding genes harboring significantly associated genetic variants . We then integrated the P-value of the association , effect size , and the number of GWAS the gene was implicated in on either or both chromosomes to refine our list of putative candidates . We narrowed our list further by limiting ourselves to genes with documented expression in the ovaries [99–103] . Our final candidate gene list ( Table 3 ) includes eleven genes from the e ro GWA , five genes from the y v GWA and four genes that were found in both . There was more than one significantly associated genetic variant in 8 of our 20 candidate genes ( CG1273 , CG4440 , CG7196 , dpr6 , Eip75B , jing , Ptp61F and Ubx ) with jing and Ptp61F having the most significantly associated variants ( 17 and 13 respectively ) . The full list of variants within these genes and associated P-values are listed in S13 Table and the genotypes of each DGRP line at these variants are listed in S14 Table . If these identified candidate genes mediate recombination rate in some way , we expect that perturbing these genes will affect recombination rate . We used both mutant analysis and RNAi to either knock out or knock down expression of each of these genes , and compared recombination rate in the knock out/down lines to an appropriate genetic background control . We measured recombination rate in the e ro and y v intervals for available mutants and RNAi lines for all 20 candidate genes in the same way as described earlier . We used a combination of P-element insertions , chromosomal deletions , as well as any available RNAi lines . For the RNAi experiments , we used a nanos GAL4 driver , which should target the effects of knockdown to oogenesis . For assessment using the e ro markers , the only line tested that produced a significant difference from control line was a deletion line , Df ( 3R ) ED2 ( P = 0 . 004 , Dunnett’s test ) ( Fig 4A; S15 Table ) ; this line shows a significant increase in recombination frequency relative to the genetic background control . This deletion encompasses 71 full genes and part of 1 additional gene , including two of our candidate genes: cdi and CG10864 . It should also be noted that this deletion is on chromosome 3R , spanning the cytological region 91A5 to 91F1 ( for reference e is at 93C7-93D1 and ro is at 97D4-97D5 ) . Using the y v markers , seven lines tested show a significant deviation in recombination frequency relative to the appropriate control ( Fig 4B; S16 Table ) . These included alph , CG9650 , CG33970 , Eip75B , grp , lola , and Ptp61F ( P < 0 . 05 , all comparisons , Dunnett’s test ) . Eip7B , and CG9650 showed a decrease in recombination relative to the control while alph , CG33970 , Eip75B , lola , and Ptp61F showed an increase in recombination relative to the control . Interestingly , one P-element insertion in grp showed a significant increase of recombination while a different P-element insertion in grp showed a significant decrease of recombination . While the mutant/RNAi analysis provides insight into whether the candidate genes function in some way to mediate recombination , we also wanted to test whether these candidate genes show significant differences at the allelic level . We hypothesized that the effects of these genes on recombination rate were mediated by expression level differences and thus tested for differences in gene expression in ovaries between allelic variants of our 20 candidate genes . We measured gene expression as mRNA abundance using quantitative RT-PCR ( qPCR ) . For each of our twenty candidate genes , we selected three DGRP lines containing the major allele and three lines containing the minor allele ( S17 Table ) . For candidate genes that had that multiple significantly associated variants , all attempts were made to include lines in which all minor alleles were present . The genotypes of these lines at the gene surveyed can found in S18 Table . Once a line was selected to assess a candidate gene , it was not used to assess another candidate gene . RNA was extracted from dissected ovaries from virgin DGRP females . The qPCR data ( normalized to GAPDH ) reveal significant differential expression for 11 of our 20 candidate genes ( Fig 5; S19 Table ) . DGRP lines with the major alleles of CG4440 , CG15365 , CG33970 , and Ptp61F ( P < 0 . 003 , all comparisons , t-test ) display higher expression levels than lines with the minor alleles . Conversely , DGRP lines with the major alleles of CG1273 , CG10864 , dpr6 , Eip75B , lola , Oaz , and Ubx ( P < 0 . 05 , all comparisons , t-test ) display lower expression levels than lines with the minor alleles . It should be noted for variants in these eleven candidate genes , all minor alleles are associated with reduced rates of recombination . Comparisons of un-normalized data ( given potential concern over unstable housekeeping gene expression [104 , 105] ) largely confirm these results ( S20 Table; S7 Fig ) .
Here we report the largest population-level survey of recombination rate variation in Drosophila to date . We find significant genetic variation for recombination rate in this North American population of D . melanogaster for two independent genomic intervals . At the broadest scope , these data are consistent with previous work from other systems . Indeed , a wealth of data indicate that recombination rate varies between and within populations in species such as Drosophila [12 , 13] , mice [23] , and humans [5 , 20 , 51 , 106] . The magnitude of population-level variation in recombination rate exposed by our survey is comparable to what has been previously shown in D . melanogaster . For instance , we observe 1 . 67 fold-variation for the y v interval , and previous work in this interval shows ~1 . 2-fold variation [13 , 72] . Other genomic regions in Drosophila consistently show 1–2 fold variation in crossover frequency among strains [13] . Although measured with a different approach , work from heterogeneous stock mice indicates that crossover frequency varies ~2-fold in both males and females [23] . Work from cattle indicates that average genome-wide recombination rate varies ~1 . 7 fold in males [63] , which aligns well with our survey . Similarly , humans show ~2-fold variation in crossover frequency in both males and females [62 , 107] . It should be noted that the ~2 fold variation in recombination frequency that we report above is biased downward and is not truly reflective of segregating natural variation in recombination rate in Drosophila . When we include lines with inversions , which clearly segregate in natural populations , we see a much greater span in recombination rates in the DGRP: 5 . 2-fold for the e ro interval ( excepting lines with an inversion on 3R ) and 3 . 5-fold for the y v interval . This range of variation in recombination frequency is remarkable , nearly doubling previous estimates from Drosophila , mouse and humans . However , it also bears mentioning that we cannot exclude that our estimates of recombination may be biased by the weak viability effects associated with our visible markers ( see above ) . Our results indicate that recombination rate at the two intervals surveyed are uncorrelated in the DGRP . It is certainly possible that the weaker genetic component of phenotypic variation in recombination rate in the e ro interval as compared to the genetic component of phenotypic variation in recombination rate in the y v interval is driving the lack of correlation between recombination rates in the two intervals . In contrast to what we observe here , previous work in humans showed a significant positive correlation between the number of maternal recombination events on individual chromosomes and the number of maternal recombination events in the remaining genome complement for 20 out of 23 chromosomes , as well as a strong , significant correlation for the first eight chromosomes compared to chromosomes nine through twenty-two and the X chromosome [3] . Other work in Drosophila is suggestive that two lines with less crossing over in one interval relative to four other lines generally had less crossing over in other intervals relative to the same four lines [13] , though this is anecdotal at best . The putative difference between Drosophila and humans with regard to correlations in recombination rates across chromosomes is interesting , and may point to different genetic architectures of this trait in these systems . Certainly , the molecular mechanics of meiotic recombination have diverged markedly between humans and Drosophila ( e . g . [108] ) and the recombinational landscapes in humans and flies are qualitatively different as well . Previous work has estimated heritability for recombination rate in many different species . While estimates of heritability are necessarily population-specific , mammalian estimates encompass a wide range , from as small as 0 . 14 [109] and 0 . 30 [110] in humans to as large as 0 . 46 in mice [23] . In maize , heritability of recombination frequency is considerably higher ( broad sense heritability 0 . 21–0 . 69; [111] ) . Insects show a wide range as well , with estimates of narrow sense heritability of recombination rate ranging from 0 . 16 in Tribolium [112] to 0 . 27–0 . 49 in grasshoppers [113] . Early estimates of narrow sense heritability of recombination frequency in Drosophila based on parent-offspring regression are comparable to ours ( 0 . 09–0 . 38; [114] ) , and selection based approaches yield a narrow sense heritability of 0 . 12 [79] . That estimates of heritability of recombination are low indicates that much of the observed variation in recombination frequency cannot be ascribed to genetic differences along lines . This is consistent with the remarkable phenotypic plasticity in recombination frequency in Drosophila , evidenced in response to temperature [115–122] , maternal age [72 , 115–117 , 123–133] , nutrition [126 , 127] , parasite pressure [134] and other environmental factors . This phenotypic plasticity could also drive the lower than expected correlations between replicates observed in this experiment ( see S3 Table ) and also reduce heritability . Wolbachia pipientis is a common endosymbiont that infects the reproductive tissues of many arthropods [135] . Evidence indicates that over 40% of arthropods are infected with W . pipientis [136–138] . Approximately 29% of Drosophila stocks from Bloomington Drosophila Stock Center [139] are infected , along with 76% of the Drosophila Population Genomics Project ( n = 116 ) [140] . In the DGRP , 108 of 205 ( 53% ) lines are infected with W . pipientis [91] . In Drosophila , there is clear infection in the ovaries [141 , 142] and infection has been shown to reduce egg production [143] . Interestingly , we see a significant association between Wolbachia infection and crossover rates in the y v interval but not in the e ro interval . This discrepancy between the two intervals surveyed is difficult to explain , and merits further investigation . More curious yet is the contrast with previous results . It has been shown that Wolbachia infection has no effect on rates of crossing over in the w ct interval ( 18 . 5 cM ) in the laboratory wild-type strain Canton S [144] . The w ct interval is actually within the y v interval surveyed in this study , so the discrepancy between the two studies is puzzling . It may be that the effect of Wolbachia infection on recombination frequency is sufficiently minor that the previous study , using a single genetic background and smaller sample sizes than the present study , was underpowered to detect this small effect ( an average increase of 1 . 8 cM associated with Wolbachia infection in our study ) . Our results , coupled with previous findings , suggest that W . pipientis might have differential effects on recombination frequencies in different parts of the genome . Testing explicitly for this heterogeneity will be a topic of future exploration . In the future , it will also be interesting to see if infecting DGRP lines with Wolbachia causes an increase of crossover rates and if curing DGRP lines via tetracycline yields a corresponding decrease in crossover rates . The DGRP allows us to couple phenotypic variation with genetic variation such that the genetic basis of complex traits of interest can be dissected . One benefit of this association mapping approach is that it is unbiased , which means that new genes , outside of known pathways playing a role in the phenotype of interest , can be identified . For example , a recent study using the DGRP dissecting the genetic architecture of abdominal pigmentation yielded associations with several variants in the known pigmentation pathway but importantly , also functionally validated seventeen out of twenty-eight candidate genes that had not been previously associated with pigmentation [145] . Because nothing was known regarding the genetic basis of population-level variation in recombination rate in Drosophila and because Drosophila lacks homologs of all genes associated with recombination rate variation in other systems , we were eager to leverage this unbiased approach to gain novel insight into the genetic architecture of this fundamentally important trait . Consistent with the power of GWAS to uncover novel genes associated with phenotypic variation , our top candidate genes significantly associated with recombination rate variation contain genes outside of the meiotic recombination pathways , which have been characterized in exquisite detail ( see [146] for review ) . Among the top 20 candidates for functional assessment , seven are computationally predicted genes that have no clearly defined biological function or human orthologs . Interestingly , four of our candidate genes have Cys2His2 zinc fingers ( CG9650 , jing , lola , and Oaz ) . This is particularly intriguing due to the link between the zinc-finger domain containing PRDM9 and hotspot determination , and it is tempting to speculate that these proteins bind to DNA and designate crossover sites in a way that is vaguely reminiscent of the role of PRDM9 in mammalian recombination [43–45] . Moreover , the D . pseudoobscura ortholog of Oaz , GA14502 , was previously identified as a possible candidate gene involved in recombination as the frequency of its zinc finger binding motif was significantly negatively associated with recombination on a broad scale [58] . Consistent with a role for zinc-finger DNA binding in Drosophila recombination , Trem , which also contains zinc fingers , was recently shown to be necessary along with Mei-W68 and Mei-P22 for the formation of double-strand breaks in Drosophila [147] . We chose two methods for functional assessment of our candidate genes . The first method is a gene-level approach and asks whether perturbation of candidate genes perturbs recombination frequencies . To complement this approach , we also compared expression levels of the different alleles in these candidate genes using qPCR . Significant differential expression of the major versus minor alleles of our candidate genes in the ovaries would be consistent with gene expression differences underlying differences in rates of crossing over . Overall , there were 5 genes ( bru-2 , CG4440 , jing , MESR3 , and pk ) which showed neither a change in recombination frequency in the e ro or y v intervals when perturbed nor a difference in expression level between the major and minor allelic variants . However , lack of functional confirmation does not imply that a candidate gene has no role in modulating recombination rate in Drosophila . Indeed , validation of candidate genes is challenging . The effect sizes of the genetic variants are moderate at best ( S4A and S4B Fig ) , making detection of these changes quite difficult in the absence of very large sample sizes . Additionally , recombination rate variation is likely to be a polygenic trait [77 , 78] , and our results confirm this . Further , it has been suggested that in many quantitative traits within the DGRP , there is pervasive epistasis [148 , 149] . Epistatic interactions may similarly contribute to recombination rate variation in Drosophila . Consistent with this is the observation that for one P-element insertion of grp , there is an increase in recombination relative to the appropriate background and a decrease in recombination rate for another P-element insertion ( though we note that this observation is also consistent with variation in allelic effects at a single locus if the two P-elements were inserted into different locations ) . Finally , the process of recombination is likely to be highly buffered , and one could hypothesize that there is redundancy for maintaining the number of crossovers required . It is also possible that these statistical associations are false positives due to our lenient P-value . However , integrating across both the gene- and allele-level functional analysis , we find five high quality candidate genes for further investigation . These genes show significant perturbations in recombination frequency relative to the appropriate genetic background control in addition to differential expression specifically in ovaries between allelic variants at these loci . These were CG10864 , CG33970 , Eip75B , lola , and Ptp61F . Two of these ( Eip75B and Ptp61F ) were identified in GWAS in both the e ro and y v interval . CG10864 is involved in potassium channel function [150] . In humans , another protein involved in potassium channel function , KCNQ1 , has been shown to somatically imprint regions of the genome with higher rates of recombination [151] . While imprinting appears to be less common in Drosophila females [152] , it is unclear if CG10864 is participating in a similar role as compared to KCNQ1 . CG33970 is predicted to be involved with ATP binding and transporter activity [98] . A direct link between ATP binding and meiotic recombination has yet to be shown , but there have been some hints of connections in the literature . For example , mutations in the ATP-binding domain of RecA [153] in Escherichia coli , DMC1 [154] , Rad51 and Rad55 in yeast [155 , 156] and XRCC3 in humans [157] cause defects in homologous recombination and meiosis . While speculative , this gives credence to the idea that the putative ATP-binding ability of CG33970 may contribute to meiotic recombination . Further work is aimed at dissecting this link . Eip75B ( Ecdysone-induced protein 75B ) is involved in mediating ecdysone signaling , a steroid hormone . Defective ecdysone signaling affects the early germarium , causing defects with meiotic entry [158] . Interestingly , ecdysone signaling is important for female fertility but not for male fertility [159–161] . Drosophila males do not undergo meiotic recombination [162 , 163] . It remains to be seen whether the connection between recombination , fertility and ecdysone signaling is merely coincidence; however , the role of Eip75B in oogenesis makes it a particularly exciting candidate for further work . lola , or longitudinals lacking , is BTB zinc finger-containing transcription factor that is required for axon growth and guidance [164 , 165] . As noted above , DNA binding ability along with zinc fingers is exciting as a possible link with recombination . The predicted human ortholog , ZBTB46 or BZEL , was shown to repress a desumoylase [166] . Sumoylation has been linked to DNA repair [167] and therefore it is possible that lola is involved in early processes that could ultimately lead to crossover formation . Ptp61F ( Protein tyrosine phosphatase 61F ) is a member of the protein tyrosine phosphatase family . Ptp61F is an induced antagonist of the JAK/STAT pathway [168 , 169] and has been directly implicated in oogenesis [170] . In the female germline , expression of Ptp61F is targeted to the nucleus and cytoplasmic organelles [171] and this gene is required for normal female fecundity [172] . Tentative links between Ptp61F and DNA damage can be made in mammals; Ptp61F is the Drosophila homolog of human PTP1B and knockout PTP1B mice show a higher sensitivity to irradiation and an upregulation of many genes in the DNA excision/repair pathway [173] . Homologous recombination , base excision repair , and nucleotide excision repair are the primary pathways by with DNA damage are repaired in Drosophila . While the role for Ptp61F in meiotic recombination is not obvious , the clear function of this gene in oogenesis coupled with its tentative connection to DNA damage repair is promising . In conclusion , we have quantified the extent of recombination rate variation in a natural population of D . melanogaster and have shown that genetic background significantly drives phenotypic variation in this critically important phenotype . The magnitude of observed phenotypic variation in recombination rate is large , with almost 2-fold variation present in each genomic interval analyzed . We demonstrate that inversions play a large role in mediating rates of recombination , indicative of the interchromosomal effect , and provide the first evidence that Wolbachia infection can significantly increase rates of recombination . Through our GWA approach , we show that recombination rate is a highly polygenic trait , with many genetic factors of small effect associating with phenotypic variation . We show that a subset of our candidate genes ( CG10864 , CG33970 , Eip75B , lola , and Ptp61F ) play putative roles in modulating recombination rate variation in Drosophila through both gene-level and expression-level functional assessment . Future work will be aimed at determining the role of these candidate genes in the molecular process of recombination .
The Drosophila Genetic Reference Panel is a collection of 205 fully-sequenced inbred lines [90 , 91] . Mated , gravid Drosophila melanogaster females were originally collected in Raleigh , NC , USA in 2003 . Their progeny were subjected to 20 generations of full-sibling matings . The resulting inbred lines were then fully sequenced . A total of 4 , 853 , 802 single nucleotide polymorphisms ( SNPs ) and 1 , 296 , 080 non-SNP variants were identified among these lines [91] . To assay recombination rate , we took advantage of visible , recessive markers in D . melanogaster . To measure recombination rates on the 3R chromosome , we used a strain marked with ebony ( e4 ) and rough ( ro1 ) ; these markers are 20 . 4 cM apart [95] . To measure recombination on the X chromosome , we used a strain marked with yellow ( y1 ) and vermillion ( v1 ) ; these markers are 33 cM apart [94] . These markers were chosen to examine due to the genetic distance between them , ease of scoring and also their apparent lack of viability defects [94 , 95] . Each of the doubly marked chromosomes was substituted into a wild-type isogenic Samarkand genetic background , free of P-elements [174] , to allow for continuity between assays and to minimize marker genetic background effects . To assay recombination rate variation in the DGRP , we used a classic two-step crossing scheme ( Fig 1 ) . All crosses were executed at 25°C with a 12:12 hour light:dark cycle on standard media using virgin females aged roughly 24 hours . We conducted three replicate assays for each interval ( either e ro or y v ) . For each replicate , all 205 lines were crossed simultaneously to avoid conflating block effects with variation among lines . This yielded three replicate estimates of recombination frequency per line per interval . For the first cross , ten virgin females from every DGRP line were crossed to ten doubly-marked males ( either e ro or y v ) in eight ounce bottles . Males and females were allowed to mate for five days , after which all adults were cleared from the bottles . F1 females resulting from this cross are doubly heterozygous; these females are the individuals in which recombination is occurring . To uncover these recombination events we backcross F1 females to doubly-marked males . For this second cross , twenty heterozygous virgin females were collected and backcrossed to twenty doubly-marked males . Males and females were allowed to mate for five days , after which all adults were cleared from the bottles . After eighteen days , BC1 progeny were collected , frozen , and scored for sex and for visible phenotypes . Previous work in our lab has demonstrated that freezing flies has no effect on the visible markers we scored . Recombinant progeny were then identified as having only one visible marker ( m1 + or + m2 ) . For each replicate , recombination rates were estimated by taking the ratio of recombinant progeny to the total number of progeny . Double crossovers cannot be recovered with this assay , so our estimates of recombination frequency are likely to be biased downwards slightly . The estimated recombination for a given strain and interval was calculated as the average across the three replicates . Freeze 2 of the DGRP contains information relating to 16 segregating autosomal inversions verified by cytological methods [91] . We therefore performed association mapping in three different ways for each interval . The X chromosome ( in this population of flies ) lacks inversions while 49 lines contain an inversion on chromosome arm 3R which spans at least part of the e ro interval used to assay recombination rate [98] . We thus completely exclude these lines when analyzing recombination rate data for the 3R interval . The three datasets used for the 3R analyses were: 1 ) lines with no inversion on 3R ( n = 156 ) , 2 ) lines with neither 3R inversions nor inversion polymorphisms elsewhere in the genome ( n = 130 ) , and 3 ) lines with the standard karyotype ( n = 112 ) . The three datasets used for the X chromosome analyses were: 1 ) all lines ( n = 205 ) , 2 ) lines without inversion polymorphisms ( n = 152 ) and 3 ) lines with a standard karyotype ( n = 112 ) . To estimate the broad-sense heritability ( H2 ) of recombination rate , we used an ANOVA framework on line means ( the average across the three replicates for each line for each interval ) . The ANOVA followed the form of Y = μ + L + ϵ for each chromosome assayed where Y is recombination rate , μ is the overall mean , L is the random effect of line and ϵ is the residual . Additionally , we ran a similar ANOVA , adding the genomic region as a fixed factor , to test for a significant interaction between line and genomic region . That ANOVA followed the form of Y = μ + L + R + L × R + ϵ , with the terms the same as above and R is the genomic region assayed . To estimate H2 , we follow the formula H2 = σ2L / ( σ2L + σ2ϵ ) where σ2L is the variance component among lines and σ2ϵ is the residual variance or variance component attributed to error . The variance components were calculated using REML . All H2 estimates were calculated using R Statistical Software , v3 . 2 . 1 and RStudio v0 . 99 . 467 . To test for a significant effect of Wolbachia infection , we used an ANOVA framework as well . The ANOVA follows the form Y = μ + W + ϵ for each chromosome assayed where Y is recombination rate ( measured in cM ) , μ is the overall mean , W is fixed effect of Wolbachia infection status and ϵ is the residual , including all individual measurements . To identify genetic variants that are associated with differences in mean crossover number in two different intervals of the Drosophila genome , we performed a GWAS using the established web-based pipeline developed by the Mackay lab at NC State University , Raleigh , NC ( http://dgrp2 . gnets . ncsu . edu/ ) [90 , 91] . The first step in conducting the GWAS was to adjust line means for the effects of Wolbachia pipientis infection as well as the presence of inversions that are segregating in the DGRP . The adjusted line means are then used to fit a linear mixed model , Y = Xb + Zu + e . Y is the adjusted phenotypic value , X is the design matrix for the fixed variant effect b , Z is the incidence matrix for the random polygenic effect u and e is the residual . The vector of polygenic effects u has a covariance matrix in the form of Aσ2 , where σ2 is the polygenic variance component and A is the genomic relatedness . Additionally , Manhattan plots were constructed using the qqman package in R [175] . As described in the text , we selected 20 candidate genes to functionally assess that contained at least one significantly associated genetic variant within them . We selected these genes based on P-value of the variant located within or near the gene , effect size of the variant , the number of GWAS that a variant within or near the gene was implicated in and available expression data . To functionally explore these candidate genes with respect to their roles in recombination , we took advantage of available P-element insertion lines and chromosomal deletions as well as RNAi lines ( S21 Table ) . Lines containing a P-element insertion or chromosomal deletion ( deleting the candidate gene ) as well as appropriate controls ( genetic background used to generate P-element insertion or chromosomal deletion ) were used in the same crossing scheme ( Fig 1 ) detailed above . For the first cross , ten virgin females from every line containing a P-element insertion or chromosomal deletion were crossed to ten doubly-marked males ( either e ro or y v ) in eight oz . bottles . Males and females were allowed to mate for five days , after which all adults were cleared from the bottles . For the second cross , ten virgin heterozygous females were collected and backcrossed to ten doubly-marked males in vials . Males and females were allowed to mate for five days , after which all adults were cleared from vials . BC1 progeny were collected from each vial , frozen , and scored for sex and for visible phenotypes . For each P-element insertion or chromosomal deletion , there were 30 replicates . For each replicate , recombination rates were estimated by taking the ratio of recombinant progeny to the total number of progeny . The RNAi lines followed an identical crossing scheme except for the males used in the F0 cross . These males contained the doubly-marked chromosome ( e ro ) along with nanos GAL4 driver [176 , 177] . nanos is expressed throughout Drosophila oogenesis [178] . All P-element insertions , chromosomal deletions or RNAi lines were compared to appropriate controls using Dunnett’s Test [179 , 180] using both the raw recombination proportions as well as arcsined transformed data . Statistics were performed in JMP Pro 11 . 2 . 0 . To test the hypothesis that gene expression differences between alleles drive phenotypic variation in recombination rate , we analyzed ovarian mRNA abundance differences between the major and minor allele for each of our 20 candidate genes using quantitative RT-PCR ( qPCR ) . For each candidate gene , three DGRP lines containing the major allele and three DGRP lines containing the minor allele were chosen ( S17 Table ) . For the eight genes that had multiple significant genetic variants associated within the gene region , DGRP lines that contained the most major/minor alleles were selected ( S18 Table ) . For each candidate gene , virgin females were collected from the six DGRP lines contemporaneously to minimize the effects of environmental variation . Females were aged three days in vials with ~0 . 5 mL of yeast paste . Ovaries were then dissected from anesthetized females in a solution of 1X PBS and stored in Life Technologies RNAlater solution ( Life Technologies ) . For each line , four replicates of ten pairs of ovaries were dissected . Total RNA was extracted from homogenized ovaries using Trizol ( Life Technologies ) following manufacturer’s instructions . cDNA was generated using Bio-Rad iScript cDNA Synthesis and following manufacturer’s instructions . Primers for candidate genes were generated using FlyPrimerBank [181] ( S22 Table ) . qPCR was run a BioRad CFX384 machine using Bio-Rad iQ SYBR Green following manufacturer's instructions . Four technical replicates for each sample were run on the same 384 plate , minimizing the contribution of between plate variation . Samples were analyzed using GAPDH for normalization due to its relatively consistent expression [182] . For each candidate gene , there were six lines analyzed , three that contained the major allele and three that contained the minor allele identified in our GWAS . For each line , we collected four biological replicates of RNA . We ran four technical replicates of each RNA sample ( converted to cDNA ) . Therefore , for each line , there are a total of 16 qPCR measurements for the candidate gene of interest and 16 qPCR measurements for the GAPDH control . Measurements from each DGRP line were normalized by dividing by the average Cq value of GAPDH for the corresponding DGRP line , modeled after common normalization procedures [183] . These 96 measurements ( 48 measurements for the major allele and 48 measurements for the minor allele ) were then analyzed by comparing the means of the lines containing the major allele to the means of the lines containing the minor allele via a students t-test using JMP Pro 11 . 2 . 0 . In addition , the raw Cq values ( before normalization ) were also analyzed to ensure that potential differential GAPDH expression was not biasing results . | During meiosis , homologous chromosomes exchange genetic material through recombination . In most sexually reproducing species , recombination is necessary for chromosomes to properly segregate . Recombination defects can generate gametes with an incorrect number of chromosomes , which is devastating for organismal fitness . Despite the central role of recombination for chromosome segregation , recombination is highly variable process both within and between species . Though it is clear that this variation is due at least in part to genetics , the specific genes contributing to variation in recombination within and between species remain largely unknown . This is particularly true in the model organism , Drosophila melanogaster . Here , we use the D . melanogaster Genetic Reference Panel to determine the scale of population-level variation in recombination rate and to identify genes significantly associated with this variation . We estimated rates of recombination on two different chromosomes in 205 strains of D . melanogaster . We also used genome-wide association mapping to identify genetic factors associated with recombination rate variation . We find that recombination rate on the two chromosomes are independent traits . We further find that population-level variation in recombination is mediated by many loci of small effect , and that the genes contributing to variation in recombination rate are outside of the well-characterized meiotic recombination pathway . | [
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] | 2016 | The Genetic Architecture of Natural Variation in Recombination Rate in Drosophila melanogaster |
Insulin provides important information to tissues about feeding behavior and energy status . Defective insulin signaling is associated with ageing , tissue dysfunction , and impaired wound healing . In the liver , insulin resistance leads to chronic damage and fibrosis , but it is unclear how tissue-repair mechanisms integrate insulin signals to coordinate an appropriate injury response or how they are affected by insulin resistance . In this study , we demonstrate that insulin resistance impairs local cellular crosstalk between the fibrotic stroma and bipotent adult liver progenitor cells ( LPCs ) , whose paracrine interactions promote epithelial repair and tissue remodeling . Using insulin-resistant mice deficient for insulin receptor substrate 2 ( Irs2 ) , we highlight dramatic impairment of proregenerative fibroblast growth factor 7 ( Fgf7 ) signaling between stromal niche cells and LPCs during chronic injury . We provide a detailed account of the role played by IRS2 in promoting Fgf7 ligand and receptor ( Fgfr2-IIIb ) expression by the two cell compartments , and we describe an insulin/IRS2-dependent feed-forward loop capable of sustaining hepatic re-epithelialization by driving FGFR2-IIIb expression . Finally , we shed light on the regulation of IRS2 and FGF7 within the fibrotic stroma and show—using a human coculture system—that IRS2 silencing shifts the equilibrium away from paracrine epithelial repair in favor of fibrogenesis . Hence , we offer a compelling insight into the contribution of insulin resistance to the pathogenesis of chronic liver disease and propose IRS2 as a positive regulator of communication between cell types and the transition between phases of stromal to epithelial repair .
Successful wound healing requires coordination between stromal and epithelial cell compartments . Stromal activation lays the groundwork for epithelial repair , producing the appropriate microenvironment and growth factors to facilitate proliferation and remodeling of epithelia [1] . Metabolic disease is associated with a spectrum of chronic comorbidities , including cardiovascular disease and liver disease , as well as defects in wound healing [2 , 3] . However , it remains unclear whether insulin resistance affects injury-repair mechanisms in target organs such as the liver , which are otherwise at the forefront of insulin's metabolic actions . The liver parenchyma is highly regenerative and can undergo dramatic tissue remodeling to maintain parenchymal function in the face of chronic injury . Such remodeling is shaped in part by the activation of perisinusoidal cells , such as hepatic stellate cells ( HSCs ) , and periportal mesenchymal cells , such as portal fibroblasts ( PFs ) [4] , that expand to produce a fibrotic milieu capable of directing epithelial repair but that also contribute to long-term risk of scarring/fibrosis and hepatic dysfunction . To date , the impact of insulin resistance on the fibrotic stroma , including how it affects the ability of mesenchymal cells to communicate repair signals to the hepatic epithelia , remains unknown [5] . In this study , we investigate how insulin resistance affects stromal–epithelial repair mechanisms in the liver during chronic injury by knockout of insulin receptor substrate 2 ( Irs2 ) , a key adaptor protein that couples the insulin and insulin-like growth factor 1 ( IGF-1 ) receptors to intracellular signaling pathways . Irs2−/− mice have normal liver development but severe peripheral insulin resistance that leads to late-onset type II diabetes [6] . IRS2 is the principle regulator of insulin sensitivity in hepatocytes [7] , cooperating closely with IRS1 to mediate the metabolic response to feeding [8] . Aberrant IRS2 expression has been associated with hepatic insulin resistance [9] and progression of chronic liver diseases , including nonalcoholic steatohepatitis ( NASH ) [10] , hepatitis C [11] , and hepatocellular carcinoma [12] , in which increased IRS2 expression is associated with proliferation , increased cell survival , and disruption of cell-fate signals controlling hepatocyte metaplasia and the expansion of bipotent liver progenitor cells ( LPCs ) [13 , 14] . Nevertheless , it remains to be established whether IRS2 plays any role in hepatic wound healing . Fibroblast growth factor 7 ( FGF7 ) is an important paracrine regulator of tissue morphogenesis during development [15 , 16] and during re-epithelialization of cutaneous lesions [17] . In the liver , FGF7 is expressed by HSCs [18] and Thy1 T-cell surface antigen ( Thy1 ) -expressing PFs [4 , 19] that produce stromal niche signals to drive epithelial remodeling in response to chronic injury . Fgf7-knockout mice have reduced survival due to liver failure when fed a hepatotoxic diet containing 0 . 1% 3 . 5-diethoxycarbonyl-1 . 4-dihydrocollidine ( DDC ) because they are unable to support the expansion of adult liver stem cells/LPCs required for the adaptive response to injury [19 , 20] . LPCs are bipotent epithelial precursors capable of differentiating into cholangiocytes or parenchymal hepatocytes [21] . During DDC liver injury , they proliferate to form duct-like structures within periportal tracts , surrounded by fibrotic stromal cells expressing Fgf7 . LPCs express Fgfr2-IIIb [19] , the receptor for Fgf7 , which is exclusively expressed in epithelia . Amplification of Fgfr2-IIIb occurs in LPCs within the expanding nonparenchymal cell ( NPC ) fraction during DDC injury [22] , and potentiation of Fgfr2-IIIb signaling by overexpression of Fgf7 [19] or Fgf10 [22] drives the dramatic expansion of LPCs and immature hepatocytes within the liver . Fgf7/Fgfr2-IIIb interactions therefore communicate proregenerative signals between stroma and epithelia , with a range that is strictly limited to local paracrine effects because of the high affinity of Fgf7 for heparin-sulphate proteoglycans in the extracellular matrix and neighboring cells [23] . Usually , insulin signals are systemic , but in model organisms such as Drosophila , they also coordinate short-range communication between niche cells and stem cells [24] . This allows for tissue-specific responses to changing environmental conditions [25] . In this study , we show that integration of insulin/IRS2 signals by HSCs and LPCs promotes paracrine crosstalk between the fibrotic stroma and LPCs via Fgf7 . We demonstrate that loss of Irs2 has a negative impact on hepatic wound healing , reducing the capacity of HSCs and LPCs to produce and respond to Fgf7 respectively .
We examined how Irs2 deletion affected the chronic liver-injury response in adult mice during 0 . 1% DDC feeding , in which paracrine Fgf7 signaling plays a central role in coordinating the LPC response and epithelial repair ( Fig 1A ) . Transient induction of Irs2 occurred in wild-type ( WT ) livers on day 7 of the DDC diet ( Fig 1B ) , correlating with a peak in liver injury as judged by serum aspartate/alanine transaminase ( AST/ALT ) activity ( Fig 1C ) . No differences in serum bilirubin or liver tissue bile acid levels were observed between the two groups ( Fig 1D ) , and initial induction of AST/ALT was comparable on day 7 , suggesting the cholestatic effects of the DDC diet were equivalent in WT and Irs2−/− mice . However , past day 7 , serum transaminases were reduced in controls ( days 14–21 ) , whereas in the Irs2−/− mice , they remained significantly elevated ( Fig 1C ) , suggesting a role for Irs2 in the attenuation of liver damage . We observed robust Fgf7 activation in WT livers ( days 14–21 ) , which coincided with the significant reductions in AST/ALT recorded at these time points ( Figs 2A and 1B ) , consistent with the proregenerative role played by Fgf7 in the response to DDC injury . Fgf7-expressing stromal cells surrounded duct-like structures in periportal tracts that stained positive for LPC markers including epithelial cell adhesion molecule ( EpCAM ) and osteopontin ( secreted phosphoprotein 1 , Spp1 ) ( Fig 2B ) . In Irs2−/− mice , we observed a striking failure to induce Fgf7 during DDC feeding ( Fig 2A ) . We also observed modest but significant down-regulation of Fgf10 but no change in Fgf22 ( S1 Fig ) , suggesting other stromal Fgfr2-IIIb ligands were also affected . Failure to induce Fgf7 expression in Irs2−/− livers occurred in parallel with a delay in the overall increase in hepatocyte nuclear factor 4-alpha ( HNF4α ) − NPCs ( S2A and S2B Fig ) , as well as a specific delay in the induction of LPC genes EpCAM and osteopontin/Spp1 ( Fig 2A ) and decreased ductular immunostaining in periportal areas ( Fig 2B ) . Proliferative expansion of cells expressing osteopontin/Spp1 was also diminished ( day 14 ) , as judged by Ki67 immunostaining ( Fig 2C ) , and ducts containing LPCs appeared disorganized ( Fig 2B ) . Parenchymal cell depletion was exacerbated in Irs2−/− mice during DDC feeding , based on quantification of HNF4α+ hepatocyte nuclei ( S2A and S2C Fig ) . This was characterized by failure to sustain numbers of so-called "small" hepatocytes ( nuclear area < 75 μm2 ) , parenchymal cells attributed with the greatest regenerative potential ( S2D Fig ) [26 , 27] . Using an original methodology to estimate ploidy ( described in Materials and methods ) , we analyzed changes in hepatocyte DNA content in whole-liver tissue sections during DDC injury ( S3A and S3B Fig ) . We found that the population of small hepatocytes with approximately 2n DNA content ( 2c ) was increased in the WT group during the later stages of DDC injury ( days 14–21 , 1 . 6-fold P = 0 . 0335 ) , consistent with a regenerative response ( S3C Fig ) . During the same period , 2c hepatocytes declined in numbers in the livers of Irs2−/− mice , indicating a failure to maintain parenchymal tissue homeostasis during chronic injury . In rodent and human livers , Fgf7 is expressed by HSCs during injury [18] . However , in the DDC mouse model , resident Thy1+ PFs have also been identified as important Fgf7-expressing mesenchymal cells and first responders to cholestatic liver damage [4 , 19] . Profiling of stromal gene expression in WT mice during DDC injury revealed sustained induction of PF/myofibroblast genes Thy1 , Elastin , Vimentin , and alpha-smooth muscle actin ( αSma/Acta2 ) , together with profibrogenic gene Connective tissue growth factor ( Ctgf ) ( Fig 3A ) , whereas in Irs2−/− mice , the activation profile of these genes was either blunted ( Thy1 , Elastin ) , less sustained ( Acta2 ) , or down-regulated from day 7 ( Vimentin , Ctgf , and Thy1 ) , suggesting a negative impact on the PF/myofibroblast population within the activated stroma . Interestingly , we also observed a decline in expression of the HSC marker glial fibrillary acidic protein ( Gfap ) from day 7 ( Fig 3A ) , coincident with significant reductions in mesenchymal marker Vimentin and in Ctgf , a regulator of epithelial mesenchymal transition . From day 7 , a significant decrease in Gfap immunostaining in Irs2−/− livers was also observed ( Fig 3B and 3C ) . This reduction in Gfap+ cells was apparent throughout the parenchyma and in periportal areas where it colocalized with Fgf7-expressing stroma ( Fig 3C ) . In contrast with the results of messenger RNA ( mRNA ) analysis ( Fig 3A ) , immunostaining for Thy1 , Elastin , and αSMA tended towards increased expression in Irs2−/− livers on day 21 , while mesenchymal intermediate filament protein vimentin was similarly induced ( S4 Fig ) . Thy1+ stroma were observed in close contact with Spp1+/Epcam+ LPCs in both WT and Irs2−/− mice as previously described [19] ( S5A Fig ) , and a similar relationship between αSma-expressing myofibroblasts and Spp1+ LPCs was also apparent in both groups ( S5B Fig ) , indicating that failure to induce Fgf7 was not due to the lack of myofibroblast-like cells in the LPC niche of Irs2−/− livers per se . In contrast , lack of contact between Gfap+ HSCs and LPCs was observed in Irs2−/− livers ( S5B Fig ) . Hence , failure to induce Fgf7 coincided with an overall loss of Gfap+ HSCs and loss of association between HSCs and LPCs in portal tracts , whereas we were unable to confirm that stromal PFs were negatively influenced by Irs2 deletion . However , increased Thy1 staining in Irs2−/− livers was partially attributable to the increase in markers of bone-marrow–derived cells; cd45/protein tyrosine phosphatase receptor type C gene encoding CD45 cell surface antigen ( Ptprc ) and cd117/Proto-oncogene c-kit ( Kit ) , on day 7 , which coincided with early activation of matrix remodeling factors ( tissue inhibitor of metalloproteinase 1 [Timp1] and Matrix metallopeptidase 9 [Mmp9] ) and MYC proto-oncogene ( cMyc ) and a dramatic early peak in transforming growth factor beta ( Tgfβ ) —a master regulator of fibrogenesis and mobilizer of circulating populations of collagen-expressing fibrocytes [28] ( S6A Fig ) . Consistent with this , we also observed greater Thy1/Cd45 colocalization in Irs2−/− livers on day 21 ( S6B Fig ) , suggesting more extensive incorporation of bone-marrow–derived cells into the stromal niche . Furthermore , the numbers of Cd3+ T cells were also increased in Irs2−/− livers on day 21 ( S6C Fig ) , suggesting that Thy1+ cells from the bone marrow , whose expression of Fgf7 is lower than that of resident Thy1+ fibroblast populations [4] , played a more significant role in the altered stromal response to DDC injury in Irs2−/− mice . We assessed the survival of Fgf7-expressing stroma during the early phase of DDC injury , when Irs2 was maximally expressed in WT livers ( day 7 ) and before significant loss of Gfap+ HSCs was observed . Analysis of cleaved caspase 3 ( c-Casp3 ) staining revealed a 3 . 0-fold ( P = 0 . 0395 ) increase in apoptosis in Fgf7-expressing cells in Irs2−/− livers at this time , confirming accelerated loss of this subpopulation within the fibrotic stroma during injury ( Fig 4A ) . In order to test the hypothesis that IRS2 promoted survival of Fgf7-expressing stroma , we performed stable knockdown of IRS2 in the human HSC ( hHSC ) cell line LX-2 using lentiviral short hairpin RNA ( shRNA ) ( Fig 4B and 4C ) . Silencing of IRS2 had no direct impact on LX-2 cell viability , Fgf7 expression , or fibrogenic gene expression under standard culture conditions ( S7 Fig ) . However , exposure to cytotoxic alkylating agent mitomycin C ( MitoC ) resulted in cell-cycle arrest and apoptotic cell death , during which IRS2 promoted survival ( Fig 4B ) by significantly reducing P53 expression and caspase 3 cleavage ( Fig 4C ) . We confirmed a positive role for IRS2 in protecting HSCs from apoptosis by treating primary hHSC cultures with an allosteric inhibitor of IRS proteins ( NT-157 ) . NT-157 triggers proteasomal depletion of IRS1/IRS2 [29] , which resulted in dramatic activation of P53/caspase 3 cleavage and apoptosis in hHSCs ( Fig 4D ) . We therefore concluded that IRS2 plays an indirect role in driving Fgf7 expression during DDC injury by protecting HSCs from apoptosis . In addition to the defect in stromal Fgf7 expression , Irs2 deletion also had a negative impact on sensitivity to Fgf7 in epithelial cells . Stromal Fgf7 drives re-epithelialization during skin wound healing , signaling exclusively to cells expressing the epithelial isoform of Fgfr2 ( Fgfr2-IIIb ) , which is highly expressed by LPCs during DDC feeding [19] . In Irs2−/− livers , we noted a sharp decline of Fgfr2-IIIb expression in the latter stages of DDC feeding ( days 14–21 ) , which contrasted with the steady increase in Fgfr2-IIIb observed in the WT group ( Fig 5A ) . In WT mice , we observed increased antibody staining for Fgfr2 in periportal ducts surrounded by Fgf7+ stroma ( Fig 5B ) . No such induction was observed in Irs2−/− livers , consistent with a failure of FGFR2-IIIb expression in LPCs . We confirmed that diminished Fgfr2-IIIb expression resulted in reduced Fgf7-sensitivity in injured Irs2−/− livers by testing the signaling response to intraperitoneal ( i . p . ) injection of recombinant Fgf7 ( rFgf7 ) protein . In WT mice , rFgf7 robustly activated Fgf7/Fgfr2 signaling as assessed by western blot , demonstrating increased in vivo phosphorylation of extracellular signal-regulated kinases ( ERK ) 1/2 in whole-liver lysates ( Fig 5C ) . This response to rFgf7 was abrogated in the Irs2−/− livers , indicating that they were refractory to Fgf7 . Taken together , our findings suggested that Irs2 played a dual role in ( i ) promoting stromal expression of Fgf7 and ( ii ) sustaining sensitivity of the hepatic epithelia to Fgf7 during chronic DDC liver injury by promoting Fgfr2-IIIb expression ( Fig 5D ) . Interestingly , the timing of Fgfr2-IIIb loss in Irs2−/− mice ( days 14–21 ) corresponded with a period during which the parenchymal architecture failed to re-epithelialize , as judged by quantitative analysis of β-catenin immunostaining in hepatocytes ( Fig 5E ) , suggesting that in the later stages of DDC injury , Irs2 was required upstream of Fgf7–Fgfr2-IIIb signaling to driving adaptive epithelial remodeling . We examined the possibility that IRS2 played a direct role in regulating LPC sensitivity to FGF7 and epithelialization using a human model of bipotent adult liver progenitor cells ( HepaRG ) [30] . Upon reaching confluence , HepaRG cells spontaneously differentiate in media supplemented with insulin to produce phase-bright epithelial “islands” of hepatocyte-like cells ( Fig 6A and S8 Fig ) . Within these “islands , ” cells express hepatocyte genes such as albumin , HNF4α , cytochrome P450 3A4 ( CYP3A4 ) , and apolipoprotein A2 ( APOA2 ) ( S8 Fig ) . Using this model , we found that IRS2 was induced prior to FGFR2-IIIb and hepatocyte-specific genes such as APOA2 ( Fig 6B ) . Using a human IRS2-promoter construct to visualize the pattern of IRS2 expression via green fluorescent protein ( pIRS2-GFP ) , we also demonstrated colocalization of IRS2 with albumin and FGFR2 in the early stages of hepatocyte island formation ( Fig 6C ) . These data were consistent with an early role for IRS2 in the patterning of FGF7 sensitivity during LPC differentiation . Removal of insulin from the media ( S8B and S8C Fig ) or stable silencing of IRS2 ( Fig 6D , S8D–S8F Fig ) resulted in a striking failure to generate hepatocytic epithelia within the cultures , together with concomitant loss of FGFR2-IIIb expression ( Fig 6E ) . Importantly , the formation of epithelial islands and FGFR2-IIIb were restored by exogenous expression of a mouse Irs2 not targeted by the human IRS2 shRNA construct ( Fig 6D and 6E ) ; hence , we confirmed a cell-autonomous role for IRS2 in promoting FGFR2-IIIb expression and differentiation in LPC-like cells . Culturing of HepaRG cells with recombinant human FGF7 ( rhFGF7 ) accelerated the formation of islands and increased hepatocyte differentiation , as determined by increased albumin/HNF4α and E-cadherin ( E-cad ) immunostaining , human APOA2-promoter ( pAPOA2 ) -GFP reporter activity ( Fig 6F ) , and analysis of hepatocyte-specific gene expression ( Fig 6G ) . This occurred downstream of a rapid ( 3–6 h ) and sustained induction in osteopontin/SPP1 ( S9 Fig ) , consistent with our in vivo observation that osteopontin/Spp1 and Fgf7 were both impaired in Irs2−/− livers during injury ( Fig 2 ) . Importantly , the ability of rhFGF7 to promote expression of osteopontin/SPP1 , as well as hepatocyte-specific genes such as albumin and APOA2 , was both insulin and IRS2 dependent ( Fig 6G ) , revealing synergy between these two pathways in driving the differentiation of bipotent progenitor cells . Stable silencing of IRS2 or omission of supplemented insulin from the medium was sufficient to abrogate the positive long-term effects of rhFGF7 on the hepatogenic program , thus demonstrating a functional requirement for insulin/IRS2 upstream of paracrine FGF7 signaling . Interestingly , rhFGF7 treatment also increased FGFR2-IIIb expression , highlighting the potential for positive feedback between ligand and receptor—an effect that was also insulin/IRS2 dependent ( Fig 6G ) and hence reinforced the conclusion that insulin/IRS2 signaling in LPC-like cells primed FGF7 sensitivity . Our data supported the hypothesis that insulin/IRS2 signaling was functionally upstream of FGF7-mediated epithelialization of LPCs and highlighted a cell-intrinsic role for IRS2 in promoting sensitivity to paracrine FGF7 produced by the fibrotic stroma . However , they also revealed that loss of stromal FGF7 could by itself be sufficient to explain a decline in FGFR2-IIIb expression and epithelialization in a non-cell–autonomous manner as a consequence of reduced positive feedback between ligand and receptor . Our results highlighted the potential for a paracrine feed-forward loop in which FGF7 drove LPC differentiation and FGFR2-IIIb expression , potentiating both epithelialization and FGF7 sensitivity in an insulin/IRS2-dependent manner . To test this hypothesis further and to more rigorously address the role of insulin/IRS2 signaling in the fibrotic stroma , we performed coculture experiments with hHSCs and HepaRG cells , seeding the two cell types together in order to model the intimate heterotypic cell–cell interactions observed between LPCs and their stromal niche in periportal tracts during DDC injury ( Fig 7A ) . LX-2 control ( sh-luc ) and IRS2-knockdown ( sh-IRS2 ) cells were used to simulate "normal" versus "IRS2-deficient" fibrotic stroma and gauge how stromal insulin/IGF-1 resistance influenced the LPC response in a non-cell–autonomous manner . Consistent with the hypothesis that FGF7 was indirectly regulated by stromal IRS2 , we observed similar levels of FGF7 in the early stages of LX-2/HepaRG coculture ( Fig 7B ) . However , while FGF7 expression was sustained in cocultures containing normal LX-2 stroma , a significant long-term decline was observed in those that contained IRS2-deficient LX-2 ( Fig 7B ) , demonstrating an indirect role for stromal IRS2 in promoting FGF7 expression in coculture . The decline FGF7 expression in the IRS2-deficient stromal cocultures also coincided with a striking failure to induce FGFR2-IIIb on day 14 ( Fig 7B ) . Interestingly , we also observed blunted induction of FGF10 within cocultures containing IRS2-deficient stroma ( days 10–14 ) , whereas FGF22 expression was unaffected ( S10A Fig ) . These data reinforced the notion that FGFR2-IIIb ligands such as FGF7/FGF10 were required to drive expression of their epithelial receptor in LPCs both in vivo and in vitro . Using the coculture method , we also found that FGFR2-IIIb and APOA2 induction by LPCs required insulin signaling via IRS2 within the fibrotic stroma . Knockdown of IRS2 in LX-2 cells completely abrogated the ability of insulin to potentiate epithelial sensitivity to FGF7 and hepatocyte differentiation within the cocultures , as judged by loss of time-dependent induction of FGFR2-IIIb and APOA2 , respectively ( Fig 7C ) . These results demonstrated a requirement for stromal IRS2 in the non-cell–autonomous potentiation of FGF7 target genes in HepaRG cells . Moreover , we observed localized expression of FGFR2 in HepaRG at sites of contact with LX-2 cells , suggesting regulation of LPC differentiation by the stroma at close range , consistent with the short-rage paracrine actions of FGF7 ( Fig 7D ) . In order to further test whether paracrine FGF7 signaling was downstream of IRS2-dependent stromal–epithelial crosstalk in hHSC/HepaRG cocultures , we used primary hHSCs , which expressed higher levels of endogenous FGF7 than LX-2 ( Fig 8 ) . Interestingly , we found that both FGF7 and IRS2 expression were significantly increased in hHSCs following MitoC-induced cell-cycle arrest ( Fig 8A ) . This observation provided us with a useful means to modulate endogenous FGF7 expression prior to coculturing the cells with HepaRG . Consistent with the increase in FGF7 expression , we observed a dramatic enhancement of APOA2 expression in HepaRG cocultures using hHSCs pretreated with MitoC ( Fig 8B ) . Moreover , inclusion in the medium of a competitive FGF7 inhibitor , consisting of a chimeric rFGFR2-IIIb extracellular domain/Human IgG1 crystallizable fragment ( Fc ) domain protein ( rhFGFR2-IIIb-Fc ) , significantly inhibited APOA2 expression by HepaRG , confirming APOA2 as a downstream target of paracrine FGF7 and demonstrating a role for FGF7 signaling in driving hepatocyte differentiation in coculture . The diffusion of FGF7 is highly restricted because of its affinity for heparin-sulphate proteoglycans in extracellular matrix and cell membranes [23] . Consistent with this short-range mode of action , we found that hepatocyte differentiation within the cocultures ( judged by HNF4α immunostaining ) was spatially limited to sites of contact between HepaRG and vimentin+ hHSCs ( Fig 8C ) . To explore the properties of the HSC signal driving differentiation within the cocultures further , we developed a short-term bioassay using APOA2 expression as a functional readout for FGF7 activity . Stimulation of HepaRG monocultures with a single dose of rhFGF7 rapidly induced APOA2 at 48 h ( Fig 8D ) . Hence , we collected conditioned medium from MitoC-treated hHSCs and added it to HepaRG cells; however , no induction of APOA2 was observed , suggesting the factor was either labile or unavailable in the cellular supernatant because of cell/matrix binding ( Fig 8E ) . Using a similar approach , we were able to confirm that direct contact between HepaRG and hHSCs ( achieved by short-term 48 h coculture ) was sufficient to induce APOA2 expression in an FGF7-dependent manner , an effect that was blocked by inclusion of the competitive FGF7 inhibitor ( Fig 8F ) . Importantly , the ability of hHSCs to induce APOA2 within 48 h in this model was conditional upon the pretreatment of hHSCs with MitoC , confirming that paracrine FGF7 signaling in hHSCs was enhanced by mitotic inactivation . Taken together , these data demonstrated a positive role for paracrine FGF7 signaling by human HSC in supporting FGF7 sensitivity and hepatocyte differentiation in adjacent epithelial progenitors . The stromal injury response precedes epithelial repair . In the liver , this is characterized by HSC-derived myofibroblast proliferation and the laying down of extracellular matrix , which is followed by cell-cycle exit , fibrogenic reversion , and apoptotic cell clearance that accompanies tissue repair . The equilibrium within the fibrotic stroma between activated and reverted HSCs is therefore central to the regulation of the stromal injury response and has important consequences for liver fibrosis and scarring in the context of chronic injury . Our data showed that both FGF7 and IRS2 expression were induced in hHSCs upon triggering of cell-cycle arrest using MitoC ( Fig 8A ) . MitoC treatment was also accompanied by a phenotypic shift in hHSCs and LX-2s consistent with fibrogenic reversion ( Fig 9A ) , as judged by loss of Collagen 1A1 ( COL1A1 ) expression and activation of antiapoptotic heat shock 70 kDa protein 1B ( HSPA1B ) , previously identified as a biomarker of HSC reversion [31] . Importantly , the down-regulation of myofibroblast marker protein αSMA upon cell-cycle exit was delayed in LX-2 cells in which IRS2 was silenced ( Fig 9B ) , suggesting impaired transition from an activated to “reverted” state . These in vitro data , which suggested that IRS2 could participate in the transition of HSCs to a “reverted” phenotype , were consistent with the in vivo observation that DDC-treated Irs2−/− mice had increased periportal and perisinusoidal fibrosis , judged by Sirius Red collagen morphometry and αSMA immunostaining ( Fig 9C ) . We therefore returned to the LX-2/HepaRG cocultures to assess changes in fibrogenic markers . Using this method , we found that upon coculture with LPC-like cells , control ( sh-luc ) LX-2 cells underwent a process of time-dependent fibrogenic reversion , during which fibrillar collagen ( COL1A1 ) expression was significantly down-regulated and αSMA/ACTA2 dampened ( Fig 9D ) . In striking contrast to this , we found that silencing of IRS2 in LX-2 cells abrogated their ability to suppress COL1A1 in coculture , and we instead observed a dramatic induction of nonfibrillar type III collagen ( COL3A1 ) indicative of an activated stromal injury response . Time-dependent switching from myofibroblast marker THY1 to LPC-associated SPP1 was also dampened by silencing of IRS2 in the stroma ( S10B Fig ) , and the numbers of GFP-expressing LX-2 cells at the end of the cocultures was increased ( S10C Fig ) . Interestingly , unlike LPC and epithelial repair genes ( SPP1/APOA2/FGFR2-IIIb ) , whose expression in coculture was both insulin and IRS2 dependent ( Fig 7C and S10 Fig ) , changes in fibrogenic/myofibroblast genes COL1A1 , COL3A1 , ACTA2 , and THY1 ( Fig 9D and S10 Fig ) were largely unaffected by omission of insulin from the medium , suggesting the ability of IRS2 to restrain activation of the fibrogenic stroma was insulin independent . We concluded that silencing of IRS2 in HSCs prolonged their fibrogenic activation in coculture while slowing fibrogenic reversion by MitoC and reducing the survival of mitotically inactivated cells in which FGF7 expression was increased . Thus , IRS2 signaling in the fibrotic stroma served as a switch between states of stromal activation and fibrogenic reversion that potentiated heterotypic paracrine signaling between the two cell types and drove epithelial FGF7 sensitivity in LPCs . Together with our results demonstrating a cell-intrinsic role for IRS2 in LPCs in promoting FGF7 sensitivity , we proposed a model in which IRS2 exerted multiple proregenerative effects in both stromal and LPC cell compartments that favored epithelial repair over fibrogenesis during chronic liver injury ( Fig 10 ) .
In this study , we provide compelling evidence that Irs2 is required for hepatic wound healing and epithelial repair during chronic DDC feeding in mice and for sustaining local Fgf7/Fgfr2-IIIb signaling . Using human cell models , we have shown that IRS2 can affect multiple processes in both the fibrotic stroma and bipotent LPC-like cells that impinge upon their abilities to communicate with one another via FGF7 signaling . In addition , coculture studies confirm that IRS2 deficiency in HSCs disrupts paracrine crosstalk between the stromal niche and LPCs , resulting in failure to induce FGFR2-IIIb and epithelialization while simultaneously prolonging fibrogenic activation . Our data therefore support the hypothesis that insulin resistance could lead to defects in hepatic injury repair that exacerbate liver damage and progression of chronic liver disease because of reduced paracrine FGF signaling . These data also have profound implications for our understanding of how systemic insulin signals are interpreted by local injury-repair mechanisms and demonstrate that defects in insulin sensitivity can influence short-range communication between distinct cell types within a tissue , notably between the so-called “niche” and adult stem cells responsible for producing new epithelia . Interestingly , insulin resistance in human liver disease is associated with increased fibrosis and the appearance of reactive periportal lesions called "ductular reactions" ( DRs ) , in which intermediate “hepatobiliary” progenitors expand surrounded by fibrotic stroma [32] . These lesions exhibit striking similarities with those observed in the DDC-injury model [33] , although DRs are a feature of disrepair because of their correlation with poor prognosis . The association between insulin resistance and DRs has been demonstrated in liver disorders including NASH and chronic hepatitis C [32 , 34] , in which defective IRS2 expression has also been reported [10 , 11] . Our results suggest that reduced sensitivity to insulin in patients with chronic liver disease could alter the intrinsic abilities of LPCs to contribute to regenerative processes and lead to a derangement of heterotypic cell–cell interactions that mediate their expansion and differentiation , leading to DRs . Our discovery that IRS2 silencing in HSCs also promoted fibrogenesis while limiting hepatocyte differentiation and FGFR2-IIIb expression in coculture recapitulated the increase in liver fibrosis and reduced Fgf7/Fgfr2-IIIb expression described in Irs2−/− mice during DDC treatment . Hence , our data suggest that lRS2 signaling may serve as a switch that regulates the transition between phases of stromal and epithelial expansion within DRs by promoting reversion and survival of FGF7-expressing stroma while simultaneously priming LPCs to express FGFR2-IIIb ( Fig 10 ) . There is precedent that IRS proteins play an important role in driving epithelial growth via modification of the stromal microenvironment in the liver . NT-157 , the allosteric inhibitor of IRS1/2 , was recently found to limit colorectal cancer metastasis to the liver by reducing the trophic support received by stromal myofibroblasts or HSCs [35] . These findings exhibit striking parallels with our results and highlight the potential future impact of our feed-forward paracrine model in the field of cancer biology , in which subversion of injury-repair mechanisms by tumors are well documented . Consistent with this , Irs2 was also recently implicated in a positive feedback loop driving disease progression and hepatocarcinogenesis in rodent models of nonalcoholic fatty liver disease ( NAFLD ) [13] . Interestingly , the study identified hepatic Irs2 expression as a downstream target of Yes-associated protein ( YAP ) and Tafazzin ( TAZ ) —two oncogenic effectors of the Hippo signaling pathway that reversibly control hepatocellular fate [36] , LPC activation [37] , and liver regeneration [38] . Hence , it seems likely that subversion of Irs2-dependent aspects of the LPC response identified in our study also contribute to the pathophysiology of metabolic liver disease and cancer . Insulin and FGF7 synergized via IRS2 to promote osteopontin/SPP1 expression and sustain hepatocyte differentiation , thus revealing a cell-intrinsic role for insulin/IRS2 in priming and potentiating LPC sensitivity to paracrine FGF7 and other IIIb ligands such as FGF22 and FGF10 . Insulin/IRS2 signaling was required for the formation of “islands” in HepaRG cultures , which expanded because of a process of hepatocyte differentiation and epithelialization and conferred FGF7 sensitivity to the cultures by promoting FGFR2-IIIb expression . Conversely , in DDC-treated Irs2−/− mice , the numbers of hepatocytes declined , and the process of re-epithelialization was impaired , as manifested by delayed restoration of the parenchymal architecture , delayed induction of Epcam , failure to sustain Fgfr2-IIIb , and failure to expand numbers of small hepatocytes . Thus , it is tempting to speculate that IRS2 promotes hepatocyte differentiation of LPCs in vivo , although this would need to be empirically tested . We have identified positive feedback between FGF7 and its receptor in HepaRG monocultures as well as HepaRG/HSC cocultures and demonstrated a role for IRS2 in both cell compartments upstream of FGFR2-IIIb amplification ( Fig 10 ) . These data were highly consistent with the observed dual failure of Fgf7/Fgfr2-IIIb in Irs2−/− mice during injury and provide new insight into the underlying synergies that help promote epithelial sensitivity to proregenerative stromal growth factors such as Fgf7 . Positive feedback between Fgf10 and Fgfr2-IIIb has previously been reported during salivary gland development [15] , and in nonmalignant tumors , Fgf7-mediated FGFR2-IIIb expression is thought to play a tumor-suppressive role by promoting epithelial differentiation [23 , 39] . Interestingly , synergy between Fgfr2-IIIb ligands and their receptor has previously been linked to crosstalk with phosphoinositide 3-kinase ( PI3K ) —one of the principle effectors of insulin/IGF-1 signaling recruited by IRS2-binding [15] . Indeed , inhibition of PI3K/Protein kinase B ( AKT ) limits hepatocellular reprogramming by Fgf10/Fgfr2-IIIb during DDC injury in vivo [22] , suggesting Irs2 could act upstream of this pathway in promoting cellular plasticity in the liver . Further to impairment of Fgf7 , we also describe reductions in Fgf10 during DDC injury and in LX-2/HepaRG cell coculture . Hence , we cannot rule out the possibility that the IRS2-dependent paracrine feed-forward loop we have identified also involves other stromal FGFR2-IIIb ligands . HSC-derived Fgf10 plays an important role in the expansion of bipotent LPCs during liver development [40] , and both FGF10 and 22 are induced by the fibrotic stroma during DDC injury [22] . A deeper understanding of the indirect nature of how IRS2 regulates these genes within the stroma is therefore needed . In recent years , FGFs have received increasing attention for their unexpected role in metabolic disease and for their potential to treat metabolic disorders by helping restore insulin sensitivity . FGF1 , 19 , and 21 have potent ( albeit poorly understood ) effects on metabolism and insulin signaling , largely through their putative actions in white adipose tissue and the liver [21 , 41] , whereas impaired FGF-receptor signaling is associated with insulin resistance , liver disease [41 , 42] , and impaired hepatic regeneration [43 , 44] . Blunted expression of FGF7 has also been previously linked to delayed skin wound healing in genetically diabetic leptin deficient ( db/db ) mice [45 , 46] . Our data therefore raise the intriguing possibility that a comparable wound-healing defect may also exist in the livers of Irs2−/− mice following injury , resulting in a breakdown in stromal–epithelial paracrine signaling and consequent failure to coordinate the injury response . Combined future strategies to potentiate hepatic insulin sensitivity and FGF7 signaling may therefore improve patient outcomes by promoting epithelial repair in the liver . The impact of insulin resistance on the stromal microenvironment in the liver during injury is poorly understood [5] , as is the role played by IRS proteins in regulating HSC biology , which involves injury-dependent cycles of myofibroblast activation and cell clearance during so-called "fibrogenic reversion . " The model we present to explain impaired stromal induction of Fgf7 in the Irs2−/− mice ( Fig 10 ) incorporates how IRS2 influenced fibrogenic reversion and survival of HSCs—only one of several FGF7-expressing stromal subpopulations that respond to DDC injury . However , we also provide evidence that the cellular composition of the fibrotic milieu was different in the livers of Irs2−/− mice and leave open the possibility that Irs2-deletion affected other resident FGF7-expressing cells such as PFs . Finally , we observed a greater increase in T cells and the incorporation of bloodborne mesenchymal cells , possibly to the detriment of resident stromal cells or simply to compensate for the failure of FGF7-expressing stroma . Fibrocytes from type II diabetic patients have recently been shown to display altered migratory and inflammatory characteristics that could also have contributed to the altered pattern of stromal gene expression and impaired wound healing in our model [47] . We show that DDC injury had a negative impact on Gfap+ HSCs in Irs2−/− mice . Gfap is associated with quiescent or inactivated HSCs; hence , our data are consistent with accelerated turnover/loss of these cells in the livers of Irs2−/− mice due to a combination of reduced survival and/or reduced myofibroblast reversion ( Fig 10 ) . Our investigation using hHSCs revealed that IRS2 protected cells from apoptosis following mitotic inactivation using MitoC , providing a mechanism to explain the dampening of Fgf7 and increased apoptosis observed in the livers of Irs2−/− mice during injury . Interestingly , both IRS2 and FGF7 were increased in primary hHSCs upon cell-cycle arrest and fibrogenic reversion while enhancing their ability to drive epithelial differentiation of HepaRG cells in an FGF7-dependent manner in coculture . Similarly , IRS2 was required in LX-2 cells to sustain both FGF7 expression and to allow a process of fibrogenic reversion to proceed in the presence of HepaRG cells . These data provide new evidence to suggest that epithelial repair signals produced by HSCs are increased by a program of cell-cycle arrest or cell death , two processes that are more closely aligned with stromal reversion than myofibroblast activation . Given that the regulation of FGF7 expression by the fibrotic stroma is poorly understood , we propose the hypothesis that fibrogenic reversion , rather than myofibroblast activation , drives FGF7 expression during injury and that IRS2 preferentially favors reversion and survival of HSCs tasked with stimulating epithelial repair . Future work by this laboratory will seek to further test this hypothesis .
Mice were housed in the facility of the CIPF ( Valencia , Spain ) , which is registered as an experimental animal Breeding , User , and Supplier Center ( reg . no . ES 46 250 0001 002 ) under current applicable Spanish regulations ( RD 53/2013 ) . Animals were kept in ventilated racks with microisolators under specific-pathogen–free ( SPF ) conditions and handled by accredited personnel and treated in accordance with EU legislation and welfare guidelines . Procedures were approved by the CIPF "Comite Ético de Experimentación Animal" ( CEEA ) and the ministry for "Agricultura , pesca , alimentacion y agua" of the Generalitat Valenciana ( Valencia , Spain ) , under the authorization code 2014/VSC/PEA/00062 tipo 2 . Mice were killed by fentanil/pentobarbital overdose followed by cervical dislocation . WT and Irs2−/− C57BL/6 littermates ( females aged 10–12 weeks , fasting blood glucose < 100 mg/dL ) were fed a modified LabDiet ( 5015 ) containing 0 . 1% DDC ( TestDiet , St . Louis , MO , USA ) . Animals were killed at intervals up to 21 days during the light cycle . FGF7 stimulation was performed by i . p . injection of ( 0 . 3 mg/kg ) recombinant mouse KGF ( Taper BioLegend , San Diego , CA , USA ) . Serum transaminases were measured by ELISA according to manufacturer’s instructions ( MilliporeSigma , St . Louis , MO , USA ) , and serum total bilirubin ( TBIL ) was measured by colorimetric assay according to the Jendrassik-Grof method ( MilliporeSigma ) . Bile acids were measured by metabolomic analysis . Briefly , samples were extracted from whole-liver tissue using the metanol-chloroform-H2O method [48] and analyzed on a 600 MHz NMR spectrometer equipped with a cryoprobe . Metabolites were identified with the aid of the spectral databases HMBD [49] and BMRB [50] and quantified using MestreNova8 . HepaRG cells ( kind gift of Anne Corlu and Cristiane Guillouzo ) were passaged and differentiated as previously described [30] using Williams E medium ( 10% fetal calf serum , 50 μM hydrocortisone hemisuccinate , 0 . 88 μM insulin , and 2 mM L-glutamine and penicillin-streptomycin ) . DMSO ( 2% ) was added from day 14 . Insulin supplement was omitted from the medium where indicated 24 h postplating . rhFGF7 ( 50 ng/ml; Cell Guidance Systems , St . Louis , MO , USA ) or rhFGFR2-IIIb Fc Chimera ( 100 ng/ml; R&D Systems , Minneapolis , MN , USA ) were added to media where indicated every 48 h . Cryopreserved primary hHSCs were obtained from a healthy 15-year-old Caucasian female donor using a previously described isolation method [51] ( Innoprot , Derio , Spain ) . LX-2 cells ( kind gift of Scott Friedman ) and primary hHSCs were cultured in DMEM ( 2% fetal calf serum ) , and primary HSCs were used within 5 passages . LX-2/HSCs were inactivated for 3 h with 1mg/ml MitoC ( MilliporeSigma ) before seeding onto 0 . 1%-gelatin–coated plates . After 24 h , activated/inactivated HSC cultures were switched to HepaRG medium until the end of the experiments . Stable cell lines were generated by lentiviral transduction ( vectors summarized in S1 Table ) using a multiplicity of infection of 0 . 5–20 TU/cell in the presence of 8 μM polybrene . Infections were performed 16 h postplating for 6 h in serum-free medium . Where applicable , cells were selected for 7 days in media containing 2 . 5 μg/ml puromycin . MTT assay: 3- ( 4 , 5-dimethyl-2-thiazolyl ) -2 , 5-diphenyl-2-H-tetrazolium bromide ( 5 mg/ml , MilliporeSigma ) was added to media in a 1:5 ratio and incubated in the dark for 4 h . Resulting formazan crystals were resuspended in DMSO , and optical density ( OD ) was measured at 570 nm using an automated microplate reader ( Victor; PerkinElmer , Waltham , MA , USA ) . Fresh frozen liver tissue sections ( 6 μm , mounted in OCT , MilliporeSigma ) or cells were fixed using 4% paraformaldehyde–PBS , washed , and permeabilized with 0 . 5% Triton-X100 before blocking ( 1% BSA , 5% horse serum , 0 . 2% Triton-X100 ) . Primary antibodies ( S2 Table ) were incubated over night at 4 °C . Alexa-conjugated secondary antibodies ( Invitrogen , Carlsbad , CA , USA ) were applied for 1 h together with 5 μg/ml Hoechst 33342 prior to mounting . Confocal images were obtained using a Leica TCS-SP6 ( Leica , Wetzlar , Germany ) . Fluorescence image analysis was performed using INCell Analyzer 1000 ( GE Healthcare , Chicago , IL , USA ) as outlined below . Formalin-fixed paraffin-embedded tissue sections ( 4 μm ) were processed automatically using the Autostainer Link 48 ( Agilent , Santa Clara , CA , USA ) with EnVision FLEX reagents according to manufacturers’ instructions ( Agilent ) . Antigen retrieval was performed using high-pH target retrieval solution ( Agilent ) . Antibodies are provided in S2 Table . Collagen was visualized in PFE liver sections using the Picro Sirius Red technique ( Abcam , Cambridge , UK ) according to manufacturer’s instructions . Following imaging , threshold analysis was performed using FIJI software to quantify staining in a total area comprising >10 portal fields per animal . Fluorescence immunostained cells/cryosections were analyzed using the “INcell 1000” imaging platform and software ( GE Healthcare ) where indicated . Individual cells were identified by Hoechst staining . Threshold analysis was used to determine the expression of the following markers: pAPOA2-GFP , albumin , CYP3A4 , HNF4α , Ki67 , Spp1 , and Vimentin . For replicate well/tissue sections , at least 20 fields were randomly analyzed for each replicate . PFE tissue sections were imaged using Pannoramic Viewer ( 3DHISTEC ) and analyzed with FIJI software; β-catenin staining was quantified using FIJI ( histogram function ) to calculate modal pixel intensity . Interpolation of DNA content in HNF4α/Hoechst-stained liver sections was performed using a methodology developed by our laboratory using INcell 1000 ( GF Healthcare ) that enabled in situ approximation of hepatocyte ploidy ( S3 Fig ) . DNA content was calculated for all circular hepatocyte nuclei ( nuclear elongation value > 0 . 8 ) as a combined function of Hoechst nuclear fluorescence intensity ( DNA density ) and nuclear volume ( calculated as a spherical function of nuclear radius ) . Hepatocyte ploidy was then calibrated using circular HNF4α− NPC cell population within the tissue as a 2n control . The 2c hepatocyte population was defined as the population of HNF4α+ nuclei within the 19 . 99 μm2–34 . 99 μm2 nuclear size range with a nuclear circularity index >0 . 8 . Tissues were lysed in RIPA buffer containing complete protease and phosphatase inhibitors ( Roche , Basel , Switzerland ) . Protein ( 20 μg/well ) was separated using standard SDS-PAGE . Transferred PVDF membranes were blocked ( TBS–Tween 3% BSA ) and probed with primary antibodies ( S2 Table ) . Total RNA was extracted using RNeasy Mini Kit ( Qiagen , Venlo , The Netherlands ) with DNaseI Digestion . Liver tissue was preprocessed using Trizol ( MilliporeSigma ) . For experiments using cells , RNA from duplicate wells were analyzed . First-strand synthesis was performed using EcoDry Premix ( Takara , Kusatsu , Japan ) , and real time-PCR was carried out in LightCycler 480 II ( Roche ) using SYBR PreMix ExTaq ( Mi RNaseH Plus , Takara ) . Reverse transcriptase-quantitative PCR ( RT-qPCR ) was performed in triplicates for all RNAs analyzed . Primers are listed in S3 Table . Relative gene expression was calculated by normalization to Glyceraldehyde-3-Phosphate Dehydrogenase ( GAPDH ) in mouse liver and to Ribosomal Protein L19 ( RPL19 ) in human cell lines . For experiments using animals , values of n reflect the number of animals per cohort . Cohort size was based on previous studies and expertise using the Irs2−/− model . For nonanimal experiments , values of n reflect the number of independent experiments performed . When possible , automated methods of image analysis were employed to quantify immunostainings using the “INcell 1000” imaging platform and software ( GE Healthcare ) . Statistical analyses were performed with Prism version 7 . 00 for Windows , GraphPad Software , La Jolla , CA , USA , www . graphpad . com . One-way ANOVA was used to compare multiple means with one variable . Multiple comparisons were obtained applying Tukey’s test . Ordinary two-way ANOVA was used to analyze data sets with several variables and Bonferroni’s test for multiple comparisons . When two data sets were compared , unpaired Student t test was used . Results were represented as mean + SEM considering statistical significance: *P < 0 . 05 , **P < 0 . 01 , and ***P < 0 . 001 . | “Insulin resistance” is a chronic state of reduced sensitivity to the effects of circulating insulin . It is one of the hallmarks of metabolic disease and a consequence of ageing , but insulin resistance is also observed in otherwise healthy individuals after severe trauma/hemorrhage/sepsis , suggesting that it plays a physiological role in modulating the response to injury . Defective insulin signals are linked to impaired wound healing , yet it remains unclear how systemic changes affect locally the cells that coordinate tissue repair . In this study , we used the liver to assess how insulin resistance impacts the injury response in mice . We provide proof of concept that insulin signals are locally integrated by the fibrotic microenvironment surrounding the adult liver stem cells during chronic injury , resulting in the increased expression of epithelial repair signals . Insulin also simultaneously primes stem cells to respond to these stromal growth factors , leading to an increased participation in epithelial repair . Insulin resistance disrupts this local paracrine circuit , resulting in a blunted epithelial response to chronic injury that exacerbates tissue damage . Our model highlights a potential role for insulin in switching the hepatic injury response from a stromal repair process to an epithelial repair process . To our knowledge , our data provide a new perspective from which to reassess how insulin resistance influences fibrosis , wound healing , and tissue remodeling during injury . | [
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] | 2019 | Insulin resistance disrupts epithelial repair and niche-progenitor Fgf signaling during chronic liver injury |
Pseudomonas aeruginosa is the causative agent of chronic respiratory infections and is an important pathogen of cystic fibrosis patients . Adaptive mutations play an essential role for antimicrobial resistance and persistence . The factors that contribute to bacterial mutagenesis in this environment are not clear . Recently it has been proposed that cationic antimicrobial peptides such as LL-37 could act as mutagens in P . aeruginosa . Here we provide experimental evidence that mutagenesis is the product of a joint action of LL-37 and free iron . By estimating mutation rate , mutant frequencies and assessing mutational spectra in P . aeruginosa treated either with LL-37 , iron or a combination of both we demonstrate that mutation rate and mutant frequency were increased only when free iron and LL-37 were present simultaneously . Colistin had the same effect . The addition of an iron chelator completely abolished this mutagenic effect , suggesting that LL-37 enables iron to enter the cells resulting in DNA damage by Fenton reactions . This was also supported by the observation that the mutational spectrum of the bacteria under LL-37-iron regime showed one of the characteristic Fenton reaction fingerprints: C to T transitions . Free iron concentration in nature and within hosts is kept at a very low level , but the situation in infected lungs of cystic fibrosis patients is different . Intermittent bleeding and damage to the epithelial cells in lungs may contribute to the release of free iron that in turn leads to generation of reactive oxygen species and deterioration of the respiratory tract , making it more susceptible to the infection .
Pseudomonas aeruginosa is an important opportunistic pathogen involved in chronic respiratory and hospital-acquired infections [1] . In cystic fibrosis ( CF ) patients , one of the most common genetic diseases in humans , this bacterium causes chronic lung infections that result in significant morbidity and mortality [2] . P . aeruginosa infections are difficult to treat due the inherent resistance to many drug classes , its ability to acquire resistance , via mutations , to all relevant treatments and its high and increasing rates of resistance locally [3] . Mutagenesis plays a crucial role in adaptation of this pathogen for persistence and antibiotic resistance acquisition in CF [4] , being found a high proportion of hypermutable bacteria among P . aeruginosa isolates [5] . Recently , Limoli et al [6] reported an increased mutant frequency after treatment of P . aeruginosa with the human cationic peptide LL-37 . Based on this finding they proposed that cationic peptides elevated bacterial mutation rates . In a recent study , we reported mutation rates of E . coli in the presence of cationic antimicrobial peptides ( cAMPs ) and antibiotics . LL-37 was present in the panel of AMPs that we tested and we did not find any increase in mutation rates . We also used transcription reporter assays and qRT-PCR and showed that none of the AMPs elicited the main mutagenic stress pathways of bacteria SOS or rpoS [7] . Here , we aim to resolve this apparent contradiction based on the observation that the different studies used different media . Limoli et al . [6] used M63 for P . aeruginosa and LB for E . coli , while we used non-cation adjusted Mueller-Hinton Broth ( MHB ) , commonly used for cAMP susceptibility testing . The most striking difference in culture media between the two studies is that both , M63 [8] and LB are iron-rich [9 , 10] , while MHB is not [11] . Fe2+ catalyses hydroxyl radical formation by reacting with hydrogen peroxide both intra- and extra-cellularly , the Fenton reaction [12] . Without free iron hydrogen peroxide reactivity is low at physiological pH and iron metabolism is strictly controlled to avoid DNA and other damage caused by oxygen radicals [13 , 14] . In most natural situations iron is in short supply , but in cystic fibrosis [15 , 16] due to accult bleeding of highly vascularised lung tissue and haemoptysis particularly during acute exacerbations , and damage of the respiratory tract epithelium where Fe2+ is present intermittently . Although ferrous iron is prone to oxidation , the anaerobic growth of at to high density of bacteria [17] may contribute to stabilise this metal in the reduced state . Many cationic antimicrobial peptides change membrane permeability properties [18] , and this led us to hypothesize that sub-inhibitory concentrations of LL-37 increase uncontrolled iron transport from the extracellular space to the cytoplasm without the intervention of the bacterial iron trafficking system . An intracellular surplus of iron should then result in DNA damage caused by the Fenton reaction . Here , we ( i ) estimate the mutation rate of P . aeruginosa in the presence of LL-37 , iron or both; ( ii ) we then investigate the hypothesis that ferrous iron ( Fe+2 ) is causal to an increase in mutation rate and that LL-37 and colistin facilitate this process; and finally ( iii ) we investigate the mutational spectra to find out if promoted mutations match with any of the molecular signatures of Fenton reactions .
First we determined the mutation rate of P . aeruginosa using a fluctuation assay , in the presence of either LL-37 ( 32 μg/ml ) , Fe2+ ( 40μM ) , or both ( Fig 1 ) . Mutation rate was only increased when LL-37 and Fe2+ were added simultaneously , while addition of LL-37 or Fe2+ separately did not produce the same effect . To investigate the effect of different concentrations of LL-37 and Fe2+ , we estimated mutant frequencies . None of the LL-37 concentrations tested , ranging from 4 to 32 μg/ml , yielded any detectable changes in mutant frequency ( S1 Fig ) . Fe2+ alone , at different concentrations , also did not increase the mutant frequencies in comparison with the control group ( S2 Fig ) . Subsequently , we assayed the MIC50 ( 32 μg/ml ) of LL-37 with several iron concentrations and measured the impact on rifampicin mutant frequency of P . aeruginosa strain PAO1 . We found that added concentrations of 10 , 20 , 40 and 80 μM of Fe2+ , increased the mutant frequency between three to five times ( S3 Fig ) . Limoli et al . [6] observed increased mutant frequencies in both , P . aeruginosa and E . coli , proposing that LL-37 induces mutagenesis in these bacteria . Taken at face value , this contrasts with our work , where we did not find such effect in E . coli [7] . P . aeruginosa in the Limoli et al . study however , was cultured in an iron-rich medium , M63 [8] , which contains 0 . 5 mg of iron sulphate per litre ( Fe2+ ) . To confirm the results in a different bacterial model , the experiment was repeated with E . coli using LB as a culture medium . However , LB is also iron-rich ( ~16 μM of iron ) [9 , 10] . In these experiments , just before the treatment with LL-37 in a saline solution , bacteria were washed . It has , however , been shown that Gram-negative bacteria can actively accumulate iron in the periplasmic space [19] . This led us to hypothesise that sub-lethal concentrations of cationic antimicrobial peptides can facilitate iron transport into the cells . To confirm that iron is causing the observed increase in mutagenesis , we first repeated the assay in the presence of an iron chelator , 2–2′ bipyridyl . 2–2′ bipyridyl completely abolished the increase in mutant frequency in the tested concentration of LL-37+Fe2+ combination . The sole action of the chelator plus Fe2+ did not increase the mutant frequency ( Fig 2 ) . Bacteria have a number of mechanisms to keep free iron as low as possible inside the cell . One of these is Dps , a natural iron chelating protein . To confirm the results obtained with 2–2′ bipyridyl , we used a Dps over-expressing strain by cloning the dps gene into a multi-copy plasmid under a constitutive promoter . The mutagenesis was not completely reverted in comparison with non-treated controls , but there was a 1 . 8 fold-reduction ( Fig 2 ) . The iron uptake assay showed that there was a significant difference ( Mann-Whitney U test , P = 0 . 00015 ) in total iron content in bacterial cells between LL-37+ Fe2+ ( 17 . 50 ± 5 . 10 nmoles/ml , mean ± standard deviation ) and iron alone ( 5 . 65 ± 4 . 13 nmoles/ml , mean ± standard deviation ) treatments after 30 minutes of incubation , indicating that LL-37 indeed promotes a non-physiological free iron entrance . We also tested a further AMP: colistin , which is of bacterial origin , and is one of the most effective agents against P . aeruginosa in CF infections [20] . We treated cultures of P . aeruginosa PAO1 with MIC50 of colistin ( 1 . 8 μg/ml ) in high and low concentrations of iron in the media . The effects of colistin were very similar to the effects we found for LL-37 ( Fig 3 ) . Although the mechanism of action of colistin is not fully understood [21] , our results suggest pore forming mechanism or permeability changes in the cell as likely mechanisms . It has been demonstrated that colistin promotes the uptake of hydrophilic antibiotics , explaining their synergism with them [22] . Previous work has suggested that iron is involved in Pseudomonas aeruginosa pathoadaptation and antibiotic resistance acquisition [15] . All experiments were carried out with the more soluble Fe2+ because ferrous iron is abundant in the CF lung ( ~39 μM on average for severely ill patients ) and significantly correlates with disease severity [23] . Moreover , iron activates the two-component signal transduction system BqsRS in P . aeruginosa , which is transcriptionally active in CF sputum , and promotes tolerance to cationic stressors [24] . This increases the tolerance to both host peptides such as LL-37 and colistin , which is administered in CF therapy . The new mechanism proposed here has almost certainly important implications . Under specific pathologies such as cystic fibrosis or other respiratory chronic diseases , iron that is usually in short supply , is abundant . Disturbances in iron metabolism have been shown to promote evolution of antibiotic resistance in E . coli [25] . Given that cationic antimicrobial peptides usually induce membrane permeability in all susceptible microbes and Fenton chemistry is universal , the mechanism we propose here is likely applicable to other AMPs . For example , beta-defensin-2 was shown to double the mucoid conversion rate , a mutagenic process , of P . aeruginosa [6] . Yet free iron is rarely available in most physiological situations but for a few pathologies such as cystic fibrosis Additionally , it has been reported that some cationic antimicrobial peptides interact with bacterial membrane proteins and delocalise them [26] . It is conceivable then that LL-37 may interact and interfere with iron transport systems , which in turn may contribute to iron homeostasis disruption and enhance mutagenesis . This possibility requires additional investigation and will be the goal for future studies . In the context of cystic fibrosis , the increase in salt concentration may lead to the reduced activity or complete inactivation of other antimicrobial peptides , as observed for human beta-defensin-1 [27] , while other components could eventually contribute to mutagenesis in the same way that LL-37 does . Moreover , the PhoP-PhoQ and PmrA-PmrB two-component regulatory systems of P . aeruginosa may play an important role in antimicrobial peptide tolerance . This resistance is reproduced in vitro when magnesium concentrations are low [28] . Our experimental conditions , where divalent cations are depleted or in low concentrations , seem comparable . In the light of our results this suggest that bacteria under certain conditions that elicit the expression of these two component systems alter the lipidA structure resulting in increased resistance to colistin [29] . The same systems up-regulate ferrous iron uptake which is mediated by feoAB operon [28 , 30] . This phenomenon could potentially contribute to saturate intracellular iron storage systems and to generate an excess of iron that eventually can participate in Fenton reaction operating by the mechanisms that we propose here . How would the mechanism we propose here enhance the overall mutation rate of bacterial populations ? In the context of cystic fibrosis , there is a high proportion of hypermutable bacteria due to the inactivation of their DNA miss-match repair ( MMR ) genes [31] . We may expect a synergistic effect of both types of mutagenesis as we proposed in the past for the mutagenic effect of cystic fibrosis lung environment and the intrinsic mutagenesis of P . aeruginosa [4] . It can be speculated that iron-mutagenesis can facilitate the rise of mutator bacteria by enhancing the inactivation of MMR genes . This could then weaken genetic constraints that impede the evolution of bacteria to resist antibiotics by multiple pathways as previously described [32] . The results above strongly suggest that sub-lethal concentrations of cationic antimicrobial peptides facilitate the access of free iron to the cytoplasmic compartment of P . aeruginosa . Given that the Fenton reaction results in DNA damage [33] and reactive oxygen species ( ROS ) damages to the DNA display specific molecular fingerprints , we investigated if there was evidence of the Fenton reaction effects on the mutational spectrum . It would be very difficult to investigate the mutational spectrum in rpoB ( sub-unit of RNA polymerase ) that confers resistance to rifampicin in P . aeruginosa PAO1 . This gene is essential and detectable mutations are mostly point mutations , which would constrain the analysis to a few types of changes . We therefore decided to use another strain , PA14 , where mutations that confer resistance to fosfomycin are well characterized [34–36] . Resistance mutations to fosfomycin in P . aeruginosa PA14 depend on a single non-essential locus encoding the glycerol-3-phosphate transporter GlpT , making it a much better marker for studying the entire repertoire of mutations compared to rpoB [36 , 37] [38] . The treatment of P . aeruginosa P14 , which shows a similar sensitivity to LL-37 as the PAO1 strain used above , with the mutagenic combination of LL-37+Fe2+ showed almost a four fold-increase in mutant frequency ( S4 Fig ) . To assess how addition of free iron and antimicrobial peptides affect the mutation spectrum of the glpT gene of PA14 strain we exposed bacteria to either free iron , antimicrobial peptide LL-37 , or both . All of the resulting resistant mutants contained non-synonymous substitutions or deletions in glpT that potentially affect the stability of GlpT transporter and likely disrupt the function of the protein ( Figs 4 , S5 and S6 and S1 Table ) . LL-37+Fe2+-treated bacteria displayed striking differences in mutational spectra when compared to the other treatments ( Fig 4 and S1 Table ) . We used Monte Carlo hypergeometric test implemented in iMARS , a mutation analysis reporting software [39] , to assess the overall differences between mutational spectra . The probability of the mutational spectra to be the same stood at 0 . 554706 ( P-value confidence limits 0 . 531080–0 . 578 ) when the iron treatment was compared to LL-37 , but was below 0 . 0000001 when either were compared with LL-37+Fe2+ treatment . Moreover , we found a mutation hotspot ( R93 to W change , 10/20 clones ) in the LL-37+Fe2+ treatment group that was significantly ( P = 0 . 0004 , two-tailed Fisher’s exact-test ) different from the two other treatments ( S2 , S3 and S4 Tables ) . This mutation hotspot is a single nucleotide transition from C to T , which is one of the most frequent types of mutations caused by ROS [40] . We found that twelve out of twenty clones from the LL-37+Fe2+ condition had C to T transitions , whereas none were present in either iron or LL-37- treated bacteria ( two-tailed Fisher's exact-test ( P< 0 . 0001 ) ( S7 Fig ) . It is striking that although several C to T mutations can potentially lead to glpT inactivation are in principle possible , that the majority are concentrated in a single point . Such mutation hotspots can be driven by a specific topology on the chromosome [41] , or a particular sequence prone to double strand breaks resulting in mutations after repair [42] . In general , observable mutations are the results of mutation-repair balance and not all mutations are repaired with the same efficiency . A good example in P . aeruginosa is the mutational inactivation of the anti-sigma factor gene , mucA , with the mutated allele mucA22 most prevalent ( 25 to 40% ) . This inactivation seems to be spectrum dependent [43] . Interestingly , C to T transitions were one of the most frequent types of single nucleotides changes in the genomes of Salmonella typhimurium evolved to increasing concentrations of LL-37 in modified LB medium short of sodium chloride and anions [44] , which is fully consistent with our results . A number of studies [45–48] , which caused some controversy [49–51] , suggested that hydroxyl radicals can be generated as a consequence of antibiotic treatments and this aggressive by-product may take part in the killing mechanism of bactericidal drugs or promote mutagenesis . Most of these studies were carried out in the iron-rich LB medium and whether processes as described here for the interaction between an AMP and iron apply to antibiotics remains to be explored . Our results support the notion that under certain pathological situations , sub-inhibitory concentrations of cAMPs facilitate uncontrolled uptake of free iron by bacterial cells , which results in increased mutagenesis by Fenton reaction ( Fig 5 ) . According to our results , this could be a general mechanism underlying mutagenesis by joint action of antimicrobial peptides and free iron in specific situations where iron is not limited . Free iron levels are kept as low as possible due to its toxic effect for all living beings but especially in bacteria that lack proper cell compartments . In fact , iron withdrawal is part of the natural innate immune response in inflammation that makes free iron even scarcer . During inflammation and infection a “hypoferremic response” ( anaemia of inflammation ) is observed [52 , 53] . Many chelating proteins such as transferrin and ferritin exhibit antibacterial activity simply by making iron availability incompatible with bacterial proliferation [54] . Despite showing that many cationic antimicrobial peptides are unable to increase mutation rates [7] , the particular situation of iron-induced mutagenesis can be of great interest for certain types of infections where iron homoeostasis is compromised . In cystic fibrosis , bleeding happens frequently . Considering that P . aeruginosa is one of the most common pathogens that acquire all antibiotic resistance by mutations , the mechanism proposed here is likely very relevant for pathoadaptation . Finally , our work has potential implications for the development of future treatment of chronic respiratory infections by Pseudomonas aeruginosa . For example , modulation of iron-chelating agent in CF therapy could potentially slow down the pathoadaptation and development of resistance in CF , and diminish lung damaging by ROS .
The P . aeruginosa PAO1 wild-type strain was kindly provided by M . A . Jacobs . The P . aeruginosa PA14 was kindly provided by Nicole T . Liberati and Frederick Ausubel . All bacterial strains were cultured in Mueller-Hinton Broth , non-cation adjusted ( Sigma ) , with total iron content 0 . 22 ± 0 . 02 μM , following recommendations of CLSI for cationic antimicrobial peptide susceptibility testing . The MHB pH was adjusted to 6 in all cases with acetic acid to enhance solubility of both , LL-37 and iron compound . All experiments were performed at 37°C , under agitation in liquid culture . For genetic manipulation , Escherichia coli DH5α strain was used and routinely cultured in Lysogeny Broth ( LB medium ) , supplemented with antibiotic when appropriate . MICs were determined according to CLSI recommendations by a microdilution method with some modifications for antimicrobial peptides . The MIC was defined as the antimicrobial concentration that inhibited growth after 24 hours of incubation in liquid MHB medium at 37°C . Polypropylene non-binding multi-well plates ( Th . Geyer , Germany ) were used for all experiments . The MIC50s for all antimicrobials were determined by inoculating strains grown to mid-log phase into the wells of a 96-microwell plate . Approximately 102 cells from overnight cultures of PAO1 and PA14 strains were inoculated into 50 ml tubes containing 10 ml of MBH and incubated at 37°C with strong agitation until the mid-log phase of growth ( approximately 108 cfu/ml ) . Then , 100 μl of 2 ×108 cells from these cultures were inoculated in each well of polypropylene non-binding 96-multiwell plates containing 100 μl of fresh Mueller-Hinton medium with growing concentration of serially diluted LL-37 . The plates were incubated at 37°C during four hours with continuous agitation in a plate reader ( Synergy HT , BioTeK ) . Four replicates per concentration were prepared and the experiments were repeated twice . MIC50s at 4 hours were defined as the concentrations at which 50% of growth reduction in comparison to the control at OD600 were observed . For spontaneous-mutation rate measurements of PAO1 strain , 1/100 dilutions of overnight cultures were inoculated into four tubes per group , each containing two ml of MHB medium . The cultures were incubated at 37°C with strong agitation to reach ~108 cfu/ml . At this point , appropriate concentration of LL-37 , colistin , iron sulphate ( FeSO4 ) or combinations of antimicrobial peptides and iron , were added to the cultures . The tubes were allowed to continue their normal growth overnight until saturation . In the experiments with colistin , the bacterial suspensions were washed twice with saline solution 0 . 9% NaCl before plating . The cultures were appropriately diluted and plated on MHB agar plates with or without rifampicin ( 100 μg/ml ) . The mutant frequency was estimated by the number of colonies growing on rifampicin divided by the number of total cfu/ml . To confirm the results , relevant concentrations were also assayed with ten replicates to see the influence of the treatment on the population mutation rates ( the number of mutations per cell per generation ) . Mutation rates were calculated by maximum verisimilitude method and data were processed using the on-line web-tool Falcor ( http://www . mitochondria . org/protocols/FALCOR . html ) as recommended [55 , 56] . Falcor software was used to estimate the mutant frequency too . The mutation rates for the strain PA14 under the selected treatments were determined in the same way as described for PAO1 , but fosfomycin ( 128 μg/ml ) was used instead of rifampicin . To assess the effects of iron and antimicrobial peptides and their combination on the mutation spectrum of P . aeruginosa strain PA14 , the glpT gene of twenty randomly selected Fos-R clones from independent cultures for each treatment group ( MHB supplemented either with 40 μM Fe2+ , LL-37 ( 32 μg/ml ) or a combination of LL-37+Fe2+ at same concentrations for both ) , was amplified by colony PCR using glpT-P14-F1 ( 5-AGCGGAGCTCGCGATGTTC-3 ) and glpT-P14-R1 ( 5-TCAGCCGGCTTGCTGCGG-3 ) primers [36] and Kapa2G Fast ReadyMix PCR with dye kit ( KAPA Biosystems , Boston , US ) . Cycling conditions were as follows: 95°C 7’/ ( 95°C 15”/60°C 15”/72°C 40” ) x 35/72°C 7’/4°C hold . The PCR products were purified and sequenced at Macrogen Europe using the forward and reverse primers described above . Sequences were assembled using SeqTrace software . Assembled sequences were imported into CLC Sequence Viewer 6 and aligned using default settings . Low quality flanking sequences were removed and the alignment was trimmed to the 1160 bp fragment ( the 52–1212 bp region relative to the A in the start ATG codon of the 1347bp-long glpT ORF ) . A tridimensional homology model of GlpT was generated using Cn3D software by performing a BLASTP search using PA14 strain GlpT protein sequence as a query and mapping the resulting alignment against the experimentally determined Escherichia coli K-12 GlpT protein structure . To evaluate the potential effects of amino acid substitution on protein stability , the online tool I-Mutant ( http://gpcr2 . biocomp . unibo . it/cgi/predictors/I-Mutant3 . 0/I-Mutant3 . 0 . cgi ) was used . TMHMM server v . 2 . 0 ( http://www . cbs . dtu . dk/services/TMHMM/ ) was used for prediction of transmembrane helices in glpT protein sequence . Mutational spectrum differences were analysed using the software iMARS [39] . The effect of 2–2′ bipyridyl , an iron chelating agent , on LL-37+Fe2+ mutagenesis was determined by measuring its influence on mutant frequency on a selected concentration of LL-37+Fe2+ combination ( 32 μg/ml and 40 μM of Fe2+respectively ) , where mutagenesis was observed . The experiment consisted of adding a titrating concentration of 2–2′ bipyridyl ( 114 μM ) to chelate 95% of the added iron of treated cultures , in order to make the treatment compatible with bacterial growth . Cultures with the described LL-37+Fe2+ combination with no addition of 2–2′ bipyridyl were used as a control . The mutant frequencies of both groups were determined as described elsewhere in this section . LL-37 , iron and 2–2′ bipyridyl were simultaneously added to the exponentially growing cultures . DNA fragment containing the PAO1 dps gen from genomic DNAs was amplified by PCR using the oligonucleotides PA-DPS-F1 ( 5′-ATGGAAATCAATATCGGAATCG-3′ ) and PA-DPS-R1 ( 5′-CTACTCAAATCAAGCGGTTGGC-3′ ) as forward and reverse primers , respectively . The fragment contains the ATG codon and 50 nucleotides downstream of the stop codon . The PCR product was directly cloned into the SmaI-digested and T-tailed pUCP24 plasmid vector ( replicative in both P . aeruginosa and E . coli ) , which harbours Gentamicin resistance markers [57] . E . coli DH5α was used following standard protocols for genetic manipulations . The resulting plasmids , termed pUCP24-DPS , were introduced by electroporation into PAO1 wild type strain . The cloning vector was also transformed into the same strain as control . An experiment similar to the one designed for 2–2′ bipyridyl was carried out . A mutagenic combination of LL-37+Fe2+ was assayed in the strains carrying pUCP24-DPS plasmid or the empty vector pUCP24 and mutant frequencies were determined for both groups . Total iron quantification was carried our as previously described with minor modifications [58 , 59] . Cultures of P . aeruginosa PAO1 were grown to an OD600 of approximately 0 . 5 at 37°C with agitation in a volume of two ml in MHB . The cultures were centrifuged at 4000 g during ten minutes at 20°C . The pellets were re-suspended in fresh MHB and three different groups were prepared . The treatments consisted of LL-37 ( 32 μg/ml ) ( I ) , iron sulphate to a final concentration of 40 μM ( II ) , a combination iron sulphate and LL-37 ( III ) , both of them at the same concentrations of their respective group I and II , and a control group ( IV ) to which the proportional amounts of LL-37 and iron sulphate solvent were added ( sterile dH20 and dH20 , pH = 5 respectively ) . The cultures were incubated for up to 30 minutes and harvested by centrifugation as before but at 4°C . The cell pellets were washed twice with ice-cold phosphate-buffered saline ( PBS ) and re-suspended in 1 ml of TE buffer containing 5 mg/ml of egg lysozyme ( Sigma ) and incubated during 10 minutes at room temperature . To quantify total iron , the lysate ( one ml ) was mixed with one ml of HCl 10 mM and 1 ml of iron-releasing reagent containing HCl 1 . 4 M + 4 . 5% ( weight/volume ) aqueous solution of KMnO4; 1/1 and incubated at 60°C for two hours . After cooling , 0 . 06 ml iron-detection reagent ( 6 . 5 mM ferrozine , 6 . 5 mM neocuproine , 2 . 5 M ammonium acetate , 1 M ascorbic acid in water ) was added and the sample absorbance was read at 550 nm in a plate reader Synergy HT ( Biotek ) . The iron concentrations were determined based on a standard curve obtained with increasing concentrations of ferric chloride and normalized to protein concentration of the lysates . Each group consisted of five cultures . We determined the content of total iron in our cultures media MHB and LB , using the same procedure describe above , starting by the addition of 1 ml of HCl 10 mM and 1 ml of the iron-releasing reagent . Under the suspicion that MHB had lower iron content , the samples of this medium were prepared ten-fold concentrated . An unpaired Student's t test or Mann-Whitney U test was used where appropriate for statistical analysis , according to the nature of the data ( parametric or nonparametric adjustment ) . Two-tailed Fisher’s exact test or Monte Carlo hypergeometric test were used to calculate statistical significance of differences in mutation frequencies at each codon site of the alignment between treatments in the mutational spectrum analysis . P values less than 0 . 05 were considered statistically significant . All tests were performed with statistic software R except for mutational spectrum analysis where iMARS [39] was used instead . | Cationic antimicrobial peptides ( cAMPs ) are small proteins naturally produced by the immune system to limit bacterial growth mainly through pore formation in the membrane . It has recently been suggested that sub-inhibitory concentrations of cAMPs promote bacterial mutagenesis , similarly to antibiotics . However , we previously reported that cAMPs do not increase mutation rate and do not activate bacterial stress responses . Here we resolve this contradiction . We report that free iron in the culture medium increases mutagenesis in the presence of cAMPs . We show that sub-inhibitory concentrations of cAMPs facilitate entry of free iron into bacterial cells , where it interacts with hydrogen peroxide , thereby resulting in production of DNA-damaging reactive oxygen species and increased mutagenesis . Moreover , these results may have clinically-relevant implications: while very little free iron is normally present in healthy individuals , this is not the case in patients suffering from cystic fibrosis , where elevated bacterial mutagenesis promotes antibiotic resistance and contributes to persistence and severity of infection . Thus , an intervention aimed at reduction of free iron in the lungs could reduce the cAMPs-facilitation of iron-mediated mutagenesis; hence antibiotic resistance and pathoadaptation . | [
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] | [] | 2015 | Cationic Peptides Facilitate Iron-induced Mutagenesis in Bacteria |
Achieving global food security for the estimated 9 billion people by 2050 is a major scientific challenge . Crop productivity is fundamentally restricted by the rate of fixation of atmospheric carbon . The dedicated enzyme , RubisCO , has a low turnover and poor specificity for CO2 . This limitation of C3 photosynthesis ( the basic carbon-assimilation pathway present in all plants ) is alleviated in some lineages by use of carbon-concentrating-mechanisms , such as the C4 cycle—a biochemical pump that concentrates CO2 near RubisCO increasing assimilation efficacy . Most crops use only C3 photosynthesis , so one promising research strategy to boost their productivity focuses on introducing a C4 cycle . The simplest proposal is to use the cycle to concentrate CO2 inside individual chloroplasts . The photosynthetic efficiency would then depend on the leakage of CO2 out of a chloroplast . We examine this proposal with a 3D spatial model of carbon and oxygen diffusion and C4 photosynthetic biochemistry inside a typical C3-plant mesophyll cell geometry . We find that the cost-efficiency of C4 photosynthesis depends on the gas permeability of the chloroplast envelope , the C4 pathway having higher quantum efficiency than C3 for permeabilities below 300 μm/s . However , at higher permeabilities the C4 pathway still provides a substantial boost to carbon assimilation with only a moderate decrease in efficiency . The gains would be capped by the ability of chloroplasts to harvest light , but even under realistic light regimes a 100% boost to carbon assimilation is possible . This could be achieved in conjunction with lower investment in chloroplasts if their cell surface coverage is also reduced . Incorporation of this C4 cycle into C3 crops could thus promote higher growth rates and better drought resistance in dry , high-sunlight climates .
Global food consumption is estimated to increase by over 70% by 2050 [1 , 2] . To ensure global food security within the context of detrimental climate change it will be essential to achieve a substantial increase in agricultural productivity per hectare over the next couple of decades , combined with a switch to sustainable farming practices and a change in dietary habits [1] . Current yields increase per year of wheat and rice are 0 . 9% and 1% respectively [3]; however , sustained annual productivity increases of the order of 1 . 5-2% will be required ( depending on the balance and success of other solutions ) to ensure food safety [3] . As current methods of increasing yield saturate , development of new technologies that directly address the limiting factors of plant productivity is necessary [4] . The most fundamental factor limiting plant productivity , or the carbon assimilation rate , is the poor efficacy of the main CO2 fixing enzyme , Ribulose-1 , 5-bisphosphate-Carboxylase-Oxygenase ( RubisCO ) . This enzyme evolved prior to the great oxygenation of the earths atmosphere [5] when CO2 was abundant , and it not only catalyses the fixation ( carboxylation ) of CO2 into sugars in the Calvin-Benson cycle , but also an oxygenation reaction using O2 . This oxygenation reaction results in toxic compounds and removal of carbon from the Calvin-Benson cycle , which are resolved through an energetically costly chain of reactions known as photorespiration . The error rate ( i . e . the relative frequency of oxygenation ) in a typical C3 plant exceeds 20% . Attempts to improve RubisCO have met with limited success , as increasing reaction speed compromises enzyme specificity between CO2 and O2 , and both of these factors affect assimilation efficiency . RubisCO thus lies on its Pareto front [6] . Attention has hence shifted to carbon concentrating mechanisms ( CCM ) that have evolved in several plant lineages , algae and cyanobacteria . CCMs increase the concentration of CO2 in RubisCO’s vicinity , thereby increasing the rate of carbon assimilation . In C4 plants , for instance , a highly efficient enzyme , Phospho-enol-pyruvate Carboxylase ( PEPC ) , is used to initially fix CO2 ( in its hydrated form , HCO3- ) , sequestering the carbon in an intermediary ( a C4 acid such as malate ) , and releasing the CO2 in the proximity of RubisCO . This process , called the C4 cycle , is essentially a biochemical CO2 pump . C4 plants typically have more energy efficient carbon assimilation than C3 plants ( i . e . require fewer photons to assimilate the same amount of carbon into sugars ) thus making the C4 cycle a prime candidate for crop improvement [7] . The C4 cycle however consumes energy . Improving plant productivity by introducing the cycle into C3 crops is therefore a question of balancing the pumps’ costs against the efficacy of the pump ( the leakage current ) and the impact of the pump on the efficacy of RubisCO . This is a complex question , involving transport and biochemical issues within the context of a plant’s anatomy . Mathematical modelling is needed to address these issues and identify the factors determining the assimilation rate and photosynthetic efficacy . C4 photosynthesis has evolved over sixty times in higher plants [8] . It typically appears in conjunction with so-called Kranz anatomy in which concentric layers of bundle sheath and then mesophyll cells cooperate in the photosynthetic process . Photosynthesis in these C4 plants is associated with multiple cell walls acting as diffusion barriers to CO2 , preventing its escape and thereby boosting its concentration around RubisCO [8] . However , in a small number of species , the C4 cycle is contained within individual mesophyll cells ( e . g . Suaeda aralocaspica , Bienertia cycloptera [9 , 10] ) . It is thought that the spatial separation between the primary and the secondary carboxylases ( PEPC of the C4 cycle and RubisCO ) in the enlarged mesophyll cells of these plants mirrors the physical diffusion barriers found in Kranz-anatomy C4 plants [11] . A single-cell version of the C4 cycle may appear easier to engineer in C3 plants than the Kranz anatomy C4 cycle because the substantial anatomical remodelling of leaves and cellular architecture associated with Kranz anatomy could be avoided . However , even single-cell C4 plants feature notable modifications to the architecture of mesophyll cells , which facilitate the large spatial separation of the carboxylases [11] . Re-engineering the single-cell C4 intracellular architecture may thus also pose considerable challenges . This raises the question of whether there is a workable solution that does not require substantial anatomical changes . Spatial separation between PEPC and RubisCO in single-cell C4 plants aids the C4 pump by providing increased diffusive resistance and essentially underpins C4 photosynthetic efficacy in these plants [12] . However , it is not clear if such cell-scale spatial separation is strictly necessary . To investigate this , we look at a hypothetical minimal C4 pathway operating in an unaltered C3 mesophyll cell geometry . The pathway would draw carbon from the cytoplasm and concentrate it within the chloroplast stroma . It would require targeted expression of the pathway enzymes in the cytoplasm and the stroma , a change in the expression of transporters in the chloroplast envelope to transport C3 and C4 acids , and a C4 regulatory mechanism to switch it off when energy/reductant availability is low . But no anatomical modifications . This minimal C4 photosynthetic system has previously been discussed by von Caemmerer and Furbank [13 , 14] who modelled it within a compartmental paradigm . Their conclusions suggested that although a C4 cycle could result in higher CO2 assimilation rates , this would come at the expense of a substantially lower energetic efficiency of photosynthesis . However , this analysis assumed a relatively high conductance of the chloroplast envelope , the cell wall , and the plasmalemma ( 0 . 8 mol/bar m2s [13] , which is at the upper end of most experimental estimates [15] ) . Due to a small spatial separation ( ∼ 1 μm ) between the carboxylase and decarboxylase of the proposed C4 pump ( which is well below the threshold separation ( ∼ 10 μm ) for cost efficient single-cell C4 photosynthesis [12] ) the viability of a single-cell based system would be strongly influenced by the permeability of the chloroplast envelope since this determines the CO2 leakage current . The results of von Caemmerer and Furbank should thus be revisited with a spatial model of photosynthesis , with a view of establishing design parameters for a C4 pump enhanced C3 plant . We developed a spatial transport-assimilation model of steady-state photosynthesis to address this question . It focuses primarily on the effect of the intracellular geometry on the diffusive transport of photosynthetically relevant gases ( O2 , CO2 , and its hydrated form HCO3- ) . The diffusion of these species is a limiting factor for both C3 and C4 photosynthesis . Light capture , ATP/NADPH production , Calvin-Benson cycle , and photorespiration are each assumed to function optimally . Linear and cyclic electron transfer are further coordinated to meet ATP/NADPH demand , but no coordination is assumed between the C3 and C4 cycles . The model is similar in some respects to the 3D model of C3 photosynthesis presented by Tholen and Zhu [16] ( recently expanded to model Kranz-anatomy bioengineering [17] ) , but there are notable differences . Most importantly , we include C4 biochemistry , but we also explicitly treat oxygen’s kinetics and diffusion , whilst on a computational level we utilise the system’s symmetry to reduce the computational burden , permitting a thorough investigation of the parameter space . By examining how photosynthesis is affected by variation in cell geometry and biochemistry , we determine when the C4-pump is viable . This paper is organised as follows . We first briefly present our Model , with additional mathematical details in supplement ( S1 Appendix ) . In Results we examine the performance of C3 and C4 photosynthesis , profiling it in terms of the carbon assimilation rate and photon usage , across the range of possible values of relevant biophysical parameters . In some cases this addresses parameter uncertainty where there is a large spread in the values reported in the literature ( e . g . the gas permeability of chloroplast envelope ) , in others it accounts for environmental variation ( intra-leaf CO2 pressure ) or examines possible synergy gain if a cellular feature is also modified ( e . g . chloroplast size , chloroplast cell-surface coverage ) . We also assess the ability of a chloroplast to absorb and utilise photons for carbon assimilation—the light-harvesting capacity—which could limit assimilation of the proposed C4 system and thus attainable yields . In Discussion we propose a sequence of modifications to realise the predicted gains .
We use a reaction-diffusion framework to model the diffusion of CO2 , O2 and HCO3- inside a cell , solving for their position-dependent steady-state concentration profiles in order to derive photosynthetic currents . The equations are of the form D i ∇ 2 n i - r i ( n ) + s i = 0 ( 1 ) where the index i stands for CO2 , O2 , and HCO3- , labelled respectively as C , O , and B , in the following equations . ni is the spatially varying concentration of species i , Di is the compartment-dependent diffusion coefficient , and ri and si are the reaction and source terms for that species . The system is solved on a region divided into 3 compartments: chloroplast stroma , cytoplasm , and vacuole , as in Fig 1 ( a ) , with interdividing membranes modelled as low diffusion layers . Below we discuss the geometry , and the various biochemical reactions behind the reaction and source terms . Additional mathematical details are provided in S1 Appendix . A typical C3 mesophyll cell has one large central vacuole that occupies the majority of the cell volume with other organelles and cell’s cytoplasm located around the cell’s periphery . Chloroplasts in particular , press against the cell membrane in regions adjacent to the intercellular airspace ( IAS ) . Their density is high , with around 50%-70% of the cell surface covered by chloroplasts in a roughly hexagonal lattice arrangement ( Fig 1 ( b ) ) [18 , 19] . The much smaller mitochondria can move freely within the peripheral cytoplasm . As both the sources and the sinks for CO2 and O2 are located at the cell’s periphery , the central vacuole space should play only a minor role in their transport . We therefore focus on a single , typical peripheral chloroplast and its immediate environment ( the spatial region closer to this chloroplast than to its neighbours ) , approximating this roughly hexagonal region as a cylinder ( Fig 1 ( a ) and 1 ( b ) ) that contains one axially-centred semi-spherical chloroplast . The radius of the cylinder determines the chloroplast surface coverage fraction ( the fraction of the cell surface covered with chloroplasts ) —this parameter quantifies chloroplast density and thus determines the cell- and leaf- level assimilation rates . The mitochondria are mobile , so their contribution is averaged spatially and temporally at the steady state . The peripheral cytoplasm is therefore treated as a homogeneous photorespiring medium . There is little quantitative data on the precise positioning of mitochondria within the cytoplasm . Ideally , mitochondria would be positioned behind the chloroplasts ( between a chloroplast and the vacuole ) , which might promote capture of photorespirated carbon . Such positioning is visible in micrographs of rice leaves [20] , but the anatomy of rice mesophyll cells with their protruding chloroplasts is not typical for C3 plants . By assuming more evenly spread out mitochondria , we model photosynthesis under less ideal conditions . We focus on transport of three inorganic species—O2 , CO2 , and HCO3- . Whereas other metabolites are constrained to the liquid phase and typically do not pass through inter-compartmental boundaries except via dedicated channels , O2 and CO2 are gases and readily diffuse within and between cellular compartments , and between the cell interior and outside airspace . Because of this gaseous exchange , the efficacy of both C3 and C4 photosynthesis will essentially be determined by their diffusion dynamics . Diffusion within particular cellular compartments is affected by the local viscosity , while diffusion across the inter-compartmental barriers is characterised by barrier permeabilities . Diffusing gases enter and exit the simulated region through the cylinder end representing the inner surface of the cell membrane ( Fig 1 ( a ) ) . The permeability of a barrier to the diffusion of a metabolite is defined as a multiplicative factor , σ , connecting the current of the metabolite through the barrier ( per-unit-area ) , jn , with the difference in the metabolite concentrations on the two sides of the barrier , n1 and n2 , ( Fick’s law ) , j n ( 1 → 2 ) = σ ( n 1 - n 2 ) ( 2 ) Note that the permeability ( units of μm/s ) is related to the leaf-level gas conductivity associated with the same barrier , g , ( units of mol/bar m2s ) as g = ϕHσ , where H is the Henry constant of the gas , and ϕ is the ratio of the barrier ( i . e . mesophyll or chloroplast ) surface and leaf surface . Although intracellular membranes are essentially impermeable to HCO3- , its spatial dynamic also has to be treated explicitly as it strongly couples to the CO2 pool in the chloroplast stroma and in the cytoplasm , where we assume carbonic anhydrase ( CA ) is present . The CA-assisted interconversion between CO2 and HCO3- is modelled as a boost to the base pH-dependent interconversion rates , Fig 1 ( d ) . v C → B , ( CA ) ( pH ) = η C A v C → B ( base ) ( pH ) = η C A ( k C O 2 + k O H - K w / 10 - pH M ) ( 3 ) v B → C , ( CA ) ( pH ) = η C A v B → C ( base ) ( pH ) = η C A ( k d · 10 - pH M + k H C O 3 - ) ( 4 ) where the k-factors determine the base rates of CO2+H2O↔HCO3-+H+ and CO2+OH-↔HCO3- reactions [21] . The dimensionless activity factor , ηCA , accounts for both the efficiency of CA and its concentration . The simple scaling relation is possible because the enzyme-mediated reaction is reversible , thus satisfying detailed balance ( see S1 Appendix ) . We do not consider possible changes in compartmental pH due to HCO3- level shifts , since the pH in cytosol and chloroplast stroma is strongly buffered by phosphates and phosphate esters , with buffer capacities in 20 − 80 mM H+ per pH unit range [22–24] whilst our results show that the shifts in HCO3- concentration seldom exceed 0 . 2 mM ( Fig A in S1 Figures ) . The biochemistry of carbon assimilation is well established and has been a subject of numerous mathematical models [25–27] . It is briefly summarised and discussed in the context of our model equations in the following paragraphs . The reaction of HCO3- with the PEPC-bound PEP in the cytoplasm is the entry point of carbon in the C4 cycle . The PEP carboxylation rate determines at steady state the rate of CO2 release from C4-acid decarboxylation in the stroma . We assume that the levels of C3/C4 intermediaries are large enough not to impede carbon transfer , so that intermediary steps in the C4 pathway need not be explicitly modelled . The concentrations of the C4 enzymes involved in the these parts of the pathway are likewise assumed non-limiting and sufficient at all concentrations of cytoplasmic PEPC , which we use as a measure of C4 pathway expression . The Calvin-Benson cycle’s main function is to generate glucose , a 6-carbon compound from 6 CO2 . To sequentially increase the carbon content it uses ribulose bisphosphate ( RuBP ) , a 5-carbon compound . RubisCO catalyses the reaction between RuBP and CO2 to generate two 3-phosphoglycerate molecules ( 3-carbon compounds ) that are subsequently utilised to regenerate RuBP and generate glucose . RubisCO also catalyses a reaction between RuBP and O2 , creating one 3-phosphoglycerate and one 2-phosphoglycolate molecule . 2-phosphoglycolate is recycled via the photorespiration pathway; for every two molecules one 3-phosphoglycerate molecule is reformed and one CO2 molecule is released in mitochondria . The competing RuBP carboxylation and oxygenation reactions occur in the chloroplast stroma . The oxygenation rate determines at steady state the rate of mitochondrial release of photorespirated CO2 in the peripheral cytoplasm . Both carboxylation and oxygenation determine the net rate of carbon assimilation . The consumption of the reductant ( NADPH ) by the Calvin-Benson cycle and photorespiration must be matched by its production via linear electron transfer chain . ( We ignore the contribution from the mitochondrial electron transfer chain , which in lit conditions will be small in comparison . ) This couples O2 production in the chloroplast thylakoid with RuBP carboxylation and oxygenation rates ( at steady state ) . We initially assume RuBP is not limited , later imposing a limitation on its regeneration to reflect a light harvesting cap ( ATP and NADPH then being limiting ) . The reaction terms ri for the three species , Eq ( 1 ) , comprising all the above described processes are r C ( n ( r ) , r ) = χ p ( r ) v C c R n C ( r ) n C ( r ) + n O ( r ) K C / K O + K C + v C → B ( r ) n C ( r ) − v B → C ( r ) n B ( r ) ( 5 ) r O ( n ( r ) , r ) = χ p ( r ) v O c R n O ( r ) n O ( r ) + n C ( r ) K O / K C + K O ( 6 ) r B ( n ( r ) , r ) = χ c ( r ) v B c P n B ( r ) n B ( r ) + K B - v C → B ( r ) n C ( r ) + v B → C ( r ) n B ( r ) ( 7 ) In the preceding equations we have used characteristic functions χp ( r ) , χc ( r ) , and χv ( r ) to demarcate the spatial regions corresponding to the chloroplast ( plastid ) interior , the cytosol , and the vacuole interior respectively . cR and cP are concentrations of RuBP-primed stromal RubisCO and PEP-primed cytosolic PEPC . vi and Ki are Michaelis-Menten parameters for the modelled enzymatic reactions . The source terms si corresponding to photorespiratory CO2 release in the cytosol mitochondria , C4 cycle CO2 release inside chloroplast stroma , and photosynthetic O2 production on chloroplast thylakoids are given in terms of currents ( at steady state ) s C ( r ) = χ p ( r ) J C 4 V p + χ c ( r ) 1 2 J p h r e s p V c ′ ( 8 ) s O ( r ) = χ p ( r ) J C a l v i n + J p h r e s p V p ( 9 ) where Vi = ∫ χi ( r ) d3r and the reaction currents are defined as J C a l v i n = ∫ v C c R n C ( r ) n C ( r ) + n O ( r ) K C / K O + K C χ p ( r ) d 3 r ( 10 ) J p h r e s p = ∫ v O c r n O ( r ) n O ( r ) + n C ( r ) K O / K C + K O χ p ( r ) d 3 r ( 11 ) J C 4 = ∫ v B c P n B ( r ) n B ( r ) + K B χ c ( r ) d 3 r ( 12 ) The rate of assimilation is expressed on a cell-surface-area basis . The photon cost of carbon fixation ( the number of photons needed per assimilated carbon atom to cover the costs of the Calvin-Benson cycle , photorespiration , and the C4 cycle ) is quantified assuming optimal usage of the linear and cyclic electron transfer chains [28 , 29] , as detailed in the following paragraph . Linear electron transfer allows for reduction of NADP+ to NADPH , needed in the photorespiratory and Calvin-Benson cycle . Absorption of 4 photons will result in reduction of one NADP+ molecule , while also transporting 6 protons into the thylakoid lumen . The proton gradient is used to run ATP-synthase , which produces one ATP for every 4 protons exiting the lumen . The stoichiometry of the linear electron transfer perfectly matches that of the Calvin-Benson cycle , which requires 3 ATP and 2 NADPH to fix one CO2 molecule and regenerate the RuBP substrate . Photorespiration and the C4 cycle however require additional ATP ( 3 . 5 ATP and 2 NADPH per oxygenated RuBP molecule , and 2 ATP ( and no NADPH ) per C atom transferred via the C4 cycle ) . The energy for this additional ATP production is provided by cyclic electron transfer , which is more efficient than linear transfer at generating the proton gradient . It transfers 2 protons per photon , but does not reduce NADP+ . Note however that these are estimates , particularly for the efficiencies of the cyclic transfer and the ATP synthase . For instance , it is not clear if the proton-to-ATP stoichiometry of a chloroplast ATP-synthase is 12:3 or 14:3 [30] . A recent work [31] has shown that while the structural ( i . e . binding site ) stoichiometry of the spinach chloroplast ATP-synthase is 14:3 , the thermodynamic ratio is 12:3 , i . e . four protons are transported per ATP . The 12:3 ratio is also commonly used in the modelling literature [28 , 32] , so by using this ratio in our model we ensure that the results are comparable with extant modelling literature . However , both stoichiometries produce similar results in our model ( Fig B in S1 Figures ) . Optimal light use would thus amount to 8 photons per CO2 molecule fixed , 9 photons to deal with each RuBP oxygenation event , and 4 photons per carbon atom transferred by the C4 cycle . This optimal use requires that the plant adjusts the current through the linear and the cyclic electron chain according to need and assumes that NADPH is used predominantly for photosynthesis . The ability of C3 plants to adjust the balance of the linear and cyclic electron fluxes has been demonstrated experimentally [33] , and modelling has suggested that such adjustments might be directed by a straightforward change in metabolite demand [34 , 35] . Total energy consumption cannot exceed the light-harvesting capacity of chloroplasts , defined here as the combined capacity to absorb light and to use the absorbed energy to generate ATP and replenish NADPH ( thus it encompasses both the capacity of chlorophyll antennae and the linear/cyclic electron pathways ) . We express the energy consumption and the light-harvesting capacity in terms of ( photosynthetically active ) photons absorbed per stroma volume in unit time ( units of mol m-3s-1 ) , as in Xiao et al [36] . We use this measure , instead of e . g . light consumption per chloroplast or per cell- or leaf- surface , because we want to examine how the changes in the chloroplast surface coverage or chloroplast size affect the photosynthetic efficiency . If the chloroplast anatomy is preserved , a unit of stromal volume will on average contain a certain fixed amount of thylakoid . Hence , the per-volume measure of light use and harvesting capacity is an accurate proxy for the required photosynthetic activity and capacity of the thylakoid . As an illustration , with the default chloroplast geometry parameters ( Table 1 ) and 50% cell-surface coverage , a photon consumption rate of 40 mol m-3s-1 corresponds to the absorption by the chloroplast array in the peripheral cytoplasm ( Fig 1c and 1d ) of 20 μmol m-2s-1 of photons incident on the cell surface , which is 1% of the peak photosynthetically active solar flux ( 2 mmol/m2s [37] ) . This comparison however does not extrapolate easily to the leaf level , as various structures within the leaf will scatter and absorb the incoming light , so individual chloroplasts experience varied light environments [36] . Limited light availability or light-harvesting capacity ( LHC ) is modelled by iteratively scaling-down the concentrations of the substrate-primed enzymes involved in photosynthesis ( i . e . RuBP-primed RubisCO and PEP-primed PEPC ) if energy requirements exceed the supply limit , so that a self-consistent solution is found where photosynthetic energy use exactly matches the available light-harvesting capacity . The adjustment reflects the limited substrate availability caused by energy scarcity . The concentrations of RuBP-primed RubisCO and PEP-primed PEPC are scaled proportionally , so that the ratio of their carboxylation capacities stays fixed . This proportional scaling corresponds to a non-discriminate use of ATP by the Calvin-Benson and the C4 cycle , thus no coordination is assumed between the two cycles . A plant with optimised control mechanisms would be able to alter the activity of PEPC as required to improve on this performance . Our predictions would then be underestimates .
