--- _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: 2024-03-25T23:30:14Z 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' ... --- _id: '1823' abstract: - lang: eng text: Abstract Drug combinations are increasingly important in disease treatments, for combating drug resistance, and for elucidating fundamental relationships in cell physiology. When drugs are combined, their individual effects on cells may be amplified or weakened. Such drug interactions are crucial for treatment efficacy, but their underlying mechanisms remain largely unknown. To uncover the causes of drug interactions, we developed a systematic approach based on precise quantification of the individual and joint effects of antibiotics on growth of genome-wide Escherichia coli gene deletion strains. We found that drug interactions between antibiotics representing the main modes of action are highly robust to genetic perturbation. This robustness is encapsulated in a general principle of bacterial growth, which enables the quantitative prediction of mutant growth rates under drug combinations. Rare violations of this principle exposed recurring cellular functions controlling drug interactions. In particular, we found that polysaccharide and ATP synthesis control multiple drug interactions with previously unexplained mechanisms, and small molecule adjuvants targeting these functions synthetically reshape drug interactions in predictable ways. These results provide a new conceptual framework for the design of multidrug combinations and suggest that there are universal mechanisms at the heart of most drug interactions. Synopsis A general principle of bacterial growth enables the prediction of mutant growth rates under drug combinations. Rare violations of this principle expose cellular functions that control drug interactions and can be targeted by small molecules to alter drug interactions in predictable ways. Drug interactions between antibiotics are highly robust to genetic perturbations. A general principle of bacterial growth enables the prediction of mutant growth rates under drug combinations. Rare violations of this principle expose cellular functions that control drug interactions. Diverse drug interactions are controlled by recurring cellular functions, including LPS synthesis and ATP synthesis. A general principle of bacterial growth enables the prediction of mutant growth rates under drug combinations. Rare violations of this principle expose cellular functions that control drug interactions and can be targeted by small molecules to alter drug interactions in predictable ways. article_number: '807' author: - first_name: Guillaume full_name: Chevereau, Guillaume id: 424D78A0-F248-11E8-B48F-1D18A9856A87 last_name: Chevereau - 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, Bollenbach MT. Systematic discovery of drug interaction mechanisms. Molecular Systems Biology. 2015;11(4). doi:10.15252/msb.20156098 apa: Chevereau, G., & Bollenbach, M. T. (2015). Systematic discovery of drug interaction mechanisms. Molecular Systems Biology. Nature Publishing Group. https://doi.org/10.15252/msb.20156098 chicago: Chevereau, Guillaume, and Mark Tobias Bollenbach. “Systematic Discovery of Drug Interaction Mechanisms.” Molecular Systems Biology. Nature Publishing Group, 2015. https://doi.org/10.15252/msb.20156098. ieee: G. Chevereau and M. T. Bollenbach, “Systematic discovery of drug interaction mechanisms,” Molecular Systems Biology, vol. 11, no. 4. Nature Publishing Group, 2015. ista: Chevereau G, Bollenbach MT. 2015. Systematic discovery of drug interaction mechanisms. Molecular Systems Biology. 11(4), 807. mla: Chevereau, Guillaume, and Mark Tobias Bollenbach. “Systematic Discovery of Drug Interaction Mechanisms.” Molecular Systems Biology, vol. 11, no. 4, 807, Nature Publishing Group, 2015, doi:10.15252/msb.20156098. short: G. Chevereau, M.T. Bollenbach, Molecular Systems Biology 11 (2015). date_created: 2018-12-11T11:54:12Z date_published: 2015-04-01T00:00:00Z date_updated: 2021-01-12T06:53:26Z day: '01' ddc: - '570' department: - _id: ToBo doi: 10.15252/msb.20156098 ec_funded: 1 file: - access_level: open_access checksum: 4289b518fbe2166682fb1a1ef9b405f3 content_type: application/pdf creator: system date_created: 2018-12-12T10:14:34Z date_updated: 2020-07-14T12:45:17Z file_id: '5087' file_name: IST-2015-395-v1+1_807.full.pdf file_size: 1273573 relation: main_file file_date_updated: 2020-07-14T12:45:17Z has_accepted_license: '1' intvolume: ' 11' issue: '4' language: - iso: eng month: '04' oa: 1 oa_version: Published Version project: - _id: 25E9AF9E-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: P27201-B22 name: Revealing the mechanisms underlying drug interactions - _id: 25EB3A80-B435-11E9-9278-68D0E5697425 grant_number: RGP0042/2013 name: Revealing the fundamental limits of cell growth - _id: 25E83C2C-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '303507' name: Optimality principles in