Variable time of onset ofClostridium difficiledisease initiated by antimicrobial treatment in hamsters

Antibiotic-Induced Dysbiosis Predicts Mortality in an Animal Model of Clostridium difficile Infection

Antibiotic disruption of the intestinal microbiota favors colonization by Clostridium difficile Using a charcoal-based adsorbent to decrease intestinal antibiotic concentrations, we studied the relationship between antibiotic concentrations in feces and the intensity of dysbiosis and quantified the link between this intensity and mortality. We administered either moxifloxacin (n = 70) or clindamycin (n = 60) to hamsters by subcutaneous injection from day 1 (D₁) to D₅ and challenged them with a C. difficile toxigenic strain at D₃ Hamsters received various doses of a charcoal-based adsorbent, DAV131A, to modulate intestinal antibiotic concentrations. Gut dysbiosis was evaluated at D₀ and D₃ using diversity indices determined from 16S rRNA gene profiling. Survival was monitored until D₁₆ We analyzed the relationship between fecal antibiotic concentrations and dysbiosis at the time of C. difficile challenge and studied their capacity to predict subsequent death of the animals. Increasing doses of DAV131A reduced fecal concentrations of both antibiotics, lowered dysbiosis, and increased survival from 0% to 100%. Mortality was related to the level of dysbiosis (P < 10^-5 for the change of Shannon index in moxifloxacin-treated animals and P < 10^-9 in clindamycin-treated animals). The Shannon diversity index and unweighted UniFrac distance best predicted death, with areas under the receiver operating curve (ROC) of 0.89 (95% confidence interval [CI], 0.82, 0.95) and 0.95 (0.90, 0.98), respectively. Altogether, moxifloxacin and clindamycin disrupted the diversity of the intestinal microbiota with a dependency on the DAV131A dose; mortality after C. difficile challenge was related to the intensity of dysbiosis in similar manners with the two antibiotics.

Predictive values of models of Clostridium difficile infection

In vivo and in vitro models are widely used to simulate Clostridium difficile infection (CDI). They have made considerable contributions in the study of C difficile pathogenesis, antibiotic predisposition to CDI, and population dynamics as well as the evaluation of new antimicrobial and immunologic therapeutics. Although CDI models have greatly increased understanding of this complicated pathogen, all have limitations in reproducing human disease, notably their inability to generate a truly reflective immune response. This review summarizes the most commonly used models of CDI and discusses their pros and cons and their predictive values in terms of clinical outcomes.