Adaptive Landscapes of Resistance Genes Change as Antibiotic Concentrations Change

Mira, Portia; Meza, Juan C.; Nandipati, Anna; Barlow, Miriam

doi:10.1093/molbev/msv146

Cited by 58 publications

(64 citation statements)

References 25 publications

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“…Previous work with antibiotic resistance in bacteria has shown that the shape of fitness landscapes can be strongly dependent on antibiotic concentrations [31]. Interestingly, we found that the negative interactions observed between beneficial mutations at lower concentrations of nystatin reversed sign and became increasingly positive at higher concentrations of nystatin.…”

Section: Introductionmentioning

confidence: 53%

Widespread Genetic Incompatibilities between First-Step Mutations during Parallel Adaptation of Saccharomyces cerevisiae to a Common Environment

2017

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Independently evolving populations may adapt to similar selection pressures via different genetic changes. The interactions between such changes, such as in a hybrid individual, can inform us about what course adaptation may follow and allow us to determine whether gene flow would be facilitated or hampered following secondary contact. We used Saccharomyces cerevisiae to measure the genetic interactions between first-step mutations that independently evolved in the same biosynthetic pathway following exposure to the fungicide nystatin. We found that genetic interactions are prevalent and predominantly negative, with the majority of mutations causing lower growth when combined in a double mutant than when alone as a single mutant (sign epistasis). The prevalence of sign epistasis is surprising given the small number of mutations tested and runs counter to expectations for mutations arising in a single biosynthetic pathway in the face of a simple selective pressure. Furthermore, in one third of pairwise interactions, the double mutant grew less well than either single mutant (reciprocal sign epistasis). The observation of reciprocal sign epistasis among these first adaptive mutations arising in the same genetic background indicates that partial postzygotic reproductive isolation could evolve rapidly between populations under similar selective pressures, even with only a single genetic change in each. The nature of the epistatic relationships was sensitive, however, to the level of drug stress in the assay conditions, as many double mutants became fitter than the single mutants at higher concentrations of nystatin. We discuss the implications of these results both for our understanding of epistatic interactions among beneficial mutations in the same biochemical pathway and for speciation.

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Section: Introductionmentioning

confidence: 53%

Widespread Genetic Incompatibilities between First-Step Mutations during Parallel Adaptation of Saccharomyces cerevisiae to a Common Environment

2017

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“…Another consideration is the potential of the core gene models for identifying compensatory or epistatic changes throughout the genome. These mutations occur in non-AMR genes in order to accommodate the potentially reduced fitness cost of maintaining the primary AMR conferring genes or SNPs [67][68][69][70] . The results of this study, and previous AI studies focusing on AMR 25,27 , suggest that these changes could be widespread throughout the genome.…”

Section: Discussionmentioning

confidence: 99%

Predicting Antimicrobial Resistance Using Conserved Genes

Nguyen

Olson

Shukla

et al. 2020

Preprint

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Abbreviations AIartificial intelligence CI confidence interval ME major error, susceptible genomes predicted to be resistant MIC minimum inhibitory concentration PATRIC Pathosystems resource integration center PLF PATRIC local protein family RAST Rapid annotation using subsystem technology RF Random Forest SR susceptible and resistant VME very major error, resistant genomes predicted to be susceptible XGB XGBoost Abstract A growing number of studies have shown that machine learning algorithms can be used to accurately predict antimicrobial resistance (AMR) phenotypes from bacterial sequence data. In these studies, models are typically trained using input features derived from comprehensive sets of known AMR genes or whole genome sequences. However, it can be difficult to determine whether genomes and their corresponding sets of AMR genes are complete when sequencing contaminated or metagenomic samples. In this study, we explore the possibility of using incomplete genome sequence data to predict AMR phenotypes. Machine learning models were built from randomly-selected sets of core genes that are held in common among the members of a species, and the AMR-conferring genes were removed based on their protein annotations. For Klebsiella pneumoniae, Mycobacterium tuberculosis, Salmonella enterica, and Staphylococcus aureus, we report that it is possible to classify susceptible and resistant phenotypes with average F1 scores ranging from 0.80-0.89 with as few as 100 conserved non-AMR genes, with very major error rates ranging from 0.11-0.23 and major error rates ranging from 0.10-0.20. Models built from core genes have predictive power in the cases where the primary AMR mechanism results from SNPs or horizontal gene transfer. By randomly sampling non-overlapping sets of core genes for use in these models, we show that F1 scores and error rates are stable and have little variance between replicates. Potential biases from strainspecific SNPs, phylogenetic sampling, and imbalances in the phylogenetic distribution of susceptible and resistant strains do not appear to have an impact on this result. Although these small core gene models have lower accuracies and higher error rates than models built from the corresponding assembled genomes, the results suggest that sufficient variation exists in the core non-AMR genes of a species for predicting AMR phenotypes. Overall this study suggests that building models from conserved genes may be a potentially useful strategy for predicting AMR phenotypes when genomes are incomplete.

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“…Following [74], datasets reporting growth rates [4,11,14,16,23,25,77] or drugresistance phenotypes [9,37,42,43,49,73] were log-transformed before analysis. Following [49], negative two was used in place of log-transformed values when growth rate or drug resistance phenotypes of zero were observed.…”

Section: Empirical Datasetsmentioning

confidence: 99%

The influence of higher-order epistasis on biological fitness landscape topography

Weinreich

Lan

Jaffe

et al. 2017

Preprint

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The effect of a mutation on the organism often depends on what other mutations are already present in its genome. Geneticists refer to such mutational interactions as epistasis. Pairwise epistatic effects have been recognized for over a century, and their evolutionary implications have received theoretical attention for nearly as long. However, pairwise epistatic interactions themselves can vary with genomic background. This is called higher-order epistasis, and its consequences for evolution are much less well understood. Here, we assess the influence that higher-order epistasis has on the topography of 16 published, biological fitness landscapes. We find that on average, their effects on fitness landscape declines with order, and suggest that notable exceptions to this trend may deserve experimental scrutiny. We explore whether natural selection may have contributed to this finding, and conclude by highlight opportunities for further work dissecting the influence that epistasis of all orders has on the efficiency of natural selection.

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Adaptive Landscapes of Resistance Genes Change as Antibiotic Concentrations Change

Cited by 58 publications

References 25 publications

Widespread Genetic Incompatibilities between First-Step Mutations during Parallel Adaptation of Saccharomyces cerevisiae to a Common Environment

Widespread Genetic Incompatibilities between First-Step Mutations during Parallel Adaptation of Saccharomyces cerevisiae to a Common Environment

Predicting Antimicrobial Resistance Using Conserved Genes

The influence of higher-order epistasis on biological fitness landscape topography

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