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The PRMT response to inflammation : substrate methylation and alternative splicing Vhuiyan, Md. Mynol Islam 2016

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The PRMT response to inflammation: substrate methylation and alternative splicing by  Md. Mynol Islam Vhuiyan  M. Pharm., The University of Dhaka, Bangladesh, 2008  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Pharmaceutical Sciences)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)   April 2016  © Md. Mynol Islam Vhuiyan, 2016 ii  Abstract Protein arginine N-methyltransferases (PRMTs) are a family of enzymes involved in signaling pathways and gene expression by methylating arginine residues of substrate proteins. PRMT2 has been demonstrated to play a role in the NF-κB signaling pathway. Moreover, our lab recently revealed association using proteomic techniques between PRMT2 and splicing factors including Src-associated in mitosis 68 kDa protein (SAM68) that mediates the alternative splicing of BCL-X involved in the NF-κB mediated inflammatory pathway. I wanted to investigate if the PRMT activity plays a role in response to inflammation under the treatment of inflammatory cytokine tumor necrosis factor-α (TNF-α) or pro-inflammatory bacterial lipopolysaccharide (LPS) in A549 cells.  My proteomic experiments revealed that TNF-α and LPS cause similar changes to arginine methylation for proteins primarily involved in mRNA processing, RNA splicing, and nuclear transport, indicating that these two inflammatory stimuli share mutual downstream pathways involving methyltransferase activity. Among the proteins that showed hypermethylation upon treatment relative to control in mass spectrometry analysis, GAP SH3 domain-binding protein 2 (G3BP2) showed consistently in all three replicates on average a 1.5-fold increase in methylation at the R468 site in both TNF-α and LPS-treated cells. G3BP2 binds to IκB-α and prevents NF-κB translocation into the nucleus for subsequent signaling. G3BP2 methylation was necessary for signaling to occur in the Wnt/β-catenin signaling pathway. Methylation of G3BP2 might also have similar role in the inflammatory pathway that demands further study. Moreover, consistent with an inflammatory response, proteins particularly involved in innate immunity and viral response increased upon TNF-α treatment that could be related to the observed change in methylation of proteins involved in RNA processing.  iii  Our finding that PRMT2 interacts with SAM68, prompted me to investigate the potential role of PRMT2 in BCL-X alternative splicing. I found that reduced expression of PRMT2 by siRNA caused a decrease in the BCL-X(L)/BCL-X(s) ratio, suggesting that PRMT2 may contribute to BCL-X alternative splicing. This effect was replicated in TNF-α or LPS stimulated cells when PRMT2 expression was reduced by shRNA, and reversed when PRMT2 expression was increased. These results indicate that PRMT2 may play a role during inflammation in alternative splicing regulation.   iv  Preface This dissertation is based on the work conducted in Dr. Adam Frankel’s laboratory at the University of British Columbia. In Chapter 2, with the exception of liquid chromatography tandem mass spectrometry (LC-MS/MS) analyses, I performed all of the experiments. In Chapter 3, I performed all of the experiments except those related to Figure 3.1 (page 75), 3.2A, B and E panels (page 77). In Chapter 2, I wanted to investigate if methylation level of proteins changes upon TNF-α or LPS stimulation of A549 cells. I determined using Western blot that there is no recognizable difference in the methylation level of proteins between the control and TNF-α or LPS treated cells (page 47). I conducted all the cell culture work including growing the cells in different specialized media, treating the cells with TNF-α and LPS and preparing the samples for the triple peptide labeling and SILAC-based proteomic studies. The LC-MS/MS analysis of the samples were planned and executed in collaboration with Nicolas E. Scott, a former postdoctoral researcher in the laboratory of Dr. Leonard Foster (Director of the UBC Proteomics Core Facility, Department of Biochemistry & Molecular Biology, UBC). After I isolated purified proteins from treated cells, N.E. Scott performed the sample digestion, peptide labeling, hydrophilic interaction liquid chromatography (HILIC) separation, LC-MS/MS, and data analysis using MaxQuant. I analyzed and presented the MaxQuant-generated data using GeneMania and Cytoscape. I conducted the Western blot experiments to show changes in the protein expression levels of different proteins upon TNF-α and LPS treatments observed in the SILAC experiment. I also conducted in vivo methylation assay and immunoprecipitation experiments to investigate the change in the methylation level of G3BP2 observed in the SILAC experiment. v  In Chapter 3, I investigated PRMT2’s interaction with SAM68 in cultured cells and PRMT’s role in the alternative splicing of BCL-X. In order to confirm the association of HA-PRMT2 and endogenous SAM68 shown by the former Frankel laboratory graduate student Dr. Lam Pak [Figure 3.1 (page 75), 3.2A, B (I prepared the HA-SAM68 clone for this experiment) and E panels (page 77)], I separated the cell lysate into nuclear and cytoplasmic fractions prior to co-immunoprecipitating SAM68 with HA-PRMT2. I was able to demonstrate that HA-PRMT2 immunoprecipitated SAM68 in the nuclear and not in the cytoplasmic fraction (Figure 3.2C and 3.2D). I have conducted all the cell culture work including the siRNA and shRNA transfection to decrease the level of PRMT2 to show that PRMT2 affects the alternative splicing of BCL-X. I conducted the IncuCyteZoom imaging to confirm the transfection of shRNAs. I performed all the qRT-PCR experiments and data analysis mentioned in this Chapter.  vi  Table of Contents Abstract .......................................................................................................................................... ii Preface ........................................................................................................................................... iv Table of Contents ......................................................................................................................... vi List of Tables ................................................................................................................................ ix List of Figures .................................................................................................................................x List of Abbreviations .................................................................................................................. xii Acknowledgements .................................................................................................................... xvi Chapter 1: Introduction ................................................................................................................1 1.1 Protein arginine N-methyltransferase activity and classification.................................... 1 1.2 PRMT structure ............................................................................................................... 2 1.3 PRMT substrates and substrate specificity ..................................................................... 6 1.4 Involvement of PRMTs in different molecular pathways............................................. 10 1.4.1 RNA processing ........................................................................................................ 10 1.4.2 Transcriptional coactivation...................................................................................... 11 1.4.3 Transcriptional corepression ..................................................................................... 12 1.4.4 Signal transduction.................................................................................................... 13 1.4.5 Nucleocytoplasmic shuttling ..................................................................................... 14 1.4.6 DNA repair................................................................................................................ 15 1.4.7 Involvement in Wnt/-catenin signaling .................................................................. 15 1.5 Methylation as a regulator of protein-protein interactions ........................................... 17 1.6 Implications of arginine methylation in health and disease .......................................... 18 1.6.1 Physiological functions of PRMTs revealed from PRMT knockout mice ............... 18 vii  1.6.2 PRMTs in diseases .................................................................................................... 19 1.6.2.1 Cancer ............................................................................................................... 19 1.6.2.2 Cardiovascular disease ...................................................................................... 21 1.6.2.3 Inflammatory diseases ...................................................................................... 21 1.6.2.4 Viral infection ................................................................................................... 23 1.6.2.5 Multiple sclerosis .............................................................................................. 23 1.6.2.6 Spinal muscular atrophy ................................................................................... 23 1.7 Regulation of PRMT activities ..................................................................................... 24 1.8 Hypothesis and research objectives .............................................................................. 25 Chapter 2: Global changes to arginine methylation in response to inflammation ................27 2.1 Introduction ................................................................................................................... 27 2.2 Methods......................................................................................................................... 28 2.2.1 Tissue culture and treatment with TNF-α or LPS ..................................................... 28 2.2.2 Western Blot ............................................................................................................. 29 2.2.3 Triple peptide labeling by formaldehyde and analysis by MS ................................. 30 2.2.4 SILAC experiment, sample processing, mass spectrometry and data analysis ........ 30 2.2.5 In vivo methylation assays ........................................................................................ 32 2.3 Results ........................................................................................................................... 34 2.3.1 Altered expression of proteins upon TNF-α or LPS stimulation .............................. 34 2.3.2 Changes in the methylation level of proteins upon TNF-α or LPS stimulations ...... 45 2.4 Discussion ..................................................................................................................... 64 2.4.1 Effect of TNF-α and LPS on protein expression ...................................................... 64 2.4.2 TNF-α or LPS changes the methylation level of proteins ........................................ 66 viii  Chapter 3: PRMT2’s role in splicing of BCL-X, an inflammatory pathway target gene ......68 3.1 Introduction ................................................................................................................... 68 3.2 Methods......................................................................................................................... 69 3.2.1 DNA .......................................................................................................................... 69 3.2.2 Tissue culture ............................................................................................................ 70 3.2.3 Co-immunoprecipitations ......................................................................................... 71 3.2.4 Western blotting ........................................................................................................ 72 3.2.5 Total RNA extraction and qRT-PCR ........................................................................ 72 3.3 Results ........................................................................................................................... 72 3.3.1 PRMT2 associates with SAM68 in cells .................................................................. 72 3.3.2 PRMT2 influences the alternative splicing of BCL-X .............................................. 76 3.4 Discussion ..................................................................................................................... 82 3.4.1 PRMT2 SH3 domain selectivity ............................................................................... 82 3.4.2 PRMT2-mediated effects on BCL-X alternative splicing ......................................... 83 Chapter 4: Conclusion and future directions ............................................................................86 References .....................................................................................................................................92 Appendix .....................................................................................................................................119  ix  List of Tables Table 2.1 Changes in the expression level of proteins (expressed in log2 value) upon TNF-α or LPS treatment................................................................................................................................ 37 Table 2.2 Changes in the DMA and MMA level (expressed as log2 value) on peptide(s) of the proteins upon TNF-α or LPS treatment. ....................................................................................... 57 Table A.1 Identified proteins that associate with the SH3 domain of PRMT2 from gel slices...119 x  List of Figures Figure 1.1 PRMT activities. ............................................................................................................ 2 Figure 1.2 PRMT family members. ................................................................................................ 4 Figure 1.3 Structural alignment of PRMTs..................................................................................... 5 Figure 2.1 Response to inflammation by LPS and TNF-α. .......................................................... 35 Figure 2.2 Proteomic network....................................................................................................... 36 Figure 2.3 Proteomic network of immunity and viral response related proteins upon TNF-α stimulation..................................................................................................................................... 42 Figure 2.4 Proteomic network of proteins involved in cell cycle processes upon TNF-α stimulation..................................................................................................................................... 43 Figure 2.5 Change in protein expression under TNF-α and LPS stimulation............................... 44 Figure 2.6 Sample processing for detecting methylation changes. ............................................... 46 Figure 2.7 Protein ADMA level upon TNF-α and LPS stimulation of lung epithelial cells. ....... 46 Figure 2.8 Steps to identifying hypermethylated proteins upon TNF-α or LPS treatment by stable isotope dimethyl labeling. ............................................................................................................. 48 Figure 2.9 Protein arginine methylation response to inflammation. ............................................. 49 Figure 2.10 Experimental steps for the SILAC experiment. ........................................................ 51 Figure 2.11 Venn diagrams representing the number of identified methylated peptides and proteins by SILAC. ....................................................................................................................... 54 Figure 2.12 Response to inflammation by TNF-α and LPS. ........................................................ 55 Figure 2.13 Methylarginine network. ........................................................................................... 56 Figure 2.14 Methylarginine network of proteins involved in mRNA processing obtained from A549 cells treated with TNF-α. .................................................................................................... 60 xi  Figure 2.15 Methylarginine network of proteins involved in mRNA processing obtained from A549 cells treated with LPS. ........................................................................................................ 61 Figure 2.16 Methylated level of G3BP2 upon TNF-α stimulation. .............................................. 63 Figure 2.17 C-terminal sequence of G3BP2 showing potential arginine methylation sites. ........ 64 Figure 3.1 Identification of PRMT2 SH3 domain-associated proteins......................................... 73 Figure 3.2 The interaction between SAM68 and PRMT2 is dependent on the PRMT2 SH3 domain........................................................................................................................................... 75 Figure 3.3 The effect of PRMT2 expression levels on BCL-X alternative splicing. .................... 76 Figure 3.4 IncuCyte Zoom images of shRNA-transfected HEK293T cells. ................................ 77 Figure 3.5 The effect of PRMT2 expression levels on BCL-X alternative splicing under inflammatory conditions. .............................................................................................................. 78 Figure 3.6 The effect of added PRMT2 on BCL-X alternative splicing upon TNF-stimulation........................................................................................................................................................ 80 Figure 3.7 The effect of added PRMT2 on BCL-X alternative splicing upon LPS stimulation. .. 81 Figure 3.8 The effect of PRMT2 expression on total transcript level of BCL-X. ......................... 84 Figure 3.9 Proposed model on the role of PRMT2 in the alternative splicing of BCL-X. ............ 85 Figure A.1. Methylarginine quantitation…………………………………………………….....123 Figure A.2. Differential inhibition of cancer cell proliferation………………………………...125  xii  List of Abbreviations ADMA -NG,NG-asymmetric dimethylarginine AdoHcy S-adenosyl-L-homocysteine AdoMet S-adenosyl-L-methionine AdOx adenosine dialdehyde Bcl-2 B-cell lymphoma 2 BJ1 human foreskin fibroblasts CARM1 coactivator-associated arginine methyltransferase 1 CBP CREB binding protein CREB cAMP response element-binding protein DDAH dimethylarginine dimethylaminohydrolase DNA deoxyribonucleic acid EBNA Epstein-Barr virus nuclear antigen EEN extra eleven nineteen eIF3I initiation factor 3 subunit I eIF4E2 initiation factor 4E type 2 ER estrogen receptor G3BP2 GAP SH3 domain-binding protein 2 GAR glycine- and arginine-rich HCMV Human cytomegalovirus HILIC hydrophilic interaction liquid chromatography HIV human immunodeficiency virus xiii  hnRNP A1 heterogeneous ribonucleoprotein A1 IKK IκB kinase IκB inhibitor of κB IκBα nuclear factor of κ light polypeptide gene enhancer in B-cells inhibitor, α K LC-MS/MS Lysine liquid chromatography tandem mass spectrometry LPS Lipopolysaccharide MEF mouse embryonic fibroblast cells MLL mixed lineage leukemia MMA -NG-monomethylarginine MRN MRE11-RAD50-NBS1 complex mRNP mRNA binding protein MS mass spectrometry NFκB nuclear factor-κB NO nitric oxide p53 tumor suppressor 53 PAD4 peptidylarginine deiminase 4 PBS Phosphate-buffered saline PCNA proliferating cell nuclear antigen PGM Proline-, glycine- and methionine-rich  PML promyelocytic leukemia protein Pol DNA polymerase xiv  PPARγ peroxisome proliferators-activated receptor gamma PRMT protein arginine methyltransferase PTM qRT-PCR post-translational protein modification quantitative reverse transcription polymerase chain reaction R Arginine Rb retinoblastoma protein RBPs RNA binding proteins RNA ribonucleic acid SAM68 Src-associated substrate in mitosis of 68 kDa SDMA -NG,NG-symmetric dimethylarginine SILAC stable isotope labeling by amino acids in cells Sm small nuclear ribonucleoprotein-associated protein SMA spinal muscular atrophy Smac second mitochondria-derived activator of caspase SmB/B´ small nuclear ribonucleoprotein-associated proteins B and B´ SMN survival of motor neurons snRNA small nuclear RNA snRNPs small nuclear ribonucleoproteins snRNPs small nuclear ribonucleoproteins SPF30 splicing factor 30 kDa SR serine/arginine-rich protein Src Rous sarcoma oncogene xv  STAT1 signal transducers and activators of transcription 1 STAT3 signal transducers and activators of transcription 3 TAF15 TATA-binding protein-associated factor 2N Tat human immunodeficiency virus (HIV)  trans-activator protein TDRD3 tudor domain-containing 3 TGF-β transforming growth factor-β TPR  tetratricopeptide repeat motif TNF-α tumor necrosis factor-α WDR5 WD repeat-containing protein 5  xvi  Acknowledgements First of all, I would like to thank my supervisor Dr. Adam Frankel for offering me the opportunity to conduct research in his laboratory and providing his continuous support and motivation. It has been a great experience to work under his supervision. His encouragement and optimism always motivated me during my stay in his laboratory.   Additionally, I would like to thank my committee members: Dr. Ujendra Kumar, Dr. Fawziah Marra, Dr. Issy Laher and Dr. Thibault Mayor for their valuable advice during my research. I also extend thanks to Dr. Kathleen Macleod and her research group for their help for Western blotting experiments.   I am very grateful to Dr. Magnolia L. Pak who trained me on many techniques including cell based work. I would also like to thank my colleagues, especially Drs. Dylan Thomas and Ted Lakowski for their useful advice during my thesis.  Last but not least, I would like to thank my family and friends for their love, understanding and emotional support.  I give a special thanks to my parents (Mr. A. R. Vhuiyan, Ms. Deloara Begum), brother and sisters for their encouragement and help throughout these years. I cannot express enough gratitude to my brother Mr. Md. Nura Alom Bhuyan, who always provided me moral support and carried out all the responsibilities of looking after our parents while I was away pursuing advanced studies. I am greatly thankful to my wife Ms. Faria Binte Firoz for her endless support, patience and encouragement. Without all of their support, I would not have been able to complete this research. 1  Chapter 1: Introduction 1.1 Protein arginine N-methyltransferase activity and classification Protein arginine N-methyltransferases (PRMTs) are a family of enzymes involved in post-translational modification, transfer methyl groups from S-adenosyl-L-methionine (AdoMet) to the terminal guanidino nitrogen of arginine residues of the substrate proteins to form a methylarginine residue and S-adenosyl-L-homocysteine (AdoHcy) (1, 2). Protein arginine methylation activity was first identified by Paik and Kim in 1967 (3). They purified crude enzymes that they called protein methylase I capable of methylating protein from calf thymus. In 1996, Lin et al. cloned the first methyltransferase enzyme called “Protein arginine N-methyltransferases 1 (PRMT1)” (4). Currently nine family members of mammalian PRMTs have been identified that are divided into three groups depending on the final product of their activity. PRMT1, PRMT2, PRMT3, coactivator-associated arginine methyltransferase 1 (CARM1/PRMT4), PRMT6 and PRMT8 produce -NG-monomethylarginine (MMA) and -NG,NG-asymmetric dimethylarginine (ADMA) and are considered as type I enzymes (5–8) (Figure 1.1). PRMT5 and PRMT9, on the other hand, catalyze the formation of -NG-monomethylarginine (MMA) and -NG,NG-symmetric dimethylarginine (SDMA) and are classified as type II enzymes (9). PRMT7 produces -NG-monomethylarginine (MMA) and is considered a type III enzyme (10).  2   Figure 1.1 PRMT activities.  PRMT reactions are presented. Type III PRMTs produce only MMA. Type I PRMTs produce MMA and ADMA, whereas type II PRMTs generate MMA and SDMA.  1.2 PRMT structure  In terms of evolutionary origin, PRMTs existed since the start of the eukaryote lineage, and no PRMT homolog has been found in prokaryotes or archaebacteria (11). Homologs of PRMT1, PRMT5 and PRMT8 can be found in almost all eukaryotes, whereas PRMT3 are reported in all eukaryotes except saccharomycotine fungi, nematodes and basal eukaryotes. Homologs of CARM1 and PRMT6 can be found in most chordates and plants. Additionally all arthropods contains CARM1. Homologs of PRMT9 can only be found in animals, while PRMT7 exists in both animals and plants (11, 12).  