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. 2024 Oct 21;27(11):111228.
doi: 10.1016/j.isci.2024.111228. eCollection 2024 Nov 15.

Transcriptional profile of Mycobacterium tuberculosis infection in people living with HIV

Burcu Tepekule  1   2   3 , Lisa Jörimann  1   2 , Corinne D Schenkel  1   2 , Lennart Opitz  4 , Jasmin Tschumi  1   2 , Rebekka Wolfensberger  1 , Kathrin Neumann  1   2 , Katharina Kusejko  1 , Marius Zeeb  1   2 , Lucas Boeck  5 , Marisa Kälin  1 , Julia Notter  6 , Hansjakob Furrer  7 , Matthias Hoffmann  8 , Hans H Hirsch  9   10   11 , Alexandra Calmy  12 , Matthias Cavassini  13 , Niklaus D Labhardt  14   15 , Enos Bernasconi  12   16   17 , Gabriela Oesch  18 , Karin J Metzner  1   2 , Dominique L Braun  1   2 , Huldrych F Günthard  1   2 , Roger D Kouyos  1   2 , Fergal Duffy  19 , Johannes Nemeth  1 ; Swiss HIV Cohort Study
Affiliations

Transcriptional profile of Mycobacterium tuberculosis infection in people living with HIV

Burcu Tepekule et al. iScience. .

Abstract

In people with HIV-1 (PWH), Mycobacterium tuberculosis (MTB) infection poses a significant threat. While active tuberculosis (TB) accelerates immunodeficiency, the interaction between MTB and HIV-1 during asymptomatic phases remains unclear. Analysis of peripheral blood mononuclear cells (PBMC) transcriptomic profiles in PWH, with and without controlled viral loads, revealed distinct clustering in MTB-infected individuals. Functional annotation identified alterations in IL-6, TNF, and KRAS pathways. Notably, MTB-related genes displayed an inverse correlation with HIV-1 viremia, at both individual and signature score levels. These findings suggest that MTB infection in PWH induces a shift in immune system activation, inversely related to HIV-1 viral load. These results may explain the observed enhanced antiretroviral control in MTB-infected PWH. This study highlights the complex interplay between MTB and HIV-1, emphasizing the importance of understanding their interaction for managing co-infections in this population.

Keywords: Health sciences; Microbiology; Transcriptomics.

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Conflict of interest statement

A.C. received grants from Merck Sharp & Dohme (MSD), ViiV Healthcare, and Gilead Sciences for unrelated research. R.D.K. received grants from Gilead Sciences and National Institutes of Health (NIH) for unrelated research. D.L.B. received honoraria for working on the advisory board of Gilead Sciences, Merck, ViiV, Pfizer, and AstraZeneca. D.L.B. received honoraria for presentations from Gilead Sciences and Merck. E.B. received grants from MSD for unrelated research. E.B. received payments for travel reimbursement from ViiV, MSD, Gilead Sciences, Pfizer, and Abbvie. E.B. received honoraria for working on the advisory board of ViiV, MSD, Pfizer, Gilead Sciences, AstraZeneca, and Ely Lilly. H.H.H. received honoraria for working on the advisory board of AiCuris, Merck, Vera Dx, and Molecular Partners. H.H.H. received honoraria for presentations from Merck, Gilead Sciences, Biotest, and Vera Dx. J.N. received honoraria for presentations from Oxford Immunotec, Gilead and ViiV. H.FG. received honoraria for working on the advisory board of Gilead Sciences, Merck, ViiV, Janssen, Johnson and Johnson, Novartis, and GlaxoSmithKline (GSK). H.F.G. received payments for travel reimbursements from Gilead Sciences. H.F.G. received grants from NIH, Yvonne Jacob Foundation, and Gilead Sciences.

Figures

None
Graphical abstract
Figure 1
Figure 1
MTB Perturbations in suppressed PWH (A) Dimensionality reduction and clustering analysis for HIV-suppressed individuals with and without MTB infection (MTB+ HIVsupp and MTB- HIVsupp). Genes with a p value lower than 0.01 are used for the analysis, corresponding to 293 genes out of the 21493 analyzed. In MTB+ patients compared to MTB- patients, 126 out of 293 genes were upregulated and 167 were downregulated. (B) First 20 pathways with the highest normalized enrichment score (NES) based on the gene-set enrichment analysis (GSEA). (C) Heatmap displaying the leading edge genes of these pathways filtered by p value <0.01. The color scale ranges from dark blue (representing downregulated genes) to dark red (representing upregulated genes), with lighter tones indicating intermediate values. Rows represent genes, and columns represent samples, annotated by MTB status (N for negative, P for positive). The data are log2-transformed and clustered by genes. The scale bar indicates log2 expression levels ranging from −4 to 4. (D) Fold change (Log2 ratios) of the differentially expressed genes.
Figure 2
Figure 2
MTB Perturbations in viremic PWH (A) Dimensionality reduction and clustering analysis for HIV-viremic individuals with and without MTB infection (MTB+ HIVvir and MTB- HIVvir). Genes with a p value lower than 0.01 are used for the analysis, corresponding to 166 genes out of the 21467 analyzed. In MTB+ patients compared to MTB- patients, 68 out of 166 genes were upregulated and 98 were downregulated. (B) First 20 pathways with the highest normalized enrichment score (NES) based on the gene-set enrichment analysis (GSEA). (C) Heatmap displaying the leading edge genes of these pathways filtered by p value <0.01. The color scale ranges from dark blue (representing downregulated genes) to dark red (representing upregulated genes), with lighter tones indicating intermediate values. Rows represent genes, and columns represent samples, annotated by MTB status (N for negative, P for positive). The data are log2-transformed and clustered by genes. The scale bar indicates log2 expression levels ranging from −4 to 4. (D) Fold change (Log2 ratios) of the differentially expressed genes.
Figure 3
Figure 3
Correlation between HIV-1 viral load and the expression levels of conserved transcripts (A) Correlation between HIV-1 viral load and the expression levels of conserved transcripts across different fold-change thresholds. Data are represented as boxplots where the central line represents the median, and the boxes extend from the 25th to the 75th percentiles (interquartile range). Individual data points are plotted on top using jitter for clarity. A one-sided t-test was performed to compare groups, and p values are shown. (B) Percentages of different immune cells known to express the individual genes from the conserved transcript set with a negative correlation to viral load (20 transcripts out of 32), according to the human protein atlas.
Figure 4
Figure 4
Signature score distributions for conserved transcripts (A) Signature score distributions of all conserved transcripts by HIV state. Data are represented as boxplots where the central line represents the median, and the boxes extend from the 25th to the 75th percentiles (interquartile range). Individual data points are plotted on top using jitter for clarity. A one-sided t-test was performed to compare groups, and p values are shown. (B) Signature Score Distributions in active TB. Signature score distributions including all 9 conserved transcripts that were present in our study and in the African cohort. Data are represented as boxplots where the central line represents the median, and the boxes extend from the 25th to the 75th percentiles (interquartile range). Individual data points are plotted on top using jitter for clarity. A one-sided t-test was performed to compare groups, and p values are shown.
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