Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

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High throughput sequencing identifies alternative splicing events regulated by PRMT9

ABSTRACT: The human genome encodes a family of nine protein arginine methyltransferases (PRMT1-9). Different members of this enzyme family catalyze different types of protein methylation at the terminal nitrogen atoms of arginine residues, forming monomethylated arginine (MMA), asymmetrically dimethylated arginine (ADMA) and symmetrically dimethylated arginine (SDMA). The last member of this family, PRMT9, is characterized in detail here. We identify two spliceosome-associated proteins, SAP145 (SF3B2) and SAP49 (SF3B4), as PRMT9 binding partners, linking PRMT9 to U2snRNP maturation. We show that SAP145 is methylated by PRMT9 at arginine 508 (R508). Amino acid analysis and a methyl-specific antibody revealed the formation of MMA and SDMA, and PRMT9 thus joins PRMT5 as the only mammalian enzymes that can deposit the SDMA mark. Methylation of the SAP145R508 generates a binding site for the Tudor domain of SMN, and RNA-seq analysis reveals gross splicing changes when PRMT9 levels are attenuated. These studies identify PRMT9 as a non-histone methyltransferase that primes the U2snRNP for interaction with SMN. RNA sequencing was carried out using RNA samples from two biological replicates of control and PRMT9 knockdown HeLa cells.

ORGANISM(S): Homo sapiens

SUBMITTER: Zheng Xia

PROVIDER: E-GEOD-63953 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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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.

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High throughput sequencing identifies alternative splicing events regulated by PRMT9

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