Reverse Chemical Genetics: Comprehensive Fitness Profiling Reveals the Spectrum of Drug Target Interactions

The emergence and prevalence of drug resistance demands streamlined strategies to identify drug resistant variants in a fast, systematic and cost-effective way. Methods commonly used to understand and predict drug resistance rely on limited clinical studies from patients who are refractory to drugs...

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Published inPLoS genetics Vol. 12; no. 9; p. e1006275
Main Authors Wong, Lai H, Sinha, Sunita, Bergeron, Julien R, Mellor, Joseph C, Giaever, Guri, Flaherty, Patrick, Nislow, Corey
Format Journal Article
LanguageEnglish
Published United States Public Library of Science 02.09.2016
Public Library of Science (PLoS)
Subjects
Alleles
Bayes Theorem
Biology and Life Sciences
Cancer
Cancer therapies
Colleges & universities
Dihydrofolate reductase
Drug interactions
Drug resistance
Drug Resistance, Neoplasm - genetics
Drug therapy
Folic Acid Antagonists - therapeutic use
Funding
Genetics
Humans
Library collections
Medicine and Health Sciences
Methotrexate - therapeutic use
Mutation
Neoplasms - drug therapy
Neoplasms - genetics
Observations
Pharmaceutical sciences
Pharmaceuticals
Research and Analysis Methods
Tetrahydrofolate Dehydrogenase - genetics
Tetrahydrofolate Dehydrogenase - metabolism
Massachusetts
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Abstract The emergence and prevalence of drug resistance demands streamlined strategies to identify drug resistant variants in a fast, systematic and cost-effective way. Methods commonly used to understand and predict drug resistance rely on limited clinical studies from patients who are refractory to drugs or on laborious evolution experiments with poor coverage of the gene variants. Here, we report an integrative functional variomics methodology combining deep sequencing and a Bayesian statistical model to provide a comprehensive list of drug resistance alleles from complex variant populations. Dihydrofolate reductase, the target of methotrexate chemotherapy drug, was used as a model to identify functional mutant alleles correlated with methotrexate resistance. This systematic approach identified previously reported resistance mutations, as well as novel point mutations that were validated in vivo. Use of this systematic strategy as a routine diagnostics tool widens the scope of successful drug research and development.
AbstractList The emergence and prevalence of drug resistance demands streamlined strategies to identify drug resistant variants in a fast, systematic and cost-effective way. Methods commonly used to understand and predict drug resistance rely on limited clinical studies from patients who are refractory to drugs or on laborious evolution experiments with poor coverage of the gene variants. Here, we report an integrative functional variomics methodology combining deep sequencing and a Bayesian statistical model to provide a comprehensive list of drug resistance alleles from complex variant populations. Dihydrofolate reductase, the target of methotrexate chemotherapy drug, was used as a model to identify functional mutant alleles correlated with methotrexate resistance. This systematic approach identified previously reported resistance mutations, as well as novel point mutations that were validated in vivo. Use of this systematic strategy as a routine diagnostics tool widens the scope of successful drug research and development.
  The emergence and prevalence of drug resistance demands streamlined strategies to identify drug resistant variants in a fast, systematic and cost-effective way. Methods commonly used to understand and predict drug resistance rely on limited clinical studies from patients who are refractory to drugs or on laborious evolution experiments with poor coverage of the gene variants. Here, we report an integrative functional variomics methodology combining deep sequencing and a Bayesian statistical model to provide a comprehensive list of drug resistance alleles from complex variant populations. Dihydrofolate reductase, the target of methotrexate chemotherapy drug, was used as a model to identify functional mutant alleles correlated with methotrexate resistance. This systematic approach identified previously reported resistance mutations, as well as novel point mutations that were validated in vivo. Use of this systematic strategy as a routine diagnostics tool widens the scope of successful drug research and development.
The emergence and prevalence of drug resistance demands streamlined strategies to identify drug resistant variants in a fast, systematic and cost-effective way. Methods commonly used to understand and predict drug resistance rely on limited clinical studies from patients who are refractory to drugs or on laborious evolution experiments with poor coverage of the gene variants. Here, we report an integrative functional variomics methodology combining deep sequencing and a Bayesian statistical model to provide a comprehensive list of drug resistance alleles from complex variant populations. Dihydrofolate reductase, the target of methotrexate chemotherapy drug, was used as a model to identify functional mutant alleles correlated with methotrexate resistance. This systematic approach identified previously reported resistance mutations, as well as novel point mutations that were validated in vivo . Use of this systematic strategy as a routine diagnostics tool widens the scope of successful drug research and development. One of the most profound outcomes of fast, reliable genome sequencing is the ability to tailor drug therapy to an individual’s genotype. This ‘personalized’ or ‘precision medicine’ is the realization of a decades-long effort to maximize drug effect and limit unwanted side effects. An undesirable consequence of such targeted therapies, however, is the emergence of drug resistance. This outcome is the result of an evolutionary process where mutations in the drug target render the drug perturbation allow such mutant cells to proliferate. Because of the unbiased, and stochastic nature of the emergence of drug resistance, it is impossible to predict. We developed a test where hundreds of thousands of mutant cells are exposed to a drug simultaneously and those cells that modulate resistance survive. This method is innovative because it partners a high-throughput experimental protocol with a tailored statistical model to identify all mutations that modulate resistance. Finally, we used synthetic biology to re-create these mutations and demonstrate that they were, in fact, bona fide drug-resistant variants. These mutations were further extended and confirmed to also be resistant in the human orthologue. This combined biological-computational approach allows one to identify drug’s degree of resistance to both guide treatments and future drug discovery.