3  All PRMTs have a highly conserved sequence of  approximately 310 amino acids containing the catalytic core and AdoMet binding domain (13) (Figure 1.2). Their catalytic core is characterized by having a signature double E loop, a THW loop and a Rossmann fold composed of beta-sheet structure having four conserved motifs called motif I, post-I, II and, III (9, 13). Two conserved glutamic acid residues present in the double E loop are considered to play a critical role in the catalytic activity of PRMTs by deprotonation and activation of a guanidino nitrogen that accepts the methyl group (14). Some PRMTs have unique sequences outside the conserved core region of varying length (15). Unlike other PRMTs, PRMT9 contain two conserved core regions instead of one, which could be the result of gene duplication (15). Sequence beyond the conserved region of PRMTs might be important for differential regulation like subcellular localization, protein-protein interaction and/or enzymatic activity (15, 16). In fact, the amino-terminal zinc finger present in PRMT3 has been implicated in protein-protein interactions (16–18). In the case of CARM1, the unique N- and C-terminal regions are required for its transcriptional activation function (19). The unique N-terminal myristoylation motif of PRMT8 is responsible for its unique localization in the plasma membrane (20).  The remaining PRMTs, except PRMT5 and PRMT6, are reported to be present both in the cytoplasm and nucleus in HEK293 cells. PRMT5 is found completely in the cytoplasm while PRMT6 is mostly present in the nucleus (21). Certain PRMTs might show variability in subcellular localization in different cell types. For instance, PRMT3 and CARM1 are exclusively found in the cytoplasm and nucleus, respectively, in HeLa cells (22). Furthermore, while other PRMTs are ubiquitously expressed, PRMT8 is specifically found in the brain (20).  4   Figure 1.2 PRMT family members.  All known PRMTs are shown for a comparison of their relative sizes and the position of their conserved signature motifs (adapted from (8)).  Crystal structures of PRMT1, PRMT3, CARM1, PRMT5 and PRMT6 reveal that PRMTs share similar structural features including N-terminal helices, an AdoMet binding site, a beta barrel domain and a dimerization arm (13, 15, 23–28) (Figure 1.3). While the AdoMet binding domain is also conserved among other AdoMet-dependent methyltransferases, the beta barrel domain is only found in PRMTs (23). 5   Figure 1.3 Structural alignment of PRMTs. A structural alignment (r.m.s.d. between 0.68-1.141 Å over aligned sequences) reveals similar folds for the catalytic cores of R. norvegicus PRMT1 (PDB 1OR8) in white, R. norvegicus PRMT3 (PDB 1F3L) in light green, M. musculus CARM1 (PDB 2V74) in green, C. elegans PRMT5 (PDB 3UA4) in cyan, and H. sapiens PRMT6 (PDB 4HC4) in dark green.  Crystal structures have shown that PRMTs can form homodimers through the interaction between the dimerization arm and AdoMet binding domain, which creates a ring like structure with 2-fold symmetry (13, 23). Dimer formation has been found to be essential to facilitate AdoMet binding for PRMT methyltransferase activity (23, 25, 29). The central cavity of PRMT dimers, which allows the access for the substrate and AdoMet, are not the same size in each PRMT. For example, the central cavity of CARM1 is larger than those of PRMT1 or PRMT3, which is thought to be required to provide extra space for CARM1’s unique C-terminal extension 6  (30). The charge distribution of the central cavity of CARM1 is also different than other PRMTs (30). Instead of the negatively charged surface found in PRMT1 and PRMT3, which is suggested to offer optimum binding surface for positively charged arginine rich substrates, CARM1 has a neutral surface due to the lack of acidic residues around the dimer cavity (30). The crystal structure of PRMT5 shows that, unlike other PRMTs, its unique TIM-barrel and beta-barrel domain is also involved in dimerization in addition to its dimerization arm (28). Higher order oligomers have also been reported for methyltransferases. Crystal structures of the yeast homologue of PRMT1, Hmt1 (Rmt1), shows that it exists as a hexamer (25). Though the crystal structure of PRMT1 captured its dimer form, gel filtration and crosslinking studies show that both Hmt1 and PRMT1 are capable of forming hexamers in solution (23, 25).  1.3 PRMT substrates and substrate specificity  Heterogeneous nuclear ribonucleoproteins have long been considered as the major substrates of PRMTs (31). The presence of glycine and arginine-rich (GAR) regions on those proteins gave rise to the notion that the Arg-Gly-Gly sequence is the general target of PRMTs. Recently it has been shown that arginine residues sitting within other amino acid sequence contexts can be methylated by PRMTs. For instance, Wooderchak et. al. identified eleven peptide sequences methylated by PRMT1 from a fibrillarin-based peptide library in which the target arginine residue is followed by amino acids other than two glycines (RLG, RYG, RFG, RTG, RKG, RGA, RGL, RGF, RGT, RGK, RGS) (32).  . Surprisingly, PRMT1 demonstrated a preference for an arginine residue preceded by a proline residue in nuclear PABPN1 and a corresponding peptide (33). Among the remaining PRMTs, PRMT2, PRMT3, and PRMT6 generally methylate proteins harboring a GAR motif, while CARM1 and PRMT5 can methylate arginine residues in both GAR and Proline-, glycine- and methionine-rich (PGM) motifs (34–7  37). PRMT7 appears to methylate  arginine residues residing in GAR and RXR (X is any amino acid residue) motifs (10, 38, 39).   Proteomic techniques have accelerated the detection of new PRMT substrates and substrate specificities. Recently, Low et al. identified 85 new methylarginine-containing proteins by tandem mass spectrometry in Saccharomyces cerevisiae (40). Most of these proteins are involved in functions similar to mammalian PRMT substrates such as RNA processing, protein folding, nuclear transport and carbohydrate metabolism. Interestingly, Low et al. have identified fifteen new methylarginine sites using LC-MS/MS in five of those methylated proteins. Jackson el al. used a different approach where they identified Hmt1 binding partners using LC-MS/MS, and used bioinformatics algorithms to predict methylation sites in proteins identified to associate with Hmt1. With that experiment, they identified 108 binding partners of Hmt1 and found that the top hit identified by bioinformatics was, in fact, an Hmt1 substrate (41). Sylvestersen et al. used a method that includes immuno-enrichment of methylated peptides using a combination of two anti-MMA antibodies followed by high resolution mass spectrometry to identify arginine monomethylation sites in HEK293T cells (42). They identified more than 1,000 MMA sites in approximately 500 proteins, including many novel MMA targets. A large numbers of identified MMA substrates from this experiment were found to be involved in RNA processing, transcription and chromatin remodeling (42). Guo et al. identified around 1,000 arginine methylation sites in human cell line (HCT116) and mouse tissue using immunoaffinity purification of methylated peptides followed by LC-MS/MS analysis (43). Many arginine-methylated proteins that they identified were found to be involved in RNA processing and transcriptional regulation.  8  Recently, a proteomic technique called stable isotope labeling by amino acids in cells (SILAC) has been reported to be very effective in identifying methylated substrates (44). In this technique, non-radioactive stable isotope-containing amino acids are incorporated in the newly synthesized proteins through normal cellular metabolic process resulting in labeling whole cellular proteomes (45). This is achieved by growing the cells in the growth medium that contains stable isotope containing amino acids (heavy) instead of natural (light) amino acids. Cells are grown in this media for approximately five doublings to allow full incorporation of heavy amino acids. Cells or proteins isolated from cells grown in the heavy and light medium are than mixed in 1:1 ratio. When analyzed by MS, peptides from these cell populations remain distinguishable due to the mass difference between the light and heavy amino acids present in the peptides. Relative MS signal intensities represent relative protein abundances in the respective cell populations.  Several enrichment techniques are used to isolate methylated arginine(s) containing proteins or peptides before MS analysis, including immunoprecipitation (e.g., use of anti-mono and dimethylarginine antibody), cation exchange chromatography, isoelectric focusing or hydrophilic interaction liquid chromatography (HILIC). All these methods except the antibody-based technique utilize highly basic and/or hydrophilic nature of methylated arginine-containing peptides. HILIC is an efficient method for enriching hydrophilic peptides and was found to be most effective in enriching methylated arginine-containing peptides among all these methods (46). Combination of SILAC with HILIC enrichment method has been reported to be very efficient in identifying PRMT substrates and unique arginine methylation sites. Uhlmann et. al. used SILAC to identify arginine methylation sites in Jurkat cells and human CD4+ lymphocytes 9  in which cells were given [13CD3]-methionine to convert into the methyl donor S-adenosyl-L-[13CD3]-methionine (46). They coupled SILAC experiments with different enrichment techniques to isolate arginine methylated peptides. They identified 215 arginine methylation sites (including 171 novel arginine methylation sites) that are approximately 3 to 5 times higher than those identified using other enrichment techniques including anti-mono and dimethyl arginine antibody (41 methylation sites including 6 novel sites), cation exchange chromatography (39 methylation sites including 25 novel sites) and isoelectric focusing (66 methylation sites including 34 novel sites). The SILAC method combined with the above mentioned enrichment techniques enabled them to identify 249 arginine methylation sites in 131 proteins including 190 novel methylation sites and 93 previously unreported methylated proteins (46). The arginine methylation sites identified by the antibody-based enrichment technique were mostly at RG sites similar to other findings, indicating the limitation of this technique for a proteomic level of study that can be overcome by using HILIC (46, 47). Approximately, 50% (87) of the identified new arginine methylation sites found using HILIC were at non-RG motifs (46). A similar SILAC experiment was conducted by Geoghegan et. al. in Jurkat  and primary T cells where they used [13CD4]-methionine instead of [13CD3]-methionine to improve the detection of methylated peptide (48). Proteins were digested using three different proteases and arginine methylated peptides were enriched using two different anti-MMA antibodies before analyzing with MS. They identified 2,502 unique arginine methylation sites from 1,257 unique proteins from the two cell lines combined. Many of those identified proteins are reported to be involved in regulation of transcription, translation and chromatin re-modeling (48).      In summary, combining different enrichment strategies ranging from immunoprecipitation using methyl-specific antibodies to chromatographic separations of peptides (46, 47, 49), SILAC 10  has revealed methylarginine residues in proteins involved in chromatin remodeling, transcription, splicing, translation, endosomal trafficking, and cytoskeletal rearrangement. Since arginine methylation is viewed as a stable post-translational modification, its dynamics in different cellular contexts have remained relatively unexplored despite the availability of techniques like SILAC that can provide quantitative comparisons of proteomes (45, 47, 48).  1.4 Involvement of PRMTs in different molecular pathways  1.4.1 RNA processing RNA binding proteins (RBPs) are responsible for appropriate processing, folding and localization of RNAs and thus ensuring proper mRNA translation (50). RBPs are considered as the major substrates for PRMTs because of the presence of GAR motifs in most of the heterogeneous ribonucleoprotein particles (hnRNPs) (50, 51). Thus methylation status of RBPs might influence their function in RNA processing including mRNA transcription, splicing, localization, translation and turnover. In fact, splicing of adenovirus major late transcripts was shown to be efficiently inhibited by SDMA-specific antibody or hypomethylated nuclear extracts in vitro (52). The decreased methylation levels of splicing proteins including small nuclear ribonucleoprotein-associated proteins B and B´ (SmB/B´) as well as the impaired formation of spliceosomal complexes observed in hypomethylated nuclear extract might be responsible for this inhibition, indicating that arginine methylation is important for splicing (52). Moreover, CARM1 that methylates several splicing factors including CA150, SAP49, SmB and U1C has been reported to regulate alternative splicing by promoting exon skipping (35). Src-associated substrate in mitosis of 68 kDa (SAM68) along with other RBPs are found to be mis-localized when they are hypomethylated, which gives rise to the notion that methylation of RBPs works as a maturation signal. It has been suggested that maturation of several RBPs 11  including Sm B, B’, D1 and D3 to mature snRNPs is facilitated by arginine methylation. In addition, ribosomal biosynthesis is influenced by the methylation status of ribosomal protein S2, which is conserved in both yeast and human (reviewed in (53)). Arginine methylation of RBPs might influence RNA-RBP interactions because of the possible positive or negative effect of the added methyl group to arginine binding (54). Arginine residues within RBPs are considered vital in governing the interaction between RNA and proteins (54, 55). When unmodified, the nitrogen atoms of the arginyl guanidino group promote hydrogen and van der Waals bonding, favoring RBP-RNA interactions (54). This interaction could be negatively affected by steric hindrance produced by the presence of methyl group that could ultimately prevent hydrogen bonding. On the other hand, methylation could enhance RNA-protein binding by making the arginine more hydrophobic that could facilitate the stacking with the bases of the RNA (53). Methylation of hnRNPA1 in vitro resulted in decreased affinity for single stranded nucleic acid (56). However, yeast protein Hrp1p methylation was shown to have no effect on its binding ability to AU-rich substrate (57). Thus, the effect of methylation on RNA binding might be determined by the specific interface of the RNA-proteins complex (58).  1.4.2 Transcriptional coactivation CARM1 was the first PRMT reported to have nuclear receptor coactivation function in the presence of p160 family of coactivators (59). Its ability to methylate histone H3 indicated that other PRMTs, which have also been shown to methylate histones, might also have a coactivation function.   Later, CARM1 and PRMT1 were found to synergistically enhance p53 mediated gene activation in the presence of p300 (60). CARM1 and PRMT1 were also reported to work synergistically in steroid hormone receptor mediated gene expression (61). To date PRMTs have been shown to act as coregulators of many transcription factors (e.g. NF-B, p53, YY1, PPAR, 12  RUNX1, E2F1), which indicates general coactivator function of CARM1 and PRMT1 (53, 62, 63). One of the possible mechanism by which PRMTs might act as coactivators could be by promoting the dissociation of a repressor from a transcription factor. For example, it has been shown that the transcriptional repressor SIN3A dissociates from transcription factor RUNX1 upon methylation of RUNX1 by PRMT1, thus facilitating RUNX1-mediated transcription (6, 64). While PRMTs can directly influence gene regulation by methylating histones, their indirect effect on gene expression is evident from the altered histone acetyltransferase activity of several methylated coactivators including p300, CBP and SRC3(65–68). For instance, CARM1 mediated methylation of the histone acetyltransferase and transcriptional co-activator, CBP has been reported to play a critical role in hormone induced gene expression (67). 1.4.3 Transcriptional corepression Several PRMTs have been found to have corepressor activities. Among them, PRMT5 was the first PRMT reported to show corepressor activity synergistically with the transcription factor E2F1 (69). Later PRMT5 has established itself as a general transcriptional repressor that works with many repressor complexes and transcription factors (e.g. BRG1, hBRM, Blimp 1 and Snail) (reviewed in (6)). Though the mechanism of its repression activity is yet to be fully understood, PRMT5 might show this effect by its ability to symmetrically dimethylate H3R8 that might prevent or inhibit the accessibility of activating acetyltransferases to H3K9 due to the steric hindrance generated by the dimethylated H3R8 (70).PRMT5 also has been reported to symmetrically dimethylate H4R3. Interestingly, PRMT1 asymmetrically dimethylates H4R3, which promotes transcriptional activation (70–72). It is important to note that PRMT5 might not function purely as a corepressor because it has been reported that lack of PRMT5 results in 13  decreased expression of IL-2, and the presence of PRMT5 is required for myogenin transcription in muscle differentiation (73, 74).   PRMT6 can also function as a transcriptional repressor by methylating H3R2, which prevents the recruitment of transcriptional activators to the active gene (75). PRMT7 has also been reported to methylate H4R3, a major regulator of DNA methylation of the imprinting control region (ICR), which results in the recruitment of Dnmt3a and Dnmt3b (DNA methyltransferases) (76, 77).  1.4.4 Signal transduction Arginine methylation can regulate signaling pathways by altering protein-protein interactions. Both positive and negative influences of methylarginine on protein interactions have been reported. Interactions mediated by different domains are affected by arginine methylation at different levels (7). For example, proline-rich regions of SAM68 interact with the SH3 and WW domain containing proteins and it has been reported that interactions facilitated by WW domain  are unchanged upon arginine methylation of proline-rich motifs of SAM68, while SH3 domain mediated interactions are sensitive to methylation by PRMTs (78).  To date several cell receptor-mediated pathways have been shown to use methylation for cell signaling, including cytokine receptors, interferon receptor, nerve growth factor receptor, T cell receptor and dopamine receptor (53, 79, 80). PRMT1 was found to bind to the cytoplasmic region of the interferon receptor type-I and depletion of PRMT1 by antisense oligonucleotide negatively affected interferon regulated signal, providing the first evidence of a PRMT involved in signal transduction (81). PRMT1-mediated methylation also promotes cytokine induced gene expression by directing the interaction between Nuclear factor of activated T-cells (NFAT) and NFAT interacting protein 45 (NIP45) (82). It has also been suggested that PRMT1 plays a role in 14  insulin signaling, glucose metabolism and estrogen signaling (83, 84). Moreover, plasma membrane-associated PRMT8 has been proposed to act as a signaling hub due to the presence of a proline-rich domain in its structure that can interact with SH3 domains (6). Recently, Likhite et. al. have reported that PRMT5 methylates D2 dopamine receptor that enhanced the D2 receptor mediated suppression of cyclic adenosine monophosphate (cAMP) signaling in HEK293T cells (80). Thus the ability of arginine methylation to positively affect D2 receptor mediated signaling indicates that arginine methylation could be a potential target in treating different diseases associated with dopaminergic dysfunction including schizophrenia and Parkinson’s disease (80). 1.4.5 Nucleocytoplasmic shuttling RBPs are the major substrates for PRMTs because of the presence of GAR and PGM motifs (6). It has been found that RBPs including snRNPs and hnRNPs constitute a major sources of ADMA in the nucleus (53). Shuttling between nucleus and cytoplasm is a common phenomenon for many of these RBPs and it has been found that the absence of Hmt1 (a yeast type I PRMT enzyme) interferes with this shuttling and traps some of the RBPs (e.g., Np13 and Hrp1) in the nucleus (85). The nuclear localization of high molecular weight basic fibroblast growth factor (FGF2) and SAM68 were decreased upon reduction of their methylation levels (86, 87). Similarly, Sinha et al. reported that methylation of serine/arginine-rich splicing factor 1 (SF2/ASF) affects its cellular localization and function (88). SF2/ASF is methylated at three arginine residues (R93, R97 and R109). Prevention of methylation of these residues by mutating arginine to alanine caused reduced nuclear accumulation of SF2/ASF that negatively affected SF2/ASF mediated splicing (88). Furthermore, the methylated adenovirus 100K protein is localized in the nucleus, while inhibition of methylation of 100K by inhibitors blocked its 15  nuclear accumulation (89). However, the exact mechanism of regulation of shuttling between nucleus and cytoplasm by methylation has not been fully understood yet (6). 1.4.6 DNA repair Mre11 is a component of Mre11/Rad50/NBS1 (MRN) complex that plays an important role in homologous recombination repair (HRR) upon DNA double strand breaks (DSBs) during ionization radiation induced damage or DNA replication. PRMT1 methylates Mre11 within its GAR motif (90, 91). It has been reported that arginine  methylation of the GAR motif of Mre11 is required for its DNA binding and  3’ to 5’ exonuclease activity (92). The recruitment of Mre11 to DSBs in response to etoposide, a DNA damaging agent, was prevented upon treatment with the global methyltransferase inhibitor adenosine dialdehyde (AdOx) (92).  DNA polymerase beta (Pol-) is involved in DNA base excision repair. PRMT1 and PRMT6 have been shown to associate with the N-terminus of DNA polymerase beta (Pol-) and regulate Pol- function. PRMT1 was reported to block the interaction of Pol- with proliferating cell nuclear antigen (PCNA) by methylating R137 of Pol-However, the polymerase activity of Pol- was not affected by PRMT1 activity. PRMT6 was found to enhance polymerase activity of Pol- by methylating it at R83 and R152 (93, 94).  1.4.7 Involvement in Wnt/-catenin signaling PRMTs have been found to regulate different cell signaling pathways including Wnt/-catenin signaling that is involved in several essential processes such as regulation of cell proliferation, tissue homeostasis and embryonic development (95, 96). Moreover, dysregulation of Wnt/-catenin signaling was found to relate to many diseases including cancer. Human tumor suppressor adenomatous polyposis coli (APC) protein can form a multimeric destructive 16  complex in the absence of Wnt activation that can facilitate cytoplasmic -catenin degradation, and thus maintain a low level of cytoplasmic -catenin. On the other hand, Wnt activation prevents formation of the destructive complex resulting in increased level of cytoplasmic -catenin that translocates to nucleus. Nuclear -catenin can bind with the transcription factors LEF1 and TCF, and convert LEF1 into a transcriptional activator resulting in expression of target genes (34, 97–99). It has been shown that CARM1 interacts with -catenin and ultimately affects Wnt/-catenin mediated target gene expression. Knockdown of CARM1 resulted in decreased expression of Wnt target genes and reduced β-catenin mediated tumorigenesis. Furthermore, -catenin is reported to be associated with different chromatin remodeling complexes such as CBP/p300, which is a substrate for CARM1 indicating possible association of CARM1 in the Wnt/-catenin signaling (34, 100). PRMT2 has also been associated with the Wnt target gene expression. catenin recruits PRMT2 that leads to histone H3R8 methylation at Wnt target gene sites during the early embryo development stage, and lack of maternal PRMT2 in the embryos shows decreased levels of Wnt-target gene expression, leading to unusual embryo development  (34, 101). Wnt activation has also been reported to affect PRMT1’s activity towards Ras GTPase-activating protein-binding protein 1 (G3BP1). Wnt stimulation was shown to enhance the interaction between G3BP1 with PRMT1 concomitant with an increase in G3BP1 arginine methylation (102). Arginine methylation of G3BP2 has also been shown to be a positive regulator in the Wnt signaling pathway (103, 104).  17  1.5 Methylation as a regulator of protein-protein interactions The Tudor domain is an approximately 60-amino acid long structure that was initially reported to be present in nucleic acid-associated proteins (105). Tudor domains are the major group of protein modules that are reported to bind methylated arginine residues (106). Tudor domain-containing proteins have diverse important cellular functions including histone modification, RNA metabolism and DNA damage response (106). The interaction of Tudor domain with GAR and PGM motifs are governed by arginine methylation. For example, the interaction of Tudor domains of splicing factor 30 kDa (SPF30), spinal muscular atrophy protein SMN and Tudor domain-containing 3 (TDRD3) with SmB requires the symmetric dimethylation of SmB by PRMT5 (107). The interaction of SM proteins including SmB with SMN promotes the assembly of mature small nuclear ribonucleoproteins (snRNP) that is important for pre-mRNA splicing (106).  