Evolutionary History and Strength of Selection Determine the Rate of Antibiotic Resistance Adaptation
Por:
Cisneros-Mayoral, Sandra, Grana-Miraglia, Lucia, Perez-Morales, Deyanira, Pena-Miller, Rafael, Fuentes-Hernandez, Ayari
Publicada:
1 sep 2022
Resumen:
Bacterial adaptation to stressful environments often produces
evolutionary constraints whereby increases in resistance are associated
with reduced fitness in a different environment. The exploitation of
this resistance-cost trade-off has been proposed as the basis of
rational antimicrobial treatment strategies designed to limit the
evolution of drug resistance in bacterial pathogens. Recent theoretical,
laboratory, and clinical studies have shown that fluctuating selection
can maintain drug efficacy and even restore drug susceptibility, but can
also increase the rate of adaptation and promote cross-resistance to
other antibiotics. In this paper, we combine mathematical modeling,
experimental evolution, and whole-genome sequencing to follow
evolutionary trajectories towards beta-lactam resistance under
fluctuating selective conditions. Our experimental model system consists
of eight populations of Escherichia coli K12 evolving in parallel to a
serial dilution protocol designed to dynamically control the strength of
selection for resistance. We implemented adaptive ramps with mild and
strong selection, resulting in evolved populations with similar levels
of resistance, but with different evolutionary dynamics and diverging
genotypic profiles. We found that mutations that emerged under strong
selection are unstable in the absence of selection, in contrast to
resistance mutations previously selected in the mild selection regime
that were stably maintained in drug-free environments and positively
selected for when antibiotics were reintroduced. Altogether, our
population dynamics model and the phenotypic and genomic analysis of the
evolved populations show that the rate of resistance adaptation is
contingent upon the strength of selection, but also on evolutionary
constraints imposed by prior drug exposures.
Filiaciones:
Univ Nacl Autonoma Mexico, Ctr Ciencias Genom, Programa Biol Sintet, Cuernavaca 62210, Morelos, Mexico
Univ Toronto, Dept Cell & Syst Biol, Toronto, ON, Canada
Univ Nacl Autonoma Mexico, Ctr Ciencias Genom, Programa Biol Sistemas, Cuernavaca 62210, Morelos, Mexico
Green Published, gold, All Open Access, Gold, Green
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