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

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Model Identifier
BIOMD0000000583
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.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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 , PubMed ID: 26230099
  • 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
hasTaxon
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

Tags
Name Description Size Actions

Model files

BIOMD0000000583_url.xml SBML L2V4 representation of Leber2015 - Mucosal immunity and gut microbiome interaction during C. difficile infection 125.01 KB Preview | Download

Additional files

BIOMD0000000583.sci Auto-generated Scilab file 154.00 Bytes Preview | Download
BIOMD0000000583.vcml Auto-generated VCML file 897.00 Bytes Preview | Download
BIOMD0000000583-biopax3.owl Auto-generated BioPAX (Level 3) 74.57 KB Preview | Download
BIOMD0000000583-biopax2.owl Auto-generated BioPAX (Level 2) 50.55 KB Preview | Download
BIOMD0000000583.pdf Auto-generated PDF file 361.97 KB Preview | Download
BIOMD0000000583.xpp Auto-generated XPP file 12.05 KB Preview | Download
BIOMD0000000583.png Auto-generated Reaction graph (PNG) 1.40 MB Preview | Download
BIOMD0000000583.m Auto-generated Octave file 16.60 KB Preview | Download
BIOMD0000000583_urn.xml Auto-generated SBML file with URNs 124.16 KB Preview | Download
BIOMD0000000583.svg Auto-generated Reaction graph (SVG) 126.52 KB Preview | Download

  • Model originally submitted by : Andrew Leber
  • Submitted: 20-Jul-2015 18:34:49
  • Last Modified: 28-Aug-2015 13:18:21
Revisions
  • Version: 2 public model Download this version
    • Submitted on: 28-Aug-2015 13:18:21
    • 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: 20-Jul-2015 18:34:49
    • Submitted by: Andrew Leber
    • With comment: Original import of C. difficile Host Interactions Model

(*) You might be seeing discontinuous revisions as only public revisions are displayed here. Any private revisions unpublished model revision of this model will only be shown to the submitter and their collaborators.

Legends
: Variable used inside SBML models


Species
Species Initial Concentration/Amount
E i

epithelial cell
0.0 item
M LP

lamina propria ; macrophage
3250.0 item
eDC LP

dendritic cell ; lamina propria
0.0 item
M0

macrophage
1714285.71428571 item
N LP

lamina propria ; neutrophil
714285.714285714 item
Commensal Dead

Bacteria
5.0E10 item
Commensal Harmful

Bacteria
1.5E10 item
N Lum

neutrophil ; Lumen of intestine
0.0 item
E

epithelial cell
1052500.0 item
E d

epithelial cell
0.0 item
Reactions
Reactions Rate Parameters
E_i => E_d; E_i, E_i Epithelium*k1*E_i k1=2.5
M0 => M_LP; Th17_LP, Cdiff, iTreg_LP, M0, Th17_LP, Cdiff, iTreg_LP, M0, Th17_LP, Cdiff, iTreg_LP K*M0*((e1*Th17_LP+Cdiff)-e2*iTreg_LP) K=4.5E-5; e2=0.092308585205372; e1=2.0
eDC_LP => eDC_MLN; eDC_LP, eDC_LP k1*eDC_LP k1=10.5
iDC_E + Cdiff => eDC_LP; Commensal_Dead, Commensal_Beneficial, Cdiff, Cdiff k*Cdiff k=0.55
N_LP => N_Lum; Cdiff, E_d, Th17_LP, iTreg_LP, N_LP, Cdiff, E_d, Th17_LP, iTreg_LP, N_LP, Cdiff, E_d, Th17_LP, iTreg_LP v*N_LP*(Cdiff*(k1*E_d+k2*Th17_LP)-k3*iTreg_LP) k3=0.129717307334483; v=5.29827880572231E-5; k1=0.120935308788409; k2=0.171190728888258
Commensal_Beneficial => Commensal_Dead; N_Lum, E_i, Commensal_Beneficial, N_Lum, E_i, Commensal_Dead, Commensal_Beneficial, N_Lum, E_i, Commensal_Dead Lumen*(k1*Commensal_Beneficial*N_Lum*E_i-k2*Commensal_Dead) k2=0.156287382551622; k1=4.5E-10
Commensal_Dead => ; Commensal_Dead, Commensal_Dead Lumen*k1*Commensal_Dead k1=0.0933277452272273
Commensal_Harmful => ; N_LP, E_i, Commensal_Harmful, N_LP, E_i, Commensal_Harmful, N_LP, E_i Lumen*K*Commensal_Harmful*(N_LP*A1+E_i*A2) A1=0.00478; K=2.33225E-5; A2=0.18
N_Lum => ; Commensal_Beneficial, N_Lum, Commensal_Beneficial, N_Lum, Commensal_Beneficial Lumen*K*N_Lum*Commensal_Beneficial K=2.35932924820229E-7
E => E_d; N_Lum, Th17_LP, M_LP, E, N_Lum, Th17_LP, M_LP, E, N_Lum, Th17_LP, M_LP Epithelium*v*E*(k1*N_Lum+k2*Th17_LP+k3*M_LP) k3=62.5911647602982; v=1.59920673150176E-6; k1=1.1E-5; k2=2.3381277077344E-6
E => E_i; Cdiff, E, Cdiff, E, Cdiff Epithelium*K*E*Cdiff K=1.71079818745428E-4
E_d => E; E_d, E_d Epithelium*k1*E_d k1=4000.0
E_i => E_d; N_Lum, Th17_LP, M_LP, E_i, N_Lum, Th17_LP, M_LP, E_i, N_Lum, Th17_LP, M_LP Epithelium*v*E_i*(k1*N_Lum+k2*Th17_LP+k3*M_LP) k1=0.006; k3=1.16013457036959E-6; k2=0.0106698310809694; v=0.065
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.