| 1. |
- Jerlström-Hultqvist, Jon, 1982-, et al.
(författare)
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A bacteriophage enzyme induces bacterial metabolic perturbation that confers a novel promiscuous function
- 2018
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Ingår i: Nature Ecology & Evolution. - : Springer Science and Business Media LLC. - 2397-334X. ; 2:8, s. 1321-1330
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Tidskriftsartikel (refereegranskat)abstract
- One key concept in the evolution of new functions is the ability of enzymes to perform promiscuous side-reactions that serve as a source of novelty that may become beneficial under certain conditions. Here, we identify a mechanism where a bacteriophage-encoded enzyme introduces novelty by inducing expression of a promiscuous bacterial enzyme. By screening for bacteriophage DNA that rescued an auxotrophic Escherichia coli mutant carrying a deletion of the ilvA gene, we show that bacteriophage-encoded S-adenosylmethionine (SAM) hydrolases reduce SAM levels. Through this perturbation of bacterial metabolism, expression of the promiscuous bacterial enzyme MetB is increased, which in turn complements the absence of IlvA. These results demonstrate how foreign DNA can increase the metabolic capacity of bacteria, not only by transfer of bona fide new genes, but also by bringing cryptic bacterial functions to light via perturbations of cellular physiology.
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| 2. |
- Knopp, Michael, et al.
(författare)
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No beneficial fitness effects of random peptides
- 2018
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Ingår i: Nature Ecology & Evolution. - : Springer Science and Business Media LLC. - 2397-334X. ; 2:7, s. 1046-1047
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)
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| 3. |
- Pinheiro, Fernanda, et al.
(författare)
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Metabolic fitness landscapes predict the evolution of antibiotic resistance
- 2021
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Ingår i: Nature Ecology & Evolution. - : Springer Nature. - 2397-334X. ; 5:5, s. 677-687
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Tidskriftsartikel (refereegranskat)abstract
- This study develops metabolic fitness models that integrate drug action with evolutionary response to predict growth rates of resistance mutations and prevalent mechanisms of antibiotic resistance in Escherichia coli. 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.
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