| Literature DB >> 35670099 |
Urszula Łapińska1,2, Margaritis Voliotis1,3, Ka Kiu Lee1,2, Adrian Campey1,2, M Rhia L Stone4,5, Brandon Tuck1,2, Wanida Phetsang4, Bing Zhang4, Krasimira Tsaneva-Atanasova1,3,6,7, Mark A T Blaskovich4, Stefano Pagliara1,2.
Abstract
Phenotypic variations between individual microbial cells play a key role in the resistance of microbial pathogens to pharmacotherapies. Nevertheless, little is known about cell individuality in antibiotic accumulation. Here, we hypothesise that phenotypic diversification can be driven by fundamental cell-to-cell differences in drug transport rates. To test this hypothesis, we employed microfluidics-based single-cell microscopy, libraries of fluorescent antibiotic probes and mathematical modelling. This approach allowed us to rapidly identify phenotypic variants that avoid antibiotic accumulation within populations of Escherichia coli, Pseudomonas aeruginosa, Burkholderia cenocepacia, and Staphylococcus aureus. Crucially, we found that fast growing phenotypic variants avoid macrolide accumulation and survive treatment without genetic mutations. These findings are in contrast with the current consensus that cellular dormancy and slow metabolism underlie bacterial survival to antibiotics. Our results also show that fast growing variants display significantly higher expression of ribosomal promoters before drug treatment compared to slow growing variants. Drug-free active ribosomes facilitate essential cellular processes in these fast-growing variants, including efflux that can reduce macrolide accumulation. We used this new knowledge to eradicate variants that displayed low antibiotic accumulation through the chemical manipulation of their outer membrane inspiring new avenues to overcome current antibiotic treatment failures.Entities:
Keywords: antibiotic resistance; antibiotic uptake; antibiotics; burkholderia cenocepacia; efflux; escherichia coli; infectious disease; membrane transport; microbiology; microfluidics; phenotypic heterogeneity; physics of living systems; pseudomonas aeruginosa; single-cell analysis; staphylococcus aureus
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Year: 2022 PMID: 35670099 PMCID: PMC9173744 DOI: 10.7554/eLife.74062
Source DB: PubMed Journal: Elife ISSN: 2050-084X Impact factor: 8.713