Unpredictability of the fitness effects of antimicrobial resistance mutations across environments in <i>Escherichia coli</i>
Bibliographic record
Abstract
Abstract The evolution of antimicrobial resistance (AMR) in bacteria is a major public health concern. When resistant bacteria are highly prevalent in microbial populations, antibiotic restriction protocols are often implemented to reduce their spread. These measures rely on the existence of deleterious fitness effects (i.e., costs) imposed by AMR mutations during growth in the absence of antibiotics. According to this assumption, resistant strains will be outcompeted by susceptible strains that do not pay the cost during the period of restriction. Hence, the success of a given intervention depends on the magnitude and direction of fitness effects of mutations, which can vary depending on the genetic and environmental context. However, the fitness effects of AMR mutations are generally studied in laboratory reference strains and estimated in a limited number of environments, usually a standard laboratory growth medium. In this study, we systematically measure how three sources of variation impact the fitness effects of AMR mutations: the type of resistance mutation, the genetic background of the host, and the growth environment. We demonstrate that while AMR mutations are generally costly in antibiotic-free environments, their fitness effects vary widely and depend on complex interactions between the AMR mutation, genetic background, and environment. We test the ability of the Rough Mount Fuji genotype-fitness model to reproduce the empirical data in simulation. We identify model parameters that reasonably capture the variation in fitness effects due to genetic variation. However, the model fails to accommodate variation when considering multiple growth environments. Overall, this study reveals a wealth of variation in the fitness effects of resistance mutations owing to genetic background and environmental conditions, that will ultimately impact their persistence in natural populations. Author’s Abstract The emergence and spread of antimicrobial resistance in bacterial populations poses a continuing threat to our ability to successfully treat bacterial infections. During exposure to antibiotics, resistant microbes outcompete susceptible ones, leading to increases in prevalence. This competitive advantage, however, can be reversed in antibiotic-free environments, due to deleterious fitness effects imposed by resistance determinants, a concept referred to as the ‘cost of resistance’. The extent of these fitness effects is an important factor governing the prevalence of resistance in natural populations. However, predicting the fitness effects of resistance mutations is challenging, since their magnitude can change depending on the genetic background in which the mutation arose and the environmental context. Comprehensive data on these sources of variation is lacking, and we address this gap by determining the fitness effects of resistance mutations introduced in a range of Escherichia coli clinical isolates, measured in different antibiotic-free environments. Our results reveal wide variation in the fitness effects, driven by irreducible interactions between resistance mutations, genetic backgrounds, and growth environments. We evaluate the performance of a fitness landscape model to reproduce the data in simulation, highlight its strengths and weaknesses, and call for improvements to accommodate these important sources of variation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".