Modeling the effects of prebiotic interventions on luminal and mucosa-associated gut microbiota without and with challenge .

Prebiotics can modulate the gut microbial community composition and function for improved (gut) health and increase resilience against infections. models of the gut facilitate the study of intervention effects on the gut microbial community relevant to health. The mucosa-associated gut microbiota, which thrives in close contact with the host plays a pivotal role in colonization resistance and health. Therefore, we here introduce the Mi-screen, an experimental approach implementing a 96-well plate equipped with a mucus agar layer for the additional culturing of mucosa-associated microbiota . In this study, we screened the effects of 2'-Fucosyllactose (2'-FL), fructooligosaccharides (FOS), and inulin within a complex microbiota without and with infection with the strains ATCC 43599 (Ribotype 001) or ATCC BAA-1870 (Ribotype 027). We analyzed the microbial community composition and short-chain fatty acid levels after 48 h of incubation. The inclusion of an additional substrate and surface in the form of the mucus agar layer allowed us to culture a microbial richness ranging between 100-160 in Chao index, with Shannon indices of 5-6 across culture conditions, indicative of a microbial diversity of physiological relevance. The mucus agar layer stimulated the growth of characteristic mucosa-associated bacteria such as . The prebiotic interventions affected luminal and mucosal microbial communities cultured and stimulated short-chain fatty acid production. FOS, inulin and 2'-FL promoted the growth of within the mucosa-associated microbiota cultured . When spiking the untreated conditions with pathogenic , the strains thrived within the luminal and the mucosal sample types, whereas prebiotic treatments exhibited inhibitory effects on growth and prevented colonization. In conclusion, the Mi-screen facilitates the screening of luminal and mucosa-associated gut microbial community dynamics and therefore fills an important gap in the field of modeling.