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      Capturing site-specific heterogeneity with large-scale N-glycoproteome analysis

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          Abstract

          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.

          Abstract

          Mass spectrometry facilitates large-scale glycosylation profiling but in-depth analysis of intact glycopeptides is still challenging. Here, the authors show that activated ion electron transfer dissociation is suitable for glycopeptide fragmentation and improves glycoproteome coverage.

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          Byonic: advanced peptide and protein identification software.

          Byonic is the name of a software package for peptide and protein identification by tandem mass spectrometry. This software, which has only recently become commercially available, facilitates a much wider range of search possibilities than previous search software such as SEQUEST and Mascot. Byonic allows the user to define an essentially unlimited number of variable modification types. Byonic also allows the user to set a separate limit on the number of occurrences of each modification type, so that a search may consider only one or two chance modifications such as oxidations and deamidations per peptide, yet allow three or four biological modifications such as phosphorylations, which tend to cluster together. Hence, Byonic can search for tens or even hundreds of modification types simultaneously without a prohibitively large combinatorial explosion. Byonic's Wildcard Search allows the user to search for unanticipated or even unknown modifications alongside known modifications. Finally, Byonic's Glycopeptide Search allows the user to identify glycopeptides without prior knowledge of glycan masses or glycosylation sites.
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            The Structure and Function of the Na,K-ATPase Isoforms in Health and Disease

            The sodium and potassium gradients across the plasma membrane are used by animal cells for numerous processes, and the range of demands requires that the responsible ion pump, the Na,K-ATPase, can be fine-tuned to the different cellular needs. Therefore, several isoforms are expressed of each of the three subunits that make a Na,K-ATPase, the alpha, beta and FXYD subunits. This review summarizes the various roles and expression patterns of the Na,K-ATPase subunit isoforms and maps the sequence variations to compare the differences structurally. Mutations in the Na,K-ATPase genes encoding alpha subunit isoforms have severe physiological consequences, causing very distinct, often neurological diseases. The differences in the pathophysiological effects of mutations further underline how the kinetic parameters, regulation and proteomic interactions of the Na,K-ATPase isoforms are optimized for the individual cellular needs.
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              pGlyco 2.0 enables precision N-glycoproteomics with comprehensive quality control and one-step mass spectrometry for intact glycopeptide identification

              The precise and large-scale identification of intact glycopeptides is a critical step in glycoproteomics. Owing to the complexity of glycosylation, the current overall throughput, data quality and accessibility of intact glycopeptide identification lack behind those in routine proteomic analyses. Here, we propose a workflow for the precise high-throughput identification of intact N-glycopeptides at the proteome scale using stepped-energy fragmentation and a dedicated search engine. pGlyco 2.0 conducts comprehensive quality control including false discovery rate evaluation at all three levels of matches to glycans, peptides and glycopeptides, improving the current level of accuracy of intact glycopeptide identification. The N-glycoproteome of samples metabolically labeled with 15N/13C were analyzed quantitatively and utilized to validate the glycopeptide identification, which could be used as a novel benchmark pipeline to compare different search engines. Finally, we report a large-scale glycoproteome dataset consisting of 10,009 distinct site-specific N-glycans on 1988 glycosylation sites from 955 glycoproteins in five mouse tissues.
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                Author and article information

                Contributors
                jcoon@chem.wisc.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                21 March 2019
                21 March 2019
                2019
                : 10
                : 1311
                Affiliations
                [1 ]ISNI 0000 0001 2167 3675, GRID grid.14003.36, Genome Center of Wisconsin, , University of Wisconsin-Madison, ; Madison, WI 53706 USA
                [2 ]ISNI 0000 0001 2167 3675, GRID grid.14003.36, Department of Chemistry, , University of Wisconsin-Madison, ; Madison, WI 53706 USA
                [3 ]ISNI 0000 0001 2167 3675, GRID grid.14003.36, Department of Biomolecular Chemistry, , University of Wisconsin-Madison, ; Madison, WI 53706 USA
                [4 ]ISNI 0000 0001 2167 3675, GRID grid.14003.36, Morgridge Institute for Research, ; Madison, WI 53715 USA
                [5 ]ISNI 0000000419368956, GRID grid.168010.e, Present Address: Department of Chemistry, , Stanford University, ; Stanford, CA 94305 USA
                Author information
                http://orcid.org/0000-0002-1536-2966
                Article
                9222
                10.1038/s41467-019-09222-w
                6428843
                30899004
                6e1cfae2-4cf8-4b7a-a8c5-f77c0606f2e0
                © 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/.

                History
                : 1 June 2018
                : 19 February 2019
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