Gut microbes engage in competitive and cooperative interactions that are essential for microbiome functions in health and disease. These interactions are context-dependent, influenced by diet, the host, and the microbial environment. The gut microbiota is also highly spatially structured, with microbes clustering in different gut regions, around dietary particles or within the host’s mucus layer. This spatial organization is believed to shape metabolic interactions among microbes. Yet our understanding of how physical structure affects proximity-dependent microbial interactions remains limited.
Our collaborative project aims to address the hypothesis that bacterial pathways, structures, and metabolites governing microbial interactions in the gut vary across space and time. To test this, we will utilize a well- characterized synthetic gut bacterial model community (OMM12) that mimics key microbiome functions in gnotobiotic mice. We will combine this model with spatial cultivation methods and high-throughput (meta-)proteomics and metabolomics analysis. Building on our comprehensive understanding of keystone functions, metabolic and strain-strain interactions in this Syncom model, we aim to further explore how these dynamics are controlled by spatial arrangement of microbial cells. We will trace metabolic interactions and uncover proteins and metabolites governing functionally relevant interactions using high-throughput (meta)proteome acquisition and analysis methods as well as differential incorporation of amino acids (DILAC). Further, we will uncover unknown gene functions and improve the functional annotation of understudied bacterial genomes, mostly using protein covariation analyses on the basis of the generated proteome data.
We expect this highly interdisciplinary project to reveal the dynamics of metabolite exchange interactions and their dependency on spatial localisation of the communities in vivo, as well as to exploit the untapped potential of metaproteomics to associate functional terms to previously uncharacterized genes in the understudied bacterial genomes.