skip to main content


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

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
 [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:
Grant/Contract Number:
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
Public Library of Science
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)
Country of Publication:
United States
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
OSTI Identifier: