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 Article has an altmetric score of 144

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

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 2

Gene expression in TB and sarcoidosis has significant overlap.

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Gene expression in TB and sarcoidosis has significant overlap. (A) PCA p...
(A) PCA plot of whole transcriptome data demonstrates separation of the control group from diseased lymph nodes (purple), while the TB (orange) and sarcoidosis (blue) samples overlap. (B) Hierarchical clustering heat map of top 50 most variable genes using Spearman correlation and complete linkage. Control samples (purple) cluster separately to the TB (orange) and sarcoidosis (blue) samples, which show no differentiation. (C) Venn diagram of the number of upregulated genes in TB (orange) and sarcoidosis (blue) relative to control, confirming numerous shared genes (log2 fold change ≥ 1.5, adjusted P value < 0.05). (D) Venn diagram of the number of downregulated genes in TB (orange) and sarcoidosis (blue) relative to control (log2 fold change ≥ 1.5, adjusted P value < 0.05). (E and F) Volcano plots of differentially expressed genes in TB (E) and sarcoidosis (F) plotting the log2 fold change on the x axis and adjusted P value on the y axis. Gray: similarly expressed genes; green: genes with absolute log2 fold change ≥ 1.5; blue: genes with adjusted P value < 0.05; red: genes exceeding both thresholds. More genes are upregulated in TB, and to a higher fold change, than in sarcoidosis. Horizontal dashed line denotes adjusted P value of 0.05, vertical dashed line denotes absolute log2 fold change of 1.5.