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Title: Computational and empirical studies predict Mycobacterium tuberculosis-specific T cells as a biomarker for infection outcome

Abstract

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 tomore » 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. As a result, we emphasize that pairing wetlab and computational approaches holds great promise to accelerate TB biomarker discovery.« less

Authors:
 [1];  [2];  [1];  [3];  [1];  [4];  [5];  [2];  [1]
  1. Univ. of Michigan Medical School, Ann Arbor, MI (United States)
  2. Univ. of Pittsburgh, Pittsburgh, PA (United States)
  3. Univ. of Maryland, College Park, MD (United States)
  4. Univ. of Pittsburgh of UPMC, Pittsburgh, PA (United States)
  5. Univ. of Michigan, Ann Arbor, MI (United States)
Publication Date:
Research Org.:
Univ. of Michigan, Ann Arbor, MI (United States)
Sponsoring Org.:
USDOE Office of Science (SC); National Energy Research Scientific Computing Center (NERSCC); National Science Foundation (Open Science Grid); National Institutes of Health (NIH)
OSTI Identifier:
1262275
Grant/Contract Number:
AC02-05CH11231
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
PLoS Computational Biology (Online)
Additional Journal Information:
Journal Name: PLoS Computational Biology (Online); Journal Volume: 12; Journal Issue: 4; Journal ID: ISSN 1553-7358
Publisher:
Public Library of Science
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; immune-response; cynomolgus macaques; active tuberculosis; protective immunity; latent tuberculosis; granuloma-formation; antibody profiles; systems biology; dendritic cells; memory; T cells; granulomas; blood; tuberculosis; cytotoxic T cells; mycobacterium tuberculosis; biomarkers

Citation Formats

Marino, Simeone, Gideon, Hannah P., Gong, Chang, Mankad, Shawn, McCrone, John T., Lin, Philana Ling, Linderman, Jennifer J., Flynn, JoAnne L., and Kirschner, Denise E. Computational and empirical studies predict Mycobacterium tuberculosis-specific T cells as a biomarker for infection outcome. United States: N. p., 2016. Web. doi:10.1371/journal.pcbi.1004804.
Marino, Simeone, Gideon, Hannah P., Gong, Chang, Mankad, Shawn, McCrone, John T., Lin, Philana Ling, Linderman, Jennifer J., Flynn, JoAnne L., & Kirschner, Denise E. Computational and empirical studies predict Mycobacterium tuberculosis-specific T cells as a biomarker for infection outcome. United States. doi:10.1371/journal.pcbi.1004804.
Marino, Simeone, Gideon, Hannah P., Gong, Chang, Mankad, Shawn, McCrone, John T., Lin, Philana Ling, Linderman, Jennifer J., Flynn, JoAnne L., and Kirschner, Denise E. Mon . "Computational and empirical studies predict Mycobacterium tuberculosis-specific T cells as a biomarker for infection outcome". United States. doi:10.1371/journal.pcbi.1004804. https://www.osti.gov/servlets/purl/1262275.
@article{osti_1262275,
title = {Computational and empirical studies predict Mycobacterium tuberculosis-specific T cells as a biomarker for infection outcome},
author = {Marino, Simeone and Gideon, Hannah P. and Gong, Chang and Mankad, Shawn and McCrone, John T. and Lin, Philana Ling and Linderman, Jennifer J. and Flynn, JoAnne L. and Kirschner, Denise E.},
abstractNote = {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. As a result, we emphasize that pairing wetlab and computational approaches holds great promise to accelerate TB biomarker discovery.},
doi = {10.1371/journal.pcbi.1004804},
journal = {PLoS Computational Biology (Online)},
number = 4,
volume = 12,
place = {United States},
year = {Mon Apr 11 00:00:00 EDT 2016},
month = {Mon Apr 11 00:00:00 EDT 2016}
}

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  • Lack of an effective vaccine results in 9 million new cases of tuberculosis (TB) every year and 1.8 million deaths worldwide. While many infants are vaccinated at birth with BCG (an attenuated M. bovis), this does not prevent infection or development of TB after childhood. Immune responses necessary for prevention of infection or disease are still unknown, making development of effective vaccines against TB challenging. Several new vaccines are ready for human clinical trials, but these trials are difficult and expensive; especially challenging is determining the appropriate cellular response necessary for protection. The magnitude of an immune response is likelymore » key to generating a successful vaccine. Characteristics such as numbers of central memory (CM) and effector memory (EM) T cells responsive to a diverse set of epitopes are also correlated with protection. Promising vaccines against TB contain mycobacterial subunit antigens (Ag) present during both active and latent infection. We hypothesize that protection against different key immunodominant antigens could require a vaccine that produces different levels of EM and CM for each Ag-specific memory population. We created a computational model to explore EM and CM values, and their ratio, within what we term Memory Design Space. Our model captures events involved in T cell priming within lymph nodes and tracks their circulation through blood to peripheral tissues. We used the model to test whether multiple Ag-specific memory cell populations could be generated with distinct locations within Memory Design Space at a specific time point post vaccination. Boosting can further shift memory populations to memory cell ratios unreachable by initial priming events. By strategically varying antigen load, properties of cellular interactions within the LN, and delivery parameters (e.g., number of boosts) of multi-subunit vaccines, we can generate multiple Ag-specific memory populations that cover a wide range of Memory Design Space. As a result, given a set of desired characteristics for Ag-specific memory populations, we can use our model as a tool to predict vaccine formulations that will generate those populations.« less
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  • Beta-lactam antibiotics target penicillin-binding proteins including several enzyme classes essential for bacterial cell-wall homeostasis. To better understand the functional and inhibitor-binding specificities of penicillin-binding proteins from the pathogen, Mycobacterium tuberculosis, we carried out structural and phylogenetic analysis of two predicted D,D-carboxypeptidases, Rv2911 and Rv3330. Optimization of Rv2911 for crystallization using directed evolution and the GFP folding reporter method yielded a soluble quadruple mutant. Structures of optimized Rv2911 bound to phenylmethylsulfonyl fluoride and Rv3330 bound to meropenem show that, in contrast to the nonspecific inhibitor, meropenem forms an extended interaction with the enzyme along a conserved surface. Phylogenetic analysis shows thatmore » Rv2911 and Rv3330 belong to different clades that emerged in Actinobacteria and are not represented in model organisms such as Escherichia coli and Bacillus subtilis. Clade-specific adaptations allow these enzymes to fulfill distinct physiological roles despite strict conservation of core catalytic residues. The characteristic differences include potential protein-protein interaction surfaces and specificity-determining residues surrounding the catalytic site. Overall, these structural insights lay the groundwork to develop improved beta-lactam therapeutics for tuberculosis.« less