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Multiplex Assessment of Protein Variant Abundance by Massively Parallel Sequencing

Determining the pathogenicity of genetic variants is a critical challenge, and functional assessment is often the only option. Experimentally characterizing millions of possible missense variants in thousands of clinically important genes requires generalizable, scalable assays. We describe Variant...

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Autores principales: Matreyek, Kenneth A., Starita, Lea M., Stephany, Jason J., Martin, Beth, Chiasson, Melissa A., Gray, Vanessa E., Kircher, Martin, Khechaduri, Arineh, Dines, Jennifer N., Hause, Ronald J., Bhatia, Smita, Evans, William E., Relling, Mary V., Yang, Wenjian, Shendure, Jay, Fowler, Douglas M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5980760/
https://www.ncbi.nlm.nih.gov/pubmed/29785012
http://dx.doi.org/10.1038/s41588-018-0122-z
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author Matreyek, Kenneth A.
Starita, Lea M.
Stephany, Jason J.
Martin, Beth
Chiasson, Melissa A.
Gray, Vanessa E.
Kircher, Martin
Khechaduri, Arineh
Dines, Jennifer N.
Hause, Ronald J.
Bhatia, Smita
Evans, William E.
Relling, Mary V.
Yang, Wenjian
Shendure, Jay
Fowler, Douglas M.
author_facet Matreyek, Kenneth A.
Starita, Lea M.
Stephany, Jason J.
Martin, Beth
Chiasson, Melissa A.
Gray, Vanessa E.
Kircher, Martin
Khechaduri, Arineh
Dines, Jennifer N.
Hause, Ronald J.
Bhatia, Smita
Evans, William E.
Relling, Mary V.
Yang, Wenjian
Shendure, Jay
Fowler, Douglas M.
author_sort Matreyek, Kenneth A.
collection PubMed
description Determining the pathogenicity of genetic variants is a critical challenge, and functional assessment is often the only option. Experimentally characterizing millions of possible missense variants in thousands of clinically important genes requires generalizable, scalable assays. We describe Variant Abundance by Massively Parallel Sequencing (VAMP-seq), which measures the effects of thousands of missense variants of a protein on intracellular abundance simultaneously. We apply VAMP-seq to quantify the abundance of 7,801 single amino acid variants of PTEN and TPMT, proteins in which functional variants are clinically actionable. We identify 1,138 PTEN and 777 TPMT variants that result in low protein abundance, and may be pathogenic or alter drug metabolism, respectively. We observe selection for low-abundance PTEN variants in cancer, and reveal that p.Pro38Ser, which accounts for ~10% of PTEN missense variants in melanoma, functions via a dominant negative mechanism. Finally, we demonstrate that VAMP-seq is applicable to other genes, highlighting its generalizability.
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spelling pubmed-59807602018-11-21 Multiplex Assessment of Protein Variant Abundance by Massively Parallel Sequencing Matreyek, Kenneth A. Starita, Lea M. Stephany, Jason J. Martin, Beth Chiasson, Melissa A. Gray, Vanessa E. Kircher, Martin Khechaduri, Arineh Dines, Jennifer N. Hause, Ronald J. Bhatia, Smita Evans, William E. Relling, Mary V. Yang, Wenjian Shendure, Jay Fowler, Douglas M. Nat Genet Article Determining the pathogenicity of genetic variants is a critical challenge, and functional assessment is often the only option. Experimentally characterizing millions of possible missense variants in thousands of clinically important genes requires generalizable, scalable assays. We describe Variant Abundance by Massively Parallel Sequencing (VAMP-seq), which measures the effects of thousands of missense variants of a protein on intracellular abundance simultaneously. We apply VAMP-seq to quantify the abundance of 7,801 single amino acid variants of PTEN and TPMT, proteins in which functional variants are clinically actionable. We identify 1,138 PTEN and 777 TPMT variants that result in low protein abundance, and may be pathogenic or alter drug metabolism, respectively. We observe selection for low-abundance PTEN variants in cancer, and reveal that p.Pro38Ser, which accounts for ~10% of PTEN missense variants in melanoma, functions via a dominant negative mechanism. Finally, we demonstrate that VAMP-seq is applicable to other genes, highlighting its generalizability. 2018-05-21 2018-06 /pmc/articles/PMC5980760/ /pubmed/29785012 http://dx.doi.org/10.1038/s41588-018-0122-z Text en Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature.com/authors/editorial_policies/license.html#terms
spellingShingle Article
Matreyek, Kenneth A.
Starita, Lea M.
Stephany, Jason J.
Martin, Beth
Chiasson, Melissa A.
Gray, Vanessa E.
Kircher, Martin
Khechaduri, Arineh
Dines, Jennifer N.
Hause, Ronald J.
Bhatia, Smita
Evans, William E.
Relling, Mary V.
Yang, Wenjian
Shendure, Jay
Fowler, Douglas M.
Multiplex Assessment of Protein Variant Abundance by Massively Parallel Sequencing
title Multiplex Assessment of Protein Variant Abundance by Massively Parallel Sequencing
title_full Multiplex Assessment of Protein Variant Abundance by Massively Parallel Sequencing
title_fullStr Multiplex Assessment of Protein Variant Abundance by Massively Parallel Sequencing
title_full_unstemmed Multiplex Assessment of Protein Variant Abundance by Massively Parallel Sequencing
title_short Multiplex Assessment of Protein Variant Abundance by Massively Parallel Sequencing
title_sort multiplex assessment of protein variant abundance by massively parallel sequencing
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5980760/
https://www.ncbi.nlm.nih.gov/pubmed/29785012
http://dx.doi.org/10.1038/s41588-018-0122-z
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