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Computational and Empirical Studies Predict Mycobacterium tuberculosis-Specific T Cells as a Biomarker for Infection Outcome

Marino, S and Gideon, HP and Gong, C and Mankad, S and McCrone, JT and Lin, PL and Linderman, JJ and Flynn, JAL and Kirschner, DE (2016) Computational and Empirical Studies Predict Mycobacterium tuberculosis-Specific T Cells as a Biomarker for Infection Outcome. PLoS Computational Biology, 12 (4). ISSN 1553-734X

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Abstract

© 2016 Marino et al. 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.


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Details

Item Type: Article
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Marino, S
Gideon, HP
Gong, C
Mankad, S
McCrone, JT
Lin, PLpll7@pitt.eduPLL7
Linderman, JJ
Flynn, JAL
Kirschner, DE
Contributors:
ContributionContributors NameEmailPitt UsernameORCID
EditorBeauchemin, Catherine A.A.UNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Date: 1 April 2016
Date Type: Publication
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Journal or Publication Title: PLoS Computational Biology
Volume: 12
Number: 4
DOI or Unique Handle: 10.1371/journal.pcbi.1004804
Institution: University of Pittsburgh
Schools and Programs: School of Medicine > Microbiology and Molecular Genetics
School of Medicine > Pediatrics
Refereed: Yes
ISSN: 1553-734X
Date Deposited: 23 Aug 2016 13:54
Last Modified: 04 Feb 2019 15:57
URI: http://d-scholarship.pitt.edu/id/eprint/28501

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