Fig 2 shows how the photon cost and assimilation rate depend on the envelope permeability and the PEPC concentration ( i . e . the pump activity ) . There is an envelope permeability ( σp ) efficacy threshold around 300 μm/s , such that for envelope permeabilities below threshold the photon cost decreases when the pump is operational , whilst above threshold C3 photosynthesis is more efficient than the enhanced C4 system . Both photon cost and assimilation rate begin to change notably when PEPC concentration reaches 10−2 − 10−1 mM . By 1 mM PEPC , these two efficacy measures essentially saturate as the pump reaches full activity . Taking into account the volume of the chloroplast and the surrounding cytoplasm , the PEPC concentration range of 10−2 − 10−1 mM corresponds to a PEPC-to-RubisCO carboxylation capacity ratio between 0 . 1 and 1 , while saturation occurs at ratios close to 10 . By comparison , the PEPC/RubisCO activity ratio in C4 plants is between 2 and 6 . 5 [11] . Saturation in photosynthetic activity at high PEPC concentrations occurs because of a limited carbon supply—either the CA-assisted CO2↔HCO3- conversion rate becomes insufficient or the diffusion of CO2 from internal airspaces ( IAS ) through the cell wall reaches its limit . The relevant rates are the CO2→HCO3- conversion rate and the volume-adjusted rate of CO2 diffusion from IAS ( A C V C σ c , where AC is the cell surface area , VC is the volume of the peripheral cytoplasm , and σC is the permeability of the cellular boundary ) . For the default choice of parameter values ( including ηCA = 106 ) , these are roughly 4 ⋅ 104 s-1 and 500 s-1 , so the diffusion of CO2 from IAS is limiting . At ηCA = 104 the conversion rate is only 400 s-1 so it becomes limiting instead . Realistically however , we can expect that energy expenditure will limit photosynthesis before that , as we demonstrate later . Establishing whether the envelope permeability is above or below the efficacy threshold is particularly important as it determines if the C4 pump is more efficient than C3 photosynthesis . We use constraints on the efficacy of C3 photosynthesis ( negligible PEPC concentration in Fig 2 ) to constrain the envelope permeability . With the cell wall and membrane permeability fixed at 20 μm/s , the photon cost of C3 photosynthesis reaches 20/C ( known quantum efficiency of regular C3 photosynthesis [50] ) for an envelope permeability σp ≈ 600 μm/s ( Fig 2 ) . This indicates that the permeability of the chloroplast envelope is higher than the efficacy threshold ( estimated at 300 μm/s ) and the photon cost of photosynthesis in a C4-pump enhanced cell is thus higher than for C3 photosynthesis alone ( Fig 2 ( a ) ) . We note however that the permeability efficacy threshold is dependent on CO2 pressure in the internal airspaces , moving to higher values as the pressure decreases ( see Fig C in S1 Figures ) . Consequently , even for envelope permeabilities of several hundred μm/s the proposed pathway can become a cost-efficient strategy under conditions of CO2 deprivation ( IAS CO2 pressure p C O 2 < 150 μbar ) , such as may occur during prolonged stomata closure . Although the C4 cycle may not be cost-effective in terms of quantum efficiency , it always increases the assimilation rate at sufficiently high PEPC activities . The assimilation gain can be substantial—up to several-fold at high PEPC concentrations—assuming photosynthesis is not limited by light ( Fig 2 ( b ) ) . The light harvesting capacity of chloroplasts can be estimated by examining the energy consumption of C3 photosynthesis when the photon cost is 20/C or less , which is the case for σp ⪆ 600 μm/s ( see Fig 2 ( b ) ) . In this parameter region the light-harvesting-capacity of chloroplasts is larger than 30 mol m-3s-1 of photosynthetically active photons per stromal volume . We hence take 40 mol m-3s-1 as an estimate of the actual , or at least achievable light-harvesting capacity of an average chloroplast . Substantial assimilation gains ( ≥ 15% ) are feasible at this light-harvesting capacity , as we demonstrate later . Light use and photon cost are appropriate measures of photosynthesis costs and efficiency , since the required energy ultimately comes from sunlight . However , as explained in the Model section , the C4 cycle and the Calvin-Benson cycle do not consume ATP and NADPH in the same ratio: the C4 cycle does not require reductants so the ATP it requires can be provided by cyclic electron transfer . To achieve the required light-harvesting capacity we thus have to not only boost the linear electron transfer capacity ( needed for Calvin-Benson cycle ) but also change the balance between cyclic and linear transfer . Fig 3 ( a ) re-expresses the results of Fig 2 in terms of ATP use . The increase in photosynthesis costs due to C4 cycle operation is more pronounced when expressed in ATP , but this is offset by up to 20% cheaper photon cost of ATP production when cyclic electron transfer is also used ( Fig 3 ( b ) ) . The cyclic transfer usage would have to increase substantially ( accounting for more than 50% of PS-I current when PEPC carboxylation capacity equals that of RubisCO; Fig 3 ( c ) ) . The required increase in linear electron transfer current ( Fig 3 ( d ) ) is however less pronounced than the increase in light use as linear transfer is not used to supply energy to the C4 cycle and the latter suppresses costly RuBP oxygenation . The cytoplasmic and stromal CA activity affects the efficiency of both C3 and C4 photosynthesis . It has been conjectured that the stromal CA’s purpose is to boost CO2 diffusion within the chloroplast , or to facilitate CO2 transfer through the envelope by generating a larger CO2 gradient across this diffusion barrier [56] . Previous modelling has shown a minor positive impact on the assimilation rate attributable to stromal CA [16] . Our results support these findings , showing an increase to C3 photosynthetic efficiency and assimilation rate at CA conversion efficiencies ( ηCA ) above 103 ( Fig D in S1 Figures ) . The gain reaches 10% at ηCA = 106 and saturates at larger ηCA . Interestingly , the effect is essentially independent of the envelope permeability value , as long as we are not close to the compensation point ( where assimilation equals zero; Fig E in S1 Figures ) . The results are similar when CA is present both in the chloroplast stroma and in the cytoplasm , but with a somewhat larger increase in C3 efficiency and assimilation ( ∼ 14% at ηCA = 106 , Fig D in S1 Figures ) . With the C4 cycle present , changing the efficacy of the cytoplasmic CA ( keeping the efficacy of stromal CA at 106 ) can greatly affect photosynthesis ( Fig F in S1 Figures ) . Cytoplasmic CA activity acts as one of the bottlenecks to the pump throughput , as the C4 cycle uses bicarbonate ( the substrate for PEPC ) . Hence a fast conversion of CO2 into HCO3- is needed . For ηCA < 104 the C4 pump is effectively non-operational and varying the PEPC level produces no noticeable change in the assimilation rate . For ηCA beyond 106 , CA ceases to be a limiting factor at PEPC concentrations below 1 mM . The impact of the vacuole membrane permeability or the thickness of the peripheral cytoplasmic layer on the C4 cycle efficiency is minimal ( Fig G in S1 Figures ) . Changing the cytoplasm thickness does change the PEPC concentration at which a particular efficiency or gain is achieved ( Fig G ( c ) ) , showing that it is the ratio of PEPC-to-RubisCO activity that matters ( Fig G ( d ) ) . Changing the permeability of the cell wall and plasmalemma results in significant changes to the photon cost and the assimilation rate ( Fig H in S1 Figures ) . The efficacy of the C4 cycle ( that is , its advantage or disadvantage over C3 photosynthesis ) is only slightly affected , however . At very high cell wall and plasmalemma permeability , the C4 cycle allows for a several-fold higher assimilation rate , as the bottleneck due to diffusion of CO2 through the cell wall is removed , but a concurrent increase in the photon cost means the chloroplast light-harvesting capacity would be limiting ( this is evident from the capacity thresholds which follow the assimilation rate level-lines at high cell boundary permeability in Fig H ( b ) in S1 Figures ) . Diffusion of bicarbonate through the chloroplast envelope might impact photosynthetic efficiency if the permeability of the envelope to HCO3- is not negligible [16] . Recent experiments estimate the HCO3- permeability between 10-3 and 10-2 μm/s [48] . We find the bicarbonate permeation has no effect on the efficacy of photosynthesis ( both C3 and C4 ) for envelope permeabilities less than 10-1 μm/s , and that for permeabilities up to 10 μm/s the effect is only marginal ( Fig I in S1 Figures ) . Bicarbonate diffusion can thus be safely neglected . Changing the chloroplast surface coverage ( by changing the spacing between the chloroplasts while keeping their size fixed; Fig 4 ) alters the efficacy of the C4 cycle . Photon cost rises with the activation of the C4 pump ( if the envelope permeability is above the efficacy threshold ) , but it also rises with surface coverage if the pump is inactive ( C3 regime ) . This , coupled with the fact that the C4 pump provides a much stronger boost to assimilation rate at lower surface coverages ( 30% − 50% ) , leads to a remarkable and non-intuitive result that C4 photosynthesis allows for a higher assimilation rate per cell surface area ( and hence per leaf-surface area , assuming a fixed mesophyll-to-leaf surface ratio ) at lower chloroplast surface coverage , i . e . at a lower investment in chloroplasts ( Fig 4 ( b ) ) , while maintaining the same level of quantum efficiency . Increasing the chloroplast size ( and hence RubisCO amount ) while keeping the cell surface coverage constant ( Fig 5 ) means more RubisCO per cell surface area and hence a higher assimilation rate , but also a higher photon cost because of the increased RuBP oxygenation in the case of C3 photosynthesis . The C4 cycle , at high enough PEPC concentrations , can reverse this negative trend: at PEPC-to-RubisCO capacity ratios above 3 , C4 photosynthetic efficiency increases with chloroplast size ( for very large chloroplasts C4 photosynthesis is even more efficient than C3 ) . This results in a higher assimilation rate per cell-surface area combined with lower demands on the light-harvesting capacity ( Fig 5 ( b ) ) . We now examine what gains are achievable when energy is a constraining factor . This could be either due to limited light availability or limited light-harvesting capacity . We expect that at energy inputs below the level needed to operate C3 photosynthesis , activating the C4 pump would negatively affect the assimilation rate . Therefore we consider only situations where the energy constraints do not limit C3 photosynthesis . This will be the case at light-utilisation caps of 40 mol m-3s-1 or more ( see e . g . Fig 2 ( b ) ) . If the thylakoid surface area is not the constraining factor in C3 photosynthesis , it should be possible to boost the chloroplast light-harvesting capacity beyond 40 mol m-3s-1 by over-expressing the photosystem complexes and associated proteins on the thylakoid ( this may present a significant engineering challenge however , and there might be engineering obstacles or physical constraints forbidding a much higher light-harvesting capacity ) . To gain an understanding of system behaviour , we proceed with an optimistic prospect that the light-harvesting capacity can be doubled . We thus examine photosynthesis under a realistic light-utilisation cap of 40 mol m-3s-1 , and under an optimistic one of 80 mol m-3s-1 . Fig 6 ( a ) shows how assimilation changes with the PEPC concentration at different envelope permeabilities , when the 40 mol m-3s-1 cap is imposed . The steady-state operation is not affected as long as energy use remains below the cap , so assimilation grows with C4 cycle activity . When energy becomes limiting , the C4 cycle and Calvin-Benson cycle enzymes start to compete for resources , resulting in an increase in futile cycles and reduced net assimilation at high PEPC concentrations . We might expect that the optimal assimilation under an energy constraint is then achieved exactly at the threshold where the energy usage reaches the cap . This is true for 80 mol m-3s-1 light-harvesting capacity , but not for 40 mol m-3s-1 . As the C4 cycle changes the operating conditions in the stroma ( i . e . CO2 levels ) , a situation is possible where a lower RubisCO-bound RuBP concentration ( due to energy constraints ) results in a higher net assimilation . The comparison of the assimilation gains ( with respect to C3 photosynthesis ) at the threshold PEPC concentration where the energy consumption reaches the cap and at the PEPC concentration where the assimilation is maximal is shown in Fig 6 ( c ) . The respective photon costs and PEPC concentrations are shown in Fig 6 ( d ) and 6 ( e ) . It is evident that the C4 cycle activity has to be tuned to obtain the maximal benefit under conditions of limited and variable energy availability . Given that light supply fluctuates continually , dynamic control of the C4 cycle activity would have to be implemented . Alternatively , under-operating the cycle ( i . e . having its activity level below the speculated optimum ) may be a beneficial strategy . Even without a fine-tuned C4 cycle a sizeable gain in the assimilation rate can be expected as long as envelope permeability is not too large . Looking at the photosynthetic performance at the threshold where the energy consumption reaches the 40 mol m-3s-1 cap ( the green dotted line in Fig 6 ( c ) ) , we predict that up to 20% gain in carbon assimilation at the envelope permeability of 600 μm/s may be achieved , with the photon cost rising by less than 10% ( Fig 6 ( d ) . With 80 mol m-3s-1 capacity ( and sufficient sunlight ) large gains are possible over the entire range of the envelope permeability values . Assimilation could even be doubled . Stomatal conductance is continually tuned to the environment and when conductances are low photosynthesis is frequently CO2 deprived . Assimilation gains from using the C4 pump are much more notable at low CO2 pressures in the intra-leaf airspaces , Fig 7 ( a ) . At 120 μbar CO2 the assimilation could be doubled , while still not exceeding the 40 mol m-3s-1 light-utilisation cap ( Fig 7 ( c ) ) . In contrast , at 400 μbar no gain is possible with that energy cap .
We modelled a hypothetical cytoplasm-to-stroma C4 cycle in a C3 mesophyll cell geometry , and quantified carbon assimilation and photosynthetic efficiency . The proposed C4 pump would lead to an increase in the assimilation rate whenever there is sufficient light-harvesting capacity and excess light is available . The magnitude of this gain is highly dependent on CO2 permeability of the chloroplast envelope and on operating conditions , such as the internal airspace CO2 pressure and light availability . At medium envelope permeability ( 600 μm/s ) , CO2 pressure ( 250 μbar ) , and light-harvesting capacity ( 40 mol m-3s-1 ) , the gain is moderate ( 20% ) . At low CO2 pressure ( 125 μbar ) , or at high light availability and harvesting capacity ( 80 mol m-3s-1 ) , the gain is substantial ( 85% ) , Fig 7 ( c ) . The assimilation boost comes at the price of higher photon cost ( except when mesophyll is CO2 deprived ) , which may explain why this C4 photosynthesis strategy is not found in nature , ( i . e . there is likely strong selection pressure to improve the C4 efficacy ) . Modelling the competitive evolution of single-cell vs two-cell C4 photosynthesis analogous to the modelling of Kranz-type C4 photosynthesis evolution by Heckmann et al [57] may provide more definite answers . Heckmann et al [57] determined the most likely order of mutations leading to two-cell C4 photosynthesis assuming a ‘greedy’ evolutionary algorithm . By also considering mutations leading to single-cell C4 varieties , it should be possible to establish which conditions would favour the evolution of single-cell C4 photosynthesis . Due to the design of the model , which assumes optimal functioning of the C3/C4 enzymatic pathways , our predictions always represent the best case scenario . Even so , the large predicted assimilation advantage under conditions of CO2 deprivation is likely robust . As CO2 deprivation is a common hazard facing plants in dry and warm climates—which are typically well-lit—the development of the proposed C4 pathways could be very beneficial for creating drought-resistant high-yield crop strains . It is interesting to note that terrestrial species that have evolved single-celled C4 photosynthesis ( e . g . Suaeda aralocaspica , Bienertia cycloptera ) grow in salty depressions in semi-arid regions—the conditions that would likely lead to low CO2 within the leaf [9 , 10] . Our conclusions are generally in qualitative agreement with von Caemmerer [14] , but the more accurate accounting of energy use and the treatment of gas diffusion in our model produces more optimistic results . Specifically , although we agree with von Caemmerer [14] that the C4 cycle will be cost-inefficient , our results show the difference between carbon assimilation costs in C3 and C4 photosynthesis is smaller at lower envelope permeability or CO2 level , so the operation of a C4 cycle need not be prohibitively expensive . This means higher gains are possible as long as there remains some unused light-harvesting capacity , as Fig 6 ( c ) demonstrates . To understand the reasons for the differences in our conclusions , we attempt a more direct comparison with the results of von Caemmerer and Furbank [13] . At 200 ppm CO2 in the IAS , they predict that operating the C4 pump at 1:1 PEPC-to-RubisCO carboxylation capacity ratio would result in a 40% increase in the assimilation rate and a 70% increase in energy cost per assimilated carbon ( Fig 5 in von Caemmerer and Furbank [13] ) . Their model expresses gas conductances and enzyme catalytic capacities per leaf-surface area , so a comparison to our diffusion model requires an assumption of the mesophyll-to-leaf surface area ratio . For a ratio of 13 . 5 ( similar to values observed in A . thaliana ( 8-10 ) [19] ) , the RubisCO catalytic capacities in the two models match , so we use this value for the comparison . Their conductances would then correspond to the permeabilities of the envelope , and of the cell wall and plasmalemma , of approximately 103 μm/s each . With the same parameters we get a 50% increase in the assimilation rate with a 30% increase in the photon cost ( from 17/C to 22/C ) . There is a significant difference in the predictions of the energy cost of C4 photosynthesis . The difference in part stems from different accounting methods . von Caemmerer and Furbank [13] considers ATP consumption whereas our quantification in terms of light-use takes into account in the fact that the C4 cycle does not need a reductive agent and hence its ATP requirements can be met more efficiently ( up to 20% , conf . Fig 3 ( b ) ) by cyclic electron transfer . In terms of ATP we see a 50% increase in cost . The difference between this value and the 70% increase in von Caemmerer and Furbank [13] is attributable to spatial effects and diffusion . Another promising result is that the pathway’s beneficial effects can be increased further by reducing the chloroplast surface coverage , bringing it into the region in Fig 4 ( a ) where the rise in the photon cost when the C4 pump is active is less pronounced . This minor change to the cell anatomy would allow for the same assimilation rate to be achieved with a reduced chloroplast investment , translating into an even higher plant growth rate . One way this could be accomplished might be to arrest or slow down the chloroplast division cycle . A possible side-effect would be an increase in the average chloroplast size , which would further benefit C4 photosynthesis ( Fig 5 ) . An illustration of possible benefits from a design strategy that combines the implementation of a C4 cycle with alterations in the chloroplast surface coverage is presented in Fig 8 . The design steps are broadly outlined in Fig 8 ( b ) . Fig 8 ( a ) shows how the assimilation rate varies with the surface coverage ( assuming no changes in the chloroplast size ) for C3 photosynthesis , and C4 photosynthesis at 40 mol m-3s-1 and 80 mol m-3s-1 light utilisation thresholds ( compare with Fig 4 ( b ) ) . Starting with C3 photosynthesising chloroplasts at 50% cell surface coverage ( a0 ) , implementing the C4 pump and boosting the light-harvesting capacity to 40 mol m-3s-1 ( a1 ) or 80 mol m-3s-1 ( a2 ) would result in a 15% or an 85% increase in the assimilation rate respectively . Alternatively , at 40 mol m-3s-1 light-harvesting capacity , the number of chloroplasts could be reduced by 20% ( b1 ) without any loss in assimilation compared to C3 photosynthesis . Boosting the light-harvesting capacity to 80 mol m-3s-1 would allow for an even larger reduction in the number of chloroplasts while still maintaining or increasing assimilation ( b2 , c2 ) . Fig 8 ( c ) and 8 ( d ) illustrate how the suggested modifications would move the system on the photon cost and assimilation rate landscapes . If the chloroplasts are also enlarged in the process , even larger gains may be possible . The level of required C4 cycle expression , quantified by the PEPC/RubisCO carboxylation capacity ratio , would not exceed the observed level of C4 cycle activity in C4 plants ( 2-7 [11] ) , even at 80 mol m-3s-1 light-harvesting capacity ( Fig 8 ( a ) ) . A regulation mechanism would have to be incorporated however , to moderate the activity of the C4 pump based on the energy availability , so as to prevent it from competing adversely with the Calvin-Benson cycle in low-light conditions . Regulation of the C4 cycle based on the ambient light levels and CO2 availability is already present in Kranz-type C4 plants [58] , so implementing existing C4 regulatory mechanisms may allow this . The relative expression of the two photosystems would also need to be rebalanced , to allow for a larger cyclic electron current through PS-I ( Fig 8 ( a ) ) . The cyclic current would constitute ∼ 30% of total electron current through PS-I at 40 mol m-3s-1 , and ∼ 60% at 80 mol m-3s-1 . Such large cyclic current fractions are not commonly seen in C3 plants ( though they are normal in C4 plants ) , however C3 plants can adapt to use cyclic transfer more ( up to 50% of electron current through PS-I ) if circumstances so require [33] . The optimal modification strategy when introducing the C4 cycle would be the one that maximises the return on resource investment . To calculate this however , the maintenance costs also need to be established . Quantifying the return-on-investment and deciding the optimal strategy will require additional research . | Feeding the estimated world population of 9 billion people by 2050 presents a major challenge . Crop yields currently increase by about 1% each year . They would need to grow almost twice as fast to ensure global food security . New technologies that boost plant productivity are needed . A fundamental factor limiting plant growth is the speed with which plants can carry out photosynthesis . Few plants have evolved additional ‘pre-processing’ steps—the C4-cycle is one—that improve their photosynthetic efficiency and increase their drought resistance . Efforts are being made to introduce some form of C4-cycle into crops without it , but they encounter serious challenges: one should alter not only the plant’s biochemistry but also the anatomy of its leaves and cells to match that of C4-cycle plants . The question is , are all the alterations actually needed ? We have used computational modelling to examine a scenario where the C4-cycle is introduced in an alternate way , with no anatomy changes made . We find that even this ( previously thought unpromising ) implementation strategy can substantially boost plant photosynthesis , and hence growth rate , especially in the case of plants in dry , high-sunlight climates . | [
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] | 2019 | Computational modelling predicts substantial carbon assimilation gains for C3 plants with a single-celled C4 biochemical pump |
The Asian tiger mosquito ( Aedes albopictus ) is an invasive species and important arbovirus vector that was introduced into the U . S . in the 1980's where it continues to expand its range . Winter temperature is an important constraint to its northward expansion , with potential range limits located between the 0° and -5°C mean cold month isotherm . Connecticut is located within this climatic zone and therefore , Ae . albopictus was monitored statewide to assess its northern range expansion and to delineate where populations can stably persist . Ae . albopictus females were monitored at fixed trapping sites throughout Connecticut from June-October over a 20-year period , 1997–2016 . In addition , Ae . albopictus larvae and pupae were collected from tire habitats and tires were retrieved from the field in the spring and flooded to evaluate overwintering success of hatching larvae . Ae . albopictus was first detected during statewide surveillance when a single adult female was collected in 2006 . This species was not collected again until 2010 and was subsequently detected each successive year with increasing abundance and distribution except following the unusually cold winters of 2014 and 2015 . Ae . albopictus mosquitoes were most abundant in urban and suburban locations along the southwestern shoreline of Connecticut; however , single specimens were occasionally detected in central parts of the state . Field-collected females were also screened for arbovirus infection yielding two isolations of Cache Valley virus and one isolation of West Nile virus , highlighting the threat posed by this mosquito . Ae . albopictus overwintered in Connecticut under mild winter conditions as shown by recovery of hatched larvae from field collected tires in spring and by early season detection of larvae and pupae . This study documents the establishment and expansion of Ae . albopictus at the northern boundary of its range in the northeastern U . S . and provides a baseline for monitoring the future spread of this species anticipated under climate change .
The Asian tiger mosquito , Aedes albopictus ( Skuse ) , is an invasive species that has spread from East Asia to the Americas , Africa , Europe , and the Middle East primarily by the global tire trade [1 , 2] . This mosquito inhabits urbanized environments [3] , develops in artificial , water-holding containers [4] , and feeds readily on humans , making it a major pest species throughout its range [5–7] . Ae . albopictus is also an important vector of dengue virus ( DENV ) , chikungunya virus ( CHIKV ) , and Zika virus ( ZIKV ) in many parts of the world . This includes its primary role in outbreaks of DENV in China [8] , CHIKV in the Indian Ocean Islands [9] , ZIKV in central Africa [10] , and autochthonous transmission of DENV and CHIKV in southern Europe [11–13] . In addition , Ae . albopictus is competent to transmit 23 other arboviruses [14] . This includes eastern equine encephalitis virus , West Nile virus ( WNV ) , and La Crosse virus that have also been isolated from this species during field investigations in the U . S . [3 , 15 , 16] . In the continental U . S . , the first established population of Ae . albopictus was documented in Houston , Texas in 1985 [17] , followed by its rapid expansion throughout southeastern and parts of northeastern and northcentral U . S . [2 , 18] . Ae . albopictus has been occasionally collected in locations as far north as Minnesota , New Hampshire , and Ontario , Canada , but these records most likely represent seasonal introductions rather than permanent resident populations [19 , 20] . The northern distribution limit for overwintering populations was conservatively estimated to be at the 0°C cold-month isotherm based on its distribution in Asia [21] . The southern coast of Connecticut is at this thermal limit of Ae . albopictus and possesses suitable habitat for colonization by this species . In 2003 , Ae . albopictus ( a single female ) was first collected during field studies in southern Connecticut but was not further detected that same year during statewide mosquito surveillance [22] . An infestation of this species was then discovered at a tire recycling plant in northeastern Connecticut in 2006 , yet the population failed to overwinter and establish at the site [23] . Clearly , continued monitoring for this species is warranted in this region , particularly in urban and suburban locations and under milder winter conditions that are anticipated to increase in frequency due to climate change . In this study , we document the northern range expansion of Ae . albopictus by continuous monitoring of mosquito populations over a 20 year period in Connecticut . Mosquitoes were collected at 91 fixed trapping locations from June-October , identified to the species level , and tested for arbovirus infection as a part of the statewide mosquito arbovirus surveillance program . Mosquito larvae and pupae were also monitored in a used tire habitat in Stratford , ( Fairfield County ) Connecticut at a known locality for Ae . albopictus reproduction . Tires were also retrieved from the field in the spring and flooded to evaluate overwintering success of Ae . albopictus . From this effort , we describe the initial detection of Ae . albopictus in 2006 , its annual reemergence and population expansion in the state from 2010–2016 , and its local overwintering under mild winter conditions .
Mosquitoes were collected at 91 trapping locations statewide ( Figs 1 and 2 ) as a part of the Connecticut Mosquito and Arbovirus Surveillance Program from June through October [24] . Thirty-six of the sites have been monitored since 1997 with the balance of sites in continuous operation since 2000 . About half of the sites are located in surburban and urban locations , primarily in the southwestern and central parts of the state , which are focal areas for WNV . Specific trapping locales include neighborhood parks , school grounds , sewage treatment plants , municipal dumping stations , industrial parks , and fragmented woodlots . The remaining sites are located in more rural settings that are associated with eastern equine encephalitis virus transmission and include permanent fresh-water swamps and bogs , coastal salt marshes , and mixed woodlands . Each trapping site was sampled on average weekly and at least once every 10 days by setting a CO2-baited CDC light trap and a gravid trap ( John W . Hock Co . , Gainesville , FL ) baited with a hay/yeast/lactalbumin infusion [25] . In addition , BG Sentinel ( BGS ) traps baited with the BG–Lure but without CO2 ( Biogents AG , Regensburg , Germany ) were added after the detection of Ae . albopictus at a site . Traps were operated overnight and adult mosquitoes were transported back to the laboratory alive in an ice chest . Mosquitoes were immobilized with dry ice , sorted , and identified to species on chill tables with the aid of a stereo microscope and taxonomic keys [22] . Female mosquitoes were combined into pools of 50 or fewer individuals by location , trap type , species , and collection date in 2 mL microcentrifuge tubes containing a copper BB , and stored at -80°C until virus testing . Mosquito pools were prepared for virus isolation by adding 1 mL of PBS-G ( phosphate buffered saline , 30% heat-inactivated rabbit serum , 0 . 5% gelatin , and antibiotic/antimycotic ) to each tube . Mosquito pools were homogenized using the MM300 Mixer Mill ( Retsch Laboratory , Hann , Germany ) set for 4 min at 25 cycles/sec . Mosquito homogenates were centrifuged for 6 minutes at 7 , 000 rpm at 4°C and 100 μL of the supernatant was inoculated into Vero cell cultures- clone E6 ( provided by Shirley Tirrell , Yale University ) growing in 25 cm2 flasks . Vero cells were maintained at 37°C and 5% CO2 and examined daily for cytopathic effect from days 3–7 post-inoculation . RNA was extracted from infected cell supernatants and the corresponding mosquito pool using the viral RNA kit ( Qiagen , Valencia , CA ) . Virus isolates were identified as WNV or Cache Valley virus ( CVV ) using PCR-based assays as previously described [26 , 27] . From 2013–2016 , larvae and pupae were sampled from a location in Stratford , Connecticut . This site is a small woodlot , located in a mixed residential and commercial neighborhood with an abandoned pile of about 30 waste tires concealed in the woods . The majority of the tires at the pile were not available for oviposition and larval development because they were buried or completely filled with organic material . Seven tires were flagged , positioned upright , and sampled weekly from May-October . Water samples were removed from tires with a small plastic container and fresh water was added back to maintain water levels in the tires . Mosquito larvae were identified using taxonomic keys under a compound microscope and pupae were allowed to emerge as adults prior to their identification . To evaluate mosquito overwintering from 2013–2016 , four tires were retrieved from the Stratford site in late April , and residual water and sediments were removed and inspected for larvae . The tires were then placed in a greenhouse , exposed to natural light and photoperiod , oriented in the same position as in the field , and flooded with water . Tires were inspected daily and larvae were identified as described above . The tires were then emptied and re-flooded a second time after a 2–3 week period when larvae were no longer observed , to evaluate if there was a delayed hatch . To assess the most effective method for collecting Ae . albopictus , we compared numbers of females collected in CDC-light , BGS , and gravid traps . Trap evaluations were performed only when all three trap types were in operation from 2010–2016 ( n = 474 trap-nights ) . The mean number of Ae . albopictus collected per trap night was calculated for each trap type and compared by Krustal-Wallis , one-way analysis of variance ( ANOVA ) of ranks ( Prism 7 . 0 , GraphPad software ) . Dunn's multiple comparison post-tests were then applied to identify which pairs of trap types significantly differed from each other . Daily weather summaries were obtained from the National Oceanic and Atmospheric Administration National Centers for Environmental Information ( https://www . ncdc . noaa . gov/cdo-web/datatools/selectlocation ) for four locations in coastal Fairfield and New Haven counties: Bridgeport , Success Hill ( 41 . 200°N , 73 . 157°W ) , Stamford , 5N ( 41 . 125°N , 73 . 548°W ) , Stratford , Sikorsky Airport ( 41 . 158°N , 73 . 129°W ) , and New Haven , Tweed Airport ( 41 . 264°N , 72 . 887°W ) . Mean monthly temperatures were calculated for the winter months ( Fig 3 ) , and the lowest temperature recorded at each site per month was noted .
Figs 1 and 2 depict the spatial-temporal distribution and abundance of female Ae . albopictus collected during statewide mosquito trapping efforts in Connecticut . The first female Ae . albopictus was collected from a gravid trap located in New Haven County during 2006 , with no additional collections until 2010 ( N = 2 ) and 2011 ( N = 12 ) . The number of Ae . albopictus increased substantially during 2012 ( N = 245 , 11 locations ) and 2013 ( N = 547 , 11 locations ) . During these years , positive sites were located in southwestern coastal Connecticut with the exception of one adult female collected in the central part of the state . Ae . albopictus collections decreased during 2014 ( N = 133 , 9 locations ) and 2015 ( N = 220 , 10 locations ) following winters with mean monthly temperatures well below the 0°C threshold and then increased sharply during 2016 ( N = 936 , 24 locations ) following a more moderate winter ( Fig 3 ) . Ae . albopictus were detected primarily in densely-populated communities along the southwestern portion of the state; however , single specimens were also collected from a few sites in central Connecticut . To evaluate the effectiveness of commonly implemented mosquito traps and methodologies , we compared Ae . albopictus collections for each trap configuration used in this study . The mean number of Ae . albopictus collected per trap night differed among the BGS trap with BG Lure ( mean = 1 . 6/trap night ) , CDC light trap with CO2 ( mean = 1 . 1/trap night ) and gravid trap with hay infusion ( mean = 0 . 4/trap night ) ( Krustal-Wallis ANOVA p<0 . 0001 ) . Pairwise comparisons revealed significant differences in trapping efficiency between the BGS trap and gravid trap ( Dunn's post-test p<0 . 0001 ) and the CDC-light trap and gravid trap ( p<0 . 01 ) , but not between the BGS and CDC-light traps ( p = 0 . 7 ) . Female Ae . albopictus were processed and screened for arbovirus infection by Vero cell culture assay . There were two isolations of CVV from Ae . albopictus collected in Stratford in 2014 and one isolation of WNV from Bridgeport in 2016 . WNV infection was then reconfirmed by directly testing the positive mosquito pool by real-time RT-PCR ( Ct = 22 . 9 ) . Ae . albopictus larvae and pupae were sampled weekly from tire habitats in Stratford , CT and compared to adult trap collections from the same site ( Fig 4 ) . Larvae and pupae were collected much earlier in the season during 2013 following a mild winter than in subsequent years ( Fig 3 ) . During 2013 , the numbers of juvenile Ae . albopictus fluctuated throughout the season with peak abundances occurring during early May , August , and September . Adult collections lagged behind the larval- and pupal-cohorts with peaks occurring in early and late September . During 2014 and 2015 , Ae . albopictus immatures and adults closely tracked each other , appearing later in the season and in fewer numbers than during 2013 . Ae . albopictus populations rebounded during 2016; however , larvae appeared later in the season starting in July and peaked in late August to early September in parallel with adult collections . To determine whether Ae . albopictus may survive winters in Connecticut , we retrieved four tires from the Stratford site in April and flooded them twice with water to recover hatched larvae . A total of 51 Ae . albopictus larvae were recovered during 2013 , but none were collected from tires retrieved from the site in late April during 2014 , 2015 , or 2016 . Other mosquito species hatching from the tires included Aedes japonicus ( Theobald ) ( 2013 N = 39 and 2014 N = 5 ) and Aedes triseriatus ( Coquillett ) ( 2013 N = 60 and 2014 N = 22 ) ; these species were not enumerated during 2015 and 2016 .
This study documents the establishment and expansion of Ae . albopictus at the northern boundary of its range in southern New England . This species was first detected during statewide mosquito surveillance in 2006 and then reemerged every summer starting in 2010 . Ae . albopictus mosquitoes were most abundant in urban and suburban locations along the shoreline of southwestern Connecticut; however , single specimens were occasionally detected in central parts of the state . Mosquito abundance and the number of positive traps increased every year starting in 2010 except following the cold winters of 2013–2014 and 2014–2015 . These changes in abundance and distribution are not an artifact of sampling effort because mosquito populations were consistently sampled at fixed trapping sites over a 20 year period . We conclude that Ae . albopictus is expanding northward in the northeastern U . S . and this trend is anticipated to accelerate under conditions of climate change as previously noted [28 , 29] . We also showed that Ae . albopictus was able to overwinter in Connecticut during 2012–2013 . Local overwintering was demonstrated by recovery of larvae hatching in late April from tires that were left outside during winter and by early seasonal detection of Ae . albopictus larvae and pupae . This finding , however , could not be reproduced during the following two winters that were exceptionally cold ( January and February mean temperatures -2 . 2 to -7 . 2°C ) or following a more mild winter during 2015–2016 ( Fig 3 ) . The performance of overwintering populations is affected by the number diapause-conditioned eggs laid during the previous fall , and overwintering temperatures and conditions impacting egg mortality . The lack of evidence for overwintering success observed in the spring of 2016 following a warmer winter may be explained , in part , by a lower abundance of females in the previous fall , leading to a lower number of diapaused eggs deposited for overwintering . It is also noteworthy that temperatures dropped to an absolute low of -21°C during a cold snap in February 2016 despite warmer mean temperatures for the entire month as shown in Fig 3 . This is well below the 50% survival threshold estimated for diapausing Ae . albopictus eggs which ranges from -5 to -13°C for a 24 hour exposure period and approaches the -26°C supercooling point that is 100% lethal under brief exposures [30 , 31] . The primary mechanism driving the annual reemergence of Ae . albopictus in Connecticut is unknown , but may be due to local overwintering of mosquito eggs , annual reintroduction of mosquitoes from southern locations , or some combination of both of these processes . Environmental conditions appeared to be lethal for diapausing eggs during three of the four years of this study based on recovery of Ae . albopictus from overwintering tires during the spring . Nevertheless , we cannot preclude the possibility that this species may survive at low levels in microhabitats that were not represented in our study . Ae . albopictus eggs were shown to survive winter temperatures as low as -19°C in northern Indiana , presumably due to the insulating effects of snow cover [32] . Snow drifts , sheltered structures , and urban heat transfers may enhance overwintering survival of this species at the northern distribution limit . Further studies on the field survival of diapause eggs under different microhabitat conditions and temperature regimes are needed to better understand the conditions affecting its overwintering success . Ae . albopictus is readily dispersed over long distances by the transport of dormant eggs and emergent larvae and pupae in tires and other water-holding containers [33 , 34] . This may be an important mechanism for reestablishing populations after severe winter conditions . Potential reintroduction would most likely be human mediated as Ae . albopictus is a short distance disperser where most females remain within 300 m of the larval habitat from which they emerge [35] . The observation of late season emergence of Ae . albopictus in our study sites may reflect either reintroduction of mosquitoes from more southerly locations or perhaps low levels of mosquito overwintering and hatching in the spring that could not be readily detected until later in the summer when the populations rebounded . We cannot distinguish from these possibilities based on our current data but show direct evidence for mosquito overwintering at least under mild winter conditions . A number of studies have estimated the geographic distribution of Ae . albopictus by correlating current thermal conditions with mosquito distribution records and using this data to develop spatial models to predict its potential range . Our findings are in close agreement with predicted northern range limits along the southern margin of Connecticut , Rhode Island , and Massachusetts based on mean annual and winter temperatures [21 , 28 , 36] . Other studies show the distribution limit extending further north well into New England , including most of Connecticut [37 , 38] , yet we did not find stable populations in the interior part of the state . Mean winter temperature was identified as the most significant environmental factor predicting its current range within northeastern U . S . [28] , with thermal limits estimated between the 0°C and -5°C cold month isotherm [21 , 36] . The observed distribution of Ae . albopictus populations in Connecticut aligns more closely to the 0°C isotherm; however , we anticipate future range expansion as founding populations continue to build , along with milder winters and hotter summers that are projected to increase in frequency under climate change [39] . Our current trapping methods for mosquito monitoring rely mainly on the deployment of CO2-baited CDC light and gravid traps . CDC light traps are effective for collecting a diversity of crepuscular and nocturnal feeding Anopheline and Culicine species , whereas gravid traps are more effective for trapping gravid Culex species . Neither trap type is particularly effective for collecting diurnal feeding and container-inhabiting Aedes species so we supplemented our collections by deploying BGS traps that have shown promise for the collection of Ae . albopictus . In this study , the BGS trap did not significantly improve capture of Ae . albopictus over the CDC light trap but both of these traps were significantly better than the gravid trap . These findings contrast with trap evaluations that were performed in locations with more established Ae . albopictus populations [40 , 41] . In these studies , BGS traps outperformed CDC light traps that may be explained by their combined use of CO2 and BG-lures and/or the overall density of Ae . albopictus at the trapping site . In this study , we isolated WNV and CVV from Ae . albopictus collected in Connecticut , raising concerns about its role as a potential arbovirus vector . Both of these arboviruses have been detected in field-collected Ae . albopictus from other states , including Pennsylvania and New Jersey [3 , 42 , 43] . Ae . albopictus has also been implicated as a potential bridge vector of WNV based on its vector competence [44] and feeding behavior that occasionally includes blood meals from virus-competent birds [45 , 46] . However , in another study , WNV infection was not detected in more than 30 , 000 Ae . albopictus collected in New Jersey despite concurrent WNV amplification in the region , suggesting a limited role for this species as a vector [43] . It is striking that we isolated both CVV and WNV from a limited sample of Ae . albopictus , highlighting its potential threat , but further sampling and testing are required to assess its overall contribution . In addition , exotic arboviruses such as CHIKV , DENV , and ZIKV are frequently introduced into the U . S . by infected travelers returning from endemic countries necessitating research on the vector competence and capacity of local Ae . albopictus populations for these pathogens . This is an important priority given the central role that this species had in the autochthonous transmission of DENV and CHIKV in unlikely places such as Hawaii , France , Italy , and Japan [11 , 12 , 47 , 48] . | The Asian tiger mosquito ( Aedes albopictus ) is a highly invasive species and an important disease vector that is undergoing rapid range expansion in many countries including the U . S . Winter temperature is an important limit to its northward expansion with Connecticut situated near the northern boundary of its potential range . In this study , we sampled mosquitoes at fixed trapping sites located statewide to track the establishment and range expansion of Ae . albopictus in this region . In addition , mosquito larvae were monitored in tire habitats to evaluate overwintering success of local populations . From this effort , we describe the initial detection of Ae . albopictus in 2006 , its annual reemergence and population expansion in southwestern Connecticut from 2010–2016 , and its local overwintering under mild winter conditions . Together , this study documents population changes in Ae . albopictus at the northern boundary of its range and provides a baseline for monitoring future range expansion anticipated under climate change . | [
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] | 2017 | Northern range expansion of the Asian tiger mosquito (Aedes albopictus): Analysis of mosquito data from Connecticut, USA |
The functional HIV-1 envelope glycoprotein ( Env ) trimer , the target of anti-HIV-1 neutralizing antibodies ( Abs ) , is innately labile and coexists with non-native forms of Env . This lability and heterogeneity in Env has been associated with its tendency to elicit non-neutralizing Abs . Here , we use directed evolution to overcome instability and heterogeneity of a primary Env spike . HIV-1 virions were subjected to iterative cycles of destabilization followed by replication to select for Envs with enhanced stability . Two separate pools of stable Env variants with distinct sequence changes were selected using this method . Clones isolated from these viral pools could withstand heat , denaturants and other destabilizing conditions . Seven mutations in Env were associated with increased trimer stability , primarily in the heptad repeat regions of gp41 , but also in V1 of gp120 . Combining the seven mutations generated a variant Env with superior homogeneity and stability . This variant spike moreover showed resistance to proteolysis and to dissociation by detergent . Heterogeneity within the functional population of hyper-stable Envs was also reduced , as evidenced by a relative decrease in a proportion of virus that is resistant to the neutralizing Ab , PG9 . The latter result may reflect a change in glycans on the stabilized Envs . The stabilizing mutations also increased the proportion of secreted gp140 existing in a trimeric conformation . Finally , several Env-stabilizing substitutions could stabilize Env spikes from HIV-1 clades A , B and C . Spike stabilizing mutations may be useful in the development of Env immunogens that stably retain native , trimeric structure .
For an HIV/AIDS vaccine to be effective , it is widely thought that it should elicit high titers of broadly neutralizing antibody ( Ab ) [1] , [2] . HIV-1 neutralizing Abs target the envelope glycoprotein ( Env ) spike , which is a trimer containing three copies each of the surface subunit , gp120 , and the transmembrane subunit , gp41 [3] . A major confounding issue in the rational development of Env as a vaccine is that fusion-competent Env trimers are often labile and heterogeneous , so distinguishing fusogenic from other forms of Env can be challenging [4]–[8] . Non-native forms of Env include dissociated gp120 monomers and dimers , gp41 stumps , monomers and oligomers of unprocessed gp160 , as well as Env with aberrant disulfides and heterogeneous glycosylation [6] , [7] , [9]–[11] . In particular , non-native forms of Env may serve as immune decoys and elicit non-neutralizing Abs [6] , [12]–[14] . Envs that are truncated prior to the gp41 transmembrane ( TM ) domain have in some cases been engineered as trimers , but these are not in a native conformation as , unlike native Env , they are typically recognized by non-neutralizing Abs and also elicit non-neutralizing Abs after immunization [15]–[20] . Thus , limiting exposure to the immune system of non-fusogenic forms of Env through stabilization of the native structure may facilitate HIV-1 vaccine design . HIV-1 Env spikes are held together by non-covalent interactions among its subunits . Mutations that accelerate spontaneous or CD4 receptor-induced dissociation of gp120 from the HIV-1 Env complex can be found in various regions including the N-heptad repeat ( NHR ) [21] , the disulfide loop ( DSL ) [22] and C-heptad repeat ( CHR ) regions [21] , [23] of gp41 , as well as in the C1 [24] , V3 [25] , β3–β5 loop of C2 [26] , and C5 [27] regions of gp120 . This may be expected on chance , as random mutations are much more likely to disrupt than stabilize the structure-function of a protein . Indeed , mutations that would stabilize Env trimers in the active membrane-anchored form have not been forthcoming or even reportedly sought after . One potential solution has been the introduction of a disulfide-bond between gp120 C5 and the DSL of gp41 ( e . g . 501C and 605C; known as “SOS” ) , which , when exposed to a reducing agent , breaks and allows for productive entry of SOS-modified HIV-1 into target cells [28] , [29] . However , the disulfide bond is subject to exchange and can also leave many non-neutralizing epitopes exposed , at least in soluble forms of the SOS molecule [30] , [31] . Thus , we envisioned an alternative strategy that allows the virus to select for mutations that stabilize the Env trimer naturally , without compromising native structure or antigenicity . We have shown previously that , depending on the viral isolate , virion associated Env can have different levels of heterogeneity and can have a range in stabilities to conditions such as elevated temperature , prolonged incubation at 37°C or exposure to denaturants [6] , [7] . Env from the clade B isolate ADA is both labile and heterogeneous [7] . An ADA variant , AD8 , has recently been associated with rapid elicitation of broadly neutralizing Abs in a macaque model and so is relevant to vaccine research [32] . Heterogeneous and labile Envs can be problematic to study because non-fusogenic forms of Env can accumulate and confound measurements [7] . Diversity in glycosylation may account for some of the observed heterogeneity , even in functional Env , as certain cloned isolates of HIV-1 are incompletely neutralized by glycan-sensitive Abs such as PG9 and PG16 [10] . Directed evolution is often used in virology to study cellular tropism or resistance to neutralizing antibodies or antiviral drugs . HIV-1 has been selected to resist spontaneous inactivation of Env at cold temperatures or to overcome functional defects associated with truncated Envs [33] , [34] . Increasing the stability and homogeneity of native Env might also improve Env-based vaccines by limiting potentially distracting non-neutralizing and immunogenic surfaces of Env , improve correlations between observed Env structures and their associated functions , as well as inform the design of more molecularly defined immunogens [4]–[6] . We therefore devised a strategy in which Env is selected specifically for increased stability in its unliganded , functional state . Thus , HIV-1 is subjected to iterative cycles of harsh destabilizing conditions and subsequent viral expansion . We show that this approach can overcome as well as help understand the molecular heterogeneity and lability of viral spikes , which may have implications for the design of immunogens based on functional , unliganded and membrane-anchored Env of multiple clades of HIV-1 .
We previously showed that Env from the clade B , R5 isolate ADA was relatively heterogeneous as well as labile to heat , guanidinium hydrochloride ( GuHCl ) and to spontaneous inactivation at physiological temperature [7] . We generated mutant pools of HIV-1 ADA using site-directed mutagenesis targeted to regions of Env shown previously to affect Env trimer stability ( Figure S1A ) . However , random mutant clones were found to be mostly non-infectious and when library DNAs were transfected into 293T cells , the virus produced was of very low titer , most likely because of the high number of mutations targeted to conserved regions of Env ( Figure S1B and C ) . Nevertheless , extensive passaging in MT2-CCR5ΔCT cells yielded high titer virus with each pool ( Figure S1D ) . To select for stable ADA variants , the pools of virions were treated separately with incremental concentrations of GuHCl , urea , hyper-physiological temperatures or prolonged incubations at physiological temperature that inactivates wild-type ADA [7] . After destabilizing treatment , the surviving infectious viruses were rescued on MT2-CCR5ΔCT cells . Following three rounds of selection , some virion pools were more stable than ADA wild-type to GuHCl and heat treatment ( e . g . B21 and B22 ) , one was resistant to heat ( e . g . C11 ) , and two pools were resistant to 37°C decay ( e . g . C12 and M2; Figure 1 ) . None of the selected library pools were found to have increased stability to urea . To identify individual Env mutants of increased stability , viral RNA was purified from the stability-enriched pools and env was amplified using RT-PCR . Only the ectodomain portion of Env was subcloned back into the pLAI display vector in order to rule out mutations in the gp41 TM and cytoplasmic tail ( CT ) domains that might affect interactions below the viral membrane , such as between the gp41 CT and Gag [35] , and would add further complexity to the analysis . Individual Env clones were picked from each pool and the corresponding virions were assayed for resistance to each of the selection conditions ( Figure 2 ) . A variety of stability phenotypes were observed . Notably , some clones from the GuHCl and heat-treated pools fully recapitulated the stability phenotype of the originating viral populations ( Figure 2A , B , and C ) . For virion pools that showed resistance to decay at 37°C ( i . e . PM and PC1 ) , none of the cloned Envs approached the stability of the corresponding pool ( Figure 2D and data not shown ) . We therefore took an alternative limiting dilution approach to identify stable mutants . Using MT2-CCR5ΔCT cells as target cells , six limiting dilution wells for each pool were found to be equal in stability to the parental pools ( Figure 2E and F ) . In summary , we successfully identified HIV-1 mutant clones that were stable to the same conditions as the pools from which they were derived . To understand the basis of the hyperstable phenotypes of the rescued mutant clones , we determined the primary Env sequences . Mutations were identified , remarkably , only in regions of ADA Env not targeted by our mutagenesis procedure , most likely because the targeted mutations negatively impacted infectivity and any mutants were out-competed by the small number of virions incorporating mostly wild-type Env sequence . From the heat-stable library pool HC11 , all of the clones contained the substitution S649A in the CHR region of gp41 , whereas some but not all clones contained two substitutions in gp120 C5 ( S462G and D474N ) and an alteration to gp120 V1 ( i . e . deletion of N139/I140 plus an N142S substitution that will hereby be referred to as “V1alt” ) . These mutations in the HC11 clones appeared to arise de novo . ( Figure 3A ) . Sequencing of Env from the GuHCl-stable and heat-stable library pools , G and HB2 , surprisingly revealed mutations to residues in common with the LAI strain that was used in engineering the display vector , pLAI ( Figure 3B and 3C ) . In these clones , recombination events involving a very small amount of LAI DNA appear to have occurred during the PCR used in library production . Importantly , all of the selected clones were more stable than either the parental strain , ADA , or the LAI strain ( Figure 3C ) . Multiple recombination events appear to have taken place during the selection process resulting in virions containing different amounts of LAI-derived gp120 sequence , and among the most stable clones , LAI-derived sequence from gp41 , but not from gp120 , was associated with improved Env stability . Thus , the presence of LAI gp120 was significantly inversely correlated with Env stability ( Figure 4 ) and the most stable clone ( GB21-6 ) contained full LAI gp41 and ADA gp120 . In total , 9 amino acid residues in the gp41 ectodomain differ between ADA and LAI ( Figure 3 ) . In addition , a tenth mutation , K574R , arose de novo and was conserved amongst the stable clones . The stability of the selected Env clones can either be specific to the selection conditions , or can impart a broader resistance to multiple destabilizing treatments . To investigate , the most stable clones from the GuHCl and heat-selected pools , GB21-6 and HC11-1 , were subjected in parallel to GuHCl , heat and prolonged incubation at physiological temperature . Both GB21-6 and HC11-1 maintained infectivity with increased resistance to each of these conditions relative to wild-type ADA ( Figure 5 ) . Clone GB21-6 was consistently the most stable in each case . In keeping with our prior observation of a negative correlation between the number of LAI gp120 residues and Env stability ( Figure 4 ) , LAI was less stable than GB21-6 when treated with heat or GuHCl , but notably the two viruses displayed similar rates of decay at physiological temperature ( Figure 5 ) . To determine the relationship between functional stability and oligomeric stability of the unliganded mutant Env trimers , we turned to BN-PAGE . Two GuHCl resistant and two heat resistant clones were chosen for the analysis: GB21-6 , GB22-1 , HB22-5 and HC11-4 , the latter of which shares similar stability and most of the same mutations as HC11-1 ( Figure 3 ) . The clones were subjected to increasing temperature or GuHCl concentrations , and samples were analyzed for infectivity and by BN-PAGE . In each case , dissociation of the Env trimer on BN-PAGE closely correlated with the loss of infectivity , with stability selected Envs clearly maintaining trimeric association under conditions that caused dissociation of wild-type ADA trimers ( Figure 6 ) . HC11-1 , used above and in subsequent analyses , was also found to be more stable ( data not shown ) . Importantly , the stable Env variants also appear more homogeneous than that of wild-type ADA as non-trimeric species in the former are much less apparent than in the latter . Certain neutralizing monoclonal Abs ( mAbs ) and other inhibitory ligands , such as soluble CD4 ( sCD4 ) , can destabilize and irreversibly inactivate the Env trimer upon binding [36]–[38] . We used two approaches to determine whether the stabilized clones of HIV-1 Env would resist ligand destabilization . In the first approach , we employed a modified virus capture assay ( VCA ) that was designed to crudely mimic Env destabilization through host receptor engagement . Thus , GB21-6 or HC11-1 virions were incubated in solution with increasing concentrations of sCD4 for 15 min , and then overlaid on microwells coated with either mAb hNM01 ( anti-V3 ) or X5 ( anti-coreceptor binding site ( CoRbs ) ) . Unbound virus was then washed away and TZM-bl target cells were overlaid to measure remaining infectivity . Binding of sCD4 to the Env trimer initially exposes the V3 loop and CoRbs in a fusion-active state [39] , but after a short period the sCD4-bound trimer decays to an inactive state [36] . The VCA design provides an aggregate measure of induction of conformational changes by sCD4 and the functional stability of the sCD4-activated state . As expected , capture of infectious wild-type ADA was increased by low concentrations of sCD4 that promotes exposure of the epitopes of the capture mAbs , but decreased at higher concentrations as virions were inactivated ( Figure 7 ) . By contrast , when using the same initial concentrations of sCD4 capture efficiency of infectious virus was increased for both GB21-6 and HC11-1 , and , at high concentrations of sCD4 that inactivated wild-type ADA , the infectivities of GB21-6 and HC11-1 were still intact . We note that in the absence of sCD4 mAbs X5 and hNM01 captured lower levels of GB21-6 and HC11-1 relative to wild-type ADA ( Figure 7 ) . This may be related to the BN-PAGE results showing that these viruses display more homogeneous trimers ( Figure 6 ) , as mAb X5 does not appear to bind the unliganded trimers of most primary isolates and likely captures virions via non-native Env [6] . If an inhibitor binds and inactivates Env , the IC50 of that inhibitor is expected to decrease over time [37] . In a second approach to assay ligand-induced Env destabilization , GB21-6 and HC11-1 viruses were pre-incubated with mAbs or inhibitors for two different periods of time prior to measuring viral infectivity on target cells . Of the inhibitors we tested , mAbs b12 ( anti-CD4 binding site; CD4bs ) , 4E10 and 2F5 ( anti-membrane proximal external region; MPER ) , as well as sCD4 have all been shown to destabilize Env trimers [36] , [37]; mAb VRC01 ( anti-CD4bs ) has a much weaker destabilizing effect [40]; mAb PG9 ( anti-V2/V3 ) has not been well studied in this context [41]; C34 ( anti-NHR ) should have no activity towards unliganded Env as it only binds Env post-CD4 engagement [42]; and PF-348089 is an analogue of BMS-378806 that binds to gp120 and prevents CD4-induced conformational changes [43] , [44] . Consistent with the VCA results above , both GB21-6 and HC11-1 resisted inactivation by sCD4; similar resistance was also observed using b12 , 4E10 and 2F5 ( Figure 8A; Table S1 ) . Thus , whereas the IC50s of these inhibitors against wild-type ADA decreased 9–15-fold from the first ( 1 h ) to the second ( 20 h ) pre-incubation time point , the IC50 decreases with GB21-6 and HC11-1 were only 3–6-fold and 4–8-fold , respectively . Notably , VRC01 affected all three viruses equivalently . PG9 inactivated both wild-type ADA and clone GB21-6 with IC50 decreases of ∼10-fold between pre-incubation times . In contrast , HC11-1 resisted PG9 inactivation , but this clone contains an alteration in V1 that may directly affect the PG9 epitope . Finally and as expected , C34 and PF-348089 did not inactivate any of the viruses over time . The above inhibition data can also be expressed as a ratio of IC50s of the mutants to that of wild-type ADA using the standard 1 h pre-incubation time ( Figure 8B and Table S1 ) . Expressed in this way , GB21-6 is 12–30-fold more resistant than wild-type virus to sCD4 , b12 , 4E10 and 2F5 , while HC11-1 is also resistant to these inhibitors , but to a lesser extent . The same two mutants are not generally more resistant to VRC01 , PG9 , and C34 , with some exceptions that are most likely due to sequence changes that directly affect ligand binding ( Figure 3 ) . Hence , the stable clones tend to be more resistant to ligand-induced inactivation , except when the ligand is not of the destabilizing type ( e . g . VRC01 and C34 ) or the inhibitor epitope is affected . Interestingly , both GB21-6 and HC11-1 were 5–10-fold more sensitive to PF-348089 , an inhibitor that prevents CD4-induced conformational changes presumably by stabilizing the CD4-unbound state . It has been shown that mAbs PG9 and PG16 can neutralize ∼92% of HIV-1 isolates in a large multi-clade viral panel but can occasionally give rise to inhibition curves that plateau below 100% neutralization for certain sensitive isolates [10] , [41] . The latter phenomenon is thought to be caused by heterogeneity in glycosylation on the Env trimer [10] . We observed such a phenomenon with wild-type ADA in which PG9 and PG16 exhibited a maximal percent inhibition ( MPI ) of 74% and 90% , respectively ( Figure 9 ) . Interestingly , both GB21-6 and HC11-1 exhibited higher MPIs against both mAbs; PG9 and PG16 had 90% and 96% MPI , respectively . Thus , not only do these two mutants of ADA exhibit decreased levels of heterogeneity and “cleaner” trimer bands on BN-PAGE , but they also show less heterogeneity within the pool of functional trimers as probed by mAbs PG9/16 . Based on the stable mutant Env sequences we selected single amino acid residue changes to introduce into wild-type ADA and examined their effect on Env stability . H625N and T626M were introduced as a double substitution , as these residues were adjacent to one another and seemed to co-vary . Although none of the point mutants completely recapitulated the phenotype of the stable clones , stabilizing effects were clearly observed and could be narrowed down to a few residues in each case ( Figure 10 ) . Thus , from the B2 pools , I535M , L543Q , and K574R in the NHR and H625N/T626M in the CHR each partially stabilized ADA Env to both heat and GuHCl treatment . In the case of the HC11 clones , the CHR mutation S649A played the largest role in stabilization and the V1alt substitution provided a more limited increase in Env stability . Among the gp41 amino acid changes identified in the GB2 and HB2 virion pools that increased functional trimer stability , K574R was the only mutation that was not of LAI origin . The conserved Lys at position 574 residue has previously been shown to be crucial for stability of the six-helix bindle ( 6HB ) protein that is a mimetic of gp41 in a post-fusion form [45] . Other studies have shown that mutations to the NHR of gp41 can affect neutralization sensitivity of HIV-1 [46] , [47] , and as shown above Env trimer stability can also alter sensitivity to certain inhibitors . To further characterize the relationship between Env stability and neutralization sensitivity due to mutation in the NHR , we examined how non-conservative mutation at position 574 affects trimer stability . We first performed this analysis using the mutant K574A in the LAI strain , which was generated in a previous study [48] . The K574A mutation profoundly decreased the T90 ( the temperature at which viral infectivity is diminished by 90% in one hour ) of HIV-1 LAI by ∼4°C and also globally increased sensitivity to a number of Env-destabilizing ligands ( e . g . b12 , sCD4 , 2F5 , 4E10 , and b6; Figure 11A and Table 1 ) . The most profound effect was observed with the weakly neutralizing CD4bs mAb b6 which was 250-fold more potent against K574A than wild-type LAI . In contrast , the substitution K574A had a less pronounced effect on neutralization by the non-destabilizing mAb 2G12 [37] , and was only 2-fold more sensitive to mAbs and inhibitors that target Env in a pre-fusion intermediate state . We also introduced the K574A mutation into the relatively stable primary isolate , JR-FL . Again , K574A destabilized JR-FL ( i . e . T90 decreased by ∼3°C ) and also resulted in broader sensitivity to a variety of destabilizing inhibitors ( Figure 11B; Table 1 ) . In particular , K574A made JR-FL ∼50-fold more sensitive to b6 . Overall , our results suggest that residue K574 also plays a crucial role in regulating stability of the receptor-naive Env trimer . To see if the stabilizing substitutions we identified could stabilize other viral Envs , we first targeted the relatively stable and homogeneously trimeric Env , JR-FL . L543Q , K574R , S649A , and V1alt were introduced , and , since JR-FL already contained M535 and N625/M626 , the reverse mutations M535I and NM625/6HT were introduced to see if they would destabilize the trimer . When the mutants were tested for stability in the heat gradient assay , L543Q , K574R , and S649A all increased the T90 of JR-FL by 0 . 7–1 . 5°C , while M535I and NM625/6HT both decreased the T90 of JR-FL by ∼2°C ( Table 2 ) . V1alt did not have any effect on JR-FL stability , as might be expected due to the extreme sequence variability in this region . Thus , substitutions to these amino acid residues in the heterologous isolate JR-FL have similar effects on Env stability as in ADA . Next , we introduced stabilizing substitutions into isolates from multiple clades that have previously been shown to be labile , including Q769 . b9 ( clade A ) , RHPA4259 ( clade B ) and ZM109F ( clade C ) [7] . Substitutions I535M , K574R and S649A were introduced into all three strains; L543Q was only introduced into RHPA4259 , as Q769 . b9 and ZM109F already contained Q543; all three isolates already contained N625/M626 . All mutations inserted into the clade B isolate RHPA4259 increased its T90 by 0 . 8–1 . 3°C ( Table 2 ) . Substitutions in the non-clade B isolates showed mixed effects . Thus , K574R increased the T90 of both isolates , S649A increased the T90 of Q769 . b9 but not ZM109F , and I535M did not affect the stability of either strain . Thus , the stabilizing mutations identified in this study appear to have a similar effect on all clade B isolates tested , and some of the substitutions ( i . e . K574R and S649A ) impart a stabilizing phenotype on Envs across the three major clades of HIV-1 . In an attempt to reconstitute the phenotypes of the most stable variants , we combined the stabilizing mutations identified from the GB2 and HC11 viral pools to produce Gmut ( I535M , L543Q , K574R and H625N/T626M ) and Hmut ( S649A and V1alt ) . We also generated comb-mut , which combines both sets of consensus mutations from the two unrelated stable clones . When challenged with GuHCl , both Gmut and Hmut clearly recapitulated the phenotype of the clones from which they were derived . Notably , comb-mut was even more stable than either variant , being resistant to GuHCl at 2 M ( Figure 12A ) . Similar results were seen with heat treatment , although in this case comb-mut was only slightly more stable than the others . Similarly , following prolonged incubation at 37°C , all of the stabilized mutants showed much improved half-lives compared to wild-type ADA , but no significant differences could be seen between the mutants . Loss of infectivity due to heat or 37°C incubation involves multiple viral components and we have seen that functional Env stability beyond a T90 of ∼50°C or a half-life of ∼20 h at 37°C cannot be quantified under the conditions of the assay [7] . We further tested the functional stability of comb-mut Env using destabilizing ligands . ADA wild-type or comb-mut virus was pre-incubated with the same mAbs and inhibitors used in Figure 8 for two different time periods . The inhibition data was again plotted both as the ratio of IC50s after a one hour incubation to that of a 20 hour incubation , as well as the ratio of IC50s of comb-mut to that of wild-type ADA using just the one hour incubation time . Comb-mut resisted destabilization by sCD4 , b12 , 4E10 , and 2F5 , as seen previously with GB21-6 , and likewise was more sensitive than wild-type ADA to PF-348089 ( Figure 12B and C ) . However , comb-mut was even more resistant to inhibition by sCD4 than GB21-6 , exhibiting an 80-fold increase in IC50 relative to wild-type virus . In order to verify that resistance to destabilizing mAbs is not due to changes in binding site accessibility or integrity , we measured binding of a panel of mAbs to virion-displayed Env in a simplified virus ELISA format [11] . From this panel , we found that all of the broadly neutralizing mAbs to gp120 bound at least somewhat more strongly to comb-mut virus than to ADA ( Table S2 ) . In particular , neutralizing mAbs that bind the outer surface of gp120 ( e . g . PGT128 , PG9 , PG16 , and 2G12 ) bound ∼10–50-fold more strongly to comb-mut than to ADA , while mAbs against the more recessed CD4bs bound ∼4-fold better . Binding of neutralizing mAbs to gp41 was equivalent between comb-mut and wild-type ADA . In contrast , all of the non-neutralizing mAbs tested in this assay exhibited somewhat reduced binding affinity to comb-mut virus relative to ADA . In particular , mAb 7B2 against the immunodominant disulfide loop region of gp41 showed 20-fold lower binding to comb-mut . These results show that functional comb-mut Env trimers are resistant to destabilization by various inhibitors and the binding sites of these inhibitors appear to be intact on virions , while the epitopes of non-neutralizing mAbs appear to be diminished on virions , although not eliminated . We further examined the direct effect of heat on Env trimer dissociation by comparing wild-type ADA and comb-mut using BN-PAGE . As expected , the Env trimer of wild-type ADA dissociated on heat treatment at a temperature slightly above the T90 of ADA ( Figure 13A and B ) [7] . In contrast , the Env trimer of comb-mut was much more resistant to this treatment and did not significantly dissociate until an incubation temperature of 63 . 6°C . The observed increase in oligomeric stability of mutant Env trimers might be due at least in part to an increase in the level of uncleaved gp160 incorporated into the virus . We therefore analyzed the relative levels of cleaved gp120 and uncleaved gp160 associated with each virus by reducing SDS-PAGE . We observed no effect on cleavage as a result of the stabilizing mutations , as all Env variants appear to be ∼95% cleaved ( Figure S2 ) . Immunization with virus particles typically does not elicit neutralizing Abs to the autologous virus , suggesting that native Env might be degraded rapidly in vivo . In addition to spontaneous dissociation , a possible cause of Env degradation is proteolysis . To explore their protease sensitivity , we treated ADA and comb-mut virions with a cocktail of trypsin , chymotrypsin , and proteinase K and then measured virus infectivity over a time course at physiological temperature . We note that with concentrated ( 500-fold ) virus , the infectivities of ADA and comb-mut decreased much more rapidly than with unconcentrated virus , possibly due to an effect of lysosomal proteases or other cellular agents that might pellet with the virus . After normalizing for this effect , we found that ADA infectivity was reduced by ∼50% immediately upon treatment and the virus was almost completely inactivated after two hours ( Figure 12D ) . In contrast , comb-mut was significantly more resistant to this treatment and viral infectivity did not drop below 60% of the untreated virus control over the same time period . When analyzed using BN-PAGE , the majority of protease-treated ADA Env trimer was already consumed at the earliest time point analyzed , while the effect on comb-mut Env trimers was significantly delayed and less complete ( Figure 13C and D ) . Thus , the trimer-stabilizing mutations in comb-mut appear to make the Env complex less susceptible to degradation by a cocktail of different protease specificities . While membrane-anchored Env is arguably most relevant for structural studies and vaccine development , truncated gp140 trimers are also of considerable interest . However , the two forms are likely to have stability requirements that are at least somewhat different since the TM and viral membrane play critical roles in stabilizing native spikes . In addition , techniques employed to artificially trimerize gp140s have typically altered its conformation , which poses a conundrum as to which truncated forms of Env trimer to use to evaluate mutations that were selected in the membrane-anchored context . Rather than investigate artificial trimerization motifs , disulfide bonds , or cleavage site knockout mutations , we decided to determine the oligomerization state of secreted gp140s without further genetic modification . We chose to produce gp140s by transient transfection of 293S ( GnTI−/− ) cells that result in relatively homogeneous glycosylation ( i . e . only Man5 , Man8 , and/or Man9 ) , as ADA gp140 produced in GnTI−/− cells has been shown to form trimers , at least in the uncleaved form [49] . We generated cleavage-competent ADA and comb-mut Envs that were truncated after amino acid position 664 , since trimers with this truncation have been shown to be relatively well-behaved [50] . We observed that ADA and comb-mut gp140 produced in 293S cells was indeed at least partially trimeric as measured by BN-PAGE , whereas the trimeric fraction was negligible when produced for comparison in 293T cells ( Figure S3A ) . The Env constructs have an intact cleavage site between gp120 and gp41 , so we wished to determine the actual level of cleavage in the soluble Env preparations using SDS-PAGE . We observed that both ADA and comb-mut soluble gp140s were approximately 50% processed ( Figure S3B ) . When the oligomeric states of secreted comb-mut and ADA gp140s were compared using BN-PAGE , we observed a statistically significant increase over wild-type in the proportion of comb-mut Env that spontaneously formed trimers ( 51% trimer for comb-mut and 25% trimer for ADA , p = <0 . 0001 , n = 10 ) , which was accompanied by a corresponding decrease in bands corresponding to non-trimeric Env ( Figure 14A , B , and C ) . To rule out the possibility that this apparent increase in the trimeric population could be an artifact of BN-PAGE/Western blots , we used the same Env preparations in ELISA to analyze binding to PG9 - that has a strong preference for trimeric Env [41] - along with several control mAbs . With the control mAbs , we observed strong binding by both neutralizing ( e . g . 2G12 and b12 ) and non-neutralizing ( e . g . b6 and 7B2 ) control mAbs , with no change in binding between the two Envs with these or other mAbs ( Figure 14D and data not shown ) . However , much weaker but highly reproducible binding was observed using PG9 against both ADA and comb-mut soluble Env , and , consistent with the BN-PAGE data , there was a statistically significant two-fold increase in PG9 binding to comb-mut relative to ADA ( Figure 14D ) . Thus , the secreted Env is comprised of Env trimers that either lack certain antigenic features of native Env , or those Env trimers with native antigenicity would have to be a relatively minor constituent of the total Env population . However , in addition to slightly enhancing trimerization of soluble gp140 , the stabilizing mutations in comb-mut also cause a small but significant increase in the proportion of PG9-reactive molecules , which are both sought after features in Env immunogen design . To determine the stability of soluble trimers of comb-mut and wild-type gp140s , we assayed heat induced trimer dissociation using BN-PAGE ( Figure 14E ) . Interestingly , the lower molecular weight species of Env disappeared from the Western blot at intermediate temperatures and appeared to form larger complexes , while the trimer band disappeared at higher temperatures . Corresponding monomeric dissociation byproducts did not appear concomitantly with the disappearance of the trimer band and all staining became undetectable after treatment at higher temperatures , presumably due to product aggregation . Under these conditions the comb-mut trimer did appear more stable than ADA , as it consistently disappeared from the blot at a higher temperature ( i . e . 50% trimer disappearance at 68 . 4°C for comb-mut vs . 62 . 3°C for ADA; Figure 14E and F ) . Importantly , both ADA and comb-mut soluble gp140 trimers were much more thermostable than their functional Env trimer counterparts on the virion , as the gp140s retained a significant amount of trimer following incubation at 68°C for one hour . Because the gp140s are largely unprocessed ( Figure S3B ) , and because uncleaved Env has been shown to be more stable than its cleaved counterpart on the membrane surface [6] , we wished to determine the stabilities of uncleaved and cleaved gp160s of ADA and comb-mut . We compared replication-competent virions that display mostly cleaved Env , pseudotyped virus particles that display mostly uncleaved gp160 , and Env produced by DNA transfection in the absence of viral backbone that is essentially uncleaved ( Figure S4B and D ) . When subjected to heat and visualized by BN-PAGE , the uncleaved gp160 formed a less discrete oligomeric band at 57°C , which appeared to increase in size at higher temperatures that cause cleaved ADA Env trimers to dissociate ( Figure S4A and C ) . These results are quite similar to what was observed when soluble gp140s were exposed to heat , suggesting that uncleaved gp140 trimers may share some stability features with their uncleaved gp160 counterparts . Because the stability of native Env trimers is dependent on interactions with the membrane [7] , we wished to investigate comb-mut Env stability in detergent . We previously showed that fully mature , virion-associated Env of a clade B primary isolate , JR-FL , dissociated at physiological temperature in under four hours following solubilization in the mild detergent , DDM [7] . We therefore used BN-PAGE to analyze the stability of ADA wild-type and comb-mut trimers in DDM over time at 37°C . ADA Env trimers quickly dissociated under these conditions and had almost completely decayed after one hour ( Figure 13E and F ) . However , under identical conditions Env from comb-mut retained a substantial fraction of trimeric Env ( 30% ) a full 24 hours following DDM treatment . Hence , the comb-mut Env spike is not only relatively resistant to heat , proteolysis and GuHCl treatment but also exhibits greatly increased stability after being detergent-solubilized .
HIV-1 has been experimentally subjected to various evolutionary selection pressures in order to study its fitness , tropisms , and various aspects of Env structure-function including mechanisms of escape from drugs and neutralizing antibodies [30] , [33] , [34] , [51] . Here , we used directed evolution to identify amino acid changes in HIV-1 Env that increase the stability and homogeneity of the unliganded spike without grossly altering its function or its antigenic properties , and without the aid of structural data . Previous engineering approaches have sought to stabilize Env trimer-based immunogens using intermolecular disulfides , cleavage site knockouts , and artificial trimerization domains , but each approach has adversely affected the function and antigenic profile of the cognate native trimer [18] , [28] , [52] , [53] . Selection strategies may be devised to identify HIV-1 Env trimers with even greater stabilities than we observed here . Env requires a degree of conformational flexibility in order to mediate fusion of the viral membrane with the target cell membrane [54] . Screening for stable Env trimers in the absence of an infectivity requirement may therefore identify a greater diversity of Env-stabilizing mutations . Nevertheless , such screens can also lead to non-native conformations of Env so specific counter-screens may also be necessary . In our screen for Env stability , several trimer-stabilizing mutations were identified in the NHR of gp41 . A prior study identified substitutions I535M and L543Q in the NHR that led to decreased levels of non-trimeric Env on pseudotyped virus , but native Env trimer stability was not explicitly measured [55] . In the current study we show that these substitutions in the NHR stabilize the functional form of HIV-1 Env . The K574R substitution we identified affects a highly conserved residue in the NHR , with >99 . 5% of group M isolates having a Lys at position 574 in the LANL HIV sequence database . Non-conservative substitutions with K574 tend to destabilize the post-fusion ( 6HB ) conformation of gp41 [45] , but their effect on unliganded trimer stability has not previously been studied . We show here that mutations to K574 can either stabilize ( e . g . K574R ) or destabilize ( e . g . K574A ) the unliganded , native trimer . Thus , position 574 appears to have a pivotal role in regulating multiple conformations of Env , which may explain the high degree of sequence conservation at this position . Which interactions this residue makes as it transitions between the unliganded and receptor primed forms of Env is unclear as the structural details of these states are currently lacking . Post-CD4 engagement , the NHR region of gp41 forms a homotrimeric coiled-coil that is transiently accessible to peptide inhibitors and Abs [36] , [56] . However , details of NHR structure in the unliganded ( ground ) state have not been described . The NHR appears to form a homo-trimer in receptor activated and post-fusion conformations of Env and has even been implicated in trimerization of the unliganded Env complex , though apparently not by equivalent mechanisms [55] , [57] . The NHR mutations we identified here may enhance subunit-subunit interactions within Env , which could resist structural transitions out of the native state and into CD4-bound , antibody-bound , and other inactive states ( see below ) . We speculate that while the K574R mutation maintains hydrophilicity and charge , the guanidino group may enhance electrostatic interactions or hydrogen bonds with adjacent elements on Env . The mutation L543Q , and to a lesser extent I535M , involve the substitution of a hydrophobic side-chain with a polar residue that is more likely to be found on the surface of the protein , suggesting that this portion of the NHR might be at least somewhat solvent-exposed and poised to interact with other hydrophilic elements . Cryo-electron microscopic ( cryo-EM ) structures of the Env trimer have shown the presence of a hole in the center of the trimer [58] , [59] , and a recent study suggests that the NHR helices may line this cavity [60] . We also identified Env stabilizing mutations in the CHR region of gp41: H625N , T626M and S649A . Residues N625 and M626 occur commonly among HIV-1 isolates . However , S649 is conserved in group M ( 95 . 3% of isolates ) , while A649 predominates in groups N and O and SIVcpz . A number of studies have implicated the CHR of gp41 in Env subunit-subunit interactions . Thus , mutations in the CHR can disrupt gp120-gp41 interactions and increase spontaneous shedding of gp120 [23] . In addition , a peptide corresponding to the DSL and CHR regions of gp41 can bind to monomeric gp120 through interactions with the C5 and C1 regions of gp120 [61] . Another study showed that peptides corresponding to gp120 C4 can interact with the peptide fusion inhibitor T-20 , the latter of which is comprised mostly of CHR residues [62] . Residues in the CHR immediately N-terminal to position 646 have also been shown to contribute to gp41 trimerization [63] . Thus , the CHR in the unliganded , native trimer could conceivably interact with the inner domain and/or base of gp120 as well as with other gp41 protomers [58] . The presence of the dipeptide motif HT at positions 625/626 has been shown to increase virion infectivity in a CD4-independent manner and makes HIV-1 more sensitive to sCD4 and cold inactivation [64] . We note that , the substitution of NM for HT at positions 625/626 causes a putative N-glycosylation site ( PNGS ) to shift from N624 to the new N625 . Studies have shown that this glycosylation site is occupied [50] , [65] , so its alteration might explain at least part of the stabilizing effect of this mutation . The S649A substitution involves a hydrophilic to hydrophobic residue change that could mean that this residue is in a more hydrophobic environment ( i . e . buried ) in the unliganded trimer . The only stabilizing mutation identified in gp120 was the V1 alteration ( N139/I140 deletion , N142S ) . In cryo-EM models , V1V2 is located at the apex of the trimer where it may interact with adjacent protomers by contacting other V1V2s [58] , V3 [66] and/or other elements nearby on Env . V1 is heavily N-glycosylated and likely O-glycosylated as well [67]–[69] . The N142S mutation we identified eliminates a PNGS in V1 , so glycosylation at this site and glycosylation proximal to this site may contribute to Env trimer stability as well . The Env mutants that we identified in the B2 and HC11 library pools have distinct sequences and yet possess a stability phenotype that appears to be largely independent of the method of destabilization ( i . e . GuHCl , heat , prolonged incubation at 37°C , destabilizing ligands , proteolysis , and detergent ) . Moreover , both Env mutants are hyper-sensitive to an entry inhibitor that opposes conformational changes in trimeric Env ( i . e . PF-348089 ) . Collectively , the results suggest that more than one element within Env may cooperate to resist trimer-destabilizing treatments , and that the different treatments may inactivate Env through a cooperative mechanism . We previously showed a correlation between heat stability and resistance of HIV-1 to 37°C decay , and , for the isolates JR-CSF and ADA , resistance to GuHCl also correlated with resistance to heat and spontaneous decay [7] . Recently , residues in gp41 including H625/T626 were found to increase CD4 independent infection , global neutralization sensitivity , and sensitivity of Env to cold inactivation [64] . Conformational changes in the Env trimer that lead either to infection or inactivation both involve an irreversible transition over an activation energy barrier [54] , [70] , [71] . The stability of native Env can either be increased by reducing the Gibbs free energy of the unliganded Env trimer or by increasing the free energy of the transition state that leads to a new state . It seems likely that the mutations we selected tighten interactions between subunits in the unliganded trimer . However , the mutations identified here may also destabilize the transition state that leads to inactive conformations , thus making it less likely that Env will decay . Elucidation of the mechanisms of Env stabilization might reveal structural distinctions between functional forms of HIV-1 Env trimers . In addition to increasing the stability of Env , the mutations identified here also made the native Env trimer less sensitive to protease digestion . The mutations introduced into comb-mut are not expected to significantly impact the preferred cleavage sites of the enzymes tested . Most likely then the mutations in Env cause it to assume a more closed conformation in which protease cleavage sites are less accessible . It is possible that digestion of Env immunogens in vivo restricts elicitation of certain Abs [72] , [73] , so incorporation of comb-mut mutations into Env trimer-based immunogens might offer a level of protection against such degradation . We note that the specific enzymes used here would not be encountered at the site of vaccination , but comb-mut Env trimers are resistant to a cocktail of multiple proteases with different specificities so the effect may be more general . Binley and colleagues have shown that sequential glycosidase-protease digests can degrade non-functional Env species with greater efficiency than native Env trimers , but the process does reduce viral infectivity by ∼70% suggesting some effect on functional Env [11] , [13] . By including stabilizing mutations in Env it may be possible to remove irrelevant Env without loss of native trimer . As Env stability and Env homogeneity are not always correlated [6] , [7] , it is notable that the stable Env mutants selected in this study were also homogeneously trimeric on BN-PAGE . In support of the BN-PAGE results , the stabilized mutant Env virions were also captured with much lower efficiency than wild-type ADA using mAbs that bind poorly to unliganded native trimers ( i . e . X5 and hNM01 ) [6] , suggesting that less non-native Env exists on the stable ADA mutants . Reasons for the decrease in non-native Env may include less cellular biosynthesis of aberrantly folded or improperly glycosylated forms of Env prior to incorporation of Env onto the budding virion and/or slower decay of the folded Env trimer , both prior to budding from the cell and on the virus surface [7] , [9] , [13] . We found that mAbs PG9/16 neutralized the stable Env mutants of ADA more completely than the wild-type virus [10] . When certain PG9/16 resistant viruses are produced in GnTI−/− cells , PG9/16 can neutralize the resulting viruses more efficiently and completely , due at least in part to changes in glycans on the variable loops of gp120 that become enriched in Man5GlcNAc2 and oligomannose structures [10] . The stabilizing mutations therefore seem to reduce glycan heterogeneity within the functional population of Env . Mutations that stabilize the native trimer might increase the packing density of glycans and affect the ability of glycosylation enzymes to trim high mannose residues and add complex glycans [74] , despite being distal from the actual glycosylation sites . It is notable that both GB21-6 and HC11-1 have completely different mutations , but both increase the proportion of PG9/16 sensitive Env . Env spikes with high functional stability and high structural homogeneity might be useful for immunization studies [5]–[7] , [13] . However , many factors besides trimer stability can influence immunogenicity of Env including mode and density of display , choice of adjuvant , ability to elicit T-cell help , and the capacity to stimulate the appropriate germline B cells and drive affinity maturation . Virus particles , while displaying native Env trimers , do so at low levels ( ∼10 copies/virion ) and typically induce only weak neutralizing Ab titers [14] , [75] . Env displayed at higher density ( e . g . as soluble protein or on nanoparticles ) may lower the affinity threshold of BCR activation by taking advantage of the avidity effect [76] . The Env-stabilizing mutations we identified increased both the trimerization and stability of secreted gp140s , although these effects were quite modest . However , we also show that this trimeric truncated form of Env is largely uncleaved and is more thermostable than that of native Env . Uncleaved Env trimers , whether membrane-anchored or soluble , can be relatively stable but have antigenic features that are not native-like ( i . e . binding of non-neutralizing mAbs ) [6] . Described soluble gp140 trimers that include artificial stabilizing alterations also differ antigenically from native Env [15]–[20] , [49] , [77]–[79] . One factor that might contribute to the non-native properties of secreted Env is that it may traffic in the cell differently from membrane-anchored protein , resulting in differences in folding , processing , and post-translational modification [74] . Other stabilizing modifications or re-routing of soluble gp140 through specific folding and processing pathways may be required to compensate for the gp41 TM/CT truncation . As an alternative to membrane-anchored or soluble gp140s , detergent-solubilized Env spikes may be purified from virions and used in immunization or structural studies . A detergent solubilization strategy was recently used to prepare Env trimers for cryo-EM analysis , but uncleaved rather than cleaved Env trimers were used due to instability of the latter form of Env [80] . The mutations we identified here greatly increased the stability of fully cleaved virion spikes in detergent raising the possibility that cleaved Env trimers could also be purified . Future studies will be directed at how to incorporate native Env-stabilizing mutations into an immunogen that can elicit neutralizing Ab .
Virus was produced from 293T cells by transient transfection using the polyethylene imine ( PEI ) as previously described [6] . When virus was amplified in MT2-CCR5ΔCT cells , cells were infected at an m . o . i . of 0 . 01 . Every 2–3 days , one half of the cells and virus-containing media was removed and replaced with media containing fresh cells . This procedure was continued for 10–12 days . Oligonucleotide directed mutagenesis was used to generate HIV-1 ADA mutant pools by targeting four different regions of Env that have been shown to be involved in subunit-subunit interactions in Env . Mutagenesis was targeted to the C1 region of gp120 ( pools C1 and C2 ) , the β3–β5 loop of gp120 ( pools B1 and B2 ) , the disulfide loop region ( DSL ) of gp41 ( pools D1 , D2 , and D3 ) , and the membrane proximal external region ( MPER ) of gp41 ( pool M ) ( Figure S1A ) . Mutagenesis was restricted to amino acid residues found to naturally occur in the Los Alamos National Laboratory ( LANL ) HIV Sequence Database . The libraries were created using two PCRs: one 3′ PCR using a primer containing degenerate codons in the region targeted and a 5′ PCR that would partially overlap with the 3′ PCR upstream of targeted region . These two PCR products were then joined by splicing-overlap-extension PCR and the mutant Envs were subcloned into the molecularly cloned HIV-1 Env display vector , pLAI-ADA . Randomly selected test clones were sequenced and each was found to be a unique variant containing between 1 and 8 mutations in the targeted region ( Figure S1B and C ) . The bulk ligated DNA was used to transfect 293T cells and virus-containing cell culture supernatant was used to infect MT2-CCR5ΔCT cells at an m . o . i . of 0 . 01 to produce pools of replication-competent viruses . Incremental concentrations of denaturant ( 0 . 25–2 M GuHCl or 0 . 5–4 M Urea ) , hyper-physiological temperatures ( 45 . 7–53 . 6°C ) , and incubation time periods at 37°C ( 4–6 days ) , designed to be in the range that inactivates wild-type ADA [7] , were used separately to select the 8 virion pools in duplicate . In the case of denaturants , treated viruses were pelleted by centrifugation and the denaturant was washed away prior to the infection step . After the destabilizing treatment , an aliquot of each viral pool was analyzed for infectivity in TZM-bl cells to determine the proportion of virus inactivated , and the remaining virus was rescued on MT2-CCR5ΔCT cells . A total of three such rounds were performed for each virion pool . Following 3 rounds of selection , individual clones were rescued from each stability-enhanced library pool . Whole RNA was isolated from virions in culture supernatant using the QIAamp Viral RNA kit ( Qiagen ) . SMARTScribe Reverse Transcriptase ( Clontech ) was used to produce cDNA from the viral RNA using the primer NefOR ( AGGCAAGCTTTATTGAGG; donated by D . Mosier , TSRI ) which binds downstream from env . Next , env was amplified using the Expand High Fidelity PCR System ( Roche ) and Env-specific primers ( i . e . pLAI5EnvF 5′-TAGGCATCTCCTATGGCAGGAAG-3′ and pLAI3EnvR 5′-GTCTCGAGATGCTGCTCCCACCC-3′ ) . Amplified env was subcloned into pLAI-ADA using a BamH I and Bgl I restriction sites . Individual plasmid DNA , amplified in E . coli , was purified and full-length env was sequenced . A serial 5-fold dilution was performed for each stability-enhanced virion pool and the virions were added to MT2-CCR5ΔCT cells . After 24 h , the media was replaced , and following a 7 day incubation , cell culture supernatants were harvested and tested for the presence of infectious virus in the TZM-bl assay . The media from the highest dilution to produce infectious virus was saved for stability tests . Virions were exposed to incremental concentrations of GuHCl or urea for 1 h , increasing temperature for 1 h , or extended incubation at 37°C . Samples treated with denaturants were pelleted in a microcentrifuge ( 20 , 000×g at 4°C ) and were washed with fresh media twice before being resuspended in an equal concentration of media . Virus was then added to TZM-bl cells and luciferase activity was determined 72 h later using the Bright-Glo System ( Promega ) and an Orion microplate luminometer ( Berthold Instruments ) . Residual infectivity was determined , and results are expressed relative to untreated virus . All experiments were performed in triplicate . Virus used for BN-PAGE was pelleted in an Optima ultracentrifuge ( Beckman; 60 , 000×g at 4°C ) and resuspended 100-fold concentrated in PBS . Virions were exposed to destabilizing conditions as above . BN-PAGE was performed as previously described [7] . Briefly , samples were treated with 1% DDM for 20 min on ice , and then electrophoresed on 4–16% NativePAGE Bis-Tris gels ( Invitrogen ) . Proteins in the gel were then transferred to a PVDF membrane , membranes were blocked in 5% non-fat dry milk and blotted overnight at 4°C using a cocktail of mAbs to gp120 ( b12 , 2G12 and F425-B4e8 , 2 µg/ml ) or to gp41 ( 2F5 , 4E10 and Z13e1 each at 1 µg/ml ) . After washing , membranes were probed for 30 min at room temperature with a goat anti-humanFc-HRP conjugated Ab ( Jackson ) , and peroxidase activity was assayed using Super Signal West Pico Chemiluminescence ( Pierce ) . HIV-1 virions were concentrated 500-fold . The following proteases were added in Trypsin buffer ( 50 mM Tris-HCl , 20 mM CaCl2 , pH 8 . 0 ) : trypsin ( 50 µg/ml ) , chymotrypsin ( 50 µg/ml ) , and proteinase K ( 1 mg/ml; all NEB ) . Virions were incubated at 37°C for the indicated time periods and the digestion was stopped by addition of Complete Protease Inhibitor Cocktail ( Roche ) and stored at −80°C until analyzed . Samples were analyzed for infectivity and by BN-PAGE . Env was solubilized from virus particles by addition of n-Dodecyl β-D-maltoside ( DDM ) to a final concentration of 1% at 37°C . Samples were removed at different time points and analyzed using BN-PAGE as described above . VCAs were modified from a previously detailed protocol [6] . Microtiter wells were coated overnight at 4°C with capture mAb ( 5 µg/ml in 50 µl of PBS ) . Wells were blocked using 4% non-fat dry milk ( NFDM ) in PBS for 1 h at 37°C . Incremental concentrations of soluble CD4 were added to 50 µl of virus in cell culture supernatant and , after a 15 min incubation , virions were added to the blocked wells and incubated for 2 h at 37°C . Wells were washed 6 times with PBS , and TZM-bl target cells were overlaid ( 104 cells/well ) . Luciferase activity was determined after a 72 h incubation as described above . HIV-1 infectivity and neutralization was determined as described previously [6] . Briefly , TZM-bl reporter cells were seeded in 96-well plates at 104 cells per well in 100 µl complete DMEM and incubated for 24 h at 37°C . Virus samples were incubated with mAbs or inhibitors for 1 h or 20 h at 37°C , and the mixture was added to cells in a total volume per well of 200 µl . Cells were harvested 72 h post-infection , luciferase activity in the cells was determined as above . The virus ELISA was adapted from a previously described protocol [11] . Virions were immobilized directly on microtiter wells for 2 hours at 37°C ( 2 ng p24 equivalents per well ) . Plates were washed ( all washes were performed using PBS without detergent ) and wells were blocked using 4% NFDM in PBS for 1 h at 37°C . After washing , primary Abs were added in PBS containing 0 . 4% NFDM for 1 h at 37°C . Plates were washed again , goat anti-human-Fcγ-HRP secondary Ab ( Jackson ) was added , and the plates incubated for 45 min at 37°C . Following another wash , TMB substrate ( Pierce ) was added and absorbance read at 450 nm . ADA wild-type and comb-mut gp140 Env expression vectors were generated by introducing mutations D664G and K665stop in pcDNA-ADA using Quikchange site-directed mutagenesis ( Agilent ) . Cleavage-competent gp140 proteins were produced by transient transfection of 293T and 293S ( GnTI−/− ) cells as described above for virus production . The oligomeric state of soluble gp140 in cell culture supernatant was analyzed by BN-PAGE and Western blot using only the anti-gp120 mAb cocktail , because the anti-gp41 mAbs used for Western blot staining bind to the region of gp41 removed by the truncation after position 664 . The identity of the Env trimer band was verified by comparison with KNH1144 SOSIP [104] and JRFL-foldon soluble trimers [17] ( gifts from I . Wilson and R . Wyatt ( TSRI ) , respectively ) . Relative density of the BN-PAGE bands was analyzed using ImageJ software ( NIH ) and compared by t-test using GraphPad Prism . Microtiter wells were coated with Galanthus nivalis lectin ( GNL; Sigma ) at 5 µg/ml in PBS overnight at 4°C . Plates were then washed using PBS containing 0 . 05% Tween ( PBST ) ; all washes are with PBST . Plates were blocked with 4% non-fat dry milk ( NFDM ) in PBS for 1 h at 37°C . Next , plates were washed and gp140 cell culture supernatant was added for 2 h at 37°C . Following this incubation , plates were washed and mAb binding was assayed as with the Virus ELISA above , except that 0 . 05% Tween was included in all steps . | A vaccine is needed to prevent HIV/AIDS but eliciting potent neutralizing antibodies ( Abs ) against primary isolates has been a major stumbling block . The target of HIV-1 neutralizing antibodies is the native envelope glycoprotein ( Env ) trimer that is displayed on the surface of the virus . Virion associated Env typically elicits antibodies that cannot neutralize primary viruses . However , because native Env trimers can dissociate and coexist with non-fusogenic forms of Env interpreting these results are difficult . Here , we used directed evolution to select for virions that display native Env with increased stability and homogeneity . HIV-1 virions were subjected to increasingly harsh treatments that destabilize Env trimers , and the variants that survived each treatment were expanded . We could identify seven different mutations in Env that increased its stability of function in the face of multiple destabilizing treatments . When these mutations were combined , the resulting mutant Env trimers were far more stable than the original Env protein . Incorporating trimer-stabilizing mutations into Env-based immunogens should facilitate vaccine research by mitigating the confounding effects of non-native byproducts of Env decay . A similar approach may be used on other pathogens with potential vaccine targets that are difficult to isolate and maintain in a native form . | [
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] | 2013 | Increased Functional Stability and Homogeneity of Viral Envelope Spikes through Directed Evolution |
Brucella is an intracellular pathogen able to persist for long periods of time within the host and establish a chronic disease . We show that soon after Brucella inoculation in intestinal loops , dendritic cells from ileal Peyer's patches become infected and constitute a cell target for this pathogen . In vitro , we found that Brucella replicates within dendritic cells and hinders their functional activation . In addition , we identified a new Brucella protein Btp1 , which down-modulates maturation of infected dendritic cells by interfering with the TLR2 signaling pathway . These results show that intracellular Brucella is able to control dendritic cell function , which may have important consequences in the development of chronic brucellosis .
The immune response to bacterial infection relies on the combined action of both the innate and adaptive immune systems . Dendritic cells ( DCs ) are known as mediators of pathogen recognition and are strategically located at the typical sites of bacterial entry and have the ability to migrate from peripheral tissues to secondary lymphoid organs to elicit primary T cell responses and initiate immunity . DCs express several pathogen recognition receptors such as C-type lectins and Toll-like receptors ( TLRs ) that recognise molecular patterns expressed by pathogens and determine the type of immune response . Microbial stimuli induce significant morphological and biochemical changes , such as IL-12 secretion and increased surface expression of many co-stimulatory and MHC class II molecules . This activation of DCs , known as maturation , is required for efficient T-cell-priming and pathogen elimination . However , several bacterial pathogens have developed mechanisms to subvert DC function and evade the immune system . Mycobacterium tuberculosis interferes with TLR signalling via the C-type lectin DC-SIGN , blocking DC maturation and IL-12 production [1] . The immune response is instead directed towards immune suppression with secretion of IL-10 , which seems to contribute to the chronic carriage of this pathogen . Similarly , Bordetella pertussis-infected DCs secrete IL-10 and activate T regulatory cells that suppress a protective immune response and enhance colonisation of the lower respiratory tract [2] . In addition , several studies have shown that while Salmonella typhimurium induces normal maturation of DCs and secretion of pro-inflammatory cytokines [3 , 4] it is able to block MHC class II antigen presentation in bone marrow-derived DCs [5 , 6] . Another bacterial pathogen , Francisella tularensis , which also induces phenotypic maturation of DCs , has been shown to inhibit secretion of pro-inflamatory cytokines such as TNF-α while eliciting production of immunosuppressive cytokines that facilitate pulmonary infection [7] . F . tularensis has also been shown to replicate efficiently within DCs [7] in contrast to many other bacterial pathogens ( including mycobacteria , Bordetella , Salmonella and Legionella pneumophila ) for which DCs seem to restrict their intracellular growth . Human brucellosis is a zoonotic disease that results from direct contact with infected animals or ingestion of contaminated food products . It is usually presented as a debilitating febrile illness that can progress into a chronic disease with severe complications such as infection of the heart and bones . It has been previously shown that B . abortus infection in calves using an intestinal loop model occurred via Peyer's patches [8] . Several studies using animal models have established macrophages and placental trophoblasts as two key targets of Brucella infection [9] but a more recent study has shown that Brucella can replicate in vitro within human monocyte-derived DCs [10] . DCs may therefore constitute an important cellular niche to promote infection . Brucella virulence is dependent on its ability to survive and replicate within host cells . Once internalised , Brucella is found within a compartment , the Brucella-containing vacuole ( BCV ) that transiently interacts with early endosomes [11] . However , BCVs do not fuse with lysosomes and instead fuse with membrane from the endoplasmic reticulum ( ER ) , to establish a vacuole suited for replication [11 , 12] . An important virulence factor of Brucella is its unconventional lipopolysaccharide ( LPS ) that is necessary for entry and early development of BCVs within host cells [13] . Brucella LPS also has important immuno-modulatory activities by forming non-functional complexes with MHC class II at the cell surface of macrophages [14] . More recently , the periplasmic Brucella cyclic β-1 , 2-glucan , which interacts with lipid domains , has been implicated in the early biogenesis of BCVs [15] . At later stages of BCV trafficking , the VirB type IV secretion system enables interactions with the ER and formation of an ER-derived compartment suitable for Brucella intracellular replication [12 , 16 , 17] . However , no secreted effector molecules of the VirB type IV secretion system have yet been identified . Although there is increasing knowledge about the virulence factors that enable Brucella to survive inside host cells , it is still unclear how Brucella is able to remain hidden from the immune system and cause chronic disease . The escape from the lysosomal pathway and fusion with the ER may interfere with the ability of host cells to mount an efficient immune response against Brucella . In this study , we used a murine intestinal loop model of infection and found that intestinal DCs from the region underlying the follicle associated epithelium ( FAE ) of Peyer's patches were infected by B . abortus soon after inoculation . To better characterise the consequences of B . abortus infection on the immune functions of DCs , we have used murine bone marrow-derived dendritic cells ( BMDCs ) as a model system . B . abortus replicates within BMDCs and inhibits the process of DC maturation , ultimately leading to a reduction of cytokine secretion and antigen presentation . We have also identified a Brucella protein , Btp1 , which contributes to inhibiting maturation of infected DCs in vitro by targeting the TLR2 signalling pathway , highlighting a new mechanism used by Brucella to control the immune response of the host .
We used a mouse intestinal loop model to characterise the cellular targets of B . abortus by confocal microscopy during early stages of the infection . One hour after inoculation in a loop containing at least one Peyer's patch , B . abortus mainly penetrated the epithelium through the FAE of Peyer's patches and were either associated or internalized by cells presenting a “dendritic-like” morphology and identified as DCs on the basis of their positivity for CD11c ( Figure 1 ) . On more than one hundred cells associated with B . abortus we observed , 50% were CD11c+ cells , the others being either FAE cells ( 30% ) or not determined . Few bacteria were also observed in the lamina propria of adjacent villi ( Figure 1 ) . These were always associated with CD11c+ cells . Thus , in the mouse model , DCs are a cellular target of B . abortus during enteric infection . Having established the relevance of DCs in the context of Brucella infection we used BMDCs as cellular model to characterise the intracellular fate of Brucella . We first analysed the survival of the wild-type B . abortus 2308 strain in murine BMDCs by enumerating the colony forming units ( CFU ) at different times after infection . We observed an increase in the number of intracellular bacteria up to 48 h after inoculation , showing that the wild-type B . abortus is able to survive within BMDCs ( Figure 2A ) . Equivalent results were obtained in CD11c-positive cells ( Figure 2A , graph on the right ) . Intracellular replication was confirmed by microscopic examination of infected cells . At 2 h after infection cells contained on average 1 . 2 ± 0 . 1 bacteria per cell in contrast to 17 . 7 ± 3 . 4 bacteria per cell at 48h . Although high intracellular bacterial loads were observed , viability of BMDCs was not affected up to 48 h after infection ( Figure S1 ) . At 24 and 48 h we found that only a small percentage of infected cells ( approximately 12 and 25% , respectively ) contained more than 10 bacteria , indicative of intracellular replication . This observation is similar to what was previously described for murine bone marrow-derived macrophages [12] and HeLa cells [11] . In agreement with previous reports , we did not observe replication with the wild-type Salmonella typhimurium [18] . In the case of a strain lacking the type IV secretion component virB9 , there was a continuous decrease in CFU numbers , suggesting that this system is necessary for the establishment of the Brucella replicative niche in DCs . Surprisingly , in contrast to other cell types [15 , 19] the cyclic β-1 , 2 glucan synthesized by Brucella was not required for virulence during DC infection since cgs− mutants replicated as efficiently as the wild-type strain ( Figure 2A ) . Loss of lysosomal and acquisition of ER markers on BCVs are hallmarks of Brucella infection . We therefore analysed the recruitment of lysosomal associated membrane protein 1 ( LAMP1 ) and of the ER-specific KDEL bearing molecules in MHC II expressing CD11c-positive DCs by immunofluorescence confocal microscopy ( Figures 2 and S2 ) . As expected , virB9− mutant BCVs retained LAMP1 whereas wild-type BCVs progressively lost this marker ( Figure 2B ) . At 24 h after infection only a small percentage of wild-type BCVs contained LAMP1 in contrast to the virB9− mutant ( Figure 2C ) . Concomitant to LAMP1 decrease , a significant proportion of wild-type BCVs became positive for the ER retention and retrieval epitope KDEL ( 50 . 7 ± 3 . 5% , Figure 2D ) , albeit to a lower level than in macrophages [12] . Wild-type BCV fusion with ER membranes was confirmed through ultrastructural examination by electron microscopy of infected DCs . BCVs were often outlined by ribosomes ( Figure 2E ) and cytochemical detection of glucose 6-phosphatase activity , an enzyme mostly found in the ER lumen , revealed that the majority of BCVs were positive for this enzymatic activity at 24 h after infection . In cryosections of infected DCs we observed ER membranes directly fusing with BCVs ( Figure 2F , blue arrows ) , which were also decorated with an antibody against the ER resident protein calnexin ( Figure 2G ) . In addition to the ER cisternae , which were extensively labelled ( data not shown ) , gold particles were present on the vacuolar membranes surrounding Brucella and on tubular-like ER membranes connecting with the vacuoles ( blue arrows ) as well as on vesicles in the vicinity of BCVs ( red arrow ) . Together , these results confirm that Brucella is dependent on its VirB type IV secretion system to replicate within DCs in an ER-derived compartment that evades the lysosomal degradative pathway , similar to what has been observed for other cell types , in particular within bone marrow-derived macrophages [12] . Most studies have shown that infection of DC is generally associated with their activation and a mature phenotype . This phenotype is characterized not solely by the up-regulation of co-stimulatory and MHC class II molecules at the cell surface , but also by the intense clustering of multivesicular bodies ( MVB ) around the microtubule organizing center ( MTOC ) [20] , as well as the formation of large cytosolic dendritic cell aggresome-like induced structures ( DALIS ) , which are made of defective newly synthesised ubiquitinated proteins [21] . This situation is well illustrated by Salmonella-infected DCs ( Figure 3A ) , in which MHC class II molecules mostly localize on the cell surface and MVBs , labelled with LAMP1 , are often tightly concentrated at the MTOC . Surprisingly , most of Brucella-infected DCs did not show any sign of maturation , since Brucella-bearing cells rarely displayed lysosomal clustering or MHC II surface accumulation ( Figure 3A ) . Instead , MHC II molecules remained mostly intracellular co-localising with LAMP1 as observed in non-activated DCs . At 24 h after inoculation , only 14 . 3 ± 0 . 5% of Brucella-infected DCs ( versus 31 . 9 ± 8 . 3% for Salmonella ) displayed a mature phenotype in terms of MHC II molecules and LAMP1 distribution , suggesting that Brucella does not promote DC activation . We then tested if DALIS formation was also impaired in infected cells . Although the function of DALIS is still unclear , these structures are easily detectable using the FK2 antibody that recognises both mono- and polyubiquitinated proteins , and therefore provide an effective way of monitoring DC maturation by microscopy . Thus , we compared the kinetics of DALIS formation in DCs infected with either Brucella or Salmonella , previously shown to induce normal maturation of DCs [3 , 6] ( Figure 3B–3D ) . In the case of Salmonella , formation of DALIS began after 4 h ( as reported with E . coli LPS [21] ) , with 90% of infected cells containing large and numerous DALIS 24 h after infection ( Figure 3B and 3C ) , while only 20% of control cells contained DALIS , probably due to mechanical or spontaneous maturation . Although significantly higher than the control ( p = 0 . 005 ) , only 43% of Brucella-infected DCs contained DALIS 24 h after infection and this number remained stable a later time-points ( Figure 3C ) . Furthermore , the average size of Brucella-induced DALIS was always considerably smaller than that of DALIS formed in the presence of Salmonella ( Figure 3B ) . These observations suggest the Brucella replication in DC could be restricting the maturation process . A significantly higher proportion of DCs infected with virB9− , a mutant that does not replicate in DCs , contained DALIS at 24 h after infection ( p = 0 . 006 , Figure 3D ) . Similar results were obtained when analysing DCs activated with heat-killed B . abortus ( p = 0 . 018 , Figure 3D ) , consistent with the hypothesis that B . abortus is actively inhibiting the maturation of DCs . The extent of this inhibition was monitored by flow cytometry of CD11c-positive DCs infected with GFP-expressing Brucella or Salmonella , for surface expression of classical maturation markers ( eg CD80 , CD86 and MHC molecules ) . Analysis of the median of fluorescence ( Figure 4A ) or the geometric mean ( data not shown ) in the CD11c+GFP+ populations showed a consistently lower surface expression of all co-stimulatory and MHC class II molecules in cells infected with B . abortus than in Salmonella-infected cells ( Figure 4A and 4B ) . However , statistical difference between B . abortus and Salmonella was only observed for CD86 expression ( p = 0 . 011 ) . This is probably due to the fact that in the case of Brucella-infected DCs two populations were observed ( overlay histograms , Figure 4A and 4B ) , one with a moderately increased surface levels in comparison to control cells and the other with similar levels of expression to Salmonella-activated DCs . This is in agreement with microscopy observations where a high proportion of MHC class II molecules remained intracellular in Brucella-infected cells ( Figure 2A ) . Surprisingly , we did not detect any significant difference in the surface expression of MHC class I molecules in cells infected by the two pathogens ( Figure 4B ) , thus suggesting the expression of MHC class I molecules is controlled differently than MHC class II and co-stimulatory molecules . Together , these data confirm that maturation of Brucella-infected DCs is impaired . In addition , we have found that Brucella can only establish its intracellular replicative niche in immature DCs ( Figure S3 ) . When E . coli LPS was added to cells ( to induce maturation ) early after infection ( 0 . 5 to 6 h ) , there was a significant inhibition of bacterial growth ( p < 0 . 001 , Figure S3 ) . However , bacterial CFU counts did not decrease when LPS was added at 12 h after infection , suggesting that Brucella survival is not affected by DC maturation at a time point that corresponds to its arrival in the ER [11 , 12] . In contrast , addition of E . coli LPS had no significant effect on the intracellular survival of Salmonella in DCs ( data not shown ) , thus further underlining the need for Brucella to avoid DC maturation . DCs infected with B . abortus do not achieve full maturation . We therefore investigated the consequences of Brucella infection on the ability of DCs to secrete cytokines , by analysing the supernatants of infected cells . Levels of TNF-α , IL-12 ( p40/p70 ) , IL-6 and IFN-β were consistently and considerably lower in supernatants from Brucella- than from Salmonella-infected cells ( Figure 5A ) . This effect was observed at 24 h as well as at 48 h after infection ( data not shown ) suggesting that Brucella infection inhibits the secretion of immuno-stimulating cytokines . Intracellular IL-12 ( p40/p70 ) expression was further analysed by flow cytometry . DCs incubated with either media alone or infected with GFP-expressing Brucella or Salmonella were labelled at 4 and 24 h post-inoculation with anti-CD11c and IL-12 ( p40/p70 ) antibodies . Analysis of IL-12 expression was carried out on CD11c+ cells ( Figure 5B ) . IL-12 expression in Salmonella-infected DCs peaked after 4 h , similarly to what observed with E . coli LPS ( data not shown ) , whereas in Brucella-infected DCs , IL-12 expression was only detected after 24 h ( Figure 5B ) . Similar results were obtained when analysing GFP-positive populations for each pathogen ( data not shown ) . These results indicate that Brucella , like Francisella , is capable of interfering with the ability of DCs to secrete cytokines . Interestingly , Brucella also reduced the capacity of DCs to present exogenous ovalbumin to specific T cells in the context of MHC class I and class II ( Figure 5C ) suggesting that Brucella is generally down-modulating the immuno-stimulatory properties of maturing DCs both at the cytokine production level and also at the antigen processing and presentation level . The Brucella genome was searched for candidate proteins responsible for interfering with DC maturation . We identified one candidate protein: BAB1_0279 ( YP_413753 ) , a 250-amino acid protein containing a c-terminal 130-amino acid domain with significant sequence similarity to Toll/interleukin 1 receptor ( TIR ) domain family ( smart00255 , e-value of 4E−11 ) . Sequence comparison with the human TIR containing proteins TLR2 , TLR4 , SIGIRR , MyD88 and IL-1R acP indicated similarity particularly in box 1 , the signature sequence of the TLR family , and in box 2 which is reported to be important for signaling [22] ( Figure 6A ) . This Brucella protein , which we have designated Btp1 ( Brucella-Tir-Protein 1 ) , is also homologous to TlpA , a Salmonella effector that interferes with TLR signaling in vitro [23] . The btp1 gene is located in a 20-Kb genomic island on chromosome I and is present in B . abortus and B . melitensis but absent from B . suis . Interestingly , the island appears to have been acquired recently via a phage-mediated integration event [24] . In order to test if this protein could be responsible for Brucella immunomodulatory properties , we infected DCs with Brucella knock-out mutants of btp1 . We first observed that the mutant strain replicated as efficiently as the wild type Brucella in DCs ( Figure 6B and 6C ) . Next , the degree of DC activation after infection with this mutant was monitored by flow cytometry and microscopy . Interestingly , the level of surface expression of MHC II and CD80 were augmented in DCs infected with the btp1 mutant when compared the wild-type . However , CD40 and CD86 were not significantly affected ( Figure S4 ) . In contrast , a significantly higher percentage of DCs infected with btp1− contained DALIS at 24 h after infection ( p < 0 . 001 ) , in comparison to DCs infected with the wild-type strain ( Figure 6D ) . Although , btp1− did not induce formation of DALIS to the same level than Salmonella ( Figure 3B ) , it was equivalent to heat-killed B . abortus ( HKB , Figure 6D ) and could be partially complemented by expressing btp1 ( btp1− pbtp1 , Figure 6D ) . Therefore , the ability of Brucella to inhibit formation of DALIS is dependent on Btp1 . In order to exclude a potential role of Btp1 on LPS immunostimulation properties , LPS extracted from Brucella wt and the Btp1 mutant were tested on DCs . Neither LPS from wt or btp1 mutant was found to promote DC activation ( Figure S5A and S5B ) . Therefore , the increase of maturation in infected cells with the btp1 mutant is not due to modification of its LPS . In addition , an increase in the level of expression and secretion of TNF-α and to a lesser extent IL-12 ( Figure 6E and 6F ) was observed at both 24 and 48 h after infection , consistent with a role for Btp1 in controlling DC function . However , the IL-12 levels were lower than the levels obtained with heat-killed B . abortus suggesting that other Brucella factors might block DC activation . Complementation of the mutant phenotype was also attained for both TNF-α and IL-12 ( Figure S5C ) . TLR signal transduction pathways are essential for the induction of DC maturation and secretion of pro-inflammatory cytokines . The most important TLRs in the context of a bacterial infection are TLR2 , TLR4 , TLR5 and TLR9 that recognize lipoproteins , LPS , flagellin and CpG DNA , respectively . However , recent work showed that DCs poorly respond to flagellin due to limited TLR5 expression in these cells [25] so we therefore excluded this pathway . To determine if Btp1 was interfering with a specific TLR in the context of an infection we used DALIS formation as a read-out , since the increased number of DCs with DALIS was the clearest phenotype for the btp1 mutant when compared to wild-type Brucella . DCs from different TLR knockout mice were infected with Brucella wild-type or the btp1 mutant and analysed by confocal immunofluorescence microscopy . We observed that DCs from wt mice infected with btp1− contained a very high number of DALIS , contrasting with infected TLR2−/− DCs ( Figure 7A ) , a phenotype reminiscent of DCs treated with the TLR2 ligand PAM . This difference was not noticeable in other TLR knockout mice used . Indeed , quantification of the number of infected DCs containing DALIS confirmed that DCs infected with the btp1 mutant showed increased DALIS formation in wild-type , TRIF−/− , TLR4−/− , TLR7−/− and TLR9−/− DCs but not in TLR2−/− and to a lesser extent in MyD88−/− DCs ( Figure 7B ) . These results suggest that inhibition of DC maturation by Btp1 is at least in part dependent on the TLR2 pathway . Appropriate ligands were used as positive controls for the assay ( Figure S6 ) . In addition , we found that DC maturation induced by heat-killed Brucella , as measured by DALIS formation , was also dependent on TLR2 ( Figure S7 ) . This result is consistent with previous reports showing that TNF-α secretion induced by heat-killed Brucella in DCs is partially dependent on TLR2 and MyD88 pathways [26] . We then investigated if the ability of Brucella to reduce DC maturation was dependent on the TLR2 pathway . DCs were infected with wild-type B . abortus for 24 h to allow establishment of a replication niche and then incubated with either LPS or PAM for a further 24 h . We found that DCs infected with wild-type Brucella and treated with E . coli LPS were very activated and contained very large DALIS , similar to what has been observed in LPS-treated cells . In contrast , infected cells treated with PAM contained only small DALIS similar to untreated infected cells . However , non-infected cells treated with PAM contained large and numerous DALIS ( Figure S8 ) . These results confirm that Brucella is able to control TLR2-dependent DC maturation . To determine if Btp1 had the ability to specifically block TLR2 signalling we expressed increasing amounts of myc-tagged Btp1 in HEK293T cells along with a constant amount of either TLR2 or TLR9 ( as a negative control ) and a luciferase NF-κB reporter plasmid . We then analysed luciferase activity in the presence of the appropriate ligands . A Salmonella effector protein , myc-PipB2 , was also included as a negative control as it is known to affect kinesin recruitment rather than cell signalling [27] . We found that Btp1 efficiently inhibited TLR2 signalling , in a dose dependent manner but not TLR9 ( Figure 7C ) . In conclusion , Btp1 is able to interfere with the TLR2 to reduce the progression of maturation in infected DCs .
Brucella enteric infection has been found to occur in calves' Peyer's patches [8] . Using a murine intestinal loop model , we found that after penetrating the epithelium , B . abortus localized in DCs just below the FAE of Peyer's patches . Interestingly , a few bacteria were also observed in the inter-follicular region of Peyer's patches or in the lamina propria of adjacent villi , were they were always associated with DCs . Unlike most pathogens , Brucella was able to establish a replication niche within DCs cultured in vitro in a very similar manner to what has been described in macrophages and non-phagocytic cells . DCs therefore constitute a potentially important cellular target for Brucella infection . Interestingly , we have found that although the VirB type IV secretion system is required for virulence in DCs , the Brucella cyclic β-1 , 2-glucan is dispensable for early events of BCV biogenesis . The Brucella cyclic β-1 , 2-glucan , which modulates lipid microdomain organisation [15] , is essential for preventing fusion between BCVs and lysosomes in macrophages and non-phagocytic cells . It is possible that within DCs early BCVs have a different membrane composition than in macrophages . In the case of human monocyte-derived DCs , Brucella strongly induces the formation of veils on the plasma membrane during internalisation , a phenomenon not observed in macrophages [10] . Alternatively , the proteolytic and bactericide activity of DC endosomes and phagosomes , which has been shown to be considerably limited when compared to macrophages [28 , 29] , potentially eliminates the need for cyclic β-1 , 2-glucan during infection . Further work will be required to characterise in detail the molecular mechanisms of Brucella internalisation by DCs . An important aspect of Brucella pathogenesis is its ability to evade the immune system and persist within the host . In vitro studies have illustrated that Brucella is efficient at remaining unnoticed by host cells , notably by having an atypical LPS [30] , which is several hundred times less toxic than Salmonella or E . coli LPS . Although , shown to signal through the TLR4 pathway , Brucella LPS is not a potent inducer of pro-inflammatory cytokines and anti-microbial proteins such as the IFN-γ inducible p-47 GTPases [31] . DCs treated with up to 10 μg/ml of purified Brucella LPS , did not induce phenotypic maturation nor significant secretion of pro-inflammatory cytokines ( Figure S4A and data not shown ) . Conversely , DCs incubated with heat-killed B . abortus showed significant maturation indicating that host cells can detect Brucella pathogen associated molecular patterns ( PAMPs ) , other than LPS . A recent study has shown that a lumazine synthase from Brucella can activate DCs via TLR4 [32] . In addition , heat-killed Brucella can induce TLR9 and promote Th1-mediated responses in both DCs and macrophages [33] probably after bacterial degradation in lysosomes . Immunity against Brucella requires cell-mediated mechanisms that result in the production of cytokines such as IL-12 and IFN-γ . In this study , we have shown that Brucella inhibits or delays the process of DC maturation to establish a replicative niche , which results in reduced cytokine secretion and disabled antigen presentation . It is likely that impairement of DC function and cytokine secretion by Brucella favours infection and/or promotes the establishment of the chronic phase of the disease . Although , we did not detect any significant secretion of IL-10 in infected DCs it is possible that , by preventing their full activation , Brucella uses the tolerogenic properties of DCs to subvert the immune response . There is growing evidence that induction of tolerance is not restricted to immature DCs . Recent studies have shown that migrating DCs , which are mature or in the process of maturing ( bearing considerable surface expression of MHC class II molecules ) are capable of inducing tolerance [34–36] . Brucella-infected DCs , which show an intermediate level of maturation could therefore contribute to tolerance induction in tissues and establishment of chronic infection . Consistent with this hypothesis , reduction of IL-10 levels in mice has been shown to improve host resistance to Brucella infection [37] . In addition , a new Brucella virulence protein ( PrpA ) was recently identified as a potent B-cell mitogen and IL-10 inducer [38] . Indeed , this protein is necessary for the early immuno-supression observed in Brucella-infected mice and the establishment of chronic disease . Brucellosis development is a complex process that involves many virulence factors some of them , such as the VirB type IV secretion system , are required for virulence in different cell types , whereas others may function in specific cell types , such as the Brucella cyclic β-1 , 2-glucan in macrophages and PrpA in B-cells . A recent study has shown that Brucella-infected human myeloid DCs do not mature extensively and instead secrete low levels of IL-12 and TNF-α and have an impaired capacity to present antigens to naïve T cells [39] . These results are consistent with our data in murine DCs . However , this study implicates the Brucella outer membrane protein Omp25 and the two-component regulatory system BvrRS in the control of DC maturation through blockage of TNF-α secretion . This is not surprising as both these mutants have significant modifications of their outer membrane and LPS structure , which are an essential feature of Brucella virulence enabling bacteria to escape pathogen recognition [40 , 41] . Therefore the effects observed are more likely to be pleiotropic than specific to DC maturation . Furthermore , in the case of the bvrR mutant , which they show induces higher maturation than the omp25 mutant , the increased maturation is most likely due to its degradation in lysosomal compartments ( the equivalent results can be obtained with heat-killed Brucella ) since this mutant was previously shown to be unable to replicate within human DCs by the same group [10] . In this study , we have identified a new Brucella protein , Btp1 , which contributes to modulation of TLR signalling within host cells . A mutant lacking this protein showed increased levels of DALIS formation and also an increase in secretion of pro-inflammatory cytokines . However , the level of surface expression of some co-stimulatory molecules was not significantly increased in cells infected with the mutant strain . Thus the existence of other bacterial molecules , capable of further inhibiting the antigen presentation capability of DCs , is highly probable and additional studies are required to identify and characterize these putative new factors . By ectopically expressing Btp1 , we found that it can inhibit TLR signalling , particularly TLR2 , in a manner consistent with observations in DCs obtained from different knock-out mice infected with the btp1 mutant strain . Interestingly , we did not observe any inhibition of human TLR2 when using the same concentrations of Btp1 . It is possible that higher amounts of Btp1 are required to block human TLR2 in this system or it may be that Btp1 specifically recognizes the mouse TLR2 Tir domain . Further work is now required to identify the cellular target of Btp1 and to analyse its role during infection in vivo . Previous studies have highlighted the importance of TLR signalling in the control of Brucella infection , namely the role of the MyD88 adaptor protein in the clearance of the S19 Brucella vaccine strain in macrophages at late stages of the infection [42] . It is interesting that Brucella produces at least one protein that can act as negative regulator of TLR signalling , which constitutes an essential link between the innate and adaptive immune systems . Along with the Salmonella TIR-containing protein TlpA , they may constitute a novel class of intracellular bacterial virulence factors that interfere with TLR signalling and control specific steps of the host immune response . We hypothesize that Btp1 is secreted into the host cytosol were it interacts with either the TLR2 directly and/or with its adaptor proteins resulting in reduction of TLR2 signalling . In the case of Salmonella , TlpA is required for virulence in macrophages and in the mouse model of infection . This is not the case for Btp1 , since a btp1 mutant is not significantly attenuated in the mouse model of brucellosis ( Marchesini , Comerci and Ugalde , unpublished results ) . Interestingly , Salmonella infection results in phenotypic activation of DCs and high secretion of pro-inflammatory cytokines probably due to its LPS ( since addition of Salmonella LPS at 100 ng/ml to DCs results in DC activation; data not shown ) . Nonetheless , live Salmonella is able to restrict MHC class II-dependent antigen presentation [6] , however no study has yet been carried out with the tlpA mutant in this context . It is possible that the Tir-containing protein TlpA is involved in the control of DC function but no mechanism has yet been proposed and it remains unclear how Salmonella interferes with antigen presentation in DCs . When comparing Salmonella and Brucella , it is important to consider that these two pathogens cause very distinct diseases in susceptible mice; S . typhimurium infection is characterised by fast systemic spread of the bacteria whereas B . abortus establishes a chronic-like non-fatal disease . It is therefore likely that DCs play a very distinct role in the pathogenesis of these two bacteria and that these pathogens have developed specific mechanisms to control the immune response . For example , in the case of Salmonella , activation of DCs does not affect its intracellular survival ( data not shown ) whereas Brucella cannot reach its ER-derived replication niche in fully matured DCs , which impact on its survival . In the case of Brucella , infected DCs remain in an intermediate maturation stage . Although its atypical LPS enables bacteria to remain less noticeable by the host , it is interesting that Brucella expresses at least one protein that interferes to a certain level with DC maturation and particularly with secretion of pro-inflammatory cytokines by DCs . Therefore , it is possible that Btp1 contributes to the establishment of chronic brucellosis by controlling in the host immune response within specific tissues . Overall , Brucella infection of DCs may therefore be required to control the anti-bacterial immune response , in addition to providing a productive replication niche . Alternatively , the migration properties of DCs could facilitate infection spreading , as suggested by the rapid interaction of DCs with FAE penetrating Brucella .
