Computational methods for the identification of drug-metabolizing species and enzymes in the human gut microbiome

It is well established that the composition of a human’s microbiome can contribute to variations in drug metabolism. Indeed, from cumulative research efforts over the past sixty years, hundreds of drugs are now known to be altered in the presence of the gut microbiome, and many of these changed therapeutic profiles are due to direct metabolism by bacterial enzymes. While studies exploring chemical transformations within the human gut microbiome increasingly employ high-throughput methods, determining metabolite identities and the genetic elements responsible for their production is still a low-throughput process. What results are large knowledge gaps that must be overcome before the physiological and clinical relevance of a given bacterial drug metabolism event can be determined. In this thesis, I demonstrate that computational techniques can help alleviate such knowledge gaps. Attempts have been made in the past to computationally predict which bacterial species and enzymes are responsible for chemical transformations in the gut environment, but with limited utility and low accuracy. This dissertation begins with an overview of current computational approaches for exploring single-step xenobiotic transformations in the microbiome. I highlight the strengths and weakness of current approaches and make architectural recommendations for future tools. Based on these observations and recommendations, I present an in silico approach that employs chemical and protein Similarity algorithms that Identify MicrobioMe Enzymatic Reactions (SIMMER). I show that SIMMER predicts the chemistry and responsible species and enzymes for a queried reaction with high accuracy, unlike previous methods. I demonstrate SIMMER use cases in the context of drug metabolism by predicting previously uncharacterized enzymes for 88 drug transformations known to occur in the human gut. Bacterial species containing these enzymes are enriched within human donor stool samples that metabolize the query compound. After demonstrating its utility and accuracy, I chose to make SIMMER available as both a command-line and web tool, with flexible input and output options for determining chemical transformations within the human gut. Lastly, we demonstrate an experimental use-case of SIMMER by performing the first species-level characterization of methotrexate metabolism in the microbiome of Rheumatoid Arthritis patients.I present SIMMER as a computational addition to the microbiome researcher’s toolbox, enabling them to make informed hypotheses before embarking on the lengthy laboratory experiments required to characterize novel bacterial enzymes that can alter human ingested compounds. Beyond pharmaceutical applications, SIMMER can additionally be employed to determine bacterial enzymes responsible for breaking down non-therapeutics, such as dietary compounds or environmental pollutants. The method can also be extended in the future to make predictions on microbes in other body sites or environments

How to Determine the Role of the Microbiome in Drug Disposition.

With a paradigm shift occurring in health care toward personalized and precision medicine, understanding the numerous environmental factors that impact drug disposition is of paramount importance. The highly diverse and variant nature of the human microbiome is now recognized as a factor driving interindividual variation in therapeutic outcomes. The purpose of this review is to provide a practical guide on methodology that can be applied to study the effects of microbes on the absorption, distribution, metabolism, and excretion of drugs. We also highlight recent examples of how these methods have been successfully applied to help build the basis for researching the intersection of the microbiome and pharmacology. Although in vitro and in vivo preclinical models are highlighted, these methods are also relevant in late-phase drug development or even as a part of routine after-market surveillance. These approaches will aid in filling major knowledge gaps for both current and upcoming therapeutics with the long-term goal of achieving a new type of knowledge-based medicine that integrates data on the host and the microbiome

SIMMER employs similarity algorithms to accurately identify human gut microbiome species and enzymes capable of known chemical transformations.

Bacteria within the gut microbiota possess the ability to metabolize a wide array of human drugs, foods, and toxins, but the responsible enzymes for these chemical events remain largely uncharacterized due to the time-consuming nature of current experimental approaches. Attempts have been made in the past to computationally predict which bacterial species and enzymes are responsible for chemical transformations in the gut environment, but with low accuracy due to minimal chemical representation and sequence similarity search schemes. Here, we present an in silico approach that employs chemical and protein Similarity algorithms that Identify MicrobioMe Enzymatic Reactions (SIMMER). We show that SIMMER accurately predicts the responsible species and enzymes for a queried reaction, unlike previous methods. We demonstrate SIMMER use cases in the context of drug metabolism by predicting previously uncharacterized enzymes for 88 drug transformations known to occur in the human gut. We validate these predictions on external datasets and provide an in vitro validation of SIMMER's predictions for metabolism of methotrexate, an anti-arthritic drug. After demonstrating its utility and accuracy, we made SIMMER available as both a command-line and web tool, with flexible input and output options for determining chemical transformations within the human gut. We present SIMMER as a computational addition to the microbiome researcher's toolbox, enabling them to make informed hypotheses before embarking on the lengthy laboratory experiments required to characterize novel bacterial enzymes that can alter human ingested compounds

