Pharmaceutical Sciences, Faculty of
DSpace
UBCV
Brown, Jennifer
Doctor of Philosophy - PhD
University of British Columbia
Protein arginine N-methyltransferases (PRMTs) catalyze the transfer of methyl groups from the methyl donor S-adenosyl-L-methionine (SAM) to polypeptide substrates. PRMTs are conserved among eukaryotes and many of their biological roles, including transcriptional regulation, DNA repair, and RNA processing, have been well elucidated. Another emerging research area is their role in stress pathways. Because of their significance in biological processes, PRMTs are promising drug targets for many human diseases. Knowledge of an enzyme’s kinetic mechanism and roles in cellular pathways can contribute to effective design of PRMT inhibitors. The purpose of this project was to examine how PRMTs bind to and methylate their substrates, and explore their roles in the stress response using Saccharomyces cerevisiae as a model organism. Using a combination of steady-state enzyme kinetics and biophysical assays, including differential scanning fluorimetry (DSF), we show that the predominant mammalian PRMT binds its substrates in a sequential manner where target substrate binding follows cofactor binding. By using knockout strains, we demonstrate that methylarginines are expelled from cells undergoing stress and that methylarginine expulsion is driven by autophagy. Further, we demonstrate that yeast do not take up methylarginines from their environment because methylarginines can inhibit nitric oxide production which is important for long-term cell survival under stressful growth conditions. Last, through studying in vitro methylation of yeast lysates using recombinantly expressed enzymes, we identify putative yeast methyltransferase Ynl092wp as a protein histidine N-methyltransferase that binds its substrates using a sequential mechanism and predict that Ykl162cp is an RNA methyltransferase. Here, we use a novel approach to examine the PRMT binding mechanism and we demonstrate that this novel technique is applicable to other enzyme families. Further, we demonstrate how yeast cells can evade cell toxicity by controlling methylarginine flux. Therefore, the research described herein encompasses the full importance and impact of PRMTs and their substrates in eukaryotic biological pathways.
MECHANISTIC INVESTIGATIONS OF PROTEIN ARGININE N-METHYLTRANSFERASES AND ELUCIDATION OF THEIR IMPLICATIONS IN THE YEAST STRESS RESPONSE by Jennifer Brown B.Sc. (Hons), Trent University, 2014 B.Ed., Queen’s University, 2015 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) September 2021 © Jennifer Brown, 2021 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: MECHANISTIC INVESTIGATIONS OF PROTEIN ARGININE N-METHYLTRANSFERASES AND ELUCIDATION OF THEIR IMPLICATIONS IN YEAST STRESS RESPONSE submitted by Jennifer Brown in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Pharmaceutical Sciences Examining Committee: Dr. Adam Frankel, Faculty of Pharmaceutical Sciences, University of British Columbia Supervisor Dr. Brent Page, Faculty of Pharmaceutical Sciences, University of British Columbia Supervisory Committee Member Dr. Calvin Yip, Department of Biochemistry and Molecular Biology, University of British Columbia University Examiner Dr. Kirsten Wolthers, Department of Chemistry, University of British Columbia, Okanagan University Examiner Additional Supervisory Committee Members: Dr. LeAnn Howe, Department of Biochemistry and Molecular Biology, University of British Columbia Supervisory Committee Member Dr. Thibault Mayor, Department of Biochemistry and Molecular Biology, University of British Columbia Supervisory Committee Member iii Abstract Protein arginine N-methyltransferases (PRMTs) catalyze the transfer of methyl groups from the methyl donor S-adenosyl-L-methionine (SAM) to polypeptide substrates. PRMTs are conserved among eukaryotes and many of their biological roles, including transcriptional regulation, DNA repair, and RNA processing, have been well elucidated. Another emerging research area is their role in stress pathways. Because of their significance in biological processes, PRMTs are promising drug targets for many human diseases. Knowledge of an enzyme’s kinetic mechanism and roles in cellular pathways can contribute to effective design of PRMT inhibitors. The purpose of this project was to examine how PRMTs bind to and methylate their substrates, and explore their roles in the stress response using Saccharomyces cerevisiae as a model organism. Using a combination of steady-state enzyme kinetics and biophysical assays, including differential scanning fluorimetry (DSF), we show that the predominant mammalian PRMT binds its substrates in a sequential manner where target substrate binding follows cofactor binding. By using knockout strains, we demonstrate that methylarginines are expelled from cells undergoing stress and that methylarginine expulsion is driven by autophagy. Further, we demonstrate that yeast do not take up methylarginines from their environment because methylarginines can inhibit nitric oxide production which is important for long-term cell survival under stressful growth conditions. Last, through studying in vitro methylation of yeast lysates using recombinantly expressed enzymes, we identify putative yeast methyltransferase Ynl092wp as a protein histidine N-methyltransferase that binds its substrates using a sequential mechanism and predict that Ykl162cp is an RNA methyltransferase. Here, we use a novel approach to examine the PRMT binding mechanism and we demonstrate that this novel technique is applicable to other enzyme families. Further, we demonstrate how yeast cells can evade cell toxicity by controlling methylarginine flux. Therefore, iv the research described herein encompasses the full importance and impact of PRMTs and their substrates in eukaryotic biological pathways. v Lay Summary Members of the protein arginine N-methyltransferase (PRMT) enzyme family are key players in many cellular processes, and because of this, their functions are crucial for human health. This research provides new knowledge of how PRMTs connect with their interacting partners and identifies new ways that PRMTs contribute to stress in cells. We use a new technology to explore how PRMTs bind to their partners and we show that this can be adopted by researchers to study different protein families. Further, we use baker’s yeast to show that metabolized byproducts of PRMT reactions can interfere with an important process for yeast cell survival and show that yeast expel these products to their environment in order to evade death. Since yeast and human cells have many biological pathways in common, results from these studies reveal new aspects to our understanding of PRMTs. vi Preface A version of Chapter 2 has been published and adapted here with permission from the publisher, John Wiley & Sons, Inc. This paper was selected as the cover feature of the journal issue and labeled and advertised as a “Very Important Paper” due to its high scientific quality. • Brown, J.I., Koopmans T., van Strien, J., Martin, N.I., and Frankel, A. Kinetic Analysis of PRMT1 Reveals Multifactorial Processivity and a Sequential Ordered Mechanism, Chembiochem. 19 (2018) 85-99. I designed and performed all biological experiments, analyzed all data, and drew conclusions from the data. I created all figures and was the primary author of the manuscript with editing form Dr. Adam Frankel. My collaborators T. Koopmans, J. van Strien, and N.I. Martin were responsible for the synthesis of modified peptides. Dr. Adam Frankel and I contributed to the experimental design. András Szeitz provided mentorship and expertise in mass spectrometry during this project. A version of Chapter 3 has been published and adapted here with permission from the publisher, Elsevier. This paper was selected as the cover feature of the special issue “Biological and chemical approaches to understanding protein arginine methylation” for which Dr. Adam Frankel was invited as the guest editor. • Brown, J.I., Page, B.D.G., and Frankel, A. The application of differential scanning fluorimetry in exploring bisubstrate binding to protein arginine N-methyltransferase 1, Methods. 15:175 (2020) 10-23 I planned, designed, and performed all experiments and analyzed and drew conclusions from all data. I created all figures and was the primary author of the manuscript with editing from vii Dr. Brent Page and Dr. Adam Frankel. Dr. Adam Frankel, Dr. Brent Page and I contributed to the experimental design. Dr. Brent Page provided guidance in setting up this new assay in our lab. For Chapter 4, I planned, designed, and performed most experiments and analyzed and drew conclusions from all data. I wrote the entirety of the chapter with edits from Dr. Adam Frankel. Dr. Adam Frankel created the figure panel detailing the scheme of succinimide formation. Dr. Jennifer Chiang from Dr. Corey Nislow’s lab at the University of British Columbia gifted us all yeast strains used in this study. Our summer student Erica Zhu assisted in the confirmation of yeast knockout strains in 2018. Our Visiting International Research Student (VIRS) Anke Gremmel assisted in purification of Ynl092wp in 2020. Jason Rogalski at the University of British Columbia Proteomics Core Facility performed mass spectrometry to identify post-translational modifications and assisted with mass spectra analyses. Dr. Adam Frankel and I contributed to the experimental design. For Chapter 5, I planned, designed, and performed most experiments and analyzed and drew conclusions from all data. I wrote the entirety of the chapter with edits from Dr. Adam Frankel. Dr. Adam Frankel assisted in analysis of methylarginine efflux from putative methylarginine knockout strains and assisted in making correlation figures to show these data. Dr. Jennifer Chiang from Dr. Corey Nislow’s lab at the University of British Columbia for gifted us most yeast strains used in this study. Our summer student Fraser Ball (PharmD) assisted in performing yeast growth and stress experiments in 2016. Our summer student and directed studies student Jenah Alibhai (PharmD) assisted in performing yeast stress experiments and yeast viability assays for autophagy-deficient mutants. Jenah also specifically performed and analyzed acetic acid stress experiments in 2017-2018. Our summer student Erica Zhu assisted in the confirmation of yeast knockout strains and performed Western blot assays to monitor GFP-Atg8p in 2018. Our viii summer student Diana Vasileva assisted in confirming yeast knockout strains in 2019-2020. Dr. Adam Frankel and I contributed to the experimental design. ix Table of Contents Abstract ......................................................................................................................................... iii Lay Summary .................................................................................................................................v Preface ........................................................................................................................................... vi Table of Contents ......................................................................................................................... ix List of Tables ................................................................................................................................xx List of Figures ............................................................................................................................. xxi List of Abbreviations ............................................................................................................... xxiv Acknowledgements ....................................................................................................................xxx Dedication ................................................................................................................................ xxxii Chapter 1: Introduction ............................................................................................................... 1 1.1 Epigenetic regulation of gene expression via methylation ......................................... 1 1.1.1 DNA methylation .................................................................................................... 1 1.1.2 RNA methylation .................................................................................................... 3 1.1.3 Protein methylation ................................................................................................. 5 1.2 Substrate methylation is enzymatically mediated by SAM-dependent methyltransferases................................................................................................................... 9 1.2.1 Rossmann-domain methyltransferases.................................................................. 10 1.2.2 SPOUT-domain methyltransferases...................................................................... 12 1.2.3 SET-domain methyltransferases ........................................................................... 12 1.2.4 Eukaryotic and prokaryotic methylation ............................................................... 14 x 1.3 Mammalian protein arginine N-methyltransferases (PRMTs) .................................. 16 1.3.1 Structural features of conserved PRMT enzymes contributing to enzymatic function ............................................................................................................................. 19 1.3.1.1 Cofactor binding ........................................................................................... 19 1.3.1.2 Active site ..................................................................................................... 20 1.3.1.3 Dimerization arm .......................................................................................... 22 1.3.2 Structural features of PRMT enzymes contributing to substrate specificity ........ 24 1.3.3 Biological significance of mammalian PRMTs and their substrates .................... 28 1.3.3.1 Biological significance of histone arginine methylation .............................. 28 1.3.3.2 Biological significance of arginine methylation on nucleic acid- binding proteins………………………………………………………………………………...29 1.3.3.3 Implications of PRMT deregulation ............................................................. 32 1.4 Protein arginine methylation in Saccharomyces cerevisiae ...................................... 34 1.4.1 Yeast PRMT enzymes........................................................................................... 34 1.4.2 Yeast PRMT substrates ......................................................................................... 37 1.4.3 Yeast arginine methyltransferasome ..................................................................... 39 1.4.4 Saccharomyces cerevisiae as a model organism to study the biological roles of PRMTs in stress ................................................................................................................ 40 1.4.4.1 Conserved eukaryotic signaling pathways .................................................... 40 1.4.4.2 Methylarginine regulation in yeast ............................................................... 42 1.5 Thesis outline and rationale ...................................................................................... 43 Chapter 2: Kinetic analysis of PRMT1 reveals multifactorial processivity and a sequential ordered mechanism ..................................................................................................................... 46 xi 2.1 Chapter Synopsis ...................................................................................................... 46 2.2 Introduction ............................................................................................................... 46 2.3 Materials and Methods .............................................................................................. 52 2.3.1 Peptides ................................................................................................................. 52 2.3.2 Expression and purification of PRMT1 ................................................................ 53 2.3.3 PRMT1 Michaelis-Menten enzyme kinetics ........................................................ 54 2.3.3.1 P81 filter binding assay ................................................................................. 54 2.3.3.2 Radioactive tricine gel assay ......................................................................... 55 2.3.4 Peptide methylation analyses by mass spectrometry ............................................ 56 2.3.5 FRET assay ........................................................................................................... 58 2.3.6 PRMT1 product and substrate inhibition .............................................................. 59 2.4 Results ....................................................................................................................... 60 2.4.1 PRMT1 Michaelis-Menten kinetics ...................................................................... 60 2.4.2 PRMT1 degree of processivity ............................................................................. 65 2.4.3 PRMT1 mechanistic investigation using product inhibition studies .................... 70 2.5 Discussion ................................................................................................................. 80 2.5.1 PRMT1 kinetics are dependent on substrate sequence ......................................... 80 2.5.2 PRMT1 degree of processivity increases with both cofactor and enzyme concentration ..................................................................................................................... 82 2.5.3 PRMT1 uses a sequential ordered Bi-Bi mechanism of action ............................ 84 2.6 Conclusion ................................................................................................................ 87 Chapter 3: The application of differential scanning fluorimetry in exploring bisubstrate binding to protein arginine N-methyltransferase 1 ................................................................. 88 xii 3.1 Chapter Synopsis ...................................................................................................... 88 3.2 Introduction ............................................................................................................... 89 3.2.1 Protein arginine N-methyltransferases .................................................................. 89 3.2.2 Thermal shift assay: background and methods ..................................................... 90 3.2.3 Thermal shift assays to study PRMTs .................................................................. 93 3.3 Materials and Methods .............................................................................................. 93 3.3.1 Overview of protocol and consideration of controls............................................. 93 3.3.2 Materials ............................................................................................................... 94 3.3.3 Expression and purification of PRMT1 ................................................................ 94 3.3.4 Differential scanning fluorimetry thermal shift assay .......................................... 96 3.3.5 In vitro thermal shift assay .................................................................................... 98 3.3.6 Enzyme activity assay ........................................................................................... 98 3.3.7 Structural modelling.............................................................................................. 99 3.4 Results ..................................................................................................................... 101 3.4.1 Optimization of the differential scanning fluorimetry assay with PRMT1 ........ 101 3.4.2 Stabilization of PRMT1 by cofactor ................................................................... 102 3.4.3 Destabilization of PRMT1 by peptide substrates................................................ 106 3.4.4 Effect of inhibitor MS023 on PRMT1 thermostability ....................................... 112 3.5 Discussion ............................................................................................................... 116 3.5.1 Relative binding affinity and common ligand interactions as detected by DSF . 116 3.5.2 Ligand interactions with SYPRO™ Orange as a limitation of the assay ........... 119 3.5.3 Sequential order of substrate binding to PRMT1 ............................................... 120 3.6 Conclusion .............................................................................................................. 123 xiii Chapter 4: Identification of methyltransferase activity from the putative yeast methyltransferases Ykl162cp and Ynl092wp ......................................................................... 125 4.1 Chapter Synopsis .................................................................................................... 125 4.2 Introduction ............................................................................................................. 126 4.3 Materials and Methods ............................................................................................ 134 4.3.1 Recombinant expression and purification of putative yeast methyltransferases 134 4.3.2 In vitro methylation of yeast lysates ................................................................... 136 4.3.3 Analysis of Ynl092wp automethylation via mass spectrometry......................... 137 4.3.4 Ynl092wp methylation activity linear range ....................................................... 140 4.3.5 Inhibition of automethylation ............................................................................. 141 4.3.6 Differential scanning fluorimetry (DSF) thermal shift assay ............................. 142 4.3.7 In vitro thermal shift assay .................................................................................. 144 4.4 Results ..................................................................................................................... 145 4.4.1 In vitro methylation of yeast lysates with putative yeast methyltransferases ..... 145 4.4.2 Analysis of Ynl092wp automethylation via mass spectrometry......................... 152 4.4.3 Inhibition of Ynl092wp automethylation by substrate amino acids and peptide 156 4.4.4 Thermal stability of Ynl092wp in the presence of cofactor ............................... 162 4.4.5 Thermal stability of Ynl092wp in the presence of amino acid and peptide substrates …………………………………………………………………………………..167 4.4.6 Thermal stability of Ynl092wp in the presence of cofactor and substrate combinations ................................................................................................................... 173 4.5 Discussion ............................................................................................................... 178 4.5.1 Ykl162cp is a suspected putative RNA methyltransferase ................................. 178 xiv 4.5.2 Ynl092wp characterization ................................................................................. 180 4.5.2.1 Ynl092wp substrate characterization .......................................................... 180 4.5.2.2 Ynl092wp substrate binding ....................................................................... 182 4.6 Conclusions ............................................................................................................. 185 Chapter 5: Exploration of the fates of methylarginines in the yeast stress response ......... 187 5.1 Chapter Synopsis .................................................................................................... 187 5.2 Introduction ............................................................................................................. 188 5.3 Materials and Methods ............................................................................................ 193 5.3.1 Yeast strains and media ...................................................................................... 193 5.3.2 Heat-shock and stationary phase treatment to observe methylarginines on proteins …………………………………………………………………………………..196 5.3.3 Starvation in water .............................................................................................. 197 5.3.4 Rapamycin treatment .......................................................................................... 197 5.3.5 Yeast viability assay ........................................................................................... 197 5.3.6 Preparation of samples for quantification of methylarginines by mass spectrometry …………………………………………………………………………………..198 5.3.7 Quantification of methylarginines by mass spectrometry .................................. 199 5.3.8 Transformation of plasmids into yeast for protein expression ............................ 200 5.3.9 Monitoring autophagy in yeast via Western blot ................................................ 201 5.3.10 Monitoring subcellular localization of proteins via microscopy .................... 202 5.3.11 Radioactive monitoring of methylarginine influx........................................... 203 5.3.12 Measuring nitric oxide synthase activity in yeast lysates ............................... 204 5.4 Results and Discussion ........................................................................................... 205 xv 5.4.1 Quantitating methylarginines on proteins in yeast under stress and aging conditions …………………………………………………………………………………..205 5.4.2 Methylarginines are expelled from yeast under water starvation ....................... 209 5.4.3 Quantification of autophagy in water incubation assay ...................................... 217 5.4.4 Transport of methylarginine species across yeast cell membranes .................... 221 5.4.5 Inhibition of yeast nitric oxide synthase by methylarginines in yeast lysates .... 229 5.5 Conclusions ............................................................................................................. 234 Chapter 6: Conclusions and Future Directions...................................................................... 236 6.1 Major Thesis Findings, Overall Significance, and Impact ..................................... 236 6.1.1 The kinetic mechanism of human PRMT1 ......................................................... 236 6.1.2 Activity of yeast putative methyltransferases ..................................................... 239 6.1.3 Biological significance of methylarginines in yeast stress response .................. 241 6.2 Future Directions .................................................................................................... 242 6.2.1 The application of DSF to other PRMTs and other enzyme families ................. 242 6.2.2 Characterization of Ykl162cp activity and identification of a native yeast substrate …………………………………………………………………………………..243 6.2.3 Characterization of Ynl092wp activity and identification of a native yeast substrate …………………………………………………………………………………..244 6.2.4 The roles and implications of methylarginines in yeast ...................................... 245 6.3 Conclusion .............................................................................................................. 248 References ...................................................................................................................................249 Appendices ..................................................................................................................................303 Supplemental data for Chapter 2 .................................................... 303 xvi A.1 Characterization of peptides in this study ............................................... 303 A.2 Purified recombinant PRMT1 proteins ................................................... 304 A.3 Linear range of PRMT1 with unmodified peptides ................................ 305 A.4 PRMT1 methylation of R1 peptide ........................................................ 306 A.5 PRMT1 concentration effect on H4-OH peptide methylation ................ 307 A.6 Nonlinear regression analyses ................................................................ 308 A.7 Estimated axes intercepts for substrate inhibition .................................. 315 Supplemental data for Chapter 3 .................................................... 316 B.1 Purity and linear range of P81 filter-binding assay for PRMT1 ............. 316 B.2 Raw fluorescence of SYPRO™ orange dye is plotted against reaction temperature ..................................................................................................... 317 B.3 Normalized fluorescence of SYPRO™ orange dye is plotted against reaction temperature........................................................................................ 318 B.4 Derived melting temperature of PRMT1 is dependent on protein concentration. .................................................................................................. 319 B.5 Summary of dye and protein concentration array data ........................... 320 B.6 Summary of PRMT1 with HCl and cofactor negative control data ....... 323 B.7 Summary of PRMT1 and cofactor data .................................................. 323 B.8 Untruncated melt curves for cofactor with PRMT1 ............................... 324 B.9 Summary of sinefungin and PRMT1 data .............................................. 324 B.10 Untruncated melt curves for sinefungin with PRMT1 .......................... 325 B.11 Summary of RKK and H4 peptides with PRMT1 data ........................ 326 B.12 Untruncated melt curves for RKK and H4 peptides with PRMT1 ....... 327 xvii B.13 Summary of varying RKK with saturating SAH and varying SAH with saturating RKK with PRMT1 ......................................................................... 328 B.14 Untruncated melt curves for RKK and SAH with PRMT1 .................. 330 B.15 Untruncated melt curves for H4 and SAH with PRMT1 ...................... 331 B.16 Summary of varying H4 with saturating SAH and varying SAH with saturating H4 with PRMT1 data ..................................................................... 332 B.17 Interactions of MS023 within the PRMT1 active site .......................... 333 B.18 Visualization of the pan type-I PRMT1 inhibitor, MS023 (pink) with PRMT1 and SAH (blue) ................................................................................. 334 B.19 Summary of DMSO with PRMT1 and with SAH data ........................ 335 B.20 Untruncated melt curves for DMSO with PRMT1 ............................... 336 B.21 Untruncated melt curves for MS023 with PRMT1 ............................... 336 B.22 Summary of MS023 with PRMT1 alone and with SAH data............... 337 B.23 Comparing the interactions of crystalized SAH with docked SAM and sinefungin within the cofactor-binding site of PRMT1 homology model ...... 338 B.24 The interactions of SAH, SAM, and sinefungin within the cofactor-binding site of PRMT1.................................................................................... 339 Supplemental data for Chapter 4 .................................................... 340 C.1 Purification of yeast His6-Ynl092wp and His6-Ykl162cp ...................... 340 C.2 PCR deletion primer pairs ....................................................................... 341 C.3 Unique peptides generated by trypsin and Glu-C digests ....................... 342 C.4 Summary of increasing Ynl092wp with 7.5x SYPRO™ Orange DSF data. …….………………………………………………………………………….343 xviii C.5 Summary of Ynl092wp with HCl and cofactor negative control DSF data . …….………………………………………………………………………….344 C.6 Summary of Ynl092wp with SAM and SAH cofactor DSF data ........... 345 C.7 Untruncated melt curves for SAM and SAH cofactor with Ynl092wp .. 346 C.8 Summary of Ynl092wp with carnosine, histidine, and CARN peptide DSF data………………………………...…………………………………………347 C.9 Untruncated melt curves for amino acids and peptide substrates with Ynl092wp ........................................................................................................ 349 C.10 Summary of Ynl092wp with constant SAH and increasing carnosine, histidine, and CARN peptide .......................................................................... 350 C.11 Summary of Ynl092wp with constant carnosine or CARN peptide and increasing SAH ............................................................................................... 352 C.12 Untruncated melt curves for combinatorial amino acids and peptide substrates with cofactor and Ynl092wp .......................................................... 353 Supplemental data for Chapter 5 .................................................... 354 D.1 PCR primer pairs .................................................................................... 354 D.2 Summary of methylarginine fold change at beginning and end of water incubation assay for putative methyltransferase knockouts............................ 356 D.3 Growth curves for putative methyltransferase knockouts ...................... 358 D.4 Summary of methylarginines in SC media and water for methyltransferase knockouts ........................................................................................................ 359 D.5 Summary of methylarginine fold change at end and beginning of water incubation assay for autophagy-related strains ............................................... 363 xix D.6 Summary of methylarginines at beginning and end of water incubation assay for autophagy-related strains ................................................................. 364 D.7 Summary of methylarginine fold change at end and beginning of water incubation assay for permease-deficient strains ............................................. 366 D.8 Summary of methylarginines at the beginning and end of water incubation assay for permease-deficient strains ............................................................... 367 D.9 Summary of [3H]-arginine and [14C]-methylarginine incorporation into yeast cells ........................................................................................................ 369 D.10 Lysis buffer background controls ......................................................... 371 xx List of Tables Table 2.1. Peptide substrates ......................................................................................................... 61 Table 2.2. Apparent enzyme kinetic parameters of PRMT1 ........................................................ 64 Table 2.3. PRMT1 degree of processivity for substrate peptides. ................................................ 67 Table 2.4. PRMT1 degree of processivity with varying enzyme concentrations. ........................ 69 Table 2.5. Product inhibition patterns. .......................................................................................... 71 Table 4.1. Known and putative yeast methyltransferases are upregulated in response to stress. 130 Table 4.2. Proteomic data scoring............................................................................................... 156 Table 5.1. List of haploid, homozygous diploid, and heterozygous diploid KOs used in this study...................................................................................................................................................... 194 xxi List of Figures Figure 1.1. The most common SAM-dependent methyltransferases. ........................................... 14 Figure 1.2. PRMT methylation patterns. ...................................................................................... 18 Figure 1.3. Structural features of mammalian PRMTs. ................................................................ 23 Figure 1.4. Sequence alignments of human and yeast PRMT homologs. .................................... 36 Figure 2.1. Production of methylarginine species. ....................................................................... 48 Figure 2.2. Cleland notations for the predominant kinetic mechanisms for PRMTs. .................. 50 Figure 2.3. Michaelis-Menten enzyme kinetics of PRMT1 methylation of peptide substrates. ... 63 Figure 2.4. PRMT1 degree of processivity for substrate peptides. ............................................... 67 Figure 2.5. PRMT1 degree of processivity, varying enzyme concentration. ............................... 68 Figure 2.6. Cofactor and peptide contribution to PRMT1 dimerization. ...................................... 70 Figure 2.7. Product inhibitor results of PRMT1 with SAH inhibitor. .......................................... 73 Figure 2.8. Product inhibitor results of PRMT1 with RKK-aDMA inhibitor. ............................. 76 Figure 2.9. Hydroxy-substituted H4 peptide inhibits PRMT1 activity through formation of a dead-end BE complex. ........................................................................................................................... 79 Figure 3.1. PRMT1 crystal structure and principle of differential scanning fluorimetry assay. .. 92 Figure 3.2. Optimization of the differential scanning fluorimetry assay. ................................... 102 Figure 3.3. Control experiments for cofactor analogues SAH, SAM, and sinefungin. .............. 104 Figure 3.4. Effect of cofactor on PRMT1 thermostability. ......................................................... 106 Figure 3.5. DSF negative controls for RKK and H4................................................................... 108 Figure 3.6. Effect of peptide substrates on PRMT1 stability with and without cofactor. .......... 111 Figure 3.7. The effect of MS023 on PRMT1 thermostability with and without cofactor. ......... 115 xxii Figure 4.1. Alignment of yeast putative methyltransferases with their homologs. .................... 133 Figure 4.2. In vitro methylation activity of Ynl092wp. .............................................................. 147 Figure 4.3. In vitro methylation activity of Ykl162cp. ............................................................... 151 Figure 4.4. Mass spectrometry data from Ynl092wp proteolysis. .............................................. 155 Figure 4.5. Inhibition of Ynl092wp automethylation by amino acids as measured by a gel-based radiometric assay. ....................................................................................................................... 158 Figure 4.6. Inhibition of Ynl092wp automethylation by CARN peptide. .................................. 160 Figure 4.7 In vitro methylation of human actin by Ynl092wp. .................................................. 162 Figure 4.8. Identification of differential scanning fluorimetry assay conditions. ....................... 164 Figure 4.9. Control DSF experiments for cofactor. .................................................................... 165 Figure 4.10. Thermal stability of Ynl092wp in the presence of SAM or SAH cofactor. ........... 