Leber2015 - Mucosal immunity and gut microbiome interaction during C. difficile infection

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Short description
Leber2015 - Mucosal immunity and gut microbiome interaction during C. difficile infection

This model is described in the article:

Leber A, Viladomiu M, Hontecillas R, Abedi V, Philipson C, Hoops S, Howard B, Bassaganya-Riera J.
PLoS ONE 2015; 10(7): e0134849

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.

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Format
SBML (L2V4)
Related Publication
  • Systems Modeling of Interactions between Mucosal Immunity and the Gut Microbiome during Clostridium difficile Infection.
  • Leber A, Viladomiu M, Hontecillas R, Abedi V, Philipson C, Hoops S, Howard B, Bassaganya-Riera J
  • PloS one , 0/ 2015 , Volume 10 , pages: e0134849
  • The 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.
  • 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.
Contributors
Andrew Leber

Metadata information

is
BioModels Database MODEL1507200000
BioModels Database BIOMD0000000583
isDescribedBy
PubMed 26230099
isVersionOf
hasProperty
Human Disease Ontology Clostridium difficile colitis
Curation status
Curated
Original model(s)
Systems modeling of interactions between mucosal immunity and the gut microbiome during Clostridium difficile infection
  • Model originally submitted by : Andrew Leber
  • Submitted: Jul 20, 2015 6:34:49 PM
  • Last Modified: Aug 28, 2015 1:18:21 PM
Revisions
  • Version: 2 public model Download this version
    • Submitted on: Aug 28, 2015 1:18:21 PM
    • Submitted by: Andrew Leber
    • With comment: Current version of Leber2015 - Mucosal immunity and gut microbiome interaction during C. difficile infection
  • Version: 1 public model Download this version
    • Submitted on: Jul 20, 2015 6:34:49 PM
    • Submitted by: Andrew Leber
    • With comment: Original import of C. difficile Host Interactions Model
Curator's comment:
(added: 21 Aug 2015, 18:29:57, updated: 21 Aug 2015, 18:29:57)
Figure 3 of the reference publication has been reproduced here. The difference in the y-axis measurement between the plots generated by the model and that of the paper is because the model is designed to be a scale-able representation of a 50 mg section of tissue and in the paper it is the measured values of biological quantities within the in vivo mode. The model was simulated using SBMLSimulator.