iScience (Mar 2024)

An integrative multi-omics approach to characterize interactions between tuberculosis and diabetes mellitus

  • Caian L. Vinhaes,
  • Eduardo R. Fukutani,
  • Gabriel C. Santana,
  • María B. Arriaga,
  • Beatriz Barreto-Duarte,
  • Mariana Araújo-Pereira,
  • Mateus Maggiti-Bezerril,
  • Alice M.S. Andrade,
  • Marina C. Figueiredo,
  • Ginger L. Milne,
  • Valeria C. Rolla,
  • Afrânio L. Kristki,
  • Marcelo Cordeiro-Santos,
  • Timothy R. Sterling,
  • Bruno B. Andrade,
  • Artur T.L. Queiroz

Journal volume & issue
Vol. 27, no. 3
p. 109135

Abstract

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Summary: Tuberculosis-diabetes mellitus (TB-DM) is linked to a distinct inflammatory profile, which can be assessed using multi-omics analyses. Here, a machine learning algorithm was applied to multi-platform data, including cytokines and gene expression in peripheral blood and eicosanoids in urine, in a Brazilian multi-center TB cohort. There were four clinical groups: TB-DM(n = 24), TB only(n = 28), DM(HbA1c ≥ 6.5%) only(n = 11), and a control group of close TB contacts who did not have TB or DM(n = 13). After cross-validation, baseline expression or abundance of MMP-28, LTE-4, 11-dTxB2, PGDM, FBXO6, SECTM1, and LINCO2009 differentiated the four patient groups. A distinct multi-omic-derived, dimensionally reduced, signature was associated with TB, regardless of glycemic status. SECTM1 and FBXO6 mRNA levels were positively correlated with sputum acid-fast bacilli grade in TB-DM. Values of the biomarkers decreased during the course of anti-TB therapy. Our study identified several markers associated with the pathophysiology of TB-DM that could be evaluated in future mechanistic investigations.

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