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Leber et al., (2015). Systems Modeling of Interactions between Mucosal Immunity and the Gut Microbiome during Clostridium difficile Infection.

March 2016, model of the month by Florent Yvon
Original model: BIOMD0000000583.


Clostridium difficile is a Gram-positive spore-forming anaerobic bacterium, which may be naturally present in human colon. Despite it being harmless in balanced proportions (2-5%), an alteration in the gut flora may cause an extensive colonisation of C. difficile, which leads to a severe colitis. The disruption of the commensal flora often occurs after a long-term antibiotics treatment, and the opportunistic domination of C. difficile is enhanced by the resistance of the most virulent strains to common antibiotics [1]. C. difficile infection (CDI) is usually treated using more antibiotics. As CDI is caused by over-utilisation of antibiotics, this approach is likely to contribute to the high rate of recurrence of this infection. Alternatives strategies can be used, such as using toxin-targeting antibodies and gut flora reconstitution.

The most important virulence determinants of CDI are the toxin A (TcdA) and the toxin B (TcdB). Those toxins trigger the apoptosis of the epithelial cells and the recruitment of neutrophils and monocytes into the intestinal lumen. The pathogen also interacts with dendritic cells (DC) that are present in the lumen. The resulting maturation of the DC triggers the differentiation of naive CD4+ T cells to regulatory T helper (Treg) cells, which have an essential role in the adaptive immune response. It is also known that naturally harmless commensal bacteria play an important role in the outcome of the infection.

The model by Leber et al. [2] presented here describes the interactions between C. difficile, the commensal flora and the host in the colon during CDI.

The model:

The model uses a system of ordinary differential equations. The topology of the network is shown on figure 1. The model describes 4 compartments, 23 species, 30 reactions and 49 parameters. An in-vivo CDI experiment was performed on mice to determine the values of the parameters.

Figure 2

Figure 2In silico simulation showing commensal bacteria (CommB), C. difficile (Cdiff), activated neutrophils in the lumen (Nlum) and Treg cells in the lamina propria (iTreg_lp) during Clostridium difficile infection. Four screnarios were considered: neutrophils and epithelial cells present (N and E), only neutrophils (N), only epithelial cells (E) and neither (None). Figure taken from [2].

Figure 1

Figure 1Network topology of the model created via CellDesigner. Figure taken from [2].


The model has been able to predict the very important role of CD4+ T cells in the immune response during the CDI and an increase in the Th17 effector cell subset during the pro-inflammatory response at the early stage of the infection. Also, the commensal bacteria plays a substantial role in the clearance of C. difficile. Sensitivity analyses showed that the damage on the epithelium is minimised by the actions of Th17 cells and the commensal bacteria.

The in vivo experiments on mice have shown that antibiotics have also a negative impact on protective commensal bacteria population. Moreover, the authors highlighted that the epithelial cells synthesise anti-microbial peptides during the inflammatory response. Those peptides inhibit the commensal bacteria regrowth. Using simulations where epithelial cells were removed (figure 2), it has been shown that the inhibition of the commensal flora regrowth delays the clearance of C. difficile in the colon.


The authors have shown the ability of systems biology to perform in modelling of host-microbiota interactions. This strategy can potentially be applied for any colonic bacterial infection. The model can be used to determine key time points when data can be collected, improving the efficiency of clinical studies and decreasing the cost of wet-lab experiments. The model highlights the important role of commensal bacteria in the clearance of the pathogen. This aspect can be investigated further away to develop more specific pro-biotic treatments to limit the duration of the infection.

Bibliographic references

  1. Avila et al. Recent Advances in the Diagnosis and Treatment of Clostridium Difficile Infection. F1000Res. 2016 Jan 29;5. pii: F1000 Faculty Rev-118. doi: 10.12688/f1000research.7109.1. eCollection 2016.
  2. Leber et al. Systems Modeling of Interactions between Mucosal Immunity and the Gut Microbiome during Clostridium difficile Infection. PLoS ONE 2015; 10(7): e0134849