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Convergent phenotypic evolution towards fosfomycin collateral sensitivity of Pseudomonas aeruginosa antibiotic‐resistant mutants
The rise of antibiotic resistance and the reduced amount of novel antibiotics support the need of developing novel strategies to fight infections, based on improving the use of the antibiotics we already have. Collateral sensitivity is an evolutionary trade‐off associated with the acquisition of ant...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8867969/ https://www.ncbi.nlm.nih.gov/pubmed/33960651 http://dx.doi.org/10.1111/1751-7915.13817 |
Sumario: | The rise of antibiotic resistance and the reduced amount of novel antibiotics support the need of developing novel strategies to fight infections, based on improving the use of the antibiotics we already have. Collateral sensitivity is an evolutionary trade‐off associated with the acquisition of antibiotic resistance that can be exploited to tackle this relevant health problem. However, different works have shown that patterns of collateral sensitivity are not always conserved, thus precluding the exploitation of this evolutionary trade‐off to fight infections. In this work, we identify a robust pattern of collateral sensitivity to fosfomycin in Pseudomonas aeruginosa antibiotic‐resistant mutants, selected by antibiotics belonging to different structural families. We characterize the underlying mechanism of the collateral sensitivity observed, which is a reduced expression of the genes encoding the peptidoglycan‐recycling pathway, which preserves the peptidoglycan synthesis in situations where its de novo synthesis is blocked, and a reduced expression of fosA, encoding a fosfomycin‐inactivating enzyme. We propose that the identification of robust collateral sensitivity patterns, as well as the understanding of the molecular mechanisms behind these phenotypes, would provide valuable information to design evolution‐based strategies to treat bacterial infections. |
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