The Tudor domain of SMN can also bind with CA150, a transcription factor, upon asymmetric demethylation by CARM1 (35). These binding interactions indicate that Tudor domains can act as specific binding modules to sequences containing aDMA or sDMA residues for specific cellular processes (6).  Certain protein-protein interactions may also be negatively regulated by PRMT activity.  For instance, the binding of SH3 domains to proline-rich motifs in SAM68 peptides can be  blocked by methylation of adjacent arginine residues, whereas the binding of WW domains to the proline-rich motifs is not affected (78). Another example is the arginine methylation of H3R2. H3R2 has been reported to be methylated by CARM1 and PRMT6 (108, 109). Lysine methylated H3K4 provides a binding site for the double chromodomains of CHD1 (chromo-helicase/ATPase DNA-binding protein 1). Methylation of H3R2 decreases the binding affinity of lysine methylated H3K4 by four fold (110). WD repeat-containing protein 5 (WDR5) is a 18  ubiquitous subunit found in various lysine methyltransferase complexes and particularly associates with di- or tri-methylated H3K4. WDR5 is needed for binding of lysine methyltransferase complex to the di-methylated H3K4 and for tri-methylation of H3K4 (111, 112). H3R2 methylation also prevented binding of WDR5 to methylated H3K4 (111).  1.6 Implications of arginine methylation in health and disease 1.6.1 Physiological functions of PRMTs revealed from PRMT knockout mice PRMT knockout mice show different phenotypes and different degrees of severities thus indicating that PRMTs have non-redundant functions in tissue homoeostasis and embryogenesis (113). PRMT1 knockout mice died at early embryonic stage demonstrating that PRMT1 has essential function in the early embryo development (113). PRMT1 might not be essential for cell survival because of the finding that viable embryonic stem (ES) cells can be collected from the PRMT1 homozygous mutant embryos (113). However, there is some evidence that PRMT1 activity has an important role in cell proliferation and genome integrity as PRMT1 knockout mouse embryonic fibroblasts display several hypomethylated proteins along with delays in cell cycle progression, increased DNA damage, and checkpoint defects (114).  Though PRMT2 knockout mice do not show a severe phenotype in the embryonic stage similar to PRMT1 knockout mice, they were found to be lean and hypophagic, as well as having a significantly lower serum leptin compared to normal littermates due to the imbalanced STAT3-leptin signaling resulting from a lack of PRMT2-dependent methylation of STAT3 (115). PRMT2 knockout fibroblasts show enhanced NF-B activity and decreased levels of apoptosis (116). Knockout of PRMT3 resulted in small sized embryos where a known substrate of PRMT3, ribosomal protein S2 (rpS2), was hypomethylated. In addition to the small size phenotype found in PRMT3 knockout embryos, CARM1 knockout embryos die soon after birth (117, 118). 19  Moreover, CARM1 knockout mice were reported to have decreased levels of brown fat, indicating CARM1’s potential function in adipocytes (119). PRMT5 knockout was also found to be lethal at the early embryonic stage (34, 120).  1.6.2 PRMTs in diseases 1.6.2.1 Cancer Altered expression of PRMTs has been reported in different human cancer types. PRMTs have diverse range of substrates including histones, tumor suppression gene products and oncogenic proteins. Altered activity of PRMTs can cause dysregulated methylation of histones, oncogenes or tumor suppressor genes leading to cancer (34). Moreover, overexpression of PRMTs might also lead to cancer due to their ability to act as transcriptional coactivators. In prostate cancer, the methylation level of histone H4R3 has been found to be positively related to the increasing cancer grade and poor clinical outcome (121). Likewise colon and breast cancer has been correlated with the expression of one of the splice variants of PRMT1 (122, 123). Moreover, PRMT1 and PRMT6 have been found to be overexpressed in several types of cancer such as breast cancer, bladder cancer and lung cancer. Depletion of PRMT1 and PRMT6 by siRNA significantly inhibited growth of lung and bladder cancer cell lines indicating their potential role in those cancer types (34).  Mixed lineage leukemia (MLL) fusion protein such as MLL-EEN is a well characterized oncogene (124). Its C-terminal EEN contains an SH3 domain that interacts with PRMT1 through SAM68 (SAM68 interacts with PRMT1 and other SH3 domain containing proteins) (87, 125). While the ectopic expression of both MLL-Sam68 and MLL-PRMT1 was able to enhance methylation of histone H4R3, causing transformation of primary myeloid progenitors, histone 20  H4R3 methylation was inhibited by the knockdown of Sam68 or PRMT1. This finding suggests the important role of PRMTs in MLL mediated oncogenesis (126). It has been reported that estrogen receptor (ER)-αis hypermethylated in some types of breast cancers (127). It is known that PRMT1, under estrogen stimulation, methylates arginine 260 within the DNA-binding domain of ER-α. This activity is important for ER-α to recruit binding partners for estrogen signaling. Presence of hypermethylated ER-α in a subset of breast cancer indicates that PRMT1 might be involved in certain breast cancers (127) Studies revealed that the level of CARM1 is increased in prostate, breast and prostate cancers (34, 128–130). siRNA-mediated silencing of CARM1 strongly reduced estrogen mediated proliferation of  ER-α positive breast cancer cells (MCF-7), suggesting that CARM1 has a role in the cell cycle in response to estrogen (131). CARM1 has been reported to control cell proliferation by regulating E2F1, a major regulator of cell cycle. Knock down of CARM1 significantly reduced estrogen-mediated increase of E2F1 and E2F1-responsive genes in MCF-7 cells (132). The role of CARM1 in controlling estrogen-induced E2F1 expression has been reported to be mediated by its ability to methylate H3R17 at the E2F1 promoter (132). However, another study found that instead of E2F1, the expression of several negative regulators of cell proliferation such as p27kip and p21cip1 were increased upon overexpression of CARM1, leading to inhibition of estrogen induced cell growth (133). Furthermore, estrogen-induced tumor growth was increased upon knockdown of CARM1, and CARM1 expression levels were inversely correlated with tumor grade in ER-positive breast cancer samples (134). Further studies are required to confirm CARM1’s role in cancer. PRMT5 levels are overexpressed in lymphoid cancer cell lines and clinical samples of mantle lymphoma, suggesting its role in cancer (34). It has been shown that several tumor 21  repressor genes such as ST7 and N23 can be downregulated by the overexpression of PRMT5, leading to increased cell differentiation and transformation (135). PRMT5 can inhibit acetylation of histone H3K9 and increase transformation of NIH3T3 cells by methylating histone H3R8 at the promoter region of ST7 and N23 (135). In fact, lymphoid cancer cell lines and mantle cell lymphoma clinical samples were found to contain hypermethylated histone H3R8 in the promoter region of ST7 (136). Cell growth was decreased upon knockdown of PRMT5 in the lymphoid cancer cell lines (137). Moreover, it has also been found that altered activity of PRMT5 can contribute to the neoplastic transformation (34). 1.6.2.2 Cardiovascular disease Nitric oxide synthase (NOS), an enzyme that produces nitric oxide (NO) from L-arginine can be inhibited by the amino acids ADMA and MMA (53). These amino acids can be formed endogenously by the proteolysis of arginine methylated proteins. Nitric oxide plays an important roles in different physiological systems including cardiovascular, pulmonary and immune systems (138). Thus PRMTs are indirectly responsible for producing inhibitors of NOS by their abilities to produce ADMA or MMA on proteins. ADMA levels are eliminated in the body by dimethylarginine dimethylaminohydrolase (DDAH), an enzyme that metabolizes ADMA to citrulline and dimethylamine through hydrolytic degradation (139). Dysregulation of either PRMT or DDAH may lead to altered production of NO, leading to cardiovascular diseases. In fact, studies found increased levels of ADMA and reduced NO mediated signaling in DDAH1 knockout mice, which can lead to cardiovascular dysfunction (140).  1.6.2.3 Inflammatory diseases The implication that a PRMT may be involved in lung inflammation stems from studies that show increases in both PRMT2 protein expression and free ADMA levels in murine lung 22  epithelium upon exposure of mice to chronic hypoxic conditions or  ovalbumin that triggered allergic airway inflammation resembling asthma in mice (141, 142). These findings suggest that PRMT2 might participate in cellular inflammation, but its precise role as a methyltransferase has yet to be defined. PRMTs may affect lung function by mediating the formation of ADMA and MMA in the body. In the lung, NO has been shown to control multiple pulmonary functions, including pulmonary artery vasodilation, bronchoconstriction, and macrophage activity (138). As mentioned earlier, ADMA and MMA are naturally occurring amino acid derivatives of L-arginine that act as potent competitive inhibitors of all three isoforms of NOS including the inflammatory inducible iNOS (143). Indeed, increased levels of ADMA have been detected in the lungs and sputum samples of human asthmatic patients at concentrations capable of inhibiting NOS (144). In lung epithelial tissue ADMA has been shown to cause nitric oxide deficiency and induce the production of oxidative and nitrosative stress through NOS uncoupling (139, 145). Additionally, a recent study demonstrated that circulating ADMA levels affected the degree of lung inflammation in a mouse model of allergic asthma (146). In the absence of lung inflammation, ADMA infused subcutaneously into mice has also been shown to negatively affect airway physiology by contributing to increased collagen deposition and attenuated lung function (147). Therefore, ADMA is directly contributing to the pathophysiology of several pulmonary dysfunctions.  PRMT2 has also been shown to play a role in inflammatory pathway by opposing NF-B-dependent transcription upon tumor necrosis factor-α (TNF-α) signaling by sequestering the NF-B inhibitor IB-α in the nucleus where NF-B functions (116, 148). Since NF-B plays a key role in the pathogenesis of inflammation and cancer (149), the involvement of PRMT2 in controlling its signaling suggests that PRMT2 may contribute to inflammation. 23  1.6.2.4 Viral infection PRMTs methylate many viral proteins such as hepatitis C virus protein NS3, the Epstein-Barr virus nuclear antigen 2 (EBNA2), herpes simplex virus 1 nuclear regulatory protein ICP27, adenovirus E1B-AP5 and human immunodeficiency virus (HIV) trans-activator protein (Tat) (150–154). Increased HIV gene expression correlated with inhibition of PRMT6-mediated methylation leading to the hypothesis that protein arginine methylation may help protect against infection caused by HIV (150). On the other hand, hepatitis replication was found to be prevented by inhibiting arginine methylation of the small form of hepatitis delta antigen (a viral protein in the hepatitis delta virus). Finally methylation of 100K protein (an adenovirus protein) was reported to be necessary for adenovirus infection, providing evidence that arginine methylation may be a useful target to prevent or reduce some viral infections (155, 156). 1.6.2.5 Multiple sclerosis Myelin basic protein (MBP) is considered as an autoantigen in multiple sclerosis. It has been reported to be monomethylated or dimethylated at position R107 (157). Later PRMT5 was found to symmetrically dimethylate MBP at that position. A 3-fold increase in methylation of MBP was reported before the first sign of the disease in a mouse model using capillary electrophoresis mass spectrometry, suggesting that arginine methylation may have an early role in this disease condition (158).  1.6.2.6 Spinal muscular atrophy Loss of SMN1 (survival motor neuron) gene function is the characteristic feature of spinal muscular atrophy (SMA), a leading genetic disease (159). The SMN protein works as a chaperone for the proteins that are methylated by PRMTs. SMN harbors a Tudor domain, a module that binds to methylarginine-containing motifs, and has been reported to interact with 24  many CARM1 and PRMT5 substrates (35, 160). Around 3.4% of SMA patients are reported to have mutations in SMN1 gene (159, 161). The presence of point mutations in the Tudor domain of SMN1 in some of the SMA patients that had one copy of the SMN1 gene indicates a possible correlation between SMA and methyl-binding capacity of SMN (159).  1.7 Regulation of PRMT activities  Though recombinant PRMTs are active in vitro, their activities have been suggested to be regulated in different ways in vivo. Methyltransferase activity can be regulated by PRMT-binding proteins that can modify PRMT activity or change the substrate specificity of PRMTs. For instance, BTG1 and TIS2/BTG2 bind to PRMT1 to stimulate its methyltransferase activity toward certain substrates (4). hCAF1, a BTG1 binding protein, interacts with PRMT1 and controls its methyltransferase activity towards histone H4 and SAM68 (162). The tumor suppressor DAL-1/4.1B has been found to inhibit the methyltransferase activity of PRMT3 by interacting with it (163).  PRMTs function can also be regulated by their post translational modification. Several PRMTs such as PRMT1, CARM1, PRMT6 and PRMT8 can automethylate themselves (164). Though the implication of this modification is still not clear, it has been reported that automethylation of CARM1 at R551 is important for its transcriptional and splicing activity, but not for its enzymatic activity, indicating the role of this modification in regulating PRMTs activity (164, 165). CARM1 can also be phosphorylated at Serine-228 that can prevent its homodimerization and inhibit its methyltransferase activity (29). Mutation at the phosphorylation site (serine to glutamic acid) of CARM1 that mimics phosphorylated serine decreased CARM1’s ability to bind the AdoMet and reduced its methylation activity towards histone. That mutation also inhibited CARM1 mediated transactivation of ER-dependent transcription (29).  25  The ability of PRMTs to methylate their substrates can also be regulated by other post-translational modifications adjacent to or on the methylation sites. H3R8 methylation by PRMT5 and lysine acetylation of H3K9 can be a good example of this type of interaction (135, 166). Whereas H3K9ac prevents H3R8 methylation, dimethylated H3R8 prevents H3K9 methylation (135, 167). Furthermore, methylated H3K4 can inhibit the methylation of H3R2 by PRMT6 (168). Methylation of arginine residues can also be blocked by peptidyl arginine deiminases (PAD) that convert arginine to citrulline residues (169). Histone H2A, H3 and H4 are the major deiminated proteins (170). Moreover, a Jumonji domain containing protein JMJD6 has been reported to have arginine demethylase activity; it was found to demethylate H3R2me2 and H4R3me2 (171). However, further studies raised controversy on its demethylation activity and revealed that JMJD6 is a lysine hydroxylase enzyme that plays a role in splicing and other gene expression related processes (172, 173).  1.8 Hypothesis and research objectives PRMT2 has already been demonstrated to play a role in the NF-κB signaling pathway in response to inflammatory cytokines (116, 148), and Dr. Lam Pak (a former graduate student in our lab) recently identified (using proteomic techniques) sets of PRMT2-binding proteins that have been shown to control gene expression upon extracellular stimuli through controlling transcription, alternative splicing and signal transduction (Table A.1) (174). One of the identified splicing factors was SAM68. SAM68, a known arginine methylation substrate, is a trans-acting factor that mediates the alternative splicing of the BCL-X transcript involved in the NF-κB mediated inflammatory pathway and is a regulator of programmed cell death (175–177). Thus PRMT2’s ability to interact with SAM68 suggests that PRMT2 could participate in the 26  inflammatory pathway by controlling the alternative splicing of the BCL-X. Additional preliminary evidence from work in our laboratory indicates that PRMT2 can interact with and activate PRMT1 to produce elevated levels of ADMA on proteins (178). Increased levels of free ADMA has been reported in some lung inflammatory diseases like asthma (141, 142) that could result from increased PRMT activity. Therefore, it could be predicted that PRMT2’s ability to increase the activity of PRMT1 can be another way by which PRMT2 might be involved in inflammation. Thus, I hypothesize that PRMT activity plays a role in the cellular response to inflammation in cultured cells. In order to address my research hypothesis, my first research objective was to identify PRMT substrates that change their methylation level upon inflammatory stimulation. I treated A549 lung epithelial cells with pro-inflammatory cytokine TNF-α and the pro-inflammatory agent bacterial lipopolysaccharide (LPS) and identified PRMT substrates that change their methylation level upon these treatments using different techniques like Western blot and mass spectrometry. Once proteins with altered methylation levels were identified, I used networking tools (e.g., GeneMANIA) to determine if methylated proteins and their protein networks are involved in specific biological processes. My second aim was to explore a role for PRMT2 as a factor in the inflammatory response by looking at its effect on BCL-X alternative splicing. I depleted PRMT2 levels using siRNA or shRNA in HEK293 and investigated if BCL-X splicing has been affected. Then I overexpressed PRMT2 to investigate if it can reverse the effect of PRMT2 silencing on BCL-X splicing to provide evidence that PRMT2 in fact mediates alternative splicing of BCL-X.  27  Chapter 2: Global changes to arginine methylation in response to inflammation 2.1 Introduction  In this study, I investigated the effect of inflammatory stimulation on the methylation status of proteins in A549 cells. I used Western blot to detect changes in the methylation level of proteins upon inflammatory stimulation with the pro-inflammatory cytokine TNF-α and the pro-inflammatory agent LPS. However, due to the inability to capture any recognizable difference in protein methylation by Western blot using anti-ADMA antibodies in whole cell lysate (discussed in the result section 2.3.2), we decided to use a more sensitive proteomic technique (peptide labeling technique for proteomic MS) to identify proteins with altered methylation levels. The planning and execution of this effort was performed in collaboration with N.E. Scott. Though we were able to identify >200 unique dimethylarginine-containing peptides in each replicate, the level of variation between the three experiments was too great to be confident in quantifying methylation changes (discussed in the result section 2.3.2). To avoid this variability, we used a different proteomic technique called SILAC coupled with HILIC based enrichment strategy prior to sample analysis by MS.  SILAC involves labeling the proteins in cells instead of peptides as a means to eliminate any variability during sample processing (45). After initial optimizations, A549 cells were grown for an appropriate amount of time in the presence of tissue culture media specifically modified for SILAC experiments. Specifically, the cells were supplemented with differentially labeled L-lysine and L-arginine to differentiate treated from untreated samples (samples mixed for relative quantitation purposes) in the mass spectrometer (45, 179) (discussed in the method section). 28  In our SILAC experiments, we found that TNF-α and LPS caused similar changes to arginine methylation for proteins primarily involved in mRNA processing, RNA splicing, and nuclear transport, indicating that these two inflammatory stimuli share mutual downstream pathways involving methyltransferase activity. Moreover, the change in the methylation level of proteins involved predominantly in mRNA processing, RNA splicing, and nuclear transport indicates that methylation might exhibit their role in inflammatory condition by altering expression level of proteins relevant to inflammatory pathways. Although TNF-α and LPS treatment did not generally impact protein expression, proteins involved in innate immunity and viral response did increase upon TNF-α treatment consistent with an inflammatory response. Altogether, our findings indicate that TNF-α and LPS caused changes in the methylation level of proteins particularly involved in mRNA processing, RNA splicing, and nuclear transport.  2.2 Methods 2.2.1 Tissue culture and treatment with TNF-α or LPS  A549 cells (human alveolar basal epithelial cells) (ATCC) were cultured in Dulbecco’s Modified Eagle’s Medium (DMEM) (Sigma) supplemented with 10% fetal bovine serum (FBS) (Gibco), 50 units/mL penicillin (Gibco), and 50 µg/mL streptomycin (Gibco) at 37°C in 5% CO2. Cells were seeded in 75 cm2 flasks (Corning) 1 day prior to the treatment in standard growth medium so that the cells grow at a confluence level of around 70% on the day of treatment. On the day of experiment, the growth medium was first replaced with fresh growth medium and then the cells were treated with either TNF-α (25 ng/ml) (BD Pharmingen) or LPS (1 µg/ml) (Sigma-Aldrich) for 24 h before they were harvested. 29  2.2.2 Western Blot After TNF-α or LPS treatment, A549 cells were harvested using 0.05% trypsin (Sigma) and lysed by repeated (three times) freezing and thawing in hypotonic lysis buffer [20 mM HEPES-KOH pH 7.4, 2 mM MgCl2, 0.2 mM EGTA, 10% glycerol, 1x protease inhibitor cocktail (Roche, 04693132001)]. The concentration of NaCl was then adjusted to 400 mM. The cell lysate was incubated for 5 minutes on ice and then was centrifuged at 14,000 rpm using an Eppendorf centrifuge (5417 R) at 4 ºC for 15 minutes to separate proteins from the rest of the cell components. The soluble supernatant was aliquoted and protein concentration was measured using Bradford reagent (Bio-Rad, catalog number 500-0002).  Proteins were separated on 10% SDS-PAGE gels and transferred to nitrocellulose or PVDF membranes. The membranes were then blotted with anti-ADMA antibody (39231, Active Motif). Goat anti-rabbit IgG-HRP (sc-2054, Santa Cruz) and ECL Western blotting detection reagents (GE Healthcare) were used for protein visualization. The membranes were then stripped and re-blotted with anti-Actin antibody (sc-1615, Santa Cruz) and were visualized using rabbit anti-goat IgG-HRP (sc-2922, Santa Cruz) following the method described above.  For investigating the expression of MX1, ICAM1, HMGI-C, Rab 27b and RABAC1, (sc-162033, Santa Cruz), the cell lysates were obtained after TNF-α or LPS treatment following the method described earlier. The membrane was blotted against respective antibodies (MX1 (ab95926, Abcam), ICAM1 (sc-7891, Santa Cruz), HMGI-C (sc-30223, Santa Cruz), Rab 27b (sc-22991, Santa Cruz), and RABAC1 (sc-162033, Santa Cruz)) and visualized following the method described above. The membranes were then stripped and re-blotted with anti-Actin antibody (sc-1615, Santa Cruz) following the method described earlier. Images were processed 30  and analyzed using ImageJ 1.47v image processing and analyzing software (http://imagej.nih.gov/ij/). 2.2.3 Triple peptide labeling by formaldehyde and analysis by MS A549 cells were treated in triplicate for 24 h with TNF-α or LPS to stimulate inflammation following the method described under the section “Tissue culture and treatment with TNF-α or LPS”. No-treatment samples were included as a negative control. After TNF-α or LPS treatment, A549 cells were harvested and proteins were isolated following the procedure mentioned under the previous section. Protein concentration was measured using Bradford reagent (Bio-Rad, catalog number 500-0002). After digesting an equal amount (1 mg) of proteins with trypsin, TNF-α treated samples were labeled with medium label (12CD2O, +32 Da), LPS treated samples with heavy label (13CD2O, +36 Da), and control samples with light label (12CH2O, +28 Da). The labeled peptides were then separated by HILIC to isolate arginine-containing peptides prior to sample analysis on a Thermo Scientific Q Exactive Orbitrap LC-MS/MS System housed in the Pharmaceutical Sciences Mass Spectrometry Facility, UBC.  2.2.4 SILAC experiment, sample processing, mass spectrometry and data analysis The SILAC experiments were divided into two phases, the adaptation phase and the experiment phase. In the adaptation phase of the SILAC experiment, A549 cells were grown in parallel in three differentially-labeled amino acids containing SILAC medium at 37°C in 5% CO2 to allow full incorporation of the amino acid into proteins (45). The SILAC medium contained SILAC DMEM lacking arginine and lysine (Caisson labs, product number: DML07), 10% dialyzed FBS, 50 units/mL penicillin, and 50 µg/mL streptomycin (Gibco) along with differentially labeled L-arginine and L-lysine. The heavy labeled SILAC medium contained Arg-31  10 and Lys-8, the medium labeled SILAC medium contained Arg-6 and Lys-4, and the light medium contained regular (unlabeled) L-arginine and L-lysine. The final concentrations of L-arginine and L-lysine in the SILAC media were 0.398 mM and 0.798 mM, respectively.  