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

Marino, Simeone; Gideon, Hannah P.; Gong, Chang; Mankad, Shawn; McCrone, John T.; Lin, Philana Ling; Linderman, Jennifer J.; Flynn, JoAnne L.; Kirschner, Denise E.

doi:10.1371/journal.pcbi.1004804

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

Journal Article · Mon Apr 11 00:00:00 EDT 2016 · PLoS Computational Biology (Online)

DOI:https://doi.org/10.1371/journal.pcbi.1004804· OSTI ID:1262275

Marino, Simeone ^[1]; Gideon, Hannah P. ^[2]; Gong, Chang ^[3]; Mankad, Shawn ^[4]; McCrone, John T. ^[3]; Lin, Philana Ling ^[5]; Linderman, Jennifer J. ^[6]; Flynn, JoAnne L. ^[2]; Kirschner, Denise E. ^[3]

Univ. of Michigan Medical School, Ann Arbor, MI (United States); University of Michigan
Univ. of Pittsburgh, Pittsburgh, PA (United States)
Univ. of Michigan Medical School, Ann Arbor, MI (United States)
Univ. of Maryland, College Park, MD (United States)
Univ. of Pittsburgh of UPMC, Pittsburgh, PA (United States)
Univ. of Michigan, Ann Arbor, MI (United States)

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.

View Accepted Manuscript (DOE)

Research Organization:: Univ. of Michigan, Ann Arbor, (United States)

Sponsoring Organization:: NIH; National Energy Research Scientific Computing Center (NERSCC); National Science Foundation (Open Science Grid); USDOE Office of Science (SC)

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 1262275

Journal Information:: PLoS Computational Biology (Online), Journal Name: PLoS Computational Biology (Online) Journal Issue: 4 Vol. 12; ISSN 1553-7358

Publisher:: Public Library of ScienceCopyright Statement

Country of Publication:: United States

Language:: English

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59 BASIC BIOLOGICAL SCIENCES
T cells
active tuberculosis
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biomarkers
blood
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dendritic cells
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granulomas
immune-response
latent tuberculosis
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Computational and empirical studies predict Mycobacterium tuberculosis-specific T cells as a biomarker for infection outcome

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