--- _id: '1619' abstract: - lang: eng text: The emergence of drug resistant pathogens is a serious public health problem. It is a long-standing goal to predict rates of resistance evolution and design optimal treatment strategies accordingly. To this end, it is crucial to reveal the underlying causes of drug-specific differences in the evolutionary dynamics leading to resistance. However, it remains largely unknown why the rates of resistance evolution via spontaneous mutations and the diversity of mutational paths vary substantially between drugs. Here we comprehensively quantify the distribution of fitness effects (DFE) of mutations, a key determinant of evolutionary dynamics, in the presence of eight antibiotics representing the main modes of action. Using precise high-throughput fitness measurements for genome-wide Escherichia coli gene deletion strains, we find that the width of the DFE varies dramatically between antibiotics and, contrary to conventional wisdom, for some drugs the DFE width is lower than in the absence of stress. We show that this previously underappreciated divergence in DFE width among antibiotics is largely caused by their distinct drug-specific dose-response characteristics. Unlike the DFE, the magnitude of the changes in tolerated drug concentration resulting from genome-wide mutations is similar for most drugs but exceptionally small for the antibiotic nitrofurantoin, i.e., mutations generally have considerably smaller resistance effects for nitrofurantoin than for other drugs. A population genetics model predicts that resistance evolution for drugs with this property is severely limited and confined to reproducible mutational paths. We tested this prediction in laboratory evolution experiments using the “morbidostat”, a device for evolving bacteria in well-controlled drug environments. Nitrofurantoin resistance indeed evolved extremely slowly via reproducible mutations—an almost paradoxical behavior since this drug causes DNA damage and increases the mutation rate. Overall, we identified novel quantitative characteristics of the evolutionary landscape that provide the conceptual foundation for predicting the dynamics of drug resistance evolution. article_number: e1002299 author: - first_name: Guillaume full_name: Chevereau, Guillaume id: 424D78A0-F248-11E8-B48F-1D18A9856A87 last_name: Chevereau - first_name: Marta full_name: Dravecka, Marta id: 4342E402-F248-11E8-B48F-1D18A9856A87 last_name: Dravecka orcid: 0000-0002-2519-8004 - first_name: Tugce full_name: Batur, Tugce last_name: Batur - first_name: Aysegul full_name: Guvenek, Aysegul last_name: Guvenek - first_name: Dilay full_name: Ayhan, Dilay last_name: Ayhan - first_name: Erdal full_name: Toprak, Erdal last_name: Toprak - first_name: Mark Tobias full_name: Bollenbach, Mark Tobias id: 3E6DB97A-F248-11E8-B48F-1D18A9856A87 last_name: Bollenbach orcid: 0000-0003-4398-476X citation: ama: Chevereau G, Lukacisinova M, Batur T, et al. Quantifying the determinants of evolutionary dynamics leading to drug resistance. PLoS Biology. 2015;13(11). doi:10.1371/journal.pbio.1002299 apa: Chevereau, G., Lukacisinova, M., Batur, T., Guvenek, A., Ayhan, D., Toprak, E., & Bollenbach, M. T. (2015). Quantifying the determinants of evolutionary dynamics leading to drug resistance. PLoS Biology. Public Library of Science. https://doi.org/10.1371/journal.pbio.1002299 chicago: Chevereau, Guillaume, Marta Lukacisinova, Tugce Batur, Aysegul Guvenek, Dilay Ayhan, Erdal Toprak, and Mark Tobias Bollenbach. “Quantifying the Determinants of Evolutionary Dynamics Leading to Drug Resistance.” PLoS Biology. Public Library of Science, 2015. https://doi.org/10.1371/journal.pbio.1002299. ieee: G. Chevereau et al., “Quantifying the determinants of evolutionary dynamics leading to drug resistance,” PLoS Biology, vol. 13, no. 11. Public Library of Science, 2015. ista: Chevereau G, Lukacisinova M, Batur T, Guvenek A, Ayhan D, Toprak E, Bollenbach MT. 2015. Quantifying the determinants of evolutionary dynamics leading to drug resistance. PLoS Biology. 13(11), e1002299. mla: Chevereau, Guillaume, et al. “Quantifying the Determinants of Evolutionary Dynamics Leading to Drug Resistance.” PLoS Biology, vol. 13, no. 11, e1002299, Public Library of Science, 2015, doi:10.1371/journal.pbio.1002299. short: G. Chevereau, M. Lukacisinova, T. Batur, A. Guvenek, D. Ayhan, E. Toprak, M.T. Bollenbach, PLoS Biology 13 (2015). date_created: 2018-12-11T11:53:04Z date_published: 2015-11-18T00:00:00Z date_updated: 2024-03-27T23:30:28Z day: '18' ddc: - '570' department: - _id: ToBo doi: 10.1371/journal.pbio.1002299 ec_funded: 1 file: - access_level: open_access checksum: 0e82e3279f50b15c6c170c042627802b content_type: application/pdf creator: system date_created: 2018-12-12T10:09:00Z date_updated: 2020-07-14T12:45:07Z file_id: '4723' file_name: IST-2016-468-v1+1_journal.pbio.1002299.pdf file_size: 1387760 relation: main_file file_date_updated: 2020-07-14T12:45:07Z has_accepted_license: '1' intvolume: ' 13' issue: '11' language: - iso: eng month: '11' oa: 1 oa_version: Published Version project: - _id: 25EB3A80-B435-11E9-9278-68D0E5697425 grant_number: RGP0042/2013 name: Revealing the fundamental limits of cell growth - _id: 25E9AF9E-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: P27201-B22 name: Revealing the mechanisms underlying drug interactions - _id: 25E83C2C-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '303507' name: Optimality principles in responses to antibiotics publication: PLoS Biology publication_status: published publisher: Public Library of Science publist_id: '5547' pubrep_id: '468' quality_controlled: '1' related_material: record: - id: '9711' relation: research_data status: public - id: '9765' relation: research_data status: public - id: '6263' relation: dissertation_contains status: public scopus_import: 1 status: public title: Quantifying the determinants of evolutionary dynamics leading to drug resistance tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 13 year: '2015' ...