The bacterial strains used in this study were S . enterica serovar Typhimurium strain 12023 , smooth virulent B . abortus strain 2308 [11] and the isogenic mutants virB9− [43] , cgs− BvI129 [15] and btp1− ( this study , see bellow ) . In the case of Brucella , green fluorescent protein ( GFP ) -expressing derivatives contain a pBBR1MCS-2 derivative expressing the gfp-mut3 gene under the control of the lack promoter . The plasmid pVFP25 . 1 carrying the gfp-mut3 under the control of a constitutive promoter was used for Salmonella . Brucella strains were grown in tryptic soy broth ( TSB; Sigma-Aldrich ) and Salmonella in Luria Bertani ( LB ) medium . For infection , we inoculated 2 ml of media for 16 h at 37 °C up to an optical density ( OD600nm ) of approximately 2 . 0 [12] . Salmonella strains were cultured 16 h at 37 °C with aeration to obtain stationary phase cultures . A PCR product of 1 . 257 Kbp containing the btp1 gene ( BAB1_0279 ) was amplified using primers 5′-caaaactctttcccgcatgcga-3′ and 5′-tcagataagggaatgcagttct-3′ and ligated to pGem-T-Easy vector ( Promega ) to generate pGem-T-btp1 . The plasmid was linearized with EcoRV . Linearized pGem-T-btp1 was ligated to a 0 . 7 Kpb SmaI fragment containing a gentamicin resistance cassette to generate pGem-T-btp1::Gmr . Plasmids were electroporated into B . abortus S2308 where they are incapable of autonomous replication . Homologous recombination events were selected using gentamicin and carbenicillin sensitivity . PCR analyses showed that the btp1 wild-type gene was replaced by the disrupted one . The mutant strain obtained was called B . abortus btp1::Gmr ( btp1− ) . A 1 . 257 Kpb EcoRI fragment containing the btp1 gene was excised from pGem-Tbtp1 and ligated to the EcoRI site of pBBR1-MCS4 [44] . The resulting plasmid was conjugated into btp1 mutant strain by biparental mating . C57/BL/6 and BALB/c mice were starved 24h before anaesthesia . After a small incision through the abdominal wall was done , a loop starting from the ileoocaecal junction and containing 2 to 3 Peyer's patches was formed taking care to maintain blood supply . Before closing the loop , 200μL of a 108 CFU/mL culture of B . abortus expressing GFP was injected . The intestine was then returned to the abdominal cavity for one hour before the mice were killed and the intestinal loops were removed , opened flat and washed extensively with PBS . Peyer's patches were then fixed with 3 . 2% paraformaldehyde for 60 minutes , rinsed in PBS , infused overnight in 35% sucrose and frozen in OCT compound . Immunofluorescence labelling was performed on 8 to 10 μm thick cryostat tissue sections overnight at 4°C using hamster anti CD11c ( N418 ) and rabbit anti Brucella abortus LPS followed by incubation with goat anti-rabbit Alexa Fluor 546 and goat anti-hamster Cy5 for 1h at room temperature . BMDCs were prepared from 7–8 week-old female C57BL/6 mice or TLR2−/− [45] , TLR4−/− [46] , MyD88−/− [47] , TLR9−/− [48] , TRIF−/− [49] , TLR7−/− [50] knockout mice , as previously described [51] . Infections were performed at a multiplicity of infection of 50:1 for flow cytometry experiments and 20:1 for all remaining experiments . Bacteria were centrifuged onto BMDCs at 400 g for 10 min at 4 °C and then incubated for 30 min at 37 °C with 5% CO2 atmosphere . Cells were gently washed twice with medium and then incubated for 1 h in medium supplemented with 100 μg/ml streptomycin to kill extracellular bacteria ( or gentamicin for Salmonella ) . Thereafter , the antibiotic concentration was decreased to 20 μg/ml . Control samples were always performed by incubating cells with media only and following the exact same procedure for infection . To monitor bacterial intracellular survival , infected cells were lysed with 0 . 1% Triton X-100 in H2O and serial dilutions plated onto TSB agar to enumerated CFUs . When stated cells were previously selected based on CD11c-labeling using CD11c MicroBeads ( MACS , Miltenyi Biotec ) following the manufacture's instructions . The primary antibodies used for immunofluorescence microscopy were: cow anti-B . abortus polyclonal antibody; hamster anti-CD11c ( N418; Biolegend ) ; affinity purified rabbit Rivoli antibody against murine I-A [20]; rat anti-mouse LAMP1 ID4B ( Developmental Studies Hybridoma Bank , National Institute of Child Health and Human Development , University of Iowa ) ; mouse antibody FK2 ( Biomol ) ; mouse anti-KDEL ( Stressgen ) . Monoclonal anti-calnexin antibody was kindly provided by Dr . D . Williams ( University of Toronto ) . For flow cytometry allophycocyanin conjugated-anti-CD11c antibody ( HL3 ) was used in all experiments along with either phycoerythrin-conjugated CD40 , CD80 , CD86 , IA-IE ( MHC class II ) or H2-2Kb ( MHC class I ) all from Pharmingen . Appropriate isotype antibodies were used as controls ( data not shown ) . For intracellular labelling of IL12 the phycoerythrin-conjugated IL-12 ( p40/p70 ) monoclonal from Pharmingen was used . The following TLR ligands from Invivogen were used: ODN1826 , Poly ( I:C ) , Pam2CSK4 and E . coli K12 LPS . Cells were fixed in 3% paraformaldehyde , pH 7 . 4 , at 37 °C for 15 min and then processed for immunofluorescence labelling as previously described [11] . Samples were either examined on a Leica DMRBE epifluorescence microscope or a Zeiss LSM 510 laser scanning confocal microscope for image acquisition . Images of 1024 × 1024 pixels were then assembled using Adobe Photoshop 7 . 0 . In all experiments we used an antibody against a conserved cytoplasmic epitope found on MHC-II I-A ß subunits since it strongly labels BMDCs [20] while no significant labelling was detected in bone marrow-derived macrophages ( Figure S2 ) . All BMDCs significantly expressing MHC II were also labelled with an anti-CD11c antibody confirming that they are indeed DCs ( Figure S2 ) . Quantification was always done by counting at least 100 cells in 4 independent experiments , for a total of at least 400 host cells analysed . For flow cytometry , infected DCs were collected and stained immediately before fixation . Isotype controls were included as well as DCs infected with non-gfp B . abortus as control for autofluorescence . Cells were always gated on CD11c for analysis and at least 100 , 000 events were collected to obtained a minimum of 10 , 000 CD11c-positive and GFP-positive events for analysis . A FACScalibur cytometer ( Becton Dickinson ) was used and data was analysed using FlowJo software ( Tree Star ) . The detection of glucose-6-phosphatase activity was performed by electron microscopy cytochemistry as previously described [52] with minor modifications . Cells were pre-fixed in 1 . 25% ( vol/vol ) gluteraldehyde in 0 . 1 M Pipes , pH 7 . 0 , containing 5% ( wt/vol ) sucrose for 30 min on ice , washed three times for 3 min in 0 . 1 M Pipes , pH 7 . 0 , containing 10% ( wt/vol ) sucrose and then briefly in 0 . 08 M Tris-maleate buffer , pH 6 . 5 . Cells were incubated in 0 . 06 M glucose-6-phosphate ( Sigma-Aldrich ) , 0 . 1% lead nitrate ( wt/vol ) in 0 . 08 M Tris-maleate buffer , pH 6 . 5 for 2 h at 37 °C . After 3 washes in 0 . 08 M Tris-maleate buffer and 3 washes in 0 . 1 M cacodylate buffer , pH 7 . 2 , containing 0 . 1 M sucrose , 5 mM CaCl2 and 5 mM MgCl2 , cells were post-fixed in 1 . 25% ( vol/vol ) gluteraldehyde in the same buffer for 1 h at 4 °C , and the , with 1% OsO4 in the same buffer devoid of sucrose for 1 h at room temperature . Samples were processed as previously described [52] . For immunoelectron microscopy samples were fixed in 4% paraformaldehyde in 0 . 1 M cacodylate buffer , pH 7 . 2 , containing 0 . 1 M sucrose , 5 mM CaCl2 and 5 mM MgCl2 , for 1 h at room temperature followed by 8% paraformaldehyde in the same buffer overnight at 4 °C . Cells were scraped , pelleted , and embedded in 10% bovine skin gelatin in phosphate Sorensen 0 . 1 M . Fragments of the pellet were infiltrated overnight with 2 . 3 M sucrose in PBS at 4 °C , mounted on aluminium studs and frozen in liquid nitrogen . Sectioning was done at −110 °C in an Ultracut cryo-microtome ( Leica ) . The 60 nm thick sections were collected in 1:1 mixture of 2 . 3 M sucrose and 2% methyl cellulose , transferred onto Formvar-carbon-coated nickel grids and incubated for 2 min with PBS-glycine 50 mM and then for 20 min with PBS-1% BSA . Sections were then incubated for 50 min with primary antibody in PBS-1% BSA , washed 5 times and then labelled with 10 nm protein A-gold particles in PBS-1% BSA for 20 min . Sections were finally washed 10 times in PBS , fixed for 5 min with 1% gluteraldehyde in PBS , rinsed in distilled water and incubated for 5 min with uranyl oxalate solution . Grids were then rinsed in distilled water , incubated with 2% methyl cellulose ( Sigma-Aldrich ) and 0 . 4% uranyl acetate for 5 min on ice and then dried at room temperature . Samples were analysed with a Zeiss 912 electron microscope and images were then processed using Adobe Photoshop 7 . 0 . Measurement of lactate dehydrogenase ( LDH ) release in the supernatant of cells infected with different strains was carried out using the Detection Kit ( Roche ) as indicated by the manufacturer . The percentage of cytotoxicity corresponds to the ratio between the experimental value subtracted by the negative control ( spontaneous LDH release ) and the maximum LDH release ( triton lysed cells ) subtracted by the negative control . For detection of 7-AAD , cells were infected with GFP-expressing strains as described above and collected at 24 h in cold PBS . Cells were then labelled first for CD11c-APC , washed several times in cold PBS and then incubated with 7-AAD following the manufacturer's instructions ( BD Pharmingen ) . The flow cytometric analysis were performed on fixed cells within 20 min . Sandwich enzyme-linked immunosorbent assays ( ELISA ) from Abcys were used to detect IL-12 ( p40/p70 ) , TNFα and IL-6 from supernatants of BMDCs infected with different Brucella strains . The ELISA kit for IFNβ measurement was obtained from PBL Biomedical Laboratories . DCs were seeded at 1 × 105 cells per well in 96-well flat bottom plates and infected for 30 min as described above . Cells were then washed and ovabulmin ( Sigma-Aldrich ) was added at a final concentration of 50 μg/ml for 2 h in media containing 100 μg/ml of streptomycin to kill extracellular bacteria . Antigen was then removed by changing media and DCs were co-cultured with splenic CD4+ T cells prepared from OTI and OTII transgenic mice ( added at a 1:1 ratio ) for a remaining 48 h in media containing 20 μg/ml of streptomycin . Supernantants were then collected and IL-2 production was assessed by evaluating the growth of IL-2-dependent CTLL-2 cell line ( 104 cells/well ) after 24 h and incorporation of 3H-thymidine ( NEN , 0 . 5 μCi per well ) during 8 h of incubation . All experiments were done in triplicates and the data presented corresponds to a representative experiment of three independent repeats . Data are expressed as the arithmetic mean counts per minute . Brucella did not induce cytotoxicity of DCs ( Figure S2 ) and did not affect uptake of fluorescently labelled OVA ( data not shown ) . HEK 293T cells were transiently tranfected using Fugene ( Roche ) for 24 h , according to manufacturer's instructions , for a total of 0 . 4 μg of DNA consisting of 50 ng TLR plasmids , 200 ng of pBIIXLuc reporter plasmid , 5 ng of control Renilla luciferase ( pRL-null , Promega ) and indicated amounts of Btp expression vectors . The total amount of DNA was kept constant by adding empty vector . Were indicated , cells were treated with E . coli LPS ( 1 μg/ml ) , Pam2CSK4 ( 1 ng/μl ) or CpG ODN1826 ( 10 μM ) for 6 h and then cells were lysed and luciferase activity measured using Dual-Glo Luciferase Assay System ( Promega ) . The TLR2 construct was obtained by PCR amplification from cDNA of bone marrow-derived macrophages and subcloning in the pCDNA3 . 1 expression vector ( Promega ) . All experiments were carried out at least 3 independent times and all the results correspond to the means ± standard errors . Statistical analysis was done using two-tailed unpaired Student's t test and p ≥ 0 . 05 were not considered significant .
The Entrez Protein ( http://www . ncbi . nlm . nih . gov/sites/entrez ? db=protein ) accession number for Btp1 ( BAB1_0279 ) is GI: 82699179 . | A key determinant for intracellular pathogenic bacteria to induce infectious diseases is their ability to avoid recognition by the host immune system . Although most microorganisms internalized by host cells are efficiently cleared , Brucella behave as a Trojan horse causing a zoonosis called brucellosis that affects both humans and animals . Here we show that pathogenic Brucella are able to target host cell defense mechanisms by controlling the function of the sentinels of the immune system , the dendritic cells . In particular , the Brucella TIR-containing protein ( Btp1 ) targets the Toll-like receptor 2 activation pathway , which is a major host response system involved in bacterial recognition . Btp1 is involved in the inhibition of dendritic cell maturation . The direct consequence is a control of inflammatory cytokine secretion and antigen presentation to T lymphocytes . These bacterial proteins are not specific for Brucella and have been identified in other pathogens and may be part of a general virulence mechanism used by several intracellular pathogens to induce disease . | [
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] | 2008 | Brucella Control of Dendritic Cell Maturation Is Dependent on the TIR-Containing Protein Btp1 |
The mechanisms that regulate post-natal growth of the craniofacial complex and that ultimately determine the size and shape of our faces are not well understood . Hippo signaling is a general mechanism to control tissue growth and organ size , and although it is known that Hippo signaling functions in neural crest specification and patterning during embryogenesis and before birth , its specific role in postnatal craniofacial growth remains elusive . We have identified the transcription factor FoxO6 as an activator of Hippo signaling regulating neonatal growth of the face . During late stages of mouse development , FoxO6 is expressed specifically in craniofacial tissues and FoxO6-/- mice undergo expansion of the face , frontal cortex , olfactory component and skull . Enlargement of the mandible and maxilla and lengthening of the incisors in FoxO6-/- mice are associated with increases in cell proliferation . In vitro and in vivo studies demonstrated that FoxO6 activates Lats1 expression , thereby increasing Yap phosphorylation and activation of Hippo signaling . FoxO6-/- mice have significantly reduced Hippo Signaling caused by a decrease in Lats1 expression and decreases in Shh and Runx2 expression , suggesting that Shh and Runx2 are also linked to Hippo signaling . In vitro , FoxO6 activates Hippo reporter constructs and regulates cell proliferation . Furthermore PITX2 , a regulator of Hippo signaling is associated with Axenfeld-Rieger Syndrome causing a flattened midface and we show that PITX2 activates FoxO6 expression . Craniofacial specific expression of FoxO6 postnatally regulates Hippo signaling and cell proliferation . Together , these results identify a FoxO6-Hippo regulatory pathway that controls skull growth , odontogenesis and face morphology .
Hippo signaling is a major determinant in regulating organ size and tissue regeneration . Several lines of evidence indicate that developing organs possess intrinsic mechanisms that modulate their final size [1 , 2] . Genetic studies have established that the Hippo pathway plays a crucial role in organ size , controlling cell number by modulating cell proliferation and apoptosis [3–8] . This pathway is triggered by the binding of extracellular ligands , which activate Mst1/2 . Active Mst1/2 phosphorylates the Lats1/2 kinase [9] , which is in turn activated , and subsequently phosphorylates and inactivates the Yes-associated protein ( YAP ) 1 transcriptional co-activator , a major downstream effector of the mammalian Hippo pathway [9–11] and TAZ , causing them to accumulate in the cytoplasm [12–14] . Therefore , upon activation of Lats1/2 , the expression of target genes related to cell survival are inhibited due to the retention of YAP and TAZ in the cytoplasm . In contrast , the unphosphorylated ( i . e . active ) forms of YAP and TAZ associate with transcription factors ( TFs ) of the TEAD/TEF family in the nucleus , activating the expression of target genes , and thereby promoting cell proliferation and inhibiting apoptosis [8 , 15 , 16] . Thus , Hippo signaling represses cell proliferation and stimulates apoptosis . The role of Hippo signaling in craniofacial development was recently shown through the inactivation of Yap and Taz in early neural crest-derived structures of the craniofacial complex [17 , 18] . This study identified how Hippo signaling was involved in neural crest specification and patterning during embryogenesis and before birth . However , the transcriptional mechanisms that regulate Hippo signaling components in postnatal craniofacial development are not well understood . We have identified a new transcriptional regulator of Hippo signaling specifically expressed in the craniofacial complex . The molecular mechanisms that control species-specific craniofacial growth and give rise to the different vertebrate head sizes and morphology include signaling and growth factors [19–31] . In particular , the Wnt , Fgf , Bmp , Shh , and Tgf-β signaling pathways control the early patterning and growth of the craniofacial skeleton by regulating the migration , proliferation , differentiation and transformation of cells derived from the mesoderm and cranial neural crest [32–41] . These factors and pathways interact and intersect to control development of the brain and skull [21 , 22 , 25 , 26 , 40 , 42–44] . Tissue-tissue interactions that give rise to cell fate decisions are fundamental to the development of head structures , especially for the patterning and morphogenesis of craniofacial structures [45–47] . These early developmental cues drive the morphogenesis and patterning of perinatal craniofacial tissues . However , regulation of postnatal craniofacial growth is less well known , and although members of the early developmental pathways above are likely implicated , other less well-known factors could also play significant roles in determining adult head size and shape . Continued growth of the head and face after birth are critical for healthy ontogeny . Yet , it has been unclear how Hippo signaling affects these growth and expansion processes of the craniofacial complex after birth . We provide evidence that Hippo signaling components regulate this postnatal growth , which is distinct from early patterning events . FoxO6 is a TF that contains a Forkhead ( winged helix ) domain and is encoded by one of the FoxO class genes [48 , 49] . Mammals have four FoxO members ( FoxO1 , FoxO3 , FoxO4 and FoxO6 ) [50] . In humans , single nucleotide polymorphisms in FOXO1 and FOXO3 have been associated with increased longevity and in invertebrates the expression of FoxO proteins can increase their life span [51–55] . FoxO6 is the most recently identified FoxO-encoding gene and in mammals it was initially observed in the CNS [56–58] . FoxO6 is expressed in the hippocampus [56–58] , is negatively regulated by insulin/IGF signaling via the PI3K-Akt pathway , and is phosphorylated in contrast to other FoxO factors . However , its nuclear localization is not affected by phosphorylation [56 , 59] . In this report we demonstrate specific FoxO6 expression patterns in craniofacial structures and show that localized transduction of Hippo signaling controls growth of the anterior craniofacial region . FoxO6 was identified in our bioinformatics analysis of transcription factor gene regulatory networks that are active during craniofacial development . FoxO6 expression is specific and spatially restricted to the anterior regions of the brain and face . Cell-based studies showed that FoxO6 regulated components of Hippo signaling . Our subsequent analyses of FoxO6 mutant mice showed that FoxO6 loss-of-function led to enhanced head and craniofacial growth . This growth largely affected the anterior-posterior axis and depended on a Hippo pathway that controls the sizes of the brain , face , jaw and incisors during late stages of development . Fox genes have been shown to play different roles in directing facial growth through skeletal and dental tissue components [60 , 61] . Thus , the specific expression and function of FoxO6 in craniofacial tissues in postnatal stages controls growth of the face and subtle changes in FOXO6 expression controlling Hippo signaling may define facial morphology .
Multiple bioinformatics analyses from different stages of murine craniofacial development comparing WT embryonic and neonate mice to 8 different mouse gene knockout mice identified FoxO6 as a new gene in craniofacial development . We have identified several new transcription factors involved in craniofacial and dental development based on these data . We generated FoxO6-lacZ knock-in mice ( FoxO6+/- ) from KOMP FoxO6 gene targeted ES cells ( Fig 1A ) and performed X-gal staining on staged embryos ( Fig 1B–1E ) . Analysis of whole-mount embryos revealed that at E10 . 5 , FoxO6 was not expressed at detectable levels ( Fig 1B ) ; at E12 . 5 it was expressed within the brain ( e . g . frontal lobes , cerebellum primordium and trigeminal ganglion ) , somites and craniofacial region ( Fig 1C ) ; at E14 . 5 , expression was present in the brain and somites , as well as in the posterior regions of the maxilla and mandible ( Fig 1D ) ; at E18 . 5 , expression was increased in the craniofacial region ( including the maxilla , mandible , incisor , molar and palate ) ( Fig 1E ) . To determine if formation of the cranial bones was affected during late stages of embryonic development , we stained FoxO6-/- embryos with Alcian Blue/Alizarin Red . This staining revealed that in E18 . 5 embryos , ossification ( red stain ) of the interparietal bone ( INT ) , exoccipital bone ( EXO ) and nasal bone ( NB ) appeared delayed in FoxO6-/- embryos ( Fig 2A ) . At P1 , the frontal ( FB ) , parietal ( PB ) and occipital bones ( OB ) were slightly larger in the FoxO6-/- mice , with a flattened dorsal skull ( Fig 2B ) . Taken together , these bone staining data indicate that osteogenesis , and in particular endochondral ossification , was modestly delayed in the FoxO6-/- neonate mice . At birth , the growth of the FoxO6-/- mice appear normal compare to FoxO6+/- mice . However , as growth of the mice continued , a significant growth increase of the anterior region of the mandible , maxilla and skull was observed in the FoxO6-/- mice shown at 2 months of age ( Fig 2C ) . In contrast , body growth of the FoxO6-/- mice is normal compared to FoxO6+/- mice . The submandibular gland was enlarged in the 6-month old FoxO6-/- mouse ( Fig 2D ) . Magnetic resonance imaging ( MRI ) analyses of the FoxO6-/- mice and subsequent volumetric measurement indicated that specific areas of the brain and craniofacial regions differed with respect to growth effects ( Fig 2E ) . The frontal cortex , olfactory system and pituitary , regions of high FoxO6 expression , were all clearly larger in the FoxO6-/- mice , while midbrain and medulla were slightly larger than in their FoxO6+/- littermates ( Fig 2E ) . In contrast , the cerebral cortex , cerebellar cortex , thalamus and pons were all smaller in FoxO6-/- mice than in controls ( Fig 2E ) . These measurements are consistent with findings from an earlier study on the consequences of reduced hippocampal function for memory and synaptic function in FoxO6-/- mice [58] . Because FoxO6 is expressed mainly in the brain and craniofacial region , it acts as a regulator of head morphology . Furthermore , FoxO6 is expressed at later embryonic stages ( after E10 . 5 ) and does not appear to affect cell/tissue-specific patterning . Microcomputed tomography ( μCT ) analysis of whole heads of 2 month-old FoxO6-/- and WT mice identified specific changes in growth patterns in the context of loss of FoxO6 function ( Fig 3 ) . Lengths of the nasal and facial bones were approximately 10% greater in the mutant animals , N = 3 ( Fig 3B ) . Mandibular length and height were larger by 5 . 89% and 8 . 37% respectively , N = 3 ( Fig 3B ) . The lengths of the frontal bone , parietal bone and skull overall were ~4 . 8% greater ( N = 3 ) ( Fig 3C ) . A midsagittal section measuring the cranial base angle showed that it was similar in the FoxO6-/- mice compared to WT , N = 3 ( Fig 3D ) . Overall the base length of the cranium was ~7% larger , with increased lengths in the palate , cranial and lower incisors ( Fig 3B ) . Quantitation of other specific craniofacial growth measurements show growth of the palate and lower incisor , however the molars are not significantly increased in length or breadth ( S1 Table ) . These increases in anterior growth became apparent immediately after birth and continued through 2 months of age . These data suggest that FoxO6 functions after neural-crest migration has taken place and once tissue identity , including that of epithelial tissues , has been specified . MicroCT analyses of the lower incisor showed a 4 . 21% increase in lower incisor length ( Fig 4A , measurements not shown , N = 3 ) . Although the molars of 1 month-old FoxO6-/- mice were normal , the lower incisor mineralization was delayed and displaced in the anterior direction due to the overall increase in mandible length ( Fig 4B and 4C ) . Moreover , lower incisor mineralization was not detected in the area of the mesial apex of the second molar , where in control mice enamel mineralization is clearly seen ( Fig 4B and 4C , see arrows ) . In the posterior region , enamel is not detected near the mesial bifurcate root of the first molar in the FoxO6-/- mice and dentin is thinner and less mineralized ( Fig 4D , 4E , 4F , and 4G ) . Thus , the lower incisor is longer in the FoxO6-/- mice , and the zone of incisor formation and appositional enamel growth is extended and positioned further anterior relative to the molars , reflecting the increase in anterior growth of the jaw . We used the incisor as one model to determine the role of FoxO6 in craniofacial development as incisor development is linked with craniofacial development . The mouse incisor is a useful model , since it grows continuously throughout the life-time of the animal , relying on a stem-cell niche in the labial cervical loop ( LaCL ) . Initially , during development , the teeth grow with the mandible and maxilla . However , as the incisors are worn down , the tooth structure is regenerated by stem-cell proliferation and differentiation in the posterior region of the tooth . Fig 5A depicts the rodent lower incisor , including the structures that give rise to the enamel ( En ) -forming ameloblast , i . e . the: labial cervical loop ( LaCL , outlined by dashed line ) , outer enamel epithelium ( OEE ) , and inner enamel epithelium ( IEE ) . The dental mesenchyme ( Mes ) also contains stem cells; these give rise to odontoblasts , which produce dentin ( De ) . We analyzed gene expression and epithelial-cell proliferation and differentiation in the lower incisors of FoxO6-/- mice and WT littermates at E16 . 5 , E18 . 5 and P0 to study defects in incisor morphogenesis . Bioinformatics analyses of the lower incisors , mandible and maxilla regions demonstrated genes involved in Hippo signaling were regulated by FoxO6 . Gene expression data has been deposited ( GSE117013 ) in the NIH GEO repository . A heat map of selected genes including Lats1 and Last2 are shown decreased in the FoxO6-/- mandibles ( S1A Fig ) . A gene ontology map of biological processes shows that FoxO6 regulates transcription ( S1B Fig ) . A gene set enrichment analysis ( GSEA plot ) shows that the Hippo pathway is down regulated in the FoxO6-/- mandible ( Fig 6 ) . A volcano plot of the RNA-Seq data reveals several genes regulated by FoxO6 , including the Hippo pathway ( Fig 6 ) . Lats1 is a major component of the Hippo pathway and it was significantly decreased ( P< 0 . 01 ) in the FoxO6 null mouse mandible tissue ( Figs 6 and S1; RNA from 4 biological samples combined for bioinformatics analyses ) . We used X-gal antibody to probe FoxO6 expression in E18 . 5 sagittal sections of the FoxO6+/- mouse head . X-gal was highly expressed in the dental epithelium ( both incisor and molar ) ( Fig 5C and 5E ) oral epithelium and craniofacial mesenchyme ( Fig 5G ) . This is consistent with a role for FoxO6 in both odontogenesis and development of the mandible and maxilla . Notably , FoxO6 was detected in transit amplifying cells ( TACs ) [74] of the dental mesenchyme as well ( Fig 5C and 5E ) . We further performed immunostaining using a FoxO6 antibody to probe for FoxO6 expression and compare to the X-gal staining . FoxO6 was specifically expressed in the oral epithelium ( OE ) , dental lamina ( DL ) and lower incisor dental epithelium ( S2A–S2D Fig ) . Low level of expression was observed in the dental mesenchyme and no FoxO6 expression was seen in the FoxO6-/- embryos . RNA extracted from WT and FoxO6-/- mandibles shows no FoxO6 transcripts were present in the FoxO6-/- embryos ( S2E Fig ) . To demonstrate a role for FoxO6 in the direct regulation of Hippo signaling we used a Hippo reporter construct ( HOP ) that contains several TEAD binding sites to activate luciferase expression in CHO cells . A Hippo reporter with mutations in the TEAD binding sites ( HIP ) acted as a control reporter . Cotransfection of FoxO6 with the HOP reporter resulted in decreased HOP activation , while the HIP reporter was not affected ( S2F Fig ) . In contrast knockdown of FoxO6 expression ( shFoxO6 ) resulted in an increase in HOP reporter activity ( S2F Fig ) . As controls the constitutively active YAP 5SA construct was cotransfected with HOP and HIP and showed that YAP 5SA activated the HOP reporter as expected ( S2F Fig ) . Cotransfection of WT YAP had only a modest effect on HOP activity . These results demonstrate that FoxO6 has a direct effect on Hippo signaling through regulating YAP activation of the HOP reporter . Given that our analysis of gene expression arrays had shown Hippo signaling in FoxO6-/- mice to be defective , and that our bioinformatics analyses demonstrated that Lats1 , Yap and pYap were expressed during incisor development ( S1 Fig ) , we sought to confirm the dental effects of defective Hippo signaling in FoxO6-/- mice . Immunofluorescence assays demonstrated that Lats1 is normally expressed in the lower incisor ( LI ) of the P1 mouse , and that Yap is phosphorylated at this time . In WT , sagittal sections of the lower incisor , shows Lats1 expression is predominantly localized to the LaCL region and to some extent in the dental mesenchyme ( Fig 5H and 5I ) . As expected , in the FoxO6-/- P1 mouse this expression was lower ( Fig 5J and 5K ) . Examination of pYap staining in sections of WT LI’s revealed that Yap was activated in the LaCL , differentiating ameloblasts and mesenchymal cells ( Fig 5L and 5M ) . In the FoxO6-/- P1 mice , this expression was lower ( Fig 5N and 5O ) . These experiments suggest that FoxO6 directly activates Lats1 and Yap in vivo to regulate Hippo signaling during incisor development . Consistent with a role for FoxO6 in regulating Hippo signaling , we observed an increase in the size of the lower incisors of E16 . 5 and E18 . 5 ( Fig 7A–7H ) , as well as of the LaCL , in FoxO6-/- embryos ( LaCl is outlined in G and H ) . These differences persisted in P0 FoxO6-/- mice ( Fig 7I–7L ) , at which point structural defects in ameloblasts ( dental epithelium , Am ) and odontoblasts ( dental mesenchyme , Od ) were also noted in the FoxO6-/- mice , with neither cell layer as uniform as in WT mice ( Fig 7M and 7N ) . We asked if the ameloblasts in the FoxO6-/- incisors indeed express amelogenin , the most abundant structural enamel matrix protein that is required for the formation of enamel and that marks the differentiation of this cell type . Immunofluorescence analysis using an amelogenin antibody revealed greater overall expression of this protein , due to an increase in length of this structure and a corresponding increase in the number of differentiated ameloblasts ( Fig 7O–7R ) . Thus , whereas the ameloblast layer is less structured in FoxO6-/- mice , the cells differentiate as ameloblasts . Thus , FoxO6-mediated , Hippo-dependent regulation of incisor size in coordination with craniofacial growth may be due to the control of LaCL stem-cell proliferation . Hippo signaling is known to regulate organ size by modulating cell proliferation [8 , 75] . To determine if the observed increase in incisor length in of FoxO6-/- mice was due to an increase in cell proliferation , E18 . 5 FoxO6-/- and WT embryos were sectioned and proliferation was assessed using the Ki67 antibody . This analysis revealed an increase in Ki67-positive epithelial cells in mutant vs . WT incisors ( Fig 8A and 8B ) , specifically within the LaCL ( Fig 8C and 8D ) . Quantitative analysis revealed that this increase applied to the epithelium ( IEE ) and mesenchyme ( Fig 8E ) , indicating that dental-cell proliferation is up-regulated in FoxO6-/- incisors . BrdU labeling in E17 . 5 FoxO6-/- incisors confirmed this increase in cell proliferation ( Fig 8F–8I ) . Quantitation of the BrdU-positive cells demonstrated that the proliferation of both epithelial and mesenchyme cells was increased ( Fig 8J ) . BrdU labeling was subsequently performed in P7 mice to show increase cell proliferation at later stages . Analyses of the lower incisor shows an increase in cell proliferation of the transit amplifying cells of the dental epithelium in FoxO6-/- mice compared to WT mice ( S3A and S3B Fig ) ; white line denotes transit amplifying cells of the lower incisor ) . Quantitation of cell proliferation demonstrates a 20% increase in epithelial cell proliferation ( S3C Fig ) . To independently show that FoxO6 regulates cell proliferation , CHO cells were transfected with either FoxO6 , a short hairpin FoxO6 inhibitor ( pshFoxO6 ) or empty vector DNA plasmids . The over expression of FoxO6 inhibited cell proliferation while inhibition of FoxO6 increased cell proliferation ( S3D Fig ) . The activation of Hippo signaling inhibits cell proliferation , and we have shown that FoxO6 activates Lats1 expression and increases the phosphorylation of YAP . This modification is known to cause Yap to be sequestered in the cytoplasm and to thereby down-regulate the expression of genes required for both cell proliferation and anti-apoptotic activity . Thus , in the absence of FoxO6 the Hippo pathway is not stimulated , and this leads to specific tissue responses involving cell proliferation . In order to find genes downstream of FoxO6 that mediate craniofacial development , we assessed gene expression using arrays generated from the mandibular and maxillary tissue from E18 . 5 WT and FoxO6-/- mouse embryos . Subsequent gene ontology ( GO ) analysis of biological processes indicated that FoxO6 plays a role in regulating transcription and development . Expression of Lats1 , an important component of the Hippo signaling pathway , was ~20-fold lower in FoxO6-/- mice compared to WT ( Figs 6 and S1 ) . To validate these array data and characterize other genes involved in regulating cell proliferation and differentiation , we performed RT-qPCR using the same tissues ( Fig 9A ) . These analyses revealed reduced expression of the following genes: Lats1 and Lats2 ( ~80% and 70% reduced , respectively ) , Runx2 ( 85% reduced ) , and Shh ( 80% reduced ) . The observed change in expression in Runx2 is consistent with the roles of the encoded transcription factor in osteoblast differentiation and skeletal morphogenesis , as well as with the decrease in ossification observed at E18 . 5 in FoxO6-/- mice ( Fig 2A and 2B ) . The difference in Shh expression is likewise consistent with the role of its product in regulating the size and shape of the face [22] . Genes whose products were expressed at higher levels were amelogenin and cyclin D1 . The former is a marker for the differentiation of ameloblasts and the formation of dental enamel , and the latter is a positive regulator of cell proliferation . We next used an in vitro assay to test whether FoxO6 regulates Hippo signaling through Lats1 . To this end , we over-expressed FoxO6 in CHO and LS-8 cells ( oral epithelial-like cells ) by transient transfection . This led to an increase in Lats1 expression in both cell lines ( Fig 9B and 9C ) , and the specificity of the effect was supported by the responsiveness to Fox06 dosage in the CHO cells ( Fig 9B ) . Notably , the level of Yap phosphorylation was also higher in both of the transfected cell lines ( Fig 9B and 9C ) even though overall levels of Yap expression did not differ ( Fig 9B and 9C ) . Subsequent analysis of the effects of FoxO6 knockdown on Lats1 and Lats2 in ET-16 cells ( oral epithelial cells in which FoxO6 is highly expressed ) by real-time PCR revealed that a 70% reduction in Fox06 was accompanied by a 60% decrease in Lats1 ( Fig 9D ) . These data suggest that FoxO6 controls Hippo signaling by regulating Lats1 expression . Sequence analysis of the Lats1 5’flanking region identified a consensus FoxO6 binding site approximately 2 , 471 bp upstream of the Lats1 transcription start site ( Fig 10A ) [58] . Chromatin immunprecipitation ( ChIP ) assays demonstrated that endogenous FoxO6 binds to this consensus binding site ( Fig 10B , lane 2 ) . The chromatin input is shown in lane 1 , and the control IgG , which failed to pull down the chromatin , in lane 3 ( Fig 10B ) . Specificity test of the ChIP assay was carried out using primers to an upstream region of the Lats1 promoter that does not contain a FoxO6 binding element . The FoxO6 antibody did not immunoprecipitate chromatin that does not contain a FoxO6 binding site ( Fig 10B , lane 7 ) ; chromatin input and IgG control are also shown ( Fig 10B , lane 6 and 8 , respectively ) . Quantitative PCR demonstrated an 8-fold enrichment of the FoxO6 ChIP product over that of the IgG control ( Fig 10C ) . The Lats1 promoter was cloned ( 1 . 7Kb ) upstream of the luciferase gene to measure promoter activity . Co-transfection of the Lats1 promoter with FoxO6 resulted in 15-fold activation ( Fig 10D; comparison is to transfection with empty vector transfection ) . Mutation of the FoxO6 binding site abolished this activation ( Fig 10E ) . These results indicate that FoxO6 directly activates the Lats1 promoter . Pitx2 regulates a hierarchical gene regulatory network to control tooth initiation , patterning and growth [62 , 63 , 76–83] . Human PITX2 mutations are associated with Axenfeld-Rieger Syndrome ( ARS ) and these individuals have a flattened mid-face phenotype and tooth agenesis [84 , 85] . Pitx2 is also expressed in the oral epithelium , and our bioinformatics analysis of the consequences of the overexpression of PITX2 in mandibular and maxillary tissues show that PITX2 activates FoxO6 expression ( Fig 11A ) . Consistent with these findings , real-time PCR analysis of PITX2-transfected LS-8 and MDPC cells showed an increase in FoxO6 transcripts ( Fig 11B ) , and Western blot analyses of PITX2-transfected cells showed a corresponding increase in FoxO6 protein ( Fig 11C and 11D ) . Sequence analyses of the FoxO6 5’-flanking region identified a consensus PITX2 binding element 1504 bp upstream of the transcription start site ( Fig 12A ) , and a ChIP assay revealed that endogenous Pitx2 bound to the FoxO6 chromatin in LS-8 cells ( Fig 12B ) but not to DNA from an upstream region of the FoxO6 promoter that lacks a Pitx2 binding site ( Fig 12C ) . The enrichment of chromatin achieved using the Pitx2 antibody represented a 37-fold increase ( Fig 12D; comparison is to control IgG ) . A luciferase reporter assay using a construct bearing the FoxO6 promoter ( 4Kb ) showed that co-transfection of LS-8 cells with PITX2 led to an ~60-fold increase in reporter activity over transfection with the reporter construct alone ( Fig 12E ) . This effect was specific , as shown using enhancer constructs containing either the intact 70-bp FoxO6 PITX2 enhancer or the same site with a mutation in the PITX2 binding site ( Mut enhancer ) within the luciferase vector ( has Thymidine Kinase ( TK ) minimal promoter ) . These enhancer constructs were co-transfected into LS-8 cells with the PITX2 cDNA or empty vector , and mutation of the PITX2 binding element abolished PITX2 activation ( Fig 12F ) . We have recently shown that Pitx2 regulates Hippo signaling through a direct interaction with Yap to control heart regeneration [86] . These data demonstrate another role for Pitx2 regulating FoxO6 , which activates Lats1 expression .
This report describes how restricted expression of FoxO6 in the brain and craniofacial region activates Hippo signaling to regulate growth of the anterior part of the skull , brain , maxilla , mandible and teeth . Because Hippo signaling is ubiquitous in the developing embryo , specific factors must modulate its activity in a tissue- and organ-specific manner at particular points in development . Our MRI analyses have identified specific regions that are expanded in the FoxO6-/- mice: the frontal cortex , the olfactory system , and the pituitary regions . It is notable that all of these regions are linked to growth of the frontonasal prominence . The unique expression pattern of FoxO6 appears to control the development and size of the craniofacial skeleton and anterior structures after birth . The temporal , spatial and tissue-specific timing of expression of specific factors drives patterning and growth of the facial prominences . In humans , facial characteristics vary by ethic and cultural background . Individuals can present with maxillary and/or mandibular prognathism ( increased growth of the upper and lower jaw , respectively ) , micrognathia ( small jaw ) or retrognathia ( retracted jaw ) . Each of these conditions affects the development of the dentition demonstrating that odontogenesis and jaw growth are directly linked . Early in development ( E8 . 5 to E10 . 5 in mice ) , cranial neural crest ( CNC ) migrate to populate the frontonasal prominence and the first pharyngeal arch [87–90] . Modulation of gene expression in the CNC , by numerous factors has been shown to guide craniofacial development [19 , 42 , 91 , 92] . The head ectoderm contains cells and centers necessary for the early development , patterning and specification of the craniofacial skeleton . The epithelium of the frontonasal prominence and first pharyngeal arch express several signaling proteins that induce transcription factor expression in the neural crest-derived mesenchyme . For example , Wnt signaling in the frontonasal process induces growth and morphogenesis [21 , 31 , 93–105] , and Shh expression in the facial ectoderm regulates morphogenesis of the head and face as well as development of the maxillary process [21 , 106] . These molecular mechanisms all drive early patterning ( initial patterning and segregation of tissues ) within the craniofacial region . However , little is known about factors that influence later stages in the development of facial morphology , FoxO6 is a good candidate , because it is expressed at later embryonic stages and does not appear to affect cell and tissue-specific patterning . The evolution of the mammalian skull and its morphologically complex traits has been extensively studied as the development of three distinct modules: the calvarium , the cranial base , and the face [25 , 107–109] . The hypothesis that growth of the brain and face are linked is supported by some evidence from mouse models [25 , 26 , 110 , 111] . This hypothesis is strengthened by work showing that development of the cerebellar structure is associated with isolated cleft lip and/or palate [112] . The face forms from the maxillary , mandibular and frontonasal prominences and a complex gene regulatory network that tightly controls development of these tissues [21 , 113 , 114] . As the brain and skull grow , the facial prominences also grow and both converge to form the face . The rate of growth of each can influence outgrowth of the midface and produce either prognathism or retrognathism . An elegant study demonstrated that forebrain and facial shape differ between mouse strains [25] . Although the variations noted in that study were limited to developmental stages with complex developmental processes and molecular mechanisms that remain undefined , it is clear from that study that epigenetic integration and sensitivity to gene dosage can affect facial morphology [25 , 26 , 44 , 115] . Growth of the craniofacial complex ( skull and brain ) involves the integration of signaling pathways that pattern both during development [116] . Crosstalk between the molecular components of the TGF-β , WNT , Notch , Hedgehog , Fgf and Hippo pathways influences the control of both development and cell proliferation [2 , 117–139] . Interacting components of one such set of pathways are Smad ( of the TGF-β pathway ) and β-catenin ( of the WNT pathway ) : both accumulate in the nucleus to activate shared targets , and interact with Lef/TCF transcription factors in inducing gene expression and controlling cell fate [124 , 125 , 140] . Hippo is another interface for these two pathways; its activation results in cytoplasmic retention of TAZ/YAP proteins , which can also interact with Smads to inhibit their activity , and thus Hippo activity antagonizes WNT signaling [2 , 135–137 , 141] . TAZ/YAP can also interact with β-catenin to activate gene expression during development [2 , 142 , 143] . Pitx2 is the earliest transcriptional marker of tooth development , and interacts with β-catenin to regulate gene expression [77 , 81 , 82 , 144 , 145] . Pitx2 was identified as a potential modulator of craniofacial morphogenesis involving ephrin-B1 signaling and cell proliferation [34] . Given that this protein is expressed in both the brain and craniofacial regions , it may link brain and craniofacial development by activating FoxO6 . Hippo signaling regulates Runx2 expression , and FoxO6 loss-of-function mice have decreased levels of Runx2 , a protein that can interact with TAZ/YAP to activate gene expression . Several studies have demonstrated a role for Runx2 in modulating facial size , and length [146–148] . Mutations and polymorphisms contribute to variations in facial morphology and evolution of face shape changes in carnivoran species [149] . Thus , FoxO6 may directly regulate Runx2 independent of Hippo signaling to delay osteogenesis in relation to increased skeletal growth . However , the decrease in Lats1 expression in the FoxO6-/- mouse would lead to activation of cell proliferation by promoting the nuclear accumulation of TAZ/YAP and an increase in the expression of WNT/β-catenin target genes . Lats1/2 phosphorylates YAP and TAZ , leading to the retention of these proteins in the cytoplasm and preventing them from activating gene expression . TAZ inhibits Wnt signaling by suppressing DVL2 and preventing β-catenin from entering the nucleus to stimulate gene expression [2] . Furthermore , TAZ/YAP and TGF-β/Smads can interact with β-catenin in the nucleus , thereby activating a gene regulatory network that controls cell proliferation and organ size . Thus , the direct regulation of Lats1 by FoxO6 is a new mechanism for controlling craniofacial growth , by inhibiting the cell proliferation controlled by the Hippo signaling pathway . In FoxO6-/- mice , the incisors had low Lats1/2 expression compared with control mice and were expanded in length and size . During development of the mouse incisor , the stem cells proliferate ( LaCL ) and populate the transient amplifying ( TA ) zone of the inner enamel epithelium ( IEE ) and migrate to differentiate into ameloblasts that secret enamel [150] . We conclude that the increased amelogenin expression in FoxO6-/- mice may be caused by the observed increase in proliferation of the dental stem cells , as shown by Ki67 staining and BrdU labeling . However , Pitx2 expression was not changed and Sox2 , a marker of dental epithelial stem cells [71 , 150 , 151] , was slightly up-regulated in FoxO6-/- embryos . The increase in incisor size correlates with an increase in the size of the anterior region of the mandible , whereas size of the molar was not affected . Dental patterning and tooth number were also not affected , suggesting that FoxO6-mediated regulation of Hippo signaling occurred later in development , after tooth development was initiated . However , we report a defect in pre-ameloblast polarization and differentiation , and this correlates with the role of Hippo signaling in the regulation of cell polarity [152] . A previous study of YAP over-expression in mice reported that the enamel organ was deformed and the dental lamina was widened [153] , but that in the dental epithelium , proliferation was only slightly reduced , Shh , Fgf and Wnt levels were decreased , and epithelial cell movement and/or polarization was defective [153] . Interestingly , integration of the Hedgehog and Hippo signaling pathways is important in the proliferation of stem cells of Drosophila ovarian follicles , where Shh induces the expression of a transcriptional coactivator of the Hippo pathway [154] . Because Hippo signaling regulates stem-cell proliferation , self-renewal and differentiation , we speculate that crosstalk between the FoxO6 , Hippo and Shh pathways may contribute to regulation of the proliferation and maintenance of dental stem cells [155] . The human face has many complex geometric variations . GM methods applied to both two and three dimensional craniofacial images , allow the exploration of specific patterns of craniofacial shape [156] that could be associated with genetic variation . There are three SNPs with high association results are known eQTLs for FOXO6 . In addition , Pitx2 has been linked to Hippo signaling through its interaction with Yap [86] and our results show another role for Pitx2 in Hippo signaling through its regulation of FoxO6 expression . Our results particularly support a role for FOXO6 in variation related to horizontal projection of maxillary and mandibular structures . These features partly resemble those of the FoxO6-/- mice . The activation of the Hippo pathway would impede growth in these specific regions and cause maxillary retrusion . Thus , mutations in FOXO6 may define subtle shape alteration patterns that control the anterior growth of the human face . This study of the FoxO6-/- mouse revealed increases in growth of the cerebral cortex and the incisors in the anterior parts of the face , with an overall increase in the length of the maxilla and mandible . These data suggest that FoxO6 controls the anterior-posterior growth of the craniofacial skeleton , and that differences in FoxO6 expression my account for the differences in face morphology seen among mammals and other vertebrates . Furthermore , because FoxO6 regulates Lats1/2 to control Hippo signaling independent of ligands or stimuli , this level of regulation may play important roles in other cell processes . A model for the role of FoxO6 is shown ( Fig 13 ) . We are currently exploring other transcriptional interactions and regulators of Hippo signaling during development .
"Institutional ethics ( IRB ) approval was obtained at each recruitment site and all subjects gave their written informed consent prior to participation ( University of Pittsburgh Institutional Review Board #PRO09060553 and #RB0405013; UT Health Committee for the Protection of Human Subjects #HSC-DB-09-0508; Seattle Children’s Institutional Review Board #12107; University of Iowa Human Subjects Office/Institutional Review Board #200912764 , #200710721 and #200811701 ) . Animal care and use was approved by the University of Iowa Institutional Animal Care and Use Committee ( IACUC ) , Protocol #1207146 . " All animals were housed at the University of Iowa , Program of Animal Resources and were handled in accordance with the principles and procedures specified in the Guide for the Care and Use of Laboratory Animals . All experimental procedures were approved by the University of Iowa IACUC guidelines . FoxO6 knockout mice were generated using a gene-trap strategy . In these mice 19 kb of FoxO6 genomic DNA ( a stretch that contains the two FoxO6 exons ) were replaced with a LacZ cassette and a neomycin cassette surrounded by loxP sites for future excision . Briefly , FoxO6 knockout embryonic stem ( ES ) cells ( derived from C57BL/6 mice ) were purchased from Knockout Mouse Project ( KOMP ) ( project number: VG12465 ) and injected into blastocysts ( from BALB/cJ ) by the Texas A&M University Institute for Genomic Medicine . In two chimeras generated from ES cell injection , the mutant allele was passed through the germ line , and these animals produced heterozygous progeny . K14-Pitx2 transgenic mice were previously reported [62] . Mice were maintained on a C57BL/6 background . Observation of a vaginal plug was counted as embryonic ( E ) day 0 . 5 , and embryos were collected at E14 . 5 , E16 . 5 , E18 . 5 , P0 and P1 . Mice and embryos were genotyped based on PCR carried out on DNA extracted from tail biopsies ( WT primers: sense: 5’ACCTCATCACCAAAGCCATC3’ , antisense: 5’GTCACCCTACCAGACCTCCA3’; KO primers: sense: 5’CCTGCAGCCCCTAGATAACTT3’ , antisense: 5’GGTTGCTGGCTTCGTGTGGTG3’ ) . Mouse embryos or heads were dissected in phosphate-buffered saline ( PBS ) . Embryos were fixed with 4% paraformaldehyde-PBS solution for 0 . 5–4 hours . Following fixation , samples were dehydrated through graded ethanol , embedded in paraffin wax and sectioned ( 7 μm ) . Standard Hematoxylin and Eosin was used to examine tissue morphology as previous described [157] . For immunofluorescence ( IF ) assays , slides were boiled in 10mM sodium citrate solution ( pH 6 . 0 ) for 20 minutes for antigen retrieval . They were then incubated with 20% goat serum-PBST for 30 min at room temperature , and then with antibodies against Ki67 ( Abcam , 1:200 ) , amelogenin ( Santa Cruz , 1:200 ) , Lats1 ( Cell signaling , 1:200 ) and pYap ( Cell signaling , 1:200 ) and Beta-galactosidase ( Abcam , ab9361 , 1:50 ) at 4 oC overnight . The slides were treated with FITC ( Alexa-488 ) - or Texas Red ( Alexa-555 ) -conjugated Secondary antibody for 30 minutes at room temperature for detection ( Invitrogen , 1:500 ) . Nuclear counterstaining was performed using DAPI-containing mounting solution . Mouse heads were stained for β-galactosidase activity according to standard procedures [63] . Embryos ( from E12 . 5-E16 . 5 ) or embryo heads ( from E17 . 5 to postnatal stage ) were fixed for 30–60 min at RT in 0 . 2% glutaraldehyde in PBS . Fixed embryos were washed three times ( 1M MgCl2 , 0 . 5M NaH2PO4 , 0 . 2% Nonidet P-40 and 0 . 01% sodium deoxycholate in PBS ) and stained 24–48 hours at 37°C using standard staining solution ( 5 mM potassiumferricyanide , 5 mM potassium ferrocyanide , 0 . 1% X-gal in wash buffer ) . On the next day , the samples were rinsed in PBS , photographed , and post-fixed in 4% formaldehyde for 0 . 5–4 hours . The fixed samples were dehydrated in a graded series of ethanol solutions , embedded in paraffin wax and sectioned . Sections were cut at 12-μm thickness and lightly counterstained with eosin . FoxO6 expression plasmid was cloned into pcDNA-myc 3 . 1 vector ( Invitrogen ) using the following primers: 5'- GCCTACATACCTCGCTCTGC -3' and 5'- ATCATAAGCTTGATTGGAGTTGGGTGGCTTA -3' . A 4kb DNA fragment in which the Pitx2 binding site was incorporated upstream of the FoxO6 gene was cloned into the pTK-Luc vector ( Promega ) using the following primers: 5'-GGAAGCAATTGAAGTGGCCTTAGAT -3' and 5'- CAGATCCCAGAGCCGCGC-3' . A 1 . 7kb DNA fragment in which the FoxO6 binding site was incorporated upstream of the Lats1 gene was cloned after the luciferase gene in the pTK-Luc vector ( Promega ) using the following primers: 5'- TCAGTGGATCCAGATCCCCTGAAGCTGGAGT -3' and 5'-TGACTGGATCCCAACATTGGGCACTGACATT -3' . The FoxO6 shRNA targets the 5’- CTCCAATCTGGTTCTCAAATGACAC -3’ sequence of FoxO6 mRNA was cloned into pSilencer 4 . 1 ( Life Technologies ) . The 5’-CCTAAGGTTAAGTCGCCCTCG-3’ sequence was cloned into pSilencer 4 . 1 to generate a scrambled shRNA control . The Hippo reporter constructs HOP-Flash ( 8x TEAD binding sites ) and HIP-Flash ( mutant TEAD binding sites ) were obtained from Addgene [158] . CHO , LS-8 cells ( oral epithelial-like cell ) , HEPM ( ATCC , Human Embryonic Palatal Mesenchyme ) and ET-16 cells ( dental epithelial cell ) were cultured in DMEM supplemented with 10% fetal bovine serum ( FBS ) and penicillin/streptomycin and were transfected by electroporation . Cultured cells were fed 24 h prior to transfection , resuspended in PBS and mixed with 5 μg expression plasmids , 10 μg reporter plasmid and 0 . 5 μg SV-40 ß-galactosidase plasmid . Electroporation of CHO , LS-8 cells and ET-16 cells was performed at 400 V and 750 microfarads ( μF ) ( Gene Pulser XL , Bio-Rad ) . Transfected cells were incubated for 24–48 h in 60 mm culture dishes and fed with 10% FBS and DMEM and then lysed and assayed for reporter activity and protein content by Bradford assay ( Bio-Rad ) . FOX06 , shFOX06 , YAP5SA , and YAP WT were transfected into CHO cells at differing ratios of cDNA/reporter/ß-gal using PEI . 20 h after transfection , cells were placed in low serum media ( 0 . 5% ) and harvest in reporter lysis buffer 40h after transfection . Luciferase was measured using reagents from Promega . β-galactosidase was measured using the Galacto-Light Plus reagents ( Tropix Inc . ) . All luciferase activities were normalized to β-galactosidase activity . For Western blot assay , cell lysates ( 10 μg ) were separated on a 10% SDS–polyacrylamide gel and the proteins were transferred to PVDF filters ( Millipore ) , and immunoblotted using the following antibodies FoxO6 , ( Abcam , 1:1000 ) , Lats1 ( Cell signaling , 1:1000 ) , Yap ( Cell signaling , 1:1000 ) , pYap ( Cell signaling , 1:1000 ) , PITX2 ( Capra Sciences , 1:1000 ) , GAPDH ( Santa Cruz , 1:1000 ) or β-Tubulin ( Santa Cruz , 1:2000 ) . ECL reagents from GE HealthCare were used for detection . The ChIP assays were performed as previously described using the ChIP Assay Kit ( Upstate ) with the following modifications [159] . LS-8 cells were plated in 60 mm dishes and fed 24 h prior to the experiment , harvested and plated in 60 mm dishes . Cells were cross-linked with 1% formaldehyde for 10 min at 37°C . Cross-linked cells were sonicated three times to shear the genomic DNA to DNA fragments average ranged between 200 and 1000 bp . DNA/protein complex were immunoprecipitated with specific antibody ( Pitx2 antibody , Capra Sciences , PA-1023; FoxO6 antibody , Abcam , ab48730 ) . DNAs from the precipitants were subject to PCR to evaluate the relative enrichment . The following primers were used to amplify the FoxO6 promoter region , which contains a Pitx2 binding site: The sense primer ( 5- GGGGAGGAACTCACTCGTTT -3 ) and the antisense primer ( 5- GAGCTGCTCCCTTTGAGGTC -3 ) . The following primers were used to amplify the Lats1 promoter region , which contains a FoxO6 binding site: the sense primer ( 5- TTCCCAGCAGGACTCTGTCT -3 ) and the antisense primer ( 5- CAAATGCCACTTTCTGGTGA -3 ) . All PCR reactions were done under an annealing temperature of 60°C . All the PCR products were evaluated on a 2% agarose gel for appropriate size and confirmed by sequencing . As controls the primers were used in PCRs without chromatin; normal rabbit IgG was used replacing the specific antibody to reveal nonspecific immunoprecipitation of the chromatin . 3 parallel Realtime PCRs were also performed in triplicates using these primers to quantify the enrichment of DNA pulled down by specific antibody over the DNA pulled down by IgG control . Primers designed according to 3 . 5 kbp upstream ( Sense: 5- TGTGCTCCAGGACTCCTCTT -3 , antisense: 5- GCCATGGTCTAGCTCTGTCC -3 ) of the FoxO6 promoter region containing no Pitx2 binding sites were used as negative controls . Primers located 4 . 9 kb upstream ( Sense: 5- ATGGATCTCTCTGGCATTGG -3 , antisense: 5- TCCTAGGCAGAGGCAGGTAA -3 ) of the Lats1 promoter region containing no FoxO6 binding sites were used as negative controls . BrdU was injected into the pregnant mice ( 10μl/g of body weight , Invitrogen , 00–0103 ) 2 h prior to harvesting of E17 . 5 embryos . BrdU was directly injected into P7 mice 2 hour prior to harvesting , heads were decalcified and sectioned for staining . Embryos were embedded and sectioned as described previously [157] , and sections were mounted and rehydrated with sequential concentrations of alcohol , followed by immersion in 3% H2O2 to block the endogenous peroxidase activity . Antigen retrieval was carried out by treating sections with 10 mM Sodium Citrate solution for 15 min at a slow boiling state . Sections were hydrolyzed for 30 min in 2 N HCl , neutralized for 10 min in 0 . 1 M sodium borate ( pH 8 . 5 ) , rinsed , blocked for 1 h in 10% goat serum , and immunostained with rat anti-BrdU antibody ( 1:250 , ab6326 , Abcam ) . FoxO6-/- mice and WT sections were placed on the same slide and processed together for identical time periods . For staining and visualization of whole skeletons , mice were dissected and skeletons were stained with alizarin red S and alcian blue 8G ( Sigma ) , as previously described [160] . Gene expression DNA microarray analyses were performed by LC Sciences ( Houston , TX ) using GeneChip Mouse Genome Mouse 430 . 2 . 0 Arrays . Total RNA was extracted from mandible and maxilla tissue of WT and FoxO6-/- embryos using RNeasy Mini Kit from Qiagen ( 4 biological replicates were combined ) . Reverse transcription was performed according to the manufacturer's instruction ( BIO-RAD iScript Select cDNA Synthesis Kit ) using oligo ( dT ) primers . cDNAs were adjusted to equal levels by PCR amplification with primers to beta-actin ( primers for beta-actin are 5'- GCCTTCCTTCTTGGGTATG-3’ and 5'- ACCACCAGACAGCACTGTG-3' ) . Primers for FoxO6 q-PCR are: 5'- AAGAGCTCCCGACGGAAC -3' and 5'- GGGGTCTTGCCTGTCTTTC -3' . Primers for Lats1 q-PCR are: 5'- TAGAATGGGCATCTTTCCTGA-3' TGCTATCTTGCCGTGGGT . Primers for Lats2 q-PCR are: 5'-GACGATGTTTCCAACTGTCGCTGTG-3’ and 5'- CAACCAGCATCTCAAAGAGAATCACAC-3' . Primers for detecting Runx2 , Shh , Amelogenin , CCD1 and Sox2 were previously described [65] . All of the PCR products were sequenced to verify that the correct band was amplified . Magnetic resonance imaging ( MRI ) was performed on a 4 . 7-T Varian small-bore scanner . All acquisitions utilized a 25-mm diameter transmit/receive coil for high-resolution imaging . Mice were anesthetized with isoflurane ( 3% induction , 1 . 5% maintenance ) and transferred to the scanner for imaging . After a series of three-localizer scans ( each about 5 sec long ) , a set of T2-weighted fast spin-echo images was acquired in the axial plane . The protocol parameters were TR/TE . 2 , 100/60 msec , echo train length of eight , 0 . 5-mm thick contiguous slices with in-plane resolution of 0 . 16mm X 0 . 16mm over a 256 X 256 matrix using 12 signal averages . The total time for the entire protocol was about 40 min . All MRI data were processed using BRAINS software developed locally at the University of Iowa [161] . The mouse brain atlas used for segmentation purposes was the mouse Biomedical Informatics Research Network ( mBIRN ) atlas , which was constructed using T2-weighted magnetic resonance microscopy ( MRM ) from 11WTC57BL/6J mice at the University of California , Los Angeles [162] . For our pipeline process we employed a directed acyclic pipeline architecture using Nipype [163] , a Pythonbased wrapping library for neuroimaging applications . ThemBRIN atlas was registered to the input T1 file using b-spline warping within BRAINSFit [164] , a mutual information driven application developed under the ITK framework . The atlas was then resampled using BRAINSResample to match the voxel lattice of the T1 image , thereby allowing one-to-one correspondence between atlas and image . Finally , we computed the volume measurements for each desired region of our atlas as the sum of voxels within a given label times the volume of a voxel . The atlas defined 43 regions of interest . The regions were then grouped into the following areas: amygdala , hypothalamus , pituitary , thalamus , total brain volume , basal ganglia , brainstem , cerebrospinal fluid ( CSF ) , cerebellum , hippocampus , white matter tracts , anterior cortex , and posterior cortex . The anterior cortex was further subdivided into the olfactory and frontal cortices . The posterior cortex was similarly subdivided into the posterior , entorhinal , and perirhinal cortices . Volumes were reported in mm3 . All analyses were performed using Statistical Package for Social Science ( SPSS ) , version 19 . 0 for Windows ( SPSS , Inc . , Chicago , IL ) . Due to the small sample size , non-parametric analysis using the rank of all measures was utilized in order to minimize any effects of outliers . Analysis of variance ( ANOVA ) was used to evaluate total brain volume across groups . The remainder of the brain regions was compared across the two groups using Analysis of Covariance ( ANCOVA ) , controlling for total brain volume . To analyze variation in gross craniomandibular dimensions , we imaged n = 3 WT and n = 2 Fox06-/- mice using a Siemens Inveon Micro-CT/PET scanner . Skulls were scanned at 60kVp and 500mA with a voxel size of 30μm and reconstructed images were imported into Osirx DICOM imaging software [165] for morphometric analysis . Using two- and three- dimensional renderings we collected a series of linear anterior-posterior and transverse skeletal and dental measurements defined in Fig 3 . Hemimandibles with soft tissues removed were scanned in a μCT-40 ( Scanco , Brüttisellen , Switzerland ) at 70 kV , 114 mA , and 6 μm resolution . Images were processed with μCT-40 evaluation software and FIJI ( https://fiji . sc/ ) was used to orient the mandibles in a standardized way based on anatomical landmarks to clearly observe and compare enamel mineralization in two planes 1 ) through the first molar , in a coronal plane through the distal root and , extending this plane , in the early maturation stage of the developing incisor and , 2 ) in the maturation stage incisor , in coronal plane through the mandible . For each condition , three experiments were performed with results are presented as the mean ± SEM . The differences between two groups of conditions were analyzed using an independent , two-tailed t-test . Gene expression data were normalized by MAS5 algorithms . Differential expression genes were identified using Limma package in R . To identify altered pathways in FoxO6 knockout mice . We ran Gene Set Enrichment Analysis ( GSEA ) with Broad Institute GSEA software ( GSEA Preranked command line with default parameters ) [166] . We ranked all genes based on its fold change between FoxO6 knockout and wild type mandible and maxilla tissue . P value was calculated by permutation test . P value was adjusted by Benjamini & Hochberg's False Discovery Rate ( FDR ) . RNA was pooled from 4 biological replicates for WT and FoxO6 mutant mice including null and heterozygotes ( RIN numbers were >9 for the samples ) . | The basic question of how human faces develop , undergo morphogenesis and grow after birth to define our final characteristic shape has been studied from the earliest days of comparative vertebrate developmental research . While many studies have shown the factors and mechanisms that contribute to the cells and tissues of the face during embryology , fewer studies have determined mechanisms that promote face growth after birth and into childhood . In our quest to understand developmental mechanisms of facial growth we used murine gene expression and bioinformatics analyses combined with human 3D facial variations and genome-wide association studies to identify genes and variants controlling post-natal face growth . Bioinformatics analyses of mouse craniofacial gene expression identified FoxO6 as a transcription factor expressed at late stages of face development . Ablation of FoxO6 in the mouse resulted in specific anterior growth of the mouse face . The increased FOXO6 expression activated Hippo signaling to reduce face growth . These data indicate that changes in FOXO6 expression control face growth during early childhood . | [
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] | 2018 | FoxO6 regulates Hippo signaling and growth of the craniofacial complex |
CD36 is the major receptor mediating nonopsonic phagocytosis of Plasmodium falciparum-parasitized erythrocytes by macrophages . Its expression on macrophages is mainly controlled by the nuclear receptor PPARγ . Here , we demonstrate that inflammatory processes negatively regulate CD36 expression on human and murine macrophages , and hence decrease Plasmodium clearance directly favoring the worsening of malaria infection . This CD36 downregulation in inflammatory conditions is associated with a failure in the expression and activation of PPARγ . Interestingly , using siRNA mediating knock down of Nrf2 in macrophages or Nrf2- and PPARγ-deficient macrophages , we establish that in inflammatory conditions , the Nrf2 transcription factor controls CD36 expression independently of PPARγ . In these conditions , Nrf2 activators , but not PPARγ ligands , enhance CD36 expression and CD36-mediated Plasmodium phagocytosis . These results were confirmed in human macrophages and in vivo where only Nrf2 activators improve the outcome of severe malaria . Collectively , this report highlights that the Nrf2 transcription factor could be an alternative target to PPARγ in the control of severe malaria through parasite clearance .
Mononuclear phagocytes represent the first line of innate immune defense against pathogens through mechanisms involving recognition by pattern-recognition receptors ( PRRs ) of highly structurally conserved microbial structures , known as pathogen-associated molecular patterns [1] . Among the PRRs family , the class B scavenger receptor CD36 , initially known as a receptor for the uptake of oxidatively low density lipoprotein , also mediates the recognition and the elimination of apoptotic cells and bacteria [2] , [3] . Additionally , the CD36 receptor specifically recognizes Plasmodium falciparum parasitized-erythrocytes ( PfPEs ) , resulting in a CD36-dependent nonopsonic phagocytosis of PfPEs and a decrease in parasite-induced TNF-α secretion [4] . Consistently , CD36-deficient macrophages displayed a marked phagocytic defect for parasitized erythrocytes compared with wild-type macrophages [5] . Furthermore , a recent study demonstrates in vivo the importance of CD36 receptor expression on macrophages during malaria infection . Indeed , CD36−/− mice present a defect in parasite clearance [5] . CD36 expression is under the transcriptional control of a PPARγ nuclear receptor . As a consequence , PPARγ ligands , such as thiazolidinediones , or IL4 and IL13 Th2 cytokines , promote CD36 expression on macrophages [6]–[8] . Moreover , rosiglitazone and IL13 have been shown to promote in vitro an increase in CD36-mediated phagocytosis and a decrease in malaria parasite-induced TNF-α release both on murine macrophage and human monocytes [4] , [8] , [9] . More recently , rosiglitazone treatment has been shown in vivo to reduce parasitemia level in the Plasmodium chabaudi chabaudi AS murine experimental model through the CD36 pathway [9] . Pharmacological modulation of CD36 expression on macrophages might therefore contribute to enhance parasite elimination and limit host inflammatory deleterious response to malaria infection . Nevertheless , much of the pathology associated with malaria infection is a result of excessive and uncontrolled production of proinflammatory markers and cytokines [10] , [11] . In this acute malaria inflammatory context , we previously demonstrated that CD36 receptor expression was reduced on the surface of circulating monocytes from P . falciparum infected patients [12] . In line with this , Th1 cytokines , such as TNF-α and IFNγ decrease CD36 expression both on monocytes and macrophages [13] , [14] . Interestingly , this CD36 downregulation was correlated with a marked reduction in PPARγ activation upon TNF-α stimulation [13] . Collectively , all these data suggest that inflammatory processes might negatively regulate PPARγ expression and activation in macrophages . Surprisingly , PPARγ−/− macrophages did not present a totally abolished CD36 phenotype [8] , [15] . This data suggests the existence of alternative pathways controlling CD36 expression on macrophages . In this study , we focused on NF-E2 related factor 2 ( Nrf2 ) , a transcription factor involved in the prevention of severe inflammatory diseases [16] , that is activated in response to oxidative stress and electrophiles agents , such as sulforaphane or diethylmaleate . We previously demonstrated that the anti-TNFα antibody treatment increased CD36 expression on human monocytes through the enhancement of reactive oxygen species production independently of PPARγ [13] . Nrf2 was also shown to play an important role in the regulation of CD36 expression [17]–[19] . We therefore postulated that Nrf2 transcription factor might substitute PPARγ to promote CD36 expression and hence CD36-mediated phagocytosis of PfPEs during acute inflammatory processes . In this study , we show in vitro on murine and human monocytes-derived macrophages ( hMDMs ) and in vivo in murine inflammatory-induced severe malaria model , that inflammatory processes downregulate CD36 expression and CD36-mediated Plasmodium clearance , exacerbating the development of severe malaria infection . In acute inflammatory conditions , PPARγ ligands were unable to promote CD36 expression and subsequently to restore the loss of CD36-mediated Plasmodium clearance . Interestingly , we demonstrate the existence of an alternative pathway controlling CD36 expression in inflammatory conditions independently of PPARγ both on murine and human inflammatory macrophages . We established in vitro and in vivo that the Nrf2 transcription factor is essential to promote CD36 expression and Plasmodium clearance and therefore control malaria infection . This report highlights that Nrf2 transcription factor could be a therapeutic target in the control of severe malaria infection .
To determine the effect of inflammation on the modulation of CD36 expression , murine peritoneal macrophages were treated during 24 h for the quantification of CD36 protein level and during 5 h for mRNA level with TNF-α , peptidoglycan ( PGN ) , or were incubated in presence of P . falciparum culture supernatant ( P . f . cs ) to mimic a more physiological malaria inflammatory context . The analysis of CD36 protein level on cells was evaluated on a selected R1 region , in which 92 , 2% of the cells were F4/80 and CD36 double-positive ( Fig . S1A ) . Fig . 1A shows that TNF-α , PGN and P . f . cs treatments significantly decreased CD36 protein level . Consistent with this data , CD36 mRNA level was also downregulated following TNF-α , PGN and P . f . cs treatments ( Fig . 1B ) . To assess whether TLR signaling directly leads to reduced CD36 expression or if the effects mediated by PGN or P . f . cs are dependent on TNF-α production , we evaluated CD36 protein level in presence of an anti-TLR2 antibody or etanercept , a potent TNF-α inhibitor . We demonstrated that PGN and P . f . cs treatments downregulate CD36 expression through TLR2 ( Fig . 1C ) . The use of etanercept , unequivocally prove that the effects mediated by TLR2 activators on macrophage CD36 expression are independent of TNF-α production ( Fig . 1C ) . We then evaluated whether rosiglitazone and IL13 , known to promote CD36 expression via an activation of the nuclear receptor PPARγ , could reverse the downregulation of CD36 receptor induced by inflammatory conditions . Murine peritoneal macrophages were firstly treated for 24 h with TNF-α , peptidoglycan ( PGN ) or P . f . cs and were then incubated for an additional time with rosiglitazone or IL13 . Fig . 1A and FACS profiles ( Fig . S1B , S1C ) showed that rosiglitazone and IL13 increased CD36 protein level in noninflammatory conditions ( control ) . However , in an inflammatory context both rosiglitazone and IL13 treatments failed to increase the CD36 protein and mRNA levels ( Fig . 1A , 1B , S1B , S1C ) . These data demonstrate that inflammation negatively regulates CD36 expression and reveals the failure of PPARγ ligands to promote CD36 expression on macrophages in these conditions . To determine whether these inflammatory conditions were also associated with an impaired P . falciparum clearance , the ability of macrophages to phagocytose PfPEs was assessed . Fig . 1D showed a significantly reduced ability of macrophages to eliminate PfPEs in inflammatory conditions ( TNF-α , PGN , P . f . cs ) . In addition , IL13 was not able to restore the decrease of CD36-mediated phagocytosis of PfPEs observed in inflammatory conditions ( Fig . 1D ) . Therefore , the failure of PPARγ activators to promote CD36 expression and its PfPEs phagocytosis-associated function in inflammatory conditions strongly suggests that PPARγ is no longer able to exert its transcriptional activity on the CD36 promoter . To investigate whether the failure in CD36-dependent PPARγ transcriptional activity was correlated with a lower level of PPARγ expression , we evaluated PPARγ protein and mRNA levels in an inflammatory context . Interestingly , TNF-α , PGN and P . f . cs significantly decreased PPARγ protein and mRNA expressions ( Fig . 1E ) . All these observations indicate that the inability of PPARγ ligands to enhance CD36 expression and its antimalarial associated functions in inflammatory conditions was associated with a marked decrease of PPARγ expression . We hypothesized that the Nrf2 transcription factor , recently known to control the CD36 expression , could substitute the deficiency of PPARγ in acute inflammatory conditions to enhance the expression of the CD36 receptor . Murine macrophages were firstly treated over 24 h with TNF-α , PGN or P . f . cs and then incubated for an additional time with Nrf2 activators sulforaphane ( SFN ) or diethylmaleate ( DEM ) . Fig . 2A and FACS profiles ( Fig . S2A , S2B ) demonstrate that sulforaphane ( SFN ) and diethylmaleate ( DEM ) increase the CD36 protein level both in noninflammatory ( control ) and in an inflammatory context ( TNF-α , PGN or Pfcs ) . Consistent with this data , the same profiles of CD36 mRNA were obtained ( Fig . 2B ) . Then , the study of CD36 protein level after the administration of Nrf2 or PPARγ activators before the onset of inflammation demonstrate that both Nrf2 and PPARγ ligands prevent the downregulation of CD36 expression . ( Fig . S2C ) . In parallel , we showed that Nrf2 activators also up-regulate PPARγ mRNA levels in an Nrf2-dependent manner ( Fig . S2E , S2F ) . However , no synergistic effect between PPARγ and Nrf2 activators has been observed in vitro on macrophage CD36 expression in inflammatory conditions ( Fig . S2D ) . We then explored whether this CD36 up-regulation by Nrf2 activators during inflammatory conditions could restore the decrease of CD36-mediated phagocytosis of PfPEs observed during inflammation . Fig . 2C reveals that an SFN treatment both in noninflammatory ( control ) and inflammatory conditions ( TNF-α ) enhanced the phagocytosis of PfPEs . These inductions were abolished by the use of a CD36 specific antibody ( α-CD36 ) , demonstrating that these phagocytic processes were dependent of the CD36 receptor . To validate that the transcriptional activity of Nrf2 was still effective under inflammatory conditions , we next studied the modulation of HO-1 gene expression , a specific Nrf2 target gene [18] . Both in noninflammatory ( control ) and inflammatory ( TNF-α ) conditions , SFN and DEM significantly increased HO-1 mRNA expression , while PPARγ activators ( IL13 or rosiglitazone ) did not change HO-1 mRNA level ( Fig . 2D ) , demonstrating that Nrf2 transcriptional activity was still efficient in inflammatory conditions . Altogether , these results suggest that the Nrf2 transcriptional factor could be involved in the regulation of CD36 expression and hence in CD36-mediated PfPEs phagocytosis in inflammatory conditions . To establish that the Nrf2 transcription factor may promote CD36 expression in absence of the PPARγ nuclear receptor , we performed experiments on the RAW 264 . 7 macrophage murine cell line which expresses a very low level of PPARγ , as demonstrated in Fig . 3A . Fig . 3B shows that PPARγ specific activators , IL13 and rosiglitazone , did not change the CD36 protein level , whereas the Nrf2 activators ( SFN or DEM ) strongly enhanced CD36 protein expression ( Fig . 3B ) . These results show that in absence of PPARγ , Nrf2 activators are able to promote CD36 expression . To further confirm that PPARγ was not involved in the regulation of CD36 by Nrf2 activators , murine macrophages were incubated in the presence of GW9662 or T007 , two specific irreversible antagonists of PPARγ . Fig . 3C shows that the macrophages treated by GW9662 or T007 failed to up-regulate CD36 expression after exposure to IL13 . In contrast , GW9662 or T007 treatments did not affect CD36 over-expression observed with SFN or DEM treatments . Altogether these data prove that the induction of CD36 by Nrf2 activators is independent of PPARγ . Finally , to unequivocally prove that Nrf2 activators could promote CD36 expression in absence of PPARγ , we studied CD36 mRNA expression in macrophages in which PPARγ had been selectively disrupted . As expected , the expression of CD36 was promoted by rosiglitazone , IL13 , SFN or DEM treatments in macrophages from PPARγ+/+ mice . Interestingly , the CD36 over-expression following rosiglitazone or IL13 treatments failed in PPARγ deficient macrophages ( PPARγ−/− ) , while SFN or DEM treatments enhanced CD36 expression in PPARγ−/− cells ( Fig . 3D ) . In line , the PfPEs phagocytosis level by PPARγ−/− macrophages was only enhanced following SFN or DEM treatments and not by rosiglitazone or IL13 ( Fig . 3E ) . Altogether these data establish that only Nrf2 activators contribute to the enhancement of CD36 expression and CD36 mediated-P . falciparum phagocytosis in absence of PPARγ . To confirm the specific involvement of Nrf2 transcription factor in the regulation of CD36 expression under inflammatory conditions , TNF-α activated macrophages were transiently transfected with siRNA specifically targeting Nrf2 . As predicted , the siRNA-mediated knock down of Nrf2 decreased HO-1 expression in TNF-α treated macrophages following SFN or DEM treatments ( Fig . S3A , S3B ) . Interestingly , the increase of CD36 mRNA level both in TNF-α treated macrophages ( Fig . 4A ) and RAW cells ( Fig . 4B ) following SFN or DEM treatments was abolished after transfection with siRNA targeting Nrf2 . These data strongly suggest that the up-regulation of CD36 in inflammatory context is mediated by Nrf2 transcription factor . To consolidate our hypothesis , we studied CD36 and HO-1 mRNA levels in macrophages from Nrf2−/− mice . The induction of CD36 ( Fig . 4C ) and HO-1 ( Fig . S3C ) mRNA levels by SFN or DEM treatments in macrophages from Nrf2+/+ mice both in normal ( control ) and in inflammatory conditions ( TNF-α ) was not observed in macrophages from Nrf2−/− mice . Consistently , the levels of P . falciparum phagocytosis were not enhanced by SFN or DEM treatments in Nrf2−/− macrophages ( Fig 4D ) . Altogether , these data clearly revealed that Nrf2 plays a crucial role in the activation of CD36 macrophage gene expression and in its phagocytosis-associated functions during acute inflammatory processes . To evaluate the DNA-binding activity of Nrf2 transcription factor in inflammatory conditions , a DNA-binding ELISA-based assay using a specific Nrf2 antibody was performed . Fig . 5A demonstrates that Nrf2 was specifically activated by SFN treatment and bound to its ARE-binding sequence both in noninflammatory and in inflammatory ( TNF-α ) conditions . Fig . 5B reveals that the Nrf2 protein level was increased in the nucleus of SFN-treated cells both in noninflammatory and in inflammatory ( TNF-α ) conditions . Then , to evaluate this nuclear localization of Nrf2 following SFN treatment , confocal laser scanning microscopy analysis was performed . Nrf2 transcription factor was localized both in the nucleus and in the cytoplasm of control and TNF-α treated cells ( Fig . 5C ) . Interestingly , Nrf2 was exclusively located in the nucleus following SFN treatment both in noninflammatory and in inflammatory ( TNF-α ) conditions . Altogether these results confirm that after SFN treatment Nrf2 translocates to the nucleus and exerts its transcriptional activity during inflammatory conditions . To extend our results to human monocytes-derived macrophages ( hMDMs ) , we evaluated the CD36 protein level on inflammatory hMDMs following rosiglitazone , IL13 , SFN or DEM treatments . Cells were gated on the R1 region , corresponding to the hMDMs population highly expressing CD36 ( Fig . S4C ) . TNF-α , PGN and P . f . cs downregulated CD36 protein and mRNA levels on hMDMs ( Fig . 6A , S4A , S4B ) . The increase of CD36 protein level in control hMDMs following treatments by rosiglitazone , IL13 , SFN and DEM was only observed in TNF-α and PGN treated hMDMs after SFN or DEM treatments . Indeed , rosiglitazone and IL13 did not promote CD36 protein level in these hMDMs ( Fig . 6A , S4D ) . These data confirm in humans that during inflammatory processes only Nrf2 activators were able to promote CD36 expression . The failure to promote CD36 expression on hMDMS via the PPARγ signaling pathway was associated with a marked reduction of PPARγ mRNA and protein levels ( Fig . 6B ) during acute inflammatory processes . Finally , the ability of hMDMs to mediate the phagocytosis of PfPEs under acute inflammatory processes was determined . Fig . 6C reveals that SFN treatment promoted the phagocytosis of P . falciparum both in noninflammatory ( control ) and inflammatory ( PGN ) hMDMs . Phagocytosis both in normal and inflammatory contexts was significantly inhibited by a CD36 specific antibody , demonstrating that the induction of P . falciparum phagocytosis by SFN treated hMDMs was dependent of CD36 receptor ( Fig . 6C ) . These data indicate that in humans during inflammatory processes , only Nrf2 activators up-regulate CD36 expression on hMDMs and P . falciparum phagocytosis-associated function . Since we have demonstrated that Nrf2 regulates similarly CD36 receptor expression in inflammatory conditions both in human monocytes-derived macrophages and in Swiss murine peritoneal macrophages , we developed an induced-inflammatory severe malaria model in Swiss genetic background to validate that Nrf2 could be a therapeutic target in the prevention of severe malaria . To establish this malaria model , Swiss mice were pre-treated with peptidoglycan ( PGN ) , a TLR2 activator , and then infected with P . berghei . As expected , PGN-treated mice had a significantly lower survival rate compared with control mice ( p = 0 , 043 ) which did not succumb in the early phase of infection , demonstrating that PGN-induced inflammatory processes strongly worsen the outcome of infection in the Swiss mice model ( Fig . 7A ) . In addition , PGN treated mice presented significant higher parasitemia levels than control animals ( Fig . 7B ) , but did not succumb from anemia ( data not shown ) . Altogether , these results indicate that installed acute inflammatory processes before infection by P . berghei in mice clearly worsen the severity of the infection . To determine whether the PGN-induced increase in parasite burden observed in our severe malaria model was associated with macrophage CD36 downregulation , we evaluated the CD36 protein level on peritoneal macrophages . PGN treatment significantly decreased CD36 protein level ( Fig . 7E ) . Altogether these data demonstrate that inflammatory processes induced by PGN treatment in infected mice downregulate the macrophage CD36 expression and contribute to worsen the severity of malaria infection in mice . To assess whether Nrf2 activators or PPARγ ligands were able to improve the outcome of severe malaria , PGN-induced severe malaria mice were treated with SFN or rosiglitazone . The in vivo oral treatment of mice with SFN initiated the day of infection and followed 5 days post infection greatly increases the survival rates of mice ( p = 0 , 0001 ) ( Fig . 7C ) and contributes to limit the parasite burden in the first days of infection ( Fig . 7D ) . The rosiglitazone treatment increases the survival rates of PGN treated mice ( p = 0 , 032 ) ( Fig . 7C ) and does not affect their blood parasitemia level ( Fig . 7D ) . Interestingly , the CD36 protein level on macrophages only increased after in vivo SFN treatment ( Fig . 7F ) , demonstrating that in an in vivo acute inflammatory context only Nrf2 activators and not PPARγ ligands are able to up-regulate CD36 expression . Finally , we demonstrate that macrophages from infected SFN-treated mice enhance P . berghei clearance ( Fig . 7H ) . The use of anti-CD36 and anti-FcR antibodies demonstrates that P . berghei-PEs were internalized in a CD36 dependent manner ( Fig . 7G ) . Altogether , these results strongly suggest that targeting Nrf2 in vivo contributes to improve the outcome against inflammatory-induced severe malaria in mice .
Mononuclear phagocytes play an important role in the clearance of blood-stage malaria parasites resulting in an early control of parasite proliferation during acute infection [5] , [20] . The importance of CD36 receptor expression on macrophages in malaria parasite clearance and in the regulation of parasite-induced inflammatory processes has been demonstrated both in vitro and in vivo on CD36−/− mice [5] . Recent data provide evidence that rosiglitazone PPARγ ligand can in vitro and in vivo improves the outcome of experimental malaria in mice , enhancing CD36-mediated PEs phagocytic processes and limiting parasite-induced inflammatory processes [9] . Nevertheless , much of the pathology associated with malaria infections is a result of excessive and uncontrolled production of proinflammatory markers [11] , [21] . In this study , we demonstrated that inflammatory processes induced by TNF-α or TLR2 ligands downregulate CD36 expression on Swiss murine and human macrophages . These data are consistent with other studies , highlighting the deleterious effect of LPS , IFNγ or TNF-α treatment on CD36 expression [13] , [14] , [22] , [23] . We also show that a similar CD36 downregulation was observed on murine and human macrophages following Pfcs treatment , which contains soluble factors released from infected erythrocytes rupture such as PfGPI anchors ( glycophosphatidylinositol ) , described as TLR2 ligands [24] . In addition , we have previously demonstrated that circulating human monocytes presented a loss of CD36 expression during the acute phase of plasmodial infection [12] . In parallel , we demonstrated that PGN and P . f . cs treatments downregulate CD36 expression specifically through TLR2 . Surprisingly , the TLR2 activators-mediated CD36 dowregulation is independent of TNF-α release . In addition , we demonstrate in this study that this decrease in CD36 expression on macrophages impairs CD36-mediated PfPEs phagocytosis . Consistent with these data , prostaglandin E2 , a pro-inflammatory eicosanoid , was shown to downregulate CD36 expression on macrophage directly resulting in a reduced CD36-phagocytic ability leading to the development of endometriosis [25] . PPARγ ligands thiazolidinediones and IL4 or IL13 , two Th2 cytokines known to activate PPARγ , were previously shown in vitro to promote CD36 expression and enhance CD36-mediated PEs phagocytosis [4] , [6] , [8] , [9] , [26] . Surprisingly , our current results revealed that PPARγ ligands and IL13 have no effect on the modulation of CD36 expression in inflammatory macrophages . Consistent with this , TNF-α was previously shown to downregulate CD36 expression on human monocytes involving a direct reduction in PPARγ activation [13] . In line with these results , it has been reported that inflammatory processes reduce PPARγ expression and activation , highlighting the deleterious effect of inflammatory processes on PPARγ [27]–[29] . Collectively , our data provides compelling evidence that inflammatory processes negatively regulate the expression of PPARγ in swiss murine macrophages and human monocyte-derived macrophages , resulting in a failure to trigger this pathway to promote CD36 expression and Plasmodium clearance . Interestingly , inflammatory stimuli did not negatively regulate the expression of PPARγ and hence CD36 expression in C57BL/6 murine macrophages , suggesting the importance of genetic background in their regulation . Therefore , it seems that the Swiss mice model better mimics the human model . Indeed , inflammatory processes inhibit PPARγ and CD36 in hMDMS , and monocytes from Plasmodium-infected patients exhibit a CD36 dowregulation [12] . In our study , we demonstrated that PPARγ−/− macrophages did not present a totally abolished CD36 phenotype , suggesting the existence of alternative pathways controlling CD36 expression on macrophages . We also previously demonstrated that the increase of the CD36 receptor following an anti-TNF-α antibody treatment occurred independently of PPARγ and involved radical oxygen species production ( ROS ) via NADPH oxidase activation [13] . The ROS are potent inducers of Nrf2 activation , a transcription factor involved in the prevention of severe inflammatory diseases [16] . Interestingly , this transcription factor was shown to play an important role in the regulation of CD36 expression on murine macrophages [18] . Thus , we postulated that Nrf2 transcription factor might substitute PPARγ to promote CD36 expression during acute inflammatory processes . Consistent with this hypothesis , we demonstrated that SFN or DEM , Nrf2 activators , upregulate CD36 expression both on murine and hMDMs during inflammatory processes . This CD36 overexpression leads to a higher elimination rate of PfPEs by macrophages . CD36 induction following SFN or DEM treatments was shown to be PPARγ independent and Nrf2 dependent . Altogether , these data unequivocally demonstrate that the Nrf2 transcription factor regulate CD36 expression in absence or in the presence of PPARγ nuclear receptor . Serghides et al . have recently focused on the effects of PPARγ ligand treatment in an inflammatory cerebral malaria murine model . The rosiglitazone treatment improves the survival rate of mice with cerebral malaria . Nevertheless , time course administration of rosiglitazone revealed that the rosiglitazone treatment was a lot more efficient when administered one week before infection than one day after the onset of the disease [9] . In our induced-inflammatory severe malaria model , we demonstrated that rosiglitazone administered after the onset of infection , when the inflammatory processes are already triggered , has a very slight effect compared with SFN effect on mice survival rate and no effect on the modulation of parasitemia level . The positive in vivo effect of rosiglitazone on Swiss mice survival may be associated to a refractory population of macrophages to the inflammatory mediated CD36 downregulation . The differences observed between our study and the data published by the Kain's group are certainly related to the genetic background of the murine models used . Indeed , we did not in fact observe in vitro the downregulations of PPARγ and CD36 on macrophages from C57BL/6 mice in inflammatory conditions while PPARγ and CD36 were greatly impaired in swiss macrophages , as in hMDMs . These data suggest that the benefit of using rosiglitazone depends on the modulation of PPARγ expression which seems to be dependent on individual genetic background . The variability of rosiglitazone effectiveness in inflammatory processes has already been observed in other studies . Preventive administration of thiazolidinediones did in fact provide beneficial effects in murine models of ulcerative colitis but was less efficient when administered after the onset of the disease because PPARγ was shown to be downregulated by colitis-induced inflammatory processes [30] . Similar results were observed in human patients with colitis , which present a modest improvement in the outcome of the disease after rosiglitazone treatment [31] . This rosiglitazone inefficiency could be directly related to an impairment of PPARγ expression observed in patients with inflammatory colitis [32] . Interestingly , as opposed to the rosiglitazone treatment which slightly decreases severe malaria infection , we demonstrated here for the first time that the SFN , an Nrf2 activator , strongly contributes to control the elevation of parasite burden and hence consistently improves the outcome of severe inflammatory induced-malaria . This was correlated to an induction of CD36 expression on macrophage following SFN treatment and with a higher phagocytic capacity for PEs . In addition , SFN treatment did not alter major pro- and anti-inflammatory cytokines involved in malaria physiopathology ( Fig . S5A , S5B ) , suggesting that SFN-induced protection is essentially due to the uptake of parasites through CD36 receptor . In addition , we showed that SFN treatment also reduces parasitemia in Swiss infected mice without prior PGN treatment ( data not shown ) , indicating that the Nrf2 pathway is also important under more natural infection conditions . Recently , a clinical study in humans described the effectiveness of rosiglitazone as an adjunct treatment to standard therapy for non severe malaria [33] . Nevertheless , although the effects of rosiglitazone in combination with conventional antimalarial treatments were effective in a non severe P . falciparum infection associated with moderated and controlled inflammatory processes , one question may subsist on the effectiveness of rosiglitazone when administered during severe malaria when acute inflammatory processes are engaged . We showed on human inflammatory MDMs that the rosiglitazone treatment was ineffective on CD36-mediated parasite clearance due to a downregulation of PPARγ . Interestingly , the Nrf2 activators treatments were able to promote CD36 expression on human MDMs and hence participate actively to the clearance of Plasmodium . In conclusion , the present results provide direct evidence that inflammatory processes and particularly malaria parasite-induced inflammatory processes impair CD36 expression on Swiss murine as on human MDMs and CD36-mediated phagocytosis , favoring the worsening of malaria infection . This observation is correlated to a failure in PPARγ expression and activation . However , in inflammatory conditions , we demonstrated that Nrf2 pathway controls CD36 expression and improves the outcome of severe malaria independently of PPARγ . Thus , the results suggest the possibility that Nrf2 may be a therapeutic target for the control of severe malaria .
This study was carried out in accordance with Approval No B3155503 and all animal experiments followed the guiding principles of animal care and use defined by the Conseil Scientifique du Centre de Formation et de Recherche Experimental Médico Chirurgical ( CFREMC ) with the rules of Decree 87–848 dated 10/19/1987 ( modified by Decree 2001-464 and Decree 2001-131 relative to European Convention , EEC Directive 86/609 dated 24/11/1986 ) . The experiments were approved by the ethics board of the Midi-Pyrénées ethic committee for animal experimentation ( Experimentation permit number 31-067 ) . Monocytes were obtained from healthy blood donors ( Etablissement Français du Sang , Toulouse ) . Written informed consents were obtained from the donors under EFS contract n°21/PVNT/TOU/UPS04/2010–0025 . Following articles L1243-4 and R1243-61 of the French Public Health Code , the contract was approved by the French Ministry of Science and Technology ( agreement n°AC 2009-921 ) . Female Swiss and C57BL/6 transgenic 6–12-week-old mice were used both for in vitro and in vivo experiments . The generation of the PPARγ−/− [8] and Nrf2−/− [34] mouse lines were previously described . Resident peritoneal cells were harvested by washing the peritoneal cavity with sterile NaCl 0 , 9% . Collected cells were centrifuged and the cell pellet was suspended in Macrophage-Serum Free Medium ( M-SFM ) ( Gibco Invitrogen ) . Cells were allowed to adhere for 2 h at 37°C , 5% CO2 . Non-adherent cells were then removed by washing with PBS . Human peripheral blood mononuclear cells were isolated from the blood of healthy volunteers by a density gradient centrifugation method on Lymphoprep ( Abcys ) . Monocytes were isolated by adherence to plastic for 2 h in M-SFM at 37°C , 5% CO2 . Monocytes were cultured for 5–7 supplementary days in M-SFM containing 50 ng/mL M-CSF ( eBiosciences ) to allow for differentiation into human monocyte-derived macrophages . The laboratory P . falciparum strain FcB1-Columbia presenting the phenotype Knobs+ at the erythrocyte surface was continuously cultured according to Trager and Jensen [35] with modifications [36] . To obtain P . falciparum culture supernatant ( P . f . cs ) for in vitro stimulations , parasites were highly synchronized by 5% D-Sorbitol treatment ( Sigma ) . After schizont stage-infected erythrocyte rupture occurred , the culture medium was collected and centrifuged before being used for in vitro experiments . For phagocytosis assays , trophozoite-stage infected erythrocytes were washed and used at a PEs macrophage ratio of 20∶1 . For P . berghei phagocytosis experiments , we used a PEs macrophage ratio of 10∶1 . Murine peritoneal macrophages and hMDMs were stimulated by TNF-α ( 10 ng/mL ) ( eBiosciences ) , LPS ( 100 ng/mL ) ( Sigma Aldrich ) , Peptidoglycan ( PGN ) ( 1 µg/mL ) ( Sigma Aldrich ) , rosifoglitazone ( 5 µM ) ( Cayman Chemical ) , IL-13 ( 50 ng/mL ) ( eBiosciences ) , sulforaphane ( SFN ) ( 10 µM ) ( Sigma Aldrich ) , diethylmaleate ( DEM ) ( 100 µM ) ( Sigma Aldrich ) , and by 500 µL of P . f . cs . Macrophages were incubated with the specific inhibitors of PPARγ , GW9662 ( 5 µM ) and T0070907 ( 2 µM ) ( Cayman Chemical ) , 30 min before the addition of PPARγ or Nrf2 activators . Murine macrophage surface expression of CD36 was detected as previously described [37] . Briefly , murine macrophage CD36 expression was detected using a PE-monoclonal CD36 antibody ( Santacruz , sc-13572 ) and compared with an irrelevant appropriate isotype control ( Santa Cruz , sc-3600 ) . hMDMs macrophages were stained with mouse IgM , κ CD36-APC antibody ( BD Pharmingen ) . A mouse IgM , κ isotype-matched antibody conjugated to APC ( BD Pharmingen ) was used as a control . A minimum population of 3000 cells was analyzed for each data point . All analyses were performed on a FACScan using CellQuestPro software ( Becton Dickinson ) . RNA and cDNA preparation were as described [38] . Quantitative RT-PCR was performed on a LightCycler 480 system using LightCycler 480 SYBR GREEN I MASTER ( Roche Diagnostics ) . β-actin was used as the invariant control . The sequences of primers were listed in supplementary Table S1 . The N-fold differential expression of mRNA gene samples was expressed as 2ΔΔCt . Cells were fixed with PBS containing 4% paraformaldehyde and were then incubated in a glycine solution ( 100 mM ) . After permeabilization and blocking , cells were then incubated overnight at 4°C with anti-Nrf2 antibody ( Santacruz , sc-13032 ) . Cells were then incubated with Alexa 488-conjugated anti rabbit antibody ( Invitrogen ) for 1 h at room temperature . Nuclei were stained with DAPI . Treated cells were covered with glass slips using Perma Fluor ( Thermo Scientific ) . All microscopy imagery was performed with a LEICA SP2 laser scanning confocal microscope . For each condition , 40–50 cells were analyzed . The staining is representative of three independent experiments . Phagocytosis assays were performed as already described [8] . The phagocytic index was calculated as the percentage of macrophages with PEs phagocytosed . Nrf2 siRNA ( Santacruz sc-37049 ) and control siRNA ( Santacruz sc-37007 ) were transfected into RAW cells or Swiss murine peritoneal macrophages using Lipofectamine 2000 reagent ( Invitrogen ) as described in the manufacturer's protocol . Nuclear proteins were isolated with NE-PER kit ( Thermo Scientific ) . Nrf2 TransAM ELISA-kit ( Active Motif ) was used to evaluate Nrf2 DNA-binding activity . The final A450 was read on a microplate reader ( Wallac 1420 Victor2 ) . Westen Blot experiments were performed as described [38] . Rabbit polyclonal IgG anti-PPARγ ( Santa Cruz Biotechnology , sc-7196 ) , rabbit polyclonal IgG anti-Nrf2 ( Santacruz Biotechnology , sc-13032 ) and goat polyclonal IgG anti-actin ( Santacruz biotechnology , sc-1615 ) were used . Secondary polyclonal anti-goat or -rabbit IgG HRP coupled Abs were used ( Cell Signaling ) . In vivo assays were performed with Plasmodium berghei . Parasites were administered intraperitoneally ( 1 . 106 parasites/mouse ) in Swiss female 12 week-old mice . Studies were performed on separate groups of 7 mice each infected and were repeated twice . Two days before infection , mice were treated subcutaneously with PGN ( 200 µg/mouse ) . Mice were then treated by oral route with d , L-sulforaphane ( Santacruz Bio ) ( 75 mg/kg ) , rosiglitazone ( 3 mg/kg ) or with vehicule 5 days following the infection . The mice were then monitored daily for parasitemia levels with thin blood smears and survival was assessed twice daily . For each in vitro experiments , the data were subjected to one-way analysis of variance followed by the means multiple comparison method of Bonferroni-Dunnet . p<0 . 05 was considered as the level of statistical significance . Survival studies were done using 7 mice per group and were repeated twice . Statistical significance was determined by a log-rank test . | Severe and fatal malaria is still increasing both in incidence and in its resistance to antimalarial agents . The improved understanding of immune mechanisms mediating Plasmodium elimination might therefore offer a complementary way to conventional therapeutic interventions . The main host innate immune defense mechanism against Plasmodium falciparum is the engulfment by macrophages through CD36 , the macrophage receptor recognizing infected erythrocytes . The up-regulation of CD36 on macrophages therefore represents an alternative way to favor parasite clearance during infection . Severe malaria infection is associated with an excessive production of proinflammatory markers and an inability to control parasite proliferation . We demonstrate here that malaria-induced inflammation down regulates CD36 expression on macrophages and favors the worsening of malaria infection . The conventional way to promote CD36 expression through PPARγ nuclear receptor is inefficient under malaria inflammatory processes . Interestingly , we establish that the Nrf2 transcription factor may substitute PPARγ to promote CD36 expression and its associated functions in inflammatory conditions . As a consequence , only Nrf2 but not PPARγ activators improve the outcome of severe malaria in vivo . This paper which highlights a new area of application for Nrf2 activators in infectious diseases , heralds the emergence of a new therapeutic strategy against severe malaria . | [
"Abstract",
"Introduction",
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] | [
"medicine",
"biology"
] | 2011 | Nrf2, a PPARγ Alternative Pathway to Promote CD36 Expression on Inflammatory Macrophages: Implication for Malaria |
Long interspersed ( L1 ) and Alu elements are actively amplified in the human genome through retrotransposition of their RNA intermediates by the ∼100 still retrotranspositionally fully competent L1 elements . Retrotransposition can cause inherited disease if such an element is inserted near or within a functional gene . Using direct cDNA sequencing as the primary assay for comprehensive NF1 mutation analysis , we uncovered in 18 unrelated index patients splicing alterations not readily explained at the genomic level by an underlying point-mutation or deletion . Improved PCR protocols avoiding allelic drop-out of the mutant alleles uncovered insertions of fourteen Alu elements , three L1 elements , and one poly ( T ) stretch to cause these splicing defects . Taken together , the 18 pathogenic L1 endonuclease-mediated de novo insertions represent the largest number of this type of mutations characterized in a single human gene . Our findings show that retrotransposon insertions account for as many as ∼0 . 4% of all NF1 mutations . Since altered splicing was the main effect of the inserted elements , the current finding was facilitated by the use of RNA–based mutation analysis protocols , resulting in improved detection compared to gDNA–based approaches . Six different insertions clustered in a relatively small 1 . 5-kb region ( NF1 exons 21 ( 16 ) –23 ( 18 ) ) within the 280-kb NF1 gene . Furthermore , three different specific integration sites , one of them located in this cluster region , were each used twice , i . e . NM_000267 . 3 ( NF1 ) :c . 1642-1_1642 in intron 14 ( 10c ) , NM_000267 . 3 ( NF1 ) :c . 2835_2836 in exon 21 ( 16 ) , and NM_000267 . 3 ( NF1 ) :c . 4319_4320 in exon 33 ( 25 ) . Identification of three loci that each served twice as integration site for independent retrotransposition events as well as 1 . 5-kb cluster region harboring six independent insertions supports the notion of non-random insertion of retrotransposons in the human genome . Currently , little is known about which features make sites particularly vulnerable to L1 EN-mediated insertions . The here identified integration sites may serve to elucidate these features in future studies .
Long interspersed nuclear elements ( LINE-1 or L1 elements ) and Alu sequences belonging to the family of short interspersed nuclear elements ( SINEs ) still actively amplify in the human genome , by a process called retrotransposition . L1 elements comprise ∼17% of the human genome sequence [1] but of the ∼500 . 000 L1 copies only ∼80–100 are still fully capable of active retrotransposition [2] . Equally , only a small minority of the >1 million Alu elements comprising more than 10% of the human genome can retrotranspose in a non-autonomous process , using proteins encoded by L1 elements to mediate their mobility [3] . The active Alu elements are named master or source Alu elements [4]–[5] . L1 elements are transcribed by RNA polymerase II whereas polymerase III transcribes Alu elements . Both elements are transcribed from an internal promoter [6]–[7] . While L1 transcripts are polyadenylated after transcription , the poly ( A ) tail of Alu transcripts may be encoded directly from the genomic site of transcription [8] . Alu transcripts are then terminated at the 3′ end with a short run of U's [8] . L1 elements are autonomous retrotransposons . Active L1 elements are typically 6 kb in length and contain two non-overlapping open reading frames ORF1 and ORF2 [9]–[10] . The latter encodes a protein with endonuclease ( L1 EN ) and reverse transcriptase ( L1 RT ) activities [11]–[12] . It is generally accepted that L1 EN forms a nick at the insertion site of L1 elements and the L1 transcripts are reverse transcribed using the 3′ overhang of the nick as a primer [11] . The consensus cleavage site of L1 EN 3′-AA/TTTT-5′ ( and derivates thereof ) [11] , [13] which usually cleaves at the bottom strand allows the T's at the 3′ terminus of the nick to prime reverse transcription from the poly ( A ) end of a L1 transcript . There is evidence that Alu elements are reverse transcribed by the same process called “target primed reverse transcription” ( TPRT ) , but they need to “borrow” the factors for TPRT from L1 elements [14] and are , hence , called non-autonomous retrotransposons . Integration of the generated cDNA is less well understood . Generally , cleavage of the second DNA strand occurs a few base pairs ( typically 7–20 bp ) downstream of the first nick , causing target site duplications ( TSD ) and the free 3′ end at the second cleavage site is used to prime the second strand cDNA synthesis . The whole process results in the formation of a new DNA copy of an L1 or Alu element including the poly ( A ) tail flanked by short direct repeats of the duplicated target site . For a detailed description of the mechanisms of autonomous and non-autonomous retrotransposition see [8] , [15] and papers cited therein . Although retrotransposable elements have no immediate function in the cell , their motility can be important for genome plasticity and the creation of genetic variation [16] . Most recent studies [17]–[20] demonstrate that L1 and Alu elements dimorphic with respect to presence/absence at a given site contribute significantly to structural variation of the human genome . Furthermore , these studies show that current activity of mobile elements is likely to be higher than previously appreciated . L1 and Alu elements are believed to insert randomly into the human genome . Hence , de novo transposition of an element may occasionally cause an inherited disease when the element is inserted into ( or at proximity of ) the coding region of a functional gene [21] . To date , only ∼65 cases of de novo L1 EN-mediated insertions of retrotransposable elements causing genetic diseases have been reported [22]–[23] . A systematic analysis of retrotranspositional events causing genetic disorders lists 48 such mutations consisting of 26 Alu , 15 L1 , four SINE/VNTR/Alu ( SVA ) composite element insertions as well as three simple poly ( A ) insertions [24] . These de novo insertions were found in 31 different genes including one insertion in the NF1 gene [25] . It has long been suspected that L1 EN-mediated retrotranspositional events are underreported as disease causing mutations , since they may be overlooked by the most commonly used mutation detection methods that rely on PCR amplification of small amplicons , i . e . exons from genomic DNA [26] ( and reviewed later on in [27]–[28] ) . Here we report 18 novel L1 EN-mediated insertions in the NF1 gene: one poly ( T ) , three L1 and 14 Alu insertions were all identified using an RNA-based core assay as the starting point for comprehensive NF1-gene analysis illustrating the strength of this approach in identifying also this complex type of mutations . Of note , six of the integration sites were located within a relatively small genomic region of 1500 bp between NF1 exons 21 ( 16 ) and 23 ( 18 ) ( exons are numbered consecutively according to the NCBI reference sequence NM_000267 . 3; in addition , the more widely known historical legacy numbers , originally designated by the international NF1 consortium , are given in parentheses ) . Even more striking , three of the integration sites within the NF1 gene were used twice . Our results indicate and confirm that some genomic locations may be especially prone to L1 EN mediated retrotransposition .
Direct cDNA sequencing used as the core assay of comprehensive NF1 mutation analysis in the two centers ( UAB and MUI ) uncovered heterozygous ( ∼50% of the transcripts affected ) splicing alterations in 18 NF1 index patients ( Table 1 ) that could neither be explained by a heterozygous mutation of the splice regulatory elements of the affected exon and/or the flanking intronic sequences nor by a deletion of the genomic DNA . However , fragment analysis of the gDNA amplicons of the affected exons showed in some cases a faint extra band of larger size absent in the PCR products of a control DNA ( Figure S2A ) . Sequence analysis of these PCR products revealed a faint additional sequence , besides the wild type sequence , starting at the position where the retroelement was inserted ( Figure S2B ) . Manual reading of the background sequences revealed the presence of Alu elements inserted within the exonic sequences . In addition to the approximately 280-bp long Alu elements a 60–120-bp long poly ( A ) tail -or a poly ( T ) tail- was inserted depending on whether the Alu element was inserted in the sense or antisense orientation with respect to the NF1 reading frame ( Figure S2B ) . As expected for de novo retrotransposed Alu elements the inserted sequences were flanked by short duplicated sequences derived from the insertion sites , i . e . target site duplication ( TSD ) . We reasoned that the substantial increase in size of the mutant exons containing the Alu/L1 insertions , compared to the wild type exons caused allelic drop-out under standard PCR conditions . We increased the extension time of the PCR reactions and/or increased the size of the PCR amplicons to enhance the amplification of the larger mutant alleles and , hence , facilitate the detection of possible de novo Alu insertions . Amplification of all exons with so far unexplained splicing alterations using PCR conditions optimized to amplify larger PCR products ( Figure 1A and 1B ) led to the identification of 14 different de novo Alu insertions ( Table 1 ) as well as an approximately 120-bp poly ( T ) stretch in exon 25 ( 19b ) resulting in skipping of the last 40 nucleotides of this exon in the mRNA transcripts . To further confirm the presence of the Alu elements within the mutant NF1 alleles and to determine precisely the inserted sequence , the PCR products showing the extra bands of increased size were cloned and sequenced ( Figure 1B ) . Additionally or alternatively , Alu-insertion-specific primers were used together with the regular exon primer at the opposite site of the exon to specifically amplify and subsequently sequence the mutant alleles ( Figure S3 ) . All 14 Alu insertions identified are listed in Table 1 . Alignment of the inserted Alu sequences with the consensus sequence of the different Alu families ( as deposited in Repbase Giri [29] ) showed that two Alu elements belong to the AluY , six to the AluYa5 and six to the AluYb8 family ( Figure S4 ) . Three of the Alu insertions were truncated and lacked at their 5′ end 17 , 20 and 39-bp , respectively , ( see patients MUI-2 , UAB-R07118 and UAB-R869001 in Figure S4 ) . Six of the Alu elements were inserted in sense and eight in anti-sense orientation . Increase of the amplicon size and the PCR extension time of exons 39 ( 30 ) , 23 ( 18 ) and 9 ( 7 ) did not allow resolution of the cause of their mis-splicing in the patients UAB-R01429 , UAB-R316001 and UAB-R91409 , respectively . Use of a Taq polymerase enabling amplification of extremely long PCR products ( up to >6 kb ) was needed to amplify the mutant exons which had 10- and 17-times increased in size compared to their wild type normal allele in patients UAB-R316001 and UAB-R01429 , respectively . A 6-kb full-length L1 element inserted in sense orientation into exon 39 ( 30 ) was identified in patient UAB-R01429 ( Figure 2A ) . Full sequencing of the inserted L1 element showed it to belong to the youngest L1 ( Ta-1d ) subset since it carried all sequence variants that distinguish this L1 ( Ta ) group from others [2] , [30] . It contained 10 deviations from the consensus sequence of hot L1 sequences as given by [2] ( Figure S5 ) . Sequence alignment with all known intact L1s of the Ta-1d subset [31] showed that the closest related hot L1 ( Ta-1d ) element is contained in NCBI sequence AL356438 . Only six nucleotides differentiate the full-length L1 identified in exon 39 ( 30 ) from this possible precursor sequence . A 5′-truncated L1 element was found to be inserted in sense into exon 23 ( 18 ) in patient UAB-R316001 . The latter sequence contained 1753 bp of the 3′-half of ORF2 and the poly ( A ) tail but lacked the ORF1 and the promoter region of L1 elements . Furthermore , it was preceded by a short 8-bp sequence neither derived from the target sequence nor from the inserted L1 element , a finding that previously has been observed for L1 retrotransposition events in cultured cells [32]–[34] . This L1 element belongs to the L1 ( pre-Ta ) subset since it carries a 3-bp ACG at the site discriminating the L1 ( Ta ) and L1 ( pre-Ta ) subsets [35] . In intron 9 ( 7 ) we found a sequence inserted that contained a poly ( T ) stretch at the 5′-end indicating that the poly ( A ) tail of the inserted retrotransposon transcript had annealed to the sense strand where reverse transcription from the template started . Sequencing from the reverse strand showed , however , that the 3′-end of the inserted sequence contained part of an L1 ORF2 sequence ( see Figure S6 ) . This sequence was inserted in sense orientation with regard to the NF1 coding sequence suggesting that during the process of retrotransposition the orientation of the reverse transcription from the L1-RNA template that started at the sense strand of the NF1 gene switched and continued from the anti-sense strand . Due to experimental difficulties and lack of sufficient patient's DNA it was not possible to assess the full sequence of the inserted L1 element and , therefore , the breakpoint of inversion could not be defined . Alignment of the sequences at the integration sites ( Table 2 ) shows that all integration sites match the reported L1 EN consensus cleavage site [13] , indicating that all insertions arose via L1 EN-mediated retrotransposition . The exact insertion site of an AluY element into intron 10 ( 8 ) cannot be determined due to a 71-bp deletion associated with this insertion that causes also lack of a TSD . Nevertheless , the most likely insertion site matching a L1 EN cleavage site is tentatively given in Table 2 . The alignment of the integration sites also shows that the 18 retroelements inserted in 15 different integration sites with three of them used twice independently . We found in two non-related patients ( UAB-R10408 and UAB-R164201 in Table 1 ) an AluY and AluYb8 element inserted into the splice acceptor site of intron 14 ( 10c ) . The same L1 EN cleavage site was used once at the anti-sense strand to insert the AluY element in sense orientation with respect to the coding sequence of the NF1 gene ( integration site according to HGVS nomenclature c . 1642-1_1642 ) and in an unrelated patient once at the sense strand to insert the AluYb8 element in anti-sense orientation ( integration site according HGVS nomenclature c . 1642-12_1642-11 ) . Furthermore , we found in two non-related patients ( UAB-R81017 and UAB-R340101 in Table 1 ) an AluYa5 and an AluYb8 element , respectively , inserted at the identical integration site , c . 4319_4320 , in exon 33 ( 25 ) . Both Alu elements were inserted in sense orientation and flanked by the same sized TSD ( Table 2 ) . Finally , in two non-related patients ( UAB-R37616 and UAB-R37305 in Table 1 ) -one of them being a sporadic patient proven to have a de novo insertion- an AluYa5 element inserted at the identical position , c . 2835_2836 , in exon 21 ( 16 ) . The length of TSDs flanking the two insertions differed between both patients ( Table 2 ) confirming further that two independent events led to the formation of these two mutant alleles . This site was located within a 1 . 5-kb genomic region containing exons 21 ( 16 ) , 22 ( 17 ) and 23 ( 18 ) that harbor a total of six independent retrotranspositions . The remaining eight integration sites of de novo insertions were distributed over the entire NF1 gene from exon 6 ( 4c ) to intron 48 ( 39 ) . Four out of 14 Alu elements ( UAB-R10408 , UAB-R164201 , UAB-R07118 and UAB-R119201 in Table 1 ) were inserted into the canonical AG-dinucleotide ( 1/4 ) or the polypyrimidine tract ( 3/4 ) of the 3′ splice sites of introns 14 ( 10c ) , 48 ( 39 ) , and 10 ( 8 ) , respectively . Disruption of these splice sites readily explains the observed skipping of the downstream exons ( type 1 splicing effect in Figure 3 ) . Skipping of the affected exon was also observed in five other patients with Alu insertions , i . e . two insertions at the identical site in exon 33 ( 25 ) , two insertions at different integration sites in exon 22 ( 17 ) and one insertion in exon 6 ( 4c ) ( type 2 effect in Figure 3 ) . Two AluYa5 elements ( UAB-R37305 and UAB-R37616 in Table 1 ) inserted at the identical integration site c . 2835_2836 in exon 21 ( 16 ) in two unrelated patients causing skipping of the last 233 nucleotides of this 441-bp exon . This partial exon skipping results from the use of a cryptic 5′ splice site located upstream of the integration site ( type 3 effect in Figure 3 ) . Similarly , integration of an AluYb8 element at position c . 2439_2440 in the 5′ half of exon 21 ( 16 ) , 30 nucleotides downstream of the intron-exon 21 ( 16 ) border , resulted in the use of a cryptic 3′ splice site downstream of the integration site and loss of the first 229 bp of the exon 21 ( 16 ) ( mutation UAB-R39428 in Table 1; type 4 effect in Figure 3 ) . Insertion of the approximately 120-bp poly ( T ) stretch at c . 3312_3313 in exon 25 ( 19b ) , 3 nucleotides upstream of the exon-intron border also led to use of a cryptic 5′ splice site and loss of the last 40 nucleotides of this exon ( mutation UAB-R75103; type 3 effect in Figure 3 ) . Insertion of an AluYa5 element into exon 47 ( 38 ) resulted in at least two different splicing effects , i . e . skipping of the exon and use of an exonic cryptic 5′ splice site upstream of the integration site ( mutation MUI-2; type 2 and 3 splicing effect in Figure 3; see also Figure S1 ) . Insertion of an AluYb8 element into exon 12 ( 10a ) also resulted in a more complex splicing defect . This element lacks the first 39 bp at its 5′ end ( UAB-R869001 in Table 1 and Figure S4 ) . A cryptic 5′ splice site within this truncated Alu element is used instead of the natural one of the exon 12 ( 10a ) , leading to transcripts containing the first 61 nucleotides of this Alu element but lacking the 3′ half of exon 12 ( 10a ) , downstream of the Alu integration site ( type 5 effect in Figure 3 ) . Insertion of the 5′-truncated L1 element into exon 23 ( 18 ) also caused simple exon skipping in the transcripts of the patient UAB-R316001 ( type 2 splicing effect in Figure 3 ) . The insertion of the 5′-inverted L1 element 195 nucleotides downstream of exon 9 ( 7 ) caused skipping of exon 9 ( 7 ) as well as inclusion of a 130-bp cryptic exon embedded within the inserted L1 element ( patient UAB-R91409; type 6 effect in Figure 3; see also Figure S6 ) . Finally , a 5′ splice site within the full-length L1 element inserted into exon 39 ( 30 ) is used instead of the natural 5′ splice site of exon 39 ( 30 ) , leading to a transcript lacking the 143 nucleotides of the exon downstream of the integration site and containing instead the first 96 nucleotides of the L1 element ( patient UAB-R01429; type 5 effect in Figure 3 ) . Taken together , all Alu and L1 elements as well as the insertion of a poly ( T ) element lead to altered splicing of the transcripts from the mutant allele . None of the inserted Alu or L1 elements was fully included into the transcripts .
