The mobilization, transfer, and promotion of new antibiotic-resistance genes

Bacteria become resistant to antibiotics through changes in their genome, often from the acquisition of antibiotic resistance genes (ARGs).

New ARGs are constantly transferred from commensal and environmental bacteria into pathogens, threatening the potency of both existing and future antibiotics.

We lack, however, fundamental knowledge about the ARGs maintained by bacterial communities, the factors that govern their mobilization and transfer into pathogens, and in what environments these events are most likely to occur.In this project, we will use large-scale data analysis to study the mobilization, transfer, and promotion of new ARGs.

We will screen bacterial and metagenomes for new, previously uncharacterized ARGs, using a newly developed alignment-free method that uses deep learning and take protein structure into account.

We will then use machine learning to assess factors that influence the horizontal transfer of the new ARGs and predict current and future hosts.

Analysis of metagenomic data from bacterial communities will, finally, be used to evaluate the diversity and spread of new ARGs and pinpoint risk environments for their promotion.

Identified emerging ARGs will be experimentally validated and functionally characterized.This project will lead to new insights into the evolution of antibiotic resistance and has direct implications for human health. The methodologies developed within the project are general with broad applicability in many areas of microbiology.