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Metabolites enhance innate resistance to human Mycobacterium tuberculosis infection
Deepak Tripathi, … , Vijaya Lakshmi Valluri, Ramakrishna Vankayalapati
Deepak Tripathi, … , Vijaya Lakshmi Valluri, Ramakrishna Vankayalapati
Published November 22, 2022
Citation Information: JCI Insight. 2022;7(22):e152357. https://doi.org/10.1172/jci.insight.152357.
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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 3

Whole-transcriptome sequencing analysis of ESAT-6– and CFP-10–cultured PBMCs from HHCs of patients with TB.

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Whole-transcriptome sequencing analysis of ESAT-6– and CFP-10–cultured P...
(A) PBMCs were isolated from age-matched, epidemiological risk–matched, healthy (no comorbid conditions and no immunosuppressive drugs) nonconverters (n = 3) and converters (n = 3) at baseline (during study enrollment, when all participants were LTBI–) and after 24 months. Freshly isolated PBMCs were cultured with or without ESAT-6 and CFP-10 (10 μg/mL each), as described in the Methods section. After 96 hours, RNA was extracted, cDNA libraries were prepared, and whole-transcriptome sequencing was performed. The numbers of unique and relative transcript changes in unstimulated and ESAT-6 + CFP-10–stimulated PBMCs of converters and nonconverters at baseline and follow-up are shown. (B) A representative heatmap is shown. Transcripts differentially expressed in the PBMCs of LTBI– compared with LTBI+ and active TB (P < 0.05, ANOVA). Diagram showing differentially expressed transcripts in nonconverters compared with converters. (C) PBMCs were obtained from nonconverters (n = 10) and converters (n = 10) at baseline and follow-up and from unexposed healthy controls (n = 10) and cultured in the presence of ESAT-6 + CFP-10 (10 μg/mL each), as described in the Methods section. After 96 hours, RNA was extracted, and the mRNA expression levels of Siglec-14, CES1, RPS-26, ANXA1, and RGCC1 were determined by quantitative real-time PCR. (D) PBMCs were obtained from healthy donors (n = 5) and cultured in the presence or absence of γ-M. tuberculosis (10 μg/mL). After 96 hours, various immune cell populations were sorted, and the relative mRNA expression levels of Siglec-14, CES1, RPS-26, ANXA1, and RGCC1 were determined by quantitative real-time PCR. (E) PBMCs were obtained from healthy donors (n = 3) and cultured in the presence or absence of γ-M. tuberculosis (10 μg/mL). After 96 hours, the expression of Siglec-14 in various immune populations was determined by flow cytometry. The P values were determined by 1-way ANOVA with Tukey’s multiple comparisons test. Mean values, SDs, and P values are shown.

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