167 Figure 4.11. Control DSF experiments for carnosine, histidine, and CARN peptide................. 169 Figure 4.12. Thermal stability of Ynl092wp in the presence of carnosine, histidine, or CARN peptide. ........................................................................................................................................ 172 Figure 4.13. Thermal stability of Ynl092wp in the presence of increasing carnosine, histidine, or CARN peptide pre-incubated with excess SAH. ........................................................................ 175 Figure 4.14. Thermal stability of Ynl092wp in the presence of increasing SAH pre-incubated with excess carnosine or CARN peptide............................................................................................. 177 Figure 4.15. Ynl092wp structure, sequence, and substrate binding mode. ................................ 183 Figure 5.1. Change in methylarginines on proteins in WT and putative KO strains during treatments .................................................................................................................................... 207 Figure 5.2 Change in methylarginine species from SC media into water for putative methyltransferase KO strains. ..................................................................................................... 211 xxiii Figure 5.3. Methylarginine efflux changes during incubation SC media and water in autophagy-related gene deletion yeast strains............................................................................................... 216 Figure 5.4. Quantification of autophagy by monitoring GFP-Atg8p.......................................... 219 Figure 5.5. Methylarginine efflux changes during incubation in SC media and water in permease-deficient yeast strains. ................................................................................................................. 223 Figure 5.6. Monitoring amino acid influx into yeast cells. ......................................................... 228 Figure 5.7. Correlation of change in basic amino acid efflux to change in methylarginine efflux...................................................................................................................................................... 229 Figure 5.8. Nitric oxide synthase activity in yeast lysates. ......................................................... 233 xxiv List of Abbreviations [14C]SAM S-adenosyl-L-[methyl-14C]-methionine 3'UTR 3' untranslated region 5hmC 5-Hydroxymethylcytosine 5mC 5-Methylcytosine aDMA ω-NG, NG-asymmetric dimethylarginine Alp1p basic amino acid permease 1 Arp1p centractin AMP adenosine monophosphate Atg Autophagy related protein Atg1/8/11/13 /17/22/33p autophagy-related protein 1/8/11/13/17/22/33 ATP adenosine triphosphate Avt3/4p Amino acid Vacuolar Transporter 3/4 BCA Bicinchoninic acid assay BCL-2 B-cell lymphoma 2 BEH Ethylene Bridged Hybrid BER base excision repair BLAST Basic Local Alignment Search Tool BOP benzotriazol-1-yloxytris(dimethylamino)phosphonium hexafluorophosphate Bq bequerels Bre5p Ubiquitin-protease cofactor brefeldin A sensitivity BSA Bovine serum albumin cAMP-PKA cyclic AMP-PKA pathway Can L-canavanine Can1p Canavanine resistance arginine permease CARM1 coactivator associated arginine methyltransferase 1 (PRMT4) CARME Carnosine N-Methyltransferase 1 CARN Ynl092wp peptide Cdc12p Cell Division Cycle 12 CETSA cellular thermal shift assay CpG cytosine-guanine CSI Cold-Spray Ionization DAF-FM DA 4-Amino-5-Methylamino-2',7'-Difluorofluorescein Diacetate DAPI 4′,6-diamidino-2-phenylindole DDAH Dimethylargininase DIPEA N,N-Diisopropylethylamine DMF dimethylformamide xxv DMSO dimethylsulfoxide DNA deoxyribonucleic acid DPM disintegrations per minute DSF differential scanning fluorimetry DTT dithiothreitol EDTA ethylenediaminetetraacetic acid eIF4A1 eukaryotic initiation factor 4A1 ERα estrogen receptor alpha ESI electrospray ionization eV electron volt FM 4-64 N-(3-Triethylammoniumpropyl)-4-(6-(4-(Diethylamino) Phenyl) Hexatrienyl) Pyridinium Dibromide FRET Förster resonance energy transfer GAMT Guanidinoacetate N-Methyltransferase Gap1p General amino acid permease 1 GAR glycine and arginine rich GFP green fluorescent protein Gyp1p GTPase-activating protein 1 HA Human influenza hemagglutinin His6 hexahistidine tag HNRNPC Heterogeneous Nuclear Ribonucleoprotein C Hog1p High Osmolarity Glycerol response mitogen activated kinase HPLC High-performance liquid chromatography Hpm1p Histidine Protein Methyltransferase HRP horseradish peroxidase Hrp1p Heterogenous nuclear RibonucleoProtein 1 HSE heat shock element Hsf1p heat shock factor 1 Hsl7p histone synthetic lethal 7 Hsp90p heat shock protein 90 HuD Hu antigen D Hz hertz IAA Iodoacetamide IC50 half-maximal inhibitory concentration ID isotope depletion IGF2BP1-3 insulin-like growth factor 2 mRNA-binding proteins 1-3 IgG Immunoglobulin G Ioc4p ISWI one complex protein 4 IPTG isopropyl β-d-1-thiogalactopyranoside ITC isothermal titration calorimetry xxvi JMJD6 Jumonji domain-containing 6 Kar4p Karyogamy protein 4 kcat turnover number KD equilibrium dissociation constant Ki inhibitor constant KM Michaelis constant Kme monomethyllysine Kme2 dimethyllysine Kme3 trimethyllysine KMT2E Lysine methyltransferase 2E KO knockout KRK fibrillarin-derived peptide with basic residues in Lys-Arg-Lys orientation LC liquid chromatography L-NAME NG-nitroarginine methylester lncRNA long non-coding RNA LP Log phase m/z mass to charge ratio m6A N6-Methyladenosine MAPK mitogen-activated protein kinase mCer-PRMT1 monomeric Cerulean-PRMT1 mCit-PRMT1 monomeric Citrine-PRMT1 MEP50 methylosome protein 50 METTL14 N6-adenosine-methyltransferase non-catalytic subunit MidA Protein arginine methyltransferase NDUFAF7 homolog, mitochondrial MMA monomethylarginine mRNA messenger RNA MS mass spectrometry Msn2/4p Multicopy suppressor of SNF1 mutation 2 and 4 MT methyltransferase Mtf1p Metal Regulatory Transcription Factor 1 NAD+/NADH Nicotinamide adenine dinucleotide NADPH Nicotinamide adenine dinucleotide phosphate NCBI National Center for Biotechnology Information Nde1/2p NADH Dehydrogenase, External 1/2 NDUFAF7 NADH:Ubiquinone Oxidoreductase Complex Assembly Factor 7 NDUFS2 NADH:Ubiquinone Oxidoreductase Core Subunit S2 NF-κB nuclear factor kappa-light-chain-enhancer of activated B cells NO nitric oxide NOS nitric oxide synthase xxvii Npl3p Nuclear Protein Localization 3 Npp2p ectonucleotide pyrophosphatase/phosphodiesterase 2 NUDT5 Nudix Hydrolase 5 Nvj1p Nucleus-vacuole junction 1 NXF1 Nuclear RNA Export Factor 1 OD optical density OD600 optical density at 600 nm Oms1p OXA1 Multicopy Suppressor 1 ORF open reading frame OTOF orthogonal time-of-flight PAS Phagophore assembly site PAD4 Peptidyl arginine deiminase, type IV PBS phosphate buffered saline PCAF P300/CBP-associated factor PCR polymerase chain reaction PDB protein data base PE phosphatidylethanolamine Pep4p vacuolar proteinase A PHD plant homeodomain pI isoelectric point PKA protein kinase A pKa acid dissociation constant PMSF phenylmethylsulfonyl fluoride ppm parts per million PRMT protein arginine N-methyltransferase PRMT1-9 protein arginine N-methyltransferase 1-3, 5-9 Ptc6p Protein phosphatase PTM post-translational modification PVDF Polyvinylidene fluoride R2 Correlation coefficient R1 synthetic peptide containing one arginine residue RKK fibrillarin-derived peptide with basic residues in Arg-Lys-Lys orientation Rmt1p/Hmt1p arginine methyltransferase 1/hnRNP methyltransferase 1 Rmt2p arginine methyltransferase 2 RNA ribonucelic acid ROS Reactive oxygen species Rpl3p Ribosomal Protein L3 RPM Revolutions per minute RPS2 Ribosomal Protein S2 rRNA ribosomeal RNA xxviii SAH S-adenosyl-L-homocysteine SAM S-adenosyl-L-methionine SC synthetic complete SC-3% glycerol SC media with 3% glycerol SC-N SC-nitrogen dropout media sDMA ω-NG,NG’-symmetric dimethylarginine SDS sodium dodecyl sulfate SDS-PAGE sodium dodecyl sulphate–polyacrylamide gel electrophoresis SET Su(var)3-9, Enhancer of zeste, and Trithorax Set3/4/6p SET-domain containing protein 3/4/6 SETD5 SET-domain containing protein 5 SF3B2/SAP145 Splicing Factor 3b Subunit 2 Sfm1p SPOUT family methyltransferase 1 SH3 Src-homology 3 Sm spliceosomal machinery SMN survival of motor neuron SNF1 Sucrose NonFermenting kinase snRNA small nuclear RNA SP Stationary phase SPF splicing factor 30 SPOUT SpoU and tRNA methyltransferase D SPPS solid-phase peptide synthesis STRE stress response element Tah18p NAPDH-dependent diflavin reductase TBS-T Tris-buffered saline with 0.1% Tween TDRD Tudor domain-containing protein TFA trifluoracetic acid Th thomson TM melting temperature TOR target of rapamycin tRNA transfer RNA TSA thermal shift assay Ubp3p Ubiquitin protease UHPLC-MS/MS ultra-high performance liquid chromatography-tandem mass spectrometry Ump1p Ubiquitin-Mediated Proteolysis proteasome maturation factor UPS ubiquitin-proteasome system Vac8p Vacuole-related protein 8 WT wild type YKO Yeast knockout collection YPAD YPD media + adenine xxix YPD yeast-peptone-dextrose YTHDC YTH domain-containing protein YTHDF YTH domain-containing family protein β-Me β-mercaptoethanol δMMA Nδ-monomethylarginine ηMMA Nη-monomethylarginine xxx Acknowledgements I sincerely thank all current and previous members of my supervisory committee for their guidance and feedback throughout my graduate studies. I thank previous members Dr. Tara Klassen and Dr. David Grierson for their enthusiastic dedication to my success from the beginning. I thank Dr. LeAnn Howe for lending her much-needed yeast expertise to me, a yeast novice. I thank Dr. Thibault Mayor for lending me his advice and reagents as I journeyed into the unfamiliar world of autophagy and for challenging me by asking the tough questions. Last, I thank Dr. Brent Page for his collaborations, professional and career guidance, and friendship. I thank all current and previous Frankel lab personnel during my tenure as a graduate student for creating a positive and productive working environment that has immensely added to my graduate school experience. I thank Shona Sedlock and Dr. Mynol Vhuiyan for sharing their knowledge and training when I was a new graduate student. I thank all undergraduate trainees, Fraser Ball, Jenah Alibhai, Erica Zhu, Anke Gremmel, Diana Vasileva, Jasmin Gill, and Alana Hitsman for their eager contributions to both science and camaraderie in the Frankel lab. I thank M.Sc students Shireen Anbardan and Riley Prout-Holm and PhD candidate Michael Rowley for their selfless dedication to joint Frankel lab success and for being generous, kind, and exuberant colleagues. Above all else, I thank the Frankel lab members for their friendship and support. My graduate student experience would not have been the same without this wonderful group of people. I thank the many individuals in the Faculty who were instrumental in my research training. I thank Dr. Alex Smith for being my unofficial support and trainer during the early years of graduate studies. I thank Dr. Tara Klassen for her in silico support. I thank Dr. Rishi Somvanshi and the Kumar lab for introducing me to microscopy and for generously allowing me to use the Alpha Imager. I thank the Williams lab for training and support in using their microscope. I thank xxxi András Szeitz for his comprehensive mass spectrometry training and Dickson Lai for mass spectrometry support in later years. I thank Jennifer Chiang and the Nislow lab for the generous donation of yeast strains and for answering all my questions when I was new to the yeast research community. I thank Jeff McCarthy from AdMare for ongoing access to their phosphor imager. I was fortunate to receive very generous funding and grant support from the University of British Columbia (the Shaughnessy Hospital Volunteer Society Fellowship in Health Care, the Walter C. Koerner Fellowship, and the University of British Columbia Four-Year Doctoral Fellowship) and from the Natural Sciences and Engineering Research Council of Canada (NSERC) (CGS-M and CGS-D) throughout the entirety of my graduate studies. I owe very special thanks to my friends and colleagues within and outside the Faculty, within the CTLT, and around the world. I simply cannot list every single person who encouraged and supported me throughout these projects. I am incredibly appreciative of the love and kindness they have shown me. My dearest parents, selfless sister, and loving partner have been my biggest supporters throughout this journey. I am eternally grateful for their emotional support and for always encouraging me to pursue my goals, even though it has pulled me across the country to do so. Their incredibly selfless love has kept me going. Above all else, I truly thank my marvelous supervisor Dr. Adam Frankel who mentored, encouraged, and inspired me every day. I thank Adam for his gentle guidance through the many twists and turns of these projects, his unflappable calmness in times of stress, and his encouragement to pursue my research and teaching passions. In addition to acting as my research supervisor, Adam also fulfilled the roles of therapist, confidante, and friend. I feel very fortunate to have had an extremely positive PhD experience, much of which is owed to Adam. I’m quite glad I stuck around for so long. xxxii Dedication For my parents, Janice Brown and George Pierce, who, with their endless love, unwavering support, and unshakable faith in my ability, have given me every tool I need to pursue my goals. And for my emotional anchor, my biggest cheerleader, and my love, Andrew Hoover. Chapter 1: Introduction 1.1 Epigenetic regulation of gene expression via methylation Methylation is a common modification of nucleic acids, amino acids, lipids, and other small molecules. A testament to the functionality of methylation can be seen in the sheer volume of methyltransferases in the genome; it has been estimated that over 1% of the mammalian genome codes for methyltransferases targeting a wide range of substrates [1]. Methyltransferase enzymes deposit one or more methyl groups from a methyl-donor cofactor onto their substrates at a nucleophilic atom, such as sulfur, nitrogen, oxygen, or carbon through a substitution, nucleophilic, bimolecular (SN2)-like reaction, where the nucleophilic atom attacks the electrophilic methyl-donor, resulting in methyl transfer [2]. Methylation of a substrate can regulate how that substrate interacts with other molecules. Methylation of nucleic acids and amino acids in eukaryotes is often dynamic and the reversibility of methylation status emphasizes the important regulatory role for this modification. Thus, methyltransferase enzymes have an important role in regulating function and molecular interactions of a wide array of substrates in a range of biological functions [3]. Specifically, methylation of DNA, RNA, and protein substrates can epigenetically impact gene expression and regulation at transcriptional, translational, and post-translational levels. 1.1.1 DNA methylation In mammals, DNA methylation primarily occurs on cytosine residues within the context of cytosine-guanine dinucleotides, otherwise named CpG sites (where p represents the phosphate group between each base) [4]. Methylation of cytosine occurs on the C5 position of the pyrimidine ring to form 5-methylcytosine (5mC). This addition does not significantly impact overall hydrophilicity of the nitrogen base, nor does it interfere with Watson and Crick base pairing [5]. 2 However, the addition does add a site of local hydrophobicity that lies within the major groove of the DNA double-helix, which is accessible by chromatin-interacting proteins, such as transcription factors or other DNA-modifying enzymes, thus impacting local chromatin structure [5]. In mammals, DNA methylation is also observed on adenine at the N6 position of the purine ring. This modification is more prevalent in bacterial and plant species and has only recently been observed in mammalian DNA where it may play a role in gene silencing [6]. The impact of DNA methylation status depends on the location of CpG sites within a gene. In mammals, an estimated 60-90% of CpG sites are methylated [5]. The methylation status of genes plays a role in development, with the global methylation patterns changing throughout mammalian embryonic development. Aberrant changes in these methylation patterns can have deleterious effects on embryonic development [7,8]. Methylation of CpG sites within gene bodies is associated with active genes and it is suggested that hypomethylation may play an important role in gene silencing [9]. For example, in X-inactivation, the silenced X chromosome is globally hypomethylated compared to its active counterpart [10]. Despite broad methylation of CpG sites throughout an active gene, significant CpG hypomethylation is consistently seen in the promoter regions of these active genes, specifically within GC-rich regions, denoted as CpG islands [4]. Hypermethylation of CpG islands in silent gene promoters is associated with stable transcriptional repression maintenance, by both recruiting binding partners that work to repress transcription and by preventing the binding and assembly of transcription factors necessary for gene transcription [4]. Therefore, DNA methylation of CpG sites, especially CpG islands in gene promoters, serves as an epigenetic regulator in part by modulating DNA accessibility for gene transcription. In mammals, DNA demethylation is primarily mediated enzymatically through rigorous DNA repair mechanisms by excising the modified 5mC so that it can be replaced with an 3 unmodified cytosine via the base excision repair (BER) pathway [11]. In one mechanism, 5mC is deaminated by cytosine deaminases to produce thymine to create a mismatched thymine-guanine pair. The intermediate thymine is then subsequently removed and replaced with cytosine via BER [11,12]. In another mechanism, 5mC is oxidized by translocation methylcytosine dioxygenases to 5-hydroxymethylcytosine (5hmC). This is considered a stable DNA modification with several regulatory roles in itself [13]. However, this mark can further be metabolized through subsequent conversion to thymine or uracil via deaminating enzymes or to more oxidized versions where it is also subjected to base excision repair [11]. Thus, dynamic DNA methylation and demethylation is important for switching between activated and repressed transcriptional gene function [4]. 1.1.2 RNA methylation Unlike in DNA, the most widespread methylation mark in RNA is methylation of adenosine on the N6 position of the purine ring to form N6-methyladenosine (m6A). Adenosine methylation decreases molecule polarity and topological polar surface area, and, as in 5mC, adds a local hydrophobic moiety that impacts RNA secondary structure. The presence of the methyl group on adenosine contributes to strong base stacking in single-stranded RNA sequences, with single-stranded RNA containing m6A being considerably more stable than its unmethylated counterpart [14]. Base-pairing with uracil in RNA or thymine in DNA is still possible, but the duplex is much less stable than unmethylated base-pairing. In m6A paired with uracil, the methylamine moiety must rotate to an energetically unfavourable position (anti conformation) to successfully hydrogen bond with its pyrimidine partner [14]. Therefore, the presence of an m6A mark strongly favours a local single-stranded RNA structure, thus influencing RNA secondary structure and disturbing RNA-DNA complexes. Much like in 5mC in DNA, m6A also serves as a 4 recruitment point for a variety of methyl-RNA readers [15]. Although this mark has been identified in tRNA, rRNA, lncRNA, and snRNA, it is most prevalent in mRNA. In particular, anywhere from 25-60% of mRNA transcripts contain at least one m6A base [16,17] which are usually enriched in the last exon and the three prime untranslated region (3’UTR ) regardless of mammalian cell line and tissue type [16,18]. Methylation of adenosine occurs co-transcriptionally on pre-mRNA and impacts gene expression by affecting pre-mRNA processing, nuclear export, and mRNA stability [16]. The m6A-induced RNA unfolding promotes a single-stranded structure, meaning that certain RNA-binding proteins which bind to single-stranded RNA preferentially bind to m6A-containing mRNA. Two such mammalian binders are the heterogeneous nuclear ribonucleoproteins C1/C2 (hnRNPC) which plays a role in spliceosome assembly, specifically splicing of bound pre-mRNA [15,19]. Further, the methyl mark recruits YTH-domain containing (YTHDC) proteins such as YTHDC1, whose specific binding can lead to alternative splicing of pre-mRNA and polyadenylation of the mRNA tail [20]. This protein further promotes expedited nuclear export via association with nuclear RNA export factor 1 (NXF1) [21]. Considering this, pre-mRNA containing m6A may be more readily spliced and processed for nuclear export prior to translation. In the cytosol, several interacting proteins that promote cytosolic stability have been found to bind to either m6A-RNA specifically or to single-stranded RNA, including insulin-like growth factor 2 mRNA-binding proteins 1-3 (IGF2BP1-3), which are not only responsible for transient mRNA storage, but also responsible for recruiting mRNA to the translational machinery [22]. Association of m6A-RNA with m6A-readers YTHDF proteins modulate 3’UTR length, which further impacts translation efficiency [20]. Alternatively, YTHDF proteins can sequester m6A RNA to cytosolic P-bodies under stressful conditions, storing and protecting the transcripts for later translation [23]. 5 Therefore, RNA methylation of adenosine serves as an epigenetic regulator of gene expression by modulating post-transcriptional mRNA processes impacting translation. Two mammalian enzymes responsible for m6A RNA demethylation have been identified, both of which function as oxygenases that add a hydroxy group to m6A to form a methoxy intermediate. The resulting structure is highly unstable and spontaneously decomposes in an aqueous environment to produce a formaldehyde group and the resulting demethylated adenosine [23]. Post-transcriptional demethylation of mRNA primarily occurs in the nucleus and thus is considered an important element in mRNA processing that can negatively impact mRNA splicing and nuclear export, delaying translation. Demethylation of mRNA plays an important role in regular biological function because upregulation of RNA demethylases is characteristic in several diseases, including cancer [16]. Therefore, dynamic methylation of mRNA contributes to gene expression at the post-transcriptional, pre-translational, and translational levels. 1.1.3 Protein methylation Like nucleic acid methylation, protein methylation regulates gene expression at the transcriptional and translational levels through post-translational modification of transcription factors, RNA-processing or RNA-binding proteins, and histones. Proteins are commonly methylated on the side-chains of basic residues. Lysine and arginine are the most common substrates of protein methylation enzymes, but histidine N-methylation has been observed on both N1 and N3 positions of the imidazole sidechain of histidine, though these modifications are more rarely studied [24]. Lysine can accept up to three methyl additions on the side chain amino group to form methyllysine (Kme), dimethyllysine (Kme2), or trimethyllysine (Kme3). Addition of the methyl moieties to the lysine side chain consumes a possible hydrogen bond donor and adds 6 considerable hydrophobic and steric bulk, particularly as the number of methyl groups increases. Arginine can accept one or two methyl additions on the guanidino moiety of the side chain in several patterns. Methylation of a terminal Nη atom of the guanidino group results in Nη-monomethylarginine (ηMMA), but a less common monomethylation of the internal Nδ nitrogen of the guanidino group has been observed and is termed Nδ-monomethylation (δMMA). This rare modification has been mostly observed in yeast species, but one group has identified it in small amounts in mammals [25,26]. Dimethylation of terminal nitrogen atoms can also occur with both methyl groups posited on the same nitrogen atom to form ω-NG,NG-asymmetric dimethylarginine (aDMA) or by each being added to separate terminal nitrogen atoms to form ω-NG,NG’-symmetric dimethylarginine (sDMA). Like lysine, the methyl additions consume a possible hydrogen bond donor or acceptor and add considerable steric bulk. Protein methylation may impact protein-protein or protein-ligand interactions since both lysine and arginine are important interacting amino acids. They have the highest side chain pKa values of all amino acids (12-14 for arginine [27] and ~10.5 for lysine [28]), making them intrinsically positively charged across all physiological pH values and capable of forming strong electrostatic interactions. The presence of each methyl group on the basic side chains removes a possible hydrogen-bonding site, of which lysine has three and arginine has five, which could unfavourably affect protein interactions through hydrogen-bonding and through steric hindrance [29,30]. Further, the pKa of lysine side chain decreases marginally with the addition of methyl groups which detracts from electrostatic interactions [31]. Interestingly though, methylation of the guanidino group does not significantly alter its pKa and therefore, does not interfere with any electrostatic interactions [32,33]. 7 Methyllysines and methylarginines additionally create new interaction opportunities as docking sites for proteins containing methyl-binding domains. While eight different protein domains are able to specifically bind to methyllysines (such as plant homeodomain (PHD) and the WD-repeat domains), proteins containing a Tudor domain are the primary readers of methylarginines [34,35]. Proteins within the Tudor family are able to bind to both methyllysine and methylarginine marks; however, each individual protein is specific to either methyllysine or methylarginine [34]. Probing the structural basis for methylarginines or methyllysines and Tudor-domain interactions revealed that these interactions are facilitated by a conserved aromatic cage or cavity on the Tudor protein [36–39]. In methylarginine-reading Tudor-domain containing (TDRD) proteins, aromatic residues such as tyrosine, phenylalanine, and tryptophan form a cuboid structure, with additional support from a conserved asparagine, that allows for proper positioning of methylarginine within the cage. Each individual cage from different proteins is selective for different methylarginine marks. Selectivity is determined by the steric bulk of the methyl additions, which guides the methylarginine to be properly positioned within the aromatic cage so that the guanidino cation interacts with the faces of the cage to form cation-π interactions [36–39]. Therefore, the size and shape of the aromatic cage determines which methylarginine mark is best accommodated and stabilized within the structure to form strong cation-π interactions. While the aromatic cage is also important for methyllysine-binding activity, the aromatic cage in methyllysine-reading TDRD proteins is much wider than methylarginine-binding species in order to accommodate the tetrahedral methyllysine [37,39]. The interaction of methylarginines or methyllysines with these Tudor-domain containing proteins can recruit these methyl readers to specific subcellular locations, such as histones, to exact their functions. The implications and biological relevance of these proteins will be further discussed in section 1.3.3. 8 Many methylated proteins contribute to regulation of gene expression. First, histones are frequently methylated on their N-terminal tails on lysine and arginine residues. Depending on the site of methylation, the methylation pattern, and crosstalk with additional histone modifications (e.g. methylation, acetylation, phosphorylation, ubiquitination, etc.), the methyl mark may be either activating or repressive. For example, in mammals, asymmetric dimethylation of histone H4 at arginine 3 (H4R3me2a) is associated with active chromatin and functions as a transcriptional activating mark [40], yet symmetric dimethylation of the same residue (H4R3me2s) is found in repressed promoter regions, serving as a repressive mark [41]. These marks are subject to significant crosstalk between histone modifications as well, with the H4R3me2a mark leading to recruitment of lysine acetyltransferases and subsequent downstream addition of transcriptional activating acetylation marks, but not the H4R3me2s mark [42]. Conversely, acetylation of local lysine residues on histone H4 influence H4R3 methylation and promote formation of symmetric dimethylation, favouring transcriptional repression [43]. Protein methylation can also be an epigenetic regulator by way of altering activity of transcription factors. One such example is the transcription factor NF-κB, which can either be asymmetrically or symmetrically dimethylated at arginine 30 of the p65 subunit to either repress or enhance its DNA binding, respectively [44,45]. Especially considering the pathological role of NF-κB in many cancers [46], its activation or repression of transcriptional activity by arginine methylation demonstrates how these methyl marks can epigenetically contribute to the regulation of cancer. Lastly, protein methylation of ribonucleoproteins further contributes to RNA processing activities, including pre-mRNA splicing. For example, global loss of methylarginine-producing enzymes resulted in significant changes in alternative splicing of mRNA [47]. Similarly, loss of these enzymes resulted in hypomethylation of arginine on both ribosomal subunits and led to decreased protein synthesis 9 [47]. Therefore, protein methylation can impact gene expression at transcriptional, post-transcriptional, and translational levels. Many lysine demethylases have been characterized which, like RNA demethylases, demethylate lysines through oxidation of the methyl groups to form a methoxy intermediate followed by subsequent dissociation of a formaldehyde group. Uniquely, the Jumonji domain-containing 6 (JMJD6) protein has been shown to exhibit dual functionality as both an arginine demethylase and a lysine hydroxylase in mammals [48,49]. However, unlike lysine demethylation, arginine demethylation has not been widely detected. In lieu of widespread true arginine demethylation activity, other enzymes, such as peptidyl arginine deiminase, type IV (PAD4) can convert arginine and monomethylarginine to citrulline in mammals [50]. Although this dynamic arginine methylation has been occasionally observed in mammals, no arginine demethylation nor deiminase activity has yet been observed in lower eukaryotes such as yeast, yet lysine demethylation activity is conserved [51]. Therefore, nucleic acid and amino acid methylation is dynamic. Gene expression at the transcriptional, post-transcriptional, and translational level is maintained by the opposing functions of methyltransferases and demethylases. 1.2 Substrate methylation is enzymatically mediated by SAM-dependent methyltransferases Most methyltransferase enzymes use S-adenosyl-L-methionine (SAM) as the methyl donor [52]. During the methylation process, S-adenosyl-L-homocysteine (SAH) is produced as a potent product inhibitor [53]. SAM synthetase consumes adenosine triphosphate (ATP) to enzymatically link adenosine to methionine [54]. Cells can source methionine from the environment or can 10 synthesize methionine. One such method of methionine synthesis is from the loss of the adenosyl group from SAH to produce homocysteine, which can then enzymatically accept a methyl group from a folate source to produce methionine [54]. SAM-dependent methyltransferases are categorized based on their structural fold and corresponding mechanism of catalysis, of which there are nine identified classes, including: the SSo0622-like, mainly methylating RNA; the protein methylating DNA/RNA-binding-3-helical bundle; the small molecule-methylating TIM beta/alpha-barrel; the small molecule-methylating Thymidylate synthetase methyltransferases; the broad-substrate Transmembrane methyltransferases; the broad substrate Tetrapyrrole methylase methyltransferases; the Rossmann domain-containing broad substrate methyltransferases; the mainly RNA-methylating the SpoU and tRNA (SPOUT) methyltransferases; and the Su(var)3-9, Enhancer of zeste, and Trithorax (SET) domain-containing methyltransferases [55]. The structural fold of most eukaryotic methyltransferases can fit into one of the latter three classes, whose features and functions will be elaborated below. 1.2.1 Rossmann-domain methyltransferases The seven beta strand methyltransferases (Class I; also referred to as Rossmann) are the largest class of eukaryotic methyltransferases with ~130 identified members in humans (Figure 1.1A) [1]. This broad group includes enzymes with a wide range of methyltransferase functions, including those that methylate DNA, RNA, small molecules, lipids, proteins, and some with still yet unidentified substrate specificity [56]. Despite the comprehensive list of variable substrates, these enzymes display conserved sequence and structural similarity of three motifs comprising the Rossmann N-terminal SAM-binding domain, where a seven-stranded anti-parallel β sheet is 11 flanked by α helices to form a sandwich structure [57]. These enzymes differ in their variable C-termini that allow for substrate specificity and brings the substrate binding site in close proximity with the methyl donor cofactor. Four motifs have been identified as responsible for SAM binding in protein methyltransferases. The first region, motif I, which has also been identified in DNA methyltransferases, is a 9-membered glycine rich loop region with the general consensus sequences ΨΨ(D/E)ΨGXGXG for protein methyltransferases and ΨΨ(D/S)(L/P)FXGXG for nucleic acid methyltransferases, where Ψ represents a hydrophobic residue and X represents any amino acid [58,59]. At least one glycine residue interacts with the amino moiety of SAM. This region is also followed by a short largely hydrophobic consensus ΨΨXΨ(D/E), termed post-I motif, where the acidic residue coordinates the position of the ribose moiety within the binding site [58,60].The second region, motif II, is an 8-amino acid region with the consensus sequence (P/G)(Q/T)(F/Y/A)DA(I/V/Y)(F/I)(C/V/L), with a special note that the middle aspartic acid residue plays an important role in function as it is unchanging. This region also usually features a disproportionately large number of surrounding aromatic residues [58]. This region, along with motif III (a 10 amino-acid block with the consensus sequence LL(R/K)PGG(R/I/L)(L/I)(L/F/I/V)(I/L)) form parallel β-strands which work together to stabilize the β-sheet, contributing to stable formation of the SAM-binding site [58,60]. Although many Rossmann fold-containing methyltransferases may form higher ordered oligomeric structures, most enzymes function as monomers or dimers [57]. 12 1.2.2 SPOUT-domain methyltransferases The next largest class of methyltransferases are the SPOUT domain (Class IV) methyltransferases (Figure 1.1B). The SPOUT domain is unique in several ways. First, the binding site for SAM in SPOUT family methyltransferases is formed by the C-terminal end folded through the protein to form a knot. Aside from the core trefoil knot, very little amino acid sequence and structural similarity amongst SPOUT family members exists, making identification and characterization of new members a challenge. Despite this variability, the SAM cofactor adopts the same conformation around the knot in each structure, forming a sharp bend with a twist through the ribose moiety [61]. Further, the SAM-binding domain around the trefoil knot is Rossmann-like in nature, with the obvious antiparallel five β-strand and α-helical structure being apparent. Despite this conserved topological structure, the consensus sequence similar to motif I in Class I methyltransferases is less conserved, but a glycine-rich stretch within the β-strand sandwich interacts with SAM amino group in a similar way [62]. Enzymes in this family are also unique in that nearly all identified members (except for two, one of which is the yeast arginine methyltransferase Sfm1[63,64]) are homodimers and the substrate binding site is formed at the junction of each monomer [65]. All characterized SPOUT methyltransferases in mammals methylate RNA, but many predicted SPOUT-domain proteins are uncharacterized due to limited sequence similarity between SPOUT-domain families; identification instead relies on tertiary structure [65]. 1.2.3 SET-domain methyltransferases Finally, the third largest class of methyltransferases is the SET-domain family (Class V) and in humans contains ~56 members (Figure 1.1C) [1]. All identified members of this family in 13 all eukaryotes are known to methylate proteins, specifically at lysine residues. The conserved structure of the SET domain is composed of eight β-strands that form very few sheets and mostly loops and turns, three α-helices, and a conserved C-terminal tyrosine residue needed for catalysis [66]. Many SET-domain containing proteins also have conserved adjacent pre-SET and post-SET regions. The pre-SET domain is cysteine rich; these residues coordinate zinc ions and together, the pre-SET domain contributes to stabilization of the SET domain [67]. The post-SET domain is important for catalytic activity and brings the SAM binding site into close proximity with the target substrate [68]. Interestingly, the target lysine binding site and SAM binding site are located on opposite faces of the enzyme and are only in close proximity through a connecting narrow channel [66]. Unlike both Class I and Class IV methyltransferases which bind SAM using a Rossmann-like fold, SAM-binding sites of SET-domain methyltransferases bear very little resemblance. As a result of this unique SAM-binding domain, bound SAM assumes a distinct horseshoe conformation in SET-family methyltransferases [69]. This bent shape allows for proper protrusion of the SAM methylsulfonium cation into the narrow substrate binding pocket for subsequent catalysis [66]. Many SET-domain containing methyltransferases function as monomers, but certain lysine methyltransferases, such as G9a, function as part of homodimeric, heterodimeric, or higher-ordered oligomeric structures [70,71]. 14 Figure 1.1. The most common SAM-dependent methyltransferases. A) Human coactivator-associated arginine methyltransferase 1 (CARM1/PRMT4) homodimer is a Rossmann-like methyltransferase (PDB: 2Y1X). B) SPOUT family methyltransferase 1 (Sfm1) from Saccharomyces cerevisiae in its active homodimeric state is a SPOUT methyltransferase (PDB: 5C77). C) Human euchromatic histone-lysine N-methyltransferase 2 (EHMT2/G9a) homodimer is a SET-domain containing protein (PDB: 4NVQ). All structures are shown containing the unmethylated cofactor product S-adenosyl-L-homocysteine (pink) and the approximate substrate binding site (red circle) together in close proximity. 