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

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.

Sensitivity Analysis of an ENteric Immunity SImulator (ENISI)-Based Model of Immune Responses to Helicobacter pylori Infection

“…The particular strain in this experiment is the Cag Pathogenicity Island (CagPAI)+ H. pylori strain 26695 [ 36 – 38 ]. However, it is important to indicate that this analysis can be applied to an array of complex biological networks including the model of CD4+ T cell differentiation [ 49 – 51 ], network dynamics of T helper 17 induction and differentiation [ 52 ], and network of interactions between mucosal immunity and the gut microbiome during Clostridium difficile infection [ 53 ].…”

Modeling the Role of Lanthionine Synthetase C-Like 2 (LANCL2) in the Modulation of Immune Responses to Helicobacter pylori Infection

“…In COPASI, the interactions and transitions were assigned ordinary differential equations representing multiple kinetics including mass action, simple activation and Hill-type activation and inhibition, available in S1 File. The resulting parameters in the tissue level model were estimated using Particle Swarm and Genetic algorithms with time course data generated through the mouse model at various time points post-infection utilizing methods as previously described [27,29]. The parameter search algorithms seek to minimize the sum of squares for the calibration dataset.…”

“…Similar models have generated hidden informative insights into the immune system’s role in IBD and C . difficile infection [29,30]. The successes of computational approaches in modeling mucosal immune responses also extend to a finer scale of resolution with the ability to assess intracellular mechanisms controlling differentiation of cell types including the role of NLRX1 in the differentiation of inflammatory macrophages in response to H .…”

Host‐microbe interaction in the gastrointestinal tract

“…Such computational models have been used to describe the spatiotemporal interactions between pathogens, T cells, macrophages, dendritic cells and epithelial cells during infection (Wendelsdorf et al, 2012;Alam et al, 2015). Simulations of experimentally inaccessible scenarios have for instance predicted that the removal of neutrophils and epithelialderived anti-microbial compounds would enhance commensal bacteria growth and promote recovery against Clostridium difficile infection (Leber et al, 2015). Systems of ordinary (Arciero et al, 2013) and partial differential equations (Barber et al, 2013) have been proposed to model the epithelial and inflammatory responses to the microbiota driving the progression of necrotising enterocolitis in premature infants.…”