10.5061/DRYAD.J3TX95X92
Dean, Ziah
University of Michigan–Ann Arbor
Maltas, Jeff
University of Michigan–Ann Arbor
Wood, Kevin
University of Michigan–Ann Arbor
Antibiotic interactions shape short-term evolution of resistance in
Enterococcus faecalis
Dryad
dataset
2020
evolution of antibiotic resistance
antimicrobial combinations
National Science Foundation
https://ror.org/021nxhr62
1553028
National Institutes of Health
https://ror.org/01cwqze88
National Institute of General Medical Sciences
https://ror.org/04q48ey07
1R35GM124875
Hartwell Foundation
https://ror.org/038cgyc59
2020-03-20T00:00:00Z
2020-03-20T00:00:00Z
en
https://doi.org/10.1371/journal.ppat.1008278
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CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
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 identified, much
less is known about how different 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
different 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
specific interplay between drug interaction and drug resistance profiles.
For example, selection in a dual \textbeta-lactam combination leads to
accelerated growth adaptation compared to selection with the individual
drugs, even though the resulting resistance profiles are nearly
identical. On the other hand, populations evolved in an aminoglycoside
and \textbeta-lactam combination exhibit decreased growth adaptation and
resistant profiles that depend on the specific 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 profiles selected by drug combinations are nearly
growth-optimized along a contour connecting profiles selected by the
component drugs. Our results highlight trade-offs between drug
interactions and resistance profiles during the evolution of multi-drug
resistance and emphasize evolutionary benefits and disadvantages of
particular drug pairs targeting enterococci.
Data includes growth rate and drug IC50 data from lab evolution
experiments in multiple populations exposed to antibiotic combinations.
README file is included.