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Journal ArticleDOI: 10.1038/S41559-021-01397-0

Metabolic fitness landscapes predict the evolution of antibiotic resistance.

04 Mar 2021-Nature Ecology and Evolution (Nature Publishing Group)-Vol. 5, Iss: 5, pp 677-687
Abstract: Bacteria evolve resistance to antibiotics by a multitude of mechanisms. A central, yet unsolved question is how resistance evolution affects cell growth at different drug levels. Here, we develop a fitness model that predicts growth rates of common resistance mutants from their effects on cell metabolism. The model maps metabolic effects of resistance mutations in drug-free environments and under drug challenge; the resulting fitness trade-off defines a Pareto surface of resistance evolution. We predict evolutionary trajectories of growth rates and resistance levels, which characterize Pareto resistance mutations emerging at different drug dosages. We also predict the prevalent resistance mechanism depending on drug and nutrient levels: low-dosage drug defence is mounted by regulation, evolution of distinct metabolic sectors sets in at successive threshold dosages. Evolutionary resistance mechanisms include membrane permeability changes and drug target mutations. These predictions are confirmed by empirical growth inhibition curves and genomic data of Escherichia coli populations. Our results show that resistance evolution, by coupling major metabolic pathways, is strongly intertwined with systems biology and ecology of microbial populations.

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7 results found


Open accessPosted ContentDOI: 10.1101/2020.08.25.267484
Sarah M. Ardell1, Sergey Kryazhimskiy1Institutions (1)
25 Aug 2020-bioRxiv
Abstract: Pleiotropic fitness tradeoffs and their opposite, buttressing pleiotropy, underlie many important phenomena in ecology and evolution. Yet, predicting whether a population adapting to one (“home”) environment will concomitantly gain or lose fitness in another (“non-home”) environment remains challenging, especially when adaptive mutations have diverse pleiotropic effects. Here, we address this problem using the concept of the joint distribution of fitness effects (JDFE), a local measurable property of the fitness landscape. We derive simple statistics of the JDFE that predict the expected slope, variance and co-variance of non-home fitness trajectories. We estimate these statistics from published data from the Escherichia coli knock-out collection in the presence of antibiotics. We find that, for some drug pairs, the average trend towards collateral sensitivity may be masked by large uncertainty, even in the absence of epistasis. We provide simple theoretically grounded guidelines for designing robust sequential drug protocols.

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Topics: Fitness landscape (62%), Population (53%), Epistasis (52%) ... show more

6 Citations


Open accessPosted ContentDOI: 10.1101/2020.08.25.267484
Sarah M. Ardell1, Sergey Kryazhimskiy1Institutions (1)
22 Aug 2021-bioRxiv
Abstract: Resistance mutations against one drug can elicit collateral sensitivity against other drugs. Multi-drug treatments exploiting such trade-offs can help slow down the evolution of resistance. However, if mutations with diverse collateral effects are available, a treated population may evolve either collateral sensitivity or collateral resistance. How to design treatments robust to such uncertainty is unclear. We show that many resistance mutations in Escherichia coli against various antibiotics indeed have diverse collateral effects. We propose to characterize such diversity with a joint distribution of fitness effects (JDFE) and develop a theory for describing and predicting collateral evolution based on simple statistics of the JDFE. We show how to robustly rank drug pairs to minimize the risk of collateral resistance and how to estimate JDFEs. In addition to practical applications, these results have implications for our understanding of evolution in variable environments.

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Topics: Population (53%)

4 Citations


Open accessJournal ArticleDOI: 10.1016/J.COISB.2021.100365
Abstract: Antibiotic resistance is a growing public health problem. To gain a fundamental understanding of resistance evolution, a combination of systematic experimental and theoretical approaches is required. Evolution experiments combined with next-generation sequencing techniques, laboratory automation, and mathematical modeling are enabling the investigation of resistance development at an unprecedented level of detail. Recent work has directly tracked the intricate stochastic dynamics of bacterial populations in which resistant mutants emerge and compete. In addition, new approaches have enabled measuring how prone a large number of genetically perturbed strains are to evolve resistance. Based on advances in quantitative cell physiology, predictive theoretical models of resistance are increasingly being developed. Taken together, a new strategy for observing, predicting, and ultimately controlling resistance evolution is emerging.

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Open accessPosted ContentDOI: 10.1101/2021.09.27.461914
27 Sep 2021-bioRxiv
Abstract: Fitness landscape mapping and the prediction of evolutionary trajectories on these landscapes are major tasks in evolutionary biology research. Evolutionary dynamics is tightly linked to the landscape topography, but this relation is not straightforward. Models predict different evolutionary outcomes depending on mutation rates: high-fitness genotypes should dominate the population under low mutation rates and lower-fitness, mutationally robust (also called 9flat9) genotypes - at higher mutation rates. Yet, so far, flat genotypes have been demonstrated in very few cases, particularly in viruses. The quantitative conditions for their emergence were studied only in simplified single-locus, two-peak landscapes. In particular, it is unclear whether within the same genome some genes can be flat while the remaining ones are fit. Here, we analyze a previously measured fitness landscape of a yeast tRNA gene. We found that the wild type allele is sub-optimal, but is mutationally robust (9flat9). Using computer simulations, we estimated the critical mutation rate in which transition from fit to flat allele should occur for a gene with such characteristics. We then used a scaling argument to extrapolate this critical mutation rate for a full genome, assuming the same mutation rate for all genes. Finally, we propose that while the majority of genes are still selected to be fittest, there are a few mutation hot-spots like the tRNA, for which the mutationally robust flat allele is favored by selection.