1.2.4 Eukaryotic and prokaryotic methylation Nucleic acid methylation is highly prevalent in both eukaryotes and prokaryotes, yet the functional role and primary methylation sites differ. DNA methylation in bacteria also plays a role 15 in epigenetic transcriptional regulation, but not through methylation of CpG islands, like in eukaryotes (bacterial genes have very little to no methylated CpG islands) [72]. Instead, methylation of adenine contributes to epigenetic transcriptional regulation by disrupting DNA-protein interactions necessary for transcriptional machinery [73]. However, the predominant role for bacterial DNA methylation is as part of the bacterial restriction modification system. Briefly, bacteria methylate their own genomic DNA at certain consensus sequences which protects it from degradation via intrinsic endonuclease activity. Bacteria do not methylate foreign DNA at the same sequences, leaving them subject to cleavage. Therefore, DNA methylation in bacteria is primarily a defense mechanism to protect against bacteriophages [72]. Adenine methylation of mRNA is also found widespread in bacteria and is anticipated to play functional roles, yet these have not yet been clearly identified [74]. Like nucleic acid methylation, amino acid methylation differs widely between eukaryotes and prokaryotes. Lysine methylation has primarily been observed in eukaryotes, although a few known methyllysine marks have been observed on bacterial flagellar and ribosomal proteins [75,76]. Lysine methylation is conserved among higher and lower eukaryotes and is specifically the most prominent site of protein methylation in lower eukaryotes such as yeast [77]. Arginine methylation is also conserved among higher and lower eukaryotes, with ~0.5% of all arginine residues being methylated in mammals, and appreciable amounts detected in yeast [77,78]. Unlike lysine methylation, arginine methylation is not detected in prokaryotes like E. coli. Methylhistidine has been identified in mammalian, yeast, and some archaea prokaryotic cells, yet detectable levels are low and the functional roles are under investigated [79]. In prokaryotes, the most prevalent protein methylation type is N-terminal methylation [77]. Between higher and lower eukaryotes, the functional role of protein methylation is largely conserved, with similar and often conserved 16 proteins serving as substrates in each, such as histones, transcription factors, and RNA-binding proteins. The similarities and differences in protein arginine methylation between higher and lower eukaryotes will be discussed in section 1.4. However, since the global landscape of prokaryotic methylation differs wildly from that of eukaryotes, the functional roles of prokaryotic protein methylation are not considered conserved. Like in eukaryotes, components of prokaryotic translational machinery are prominent methylated substrates, yet their functional roles are still unclear [80]. Since bacteria do not possess canonical protein methylation though, it allows for robust recombinant expression of eukaryotic methyltransferases in bacterial systems for in vitro functional and activity assays without fear of bacterial contamination. 1.3 Mammalian protein arginine N-methyltransferases (PRMTs) Protein arginine N-methyltransferases (PRMTs) are a family of eukaryotic enzymes that are responsible for the methylation of arginine residues within protein substrates. In humans, there is a family of nine highly-conserved PRMTs (PRMT1-9; PRMT4 is also known as CARM1) and an additional, less conserved tenth enzyme, NADH:ubiquinone complex assembly factor 7 (NDUFAF7) [3]. All mammalian PRMTs fall into the Rossmann-like family of SAM-dependent methyltransferases which bind SAM using a five-stranded β-sheet rather than a canonical seven-stranded Rossmann domain. These enzymes bind both the SAM cofactor and the target arginine to be methylated in close proximity. The target arginine performs a nucleophilic attack on the methylsulphonium of SAM, resulting in methyl transfer to form the methylated substrate and the unmethylated product SAH [52]. These enzymes are sub-divided into four types based on their propensity to form each of the methylarginine species (Figure 1.2). The types of methylarginine species have been described above in section 1.1.3. 17 Type I PRMTs can transfer one methyl group to a terminal nitrogen to form ηMMA or they may go on to catalyze the transfer of an additional methyl group to the same terminal nitrogen to form aDMA. Type I PRMTs include PRMTs 1-3, CARM1, PRMT6, and PRMT8. PRMT1 is the predominant aDMA-producing mammalian enzyme and is ubiquitously expressed in most tissue types, has a wide range of substrates, and is critical for cell survival [81–84]. PRMT2 has been shown to display remarkably low in vivo and in vitro activity relative to other members of this family but does provide an important dimethylarginine epigenetic regulation mark at histone H3R8 [53,85]. PRMT2 is also unique in that is the only PRMT enzyme with an N-terminal SH3 domain, which plays a role in protein-protein interactions [86]. PRMT3 is responsible for very little MMA and aDMA formation and is specifically localized to the cytoplasm only [87]. It is also unique because it harbours an additional N-terminal zinc-finger domain that serves as an anchor to its substrates [88]. CARM1, like PRMT1, is crucial for cell survival but has a much more restricted substrate specificity than PRMT1 [89,90]. CARM1 has an extended C-terminal region which is postulated to play a role in interactions with functional partners [91]. PRMT6 has a small number of known substrate recognition sites and is found to be localized to the nucleus [92]. PRMT8 is highly similar to PRMT1, but its expression is limited to the brains of vertebrate-species and is typically anchored to cell membranes [93]. Type II PRMTs, like type I, can also form ηMMA but may instead proceed with the transfer of a second methyl group to the other terminal nitrogen to form sDMA. Type II enzymes include PRMT5, PRMT9, and the relatively uncharacterized NDUFAF7, but PRMT5 is the major sDMA-producing enzyme and the best-studied type II PRMT [84]. PRMT9 methylates substrates that are distinct from PRMT5, showing that its functional role is nonredundant, although only one substrate for PRMT9 has been identified to date [94,95]. PRMT9 is also believed to function as a pseudo-18 dimer [94]. Although less conserved to the canonical PRMT family members, NDUFAF7 is a newly identified mitochondrial enzyme that has been shown to produce sDMA on a subunit of human complex I of the electron transport chain [96,97]. A type III enzyme is restricted to a single methyl transfer to one terminal nitrogen to form ηMMA, of which PRMT7 is the lone mammalian member [98]. The PRMT7 gene uniquely contains two adjacent methyltransferase domains so that each monomer is a pseudo-dimer, although cofactor is only bound in one domain [99]. Last, a type IV enzyme transfers one methyl group to the internal ε-nitrogen to form δMMA. In mammals, δMMA has been detected in small amounts by one study, but no mammalian type IV enzyme has been identified [26]. Figure 1.2. PRMT methylation patterns. PRMTs mono- or dimethylate L-arginine to produce Nη-monomethylarginine (ηMMA, Type I, II, or III PRMTs), Nδ-monomethylarginine (δMMA, Type IV PRMTs), Nη,Nη-dimethylarginine (aDMA, Type I PRMTs), or Nη1, Nη2-dimethylarginine (sDMA, Type II PRMTs). 19 1.3.1 Structural features of conserved PRMT enzymes contributing to enzymatic function 1.3.1.1 Cofactor binding Several conserved regions within the catalytic core of the PRMT family are important for catalysis, including the dimerization arm and the catalytic site. The catalytic core of most human PRMTs share a high degree of sequence and structural similarity, which includes conserved sites for cofactor and substrate binding. The less similar NDUFAF7 still shares some similarity within the cofactor binding domain. The cofactor binds to these enzymes within the conserved Rossmann-like fold and directly interacts with residues within motif I and Post-I, as described previously in section 1.2.1. In PRMTs, the conserved motif I has been defined as (V/I/L)(L/V)(D/E)(V/I)G(G/C)G(T/P)G and the conserved post-I appears as ΨΨ(A/G)Ψ(D/E), where h represents a hydrophobic residue [58]. Figure 1.3A demonstrates the high sequence similarity between human PRMT1-9, and the less conserved NDUFAF7 within the SAM-binding domain and a representative image of the cofactor-enzyme interactions between SAM and the PRMT SAM-binding site. In each PRMT family member, the GXGXG pattern important for SAM-binding in all Rossmann-like folds is consistent and invariable. Structurally, the first repeating glycine residue at position 5 of the 9-membered motif makes a hydrogen bond with the SAM amino group. This glycine-rich region also includes an acidic aspartate or glutamate residue at position 3 that interacts with the ribose region of the cofactor, further stabilizing the cofactor interactions. Within PRMTs, the cofactor binding site is buried deep within the protein and is occluded by a flexible αX/αY helical “lid” that stabilizes upon cofactor binding (Figure 1.3B) [100–102]. Several pieces of structural evidence suggest that cofactor binding induces a conformational change within the enzyme, whereby an N-terminal α-helix buries it within the 20 interior of the protein and, as a consequence, completes the formation of the nearby peptide-binding groove that positions the substrate to that the target arginine penetrates deep into the binding pocket [101,103–107]. The order of substrate binding will be more deeply explored in Chapters 2 and 3. Some conserved residues exist on these helices, such as the presence of a conserved methionine and arginine residue on the αY helix in all mammalian PRMT enzymes except PRMT5, which are instead occupied by a phenylalanine and lysine, respectively [108]. In PRMT5, the phenylalanine residue (F379) is partially responsible for sDMA-specific product formation because a phenylalanine-to-methionine mutation to more resemble a type I enzyme resulted in formation of both aDMA and sDMA [108]. 1.3.1.2 Active site The arginine-binding pocket is formed by conserved active site residues, including a “THW loop” and “double-E loop)”. A well-conserved 10-membered “double-E loop” with an invariant glutamate at each terminus (Glu144 and Glu153 in PRMT1) is important for PRMT catalysis. Glutamate-to-glutamine conversions within the double-E loop have been shown to compromise enzyme activity in different PRMTs [86,109–111], underscoring the functional importance of this conserved sequence motif. This loop region coordinates the guanidine moiety of the arginine substrate for methyl transfer and also plays a role in determining product specificity [110]. Specifically, the two glutamate residues form hydrogen bonds with the guanidino group to position it within the active site. Additionally, amino acid differences within this loop also contribute to product specificity. In type I PRMTs, this loop is highly conserved and specifically includes a tryptophan at position 2, an invariable methionine at position 3 and glycine at position 4, a highly conserved tyrosine at position 5, and an invariable leucine at position 7 of the loop (Figure 1.3C). 21 In type II PRMTs, there is no consensus between PRMT5 and PRMT9, but the PRMT5 double-E loop contains the conserved position 4 glycine and the PRMT9 double-E loop contains the conserved position 7 leucine. In the lone type III enzyme PRMT7, only the conserved position 7 leucine is present and there is otherwise no similarity to other PRMT enzymes. Comparing the spatial organization of the active site between enzyme types, the double-E loop region in type I enzymes is larger and more open than both type II and III enzymes to accommodate the asymmetric bulk of two methylations on the same nitrogen atom [109]. Mutations in the “double-E loop” also result in changes to product formation selectivity. In TbPRMT7, a glutamate-to-aspartate variant expanded this enzyme’s product formation to include aDMA [109,112]. Variations on the THW sequence motif contribute to the product specificity of enzyme types by contributing to differences in active site size and sterics [64,99,102,113]. For example, in the type III PRMT7 from Caenorhabditis elegans (CePRMT7) and Trypanosoma brucei (TbPRMT7) (32% and 45% similarity to human PRMT7, respectively [114,115]), the sequence of the canonical “THW loop” is Met-Gln-Trp [109]. The methionine residue in the “THW loop” forms interactions with both the αY helix and double-E loop that brings these three regions into close proximity, which results in an arginine binding pocket that is significantly narrower than the type I and type II enzymes, PRMT1 and PRMT5, respectively (Figure 1.3D) [116,117]. The binding pockets of both CePRMT7 and TbPRMT7 are simply not large enough to accommodate dimethylarginine, thus providing a structural explanation for the sole formation of MMA in PRMT7. Mutation studies have demonstrated in TbPRMT7 that opening the active site by introducing active site mutations that decrease steric bulk can change product specificity. A dual mutation of glutamine to alanine in the “THW loop” and a glutamate to aspartate in the “double-E loop” results in an enzyme capable of forming sDMA [109]. Conversely, mutating a cysteine 22 within the “THW loop” of PRMT9 to a bulkier histidine shifts the product formation from sDMA to MMA [109]. The product formation of PRMT1 was tuned to produce both aDMA and sDMA by mutating the histidine of the “THW loop” to a serine in conjunction with a methionine-to-phenylalanine mutation in the αX helix, resulting in a landscape resembling type II PRMT5 [118]. 1.3.1.3 Dimerization arm Another conserved PRMT structure is the dimerization arm harboured by all PRMTs with the exception of PRMT7, which instead functions as a pseudo-homodimer that contains two tandem copies of the PRMT catalytic unit, one being functional and the other being degenerate [119]. Aside from this PRMT outlier, all PRMT enzymes function as a dimer; deletion of the dimerization arm blocks PRMT dimerization and prevents cofactor binding, thereby abolishing enzymatic activity [110,120]. The helical dimerization arm extends from the C-terminal β-barrel domain (Figure 1.3D). Crystal structures show that the dimerization arm of one subunit makes reciprocal contacts with the N-terminal SAM-binding domain of the other subunit to form an overall toroidal shape in which the two active sites face inward and across from one another [99,102–104,106,108,110,116,117,121,122]. In the absence of a dimerization arm, recombinantly expressed versions of mammalian PRMT1 and its yeast homolog Rmt1p/Hmt1p are monomeric, unable to bind to SAM, and inactive [110,120]. CARM1 phosphorylation at a conserved serine residue near the dimer interface was shown to inhibit dimerization and prevent SAM binding, thereby regulating enzyme function during cell cycle progression [123]. In addition to dimerization being a requirement for SAM binding, SAM and SAH both influence the dissociation constants for PRMT1 and PRMT6 homodimers [124]. PRMT dimerization and its relationship with cofactor binding, therefore, are undoubtedly important aspects of enzyme activity. 23 Figure 1.3. Structural features of mammalian PRMTs. A) The SAM-binding domain (motif I) of PRMT1-9 and NDUFAF7 are aligned (top panel). Highlighted in yellow are the three invariant glycine residues from the GXGXG sequence that are important for SAM binding. Highlighted in red are the regions of conserved small hydrophobic residues within the SAM-binding domain that are present in all mammalian PRMTs. The phylogenetic tree shows highest motif I similarity between the highly homologous PRMT1 and PRMT8, the two pseudo-dimer members PRMT7 and PRMT9, and two type II methyltransferases PRMT5 and NDUFAF7 (bottom panel). B) Overlay of superimposed SAH from human PRMT1 (PDB: 6NT2), mouse PRMT2 (PDB: 5JMQ), mouse PRMT3 (PDB: 1F3L), human CARM1 (PDB: 2Y1X), human PRMT6 (PDB: 4Y30), mouse PRMT7 (PDB: 4C4A), and human PRMT8 (PDB: 5DST) in the PRMT1 SAM-binding site [99,102,105,125–127]. C) The double-E loops of PRMT1-9 are aligned (top panel). Highlighted in yellow are the completely conserved glutamic acid residues that terminate each end of the double-E loop. Highlighted in red is the conserved position of small hydrophobic residues and highlighted in green is the moderately conserved position 24 of glycine residues. The phylogenetic tree shows highest double-E loop similarity between PRMT7 and PRMT9, PRMT1 and PRMT8, and PRMT3 and PRMT6 (bottom panel). All alignments were created using CLUSTAL [128]. D) Conserved and catalytically important PRMT features are highlighted in PRMT1 dimer (PDB: 6NT2). Features include the THW loop (dark green), the double-E loop (yellow), the dimerization arm (orange), the αX helix (red), and the αY helix (cyan). SAH is shown in the cofactor binding site as a ball and stick model. 1.3.2 Structural features of PRMT enzymes contributing to substrate specificity Despite the high degree of conservation, the mammalian PRMT family methylates a wide range of substrates. Unique substrates have been identified for many enzymes with only a small amount of substrate overlap. There is additionally some consensus methylation sequence. In type I PRMTs, a general “RGG” sequence that is rich in glycine and arginine has been identified and can be methylated in vitro by PRMT1-3, 6, and 8 [88,92,93,129–131]. Although PRMT6 is able to methylate substrates in glycine- and arginine-rich (GAR) regions, it has a much higher propensity for methylating arginine with RG motifs rather than the canonical RGG [132]. This GAR motif was identified in the endogenous PRMT1 substrate fibrillarin, whose N-terminal region contains several clusters of arginine and glycine [133]. This sequence is the basis for one of the most common and well-methylated in vitro substrates GST-GAR, which is a polypeptide heterogenous substrate containing glycine and arginine repeats of different peptide sizes [87]. Profiling of in vivo substrates led to an expanded understanding of type I substrate recognition. For example, histone H4 tail at R3 is a notable PTM in vivo. In this substrate, the target arginine lies within the peptide sequence SGRGKGGKG (NCBI Reference sequence NP_003486.1). Mutation of this sequence to generate substrates within new sequence landscapes reveals that many type I enzymes can methylate substrates in vitro within an RGG/RXG/RGX sequence [134]. Unique to type I enzymes, CARM1 was found to not methylate arginine in GAR regions [135], although our laboratory has observed such activity, albeit at reduced levels compared to preferred 25 substrate sequences (unpublished results). CARM1 does methylate arginines that neighbour proline-rich sequences, particularly PGM sequences, as seen in profiling of CARM1 in vivo substrates [89,135,136]. Interestingly, both the PRMT1-specific histone H4R3 substrate [137] and the CARM1-specific, PGM-containing splicing factors substrates, such as hnRNP polypeptide B (SmB) [136] are also substrates of PRMT5, demonstrating that the type II enzyme is capable of methylating both proline- and glycine-and-arginine-rich sequences. The other type II enzyme PRMT9 is unable to methylate GAR and only has one known substrate, an RNA splicing factor SF3B2 otherwise known as SAP145, which it methylates within a KRK context; interestingly, PRMT9 is unable to methylate other KRK peptide substrates [94]. The lone type III enzyme PRMT7, like CARM1, is also unable to methylate GAR sequences in either fibrillarin or GST-GAR, and is unable to methylate other classic “RGG” PRMT substrates [138]. It instead behaves more like CARM1 in that it is able to methylate a synthetic proline-, arginine-, and glycine-rich peptide GGPGGRGGPGG [138]. It also uniquely has a propensity for methylating arginine within RXR sequences that are embedded within additional basic peptides, such as in its endogenous substrate histone H2B [139]. Therefore, specific methylation motifs emerge for different PRMT types, thereby contributing to diversity within the protein family and their substrates. However, substrate specificity extends beyond a simple recognition sequence and is also heavily influenced by both proximal and distal residues to the target arginine within a substrate sequence. For example, although PRMT1 is known to classically methylate RGG/RGX/RXG sequences to a high degree, mutation of RGG to RSG in a fibrillarin-based peptide was not methylated by PRMT1 in vitro, but the RSG sequence within a peptide derived from eIF4a1 was, demonstrating that local substrate sequence is important for substrate binding [134]. Further, using a histone H4-based peptide, it was shown that truncated peptides are poorer in vitro substrates for 26 both PRMT1and PRMT5, suggesting the importance of a minimum peptide length for substrate binding regardless of enzyme type [137,140]. The importance of regional sequence was also demonstrated in PRMT5, where the addition of possible methylation targets in a histone H4-derived peptide substrate did not change the target arginine in the peptide sequence [137]. The identity of the substrate can also influence the relative production of methylarginine species in dimethylarginine-producing enzymes. Specifically, PRMT1 forms variable ratios of MMA/aDMA depending on substrate identity (which will be explored in more detail in Chapter 2) [141,142]. Therefore, the impact of substrate sequence identity on PRMT function is complex. Structural determinants of substrate specificity have emerged for many PRMTs and often lies in the N-terminal extension that is unique to each PRMT. In PRMT1, acidic residues in the N-terminal region have been identified as important for substrate binding through mutational studies. Mutating surface glutamate residue 47 located on the αY helix and residue 129 on a loop region neighbouring the active site to lysine resulted in different patterns of substrate methylation in cell extracts [143]. Further, the E47K mutant showed wild-type activity towards substrate GST-GAR, but drastically reduced activity towards histone H4, whereas the E129K mutant had decreased activity towards both peptide substrates, underscoring the impact of both enzyme and substrate sequence on substrate specificity [143]. In PRMT3 mutants lacking the unique zinc finger domain, not only is there reduced enzymatic activity but also abolished methylation of lysates when RNase was added, suggesting that the zinc finger domain plays a role in associating with its substrates, which for PRMT3, include RNA-associated proteins [88]. It was also separately shown that full length PRMT3 can methylate GST-GAR, but this ability is removed without the zinc finger domain, yet other methylation activity is still detected [87]. The N-terminus has also been shown to be important for substrate recognition in CARM1, as deletion of the N-terminus in vivo resulted 27 in abolished interaction of known substrates via immunoblotting and also reduced in a global loss of methylation in mammalian cells [89]. Although not a classical N-terminal extension, PRMT5 functionality depends on a binding partner MEP50. The PRMT5:MEP50 interface includes key interactions in the PRMT5 N-terminus [144,145]. Interestingly, MEP50 alone is able to interact with and pull-down known PRMT5 substrates from lysates, providing a possible mechanism by which PRMT5 is able to interact with its substrates [144]. Since PRMT7 is unique in its function as a pseudo-dimer, residues outside the N-terminal region have been identified as important for substrate specificity. Specifically, mammalian PRMT7 has a unique “double-E loop” (as described above) that includes two acidic residues; all other PRMTs except for PRMT9 do not have acidic residues within the “double-E loop” (Figure 1.3C). In PRMT7, mutation of these acidic residues to glycine (to imitate other PRMTs) and methionine (to resemble TbPRMT7) resulted in enzymes that retained high automethylation activity, yet were unable to methylate a histone H2B derived peptide, showing that these residues are crucial for substrate recognition but not methylation activity [146]. Therefore, structural determinants of substrate specificity cannot be distilled to a specific domain but rather is dependent on residues both inside and outside the active site. Despite the combination of enzyme and substrate sequence on substrate specificity contributing to unique substrates for PRMT family members, loss of PRMT1 results in substrate scavenging by other PRMTs suggesting that some PRMT enzymes are opportunistic and may have the ability to methylate otherwise non-preferred and non-endogenous substrates in vivo [78]. Therefore, factors contributing to PRMT substrate specificity appear to be complex, but we do know that identifying PRMT substrates on consensus sequence alone such as RGG does not accurately describe the breadth of PRMT substrates. 28 1.3.3 Biological significance of mammalian PRMTs and their substrates The expression of PRMTs in mammals is ubiquitous and methylarginine-containing proteins have been identified in virtually all tissue types, biological systems, and subcellular localizations [147]. Although exhaustive profiling of PRMT substrates will not be discussed here, an overview of their roles in some of their notable biological processes will be presented, including in gene expression, RNA processing, and stress response. 1.3.3.1 Biological significance of histone arginine methylation As discussed in section 1.1.3, all histone tails are endogenous substrates of specific PRMTs. Methylation of the N-terminal histone tails contribute to activation or repression of transcription by recruiting methylarginine readers specific to the methyl mark and methylation location. For example, PRMT5-mediated symmetric dimethylation has a repressive effect on histone H3R8 and histone H4R3 through various mechanisms, such as by recruiting histone deacetylases (associated with a closed chromatin state), or by recruiting DNA methyltransferases to methylate and subsequently repress the transcriptional start site [41,148]. The transcriptional repression activity of PRMT5 is enhanced by antecedent PRMT7-dependent monomethylation of H4R17, which activates subsequent symmetric methylation of H4R3 [149]. However, sDMA has also been observed as a mark of transcriptional activation, particularly on histone H3R2. In this case, the sDMA mark is enriched in active chromatin, preventing binding of known co-repressors and instead, increasing the binding of coactivator complexes [150,151]. Conversely, asymmetric dimethylarginine on histone H3R8 and on histone H4R3, primarily mediated by PRMT2 and PRMT1, respectively, are enriched in active chromatin, and for H3R8 aDMA in particular, is a necessary component for activation of its targets [85]. Methylation activity of PRMT1 towards 29 histone H4R3 is also subject to crosstalk, in that preceding H4K20 trimethylation decreasing PRMT1 association with histone H4 and results in arginine hypomethylation [140]. One proposed mechanism for transcriptional activation is that the aDMA marks serve as a binding site for transcriptional coactivators such as PCAF, whose recruitment results in downstream histone acetylation, leading to open chromatin formation [152]. The histone H3 tail is also asymmetrically dimethylated at arginine residues 17 and 26 [135,153]. These residues, which are not within a GAR context, are uniquely modified by CARM1. Methylation of these residues are both associated with transcriptional activation because the presence of the methyl marks inhibits binding of corepressors and a chromatin remodeling and deacetylase complex, which would otherwise work to repress transcription [154]. However, the aDMA mark is not always associated with transcriptional activation, as seen at H3R2 and H2AR29, which are methylated by PRMT6, preventing subsequent recruitment of activator complexes [155,156]. Monomethylation deposited by PRMT7 in the RXR motifs on histone H2BR29, H2BR31, and H2BR33 have also been identified in vivo, but the epigenetic effects of these PTMs are not yet elucidated [139]. Therefore, the effect of arginine methylation on gene transcription is complex and depends on several factors, including methylation type, local sequence, gene locus, methyl-reader interacting partners, and crosstalk between other PTMs. 1.3.3.2 Biological significance of arginine methylation on nucleic acid- binding proteins The majority of non-histone PRMT substrates are RNA-binding proteins that are associated with RNA or are responsible for various RNA processing mechanisms [157]. Many RNA-binding proteins contain RGG motifs, the classical methylation target of many PRMTs [129]. The presence of basic residues arginine and lysine are important for protein binding to negatively charged RNA. 30 Methylation of arginine residues on RNA-binding proteins can both attenuate and potentiate RNA binding ability by either disrupting hydrogen bonding that can contribute to RNA-protein interactions or by contributing to van der Waals interactions by increasing local hydrophobicity of methylarginine residues [158]. Many RGG-containing proteins are members of the hnRNP family which play roles in mRNA splicing, mRNA stabilization, mRNA export, and mRNA translation, and contain high levels of arginine methylation. For example, a well-characterized PRMT substrate is splicing factor SAP145, which is monomethylated and symmetrically dimethylated by PRMT9 [95]. The methylarginine serves as an important recruitment mark for TDRD-containing survival of motor neuron (SMN), which in turn is a mandatory prerequisite for the formation of the spliceosomal machinery (Sm) [95]. Loss of PRMT9 function results in ineffective alternative splicing; however, the functional role of the broader PRMT family is also implicated in alternative splicing as global PRMT inhibition results in aberrant alternative splicing [47,95]. The RNA-binding protein HuD binds to and stabilizes mRNA in the cytoplasm [159]. The interactions between HuD and mRNA are impacted by CARM1-dependent methylation; methylated HuD does not form as tight a complex with mRNA as its unmethylated counterpart, thus impacting transcript half-life [160]. Arginine methylation also impacts mRNA-interactions with nuclear REF protein, a shuttle protein which is responsible for transiently binding mRNA for subsequent handoff to other interacting partners for mRNA export from the nucleus [161]. Similarly to HuD:mRNA interactions, unmethylated mRNA binds tightly to REF. Arginine methylation of REF is needed to release the transcript for subsequent nuclear export [162]. Core ribosomal subunits have been found to contain extensive arginine methylation and has been shown to play a functional role. Inhibition of both PRMT1 and CARM1 heavily quells translation [47]. Further, arginine-substituted mutants of the methylarginine-containing ribosome subunit RPS2 were deficient in 31 translation [47]. Therefore, methylarginine in RNA-binding proteins plays a functional role in a wide range of RNA processing mechanisms by stabilizing oligomeric protein complexes and by modulating direct protein-RNA interactions. Many important signaling pathways include PRMTs as key players, including the mammalian stress response. In response to various environmental stressors in eukaryotes, stress granules form as large ribonucleoprotein particles. A purpose of stress granules is to store and protect pre-translational mRNA for translation re-initiation upon exit from stressful conditions [163]. Considering that stress granules are RNA and protein heterogeneous aggregations, it is unsurprising that many proteins associated with stress granules are PRMT targets and accordingly, contain all methylarginine species [164]. Most sDMA-containing proteins within the granules are associated with pre-mRNA splicing, suggesting a functional link between the spliceosome and stress granules, perhaps as a way to modify gene expression in response to stress [164]. However, the methylarginine-dependent regulation of stress granule formation is complex, as PRMT1-mediated methylation of certain RNA-binding proteins actually prevents stress granule formation in the stress response [165,166]. Conversely, inhibition of PRMT7 results in hypomethylation of a stress-responsive chaperone, which in turn inhibits stress granule formation, therefore showing that PRMT7 methylation activity positively modulates stress granule formation [167]. Like PRMT7, PRMT5 also plays a positive role in the mammalian stress response, in part by symmetrically dimethylating and subsequently activating NF-κB, a transcription factor that is involved in both the mammalian stress and immune responses [45]. Upon activation, NF-κB initiates the transcription of many stress-induced genes [168]. In plants, PRMT5 activity is enhanced in response to increased nitric oxide levels in stress [169] and it is known that the NF-32 κB stress response pathway is redox-sensitive [168], therefore suggesting that PRMT5 and sDMA provide an important functional role in activation of the stress response in humans. 1.3.3.3 Implications of PRMT deregulation Considering the important roles of PRMTs in crucial cellular functions that contribute to gene expression, RNA processing, and stress response, it is unsurprising that deregulation of PRMTs and subsequent altered methylation marks have been identified in various cancers. For example, the transcriptionally active aDMA mark on histone H4R3 is globally abundant in prostate cancer tissues and is associated with high tumor grade and increased likelihood of cancer reoccurrence [170]. Overexpression of PRMT5 is observed in several cancers, including ovarian, lung, melanoma, gastric, and bladder cancer, among many others, suggesting its role as an oncogene its sDMA-producing activity [49]. Specifically, PRMT5 hyperactivity leads to downstream overactivation of NF-κB and its subsequent transcriptional activation of oncogenes related to cell proliferation and metastasis in a range of cancers [171]. The hyperactivity of NF-κB in many cancers is directly linked to PRMT5 methylation as pharmacologic inhibition of PRMT5 resulted in decreased NF-κB activation and tumor progression in both prostate and colorectal cancers [172]. Therefore, the anomalous overexpression of many PRMTs is directly linked to methylation activity in cancer by modulating gene expression through many pathways, leading to cancer progression. Subsequently, many groups are developing PRMT inhibitors as potential therapies for various cancers [173]. Since many PRMT-related disease states result from aberrant PRMT overexpression or upregulation, precise metabolism of methylarginine-containing proteins is important for proper cellular function. As discussed in section 1.1.3, methylarginine-containing proteins in mammals 33 may be demethylated by JMJD6 to reverse the effects of methylation [48]. JMJD6 has been reported to interact with and demethylate the methylated estrogen receptor α (ERα), inhibiting its signal transduction cascade [174]. However, the identification of JMJD6 as a bona fide arginine demethylase has been contested, with at least one group being unsuccessful in observing demethylation activity [175]. As an alternative to true demethylation activity, methylarginine-containing proteins are also substrates of protein arginine deiminase (PAD) enzymes which convert monomethylarginines to citrulline [176]. Specifically, PADI4 deiminates the histone H3 tail, preventing downstream CARM1-mediated asymmetric dimethylation which serves as a transcriptional activating mark on histone H3 [177]. However, mammalian arginine demethylase activity is low and PAD enzymes perform citrullination on only arginine and monomethylarginine, and not dimethylarginine [176,178]. Therefore, the majority of methylarginines are removed through proteolytic cleavage into free amino acids which are then processed further or excreted from the body through the kidneys [179]. Mammals have dimethylarginine dimethylaminohydrolase (DDAH), which converts MMA or aDMA into citrulline and monomethylamine or dimethylamine, respectively via hydrolysis [180]. Unlike MMA and aDMA, sDMA is not metabolized by DDAH, so its only form of removal is through renal excretion [179]. Not only is the maintenance of methylarginines on proteins important for cellular function, maintenance, and metabolism of free methylarginines must be tightly controlled as well. In mammals, L-arginine is used as a reactant in the production of nitric oxide (NO), a crucial cell signaling molecule; however, this activity is inhibited by free MMA and aDMA [178]. The activity of DDAH functions to remove free MMA and aDMA, since their metabolites do not inhibit nitric oxide synthase activity [178]. Therefore, mammalian cells have multiple distinct processes for strictly regulating methylarginines on and out of proteins. 34 The impact of protein arginine methylation on diverse cellular processes is significant. Although most current work is focused on deciphering the roles of PRMTs in human disease and exploring PRMTs as drug targets, there is still much to be learned about the functions of PRMTs and their roles in healthy-functioning cells, especially within the context of their non-histone substrates. A deeper investigation into these specifics will contribute to a more comprehensive understanding of PRMT function, methylarginine utility, and substrate roles in cellular processes and disease. 