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 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.
authors.affiliation: Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, Michigan, United States of America.
authors.forename: Simeone
authors.initials: S
authors.lastname: Marino
authors.affiliation: Department of Microbiology and Molecular Genetics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.
authors.forename: Hannah P
authors.initials: HP
authors.lastname: Gideon
authors.affiliation: Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, Michigan, United States of America.
authors.forename: Chang
authors.initials: C
authors.lastname: Gong
authors.affiliation: Robert H. Smith School of Business, University of Maryland, College Park, Maryland, United States of America.
authors.forename: Shawn
authors.initials: S
authors.lastname: Mankad
authors.affiliation: Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, Michigan, United States of America.
authors.forename: John T
authors.initials: JT
authors.lastname: McCrone
authors.affiliation: Department of Pediatrics, Children's Hospital of the University of Pittsburgh of UPMC, Pittsburgh, Pennsylvania, United States of America.
authors.forename: Philana Ling
authors.initials: PL
authors.lastname: Lin
authors.affiliation: Department of Chemical Engineering, University of Michigan, Ann Arbor, Michigan, United States of America.
authors.forename: Jennifer J
authors.initials: JJ
authors.lastname: Linderman
authors.affiliation: Department of Microbiology and Molecular Genetics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.
authors.forename: JoAnne L
authors.initials: JL
authors.lastname: Flynn
authors.affiliation: Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, Michigan, United States of America.
authors.forename: Denise E
authors.initials: DE
authors.lastname: Kirschner
mesh_terms: D000465:Algorithms; D000818:Animals; D015415:Biomarkers / Q000097:blood; D015496:CD4-Positive T-Lymphocytes / Q000276:immunology / Q000382:microbiology; D018414:CD8-Positive T-Lymphocytes / Q000276:immunology / Q000382:microbiology; D019295:Computational Biology; D003198:Computer Simulation; D016207:Cytokines / Q000096:biosynthesis; D016208:Databases, Factual; D006801:Humans; D008168:Lung / Q000276:immunology / Q000382:microbiology; D008252:Macaca fascicularis; D018448:Models, Immunological; D009169:Mycobacterium tuberculosis / Q000276:immunology*; D013601:T-Lymphocytes / Q000276:immunology* / Q000382:microbiology*; D014397:Tuberculosis, Pulmonary / Q000097:blood / Q000276:immunology / Q000382:microbiology
chemical_list: D015415:Biomarkers; D016207:Cytokines