Computational and Empirical Studies Predict Mycobacterium tuberculosis-Specific T Cells as a Biomarker for Infection Outcome
[['Mycobacterium tuberculosis ( Mtb ) continues to be a global public health threat , responsible for 1 . 3 million deaths due to tuberculosis ( TB ) and 8 . 6 million new infections in 2013 [1] .', 'While only 10% of infected individuals develop clinically active TB , the other 90% harbor bacteria and are considered to be clinically latent [2] ( latent TB , or LTBI ) .', 'Clinically latent individuals can undergo reactivation to active TB , and thus serve as a large reservoir for disease transmission .', 'A major hurdle in controlling TB is the lack of accurate biomarkers that correlate prognosis and progression to infection [3 , 4 , 5 , 6] .', 'Identification of biomarkers for both infectious and non-infectious diseases is a focus of much current biomedical research .', 'While blood or urine can be obtained from patients to measure biomarkers , events occurring in these physiological compartments may not accurately reflect dynamics at sites of infection , such as lungs [7] .', 'We recently showed that during Mtb infection , T cell responses in blood do not consistently reflect T cell responses within granulomas , sites of Mtb infection in lungs [8] .', 'Biomarkers associated with the observed spectrum of infection , ranging from control of infection ( LTBI ) to clinically active TB [9] , are unknown .', 'This lack of understanding is present in settings of both natural and vaccine-induced immunity [6] .', 'Here we focus our study in a natural immunity setting .', 'Non-specific markers of inflammation , when considered alone , do not have sufficient predictive value for clinical use in TB [3 , 4] .', 'For decades , the Tuberculin Skin Test ( TST ) has been the most common diagnostic tool for Mtb exposure .', 'However , lack of specificity for detection of active TB disease , inability to distinguish between BCG vaccination and Mtb infection , and inability to provide insight into disease progression limit the predictive power of TST [3 , 4 , 5] .', 'IFN-γ Release Assays ( IGRA ) , which measure Mtb-specific release of IFN-γ from blood cells , have higher specificity for detection an ongoing TB infection ( ~80% ) [10] but fail both as a useful correlate of vaccine-induced protection and a reliable predictor of disease progression ( i . e . , due to low sensitivity or true positive rate ) [3 , 4] .', 'Recent association studies suggest that ratios of various T cell subpopulations in blood ( e . g . , CD4+ vs CD8+ T cells ) may help distinguish among stages of TB progression [2 , 11 , 12 , 13] .', 'Time course data in humans are typically only available from blood , and not from sites of Mtb infection ( lungs or lymph nodes ( LNs ) .', 'In addition , it is challenging to determine the time of exposure and infection progression status in humans , limiting the ability to use these samples for predictive studies .', 'Gene expression profiling [2 , 14 , 15] , microRNA and metabolism-based discovery [16 , 17] , and plasma antibody profiling in Mtb infected humans [18] and animal models [3 , 19] have been paired with data mining and machine learning tools to uncover potential TB biomarkers .', 'Imaging has been recently used successfully as a diagnostic and prognostic tool in TB studies ( i . e . , PET/CT scan ) , both in the context of natural infection [20 , 21] and drug-treatment [22 , 23] .', 'While informative , these studies have thus far been unable to determine either a single or suite of practical biomarkers , or predictive correlates of protection that would be useful in clinical practice , particularly in developing countries where TB is most prevalent .', 'This is likely due to the complexity of TB disease , that blood may not reflect lung infection dynamics [8] , intrinsic limitations in ex vivo studies and limited availability of longitudinal in vivo data .', 'Moreover , a spectrum of infection outcomes overlying binary classifications of active TB and latent infection has been identified , making biomarker discovery for infection status even more challenging [9] .', 'Here we present an integrated experimental and computational modeling approach toward the discovery of TB biomarkers with the goal of predicting Mtb infection outcomes .', 'Fig 1 shows a methodology roadmap of the different datasets generated and analyses performed in this study .', 'First , using machine learning , immunologic data from blood of Mtb-infected cynomolgus macaques was interrogated for biomarkers that would predict infection outcome .', 'The analysis of the NHP blood datasets did not identify any potential TB biomarker .', 'A separate dataset was then used to help build and calibrate a computational model of the immune response during Mtb infection .', 'Our unique multi-scale and multi-physiological compartmental computational model generates in silico data on dynamics of infection in both blood and lung , capturing formation of independent granulomas in lungs and at the same time T cell profiles in blood .', 'We then constructed virtual non-human primate hosts based on granuloma data and infection status from macaques , and used these models to identify potential biomarkers that can predict infection outcome .', 'We found that Mtb-specific frequencies of effector T cell phenotypes ( i . e . , both CD4+ and CD8+ ) in the blood can be targeted to distinguish infection outcomes , with a clear separation between median trajectories of active versus latent TB late during infection progression ( ~300 days ) .'], ['We assessed levels of CD4+ and CD8+ T cells ( and memory subsets based on the expression of the two markers CD45RA and CD27 , see Table 1 for details ) and their cytokine production in the blood of 28 non-human primates ( NHPs ) ( cynomologus macaques ) at 10 time points over the course of experimental Mtb infection ( up to 6 months post infection ) ( Fig 2A , Table 1 , S1–S6 Tables ) .', 'Such an extensive time course of sampling in the NHP model of TB , which recapitulates human Mtb infection , has not been previously available for biomarker studies .', 'These NHPs were clinically classified as having either latent infection or active TB disease as previously described [24 , 25] , but exhibit a range of outcomes encompassing these clinical classifications [26] .', 'S1 Fig shows representative flow cytometry plots outlining gating strategies employed in assessing T cell levels .', 'To interrogate these unique and extensive NHP infection datasets for biomarkers ( step 1 in Fig 1 ) , we applied four supervised classification algorithms: classical and penalized discriminant analysis ( LDA and PLDA ) , quadratic discriminant analysis ( QDA ) and logistic regression ( full details are given in the Methods and Supplemental materials ) .', 'Sensitivity , specificity and misclassification error rates ( MER ) are shown in Table 2 for both training and test sets .', 'Results of the training dataset show high accuracy , even if overfitting is likely driving it ( since we have 28 samples for more than 150 features ) .', 'However , similar results were not observed in the test data set , where none of the four methods used was able to discriminate disease outcomes ( MERs for the test set ~50% , Table 2; S2 Fig ) .', 'The stark differences in predictive accuracy between the training and test datasets indicate over-fitting , which could potentially be mitigated by increasing sample size .', 'We next re-analyzed the blood datasets using unsupervised clustering algorithms ( multidimensional scaling , Ward’s method and other hierarchical methods , see Materials and Methods for full details ) .', 'These algorithms suggested that the 28 macaques can be subdivided into 3–25 optimal clusters ( S7 Table ) , but there was no agreement among the methods in terms of what the clustering should be .', 'The clusters contain mixtures of NHPs with active disease and latent infection , with no clear separation between subjects and no distinct delineator of infection stage .', 'This is likely due to the overlapping spectrum of pathology present in animals that are clinically classified as having active disease or latent infection [8 , 9 , 21 , 22 , 25] , as also described for humans [27] .', 'Overall , our multiple attempts to use longitudinal blood T cell data obtained from NHPs to predict the clinical binary classification ( i . e . latent infection or active TB ) were not successful .', 'Our NHP blood dataset may not be predictive of infection outcomes if blood does not reflect lung infection dynamics ( as suggested by our previous work [8] ) and/or if the sample size does not have the necessary statistical power to discriminate binary outcomes .', 'In addition , TB exists on a spectrum and identifying binary outcomes may be an artificially imposed constraint and not practical [9] .', 'These shortfalls could be mitigated using a computational model that describes the immune response to Mtb infection in three physiological compartments capturing blood , LNs and lung dynamics , calibrated with our NHP datasets ( step 