Evidence map›Paper›PMID 33370784›Full record

ArticlePLoS computational biology2020

Systems biology predicts that fibrosis in tuberculous granulomas may arise through macrophage-to-myofibroblast transformation.

Stephanie Evans, J Russell Butler, Joshua T Mattila, Denise E Kirschner

Open access · goldAbstract read
In one paragraph

Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed
0.7field-weighted citation impact, top 17% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

27 citing papers in PubMed, 48 citations in OpenAlex.

  1. Review
  2. Review
  3. Review
  4. Article
  5. Review
  6. Article
  7. Review
  8. Macrophages Transdifferentiate Into Myofibroblasts During Ocular Surface Inflammation.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2025
    Article
  9. Article
  10. Experimental dissection of tuberculosis protective immunity: a human perspective.Frontiers in cellular and infection microbiology · 2025
    Review
  11. Review
  12. Article
  13. Review
  14. Article
  15. Characterizing the immune response toFrontiers in immunology · 2024
    Review
  16. Article
  17. Article
  18. Article
  19. Frontiers in veterinary science · 2023
    Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 3 institutions in 1 country.

Stephanie EvansMicrobiology and Immunology, School of Medicine, University of Michigan Medical School, Ann Arbor, Michigan, United States of America.ORCID 0000-0002-2345-5249
J Russell ButlerDivision of Biological Sciences, Advent Health University, Orlando, Florida, United States of America.
Joshua T MattilaDepartment of Infectious Disease and Microbiology, Graduate School of Public Health, University of Pittsburgh, Pennsylvania, United States of America.
Denise E KirschnerMicrobiology and Immunology, School of Medicine, University of Michigan Medical School, Ann Arbor, Michigan, United States of America.
University of Michigan · USAdventist University of Health Sciences · USUniversity of Pittsburgh · US

Funding

Predicting immune responses that correlate with protection against tuberculosisR01HL110811 · NHLBI · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI FLYNN, JOANNE L., KIRSCHNER, DENISE E · 2012 to 2016
$5.9M
Predicting protective T-cell responses in Tuberculosis using a systems biology approachR01AI123093 · NIAID · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI FLYNN, JOANNE L., KIRSCHNER, DENISE E · 2016 to 2020
$3.6M
NHLBI NIH HHS R01 HL110811NIAID NIH HHS R01 AI123093
6 · The paper itself

Abstract

Mycobacterium tuberculosis (Mtb) infection causes tuberculosis (TB), a disease characterized by development of granulomas. Granulomas consist of activated immune cells that cluster together to limit bacterial growth and restrict dissemination. Control of the TB epidemic has been limited by lengthy drug regimens, antibiotic resistance, and lack of a robustly efficacious vaccine. Fibrosis commonly occurs during treatment and is associated with both positive and negative disease outcomes in TB but little is known about the processes that initiate fibrosis in granulomas. Human and nonhuman primate granulomas undergoing fibrosis can have spindle-shaped macrophages with fibroblast-like morphologies suggesting a relationship between macrophages, fibroblasts, and granuloma fibrosis. This relationship has been difficult to investigate because of the limited availability of human pathology samples, the time scale involved in human TB, and overlap between fibroblast and myeloid cell markers in tissues. To better understand the origins of fibrosis in TB, we used a computational model of TB granuloma biology to identify factors that drive fibrosis over the course of local disease progression. We validated the model with granulomas from nonhuman primates to delineate myeloid cells and lung-resident fibroblasts. Our results suggest that peripheral granuloma fibrosis, which is commonly observed, can arise through macrophage-to-myofibroblast transformation (MMT). Further, we hypothesize that MMT is induced in M1 macrophages through a sequential combination of inflammatory and anti-inflammatory signaling in granuloma macrophages. We predict that MMT may be a mechanism underlying granuloma-associated fibrosis and warrants further investigation into myeloid cells as drivers of fibrotic disease.

Indexed as

Systems BiologyFibrosisGranulomaHumansMacrophagesMycobacterium tuberculosisMyofibroblastsSTAT1 Transcription FactorSTAT3 Transcription FactorTuberculosisSTAT1 protein, humanSTAT1 Transcription FactorSTAT3 protein, humanSTAT3 Transcription Factor

Identifiers

PMID33370784
PMCPMC7793262
OpenAlexW3118222884

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.