Wong, L.H., Sinha, S., Bergeron, J.R. orcid.org/0000-0002-2841-9511 et al. (4 more authors) (2016) Reverse Chemical Genetics: Comprehensive Fitness Profiling Reveals the Spectrum of Drug Target Interactions. PLOS Genetics, 12 (9). e1006275. ISSN 1553-7390
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2016 Wong et al. 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. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Science (Sheffield) > School of Biosciences (Sheffield) > Department of Molecular Biology and Biotechnology (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 15 Feb 2018 12:48 |
Last Modified: | 15 Feb 2018 12:48 |
Published Version: | https://doi.org/10.1371/journal.pgen.1006275 |
Status: | Published |
Publisher: | Public Library of Science |
Refereed: | Yes |
Identification Number: | 10.1371/journal.pgen.1006275 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:127345 |