Evidence map›Paper›PMID 33664488›Full record

ArticleNature ecology & evolution2021

Metabolic fitness landscapes predict the evolution of antibiotic resistance.

Fernanda Pinheiro, Omar Warsi, Dan I Andersson, Michael Lässig

Abstract read
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In one paragraph

Article in Nature ecology & evolution, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
42citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

42 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Fernanda Pinheiro *Institute for Biological Physics, University of Cologne, Cologne, Germany.ORCID http://orcid.org/0000-0002-0132-0562
Omar Warsi *Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden.ORCID http://orcid.org/0000-0003-3175-1184
Dan I AnderssonDepartment of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden. dan.andersson@imbim.uu.se.ORCID http://orcid.org/0000-0001-6640-2174
Michael LässigInstitute for Biological Physics, University of Cologne, Cologne, Germany. mlaessig@uni-koeln.de.ORCID http://orcid.org/0000-0002-3575-6719

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Bacteria evolve resistance to antibiotics by a multitude of mechanisms. A central, yet unsolved question is how resistance evolution affects cell growth at different drug levels. Here, we develop a fitness model that predicts growth rates of common resistance mutants from their effects on cell metabolism. The model maps metabolic effects of resistance mutations in drug-free environments and under drug challenge; the resulting fitness trade-off defines a Pareto surface of resistance evolution. We predict evolutionary trajectories of growth rates and resistance levels, which characterize Pareto resistance mutations emerging at different drug dosages. We also predict the prevalent resistance mechanism depending on drug and nutrient levels: low-dosage drug defence is mounted by regulation, evolution of distinct metabolic sectors sets in at successive threshold dosages. Evolutionary resistance mechanisms include membrane permeability changes and drug target mutations. These predictions are confirmed by empirical growth inhibition curves and genomic data of Escherichia coli populations. Our results show that resistance evolution, by coupling major metabolic pathways, is strongly intertwined with systems biology and ecology of microbial populations.

Indexed as

Drug Resistance, BacterialEscherichia coli ProteinsAnti-Bacterial AgentsEscherichia coliMutationAnti-Bacterial AgentsEscherichia coli Proteins

Identifiers

PMID33664488

What OpenQuestion holds

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Registered trials

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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.