Impact of a 7-day homogeneous diet on interpersonal variation in human gut microbiomes and metabolomes.

Gut microbiota metabolism of dietary compounds generates a vast array of microbiome-dependent metabolites (MDMs), which are highly variable between individuals. The uremic MDMs (uMDMs) phenylacetylglutamine (PAG), p-cresol sulfate (PCS), and indoxyl sulfate (IS) accumulate during renal failure and are associated with poor outcomes. Targeted dietary interventions may reduce toxic MDM generation; however, it is unclear if inter-individual differences in diet or gut microbiome dominantly contribute to MDM variance. Here, we use a 7-day homogeneous average American diet to standardize dietary precursor availability in 21 healthy individuals. During dietary homogeneity, the coefficient of variation in PAG, PCS, and IS (primary outcome) did not decrease, nor did inter-individual variation in most identified metabolites; other microbiome metrics showed no or modest responses to the intervention. Host identity and age are dominant contributors to variability in MDMs. These results highlight the potential need to pair dietary modification with microbial therapies to control MDM profiles.

A metabolomics pipeline for the mechanistic interrogation of the gut microbiome.

Gut microorganisms modulate host phenotypes and are associated with numerous health effects in humans, ranging from host responses to cancer immunotherapy to metabolic disease and obesity. However, difficulty in accurate and high-throughput functional analysis of human gut microorganisms has hindered efforts to define mechanistic connections between individual microbial strains and host phenotypes. One key way in which the gut microbiome influences host physiology is through the production of small molecules1-3, yet progress in elucidating this chemical interplay has been hindered by limited tools calibrated to detect the products of anaerobic biochemistry in the gut. Here we construct a microbiome-focused, integrated mass-spectrometry pipeline to accelerate the identification of microbiota-dependent metabolites in diverse sample types. We report the metabolic profiles of 178 gut microorganism strains using our library of 833 metabolites. Using this metabolomics resource, we establish deviations in the relationships between phylogeny and metabolism, use machine learning to discover a previously undescribed type of metabolism in Bacteroides, and reveal candidate biochemical pathways using comparative genomics. Microbiota-dependent metabolites can be detected in diverse biological fluids from gnotobiotic and conventionally colonized mice and traced back to the corresponding metabolomic profiles of cultured bacteria. Collectively, our microbiome-focused metabolomics pipeline and interactive metabolomics profile explorer are a powerful tool for characterizing microorganisms and interactions between microorganisms and their host.

Bringing microbiome-drug interaction research into the clinic.

Our understanding of the scope and clinical relevance of gut microbiota metabolism of drugs is limited to relatively few biotransformations targeting a subset of therapeutics. Translating microbiome research into the clinic requires, in part, a mechanistic and predictive understanding of microbiome-drug interactions. This review provides an overview of microbiota chemistry that shapes drug efficacy and toxicity. We discuss experimental and computational approaches that attempt to bridge the gap between basic and clinical microbiome research. We highlight the current landscape of preclinical research focused on identifying microbiome-based biomarkers of patient drug response and we describe clinical trials investigating approaches to modulate the microbiome with the goal of improving drug efficacy and safety. We discuss approaches to aggregate clinical and experimental microbiome features into predictive models and review open questions and future directions toward utilizing the gut microbiome to improve drug safety and efficacy.

The human gut chemical landscape predicts microbe-mediated biotransformation of foods and drugs.

Microbes are nature's chemists, capable of producing and metabolizing a diverse array of compounds. In the human gut, microbial biochemistry can be beneficial, for example vitamin production and complex carbohydrate breakdown; or detrimental, such as the reactivation of an inactive drug metabolite leading to patient toxicity. Identifying clinically relevant microbiome metabolism requires linking microbial biochemistry and ecology with patient outcomes. Here we present MicrobeFDT, a resource which clusters chemically similar drug and food compounds and links these compounds to microbial enzymes and known toxicities. We demonstrate that compound structural similarity can serve as a proxy for toxicity, enzyme sharing, and coarse-grained functional similarity. MicrobeFDT allows users to flexibly interrogate microbial metabolism, compounds of interest, and toxicity profiles to generate novel hypotheses of microbe-diet-drug-phenotype interactions that influence patient outcomes. We validate one such hypothesis experimentally, using MicrobeFDT to reveal unrecognized gut microbiome metabolism of the ovarian cancer drug altretamine.