Firstly , mutations due to retrotransposition have long been thought to be underestimated , since they may be missed by mutation detection methods relying on PCR amplification of small amplicons , i . e . exons , from genomic DNA [26]–[28] and references cited therein . Indeed , all insertions reported here were identified because they altered splicing of the transcripts as detected by direct cDNA sequencing [37] . In order to identify the cause of the splicing defects , i . e . the Alu , L1 and poly ( T ) stretch insertions , it was necessary in most of the cases to modify the PCR conditions to prevent allelic drop-out of the substantially larger mutant allele . Hence , most of these mutant alleles would be missed by an exon by exon-sequencing approach . Similar findings were reported for the BRCA1 and BRCA2 genes [38] and even for genes involved in X-linked diseases [39] although allelic drop-out is expected to be less severe in X-linked diseases due to hemizygosity of the mutant allele in the affected males . Thus , our results demonstrate that RNA-based mutation analysis protocols are more apt to uncover L1 EN-mediated retrotransposon insertions . Secondly , it is possible that the NF1 gene is particularly susceptible to retrotransposon integration . It is striking that three specific sites within the NF1 exons 21 ( 16 ) and 33 ( 25 ) and the splice acceptor site of intron 14 ( 10c ) hosted each two independently retrotransposed elements . Moreover , one of these sites is located in a small region of approximately 1 . 5 kb within the ∼280-kb NF1 gene where six of the 18 insertions cluster . Together , these finding strongly support the notion of an non-random de novo insertion of retrotransposons in the human genome [24] . A systematic analysis of 48 previously reported simple L1 EN-dependent insertions showed that some genes , e . g . F8 and F9 mutated in hemophilia B , may have hotspot regions for retrotransposon integration [24] . Furthermore , three integration sites in three other genes , i . e APC , F9 and BTK , have been reported to be used twice independently for L1 EN-mediated retrotransposon insertions [24] , [39] . Taken together with our findings in the NF1 gene , a tenth ( 6/66 ) of the well characterized sites that harbor disease causing retrotransposon insertions have been used multiple times with half of them identified in the current study . It remains to be elucidated which features make these sites particularly vulnerable to L1 EN-mediated insertions . The fact that some of these sites are embedded in a larger sequence context that appears to be a hotspot region for insertions may indicate that flanking sequences and possibly also the chromatin structure in these regions may play a role . It has been suggested that Alu and L1 elements have a similar retrotransposition efficiency via-à-vis the molecular retrotransposition machinery [40] . However , as in the NF1 gene also in previously published disease causing retrotranspositional insertions a preponderance of Alu over L1 insertions can be observed . As a possible explanation Dewannieux et al . [3] propose that L1 elements harboring a disrupted ORF1 , but retaining a functional ORF2 , should still be competent for trans mobilization of Alu elements even if they are unable to promote their own transposition . In other words , there appear to be more potential “drivers” for Alu retrotransposition than there are for L1 retrotransposition [4] potentially explaining the preponderance of disease-causing Alu over L1 insertions [24] . With this report we substantially increase the number of known disease causing Alu , L1 and simple poly ( T ) insertions . Hence , our data confirm and extend several observations deduced from 48 previously analyzed pathological insertions [24] . All inserted retrotransposons in our study contained a poly ( A ) /poly ( T ) tail with a size in the range from 60–178 bp and all but one were flanked by TSDs with a size in the range of 6–17 bp ( see Table 1 and Table 2 ) . All integration sites ( Table 2 ) matched the previously reported consensus sequence of the L1 EN cleavage site [13] , [41] and there was no preferential orientation of the inserts with respect to the NF1 coding sequence . All Alu insertions belong to the evolutionary youngest “Y” Alu family ( see Figure S4 ) . Three of them ( MUI-2 , UAB-R07118 and UAB-R869001 in Table 1 ) lacked 17–39 bp at their 5′ end when compared to the Alu consensus sequence ( Figure S4 ) . According to [42] these three elements would fall into the Group II Alu inserts which represent ∼8% of all Alu elements polymorphic with respect to presence or absence in the human genome . The poly ( T ) sequence inserted into the NF1 exon 25 ( 19b ) may represent a severely truncated Group III Alu insertion [42] . Equally , this poly ( T ) stretch may result from a severely truncated L1 element or even a processed pseudogene . One AluY element inserted into intron 10 ( 8 ) was accompanied by a 71-bp deletion and lacked a TSD . Different possible mechanisms for the loss of genomic sequences in association with EN-dependent retroelement insertions have been discussed [24] . One suggested mechanism depicted in Figure 6A in [33] assumes that the second-strand nick at the top strand of the L1 target site is made a few bp to the “left” of the initial nick on the bottom strand rather than to the “right” causing the loss of a few bp at the insertion site . A model that would explain the larger deletions of several kb assumes that the reverse-transcribed cDNA strand of the retroelement is involved in double-strand break processing and invades a double-strand break to the “left” of the first-strand nick ( see Figure 6B in [33] ) . Both models may theoretically apply to the 71-bp deletion associated with the AluY insertion into NF1 intron 10 ( 8 ) . In both models the first-strand L1 EN-mediated nick would have occurred at position c . 1186-85_1186-86 in intron 10 ( 8 ) ( as tentatively given in Table 1 and Table 2 ) . However , it is also possible that an EN-independent integration mechanism as previously observed in certain Chinese hamster ovary ( CHO ) cell models [41] , [43] has led to the TSD-lacking and deletion-associated integration of this AluY element . According to Kojima [44] L1 elements of the human genome can be classified into three categories: full-length , 5′-truncated and 5′-inverted , comprising respectively 25 . 5% , 43 . 8% and 30 . 7% of all L1 elements . Here we found an example of each category . The full-length element belonging to the youngest L1 ( Ta-1d ) subset was a simple insertion in sense orientation with regard to the NF1 coding sequence flanked by a 12-bp TSD . The 5′-truncated element lacked the promoter region , the entire ORF1 and the first 2080 bp of the 3825-bp ORF2 . Of note , this is only the second reported pathogenic insertion of an older and less active L1 ( pre-Ta ) element [2]; the first was found in the F8 gene in a hemophilia A patient [21] . The 5′ inverted L1 element contained a poly ( T ) tail at the 5′ end but the analyzed sequence at the 3′ end was inserted in sense orientation and contained 1088 bp from the center of the 3825-bp ORF2 ( Figure S6 ) . This suggests that during the process of retrotransposition the orientation of the reverse transcription from the L1-RNA template that started at the sense strand of the NF1 gene switched and continued from the anti-sense strand . The presence of a 2-bp micro-homology that may have promoted strand switching due to secondary binding between the RNA-template and the 3′-end of the 5′-overhang at the antisense strand supports the model of twin priming [45] ( depicted also in Figure 1 in [44] ) as the underlying mechanism leading to the insertion of this 5′ inverted L1 element . Only four of the Alu insertions identified here directly affect splice sites . Nevertheless , all L1 EN-mediated insertions in this study affected NF1 splicing in the patients . The predominant effect of the 13 exonic Alu , L1 and poly ( T ) insertions was exon skipping ( 6/13 cases ) or use of a cryptic splice site -either a cryptic 5′ splice site upstream ( 3/13 cases ) or a cryptic 3′ splice site downstream of the integration site ( 1/13 cases ) . In one of 13 cases both of these splice effects were observed in the same patient . In only two cases part of the Alu or L1 element were contained in the mRNA transcripts , inserted into exon 12 ( 10a ) and 39 ( 30 ) respectively . Overall , we do not believe that ascertainment bias can explain why exon skipping and/or use of cryptic splice site are the main effects of the de novo Alu and L1 insertions in exonic sequences . Firstly , because we have no evidence that the long-range RT-PCR reactions used in the applied assays would miss Alu insertions in exons or introns that lead to full or partial exonisation of the inserted sequences in the mRNA transcripts . On the contrary , duplications of single or multiple exons which lead to a similar size increase of the RT-PCR products were readily detected by our assays [46] . Secondly , in agreement with our observations , exon skipping was also the reported effect of 5/7 L1 EN-mediated integrations for which RNA data were available in the literature ( see Table 1 in [28] ) . Hence , following an exon definition model of splicing [47] , our data indicate that the main effect of exonic Alu and L1 insertion is weakening of the exon definition resulting in altered splicing of the affected exon . Currently it is not fully evaluated by which mechanisms the inserted sequences reduce exon recognition by the splicing machinery . It has been proposed that inserted sequences may disrupt specific cis-acting exonic splice elements , such as exonic splicing enhancers ( ESEs ) ( see [28] and references cited therein ) . However , it is unlikely this pertains to all or to the majority of exonic de novo insertions . Therefore , we favor the hypothesis that the insertion of a relatively large number of nucleotides ( 300–2000 bp ) weakens exon definition simply by increasing the size of the affected exon and/or by disrupting the exonic structure of cis acting elements in a more general sense , e . g . by increasing the distance of exonic cis regulatory elements and the splice sites of the exon . In this respect it is of note that the use of cryptic 5′- and 3′- splice sites in exons 21 ( 16 ) and 25 ( 19b ) is also observed in transcripts from mutant NF1 alleles that carry single nucleotide alterations destroying the respective natural 5′- and 3′- splice sites of these exons ( [48] and Messiaen unpublished results ) . This observation may indicate that some of the inserted sequences weaken particularly the downstream or upstream splice site while the definition of the respective other splice site is unaffected which in certain instances favors use of a cryptic splice over skipping of the affected exon [48]–[49] . The integration of an inverted L1 element into intron 9 ( 7 ) caused a complex splicing effect , i . e . insertion of a cryptic exon embedded in the inserted L1 element and skipping of the preceding exon 9 ( 7 ) . A similar splicing defect was described in a chronic granulomatous disease patient who carried a truncated L1 element in intron 5 of the CYBB gene [50] . As in the NF1 gene , the cryptic exon inserted in the transcripts of CYBB was not better defined by the splice sites compared to the flanking natural exons that were skipped in those transcripts containing the L1 derived cryptic exon . Hence , it remains to be explained why insertion of these L1 elements lead to exonisation of the cryptic exon on the account of the adjacent natural ones . Our results clearly show that RNA-based mutation analysis strategies have the potential to detect disease-causing L1 and Alu insertions . In addition , the RNA-based comprehensive NF1 mutation detection approaches unambiguously identify and functionally characterize other classes of mutations usually missed by DNA-based mutation detection strategies , such as intronic alterations outside the canonical splice site dinucleotides ( GT-AG ) ( including deep intronic mutations ) , as well as silent and missense mutations with an effect on splicing [48] , [51] . Still this approach may underestimate L1 EN-mediated insertions . De novo retrotransposon insertions within the 3′- or 5′-UTR can lead to reduced expression by the disruption of gene regulatory elements [52]–[53] . Furthermore , insertion of L1 elements within introns has been shown to reduce mRNA transcript levels . This phenomenon is related to RNA polymerase II elongation defects and/or premature polyadenylation caused by the L1 elements [54]–[55] . Thus , it is possible that insertions especially in the regulatory or intronic regions of the gene may still be missed by the here applied NF1 mutation analysis assay .
All mutations reported were uncovered in samples from unrelated index patients sent for clinical NF1 testing to two centers , i . e . the Medical Genomics Laboratory at the University of Alabama in Birmingham ( UAB ) and the Division of Human Genetics , Medical University Innsbruck ( MUI ) . Informed consent was obtained from all patients . This study was approved by the ethical committee from both institutions . In both laboratories the primary assay for comprehensive NF1 mutation analysis is a direct cDNA sequencing approach that is based on the amplification of the entire NF1 coding region in three ( Birmingham , AL ) or five ( Innsbruck ) overlapping RT-PCR fragments and subsequent sequencing of the entire PCR products with 18 ( 20 ) internal primers . Details on the RT-PCR reactions and primers can be found in [37] , [56] . To avoid illegitimate splicing , known to lead to multiple aberrant splice variants that impede the detection of mutations in an RNA-based approach [51] , [57] , total RNA is extracted from phytohemagglutinin ( PHA ) -stimulated short-term lymphocyte cultures treated with 200 µg/ml puromycin for 4 h prior to cell harvest to prevent the nonsense-mediated RNA decay [51] . Details on cell culture , RNA-extraction and cDNA synthesis can be found in [37] . BigDye Terminator Cycle Sequencing chemistry was used for sequencing ( Applied Biosystems , Foster City , CA ) . The sequencing reactions were subsequently run on an automated capillary sequencer and analyzed using the sequence analysis program SeqScape v2 . 5 ( Applied Biosystems , Foster City , CA ) and/or SequencePilot ( JSI Medical Systems , Kippenheim , Germany ) . In some instances it was necessary to manually analyze ( read ) the aberrant sequences in order to determine all splicing defects deducible from multiple overlaying sequences ( see Figure S1 ) . NF1 nucleotide numbering is based on GenBank reference sequence NM_000267 . 3 with the A of the ATG start codon being nucleotide position c . 1 . The integration sites are given according to the HGVS nomenclature after the first duplicated sequence regardless of whether the first nick occurred at the sense or the anti-sense strand and the thereof resulting orientation of the insertion with respect to the NF1 coding sequence . Exons are numbered according to the reference sequence with the widely known legacy numbering given in parenthesis . Exons and flanking intronic sequences affected by splicing defects were analyzed by sequencing from genomic DNA and by multiplex ligation dependent probe amplification ( MLPA ) with the current SALSA-MLPA-kit P081-B1 and/or P082-B1 ( MRC-Holland , Amsterdam , the Netherlands ) . When MLPA results were negative for a genomic deletion of the respective exon and no alteration was identified explaining the observed splicing defect , sequences were meticulously reanalyzed to uncover a possible Alu/L1 insertion within or in close vicinity of the exon ( see Figure S2 ) . To diminish allelic drop-out of the mutant allele due to the increased exon size by an Alu/L1 insertion , the PCR conditions were modified to allow for the amplification of larger sequences from gDNA of the patients . Primers and PCR conditions used to amplify the affected exons are listed in Table S1A and S1B . Mutant exons containing Alu elements were amplified using Takara Ex Taq ( Takara Biotechnology Co . LTD , Madison , WI ) or Taq DNA Polymerase ( Invitrogen , Carlsbad , CA ) . To amplify the mutant alleles containing a truncated 1753-bp and a full-length 6-kb L1 element , respectively , Expand Long PCR Taq ( Roche Diagnostics GmbH , Roche Applied Science , Mannheim , Germany ) was used . To determine the precise sequence of the inserted Alu element as well as the duplicated sequence at the insertion site the PCR products showing a band of increased size were cloned according to the manufacturer's instructions into the TOPO-TA cloning vector pCR 4-TOPO ( Invitrogen , Carlsbad , CA ) and individually sequenced . Alternatively or in addition , primers were designed to specifically amplify only the mutant alleles containing the Alu insertions . These primers contained at their 5′-end the exonic sequences immediately upstream/downstream of the insertion site and at the 3′-end a few nucleotides of either the Alu or the poly ( A/T ) stretch . All primers are listed in Table S1A . PCR products generated with these primers used together with the regular exon primer at the opposite site of the exon were subsequently sequenced ( see as an example Figure S3 ) . Alignment of the identified Alu sequences with the consensus sequences of the different Alu families ( as deposited in Repbase Giri [29] ) was performed with the program ClustalW v . 1 . 83 [58] . To amplify the much larger mutant alleles containing a truncated 1753-bp and a full-length 6-kb L1 element , the Expand Long PCR Taq kit ( Roche Diagnostics GmbH , Roche Applied Science , Mannheim , Germany ) specifically designed to amplify fragments up to around 20 kb , was used . To sequence the 5′-end of a full length L1 element integrated in exon 39 ( 30 ) a L1-specific reverse primer ( L1-5_39r in Table S1 ) was used together with the exon 39 ( 30 ) forward primer ( 39f ) . Thereafter , the entire 6021-bp L1 sequence was characterized by sequencing of the PCR product generated with primers 39f and 39r ( Figure 2 ) using L1-sequence specific internal primers ( Table S1 ) . Similarly , to amplify a mutant 1800-bp fragment containing a truncated L1 element in exon 23 ( 18 ) , and thereafter sequence the 5′ and 3′ends of this mutant fragment , we used the primers 23f and 23r . In addition , sequence analysis was performed with a L1-specific primer , L1_Fam ( Table S1 ) , located 86 bp upstream of a 3-bp diagnostic site that distinguishes the young L1 subfamilies ( Ta ) and pre- ( Ta ) from the older ones [35] . To determine the sequence inserted into intron 9 ( 7 ) that lead , at the transcript level , to loss of exon 9 ( 7 ) and concomitant insertion of an L1-derived 130-bp sequence between exons 8 ( 6 ) and 10 ( 8 ) in patient UAB-R91409 , a reverse and a forward primer ( Line9_r and Line10_f in Table S1 ) were used in two PCR reactions together with the exon 9 ( 7 ) forward ( 9f ) and the exon 10 ( 8 ) reverse ( 10r ) primer , respectively . The resulting PCR products were sequenced in both directions . | Repetitive retrotransposable elements , including LINE1 and Alu elements accounting for more than one fourth of the human genome , are still actively amplifying . It is widely believed that retroelements insert randomly in the genome . Retroelements newly inserted in the germ line may cause genetic disease , if a functional gene is disrupted . Up to now , only ∼65 well-characterized pathogenic retroelement insertions in 31 different human genes have been reported . Therefore , retrotransposition is suspected to be underdiagnosed as disease-causing mutation mechanism . Reporting 18 novel insertions in the NF1 gene , all identified by a comprehensive RNA–based mutation analysis protocol , we show that L1 and Alu insertions represent 0 . 4% of all NF1 mutations . Strikingly , we found three integration sites within this 280-kb gene that were used twice independently to insert a retroelement . One of these sites was located in a 1 . 5-kb “hotspot” region where four additional integration sites clustered . These findings , together with three additional integration sites used multiple times independently to insert retroelements in other genes , indicate that some genomic sites may be especially prone to host newly retrotransposed elements . As some of these sites are embedded in “hotspot” regions , larger flanking sequences may play a role in making these sites particularly vulnerable . | [
"Abstract",
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] | [
"medicine",
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] | 2011 | The NF1 Gene Contains Hotspots for L1 Endonuclease-Dependent De Novo Insertion |
We explore the relationship among experimental design , parameter estimation , and systematic error in sloppy models . We show that the approximate nature of mathematical models poses challenges for experimental design in sloppy models . In many models of complex biological processes it is unknown what are the relevant physical mechanisms that must be included to explain system behaviors . As a consequence , models are often overly complex , with many practically unidentifiable parameters . Furthermore , which mechanisms are relevant/irrelevant vary among experiments . By selecting complementary experiments , experimental design may inadvertently make details that were ommitted from the model become relevant . When this occurs , the model will have a large systematic error and fail to give a good fit to the data . We use a simple hyper-model of model error to quantify a model’s discrepancy and apply it to two models of complex biological processes ( EGFR signaling and DNA repair ) with optimally selected experiments . We find that although parameters may be accurately estimated , the discrepancy in the model renders it less predictive than it was in the sloppy regime where systematic error is small . We introduce the concept of a sloppy system–a sequence of models of increasing complexity that become sloppy in the limit of microscopic accuracy . We explore the limits of accurate parameter estimation in sloppy systems and argue that identifying underlying mechanisms controlling system behavior is better approached by considering a hierarchy of models of varying detail rather than focusing on parameter estimation in a single model .
Mathematical models play an important role in understanding complex biological systems . Mathematical models often synthesize a large amount of information about a system into a single representation that can be used to give both conceptual insights into mechanisms and to make predictions about different experimental conditions . As our mechanistic understanding of the underlying biological processes grows , so too do the scope and complexity of mathematical models used to describe them . However , mathematical models are never a complete representation of a biological system . This is a strength , not a weakness , of mathematical modeling . Mathematical models always include simplifying approximations and abstractions that provide insights into which components of the system are ultimately responsible for a particular behavior [1] . Mathematical models , therefore , ought to represent the judicious distillation of the essence of the behavior in question . Indeed , biological models cover an enormous range of scales and scopes and mechanistic models are usually formulated in terms of the immediately underlying physical components . Molecular mechanisms , for example , are modeled as dynamic simulations of complex macromolecules , and systems biology models of gene regulation and protein interactions involve ordinary differential equations for the time evolution of chemical kinetics . Larger scale phenomenon , such as tumor growth or tissue response to radiation treatments involve models that are more removed from fundamental physics , but nevertheless attempt to reflect the effective mechanisms driving a particular behavior . This approach is appropriate; it is both impractical and theoretically unsatisfying to “model bulldozers with quarks” [2] . Unfortunately , it is very difficult to identify a priori which components of a complex system can be ignored , i . e . , which degrees of freedom are irrelevant . It is therefore common for mathematical models to be very complex and include more mechanisms than are strictly necessary to explain a phenomenon . When overly complex models are fit to data , the parameters associated with the irrelevant mechanisms are difficult to infer from observations . These parameters are said to be ( practically ) unidentifiable . Parameter identifiability is ( locally ) measured by the Fisher Information Matrix ( FIM ) [3–5]: I μ ν = - ∂ 2 log P ( ξ | θ ) ∂ θ μ ∂ θ ν = ∂ log P ( ξ | θ ) ∂ θ μ ∂ log P ( ξ | θ ) ∂ θ ν , ( 1 ) where P ( ξ|θ ) is the probability distribution for random variable ξ given parameters θ and 〈⋅〉 means expectation value . ( Both ξ and θ can be vector quantities . ) A small eigenvalue of the FIM indicates that a combination of parameters ( given by the corresponding eigenvector ) can vary by a large amount without affecting the behavior of the system . In many cases , particularly in the context of modeling chemical kinetics in systems biology , the eigenvalues of a model’s FIM are “sloppy” , i . e . , have a uniform spacing of FIM eigenvalues on a log scale spread over many orders of magnitude [6–10] . However , the phenomenon is not unique to biochemical kinetics models; sloppiness has been observed in a wide variety of models in biology , physics , and engineering [8–12] . The exponential eigenvalue distribution of sloppy models quantifies that several parameter combinations are exponentially less important for explaining system behavior than others . Practically all of the system behavior can be controlled by tuning a small number of stiff parameter combinations ( i . e . , eigendirections with largest FIM eigenvalues ) while varying the sloppy parameter combinations has relatively little effect on the model behavior . Because of this , sloppiness is closely related to parameter identifiability and is often ( incorrectly ) used as synonym for practical unidentifiability . In principle , however , these two concepts are distinct , as we illustrate in Fig 1 . Fig 1 illustrates different cases categorized by their sloppiness and identifiability properties . Sloppy models are characterized by a logarithmic hierarchy of FIM eigenvalues ( independent of scale ) while unidentifiable models have small eigenvalues ( in a sense to be made more precise shortly ) . Examples of all four possible combinations illustrated in Fig 1 can be found in science . Examples of sloppy , unidentifiable models ( first column ) abound in the systems biology literature ( see references [8 , 9 , 12] for several examples ) . Because of the ubiquity of sloppiness in the systems biology , it was suggested that accurate parameter estimation in sloppy models was impossible ( or at least impractical ) because of the unreasonable data requirements [9] . However , there are cases of identifiable sloppy models ( second column , see for example references [13–15] ) . Unidentifiable models that are not sloppy ( third column ) are often characterized by a “small parameter” . A small parameter is a small dimensionless number appearing in a model that renders certain aspects of the model unimportant . The canonical example is a system with well-separated time scales [16] . In this case , the small-parameter is the ratio of time scales , and singular perturbation theory makes explicit the approximation in which the fast dynamics are slaved to the slow variables . In general , the small parameter separates which mechanisms can be ignored from those that are relevant . For a sufficiently small parameter , the gap between the identifiable and unidentifiable parameters becomes very large as in the third column . Identifiable , non-sloppy models ( fourth column ) are those in which all parameters are more-or-less equally easily to infer from data . In our experience , linear least squares models often fall into this category . However , models from other fields can also fit this description for appropriate observations [15] or if the model is reduced [17 , 18] . It is important to note that being unidentifiable does not mean a model is not predictive . In many cases , models with very large uncertainties in their parameters may nevertheless make falsifiable predictions [19] . Indeed , it has been argued that the irrelevance of microscopic complications enables effective modeling at different scales without the need to accurately account for all details [20] . It is therefore possible the unidentifiability is necessary for rather than a obstacle to predictive modeling . Analyses based the eigenvalues of the FIM are limited for several reasons . First , the FIM is really only meaningful in the asymptotic limit , i . e . , the limit of infinite data and identifiable models . Second , the FIM eigenvalues are dependent on parameterization . Specifically , re-parameterizing a model with parameters θ = f ( ϕ ) where f is some ( non-singular ) function leads to a new FIM related to the first by I ϕ = ∂ f ∂ ϕ T I θ ∂ f ∂ ϕ , ( 2 ) where ∂f/∂ϕ is the Jacobian matrix of the parameter transformation , Iθ is the FIM for the θ parameterization and Iϕ is the FIM for the ϕ parameterization . For an appropriate choice of f ( ϕ ) , it is possible to transform eigenvalues of Iϕ to be any positive numbers . A simple example of a possible reparameterization is changing the units of parameters ( e . g . , from meters to nanometers ) , although in principle any nonsingular f is permissible . It was shown in reference [21] using information geometry [22–24] that the FIM eigenvalues often reflect a global , parameterization-independent property of the model . In this approach , the set of all possible model outputs ( found by varying the parameters over all physically possible values ) generates a manifold of possible predictions with the FIM acting as a Riemannian metric . This idea is illustrated in Fig 2 . For sloppy models , this manifold is often bounded by a hierarchy of widths , reminiscent of the hierarchy of sloppy eigenvalues . Indeed , when the model is parameterized by dimensionless parameters ( e . g . , using log-parameters ) , the widths are approximately given by the square root of the eigenvalues W μ ≈ λ μ , as in reference [21 , Fig 3] . The concept of manifold widths makes the notion of identifiability distinct from issues related to model parameterization . For example , a small FIM could be an artifact of a poorly chosen parameterization . The value of the parameters could indeed vary over a numerically large range , but the apparent unidentifiability may just be a consequence of poorly chosen units ( for example ) . If this is the case , sufficiently large variations in the parameters will change the model predictions in a statistically significant way . In contrast , the existence of manifold widths demonstrates that there are parameter combinations that can vary infinitely without appreciably affecting the model behavior . These parameter combinations are truly unidentifiable . We describe parameters that are unidentifiable as being irrelevant or unimportant . These parameters correspond to manifold widths much less than the scale of the experimental noise , i . e . , FIM eigenvalues much less than one . These parameter combinations could be fixed to arbitrary values or removed from the model without affecting the ability of the model to give a good fit to the data . In contrast , parameters with widths much greater than the experimental noise need to be tuned to reproduce the observed behavior . These parameters can be accurately inferred from data , and we call them relevant or important . Parameters between these two extremes we call marginal . Optimal experimental design has been proposed as a way to improve the identifiability of model parameters [13 , 14 , 25–33] . Experimental design is a broad subject; in this paper , we use the term to refer to the general process of using numerical simulations to identify potential experiments to render model parameter identifiable . Many different approaches are available [34–41] . Consider , for example , the model of Brown et al . [6] of EGFR signaling . This model has 48 unknown parameters ( mostly reaction rates and Michaelis constants ) and is the seminal example of a sloppy model [6 , 7 , 9] . Apgar et al . [13] identified five experiments ( from a candidate pool of 164 , 000 ) for which , if performed , the FIM would have no small eigenvalues and all parameters could be identifiable . Notably , the model was unidentifiable for each of the experiments individually; however , the unidentifiable parameter combinations were different for each experiment . When the experiments were fit collectively , therefore , the parameters could all be estimated to within 10% , so the experiments were described as complementary [13 , Fig 1] . Although these optimal experiments still require considerably more data than is typical in order to have the desired effect [14] , the dramatic reduction in the range of FIM eigenvalues ( and manifold widths ) is nevertheless encouraging . Subsequent work [5 , 15] has confirmed this result: carefully chosen , complementary experiments can have a dramatic effect on parameter identifiability . When selecting optimal experiments , it is of course important to limit the search to those experiments for which the model is valid . However , for in many cases it is difficult to know a priori which experiments these will be . In practice the true distribution ( red dot ) in Fig 2 does not lie on the model manifold . There are always some systematic errors that result from approximations in the model . However , if the model contains all of the relevant parameters , the systematic errors will not be larger than the experimental noise and can primarily be ignored . By including additional mechanisms in the model , the model manifold will come closer to the true distribution , usually at the cost of including additional irrelevant parameters . We use the terms “model discrepancy” and “model error” to mean the degree to which the true distribution is realizable by the model . In this paper we consider the effects of model discrepancy on parameter estimation in sloppy models , i . e . , what effect the approximations inherent to the model have on the prospect of accurate parameter estimation . Although all mathematical models employ some simplifying approximations , in most well-understood examples , the validity of the approximation can be traced to the existence of a small parameter in the system as discussed above . In addition to suppressing irrelevant details , the small parameter also helps identify under which experimental regime the approximation is valid ( those experiments for which the parameter is small ) . For example , thermodynamics becomes exact in the limit of large system size . As long as experimental probes are restricted to sufficiently large systems , the corrections due to statistical mechanical fluctuations will be small and the approximate model can be treated , for all intents and purposes , as being an exact surrogate of the physical reality . However , in many physical systems there is no obvious small parameter , making it difficult to know a priori which physical details are relevant [12] . ( This is one reason that models often include unnecessary details . ) What is more problematic , the near-uniform spacing of FIM eigenvalues in sloppy models suggests that there is no clear separation between relevant and irrelevant details . If more mechanisms were added to a sloppy model , there would be more FIM eigenvalues . However , this new model will likely be sloppy too , so that the new eigenvalues will not be much smaller ( on a log scale ) than the those of the original model . Optimal experimental design chooses complementary experiments so as to make the small eigenvalues become larger . What then is the effect of this process on the eigenvalues of the more accurate model ? Do they also become larger ? If so , the approximate model will not be able to fit the data accurately , leading to less predictive models . This question is illustrated in Fig 3 and the primary purpose of this paper is to explore this possibility . In this paper we demonstrate the complicated relationship between models and experimental conditions using two models of EGFR signaling . We then introduce a simple hyper-model to quantify the systematic error in the model . We next consider models of DNA repair as a second example . We find in both cases that the model’s predictive power is appreciable reduced after fitting to optimally chosen experiments . This loss in predictive power is the result of larger systematic errors which come in spite of more accurately constrained parameter values .
We begin with an illustrative example; consider a model of EGFR signaling due to Brown et al . [6] . The model contains 48 unknown parameters ( reaction rates and Michaelis constants ) that were originally fit to 63 data points [6] . These data gave limited measurements of activity levels a few proteins after EGF and NGF stimulation in conjunction with a few knockout perturbations . Notably , this model gave a reasonable fit to experimental ( not simulated ) data . Furthermore , the model also made accurate , falsifiable predictions for the system behavior under novel perturbation experiments that were subsequently verified . From this , we conclude that the model likely contained all of relevant biology and chemistry necessary for giving a mechanistic explanation of these observations . However , the parameters were largely unconstrained when fit to this data , with relative uncertainties ranging from a factor of 50 up to a factor of a million . In order to constrain the parameter estimates , Apgar et al . [13] proposed a set of five experimental conditions specifically for the model of Brown et al . These experiments were selected from a candidate pool of more than 160 , 000 possible experiments that included EGF and NGF stimulations at various levels and in various combinations , as well as potential knockout and over-expression perturbations . The five optimal experiments were chosen according to a “greedy” algorithm to maximize the smallest eigenvalue of the FIM . Under these experimental conditions , the authors expected to estimate all parameters to better than 10% accuracy . The experiments were simulated and not physically performed . Rather than actually perform the five experiments proposed by Apgar et al . , we create a second model to act as a surrogate for reality . The EGFR system is well studied and a model that reflects our current understanding of the system [42] would contain far more than the 48 parameters of Brown et al . As a reasonable next step along the ladder of realism , we replace the Michaelis-Menten approximations of the Brown et al . model by the mechanistic model from which this approximation was derived . The mechanistic model of ( Henri- ) Michaelis-Menten is two step enzyme-catalyzed reaction obeying mass-action kinetics: E + S ⇌ ES → E + P . This change introduces several new parameters ( from 48 to 70 ) as well as several new chemical species corresponding to the intermediate enzyme-substrate complexes . Note that there are a large class of mechanisms that could result in the same approximate Michaelis-Menten equation , so implementing the mechanistic model described above also makes several other simplifying assumptions . For example , a mechanistic model could also be written as E + S ⇌ ES ⇌ EP → E + P , i . e . , with an isomerisation step for the enzyme-substrate complex . Indeed , there is a hierarchy of refining approximations one could make to this model . Our choice represents what is likely the simplest next step in refining the mechanistic description of Brown et al . For brevity , we refer to the original model of Brown et al . that implements the Michaelis-Menten approximation as the approximate model . We refer to the model of the Michaelis-Menten mechanism with mass-action kinetics as the mechanistic model . Using the model and parameter values of Brown et al . , we simulate the experimental conditions from reference [6] and add random noise to generate an initial data set characteristic of that in reference [7] . We then fit the 70 parameter , mechanistic EGFR model to this initial data . The resulting fit is both sloppy and unidentifiable as can be seen in Fig 4 ( second column ) . Notably , the 70 parameter model has 22 more eigenvalues than the 48 parameter model , but there is not a clear separation between the largest 48 and the smallest 22 eigenvalues . Furthermore , it is not possible to equate the largest 48 eigenvalues with the 48 parameters of the approximate model and the 22 smallest eigenvalues with the new parameter combinations introduced by the more detailed kinetics . Next , we simulate artificial data for each of the experiments suggested by Apgar et al . using the 70 parameter mechanistic model and parameter values estimated from the first fit . We add random noise to these simulations to create a second data set . This second data set , having come from the more complicated model , acts as a surrogate for real experimental data . We then fit the 48 parameter , approximate model to the second artificial data . The use of Michaelis-Menten kinetics in models of protein networks is somewhat controversial . In many practical cases , such as the original experiments of Brown et al . , the model seems to work . However , the actual condition that must be satified is shown rigorously in reference [43] to be [ E ] [ S ] + K M ≪ 1 , ( 3 ) where [E] and [S] are the enzyme and substrate concentrations respecively and KM = ( kr + kcat ) /kf is the Michaelis constant . From this condition we see that it is not strictly necessary for [E] ≪ [S] as is often ( incorrectly ) asserted . However , this condition is derived for a single enzyme-substrate reaction in isolation and , as we will see below , is generally not a sufficient condition for the Michaelis-Menten approximation to hold in a network context . Nevertheless , Eq ( 3 ) suggests that the Michaelis-Menten model could be valid provided KM is very large , even if E ∼ S . In choosing parameters for the mechanistic model , we therefore choose parameters such that the combination KM = ( kr + kcat ) /kf is very large . This is always possible because the model is unidentifiable when fit to the initial data . We were able to choose parameter values for all reaction rates such that the Michaelis-Menten approximation would not give errors larger than 10% for any of the reactions given the conditions of the network . This means that there is no a-priori reason to think that the approximate model of Brown et al . would be a poor approximation to the mechanistic model for our parameter values . Even when enforcing the constraint that KM is large , we find that the approximate model cannot give a reasonable fit to the data generated by the mechanistic model for the Apgar experiments . This is illustrated in Fig 5 where the systematic errors in the fit are clearly much larger than random noise . Because the error in the fit is large , we conclude that approximate Michaelis-Menten kinetics do not contain the relevant mechanisms to explain the observations under the expanded experimental conditions . This can be seen from the FIM eigenvalues in Fig 4 . In particular , observe that the FIM for the mechanistic mass-action model under the expanded experimental condition contains approximately 60 eigenvalues larger than 1 , so that there are about 60 directions on the model manifold with widths larger than the experimental noise . This indicates that a minimal model would require about 60 parameters to fit this data; the 48 parameter model of Brown et al . is clearly insufficient . Furthermore , the parameters of the mechanistic mass-action model are unidentifiable for the experimental conditions of Apgar et al . If the approximate model were replaced by the mechanistic model , it would require another round of experimental design in order to find accurate parameter estimates . Because the fit of the mechanistic mass action kinetics to the original experiment is unidentifiable , there are many possible parameter values that would give equivalent fits . We generated an ensemble of parameter values for the mechanistic model consistent with the original experiments . When these ensembles generate data for the optimal experiments , we consistently find that the best fits for the approximate model have large errors similar to that in Fig 5 . The discrepancy between the data and the fit in Fig 5 is reminiscent of over-fitting . This is not the case however . Over-fitting occurs when the fit to a training data set is very good ( i . e . , too good ) so that the predictions on a test set suffer as a result . In this language , the fit in Fig 5 is the training set ( not the test set ) , so this is not an instance of over-fitting . Rather it is a demonstrationg that the model cannot fit the data . Motivated by the example of optimal experimental design in EGFR signaling described above , we now propose a simple method to quantify model descrepancy when fitting data to approximate models . We assume that parameters are estimated by least squares regression , although many of our methods and results generalize . Least squares regression is justified by the assumption that the data are generated from a model with additive Gaussian noise: d i = y i ( θ ) + σ i ξ i ( 4 ) where di is the i-th data point , yi is the model prediction for the i-th data point , θ is a vector of parameters , ξi is a random variable with zero mean and unit variance , and σi is the scale of the noise . The random variable may account for any intrinsic stochasticity in the system , e . g . , thermal fluctuations in particle number or inconsistencies in experimental measurements . It may also accommodate systematic errors such as model approximations , i . e . , mechanisms that have been left out of the model . The field of uncertainty quantification has begun to explore methods to account for the inadequacy of the model [44–49] . Minimizing the weighted sum of square errors χ 2 ( θ ) = ∑ i ( d i - y i ( θ ) ) 2 σ i 2 ( 5 ) corresponds to a Maximum Likelihood Estimate ( MLE ) of the parameters for the model in Eq ( 4 ) . The FIM for the model in Eq ( 4 ) is calculated as I μ ν = ∑ i 1 σ i 2 ∂ y i ∂ θ μ ∂ y i ∂ θ ν . ( 6 ) The FIM is the inverse covariance matrix for the parameter estimates , so that the square root diagonal elements of the inverse FIM correspond to the one-standard deviation statistical uncertainties in the inferred parameters . It therefore follows that a well-conditioned FIM is necessary for accurate parameter inference . Because mathematical models are always approximations , a model’s discrepancy from physical reality can always be improved by including additional physical mechanisms . This increased realism usually comes at the cost of increased complexity , often in the form of additional parameters , additional physical degrees of freedom , and computational cost . Therefore , a modeler is often faced with choosing from a hierarchy of models of increasing realism and growing complexity . Overly complex models increase the possibility of over-fitting data or over-explaining behavior while excessively simple models may lack all the relevant mechanisms . Effective models strike a balance between these two extremes . To formalize this concept and facilitate later discussion , we introduce the concept of a sloppy system . We define a sloppy system as a physical system and a set of experimental protocols that can be approximated by a hierarchy of mechanistic , mathematical models of growing complexity that become sloppy in the limit of microscopic accuracy . To make this definition more concrete , consider a model of a biological system . Of necessity , the model does not include every mechanism that is present in the true physical system . It is therefore possible to augment the model with additional mechanistic details , resulting in a more realistic , but more complex model . By repeating this process , one can generate a sequence of models of increasing realism that are better approximations to the actual physical system . The limit of this sequence converges on a model indistinguishable from the physical system , i . e . , the limit of microscopic accuracy . Naturally , this sequence of models will introduce many parameters that are unidentifiable . However , if the sequence of models is not just unidentifiable , but is also sloppy ( i . e . , as in the first column of Fig 1 rather than the third column ) , then we say that the system is sloppy . As an aside , we have introduced the concepts of “sloppy systems” and “limits of microscopic accuracy” as useful abstractions . In practice , constructing more detailed mechanistic models may have a number of challenges . For example , it may be necessary to include parameters describing the experimental apparatus but that are separate from the biological system of interest . These potential complications are beyond the scope of this paper which instead uses these idealized concepts to explain our reported results . The significance of a sloppy system is that models will always include marginal parameters so that there is always a trade off between model identifiability and model predictivity . For a fixed set of experimental protocols , there will always be some mechanistic details that are not entirely identifiable but still facilitate predictivity in the model . The concept of a sloppy system brings together a number of concepts that are each well-known to modelers of complex systems . Considering multiple models of a single physical system is as old as science , but has recently been employed with new sophistication in the context of ensemble modeling [50] and multi-scale modeling [51–53] . The literature for parameter identifiability and the related concept of sloppiness was discussed in the introduction . By bringing these concepts together , we seek to explain the results of our EGFR simulations above and argue that sloppy systems pose unique challenges for predictive modeling . We now demonstrate that the EGFR system is a sloppy system . Fig 6 shows the FIM eigenvalues for several models of EGFR signaling under the experimental conditions of Brown et al . [6] . Including more microscopic realism in the model requires additional parameters that render the model less identifiable . Note that this sequence of models was constructed in reverse–beginning from a sloppy model , irrelevant parameters were removed one at a time using the manifold boundary approximation method [17 , 18] . Here we reinterpret this result as a demonstration of the existence of sloppy systems and emphasize the trade-off between mechanistic accuracy and model simplicity . In general , all the models that belong to a sloppy system cannot be ordered in a simple sequence as in Fig 6 . There will often be a complex , hierarchical relationship among models similar to that described by the adjacency graphs in reference [54] . In order to account for errors due to ignoring marginal parameters , we need to refine the assumptions underlying Eqs ( 4 ) – ( 6 ) . If the stochastic term in Eq ( 4 ) is dominated by experimental noise , then σi can be estimated by repeated observations of data point i , in which case σi scales like σ i ∼ 1 / n i where ni is the number of repeated observations of data i . In this case , σi becomes the standard deviation of the observations and is a measure of experimental reproducibility . Henceforth , we assume that σi denotes the experimental uncertainty and is known from experimental observations . We modify eq ( 4 ) to also include an error term δi due to approximations in the model d i = y i ( θ ) + σ i ξ i + δ i . ( 7 ) The model error term δi will be a type of “hyper-model” that accounts for and quantifies errors in the model in a phenomenological way without including additional mechanisms . We adopt a simple hyper-model of the systematic error given by δ i = f σ i ξ i ′ ( 8 ) where f is a hyper-parameter that will be estimated from the data , and ξ i ′ is another Gaussian random variable with zero mean and standard deviation of one . We illustrate this concept geometrically in Fig 7 . When this ansatz breaks down , it is an indication that relevant mechanisms are missing from the model , i . e . , that the unfit data has structure that could be modeled and predicted . Care must be taken in the interpretation of ξ i ′ . We have modeled the systematic error as a random variable . Unlike experimental noise , the size of this uncertainty cannot be decreased by repeated observations . Rather , the stochastic element in the model error represents the ( unknown ) approximations in the model . The relevant statistical ensemble is the set of all possible model refinements that could be made to correct the model shortcomings . We have assumed that the model errors ξ i ′ are uncorrelated among data points . We also assume that the model is likely to give worse predictions for data points that also have large experimental variation . These choices are convenient and constitute what is likely the simplest possible such hyper-model . More sophisticated models could be used , and the meta-problem of modeling the error in the model has been addressed in the context of uncertainty quantification [44–49] . In the present context , these assumptions will give us a simple way of estimating the error of the model from data . These assumptions will be valid provided that δi is small compared to experimental noise . We will now use our hyper-model to provide a criterion for including additional mechanisms in the model . The negative log-likelihood for the model of Eqs ( 7 ) and ( 8 ) is - l ( θ , f ) = ∑ i ( d i - y i ( θ ) ) 2 2 σ i 2 1 + f 2 + log σ i + 1 2 log 1 + f 2 + 1 2 log 2 π . ( 9 ) The best fit values for the parameters θ are unchanged by Eqs ( 7 ) and ( 8 ) , and an unbiased estimate of f is given by f = χ 2 M - N - 1 ( 10 ) where χ2 is the sum of squared error defined in Eq ( 5 ) , M is the number of data points , and N are the number of parameters in the vector θ . As a practical matter , the model can be fit using Eq ( 5 ) as though there were no approximations in the model . This is due to our convenient choice of δi in Eq ( 8 ) . The augmented FIM is given by I θ f = 1 1 + f 2 I 0 0 2 f 1 + f 2 2 ( M - N ) , ( 11 ) where I in the first entry is the N × N FIM in Eq ( 6 ) . The zeros in the off-digonal terms are 1 × N and N × 1 zero vectors . It follows that the parameter covariance matrix must be modified according to C o v ( θ ) = χ 2 M - N I - 1 , ( 12 ) where I is the FIM in Eq ( 6 ) and χ2 is the best fit cost that minimizes Eq ( 5 ) . It is worth noting that the parameter f contains the same information as the likelihood function as is made explicit by Eq ( 10 ) . Indeed , Eq ( 12 ) is a standard statistical formula for estimating parameter uncertainties in ordinary least squares regression in which the scale of the noise is unknown . The standard deviation of the estimate in f is given by σ f = 1 + f 2 f 2 ( M - N ) . ( 13 ) We now consider how large an f can be acceptable . We seek a model’s whose approximations do not limit is predictive power . That is to say , the model error should be small compared to the experimental noise ( f < 1 ) and unidentifiable from experimental observations . If the MLE of model error is f , then the statistical uncertainty in that estimate should satisfy δf ∼ f < 1 . If this is the case , then the systematic error will be relatively small and not significantly limit the model’s predictive ability . This criterion gives f = 1 2 ( M - N ) - 1 ( 14 ) as an acceptable value for f . With this background , we can now revisit the EGFR model above . The experimental conditions of Apgar et al . , included 7000 data points . If the approximate model were a good approximation , we would expect the fit to have a sum of squares error of approximately 7000 ± 84 where the range is one standard deviation of the Chi-squared distribution . However , fitting the artificial data typically led to a best fit error greater than 100 , 000 and was never less than 96 , 000 . This error corresponds to an estimated value of f = 3 . 7 with δf = 0 . 03 . The statistical uncertainty in the model parameters is larger than expected by a factor 1 + f 2 = 3 . 8 for f = 3 . 7 . The optimal experiments were designed to give less than 10% error in parameter estimates , so that modified uncertainty would now be less than 40% . Considering several parameters were initially unknown to a factor of a million , 40% appears to be a significant reduction . Although the parameter estimates remain somewhat constrained , the predictive power of the model is completely lost . This is because the effective error bars on the data are also larger by a factor of 3 . 8 . In our simulations we assumed that the fractional activity levels of all proteins were measured to 10% accuracy . With 40% effective error bars , one standard deviation on either side of the mean covers almost the entire range of possible predictions . In addition to having a large value for f , the ansatz of Eq ( 8 ) breaks down for the fitting the EGFR model . This is clearly seen by inspecting Fig 5 . We speculate that it may be possible to rescue some of the predictive power of the model by implementing a more sophisticated hyper-model , such as introducing a separate f parameter for each time series or including phenomenological parameters to account for correlations in systematic errors . However , this possibility is beyond the scope of this work , but has been explored in the uncertainty quantification literature [44 , 45 , 47] .
It is perhaps surprising that the approximate Michaelis-Menten model is inadequate even if Eq ( 3 ) is satisfied . However , one should remember that this condition was derived for a single enzyme-substrate reaction in isolation . One possible explanation of our results is that approximate Michaelis-Menten kinetics are not valid in a network . This explanation is problematic however , because the approximate Michaelis-Menten model had been used previously to fit real experimental data and make falsifiable predictions for new experiments . Indeed , in spite of its dubious status , the Michaelis-Menten approximation is often used with much success in many systems biology models . Therefore , while it is true that the Michaelis-Menten approximation is not generally valid , there is considerable evidence that it may sometimes be an safe approximation . To complicate matters , we have forced Eq ( 3 ) to be satisfied by requiring KM to be very large . Naively , one would expect this restriction to lead to Km being unidentifiable . While this would also be true for measurements of a single reaction , it does not generalize to the network case , as our results demonstrate . Furthermore , as the DNA repair results show , our results are not specific to the question of approximate Michaelis-Menten kinetics . Rather , we have shown that the general question of which physical details are necessary to include in a sloppy model can depend strongly , and in unexpected ways , on which combinations of experiments the model is to explain . A common use for optimal experimental design is model falsification . Demonstrating the shortcomings of model is hopefully accompanied by new insights into the system’s behavior . Since none of models we have considered here were considered “correct” in the reductionist sense , demonstrating that they are incomplete is not profound in itself . We suggested above that errors in the fit could be used to motivate new hypotheses about microscopic mechanisms . This possibility is beyond the scope of the current work that focuses on the implications for parameter estimation in sloppy models . A potential alternative to experimental design for parameter estimation , is experimental design to constrain model predictions [28 , 32] . Rather than constrain parameter estimates , one seeks to identify a small number of experimental observations that are controlled by the same few parameter combinations as the prediction one would like to make . In this approach , the model parameters remain sloppy , but the model may be predictive in spite of uncertainty about microscopic details . It may be surprising that a model may be more predictive in the unidentifiable regime than in the identifiable regime . The predictivity of the an unidentifiable model is enabled by the narrow widths of the model manifold in Fig 2 . The narrow widths guarantee that even infinite fluctuations in parameters do not correspond to large fluctuations in predictions . It has been suggested elsewhere that “sloppiness” can explain why models that make many uncontrolled approximations may be usefully predictive [9 , 20] . Our results lend some support to this hypothesis; for our test cases removing sloppiness was always accompanied by a decrease in the predictive ability of the model . In previous work , sloppiness has been viewed as a challenge to be overcome or as a disease to be cured [5 , 13 , 15] . From this perspective the major challenge of sloppy models has been assumed to be the small eigenvalues of the FIM corresponding to practically unidentifiable parameter combinations . This in turn has led ( incorrectly ) to the conflation of sloppiness and practical unidentifiability . As we have argued here , the near uniform spacing of the eigenvalues ( on a log scale ) also pose unique challenges for parameter estimation because there is no clear cutoff between relevant and irrelevant mechanisms . In order for an approximate model to be effective , it is important that the microscopic details ommited from the model be irrelevant , i . e . , unidentifiable . When modeling systems for which all the relevant mechanisms are known , the validity of the model can usually be justified by small parameters , e . g . , separated scales or distance from a critical point . The small parameter guarantees that the FIM eigenvalues for the irrelevant mechanisms are well-separated from those of the relevant mechanisms ( e . g . , column 3 in Fig 1 ) . Some amount of unidentifiability in the physical system is therefore important for effective modeling . For many complex systems , no such ( known ) small parameter exists and sloppy model analysis reveals that there is no sharp distinction between the relevant and irrelevant mechanisms . We speculate that in many cases the system ( not just the model ) is intrinsically sloppy because there is no intrinsic scale separation to suppress irrelevant mechanisms in the system . Therefore , a sequence of mechanistically more realistic models would have an eigenvalue structure closer to that in column 1 in Fig 1 rather than Fig 3 . If that is the case , then one should not expect there to exist a mathematical model that can both be accurately calibrated and accurately predict the system behavior . There will always be several parameters that are marginal , i . e . , not tightly constrained by data but are nevertheless necessary to explain the system behavior . In this case there is a fundamental limit to the efficacy of optimal experimental design: attempting to constrain the marginal parameters of a model of a sloppy system reduces the accuracy of the model and limits its predictive ability as we have seen . Rather than posing a problem for parameter identification in models of complex systems , we argue here that sloppiness is important for successful modeling . Sloppy model analysis reveals that in many cases a behavior of interest is controlled by only a small number of parameter combinations . This observation has been used to explain why relatively simple models can make useful predictions . Indeed , it has been argued elsewhere that sloppiness may help explain why the world in its microscopic complexity is comprehensible at different scales [20] . Our results give credence to this position since removing sloppiness from a model reduced its predictive ability . Another approach is to remove the sloppy parameters from the model . In principle , another simple model may exist whose parameters correspond to the few relevant parameter combinations in the sloppy model . Parameter estimation in such a model would be relatively straightforward . Recent advances in model reduction suggest that systematic construction of simple models from complex representations may be generally possible [17 , 18 , 20] . In some branches of physics , the distinction between relevant , irrelevant , and marginal parameters is defined rigorously in terms of the stability of the collective behavior to microscopic variations in mechanistic details as measured by a renormalization group flow . In that context , relevant parameters correspond to degrees of freedom that must be tuned to achieve a behavior . In this work , we have used relevant and irrelevant less precisely as synonyms for identifiable and unidentifiable as measured by the FIM eigenvalues . This equivalence is reasonable because the the identifiable parameters are those that must be tuned to reproduce a behavior . The equivalence of these definitions was demonstrated in reference [12] . However , one of the hallmark features of sloppy models is the roughly uniform spacing of FIM eigenvalues , making it difficult to make a clear delineation between relevant and irrelevant parameters . Lacking a clear cutoff between important and unimportant parameter directions means that some physical mechanisms may be either relevant or irrelevant depending on the experimental conditions . We have shown this explicitly for the two cases considered here . The model of Brown et al . [6] contained all of the relevant mechanisms ( and many irrelevant ones ) for explaining several experimental observations of an EGFR pathway . In contrast , it did not contain all of the relevant mechanisms for experimental conditions proposed by Apgar et al . [13] . Similarly , the LPL model is sufficient for modeling single radiation doses , while the RS model is necessary for modeling sequences of varied radiation doses and neither contains all the relevant mechanisms for modeling the experiments described in this work . These results demonstrate the need for a theory of modeling and approximation that identifies which physical mechanisms are relevant for explaining different collective system behaviors . We have described two approaches that could be the beginnings of such a theory . First , we have introduced the concept of a sloppy system in which multiple models of varying complexity describe the same observations . Second , we have used a hyper-model to quantify the limitations of a model . Although , most of these ideas have existed in some form in the literature , the unique contribution of this work is synthesizing the concepts to explain why sloppy models pose unique challenges for system identification and why these problems are not shared by unidentifiable models that are not sloppy . Because simple models are not complete , they cannot make accurate predictions for all experimental conditions . Of course , it is possible to extend a model by including more details in order to extend its range of validity . In principle , a single , monolithic model could accurately predict the outcome of all possible experiments . This possibility underlies the concept of a sloppy system . Microscopically complete models effectively act as numerical experiments and are a precursor to a more complete theory . In advancing to a more complete understanding of a system , we believe it is useful to consider multiple models of varying complexity and try to understand their limitations . Simultaneously considering multiple representations creates a rich and insightful theory into the mechanisms driving behavior that allow for abstraction and generalization . We believe that accounting for the approximations and context of a model are essential for successful modeling . In this paper we have proposed a “sloppy system hypothesis . ” We speculate that the prevalance of sloppy models in complex biological systems ( and other areas of science ) is not due to a limitation in the measurement structure of the system , but reflects a property intrinsic to the system itself . Because many complex systems lack an intrinsic scale separation ( i . e . , “small parameter” as discussed in the introduction ) , there is no mechanism whereby irrelevant details are necessarily suppressed in the model . Consequently , corrections to the mathematical model are relevant at all scales so that an accurate model will necessarily have several “marginal parameters” as in Fig 2 . This hypothesis suggests that there is a fundamental limitation of optimal experimental design in sloppy systems due to these marginal parameters; attempting to constrain the marginal parameters of a model of a sloppy system reduces the accuracy of the model and limits its predictive ability . We have demonstrated this phenomenon on two complex biological systems , EGFR signaling and DNA repair . Mathematical modeling in the face of structural uncertainty is a problem of growing importance across science [44–49] . Because mathematical models by their very nature are not exact replicas of physical processes , it is essential that they include the physical details relevant to the behavior of interest . In some branches of science , most notably several areas in physics , the equations governing some phenomena are sufficiently well-understood that numerical simulations come very near to being surrogates for real experiments . When this is the case , accurate parameter estimates reduce uncertainty in the model’s predictions . However , in many areas of complexity science , particularly for systems with fewer symmetries and less homogeneity , which physical details are relevant for explaining a particular behavior remains the theoretical bottleneck . Our results suggest that there is a need for better understanding of and accounting for the approximations in complex models . In particular , optimal experimental design methods should limit their search space to those experiments for which the model is an accurate approximation . In spite of the growing documentation of microscopic biological mechanisms , it is difficult to predict how the errors introduced by a given approximation ( such as the steady state approximation ) will propogate to the predictions for the system’s collective behavior . In other words , it is difficult to know a priori which mechanisms are relevant for a particular behavior . We believe that better quantification of uncertainty will enable improved methods of experimental design and the development of accurate models for predicting behavior in complex systems .
Animals were maintained in an Association for Assessment and Accreditation of Laboratory Animal Care approved facility , and in accordance with current regulations of the United States Department of Agriculture and Department of Health and Human Services . The experimental protocol was approved by , and in accordance with , institutional guidelines established by the Institutional Animal Care and Use Committee . UT MD Anderson Cancer Center ACUF #: 00001061 RN00 Date Approved: 1/24/2014 Expiration Date: 1/2/2017 We use the model of EGFR signaling due to Brown et al . [7] formulated in terms of approximate Michaelis-Menten kinetics . We also constructed a similar model using mechanistic mass action kinetics . This replaces each approximate Michaelis-Menten reaction with two mass-action steps: E + S ⇌ ES → E + P . We first model each chemical reaction as an enzyme and substrate reversibly binding into an enzyme-substrate complex , and then dissociating to yield the original enzyme and the product . This gives four nonlinear ordinary differential equations ( ODEs ) for each enzyme substrate ppreaction , including one each for the changes in concentration of the enzyme , the substrate , the enzyme-substrate complex , and the resulting product . In total , modeling the EGFR network using the same topology as the Brown model by means of mechanistic mass action kinetics requires 54 independent , nonlinear ODES with 70 parameters . These equations are given in the supplement along with an sbml implementation of the mechanistic model and are available on github [60] . All models were simulated using in-house C , FORTRAN and python routines , including methods to automatically calculate parameter sensitivities , included in the supporting information . We use the approximate Michaelis-Menten model to simulate the original seven laboratory experiments performed by Brown et al . using parameter values from reference [7] . We then add random noise to results of this simulation and treat the results as if they were actual laboratory results for the experiments . Finally , we fit this data to the mechanistic mass-action model using the geodesic Levenberg-Marquardt algorithm [21 , 61] . In order to help avoid complications in which the fit is prematurely stuck at manifold boundaries ( as described in [21] ) , all fits were done with regularizing terms to keep parameters from drifting to infinite values . We use regularizing terms that take the form wi ( log xi/xi0 ) 2 for each parameter xi . Fits were repeated for many different values of xi0 ( i . e . , the point at which the regularization was centered ) and weights wi . Weights were varied over four orders of magnitude ( 0 . 01 to 100 ) , and we observe that our final fits were robust to these choices , suggesting that our results are not an artifact of having converged to a local optimum . Using the mechanistic model with parameters from fitting the Brown experiments , we simulate the five experimental conditions proposed by Apgar et al . [13] and add random noise . We then fit the approximate model to the these data as before . Experimental methods are the same as in reference [55] . C3Hf/KamLaw mice were exposed to whole body irradiation using 300 kVp X-rays at a dose rate of 1 . 84 Gy/min , and the number of viable jejunal crypts was determined using the microcolony assay . 14 Gy total dose was split into unequal first and second fractions separated by 4 h . Data were analyzed using the LQ model , the lethal potentially lethal ( LPL ) model , and a repair-saturation ( RS ) model . | Sloppy models are often unidentifiable , i . e . , characterized by many parameters that are poorly constrained by experimental data . Many models of complex biological systems are sloppy , which has prompted considerable debate about the identifiability of parameters and methods of selecting optimal experiments to infer parameter values . We explore how the approximate nature of models affects the prospect for accurate parameter estimates and model predictivity in sloppy models when using optimal experimental design . We find that sloppy models may no longer give a good fit to data generated from “optimal” experiments . In this case , the model has much less predictive power than it did before optimal experimental selection . We use a simple hyper-model of model error to quantify the model’s discrepancy from the physical system and discuss the potential limits of accurate parameter estimation in sloppy systems . | [
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] | 2016 | The Limitations of Model-Based Experimental Design and Parameter Estimation in Sloppy Systems |
Cats ( Felis catus ) are reservoirs of several pathogens that affect humans , including Toxoplasma gondii . Infection of pregnant women with T . gondii can cause ocular and neurological lesions in newborns , and congenital toxoplasmosis has been associated with schizophrenia , epilepsy , movement disorders , and Alzheimer’s disease . We compared seroprevalence of T . gondii and risk factors in people on seven islands in Mexico with and without introduced cats to determine the effect of cat eradication and cat density on exposure to T . gondii . Seroprevalence was zero on an island that never had cats and 1 . 8% on an island where cats were eradicated in 2000 . Seroprevalence was significantly higher ( 12–26% ) on the five islands with cats , yet it did not increase across a five-fold range of cat density . Having cats near households , being male and spending time on the mainland were significant risk factors for T . gondii seroprevalence among individuals , whereas eating shellfish was protective . Our results suggest that cats are an important source of T . gondii on islands , and eradicating , but not controlling , introduced cats from islands could benefit human health .
Cats ( Felis catus ) are reservoirs of many pathogens that affect humans , including the parasite Toxoplasma gondii [1] . Cats are also the second most widespread introduced predator found on islands [2 , 3] , and have contributed to 14% of global bird , reptile and mammal extinctions on islands [2] . The dual impact of introduced cats on wildlife and human health increases the potential benefits of eradicating cats from islands [4 , 5] . Introduced cats have been eradicated for conservation reasons from 80 islands globally [6] , resulting in rapid recoveries of native species on many of those islands [3] . Fifteen of these cat eradications were on islands with permanent human settlements [6] . A key gap in our knowledge is whether eradication or control of introduced species also result in public health benefits . Toxoplasmosis is one of the most widespread zoonotic diseases with a significantly greater burden in low-income countries , and cats are a key reservoir host [7] . Domestic cats and wild felids are the only known definitive hosts for T . gondii [8] . Cats can become infected after ingesting T . gondii bradyzoites found in tissue cysts of infected intermediate hosts ( i . e . prey such as rodents or birds ) [9] . Acutely infected cats host the sexual cycle of the parasite and subsequently shed millions of T . gondii oocysts in their feces [9–11] , thereby contaminating the soil or bodies of water [12] . Oocysts sporulate in the environment and become infectious to intermediate hosts and people [8] . The burden of toxoplasmosis tends to be highest in low-income countries from tropical regions , with prevalence rates ranging between 35 . 8% and 85 . 4% [13–20] . Women exposed to T . gondii during pregnancy can transmit the parasite to their fetus , which can lead to miscarriage or congenital toxoplasmosis [7] . Congenital toxoplasmosis can result in severe ocular and neurological lesions in newborns [21–23] and has been linked to schizophrenia , epilepsy , movement disorders and Alzheimer’s disease [24 , 25] . Furthermore , T . gondii infection can be acquired postnatally leading to vision loss [23] and systemic disease in immunocompromised individuals [26] . There is currently no vaccine against T . gondii and treatment is commonly restricted to acute infections , particularly for women infected during pregnancy or immunosuppressed patients [10 , 21] . Most islands do not have native felid species [27] , creating the potential to reduce the burden of T . gondii infection in people living on islands by reducing or eliminating introduced cat populations . Local sources of T . gondii on islands may include contact with shedding cats , oocyst-contaminated soil or consumption of local shellfish that have been contaminated by runoff that carries T . gondii oocysts from land to sea [28 , 29] . External sources of infection include consumption of contaminated meat and vegetable products that are imported from the mainland , and exposure during travel to a region where T . gondii is endemic . Although there have been many studies that attempt to correlate prevalence of T . gondii in soil , or T . gondii seroprevalence in pigs , humans , rodents , or cats with some measure of cat abundance or exposure [30–39] , only one of these studies [32] estimated cat density using a standardized approach , and none attempted to determine the quantitative relationship between cat density and T . gondii exposure in humans . This relationship is needed to determine how low cat density must be reduced to achieve a reduction in T . gondii exposure in humans . Thus , our goal was to determine if reducing or eliminating populations of introduced cats could reduce T . gondii exposure in human populations on islands . We examined risk factors and the seroprevalence of T . gondii exposure in people on seven human inhabited islands located off the coast of the Baja California Peninsula , Mexico ( Fig 1 ) . These islands do not harbor native felids and have a range of introduced cat densities , including one where cats have never been present and another where cats were present but eradicated in 2000 .
We systematically visited households and we visited community aggregation centers ( i . e . schools , convenience stores , administrative offices of the fish cooperatives and a gypsum mine ) , where we distributed information pamphlets about the study . To determine T . gondii seropositivity and examine possible risk factors we collected blood samples and applied a questionnaire to people who had given informed consent , and in the case of underage children , if they were accompanied by their parent or tutor and had given informed consent . We used the fingerprick method to collect approximately 10 μL of blood on Guthrie cards ( filter paper ) and refrigerated cards at 4°C until we analyzed them at the Laboratorio de Inmunología Experimental of the Instituto Nacional de Pediatría , México . We tested all samples for presence of IgG antibodies against T . gondii using an indirect ELISA [44] . We determined the cutoff value for seropositivity in each ELISA run as the average optical absorbance of the negative controls plus three standard deviations of the absorbance from negative samples [45] . We ran each sample in duplicate and considered it as positive if the average optical absorbance was greater than the cutoff value . We estimated crude T . gondii antibody prevalence and created age-adjusted estimates using the direct method [46] with the age-structure of the 2015 population of the States of Baja California and Baja California Sur , Mexico [47] . To examine associations between risk factors and T . gondii exposure , we interviewed participants using a standardized questionnaire [48 , 49] adapted to the social context of communities in the islands of Baja California ( S1 Appendix ) . For each individual we recorded gender; age; educational level; source of drinking water; whether they had contact with soil through outdoor activities; consumption of raw or undercooked meat or poultry; annual frequency of meat ( including poultry and pork ) , and shellfish consumption; annual frequency of travel outside the island; and fraction of time spent outside the island . We asked people if they had cats; whether cats were allowed indoor , outdoor , or both; if they were in contact with cat feces when cleaning their household; and we asked them to estimate the number of cats observed near their house . To better understand cat ownership and the relationship between people and cats in the islands , we asked people if they fed the cats that roamed near their household and whether they fed them food scraps or cat food . We also asked people whether they owned a dog , and if it was allowed indoor , outdoor , or both , as dogs may act as carriers of T . gondii oocysts in their fur [50] . We excluded water as a potential source of T . gondii exposure because all islands obtain water from local desalination plants or from fishing boats with desalination equipment ( El Pardito ) , from which water is delivered to each household through water pipes or barrels . We interviewed and collected blood samples from all 13 residents on El Pardito , and 59–325 participants on each of the six remaining islands ( representing 25–75% of each population ) , for a total of 724 participants of ages 9 to 70 ( Table 1 and S2 Appendix ) . We used distance sampling along transects ( 178–433 meters in length ) on each island to measure cat density ( S3 Appendix ) [51] . We placed transects in human-occupied areas , including main roads in towns . We walked transects between 30 and 90 minutes after dawn on each of two consecutive days , counted all cats , and estimated the distance to each cat with the aid of a rangefinder ( Bushnell Yardage Pro Sport 450 ) . We used the ds function in the Distance package in R [52 , 53] to estimate cat density on each island . We selected the best fitting detection function on each island using Akaike’s Information Criterion with correction for small sample sizes ( AICc ) and used the Cràmer-von Mises test to assess the goodness of fit of the best fitting function [53] ( S3 Appendix ) . To examine the relationship between T . gondii seroprevalence and cat density among islands , we fit a nonlinear saturating function ( Seroprevalence = Yint+c0* ( 1-e ( -c1*Cat Density ) ) to the data with a binomial distribution using the mle2 function in the bbmle package in R . In this model , Yint is the seroprevalence when cat density is zero , ( c0+Yint ) is the asymptote ( the seroprevalence at high cat densities ) , and c1 is the slope parameter describing the increase in seroprevalence with cat density . We used R version 3 . 3 . 3 to run all statistical analysis [52] . We used logistic regression models to compare age-adjusted seroprevalence among islands . We used Fisher’s Exact Tests to compare seroprevalence among islands for children born after 2000 , when cat eradication took place in Natividad . For the risk factor analysis , the predictor variables associated to cats were correlated ( r>0 . 3 ) . To avoid including many collinear variables , we ran two generalized linear mixed effects models ( with a binomial distribution and a logit link ) including data from all the islands , with island as a random effect . In the first model , we included the presence/absence of cats reported near households , and all non-cat related predictor variables , but removed all other variables related to cats . We then fit a second model for the subset of people that reported having cats near their households that included all non-cat related variables , exposure to cat feces and the number of cats a person reported having near their house . All research was performed under the human subjects protocols CONBIOETICA02CEI00520131206 and CONBIOETICA03CEI00120131203 approved by the Human Subjects Research review committees of the State of Baja California and Baja California Sur , Mexico , and protocol HS2385 approved by the Office of Research Compliance Administration of the University of California Santa Cruz . All adult subjects provided written informed consent , and in the case of underage children who participated in the study , a parent or guardian provided written informed consent on the child’s behalf .
We sampled a total of 724 participants of ages 9 to 70 ( Table 1 and S2 Appendix ) . All 13 inhabitants from El Pardito , where cats have never been present , were seronegative for T . gondii IgG antibodies . The age-adjusted seroprevalence was 1 . 8% on Natividad , where cats were eradicated in the year 2000 . Age-adjusted seroprevalence on the remaining five islands varied from 11 . 6% to 25 . 7% , which was significantly higher than on the two cat-free islands ( Table 1 and Fig 2 ) . Seroprevalence varied with age across the islands , with seroprevalence being significantly greater in age groups 26–35 , and 46 and older , than 9–16 year olds ( Fig 2 and Table 2 ) . The island where cats were eradicated ( Natividad ) had significantly lower seroprevalence in all age groups compared to most islands with cats . Seroprevalence in the 30 children 9–15 years of age ( who were born after cats were eradicated ) on Natividad was 0% , which was significantly lower than in children of the same age from three other islands ( Fisher’s exact tests: Cedros: 12 . 5% ( 20/160 ) , P = 0 . 04; Guadalupe: 31 . 2% ( 5/16 ) , P = 0 . 01; San Marcos: 21 . 4% ( 6/28 ) , P = 0 . 03 ) but not in the two others where sample sizes were very small ( Margarita: 0% ( 0/3 ) , P = 1; Magdalena: 33 . 3% ( 1/3 ) , P = 0 . 13 ) . In addition , seroprevalence in people born when cats were still present on Natividad ( 16 years and older ) , was significantly lower ( 3 . 1% , 2/64 , 95% CI = 0 . 3–10 . 8% ) than that of people of the same age from islands with cats ( 21 . 87% , 89/407 , 95% CI = 17 . 9–26 . 2% ) . We found that interactions with cats , gender , visitation to the mainland , and diet were important factors influencing T . gondii seroprevalence . The odds of seropositivity to T . gondii were 4 . 8-fold higher in people that had cats near their homes . For the subset of people that reported having cats near their homes , T . gondii seroprevalence decreased with the number of cats reported ( S4 and S5 Appendices ) . The odds of seropositivity were 1 . 6-fold higher in men ( Table 2 ) , 1 . 03-fold higher for every percent increase of time spent on the mainland , and 0 . 97-fold lower for every percent increase of shellfish consumption ( Table 2 ) . The fitted model indicated that seroprevalence of T . gondii was 4 . 3% ( 1 . 7% - 6 . 4% ) higher in men than women for the most common age classes of each island ( S6 Appendix ) . T . gondii seroprevalence increased with cat density , but the best fitting model showed a sharp rise in seroprevalence between zero cats and the lowest non-zero cat density island , and no change thereafter ( Fig 3 ) .
We found evidence that introduced cats are a key source of human exposure to T . gondii on islands and that eradication , but not control , of cats can reduce the burden of this zoonotic disease . Seroprevalence of T . gondii was near or equal to zero and significantly lower on the two islands where cats were absent . Moreover , children born after cat eradication were all seronegative . Further , we found that the odds of being seropositive for T . gondii were nearly fivefold greater for people that reported having cats near their households . However , we found seroprevalence did not increase with overall cat density on islands with cats , and we were surprised to find that for the subset of people that reported having cats near their homes , risk of seropositivity to T . gondii actually decreased with number of cats reported . Although our findings are based on a relatively small number of study populations from a single geographical region , our focus on island populations ( including an island where cats were eradicated ) allowed us to examine the effect of cat presence and density , exposure routes , and age more easily than in mainland populations . Our results suggest that exposure to T . gondii occurs both in children and in young adults , but may have occurred through different exposure routes . Seroprevalence was lowest in 9–15 year-olds ( the youngest group we sampled ) with a predicted seroprevalence across all islands of 14% , which increased to 17 . 0% , 25 . 8% , 20 . 3% , and 24 . 3% in the next four age classes . Studies have suggested that exposure to T . gondii in children occurs via exposure to oocysts when children play with soil that is contaminated with T . gondii [18 , 54 , 55] . Exposure of young adults may occur through ingestion of bradyzoite cysts in raw or undercooked meat and contaminated produce , likely imported from the mainland [18 , 55] . To better understand the main sources of T . gondii infection would require the use of serological tests designed specifically for detecting antibodies against T . gondii oocysts ( e . g . [56] ) . Finally , we also found older individuals from the island where cats were eradicated ( Natividad ) to be seronegative to T . gondii , which may suggest that T . gondii antibodies wane without antigenic stimulation , as has been suggested elsewhere [57] . Gender , diet , and travel also influenced the risk of T . gondii exposure . Men had higher risk of T . gondii seropositivity than women , suggesting that men may engage in activities that increase risk of exposure to T . gondii ( S6 Appendix ) . We also found that spending time on the mainland was an important risk factor for being exposed to T . gondii . People may be exposed to contaminated soil , meat or vegetables when traveling to the mainland . The majority of people ( 83 . 3% ) reported travelling to the northwestern states of Mexico , where the average prevalence of T . gondii is 39 . 9% ± 12 . 9 ( Range 20–59 . 9% ) [58] . In contrast , eating shellfish was associated with reduced T . gondii seroprevalence . How shellfish consumption reduces T . gondii seroprevalence is unknown , because consumption of shellfish was positively ( r = 0 . 23; N = 724; P < 0 . 001 ) , not negatively correlated with meat , poultry and pork consumption ( potential sources of T . gondii [10 , 58 , 59] ) . Our finding that T . gondii seroprevalence decreased with the number of cats reported for people with cats near their homes suggests that greater cat abundance reduces T . gondii transmission . While this may seem counterintuitive , higher numbers of cats near a home could actually decrease rodent populations through predation as well as impose sub-lethal effects on rodents through fear and lower fecundity [60] . Reduced rodent density could reduce exposure of T . gondii in cats by interrupting the predator-prey transmission route of the parasite [9] . Determining whether increased cat abundance reduces transmission of T . gondii by reducing rodent abundance would require measuring T . gondii shedding ( or at least seroprevalence ) in cats as well as rodent abundance across a range of cat densities . Regardless , management or eradication of cat populations should also incorporate management of rodent populations to avoid an increase in rodents as cat predators are removed [61 , 62] . Increases in rodent population could lead to increasing outbreaks of rodent-borne diseases as well as increased rodent predation on native species [61 , 62] . Interestingly , we found no increase in seroprevalence with cat density among islands where there were cats . Initially , this appears to contrast with several studies that have found higher T . gondii seroprevalence in pigs , or humans or T . gondii in soil in areas with “high” cat density than “low” cat density , or nearer to farms with cat populations than farther away [31–35 , 37–39] . However , none of these studies examined seroprevalence across a continuous range of cat densities ( all treated cat abundance as a categorical variable ) , and the combination of multiple T . gondii exposure routes ( e . g . soil , food , direct contact with cats ) makes studies of human exposure critical . As a result , the actual relationship between cat density and T . gondii transmission to humans is very poorly understood . The lack of a relationship between cat density and T . gondii seroprevalence on islands with cats could result from focal aggregation of T . gondii-contamination in common latrine areas [32 , 35 , 63] where cats defecate , but only limited T . gondii-contamination outside these areas . Spatial sampling of T . gondii in soil at sites across a range of cat densities would provide data to test this hypothesis . In addition , our estimates of cat density came from a single point in time , and cat densities have likely varied over time . Temporal variation in cat density would make it more difficult to detect a relationship between cat density and seroprevalence . Likewise , the demographic structure of cat populations , which can also vary temporally and as a result of cat population control [62] , may also influence transmission dynamics among cats and subsequently exposure to people . This is because kittens lose maternal antibodies against T . gondii after being weaned and as they begin to consume potentially infected intermediate hosts they are more likely to become infected and shed T . gondii oocysts [9 , 64] . Overall , our results suggest that there are opportunities to achieve measurable public health benefits from cat eradications on islands . In contrast , we found little evidence to indicate that controlling cat abundance on islands is an effective tool to reduce human T . gondii exposure . It remains to be determined how T . gondii transmission to humans varies with cat density in continental populations , and whether control of feral cat colonies will result in public health benefits without complete or near eradication . Regardless , eradicating zoonotic diseases such as T . gondii by eliminating their introduced reservoir hosts is much more feasible on islands than in continental populations , and eradicating introduced cats from islands contributes to a “One Health” approach in that this intervention simultaneously benefits human health and native biodiversity [42 , 65 , 66] . | Infection with T . gondii can cause miscarriage or severe ocular and neurological lesions in newborns , systemic disease in immunocompromised individuals , and has been linked to mental disorders and neurodegenerative diseases such as schizophrenia , Alzheimer’s and movement disorders in adults . On the majority of islands , introduced cats are the only species capable of maintaining the sexual phase of the life cycle of Toxoplasma gondii . Introduced cats on islands are also responsible for 14% of all bird , mammal and reptile extinctions . Their management , which has been implemented as a biodiversity conservation measure , has the potential to reduce or eliminate the burden of diseases caused by T . gondii in island communities via control of its definitive host . To examine if management of introduced cats could reduce risk of infection with T . gondii , we compared the seroprevalence and risk factors associated with T . gondii exposure in people on seven islands with variation in cat density , including one island in which cats were eradicated in the year 2000 , and another island in which cats had never been present . We found that eradication of introduced cats on islands could significantly reduce human risk of exposure to T . gondii . Seroprevalence of T . gondii was zero on the island that never had cats and near zero on the island where cats were eradicated . Furthermore , all island resident children born after cats were eradicated showed no evidence of exposure to the parasite . The odds of seropositivity to T . gondii were nearly five-fold higher in people that had cats near their homes . On islands with cats , we found no association between local cat density and T . gondii seroprevalence , suggesting that complete eradication rather than control of cat population densities is necessary to reduce public health impacts of toxoplasmosis . | [
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] | 2019 | Potential public health benefits from cat eradications on islands |
Previous genome-wide association studies ( GWAS ) have identified hundreds of genetic loci to be associated with body mass index ( BMI ) and risk of obesity . Genetic effects can differ between individuals depending on lifestyle or environmental factors due to gene-environment interactions . In this study , we examine gene-environment interactions in 362 , 496 unrelated participants with Caucasian ancestry from the UK Biobank resource . A total of 94 BMI-associated SNPs , selected from a previous GWAS on BMI , were used to construct weighted genetic scores for BMI ( GSBMI ) . Linear regression modeling was used to estimate the effect of gene-environment interactions on BMI for 131 lifestyle factors related to: dietary habits , smoking and alcohol consumption , physical activity , socioeconomic status , mental health , sleeping patterns , as well as female-specific factors such as menopause and childbirth . In total , 15 lifestyle factors were observed to interact with GSBMI , of which alcohol intake frequency , usual walking pace , and Townsend deprivation index , a measure of socioeconomic status , were all highly significant ( p = 1 . 45*10−29 , p = 3 . 83*10−26 , p = 4 . 66*10−11 , respectively ) . Interestingly , the frequency of alcohol consumption , rather than the total weekly amount resulted in a significant interaction . The FTO locus was the strongest single locus interacting with any of the lifestyle factors . However , 13 significant interactions were also observed after omitting the FTO locus from the genetic score . Our analyses indicate that many lifestyle factors modify the genetic effects on BMI with some groups of individuals having more than double the effect of the genetic score . However , the underlying causal mechanisms of gene-environmental interactions are difficult to deduce from cross-sectional data alone and controlled experiments are required to fully characterise the causal factors .
Gene-environment interactions result from individuals responding differently to environmental stimuli depending on their genotype , or from genetic effects that vary between groups of individuals depending on their lifestyles . In humans , the most famous examples include skin color and risk of melanoma in response to ultra-violet rays , and phenylketonuria ( PKU ) in response to foods containing phenylalanine in individuals who carry mutations that lead to phenylalanine hydroxylase deficiency [1] . Gene-environmental interactions are likely to exist for complex human traits and identifying gene-environment interactions can potentially improve risk-assessment for disease and help unravel underlying biological pathways [1] . Obesity and being overweight are serious public health issues due to their strong associations with diseases such as cardiovascular disease , type 2 diabetes and cancer . In addition , their global prevalence has increased dramatically over the latter part of the 20th century and up to the present day [2] . Body mass index ( BMI ) is a standardised measure of human body size that is calculated from weight and height . Twin studies have demonstrated a heritable component of BMI and genome-wide association studies ( GWAS ) have shown that BMI is influenced by hundreds of common genetic variants [3–5] . Recently , a GWAS for BMI on 339 , 224 individuals , reported 97 genetic loci to be associated with variation in BMI [4] . However , only a few studies have investigated the effect of gene-environment interactions on BMI . Previous studies have reported physical activity to attenuate the effect of genetic factors on BMI , including the effects of genetic variants within the FTO locus [6–9] . Identification of gene-environment interactions for complex human traits poses several challenges . For instance , most GWAS of complex traits have been performed by large-scale meta-analyses of multiple cohorts , which complicate a harmonised collection of lifestyle and environmental data . Also , the effects of genetic variants identified through GWAS are generally small [4] , and differences in the effects of genetic variants between groups exposed to different lifestyle factors may be difficult to detect in smaller cohorts due to lack of statistical power . Initiatives such as the UK Biobank provide a unique opportunity to study interactions between genetic and lifestyle factors . Data collection in UK Biobank has been performed in a standardised manner and data include a large number of lifestyle and environmental factors collected from approximately half a million UK citizens , as well as comprehensive , genome-wide genotyping [10] . Recent studies on the UK Biobank found that the effect of the FTO locus variant , rs1421085 , interacts with several lifestyle risk factors such as alcohol consumption , sleep patterns , diet and physical activity [9] . Another recent study on the UK Biobank determined that the effect of a genetic risk score for BMI is modified by socioeconomic status [11] . Here , we study the effects of gene-environment interactions on BMI , by analysing 131 lifestyle factors assessed by touchscreen questionnaires . These factors include diet , smoking , alcohol consumption habits , physical activity , socioeconomic status , mental health , sleep , general health as well as factors that are specific to females such as number of live births . For the purpose of our analyses , we constructed a genetic score for BMI ( GSBMI ) which was composed of 94 single-nucleotide polymorphisms ( SNPs ) that have previously been associated with BMI in a GWAS [4] .
We utilised data from the UK Biobank Resource ( http://www . ukbiobank . ac . uk/about-biobank-uk/ ) [10] for all analyses . UK Biobank has recruited more than 500 , 000 individuals aged 40–69 from the United Kingdom during the years 2006–2010 . Participants underwent standardised measurements of anthropomorphic traits , and additionally provided biological samples and detailed information about themselves via touchscreen questionnaires . Genotyping had been performed using two custom-designed UK Biobank Axiom Arrays with 820 , 967 and 807 , 411 SNPs respectively ( BiLEVE and Axiom ) . Genotypes that were not directly assayed had been imputed [12] using a combined set consisting of the UK10K [13] haplotype reference panel and the 1000 Genomes phase 3 reference panel [14] . We utilized the initial release of genotype data ( data accessed January 2016 ) as a discovery cohort , and the remaining participants with genotype data available in the second release as a replication cohort ( data accessed July 2017 ) . In the initial release , data were available for 73 , 355 , 667 SNPs in 152 , 249 UK Biobank participants . To identify related individuals , we used information provided by UK Biobank ( Data-Field: 22011—Genetic relatedness pairing ) . Briefly , kinship coefficients had been calculated for each pair of participants in the cohort using the genetic data and pairs of related individuals had been identified ( at least 3rd degree relatives = kinship coefficient > 0 . 044 ) . In addition , only people who self-identified as white British ( Data-Field 21000 ) and that were classified as Caucasians based on the genetic principal components ( Data-Field 22006 ) were included . After filtering , 116 , 138 individuals remained for the analysis in the discovery cohort . The same filtering was applied to the replication cohort , leaving 246 , 358 participants for the replication . All participants had provided signed consent to participate in UK Biobank [15] . UK Biobank has been given ethical approval to collect participant data by the North West Multicentre Research Ethics Committee , which covers the UK; the National Information Governance Board for Health & Social Care , which covers England and Wales , and the Community Health Index Advisory Group , which covers Scotland . UK Biobank possesses a generic Research Tissue Bank approval granted by the National Research Ethics Service ( http://www . hra . nhs . uk/ ) , which lets applicants conduct research on UK Biobank data without obtaining separate ethical approvals . Access to UK Biobank genetic and phenotypic data was granted under application no . 15152: “Interaction between diet , food preference and lifestyle with genetic factors influencing body mass , body adiposity and obesity” . Written consent was obtained from all participants . Participants’ weights were assessed by a variety of means during the initial UK Biobank assessment centre visit . For weight , we utilised data-field 21002 , which is an amalgate of all weight values into a single item . Standing height was measured on a SECA 240 Height Measure . BMI was constructed from height and weight measurements during participants’ initial visit to assessment centers . For most analyses , BMI was transformed using rank-based inverse normal transformation . Lifestyle factors have primarily been collected via self-report touchscreen questionnaire . All lifestyle variables that had been assessed in more than 20 , 000 of the participants were included for analyses . This resulted in 131 quantitative , ordinal and categorical measurements of lifestyle factors representing dietary habits , general health , sleep , smoking and alcohol consumption , physical activity , mental health and socioeconomic status ( S1 Table ) . We aimed to use linear regression models to test for interaction between lifestyle factors and genetic factors on BMI . To this end , “Prefer not to answer” and “I don’t know” were set to “missing” in our analyses . We removed the 99th percentile of quantitative phenotypic variables , such as , for example: “Average weekly red wine intake” in number of glasses , and “Duration of moderate physical activity” in minutes , to reduce the effect of outliers . We analysed ordinal phenotypic data as quantitative variables . For example , data-field 1558—frequency of alcohol intake: which is coded as: 1 = “Daily or almost daily” , 2 = “Three or four times a week” , 3 = “Once or twice a week” , 4 = “One to three times a month” , 5 = “special occasions only” , 6 = “Never” . Data field 20126 represents bipolar and major depression status among participants . This variable was derived from self-report questionnaire data [16] . Very few patients were assessed to have bipolar disorder type I and II ( n = 808 & 807 respectively ) and these were designated missing . Severity of depression was assessed as 0 = “No depression” , 1 = “Single probable major depression episode” , 2 = “Probable recurrent major depression ( moderate ) ” , 3 = “Probable recurrent major depression ( severe ) ” . Categories 1 to 3 were combined and this field was recoded as “No depression” = 0 , and “Probable depression” = 1 . ‘Had menopause’ ( Data-field 2724 ) was recoded to better represent linearity: participants who were uncertain due to having undergone a hysterectomy were designated “missing” . Data field 680: “Own or rent accommodation lived in” , was recoded to better represent linearity with regard to socioeconomic status: 1 = “Own outright” , 2 = “Own with mortgage” , 3 = “Rent , from local authority” , 4 = “Rent , from private landlord or letting agency” . Categories 5: “Pay rent and part mortgage ( shared ownership ) ” and 6: “Live in accommodation rent free” , were set to missing due to the low number of participants in these groups ( N = 303 and N = 735 , respectively ) . Self-reported drinking habits were converted to amounts in ml alcohol per week using standard sizes for serving and percentages: red wine—125 ml per glass , 13 . 5% alcohol; white wine– 125 ml per glass , 12 . 0% alcohol; beer and cider– 570 ml per pint , 5 . 5% alcohol; spirits– 30 ml per measure , 41 . 5% alcohol; fortified wine– 58 ml per glass , 19% alcohol . Amounts of exercise per week for specific exercise-types , e . g . , “10+ minute walks” , “walking for pleasure” , “moderate physical activity” , and “vigorous physical activity”; were calculated by multiplying the exercise frequency per week with the duration of activity in minutes . Genotype data for 97 SNPs that have previously been identified to be associated with BMI [4] were considered for our analyses ( S2 Table ) . One SNP , rs2033529 was not part of the UK biobank dataset and was replaced by another linked SNP rs751414 ( r2 = 0 . 99 , D’ = 1 ) . Three SNPs were removed due to deviation form Hardy Weinberg equilibrium , which left 94 SNPs for the analyses . Since many of these variants have not been replicated in an independent cohort , we first tested for association in the initial release of genotype data from the UK Biobank cohort . This was done using linear regression models with BMI as a response variable . BMI was first transformed using a rank-based inverse normal transformation similar to the discovery study [4] . The UK Biobank participants were genotyped on two different genotyping arrays: ( BiLEVE and Axiom ) , and a variable to adjust for this was included as a covariate in addition to sex , age , age2 and the first 15 genetic principal components ( PCs ) . Some of the SNPs were identified as being associated with BMI in females or males separately in the previous study [4] . We therefore also tested for association in males and females separately and compared whether there was a significant difference in the estimates between males and females . Genotype data was used in dosage format , where SNP genotypes were represented by the number of copies of the effective allele . To calculate GSBMI , regression coefficients ( β-estimates ) were retrieved from the GIANT consortium meta-analysis for BMI for the European populations with males and females combined [4] . Weighted GSBMI were then calculated for each individual by multiplying the number of effective alleles for each of the 94 SNPs ( all SNPs in HWE in UK biobank ) with the respective β-estimates ( i . e . , β^SNP , i , i=1 , … , 94 ) and calculating the sum over all SNPs ( S2 Table ) : GSBMI=∑i=194βSNP , i*SNPi ( 1 ) Linear regression modeling was used to estimate the effect of gene-environment interaction ( GSBMI × E ) on BMI , for 131 lifestyle factors ( E ) separately . In addition to the GSBMI × E interaction term , each of the 131 models was adjusted for covariates: age , age2 , sex , PCs , and genotyping array ( batch ) . Interaction terms for GSBMI with age , age2 , and sex as well as interaction terms for the lifestyle factor with age , age2 , and sex were also included in order to properly control for possible confounding effects of these interactions , in accordance with previously published recommendations [17] , such that: BMI=β0+β1GSBMI+β2E+β3GSBMI×E+β4Age+β5Age2+β6Sex+β7GSBMI×Age+β8GSBMI×Age2+β9GSBMI×Sex+β10E×Age+β11E×Age2+β12E×Sex+β13Batch+∑i=115βPC , iPCi+ε ( 2 ) We assume that the error term ε ~ NID ( 0 , σ2 ) . The models also included 15 principal components ( PCs ) to account for effects of population stratification in UK Biobank . In the primary analyses , models for each of the 131 lifestyle factors were analysed separately . The aim of this study was to investigate the effect of the interaction term GSBMI×E on BMI . For this purpose , we focused our attention on the estimate of the coefficient β3 in ( 2 ) , and more specifically whether this estimate significantly deviated from zero . The null hypothesis H0: β3 = 0 was either accepted or rejected , depending on the outcome of a two-sided marginal student’s t-test , which in this case ( i . e . , one degree-of-freedom difference between the nested models and normal regularity conditions ) is equivalent to a likelihood-ratio test of the hypothesis H0: β3 = 0 . P-values lower than the significance level α = 0 . 05/131 ≈ 3 . 82*10−4 were considered significant to account for the family-wise error rate using the Bonferroni method . Interaction effects that were considered significant in the discovery cohort were then tested in the replication cohort using the same covariates as well as 15 PCs . Calculations were performed in R version 3 . 3 . 0 [18] using the “lm” function included in the stats package . In order to visualise and make it easier to interpret the significant interactions , we also estimated the effect of GSBMI and of individual SNPs on BMI in different subgroups with regards to lifestyles , e . g . , the genetic effect in participants with different frequencies of alcohol consumption . In these analyses , linear regression was performed in the subgroups using the same covariates as above , but with untransformed BMI values as a response variable , for easier interpretability of the regression coefficients ( presented in kg/m2 ) . Here the differences in effect between the subgroups reflect the interaction term from the previous analyses . Interactions were visualised with bar graphs using the ggplot2 package in R . We also used the plot3D R package to visualise interactions in 3D-plots . Genetic variants within intron one of FTO have consistently been shown to be the strongest genetic factors associated with BMI [3–5 , 19 , 20] . We therefore constructed a genetic score that excluded the FTO-linked SNP rs1558902 ( GSBMI’ ) , and performed linear regression modeling in exactly the same manner as previously . We also performed additional analyses to assess how individual SNPs interacted with lifestyle factors . SNP-interactions that were considered significant in the discovery cohort were further tested in the replication cohort . We also performed sensitivity analyses by including TDI and its interactions with age , age2 , sex , GSBMI , as well as each of the lifestyle factors in the model , in addition to the variables described in Eq ( 2 ) . For calculating GSBMI , we used the regression coefficients from the GIANT consortium . However , in a discovery GWAS , the regression coefficients are often overestimated , which will introduce a bias in the GSBMI . For this reason , we also performed additional analyses using the regression coefficients estimated in UK Biobank , for the sake of comparison . To determine which of the interacting lifestyle factors had an independent contribution in the regression model for BMI , we performed stepwise linear regression ( SLR ) using the ‘step’ function included in the ‘stats’ package in R [18] . This function uses the Akaike information criterion ( AIC ) to select variables for the model . A base-model for BMI was constructed that included GSBMI , age , age2 , sex , a batch variable to control for the two genotyping platforms used in UK Biobank , as well as 15 principal components . Variables that were significant after replication were included in SLR . SLR was performed using ‘both’ directions so that variables were either added or dropped depending on how they improved AIC . The process is repeated until no improvement in AIC can be made . Individuals with any missing data were excluded from the analyses , and in order to maintain a large sample size for the analyses , we performed SLR on a combined set of the discovery and replication cohorts . Individuals with missing data in any of the tested factors were excluded before running SLR , which resulted in 290 , 441 participants remaining after filtering . All secondary interactions between variables were included in the analysis to control for potential confounding , in accordance with recommendations by Keller [17] .
To test whether interactions were driven primarily by the FTO-SNP rs1558902 , we constructed a genetic score for BMI with rs1558902 excluded ( GSBMI’ ) . In the discovery analysis , all Bonferroni significant interaction terms from the previous analyses remained significant when we used GSBMI’ , except for smoking status and number of treatments/medications taken ( Table 2 , S4–S9 Tables ) . Effect estimates for all significant interactions were in the same direction and within 15% of the interaction effects with the previous GSBMI . We also utilized GSBMI’ in the replication cohort , and replication was successful for all 15 interactions observed with GSBMI , except for frequency of depressed mood and smoking status ( Table 3 ) . We also performed sensitivity analyses by including TDI and its interaction terms as covariates in linear regression models . These results were highly correlated with previous results ( Pearson’s r = 0 . 98 for effect estimates of interaction terms ) . Including TDI as a covariate led to a general slight decrease in effect sizes of interaction terms . The largest decreases were seen for factors related to socioeconomic status ( S11 Table ) and smoking status , which is consistent with the highly significant correlation between these variables and TDI ( S12 Table ) . In the present study , we utilised SNP effect estimates from a previous GWAS by the GIANT consortium [4] to calculate the genetic score for BMI . These estimates may be somewhat overestimated due to the “winner’s curse” [21] . Using overestimated effect sizes results in a GSBMI that is associated with a slightly lower BMI-increase in UK Biobank , compared to when using the correct effect size estimates . This can clearly be seen in our data since a one-unit increase in the GSBMI results in a 0 . 82 unit increase in the rank transformed BMI in UK Biobank . We therefore also tested interaction effects using a genetic score composed of SNP effect estimates calculated in the UK Biobank cohort ( GSBMI_UKBB ) , so that a single-standard-unit increase in the GSBMI_UKBB results in an exactly 1 . 00 unit increase in the rank-transformed BMI , which is 22% higher compared to 0 . 82 for GSBMI . The interaction results were also strongly correlated between using GSBMI_UKBB or GSBMI ( Pearson’s r = 0 . 99 for effect estimates of interaction terms; S11 Table ) . However , the effect estimates for FDR-significant interaction terms were , not surprisingly , on average 16% larger when we utilised GSBMI_UKBB ( S11 Table ) . Several of the exposures that were found to interact with GSBMI in our primary analysis showed highly significant evidence for correlation with one another ( S12 Table ) . In order to identify the most informative interacting variables and interaction terms for a predictive model for BMI , we performed SLR on a combined set of the discovery and replication cohort . SLR was performed in both directions using the 15 lifestyle factors whose interactions with GSBMI were replicated ( Table 3 ) . This resulted in inclusion of 290 , 441 participants with non-missing data when combining the discovery and replication cohorts . The final model generated by SRL included gene-environment interaction terms for ten lifestyle factors ( S10 Table , supplementary Data ) , of which eight were nominally significant: alcohol intake frequency ( p = 2 . 82*10−15 ) , usual walking pace ( p = 1 . 55*10−14 ) , frequency of 10+ minute walks ( p = 6 . 26*10−4 ) , smoking status ( p = 1 . 45*10−3 ) , frequency of vigorous exercise ( p = 2 . 28*10−3 ) , number of vehicles in household ( p = 1 . 03*10−2 ) , TDI ( p = 3 . 02*10−2 ) and frequency of tiredness/lethargy ( p = 3 . 87*10−2 ) and . The adjusted R2 value for the final model was 0 . 1957 .
In this study , we performed a gene-environment interaction study using genetic variants and self-reported lifestyle data . We identified several lifestyle factors that influence the effect of genetic variants on BMI . Interactions were observed for factors related to alcohol intake , physical activity , socioeconomic status , mental health and sleeping patterns . Interactions were seen for factors related to physical activity , where a more active lifestyle attenuated the genetic effects , which is consistent with previous reports [6–9] . Interactions were observed for light , moderate intensity , and vigorous physical activity . However , we observed that the interaction between physical activity and the genetic score was strong for frequencies of physical activity , in contrast to durations in minutes/day . Strong evidence was also observed for an interaction with frequency of alcohol intake . The genetic effect was attenuated with higher frequency of alcohol intake in an almost dose-dependent manner with twice as large effects in non-drinkers compared to daily drinkers . Alcohol consumption is common in western societies , where also most previous GWAS have been performed . Our results indicate that the interaction associated with alcohol intake frequency may have partially attenuated the full effect of BMI-associated genetic variants observed in previous association studies . Alcohol intake frequency was also associated with lower average BMI . This is consistent with clinical reports of lower BMI and fat mass in severely alcoholic patients [22–24] . In vitro and in vivo experiments have also shown ethanol exposure to increase lipolysis and reduce white adipose tissue mass [25 , 26] . This can also be compared to data from the National Institute on Alcohol Abuse and Alcoholism ( NIAAA ) [27] , which suggests that moderate daily consumption of alcoholic beverages , 1–2 drinks per day , reduces the risk of myocardial infarction as well as all-cause mortality [27] . In addition , a cohort study on 38 , 077 male health professionals reported that alcohol consumption frequency , rather than total amounts , was the primary determinant of the inverse association between alcohol consumption and risk of myocardial infarction [28] . Unfortunately , we do not have data on UK Biobank participants’ daily consumption amounts and we are unable to determine how this factors into the association between alcohol consumption frequency , BMI and the interaction between alcohol intake frequency and GSBMI . In a previous study on gene-environment interactions in the UK Biobank , Tyrrell et al . used a genetic score composed of 69 BMI-associated variants to study interactions with measurements of the obesogenic environment , with focus on physical activity , diet and socioeconomic status [29] . Interactions were identified with measurements of physical activity and socioeconomic status ( TDI ) [29] which were consistent with the current study . TDI serves as a proxy for environmental and lifestyle factors that are correlated with income and social position . The study by Tyrell et al . study contrasts the current in the selection of twelve obesogenic factors . The current study instead utilised a hypothesis-free approach to test interactions between GSBMI and 131 factors , which allows us to contrast aspects of the same behaviour , e . g . , between amounts of physical activity and frequency and also gives us the potential to identify new gene-environment interactions . A drawback to this approach is the increased statistical power required in order to correct for the family-wise error rate . In our primary analyses , we have investigated interactions between lifestyle factors and a genetic score composed by 94 independent SNPs , located in different loci . SNPs were combined into genetic scores that explain a greater amount of the variation in BMI compared to the individual SNPs , in order to gain statistical power ( S1 Supporting Information ) . These SNPs have previously been shown to influence BMI [4] . However , combining them into a genetic score before testing for interactions with lifestyle factors assumes that the interaction effect of the BMI-increasing alleles are all in the same direction: e . g . , that alcohol intake frequency decreases the genetic effects of all SNPs rather than some genetic effects being larger among frequent drinkers and others larger among non-drinkers . For alcohol intake frequency , we have the statistical power to detect an interaction if interaction effects in the same direction are present for at least 37 of the SNPs ( S1 Supporting Information ) . However , if some SNPs are interacting in the opposite direction , our power will decrease dramatically ( S1 Supporting Information ) . It is therefore possible that there are gene-environment interactions that are masked by SNPs having interaction effects with the same environmental factor , but in opposite directions . For this reason , we also performed follow-up analyses of individual SNPs . These analyses revealed that the FTO-linked SNP rs1558902 , in addition to interacting with alcohol intake frequency , also interacts with average total household income and physical activity . For BMI , as well as for other complex traits , knowledge on the biological implications of associated genetic variation is limited , which impedes deduction of causal mechanisms underlying gene-environment interactions . The FTO variant , rs1558902 , is associated with the expression of two upstream genes ( IRX3 and IRX5 ) which affect adipocyte “browning” , i . e . the occurrence of thermogenic ‘beige’ adipocytes in white adipose tissue depots . This may partly explain the observed interaction between rs1558902 and frequency of alcohol intake , as in vitro experiments have shown that ethanol exposure interferes with mobilization of glucose transporters to the adipocyte cellular membrane in response to insulin [30] . Beige adipocytes , on the other hand , are able to take up glucose from the circulation in an insulin-independent manner [31] . The altered lipolysis in white adipose tissue due to ethanol exposure , in combination with an altered rate of thermogenesis due to differential propensity for adipocyte browning between individuals with different rs1558902 genotypes may explain the interaction between this SNP and frequency of alcohol intake . Enrichment analyses from previous GWAS have also implicated central nervous system processes to play an important role in BMI [4 , 5] . The central nervous system contains regions that regulate several functions related to BMI , such as appetite , homeostasis , reward , and motivation . Ethanol confers several well-known behavioural effects on humans , but also acts in a bi-phasic manner as a central nervous system stimulant at low doses , and a general depressant at higher doses [32] . BMI-associated genetic variants that affect BMI-related central nervous system function may also factor into the observed interaction between alcohol consumption frequency and GSBMI . A possible limitation to our study is responder bias in the self-report questionnaire data . This may be more likely for factors pertaining to self-image such as alcohol , tobacco use and physical activity . The lack of an interviewing person , and assuring participants of the confidentiality and anonymity of their data aim to reduce the likelihood of responder bias [33] . We tested the validity of factors related to alcohol consumption and physical activity by comparing these to data collected through a 24-hour recall questionnaire . We observe that both frequency and amounts of alcohol consumption , as well as measurements of frequency and duration of physical exercise , agreed well with 24-hour recall data , which supports the validity of these measurements ( S2 Supporting Information ) . In this study , we primarily investigated associations , and the underlying causal mechanisms behind gene-environment interactions are difficult to deduce from cross-sectional data alone . We constructed separate models for each environmental exposure or lifestyle factor . As such , it is important to be aware that confounding effects of factors that are not included in the models , or that are unknown , may be present in the results from these tests . In order to fully correct for confounding factors and correctly characterise causal factors , controlled experiments such as clinical trials in controlled settings serve as the gold standard . We have attempted to correct for confounding by including TDI and the interaction term TDI*GSBMI as covariates in all analyses , which resulted in very little effect on the main results . To identify factors with the highest predictive value , we also performed SLR , which showed evidence for interactions between GSBMI and alcohol consumption frequency , physical activity , smoking , and socioeconomic status all contributed independently to a predictive model for BMI . In conclusion , the standardised collection of genetic and lifestyle data in UK Biobank has enabled us to identify several factors that modify the effect of BMI-associated genetic variants . The most significant interactions were observed between GSBMI and frequency of alcohol intake , frequency of physical activity and socioeconomic status . Previous studies have reported interactions between genetic variants at the FTO locus and environmental factors [6–9] . However , most interactions were still observed even when the FTO locus was excluded from the genetic score , which indicates that the individual interactions are not solely dependent on FTO variants . We can therefore conclude that the presence of genetic interactions is more general and will be identified to a higher degree for individual SNPs once the sample size increases even more and reaches sufficient power . | Genome-wide association studies ( GWAS ) have identified hundreds of genes as being associated with body mass index ( BMI ) . How these genetic effects are modulated by lifestyle factors has not been extensively investigated previously . Here we utilise data from approximately 360 , 000 participants from the UK Biobank , aged 40–69 years old , to identify interactions between genetic and lifestyle factors in relation to BMI . We investigated 131 lifestyle factors , of which 15 influence the genetic effects on BMI . The most significant factors were those related to physical activity , alcohol consumption , and socioeconomic status . For example , the effect of a genetic score for BMI was almost twice as high in participants who reported never drinking alcohol compared to every-day drinkers . Similarly , the effect of the genetic score for BMI was 2 . 5 times higher in participants who reported having a slow walking pace compared to participants who reported having a brisk walking pace . Our results show that many lifestyle factors influence the genetic effects , which suggests that changing our lifestyle may be a way to influence our genetic risk for obesity and other common human disorders . | [
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] | 2017 | Gene-environment interaction study for BMI reveals interactions between genetic factors and physical activity, alcohol consumption and socioeconomic status |
Gamma rhythms ( 30–100 Hz ) are an extensively studied synchronous brain state responsible for a number of sensory , memory , and motor processes . Experimental evidence suggests that fast-spiking interneurons are responsible for carrying the high frequency components of the rhythm , while regular-spiking pyramidal neurons fire sparsely . We propose that a combination of spike frequency adaptation and global inhibition may be responsible for this behavior . Excitatory neurons form several clusters that fire every few cycles of the fast oscillation . This is first shown in a detailed biophysical network model and then analyzed thoroughly in an idealized model . We exploit the fact that the timescale of adaptation is much slower than that of the other variables . Singular perturbation theory is used to derive an approximate periodic solution for a single spiking unit . This is then used to predict the relationship between the number of clusters arising spontaneously in the network as it relates to the adaptation time constant . We compare this to a complementary analysis that employs a weak coupling assumption to predict the first Fourier mode to destabilize from the incoherent state of an associated phase model as the external noise is reduced . Both approaches predict the same scaling of cluster number with respect to the adaptation time constant , which is corroborated in numerical simulations of the full system . Thus , we develop several testable predictions regarding the formation and characteristics of gamma rhythms with sparsely firing excitatory neurons .
Synchronous rhythmic spiking is ubiquitous in networks of the brain [1] . Extensive experimental evidence suggests such activity is useful for coordinating spatially disparate locations in sensory [2] , motor [3] , attentional [4] , and memory tasks [5] . In particular , network spiking in the gamma band ( 30–100 Hz ) allows for efficient and flexible routing of neural activity [6] . Groups of neurons responding to a contiguous visual stimulus can synchronize such fast spiking to within milliseconds [7] . The processing of other senses like audition [8] and olfaction [9] has also been shown to employ synchronized gamma rhythms , suggesting this fast synchronous activity is indispensable in solving perceptual binding problems [10] . Aside from sensation , gamma band activity has been implicated in movement preparation in local field potential recordings of macaque motor cortex [3] and electroencephalogram recordings in humans [11] . Also , there is a boost in power of the gamma band in both sensory [12] and motor [13] cortices during an increase in attention to related stimuli , which may serve as a gain control mechanism for downstream processing [4] . Short term memory is another task shown to consistently use gamma rhythms in experiments where humans must recall visual stimuli [14] . Thus , there are a myriad of studies showing gamma band synchrony appears in signals of networks performing neural processing of a variety of tasks and information . This suggests an understanding of the ways in which such rhythms can be generated is incredibly important to understanding the link between single neuron activity and network level cognitive processing . Many theoretical studies have used models to generate and study fast , synchronous , spiking rhythms in large neuronal networks [15]–[17] . One common paradigm known to generate fast rhythms is a large network of inhibitory neurons with strong global coupling [16] . Periodic , synchronized rhythms are stable because all cells must wait for global inhibition to fade before they may spike again . This observation lends itself to the theory that gamma rhythms can be generated solely by such mutual inhibition , the idea of interneuron network gamma ( ING ) oscillations [18] . Of course , this idea can be extended to large networks where excitatory neurons strongly drive inhibitory neurons that in turn feedback upon the excitatory population for a similar net effect [19] , [20] ( see also Fig . 5 of [21] ) , known as pyramidal–interneuron network gamma ( PING ) oscillations [18] , [22] . Even when coupling is sparse and random , it is possible for large networks with some inhibitory coupling to spontaneously generate a globally synchronous state [19] , [23] . The primary role of inhibitory neurons in gamma rhythms has been corroborated in vivo by [24] , using optogenetic techniques . Light-driven activation of fast-spiking interneurons serves to boost gamma rhythms , whereas driving pyramidal neurons only increases the power of lower frequencies . Depolarization of interneurons by activating channelrhodopsin-2 channels has also been shown to increase gamma power in local field potentials [25] . Still , no conclusive evidence exists to distinguish between PING or ING being more likely , and [26] suggests that weak and aperiodic stimulation of interneurons is the best protocol to make this distinction . Nonetheless , it is clear that recent experiments have verified much of the extensive theory developed regarding the mechanism of gamma rhythms . One particularly notable experimental observation of the PING mechanism for gamma rhythms is that constituent excitatory neurons fire sparsely and irregularly [12] , [27] , while inhibitory neurons receive enough excitatory input to fire regularly at each cycle . Due to their possessing slow hyperpolarizing currents , pyramidal neurons spike more slowly than interneurons [28] , so this partially explains their sparse participation in a fast rhythm set by the interneurons . Modeling studies have accounted for the wide distribution of pyramidal neuron interspike intervals by presuming sparse random coupling in network connections [29] or by including some additive noise to the input drive of the population [30] . From this standpoint , the excitatory neurons are passive participants in the generation of fast rhythms , so their statistics have no relation cell to cell . The requirement , in these cases , is a high level of variability in the structure and drive to the network . However , an alternative explanation of sparse firing might suggest that excitatory neurons assemble into subpopulations , clusters , that fire in a more regular pattern for a transient period of time . This may be accomplished without the need for strong variability hardwired into a network . One cellular mechanism that has been largely ignored in network models of fast synchronous spiking rhythms is spike frequency adaptation [30] , [31] . Slowly activated hyperpolarizing currents known to generate spike frequency adaptation have been shown in many different populations of regular spiking cells within cortical areas where gamma rhythms arise . In particular , pyramidal neurons in visual cortex exhibit slow sodium and calcium activated afterhyperpolarizing current , proposed to play a major role in generating contrast adaptation [32] . Regular spiking cells in rat somatosensory cortex also have adaptive currents . Furthermore , they exhibit a type 1 threshold , where they can fire regularly at very low frequencies [33] . Also , recent experiments in primate dorsolateral prefrontal cortex reveal significant increases in interspike intervals due to spike frequency adaptation [34] . Synchronous spiking in the gamma range has been observed in visual [2] , [12] , somatosensory [35] , [36] , and prefrontal [14] cortex , all areas with neurons manifesting adaptation . Also , adaptation may promote a low resonant frequency in regular spiking neurons that participate in gamma rhythms , as revealed by optogenetic experiments [24] . Therefore , adaptation not only slows the spike rate of individual regular spiking neurons , but can play a role in setting the frequency of network level spiking rhythms . Thus , we propose to study a paradigm for the generation of a network gamma rhythm in which excitatory neurons form clusters . This accounts for the key observation that excitatory cells do not fire on every cycle of the rhythm . The essential ingredients of the network are spike frequency adaptation and global inhibitory coupling . Spike frequency adaptation produces the slow firing of individual cells . The restrictions on the sparsity of coupling and the level of noise in the network are much looser than [30] . After identifying these properties of the network , we can extract several relationships between parameters of our model and attributes of the resulting clustered state of the network . One result of considerable interest is the relationship between the time constant of adaptation and the number of clusters that can arise in the network . Using two different methods of analysis , we can predict the cluster number to scale with adaptation time constant as . The paper employs both a detailed biophysical model as well as an idealized model that we study for the formation of cluster states . Our results begin with a display of numerical simulations of cluster states in the detailed model . The main point of interest is that excitatory neurons possess a spike frequency adaptation current whose timescale appears to influence the number of clusters that can arise . To begin to understand how this happens , we analyze the periodic solution of a single adapting neuron , in the limit of large adaptation time constant , for an idealized model of adapting neurons . Using singular perturbation theory , we can derive an approximate formula for the period of a single neuron and thus an estimate of the number of clusters in a network of neurons . Then , an exact expression is derived for the periodic solution of an equivalent quadratic integrate and fire model with adaptation as well as its phase-resetting curve . Next , we employ a weak coupling assumption to predict the number of synchronized clusters that will emerge in the network as the amplitude of additive noise is decreased . The number of clusters in the predicted state is directly related to a Fourier decomposition of the phase-resetting curve . Our main result is that both the singular perturbation theory and weak coupling analysis predict the same power law relating cluster number to adaptation time constant . Finally , we compare our predictions made using singular perturbation theory and the weak coupling approach to numerical simulations of the idealized model and the detailed biophysical model .
For our initial numerical simulations , we use a biophysical model developed by Traub for a network of excitatory and inhibitory spiking neurons [37] . Parameters not listed here are given in figure captions . The membrane potentials of each excitatory neuron and each inhibitory neuron satisfy the dynamics:with synaptic currentswhere , , , and are random binary matrices such thatand the synaptic gating variables are givenwhereThe ionic currents of each excitatory and each inhibitory neuron are givenwhere gating variables evolve aswhere . The biophysical functions associated with the gating variables areCalcium concentration associated with the hyperpolarizing current responsible for spike frequency adaptation in excitatory neurons follows the dynamicsBias currents to both excitatory and inhibitory neurons have a mean and fluctuating partwhere fluctuations are given by a white noise process such thatFinally , the fixed parameters associated with the network model areRandom initial conditions are used for the simulations of the model , and we wait until the system has settled into a steady state to make calculations of the statistics . We evolve this model numerically , using the Euler-Maruyama method , with a time step of dt = 0 . 0001 . The majority of our analysis uses an idealized spiking neuron model to study the mechanism of clustering associated with a network of adapting neurons . The Traub model for a single neuron exhibits a saddle-node on an invariant circle ( SNIC ) bifurcation . It is possible to exploit this fact to reduce the Traub model to a theta neuron model with adaptation , if the system is close to the bifurcation and the adaptation is small and slow [38] . In [39] , an alternative conductance based model with an afterhyperpolarizing ( AHP ) current was reduced using phase reduction type techniques , where the AHP gating variable was taken to evolve slowly . In particular , Fig . 3 ( c ) of [39] shows that the associated phase-resetting curve has a characteristic skewed shape . We also eliminate the inhibitory cells from the idealization of this section by slaving their synaptic output to the total firing of the excitatory cells . To our knowledge , there is no rigorous network level reduction that would allow us to reduce the excitatory-inhibitory conductance based network to the idealized one we present here . We do not provide a meticulous reduction from the Traub network model to the network analyzed from here on . We do wish to preserve the essential aspects of the biophysical model described in the previous section , spike frequency adaptation and inhibitory feedback . Therefore , we consider a system of spiking neurons , each with an associated adaptation current , globally coupled by a collective inhibition current ( 1a ) ( 1b ) ( 1c ) for . Equation ( 1a ) describes the evolution of a single spiking neuron with input , in the presence of spike frequency adaptation with strength and global inhibition with strength . Each neuron's input has the same constant component and a unique noisy component with amplitude where is a white noise process such that and for . The adaptation current associated with each neuron is discretely incremented with each spike and decays with time constant , according to equation ( 1b ) . Global inhibitory synaptic current is incremented by with each spike and decays with time constant . Notice , in the limit of pulsatile synapses ( ) , the equation ( 1c ) for inhibitory synaptic current becomesWe will make use of this reduction for some calculations relating cluster number to model parameters . The membrane time constant of neurons is usually approximated to be between 1–5 ms , so even though time has been nondimensionalized , its units could be deemed to be between 1–5 ms . In addition , experimental results suggest that the hyperpolarizing currents that generate spike frequency adaptation decay with time constants roughly 40–120 ms [40] , [41] , indicating that . This observation will be particularly helpful in calculating a number of results . Note , we consider this model as an idealization of adapting excitatory spiking neurons coupled to a smaller population of inhibitory neurons that then collectively connect to the excitatory population . Our approximation is reasonable , considering inhibitory neurons evolve on a faster timescale than the adapting excitatory neurons , as they did in the more detailed biophysical Traub model . For our numerical simulations of this model , we employ the Euler-Maruyama method , with a time-step of dt = 0 . 0001 . To display the spikes from our simulations of the Traub model ( see Figs . 1 and 2 ) , we employ the following sorting technique . First , to better illustrate the formation of clusters , we sort the simulations displayed in Fig . 1 in order of increasing voltage at the end of the simulation using MATLAB's sort function . Similarly , we sort the neurons in Fig . 2a in decreasing order , according to their spike time closest to ms also using the sort function . We do not resort the neurons between the left and right panel , which displays the mixing effects of cycle skipping . We use standard techniques for computing the interspike interval ( ISI ) and correlation coefficient ( CC ) for the population of spike trains . Calculations of the ISI take spike times of each neuron ( ) and compute their difference ( ) . Interspike intervals of all excitatory neurons are then combined into one vector and a histogram is then computed with MATLAB's hist function for a bin width of . We compute the CC for all possible pairs of excitatory neurons to ensure the best possible convergence . We first digitize two neurons' ( and ) spike trains into bins of and then use MATLAB's xcorr function to compute an unnormalized correlation function . This is then normalized by dividing by the geometric mean of both neuron's total firing and over the time interval . For the calculations displayed in Fig . 2 , we use a total run time of . The extensive singular perturbation theory analysis we carry out on the idealized network suggests that there is a clear cut scaling for the relationship between the number of clusters arising in a network and the adaptation time constant ( We also use the following least squares method to fit data relating to attained from numerical simulations of the Traub model ) . To compare this result with the relations between and derived using a weak coupling assumption , we consider the function determined by ( 23 ) . This gives the number of clusters associated with a particular and so must be an integer number . Since ( 6 ) is a continuous function , we wish to remove the stepwise nature of to make a comparison . Thus , we first generate the vector and matrixwhere and are the minimum and maximum number of clusters attained in the given range of . The function gives the minimal value of such that ; in other wordsNote that and . Now , we solve for the coefficients of the power function fit by solvingas an overdetermined least squares problem for the coefficient vector . We find the points are well fit by the specific case . To generate the inset plot , we simply compute the residualfor . This shows the global minimum is in very close proximity to . As a means of comparison with our theory , we perform simulations of the idealized model by starting the system ( 1 ) at random initial conditionswhere are uniformly distributed random variables on , is given in Text S1 , and . As suggested by our weak coupling analysis , we start the system with high amplitude noise ( ) , where clusters are not well defined , and incrementally decrease as the system evolves until noise is relatively weak ( ) . For low noise , each cluster is particularly well defined , especially when there are fewer clusters present . We now describe the attainment of the data points corresponding to minimal ( for our calculations of the Traub model ) to attain clusters for numerical simulations . These are computed by , first , simulating 20 realizations for each value of ( for the Traub model ) , starting with random initial conditions ( 2 ) and high noise , reducing noise and stopping after 20000 time units ( 20000 ms for the Traub model ) , and finally recording the number of clusters in the network for each realization . The points we then plot correspond to the first value of whose median cluster number is larger than the median for the previous ( ) value . Increments in between neighboring ( ) values are always no more than one .