The cells were grown in the light and medium SILAC media for nine replication cycles (each replication cycle was of approximately 24 h measured with IncuCyte Zoom live cell imaging system (Essen Bioscience)) in 25 cm2 flasks (Corning) to assure >97% incorporation (45). Medium was changed every 2-3 days and the cells were passaged to a new flask when they reached around 80% confluency. Cells were then harvested, lysed and proteins were analyzed by MS for measuring amino acid incorporation to ensure that approximately 97% of the proteins contained labeled amino acids in the cells following previously described method (49). After ensuring the required level of incorporation of amino acids has been achieved, the three cell populations were differentially treated for the experiment phase. Cells were seeded in 75 cm2 flasks (Corning) 1 day prior to the treatment so that the cells grow at a confluence level of around 70% on the day of treatment. On the day of the experiment, the growth medium was first replaced with fresh growth medium. I treated cells in triplicate for 24 h with TNF-α (25 ng/ml) (BD Pharmingen) in medium label (Arg-6, Lys-4) or LPS (1 µg/ml) (Sigma-Aldrich) in heavy label (Arg-10, Lys-8) to stimulate inflammation. Cells grown in the absence of treatment without labelled amino acids served as the control group. Cells were harvested and lysed by repeated freezing and thawing in hypotonic lysis buffer [(20 mM HEPES-KOH pH 7.4, 2 mM MgCl2, 0.2 mM EGTA, 10% glycerol, 1x protease inhibitor cocktail (Roche, 04693132001)]. The cell lysate was incubated for 5 minutes on ice and then was centrifuged at 14,000 rpm using an Eppendorf centrifuge (5417 R) at 4 ºC for 15 minutes to separate proteins from the rest of the cell components. The soluble protein containing supernatant was aliquoted and protein 32  concentration was measured using Bradford reagent (Bio-Rad, catalog number 500-0002). After combining equal amounts of protein (1 mg) for each sample and digesting with trypsin, the resulting peptides were separated by hydrophilic interaction liquid chromatography to isolate arginine-containing peptides prior to sample analysis on the Q Exactive LC-MS/MS. Data analysis was done using MaxQuant.  Gene Ontology terms of individual proteins based on their involvement in biological processes were obtained from UniProt database at http://www.uniprot.org/, as well as from references herein. Physical- and pathway-based interaction maps for the identified proteins were generated using the GeneMANIA algorithm at http://www.genemania.org/. Data obtained from GeneMANIA are presented based on the expression or methylation level using Cytoscape (http://www.cytoscape.org/).  2.2.5 In vivo methylation assays Myc-G3BP2 plasmid was a gift from Dr. Rama Kamesh Bikkavilli in the Department of Pulmonary, Critical Care, Sleep, and Allergy at the University of Illinois at Chicago, Chicago, IL, USA. For transfections with pCMV-Myc (empty vector) or Myc-G3BP2, A549 cells were seeded in 75 cm2 flasks (Corning) containing standard growth medium 1 day prior to transfection so that they become approximately 70% confluent on the day of treatment. On the day of the experiment, the growth medium was first replaced with Opti-MEM medium (Invitrogen), and the A549 cells were transfected with 8.0 µg of plasmid DNA using 35.0 µL of Lipofectamine 3000 and 16.0 µL of P3000 reagent (Invitrogen) per the manufacturer’s instructions. The Opti-MEM medium was replaced with the standard growth medium (DMEM supplemented with 10% FBS, 50 units/mL penicillin, and 50 µg/mL streptomycin) 8-hours post-transfection. After 24 hours of transfection, cells were washed twice with PBS and treated with 33  cycloheximide (100 µg/ml) and chloramphenicol (40 µg/ml) in cell culture medium (DMEM with 10% FBS) for 30 minutes at 37ºC to inhibit protein translation (103). The A549 cells were than washed twice with methionine-free DMEM (Gibco) after 30 minutes. The cell labeling mixture containing cycloheximide (100 µg/ml), chloramphenicol (40 µg/ml) and L-[methyl-3H]methionine (20 µCi/ml) (Perkin Elmer, NET061X005MC) in methionine-free DMEM was added to the cells in the presence or absence of TNF-α (25 ng/ml) and cultured for an additional 24 hours before they were harvested (103, 180).  Cells were lysed by repeated freezing and thawing in hypotonic lysis buffer [20 mM HEPES-KOH pH 7.4, 2 mM MgCl2, 0.2 mM EGTA, 10% glycerol, 1x protease inhibitor cocktail (Roche, 04693132001)]. The cell lysate was adjusted to 400 mM NaCl and incubated on ice for 5 minutes before centrifugation at 14,000 rpm for 15 minutes at 4 ºC. The soluble supernatant was aliquoted and protein concentration was measured by using Bradford reagent (Bio-Rad, catalog number 500-0002).  For immunoprecipitation of Myc-G3BP2, cell lysate (1 mg protein) was aliquoted and the buffer was adjusted (50 mM HEPES-KOH, 150 mM NaCl). Monoclonal anti-c-Myc antibody (9E10, Sigma) was then added to the aliquoted cell lysate, and the volume was adjusted to 500 μL with co-IP buffer [150 mM NaCl, 50 mM HEPES-KOH, pH 7.4 and 1x protease inhibitor cocktail (Roche, 04693132001)]. The protein-antibody mixtures were incubated for 4 hours with rotation at 4ºC. After 4 hours the cell lysate-antibody mixture was added to 80 μL protein G-sepharose (Invitrogen) prewashed with PBS and was incubated with rotation at 4 ºC for 16 h. The resin was then washed thoroughly with 0.05% (v/v) Tween 20 in PBS five times. The bound proteins were eluted by adding 2X SDS-PAGE sample loading buffer and boiling for 10 minutes. Proteins were separated on 10% SDS-PAGE gel and the gel was transferred to a PVDF membrane. The membranes were then sprayed with EN3HANCE 34  (PerkinElmer) for autoradiography enhancement per the manufacturer’s instructions. The dried gel was then placed against an X-ray film (Kodak). The film was exposed for one week at -80ºC to achieve the desired signal level and then the image was developed using the autoradiography developer and fixer (Sigma, P7042 and P7167).  2.3 Results 2.3.1 Altered expression of proteins upon TNF-α or LPS stimulation  The SILAC experiments allowed us to identify around 4000 proteins in all three replicates. We found that TNF-α and LPS treatment did not generally impact protein expression, however proteins involved in innate immunity and viral response did increase upon TNF-α treatment (Figure 2.1 and Table 2.1) consistent with an inflammatory response. Compared to TNF-α, LPS treatment had much less impact on protein expression level in terms of both quantity and magnitude.  Expression of around 130 proteins was found to be upregulated, whereas 140 proteins expression were downregulated upon TNF-α stimulation (Table 2.1). Nine proteins showed more than 4-fold increase in their expression level in all replicates (Table 2.1). Most of these proteins such as ICAM1 and MX1 are involved in the immunity-related pathway according to GO. Proteins that showed 2- to 4-fold increase in expression level (around 30 proteins) are also mostly involved in immunity pathways with some of them involved in cell cycle and inflammatory pathways. Around 90 proteins expression level increased by 1.4- to 2- fold upon TNF-α stimulation. Proteins related to immunity and inflammatory pathways represent the majority of proteins in this group. Noticeably, downregulated proteins are mostly involved in cell cycle processes.  35   Figure 2.1 Response to inflammation by LPS and TNF-α.  Changes in the amounts of proteins were quantified using SILAC-MS for treated A549 cells (n = 3). Gene ontology (GO) terms are indicated.  36    Figure 2.2 Proteomic network. The physical and pathway based interactions between proteins from cells stimulated with either TNF-α or LPS identified by proteomics and their common biological functions are shown using the GeneMANIA algorithm and rendered with Cytoscape version 3.0.1.   37    Table 2.1 Changes in the expression level of proteins (expressed in log2 value) upon TNF-α or LPS treatment.  Log2 value (x) TNF-α treatment  LPS treatment  x > 2 immunity  DDX58 IFIT1 ASS1  ICAM1 MX1 GBP1  inflammatory response  SERPINE1 TRAF1   superoxide metabolic process  SOD2    2 ≥ x> 1 apoptotic process  CLIC4 BID PLEC  FOSL2 CASP7 PPIF ANXA6 DFNA5 PTTG1IP  cell cycle  HMGA2 EGFR PSME2 RB1 PSMB9 PSMB10 SMAD3 DAPK3 PSMB8  cell differentiation  CSRP2 JUNB PDLIM7  cell adhesion  ITGA2 FERMT2 ITGAV  cell migration  AVL9 PEAK1   cytoskeleton organization  PALLD SDCBP   endocytosis EHD1 EHD4   exocytosis 38  Log2 value (x) TNF-α treatment  LPS treatment  TNFAIP2   RAB27B NF-kappaB signalling     CARD8 others LUZP1 PLOD2 KIAA1217 EML2 AMPD3 RPL22L1  MMTAG2  BPGM OTUD4   1 ≥ x  > 0.5 Immunity HLA-A IFI16 TRIM5 MX1 JUN MAP2K2 ERAP1  NFKB2 OAS3 PML  B2M SERPINB9   immunity; inflammatory response IRAK2 ISG20 NFKB1 IRAK2 RIPK2 OASL CXCL3 ISG20 TNIP1 RELB DPYSL3  GBP2 STAT5A OPTN  IFIT3 BIRC2   inflammatory response PTGS2 CEBPB   APOL2 NMI   metabolic process ACO1 DBI NAMPT  AKR1B1 KYNU CA8  C14orf149 MVD   signal transduction AFAP1 FGD6 GRB10  AKAP12 NPC1   DOCK4 PLEK2   Transcription DENND4A GLS   peptidyl-proline hydroxylation P4HA1 P4HA2   negative regulation of endopeptidase activity SERPINB8    platelet aggregation CSRP1    protein transport SCAMP1    RNA splicing QKI    39  Log2 value (x) TNF-α treatment  LPS treatment  cell cycle    RAD21 Others SHB TBC1D10A TRIM21 WDR59 SLFN5 TGM2 TUBB2B EIF2B5 SMTN TJAP1 UAP1 CA8 SQRDL TLE4 UBE2L6 EXOC3 STK38L TMOD1 UMPH1 GALT STX4 TOM1 USF2 GTF2H2C TAGLN TPM1  KIF15    LENG8    NDRG1    RPL22L1    RPN1 RPS6KA4 -1 ≤ x < -0.5 cell cycle AURKB KPNA2 SKA3  CCNB1 LIG1 SPAG5  CENPF MASTL SPC24  CIT NUSAP1 TIPIN  DHFR PBK TPX2  DLGAP5 PLK1 TTK  ECT2 POLE2 UBE2C  KANK2 PPP1R9B ZWINT  KIF11 RACGAP1 BANF1  KIF22 RBL1   KIF23 RRM2   immunity CLU PTK2B  TAB2 MAP2K6 RNF31  IFIT1    C8ORF59 immunity; inflammatory response FOS    inflammatory response HMOX1    cell proliferation RRM1     DNA ligation MGMT TOP2A   DNA conformation change HIST1H4A HJURP   protein binding 40  Log2 value (x) TNF-α treatment  LPS treatment  MKI67    metabolic process    ALAD    HMOX1    ACADM    ALDH4A1 endocytic recycling    ARL4C proteolysis    CLPP transcription    CHCHD2 Others AGFG2 GPX2 PLIN2 VPS37B AGR2 HADH PPFIBP2 CLU ALDH3A1 HELLS PRC1 EPM2AIP1  ALDH3B1 HEXB PROCR GRAMD1A AMZ2 HEXIM1 RAB11FIP1 ID1 ANK1 HGD RNASEH2B MRPL13 ARAP3 HIRIP3 SAMD1 NEIL2 ARHGAP18 HIST1H1C SARG PIN4 ARRB1 HIST1H1D SCP2 PLEKHF2 ASPH HIST1H1E SDPR UTP23 C19ORF21 HMMR SHMT1  CEACAM6 HSD11 SLC12A2  CHCHD2 ISYNA1 SSH3  CKAP2 ITGB4 STRBP  CTSZ IVNS1ABP SULT1A3  DAPK1 KCTD15 TACC3  DENND5B LEMD3 TFEC  DHRS3 LLGL2 TJP3  DIAPH3 LMNB1 TMPO  DMC1 LXN TOR1AIP1  DNM3 MAP4K2 TTC38  DPP7 MEAF6 TUBB4A  EPB41L1 MTDH UACA  EPB49 MTUS1 UBE2T   EPN1 MYO15B UTP23  EPS8L2 MYO18A VGLL4  FAM195A NFIA WDHD1  FANCI PCM1 ZMYM4  GABARAP PFKFB3 ZNF511  41  Log2 value (x) TNF-α treatment  LPS treatment  GATSL1 PIN4   GPCPD1 PKP3   -3 ≤ x < -1 cell cycle TYMS    peroxisome proliferator activated receptor signaling pathway    STARD10 others CDCA2 PEG10 SYNE2  ENO3 PLCH1 TPD52L1  FTL RRBP1   ID1 SELENBP1   x < -3 vesicle formation RABAC1   RABAC1  We further explored the network of proteins involved in innate immunity and viral responses represented in Figure 2.3. Most of these proteins showed increased expression level upon TNF-α treatment. We also explored the interactions of proteins involved in cell cycle process and found that this group of proteins comprises proteins with diverse functions including positive and negative regulator of cell cycle (Figure 2.4).   42   Figure 2.3 Proteomic network of immunity and viral response related proteins upon TNF-α stimulation. The interactions between proteins from cells stimulated with TNF-α by proteomics and their common biological functions are shown using the GeneMANIA algorithm and rendered with Cytoscape version 3.3.0.  43   Figure 2.4 Proteomic network of proteins involved in cell cycle processes upon TNF-α stimulation. The interactions between proteins from cells stimulated with TNF-α by proteomics and their common biological functions are shown using the GeneMANIA algorithm and rendered with Cytoscape version 3.3.0. As mentioned earlier, LPS treatment had much less of an impact on protein expression levels in terms of both quantity and magnitude compared to that of TNF-α. We identified around 40 proteins that changed their expression level by at least 1.4-fold (Table 2.1). Similar to the TNF-α stimulation, immunity related proteins showed increased expression under LPS treatment.  We were also able to observe increases in the human myxovirus resistance gene A (MX1 or MXA), intercellular adhesion molecule 1 (ICAM1), and high mobility group protein (HMGI-C) expression in TNF-α treated cells by Western blot that corresponded to the changes seen in 44  the SILAC experiment (Figure 2.5A-C). These proteins were used as markers to confirm that inflammation was elicited in TNF-α treated cells.   Figure 2.5 Change in protein expression under TNF-α and LPS stimulation. A549 cells were stimulated with TNF-α (25 ng/ml) (A-C) or LPS (1 µg/ml) (D) for 24 hours before they were harvested. Expression level of Icam1 (A), MX1 (B), HMGI-C (C) and RABAC1 (D) were analyzed by Western blot where actin is a loading control. We also used Western blot to show the change in the expression level of some of the proteins that we observed in our SILAC experiment upon LPS treatment. We observed increased expression of Rab 27b (a Ras-related protein) and decreased expression of RABAC1 (Prenylated Rab acceptor protein 1; PRAF1; PRA1) under LPS stimulation by SILAC. However, we could not find any overexpression of Rab27a upon LPS treatment by Western blot (data not shown), which could be attributed to the quality of the antibody and/or low expression of Rab27a in A549 cells (181). We observed a slightly decreased expression of RABAC1 (44.5 ± 12.02 %) upon LPS treatment by Western blot (Figure 2.5D). Altogether our results indicate that proteins 45  involved in innate immunity and viral response increases upon TNF-α or LPS treatment though LPS has less impact on protein expression levels. 2.3.2 Changes in the methylation level of proteins upon TNF-α or LPS stimulations Initially I wanted to establish the cellular response to the pro-inflammatory cytokine TNF-α and the pro-inflammatory agent LPS in A549 lung epithelial cells by measuring ADMA-containing proteins using Western blot analysis. If we observed any hypermethylated band(s), our plan was to cut the band(s) from a gel corresponding to the molecular weight of hypermethylated bands on Western blot and identify hypermethylated proteins by proteomic approaches (Figure 2.6). A549 cells were stimulated with the TNF-α or LPS for 24 hours and ADMA content in proteins was analyzed by Western blot. However, we could not observe recognizable difference in protein methylation by Western blot (Figure 2.7). It could be that inflammation might change the methylation status of only a few specific proteins, and capturing such small changes in methylation by Western blot with the whole cell lysate might not be possible because of the limitation of sensitivity inherent for the method. So we decided to use a more sensitive proteomic technique to identify specific proteins that get hypermethylated upon inflammatory stimulation (182) in collaboration with N.E. Scott. 46   Figure 2.6 Sample processing for detecting methylation changes. Here is an overview of the proposed process of identifying hypermethylated substrates upon TNF-α or LPS treatment by Western blot and proteomic analysis.  Figure 2.7 Protein ADMA level upon TNF-α and LPS stimulation of lung epithelial cells. A549 cells were stimulated with TNF-α (25 ng/mL) or LPS (1 µg/ml) for 24 hours (A) or 48 hours (B). ADMA levels were assessed by Western blot where actin is a loading control.  We treated A549 cells in triplicate for 24 h with TNF-α or LPS to stimulate inflammation, as well as included no-treatment (control) samples. After digesting an equal amount of proteins 47  with trypsin, the resulting peptides were labeled by triple peptide labeling technique by formaldehyde (Figure 2.8). This technique involves converting primary amines of a peptide to dimethylamines using differentially labeled formaldehyde and cyanoborohydride (183). TNF-α treated samples were labeled with medium label (CD2O, +32 Da), LPS treated samples with heavy label (13CD2O, +36 Da), and control samples with light label (CH2O, +28 Da). The labeled peptides were then separated by HILIC to isolate arginine-containing peptides prior to sample analysis on a Thermo Scientific Q Exactive Orbitrap LC-MS/MS System. As shown in Figure 2.9A, 1682 and 1804 peptides identified in the TNF-α and LPS treatment groups, respectively, show that methylation of the majority of peptides during inflammation change within 2-fold, whereas others exhibit larger changes. Additionally, many of the peptide methylation changes between TNF-α and LPS treatment groups are distinct (Figure 2.9B), indicating that these treatments create different methylation profiles. Despite these differences, the proteins whose methylation changes during inflammation belong to cellular pathways key for adaptation to environmental changes (Figure 2.9C). 48   Figure 2.8 Steps to identifying hypermethylated proteins upon TNF-α or LPS treatment by stable isotope dimethyl labeling.  49   Figure 2.9 Protein arginine methylation response to inflammation. (A) Peptide changes in methylation. (B) A comparison of methylation changes on peptides between treatment groups. (C) Major cellular pathways affected by treatments (the numbers of identified proteins are indicated). Though we were able to identify >200 dimethylarginine-containing peptides in each replicate, the level of variation between the three experiments was too great to be confident in quantifying methylation changes. We selected dimethylated peptides that were identified in all three replicates to investigate the variability among the replicates. We identified 27 and 25 di-methylated peptides in TNF-α and LPS treated cells, respectively, that were detected in all three of the experimental replicates, as well as measured the standard deviation between the values of methylation changes of those peptides. The average log2 values of the methylation change for all 50  of these peptides compared to control were -0.303 and -0.736, respectively for TNF-α and LPS treated samples. The average standard deviations of log2 values of the methylation change between three replicates were 1.14 and 1.051, respectively for TNF-α and LPS treated samples, indicating that most of the methylated peptides (about 68%) exhibit on average up to a 2.2-fold change from one biological replicate to another, which is huge compared to the average methylation change of 0.81- and 0.60-fold upon TNF-α and LPS treatments, respectively. Moreover, this result also indicates that another 27% of the methylated peptides might show 2.2- to 4.4-fold change from one biological replicate to another without any change in the treatment. For example, we observed large variably in the methylation changes among the replicates in multiple cases. For example, we observed around a 15-fold decrease in methylation of one of the peptides (NR(di)PAIAR(di)GAAGGGGR) of  THO complex subunit 4 (ALYREF) under LPS stimulation in two experiments, but saw only 0.71-fold decrease in the third replicate. Another example is the methylation change of the one of the peptides (GGNFSGR(di)GGFGGSR) of HNRNPA1 that showed decreased methylation by 10.63- and 1.17-fold in the two experiments, whereas it showed increased methylation by 1.16-fold in the third replicate upon TNF-α stimulation. We reasoned that this method involves several sample processing steps before MS analysis of the labeled peptides that could introduce variability in achieving equal amounts of labeled peptide from each treatment during the final analysis by MS as reported previously (45). To avoid this variability, we used SILAC (45), which involves labeling the proteins in cells instead of peptides as a means to eliminate any variability during sample processing (Figure 2.10). We treated A549 cells in triplicate for 24 hours with TNF-α in medium label (Arg-6, Lys-4) or LPS in heavy label (Arg-10, Lys-8) to stimulate inflammation, as well as no treatment without label (control sample (Arg-0, Lys-0)) (discussed in the method section). After combining 51  equal amounts of protein for each sample and digesting with trypsin, the resulting peptides were separated by HILIC to isolate arginine-containing peptides prior to sample analysis on the Q Exactive LC-MS/MS.  Figure 2.10 Experimental steps for the SILAC experiment. Figure 2.11 represents the numbers of identified MMA and DMA containing peptides in each of the three replicates in TNF-α and LPS treated cells. The majority of DMA-containing peptides contain the modified residue flanked by glycines. We identified 149 unique arginine methylated proteins combined from three experiments. We found that 43 of these methylated proteins were common in all of the replicates that were identified by 46 and 21 unique DMA- and MMA-containing peptides, respectively. We selected dimethylated peptides (46) that were 52  identified in all three replicates to investigate the variability among the replicates. We measured the standard deviation between the values of methylation changes of those peptides upon treatment with TNF-α or LPS in each of the three experiments. The average log2 values of the methylation change of all these peptides compared to control were -0.0106 (indicating on average 0.99-fold change) and -0.023 (indicating on average 0.98-fold change), respectively for TNF-α and LPS treated samples. The average standard deviations of log2 values of the methylation change between three replicates were 0.106 and 0.099, respectively for TNF-α and LPS treated samples indicating that most of the methylated peptides (about 68%) exhibit only up to 1.07-fold change from one biological replicate to another without any change in the treatment. This small variation between each replicate represents the strength of the SILAC method compared to the peptide labeling method similar to what has been mentioned in the literature (45). TNF-α and LPS caused similar changes to arginine methylation for proteins primarily involved in mRNA processing, RNA splicing, transcription regulation, chromatin modification and nuclear transport (Figure 2.12 and 2.13), indicating that these two inflammatory stimuli share mutual downstream pathways involving methyltransferase activity. Identified methylated proteins with altered methylation status under TNF-α and LPS treatment are also found to be involved in some other biological processes including translation regulation, NF-κB signaling, post-transcriptional regulation, intracellular transport and viral processes (Table 2.2). In total 64 proteins exhibited altered methylation levels upon TNF-α stimulation. Among them, 24 proteins are reported to be involved in mRNA processing and RNA splicing that covers approximately 10% of the total number of proteins involved in these biological processes according to GeneMANIA algorithm. More than 10 of the identified RNA splicing proteins are 53  found to be part of the spliceosomal complex. Approximately 5 and 10 of the identified proteins with changed methylation level are involved in transcription and posttranslational regulation of gene expression, respectively (Table 2.2).  In total 60 proteins exhibited altered methylation levels upon LPS stimulation. Among them, around 20 proteins are reported to be involved in mRNA processing and RNA splicing. Similar to the TNF-α treated cell, 11 of the identified RNA splicing proteins are found to be the part of spliceosomal complex. Approximately 5 and 10 of the identified proteins with changed methylation level are involved in the processes related to viral life cycle and posttranslational regulation of gene expression, respectively. We also found that 5 and 4 of the identified proteins are involved in nuclear transport and transcription, respectively (Table 2.2). Interestingly, when I tried to find out using GeneMANIA if the identified methylated proteins interact with each other, we found that approximately 50 of the identified proteins from both TNF-α and LPS treated cells form physical and pathway-based networks, indicating cross-talk among these methylated proteins (Figure 2.13). Most of these proteins are involved in mRNA processing similar to the findings with all the proteins with altered methylation levels discussed earlier (Table 2.2 and Figure 2.12). The proteins in the network are also involved in some other biological processes including histone modification, gene expression, transcription and translation regulation, NF-κB signaling, and nuclear transport. Most of the proteins related to mRNA processing, nuclear transport, translation, NF-κB signaling, translation regulation showed increased methylation levels, while proteins involved in transcription regulation showed no change or decreased arginine methylation (Figure 2.13).    