Heavy Methyl SILAC Metabolic Labeling of Human Cell Lines for High-Confidence Identification of R/K-Methylated Peptides by High-Resolution Mass Spectrometry.

Protein methylation is a widespread post-translational modification (PTM) involved in several important biological processes including, but not limited to, RNA splicing, signal transduction, translation, and DNA repair. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) is considered today the most versatile and accurate technique to profile PTMs with high precision and proteome-wide depth; however, the identification of protein methylations by MS is still prone to high false discovery rates. In this chapter, we describe the heavy methyl SILAC metabolic labeling strategy that allows high-confidence identification of in vivo methyl-peptides by MS-based proteomics. We provide a general protocol that covers the steps of heavy methyl labeling of cultured cells, protein sample preparation, LC-MS/MS analysis, and downstream computational analysis of the acquired MS data.

ProMetheusDB: An In-Depth Analysis of the High-Quality Human Methyl-proteome.

Protein arginine (R) methylation is a post-translational modification involved in various biological processes, such as RNA splicing, DNA repair, immune response, signal transduction, and tumor development. Although several advancements were made in the study of this modification by mass spectrometry, researchers still face the problem of a high false discovery rate. We present a dataset of high-quality methylations obtained from several different heavy methyl stable isotope labeling with amino acids in cell culture experiments analyzed with a machine learning-based tool and show that this model allows for improved high-confidence identification of real methyl-peptides. Overall, our results are consistent with the notion that protein R methylation modulates protein-RNA interactions and suggest a role in rewiring protein-protein interactions, for which we provide experimental evidence for a representative case (i.e., NONO [non-POU domain-containing octamer-binding protein]-paraspeckle component 1 [PSPC1]). Upon intersecting our R-methyl-sites dataset with the PhosphoSitePlus phosphorylation dataset, we observed that R methylation correlates differently with S/T-Y phosphorylation in response to various stimuli. Finally, we explored the application of heavy methyl stable isotope labeling with amino acids in cell culture to identify unconventional methylated residues and successfully identified novel histone methylation marks on serine 28 and threonine 32 of H3. The database generated, named ProMetheusDB, is freely accessible at https://bioserver.ieo.it/shiny/app/prometheusdb.

A Mass Spectrometry-Based Proteomics Approach for Global and High-Confidence Protein R-Methylation Analysis.

Protein Arginine (R)-methylation is a widespread protein post-translational modification (PTM) involved in the regulation of several cellular pathways, including RNA processing, signal transduction, DNA damage response, miRNA biogenesis, and translation. In recent years, thanks to biochemical and analytical developments, mass spectrometry (MS)-based proteomics has emerged as the most effective strategy to characterize the cellular methyl-proteome with single-site resolution. However, identifying and profiling in vivo protein R-methylation by MS remains challenging and error-prone, mainly due to the substoichiometric nature of this modification and the presence of various amino acid substitutions and chemical methyl-esterification of acidic residues that are isobaric to methylation. Thus, enrichment methods to enhance the identification of R-methyl-peptides and orthogonal validation strategies to reduce False Discovery Rates (FDR) in methyl-proteomics studies are required. Here, a protocol specifically designed for high-confidence R-methyl-peptides identification and quantitation from cellular samples is described, which couples metabolic labeling of cells with heavy isotope-encoded Methionine (hmSILAC) and dual protease in-solution digestion of whole cell extract, followed by off-line High-pH Reversed Phase (HpH-RP) chromatography fractionation and affinity enrichment of R-methyl-peptides using anti-pan-R-methyl antibodies. Upon high-resolution MS analysis, raw data are first processed with the MaxQuant software package and the results are then analyzed by hmSEEKER, a software designed for the in-depth search of MS peak pairs corresponding to light and heavy methyl-peptide within the MaxQuant output files.

Pharmacological inhibition of LSD1 triggers myeloid differentiation by targeting GSE1 oncogenic functions in AML.