1.4 Protein arginine methylation in Saccharomyces cerevisiae 1.4.1 Yeast PRMT enzymes There is a high degree of similarity between the human and baker’s yeast methyltransferasomes. In S. cerevisiae, there are four confirmed arginine methyltransferases and three of them, like all mammalian PRMTs, have a Rossmann-like structural domain with conserved “THW loop”, “double-E loop”, and canonical motif (I, post-I, II, and III) features. Also like their mammalian homologs, these enzymes operate as functional dimers as mediated by a dimerization arm that is needed for enzymatic activity. Arginine methyltransferase 1 (Rmt1p, also known as HnRNP methyltransferase 1, Hmt1p) is the functional homolog of human PRMT1, with 51% amino acid sequence identity and strong structural similarity; like PRMT1, it is the predominant type I enzyme in budding yeast, being responsible for 66% MMA and 89% aDMA (Figure 1.4A and Figure 1.4B) [181]. Human PRMT1 can methylate yeast Rmt1p substrates in vitro, further showing a functional similarity [181]. However, unlike its mammalian homolog, the RMT1 gene is not essential and no notable metabolic phenotypic changes have been observed [182]. Histone synthetic lethal 7 (Hsl7p) can form MMA and sDMA, and is the yeast homolog for 35 human PRMT5, for which they share 27% amino acid sequence identity (Figure 1.4C) [183]. This protein was identified in a synthetic-lethal screen of gene deletions in conjunction with deletion of histone H3 N-terminal sequence [184]. Limited in vivo sDMA-producing activity for Hsl7p has been detected and sDMA has only been detected on yeast proteins in small quantities [185,186]. Arginine methyltransferase 2 (Rmt2p) is the only known eukaryotic methyltransferase uniquely capable of forming δMMA, monomethylation of the internal nitrogen of the guanidino group [187]. Although no functional homolog has been identified in mammals, Rmt2p does share 28% sequence identity to mammalian guanidinoacetate methyltransferase (GAMT), an enzyme which also methylates the δ-nitrogen of the guanidine group in guanidinoacetate (Figure 1.4D) [187]. The final confirmed arginine methyltransferase, Sfm1p, only forms ηMMA and is the only known arginine methyltransferase that harbours a SPOUT methyltransferase domain rather than a Rossmann-like fold [188]. The relative proportion of methyl marks in log-phase yeast has been determined, with aDMA (~91%) being the most abundant modification, followed by MMA (~8%), and sDMA (<1%) [185]. Figure 1.4. Sequence alignments of human and yeast PRMT homologs. A) Human PRMT1 and yeast Rmt1p share 51% sequence identity. B) Structure alignment of human PRMT1 (pale blue; PDB: 6NT2) and its yeast homolog Rmt1p (pale pink; PDB: 1G6Q) show highly conserved structural similarity [120,125]. Also shown are SAH (yellow) and an active site inhibitor GSK3368715 (green) from human PRMT1 crystal structure. The active site double-E loop from human PRMT1 and yeast Rmt1p is coloured in dark blue and pink, respectively. Structural alignment was performed using Discovery Studio (Biovia) [189]. C) Human PRMT5 and yeast Hsl7p share 27% sequence identity. D) Human GAMT and yeast Rmt2p share 28% sequence identity. Sequences were aligned using Blastp (NCBI) [190].1.4.2 Yeast PRMT substrates Targets of PRMTs in yeast are similar to targets in mammals. Like in mammals, most methylarginine-containing proteins in yeast are RNA-binding proteins that have roles in mRNA processing, mRNA storage and stabilization, or in translation [51,191]. Methylation of several PRMT substrates has been shown to play a functional role. For example, two known targets of Rmt1p are Npl3p and Hrp1p, two RNA-binding proteins that shuttle between the nucleus and cytoplasm [182,192]. Both Npl3p and Hrp1p have similar features of the PRMT substrates mammalian hnRNPs in that they contain RGG-rich motifs as plausible methylarginine targets [193,194]. Npl3p plays an important role in mRNA export from the nucleus; Δnpl3 cells are unable to export mRNA from the nucleus [195]. When arginines within Npl3p RGG sequences were mutated to lysine, cells had growth defects and Npl3p displayed different nuclear to cytoplasm translocation, suggesting that methylarginine on Npl3p may mediate its function [194]. When yeast cells are deficient in both Npl3p and Rmt1p activity, then cells are non-viable, suggesting some synergistic and functional role for the Rmt1p in mRNA export processes [196]. Hrp1p plays a role in mediating poly-adenylation of mRNA tail length and its role as an RNP is impacted by Rmpt1p, as in Δrmt1 cells, the genome binding of Hrp1p changes markedly [182]. Further, knockout of Rmt1p severely impedes translocation of both Npl3p and Hrp1p from the nucleus to the cytoplasm, showing that arginine methylation of these substrates not only affects protein function, but also subcellular localization [197]. Identification of yeast proteins containing δMMA has not been incredibly fruitful, with only one bona fide substrate being identified, L12, a ribosomal protein important for ribosome assembly [198]. Similarly, only one in vivo substrate for Sfm1 has been identified, protein S3, which is also a component of the eukaryotic ribosome; however, a functional role for the methylation of protein S3 has not been identified as cells with 38 hypomethylation of protein S3 show no notable phenotypic changes [188]. No non-histone sDMA-containing substrates have been identified in yeast. In addition to RNA-binding proteins, histones are also methylated in yeast, although the knowledge of methylation targets and functional roles are limited. In vivo histone methylation activity has been routinely shown for Rmt1p and once for Hsl7p, but not for the other known PRMTs. As described above in section 1.1.3, asymmetric dimethylation of mammalian histone H4R3 is associated with transcriptional activation, but interestingly in yeast, Rmt1p-mediated aDMA deposition on histone H4R3 is a sign of transcriptional repression; in Δrmt1 cells, areas of silent chromatin show increased transcriptional activity associated with hypomethylation of histone H4R3 [199]. Further, histone H4 is a better Rmt1p substrate when it is acetylated and asymmetric dimethylation of histone H4R3 recruits deacetylase complexes, which works to close chromatin, thereby suggesting that histone H4 methylation and acetylation crosstalk contributes to silent chromatin maintenance [200]. But like the aDMA mark on histone H3R2 in mammals, this mark is also repressive in yeast [201]. Recently in vivo Hsl7p symmetric dimethylation activity was identified on histone H4R3, which like in mammals, is associated with repressed transcription by associating with a histone deacetylase to silence gene expression [186]. Previously, Hsl7p was only shown to methylate mammalian histone H2A in vitro and is unable to methylate the canonical GAR motif substrate GST-GAR [183]. Although the extent and function of histone PTMs in yeast is not fully elucidated, it is clear that like in mammals, the role arginine methylation on gene transcription is complex and multifactorial, although not entirely conserved between higher and lower eukaryotes. 39 1.4.3 Yeast arginine methyltransferasome The yeast protein arginine methyltransferasome is thus far incomplete. First, although many methylarginine-containing in vivo substrates have been identified in yeast, the depositing enzyme for many has not been identified. In yeast rmt1/rmt2/hsl7 triple knockouts, 50 methylarginine-containing proteins are still found [191]. Further, the enzyme responsible for the conserved repressive aDMA mark on histone H3R2 is elusive; Rmt1p can asymmetrically dimethylate H3R2 in vitro, but H3R2me2a is unchanged in Δrmt1 yeast [202]. Knockout of Rmt1p does not abolish all aDMA activity, yet no other known yeast PRMT has been shown to produce aDMA, indicating that there is at least one other unidentified PRMT capable of producing aDMA [181]. Similarly, Hsl7p is the only known yeast PRMT capable of forming sDMA, yet in Δhsl7 cells, sDMA is still detected on yeast proteins, especially under stressful environmental conditions, suggesting a yet unidentified sDMA-producing enzyme in yeast [185]. With the surprising identification of Sfm1p as a noncanonical protein methyltransferase comes the realization that additional nontraditional PRMT enzymes are yet to be discovered. In yeast, there are 86 genes encoding known and putative methyltransferases that are predicted to methylate nucleic acids, proteins, or other small molecules based on global isoelectric point (pI) as a measure of surface charge, methyltransferase fold, enzyme expression patterns, and subcellular localization; of these putative enzymes, 19 have not demonstrated in vivo activity towards a substrate [203,204]. The putative methyltransferases will be explored in more detail in Chapter 4. Further, since global levels of all methylarginines have been shown to change drastically in response to certain environmental stressors, it is possible that PRMT activity in yeast may be related to yeast stress pathways. Therefore, the unaccounted-for yeast methylarginines, the large number of unidentified 40 methyltransferases in the yeast genome, and the new implications of stress on yeast methylarginine indicate a need to more comprehensively study the yeast methyltransferasome. 1.4.4 Saccharomyces cerevisiae as a model organism to study the biological roles of PRMTs in stress 1.4.4.1 Conserved eukaryotic signaling pathways In addition to high similarity between mammalian and yeast arginine methylation enzymes and substrates, there is also significant similarity and overlap in the biological pathways and processes in which PRMTs are involved, specifically within the stress and aging pathways. In both mammals and S. cerevisiae, the response to environmental stressors is mediated through a variety of mitogen-activated protein kinase (MAPK) pathways, which rely on the successive phosphorylation and activation of downstream effectors to form a kinase cascade to activate a variety of targets, including transcription factors [205]. Of these MAPK-activated transcription factors, three key players, multicopy suppressor of SNF1 mutation 2 and 4 (Msn2/4p) and heat shock factor 1 (Hsf1p) activate transcription of genes in budding yeast in response to heat shock, oxidative, osmotic, and general stress. Msn2/4p mediates the general stress response, including stress caused by low glucose levels, heat shock, and osmotic stress, while Hsf1p activation is specific to low glucose levels and heat shock. Msn2/4p is activated in response to glucose deprivation and heat shock through the cyclic adenosine monophosphate-protein kinase A (cAMP-PKA) pathway. Both glucose depletion and heat shock leads to inactivation of PKA, which normally functions to phosphorylate cytosolic Msn2/4p. Unphosphorylated Msn2/4p can subsequently translocate to the nucleus for its transcriptional role [206,207]. Glucose and nitrogen depletion also regulates Msn2/4p through the target of rapamycin (TOR) pathway; in high nutrient 41 conditions, TOR causes Msn2/4p to associate with a cytoplasmic protein, preventing Msn2/4p nuclear uptake and preventing transcription of its targets [208]. Msn2/4p are also directly activated by extracellular osmotic stress signals by high osmolarity glycerol 1 (Hog1p) [209]. Upon nuclear activation of Msn2/4p, they bind to a regulatory element located in the promoter region of target genes called the stress responsive element (STRE) [210]. Conversely, trimeric Hsf1p binds to heat shock transcription elements (HSE) in gene promoters; some trimeric Hsf1p is constitutively bound while some binds in an inducible manner in response to heat shock and glucose depletion [211,212]. Similarly to Msn2/4p, when glucose is depleted, nuclear Hsf1p becomes hyperphosphorylated via the cAMP-PKA pathway, thus increasing Hsf1p transcriptional activity [206]. Further, in response to heat shock, cytosolic Hsf1p, which is primarily bound to chaperone heat shock protein 90 (Hsp90p), dissociates from Hsp90p, allowing its nuclear localization, hyperphosphorylation, and subsequent transcriptional regulation [213]. These stress response pathways are tightly linked to aging. When yeast are grown into stationary phase, they can survive in an environment devoid of necessary nutrients, leading to cessation of cell proliferation, much like a typical eukaryotic cell that can only undergo a finite number of replications. Chronological age describes the lifespan of a cell in its non-dividing state whereas the replicative age describes how many times a cell can divide [214]. Further, in yeast mutants incapable of surviving in stationary phase, the expression of human B-cell lymphoma 2 (Bcl-2), a typical anti-apoptotic protein, is able to induce cellular survival, suggesting the mechanisms of cellular aging and survival are conserved among eukaryotes [215]. Therefore, non-proliferative budding yeast in stationary phase can be a model for human aging. Similar to various stresses, both TOR and PKA have been implicated in yeast senescence. When both TOR and PKA activities were individually repressed, both the replicative and chronological lifespans of yeast 42 cells increased [216,217]. Furthermore, restriction of amino acids or glucose depletion increases yeast life span, which is consistent with the idea that calorie restriction can increase life span in mammals [218,219]. Therefore, together this information suggests a relationship between stress and aging that is conserved between yeast and mammals. 1.4.4.2 Methylarginine regulation in yeast Like in mammals, tight regulation of methylarginines in yeast is important to maintain protein subcellular localization, protein interactions, and protein function. Unlike in mammals though, there is no arginine demethylase or deiminase activity to remove methylation from arginine in proteins and no DDAH to remove free methylarginines from the cytosol [220]. Therefore, the only way to metabolize methylarginines is through protein degradation pathways and subsequent expulsion of free methylarginines. Protein degradation is managed through two distinct systems: the ubiquitin-proteasome system (UPS), which is highly conserved among eukaryotes [221], and the autophagy pathway, which is highly homologous between higher and lower eukaryotes [222]. The UPS targets ubiquitinated proteins for degradation by the proteasome. Ubiquitylation of targets occurs using a sequential relay system of first a ubiquitin-activating enzyme (E1), then a ubiquitin-conjugating enzyme (E2), and a last ubiquitin-ligase enzyme (E3) [223]. The eukaryotic 26S proteasome is composed of a proteolytic 20S core and two 19S regulatory units [221]. Proteins are targeted for degradation via the UPS as part of regular cell maintenance and protein turnover, but also in response to damaged, aggregated, or misfolded proteins [223]. Conversely, autophagy imports either targeted cellular material or bulk cytosol into the vacuole (or lysosome in mammals) for proteolytic degradation [224]. Cytosolic material is encapsulated in a phagophore, which then fuses to the vacuole to unload its contents. Autophagy 43 is largely regulated by the major nutrient sensing TOR pathway and is only induced when the TOR pathway is inactive, such as under starvation conditions [225]. Therefore, autophagy is typically activated in response to stressors as a method to recycle cellular machinery or degrade unnecessary organelles, such as mitochondria under energy-preserving stress conditions [224]. The autophagy pathway will be explored in more detail in Chapter 5. Although yeast do not employ the same mechanisms as mammals to reverse arginine methylation, they share conserved mechanisms for protein degradation. 1.5 Thesis outline and rationale Many roles of PRMTs and their methylated substrates have been described here. In both mammals and yeast, we know that strict regulation of PRMT activity is important for cell function. We also know that in mammals, knockout of major PRMTs is lethal, underscoring their importance in mammalian development. As key modulators in many cancers, PRMTs have become popular drug targets, and inhibitors are being actively developed. For example, overexpression of PRMTs has been observed in many cancers [49] and successful pharmacologic inhibition of PRMT1, CARM1, and PRMT5 has been shown to have anti-tumor activity in renal cell carcinoma, myeloma, and lymphoma, respectively [226–228]. Knowledge of PRMT kinetic mechanisms and how they interact with substrates is important in designing and testing novel inhibitors. Basic kinetics of PRMTs have been studied but conclusions are indecisive and often divisive. A more robust and comprehensive characterization of PRMT biochemistry is required to bolster and supplement discovery of PRMT therapeutic agents. While PRMTs have long been studied for their roles in gene expression and their involvement in cancers, elucidation of their roles in stress pathways is a relatively new interest. 44 For example, the clear role of PRMT5 in the activation of NF-κB has been studied in the context of cancer, but less so in the context of NF-κB-mediated stress responses. Cellular stress is also an important component of many cancers, as the tumor microenvironment may starve surrounding cells and may increase the local inflammation response, including increased temperature and oxidation. In a genome-wide study of genes activated in response to osmotic stress in yeast, the human orthologues of the identified upregulated genes were largely found to be implicated in cancer, underscoring the importance of understanding the stress response and how it relates to cellular development [229]. Yeast offers an opportunity to study the role of highly conserved methylarginines in highly conserved stress pathways. Methylarginines in yeast have almost exclusively been explored under nutrient-rich rather than stress conditions, which I postulate has led to a misunderstanding of the full importance and function of methylarginines. Considering the high degree of homology between the mammalian and yeast methyltransferasome, probing for yet unidentified yeast PRMTs may also lead to new knowledge of mammalian homologs. The identification of non-canonical PRMTs in budding yeast together with the recent interest of PRMT involvement in the stress response demonstrates that there is much left to learn about the impact of methylarginines. There are four goals of this thesis: to investigate the kinetic mechanism of PRMTs; to explore a novel application of differential scanning fluorimetry (DSF) to enzyme kinetics; to characterize putative novel yeast methyltransferases using DSF and other techniques; and, to explore the fates of intercellular methylarginines in yeast under stress conditions. Based on literature and previous research from our group, we hypothesize that PRMT1 uses a sequential ordered mechanism to bind and methylate its substrates. In Chapter 2, we investigate the methylation and kinetic mechanism of PRMT1 and comprehensively review reasons for 45 inconsistency in previous research. We further hypothesize that DSF, a routine technique in exploring protein-ligand interactions, can be used in a new way to reveal the order of substrate binding to methyltransferase enzymes. This technique may serve as a complement to mechanistic investigations and in Chapter 3, we demonstrate that DSF is a novel biophysical method for determining the kinetic mechanism of PRMT1 as a proof-of-concept. We next hypothesize that uncharacterized putative yeast enzymes Ykl162cp and Ynl092wp are methyltransferases and predict that they may be arginine methyltransferases. In Chapter 4, using our developed DSF methodology, we characterize histidine-methyltransferase activity from Ynl092wp and predict that Ykl162cp may be an RNA-methyltransferase, contributing to the knowledge of the largely incomplete yeast proteome. Last, we hypothesize that methylarginine production in yeast is impacted by stress conditions and that free methylarginines may be toxic to yeast cells. In Chapter 5, we explore the role of PRMTs and their substrates in the stress response in the yeast model organism by exploring how methylarginine levels change and how yeast are able to eliminate or recycle them in response to stress. These novel approaches contribute to a more thorough understanding of PRMT roles and methylarginine effects. Identification and kinetic and mechanistic characterization of novel yeast methyltransferases highlights the similarities and differences between eukaryotic methyltransferase enzymes and may reveal insights into their human homologs. 46 Chapter 2: Kinetic analysis of PRMT1 reveals multifactorial processivity and a sequential ordered mechanism1 2.1 Chapter Synopsis Arginine methylation is a prevalent post-translational modification in eukaryotic cells. Two notable debates exist within the field: do these enzymes dimethylate their substrates in a processive or distributive manner, and do these enzymes operate using a random or sequential method of binding to its two substrates? We revealed that human protein arginine N-methyltransferase 1 (PRMT1) enzyme kinetics are dependent on substrate sequence. Further, peptides containing an Nη-hydroxyarginine generally demonstrated substrate inhibition and had improved KM values, which evoked a possible role in inhibitor design. We also revealed that the perceived degree of enzyme processivity is a function of both cofactor and enzyme concentration, suggesting that previous conclusions about PRMT sequential methyltransfer mechanisms require assessment. Finally, we demonstrated a sequential ordered Bi–Bi kinetic mechanism for PRMT1, based on steady-state kinetic analysis. Together, our data indicate a PRMT1 mechanism of action and processivity that might also extend to other functionally and structurally conserved PRMTs. 2.2 Introduction Arginine-methylated proteins are involved in many important cellular functions, including RNA processing, epigenetic transcription regulation, signal transduction, and DNA repair [3]. Considering that approximately 0.5% of arginine residues in the human proteome are methylated, 1 Adapted with permission from Brown, J.I., Koopmans, T., van Strien, J., Martin, N.I., and Frankel, A. Kinetic Analysis of PRMT1 Reveals Multifactorial Processivity and a Sequential Ordered Mechanism, ChemBioChem. 19:1 (2018) 85-99 47 this post-translational modification is thought to be a major regulatory element [230,231]. Protein arginine N-methyltransferase (PRMT) enzymes are responsible for post-translationally methylating the guanidine moiety of specific arginine residues in target protein substrates. Nine members of the human PRMT family are categorized based on the type of methylated product(s) each forms: type I enzymes (PRMT1–4, PRMT6, and PRMT8) catalyze the formation of monomethylarginine (MMA) and ω-NG,NG-asymmetric dimethylarginine (aDMA); type II enzymes (PRMT5 and PRMT9) catalyze the formation of MMA and ω-NG,NG’-symmetric dimethylarginine (sDMA); and the type III enzyme PRMT7 is only able to form MMA (Figure 2.1) [87,92,93,98,130,232–235]. Many groups have demonstrated that active site steric interactions surrounding the substrate arginine residue contribute to enzyme product specificity. For example, changes to one or two residues within the PRMT1 active site have been shown to either alter the distribution of MMA and aDMA products or shift the enzymatic regioselectivity from aDMA to sDMA [118,236,237]. This regioselective shift from one or two amino acid changes was also observed in PRMT7 from Trypanosoma brucei, in which an E181D variant expanded this enzyme’s product formation to include aDMA, and a E181D/Q329A double variant formed sDMA [109,112]. Two native glutamate residues in PRMT7 (E181 and E172) demarcate an invariant “double-E loop” that coordinates the positive charge of arginine within the active site. Glutamic acid-to-glutamine conversions within the double E loop have been shown to compromise enzyme activity in different PRMTs [86,109–111], underscoring the functional importance of this conserved sequence motif. 48 Figure 2.1. Production of methylarginine species. PRMTs use S-adenosyl-L-methionine (SAM) as a cofactor to donate methyl groups (circle) to produce monomethylated arginine (MMA) and the spent cofactor S-adenosyl-L-homocysteine (SAH). Substrates may undergo subsequent rounds of methylation to form ω-NG,NG-asymmetric or ω-NG,NG’-symmetric dimethylarginine (aDMA or sDMA). Another conserved and notable sequence motif within the PRMT active site is referred to as the THW loop. Variations on this sequence motif specific for each PRMT product type were demonstrated in the mutagenesis study of TbPRMT7, as well as identified in a recent structural study of PRMT6 [101,109]. As product specificities of PRMTs appear tunable through mutagenesis and diverge merely due to a small number of active site residue differences, it is likely that all PRMTs share a common catalytic mechanism, regardless of enzyme type. 49 The presence of conserved structural motifs in PRMTs provides further evidence to support a common mechanism. PRMTs contain a seven-β-strand structural motif that is representative of the largest superfamily of SAM-dependent methyltransferases [238]. This Rossmann-like structural core makes several conserved contacts with SAM and, in PRMTs, several pieces of structural evidence suggest that cofactor binding induces a conformational change within the enzyme, whereby an N-terminal α-helix buries it within the interior of the protein and, as a consequence, completes the formation of the nearby peptide-binding groove [101,103–107]. Binding studies between PRMTs and either peptides or small-molecule inhibitors (ligands that bind in the peptide-binding groove) using isothermal titration calorimetry (ITC) have demonstrated cofactor (SAM or SAH) dependence in order to elicit a binding isotherm, which supports a sequential order of substrate binding to enzyme [105,113,117,239–241]. Despite mechanistic implications surrounding these observations, enzyme kinetic studies of different PRMTs have generated conflicting data as to whether SAM binding precedes peptide substrate binding (i.e., sequential ordered mechanism) or not (i.e., random mechanism). Interpretations of kinetic and molecular modeling data for PRMT1 [130,239,242–244], CARM1 [105,153], PRMT5 [108,137], and PRMT6 [101,117,245,246] have varied between sequential and random bi-bi mechanisms, in which the reaction system contains two substrates and two products. One predominant theory is a rapid equilibrium random mechanism with dead-end EAP/EBQ complexes, in which either SAM or substrate binding can occur first and either SAH or product release can occur first. Further, complexes containing enzyme, SAM, and methylated product (EAP) or enzyme, target substrate, and SAH (EBQ) can form and are termed “dead-end” as they are not conducive to methyl transfer (Figure 2.2A). The second predominant theory is a sequential ordered mechanism in which SAM binding precedes target substrate binding methylated product release precedes SAH release (Figure 50 2.2B). Further kinetic and structural research into these enzymes needs to be performed while considering the extent of the published literature to help settle this debate and establish consensus on a shared PRMT mechanism. Figure 2.2. Cleland notations for the predominant kinetic mechanisms for PRMTs. (A) Random equilibrium random mechanism with dead-end EAP/EBQ complexes. Either SAM (A) or target substrate (B) may bind to the enzyme (E) first to form the enzyme, SAM, and target substrate complex (EAB) for methyl transfer, producing the enzyme, methylated target, and SAH complex (EPQ). Similarly, either SAH (Q) or the methylated target (P) may be released from the enzyme first. Dead-end complexes comprising enzyme, SAM, and methylated product (EAP) or enzyme, target substrate, and SAH (EPQ) may be formed and are not conducive to methyl transfer. (B) Sequential ordered mechanism. SAM binding (A) precedes target substrate binding (B) to form the enzyme, SAM, and target substrate complex (EAB) for methyl transfer, producing the enzyme, methylated target, and SAH complex (EPQ). Methylated target (P) is released from the complex prior to SAH (Q). The dimerization arm that extends from the C-terminal β-barrel domain is another shared structural motif within the PRMT enzyme family whose function suggests a common mechanism. For most PRMTs, crystal structures show that the dimerization arm of one subunit makes reciprocal contacts with the other subunit to form an overall toroidal shape in which the two active 51 sites face inward and across from one another [99,102–104,106,108,110,116,117,121,122]. In the absence of a dimerization arm, recombinantly expressed versions of mammalian PRMT1 and its yeast homolog Rmt1p/Hmt1p are monomeric, unable to bind to SAM, and inactive [110,120]. CARM1 phosphorylation at a conserved serine residue near the dimer interface was shown to inhibit dimerization and prevent SAM binding, thereby regulating enzyme function during cell cycle progression [123]. In addition to dimerization being a requirement for SAM binding, our group found that SAM and SAH influenced the dissociation constants for PRMT1 and PRMT6 homodimers [124]. PRMT dimerization and its relationship with cofactor binding, therefore, are undoubtedly important aspects of enzyme activity. One of the aspects of enzyme activity for which consensus has not been established in the literature is in the sequential transfer of methyl groups onto a single substrate arginine residue (i.e., dimethylation). A processive enzyme, in this instance, does not release the substrate prior to the second methylation in aDMA or sDMA formation, whereas a distributive enzyme releases substrate after the first methylation to give MMA as the predominant product. Several studies offer compelling data and rationales in support of processive or distributive mechanisms for PRMT1 [130,140,141,239,243–245,247,248], PRMT2 [130], PRMT3 [248], CARM1 [153], PRMT5 [137,145,249,250], PRMT6 [92,245,246], PRMT7 [116], and PRMT9 [109]. The purpose of the current research was to use steady-state enzyme kinetics to further explore PRMT1’s methylation activity, in terms of catalytic mechanism and degree of processivity, with several biologically relevant peptide substrates. Our lab previously studied the effect of univalent isosteric replacement of the methyl group in MMA with a hydroxy substituent, which lowered the pKa of the arginine guanidino group from 12.5 to 8.7, and found that it was amenable to methylation [251]. Here, we demonstrate that hydroxy-substituted substrates behave 52 similarly to monomethylated substrates and, in the context of the H4 peptide, result in the strongest substrate inhibition. We also demonstrate, in congruence with previous findings, that the degree of enzyme processivity is substrate dependent. Additionally, we show, for the first time, that the degree of processivity was dependent on cofactor and enzyme concentrations. Finally, we demonstrate that PRMT1 acts in a sequential ordered Bi–Bi mechanism, which differs from recently reported findings [153,243]. These results are significant in that they provide for a better understanding of how PRMT1 catalyzes sequential methylation reactions. These insights likely extend to other PRMT family members. 2.3 Materials and Methods 2.3.1 Peptides The various PRMT1 substrate peptides (Table 2.1 and Appendix A.1) were prepared following standard Fmoc solid-phase peptide synthesis (SPPS) protocols. The requisite Nη-hydroxy-L-arginine and Nη-methyl-L-arginine building blocks were prepared as previously described [251]. Peptides were assembled on 2-chlorotrityl resin (0.25 mmol scale). With the exception of the modified L-arginine building blocks, peptide couplings were performed by using protected Fmoc amino acid (4.0 equiv), BOP reagent (4.0 equiv), and DIPEA (8.0 equiv) in 10 mL total volume DMF at ambient temperature for 1 h. Alternatively, incorporation of the modified L-arginine building residues was performed by using modified L-arginine building blocks (2.0 equiv), BOP reagent (2.0 equiv), and DIPEA (4.0 equiv) in 10 mL total volume DMF at ambient temperature overnight. Peptide couplings were verified by using the Kaiser and Bromophenol Blue tests. Upon completion of SPPS, peptides were cleaved from the resin and deprotected by using 53 TFA/TIS/H2O (95:2.5:2.5), followed by Et2O precipitation to yield the crude peptides. Each peptide was purified to homogeneity by RP-HPLC, and its identity was confirmed by MS analysis. The absorbance at 280 nm was used to estimate the concentrations of KRK, RKK, and R1 peptide solutions by using an extinction coefficient of 5500 M-1 cm-1 for KRK and RKK, and 13980 M-1 cm-1 for R1. The absorbance at 205 nm was used to estimate the concentrations of H4 and eIF4A1 peptides by using an extinction coefficient of 31 mg mL-1 cm-1 [252]. The monomethylated and asymmetrically dimethylated RKK peptides were purchased from Canada Peptide. 2.3.2 Expression and purification of PRMT1 A rat PRMT1 H161Y mutant was generated to create human PRMT1 [124]. Construction of the pET28a(+)-His6-PRMT1 and pET28a(+)-mCerulean/mCitrine-His6-PRMT1 plasmids was previously described, and PRMT1 (UniProt Accession ID: Q99873) proteins were expressed and purified according to established protocols [124]. Briefly, the plasmids were transformed into BL21(DE3) plysS (Stratagene) cells, and protein expression was induced at an OD600 of 0.8 with 1.0 mM isopropyl β-D-1-thiogalactopyranoside (IPTG) at 30°C with shaking at 250 rpm for 16 h. Cells were harvested by centrifugation at 10000 x g for 15 min at 4°C , and cell pellets were frozen at -80°C. Thawed pellets were resuspended in lysis buffer (2 mL lysis buffer per gram of cell pellet): 50 mM HEPES·KOH pH 7.6, 1.0 M NH4Cl, 10 mM MgCl2, 0.1% v/v Triton X-100, 0.1% w/v lysozyme, 25 U mL-1 DNAse 1, 7.0 mM β-mercaptoethanol (β-Me), 1.0 mM phenylmethanesulphonyl fluoride (PMSF), and 1.0 mM EDTA-free protease inhibitor cocktail (Roche: 04693132001). Cells were further lysed at 4°C by using a Branson Sonifier 450 with eight 30 s pulses at 50% duty cycle with 30 s pauses. The lysate was clarified through centrifugation at 54 35000 x g for 1 h at 4°C and filtered through 0.45 μm low protein binding Durapore membrane (Millipore). The lysate was applied to a pre-equilibrated HisTrap FF column (GE Healthcare) with wash buffer [50 mM HEPES, pH7.6, 1.0 M NH4Cl, 10 mM MgCl2, 7.0 mM β-Me, 10 mM imidazole, 1.0 mM PMSF]. The bound fraction was eluted by using the same buffer with 400 mM imidazole added. Eluted fractions containing PRMT1 were pooled and applied to a pre-equilibrated HiLoad 26/600 Superdex 200 pg size-exclusion column (GE Healthcare). His6-PRMT1 was applied by using a Tris running buffer [50 mM Tris·HCl pH7.5, 100 mM NaCl]. Eluted fractions were pooled and concentrated by using Amicon Ultra 15 mL filters with 30-kDa molecular weight cut-off (MWCO; Millipore). Fractions appeared to be greater than 90% pure (Appendix A.2). His6-PRMT1 was further exchanged into storage buffer [100 mM HEPES·KOH, pH8, 200 mM NaCl, 1.0 mM DTT, 10% v/v glycerol, and 2.0 mM EDTA]. The concentration of His6-PRMT1 was estimated according to the Edelhoch method, in which absorbance at 280 nm of the denatured protein was measured in 6.6 M guanidine·HCl in 40 mM potassium phosphate buffer, pH 6.5 [253]. Concentrations of mCit- and mCer-PRMT1 were quantified by measuring the absorbance of the protein at 516 and 434 nm, respectively, and with extinction coefficients of 77000 and 43000 M-1 cm-1, respectively [254]. 2.3.3 PRMT1 Michaelis-Menten enzyme kinetics 2.3.3.1 P81 filter binding assay Reactions containing 200 nM PRMT1, 50 μM S-adenosyl-L-[methyl-14C]-methionine ([14C]SAM; 58mCi mmol-1 in H2SO4/ethanol (9:1, 10 mM); PerkinElmer NEC363050UC), and increasing concentrations of unmethylated, monomethylated, or hydroxylated substrate peptide (0.5–100 μM) were incubated for 1 h at 37°C in 27 μL total volume methylation buffer [50 mM 55 HEPES, pH 8.0, 10 mM NaCl, 1.0 mM DTT]. The linear range was determined by incubating 200 nM PRMT1 and 50 μM [14C]-SAM with 100 μM of each unmodified peptide for 20-180 min (Appendix A.3). A P81 filter binding assay was used to measure PRMT1 activity in the presence of substrate, in which the positively charged peptides were immobilized onto Whatman P81 phosphocellulose filter paper (Fisher Scientific 3698-915) [255]. Each reaction was spotted onto the filter paper in duplicate (12 μL each) and dried at ambient temperature (25°C). Dry filter papers were washed by vortexing and gentle shaking five times in 5 mL of sodium carbonate-bicarbonate buffer (pH 9.0) for 5 min each time in culture tubes (Sarstedt 62.515.006). Filter papers were dried overnight at 37°C. Dry filter papers were transferred to 6 mL scintillation vials (PerkinElmer PA6000292) with 2.5 mL Scintiverse E scintillation cocktail (Fisher Scientific SX16-4). Disintegrations per minute (DPM) of the samples were quantified for 1 min by using a Tri-Carb 3110TR (PerkinElmer) liquid scintillation counter. Linear ranges were determined by plotting methyl transfer versus time. The observed reaction rates were fitted to the Michaelis–Menten equation (Equation 2.1) or Michaelis–Menten substrate inhibition equation (Equation 2.2) by using SigmaPlot 12 (Systat), from which the apparent KM, kcat, Vmax, and standard error values were estimated. Equation 2.1 v = (Vmax[S])/(KM+[S]) Equation 2.2 v = (Vmax[S])/(KM+[S](1+[S]/Ki)) 2.3.3.2 Radioactive tricine gel assay To estimate apparent PRMT1 enzyme kinetics with the R1 peptide, a radioactive gel assay was used. Reactions containing 200 nM PRMT1, 50 μM [14C]SAM, and 0-100 μM R1 peptide in 100 μL total methylation buffer were incubated for 1 h at 37°C. Reactions were concentrated by using a Savant ISS110 SpeedVac Concentrator (Thermo Scientific) on high temperature and speed 56 for 1 h. Reactions were reconstituted in 21 μL of tricine sample buffer (50 mM Tris·HCl, pH6.8, 13% v/v glycerol, 0.2% w/v SDS, 0.02% w/v Coomassie Brilliant Blue G-250, and 0.4% v/v β-me]. After loading 15 μL of each reaction in duplicate on a 16.5% tricine gel, sample proteins and peptides were electrophoretically separated. After separation, peptides were fixed in gels by using 5% v/v glutaraldehyde in 0.5 M boric acid, pH 6.2. The gels were stained with 0.025% w/v Coomassie Brilliant Blue. Each lane was excised and dissolved in 4 mL of 30% v/v hydrogen peroxide at 70°C for 4h in 20 mL scintillation vials (PerkinElmer 6000477) [256]. Afterwards, 10 mL of Scintiverse E scintillation cocktail was added, and the mixture was stirred until a gel formed. The DPM of each sample was measured, and apparent enzyme kinetic parameters were estimated as described above. 2.3.4 Peptide methylation analyses by mass spectrometry Methylation reactions containing PRMT1, SAM (in 0.5 mM HCl), and unmodified peptide in methylation buffer were incubated for 1 h at 37°C. To stop the reaction and remove confounding automethylation, PRMT1 was separated from reactions by filtration through a 30-kDa MWCO filtration device (VWR 82031-352) that was prewashed with 1 mL deionized water. Reactions were then transferred to 6x50 mm Pyrex glass tubes (Corning 9820-6) and dried in a Savant ISS110 SpeedVac Concentrator on high temperature and speed for 1.5 h. Dried reaction tubes were transferred to a reaction vial assembly (Eldex 1163), with 200 μL of 6N HCl in the bottom of the reaction vial. Samples were acid hydrolyzed in vacuo for 20– 24 h at 110°C by using a Waters Pico•Tag work station. Each reaction was resuspended in 100 μL mobile phase A [0.1% v/v formic acid and 0.05% v/v trifluoroacetic acid (TFA) in water], and all insoluble debris was removed by centrifugation to enable UHPLC- MS/MS analysis of MMA and aDMA production. 