Microbial talking between members of the gut community
Article highlights
A community-level network composed of 570 microbial species populating 3 human cell types metabolically interacting through 4400 small-molecule transport and macromolecule degradation events
Distinct segregation of microbial taxa in the gut according to their transportable metabolites and macromolecular degradation products.
Assignment of biochemical reactions to specific microbial taxa and host cells pertaining analysis of interspecies and host-microbes interactions.
The increasing availability of high throughput biological data necessitates new approaches for deeper data analysis and interpretation. In this study, starting with a phylogeneitc analysis of fecal metagenomes from Chinese individuals, the authors of ‘Global metabolic interaction network of the human gut microbiota for context-specific community-scale analysis’ identified microbial members of the community network. Next, they compiled a curated list of experimentally validated small metabolites that are imported/exported by microbial members of the community network. Interestingly, the authors have also included degradation products of microbes and macromolecules which the microbes are known to degrade (cellulose, hemicellulose, inulin, starch and mucin, D-glucose and cellobiose from cellulose, N-acetylglucosamine, N-acetylneuraminate, L-fucose and sulfate from mucin). Three different host cells were considered as the environment of the reconstructed microbial community network: 1) colonocyte, 2) goblet cell (for mucin secretion) and the 3) hepatocyte (for glycine- or taurine-conjugated bile acid export).
The resulting network is thus an extensive data resource composed of 570 microbial species and 3 human cell types metabolically interacting through >4,400. While correlation-based inference networks between taxa abundance are hardly informative in terms of mechanistic or causal linkages, directed linkages between a microbe and a specific metabolite or macromolecule in the presented network would map specific microbes to metabolites and macromolecules it can transport and degrade, respectively.
Another not less important contribution of the network presented here is the clustering of the microbes into functional groups according to their linked metabolites and degradative products. This has a direct implication for elucidating types of interactions like competition, cooperation or cross-feeding between microbial community members.
Although the direct application of such a community scale network, as introduced in the paper, is to identify characteristic metabolic features (e.g. identity and frequency of release of secondary metabolites) of representative microbial communities in defined populations (e.g. disease cohorts), in its foundational structure, the network provides a comprehensive resource profiling microbes existing in the gut along with their metabolic interconnections and intra-connections (with the host). Further, the network provides a context for integrative analysis of disparate data levels where patterns of interactions could be hard to capture otherwise.
Overall, the availability of such a global interaction network paves the way for integrating disparate data types that would lead to deep insights on interaction patterns between members of the microbial population as well as between the microbes and their hosting cells.
Global metabolic interaction network of the human gut microbiota for context-specific community-scale analysis
Abstract
A system-level framework of complex microbe–microbe and host–microbe chemical cross-talk would help elucidate the role of our gut microbiota in health and disease. Here we report a literature-curated interspecies network of the human gut microbiota, called NJS16. This is an extensive data resource composed of ∼570 microbial species and 3 human cell types metabolically interacting through >4,400 small-molecule transport and macromolecule degradation events. Based on the contents of our network, we develop a mathematical approach to elucidate representative microbial and metabolic features of the gut microbial community in a given population, such as a disease cohort. Applying this strategy to microbiome data from type 2 diabetes patients reveals a context-specific infrastructure of the gut microbial ecosystem, core microbial entities with large metabolic influence, and frequently produced metabolic compounds that might indicate relevant community metabolic processes. Our network presents a foundation towards integrative investigations of community-scale microbial activities within the human gut.
Reference
Jaeyun Sung, Seunghyeon Kim, Josephine Jill T. Cabatbat, Sungho Jang, Yong-Su Jin, Gyoo Yeol Jung, Nicholas Chia & Pan-Jun Kim. Global metabolic interaction network of the human gut microbiota for context-specific community-scale analysis. Nature Communications 8: 15393 (2017). doi:10.1038/ncomms15393