Clustering of spiking activity in a network of neurons is the phenomenon in which only neurons belonging to the same cluster spike together , and two or more clusters spike each period of the population oscillation . The emergence of cluster states has been studied in globally coupled networks of phase oscillators with additive noise [42] , where clusters can be identified using stability analysis of an associated continuity equation . Phase oscillator networks may also develop clustering in the presence of heterogeneous coupling [43] or time delays [44] , [45] . Golomb and Rinzel extended early work in phase oscillators to show cluster states can arise in biologically-inspired networks of Wang–Rinzel spiking neurons [46] . They employed a stability analysis of periodic solutions to their network , using Floquet multipliers to identify which cluster state could arise for a particular set of parameters . Networks of leaky integrate-and-fire neurons can also exhibit clustering if coupled with fast inhibitory synapses [47] or there is sufficient heterogeneity in each neuron's intrinsic frequency [48] . In Hodgkin-Huxley type networks clustering has been witnessed due to a decrease in the amplitude of a delayed rectifier current [16] or by simply including a delay in synaptic coupling [44] . The addition of a voltage dependent potassium current to an excitatory-inhibitory network has also been shown to form two cluster states in detailed simulations [49] . In this section , we show clustering can arise in a detailed biophysical model network of spiking neurons developed by Traub ( see Methods ) . The network consists of excitatory and inhibitory neurons , but only excitatory neurons possess a slow calcium activated hyperpolarizing current , representative of spike frequency adaptation . The connectivity structure is dense but random , where each pair of neurons has a set probability of being connected to one another , according to their type . Here , we present the results of numerical simulations of this model , showing the behavior of cluster states in the network . More specifically , we are interested in the way that spike frequency adaptation helps to generate these states . In later sections , we look at cluster states in an idealized network model in order to analytically study the role of adaptation in the onset of clustering . We first present spike times of a model network of 200 excitatory and 40 inhibitory Traub neurons in Fig . 1 for two different time constants of the calcium-induced hyperpolarizing current . In particular , we find that , for slower adaptation , there is an increase in the number of clusters , but the overall frequency of the network decreases . This relationship persists over a wide range of model parameters , like network connectivity , synaptic strength , and input to neurons . To aid in the visualization of the clusters , we sort the neurons according to their voltage's value at the end of the simulation ( see Methods ) . Although the size of the clusters is fairly invariant over time , neurons do not remain in the same clusters indefinitely . In fact , by examining the state of neurons at times significantly before of after the time we sort them according to spike times ( see Methods ) , we find that units of clusters begin to mix with one another , shown in Fig . 2 ( a ) . Neurons jump from one cluster to another . The mechanisms by which this can occur are that either a neuron fails to fire with its current cluster and fires with the next cluster or the neuron fires with the previous cluster . This is exemplified by the additional peaks in the interspike interval distribution shown in Fig . 2 ( c ) . The correlation coefficient is relatively low on short time scales and decreases significantly over long time scales since neurons skip cycles or spike early due to fluctuations in drive to the network ( see Fig . 2 ( d ) ) . As pictured in Fig . 2 ( e ) , on short timescales , excitatory neuron spike times are weakly correlated between clusters , before cycle hopping takes effect . We have found that higher amplitude noise leads to more frequent switching of neurons between clusters . In addition , as the number of clusters increases , each individual cluster appears to be less stable and neurons also hop from one cluster to the next more frequently . We have considered architectures for which the cross correlations between neurons decay more quickly due to sparser connectivity . The main goal of our study , though , is to examine clustering as a complementary mechanism to irregular input and random connectivity for generating sparse firing . This can be contrasted with the degradation of correlations between excitatory neurons on fast timescales in [30] , due to strong fluctuations and sparse connectivity in their excitatory-inhibitory network . Thus , the cluster state that arises in this biophysically based network of spiking neurons appears to be a stable state that exists over a large range of parameters . The essential ingredients are a slow adapting current and inhibitory neurons that only fire when driven by excitatory neurons . The key feature of the detailed biophysical model that makes excitatory neurons susceptible to grouping into clusters is spike frequency adaptation . Few studies have examined the effects of adaptive mechanisms on the dynamics of synchronous states in spiking networks . In a study of two coupled adapting Hodgkin-Huxley neurons , their excitatory synapses transitioned from being desynchronizing to synchronizing as the strength of their spike frequency adaptation was increased [50] . In a related study , spike frequency adaptation was shown to shift the peak of an idealized neuron's phase-resetting curve , creating a nearly stable synchronous solution [51] . The effects of this on network level dynamics were not probed , and , in general , studies of the effects of adaptation on dynamics of large scale neuronal networks are fairly limited . A large excitatory network with adaptation can exhibit synchronized bursting , followed by long periods of quiescence set by the adaptation time constant [52] . Spike adaptation must build up slowly and be strong enough to keep neurons from spiking at all . More aperiodic rhythms were studied in populations of adapting neurons by [53] , who showed the population frequency could be predicted by the preferred frequency of a single adapting cell . Adaptation has also been posed as a mechanism for disrupting synchronous rhythms in [54] , where increasing the conductance of slow hyperpolarizing currents transitions a network to an asynchronous state . There remain many open questions as to how the strength and timescale of adaptive processes in neurons contribute to synchronous modes at the network level . We therefore proceed by studying several characteristics of the cluster state as influenced by spike frequency adaptation . First , we study how the period of a single neuron relates to the strength and time scale of adaptation . Then , we find how these parameters bear upon the number of clusters arising in the network of adapting neurons with global inhibition . Approximate relations are derived analytically and then compared to the results of simulations of ( 1 ) as well as the Traub model . We first present a calculation of the approximate period of a single adaptive neuron , uncoupled from the network . The singular perturbation theory we use relies upon the fact that the periodic solution is composed of three different regions in time: an initial inner boundary layer; an intermediate outer layer; and a terminal inner boundary layer . In this case , the initial and terminal boundary layers correspond to what would be the back and front of an action potential in a biophysical model of a spiking neuron , such as the Traub model . The intermediate layer corresponds to a refractory period imposed by the strong slow afterhyperpolarizing current . An asymptotic approximation to the periodic solution is pictured in Fig . 3 , showing the fast evolution of in boundary layers and slow evolution in the outer layer . The slow timescale arises due to the fact that , so we shall use the small parameter in our perturbation theory . Key to our analysis is the fact that the end of the outer layer comes in the vicinity of a saddle-node bifurcation in the fast subsystem , determined by the equation ( 1a ) . It then turns out that , as a result , we must rescale time to be in the terminal boundary solution . Such an approach has been studied extensively by Guckenheimer in the Morris-Lecar and Hodgkin-Huxley neurons with adaptation , as well as general systems that support canards of this type [55] , [56] . Nonetheless , we proceed by carrying out a similar calculation here and use it to derive an approximate formula for the period of the solution . We find that it matches the numerically computed solution remarkably well . In addition , we can use the expression for the period to explain why the number of clusters arising in the network ( 1 ) , when compared to the adaptation time constant , will scale as . To initially approximate the interspike interval for a deterministically–driven adaptive neuron , uncoupled from the network ( 2 ) we shall use singular perturbation theory . In particular , we exploit the fact that the adaptation time constant is large in comparison to the membrane time constant of a spiking neuron . Guckenheimer has carried out several other studies examining relaxation oscillations and canards in the vicinity of fold singularities [55] , [56] . The usual approach is to decompose the full system into a fast and slow part and then use standard methods of bifurcation analysis to analyze constituent parts [57] . We are particularly interested in computing the approximate form of a periodic solution . The details of this calculation are carried out in Text S1 . Our analysis exploits the fact that the fast subsystem , defined by the equation of the system ( 2 ) , exhibits a saddle-node on an invariant cycle ( SNIC ) bifurcation . Thus , we have an approximate periodic solution that is split into two time regions , one before the subsystem reaches the SNIC at time and the other after , so ( 3 ) andwhere the parameters and are defined in Text S1 while and are Airy functions of the first and second kind . We plot this solution along with numerical simulations in Fig . 4 . The location of the saddle-node bifurcation point of the fast subsystem correlates biophysically to the end of the refractory period imposed by the afterhyperpolarizing current . Notice that there is a cusp at the point where the outer and terminal boundary solution come together . In addition , the perturbative solution's phase arrives at zero before the actual solution's . This suggests that there are finer scaled dynamics arising from the phase variable being small in the vicinity of the saddle-node bifurcation of the fast subsystem . Such effects could potentially be explored with higher order asymptotics . For the purposes of this study , it suffices to truncate the expansion to two terms . The resulting formulae can be utilized extensively in the explanation of network dynamics . In deriving our approximation to the periodic solution , we were able to calculate a relatively concise formula relating the period of the solution to the remainder of the parameters ( 4 ) where is the minimal solution to ( 5 ) such that ( see Text S1 ) . We illustrate the accuracy of this approximation over a wide range of adaptation time constants in Fig . 5 . The approximation is fairly accurate for a substantial region of parameter space , but improves appreciably as and are increased . We conclude our study of the periodic solution to ( 2 ) by using our formula for the period ( 4 ) to roughly calculate the number of clusters admitted by a network of adapting neurons with pulsatile inhibitory coupling . This also provides us with an estimate of the population spike frequency . Any inputs delivered to the neuron during the initial or the outer layer stage of the solution , equation ( 3 ) , will have little or no effect on its firing time . During this interval , the adaptation variable constrains the phase so that it simply relaxes back to the same point on the trajectory following a perturbation . Once the terminal layer begins , the input is above a threshold such that the phase can increase at an accelerating rate . However , it is possible to hold the phase back with a negative perturbation . A neuron that has already begun its terminal phase when another cell spikes will always be forced to delay its own spike . As a result , over time , in a network , clusters of neurons would be forced apart to about the time length of the terminal layer . Therefore , the number of clusters will be roughly determined by the length of this terminal layer as compared with the total length of the period ( 6 ) Therefore , as the adaptation time constant increases , the number of clusters will scale as . While our main interest in this formula is its relationship to the adaptation time constant , there are also nonlinear relationships derived here between cluster number and other parameters . We shall compare this formula further with the predictions we calculate using weak coupling and the phase-resetting curve . Since the perturbative solution ceases its slow dynamics briefly before the numerical solution ( see Fig . 4 ) , we expect that this asymptotic formula ( 6 ) approximating cluster size may be a slight underestimate . Nonetheless , it allows us to concisely approximate how the population frequency depends on the adaptation time constant as well as the cluster number . Since each neuron spikes with a period given by equation ( 4 ) and there are clusters of such neurons , the frequency of populations spikes in the network are given by ( 7 ) We plot this function versus as well as in Fig . 6 . Notice , networks with neurons whose spike frequency adaptation have a longer time constant support synchronous spiking rhythms with lower frequencies , as in the Traub network ( see Fig . 1 ) . Also , by our mechanism , as more clusters are added , the population frequency decreases . This is due to the period of individual neuron spiking scaling more steeply with adaptation time constant than the cluster number . We have identified general relationships between the adaptation time constant and two quantities of the idealized spiking network ( 1 ) : the period of a single neuron and the cluster number of the network . These relationships help characterize the behavior of the cluster state in the adaptive network . In particular , the bifurcation structure of the fast-slow formulation of the single neuron system guides the identification of a timescale of the spike phase , which evidently guides network level dynamics . Singular perturbation theory is indispensable in making this observation . As a means of studying the susceptibility of a single neuron to synchronizing to input from the network , we shall derive the phase-resetting curve of a neuron with adaptation . Biophysically , the phase-resetting curve corresponds to the amount that brief inputs to a tonically spiking neuron delay or advance the time of the next spike . First , we make a change of variables to the system ( 2 ) , so the state of the neuron is now described by the quadratic integrate and fire ( QIF ) model with adaptation [58] ( 8 ) We show in Text S1 that by using a sequence of further changes of variables , we are able to express the periodic solution to this system in terms of special functions . As has been shown previously , the solution to the adjoint equations of a system that supports a limit cycle is the infinitesimal phase-resetting curve ( PRC ) of the periodic orbit [59] . Therefore , with the function form of in hand , we can derive the adjoint equations by first linearizing the system ( 8 ) about the limit cycle solution so ( 9 ) The adjoint equations , under the inner product ( 10 ) will be ( 11 ) ( 12 ) Since is known , it is straightforward to integrate ( 11 ) , to solve for the first term of the adjointBy plugging in ( see Text S1 ) , we find we can further specifywhere is given up to a scaling factor in Text S1 . It is now straightforward to plot the PRC of the QIF model with adaptation . To our knowledge , this is the first exposition of an analytic calculation of the PRC of the QIF model with adaptation . Although , the bifurcation structure of more general QIF models with adaptation has been analyzed in previous work by [60] , [61] . The exact period can be computed using the right boundary condition given in Text S1 , which can then be used to determine the initial condition for the adaptation variableWe then must plot a function which involves a Bessel function of imaginary order and imaginary argument ( 13 ) In Fig . 7 , this is shown along with the numerically computed PRC , where pulsatile inputs are applied at discrete points in a simulation . Time is also normalized by the period to yield the phase variable . We find an excellent match between the two methods . One can also derive a very accurate representation of the PRC by numerically solving the adjoint equations ( 11 ) and ( 12 ) . This is also useful because Bessel functions with pure imaginary order and argument are particularly difficult to approximate as the magnitude of the order and argument become large . Accurate asymptotic approximations for this class of special functions are lacking , although [62] provides some useful formulae along these lines . Thus , we compute the PRC using numerical solution of the QIF system ( 8 ) and the adjoint equation ( 11 ) , pictured in Fig . 8 for several different values . Time is normalized here , as in Fig . 7 , so the phase variable goes between zero and one . This also eases comparison for different time constants . We find that , as we would suspect from our singular perturbation theory calculations , the region in which the neuron is susceptible to inputs shrinks as increases . This skewed shape to the PRC has been revealed previously in other studies of spiking models , where adaptation currents were treated in alternative ways [51] , [63] . We also compute the PRC for the theta model numerically using the adjoint equations . To derive them , we linearize the system ( 2 ) about the limit cycle solution soThe adjoint equations , under the inner product ( 10 ) will be ( 14 ) ( 15 ) By solving ( 2 ) numerically , we can use the solution to then numerically integrate ( 14 ) to solve for the first term of the adjoint , which is the PRC of the theta model . We show this alongside the numerically calculated PRCs of the QIF model . Notice they are quite alike , save for the theta model's PRC being nonzero at . In the theta model's PRC , the change of variables creates a discontinuity . Therefore , as revealed by an analytic formula and numerical method for computing the PRC , we find that spike frequency adaptation creates a lengthy time window during which the neuron is insensitive to inputs . As the time constant of adaptation is increased , this window occupies more of the solution period . With these formulations of the PRC in hand , we may carry out a weak coupling analysis of the network to quantitatively study predictions regarding solutions that emerge from instabilities of the incoherent state . Due to large scale spiking network models usually being analytically intractable , a weak coupling assumption is commonly used to study their resulting activity patterns . This allows the reduction of each cell's set of equations to a single one for the phase [38] . Based on the averaging theorem , this reduction is valid as long as parameters of the model are such that each unit supports a limit cycle , their firing rates are not too heterogeneous , and coupling between units is not too strong [15] , [59] . This also allows us to place our work in the context of previous studies of clustering in phase models [42]–[44] . Presuming the cells receive enough input to spontaneously oscillate and that they are weakly coupled , we can reduce the system to a collection of limit cycle oscillators [38] . Each oscillator will have some constant frequency , where we use the period computed using the exact solution ( see Text S1 ) for a particular set of parameters . Thus , the network becomes ( 16 ) where is the coupling function attained by convolving the PRC with the synaptic timecourse ( 17 ) and is a white noise process such that and . To analyze the system ( 16 ) , we consider the mean field limit . Mean field theory has been used extensively to study ( 16 ) when [64]–[66] , but much less so when [67] , [68] . Following such previous studies , we can employ a population density approach where oscillators are distributed in a continuum of phases so that denotes the fraction of oscillators between and at time . Thus , is nonnegative , -periodic in , and normalizedTherefore , evolves according to the Fokker-Planck equation [64] , [65] ( 18 ) where the instantaneous velocity of an oscillator isthe continuum limit of . Now , in order to examine the effect that the phase-resetting curve has upon the solutions to ( 16 ) , the weak coupling approximation to ( 1 ) , we shall study instabilities of the uniform incoherent state of ( 18 ) , given by . It is straightforward to check that this is indeed a solution by plugging it into ( 18 ) . Since this is always a solution , for all parameters , we can examine the solutions that emerge when it destabilizes by studying its linear stability . We will show that for sufficiently large , the incoherent state is stable , but as is reduced , the solution destabilizes , usually at a unique Fourier eigenmode . We begin by lettingwhere . Expanding the continuity equation ( 18 ) to first order in , we arrive at an equation for the linear stability of the incoherent state ( 19 ) where . Expressing as a Fourier seriesand specifically taking , we can compute the eigenvalue of the th mode of using the spectral equation of the linear system ( 19 ) , soApplying the change of coordinates , we have a general equation for the th eigenvalue ( 20 ) We can evaluate the integral term by considering the Fourier series expansion ( 21 ) so thatUpon plugging this into ( 20 ) , we find the eigenvalue associated with the th mode of is related to the Fourier coefficients of by ( 22 ) Thus , as is reduced towards zero , the first eigenmode to destabilize will be the one whose eigenvalue crosses from the left to the right half of the complex plane first . Using equation ( 22 ) , we can identify this mode as the first to have Re orThis corresponds to the for which is maximal . For the critical value at which the first eigenvalue has positive real part , we show plots of as a function of for several different parameters in Fig . 9 . Notice that as the adaptation time constant is increased , and other parameters are held fixed , the critical increases . As the synaptic time constant is increased and other parameters are held fixed , the critical decreases . We contrast this with the case of excitatory coupling ( ) in the system ( 1 ) , where the PRC is nonnegative . In this case , the critical is fairly insensitive to changes in the time constants , virtually always predicting the mode becomes unstable first ( not shown ) . Therefore , our weak coupling calculation approximates the number of clusters for a given set of parameters using the coupling function ( 17 ) with the Fourier expansion ( 21 ) so that ( 23 ) To compare with our singular perturbation theory results , we compute the approximate number of clusters using the weak coupling assumption for pulsatile synapses . In the limit , the coupling function becomes . Therefore , the Fourier coefficients are calculated directly from the PRC of the theta model . In Fig . 10 , we plot the number of clusters as a function of , calculated using equation ( 23 ) along with the asymptotic approximation to the number of clusters ( see equation ( 6 ) ) . Notice that the singular perturbation theory slightly underestimates as compared with weak coupling . This may be due to the fact that the singular perturbative solution reaches the saddle-node point slightly before the actual solution does , underestimating the length of the quiescent phase of the PRC . Nonetheless , both curves have a characteristic sublinear shape . We show in Fig . 11 that the weak coupling dependence upon scales as a power law , just as predicted by singular perturbative theory . Thus , even though our asymptotic approximation ( 6 ) is an underestimate , it provides us with the correct scaling for cluster number dependence upon adaptation time constant . The same power law scaling is reflected in networks with exponentially decaying synapses , as shown in Fig . 12 . We plot predictions based on our weak coupling assumption for . As the synaptic time constant is increased , the number of clusters is diminished , since feedback inhibitory inputs relax more slowly . Therefore , we speculate an improved asymptotic approximation of cluster number that accounts for synaptic timescale might include an inverse dependence upon . In this section , we present results of numerical simulations of the idealized network ( 1 ) of theta neurons with global inhibition and adaptation . In addition , we compare the scaling law predicted for the idealized model to the number of clusters arising in numerical simulations of the more detailed Traub model . We find that the qualitative predictions of our singular perturbation theory and weak coupling approximations are reflected in the dependence of the state of the network on model parameters . The quantitative relationship between adaptation time constant and cluster number is sensitive to the strength of global inhibitory feedback , holding for small values only . One would expect this , since approximations were made considering weak coupling . In Fig . 13 , we show the results of simulations for various adaptation time constants in the case of pulsatile synapses ( ) . As predicted by the formulae of both our singular perturbation theory approximation ( 6 ) and weak coupling assumption ( 23 ) , cluster number increases sublinearly with adaptation time constant . Notice in Fig . 13 ( c ) , when there are seven clusters , neurons of each cluster do not spike in as tight of a formation as can be found in simulations with four and six clusters . We conjecture that this is due to fewer neurons participating in each cluster and so less global inhibition is recruited each time a set of neurons fires . This smears the boundary between each cluster . In Fig . 14 , we show the results of simulations in the case of exponentially decaying synapses with time constant . As predicted by our weak coupling analysis , the smoothing of the synaptic signal leads to there being fewer clusters on average for a particular value . Notice in both the pulsatile and exponential synapse cases , as the number of clusters increases , the interspike intervals are prolonged , as predicted by our approximation of the period ( 4 ) . Therefore , the resulting frequency of population activity decreases , on average , with . To quantitatively compare our theoretical predictions with numerical simulations of ( 1 ) , we plot the minimal necessary to generate the number of clusters for each method . Theoretical calculations include both the singular perturbation approach ( 6 ) and the weak coupling approximation ( 23 ) . The points we then plot in Fig . 15 correspond to the first value of whose median cluster number is larger than the median for the previous value ( see Methods ) . Remarkably , the theoretical calculation using the weak coupling approach give a reasonable approximation to the behavior of the simulations . Comparing the result of pulsatile versus exponentially decaying synapses , the increase in with is clearly larger for the pulsatile synapse case . This can be contrasted with the results of van Vreeswijk , who found in simulations of inhibitory integrate and fire networks that median cluster number increased with synaptic timescale [47] . One particular aspect of simulations of the full model ( 1 ) that may escape our theoretical formulae ( 6 ) and ( 23 ) is the effect of different synaptic strengths . To produce fairly well resolved clusters , it was necessary to take , not very weak . Additionally , as the number of clusters increases , the strength of inhibitory impulses decreases . Both of these facts may bear upon potential cluster number and account for the nonlinear shape of the numerically developed relationship between and . Finally , we return to the original detailed biophysical model to compare the predictions of cluster scaling made in the idealized model . Exchanging the idealized adaptation time constant for the time constant for calcium dynamics in the Traub model , , we examine how well the scaling holds in numerical simulations of the detailed model . We use the same method as that employed for the idealized model to identify the minimal at which a certain number of clusters appears ( see Methods ) . Our results are summarized in Fig . 16 and show that , in fact , cluster number does approximately follow the adaptation time constant scaling predicted from the idealized model . This makes sense , since one can relate the Traub model to the idealized theta model using a normal form reduction , so their phase-resetting properties will be similar to a first approximation [38] . The quiescence invoked by strong adaptation will lead to sharp narrow peaks in the PRC for the Traub model ( as shown for the idealized model in Fig . 8 ( b ) ) . Therefore , our analysis of the theta model leads to an excellent prediction of the effects of adaptation upon the cluster state in the network of Traub neurons .
In this paper , we have studied the formation of cluster states in spiking network models with adaptation . We theorize clustering may be an alternative , or at least contributing , mechanism for the sparse firing of pyramidal cells during gamma rhythms [12] . Sparse gamma rhythms may , therefore , not rely solely upon the effects of input and connectivity heterogeneities [30] . Besides spike frequency adaptation , the other essential property for the formation of clusters in the network is feedback inhibition . Empirically , we observe the number of clusters increases with the time constant of adaptation in a detailed biophysical spiking network and a more idealized model . We can carry out a number of analytical calculations on the idealized model that help uncover the mechanisms of clustering . Results of a singular perturbative approximation of a single neuron's periodic spiking solution confirm that adaptation with longer timescales will shorten the relative length of time a neuron is susceptible to inputs . This is revealed in a compact expression ( 4 ) relating the period of the neuron to parameters . In particular , we can estimate the number of clusters generated in the network for a particular value of adaptation time constant and find they will scale as . We then compare this result to a formula that can be derived in the context of a phase model , where , incidentally , the phase-resetting curve can be computed exactly . In the weak coupling limit , the number of clusters is related to the Fourier modes of the phase-resetting curve . In fact , we can fit the number of clusters to a power law . These results are confirmed in simulations of the full idealized model ( 1 ) and are well matched to simulations of the detailed biophysical model . Our results suggest a number of experimentally testable predictions . We have suggested that clustered states may be an organized synchronous state capable of generating sparse gamma rhythms [1] . Rather than a rhythm generated by a balanced network containing neurons with driven by high amplitude noise [30] , gamma may be a rhythm generated by slow excitatory neurons that cluster into related groups temporarily but dissociate from one another after some length of time . This could be probed using multiunit recordings to look for clustering of pyramidal neurons on short timescales . Large networks that exhibit clustering may do so through this combination of adaptation and inhibition . This suggests that it may be possible to identify in vitro or in vivo clustering that depends upon spike frequency adaptation by examining the effects of curtailing calcium dependent potassium currents using cadmium , for example [69] . Our model suggests weakening spike frequency adaptation should lead to a decrease in cluster number . In addition , there are a growing number of ways to experimentally measure the PRC of single neurons [63] , [70] . Since pyramidal cells are known to often possess adaptation currents , it may be possible to study the ways in which modulation of those currents' effects bears on a neuron's associated PRC . Our analysis indicates that stronger and slower spike frequency adaptation leads to PRCs with a steep peak at the end . Thus , different aspects of the cluster state shown here may be studied experimentally in several ways . Clustering through intrinsic mechanisms may in fact be a way for networks to generate cell assemblies spontaneously [71] . If clustering is involved in the processing of inputs , shifting neurons from one cluster to another might disrupt the conveyance of some memory or sensation [10] , [14] . In more specific networks , underlying heterogeneous network architecture may provide an additional bias for certain neurons to fire together . Alternatively , cell assemblies may be formed due to bias in the input strength to a recurrent excitatory-inhibitory network , as shown in [49] . They found that the inclusion of hyperpolarizing current could generate slow rhythms in the excitatory neurons with increased input . Our model does rely on a hyperpolarizing current but does not require a heterogeneity in the input . Also , each assembly possesses its own beta rhythm whereas the entire network possesses a gamma rhythm . In the future , it would be interesting to pursue a variety of the theoretical directions suggested by our results . The singular perturbation calculation follows along the lines of a few previous studies of canards in the vicinity of fold singularities [55]–[57] , [72] . Carrying out an even more detailed study of the bifurcation structure of the fast-slow system of the single neuron ( 2 ) may allow for a more exact calculation of how the period relates to the parameters . In particular , we may be able to compute the dynamics of relaxation time in the vicinity of the bottleneck near the saddle-node bifurcation of the fast system ( see Fig . 3 ) . We could also extend this calculation to other idealized spiking models with adaptation such as Morris-Lecar [55] or the quartic integrate and fire model [60] . In addition , we have considered examining the types of dynamics that may result in inhibitory leaky integrate and fire networks with adaptation . Excitatory integrate and fire networks have previously been shown to support synchronized bursting when possessing strong and slow enough adaptation [52] . It has also been shown that inhibitory integrate and fire networks without adaptation support clustering in the case of alpha function synapses [47] . In preliminary calculations , we find that a single integrate and fire neuron with strong and slow adaptation does not have the same steep peaked PRC as the theta model , due to there being no spike signature in the model . Therefore , it may not support clustered states through the same mechanism as the system we have studied . We have also mentioned that clustering arises in the network ( 1 ) through the application of a homogenous deterministic current with some additive noise . Therefore , applying an input with more temporal structure , for example at the frequency of the network or individual neurons , may lead to interesting variations of the clustered state . Finally , we seek to study other potential negative feedback mechanisms for generating clusters . In a large competitive spiking network , it may be possible for a subset of neurons to suppress the rest until synaptic depression exhausts inhibition . Multistable states supported with such mechanisms have been shown in small spiking networks [73] , [74] , but theory has yet to be extended to large scale synchronous states like clustering . | Fast periodic synchronized neural spiking corresponds to a variety of functions in many different areas of the brain . Most theories and experiments suggest inhibitory neurons carry the regular rhythm while being driven by excitatory neurons that spike more sparsely in time . We suggest a simple mechanism for the low firing rate of excitatory cells – spike frequency adaptation . Combining this mechanism with strong global inhibition causes excitatory neurons to group their firing into several clusters and , thus , produce a high frequency global rhythm . We study this phenomenon in both a detailed biophysical and an idealized model that preserves these two basic mechanisms . Using analytical tools from dynamical systems theory , we examine why adaptation causes clustering . In fact , we show the number of clusters relates to a simple function of the adaptation time scale over a broad range of parameters . This allows us to develop several predictions regarding the formation of fast spiking rhythms in the brain . | [
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] | 2011 | Sparse Gamma Rhythms Arising through Clustering in Adapting Neuronal Networks |
The primate connectome , possessing a characteristic global topology and specific regional connectivity profiles , is well organized to support both segregated and integrated brain function . However , the organization mechanisms shaping the characteristic connectivity and its relationship to functional requirements remain unclear . The primate brain connectome is shaped by metabolic economy as well as functional values . Here , we explored the influence of two competing factors and additional advanced functional requirements on the primate connectome employing an optimal trade-off model between neural wiring cost and the representative functional requirement of processing efficiency . Moreover , we compared this model with a generative model combining spatial distance and topological similarity , with the objective of statistically reproducing multiple topological features of the network . The primate connectome indeed displays a cost-efficiency trade-off and that up to 67% of the connections were recovered by optimal combination of the two basic factors of wiring economy and processing efficiency , clearly higher than the proportion of connections ( 56% ) explained by the generative model . While not explicitly aimed for , the trade-off model captured several key topological features of the real connectome as the generative model , yet better explained the connectivity of most regions . The majority of the remaining 33% of connections unexplained by the best trade-off model were long-distance links , which are concentrated on few cortical areas , termed long-distance connectors ( LDCs ) . The LDCs are mainly non-hubs , but form a densely connected group overlapping on spatially segregated functional modalities . LDCs are crucial for both functional segregation and integration across different scales . These organization features revealed by the optimization analysis provide evidence that the demands of advanced functional segregation and integration among spatially distributed regions may play a significant role in shaping the cortical connectome , in addition to the basic cost-efficiency trade-off . These findings also shed light on inherent vulnerabilities of brain networks in diseases .
The large-scale network of cortical areas in the brain supports and integrates brain functions that are distributed across spatially segregated regions [1] . It has been known for a long time that different regions have specialized roles in brain function [2 , 3] and such segregated functions also need to be integrated , a finding supported by neuroimaging studies [4–12] . The primate brain connectome is a physical network with nodes ( cortical regions ) and connections ( fiber projections ) embedded in Euclidean space [13 , 14] . Exploring the factors that shape the spatial layout and the topological organization of the connectome is important for understanding the organization principles and structure-function relationships in the brain that support both segregated and integrated functioning at the systems level . It has been generally believed that the brain connectome reflects a trade-off between physical cost and functional requirements [13] . Early work mainly focused on testing the hypothesis of wiring cost minimization using component placement optimization [15–17] , by fixing the topological connectivity of the network . While the placement of regions in some sub-systems ( e . g . , macaque monkey frontal cortex and C . elegans ganglia ) appeared to be optimal [15–18] , the global network appears not to minimize wiring cost , and comprises a substantial admixture of long-distance connections [13] . A scheme of wiring cost minimization with fixed network topology does not permit to explicitly study the competition between physical cost and network functionality . However , it is also an unresolved question of how to quantify the functional requirements of the brain . In such a spatially embedded signal communication brain network , the interregional connection patterns may need to achieve a balance between reducing cost by sending most projections to spatially close nodes while still maintaining efficient communication among spatially distant components , which may be a possible function of some of the long-distance connections [14 , 18] . The processing efficiency of a network , as measured by the inverse of shortest graph path-length , thus , may be employed to partially capture the network functionality for a quantitative study of the trade-off with the wiring cost minimization . This approach is indeed reasonable , since it has been found that the processing efficiency of the neural network is related to cognitive performance and dysfunction for brain disorders and diseases [19–21] . Recently , a great amount of attention has been given to characterizing important features of the organization of neural networks [14 , 22–27] and to exploring possible basic factors or simple rules which may play a role in generating such features . Graph theoretical approaches have revealed characteristic topological features of the cortical connectome , such as densely connected modules [28 , 29] , hubs with a large number of connections [30 , 31] , and rich-clubs with dense connections among hubs [32] . Our recent work considered a trade-off between wiring cost and processing efficiency by fixing the spatial layout of the cortical areas [27] and showed that prominent network properties , such as module divisions and spatial positions of the hubs may be determined by such a trade-off [27] . Generative models combining observed spatial features ( e . g . , connectivity decaying with distance ) and topological features ( e . g . , matching index measuring similar input and output patterns of nodes ) [14 , 22–26] can reproduce some other statistical properties of the real connectivity , such as the distribution of degree , clustering , betweenness centrality and edge length . However , as shown in S1 Fig , it is still challenging to reproduce the degree sequence of individual cortical nodes by the generative model . The heterogeneous degrees may be also affected by the cost-efficiency trade-off , as the degrees from the cost-efficiency optimized model are significantly correlated with the real degrees in different nervous systems ( macaque and C . elegans ) [27] . More importantly , the different spatial and topological features of brain networks may not be independent , but rather be essentially related to basic physical constraints ( e . g . , wiring cost minimization ) [22] . For instance , the topological similarity ( in terms of common neighbors ) of different regions is also affected by the wiring cost [25] . Although the important statistical features in the neural networks may be captured by the combination of a few observed factors [13 , 14 , 33 , 34] , a fundamental but still not well understood question is how the regional connectivity profiles as well as individual connections are shaped by some basic principles , for instance , wiring economy in trade-off with efficient processing . Which model can better capture the rules that shape the regional connectivity and its relationship to functions , a generative model combining different observed features or a trade-off model with basic factors of the wiring cost and processing efficiency ? Is the arrangement of wiring-costly long-distance connections among different regions related to the segregation and integration of diverse brain functions ? Exploring how such basic rules shape the cortical connectivity and its relationship to advanced functional requirements deepens the understanding of the relationships among the underlying design principles , wiring diagrams , functional performance , and potential vulnerability in the primate brain connectome . Here we studied how the trade-off between two basic factors , the wiring cost and the topological processing efficiency , affects other topological features and regional connectivity profiles of the primate cortex structural connectome , and explored the relationship between the long-distance connections and functional requirements of segregation and integration . Previously , it was found that the degree distribution may be related to different factors , such as the cost-efficiency trade-off [27] , or the combination of wiring distance and matching index [14] ( S1 Fig ) . Though it is significant , the relatively low correlation between the degrees in the real network and that from the cost-efficiency trade-off model ( without fixing degrees ) implies that the degree sequence of individual nodes is also shaped by other factors . Indeed , even though the generative model [14] can closely recover the degree distribution , it cannot reliably reproduce the degree sequence of the nodes ( S1 Fig ) . Therefore , in this study , the input and output degrees of the nodes are fixed as a constraint together with the spatial layout in the cost-efficiency trade-off model to obtain reconstructed networks from the combined optimization of wiring cost and processing efficiency . For comparison , we also consider the objective function of a generative model [14 , 24] explicitly aimed at reproducing multiple statistical features of the connectome . We extended the generative model to consider the constraint of fixed degree as in the real network for a fair comparison with the trade-off model . We searched the best generative model with the most similar distribution of several features compared to the real macaque cortical network , and compared the performance of the optimized networks from the models in the recovery of topological features , regional connectivity profiles and individual connections . Our analyses showed that the trade-off outperforms the generative model in these aspects . The detailed comparison of real connectivity with the trade-off model for each region allowed us to determine ( 1 ) how much connectivity of the whole network and each region can be explained by the cost-efficiency trade-off , ( 2 ) how the unexplained connections are distributed among different functional regions and domains , and ( 3 ) what relationships exist between regions possessing many long-distance connections and the overarching functional features of segregation and integration . Our analyses provide evidence that the wiring cost-efficiency trade-off plays an essential role in determining regional cortical connectivity . However , additional requirements for proper segregation and integration of functions can strongly violate the cost minimization for some cortical regions and induce many long-range connections . In contrast to typical hubs identified by previous studies , these regions are typically non-hubs , but concentrate the long-distance projections in the network , acting as long-distance connectors ( LDCs ) . While the organization of LDCs could reduce the metabolic and functional burden of outstanding hubs , such regions may also become spots of vulnerability in the primate brain network . Overall , the results provide support for , and insights into , the trade-off between physical cost and functional values of the primate brain connectome organization .
Fig 1A shows that the real network indeed displays a trade-off between wiring cost and processing efficiency . In this work , the cost-efficiency model refers to the case with fixed degrees as in the real network . Optimizing wiring cost on its own leads to a physically cheaper network , which is , however , less efficient; with the wiring cost lp reduced to 77 . 1% and the efficiency lg increased to 105% of the real network . By contrast , optimizing the processing efficiency on its own increases efficiency ( lg reduced to 94 . 7% ) , but at the expense of wiring cost ( lp increased to 113 . 7% ) . The model combining wiring cost and efficiency brings both lp and lg closer to the real network , as seen by the configuration that maximally recovers the real connectivity . The rates of link recovery with respect to α in Fig 1B–1D clearly show that a trade-off between wiring cost and processing efficiency can give a better account of the real connectivity than the individual factors . We computed the recovery rate r1 for the connected pairs ( Aij = 1 ) and r0 for the unconnected pairs of regions ( Aij = 0 ) , respectively ( Fig 1B ) ( see Material and methods ) . The maximal recovery occurs at α = 0 . 006 ( r1 = 0 . 67 , r0 = 0 . 91 ) , which is better than the recovery only for efficiency optimization at α = 0 ( r1 = 0 . 45 , r0 = 0 . 84 ) or only for wiring cost optimization at α = 1 . 0 ( r1 = 0 . 61 , r0 = 0 . 89 ) . Both recovery rates are clearly higher than that ( r1 = 0 . 52 , r0 = 0 . 80 ) for the synthetic network produced by the original generative model [14] with the optimized parameters but without fixing degrees , and are also higher than that ( r1 = 0 . 56 , r0 = 0 . 82 ) for extended generative model with fixed degrees . Since the extended generative model and the cost-efficiency model both have the same degree sequence as the real network , further comparison will be performed between these two models . Optimizing the wiring cost by itself at α = 1 can recover most of the short-distance connections ( r1≈ 1 for small distance , Fig 1C ) while suppressing the long-distance connections ( r0≈ 1 for large distance , Fig 1D ) . However , by combination with the processing efficiency constraint at α = 0 . 006 ( see the Discussion for why such a small value is needed ) , the model network can clearly better recover the long-distance links ( Fig 1C , larger r1 at long distances , e . g . , x > 30 mm , especially as in the two longest bins in dashed circle ) and also avoid false-positive links of pairs in the spatial neighborhood ( Fig 1D , larger r0 at shorter distance , e . g . , x < 10 mm ) . Changes of the recovery rates r1 for long distance connections ( > 30 mm ) and r0 for short-distance pairs ( <30 mm ) with respect to α in S2 Fig showed that the improvement of the recovery of long distance links is quite significant , although the absolute increase of the recovery rate r1 from 0 . 61 to 0 . 67 appears not as a big change . In comparison , the cost-efficiency trade-off model has a clearly higher recovery rate ( larger r1 ) and fewer false-positive links ( larger r0 ) than the generative model ( Fig 1C and 1D ) . The cost-efficiency trade-off model better recovers the middle-range connections and performs especially well in avoiding the false-positive links at short distance , which clearly reflects the competition of wiring cost and processing efficiency , respectively , that is associated with short and long-distance connections . These results show that efficient processing requires long-distance connections , but the real network possesses even more long-distance connections than the best trade-off model provides , suggesting that further functional factors are at work in the real network . In previous work using a generative model with link-generating rules combining spatial distance and topological similarity ( i . e . , matching index ) , several different topological features were well recovered for the brain networks of different species [14 , 24 , 25] . Here we also applied statistical measures to quantify the performance of this generative model on the macaque connectome with optimized energy ( see Methods ) , and compared the outcome with reconstructed networks produced by the cost-efficiency trade-off model across different weight parameters α ( Fig 2 ) . For the macaque cortical network , the synthetic network of the original generative model [14] with optimal parameter values was obtained by combining the spatial distance and the matching index , achieving the minimal energy E = 0 . 14 , which was quite similar to the previous result for the human connectome [14] . As for the extended model with fixed degrees , the minimal energy E = 0 . 2 . This is plausible since under the additional constraint of fixed degrees the generative rules may have reduced freedom to recover the other statistical features . As for the cost-efficiency trade-off model , the energy E clearly decreased with α , reaching a minimum at α = 0 . 006 and the E value was very close to that of the optimal generative model ( Fig 2A ) . The result of the similarity of each statistical measure of clustering , betweenness centrality and edge length shown in Fig 2B–2D provides further support that the best trade-off model performs as least as well as , generally slightly better than the optimal generative model ( fixed degrees ) . The cumulative distributions of the three measures were similar for the real cortical network , the optimal generative model and the best cost-efficiency trade-off model ( insets of Fig 2B , 2C and 2D ) . Taken together , the results showed that a proper trade-off between cost and efficiency not only better recovers the individual links , but also simultaneously reproduces multiple statistical features of the real network as done by the generative model which explicitly targets such measures as its objective function . Recovery of connectivity by the trade-off model is not uniform and appears related to the functional network organization and spatial layout ( Fig 3 ) . Connectivity within the group of visual regions and within the whole frontal cortex is almost fully recovered ( Fig 3A , red ) . There is also a significant portion of inter-modal links that can be recovered ( mainly between frontal and other sub-systems ) . On average , 72 . 5% of links within functional systems can be recovered , but the links between functional systems have a lower recovery rate of 56 . 3% . The recovered links are typically short-distance ( Fig 3C , red bars ) . Overall , there are 33% links in the real network that cannot be reliably recovered with the best cost-efficiency trade-off model ( blue links in Fig 3A ) . The unrecovered links are mainly found within the visual system ( 38 . 6% ) and between the somatosensory and frontal systems ( 25 . 6% ) ; see Fig 3B for schematics of the distribution of unrecovered ( blue ) links in the real network . The unrecovered links are typically long-distance ( Fig 3C , blue bars ) . In the reconstructed network , these unrecovered links are redistributed ( see S3 Fig for the adjacency matrix A¯ of the reconstructed network and the schematics of redistribution of unrecovered links on the cortex ) , typically becoming short-distance links ( Fig 3C , green bars ) . Visual inspection of Fig 3A suggests that these unrecovered links ( blue ) are not uniformly distributed in the connectivity matrix , but are mainly concentrated on a few regions in the visual and somatosensory systems . To investigate the region-dependent recovery , we obtained the node recovery rate Rrecon ( i ) of the total links ( input and output ) of each region i by the reconstructed network ( Fig 4A ) . We further quantified the significance of Rrecon ( i ) with respect to that of random networks by the Z-score ( Fig 4B ) . ZR ( i ) > 1 . 65 ( corresponding to p<0 . 05 ) means that the reconstructed network shows a significant recovery of real links of a region relative to coincident recovery in random networks . Regions with low recovery rate in Fig 4A in general have an insignificant Z-score in [−1 . 65 , 1 . 65] . From Figs 3A , 4A and 4B , we observed the following features . ( 1 ) All frontal regions have high and significant link recovery rate , and only links with some somatosensory regions cannot be recovered ( Fig 4A ) ; ( 2 ) Most of the visual regions have ZR ( i ) > 1 . 65 . The majority of the unrecovered links of visual regions are within the visual system , except for a few links with frontal or somatosensory regions ( Fig 4A ) ; ( 3 ) Several somatosensory , temporal and motor regions have poor and insignificant recovery rate ( Z-score in [−1 . 65 , 1 . 65] ) . The insignificant recovery rate of regions is mostly due to a small total degree: 18 regions with Z-score in [−1 . 65 , 1 . 65] are within the bottom 22 regions ranked by total degree ( left to the vertical dashed line in Fig 4D , degree < 20 ) . However , the overall recovery rate for the regional connectivity profiles is quite low for many regions in the optimal generative model ( S4A Fig ) . Apart from the insignificant recovery rate of regions with degree <20 as in the cost-efficiency trade-off model , there are 33 regions with ZR ( i ) <1 . 65 ( p>0 . 05 ) ( S4B Fig ) , whose connectivity cannot be recovered by the generative model better than coincidence in random benchmarks ( all with the same fixed degrees ) . This number is almost three times as the number 12 of the cost-efficiency trade-off model ( Fig 3 ) . Specifically , the recovery rate for the hub regions is high with a large Z-score in the cost-efficiency trade-off model ( Fig 4D ) , but is quite low for the generative model ( S4C Fig ) . Since the cost-efficiency trade-off model not only better recovered the individual links ( Fig 1 ) , but also the regional connectivity profiles ( Fig 4 ) , the analysis in the following sections focuses on the cost-efficiency trade-off model . Interestingly , there are six regions with ZR ( i ) < − 1 . 65 in Fig 4B ( denoted by red stars ) , indicating that the connectivity of these regions in the real network is quite special: the best cost-efficiency trade-off model performs significantly worse than random coincidence for recovering the actual links of these regions . We systematically identified such regions with ZR ( i ) < − 1 . 65 in the real network when compared to model networks at different α . It was found that the number of such regions is minimal at α = 0 . 006 where the overall recovery rates r1 and r0 are maximal ( Fig 1A ) , while it is maximal at α = 1 ( S5A Fig ) . The total number of regions with insignificant recovery rate ( ZR ( i ) < 1 . 65 ) also shows a minimum at α = 0 . 006 ( S5B Fig ) . The six core regions in the real network whose connectivity profile is unexplained by cost-efficiency trade-off , identified by comparison with the best trade-off model at α = 0 . 006 , are visual ( 46 , FEF ) and somatosensory ( 23 , 29 , 3b , 5 ) regions . They are also among the regions with ZR ( i ) < − 1 . 65 at different α . Except for region 46 , five regions have intermediate degree ranks ( Fig 4D , red stars , 41 < degree < 70 ) . In total , there are 381 links from these six core regions , but they contribute 266 ( 31 . 4% ) of unrecovered connections . In Fig 4D , we also marked out six more regions ( 2 , AITD , STPa , 3a , SII and 7b ) , having intermediate or large degrees , but with a recovery rate not significantly lower than for random benchmarks ( − 1 . 65 < ZR ( i ) < 1 . 65 ) , by blue stars . Together with the former core six regions , these 12 regions ( see Fig 4E for the positions ) with intermediate or large degrees but poor recovery contribute about 60% of the unrecovered links . The connectivity of the six core regions and the other six regions in the real network and in the best cost-efficiency trade-off model at α = 0 . 006 is shown in Fig 5 . In the real network , these regions tend to connect with remote targets , but in the reconstructed network they mainly connect to spatially neighboring regions . Therefore , for all the 12 regions , the wiring length lp in the real network is large , very close to , or even larger than lpran in the random networks ( Fig 4C , lp/lpran ≳ 1 for the stars ) . Strikingly , the six core regions involve almost all of the most distant connections in the real network ( Fig 4F , red bars ) and the other six regions occupy a significant portion of intermediate-to-distant links ( Fig 4F , blue bars ) . Overall , the six core regions and the 12 regions contribute 38 . 1% and 64 . 6% of the long-distance links ( x > 30 mm ) , respectively . The above analysis clearly shows that the real network possesses more long-distance links and that they are distributed highly non-uniformly , concentrated on few regions , forming long-distance connectors ( LDCs ) . Previous studies have focused on hubs with a large number of connections [12 , 32 , 35 , 36] . Interestingly , however , the ratio of the wiring length lp of the hubs in the real network to lpran in the random network , which is just around 1 , is not as high as for the LDCs ( Figs 4C and 6A ) . Especially for the input connections , most hubs have relatively low lp/lpran ( ~65% ) . Although the hub regions have a large number of connections , the long-distance links represent only a small fraction of connections for the hubs ( < 0 . 4 , Fig 6B ) . Thus , the total wiring distance for the hub regions is more or less the same as for the LDCs , some of which is even less than LDCs ( Fig 6C ) . What could be the functional impact of the LDC regions which strongly violate the wiring cost economy of the network ? In neural systems it is desirable to achieve a balance of functional segregation and integration [37 , 38] . Network substrates providing such a balance may be densely connected modules which are not too strongly affected by other systems but are still properly interlinked by sparser connections among the modules [39 , 40] . More generally , a hierarchy of modules can provide segregation and integration across scales of organization [41] . Here , in order to explore the functional influence of the LDCs , we compared the functional segregation and integration of three types of networks . They were the real biological network , the reconstructed network ( at α = 0 . 006 ) with only about 30% of total links different from the real network , and the R-network ( see Methods ) in which the concentration of long-distance links on LDCs was destroyed , but the wiring cost was the same as in the real network . The primate cortical network analyzed in the present study was based on a previous dataset collated from the anatomical literature [18 , 43] , and there could be concern that the connections of some cortical regions may be incompletely characterized . Recently , a more systematic tract-tracing compilation of cortical connectivity of the non-human primate brain , including the relative weights of fiber projections was published , but only a partial square matrix dataset for 29 out of 91 targeted regions was presented [44 , 45] . While this relatively small dataset is not quite suitable for the trade-off modeling approach , it still allowed us to assess the reliability of our main findings ( see Material and methods ) . First , the primary dataset and the Markov et al . dataset [45] are consistent in different aspects when considering the overlapping subsets of nodes . ( i ) Most links within functional systems largely overlap in two datasets . However , the new dataset revealed more intermediate and long distance connections ( Fig 10A ) , which mostly consist of links among different sub-functions , e . g . , between visual and frontal systems; ( ii ) the average weight for the links of a given physical distance decays with the distance for the links found in both datasets and the links overlapping with the present dataset appear much stronger in weight than those newly found links revealed in [46] ( Fig 10B ) . This comparison confirmed that our dataset collated from the literature is reliable when compared to the more systematic new dataset , since it captured the significant and strong projections . The weaker links revealed in the new dataset would not make much contribution to the overall wiring cost in terms of neuron-to-neuron projection . Next , we examined whether the observed concentration of the long-distance links on a few LDC regions from the global network of our dataset , i . e . , the formation of LDCs , is also apparent in the new dataset . In particular , 4 of the 12 LDCs were examined in [45] , including 2 core LDC regions ( 5 and 46 ) and non-core LDC regions ( 2 and 7b ) . Similar to Fig 4F , we checked how much fraction of the projection weights in the distance bin was concentrated on these 4 regions in the new dataset ( Fig 10C ) . It is very interesting to see that , although the new dataset contains only partial connections ( i . e . , only the input projections ) , the distribution pattern in Fig 10C is very similar to that of the global network in our data in Fig 4F: the two core LDC regions 5 and 46 ( 1/12 of the 24 target regions ) , occupy 21 . 5% of the weight of long-distance projections ( > 30mm ) in [45] . The other two regions , 2 and 7b , occupy a significant portion of intermediate-distance projections ( 20 ∼ 35 mm ) . In all , the 4 regions ( 1/6 of 24 regions ) contribute 54 . 8% of the weight of long-distance projections ( > 30 mm ) . It is shown in Fig 4C in our global dataset that for the LDC regions , the total connection cost lp of the region in the real network with respect to that in the corresponding randomized networks , lp/lpran , is close to , or larger than , 1 . In the Markov et al . data [45] , we first calculated the wiring cost , then calculated a similar weighted ratio lp/lpran after incorporating the projection weights ( see Methods ) . In this case , the weight value of links to the same targeted region is comparable and reflects the projection density from different sources . The wiring cost in the new dataset was calculated by the weighted distance . Since weight information reflects the number of fiber projections , the weighted distance more accurately describes the corresponding wiring cost . However , the weighted network has incomplete cortical coverage ( only about 1/3 of the cortex ) ; thus , we can only check the consistency by comparing the weighted ratio for the 24 targeted regions to the ratio in our dataset ( Fig 4C ) which was obtained from binary global networks , as shown in Fig 10D . Interestingly , the ratio lp/lpran of most regions in our data is proportional to that in [45] . Except for region 46 , the remaining 3 LDC regions 5 , 2 and 7b appearing in the 24 targeted regions in [45] all have the largest lp/lpran values , which is also clearly illustrated in Fig 10E . As stated in Material and Methods , three regions ( 9/46d , 9/46v , 46d ) in the [45] data correspond to region 46 in our data . Thus , the calculation of the fraction of region 46 in [45] actually involves three targeted regions , thus may have some ambiguity . Besides the three LDC regions 5 , 2 and 7b , there are several regions with a large ratio of the wiring cost in the actual network relative to random benchmarks , such as the motor regions 6vb and 4 , and the visual regions TPOc and TPOr . In our global network data , these regions have small degrees , and the link recovery by the trade-off model is not strongly significant ( Z-score in [− 1 . 65 , 1 . 65] ) . To summarize , the comparison of our global dataset with the more recent , partial dataset of Markov et al . [45] showed that our main results are reliable and robust . Particularly , the connections in our data correspond to strong projections in the new data . The new study revealed that ( 1 ) there are even more long-distance connections , which are typically much weaker ( Fig 10A and 10B ) ; ( 2 ) a few LDCs identified in our dataset of the global network are included in the new dataset ( regions 2 , 5 , 7b , 46 ) , and reassuringly , these four regions also contain most of the long-distance connections in the new dataset ( Fig 10C ) , up to 54 . 8% of the total weights of long-distance links ( x > 30 mm ) ; ( 3 ) the total projection cost of these regions ( except for region 46 that is represented with some ambiguity ) is quite large with respect to random benchmarks in the data of [45] ( Fig 10D and 10E ) . These observations give us confidence that our main finding , of LDC regions violating the cost-efficiency trade-off , likely still holds when the dataset of Markov et al . [45] is expanded to an even more complete connectivity matrix in the future .
Recently , research using generative models has made progress in understanding complex brain network features , by employing a few simple spatial embedding and topological connections rules [13 , 14 , 24–26] . These generative models produce synthetic networks by systematically searching for parameters that explicitly attempt to reproduce multiple statistical features of the real connectome , namely the distribution of clustering , betweenness centrality , edge length , and so on . Generative models showed that projecting connections following a decay of the connectivity probability with distance as observed in the data can recover some statistical properties of real brain networks [24–26] . Some studies also found that the combination of various topological or spatial features well recovers the macroscopic topological properties [14 , 24 , 47] . Notably , these observed features may be results of basic constraints and organization principles [22] . For example , the topological feature of common neighbors may be rooted in the wiring cost dependence of connections in spatially embedded networks [25] . The connection probability following a distance decay could be the consequence of wiring cost economy , together with the functional requirement of maintaining efficient propagation of signals [14] . Different from approaches using some observed features to recover the other statistical properties of primate brain connectomes , the aim of the present study was to explore the role played by the two fundamental factors of wiring cost and global processing efficiency on the statistical features , the regional connectivity profiles as well as individual links of the macaque cortical network . Remarkably , we showed that the cost-efficiency trade-off model that has only one parameter and that does not integrate multiple topological and statistical features into the objective function can also recover these multiple features as well as the optimal generative model ( Fig 2 ) , and in addition can much better recover the connectivity matrix ( Fig 1 ) . In the cost-efficiency trade-off model , short and long-distance links can be more clearly related to the trade-off between cost and processing efficiency , and the connectivity profiles of most of the regions can be well recovered ( Fig 4 ) , except for a few special LDC areas with a particularly large number of long-distance links . The finding of the LDCs revealed new organization features of the cortical connectome and pointed at additional functional requirements of segregation and integration . The generative model , by contrast , did not well recover both short and long-distance links of many areas ( Fig 1C and 1D ) , and it is not as intuitive as for the trade-off model how to associate those uncovered links to functional values . A good recovery across multiple statistical features , regional connectivity profiles and individual connections in the trade-off model provides strong evidence to support the hypothesis of a trade-off of physical cost and functional values in the brain connectome . Our findings went beyond previous observations on structural and functional constraints on cortical networks . We found that some subsystems , such as the frontal subsystem , are almost fully recovered by the wiring cost minimization only ( α = 1 ) , with a recovery rate of 0 . 98 ( Figs 3A and 4A ) , consistent with previous reports based on component placement optimization [18 , 48] ) . However , several other studies showed that connections in most subsystems are clearly not optimized for wiring cost , and the whole network is not minimally wired [18] when the network topology is fixed in component placement optimization . Many essential topological properties of brain networks , such as the coexistence of modules and hubs , may be shaped by a trade-off between the wiring cost and processing efficiency when the reconstructed network is only required to have the same total number of links as the real network , as shown in our previous study [27] . Notably , the original generative model [14] with the combination of spatial distance and topological factors was not able to reliably recover the individual degrees ( S1A Fig ) , although it could better recover the degree distribution ( S1C Fig ) than the cost-efficiency trade-off model [27] ( S1D Fig ) . The trade-off model without fixing the degree [27] can also generate heterogeneous degrees due to the inhomogeneous spatial layout of the areas , and the degrees in the model network are significantly correlated with the degrees in the real network [27] ( S1B Fig ) . However , the correlation value ( 0 . 28 ) was not very high . Thus , these results suggested that the degrees are partially affected by the cost-efficiency trade-off , but it is still likely that the degrees are also affected by other functional requirements . In the present work , we thus fixed the input and output degrees of each node as in the real network , both for the cost-efficiency trade-off model and for the generative model . Preserving the degrees of each region thus already put some effects of the cost-efficiency trade-off on the benchmarks for reconstructing networks . Similar schemes of generating random benchmarks while preserving the node degrees have been widely used in network analysis . When fixing the degrees , some areas with a large degree are forced to have some long-distance connections , which increases the global efficiency and limits the freedom of variation of the network organization . Indeed , the graph distance Lg ( the reverse of efficiency ) varies much less ( 1 . 58~1 . 74 ) between efficiency optimal ( α = 0 ) and cost optimal ( α = 1 ) networks for fixed degrees , just half the range ( 1 . 62~1 . 91 ) compared to the model without fixing degrees [27] . Thus , the cost minimization ( α = 1 ) under fixed degrees already effectively reflects some trade-off between cost and efficiency , and the overall recovery rate of connections is already larger than 60% in the macaque cortical network . In the present work , a further trade-off with efficiency refined the cost-efficiency trade-off and further increased the recovery rates . In this case , rewiring a link from short to long distance more strongly affected the wiring cost Lp , but only slightly decreased the graph distance Lg ( or increase the processing efficiency ) . Thus , more weight was put on the efficiency to achieve a trade-off , corresponding to an α value close to zero ( 0 . 006 ) . However , it is important to stress that cost minimization is still effective here . The efficiency optimal network without the cost constraint ( α = 0 ) had a much larger wiring cost ( Fig 1A ) and much lower recovery rate ( Fig 1B , inset ) . The further trade-off with the processing efficiency clearly improved the recovery of some long-distance connections in the network , as shown in Fig 1C and S2 Fig , and simultaneously abolished many false-positive links at short distances ( Fig 1D ) . We further studied the distribution of the long-distance connections on different regions beyond the basic cost-efficiency trade-off and its relationship with functional segregation and integration . Except for regions with low degrees and unreliable statistics , there are only a few regions with intermediate and large degrees , whose connectivity profiles are not well explained by the basic cost-efficiency trade-off model . Our findings elucidated that these regions ( LDCs ) appear to be crucial for maintaining advanced functional requirements of segregation and integration . As for functional integration , previous studies have predominantly focused on hubs with the largest number of connections [35 , 49] . Recently , the grouping of such high-degree regions as a densely connected core or “rich-club” in the human and non-human primate brains has attracted great attention [11 , 32 , 50] . Consistent with the previous work , the hubs for the macaque cortical network , especially most frontal regions , such as 11 , 12o , 12l , 13a , 24 , and LIP , form a rich-club and have high diversity coefficient among different functions ( Fig 9A ) . Interestingly , these regions project most connections at short distance , and can be recovered well by the trade-off model ( black triangles in Fig 4 ) . Together with our previous findings that hubs are located close to the regional geographical centers , such an organization is wiring economical for the high-degree hubs . Previous study has also revealed that the regions with long-distance connections have a high diversity coefficient among different modules in the mouse brain connectome [26] . Here we showed consistent results in the macaque connectome that there are also LDC areas possessing a high diversity coefficient , but these areas mainly have intermediate degrees and are non-hubs structurally , while also forming a strong dense group . These regions appear to be crucial for integration among functional clusters of the macaque brain connectome . Indeed , most LDCs correspond to functional hubs , some of which belong to the default mode network . In fMRI studies , functional hubs are detected as regions of a high density of functional connectivity with other regions [11 , 36] . Previous studies on the intrinsic activity of the brain identified functional hubs such as the precuneus , posterior and anterior cingulate gyrus , dorsomedial frontal cortex , as well as inferior parietal regions [31 , 51 , 52] . Among the LDCs that we identified for the Macaque structural network , there are several regions overlapping with the functional hubs in human or macaque brain , such as areas 7b ( inferior parietal cortex ) [4] , 23 ( posterior cingulate cortex ) [53] , 46 ( DLPFC ) [6] , and AITd ( anterior inferior temporal cortex ) . Notably , area 23 and 46 are also hubs in the human structure network [30 , 54] . Some of the LDC regions , for instance , areas 23 , SII and 46 , also overlap with the default mode network [55 , 56] . The overlapping of some LDCs , consisting mainly of non-hubs and a few hubs in structural connectivity , with functional hubs and the default mode network , suggests that concentration of long-distance connections on LDCs may constitute an anatomical substrate for functional integration , in addition to possessing an intermediate or large number of connections . It would be interesting to extend a similar analysis to human brain to investigate whether there exist long-distance connections concentrated on LDCs that also play important roles in functional performance . However , counter-intuitively , the formation of LDCs also appears important for proper functional segregation . Recent fMRI studies showed that cognitive functional domains are segregated into different clusters of functional connectivity [4–8 , 10–12 , 57 , 58] . Different from these studies , the current work did not involve functional imaging data for macaque . The clustering analysis here is applied to the structural cortical network and compared with the anatomical functional domains distinguished by cytoarchitectonic and myeloarchitectonic features in previous studies [2 , 3] . In the future , it will be interesting to compare the structural clusters with functional modules [6 , 8] . Our results demonstrated that , under the basic cost-efficiency trade-off , the modeled connectome has a much smaller number of long-distance connections , and only possesses two large functional domains , namely , visual and frontal systems ( Fig 7D ) , mixed with areas from other systems . It appears somewhat counter-intuitive that the real network with many more long-distance connections possesses an intricate segregation of the system into visual , frontal , somatosensory and motor functional subsystems ( Fig 8 ) . Here the important point is that the long-distance connections are largely concentrated to LDCs , so that modules can be better preserved by the intra-modular , short-distance connections , and are not strongly mixed by the inter-module long-distance connections . Indeed , if we preserve a similar number of long-distance connections in the real network , but rewire them to avoid high concentration on particular nodes ( i . e . , abolishing LDCs ) , the partition of modules ( clusters ) and the matching with functional divisions is destroyed ( R-network , Figs 7 and 8 ) . Importantly , our findings provide new insights on the organizing principles of primate cortical connectivity . ( 1 ) A large number of white matter projections follow the trade-off between the parsimonious requirements of economical wiring and efficient processing , to group the cortical areas into spatially segregated functional domains . ( 2 ) To support integrated functional performance , the hubs ( e . g . , mostly in the frontal system and visual systems ) with short-distance connections link the segregated functions of neighboring regions . ( 3 ) Furthermore , to integrate the multimodal functions of remote regions , it appears to be necessary to sacrifice the wiring economy of a few regions ( i . e . , LDCs ) . Indeed , LDCs concentrate nearly 2/3 of long-distance connections ( Fig 4F ) and form a dense group ( Fig 9C ) , although most LDCs have average degrees ( Fig 4D ) . In this way , the organization of intermediate-degree LDCs not only shares the load of integrating spatially segregated functions , but also reduces the wiring cost burden of the outstanding hub nodes by absorbing most of the long-distance connections ( Fig 6 ) . Furthermore , concentrating the long-distance connections on LDCs also allows to maintain a proper segregation of the system into intricate subsystems for specialized processing . These new findings provide further support for the hypothesis of a trade-off between physical cost and functional values in the brain network organization . Importantly , LDCs might be spots of vulnerability in the brain network . Recent studies suggested that functional hubs partially overlap with regions of high metabolic rate and deposition of disease-related agents ( such as Amyloid plaque ) observed from PET data [31 , 36 , 59] , and are also vulnerable in various neurodegenerative diseases and mental disorders [60–62] . Several studies have started to reveal the importance of long-distance connections for the energy consumption in human functional networks [63] . Other work also indicated relationships between structural hubs and brain disorders or diseases [19] . Intuitively , structural hub regions with a large number of fiber connections may have high metabolic demands , and therefore become vulnerable in disorders and diseases . On the one hand , attaching a large portion of the long-distance links to intermediate-degree LDCs may reduce the risk of energy deficits in hubs . On the other hand , non-hub LDCs , carrying a strong inter-functional communication and integration load based on long-distance connections , might also become metabolically highly demanding , and may also be vulnerable to disruptions in energy supply and other attacks . Indeed , some LDCs correspond to vulnerable regions , such as areas 23 and 46 , in chronic progressive neurodegenerative diseases [36 , 64] . Our work , thus , provides a fresh perspective for investigating the relationship between multiple constraints and disease vulnerability in the structural and functional networks of the human brain . In the current work , our results are subject to some inherent limitations imposed by the experimental data . For example , recent data [45] provided weighted macaque cortical connectivity , which we used to test the reliability of our findings ( Fig 10 ) . However , the weighted dataset , while more detailed than previous data collations , is far from complete . Thus , it will be promising to re-explore basic factors on structural connectivity once a more complete weighted connectome is available . Presently , there may be concern that incompleteness of the CoCoMac database may contribute to our finding that most of the LDCs are not high-degree hubs of the network . The Markov et al . data [37] showed that many brain regions are connected by very weak fiber projections , leading to high density of binary connectivity at the area level , which might imply that there are not really any outstanding hubs . Measuring degrees by binary connectivity , that is , regarding many very weak links as equally important as the strong ones appears not very appropriate , given that the weight values span orders of magnitude . Consequently , the total weight of a node may be a more reasonable measure of degree ( appropriately reflecting connectivity at the level of the relative fiber density ) , and the total weights are mainly contributed by the strong links . Indeed , the total input weight of the areas covered so far is quite heterogeneous . Thus , it is likely that the network still has hubs in terms of total node weight , if the Markov et al . data are extended to provide complete cortical coverage . Our detailed comparison between the two data sets ( Fig 10 ) showed that our primary dataset , which was collated and further developed from CoCoMac , is reliable and captures the majority of the significant and strong projections for the corresponding areas covered in the Markov et al . data . Therefore , measuring the binary degree in our un-weighted global network appears as a reasonable estimation of the total weights of a node . Our observation , that four of the LDCs identified in our dataset also concentrate the majority of the long-distance projection weights in the Markov et al . data , suggested that the LDCs would remain as LDCs if the new data set would be completed . It is also not very likely that these areas will be drastically changed to hubs in terms of total weights , which could happen only if many and a large portion of strong links were missed for these areas in the CoCoMac data . Therefore , it will be interesting to test if LDCs can indeed be identified as non-hubs of the structural connectivity once a more complete weighted connectome becomes available . The cost-efficiency trade-off model with fixed degrees can recover most of the connections of the whole macaque cortical network , which allows to reveal new organization features by further analysis of a few regions with low recovery rates by the model . However , fixing degrees limits the capacity of the model to explore the mechanisms underlying important features in the degree sequences , such as degree distribution , hubs and degree correlations . Further studies of the cost-function trade-off may need to develop more sophisticated quantification of the functional values related to degrees in order to better elucidate the underlying mechanisms . While the global efficiency of the interregional cortical network may partially capture the basic functionality of the brain at the systems level by using the strongly simplified assumption of identical network nodes , it should be stressed that actual brain functions are implemented by highly specialized cortical areas , comprising heterogeneous local circuits and displaying complex dynamical activity . Interestingly , these two levels of organization are also interrelated . For example , similarity in regional neuronal density is closely related to the probability of interregional connection [65–68] , and the regional synaptic spine density ( and consequently the response time of the local circuit ) is related to a gradient of cortical areas [69] ( which is also roughly related to the total degrees of areas ) . Therefore , most likely the basic principle of a trade-off between physical cost and functional values could be operating to shape the network structure and dynamics across different levels . Our own recent work [70] showed that the co-organization of salient multi-scale dynamical features as typically observed in electrophysiological experiments , including irregular firing of individual neurons , clustered firing of neuron groups in the form of critical avalanches and the emergence of stochastic oscillations of the population , indeed reflects a cost-efficient neuronal information capacity with economical firing rates . In the future , it will be important to study cost-efficiency trade-offs in an integrated manner in terms of both neuronal connectivity and activity and in specific neuronal information processing tasks across multiple levels of brain organization . Our study suggests that primate anatomical connectivity , comprising characteristic topological features as well as specific regional connectivity profiles and individual connections , is shaped by a basic cost-efficiency trade-off as well as advanced functional requirements , reflected by a special group of long-distance connector regions that are crucial for functional segregation and integration . Together , these findings support the hypothesis of a trade-off between physical cost and functional values in brain network organization [13] . Our work , moreover , illuminates the potential inherent vulnerability of the cortical connectome as a result of the competition between energy cost and functional values , which were not identified by previous topological analyses of cortical connectivity .
We analyzed the connectivity of the macaque cortical network and its relationship with the three-dimensional spatial layout of the network components and compared the original network to various reconstructed networks in order to understand the impact of multiple structuring factors . The analyzed macaque connectivity data was based on anatomical tract-tracing and adapted from a dataset of 94 cortical regions and 2 , 390 directed projections among them [18] . The connectivity data and three-dimensional spatial positions ( the average surface coordinate ) of each cortical region were obtained from http://www . biological-networks . org . However , the dataset did not provide complete coverage of cortical regions . Especially the divisions of motor regions were quite coarse , with incomplete connection data of several regions ( e . g . , motor regions 4 and 6 which cover a large territory , 6 . 5% of neocortex ) . In a previous study [27] , we improved and expanded the dataset to 103 regions using a more detailed parcellation of the motor regions based on the CoCoMac database [43] . The spatial positions of the newly added regions were taken as the average surface 3-D coordinate estimated from surface parcellation using the CARET software ( http://sumsdb . wustl . edu/sums/index . jsp ) . Consequently , the improved dataset was also used in the present study . The cortical network of the nonhuman primate ( macaque monkey ) studied here has N = 103 regions and K = 2518 connections in total [27] . The labels of the regions are listed in S1 Table of SI . This network was also compared to a recent systematic tract-tracer study [45 , 71 , 72] , for which , however , only an incomplete square matrix ( for only 29 out of 91 candidate regions ) is currently available; see details in the final section of Results . We reconstructed the cortical network connections based on a variety of objective functions , while preserving the spatial positions as well as both the input and output degree of the regions as in the real cortical network . Network connectivity can be described as a matrix {Aij} with Aij = 1 if there is a link from region j to i , and Aij = 0 otherwise . The reconstructed networks were obtained by minimizing an objective function combining the wiring cost and processing efficiency , L= ( 1−α ) Lg+αLp , ( 1 ) where α is a parameter to represent the relative weight of the normalized physical length Lp=lp/lpmax which reflects the influence of the wiring cost , and the normalized graph length Lg=lg/lgmax , representing the influence of the processing efficiency . Here lp is the total wiring length of the links and lg is the sum of the shortest path lengths between all pairs of nodes in the network . lpmax is obtained at α = 0 when minimizing lg without considering the wiring cost , and lgmax is obtained at α = 1 when minimizing lp without considering the efficiency . In the simulation , we computed lg as the reciprocal value of the global network efficiency , lg = 1/eg , where eg is defined as eg=1N ( N−1 ) Σi≠j∈G1lij where lij is the shortest pathlength between the nodes i and j [73] . In this way , we avoided the numerical problem of isolated nodes ( where some path lengths would be ∞ ) . Disconnection of nodes can be naturally avoided in the optimization processes for α < 1 , because disconnection leads to large lg The fiber length between the regions was estimated by Euclidean distance between the spatial positions of the regions . Euclidean distance is inexact , because the fiber tracts do not strictly follow the straightest trajectory . However , based on the linear proportional relationship between the fiber length and Euclidean distance within a hemisphere [74] , the Euclidean distance is a good approximation of fiber length , and has been widely applied for the primate brain [49 , 75 , 76] . The wiring length lp is taken as the sum of the distances between connected areas . We applied a simulated annealing optimization algorithm [77] to search for network configurations that minimize the objective function L . The algorithm was implemented as follows: starting with a random network and a high temperature T0 , the temperature was reduced asTn+1 = Tn/n . At each temperature level , the network was rewired for 1000 steps by exchanging the connections of two pairs of randomly selected notes ( disconnected networks were discarded ) . If L after switching was smaller than before switching , i . e . , ΔL<0 , the switching was accepted; otherwise , the operation of switching was accepted with a probability exp ( −ΔL/T ) . The program was terminated whenΔL ≤ 10−5 [27] . Reconstructed connectivity {Aij¯} was obtained for each α from 50 realizations of the optimized networks from different initial random networks . The probability for finding a link is Pij = Nij/50 , where Nij is the number of realizations with a link from area j to i . The reconstructed connectivity is {Aij¯}=1 if Pij ≥ PT and Aij = 0 otherwise , where the threshold PT is set for a given α such that the total number of links of the reconstructed network is the same as the real network K = 2518 ( in fact , the closest possible value to K , due to discreteness of Pij from 50 realizations ) . The thresholds differ slightly at different α , but all are larger than 0 . 5 , which means that the corresponding link is appearing in more than 50% of realizations , showing good consistency of the optimization algorithm . In this part of the study , we compared the performance of recovering different statistical features , regional connectivity profiles as well as individual connections of the real macaque connectome by the cost-efficiency trade-off model and a recently proposed generative model [14] . This generative model aims to generate model networks by combing spatial and topological rules , and searches for parameters that can best reproduce multiple statistical features of the real network . Starting with a sparse seed network ( 464 edges among 16 regions , both randomly selected , about 15% of the nodes and links of the real network [14] ) , edges were added one at each time over a series of steps until the remaining M = 2518–464 = 2054 total connections were added . At each step , the unconnected nodes , u and v , were connected with a probability P ( u , v ) , which is given by: P ( u , v ) =E ( u , v ) η×K ( u , v ) γ , ( 2 ) where E ( u , v ) denotes the Euclidean distance between brain regions u and v , and K ( u , v ) represents a non-geometric relationship between nodes u and v , which contains 12 different generative models as listed in S3 Table . We applied the matlab function “generative model . m” of the Brain Connectivity toolbox ( https://www . nitrc . org/projects/bct/ ) to generate the synthetic networks at different control parameters η and γ . The optimal generative model can closely recover the degree distribution ( S1C Fig ) , but it cannot reliably reproduce the degree sequence , since the ( total ) degree of each node from the model is not significantly correlated with that of the real network ( S1A Fig ) . On the other hand , although the cost-efficiency model without fixing the degrees does not recover the degree distribution as well as the generative model , it could slightly better recover the degrees of nodes , and the correlation between the degree of model and the real network becomes significant ( S1B and S1D Fig ) . To have a fair comparison of the generative model with the cost-efficiency model under fixed degrees , we extended the generative model to have the constraint of fixed degrees as in the real network . The model network is obtained by rewiring initial random network while fixing degrees to approach the wiring probability as that in the generative model ( Eq 2 ) . Starting with a random network with the degrees fixed as that in the real macaque brain network , randomly pick two ( directed ) links connecting two pairs of regions ( u1 , v1 and u2 , v2 ) , given that there are no crossing-connections between the two groups , i . e . , A ( u1 , v1 ) = 1 , A ( u2 , v2 ) = 1 , A ( u1 , v2 ) = 0 , and A ( u2 , v1 ) = 0 . Then with a probability P ( u1 , v2 ) * P ( u2 , v1 ) , we exchanged the connections for these two pairs of region , namely A ( u1 , v1 ) = 0 , A ( u2 , v2 ) = 0 , A ( u1 , v2 ) = 1 , and A ( u2 , v1 ) = 1 . Here P ( u1 , v2 ) *P ( u2 , v1 ) =E ( u1 , v2 ) η×K ( u1 , v2 ) γ*E ( u2 , v1 ) η×K ( u2 , v1 ) γ ( 3 ) is the probability to place simultaneously two independent links following the generative rules ( Eq 2 ) as described above . The rewiring will repeat for large enough time steps until 200 , 000 pairs rewired . So the network is supposed to follow the generative rules , but maintaining the input and output degrees as in the real network . K ( u , v ) reflecting topological relationship also contains 12 different generative models as listed in S3 Table . We generated the rewired networks at different control parameters η and γ as in the case without fixing the degrees . In the previous work [14] , the optimized generative network was obtained by searching the parameter space ( η , γ ) to achieve the lowest energy , which quantifies the similarity of the generated network to different features in the real network . The energy of the generated network was defined as: E=max ( KSdegree , KSclustering , KSbetweenness , KSedgelength ) , where KS is the Kolmogorov-Smirnov statistics quantifying the discrepancy between the synthetic and the real macaque cortical network in terms of their statistical distribution of degree , clustering , betweenness centrality , and edge length . For the extended generative model with fixed degrees as in the real network , KSdegree = 0 , which does not affect the definition of energy as the upper bound of the measures . Clustering measures the fraction of a node’s neighbors that are connected to each other . Betweenness centrality of a node is the number of the shortest paths in the network that pass through the given node . Edge length refers to the Euclidean distance between two regions of the connection . Thus , the optimization process searches for the network configuration under the generative rules that maximizes the similarity to multiple statistical features of the real network . When applied to the macaque connectome , the generative model with the lowest energy is obtained when the spatial distance is combined with the matching index ( i . e . , K ( u , v ) , which is the ratio of common neighbors to total neighbors of two nodes u and v ) , as is consistent with a previous study of the human connectome [14] ( Fig 1B ) . The different KS measures were also applied to the reconstructed network from the cost-efficiency trade-off model , and compared with KS for the optimized synthetic network from the generative model ( Fig 2 ) . The adjacency matrices A¯ of the reconstructed networks ( from both generative model and cost-efficiency trade-off model ) were compared to A of the real network in different ways . ( 1 ) We counted the number of overlapping entries between A¯ and A , obtaining Kr1 and Kr0 respectively for the connected pairs ( Aij = 1 ) and unconnected pairs ( Aij = 0 ) in the real network . The corresponding recovery rates were r1 = Kr1/K1 and r0 = Kr0/K0 for the K1 = K entries Aij = 1 and K0 = N ( N − 1 ) − K non-diagonal entries Aij = 0 . The values r1 and r0 were also obtained , to measure the recovery rates for pairs of areas separated within a range of distances ( Fig 1B and 1C ) , and to quantify the recovery of connectivity of each area in the network ( Fig 4 ) . Recovery rates from the reconstructed networks were compared to random benchmarks where the number of input and output links of each area was preserved as in the real network , but the connections were randomly rewired by exchanging links of two pairs of randomly selected areas . As detailed in the Results , there are about 33% of connections in the real networks which cannot be recovered by the best reconstructed network from the cost-efficiency trade-off model . The unrecovered connections are mainly long-distant links and are concentrated on a few special areas termed long distance connectors ( LDCs ) . To study the impact of the concentration of links on LDCs , we randomly rewired the unrecovered links of LDCs to obtain an ‘R-network’ while preserving the distribution of physical distances as in the real network . In this way , only the concentration of long-distance links on LDCs was destroyed while the wiring cost remained the same . Thus , comparing the real network with R-networks , we could explore the functional influence of LDCs while excluding the contribution of the wiring cost . The R-network had similar efficiency ( lg = 1 . 81 ) as the real network ( lgreal = 1 . 85 ) , but its long-distance connections were not concentrated on the LDCs . Applying established modular division methods [78] , we found that there are only two modules in the real macaque cortical network [27] , roughly separating the visual and frontal cortex , both mixed with areas from other functional systems . The reason , why the traditional modular division does not well capture the functional segregation , may be because both the size and the intra-connection density for different functional subsystems are quite heterogeneous . To better illustrate and detect the relationship between the clustering of anatomical connectivity and functional segregation , we studied the clustering of the brain cortical areas by analyzing the hierarchical tree of the connectivity matrix . The hierarchical tree ( Fig 7 ) was obtained using the similarity measure {Sij} computed from the connectivity matrix A , Sij = Mij/ ( Ki + Kj − Mij ) , namely , the ratio of common neighbors Mij over the total distinct neighbors of two nodes i and j . The MATLAB function pdist was used to obtain the hierarchical tree ( dendrogram ) using the dissimilarity 1 − Sij . The connectivity modules from the sub-trees cut out at different thresholds ( see below ) were compared to functional subdivisions of the network as described in the next section . The limitation of the traditional modular division not well capturing the functional segregation is due to the fact that a uniform threshold is chosen to maximize the modularity , which however mixes different trees . To calculate the degree of matching between the hierarchical trees and brain functions , we first identified sub-trees dominated by a certain function . We cut the hierarchical tree from the top , with the threshold varying from 1 to 0 , to obtain different sub-trees dominated by the corresponding functions . At each threshold , we calculated for each sub-tree the fraction of regions from different functional subsystem with respect to the total number of regions in the tree . Once the fraction for some functional subsystem in specific sub-tree was larger than 0 . 5 , this sub-tree was retained and not divided further . We cut the hierarchical trees until we found all sub-trees with the fraction of regions from some functional subsystem to all regions in the sub-tree larger than 0 . 5 and the number of regions from a dominating function larger than 3 , since further partition would make too many fragmental and small trees . We illustrate the method by detecting the subtrees of the hierarchical tree in the real network in Fig 8A . At threshold value 0 . 67 , the first sub-tree ( Tree 1 ) was obtained containing only motor areas ( matching rate 100% ) . The other large branch at this threshold at the right part of Fig 8A contains cortical regions from different functional systems and is not dominated by one of the functions . Then we proceeded to cut this branch further into two major branches at a lower threshold of 0 . 65 and obtained the second sub-tree ( Tree 2 ) containing only visual regions ( matching rate 100% ) . Now the remaining middle branch still contains regions from different functions , and the process continued until we obtained all sub-trees where the number of regions from a dominating function was larger than 3 . The remaining regions that were not included in the trees identified above were put into a group called”non-clusters” . In this way , a total of 6 sub-trees were obtained for the real network , as indicated in Fig 8A , and they are graphically presented in Fig 8B of the main text . The ratio of dominance for a certain function in each of the hierarchical trees in the real network is very high , 100% for 5 trees and 86% for tree 4 ( with just 1 region mismatched ) . For each region , we calculated how the links were distributed among the five functional systems ( visual , somatosensory , motor , temporal and frontal ) , pi ( J ) =ki , Jki , where ki is the total degree of region i and ki , J the number of connections linking region i with regions of functional system J . The functional diversity coefficient of a region can be indexed by the entropy of the link distribution as: Ci=−∑J=15pi ( J ) ln[pi ( J ) ] , as applied in [79] . If node i ∈ J has only internal links within a functional system J , then Pi ( J ) = 1 and Pi ( J′ ) = 0 for any other J′≠ J , hence the functional diversity coefficient Ci = 0 . For the opposite case , if the links of a region i are uniformly distributed in 5 different function regions , then Ci achieves the maximum value ln 5 = 1 . 61 . A set of nodes in the network is said to form a dense group when the connection density θ among the nodes is significantly larger ( by above one standard deviation ) than the expected connection density due to the nodes′ degrees in randomized networks . The calculation of the density is exactly the same as done for rich-clubs of the degree-rich subsets . Such dense groups formed by degree-rich hubs are called rich-clubs [32] . In this study , we assessed the properties for the groups formed by LDCs , which mainly are non-hubs . The connection density θ among LDCs is significantly higher than for random benchmarks ( Fig 9B ) , thus they form a “dense group” . The primate cortical network used in this study has 103 regions and 2518 connections in total . However , such a dataset established from the collation of several individual studies available in the literature may have some limitations; for example , the connections of some cortical regions may not be as well characterized as others . Thus , it is important to verify the reliability of the data with reference to other datasets and test the robustness of our findings . Specifically , the group of Kennedy has recently undertaken a systematic effort to gather cortical connectivity data for the macaque monkey using retrograde tracer injections , resulting in a series of papers [30 , 45 , 71 , 72] about the partial network of 1615 connections formed by afferent ( input ) links to a subset of 29 targeted regions from a total of 91 cortical regions . These targeted regions are sampled from different functional systems . This dataset provides the weight index ( FLNe index ) for region-to-region connections , which represents the fraction of labeled neurons in a source region relative to the total number of labeled neurons from all possible source regions extrinsic to a targeted region . However , there are two major limitations and challenges in directly employing this dataset in the present cost-efficiency trade-off study . Firstly and most importantly , the dataset is still far from being complete ( providing only about 1/3 of coverage of the whole cortex , and only the input connections are complete for the covered areas ) ; thus , is not representative for applying the cost-effciency trade-off model which considers the wiring cost and processing efficiency of the global network . Secondly , the weight index provides the relative strength of the input of a targeted region from different sources , and the efficiency measure and the rewiring procedure would need to consider the projections weights . Note that the projection weights in macaque have a very broad range , spanning 5-order of magnitudes [37] . While very weak links between areas could still be functionally useful , at least at the neuronal level , a simple linear measure of the contribution of the connection weights to the efficiency would not be able to capture its subtle functionality . This issue needs further exploration in the future . Our detailed comparison between the two data sets ( Results , Fig 10 ) showed that our dataset collated and developed from CoCoMac is reliable when compared to the Markov et al . dataset [80] , since it captures the significant and strong projections of the corresponding areas covered in the Markov et al . dataset . Therefore , the CoCoMac data , providing relatively complete cortical coverage , but incomplete characterization of some of the moderately or very weak links , are more suitable at this stage for applying the cost-efficiency trade-off model . While the dataset is expected to become more complete in the future and an improved cost-efficiency trade-off analysis including the weighted information could then be developed , the current partial information may still be useful for some comparison with our dataset to provide an indication of the reliability and robustness of our findings . The parcellation of the data by [45] was based on the Felleman and van Essen ( 1991 ) atlas . The present data is also based on the Felleman and van Essen ( 1991 ) atlas , combined with the Lewis and van Essen ( 2000 ) atlas for a more detailed parcellation of the motor system [42] . By comparing the different atlases in CARET , we found that the 91 regions [45] correspond to 74 regions in our data . The 29 targeted regions correspond to 24 regions in our dataset , while the regions STPr , ProM , 10 have no correspondence in our data , and regions 9/46d , 9/46v , 46d all correspond to region 46 in our dataset . We carried out more detailed analyses of the common sub-matrices of 74 × 24 nodes in both datasets . The links of our data in the 74 × 24 sub-matrix highly overlap ( 78 . 6% ) with those by [45] . For this sub-matrix , only few links in our data do not appear in [45] . In this study , we evaluated the wiring cost in the real network by comparing it to that of the random network with the same input and output degrees as in the real network . The fraction of wiring cost with respect to random benchmarks was calculated by lp/lpran both in 24 regions of data by [45] ( afferents from 74 regions ) and our global network data ( afferents from 103 regions ) for comparison . Importantly , since the data by [45] contain the afferent projections to the 24 regions , we only consider the afferent direction in calculating lp/lpran for both datasets . For the weighted links , we can obtain the projection cost ( weighted distance ) for each targeted region i as lpi=∑jwij*Aij*dij , where Aij = 0 or 1 represents the existence of a projection to region i from region j , wij is the weight and dij is the Euclidean distance between region i and j . To obtain corresponding random networks , the weighted link wij was randomly shuffled for the index j among the 74 cortical regions and the cost lprani calculated accordingly . For unweighted links in our data , wij = 1 for all the links . | The intricate primate structural connectome , as the network substrate for distributed and integrated brain function , is shaped by fundamental physical factors and functional requirements . We addressed a trade-off between two competing basic factors of wiring cost and processing efficiency as well as additional requirements of functional segregation and integration on regional structural connectivity profiles by applying cost–efficiency trade-off model to reconstruct the macaque cortical network . We also compared this model with a generative model combining spatial distance and topological similarity . The trade-off model balancing the two basic factors recovered the main topological features that were explicitly specified as the objective functions in the generative model . Moreover , 67% of all connections and most regional connectivity profiles were recovered by the cost-efficiency trade-off , substantially outperforming the generative model . Most long-distance links , unexplained by the cost-efficiency trade-off , are concentrated on a few special regions constituting long-distance connectors ( LDCs ) . Departing from previous findings , LDCs are mostly non-hubs , but are crucial for supporting functional integration and , counter-intuitively also for proper segregation across different hierarchical scales of the cortical network . The perspective of a trade-off between basic factors on brain structural organization sheds light on the organization principles and potential vulnerabilities of the cortical connectome . | [
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Globally there are an estimated 390 million dengue infections per year , of which 96 million are clinically apparent . In Cambodia , estimates suggest as many as 185 , 850 cases annually . The World Health Organization global strategy for dengue prevention aims to reduce mortality rates by 50% and morbidity by 25% by 2020 . The adoption of integrated vector management approach using community-based methods tailored to the local context is one of the recommended strategies to achieve these objectives . Understanding local knowledge , attitudes and practices is therefore essential to designing suitable strategies to fit each local context . A Knowledge , Attitudes and Practices survey in 600 randomly chosen households was administered in 30 villages in Kampong Cham which is one of the most populated provinces of Cambodia . KAP surveys were administered to a sub-sample of households where an entomology survey was conducted ( 1200 households ) , during which Aedes larval/pupae and adult female Aedes mosquito densities were recorded . Participants had high levels of knowledge regarding the transmission of dengue , Aedes breeding , and biting prevention methods; the majority of participants believed they were at risk and that dengue transmission is preventable . However , self-reported vector control practices did not match observed practices recorded in our surveys . No correlation was found between knowledge and observed practices either . An education campaign regarding dengue prevention in this setting with high knowledge levels is unlikely to have any significant effect on practices unless it is incorporated in a more comprehensive strategy for behavioural change , such a COMBI method , which includes behavioural models as well as communication and marketing theory and practice . ISRCTN85307778 .
With up to 3 . 9 billion people in 128 countries at risk of the disease , dengue affects most of the world’s tropical and sub-tropical regions and has become the most rapidly spreading mosquito-borne viral disease[1 , 2] . There are an estimated 390 million infections per year , of which 96 million are clinically apparent [3] . There is currently no cure available for dengue . In 2015 , the first ever dengue vaccine , Dengvaxia ( Sanofi-Pasteur ) , came on the market despite having 60% efficacy and inducing very low protection against DENV-2[4 , 5] . It will also likely be several years before the vaccine is made available in low-income countries such as Cambodia . Due to the lack of a readily available vaccine or therapeutics , vector control is the only means of dengue prevention . There are an estimated 185 , 850 dengue cases in Cambodia annually [6 , 7] . Since the early 1990s , the primary means of vector control by the Cambodian National Dengue Control Program ( NDCP ) has been the use of the organophosphorous larvicide temephos , under the trade name Abate , applied in water storage containers [8] . However , evidence of Ae . aegypti resistance to temephos has been found in Cambodia and in other parts of Southeast Asia since 2001 [9–13] . Thus , it is clear that alternative vector control strategies are needed . Previous studies have demonstrated the most effective vector control approaches used community-based methods tailored to the local context [14–17] . In order to develop a successful strategy , it is therefore crucial to gain an understanding of current knowledge and practices regarding vector control and dengue fever in the communities . This study aimed to evaluate people’s knowledge of the dengue vector and control methods , their perceived risk of dengue fever , and to compare reported versus observed household practices . Knowledge , Attitudes and Practices ( KAP ) , and entomology surveys were conducted to ascertain these measures and respective relations .
The study took place in Kampong Cham , a large and populous province located in central Cambodia , chosen for its high dengue incidence [18] . Being a largely rural province , the main occupation is agriculture , and it has literacy rates of 74 . 8% for women and 81 . 3% for men[19] . The data were collected as part of a larger cluster randomized trial set up to evaluate the effect of placing guppy fish and WHO-approved insect growth regulators in household water containers on adult female Aedes aegypti mosquito densities . The detailed protocol can be found in a previously published manuscript[20] . Briefly , 30 clusters containing one or more villages were randomly assigned to three different arms . Arm 1 received guppy fish in large water containers , the insect growth regulator in smaller containers and communication activities to promote community engagement and uptake , based on the Communication and Behaviour Impact ( COMBI ) approach ( 20 ) . Arm 2 received only guppy fish and COMBI activities . The third arm , the control , received only standard vector control activities from the Ministry of Health which includes outbreak response in villages with three or more cases . Prior to the trial , a baseline KAP survey was administered to one participant from each of 600 randomly selected household ( 20 HHs per cluster in 30 clusters ) . Additionally , baseline entomology surveys were conducted in 1200 households ( 40 per cluster in 30 clusters ) . The KAP survey was administered to participants in September 2015 , prior to the start of the interventions . The survey questionnaire ( S1 Survey ) was formulated using data previously collected as part of focus group discussions and in-depth interviews in a neighbouring community . The survey was piloted in a village in Kampong Cham not involved with the study ( 20km from study site ) to assess comprehensibility and refine the formulation of questions . Following written informed consent , the questionnaire was administered face-to-face in Cambodian language to all participants , and included both structured and open-ended questions about participants’ knowledge of the dengue vector , the vector’s breeding sites and breeding prevention methods as well as dengue symptoms and treatment . The KAP survey also measured household wealth using a set of questions on asset ownership based on those included in the national Demographic and Health Survey ( DHS ) and adapted to the local context [21] . This information was used to generate a measure of socio-economic status ( SES ) which is further described below . A detailed description of the methods for both the entomology and KAP survey is provided in a previously published paper [20] . The baseline entomology surveys were done at the same time as the KAP surveys . The methodology used was based on the WHO guidelines for entomological collections [2] . All containers in surveyed households were inspected . Larvae and pupae collection in containers larger that 50L was conducted using the five sweep method [22] . The contents of smaller containers were emptied out into the sweep net . Resting adults were caught with a portable aspirator ( Camtech , Phnom Penh , Cambodia ) : the walls in bedrooms and living spaces were aspirated in a clockwise manner up and down the wall for 10 minutes per house . Data were double entered into EpiData ( EpidData Association Denmark ) by an experienced external data entry team . Textual data collected in open-ended questions was translated into English and coded . All analysis was done using the statistical analysis software Stata 14 . 1 . Principal component analysis ( PCA ) using durable asset ownership was performed in order to classify participants into socio-economic quintiles [23 , 24] . A descriptive analysis of the different asset variables was carried out to determine their frequency and standard deviation . Variables with very low counts ( <0 . 01% ) were excluded from further analysis . A co-variance matrix was generated for the PCA analysis as all the variables were standardized to the same unit ( binary yes = 1/no = 0 ) . From this matrix a PCA analysis was performed using the Stata PCA command . The results of this analysis were used to generate a wealth score , which was then used to classify the participants into socio-economic quintiles . In order to determine the factors influencing knowledge , Chi squared tests as well as univariate followed by multivariate logistic regressions were performed . The multivariate regression models were built using a backward elimination approach . Variables found to be have a p-value<0 . 1 were kept in the model . Linear regression was used to analyse the relation between knowledge of mosquito breeding levels and reported behaviours and their observed behaviours . Knowledge of mosquito breeding was defined as the mean number of miscellaneous mosquito breeding containers identified per cluster . A negative binomial regression was used to analyse relationship between adult mosquito densities and reported behaviours . Where appropriate , robust standard errors were used to account for intra-cluster correlation . This study received approval from the Cambodian National Ethics Committee for Health Research on 9 October 2014 ( reference number 0285 ) . Additionally ethics approval was also received from the London School of Hygiene and Tropical Medicine ethics review board ( reference number 10704 ) . The trial was registered with the International Standard Randomized Controlled Trial Number Register: ISRCTN85307778 .
Of the 600 participants who were administered the KAP survey , the majority were female ( 77 . 8% ) , most likely because the survey was conducted during the day when the men are out working ( Table 1 ) . The ages of the participants ranged from 17 to 88 years with the mean age being 44 . One per cent of participants were less than 20 years of age . In Cambodia it is common for people to marry early . Indeed , the most recent Demographic and Health Survey ( 2014 ) showed that 4 . 4% of women were married by the age of 15 and 45% by the age of 20 [19] . The highest level of education attained for most of the respondents was primary school ( 59 . 2% ) . Near 20% of participants had no formal education or had received the alternative education and training during the Khmer Rouge rule . The primary occupation of people surveyed was farming ( 70 . 2% ) , followed by manual labour ( 12% ) . Amongst the survey participants , knowledge regarding dengue transmission , prevention methods and symptoms was high . The vast majority of people ( 96 . 7% ) were able to identify mosquitoes as being the dengue vector ( Table 2 ) . The majority of people surveyed were also correctly able to identify the dengue mosquito biting times , although , 17 . 8% of participants believed that the dengue vector bites at night . The majority of respondents ( 95 . 5% ) were able to correctly identify at least one breeding site . Water storage jars , coconut shells/cans were the most commonly cited breeding sites ( 85 . 1% and 78 . 6% respectively ) . When asked about mosquito breeding prevention methods , 93 . 9% of participants knew of at least one mosquito breeding prevention method ( Table 2 ) . Indeed , in response to an open-ended question about the types of breeding prevention methods , the most commonly cited method was the use of Abate ( 73% of respondents ) ; 12 . 6% of respondents only cited Abate , while 53 . 7% also mentioned changing water frequently in storage jars . Almost all participants ( 94 . 1% ) knew at least one mosquito-bite prevention method . The use of nets during the day was the most commonly cited method ( 59% ) . Certain methods were strongly associated with participant’s SES level . For example , 45% ( 95% CI 33 . 24–56 . 59 ) of respondents from the highest SES quintile mentioned the use of fans compared with only 6% ( 95% CI 2 . 56–15 . 03 ) from the lowest quintile . Knowledge of dengue symptoms was much less common ( Table 2 ) . In order to distinguish dengue from other febrile illnesses , WHO recommends looking for the presence of high fever and at least two other symptoms such as rash , aches and pains , mucosal bleeding and nausea/vomiting [2] . Although 92 . 1% of people mentioned fever as one of the symptoms of dengue , only 42 . 7% could name three or more symptoms . Education was the main predictor of knowledge amongst participants ( Table 3 ) . After adjusting for age , respondents with at least 6 years of education had almost seven times the odds of knowing mosquitoes are the vector of dengue and four times the odds of being able to name at least one mosquito breeding site . Gender was found to be a strong predictor of knowledge regarding dengue symptoms: after adjusting for education and socio-economic status , female participants had a 63% higher odds of being able to name three or more dengue symptoms . If the participants or a member of their family developed a fever , just over 32 . 2% of them would seek care from a health facility or private provider ( Table 4 ) . The majority of people would self medicate either by getting drugs from the pharmacy ( i . e . paracetalmol ) ( 18 . 6% ) or using cold compresses ( 26 . 4% ) . Four percent of respondents mentioned they would practice “scratching” , a traditional practice that involves scratching the body with a coin to cure illness . Men were found to be more likely to seek medical attention from a health facility or private provider . Amongst the men surveyed , 43 . 7% said they would seek care , compared to only 29% of women ( χ2 test , P<0 . 01 ) . Linear regression analysis revealed strong evidence of a negative correlation between a person’s SES and the amount of time waited before seeking care if they or a member of their family gets a fever ( Coeff: -0 . 08 , 95%CI: -0 . 12–-0 . 05 , P<0 . 001 ) . People in the lowest SES quintile stated that they would wait on average 2 . 35 ( 95% CI: 2 . 13–2 . 56 ) days whereas respondents in the highest SES quintile would wait 1 . 80 days ( 95% CI: 1 . 61–1 . 99 ) ( Table 4 ) . Almost all participants surveyed believed they are at risk of getting dengue ( 97 . 5% ) and the majority also believed that the disease can be prevented ( 78% ) ( Table 5 ) . No significant correlation was found between risk awareness and socio-cultural variables such as education , SES , gender , occupation and age . The effect of knowledge that miscellaneous containers such as soda cans , coconut shells and tyres are breeding sites on observed practices was assessed ( Table 6 ) . It was found that the mean levels of knowledge per cluster had no effect on the proportion of households found to have such containers . Furthermore , no correlation was found between village knowledge levels of miscellaneous containers serving as breeding sites and proportion of households found to have pupae/larvae in miscellaneous containers ( Table 6 ) . Analysis was also performed on the proportion of households per village reporting disposing of miscellaneous containers and proportion of households found to have these containers . No correlation was found between these factors either . Nor was there any correlation between the levels of self-reported disposal of containers and the proportion of households with pupae/larvae in miscellaneous containers ( Table 6 ) . There was however a strong positive correlation between the proportion of households per village who reported clearing these containers and the mean number of resting female Aedes aegypti mosquitoes per household per village ( coeff . 5 . 28 , standard error 2 . 1 , p-value 0 . 02 ) ( Table 6 ) . Interestingly , the higher the number of households reporting the practice per cluster , the higher the mean number of resting mosquitoes per household was found .
Knowledge regarding dengue transmission and prevention methods was very high amongst participants , of which education was the main predictor . Villagers with at least 6 years of education had higher odds of being able to identify the Aedes biting times , biting prevention methods as well as Aedes breeding prevention methods . Although the results suggest knowledge regarding vector control measures was high , this did not translate into practice . In this setting with such high knowledge levels , an educational campaign is unlikely to have any real impact on practices . Instead a sustained behaviour change approach such as COMBI would be more appropriate . This method uses behavioural models , as well as communication and marketing theory and practice to instil change in practices . As Sokrin and Manderson stated , for any intervention to be successful , community involvement is crucial [50] . Awareness of dengue symptoms was found to be lower . To improve this , any dengue prevention program should also include education regarding dengue symptoms . Indeed , early testing and diagnosis is crucial to improving dengue outcomes , and mortality rates can be reduced when people are able to correctly identify dengue symptoms [51] . If improvements with regards to dengue prevention practices as well as symptom knowledge can be achieved , it could significantly improve dengue health outcomes in Cambodia . The views expressed are those of the authors and do not necessarily represent the official policy or position of the Department of the Navy , Department of Defense , or the U . S . Government . | The global incidence of dengue has grown dramatically over the last few decades and has become the most rapidly spreading mosquito-borne disease . To date , there is no specific treatment . A vaccine came on the market in 2015 , but it will be several years before it becomes widely available and its efficacy is limited . Therefore vector control is the most important means of dengue prevention at the current time . The World Health Organization recommends the adoption of an integrated vector management approach using community-based methods tailored to the local context . In order to design appropriate strategies , it is essential to understand local knowledge , attitudes and practices regarding dengue vector control . We conducted a survey in the Cambodian province of Kampong Cham , to investigate the local knowledge levels as well as self-reported vector control practices and observed practices . We found a high knowledge of dengue transmission , and Aedes breeding and biting prevention methods . However , no correlation was found between self-reported vector control practices and observed practices . Additionally , knowledge levels did not correlate with actual vector control practices . | [
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] | 2018 | Dengue knowledge, attitudes and practices and their impact on community-based vector control in rural Cambodia |
Defining the components of an HIV immunogen that could induce effective CD8+ T cell responses is critical to vaccine development . We addressed this question by investigating the viral targets of CD8+ T cells that potently inhibit HIV replication in vitro , as this is highly predictive of virus control in vivo . We observed broad and potent ex vivo CD8+ T cell-mediated viral inhibitory activity against a panel of HIV isolates among viremic controllers ( VC , viral loads <5000 copies/ml ) , in contrast to unselected HIV-infected HIV Vaccine trials Network ( HVTN ) participants . Viral inhibition of clade-matched HIV isolates was strongly correlated with the frequency of CD8+ T cells targeting vulnerable regions within Gag , Pol , Nef and Vif that had been identified in an independent study of nearly 1000 chronically infected individuals . These vulnerable and so-called “beneficial” regions were of low entropy overall , yet several were not predicted by stringent conservation algorithms . Consistent with this , stronger inhibition of clade-matched than mismatched viruses was observed in the majority of subjects , indicating better targeting of clade-specific than conserved epitopes . The magnitude of CD8+ T cell responses to beneficial regions , together with viral entropy and HLA class I genotype , explained up to 59% of the variation in viral inhibitory activity , with magnitude of the T cell response making the strongest unique contribution . However , beneficial regions were infrequently targeted by CD8+ T cells elicited by vaccines encoding full-length HIV proteins , when the latter were administered to healthy volunteers and HIV-positive ART-treated subjects , suggesting that immunodominance hierarchies undermine effective anti-HIV CD8+ T cell responses . Taken together , our data support HIV immunogen design that is based on systematic selection of empirically defined vulnerable regions within the viral proteome , with exclusion of immunodominant decoy epitopes that are irrelevant for HIV control .
Only two HIV vaccines designed to elicit protective T cell responses have reached clinical efficacy testing , both with disappointing results [1][2][3] . The reasons for this are not completely understood , despite much accumulated knowledge regarding the characteristics of cell-mediated immune responses associated with HIV and SIV control . The limited magnitude and breadth of vaccine-induced T cell responses , particularly when compared with responses to similar vaccines in non-human primate models , the modest cytotoxic capacity of CD8+ T cells , waning of responses over time , bias towards targeting of more variable regions of the viral proteome and the modest immunogenicity of the vaccine vector regimens are all likely contributing factors [2][4][5][6][7][8] . A critical first step towards addressing this is to determine whether the antiviral efficacy of CD8+ T cells is a function of their specificity . The HVTN 502 ( Step ) and 503 ( Phambili ) trials were a test-of-concept for induction of protective T cell responses that collectively evaluated Merck’s trivalent adenovirus type 5 HIV-1 Gag/Pol/Nef vaccine in ∼3800 subjects at high risk of HIV acquisition [1][9] . Post-hoc analyses of HVTN 502 have shown that individuals in whom vaccine-induced responses targeted ≥3 epitopes in Gag achieved a lower viral load after HIV infection than subjects without Gag responses; it is striking , however , that these subjects were a small minority among the vaccinees ( <7% ) [6] . While this confirms several observational studies that showed an association between HIV control and preferential recognition of Gag epitopes [10][11] , the question remains as to why vaccines that express full-length Gag proteins have so far failed to induce responses that can impact on HIV replication after infection . The answer may be two-fold: first , immunodominance hierarchies of the T cell responses elicited by these vaccines often mimic those of natural infection , with ‘hotspots’ in variable and least vulnerable regions of the viral proteome [12]; second , even within Gag and other conserved proteins , not all epitopes are equal in terms of vulnerability to immune pressure , or ‘fragility’ , which is defined by the capacity to maintain function in the face of genetic mutations [13] . Thus , the efficacy of cell-mediated immune responses may depend on the specific epitopes targeted , both within and outside Gag . This was demonstrated in an observational study of 950 clade B- and C-infected individuals , in whom responses to overlapping peptides ( OLP ) spanning the entire viral proteome were systematically analysed [14] . A ‘protective ratio’ ( PR ) was calculated for each OLP from the ratio of the median viral load in subjects who failed to respond to the OLP to responders . OLP with a protective ratio >1 were defined as ‘beneficial’ . Of note , Gag proteins contained the majority of the beneficial regions , though not all of them , and also contained regions that were not targeted by protective responses . Together , these data support the ‘decoy’ hypothesis , which proposes that certain epitopes within the viral proteome elicit dominant yet irrelevant responses that serve to undermine effective targeting of regions of vulnerability [15] . This question will only be adequately addressed by clinical testing of rationally designed immunogens based on ‘beneficial’ regions , as proposed by Rolland et al . and Mothe et al . [15][14] . Aside from identifying specific beneficial targets , the precise mechanisms and effector functions of antiviral T cell responses that underlie heterogeneity in HIV control among infected individuals need to be defined . We showed in a prospective study that CD8+ T cell viral inhibitory activity in vitro strongly correlated with HIV control in vivo , reflected in both viral load set-point and CD4+ cell decline over time [16] . This indicates that CD8+ T cell viral inhibitory activity is expressed on a continuum and is not a discrete function that is unique to HIV controllers with protective HLA alleles , providing scope for induction of effective CD8+ T cell responses by vaccination of subjects who do not have a favourable genotype . Viral inhibition assays that use polyclonal T cell populations provide a composite measure of lytic and non-lytic activity of all circulating HIV-specific CD8+ T cells , which may be heterogeneous in their functional capacity [17][18][19][20][21][22] . This activity is detectable in acute infection in a minority but rapidly wanes , likely as a result of viral escape and / or functional impairment [23][24][21][25] . Low level activity has also been detected in HIV-naïve recipients of DNA and adenovirus type 5-vectored vaccines encoding full-length HIV proteins even though such vaccines are capable of eliciting substantial numbers of Gag- and Pol-specific cytokine-secreting T cells [23][26][3] . These observations underscore the need for better understanding of the factors that determine the potency of CD8+ T cell viral inhibitory activity . We also showed previously that CD8+ T cell viral inhibition in chronically infected individuals did not correlate with the total magnitude of IFN-γ-positive T cell response to any single HIV protein , including Gag [16] . This was surprising , given the known associations between Gag responses and HIV control , and led us to propose the hypothesis that potent viral inhibition depends on preferential targeting of selected regions that are not limited to Gag nor predicted by conservation score alone . We hypothesised that responses to such critical regions are generally subdominant and that this may explain the lack of efficacy of T cell-inducing vaccines . To this end , we investigated CD8+ T cell-mediated inhibitory activity in a subset of HIV-positive HVTN 502 and 503 vaccine trial participants . This comprised recipients of both the vaccine and placebo who were sampled at the same time during early HIV infection ( 1 year ) . They were naïve to antiretroviral therapy ( ART ) , with CD4 cell counts >350 cells/μl , and were not selected for low virus loads or protective HLA class I alleles . In parallel , we studied ART-naïve subjects who showed spontaneous long-term control of HIV , with plasma viral loads consistently <5000 copies/ml ( viremic controllers , VC ) . They were sampled later in infection ( median 4 . 5 years ) and were included as a reference cohort , as potent CD8+ T cell antiviral activity has been reported in such individuals [23][26][16] .
CD8+ T cell antiviral activity was measured in 34 HIV-positive HVTN 502 & 503 trial participants , who were infected with clade B and C viruses respectively . They were aligned for duration of infection , early post-infection viral load and CD4+ cell counts . Only a minority had either a protective HLA class I allele ( n = 7 , 20% ) or evidence of spontaneous viremia control , indicated by plasma viral loads consistently below 5000 copies/ml ( n = 5 , 15% ) ( Table 1 ) . We included both vaccinees and placebos in order to maximise the number of subjects with samples available for analysis . Fourteen VC with viral loads <5000 copies/ml were studied in parallel as a reference cohort . The estimated duration of HIV infection in latter ranged from 1–11 years . Six ( 43% ) had a protective HLA class I allele and all were presumed clade B-infected ( Table 2 ) . The inclusion of clade B and C cohorts enabled us to ascertain whether the association between CD8+ T cell inhibitory activity and HIV control was clade-independent , as suggested by our previous results [16] . However , a major goal of this study was to explore the extent of cross-clade inhibition ( breadth ) using a panel of laboratory-adapted and primary HIV isolates representing clades A , B and C strains , as this had not been systematically examined in HIV-positive individuals before . CD8+ T cells from HIV-positive HVTN 502 & 503 participants were tested according to PBMC availability , using at least one clade B and one clade C virus , while all VC were tested against five viral isolates ( S1 Table ) . Among the HIV-positive trial participants in whom viral inhibitory activity against a clade-matched virus was analysed at a CD8+/CD4+ cell ratio of 2:1 , it was not significantly different between vaccinees ( n = 20 ) and placebo ( n = 8 ) recipients ( ranges 0–87% vs . 0–93% , p = 0 . 32; Fig . 1A ) . Because no difference was observed , analyses presented in the main were performed by combining data from both the vaccinee and placebo groups . However , the vaccinees were also analysed independently as they accounted for two-thirds of the HVTN cohorts . The data are shown in Supplementary Results , S1 Text . Whether data were combined or independent , the results were similar . Inhibition of a clade-matched virus was significantly higher among VC at CD8+/CD4+ T cell ratios of both 2:1 ( medians 85% and 37% respectively , p <0 . 0001 ) ( Fig . 1B ) and 1:10 ( medians 61% and 0% respectively , p <0 . 0001 ( Fig . 1C ) . VC also showed more potent cross-clade inhibition than HVTN 502 participants when tested using a clade C virus ( CD8+/CD4+ T cell ratio of 2:1—medians 60% vs . 14% , p = 0 . 002 ) ( Fig . 1D ) . These differences remained significant when the placebos were excluded from the analyses ( Supplementary Results , S1 Text ) . Cross-clade activity was analysed further using at least 3 viruses in 14 HVTN 502 & 503 participants and 14 VC . Differences in the potency and breadth of CD8+ T cell-mediated inhibitory responses in these groups are highlighted in the heatmap ( Fig . 1E ) . We have previously reported a significant inverse relationship between CD8+ T cell antiviral activity measured 6 months post-infection in a primary HIV infection cohort and viral load set-point , a known predictor of the rate of progression to AIDS [16] . In the present study , CD8+ T cell inhibitory activity was measured later . Nevertheless , there was still a significant inverse correlation between CD8+ T cell inhibition of a clade-matched virus and viral load set-point ( which was attained within 100 days of infection in the HVTN trial participants ) or current viral load in the VC ( r = -0 . 49 , p = 0 . 0009 , S1 Fig . ) . The finding that HIV-positive trial participants showed less potent inhibition of a clade-matched virus isolate than VC was consistent with results from previous studies of early infected individuals [16][24] . Here , we extended these observations to clade-mismatched viruses . The broader CD8+ T cell inhibitory responses in VC suggested that they preferentially recognised conserved viral epitopes . However , when examining responses within the groups , we observed more potent inhibition of clade-matched than mismatched viruses in VC and HIV-positive trial participants alike . This indicated that CD8+ T cells targeting clade-specific viral epitopes must contribute to the overall potency of the response . To investigate this further , we used ex vivo IFN-γ Elispot assays to measure the magnitude of responses to two sets of overlapping 15-mer peptides . The first corresponded to the beneficial regions that were defined by Mothe et al . in clade B and clade C-infected populations ( S2 and S3 Tables ) and the second to a set of ‘conserved elements’ ( CE ) peptides that were originally defined by Rolland et al . and consisted of 7 regions in Gag p24 ( S4 Table ) [14][27][28] . The peptides representing beneficial regions were constituted in pools according to their previously defined protective ratio , with the first pool of each protein containing the peptides with the highest protective ratio ( higher number indicating lower viral load in responders compared with non-responders ) [14] . CE peptides were divided into pools A & B , also in accordance with previously observed associations with low virus loads [29] ( S4 Table ) . To match their infecting clade , VC and HVTN 502 participants were tested with a peptide set representing beneficial regions in Clade B and HVTN 503 subjects were tested with the Clade C beneficial peptide set . All three groups were tested with the same CE peptide set . For all Elispot assays , CD8+ T cells were obtained from the same sample as that used in the viral inhibition assay ( except for 2 VC in whom it was necessary to use an additional sample obtained within 1 year of the original bleed ) . Summed frequencies of IFN-γ-producing CD8+ T cells targeting the beneficial and CE peptides are shown in Fig . 2 . The median response to beneficial peptides was 190 and 262 SFU/million CD8+ T cells for HVTN 502 and 503 groups respectively and 210 SFU/million CD8+ T cells for the VC ( Fig . 2A ) . The median response to the CE peptides was 60 SFU/million CD8+ T cells for the combined HVTN groups and 35 SFU/million CD8+ T cells for the VC ( Fig . 2B ) . These differences were not statistically significant , nor were there significant differences between vaccinees and placebos in terms of the magnitude of response to either beneficial ( medians 198 vs . 415 SFU/million CD8+ T cells , p = 0 . 99 ) or CE peptides ( medians 55 vs 60 SFU/million CD8+ T cells , p = 0 . 6 ) . This group of VC did not show significantly higher responses to beneficial or CE peptides than the HVTN subjects . This was unexpected in the light of previous reports but likely reflected the longer duration of infection ( median 4 . 5 years vs . 1 year ) , which may be associated with loss of responses to epitopes within the regions studied , due to mutational escape [14][29][30][31][32] . For example , the two VC who were HLA-B*5701-positive did not make detectable responses to the beneficial or CE peptide pools that contained immunodominant Gag epitopes restricted by this allele ( TW10 and KF11 ) . We next explored the relationship between virus inhibition and the magnitude of CD8+ T cell responses to the beneficial and CE regions in the HVTN subjects . We observed a strong correlation between the magnitude of T cell responses to beneficial regions and CD8+ T cell-mediated inhibition of a clade-matched virus ( r = 0 . 69 , p = 0 . 0001 for a CD8+/CD4+ cell ratio of 2:1 ) ( Fig . 2C ) . This relationship was also confirmed using a lower CD8+/CD4+ cell ratio of 1:1 ( r = 0 . 5 , p = 0 . 01 ) and importantly , was maintained after removal of subjects with protective HLA class I alleles ( HLA-B*27 , B51 , B*5701/03 , B*5801 , B*81 ) ( r = 0 . 71 , p = 0 . 0005 ) ( Fig . 2D ) . Furthermore , these correlations remained statistically significant after exclusion of placebos ( Supplementary Results , S1 Text ) . Taken together , these analyses suggested that CD8+ T cell viral inhibition of >85% ( i . e . the median response in VC ) was associated with a beneficial peptide response threshold of ∼1300 SFU/million CD8+ T cells . Additional support for the relationship between CD8+ T cell viral inhibition and magnitude of T cell responses to beneficial regions was obtained in a subset of subjects ( n = 15 ) in which individual peptides were tested in cultured Elispot assays . The highest viral inhibition also correlated with the higher magnitude T cell responses to individual beneficial peptides ( r = 0 . 61 , p = 0 . 02 , Fig . 2E ) . Unexpectedly , there was a weaker association between the magnitude of T cell responses to the conserved elements pools and CD8+ T cell viral inhibition ( r = 0 . 41 , p = 0 . 04 ) ( Fig . 2F ) . This positive relationship was also maintained after exclusion of placebos ( Supplementary Results , S1 Text ) and was largely driven by responses to the conserved elements pool B , containing peptides spanning CE 4 , 5 and 6 . We also analysed the frequency of T cell responses to the total HIV proteome as these had been measured previously by intracellular staining for IFN-γ ( at a median of 5 weeks after HIV infection ) after stimulation of PBMC with clade B consensus potential T cell epitope ( PTE ) peptide sets [2][30] . These were selected to optimise the detection of CD8+ T cell responses to circulating viruses and thus ensure accurate measurement of the maximum response [33] . The total proteome response ( median ) was 1 . 81% CD8+ T cells ( Fig . 3A ) , with no significant difference between HVTN 502/503 vaccinees and placebos ( median 2 . 1% and 1 . 5% of CD8+ T cells respectively , p = 0 . 23 ) , which is similar to data obtained from chronic infection cohorts [31] . There was no correlation between CD8+ T cell antiviral activity and responses to the whole proteome , either for HVTN subjects as a whole ( r = 0 . 14 , p = 0 . 5 ) ( Fig . 3B ) or for the vaccinees only ( Supplementary Results , S1 Text ) . In view of the strong correlation between CD8+ T cell antiviral activity and recognition of beneficial peptides , we explored this relationship further using a series of univariate and multivariable regression models , with CD8+ T cell antiviral activity as the dependent variable . We investigated associations with the following independent variables: 1 ) the Shannon entropy score for each beneficial region as a measure of its variability at the population level; 2 ) the magnitude of responses to beneficial regions ( ‘total beneficial’ response ) ; 3 ) the magnitude of the Gag component of the beneficial regions ( ‘beneficial Gag’ response ) , in order to ascertain how much this contributed to the total beneficial response; 4 ) the magnitude of responses to CE peptides; 5 ) the ratio of magnitude of responses to beneficial regions to the total proteome response ( relative magnitude or immunodominance ) and 6 ) the presence of protective ( ‘good’ ) or non-protective ( ‘bad’ ) HLA class I alleles . For our first set of models , entropy was used as the primary independent ( or predictor ) variable of interest since both the beneficial regions and CE regions were largely derived from conserved , i . e . low entropy regions in the viral proteome [14][28] . Thus , our first regression model included entropy as the only independent variable . Total beneficial responses , beneficial Gag responses or CE responses were then each added separately to this baseline model to ascertain whether they improved the fit of the model ( as captured by a change in the model r2 ) and , thus , whether they were independently associated with CD8+ T cell activity . Entropy alone explained 13 . 5% of the variance in inhibition . Addition of the total beneficial response or the beneficial Gag response each improved the fit of the model ( by 46% and 24% respectively ) and the contribution of each of these was statistically significant ( Table 3 ) . By contrast , addition of the CE response had no effect ( increase in model r2 of 0 . 1% ) . We also constructed three multivariable regression models that included various combinations of the following factors: magnitude of total beneficial responses , relative magnitude or entropy of beneficial regions and good or bad HLA class I alleles . The combinations of covariates for these models were chosen to allow us to investigate several potential pathways for any associations , based on hypothesised interactions between absolute and relative magnitude of responses and between certain HLA alleles and entropy of epitopes restricted by these alleles . These models explained 39–49% of the variance in CD8+ T cell inhibition and all were significant as a whole . However , in each case the magnitude of the response to beneficial regions made the strongest unique contribution whereas the contribution of the other variables was not statistically significant ( Table 4 ) . Given that responses to beneficial regions were subdominant in HIV-infected individuals , we next investigated whether this was also the case for responses that are primed in HIV-naïve individuals by vaccines encoding full-length HIV proteins . Data on responses that developed post-vaccination and prior to HIV acquisition were available for 13/20 of the HVTN 502 trial participants in this analysis ( sampled 4 weeks after the second vaccination ) [6] . We compared the magnitudes of vaccine-induced responses to peptides spanning the entire Gag/Pol/Nef immunogen with beneficial and CE regions . Vaccination induced responses to beneficial regions in 5/13 patients and to CE regions 3/13 patients , while no response to any of these regions was detected in 5 subjects . Overall , vaccine-induced responses to beneficial regions accounted for a median ( range ) of 0% ( 0–43% ) of the response to the entire immunogen in these subjects , despite representing 36% of the immunogen sequence ( Fig . 4A ) . Finally , we investigated whether natural immunodominance hierarchies were maintained or altered following the administration of a Gag immunogen as a therapeutic vaccine in chronic HIV infection . We mapped T cell responses to beneficial and non-beneficial regions before and after vaccination with an immunogen , ‘HIVA’ comprising full-length Gag p24/p17 sequences fused to a multiepitope string , delivered as a modified vaccinia virus Ankara-vectored vaccine to chronic ART-treated HIV-positive subjects with suppressed viremia [34][35] . Epitope mapping was performed in 9 subjects using overlapping 15-mers spanning p24 and p17 , together with optimal 8–10-mer peptides for epitopes that had been defined previously ( Table 5 ) [36] . We confined our analysis to responses to the Gag component of the immunogen , since the epitope string was , by definition , designed to focus responses on selected regions of the proteome . Prior to vaccination , the magnitude of summed responses to beneficial regions was lower than for non-beneficial Gag regions , although the difference was not statistically significant ( median 205 and 615 SFU/million PBMC respectively , p = 0 . 27 ) . MVA . HIVA vaccination significantly boosted T cell responses to the beneficial Gag regions ( median change +150 SFU/million PBMC , p = 0 . 03 ) . However , responses to non-beneficial Gag regions were preferentially expanded ( median change +845 SFU/million PBMC , p = 0 . 004 ) ( Fig . 4B , Table 5 ) . Taken together , these data suggest that vaccines encoding full- or near full-length HIV proteins mimic natural HIV infection by eliciting responses that are biased towards non-beneficial targets , regardless of whether they are administered to HIV-naïve or primed individuals .