54   Figure 2.11 Venn diagrams representing the number of identified methylated peptides and proteins by SILAC.  Number of identified MMA and DMA containing peptides from each replicate are represented in the diagrams on the left.  Total numbers of proteins identified are marked in blue. Venn diagram on the right represents number of MMA and DMA containing proteins identified in each replicate.  55    Figure 2.12 Response to inflammation by TNF-α and LPS.Changes in the amounts of MMA and DMA were quantified using SILAC-MS for treated A549 cells (n = 3). The log2 values for changes in methylation were normalized to the expression of the proteins. Gene ontology (GO) terms are indicated.  56    Figure 2.13 Methylarginine network. The physical interactions between arginine-methylated proteins from cells stimulated with either TNF-α or LPS identified by proteomics and their common biological functions are shown using the GeneMANIA algorithm and rendered with Cytoscape version 3.0.1.       57    Table 2.2 Changes in the DMA and MMA level (expressed as log2 value) on peptide(s) of the proteins upon TNF-α or LPS treatment. Log2 value (x) TNF-α treatment (DMA) LPS treatment (DMA) TNF-α treatment (MMA) LPS treatment (MMA) x > 0.5  mRNA processing; RNA splicing SFPQ  SFPQ SFPQ   PABPC1  mRNA processing; RNA splicing; nuclear transport THOC4    nuclear transport G3BP2 G3BP2   regulation of translation RBM3    CSDA    unknown  CRIP1 CRIP1 0 < x < 0.5       gene expression MED12 MED12   PATL1    mRNA processing EIF4G1 EIF4G1   mRNA processing; RNA splicing FUS FUS   HNRNPA0 HNRNPA0   HNRNPA2B1 HNRNPA2B1 HNRNPA2B1 HNRNPA2B1 HNRNPD HNRNPD   HNRNPH2 HNRNPH2   HNRNPU HNRNPU   HNRNPUL1 HNRNPUL1   PABPC1 PABPC1   TRA2B TRA2B   PABPN1 SFPQ   HNRNPA1  HNRNPA1  HNRNPH1    QKI    mRNA processing; nuclear transport  KHDRBS1   mRNA processing; RNA splicing; nuclear transport 58  Log2 value (x) TNF-α treatment (DMA) LPS treatment (DMA) TNF-α treatment (MMA) LPS treatment (MMA)  HNRNPA1    PABPN1    THOC4   nuclear transport G3BP1 G3BP1   transcription & transcription regulator TAF15 TAF15   EWSR1 EWSR1   HNRNPAB HNRNPAB HNRNPAB  ELMSAN1 YLPM1    BPTF    HCFC1   NF-kappaB signaling regulator TFG TFG TFG TFG others XRN2 XRN2 CCT7 CCT7 HNRPDL HNRPDL CAPN2 CAPN2 AKAP8L RBM3 G3BP2 G3BP2 CHTF8 TAF15 QK  HNRNPUL2 YLPM1   RNF214 CHTF8    HNRNPUL2     CSDA   0 > x > -0.5 gene expression EEF1A1 EEF1A1 EEF1A1 EEF1A1  PAIP1  RAI1  PATL1   mRNA processing SUPT5H SUPT5H   CPSF6 CSTF2    HNRNPK    SNRPB   mRNA processing; RNA splicing HNRNPH1 HNRNPH1 HNRNPH1 HNRNPH1 HNRNPH3 HNRNPH3  QKI HNRNPA1 QKI HNRNPA1 HNRNPA1 HNRNPK SF3B2 SF3B2 SF3B2 HNRNPU    PABPN1    RBMX    SNRPB    59  Log2 value (x) TNF-α treatment (DMA) LPS treatment (DMA) TNF-α treatment (MMA) LPS treatment (MMA) XRN2    CSTF2    transcription & transcription regulator GATAD2A GATAD2A HCFC1 HCFC1 NCOR2 NCOR2   EWSR1 EP300   TAF15 MLL2   ZNF579 SRRT       postransriptional regulation of gene GIPC1 GIPC1   ILF3    SRRT    intracellular transport KIF1C KIF1C   SEC24A SEC24A           histone acetylation CTBP1 CTBP1   EP300    HCFC1    NCOA3    viral process  RPS10   others LCT AKAP8L CCT7  CHTF8  EEF2 EEF2 FAM120A    x < -0.5 mRNA processing; nuclear transport KHDRBS1    transcription regulation MLL2  RAI1  others   CHST2 CHST2    CCT7  We wanted to investigate more on the function of different proteins related to mRNA processing that changed their methylation levels upon TNF-α or LPS stimulation because of their 60  abundance on the methylated protein group in our experiment. We used GeneMANIA to categorize these proteins further according to their involvement in common biological processes, which is then rendered using Cytoscape, and most of them showed increased levels of methylation upon cytokine or LPS treatment (Figure 2.14 and 2.15). We found that these proteins can be divided into different subcategories depending on their involvement in biological processes such as nuclear export, alternative mRNA splicing and post-transcriptional regulation. Interestingly, almost all proteins under these three subcategories showed hypermethylation upon TNF-α or LPS stimulation. Moreover, most of the mRNA processing related proteins are involved in mRNA or RNA splicing.   Figure 2.14 Methylarginine network of proteins involved in mRNA processing obtained from A549 cells treated with TNF-α. The physical interactions between arginine-methylated proteins from cells stimulated with 61  TNF-α identified by proteomics and their common biological functions are shown using the GeneMANIA algorithm and rendered with Cytoscape version 3.3.0.   Figure 2.15 Methylarginine network of proteins involved in mRNA processing obtained from A549 cells treated with LPS. The physical interactions between arginine-methylated proteins from cells stimulated with LPS identified by proteomics and their common biological functions are shown using the GeneMANIA algorithm and rendered with Cytoscape version 3.3.0.  Among the proteins that showed hypermethylation upon treatment relative to control in MS analysis, GAP SH3 domain-binding protein 2 (G3BP2) showed consistently in all three replicates on average a 1.5-fold increase in arginine methylation at the R468 site in both TNF-α and LPS-treated cells. G3BP2 is a 54 kDa protein consisting of 482 amino acids. It binds to IκB-α and retains the IκB-α/NF-κB complex in the cytoplasm to prevent NF-κB translocation into the nucleus for subsequent signaling (184). In the Wnt/β-catenin signaling pathway, G3BP2 62  methylation was necessary for signaling to occur (185), so it may serve a similar function in other pathways. We wanted to confirm the increased methylation level by Western blot, but could not capture the difference between treatment and control cells (data not shown).  We also used metabolic labeling of cells with L-[methyl-3H]methionine for in vivo detection of arginine methylation (185) where cells were transiently transfected with Myc or Myc-G3BP2 and metabolically labeled and treated with TNF-α. Myc-G3BP2 was immunoprecipitated from cell lysates using anti-Myc antibody and resolved by the SDS-PAGE gel electrophoresis. The fluorography showed no difference in methylation of G3BP2 in cells with or without TNF-α treatment (Figure 2.16). Our inability to confirm an increase in G3BP2 methylation upon TNF-α treatment could be attributed to the fact that there are six arginine residues in the C-terminal GAR motif of G3BP2 (residues R418, R432, R438, R452, R457, and R468) that could be potential methylation sites, and we were only able to detect R468 as the hypermethylated site by proteomics (Figure 2.17). R457 was found to be slightly hypomethylated upon TNF-α and LPS treatment in one out of three replicates, and its status in the other two replicates could not be determined. Previously, R457 and R468 of G3BP2 have been shown to be dimethylated using SILAC experiment in HeLa cells (47). Moreover, Bikkavilli et. al. generated several methylation-deficient mutants of G3BP2 where they substituted R with K in the C-terminal GAR motif of G3BP2 (R418K, R432K, R438K, R452K, R457K and R468K) based on the similarity with the isoform G3BP1 and previously reported methylation sites of G3BP2 (47) to find the role of G3BP2 methylation in the Wnt signaling (103). They found that methylation at R432, R438, R452 and R468 were crucial for G3BP2 mediated activation of Wnt/β-catenin signaling, and R418 and R457 had a small effect in this signaling (103), indicating that all of these arginine residues might be methylated in vivo. In our 63  study, the status of four of these arginine residues (R418,R432, R438 and R452) could not be confirmed by MS analysis. It could be that the methylation status of other arginine residues is masking the difference in methylation level of our target arginine (R468). If we consider that all six arginine residues are methylated in vivo in the control cells and their methylation levels do not change upon TNF-α and LPS stimulation except the R468 (it changed its methylation level by 1.5-fold on average in our experiments), we can calculate the overall change in the methylation level of G3BP2. For instance, if x is the level of methylation of each of the six arginine residues of certain number of G3BP2 proteins in the control cells, then the total methylation level of that number of G3BP2 proteins would be 6x in the control cells, while the total level of methylation of same number of G3BP2 proteins in treated cells will be 6.5x (considering 1.5-fold increase in the methylation level of R468). Thus we are supposed to observe only a 1.08-fold (6.5x/6x) change in total methylation level of G3BP2. This slight change in the overall methylation level of G3BP2 could be the reason of our inability to observe the change in the methylation level by Western blot or metabolic labeling of cells.  Figure 2.16 Methylated level of G3BP2 upon TNF-α stimulation. Myc-G3BP2 transfected A549 cells were treated with TNF-α (25 ng/ml) for 24 hours in the presence of cell labeling mixture containing L-[methyl-3H] methionine before they were harvested. Myc-G3BP2 was then immunoprecipitated using anti-Myc antibody, transferred to a PVDF membrane and auto-radiographed. 64   Figure 2.17 C-terminal sequence of G3BP2 showing potential arginine methylation sites.  Hypermethylated arginine containing peptide identified in SILAC experiment is highlighted green.  Slightly hypomethylated peptide is highlighted yellow. Asterisks indicate other potential arginine methylation sites.  2.4 Discussion 2.4.1 Effect of TNF-α and LPS on protein expression As we expected, TNF-α and LPS treatment did not cause global change in the protein expression in A549 cells, instead, expression of proteins related to innate immunity and inflammatory response increased upon TNF-α stimulation (Figure 2.12 - 2.15 and Table 2.2) consistent with the function of TNF-α as an inflammatory mediator. Changes in the methylation level of predominantly mRNA processing, RNA splicing and nuclear transport related proteins observed in our SILAC experiment (discussed in the earlier section) may be involved in mediating some of these changes in the expression of these immunity and inflammatory response related proteins, which demands further investigation. A similar SILAC experiment can be carried out in the presence of global methyltransferase inhibitor AdOx (adenosine dialdehyde) to confirm the effect of methylation on the expression of immunity and inflammatory response related proteins. We observed that downregulated proteins upon TNF-α treatments are dominated by the proteins involved in cell cycle process indicating the role of TNF-α in the cell cycle regulation G3BP2  …….R*(418)GGGDDRRDIRRNDR*(432)GPGGPR*(438)GIVGGGMMRDRDGR*GPPPR(457)GGMAQKLGSGR(468)GTGQMEGRFTGQRR 65  (186–188). By Western blot we were able to observe increases in MX1 (MXA), ICAM1, and HMGI-C expression in TNF-α treated cells that corresponded to changes seen with SILAC (Figure 2.5A-C). MxA, a type I interferon-inducible protein, shows antiviral activity against different RNA and DNA viruses (e.g., Influenza, Hepatitis B) partly by enhancing cell death induced by endoplasmic reticulum stress. It works through both caspase-dependent and –independent mechanisms (189, 190). ICAM1 is inducible by inflammatory cytokines (TNF-α and IL-1) and plays an important role in immune responses by mediating activation of T cell and recruitment of leukocyte to the site of inflammation (191). HMGA2 is a transcriptional regulator having important function in cell cycle regulation. HMGI-C has been found to be overexpressed in several human tumors and thought to be involved in neoplastic transformation (192). We also wanted to use Western blot to show the change in the expression level of proteins involved in inflammatory pathways observed in our SILAC experiment upon LPS treatment. We observed increased expression of Rab 27b and decreased expression of RABAC1 under LPS stimulation by SILAC experiment. The low molecular weight GTPase Rab family of proteins is a major regulator of membrane trafficking (193). Rab27a regulates diverse processes involving lysosome-related organelles including lytic granule release in cytotoxic T lymphocytes (194). Rab 27b was found to be over expressed upon Human cytomegalovirus (HCMV) infection in human foreskin fibroblasts (BJ1) cells (195) and upon LPS treatment in RAW264.7 macrophages (193). However, we could not find any change in the expression level of Rab27a upon LPS treatment by Western blot, which could be attributed to the quality of the antibody and/or low expression of Rab27a in lung cells (181). RABAC1 is a Rab protein regulator responsible for the vesicle formation from Golgi complex and implicated in NF-κB signaling (196, 197). It was shown to be down-regulated upon administration of Human papillomavirus 66  (HPV) virus-like particle (VLP) vaccines (198). We observed a slightly decreased expression of RABAC1 upon LPS treatment by Western blot (Figure 2.5D). Our study suggests that TNF-α has more impact on protein expression in the A549 cells compared to LPS.  2.4.2 TNF-α or LPS changes the methylation level of proteins   We have found in our SILAC experiment that proteins that have altered methylation status upon TNF-α or LPS treatment are mostly involved in mRNA processing, RNA splicing, transcription regulation, chromatin modification and nuclear transport (Figure 2.12 - 2.15 and Table 2.2). In fact, these biological functions represent some of the major biological processes that PRMTs are involved in. PRMTs are implicated in diverse biological pathways including RNA processing, nuclear transport and transcriptional regulation (discussed in the introduction section).  They take part in these diverse functions, in part, by their ability to change the methylation status of their various substrates including RBPs and transcription factors. The methylation status of these proteins may affect their functions by a number of ways such as changing their localization, or altering their interactions with other protein(s) or RNA (6, 53).  G3BP2 that showed consistently in all three replicates a roughly 1.5-fold increase in arginine methylation at the R468 site in both TNF-α and LPS-treated cell has been known to be involved in the NF-κB signaling pathway. G3BP2 binds to IκB-α and retains the IκB-α/NF-κB complex in the cytoplasm to prevent NF-κB translocation into the nucleus for subsequent signaling (184). It has also been implicated in the Wnt/β-catenin signaling pathway where G3BP2 methylation was necessary for signaling. Our inability to confirm an increase in G3BP2 methylation upon TNF-α treatment by Western blot could be attributed to the fact that there are six arginines in the C-terminal glycine- and arginine-rich motif of G3BP2 (R418, R432, R438, R452, R457, R468) that could be 67  potential methylation sites, and we were only able to detect R648 as the hypermethylated site. R457 was found to be slightly hypomethylated upon TNF-α and LPS treatment in one out of three replicates, and its status in the other two replicates could not be determined. The status of the other four arginines could not be confirmed by MS analysis. It could be that methylation status on other arginines is masking the difference in methylation level of our target arginine when analyzed by Western blot using non-specific anti-ADMA antibodies. Developing sequence-specific anti-ADMA and anti-SDMA antibodies against a methylated R468-containing peptide could solve this problem. Moreover, an experiment could be performed in which the status of G3BP2: NF-κB: IκB-α complex can be determined upon PRMT1 knock down (PRMT1 was found to methylate G3BP2 (185)) with or without TNF-α or LPS treatment. Any changes to the complex will suggest the importance of methylation of G3BP2 on the NF-κB signaling pathway under inflammatory conditions. Moreover, NF-κB itself has recently been reported to be dimethylated on R30 of its p65 subunit by PRMT5 in response to cytokine interleukin (IL)-1β in 293IL1R cells (199). This methylation has been shown to have significant positive effects on NF-κB’s ability to bind to DNA and NF-κB-dependent gene expression, further indicating a role for PRMTs in the NF-κB signaling pathway (199). As release of IκB from NF-κB occurs upstream to the PRMT5-mediated methylation of NF-κB according to the suggested model (199), it would be also interesting to see if inhibition of G3BP2 methylation has any effect on subsequent methylation of NF-κB in the NF-κB signaling pathway under inflammatory stimulation with TNF-α or LPS. Overall, our findings that pathway specific proteins changed their methylation status upon TNF-α or LPS treatment indicates that PRMTs activities might play a key role in elucidating inflammatory action of inflammatory molecules like TNF-α or LPS. 68  Chapter 3: PRMT2’s role in splicing of BCL-X, an inflammatory pathway target gene 3.1  Introduction  Our findings described in the previous chapter indicated that catalytic activities of PRMTs may be involved in the inflammatory pathways associated with TNF-α and LPS. In this study, we wanted to investigate the role of PRMT2 as an inflammatory mediator through its binding interactions between its SH3 domain and other proteins. PRMT2’s in vivo catalytic activity against any endogenous substrate has not been reported yet (our lab has reported very weak catalytic activity of PRMT2 in vitro towards histone H4 (36)). PRMT2 has been demonstrated to play a role in the NF-κB signaling pathway in response to inflammatory cytokines by its ability to sequester IκB-α in the nucleus to inhibit NF-κB dependent transcription (116). We have previously demonstrated that PRMT2 interacts with PRMT1 and increases its activity (200). Thus PRMT2 might facilitate the interaction between PRMT1 and its substrate(s) to regulate a particular cellular function. Moreover, increased levels of free ADMA has been reported in some lung inflammatory diseases like asthma (141, 142) that could result from increased PRMT activity. Therefore, PRMT2’s ability to increase the activity of PRMT1 can be another way by which PRMT2 might be involved in inflammation. Recently using proteomic techniques our lab identified several PRMT2-binding proteins from HeLa cell lysate that are involved in splicing, including Sm core snRNP protein SmB/B’, splicing regulators such as hnRNPs, components of major and minor snRNPs, and other splicing-related proteins (174). One of the identified splicing factors was SAM68. SAM68, a known PRMT1 substrate (87), is a trans-acting factor that mediates the alternative splicing of the BCL-X 69  transcript involved in the NF-κB mediated inflammatory pathway and is a regulator of programmed cell death (175–177). BCL-X belongs to the family of Bcl-2 proteins that play central roles in caspase-mediated cell death by regulating the integrity of the mitochondrial and endoplasmic reticulum (ER) membranes. BCL-X has two splice variants, the long isoform BCL-X(L) that shows anti-apoptotic activity, and the short form BCL-X(s) that promotes apoptosis (175–177).  We found that 1) SAM68 and PRMT2 interact in vitro and in cultured human cells (contributors: Lam Pak and me) and 2) the co-localization of SAM68 and PRMT2 in cells is dependent on the PRMT2 SH3 domain (contributor: Lam Pak). Considering that PRMT2 and SAM68 interact, I investigated if PRMT2 plays a role in the alternative splicing of BCL-X as part of the inflammatory response. I found that reduced expression of PRMT2 by siRNA caused a decrease in the BCL-X(L)/BCL-X(s) ratio, suggesting that PRMT2 may contribute to BCL-X alternative splicing. This effect was replicated in TNF-α or LPS stimulated cells when PRMT2 expression was reduced by shRNA, and reversed when PRMT2 expression was increased. These results indicate that PRMT2 may play a role during inflammation in alternative splicing regulation. 3.2 Methods 3.2.1 DNA Four pGFP-V-RS plasmid vectors and a control vector containing a scrambled sequence were acquired from OriGene. Each vector was designed to harbor a unique shRNA expression cassette targeting the 3’ untranslated region (3’UTR) of PRMT2 mRNA. The manufacture of pcDNA3.1-HA-PRMT2 was previously described (200).   70  3.2.2 Tissue culture HeLa and HEK293T cells were cultured in Dulbecco’s Modified Eagle’s Medium (DMEM) (Sigma) supplemented with 10% fetal bovine serum (FBS) (Gibco), 50 units/mL penicillin, and 50 µg/mL streptomycin (Gibco) at 37 ºC in 5% CO2. For transfections with pcDNA3.1-HA-PRMT2, approximately 0.6 X 106 HeLa cells were seeded in each well of a 6-well plate containing standard growth medium 1 day prior to transfection. On the day of the experiment, the growth medium was first replaced with Opti-MEM medium (Invitrogen), and the HeLa cells were transfected with 4.0 µg of the desired constructs using 8.0 µL of Lipofectamine 2000 (Invitrogen) according to the manufacturer’s instructions. The Opti-MEM medium was replaced with the standard growth medium (DMEM supplemented with 10% FBS, 50 units/mL penicillin, and 50 µg/mL streptomycin). The transfected cells were cultured for an additional 16 h before they were harvested. In order to reduce PRMT2 expression by siRNA, HEK293T cells at approximately 30% confluence were transfected with 50 nM of PRMT2-specific siRNAs (siRNA-A (Invitrogen, 10620310), siRNA-B (Sigma, PDSIRNA2D)) or scramble RNA (Santa Cruz, sc-37007) as a control using Opti-MEM and Lipofectamine RNAiMAX (Invitrogen) according to the manufacturer’s protocol. Opti-MEM was replaced with the standard growth medium 8 h post-transfection, and transfections were performed for 48 h.  For transfections with shRNA constructs, approximately 0.6 X 106 HEK293T cells were seeded in each well of a 6-well plate containing standard growth medium 1 day prior to transfection. On the day of experiment, the growth medium was first replaced with Opti-MEM medium (Invitrogen), and the cells were transfected with 2.5 µg of the desired constructs using 5.0 µL of Lipofectamine 3000 (Invitrogen) per the manufacturer’s instructions. The Opti-MEM 71  medium was replaced with the standard growth medium (DMEM supplemented with 10% FBS, 50 units/mL penicillin, and 50 µg/mL streptomycin) containing puromycin (3 µg/ml) 24-h post-transfection. The transfected cells were passed to the new plate every 3-4 days and monitored by IncuCyte Zoom live cell imaging system (Essen Bioscience) for GFP positive cells to confirm puromycin selection. For rescue experiments to reintroduce PRMT2 into shRNA-containing HEK293T cells, the transfection of pcDNA3.1, pcDNA3.1-HA-PRMT2 and pcDNA3.1-HA-PRMT2 E220Q were performed as described above. The Opti-MEM medium was replaced with the standard growth medium (DMEM supplemented with 10% FBS, 50 units/mL penicillin, and 50 µg/mL streptomycin) 24-h post-transfection. The cells were then treated with either TNF-α (100 ng/ml) (BD Pharmingen) or LPS (1 µg/ml) (Sigma-Aldrich) for 24 h before they were harvested. 3.2.3 Co-immunoprecipitations  Cell lyates were fractionated into cytoplasmic and nuclear fractions following the published procedure (201). In this case, cell lysates (250 µg protein for the nuclear fraction and 700 µg protein for the cytoplasmic fraction) were taken into a 2 ml tube and the buffer of the cell lysates was adjusted to 50 mM HEPES-KOH and 150 mM NaCl before adding 2.0 µg of monoclonal anti-HA antibody (H3663, Sigma) or mouse IgG (negative control) (Sigma) in each tube. Co-IP buffer (50 mM HEPES-KOH, pH 7.4, 150 mM NaCl, 1x protease inhibitor cocktail (Roche #04693132001)) was used to adjust the sample volume to 0.6 mL, and the mixtures were incubated for 16 h at 4 ºC with rotation. The mixtures were added to 50-µL pre-washed protein G-sepharose (Invitrogen), and then incubated at 4 ˚C for 2 h with rotation. The bound proteins were eluted in SDS-PAGE sample buffer after washing the resin five times with 0.05% (v/v) Tween 20 in PBS (Gibco). 72   3.2.4 Western blotting  Proteins were separated on 10% SDS-PAGE gels, transferred to nitrocellulose or PVDF membranes, and blotted with anti-SAM68 (sc-333, Santa Cruz), anti-PRMT2 (sc-135010, Santa Cruz), anti-Actin (sc-1615, Santa Cruz), anti-Fibrillarin (ab5821, abcam), or anti-HA (H3663, Sigma) antibodies. Proteins were visualized using goat anti-mouse IgG-HRP (sc-2005) or goat anti-rabbit IgG-HRP (sc-2054, Santa Cruz) and ECL Western blotting detection reagents (GE Healthcare). 3.2.5 Total RNA extraction and qRT-PCR  Total RNA was extracted from HEK293T cells using Trizol reagent (Life Technologies) following the manufacturer’s instructions. RNA was resuspended in DEPC-treated water (Life Technologies) and stored at -80 ˚C. First strand synthesis reverse transcriptase reactions were performed using 2.0 µg of total RNA and SuperScript VILO MasterMix (Life Technologies) according to the manufacturer’s instructions. For evaluation of PRMT2 levels and BCL-X splice variant expression, cDNA was subjected to qPCR using TaqMan PRMT2-, BCL-X(L)-, BCL-X(s)- and GAPDH-specific primer/probe sets (Life Technologies) and TaqMan Universal PCR Master Mix (Life Technologies). Samples were analyzed using a StepOnePlus real-time PCR system (Applied Biosystems). PRMT2, BCL-X(L) and BCL-X(s) transcript levels were calculated using the standard curve method and were normalized to GAPDH for the corresponding samples. 