Analysis of the evolution of resistance to multiple antibiotics enables prediction of the Escherichia coli phenotype-based fitness landscape

Iwasawa, Junichiro; Maeda, Tomoya; Shibai, Atsushi; Kotani, Hazuki; Kawada, Manabu; Furusawa, Chikara

doi:10.1371/journal.pbio.3001920

Cited by 15 publications

(14 citation statements)

References 53 publications

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“…Indeed, the drop in the native fitnesses P nat , has a strong out-of-equilibrium nature; whereas applying a smooth selection pressure relaxes the constraint over the two interacting faces and allows for a monotonically (decreasing) evolution of P nat . This observation signals the existence of a minimal time for adaptation to new constraints, in agreement with experimental findings for bacterial evolution under stressful conditions [29][30][31].…”

Section: Discussionsupporting

confidence: 90%

Evolutionary dynamics of a lattice dimer: a toy model for stability vs. affinity trade-offs in proteins

Loffredo,

Vesconi,

Razban

et al. 2023

J. Phys. A: Math. Theor.

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Understanding how a stressor applied on a biological system shapes its evolution is key to achieving targeted evolutionary control. Here we present a toy model of two interacting lattice proteins to quantify the response to the selective pressure defined by the binding energy. We generate sequence data of proteins and study how the sequence and structural properties of dimers are affected by the applied selective pressure, both during the evolutionary process and in the stationary regime. In particular we show that internal contacts of native structures lose strength, while inter-structure contacts are strengthened due to the folding-binding competition. We discuss how dimerization is achieved through enhanced mutability on the interacting faces, and how the designability of each native structure changes upon introduction of the stressor.

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Section: Discussionsupporting

confidence: 90%

Evolutionary dynamics of a lattice dimer: a toy model for stability vs. affinity trade-offs in proteins

Loffredo,

Vesconi,

Razban

et al. 2023

J. Phys. A: Math. Theor.

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“…Our findings support that such ideas may be feasible by demonstrating that there are not as many unique fitness tradeoffs as there are mutations. Our work – showing that 774 mutants fall into a much smaller number of groups – contributes to growing literature suggesting that the phenotypic basis of adaptation is not as diverse as the genetic basis (Kinsler et al 2020; Iwasawa et al 2022; Petti et al 2023). This winnowing of diversity may make evolutionary processes, for example, whether an infectious population will adapt to resist a drug, somewhat more predictable (Rodrigues et al 2016; Lässig et al 2017; Kinsler et al 2020; Yoon et al 2021; King et al 2022; Wortel et al 2023).…”

Section: Introductionmentioning

confidence: 77%

“…But previous work suggests that the phenotypic basis of adaptation is less diverse than the genotypic basis. This is important because it means that evolutionary outcomes are more predictable at the level of phenotype (Kinsler et al 2020; Brettner et al 2022b; Iwasawa et al 2022). Even so, every mutant could have a slightly different fitness profile if each affects the same handful of molecular-level phenotypes but to relatively different degrees.…”

Section: Resultsmentioning

confidence: 99%

Distinguishing mutants that resist drugs via different mechanisms by examining fitness tradeoffs

Schmidlin,

Apodaca,

Newell

et al. 2023

Preprint

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There is growing interest in designing multidrug therapies that leverage tradeoffs to combat drug resistance. Tradeoffs are common in evolution and occur when, for example, resistance to one drug results in sensitivity to another. Major questions remain about the extent to which tradeoffs are reliable, specifically, whether the mutants that provide resistance to a given drug all suffer similar tradeoffs. This question is difficult because the drug-resistant mutants observed in the clinic, and even those evolved in controlled laboratory settings, are often biased towards those that provide large fitness benefits. Thus, the mutations (and mechanisms) that provide drug resistance may be more diverse than current data suggests. Here, we perform evolution experiments utilizing lineage-tracking to capture a fuller spectrum of mutations that give yeast cells a fitness advantage in fluconazole, a common antifungal drug. We then quantify fitness tradeoffs for each of 774 evolved mutants across 12 environments, finding these mutants group into six classes with characteristically different tradeoffs. Their unique tradeoffs may imply that each group of mutants affects fitness through different molecular mechanisms. Some of the groupings we find are surprising. For example, we find some mutants that resist single drugs do not resist their combination, and some mutants to the same gene have different tradeoffs than others. These findings, on one hand, demonstrate the difficulty in relying on consistent or intuitive tradeoffs when designing multidrug treatments that thwart resistance. On the other hand, by demonstrating that hundreds of adaptive mutations can be reduced to a relatively smaller number of groups, our findings suggest that resistance evolves through a relatively small number of mechanisms such that multidrug strategies that hinder resistance may be possible. Finally, by grouping mutants that likely affect fitness through similar underlying mechanisms, our findings also guide efforts to map the phenotypic impacts of mutation and predict the impacts of some mutations from others.

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“…In vitro implementations of reinforcement learning-based drug cycle optimization systems are needed to address this potential shortcoming. Another potential alternative would be to use the comparatively low-dimensional phenotype landscape of drug resistance ( 59 ).…”

Section: Discussionmentioning

confidence: 99%

Reinforcement learning informs optimal treatment strategies to limit antibiotic resistance

Weaver,

King,

Maltas

et al. 2024

Proc. Natl. Acad. Sci. U.S.A.

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Antimicrobial resistance was estimated to be associated with 4.95 million deaths worldwide in 2019. It is possible to frame the antimicrobial resistance problem as a feedback-control problem. If we could optimize this feedback-control problem and translate our findings to the clinic, we could slow, prevent, or reverse the development of high-level drug resistance. Prior work on this topic has relied on systems where the exact dynamics and parameters were known a priori. In this study, we extend this work using a reinforcement learning (RL) approach capable of learning effective drug cycling policies in a system defined by empirically measured fitness landscapes. Crucially, we show that it is possible to learn effective drug cycling policies despite the problems of noisy, limited, or delayed measurement. Given access to a panel of 15 β -lactam antibiotics with which to treat the simulated Escherichia coli population, we demonstrate that RL agents outperform two naive treatment paradigms at minimizing the population fitness over time. We also show that RL agents approach the performance of the optimal drug cycling policy. Even when stochastic noise is introduced to the measurements of population fitness, we show that RL agents are capable of maintaining evolving populations at lower growth rates compared to controls. We further tested our approach in arbitrary fitness landscapes of up to 1,024 genotypes. We show that minimization of population fitness using drug cycles is not limited by increasing genome size. Our work represents a proof-of-concept for using AI to control complex evolutionary processes.

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Analysis of the evolution of resistance to multiple antibiotics enables prediction of the Escherichia coli phenotype-based fitness landscape

Cited by 15 publications

References 53 publications

Evolutionary dynamics of a lattice dimer: a toy model for stability vs. affinity trade-offs in proteins

Evolutionary dynamics of a lattice dimer: a toy model for stability vs. affinity trade-offs in proteins

Distinguishing mutants that resist drugs via different mechanisms by examining fitness tradeoffs

Reinforcement learning informs optimal treatment strategies to limit antibiotic resistance

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