Speaker
Description
Microbes living in natural communities develop antimicrobial resistance (AMR) through complex evolutionary trajectories. Fundamental features of this process emerge only in natural settings and therefore remain poorly understood. How are real-world AMR-associated genetic traits distributed between genes, intergenic regulatory regions, and mobile elements? Following an antibiotic exposure, are AMR-associated genetic changes transient or do they persist after the end of the disturbance? In this talk we will present a new tool that tracks evolutionary dynamics in natural microbial communities evolving under a short period of selective pressure. The tool recovers microbial genomes and their genetic variants from temporal DNA shotgun sequencing data. We overcome sequencing errors and assembly complications to identify true strain-level genetic variants within genomes, including single point polymorphisms (SNPs), local insertions/deletions and traces of large-scale genome rearrangements. By focusing on variants that dramatically increase in their frequency over time the tool pinpoints genetic changes that are associated with a fitness advantage during the disturbance. This approach allows to map out the genomic fitness landscape in natural settings. We will present unpublished data generated from ~50 healthy human subjects that were exposed to antibiotics. The analysis identified thousands of AMR-associated genetic variants that rose in frequency during the disturbance. We will show how most of these events are transient and subside after the end of the antibiotic treatment, and some persist until the end of the sampling period. While a fraction of the variants involves well-studied AMR genes, the majority is poorly characterized and potentially harbors novel mechanisms of resistance. To the best of our knowledge this is the most comprehensive assessment of evolution towards AMR in natural settings to date. This work paves the road to study short-term evolutionary dynamics in natural microbial communities.
