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

Abstract

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.

Keywords: Bioinformatics; Biological sciences; Health sciences; Internal medicine; Medical informatics; Medical specialty; Medicine; Natural sciences.

Graphical abstract

Figure 1

Distinct multi-omic expression profiles identified tuberculosis regardless the glycemic status Right panel. A hierarchical cluster analysis (Ward method with 100 × bootstrap) was employed to test the overall expression of plasma cytokines, gene expression and urinary eicosanoids in the study population. Dendrograms represent Canberra distance. Left panel. Differential expression analysis was used to calculate the fold-changes and show differences in biomarkers levels for each clinical group (TB, TB-DM, and DM) versus control. Differences that reached statistical significance after adjustment for multiple comparisons (adjusted p < 0.05) are represented in colored bars.

Figure 2

A distinct multi-omic expression and correlation profile between AFB grade and markers among TB groups (A) Left panel. A hierarchical cluster analysis (Ward method with 100 × bootstrap) was employed to evaluate multi-omic marker expression according to the AFB smear grade in TB and TB-DM, as indicated. Right panel. A Spearman correlation analysis was used to study the influence of the AFB smear grade on multi-omic marker expression. The rho values are shown. Red bars indicate correlation with p value < 0.05. (B) Spearman correlation plots demonstrating the associations between the expression levels of the indicated genes and the AFB smear grade. Line represent linear regression. R: Spearman rho value.

Figure 3

Changes in multi-omic expression after anti-tuberculosis therapy initiation (A) A hierarchical cluster analysis (Ward method with 100 × bootstrap) was employed to evaluate multi-omic marker expression in TB and TB-DM after anti-tuberculosis therapy initiation. (B) A boxplot was used to test the changes in multi-omic levels in months 2 and 6 after ATT. In the graphs, dots represent median and whiskers represent interquartile range values.

Figure 4

Accuracy of a new transcriptomic signature to detect TB-DM Accuracy of the gene signature detected in the random forest analysis to classify TB, with or without DM in Brazil (A), India (B), Romania (C) and South Africa (D). Receiver operator characteristics (ROC) curve analysis was used to check the accuracy of the signature genes identified by the random forest model to classify the TB, TB-DM, and DM groups in each clinical site as indicated with respect to TB disease.