Study Overview 
Investigator: Andrew Leber, Raquel Hontecillas, Josep Bassaganya-Riera Grant: MIEP


This study was created to strengthen and inform a computational model of the host-C. difficile interactions. 20 to 25-week old WT mice were infected with 10^7 cfu/mouse of C. difficile by orogastric gavage. A control uninfected group was included. The C difficile strain used was VPI10463 (ATCC 43255). Mice were weighed and scored daily for mortality and morbidity and the presence of diarrhea and other symptoms. Animals judged to be in a moribund state were euthanized and tissue samples were collected. Tissues was collected at days 4, 7, and 10 post-infection and processed for cell immunophenotyping and bacterial loads.

[Cdiff32 NecropsyForm.docx] Study Protocol Document

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

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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