Genome-wide identification of fitness-genes in aminoglycoside-resistant Escherichia coli during antibiotic stress

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Resistance against aminoglycosides is widespread in bacteria. This study aimed to identify genes thatare important for growth of E. coli during aminoglycoside exposure, since such genes may be targetedto re‑sensitize resistant E. coli to treatment. We constructed three transposon mutant libraries eachcontaining > 230.000 mutants in E. coli MG1655 strains harboring streptomycin (aph(3″)-Ib/aph(6)-Id),gentamicin (aac(3)-IV), or neomycin (aph(3″)-Ia) resistance gene(s). Transposon Directed Insertion‑siteSequencing (TraDIS), a combination of transposon mutagenesis and high‑throughput sequencing,identified 56 genes which were deemed important for growth during streptomycin, 39 duringgentamicin and 32 during neomycin exposure. Most of these fitness‑genes were membrane‑located(n = 55) and involved in either cell division, ATP‑synthesis or stress response in the streptomycin andgentamicin exposed libraries, and enterobacterial common antigen biosynthesis or magnesiumsensing/transport in the neomycin exposed library. For validation, eight selected fitness‑genes/gene‑clusters were deleted (minCDE, hflCK, clsA and cpxR associated with streptomycin and gentamicinresistance, and phoPQ, wecA, lpp and pal associated with neomycin resistance), and all mutants wereshown to be growth attenuated upon exposure to the corresponding antibiotics. In summary, weidentified genes that are advantageous in aminoglycoside‑resistant E. coli during antibiotic stress.In addition, we increased the understanding of how aminoglycoside‑resistant E. coli respond toantibiotic exposure.
OriginalsprogEngelsk
Artikelnummer4163
TidsskriftScientific Reports
Vol/bind14
Antal sider16
ISSN2045-2322
DOI
StatusUdgivet - 2024

Bibliografisk note

Funding Information:
We acknowledge Tom Gilbert and Julie Bitz-Thorsen (University of Copenhagen) for allowing us access to Covaris M220. We would also like to thank Yibing Ma, Jennifer Moussa and Line E. Thomsen (University of Copenhagen) for lambda red protocol optimization and productive discussions. Furthermore, we thank Natasha C. Pedersen (University of Copenhagen) for technical support. Ana Herrero-Fresno acknowledges the “Ministerio de Universidades”, Spain for her grant (BG22/00150–Beatriz Galindo program).

Funding Information:
This research has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement number 956154.

Funding Information:
We acknowledge Tom Gilbert and Julie Bitz-Thorsen (University of Copenhagen) for allowing us access to Covaris M220. We would also like to thank Yibing Ma, Jennifer Moussa and Line E. Thomsen (University of Copenhagen) for lambda red protocol optimization and productive discussions. Furthermore, we thank Natasha C. Pedersen (University of Copenhagen) for technical support. Ana Herrero-Fresno acknowledges the “Ministerio de Universidades”, Spain for her grant (BG22/00150–Beatriz Galindo program).

Publisher Copyright:
© The Author(s) 2024.

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