[{"issue":"4","title":"Systematic discovery of drug interaction mechanisms","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publisher":"Nature Publishing Group","oa_version":"Published Version","quality_controlled":"1","ddc":["570"],"oa":1,"date_created":"2018-12-11T11:54:12Z","ec_funded":1,"license":"https://creativecommons.org/licenses/by/4.0/","scopus_import":1,"has_accepted_license":"1","abstract":[{"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.","lang":"eng"}],"file":[{"file_name":"IST-2015-395-v1+1_807.full.pdf","date_created":"2018-12-12T10:14:34Z","content_type":"application/pdf","file_id":"5087","relation":"main_file","access_level":"open_access","file_size":1273573,"checksum":"4289b518fbe2166682fb1a1ef9b405f3","creator":"system","date_updated":"2020-07-14T12:45:17Z"}],"project":[{"grant_number":"P27201-B22","_id":"25E9AF9E-B435-11E9-9278-68D0E5697425","call_identifier":"FWF","name":"Revealing the mechanisms underlying drug interactions"},{"grant_number":"RGP0042/2013","_id":"25EB3A80-B435-11E9-9278-68D0E5697425","name":"Revealing the fundamental limits of cell growth"},{"grant_number":"303507","_id":"25E83C2C-B435-11E9-9278-68D0E5697425","call_identifier":"FP7","name":"Optimality principles in responses to antibiotics"}],"tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"citation":{"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.","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.","ama":"Chevereau G, Bollenbach MT. Systematic discovery of drug interaction mechanisms. Molecular Systems Biology. 2015;11(4). doi:10.15252/msb.20156098","ista":"Chevereau G, Bollenbach MT. 2015. Systematic discovery of drug interaction mechanisms. Molecular Systems Biology. 11(4), 807.","short":"G. Chevereau, M.T. Bollenbach, Molecular Systems Biology 11 (2015).","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","ieee":"G. Chevereau and M. T. Bollenbach, “Systematic discovery of drug interaction mechanisms,” Molecular Systems Biology, vol. 11, no. 4. Nature Publishing Group, 2015."},"volume":11,"type":"journal_article","status":"public","pubrep_id":"395","publication_status":"published","publication":"Molecular Systems Biology","doi":"10.15252/msb.20156098","intvolume":" 11","language":[{"iso":"eng"}],"publist_id":"5283","file_date_updated":"2020-07-14T12:45:17Z","author":[{"id":"424D78A0-F248-11E8-B48F-1D18A9856A87","first_name":"Guillaume","last_name":"Chevereau","full_name":"Chevereau, Guillaume"},{"full_name":"Bollenbach, Mark Tobias","orcid":"0000-0003-4398-476X","id":"3E6DB97A-F248-11E8-B48F-1D18A9856A87","first_name":"Mark Tobias","last_name":"Bollenbach"}],"year":"2015","date_updated":"2021-01-12T06:53:26Z","day":"01","department":[{"_id":"ToBo"}],"_id":"1823","date_published":"2015-04-01T00:00:00Z","month":"04","article_number":"807"},{"date_published":"2015-11-18T00:00:00Z","month":"11","article_processing_charge":"No","department":[{"_id":"ToBo"}],"_id":"9711","day":"18","related_material":{"record":[{"status":"public","id":"1619","relation":"used_in_publication"}]},"year":"2015","date_updated":"2023-02-23T10:07:02Z","date_created":"2021-07-23T11:53:50Z","author":[{"full_name":"Chevereau, Guillaume","id":"424D78A0-F248-11E8-B48F-1D18A9856A87","first_name":"Guillaume","last_name":"Chevereau"},{"orcid":"0000-0002-2519-8004","full_name":"Lukacisinova, Marta","last_name":"Lukacisinova","id":"4342E402-F248-11E8-B48F-1D18A9856A87","first_name":"Marta"},{"last_name":"Batur","first_name":"Tugce","full_name":"Batur, Tugce"},{"first_name":"Aysegul","last_name":"Guvenek","full_name":"Guvenek, Aysegul"},{"full_name":"Ayhan, Dilay Hazal","first_name":"Dilay Hazal","last_name":"Ayhan"},{"full_name":"Toprak, Erdal","first_name":"Erdal","last_name":"Toprak"},{"last_name":"Bollenbach","first_name":"Mark Tobias","id":"3E6DB97A-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0003-4398-476X","full_name":"Bollenbach, Mark Tobias"}],"doi":"10.1371/journal.pbio.1002299.s001","title":"Excel file containing the raw data for all figures","type":"research_data_reference","status":"public","publisher":"Public Library of Science","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","oa_version":"Published Version","citation":{"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.","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.","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","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.","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","short":"G. Chevereau, M. Lukacisinova, T. Batur, A. Guvenek, D.H. Ayhan, E. Toprak, M.T. Bollenbach, (2015).","ieee":"G. Chevereau et al., “Excel file containing the raw data for all figures.” Public Library of Science, 2015."