Periodic Reporting for period 1 - GutTransForm (Gut microbiota drug biotransformation as a tool to unravel the mechanisms of metabolic microbiota-host interactions)
Periodo di rendicontazione: 2023-05-01 al 2025-10-31
Our gut is home to trillions of microbes that vary greatly between people—much more than our genes do. These microbes influence our health in many ways, especially through metabolism (how our body processes food and drugs). But today, scientists mostly describe these effects without truly understanding how they work. This is partly because most studies only show connections between gut microbes and health, without explaining the underlying processes. It’s also hard to tell whether certain molecules in the body come from us or from the microbes. To tackle this, this project proposes a new approach: using drugs as tools to study how gut microbes and the human body interact at a molecular level. Since drugs are foreign to the body and chemically diverse, they’re ideal for pinpointing how microbes change them and how that affects our health.The plan includes growing many different gut bacteria in the lab, using advanced genetic and chemical analysis tools, studying specially raised mice with defined microbes, and building computer models. Together, these methods aim to connect differences in people's gut microbes to differences in how their bodies process substances, including medications. By revealing how gut microbes affect drug responses and metabolism, this research could help doctors better predict how individuals will react to medicines—and move us closer to truly personalized healthcare.
1) Mapping and modeling interpersonal differences in gut microbial biotransformation activity
We integrated high-throughput metabolomics and metagenomics to assess how 265 drugs are biotransformed by 87 gut microbiota communities from humans and preclinical models. Our large-scale, time-resolved screen of over 23,500 drug-microbiota interactions revealed that over 90% of drugs were metabolized (≥25%) by at least one community, underscoring the pervasiveness of microbial drug metabolism. These include novel biotransformation events, supporting the idea that community-level traits emerge beyond isolated strains. This highlights the power of our assay system to uncover hidden metabolic capacities. While chemical similarity explained some patterns, specific functional groups alone were insufficient predictors, revealing the complexity of interactions.
To link interpersonal gut microbiota composition with biotransformation activity, we developed a predictive framework—from linear to non-linear, taxonomically complete models using microbial composition and enzymatic profiles. These showed mid-to-high accuracy for several drugs, validating that microbiota function can be computationally inferred. The models often indicated multi-species contributions rather than single-strain effects, supporting the utility of ensemble approaches.
Despite sample size and modeling limits, our work offers a scalable platform for hypothesis generation and model development. The unified metabolomics workflow and conservative thresholds (25–90% biotransformation) enhance reproducibility amid variability. Our dataset supports AI-driven exploration of microbial drug metabolism and emphasizes the need to consider nuanced chemical and ecological features for accurate predictions.
2) Establishing an in vitro pipeline for the high-throughput quantification of epithelial permeability of gut bacterial metabolites
We developed a scalable workflow to study how gut microbial activity affects epithelial drug and metabolite absorption. This system combines anaerobic bacterial cultures with human Caco2 epithelial monolayers in a transwell setup, integrating untargeted metabolomics to track flux. A novel feature is our marker compound panel for real-time monitoring of epithelial barrier integrity, revealing subtle disruptions missed by TEER.
Using this approach, we assessed the permeability of 482 drugs and 172 microbial metabolites, identifying 33 new bacteria-drug pairs where microbial transformation altered absorption. The workflow recapitulated known biotransformations (e.g. prodrug deacetylation) and revealed new host-microbiota synergies.
We extended the platform to map the permeability of 397 abundant bacterial metabolites from 7240 detected features. Over 79% did not cross the epithelium, reinforcing its selective gatekeeping role. Some metabolites vanished apically without appearing basolaterally, suggesting Caco2 metabolism—supported by 73 newly detected basolateral metabolites.
Notably, bacterial supernatants affected barrier integrity without harming cell viability, implicating microbial metabolites—possibly via Toll-like receptor signaling—in tight junction modulation. The system is compatible with future enhancements (e.g. air-liquid interface, co-cultures), enabling broader gut-organ interaction studies.
This workflow provides a robust, adaptable tool for dissecting microbiota-drug-host interactions, with applications in pharmacokinetics, toxicology, and drug development.
We integrated high-throughput metabolomics and metagenomics to assess how 265 drugs are biotransformed by 87 gut microbiota communities from humans and preclinical models. Our large-scale, time-resolved screen of over 23,500 drug-microbiota interactions revealed that over 90% of drugs were metabolized (≥25%) by at least one community, underscoring the pervasiveness of microbial drug metabolism. These include novel biotransformation events, supporting the idea that community-level traits emerge beyond isolated strains. This highlights the power of our assay system to uncover hidden metabolic capacities. While chemical similarity explained some patterns, specific functional groups alone were insufficient predictors, revealing the complexity of interactions.