The histone demethylase LSD1 is over-expressed in hematological tumors and has emerged as a promising target for anticancer treatment, so that several LSD1 inhibitors are under development and testing, in preclinical and clinical settings. However, the complete understanding of their complex mechanism of action is still unreached. Here, we unraveled a novel mode of action of the LSD1 inhibitors MC2580 and DDP-38003, showing that they can induce differentiation of AML cells through the downregulation of the chromatin protein GSE1. Analysis of the phenotypic effects of GSE1 depletion in NB4 cells showed a strong decrease of cell viability in vitro and of tumor growth in vivo. Mechanistically, we found that a set of genes associated with immune response and cytokine-signaling pathways are upregulated by LSD1 inhibitors through GSE1-protein reduction and that LSD1 and GSE1 colocalize at promoters of a subset of these genes at the basal state, enforcing their transcriptional silencing. Moreover, we show that LSD1 inhibitors lead to the reduced binding of GSE1 to these promoters, activating transcriptional programs that trigger myeloid differentiation. Our study offers new insights into GSE1 as a novel therapeutic target for AML.

Systematic Analysis of the Impact of R-Methylation on RBPs-RNA Interactions: A Proteomic Approach.

RNA binding proteins (RBPs) bind RNAs through specific RNA-binding domains, generating multi-molecular complexes known as ribonucleoproteins (RNPs). Various post-translational modifications (PTMs) have been described to regulate RBP structure, subcellular localization, and interactions with other proteins or RNAs. Recent proteome-wide experiments showed that RBPs are the most representative group within the class of arginine (R)-methylated proteins. Moreover, emerging evidence suggests that this modification plays a role in the regulation of RBP-RNA interactions. Nevertheless, a systematic analysis of how changes in protein-R-methylation can affect globally RBPs-RNA interactions is still missing. We describe here a quantitative proteomics approach to profile global changes of RBP-RNA interactions upon the modulation of type I and II protein arginine methyltransferases (PRMTs). By coupling the recently described Orthogonal Organic Phase Separation (OOPS) strategy with the Stable Isotope Labelling with Amino acids in Cell culture (SILAC) and pharmacological modulation of PRMTs, we profiled RNA-protein interaction dynamics in dependence of protein-R-methylation. Data are available via ProteomeXchange with identifier PXD024601.

Biochemical and Computational Approaches for the Large-Scale Analysis of Protein Arginine Methylation by Mass Spectrometry.

The absence of efficient mass spectrometry-based approaches for the large-scale analysis of protein arginine methylation has hindered the understanding of its biological role, beyond the transcriptional regulation occurring through histone modification. In the last decade, however, several technological advances of both the biochemical methods for methylated polypeptide enrichment and the computational pipelines for MS data analysis have considerably boosted this research field, generating novel insights about the extent and role of this post-translational modification. Here, we offer an overview of state-of-the-art approaches for the high-confidence identification and accurate quantification of protein arginine methylation by high-resolution mass spectrometry methods, which comprise the development of both biochemical and bioinformatics methods. The further optimization and systematic application of these analytical solutions will lead to ground-breaking discoveries on the role of protein methylation in biological processes.

PRMT1 Is Recruited via DNA-PK to Chromatin Where It Sustains the Senescence-Associated Secretory Phenotype in Response to Cisplatin.

Protein arginine methyltransferase 1 (PRMT1) is overexpressed in various human cancers and linked to poor response to chemotherapy. Various PRMT1 inhibitors are currently under development; yet, we do not fully understand the mechanisms underpinning PRMT1 involvement in tumorigenesis and chemoresistance. Using mass spectrometry-based proteomics, we identified PRMT1 as regulator of arginine methylation in ovarian cancer cells treated with cisplatin. We showed that DNA-dependent protein kinase (DNA-PK) binds to and phosphorylates PRMT1 in response to cisplatin, inducing its chromatin recruitment and redirecting its enzymatic activity toward Arg3 of histone H4 (H4R3). On chromatin, the DNA-PK/PRMT1 axis induces senescence-associated secretory phenotype through H4R3me2a deposition at pro-inflammatory gene promoters. Finally, PRMT1 inhibition reduces the clonogenic growth of cancer cells exposed to low doses of cisplatin, sensitizing them to apoptosis. While unravelling the role of PRMT1 in response to genotoxic agents, our findings indicate the possibility of targeting PRMT1 to overcome chemoresistance in cancer.

PRMT1-mediated methylation of the microprocessor-associated proteins regulates microRNA biogenesis.