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Topics: Mutation (genetic algorithm) (61%), Mutation rate (61%), Fitness landscape (60%) ... show more

References
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80 results found


Journal ArticleDOI: 10.1146/ANNUREV.MI.03.100149.002103
Abstract: The study of the growth of bacterial cultures does not constitute a specialized subject or branch of research: it is the basic method of Microbiology. It would be a foolish enterprise, and doomed to failure, to attempt reviewing briefly a \"subject\" which covers actually our whole discipline. Unless, of course, we considered the formal laws of growth for their own sake, an approach which has repeatedly proved sterile. In the present review we shall consider bacterial growth as a method for the study of bacterial physiology and biochemistry. More precisely, we shall concern ourselves with the quantitative aspects of the method, with the interpretation of quantitative data referring to bacterial growth. Furthermore, we shall considerz exclusively the positive phases of growth, since the study of bacterial \"death,\" i.e., of the negative phases of growth, involves distinct problems and methods. The discussion will be limited to populations considered genetically homogeneous. The problems of mutation and selection in growing cultures have been excellently dealt with in recent review articles by Delbriick (1) and Luria (2). No attempt is made at reviewing the literature on a subject which, as we have just seen, is not really a subject at all. The papers and results quoted have been selected as illustrations of the points discussed.

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3,787 Citations


Open accessJournal ArticleDOI: 10.1038/NM1145
Stuart B. Levy1, Bonnie Marshall1Institutions (1)
01 Dec 2004-Nature Medicine
Abstract: The optimism of the early period of antimicrobial discovery has been tempered by the emergence of bacterial strains with resistance to these therapeutics. Today, clinically important bacteria are characterized not only by single drug resistance but also by multiple antibiotic resistance--the legacy of past decades of antimicrobial use and misuse. Drug resistance presents an ever-increasing global public health threat that involves all major microbial pathogens and antimicrobial drugs.

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Topics: Drug resistance (61%), Antimicrobial (53%)

3,094 Citations


Open accessBook
01 Jan 1992-
Abstract: A sequel to Experiments in Molecular Genetics (Cold Spring Harbor Lab. Press, 1972) for those doing genetic or recombinant DNA work with E. coli or similar organisms. The spiral-bound manual includes 34 detailed experiments with step-by-step protocols and clear diagrams that demonstrate major concep

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Topics: Bacterial genetics (57%)

2,358 Citations


Journal ArticleDOI: 10.1126/SCIENCE.1123539
07 Apr 2006-Science
Abstract: Five point mutations in a particular β-lactamase allele jointly increase bacterial resistance to a clinically important antibiotic by a factor of ∼100,000. In principle, evolution to this high-resistance β-lactamase might follow any of the 120 mutational trajectories linking these alleles. However, we demonstrate that 102 trajectories are inaccessible to Darwinian selection and that many of the remaining trajectories have negligible probabilities of realization, because four of these five mutations fail to increase drug resistance in some combinations. Pervasive biophysical pleiotropy within the β-lactamase seems to be responsible, and because such pleiotropy appears to be a general property of missense mutations, we conclude that much protein evolution will be similarly constrained. This implies that the protein tape of life may be largely reproducible and even predictable.

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Topics: Pleiotropy (52%), Fisher's geometric model (52%)

1,211 Citations


Open accessJournal ArticleDOI: 10.1371/JOURNAL.PPAT.1002158
Erik Gullberg1, Sha Cao1, Otto G. Berg1, Carolina Ilbäck1  +3 moreInstitutions (1)
21 Jul 2011-PLOS Pathogens
Abstract: The widespread use of antibiotics is selecting for a variety of resistance mechanisms that seriously challenge our ability to treat bacterial infections. Resistant bacteria can be selected at the high concentrations of antibiotics used therapeutically, but what role the much lower antibiotic concentrations present in many environments plays in selection remains largely unclear. Here we show using highly sensitive competition experiments that selection of resistant bacteria occurs at extremely low antibiotic concentrations. Thus, for three clinically important antibiotics, drug concentrations up to several hundred-fold below the minimal inhibitory concentration of susceptible bacteria could enrich for resistant bacteria, even when present at a very low initial fraction. We also show that de novo mutants can be selected at sub-MIC concentrations of antibiotics, and we provide a mathematical model predicting how rapidly such mutants would take over in a susceptible population. These results add another dimension to the evolution of resistance and suggest that the low antibiotic concentrations found in many natural environments are important for enrichment and maintenance of resistance in bacterial populations.

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Topics: Antibiotic resistance (56%), Antibiotics (54%), Bacteria (52%) ... show more

1,082 Citations


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