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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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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. |
format | Online Article Text |
id | pubmed-6657027 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
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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