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Systematic analysis of the intersection of disease mutations with protein modifications

BACKGROUND: Perturbed posttranslational modification (PTM) landscapes commonly cause pathological phenotypes. The Cancer Genome Atlas (TCGA) project profiles thousands of tumors allowing the identification of spontaneous cancer-driving mutations, while Uniprot and dbSNP manage genetic disease-associ...

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Autores principales: Simpson, Claire M., Zhang, Bin, Hornbeck, Peter V., Gnad, Florian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6657027/
https://www.ncbi.nlm.nih.gov/pubmed/31345222
http://dx.doi.org/10.1186/s12920-019-0543-2
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author Simpson, Claire M.
Zhang, Bin
Hornbeck, Peter V.
Gnad, Florian
author_facet Simpson, Claire M.
Zhang, Bin
Hornbeck, Peter V.
Gnad, Florian
author_sort Simpson, Claire M.
collection PubMed
description BACKGROUND: Perturbed posttranslational modification (PTM) landscapes commonly cause pathological phenotypes. The Cancer Genome Atlas (TCGA) project profiles thousands of tumors allowing the identification of spontaneous cancer-driving mutations, while Uniprot and dbSNP manage genetic disease-associated variants in the human population. PhosphoSitePlus (PSP) is the most comprehensive resource for studying experimentally observed PTM sites and the only repository with daily updates on functional annotations for many of these sites. To elucidate altered PTM landscapes on a large scale, we integrated disease-associated mutations from TCGA, Uniprot, and dbSNP with PTM sites from PhosphoSitePlus. We characterized each dataset individually, compared somatic with germline mutations, and analyzed PTM sites intersecting directly with disease variants. To assess the impact of mutations in the flanking regions of phosphosites, we developed DeltaScansite, a pipeline that compares Scansite predictions on wild type versus mutated sequences. Disease mutations are also visualized in PhosphoSitePlus. RESULTS: Characterization of somatic variants revealed oncoprotein-like mutation profiles of U2AF1, PGM5, and several other proteins, showing alteration patterns similar to germline mutations. The union of all datasets uncovered previously unknown losses and gains of PTM events in diseases unevenly distributed across different PTM types. Focusing on phosphorylation, our DeltaScansite workflow predicted perturbed signaling networks consistent with calculations by the machine learning method MIMP. CONCLUSIONS: We discovered oncoprotein-like profiles in TCGA and mutations that presumably modify protein function by impacting PTM sites directly or by rewiring upstream regulation. The resulting datasets are enriched with functional annotations from PhosphoSitePlus and present a unique resource for potential biomarkers or disease drivers. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12920-019-0543-2) contains supplementary material, which is available to authorized users.
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spelling pubmed-66570272019-07-31 Systematic analysis of the intersection of disease mutations with protein modifications Simpson, Claire M. Zhang, Bin Hornbeck, Peter V. Gnad, Florian BMC Med Genomics Research BACKGROUND: Perturbed posttranslational modification (PTM) landscapes commonly cause pathological phenotypes. The Cancer Genome Atlas (TCGA) project profiles thousands of tumors allowing the identification of spontaneous cancer-driving mutations, while Uniprot and dbSNP manage genetic disease-associated variants in the human population. PhosphoSitePlus (PSP) is the most comprehensive resource for studying experimentally observed PTM sites and the only repository with daily updates on functional annotations for many of these sites. To elucidate altered PTM landscapes on a large scale, we integrated disease-associated mutations from TCGA, Uniprot, and dbSNP with PTM sites from PhosphoSitePlus. We characterized each dataset individually, compared somatic with germline mutations, and analyzed PTM sites intersecting directly with disease variants. To assess the impact of mutations in the flanking regions of phosphosites, we developed DeltaScansite, a pipeline that compares Scansite predictions on wild type versus mutated sequences. Disease mutations are also visualized in PhosphoSitePlus. RESULTS: Characterization of somatic variants revealed oncoprotein-like mutation profiles of U2AF1, PGM5, and several other proteins, showing alteration patterns similar to germline mutations. The union of all datasets uncovered previously unknown losses and gains of PTM events in diseases unevenly distributed across different PTM types. Focusing on phosphorylation, our DeltaScansite workflow predicted perturbed signaling networks consistent with calculations by the machine learning method MIMP. CONCLUSIONS: We discovered oncoprotein-like profiles in TCGA and mutations that presumably modify protein function by impacting PTM sites directly or by rewiring upstream regulation. The resulting datasets are enriched with functional annotations from PhosphoSitePlus and present a unique resource for potential biomarkers or disease drivers. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12920-019-0543-2) contains supplementary material, which is available to authorized users. BioMed Central 2019-07-25 /pmc/articles/PMC6657027/ /pubmed/31345222 http://dx.doi.org/10.1186/s12920-019-0543-2 Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Simpson, Claire M.
Zhang, Bin
Hornbeck, Peter V.
Gnad, Florian
Systematic analysis of the intersection of disease mutations with protein modifications
title Systematic analysis of the intersection of disease mutations with protein modifications
title_full Systematic analysis of the intersection of disease mutations with protein modifications
title_fullStr Systematic analysis of the intersection of disease mutations with protein modifications
title_full_unstemmed Systematic analysis of the intersection of disease mutations with protein modifications
title_short Systematic analysis of the intersection of disease mutations with protein modifications
title_sort systematic analysis of the intersection of disease mutations with protein modifications
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6657027/
https://www.ncbi.nlm.nih.gov/pubmed/31345222
http://dx.doi.org/10.1186/s12920-019-0543-2
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