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Metabolites enhance innate resistance to human Mycobacterium tuberculosis infection
Deepak Tripathi, Kamakshi Prudhula Devalraju, Venkata Sanjeev Kumar Neela, Tanmoy Mukherjee, Padmaja Paidipally, Rajesh Kumar Radhakrishnan, Igor Dozmorov, Abhinav Vankayalapati, Mohammad Soheb Ansari, Varalakshmi Mallidi, Anvesh Kumar Bogam, Karan P. Singh, Buka Samten, Vijaya Lakshmi Valluri, Ramakrishna Vankayalapati
Deepak Tripathi, Kamakshi Prudhula Devalraju, Venkata Sanjeev Kumar Neela, Tanmoy Mukherjee, Padmaja Paidipally, Rajesh Kumar Radhakrishnan, Igor Dozmorov, Abhinav Vankayalapati, Mohammad Soheb Ansari, Varalakshmi Mallidi, Anvesh Kumar Bogam, Karan P. Singh, Buka Samten, Vijaya Lakshmi Valluri, Ramakrishna Vankayalapati
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Research Article Immunology

Metabolites enhance innate resistance to human Mycobacterium tuberculosis infection

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Abstract

To determine the mechanisms that mediate resistance to Mycobacterium tuberculosis (M. tuberculosis) infection in household contacts (HHCs) of patients with tuberculosis (TB), we followed 452 latent TB infection–negative (LTBI–) HHCs for 2 years. Those who remained LTBI– throughout the study were identified as nonconverters. At baseline, nonconverters had a higher percentage of CD14+ and CD3–CD56+CD27+CCR7+ memory-like natural killer (NK) cells. Using a whole-transcriptome and metabolomic approach, we identified deoxycorticosterone acetate as a metabolite with elevated concentrations in the plasma of nonconverters, and further studies showed that this metabolite enhanced glycolytic ATP flux in macrophages and restricted M. tuberculosis growth by enhancing antimicrobial peptide production through the expression of the surface receptor sialic acid binding Ig-like lectin–14. Another metabolite, 4-hydroxypyridine, from the plasma of nonconverters significantly enhanced the expansion of memory-like NK cells. Our findings demonstrate that increased levels of specific metabolites can regulate innate resistance against M. tuberculosis infection in HHCs of patients with TB who never develop LTBI or active TB.

Authors

Deepak Tripathi, Kamakshi Prudhula Devalraju, Venkata Sanjeev Kumar Neela, Tanmoy Mukherjee, Padmaja Paidipally, Rajesh Kumar Radhakrishnan, Igor Dozmorov, Abhinav Vankayalapati, Mohammad Soheb Ansari, Varalakshmi Mallidi, Anvesh Kumar Bogam, Karan P. Singh, Buka Samten, Vijaya Lakshmi Valluri, Ramakrishna Vankayalapati

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Figure 5

Nonconverters exhibit differential plasma metabolomic signatures.

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Nonconverters exhibit differential plasma metabolomic signatures.
Lyophi...
Lyophilized plasma from nonconverters (n = 5) and converters (n = 5) at baseline (0, at enrollment) and follow-up (24 months) was analyzed using LC-MS. (A) A representative score plot of the partial least squares discriminant analysis (PLS-DA) was generated using MetaboAnalyst. PLS-DA models were validated using R2 and Q2 based on leave-one-out cross-validation; the 4-component model was selected as the optimized model with R2 = 0.95 and Q2 = 058. The significance of the model was demonstrated by a permutation test with 100 testing iterations using a separation distance of P < 0.01 (95% confidence interval). Blue: nonconverter baseline, light blue: nonconverter follow-up, red: converter baseline, green: converter follow-up. (B) An FDR-corrected heatmap of selected metabolites is shown, q = 0.05. (C) Representation of 25 metabolites with variable importance of projection (VIP) scores based on PLS-DA and considered significant. On the extreme right, red indicates high levels, and green indicates low levels of metabolites in the respective groups. (D) Quantitative metabolite set enrichment overview using metabolite set enrichment analysis, with the fold change showing metabolic pathways of 25 metabolites selected based on VIP scores (FDR-corrected q = 0.05).

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