2 in Fig 1 and Fig 2B ) .', 'Building on previous work from our group , we developed a multiscale and multicompartment model by linking our computational model GranSim that captures individual granuloma formation and function in the lungs using an agent-based model framework [28 , 29 , 30 , 31 , 32 , 33] with 2 additional non-linear ODE models capturing dynamics in both LN and blood [28 , 29] ( Fig 2 , S1 Text ) .', 'Figs 3 and 4 illustrate the calibration of our in silico model to our NHP dataset , with both spatial and temporal fits to distinct data from individual granulomas in lungs ( Fig', '3 ) and blood ( Fig', '4 ) ( ranges for parameter values given in S8 and S9 Tables ) .', 'In addition , bacterial burden per granuloma ( in colony forming units , CFU ) in the lung are calibrated to recently published [8 , 22] and unpublished NHP datasets ( Fig 3A , S1 Table ) .', 'Time courses for the number of Mtb bacteria from a representative contained granuloma are shown in Fig 3B .', 'We also show a comparison between granuloma spatial distribution ( e . g . , location of immune cells , bacteria , necrotic center ) from two NHP granuloma images and two in silico granuloma snapshots with similar Mtb bacteria levels and lesion size ( Fig 3C ) .', 'NHP T cell dynamics ( S5 Table ) from 9 animals were used to calibrate our in silico blood dynamics , and in silico trajectories fall within the levels of the CD4+ and CD8+ T cells from NHP blood ( Fig 4 ) .', 'Currently , Mtb-specificity of T cells in the NHP model cannot be directly measured ( for example , tetramers are not available ) .', 'As a surrogate , we used our NHP blood dataset to calculate frequency of T cells that produce any of 6 cytokines measured ( IFN-γ IL-2 , IL-6 , IL-10 , IL-17 and TNF ) ( S6 Table ) following stimulation with two Mtb-specific immunodominant antigens ESAT-6 and CFP-10 ( S6 Table ) .', 'These T cell frequencies are then used as proxies for the frequencies of Mtb-specific T cells ( at least from the perspective of a functional response to these antigens ) .', 'This does not provide a direct measure of cells that are truly Mtb-infection specific .', 'The NHP used in this study are outbred monkeys that could have been exposed to non-tuberculous mycobacteria ( NTMs , or environmental exposure ) and would nonetheless respond to stimulation with these antigens prior to our infecting them with Mtb .', 'This may explain the non-zero frequencies in the NHP dataset present in many of the memory T cell phenotypes observed early during infection ( red dots , Fig 5 ) .', 'The computational model does not account for these potential effects .', 'We next used our calibrated in silico model to predict the spatio-temporal dynamics of Mtb-specific T cells ( see Materials and Methods section for details ) .', 'To parallel our computational model of virtual NHPs that would also previously be exposed to mycobacterial antigens , our simulations start with non-zero initial conditions in the same frequencies as the NHP data .', 'Our in silico model predictions for frequencies of Mtb-specific Central Memory ( Fig 5A and 5C ) and Effector T cells ( Fig 5B and 5D ) are within the variation of the NHP data that derived as proxies for Mtb-specific T cell responses .', 'We next apply data mining techniques and correlation methods on these in silico datasets for discovery of potential biomarkers ( step 4 in Fig 1 ) .', 'Since our computational model can predict dynamics of Mtb-specific cells in blood , lung and LNs , we use our model to generate large in silico datasets ( 10 , 000 virtual granuloma ) , pairing lung outputs ( i . e . , CFU/granuloma dynamics ) with blood measures ( immune cell dynamics ) over the time course of infection ( step 3 in Fig 1 ) .', 'We first apply principal component analyses ( PCA ) to the in silico blood readouts only ( S3 Fig ) , and then we extend the analysis to the 3 compartment readouts combined together ( e . g . , blood , lymph node and single granuloma in the lung , see S11 Table and S4 and S5 Figs ) .', 'If the analysis is performed only on Mtb-specific T cell variables in the blood , the top 2 principal components alone explain ~70% of dataset variability ( S3A Fig ) .', 'Even if no cluster emerges ( S3B Fig ) , the top 2 principal components are dominated by Mtb-specific effector CD4+ and effector CD8+ T cells , as early as 42 days post infection , as well as Mtb-specific Central Memory CD4+ and CD8+ T cells later during infection ( S3C Fig ) .', 'To explore this finding further , we narrowed the analysis of the in