Dataset Information

Associations between sensitivity to antibiotics, disinfectants and heavy metals in natural, clinical and laboratory isolates of Escherichia coli.

ABSTRACT: Bacteria in nature often encounter non-antibiotic antibacterials (NAAs), such as disinfectants and heavy metals, and they can evolve resistance via mechanisms that are also involved in antibiotic resistance. Understanding whether susceptibility to different types of antibacterials is non-randomly associated across natural and clinical bacteria is therefore important for predicting the spread of resistance, yet there is no consensus about the extent of such associations or underlying mechanisms. We tested for associations between susceptibility phenotypes of 93 natural and clinical Escherichia coli isolates to various NAAs and antibiotics. Across all compound combinations, we detected a small number of non-random associations, including a trio of positive associations among chloramphenicol, triclosan and benzalkonium chloride. We investigated genetic mechanisms that can explain such associations using genomic information, genetic knockouts and experimental evolution. This revealed some mutations that are selected for by experimental exposure to one compound and confer cross-resistance to other compounds. Surprisingly, these interactions were asymmetric: selection for chloramphenicol resistance conferred cross-resistance to triclosan and benzalkonium chloride, but selection for triclosan resistance did not confer cross-resistance to other compounds. These results identify genetic changes involved in variable cross-resistance across antibiotics and NAAs, potentially contributing to associations in natural and clinical bacteria.

SUBMITTER: Bischofberger AM

PROVIDER: S-EPMC7384044 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Associations between sensitivity to antibiotics, disinfectants and heavy metals in natural, clinical and laboratory isolates of Escherichia coli.

Bischofberger Anna M AM Baumgartner Michael M Pfrunder-Cardozo Katia R KR Allen Richard C RC Hall Alex R AR

Environmental microbiology 20200325 7

Bacteria in nature often encounter non-antibiotic antibacterials (NAAs), such as disinfectants and heavy metals, and they can evolve resistance via mechanisms that are also involved in antibiotic resistance. Understanding whether susceptibility to different types of antibacterials is non-randomly associated across natural and clinical bacteria is therefore important for predicting the spread of resistance, yet there is no consensus about the extent of such associations or underlying mechanisms. ...[more]

PMID: 32162766

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Project description:The spread of antibiotic resistance is driving interest in new approaches to control bacterial pathogens. This includes applying multiple antibiotics strategically, using bacteriophages against antibiotic-resistant bacteria, and combining both types of antibacterial agents. All these approaches rely on or are impacted by associations among resistance phenotypes (where bacteria resistant to one antibacterial agent are also relatively susceptible or resistant to others). Experiments with laboratory strains have shown strong associations between some resistance phenotypes, but we lack a quantitative understanding of associations among antibiotic and phage resistance phenotypes in natural and clinical populations. To address this, we measured resistance to various antibiotics and bacteriophages for 94 natural and clinical Escherichia coli isolates. We found several positive associations between resistance phenotypes across isolates. Associations were on average stronger for antibacterial agents of the same type (antibiotic-antibiotic or phage-phage) than different types (antibiotic-phage). Plasmid profiles and genetic knockouts suggested that such associations can result from both colocalization of resistance genes and pleiotropic effects of individual resistance mechanisms, including one case of antibiotic-phage cross-resistance. Antibiotic resistance was predicted by core genome phylogeny and plasmid profile, but phage resistance was predicted only by core genome phylogeny. Finally, we used observed associations to predict genes involved in a previously uncharacterized phage resistance mechanism, which we verified using experimental evolution. Our data suggest that susceptibility to phages and antibiotics are evolving largely independently, and unlike in experiments with lab strains, negative associations between antibiotic resistance phenotypes in nature are rare. This is relevant for treatment scenarios where bacteria encounter multiple antibacterial agents.IMPORTANCE Rising antibiotic resistance is making it harder to treat bacterial infections. Whether resistance to a given antibiotic spreads or declines is influenced by whether it is associated with altered susceptibility to other antibiotics or other stressors that bacteria encounter in nature, such as bacteriophages (viruses that infect bacteria). We used natural and clinical isolates of Escherichia coli, an abundant species and key pathogen, to characterize associations among resistance phenotypes to various antibiotics and bacteriophages. We found associations between some resistance phenotypes, and in contrast to past work with laboratory strains, they were exclusively positive. Analysis of bacterial genome sequences and horizontally transferred genetic elements (plasmids) helped to explain this, as well as our finding that there was no overall association between antibiotic resistance and bacteriophage resistance profiles across isolates. This improves our understanding of resistance evolution in nature, potentially informing new rational therapies that combine different antibacterials, including bacteriophages.

Dataset Information

Associations between sensitivity to antibiotics, disinfectants and heavy metals in natural, clinical and laboratory isolates of Escherichia coli.

Publications

Associations between sensitivity to antibiotics, disinfectants and heavy metals in natural, clinical and laboratory isolates of Escherichia coli.

Similar Datasets

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