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Quantifying the Determinants of Evolutionary Dynamics Leading to Drug Resistance
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 evolut...
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| Published in: | PLoS biology 2015-11, Vol.13 (11), p.e1002299-e1002299 |
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| creator | Chevereau, Guillaume Dravecká, Marta Batur, Tugce Guvenek, Aysegul Ayhan, Dilay Hazal Toprak, Erdal Bollenbach, Tobias |
| description | 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. |
| doi_str_mv | 10.1371/journal.pbio.1002299 |
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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.</description><identifier>ISSN: 1545-7885</identifier><identifier>ISSN: 1544-9173</identifier><identifier>EISSN: 1545-7885</identifier><identifier>DOI: 10.1371/journal.pbio.1002299</identifier><identifier>PMID: 26581035</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Algorithms ; Anti-Bacterial Agents - pharmacology ; Antibiotics ; Bacteria ; Drug dosages ; Drug resistance ; Drug Resistance, Bacterial ; Drug Resistance, Multiple, Bacterial ; E coli ; Escherichia coli - drug effects ; Escherichia coli - genetics ; Escherichia coli - growth & development ; Escherichia coli - metabolism ; Escherichia coli K12 - drug effects ; Escherichia coli K12 - genetics ; Escherichia coli K12 - metabolism ; Escherichia coli Proteins - genetics ; Escherichia coli Proteins - metabolism ; Evolution, Molecular ; Experiments ; Gene Deletion ; Genetic Fitness - drug effects ; Genomes ; Growth rate ; Microbial Sensitivity Tests ; Models, Genetic ; Mutagens - pharmacology ; Mutation ; Mutation - drug effects ; Mutation Rate ; Nitrofurantoin - pharmacology ; Population genetics ; Reproducibility of Results</subject><ispartof>PLoS biology, 2015-11, Vol.13 (11), p.e1002299-e1002299</ispartof><rights>2015 Chevereau et al 2015 Chevereau et al</rights><rights>2015 Public Library of Science. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited: Chevereau G, Dravecká M, Batur T, Guvenek A, Ayhan DH, Toprak E, et al. (2015) Quantifying the Determinants of Evolutionary Dynamics Leading to Drug Resistance. 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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.</description><subject>Algorithms</subject><subject>Anti-Bacterial Agents - pharmacology</subject><subject>Antibiotics</subject><subject>Bacteria</subject><subject>Drug dosages</subject><subject>Drug resistance</subject><subject>Drug Resistance, Bacterial</subject><subject>Drug Resistance, Multiple, Bacterial</subject><subject>E coli</subject><subject>Escherichia coli - drug effects</subject><subject>Escherichia coli - genetics</subject><subject>Escherichia coli - growth & development</subject><subject>Escherichia coli - metabolism</subject><subject>Escherichia coli K12 - drug effects</subject><subject>Escherichia coli K12 - genetics</subject><subject>Escherichia coli K12 - metabolism</subject><subject>Escherichia coli Proteins - genetics</subject><subject>Escherichia coli Proteins - metabolism</subject><subject>Evolution, Molecular</subject><subject>Experiments</subject><subject>Gene Deletion</subject><subject>Genetic Fitness - drug effects</subject><subject>Genomes</subject><subject>Growth rate</subject><subject>Microbial Sensitivity Tests</subject><subject>Models, Genetic</subject><subject>Mutagens - pharmacology</subject><subject>Mutation</subject><subject>Mutation - drug effects</subject><subject>Mutation Rate</subject><subject>Nitrofurantoin - pharmacology</subject><subject>Population genetics</subject><subject>Reproducibility of Results</subject><issn>1545-7885</issn><issn>1544-9173</issn><issn>1545-7885</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><sourceid>DOA</sourceid><recordid>eNpVUctu2zAQJIoWTZr2D4pWx1zscMWHxEuBIk6bAAb6QHsmViTl0JBIl5QC-O8r20qQnEjszs7O7BDyEegSWAVX2zimgN1y1_i4BErLUqlX5BwEF4uqrsXrZ_8z8i7n7RFT1m_JWSlFDZSJc_Lz14hh8O3eh00x3Lti5QaXeh-mai5iW9w8xG4cfAyY9sVqH7D3Jhdrh_Y4EYtVGjfFb5d9HjAY9568abHL7sP8XpC_327-XN8u1j--311_XS9Mxdiw4EIahSBbZFaYRnFGAai12CqnkE3NxjaVUVDVlkugCKpxsikNZ5NyqtgF-Xzi3XUx6_kYWUPFlRSqBjYh7k4IG3Grd8n3kwUd0etjIaaNxjR40zkNRpWCUxSqAs5lrRS3CpvatK0TQpUT15d529j0zhoXhoTdC9KXneDv9SY-aC4FMMkngsuZIMV_o8uD7n02ruswuDgedDOhKIejM36CmhRzTq59WgNUH6J_dKsP0es5-mns03OJT0OPWbP_FsmtfQ</recordid><startdate>20151101</startdate><enddate>20151101</enddate><creator>Chevereau, Guillaume</creator><creator>Dravecká, Marta</creator><creator>Batur, Tugce</creator><creator>Guvenek, Aysegul</creator><creator>Ayhan, Dilay Hazal</creator><creator>Toprak, Erdal</creator><creator>Bollenbach, Tobias</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><scope>CZG</scope></search><sort><creationdate>20151101</creationdate><title>Quantifying the Determinants of Evolutionary Dynamics Leading to Drug Resistance</title><author>Chevereau, Guillaume ; 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Performed the experiments: GC MD TBa AG DHA TBo. Analyzed the data: GC MD TBo. Wrote the paper: GC MD ET TBo.</notes><notes>Current Address: University of Massachusetts Amherst, Molecular and Cellular Biology Graduate Program, Amherst, MA</notes><notes>The authors have declared that no competing interests exist.</notes><notes>Current Address: University of Texas Southwestern Medical Center, Green Center for Systems Biology, Dallas, TX</notes><notes>Current Address: INSA de Strasbourg, 67084 Strasbourg cedex, France</notes><abstract>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.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>26581035</pmid><doi>10.1371/journal.pbio.1002299</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Algorithms Anti-Bacterial Agents - pharmacology Antibiotics Bacteria Drug dosages Drug resistance Drug Resistance, Bacterial Drug Resistance, Multiple, Bacterial E coli Escherichia coli - drug effects Escherichia coli - genetics Escherichia coli - growth & development Escherichia coli - metabolism Escherichia coli K12 - drug effects Escherichia coli K12 - genetics Escherichia coli K12 - metabolism Escherichia coli Proteins - genetics Escherichia coli Proteins - metabolism Evolution, Molecular Experiments Gene Deletion Genetic Fitness - drug effects Genomes Growth rate Microbial Sensitivity Tests Models, Genetic Mutagens - pharmacology Mutation Mutation - drug effects Mutation Rate Nitrofurantoin - pharmacology Population genetics Reproducibility of Results |
| title | Quantifying the Determinants of Evolutionary Dynamics Leading to Drug Resistance |
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