Abstract
BackgroundThe human gut microbiome performs important functions in human health and disease. A classic example for host-gut microbial co-metabolism is host biosynthesis of primary bile acids and their subsequent deconjugation and transformation by the gut microbiome. To understand these system-level host-microbe interactions, a mechanistic, multi-scale computational systems biology approach that integrates the different types of omic data is needed. Here, we use a systematic workflow to computationally model bile acid metabolism in gut microbes and microbial communities.ResultsTherefore, we first performed a comparative genomic analysis of bile acid deconjugation and biotransformation pathways in 693 human gut microbial genomes and expanded 232 curated genome-scale microbial metabolic reconstructions with the corresponding reactions (available at https://vmh.life). We then predicted the bile acid biotransformation potential of each microbe and in combination with other microbes. We found that each microbe could produce maximally six of the 13 secondary bile acids in silico, while microbial pairs could produce up to 12 bile acids, suggesting bile acid biotransformation being a microbial community task. To investigate the metabolic potential of a given microbiome, publicly available metagenomics data from healthy Western individuals, as well as inflammatory bowel disease patients and healthy controls, were mapped onto the genomes of the reconstructed strains. We constructed for each individual a large-scale personalized microbial community model that takes into account strain-level abundances. Using flux balance analysis, we found considerable variation in the potential to deconjugate and transform primary bile acids between the gut microbiomes of healthy individuals. Moreover, the microbiomes of pediatric inflammatory bowel disease patients were significantly depleted in their bile acid production potential compared with that of controls. The contributions of each strain to overall bile acid production potential across individuals were found to be distinct between inflammatory bowel disease patients and controls. Finally, bottlenecks limiting secondary bile acid production potential were identified in each microbiome model.ConclusionsThis large-scale modeling approach provides a novel way of analyzing metagenomics data to accelerate our understanding of the metabolic interactions between the host and gut microbiomes in health and diseases states. Our models and tools are freely available to the scientific community.
Highlights
The human gut microbiome performs essential functions for human health and is directly implicated in the pathogenesis of complex diseases, such as inflammatory bowel disease, obesity, and type II diabetes [1]
One important function carried out by the human gut microbiome is the deconjugation of human primary bile acids and their subsequent biotransformation to secondary bile acids with implications for human health [3]
We expanded bile acid metabolism pathways captured in 232 gut microbial reconstructions using state-of-the-art comparative genomics methods
Summary
The human gut microbiome performs essential functions for human health and is directly implicated in the pathogenesis of complex diseases, such as inflammatory bowel disease, obesity, and type II diabetes [1]. To understand the interplay between the factors underlying the disease network, such as genome, microbiome, and diet, computational systems biology approaches are necessary to integrate the different -omes, such as metagenome and metabolome, and to identify key interactions in an unbiased manner [1]. One important function carried out by the human gut microbiome is the deconjugation of human primary bile acids and their subsequent biotransformation to secondary bile acids with implications for human health [3]. A classic example for host-gut microbial co-metabolism is host biosynthesis of primary bile acids and their subsequent deconjugation and transformation by the gut microbiome To understand these system-level host-microbe interactions, a mechanistic, multi-scale computational systems biology approach that integrates the different types of omic data is needed. We use a systematic workflow to computationally model bile acid metabolism in gut microbes and microbial communities
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
