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CORDIS - Résultats de la recherche de l’UE
CORDIS

Mechanistic study of gut microbial biotransformation reactions and their physiological role.

Periodic Reporting for period 1 - BiPMeM (Mechanistic study of gut microbial biotransformation reactions and their physiological role.)

Période du rapport: 2023-04-01 au 2025-03-31

The human gut microbiota, the trillions of microbes inhabiting our gastrointestinal tract, encodes an enormous number of metabolic enzymes, responsible for diverse biochemical reactions that determine metabolic microbiota-host interactions. Bacterial biotransformation reactions, the chemical modification of compounds by bacteria, seem to play a central role in such metabolic interplay between the gut microbiota and its human host. A number of gut microbial biotransformations such as reductive and hydrolytic reactions, as well as their metabolic enzymes, have been recently described. However, our mechanistic and physiological understanding remains still limited for many gut microbial biotransformation reactions, such as chemical conjugation. To fill this knowledge gap, this project presents a multidisciplinary plan to systematically investigate conjugative biotransformation reactions of gut bacteria.
• Developed a mass spectrometry-based computational pipeline to identify conjugative biotransformation on xenobiotics generated by gut bacteria.
• Successfully identifying gut bacterial enzymes responsible for chemical conjugation by applying high-throughput gut bacterial genetics using gain-of-function library approaches.
• Utilized gnotobiotic mouse models to assess the impact of conjugative enzymes on drug absorption, distribution, metabolism, and excretion (ADME).
This project has significantly expanded our understanding of gut microbiome conjugative biotransformation. The identification of novel bacterial enzymes responsible for chemical conjugation presents potential advancements in personalized medicine, particularly in tailoring drug treatments based on individual microbiome profiles. These findings could lead to the development of predictive models for drug efficacy and safety, offering valuable insights for future research and clinical applications.
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