Patterns of Epistasis between Beneficial Mutations in an Antibiotic Resistance Gene
By Martijn F. Schenk, I. Szendro, M. Salverda, Joachim Krug, J. Arjan, G. D. De Visser, Miriam Barlow. 2013
132 citations recorded by the source corpus.
Understanding epistasis is central to biology. For instance, epistatic interactions determine the topography of the fitness landscape and affect the dynamics and determinism of adaptation. However, few empirical data are available, and comparing results is complicated by confounding variation in the system and the type of mutations used. Here, we take a systematic approach by quantifying epistasis in two sets of four beneficial mutations in the antibiotic resistance enzyme TEM-1 β-lactamase. Mutations in these sets have either large or small effects on cefotaxime resistance when present as single mutations. By quantifying the epistasis and ruggedness in both landscapes, we find two general patterns. First, resistance is maximal for combinations of two mutations in both fitness landscapes and declines when more mutations are added due to abundant sign epistasis and a pattern of diminishing returns with genotype resistance. Second, large-effect mutations interact more strongly than small-effect mutations, suggesting that the effect size of mutations may be an organizing principle in understanding patterns of epistasis. By fitting the data to simple phenotype resistance models, we show that this pattern may be explained by the nonlinear dependence of resistance on enzyme stability and an unknown phenotype when mutations have antagonistically pleiotropic effects. The comparison to a previously published set of mutations in the same gene with a joint benefit further shows that the enzyme's fitness landscape is locally rugged but does contain adaptive pathways that lead to high resistance.
Subfields: Epidemiology, Genetics, Public Health, Environmental and Occupational Health
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In Epidemiology
In Genetics
- Courtney E. Gonzalez
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Citations in the corpus
- Low-N protein engineering with data-efficient deep learning (2020, background): “Additionally, their in silico evolutionary trajectories were consistent with the navigation of a rugged, epistatic fitness landscape 51 (Supplementary Fig.”
- Epistasis and Adaptation on Fitness Landscapes (2022, background): “Whereas laboratory studies in bacteria support that more strongly selected mutations have stronger interactions (e.g. Schenk et al. 2013), it remains unknown whether epistatic trajectories in nature are enriched for individually strongly selected substitutions.”
- Direct coupling analysis of epistasis in allosteric materials (2018, background): “Indeed, high-cost mutations exhibit stronger epistasis than low-cost ones (as also seen in RNA sequences [33,40], in the enzyme TEM-1 β-lactamase [11] and in previous in silico evolution work [29]), and are well-predicted by DCA.”
- Pervasive pairwise intragenic epistasis among sequential mutations in TEM-1 β-lactamase (2019, background): “[18] looked exclusively at combinations of beneficial mutations, quantifying epistasis in sets of four single mutations that had a known “large effect” or “small effect” on improving antibiotic resistance.”