MicroRNA (miRNA) biogenesis is a tightly controlled multi-step process operated in the nucleus by the activity of the Microprocessor and its associated proteins. Through high resolution mass spectrometry (MS)- proteomics we discovered that this complex is extensively methylated, with 84 methylated sites associated to 19 out of its 24 subunits. The majority of the modifications occurs on arginine (R) residues (61), leading to 81 methylation events, while 30 lysine (K)-methylation events occurs on 23 sites of the complex. Interestingly, both depletion and pharmacological inhibition of the Type-I Protein Arginine Methyltransferases (PRMTs) lead to a widespread change in the methylation state of the complex and induce global decrease of miRNA expression, as a consequence of the impairment of the pri-to-pre-miRNA processing step. In particular, we show that the reduced methylation of the Microprocessor subunit ILF3 is linked to its diminished binding to the pri-miRNAs miR-15a/16, miR-17-92, miR-301a and miR-331. Our study uncovers a previously uncharacterized role of R-methylation in the regulation of miRNA biogenesis in mammalian cells.

Therapeutic Targeting of RNA Splicing Catalysis through Inhibition of Protein Arginine Methylation.

Cancer-associated mutations in genes encoding RNA splicing factors (SFs) commonly occur in leukemias, as well as in a variety of solid tumors, and confer dependence on wild-type splicing. These observations have led to clinical efforts to directly inhibit the spliceosome in patients with refractory leukemias. Here, we identify that inhibiting symmetric or asymmetric dimethylation of arginine, mediated by PRMT5 and type I protein arginine methyltransferases (PRMTs), respectively, reduces splicing fidelity and results in preferential killing of SF-mutant leukemias over wild-type counterparts. These data identify genetic subsets of cancer most likely to respond to PRMT inhibition, synergistic effects of combined PRMT5 and type I PRMT inhibition, and a mechanistic basis for the therapeutic efficacy of PRMT inhibition in cancer.

Proteomics profiling of arginine methylation defines PRMT5 substrate specificity.

Protein arginine methyltransferases (PRMTs) catalyze arginine methylation on both chromatin-bound and cytoplasmic proteins. Accumulating evidence supports the involvement of PRMT5, the major type II PRMT, in cell survival and differentiation pathways that are important during development and in tumorigenesis. PRMT5 is an attractive drug target in various cancers, and inhibitors are currently in oncological clinical trials. Nonetheless, given the complex biology of PRMT5 and its multiple nonhistone substrates, it is paramount to fully characterize these dynamic changes in methylation and to link them to the observed anticancer effects to fully understand the functions of PRMT5 and the consequences of its inhibition. Here, we used a newly established pipeline coupling stable isotope labeling with amino acids in cell culture (SILAC) with immunoenriched methyl peptides to globally profile arginine monomethylation and symmetric dimethylation after PRMT5 inhibition by a selective inhibitor. We adopted heavy methyl SILAC as an orthogonal validation method to reduce the false discovery rate. Through in vitro methylation assays, we validated a set of PRMT5 targets identified by mass spectrometry and provided previously unknown mechanistic insights into the preference of the enzyme to methylate arginine sandwiched between two neighboring glycines (a Gly-Arg-Gly, or "GRG," sequence). Our analysis led to the identification of previously unknown PRMT5 substrates, thus both providing insight into the global effects of PRMT5 and its inhibition in live cells, beyond chromatin, and refining our knowledge of its substrate specificity.

hmSEEKER: Identification of hmSILAC Doublets in MaxQuant Output Data.

Heavy methyl Stable Isotope Labeling with Amino acids in Cell culture (hmSILAC) is a metabolic labeling strategy employed in proteomics to increase the confidence of global identification of methylated peptides by MS. However, to this day, the automatic and robust identification of heavy and light peak doublets from MS-raw data of hmSILAC experiments is a challenging task, for which the choice of computational methods is very limited. Here, hmSEEKER, a software designed to work downstream of a MaxQuant analysis for in-depth search of MS peak pairs that correspond to light and heavy methyl-peptide within MaxQuant-generated tables is described with good sensitivity and specificity. The software is written in Perl, and its code and user manual are freely available at Bitbucket (https://bit.ly/2scCT9u).