Dataset Information

Human Arginine Methylome

ABSTRACT: Identification of arginine methylation sites in human HEK293 cells, yielding a total of >8.000 high-confident modification sites on >3.300 proteins

INSTRUMENT(S): Q Exactive

ORGANISM(S): Homo Sapiens (ncbitaxon:9606)

SUBMITTER: Michael Lund Nielsen

PROVIDER: MSV000080836 | MassIVE | Fri Mar 31 18:24:00 BST 2017

SECONDARY ACCESSION(S): PXD003700

REPOSITORIES: MassIVE

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Publications

Combined Protein-Rich Diet With Resistance Exercise Intervention to Counteract Sarcopenia: A Qualitative Study on Drivers and Barriers of Compliance.

Herrema Annemarthe L AL Westerman Marjan J MJ van Dongen Ellen J I EJI Kudla Urszula U Veltkamp Martijn M

Journal of aging and physical activity 20171209 1

Interventions combining protein-rich diets with resistance exercises seem a promising avenue in helping to prevent sarcopenia. However, compliance to health interventions is generally low. The aim of the present study was to provide qualitative insights into the drivers and barriers that older adults experience when trying to comply with a combined dietary and physical exercise intervention. Semi-structured interviews with 18 older adults participating in such an intervention were conducted and ...[more]

PMID: 28595022

Similar Datasets

Project description:The covalent attachment of methyl groups to the side-chain of arginine residues is known to play essential roles in regulation of transcription, protein function and RNA metabolism. The specific N-methylation of arginine residues is catalyzed by a small family of gene products known as protein arginine methyltransferases; however, very little is known about which arginine residues become methylated on target substrates. Here we describe an unbiased methodology that combines single-step immunoenrichment of methylated peptides with high-resolution mass spectrometry to identify endogenous arginine mono-methylation (MMA) sites. We thereby identify 1,027 site-specific MMA sites on 494 human proteins, discovering numerous novel mono-methylation targets and confirming the majority of currently known MMA substrates. Nuclear RNA-binding proteins involved in RNA processing, RNA localization, transcription, and chromatin remodeling are prominently found modified with MMA. Despite this, MMA sites prominently are located outside RNA-binding domains as compared to the proteome-wide distribution of arginine residues. Quantification of arginine methylation in cells treated with Actinomycin D uncovers strong site-specific regulation of MMA sites during transcriptional arrest. Interestingly, several MMA sites are down-regulated after a few hours of under transcriptional arrest. In contrast, the corresponding di-methylation or protein expression level is not altered in expression, confirming that MMA sites contain regulated functions on their own. Collectively, we present a site-specific MMA dataset in human cells and demonstrate for the first time that MMA is a dynamic post-translational modification regulated during transcriptional arrest by a hitherto uncharacterized arginine demethylase. Data analysis: All raw data analysis was performed with MaxQuant software suite version 1.2.6.20 supported by the Andromeda search engine. Data was searched against a concatenated target/decoy (forward and reversed) version of the UniProtKB Human database encompassing 71,434 protein entries. Mass tolerance for searches was set to maximum 7 ppm for peptide masses and 20 ppm for HCD fragment ion masses. Data was searched with carbamidomethylation as a fixed modification and protein N-terminal acetylation, methionine oxidation and mono-methylation on lysine and arginine as variable modifications. A maximum of three mis-cleavages was allowed while requiring strict trypsin specificity, and only peptides with a minimum sequence length of seven were considered for further data analysis. Peptide assignments were statistically evaluated in a Bayesian model on the basis of sequence length and Andromeda score. Only peptides and proteins with a false discovery rate (FDR) of less than 1% were accepted, estimated on the basis of the number of accepted reverse hits. Protein sequences of common contaminants such as human keratins and proteases used were added to the database.