Pervasive genotype-by-environment interactions shape the fitness effects of antibiotic resistance mutations

Abstract

The fitness effects of antibiotic resistance mutations are a major driver of resistance evolution. While the nutrient environment affects bacterial fitness, experimental studies of resistance typically measure fitness of mutants in a single environment only. We explored how the nutrient environment affected the fitness effects of rifampicin-resistant rpoB mutations in Escherichia coli under several conditions critical for the emergence and spread of resistance—the presence of primary or secondary antibiotic, or the absence of any antibiotic. Pervasive genotype-by-environment (GxE) interactions determined fitness in all experimental conditions, with rank order of fitness in the presence and absence of antibiotics being strongly dependent on the nutrient environment. GxE interactions also affected the magnitude and direction of collateral effects of secondary antibiotics, in some cases so drastically that a mutant that was highly sensitive in one nutrient environment exhibited cross-resistance to the same antibiotic in another. It is likely that the mutant-specific impact of rpoB mutations on the global transcriptome underpins the observed GxE interactions. The pervasive, mutant-specific GxE interactions highlight the importance of doing what is rarely done when studying the evolution and spread of resistance in experimental and clinical work: assessing fitness of antibiotic-resistant mutants across a range of relevant environments.

Genotype-by-environment interactions govern fitness changes associated with adaptive mutations in two-component response systems

“…To determine changes in fitness, we calculated the area under the curve (AUC) for each growth curve. We used AUC as a proxy for fitness as it represents all the features of the bacterial growth curve, including lag time, the slope of exponential phase (growth rate) and the final optical density (24-hour cell yield) as a single comparable metric (Gifford et al, 2016;Dunai et al, 2019;Shea et al, 2020;Arrieta-Ortiz et al, 2023;Soley et al, 2023). We also calculated each of the three individual metrics and they are reported in Table 1.…”

“…We also performed an Analysis of Variance (ANOVA) utilizing the General Linear Models in IBM SPSS v29 to evaluate changes in final 24-hour optical densities and reported p-values. To determine fitness changes relative to the WT, we calculated the area under the curve (AUC) for the 24-hour growth curves (Soley et al, 2023), the AUC along with the standard error and N (N is defined as df + 1 where df is the number of data points for that group minus the number of hours) were then plotted. A one-way ANOVA was then used to calculate statistical difference between each AUC to 1) the WT in DMB, 2) the WT in the same respective environment or 3) to their respective mutant in their respective media in absence of silver nitrate, we then reported p-values.…”

“…These EE studies also support the idea that several possible mutations will be acted upon by positive natural selection to confer resistance in a particular environment and it is likely that not all these variants will confer the same level of resistance. Furthermore, we expect that the fitness of these variants will differ by environment (Zar, 1999;Martin and Lenormand, 2015;Gifford et al, 2016;Soley et al, 2023). This phenomenon is known as genotype-by-environment (GxE) interaction which are the non-additive effects of an organism's genotype and environment on the expression of a trait (Remold and Lenski, 2001).…”