Quantitative systems pharmacology and the personalized drug–microbiota–diet axis

Precision medicine is an emerging paradigm that aims at maximizing the benefits and minimizing the adverse effects of drugs. Realistic mechanistic models are needed to understand and limit heterogeneity in drug responses. While pharmacokinetic models describe in detail a drug's absorption and metabolism, they generally do not account for individual variations in response to environmental influences, in addition to genetic variation. For instance, the human gut microbiota metabolizes drugs and is modulated by diet, and it exhibits significant variation among individuals. However, the influence of the gut microbiota on drug failure or drug side effects is under-researched. Here, we review recent advances in computational modeling approaches that could contribute to a better, mechanism-based understanding of drug–microbiota–diet interactions and their contribution to individual drug responses. By integrating systems biology and quantitative systems pharmacology with microbiology and nutrition, the conceptually and technologically demand for novel approaches could be met to enable the study of individual variability, thereby providing breakthrough support for progress in precision medicine

A systems biology approach to studying the role of microbes in human health.

Host-microbe interactions play a crucial role in human health and disease. Of the various systems biology approaches, reconstruction of genome-scale metabolic networks combined with constraint-based modeling has been particularly successful at in silico predicting the phenotypic characteristics of single organisms. Here, we summarize recent studies, which have applied this approach to investigate microbe-microbe and host-microbe metabolic interactions. This approach can be also expanded to investigate the properties of an entire microbial community, as well as single organisms within the community. We illustrate that the constraint-based modeling approach is suitable to model host-microbe interactions at molecular resolution and will enable systematic investigation of metabolic links between the human host and its microbes. Such host-microbe models, combined with experimental data, will ultimately further our understanding of how microbes influence human health

Systems-level characterization of a host-microbe metabolic symbiosis in the mammalian gut.

The human gut microbiota consists of ten times more microorganisms than there are cells in our body, processes otherwise indigestible nutrients, and produces important energy precursors, essential amino acids, and vitamins. In this study, we assembled and validated a genome-scale metabolic reconstruction of Bacteroides thetaiotaomicron (iAH991), a prominent representative of the human gut microbiota, consisting of 1488 reactions, 1152 metabolites, and 991 genes. To create a comprehensive metabolic model of host-microbe interactions, we integrated iAH991 with a previously published mouse metabolic reconstruction, which was extended for intestinal transport and absorption reactions. The two metabolic models were linked through a joint compartment, the lumen, allowing metabolite exchange and providing a route for simulating different dietary regimes. The resulting model consists of 7239 reactions, 5164 metabolites, and 2769 genes. We simultaneously modeled growth of mouse and B. thetaiotaomicron on five different diets varying in fat, carbohydrate, and protein content. The integrated model captured mutually beneficial cross-feeding as well as competitive interactions. Furthermore, we identified metabolites that were exchanged between the two organisms, which were compared with published metabolomics data. This analysis resulted for the first time in a comprehensive description of the co-metabolism between a host and its commensal microbe. We also demonstrate in silico that the presence of B. thetaiotaomicron could rescue the growth phenotype of the host with an otherwise lethal enzymopathy and vice versa. This systems approach represents a powerful tool for modeling metabolic interactions between a gut microbe and its host in health and disease

A functional metabolic map of Faecalibacterium prausnitzii, a beneficial human gut microbe

The human gut microbiota plays a central role in human well-being and disease. In this study, we present an integrated, iterative approach of computational modeling, in vitro experiments, metabolomics, and genomic analysis to accelerate the identification of metabolic capabilities for poorly characterized (anaerobic) microorganisms. We demonstrate this approach for the beneficial human gut microbe Faecalibacterium prausnitzii strain A2-165. We generated an automated draft reconstruction, which we curated against the limited biochemical data. This reconstruction modeling was used to develop in silico and in vitro a chemically defined medium (CDM), which was validated experimentally. Subsequent metabolomic analysis of the spent medium for growth on CDM was performed. We refined our metabolic reconstruction according to in vitro observed metabolite consumption and secretion and propose improvements to the current genome annotation of F. prausnitzii A2-165. We then used the reconstruction to systematically characterize its metabolic properties. Novel carbon source utilization capabilities and inabilities were predicted based on metabolic modeling and validated experimentally. This study resulted in a functional metabolic map of F. prausnitzii, which is available for further applications. The presented workflow can be readily extended to other poorly characterized and uncharacterized organisms to yield novel biochemical insights about the target organism

Generation of genome-scale metabolic reconstructions for 773 members of the human gut microbiota

Genome-scale metabolic models derived from human gut metagenomic data can be used as a framework to elucidate how microbial communities modulate human metabolism and health. We present AGORA (assembly of gut organisms through reconstruction and analysis), a resource of genome-scale metabolic reconstructions semi-automatically generated for 773 human gut bacteria. Using this resource, we identified a defined growth medium for Bacteroides caccae ATCC 34185. We also showed that interactions among modeled species depend on both the metabolic potential of each species and the nutrients available. AGORA reconstructions can integrate either metagenomic or 16S rRNA sequencing data sets to infer the metabolic diversity of microbial communities. AGORA reconstructions could provide a starting point for the generation of high-quality, manually curated metabolic reconstructions. AGORA is fully compatible with Recon 2, a comprehensive metabolic reconstruction of human metabolism, which will facilitate studies of host–microbiome interactions