Extreme antagonism arising from gene-environment interactions

TL;DR

This study uncovers extreme antagonistic gene-environment interactions in E. coli, where mutations deleterious in one environment become beneficial in another, revealing potential targets to combat antibiotic resistance.

Contribution

The paper introduces a systematic approach using adaptive evolution to identify extreme antagonistic gene-environment interactions in bacteria.

Findings

Mutations beneficial with rifampicin are deleterious without it.

Antagonistic mutations repress a stringent response-like transcriptional program.

Non-antagonistic mutations show opposite transcriptional profiles.

Abstract

Antagonistic interactions in biological systems, which occur when one perturbation blunts the effect of another, are typically interpreted as evidence that the two perturbations impact the same cellular pathway or function. Yet, this interpretation ignores extreme antagonistic interactions wherein an otherwise deleterious perturbation compensates for the function lost due to a prior perturbation. Here, we report on gene-environment interactions involving genetic mutations that are deleterious in a permissive environment but beneficial in a specific environment that restricts growth. These extreme antagonistic interactions constitute gene-environment analogs of synthetic rescues previously observed for gene-gene interactions. Our approach uses two independent adaptive evolution steps to address the lack of experimental methods to systematically identify such extreme interactions. We apply the approach to Escherichia coli by successively adapting it to defined glucose media without and with the antibiotic rifampicin. The approach identified multiple mutations that are beneficial in the presence of rifampicin and deleterious in its absence. The analysis of transcription shows that the antagonistic adaptive mutations repress a stringent response-like transcriptional program, while non-antagonistic mutations have an opposite transcriptional profile. Our approach represents a step toward the systematic characterization of extreme antagonistic gene-drug interactions, which can be used to identify targets to select against antibiotic resistance.