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Dryad

Allele-specific collateral and fitness effects determine the dynamics of fluoroquinolone-resistance evolution

Cite this dataset

Liakopoulos, Apostolos et al. (2022). Allele-specific collateral and fitness effects determine the dynamics of fluoroquinolone-resistance evolution [Dataset]. Dryad. https://doi.org/10.5061/dryad.c2fqz61b4

Abstract

Collateral sensitivity (CS), which arises when resistance to one antibiotic increases sensitivity towards other antibiotics, offers novel treatment opportunities to constrain or reverse the evolution of antibiotic resistance. The applicability of CS-informed treatments remains uncertain, in part because we lack an understanding of the generality of CS effects for different resistance mutations, singly or in combination. Here we address this issue in the Gram-positive pathogen Streptococcus pneumoniae by measuring collateral and fitness effects of clinically relevant gyrA and parC alleles, and their combinations, that confer resistance to fluoroquinolones. We integrated these results in a mathematical model which allowed us to evaluate how different in silico combination treatments impact the dynamics of resistance evolution. We identified common and conserved CS effects of different gyrA and parC alleles; however, the spectrum of collateral effects was unique for each allele or allelic pair. This indicated that allelic identity can impact the evolutionary dynamics of resistance evolution during monotreatment and combination treatment. Our model simulations, which included the experimentally derived antibiotic susceptibilities and fitness effects, and antibiotic-specific pharmacodynamics, revealed that both collateral and fitness effects impact the population dynamics of resistance evolution. Overall, we provide evidence that allelic identity and interactions can have a pronounced impact on collateral effects of different antibiotics and suggest that these need to be considered in models examining CS-based therapies.

Funding

JPI-EC-AMR, Award: 547001002

ZonMW Off Road, Award: 451001033