57 The UHPLC-MS/MS procedure closely followed a previously described method [130]. A Water Acquity UHPLC bridged ethylene hybrid C18 column (2.1 mmx100 mm) was used at a flow rate of 0.15 mL min-1 at 45°C. Mobile phase A (described above) and mobile phase B [0.1% v/v formic acid, 0.05% v/v TFA, and 30% v/v methanol in water] were used in a linear gradient of 0–100% phase B over 2.90 min. A Linear Ion Trap Quadrupole QTRAP 5500 mass spectrometer (Sciex 1024945-AM) was operated in positive-ion mode with electrospray ionization in multiple reaction monitoring mode to detect parent and product ions of MMA and aDMA. For MMA, a cone voltage of 30 V and 17 eV collision energy was used to detect the parent ion (189 m/z) and product ion (74 m/z). For aDMA, a 30 V cone voltage and 20 eV collision energy were used to detect the parent ion (203 m/z) and product ion (46 m/z). A sample injection volume of 5.0 μL was used. MMA and aDMA standards were initially reconstituted in deionized water and were diluted in mobile phase A to prepare linear standard curves (0.02– 5.0 μM). Concentrations of MMA and aDMA present in each reaction were interpolated from the standard curves, and the aDMA/MMA ratios were calculated. To observe methylation ratios of unmodified peptides under conditions used in the Michaelis–Menten enzyme kinetic assays, 200 nM PRMT1, 50 μM SAM, and 100 μM of each peptide in a 60 Ul total reaction volume were incubated at 37°C for 1 h. Reactions were subsequently subjected to PRMT1 removal, acid hydrolysis, and UHPLC-MS/MS analysis. To observe methylation ratios of unmodified peptides with variable substrate concentrations, all reactions contained 5.0 μM PRMT1 and one of the following conditions: limiting SAM (1.0 μM) and excess substrate, corresponding to a concentration at least ten times higher than the KM value of each peptide (25 μM for H4, 300 μM for eIF4A1, 35 μM for KRK, and 130 μM for RKK); intermediate SAM (10 μM) and excess substrate; or limiting peptide substrate (10 μM), and excess 58 SAM (100 μM) in triplicate in a total reaction volume of 60 μL. To observe methylation ratios of unmodified peptides with increasing enzyme concentrations, reactions contained constant saturating concentrations of SAM (100 μM), substrate concentrations at least ten times higher than the KM value as before, and varying PRMT1 concentrations (0.2, 1.0, or 5.0 μM) in at least duplicate in total reaction volume of 60 μL. 2.3.5 FRET assay Reactions containing 100 nM mCerulean-PRMT1 (mCer-PRMT1) and 100 nM mCitrine-PRMT1 (mCit-PRMT1) fluorescent proteins in methylation buffer were incubated with varying concentrations of KRK peptide, SAM, or SAH (0, 2.5, 5.0, 10, 20, 40, 50 μM) at 37°C for 1 h in 80 μL initial reaction volume. The buffer-only control showed that small changes to the volume from the addition of stock solutions had a negligible impact on changes to FRET signal. Reactions were also prepared containing varying KRK peptide concentrations in the presence of 50 μM SAM and varying SAM concentrations in the presence of 50 μM KRK peptide. PRMT1 dimerization was assessed by exciting reactions in individual wells in a 384-well black polystyrene non-binding surface microplate (Corning #3575) at 434 nm and measuring the fluorescence at 475 and 529 nm by using a Synergy MX microplate reader (BioTek) with excitation and emission slit widths of 9 nm and sensitivity adjusted with an 8 mm height correction from the upper plane of the sample wells. The ratios of fluorescence at 529 nm and 475 nm were calculated to measure FRET for each equilibrated reaction mixture. 59 2.3.6 PRMT1 product and substrate inhibition A radioactive P81 filter binding assay (described above) was used to determine the kinetic mechanism of PRMT1 by using SAM or RKK substrate with SAH or RKK-aDMA inhibitor and 200 nM PRMT1 in methylation buffer. Assays that contained constant RKK peptide (25 or 130 μM) were performed with various SAM (0.5, 1.0, 5.0, 10, 25, 50 μM) and various SAH (0, 0.5, 2.0, 5.0, 10, 25 μM) or various RKK-aDMA (300, 450, 600, 800, 1000 μM) concentrations. Assays that contained constant SAM (5.0 or 50 μM) were performed with various RKK peptide (5.0, 10, 15, 25, 50, 100 μM) and variable SAH (0, 0.5, 2.0, 5.0, 10, 25 μM) or various RKK-aDMA (300, 450, 600, 800, 1000 μM) concentrations. Data were interpreted through both quantitative nonlinear and qualitative linear analyses. For quantitative analysis, each data set was fit to equations corresponding to competitive (Equation 2.3), uncompetitive (Equation 2.4), noncompetitive (Equation 2.5), and mixed (Equation 2.6) inhibition by using SigmaPlot 12 (Systat Software, Inc.), Equation 2.3 v = (Vmax[S])/(KM (1+[I]/Ki )+[S]) Equation 2.4 v = (Vmax[S])/(KM+[S](1+[I]/Ki)) Equation 2.5 v = (Vmax[S])/(KM(1+[I]/Ki)+[S](1+[I]/Ki)) Equation 2.6 v = (Vmax[S])/(KM(1+[I]/Kia)+[S](1+[I]/Kib)) in which Vmax and KM were held constant. Best fitting of the data was determined by comparing standard errors of the fits. For qualitative analysis, Eadie–Hofstee (Equation 2.7), Lineweaver–Burk (Equation 2.8), and Hanes–Woolf (Equation 2.9) plots were generated directly from product inhibition data sets by using linear regression (i.e., transformations of nonlinear models were not employed). These linear plots were used to visualize data and supplement nonlinear analyses. 60 Equation 2.7 v = Vmax/((1+[I]/Kib))-(((1+[I]/Kia)KM)/((1+[I]/Kib)))(v/([S])) Equation 2.8 1/v = (((1+[I]/Kia)KM)/Vmax)(1/([S]))+((1+[I]/Kib))/Vmax Equation 2.9 ([S])/v = (((1+[I]/Kib))/Vmax)[S]+((1+[I]/Kia)KM)/Vmax A radioactive P81 filter binding assay was similarly used to observe substrate inhibition of 200 nM PRMT1 by using various concentrations of H4-OH substrate peptide (2, 5, 10, 25, 50, and 100 μM) with various concentrations of SAM (1, 2, 5, 10, and 50 μM). A Lineweaver–Burk (Equation 2.8) plot was directly generated from the data and used to observe apparent enzyme kinetic parameters under such conditions. 2.4 Results 2.4.1 PRMT1 Michaelis-Menten kinetics We first sought to investigate single and double methyl transfers by PRMT1 on well-characterized peptide substrates due to a lack of consensus regarding the enzyme kinetic mechanism of PRMT1. Unmethylated, monomethylated, and hydroxylated versions of peptides representing the following PRMT1 substrates were studied: histone H4 (H4), eukaryotic initiation factor (eIF4A1), and two fibrillarin-based peptides (KRK and RKK) [133,134,257–259]. An acetylated version of the fibrillarin-based (R1) peptide from our previous study on arginine isosteres [251] was used as a reference substrate for comparison with other peptide sequences (Table 2.1). The hydroxylated peptides were used to investigate the differences in PRMT1 kinetics between monomethylated and hydroxylated isosteric substrates. 61 Table 2.1. Peptide substrates synthesized to contain either an unmodified arginine residue or an Nη-hydroxyl-,Nη-monomethyl-, or Nη,Nη-dimethyl-substituted arginine residue (shown in bold). Peptide Sequence H4 Ac-SGRGKGGKGLGKGGAKR H4-CH3 Ac-SGRmeGKGGKGLGKGGAKR H4-OH Ac-SGROHGKGGKGLGKGGAKR eIF4A1 Ac-YIHRIGRGGR eIF4A1-CH3 Ac-YIHRIGRmeGGR eIF4A1-OH Ac-YIHRIGROHGGR KRK Ac-KGGFGGRGGFGGKW KRK-CH3 Ac-KGGFGGRmeGGFGGKW KRK-OH Ac-KGGFGGROHGGFGGKW RKK Ac-GGRGGFGGKGGFGGKW RKK-CH3 Ac-GGRmeGGFGGKGGFGGKW RKK-OH Ac-GGROHGGFGGKGGFGGKW RKK-(CH3)2 Ac-GGRme2aGGFGGKGGFGGKW R1 Ac-WGGYSRGGYGGW The rate of methyl transfer was assessed by quantifying the incorporation of radiolabeled methyl groups into peptides in a p81 filter binding assay, except in the case of the R1 peptide, where we measured radioactivity in gel slices. In all cases, 200 nM of purified PRMT1 (Appendix A.2) and 50 mM SAM were used, which is a saturating condition (the KM value for SAM is 1.0 μM) [130]. Reactions were performed for one hour, within the linear range of the enzyme (Appendix A.3). The rate values were fitted to Michaelis–Menten Equation (Equation 2.1) or (Equation 2.2), from which the apparent Vmax (pmol min-1, normalized to nmol enzyme), KM, and kcat values were estimated. We found that for almost all the peptide substrates, PRMT1 displays the highest activity towards the unmodified peptides. Specifically, unmodified eIF4A1, KRK, and RKK peptides exhibit the highest Vmax values, whereas the monomethylated and hydroxylated isosteres reveal comparably lower Vmax values (Figure 2.3 and Appendix A.4). However, 62 unmodified and monomethylated H4 peptides exhibited similarly high apparent Vmax values, whereas the value for the hydroxylated peptide was unexpectedly and appreciably lower. Correspondingly, both the unmodified and modified H4 substrates had comparable KM and kcat values (Table 2.2). Furthermore, although PRMT1 appears to display approximately two-fold higher catalytic efficiency (kcat/KM) towards hydroxylated H4 and eIF4A1 peptide substrates compared to their monomethylated counterparts, PRMT1 catalysis is less efficient for the hydroxylated KRK and RKK substrates (Table 2.2). A consistently observed trend, however, is that PRMT1 requires less hydroxylated substrate to achieve half-maximal velocity compared to its unmodified and monomethylated counterparts, as observed by the lower apparent KM values for each hydroxylated peptide. 63 Figure 2.3. Michaelis-Menten enzyme kinetics of PRMT1 methylation of peptide substrates. Reactions containing 200 nM PRMT1, 50 μM SAM, and varying concentrations of unmodified, monomethylated, or hydroxylated (A) H4, (B) eIF4A1, (C) KRK or (D) RKK peptides were monitored for methyl transfer activity over 1 h at 37oC. Data points were fit to the Michaelis-Menten equation. Error bars represent standard error (n = 2). 64 Table 2.2. Apparent enzyme kinetic parameters of PRMT1 with unmethylated, monomethylated, and hydroxylated H4, eIF4A1, KRK, and RKK peptides, as well as unmodified R1 peptide. The standard error of two replicates is shown. Peptide KM peptide (μM) kcat (s-1) (x10-4) kcat/KM (M-1 s-1) Ki peptide (μM) H4 2.30 ± 0.36 7.60 ± 0.27 330 ± 53 – H4-CH3 3.94 ± 0.58 7.92 ± 0.28 202 ± 30 – H4-OH 2.03 ± 0.49 7.20 ± 0.73 355 ± 93 47.6 ± 12 eIF4A1 25.9 ± 2.6 3.30 ± 1.3 12.7 ± 5.3 – eIF4A1-CH3 22.0 ± 3.9 1.53 ± 0.10 6.97 ± 1.3 – eIF4A1-OH 6.17 ± 1.1 0.92 ± 0.05 14.9 ± 2.8 [a] KRK 2.64 ± 0.40 3.80 ± 0.23 144 ± 67 250 ± 1.2 x 102 KRK-CH3 4.48 ± 1.8 1.75 ± 0.33 39 ± 17 72.5 ± 34 KRK-OH 3.35 ± 1.1 0.97 ± 0.08 28.8 ± 10 [a] RKK 11.3 ± 1.3 4.63 ± 1.5 41.0 ± 14 – RKK-CH3 2.70 ± 1.1 2.67 ± 0.48 98.8 ± 43 56.5 ± 25 RKK-OH 2.25 ± 1.6 1.85 ± 0.45 82.2 ± 60 309 ± 4.7 x 102 R1 141 ± 30 4.57 ± 0.65 3.23 ± 0.82 – H4 2.30 ± 0.36 7.60 ± 0.27 330 ± 53 – [a]Data did not fit to inhibition model. It was further observed that some datasets fit better to a modified Michaelis–Menten inhibition model (Equation 2.2). As seen in Figure 2.3 and Table 2.2, H4-OH, eIF4A1-OH, KRK, KRK-CH3, KRK-OH, RKK-CH3, and RKK-OH display an inhibitory effect on PRMT1 catalysis at higher substrate concentrations. The hydroxylated KRK and eIF4A1 peptides exhibit some substrate inhibition but did not adequately fit to the inhibition model from which Ki values were derived. Generally, Ki values for peptides are high, but variable depending on the peptide sequence. It is unclear why some peptides lead to an inhibitory effect, but we have previously observed that the substrate inhibition effect can be mitigated by increasing enzyme concentration [260]. Therefore, we performed steady state kinetics experiments on the H4-OH peptide at different PRMT1 concentrations and found that doubling or quadrupling the enzyme concentration to 400 65 or 800 nM eradicates substrate inhibition (Appendix A.5). Therefore, these data together show that PRMT1 displays differential apparent kinetic parameters based on substrate identity. 2.4.2 PRMT1 degree of processivity To test if different peptide substrates result in different levels of methylated product, we aimed to discern relative amounts of MMA and aDMA produced on each unmodified substrate under various experimental conditions. We first investigated the ratio of MMA and aDMA produced on saturating concentrations of unmodified peptide substrates under conditions used for steady-state kinetics experiments described above (methylation reactions for 1 hour with 200 nM PRMT1, 50 μM SAM, 100 μM peptide). We expected to see different aDMA/MMA ratios if PRMT1 activity was dependent on substrate sequence, which has previously been shown for these peptides [141]. We found that for eIF4A1, KRK, and RKK peptide substrates, approximately 1.4-fold more MMA was produced than aDMA after 1 hour, whereas 4.4-fold more MMA was produced than aDMA on the H4 peptide substrate (data not shown). These different observed ratios brought into question the effect of substrate sequence on enzyme processivity. Given the experimental conditions and reaction time within the linear range, a processive PRMT1 would predominantly produce dimethylarginine, whereas a distributive PRMT1 would mostly produce monomethylarginine, with the dimethylarginine species accumulating only after the concentration of monomethylated substrate rose to the level of the unmodified substrate concentration. An enzyme that produces an appreciable amount of both the monomethylated and dimethylated species might be described as acting semi-processively [243]. Using these definitions, we find PRMT1 acting distributively towards the H4 peptide and semi-processively towards the eIF4A1, KRK, and RKK peptides under the described experimental conditions. 66 We designed additional experiments to measure the amount of enzyme processivity under limiting or excess concentrations of SAM and unmodified peptides. Methylation reactions were performed in which SAM was limiting (1.0 μM) and peptides were in excess (10•KM), SAM was two-fold (10 μM) in excess of enzyme concentration and peptides were in excess (10•KM), and finally, SAM was in excess (100 μM) and peptides were limiting (10 μM). The degree of processivity was assessed by measuring aDMA/MMA ratios. Enzyme was removed from each methylation reaction and the remaining methylated targets were acid hydrolyzed to individual amino acid components and ratios were quantified by UHPLC-MS/MS. The limit of detection for aDMA and MMA from the 60-mL reaction volume with this method is 1.2 pmol and 0.6 pmol, respectively. To ensure that we observed enough product to accurately quantify, we used 5.0 μM PRMT1 in these reactions. For each peptide, MMA and aDMA were produced with various aDMA/MMA ratios by PRMT1 under different conditions tested (Figure 2.4 and Table 2.3). The H4 peptide yielded aDMA/ MMA ratios of 5.0:1 and 7.2:1 for SAM-intermediate and SAM- excess conditions, respectively, which is an appreciably higher ratio than what we see for the other three substrates. These conditions suggest that PRMT1 acts more processively towards the H4 substrate peptide than it does towards other tested substrates. Under SAM-limiting conditions, the level of PRMT1 processivity was strikingly different from SAM-intermediate and SAM-excess conditions. In this case, both the H4 and KRK peptide substrates yielded approximately two-fold more mono- methylated than dimethylated product, whereas eIF4A1 and RKK peptide substrates bear approximately equal levels of both methylarginine species. PRMT1 appears to behave less processively towards H4 and KRK under SAM limiting conditions. Together, these results 67 demonstrate that relative SAM concentration directly affects PRMT1 processivity, whereas the peptide concentration does not. Figure 2.4. PRMT1 degree of processivity for substrate peptides. Reactions containing 5.0 μM PRMT1 and the indicated amounts of SAM and unsubstituted peptides were incubated at 37˚C for 1 h. PRMT1 was removed from the sample to eliminate automethylation and remaining methylated targets were acid hydrolyzed. Concentrations of aDMA (dark grey) and MMA (light grey) were determined using UHPLC-MS/MS. Error bars represent standard deviation (n = 3). Table 2.3. PRMT1 degree of processivity for substrate peptides. Ratios are reported as aDMA/MMA and were determined for reactions as described in Figure 2.4. Values are listed as nanomolar concentrations with the standard deviation of three replicates. Limiting SAM, excess peptide Intermediate SAM, excess peptide Excess SAM, limiting peptide Peptide aDMA MMA Ratio aDMA MMA Ratio aDMA MMA Ratio H4 33.6 ± 5.5 58.8 ± 3.7 1/1.8 543 ± 22 108 ± 5.5 5.0/1 657 ± 15 91.0 ± 7.0 7.2/1 eIF4A1 57.3 57.8 1/1.0 294 ± 15 115 ± 9.6 2.6/1 316 ± 21 118 ± 19 2.7/1 KRK 35.5 ± 4.5 71.1 ± 9.4 1/2.0 290 ± 54 120 ± 14 2.4/1 277 ± 38 101 ± 9.5 2.7/1 RKK 66.5 ± 5.2 85.2 ± 1.3 1/1.3 374 ± 7.6 127 ± 15 2.9/1 271 ± 21 98.1 ± 16 2.8/1 68 We turned our attention to the effect of enzyme concentration on processivity by testing different PRMT1 concentrations with constant saturating levels of SAM and peptide. Our results show that the aDMA/MMA ratio increases for each peptide with a concomitant increase in PRMT1 concentration (Figure 2.5 and Table 2.4), demonstrating that PRMT1 processivity is directly linked to enzyme concentration. Additionally, these results show that the increase in aDMA disproportionally contributes to the overall increase in methylated product as compared to MMA at the highest enzyme concentration tested. This result supports the notion that PRMT1 processivity is a function of enzyme concentration. Figure 2.5. PRMT1 degree of processivity, varying enzyme concentration. Reactions containing 0.2, 1.0, or 5.0 μM PRMT1, 100 μM SAM, and at least 10•KM peptide substrate (25 μ H4, 300 μM eIF4A1, 35 μM KRK, and 130 μM RKK) were incubated for 1 h at 37oC. Methylated peptide targets were acid hydrolyzed and analyzed via UHPLC-MS/MS to quantify aDMA (dark grey) or MMA (light grey). Error bars represent standard deviation (n = 3). 69 Table 2.4. PRMT1 degree of processivity with varying enzyme concentrations. Ratios are reported as aDMA/MMA and were determined for reactions as described in Figure 2.5. Values are listed as nanomolar concentrations with the standard deviation of three replicates. 0.2 μM PRMT1 1.0 μM PRMT1 5.0 μM PRMT1 Peptide aDMA MMA Ratio aDMA MMA Ratio aDMA MMA Ratio H4 11.1 ± 1.5 25.4 ± 5.2 1/2.3 215 ± 35 150 ± 4.5 1.4/1 529 ± 38 162 ± 34 3.3/1 eIF4A1 10.2 ± 0.31 21.6 ± 4.0 1/2.12 63.4 ± 19 128 ± 5.7 1/2.0 332 ± 8.5 215 ± 21 1.6/1 KRK 8.65 ± 3.4 19.9 ± 8.0 1/2.3 50.4 ± 3.5 21.4 ± 8.9 1/1.4 431 ± 69 121 ± 7.0 3.6/1 RKK 7.05 ± 2.0 23.8 ± 3.3 1/3.4 98.9 ± 11 132 ± 10 1/1.3 535 ± 71 166 ± 5.9 3.2/1 Our observations that both cofactor and enzyme concentration affect PRMT1 processivity led us to question what influences the degree to which PRMT1 acts processively. From a mechanistic point of view, we know that higher concentrations of PRMT1 revealed a correspondingly increased level of dimerization and higher order oligomers [124,242]. Therefore, we hypothesized that factors that enhance enzyme dimerization might in turn increase processivity. We previously showed through FRET measurements that the addition of excess SAM lowered the KD value of dimerization between mCer-PRMT1 and mCit-PRMT1 [124]. Here, we used this FRET pair to determine how dimerization was affected by different concentrations of cofactor and peptide alone or in combination in an effort to better understand PRMT1 behavior and establish a model for its activity (Figure 2.6. ). To our surprise, we found that PRMT1 dimerization increased in a concentration-dependent manner in response to SAM or KRK peptide and increased in response to SAH, but the impact was not concentration-dependent. At the highest concentrations, KRK appeared to cause the greatest increase in FRET signal as compared to SAM or SAH cofactor. Interestingly, the addition of both KRK peptide and SAM reveal an additive effect on FRET signal. 70 Figure 2.6. Cofactor and peptide contribution to PRMT1 dimerization. The FRET signal for PRMT1 dimerization, as measured using both mCer- and mCit-PRMT1 (100 nM each), increases in the presence of KRK (grey diamond), SAM (grey square), or SAH (open triangle) alone, as well as with increasing KRK pre-incubated with 50 μM SAM (plus sign), or increasing SAM pre-incubated with 50 μM KRK (x symbol). Buffer control (closed circle). Error bars represent standard deviation (n = 3). 2.4.3 PRMT1 mechanistic investigation using product inhibition studies A series of product inhibition experiments were designed to observe the effect of SAH and RKK-aDMA inhibitors on PRMT1 activity in the presence of varying SAM or RKK substrates by using the radiometric p81 filter binding assay. Transferred methyl groups were used to calculate the enzyme rate of reaction. Each dataset was fit to nonlinear models for competitive (Equation 2.3), uncompetitive (Equation 2.4), noncompetitive (Equation 2.5), and mixed (Equation 2.6) inhibition. Data sets were assessed as best fitting to models based on the best coefficient of determination and the lowest error. Data were also qualitatively presented in Eadie–Hofstee plots 71 (rate versus rate/substrate), double-reciprocal Lineweaver–Burk plots (reciprocal rate versus reciprocal substrate), and Hanes–Woolf plots (substrate/rate versus substrate) for each product inhibitor concentration. These plots aided in determining which model best fit the data in some instances in which nonlinear regression analyses provided similar accuracy of fit and errors for multiple inhibition equations. The combination of these three plots gave a clear indication of the observed trend, rather than relying solely on the commonly used Lineweaver–Burk plot, which overemphasizes the data values collected at various low substrate concentrations, thereby heavily weighting regression of the line and enlarging the error [261]. Two leading kinetic mechanisms - rapid equilibrium random mechanism with dead-end EAP/EBQ complexes (ternary complexes comprising enzyme bound to one substrate and one product) and sequential ordered mechanism - were considered (Table 2.5). These kinetic mechanisms both have well-established and characteristic product inhibition patterns [262]. Our product inhibition data were analyzed to determine which mechanism was best supported by our results. Table 2.5. Product inhibition patterns. Inhibition patterns for rapid equilibrium random with dead end EAP/EBQ complexes and sequential ordered Bi-Bi mechanisms under various SAM, peptide, SAH, and dimethylated peptide product concentrations are listed. In Cleland notation, A refers to SAM, B is the unmethylated peptide, P is the methylated peptide, Q is SAH, and E is PRMT1. Rapid equilibrium random (with dead end EAP/EBQ) Varied SAM Varied peptide Product inhibitor Low peptide High peptide Low SAM High SAM peptide product Mixed No inhibition Competitive Competitive SAH Competitive Competitive Mixed No inhibition Sequential Ordered Varied SAM Varied peptide Product inhibitor Low peptide High peptide Low SAM High SAM peptide product Mixed Uncompetitive Mixed Mixed SAH Competitive Competitive Mixed No inhibition 72 First, with increasing concentrations of SAH and varying SAM concentrations at 25 and 130 μM concentrations of RKK peptide, data sets best fit to a competitive model, suggesting that SAH displays competitive inhibition towards PRMT1 under these conditions (Appendix A.6). Consistent with this analysis, all three linear plots also reveal a competitive inhibition pattern (Figure 2.7A and Figure 2.7B), in which apparent Vmax does not change, but apparent KM increases with increasing inhibitor concentration. As SAM and SAH bind to the same binding site on PRMT1, product inhibition with SAH is expected to demonstrate classic competitive inhibition when varying SAM. Second, with increasing concentrations of SAH and varying RKK peptide concentrations at 50 μM SAM, the data sets fit equally well to noncompetitive and mixed models (Appendix A.6), which is also seen on each linear plot (Figure 2.7C). In this case, the apparent Vmax decreases and apparent KM remains constant with increasing inhibitor concentration. An inhibition experiment with a high SAM concentration and varying RKK peptide was not conducted because it would not help distinguish between sequential ordered and random mechanisms. Overall, the results from SAH product inhibitor data sets were consistent with both a sequential ordered Bi–Bi mechanism and a rapid equilibrium random Bi–Bi mechanism with dead-end EAP/EBQ complexes. Further experiments with the RKK-aDMA product inhibitor were pursued to distinguish between these two mechanisms. 73 Figure 2.7. Product inhibitor results of PRMT1 with SAH inhibitor. Datasets are represented as Eadie-Hofstee (left; rate versus rate/variable substrate concentration), Lineweaver-Burk (middle; reciprocal rate versus reciprocal substrate concentration), and Hanes-Woolf (right; substrate/rate versus substrate concentration) plots. Linear regression analysis was applied to construct lines of best fit for the data. Methylation reactions containing 200 nM PRMT1 were performed with different substrate and product inhibitor concentrations. Variable concentrations of SAH used are 0 (black circle), 0.5 (grey square), 2.5 (plus sign), 5.0 (grey diamond), 10 (x symbol), and 25 (open triangle) M. (A) Constant RKK peptide (25 M) was incubated with variable SAH (as indicated) and variable SAM (0.5, 1.0, 5.0, 10, or 50 M). (B) Constant RKK peptide (130 M) was incubated with variable SAH (as indicated) and variable SAM (0.5, 1.0, 5.0, 10, or 50 M). (C) Constant SAM (50 M) was incubated with variable SAH (as indicated) and variable RKK peptide (5.0, 10, 25, 50, or 100 M). Some rates are reported in arbitrary units from disintegrations per minute (DPM). Error bars represent standard deviation (n = 2). 74 Data sets corresponding to increasing RKK-aDMA inhibitor with varying SAM concentrations and 25 μM RKK peptide fit equally well to uncompetitive, noncompetitive, and mixed inhibition models (Appendix A.6). As a consequence of assaying PRMT1 at this peptide concentration close to its KM value, the resulting low activity of the enzyme contributed to error in these measurements. To aid in analysis, we looked to the linear plots, which demonstrate a noncompetitive/mixed inhibition pattern (Figure 2.8A), consistent with both the random and sequential mechanisms. Together, these results best support that RKK-aDMA acts as a noncompetitive/mixed inhibitor towards PRMT1 under these conditions. Next, with increasing concentrations of RKK-aDMA and varying SAM concentrations at 130 μM RKK peptide substrate (≈10•KM), the data sets fit best to the noncompetitive and mixed inhibition models. However, the data sets also fit appreciably well, with relatively low error, to the uncompetitive model (Appendix A.6). Each plot is also consistent with this result in that it is difficult to confidently discern between a noncompetitive, mixed, or uncompetitive inhibition pattern when both apparent Vmax and KM values decreased with increasing inhibitor concentration (Figure 2.8B). Taken together, this evidence best suggests that RKK-aDMA displays mixed/noncompetitive/ uncompetitive inhibition under these conditions. Although it is difficult to assign a single inhibition model, it is important to remark that, under a sequential ordered mechanism, we would expect to see uncompetitive inhibition under these conditions, but under a rapid equilibrium random mechanism with dead-end EAP/EBQ complexes, we would expect to see no inhibition [262]. Finally, with increasing concentrations of RKK-aDMA and various RKK concentrations at both 5.0 and 50 μM concentrations of SAM, these data sets reveal similarly low errors and 75 coefficients of determination for noncompetitive and mixed inhibition models (Appendix A.6), which is also consistently seen on the linear plots (Figure 2.8C and Figure 2.8D). These results, therefore, suggest that RKK-aDMA displayed mixed/noncompetitive inhibition under these conditions. It is important to also discern that under a sequential ordered mechanism, we would expect to see mixed inhibition under these conditions, but under a rapid equilibrium random mechanism with dead-end EAP/EBQ complexes, we would expect to see competitive inhibition [262]. These data sets have much lower coefficients of determination when fit to the competitive model, accompanied by appreciably higher estimates of error. We also observed high Ki values in the range of 400 to 1000 μM for the RKK-aDMA inhibitor (Appendix A.6), which suggests that RKK-aDMA is a weak PRMT1 inhibitor. Accordingly, large inhibitor concentrations were used. 76 Figure 2.8. Product inhibitor results of PRMT1 with RKK-aDMA inhibitor. Datasets are represented as Eadie-Hofstee (left; rate versus rate/variable substrate concentration), Lineweaver-Burk (middle; reciprocal rate versus reciprocal substrate concentration), and Hanes-Woolf (right; substrate/rate versus substrate concentration) plots. Linear regression analysis was applied to construct lines of best fit for the data. Methylation reactions containing 200 nM PRMT1 were performed with different substrate and product inhibitor concentrations. Variable concentrations of RKK-aDMA used were 0 (black circle), 100 (grey square), 77 300 (grey diamond), 450 (plus sign), 600 (open triangle), 800 (x symbol), and 1000 (black diamond) M. (A) Constant RKK peptide (25 M) was incubated with variable RKK-aDMA (as indicated) and variable SAM (0.5, 1.0, 5.0, 10, or 50 M). (B) Constant RKK peptide (130 M) was incubated with variable RKK-aDMA (as indicated) and variable SAM (1.0, 5.0, 10, 25, or 50 M). (C) Constant SAM (5.0 M) was incubated with variable RKK-aDMA (as indicated) and variable RKK peptide (10, 15, 25, 50, or 100 M). (D) Constant SAM (50 M) was incubated with variable RKK-aDMA (as indicated) and variable RKK peptide (5.0, 10, 25, 50, or 100 M). Some rates are reported in arbitrary units from DPM. Error bars represent standard deviation (n = 2). Since we saw that cofactor and peptide could independently bind to PRMT1 when measuring dimerization by FRET (Figure 2.6. ) and since we observed that some substrates led to enzyme inhibition at high concentrations (Figure 2.3), we questioned if this was due to the formation of dead-end EBQ, BEB, or BE complexes. The EBQ dead-end complex would be formed when the peptide substrate bound to the catalytically inactive PRMT1:SAH complex. The latter complexes would be theoretically formed when one or two peptide substrates bound to PRMT1 (BE and BEB, respectively) and prevented cofactor binding. To distinguish between these dead-end complexes, we conducted a series of experiments with various concentrations of H4-OH peptide (2, 5, 10, 25, 50, and 100 μM) and various concentrations of SAM (1, 2, 5, 10, and 50 μM). A double-reciprocal plot of reciprocal rate versus reciprocal SAM concentration was used to reveal which dead-end complexes were formed [263]. We observe that, at low H4-OH concentrations (1–25 μM), the data sets intersect on the x-axis and share the same apparent KM value, but at high H4-OH peptide concentrations (50–100 μM), the data sets intersect along the y-axis and share the same apparent Vmax value (Figure 2.9A and Appendix A.7). This pattern is characteristic of the formation of dead-end BE and BEB complexes. If we were observing inhibition by the formation of EBQ complexes, we would expect to see a decrease in KM/Vmax to a minimum as substrate concentrations increase. After this point, data sets at higher substrate concentrations would maintain the minimum 78 KM/Vmax, but would show an increasing reciprocal rate [263]. Therefore, our evidence does not support the formation of an EBQ complex as an underlying mechanism for observed substrate inhibition. For a BEB complex to form, A (SAM) and B (peptide) must be structurally similar for B to indiscriminately bind the enzyme at A and B binding sites. This mode of substrate inhibition seems unlikely, as no additional evidence exists to support such a mechanism. The alternative mechanism supported by kinetic data (Figure 2.9A) is formation of the dead-end BE complex, in which peptide binds the enzyme at the B binding site, such that binding of A is prevented from occurring (Figure 2.9B). 79 Figure 2.9. Hydroxy-substituted H4 peptide inhibits PRMT1 activity through formation of a dead-end BE complex. (A) Datasets are represented in a Lineweaver-Burk (reciprocal rate versus reciprocal substrate concentration) plot. Linear regression analysis was applied to construct lines of best fit for the data. Methylation reactions containing 200 nM PRMT1 were performed with varying SAM and H4-OH substrate concentrations. Variable concentrations of H4-OH used were 2.0 (plus sign), 5.0 (black circle), 10 (light grey diamond), 25 (x symbol), 50 (grey square), and 100 (grey triangle) μM. Variable concentrations of SAM used were 1, 2, 5, 10, and 50 μM. (Inset) A zoom-in to observe intersecting lines along the x-axis (2, 5, 10, and 25 μM; solid lines) and y-axis (50 and 100 μM; dashed lines). Error bars represent standard deviation (n = 2). (B) Cleland notation for a sequential ordered reaction mechanism with a dead-end BE complex where A is SAM, B is peptide substrate, P is the methylated peptide product, Q is SAH, and E is PRMT1. 80 2.5 Discussion 2.5.1 PRMT1 kinetics are dependent on substrate sequence We observed different PRMT1 kinetics depending on substrate identity. For eIF4A1, KRK, and RKK peptides, PRMT1 behaved similarly, behaving most potently towards the unmodified peptides and less active towards the hydroxylated and monomethylated peptides. These observations were expected, because monomethylated and hydroxylated substrates are sterically similar and allow for only one methyl transfer at the methylation site. It was surprising then to find that PRMT1 had similar catalytic activity towards unmodified and modified H4 substrates alike. Further, we observed that the hydroxy-addition to each peptide resulted in lower KM values relative the unmodified counterpart, yet the failure to observe a predicted increase in turnover number for all hydroxylated peptides suggested that the presence of the Nη-hydroxyarginine had differential effects on PRMT1catalysis, based on peptide sequence context, underscoring substrate-dependent effects on PRMT1 enzyme kinetics seen previously with unmodified and monomethylated forms of these peptides [141]. PRMT1 kinetic data with the unsubstituted H4 peptide presented here are consistent with data presented by Dillon et al., with all calculated apparent enzyme kinetic values falling within the same order of magnitude [264]. This previous publication used a radioactive gel-based assay, comparable to what we used to calculate apparent kinetic data for the R1 peptide. The apparent KM, kcat, and kcat/KM values for unmodified and monomethylated eIF4A1, KRK, and RKK peptides differed compared to what has been reported by Hevel and co-workers by an order of magnitude [141]. Whereas Gui et al. reported an apparent higher catalytic efficiency for each monomethylated peptide compared to its unmethylated counterpart [141], we only observed a higher catalytic efficiency for the monomethylated RKK peptide. Many experimental design variations could 81 contribute to these differences. Our methodology included a radioactive assay approach, compared to a continuous spectrophotometric assay. In our assay, all background automethylation of the enzyme was subtracted based on an enzyme-only reaction, whereas the continuous spectrophotometric assay accounted for both substrate methylation and enzyme automethylation. The limit of detection of our radiometric assay was approximately 0.1 pmol of methyl groups transferred, compared to a10 pmol limit of detection (10 μM in a 1.0-cm cuvette) reported for the continuous spectrophotometric assay [141]. The lower limit of detection for our assay allowed us to collect data over abroad range of substrate concentrations, leading to a comprehensive curve when fit to the Michaelis–Menten equation. Furthermore, the enzyme concentrations used (200 nM in this study versus 4.0 μM) might also contribute to these differences, since we observed here that different PRMT1 concentrations display different kinetic parameters specifically towards the H4-OH peptide, similar to what has been shown for methylation activity towards unmodified histone H4 and Tat peptides [242,260]. Finally, Gui et al. used SAH nucleosidase and adenine deaminase to decompose SAH and prevent its feedback inhibition of PRMT1 in their assay, which might have also contributed to differences between our apparent kinetic parameters. Our substrate inhibition data support, in part, an inhibition theory proposed by Zheng and co-workers, who suggested that substrate inhibition could be due to the accumulation of a binary or ternary complex with the enzyme that has not undergone a conformational change and is, therefore, catalytically inactive [239]. Their analysis did not consider that substrate inhibition was observed at low enzyme concentrations. We have previously argued that PRMT1 in solution contains concentration-dependent enzyme populations in various oligomeric states whose kinetic behaviors differ [260]. At a lower enzyme concentration, PRMT1 was susceptible to substrate inhibition, whereas at a high concentration - well above the KD value of dimerization (110 nM) 82 [124] - a kinetically distinct PRMT1 population was no longer susceptible to substrate inhibition. As the inhibition effect on H4-OH peptide is alleviated at higher enzyme concentrations, our results further support our prevailing theory. In summary, PRMT1 displays the highest catalytic activity towards unmodified peptide substrates, whereas it methylates hydroxy-substituted peptides comparably to their monomethylated isosteres (with the exception of the H4 peptide). For some peptide substrates, including some hydroxylated peptides, PRMT1 kinetics modelled to substrate inhibition. Taken together, these data suggest that the enzyme kinetics of PRMT1 vary and are affected by substrate sequence, which complements what was seen by Hevel and co-workers [141], and arginine modification. The finding that arginine hydroxylation generally led to an inhibitory effect, as well as to a largely improved KM value compared to unmodified counterparts, could suggest a benefit to using hydroxy groups in potential PRMT1 active site inhibitor design. 2.5.2 PRMT1 degree of processivity increases with both cofactor and enzyme concentration We observed that the PRMT1 degree of processivity not only depends on the substrate identity, but also on enzyme and cofactor concentration. Although we were the first group to identify that SAM concentration affects the level of PRMT processivity, this study is not the first published example. Other studies of PRMT1 and PRMT5 have shown that higher SAM concentrations can lead to more aDMA and sDMA, respectively, even though SAM was not implicated as a possible cause [141,249]. Similarly, although we were the first to explicitly show that enzyme concentration affects processivity, our conclusion is actually well represented in published studies that have classified different PRMT enzymes as acting processively or 83 distributively [92,109,246–249,130,137,140,141,153,239,243,244]. Not only does the lack of consensus in the literature suggest that PRMT kinetics could be dependent on substrate identity, but our current findings also demonstrate that inherent differences in experimental design, such as enzyme and cofactor concentration, contribute to observed differences regarding distributive or processive mechanisms. For example, our group, as well as Wahle and co-workers, have concluded that PRMT1 acts distributively when tested at low nanomolar concentrations [130,248]. Other groups found PRMT1 to act semi-processively at higher nanomolar and micromolar concentrations [140,141]. Therefore, conclusions drawn about PRMT processivity in the literature are largely consistent and predictable, based on cofactor and enzyme concentrations used in the assay. Based on our product formation and FRET results, we postulate that the PRMT1:SAM:peptide complex best promotes an equilibrium shift in favor of PRMT1 dimerization which may explain how the enzyme can perform processively. Most processive enzymes are toroidal in shape and bear a central cavity containing one or more active sites [265]. PRMT dimers share this structural feature [100,110,120] and are capable of processive arginine dimethylation under certain assay conditions, as discussed above. It is, however, uncertain exactly how PRMT dimers behave processively. With two active sites per PRMT dimer, it is tempting to speculate that each site contributes to arginine dimethylation. This notion is supported by the fact that the stoichiometry of ligand binding to PRMT6 was shown by native mass spectrometry to be 2:2:1 for the PRMT6:SAH:peptide ternary complex [106]. However, we previously showed that mixed dimers of 25 nM active PRMT1 and 750 nM inactive (E153Q) PRMT1 formed approximately twofold more aDMA than MMA on full-length histone H4 after a 1-hour reaction, providing some evidence that processive dimethylation can occur at one active site [86]. More recently, different amino acid residue changes within the one viable active site of PRMT7 84 converted it from a type III enzyme to a type I or type II enzyme [109,112], thus providing additional evidence that dimethylation can occur at one active site. What remains unclear is how the target arginine can remain in one active site for two rounds of methylation, which might not necessarily be a prerequisite for processivity. PRMT1 has been shown to contain, in addition to the peptide binding groove adjacent to the active site, two additional exosite binding grooves that interact with substrate polypeptides [110]. It is plausible that these exosites could prevent substrates from completely dissociating from the enzyme after the first methylation event. This rationale is supported by the observation that histone H4 peptide length and distal elements were shown to strongly influence the catalytic efficiency of PRMT1 [140], indicating that binding interactions outside the active site are important for methylation. Overall, the results from this set of experiments shine a new light on our understanding of PRMT1 processivity. We conclude that PRMT1 processivity is a multifactorial effect. Although the degree of PRMT1 processivity can depend on peptide substrate sequence identity [141], we found that it is not affected by peptide concentration. Instead, we determined that enzyme and cofactor concentrations are the primary variables affecting apparent enzyme processivity. Although we only report this effect for PRMT1, it is logical to extend these considerations in determining processivity of other type I and type II arginine methyltransferases. 