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

Submit a comment

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

×

Guidelines

The Editorial Board will only consider comments that are deemed relevant and of interest to readers. The Journal will not post data that have not been subjected to peer review; or a comment that is essentially a reiteration of another comment.

  • Comments appear on the Journal’s website and are linked from the original article’s web page.
  • Authors are notified by email if their comments are posted.
  • The Journal reserves the right to edit comments for length and clarity.
  • No appeals will be considered.
  • Comments are not indexed in PubMed.

Specific requirements

  • Maximum length, 400 words
  • Entered as plain text or HTML
  • Author’s name and email address, to be posted with the comment
  • Declaration of all potential conflicts of interest (even if these are not ultimately posted); see the Journal’s conflict-of-interest policy
  • Comments may not include figures
This field is required
This field is required
This field is required
This field is required
This field is required
This field is required

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

Sign up for email alerts