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Capturing site-specific heterogeneity with large-scale N-glycoproteome analysis
Protein glycosylation is a highly important, yet poorly understood protein post-translational modification. Thousands of possible glycan structures and compositions create potential for tremendous site heterogeneity. A lack of suitable analytical methods for large-scale analyses of intact glycopepti...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6428843/ https://www.ncbi.nlm.nih.gov/pubmed/30899004 http://dx.doi.org/10.1038/s41467-019-09222-w |
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author | Riley, Nicholas M. Hebert, Alexander S. Westphall, Michael S. Coon, Joshua J. |
author_facet | Riley, Nicholas M. Hebert, Alexander S. Westphall, Michael S. Coon, Joshua J. |
author_sort | Riley, Nicholas M. |
collection | PubMed |
description | Protein glycosylation is a highly important, yet poorly understood protein post-translational modification. Thousands of possible glycan structures and compositions create potential for tremendous site heterogeneity. A lack of suitable analytical methods for large-scale analyses of intact glycopeptides has limited our abilities both to address the degree of heterogeneity across the glycoproteome and to understand how this contributes biologically to complex systems. Here we show that N-glycoproteome site-specific microheterogeneity can be captured via large-scale glycopeptide profiling methods enabled by activated ion electron transfer dissociation (AI-ETD), ultimately characterizing 1,545 N-glycosites (>5,600 unique N-glycopeptides) from mouse brain tissue. Our data reveal that N-glycosylation profiles can differ between subcellular regions and structural domains and that N-glycosite heterogeneity manifests in several different forms, including dramatic differences in glycosites on the same protein. Moreover, we use this large-scale glycoproteomic dataset to develop several visualizations that will prove useful for analyzing intact glycopeptides in future studies. |
format | Online Article Text |
id | pubmed-6428843 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-64288432019-03-25 Capturing site-specific heterogeneity with large-scale N-glycoproteome analysis Riley, Nicholas M. Hebert, Alexander S. Westphall, Michael S. Coon, Joshua J. Nat Commun Article Protein glycosylation is a highly important, yet poorly understood protein post-translational modification. Thousands of possible glycan structures and compositions create potential for tremendous site heterogeneity. A lack of suitable analytical methods for large-scale analyses of intact glycopeptides has limited our abilities both to address the degree of heterogeneity across the glycoproteome and to understand how this contributes biologically to complex systems. Here we show that N-glycoproteome site-specific microheterogeneity can be captured via large-scale glycopeptide profiling methods enabled by activated ion electron transfer dissociation (AI-ETD), ultimately characterizing 1,545 N-glycosites (>5,600 unique N-glycopeptides) from mouse brain tissue. Our data reveal that N-glycosylation profiles can differ between subcellular regions and structural domains and that N-glycosite heterogeneity manifests in several different forms, including dramatic differences in glycosites on the same protein. Moreover, we use this large-scale glycoproteomic dataset to develop several visualizations that will prove useful for analyzing intact glycopeptides in future studies. Nature Publishing Group UK 2019-03-21 /pmc/articles/PMC6428843/ /pubmed/30899004 http://dx.doi.org/10.1038/s41467-019-09222-w Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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 images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Riley, Nicholas M. Hebert, Alexander S. Westphall, Michael S. Coon, Joshua J. Capturing site-specific heterogeneity with large-scale N-glycoproteome analysis |
title | Capturing site-specific heterogeneity with large-scale N-glycoproteome analysis |
title_full | Capturing site-specific heterogeneity with large-scale N-glycoproteome analysis |
title_fullStr | Capturing site-specific heterogeneity with large-scale N-glycoproteome analysis |
title_full_unstemmed | Capturing site-specific heterogeneity with large-scale N-glycoproteome analysis |
title_short | Capturing site-specific heterogeneity with large-scale N-glycoproteome analysis |
title_sort | capturing site-specific heterogeneity with large-scale n-glycoproteome analysis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6428843/ https://www.ncbi.nlm.nih.gov/pubmed/30899004 http://dx.doi.org/10.1038/s41467-019-09222-w |
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