Canine Mucosal Artificial Colon: development of a new colonic in vitro model adapted to dog sizes

Abstract

Differences in dog breed sizes are an important determinant of variations in digestive physiology, mainly related to the large intestine. In vitro gut models are increasingly used as alternatives to animal experiments for technical, cost, societal, and regulatory reasons. Up to now, only one in vitro model of the canine colon incorporates the dynamics of different canine gut regions, yet no adaptations exist to reproduce size-related digestive parameters. To address this limitation, we developed a new model of the canine colon, the CANIne Mucosal ARtificial COLon (CANIM-ARCOL), simulating main physiochemical (pH, transit time, anaerobiosis), nutritional (ileal effluent composition), and microbial (lumen and mucus-associated microbiota) parameters of this ecosystem and adapted to three dog sizes (i.e., small under 10 kg, medium 10–30 kg, and large over 30 kg). To validate the new model regarding microbiota composition and activities, in vitro fermentations were performed in bioreactors inoculated with stools from 13 dogs (4 small, 5 medium, and 4 large). After a stabilization period, microbiota profiles clearly clustered depending on dog size. Bacteroidota and Firmicutes abundances were positively correlated with dog size both in vitro and in vivo, while opposite trends were observed for Actinobacteria and Proteobacteria. As observed in vivo, microbial activity also increased with dog size in vitro, as evidenced from gas production, short-chain fatty acids, ammonia, and bile acid dehydroxylation. In line with the 3R regulation, CANIM-ARCOL could be a relevant platform to assess bilateral interactions between food and pharma compounds and gut microbiota, capturing inter-individual or breed variabilities.Key points• CANIM-ARCOL integrates main canine physicochemical and microbial colonic parameters• Gut microbiota associated to different dog sizes is accurately maintained in vitro• The model can help to move toward personalized approach considering dog body weight