The lack of a reliable correlate of protective immunity against HIV is a significant obstacle to systematic evaluation of vaccine candidates . Consequently , efforts to develop a T cell-based vaccine have focused broadly on recapitulating the immunological phenotype of HIV controllers , using immunogens incorporating near-complete gene sequences for many proteins . Recently , there has been greater emphasis on rationally designed immunogens , in particular , those that aim to maximise coverage of variable viral epitopes ( mosaics ) or avoid them altogether ( conserved regions ) [15][37][38][39][8] . CD8+ T cell-mediated viral inhibition was found to correlate with the frequency of T cells targeting conserved epitopes in HIV-uninfected vaccinees [8][40] . However , no vaccine candidate has yet been shown to elicit viral inhibitory activity of similar potency to that observed in HIV controllers . Here , we report that the total viral inhibitory capacity of anti-HIV CD8+ T cells is highly dependent on their specificity and we provide a mechanism to explain why conventional HIV immunogens elicit largely ineffective CD8+ T cell responses . We reported previously that ex vivo CD8+ T cell-mediated viral inhibitory activity is inversely correlated with viral load set-point; we confirmed this finding here in genetically unrelated cohorts infected with different viruses [16] . While this is consistent with well-established associations between primary CD8+ T cell responses to HIV-1 and control of acute viraemia [41][42][32][43] , the time interval between attainment of viral load set-point and sampling for the viral inhibition assay was longer in the present study , thus we cannot rule out the possibility that early control of viraemia was the cause rather than the consequence of the level of antiviral activity . It is also conceivable that a viral inhibition ‘set-point’ is attained soon after infection; this could explain the findings of Lecuroux et al . , who reported that most HIV-infected individuals showed modest CD8+ T cell inhibitory activity throughout acute and early infection [24] . Nevertheless , our data give insight into the level of inhibitory activity that might be used as a benchmark to assess vaccine candidates: for example , inhibition of a clade-matched virus by ≥ 85% ( observed in 50% of VC subjects but only 7% of HVTN trial participants ) was associated with a median viral load of ∼ 2000 copies/ml . This suggests that the bar must be set very high if such assays are to be used to identify vaccine strategies that could clear HIV infection or reduce viral loads to undetectable levels [44] . We report for the first time , to our knowledge , that the breadth of inhibitory activity , indicated by inhibition of clade-mismatched viruses , was significantly greater in VC than subjects with uncontrolled viraemia . This suggested two non-mutually exclusive explanations: enrichment of the HIV-specific repertoire in VC for T cells recognising conserved epitopes and / or high frequencies of circulating cross-reactive CD8+ T cells that can tolerate epitope variation . However , potent clade-specific viral inhibitory activity , together with differential inhibition of diverse viruses was evident in both study groups . This led us to hypothesise that factors other than epitope conservation must play a role in the control of viral replication . We found that CD8+ T cell antiviral activity in HVTN subjects was highly correlated with the frequency of CD8+ T cells targeting selected peptides that had been shown in an independent study of two large cohorts to associate with control of viraemia [14] . This correlation was independent of protective HLA class I alleles , which suggests that effective CD8+ T cell responses may be restricted by a broader range of HLA class I alleles than previously suspected , as was also proposed by Mothe et al [14] . While the viral regions that were defined as beneficial were predominantly of low entropy , our regression analysis indicated that the magnitude of these responses accounted for a significantly greater proportion of the variation in viral inhibition than entropy alone . The Gag component of these regions explained nearly two-thirds of the effect . Interestingly , T cell responses to conserved elements peptides were weakly correlated with viral inhibition and this effect was driven by only three of the seven conserved regions tested . This is consistent with other studies showing that high population-level conservation per se does not necessarily predict viral fitness and may reflect the presence of invariant regions that are immunologically inert [27][45] . Collectively , these observations are not only reconcilable with previously described associations between broad Gag-specific T cell responses and reduced viral loads at the population level but also point to a mechanism that could explain them with greater precision [10][14][6] . The greater the breadth of responses to Gag , the higher the probability of targeting the most vulnerable epitopes , even though there is also the possibility of targeting the non-beneficial regions . The lack of responses to beneficial regions in some of the VC studied is quite likely explained by the small sample size studied and / or the extended time of untreated HIV infection which may have led to elimination of some of these T cell responses , or possibly that these VC made responses to other critical epitopes that were not represented in our peptide sets [32][46][47] . However , this does raise questions as to how long the effect of responses to beneficial regions lasts , in the face of ongoing viral escape . The rate of escape from CD8+ T cell responses is determined by the net effect on viral fitness of all escape mutations and is significantly slower in chronic than acute infection [48] . The association between the prevalence of T cell responses to beneficial regions and population-level viral load was made in chronically infected cohorts and suggests , therefore , that even though these beneficial responses may drive viral escape , the net effect is an overall impairment of viral fitness . This is consistent with observations made by Boutwell et al . who showed that CD8+ T cell escape mutations in HIV-1 Gag frequently impair viral fitness; many of the susceptible epitopes in their study were located in the beneficial regions [49] . It is possible that we have overlooked functional characteristics of Gag-specific CD8+ T cells such as the capacity to produce multiple cytokines simultaneously , as these have also been associated with control of viraemia [50][51] . However , viral inhibition assays arguably provide the most direct and complete measure of antiviral function , whereas the cytokines that are typically detected in assays of T cell polyfunctionality provide an indirect assessment . Our analysis indicated that individuals with potent viral inhibitory responses are rare , as was reported by others [24] , and furthermore highlighted that responses to beneficial regions within the HIV proteome are both infrequent and subdominant . This is consistent with a previous study that showed infrequent targeting of epitopes in these regions in acute infection [32] . As spontaneous control of viraemia is itself a rare event , this provides further evidence that viral inhibitory activity in vitro accurately reflects immune control in vivo . It also raises questions as to whether long-term control or even clearance of infection can be achieved by vaccines that mimic priming by HIV . Responses elicited by the Ad5-HIV vaccine in HVTN 502 trial participants were shown previously to be limited in breadth , with a bias towards variable regions [2][7] . Our retrospective analysis of a subset of HVTN 502 vaccinees indicated preferential targeting of non-beneficial regions , which was concerning given that the Gag/Pol/Nef immunogen contained the majority of the previously described beneficial regions [14] . We observed a similar skewing of responses in HIV-positive subjects who received a therapeutic MVA vaccine encoding the immunogen , HIVA , which included 9 of the identified beneficial regions within Gag . Newer vaccine candidates such as Ad35-GRIN and Ad35-ENV , which comprise Gag , Reverse transcriptase , Integrase and Nef and Env sequences , induced responses to a median of one Gag epitope in HIV-uninfected healthy volunteers [40] . The common factor among these immunogens is the inclusion of full or near-full-length Gag sequences . A non-human primate study showed that full-length HIV immunogens induced responses to conserved regions that were of similar breadth to those elicited by non-native conserved region immunogens [52]; by contrast , Kulkarni et al . compared vaccination with p55 Gag and a conserved elements-only immunogen and showed better recognition of conserved elements epitopes with the latter approach [28 , 53] . Taken together , these observations highlight the need for vaccines to overcome natural immunodominance hierarchies in humans through the development of immunogens that focus responses on specific critical regions of the viral proteome . Additional refinements , such as inclusion of sequences that pre-empt predictable escape mutations , should also be considered [54] . Vaccine-mediated clearance of an AIDS virus infection in the non-human primate model was recently demonstrated for the first time with a persistent rhesus CMV SIV vaccine [55 , 56][57] . It is noteworthy that the responses elicited were unique in terms of their unprecedented breadth , absence of immunodominance and specificity for non-canonical viral epitopes , although the immunogen comprised entire proteins . While this may reflect unusual properties of the CMV vector and the specific mechanisms that contributed to virus eradication have yet to be resolved , such studies may provide vital lessons for human vaccine development . In summary , these data provide several new insights that should inform HIV vaccine design . First , they suggest that induction of effective anti-HIV CD8+ T cell responses could be achieved with an immunogen comprising only a few selected regions of the viral proteome . In addition to the regions defined by Mothe et al . , which were identified in chronically infected individuals , comprehensive analyses of responses that arise during acute / early HIV infection may yield viral targets that are critical to early and sustained control [32][58] . Secondly , we have identified a possible threshold for the magnitude of responses to these critical regions that should be attained in order to have a meaningful impact on viral replication . Our analysis of responses to vaccination with Ad5 Gag/Pol/Nef in a small subset of HVTN 502 subjects prior to HIV infection , together with other post-hoc studies , suggests that this is extremely unlikely to be achieved using immunogens that comprise full-length proteins . Exclusion of irrelevant decoy regions that when present , often induce immunodominant T cell responses , may be essential to prevent the development of such non-protective responses . Finally , our previous experience with potent heterologous viral vector combinations has shown that it is feasible to induce HIV-specific T cell responses in human subjects of the order of magnitude that we have proposed here [8]; rationally designed immunogens that exploit these vectors should be prioritised for clinical development .
Approval was obtained from the Oxford Tropical Research Ethics Committee for analysis of anonymised PBMC samples that were made available to University of Oxford , UK by Fred Hutchinson Cancer Research Center via a Material Transfer Agreement ( ‘HVTN 502/Merck 023—HVTN 503 Ancillary Study’ ) following approval of the study by HVTN Protocol Committee . The PBMC samples were gathered and obtained from a collection held by HVTN . Viremic controllers ( VC ) were recruited at Duke University Medical Center with IRB approval and after obtaining written informed consent . The HVTN 502 and 503 studies have been described previously [1][9] . PBMC sampled from 36 HIV-positive HVTN 502 and 503 participants who were still naïve to ART 12 months after HIV acquisition , with CD4+ cell counts >350 cells/μl , were provided through the HVTN 502 Oversight Committee . Plasma viral load data were provided by SCHARP and set-point was determined using the method described by Fellay et al . [59] . Participants’ characteristics are given in Table 1 . Criteria for enrolment of VC were plasma viremia consistently <5000 copies/ml for at least one year and a CD4+ cell count >400 cells/μl in the absence of ART . However , one subject was included despite a CD4+ cell count <400 cells μl because of viral loads consistently <2280 copies/ml for 5 years prior to enrolment; this individual maintained viral loads <448 copies/ml during the study . Two subjects had transient viraemia >5000 copies/ml which was subsequently spontaneously controlled . Patients’ characteristics are given in Table 2 . All VC had presumed clade B infection , due to the geographical location . Therapeutic vaccine trial participants were patients with chronic HIV infection , receiving effective ART for at least 12 months , with CD4+ cell counts >350 cells/μl , who received two intramuscular immunisations of MVA . HIVA 5x107 pfu 4 weeks apart [34][60] . HLA typing was performed as described previously [6] . Virus subtyping was performed by near full-length genome sequencing , as described previously [61] or by bulk sequencing of p17 Gag and analysis using REGA HIV-1 & 2 Automated Subtyping Tool ( Version 2 . 0 ) [62][63] . HIV-1 isolates were obtained from the Programme EVA Centre for AIDS Reagents , National Institute for Biological Standards and Control ( NIBSC ) , a centre of the Health Protection Agency , UK . The virus panel comprised two laboratory-adapted clade B isolates , BaL ( CCR5-tropic ) and IIIB ( CXCR4-tropic ) and three primary isolates , ES X-1936 ( clade C , CCR5-tropic ) , 92UG029 ( clade A , CCR5 / CXCR4 dual-tropic ) and RW93024 ( clade A , CXCR-tropic ) . All virus propagation was performed using primary CD4+ cells and 50% tissue culture infectious doses ( TCID50 ) for each virus was calculated as described previously [64] . Clades B and C consensus peptides spanning the entire HIV proteome ( 15-mers overlapping by 11 amino acids ) were obtained from the NIH Aids Reagent Programme . 10mg/ml stocks were stored at -80°C until required , then were diluted to generate working stocks . One or more 15-mer peptides that matched most closely the beneficial OLP described by Mothe et al . and the CE peptides described by Kulkarni et al . were selected for use in Elispot assays [14][28] ( Tables 3–5 ) . The viral inhibition assay has been described in detail elsewhere [16 , 65] . Briefly , CD8+ T cells were isolated from cryopreserved PBMC by magnetic bead selection ( Miltenyi Biotec ) and retained for use in IFN-γ Elispot assays . CD8-depleted cells ( hereafter referred to as CD4+ T cells ) were stimulated with PHA ( 5 μg/mL ) in RPMI 1640 medium supplemented with 10% fetal calf serum ( R10 ) for 3 days , washed , and infected with HIV-1 isolates at pre-determined optimal MOI ( National Institute for Biological Standards and Control , United Kingdom ) . To assess viral inhibition , HIV-superinfected CD4+ T cells ( 5 × 104 ) were cultured in triplicate in R10 with interleukin 2 ( 20 IU/mL ) in 96-well round-bottomed plates , alone or together with unstimulated ex vivo CD8+ T cells , obtained by positive bead selection of PBMCs from a second freshly thawed vial on day 3 . CD8+ T cells were confirmed as >98% pure by staining for CD3 , CD8 , and CD56 . CD8+ and CD4+ T cells were co-cultured for 6 days for all virus isolates except clade A2 , for which the peak of virus replication is attained after 3 days [65] . CD8+/CD4+ ratios of 2:1 , 1:1 and 1:10 were tested , according to cell availability . On the day of harvest , cells were stained first with Aqua Live/Dead Fixable stain ( Invitrogen ) , fixed with 1% paraformaldehyde/20 μg/mL lysolecithin at RT , permeabilized with cold 50% methanol followed by 0 . 1% Nonidet P-40 , and finally stained with p24 antibody ( KC-57-FITC; Beckman Coulter ) and antibodies to CD3 , CD4 , and CD8 ( conjugated to APC-Cy7 , PerCP , and APC , respectively; BD Biosciences ) . Samples were acquired on a CyAn flow cytometer . Data were analyzed using FlowJo software . Antiviral suppressive activity was expressed as percentage inhibition and determined as follows: [ ( fraction of p24 + cells in CD4 + T cells cultured alone ) – ( fraction of p24 + in CD4+ T cells cultured with CD8+ cells ) ]/ ( fraction of p24 + cells in CD4 + T cells cultured alone ) × 100 . Purified CD8+ T cells from the PBMC sample that was used to isolate CD4+ T cells for the viral inhibition assay were tested in IFN-γ Elispot assays with pools of beneficial or CE peptides ( final concentration 2μg/ml ) as described previously [16] . Mapping of responses to epitopes in the Gag component of the HIVA immunogen was performed using PBMC sampled pre- and 2 or 4 weeks post-vaccination , with overlapping 15-mer peptides ( final concentration 4μg/ml ) spanning the entire immunogen sequence , with confirmation using optimal 8–10-mer peptides where available [60] . Elispot assays with CD8-depleted PBMC were performed to confirm that these responses were CD8+ T cell-mediated . In selected assays , CD8+ T cells were recovered from the Elispot plate after overnight incubation with peptides , washed and cultured ( 2x106/ml ) in R10 medium ( RPMI with 10% fetal calf serum ) plus IL-7 ( 25ng/ml ) . Cultures were supplemented with IL-2 ( 1 . 8 x103 units/ml ) on day 3 and R10/IL-7/IL-2 medium was replaced on day 7 . Cells were starved of IL-2 for 30 hours on day 10 and then used in cultured IFN-γ Elispot assays with individual peptides ( 2μg/ml ) . Intracellular cytokine staining was performed as described previously , typically at the second visit after HIV infection had been confirmed [66][2] . Group comparisons were performed using the Mann Whitney test and correlations were investigated by determination of Spearman’s rank coefficient , using Graphpad Prism software , version 6 . Models to explore predictors of inter-subject variation in viral inhibition by CD8+ T cells were tested using univariate and multivariable linear regression . Analyses were performed using SPSS version 22 . | Attempts to develop an HIV vaccine that elicits potent cell-mediated immunity have so far been unsuccessful . This is due in part to the use of immunogens that appear to recapitulate responses induced naturally by HIV that are , at best , partially effective . We previously showed that the capacity of CD8+ T cells from patients to block HIV replication in culture is strongly correlated with HIV control in vivo , therefore , we investigated the virological determinants of potent CD8+ T cell inhibitory activity . We observed that CD8+ T cells from patients with naturally low plasma viral loads ( viremic controllers ) were better able to inhibit the replication of diverse HIV strains in vitro than CD8+ T cells from HIV-noncontroller patients . Importantly , we also found that the potency of the antiviral activity in the latter group was strongly correlated with recognition of selected regions across the viral proteome that are critical to viral fitness . Vaccines that encode full-length viral proteins rarely elicited responses to these vulnerable regions . Taken together , our results provide insight into the characteristics of effective cell-mediated immune responses against HIV and how these may inform the design of better immunogens . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [] | 2015 | Identification of Effective Subdominant Anti-HIV-1 CD8+ T Cells Within Entire Post-infection and Post-vaccination Immune Responses |
Mutations in whirlin cause either Usher syndrome type II ( USH2 ) , a deafness-blindness disorder , or nonsyndromic deafness . The molecular basis for the variable disease expression is unknown . We show here that only the whirlin long isoform , distinct from a short isoform by virtue of having two N-terminal PDZ domains , is expressed in the retina . Both long and short isoforms are expressed in the inner ear . The N-terminal PDZ domains of the long whirlin isoform mediates the formation of a multi-protein complex that includes usherin and VLGR1 , both of which are also implicated in USH2 . We localized this USH2 protein complex to the periciliary membrane complex ( PMC ) in mouse photoreceptors that appears analogous to the frog periciliary ridge complex . The latter is proposed to play a role in photoreceptor protein trafficking through the connecting cilium . Mice carrying a targeted disruption near the N-terminus of whirlin manifest retinal and inner ear defects , reproducing the clinical features of human USH2 disease . This is in contrast to mice with mutations affecting the C-terminal portion of whirlin in which the phenotype is restricted to the inner ear . In mice lacking any one of the USH2 proteins , the normal localization of all USH2 proteins is disrupted , and there is evidence of protein destabilization . Taken together , our findings provide new insights into the pathogenic mechanism of Usher syndrome . First , the three USH2 proteins exist as an obligatory functional complex in vivo , and loss of one USH2 protein is functionally close to loss of all three . Second , defects in the three USH2 proteins share a common pathogenic process , i . e . , disruption of the PMC . Third , whirlin mutations that ablate the N-terminal PDZ domains lead to Usher syndrome , but non-syndromic hearing loss will result if they are spared .
Usher syndrome manifests as both retinal degeneration and hearing loss [1] , [2] . It is classified into type I , II , and III based on clinical features of the hearing defects [3]–[8] . Usher syndrome type I ( USH1 ) presents with profound congenital deafness and vestibular dysfunction . USH2 is the most common form and is characterized by moderate non-progressive hearing loss without vestibular dysfunction . USH3 is distinguished from USH2 by the progressive nature of its hearing loss and occasional vestibular dysfunction . There is further genetic heterogeneity within each clinical type of Usher syndrome . For example , three distinct gene loci , referred to as USH2A , USH2C and USH2D , are known to underlie USH2 . These three genes encode the USH2A protein ( also known as usherin ) , Very Large G protein-coupled Receptor-1 ( VLGR1 ) and whirlin , respectively . Among these , mutations in USH2A account for over 70% of USH2 patients whereas USH2C and USH2D are responsible for the remainder . A previously proposed USH2B locus was subsequently shown to be in error and has been withdrawn [9] . Genetic defects in the whirlin gene have long been known as a cause of nonsyndromic deafness DFNB31 [10] , [11] and , more recently , were found to underlie USH2D [12] . Whirlin R778X and c . 2423delG mutations ( Figure 1A ) that truncate the protein close to its C-terminus cause profound prelingual hearing impairment in humans . In the naturally occurring whirler mouse , from which the name whirlin was derived , a large deletion was found in the middle of the whirlin gene ( Figure 1A ) . Similar to human patients with DFNB31 , the whirler mouse suffers from inner ear defects [10] . Neither patients with DFNB31 nor the whirler mouse manifest any retinal deficits . The whirlin gene defect underlying USH2D arises from compound heterozygosity of a Q103X mutation and a c . 837+1G>A mutation [12] , which are positioned in the first and second exon of the whirlin gene , respectively ( Figure 1A ) . Therefore , different mutations of the whirlin gene account for a spectrum of hearing and vision defects although the mechanism underlying the variable disease expression of different mutations in the whirlin gene is not known . Multiple whirlin transcript variants were found in the inner ear [10] , [13] , [14] . They are conceptually translated into two groups of proteins , the long and short isoforms ( Figure 1A ) . The whirlin long isoform contains two N-terminal PDZ domains , a proline-rich domain and a third PDZ domain near the C-terminus . Heterogeneity in the whirlin short isoform arises from use of alternative transcriptional start sites and/or splicing sites of the whirlin gene , which generates several variants with different N-termini . The short isoform has no N-terminal PDZ domains but retains the proline-rich region and the third C-terminal PDZ domain . Both the PDZ domain and proline-rich region are modular protein interaction domains . PDZ domains bind to a short conserved sequence , known as a PDZ-binding motif , present at the C-terminus of proteins or found as an internal motif [15] . A proline-rich region usually binds to WW and SH3 domains [16] . With these two types of protein interaction domains , whirlin is believed to be engaged in the assembly of supramolecular complexes at specific subcellular locations . A series of in vitro analyses have found that whirlin is able to interact with usherin [17] and VLGR1 [14] , the two causative proteins for other forms of USH2 [18]–[20] . A recent report demonstrates that these interactions probably exist at the ankle-link complex in developing hair cells [21] . A few reports have been published which examined the localization of whirlin in photoreceptors [14] , [22] , [23] . Whirlin has been reported to localize to the apical inner segment collar , the ciliary apparatus , the adherens junctions and the synaptic region of photoreceptors [14] , [23] . However , there is no consensus from these reports on where the whirlin protein is localized in photoreceptors . As the photoreceptors are highly polarized neurons and are well organized into stratified layers of the retina , whether a protein is localized to the apical inner segment vs . the synaptic layer has completely different implication for its putative functions . More importantly , there has been no in vivo study of any kind on the association among the three USH2 proteins in photoreceptors . To fill in this knowledge gap , we carried out targeted disruption of the whirlin gene in mice at the 5′-terminal region . This disruption abolishes the long isoform and simulates the human mutations that cause USH2D . This mutant line of mice reiterated the vision and hearing defects of human USH2 patients . Using this mouse line and the Ush2A and Ush2C mutant mouse lines that had been previously generated , we analyzed the expression , localization and function of whirlin in the retina and compared them with those in the inner ear cochlea . We further analyzed the interaction among the USH2 proteins using those mouse lines as in vivo model systems . Our data provide new insight into the function of whirlin and other USH2 proteins and point to a possible disease mechanism for USH2 . The data also help to explain the molecular basis for the variable disease expression caused by mutations in different regions of the whirlin gene .
A whirlin mutant mouse line was generated by replacing a portion of exon 1 , which included the translation start codon for the whirlin long isoform , with a Neor expression cassette ( Figure 1B ) . The targeted allele was confirmed by amplifying the genomic DNA fragments containing the junctional sequences between the whirlin gene and the Neor expression cassette . To determine if expression of whirlin was abolished in the mutant mice , we conducted RT-PCR and western blotting analyses in the retina . RT-PCR analysis verified that the first exon of whirlin transcripts was absent in the homozygotes ( Figure 1D ) . Western blotting analysis showed that the whirlin long isoform , normally migrating at an apparent molecular weight of about 110 kDa , was completely absent in the retina of homozygous mice ( Figure 1E ) . Thus , this targeted allele of whirlin is a null allele for the whirlin long isoform . To distinguish it from the existing whirler mice , we refer to this line of mutant mice as the whirlin knockout mouse . Whirlin knockout mice appeared viable and comparable to their wild-type littermates in growth characteristics , reproductive performance and general health . To examine the normal expression of whirlin isoforms at the protein level in the retina , we generated a series of antibodies against whirlin and used two whirlin mutant mouse lines , whirlin knockout and whirler mice , as negative controls . In whirlin knockout mice , deletion of the first exon ablates the long isoform , while mutation in whirler mice eliminates the short isoform [10] ( Figure 1A ) . Rabbit PDZIE , chicken PDZIE , and CIP98 [24] antibodies are directed against epitopes common in both the whirlin long and short isoforms ( Figure 1A ) . Western blotting using these antibodies detected only the whirlin long isoform in the wild-type ( WT ) retina ( Figure 2A ) , suggesting that the short isoform was either not expressed or was expressed at such a low level that was beneath the threshold of detection by this assay . To confirm this result , we enriched the whirlin protein ( s ) from the retinal lysate by immunoprecipitation using the rabbit PDZIE antibody , and then performed western blotting analysis of the precipitates using the chicken PDZIE antibody . While we found significant enrichment of the whirlin long isoform , we again did not detect the short isoform . As a positive control , we found both isoforms were enriched and readily detectable in the cochlear immunoprecipitate ( Figure 2B ) . Therefore , the whirlin short isoform in the retina is a rare variant if expressed at all . In addition to the long and short variants reported previously , we found a distinct N-terminal transcript of whirlin in the retina by screening a mouse retinal cDNA library . This transcript terminates in the middle of the second PDZ domain such that if translated , it would produce a whirlin protein that includes only the first N-terminal PDZ domain . This transcript is therefore not affected by the whirler mutation or by the corresponding human mutations causing DFNB31 ( Figure 1A ) . To examine whether this N-terminal whirlin isoform was abundant at the protein level in the retina , we performed immunoprecipitation using the rabbit PDZ320 antibody , whose antigen is the N-terminal 320 amino acids of whirlin ( Figure 1A ) . Again only the whirlin long isoform was detected by western blotting using the chicken PDZ320 antibody ( Figure 2B ) , suggesting this N-terminal whirlin isoform is not an abundant variant either . Nevertheless the presence of an N-terminal whirlin variant may be of functional significance . Taken together , these results clearly demonstrate that the whirlin long isoform is the predominant variant expressed in the retina . Photoreceptors are highly polarized sensory neurons consisting of three major subcellular compartments , the outer segment , the inner segment and the synaptic terminus . Linking the light sensing outer segment and the biosynthetic inner segment is a thin bridge known as the connecting cilium . By immunofluorescence whirlin was found at the vicinity of the connecting cilia ( Figure 3A and 3B ) but not in the photoreceptor synaptic layer ( the outer plexiform layer , data not shown ) . RPGR ( retinitis pigmentosa GTPase regulator ) and RP1 ( retinitis pigmentosa 1 ) are proteins known to be localized at the connecting cilia and at the axonemal microtubules distal to the connecting cilia , respectively [25] ( see Discussion ) . Double staining of whirlin with either RPGR or RP1 showed whirlin to localize adjacent to RPGR ( Figure 3A ) but proximal to RP1 ( Figure 3B ) . However , unlike RPGR , RPGRIP1 and other ciliary proteins , immunostaining of dissociated photoreceptors , which include the outer segments and the connecting cilia , could not detect any whirlin signals at the connecting cilia ( data not shown ) . This indicated that whirlin was not a core component of the connecting cilia . Immunoelectron microscopy from both longitudinal ( Figure 3C ) and cross ( Figure 3D ) sections found the immunogold labels of whirlin at a plasma membrane microdomain in the apical inner segment , which wraps around the connecting cilium and is usually destroyed in the dissociated photoreceptors . Thus , data from immunofluorescent staining and immunoelectron microscopy were consistent with whirlin localizing to a membrane microdomain that surrounds the connecting cilia , a location that is identical to that of usherin [26] ( see Discussion ) . The distribution pattern of whirlin in mouse photoreceptors was reminiscent of a structure called the periciliary ridge complex ( PRC ) found in frog photoreceptors [27] . The PRC was defined by a morphological feature , which includes a set of ridges and grooves with a nine-fold symmetry , seen by scanning electron microscopy . It marks a specialized domain on the plasma membrane of the inner segment that surrounds the base of the connecting cilium . To examine whether whirlin was localized at this structure in frogs , we generated an antibody against the C-terminus of frog whirlin . Double staining of whirlin with γ–tubulin and acetylated α–tubulin , markers of basal bodies and axonemal microtubules , respectively , showed that whirlin was localized immediately above the basal bodies ( Figure 3E ) and beneath the axonemal microtubules ( Figure 3F ) . This is similar to the findings in mouse photoreceptors . In a cross sectional view , the signals of whirlin appeared as circles surrounding the basal bodies ( Figure 3G ) . The diameter of these circles was approximately 2 µm , which is in the range of the previously determined diameter of the PRC [27] . Both rod and cone photoreceptors had the same distribution of whirlin ( Figure 3F and 3H ) . These data suggest that whirlin is a resident protein at the PRC in frogs . The PRC as a morphologically distinct structure is not present in mammalian photoreceptors [28] . However , the conserved whirlin distribution in frog and mouse photoreceptors suggests that a functionally equivalent structure , delineated by the presence of whirlin , exists in the latter . We refer to this PRC-homologous membrane microdomain as the periciliary membrane complex ( PMC ) . Thus , whirlin is a marker of the mammalian photoreceptor PMC . The distribution of whirlin in photoreceptors was similar to that of USH2A protein ( usherin ) , which was previously reported by our laboratory [26] . Usherin is predicted to have a PDZ-binding motif at its C-terminus [19] . We investigated whether whirlin and the cytoplasmic C-terminus of usherin interacted with each other . Yeast two-hybrid analysis demonstrated their interaction and the involvement of the first and second PDZ domains of whirlin in this protein binding ( Figure 4A ) . We then sought further confirmation of their interaction by performing GST pull-down assays . We generated frog and mouse usherin-GST fusion protein constructs using either intact or mutant versions of the usherin C-terminal ( intracellular ) domain . The mutant usherin C-terminal domain lacked a functioning PDZ-binding motif . The expressed GST fusion proteins were incubated with mouse retinal lysate in an attempt to pull down whirlin . The results showed that the intact but not the mutant usherin C-termini were able to pull down endogenous whirlin from retinal lysate ( Figure 4B ) . Therefore , our studies demonstrated that whirlin and usherin directly interacted with each other through the two N-terminal PDZ domains of whirlin and the C-terminal PDZ-binding motif of usherin . Our data support the findings of others reported in recent publications [14] , [17] . We next evaluated the in vivo interaction between whirlin and usherin by double labeling immunofluorescence . In WT mouse photoreceptors , these two proteins colocalized fully at the PMC ( Figure 5A ) . Examination of their distribution in the retinas of whirlin knockout , whirler and Ush2a knockout mice revealed profound perturbation of their localization pattern . In Ush2a knockout mice whirlin disappeared from the PMC . In whirlin knockout mice , usherin signals was largely absent from the PMC . In whirler mice , usherin staining was greatly reduced though not extinguished; trace amount of usherin staining was seen uniformly distributed at the PMC of all photoreceptors ( Figure 5B ) . These results suggest that the normal localization of whirlin and usherin at the PMC depends on each other . Thus , ablation of usherin disrupts the normal localization of whirlin , and vice versa . The observation that usherin localization at the PMC was only partially disrupted in whirler mice is consistent with the lack of an overt retinal phenotype in these mice , and can be explained on the basis that the N-terminal PDZ domains of whirlin is not disrupted by the whirler mutation ( see Discussion ) . Loss of binding partners also appeared to destabilize these two proteins . Western blotting analysis showed a reduction in the amount of usherin by 80% in the whirlin knockout mice ( Figure 5C ) , and a reduction in whirlin by 50% in the Ush2a knockout mice ( Figure 5D ) . VLGR1 is the third known protein to be implicated in the USH2 etiology , and was previously reported to interact with whirlin in vitro [14] . Therefore , we studied whether VLGR1 was in the complex of whirlin and usherin in photoreceptors . Double staining of VLGR1 with either whirlin or usherin in the retina found VLGR1 to colocalize with both whirlin and usherin at the PMC in photoreceptors ( Figure 6A and 6B ) . VLGR1 localization at the PMC in mouse rod and cone photoreceptors was further verified by immunoelectron microscopy ( Figure 6C ) . Moreover , immunostaining demonstrated a decrease in VLGR1 signals at the PMC in whirlin and Ush2a knockout retina ( Figure 6F ) , and an absence of whirlin ( Figure 6D ) and usherin ( Figure 6E ) proteins at the PMC in the Vlgr1 knockout retina . These results indicate that whirlin , VLGR1 and usherin form a multi-protein complex in vivo at the PMC in photoreceptors and that functional deficits in any of these three known USH2 proteins destabilize this complex and disrupt its function . Along the cochlear spiral , there are one row of inner hair cells and three rows of outer hair cells . The inner hair cells are responsible for mechanoelectric transduction , whereas the electromotile outer hair cells also perform an electromechanical transduction , thereby amplifying the sound-evoked vibrations of the entire sensory epithelium . Both types of hair cells have stereocilia on their apical surfaces , which are modified microvilli filled with bundles of actin filaments . The tips of the stereocilia are for the sites of the mechanoelectric transduction channels . Because of the involvement of USH2 proteins in hearing impairment in humans , we studied their localization in the cochlea . Double staining of the cochleas from mice aged at postnatal day ( P ) 3–6 showed VLGR1 colocalized with whirlin and usherin in the stereocilia bundles of both inner ( data not shown ) and outer hair cells ( Figure 7A and 7B ) . The three USH2 proteins are localized to the ankle-link complex of the hair cell stereocilia [21] . This ankle-link complex appears as fine extracellular fibers at the base of the stereocilia bundle during development ( P2–P12 ) [29]; however , its exact function is not clear . To study whether the three USH2 proteins are interdependent at the ankle-link complex as at the PMC , we examined their distribution in hair cells in whirlin and Ush2a knockout mice at P3–P6 . The signals of whirlin and VLGR1 were decreased in Ush2a knockout mice and the signals for usherin and VLGR1 were decreased in whirlin knockout mice ( Figure 7C ) . These data are consistent with the reported findings of mislocalization of USH2 proteins in whirler mice and one line of the Vlgr1 mutant mice [21] , and support the notion that whirlin , usherin and VLGR1 also form a multi-protein complex at the ankle-link complex of the stereocilia in hair cells , and the normal subcellular localizations of these three proteins are , to some extent , dependent on one another in the cochlea . Retinal function tested by electroretinogram ( ERG ) , a recording of the retinal electrical response to flashes of light , and histology examined by light microscopy did not reveal overt retinal degeneration in whirlin knockout mice up to 24 months of age ( data not shown ) . However , morphological defects were evident at the ultrastructural level as early as 5 months of age . Examination by electron microscopy found membrane fusion between the apical inner segment and the connecting cilium and accumulation of vacuoles next to the PMC in the apical inner segment ( Figure 8 ) . The synaptic terminus of photoreceptors appeared normal ( data not shown ) . Further analysis of whirlin knockout mice aged from 28 to 33 months found that the amplitudes of both a- and b-waves of dark-adapted ERG recordings significantly decreased compared to their heterozygous littermate controls . The light-adapted ERG amplitudes also decreased although the difference did not reach statistical significance ( Figure 9A and Table 1 ) . Histological examination of the eyes from this cohort of animals found that the photoreceptor nuclear layer was significantly thinner and the outer segments shortened in the whirlin knockout mice ( Figure 9B and 9D ) , which are signs for retinal degeneration . Thus both functional and morphological assays indicate that the whirlin knockout mice develop late-onset retinal degeneration . In contrast , histological examination of the whirler mouse retina from 28 to 33 months of age did not find any abnormalities compared with age-matched wild-type controls ( Figure 9C and 9E ) . We measured distortion product otoacoustic emissions ( DPOAE ) to assay cochlear function in two groups of whirlin knockout mice at 2 and 9 months of age , respectively ( Figure 10A ) . At both ages , knockouts showed no cochlear responses ( i . e . thresholds were above the measurement ceiling at which the system produces its own distortion components ) , thus demonstrating a profound congenital hearing loss across all cochlear frequencies . Light microscopic evaluation of whirlin knockout ears at 2 months of age ( data not shown , n = 2 ) showed only sporadic loss of hair cells in the mid-basal turn . All other accessory structures of the inner ear , including spiral ligament and stria vascularis , appeared normal . There did not appear to be a substantial loss of cochlear neurons . Scanning electron microscopy was performed to examine the morphology of cochlear stereocilia . Throughout the cochlear spiral , hair bundles on outer hair cells were abnormally compressed in the spiral dimension , i . e . the angle between the two limbs of the “V” shaped formation was smaller in the knockout ears ( Figure 10B ) . In general , the hair bundle formation exhibits a “U” shape , which is a morphological defect characteristic of USH2 mutant mice [21] , [26] , [30] . Closer examination ( Figure 10C ) showed that , although some outer hair cell stereocilia bundles were normal with obvious interstereocilia links , many showed a patchy loss of stereocilia from the innermost ( shortest ) row of stereocilia . The inner hair cell stereocilia were normal in appearance throughout the cochleas . Given the critical role of outer hair cells in cochlear amplification and the production of DPOAEs , these stereocilia abnormalities in whirlin knockouts could explain the cochlear dysfunction .
The whirlin knockout mice characterized in this study have a late-onset retinal degeneration and a congenital , non-progressive hearing impairment . This phenotype reiterates the clinical features of USH2D disease in humans [12] . Therefore , this whirlin knockout mouse line is an appropriate animal model for studying the pathogenesis of this disease . In this study , we have provided definitive evidence on the in vivo interaction of whirlin with usherin and VLGR1 in both the retina and the inner ear . Because these three proteins are all involved in USH2 , this finding suggests that the USH2 proteins function coordinately as a multi-protein complex in vivo . Usherin and VLGR1 are proteins with an extremely large extracellular region containing multiple repeats of a number of known cell adhesion motifs . It is believed that usherin and VLGR1 may participate in the linkage to various extracellular matrix proteins and/or cell adhesion proteins . Thus , it is essential that they be anchored at specific plasma membrane microdomains of the cells to fasten these linkages . Their interaction with whirlin appears to provide this anchorage by binding them to a submembrane protein supramolecular complex . Moreover , the localization of the protein supramolecular complex at the plasma membrane of the PMC requires binding of whirlin to usherin and VLGR1 . As a result , the three proteins are interdependent for their normal subcellular localization and stability in photoreceptors ( Figure 11 ) and hair cells . In the absence of one USH2 protein , the other two USH2 partner proteins are dispersed and destabilized , and are presumed to no longer function normally . This observation has important implications for understanding the disease mechanisms of USH2 . First , all three USH2 subtypes , despite their genetic heterogeneity , affect the same subcellular target in photoreceptors and hair cells . Second , loss of one USH2 gene function is predicted to be functionally close to loss of all three . Third , the photoreceptor degeneration in USH2 disease arises from dysfunction of the PMC , a subcellular structure that is conserved from amphibian to mammalian photoreceptors . The PRC , the analogous structure of the PMC in frog photoreceptors , is a set of nine symmetrically arrayed ridges and grooves , seen by scanning electron microscopy , at the apical inner segment membrane surrounding the connecting cilium . Originally discovered over 20 years ago [31] , [32] , the molecular components of the PRC had remained unknown . In the present study , we show that whirlin is a component of the PRC , the first identified marker for this complex in frogs . Although a morphologically apparent PRC structure in mammalian photoreceptors has not been seen , the similar localization pattern of whirlin in frog and mouse photoreceptors strongly suggests that a functional equivalent structure of the PRC exists in mammalian photoreceptors . Hence , we propose that the mammalian equivalent of the PRC be designated the periciliary membrane complex ( PMC ) . Our group was the first to propose the concept of a PRC equivalent structure in mammalian photoreceptors based on the subcellular distribution of whirlin [33] . Our findings in the present study of the subcellular localization and functional interaction among whirlin , usherin and VLGR1 in mouse photoreceptors further strengthen this argument . In frogs , numerous rhodopsin-containing vesicles are present in the surrounding cytoplasm of the PRC , suggesting that the PRC may be a docking site of vesicles transporting newly synthesized rhodopsin from the Golgi [32] . Consistent with this theory , we found accumulation of vacuoles around the PMC in a small proportion of photoreceptors in whirlin knockout mice . However , in both whirlin knockout and Ush2a knockout mice [26] , polarized distribution of rhodopsin to the outer segments was not measurably disrupted as shown by immunofluorescence . This observation suggests that either loss of these proteins is not sufficient to abolish completely the organization and function of the PMC , or alternative routes exist in mammalian photoreceptors for targeting rhodopsin to the outer segments . It is also possible that the USH2 protein complex is not involved directly in protein trafficking but plays only a structural role . Interestingly , we have found that the spacing between the PMC and the connecting cilium became irregular in whirlin knockout mice and there was frequent occurrence of membrane fusion between the PMC and the connecting cilium . These findings indicate that the USH2 proteins are important in maintaining the integrity of the spatial relationship between the PMC and the juxtaposing connecting cilium . A series of in vitro analyses have found that whirlin is able to interact with calmodulin-dependent serine kinase [34] , NGL-1 [24] , SANS [23] , myosin VIIa [24] as well as usherin [17] and VLGR1 [14] . Among these proteins , SANS and myosin VIIa are involved in human Usher syndrome type I [20] , [35] . They have been reported to localize at or in the vicinity of the connecting cilium in photoreceptors [23] , [36] . In inner ear hair cells , immunostaining , biochemical and cellular analyses suggest that the interaction between whirlin and myosin XVa through the PDZ domains of whirlin is required for delivery of whirlin to the tip of stereocilia [13] , [24] , [37] . Additionally , whirlin has been shown to interact with p55 in hair cells [38] . Therefore , some of these proteins might be candidate components of the PMC , although further studies are necessary to verify their presence in the PMC in vivo , Such studies could lead to a more comprehensive understanding of this specialized membrane domain . In the inner ear hair cells , the interaction among whirlin , usherin and VLGR1 plays a similar role in localizing the USH2 protein complex at their normal subcellular location , i . e . , the stereocilia . Here , the interactions among the three proteins may be subtly different from those in photoreceptors . The three proteins may not be completely dependent on one another for their normal localization in hair cells , as indicated by the incomplete loss of the complex from stereocilia in whirlin and Ush2a single knockouts . Usherin and VLGR1 have been demonstrated in this study and a recent study [14] to bind to the N-terminal two PDZ domains in whirlin . In the inner ear , the high abundance of whirlin short isoform , which lacks these two PDZ domains , may make the interaction of whirlin long isoform with usherin and VLGR1 partially redundant . Additionally , there may be different proteins participating in the formation and localization of the multi-protein complex containing whirlin , usherin and VLGR1 between photoreceptors and hair cells . For example , a unique exon in the cytoplasmic region of usherin in hair cells , which is missing in photoreceptors [17] , may provide a platform for binding yet unidentified proteins in hair cells . In contrast to previous studies on the localization of USH2 proteins in photoreceptors [14] , [22] , [23] , we localized whirlin and VLGR1 only to the PMC in photoreceptors as what we have found for usherin in one of our recent publications [26] . To further confirm this finding , we rigorously exploited two approaches , double immunostaining of whirlin with different subcellular structure markers in two different species and immunoelectron microscopy . We determined the specificity of our antibodies of USH2 proteins in western blotting and immunostaining using USH2 mutant mice as valid negative controls . Additionally , ultrastructural examination of whirlin knockout mice found various defects only around the PMC but not in other regions , such as the synaptic terminus , in photoreceptors . Therefore , our study presents strong evidence that the USH2 proteins are only located at the PMC in photoreceptors . Comparison of whirlin knockout mice generated in this study with the whirler mice demonstrates that whirlin long isoform plays an essential role in photoreceptors . In our whirlin knockout mice , whirlin long isoform including the first and second PDZ domains , which bind to usherin , have been disrupted . By immunofluorescence , usherin is lost from the PMC ( Figure 5B ) . Since usherin is required for maintaining the long term viability of photoreceptors [26] , the absence of usherin from the PMC could be responsible , at least in part , for the late-onset retinal degeneration in whirlin knockout mice . In whirler mice , a large deletion in the whirlin gene ( Figure 1A ) removes all predicted translational start codons of the short isoform and a portion of the proline-rich region . This mutation , therefore , is believed to completely ablates the short isoform and truncates the long isoform leaving only the N-terminal PDZ domains intact . Furthermore , an N-terminal whirlin transcript that we have found in abundance by cDNA library screening is predicted to produce a protein that retains the first PDZ domain . These N-terminal whirlin protein variants appeared to partially compensate for the loss of the intact whirlin long isoform . Indeed , in whirler mice a reduced amount of usherin is still found at the PMC in photoreceptors ( Figure 5B ) . This residual whirlin/usherin function appears to be sufficient in maintaining photoreceptor viability , and hence no photoreceptor degeneration was found in whirler mice . Our finding that whirlin long isoform protein alone is expressed in the retina further supports the notion that the long but not the short variant of whirlin is required in photoreceptors . The differences in hearing and vestibular dysfunction and in hair cell stereocilia defects between whirlin knockout and whirler mice suggest that whirlin long and short isoforms may function differently in hair cells . In whirlin knockout mice , only the outer hair cell stereocilia exhibit an abnormal ‘U’ shape formation , while the inner hair cell stereocilia appear normal . These whirlin knockout mice are partially deaf and have no circling behavior ( no vestibular defect ) . But in whirler mice , besides the abnormal ‘U’ shape stereocilia formation in the outer hair cells , the inner hair cells have significantly shortened stereocilia [10] , [39] , [40] . These mice are completely deaf and exhibit a vestibular balance problem . The position-dependent outcome of whirlin gene mutations observed in mice is also apparent in humans . In a German USH2 family , compound heterozygosity of a nonsense mutation p . Q103X and a mutation in the splice donor site , c . 837+1G>A , which are in the 5′-terminal region of the whirlin gene , was found to cause USH2 [12] . In addition , a nonsense mutation , p . R778X , and a single nucleotide deletion , c . 2324delG , leading to a deletion of the C-terminus of the whirlin protein were found responsible for deafness DFNB31 [10] , [11] . Therefore , in both humans and mice , mutations at the N-terminus of the whirlin protein cause both vision and hearing impairments ( our study and [12] ) , while mutations at the C-terminus of the whirlin protein cause more severe hearing defects only [10] , [11] . These data support our conclusion that the long isoform plays an essential role in photoreceptors , while the short isoform functions primarily in hair cells . In summary , this study provides strong evidence that USH2 proteins form a multi-protein complex in which the whirlin long isoform plays a key role . This complex is localized at the PMC in photoreceptors and the stereocilia in hair cells . Disruption of this USH2 protein complex could be the common pathogenic mechanism underlying all three subtypes of human USH2 disease .
Two genomic DNA fragments ( 2 . 8 and 6 kb ) flanking the 3′ portion of the first exon of whirlin were amplified from 129/Sv mouse genomic DNA by PCR , and inserted separately as the short and long arm into a modified pGT-N29 vector , which contained a diphtheria toxin expression cassette as a negative selection marker ( Figure 1B ) . The targeting vector was linearized and electroporated into R1 embryonic stem ( ES ) cells . An ES clone was found to have the partial replacement of the first exon of whirlin by the Neor gene , and was microinjected into C57BL/6 blastocysts . The resulting chimeras were crossed with C57BL/6 mice . Heterozygous and homozygous knockout mice were identified with respect to the targeted allele by PCR ( Figure 1C ) . The MEEI institutional guidelines were followed on all animal procedures . A tiny piece of the mouse tail ( about 2 mm long ) was lysed by proteinase K at 50°C overnight in tissue lysis buffer ( 100 mM Tris-HCl pH 8 . 0 , 200 mM NaCl , 5 mM EDTA , and 0 . 2% SDS ) . The genomic DNA was precipitated from the resulting lysate by adding the same volume of isopropanol and centrifugation . The pellet was finally dissolved in TE buffer . The total RNA was isolated using TRIzol Reagent ( Invitrogen ) according to the manufacturer's instruction . RT ( ThermoScript™ RT-PCR system , Invitrogen ) and PCR ( Expand long template PCR system , Roche Diagnostics ) reactions were performed following the manufacturer's instructions . Mouse whirlin cDNA fragments ( PDZ320 , 1–320 aa; PDZIE , 315–580 aa , accession number , NP_082916 ) and frog whirlin cDNA fragment ( analogous to mouse whirlin 816–907 aa ) were inserted into the expression vector pET28 ( Novagen ) . Recombinant proteins were expressed as His-tagged fusion proteins in Escherichia coli host BL21-CodonPlus ( DE3 ) -RIPL . The recombinant proteins were purified through a Ni2+-charged nitriloacetic acid agarose column and were used to immunize rabbits and chickens . Whirlin-specific antibodies were affinity-purified from antisera or egg yolk extracts . Usherin antibodies used in this study were raised against the N-terminal and C-terminal domains of the protein [26] . RP1 , RPGR and CIP98 antibodies were as described previously [25] , [34] , [41] , [42] . VLGR1 antibody was kindly provided by Dr . Perrin C . White ( University of Texas Southwestern Medical Center , Dallas , Texas ) . Mass1 ( C20 ) antibody was purchased from Santa Cruz Biotechnology , Inc . Monoclonal anti-γ-tubulin and anti-acetylated α-tubulin antibodies were obtained from Sigma-Aldrich . Alexa fluorochrome-conjugated phalloidin and secondary antibodies , and Hoechst dye 33342 were obtained from Molecular Probes , Inc . Mouse whirlin and its fragments ( full-length , 3–907 aa; N-terminus , 3–472 aa; C-terminus , 438–907 aa , accession number , NP_082916 ) and mouse usherin fragment ( 5053–5193 aa , accession number , NP_067383 ) were amplified from the retina and individually cloned into both pGBKT7 and pGADT7 vectors . Yeast two-hybrid analysis was performed as described previously [43] . Briefly , a protein/peptide in pGBKT7 vector was co-transformed with its putative interacting protein/peptide in pGADT7 vector . Empty pGBKT7 and pGADT7 vectors were used as negative controls . Co-transformants were grown on both SD-4 ( -Leu , -Trp , -Ade , and -His ) and SD-2 ( -Leu and -Trp ) plates . The growth on SD-4 plates indicated an existence of interaction between the two co-transformed proteins/peptides . In our experiments , all co-transformants were able to grow on SD-2 plates indicating a successful co-transformation . GST pull-down assay: cDNA fragments of intact ( mouse: 5053–5193 aa , NP_067383; frog: analogous to mouse usherin 5053–5193 aa ) and mutant ( without PDZ-binding domain , mouse: 5053–5186 aa , NP_067383; frog: analogous to mouse usherin 5053–5189 aa ) C-terminal usherin were amplified from frog and mouse retinas and cloned into the pGEX4T-1 vector . The GST-fused intact and mutant usherin were expressed in BL21-CodonPlus ( DE3 ) -RIPL cells and then incubated with mouse retinal lysate and glutathione sepharose beads for 2 hours at 4°C . Subsequently , the sepharose beads were washed with lysis buffer ( 50 mM Tris-HCl pH8 . 0 , 150 mM NaCl , 0 . 5% TritonX-100 , 5 mM EDTA , 0 . 5 mM PMSF , 1× protease inhibitor , and 1 mM DTT ) for three times and boiled in Laemmli sample buffer for 10 minutes . All the procedures were performed at 4°C . Retinal lysates incubated with glutathione sepharose beads and GST or only with GST were used as negative controls . Immunoprecipitation: Dissected retinal or inner ear tissues were homogenized and incubated for about 60 minutes in lysis buffer . After centrifugation at 18 , 000 g for 10 minutes , the supernatants were precleared by incubation with protein G sepharose ( Amersham Biosciences ) for 1 hour . Subsequently , they were incubated with the primary antibodies for 3 hours and then centrifuged at 18 , 000 g for 10 minutes . The resulting supernatants were incubated with protein G sepharose for an additional 1 hour . After a brief centrifugation at 2000 g , the pellets were washed with lysis buffer for four times and then boiled in Laemmli sample buffer . All the procedures were performed at 4°C . A non-immune rabbit IgG served as a negative control . Western blotting was carried out as described previously [43] . Immunofluorescence: Eyes were enucleated , frozen immediately and sectioned at 10-µm thick . Sectioned tissues were fixed in 4% formaldehyde/PBS for 10 minutes ( for usherin staining , 2% formaldehyde/PBS for 5 minutes ) , and permeabilized by 0 . 2% Triton X-100/PBS for 5 minutes at room temperature . Pup heads on postnatal day 3–6 were fixed in 4% formaldehyde/PBS for about 36 hours , switched to 30% sucrose/PBS for several days , and sectioned at 30-µm thick . The subsequent steps of blocking and incubation with primary and secondary antibodies were as described previously [43] . Alexa 488- and 594-conjugated secondary antibodies were routinely used for tissue double-labeling . Stained sections were viewed and photographed on a fluorescent microscope ( Olympus , model 1X70 ) equipped with a digital camera ( Carl Zeiss MicroImaging , Inc . ) or on a confocal laser scanning microscope ( Leica , model TCS SP2 ) . Immunoelectron microscopy: Eyes were enucleated . Their anterior segments and lens were removed . Dissected retina was fixed with 4% formaldehyde/PBS ( whirlin ) or 2% formaldehyde/0 . 1% glutaraldehyde/PBS ( VLGR1 ) for 30 minutes , washed with TTBS buffer ( Tween/Tris-buffered saline ) , blocked in 5% goat serum/TTBS for 1 hour , and incubated with the primary antibodies at 4°C overnight . After rinses , the retina was incubated with Nanogold goat anti-rabbit antibody ( Aurion , Wageningen , The Netherlands ) , post-fixed sequentially with 1% formaldehyde/2 . 5% glutaraldehyde/0 . 1 M cacodylate buffer and 2% osmium tetroxide . Later , it was silver-enhanced , dehydrated , embedded in Epon , and sectioned at 70 nm thickness . In an alternative protocol , the retina was fixed in 2% formaldehyde/0 . 1% glutaraldehyde/PBS for 30 minutes , frozen and cut to 10 µm sections prior to staining with primary and secondary antibodies . Staining was done while floating in a dish . After final wash , the sections were post-fixed and processed for immunoEM as above . ImmunoEM for whirlin were studied with both methods which yielded the same results . ImmunoEM for VLGR1 used the alternative protocol . Transmission electron microscopy and scanning electron microscopy was performed as described previously [26] , [43] . Measurements of photoreceptor outer segment length and outer nuclear layer thickness were made along the vertical meridian ( superior to inferior ) at five locations to each side of the optic nerve head separated by approximately 600 µm each . Measurements began at approximately 200 µm from the optic nerve head and ended at approximately 200 µm from the retinal periphery . For the analysis in the whirlin knockout mice , seven whirlin knockouts and five whirlin heterozygous littermates from 28–33 months of age were included . For the analysis in the whirler mice , three whirler mice aged from 28–33 months and two age-matched wild-type mice were included . Photoreceptors with abnormal morphology around the PMC were counted at the retinal mid-periphery . Abnormal morphology was defined as membrane fusion between the apical inner segment and the distal connecting cilium or vacuole accumulation in the apical inner segment around the PMC . The presence of at least 3 large vacuoles ( diameter is larger than 200 nm ) or 4 small vacuoles ( diameter is about 100 nm ) was considered as vacuole accumulation . Four wild-type and six whirlin knockout mice at the age from 5 to 24 months were included in this experiment . The Student's t-test was conducted to compare the measured values of whirlin knockout and control mice . A P value of less than 0 . 05 was considered to indicate a significant difference between the two groups . ERG and DPOAE recordings were performed as described previously [26] , [44] . | Usher syndrome is a devastating genetic disorder affecting both vision and hearing . It is classified into three clinical types . Among them , type II ( USH2 ) is the predominant form accounting for about 70% of all Usher syndrome cases . Three genes , USH2A , USH2C , and USH2D , underlie the development of USH2; and they encode usherin , Very Large G protein-coupled Receptor-1 ( VLGR1 ) , and whirlin , respectively . In this study , we show that the long whirlin isoform organizes the formation of a multi-protein complex in vivo that includes usherin and VLGR1 . Targeted disruption of whirlin long isoform abolishes the normal cellular localization of the two partner USH2 proteins in the retina and in the inner ear and causes visual and hearing defects . We present the first definitive evidence that the USH2 proteins mark the boundary of the periciliary membrane complex , which was first described in frog photoreceptors and is thought to play a role in regulating intracellular protein transport . We propose that defects in all USH2 proteins share a common pathogenic pathway by disrupting the periciliary membrane complex in photoreceptors . | [
"Abstract",
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] | [
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] | 2010 | Ablation of Whirlin Long Isoform Disrupts the USH2 Protein Complex and Causes Vision and Hearing Loss |
HIV protease , an aspartyl protease crucial to the life cycle of HIV , is the target of many drug development programs . Though many protease inhibitors are on the market , protease eventually evades these drugs by mutating at a rapid pace and building drug resistance . The drug resistance mutations , called primary mutations , are often destabilizing to the enzyme and this loss of stability has to be compensated for . Using a coarse-grained biophysical energy model together with statistical inference methods , we observe that accessory mutations of charged residues increase protein stability , playing a key role in compensating for destabilizing primary drug resistance mutations . Increased stability is intimately related to correlations between electrostatic mutations – uncorrelated mutations would strongly destabilize the enzyme . Additionally , statistical modeling indicates that the network of correlated electrostatic mutations has a simple topology and has evolved to minimize frustrated interactions . The model's statistical coupling parameters reflect this lack of frustration and strongly distinguish like-charge electrostatic interactions from unlike-charge interactions for of the most significantly correlated double mutants . Finally , we demonstrate that our model has considerable predictive power and can be used to predict complex mutation patterns , that have not yet been observed due to finite sample size effects , and which are likely to exist within the larger patient population whose virus has not yet been sequenced .
Proteins evolve through random mutagenesis and their evolutionary selection is constrained by structural , functional and environmental factors [1] . Thermodynamic stability is by far the most important structural factor , as most proteins need to be folded in order to function . The stability range for each protein , however , is narrow and is estimated experimentally to be around 10 kcal/mol , which is of the order of three hydrogen bonds [2] . As a result of this marginal stability , proteins operate “on a knife's edge” [3] , whereby a single highly deleterious mutation could potentially lead to decreased stability and loss of activity [4] . By the same token , a single stabilizing mutation could be advantageous from an evolutionary point of view . For example , more stable forms of cytochrome P450 allowed for greater exploration of mutational space in directed evolution experiments than sequences without stabilizing mutations [5] . This increased “evolvability” is not just limited to directed evolution experiments , but may be a general property of proteins evolving under selective pressure [6] . In fact , recent experimental work on HIV protease has shown that accessory mutations compensate for the loss of stability due to destabilizing primary drug resistance mutations , helping the virus evade drugs [7] . This stabilizing effect can have an external source as well: Hsp90 , a molecular chaperone , buffers deleterious mutations , allowing for polymorphisms to appear and new traits to evolve [8] . As a result of this work and prior research by other groups , it is now widely recognized that thermodynamic stability is intimately linked with the evolvability of a protein [9]–[11] . Even though the process of mutagenesis is random , the genetic and structural constraints mentioned above , coupled with functional selection , ensure that certain mutations in evolving proteins are associated with each other in a highly non-random fashion [12] . These correlated mutations are an inherent property of evolving amino acid sequences , and an evolutionary signature of viable proteins . A multitude of methods have been developed to identify such pairs and groups of mutations [13] , some of which have been applied to HIV protease sequences to locate pairs or groups of coevolving residues [14]–[16] . Our previous work on higher-order correlations showed that for HIV-1 protease , including at least pair correlations is essential for reproducing statistical patterns of primary and accessory mutations observed in protease sequences from patients undergoing anti-retroviral therapy [17] . It is tempting to attribute sequence correlations to effects arising from protein stability constraints [18] , and several groups have tried to connect sequence correlations with protein energetics on a detailed atomic level . For example , Zhang et al . applied Bayesian networks to infer therapeutically relevant and conditionally dependent sets of resistance mutations in HIV protease and reverse transcriptase and then used molecular simulations to model the specific interactions that cause resistance [19] . Ranganathan et al . have attempted to explain mutational coevolution by connecting statistical free energies from multiple sequence alignments to differences in experimental folding free energies [20] . Some of these results have been difficult to replicate [21] , and are still a topic of active debate in the community [22] , [23] . Thus while studies that link mutational correlations to thermodynamic constraints have made great progress [12] , [19] , [24]–[26] , a consensus linking protein energetics and mutational correlation patterns has not yet emerged . These observations have motivated us to explore how correlated mutations in HIV protease are connected to energetics via their impact on protein stability . Since current methods for predicting stability changes upon mutation based on detailed atomic models are not sufficiently accurate [27] , we have chosen to focus instead on the electrostatic part of the total energy for which a coarse-grained model of electrostatics is appropriate . We find that this model captures many important effects of mutations on energetics and stability of HIV protease . We show that the average electrostatic stabilization of HIV protease increases with the number of electrostatic mutations ( an electrostatic mutation changes the charge of that mutating residue relative to the wild-type residue ) , consistent with the hypothesis that accessory electrostatic mutations buffer the destabilizing effects of primary drug-resistance mutations , most of which are non-electrostatic mutations and are therefore not modelled here . We demonstrate that correlations among electrostatic mutations are critical for stabilization; uncorrelated mutations would strongly destabilize the protein . We show that our method , which employs both electrostatic calculations and sequence analysis based on statistical inference techniques , can be used as a predictive tool for novel mutational patterns that have not yet been observed . Finally , we comment on the structure of the electrostatic mutation network of HIV protease . Energy landscape theory , which provided the framework for understanding protein folding through funnels , introduced the concept of a smooth , minimally frustrated landscape for foldable , natural proteins [28] , [29] . Our results indicate that the electrostatic interaction network is minimally frustrated as is evident in the derived statistical coupling parameters which strongly predict the underlying charge patterns , providing additional evidence that proteins have evolved to minimize frustrated interactions .
Our analysis of electrostatic mutation patterns is based on the alignment of HIV protease sequences from Christopher Lee's HIV Positive Selection Mutation Database ( http://bioinfo . mbi . ucla . edu/HIV ) [30] . Each amino acid sequence in the Lee database is converted into a charge signature , which is a three letter alphabet representation of that sequence ( + , − , n ) corresponding to positively-charged , negatively-charged , and neutral residues . These charge signatures are compared to the wild-type charge signature to determine electrostatic mutations . We examined all primary , accessory and polymorphic drug resistance mutation positions ( as designated by the Stanford HIV database [31] ) and limited our analysis to a subset of 18 positions whose charged state mutates above a threshold frequency of 0 . 01% . Our model therefore includes more than 380 million states or unique charge signatures involving these 18 positions ( Figure 1 ) . Of the 18 , 9 are sites which have been characterized as primary or accessory drug resistance mutations while the rest are sites labeled as polymorphic mutations . Mutations are labelled “polymorphic” if they are observed to mutate in the absence of drugs and whose compensatory effect has not yet been experimentally verified , even though drugs may have a significant affect on their correlations with other mutating residues [31] . If we divide this database of charge signatures into subsets with 1 , 2 , 3… electrostatic mutations and calculate the electrostatic contribution to the average folding free energy , for each subset , we find that on average the stability of the folded state increases by kcal/mol from 1 to 3 mutations and maintains this level of stabilization beyond 3 mutations ( Figure 2 , black curve ) . Since selective pressure in the presence of inhibitors often leads to destabilizing primary drug resistance mutations [32] , [33] , the observed increase in electrostatic stability is due to energetic compensation: destabilizing mutations occur due to selective pressure and electrostatically active residues provide a “reservoir of stability” . The observed stabilization requires not only the correct frequencies of occurrence of each of the three possible charge states at each position , but also the presence of correlations . Generating random sequences with equal mutation frequencies for the three charge states results in a substantial destabilization of the protein ( Figure 2 , red curve ) . Introducing observed frequencies of occurrence of each charge at every position improves the stabilization relative to the previous model with equal mutation frequencies , but still results in substantial destabilization ( Figure 2 , blue curve ) . We refer to this latter model as the independent model as it generates an alignment in which mutations at each position occur independently with correct frequencies . If pair correlations are introduced by preserving the observed joint mutation frequencies ( see Methods ) , substantial protein stabilization occurs , and the energies predicted by this pair correlation model ( Figure 2 , green curve ) become comparable to the energies of the observed sequences . The magnitude of the difference between the observed and pair correlation model average energies is less than 2 kcal/mol for sequences with mutations , suggesting that introducing pair correlations is sufficient for explaining the observed energetic stabilization trends . Overall , the difference between the independent model and the pair correlation model is statistically significant given a sample size corresponding to the numbers of sequences observed in the database with those number of mutations ( e . g . for sequences with 4 or fewer mutations ) . We find that the observed electrostatic stabilization can be attributed to a relatively small number of low-energy signatures which are highly unlikely under the independent model but become very probable once pair correlations are introduced ( Figure S1 ) . For example , the well-studied pair of primary drug resistance mutations , D30N-N88D [15] , [32] , which occurs 2220 times in the Lee database , contributes to the kcal/mol stabilization of the pair correlation model relative to the independent model shown in Figure 2 . Together , these top 10 double mutants account for of the stabilization of the pair correlation model relative to the independent model . Table S3 lists the most statistically deviated pairs and Figure S7 depicts the distance between the pairs on the structure of HIV protease . It is interesting to note that 4 out of the top 10 most correlated pairs are greater than 10 Angstroms apart . With increasing numbers of mutations however , the stabilization spreads among multiple patterns ( Figure S1 ) . For 3 electrostatic mutations , the top contributor D30N-N37D-N88D is responsible for 20% , while the top 10 signatures account for 64% of the kcal/mol stabilization of the pair correlation model relative to the independent model . For 4 mutations , K20I-D30N-N37D-N88D accounts for 10% , while the top 10 signatures account for 33% of the stabilization . For 5 mutations , K20I-D30N-E35Q-N37D-N88D accounts for 17% and the top 10 signatures account for 36% of the stabilization . These stabilizing charge patterns are also strongly associated with protease inhibitor therapies , as determined by our drug association analysis ( see Text S1 ) . Most protease drug association studies focus on point mutations or pairs of mutations [30] , [34] . Our drug association analysis allows us to examine the significance of drug association for patterns of more than two mutations . Table S1 lists the most significant associations between drugs and charge patterns of 2 , 3 and 4 electrostatic mutations with the highest probabilities as predicted by the pair correlation model . Most of the patterns listed are strongly associated with at least one drug and several are associated with many drugs . For example the D30N-N88D double mutant and the D30N-N37D-N88D triple mutant are both strongly associated with Nelfinavir monotherapy and Indinavir-Nelfinavir combination therapy with . We also find strong association between drugs and patterns predicted by the pair correlation model with more than three mutations . For example K20I-D30N-N37D-N88D and K20I-D30N-E35Q-N88D are both associated with Indinavir-Nelfinavir combination therapy while K20I-D30N-H69Q-N88D is associated with Ritonavir-Nelfinavir therapy with . The ability to predict drug resistant mutation patterns is of great therapeutic relevance . Approaches to predicting drug resistance mutations based on biophysical modeling have recently been proposed [35] , [36] . In contrast , our statistical-inference based approach which includes pair correlations among mutations , allows us to predict the probabilities of arbitrary charge signatures , many of which have not yet been experimentally observed . Figure S2 shows that most of the sequences with less than 5 mutations , whose probabilities are significantly enhanced by pair correlations , are observed in the Lee database , indicating that these mutational patterns are routinely utilized by the virus . However , for 6 mutations the most stabilizing pattern , K20I-D30N-E35Q-N37D-Q58E-N88D , was not observed ( Figure S1 ) . The probability of this pattern under the pair correlation model is , too small to appear frequently in a database of sequences due to finite sample size effects . However , if the size of the database were to increase five-fold , the probability of observing at least one copy of this pattern would be . The proportion of sequences not observed in the Lee database with significantly enhanced pair correlation model probabilities increases greatly with the number of mutations , of which it is likely that many are not observed because of finite sample size effects ( Figure S2 ) . In order to test our ability to predict novel patterns of favorable electrostatic mutations unobserved in the Lee database due to finite sample size effects , we examined the contents of a separate database , the drug-annotated Stanford database which contains HIV protease subtype B sequences from various sources [31] . Figure 3 plots the probabilities of sequences using the pair correlation model , , as a function of the observed probabilities in the Lee database . Sequences are also shaded according to a gradient that represents how often the sequence occurs in the Stanford database , relative to the Lee database . The plot shows that sequences with the highest predicted probabilities that are unobserved in the Lee database are largely shaded red , indicating that most are observed in the Stanford database . In fact , of the top 25 most probable sequences predicted by the pair correlation model that are not found in the Lee database , 19 are present in the Stanford database ( Table S2 ) . Most of these sequences are also significantly associated with drug therapies ( e . g . ) . As the predicted probability decreases , the shading of the dots gradually changes to green , indicating that sequences with the lowest predicted probabilities are unobserved in both the Lee and the Stanford database . Thus our approach exhibits considerable predictive power . If we examine other sequences in the tail of the Lee probability distribution that are observed once , twice , three times etc in the Lee database , we notice a similar trend; sequences with higher predicted probabilities are present in the Stanford database at much higher frequencies than sequences with lower predicted probabilities , even though the pair correlation model was parameterized on the Lee database . This result strongly suggests that the pair correlation model is a much better predictor of actual sequence probabilities than using the sequence counts from the databases themselves , because of finite sample size effects . In other words , the tail of the distribution is very well represented by the pair correlation model . Determining the statistical field and coupling parameters ( written as and for simplicity ) that best fit the pair correlation model given a set of observed univariate and bivariate marginals ( and ) , is known in the literature as the inverse Ising problem . As described in the Methods , we iteratively determine these parameters using a graph-theoretic inference algorithm , called belief propagation ( BP ) [37]–[39] , a method which has recently been applied by other research groups to study protein conformational entropy and protein-protein interactions [40] , [41] . Within the BP framework , we apply a mean field model which includes pair correlations in the Bethe approximation to estimate the bivariate marginals , [38] , [42] , [43] . In the statistical physics community “mean field” in often used to refer to a class of approximations whereby the free energy of the system is written in terms of the marginals up to a given order , the corresponding mean field model at the pair correlation level is the Bethe approximation . However , it is well known that while is exact and converges to on acyclic networks , only approximates and can become unstable on cyclic networks [44] . For the electrostatic correlation network of HIV protease , we observe that the belief propagation algorithm converges quickly to the observed bivariate marginals ( Figure S3 ) . The convergence towards the observed probabilities for triplets and larger multiplets is also well approximated , a result that is non-trivial since the Bethe approximation is a pair-level approximation and does not guarantee the convergence for marginals beyond pairs [45] . For trivariate marginals , the correlation coefficient between and is 0 . 98 while for four mutations , the correlation coefficient between and is 0 . 90 ( Figure S3 ) . This close correlation between observed and predicted marginals argues for a simple network structure and suggests that for this system , the Bethe approximation is a good approximation and by implication the electrostatic mutation network is minimally frustrated . Another strong indicator of the lack of frustration in the electrostatic mutation network of HIV protease is that the statistical coupling parameters of the ( Bethe ) mean field model are able to distinguish like-charge patterns from unlike-charge patterns with high accuracy ( Figure 4 ) . We find that the sign of , a quantity derived from the sequence analysis alone , is able to correctly predict the charge patterns for of the top 35 most significantly correlated charge pairs ( . ) , reflecting the evolutionary optimization of the protein electrostatic interaction network ( Figure 4 ) . Moreover , Figure 4 also indicates that the magnitude of correlates with the spatial distance between residues . Of the top ten pairs of residues with the largest statistical coupling parameters , nine are situated close to one another ( Å ) in the folded structure of the HIV protease homodimer . In this context , we note that Morcos et al . [46] used a similar approach to infer spatial contacts between residues of many proteins , through an analysis of the coupling parameters of a corresponding mean field model .
Our results suggest that electrostatic interactions play an important role in the coevolution of mutations in HIV protease . The extent to which electrostatics influences protein stability has been the subject of debate in the literature [47]–[53] . One experimental study of protease has minimized the role of electrostatics in favor of the impact of compensatory mutations on protein flexibility [54] . Others have suggested that buried charges play a more important role in protein function than stability [55] , [56] . We recognize that electrostatics is only part of the total energy , and that contributions to stability from van der Waals interactions , hydrogen bonding and hydrophobic effects are significant . Nonetheless , long-range electrostatics is likely to have a substantial effect on protein stability [50] , [52] , [57]–[59] . Our results support the proposal that the presence of correlations among electrostatic mutations arises from the contraints imposed by the need to maintain the stability of the folded protein . HIV protease is under strong selection pressure from drugs . As a result of the initial build up of drug resistance , protease becomes less stable [32] . We hypothesize that electrostatic mutations not only bring the protein back to a more viable state , but may give the protein more “breathing room” on the evolutionary fitness landscape . Manipulating the charge distribution of HIV protease is complex and we find that uncorrelated mutations would tend to strongly destabilize the enzyme , contrary to the stability gain observed in the database . Therefore , we propose that sets of electrostatic mutations occur together , increasing the “evolvability” of a protein by providing a “reservoir of stability” which allows it to escape epistatic traps along evolutionary pathways towards higher fitness [18] . The absence of frustration could reflect evolutionary optimization of the electrostatic interaction network in HIV protease under selection pressure from drugs , or it could be a general property of protein electrostatic interaction networks . Indeed , natural proteins tend not to be frustrated systems [29] , [60] – they are fine-tuned biological machines with restricted evolutionary pathways [18] . Within these pathways , proteins are highly robust and the physics underlying their folding display a kind of simplicity [29] , [61]–[63] . Our conclusions based on a coarse-grained electrostatics model combined with statistical inference techniques reflect this lack of frustration . In future work we will study our algorithm on interaction networks with a larger alphabet size and different network topologies . In this context , we note that Balakrishnan et al . used an alternative learning algorithm that a solved similar problem but determined the optimal graph topology for the network [64] . Our statistical analysis of HIV sequences captures biophysical constraints in the form of a statistical network of correlated mutations . Even though the model is based on pairwise correlations , it captures the higher-order effects and correctly predicts the probabilities of sequences found in the tail of the distribution . The fact that many of these patterns are also strongly associated with protease inhibitors from patients undergoing antiretroviral therapy , highlights the clinical relevance of our method . Other mutation patterns that we predict are likely to exist within patients whose virus has not yet been sequenced . Having knowledge of these unique , but as yet unobserved patterns , can be important for the design of future inhibitors to combat drug resistance . In this work , we go beyond a purely statistical approach to modeling patterns of electrostatic mutations , and show that the statistical results are entirely consistent when viewed in the context of a structure based energy model . Though electrostatics is only part of the total energy , our work has highlighted its importance and provided support for the proposal that correlated electrostatic mutations provide a reservoir of stability for HIV protease as it builds resistance to drugs .
45 , 161 aligned HIV-1 DNA nucleotide sequences were downloaded from Christopher Lee's HIV Positive Selection Mutation Database ( http://bioinfo . mbi . ucla . edu/HIV ) on March 4th , 2008 [30] . This database of sequences , which we call the Lee database , consists primarily of HIV-1 subtype B samples sequenced by Specialty Laboratories Inc from 1999 to mid-2002 . These sequences are not annotated . [30] . The amino acid sequences were converted into strings of characters “n” , “−” and “+” , indicating whether a given residue is neutral , negatively or positively charged at pH = 6 ( i . e . His , Arg , and Lys are positively charged , while Asp and Glu are negatively charged ) . A second database of subtype B sequences , which we call the Stanford database , was downloaded from the Stanford HIV database on April 7th , 2010 [31] . This database consists of drug annotated sequences collected primarily from more than 900 literature and GenBank references . This drug-annotated dataset was used to associate correlated mutation patterns with specific anti-retroviral therapies ( see SI ) . The univariate and bivariate marginals extracted from the Lee database and from the Stanford database are effectively the same ( correlation coefficient of 0 . 999 ) , indicating that our results would be unchanged if we used the Stanford database to parameterize the model . Moreover , at a 20 letter amino acid alphabet level , there is little redundancy between the databases as only 7 . 63% of the sequences are present in both databases . To locate electrostatic mutations , the resulting charge signatures were compared to the HIV-1 subtype B consensus sequence from the Los Alamos National Laboratory HIV sequence database . This consensus sequence was used to define the wild-type charge signature . We define an electrostatic mutation as an amino acid mutation which changes the charge at a certain position along the protein sequence , relative to the wild-type amino acid at that position ( e . g . D30N and N88D ) . In contrast , the L90M and R8K are not considered to be electrostatic mutations . We examined all the primary , accessory and polymorphic drug resistance mutations positions ( as designated by the Stanford database [31] ) and included all electrostatic mutations above a threshold frequency of 0 . 01% . The 18 positions included are the primary drug resistance mutatation sites D30 and N88 , accessory mutation sites K20 , E34 , E35 , K43 , Q58 , L63 , and Q92 , and polymorphic mutation sites Q7 , T12 , G16 , Q18 , N37 , Q61 , H69 , K70 , and I72 . The electrostatic energy of protein folding was estimated as ( 1 ) where and are electrostatic free energies of the folded and unfolded states , computed using an Analytical Generalized Born ( AGB ) model [65] . The folded state electrostatic free energy was calculated by placing unit charges corresponding to a particular charge signature onto the most-distal sidechain carbon atom of the corresponding wild-type amino acid within a dimer crystal structure ( PDB ID 1NH0 ) . All other sidechain atoms remain neutral , although a partial charge dipole of is placed on every backbone amide and carbonyl group to retain the helix dipole effects [66] . Our approximation of the denatured state is a maximally extended structural representation of chain A from 1NH0 , with backbone dihedral angles set to ( except for prolines ) and sidechain rotamer states set to all-trans . Similarly to the folded state , charges on the unfolded state are placed on the most-distal sidechain carbon atom and backbone dipoles are switched on . See SI Methods for further implementation details . As in our previous work [17] , we make use of a Potts model to capture the effects of pair interactions between residues . Since our electrostatic data consists of sequences with three possible charge states at each site , we use a 3-letter alphabet ( + , − , n ) , for positively-charged , negatively charged , and neutral residues . Including all three charge states in our study leads to possible charge signatures for positions . For each signature we calculate , the independent model probability , and , the pair correlation model probability . Specifically , we fit the frequencies of charge states at each position and the joint frequencies of charge states at pairs of positions to the 3-state Potts model: ( 2 ) where is a sequence of 's , 's or 's of length , are position indices , and are the fitting parameters for the fields and couplings , and is the partition function . The independent model , obtained by setting all , corresponds to . For the equal frequency model in Figure 2 , we set . The joint probability distribution given by the Potts model has the largest entropy constrained by the univariate ( independent model ) or both univariate ( ) and bivariate ( ) marginals from the data [67] . To solve the inverse Ising problem , we implemented an efficient graph-theoretic inference algorithm called belief propagation ( BP ) [37]–[39] . Our algorithm employs a two-step procedure: first , all the univariate and bivariate marginals are determined for a given set of and in the Bethe approximation within BP [38] , [42] , [43] . Second , the predicted marginals are compared to the observed marginals to determine updated and via gradient descent [39] . See the supporting information for further information about the algorithm . | HIV is incurable because its enzymes evolve rapidly by developing resistance mutations to retroviral inhibitors . Most of these mutations work synergistically , but the biophysical basis behind their cooperation is not well understood . Our work addresses these important issues by bridging the gap between the statistical modeling of HIV protease subtype B sequences with the energetics of mutations involving charged amino acids by showing that electrostatic stability is intimately related to correlations . Moreover , we demonstrate that our statistical model has considerable predictive power and can be used to predict complex mutation patterns that have not yet been observed due to the finite sizes of the current sequence databases . In other words , as the database size increases , our model has the ability to predict the identities of the high probability mutations patterns , which are more likely to be observed . Knowing which currently unobserved mutations are more likely to be observed can be very advantageous in combating the disease . | [
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] | 2012 | Correlated Electrostatic Mutations Provide a Reservoir of Stability in HIV Protease |
Evolutionary game theory is a powerful framework for studying evolution in populations of interacting individuals . A common assumption in evolutionary game theory is that interactions are symmetric , which means that the players are distinguished by only their strategies . In nature , however , the microscopic interactions between players are nearly always asymmetric due to environmental effects , differing baseline characteristics , and other possible sources of heterogeneity . To model these phenomena , we introduce into evolutionary game theory two broad classes of asymmetric interactions: ecological and genotypic . Ecological asymmetry results from variation in the environments of the players , while genotypic asymmetry is a consequence of the players having differing baseline genotypes . We develop a theory of these forms of asymmetry for games in structured populations and use the classical social dilemmas , the Prisoner’s Dilemma and the Snowdrift Game , for illustrations . Interestingly , asymmetric games reveal essential differences between models of genetic evolution based on reproduction and models of cultural evolution based on imitation that are not apparent in symmetric games .
Evolutionary game theory has been used extensively to study the evolution of cooperation in social dilemmas [1–3] . A social dilemma is typically modeled as a game with two strategies , cooperate ( C ) and defect ( D ) , whose payoffs for pairwise interactions are defined by a matrix of the form C D C D ( R , R S , T T , S P , P ) ( 1 ) [4 , 5] . For a focal player using a strategy on the left-hand side of this matrix against an opponent using a strategy on the top of the matrix , the first ( resp . second ) coordinate of the corresponding entry of this matrix is the payoff to the focal player ( resp . opponent ) . That is , a cooperator receives R when facing another cooperator and S when facing a defector; a defector receives T when facing a cooperator and P when facing another defector . Since the same argument applies to the opponent , the game defined by ( Eq 1 ) is symmetric . If defection pays more than cooperation when the opponent is a cooperator ( T > R ) , but the payoff for mutual cooperation is greater than the payoff for mutual defection ( R > P ) , then a social dilemma [6 , 7] arises from this game due to the conflict of interest between the individual and the group ( or pair ) . The nature of this social dilemma depends on the ordering of R , S , T , and P . Biologically , the most important rankings are given by the Prisoner’s Dilemma ( T > R > P > S ) and the Snowdrift Game ( T > R > S > P ) [4 , 7–10] . Since matrix ( Eq 1 ) defines a symmetric game , any two players using the same strategy are indistinguishable for the purpose of calculating payoffs . In nature , however , asymmetry frequently arises in interspecies interactions such as parasitic or symbiotic relationships [4] . Interactions between subpopulations , such as in Dawkins’ Battle of the Sexes Game [11–14] , also give rise to asymmetry that cannot be modeled by the symmetric matrix ( Eq 1 ) . Even intraspecies interactions are essentially always asymmetric: ( i ) phenotypic variations such as size , strength , speed , wealth , or intellectual capabilities; ( ii ) differences in access to and availability of environmental resources; or ( iii ) each individual’s history of past interactions , all affect the interacting individuals differently and result in asymmetric payoffs . The winner-loser effect , for example , is a well-studied example of effects of previous encounters on future interactions and has been reported across taxa [4 , 15] , including even mollusks [16 , 17] . Asymmetry may also result from the assignment of social roles [18–20] , such as the roles of “parent” and “offspring” [21]: cooperation may be tied to individual energy or strength , for example , which is , in turn , determined by a player’s role . In the realm of continuous strategies , adaptive dynamics has been used to study asymmetric competition , which applies to the resource consumption of plants , for instance [22–24] . In social dilemmas containing many cooperators , accumulated benefits may be synergistically enhanced ( or discounted ) in a way that depends on who or where the players are [7] , thereby making larger group interactions asymmetric . To model such interactions using evolutionary game theory , the payoff matrix must reflect the asymmetry . In the Donation Game , a cooperator pays a cost , c , to deliver a benefit , b , to the opponent , while a defector pays no cost and provides no benefit [25] . In terms of matrix ( Eq 1 ) , this game satisfies R = b − c , S = −c , T = b , and P = 0 . Provided b and c are positive , mutual defection is the only Nash equilibrium . If b > c , then this game defines a Prisoner’s Dilemma . Perhaps the simplest way to modify this game to account for possible sources of asymmetry is to allow for each pair of players to have a distinct payoff matrix; that is , the payoff matrix for player i against player j in the Donation Game is C D M i j : = C D ( b j - c i , b i - c j - c i , b i b j , - c j 0 , 0 ) ( 2 ) for some bi , bj , ci , and cj . If player i cooperates , then this player donates bi to his or her opponent and incurs a cost of ci for doing so . As before , defectors provide no benefit and pay no cost . The index i could refer to a baseline trait of the player , the player’s location , his or her history of past interactions , motivation [26] , or any other non-strategy characteristic that distinguishes one player from another . Games based on matrices of the form ( Eq 2 ) , with payoffs for both players in each entry of the matrix , are sometimes called bimatrix games . Although bimatrix games have appeared in the context of evolutionary dynamics [14 , 20 , 27] , most of the focus on these games has been in the setting of classical game theory and economics [see 28] where “matrix game” generally means “bimatrix game . ” Bimatrix games may be used to model classical asymmetric interactions such as those arising from sexual asymmetry in the Battle of the Sexes Game [29] . The asymmetric , four-strategy Hawk-Dove Game of [4] consisting of the strategies Hawk , Dove , Bourgeois , and anti-Bourgeois may also be framed as a ( 4 × 4 ) bimatrix game [see 30] . Symmetric matrix games , such as ( Eq 1 ) , are special cases of bimatrix games . We explore here the ways in which bimatrix games can be incorporated into evolutionary dynamics and used to model natural asymmetries in biological populations . We treat two particular forms of asymmetry: ecological and genotypic . Ecological asymmetry is derived from the locations of the players , whereas genotypic asymmetry is based on the players themselves . With ecological asymmetry , Mij is the payoff matrix for a player at location i against a player at location j . Since the payoffs depend on the locations of the players , this form of asymmetry requires a structured population . Ecological asymmetry is a natural consideration in evolutionary dynamics since it ties strategy success to the environment . In the Donation Game , for instance , cooperators might be donating goods or services , but the costs and benefits may depend on the environmental conditions , i . e . the location of the donor . On the other hand , players might instead differ in ability or strength , and “strong” cooperators might contribute greater benefits ( or incur lower costs ) than “weak” cooperators . This variation results in genotypic asymmetry , where each player has a baseline genotype ( strength ) and a strategy ( C or D ) . This form of asymmetry turns out to be subtler than it seems at first glance , however , since genotypes are generally represented by strategies in evolutionary game theory [4 , 31] . In particular , it might seem that the genotype and strategy of a player could be combined into a single composite strategy and that the symmetric game based on these composite strategies could replace the original asymmetric game . As it happens , whether genotypic asymmetry can be resolved by a symmetric game depends on the details of the evolutionary process . Classically , evolutionary games were studied in infinite populations via replicator dynamics [32] , and more recently these games have been considered in finite populations [33 , 34] . Because every biological population is finite , we focus on finite populations ( which , for technical reasons , we assume to be large ) . Since ecological asymmetry requires distinguishing different locations within the population , we assume that the population is structured and that a network defines the structure . Network-structured populations have received a considerable amount of attention in evolutionary game theory and provide a natural setting in which to study social dilemmas [1 , 3 , 35–38] . Compared to well-mixed populations , in which each player interacts with every other player , networks can restrict the interactions that occur within the population by specifying which players are “neighbors , ” i . e . share a link . We represent the links among the N players in the population using an adjacency matrix , ( wij ) 1 ⩽ i , j ⩽ N , which is defined by letting wij = 1 if there is a link from vertex i to vertex j and 0 otherwise ( and satisfies wij = wji for each i and j ) . In an evolutionary game , the state of a population of players is defined by specifying the strategy of each player . Each player interacts with all of his or her neighbors . The total payoff to a player is multiplied by a selection intensity , β ⩾ 0 , and then converted into fitness ( see Methods ) . Once each player is assigned a fitness , an update rule is used to determine the state of the population at the next time step [39] . For example , with a birth-death update rule , a player is chosen from the population for reproduction with probability proportional to relative fitness . A neighbor of the reproducing player is then randomly chosen for death , and the offspring , who inherits the strategy of the parent , fills the vacancy . This process is a modification of the Moran process [40] , adapted to allow for ( i ) frequency-dependent fitnesses and ( ii ) population structures that are not necessarily well mixed . The order of birth and death could also be reversed to get a death-birth update rule [1] . In this rule , death occurs at random and the neighbors of the deceased compete to reproduce in order to fill the vacancy . These two rules result in the update of a single strategy in each time step , but one could consider other rules , such as Wright-Fisher updating , in which all of the strategies are revised in each generation [41] . The rules mentioned to this point define strategy updates via reproduction and inheritance; as such , we refer to them as genetic update rules . Another popular class of update rules is based on revisions to the existing players’ strategy choices . We refer to rules falling into this class as cultural update rules . Examples include imitation updating , in which a player is selected at random to evaluate his or her strategy and then probabilistically compares this strategy to those of his or her neighbors [1] . A more localized version of this update rule is known as pairwise comparison updating , in which a player chooses a random neighbor for comparison rather than looking at the entire neighborhood [42 , 43] . Under best response dynamics , an individual adopts the strategy that performs best given the current strategies of his or her neighbors [44] . In each of these cultural processes , the strategy of a player can change , but the underlying genotype is always the same , which suggests that baseline genotype and strategy need to be treated separately . Genotypic asymmetry needs to be handled more carefully if the update rule is genetic since the nature of genotype transmission affects the dynamics of the process . In contrast to cultural processes , the genotype and strategy of a player at a given location may both change if the update rule is genetic: genotype may be inherited but not imitated . We will see that this property results in cultural and genetic processes behaving completely differently in the presence of genotypic asymmetry . Phenotype may have both genetic and environmental components [45 , 46] , and after treating the genetic ( genotypic ) and environmental components separately , these two forms of asymmetry may be combined in order to get a model in which the asymmetry is derived from varying baseline phenotypes . Thus , with a theory of both ecological asymmetry and genotypic asymmetry based on inherited genotypes , one can account for more complicated forms of asymmetry appearing in biological populations .
Here we develop a framework for ecologically asymmetric games in which the payoffs depend on the locations of the players as well as their strategies . We assume that all of the players have the same set of strategies ( or “actions” ) available to them , {A1 , … , An} . The payoff matrix for a player at vertex i against a player at vertex j is A 1A 2 ⋯ A n M i j = A 1 A 2 ⋮ A n ( a 11 i j , a 11 j i a 12 i j , a 21 j i ⋯ a 1 n i j , a n 1 j i a 21 i j , a 12 j i a 22 i j , a 22 j i ⋯ a 2 n i j , a n 2 j i ⋮ ⋮ ⋱ ⋮ a n 1 i j , a 1 n j i a n 2 i j , a 2 n j i ⋯ a n n i j , a n n j i ) . ( 3 ) That is , a player at vertex i using strategy Ar against an opponent at vertex j using strategy As realizes a payoff of a r s i j , whereas his opponent receives a s r j i . Since a r s i j depends on i and j , these payoff matrices capture the asymmetry of the game . In the simpler setting of symmetric games , the pair approximation method has been used successfully to describe the dynamics of evolutionary processes on networks [1 , 36 , 47–49] . For each r ∈ {1 , … , n} , this method approximates the frequency of strategy Ar , which we denote by pr , using the frequencies of strategy pairs in the population . Pair approximation is expected to be accurate on large random regular networks [1 , 48] , so we assume that the network is regular ( of degree k > 2 ) and that N is sufficiently large . ( For k = 2 , the network is just a cycle , which we do not treat here . ) We also take β ≪ 1 , meaning that selection is weak , which results in a separation of timescales: the local configurations equilibrate quickly , while the global strategy frequencies change much more slowly . This separation allows us to get an explicit expression for the expected change , 𝔼 [Δpr] , in the frequency of strategy Ar for each r . Incidentally , weak selection happens to be quite reasonable from a biological perspective since each trait is expected to have only a small effect on the overall fitness of a player [50–52] . Interestingly , for two genetic and two cultural update rules , weak selection reduces ecological asymmetry to a symmetric game derived from the spatial average of the payoff matrices: Theorem 1 . In the limit of weak selection , the dynamics of the ecologically asymmetric death-birth , birth-death , imitation , and pairwise comparison processes on a large , regular network may be approximated by the dynamics of a symmetric game with the same update rule and payoff matrix M ¯ : = 1 k N ∑ i , j = 1 N w i j M i j , i . e . A 1 A 2 ⋯ A n M ¯ = A 1 A 2 ⋮ A n ( a ¯ 11 , a ¯ 11 a ¯ 12 , a ¯ 21 ⋯ a ¯ 1 n , a ¯ n 1 a ¯ 21 , a ¯ 12 a ¯ 22 , a ¯ 22 ⋯ a ¯ 2 n , a ¯ n 2 ⋮ ⋮ ⋱ ⋮ a ¯ n 1 , a ¯ 1 n a ¯ n 2 , a ¯ 2 n ⋯ a ¯ n n , a ¯ n n ) , ( 4 ) where a ¯ s t : = 1 k N ∑ i , j = 1 N w i j a s t i j for each s and t . For a proof of Theorem 1 , see Methods . In Methods , we derive explicit formulas for 𝔼 [Δpr] for each r ( where pr is the frequency of strategy Ar and 𝔼 [Δpr] is the expected change in pr in one step of the process ) and show that these expectations depend on M ¯ in the limit of weak selection . If we choose an appropriate time scale and make the approximation p ˙ r≔ d p r d t = 𝔼[ Δ p r ] Δ t , ( 5 ) then the dynamics of an ecologically asymmetric process may also be described in terms of the replicator equation ( on graphs ) of [36]: If ϕ : = ∑ s , t = 1 n p s p t a ¯ s t , then p ˙ r = p r ( ∑ s = 1 n p s ( a ¯ r s + b ¯ r s ) - ϕ ) , ( 6 ) where b ¯ r s is a function of M ¯ , k , and the update rule . ( For each of the four processes , the explicit expression for b ¯ r s is provided in Methods . ) The matrix ( b ¯ r s ) r , s = 1 n accounts for local competition resulting from the population structure [see 36] . In particular , the Ohtsuki-Nowak transform , a ¯ r s r , s = 1 n → a ¯ r s + b ¯ r s r , s = 1 n , ( 7 ) which transforms the classical replicator equation into the replicator equation on graphs , also applies to evolutionary games with ecological asymmetry . Even though interactions are now governed by a symmetric game , Theorem 1 states that , in general , the dynamics depend on the particular network configuration , ( wij ) 1 ⩽ i , j ⩽ N; that is , the symmetric payoffs defined by M ¯ still depend on the network structure , or , equivalently , on the distribution of ecological resources within the population . However , somewhat surprisingly , there is a broad class of games for which this dependence vanishes: Definition 1 . If a r s i j = x r s i + y r s j for each r and s , then Mij is called a spatially additive payoff matrix . If Mij is spatially additive for each i and j , then the game is said to be spatially additive . A game is spatially additive if the payoff for an interaction between any two members of the population can be decomposed as a sum of two components , one from each player’s location . Note that spatial additivity is different from the “equal gains from switching” property [53] in that neither implies the other . However , spatial additivity is an analogue in the following sense: if two players at different locations use the same strategy against a common opponent , then the difference in these two players’ payoffs for this interaction is independent of the location of the opponent . Interchanging “location” and “strategy , ” one obtains the equal gains from switching property . The importance of spatially additive games is due to the following corollary to Theorem 1: Corollary 1 . If Mij is spatially additive for each i and j , then the expected change in the frequency of strategy Ar , 𝔼 [Δpr] , is independent of ( wij ) 1 ⩽ i , j ⩽ N for each r . In particular , the dynamics of the process do not depend on the particular network configuration . As an example , the asymmetric Donation Game is spatially additive and possesses the equal gains from switching property , which greatly simplifies the analysis of its dynamics: Example 1 . ( Donation Game with ecological asymmetry ) . The asymmetric Donation Game with payoff matrices defined by Eq ( 2 ) is spatially additive and satisfies C D M ¯ = C D ( b ¯ - c ¯ , b ¯ - c ¯ - c ¯ , b ¯ b ¯ , - c ¯ 0 , 0 ) , ( 8 ) where b ¯ = 1 N ∑ i = 1 N b i and c ¯ = 1 N ∑ i = 1 N c i . Therefore , the dynamics of the asymmetric game are the same as those of its symmetric counterpart with benefit , b ¯ , and cost , c ¯ , regardless of network configuration or resource distribution . Under death-birth ( resp . imitation ) updating , this result implies that cooperation is expected to increase if and only if b ¯ / c ¯ > k ( resp . b ¯ / c ¯ > k + 2 ) , where k is the degree of the ( regular ) network [1] . Fig 1 ( A ) compares the predicted result obtained from M ¯ to simulation data for imitation updating when benefit and cost values are distributed according to Gaussian random variables . Example 2 . ( Snowdrift Game with ecological asymmetry ) . In order to illustrate when Corollary 1 fails , we turn to cooperation in the Snowdrift Game [8 , 9] . In this game , two drivers find themselves on either side of a snowdrift . If both cooperate in clearing the snowdrift , they share the cost , c , equally , and both receive the benefit of being able to pass , b . If one player cooperates and the other defects , both players receive b but the cooperator pays the full cost , c . If both players defect , each receives no benefit and pays no cost . In order to incorporate ecological asymmetry , we assume that the benefits are all the same since they are derived from being able to pass in the absence of a snowdrift . On the other hand , the cost a player pays to clear the snowdrift may depend on his or her location: the snowdrift may appear on an incline , for example , in which case one player shovels with the gradient and the other player against it . Moreover , when two cooperators meet , they might clear unequal shares of the snowdrift . Thus , the payoff matrix for a player at location i against a player at location j should be of the form C D M i j ( α i j ) ≔ C D ( b - α i j c i , b - α j i c j b - c i , b b , b - c j 0 , 0 ) , ( 9 ) where 0 ⩽ αij ⩽ 1 and αij+αji = 1 [54] . Intuitively , when two cooperators face one other , they each begin to clear the snowdrift and stop once they meet; the quantity αij indicates the fraction of the snowdrift a cooperator at location i clears before meeting the cooperator at location j . A natural choice for αij is α i j = c j c i + c j , ( 10 ) which is the unique value that gives αij ci = αji cj for each i and j , ensuring that the game is fair , i . e . that the cooperator with the higher cost clears a smaller portion of the snowdrift than the one with the lower cost . Averaging the payoff to one cooperator against another over all possible locations gives 1 k N ∑ i , j = 1 N w i j ( b - α i j c i ) = b - 1 k N ∑ i , j = 1 N w i j ( c i c j c i + c j ) , ( 11 ) which is the upper-left entry of M ¯ . In contrast , the remaining three entries of M ¯ do not depend on ( wij ) 1 ⩽ i , j ⩽ N . Therefore , provided there are at least two locations with distinct cost values , the dynamics of an evolutionary process depend on the particular network configuration ( Theorem 1 ) . This network dependence is illustrated in Fig 2 . Suppose now that we set αij ≡ 1/2 to model ecological asymmetry in the Snowdrift Game; that is , if two cooperators meet , they each clear exactly half of the snowdrift . If there are two cost values in the population , c1 and c2 , with c1 < b < c2 < 2b , then a player who incurs a cost of c1 finds it beneficial to cooperate against a defector , but a player who incurs a cost of c2 would rather defect in this situation . Thus , based on the social dilemma implied by the ranking of the payoffs , a player who incurs a cost of c1 for cooperating is always playing a Snowdrift Game while a player who incurs a cost of c2 is always playing a Prisoner’s Dilemma . It follows that ecological asymmetry can account for multiple social dilemmas being played within a single population , even if the players all use the same set of strategies ( C and D ) . The payoff matrices of this particular game are spatially additive , so , by Corollary 1 , the dynamics do not depend on the network configuration . If q is the fraction of vertices with cost value c1 then c ¯ = q c 1 + ( 1 − q ) c 2 is the average cost of cooperation for a particular location and the dynamics are the same as those of the symmetric Snowdrift Game in which the cost of clearing a snowdrift is c ¯ ( see Fig 1 ( B ) ) . Fig 3 demonstrates that this result does not extend to stronger selection strengths , so Theorem 1 is unique to weak selection . Based on Theorem 1 and the relative rank of payoffs , the social dilemma defined by the asymmetric game ( Eq 9 ) ( for general αij ) is a Prisoner’s Dilemma if b < c ¯ and a Snowdrift Game if b > c ¯ when selection is weak . That is , microscopically , there is a mixture of Prisoner’s Dilemmas and Snowdrift Games , but , macroscopically , the process behaves like just one of these social dilemmas . Consequently , although the dynamics of this evolutionary process may depend on the network configuration , the type of social dilemma implied by this game does not . Another form of asymmetry is based on the genotypes of the players rather than their locations . Each player in the population has one of ℓ possible genotypes , and these genotypes are enumerated by the set {1 , … , ℓ} . For an n-strategy game , the payoff matrix for a player whose genotype is u against a player whose genotype is v is A 1 A 2 ⋯ A n M u v = A 1 A 2 ⋮ A n ( a 11 u v , a 11 v u a 12 u v , a 21 v u ⋯ a 1 n u v , a n 1 v u a 21 u v , a 12 v u a 22 u v , a 22 v u ⋯ a 2 n u v , a n 2 v u ⋮ ⋮ ⋱ ⋮ a n 1 u v , a 1 n v u a n 2 u v , a 2 n v u ⋯ a n n u v , a n n v u ) . ( 12 ) We explore genotypic asymmetry for cultural and genetic processes separately:
Asymmetric games naturally separate standard evolutionary update rules into cultural and genetic classes . This distinction is important because it captures biological differences that are not always apparent in models of evolution based on symmetric games . For example , consider a model player whose offspring replaces a focal player and a model player whose strategy is imitated by a focal player . For symmetric games , processes based on these two types of updates are mathematically identical; if asymmetry is present , then the fact that one update is genetic ( replacement ) and the other is cultural ( imitation ) becomes important . Thus , asymmetric games can highlight fundamental differences in evolutionary processes that are based on distinct update rules but happen to behave similarly when the underlying game is symmetric . In order to incorporate into evolutionary games the asymmetries commonly studied in classical game theory , our focus has been on games with asymmetric payoffs . Games with asymmetric payoffs arise naturally from different forms of interaction heterogeneity . Dependence of payoffs on the environment is a reasonable assumption when considering ecological variation [55] . Certain patches may provide resources or have drawbacks that influence a player’s success when using a particular strategy [56] . Asymmetric interactions may also be the result of heterogeneity in the sizes or strengths of players [57 , 58] . Whether the source of asymmetry is the environment or the players themselves , our model effectively resolves a collection of microscopically asymmetric interactions with a macroscopically symmetric game in the limit of weak selection . Figs 1 and 2 illustrate this result for three common update rules . Similar forms of asymmetry have been studied previously in evolutionary game theory: Szolnoki and Szabó [59] consider asymmetry appearing in the update rule that results in “attractive” and “repulsive” players in the pairwise comparison process . For games with population structures defined by two graphs ( “interaction” and “dispersal” graphs ) , Ohtsuki et al . [60 , 61] show that the evolution of cooperation can be inhibited by asymmetry arising from differences in these two graphs . On the other hand , Pacheco et al . [62] show that heterogeneous population structures can promote the evolution of cooperation by effectively transforming a collection of microscopic social dilemmas into a global coordination game . This result is reminiscent of our Theorem 1 , which relates the microscopic interactions to the global behavior of a process . Such heterogeneous population structures can result in asymmetric interactions even if the underlying game is symmetric [63] . These models , although somewhat different from ours , demonstrate that asymmetry ( in its many forms ) has a remarkable effect on evolutionary dynamics . Although genotypic asymmetry can always be reduced to a ( larger ) symmetric game under genetic update rules , this symmetric game can be of independent interest . For example , Eq ( 16 ) shows that if cooperators vary in size or strength , then certain cooperators may increase in the Donation Game even under birth-death updating . In contrast , cooperation never increases in the absence of cooperator variation [1] . Though defectors still eventually outcompete cooperators , the transient increase in cooperators suggests that other evolutionary processes with this form of asymmetry can behave in novel ways . If both ecological and genotypic asymmetries are present , they can be handled separately: genotypic asymmetry is reduced to either ( i ) ecological asymmetry ( if the update rule is cultural ) or ( ii ) a symmetric game with more strategies ( if the update rule is genetic ) . In either case , an evolutionary game with both ecological and genotypic asymmetries can be reduced to a game with ecological asymmetry only and hence Theorem 1 applies . Our framework handles asymmetry resulting from varying baseline traits due to both environment and genotype , which could be referred to as phenotypic asymmetry . The presence of ecological or genotypic asymmetry in an evolutionary process does not necessarily depend on the selection strength or update rule; these forms of asymmetry may be incorporated into many evolutionary processes . Theorem 1 , which effectively reduces a game with ecological asymmetry to a particular symmetric game , is stated for four common update rules in evolutionary game theory . Fig 3 demonstrates ( using the asymmetric Snowdrift Game ) that this theorem is specific to weak selection . That selection is weak is often a reasonable assumption when using evolutionary games to study populations of organisms with many traits . However , our study of the asymmetric Snowdrift Game for stronger selection strengths suggests that the behavior of asymmetric games is more complicated if selection is strong . Though more difficult to treat analytically , symmetric games under strong selection are worthy of further investigation . Asymmetry is omnipresent in nature , and any framework that is used to model evolution should take into account possible sources of asymmetry . We have formally introduced ecological and genotypic asymmetries into evolutionary game theory and have studied these asymmetries in the limit of weak selection . Asymmetry has a natural place in the Donation Game and the Snowdrift Game , but our results are applicable to any general n-strategy matrix game . Our treatment of asymmetry highlights important differences between models of cultural and genetic evolution that are not apparent in the traditional setting of symmetric games . Ecological and genotypic asymmetries cover a wide variety of background variation observed in biological populations , and , as such , our framework enhances the modeling capacity of evolutionary games .
Let 𝒮 = {A1 , … , AN} be the set of pure strategies available to each player and suppose that there are N players on a regular network of size N ( i . e . every node is occupied ) . A strategy pair ( Ar , As ) means a choice of a player using strategy Ar who has as a neighbor a player using strategy As . Let p r ≔ frequency of players using strategyAr ; ( 17a ) p r s ≔ frequency of strategy pairs ( A r , A s ) ; ( 17b ) q s | r ≔ conditional probability of finding an s player next to anrplayer . ( 17c ) We will make repeated use of the following properties of these quantities: ∑ r = 1 n p r = ∑ s = 1 n q s | r = 1 ; ( 18a ) p s q r | s = p r s = p s r = p r q s | r . ( 18b ) Strictly speaking , the equalities ps qr|s = prs = psr = pr qs|r need not hold in general . As a pathological example , one may consider the network with two nodes and a single undirected link between these nodes . If the player on the first node uses Ar , the player on the second node uses As , and r ≠ s , then prs = 1 but ps = 1/2 , which gives qr|s = 2 . However , for large random regular graphs [48] , condition ( Eq 21 ) holds approximately , and we will take this equality as given in what follows . For 𝒳 ∈ {pr , prs , qs|r}1⩽r , s⩽n; , let 𝔼 [Δ𝒳] denote the expected change in 𝒳 in one step of the process . A pair ( Ar , i ) denotes a player on vertex i using strategy Ar . Given pairs ( Ar , i ) and ( As , j ) , we denote by π ( As , j ) ( Ar , i ) the expected payoff to a player at vertex j playing strategy As given that they have as a neighbor an individual playing strategy Ar at vertex i . If β ⩾ 0 is a parameter representing the intensity of selection , then payoff , π , is converted to fitness , fβ ( π ) , via f β ( π ) ≔ exp { β π } . ( 19 ) When defined in this way , fitness is always positive . The main theorem we prove is the following: Theorem 1 . In the limit of weak selection , the dynamics of the ecologically asymmetric death-birth , birth-death , imitation , and pairwise comparison processes on a large , regular network may be approximated by the dynamics of a symmetric game with the same update rule and payoff matrix M ¯ ≔ 1 k N ∑ i , j = 1 N w i j M i j , i . e . A 1 A 2 ⋯ A n M ¯ = A 1 A 2 ⋮ A n ( a ¯ 11 , a ¯ 11 a ¯ 12 , a ¯ 21 ⋯ a ¯ 1 n , a ¯ n 1 a ¯ 21 , a ¯ 12 a ¯ 22 , a ¯ 22 ⋯ a ¯ 2 n , a ¯ n 2 ⋮ ⋮ ⋱ ⋮ a ¯ n 1 , a ¯ 1 n a ¯ n 2 , a ¯ 2 n ⋯ a ¯ n n , a ¯ n n ) . ( 20 ) where a ¯ s t ≔ 1 k N ∑ i , j = 1 N w i j a s t i j for each s and t . Theorem 1 is established for each of these four update rules separately: If an individual is playing strategy Ar at node i , As at j , and if wij ≠ 0 , then π ( As , j ) ( Ar , i ) =asrji+∑m≠iwjm∑t=1nastjmqt|s . ( 21 ) Suppose that an ( Ar , i ) individual is selected for death . The probability that ( As , j ) replaces this focal individual is proportional to fβ ( π ( As , j ) ( Ar , i ) ) . For each i , let ( i1 , … , ik ) be an enumeration of the indices j with wij ≠ 0 ( say , in increasing order ) and let sℓ be the strategy used by the player at vertex iℓ . If ( Ar , i ) is chosen for death , then the probability that it is replaced by ( Asℓ , iℓ ) is fβ ( π ( Asℓ , iℓ ) ( Ar , i ) ) ∑j=1kfβ ( π ( Asij , ij ) ( Ar , i ) ) . ( 22 ) The Taylor expansion of this term for small β is fβ ( π ( Asℓ , iℓ ) ( Ar , i ) ) ∑j=1kfβ ( π ( Asij , ij ) ( Ar , i ) ) =1k+β ( kπ ( As , iℓ ) ( Ar , i ) −∑j=1kπ ( Asij , ij ) ( Ar , i ) k2 ) +O ( β2 ) . ( 23 ) This expansion will be used frequently in the displays that follow . In the birth-death process , an individual is selected for reproduction with probability proportional to relative fitness . The offspring of the selected player then replaces a random neighbor . Rather than trying to approximate the total fitness of the population , we will simply denote this value by fpop . Since this value is positive , it does not influence the sign of the expectation values and as such we will largely ignore it . We have 𝔼[ Δpr ]=1fpopNpr ( 1N ) ∑i=1N∑si1 , … , sik=1nqsi1|r⋯qsik|rfβ ( ∑ℓ=1karsiℓiiℓ ) ∑h≠r ( ∑j=1kδsij , hk ) ( 1N ) +1fpop∑h≠rNph ( 1N ) ∑i=1N∑si1 , … , sik=1nqsi1|h⋯qsik|hfβ ( ∑ℓ=1kahsiℓiiℓ ) ( ∑j=1kδsij , rk ) ( −1N ) . ( 35 ) The local equilibrium conditions for birth-death updating turn out to be the same as those for death-birth updating ( Eq ( 32 ) ) . These local equilibrium conditions do not take into account selection as long as β is close to 0 , so they are essentially based on a neutral process in which at most one strategy is update at each time step . Therefore , it is perhaps not surprising that these conditions are the same for different processes based on one strategy update in each time step . In the following expressions , by x ∝ y we mean that x is proportional to y with positive constant of proportionality . Letting β → 0 and using the local equilibrium conditions ( as well as the same separation-of-timescales argument we used in § ) , we find that 𝔼[ Δpr ]∝βpr ( k∑i , j=1Nwij∑s=1narsijqs|r− ( k−1 ) ∑i , j=1Nwij∑s , t=1nastijqt|sqs|r−∑i , j=1Nwij∑s=1nasrijqs|r ) +O ( β2 ) ∝βpr ( k∑s=1na¯rsqs|r− ( k−1 ) ∑s , t=1na¯stqt|sqs|r−∑s=1na¯srqs|r ) +O ( β2 ) ∝βpr ( − ( k−2 ) ∑s , t=1na¯stpspt+ ( k−1 ) ∑s=1na¯rsps−∑s=1na¯srps−∑s=1na¯ssps+a¯rr ) +O ( β2 ) . ( 36 ) Just as we saw with the death-birth process , after choosing an appropriate time scale and letting b ¯ r s = ( k + 1 ) a ¯ r r + a ¯ r s - a ¯ s r - ( k + 1 ) a ¯ s s ( k - 2 ) ( k + 1 ) ; ( 37a ) ϕ = ∑ s , t = 1 n p s p t a ¯ s t , ( 37b ) we have p . r = p r ( ∑ s = 1 n p s ( a ¯ r s + b ¯ r s ) − ϕ ) , proving Theorem 1 for birth-death updating . In the imitation process , an individual is selected uniformly at random from the population to evaluate his strategy . The chosen player then compares his fitness with the fitness of each neighbor and either adopts a new strategy or retains his or her current strategy ( with probability proportional to relative fitness ) . Suppose that an individual at vertex i , playing Ar , is selected to evaluate his or her strategy . If s ≠ r , then the probability that he or she adopts strategy s is ∑ℓ=1kδsℓ , sfβ ( π ( Asℓ , iℓ ) ( Ar , i ) ) ∑j=1kfβ ( π ( Asij , ij ) ( Ar , i ) ) +fβ ( ∑j=1karsijiij ) ( 38 ) and the probability that his strategy remains unchanged is ∑ℓ=1kδsℓ , rfβ ( π ( Asℓ , iℓ ) ( Ar , i ) ) +fβ ( ∑j=1karsijiij ) ∑j=1kfβ ( π ( Asij , ij ) ( Ar , i ) ) +fβ ( ∑j=1karsijiij ) . ( 39 ) We let π ( As , j ) ( Ar , i ) be the same as it was for death-birth updating . For small β , fβ ( π ( Asℓ , iℓ ) ( Ar , i ) ) ∑j=1kfβ ( π ( Asij , ij ) ( Ar , i ) ) +fβ ( ∑j=1karsijiij ) ≈1k+1+β ( ( k+1 ) π ( Asℓ , iℓ ) ( Ar , i ) −∑j=1kπ ( Asij , ij ) ( Ar , i ) −∑j=1karsijiij ( k+1 ) 2 ) +O ( β2 ) . ( 40 ) In the pairwise comparison process , a focal individual is selected uniformly at random from the population . A model individual is then chosen uniformly at random from the neighbors of the focal individual . If πf and πm denote the payoffs to the focal and model individuals , respectively , then the focal player will adopt the strategy of the model player with probability 1 1 + e β ( π f - π m ) = f β ( π m ) f β ( π m ) + f β ( π f ) , ( 44 ) where β ⩾ 0 is a real parameter representing the intensity of selection . In addition to the expected payoff π ( As , j ) ( Ar , i ) ( defined in the same way as for death-birth updating ) , we let π ( As , i ) ≔∑j=1kassijiij ( 45 ) if ( As , i ) has as a neighborhood ( Asi1 , … , Asik ) . With this notation in place , we have 𝔼[ Δpr ]=1N∑i=1N∑h≠rph∑si1 , … , sik=1nqsi1|h⋯qsik|h×∑ℓ=1k ( 1k ) δsiℓ , r ( fβ ( π ( Ar , iℓ ) ( Ah , i ) ) fβ ( π ( Ar , iℓ ) ( Ah , i ) ) +fβ ( π ( Ah , i ) ) ) ( 1N ) +1N∑i=1Npr∑si1 , … , sik=1nqsi1|r⋯qsik|r×∑h≠r∑ℓ=1k ( 1k ) δsiℓ , h ( fβ ( π ( Ah , iℓ ) ( Ar , i ) ) fβ ( π ( Ah , iℓ ) ( Ar , i ) ) +fβ ( π ( Ar , i ) ) ) ( −1N ) . ( 46 ) As β → 0 , we have f β ( x ) f β ( x ) + f β ( y ) ≈ 1 2 + β ( x - y 4 ) + O ( β 2 ) . ( 47 ) Consequently , in the limit of weak selection , 𝔼[ Δpr ]≈βpr2kN2 ( k∑i , j=1Nwij∑s=1narsijqs|r− ( k−1 ) ∑i , j=1Nwij∑s , t=1nastijqt|sqs|r−∑i , j=1Nwij∑s=1nasrijqs|r ) +O ( β2 ) =βpr2N ( k∑s=1na¯rsqs|r− ( k−1 ) ∑s , t=1na¯stqt|sqs|r−∑s=1na¯srqs|r ) +O ( β2 ) =β ( ( k−2 ) pr2 ( k−1 ) N ) ( − ( k−2 ) ∑s , t=1na¯stpspt+ ( k−1 ) ∑s=1na¯rsps−∑s=1na¯srps−∑s=1na¯ssps+a¯rr ) +O ( β2 ) . ( 48 ) The local equilibrium conditions are exactly the same as they were for the other processes , but in this case they are not needed to arrive at this last expression for 𝔼 [Δpr] . With b ¯ r s = a ¯ r r + a ¯ r s − a ¯ s r − a ¯ s s k − 2 and ϕ = ∑ s , t = 1 n p s p t a ¯ s t , we have p . r = p r ( ∑ s = 1 n p s ( a ¯ r s + b ¯ r s ) − ϕ ) . It follows that the dynamics of the pairwise comparison process depend on M ¯ , which completes the proof of Theorem 1 . Finally , we show that the dynamics of each process are independent of the particular network configuration if the asymmetric game is spatially additive: Definition 1 . If a r s i j = x r s i + y r s j for each r and s , then Mij is called a spatially additive payoff matrix . If Mij is spatially additive for each i and j , then the game is said to be spatially additive . Corollary 1 . If Mij is spatially additive for each i and j , then the expected change in the frequency of strategy Ar , 𝔼 [Δpr] , is independent of ( wij ) 1 ⩽ i , j ⩽ N for each r . In particular , the dynamics of the process do not depend on the particular network configuration . Proof . If a r s i j = x r s i + y r s j for each r , s , i , j , then a ¯ s t = 1 k N ∑ i , j = 1 N w i j a s t i j = 1 N ∑ i = 1 N x r s i + 1 N ∑ j = 1 N y r s j , ( 49 ) which is independent of ( wij ) 1 ⩽ i , j ⩽ N . The corollary then follows directly from Theorem 1 . In each simulation , a random k-regular network ( with k = 3 ) of N = 500 vertices is generated . The selection intensity is β = 0 . 01 for Figs 1 and 2 , β = 0 . 1 for Fig 3 ( A ) , and β = 0 . 5 for Fig 3 ( B ) . The figures are generated based on data collected from a number of cycles: In each cycle , the network is given an initial configuration of cooperators by first choosing a density , d , uniformly at random from the interval [0 , 1] , and then placing a cooperator ( resp . defector ) at each vertex with probability d ( resp . 1 − d ) . The update rule is applied until either C or D fixates . ( The absorption time depends on a number of factors including the game , selection strength , and initial configuration of the population . ) Let pC ( t ) denote the frequency of cooperators at time t; pC ( 0 ) is just the initial frequency of cooperators . The frequency pC ( t+1 ) is obtained from pC ( t ) by adding to it the change in the frequency of cooperators over the next N ( = 500 ) updates . For each t , the quantity pC ( t + 1 ) − pC ( t ) is associated with pC ( t ) . Once pC ∈ {0 , 1} , a new initial configuration of cooperators is chosen and the process is repeated . After each possible value of pC has at least 105 associated data points ( changes in cooperator frequency ) , these changes are averaged , and this resulting quantity , Δ p C ¯ , is paired with the corresponding value of pC . These pairs are then plotted to obtain Figs 1 , 2 , and 3 . The results from pair approximation apply to the expected change over one update , but we can easily get a predicted result over N updates ( i . e . one Monte Carlo step ) by scaling the expressions for 𝔼[ΔpC] by a factor of N . Small deviations from the expected results are seen in each of the figures , and these deviations are due to the effects of finite selection parameter ( β ) and the finiteness of the set of possible values of pC ( ΔpC is a multiple of 1/N ) . As an example of how these properties can give rise to small deviations , consider the Donation Game under imitation updating in Fig 1 ( A ) . Eq ( 42 ) predicts that 𝔼[ΔpC] is always positive , yet we observe in Fig 1 ( A ) that this change becomes negative as pC → 0 , 1 . If pC = ( N − 1 ) /N and β > 0 , then the only defector in the population has a higher payoff than all of the other cooperators . Let f β ( j ) denote the fitness of the player at location j . Thus , with just a single defector ( at location i ) in a population of cooperators , we have f β ( i ) ⩾ f β ( j ) for each j ≠ i , with equality if and only if β = 0 . The expected change in the frequency of cooperators in the next time step is 𝔼[ ΔpC ]= ( 1N ) ( 1N ) ( 1−fβ ( i ) fβ ( i ) +∑{ j:wij=1 }fβ ( j ) ) − ( 1N ) ∑{ j:wij=1 } ( 1N ) ( fβ ( i ) fβ ( j ) +∑{ l:wjl=1 }fβ ( l ) ) . ( 50 ) The first ( resp . second ) summation runs over all of the neighbors of i ( resp . j ) . For each j ≠ i , fβ ( i ) fβ ( i ) +∑{ j:wij=1 }fβ ( j ) ⩾1k+1; ( 51a ) fβ ( i ) fβ ( j ) +∑{ l:wjl=1 }fβ ( l ) ⩾1k+1 , ( 51b ) both with equality if and only if β = 0 . Therefore , we see that 𝔼[ Δ p C ] ⩽ ( 1 N ) ( 1 N ) ( 1 - 1 k + 1 ) - ( 1 N ) ( k N ) ( 1 k + 1 ) = 0 ( 52 ) with equality if and only if β = 0 . The same argument explains the negative average changes as pC → 0 . Since pC can only take on finitely many values for a given population size , similar arguments explain the small discrepancies between the actual and expected results for intermediate values of pC ( see Fig 1 ) . | Biological interactions , even between members of the same species , are almost always asymmetric due to differences in size , access to resources , or past interactions . However , classical game-theoretical models of evolution fail to account for sources of asymmetry in a comprehensive manner . Here , we extend the theory of evolutionary games to two general classes of asymmetry arising from environmental variation and individual differences , covering much of the heterogeneity observed in nature . If selection is weak , evolutionary processes based on asymmetric interactions behave macroscopically like symmetric games with payoffs that may depend on the resource distribution in the population or its structure . Asymmetry uncovers differences between genetic and cultural evolution that are not apparent when interactions are symmetric . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [] | 2015 | Asymmetric Evolutionary Games |
Understanding the structure and dynamics of cortical connectivity is vital to understanding cortical function . Experimental data strongly suggest that local recurrent connectivity in the cortex is significantly non-random , exhibiting , for example , above-chance bidirectionality and an overrepresentation of certain triangular motifs . Additional evidence suggests a significant distance dependency to connectivity over a local scale of a few hundred microns , and particular patterns of synaptic turnover dynamics , including a heavy-tailed distribution of synaptic efficacies , a power law distribution of synaptic lifetimes , and a tendency for stronger synapses to be more stable over time . Understanding how many of these non-random features simultaneously arise would provide valuable insights into the development and function of the cortex . While previous work has modeled some of the individual features of local cortical wiring , there is no model that begins to comprehensively account for all of them . We present a spiking network model of a rodent Layer 5 cortical slice which , via the interactions of a few simple biologically motivated intrinsic , synaptic , and structural plasticity mechanisms , qualitatively reproduces these non-random effects when combined with simple topological constraints . Our model suggests that mechanisms of self-organization arising from a small number of plasticity rules provide a parsimonious explanation for numerous experimentally observed non-random features of recurrent cortical wiring . Interestingly , similar mechanisms have been shown to endow recurrent networks with powerful learning abilities , suggesting that these mechanism are central to understanding both structure and function of cortical synaptic wiring .
The patterns of synaptic connectivity in our brains are thought to be the neurophysiological substrate of our memories , and framework upon which our cognitive functions are computed . Understanding the development of micro-structure in the cortex has significant implications for the understanding of both developmental and cognitive / computational processes . Such insight would be invaluable in understanding the root causes of cognitive and developmental impairments , as well as understanding better the nature of the computations realized by the cortex . It is believed that a small population of strong synapses forms a relatively stable backbone in recurrent cortical networks—perhaps the basis of long-term memories—while a larger population of weaker connections forms a more dynamic pool with a high rate of turnover [1–3] . It has been shown that much of the lateral recurrent connectivity of the layers of the cortex is significantly non-random [4–6] , with a focus on layer 5 ( L5 ) , as this is more conventionally examined via slice studies . It is an open question which non-random features are developed as a result of direct genetic programming , neural plasticity under structured input , and spontaneous self-organization . We examine here several noted non-random features of recurrent cortical wiring that we believe can be explained as the result of spontaneous self-organization—specifically , self-organization driven by the interaction of multiple neural plasticity mechanisms . The features we will examine are the heavy-tailed , log-normal-like distribution of synaptic efficacies or dendritic spine sizes [6–10] and their associated synaptic dynamics , and the overrepresentation of bidirectional connectivity and certain triangular graph motifs [6] . The interaction of multiple plasticity mechanisms , such as synaptic scaling and Hebbian plasticity has been studied before [11–14] , with results suggesting that the interactions of such mechanisms are useful for the formation and stability of patterns of representation . However , we desire a more detailed look at how such self-organization might take place in the cortex . The predecessor to the model we use to address these issues is the Self-Organizing Recurrent Neural Network , or SORN [11] . The SORN is a recurrent network model of excitatory and inhibitory binary neurons which incorporates both Hebbian and homeostatic plasticity mechanisms . Specifically , it incorporates binarized spike timing dependent plasticity ( STDP ) , synaptic normalization ( SN ) , and intrinsic homeostatic plasticity ( IP ) . Certain variants also employ structural plasticity . It has been demonstrated to be computationally powerful and flexible for unsupervised sequence and pattern learning , presenting apparent approximate Bayesian inference and sampling-like behavior [15–17] . Additionally , it has been used to reproduce synaptic weight distributions and growth dynamics observed in the cortex [18] . In this paper , we introduce the LIF-SORN , a leaky integrate-and-fire based SORN-inspired network model that incorporates a spatial topology with a distance-dependent connection probability , in addition to more biologically plausible variants of and extensions to the plasticity mechanisms of the SORN . The LIF-SORN models a recurrently connected network of excitatory and inhibitory neurons in L5 of the neocortex , or a slice thereof . This new model is the first to reproduce numerous elements of the synaptic phenomena examined in [10 , 19] , and [18] in combination with the sort of non-random graph connectivity phenomena observed in [6] . The simultaneous reproduction of all these elements with a minimal set of plasticity mechanisms and constraints represents an unprecedented success in explaining noted features of the cortical micro-connectome in terms of self-organization .
