Untargeted metabolomics data for the publication Weiss et al. 2022 "In vitro interaction network of a synthetic gut bacterial community"

<p>This dataset&nbsp;contains the untargeted metabolomics data for the publication Weiss et al. 2022 &quot;In vitro interaction network of a synthetic gut bacterial community&quot;. The dataset has also been submitted to&nbsp;MetaboLights repository with ID &quot;MTBLS3535&quot;. Please refer to the MetaboLights repository for the most up-to-date datasets.&nbsp;</p> <p>Publication abstract:</p> <p>A key challenge in microbiome research is to predict the functionality of microbial communities based on community membership and (meta)-genomic data. As central microbiota functions are determined by bacterial community networks, it is important to gain insight into the principles that govern bacteria-bacteria interactions. Here, we focused on the growth and metabolic interactions of the Oligo-Mouse-Microbiota (OMM¹²) synthetic bacterial community, which is increasingly used as a model system in gut microbiome research. Using a bottom-up approach, we uncovered the directionality of strain-strain interactions in mono- and pairwise co-culture experiments as well as in community batch culture. Metabolic network reconstruction in combination with metabolomics analysis of bacterial culture supernatants provided insights into the metabolic potential and activity of the individual community members. Thereby, we could show that the OMM¹²&nbsp;interaction network is shaped by both exploitative and interference competition in vitro in nutrient-rich culture media and demonstrate how community structure can be shifted by changing the nutritional environment. In particular,&nbsp;<em>Enterococcus faecalis</em>&nbsp;KB1 was identified as an important driver of community composition by affecting the abundance of several other consortium members in vitro. As a result, this study gives fundamental insight into key drivers and mechanistic basis of the OMM¹²&nbsp;interaction network in vitro, which serves as a knowledge base for future mechanistic in vivo studies.</p>