2.5.3 PRMT1 uses a sequential ordered Bi-Bi mechanism of action The key difference between a rapid equilibrium random Bi-Bi mechanism with dead-end EAP/EBQ complexes and a sequential ordered Bi-Bi mechanism is whether substrate binding and product release occur in a random or ordered fashion. The latter mechanism has been assigned to PRMT2 [130] and PRMT6 [246], whereas the former mechanism has been assigned to PRMT1 85 [243], CARM1 [153], PRMT5 [137], and PRMT6 [245]. Our current nonlinear regression analyses and graphical representations provide the strongest evidence that PRMT1 operates by using a sequential ordered Bi-Bi mechanism instead of a rapid equilibrium random Bi-Bi mechanism with dead-end EAP/EBQ complexes. Specifically, if PRMT1 operated by using a rapid equilibrium random mechanism, we would have expected to see no inhibition when using the RKK-aDMA inhibitor with varied SAM concentration and a high RKK peptide concentration, but these conditions unquestionably showed inhibition. Furthermore, we would have expected to see competitive inhibition by RKK-aDMA when varying RKK peptide concentration, but both nonlinear and linear regression analyses revealed mixed/noncompetitive inhibition under these conditions. Therefore, results from our steady-state kinetic product inhibition experiments imply that it is more probable that PRMT1 uses a sequential ordered Bi-Bi mechanism rather than a random mechanism. Our product inhibition results contrast with findings from other studies [137,153,243,245]. The source of this discrepancy resides in the interpretation of product inhibition or dead-end inhibition curves involving peptide inhibitors (for example, RKK-aDMA). One issue that groups have encountered is high Ki values for inhibitor peptides, which require high enough concentrations to elicit inhibitory effects that can be accurately measured and properly interpreted. Another issue is reliance on Lineweaver-Burk double-reciprocal plots, which tend to yield unequal weighting of data points over a range of substrate concentrations. Eadie–Hofstee and Hanes–Woolf plots, on the other hand, more evenly distribute data points so that patterns can be more readily discernible by visual inspection. We found in our own data that, in some instances, single plots were not able to clearly elucidate an inhibition pattern. For example, when using the RKK-aDMA inhibitor and various RKK concentrations at a low SAM concentration (Figure 2.8C), the 86 Lineweaver–Burk plot appears unclear and could suggest an uncompetitive pattern. However, in this instance, both the Eadie–Hofstee and Hanes–Woolf plots strongly suggest a mixed inhibition pattern. This example demonstrates how the double-reciprocal plot can be subject to user interpretation and further emphasizes the importance of assessing all plots as a complement to nonlinear regression and as an interpretive tool. Finally, the transformation of nonlinear kinetic models (Equation 2.3-Equation 2.6) to a linear plot like the double-reciprocal plot allows for visual inspection of the model rather than the acquired product inhibition data. In this instance, line patterns are not reflective of data points and are forced through a single x-or y-intercept, as seen in some PRMT studies [137,245]. Furthermore, it is surprising that regression metrics are not regularly reported in literature. This information would not only provide more robust support for authors’ claims but would also invite transparency and allow readers to draw their own conclusions from the presented data. The evaluation of product and dead-end inhibition studies does bear some pitfalls for misinterpretation of kinetic data, but they can be overcome when considering these issues. Zheng and co-workers used transient kinetic measurements to establish a PRMT1 kinetic mechanism in which both SAM and histone H4 peptide were capable of independently binding to the enzyme, but the catalytically active ternary complex (PRMT1:SAM:H4) required that SAM bound first to PRMT1 before the peptide bound [239]. Despite the possibility of peptide binding first, the model presented by Zheng et al. and the evidence we present here both support the sequential ordered Bi–Bi mechanism for PRMT1. In their model, formation of a PRMT1:H4 complex was deemed catalytically inconsequential to methylation [239]. Here, we demonstrate that the formation of a BE complex prevents productive cofactor binding. This conclusion seems logical as the SAM binding site is occluded by an occupied peptide binding groove and inhabits a 87 space contiguous to the arginine binding pocket, as observed in various PRMT crystal structures. This kinetic mechanism is further supported by published ITC data suggesting that peptide substrates do not readily bind PRMTs in the apo form [113,117,239], as well as structural data that provide visual evidence for cofactor binding as a prerequisite to peptide docking [103–107]. These results, therefore, support the formation of a dead-end BE complex as an explanation for substrate inhibition observed within the oligomeric enzyme states that exist under low enzyme concentrations. 2.6 Conclusion Our study reveals that PRMT1 enzyme kinetics are largely dependent on substrate sequence, enzyme processivity is a multifactorial effect, and PRMT1 uses a sequential ordered Bi–Bi kinetic mechanism. These results have many implications for the field. Future research into PRMT processivity needs to consider the effect of enzyme and cofactor concentration on perceived enzyme dimerization and processivity. The processive nature of PRMT1 at high enzyme concentrations, however, might not be representative of conditions within a biological system. Furthermore, our data demonstrate the importance of using multiple linear plots as a complement to nonlinear analyses when interpreting kinetic data. The conflicting reports, therefore, might indicate previous misinterpretations that could be rectified through a more robust analysis of product inhibitor data sets. Our own results support published biophysical data that, although PRMTs can bind peptide substrate in the absence of cofactor, productive methylation is only accomplished if the peptide substrate binds in the presence of cofactor. In light of our findings and the structural and catalytic similarities between PRMT family members, we surmise that these enzymes share a common mechanism. 88 Chapter 3: The application of differential scanning fluorimetry in exploring bisubstrate binding to protein arginine N-methyltransferase 12 3.1 Chapter Synopsis Protein arginine N-methyltransferases (PRMTs) are a family of enzymes that catalyze mono- or di-methylation of arginine residues using S-adenosyl-L-methionine (SAM). Arginine methylation is an important post-translational modification that can regulate the activity and structure of target proteins. Altered PRMT activity can lead to a variety of health issues including neurodevelopmental disease, autoimmune disorders, cancer, and cardiovascular disease. Thus, developing a robust mechanistic understanding of PRMT function may provide insight into these various disease states and enable the development of potential therapeutic agents. Although PRMTs have been studied for nearly two decades, a consensus regarding the mechanism of action for this class of enzymes has remained noticeably elusive. To address this shortcoming, differential scanning fluorimetry (DSF) was used to gain mechanistic insight into the order of PRMT substrate and cofactor binding. This methodology confirms that PRMT cofactor binding precedes target substrate binding and supports the use of DSF to study bisubstrate enzymatic reaction mechanisms. 2 Adapted with permission from Brown, J.I., Page, B.D.G., and Frankel, A. The application of differential scanning fluorimetry in exploring bisubstrate binding to protein arginine N-methyltransferase 1, Methods. 15:175 (2020) 10-23 89 3.2 Introduction 3.2.1 Protein arginine N-methyltransferases In humans, the PRMT family consists of nine members (PRMT1-9; PRMT4 is also known as CARM1) that are important in epigenetic regulation of transcription, RNA processing, and signal transduction, and as a consequence, PRMT dysfunction is implicated in a number of disease states, including cancer [3]. PRMTs are divided into three types based on their propensity to form ω-NG,NG-asymmetric dimethylarginine (aDMA), ω-NG,NG’-symmetric dimethylarginine (sDMA), or solely monomethylarginine (MMA) on proteins (Figure 3.1A). Type I PRMTs (PRMT1-4, 6, and 8) transfer two methyl groups onto a single terminal nitrogen atom of arginine to make aDMA; type II PRMTs (PRMT5 and 9) transfer a methyl group to each of the two terminal nitrogen atoms of arginine to make sDMA; and the type III PRMT, PRMT7, transfers a single methyl group to a terminal nitrogen atom on arginine to make MMA. Regardless of enzyme type, human PRMTs share high structural similarity, including a conserved dimerization arm, cofactor-binding site, and substrate-binding site. Deletion of the dimerization arm destroys enzyme activity by preventing cofactor binding [110,120]. Upon cofactor binding, two local flexible α helices (αY and αX) stabilize to form a lid that seals the cofactor deep within the protein and subsequently forms the substrate binding groove (Figure 3.1B and Figure 3.1C) [100–102]. Two conserved active site motifs, the “THW loop” and the “double-E loop” interact with the substrate [64,99,102,113] and coordinate the target arginine side chain within the arginine-binding pocket (Figure 3.1B and Figure 3.1C) [110]. The mechanism of PRMT catalysis has been studied widely for most of the PRMT family members but has yielded conflicting results. Due to the high degree of similarity among PRMT family members, a generally conserved mechanism of action across the entire PRMT family is 90 suggested [53]. The predominant theory in the field is that SAM binding precedes target substrate binding for productive methyl transfer, which has been demonstrated for PRMT1 [130,142,239,242], PRMT2 [130], CARM1 [105], PRMT5 [108], and PRMT6 [101,246]. Conversely, some research groups suggest that the order of cofactor and substrate binding is inconsequential for PRMT1 [243,244], CARM1 [153], PRMT5 [137], and PRMT6 [245], and that catalysis follows a random binding mechanism. Typically, studies designed to determine mechanism of action are performed using product inhibition under steady-state conditions or pre-steady state to measure initial reaction rate [263]. For PRMTs, reaction monitoring has typically been achieved using radioactive or mass spectrometry-based analytical methods [131]. However, the Zheng group developed a stopped flow fluorescence technique to perform transient kinetics on PRMT1, which takes advantage of two tryptophan residues within the PRMT1 active site whose fluorescence signal is subject to the active site microenvironment and is disturbed upon ligand binding, allowing these investigators to determine dissociation constants [239]. Although this fluorescence-based method is an underutilized tool in studying PRMT kinetics, it has proven to be a powerful complement to other biochemical techniques. 3.2.2 Thermal shift assay: background and methods To further explore the mechanistic order of PRMT substrate binding, thermal shift assays (TSAs) were herein employed. These assays have been widely applied in optimization of protein purification processes and exploration of ligand-protein interactions [266]. In principle, a protein melt curve is produced by thermally denaturing the protein of interest. From this melt curve, the melting temperature (Tm) of a protein, the temperature at which the ratio of folded:unfolded protein is 1 [267], can be interpolated. Some additives, such as ligands or buffer components, may interact 91 with the protein of interest in such a way that stabilize the protein, requiring more heat to denature the protein. Typically, a ligand that tightly binds to the protein stabilizes it and causes a positive thermal shift [268]. Several methods to monitor the melting temperature in a TSA are routinely used. One effective method is differential scanning calorimetry [269], which measures the amount of heat required to raise the temperature of a protein sample. This method requires expensive, specialized equipment and each sample may take as long as 90 minutes [267]. Another method uses the understanding that an unfolded protein will aggregate and precipitate out of solution from either in vitro reactions or lysed cells. The remaining soluble fraction can be assessed by Western blot and plotted against the temperature to derive the melting temperature. An advantage of this technique is that it is effective for monitoring target engagement in cells (e.g., cellular thermal shift assay; CETSA) [270]. However, a simpler method takes advantage of certain fluorescent dyes whose fluorescence is quenched by the aqueous environment. In this TSA variation, the protein denatures in response to an increase in temperature and exposes its hydrophobic regions that are usually protected in native tertiary and quaternary structures. The hydrophobic regions bind to and shield the dye from the quenching effects of water, causing an increase in fluorescence (Figure 3.1D). This continues until the protein aggregates at high temperatures, which pushes the dye back into an aqueous environment, thereby decreasing its fluorescence. In this differential scanning fluorimetry (DSF) assay, protein unfolding is estimated by indirectly measuring the concomitant increase in fluorescence from a commonly used dye like SYPRO™ Orange, whose emission and excitation wavelength maxima are 570 and 470 nm, respectively [267]. This excitation/emission wavelength pair allows for easy adaptation of generic real-time PCR machines equipped with the appropriate filter wavelength that many laboratories routinely use. The ideal results from a DSF 92 experiment produce a sigmoidal curve demonstrating protein thermal denaturation from which the Tm can be determined. Some possible thermal shifts from ligand binding are depicted in Figure 3.1E. Figure 3.1. PRMT1 crystal structure and principle of differential scanning fluorimetry assay. (A) PRMTs di- or monomethylate arginine to produce ω-NG,NG-asymmetric dimethylarginine (aDMA), ω-NG,NG’-symmetric dimethylarginine (sDMA), or monomethylarginine (MMA). (B) The dimer structure of PRMT1, with the bound cofactor SAH shown in blue and the αX and αY helices shown in green. (C) The active site of PRMT1, with the THW loop shown in pink, the double E loop shown in yellow, the bound cofactor SAH shown in blue, and the αX and αY helices shown in green. (D) Under native protein conformation, the environmentally-sensitive SYPRO™ Orange dye (SO) is exposed to a hydrophilic environment, which quenches its fluorescence. By applying heat, the protein denatures and exposes hydrophobic regions capable of shielding the dye. Upon excitation at 470 nm, the dye will release energy at 570 nm for fluorescence detection. (E) Example melt curves of a protein alone or in conjunction with additives are shown. When a protein is incubated with a stabilizing ligand, a positive thermal shift to the right will be observed. When a protein is incubated with a destabilizing ligand, a negative thermal shift to the left will be observed. If an added ligand does not interact with the enzyme, the resulting thermal shift will be comparable to that of enzyme alone. It is also possible that the interaction of a protein and ligand does not induce detectable shifts in the Tm of the protein. 93 3.2.3 Thermal shift assays to study PRMTs Interactions between PRMTs and potential inhibitors have previously been detected using thermal shift assays where inhibitors could stabilize PRMTs against thermal melting [121,271]. Using DSF, the type I PRMT inhibitor MS023 was shown to stabilize PRMT1 at a very high concentration and in the presence of excess cofactor [272]. Stabilization of PRMT5 has also been demonstrated using CETSA, where the inhibitor EPZ015666 induced a positive thermal shift, indicating a direct interaction with the enzyme in cells [227]. While TSAs have been used to show interactions between PRMTs and potential inhibitors, they have yet to be used to study enzyme mechanisms. One group observed that the aldehyde dehydrogenase cofactor NAD+ significantly stabilized the protein, while its substrate all-trans retinal or product all-trans retinoic acid did not, concluding that this observation supported a sequential ordered binding mechanism [273]. Nevertheless, these authors did not fully explore this assay in-depth. Herein, we demonstrate that DSF can be employed to investigate PRMT ligand binding from a mechanistic perspective. Our results corroborate structural, biophysical, and biochemical reports that suggest a sequential ordered mechanism is indispensable for PRMT1 function. 3.3 Materials and Methods 3.3.1 Overview of protocol and consideration of controls When applying the concept of TSA to exploring bisubstrate binding to an enzyme, several control experiments need to be performed to rule out confounding interactions between protein and dye, as well as ligand and dye. The following experiments should be considered: • An array of protein and dye concentrations to determine optimal conditions that produce monophasic melt curves. 94 • “No-protein” controls with only ligand and dye to observe interactions between dye and ligand alone. If the ligand alters the fluorescent properties of the dye, one must consider if the produced raw fluorescence changes significantly in a concentration dependent manner. • After testing ligand and protein with dye, it is important to observe a thermal shift via a different assay that is independent of the dye to rule out confounding interactions. • It is important to observe both bisubstrate ligands incubated with protein independently and together. 3.3.2 Materials SAM and SAH were both purchased from MilliporeSigma (A7007 and A9384, respectively) and dissolved in 0.5 mM HCl to prevent hydrolysis. Sinefungin was purchased from STEMCELL Technologies (73812) and MS023 was received from Chemical Probes (Chemical Probes Portal ID: CPP63) (www.chemicalprobes.org). The RKK (Ac-GGRGGFGGKGGFGGKW) and H4 (Ac-SGRGKGGKGLGKGGAKR) peptides were purchased from Canada Peptide and were synthesized with N-terminal acetylation. The concentrations of the RKK peptide (280 = 5500 M-1 cm-1) and the H4 peptide (205 = 31 mg mL-1 cm-1) [252] were determined spectrophotometrically. SYPRO™ Orange Protein Gel Stain was purchased from ThermoFisher (S-6650) as a 5000x concentrate in dimethylsulfoxide (DMSO) and was prepared as a 500x working stock in water immediately before use. 3.3.3 Expression and purification of PRMT1 The pET28a(+)-PRMT1 plasmid (previously described [124]) was transformed into BL21(DE3) pLysS (Stratagene) and grown in LB media supplemented with 50 μg/mL kanamycin 95 and 35 μg/mL chloramphenicol. Protein expression was induced at mid-log phase (OD600 = 0.8) with the addition of isopropyl β-D-1-thiogalactopyranoside (IPTG) to a final concentration of 1.0 mM supplemented with 1.0% glucose (w/v) and grown at 30°C for 16 h. Cells were pelleted by centrifugation at 10000 x g for 15 min at 4°C and stored at -80°C [124]. Purification of His6-PRMT1 has been previously described [124]. Briefly, the cell pellet was resuspended in lysis buffer (50 mM HEPES-KOH, pH 7.6, 1.0 M NH4Cl, 10 mM MgCl2, 0.1% Triton X-100 v/v, 0.1% lysozyme w/v, 25 U/mL DNAse 1, 7.0 mM β-mercaptoethanol (β-ME), 1.0 mM phenylmethanesulphonylfluoride (PMSF), and one tablet EDTA-free Protease Inhibitor Cocktail (Roche 04693132001)) on ice using 2.0 mL lysis buffer per each gram of cell pellet. The pellet was mechanically dispersed with repetitive pipetting and lysed through sonication at 4°C using eight 30-s pulses at 50% duty cycle with a Sonifier 450 (Branson). To clarify the lysate, it was centrifuged at 35,000 x g for 1 h at 4˚C and subsequently filtered through a 0.45-μm pore membrane (MilliporeSigma). PRMT1 was purified through a two-step chromatography process as before [124]. The clarified lysate was first applied to a 5 mL HisTrap FF column (GE Healthcare) that was equilibrated with wash buffer (50 mM HEPES-KOH, pH 7.6, 1.0 M NH4Cl, 10 mM MgCl2, 7.0 mM β-ME, 10 mM imidazole). Bound His6-PRMT1 was eluted with 400 mM imidazole in wash buffer. The eluted fraction was subsequently subjected to size exclusion chromatography on a HiLoad 26/600 Superdex 200 pg column (GE Healthcare) that was pre-equilibrated (50 mM Tris-HCl, pH 7.5, 100 mM NaCl). The eluted fractions containing PRMT1 were pooled, concentrated, and exchanged into storage buffer (100 mM HEPES-KOH, pH 8.0, 200 mM NaCl, 1.0 mM dithiothreitol (DTT), 10% glycerol v/v, 2.0 mM ethylenediaminetetraacetic acid (EDTA)) in an Amicon Ultra 15 mL filter with a 30-kDa molecular weight cut-off (MilliporeSigma). PRMT1 was found to be 90% pure as measured by 96 SDS-PAGE (Appendix B.1), and its concentration was estimated using the Edelhoch method [253]. 3.3.4 Differential scanning fluorimetry thermal shift assay All reactions were made to a final volume of 20 μL in methylation buffer (50 mM HEPES, pH 8.0, 10 mM NaCl, 1.0 mM DTT) in a Fast Optical 96-well reaction plate (Applied Biosystems 4346906). All reactions contained PRMT1 and SYPRO™ Orange, and varying amounts of SAM, SAH, inhibitor, or peptide. To determine the optimal conditions to produce a melt curve for PRMT1 alone, reactions contained an array of PRMT1 concentrations (0-20 μM) and SYPRO™ Orange dye concentrations (0.5x-10x) in duplicate. All subsequent thermal shift assays contained either 15 μM or 5.0 μM PRMT1 and 7.5x SYPRO™ Orange prepared using a master mix. To determine the effect of SAM and SAH on PRMT1 thermal stability, reactions were prepared with SAM or SAH concentrations ranging from 0.5-200 μM in quadruplicate. To determine the effect of peptides, reactions were prepared with RKK or H4 peptide concentrations ranging from 0.5-300 μM in triplicate with or without saturating 200 μM SAH. The effect of MS023 (0.1-10000 nM) was similarly evaluated in triplicate with or without saturating 200 μM SAH. PRMT1 thermal stability reactions for sinefungin (0.5-400 µM) were prepared in triplicate. Prepared reaction plates were covered with an optical adhesive cover (Applied Biosystems 4360954) and briefly centrifuged at 500 x g using an MPS 1000 Microplate Spinner (Labnet). Plates were heated and analyzed using the StepOnePlus Real-Time PCR System (Applied Biosystems). The reactions were ramped from 25-99˚C at a rate of 1˚C/min and fluorescence at ex. = 490 (from SYBR® Green and FAM™ dye filter set), em. = 580 nm (from TAMRA™ and NED™ dye filter set) was measured after one-minute intervals. 97 The resulting data were normalized by converting raw fluorescence data to relative percent fluorescence using the following formula: Equation 3.1 𝒚 = (𝒎𝒂𝒙𝒏𝒆𝒘−𝒎𝒊𝒏𝒏𝒆𝒘𝒎𝒂𝒙𝒐𝒍𝒅−𝒎𝒊𝒏𝒐𝒍𝒅) (𝒙 − 𝒎𝒂𝒙𝒐𝒍𝒅) + 𝒎𝒂𝒙𝒏𝒆𝒘 where new maximum (maxnew) and minimum (minnew) are 100% and 0%, respectively, old maximum (maxold) and minimum (minold) are raw fluorescence data from the dataset, x represents the raw fluorescence to be normalized, and y represents the normalized fluorescence value. Normalized data were trimmed to 3 points beyond the maximum and 3 points beyond the minimum fluorescence values to improve fit due to excess error at low and high temperatures [274]. The melting temperature (Tm) was determined by plotting the normalized fluorescence on the y-axis against the temperature on the x-axis from the truncated datasets and fit to the Boltzmann equation [275]: Equation 3.2 𝒚 = 𝑩𝒐𝒕𝒕𝒐𝒎 +(𝑻𝒐𝒑−𝑩𝒐𝒕𝒕𝒐𝒎)𝟏+𝒆(𝑻𝒎−𝒙𝑺𝒍𝒐𝒑𝒆 ) For datasets that produced biphasic melt curves, the negative first derivative of the datasets were plotted against the temperature, where the resulting melting temperatures are the local minima. Local minimum values were determined by taking the second derivative of the biphasic melt curve datasets and identifying the temperature at which the change is zero, corresponding to the first derivative’s local minimum [267,276]. To determine dissociation constants (KD) of ligands to PRMT1, a dose-response curve of Tm plotted against ligand concentration was constructed. This curve was fit to the single site ligand binding model [275]: Equation 3.3 𝒚 = 𝑩𝒐𝒕𝒕𝒐𝒎 + (𝑻𝒐𝒑 − 𝑩𝒐𝒕𝒕𝒐𝒎)(𝟏 −𝑷−𝑲𝑫−𝒙+√(𝑷+𝒙+𝑲𝑫)𝟐−𝟒𝑷𝒙𝟐𝑷) where x is the ligand concentration, y is the Tm, and P is the protein concentration. All curve fitting was performed using GraphPad Prism 8.0.0. To test for statistical significance for thermal shifts 98 with ligand added compared to enzyme alone, an ordinary one-way ANOVA for multiple comparisons was applied. A statistical level of significance cut-off of 0.05 was used. 3.3.5 In vitro thermal shift assay Using a modified CETSA assay [270], saturating concentrations of cofactor or substrate were incubated with PRMT1 without SYPRO™ Orange Dye to determine if resulting PRMT1 thermal shifts were truly due to ligand interactions or due to the ligands interacting with fluorescent dye. In a 20-μL total volume, reactions contained 15 μM PRMT1, 200 μM SAH, 300 μM RKK, or 300 μM H4 in methylation buffer. As a loading control, 180 μg of the thermally stable recombinant NUDT5 was added to each reaction [276]. Reactions were incubated for 8 min [277] from 37-81°C in a DNA Engine Dyad Peltier Thermal Cycler (Bio-Rad) and subsequently centrifuged at 20000 x g for 20 min at 4°C. The soluble fraction was removed by pipette, and proteins (4.0 μL of sample) were separated by SDS-PAGE on a 10% gel. Bands were detected by Coomassie (Brilliant Blue G; Sigma) or Silver Staining (BioRad) and subsequently analyzed by densitometry using ImageJ (National Institutes of Health). Band intensity of PRMT1 was normalized to the loading control. 3.3.6 Enzyme activity assay To determine enzyme activity in the presence of MS023 inhibitor, 0.1-10000 nM MS023 was incubated with 1.0 μM PRMT1, 10 μM of a glycine and arginine rich substrate (GST-GAR, roughly 6 times greater than the KM; 1.7 μM) [278], and 50 μM S-adenosyl-L-[methyl-14C]-methionine ([14C]SAM; 58mCi mmol-1; PerkinElmer NEC363050UC) in a 27-μL total volume of methylation buffer. Samples were incubated at 37°C for 1 h (within the linear range of the enzyme 99 for this substrate) (Appendix B.1) and subsequently spotted (12 μL) in duplicate onto 1 cm2 Whatman P81 phosphocellulose filter paper (Fisher Scientific 3698-915). Dried filter papers were washed 5 times with 5.0 mL sodium carbonate-bicarbonate buffer (pH 9.0). Dried and washed papers were added to 2.0 mL of Scintiverse™ BD Cocktail (Fisher Scientific, SX18-4). Disintegrations per minute were detected for 1 min using a Tri-Carb 3110TR (PerkinElmer) liquid scintillation counter. For varying MS023 concentration in the presence of PRMT1 and saturating [14C]SAM, resulting data were fit to the four-parameter logistic equation, from which the half-maximal inhibitory concentration (IC50) was derived: Equation 3.4 𝒚 =𝑩𝒐𝒕𝒕𝒐𝒎+(𝑻𝒐𝒑−𝑩𝒐𝒕𝒕𝒐𝒎)(𝟏+𝑰𝑪𝟓𝟎𝒙)𝑯𝒊𝒍𝒍 𝒔𝒍𝒐𝒑𝒆 where x is the inhibitor concentration, and y is the percent activity. Curve fitting was performed using GraphPad Prism 8.0.0. The Ki can be further estimated using the IC50 value by: Equation 3.5 𝑲𝒊 =𝑰𝑪𝟓𝟎[𝑺]𝑲𝑴+𝟏 where [S] is the substrate concentration, and KM is the Michaelis constant for the substrate and the enzyme. 3.3.7 Structural modelling Current PRMT1 crystal structures are missing roughly the first forty residues, which comprise the αY and αX helices crucial for formation of the peptide binding site. Without resolution of these features, current crystal structures are not suitable for visualizing cofactor and substrate interactions within the binding site. Discovery Studio 4.5 (Biovia) was used to create a homology model of humanized rat PRMT1 [189]. The sequence for rat PRMT1 (NCBI Accession: Q63009.1) was used and a H161Y change was manually produced. Six structures were used as templates, including four resolved rat structures (PDB: 1OR8, 1ORI, 1ORH, 3Q7E) [110,236]. 100 Since resolved CARM1 structures feature complete αY and αX helices, two CARM1 structures were also used as templates (PDB: 2Y1X, 3B3F) [104,105]. The target sequence was aligned to template structures and the MODELER toolkit was used to create the model. To improve model quality, all side chains and loops were refined through simulated annealing and use of the CHARMm forcefield [279]. To constrain the amino acid side chains of the SAM and peptide binding site, SAH and the active-site pyrazole inhibitor CMPD-2 from 2Y1X were copied into the template models [105]. The selected homology model had the comparatively lowest PDF total energy (-9045.41) and the lowest DOPE score (-40284.9) out of the produced models. The resulting homology model had a verify score of 137.95 out of an expected high score of 152.14, which suggests a model that is 91% correctly folded. Sinefungin, SAM, and MS023 were docked to the PRMT1 model first using the LibDock algorithm using all default parameters. Multiple poses for each ligand were generated. The pose with the highest LibDock score for each compound was selected for subsequent docking using the CDOCKER algorithm using all default parameters. LibDock uses an algorithm that generates a hotspot map to configure the ligand in several possible conformations within the defined active site of the protein, while keeping both the ligand and protein rigid during docking [280]. After the ligand is favourably docked, an energy minimization is applied to return flexibility to the complex and the final LibDock score is generated [281]. The CDOCKER algorithm uses a molecular dynamics approach, which accounts for optimal flexibility within both the ligand and protein structures when finding favourable interactions [282]. It uses a CHARMm forcefield, which is a simulation that is designed specifically for the wide range of biologically relevant molecules and requires a wide array of computations, energy minimizations, and dynamics, among other parameters [283]. The CDOCKER algorithm, like LibDock, produces multiple poses of the 101 protein-ligand complex per ligand. For MS023, the receptor site was derived from the crystallized CMPD-2 carried over from CARM1 (PDB: 2Y1X). For sinefungin and SAM docking, the receptor site was derived from crystallized SAH. This procedure is relatively fast and is, therefore, convenient for high-throughput screening of a large number of compounds [280]. 3.4 Results 3.4.1 Optimization of the differential scanning fluorimetry assay with PRMT1 To determine the optimal conditions to produce a PRMT1 melt curve, a microarray with varying concentrations of both PRMT1 and the SYPRO™ Orange dye was performed. The resulting normalized fluorescence data were fit to the Boltzmann equation (Equation 3.2) to derive the melting temperatures. Conditions with low dye concentration (0.5x and 1x) or low enzyme concentration (1.0 and 2.0 μM) yielded melt curves that had a very low overall change in fluorescence and poor signal-to-noise ratios. (Appendix B.2 and Appendix B.3). Robust melt curves were produced using dye concentrations of 5-10x and protein concentrations between 15 and 20 μM (Appendix B.2 and Appendix B.3). Thus, subsequent experiments were generally performed at 15 μM PRMT1 with 7.5x dye to ensure clearly defined melt curves (Figure 3.2A and Figure 3.2B). This enzyme concentration consistently gave fluorescent values that were approximately 3.8-fold above background and 2.7-fold greater than the limit of detection (three standard deviations above background). Due to the small volumes of pipetting and highly active fluorophore, variability in raw fluorescence values is common (largely attributed to pipetting inconsistencies). While the raw fluorescence values are very sensitive to changes in the dye concentration, normalization of these values to the maximum and minimum fluorescence values per well alleviate much of this 102 variability. To reduce variability, it is recommended that one use a master mix containing desired enzyme, dye, and buffer volumes for plating. Melt curves produced with PRMT1 alone and SYPRO™ Orange dye were monophasic (Figure 3.2A and Figure 3.2B and Appendix B.2 and Appendix B.3), with a single inflection point, peak, and trough. A multiphasic melt curve would indicate that the various domains of the enzyme unfold discretely and might occlude the observed effects of stabilizing or destabilizing additives [267], thus complicating the curve fitting and subsequent interpretations of thermal shifts. For dye concentrations of 5x-10x, plotting the derived Tm value against the concentration of enzyme in each well surprisingly revealed a positively correlated trend (Figure 3.2C, Appendix B.4, and Appendix B.5), indicating that the enzyme may form oligomers at high concentrations. Figure 3.2. Optimization of the differential scanning fluorimetry assay. (A) Raw and (B) normalized fluorescence of SYPRO™ Orange dye is plotted against reaction temperature. 7.5x dye was incubated with varying concentrations of PRMT1: 0 (black), 1.0 (red), 2.0 (orange), 5.0 (yellow), 7.5 (green), 10 (teal), 15 (blue) and 20 μM (pink). Reactions are in duplicate and are represented by solid and dashed lines. (C) Protein concentrations from 5.0-20 μM were plotted against the derived Tm for each reaction using 7.5x dye. As protein concentration increases, the derived Tm also increases significantly, as compared to the 5-μM PRMT1 reaction. Reactions were performed in duplicate. Error bars represent the standard deviation. Statistical significance was determined using an ordinary one-way ANOVA for multiple comparisons (* = p ≤ 0.05; ** = p ≤ 0.01). 3.4.2 Stabilization of PRMT1 by cofactor To observe the effect of analogous cofactors SAM, SAH, and the pan-methyltransferase inhibitor sinefungin [137,153,243,245] on PRMT1 stability, DSF was performed using 7.5x 103 SYPRO™ Orange dye, 15 μM PRMT1, and varying concentrations of 0-200 μM SAM or SAH, or 0-400 μM sinefungin. Given the structural similarity between cofactor and sinefungin (Figure 3.3A), we speculated that the latter would behave the same as PRMT1 cofactors in DSF experiments. Both SAM and SAH were prepared in 0.5 M HCl and negative controls were included using the equivalent concentration of HCl from the highest concentration of SAM or SAH. These concentrations of HCl had minimal effect on PRMT1 Tm values due to efficient buffering capacity of the assay buffer (Figure 3.3B and Figure 3.3C and Appendix B.6). As an additional control, DSF experiments using SYPRO Orange with SAM, SAH, or sinefungin alone (no enzyme) were used to investigate possible interactions between these ligands and the dye. Curves resembling melt transitions were produced, but raw fluorescence of SAM and SAH, regardless of cofactor concentration, did not differ from dye alone (Figure 3.3D). To rule out that any melting point fluctuations could be due to interactions between SYPRO Orange and SAH or SAM, we performed a modified in vitro CETSA [270] to detect a thermal shift with PRMT1 and SAM/SAH. In this assay, PRMT1 was incubated either alone or with a high concentration of SAH over a range of temperatures. A thermally stable NUDT5 was added as a loading control [276]. Following incubation at the indicated temperatures and removing precipitated proteins by centrifugation, soluble PRMT1 levels were quantitated by densitometry. Both conditions produced distinct melt curves (Figure 3.3E and Figure 3.3F). With PRMT1 alone, a Tm of 52.4 ± 0.1˚C was observed, but with the addition of 200 μM SAH, a Tm of 57.5 ± 0.2˚C was found, representing a significant difference (adjusted p value = 0.001) as measured by a two-tailed, unpaired Student’s t-test. Since we see a positive thermal shift in PRMT1 Tm when incubated with saturating SAH compared to PRMT1 alone without any dye present, any thermal 104 shifts observed in DSF are likely due to SAH-PRMT1 interactions and not due to SAH-dye interactions. Figure 3.3. Control experiments for cofactor analogues SAH, SAM, and sinefungin. (A) Structures of SAM, SAH, and sinefungin. (B) Varying concentrations of HCl were incubated with PRMT1 and SYPRO™ Orange dye. Melt curves for 0 μM (black circle), 8.5 μM HCl (dark grey square), and 21 μM HCl (light grey triangle) are shown. (C) The experimentally determined melting temperature (Tm) for PRMT1 with each HCl concentration. An ordinary one-way ANOVA for multiple comparisons was used to determine statistical significance between each HCl concentration and PRMT1 alone (ns = not significant; * = p ≤ 0.05). (D) Raw fluorescence values for 0.5 μM SAH (green triangle), 200 μM SAH (blue triangle), 0.5 μM SAM (purple diamond), and 200 μM SAM (pink circle) do not differ from dye alone (black circle), but raw fluorescence values for 15 μM PRMT1 (red square) are substantially higher. (E) PRMT1 and NUDT5 were incubated in methylation buffer from 37-73oC, and (F) PRMT1 and NUDT5 with 200 μM SAH were incubated in methylation buffer from 37-81oC. The density of PRMT1 bands (~42 kDa) were normalized to the NUDT5 band (~35 kDa) in each lane and subsequently normalized to 37oC as 100%. All control reactions were performed in duplicate. Error bars represent the standard deviation. 