Drug-Driven Phenotypic Convergence Supports Rational Treatment Strategies of Chronic Infections

Imamovic, Lejla; Ellabaan, Mostafa Mostafa Hashim; Machado, Ana Manuel Dantas; Citterio, Linda; Wulff, Tune; Molin, Søren; Johansen, Helle Krogh; Sommer, Morten Otto Alexander

doi:10.1016/j.cell.2017.12.012

Cited by 143 publications

(231 citation statements)

References 43 publications

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“…Intriguingly, strains within the same class were not necessarily evolved in the same stress nor stress category. Similar phenotypic convergence of drug resistant strains has recently been reported for Pseudomonas aeruginosa (Imamovic et al, 2018). To elucidate characteristic gene expressions for each class, we applied linear discriminant analysis (LDA) (see STAR Methods).…”

Section: Supervised Pca Reveals Modular Phenotypic Statesmentioning

confidence: 83%

“…Quantitative studies of resistance evolution showed that these mechanisms for resistance are tightly interconnected, as demonstrated by the complicated networks of cross-resistance and collateral sensitivity among drugs (Barbosa et al, 2017;Chevereau et al, 2015;Girgis et al, 2009;Lázár et al, 2014;Suzuki et al, 2014), which is the phenomena where the acquisition of resistance to a certain drug is accompanied by resistance or sensitivity to another drug. Such interactions among resistance mechanisms result in constraints on accessible phenotypes in evolution, which could provide a basis to predict and control the resistance evolution (Furusawa et al, 2018;Imamovic et al, 2018;Lässig et al, 2017). For example, the cyclic or simultaneous use of two drugs with collateral sensitivity, to which pathogens did not easily acquire resistance simultaneously, were demonstrated to suppress resistance evolution (Munck et al, 2014;Yoshida et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

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High-throughput laboratory evolution and evolutionary constraints inEscherichia coli

Maeda

Iwasawa

Kotani

et al. 2020

Preprint

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These authors contributed equally to this work. SUMMARYUnderstanding the constraints that shape the evolution of antibiotic resistance is critical for predicting and controlling drug resistance. Despite its importance, however, a systematic investigation for evolutionary constraints is lacking. Here, we performed a high-throughput laboratory evolution of Escherichia coli under the addition of 95 antibacterial chemicals and quantified the transcriptome, resistance, and genomic profiles for the evolved strains. Using interpretable machine learning techniques, we analyzed the phenotype-genotype data and identified low dimensional phenotypic states among the evolved strains. Further analysis revealed the underlying biological processes responsible for these distinct states, leading to the identification of novel trade-off relationships associated with drug resistance. We also report a novel constraint that leads to decelerated evolution. These findings bridge the genotypic, gene expression, and drug resistance space and lead to a better understanding of evolutionary constraints for antibiotic resistance.

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Section: Supervised Pca Reveals Modular Phenotypic Statesmentioning

confidence: 83%

Section: Introductionmentioning

confidence: 99%

High-throughput laboratory evolution and evolutionary constraints inEscherichia coli

Maeda

Iwasawa

Kotani

et al. 2020

Preprint

View full text Add to dashboard Cite

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“…In recent years, significant efforts have been devoted to designing evolutionarily sound strategies that balance short-term drug efficacy with the long-term potential to develop resistance. These approaches describe a number of different factors that could modulate resistance evolution, including interactions between bacterial cells (3,4,5,6,7,8), synergy with the immune system (9), spatial heterogeneity (10,11,12,13,14,15), epistasis between resistance mutations (16,17), precise temporal scheduling (18,19,20,21), and statistical correlations between resistance profiles for different drugs (22,23,24,25,26,27,28,29,30,31).…”

Section: Introductionmentioning

confidence: 99%

Antibiotic interactions shape short-term evolution of resistance in E. faecalis

Dean

Maltas

Wood

2019

Preprint

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Antibiotic combinations are increasingly used to combat bacterial infections. Multidrug therapies are a particularly important treatment option for E. faecalis, an opportunistic pathogen that contributes to high-inoculum infections such as infective endocarditis. While numerous synergistic drug combinations for E. faecalis have been identi ed, much less is known about how di erent combinations impact the rate of resistance evolution. In this work, we use high-throughput laboratory evolution experiments to quantify adaptation in growth rate and drug resistance of E. faecalis exposed to drug combinations exhibiting di erent classes of interactions, ranging from synergistic to suppressive. We identify a wide range of evolutionary behavior, including both increased and decreased rates of growth adaptation, depending on the speci c interplay between drug interaction and cross resistance. For example, selection in a dual -lactam combination leads to accelerated growth adaptation compared to selection with the individual drugs, even though the resulting resistance pro les are nearly identical. On the other hand, populations evolved in an aminoglycoside and -lactam combination exhibit decreased growth adaptation and resistant pro les that depend on the speci c drug concentrations. We show that the main qualitative features of these evolutionary trajectories can be explained by simple rescaling arguments that correspond to geometric transformations of the two-drug growth response surfaces measured in ancestral cells. The analysis also reveals multiple examples where resistance pro les selected by drug combinations correspond to (nearly) optimized linear combinations of those selected by the component drugs. Our results highlight tradeo s between drug interactions and collateral e ects during the evolution of multi-drug resistance and emphasize evolutionary bene ts and disadvantages of particular drug pairs targeting enterococci.

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“…These anti-resistance approaches exploit different features of the population dynamics, including competitive suppression between sensitive and resistance cells (14,15), synergy with the immune system (16), precise timing of growth dynamics or dosing (17,18), responses to subinhibitory drug doses (19), and band-pass response to periodic dosing (10). Resistance-stalling strategies may also exploit spatial heterogeneity (20,21,22,23,24,25), epistasis between resistance mutations (26,27), hospital-level dosing protocols (28,29), and regimens of multiple drugs applied in sequence (28,30,18,31,19,32) or combination (33,34,35,36,37,38,39,40), which may allow one to leverage statistical correlations between resistance profiles for different drugs (41,42,43,44,39,37,45,46,47,48). As a whole, these studies demonstrate the important role of community-level dynamics for understanding and predicting how bacteria respond and adapt to antibiotics.…”

Section: Introductionmentioning

confidence: 99%

Delayed antibiotic exposure induces population collapse in enterococcal communities with drug-resistant subpopulations

Hallinen

Karslake

Wood

2019

Preprint

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Bacteria exploit a diverse set of defenses to survive exposure to antibiotics.While the molecular and genetic underpinnings of antibiotic resistance are increasingly understood, less is known about how these molecular events influence microbial dynamics on the population scale. In this work, we show that the dynamics of E. faecalis communities exposed to antibiotics can be surprisingly rich, revealing scenarios where-for example-increasing population size or delaying drug exposure can promote population collapse. Specifically, we combine experiments in computer-controlled bioreactors with simple mathematical models to reveal density-dependent feedback loops that couple population growth and antibiotic efficacy when communities include drug-resistant (β-lactamase producing) subpopulations. The resulting communities exhibit a wide range of behavior, including population survival, population collapse, or one of two qualitatively distinct bistable behaviors where survival is favored in either small or large populations. These dynamics reflect competing density-dependent effects of different subpopulations, with growth of drug-sensitive cells increasing but growth of drug-resistant cells decreasing effective drug inhibition. Guided by these results, we experimentally demonstrate how populations receiving immediate drug influx may sometimes thrive, while identical populations exposed to delayed drug influx (and lower average drug concentrations) collapse. These results illustrate that the spread of drug resistant determinants-even in a simplified single-species communities-may be governed by potentially counterintuitive dynamics driven by population-level interactions.

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Drug-Driven Phenotypic Convergence Supports Rational Treatment Strategies of Chronic Infections

Cited by 143 publications

References 43 publications

High-throughput laboratory evolution and evolutionary constraints inEscherichia coli

High-throughput laboratory evolution and evolutionary constraints inEscherichia coli

Antibiotic interactions shape short-term evolution of resistance in E. faecalis

Delayed antibiotic exposure induces population collapse in enterococcal communities with drug-resistant subpopulations

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