silico datasets by correlating virtual Mtb-specific frequencies of T-cells in blood to virtual CFU/granuloma at the site of infection ( lungs ) .', 'In silico frequencies of Mtb-specific Effector CD4+ and CD8+ T cells in blood predict well the granuloma-scale bacterial burden , with a clear separation between low vs high CFU/granuloma ( all results shown in supplement at the granuloma scale- S6 and S7 Figs , S10 Table ) .', 'However , since infection usually results in multiple granulomas within a single host , a host scale-readout , not a single granuloma-scale readout , would be more useful .', 'We provide such an analysis below .', 'We next generate host-scale predictions by combining NHP lung necropsy data and the in silico repository described in the previous section ( Fig 1 , step 5 ) .', 'Our main assumption is that host-scale clinical outcomes can be determined by the combined effect of a host’s heterogeneous granuloma bacterial burden [21] .', 'In fact , we previously demonstrated substantial heterogeneity and variability in NHP granulomas at the bacterial and immune cell levels , both among animals and within a single animal [8 , 21 , 22] .', 'Total bacterial burden in NHP lungs at necropsy correlates with infection outcome ( i . e . NHP with active disease have higher bacterial burden than those with latent infection ) [25] .', 'The contribution of individual ( and total ) granulomas in a single host to factors that can be tracked in blood is not clear , and likely is responsible for the lack of correlation of T cell responses in blood with those in granulomas or with clinical status in our previous study [8] .', 'To scale our computational model results from single lung granuloma to whole host , we first generate a large sample of virtual NHP hosts by using our in silico dataset to simulate NHP infections ( Fig 6 ) that recapitulate known CFU/granuloma data ( S1 Table ) .', 'We generated virtual hosts that contained the number of granulomas and CFU/granuloma to match our NHP dataset ( step 5 in Fig 1 ) .', 'For each virtual NHP host , we sample from our in silico repository of 10 , 000 granulomas ( see Materials and Methods section for detail ) .', 'We then combine all the sampled in silico granulomas and examine their corresponding in silico blood data on immune cell levels as generated from the computational model .', 'This technique allows us to predict time courses of many different T cell phenotypes in the blood using our computational model ( step 6 in Fig 1 ) .', 'Our goal here is to predict the collective behavior of latent and active TB groups rather than in silico blood trajectories of single NHP .', 'Thus the scaling to host analysis does not include the computation of specificity , sensitivity and misclassification rates .', 'Predicted numbers of CD4+ and CD8+ T cells ( including the subsets of Mtb-specific T cells ) in blood were generated for 43 virtual NHP ( 20 latent infection , 23 active TB as in the experimental NHP dataset ) ( Fig 7 and S8 and S9 Figs ) .', 'These predictions match the NHP blood dataset , suggesting that one cannot distinguish between active and latent TB outcomes by monitoring total T cell levels ( Fig 7A–7C and S8A and S8C–S8E Fig ) .', 'In contrast , predicted Mtb-specific T cell levels in blood do allow us to distinguish infection outcomes ( active vs latent ) by ~300 days post infection ( both CD4+ and CD8+ , Fig 7C and 7D ) .', 'The predicted frequencies of Mtb-specific Effector CD4+ and CD8+ T cells in virtual active vs latent hosts separate after 300 days post infection ( Fig 7E and 7G ) .', 'The predicted frequencies of Mtb-specific Effector Memory CD4+ and CD8+ T cells are also indicative of outcome but at later time points ( Fig 7F and 7H ) .', 'Overall , we predict that frequencies of Mtb-specific effector CD4+ and CD8+ T cells in blood are significantly higher ( from 2- to 4- fold ) in an active versus a latent Mtb-infected NHP and thus a combination of these cells and various time points post-infection should be targeted as potential biomarkers of Mtb infection progression .'], ['One of the greatest tools in disease diagnosis and treatment is a robust biomarker .', 'In TB , there been has much debate regarding whether biomarkers exist and , if so , what could serve as appropriate biomarkers [3 , 4 , 5 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 34 , 35] .', 'To date , no biomarker ( or set of biomarkers ) has been shown to be useful in discriminating the extent of infection and disease in humans .', 'One of the complications in