Genome-scale metabolic reconstruction of 7,302 human microorganisms for personalized medicine

Abstract

The human microbiome influences the efficacy and safety of a wide variety of commonly prescribed drugs. Designing precision medicine approaches that incorporate microbial metabolism would require strain- and molecule-resolved, scalable computational modeling. Here, we extend our previous resource of genome-scale metabolic reconstructions of human gut microorganisms with a greatly expanded version. AGORA2 (assembly of gut organisms through reconstruction and analysis, version 2) accounts for 7,302 strains, includes strain-resolved drug degradation and biotransformation capabilities for 98 drugs, and was extensively curated based on comparative genomics and literature searches. The microbial reconstructions performed very well against three independently assembled experimental datasets with an accuracy of 0.72 to 0.84, surpassing other reconstruction resources and predicted known microbial drug transformations with an accuracy of 0.81. We demonstrate that AGORA2 enables personalized, strain-resolved modeling by predicting the drug conversion potential of the gut microbiomes from 616 patients with colorectal cancer and controls, which greatly varied between individuals and correlated with age, sex, body mass index and disease stages. AGORA2 serves as a knowledge base for the human microbiome and paves the way to personalized, predictive analysis of host–microbiome metabolic interactions.

Human Genes Involved in the Interaction between Host and Gut Microbiome: Regulation and Pathogenic Mechanisms

“…Considering such close interaction, it is logical to assume that microbiota influence host gene expression in all body areas that it colonizes, such as skin, respiratory organs, gastrointestinal, and urogenital tract. Two main mechanisms allow the microbiota to “regulate” our genome: through direct body exposure to microorganisms [ 21 ] or those of their metabolites [ 18 , 22 ]. Indeed, we must recognize a fine distinction between microbial antigen-mediated genome modulation and metabolite-mediated one [ 19 ].…”

Flux Sampling in Genome-scale Metabolic Modeling of Microbial Communities

“…We use the Constrained Riemannian Hamiltonian Monte Carlo (RHMC), which has recently been shown to be substantially more efficient than prior sampling algorithms 41 .…”

Disease-specific loss of microbial cross-feeding interactions in the human gut

“…These models have been used to estimate metabolic exchanges between pairs of bacterial species for over a decade 12,13 . Developments in automating the reconstruction of GEMs 14 , manually curating GEMs for thousands of gut microorganisms 15,16 , and in the availability of tools to model interactions between multiple species 17 have paved the way to build metabolic models for complex microbial communities. Studies using community-wide metabolic models have found dozens to hundreds of significantly different metabolic exchanges in the gut microbiome associated with type 2 diabetes 18 and in inflammatory bowel disease 19 when compared to healthy controls.…”