We randomly populate a 1000 × 1000 μm grid with 400 LIF neurons with intrinsic Ornstein-Uhlenbeck membrane noise as the excitatory pool , and a similar ( though faster refracting ) population of 80 noisy LIF neurons as the inhibitory pool . All synapses are inserted into the network with a gaussian distance-dependent connection probability profile with a half-width of 200 μm . This particular profile is chosen as a middle ground between the results of [6] , which finds no distance dependence up to a scale of 80–100 μm , and the results of [5] , which finds an exponential distance dependence at a scale of 200–300 μm . Recurrent excitatory synapses are not populated , as they will be grown “naturally” with the structural plasticity . Excitatory to inhibitory and inhibitory to excitatory synapses are populated to a connection fraction of 0 . 1 and inhibitory recurrent synapses to a connection fraction of 0 . 5 , in approximate accordance with L5 experimental data [20] . Excitatory to inhibitory , inhibitory to excitatory , and inhibitory to inhibitory connections are given fixed efficacies and connectivities . Recurrent excitatory connectivity is begun empty and is to be grown in the course of the simulation . The relevant parameters are summarized in Tables 1 and 2 . We use the Brian spiking neural network simulator [21] . The neuron model is a leaky integrate-and-fire ( LIF ) neuron , the behavior of which is defined by: d V d t = - V - E l τ + σ ξ τ , ( 1 ) where V is the membrane potential , El is the resting membrane potential , τ is the membrane time constant , σ is the standard deviation of the intrinsic membrane noise , and ξ is the Ornstein-Uhlenbeck process which drives the noise . In our model , the variance of the noise is 5 mV . When V reaches a threshold VT , the neuron spikes , and the membrane potential V is returned to Vreset ( which may be lower than El in order to provide effective refractoriness ) . The parameters used are given in Table 3 . A simple transmitting synapse model is used , connecting neuron i to neuron j . When neuron i spikes , the synaptic weight W i j effective is added to the membrane potential V of neuron j following the conduction delay for the type of connection ( as in Table 2 ) . To improve network activity stability , this synaptic weight is modulated by a short term plasticity ( STP ) mechanism [22] implementing a rapid synaptic depression combined with a slightly slower facilitation . The STP mechanism consists of a two variable system: d x d t = 1 - x τ d , d u d t = U - u τ f . ( 2 ) Upon each presynaptic spike , the variables are updated according to the following rules: x → x ( 1 - u ) , u → u + U ( 1 - u ) ( 3 ) The synaptic weight is then modulated as W i j effective = u × x × W i j . We select U = 0 . 04 , τd = 500 ms and τf = 2000 ms as the respective depression and facilitation timescales , corresponding to approximate experimentally observed values [22 , 23] . The presence of the STP adds a significant degree of stability to network activity and provides a more robust paramter range for other mechanisms , reducing the need for parameter tuning . As in the original binary SORN , we include multiple plasticity mechanisms . The first is exponential spike timing dependent plasticity ( STDP ) , which is executed at a biologically realistic timescale [24–29] . This defines the weight change to a synapse caused by a pair of pre- and post-synaptic spikes as in Eqs 4 , 5 and 6: Δ w i j = ∑ f = 1 N f ∑ n = 1 N n W t j n - t i f ( 4 ) W ( x ) = A + exp - x / τ + , x > 0 ( 5 ) W ( x ) = A - exp x / τ - , x < 0 . ( 6 ) Here , i and j index the synapse via its pre- and postsynaptic neurons respectively , f indexes presynaptic spikes , and n indexes postsynaptic spikes . A+ and A− are the maximal amplitudes of the weight changes , and τ+ and τ− are the time constants of the decay windows . Values are set to approximate experimental data; in particular , round numbers were chosen that roughly approximate data in [24] and [25] , with τ+ = 15 ms , A+ = 15 mV , τ− = 30 ms , and A− = 7 . 5 mV . We use the “nearest neighbor” approximation in order to efficiently implement this online , in which only the closest pairs of pre- and post-synaptic spikes are used . This is implemented in an event-based fashion , using a spike memory buffer with a timestep equal to that of the simulation itself ( 0 . 1 ms ) and with the full calculation only evaluated upon a spike . In the brain , several mechanisms appear to regulate the amount of synaptic drive that a neuron is receiving . [30] demonstrated the phenomenon of synaptic normalization during long-term potentiation ( LTP ) . The overall density of postsynaptic AMPA receptors per micrometer of dendrite stays roughly constant , but the density at individual synapses increases ( for some ) while the total number of synapses per micrometer of dendrite decreases . This suggests that synaptic efficacies are mainly redistributed over the dendritic tree during the typical time course of an LTP experiment , but the sum of these efficacies ( roughly corresponding to the sum of the active zone areas ) stays approximately constant . Another phenomenon regulating the synaptic drive a neuron is receiving is homeostatic synaptic scaling [31] , which is thought to regulate synaptic efficacies in a multiplicative fashion on a very slow time scale ( on the order of days ) in order to maintain a certain desired level of neural activity . For the sake of simplicity , we use here only a multiplicative form of normalization that drives the sum of synaptic efficacies to a desired target value on a fast time scale: W i → W i 1 + η SN W total ∑ j N W i j - 1 . ( 7 ) Here , Wi is the vector of incoming weights for any neuron i , Wij are the weights of the individual synapses , Wtotal is the target total input for each neuron , and ηSN is a rate variable which , together with the size of the timestep , determines the timescale of the normalization . Wtotal is calculated before the simulation run for each of the four types of synapse ( E to E , E to I , I to E , and I to I ) by multiplying the connection fraction for that type of connection by the mean synapse strength and the size of the incoming neuron population . The timescale we use is on the order of seconds and therefore accelerated from biology; corresponding to an application of the process once per second and ηSN = 1 . 0 . We have tested it as well applying the normalization at every single simulation timestep , and with smaller values for ηSN , which , except for very small values of ηSN , has no significant effect on any of our observables . The accelerated timescale is sufficiently separated from that of the STDP , which operates on the order to tens of milliseconds , to avoid unwanted interactions while decreasing the necessary simulation time . Neuronal excitability is regulated by various mechanisms and over different time scales in the brain . On a very fast milliseconds time scale , a neuron’s refractory mechanism prevents it from exhibiting excessive activity in response to very strong inputs [32] . This is inherently included in the neuron model we use . At a somewhat slower time scale , spike rate adaptation reduces many types of neurons’ responses to continuous drive [33] . Given that our model lacks any strong external drive , we neglect this . At very slow time scales of hours to days , intrinsic plasticity mechanisms change a neuron’s excitability through the modification of voltage gated ion channels that can modify its firing threshold and the slope of its frequency-current curve in a homeostatic fashion . Additional regulation of neuronal activity has been observed over multiple timescales [34 , 35] . In order to capture the essence of such mechanisms in a simple fashion , we adopt a simple regulatory mechanism for the firing threshold , which , in combination with the previously discussed STP mechanism , phenomenologically captures the majority of these adaptive behaviors over short and medium timescales . Though relatively stable network activity can be achieved without this mechanism , it requires hand tuning of thresholds dependent on other network parameters , which we wish to avoid . The mechanism is implemented at discrete time steps in the following way: V T → V T + η IP N spikes - h IP ( 8 ) N spikes → 0 . ( 9 ) Here , VT is the threshold for an individual neuron , ηIP is a learning rate , hIP is the target number of spikes per update interval , and Nspikes is the number of times a neuron has spiked since the last time a homeostatic plasticity step was executed . The right arrow indicates that the counter is reset after each evaluation of the window . This operation is performed at a biologically accelerated timescale . The desired target rate is chosen to be 3 . 0 Hz , so hIP = 3 . 0 Hz × 0 . 1 ms = 0 . 0003 and ηIP is set to 0 . 1 mV . In our implementation , the operation is performed at every timestep of the simulation ( 0 . 1 ms ) , so Nspikes effectively becomes a binary variable and Eq 9 becomes irrelevant . In this case , the action of the mechanism is that every spike increases the threshold by a small amount , and the absence of a spike decreases it by a small amount . Like the SN process , the accelerated ( relative to biology ) timescale is sufficiently separated from the timescale of the STDP to avoid unwanted interactions while decreasing the necessary simulation time . We implement structural plasticity for the recurrent excitatory synapses via simultaneous synaptic pruning and synaptic growth . Synaptic pruning is implemented in a direct fashion in which synapses whose strength has been driven below a near-zero threshold ( 0 . 000001 mV ) by the other plasticity mechanisms are eliminated . At the same time , new synapses are stochastically added with a strength of 0 . 0001 mV , according to the distance-dependent per-pair connection probabilities , at a regular rate . This is done at an accelerated timescale by adding a random number of synapses ( drawn from an appropriately scaled and integer-rounded normal distribution ) once a second . A mean growth rate is hand-tuned to lead to the desired excitatory-excitatory connection fraction . In this case , the mean growth rate is 920 synapses per second ( with standard deviation of 920 ) and the target connection fraction is 0 . 1 [6 , 20] . The synapses are added according to pre-calculated connection probabilities determined by the gaussian connectivity profile described in the first paragraph of this section . Like the previous two plasticity mechanisms , the acceleration of the timescale from biology is justified by the principle of separation of timescales . At certain points in the Results and Supplementary Material , the results of the simulation are compared to those of a purely topological network . This is generated simply by performing the batch structural growth operation , as described , a single time , but adding instead a number of connections equal to the total number of connections at the target connection fraction .
As the fully simulated network runs , new recurrent excitatory synapses are allowed to grow and , if their strengths are driven close to zero , be pruned . The network first enters a growth phase , which lasts 100–200 seconds of simulation time , and then a stable phase in which the growth rate balances the pruning rate . The network is allowed to run for 500 seconds and the state of the excitatory connectivity and the dynamics of the connection changes during the final epoch are then examined . We first examine , alongside the smooth growth of the network , the prevalence of bidirectional connections as compared to chance , a phenomenon noted to be significantly above-chance in [4] and [6] , as shown in Fig 1 . We observe for the total connection fraction a reliable value of 0 . 1 , as selected . We observe a stable phase value of 0 . 018 for the bidirectional connection fraction , translating to a factor of 1 . 83 above chance . Our control for chance is the expected number of bidirectional connections for an Erdős-Rényi graph containing the same number of nodes and edges as the simulated network . For comparison purposes , a value of approximately 4 times chance is observed in [6] . We note that an otherwise equivalent non-topological network , in which the probability of connection between neurons is uniform rather than distance-dependent , produces a slight underrepresentation of bidirectional connections , reinforcing the well-known expectation that classical STDP , in the absence of other factors , favors unidirectional connectivity . Regarding the growth of the network and the stabilization of its activity , we note one additional thing . In Fig 2 , we observe that the distribution of interspike intervals ( ISIs ) and their coefficients of variation ( CVs ) follow the properties of an approximately Poisson-like spiking with an effective refractory period , as is observed in cortical circuits . That is to say , the distribution of ISIs follows an exponential decay with a distortion , induced by the refractory period , at the low end , and that the CVs of the ISIs tend to be close to one . We would like to briefly consider how a model using classical STDP , which is known to drive the formation of unidirectional connections , can still produce such an abundance of bidirectional connections . In this model , the existence of clustering topology strongly drives the initial overrepresentation of bidirectional connections ( as well as likely seeding higher order clustering effects , which are then selected and tuned via the plasticity mechanisms , and will be examined later ) . A simple mathematical argument will serve to demonstrate this ( and , in fact , that any inhomogeneity in unidirectional connection probability will lead to an overrepresentation of bidirectional connections ) . Consider a single neuron in the center of a two dimensional sheet ( this generalizes to volumes as well ) which is populated with additional neurons at a uniform density . Assume that the central neuron has formed distance-dependent but otherwise random connections to the other neurons as follows: There is a local neighborhood containing a fraction f of all the neurons in the sampled area which have been connected with a high probability ph , while the remaining area contains the fraction 1 − f of all neurons , which connect with a lower probability pl . We can then treat the connection probability as a random variable P which takes the value ph with probability f and pl with probability 1 − f ( this generalizes as well to additional neighborhoods , and , as the number of neighborhoods goes to infinity , to a continuous density of connection probability ) . The average overall connection probability of the neuron is then given by E[P] = ph f + pl ( 1 − f ) . We now want to consider the average probability of finding a bidirectional connection . We assume that all neurons share the same distance-dependent connection probability , and therefore , the probability that a neuron within the local neighborhood has formed a connection to the central neuron is the same ph with which the central neuron is likely to form a connection to the neuron in the local neighborhood . Thus , the probability of a bidirectional connection in the local neighborhood is p h 2 , and by the same reasoning , the probability of forming a bidirectional connection with a neuron outside the local neighborhood is p l 2 . Then , the average overall bidirectional connection probability of the neuron is given by E [ P 2 ] = p h 2 f + p l 2 ( 1 - f ) . Since the squaring operation is convex , Jensen’s inequality applies , stating that for any convex function g ( P ) of a random variable P , g ( E[P] ) ≤ E[g ( P ) ] . It then follows that with g ( P ) = P2 , E[P2] ≥ E[P]2 . Thus , bidirectional connections can occur more frequently than would be expected from the average unidirectional connection probability . Equality holds if and only if P is constant . It follows then that any inhomogeneity in unidirectional connection probability will lead to an overrepresentation of bidirectional connections . In the case of our model , the inhomogeneity is the distance-dependent connection probability , though any number of other factors could come into play . For the above argument to apply to a structurally dynamic model such as ours , all that need be true is that bidirectional connections are added at a sufficiently high rate compared to their rate of removal due to STDP and pruning . The high number of bidirectional connections in the purely topological network , the low values for the purely plastic network , and the intermediate number of bidirectional connections for the full network model in Fig 1 serve to demonstrate the competition between the distance-dependent structural plasticity , which tends to boost bidirectional connectivity , and STDP and pruning , which tend to reduce bidirectional connectivity . Furthermore , this competition can be captured and described by a simple Markov model in which each bidirectional connection pair develops independently of all the others . The model considers a pair of excitatory neurons and has three states {U , S , D} representing that the pair of neurons is either unconnected , singly connected , or doubly connected , respectively . We define transition probabilities denoting the probability of transitioning from one state to another during a fixed time interval . For example , pUS is the probability for transitioning from the unconnected state U to the singly connected state S . The transition matrix is the matrix formed by all transition probabilites and is given by: T = p U U p U S 0 p S U p S S p S D 0 p D S p D D , given the assumption that transitions from the unconnected state U to the doubly connected state D and vice versa are sufficiently unlikely to be considered negligible . Since the sum of the elements in each row of T has to equal one , T can be rewritten as: T = 1 - p U S p U S 0 p S U 1 - p S U - p S D p S D 0 p D S 1 - p D S , which depends on the four parameters pUS , pSU , pSD , and pDS . If all of them are greater than zero , then the Markov Chain is regular and we can find its stationary distribution by finding the left Eigenvector of T: ( u s d ) 1 - p U S p U S 0 p S U 1 - p S U - p S D p S D 0 p D S 1 - p D S = ( u s d ) with u + s + d = 1 . The resulting system of linear equations can be written as: u= 1 - s - ds= p U S p S U u ≡ α ud= p S D p D S s ≡ β s = α β u , where we have defined α = pUS/pSU and β = pSD/pDS . Thus , the behavior of the system depends only on the two transition probability ratios α and β . We can express u as a function of α and β to arrive at the final solution: u= 1 1 + α ( 1 + β ) s= α ud= α β u . We can now consider the conditions under which the model leads to an overrepresentation of bidirectional connections . The overall connection probability in the Markov model is p = s/2 + d . For a random graph , we then expect: u random= ( 1 - p ) 2s random= 2 p ( 1 - p ) d random= p 2 . We consider an overrepresentation of bidirectional connections to be in comparison to a random graph . Therefore , using the previously defined transition ratios and a bit of algebra , we arrive at the following expression for the overrepresentation A: A = d d random = β + α β ( 1 + β ) α 4 + α β ( 1 + β ) We can then empirically check this Markov model against our simulation . Counting and averaging connections and transitions over the last 100 seconds of a standard 500 second run of our model , we obtain α = 0 . 194 and β = 0 . 105 . This leads the Markov model to predict an overrepresentation of A = 0 . 180 , which is , in fact , the also measured value for the average overrepresentation over the observed time period . During the growth phase of the simulation , we note the reproduction of some of the results of [19] , specifically that during network growth there is a tendency for larger synaptic weights to be more likely to shrink than smaller synaptic weights , as seen in Fig 3 . Once the stable phase is reached , we observe the distribution of synaptic weights via histogramming , as previously stated , in Fig 4 . This is in qualitative agreement with the heavy-tailed , log-normal-like shape typically observed in experimental data [6–10] . Several theoretical explanations for this distribution have been proposed , including a self-scaling rich-get-richer dynamic [18] and a confluence of additive and multiplicative processes [36 , 37] , both of which are consistent with our model . We note that the topology of the network seems to have a minimal effect on this result , as would be expected from the results of [18] . We observe next the synaptic change dynamics in the stable phase of the network . We follow the format used in [10] , comparing initial synaptic weight during a test epoch to both absolute and relative changes in synaptic weight , and demonstrate in Fig 5 that strong synaptic weights exhibit relatively smaller fluctuations over time , as experimentally observed [10] . Additionally , this serves to reinforce the earlier success of [18] in modeling such synaptic dynamics as the result of self-organization , and demonstrates that such results carry over into a biologically more realistic model . We examine , as well , the distribution of synaptic lifetimes ( see Fig 6 ) . It has been predicted that the lifetimes of fluctuating synapses may follow a power law distribution [18]; our model makes this prediction as well . Recent experimental evidence supports this prediction [38] . We expand upon previous predictions with two interesting observations . In its current form , our model produces a slope of approximately 5/3 in the stable phase ( for comparison , the experimentally observed slope is approximately 1 . 38 ) . This decreases slightly in the growth phase . Secondly , we have observed as well that the slope can be modified by adjusting the balance of potentiation and depression in the STDP rule , varying between values between 1 and greater than 2 , depending on the chosen parameters . For example , doubling the amplitude of the depression term in the STDP rule leads to a slope of approximately 5/2 , while halving it leads to a slope of approximately 5/4 . This is , in retrospect , an intuitive phenomenon . A preponderance of potentiation will lead to synapses being depressed to a value below the pruning threshold less frequently , thereby decreasing the slope of the power law . Similarly , in a depression-dominated scenario , synapses will be driven below the pruning threshold more frequently , leading to a higher power law slope . Returning to the slight decrease in slope during the growth phase , this makes sense , as a reduction in the effective pruning rate is necessary for the network to continue to grow . We believe that with a more extensive investigation of the effects of other model parameters on the power law , the slope of this distribution could be used as a meaningful measure of the potentiation-depression balance in a recurrent cortical network . We subsequently examine the prevalence of triadic motifs in the graph of the simulated network . An overrepresentation of certain motifs was noted in [6] . We used a script written for the NetworkX Python module [39 , 40] to acquire a motif count for the graph of the simulated network . As the overrepresentation of bidirectional connections will trivially lead to an overrepresentation of graph motifs containing bidirectional edges , the control for chance is , in this case , a modified Erdős-Rényi graph with the same number of nodes , same number of unidirectional edges , and same number of bidirectional edges as the graph of the simulated network , with the unidirectional and bidirectional edges being independently populated . A similar control is used in [6] . We observe a similar pattern of “closed loop” triadic motifs being overrepresented in Fig 7 , as experimentally observed in [6] . We note that the results for a non-topological plastic network with classical STDP , in the absence of additional factors , does not , relatively speaking , strongly select for any particular family of motifs . We similarly note that while distance-dependent topology does select for the observed family of motifs , it does not do so at the experimentally observed level . It is only the combination of topology and plasticity that strongly selected for the desired family of motifs while simultaneously producing all other noted effects . Approximate experimental data for comparison was extracted from [6] using GraphClick [41] .
The problem of how the non-random micro-connectivity of the cortex arises is a nontrivial one with significant implications for the understanding of both cognition and development . We attempt , in this paper , to provide insight into this problem by presenting a plausible model by which such non-random connectivity arises as the self-organized result of the interaction of multiple plasticity mechanisms under physiological constraints . Some models attempt to describe elements of the graph structure of the micro-connectome in purely physiological and topological terms [42] . However , such models necessarily lack an active network , and are thus unable to simultaneously account for synaptic dynamics , as our model does . Our model is , of course , a simple model , but the degree to which it accounts for observed non-random features of the typical cortical microcircuit without detailed structural features , metabolic factors , or structured input to drive the plasticity in a particular fashion is highly suggestive in terms of what is necessary at a bare minimum to drive the development and maintenance of the complex microstructure of the brain . As mentioned in the introduction , it is hypothesized that a small backbone of strong synapses may form the basis of stable long-term memory . The fact that in our model , strong weights remain stable in the presence of ongoing plasticity and despite significant fluctuations of smaller weights ( which has been modeled as a stochastic Kesten process [37] ) , and the naturalness with which such a dynamic arises out of the interactions of known plasticity mechanisms , is both suggestive and supportive of this theory . On a related note , the heavy-tailed distribution of synaptic efficacies ( often described as log-normal or log-normal-like ) is an experimentally observed phenomenon seemingly fitting this narrative [6–10] . A theoretical explanation connecting log-normal firing rates with a log-normal synaptic efficacy distribution was one of the first proposed [43] . However , further studies have suggested that such a firing rate distribution is not necessary to create a heavy-tailed distribution of synaptic efficacies , using either a self-scaling rich-get-richer dynamic [18] or a combination of additive and multiplicative dynamics [36 , 37] . An additional noted non-random feature of cortical recordings that has been passed over in this model is the observed log-normal distribution of cortical firing rates ( touched upon in the previous paragraph ) . Our intrinsic plasticity mechanism necessarily negates this feature , which may be self-organized via mechanisms not included in our model , such as diffusive homeostasis [44 , 45] . In order to maximize simplicity , a single target firing rate is chosen for all neurons . Additional testing in which the target firing rate is drawn from a log-normal distribution produces minimal qualitative effects on the observed features ( except , trivially , the ISI distribution , see S1 Fig ) . Another issue is that as things stand , the exact statistics of the micro-connectome are difficult to discern—though strong inferences can be made in the right direction—due to inherent sampling biases in paired patch-clamp reconstructions of limited size [46] . It is our hope and belief that advances in fluorescence imaging , automated electron microscopy reconstruction [47 , 48] , and massive multi-unit array recordings will help to alleviate these biases . One might imagine that additional biases may be caused by the relatively small model size of 400 excitatory neurons , when realistic cortical densities would result in thousands of neurons in such an equivalent volume . We have tested the network at much larger sizes of up to 2000 neurons and found no notable qualitative change to our observed results ( S2 Fig; all other features remain the same as well ) , so we maintained a relatively small network size to increase computational ease . It should be noted that except for this check , all supplementary checks , tests , and additional analyses were performed with the standard 400 + 80 neuron network size . We have described the formation of the overrepresentation of bidirectional connections in terms of the competition between structural growth and structural pruning in the presence of a topological inhomogeneity . Other possibilities for increasing the prevalence of bidirectional connections include an STDP window with an integral greater than zero ( i . e . biased toward potentiation ) , or one in which the asymmetries are finely tuned so that , given the target homeostatic target firing rate , connections are , on the whole , more likely to potentiate ( making the STDP window fully symmetrical has , in our model , only a minimal effect ) . Additionally , more complicated STDP models [50 , 51] are known to produce overrepresentation of bidirectional connections in high-frequency firing regimes . One other computational study has reproduced similar motif overrepresentations , however , this model was significantly more complex and required specific structured input [49] . Some might view the fact that , in this model , the primary driver behind the overrepresentation of bidirectional connections is topology , as a shortcoming . We do not view this as a problem; after all , topology exists in the cortex and the rest of the study’s results suggest it is an important factor in the self-organization of cortical circuits . There are the previously mentioned mechanisms utilizing non-classical STDP , such as the so-called triplet and voltage rules [50 , 51] , which , in the presence of high-frequency activity , are capable of producing and maintaining bidirectional connections . Introducing such mechanisms into a similar model would be a welcome and interesting future study , and could potentially lead to an even stronger and more precise motif selectivity . To further explain the importance of the various mechanisms we have introduced in self-organization , we have included a brief analysis of the behavior of the network in the absence of individual mechanisms ( see Table 4 below and S3 and S4 Figs ) . Essentially , removal of the topology leaves the synaptic dynamics mostly unchanged , but significantly alters the connectivity structure . Removal of structural plasticity trivially leads to failure of the network to form , or , in the case of the removal of pruning only , divergent network growth . Similarly , removal of STDP leads to divergent network growth because LTD is necessary to trigger pruning . Removal of the STP leads to “epileptic” behavior , resulting in dynamic and structural disruptions . Removal of SN leads to a small subset of synapses experiencing runaway growth , with the others shrinking to near zero and being pruned . Finally , removal of the IP leads to small changes to the structural properties , but requires fine tuning of the thresholds to run in this regime . Failure to tune the thresholds in this case leads to either silent or “epileptic” networks . Additionally , with the aim of understanding the relationship between the activity correlation , the synaptic weights , and the intersomatic separation , a Spearman’s rank correlation analysis was performed on such data from an example trial ( results in S1 Table ) . In summary , a strong and highly significant positive correlation was found between the spike correlation and the synaptic weight . However , only a weak ( negative ) correlation was found between the spike correlation and the intersomatic separation , and no significant correlation was found between the intersomatic separation and synaptic weight . As a concluding point , often , models of cortical microcircuits are described as random graphs , such as the classical random balanced network [52] . However , experiments have demonstrated that the structure of cortical microcircuitry is significantly non-random [5 , 6] , suggesting that random networks may be insufficient for modeling cortical development and activity . Lacking in structural plasticity or topology , such random graph based balanced networks are incapable of producing the sort of results we have observed . Having provided a mechanism with which one may generate a cortex-like non-random structure , it would be enlightening to determine if said structure provides any significant computational or metabolic advantage as compared to a random graph . Similarly , limitations in online plasticity capabilities significantly hinder the use of such random networks and their relatives in reservoir computing [53] for unsupervised learning and inference tasks ( though progress has recently been made in this direction [54] ) , while earlier studies with the original SORN model [11 , 15] suggest that the particular combination of plasticity mechanisms in our model can endow networks with impressive learning and inference capabilities . A logical next step is therefore to study the learning and inference capabilities of LIF-SORN networks and relate them to neurophysiological experiments . Our rapidly developing ability to manipulate neural circuits in vivo suggests this as an exciting direction for future research . It is our belief that the future of modeling cortical computation and related biological processes lies in the incorporation of multiple plasticity and homeostatic mechanisms under simple sets of constraints and biases . | The problem of how the brain wires itself up has important implications for the understanding of both brain development and cognition . The microscopic structure of the circuits of the adult neocortex , often considered the seat of our highest cognitive abilities , is still poorly understood . Recent experiments have provided a first set of findings on the structural features of these circuits , but it is unknown how these features come about and how they are maintained . Here we present a neural network model that shows how these features might come about . It gives rise to numerous connectivity features , which have been observed in experiments , but never before simultaneously produced by a single model . Our model explains the development of these structural features as the result of a process of self-organization . The results imply that only a few simple mechanisms and constraints are required to produce , at least to the first approximation , various characteristic features of a typical fragment of brain microcircuitry . In the absence of any of these mechanisms , simultaneous production of all desired features fails , suggesting a minimal set of necessary mechanisms for their production . | [
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] | 2016 | Plasticity-Driven Self-Organization under Topological Constraints Accounts for Non-random Features of Cortical Synaptic Wiring |
Taenia solium inflicts substantial neurologic disease and economic losses on rural communities in many developing nations . “Ring-strategy” is a control intervention that targets treatment of humans and pigs among clusters of households ( rings ) that surround pigs heavily infected with cysticerci . These pigs are typically identified by examining the animal’s tongue for cysts . However , as prevalence decreases in intervened communities , more sensitive methods may be needed to identify these animals and to maintain control pressure . The purpose of this study was to evaluate ultrasonography as an alternative method to detect pigs heavily infected with T . solium cysts . We purchased 152 pigs representing all seropositive animals villagers were willing to sell from eight communities ( pop . 2085 ) in Piura , Peru , where T . solium is endemic . Tongue and ultrasound examinations of the fore and hind-limbs were performed in these animals , followed by necropsy with fine dissection as gold standard to determine cyst burden . We compared the sensitivity and specificity of ultrasonography with tongue examination for their ability to detect heavy infection ( ≥ 100 viable cysts ) in pigs . Compared to tongue examination , ultrasonography was more sensitive ( 100% vs . 91% ) but less specific ( 90% vs . 98% ) , although these differences were not statistically significant . The greater sensitivity of ultrasound resulted in detection of one additional heavily infected pig compared to tongue examination ( 11/11 vs . 10/11 ) , but resulted in more false positives ( 14/141 vs . 3/141 ) due to poor specificity . Ultrasonography was highly sensitive in detecting heavily infected pigs and may identify more rings for screening or treatment compared to tongue examination . However , the high false positive rate using ultrasound would result in substantial unnecessary treatment . If specificity can be improved with greater operator experience , ultrasonography may benefit ring interventions where control efforts have stalled due to inadequate sensitivity of tongue examination .
Ultrasonography is a noninvasive diagnostic tool that can detect soft-tissue larval cestode infections . It is considered a primary method for identifying hepatic cystic hydatidosis and subcutaneous cysticercosis in humans [1–5] , and has proved useful for mass screening of hydatidosis in sheep and goats [6–8] . However , an overlooked application for ultrasound is its potential use in the detection of cysticercosis in live pigs . Cysticercosis occurs in pigs infected with larval cysts of the pork tapeworm , Taenia solium . Humans are the definitive hosts of the adult-stage parasite ( taeniasis ) , which infects the small intestine and sheds eggs or gravid proglottids into the host’s feces . T . solium eggs are deposited into the environment through the stool of infected humans and later consumed by foraging pigs . Once ingested , the eggs develop into their larval form ( cysticercosis ) , which encyst in the pig’s muscle or other soft tissue . The parasite’s life cycle is completed when a larval cyst in raw or undercooked pork is consumed by a human , and subsequently develops into an adult tapeworm in the small intestine . A set of reliable diagnostic tools for porcine cysticercosis is needed to allow treatment of infected pigs prior to slaughter , to identify transmission hotspots within communities , and to monitor progress of control programs [9 , 10] . Only one study has evaluated the use of ultrasonography for detecting porcine cysticercosis [11] . While this study confirmed the ability of ultrasound to diagnose larval cysts in live pigs , only a small number of heavily infected animals were examined . Given that the vast majority of infected pigs have only a few cysts in the entire body [12–14] , the utility of ultrasonography as an adequate screening tool for food safety purposes or for monitoring progress of control programs is doubtful . However , ultrasonography could prove useful for applications in which detection of heavily infected pigs is the goal . One such application is a control intervention known as “ring-strategy” , which involves screening and treatment of humans and pigs for taeniasis and cysticercosis , respectively , if they live within 100 meters of a pig heavily infected with cysticerci [15 , 16] . The working assumption of this approach is that pigs with hundreds or thousands of infecting cysts have experienced repeated or intense exposure to T . solium eggs , suggesting that a human with taeniasis resides nearby , and that other humans and pigs in the area may be at increased risk of infection . This approach was developed in response to a small study in rural Peru that found the prevalence of taeniasis to be eight times greater among humans living within 100 meters of heavily infected pigs [17] . Studies of ring-strategy have relied on tongue examination of pigs to diagnose heavy cyst infection [16 , 18] . This method involves visual inspection and palpation of the inferior surface of the tongue in live pigs , and was traditionally performed by local buyers experienced at screening pigs prior to sale at market . Despite the appeal of tongue examination as a low-cost and locally accepted method , it suffers from a lack of sensitivity , as some studies have found that tongue examination fails to detect cysts in 80% or more of infected pigs [19 , 20] . There is evidence , however , to suggest that tongue examination performs better when used to exclusively identify heavily infected pigs . In a small Zambian study , researchers found that 35% of pigs with 100 or more cysts ( compared to 0% of pigs with <100 cysts ) were positively identified through tongue examination [19] . While more research is needed to confirm this hypothesis , it is likely that the sensitivity of tongue examination increases at even heavier cyst burdens , which can range into the thousands of cysts [12–14] . This makes it a reasonable tool for ring-strategy , which aims to identify only the most heavily infected pigs in a community . Nonetheless , a rapid diagnostic test that can be applied in the field and that improves upon the sensitivity of tongue examination is needed in later stages of control . During early stages of control interventions , when the prevalence of porcine cysticercosis is high in endemic communities , even low sensitivity methods will identify heavily infected pigs and result in screening and treatment being applied . As prevalence of both porcine cysticercosis and taeniasis decline , however , more sensitive methods are needed to identify and treat remaining pockets of infection . The purpose of this study , therefore , was to evaluate ultrasonography as an alternative to tongue examination for non-invasive detection of viable T . solium cysts in live pigs . We aimed to compare the performance of these two diagnostic tools for detecting pigs with heavy cyst burdens in the context of ring-strategy .
Our study consisted of eight villages in Piura ( total population 2 , 085 residents ) , a province in the arid northern region of Peru where T . solium is endemic . We performed a door-to-door survey of all households in the eight villages and attempted to capture all pigs older than two months of age , with age verified by the owner . Eligible pigs were manually restrained while trained study personnel performed tongue examination and collected blood samples from each pig . We collected five milliliters of blood from pigs in the field using pre-caval venipuncture , and stored samples in chilled ice coolers until they could be centrifuged in a field laboratory . 1 ml aliquots of sera were placed in microtubules , frozen at -20°C , and then shipped by air to the Cysticercosis Unit at the National Institute of Neurological Sciences in Lima for further analysis . Pig sera were analyzed by enzyme-linked immunoelectrotransfer blot ( EITB ) for presence of antibodies against T . solium cysts using methods described elsewhere [21] . Briefly , the EITB assay uses an enriched fraction of homogenized T . solium cysts containing seven T . solium glycoprotein antigens , GP50 , GP42 , GP24 , GP21 , GP18 , GP14 , GP13 . Reaction to any of these seven glycoprotein antigens is considered positive . Of the 827 pigs tested from the eight study villages , 432 ( 52% ) seropositive pigs were identified . Field teams returned to these households and offered to purchase all seropositive pigs in order to perform ultrasound examination and necropsy off-site . Due to the reluctance of villagers to sell their animals , only 152 ( 35% ) seropositive pigs were purchased and underwent further testing at the Center for Global Health in Tumbes , Peru . We purchased seropositive pigs only in order to increase the likelihood of including pigs with viable cyst infection in the subsequent evaluations using necropsy and ultrasonography . Tongue examination was performed in the study community while the pig was manually restrained . We used a wooden stick to keep the mouth open while retracting the tongue with a cloth and visually inspecting and palpating the entire inferior aspect of the tongue for the presence of viable cysts . Pigs were considered tongue-positive if one or more fluid-filled cystic structures was either seen or felt , regardless of whether a central opacity was visible . Degenerated or calcified cysts , which can be confused with scars or granulomas resulting from tongue trauma , were excluded . Ultrasound examinations were conducted in the corrals at the Center for Global Health by a trained ultrasonographer who had experience using ultrasound to screen for intra-abdominal hydatid disease among humans and ruminants . Pigs were manually restrained on the ground in dorsal recumbency by two technicians securing the front and hind-legs while the medial aspects of both thighs and brachii were inspected for viable cysts using a SonoSite plus portable machine with a L38 5 . 0–10 . 0 MHz transducer ( SonoSite , Bothell , WA , USA ) . For ultrasound examination , viable cysts were defined as cystic structures with clearly delineated borders containing clear vesicular fluid and a central opacity ( Fig 1 ) . The total number of viable cysts was recorded regardless of whether degenerating or calcified cysts were also encountered . We limited our analysis to viable cysts only as these presumably indicate more recent infection than do degenerated or calcified cysts , an important consideration in the context of ring-screening in which the goal is to identify and treat the active source of infection . To emulate field conditions , ultrasound examinations were restricted to a total of five minutes per animal . Following ultrasound examination , 0 . 1 mg/kg of xylazine combined with 5 mg/kg of ketamine was administered intravenously to provide a deep plane of anesthesia . The animal was then humanely euthanatized by injecting an overdose of sodium pentobarbital ( 100 mg/kg ) intravenously . Detailed necropsy was conducted by systematically dissecting the full carcass of all pigs , and identifying any cysts present in the brain , heart , tongue and all skeletal muscles . Fine cuts of less than 0 . 5 centimeters were used to dissect all inspected tissues and the total number of viable cysts in each pig was recorded . Viable cysts were defined as well-delineated thin-walled cystic structures containing clear vesicular fluid and a visible white protoscolex . The total number of viable cysts encountered was recorded . Degenerated and calcified cysts were not considered in this analysis . For pigs with particularly dense cyst burdens , a weighed sample of forelimb muscle was counted for cysts and extrapolated to estimate the total body burden . Data analysis was performed using Stata version 14 . 0 ( Stata Corp . , College Station , TX , USA ) . Necropsy cyst burden was stratified as negative ( no cysts ) , light for 1–9 viable cysts , moderate for 10–99 viable cysts , and heavy for those with more than 100 viable cysts . We then compared tongue examination with ultrasound detection for their ability to detect heavily infected pigs ( ≥100 viable cyst identified on necropsy ) . In an initial analysis , we found that visualizing just one viable cyst on ultrasound was responsible for 46% ( 12/26 ) of false positive pigs . Therefore , to improve specificity , we required the identification of at least two viable cysts on ultrasound to meet the definition of positive for subsequent analyses . We compared the sensitivity and specificity of the two screening methods and calculated exact binomial 95% confidence intervals for each . We also calculated and plotted positive and negative predictive values of each test under a range of hypothetical prevalences of heavily infected pigs to assess performance of each under a variety of endemic scenarios . This study was reviewed and approved by the Institutional Review Boards and Institutional Ethics Committees for the Use of Animals at Oregon Health & Science University , Portland , Oregon , USA under permit number IS00002843 , and the Universidad Peruana Cayetano Heredia , Lima , Peru under permit 61326 . Treatment of animals adhered to the Council for International Organizations of Medical Sciences ( CIOMS ) International Guiding Principles for Biomedical Research Involving Animals .
The median age of the 152 study pigs was 10 months ( range: 6 to 32 months ) . Nearly 80% ( 120/152 ) were under 1 year of age and 55% ( 84/152 ) were female . Weight ranged between 7 and 74 kilograms ( mean: 28 kg ) . Serologic results revealed that 40% ( 60/152 ) of pigs had one or two reactive bands on EITB , 34% ( 51/152 ) had three reactive bands , and 27% ( 41/152 ) had four or more reactive bands . Among the 152 pigs necropsied , 105 ( 69% ) did not have any viable cysts , 27 ( 18% ) had a light cyst burden ( 1 to 9 cysts ) , 9 ( 6% ) pigs had a moderate burden ( 10 to 99 cysts ) , and 11 ( 7% ) had a heavy burden ( ≥100 cysts ) ( Table 1 ) . Among the 11 heavily infected pigs , the cyst burden ranged from 545 to over 34 thousand cysts ( median: 2 , 827 cysts ) ; 10 ( 91% ) had over 1000 cysts . Inspection of the inferior aspect of the tongue identified cysts in 8 . 6% of pigs ( 13/152 ) . 10 out of 11 heavily infected pigs ( ≥100 viable cysts by necropsy ) were positively identified , yielding a sensitivity for detecting heavy infection of 90 . 9% ( 95% CI: 58 . 7 , 99 . 8 ) ( Table 2 ) . The pig with heavy infection that was not detected by tongue examination had the lowest cyst burden among heavily infected pigs ( 545 viable cysts on necropsy ) . Tongue examination positively identified 10 out of 10 pigs that had at least 1000 cysts . Tongue examination was positive in 2/105 ( 1 . 9% ) pigs that were negative on necropsy and 1/36 ( 2 . 8% ) pigs with light to moderate cyst burden . Combined , these positive tongue findings in lightly infected or uninfected pigs ( 3/141 ) resulted in a specificity of 97 . 9% ( 95% CI: 93 . 9 , 99 . 6 ) for detecting heavy cyst burden in pigs ( ≥100 viable cysts ) . We required a minimum of two viable cysts to be identified by ultrasound to consider the result to be positive . At least two suspected cysts were seen in 16% ( 25/152 ) of pigs . 11 out of 11 heavily infected pigs ( ≥100 viable cysts by necropsy ) were positively identified with ultrasonography , yielding a sensitivity for detecting heavy infection of 100% ( 95% CI: 71 . 5 , 100 . 0 ) ( Table 2 ) . Ultrasound was positive in 11/105 ( 10 . 5% ) pigs that were negative by necropsy and 3/36 ( 8 . 3% ) pigs with light to moderate cyst burdens . Taken together , these positive ultrasound findings in lightly infected or uninfected pigs ( 14/141 ) yielded a specificity of 90 . 1% ( 95% CI: 83 . 9 , 94 . 5 ) for detecting heavy cyst burden ( ≥ 100 viable cysts ) . The positive ( PPV ) and negative predictive values ( NPV ) for tongue examination and ultrasonography across a range of prevalence values for heavily infected pigs are shown in Fig 2 . While the NPV was similar for both methods at the low prevalences ( <10% ) typically seen in endemic areas in Peru , the PPV of tongue examination was substantially higher than that for ultrasound at all prevalence levels .
The main purpose of this study was to compare ultrasonography to tongue examination as a rapid diagnostic tool for identifying live pigs heavily infected with viable T . solium cysts , and to determine the utility of ultrasonography in the context of a ring-strategy . Although the modest improvement in sensitivity of ultrasonography , as compared to tongue examination , could potentially increase control pressure through the identification of more heavily infected pigs and associated areas of parasite transmission , the lower specificity would translate into application of substantial treatment resources in areas that may not have increased risk . Unless the specificity of ultrasonography can be improved , development of alternative methods for identifying heavy infection in pigs , such as rapid serology , should be pursued . In addition , the results of this study demonstrate that neither ultrasonography nor tongue examination accurately identify pigs with low or moderate infection burdens . This confirms that neither are adequate diagnostic tools for use in food safety monitoring or for measuring progress of control programs . The 9% difference in sensitivity between ultrasonography and tongue examination ( 100% vs . 91% ) was not statistically significant , and in absolute terms translated to just one additional heavily infected pig being detected among the 152 pigs analyzed . Nonetheless , if the greater sensitivity of ultrasound represents a true difference , it could have an impact on ring interventions given the high reproductive capacity of the adult stage tapeworm and the potential for an untreated case to reverse control gains . In a recent investigation that allocated treatment rings based on tongue-positivity in pigs , the prevalence of taeniasis at study end was 1 . 4% in the treatment arm [16] . While this was significantly less than the prevalence in the control arm ( 2 . 5% ) , the majority of remaining taeniasis carriers went untreated because they did not fall into treatment rings , suggesting a potential underdiagnosis of heavily infected pigs in this trial . To advance ring control efforts beyond what is possible when using tongue examination , a more sensitive method of diagnosing heavily infected pigs is needed . Ultrasound may also allow for the detection of pigs with slightly lower cyst burdens than tongue examination , which may be beneficial in later stages of control when there are fewer infected pigs present at any level of cyst burden . While both ultrasound and tongue examination performed extremely well in identifying pigs with massive cyst burdens ( 100% sensitivity for pigs with ≥1000 cysts ) , only ultrasound identified the one pig that was found to have 545 cysts . This may simply reflect that ultrasound allowed us to screen a greater mass of skeletal muscle than did tongue examination . The ability of ultrasound to detect infected pigs with lower thresholds of cyst burden could prompt screening and treatment intervention in areas that may otherwise have been missed . Despite ultrasound’s potential greater sensitivity compared to tongue examination , the high false positive rate we observed precludes its use in ring-screening unless specificity can be improved . Even after we required a minimum of two cysts to be detected on ultrasound to meet the definition of positive , ultrasonography had lower specificity for detecting pigs with heavy cyst burdens compared to tongue examination ( 90% vs . 98% ) , leading to an unacceptably low positive predictive value at all but the highest background prevalences for heavily infected pigs . Our concern is that by using ultrasonography as a diagnostic tool in the context of a ring strategy , unnecessary treatment rings would be created , leading to substantial over-treatment of humans and pigs as compared to tongue examination . Although we found that ultrasonography critically lacked specificity in this study , it is an operator-dependent test and performance will likely improve with experience . The ultrasound examinations in this study were performed by a single operator , who , although trained and experienced in the detection of inter-abdominal hydatid cysts in small ruminants , had no prior experience diagnosing cysticerci in pigs . With more experience , the ability to discriminate cysts from other structures may improve . Conversely , it is unlikely the performance of tongue examination is subject to significant improvement . Since ultrasonography and tongue examination have poor sensitivity at lower cyst burdens , neither is an adequate screening tool for evaluating food safety or for monitoring progress of control programs . Results from several necropsy studies in endemic villages have shown that the vast majority of pigs infected with cysticercosis have less than 10 cysts in the entire carcass [16–18] . Neither ultrasonography nor tongue examination was able to accurately diagnose pigs with burdens of infection less than 100 cysts , detecting just 8% ( 3/36 ) and 3% ( 1/36 ) of these infected pigs respectively . Serologic methods or carcass dissection , despite known limitations , continue to be the predominant methods used to monitor the effectiveness of control interventions [10 , 22] , while there is no reliable tool available for food safety applications . The development of a rapid serologic test that could detect heavily infected pigs with high specificity ( >98% ) and moderately high sensitivity ( >90% ) , could provide a viable option for use in ring strategy . Evaluating ultrasonography and tongue examination requires important qualitative comparisons . Ultrasonography incurs additional costs including the equipment and the service of a skilled technician . Although the higher cost could be justified in the later stages of control when greater sensitivity is needed , ultrasonography provides little value above tongue examination in the early stages of a ring intervention . However , ultrasonography does provide a potential benefit of creating an opportunity for education and engagement with community members intrigued by the ability to visualize cysts in their pigs on the ultrasound screen . This level of engagement is beyond that which we observed during tongue examination or serum collection , and may ultimately improve trust between community members and project staff , and increase community knowledge about cysticercosis prevention . We chose to include only seropositive pigs ( based on EITB assay ) in our sample in order to maximize the number of heavily infected pigs examined . While EITB measures antibody response , and not necessarily active cyst infection , antigen detection assays are known to cross-react with T . hydatigena which is highly prevalent in the study region . It is important to note that although the exclusion of seronegative pigs deliberately biased our sample towards including a greater proportion of pigs with viable cysts , this does not affect the sensitivity and specificity values that we report for ultrasonography or tongue examination . However , the PPV and NPV are strongly influenced by the underlying prevalence , which is why we chose to present these characteristics across a range of prevalence values . Future studies would benefit from a larger sample of pigs to increase the precision of point estimates for sensitivity and specificity in each stratum of cyst burden . While it is possible that pig age , sex and weight might influence the performance of ultrasound due to the amount of fat present in the animal , our sample included pigs across a broad range of the characteristics . Lastly , we chose the medial aspects of the fore and hind limbs for ultrasonography as these sites are readily accessible , are sparsely haired , and are known to harbor cysts . Other anatomic sites , however , may allow for clearer visualization of cysts or may have higher predilection for cyst formation , ultimately affecting the sensitivity and specificity of the test . | Taenia solium is a cestode that infects humans and pigs . The parasite causes up to one-third of epilepsy in Latin America , Asia and Africa and results in economic harm to smallholder farmers who cannot sell the contaminated pork of their infected pigs . “Ring-strategy” is an intervention being evaluated as a potential method to control the spread of infection within communities . This strategy involves identifying heavily infected pigs and targeting treatment resources to humans and pigs living nearby these animals . Tongue examination of pigs is used to provide a simple yet crude technique for identifying the most heavily infected pigs . The purpose of this study was to evaluate the ability of ultrasonography to identify T . solium infection in pigs and to compare it to traditional tongue examination methods . We found that ultrasonography may be better at detecting heavily infected pigs than traditional tongue examination methods , but has limitations such as increased cost and a high false positive rate . With improvements in training and greater operator experience , ultrasound may have the potential to contribute to control interventions based on ring-strategy . | [
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] | 2017 | Assessing Ultrasonography as a Diagnostic Tool for Porcine Cysticercosis |
Maintenance of physiologic phosphate balance is of crucial biological importance , as it is fundamental to cellular function , energy metabolism , and skeletal mineralization . Fibroblast growth factor-23 ( FGF-23 ) is a master regulator of phosphate homeostasis , but the molecular mechanism of such regulation is not yet completely understood . Targeted disruption of the Fgf-23 gene in mice ( Fgf-23−/− ) elicits hyperphosphatemia , and an increase in renal sodium/phosphate co-transporter 2a ( NaPi2a ) protein abundance . To elucidate the pathophysiological role of augmented renal proximal tubular expression of NaPi2a in Fgf-23−/− mice and to examine serum phosphate–independent functions of Fgf23 in bone , we generated a new mouse line deficient in both Fgf-23 and NaPi2a genes , and determined the effect of genomic ablation of NaPi2a from Fgf-23−/− mice on phosphate homeostasis and skeletal mineralization . Fgf-23−/−/NaPi2a−/− double mutant mice are viable and exhibit normal physical activities when compared to Fgf-23−/− animals . Biochemical analyses show that ablation of NaPi2a from Fgf-23−/− mice reversed hyperphosphatemia to hypophosphatemia by 6 weeks of age . Surprisingly , despite the complete reversal of serum phosphate levels in Fgf-23−/−/NaPi2a−/− , their skeletal phenotype still resembles the one of Fgf23−/− animals . The results of this study provide the first genetic evidence of an in vivo pathologic role of NaPi2a in regulating abnormal phosphate homeostasis in Fgf-23−/− mice by deletion of both NaPi2a and Fgf-23 genes in the same animal . The persistence of the skeletal anomalies in double mutants suggests that Fgf-23 affects bone mineralization independently of systemic phosphate homeostasis . Finally , our data support ( 1 ) that regulation of phosphate homeostasis is a systemic effect of Fgf-23 , while ( 2 ) skeletal mineralization and chondrocyte differentiation appear to be effects of Fgf-23 that are independent of phosphate homeostasis .
Maintaining physiological phosphate balance is essential , not only for skeletal mineralization but also for various important biological activities that include cellular signaling , and biochemical reactions [1] . Acute hypophosphatemia can cause myopathy , cardiac dysfunction , and hematological abnormalities , whereas chronic hypophosphatemia impairs bone mineralization , resulting in rickets and osteomalacia [2] . On the contrary , hyperphosphatemia is associated with vascular and soft tissue calcifications [3] . Understanding the molecular regulation of phosphate homeostasis has , therefore , enormous clinical and biological significance . The kidney is the major site of hormonal-dependent regulation of phosphate homeostasis , controlling urinary phosphate excretion according to the needs of the body [1] . Phosphate transport across the renal proximal tubular epithelial cells is a sodium-dependent process , driven by the gradient between extracellular and intracellular sodium concentrations , and such gradient is known to be maintained by the basolateral membrane–associated Na+/K+-ATPase [4] . The identification of distinct phosphate ( Pi ) transporters has increased our understanding of the mechanisms and regulation of renal and intestinal phosphate handling . The type II family of Na/Pi co-transporters consists of three highly homologous isoforms: type IIa ( NaPi2a ) and type IIc ( NaPi2c ) are almost exclusively expressed in the brush-border membrane of the renal proximal tubules [5]–[7] , whereas type IIb ( NaPi2b ) is expressed in the epithelial cells of the small intestine , and is thought to be involved in intestinal phosphate absorption . The NaPi2b co-transporter is not expressed in the kidney [8] . Since renal phosphate transport through NaPi2a is an important mechanism of maintaining phosphate balance , the molecules that directly or indirectly affect NaPi2a can influence phosphate homeostasis . The critical role of NaPi2a co-transporters in the maintenance of Pi homeostasis was demonstrated by genetic ablation of the murine NaPi2a gene by homologous recombination . Mice ablated for the NaPi2a gene ( NaPi2a−/− ) exhibit increased urinary phosphate excretion , resulting in hypophosphatemia [9] . Despite comparable serum levels of calcium , phosphate , PTH , and 1 , 25 ( OH ) 2D3 NaPi2a−/− mice exhibit a ricketic bone phenotype at 3 weeks of age; these mutant mice show a normal skeletal phenotype comparable to wild-type animals at 6 weeks , and have increased trabecular bone volume at 12 weeks of age [9] . FGF-23 has been shown to be an important regulator of renal Pi handling . FGF-23 inhibits renal phosphate reabsorption by suppressing the expression of NaPi2a and NaPi2c co-transporters [10] . We have recently generated fibroblast growth factor-23 null ( Fgf-23−/− ) mice which are characterized by severe hyperphosphatemia , and increased renal expression of NaPi2a [11] , [12] . In view of the fact that FGF-23 is a major regulator of phosphate homeostasis [13]–[16] , this study was designed to assess the pathophysiological significance of increased renal expression of NaPi2a in Fgf-23−/− mice . To test such significance , we have established a new mouse model by genetically ablating both Fgf-23 and NaPi2a genes in the same animal , in order to determine whether altered phosphate homeostasis in Fgf-23−/− mice is a NaPi2a-mediated process . In addition , using this model , we sought to examine phosphate-independent effects of Fgf-23 on skeletogenesis .
Heterozygous- Fgf-23 and NaPi2a mice were interbred to attain wild-type , Fgf-23−/− , NaPi2a−/− , and Fgf-23−/−/NaPi2a−/− animals at 3 and 6 weeks . Routine PCR was used to identify the genotypes of various mice as described previously [9] , [11] . All studies performed were approved by the institutional animal care and use committee at the Harvard Medical School . Bone mineral density ( BMD ) and bone mineral content ( BMC ) , were determined on 3- and 6-week-old wild-type , Fgf-23−/− , NaPi2a−/− and Fgf-23−/−/NaPi2a−/− mice using the PIXImus small animal dual-energy X-ray absorptiometry ( DEXA ) system ( Lunar ) , as described earlier [11]; BMD of the above genotypes was also measured by peripheral quantitative computerized tomography ( pQCT ) , as described previously [11] , [17] . The mineralization pattern of the skeleton was analyzed by Alizarin Red S staining in 6- week-old mice , as described by McLeod [18] . All tissues were fixed in 10% buffered formalin . Soft tissues were routinely processed and embedded in paraffin , cut into 4 µm-thick sections and stained with hematoxylin and eosin , and von Kossa . Processing of bone specimens and cancellous bone histomorphometry in the distal femoral metaphysis were performed as described [17] , [19] . The area within 0 . 25 mm from the growth plate was excluded from the measurements . Blood was obtained by cheek-pouch bleeding of 3- and 6-week-old animals . Total serum calcium , serum and urinary phosphorus , and serum and urinary creatinine levels were determined using Stanbio LiquiColor ( Arsenazo III ) , Stanbio LiquiUV , and Stanbio Creatinine kits ( Stanbio Laboratory , Boerne , TX ) , respectively . Serum PTH levels were measured using a two-sided enzyme-linked immunosorbent assay ( ELISA ) specific for intact mouse PTH ( Immunotopics , San Clemente , CA , USA ) . Serum concentrations of 1 , 25 ( OH ) 2D3 were measured using a radioreceptor assay ( Immundiagnostik , Bensheim , Germany ) . Renal tubular reabsorption of phosphorus ( TRP ) was calculated according to the formula: %TRP = [1− ( UrP×SeCrea ) / ( SeP×UrCrea ) ]×100 ( Ur , urinary; Se , serum; P , phosphorus; Crea , creatinine ) . Wild-type , Fgf-23−/− , and NaPi2a−/− mice at 4-weeks of age , were injected subcutaneously with vehicle ( saline ) , PTH peptide ( 1–34 ) , or PTH peptide ( 3–34 ) ( 50 nmol of peptide per Kg of body weight ) . Blood was collected by cheek-pouch bleeding prior to injections , as well as 2 hours post-injections , and serum phosphate levels were measured using Stanbio LiquiUV kit ( Stanbio Laboratory , Boerne , TX ) . Complementary 35S-UTP-labeled riboprobes ( complementary RNAs for collagen type X ( Col X ) , dentin matrix protein-1 ( DMP-1 ) , and osteopontin ( OPN ) ) were used for performing in situ hybridization on paraffin sections , as described previously [20] . Fresh kidney cortex was isolated from 3 week old mice , and homogenized in HbA buffer ( pH 7 . 4 ) containing 20 mM Tris base , 5 mM MgCl2 , 5 mM Na2HPO4 , 1 mM EDTA and 80 mM sucrose and protease inhibitor cocktail tablets ( Complete Mini , EDTA-free; Roche ) . Protein concentration was determined by performing BCA protein assay ( Pierce ) , using BSA as a standard . Protein samples were heated at 95°C for 5 min in sample buffer containing 2% SDS and 1% 2-mercaptoethanol , and were subjected to 10% SDS-polyacrylamide gel electrophoresis . The separated proteins in the gel were transferred electrophoretically to Hybond-P polyvinylidenedifluoride transfer membranes . After incubation in blocking solution , the membranes were further treated with diluted rabbit affinity-purified anti-type 2c NaPi co-transporter antibody ( 1∶500 ) , a generous gift of Dr Ken-ichi Miyamoto , Japan . Mouse anti-actin monoclonal antibody ( SIGMA ) was used as an internal control . Horseradish peroxidase-conjugated anti-rabbit or anti-mouse IgG was utilised as the secondary antibody ( Jackson ImmunoResearch Laboratories ) , and signals were detected by the SuperSignal West Pico Chemiluminescent Substrate system ( Pierce ) . Mouse calvarial cell culture was carried out as previously described [21] with modifications . Briefly , mouse calvarial cells were isolated from 3–5 day old C57BL/6J wild-type mice . Calvariae ( parietal bones ) were removed aseptically , and they were sequentially digested with 2 mg/ml collagenase solution containing collagenase type I and type II in 1∶3 ratio . ( collagenase type I and type II; Worthington , Newark , NJ ) . Osteoblast enriched fractions ( the last four of six fractions ) were cultured for five to seven days until confluence in α-MEM supplemented with 10% FBS and 1% Penicillin-Streptomycin ( Invitrogen Life Technologies , Baltimore , MD ) . Adherent cells were trypsinized and re-plated at a density of 2 . 5×104/cm2 in the same medium supplemented additionally with 50 µg/ml ascorbic acid and 10 mM β-glycerophosphate ( βGP ) to induce matrix mineralization with or without treatment with 10 ng/ml of human FGF-23 ( hFGF-23 ) . Alizarin Red S staining was performed 21 days after subculture in mineralization medium with or without FGF-23 treatment . Statistically significant differences between groups were evaluated by Student's t-test for comparison between two groups or by one-way analysis of variance ( ANOVA ) followed by Tukey's test for multiple comparisons . All values were expressed as mean ±SE . A p value of <0 . 05 was considered to be statistically significant . All analyses were performed using Microsoft Excel and GraphPad Prism 4 . 0 .
In vivo ablation of Fgf-23 results in significantly elevated serum phosphate levels accompanied by enhanced renal phosphate reabsorption and a significant increase in expression and activity of NaPi2a [11] , [22] , [23] . To test the hypothesis that increased NaPi2a activity is responsible for the severe hyperphosphatemia in Fgf-23−/− animals , we generated a new mouse model deficient in both the Fgf-23 and the NaPi2a genes ( Fgf-23−/−/NaPi2a−/− compound mutants ) by interbreeding heterozygous- Fgf-23 and NaPi2a mice . The mice studied were of C57BL/6J genetic background and animals examined were littermates . Compound mutants were viable and were born at the expected Mendelian frequency . In the current study , we compared and analyzed gross phenotypes , and obtained morphological and biochemical data from wild-type , Fgf-23−/− , Fgf-23−/−/NaPi2a−/− , and NaPi2a−/− animals . At birth , Fgf-23−/−/NaPi2a−/− mice appear indistinguishable from their normal littermates . At 3 weeks Fgf-23−/−/NaPi2a−/− compound mutants are larger in size than Fgf-23−/− mice ( 8 . 6±1 . 7 g vs 6 . 8±0 . 38 g ) , but are slightly smaller than wild-type ( 10 . 9±0 . 2 g ) , and similar to NaPi2a−/− single knock-out animals ( 7 . 9±1 . 1 g ) . At 6 and 12 weeks of age , compound mutants are still smaller than wild-type littermates ( 12 . 2±0 . 7 g vs 20 . 7±0 . 2 g at 6 weeks ) , but their body weight is significantly higher than that of Fgf-23−/− mice ( 6 . 5±0 . 2 g ) ( Figure 1A and B ) . Apart from the slightly reduced body size , double mutants do not show any obvious gross abnormalities with regard to movement and physical activities , whereas Fgf-23−/− littermates have severely weakened and restricted movement , as well as sluggish physical activities . In addition , Fgf-23−/−/NaPi2a−/− survive longer than Fgf-23−/− mice ( Figure 1C ) . To evaluate the effects of NaPi2a gene ablation on the skeleton of Fgf-23−/− animals , bone densitometric measurements of hind limbs from 3- and 6-week old control , Fgf-23−/− , Fgf-23−/−/NapI2a−/− , and NaPi2a−/− littermates were carried out using PIXImus and pQCT ( Figure 2 ) . PIXImus analysis showed significantly increased total body bone mineral content ( BMC ) in Fgf-23−/− mice when compared to wild-type controls at both ages ( 0 . 016±0 . 002 vs . 0 . 012±0 . 0007 at 3 weeks and 0 . 048±0 . 006 vs . 0 . 016±0 . 0003 at 6 weeks ) ( Figure 2A ) . In contrast , the BMC of Fgf-23−/−/NaPi2a−/− compound mutants was similar to control littermates at 3 weeks ( 0 . 012±0 . 002 ) but it was significantly elevated at 6 weeks ( 0 . 0225±0 . 001 ) , although it was significantly lower when compared to Fgf-23−/− mice ( Figure 2A ) . In accordance with previous reports [9] , the total body BMC of NaPi2a−/− mice at both ages was comparable to wild-type animals ( Figure 2A ) . Bone densitometric measurements using PIXImus and pQCT demonstrated decreased areal and volumetric bone mineral density ( BMD ) in the hindlimbs and in the distal femoral metaphysis of Fgf-23−/− mutant mice at both 3 and 6 weeks of age ( Figure 2B and C ) . The bones of NaPi2a−/− single knock-out mice demonstrated a significantly reduced BMD at 3 weeks , which was nearly normalized by 6 weeks ( Figure 2B and C ) , in accord with earlier published observations [9] . Areal and volumetric BMD of Fgf-23−/−/NaPi2a−/− compound mutants was not significantly different from that of control littermates at 3 weeks ( Figure 2B and C ) . However , at 6 weeks , volumetric BMD was significantly higher in compound mutants compared with Fgf-23−/− mice , but still lower than in wild-type controls ( Figure 2C ) . Phosphate , calcium , 1 , 25 ( OH ) 2D3 and parathyroid hormone ( PTH ) levels were measured in 3- and 6-week-old wild-type , Fgf-23−/− , Fgf-23−/−/NaPi2a−/− , and NaPi2a−/− animals . Fgf-23−/− mice were severely hyperphosphatemic at both 3 and 6 weeks of age ( 15 . 9±0 . 8 and 14 . 1±0 . 2 mg/dl , respectively ) when compared to control littermates ( 9 . 6±0 . 1 and 8 . 8±0 . 4 mg/dl , respectively ) . However , Fgf-23−/−/NaPi2a−/− animals were normophosphatemic at 3 weeks ( 8 . 8±0 . 6 mg/dl ) , and became hypophosphatemic with significantly lower serum phosphate levels ( 5 . 2±0 . 6 mg/dl ) by 6 weeks , comparable to those found in NaPi2a−/− animals of the same age ( 5 . 3±0 . 1 mg/dl ) ( Figure 3A ) . More importantly , decreased urinary phosphate excretion ( normalized to urinary creatinine ) in Fgf-23−/− mice ( 2 . 4±0 . 1 vs 4 . 4±0 . 2 in control littermates at 3 weeks and 2 . 5±0 . 3 vs 5 . 4±0 . 7 at 6 weeks ) was reversed in Fgf-23−/−/NapI2a−/− double mutant animals . Compound mutants showed hyperphosphaturia ( 5 . 2±0 . 1 at 3 weeks and 6 . 8±1 . 6 at 6 weeks ) , similar to the one found in NaPi2a−/− mice ( 6 . 8±0 . 3 and 5 . 3±0 . 7 respectively ) ( Figure 3D ) . In addition , Fgf-23−/−/NaPi2a−/− animals had reduced fractional renal tubular reabsorption of phosphate ( TRP ) ( 51 . 9±12 . 4 and 50 . 4±9 . 6 % at 3 and 6 weeks respectively ) when compared to Fgf-23−/− mice ( 90 . 5±3 . 2 and 80 . 4±0 . 1 % ) and wild-type littermates ( 82 . 4±6 . 8 and 67 . 9±14 . 3 % ) ( Figure 3E ) . Collectively , these results suggest that increased renal phosphate reabsorption due to increased NaPi2a activity is the major cause for abnormal hyperphosphatemia in Fgf-23−/− mice . Serum calcium levels were found to be higher in all three mutant mouse lines at 3 weeks of age . At 6 weeks , the calcium levels in NaPi2a−/− ( 11 . 1±0 . 05 mg/dl ) and Fgf-23−/−/NapI2a−/− compound mutants ( 15 . 7±1 . 3 mg/dl ) were significantly higher than in Fgf-23−/− mice ( 10 . 1±0 . 2 mg/dl ) ( Figure 3B ) . The considerable elevation in serum calcium levels in all mutants is probably due to excessive vitamin D signaling , as reflected by the significantly increased serum 1 , 25 ( OH ) 2D3 levels in these mice ( Figure 3F ) . Probably as a result of high serum 1 , 25 ( OH ) 2D3 and concomitant hypercalcemia , serum PTH was undetectable in all three mutant lines ( Figure 3G ) . The calcium-phosphate product was severely increased in 3- and 6-week-old Fgf-23−/− mice relative to wild-type controls ( Figure 3C ) . Compound mutants showed only a slight increase in the calcium-phosphate product at 3 weeks , but did not exhibit any significant difference from wild type animals at 6 weeks of age , whereas the calcium-phosphate product was significantly reduced in Napi2a−/− mice ( Figure 3C ) . Injection of bioactive PTH peptide ( 1–34 ) significantly lowered serum phosphate levels in wild-type and Fgf-23−/− treated mice , but did not reduce the serum phosphate concentration in NaPi 2a−/− mice ( Figure 3F ) . Injection of vehicle ( saline ) or inactive PTH peptide ( 3–34 ) had no effect on serum phosphate levels . To examine the mineralization pattern of the bones , Alizarin Red S staining was performed on full body skeletons of 6-week-old Fgf-23−/−/NaPi2a−/− mutants and was compared to wild-type , Fgf-23−/− , and NaPi2a−/− animals . The skeletal phenotype of Fgf-23−/−/NaPi2a−/− compound mutants resembled the one seen in Fgf-23−/− animals with typically widened ribs , whereas bones from NaPi2a−/− mutant mice were comparable to wild-type mice ( Figure 4A ) . In agreement with the bone densitometric data , histological analysis of methylmethacrylate sections from femurs showed almost normal bone architecture in Fgf-23−/−/NaPi2a−/− double mutants at 3 weeks ( Figure 4B ) . In contrast , the histological bone phenotype of Fgf-23−/−/NaPi2a−/− double mutants closely resembled that of Fgf-23−/− mice at 6 weeks ( Figure 4C ) . The bones of 6-week old Fgf-23−/− and Fgf-23−/−/NaPi2a−/− mice exhibited a decreased number of hypertrophic chondrocytes , hypermineralization adjacent to the growth plate in the primary spongiosa , and severe osteoidosis in the secondary spongiosa ( Figure 4B ) . Bones from NaPi2a−/− mice at 3 and 6 weeks appeared normal at the histological level ( Figure 4B ) . Quantitative histomorphometry ( Table 1 ) revealed a striking increase in osteoid volume and osteoid thickness in Fgf-23−/− mice at 3 and 6 weeks of age . Interestingly , osteoid thickness was normal in 3-week-old compound mutants and NaPi2a−/− animals , although osteoid volume and surface was increased in NaPi2a−/− mice relative to wild-type controls . Similar to the histological appearance of the bones , histomorphometry confirmed the severe mineralization defect in Fgf-23−/− mice and Fgf-23−/−/NaPi2a−/− compound mutants at 6 weeks of age , as evidenced by similar increases in osteoid volume and thickness relative to wild-type mice . Six-week-old NaPi2a−/− mice had normal osteoid thickness and osteoid volume . Collectively these data demonstrate that the defect in bone mineralization seen in hyperphosphatemic Fgf-23 mutants is also present in 6-week-old hypophosphatemic Fgf-23−/−/NaPi2a−/− mice despite the opposite serum phosphate levels . Thus , the mineralization defect in Fgf-23−/− mutants and Fgf-23−/−/NaPi2a−/− compound mutants appears to be due to lack of Fgf-23 gene expression rather than systemic phosphate homeostasis . Moreover , NaPi2a−/− littermates which completely resemble the serum biochemistry of Fgf-23−/−/NaPi2a−/− animals , did not exhibit any defects in bone mineralization at 6 weeks of age . To analyze the gene expression pattern of bone cells and to examine the effect of Fgf-23 and NaPi2a gene deletion on skeletogenesis , we performed in situ hybridization on paraffin sections prepared from tibias of wild-type , Fgf-23−/− , Fgf-23−/−/NaPi2a−/− , and NaPi2a−/− animals at 3 and 6 weeks of age ( Figure 5 ) . Similar to our previous findings [11] , [23] , the number of hypertrophic chondrocytes was reduced in Fgf-23−/− animals at both ages , relative to control mice , as demonstrated by the marked decrease in collagen type X expression ( Figure 5A ) . Similarly , Fgf-23−/−/NaPi2a−/− compound mutants also showed a significant reduction in the number of hypertrophic chondrocytes at 6 weeks , comparable to Fgf-23−/− animals , although collagen type X expression at 3 weeks was normal in these mice ( Figure 5A ) . In contrast , we noted a marked increase of collagen type X-positive cells in NaPi2a−/− mice , especially at 3 weeks of age ( Figure 5A ) . Furthermore , we examined expression of osteopontin ( OPN ) and dentin matrix protein ( DMP-1 ) , two members of the SIBLING protein family that exert key biological effects in skeletal mineralization . An association between FGF23 and DMP-1 has been suggested in earlier studies [24] . For instance , increased serum FGF-23 levels were found in patients with autosomal recessive hypophosphatemic rickets ( ARHR ) , a disease caused by mutation in DMP-1 gene [24] . Similarly , in Dmp-1 null mice Fgf-23 levels were high [25] . In our study , lack of Fgf-23 resulted in increased expression of DMP-1 and OPN in Fgf-23−/− and Fgf-23−/−/NaPi2a−/− compound mutants at both 3 and 6 weeks . In contrast , NaPi2a−/− animals exhibited normal DMP-1 and OPN at 3 weeks , however , the expression of both of these genes appeared to be decreased in NaPi2a−/− animals at 6 weeks , compared to wild-type controls ( Figure 5B ) . Histological examination of various soft tissues from Fgf-23−/−/ NaPi2a−/− double mutants showed that abnormalities such as intestinal atrophy and lung emphysema that are consistently observed in single Fgf-23−/− animals , were ameliorated , but not completely abolished in Fgf-23−/−/NaPi2a−/− double mutants , suggesting that these soft tissue pathological changes are partially caused by the severely increased serum phosphate levels in Fgf-23−/− mice ( Figure 6 ) . To evaluate the effect of FGF-23 on mineralization we cultured osteoblastic cells isolated from C57BL/6J wild-type calvariae in mineralization medium alone ( vehicle ) or mineralization medium containing hFGF-23 protein . Alizarin Red S staining was carried out after 21 days . A marked decrease in mineralized bone nodule formation was evident in cells treated with hFGF-23 when compared to vehicle treated cells ( Figure 7 ) . These data suggest that excess of FGF-23 in osteoblast cultures leads to an impairment of mineralization in vitro .
This is the first study using a genetic mouse model with dual ablation of the NaPi2a and Fgf-23 genes . Fgf-23−/− mice develop severe hypercalcemia , hyperphosphatemia , hypervitaminosis D , and osteomalacia starting in early life [11] , [22] . The hyperphosphatemia in Fgf-23−/− mice is associated with increased renal phosphate uptake , and increased expression of the renal Na/Pi2a co-transporter in the proximal tubular epithelial cells [26] . In our study , ablation of both NaPi2a co-transporter and Fgf-23 in the same animal resulted in reduced serum phosphate levels which were accompanied by increased urinary phosphate excretion in Fgf-23−/−/NaPi2a−/− mice , reemphasizing the fact that increased NaPi2a activity in the renal proximal tubular epithelial cells is responsible for the severe hyperphosphatemia in Fgf-23−/− mice . These results provide compelling genetic evidence of the importance of NaPi2a in regulating renal phosphate homeostasis in Fgf-23−/− mice . Deletion of NaPi2a from these animals and accompanied changes in serum phosphate levels significantly improved the abnormal phenotype associated with lack of Fgf-23 activities , indicating that the high phosphate microenvironment contributes to the development of widespread soft tissue atrophy and calcifications in Fgf-23−/− mice . Similar observations were made in recent studies in which the increased vitamin D signaling in Fgf-23−/− mice was blocked by additionally ablating the renal 1α-hydroxylase or the vitamin D receptor [23] , [27] , or in which Fgf-23−/− mice were fed a low phosphate diet [3] . In addition , our study suggests that hypervitaminosis D is toxic when associated with an increased calcium-phosphate product . The mechanisms behind the upregulation of NaPi2a expression and activity in Fgf-23−/− mice are still poorly understood . The retrieval and recruitment of NaPi2a proteins is a complex multi-factorial process , and the in vivo interactions between FGF-23 , vitamin D , PTH , and NaPi2a transporters need additional studies for comprehensive understanding . Earlier studies have shown that administration of FGF-23 increases urinary phosphate excretion by suppressing renal expression of sodium-phosphate co-transporters [28] . Therefore , the upregulation in NaPi2a protein in Fgf-23−/− mice may be a direct effect of Fgf-23 ablation . On the other hand , high 1 , 25 ( OH ) 2D3 and suppressed PTH in Fgf-23−/− mice could also be involved . PTH is a powerful inhibitor of renal phosphate reabsorption by facilitating endocytosis of the NaPi2a transporters from the brush-border membrane of proximal tubular epithelial cells for eventual lysosomal degradation [29] , [30] . To test the hypothesis that suppressed serum PTH in Fgf-23−/− mice could diminish or delay the endocytosis of the NaPi2a transporters from the proximal tubular epithelial cells , we injected vehicle ( saline ) , PTH ( 1–34 ) and PTH ( 3–34 ) into Fgf-23−/− and NaPi2a−/− mice . We found that a single injection of bioactive PTH ( 1–34 ) can significantly reduce serum phosphate levels in wild-type and Fgf-23−/− mice ( Figure 3H ) . In contrast , no effect of PTH ( 1–34 ) injection on serum phosphate levels was noted in NaPi2a−/− mice emphasizing that Napi2a is the dominant sodium phosphate co-transporter in the renal proximal tubule cells and responsible for the severe hyperphosphatemia in Fgf-23−/− mice . As expected , injections of vehicle or inactive PTH ( 3–34 ) did not have any effect on serum phosphate levels in all mice examined . NaPi2c , another sodium phosphate co-transporter in the renal proximal tubule cells was upregulated in Fgf-23−/− , Fgf-23−/−/NaPi2a−/− , and NaPi2a−/− mice when compared to wild type littermates ( Figure S1 ) . From these results we conclude that 1 ) reduced level of PTH in Fgf-23−/− mice could contribute to the upregulation of NaPi2a expression in these mice and thereby to the development of hyperphosphatemia , and 2 ) compensatory increased expression of NaPi2c cannot efficiently restore the effects of NaPi2a loss . The main source of Fgf-23 production has been shown to be the osteocyte [23] , [31] . We , therefore , analyzed the skeleton of Fgf-23−/−/NaPi2a−/− compound mutants , in which serum phosphate levels were reversed to hypophosphatemia . Surprisingly , skeletal abnormalities observed in Fgf-23−/− mice including the decrease in hypertrophic chondrocytes in the growth plate , the increased mineral deposition adjacent to the growth plate , and the osteomalacic phenotype were found to be similar in 6-week-old Fgf-23−/−/NaPi2a−/− compound mutants , despite their significantly reduced serum phosphate levels . Furthermore , our data conclusively show that the osteomalacic phenotype in Fgf-23−/− and Fgf-23−/−/NaPi2a−/− compound mutants at 6 weeks of age is not caused by changes in serum phosphate levels . Rather , our findings suggest that the increased 1 , 25 ( OH ) 2D3 serum levels , possibly in combination with elevated serum calcium-phosphate levels , cause osteomalacia in Fgf-23−/− mice . In line with this notion , studies have convincingly demonstrated that rats treated with high doses of 1 , 25 ( OH ) 2D3 have impaired bone mineralization [32] , [33] . A recent study has demonstrated that NaPi2a is expressed in mouse MC3T3-E1 and rat UMR-106 osteoblast-like cells and its expression is regulated by phosphate [34] , supporting a role of NaPi2a in mediating phosphate transport in osteoblasts . Therefore ablation of NaPi2a could affect bone mineralization . However , although both NaPi2a−/− and Fgf-23−/−/NaPi2a−/− compound mutants lack NaPi2a and have similar biochemical parameters , they exhibit a different skeletal phenotype . One obvious difference between these two mouse models however is the lack of Fgf-23 expression , suggesting that Fgf-23 has a significant role in bone mineralization . This hypothesis is strengthened by in vitro studies by Wang et al in which they show that adenoviral overexpression of FGF-23 in rat calvarial cells inhibits bone mineralization independent of systemic effects on phosphate homeostasis [35] . In addition , we have pursued ex vivo-in vitro studies by isolating and culturing mouse calvarial osteoblasts from wild-type mice and exposing them to FGF-23 treatment . Our data demonstrate that FGF-23 treatment of primary calvarial osteoblasts from wild-type mice leads to an inhibition of mineralization as shown by the decrease in Alizarin staining ( Figure 7 ) . Moreover , we could confirm the previously published data which show a reduction in mineralization using osteoblasts isolated from Hyp mice , which produce high levels of Fgf-23 , again emphasizing that FGF-23 is a potent inhibitor of mineralization [36 , 37 and data not shown] . Taken together , these results suggest that excess of FGF-23 can negatively regulate bone mineralization . However , the mechanism responsible for the effect of FGF-23 on bone mineralization , as well as the role of Klotho , if any , in the Fgf-23-specific signaling in osteoblasts in vivo remain to be determined . The expression pattern of the two sibling proteins , OPN and DMP-1 in bones of wild type , Fgf-23−/− and Fgf-23−/−/NaPi2a−/− and NaPi2a−/− at 3 and 6 weeks of age demonstrated phosphate independent effect of Fgf-23 on bone . Previous in vitro studies using wild-type murine cementoblasts , have shown phosphate-dependent regulation of DMP-1 and OPN [38] . Interestingly , however , we have found that expression of DMP-1 and OPN in bones from Fgf-23−/− and Fgf-23−/−/NaPi2a−/− compound mutants is significantly upregulated at 3 and 6 weeks of age . Thus , in the absence of Fgf-23 activity , increased expression of DMP-1 and OPN appears to be independent of circulating phosphate levels and might , therefore , be partly mediated through direct effects of Fgf-23 on these SIBLING genes , but such a hypothesis needs to be further investigated . In summary , the phenotype of Fgf-23−/−/NaPi2a−/− compound mutants demonstrates that 1 ) increased NaPi2a activity is the main cause for the severe hyperphosphatemia observed in Fgf-23−/− mice , 2 ) that the mineralization defect and the growth plate changes in Fgf-23−/− and Fgf-23−/−/NaPi2a−/− compound mutants at 6 weeks of age are partly due to lack of Fgf-23 function rather than systemic phosphate homeostasis , and 3 ) that the altered expression of the sibling proteins OPN and DMP1 in bone is independent of serum phosphate levels in mice ablated for Fgf-23 . Genetic ablation of NaPi2a from Fgf-23−/− mice reversed the hyperphosphatemia to hypophosphatemia , and partially improved the soft tissue calcifications and atrophy . Analysis of the bones from Fgf-23−/−/NaPi2a−/− compound mutants revealed that the osteomalacic bone phenotype in mice lacking Fgf-23 is not always associated with serum phosphate levels . Further analyses are needed to determine the detailed molecular interactions of Fgf-23 with genes responsible for skeletal mineralization . | Regulation of phosphate homeostasis is a tightly controlled hormonal process involving the intestine , kidneys , and bone , and imbalance of this homeostasis may influence overall mineralization . Fibroblast growth factor-23 ( FGF-23 ) is a circulating hormone produced in the bone that mainly targets the kidneys to control the activity of the sodium/phosphate co-transporters NaPi2a and NaPi2c . These transporters are responsible for actively reabsorbing phosphate ions into the body to maintain physiological serum phosphate levels . Changes in FGF-23 activity lead to human disorders associated with either phosphate wasting or retention . Genetically altered mice in which Fgf-23 activity is lost exhibit severe hyperphosphatemia accompanied by increased NaPi2a activity , and they develop abnormal bone mineralization . Here we describe a new mouse model in which we eliminated NaPi2a from Fgf-23 null mice and show reversal of hyperphosphatemia to hypophosphatemia , suggesting that NaPi2a is the major regulator of phosphate homeostasis . However , the skeletal mineralization defect observed in mice lacking Fgf-23 function remained unchanged in the absence of NaPi2a in these mice . Thus our data indicate that Fgf-23 has a role in controlling bone mineralization independent of systemic phosphate levels . | [
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] | 2008 | Genetic Evidence of Serum Phosphate-Independent Functions of FGF-23 on Bone |
The cell wall is a vital and multi-functional part of bacterial cells . For Staphylococcus aureus , an important human bacterial pathogen , surface proteins and cell wall polymers are essential for adhesion , colonization and during the infection process . One such cell wall polymer , lipoteichoic acid ( LTA ) , is crucial for normal bacterial growth and cell division . Upon depletion of this polymer bacteria increase in size and a misplacement of division septa and eventual cell lysis is observed . In this work , we describe the isolation and characterization of LTA-deficient S . aureus suppressor strains that regained the ability to grow almost normally in the absence of this cell wall polymer . Using a whole genome sequencing approach , compensatory mutations were identified and revealed that mutations within one gene , gdpP ( GGDEF domain protein containing phosphodiesterase ) , allow both laboratory and clinical isolates of S . aureus to grow without LTA . It was determined that GdpP has phosphodiesterase activity in vitro and uses the cyclic dinucleotide c-di-AMP as a substrate . Furthermore , we show for the first time that c-di-AMP is produced in S . aureus presumably by the S . aureus DacA protein , which has diadenylate cyclase activity . We also demonstrate that GdpP functions in vivo as a c-di-AMP-specific phosphodiesterase , as intracellular c-di-AMP levels increase drastically in gdpP deletion strains and in an LTA-deficient suppressor strain . An increased amount of cross-linked peptidoglycan was observed in the gdpP mutant strain , a cell wall alteration that could help bacteria compensate for the lack of LTA . Lastly , microscopic analysis of wild-type and gdpP mutant strains revealed a 13–22% reduction in the cell size of bacteria with increased c-di-AMP levels . Taken together , these data suggest a function for this novel secondary messenger in controlling cell size of S . aureus and in helping bacteria to cope with extreme membrane and cell wall stress .
Staphylococcus aureus is a very prevalent human pathogen that permanently colonizes the nares and skin of approximately 20% of the population , while another 60% are colonized transiently [1] . Infections caused by this pathogen are becoming increasingly more difficult to treat due to its resistance to antibiotic therapy . Where once methicillin was the antibiotic of choice , now only around 60% of S . aureus isolates remain sensitive to this drug . There has also been a rise in the number of community acquired methicillin resistant S . aureus ( CA-MRSA ) cases in recent years often resulting in severe skin and soft tissue infections as well as invasive diseases such as sepsis , necrotizing pneumonia or osteomyelitis [2] , [3] . The ability of S . aureus to cause such a wide range of diseases depends on many factors and is , in part , due to the diverse functions that are linked to its cell envelope . A myriad of proteins are embedded in this structure that allow bacteria to take up nutrients and adhere to diverse surfaces or niches within the human host . It also protects bacteria from environmental insults and at the same time allows the cells to sense and respond to changes in their surroundings , a function crucial for the survival of this pathogen in the host . In addition , the cell wall helps bacteria to maintain their shape and functions to counteract the high internal turgor pressure . Because the cell envelope has such essential functions , it also forms a weak point of the cell , as the inhibition of enzymes required for its synthesis is often lethal or leads to virulence defects . Therefore , this structure has been , and remains , an attractive target for therapeutic interventions . A typical cell wall of Gram-positive bacteria consists of proteins , peptidoglycan ( PG ) and the cell wall polymers wall teichoic acid ( WTA ) , which is covalently linked to PG , and lipoteichoic acid ( LTA ) , a polymer anchored to the outside of the bacterial membrane via a lipid moiety [4] , [5] , [6] . Synthesis of these cell wall components is highly coordinated and any mistakes can lead to cell lysis and death . From studies on the Gram-positive model organism Bacillus subtilis , it has emerged that PG and WTA synthesis enzymes form multi-protein complexes , which are further linked in this organism with cytoplasmic cell shape determining proteins , thereby coordinating and physically linking extracellular and intracellular synthesis processes [7] , [8] , [9] , [10] . S . aureus LTA is an anionic cell wall polymer consisting of a linear chain of glycerolphosphate repeating units that is anchored via a glycolipid to the membrane [11] . The glycerolphosphate subunits are derived from the head group of the membrane lipid phosphatidylglycerol and polymerized on the outside of the cell by the membrane-linked lipoteichoic synthase enzyme LtaS to form the backbone chain [12] , [13] , [14] , [15] . A large fraction of the glycerolphosphate subunits are substituted with D-alanine residues , a modification known to play a key role in the resistance of S . aureus and several other Gram-positive pathogens to cationic antimicrobial peptides [16] , [17] , [18] , [19] . A function for this polymer in the formation of biofilms has been identified and multiple interactions between LTA and eukaryotic cells have been described [20] , [21] . An interaction between LTA and macrophage scavenger receptors is thought to occur and help the host to clear bacterial infections [22] , [23] . In agreement with this suggestion , scavenger receptor knockout mice are more susceptible to infection with S . aureus [24] . This polymer has also been shown to act as a ligand for Draper , a phagocytic receptor in Drosophila , which upon binding of LTA results in the phagocytosis of S . aureus by Drosophila hemocytes [25] . On the other hand , LTA is also thought to act as an anti-inflammatory molecule on skin cells by suppressing the TLR-3-mediated responses upon skin injury , a key pathway in the induction of inflammation [26] . Recently defined mutants lacking the entire LTA polymer have been constructed and phenotypic analysis indicated an important role for this polymer for normal bacterial growth and morphology [13] , [27] , [28] , [29] , [30] . LTA-deficient S . aureus strains have severely impaired growth and can initially only be propagated in medium containing high salt or sucrose concentrations , which are thought to act as osmoprotectants , or at low temperature [13] , [27] . Even under conditions permissive for growth , these cells have severe morphological defects , such as an increased cell size and the tendency to clump . In addition , misplacement of cell division septa is observed , highlighting that the lack of LTA on the outside of the cell negatively affects fundamental processes in the cytoplasm of the cell . However , it is currently not known how these processes are coordinated . In this study we set out to investigate if S . aureus can find a way to survive without LTA and identified LTA-deficient suppressor strains that can grow and divide almost normally in the absence of this multi-functional cell wall polymer . Using a whole genome sequencing approach , it was determined that these strains have acquired mutations in a gene encoding a protein named GdpP ( for GGDEF domain protein containing phosphodiesterase ) . We show that as a consequence of this mutation , the intracellular levels of the novel cyclic dinucleotide c-di-AMP increase drastically in the suppressor strain and gdpP mutant laboratory or CA-MRSA strains . This provides the first experimental evidence that c-di-AMP is produced in S . aureus and that GdpP functions as a c-di-AMP phosphodiesterase in vivo . With this study we provide information on one of the first functions of this novel secondary messenger , which is in helping bacteria to cope with extreme cell wall stress in addition to controlling the cell size of S . aureus , as revealed by microscopic analysis .
In order to determine whether it is possible for S . aureus to compensate for the cell wall stresses introduced by deleting LTA , the RN4220-derived ltaS deletion strains SEJ1ΔltaSN and SEJ1ΔltaSS were created and the lack of LTA confirmed by western blot ( Figure S1A in Text S1 , data not shown ) . These strains were constructed under high osmotic conditions in medium containing 7 . 5% NaCl ( N ) or 40% sucrose ( S ) that are , as previously shown , permissive for growth of S . aureus in the absence of LTA ( Figure S1B in Text S1 ) [27] . However , in contrast to this previous study [27] , growth of our ltaS deletion strains were dependent on osmoprotectants at both 30°C and 37°C . Plating efficiencies for the ΔltaSN strain decreased from 3 . 5×108 CFU/ml on 7 . 5% NaCl containing TSA plates to 8×101 CFU/ml on TSA plates at 37°C and similar low CFUs were obtained at 30°C . SEJ1ΔltaS bacteria had aberrant cell morphologies even under conditions permissive for growth and displayed an enlarged cell size , a tendency to cluster and a misplacement of division sites ( Figure 1C ) [13] , [27] . In summary , our LTA-negative S . aureus RN4220 strains are viable under osmotically stabilizing conditions but not in TSB medium and display the expected morphological and cell division defects . When SEJ1ΔltaS strains were plated on TSA plates without the addition of sucrose or salt a small number of colonies were obtained . We hypothesized that these colonies arose from bacteria that had acquired compensatory mutation ( s ) that allow bacteria to grow in the absence of LTA . To analyze this further , five independently isolated suppressor colonies were passed four times in TSB to improve growth . As expected , LTA was still absent in the five suppressor strains 4S4 , 4S5 , 4N1 , 4N2 and 5S4 ( Figure 1A ) , however growth of these strains now more closely resembled that of the parental strain SEJ1 ( Figure 1B ) . Examination of the five suppressor strains by phase contrast and fluorescence microscopy revealed a near WT cell size and a considerable improvement in the accuracy of division site placement , although misplacement of septa still occurred in some cells ( Figure 1C ) . To further investigate the cell wall properties of these LTA-negative suppressor strains additional assays were performed . The suppressor strains were two to four-fold more susceptible to lysostaphin , nisin , vancomycin , oxacillin , penicillin G and daptomycin ( Table S1 in Text S1 ) . In addition , the suppressor strains lysed faster than the control strain in autolysis assays ( Figure S2A in Text S1 ) but had slightly reduced amounts of cell wall-associated hydrolytic enzymes as determined by zymogram assays ( Figure S2B in Text S1 ) . LTA production was restored in two suppressor strains , 4S5 and 5S4 , by introducing the complementation vector pCN34-ltaS . The amount of cell-associated hydrolytic enzymes increased in these strains ( Figure S2C in Text S1 ) , suggesting that LTA has a role in regulating autolysin levels . This is supported by the observations of Oku et al [27] who noted an even greater reduction in the amount of autolysins for their ltaS mutant strain , which could again be complemented . Taken together , S . aureus suppressor strains that can grow in the absence of LTA can be isolated readily and the morphology and cell division pattern defects are significantly improved in these strains . However , differences in autolysis and susceptibility to cell wall active antibiotics are indicative of remaining changes in the cell wall properties of these strains . It seems likely that the LTA-negative S . aureus suppressor strains 4S4 , 4S5 , 4N1 , 4N2 and 5S4 have acquired mutations elsewhere on the chromosome that allow for improved cell division and growth . A whole genome sequencing approach was used to identify such sequence alterations ( for details see materials and methods section ) . In total , ten genes within these five strains contained mutations with high confidence scores . Five of these mutations were discarded as they were also present in strain SEJ1ΔltaS pCN34-ltaS , an intermediate strain used for the construction of the ltaS deletion strains that still contains an intact copy of the ltaS gene . It is likely that these mutations were introduced during the temperature shift necessary to create the ltaS deletion . The mutations in the remaining five genes were at different positions and consisted of nonsense mutations , amino acid substitutions or DNA inversions ( Table 1 ) . The five genes included SAOUHSC_00015 , a conserved hypothetical protein with putative diguanylate cyclase and phosphoesterase activity and the only gene mutated in all five suppressor strains; SAOUHSC_01104 , encoding for the succinate dehydrogenase SdhA , a TCA cycle enzyme; SAOUHSC_01358 , which encodes for a putative permease; SAOUHSC_02001 , a conserved hypothetical protein with weak homology to a fusaric acid transporter , and SAOUHSC_02407 , a conserved hypothetical protein with homology to DisA , a DNA integrity scanning protein from B . subtilis . In each suppressor strain two to four of these five genes were mutated and all mutations were confirmed by re-sequencing all five genes in each suppressor strain . As mentioned above , the suppressor strains were passed four times in broth culture to improve growth before phenotypic and sequence analysis . Therefore , one or more of the mutations listed in Table 1 may have arisen during the passing steps to aid with growth and not as a direct consequence of assisting growth of the LTA-negative strains . To address this , chromosomal DNA was isolated from the original suppressor colonies of strains 4S4 , 4S5 , 4N1 , 4N2 and 5S4 and the five genes in question were sequenced . Only the mutations in gene SAOUHSC_00015 were present in all five strains . Thus , it appears that inactivation of SAOUHSC_00015 , already named in some S . aureus strains GdpP ( for GGDEF domain protein containing phosphodiesterase ) , compensates for the lack of LTA . The remaining mutations in the four other genes are possibly accessory and function to improve growth . If disruption of gdpP compensates for a lack of LTA , one would predict that introducing a WT copy of gdpP into a suppressor strain should be lethal , while the introduction of any of the mutant gdpP alleles present in the suppressor strains should not prevent growth . This was indeed the case , and the expression of WT gdpP from an anhydrotetracycline ( Atet ) inducible promoter containing plasmid but not the expression of the mutant gdpP alleles obtained from suppressor strains 4S4 ( inverted sequence , inframe ) , 4S5 ( point mutation ) or 4N2 ( stop codon ) prevented growth of strain 4S5 ( Figure 2B ) . As controls , introduction of the empty vector pCN34iTET or uninduced pCN34iTET-gdpP into the suppressor strain 4S5 had no effect on bacterial growth ( Figure 2A , left panel ) and expression of GdpP in WT SEJ1 also had no effect on growth , demonstrating that expression of GdpP is not toxic per se ( Figure 2A , right panel ) . These results provide further evidence that disruption of gdpP is essential for the survival of LTA-negative S . aureus suppressor strains . However , the suppressor strains contain other known mutations ( see text and Table 1 ) , therefore we set out to recreate the system in an S . aureus strain without these additional mutations . To this end , the S . aureus strain SEJ1ΔgdpP-iltaS with a silent gdpP deletion and IPTG inducible ltaS expression was created ( Figure 3A ) . Normally , the growth of an inducible ltaS strain is dependent on IPTG [13] , however this should no longer be the case upon deletion of gdpP and expression of GdpP from a plasmid should restore IPTG-dependent growth to this strain . S . aureus strains SEJ1-iltaS pCN34 ( iltaS pCN34 ) and SEJ1ΔgdpP-iltaS containing the GdpP expression plasmid ( ΔgdpP iltaS pCN34iTET-gdpP ) were used to test this experimentally . All strains grew in the presence of IPTG ( Figure 3C , black filled symbols ) , produced LTA ( Figure S3A in Text S1 ) and displayed a normal cell shape and placement of division septa ( Figure 3B and S3B in Text S1 , panels i , iv & vii ) . It is interesting to note that SEJ1ΔgdpP-iltaS bacteria , which contain a deletion of the gdpP gene , appear to have a reduced cell size ( Figure 3B , iv & vii ) and this will be analyzed in more detail later on . In the absence of IPTG , LTA was no longer produced ( Figure S3A in Text S1 ) and as expected growth of the inducible ltaS strain ceased ( Figure 3C , iltaS pCN34 – white and grey filled squares ) . Morphologically the cells appeared enlarged , clumped and showed aberrant cell division ( Figure 3B and S3B in Text S1 , panels ii & iii ) . However , as predicted growth of the inducible ltaS strain with the gdpP deletion continued even in the absence of IPTG ( Figure 3C , ΔgdpP-iltaS pCN38 – white and grey filled triangles ) and the morphology of the cells was much improved showing more regular cell sizes and division ( Figure 3B and S3B in Text S1 , panels v , vi & viii ) . Addition of Atet ( grey symbols ) for the expression of GdpP from the complementation vector restored the IPTG-dependent growth phenotype and strain SEJ1ΔgdpP-iltaS pCN34iTET-gdpP ceased to grow in the absence of IPTG ( Figure 3C , ΔgdpP iltaS pCN34iTET-gdpP - grey circles ) and cells displayed an aberrant morphology ( Figure 3B and S3B in Text S1 , panel ix ) . In summary , it can now be concluded that deleting the gdpP gene allows for the growth of an LTA-deficient strain of S . aureus . The experiments described above confirm that deletion of gdpP compensates for the lack of LTA in an RN4220-derived S . aureus strain . However , this strain is a chemically mutagenized laboratory strain that contains known mutations and defects in regulatory systems [31] , [32] , [33] , [34] . To determine if LTA is also important for growth of other S . aureus isolates , and whether mutations in gdpP can compensate for the lack of LTA , the ltaS gene was deleted in the erythromycin sensitive community-acquired MRSA ( CA-MRSA ) strain LAC* [35] . Strains LAC*ΔltaSN::erm and LAC*ΔltaSS::erm with complete ltaS deletions could be obtained on TSA plates containing 7 . 5% NaCl or 40% sucrose and the absence of LTA was confirmed by western blot ( Figure 4A ) . Both LAC*ΔltaS::erm strains could initially only grow in the presence but not in the absence of osmoprotectants ( Figure 4B ) . Identical to the RN4220 ltaS-deletion strains , LAC*ΔltaS::erm suppressor colonies could be obtained on TSA plates . These strains did not produce LTA but were now able to grow in the absence of osmoprotectants ( Figure 4 ) . To establish whether the LAC*ΔltaS::erm suppressor strains had acquired mutations within gdpP , this gene was sequenced from eight independently isolated suppressor strains . Of these eight strains , five had mutations in the gdpP gene ( Table S2 in Text S1 ) . These results show that LTA is also important for the growth of a CA-MRSA strain and indicate that , as observed in RN4220 , inactivation of gdpP provides a mechanism that allows LAC* to grow in the absence of LTA . S . aureus GdpP contains two N-terminal transmembrane helices followed by a degenerated PAS sensory domain ( Pfam00989 ) , a GGDEF domain ( Pfam00990 ) , a DHH domain ( Pfam01368 ) and a DHH-associated DHHA1 domain ( Pfam02272 ) ( Figure 5A ) . GGDEF domains are usually associated with proteins containing c-di-GMP cyclase or phosphodiesterase activity [36] , [37] and DHH/DHHA1 domain-containing proteins often function as phosphatases or phosphoesterases [38] . The B . subtilis protein YybT is a close homologue to S . aureus GdpP and recently it was shown that recombinant B . subtilis YybT has strong phosphodiesterase activity contained within the DHH/DHHA1 domains [39] . It was further suggested that the cyclic dinucleotide c-di-AMP is the physiological substrate and is converted to 5′-pApA by YybT [39] . In addition , it was found that the GGDEF domain of YybT has weak ATPase activity but no c-di-GMP or c-di-AMP cyclase activity [39] . To investigate whether S . aureus GdpP , like B . subtilis YybT , is a c-di-AMP phosphodiesterase an N-terminally His-tagged fragment of GdpP spanning amino acids 84–655 and containing the PAS , GGDEF and DHH/DHHA1 domains was expressed and purified from E . coli extracts ( Figures 5A and S4 in Text S1 ) . Incubation of c-di-AMP with the recombinant rGdpP84–655 protein resulted in the complete conversion of c-di-AMP to 5′-pApA . This was initially determined by mass spectrometry analysis ( data not shown ) and subsequently quantified by separating reaction products by HPLC and integrating the nucleotide peak areas using c-di-AMP and 5′-pApA standards as controls ( Figure 5B ) . Recombinant rGdpP84-301 protein containing only the PAS and GGDEF domains was also used in this assay and even when present in 4-fold higher amounts did not show any phosphodiesterase activity ( Figure 5B ) . Therefore , identical to B . subtilis YybT , recombinant S . aureus GdpP has in vitro c-di-AMP phosphodiesterase activity and the DHH/DHHA1 domain is essential for this activity . Purified S . aureus rGdpP84–655 did not show any , or at the most very weak , ATPase activity in vitro ( data not shown ) . However reaction conditions were not further optimized , as the phosphodiesterase activity appears to be the biologically relevant activity ( see below ) . The gdpP mutations present in three of the five sequenced RN4220 LTA suppressors ( 4N1 , 4N2 and 5S4 ) lead to stop codons in or before the DHH/DHHA1 domain , which will automatically disrupt the phosphodiesterase activity ( Figure 5A ) . On the other hand , the gdpP mutations in 4S4 and 4S5 lead to the expression of GdpP variants with a 24 amino acid inversion ( but still in frame ) and a G266D amino acid substitution . To test if these alterations affect phosphodiesterase activity , rGdpP4S4 and rGdpP4S5 variants ( comprising amino acids 84–655 ) were produced ( Figure S4 in Text S1 ) and the phosphodiesterase activity measured . While all of the input c-di-AMP was hydrolyzed by WT rGdpP84–655 in less than ten minutes , recombinant rGdpP4S4 and rGdpP4S5 variants converted less than 40% of the input substrate to 5′-pApA in one hour ( Figure 5C ) . These results provide evidence that a disruption of the phosphodiesterase function of GdpP is responsible for compensating for a lack of LTA in suppressor strains . In B . subtilis YybT , amino acids D225 and R291 in the GGDEF domain and residues D420 and D499 in the DHH/DHHA1 were identified as key residues for the weak ATPase activity and the phosphodiesterase activity , respectively [39] . Alanine substitutions at the corresponding positions D223 , R289 , D418 and D497 in S . aureus GdpP ( Figure 5A ) were made and recombinant proteins expressed and purified ( Figure S4 in Text S1 ) for use in in vitro phosphodiesterase assays or were expressed from the Atet-inducible vector pCN34iTET in S . aureus strain 4S5 in order to investigate which activity needs to be inactivated to allow for the survival of the LTA-negative suppressor strain . As expected , recombinant rGdpPD418A and rGdpPD497A with substitutions of key residues in the DHH/DHHA1 domains lacked phosphodiesterase activity ( Figure 6A ) . Interestingly , the rGdpPR289A variant with a substitution in the GGDEF domain also had reduced in vitro phosphodiesterase activity , while the activity of the rGdpPD223A enzyme was identical to WT rGdpP84–655 ( Figure 6A ) . The survival of the LTA-negative suppressor correlated with the defect in phosphodiesterase activity as expression of GdpPR289A , GdpPD418A or GdpPD497A did not affect the growth of strain 4S5 , while expression of GdpPD223A with WT phosphodiesterase activity prevented the growth of this strain ( Figure 6B ) . Therefore , the ability of S . aureus to grow in the absence of LTA seems to be independent of any potential ATPase activity GdpP may have , but the survival depends on the disruption of the phosphodiesterase activity and possibly a concurrent increase in c-di-AMP concentration in the cell . Our results thus far have shown that GdpP has in vitro c-di-AMP phosphodiesterase activity . For this activity to be of any biological significance , S . aureus needs to be able to synthesize c-di-AMP . Until now , c-di-AMP has only been described as a naturally occurring molecule in the supernatant of Listeria monocytogenes cultures and in two very recent studies in the cytoplasm of B . subtilis and Streptococcus pyogenes [40] , [41] , [42] . The c-di-AMP in the supernatant of L . monocytogenes was shown to be involved in the activation of an IFN-β-mediated host immune response [40] . In the same study , a L . monocytogenes protein thought to be essential for bacterial growth and containing a so called DisA_N or DAC domain ( Pfam02457 ) was implicated in the production of c-di-AMP and termed DacA for diadenylate cyclase A [40] . The S . aureus membrane protein SAOUHSC_02407 is homologous to this L . monocytogenes protein and the only S . aureus protein that contains a DisA_N domain . Of note , this gene is also one of the five genes found to contain mutations within two of the SEJ1ΔltaS suppressor strains ( Table 1 ) . To investigate if SAOUHSC_02407 is a c-di-AMP cyclase , this gene was cloned and expressed in E . coli , which cannot naturally produce c-di-AMP . E . coli extracts were prepared and analyzed by LC-MS/MS as previously described for the detection of c-di-GMP [43] . While no c-di-AMP was detected in E . coli extracts isolated from a strain containing the empty plasmid pET28b , extremely high levels of more than 2800 ng c-di-AMP/mg E . coli protein were detected in extracts isolated from the strain expressing SAOUHSC_02407 ( Figure 7A ) . This provides experimental evidence that the S . aureus protein SAOUHSC_02407 , renamed DacA , is a c-di-AMP cyclase and that S . aureus should be capable of producing c-di-AMP . To address experimentally if c-di-AMP is produced by S . aureus and to investigate the involvement of GdpP in adjusting nucleotide levels , cytoplasmic extracts were prepared from cultures of strains SEJ1 and the gdpP mutant SEJ1ΔgdpP::kan . C-di-AMP could be readily detected in samples isolated from both strains using an LC-MS/MS method [43] . To accurately quantify c-di-AMP levels , samples were spiked before extraction with a 13C15N isotope-labeled version of c-di-AMP of known concentration and amounts were quantified based on a c-di-AMP standard curve ( Figure S5 in Text S1 ) . A c-di-AMP concentration of 3 . 33±0 . 44 ng/mg bacterial dry weight was determined for strain SEJ1 and upon deletion of gdpP the c-di-AMP levels increased more than 13-fold to 45 . 37±3 . 83 ng/mg bacterial dry weight ( Figure 7B ) . An increase in c-di-AMP concentration of a similar magnitude was also observed when samples were prepared from the LTA-negative suppressor strain 4S5 ( Figure 7B ) . Furthermore , c-di-AMP was also detected in cytoplasmic extracts of the CA-MRSA strain LAC* and again the c-di-AMP levels increased more than 11-fold in the isogenic gdpP deletion strain LAC*ΔgdpP::kan ( Figure 7B ) . These results establish for the first time the presence of the cyclic dinucleotide c-di-AMP in S . aureus , and provide direct evidence that the S . aureus GdpP enzyme is a c-di-AMP specific phosphodiesterase in vivo . In addition , these results demonstrate that S . aureus strains that lack LTA respond by increasing cellular levels of the secondary messenger c-di-AMP . It is also important to note that no quantifiable amounts of c-di-GMP could be detected in S . aureus extracts as judged by LC-MS/MS . This is consistent with the findings by Holland et al . [44] , that GdpS ( GGDEF domain protein from Staphylococcus ) , the only staphylococcal protein with a potentially intact GGDEF domain , is unable to synthesize c-di-GMP . Taken together , our findings indicate that c-di-GMP is absent in S . aureus but underscores the importance of c-di-AMP as a secondary messenger in S . aureus . To examine whether a deletion of gdpP , and the concomitant increase in c-di-AMP , directly affects cell wall properties and could in that way compensate for the lack of LTA , the cell wall characteristics of the mutant strains were examined . Firstly the level of hydrolytic enzymes present on the cell surface of LAC* and the isogenic gdpP mutant was analyzed . The gdpP mutant strain possessed increased amounts of autolysins as judged by zymographic analysis ( Figure S6 in Text S1 ) . Next LAC* and the isogenic LAC*ΔgdpP::kan mutant strain were incubated with increasing concentrations of the cell wall or membrane targeting antimicrobials oxacillin , penicillin G , vancomycin , lysostaphin , daptomycin and nisin . LAC*ΔgdpP::kan displayed an approximately eight-fold decreased susceptibility to oxacillin and lysostaphin and a more than 32-fold decreased susceptibility to penicillin G ( Table 2 ) , indicating that changes in the cell wall/peptidoglycan structure have occurred . To investigate this further , peptidoglycan was purified from the WT and the gdpP mutant LAC* strains , digested with mutanolysin and the muropeptides analyzed by HPLC . The overall muropeptide profile of the two strains was very similar ( Figure S7A and B in Text S1 ) , however a statistically significant reduction in the amount of monomeric muropeptides and a concurrent increase in higher cross-linked muropeptides ( trimers and above ) was observed in the gdpP mutant strain ( Figure 8A ) . This increase in the amount of cross-linked peptidoglycan could potentially be responsible for the increased resistance to cell wall targeting antimicrobials observed in this strain and could aid with bacterial survival in the absence of LTA by strengthening the cell wall , however this is speculative and remains to be confirmed . Following this , the WT LAC* strain and the isogenic LAC*ΔgdpP::kan strain were grown to mid-log phase , stained with BODIPY-vancomycin and observed under the microscope . This analysis revealed a reduction of cell size by more than 13% for the gdpP mutant , which could be restored to the WT cell size upon complementation with gdpP ( Figure 8B and S7C in Text S1 ) . Based on this microscopic analysis , average diameters of 1 . 188±0 . 025 µm and 0 . 967±0 . 015 µm were determined for the LAC* and LAC*ΔgdpP::kan strains , respectively ( Table 3 ) . A similar reduction in cell size was observed for the gdpP mutant in the RN4220 strain background , where cell diameters of 1 . 138±0 . 081 µm for SEJ1 and 0 . 879±0 . 051 µm for the SEJ1ΔgdpP::kan strain were measured . These results indicate a function for c-di-AMP in controlling the cell size of S . aureus with increased cyclic dinucleotide levels leading to a statistically significant decrease in cell size . Based on the experimentally determined cell diameters and CFU counts for cultures used to prepare the cytoplasmic extracts to detect c-di-AMP ( see above Figure 7B ) intracellular cyclic dinucleotide concentrations of 2 . 8±0 . 6 µM for SEJ1 and 2 . 1±0 . 3 µM for the LAC* could be calculated , which increased approximately 15-fold to 42 . 9±9 . 0 and 31 . 5±4 . 5 µM in the isogenic gdpP mutant strains ( Table 3 ) . In a variety of bacteria an increase in intracellular levels of the related and well-characterized cyclic dinucleotide c-di-GMP , stimulates the biosynthesis of adhesins , promotes biofilm formation and inhibits various forms of motility [45] . No such functions have yet been ascribed to c-di-AMP . To investigate whether cellular levels of c-di-AMP affect the ability of S . aureus to form a biofilm , cultures of WT SEJ1 and both the silent gdpP mutant SEJ1ΔgdpP , and the marked gdpP mutant SEJ1ΔgdpP::kan , were grown without shaking in 96-well plates containing BHI 4% NaCl . Staining of adherent bacteria with crystal violet revealed that the gdpP deletion strains formed approximately 3-times more biofilm than the WT control strain ( Figure S8 in Text S1 ) . However , it should be noted that neither the WT nor the gdpP mutant LAC* strains formed robust biofilms under these conditions . Nevertheless , this indicates that increased cellular levels of c-di-AMP not only affect cell properties that allow bacteria to grow in the absence of LTA but , like c-di-GMP , also influence the production of components involved in biofilm formation at least in some S . aureus background strains .
Clear functions for the secondary messenger molecule c-di-GMP , in controlling gene expression and the switch from planktonic to sedentary lifestyles , have been established in a diverse range of bacterial species [45] . It is now well documented that this cyclic dinucleotide plays an important role in controlling biofilm formation and virulence gene expression in a range of bacteria , including important human pathogens such as Pseudomonas aeruginosa [46] . Recently , it has also been suggested that this signaling molecule , which is widespread in bacterial species but apparently not found in higher eukaryotes , can act as a danger signal in eukaryotic cells prompting studies on the immunomodulatory and immunostimulatory properties of c-di-GMP [47] , [48] . On the other hand , until very recently c-di-AMP had not been recognized as a naturally occurring molecule in any living organism . c-di-AMP was noticed for the first time in 2008 during crystallization studies of the B . subtilis DNA binding protein DisA [49] , [50] . Additional work confirmed that the N-terminal part of DisA ( DisA_N domain ) is capable of synthesizing c-di-AMP in vitro from two molecules of ATP [49] . Shortly afterwards , c-di-AMP phosphodiesterase activity was ascribed to the B . subtilis protein YybT using an in vitro assay system [39] . The first evidence for the production of c-di-AMP by living cells came from a study on L . monocytogenes , where this cyclic dinucleotide was detected in the culture supernatant and identified as the molecule that stimulates an IFN-β-mediated host immune response [40] . Very recently c-di-AMP has also been detected in cytoplasmic extracts from S . pyogenes and from B . subtilis [41] , [42] . In this study , we have identified c-di-AMP within the cytoplasm of the Gram-positive pathogen S . aureus and also quantified the amounts produced in vivo in both laboratory and clinically relevant strains ( Figure 7 and Table 3 ) . Intracellular c-di-AMP concentrations of 2 to 3 µM were detected in the S . aureus cells , which are very similar to the concentration of 1 . 7 µM reported for B . subtilis during vegetative growth [41] . We also show in this study that GdpP functions in vivo as a c-di-AMP-specific phosphodiesterase and provide experimental evidence that the S . aureus protein SAOUHSC_02407 , which was renamed DacA , is capable of producing c-di-AMP ( Figure 7 and 9 ) . Similar to what was observed with the c-di-GMP signaling molecule , this study provides a link between this novel cyclic nucleotide and cell wall properties in Gram-positive bacteria , as an increase in c-di-AMP levels allows S . aureus to grow in the absence of LTA and the CA-MRSA gdpP mutant strain shows an increased resistance to the cell wall active antimicrobials oxacillin , penicillin G and lysostaphin ( Table 2 ) and an increase in the amount of cross-linked peptidoglycan ( Figure 8A ) . Our study also revealed that c-di-AMP plays a role in controlling the cell size of S . aureus ( Figure 8B ) . Recently , Oppenheimer-Shaanan et . al have shown that c-di-AMP levels increase 3-fold in B . subtilis at the onset of sporulation and the authors suggested that this increase serves as a positive signal for sporulation to proceed [41] . Since S . aureus does not form spores , this particular function attributed to c-di-AMP cannot apply to S . aureus . However , our observation that an increase in c-di-AMP levels results in a decrease in cell size suggests perhaps a more general function for c-di-AMP in progressing the cell cycle and not just the sporulation process in Gram-positive bacteria . In this respect , it is interesting to note that , in contrast to c-di-GMP , c-di-AMP might be an essential constituent of the cell . Attempts to disrupt the c-di-AMP cyclase DacA in L . monocytogenes were unsuccessful [40] . Furthermore , screens for essential genes in Mycoplasma pulmonis , Mycoplasma genitalium , Streptococcus pneumoniae as well as S . aureus indicated that dacA is essential for cell viability [51] , [52] , [53] , [54] . The c-di-AMP cyclase DacA and the c-di-AMP phosphodiesterase GdpP are both predicted to be anchored to the bacterial membrane and it seems likely that changes in the environment , cell wall structures or in the membrane itself serve as cues to adjust the intracellular cyclic dinucleotide levels . Bioinformatic analysis of GdpP revealed that this protein contains a highly degenerated PAS domain . PAS domains usually function as sensory domains for detecting light , redox potential , oxygen , small ligands , and the overall energy level of a cell , usually by way of an associated cofactor [55] . A recent study has shown that the PAS domain of the B . subtilis YybT protein is capable of binding the cofactor heme and that this binding suppresses the phosphodiesterase activity in vitro [56] . This indicates that the PAS domain of YybT is indeed capable of sensing environmental changes and the most likely output will be a change in c-di-AMP levels . In this regard it is interesting to note that we observed a light brown discoloration of the Ni-NTA columns during the purification process of the GdpP protein , suggesting that the S . aureus protein is also capable of binding heme . GGDEF domains are typically associated with c-di-GMP cyclases or phosphodiesterases and function to synthesize c-di-GMP . Degenerated domains can also regulate the activity of associated c-di-GMP phosphodiesterase domains [57] . The GGDEF domain of GdpP has the highly divergent amino acid sequence SSDQF , with substitutions of three of the five highly conserved active site residues that are essential for GTP catalysis [58] . The GGDEF domain of B . subtilis YybT can bind ATP and slowly convert it to ADP [39] . However , this domain cannot synthesize c-di-AMP and the ATP binding had no effect on the in vitro phosphodiesterase activity contained within the DHH/DHHA1 domains [39] . The biological relevance of the ATP binding and hydrolysis activity of the degenerated GGDEF domain remains to be determined . Indeed , we provide experimental evidence in this study that argues against a role of any potential ATPase activity for the function of the S . aureus GdpP protein ( Figure 6 ) . At the same time our data suggest that this domain can influence , independently of any ATPase activity , the phosphodiesterase activity of the associated DHH/DHHA1 domains , as indicated by a decrease in in vitro phosphodiesterase activity of GdpP variants containing single point mutations in the GGDEF domain ( Figure 6 ) . Additional experiments are needed to determine the mechanism by which the GGDEF domain influences the activity of the downstream phosphodiesterase . As mentioned above , c-di-GMP is typically synthesized by GGDEF domain containing proteins and a search for this domain highlighted two S . aureus proteins that could potentially function as c-di-AMP cyclases . GdpP itself , which contains amino acid alternations in crucial resides in this domain and extrapolating from the rigorous in vitro analysis on the B . subtilis homologue YybT , does not have cyclase activity . The second protein is GdpS [44] . O'Gara and coworkers investigated the possibility that the Staphylococcus epidermidis GdpS protein is a c-di-GMP cyclase but with no success and consistent with this observation we show in this study that c-di-GMP does not appear to be present in S . aureus [44] . It is possible however , that GdpS may be involved in c-di-AMP synthesis instead , a theory that is currently under investigation . In this regard it is interesting to note that , in contrast to the gdpP deletion strain analyzed in this study that causes an increase in biofilm formation ( Figure S8 in Text S1 ) , an S . epidermidis gdpS mutant strain has a defect in biofilm formation caused by a decrease in transcription of the polysaccharide-producing ica locus [44] . While the exact number of S . aureus proteins involved in c-di-AMP production and hydrolysis remains to be determined , it is certain that GdpP and DacA are involved in this process ( Figure 7 ) . Having shown that a deletion of gdpP results in increased intracellular levels of c-di-AMP the question remains as to how this suppresses the bacterial need for LTA . Rao et al . , have demonstrated that the alarmone ppGpp , which is produced during the stringent response to cope with stress , inhibits the phosphodiesterase activity of YybT in vitro [39] . Furthermore , disruption of the GdpP homologues in Lactococcus lactis and B . subtilis renders these bacteria more resistant to acid stress [39] , [59] . The deletion in B . subtilis also increases the sporulation efficiency of cells that had been exposed to a DNA damaging agent [39] , [41] . All these observations indicate that an increase in intracellular c-di-AMP levels allows bacteria to cope better under stress conditions . Undoubtedly depleting LTA from the cell wall will place bacteria under stress . While bacteria may not naturally be able to respond to such a stress , one mechanism that allows bacteria to survive is by irreversibly disrupting the function of GdpP and in this manner increasing intracellular c-di-AMP levels ( Figure 7B ) . By analogy with c-di-GMP , we assume that c-di-AMP then acts as a secondary messenger to up- or down regulate the activity or expression of a certain set of target proteins ( Figure 9 ) [45] . Alternatively , c-di-AMP might also bind to RNA molecules to affect protein expression , as in the case of c-di-GMP-dependent riboswitches [60] . As an increase in c-di-AMP concentration in LTA depleted cells has served to improve cell growth and division ( Figure 3 ) it is tempting to speculate that c-di-AMP is involved in regulating some components of the cell division machinery . A function for c-di-AMP in controlling cell division in S . aureus is also consistent with our observations that gdpP mutant strains are smaller in size ( Figure 8B and Table 3 ) . Therefore , an increase in intracellular c-di-AMP appears to allow S . aureus cells to initiate cell division before they have reached their normal size . Furthermore , the increased resistance of a gdpP mutant LAC* strain to lysostaphin , oxacillin and penicillin G and the decrease in monomeric peptidoglycan subunits provides experimental evidence that proteins involved in peptidoglycan synthesis are regulated by c-di-AMP . It was also noted that three LAC* suppressor strains contained WT gdpP sequences ( Table S2 in Text S1 ) . It is possible that in these strains the molecular targets of c-di-AMP are mutated instead of altering the concentration of the second messenger molecule . While beyond the scope of this study , it will be interesting to identify c-di-AMP target proteins in future studies , in particular those involved in helping the bacteria cope with stress . One could suppose that by interfering with c-di-AMP synthesis cells may be less able to respond and cope with the stresses encountered during infection . By identifying a way to combine this inhibition with currently available drug treatments , it could be possible to control even the most multi-drug resistant S . aureus infections . Inactivation of GdpP was the first step that allowed all five sequenced suppressor strains to grow in the absence of LTA , however several of the mutations acquired in subsequent steps are interesting to note . Two suppressor strains acquired mutations that lead to amino acid substitutions in the c-di-AMP cyclase DacA ( Table 1 ) . These substitutions might function to improve cyclase activity and , analogous to a gdpP deletion , this may result in an overall increase in c-di-AMP levels . It is also plausible that these mutations function to reduce the drastically increased levels of c-di-AMP observed in suppressor strains , levels which could prove toxic if not regulated properly . A third suppressor mutation resulted in an amino acid substitution in the succinate dehydrogenase SdhA ( SAOUHSC_01104 ) . SdhA is a TCA cycle enzyme and it is hard to predict how alterations in a central metabolism enzyme could help LTA-deficient bacteria to survive . The remaining two mutated genes , encode for a putative permease ( SAOUHSC_01358 ) and a conserved hypothetical protein with sequence homology to a fusaric acid transporter ( SAOUHSC_02001 ) ( Table 1 ) . Premature stop codons have arisen within these two proteins presumably inactivating their function . Woodward et al have shown that over-expression of multidrug resistance transporters results in increased secretion of c-di-AMP into the supernatant of L . monocytogenes cultures [40] . It is therefore plausible that disrupting the functions of these two putative S . aureus transporters may again serve to increase intracellular c-di-AMP levels . Eukaryotic host cells often recognize essential bacterial cell components as a means of detecting an infection . One well-studied example of this is the recognition of peptidoglycan by the intracellular host proteins Nod1 and Nod2 , which upon detection results in the activation of the NF-κΒ pathway and an immune response [61] . Recently , Woodward and coworkers discovered that c-di-AMP , which is released by L . monocytogenes inside host cells , is also detected in the cytosol where it triggers a host immune response [40] . S . aureus is another pathogen that is capable of invading epithelial and endothelial cells through fibronectin binding protein-mediated adherence to the host cell integrin α5β1 and subsequent endocytosis [62] . Once inside the cell it is equally likely that S . aureus secretes this potentially essential nucleotide into the cytosol , a possibility that may then be exploited by the host immune system . The eukaryotic proteins involved in the detection of c-di-AMP and the mechanism leading to immune activation remains to be determined as our current understanding is only rudimentary [63] . Taken together we have shown that it is possible to create viable LTA-negative strains of S . aureus that compensate for the loss of this important polymer by increasing intracellular levels of the secondary messenger c-di-AMP . The unambivalent identification of c-di-AMP in the cytoplasm of S . aureus and the ability to regulate its level opens up the exciting possibility of identifying target proteins or other compounds through which this cyclic dinucleotide exerts its function and regulates cellular processes . This is especially intriguing for Gram-positive pathogens such as S . aureus , S . pneumonia , S . pyogenes and L . monocytogenes considering that c-di-AMP may be essential for cell viability . Further studies are required to fully elucidate the role of this messenger in the cell and to ultimately discover what targets are altered in response to a deletion of LTA . This will hopefully help us to more fully understand the biological significance of this polymer and to identify novel essential cellular survival mechanisms that could be exploited as therapeutic drug targets .