}},{"month":"11","date_published":"2015-11-18T00:00:00Z","_id":"9765","department":[{"_id":"ToBo"}],"article_processing_charge":"No","day":"18","related_material":{"record":[{"status":"public","id":"1619","relation":"used_in_publication"}]},"year":"2015","date_updated":"2023-02-23T10:07:02Z","date_created":"2021-08-03T07:05:16Z","author":[{"full_name":"Chevereau, Guillaume","id":"424D78A0-F248-11E8-B48F-1D18A9856A87","first_name":"Guillaume","last_name":"Chevereau"},{"first_name":"Marta","id":"4342E402-F248-11E8-B48F-1D18A9856A87","last_name":"Lukacisinova","full_name":"Lukacisinova, Marta","orcid":"0000-0002-2519-8004"},{"full_name":"Batur, Tugce","last_name":"Batur","first_name":"Tugce"},{"last_name":"Guvenek","first_name":"Aysegul","full_name":"Guvenek, Aysegul"},{"first_name":"Dilay Hazal","last_name":"Ayhan","full_name":"Ayhan, Dilay Hazal"},{"full_name":"Toprak, Erdal","last_name":"Toprak","first_name":"Erdal"},{"full_name":"Bollenbach, Mark Tobias","orcid":"0000-0003-4398-476X","first_name":"Mark Tobias","id":"3E6DB97A-F248-11E8-B48F-1D18A9856A87","last_name":"Bollenbach"}],"doi":"10.1371/journal.pbio.1002299.s008","status":"public","publisher":"Public Library of Science","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","title":"Gene ontology enrichment analysis for the most sensitive gene deletion strains for all drugs","type":"research_data_reference","oa_version":"Published Version","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","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.","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.","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.","ieee":"G. Chevereau et al., “Gene ontology enrichment analysis for the most sensitive gene deletion strains for all drugs.” Public Library of Science, 2015.","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","short":"G. Chevereau, M. Lukacisinova, T. Batur, A. Guvenek, D.H. Ayhan, E. Toprak, M.T. Bollenbach, (2015)."}},{"date_updated":"2024-03-18T23:30:29Z","year":"2015","related_material":{"record":[{"status":"public","relation":"research_data","id":"9711"},{"id":"9765","relation":"research_data","status":"public"},{"relation":"dissertation_contains","id":"6263","status":"public"}]},"day":"18","department":[{"_id":"ToBo"}],"_id":"1619","article_number":"e1002299","date_published":"2015-11-18T00:00:00Z","month":"11","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"citation":{"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","short":"G. Chevereau, M. Lukacisinova, T. Batur, A. Guvenek, D. Ayhan, E. Toprak, M.T. Bollenbach, PLoS Biology 13 (2015).","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.","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.","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.","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","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."},"pubrep_id":"468","publication_status":"published","volume":13,"type":"journal_article","status":"public","intvolume":" 13","language":[{"iso":"eng"}],"publication":"PLoS Biology","doi":"10.1371/journal.pbio.1002299","file_date_updated":"2020-07-14T12:45:07Z","author":[{"full_name":"Chevereau, Guillaume","last_name":"Chevereau","id":"424D78A0-F248-11E8-B48F-1D18A9856A87","first_name":"Guillaume"},{"full_name":"Dravecka, Marta","orcid":"0000-0002-2519-8004","first_name":"Marta","id":"4342E402-F248-11E8-B48F-1D18A9856A87","last_name":"Dravecka"},{"first_name":"Tugce","last_name":"Batur","full_name":"Batur, Tugce"},{"full_name":"Guvenek, Aysegul","last_name":"Guvenek","first_name":"Aysegul"},{"full_name":"Ayhan, Dilay","first_name":"Dilay","last_name":"Ayhan"},{"full_name":"Toprak, Erdal","first_name":"Erdal","last_name":"Toprak"},{"orcid":"0000-0003-4398-476X","full_name":"Bollenbach, Mark Tobias","last_name":"Bollenbach","first_name":"Mark Tobias","id":"3E6DB97A-F248-11E8-B48F-1D18A9856A87"}],"publist_id":"5547","scopus_import":1,"project":[{"name":"Revealing the fundamental limits of cell growth","grant_number":"RGP0042/2013","_id":"25EB3A80-B435-11E9-9278-68D0E5697425"},{"call_identifier":"FWF","name":"Revealing the mechanisms underlying drug interactions","grant_number":"P27201-B22","_id":"25E9AF9E-B435-11E9-9278-68D0E5697425"},{"name":"Optimality principles in responses to antibiotics","call_identifier":"FP7","grant_number":"303507","_id":"25E83C2C-B435-11E9-9278-68D0E5697425"}],"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."}],"has_accepted_license":"1","file":[{"file_size":1387760,"access_level":"open_access","relation":"main_file","date_updated":"2020-07-14T12:45:07Z","checksum":"0e82e3279f50b15c6c170c042627802b","creator":"system","content_type":"application/pdf","file_id":"4723","file_name":"IST-2016-468-v1+1_journal.pbio.1002299.pdf","date_created":"2018-12-12T10:09:00Z"}],"issue":"11","oa_version":"Published Version","quality_controlled":"1","title":"Quantifying the determinants of evolutionary dynamics leading to drug resistance","publisher":"Public Library of Science","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","ddc":["570"],"oa":1,"date_created":"2018-12-11T11:53:04Z","ec_funded":1}]