responses to antibiotics publication: Molecular Systems Biology publication_status: published publisher: Nature Publishing Group publist_id: '5283' pubrep_id: '395' quality_controlled: '1' scopus_import: 1 status: public title: Systematic discovery of drug interaction mechanisms 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: 11 year: '2015' ... --- _id: '9711' article_processing_charge: No author: - first_name: Guillaume full_name: Chevereau, Guillaume id: 424D78A0-F248-11E8-B48F-1D18A9856A87 last_name: Chevereau - first_name: Marta full_name: Lukacisinova, Marta id: 4342E402-F248-11E8-B48F-1D18A9856A87 last_name: Lukacisinova 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 Hazal full_name: Ayhan, Dilay Hazal 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. Excel file containing the raw data for all figures. 2015. doi:10.1371/journal.pbio.1002299.s001 apa: Chevereau, G., Lukacisinova, M., Batur, T., Guvenek, A., Ayhan, D. H., Toprak, E., & Bollenbach, M. T. (2015). Excel file containing the raw data for all figures. Public Library of Science. https://doi.org/10.1371/journal.pbio.1002299.s001 chicago: Chevereau, Guillaume, Marta Lukacisinova, Tugce Batur, Aysegul Guvenek, Dilay Hazal Ayhan, Erdal Toprak, and Mark Tobias Bollenbach. “Excel File Containing the Raw Data for All Figures.” Public Library of Science, 2015. https://doi.org/10.1371/journal.pbio.1002299.s001. ieee: G. Chevereau et al., “Excel file containing the raw data for all figures.” Public Library of Science, 2015. ista: Chevereau G, Lukacisinova M, Batur T, Guvenek A, Ayhan DH, Toprak E, Bollenbach MT. 2015. Excel file containing the raw data for all figures, Public Library of Science, 10.1371/journal.pbio.1002299.s001. mla: Chevereau, Guillaume, et al. Excel File Containing the Raw Data for All Figures. Public Library of Science, 2015, doi:10.1371/journal.pbio.1002299.s001. short: G. Chevereau, M. Lukacisinova, T. Batur, A. Guvenek, D.H. Ayhan, E. Toprak, M.T. Bollenbach, (2015). date_created: 2021-07-23T11:53:50Z date_published: 2015-11-18T00:00:00Z date_updated: 2023-02-23T10:07:02Z day: '18' department: - _id: ToBo doi: 10.1371/journal.pbio.1002299.s001 month: '11' oa_version: Published Version publisher: Public Library of Science related_material: record: - id: '1619' relation: used_in_publication status: public status: public title: Excel file containing the raw data for all figures type: research_data_reference user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf year: '2015' ... --- _id: '9765' article_processing_charge: No author: - first_name: Guillaume full_name: Chevereau, Guillaume id: 424D78A0-F248-11E8-B48F-1D18A9856A87 last_name: Chevereau - first_name: Marta full_name: Lukacisinova, Marta id: 4342E402-F248-11E8-B48F-1D18A9856A87 last_name: Lukacisinova 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 Hazal full_name: Ayhan, Dilay Hazal 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. Gene ontology enrichment analysis for the most sensitive gene deletion strains for all drugs. 2015. doi:10.1371/journal.pbio.1002299.s008 apa: Chevereau, G., Lukacisinova, M., Batur, T., Guvenek, A., Ayhan, D. H., Toprak, E., & Bollenbach, M. T. (2015). Gene ontology enrichment analysis for the most sensitive gene deletion strains for all drugs. Public Library of Science. https://doi.org/10.1371/journal.pbio.1002299.s008 chicago: Chevereau, Guillaume, Marta Lukacisinova, Tugce Batur, Aysegul Guvenek, Dilay Hazal Ayhan, Erdal Toprak, and Mark Tobias Bollenbach. “Gene Ontology Enrichment Analysis for the Most Sensitive Gene Deletion Strains for All Drugs.” Public Library of Science, 2015. https://doi.org/10.1371/journal.pbio.1002299.s008. ieee: G. Chevereau et al., “Gene ontology enrichment analysis for the most sensitive gene deletion strains for all drugs.” Public Library of Science, 2015. ista: Chevereau G, Lukacisinova M, Batur T, Guvenek A, Ayhan DH, Toprak E, Bollenbach MT. 2015. Gene ontology enrichment analysis for the most sensitive gene deletion strains for all drugs, Public Library of Science, 10.1371/journal.pbio.1002299.s008. mla: Chevereau, Guillaume, et al. Gene Ontology Enrichment Analysis for the Most Sensitive Gene Deletion Strains for All Drugs. Public Library of Science, 2015, doi:10.1371/journal.pbio.1002299.s008. short: G. Chevereau, M. Lukacisinova, T. Batur, A. Guvenek, D.H. Ayhan, E. Toprak, M.T. Bollenbach, (2015). date_created: 2021-08-03T07:05:16Z date_published: 2015-11-18T00:00:00Z date_updated: 2023-02-23T10:07:02Z day: '18' department: - _id: ToBo doi: 10.1371/journal.pbio.1002299.s008 month: '11' oa_version: Published Version publisher: Public Library of Science related_material: record: - id: '1619' relation: used_in_publication status: public status: public title: Gene ontology enrichment analysis for the most sensitive gene deletion strains for all drugs type: research_data_reference user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf year: '2015' ...