Impact of medical drugs on the evolution of human microbiome function and antimicrobial resistance development

The human gastrointestinal tract harbors trillions of microbes, known as the gut microbiota.

This microbial community helps modulating developmental, immunological, and metabolic functions of the human host and it plays an important role in medicinal drug response. Several works showed the bidirectional relationship between the gut microbiota and drugs. Medication modifies microbiome composition both in vitro and in the human gut.

Further, commonly used drugs influence the microbiome metabolic functions and increase the abundance of antimicrobial resistance (AMR) genes in human cohort studies, suggesting that non-antibiotic drugs could contribute to the emergence of AMR.

At the same time, microbiome-encoded enzymes can metabolize a wide range of medical drugs, participating in both beneficial and adverse effects.

Considering that many drugs are used over extended periods of time to treat chronic diseases, long-term exposure to drugs may likely drive microbiome evolution.

This could influence and evolve metabolic properties of gut microbes and lead to the emergence of novel AMR.This project aims at better understanding the impact of long-term exposure to medical drugs on human gut microbiome evolution at the phenotypic and metabolic level, the underlying molecular mechanism, and their consequences in vivo.

Based on previous data and in analogy to well-understood antibiotics, I hypothesize that non-antibiotic drugs might induce stress-triggered microbial evolution.

The project follows three specific objectives: (i) Quantify the impact of drug-induced evolution on microbiome metabolism and antibiotic sensitivity; (ii) Determine the role of bacterial stress responses in microbiome evolution; (iii) Assess drug-induced microbiome evolution using gnotobiotic mouse models.

If successful, this work will reveal the molecular mechanisms underlying microbiome-drug interactions and pave the way for their rational modulation to improve current and future drug therapies.