Strains used in this study are listed in Table S3 in Text S1 and primers used for cloning in Table S4 in Text S1 . E . coli and B . subtilis strains were grown in LB and S . aureus strains were grown in TSB medium at 37°C with aeration , if not otherwise stated . When required , media were supplemented with antibiotics and inducers as indicated in Table S3 in Text S1 . Details on plasmid and strain constructions are provided in the supplementary Materials and Methods section in Text S1 . WT and LTA suppressor strains were grown overnight in TSB medium . Unsuppressed ΔltaS strains were grown in TSB containing either 7 . 5% NaCl or 40% sucrose . Overnight cultures were washed three times in the appropriate medium and diluted to a starting OD600 of 0 . 05 . Cultures were incubated at 37°C with aeration and OD600 values determined at 2 h intervals . Cultures containing ltaS under IPTG inducible control were grown overnight in the presence of IPTG and the appropriate antibiotics . Bacteria were washed three times in TSB and diluted to an OD600 of 0 . 05 in 5 ml TSB with or without 1 mM IPTG and 100 ng/ml Atet as appropriate and OD600 values determined . Where stated , the cultures were diluted after 4 h 1∶100 into fresh medium with the appropriate antibiotics and inducers to maintain cultures in the exponential growth phase and growth continued for a further 6 h . The 4 h time point is then represented as T = 0 . Growth curves were performed in triplicate and representative graphs are shown . CFUs per ml culture were determined by emulsifying a colony in 1 ml TSB , normalizing the OD600 to 0 . 05 , performing serial dilutions and plating 100 µl on the appropriate plates . Plates were then incubated at 30°C or 37°C as indicated and colonies were enumerated after overnight growth . Counts were performed twice with representative figures stated in the text . For whole genome sequence determination , chromosomal DNA was isolated from the S . aureus reference strain SEJ1 and the 5 suppressor strains 4S4 , 4S5 , 4N1 , 4N2 and 5S4 . Sequences were determined using a SOLiD 3 System ( Applied Biosciences ) and DNA fragment libraries . Fifty bp fragment libraries were generated by mechanical shearing , treated and coupled to beads using emulsion PCR and deposited on glass slides following protocols supplied by Applied Biosciences . The generated reads resulted in genomic coverage of between 140x and 213x and have been submitted to the ENA Sequence Read Archive ( SRA ) under accession ERP000528 ( http://www . ebi . ac . uk/ena/data/view/ERP000528 ) . Reads from SEJ1 were aligned to the known genome sequence of S . aureus strain NCTC8325 with the BioScope mapping pipeline ( Applied Biosystems ) using an anchor length of 25 bp allowing 2 mismatches . Next the reads from the suppressor strains were individually aligned to NCTC8325 using the same alignment procedure . Using these analysis tools , large DNA deletions in place of the prophage sequences and the spa gene as well as 84 point mutations with high confidence scores were detected in all strains ( SEJ1 and the 5 suppressor strains ) when compared to the NCTC8325 genome sequence . Of these 84 mutations , 76 were identical to those listed in the recently published paper on the RN4220 genome sequence [34] . Of the 8 that differed , 7 were located in a highly repetitive genome region and are likely to represent misalignments of reads . Of the extra 45 snps identified by Nair et . al . , [34] , a number are caused by insertion or deletion events , mutations we could not detect because of limitations of the analysis software and due to the use of a fragment library rather than using mate-pairs . Ten additional mutations , identified using the BioScope diBayes pipeline ( Applied Biosystems ) and ‘high’ call stringency setting , were present in the suppressors strains but absent in strain SEJ1 and these mutations are described in detail in the results section . Suppressor strains specific differences were verified by re-sequencing the genes in question at the MRC Clinical Science Centre Sequencing Facility at Imperial College London . These assays were performed using standard procedures and details can be found in the supplementary Materials and Methods section in Text S1 . His-tagged S . aureus GdpP protein variants and the B . subtilis DisA protein were purified from 1 to 2 liter induced E . coli BL21 ( DE3 ) cultures containing the respective pET28b expression vectors ( Table S3 in Text S1 ) . Protein induction and purification was performed as described in Rao et al . with minor modifications and details can be found in Text S1 [39] . The enzymatic activities of the different purified GdpP protein variants was determined by incubating 20 µM c-di-AMP ( BioLog ) with 1 µM purified protein in buffer containing 50 mM Tris pH 8 . 5 , 20 mM KCl and 0 . 1 mM MnCl2 . Enzyme reactions were stopped at the indicated time by the addition of an equal volume of 0 . 1 M EDTA pH 8 and incubation for 3 to 5 min at 95°C . Enzymatic activity was determined by separating 15 µl of the reaction mixtures by HPLC ( Agilent LC1200 ) using a Luna 150×2 , 3 µm particle size RP C-18 column and a 0 . 1 M triethylamine acetic acid pH 6 . 1 ( Buffer A ) and 80% acetonitrile containing 20% buffer A ( Buffer B ) solvent system . The column temperature was set to 35°C and the flow rate to 0 . 25 ml/min and a constant buffer B concentration of 5% was used for the runs . Nucleotides were detected at A254 and authentic c-di-AMP and 5′-pApA ( BioLog ) were used as standards to determine nucleotide specific retention times . % c-di-AMP hydrolysis was calculated based on integrated nucleotide peak areas . Four independent experiments were performed ( using proteins from two separate purifications ) and the average and standard deviation of all four values is plotted . Overnight cultures of S . aureus cells were diluted to a starting OD600 of 0 . 05 and grown for 4 h at 37°C with aeration . Cultures were adjusted to an approximate OD600 of 2 , CFU counts determined by plating appropriate dilutions on TSA plates and bacteria from a 10 ml culture aliquot were also collected by centrifugation , washed and freeze dried to determine the dry weight , for normalization purposes . A 5 ml aliquot from the same culture was removed and bacteria collected by centrifugation at 9 , 000×g for 5 min . The pellet was suspended in 1 ml ice-cold extraction buffer ( acetonitrile/MeOH/H2O - 40∶40∶20; LC-MS gradient grade , VWR ) containing 0 . 58 µM internal 13C15N isotope labeled c-di-AMP standard . Samples were snap frozen with liquid N2 for 15 sec before being heated to 95°C for 10 min . Samples were mixed with 0 . 5 ml of 0 . 1 mm glass beads and lysed in a Fast-Prep machine 2 x for 45 sec at setting 6 ( FP120 , MP Biomedicals , LLC ) . Glass beads were separated by centrifugation at 17 , 000×g for 5 min at 4°C . The supernatant was removed and stored at 4°C and the remaining glass beads/cell debris mixture was washed with 1 ml extraction buffer without internal standard , incubated on ice for 15 min and again lysed . Samples were once again spun and the supernatant combined with the previous one . Glass beads were washed with 1 ml extraction buffer , incubated on ice for 15 min and centrifuged . All supernatants were combined and samples dried at 40°C under a stream of N2 and stored at −80°C . E . coli overnight cultures were diluted 1∶100 into fresh LB medium and grown at 37°C to an OD600 of 0 . 5 at which point 1 mM IPTG was added and cultures were grown for a further 3 h . For normalization purposes , culture aliquots corresponding to an OD600 of 2 were withdrawn , washed once in PBS pH 7 . 4 , suspended in 800 µl 0 . 1 M NaOH and heated to 95°C for 15 min . The samples were centrifuged for 5 min at 17 , 000×g and the protein content of the supernatant was determined using a BCA assay kit ( Pierce ) . Aliquots corresponding to an OD600 of 20 were withdrawn from the same cultures and centrifuged for 5 min at 9 , 000×g . The pellet was suspended in 300 µl ice-cold extraction buffer ( acetonitrile/MeOH/H2O; 40∶40∶20 ) containing cXMP ( BioLog ) as an internal standard at a final concentration of 200 ng/ml . Samples were frozen with liquid nitrogen for 15 sec and afterwards heated to 95°C for 10 min . Samples were centrifuged at 17 , 000×g for 5 min at 4°C and the supernatant removed . The pellet was suspended in 200 µl extraction buffer without cXMP , incubated on ice for 15 min and centrifuged . The supernatant was combined with the previous one and the pellet was once again suspended in 200 µl extraction buffer followed by a 15 min incubation on ice and a centrifugation step . All supernatants were combined and dried at 40°C under a flow of N2 . The c-di-AMP concentration was determined based on a c-di-AMP standard curve and values are presented as ng c-di-AMP/mg E . coli protein . For the synthesis of 13C15N isotope labeled c-di-AMP , 1 µM rDisA was incubated with 500 µM adenosine-13C15N-5′-triphosphate in 10 mM Tris-HCl , pH 7 . 5 , 10 mM MgCl2 and 0 . 1% BSA for 18 h at 30°C with gentle mixing ( 300 rpm ) . Based on an identical experiment using unlabeled ATP as the substrate , it was deduced that substrate turnover is complete under these conditions . The reaction was stopped by heating samples to 95°C for 15 min and the suspension was clarified by centrifugation ( 20 , 800×g; 10 min; 4°C ) . The concentration of 13C15N-c-di-AMP in the supernatant was determined by measuring the absorption at 259 nm ( ε259 nm = 30 , 000 M−1cm−1 ) . Further purification steps were not necessary for the use of 13C15N-c-di-AMP as an internal standard and were therefore omitted . c-di-AMP was detected and quantified by LC-MS/MS using a similar protocol as published previously for the detection of c-di-GMP [43] . Details are given in the supplementary Materials and Methods section in Text S1 . For muropeptide analysis , 1 liter TSB medium was inoculated with overnight cultures of strains LAC* ( AH1263 ) or LAC*ΔgdpP::kan ( ANG1961 ) to an OD600 of 0 . 06 . The cultures were grown at 37°C to mid-log phase ( OD600 of approximately 1 . 5 ) , cooled on ice and bacteria subsequently collected by centrifugation . Peptidoglycan was purified using a well-established method , which we have used previously [64] , [65] . Next , 5 mg purified peptidoglycan was digested in a final volume of 1 . 24 ml in 12 . 5 mM phosphate buffer pH 5 . 9 with 50 µg mutanolysin from Streptomyces globisporus ( Sigma ) for 20 h at 37°C . The suspension was subsequently boiled for 5 min and the insoluble material removed by centrifugation for 5 min at 17 , 000×g and the soluble fraction stored at 4 or −20°C . Immediately before HPLC analysis , a portion of the soluble muropeptide fraction was mixed with an equal volume of 0 . 5 M Na borate buffer pH 9 and reduced for 30 min at RT by the addition of sodium tetraborohydride . Subsequently the pH was adjusted to 2–3 with 20% phosphoric acid and 100 µl was analyzed by HPLC using a 3 µm-particle-size 120A pore size octyldecyl silane Hypersil 250×4 . 6 mm C18 column equipped with a 10×4 mm guard column made of the same material ( Thermos Electron Corporation ) . The column temperature was set to 52°C and a sodium phosphate/methanol buffer system and gradient conditions were used as previously described with the exception that the sodium azide was omitted from buffer A [64] , [66] . HPLC traces were recorded at 205 nm and the muropeptide profile from three independently grown cultures was determined and a representative graph is shown . For quantification purposes , the area of muropeptide peaks was integrated and quantified using the Agilent Technology ChemStation software and shown as percentage of the total ( min 20 and 145 ) peak area and the average values and standard deviations of the three experiments is given . The two-tailed two sample equal variance Student's t-test was used to determine statistically significant differences between muropeptide peak areas and statistically significant differences with p-values below 0 . 05 are indicated with an asterisk ( * ) . S . aureus NCTC8325: ltaS - SAOUHSC_00728; gdpP - SAOUHSC_00015; dacA - SAOUHSC_02407; gdpS - SAOUHSC_00760; gene containing suppressor mutation 2 - SAOUHSC_01104; mutation 3 - SAOUHSC_01358; mutation 4 - SAOUHSC_02001; atl – SAOUHSC_00994; B . subtilis str . 168: yybT - BSU40510; disA - BSU00880; L . monocytogenes str . EGD-e: dacA – lmo2120; Streptococcus pyogenes SF370: dacA - Spy1036 | Staphylococcus aureus is an important human pathogen that colonizes the nares and skin of both sick and healthy individuals and causes a variety of infections ranging from superficial skin to invasive infections . The ability of this bacterium to cause disease depends on many factors and is , in part , due to multi-functional cell surface structures . One such structure is lipoteichoic acid ( LTA ) , which is crucial for bacterial growth . In this study we show that LTA is also important for growth of a clinically relevant community-acquired methicillin resistant S . aureus ( CA-MRSA ) strain and not only for laboratory strains as previously described . We set out to investigate if S . aureus can find a way to survive without LTA and identified strains that can grow and divide almost normally in its absence . Using a whole genome sequencing approach , we found that alterations in one gene , gdpP , allow these strains to grow in the absence of LTA . We show that this mutation causes an increase in the recently identified signaling molecule , c-di-AMP , within the cell . Therefore , with this study we provide information on one of the first functions of this novel secondary messenger , which is in helping bacteria to cope with extreme cell wall stress . | [
"Abstract",
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"Results",
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] | [
"medicine",
"biochemistry",
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] | 2011 | c-di-AMP Is a New Second Messenger in Staphylococcus aureus with a Role in Controlling Cell Size and Envelope Stress |
Plague is an epidemic-prone disease with a potential impact on public health , international trade , and tourism . It may emerge and re-emerge after decades of epidemiological silence . Today , in Latin America , human cases and foci are present in Bolivia , Brazil , Ecuador , and Peru . The objective of this study is to identify where cases of human plague still persist in Latin America and map areas that may be at risk for emergence or re-emergence . This analysis will provide evidence-based information for countries to prioritize areas for intervention . Evidence of the presence of plague was demonstrated using existing official information from WHO , PAHO , and Ministries of Health . A geo-referenced database was created to map the historical presence of plague by country between the first registered case in 1899 and 2012 . Areas where plague still persists were mapped at the second level of the political/administrative divisions ( counties ) . Selected demographic , socioeconomic , and environmental variables were described . Plague was found to be present for one or more years in 14 out of 25 countries in Latin America ( 1899–2012 ) . Foci persisted in six countries , two of which have no report of current cases . There is evidence that human cases of plague still persist in 18 counties . Demographic and poverty patterns were observed in 11/18 counties . Four types of biomes are most commonly found . 12/18 have an average altitude higher than 1 , 300 meters above sea level . Even though human plague cases are very localized , the risk is present , and unexpected outbreaks could occur . Countries need to make the final push to eliminate plague as a public health problem for the Americas . A further disaggregated risk evaluation is recommended , including identification of foci and possible interactions among areas where plague could emerge or re-emerge . A closer geographical approach and environmental characterization are suggested .
Plague is a historic disease that is responsible for some of the most devastating epidemics in human history . Despite its low and decreasing incidence , it continues to be endemic in some countries of the world and remains a public health threat . Plague is an epidemic-prone disease with a high case-fatality rate , which can generate panic in the population , induce political tension , and impact public health systems , along with international trade and tourism . Recent plague outbreaks illustrate the capacity of this disease to re-emerge after a long quiescent period . Examples include cases in Oran , Algeria in 2003 after more than 50 years and in Mbulu , Tanzania in 2007 and 2008 after more than 30 years [1] , [2] . Plague is a zoonotic disease caused by the bacterium Yersinia pestis that involves primarily wild rodents ( reservoir ) and their fleas ( vectors ) , which occasionally transmit the disease to other animals and humans , particularly those living near natural disease foci [3]–[6] . It is an excellent example to illustrate the human-animal-ecosystem interactions and the One Health framework could be used to address health risks at this interface [7] , [8] . Plague infection can manifest in humans in three clinical forms , depending on the route of exposure: bubonic , septicaemic , and pneumonic . As per the International Health Regulations ( IHR ) ( 2005 ) , pneumonic plague is one of the notifiable diseases listed as a potential Public Health Emergency of International Concern ( PHEIC ) [9] . The first well-known worldwide pandemic happened in the Justinian period ( around 541 A . D . ) and spread around the Mediterranean Sea . The second worldwide pandemic was registered from the 14th to the 16th centuries , affected Central Asia , Africa and Europe , and was denominated “Black Death” , causing a great loss of life . Lastly , the third and more recent pandemic originated in China during the 19th century [10] , [11] . It was during the third and last pandemic that plague occurred for the first time in the Region of the Americas ( along with West Africa , South Africa , Madagascar and Indochina ) , beginning in port cities where it was probably introduced by sea or riverboat traffic [3] , [10] . After the introduction of plague to the harbor cities , large human outbreaks occurred in densely populated urban centers . The infection gradually propagated inland , generally following transportation routes . As domestic rats intermingled with wild animals in rural areas , the infection was transmitted to these wild hosts , which today serve as the main reservoir in areas where plague remains a public health problem [10] . In the Region of the Americas , several rodents have been identified as reservoirs , such as Akodon sp , Oryzomys sp , and Rattus . The main vector is the rodent flea Xenopsylla cheopis [4] . Plague is considered to be endemic in 26 countries around the world , but most cases are found in remote areas of Africa [12] . Between 2004 and 2009 , 16 countries in Africa , Asia , and the Americas reported human cases of this disease . In this time period , there was an average of around 2 , 000 cases per year , with more than 90% of them reported in Madagascar and the Democratic Republic of Congo [12] . The incidence decreased substantially after 2009 . According to the World Health Organization ( WHO ) , 400 human cases of plague and 75 deaths were recorded in 2012 , mainly in Africa [13] . In some geographic areas in the American Region considered as foci , plague appears to be maintained in nature by transmission among local susceptible rodents and flea fauna . In Latin America , human cases of plague and natural foci without evidence of human cases are present in Bolivia , Brazil , Ecuador and Peru [14] . In the western region of the United States , human cases of plague also continue to occur in rural and semi-rural areas where many types of rodent species can be involved in the plague foci [15]–[17] . In the Americas , countries that are considered endemic for plague have been conducting surveillance and implementing prevention and control measures [18]–[21] . Understanding the environmental influences in a particular geographical region where plague currently occurs is important for disease prediction , prevention and control measures [22] . Several studies have been conducted to evaluate the environmental influences in the occurrence of plague in order to better understand the complex cycle of this disease and to predict areas of potential transmission [23]–[27] . Some of the studies analyzed the relationship between precipitation and temperature with plague , while others looked at altitude , soil , and other conditions [28]–[30] . In Latin America , plague generally strikes impoverished communities in remote rural areas [31] . The main objective of this study is to identify where cases of human plague still persist in Latin America and map areas that may be at risk for disease emergence or re-emergence . Knowing when a case is unusual or unexpected is part of the verification and response of event management within the IHR . This information could also provide evidence-based information for decision makers to prioritize areas to prevent , detect , and respond to possible outbreaks of plague .
The first part of the study was a retrospective search for the historical presence of plague in Latin America . The information obtained was consolidated in order to determine where human cases of plague still persist and the potential areas where it could re-emerge . Scientific publications about plague re-emergence were used to define the time periods for the analysis . Based on this information , it was possible to describe the historic presence of plague in Latin America , by country and year , from the appearance of the first documented case in 1889 until 2012 . The different steps used in the study's data collection and analysis are described in Table 1 . Within the currently endemic countries , two parallel analyses were performed by identifying: 1 ) Areas “where plague still persists” at the second level of the political/administrative divisions that are called municipalities , cantons , or provinces in different countries , but for the purpose of this study are called counties; 2 ) Potential areas where it could emerge or re-emerge at the first level of the political/administrative divisions that are designated as departments , states , or provinces in different countries , but for the purpose of this study are called regions . This was the most disaggregated level for which information was obtained . Maps were produced to demarcate spatially the counties where plague still persists in order to be used as a baseline for the current situation . Based on the literature review , selected demographic , socioeconomic , and environmental variables were used to describe the counties where plague still persists and explore ideas for future more disaggregated studies . Lastly , a geographic analysis was carried out to map the risk for plague in Latin America , including counties where plague still persists and regions where it could emerge or re-emerge . The location of major roads , rivers , ports , and airports were also included in the map to determine whether they overlap with the counties where plague still persists . The digital cartographic data for Latin America were collected , updated , standardized and geo-processed to connect and overlap with the environmental data , using ArcGIS/Editor 10 . 1 by PAHO CHA/IR/GIS working group . The first step was to update the political/administrative boundaries available from the PAHO-UN SALB Project , following the boundary depiction compiled since 2007 in the context of the activities of the UN Geographic Information Working Group ( UNGIWG ) [66] . Diverse techniques using Geographic Information Systems ( GIS ) were applied to select units of analysis in order to classify them and to link the areas with their environmental factors:
Even though human plague cases are very localized and appear in only 18 of about 13 , 300 counties of Latin America , the risk is present and unexpected outbreaks could occur . The risk mapping suggested that there are large areas at risk of plague in the endemic countries , not only in productive areas where plague still persists with human cases , but also in neighboring counties where the disease could also emerge or re-emerge from epidemiological silence . Plague natural foci also represent a risk , even if these areas are currently not producing human cases . Attention should also be drawn to regions where plague cases are considered to be in the window of re-emergence or where there is evidence that the disease persists in nature through transmission among susceptible rodents and flea fauna and where potential transmission to humans could occur . Plague foci have been described in Argentina and Venezuela in previous decades , but no recent information has been found about active foci and no human cases have been reported since the 1950s and 60s . However , according to the literature review , these countries are close to the window of possible re-emergence [1] , [2] . The four countries considered as endemic for plague ( Bolivia , Brazil , Ecuador , Peru ) are implementing surveillance and control actions in animals; however , for most of them , plague is no longer a disease with visibility in the current epidemiological context . Even though only a few cases were reported in the last decade , the risk is still present and an unexpected outbreak could occur , as it had happened in La Libertad , Peru in 2010 , where two nosocomial infections of pneumonic plague related to the index case occurred [67] . Another example of an outbreak with pneumonic cases presenting high mortality took place in a remote Andean community in Ecuador in 1998 [68] . A further disaggregated risk evaluation is recommended , including identification of active foci and possible interactions among areas where plague could emerge or re-emerge . Demographic and poverty patterns were observed in 11 of 18 productive areas , presenting less than 200 , 000 inhabitants , more than 40% of rural population and high poverty rates . This is in accordance with the existing literature that shows that in the Region , plague affects poorer populations in rural settings [2] , [14] , [31] . In 2009 , plague was included in the list of selected neglected diseases and other poverty-related infections targeted for elimination by PAHO with member countries' agreement [69] . The goal is zero mortality and no domiciliary outbreaks [69] . Cases of human plague affect mostly vulnerable populations , supporting the regional commitment of elimination of neglected diseases and other poverty-related infections . In the Americas , plague is not only considered a poverty-related neglected infection , but also an epidemic-prone disease of potential international concern within the IHR . Behavioral factors and cultural practices might also impact the risk of human plague . In the Andean subregion , the most affected one , the cultural habits of raising guinea pigs inside homes and preparing them for cooking are risk factors for plague outbreaks [4] . In addition , household hygiene and living conditions are other key factors that influence plague prevalence among humans . Poor sanitation , mostly the accumulation of garbage , as well as the storage of harvested crops inside homes , favor rodent reproduction by providing them with a source of food and at the same time facilitate transmission of the disease to humans [4] , [40] , [42] . Furthermore , human-to-human transmission , especially among families , could also be sustained by the custom of many Andean countries of holding funeral wakes and offering the deceased's clothing to relatives [24] , [37] . Plague risk could also differ by region , depending on the reservoir and vector present locally . For example , Cavia porcellus ( the type of guinea pig used for cooking ) is an important reservoir for the Andean Area , but it is not present in Brazil . One of the limitations of this study is that we used existing information without conducting a field study . For this reason , it was not possible to determine the exact geographic location of the cases , whether the cases were imported or transmitted locally through animals and fleas , or the locations of active foci . Regarding the cases in Trujillo , Peru , interventions and epidemiological investigations have been conducted and recorded in publications [67] , [70] . The outbreak started in Ascope , La Libertad in 2009 , after 12 years of epidemiological silence in the area and subsequently , secondary cases occurred in Trujillo [67] . In the Americas , there are no secondary data available for most of the reported cases of plague , in order to determine whether the cases were imported or transmitted locally . A few productive areas have large populations and are mostly urban , for this reason , more disaggregated information is required to determine the exact location of the cases . However , this also suggests that plague remains close to large urban areas . Previous research has explored the link between plague occurrence and environmental variables [23] , [25] , [26] , [28] , [29] , [71] , [72] . Geographic distribution patterns and their associated factors are unclear [73] . Nonetheless , several studies explored altitude and topography as predictors of transmission [24] , [25] . Seasonal variation has also been used to understand the dynamics of vectors and hosts in relation to alternating cycles of temperature and precipitation [23] , [25] , [26] , [28] . More complex ecological modeling has been done using niche characterization [25] , [29] . Still , many of these studies were developed either for specific countries or in other regions and did not comprise a continental scale . They also determined that plague occurs primarily in arid-semiarid or low humidity forest types and fails to persist in moist low land areas [23] , [25] , [26] , [28] , [71] . From the selected environmental variables , we could suggest a possible pattern for the Andean countries ( Bolivia , Ecuador , and Peru ) that needs to be further studied at a larger scale ( through a closer approach ) . Most of the productive areas in the Andean countries have steeper slopes even though average altitude varies . The relationship of slope and plague has been studied before with more of an ecological perspective . In China , studies of Himalayans marmot plague foci showed that the most appropriate gradient for marmot burrows is between 5°–15° , and that the number of burrows decreases as the slope increases [74] . Steeper slopes were observed in Andean countries . In our study , 12/18 of the productive areas presented average altitudes higher than 1 , 300 meters above sea level . Higher altitudes were found in previous studies [24] , [25] , [71] . However , Brazil shows contrasting patterns; its productive areas are flatter and lower , recording slopes of 9 degrees of inclination and maximum altitudes of 805 meters above sea level . Elevational variation in the appearance of plague in the Andean countries could be related to the disease persisting long-term in native rodents and fleas at higher elevations , but occasionally spreading to village rat ( Rattus or Rattus norvegicus ) populations at lower elevations . This may occur following widespread plague epizootics in wild rodents at higher elevations or perhaps through transport of infected guinea pigs and their fleas from higher elevations to markets in lower elevations . Movements of human cases with fleas ( Pulex irritans ) in their clothes could also pose some plague risk to lower elevation sites under certain circumstances . An infected person could be a source for pneumonic outbreaks at lower elevations that are far remote from the more stable mountain plague foci . In a more integrated perspective , altitude , slope , aspect , and compound topographic index were used to characterize an Environmental Niche Model for sub-Saharan Africa , suggesting a more integrated measurement of landscape conditions than the mere altitude [29] . All of the productive areas show the presence of a drier biome within their territory , from desert and xeric shrublands to tropical and subtropical dry broadleaf forests , including biomes with montane grasslands and shrublands incapable to sustain the presence of trees and located in more mountainous zones . Nevertheless , most counties have areas with arid and semiarid conditions . As the productive areas are located in an inter-tropical zone , 11 out of 18 counties have a humid biome , tropical and subtropical moist broadleaf forests , present mostly in their flatter areas . In Latin America , 15 of the 18 productive areas present shallow/stoniness or relatively young soils with little profile development that are widely spread in arid and semi-arid areas and in mountain regions . Certain research assessing the potential role of soil in plague epidemiology showed that plague bacterium can survive at least 24 days in contaminated ground under natural conditions [73] . Furthermore , Y . pestis biotype Orientalis may remain viable and fully virulent after 40 weeks in soil [75] . Other studies have revealed that plague reservoirs have significant correlation with subsoil and topsoil characteristics such as texture , mineral contents , and pH . Plague vectors' occurrence presented significant correlation with soil depth , and mineral and organic content [76] . An additional study has demonstrated a geographic relationship between soil distribution and disease outbreaks [77] . Soil analysis may give a broader explanation about Y . pestis persistence , transmission during enzootic and epizootic cycles , or bioremediation after a natural or intentional exposure [22] . Control plans may possibly include information on the association between soil properties and plague reservoirs and vectors , in order to have a more complete environmental characterization . The information that 70 counties were identified as neighbors of productive areas could be used for future risk-based studies , to determine if they present the same environmental conditions that would be favorable for plague emergence . In addition , this information could also be used in National Plans for strengthening prevention measures and control strategies . Most of Peru , Ecuador , and Brazil's productive areas are away from international borders . In Bolivia , however , Franz Tamayo shares a border with the Peruvian region of Puno . In Peru , where plague persists in 11 counties , the analysis of 12 years of evidence of plague cases in humans suggests a geographic cluster of persistence . Most of the recent cases in Bolivia were reported in the last decade in Apolo , Franz Tamayo , a small rural village at high altitude that is well known as plague foci . The recent cases in Andres Ibanez , a highly populated area in the region of Santa Cruz , were reported as suspected . However , Santa Cruz has presented foci in previous decades . In Bolivia , the counties have extensive areas and the exact location of the suspected cases is unknown . For this reason , a more precise analysis is necessary at the third sub-national level or lower , and additional studies about the epidemiological situation of plague in Bolivia are needed . The review of the historical presence of plague in the Americas demonstrated the importance of the disease in the region . More than half of the countries in Latin America ( 14 of 25 of countries ) faced at least one plague event with human cases since the end of the 19th century . The historic spread of plague inside countries following trade and transportation is a lesson of great value , particularly in the context of the IHR [3] , [10] , [24] . Further risk analysis could be carried out to evaluate with more precision the epidemiological situation of possible risk areas close to large cities . The risk mapping shows that potential interaction by railroads , rivers , and roads may be occurring between productive areas and other zones , including highly populated cities . Neighboring counties need to be better evaluated , especially existing trade mechanisms that could potentially introduce rodents with infected fleas into new areas . In the 1990s , a plague outbreak occurred in a populated city in India , creating major public health concern , widespread panic , worldwide apprehension , and severe economic losses for India [78] . Since this was a retrospective study , we suggest that the countries develop more detailed mapping of their productive areas , silent areas , and foci , using the same definitions and methodology to evaluate the risk . Little is known about the dynamics of plague in its natural reservoir and about changing risk for humans [5] . The environmental variables in this study were selected for an exploratory purpose and to present a brief profile of the productive areas . Zones with the same environmental conditions in productive and neighboring counties may be at higher risk of plague emergence . However , this information was not disaggregated enough to geo-reference cases more precisely in order to perform this analysis . This information could be used as a stepping-stone for future studies with local participation that , within the wider context of a risk-based approach , could include the exact geographic coordinates of cases and the epidemiological investigation that was performed by the national authorities . Plague continues to be a public health threat with epidemic potential for the Americas , in spite of the low number of human cases and its persistence in fewer than 20 regions . Plague remains a neglected disease that could be addressed within the context of the One Health vision , where animals , humans , and ecosystems interrelate , and multidisciplinary teams and intersectoral collaboration is needed . The principal conclusion of the International Meeting of Plague Experts in Latin America held by PAHO in Lima , Peru in 2013 was that the preparation of a Regional Strategic Plan on Plague is crucial ( PAHO , unpublished data ) . An “ad hoc” committee was formed during the meeting with representatives from every endemic country in Latin America and PAHO as the secretary . The importance of identifying risk areas and strengthening the first level of care in these areas will be emphasized in the Plan , as well as detection , case management , community surveillance , and intersectoral collaboration for prevention and response of outbreaks . The analysis presented in this study could be used as evidence for decision-making in support of such a Plan to identify higher risk areas , and to prioritize areas for intervention , as well as to identify future studies to be conducted by the countries with international collaboration , when desired . Areas with different risk characterizations need specific actions of surveillance , prevention , and preparedness to be ready to respond to possible outbreaks . This Plan will aim to strengthen country programs and support the cooperation among countries in their common vision and goal to predict , prevent , detect , and respond to plague events . | Plague is a disease of epidemic potential that could emerge and re-emerge after decades of epidemiological silence . Today , in Latin America , human cases and natural foci are present in Bolivia , Brazil , Ecuador , and Peru . We searched for official information of where cases of human plague still persist in Latin America , mapped the areas , and briefly described selected factors . This analysis will provide evidence-based information for countries to prioritize areas for intervention . A geo-referenced database and risk map were created . One or more events of plague were found in 14 of 25 countries in Latin America in the period of 1899–2012 . There is evidence that human cases of plague still persist in 18 of the almost 13 , 300 second levels of the political/administrative divisions ( counties ) . Demographic and poverty patterns were observed in 11/18 counties . Four types of biomes are most commonly found and 12/18 counties have an average altitude higher than 1 , 300 meters above sea level . Even though human plague cases are very localized , the risk is still present , and unexpected outbreaks could occur . Countries need to make the final push to eliminate plague as a public health problem for the Americas . A further disaggregated risk evaluation and a closer environmental characterization are recommended . | [
"Abstract",
"Introduction",
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] | [
"public",
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] | 2014 | Where Does Human Plague Still Persist in Latin America? |
Host factors that facilitate viral entry into cells can , in principle , be identified from a virus-host protein interaction network , but for most viruses information for such a network is limited . To help fill this void , we developed a bioinformatics approach and applied it to hepatitis C virus ( HCV ) infection , which is a current concern for global health . Using this approach , we identified short linear sequence motifs , conserved in the envelope proteins of HCV ( E1/E2 ) , that potentially can bind human proteins present on the surface of hepatocytes so as to construct an HCV ( envelope ) -host protein interaction network . Gene Ontology functional and KEGG pathway analyses showed that the identified host proteins are enriched in cell entry and carcinogenesis functionalities . The validity of our results is supported by much published experimental data . Our general approach should be useful when developing antiviral agents , particularly those that target virus-host interactions .
The conventional approach to countering viral infections has been to develop drugs that target viral genetic material or proteins . However , two major roadblocks to this strategy exist: 1 ) the limited number of druggable viral proteins owing to small viral genomes , and 2 ) drug resistance that occurs on a relatively short time scale owing to substantial viral genomic mutation rates . To circumvent these problems , over the past decade antiviral drug development has shifted from targeting viral proteins to host proteins that interact with components of the virus [1] . For example , compounds that inhibit interactions between viral and human proteins have been identified [2] , including the compound LEDGIN , which targets the interaction between HIV integrase and human transcriptional coactivator p75 [3] . Cell-based genomic and proteomic assays that screen for host targets that interact with viral proteins have also been reported [4–6] . Nevertheless , given the large amount of biological data that has been accumulated from high-throughput omics-type experiments , development of a bioinformatics-sleuthing strategy that identifies potential antiviral host targets to complement experimental screens should be of considerable merit . Herein , we describe the development of and evaluate such a bioinformatics strategy , the premise of which is based on viral “molecular mimicry , ” an ability that viruses have developed over millions of years of evolution to antagonize their hosts [7] . Specifically , regions in viral proteins apparently can mimic short amino acid sequences found in human proteins involved in normal host protein-protein interactions ( PPIs ) , so that a virus can hijack the PPI for its own purposes , such as hijacking a cellular process ( es ) to create the cell context needed for infection [8] . Consistent with this viral strategy , their proteins often contain host-like SLiMs ( Short Linear Motifs ) allowing them to interact with complementary host proteins [9 , 10] . Viral SLiMs can be identified by sequence comparison with those with the ability to bind eukaryotic protein domains as catalogued in the database ELM ( Eukaryotic Linear Motif ) [11] . The viral SLiMs , the host proteins that contain a matched SLiM-binding domain , and these proteins’ interacting partners in the human PPI network then form a putative virus-host interaction network , which can be integrated with known functional and network properties of cellular pathways , including those involved in disease states , thereby allowing identification of host factors whose native functions may be altered or hijacked by the virus to facilitate its infection and/or another of its life cycle stages . To examine this molecular mimicry strategy and the feasibility of using human PPI network data to complement experimental studies , we focused on the hepatitis C virus ( HCV ) envelope proteins , E1 and E2 , for the following reasons: First , HCV infection is a major health problem worldwide [12] , and HCV E1 and E2 are known to play essential roles in HCV entry into human hepatocytes [13]; investigating E1 and E2 might therefore lead us to identify novel HCV entry factors as targets for drug design—an important step toward developing more effective anti-HCV drugs . Second , the complexity of the network and functional analysis required was significantly reduced because only liver cell surface proteins of the human proteome need to be considered . Third , many HCV entry–facilitating human proteins have been identified , which allowed us to compare in silico predictions with published experimental data . Using the HCV E1 and E2 sequences as examples , Fig 1 schematically depicts the four main components of our approach , which are detailed in Methods . First , conserved E1/E2 sequences from various HCV strains are identified that correspond to SLiMs found in the eukaryotic linear motif ( ELM ) database ( Fig 1A ) . Next , proteins on the surface of human hepatocytes known to bind such SLiMs are identified ( they are called VIPsdirect for Virus-Interacting host Proteins ) , as are host proteins ( VIPsindirect ) that bind VIPsdirect ( Fig 1B ) . Taking the experimentally determined interactions between VIPsdirect and VIPsindirect from the original human PPI network ( [14]; see Methods ) , a virus-host PPI network is then extracted that is constructed of the viral SLiMs and human VIPs ( Fig 1B and 1C ) . This network contains modules ( communities ) of functionally related host proteins ( nodes ) and links ( PPIs ) within and between the modules connecting interacting nodes ( Fig 1C ) . Finally , a map connecting SLiMs to known antiviral peptides ( AVPs ) and complexes containing multiple ( ≥3 ) SLiM-interacting proteins is produced ( Fig 1D ) , for which statistical analyses are carried out to find enriched functionalities and pathways that correlate with published experimental data . As shown below , the results show that the proteins we identified as possible hepatitis C virus ( HCV ) entry factors: 1 ) have a statistically significant propensity to be found in the PHISTO and EHCO lists , which contain experimentally identified HCV-interacting proteins and genes differentially expressed in HCV-induced hepatocellular carcinoma , respectively [15 , 16]; 2 ) have greater coverage of known HCV entry factors than a functional genomics screening experiment [5]; and 3 ) contain domains that can bind short linear motifs that are also present in many antiviral peptides with experimentally demonstrated activities against HCV infection . These results suggest that , to eliminate viral infection , more attention should be paid to sequence motifs involved in host protein-protein interactions because these motifs may be subject to molecular mimicry by viruses .
We identified 19 SLiMs on HCV E1 and E2 that might bind various human protein domains ( Fig 1B ) . Screening for human hepatocyte surface proteins in conjunction with the available human PPI network yielded 115 VIPsdirect containing at least one SLiM-binding domain . These proteins and their VIPsindirect , which interact with them , constitute a subset of the experimentally derived human PPI network [14] that might be directly or indirectly influenced by the mimicking HCV SLiMs . It follows , according to the premise of our molecular mimicry strategy , that the host VIPsdirect and VIPsindirect potentially facilitate or inhibit HCV entry and , along with the viral SLiMs , they formed a viral-host PPI network ( Fig 1B and 1C ) . Given a network , algorithms are available to extract network properties [17] . Using NetCarto , a tool for network module discovery [18] , we found that the resulting viral-host PPI network for HCV infection is organized into eight modules with 23 R6 ( global connector ) hubs ( Fig 2 ) . A global connector hub ( R6 ) is defined as a node with many links to most of the other network modules ( see Methods for definitions on roles of network nodes ) , and as such it is thought to play an important role in connecting different functional modules . Consistent with the definition , whereas most of the 115 VIPsdirect interact with only a few other host proteins , these 22 R6 hub proteins ( the twenty-third R6 hub is a viral SLiM ) have many interaction partners . This may imply that these R6 hubs could be important host factors for HCV infection , and could serve as targets for designing anti-HCV drugs ( see AVPs analysis below ) . Further analysis showed that 15 out of the 22 R6 hubs ( P < 2 . 2 × 10−16; S1 Fig ) were also hub proteins in the experimentally derived PPI network of human liver cell surface proteins , suggesting that most of these VIPsdirect R6 hubs have important functions for the host , irrespective of HCV infection . This is in line with the finding that viruses tend to target host hub proteins for perturbing key pathways ( or biological processes ) to benefit viral infections [19] . Interestingly , Gene Ontology ( GO ) enrichment analysis [20] and Revigo summarization [21] of the enriched GO annotations ( S1 Table ) revealed that the representative functions of seven of the eight modules belong to one , or both , of the two main functionalities: entry and carcinogenesis ( Fig 3 and S2 Table ) . As described below , much experimental data is available to support our in silico observations . For example , “Cytoskeleton organization” ( modules 1 and 7; Figs 2 and 3 ) is an essential cellular process that allows HCV to migrate to the tight junction where internalization and endocytosis of the virion occur [22] . Notably , some of the proteins of the R6 hubs are involved in this cellular process . According to our hypothesis , cellular processes involved in “Cytoskeleton organization” might be hijacked by HCV if one or more of its E1/E2 SLiMs can bind at least one of the following six R6 proteins ( in this work we use official gene symbols to represent proteins encoded by the corresponding genes ) : PIK3R1 , which enhances actin reorganization by activating PI3K-AKT signaling [23]; SRC , which induces changes in the cytoskeleton by binding and activating FAK [24]; and ABL1 [25] , GRB2 [26] , NCK1 [27] , and CTTN [28] , proteins which regulate cytoskeleton rearrangement . A function found for module 5 is “apoptosis” ( Figs 2 and 3 ) , which is an essential cellular process as it prevents HCV from spreading in the host by inducing the death of HCV-infected cells [29] . However , E2 can suppress cellular apoptosis resulting in HCV survival [30] . In our viral-host PPI network , there are five module 5-associated R6 proteins , four of them , AKT1 [23 , 31] , CHUK [32 , 33] , PRKCA [34 , 35] , and TGFBR1 [36 , 37] have a role in apoptosis and their activities and/or expressions are known to be affected by HCV infection , although the specific effects of E1 and/or E2 on CHUK , PRKCA , and TGFBR1 activities have yet to be determined . The apoptosis-regulating role of the fifth R6 protein PRKCD [38] during HCV infection has also not been examined . Another representative function of module 7 , “receptor signaling , ” contributes to HCV entry [39] and carcinogenesis [40] . Specifically , HCV infection triggers EGFR signaling and stimulates its downstream signaling , including those of HRAS and PI3K-AKT [39] . Activation of these pathways enhances HCV entry [23 , 39] and increases the proliferation of hepatocytes , which may contribute to hepatocellular carcinogenesis [40] . Three R6 proteins are associated with receptor signaling: PIK3R1 , a PI3K subunit and a crucial participant in PI3K-AKT signaling [41] , and GRB2 [42] and SHC1 [43] , two key adaptor proteins of EGFR signaling , which when silenced substantially impair HCV entry [39] . Additional published experimental data that support the relationships between the R6 proteins and the functions of the viral-host PPI network modules are summarized in S3 Table . We identified 899 VIPs using our scheme . To evaluate the validity of our findings , we compared our list of VIPs with those in the PHISTO ( Pathogen-Host Interaction Search Tool ) dataset , which contains a list of experimentally verified HCV-interacting human proteins [16] . There are a total of 698 HCV-interacting human proteins in PHISTO , of which 160 are in the set of 2 , 456 liver cell surface proteins ( Fig 1B ) . Of the 160 HCV-interacting hepatocyte surface proteins , 158 are annotated specifically as interacting with the polyprotein of HCV ( S4 Table ) , which contains E1 and E2 . As shown in S2A Fig , the 899 VIPs tended to contain members from the 158 subset of the PHISTO list , with 92 proteins overlapped between the two ( P = 9 . 2 × 10−9; S2A Fig ) . Similarly , the predicted interactions between HCV ( SLiMs ) and VIPs , i . e . the edges of the viral-host PPI network , were enriched in PHISTO ( P = 4 . 3 × 10−4 and 9 . 6 × 10−5 for direct and indirect interactions , respectively; see S3 Fig and S4 Table ) . These results indicate that our bioinformatics approach preferentially identified HCV-interacting human proteins . Four of the identified virus-host PPI network modules are associated with carcinogenic processes ( Fig 3 ) , which is a somewhat unexpected result as E1 and E2 are usually only considered to be entry proteins [13]; however , this association is consistent with known oncogenic effects of E1 and E2 [44 , 45] . To further evaluate the involvement of the VIPs in hepatocellular carcinoma ( HCC ) , we assessed if a significant number of those VIPs are found in the EHCO ( Encyclopedia of Hepatocellular Carcinoma genes Online ) [15] dataset . Of the 614 genes that are differentially expressed in HCV-caused HCC in EHCO , 194 are expressed on liver cell surface , and 91 of their encoded proteins were identified as VIPs ( P = 1 . 4 × 10−3; S2B Fig ) , further supporting the notion that E1/E2 interact with host proteins that have a role in carcinogenesis . Recently , a set of host factors for HCV entry was identified in a large-scale siRNA screening experiment reported by Li and coworkers [5] . However , the authors of that study identified only four of the 15 human proteins found on hepatocyte surface and known to be associated with HCV entry ( Table 1 ) . By comparison , we identified 11 of these entry factors known before Li and colleagues performed their study , and we also identified the four proteins found by them ( Table 1 ) . Three of the known entry factors that our study did not find , CLDN1 , SCARB1 , and CD209 , are connected to at least one VIPindirect in the human PPI network , but are not VIPs themselves ( S4A–S4C Fig ) . The fourth , NPC1L1 , which we did not identify , lacks information of interaction with any of the VIPs in the network ( S4D Fig ) . CD81 , OCLN , CLDN1 and SCARB1 are arguably the four best known HCV entry-related factors [48] . Of them , we identified CD81 and OCLN as VIPsindirect but failed to find CLDN1 and SCARB1 as noted above . Interestingly , transgenic expression of human CD81 and OCLN in mouse enabled HCV infection of mouse hepatocytes , whereas transgenic expression of human CLDN1 or SCARB1 was not necessary for mouse cell to be infected by HCV [49] . As a VIPindirect , CD81 is not predicted to possess a domain that can directly bind an E1/E2 SLiM , which might be considered to contradict a report suggesting a physical interaction between HCV E2 and CD81 [50] can occur . Nonetheless , viruses employ other strategies to interact with host proteins [7] , and indeed , substitution mutation experiments suggested that HCV E2 uses a non-sequential motif to bind CD81 [51] , which we would not have uncovered using the SLiM-based approach . Altogether , our approach found more known HCV hepatocyte entry host factors ( 15 of 19 ( 79% ) ; Table 1 ) than did the siRNA functional genomics-screening assay of Li and colleagues . Furthermore , an ROC ( Receiver Operating Characteristic [52] ) curve analysis accounting for not only sensitivity but also specificity showed that the in silico predictions yielded an AUC ( area under curve ) of 0 . 68 , which is almost as good as that ( 0 . 70 ) of Li et al . ’s experimental screening ( S5 Fig ) . Note that including protein complexes in the analysis ( see below ) significantly improved on specificity for the in silico predictions while maintaining the overall performance; similarly , in Li et al . ’s experiment , a large number of genes ( 19 , 277 ) had been removed by a genome-wide genetic screen [53] prior to the siRNA functional analysis [5] , and these were not included in the ROC curve analysis , giving rise to a much higher specificity for Li et al . ’s experiment ( S5 Fig ) . In addition , because all of those we predicted as novel ones were treated as false positives in these calculations , the actual performance of our predictions could possibly be better . Viruses are known to target protein complexes so as to heavily perturb host functions and induce disease states , e . g . , carcinogenesis [6 , 54] . It is , therefore , of interest to identify human protein complexes that might be perturbed by HCV infection , including those involving VIPsdirect and VIPsindirect . Knowledge of such protein complexes would also greatly reduce the number of VIPs needed to be considered for pathway analysis and experimentation . By mapping the VIPs to protein complexes in the HPRD [46] and CORUM [55] databases ( these databases categorize human and mammalian protein complexes , respectively ) and clustering according to shared subunits , we identified six groups of protein complexes ( Fig 4 and S6 Fig and S5 Table ) that HCV may target by interaction with the VIPs of the complexes . These six groups contain 231 of the 899 VIPs and nine of the 19 viral SLiMs , with six of the nine SLiMs capable of binding the same protein domains ( Fig 4 ) . A comparison of our results with those obtained using the same mapping procedure and the same number of randomly selected proteins showed that the tendency of the HCV SLiMs-derived VIPs to be present in these protein complexes is unlikely to occur by chance ( P < 1 . 0 × 10−5; S7 Fig ) . Furthermore , 11 of the 22 R6 hub proteins are present in these HCV-targeted complexes , ( P = 1 . 1 × 10−2; S2C Fig ) . Moreover , these 231 VIPs are significantly enriched by members of the PHISTO , EHCO , and known HCV entry factor lists ( P = 1 . 5 × 10−11 , S2D Fig; P = 1 . 8 × 10−7 , S2E Fig; and P = 4 . 8 × 10−3 , S2F Fig; respectively ) . Two known HCV entry factors , APOE and HRAS , and a novel entry factor ITGB identified by Zona and colleagues [39] are components of a single complex with CD81 [39 , 57] . Of this CD81-complex , we identified several subunits , ADAM10 , APOE , CD59 , CD9 , HRAS , ITGB1 and SCAM , as VIPs , this complex hence meets our criterion of ≥3 VIPs for an HCV-targeted host protein complex even though information of this complex was not used in our analysis because the latest compilations of CORUM and HPRD , in 2012 and 2009 , respectively , predate the work of Zona and colleagues . This example therefore illustrates the merit of our approach in general , and the inclusion of protein complexes in the analysis in particular . As detailed in the S1 Appendix , there are three main functionalities associated with KEGG pathways that are enriched in the HCV-targeted protein complexes: entry , carcinogenesis , and infectious disease; the first two overlap substantially , as is shown by the analysis of the GO enrichment terms associated with the network modules ( Fig 3 ) . Examination of the KEGG pathways enriched in complex-forming VIPs helps us to understand the roles the complexes might play during HCV infection . For example , TGF-beta signaling ( P = 1 . 2 × 10−6 ) , Endocytosis ( P = 4 . 8 × 10−11 ) , and Adherens junction ( P = 7 . 6 × 10−7 ) are among KEGG pathways enriched in group C complexes ( S6 Table ) in which the VIPsdirect TGFBR1 , TGFBR2 , ACVR1B , and ACVR1C are signaling receptors [58] and PRKCZ and PRKCI are protein kinases involved in endocytosis and adherens junction remodeling [59] . Our analysis revealed that several of the enriched pathways are involved in more than one step of HCV entry , and that the envelope proteins may regulate immune responses to HCV infection , affect hormone-related signaling pathways , and modulate HCC progression . These suggestions are in line with many experimental studies ( S1 Appendix ) , including the report that HCV E1 and E2 can alter RIG-I-like receptor signaling and Toll-like receptor signaling [60] . Finally , the presence of enriched pathways associated with infectious disease suggests that some of the VIP-containing protein complexes may also be targets of other viruses , which is consistent with reports of coinfection of two or more different viruses ( see , e . g . [61] ) . However , when compared to all the SLiM-binding hepatocyte surface proteins and their binding partners in the human PPI network , the identified HCV-targeted protein complexes were shown to be significantly enriched in KEGG pathways belonging to the functionality of cell entry ( P = 3 . 7 × 10−2 ) and carcinogenesis ( P = 2 . 7 × 10−2 ) , but not infectious disease ( P = 3 . 8 × 10−1 ) ( see S8 Fig ) . This may suggest that infectious disease is more likely than the other two functionalities to be influenced by many of these proteins with a domain that can bind other SLiMs of the ELM database . Peptides that can interfere with virus-host interactions are potential antiviral drugs [62 , 63] . The viral SLiMs that we identified may , therefore , be useful scaffolds upon which to build AVPs . A search of the AVPdb [56] returned 73 AVPs that have been examined for entry-related , anti-HCV infection ( Fig 1D ) , with more than one-third ( 29 ) harboring at least one of the nine identified SLiMs that might target a VIP residing in at least one of the six main protein complex groups . A statistical test indicated that these nine SLiMs were as likely to be also present in other AVPs ( P = 8 . 8 × 10−1 , S9A Fig ) , suggesting that , besides SLiMs , other parts of the AVP sequences are required to determine entry-related anti-HCV activities . Notably , however , many of the 29 AVPs have been shown to actively suppress HCV entry in cell-based assays ( Fig 4 , top panel ) . Although the molecular mechanisms associated with the anti-HCV activities of these AVPs have not been fully elucidated , it is tempting to speculate that , because they contain an HCV SLiM , they may interfere with an HCV-host protein interaction by preferentially binding the host protein and , thereby , inhibiting HCV entry . We present two examples to demonstrate how our bioinformatics procedures can be used to identify protein complexes known to play a role in HCV infection and/or pathology . The first example ( Fig 5A ) , involves the complex containing EGFR , SHC1 , and GRB2 ( group F complex , Fig 4 ) . This complex ( S5 Table , complex ID: 176 ) mediates HRAS signaling critical for HCV entry [39] . According to our results , HCV E1/E2 might interact directly with SHC1 and GRB2 ( both are R6 VIPsdirect ) and indirectly with EGFR ( a VIPindirect ) via the SLiMs of LIG_SH2_STAT5 and/or LIG_SH3_3 ( Fig 5A and 5C ) . Furthermore , of the 23 AVPs that contain the same SLiM ( 22 with LIG_SH2_STAT5 and one with LIG_SH3_3 ) , 20 ( 86% ) are shown to inhibit HCV entry ( Fig 5C , I and II ) , and this percentage is shown to be statistically significant ( P < 1 . 0 × 10−4 , S9B Fig ) . The second example , the AKT1-associated complex ( also a group F complex , Fig 4 ) , is presented in Fig 5B . When being a part of this complex ( S5 Table , complex ID: 180 ) , AKT1 can inhibit apoptosis induced by BAD overexpression [64] , and inhibition of BAD-mediated apoptosis contributes to HCC progression [65] . Our analysis suggests that HCV E1 and/or E2 may interact with the AKT1-associated complex by targeting one or more of its members , i . e . , AKT1 and PAK1 through SLiMs of the MOD_family ( MOD_NEK2_2 of E1; MOD_CK1_1 , MOD_CK2_1 , MOD_GSK3_1 , MOD_NEK2_1 of E2 , and MOD_ProDKin_1 of E1 and E2 ) , and SORBS2 , an SH3 domain-containing protein , through the SLiM of LIG_SH3_3 . Together with the report that HCV E2 can induce AKT phosphorylation to facilitate HCV entry [23] , these results suggest that a viral SLiM and protein domain association may be involved in viral entry and virus-induced carcinogenesis . Furthermore , as with the first example , the majority ( four out of seven; P < 1 . 0 × 10−4 , S9C Fig ) of the AVPs containing a MOD_family SLiM inhibits HCV entry ( Fig 5C ( III ) ) .
Despite recent , rapid advances in high-throughput experiments , all characterized networks of virus-host interactions remain vastly incomplete . To fill this void , several studies have incorporated bioinformatics information [66] such as that obtained by text mining experimental reports from the literature [67] . In this work , we described a strategy , identifying eukaryote-interacting SLiMs found in viruses to find human proteins that may be “hijacked” by HCV for its entry via “molecular mimicry” of the SLiMs . With the identification of these human proteins , i . e . , VIPs , we then built a virus-host PPI network by exploring the human PPI network for functions that may be modulated and/or productively used by the virus . Per our “molecular mimicry” hypothesis , more than half of the hepatocyte surface proteins could be VIPs for a SLiM from the ELM database to interact with directly or indirectly ( 1 , 320/2 , 456 , see S8 Fig legend ) , and more than one third ( 899/2 , 456 , Fig 1B ) for a SLiM harbored by HCV E1/E2 alone ( Fig 1B ) . This suggests that SLiMs by themselves are of low binding specificity to protein domains , and thus most of the predicted VIPs are likely false positives . However , as demonstrated by the results presented above , by integrating with a variety of experimental data and information , especially with network and functional analysis , the number of VIPs ( hence false positives ) predicted can be greatly reduced and a manageable list of viable candidate proteins can be extracted to complement and guide further experimental investigations . Our functional analysis shows that the SLiMs-derived VIPs of HCV infection and the related host protein complexes are involved in two major types of cellular functions , one associated with viral entry and the other with carcinogenesis and/or infectious disease ( Figs 3 and 4 ) . Although the inclusion of the second category was somewhat unexpected because HCV E1 and E2 were used to find SLiMs , a role for E1/E2 in carcinogenesis has been demonstrated [44 , 45] and multi-functionality of other viral envelope proteins has also been documented [68–70]: For example , hemagglutinin , an envelope protein of the influenza virus , is involved in viral entry but also activates NF-κB when expressed in 293T and Hela cells [69] . Many of the predicted interactions between the SLiMs found in HCV E1/E2 and the human VIPs that occupy a prominent role ( R6 , global connector hub ) in the virus-host PPI network , and the relationships between these R6 VIPs and their deduced functional modules , are supported by published experimental evidence ( Fig 3 ) . Together with the results from a similar approach used to predict HIV-interacting human proteins [71] and the report that virus-host interactions may be assisted by host-like SLiMs [10] , evidence is mounting to support the suggestion that , via SLiMs , host proteins can be “hijacked” and host functions rewired by pathogens , a phenomenon that has been extensively reviewed at the pathway level [8] . Further supporting this premise , some HCV E1/E2 SLiMs , particularly those that might interact with major protein complexes , are found in many AVPs that inhibit HCV infection ( Fig 4 ) . Although recently approved direct-acting antiviral ( DAA ) treatments have improved the virologic response rate to >90% for most types of chronic hepatitis C infections , new , hard-to-treat HCV strains including genotype 3 and DAA-resistant variants generated from DAA-treated patients , have appeared [72] . Because DAA-resistant strains are disseminated mainly through cell-to-cell transmission rather than cell-free transmission [73] , the former route will be key to eradicating HCV infection . Several known HCV entry factors for cell-free transmission , e . g . , EGFR , CLDN1 , OCLN , and SCARB1 are also involved in cell-to-cell transmission [74–76] . Additionally , cell-to-cell transmission independent of CD81 , the most well studied binding receptor for HCV E2 [13] , has been reported [77–79] . Taken together and given that HCV E1/E2 are indispensable for cell-to-cell transmission of the virus [79] , other host factors that can interact with these viral proteins need to be identified . Although the mechanism ( s ) of HCV cell-to-cell transmission is not yet understood , this type of transmission has been associated with cell adhesion molecules [80] , many of which belong to the KEGG pathway category of Cellular community . Interestingly , pathways in this category are significantly enriched in four of the six protein complex groups ( all except group A and E complexes; Fig 4 ) , suggesting that some of the complex-associated VIPs may be a good starting point to study cell-to-cell transmission of HCV . Because host-targeting antivirals are generating enthusiastic interest for the development of treatments for hard-to-treat hepatitis C infections [81] , our bioinformatics strategy is a timely approach to identify new targets for antiviral research , not only for HCV but also for other viruses as the concept of SLiM involvement in molecular mimicry is a general one .
The HCV E1/E2 sequences were scanned against sequences in the ELM database ( http://elm . eu . org/ ) [11] to find short , matching linear sequences in mammalian proteins known to interact with other mammalian proteins , which might , therefore , be mimicked by HCV E1/E2 sequences ( Fig 1A ) . E1 and E2 sequences ( from 41 and 35 HCV strains , respectively ) were extracted from the UniProtKB/SwissProt database ( http://www . uniprot . org/ ) [82] for use in our study . We required that the sequences of the matched viral SLiM needed to be conserved at a rate of at least 70% , a cutoff used for a study of HIV SLiMs by others [71] and also yielded the best performance to balance sensitivity and specificity in predicting known HCV entry factors ( S5 Fig ) . The search retrieved 26 distinct and conserved SLiMs ( Fig 1A ) , which were then used to find the human proteins ( VIPsdirect ) that they might interact with ( Fig 1B ) . As shown in S10 Fig , the number of SLiMs that can be found in HCV protein sequences is roughly proportional to protein size , and all these proteins can confer the “molecular mimicry” mechanism hypothesized and be targets of our investigation; however , as explained in Introduction , we focused on E1/E2 sequences because we were primarily concerned with the viral entry process , in addition to other considerations . Accompanying each SLiM in the ELM database is an annotation of the protein domain ( s ) to which the SLiM can bind . Of the 26 identified SLiMs , 23 have information for human proteins , and of the 23 , 19 were mapped to hepatocyte surface proteins that are present in the Human Integrated Protein-Protein Interaction Reference database [14] ( HIPPIE release v1 . 7; http://cbdm-01 . zdv . uni-mainz . de/~mschaefer/hippie/ ) , which is a constantly updated human PPI database that integrate multiple experimental PPI datasets , to derive a PPI network . The list of hepatocyte surface proteins used to develop our virus-host PPI network was collected from Human Protein Reference Database [46] ( HPRD; http://www . hprd . org ) and Human Proteinpedia Database ( http://www . humanproteinpedia . org ) [47] by using the keywords “Liver” or “Hepatocyte” for the tissue type and “Extracellular region , ” “Plasma membrane , ” “Cell surface , ” or “Cell junction” for the type of cellular component to search for potential host proteins expressed on the hepatocyte surface that might facilitate the entry of HCV . A set of 2 , 456 human proteins matched these keywords , and we termed this set “liver cell surface proteins” . Of these proteins , 115 ( VIPsdirect ) contained at least one protein domain to which one of the 19 viral SLiMs could bind which would mimic the corresponding human SLiM . A set of 784 proteins ( denoted VIPsindirect ) were identified as binding partners to the VIPsdirect . The interactions between the VIPsdirect and VIPsindirect , and those between the viral SLiMs and VIPsdirect were combined to form the virus-host network , which was then subjected to a network module/functional analysis , as described in the next step and Fig 1C . Given a network , it is useful to determine whether it is formed of modules ( i . e . , communities ) , which are often indicative of distinct functions . For this task , we applied the network analysis tool NetCarto [18] using its large-network default settings to identify modules . The roles of importance for each network node , including every VIP , could also be assigned , which ranged from the least significant , R1 ( ultra-peripheral ) and R2 ( peripheral ) , to the most significant , R6 ( global connector hub ) and R7 ( kinless hub ) ( a hub is a node connected to many nodes ) . Following NetCarto [18] , the definitions of the seven classes of network nodes and their roles in the network are summarized and schematically depicted in Fig 6 . The biological function of each network module was inferred using DAVID ( https://david . ncifcrf . gov/ ) [83] to search for enriched GO functions [20] under the category of “biological process . ” For each module , Revigo [21] was used to obtain representatives for enriched GO terms ( Benjamini–Hochberg-adjusted P < 0 . 05 ) , and a main functionality was deduced to cover these representatives . Because host VIPsdirect and VIPsindirect may be subunits of the same protein complex targeted by the virus , we mapped the VIPs to the human protein complexes in HPRD [46] and in the mammalian protein complexes database ( CORUM ) [55] . The 190 complexes containing three or more VIPs were then clustered by GAP [84] , a tool for matrix visualization and clustering , based on similarity related to the number of common subunits . The choice of three VIPs was made to reduce the total number of VIPs for enrichment analysis while maintaining their coverage of known HCV entry factors as much as possible . This procedure yielded six major protein-complex clusters , or groups ( small groups containing fewer than five complexes were excluded ) , which altogether contained 177 complexes and 231 VIPs that were linked to nine viral SLiMs . The VIPs of each of the six complex groups were then subjected to KEGG pathway enrichment analysis [85] using the clusterProfiler R package [86] . A total of 43 significantly enriched hepatocyte-expressed pathways were identified ( Benjamini-Hochberg-adjusted P < 0 . 001 ) . In addition , a search of AVPdb [56] identified 73 peptides annotated with “Hepatitis C virus” and “Virus entry” whose inhibiting activities against HCV entry have been examined by experimental screening . Of the 73 sequences , 29 matched at least one of the nine viral SLiMs . Matching of the AVP sequences and viral SLiMs was performed at the ELM database website . | Viruses recruit host proteins , called entry factors , to help gain entry to host cells . Identification of entry factors can provide targets for developing antiviral drugs . By exploring the concept that short linear peptide motifs involved in human protein-protein interactions may be mimicked by viruses to hijack certain host cellular processes and thereby assist viral infection/survival , we developed a bioinformatics strategy to computationally identify entry factors of hepatitis C virus ( HCV ) infection , which is a worldwide health problem . Analysis of cellular functions and biochemical pathways indicated that the human proteins we identified usually play a role in cell entry and/or carcinogenesis , and results of the analysis are generally supported by experimental studies on HCV infection , including the ~80% ( 15 of 19 ) prediction rate of known HCV hepatocyte entry factors . Because molecular mimicry is a general concept , our bioinformatics strategy is a timely approach to identify new targets for antiviral research , not only for HCV but also for other viruses . | [
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] | 2017 | Identification of Entry Factors Involved in Hepatitis C Virus Infection Based on Host-Mimicking Short Linear Motifs |
Chikungunya virus ( CHIKV ) has caused multiple outbreaks in tropical and temperate areas worldwide , but the clinical and biological features of this disease are poorly described , particularly in Africa . We report a prospective study of clinical and biological features during an outbreak that occurred in Franceville , Gabon in 2010 . We collected , in suspect cases ( individuals presenting with at least one of the following symptoms or signs: fever , arthralgias , myalgias , headaches , rash , fatigue , nausea , vomiting , diarrhea , bleeding , or jaundice ) , blood samples , demographic and clinical characteristics and outcome . Hematological and biochemical tests , blood smears for malaria parasites and quantitative PCR for CHIKV then dengue virus were performed . CHIKV+ patients with concomitant malaria and/or dengue were excluded from the study . From May to July 2010 , data on 270 laboratory-confirmed CHIK patients were recorded . Fever and arthralgias were reported by respectively 85% and 90% of patients , while myalgias , rash and hemorrhage were noted in 73% , 42% and 2% of patients . The patients were grouped into 4 clinical categories depending on the existence of fever and/or joint pain . On this basis , mixed forms accounted for 78 . 5% of cases , arthralgic forms 12 . 6% , febrile forms 6 . 7% and unusual forms ( without fever and arthralgias ) 2 . 2% . No cases of organ failure or death were reported . Elevated liver enzyme and creatinine levels , anemia and lymphocytopenia were the predominant biological abnormalities , and lymphocytopenia was more severe in patients with high viral loads ( p = 0 . 01 ) . During CHIK epidemics , some patients may not have classical symptoms . The existence of unusual forms and the absence of severe forms of CHIK call for surveillance to detect any change in pathogenicity .
Chikungunya fever ( CHIK ) is a neglected tropical disease caused by the Chikungunya virus ( CHIKV ) , an arthropod-borne virus belonging to the genus Alphavirus of the Togaviridae family . This virus is transmitted to humans via the bite of infected Aedes mosquitoes ( Aedes aegypti , Aedes albopictus ) . The genome consists of a single positive strand of RNA that encodes four nonstructural proteins involved in virus replication and pathogenesis , and five structural proteins that compose the virion [1] . CHIKV is subdivided into three genotypes based on phylogenetic analyses . These genotypes , based on the gene sequences of an envelope protein ( E1 ) , are Asian , East/Central/South African ( ECSA ) , and West African [2]–[6] . Over the past two decades , this virus has caused multiple outbreaks worldwide , particularly in tropical and subtropical areas . Since its initial isolation in Tanzania in 1953 , sporadic cases and numerous outbreaks have been reported in Africa , India Ocean Islands , India , and even in Italy , a temperate region . CHIKV circulated in West and East Africa at low levels until 1999–2000 , when an outbreak occurred in the Democratic Republic of the Congo ( DRC ) with around 50 , 000 cases [7] . From 2004 , successive epidemics occurred , starting in Kenya with 13 , 500 cases and then spreading through the Indian Ocean region , including the Comoros Islands , Mauritius , Mayotte , La Réunion Island , Madagascar and the Seychelles [8]–[13] . On La Réunion Island alone , which has a population of 760 , 000 inhabitants , at least 266 , 000 cases were reported [14] . Thereafter , the epidemic arrived on the Indian subcontinent in 2006–2007 and caused more than 1 . 3 million cases [15] . Genetic analyses showed that the ECSA genotype was responsible for these outbreaks [8] , [16] . Numerous cases were subsequently reported all over the world [17] , directly associated with the return of tourists from India and affected India Ocean islands [2] . The first reported European outbreak occurred in two contiguous villages of northeastern Italy [18] . A unnoticed and retrospectively diagnosed large outbreak hit Cameroon in 2006 [19] , then Gabon one year later , where a concomitant CHIKV/dengue virus- serotype 2 ( DENV-2 ) epidemic raged in the northwest and north; 20 , 000 cases were recorded and Aedes albopictus was identified as the main vector [20] . Isolates from the 2007 Gabon outbreak belonged to the ECSA phylogroup and harbor the A226V mutation [6] . Thus , the virus has proven able to expand to novel ecological niches , together with the vector Aedes albopictus [21] , [22] . Recently , autochthonous cases of dengue fever ( DF ) and CHIK were reported in southern France , where Aedes albopictus has also been detected , raising serious concerns [23] . CHIK , a word from the Bantu language , means “that which contorts or bends up” referring to the stooped posture that develops in infected patients due to severe joint pain and impaired walking ability . The clinical manifestations of CHIK are now well described . The infection is characterized by three distinct forms: asymptomatic , classical and severe . The asymptomatic form is revealed by serology , in naïve populations . In its classical form , the illness appears as a “dengue-like” disease , sometimes being confused with DF , particularly in areas where the two viruses cocirculate [3] , [22] , [24] . After an incubation period of 3–7 days , symptoms start abruptly with acute fever , followed by severe and often debilitating polyarthralgias sometimes lasting months or years . Additional symptoms include a maculopapular rash , myalgias and headaches [3] , [22] , [24] . In La Reunion Island , severe forms were reported , mainly in patients with underlying medical conditions . These forms included neurological and cardiovascular disorders , acute hepatitis , skin diseases , and respiratory and renal failure . Miscarriages and neonatal infections were also reported , and some deaths were directly attributed to CHIKV [3] , [14] , [25] , [26] . In previous studies , clinical and biological descriptions were mostly retrospective , and included a limited number of patients . Furthermore , except for the first known CHIKV outbreak in Tanganyika [27] , most studies were conducted in Asia , the Indian Ocean Islands and Europe [18] , [25] , [28] , [29] , while none have concerned Africa , where multiple pathogens co-circulate [7] , [19] , [20] , [30]–[32] . Here , we report the findings of a prospective , exhaustive clinical and biological study of 270 laboratory-confirmed cases during the simultaneous CHIKV/DENV outbreak which occurred in Gabon , a Central African country , in 2010 [33] , focusing on the high clinical variability .
A simultaneous CHIKV/DENV outbreak was reported in two provinces ( Ogooue Lolo and Haut Ogooue ) of southeast Gabon , from April to July 2010 . This study took place in Franceville , the main town of Haut Ogooue province , located 512 km south-east from Libreville ( capital of Gabon ) ( Figure 1 ) . In Franceville , there are 4 healthcare capacities , including 2 public hospitals with a total of 170 beds . The Centre International de Recherches Medicales de Franceville ( CIRMF ) , which includes a medical unit , is located in the heart of the city . The CIRMF team partnered the Ministry of Health ( MoH ) response team during this outbreak . The investigations ( epidemiological and clinical inquiries , blood sampling for laboratory confirmation ) were thus considered as part of the public health response . According to the MoH's directives , written consent was not required due to emergency diagnosis . An oral consent was obtained for each patient during interviews . The study was approved by the Regional Health Director , including individual oral consent for blood sampling ( Authorization n°189 , Figure S1 ) . The results were transmitted to patients and MoH . The study was conducted in the field by two doctors who investigated cases in all healthcare facilities of Franceville , while free medical consultations were provided by another in the CIRMF medical unit . At the time of this outbreak , all the hospitals were requested to sample suspected cases . The case definition adopted by the MoH included suspected and confirmed cases . Patients were suspected of having CHIK if they presented with at least one of the following symptoms or signs: fever ( defined as a temperature ≥38°5 , measured by a HCW ) , arthralgias , myalgias , headaches , rash , fatigue , nausea , vomiting , diarrhea , bleeding , or jaundice . A ‘confirmed’ case met the clinical case definition and was PCR-positive . Patients with suspected CHIK who were identified by the CIRMF team were examined physically , then data were collected on a standardized questionnaire , including age , sex , residence , time of onset and intensity of symptoms , and location of arthralgias ( Figure S2 ) . Analgesics and non steroidal antiinflammatory drugs ( paracetamol , ibuprofen ) were provided to patients according to MoH recommendations . If necessary , patients were hospitalized , and the length of stay was transmitted to our team . Finally , clinical data on the disease course and outcome were collected during a 3-month period . Blood samples were collected in two 7-ml EDTA Vacutainer tubes and one 7-ml dry tube ( VWR International , France ) . The tubes were stored in the dark at +4°C until arrival at the laboratory . Thick and thin blood films were stained with 20% Giemsa and examined for malaria parasites . Patients with positive test were excluded from the study . Hematological ( Hematology Analyser ACT 10 , Beckman Coulter ) and biochemical ( creatinine , AST , ALT ) tests were performed ( Automatic Analyser Hitachi model 902 , Roche Diagnostics ) . For molecular studies , RNA was extracted from 140 µL of plasma by using the QIAamp Viral RNA Mini Kit according to the manufacturer's recommended procedures ( Qiagen , Courtaboeuf , France ) . cDNA was synthesized in a 9700 thermocycler ( Applied Biosystems , Foster City , CA , USA ) , where 25 µL of extracted RNA was mixed with 25 µL of High Capacity cDNA kit ( Applied Biosystems , Foster City , CA , USA ) . Finally , 5 µL of newly synthesized cDNA was used as template in 25 µL of TaqMan Universal PCR Master Mix and then , thermocycled in a 7500 Real-Time PCR system ( Applied Biosystems ) . RNA positive and negative controls were added in each run . The TaqMan PCR products were identified by curves using a 7500 system SDS software . The quantitative PCR mix were run with 400 nM of each primer and 200 nM of probe . The E1 gene ( 208 bp ) was targeted for the CHIKV detection ( genome position , 10387–10595 ) : CHIK-S ( S = Sense ) : AAGCTYCGCGTCCTTTACCAAG; CHIK-R ( R = Reverse ) : CCAAATTGTCCYGGTCTTCCT; CHIK-P ( P = Probe ) : CCAATGTCYTCMGCCTGGACACCTTT [34] . For DENV detection , the 3′ UTR ( 107 bp ) was targeted ( genome position , 10590–10697 ) : DENt-S ( t = total , for the 4 serotypes ) : AGGACYAGAGGTTAGAGGAGA; DENt-R: CGYTCTGTGCCTGGAWTGAT; DENt-P: ACAGCATATTGACGCTGGGARAGACC [35] . The probes used for CHIKV , then for DENV assays were labeled with FAM-reporter and TAMRA-quencher ( Applied Biosystems ) . Quantified RNA transcripts and cell-culture supernatants of CHIKV and DENV were used in 10-fold dilutions as standards for viral load ( VL ) determination . The VL was determined by comparison to a standard curve . This standard curve was obtained from standard RNA which was diluted at 10 in 10 times . Exponential regression was used to determine the CHIKV viral loads from the threshold cycle . The standard linearity minimum was <101 cDNA genome equivalents/mL . DENVs were typed as previously described [35] . Co-infected patients were excluded from this study . Statistical analyses were performed using Epi Info software ( 6 . 04 , Epiconcept ) . Results were expressed as averages ( with their SD ) and percentages ( with their 95% confidence interval , CI ) . Student's t test was used to compare laboratory parameters ( samples from 50 healthy volunteers recruited at the CIRMF medical unit , with sociodemographic characteristics similar to those of the study subjects , were selected as controls ) and continuous clinical variables . For qualitative variables , the Chi square test or Fisher's exact test was used as appropriate . A p value <0 . 05 was considered to denote statistical significance .
From May to July 2010 , 2731 suspected cases were recorded in the two provinces , and 1208 cases ( 44 . 2% ) were laboratory confirmed ( Table 1 ) . There were 1139 CHIKV+ cases ( 94 . 2% ) in Haut Ogooue province . In all , 933/2063 ( 45 . 2% ) cases were confirmed in Franceville , representing 81 . 9% of the confirmed cases in the province . The first cases were confirmed on April 25 ( week 17 ) , the peak incidence was reached at weeks 21 and 22 ( 400 confirmed cases ) , and the outbreak ended at week 27 ( Figure 2 ) . Of the 2063 suspected cases detected in Franceville , 408 ( 19 . 8% ) were examined by the CIRMF medical team , of whom 289 were CHIKV+ , 19 were co-infected ( with 18 CHIKV+/DENV+ patients and 1 CHIKV+/DENV+/malaria+ ) . So , 270 CHIKV+ ( 66 . 2% ) were selected for the study ( Figure 3 ) . The 119 CHIKV- patients did not receive a diagnosis . They represented 28 . 9% of all CHIKV+ patients in Franceville . They were distributed throughout the outbreak , with 99 ( 36 . 6% ) patients included at the epidemic peak , in weeks 21 and 22 ( Figure 2 ) . The M/F sex ratio of the study population was 0 . 85 and mean age was 30±16 years ( range , 1–77 ) . Fifty-six patients ( 20 . 7% ) were under 16 years old , 70 ( 26 . 3% ) 16–30 years old , 92 ( 34 . 1% ) 31–45 years old , and 53 ( 18 . 9% ) over 45 years old . CHIK patients consulted an average of 2 days ( range , 0 to 18 days ) after the onset of symptoms , 232 ( 85 . 9% ) from day 0 to 3 , 30 ( 11 . 1% ) from day 4 to 7 , and 8 ( 3% ) after day 7 . The mean duration of symptoms in the acute phase was 7 days ( range , 1–24 days ) . Hospitalization was necessary for 42 patients ( 15 . 5% ) with more pronounced manifestations , and the mean length of stay was 2 . 6 days ( range , 1 to 6 days ) . At the time of the initial consultation , 230 ( 85% ) patients complained of fever , 246 ( 90 . 4% ) had arthralgias , 197 ( 72 . 9% ) myalgias and 194 ( 71 . 8% ) headaches ( Figure 4 ) . They all described an abrupt onset of the illness . Joint pain was mostly polyarticular , bilateral and symmetrical . On average , 7 joints per patient were affected . Among the 246 patients with arthralgias , 100 ( 40 . 7% ) had fewer than 5 affected joints , and 146 ( 59 . 3% ) had 5 or more affected joints . Arthralgias occurred in the large joints ( shoulders , elbows , wrists , knees , ankles ) in 242 ( 98 . 4% ) patients , and in the lower limbs in 220 ( 89 . 4% ) patients; the spine was affected in 146 ( 59 . 3% ) patients . Incapacitation was noted in 158 ( 64 . 2% ) patients ( Table 2 ) , and swelling of the elbows , wrists , knees or ankles was noted in one-quarter of these patients ( Figure 5A ) . Myalgias mainly affected the forearms , arms , thighs and calves , and sometimes became increasingly incapacitating . No cases of myositis were seen . Headaches were beating or weighty , and were located in the frontal , parietal , retro-orbital or , rarely , occipital regions . Skin lesions were noted in 113 ( 41 . 8% ) patients , in the form of macular or maculopapular exanthema ( Figure 5B ) , morbiliform or bullous rash in a few children ( Figure 5C ) , and was accompanied by pruritus in one-quarter of cases . A more aggressive form , with facial edema , was seen ( Figure 5D ) . Peeling of the affected skin occurred a few days after . Digestive symptoms , consisting of abdominal pain , nausea , vomiting and diarrhea ( 87 , 32% ) , were described , and mild bleeding of the nose and gums reported ( 6 , 2 . 2% ) in patients with normal platelet counts . Seizures occurred in 2 children ( CSF was not sampled ) , who recovered without sequelae . No complications or deaths were reported . The disease was classified in four forms according to the existence of fever and/or arthralgias ( plus any additional symptoms ) , namely mixed ( fever and arthralgias both present ) , pure febrile ( fever without arthralgias ) , pure arthralgic ( arthralgias without fever ) , and unusual ( neither fever nor arthralgia ) . The mixed form was found in 212 ( 78 . 5% ) patients , the arthralgic form in 34 ( 12 . 6% ) , the febrile form in 18 ( 6 . 7% ) , and the unusual form in 6 ( 2 . 2% ) ( Figure 6A ) . Patients with the unusual form mainly had digestive symptoms ( Table 3 ) . The performance indicators of the “fever and arthralgia” combination were estimated in suspected patients ( including PCR negative patients ) , taking into account the true positive ( TP ) , true negative ( TN ) , false positive ( FP ) and false negative ( FN ) results . Sensitivity was calculated as TP/ ( TP+FN ) , specificity as TN/ ( TN+FP ) , positive predictive value ( PPV ) as TP/ ( TP+FP ) and negative predictive value ( NPV ) as TN/ ( FN+TN ) . The “fever and arthralgia” combination showed a sensitivity and specificity of respectively 73 . 1% and 41% for PCR positivity , and PPV and NPV of 78 . 5% and 34 . 4% ( Table 4 ) . All four forms occurred throughout the outbreak , with a majority of patients in the mixed form between week 18 to 23 ( Figure 6B ) . There was no significant difference in the mean duration of symptoms ( p = 0 . 33 ) across the 4 clinical forms . Hospitalization was necessary for 35 ( 16 . 5% ) patients in the mixed form , 3 ( 8 . 8% ) patients in the arthralgic form , 4 ( 2 . 2% ) patients in the febrile form and none of the unusual form ( p = 0 . 33 ) , and there was no significant difference in the mean length of hospital stay ( p = 0 . 16 ) . Hematological and biochemical parameters were available for 224 patients . Mean counts of leukocytes ( 5243±1676/mm3 , range 1900–11000/mm3 ) and platelets ( 233 089±81 750/mm3 , range 52000–455000/mm3 ) did not differ from the controls . Nevertheless , anemia ( mean hemoglobin 12 . 3±1 . 7 g/dl , range 8–17 g/dl ) , and lymphocytopenia ( mean lymphocyte count 2228±216/mm3 , range 184–7150/mm3 ) were significantly frequent in CHIKV+ patients ( respective p values 0 . 0009 and <0 . 0001 ) than in the controls . Liver enzymes ( AST and ALT ) and creatinine levels were significantly higher ( respective p values 0 . 03 , 0 . 003 and <0 . 0001 ) in CHIKV+ patients than in controls ( Table 5 ) . There was no significant difference in hemoglobin rate , leukocyte , lymphocyte , or platelet count , or biochemical parameters across the four clinical forms . A total of 123 patients were selected for viral load ( VL ) assay . The selection criteria included age , sex , site of consultation , area of residence , week of disease onset , and clinical form . Their characteristics were comparable to those of the initial sample . The mean VL was 1 . 2×107 ( range , 1–4 . 4×108 ) . Fifty eight patients ( 47 . 2% ) had low VL ( <100 000 DNA copies per mL , Group 1 ) , and 65 ( 52 . 8% ) had high VL ( ≥100 000 DNA copies , Group 2 ) . There was no difference in hemoglobin rate , leukocyte or platelet count or biochemical data between the two groups . However , lymphocytopenia was significantly more frequent ( p = 0 . 01 ) in group 2 than in group 1 . Moreover , there was no difference in symptoms , affected joints ( large vs small ) , their location or intensity ( Table 2 ) . There was no difference in VL according to the day of sampling or symptom onset ( day 0 to 3 , day 4 to 7 , and after day 7 ) . VL did not differ across the four clinical forms . Of the 270 CHIKV+ patients included in the study , 225 ( 83 . 3% ) had completely recovered by day 30 . The other 45 patients complained of persistence or relapse of fever ( n = 6 ) , arthralgias ( n = 36; incapacitating arthragias were still present in 5 patients ) , myalgias ( n = 11 ) , headaches ( n = 20 ) , pruritus ( n = 5 ) or fatigue ( n = 10 ) . At day 90 , 11 patients had persistent arthralgias . Among the 5 patients with incapacitating arthralgias , 4 recovered by day 90 and one was lost to follow-up . Three patients had headaches .
During a recent concomitant CHIKV/DENV outbreak which occurred in south-east Gabon , we conducted a prospective study in the most affected town , obtaining clinical and biological descriptions of 270 laboratory-confirmed cases of CHIK . We found variable clinical manifestations , including unusual forms . This is the second large epidemic to be reported in Gabon , after the concurrent CHIKV/DENV-2 intrusion in 2007 , in which 2 provinces and 7 towns were affected . International shipping was suspected of providing the portal of entry for both viruses [20] . Inside the country , the infection spread insidiously , along a north-west/south-east axis via the railway and roads , leading to a new outbreak in 2 provinces and 10 towns , 600 km distant and 3 years later . Identified in Gabon just before 2007 , in an area where Aedes aegypti was previously predominant [36] , Aedes albopictus was the main vector of the first outbreak [20] , and the second . Given the rapid spread of this mosquito , even in temperate areas [18] , gradual invasion of the entire country by these viruses is foreseeable . This descriptive and prospective African study provides exhaustive clinical and biological data than previous outbreaks in the DRC , Republic of Congo , Cameroon and Gabon where descriptions were succinct [7] , [19] , [20] , [37] . Available descriptions have also been made in patients from la Reunion Island [25] , [26] , notably , in a prospective study [38] . A large number of patients were enrolled in this study . They represented about one-third of all laboratory-confirmed cases , and were distributed throughout the outbreak period , following the global epidemic curve . This series is therefore largely representative of the epidemic , and is as large as two other studies which included respectively 157 and 274 patients [25] , [38] . Other studies included less than 100 patients [28] , [39] , [40] . However , some patients with mild symptoms did not consult at the hospitals , and constituted the unique bias of recruitment . So , the number of enrolled patients is underestimated . This study did not provided any information on asymptomatic because patients' recruitment occurred in the hospitals ( individual with symptoms only ) . In a recent study , asymptomatic CHIKV infection was estimated at 28% [41] . With regard of the number of CHIKV negative cases , the circulation of other arboviruses during this epidemic cannot be ruled out . During the 2007 outbreak , DENV-2 was detected [20] and a fatal case of West Nile virus infection was diagnosed in Libreville [42] . Moreover , recent sero-epidemiological studies have suggested the circulation of DENV , West Nile and Rift Valley Fever viruses in rural populations [43]–[45] . We observed marked clinical variability . As previously described , fever and polyarthralgias were the most frequent manifestations , affecting nearly all the patients [3] , [14] , [22] , [25] , [38] , [41] . They were found in respectively 85% and 90% of our patients , and were both present in three-quarters of cases . We found a weak association between the “fever and arthralgia” combination and PCR positivity , with a sensitivity and specificity of 73 . 1% and 41% , while in another recent study this combination had a diagnostic sensitivity of 84% and a specificity of 89% in an epidemic setting [41] . Differences in the case definition and the assay could explain this discrepancy . In its classical form , CHIK is a painful febrile illness characterized by incapacitating arthralgias . Patients presenting with a bent gait due joint pain , mainly affecting the wrists and ankles , are easily recognizable . Incapacitating arthralgias are considered pathognomonic for the disease . When the symptoms are less severe , the clinical diagnosis becomes less clear-cut , and they may also be seen in other diseases such as malaria , DF and typhoid fever . To date , only the classical DF has been reported in Africa . To our knowledge , there is no comparative study of the two diseases . Complications such as dengue hemorrhagic fever and dengue with shock syndrome and persistent arthralgia following CHIK constitute the noteworthy differences . In our field experience , these diseases are clinically indistinguishable , and the problem of differential diagnosis may be compounded during concomitant CHIKV/DENV outbreaks in a malaria endemic area . Pure febrile and arthalgic forms were also seen , adding to the clinical variability . The other symptoms were as frequent as in previous studies [14] , [25] , [38] , [41] . Myalgias sometimes resulted in incapacity . Rashes were easily diagnosed in our darker-skinned population , and uncomplicated bullous lesions were also seen in children . The mechanism by which bleeding occurs despite normal platelet counts is unclear , but this classifies CHIK in the viral hemorrhagic fever group , in this area where ebolavirus and yellow fever virus also circulate . We identified 2 . 2% of patients who had neither fever nor arthralgias . These unusual forms were evoked in the Reunion Island . This is their first description due to a strong clinical presumption of physicians and a less sensitive and specific case definition . Furthermore , no severe forms ( requiring maintenance of vital function ) and deaths as described in the Reunion Island [14] , [26] were noted in Gabon . In a context of frequent self-medication , as in our study , many patients with unusual and non severe forms did not consult a health service , and some of those who did may have been misdiagnosed . So , the total number of cases , in this epidemic , and the proportion of unusual forms , are probably underestimated . Our findings imply that , during epidemic periods , clinicians should not focus solely on fever and arthralgias . The four clinical forms were present throughout the outbreak period , suggesting that pathogenicity did not vary markedly . We also observed rare relapses or persistence of arthralgias , as previously described [3] , [14] , [22] . The low rate of persisting arthralgia in our sampling contrasts with previous studies [46]–[48] . The little number of individuals followed up and the low mean age ( at 30 years ) of our sampling could explain it . In previous studies , the mean age of the population study was higher ( at 50 years ) and the incidence of persistent arthralgia was higher in older patients [46]–[48] . Immunonologic and genetic factors ( anti CCP antibodies , antinuclear antibodies and HLA DR alleles ) are associated with rheumatoid arthritis following Chikungunya fever [49] . Their absence in our patients could also explain low rate of persistent arthralgia . In CHIKV infections , biological abnormalities are varied , transient and nonspecific , as in many other viral diseases [25] , [38] . In our study , biological data from CHIKV+ patients were compared with those of healthy volunteers than CHIKV- , in order to avoid a bias in results interpretation . CHIKV- patients were considered as having another disease . Varied biological abnormalities were found . Anemia may have been due to parasitic coinfection ( ankylostomiasis , ascariasis ) . Lymphocytopenia correlated with viremia , as recently described [38] , and is probably due to excessive apoptosis with peripheral lymphocyte destruction . The increase in liver enzyme and creatinine levels may have been due to drug toxicity ( particularly when ibuprofen and paracetamol are associated ) , muscle damage or even rhabdomyolysis . Finally , the disease severity did not correlate with VL in our study , contrary to a recent study in which the severity criteria were different [50] . Together , these findings imply that symptoms are not directly related to the virus but rather to the immune reaction . Theoretically , PCR is the preferred method for detecting and quantifying CHIKV viral RNA , mainly during the first week after infection [4] , [5] . The detection of the virus later than 7 days after symptom onset in 3% of our patients is surprising . It is conceivable that their initial symptoms were due to another disease such as malaria or dengue , before they contracted CHIKV . The genotype of CHIV implicated in Gabonese and Cameroonian outbreaks belongs to the ESCA phylogroup and harbor the A226V mutation [20] . This mutation has also been found in samples from La Reunion Island which the genotype belongs to the Asian phylogroup . This mutation improves replication and transmission efficiency in Aedes albopictus mosquitoes [6] . To date , no genotype and no mutation have been found associated with specific clinical forms . Finally , our study showed that this outbreak , as the first one , occurred during the rainy season and Aedes albopictus was the main vector , with an extension in other parts of the country . Globally , the clinical picture did not differ from Asian , European and Indian Ocean Islands studies . The acute phase and outcome seemed similar , as biological abnormalities and treatment efficacy . In the other hand , there were some noteworthy distinctive features: the mean duration of symptoms in the acute phase is long ( 7 days reaching to 24 days ) , relapses and persistent arthralgia were rare , there were no severe forms or deaths , and unusual forms were well described . Epidemiological surveillance must continue in order to detect any change in the pathogenicity of CHIKV . | Chikungunya fever ( CHIK ) is a disease caused by a virus transmitted to humans by infected mosquitos . The virus is responsible for multiple outbreaks in tropical and temperate areas worldwide , and is now a global concern . Clinical and biological features of the disease are poorly described , especially in Africa , where the disease is neglected because it is considered benign . During a recent CHIK outbreak that occurred in southeast Gabon , we prospectively studied clinical and biological features of 270 virologically confirmed cases . Fever and arthralgias were the predominant symptoms . Furthermore , variable and distinct clinical pictures including pure febrile , pure arthralgic and unusual forms ( neither fever nor arthralgias ) were detected . No severe forms or deaths were reported . These findings suggest that , during CHIK epidemics , some patients may not have classical symptoms ( fever and arthralgias ) . Local surveillance is needed to detect any changes in the pathogenicity of this virus . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [
"medicine"
] | 2012 | Clinical Forms of Chikungunya in Gabon, 2010 |
Recent analyses of the fossil record and molecular phylogenies suggest that there are fundamental limits to biodiversity , possibly arising from constraints in the availability of space , resources , or ecological niches . Under this hypothesis , speciation rates decay over time and biodiversity eventually saturates , with new species emerging only when others are driven to extinction . This view of macro-evolution contradicts an alternative hypothesis that biodiversity is unbounded , with species ever accumulating as they find new niches to occupy . These contrasting theories of biodiversity dynamics yield fundamentally different explanations for the disparity in species richness across taxa and regions . Here , we test whether speciation rates have decayed or remained constant over time , and whether biodiversity is saturated or still expanding . We first derive a general likelihood expression for internode distances in a phylogeny , based on the well-known coalescent process from population genetics . This expression accounts for either time-constant or time-variable rates , time-constant or time-variable diversity , and completely or incompletely sampled phylogenies . We then compare the performance of different diversification scenarios in explaining a set of 289 phylogenies representing amphibians , arthropods , birds , mammals , mollusks , and flowering plants . Our results indicate that speciation rates typically decay over time , but that diversity is still expanding at present . The evidence for expanding-diversity models suggests that an upper limit to biodiversity has not yet been reached , or that no such limit exists .
We considered nine diversification scenarios , illustrated in Figure 1 ( see also Table 1 ) . In each of these scenarios , every lineage is equally likely to diversify or go extinct . Two of the scenarios ( Models 1 and 2 ) correspond to the hypothesis that diversity is saturated . Species go extinct stochastically and each extinction event is immediately followed by a speciation event , so that diversity remains constant through time . The particular case when the turnover rate ( i . e . , the rate of events in which an emerging species replaces a species going extinct ) is constant through time ( Model 1 ) is identical to Hey's model [21] . Hey's model is itself equivalent to the Moran process of population genetics , which describes the dynamics of individuals as opposed to species . Hey [21] showed that the terminal branches of phylogenies generated under his model are too short to be realistic , yet generalizations of the model to the case in which the turnover rate decays over time ( Model 2 ) may provide a better description of empirical phylogenies ( e . g . , [22] ) . Such a decay in rates is expected if species become better adapted over the course of evolution . The remaining scenarios ( Models 3–6 ) correspond to non-saturated diversity , and they feature independent speciation and extinction events . The model with time-constant speciation and extinction rates ( Model 3 ) is the classical constant-rate birth–death model of cladogenesis [20] , which reduces to the Yule process in the absence of extinction ( Model 5 ) . The other models ( Models 4a–4d and 6 ) include temporal variation in speciation and/or extinction rates [27] , [28] , [30] . Rates were assumed to vary exponentially through time , but generalization to any form of time variation is straightforward . The nine diversification scenarios we consider here represent the range of qualitative cladogenesis processes typically discussed in the cladogenesis literature [1] , [19] , [20] , [27] . These models can be divided into pairs of subsets corresponding to our competing hypotheses for diversity dynamics ( Figure 1 ) : models with expanding diversity ( in red ) versus models with saturated diversity; models with time-varying rates ( in blue ) versus models with time-constant rates; and models where extinction is present ( green ) versus models where extinction is absent . Phylogenetic trees resulting from these various diversification scenarios have distinct branch-length patterns ( Figure 2 ) . Some models produce phylogenies that can easily be distinguished from each other “by eye , ” but others produce trees that appear similar and that can be distinguished only through quantitative statistics . In all nine models , the speciation rate is assumed greater than or equal to the extinction rate at all times . To our knowledge , all models in the cladogenesis literature for which likelihood expressions are available also make this assumption . In nature , however , there is evidence that some clades have lost diversity towards the present , suggesting that extinction events are sometimes more frequent than speciation events [32] . Our coalescent likelihood expression can be used to investigate a scenario with decreasing diversity by assuming an instantaneous mass extinction event in the history of a clade . However , further work remains before the coalescent approach can accommodate general patterns of decreasing diversity ( see Materials and Methods ) .
Consider a clade with species at the present time , which has evolved according to one of the nine diversification scenarios illustrated in Figure 1 . We denote by the expected number of species at time in the past , given the model of diversification and its corresponding parameters ( e . g . , under Models 1 and 2 , and under Model 5 ) . We denote by the speciation rate at time in the past ( under Model 1 and 2 , , where is the turnover rate at time in the past ) . We consider a phylogeny of species randomly sampled in the clade at the present time . This phylogeny has internal nodes , and internode distances . The distance between node and the present is excluded because it does not correspond to a waiting time between cladogenesis events . Adapting results known for the Kingman's coalescent with deterministically varying population size [34] , the log-likelihood of the distances , , … , between nodes in the phylogeny ( nodes are numbered from the root to the tips , and is the time-length between node and node ) is given bywith ( 1 ) where is the time-length between node and the present ( see Materials and Methods ) . This expression is valid only under the assumption that is greater than or equal to ( see Materials and Methods ) . Furthermore , the stochastic number of species present at time t has been approximated by its deterministic expectation , N ( t ) . This approximation is critical to our analytical approach , as it makes the corresponding coalescent process tractable . We show below that this approximation is accurate over a broad range of parameters . The general expression above can be used to derive an approximate likelihood for the internode distances under each of the nine diversification scenarios illustrated in Figure 1 ( Appendix S1 in Text S1 ) . Given an empirical phylogeny , these expressions can then be used to estimate rates ( by maximum likelihood ) , or to compare the performance of various models . For example , the likelihood of under the simple Hey model ( Model 1 ) is ( 2 ) This equation shows that it is not possible to estimate the speciation rate and the number of species at present independently , given that the equation involves only the ratio of the two parameters . Therefore , we assume that clade size at present is known . In typical applications , accurate estimates of clade size exist for most groups . Using simulations , we tested the ability of the coalescent approach to determine the properties of the true , underlying cladogenesis process , from complete and incomplete phylogenies ( Figures 3 and S1 [in Text S1] ) . We found that the approach performed well with either complete or incomplete taxa sampling , and under both hypotheses of expanding ( Figure 3 ) or constant ( Figure S1 in Text S1 ) diversity . The method also performed well in the presence of low and high levels of extinction ( Figure 3 ) . Under the scenario with expanding diversity , a forward-time approach ( i . e . , an approach in which the process of cladogenesis is considered from the past to the present , as opposed to the backwards-time coalescent approach ) exists for estimating rates [27] , [28] , [30] . The forward-time approach has the advantage over ours that it does not require approximating diversity with a deterministic expectation , and it thus may be more accurate . However , we did not find a striking difference in the performance of the two methods . Although extinction rates were slightly overestimated with the coalescent approach , this bias was small in comparison with the large variability around expected values obtained with either the coalescent or the forward-time method ( Figure 3 ) . The forward-time approach commonly used in the literature does not simultaneously accommodate both time-varying rates and incomplete sampling of extant species ( although this could in principle be accommodated; see [28] , [36] , [37] and Discussion ) . It is well recognized that this approach should , in principle , only be used on completely sampled phylogenies . However , many empirical phylogenies omit a large proportion of extant species , and little is known about the accuracy of forward-time methods when applied to incomplete phylogenies . Our analyses indicate that such methods will produce strongly biased estimates , even when as many as 75% of extant species are present in the phylogeny . For example , in the case of the model with decaying speciation rate and constant extinction rate ( Model 4a ) , incomplete sampling leads to an underestimation of the speciation rate at the time of the most recent common ancestor , an overestimation of the decay in speciation rate , and an underestimation of the extinction rate ( Figure 3 ) . By comparison , the coalescent approach produced accurate estimates of rates when as few as 10% of the extant species were sampled . Although informative , this comparison is not entirely fair , because the coalescent approach is designed to describe the genealogy of samples , unlike the commonly used forward-time approaches ( see also [38] ) . Our simulations also show that the coalescent approach accurately identifies whether the underlying cladogenesis process is saturated or not , even under incomplete sampling . For example , out of 100 phylogenies simulated under a model with saturated diversity and constant turnover ( Model 1 , N0 = 100 ) , sampled with a fraction f = 0 . 75 , 83 were best fit ( i . e . , had the lowest second-order Akaike's Information Criterion [AICc]; see Materials and Methods ) by a model with saturated diversity ( 69 by Model 1 and 14 by Model 2 ) and , 78 were best fit by a model with constant rates ( 69 by Model 1 and nine by Model 3 ) . The coalescent approach is also able to detect decays in speciation rates . All phylogenies shown in Figure 3—generated by Model 4a , which features positive , constant extinction , and decaying speciation rates—were best fit by a model with decaying speciation rate: Models 4a , 4d , 4c , and 6 were most likely in ∼44% , ∼46% , ∼6% , and ∼4% of the simulated phylogenies , respectively . By comparison , using the approach recently proposed by Venditti et al . [31] ( see also Materials and Methods ) , 69% of phylogenies simulated with a decaying speciation rate were best fit by models in which speciation occurs at a constant rate ( i . e . , the branch-length distributions of ∼67% and ∼2% of the phylogenies were best fit by the exponential and lognormal distributions , respectively; the remainder were best fit by a Weibull distribution ) . Thus , the method of Venditti et al . is not well adapted to detecting a decay in speciation rates over absolute time . This comparison does not remove the value of the approach by Venditti et al . , which was designed to detect a dependence of speciation rates on the divergence time from an ancestral species rather than on absolute time ( see Discussion ) . We compared the performance of the nine diversification scenarios illustrated in Figure 1 in describing 289 empirical phylogenies ( see Materials and Methods ) . We found that for a large number of phylogenies ( 102 out of 289; ∼35%; Table 1 ) , the most likely model featured a time-decaying speciation rate and no extinction ( Model 6 ) . The pure-birth Yule model was the most likely model in another ∼30% of the phylogenies ( Model 5 ) . Finally , the model with saturated diversity and decaying turnover rate ( Model 2 ) was the most likely in ∼18% of the phylogenies . Each of the other models was the most likely in less than 6% of the phylogenies . In particular , the constant-rate birth–death model ( Model 3 ) was the most likely in only five out of 289 phylogenies ( less than 2% of the phylogenies ) . Sometimes the model with the smallest AICc score , among a set of candidate models , is not highly supported . This happens , for example , when both the first- and second-best models have similar AICc scores . Intuitively , the difficulty in distinguishing between models reflects the fact that different diversification scenarios may result in phylogenies with similar branch-length patterns ( Figures 2 and S2 [in Text S1]; see also [19] , [31] , [32] ) . To evaluate a model's support among a set of alternatives , we used Akaike weights—a measure of the probability of a given model among a set of candidates ( [39]; see Materials and Methods ) . For a few phylogenies , the single most likely model was highly probable ( e . g . , phylogeny of the genus Bursera; Figure 4 ) . But for many other phylogenies , the most likely model had a less than 50% chance of actually being the true model ( e . g . , the phylogenies of Bicyclus and Cicindela; Figure 4 ) . We assessed three competing hypotheses about diversity dynamics: ( 1 ) whether diversity is expanding or constant over time , ( 2 ) whether rates vary or are constant over time , and ( 3 ) whether extinctions leave a detectable signal in molecular phylogenies . To test each of these questions we used the following procedure . We first selected the model with lowest AICc in each subset . For example , to test the hypothesis of expanding versus saturated diversity , we selected for each phylogeny the model with lowest AICc among the models with expanding diversity , and the model with lowest AICc among the models with saturated diversity . We then evaluated the relative probability of these two models , based on their Akaike weights . The distribution of the relative probabilities across empirical phylogenies serves as a quantitative resolution to each of the three competing hypotheses we set out to test . This selection procedure provides a robust inference method ( Figure S3 in Text S1 ) . We found that for most phylogenies the best model with expanding diversity was more probable than the best model with saturated diversity ( ∼77% of the phylogenies; Figure 5 ) . In addition , for most phylogenies the best model with time-varying rates was more likely than the best model with time-constant rates ( ∼65% of the phylogenies ) . In particular , we typically found best-fit models that exhibit decaying speciation rates or net diversification rates . Furthermore , the best model without extinction was typically more likely than the best model with extinction ( in ∼65% of the phylogenies ) . These results were consistent across the chordate ( including birds ) , mollusk , arthropod , and magnoliophyte phylogenies , with no striking differences across phyla ( Figures S4 , S5 , S6 , S7 in Text S1 ) . The result that most phylogenies are consistent with expanding diversity and time-varying rates was robust to various tests . First , this result was not an artifact of the coalescent approach or of the model-selection procedure , since a poor fit of models with expanding diversity and time-varying rates was obtained when the procedure was performed on phylogenies simulated under a model with saturated diversity and constant rates ( Model 1; Figure S3 in Text S1 ) . Second , this result held when considering only the bird phylogenies from Phillimore and Price [6] , suggesting that it was robust to the method of phylogenetic construction ( Figure S8 in Text S1 ) . Third , this result was independent of the fraction of species sampled in the phylogenies , confirming the robustness of the coalescent inference to undersampling ( Figure S9 in Text S1 ) . Finally , inhomogeneity in diversification rates across lineages within phylogenies could have led to spurious estimates , and potentially to misleading inference [40] . However , the phylogenies we used included a narrow taxonomic range of species , which likely limited rate heterogeneity . Furthermore , we found no dependence between our results and the tree-splitting parameter , a measure of phylogenetic imbalance that reflects heterogeneity in the speed at which lineages diversify ( Materials and Methods and Figure S9 in Text S1 ) . This suggests that our observation of expanding diversity with time-decaying rates was not an artifact of inhomogeneous diversification rates . Our test of time variation in rates was conservative . We found evidence for time variation even though we allowed only exponential variations in rates . Allowing additional forms of temporal variations would , if anything , increase the number of phylogenies for which we infer some form of time variation . Furthermore , we found a positive correlation between the probability of the best model with time-varying rates and clade size ( Figure S10 in Text S1 ) , suggesting that small trees , if they were to influence the results , would influence them towards an absence of time variation in rates . Our test of expanding diversity , however , could be biased by the presence of small trees . Indeed , we found a negative correlation between the probability of the best model with expanding diversity and phylogeny size ( Figure S10 in Text S1 ) . However , the support for expanding diversity held even when considering only the phylogenies with more than ten tips ( Figure S11 in Text S1 ) or more than 50 tips ( Figure S12 in Text S1 ) , suggesting robust evidence for expanding diversity . Although models without extinction were generally more likely than models with extinction , our results suggest that extinctions can sometimes leave a detectable signal in molecular phylogenies . In those phylogenies for which extinction was detected ( 100 phylogenies , or 35% ) , the estimated ratio of present-day extinction and speciation rates was very high ( mean extinction fraction across phylogenies with positive extinction: 0 . 94±0 . 014 [1 standard error] ) . As a result , the mean extinction fraction at present across all phylogenies was nontrivial ( 0 . 32±0 . 013 ) . We observed a positive correlation between clade size and the probability that the best model features extinction ( Figure S10 in Text S1 ) . In addition , for ten of the 16 phylogenies with more than 50 tips , the best model with extinction was more likely than the best model without extinction ( Figure S11 in Text S1; see also Appendix S4 in Text S1 ) . This suggests either that species are more often subject to extinctions in big clades or that the failure to detect extinction in many phylogenies is linked to their small size . These results illustrate the potential superiority of the coalescent approach over the forward-time approach for estimating extinction rates from molecular phylogenies ( see Discussion ) . Our analysis of diversification rates has focused on the best-fit model amongst a set of nine alternative models . But this begs the question: does the best-fit model itself provide a reasonably accurate description of the empirical phylogeny ? For example , none of the models accounts for rate variation across lineages . As a result , empirical trees are typically more imbalanced than those predicted by the best-fit model ( Figure S14 in Text S1 ) . This is in agreement with previous studies showing that phylogenies arising from birth–death models are more balanced than empirical ones [19] . Nonetheless , we have verified that our best-fit model provides a good fit in at least one important respect: the gamma statistic . The gamma values of the best-fit models accurately reproduce the observed gamma values of the empirical phylogenies ( Figure S14 in Text S1 ) , even though our fitting procedure did not explicitly include any information about gamma . Thus , our modeling approach produces phylogenies with realistic branch-length patterns .
The relative importance of ecological interactions and the physical environment in driving macro-evolutionary patterns has been the subject of a long-standing debate . We have developed a coalescent-based approach to study diversity dynamics . Applying this tool across a diverse set of 289 empirical phylogenies , we found that speciation rates tend to decay over time , but that diversity is typically still expanding at present . These results suggest that diversification is the product of bursts of speciation followed by slowdowns in speciation rates as niches are filled , but not yet exhausted . The coalescent framework developed here is particularly well suited to the study of incomplete phylogenies . This is of practical importance , because fully sampled phylogenies are rarely available . By contrast , time-forward methods cannot easily accommodate missing species , which limits their practical utility . Incomplete sampling of extant species leads to a lengthening of terminal branches , modifying the series of internode distances as well as the distribution of phylogenetic branch-lengths . For example , sampling reduces gamma values , which can lead to a misleading rejection of the constant-rate birth–death model [35] . In this specific case , corrections can be made using Monte Carlo simulations [35] . In the case of phylogeny-based maximum likelihood inference , Nee et al . [28] proposed to treat sampling as a mass extinction event at present ( see also [36] , [38] and Appendix 2 of [37] ) . The corresponding likelihood expression , however , was only derived in the case of the constant-rate birth–death model . Our approach provides a more general expression for use with incomplete phylogenies , allowing rates to vary with any functional dependence over time , and clade size to vary or be constant over time . The coalescent framework can produce phylogenies with a wide range of branch-length patterns , including phylogenies with negative gamma values . In the field of macro-evolution , the coalescent has mostly been discussed in the context of Hey's model [21] . This model corresponds to the coalescent with constant population size and constant generation time , which—applied to a model of cladogenesis—produces phylogenies with short terminal branches and positive gamma values , in disagreement with empirical evidence . Our approach instead allows both population size ( i . e . , clade size in a macro-evolutionary context ) and generation time to vary over time . As we have seen , this more general approach produces phylogenies with realistic branch-length patterns , and realistic gamma values . The coalescent approach has allowed us to detect extinction in molecular phylogenies . Forward-time approaches typically produce estimates of extinction rates that are far too small to match the fossil record [27] . This discrepancy has motivated the development of models that directly incorporate species interactions [26] , or models in which extinction is forced to happen [22] . However , the absence of likelihood expressions for these more complicated models prevents comparison with simpler , more parsimonious models . Using an approach that allows model comparison , we have found that many phylogenies produce low extinction estimates , but others produce high estimates , resulting in significant inferred extinction levels overall . One explanation for the difference between our results and those obtained with the standard forward-time approach is that the coalescent approach allows diversity to take any value at the time of the most recent common ancestor . Hence , clades can reach present-day diversity even with high extinction levels . By contrast , the forward-time approach typically assumes that diversity increases monotonically from a single species to the diversity at present—which may suppress the inferred rate of extinction ( but see [23] , [24] ) . There are several potential extensions and applications of the coalescent approach in macro-evolution . First , our assumption that the speciation rate is always greater than or equal to the extinction rate could be relaxed . This would allow us to consider scenarios in which diversity decays over time , which is biologically relevant and might influence our conclusions . Such scenarios cannot be easily accommodated by the other modeling approaches in the literature [1] . Second , the coalescent framework should allow us to incorporate information from fossil data . For example , if reliable estimates of diversity at one or several points in time were available from fossil data , these estimates could be incorporated into the expression for the likelihood of internode distances , yielding more robust inferences . Finally , by adapting results on the coalescent with spatial structure ( see , e . g . , [41] ) , we could test hypotheses about both the temporal and spatial modes of diversification—e . g . , when and where did species diversify ? All of these extensions remain topics for future research . There are nevertheless limitations to the coalescent approach . We used an expression for coalescence times derived from population models with deterministically varying size . Using this expression to analyze phylogenies ( evolutionary relationship among species , not individuals ) required approximating the number of species at a given time ( a stochastic variable ) by its deterministic expectation . Within the range of parameters we tested , this approximation created only a small bias in the estimation of extinction rates ( see , e . g . , Figure 3 ) . We do not , however , exclude the possibility that this approximation may bias estimates for other parameter values [38] . We also assumed that species are randomly sampled , whereas they are often sampled with the goal of maximizing phylogenetic breath [26] . The McPeek dataset we analyzed , however , was designed to avoid this bias [26] . Another major limitation of our approach is that we did not account for rate variation across lineages . In other words , within a phylogeny and at any given time , all species were assumed equally likely to diversify , and equally likely to go extinct . This assumption is made by analytical forward-time models as well , but it is strongly violated in nature . Species colonizing new areas or acquiring beneficial traits diversify faster than others [7] , [42] , [43] , and extinctions are clustered on the phylogeny , i . e . , species within some clades are more likely to go extinct than others [44] . Consequently , empirical phylogenies are more imbalanced than predicted by models with homogeneous rates [19] , and inferences based on models with homogeneous rates might be biased [40] . Extending our approach to account for inhomogeneous rates would require considering coalescent times in a population under selection . Although mathematical solutions to this problem are typically not available , an efficient simulation approach exists ( the so-called ancestral selection graph [45] ) , which could be adapted to model differential diversification rates across lineages . Alternative approaches for modeling phylogenies with inhomogeneous rates across lineages exist , but they also have limitations . One approach that produces realistic levels of imbalance stems from the neutral theory of biodiversity [13] , [46] . Under the neutral theory with point mutation , there is a fixed probability of speciation per individual , so that speciation rates vary across lineages according to population sizes . However , the point-mutation model of speciation is highly disputable [47] . Furthermore , the neutral theory of biodiversity produces phylogenies with terminal branches that are unrealistically short , unless a steep increase in population sizes towards the present is assumed ( personal communication , Franck Jabot ) . Another approach consists of modeling cladogenesis in parallel to the evolution of species' traits , with speciation and extinction rates depending on these traits [37] , [48]–[50] . Although powerful , this approach necessitates collecting information on both the phylogenetic relationships of extant species and their traits . Furthermore , such inferences assume a priori that rate variation across species is linked to variations in their traits , as well as which traits influence diversification rates . Despite the limitations in our modeling , our empirical results strongly suggest that diversification rates vary over time in many taxa ( Figure 5 ) . The best model with time-varying rates was more likely than the best model with time-constant rates in 182 of 289 phylogenies ( ∼63% ) , and was at least three times more likely in 147 ( ∼51% ) of them . Our results on the prevalence of decaying speciation rates is apparently at odds with a recent meta-analysis of molecular phylogenies by Venditti et al . [31] , who concluded that speciation typically occurs at a constant rate over time . This comparison is somewhat inappropriate , however , because the method of Venditti et al . was designed to detect changes in speciation rates with the age of a taxon , as opposed to with absolute time . As we have demonstrated above using simulated phylogenies , our coalescent technique has greater power to detect changes in speciation rates over absolute time than the approach by Venditti et al . As a result , even though our coalescent method detects decaying speciation rates in the majority of our empirical phylogenies , the method of Venditti et al . would infer time-varying rates ( i . e . , a Weibull distribution of branch-lengths ) in only 17% of the empirical phylogenies analyzed here ( data not shown ) . Given the strong evidence we find for a decay in speciation rate over absolute time using the coalescent approach , which confirms results obtained by other inference techniques [5] , [6] , [25] , [27] , [51] , [52] , we conclude that it might be premature to reject diversification in bursts [5] , [6] , [27] , [52] ( adaptive radiations followed by a slowdown in speciation rate ) in favor of diversification at a constant pace in response to rare stochastic events [31] . Our analysis also suggests that diversity is not saturated , but rather presently expanding . This hypothesis has been tested previously using fossil data , yielding contradictory results [2] , [15] , [16] , [18] . Using molecular phylogenies , the constant-diversity hypothesis has been tested only under scenarios with time-constant rates [21] . Recently , the resurgence of the idea that diversity is limited by available resources has encouraged the development of models for cladogenesis with saturated diversity [22] . However , our findings suggest that birth–death models with time-varying rates should be favored over the classical constant-rate birth–death model or models with saturated diversity . More generally , our results suggest that an understanding of both evolutionary history ( clade age and diversification rates ) and ecological constraints ( geographical space , resource availability , and competition among species ) is necessary to explain present-day diversity and its variation across clades and regions [3] .
Consider the genealogy of individuals sampled in a population with deterministically varying size , evolving under the Wright-Fisher process ( at each generation , all individuals die and are replaced; each offspring selects a parent randomly from the previous generation ) . We number nodes from the root to the tips , denote the time-length between node and node , and the time-length between node and the present , measured in units of generation time ( time for a complete turnover of individuals ) . Using the standard coalescent approximation , Griffiths and Tavare [34] have shown that the internode distances ( e . g . , ) are distributed according to ( 3 ) where is population size at time in the past . This result has been widely used in population genetics to infer demographic history using genealogies ( e . g . , [35] , [53] ) . By analogy , replacing individuals by species , we used the coalescent approximation to infer diversity dynamics using phylogenies . In the case of an evolving population , the generation time is assumed constant over time . For a clade evolving with varying speciation rates , the generation time ( time for a complete turnover of species ) varies: intuitively , a complete turnover of species is reached faster when speciation rates are higher . If is not less than at all times , the generation time at time t in the past is given by , where is the speciation rate at time t ( in real time units ) . The change of variable ( real time units versus generation time units ) yields the likelihood expression in the text . We used forward-time simulations to construct phylogenies under different models of cladogenesis . To simulate a phylogeny of size under Models 1 and 2 , we started with an artificial phylogeny consisting of species connected to the root by a polytomy . We simulated the time of each turnover event ( i . e . , an extinction event immediately followed by a speciation event ) using the exponential distribution with rate parameter at the time of the previous turnover event ( for the first turnover event we used the initial turnover rate ) . At each event , a lineage picked at random was removed while another lineage , also picked at random , was replaced by two descendant lineages , and the turnover rate's value was updated ( in the case of Model 2 ) . The process was simulated until time exceeded a predetermined value . Note that the initial polytomy disappears as soon as all but one of the initial lineages go extinct . To simulate all other diversification scenarios ( Models 3–6 ) , we started with a single lineage and simulated events at rate ( i . e . , speciation plus extinction rates at the time of the previous event; for the first event we used the initial rate ) . At each event , a lineage picked at random was replaced by two descendant lineages with probability , and removed with probability , and the speciation and extinction rates were updated ( according to the equations in Table 1 ) . The process was simulated until time exceeded a predetermined value . To estimate speciation and extinction rates using the forward-time approach ( Figure 3 ) , we used maximum likelihood estimation as implemented in the SPVAR model of [27] , using the Laser package in R [54] . To evaluate the fits of the various models from Venditti et al . [31] on a given phylogeny , we first obtained the distribution of phylogenetic branch-lengths . Following the authors , we excluded terminal branch-lengths because they do not reflect speciation events . We fitted the exponential , Weibull , lognormal , variable rates , and normal distribution to this distribution of branch-lengths ( Table 1 of [31] ) . All models besides the Weibull correspond to scenarios of diversification in which speciation occurs at a constant rate . We obtained the maximum likelihood parameters of each model using the Nelder-Mead simplex algorithm implemented in R [55] . To measure goodness of fit , we computed the AICc: ( 4 ) in which is the log-likelihood of the branch-lengths , is the number of parameters in the model , and is the number of observations ( i . e . , the number of branch-lengths ) [39] . We used two sets of published phylogenies: 44 from Phillimore and Price [6] and 245 from McPeek [26] , for a total of 289 phylogenies ( the phylogeny of Estrildidae from Phillimore and Price [6] was not included because it is not publicly available ) . The phylogenies from Phillimore and Price [6] were exclusively bird phylogenies; they were constructed by the authors from sequence data , using a relaxed-clock Bayesian method implemented in BEAST ( see Phillimore and Price [6] for details ) . This approach yielded a distribution of trees for each clade . We first made sure that our results did not depend on the choice of the tree . Thereafter , we used for each clade a randomly chosen tree from the distribution of trees . Estimates of present-day clade richness were provided by the authors ( Table 1 of [6] ) . The phylogenies from McPeek [26] included 55 arthropod phylogenies , 140 chordate phylogenies , 11 mollusk phylogenies , and 39 magnoliophyte phylogenies , compiled by the authors from the literature ( see [4] and [26] for details ) . Small phylogenies were included in the compilation , in order to avoid the potential bias associated with analyzing only species-rich clades [1] . The authors also excluded phylogenies where the hypothesis of random sampling was obviously violated ( i . e . , phylogenies in which sampling was biased in order to maximize the breath of species sampled ) . Estimates of present-day clade richness were provided by the author . Some phylogenies had polytomies , reflecting nodes were resolution was contentious . Since the order of resolution does not matter in analyses involving only internode distances , we resolved nodes randomly . We assigned arbitrarily small internode distances between them ( 10−6 My ) , and checked that the results were robust to the arbitrary value chosen . For each phylogeny , maximum likelihood optimization for each of the nine models was performed using the Nelder-Mead simplex algorithm implemented in R [55] . To measure goodness of fit , we computed the AICc as described above ( Equation 4 ) . Here is the log-likelihood of internode distances and n is the number of internode distances included in the likelihood calculation , i . e . , , where k is the number of tips in each phylogeny ( we recall that the time between the last speciation event and the present was omitted since it does not correspond to a waiting time between two speciation events ) . To evaluate the relative performance of a given model l within a set of R candidate models , we computed the model's Akaike weight as ( 5 ) where is the difference in AICc between model l and the best model ( i . e . , the model with smallest AICc ) . The Akaike weight of a given model may be interpreted as the probability that the model is the true model , given the set of candidate models [39] . The relative probabilities of two models l and k were then calculated as and . To estimate the level of extinction in a given phylogeny , we estimated the level of extinction provided by the best-fit model for this phylogeny . To obtain a measure of extinction comparable across phylogenies , we reported the extinction fraction ( extinction rate divided by speciation rate ) at present . In the case of Models 1 and 2 ( saturated diversity models ) , the extinction fraction was assigned a value of 1 , since each speciation event is directly followed by an extinction event . In the case of Models 5 and 6 ( models without extinction ) , the extinction fraction was assigned a value of 0 . The mean extinction fraction was computed both across phylogenies where the best-fit model was a model with extinction and across all phylogenies . We adopted the broadly used gamma statistic to summarize information on phylogenetic branch-lengths [25] . The gamma statistic follows the standard normal distribution under the pure-birth Yule model , and takes negative values when phylogenetic nodes are closer to the root than expected under the Yule model . To summarize information on phylogenetic imbalance , we used the tree-splitting parameter implemented in the apTreeshape package in R [56] . The tree-splitting parameter is the maximum likelihood estimate of a single-parameter family of split distributions ( i . e . , probability distributions describing the left sister clade size conditional on the parent clade size ) encompassing the split distribution of the Yule model . The expected value of the tree-splitting parameter is zero under the Yule model , negative for trees more imbalanced than expected under the Yule model , and positive for trees more balanced than expected under the Yule model [57] , [58] . We chose this measure because , contrary to other measures , its expectation under the Yule model is independent of clade size . We assessed for each empirical phylogeny how well the best-fit model ( with associated best-fit parameters ) actually represented the data . We simulated for each empirical clade 100 phylogenies according to the model , as described above . We then randomly sampled species from each simulated phylogeny , with the sampling fraction corresponding to the empirical data . Finally , we compared empirical and simulated phylogenies using a summary statistic that reflects phylogenetic imbalance ( the tree-splitting parameter; [57] , [58] ) and a summary statistic that reflects phylogenetic branch-lengths ( the gamma statistic; [25] ) . | Is species diversity in equilibrium , or is it still expanding ? Are there ecological limits on diversity , or will evolution always find new niches for further specialization ? These are all long-standing questions about the dynamics of macro-evolution , which have been examined using the fossil record and , more recently , molecular phylogenies . Understanding these long-term dynamics is central to our knowledge of how species diversify and ultimately what controls present-day biodiversity across groups and regions . We have developed a novel approach to infer diversification dynamics from the phylogenies of present-day species . Applying our approach to a diverse set of empirical phylogenies , we demonstrate that speciation rates have decayed over time , suggesting ecological constraints to diversification . Nonetheless , we find that diversity is still expanding at present , suggesting either that these ecological constraints do not impose an upper limit to diversity or that this upper limit has not yet been reached . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"ecology/evolutionary",
"ecology",
"evolutionary",
"biology/evolutionary",
"ecology",
"evolutionary",
"biology",
"ecology",
"ecology/community",
"ecology",
"and",
"biodiversity",
"ecology/theoretical",
"ecology"
] | 2010 | Inferring the Dynamics of Diversification: A Coalescent Approach |
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