Evidence map›Paper›PMID 26230099›Full record

ArticlePloS one2015

Systems Modeling of Interactions between Mucosal Immunity and the Gut Microbiome during Clostridium difficile Infection.

Andrew Leber, Monica Viladomiu, Raquel Hontecillas, Vida Abedi, Casandra Philipson, Stefan Hoops, Brad Howard, Josep Bassaganya-Riera

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
3.3field-weighted citation impact, top 8% 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

15 citing papers in PubMed, 29 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Review
  8. Article
  9. Article
  10. Article
  11. Article
  12. The promises of quantitative systems pharmacology modelling for drug development.Computational and structural biotechnology journal · 2016
    Review
  13. Review
  14. Article
  15. Goals in Nutrition Science 2015-2020.Frontiers in nutrition · 2015
    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

8 authors at 1 institution in 1 country.

Andrew LeberThe Center for Modeling Immunity to Enteric Pathogens, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, United States of America; Nutritional Immunology and Molecular Medicine Laboratory (www.nimml.org), Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, United States of America.
Monica ViladomiuThe Center for Modeling Immunity to Enteric Pathogens, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, United States of America; Nutritional Immunology and Molecular Medicine Laboratory (www.nimml.org), Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, United States of America.
Raquel HontecillasThe Center for Modeling Immunity to Enteric Pathogens, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, United States of America; Nutritional Immunology and Molecular Medicine Laboratory (www.nimml.org), Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, United States of America.
Vida AbediThe Center for Modeling Immunity to Enteric Pathogens, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, United States of America; Nutritional Immunology and Molecular Medicine Laboratory (www.nimml.org), Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, United States of America.
Casandra PhilipsonThe Center for Modeling Immunity to Enteric Pathogens, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, United States of America; Nutritional Immunology and Molecular Medicine Laboratory (www.nimml.org), Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, United States of America.
Stefan HoopsThe Center for Modeling Immunity to Enteric Pathogens, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, United States of America; Nutritional Immunology and Molecular Medicine Laboratory (www.nimml.org), Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, United States of America.
Brad HowardNutritional Immunology and Molecular Medicine Laboratory (www.nimml.org), Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, United States of America; Department of Biological Sciences, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, United States of America.
Josep Bassaganya-RieraThe Center for Modeling Immunity to Enteric Pathogens, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, United States of America; Nutritional Immunology and Molecular Medicine Laboratory (www.nimml.org), Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, United States of America.
Virginia Tech · US

Funding

NIAID NIH HHS HHSN272201000056C
6 · The paper itself

Abstract

Clostridium difficile infections are associated with the use of broad-spectrum antibiotics and result in an exuberant inflammatory response, leading to nosocomial diarrhea, colitis and even death. To better understand the dynamics of mucosal immunity during C. difficile infection from initiation through expansion to resolution, we built a computational model of the mucosal immune response to the bacterium. The model was calibrated using data from a mouse model of C. difficile infection. The model demonstrates a crucial role of T helper 17 (Th17) effector responses in the colonic lamina propria and luminal commensal bacteria populations in the clearance of C. difficile and colonic pathology, whereas regulatory T (Treg) cells responses are associated with the recovery phase. In addition, the production of anti-microbial peptides by inflamed epithelial cells and activated neutrophils in response to C. difficile infection inhibit the re-growth of beneficial commensal bacterial species. Computational simulations suggest that the removal of neutrophil and epithelial cell derived anti-microbial inhibitions, separately and together, on commensal bacterial regrowth promote recovery and minimize colonic inflammatory pathology. Simulation results predict a decrease in colonic inflammatory markers, such as neutrophilic influx and Th17 cells in the colonic lamina propria, and length of infection with accelerated commensal bacteria re-growth through altered anti-microbial inhibition. Computational modeling provides novel insights on the therapeutic value of repopulating the colonic microbiome and inducing regulatory mucosal immune responses during C. difficile infection. Thus, modeling mucosal immunity-gut microbiota interactions has the potential to guide the development of targeted fecal transplantation therapies in the context of precision medicine interventions.

Indexed as

Immunity, MucosalMicrobiotaModels, BiologicalClostridioides difficileClostridium Infections

Identifiers

PMID26230099
PMCPMC4521955
OpenAlexW1503076745

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.