3.3 Results 3.3.1  PRMT2 associates with SAM68 in cells In order to find potential PRMT2 binding partners that bind to PRMT2 through PRMT2’s SH3 domain, L. Pak performed a GST pull-down assay using GST fused N-terminal SH3 73  domain of PRMT2 [GST-SH3(PRMT2)] and HeLa cell lysate as a bait and prey, respectively (Figure 3.1) (174). GST was used as a negative control whereas SH3 domain of the Abl tyrosine kinase fused with GST [GST-SH3(Abl)] was used as positive control. Though both SH3 domains from PRMT2 and Abl showed comparable patterns in protein binding evident from Figure 3.1, at least six bands were enriched in the pull down with GST-SH3(PRMT2). The protein contents of the six enriched bands from the GST-SH3(PRMT2) pull down lane (Figure 3.1) were digested with trypsin and analyzed by LC-MS/MS. Around thirty-seven hits were identified and interestingly most of the identified proteins are reported to be involved in mRNA processing (174). Though PRMT2 might not be responsible for methylating these proteins due to its weak enzymatic activity (36), it might serve as a scaffolding protein to mediate interactions between PRMT1 and these substrates (174, 200).  Figure 3.1 Identification of PRMT2 SH3 domain-associated proteins. Eluents of indicated GST-pull down experiments were separated by gel electrophoresis and protein bands 1-6 were isolated for proteomic analysis. Lam Pak decided to explore the interaction of PRMT2 and one of its identified binding partners, SAM68, an important regulator of alternative splicing and a known substrate of PRMT1, to investigate the significance of interaction between PRMT2 and splicing factors (78, 74  87, 202). SAM68 has already been reported to interact with the SH3 domain of PRMT2 in vitro in previous work (203), and is also recognized by many SH3 domain containing proteins (204, 205). L. Pak confirmed by Western blot that SAM68 interacts with the SH3 domain of PRMT2 (Figure 3.2A). However, she did not detect any catalytic activity of PRMT2 toward SAM68 in methylation assays analyzed by phosphor-imaging or mass spectrometry (174). She then wanted to find by co-immunoprecipitation whether full-length PRMT2 could also interact with SAM68 in HeLa cells. In order to conduct this experiment, I generated pcDNA3.1-HA-SAM68 by inserting SAM68 clone into pcDNA3.1 construct (174). L. Pak found that endogenous PRMT2 associated with ectopically expressed hemagglutinin (HA)-tagged SAM68 (Figure 3.2B). She also found that in the absence of PRMT2’s SH3 domain in ectopically expressed HA-ΔSH3PRMT2, no association between PRMT2 and endogenous SAM68 was detected (Figure 3.2E), implicating a role for the SH3 domain in mediating the interaction between the two proteins (174). However, she could not observe a specific association in a reciprocal experiment with HA-PRMT2 and endogenous SAM68 since the IgG control also showed a SAM68 signal (174).  In order to confirm the association of HA-PRMT2 and endogenous SAM68, I separated the cell lysate into nuclear and cytoplasmic fractions prior to co-immunoprecipitating SAM68 with HA-PRMT2. I was able to demonstrate that HA-PRMT2 immunoprecipitated SAM68 in the nuclear and not in the cytoplasmic fraction (Figure 3.2C and 3.2D).   75    Figure 3.2 The interaction between SAM68 and PRMT2 is dependent on the PRMT2 SH3 domain. (A) Proteins bound to GST pull-downs from HeLa cell lysate were resolved by gel electrophoresis and blotted with an anti-SAM68 antibody. Whole cell lysate from HeLa cells that expressed HA-SAM68 (B), or nuclear (C) and cytoplasmic (D) fractions from HeLa cells that expressed HA-PRMT2 were immunoprecipitated with anti-HA or mouse IgG (negative control) and blotted as indicated. (E) HeLa cell lysate from cells expressing HA-ΔSH3PRMT2 were similarly analyzed.   76   3.3.2 PRMT2 influences the alternative splicing of BCL-X The ability of PRMT2 to interact with SAM68 prompted us to investigate whether PRMT2 can influence alternative splicing of the BCL-X transcript similar to what has been observed for SAM68 (206). To explore this possibility, I carried out a siRNA-mediated knockdown of PRMT2 expression in HEK293T. We determined by qRT-PCR that PRMT2 transcript levels in HEK293T cells were reduced by roughly 4- to 5-fold compared to the scramble siRNA control (Figure 3.3A). This reduction in PRMT2 expression did result in a lowered BCL-X(L)/BCL-X(s) ratio (Figure 3.3B), offering preliminary evidence that PRMT2 levels impact BCL-X alternative splicing.   Figure 3.3 The effect of PRMT2 expression levels on BCL-X alternative splicing. HEK293T cells transfected with scramble RNA or PRMT2-targeted siRNAs were harvested 48 h post-transfection and qRT-PCR was performed to show PRMT2/GAPDH (A), and the relative amounts of BCL-X transcript ratios (B) normalized to the scramble siRNA control. Standard deviations of replicates (n = 2) are shown. A statistical comparison between the scramble control and experimental samples was analyzed using a Student’s t-test. *p<0.05; **p<0.005: versus scramble siRNA.   77  I was then interested to see if modulating PRMT2 expression levels can cause concomitant changes in the BCL-X(L)/BCL-X(s) expression ratio. Initially, I transfected HEK293T cells with different shRNAs targeting the 3’ untranslated region of PRMT2 mRNA, verified stable transfection by puromycin selection of GFP-positive cells (Figure 3.4), and assessed PRMT2 transcript expression by qRT-PCR for each shRNA construct (Figure 3.5A). The greatest decrease in PRMT2 expression (48% lower than control) occurred with shRNA-A. However, we found that shRNA-A did not significantly alter the BCL-X(L)/BCL-X(s) ratio in this experiment (Figure 3.5B).   Figure 3.4 IncuCyte Zoom images of shRNA-transfected HEK293T cells. Green fluorescence was used to qualitatively assess the transfection in HEK293T cells for shRNA A-D constructs targeting PRMT2 expression (A-D), as well as for the shRNA control (E).  78    Figure 3.5 The effect of PRMT2 expression levels on BCL-X alternative splicing under inflammatory conditions. HEK293T cells transfected with control shRNA (shControl) or PRMT2-targeted shRNAs were harvested and qRT-PCR was performed to show PRMT2/GAPDH (A) and relative amounts of BCL-X transcript ratios (B) normalized to the shControl. Control and PRMT2-reduced HEK293T cells (shRNA-A) were treated with either TNF-α or LPS and harvested after 24 h of treatment. qRT-PCR was performed to show relative amounts of BCL-X transcript ratios (C) and PRMT2/GAPDH (D) normalized to the shControl. Standard deviations of replicates (n = 2) are shown. A statistical comparison between experimental samples was analyzed using a Student’s t-test. *p<0.05: versus shControl.   79   Since BCL-X is a target gene for NF-κB (207), we decided to activate the NF-κB pathway by treating HEK293T cells with inflammatory molecules (TNF-α or LPS) to observe changes to BCL-X alternative splicing attributable to different PRMT2 levels under inflammatory conditions. Both TNF-α and LPS treatments caused significant increases in the BCL-X(L)/BCL-X(s) ratio in shRNA control groups, but these increases were reduced by over 20% in cells harboring shRNA-A (Figure 3.5C) where PRMT2 expression was reduced (Figure 3.5D). These results indicate that the loss of PRMT2 under inflammatory conditions caused the BCL-X(L)/BCL-X(s) ratio to decrease similar to the trend observed in siRNA-treated cells (Figure 3.3). In a series of rescue experiments, we transfected shRNA-containing HEK293T cells with a mammalian expression vector for HA-PRMT2 or the vector-only control (pcDNA3.1) prior to treating cells with either TNF-α or LPS. As anticipated, the increase in PRMT2 caused significant increases in the BCL-X(L)/BCL-X(s) ratio in a corresponding manner (Figure 3.6, 3.7). However, transfection of the catalytically inactive mutant of PRMT2 (HA-PRMT2 E220Q) did not cause any significant increase in the BCL-X(L)/BCL-X(s) ratio (Figure 3.6, 3.7). Taken together, these data indicate that PRMT2 promotes the formation of BCL-X(L) over BCL-X(s) and that PRMT2’s catalytic activity is required to show this splicing effect. 80   Figure 3.6 The effect of added PRMT2 on BCL-X alternative splicing upon TNF-stimulation.  shRNA-containing HEK293T cells were transfected with pcDNA3.1-HA-PRMT2, pcDNA3.1-HA-PRMT2 E220Q or control plasmid (pcDNA3.1). After 24 hours of transfection, cells were treated with TNF- and harvested after 24 hours of treatment. qRT-PCR was performed to show PRMT2/GAPDH (A), as well as relative amounts of BCL-X transcript ratios (B) normalized to the shControl. Standard deviations of replicates (n = 2) are shown. A statistical comparison between experimental samples was analyzed using a Student’s t-test.  81   Figure 3.7 The effect of added PRMT2 on BCL-X alternative splicing upon LPS stimulation.  shRNA-containing HEK293T cells were transfected with pcDNA3.1-HA-PRMT2, pcDNA3.1-HA-PRMT2 E220Q or control plasmid (pcDNA3.1). After 24 hours of transfection, cells were treated with LPS and harvested after 24 hours of treatment. qRT-PCR was performed to show PRMT2/GAPDH (A), as well as relative amounts of BCL-X transcript ratios (B) normalized to the shControl. Standard deviations of replicates (n = 2) are shown. A statistical comparison between experimental samples was analyzed using a Student’s t-test. 82  3.4 Discussion 3.4.1 PRMT2 SH3 domain selectivity SAM68 is a protein identified to interact with the PRMT2 SH3 domain in this study and in previous work (203) that is recognized by many other proteins bearing one or more SH3 domains. These proteins include multiple members of the Src tyrosine kinase family such as phosphoinositide 3-kinase, Phospholipase C-γ-1and G3BP1, several signal transduction adaptor proteins such as Growth factor receptor-bound protein 2 (Grb2), GRB2-related adapter protein, and the non-catalytic region of tyrosine kinase adaptor protein family members (204, 205).  SH3 domain-mediated interactions in cells can be regulated by phosphorylation and allosteric changes (208). Another possible regulatory mechanism for these interactions is arginine methylation. Indeed, arginine methylation within the proline-rich regions of SAM68 peptides has been reported to block ligand binding to SH3 domains (78). Although we did not observe any PRMT2 methyltransferase activity toward SAM68 in vitro, SAM68 is a known PRMT1 substrate whose arginine methylation is critical for its nuclear localization and SAM68-dependent RNA export (87). In another study, both PRMT1 and SAM68 were identified by mass spectrometry in a complex with SFPQ (PSF) and NONO (p54nrb) (126), two additional proteins that were identified in this study as PRMT2 SH3 domain-associated proteins (174). Furthermore, we have previously demonstrated that PRMT1 and PRMT2 interact in cells to produce a heteromeric complex with elevated arginine methylation activity (200). Given these facts, it seems plausible that PRMT2 facilitates the interaction between PRMT1 and its substrate SAM68 in cells. Additional experiments will be needed to better understand how PRMT2 may participate in protein complexes with trans-acting splicing factors and impact SAM68 methylation. 83  3.4.2 PRMT2-mediated effects on BCL-X alternative splicing The association of PRMT2 with SAM68 (Figure 3.2) and many other splicing-related proteins led us to explore a role for PRMT2 in pre-mRNA processing. It has been demonstrated that SAM68 together with hnRNP A1 promotes the formation of small pro-apoptotic BCL-X(s) over large anti-apoptotic BCL-X(L) via 5’ alternative splice site selection, and this effect was reversed upon tyrosine phosphorylation of SAM68 by the Src-like kinase Fyn (206). Espejo et al. have demonstrated using a protein-domain microarray that the SH3 domain of PRMT2 interacts with the proline-rich regions of SAM68 (203). It has also been shown that SAM68 is methylated by PRMT1 (87). Treating HeLa cells with methyltransferase inhibitor (AdOx) negatively affected nuclear localization of SAM68 (87), suggesting that methylation of SAM68 is important for its nuclear localization and splicing activity. Moreover, our lab has shown by immunofluorescence that AdOx treatment of HeLa cells can shift the co-localization of mCitrine-PRMT2 and SAM68 from nucleus to cytoplasm indicating PRMT2 dependence of SAM68’s nuclear localization (174).  RBM25 and Luc7L3 are two other proteins identified in our study to associate with the PRMT2 SH3 domain that are also known to promote BCL-X(s) transcript formation (209). SF3B1, a component of the U2 snRNP complex for which several other components have been identified to associate with the PRMT2 SH3 domain (Table A.1), promotes formation of BCL-X(L) over BCL-X(s) (210). Our results demonstrating that PRMT2 promotes BCL-X(L) formation (Figure 3.3, 3.5, 3.6 and 3.7) suggests that PRMT2 is a trans-acting factor involved in BCL-X alternative splicing. A role for PRMT2 as a pro-apoptotic factor has been established in work by Nabel and coworkers (116). Their experiments in Prmt2-/- MEF and HEK293 cells revealed that PRMT2 84  opposes NF-κB-dependent transcription upon TNF-α signaling by sequestering the NF-κB inhibitor IκB-α in the nucleus. In the absence of PRMT2, cells were less susceptible to apoptosis in response to cytokines and cytotoxic drugs as NF-κB-dependent transcription was unimpeded. Upon decreasing PRMT2 expression, we observed an increase in the aggregate transcript levels of BCL-X (Figure 3.8) consistent with the aforementioned finding that PRMT2 can antagonize NF-κB activity. Nevertheless, in our study we have identified a pro-survival role for PRMT2 in facilitating the formation of the larger of the two BCL-X transcripts. From our findings, we propose a model where PRMT2 interacts with spliceosome components and splicing factors including SAM68, which has an established role in mediating BCL-X alternative splicing. Through the interaction with SAM68 and/or other splicing factor(s), PRMT2 contributes to alternative splicing by promoting the formation of BCL-X(L) over BCL-X(s) (Figure 3.9).   Figure 3.8 The effect of PRMT2 expression on total transcript level of BCL-X. HEK293T cells transfected with scramble RNA or PRMT2-targeted siRNAs were harvested 48 h post-transfection and qRT-PCR was performed to 85  show total amounts of BCL-X transcript normalized to the scramble siRNA control (A).  Control and PRMT2-reduced HEK293T cells (sRNA A) were treated with either TNF-α or LPS and harvested after 24 h of treatment. qRT-PCR was performed to show total amounts of BCL-X transcript normalized to the shControl (B). Standard deviations of replicates (n = 2) are shown. Statistical comparison between experimental samples was analyzed using a Student’s t-test.     Figure 3.9 Proposed model on the role of PRMT2 in the alternative splicing of BCL-X. The PRMT2 SH3 domain interacts with spliceosome components and splicing factors including SAM68, which has an established role in mediating BCL-X alternative splicing. PRMT2 contributes to alternative splicing by promoting the formation of BCL-X(L) over BCL-X(s).  86  Chapter 4: Conclusion and future directions In Chapter 2 of this dissertation I have presented evidence that TNF-α and LPS caused changes in the methylation level of proteins primarily involved in mRNA processing, RNA splicing, and nuclear transport, indicating that these two inflammatory stimuli share mutual downstream pathways involving methyltransferase activity. It could be predicted that methylation of the proteins involved in these specific pathways might be important to carry out the signaling of inflammatory pathways induced by TNF-α or LPS. In fact, one of the proteins, G3BP2 that showed consistent hypermethylation at one arginine residue in all replicates of SILAC experiments, has been reported to be involved in inflammatory pathway mediated by NF-κB. It binds to IκB-α and retains the IκB-α/NF-κB complex in the cytoplasm to prevent NF-κB translocation into the nucleus for subsequent signaling (184). In the Wnt/β-catenin signaling pathway, G3BP2 methylation was necessary for signaling to occur (103), so it may serve a similar function in other pathways including inflammatory pathways that we are interested in. Moreover, I have also presented data showing that proteins particularly involved in innate immunity and viral response did increase upon TNF-α treatment consistent with an inflammatory response. It could be suggested that changes in the methylation level of proteins particularly involved in mRNA processing, RNA splicing, and nuclear transport under TNF-α treatment might be correlated with the expression changes of proteins involved in the inflammatory pathway that has been observed in the SILAC experiment.  In Chapter 3, we have shown PRMT2 interacts with SAM68, an important regulator of alternative splicing and a known methylation substrate (78, 87, 202). SAM68 mediates the alternative splicing of the BCL-X transcript involved in the NF-κB mediated inflammatory pathway and is a regulator of programmed cell death (175–177). I have also provided evidence 87  for the first time that PRMT2 might have a role in the alternative splicing of the BCL-X. I have shown that PRMT2 promotes formation of BCL-X(L) over BCL-X(s). Overall, we have identified a pro-survival role for PRMT2 in our study that facilitates the formation of the larger of the two BCL-X transcripts.  Future work 1. Effect of G3BP2 hypomethylation on IκB-α/ NF-κB signaling  Considering the previously established roles that G3BP2 plays in Wnt and NF-κB signaling pathways (103, 184), it seems probable that the increase in its methylation at R468 within its C-terminus that we found impacts its interactions with the IκB-α/ NF-κB complex. It would be interesting to see if preventing arginine methylation impairs its binding interactions. This experiment may require TNF-α or LPS stimulation of cells in order for G3BP2 to be methylated at R468. We can also overexpress G3BP2 as wild type or as a R468K mutant in stimulated cells and determine the interaction between overexpressed G3BP2 and the IκB-α/ NF-κB complex, the cellular localization of NF-κB by fluorescence microscopy, and proper signaling by qRT-PCR of IL-6, IKBA, and COX2 (i.e., NF-κB -dependent transcription products; GADPH as a control) as other means to demonstrate the effect of G3BP2 methylation on this pathway. Our working model predicts that interference in G3BP2 methylation will prevent G3BP2 dissociation from the IκB-α/ NF-κB complex and prevent NF-κB translocation into the nucleus for transcription of target genes. 2. PRMT responsible for G3BP2 methylation We can plan to identify the methyltransferase responsible for the increase in G3BP2 methylation. In a previous study G3BP2 was shown to associate by co-IP with PRMT1, 2, 7, and 88  8, and all of these enzymes except PRMT2 were capable of methylating G3BP2 in vitro (103). PRMT1 has been demonstrated in the Wnt/β-catenin signaling pathway to methylate G3BP1, a similarly organized isoform of G3BP2 (70% sequence similarity) (175). Additionally, overexpressed PRMT6 was recently shown to interact with and facilitate the translocation of NF-κB into the nucleus of MEF cells (211). These studies indicate that any one or a combination of enzymes could be candidates for G3BP2 methylation. PRMTs that interact with G3BP2 can be identified by co-IP and proteomic MS while A549 cells are stimulated by TNF-α or LPS. 3. The arginine methylation stress proteome  The arginine methylation proteomes for TNF-α and LPS-treated cells show similar changes (Figure 2.13) since both treatments elicit an inflammatory response. In an effort to further explore the dynamic nature of arginine methylation, SILAC-MS can be used to uncover methylation changes to cells grown under different stress conditions relative to control: genotoxic (etoposide treatment), hypoxic (2% O2), and osmotic (hypertonic medium with 300 mM sucrose added) stresses. These conditions will activate seemingly distinct pathways so that we will be able to look at pairwise comparisons of different treatments as a novel discovery tool for investigating functional overlap or dissimilarities of PRMT activities on a proteomic scale. In each case, we can examine all of the peptide sequences flanking methylated arginine residues to identify any motifs that may arise for increased or decreased methylation. Additionally, we can follow up on individual proteins whose methylation increased in response to treatment. 4. Cell-specific effect of PRMT2 on alternative splicing of BCL-X  An important aspect in determining the impact of PRMT2 on BCL-X alternative splicing is the cell line in which the experiments described above are conducted. A549 cells have been previously used as a model cell line for studying changes to the BCL-X(L)/ BCL-X(s) ratio where 89  treatment of A549 cells with cell-permeable ceramide, D-e-C(6) ceramide (an inflammatory mediator) decreased BCL-X(L)/ BCL-X(s) ratio (212). It could be that the effect of PRMT2 differs in carcinogenic and non-carcinogenic cells. So, the impact of PRMT2 on BCL-X alternative splicing may be important in other cell lines as well. 5. PRMT2-dependent mechanism for regulating alternative splicing  Based on our findings that PRMT2 plays a role in promoting the formation of the BCL-X(L) transcript over BCL-X(s) in HEK 293T cells, a model can be proposed in which PRMT2 recruits PRMT1 to SAM68 so that it can methylate its substrate (Figure 3.9). The methylation of SAM68 and perhaps other splicing factors is then the key signal that promotes BCL-X(L) 5’ splice site selection in this model. The possibility that PRMT2 mediates SAM68 methylation by PRMT1, and that the consequence of this activity is BCL-X(L) formation can be explored under this model.   We can initially look at the effect methyltransferase inhibitors (using global methyltransferase inhibitor AdOx or more targeted PRMT-selective inhibitor AMI-1) have on BCL-X alternative splicing in cells. If methylation is necessary for the formation of BCL-X(L), then inhibiting it should decrease the BCL-X(L)/ BCL-X(s) ratio similar to reducing PRMT2 expression (Figure 3.3). The results of these pharmacological experiments may lend support to our model if we see methylation-dependent changes in the ratio, but they may likely give rise to confounding data due to the compounds’ lack of selectivity for PRMT1.  An alternative way to investigate whether PRMT1 activity toward SAM68 is important in promoting BCL-X(L) 5’ splice site selection is to reduce or eliminate its expression. In the absence of PRMT1, we anticipate that the BCL-X(L)/ BCL-X(s) ratio will decrease similar to what we observed when PRMT2 expression was knocked down (Figure 3.3). We expect that in 90  rescue experiments expression of WT or catalytically inactive PRMT1 (E153Q mutant) will increase or have no effect on BCL-X alternative splicing, respectively.  We will be able to site-specifically quantify changes in methylation of SAM68 using the proteomic technique of stable isotope labeling by amino acids in cells (SILAC) (44). Once we have created cells in which the expression of either PRMT1 or PRMT2 has been reduced/ eliminated by knock down or knockout, we can then investigate whether PRMT1, PRMT2, and SAM68 reside within the same protein complex.  Despite the evidence that both PRMT2 and SAM68 affect BCL-X alternative splicing, we cannot rule out the possibility that PRMT2 may act independently of SAM68 and PRMT1. We note that PRMT2 SH3 domain binders other than SAM68 (Table A.1) can also regulate BCL-X splice site selection. The interaction between cytidine-rich elements in BCL-X exon 2 and hnRNP K has been shown to repress BCL-X(s) splicing and promote cell survival (213). We can perform binding studies (e.g., co-IP, etc.) between PRMT2 and methylated/ unmethylated hnRNP K to investigate the importance of methylation in regulating PRMT2’s interaction with hnRNP K. PRMT2 may also affect BCL-X alternative splicing in its capacity to bind proteins other than splicing factors and spliceosome components. PRMT2 has been shown to form a ternary complex with the transcription factor E2F1 and its repressor RB1 for delayed G1-S phase transition into the cell cycle (214). In addition to causing cell cycle entry and cell proliferation, E2F1 can paradoxically induce apoptosis through various mechanisms (214). It has been demonstrated that E2F1 requires the expression of its downstream gene target splicing factor SRSF2 (SC35) in response to DNA damage to induce apoptosis by switching the alternative splicing profile of several genes toward pro-apoptotic splice variants, including BCL-X (215). In this case, PRMT2 may play a transcriptional role in facilitating E2F1-dependent expression of 91  SRSF2 that contributes to BCL-X alternative splicing. Although this possibility seems remote relative to the other mechanisms discussed above, we can begin to pursue it by determining whether PRMT2 is required for SRSF2 production in response to DNA damage for cells with and without PRMT2. 6.  