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      Redox-proteomes of human NOS1-transduced versus MOCK SH-SY5Y neuroblastoma cells under full nutrition, serum-free starvation, and rapamycin treatment

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      b , a , b , a , *
      Data in Brief
      Elsevier

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          Abstract

          Upregulations of neuronal nitric oxide synthase (nNOS/NOS1) in the mouse brain upon aging and stress suggest a role of NO-dependent redox protein modifications for age-associated protein imbalances or dysfunctions. We generated a cell model, in which constitutive expression of nNOS in SH-SY5Y cells at a level comparable with mouse brain replicates the aging phenotype, that is, slowing of cell proliferation, cell enlargement, and expression of senescence markers. nNOS+ and MOCK cells were exposed to proteostasis stress by the treatment with rapamycin or serum-free starvation versus control conditions. To analyze NO-mediated S-nitrosylations (SNO) and other reversible protein modifications including disulfides and sulfoxides, we used complimentary proteomic approaches encompassing 2D-SNO-DIGE (differential gel electrophoresis), SNO-site identification (SNOSID), SNO Super-SILAC, SNO BIAM-Switch, and Redox-BIAM switch. The redox proteomes were analyzed using hybrid liquid chromatography/mass spectrometry (LC/MS). Full scan MS-data were acquired using Xcalibur, and raw mass spectra were analyzed using the proteomics software MaxQuant. The human reference proteome sets from uniprot were used as templates to identify peptides and proteins and quantify protein expression. The DiB data file contains MaxQuant output tables of the redox-modified proteins.The tables include peptide and protein identification, accession numbers, protein, and gene names, sequence coverage and quantification values of each sample. Differences in protein redox modifications in MOCK versus nNOS+ SH-SY5Y cells and interpretation of results are presented in (Valek et al., 2018).

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          1D and 2D annotation enrichment: a statistical method integrating quantitative proteomics with complementary high-throughput data

          Quantitative proteomics now provides abundance ratios for thousands of proteins upon perturbations. These need to be functionally interpreted and correlated to other types of quantitative genome-wide data such as the corresponding transcriptome changes. We describe a new method, 2D annotation enrichment, which compares quantitative data from any two 'omics' types in the context of categorical annotation of the proteins or genes. Suitable genome-wide categories are membership of proteins in biochemical pathways, their annotation with gene ontology terms, sub-cellular localization, the presence of protein domains or the membership in protein complexes. 2D annotation enrichment detects annotation terms whose members show consistent behavior in one or both of the data dimensions. This consistent behavior can be a correlation between the two data types, such as simultaneous up- or down-regulation in both data dimensions, or a lack thereof, such as regulation in one dimension but no change in the other. For the statistical formulation of the test we introduce a two-dimensional generalization of the nonparametric two-sample test. The false discovery rate is stringently controlled by correcting for multiple hypothesis testing. We also describe one-dimensional annotation enrichment, which can be applied to single omics data. The 1D and 2D annotation enrichment algorithms are freely available as part of the Perseus software.
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            SNOSID, a proteomic method for identification of cysteine S-nitrosylation sites in complex protein mixtures.

            Reversible addition of NO to Cys-sulfur in proteins, a modification termed S-nitrosylation, has emerged as a ubiquitous signaling mechanism for regulating diverse cellular processes. A key first-step toward elucidating the mechanism by which S-nitrosylation modulates a protein's function is specification of the targeted Cys (SNO-Cys) residue. To date, S-nitrosylation site specification has been laboriously tackled on a protein-by-protein basis. Here we describe a high-throughput proteomic approach that enables simultaneous identification of SNO-Cys sites and their cognate proteins in complex biological mixtures. The approach, termed SNOSID (SNO Site Identification), is a modification of the biotin-swap technique [Jaffrey, S. R., Erdjument-Bromage, H., Ferris, C. D., Tempst, P. & Snyder, S. H. (2001) Nat. Cell. Biol. 3, 193-197], comprising biotinylation of protein SNO-Cys residues, trypsinolysis, affinity purification of biotinylated-peptides, and amino acid sequencing by liquid chromatography tandem MS. With this approach, 68 SNO-Cys sites were specified on 56 distinct proteins in S-nitrosoglutathione-treated (2-10 microM) rat cerebellum lysates. In addition to enumerating these S-nitrosylation sites, the method revealed endogenous SNO-Cys modification sites on cerebellum proteins, including alpha-tubulin, beta-tubulin, GAPDH, and dihydropyrimidinase-related protein-2. Whereas these endogenous SNO proteins were previously recognized, we extend prior knowledge by specifying the SNO-Cys modification sites. Considering all 68 SNO-Cys sites identified, a machine learning approach failed to reveal a linear Cys-flanking motif that predicts stable transnitrosation by S-nitrosoglutathione under test conditions, suggesting that undefined 3D structural features determine S-nitrosylation specificity. SNOSID provides the first effective tool for unbiased elucidation of the SNO proteome, identifying Cys residues that undergo reversible S-nitrosylation.
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              MaxQuant for in-depth analysis of large SILAC datasets.

              Proteomics experiments can generate very large volumes of data, in particular in situations where within one experimental design many samples are compared to each other, possibly in combination with pre-fractionation of samples prior to LC-MS analysis. Here we provide a step-by-step protocol explaining how the current MaxQuant version can be used to analyze large SILAC-labeling datasets in an efficient way.
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                Author and article information

                Contributors
                Journal
                Data Brief
                Data Brief
                Data in Brief
                Elsevier
                2352-3409
                26 October 2018
                December 2018
                26 October 2018
                : 21
                : 1302-1308
                Affiliations
                [a ]Institute of Clinical Pharmacology, Goethe-University Hospital, Frankfurt, Germany
                [b ]Functional Proteomics, Goethe-University, SFB815 Core Unit, Frankfurt, Germany
                Author notes
                [* ]Corresponding author. tegeder@ 123456em.uni-frankfurt.de
                Article
                S2352-3409(18)31295-2
                10.1016/j.dib.2018.10.078
                6230977
                9d564a03-f6ab-46c8-bc43-abd48bf8b5c6
                © 2018 The Authors

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 11 October 2018
                : 19 October 2018
                : 23 October 2018
                Categories
                Neuroscience

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