Go to JCI Insight
  • About
  • Editors
  • Consulting Editors
  • For authors
  • Publication ethics
  • Alerts
  • Advertising
  • Job board
  • Subscribe
  • Contact
  • Current issue
  • Past issues
  • By specialty
    • COVID-19
    • Cardiology
    • Gastroenterology
    • Immunology
    • Metabolism
    • Nephrology
    • Neuroscience
    • Oncology
    • Pulmonology
    • Vascular biology
    • All ...
  • Videos
    • Conversations with Giants in Medicine
    • Author's Takes
  • Reviews
    • View all reviews ...
    • Immune Environment in Glioblastoma (Feb 2023)
    • Korsmeyer Award 25th Anniversary Collection (Jan 2023)
    • Aging (Jul 2022)
    • Next-Generation Sequencing in Medicine (Jun 2022)
    • New Therapeutic Targets in Cardiovascular Diseases (Mar 2022)
    • Immunometabolism (Jan 2022)
    • Circadian Rhythm (Oct 2021)
    • View all review series ...
  • Viewpoint
  • Collections
    • In-Press Preview
    • Commentaries
    • Research letters
    • Letters to the editor
    • Editorials
    • Viewpoint
    • Top read articles
  • Clinical Medicine
  • JCI This Month
    • Current issue
    • Past issues

  • Current issue
  • Past issues
  • Specialties
  • Reviews
  • Review series
  • Conversations with Giants in Medicine
  • Author's Takes
  • In-Press Preview
  • Commentaries
  • Research letters
  • Letters to the editor
  • Editorials
  • Viewpoint
  • Top read articles
  • About
  • Editors
  • Consulting Editors
  • For authors
  • Publication ethics
  • Alerts
  • Advertising
  • Job board
  • Subscribe
  • Contact
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.
View: Text | PDF
Research Article Infectious disease Pulmonology

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

  • Text
  • PDF
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

×

Figure 8

SphK1 regulates the host-pathogen interaction in TB and is expressed in human granulomas.

Options: View larger image (or click on image) Download as PowerPoint
SphK1 regulates the host-pathogen interaction in TB and is expressed in ...
(A) Mtb growth detected in 3D collagen model measured by luminescence (RLUs): untreated (black circles), DMSO (black squares), SphK1 inhibitor PF-543 (green triangles), and SphK1 activator K6PC-5 (orange triangles). Black arrows: drug addition on days 1 and 7. Analysis was by 2-way ANOVA; error bars indicate SD. (B) Mtb CFUs from PBMCs in 3D collagen microspheres, decapsulated and plated on Middlebrook 7H11 agar: DMSO 0.1% (black circles), SphK1 inhibitor PF-543 (green triangles), SphK1 activator (orange triangles). Difference analyzed by paired t test. Horizontal bars indicate mean, error bars indicate SD. (C) Relative fluorescence signal in human monocytes stained with pHrodo measured 5 minutes after treatment with DMSO 0.1% and Mtb infection with or without concurrent addition of 50 μM PF-543. Increased fluorescence signal indicates lower pH. Normalized data from 2 separate donors, analyzed by paired t test. Horizontal bars indicate mean, error bars indicate SD. (D) Relative fluorescence signal in human monocytes stained with pHrodo, measured at 5 minute intervals after Mtb infection for 40 minutes, treated with DMSO 0.1% (black circles) or 50 μM PF-543 (green triangles). Normalized data shown from one donor. Horizontal bars indicate mean, error bars indicate SD. (E) Secretion of CCL2 and (F) MMP-1 from 3D collagen model into tissue culture media on day 7 after Mtb infection: treated with DMSO 0.1% (black circles), SphK1 inhibitor PF-543 (green triangles), or SphK1 activator (orange triangles). Analyzed by paired t test. Horizontal bars indicate mean, error bars indicate SD. (G, H, and I) Immunohistochemistry of human lung TB granulomas stained with SphK1 antibody. No reactivity is observed without antibody (G), while SphK1 expression is demonstrated in a subset of macrophages and multinucleate giant cells (brown stain, H and I). Scale bars: 50 μm (G and H), 25 μm (I). *P < 0.05, **P < 0.01.

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

Sign up for email alerts