Audience Academic
Author Nislow, Corey
Giaever, Guri
Flaherty, Patrick
Bergeron, Julien R
Wong, Lai H
Sinha, Sunita
Mellor, Joseph C
AuthorAffiliation 2 Department of Biochemistry, University of Washington, Seattle, Washington, United States of America
1 Department of Pharmaceutical Sciences, University of British Columbia, Vancouver, Canada
3 seqWell, Inc., Beverly, Massachusetts, United States of America
Stanford University School of Medicine, UNITED STATES
4 Department of Mathematics and Statistics, University of Massachusetts, Amherst, Massachusetts, United States of America
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  orcidid: 0000-0001-8653-2734
  surname: Wong
  fullname: Wong, Lai H
  organization: Department of Pharmaceutical Sciences, University of British Columbia, Vancouver, Canada
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  givenname: Sunita
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/27588687$$D View this record in MEDLINE/PubMed
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Copyright COPYRIGHT 2016 Public Library of Science
2016 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: Wong LH, Sinha S, Bergeron JR, Mellor JC, Giaever G, Flaherty P, et al. (2016) Reverse Chemical Genetics: Comprehensive Fitness Profiling Reveals the Spectrum of Drug Target Interactions. PLoS Genet 12(9): e1006275. doi:10.1371/journal.pgen.1006275
2016 Wong et al 2016 Wong et al
2016 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: Wong LH, Sinha S, Bergeron JR, Mellor JC, Giaever G, Flaherty P, et al. (2016) Reverse Chemical Genetics: Comprehensive Fitness Profiling Reveals the Spectrum of Drug Target Interactions. PLoS Genet 12(9): e1006275. doi:10.1371/journal.pgen.1006275
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– notice: 2016 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: Wong LH, Sinha S, Bergeron JR, Mellor JC, Giaever G, Flaherty P, et al. (2016) Reverse Chemical Genetics: Comprehensive Fitness Profiling Reveals the Spectrum of Drug Target Interactions. PLoS Genet 12(9): e1006275. doi:10.1371/journal.pgen.1006275
– notice: 2016 Wong et al 2016 Wong et al
– notice: 2016 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: Wong LH, Sinha S, Bergeron JR, Mellor JC, Giaever G, Flaherty P, et al. (2016) Reverse Chemical Genetics: Comprehensive Fitness Profiling Reveals the Spectrum of Drug Target Interactions. PLoS Genet 12(9): e1006275. doi:10.1371/journal.pgen.1006275
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I have read the journal's policy and the authors of this manuscript have the following competing interests: JCM, who is CEO and founder of seqWell, Inc, participated in the sample preparation and next generation sequencing for the DFR1 amplicon libraries analyzed in this manuscript. JCM had no influence in the design or analysis of the experiments and data.
Conceptualization: LHW PF GG CN. Data curation: PF. Formal analysis: LHW PF JRB. Funding acquisition: CN GG PF. Investigation: LHW SS JCM PF. Methodology: LHW JRB PF CN. Project administration: SS CN. Resources: CN GG JCM PF. Supervision: CN. Validation: LHW. Visualization: LHW JRB PF. Writing – original draft: LHW. Writing – review & editing: SS JRB GG CN PF.
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SSID ssj0035897
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Snippet The emergence and prevalence of drug resistance demands streamlined strategies to identify drug resistant variants in a fast, systematic and cost-effective...
  The emergence and prevalence of drug resistance demands streamlined strategies to identify drug resistant variants in a fast, systematic and cost-effective...
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SubjectTerms Alleles
Bayes Theorem
Biology and Life Sciences
Cancer
Cancer therapies
Colleges & universities
Dihydrofolate reductase
Drug interactions
Drug resistance
Drug Resistance, Neoplasm - genetics
Drug therapy
Folic Acid Antagonists - therapeutic use
Funding
Genetics
Humans
Library collections
Medicine and Health Sciences
Methotrexate - therapeutic use
Mutation
Neoplasms - drug therapy
Neoplasms - genetics
Observations
Pharmaceutical sciences
Pharmaceuticals
Research and Analysis Methods
Tetrahydrofolate Dehydrogenase - genetics
Tetrahydrofolate Dehydrogenase - metabolism
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Title Reverse Chemical Genetics: Comprehensive Fitness Profiling Reveals the Spectrum of Drug Target Interactions
URI https://www.ncbi.nlm.nih.gov/pubmed/27588687
https://www.proquest.com/docview/1829446453
https://search.proquest.com/docview/1817558215
https://search.proquest.com/docview/1837330099
https://pubmed.ncbi.nlm.nih.gov/PMC5010250
https://doaj.org/article/94cea4068c87454f833a2244a4a5b03c
http://dx.doi.org/10.1371/journal.pgen.1006275
Volume 12
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