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Integrated transcriptomic analysis of human tuberculosis granulomas and a biomimetic model identifies therapeutic targets
Michaela T. Reichmann, … , Marta E. Polak, Paul Elkington
Michaela T. Reichmann, … , Marta E. Polak, Paul Elkington
Published June 15, 2021
Citation Information: J Clin Invest. 2021;131(15):e148136. https://doi.org/10.1172/JCI148136.
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Research Article Infectious disease Pulmonology

Integrated transcriptomic analysis of human tuberculosis granulomas and a biomimetic model identifies therapeutic targets

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Abstract

Tuberculosis (TB) is a persistent global pandemic, and standard treatment for it has not changed for 30 years. Mycobacterium tuberculosis (Mtb) has undergone prolonged coevolution with humans, and patients can control Mtb even after extensive infection, demonstrating the fine balance between protective and pathological host responses within infected granulomas. We hypothesized that whole transcriptome analysis of human TB granulomas isolated by laser capture microdissection could identify therapeutic targets, and that comparison with a noninfectious granulomatous disease, sarcoidosis, would identify disease-specific pathological mechanisms. Bioinformatic analysis of RNAseq data identified numerous shared pathways between TB and sarcoidosis lymph nodes, and also specific clusters demonstrating TB results from a dysregulated inflammatory immune response. To translate these insights, we compared 3 primary human cell culture models at the whole transcriptome level and demonstrated that the 3D collagen granuloma model most closely reflected human TB disease. We investigated shared signaling pathways with human disease and identified 12 intracellular enzymes as potential therapeutic targets. Sphingosine kinase 1 inhibition controlled Mtb growth, concurrently reducing intracellular pH in infected monocytes and suppressing inflammatory mediator secretion. Immunohistochemical staining confirmed that sphingosine kinase 1 is expressed in human lung TB granulomas, and therefore represents a host therapeutic target to improve TB outcomes.

Authors

Michaela T. Reichmann, Liku B. Tezera, Andres F. Vallejo, Milica Vukmirovic, Rui Xiao, James Reynolds, Sanjay Jogai, Susan Wilson, Ben Marshall, Mark G. Jones, Alasdair Leslie, Jeanine M. D’Armiento, Naftali Kaminski, Marta E. Polak, Paul Elkington

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Figure 3

TB and sarcoidosis have disease-specific gene clusters.

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TB and sarcoidosis have disease-specific gene clusters.
(A) Correlation ...
(A) Correlation analysis performed using Markov Cluster Algorithm (Pearson’s R of 0.83) with genes of absolute log2 fold change ≥ 1.5 and adjusted P value < 0.05. Each node (circle) depicts a transcript/gene, each edge (line) depicts Pearson’s correlation value. The left branches display clusters upregulated in TB and sarcoidosis, and the right branches display clusters that are downregulated. Several coregulated clusters are observed. Cluster 21 (blue) is the only cluster specific to TB and comprises the 7 annotated genes. (B) Average (mean) normalized gene expression level in Cluster 21 comparing control (n = 7), sarcoidosis (n = 10), and TB samples (n = 7). Gene expression values after TMM normalization used. Box-and-whisker plot with median values (line). Whiskers represent minimum and maximum values, boxes represent the 25th to 75th percentiles. (C) Heat map arranged according to top 10 disease-specific upregulated genes based on fold change. Using TB-specific (orange), sarcoidosis-specific (blue), and jointly regulated genes (pink), a clear distinction between control, TB, and sarcoidosis is observed. (D) Gene ontology enrichment (ReactomePA program in R, using genes with adjusted P value < 0.05) showing the top 10 upregulated REACTOME pathways in TB relative to control. Immune processes and extracellular matrix turnover are highly represented. Dot size represents number of expressed genes in the pathway, shade of color represents adjusted P value.

Copyright © 2023 American Society for Clinical Investigation
ISSN: 0021-9738 (print), 1558-8238 (online)

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