Immunometabolic signatures predict risk of progression to active tuberculosis and disease outcome
GC6-74 Consortium
(2019) Frontiers in Immunology, volume 10, issue MAR, pp.
(Article)
Abstract
There remains a pressing need for biomarkers that can predict who will progress to active tuberculosis (TB) after exposure to Mycobacterium tuberculosis (MTB) bacterium. By analyzing cohorts of household contacts of TB index cases (HHCs) and a stringent non-human primate (NHP) challenge model, we evaluated whether integration of blood transcriptional
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profiling with serum metabolomic profiling can provide new understanding of disease processes and enable improved prediction of TB progression. Compared to either alone, the combined application of pre-existing transcriptome- and metabolome-based signatures more accurately predicted TB progression in the HHC cohorts and more accurately predicted disease severity in the NHPs. Pathway and data-driven correlation analyses of the integrated transcriptional and metabolomic datasets further identified novel immunometabolomic signatures significantly associated with TB progression in HHCs and NHPs, implicating cortisol, tryptophan, glutathione, and tRNA acylation networks. These results demonstrate the power of multi-omics analysis to provide new insights into complex disease processes.
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Keywords: Biomarker, Host-pathogen interaction, Household contact, Inflammation, Metabolomics, Rhesus macaque, Transcriptomics, Tuberculosis, host-pathogen interaction, rhesus macaque, tuberculosis, biomarker, inflammation, metabolomics, transcriptomics, household contact, Humans, Transcriptome, Male, Macaca mulatta, Young Adult, Tuberculosis/genetics, Adult, Female, Tuberculosis Vaccines, Africa, Metabolome, Disease Progression, Animals, Mycobacterium tuberculosis, Adolescent, Biomarkers, Immunology and Allergy, Immunology, Journal Article, Research Support, Non-U.S. Gov't, Research Support, N.I.H., Extramural
ISSN: 1664-3224
Publisher: Frontiers Media S. A.
Note: Funding Information: This work was supported by the Bill & Melinda Gates Foundation (BMGF) Grand Challenges in Global Health (GC6-74 grant 37772, OPP1055806 and OPP1087783 in conjunction with AERAS). This work was also supported by a Strategic Health Innovation Partnership grant from the South African Medical Research Council and Department of Science and Technology/South African Tuberculosis Bioinformatics Initiative. Additional support was provided by the European Union FP7 (ADITEC, 280873 and TBVAC2020, 643381) and the National Institutes of Health [U19 AI106761 and U19 AI135976]. FD was supported by the NCDIR (National Institutes of Health [P41 GM109824]). DT and GT were supported by South African Medical Research Council SHIP funding for the South African Tuberculosis Bioinformatics Initiative to GW. Publisher Copyright: Copyright © 2019 Duffy, Weiner, Hansen, Tabb, Suliman, Thompson, Maertzdorf, Shankar, Tromp, Parida, Dover, Axthelm, Sutherland, Dockrell, Ottenhoff, Scriba, Picker, Walzl, Kaufmann, Zak and The GC6-74 Consortium. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
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