Evidence map›Paper›PMID 32119717›Full record

ArticlePLoS pathogens2020

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

Ziah Dean, Jeff Maltas, Kevin B Wood

Open access · goldAbstract read
In one paragraph

Article in PLoS pathogens, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed
3.5field-weighted citation impact, top 7% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

27 citing papers in PubMed, 38 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 1 institution in 1 country.

Ziah DeanDepartment of Biophysics, University of Michigan, Ann Arbor, Michigan, United States of America.ORCID 0000-0002-2288-5802
Jeff MaltasDepartment of Biophysics, University of Michigan, Ann Arbor, Michigan, United States of America.ORCID 0000-0001-6567-6800
Kevin B WoodDepartment of Biophysics, University of Michigan, Ann Arbor, Michigan, United States of America.ORCID 0000-0002-0985-7401
University of Michigan · US

Funding

Spatiotemporal Dynamics of Collective Antibiotic Resistance in Microbial CommunitiesR35GM124875 · NIGMS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI WOOD, KEVIN · 2017 to 2024
$2.7M
NIGMS NIH HHS R35 GM124875
6 · The paper itself

Abstract

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 β-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 β-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.

Indexed as

Drug Resistance, Multiple, BacterialAdaptation, PhysiologicalAnti-Bacterial AgentsBiological EvolutionDrug InteractionsEnterococcus faecalisGram-Positive Bacterial InfectionsHumansMicrobial Sensitivity TestsAnti-Bacterial Agents

Identifiers

PMID32119717
PMCPMC7093004
OpenAlexW3009760483

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.