105 We used DSF, we used this technique to investigate the effects of SAM, SAH, and sinefungin on the melting temperature of PRMT1. Varying concentrations of each cofactor analogue were incubated with 15 μM PRMT1 and 7.5x SYPRO™ Orange dye. Without cofactor, the Tm of PRMT1 is approximately 55˚C (Appendix B.7). All melt curves were monophasic in the presence of cofactor, as seen in the untruncated graphs (Appendix B.8). A significant positive shift was detected with various concentrations of SAH (from 10 to 200 μM, 1.85 ± 0.47oC to 6.24 ± 0.41oC) (Figure 3.4A, Appendix B.7). SAM could induce a positive thermal shift only at a high concentration (200 μM, 5.09 ± 0.72oC, Figure 3.4B and Appendix B.8). Sinefungin elicited a significant shift of 5.15 ± 0.24˚C at 400 μM (Figure 3.4C and Appendix B.9) and all melt curves were also monophasic (Appendix B.10). Thus, elevated PRMT1 Tm values positively correlate with cofactor concentration, suggesting that cofactor binding to PRMT1 has a structurally stabilizing effect. The experimentally determined Tm values were subsequently plotted against cofactor concentration and the resulting data were fit to equation (Equation 3.3) to derive the resulting dissociation constants (KD) (Figure 3.4D-F). The single-site binding model was chosen since the cofactor binding site is clearly defined within PRMT structures. The calculated KD for SAH is 15.7 ± 4.0 μM, the KD for SAM is 50.9 ± 33 μM, and the KD for sinefungin is 34.3 ± 12 μM. The data fit well to the model for both SAH and sinefungin (R2 = 0.926 and R2 = 0.837, respectively), but the SAM data fit comparatively worse (R2 = 0.740). The results from these cofactor experiments demonstrate that DSF may be a useful technique to elucidate characteristics of ligands that bind to the SAM active site of PRMT1. The observed stabilization effect of a ligand may demonstrate its importance in priming a protein or enzyme for subsequent binding events. 106 Figure 3.4. Effect of cofactor on PRMT1 thermostability. (A) Varying concentrations of SAH were incubated with PRMT1. Melt curves for 0 μM (red circle), 5.0 μM (green square), 10 μM (blue triangle), 50 μM (purple diamond), and 200 μM (grey hexagon) SAH are shown (n = 4). (B) Varying concentrations of SAM were incubated with PRMT1. Melt curves for 0 μM (red circle), 5.0 μM (green square), 10 μM (blue triangle), 50 μM (purple diamond), and 200 μM (grey hexagon) SAM are shown (n = 3). (C) Varying concentrations of sinefungin were incubated with PRMT1. Melt curves for 0 μM (red circle), 5.0 μM (green square), 10 μM (blue triangle), 50 μM (purple diamond), 200 μM (grey hexagon), and 400 μM (black cross) sinefungin are shown (n = 3). Tm values are plotted against the concentrations of SAH (D), SAM (E), and sinefungin (F) to derive their respective KD values. Error bars represent the standard deviation. 3.4.3 Destabilization of PRMT1 by peptide substrates Having confirmed a stabilizing effect of SAM-analogues on PRMT1 unfolding, we next sought to determine how PRMT1 substrates affect PRMT1 stability. The DSF assay was performed with 15 μM PRMT1 and two different peptide substrates: RKK and H4. The RKK peptide is an RGG-rich sequence from human fibrillarin, a PRMT1 substrate in vivo [259]. The H4 peptide comprises amino acids 1-17 of human histone H4, which is methylated by PRMT1 in vivo at residue R3 [257]. We and others have demonstrated that these peptides are methylated by PRMT1 in vitro with distinctive efficiencies and reactions rates [141,142]. As a control, we incubated peptides (0.5-300 μM) with dye alone. The resulting data did produce a curve, but raw 107 fluorescence of both RKK and H4 alone were not different from the data produced by dye alone, whereas the raw fluorescence of PRMT1 alone was an order of magnitude higher (Figure 3.5A). To mitigate the possible confounding effect of the dye-peptide interactions, we performed an in vitro thermal shift assay with 300 μM peptide, 15 μM enzyme and NUDT5 as a loading control and heated from 37-73˚C, as described above. The soluble fraction was analyzed by densitometry. From Figure 3.3H, PRMT1 alone produced a Tm of 52.4 ± 0.1˚C. With the addition of 300 μM RKK and H4, melting temperatures of 52.5 ± 0.2˚C and 52.0 ± 0.3˚C were observed, respectively (Figure 3.5B and Figure 3.5C). No significant change in Tm was detected. Therefore, without dye present, high peptide concentrations did not cause a positive thermal shift in PRMT1 melting temperature relative to PRMT1 alone. 108 Figure 3.5. DSF negative controls for RKK and H4. (A) Raw fluorescence values for 0.5 μM RKK (green triangle), 300 μM RKK (blue triangle), 0.5 μM H4 (purple diamond), and 300 μM H4 (pink circle) do not differ from dye alone (black circle), but raw fluorescence values for 15 μM PRMT1 (red square) are substantially higher. (B) PRMT1, NUDT5, and 300 μM RKK and (C) PRMT1, NUDT5, and 300 μM H4 were incubated in methylation buffer from 37-73oC. The density of PRMT1 bands (~42 kDa) were normalized to the NUDT5 band (~35 kDa) in each lane and subsequently normalized to 37oC as 100%. All control reactions were performed in duplicate. Error bars represent the standard deviation. To further observe the effect of peptide substrates on PRMT1 thermostability, DSF was performed with varying concentrations of peptide substrates, 15 μM PRMT1, and 7.5x SYPRO™ Orange (Appendix B.11). All melt curves were monophasic (Appendix B.12). A significant negative thermal shift of 5.5 ± 0.2˚C became pronounced at 100 μM RKK peptide (Figure 3.6A). Similarly, a significant negative thermal shift of 9.6 ± 0.7˚C was observed for H4 (300 μM), but this trend was detected with as little as 5.0 μM H4 (Figure 3.6B). The calculated Tm values for 109 PRMT1 at each RKK and H4 concentration were subsequently plotted against the concentration of substrate to derive the dissociation constants for RKK-PRMT1 and H4-PRMT1 binding [275]. The RKK data could not be fitted to the model (Figure 3.6B), but the H4 data did fit to the model (R2=0.964) to give a KD of 6.81 ± 1.62 μM (Figure 3.6D). In both cases, increasing concentration of peptide correlates with decreasing PRMT1 Tm values, indicating that PRMT1 peptides do not stabilize PRMT1. We next sought to explore how combined peptide substrate and SAH cofactor would affect PRMT1 stabilization. DSF was performed using 5.0 or 15 μM PRMT1, 7.5x SYPRO™ Orange dye, and either varying concentrations of peptide (0.5-300 μM) in the presence of saturating 200 μM SAH or varying concentrations of SAH (0.5-300 μM) in the presence of saturating 100 μM peptide. Several resulting curves appeared biphasic, so to clearly define both melt transitions, the derivative of the fluorescent units with respect to temperature was determined and plotted against temperature for all experiments using both peptide and cofactor. The multiple melt transitions can be clearly seen as local minima on the first derivative plot. When titrating RKK in the presence of saturating SAH, there was no significant change in the calculated Tm until 300 μM RKK, where a negative thermal shift of 8.45 ± 1.7oC occurred, as determined using first derivative data (Figure 3.6E, Figure 3.6F and Appendix B.13). These data could not be fitted to the one-site binding model (Equation 3.3) (Figure 3.6G). Conversely, when titrating SAH in the presence of saturating RKK, we saw a significant positive thermal shift of 4.27 ± 1.4˚C at as low as 10 μM SAH and 9.89 ± 0.9˚C at as high as 300 μM SAH, as determined using first derivative data (Figure 3.6H, Figure 3.6I, and Appendix B.13). This data was fit to the one-site binding model (Equation 3.3) to give a KD value for SAH of 14.53 ± 2.7 μM (R2 = 0.9346) (Figure 3.6J). For RKK, all melt curves were monophasic (Appendix B.14) and most derived Tm values were not statistically different using 110 either analysis method (Appendix B.13). When titrating H4 in the presence of saturating SAH, the curves were monophasic until 50 μM H4 was reached, where the curves became biphasic (Figure 3.6K, Figure 3.6L, Appendix B.15, and Appendix B.16). At 300 μM H4, we see a negative thermal shift of 21.75 ± 0.3oC for the first Tm and a positive thermal shift of 3.32 ± 0.3oC for the second Tm. Since the second melting temperatures follow the general trend observed with increasing peptide concentration, we conclude that the second melting temperature represents both peptide and cofactor bound to the enzyme. Accordingly, the first Tm data could not be fitted to the one-site binding model, but the second Tm data fit the model well to give a KD for H4 of 159.9 ± 73 μM (R2 = 0.8637) (Figure 3.6M and Figure 3.6N, respectively). When titrating SAH in the presence of saturating H4, all curves were biphasic (Appendix B.15 and Appendix B.16). The first Tm values for all SAH concentrations greater than or equal to 1.0 μM were significantly lower, with the greatest thermal shift of 5.41 ± 0.7oC occurring at 200 μM SAH (Figure 3.6O and Figure 3.6P). However, we again conclude that the second melting temperature represents both peptide and cofactor bound to the enzyme. At low SAH concentrations, we observed no significant thermal shift in the second melting temperatures, but as SAH concentration increased, we saw a steady increase in Tm with a significant positive thermal shift of 3.51 ± 0.02˚C observed at 200 μM SAH (Figure 3.6O and Figure 3.6P). As above, the first Tm data were not fit to the one-site binding model, but the second Tm data were used to derive a KD for SAH of 11.17 ± 5.7 μM (R2 = 0.7490) (Figure 3.6Q and Figure 3.6R, respectively). The results from these substrate experiments demonstrate that substrates alone do not thermally stabilize PRMT1. Stabilization of PRMT1 by substrates is only perceivable in the presence of stabilizing cofactor, further demonstrating the importance of cofactor binding for enzyme priming. 111 Figure 3.6. Effect of peptide substrates on PRMT1 stability with and without cofactor. (A) Varying concentrations of RKK were incubated with PRMT1. Melt curves for 0 μM (red circle), 5.0 μM (green square), 10 μM (blue triangle), 50 μM (purple diamond), 100 μM (grey hexagon), and 300 μM (black triangle) RKK are shown (n = 3). (B) Tm values are plotted against the concentration of RKK. (C) Varying concentrations of H4 were incubated with PRMT1. Melt curves for 0 μM (red circle), 5.0 μM (green square), 10 μM (blue triangle), 50 μM (purple diamond), 100 μM (grey hexagon), and 300 μM (black triangle) H4 are shown (n = 3). (D) Tm values are plotted against the concentration of H4 to derive the KD of H4-PRMT1 binding. (E) Varying concentrations of RKK were incubated with PRMT1 and 200 μM SAH. Melt curves for 0 μM (red circle), 5.0 μM (green square), 10 μM (blue triangle), 50 μM (purple diamond), 100 μM (grey hexagon), and 300 μM (black triangle) RKK are shown (n = 3). (F) The first derivative of the fluorescent units with respect to temperature are plotted against temperature for each of the RKK concentrations in (E). (G) Tm values determined from the 112 first derivatives are plotted against the concentration of RKK. (H) Varying concentrations of SAH were incubated with PRMT1 and 100 μM RKK. Melt curves for 0 μM (red circle), 5.0 μM (green square), 10 μM (blue triangle), 50 μM (purple diamond), 100 μM (grey hexagon), and 300 μM (black triangle) SAH are shown (n = 4). (I) The first derivative of the fluorescent units with respect to temperature are plotted against temperature for each of the SAH concentrations in (H). (J) Tm values determined from the first derivatives are plotted against the concentration of SAH. (K) Varying concentrations of H4 were incubated with PRMT1 and 200 μM SAH. Melt curves for 0 μM (red circle), 5.0 μM (green square), 10 μM (blue triangle), 50 μM (purple diamond), 100 μM (grey hexagon), and 300 μM (black triangle) H4 are shown (n = 3). (L) The first derivative of the fluorescent units with respect to temperature are plotted against temperature for each of the H4 concentrations in (K). (M) Tm1 values determined from the first derivatives are plotted against the concentration of H4. (N) Tm2 values determined from the first derivatives are plotted against the concentration of H4. (O) Varying concentrations of SAH were incubated with PRMT1 and 100 μM H4. Melt curves for 0 μM (red circle), 5.0 μM (green square), 10 μM (blue triangle), 50 μM (purple diamond), 100 μM (grey hexagon), and 200 μM (black triangle) SAH are shown (n = 3). (P) The first derivative of the fluorescent units with respect to temperature are plotted against temperature for each of the SAH concentrations in (O). (Q) Tm1 values determined from the first derivatives are plotted against the concentration of SAH. (R) Tm2 values determined from the first derivatives are plotted against the concentration of SAH. Error bars represent the standard deviation. 3.4.4 Effect of inhibitor MS023 on PRMT1 thermostability MS023 is a selective Type-I PRMT inhibitor that has been extensively studied and is known to be highly effective and cell-active, with a reported Ki of 11 nM towards PRMT1 [240]. The ethylenediamino group of the inhibitor occupies the arginine-binding pocket within Type I PRMTs and interacts with the same residues responsible for substrate binding and catalysis (Appendix B.17 and Appendix B.18) [240]. To further explore how ligands that bind to the arginine-binding pocket stabilize PRMT1, we probed the interaction of MS023 and 5.0 μM PRMT1 at nanomolar concentrations (0.1 nM-10000 nM) considering its low IC50 (30 nM) [240]; a previous study only tested the MS023-PRMT1 interaction at 200 μM inhibitor [240]. As a control, we performed DSF with varying MS023 and 7.5x SYPRO™ Orange dye alone. Resulting raw fluorescence data fell within background levels (Figure 3.7A). Since MS023 113 is prepared in DMSO, we sought to examine if the presence of DMSO affected protein stability as measured by DSF. At the highest tested concentration of MS023 (10 μM), the final concentration of DMSO in the reaction is 1%. Therefore, we incubated 1% DMSO with PRMT1 and dye to observe any shift (Appendix B.19). All melt curves produced were monophasic (Appendix B.20). We saw a statistically significant negative thermal shift of 2.5 ± 0.1oC, suggesting that in the presence of 1% DMSO, PRMT1 unfolds at a lower temperature than without DMSO (Figure 3.7B). We also incubated 1% DMSO with PRMT1 and 200 μM SAH to see if the presence of DMSO interferes with the SAH-PRMT1 interaction and resulting Tm. We saw a positive thermal shift of 9.27 ± 0.2˚C with PRMT1 and SAH, and a positive thermal shift of 8.27 ± 0.2˚C adding 1% DMSO (Figure 3.7C). Both conditions result in a positive thermal shift and stabilization of PRMT1, but the presence of 1% DMSO results in a statistically significant negative 1.0 ± 0.2˚C thermal shift compared to PRMT1 with 200 μM SAH alone, as measured by an unpaired two-tailed Student’s t-test. These results can be interpreted as DMSO destabilizing PRMT1 to a small, but noticeable extent. Regardless, the final concentration of 1% DMSO is within reported DSF guidelines [267] and is well under the final concentration of DMSO that has been used in previous PRMT inhibitor studies, which were as high as 5% DMSO [284,285]. We next incubated varying concentrations of MS023 with PRMT1 to observe how the inhibitor affects enzyme stability. All melt curves were monophasic (Appendix B.21). At the highest concentration of 10000 nM MS023, we observed a significant negative thermal shift of 2.58 ± 0.1˚C (Figure 3.7D and Appendix B.22). The magnitude of this shift does not significantly differ from that of 1% DMSO with PRMT1. Therefore, none of the observed shift here can be attributed to interactions between MS023 and PRMT1. We can conclude then that incubating 114 MS023 with PRMT1 does not stabilize the enzyme under the conditions tested and the observed destabilizing effect is likely due to the presence of 1% DMSO. We incubated different concentrations of MS023 with saturating SAH to see how the inhibitor interaction with PRMT1 changes. We observed a statistically significant positive thermal shift of 3.8 ± 0.1˚C at 1000 nM MS023 and of 7.5 ± 0.1˚C at 10000 nM (Figure 3.7E). To explore how MS023 binds to the SAH-PRMT1 complex, we again plotted the derived melting temperatures against MS023 concentration and fit the data to the one-site binding model (3). The data fit to the model very well (R2=0.951) and a KD of 1.4 ± 0.3 μM was determined. To confirm the inhibitory action of MS023 on PRMT1, a radioactive filter binding assay was conducted with 0.1-10000 nM MS023, 1.0 μM PRMT1, and excess GST-GAR. The relative radioactivity was plotted against inhibitor concentration and fitted to the four-parameter inhibition model (4) to determine an IC50 of 88 ± 12 nM (R2 = 0.992) (Figure 3.7F). The Ki was further estimated to be 14.75 ± 2.0 nM using Equation 3.5. These results demonstrate that the substrate binding site inhibitor MS023 does not interact with and stabilize PRMT1 under the biologically relevant concentrations tested without the presence of cofactor, further suggesting that cofactor binding is important for subsequent stable substrate or inhibitor binding to the substrate binding site. 115 Figure 3.7. The effect of MS023 on PRMT1 thermostability with and without cofactor. (A) Varying concentrations of MS023 inhibitor were incubated with SYPRO™ Orange dye alone. Melt curves for 0 nM (red circle), 1.0 nM (green square), 5.0 nM (blue triangle), 50 nM (purple diamond), 1000 nM (grey hexagon), and 10000 nM (black triangle) MS023 are shown. (B) DMSO was incubated with 5 μM PRMT1. Melt curves for PRMT1 with SYPRO™ Orange dye alone (red circle) and with 1% DMSO (green square). (C) DMSO was incubated with PRMT1 and 200 μM SAH. Melt curves for PRMT1 alone (red circle), PRMT1 with 200 μM SAH (green square), and PRMT1 with 200 μM SAH and 1% DMSO (blue triangle). (D) Varying concentrations of MS023 were incubated with PRMT1. Melt curves for 0 nM (red circle), 1.0 nM (green square), 5.0 nM (blue triangle), 50 nM (purple diamond), 1000 nM (grey hexagon), and 10000 nM (black triangle) MS023 are shown (n = 3). (E) Varying concentrations of MS023 were incubated with PRMT1 and 200 μM SAH. Melt curves for 0 nM (red circle), 1.0 nM (green square), 5.0 nM (blue triangle), 50 nM (purple diamond), 1000 nM (grey hexagon), and 10000 nM (black triangle) MS023 are shown (n = 3). (F) Tm values are plotted against the concentration of MS023 to derive the KD of binding to the PRMT1-SAH complex. (G) Relative enzyme activity, as determined using a radioactive filter-binding assay to track methyl transfer, is plotted against increasing concentration of MS023 (log scale). Data were fit to the four-parameter logistics Equation 3.4 to derive the IC50 (n=2). Error bars represent the standard deviation. 116 3.5 Discussion 3.5.1 Relative binding affinity and common ligand interactions as detected by DSF By exploring the effects of SAM analogues on PRMT1 thermostability, we observed relative binding affinity, not only through estimation of KD values, but also through the observed thermal shift magnitudes. The relative magnitude of the thermal shift for a set of ligands depends not only on the affinity of the enzyme for ligand, but also on the binding mechanism [286]. Therefore, for similarly-binding ligands, the relative thermal shift may represent an estimate of relative ligand affinity. For all tested compounds that bind to the SAM-binding site in PRMT1, the positive thermal shifts at 200 μM are comparable. Considering the conserved interactions of these ligands within the binding site (Appendix B.23 and Appendix B.24), it is expected that the magnitude of the thermal shift caused by each ligand is similar. Nevertheless, we do see a marginally larger PRMT1 thermal shift with SAH and sinefungin than compared to SAM, which reflects the higher affinity of SAH for PRMT1 [239]. Considering the highly similar molecular properties of sinefungin (pI = 9.74; log D7.4 = -6.85; +1 charge over physiological pH) and SAM (pI = 10.92; log D7.4 = -5.32; +1 charge over physiological pH) compared to SAH (pI = 7.21; log D7.4 = -4.03; neutral charge over physiological pH), it is surprising then that sinefungin behaves more similarly to SAH than to SAM (www.chemaxon.com; ChemAxon Chemicalize). This relative affinity is also reflected in our reported KD values for these analogues, in which KD(SAH) < KD(Sinefungin) < KD(SAM). The KD values for SAH and sinefungin are higher but within an order of magnitude of other reported values generated with other techniques, but our calculated KD for SAM was much higher [130,140,239,287]. A likely explanation is that in the presence of SAM, some level of PRMT1 automethylation would be expected under these conditions, leading to a mixed population of unmethylated and methylated enzyme, SAM, and SAH. The high degree of 117 variability seen between replicates, which is reflective in the high standard deviation for the calculated KD and the poor fit to the model, can likely be attributed to this confounding effect. We observed poor interactions between RKK and PRMT1, with only a very high RKK concentration able to induce a PRMT1 thermal shift. Additionally, the magnitude of the thermal shift with the highest concentration of H4 was greater than with the same concentration of RKK. This trend is also evident in recent literature [141,142]. Although very few thermodynamic dissociation data have been reported for the RKK and H4 peptides, they have been extensively studied in kinetic capacities. Several studies report the KM for these peptides, which is the concentration of substrate required for an enzyme to reach half-maximal reaction velocity. Although this is not a direct measure of ligand affinity, it can be used to approximate relative ligand affinity in PRMT systems. The KD is the ratio of substrate dissociation (k-1) over the rate of association (k1), whereas the KM additionally includes the rate of catalysis (kcat) in the numerator. Through a metareview of transferase enzymes, we have previously reported that PRMTs exhibit kcat values that are considerably smaller than values from other enzyme classes, so KM values will be closer to KD values (i.e., KM ≥ KD) [53]. The KM for RKK has been previously reported as an order of magnitude higher than the KM for H4 (11.3 μM and 21 μM for RKK [141,142], and 2.3 μM for H4 [142]). We were able to derive a KD for H4 in both the absence and presence of saturating cofactor, but not for RKK. However, the KD for H4 in the presence of cofactor is two orders of magnitude higher than what its reported KM. Although we can draw less definitive conclusions about relative peptide substrate affinity based on distal peptide interactions outside the active site alone [110,140], our results here do qualitatively support that PRMT1 has a markedly lower affinity for the RKK peptide than for H4. The inhibitor MS023, which binds to the arginine-binding pocket in a conserved manner, interacts with this substrate binding site more 118 strongly than both peptides, as demonstrated by its KD value. However, much like for the other tested ligands, we similarly see a higher than predicted KD value for MS023 towards PRMT1. Our reported IC50 and Ki values (Equation 3.5) match what has been reported previously (Ki was estimated using an online converter [288]) [240], but our reported KD, as measured via DSF, exceeds the reported Ki. The calculated KD values presented here do not entirely agree with what is reported in literature. This is consistent with the idea that estimation of KD values using DSF can only be considered an approximation within one order of magnitude [275]. A major contributing factor is the temperature dependence of the thermodynamic property, KD. In DSF, the KD is being determined at a higher temperature than would be representative of the relevant reaction environment. The increased temperature will then affect the total entropy and enthalpy of the system, thus increasing the total energy and the apparent KD [275]. Therefore, it is anticipated that KD values estimated via DSF will be higher than when determined by the gold-standard ITC method, which measures KD at a typically more relevant and lower temperature. Additionally, the interaction of the hydrophobic dye with the ligand of interest can lead to an apparently higher KD value than anticipated [275]. Our results further demonstrate that DSF may not be ideal in determining nanomolar dissociation constants of tight-binding ligands to PRMT1. It is true that the actual KD of MS023 is much lower than the concentration of enzyme used, but due to our relatively high concentration of enzyme, our derived KD is much greater than what is reported; however, our enzymatically determined Ki for MS023 and PRMT1, which was determined at a much lower PRMT1 concentration, is representative of what has been previously reported. Therefore, our observed discrepancies can largely be attributed to expected differences due to the 119 nature of the method. Nevertheless, this method is valuable in discerning relative ligand binding affinity. 3.5.2 Ligand interactions with SYPRO™ Orange as a limitation of the assay We observed ligand-dye interactions for all ligands tested and the produced data did fit to the Boltzmann equation. However, in all instances, the raw fluorescence of the produced curves did not depend on ligand concentration and did not differ from that of dye alone. Since the same high concentration of both RKK and H4 peptides resulted in the same negative thermal shift, it suggests that the temperature-dependent change in fluorescence at such a high peptide concentration is due to interactions between peptide molecules and dye. Despite the high degree of structural similarity of SAM and its analogues SAH and sinefungin, we observed a much stronger interaction of SAH with the dye alone to produce a strong negative thermal shift, whereas the interaction of the dye with SAM or sinefungin was comparably weaker. The log D7.4 values discussed previously indicate over an order of magnitude difference in hydrophobicity that likely accounts for the propensity of SAH and not SAM or sinefungin to interact with SYPRO™ Orange dye and cause it to fluoresce at a lower Tm. The modified CETSA qualitatively revealed the same results as the DSF in that incubation of SAH with PRMT1 produced a significantly higher Tm, and that incubation of peptide with PRMT1 did not produce a higher Tm. The observed magnitude of the shifts was not the same as in the DSF experiments, however. This is likely due to dye interactions with protein and ligands. Further, NUDT5 was used as a loading control and during optimization of this assay, when a higher concentration of NUDT5 was used, more PRMT1 remained soluble at higher temperatures (data 120 not shown). Therefore, it is likely that NUDT5 interacts with PRMT1 to provide stability that may account for the observed discrepancy. Ligand and dye alone did not produce notable melt curves and all fluorescence was within background levels. If we had observed thermal shifts in the same direction with ligand and dye alone and with ligand and enzyme, then our results would have been heavily confounded. This underscores the necessity of performing a control experiment to probe for dye and ligand interactions in the absence of enzyme. Similarly, our modified CETSA is an additional key control to remove dye as a confounder and probe for ligand-enzyme interactions in the absence of dye. We conclude that the degree of ligand hydrophobicity may be indicative of ligand-dye interactions, which is a notable limitation of this assay that needs to be considered. 3.5.3 Sequential order of substrate binding to PRMT1 Several groups have concluded a random method of bisubstrate binding for PRMTs based on kinetic studies alone [137,153,243–245]. However, biophysical and structural methods in conjunction with kinetic studies have more comprehensively revealed a sequential ordered mechanism in which cofactor binding necessarily precedes substrate binding. The stabilization effect of SAM, SAH, and sinefungin on PRMT1 are consistent with other structural information that supports a sequential mechanism. First, structural and in silico studies on CARM1 and PRMT5 demonstrate that both SAM and SAH binding stabilize an otherwise disordered region that is responsible for forming the peptide-binding groove [103–105,108]. We have also previously demonstrated that at high SAM and SAH concentrations, we see an increase in the number of dimers and oligomers formed [142]. In this study, a higher protein concentration in the absence of 121 ligand resulted in a higher Tm, suggesting that higher-ordered structuring is important for protein stability. Although we were able to observe stabilization of enzyme in the presence of cofactor analogue, we found that the interaction of peptide substrates alone with PRMT1 resulted in a negative thermal shift. The observation that peptide substrate interacts with enzyme in the absence of cofactor at all may suggest that the order of substrate binding to enzyme is not important for catalysis. In fact, we have previously noted an interaction between PRMT1 and peptide substrate in the absence of cofactor [142]. However, it has been demonstrated that there are surface sites distinct from the PRMT1 active site and postulated to make contacts with substrates [110]. In one PRMT1 mechanistic study, shorter H4 peptides were poorer PRMT1 substrates than longer peptides [140], which emphasizes the importance of distal peptide interactions with the enzyme. It is then predicted that interactions between the peptide substrate and the enzyme outside of the active site and peptide binding groove are important for productive and stable methylation. We can speculate that mechanisms by which this destabilization occurs may involve peptide interactions with PRMT1 to promote unfolding at lower temperatures [269], or preferential binding to the unfolded protein state that does not permit productive methyl transfer [289]. Nevertheless, our findings here further demonstrate that an observed interaction between enzyme and peptide substrate alone cannot be used to surmise a kinetic random binding mechanism and instead argue against it. Results from our experiments with MS023 further support a sequential mechanism and are consistent with existing biophysical data. ITC has been used to probe PRMT1 bisubstrate binding, often in the context of inhibitor design. Sack et al. observed that when measuring interactions between CARM1 and a small molecule inhibitor (that binds to the arginine binding pocket), a 122 binding isotherm was only produced when SAH was present [105]. It has also been demonstrated that titration of histone H4 peptide substrate into PRMT6 does not produce a binding isotherm in the absence of cofactor [113]. We observed the same trend, where we found no interaction between MS023 and PRMT1 in the absence of cofactor and saw a prominent stabilizing effect on PRMT1 when both MS023 and SAH were present. A previous study of MS023 showed by DSF an additive stabilizing effect on PRMT6 when both MS023 and SAH were incubated with the enzyme [240], consistent with our results for PRMT1, but also a 20oC-stabilizing effect with 200 µM MS023 alone. This MS023 concentration is more than 18000- and 250000-fold higher than the previously reported Ki values for PRMT1 and PRMT6, respectively [240]. At such an inordinately high concentration, the inhibitor may interact with the protein in such a way that is irrelevant to its inhibitory mechanism of action and elicit a positive thermal shift in the absence of cofactor. Incubation of peptide with cofactor rescued the stabilization effect, and when cofactor was incubated with moderate H4 or RKK peptide, we saw an additive stabilizing effect on PRMT1 that is greater than the stabilizing effect of SAH alone. When peptide was titrated into PRMT1 with saturating SAH, we did not see a significant change in Tm at moderate peptide concentrations due to the efficient stabilizing effect of SAH. We further observed that RKK is a relatively poor substrate of PRMT1. When titrated at high concentrations, destabilization of the enzyme occurred, showing that even with cofactor present, RKK preferentially binds to unfolded PRMT1 or promotes PRMT1 unfolding. Interestingly, a biphasic thermal denaturation of PRMT1 was observed at high concentrations of H4 when incubated together with SAH. Since Tm2 here aligns with the trending increase in Tm with H4, we postulate that this corresponds to H4 binding to SAH-PRMT1. At very high H4 concentrations, the multiple molecules of peptide might cause additional interaction with the enzyme, contributing to the two distinct melting transitions [110,140]. 123 Based on our experiments with DSF described here, we conclude that ligands that bind to the cofactor binding site of PRMT1 are stabilizing, demonstrating the importance of cofactor binding for the overall structure and stability of PRMT1. Additionally, ligands that bind to the substrate binding site do not stabilize PRMT1 in the absence of cofactor. We postulate that the same observations could be made for other PRMTs and bisubstrate enzyme classes in support of ligand binding order determination. 3.6 Conclusion Many applications for DSF have already been largely adopted, including drug discovery [268], active site characterization [290], and protein engineering [291]. However, we demonstrate here that DSF can also be applied to mechanistic investigations of enzymes. The results obtained from this study largely support many kinetic, structural, and biophysical reports on the mechanism and characterization of PRMTs. Specifically, we demonstrated through DSF that the stability of PRMT1 is dependent on concentration, which is consistent with previous findings that enzyme dimerization and oligomerization are important for optimal enzyme function [142]. This method also clearly revealed that binding of cofactor and cofactor analogues stabilized the enzyme but binding of peptide substrates and the MS023 inhibitor alone did not. We would expect that if initial binding of peptide or inhibitor was conducive to subsequent cofactor binding, we would have seen a stabilization of PRMT1. Yet, the stabilization effect was rescued when the peptide substrates and inhibitor were incubated with enzyme along with saturated cofactor. Together, this information provides further evidence that PRMTs bind to their substrates in a sequential manner, in which cofactor binding precedes target substrate binding. We propose that this assay may be useful in demonstrating sequential ordered binding, with the first-to-bind substrate exhibiting a stabilizing 124 effect on the enzyme while the second-to-bind substrate does not. Interestingly, we have also observed that DSF can be a functional tool for assessing enzyme quality, where inactive or denatured enzymes do not produce a melt curve (data not shown). We have also encountered some of the limitations of the DSF assay. First, we acknowledge that ligand interactions with the hydrophobic dye may confound thermal shift results. Second, under conditions where catalysis is occurring, DSF results may be skewed due to the introduction of mixed cofactor and substrate populations. Additionally, we failed to see substantial interaction between PRMT1 and the poor peptide substrate RKK, further suggesting that not all substrates are useful in this assay. Barring these ligand constraints and if they are properly considered, we recommend this method as a supplement to other kinetic, structural, and biophysical studies to examine enzyme-ligand interactions and binding mechanisms for PRMTs and other enzyme classes. 125 Chapter 4: Identification of methyltransferase activity from the putative yeast methyltransferases Ykl162cp and Ynl092wp 4.1 Chapter Synopsis Protein arginine methylation is largely conserved between higher and lower eukaryotes. Between mammals and the baker’s yeast Saccharomyces cerevisiae there is a high degree of sequence and functional homology between multiple protein arginine N-methyltransferase (PRMT) enzymes. However, there are yet unidentified PRMTs in yeast, as certain PRMT knockout strains still produce methylarginine. In this work to identify a novel methylarginine-producing enzyme, we sought to explore the methyltransferase activity of two yeast putative methyltransferases, Ykl162cp and Ynl092wp. Both Ykl162cp and Ynl092wp have human homologs; the human Ykl162cp homolog NDUFAF7 is a known arginine methyltransferase and the human Ynl092wp homolog CARME is a known carnosine methyltransferase. Since yeast do not produce carnosine, a dipeptide small molecule β-alanyl-L-histidine, we questioned if Ynl092wp could methylate arginine as an alternative substrate. We expressed and purified recombinant His-tagged Ykl162cp and Ynl092wp and tested their methylation activity in yeast lysates in the presence of radiolabeled methyl donor cofactor to monitor methyl transfer. We saw that Ykl162cp methylation activity was dependent on pH. We also identified a prominent Ykl162cp substrate in yeast lysates that disappeared when treated with RNase. We were unable to observe a natural Ynl092wp substrate in yeast lysates, but we did confirm substantial Ynl092wp automethylation on a histidine residue. We used differential scanning fluorimetry (DSF) in monitor Ynl092p thermal stability in the presence of cofactor and peptide substrate and propose that Ynl092wp binds its substrates using a sequential ordered mechanism in which substrate binding 126 precedes cofactor binding. Although we did not find arginine methyltransferase activity, we do provide novel evidence that Ykl162cp may be an RNA methyltransferase and we provide definitive evidence that Ynl092wp is a histidine protein methyltransferase. These valuable research findings will inform future characterization studies to contribute to the yeast methyltransferasome. 