predicting Mtb infection status is the spectrum of disease outcomes encompassed within the binary classifications of active TB and latent infection [9 , 36 , 37] .', 'This variability in disease outcome is also paralleled by heterogeneity in granuloma outcomes both between , and within , individual NHPs .', 'We previously reported that a spectrum of granulomas , in terms of numbers , types , immune responses and bacterial burden are found in individual animals and among animals with active or latent infection [8 , 22 , 25] .', 'Recent studies from our group support that progressive and healed granulomas can coexist within the same animal , with nearly all animals capable of sterilizing at least a subset of individual granulomas [21] .', 'However , animals with active TB show a subset of lesions that do not control infection , which presumably results in dissemination [22] .', 'Not surprisingly , given this heterogeneity within hosts , systemic T cell responses in the blood do not accurately reflect granuloma T cell responses [8] .', 'Here we collected a unique and extensive set of phenotypic and functional T cell data in blood from 28 NHPs experimentally infected with a low dose of Mtb and with a known clinical outcome ( active or latent TB ) .', 'We present an approach that integrates these experimental NHP data into a computational model capturing immune dynamics in 3 physiological compartments .', 'We apply classical data mining techniques to temporal datasets from blood that are derived from both NHP studies and from our computational model .', 'A major benefit of using data mining techniques on computational data is that in silico datasets are exhaustive in both time and density , allowing for greater sample sizes and statistical power .', 'This can rule out small sample size as a potential error in the corresponding animal study and point to other factors at play .', 'Standard supervised and unsupervised classification algorithms returned no clear decomposition or optimal cluster distribution .', 'This suggested that the resolution of NHP blood measures that were collected was not able to identify potential biomarkers of disease progression , possibly due to the overlap between the binary classifications of infection outcome .', 'Moreover , if we speculate that clinical outcome of hosts can ultimately be driven by the combined activity of an individual’s granuloma burden , sampling the blood will likely reflect the average dynamics of variable local immune responses to infection .', 'Ultimately , we were unable to identify biomarkers from NHP experimental data collected in a systemic compartment ( blood ) , indicating the complex nature of localized lung disease in TB .', 'To offer complementary analyses , we built a novel and unique multi-organ computational model that tracks infection dynamics in lungs , blood and LNs , using the extensive NHP datasets distinctly for model building , calibration and validation .', 'This allows generation of a large ( on the order of thousands ) parallel in silico dataset to mine for potential biomarkers .', 'One aspect of our in silico system is the ability to accurately simulate Mtb-specific T cell frequencies over time .', 'These data indicate that Mtb-specific effector CD4+ and CD8+ T cell frequencies in the blood could serve as potential biomarkers to predict infection outcome .', 'Although full ranges of Mtb-specific T cell frequencies are not currently measurable in primates , many studies are now available on Mtb protein epitopes ( e . g . , ESAT-6 , Ag85B , 16kDa , 19kDa , Hsp65 , Rv1490 ) for the most common human HLAs ( reviewed in [38] ) .', 'For each epitope , ranges for frequencies of antigenic-specific T cells span from 0 . 05% up to 1–2% .', 'If we assume that an entire Mtb-specific frequency repertoire can be represented by the sum of the responses to many of these epitopes , our new scaling-to-host methodology offers predictions that are quantitatively comparable to these ranges ( Fig 7 and S8 Fig ) .', 'Because of the variability in human HLA molecules and the large number of potential Mtb antigens , distinguishing total Mtb-specific T cell frequencies in blood is not yet feasible , especially for CD8+ T cells .', 'However , recent studies have identified novel epitope sequences from Mtb that may soon be targets to discriminate and quantify Mtb-specific T cell responses [38 , 39] .', 'This will allow prediction of infection status or treatment outcome using peripheral blood samples from infected hosts [40 , 41 , 42 , 43 , 44 , 45] .', 'For example , a recent study by Sette et al [39] provides an extensive list of Mtb-derived epitopes recognized by CD4+ T cells from healthy and LTBI individuals .', 'The authors emphasize how CD4+ T cells from both groups recognize non-tuberculous mycobacteria ( NTMs , or environmental exposure ) epitopes ( likely from previous exposure ) .', 'This might also explain the large variability identified in the NHP T cell data derived herein ( Fig 5 ) .', 'The list of peptides that reflect true Mtb-specific versus environmental responses is by no means complete , but we can reasonably speculate that more Mtb-specific epitopes will be identified in the near future .', 'By measuring these pathogen-specific responses , we will have a more adequate estimate of Mtb-specific immunity generated upon infection to correlate with protection or outcomes .', 'Moreover , by including these combinations of Mtb-specific epitopes into our computational model , we could eventually predict infection outcome earlier or discriminate among a spectrum of infection outcomes .', 'A recent study on MDR-TB patients shows how our approach can be viable in a clinical setting .', 'Riou et al [40] identified a subset of activated and proliferating Mtb-specific CD4+ T cells ( i . e . , Ki67+ HLA-DR+ ) as a potential marker in peripheral blood that predicts the time to sputum culture conversion in TB patients at the start of treatment .', 'Another recent prospective proof-of-concept study uses a novel T-cell activation marker-tuberculosis ( i . e . , TAM-TB ) assay on cryopreserved peripheral blood mononuclear cell samples to diagnose active TB in children based on ratios of CD27 phenotype of CD4 T cells producing IFNγ in response to Mtb antigens ( i . e . , ESAT6 and CFP10 ) [42] .', 'It has been also suggested that a shift of Mtb-specific Central Memory to Mtb-specific Effector Memory CD4+ T cells precedes the clinical diagnosis of active TB by many months [44] .', 'If we restrict our analysis to ratios of Mtb-specific Effector Memory to Mtb-specific Central Memory T cell frequencies ( in silico data ) , no significant difference can be shown between latent and active TB infection groups ( see S9A Fig ) .', 'However , our in silico predictions for the time courses of the ratios of Mtb-specific Effector to Mtb-specific Central Memory CD4+ T cells , and higher ratios in the active TB group ( statistically significant only after day 300 post infection ) confirm the finding of Schuetz et al ( S9B Fig and [44] ) .', 'In complex diseases such as heart disease and cancer , a suite of biomarkers best predicts disease outcomes and treatment intervention points .', 'Our studies support that for TB , a complex lifelong infection that is compartmentalized primarily in the lung , single biomarkers in blood may not be a feasible goal .', 'Coupled to the idea that infection outcome in hosts likely occurs over a spectrum , identification of a single biomarker may be a misguided goal .', 'Our novel systems biology approach combined with the ongoing progress to elucidate and winnow down Mtb-specific epitopes can significantly influence hypothesis generation for biomarker prediction for TB .', 'Any in silico prediction can then be validated on human clinical data and animal model studies , adding important knowledge to human immunity to TB and TB intervention studies .'], ['The goal of this study was to develop a methodology to identify biomarkers for TB infection outcome .', 'Our systems biology approach integrates non-human primate ( NHP ) datasets together with computational modeling , allowing the generation of virtual hosts that we use for biomarker discovery .', 'The next sections illustrate:', 'i ) the experimental datasets ,', 'ii ) classification algorithms ,', 'iii ) computational modeling framework ,', 'iv ) in silico datasets analysis ,', 'v ) model calibration and', 'vi ) virtual hosts generation .', 'Refer to Fig 1 for a general roadmap of all the different datasets that have been generated and all the many analysis that have been performed in this study .', 'All experimental manipulations , protocols , and care of the animals were approved by the University of Pittsburgh School of Medicine Institutional Animal Care and Use Committee ( IACUC ) .', 'The protocol assurance number for our IACUC is A3187-01 .', 'Our specific protocol approval numbers for this project are 11090030 and 12060181 .', 'The IACUC adheres to national guidelines established in the Animal Welfare Act ( 7 U . S . C . Sections 2131–2159 ) and the Guide for the Care and Use of Laboratory