Human microbiome signatures of differential colorectal cancer drug metabolism.

It is well appreciated that microbial metabolism of drugs can influence treatment efficacy. Microbial ß-glucuronidases in the gut can reactivate the excreted, inactive metabolite of irinotecan, a first-line chemotherapeutic for metastatic colorectal cancer. Reactivation causes adverse drug responses, including severe diarrhea. However, a direct connection between irinotecan metabolism and the composition of an individual's gut microbiota has not previously been made. Here, we report quantitative evidence of inter-individual variability in microbiome metabolism of the inactive metabolite of irinotecan to its active form. We identify a high turnover microbiota metabotype with potentially elevated risk for irinotecan-dependent adverse drug responses. We link the high turnover metabotype to unreported microbial ß-glucuronidases; inhibiting these enzymes may decrease irinotecan-dependent adverse drug responses in targeted subsets of patients. In total, this study reveals metagenomic mining of the microbiome, combined with metabolomics, as a non-invasive approach to develop biomarkers for colorectal cancer treatment outcomes.

Structure and Inhibition of Microbiome ß-Glucuronidases Essential to the Alleviation of Cancer Drug Toxicity.

The selective inhibition of bacterial ß-glucuronidases was recently shown to alleviate drug-induced gastrointestinal toxicity in mice, including the damage caused by the widely used anticancer drug irinotecan. Here, we report crystal structures of representative ß-glucuronidases from the Firmicutes Streptococcus agalactiae and Clostridium perfringens and the Proteobacterium Escherichia coli, and the characterization of a ß-glucuronidase from the Bacteroidetes Bacteroides fragilis. While largely similar in structure, these enzymes exhibit marked differences in catalytic properties and propensities for inhibition, indicating that the microbiome maintains functional diversity in orthologous enzymes. Small changes in the structure of designed inhibitors can induce significant conformational changes in the ß-glucuronidase active site. Finally, we establish that ß-glucuronidase inhibition does not alter the serum pharmacokinetics of irinotecan or its metabolites in mice. Together, the data presented advance our in vitro and in vivo understanding of the microbial ß-glucuronidases, a promising new set of targets for controlling drug-induced gastrointestinal toxicity.

OpaR controls a network of downstream transcription factors in Vibrio parahaemolyticus BB22OP.

Vibrio parahaemolyticus is an emerging world-wide human pathogen that is associated with food-borne gastroenteritis when raw or undercooked seafood is consumed. Expression of virulence factors in this organism is modulated by the phenomenon known as quorum sensing, which permits differential gene regulation at low versus high cell density. The master regulator of quorum sensing in V. parahaemolyticus is OpaR. OpaR not only controls virulence factor gene expression, but also the colony and cellular morphology associated with growth on a surface and biofilm formation. Whole transcriptome Next Generation sequencing (RNA-Seq) was utilized to determine the OpaR regulon by comparing strains BB22OP (opaR+, LM5312) and BB22TR (∆opaR1, LM5674). This work, using the published V. parahaemolyticus BB22OP genome sequence, confirms and expands upon a previous microarray analysis for these two strains that used an Affymetrix GeneChip designed from the closely related V. parahaemolyticus RIMD2210633 genome sequence. Overall there was excellent correlation between the microarray and RNA-Seq data. Eleven transcription factors under OpaR control were identified by both methods and further confirmed by quantitative reverse transcription PCR (qRT-PCR) analysis. Nine of these transcription factors were demonstrated to be direct OpaR targets via in vitro electrophoretic mobility shift assays with purified hexahistidine-tagged OpaR. Identification of the direct and indirect targets of OpaR, including small RNAs, will enable the construction of a network map of regulatory interactions important for the switch between the nonpathogenic and pathogenic states.