Nonoptimal Gene Expression Creates Latent Potential for Antibiotic Resistance
dc.contributor.author | Palmer, AC | |
dc.contributor.author | Chait, R | |
dc.contributor.author | Kishony, R | |
dc.date.accessioned | 2019-03-15T09:22:33Z | |
dc.date.issued | 2018-08-28 | |
dc.description.abstract | Bacteria regulate genes to survive antibiotic stress, but regulation can be far from perfect. When regulation is not optimal, mutations that change gene expression can contribute to antibiotic resistance. It is not systematically understood to what extent natural gene regulation is or is not optimal for distinct antibiotics, and how changes in expression of specific genes quantitatively affect antibiotic resistance. Here we discover a simple quantitative relation between fitness, gene expression, and antibiotic potency, which rationalizes our observation that a multitude of genes and even innate antibiotic defense mechanisms have expression that is critically nonoptimal under antibiotic treatment. First, we developed a pooled-strain drug-diffusion assay and screened Escherichia coli overexpression and knockout libraries, finding that resistance to a range of 31 antibiotics could result from changing expression of a large and functionally diverse set of genes, in a primarily but not exclusively drug-specific manner. Second, by synthetically controlling the expression of single-drug and multidrug resistance genes, we observed that their fitness-expression functions changed dramatically under antibiotic treatment in accordance with a log-sensitivity relation. Thus, because many genes are nonoptimally expressed under antibiotic treatment, many regulatory mutations can contribute to resistance by altering expression and by activating latent defenses. | en_GB |
dc.description.sponsorship | National Institutes of Health | en_GB |
dc.description.sponsorship | Israeli Centers of Research Excellence I-CORE Program | en_GB |
dc.description.sponsorship | European Research Council | en_GB |
dc.description.sponsorship | National Health and Medical Research Council | en_GB |
dc.identifier.citation | Vol. 35 (11), pp. 2669 - 2684 | en_GB |
dc.identifier.doi | 10.1093/molbev/msy163 | |
dc.identifier.grantnumber | R01-GM081617 | en_GB |
dc.identifier.grantnumber | 152/11 | en_GB |
dc.identifier.grantnumber | 281891 | en_GB |
dc.identifier.grantnumber | 1072965 | en_GB |
dc.identifier.uri | http://hdl.handle.net/10871/36476 | |
dc.language.iso | en | en_GB |
dc.publisher | Oxford University Press (OUP) | en_GB |
dc.rights | (C) The Author(s) 2018. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. This is an open access article distributed under the terms of the Creative Commons CC BY license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. | en_GB |
dc.subject | systems biology | en_GB |
dc.subject | antibiotic resistance | en_GB |
dc.subject | evolution | en_GB |
dc.subject | gene expression | en_GB |
dc.title | Nonoptimal Gene Expression Creates Latent Potential for Antibiotic Resistance | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2019-03-15T09:22:33Z | |
dc.identifier.issn | 0737-4038 | |
dc.description | This is the final version. Available from Oxford University Press (OUP) via the DOI in this record. | en_GB |
dc.identifier.journal | Molecular Biology and Evolution | en_GB |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en_GB |
dcterms.dateAccepted | 2018-08-28 | |
rioxxterms.version | VoR | en_GB |
rioxxterms.licenseref.startdate | 2018-08-28 | |
rioxxterms.type | Journal Article/Review | en_GB |
refterms.dateFCD | 2019-03-15T09:17:27Z | |
refterms.versionFCD | VoR | |
refterms.dateFOA | 2019-03-15T09:22:35Z | |
refterms.panel | A | en_GB |
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Except where otherwise noted, this item's licence is described as (C) The Author(s) 2018. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. This is an open access article distributed under the terms of the Creative Commons CC BY license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.