Animals ( 8th Edition ) as mandated by the U . S . Public Health Service Policy .', 'All macaques used in this study were housed at the University of Pittsburgh in rooms with autonomously controlled temperature , humidity , and lighting .', 'Animals were singly housed in caging at least 2 square meters that allowed visual and tactile contact with neighboring conspecifics .', 'The macaques were fed twice daily with biscuits formulated for non human primates , supplemented at least 4 days/week with large pieces of fresh fruits or vegetables .', 'Animals had access to water ad libitem .', 'Because our macaques were singly housed due to the infectious nature of these studies , an enhanced enrichment plan was designed and overseen by our nonhuman primate enrichment specialist .', 'This plan has three components .', 'First , species-specific behaviors are encouraged .', 'All animals have access to toys and other manipulata , some of which will be filled with food treats ( e . g . frozen fruit , peanut butter , etc . ) .', 'These are rotated on a regular basis .', 'Puzzle feeders foraging boards , and cardboard tubes containing small food items also are placed in the cage to stimulate foraging behaviors .', 'Adjustable mirrors accessible to the animals stimulate interaction between animals .', 'Second , routine interaction between humans and macaques are encouraged .', 'These interactions occur daily and consist mainly of small food objects offered as enrichment and adhere to established safety protocols .', 'Animal caretakers are encouraged to interact with the animals ( by talking or with facial expressions ) while performing tasks in the housing area .', 'Routine procedures ( e . g . feeding , cage cleaning , etc ) are done on a strict schedule to allow the animals to acclimate to a routine daily schedule .', 'Third , all macaques are provided with a variety of visual and auditory stimulation .', 'Housing areas contain either radios or TV/video equipment that play cartoons or other formats designed for children for at least 3 hours each day .', 'The videos and radios are rotated between animal rooms so that the same enrichment is not played repetitively for the same group of animals .', 'All animals are checked at least twice daily to assess appetite , attitude , activity level , hydration status , etc .', 'Following M . tuberculosis infection , the animals are monitored closely for evidence of disease ( e . g . , anorexia , weight loss , tachypnea , dyspnea , coughing ) .', 'Physical exams , including weights , are performed on a regular basis .', 'Animals are sedated prior to all veterinary procedures ( e . g . blood draws , etc . ) using ketamine or other approved drugs .', 'Regular PET/CT imaging is conducted on most of our macaques following infection and has proved very useful for monitoring disease progression .', 'Our veterinary technicians monitor animals especially closely for any signs of pain or distress .', 'If any are noted , appropriate supportive care ( e . g . dietary supplementation , rehydration ) and clinical treatments ( analgesics ) are given .', 'Any animal considered to have advanced disease or intractable pain or distress from any cause is sedated with ketamine and then humanely euthanatized using sodium pentobarbital .', 'Twenty-eight cynomolgus macaques ( Macacca fasicularis ) were infected with a low dose of Mtb ( Erdman strain , ~25–50 CFU ) and monitored clinically for signs and symptoms of TB up to six months post infection .', 'Infection was confirmed by tuberculin skin test conversion and/or lymphocyte proliferation assay six weeks post-infection .', 'The macaques were classified ( as previously described in [22 , 23 , 25] ) to have either clinically latent TB infection or active TB ( S1 Table , third column ) based on clinical , radiological and microbiologic criteria .', 'Peripheral blood was collected at time-points: pre Mtb infection ( only for 9 NHPs ) and at days 10 , 20 , 30 , 42 , 56 , 90 ( or M3 , 3 months ) , 120 ( or M4 ) , 150 ( or M5 ) and 180 ( or M6 ) post infection .', 'PBMC were isolated , stimulated with peptide pools of Mtb specific antigens ESAT-6 and CFP-10 ( 10μg/ml of each peptide ) or phorbol dibutyrate ( PDBu ) and ionomycin as positive control and media as negative control , in the presence of Brefaldin A ( BD biosciences ) for 6 hours at 37°C with 5%CO2 .', 'Multi-parametric intracellular flow cytometry was performed on fresh PBMC to assess CD4 and CD8 T cell cytokine profiles ( GMCSF ( clone: M5D12 ) , IFN-γ ( B27 ) , IL-2 ( MQ1-17H12 ) , IL-4 ( 8D4-8 ) IL-6 ( MQ2-6A3 ) , IL-10 ( JES3-9D7 ) , IL-17 ( 64CAP17 ) and TNF ( MA611 ) ) during the course of Mtb infection .', 'S1 Fig shows representative flow cytometry plots where we outline our gating strategies employed in the analysis of T cells in PBMC .', 'We generated four different datasets: a single cytokine dataset ( where each cell was labeled for a single cytokine , S2 Table ) , a multiple cytokine dataset ( where each cell was labeled for the simultaneous presence of more than 1 cytokine , S3 Table ) , a memory single cytokine dataset ( S4 Table , where the same cytokines profile of the single cytokine dataset is stratified by CD4+ and CD8+ memory sub-populations based on the expression of CD45RA and CD27 , namely Naïve-N [CD45RA+ CD27+] , Central Memory-CM [
['Introduction', 'Results', 'Discussion', 'Materials and Methods']
['Identifying biomarkers for tuberculosis ( TB ) is an ongoing challenge in developing immunological correlates of infection outcome and protection .', 'Biomarker discovery is also necessary for aiding design and testing of new treatments and vaccines .', 'To effectively predict biomarkers for infection progression in any disease , including TB , large amounts of experimental data are required to reach statistical power and make accurate predictions .', 'We took a two-pronged approach using both experimental and computational modeling to address this problem .', 'We first collected 200 blood samples over a 2- year period from 28 non-human primates ( NHP ) infected with a low dose of Mycobacterium tuberculosis .', 'We identified T cells and the cytokines that they were producing ( single and multiple ) from each sample along with monkey status and infection progression data .', 'Machine learning techniques were used to interrogate the experimental NHP datasets without identifying any potential TB biomarker .', 'In parallel , we used our extensive novel NHP datasets to build and calibrate a multi-organ computational model that combines what is occurring at the site of infection ( e . g . , lung ) at a single granuloma scale with blood level readouts that can be tracked in monkeys and humans .', 'We then generated a large in silico repository of in silico granulomas coupled to lymph node and blood dynamics and developed an in silico tool to scale granuloma level results to a full host scale to identify what best predicts Mycobacterium tuberculosis ( Mtb ) infection outcomes .', 'The analysis of in silico blood measures identifies Mtb-specific frequencies of effector T cell phenotypes at various time points post infection as promising indicators of infection outcome .', 'We emphasize that pairing wetlab and computational approaches holds great promise to accelerate TB biomarker discovery .']
['Tuberculosis ( TB ) is a disease that is caused by infection after inhaling the bacterium Mycobacterium tuberculosis .', 'Not everyone infected with TB bacteria becomes sick .', 'As a result , two TB-related conditions have been categorized: latent TB infection ( not sick but still harboring the bacteria ) and active TB disease .', 'If not treated properly , active TB disease can be fatal .', 'Almost 1 . 3 million die of TB worldwide each year , with ~8 , 6 million new infections in 2013 .', 'No effective vaccine is available to protect against TB and treatment of infection with multiple antibiotics is lengthy ( 6–9 months ) , with non-compliance being a major factor for the emergence of drug-resistant strains .', 'A key step in developing effective vaccines and possibly shorter treatment regimens is the ability to identify biomarkers that correlate prognosis and progression to infection ( similar to how cholesterol levels are a measure of heart health ) .', 'In this study we show how pairing computer modeling , statistics and mathematics with datasets derived from non-human primate studies can accelerate biomarker discovery , and offer a new approach to identifying correlates of protection that will be useful in clinical practice , particularly in developing countries where TB is most prevalent .']
['blood cells', 'medicine and health sciences', 'immune cells', 'body fluids', 'granulomas', 'immunology', 'tropical diseases', 'biomarkers', 'bacterial diseases', 'cytotoxic t cells', 'bacteria', 'infectious diseases', 'white blood cells', 'memory t cells', 'animal cells', 'tuberculosis', 't cells', 'actinobacteria', 'hematology', 'biochemistry', 'blood', 'cell biology', 'anatomy', 'mycobacterium tuberculosis', 'physiology', 'biology and life sciences', 'cellular types', 'organisms']