To link interpersonal gut microbiota composition with biotransformation activity, we developed a predictive framework—from linear to non-linear, taxonomically complete models using microbial composition and enzymatic profiles. These showed mid-to-high accuracy for several drugs, validating that microbiota function can be computationally inferred. The models often indicated multi-species contributions rather than single-strain effects, supporting the utility of ensemble approaches.
Despite sample size and modeling limits, our work offers a scalable platform for hypothesis generation and model development. The unified metabolomics workflow and conservative thresholds (25–90% biotransformation) enhance reproducibility amid variability. Our dataset supports AI-driven exploration of microbial drug metabolism and emphasizes the need to consider nuanced chemical and ecological features for accurate predictions.
2) Establishing an in vitro pipeline for the high-throughput quantification of epithelial permeability of gut bacterial metabolites
We developed a scalable workflow to study how gut microbial activity affects epithelial drug and metabolite absorption. This system combines anaerobic bacterial cultures with human Caco2 epithelial monolayers in a transwell setup, integrating untargeted metabolomics to track flux. A novel feature is our marker compound panel for real-time monitoring of epithelial barrier integrity, revealing subtle disruptions missed by TEER.
Using this approach, we assessed the permeability of 482 drugs and 172 microbial metabolites, identifying 33 new bacteria-drug pairs where microbial transformation altered absorption. The workflow recapitulated known biotransformations (e.g. prodrug deacetylation) and revealed new host-microbiota synergies.
We extended the platform to map the permeability of 397 abundant bacterial metabolites from 7240 detected features. Over 79% did not cross the epithelium, reinforcing its selective gatekeeping role. Some metabolites vanished apically without appearing basolaterally, suggesting Caco2 metabolism—supported by 73 newly detected basolateral metabolites.
Notably, bacterial supernatants affected barrier integrity without harming cell viability, implicating microbial metabolites—possibly via Toll-like receptor signaling—in tight junction modulation. The system is compatible with future enhancements (e.g. air-liquid interface, co-cultures), enabling broader gut-organ interaction studies.
This workflow provides a robust, adaptable tool for dissecting microbiota-drug-host interactions, with applications in pharmacokinetics, toxicology, and drug development.
Methods and tools. We will develop a systematic bottom-up approach to address the fundamental question of how interpersonal differences in microbiome composition can be predictively linked to differences in microbiota functions. Our developed approaches include i) ex vivo characterization of human gut communities to quantify differences in their metabolic functions across individuals, ii) in vitro and in silico methods to identify community-mediated metabolic traits at the level of species, pathways and enzymes, iii) novel machine learning-based methods to predict metabolic functions of microbial communities from their composition and molecular knowledge of metabolic pathways, iv) protocols to probe the interactions between microbial metabolites and host cells.
Scientific insights and broader impact. This project will provide mechanistic insights into the principles governing interpersonal differences in microbiota drug biotransformation activities and resulting microbiota-host interactions. This knowledge has direct medical implications, as future strategies to personalize drug treatments for improved efficacy and tolerability could be guided by individual microbiome predisposition. Additionally, I expect that we will also be able to generalize the identified principles and predictions of drug metabolic microbiota-host interaction beyond drug molecules to endogenous and food-derived compounds. Further, the outcome of this project will uniquely extend our functional understanding of the human microbiome to improve interpretation of the exponentially increasing amount of sequencing data. The possibility to model and predict metabolic microbiota-host interactions holds the promise to mechanistically explain how interpersonal variation in microbiome composition contributes to individuals’ health risks and opens opportunities for the rational design of targeted microbiome interventions for health benefits.
Altogether, the developed bottom-up strategy, multidisciplinary approaches, data integration framework, and modeling procedures will provide an innovative roadmap for understanding the microbial and chemical factors that shape metabolic microbiota-host interactions.
Scientific insights and broader impact. This project will provide mechanistic insights into the principles governing interpersonal differences in microbiota drug biotransformation activities and resulting microbiota-host interactions. This knowledge has direct medical implications, as future strategies to personalize drug treatments for improved efficacy and tolerability could be guided by individual microbiome predisposition. Additionally, I expect that we will also be able to generalize the identified principles and predictions of drug metabolic microbiota-host interaction beyond drug molecules to endogenous and food-derived compounds. Further, the outcome of this project will uniquely extend our functional understanding of the human microbiome to improve interpretation of the exponentially increasing amount of sequencing data. The possibility to model and predict metabolic microbiota-host interactions holds the promise to mechanistically explain how interpersonal variation in microbiome composition contributes to individuals’ health risks and opens opportunities for the rational design of targeted microbiome interventions for health benefits.
Altogether, the developed bottom-up strategy, multidisciplinary approaches, data integration framework, and modeling procedures will provide an innovative roadmap for understanding the microbial and chemical factors that shape metabolic microbiota-host interactions.