The interplay of microbiota with mucus

We will investigate the impact of microbiome disturbances on hydrogel properties and mucosal integrity. In so doing we will clarify the dynamics and mechanisms of bidirectional mucus-microbiome interplay, making use of antibiotic exposure as one of the most severe perturbations possible in this system. We hypothesize that upon antibiotic treatment, the microbiome shifts towards greater mucolysis, with dysbalanced glycan degradation and overall thinning of the mucus layer. Furthermore, we expect severely altered mucus properties, such as altered rheology, due to altered viscosity and swelling capabilities, and a modified composition with depleted AMPs and IgGs ultimately leading to mucosal inflammation and tissue damage. Using highly standardized murine in vivo models, we will apply antibiotics and investigate the effect on microbiome composition and mucus properties by metagenomic analysis of mucus-associated microbiota, metabolomics of the luminal content as well as by confocal microscopy using fluorescentlabelled lectins. Biochemical and viscoelastic properties of the mucus from antibiotic-treated versus untreated mice will be characterized in cooperation, as well as the composition by proteomic and glycomic approaches.
The influence on mucosal integrity will be evaluated by microscopic and transcriptomic techniques. Finally, a systems biology approach will be applied to identify mechanistic networks and interdependencies between antibiotic treatment, mucolytic activity, bacterial community structure and metabolism, and physiology of the
mucosa. These experimental findings will be projected onto a wider range of microbiome compositions by comparative genomics analyses, so as to let us predict mucolytic potential of any quantitatively measured gut microbiome. Conclusions from the animal model will be placed into the context of corresponding analysis of
mucus-associated microbiomes, metabolomes and glycomes from patient intestinal mucus and sputum samples. This will enable clinical predictions testable in a second project period.