SIMMER employs similarity algorithms to accurately identify human gut microbiome species and enzymes capable of known chemical transformations

Bacteria within the gut microbiota possess the ability to metabolize a wide array of human drugs, foods, and toxins, but the responsible enzymes for these chemical events remain largely uncharacterized due to the time-consuming nature of current experimental approaches. Attempts have been made in the past to computationally predict which bacterial species and enzymes are responsible for chemical transformations in the gut environment, but with low accuracy due to minimal chemical representation and sequence similarity search schemes. Here, we present an in silico approach that employs chemical and protein Similarity algorithms that Identify MicrobioMe Enzymatic Reactions (SIMMER). We show that SIMMER accurately predicts the responsible species and enzymes for a queried reaction, unlike previous methods. We demonstrate SIMMER use cases in the context of drug metabolism by predicting previously uncharacterized enzymes for 88 drug transformations known to occur in the human gut. We validate these predictions on external datasets and provide an in vitro validation of SIMMER’s predictions for metabolism of methotrexate, an anti-arthritic drug. After demonstrating its utility and accuracy, we made SIMMER available as both a command-line and web tool, with flexible input and output options for determining chemical transformations within the human gut. We present SIMMER as a computational addition to the microbiome researcher’s toolbox, enabling them to make informed hypotheses before embarking on the lengthy laboratory experiments required to characterize novel bacterial enzymes that can alter human ingested compounds

Human gut bacterial metabolism drives Th17 activation and colitis.

Bacterial activation of T helper 17 (Th17) cells exacerbates mouse models of autoimmunity, but how human-associated bacteria impact Th17-driven disease remains elusive. We show that human gut Actinobacterium Eggerthella lenta induces intestinal Th17 activation by lifting inhibition of the Th17 transcription factor Rorγt through cell- and antigen-independent mechanisms. E. lenta is enriched in inflammatory bowel disease (IBD) patients and worsens colitis in a Rorc-dependent manner in mice. Th17 activation varies across E. lenta strains, which is attributable to the cardiac glycoside reductase 2 (Cgr2) enzyme. Cgr2 is sufficient to induce interleukin (IL)-17a, a major Th17 cytokine. cgr2+ E. lenta deplete putative steroidal glycosides in pure culture; related compounds are negatively associated with human IBD severity. Finally, leveraging the sensitivity of Cgr2 to dietary arginine, we prevented E. lenta-induced intestinal inflammation in mice. Together, these results support a role for human gut bacterial metabolism in driving Th17-dependent autoimmunity

Human Gut Bacterial Metabolism Drives Th17 Activation and Colitis

Bacterial activation of T helper 17 (Th17) cells exacerbates mouse models of autoimmunity, but how human-associated bacteria impact Th17-driven disease remains elusive. We show that human gut Actinobacterium Eggerthella lenta induces intestinal Th17 activation by lifting inhibition of the Th17 transcription factor Rorγt through cell- and antigen-independent mechanisms. E. lenta is enriched in inflammatory bowel disease (IBD) patients and worsens colitis in a Rorc-dependent manner in mice. Th17 activation varies across E. lenta strains, which is attributable to the cardiac glycoside reductase 2 (Cgr2) enzyme. Cgr2 is sufficient to induce interleukin (IL)-17a, a major Th17 cytokine. cgr2+ E. lenta deplete putative steroidal glycosides in pure culture; related compounds are negatively associated with human IBD severity. Finally, leveraging the sensitivity of Cgr2 to dietary arginine, we prevented E. lenta-induced intestinal inflammation in mice. Together, these results support a role for human gut bacterial metabolism in driving Th17-dependent autoimmunity

Genetic basis for the cooperative bioactivation of plant lignans by Eggerthella lenta and other human gut bacteria.

Plant-derived lignans, consumed daily by most individuals, are thought to protect against cancer and other diseases1; however, their bioactivity requires gut bacterial conversion to enterolignans2. Here, we dissect a four-species bacterial consortium sufficient for all five reactions in this pathway. A single enzyme (benzyl ether reductase, encoded by the gene ber) was sufficient for the first two biotransformations, variable between strains of Eggerthella lenta, critical for enterolignan production in gnotobiotic mice and unique to Coriobacteriia. Transcriptional profiling (RNA sequencing) independently identified ber and genomic loci upregulated by each of the remaining substrates. Despite their low abundance in gut microbiomes and restricted phylogenetic range, all of the identified genes were detectable in the distal gut microbiomes of most individuals living in northern California. Together, these results emphasize the importance of considering strain-level variations and bacterial co-occurrence to gain a mechanistic understanding of the bioactivation of plant secondary metabolites by the human gut microbiome