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Front Immunol. 2019 Mar 22;10:527. doi: 10.3389/fimmu.2019.00527. eCollection 2019.

Immunometabolic Signatures Predict Risk of Progression to Active Tuberculosis and Disease Outcome.

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Center for Global Infectious Disease Research, Seattle Childrens Research Institute, Seattle, WA, United States.
Max Planck Institute for Infection Biology, Berlin, Germany.
Oregon Health and Science University, Portland, OR, United States.
Division of Molecular Biology and Human Genetics, Department of Biomedical Sciences, SAMRC-SHIP South African Tuberculosis Bioinformatics Initiative (SATBBI), Center for Bioinformatics and Computational Biology, DST/NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Stellenbosch University, Stellenbosch, South Africa.
Department of Pathology, South African Tuberculosis Vaccine Initiative, Institute of Infectious Disease and Molecular Medicine & Division of Immunology, University of Cape Town, Cape Town, South Africa.
Center for Infectious Disease Research, Seattle, WA, United States.
Translational Medicine & Global Health Consulting, Berlin, Germany.
Vaccines & Immunity Theme, Medical Research Council Unit, Fajara, Gambia.
Department of Immunology and Infection, London School of Hygiene and Tropical Medicine, London, United Kingdom.
Department of Infectious Diseases, Leiden University Medical Center, Leiden, Netherlands.


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 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.


biomarker; host-pathogen interaction; household contact; inflammation; metabolomics; rhesus macaque; transcriptomics; tuberculosis

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