PRMT2-dependent alternatively spliced transcripts other than BCL-X  From the proteomic experiment conducted by Dr. Lam Pak (Table A.1), our lab identified as possible PRMT2 SH3 domain-binding partners U1 and U2 snRNP components that respectively define 5’ and 3’ splice sites, hnRNPs that generally repress splicing, and an assortment of other splicing-related proteins that help to influence intron/exon inclusions and exclusions (216–218). Leading up to this point we have focused on experiments to understand PRMT2’s impact on BCL-X alternative splicing. As a future aim, we can investigate the overall effect PRMT2 has on the transcriptome by comparing mRNA sequencing (mRNA-Seq) data from PRMT2+/+ and PRMT2-/- cells. It can be expected that a comparison of RNA-Seq data for PRMT2+/+ and PRMT2-/- cells will reveal distinct patterns of alternative splicing that can be categorized into one of several events.   92  References 1.  Lakowski TM, Frankel A. 2008. A kinetic study of human protein arginine N-methyltransferase 6 reveals a distributive mechanism. J Biol Chem 283:10015–10025. 2.  Yang Y, Bedford MT. 2013. Protein arginine methyltransferases and cancer. Nat Rev 13:37–50. 3.  Paik WK, Kim S. 1968. Protein methylase I. Purification and properties of the enzyme. J Biol Chem 243:2108–14. 4.  Lin WJ, Gary JD, Yang MC, Clarke S, Herschman HR. 1996. The mammalian immediate-early TIS21 protein and the leukemia-associated BTG1 protein interact with a protein-arginine N-methyltransferase. J Biol Chem 271:15034–15044. 5.  Tang J, Frankel A, Cook RJ, Kim S, Paik WK, Williams KR, Clarke S, Herschman HR. 2000. PRMT1 is the predominant type I protein arginine methyltransferase in mammalian cells. J Biol Chem 275:7723–7730. 6.  Bedford MT, Clarke SG. 2009. Protein arginine methylation in mammals: who, what, and why. Mol Cell 33:1–13. 7.  McBride AE, Silver PA. 2001. State of the Arg. Cell 106:5–8. 8.  Bedford MT. 2007. Arginine methylation at a glance. J Cell Sci 120:4243–4246. 9.  Yang Y, Hadjikyriacou A, Xia Z, Gayatri S, Kim D, Zurita-Lopez C, Kelly R, Guo A, Li W, Clarke SG, Bedford MT. 2015. PRMT9 is a Type II methyltransferase that methylates the splicing factor SAP145. Nat Commun 6:6428. 10.  Feng Y, Maity R, Whitelegge JP, Hadjikyriacou A, Li Z, Zurita-Lopez C, Al-Hadid Q, Clark AT, Bedford MT, Masson J-Y, Clarke SG. 2013. Mammalian Protein Arginine Methyltransferase 7 (PRMT7) Specifically Targets RXR Sites in Lysine- and 93  Arginine-rich Regions. J Biol Chem 288:37010–37025. 11.  Krause CD, Yang Z-H, Kim Y-S, Lee J-H, Cook JR, Pestka S. 2007. Protein arginine methyltransferases: evolution and assessment of their pharmacological and therapeutic potential. Pharmacol Ther 113:50–87. 12.  Hadjikyriacou A, Yang Y, Espejo A, Bedford MT, Clarke SG. 2015. Unique Features of Human Protein Arginine Methyltransferase 9 (PRMT9) and Its Substrate RNA Splicing Factor SF3B2. J Biol Chem 290:16723–43. 13.  Zhang X, Zhou L, Cheng X. 2000. Crystal structure of the conserved core of protein arginine methyltransferase PRMT3. EMBO J 19:3509–3519. 14.  Schapira M, Ferreira de Freitas R. 2014. Structural biology and chemistry of protein arginine methyltransferases. Med Chem Commun 5:1779–1788. 15.  Cheng X, Collins RE, Zhang X. 2005. Structural and sequence motifs of protein (histone) methylation enzymes. Annu Rev Biophys Biomol Struct 34:267–94. 16.  Tang J. 1998. PRMT 3, a Type I Protein Arginine N-Methyltransferase That Differs from PRMT1 in Its Oligomerization, Subcellular Localization, Substrate Specificity, and Regulation. J Biol Chem 273:16935–16945. 17.  Frankel A, Clarke S. 2000. PRMT3 is a distinct member of the protein arginine N-methyltransferase family. Conferral of substrate specificity by a zinc-finger domain. J Biol Chem 275:32974–82. 18.  Swiercz R, Person MD, Bedford MT. 2005. Ribosomal protein S2 is a substrate for mammalian PRMT3 (protein arginine methyltransferase 3). Biochem J 386:85–91. 19.  Teyssier C. 2002. Requirement for Multiple Domains of the Protein Arginine Methyltransferase CARM1 in Its Transcriptional Coactivator Function. J Biol Chem 94  277:46066–46072. 20.  Lee J, Sayegh J, Daniel J, Clarke S, Bedford MT. 2005. PRMT8, a new membrane-bound tissue-specific member of the protein arginine methyltransferase family. J Biol Chem 280:32890–6. 21.  Herrmann F, Pably P, Eckerich C, Bedford MT, Fackelmayer FO. 2009. Human protein arginine methyltransferases in vivo--distinct properties of eight canonical members of the PRMT family. J Cell Sci 122:667–677. 22.  Frankel A, Yadav N, Lee J, Branscombe TL, Clarke S, Bedford MT. 2002. The novel human protein arginine N-methyltransferase PRMT6 is a nuclear enzyme displaying unique substrate specificity. J Biol Chem 277:3537–43. 23.  Zhang X, Cheng X. 2003. Structure of the predominant protein arginine methyltransferase PRMT1 and analysis of its binding to substrate peptides. Structure 11:509–20. 24.  Troffer-Charlier N, Cura V, Hassenboehler P, Moras D, Cavarelli J. 2007. Functional insights from structures of coactivator-associated arginine methyltransferase 1 domains. EMBO J 26:4391–4401. 25.  Weiss VH, McBride  a E, Soriano M a, Filman DJ, Silver P a, Hogle JM. 2000. The structure and oligomerization of the yeast arginine methyltransferase, Hmt1. Nat Struct Biol 7:1165–1171. 26.  Troffer-Charlier N, Cura V, Hassenboehler P, Moras D, Cavarelli J. 2007. Expression, purification, crystallization and preliminary crystallographic study of isolated modules of the mouse coactivator-associated arginine methyltransferase 1. Acta Crystallogr Sect F Struct Biol Cryst Commun 63:330–333. 95  27.  Antonysamy S, Bonday Z, Campbell RM, Doyle B, Druzina Z, Gheyi T, Han B, Jungheim LN, Qian Y, Rauch C, Russell M, Sauder JM, Wasserman SR, Weichert K, Willard FS, Zhang A, Emtage S. 2012. Crystal structure of the human PRMT5:MEP50 complex. Proc Natl Acad Sci 109:17960–17965. 28.  Sun L, Wang M, Lv Z, Yang N, Liu Y, Bao S, Gong W, Xu R-M. 2011. Structural insights into protein arginine symmetric dimethylation by PRMT5. Proc Natl Acad Sci U S A 108:20538–43. 29.  Higashimoto K, Kuhn P, Desai D, Cheng X, Xu W. 2007. Phosphorylation-mediated inactivation of coactivator-associated arginine methyltransferase 1. Proc Natl Acad Sci 104:12318–12323. 30.  Yue WW, Hassler M, Roe SM, Thompson-Vale V, Pearl LH. 2007. Insights into histone code syntax from structural and biochemical studies of CARM1 methyltransferase. EMBO J 26:4402–12. 31.  Gary JD, Clarke S. 1998. RNA and protein interactions modulated by protein arginine methylation. Prog Nucleic Acid Res Mol Biol. 32.  Wooderchak WL, Zang T, Zhou ZS, Acuña M, Tahara SM, Hevel JM. 2008. Substrate profiling of PRMT1 reveals amino acid sequences that extend beyond the “RGG” paradigm. Biochemistry 47:9456–9466. 33.  Kölbel K, Ihling C, Kühn U, Neundorf I, Otto S, Stichel J, Robaa D, Beck-Sickinger AG, Sinz A, Wahle E. 2012. Peptide backbone conformation affects the substrate preference of protein arginine methyltransferase i. Biochemistry 51:5463–5475. 34.  Cha B, Jho E-H. 2012. Protein arginine methyltransferases (PRMTs) as therapeutic targets. Expert Opin Ther Targets 16:651–664. 96  35.  Cheng D, Côté J, Shaaban S, Bedford MT. 2007. The Arginine Methyltransferase CARM1 Regulates the Coupling of Transcription and mRNA Processing. Mol Cell 25:71–83. 36.  Lakowski TM, Frankel A. 2009. Kinetic analysis of human protein arginine N-methyltransferase 2: formation of monomethyl- and asymmetric dimethyl-arginine residues on histone H4. Biochem J 421:253–261. 37.  Boisvert F-M, Chénard CA, Richard S. 2005. Protein interfaces in signaling regulated by arginine methylation. Sci STKE 2005:re2. 38.  Lee J-H, Cook JR, Yang Z-H, Mirochnitchenko O, Gunderson SI, Felix AM, Herth N, Hoffmann R, Pestka S. 2005. PRMT7, a new protein arginine methyltransferase that synthesizes symmetric dimethylarginine. J Biol Chem 280:3656–64. 39.  Feng Y, Hadjikyriacou A, Clarke SG. 2014. Substrate specificity of human protein arginine methyltransferase 7 (PRMT7): the importance of acidic residues in the double E loop. J Biol Chem 289:32604–16. 40.  Low JK, Hart-Smith G, Erce MA, Wilkins MR. 2013. Analysis of the proteome of Saccharomyces cerevisiae for methylarginine. J Proteome Res 12:3884–3899. 41.  Jackson C a, Yadav N, Min S, Li J, Milliman EJ, Qu J, Chen Y-C, Yu MC. 2012. Proteomic analysis of interactors for yeast protein arginine methyltransferase Hmt1 reveals novel substrate and insights into additional biological roles. Proteomics 12:3304–14. 42.  Sylvestersen KB, Horn H, Jungmichel S, Jensen LJ, Nielsen ML. 2014. Proteomic analysis of arginine methylation sites in human cells reveals dynamic regulation during transcriptional arrest. Mol Cell Proteomics 13:2072–88. 97  43.  Guo A, Gu H, Zhou J, Mulhern D, Wang Y, Lee KA, Yang V, Aguiar M, Kornhauser J, Jia X, Ren J, Beausoleil SA, Silva JC, Vemulapalli V, Bedford MT, Comb MJ. 2014. Immunoaffinity enrichment and mass spectrometry analysis of protein methylation. Mol Cell Proteomics 13:372–87. 44.  Ong SE, Mann M. 2006. A practical recipe for stable isotope labeling by amino acids in cell culture (SILAC). Nat Protoc 1:2650–2660. 45.  Ong S, Mann M. 2006. A practical recipe for stable isotope labeling by amino acids in cell culture (SILAC). Nat Protoc 1:2650–60. 46.  Uhlmann T, Geoghegan VL, Thomas B, Ridlova G, Trudgian DC, Acuto O. 2012. A method for large-scale identification of protein arginine methylation. Mol Cell Proteomics 11:1489–99. 47.  Ong S-E, Mittler G, Mann M. 2004. Identifying and quantifying in vivo methylation sites by heavy methyl SILAC. Nat Methods 1:119–126. 48.  Geoghegan V, Guo A, Trudgian D, Thomas B, Acuto O. 2015. Comprehensive identification of arginine methylation in primary T cells reveals regulatory roles in cell signalling. Nat Commun 6:6758. 49.  Bremang M, Cuomo A, Agresta AM, Stugiewicz M, Spadotto V, Bonaldi T. 2013. Mass spectrometry-based identification and characterisation of lysine and arginine methylation in the human proteome. Mol Biosyst 9:2231–47. 50.  Herrmann F, Bossert M, Schwander A, Akgün E, Fackelmayer FO. 2004. Arginine methylation of scaffold attachment factor A by heterogeneous nuclear ribonucleoprotein particle-associated PRMT1. J Biol Chem 279:48774–48779. 51.  Pahlich S, Zakaryan RP, Gehring H. 2006. Protein arginine methylation: Cellular 98  functions and methods of analysis. Biochim Biophys Acta 1764:1890–903. 52.  Boisvert F-M, Cote J, Boulanger M-C, Cleroux P, Bachand F, Autexier C, Richard S. 2002. Symmetrical dimethylarginine methylation is required for the localization of SMN in Cajal bodies and pre-mRNA splicing. J Cell Biol 159:957–69. 53.  Bedford MT, Richard S. 2005. Arginine Methylation. Mol Cell 18:263–272. 54.  Jones S, Daley DT, Luscombe NM, Berman HM, Thornton JM. 2001. Protein-RNA interactions: a structural analysis. Nucleic Acids Res 29:943–954. 55.  Calnan BJ, Tidor B, Biancalana S, Hudson D, Frankel AD, Series N, May N. 2007. Arginine-Mediated RNA Recognition : The Arginine Fork Arginine-Mediated RNA Recognition : The Arginine Fork Short peptides that contain the basic region of the HW-1 Tat protein bind specifically 252:1167–1171. 56.  Rajpurohit R, Paik WK, Kim S. 1994. Effect of enzymic methylation of heterogeneous ribonucleoprotein particle A1 on its nucleic-acid binding and controlled proteolysis. Biochem J 304:903–909. 57.  Valentini SR, Weiss VH, Silver PA. 1999. Arginine methylation and binding of Hrp1p to the efficiency element for mRNA 3’-end formation. RNA 5:272–280. 58.  Stetler A, Winograd C, Sayegh J, Cheever A, Patton E, Zhang X, Clarke S, Ceman S. 2006. Identification and characterization of the methyl arginines in the fragile X mental retardation protein Fmrp. Hum Mol Genet 15:87–96. 59.  Nef P, Natl P, Sci A, Res CT, Vassar R, Ngai J, Axel R, Bozza TC, Kauer JS, Neurosci J, Chen D, Ma H, Hong H, Koh SS, Huang S, Schurter BT, Aswad DW, Stallcup MR. 1999. Regulation of Transcription by a Protein Methyltransferase 284:2174–2177. 99  60.  An W, Kim J, Roeder RG. 2004. Ordered cooperative functions of PRMT1, p300, and CARM1 in transcriptional activation by p53. Cell 117:735–748. 61.  Kleinschmidt MA, Streubel G, Samans B, Krause M, Bauer U-M. 2008. The protein arginine methyltransferases CARM1 and PRMT1 cooperate in gene regulation. Nucleic Acids Res 36:3202–13. 62.  Zhao X, Jankovic V, Gural A, Huang G, Pardanani A, Menendez S, Zhang J, Dunne R, Xiao A, Erdjument-Bromage H, Allis CD, Tempst P, Nimer SD. 2008. Methylation of RUNX1 by PRMT1 abrogates SIN3A binding and potentiates its transcriptional activity. Genes Dev 22:640–53. 63.  Yadav N, Lee J, Kim J, Shen J, Hu MC-T, Aldaz CM, Bedford MT. 2003. Specific protein methylation defects and gene expression perturbations in coactivator-associated arginine methyltransferase 1-deficient mice. Proc Natl Acad Sci U S A 100:6464–8. 64.  Zhao X, Jankovic V, Gural A, Huang G, Pardanani A, Menendez S, Zhang J, Dunne R, Xiao A, Erdjument-Bromage H, Allis CD, Tempst P, Nimer SD. 2008. Methylation of RUNX1 by PRMT1 abrogates SIN3A binding and potentiates its transcriptional activity. Genes Dev 22:640–53. 65.  Ma H, Baumann CT, Li H, Strahl BD, Rice R, Jelinek MA, Aswad DW, Allis CD, Hager GL, Stallcup MR. 2001. Hormone-dependent, CARM1-directed, arginine-specific methylation of histone H3 on a steroid-regulated promoter. Curr Biol 11:1981–1985. 66.  Feng Q, Yi P, Wong J, O’Malley BW. 2006. Signaling within a coactivator complex: methylation of SRC-3/AIB1 is a molecular switch for complex disassembly. Mol Cell Biol 26:7846–57. 67.  Chevillard-Briet M, Trouche D, Vandel L. 2002. Control of CBP co-activating activity 100  by arginine methylation. EMBO J 21:5457–66. 68.  Chen D, Ma H, Hong H, Koh SS, Huang SM, Schurter BT, Aswad DW, Stallcup MR. 1999. Regulation of transcription by a protein methyltransferase. Science 284:2174–7. 69.  Fabbrizio E, El Messaoudi S, Polanowska J, Paul C, Cook JR, Lee JH, Negre V, Rousset M, Pestka S, Le Cam A, Sardet C. 2002. Negative regulation of transcription by the type II arginine methyltransferase PRMT5. EMBO Rep 3:641–645. 70.  Pal S, Vishwanath SN, Erdjument-Bromage H, Tempst P, Sif S. 2004. Human SWI/SNF-associated PRMT5 methylates histone H3 arginine 8 and negatively regulates expression of ST7 and NM23 tumor suppressor genes. Mol Cell Biol 24:9630–45. 71.  Han HS, Choi D, Choi S, Koo SH. 2014. Roles of protein arginine methyltransferases in the control of glucose metabolism. Endocrinol Metab (Seoul, Korea) 29:435–40. 72.  Wang H, Huang ZQ, Xia L, Feng Q, Erdjument-Bromage H, Strahl BD, Briggs SD, Allis CD, Wong J, Tempst P, Zhang Y. 2001. Methylation of histone H4 at arginine 3 facilitating transcriptional activation by nuclear hormone receptor. Science 293:853–7. 73.  Richard S, Morel M, Cléroux P. 2005. Arginine methylation regulates IL-2 gene expression: a role for protein arginine methyltransferase 5 (PRMT5). Biochem J 388:379–386. 74.  Dacwag CS, Ohkawa Y, Pal S, Sif S, Imbalzano AN. 2007. The protein arginine methyltransferase Prmt5 is required for myogenesis because it facilitates ATP-dependent chromatin remodeling. Mol Cell Biol 27:384–94. 75.  Hyllus D, Stein C, Schnabel K, Schiltz E, Imhof A, Dou Y, Hsieh J, Bauer U-M. 2007. PRMT6-mediated methylation of R2 in histone H3 antagonizes H3 K4 trimethylation. Genes Dev 21:3369–80. 101  76.  Jelinic P, Stehle JC, Shaw P. 2006. The testis-specific factor CTCFL cooperates with the protein methyltransferase PRMT7 in H19 imprinting control region methylation. PLoS Biol 4:1910–1922. 77.  Skaar DA, Li Y, Bernal AJ, Hoyo C, Murphy SK, Jirtle RL. 2012. The human imprintome: regulatory mechanisms, methods of ascertainment, and roles in disease susceptibility. ILAR J 53:341–58. 78.  Bedford MT, Frankel A, Yaffe MB, Clarke S, Leder P, Richard S. 2000. Arginine Methylation Inhibits the Binding of Proline-rich Ligands to Src Homology 3 , but Not WW , Domains *. J Biol Chem 275:16030–16036. 79.  Boisvert F-M. 2003. A Proteomic Analysis of Arginine-methylated Protein Complexes. Mol Cell Proteomics 2:1319–1330. 80.  Likhite N, Jackson CA, Liang M-S, Krzyzanowski MC, Lei P, Wood JF, Birkaya B, Michaels KL, Andreadis ST, Clark SD, Yu MC, Ferkey DM. 2015. The protein arginine methyltransferase PRMT5 promotes D2-like dopamine receptor signaling. Sci Signal 8:ra115–ra115. 81.  Abramovich C, Yakobson B, Chebath J, Revel M. 1997. A protein-arginine methyltransferase binds to the intracytoplasmic domain of the IFNAR1 chain in the type I interferon receptor. EMBO J 16:260–6. 82.  Mowen K a, Schurter BT, Fathman JW, David M, Glimcher LH. 2004. Arginine methylation of NIP45 modulates cytokine gene expression in effector T lymphocytes. Mol Cell 15:559–571. 83.  Iwasaki H, Yada T. 2007. Protein arginine methylation regulates insulin signaling in L6 skeletal muscle cells. Biochem Biophys Res Commun 364:1015–21. 102  84.  Le Romancer M, Treilleux I, Leconte N, Robin-Lespinasse Y, Sentis S, Bouchekioua-Bouzaghou K, Goddard S, Gobert-Gosse S, Corbo L. 2008. Regulation of estrogen rapid signaling through arginine methylation by PRMT1. Mol Cell 31:212–21. 85.  Shen EC, Henry MF, Weiss VH, Valentini SR, Silver P a, Lee MS. 1998. Arginine methylation facilitates the nuclear export of hnRNP proteins. Genes Dev 12:679–691. 86.  Pintucci G, Quarto N, Rifkin DB. 1996. Methylation of high molecular weight fibroblast growth factor-2 determines post-translational increases in molecular weight and affects its intracellular distribution. Mol Biol Cell 7:1249–58. 87.  Côté J, Boisvert F-M, Boulanger M-C, Bedford MT, Richard S. 2003. Sam68 RNA binding protein is an in vivo substrate for protein arginine N-methyltransferase 1. Mol Biol Cell 14:274–87. 88.  Sinha R, Allemand E, Zhang Z, Karni R, Myers MP, Krainer AR. 2010. Arginine methylation controls the subcellular localization and functions of the oncoprotein splicing factor SF2/ASF. Mol Cell Biol 30:2762–74. 89.  Iacovides DC, O’Shea CC, Oses-Prieto J, Burlingame A, McCormick F. 2007. Critical role for arginine methylation in adenovirus-infected cells. J Virol 81:13209–17. 90.  Boisvert F-M, Déry U, Masson J-Y, Richard S. 2005. Arginine methylation of MRE11 by PRMT1 is required for DNA damage checkpoint control. Genes Dev 19:671–6. 91.  Boisvert F-M. 2003. A Proteomic Analysis of Arginine-methylated Protein Complexes. Mol Cell Proteomics 2:1319–1330. 92.  Boisvert FM, Hendzel MJ, Masson JY, Richard S. 2005. Methylation of MRE11 regulates its nuclear compartmentalization. Cell Cycle 4:981–989. 93.  El-Andaloussi N, Valovka T, Toueille M, Steinacher R, Focke F, Gehrig P, Covic M, 103  Hassa PO, Schär P, Hübscher U, Hottiger MO. 2006. Arginine methylation regulates DNA polymerase beta. Mol Cell 22:51–62. 94.  El-Andaloussi N, Valovka T, Toueille M, Hassa PO, Gehrig P, Covic M, Hübscher U, Hottiger MO. 2007. Methylation of DNA polymerase beta by protein arginine methyltransferase 1 regulates its binding to proliferating cell nuclear antigen. FASEB J 21:26–34. 95.  Cadigan KM, Nusse R. 1997. Wnt signalling: a common theme in animal development. Genes Dev 11:3286–3305. 96.  Moon RT, Brown JD, Torres M. 1997. WNTs modulate cell fate and behavior during vertebrate development. Trends Genet 13:157–162. 97.  Polakis P. 2000. Wnt signaling and cancer Wnt signaling and cancer 1837–1851. 98.  Bienz M, Clevers H. 2000. Linking colorectal cancer to Wnt signaling. Cell 103:311–320. 99.  Klaus A, Birchmeier W. 2008. Wnt signalling and its impact on development and cancer. Nat Rev Cancer 8:387–398. 100.  Koh SS, Li H, Lee YH, Widelitz RB, Chuong CM, Stallcup MR. 2002. Synergistic coactivator function by coactivator-associated arginine methyltransferase (CARM) 1 and ??-catenin with two different classes of DNA-binding transcriptional activators. J Biol Chem 277:26031–26035. 101.  Blythe S a., Cha SW, Tadjuidje E, Heasman J, Klein PS. 2010. β-catenin primes organizer gene expression by recruiting a histone H3 arginine 8 methyltransferase, Prmt2. Dev Cell 19:220–231. 102.  Bikkavilli RK, Malbon CC. 2011. Arginine methylation of G3BP1 in response to Wnt3a 104  regulates beta-catenin mRNA. J Cell Sci 124:2310–2320. 103.  Bikkavilli RK, Malbon CC. 2012. Wnt3a-stimulated LRP6 phosphorylation is dependent upon arginine methylation of G3BP2. J Cell Sci 125:2446–56. 104.  Biggar KK, Li SS-C. 2014. Non-histone protein methylation as a regulator of cellular signalling and function. Nat Rev Mol Cell Biol 16:5–17. 105.  Cote J, Richard S. 2005. Tudor Domains Bind Symmetrical Dimethylated Arginines. J Biol Chem 280:28476–28483. 106.  Chen C, Nott TJ, Jin J, Pawson T. 2011. Deciphering arginine methylation: Tudor tells the tale. Nat Rev Mol Cell Biol 12:629–642. 107.  Cote J, Richard S. 2005. Tudor Domains Bind Symmetrical Dimethylated Arginines. J Biol Chem 280:28476–28483. 108.  Kirmizis A, Santos-Rosa H, Penkett CJ, Singer MA, Vermeulen M, Mann M, Bähler J, Green RD, Kouzarides T. 2007. Arginine methylation at histone H3R2 controls deposition of H3K4 trimethylation. Nature 449:928–932. 109.  Flanagan JF, Mi L-Z, Chruszcz M, Cymborowski M, Clines KL, Kim Y, Minor W, Rastinejad F, Khorasanizadeh S. 2005. Double chromodomains cooperate to recognize the methylated histone H3 tail. Nature 438:1181–1185. 110.  Flanagan JF, Mi L-Z, Chruszcz M, Cymborowski M, Clines KL, Kim Y, Minor W, Rastinejad F, Khorasanizadeh S. 2005. Double chromodomains cooperate to recognize the methylated histone H3 tail. Nature 438:1181–1185. 111.  Couture J-F, Collazo E, Trievel RC. 2006. Molecular recognition of histone H3 by the WD40 protein WDR5. Nat Struct &#38; Mol Biol 13:698–703. 112.  Wysocka J, Swigut T, Milne TA, Dou Y, Zhang X, Burlingame AL, Roeder RG, 105  Brivanlou AH, Allis CD. 2005. WDR5 associates with histone H3 methylated at K4 and is essential for H3 K4 methylation and vertebrate development. Cell 121:859–72. 113.  Pawlak MR, Scherer CA, Chen J, Roshon MJ, Ruley HE. 2000. Arginine N-methyltransferase 1 is required for early postimplantation mouse development, but cells deficient in the enzyme are viable. Mol Cell Biol 20:4859–4869. 114.  Yu Z, Chen T, Hebert J, Li E, Richard S. 2009. A Mouse PRMT1 Null Allele Defines an Essential Role for Arginine Methylation in Genome Maintenance and Cell Proliferation. Mol Cell Biol 29:2982–2996. 115.  Iwasaki H, Kovacic JC, Olive M, Beers JK, Yoshimoto T, Crook MF, Tonelli LH, Nabel EG. 2010. Disruption of protein arginine N-methyltransferase 2 regulates leptin signaling and produces leanness in vivo through loss of STAT3 methylation. Circ Res 107:992–1001. 116.  Ganesh L, Yoshimoto T, Moorthy NC, Akahata W, Boehm M, Nabel EG, Nabel GJ. 2006. Protein methyltransferase 2 inhibits NF-kappaB function and promotes apoptosis. Mol Cell Biol 26:3864–3874. 117.  Swiercz R, Cheng D, Kim D, Bedford MT. 2007. Ribosomal protein rpS2 is hypomethylated in PRMT3-deficient mice. J Biol Chem 282:16917–23. 118.  Yadav N, Lee J, Kim J, Shen J, Hu MC-T, Aldaz CM, Bedford MT. 2003. Specific protein methylation defects and gene expression perturbations in coactivator-associated arginine methyltransferase 1-deficient mice. Proc Natl Acad Sci U S A 100:6464–8. 119.  Yadav N, Cheng D, Richard S, Morel M, Iyer VR, Aldaz CM, Bedford MT. 2008. CARM1 promotes adipocyte differentiation by coactivating PPARgamma. EMBO Rep 9:193–198. 106  120.  Tee W-W, Pardo M, Theunissen TW, Yu L, Choudhary JS, Hajkova P, Surani MA. 2010. Prmt5 is essential for early mouse development and acts in the cytoplasm to maintain ES cell pluripotency. Genes Dev 24:2772–7. 121.  Seligson DB, Horvath S, Shi T, Yu H, Tze S, Grunstein M, Kurdistani SK. 2005. Global histone modification patterns predict risk of prostate cancer recurrence. Nature 435:1262–1266. 122.  Mathioudaki K, Papadokostopoulou A, Scorilas A, Xynopoulos D, Agnanti N, Talieri M. 2008. The PRMT1 gene expression pattern in colon cancer. Br J Cancer 99:2094–2099. 123.  Mathioudaki K, Scorilas A, Ardavanis A, Lymberi P, Tsiambas E, Devetzi M, Apostolaki A, Talieri M. 2011. Clinical evaluation of PRMT1 gene expression in breast cancer. Tumor Biol 32:575–582. 124.  Daser A, Rabbitts TH. 2005. The versatile mixed lineage leukaemia gene MLL and its many associations in leukaemogenesis. Semin Cancer Biol 15:175–188. 125.  Cheung N, Chan LC, Thompson A, Cleary ML, So CWE. 2007. Protein arginine-methyltransferase-dependent oncogenesis. Nat Cell Biol 9:1208–1215. 126.  Cheung N, Chan LC, Thompson A, Cleary ML, So CWE. 2007. Protein arginine-methyltransferase-dependent oncogenesis. Nat Cell Biol 9:1208–1215. 127.  Le Romancer M, Treilleux I, Leconte N, Robin-Lespinasse Y, Sentis S, Bouchekioua-Bouzaghou K, Goddard S, Gobert-Gosse S, Corbo L. 2008. Regulation of estrogen rapid signaling through arginine methylation by PRMT1. Mol Cell 31:212–21. 128.  Hong H, Kao C, Jeng M-H, Eble JN, Koch MO, Gardner TA, Zhang S, Li L, Pan C-X, Hu Z, MacLennan GT, Cheng L. 2004. Aberrant expression of CARM1, a 107  transcriptional coactivator of androgen receptor, in the development of prostate carcinoma and androgen-independent status. Cancer 101:83–89. 129.  El Messaoudi S, Fabbrizio E, Rodriguez C, Chuchana P, Fauquier L, Cheng D, Theillet C, Vandel L, Bedford MT, Sardet C. 2006. Coactivator-associated arginine methyltransferase 1 (CARM1) is a positive regulator of the Cyclin E1 gene. Proc Natl Acad Sci U S A 103:13351–6. 130.  Kim Y-R, Lee BK, Park R-Y, Nguyen NTX, Bae J a, Kwon DD, Jung C. 2010. Differential CARM1 expression in prostate and colorectal cancers. BMC Cancer 10:197. 131.  Frietze S, Lupien M, Silver P a, Brown M. 2008. CARM1 regulates estrogen-stimulated breast cancer growth through up-regulation of E2F1. Cancer Res 68:301–6. 132.  