Integrative Multi-Omics Reveals Serum Markers of Tuberculosis in Advanced HIV

Abstract

Tuberculosis (TB) accounts for disproportionate morbidity and mortality among persons living with HIV (PLWH). Conventional methods of TB diagnosis, including smear microscopy and Xpert MTB/RIF, have lower sensitivity in PLWH. Novel high-throughput approaches, such as miRNAomics and metabolomics, may advance our ability to recognize subclinical and difficult-to-diagnose TB, especially in very advanced HIV. We conducted a case-control study leveraging REMEMBER, a multi-country, open-label randomized controlled trial comparing 4-drug empiric standard TB treatment with isoniazid preventive therapy in PLWH initiating antiretroviral therapy (ART) with CD4 cell counts <50 cells/μL. Twenty-three cases of incident TB were site-matched with 32 controls to identify microRNAs (miRNAs), metabolites, and cytokines/chemokines, associated with the development of newly diagnosed TB in PLWH. Differentially expressed miRNA analysis revealed 11 altered miRNAs with a fold change higher than 1.4 or lower than -1.4 in cases relative to controls (p<0.05). Our analysis revealed no differentially abundant metabolites between cases and controls. We found higher TNFα and IP-10/CXCL10 in cases (p=0.011, p=0.0005), and higher MDC/CCL22 in controls (p=0.0072). A decision-tree algorithm identified gamma-glutamylthreonine and hsa-miR-215-5p as the optimal variables to classify incident TB cases (AUC 0.965; 95% CI 0.925-1.000). hsa-miR-215-5p, which targets genes in the TGF-β signaling pathway, was downregulated in cases. Gamma-glutamylthreonine, a breakdown product of protein catabolism, was less abundant in cases. To our knowledge, this is one of the first uses of a multi-omics approach to identify incident TB in severely immunosuppressed PLWH.

Keywords: HIV; biomarker; metabolomics; microRNA; multi-omics; tuberculosis.

Figure 1

Differentially expressed miRNA in cases versus controls. (A) Volcano plot from differentially expressed miRNA identified in cases versus controls based on adjusted p-value and log fold-change of miRNA expression. Red indicates differentially expressed miRNA with both a log fold change (FC) higher than 1.4 or lower than -1.4 and a false discovery ratio (FDR) of lower than 0.05. Green indicates miRNA with a log fold change higher than 1.4 or lower than -1.4, and blue indicates miRNA with a false discovery ratio lower than 0.05. Grey indicates genes without a significant FC or FDR. (B) Enrichment analysis plots from differentially expressed genes. The dot sizes represent the gene ratio in the pathway while the fill colors are the FDR values. Only statistically significant enriched pathways are displayed.

Figure 2

Boxplot of pg/ml values from serum biomarkers. Red indicates cases and blue indicates controls.

Figure 3

Decision-tree algorithm results applied in the combined multi-omics data. (A) Decision-tree from the case and control classification. (B) Dot plot from variables selected by the decision-tree with dotted lines the decision thresholds. The boxplots parallel to X-axis show the hsa-miR-215-5p variance stabilizing transformation (VST) values by group and the boxplots parallel to the Y-axis show the log2 gamma-glutamylthreonine values by group. Cases are denoted in red and controls in blue. Circles indicate correctly classified cases and controls whereas triangles indicate misclassifications. (C) Receiver operating characteristic (ROC) curve from the decision tree variables demonstrating the sensitivity, specificity, and area under the curve (AUC) of hsa-miR-215-5p and gamma-glutamylthreonine to discriminate participants by TB status.