Ecological effects of stress drive bacterial evolvability under sub-inhibitory antibiotic treatments

Vasse, Marie; Bonhoeffer, Sebastian; Frénoy, Antoine

doi:10.1101/2020.06.30.181099

Cited by 3 publications

(4 citation statements)

References 47 publications

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“…The assumed increases in the mutation rate are roughly the same order of magnitude as those reported in a recent meta-analysis focusing on common antibiotics in sub-inhibitory concentrations [45]. It seems likely that the drug-induced effects may be even more pronounced in cancer treatments given their known mutagenicity, and in clinically relevant concentrations above MIC which leads to rapid decay as assumed in our model.…”

Section: (T) Asupporting

confidence: 71%

“…Drugs with reported increases in mutation rate present a good starting point (see e.g. [15,16,45]). Our work makes concrete testable predictions, which potentially allows to robustly detect the presence of drug-induced resistance evolution without a need to try measure the actual mutation rate directly [52], but instead focusing on the dose-dependency in the observed rescue probabilities.…”

Section: Plos Computational Biologymentioning

confidence: 99%

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Drug-induced resistance evolution necessitates less aggressive treatment

et al. 2021

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Increasing body of experimental evidence suggests that anticancer and antimicrobial therapies may themselves promote the acquisition of drug resistance by increasing mutability. The successful control of evolving populations requires that such biological costs of control are identified, quantified and included to the evolutionarily informed treatment protocol. Here we identify, characterise and exploit a trade-off between decreasing the target population size and generating a surplus of treatment-induced rescue mutations. We show that the probability of cure is maximized at an intermediate dosage, below the drug concentration yielding maximal population decay, suggesting that treatment outcomes may in some cases be substantially improved by less aggressive treatment strategies. We also provide a general analytical relationship that implicitly links growth rate, pharmacodynamics and dose-dependent mutation rate to an optimal control law. Our results highlight the important, but often neglected, role of fundamental eco-evolutionary costs of control. These costs can often lead to situations, where decreasing the cumulative drug dosage may be preferable even when the objective of the treatment is elimination, and not containment. Taken together, our results thus add to the ongoing criticism of the standard practice of administering aggressive, high-dose therapies and motivate further experimental and clinical investigation of the mutagenicity and other hidden collateral costs of therapies.

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Section: (T) Asupporting

confidence: 71%

Section: Plos Computational Biologymentioning

confidence: 99%

Drug-induced resistance evolution necessitates less aggressive treatment

et al. 2021

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“…Mutation at constant rate is indeed a classic version of the complete Price equation. However, more complex mutational effects – including stress-dependent modulation of mutation rate ( Kohanski et al, 2010 ; Vasse et al, 2020 ) – could be included as a more flexible tunable term in Equation 5 .…”

Section: Discussionmentioning

confidence: 99%

Price equation captures the role of drug interactions and collateral effects in the evolution of multidrug resistance

Gjini

Wood

2021

eLife

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Bacterial adaptation to antibiotic combinations depends on the joint inhibitory effects of the two drugs (drug interaction, DI) and how resistance to one drug impacts resistance to the other (collateral effects, CE). Here we model these evolutionary dynamics on two-dimensional phenotype spaces that leverage scaling relations between the drug-response surfaces of drug sensitive (ancestral) and drug resistant (mutant) populations. We show that evolved resistance to the component drugs-and in turn, the adaptation of growth rate-is governed by a Price equation whose covariance terms encode geometric features of both the two-drug response surface (DI) in ancestral cells and the correlations between resistance levels to those drugs (CE). Within this framework, mean evolutionary trajectories reduce to a type of weighted gradient dynamics, with the drug interaction dictating the shape of the underlying landscape and the collateral effects constraining the motion on those landscapes. We also demonstrate how constraints on available mutational pathways can be incorporated into the framework, adding a third key driver of evolution. Our results clarify the complex relationship between drug interactions and collateral effects in multi-drug environments and illustrate how specific dosage combinations can shift the weighting of these two effects, leading to different and temporally-explicit selective outcomes.

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“…weighted gradient dynamics) that currently apply. We have also neglected de novo mutation and instead focused on selection driven by standing variation in the initial population, though as with the classic Price Equation, mutational effects-including stress-dependent modulation of mutation rate ( Kohanski et al, 2010 ; Vasse et al, 2020 )– may be included as an additional term in Equation 5. In addition, we have not explicitly included a fitness cost of resistance ( Andersson and Hughes, 2010 )–that is, we assume that growth rates of mutants and ancestral cells are identical in the absence of drug.…”

Section: Discussionmentioning

confidence: 99%

Price equation captures the role of drug interactions and collateral effects in the evolution of multidrug resistance

Gjini

Wood

2020

Preprint

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Bacterial adaptation to antibiotic combinations depends on the joint inhibitory effects of the two drugs (drug interaction, DI) and how resistance to one drug impacts resistance to the other (collateral effects, CE). Here we model these evolutionary dynamics on two-dimensional phenotype spaces that leverage scaling relations between the drug-response surfaces of drug sensitive (ancestral) and drug resistant (mutant) populations. We show that evolved resistance to the component drugs--and in turn, the adaptation of growth rate--is governed by a Price equation whose covariance terms encode geometric features of both the two-drug response surface (DI) in ancestral cells and the correlations between resistance levels to those drugs (CE). Within this framework, mean evolutionary trajectories reduce to a type of weighted gradient dynamics, with the drug interaction dictating the shape of the underlying landscape and the collateral effects constraining the motion on those landscapes. Our results clarify the complex relationship between drug interactions and collateral effects in multi-drug environments and illustrate how specific dosage combinations can shift the weighting of these two effects, leading to different and temporally-explicit selective outcomes.

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Ecological effects of stress drive bacterial evolvability under sub-inhibitory antibiotic treatments

Cited by 3 publications

References 47 publications

Drug-induced resistance evolution necessitates less aggressive treatment

Drug-induced resistance evolution necessitates less aggressive treatment

Price equation captures the role of drug interactions and collateral effects in the evolution of multidrug resistance

Price equation captures the role of drug interactions and collateral effects in the evolution of multidrug resistance

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