Frietze S, Lupien M, Silver P a, Brown M. 2008. CARM1 regulates estrogen-stimulated breast cancer growth through up-regulation of E2F1. Cancer Res 68:301–6. 133.  Al-Dhaheri M, Wu J, Skliris GP, Li J, Higashimato K, Wang Y, White KP, Lambert P, Zhu Y, Murphy L, Xu W. 2011. CARM1 is an important determinant of ERα-dependent breast cancer cell differentiation and proliferation in breast cancer cells. Cancer Res 71:2118–28. 134.  Al-Dhaheri M, Wu J, Skliris GP, Li J, Higashimato K, Wang Y, White KP, Lambert P, Zhu Y, Murphy L, Xu W. 2011. CARM1 is an important determinant of ERα-dependent breast cancer cell differentiation and proliferation in breast cancer cells. Cancer Res 71:2118–28. 135.  Pal S, Vishwanath SN, Tempst P, Sif S, Erdjument-bromage H. 2004. Negatively regulates expression of ST7 and NM23 tumor suppressor genes human SWI / SNF-associated PRMT5 methylates histone H3 arginine 8 and negatively regulates expression 108  of ST7 and NM23 tumor suppressor genes. Mol Cell Biol 24:9630–9645. 136.  Pal S, Baiocchi R a, Byrd JC, Grever MR, Jacob ST, Sif S. 2007. Low levels of miR-92b/96 induce PRMT5 translation and H3R8/H4R3 methylation in mantle cell lymphoma. EMBO J 26:3558–69. 137.  Pal S, Baiocchi R a, Byrd JC, Grever MR, Jacob ST, Sif S. 2007. Low levels of miR-92b/96 induce PRMT5 translation and H3R8/H4R3 methylation in mantle cell lymphoma. EMBO J 26:3558–69. 138.  Dweik RA. 2007. The lung in the balance: arginine, methylated arginines, and nitric oxide. Am J Physiol Lung Cell Mol Physiol 292:L15–17. 139.  Sharma S, Smith A, Kumar S, Aggarwal S, Rehmani I, Snead C, Harmon C, Fineman J, Fulton D, Catravas JD, Black SM. 2010. Mechanisms of nitric oxide synthase uncoupling in endotoxin-induced acute lung injury: role of asymmetric dimethylarginine. Vascul Pharmacol 52:182–90. 140.  Leiper J, Nandi M, Torondel B, Murray-Rust J, Malaki M, O’Hara B, Rossiter S, Anthony S, Madhani M, Selwood D, Smith C, Wojciak-Stothard B, Rudiger A, Stidwill R, McDonald NQ, Vallance P. 2007. Disruption of methylarginine metabolism impairs vascular homeostasis. Nat Med 13:198–203. 141.  Yildirim AO, Bulau P, Zakrzewicz D, Kitowska KE, Weissmann N, Grimminger F, Morty RE, Eickelberg O. 2006. Increased protein arginine methylation in chronic hypoxia: Role of protein arginine methyltransferases. Am J Respir Cell Mol Biol 35:436–443. 142.  Ahmad T, Mabalirajan U, Ghosh B, Agrawal A. 2010. Altered asymmetric dimethyl arginine metabolism in allergically inflamed mouse lungs. Am J Respir Cell Mol Biol 109  42:3–8. 143.  Leone  a., Moncada S, Vallance P, Calver  a., Collier J. 1992. Accumulation of an endogenous inhibitor of nitric oxide synthesis in chronic renal failure. Lancet 339:572–575. 144.  Scott JA, North ML, Rafii M, Huang H, Pencharz P, Subbarao P, Belik J, Grasemann H. 2011. Asymmetric Dimethylarginine Is Increased in Asthma. Am J Respir Crit Care Med 184:779–785. 145.  Wells SM, Holian A. 2007. Asymmetric Dimethylarginine Induces Oxidative and Nitrosative Stress in Murine Lung Epithelial Cells. Am J Respir Cell Mol Biol 36:520–528. 146.  Klein E, Weigel J, Buford MC, Holian A, Wells SM. 2010. Asymmetric dimethylarginine potentiates lung inflammation in a mouse model of allergic asthma. Am J Physiol Lung Cell Mol Physiol 299:L816–25. 147.  Wells SM, Buford MC, Migliaccio CT, Holian A. 2009. Elevated asymmetric dimethylarginine alters lung function and induces collagen deposition in mice. Am J Respir Cell Mol Biol 40:179–88. 148.  Hassa PO, Covic M, Bedford MT, Hottiger MO. 2008. Protein Arginine Methyltransferase 1 Coactivates NF-κB-Dependent Gene Expression Synergistically with CARM1 and PARP1. J Mol Biol 377:668–678. 149.  Batra S, Balamayooran G, Sahoo MK. 2011. Nuclear factor-κB: a key regulator in health and disease of lungs. Arch Immunol Ther Exp (Warsz) 59:335–51. 150.  Boulanger M-C, Liang C, Russell RS, Lin R, Bedford MT, Wainberg MA, Richard S. 2005. Methylation of Tat by PRMT6 regulates human immunodeficiency virus type 1 110  gene expression. J Virol 79:124–31. 151.  Kzhyshkowska J, Schütt H, Liss M, Kremmer E, Stauber R, Wolf H, Dobner T. 2001. Heterogeneous nuclear ribonucleoprotein E1B-AP5 is methylated in its Arg-Gly-Gly (RGG) box and interacts with human arginine methyltransferase HRMT1L1. Biochem J 358:305–14. 152.  Souki SK, Gershon PD, Sandri-Goldin RM. 2009. Arginine methylation of the ICP27 RGG box regulates ICP27 export and is required for efficient herpes simplex virus 1 replication. J Virol 83:5309–20. 153.  Shire K, Kapoor P, Jiang K, Hing MNT, Sivachandran N, Nguyen T, Frappier L. 2006. Regulation of the EBNA1 Epstein-Barr virus protein by serine phosphorylation and arginine methylation. J Virol 80:5261–72. 154.  Rho J, Choi S, Seong YR, Choi J, Im DS. 2001. The arginine-1493 residue in QRRGRTGR1493G motif IV of the hepatitis C virus NS3 helicase domain is essential for NS3 protein methylation by the protein arginine methyltransferase 1. J Virol 75:8031–44. 155.  Iacovides DC, O’Shea CC, Oses-Prieto J, Burlingame A, McCormick F. 2007. Critical role for arginine methylation in adenovirus-infected cells. J Virol 81:13209–17. 156.  Li Y-J, Stallcup MR, Lai MMC. 2004. Hepatitis delta virus antigen is methylated at arginine residues, and methylation regulates subcellular localization and RNA replication. J Virol 78:13325–34. 157.  Brostoff S, Eylar EH. 1971. Localization of methylated arginine in the A1 protein from myelin. Proc Natl Acad Sci U S A 68:765–9. 158.  Kim JK, Mastronardi FG, Wood DD, Lubman DM, Zand R, Moscarello MA. 2003. Multiple sclerosis: an important role for post-translational modifications of myelin basic 111  protein in pathogenesis. Mol Cell Proteomics 2:453–62. 159.  Cusco I, Barcelo MJ, del Rio E, Baiget M, Tizzano EF. 2004. Detection of novel mutations in the SMN Tudor domain in type I SMA patients. Neurology 63:146–149. 160.  Tadesse H, Deschênes-Furry J, Boisvenue S, Côté J. 2008. KH-type splicing regulatory protein interacts with survival motor neuron protein and is misregulated in spinal muscular atrophy. Hum Mol Genet 17:506–524. 161.  Cuscó I, López E, Soler-Botija C, Jesús Barceló M, Baiget M, Tizzano EF. 2003. A genetic and phenotypic analysis in Spanish spinal muscular atrophy patients with c.399_402del AGAG, the most frequently found subtle mutation in the SMN1 gene. Hum Mutat 22:136–143. 162.  Robin-Lespinasse Y, Sentis S, Kolytcheff C, Rostan M-C, Corbo L, Le Romancer M. 2007. hCAF1, a new regulator of PRMT1-dependent arginine methylation. J Cell Sci 120:638–647. 163.  Singh V, Miranda TB, Jiang W, Frankel A, Roemer ME, Robb VA, Gutmann DH, Herschman HR, Clarke S, Newsham IF. 2004. DAL-1/4.1B tumor suppressor interacts with protein arginine N-methyltransferase 3 (PRMT3) and inhibits its ability to methylate substrates in vitro and in vivo. Oncogene 23:7761–7771. 164.  Sayegh J, Webb K, Cheng D, Bedford MT, Clarke SG. 2007. Regulation of protein arginine methyltransferase 8 (PRMT8) activity by its N-terminal domain. J Biol Chem 282:36444–36453. 165.  Kuhn P, Chumanov R, Wang Y, Ge Y, Burgess RR, Xu W. 2010. Automethylation of CARM1 allows coupling of transcription and mRNA splicing. Nucleic Acids Res 39:2717–2726. 112  166.  Rathert P, Dhayalan A, Murakami M, Zhang X, Tamas R, Jurkowska R, Komatsu Y, Shinkai Y, Cheng X, Jeltsch A. 2008. Protein lysine methyltransferase G9a acts on non-histone targets. Nat Chem Biol 4:344–346. 167.  Rathert P, Dhayalan A, Murakami M, Zhang X, Tamas R, Jurkowska R, Komatsu Y, Shinkai Y, Cheng X, Jeltsch A. 2008. Protein lysine methyltransferase G9a acts on non-histone targets. Nat Chem Biol 4:344–346. 168.  Guccione E, Bassi C, Casadio F, Martinato F, Cesaroni M, Schuchlautz H, Lüscher B, Amati B. 2007. Methylation of histone H3R2 by PRMT6 and H3K4 by an MLL complex are mutually exclusive. Nature 449:933–7. 169.  Thompson PR, Fast W. 2006. Histone citrullination by protein arginine deiminase: is arginine methylation a green light or a roadblock? ACS Chem Biol 1:433–41. 170.  Nakashima K. 2002. Nuclear Localization of Peptidylarginine Deiminase V and Histone Deimination in Granulocytes. J Biol Chem 277:49562–49568. 171.  Chang B, Chen Y, Zhao Y, Bruick RK. 2007. JMJD6 is a histone arginine demethylase. Science 318:444–7. 172.  Böttger A, Islam MS, Chowdhury R, Schofield CJ, Wolf A. 2015. The oxygenase Jmjd6–a case study in conflicting assignments. Biochem J 468:191–202. 173.  Webby CJ, Wolf A, Gromak N, Dreger M, Kramer H, Kessler B, Nielsen ML, Schmitz C, Butler DS, Yates JR, Delahunty CM, Hahn P, Lengeling A, Mann M, Proudfoot NJ, Schofield CJ, Bottger A. 2009. Jmjd6 Catalyses Lysyl-Hydroxylation of U2AF65, a Protein Associated with RNA Splicing. Science (80- ) 325:90–93. 174.  Pak L. 2012. Insights Into a Heteromeric Protein Arginine N -Methyltransferase Complex. 113  175.  Badrichani AZ, Stroka DM, Bilbao G, Curiel DT, Bach FH, Ferran C. 1999. Bcl-2 and Bcl-XL serve an anti-inflammatory function in endothelial cells through inhibition of NF-kappaB. J Clin Invest 103:543–53. 176.  Burlacu A. 2003. Regulation of apoptosis by Bcl-2 family proteins. J Cell Mol Med 7:249–257. 177.  Eno CO, Zhao G, Olberding KE, Li C. 2012. The Bcl-2 proteins Noxa and Bcl-xL co-ordinately regulate oxidative stress-induced apoptosis. Biochem J 444:69–78. 178.  Pak ML, Lakowski TM, Thomas D, Vhuiyan MI, Husecken K, Frankel A. 2011. A Protein Arginine N-Methyltransferase 1 (PRMT1) and 2 Heteromeric Interaction Increases PRMT1 Enzymatic Activity. Biochemistry 50:8226–8240. 179.  Ong S-E, Blagoev B, Kratchmarova I, Kristensen DB, Steen H, Pandey A, Mann M. 2002. Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol Cell Proteomics 1:376–86. 180.  Tini M, Naeem H, Torchia J. 2009. Biochemical Analysis of Arginine Methylation in Transcription, p. 235–247. In . 181.  Ramalho JS, Tolmachova T, Hume AN, McGuigan A, Gregory-Evans CY, Huxley C, Seabra MC. 2001. Chromosomal mapping, gene structure and characterization of the human and murine RAB27B gene. BMC Genet 2:2. 182.  Boersema PJ, Raijmakers R, Lemeer S, Mohammed S, Heck AJ. 2009. Multiplex peptide stable isotope dimethyl labeling for quantitative proteomics. Nat Protoc 4:484–494. 183.  Boersema PJ, Aye TT, van Veen TAB, Heck AJR, Mohammed S. 2008. Triplex protein quantification based on stable isotope labeling by peptide dimethylation applied to 114  cell and tissue lysates. Proteomics 8:4624–32. 184.  Prigent M, Barlat I, Langen H, Dargemont C. 2000. IkappaBalpha and IkappaBalpha /NF-kappa B complexes are retained in the cytoplasm through interaction with a novel partner, RasGAP SH3-binding protein 2. J Biol Chem 275:36441–9. 185.  Bikkavilli RK, Malbon CC. 2012. Wnt3a-stimulated LRP6 phosphorylation is dependent upon arginine methylation of G3BP2. J Cell Sci 125:2446–2456. 186.  Yu C, Takeda M, Soliven B. 2000. Regulation of cell cycle proteins by TNF-α and TGF-β in cells of oligodendroglial lineage. J Neuroimmunol 108:2–10. 187.  Chen Y, Gu J, Li D, Li S. 2012. Time-course network analysis reveals TNF-α can promote G1/S transition of cell cycle in vascular endothelial cells. Bioinformatics 28:1–4. 188.  Darzynkiewicz Z, Williamson B, Carswell EA, Old LJ. 1984. Cell cycle-specific effects of tumor necrosis factor. Cancer Res 44:83–90. 189.  Netherton CL, Simpson J, Haller O, Wileman TE, Takamatsu HH, Monaghan P, Taylor G. 2009. Inhibition of a large double-stranded DNA virus by MxA protein. J Virol 83:2310–2320. 190.  Numajiri Haruki A, Naito T, Nishie T, Saito S, Nagata K. 2011. Interferon-inducible antiviral protein MxA enhances cell death triggered by endoplasmic reticulum stress. J Interferon Cytokine Res 31:847–856. 191.  Ren G, Zhao X, Zhang L, Zhang J, L’Huillier A, Ling W, Roberts AI, Le AD, Shi S, Shao C, Shi Y. 2010. Inflammatory cytokine-induced intercellular adhesion molecule-1 and vascular cell adhesion molecule-1 in mesenchymal stem cells are critical for immunosuppression. J Immunol (Baltimore, Md 1950) 184:2321–2328. 192.  Tessari MA, Gostissa M, Altamura S, Sgarra R, Rustighi A, Salvagno C, Caretti G, 115  Imbriano C, Mantovani R, Del Sal G, Giancotti V, Manfioletti G. 2003. Transcriptional activation of the cyclin A gene by the architectural transcription factor HMGA2. Mol Cell Biol 23:9104–9116. 193.  Mori R, Ikematsu K, Kitaguchi T, Kim SE, Okamoto M, Chiba T, Miyawaki A, Shimokawa I, Tsuboi T. 2011. Release of TNF-alpha from macrophages is mediated by small GTPase Rab37. Eur J Immunol 41:3230–3239. 194.  Zhao S, Torii S, Yokota-Hashimoto H, Takeuchi T, Izumi T. 2002. Involvement of Rab27b in the regulated secretion of pituitary hormones. Endocrinology 143:1817–1824. 195.  Fraile-Ramos A, Cepeda V, Elstak E, van der Sluijs P. 2010. Rab27a is required for human cytomegalovirus assembly. PLoS One 5:e15318. 196.  Gougeon PY, Prosser DC, Da-Silva LF, Ngsee JK. 2002. Disruption of Golgi morphology and trafficking in cells expressing mutant prenylated rab acceptor-1. J Biol Chem 277:36408–36414. 197.  Liu HP, Wu CC, Chang YS. 2006. PRA1 promotes the intracellular trafficking and NF-kappaB signaling of EBV latent membrane protein 1. EMBO J 25:4120–4130. 198.  Garcia-Pineres AJ, Hildesheim A, Dodd L, Kemp TJ, Yang J, Fullmer B, Harro C, Lowy DR, Lempicki RA, Pinto LA. 2009. Gene expression patterns induced by HPV-16 L1 virus-like particles in leukocytes from vaccine recipients. J Immunol (Baltimore, Md 1950) 182:1706–1729. 199.  Wei H, Wang B, Miyagi M, She Y, Gopalan B, Huang D-B, Ghosh G, Stark GR, Lu T. 2013. PRMT5 dimethylates R30 of the p65 subunit to activate NF-κB. Proc Natl Acad Sci U S A 110:13516–21. 200.  Pak ML, Lakowski TM, Thomas D, Vhuiyan MI, H??secken K, Frankel A. 2011. A 116  protein arginine N -methyltransferase 1 (PRMT1) and 2 heteromeric interaction increases PRMT1 enzymatic activity. Biochemistry 50:8226–8240. 201.  Ceballos-Cancino G, Espinosa M, Maldonado V, Melendez-Zajgla J. 2007. Regulation of mitochondrial Smac/DIABLO-selective release by survivin. Oncogene 26:7569–75. 202.  Matter N, Herrlich P, König H. 2002. Signal-dependent regulation of splicing via phosphorylation of Sam68. Nature 420:691–5. 203.  Espejo A, Côté J, Bednarek A, Richard S, Bedford MT. 2002. A protein-domain microarray identifies novel protein-protein interactions. Biochem J 367:697–702. 204.  Najib S, Martín-Romero C, González-Yanes C, Sánchez-Margalet V. 2005. Role of Sam68 as an adaptor protein in signal transduction. Cell Mol Life Sci 62:36–43. 205.  Huot M-E, Vogel G, Richard S. 2009. Identification of a Sam68 ribonucleoprotein complex regulated by epidermal growth factor. J Biol Chem 284:31903–13. 206.  Paronetto MP, Achsel T, Massiello A, Chalfant CE, Sette C. 2007. The RNA-binding protein Sam68 modulates the alternative splicing of Bcl-x. J Cell Biol 176:929–39. 207.  Barkett M, Gilmore TD. 1999. Control of apoptosis by Rel/NF-kappaB transcription factors. Oncogene 18:6910–24. 208.  Kay BK, Williamson MP, Sudol M. 2000. The importance of being proline: the interaction of proline-rich motifs in signaling proteins with their cognate domains. FASEB J 14:231–41. 209.  Zhou A, Ou AC, Cho A, Benz EJ, Huang S-C. 2008. Novel splicing factor RBM25 modulates Bcl-x pre-mRNA 5’ splice site selection. Mol Cell Biol 28:5924–36. 210.  Massiello A, Roesser JR, Chalfant CE. 2006. SAP155 Binds to ceramide-responsive 117  RNA cis-element 1 and regulates the alternative 5’ splice site selection of Bcl-x pre-mRNA. FASEB J 20:1680–2. 211.  Di Lorenzo A, Yang Y, Macaluso M, Bedford MT. 2014. A gain-of-function mouse model identifies PRMT6 as a NF-kappaB coactivator. Nucleic Acids Res 42:8297–8309. 212.  Chalfant CE, Rathman K, Pinkerman RL, Wood RE, Obeid LM, Ogretmen B, Hannun YA. 2002. De novo ceramide regulates the alternative splicing of caspase 9 and Bcl-x in A549 lung adenocarcinoma cells. Dependence on protein phosphatase-1. J Biol Chem 277:12587–12595. 213.  Revil T, Pelletier J, Toutant J, Cloutier A, Chabot B. 2009. Heterogeneous nuclear ribonucleoprotein K represses the production of pro-apoptotic Bcl-xS splice isoform. J Biol Chem 284:21458–21467. 214.  Iaquinta PJ, Lees JA. 2007. Life and death decisions by the E2F transcription factors. Curr Opin Cell Biol 19:649–657. 215.  Merdzhanova G, Edmond V, De Seranno S, Van den Broeck A, Corcos L, Brambilla C, Brambilla E, Gazzeri S, Eymin B. 2008. E2F1 controls alternative splicing pattern of genes involved in apoptosis through upregulation of the splicing factor SC35. Cell Death Differ 15:1815–1823. 216.  Fu XD, Ares Jr M. 2014. Context-dependent control of alternative splicing by RNA-binding proteins. Nat Rev 15:689–701. 217.  Chen M, Manley JL. 2009. Mechanisms of alternative splicing regulation: insights from molecular and genomics approaches. Nat Rev cell Biol 10:741–754. 218.  Nilsen TW, Graveley BR. 2010. Expansion of the eukaryotic proteome by alternative splicing. Nature 463:457–463. 118  219.  Lakowski TM, Szeitz A, Pak ML, Thomas D, Vhuiyan MI, Kotthaus J, Clement B, Frankel A. 2013. MS3 fragmentation patterns of monomethylarginine species and the quantification of all methylarginine species in yeast using MRM3. J Proteomics 80:43–54. 220.  Sack JS, Thieffine S, Bandiera T, Fasolini M, Duke GJ, Jayaraman L, Kish KF, Klei HE, Purandare A V, Rosettani P, Troiani S, Xie D, Bertrand JA. 2011. Structural basis for CARM1 inhibition by indole and pyrazole inhibitors. Biochem J 436:331–339. 221.  Purandare A V, Chen Z, Huynh T, Pang S, Geng J, Vaccaro W, Poss MA, Oconnell J, Nowak K, Jayaraman L. 2008. Pyrazole inhibitors of coactivator associated arginine methyltransferase 1 (CARM1). Bioorg Med Chem Lett 18:4438–41. 222.  Huynh T, Chen Z, Pang S, Geng J, Bandiera T, Bindi S, Vianello P, Roletto F, Thieffine S, Galvani A, Vaccaro W, Poss MA, Trainor GL, Lorenzi M V, Gottardis M, Jayaraman L, Purandare A V. 2009. Optimization of pyrazole inhibitors of Coactivator Associated Arginine Methyltransferase 1 (CARM1). Bioorg Med Chem Lett 19:2924–7. 223.  Wan H, Huynh T, Pang S, Geng J, Vaccaro W, Poss MA, Trainor GL, Lorenzi M V, Gottardis M, Jayaraman L, Purandare A V. 2009. Benzo[d]imidazole inhibitors of Coactivator Associated Arginine Methyltransferase 1 (CARM1)--Hit to Lead studies. Bioorg Med Chem Lett 19:5063–6.  119  Appendix Table A.1 Identified proteins that associate with the SH3 domain of PRMT2 from gel slices. The PRMT2 SH3 domain binders identified in the six protein bands analyzed by LC-MS/MS are listed (174). Gene Name Protein Name Gel Slice(s) Arginine Methylation Sm core snRNP components SNRPB Small nuclear ribonucleoprotein-associated proteins B and B´ 6 *** U1 snRNP components SNRNP70 U1 small nuclear ribonucleoprotein 70 kDa 3-5 - U2 snRNP components DDX42 ATP-dependent RNA helicase DDX42 1 - SF3A1 Splicing factor 3A subunit 1 1 - SF3A2 Splicing factor 3A subunit 2 3, 4 * SF3A3 Splicing factor 3A subunit 3 4, 5 - SF3B3 Splicing factor 3B subunit 3 1 - SNRPB2 U2 small nuclear ribonucleoprotein B'' 6 - USP39 U4/U6.U5 tri-snRNP-associated protein 2 4 - U11/U12 snRNP components PDCD7 Programmed cell death protein 7 5 - hnRNPs FUS Heterogeneous nuclear ribonucleoprotein P2 3, 5 *** HNRNPK Heterogeneous nuclear ribonucleoprotein K 4 ** HNRNPL Heterogeneous nuclear ribonucleoprotein L 4 * HNRNPR Heterogeneous nuclear ribonucleoprotein R 3 ** 120  Gene Name Protein Name Gel Slice(s) Arginine Methylation HNRNPU Heterogeneous nuclear ribonucleoprotein U 1 ** Other splicing-related proteins HNRNPUL1 Heterogeneous nuclear ribonucleoprotein U-like protein 1 1 ** HSPA1A Heat shock 70 kDa protein 1A/1B 3 - HSPA8 Heat shock cognate 71 kDa protein 3 - HSPB1 Heat shock protein beta-1 6 - KHDRBS1 Src-associated in mitosis 68 kDa protein 3 ** LUC7L3 Luc7-like protein 3 5 - NONO Non-POU domain-containing octamer-binding protein 5 - PSPC1 Paraspeckle component 1 4 * PUF60 60 kDa poly(U)-binding-splicing factor 4 - RBM25 RNA-binding protein 25 1 - RBM39 RNA-binding protein 39 3 - SF1 Splicing factor 1 2, 3 - SFPQ Splicing factor, proline- and glutamine-rich 1, 2 *** WBP11 WW domain-binding protein 11 2 - Cleavage and polyadenylation proteins CPSF5 Cleavage and polyadenylation specificity factor subunit 5 6 * CPSF6 Cleavage and polyadenylation specificity factor subunit 6 3-5 - CPSF7 Cleavage and polyadenylation specificity factor subunit 7 5 - 121  Gene Name Protein Name Gel Slice(s) Arginine Methylation Other proteins CCT3 T-complex protein 1 subunit gamma 4 - CCT4 T-complex protein 1 subunit delta 5 - CCT7 T-complex protein 1 subunit eta 5 - PRMT2 Protein arginine N-methyltransferase 2 1-6 - WASL Neural Wiskott-Aldrich syndrome protein 3 - WIPF1 WAS/WASL-interacting protein family member 1 5 *  *Novel sites of arginine methylation identified in this proteomic study.  **Human proteins annotated as arginine methylated in the Uniprot database but not identified in this proteomic study. ***Human proteins annotated as arginine methylated in both the Uniprot database and in this proteomic study.    122  Experimental details Methylarginine quantitation I analyzed methylarginine content of different peptides using LC-MS/MS as a part of the collaborative work with Dr. Jinrong Min (Principal Investigator, Chromatin Structural Biology, Structural Genomics Consortium) at the University of Toronto. Methylation reactions using different peptides and PRMTs were carried out by Dr. Jinrong Min’s lab and then were sent to our lab for the measurement of MMA, ADMA and SDMA content of those peptides. Separation of the enzyme from the substrate peptides was done by spin-filtering the samples at 14000 x g at 4 ˚C for 15 min using 10-kDa molecular weight cut-off filters (Nanosep, OD010C34). The eluates were then transferred into 300-µL glass tubes and dried using speed vacuum (Thermo Savant SC110A). The dried samples were hydrolyzed using 6 N HCl (200 µL) for 24 h at 110 ˚C in vacuo. The samples were then again dried and 100 µL of 0.5% acetic acid and 0.01% trifluoroacetic acid mixture was added for reconstitution. Samples were then analyzed by LC-MS/MS following the published protocol of multiple reaction monitoring (219), and the results are displayed in Figure A.1.    123   Figure A.1. Methylarginine quantitation. MS analysis was used to measure the MMA, ADMA and SDMA contents of different peptides resulting from the enzymatic reaction of native and mutant versions of PRMT6 (A), PRMT1 (B) and PRMT3 (C). MMA, ADMA and SDMA contents of H4 11-21R14me1 peptide upon the enzymatic activities of PRMTs were also measured using MS (D).   124  Differential inhibition of cancer cell proliferation of selected PS/DG library members This experiment was carried out as collaborative work with Dr. David Grierson (Faculty of Pharmaceutical Sciences, UBC). The objective was to find PRMT-specific inhibitors from Dr. Grierson’s library of small molecules that can affect cell proliferation. For this purpose, I tested 23 out of 82 benzisoxazolone- and pyridopyazolone-type structures in his library (PS/DG library) resembling CMPD1 and CMPD2, including five within the top 20 hits from a virtual screen against CARM1 (compounds 306, 406, 408, 412, and 417) for anti-proliferative effects on the following human cancer cell lines: A549 (lung), MCF-7 (breast), LNCaP (androgen-sensitive prostate), C4-2 (androgen-resistant prostate), and HeLa (cervical). CMPD1 and CMPD2 were identified as selective inhibitors of CARM1 (220–223). A solution of 1% DMSO served as the vehicle control, and 20 µM adenosine dialdehyde (AdOx) served as a positive control (it causes an increase in endogenous AdoHcy that inhibits all AdoMet-dependent methyltransferases). PS/DG library members were assayed at 100 µM each. Cell density over several days was monitored in an IncuCyte ZOOM live content imaging system as shown in Figure A.2. Comparing growth curves for the vehicle control to AdOx treatment indicate that A549 and LNCaP cells appear refractory to global methylation inhibition, whereas other cell lines show decreases in growth rates. The compounds selected in silico for CARM1 binding (306, 406, 408, 412, and 417) exerted their strongest anti-proliferative effects in prostate cancer cell lines, whereas 408 appeared to be the best at retarding growth in HeLa cells similar to the AdOx control. Only in a couple of instances did we see upon visual inspection cytotoxicity in the form of cell death (data not shown). Interestingly, cytotoxicity was also cell-specific and primarily limited to C4-2 cells. However we could not find any PRMT specific activity of these 125  compounds in vitro (data not shown). However, these results served as a strong impetus to further evaluate the DG/PS library and build new compounds from these active leads.    Figure A.2. Differential inhibition of cancer cell proliferation. Selected PS/DG library members were tested at 100 µM each for their abilities to reduce the proliferation of A549 (A), MCF-7 (B), LNCaP (C), C4-2 (D), and HeLa (E) cancer cell lines. A solution of 1% DMSO served as the vehicle control, and 20 µM AdOx served as a positive control. Cell density over several days was monitored in an IncuCyte ZOOM live content imaging system for percent phase object confluence. (F) A Venn diagram of cell lines susceptible to individual PS/DG compounds reveals partial overlap as well as cell-specific anti-proliferative effects. Bolded PS/DG numbers are compounds that were identified in the top 20 CARM1 binders by virtual screening. 

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