4.2 Introduction Protein arginine N-methyltransferases (PRMTs) transfer one or more methyl groups from the universal methyl donor S-adenosyl-L-methionine (SAM) to the guanidino moiety of arginine residues on polypeptide substrates to produce the methylated substrate and S-adenosyl-L-homocysteine (SAH) [3]. In Saccharomyces cerevisiae, four methylarginine species have been detected: ω-NG,NG-asymmetric dimethylarginine (aDMA) is produced by two methyl transfers to the same terminal nitrogen of the guanidino group; ω-NG,NG’-symmetric dimethylarginine (sDMA) is produced by two methyl transfers to different terminal nitrogen atoms; η-monomethylarginine (ηMMA) is produced by a single methylation of a terminal nitrogen, and; δ-monomethylarginine (δMMA) is produced by a single methylation of the internal nitrogen of the arginine guanidino group [51]. Arginine methylation is conserved between lower and higher eukaryotes, although the δMMA mark has only once been identified in mammals and the only known δMMA-producing enzyme is in S. cerevisiae [26,187]. Arginine methyltransferase activity and function is highly conserved among lower and higher eukaryotes. Two yeast PRMTs have human homologs with conserved activities. Arginine methyltransferase 1 (Rmt1p) is the only aDMA-producing and predominant MMA-producing enzyme in yeast and shares high sequence, structural, and functional similarity with the 127 predominant aDMA- and MMA-producing enzyme in mammals, PRMT1 [120,181]. Mammalian PRMT1 has in vitro methylation activity towards yeast Rmt1p substrates [181]. Further, both mammalian PRMT1 and yeast Rmt1p methylate similar substrates. For example, both enzymes are responsible for asymmetric dimethylation of histone H4R3 [200,292] and both methylate a host of RNA-binding proteins that impact mRNA export from the nucleus [197,293,294]. Additionally, the only known sDMA-producing yeast enzyme, histone synthetic lethal 7 (Hsl7p) shares 27% sequence identity with the predominant sDMA-producing mammalian enzyme, PRMT5 [183]. Like its mammalian homolog, Hsl7p also symmetrically dimethylates histone H4R3 [186,295,296]. These conserved yeast and mammalian enzymes all share a highly similar catalytic core, including a Rossmann-like SAM-binding domain and preserved active site characteristics [158]. In addition to these conserved PRMTs, S. cerevisiae also has two non-canonical enzymes that do not functionally resemble a human homolog. Arginine methyltransferase 2 (Rmt2p) has a conserved Rossmann-like SAM-binding domain, like all other mammalian PRMTs, but it is the only enzyme uniquely responsible for the formation of δMMA and no functional human PRMT homolog has been identified [187], but Rmt2p does share 28% sequence identity with a mammalian guanidinoacetate methyltransferase [187]. Another yeast enzyme, an ηMMA-producing SPOUT methyltransferase 1 (Sfm1p) is the only arginine methyltransferase detected in all eukaryotes that does not harbour a Rossmann-like SAM-binding domain. Instead, Sfm1p contains a SpoU-TrmD domain (SPOUT) and is the sole SPOUT-domain containing member that does not methylate RNA as a substrate [63,65,188]. S. cerevisiae is unique among all eukaryotes for having the only two non-canonical PRMT enzymes that have been identified. 128 Only four PRMTs have been characterized in baker’s yeast, but there is evidence to support that additional unidentified PRMTs still remain. For example, although Rmt1p is the only identified aDMA-producing enzyme in yeast, Δrmt1 lysates still have detectable aDMA on proteins, suggesting that there is at least one other enzyme capable of aDMA formation in vivo [181]. Further, our lab previously showed global changes in protein methylarginine concentrations in yeast that were grown into stationary phase. In wild-type yeast in stationary phase, sDMA production increased approximately 7-fold, aDMA production decreased 11-fold, and MMA production decreased appreciably compared to yeast grown to log phase [185]. Interestingly, in Δrmt1 and Δhsl7 strains, both aDMA and sDMA were still detected, respectively, specifically with a 20-fold increase in sDMA production in Δhsl7 cells [185]. Similar trends were also observed when yeast were stressed via heat shock at 42oC [185]. These data not only demonstrate the existence of an unidentified aDMA-producing and sDMA-producing enzyme in yeast but also suggest that methylarginines, specifically sDMA, has a biologically relevant role in the yeast stress response. It is stipulated that heat shock or growth into stationary phase leads to the accumulation of sDMA in yeast. In yeast, there are 86 genes that encode for all known and putative methyltransferases, of which 19 have not demonstrated any activity or have not been shown to have an in vivo substrate, as curated from the Saccharomyces Genome Database (SGD) [203,297]. Table 4.1 lists all the known arginine methyltransferases in yeast as well as 13 putative methylarginines that have been shown to be upregulated in response to stress according to Serial Pattern of Expression Levels Locator (SPELL) database [298]. All of the putative methyltransferases have a log2 greater than 1, corresponding to an increase in gene expression, under any stationary phase, heat shock, starvation conditions, rapamycin treatment, oxidative stress, osmotic stress or alternative carbon 129 stress conditions [298]. The likely substrates of the putative methyltransferases have been predicted by Szczepińska et al. using a probabilistic approach which considered the enzyme’s structural fold, cellular localization, expression, and isoelectric point [204]. Interestingly, the authors purport that a protein’s isoelectric point (pI) may be used as the best proxy to predict substrate type. Proteins with a high pI (≥6.5) will be positively charged on the surface and therefore bind and methylate negatively charged substrates, such as RNA. Conversely, proteins with a low pI (<6.5) will be predominantly negatively charged on the surface and will therefore bind and methylate positively charged substrates, such as basic protein substrates [204]. We hypothesize that one or more of these 13 putative methyltransferases that are upregulated in response to stress may be possible arginine methyltransferases. These putative methyltransferases include proteins from the Rossmann-domain containing structural family, to which most PRMTs belong as well as enzymes from the SET and SPOUT domain families. Although all SET domain-containing enzymes methylate proteins on lysine [66] and most SPOUT domain-containing enzymes methylate RNA [65], they are included as possible arginine methyltransferases due to the possibility of uncovering another unusual arginine methyltransferase in yeast. 130 Table 4.1. Known and putative yeast methyltransferases are upregulated in response to stress. The open reading frame (ORF), gene name (if applicable), methyltransferase (MT) structural fold, predicted substrate type, and pI are shown. Curated information has been compiled by Dzialo [203] from the SGD and the pI was determined from the gene product using ExPASy ProtParam tool [299]. The expression of each putative methyltransferase gene has been shown to have log2 > 1 under the identified stress conditions. ORF Gene Name MT Class Predicted Substrate Type Confirmed Substrate Type pI Stress Conditions Confirmed arginine methyltransferases YOR021C SFM1 SPOUT Protein Protein 4.68 Heat shock; rapamycin; amino acid starvation [300–302] YBR034C HMT1/ RMT1 Rossmann fold Protein Protein 5.15 Osmotic; alternative carbon source; stationary phase; heat shock; amino acid starvation; rapamycin [300,303–305] YBR133C HSL7 Rossmann fold Protein Protein 6.31 Stationary phase; amino acid starvation; rapamycin [302,304,305] YDR465C RMT2 Rossmann fold Protein Protein 4.19 Stationary phase; rapamycin; amino acid starvation; osmotic stress; carbon starvation [302,304,306,307] Putative methyltransferases YBR225W -- Rossmann fold RNA N/A 8.35 Nitrogen depletion; amino acid starvation [300,305] YCL055W KAR4 Rossmann fold RNA N/A 8.68 Stationary phase; amino acid starvation; rapamycin; oxidative stress [304,305,308,309] YDR316W OMS1 Rossmann fold RNA N/A 9.99 Stationary phase; heat shock; rapamycin; oxidative stress; amino acid starvation [300–302,304,305] YGR283C -- SPOUT RNA N/A 9.77 Amino acid starvation; rapamycin [302,305] YJL105W SET4 SET Protein N/A 8.85 Stationary phase; nitrogen starvation; heat shock; amino acid starvation; rapamycin; carbon source [300,302,304,305,310–312] YKL162C -- Rossmann fold RNA N/A 7.98 Heat shock; oxidative stress; amino acid starvation [300,302,313] YKR029C SET3 SET Protein N/A 9.03 Nitrogen starvation; heat shock; amino acid starvation [300,302,305] YMR209C -- Rossmann fold RNA N/A 7.65 Nitrogen starvation; heat shock; amino acid starvation [300,302] 131 ORF Gene Name MT Class Predicted Substrate Type Confirmed Substrate Type pI Stress Conditions YMR228W MTF1 Rossmann fold Protein N/A 6.52 Amino acid starvation [302,305] YMR310C -- SPOUT RNA N/A 10.02 Oxidative stress [308] YNL092W -- Rossmann fold Other N/A 5.85 Amino acid starvation; heat shock; oxidative stress; rapamycin [300,302,305,313] YPL165C SET6 SET Protein N/A 7.73 Heat shock; rapamycin; nitrogen starvation; amino acid starvation; carbon source; oxidative stress [300–302,305,308,311] 132 From this curated list, two putative methyltransferases are strong candidates for detailed investigation as possible arginine methyltransferases. The first, Ykl162cp has a Rossmann fold and pI of 7.98 which predicts RNA as a substrate. However, Ykl162cp has a suggested human arginine methyltransferase homolog named NADH:Ubiquinone Oxidoreductase Complex Assembly Factor 7 (NDUFAF7), which has been shown to produce sDMA on a subunit of complex I in human mitochondria [96,97]. Interestingly, the current version of BLAST does not identify human NDUFAF7 as a homolog, but UniProt does denote yeast Ykl162c and human NDUFAF7 homology, according to a previous version of BLAST that demonstrates 27% sequence similarity (Figure 4.1A) [314–316]. An NDUFAF7/Ykl162cp homolog in the slime mold Dictyostelium discoideum called mitochondrial dysfunction A (MidA) has also been shown to serve a similar function as is required for proper mitochondrial function [317,318]. Although both human and slime mold have a highly conserved SAM-binding domain (including the GxGxG motif I of nearly all Rossmann methyltransferases [59]), the putative SAM-binding domain of Ykl162cp is quite divergent (LLDGIPGSV) and does not indicate any similarity in sequence alignment (Figure 4.1), further underscoring the tenacious homology between the enzymes. At the time of writing, no in vitro nor in vivo activity for Ykl162cp has been identified [316]. The second candidate arginine methyltransferase Ynl092wp also has a Rossmann fold and a pI of 5.85, but its predicted substrate type is unknown. This protein is of interest because it also has a human homolog with which it shares 32% sequence identity, carnosine N-methyltransferase (CARME), which methylates the delta (otherwise named tau [319]) nitrogen atom on the imidazole ring of the dipeptide small molecule β-alanyl-L-histidine (ie. carnosine) to form β-alanyl-3-methyl-L-histidine (ie. anserine) (Figure 4.1B) [320]. Interestingly, Ynl092wp has been shown to methylate carnosine and a number of histidine-containing di- and tri-peptides [320], but its reaction 133 rate towards histidine-containing peptides is considerably slower than its rat, human, and chicken homologs, and its KM for carnosine is 21.5 ± 2.4 mM [320], an inordinately high value that is at least six times higher than other eukaryotic counterparts and does not suggest high substrate affinity. Ynl092wp has also displayed substantial automethylation, suggesting it is a protein methyltransferase rather than a small molecule methyltransferase [204]. Its low activity towards histidine and carnosine [320], the fact that no carnosine nor anserine has ever been detected in plants or fungi [321], and the lack of carnosine synthase activity in yeast [297] suggests another native substrate and methylation target. Figure 4.1. Alignment of yeast putative methyltransferases with their homologs. (A) Sequence similarity between human NDUFAF7, Dictyostelium MidA, and yeast Ykl162cp. Sequences highlighted in yellow indicate the motif I SAM-binding domain and dashes indicate gaps. Alignment was performed using CLUSTAL [128]. (B) Sequence similarity between human CARNMT1 (CARME) and yeast Ynl092wp. Dots indicate similarity and dashes indicate gaps. Sequence alignment was performed using BLAST [190]. 134 The purpose of this research was to express and purify Ykl162cp and Ynl092wp and characterize their in vitro methyltransferase activity. We found that Ykl162cp is very likely an RNA methyltransferase whose activity was only detectable using a methylation buffer at a weakly basic pH. We also confirm that Ynl092wp has strong histidine-methylating activity and identify a site of automethylation. Using differential scanning fluorimetry (DSF) to monitor Ynl092wp unfolding in the presence of cofactor and amino acid and peptide substrate, we propose that Ynl092wp binds its substrates in a sequential manner, where binding of target substrate prior to cofactor binding stabilizes the enzyme for activity to occur. Although these efforts were unsuccessful in identifying a new arginine methyltransferase, these data contribute to the overall completion of the yeast methyltransferasome. 4.3 Materials and Methods 4.3.1 Recombinant expression and purification of putative yeast methyltransferases Putative yeast methyltransferases were subcloned from the ORF expression plasmid pGB1805 into the pET28a(+) plasmid vector with an N-terminal His6 tag (YNL092W, YSC3869-202330763; YKL162C, YSC3867-202328095, from Horizon Discovery). YNL092W was cloned using NheI and HindIII forward and reverse restriction enzyme sites, respectively, using the forward primer AAACGCTAGCATGGACGAGAATGAGTTCGATAACCAAAGGG and the reverse primer GCCCGTAAGCTTTTATGATTCGTTCGTGGGGTTCGA. YKL162C was cloned using the NheI and XhoI forward and reverse restriction enzyme sites, respectively, using the forward primer GCCAGCTAGCATGATGAAAAGATTACATCCC and the reverse primer ATTGCTCGAGTCAAGTGGAGTGTAGCAC. 135 Expression and purification of His6-Ynl092wp and His6-Ykl162cp was performed as previously described [124]. The pET28a(+) plasmid was transformed into BL21 DE3 (pLysS) E. coli cells (Stratagene) for protein expression. Protein expression was induced with the addition of 1 mM isopropyl β-D-1-thiogalactopyranoside (IPTG) at an OD600 of 0.6-0.8, and cells were grown overnight at 16oC with shaking at 220 rpm. Cells were harvested at 10 000 x g for 20 min and the cell pellet was washed once with water. The cell pellet was stored at -80oC prior to lysis and purification. The thawed cell pellets were mechanically resuspended in 2 mL of lysis buffer (50 mM HEPES·KOH, pH 7.6, 1 M NH4Cl; 10 mM MgCl2, 0.1% Triton X-100 v/v, 0.1% lysozyme w/v, 25 U/mL DNAse, 7 mM β-mercaptoethanol (β-Me), 1 mM phenylmethanesulphonyl fluoride (PMSF), and 1 mM EDTA-free protease inhibitor cocktail (Roche: 04693132001)) per gram of cell pellet on ice. Resuspended cell pellet was incubated on ice for 45 min to allow lysis to proceed. Cells were further disrupted by sonication using Branson Sonifier 450 at 50% duty cycle at 4oC for 10 30-sec intervals with 30-sec ice incubations in between. The lysate was clarified by centrifugation at 35 000 x g for 1 h at 4oC. The clarified lysate was passed through a 0.22-μm Durapore filter (Millipore Sigma). His6-tagged proteins in the clarified and filtered lysates were immobilized on Ni Sepharose High Performance histidine-tagged protein purification resin (Cytiva) containing Ni2+ ions and was washed with 10 column volumes of wash buffer (50 mM HEPES, pH 7.6, 1 M NH4Cl, 10 mM MgCl2, 7 mM β-Me, 10 mM imidazole, 1 mM PMSF). Bound proteins were eluted in a stepwise gradient of 100-300 mM imidazole. Eluted samples were analyzed via SDS-PAGE and pure fractions were pooled, concentrated using a 10-kDa MWCO centrifugal filter (Millipore) and exchanged into storage buffer (100 mM HEPES·KOH, pH 8, 200 mM NaCl, 1.0 mM DTT, 10% v/v glycerol, and 2.0 mM EDTA for Ykl162cp, and 50 mM Tris, 136 pH 8, 200 mM NaCl, 1.0 mM DTT, 10% v/v glycerol, and 2.0 mM EDTA for Ynl092wp). Protein concentration was estimated using a Bradford assay. The Ynl092wp sample was estimated to be 85% pure and Ykl162cp was estimated to be 10.5% pure by densitometry (Appendix C.1). 4.3.2 In vitro methylation of yeast lysates Yeast lacking the predominant arginine methyltransferase Rmt1p were grown in Synthetic Complete media (recipe according to Sherman, 2002) [322], or in standard YPD media into log phase (OD600 0.6-0.8) or into stationary phase (11 days) to observe a media and growth dependence on yeast substrates. Yeast were harvested by collecting 10 OD units by centrifugation at 1500 x g and frozen at -80oC to facilitate lysis. Cell pellets were resuspended in 100 μL CellLytic™ Y (Sigma-Aldrich) supplemented with 10 mM PMSF and 0.5-mm glass beads were added to 50% the final volume of the resuspended pellet. Cells were mechanically homogenized using the SpeedMill PLUS (Analytik Jena) at 4oC for seven 1-min intervals separated by 1-min incubation to prevent sample heating. Lysates were removed from glass beads by pipetting and saved. The beads were washed with an additional 100 μL lysis buffer and added to the saved lysate. The concentration of the 200 μL lysates were determined using the Pierce™ Rapid Gold BCA Protein Assay Kit according to manufacturer instructions. To test for in vitro methylation activity, reactions including 10 μL of crudely purified enzyme (Ynl092wp or Ykl162cp), 40 μg yeast lysate (Δrmt1, Δykl162c, or Δynl092w) and 50 μM S-adenosyl-L-[methyl-14C]-methionine ([14C]-SAM; PerkinElmer NEC363050UC), were incubated in methylation buffer (50 mM HEPES, pH 8, 10 mM NaCl, 1 mM DTT) for a 27-μL total reaction volume . Prior to reaction preparation, some lysates were incubated at 70oC for 20 min to heat inactivate endogenous methyltransferase activity. Additionally, some reactions 137 included the addition of 5 μg/μL RNase to assess the RNA dependence of in vitro methylation activity. Additionally, Ykl162cp was incubated using different methylation buffer conditions to determine the effect of pH on activity (50 mM MES at pH 6, 6.5, or 7, 50 mM HEPES at pH 7, 7.5, 8, or 8.5, 50 mM Tricine at pH 8.5, 8.75, or 9, and 50 mM Tris pH 8.75, 9, or 9.25, all with 10 mM NaCl and 1 mM DTT). Reactions were incubated at 30oC overnight to facilitate exhaustive methylation of substrates. A volume of 13 μL from each reaction was separated by SDS-PAGE on a 10% gel in duplicate. The gel was subsequently stained with 0.025% Coomassie Brilliant Blue. The resulting gel was dried using a slab gel dryer for 45 min at 80oC and exposed to a blanked phosphor screen for at least 72 h. The resulting phosphor image was detected using a Sapphire Biomolecular Imager (Azure Biosystems) using 100-μm pixel size, highest scan rate, and intensity 3. All knockout strains were gift from Dr. Corey Nislow. Knockouts were confirmed via PCR. The primers and genotypes of the strains are listed in Appendix C.2 [323]. 4.3.3 Analysis of Ynl092wp automethylation via mass spectrometry To determine the identity of automethylated amino acids, exhaustive automethylation was performed using 50 μg of purified Ynl092wp protein in 100 μL total volume with 50 μM nonradioactive SAM in methylation buffer. Control reactions were also prepared using buffer only and using Ynk092wp without SAM. Reactions were incubated at 30oC for 16 h. The reactions were transferred to 6x50-mm Pyrex glass tubes (Corning 9820-6) and trichloroacetic acid was added to a final concentration of 25% to precipitate protein. The precipitate was pelleted via centrifugation at 20 000 x g and the supernatant was discarded. The pellet was washed with 100 μL of ice-cold acetone. The pellet was air-dried, and precipitated proteins were acid hydrolyzed in 138 vacuo into their individual amino acid components in the presence of 200 μL 6 N HCl inside a reaction vial assembly (Eldex 1163) using a Waters Pico Tag workstation for 24 h at 110oC. Amino acids were resuspended in 100% methanol and injected into a QTRAP 5500 mass spectrometer using Turbo Spray ionization. The resulting mass spectrum was inspected for m/z peaks corresponding to protonated, methylated basic amino acid residues (203.2 for dimethylarginine; 189.2 for monomethylarginine; 190.2 for trimethyllysine; 175.2 for dimethyllsyine; 161.2 for monomethyllysine; and 170.2 for methylhistidine). Further, m/z peaks corresponding to their predominant product ions were examined, including: 158.0 for asymmetric dimethylarginine and 144.2 for monomethylarginine [324]; 130.0 for monomethyl, dimethyl, and trimethyllysine [325]; and 124.2 for 1-methylhistidine and 152.9 for 3-methylhistidine [326]. A thorough proteomics approach to determining the identity of the automethylated residue(s) was performed. A reaction containing 140 μg of purified Ynl092wp, 100 μM of non-radioactive SAM in a total volume of 270 μL methylation buffer was exhaustively methylated for 16 h at 30oC. The sample was submitted to the Proteomics Core Facility at the University of British Columbia for proteomic analysis. Duplicate samples were prepared by diluting 25 μg of methylated sample to 0.5 μg/μL in 50 mM NaHCO3, pH 8 in 50 μL total volume with 0.5 μg DTT and 0.5 μg iodoacetamide (IAA). In separate reactions, either 0.5 μg trypsin or 0.5 μg Glu-C protease were added and incubated overnight at 37oC. Digestion was quenched and samples were acidified with the addition of TFA to 1%. Samples were cleaned up using the Stage-tip procedure [327] by immobilizing the samples on an 8-mm C18 column, washed with 1% TFA, and eluted with 40% acetonitrile with 0.1% TFA. Eluted samples were dried down. An Easy LC 1000 HPLC (ThermoFisher Scientific) was connected to an Aurora Series Gen2 (CSI) analytical column (25 cm x 75 μm 1.6 μm FSC C18, with Gen2 nanoZero and CSI 139 fitting; Ion Opticks) and a μ-Precolumn, (300 μm ID x 5 mm, C18 PepMap, 5 μm, 100 A, Thermo Scientific). The mobile phases of 0.1% formic acid and 2% acetonitrile in water (Buffer A) and 0.1% formic acid in 90% acetonitrile (Buffer B) were used in a gradient of 5% B to 18% B over 45 min, then to 35% B to 90 min, and last to 90% B over 2 min and held at 90% B for 13 min. Before each run, the analytical and precolumn column were conditioned with 4 and 20 μL of buffer A, respectively. The sample was applied at a flow rate of 0.35 μL/min. The analytical column was heated to 50°C using tape heater (SRMU020124, Omega.com and in house build microprocessor temperature controller) and the LC temperature was set to 7°C. A total of 0.275 μg each sample was loaded onto the column reconstituted in buffer A. The total run time per sample was 90 min. All samples were analyzed on an Impact II quadrupole time-of-flight mass spectrometer with a Captive nanospray ESI source (Bruker Daltonics). The ESI source was operated at 1900 V capillary voltage, 0.25 Bar pressure with methanol in the nanoBooster, 3 L/min drying gas, and 150°C drying temperature. Impact II was run with OTOF Control v. 4.1 (Bruker). LC and MS were controlled with HyStar 4.1 (4.1.21.2, Bruker). Spectra were obtained using a data-dependent auto-MS/MS mode, scanning from 200 to 2000 m/z (Th) at 5 Hz, followed by inactive focus to fragment the 20 most abundant ions individually at 18 Hz. The isolation window ranged from 2 to 3 Th and the collision energy ranged from 23 to 65 eV, depending on parent ion m/z. Parent ions were excluded from MS/MS and only reconsidered if their intensity increased more than 5 times. Singly charged ions were also excluded since peptides carry multiple charged in ESI mode. Resulting spectra of peptides were searched against the Uniprot protein database for Saccharomyces cerevisiae and Escherichia coli using the Byonic search Algorithm from Protein Metrics Inc, with 10 ppm and 30 ppm mass accuracies for precursor and product ion masses 140 respectively, and a 1% false discovery rate cut-off. For the Trypsin- and Glu-C-treated samples, cleavage C-terminal to lysine and arginine residues, and glutamic acid and aspartic acid residues were chosen, respectively. In both searches, methylation of H, K, and R (+14) were selected as variable modifications. 4.3.4 Ynl092wp methylation activity linear range To determine the linear range of the radiometric filter-binding and gel-based automethylation assays, 0.83 μM Ynl092wp was incubated with 50 μM [14C]-SAM in 165 μL total volume in methylation buffer. The reaction was allowed to proceed at 30oC and at time points of 0, 0.5, 1, 2, 3, 4, and 5 h, 12 μL of reaction was spotted onto Whatman p81 filter paper to bind to the positively-charged protein and 9 μL was prepared for SDS-PAGE. For the p81 filter-binding assay, nonspecifically bound radioactivity was washed away from the dried filter papers with vortexing at room temperature using 5 x 5 mL sodium carbonate-bicarbonate buffer pH 9 washes. Washed filter papers were immersed in 2.5 mL of Scintiverse™ BD Cocktail (Fisher) and immobilized radioactivity corresponding to Ynl092wp automethylation was quantified by counting disintegrations per minute (DPM) on a Tri-Carb 3110TR (PerkinElmer) liquid scintillation counter for 1 min. For the radiometric gel-based assay, 13 μL total volume of each sample was separated via SDS-PAGE on a 10% gel and resulting stained and dried gels were exposed to a phosphor screen as described above. The relative band intensity of the Coomassie stain and of the phosphorimage were quantitated using ImageJ (NIH) [328]. 141 4.3.5 Inhibition of automethylation To observe how possible amino acid substrates impact automethylation of Ynl092wp, 0.83 μM enzyme was incubated with 50 μM [14C]-SAM and 0.05-500 mM carnosine, histidine, and arginine in a total reaction volume of 24 μL methylation buffer. Reactions were incubated at 30oC for 2 h and 13 μL of each reaction was denatured at 90-100oC and loaded onto a 10% polyacrylamide gel in duplicate for SDS-PAGE. Stained and dried gels were exposed to a blank phosphor screen and the resulting phosphorimage was imaged and quantified as described above. Resulting inhibition curves were fit to the four-parameter logistic model (Equation 4.1) using GraphPad Prism 8 to determine the IC50: Equation 4.1 𝒚 = 𝑴𝒊𝒏𝒊𝒎𝒖𝒎 + (𝑴𝒂𝒙𝒊𝒎𝒖𝒎 − 𝑴𝒊𝒏𝒊𝒎𝒖𝒎) (𝟏 +𝑰𝑪𝟓𝟎𝑿)𝑯𝒊𝒍𝒍𝒔𝒍𝒐𝒑𝒆⁄ where IC50 is the concentration of amino acid that gives the half-maximal inhibition response, y is the percent inhibition, x is the inhibitor concentration, the Hillslope is the steepness of the curve, and minimum and maximum values represent the upper and lower plateaus along the y-axis. To observe how Ynl092wp methylates and binds its substrates, a 9mer N-terminal acetylated peptide representing the site of histidine automethylation (CARN peptide, Ac-LYHFENDHG; the bolded residue indicates a site of known monomethylation) was purchased from Canada Peptide. To observe methylation of the peptide substrate, 0.83 μM Ynl092wp was incubated with 50 μM [14C]-SAM and 0.5-500 μM carnosine methyltransferase peptide (CARN) in 50 μL reaction volume in methylation buffer. To observe methylation of the peptide, a radiometric p81 filter-binding assay was performed, as described above. Cofactor-only and no-peptide controls were included to measure background radioactivity. To simultaneously observe the effect of increasing peptide concentration on Ynl092wp automethylation, 13 μL of each reaction was loaded onto a 16% tricine-SDS gel in duplicate and 142 electrophoretically separated. Peptides and proteins were fixed by using 5% glutaraldehyde and Coomassie-stained. The stained gel was dried, exposed to a phosphor screen, and detected via phosphorimaging and analyzed via densitometry as described above. To further test Ynl092wp in vitro methylation activity, 0.83 μM Ynl092wp was incubated with 50 μM [14C]-SAM and 0.01-13 μM human alpha actin (abcam) in 24 μL reaction volume in methylation buffer. In duplicate, 13 μL of each reaction was loaded onto a 10% SDS-PAGE gel. The stained and dried gel was exposed to a phosphor screen, detected via phosphorimaging, and analyzed by densitometry as described above. Resulting data were fit to the inhibition model (Equation 4.1) and to the Michaelis-Menten equation (Equation 4.2): Equation 4.2 𝒗 = 𝑽𝒎𝒂𝒙[𝑺] 𝑲𝑴 + [𝑺}⁄ where v represents the rate, [S] represents the substrate concentration, Vmax represents the maximum rate, and KM represents the Michaelis constant. 4.3.6 Differential scanning fluorimetry (DSF) thermal shift assay To determine optimal DSF conditions for Ynl092wp, varying concentrations of enzyme (0.83, 1.0, 2.5, 5.0 μM) were incubated with varying concentrations of SYPRO™ Orange dye (5, 7.5, and 10x) in a final reaction volume of 20 μL in methylation buffer. Reactions were prepared in a Fast Optical 96-well plate (Applied Biosystems). Subsequent reactions included optimized reaction conditions of 0.83 μM Ynl092wp and 7.5x SYPRO™ Orange dye. In all conditions, a master mix was created to add the same amount of enzyme, methylation buffer, and SYPRO™ Orange dye to each reaction. To determine the effect of cofactor on Ynl092wp thermal stability, reactions were prepared in the presence of 0.5-200 μM SAM or SAH in quadruplicate. To observe the effect of free amino acids on Ynl092wp thermal stability, reactions were prepared using 0.05- 143 500 mM carnosine, histidine, or arginine in quadruplicate. To observe the effect of peptide substrate on Ynl092wp thermal stability, reactions were prepared using 0.5-300 μM CARN peptide. To observe the combinatorial effect of cofactor and substrate on Ynl092wp thermal stability, the range of amino acid or peptide concentrations were incubated in the presence of 200 μM SAH, which was pre-incubated with enzyme for 30 minutes on ice, and the range of SAH concentrations were incubated in the presence of saturating substrate (500 mM amino acid or 300 μM peptide), which was pre-incubated with enzyme for 30 minutes on ice. An optical adhesive cover was added to the reaction plates and the reactions were briefly spun down via centrifugation. Reactions were subjected to thermal degradation using the StepOnePlus Real-Time PCR System (Applied Biosystems) by heating from 25-99oC, increasing 1oC per min. SYPRO™ Orange fluorescence was monitored at each minute interval by excitation at 490 nm and emission at 580 nm (the SYBR® Green/FAM™ and TAMRA™/NED™ filter sets, respectively). The resulting raw fluorescent data were normalized to relative percent (0-100%) within each individual reaction (Equation 4.3): Equation 4.3 𝒚 = (𝒎𝒂𝒙𝒏𝒆𝒘−𝒎𝒊𝒏𝒏𝒆𝒘𝒎𝒂𝒙𝒐𝒍𝒅−𝒎𝒊𝒏𝒐𝒍𝒅) (𝒙 − 𝒎𝒂𝒙𝒐𝒍𝒅) + 𝒎𝒂𝒙𝒏𝒆𝒘 where x represents the raw fluorescent value, y represents the new, normalized percent value, the old maximum and minimum represent the raw fluorescent peak and trough of the melt curve, respectively, and the new maximum and minimum represent 100 and 0%. To derive the melting temperature (Tm) of each melt curve, the normalized fluorescent data were plotted against temperature and fit to the Boltzmann equation (Equation 4.4) using GraphPad Prism 8: Equation 4.4 𝒚 = 𝑴𝒊𝒏𝒊𝒎𝒖𝒎 + (𝑴𝒂𝒙𝒊𝒎𝒖𝒎 − 𝑴𝒊𝒏𝒊𝒎𝒖𝒎) (𝟏 + 𝒆(𝑻𝒎−𝒙𝑺𝒍𝒐𝒑𝒆))⁄ where x represents the temperature, y represents the normalized fluorescent data, the slope represents the steepness of the sigmoidal curve, the Tm represents the inflection point of the curve 144 where half of the protein is unfolded, and the minimum and maximum represent the plateaus along the y-axis. To facilitate fitting the sigmoidal curve, the normalized data were truncated 3 data points from the maximum and minimum values. In the cases where data produced biphasic melt curves, the first derivatives of the normalized data were plotted against temperature and the Tms corresponded to local minima. The Tms were determined using second derivative data to identify the temperatures at which the second derivative data were 0. The binding of cofactor and substrate binding to Ynl092p was estimated by plotting the Tm against ligand concentration and fitting to the single site ligand binding model (Equation 4.5)[275] using GraphPad Prism 8: Equation 4.5 𝒚 = 𝑴𝒊𝒏𝒊𝒎𝒖𝒎 + (𝑴𝒂𝒙𝒊𝒎𝒖𝒎 − 𝑴𝒊𝒏𝒊𝒎𝒖𝒎)(𝟏 −𝑷−𝑲𝑫−𝒙+√(𝑷+𝒙+𝑲𝑫)𝟐−𝟒𝑷𝒙𝟐𝑷) where x represents the ligand concentration, y represents the Tm, P represents protein concentration, KD represents the dissociation equilibrium constant that measures the separation of bound ligand from enzyme, and the minimum and maximum represent the plateaus along the y-axis. In all melt curves, an ordinary one-way ANOVA using multiple comparisons was used to measure significant changes in melting temperature compared to the no-ligand control, using a threshold of p = 0.05. 4.3.7 In vitro thermal shift assay A modified CETSA assay [270] was used to monitor protein unfolding in the absence of fluorescent dye. In each reaction, 180 μg thermally-stable NUDT5 was used as a normalizing control [276]. Reactions included 0.83 μM Ynl092wp with or without 300 mM histidine and were incubated in a DNA Engine Dyad Peltier Thermal Cycler (Bio-Rad) from 30-66oC for 8 min in a 145 20-μL total volume methylation buffer [277]. Reactions were centrifuged at 20000 x g for 20 min at 4°C to pellet the aggregated and thermally unstable protein. The soluble fraction was carefully pipetted off the top of each reaction and 4.0 μL of sample were separated by SDS-PAGE on a 10% gel. Bands were detected by incubation in 1x SYPRO™ in 7.5% acetic acid for 5 min and then imaged at 302 nm. Gels were subsequently analyzed by densitometry. Band intensity of Ynl092wp was normalized to the NUDT5 control. A student’s unpaired t-test was used to determine significance between the histidine and Ynl092wp-alone samples. 4.4 Results 4.4.1 In vitro methylation of yeast lysates with putative yeast methyltransferases To test for methylation activity and in an attempt to identify a natural yeast substrate, crudely purified Ynl092wp and Ykl162cp were incubated with yeast lysates and radioactive cofactor, either lacking the protein of interest (Δynl092w or Δykl162c, respectively) or lacking the predominant MMA- and aDMA-producing yeast enzyme, Rmt1p (Δrmt1) overnight at 30oC. The rationale was that in yeast strains lacking Ynl092wp or Ykl162cp, the natural substrate(s) would be hypomethylated. Further, the Δrmt1 would have global protein hypomethylation, theoretically introducing a large number of possible methylation sites for recombinant Ynl092wp and Ykl162cp [185]. It has been observed in mammalian cells that knockout of the predominant MMA- and aDMA producing enzyme PRMT1 allows for substrate scavenging by other PRMTs [78]. To reduce background methylation, lysates were heated at 70oC for 20 min prior to adding recombinant enzyme. Methylation was tracked through phosphorimaging after separation of proteins via SDS-PAGE. 146 To probe for substrates with variable expression under growth conditions, the yeast knockout strains were grown into log phase (t=0 d) and into stationary phase (t=11 d) in either synthetic complete (SC) or yeast-peptone-dextrose (YPD) media. As a negative control, each yeast lysate was incubated with [14C]-SAM cofactor. For Ynl092wp experiments, in all cases, no background methylation within the lysates was detected (Figure 4.2). As an additional control, one reaction also contained only Ynl092wp with [14C]-SAM cofactor and without yeast lysate. In the enzyme alone reaction, a considerable methylated band at ~45 kDa was observed corresponding to Ynl092wp automethylation. Additional methylated bands in the enzyme-alone reaction were also observed at ~35 kDa, ~30 kDa, and in the dye front, potentially corresponding to impurities from the bacterial purification or to breakdown of the enzyme. When Ynl092wp was incubated with yeast lysates, methylation of these bacterial impurities persisted. When Ynl092wp was incubated with Δrmt1 and Δynl092w lysates that were grown in SC media into log phase and stationary phase, no additional methylated bands in the yeast lysates were observed (Figure 4.2). 147 Figure 4.2. In vitro methylation activity of Ynl092wp. Exhaustive methylation of Δrmt1 (A) and Δynl092w (B) lysates by purified recombinant Ynl092wp. Reactions contained 10 μL of Ynl092wp, 50 μM [14C]-SAM, and 40 µg of yeast lysate and were incubated for 16 hours at 30oC. 15 μL reactions were separated via SDS-PAGE on 10% polyacrylamide gels in duplicate. The dried gels were exposed to a blank phosphor screen for 5 days and the resulting phosphorimage was captured. The Coomassie stain is shown on top and the phosphorimage is shown on the bottom. The banding patterns of these methylated proteins was consistent in all lysate-only controls, regardless of growth time (log phase or stationary phase), media composition (SC or YPD), or strain (Δrmt1 and Δykl162c) (Figure 4.3). Unlike the Ynl092wp-only reaction, there is no radioactivity detected in the Ykl162cp-only reaction, suggesting that Ykl162cp does not methylate itself nor any contaminating bacterial proteins. Further, in reactions containing both Ykl162cp and yeast lysate, there is no detectable methylation of any substrate at all nor background methylation that was present in the lysate-only controls. Despite heat-treating the Δrmt1 and Δykl162c lysates, 148 notable background methylation was detected in lysate-only controls in the Ykl162cp experiments, with a noticeable smear in the 30-45 kDa range. One suggestion is that Ykl162cp has a strong propensity for sequestering SAM, thus preventing background lysate methylation. Another possible suggestion is that Ykl162cp may methylate non-protein substrates that were not visible by gel separation. To observe if Ykl162cp methylation activity was dependent on pH, reactions containing 50 μM [14C]-SAM, 40 µg of Δrmt1 lysate grown into log phase in YPD media was incubated with 10 μL of Ykl162cp at 30oC overnight in methylation buffers of varying pH: 6, 6.5, 7 (MES buffer), 7, 7.5, 8, 8.5 (HEPES buffer), 8.5, 8.75, 9 (Tricine buffer), 8.75, 9, and 9.25 (Tris buffer). As before, no radioactivity was observed on specific protein bands in the gel, but radioactivity in the dye front correlated with an increase in pH, although methylation signal was detected at low levels (Figure 4.3C). For all subsequent methylation reactions with Ykl162cp, the Tris methylation buffer at pH 8.75 was selected. A series of experiments with varying amounts of Ykl162cp (7 and 14 μL), radioactive cofactor (50, 75, and 100 μM), and Δrmt1 lysate (35, 70, 105 ug) were designed to test the impact of these variables on observed radioactivity, since initial methylation detection was low (Figure 4.3D). In the no-lysate controls (7, 10, and 14 μL enzyme), very little to no methylation was observed, with the exception of a faint band at ~25 kDa that appears to increase as more enzyme was added. In the no-enzyme controls with 35, 70, and 105 µg of Δrmt1 lysate, the radioactive band at ~25 kDa was not present, but rather a faint, undefined smear of radioactivity was visible at a higher molecular weight. For increasing cofactor conditions, Ykl162cp was held constant at 7 μL and lysate was held constant at 35 μg. At 75 and 100 μM [14C]-SAM, the radioactive band at ~25 kDa was present, but it was not visible at 50 μM [14C]-SAM. For increasing Ykl162cp 149 conditions, [14C]-SAM was held constant at 50 μM and lysate was held constant at 35 μg. At 7, 10, and 14 µL Ykl162cp, the band at ~25 kDa was visible but there was no observed difference in band intensity. Last, for increasing lysate conditions, Ykl162cp was held constant at 7 μL and [14C]-SAM was held constant at 50 μM. With increasing lysate, the band at ~25 kDa did not change but a new band appeared at ~15 kDa that increased in intensity as lysate increases. Therefore, the 25-kDa band represents a bacterial contaminant from the Ykl162cp purification and the ~15 kDa band represents a yeast substrate present in the Δrmt1 lysate grown in YPD media. Since many known PRMTs interact with RNA, the lysates were treated with RNase to potentially expose new methyl-accepting sites on yeast proteins previously occluded by the binding of RNA to a target substrate and inhibit methylation activity [329]. Reactions were treated with RNase, either before incubation with protein or after incubation with protein to observe the effect of RNA removal on observed radioactivity. Further, since observed radioactivity in previous experiments was relatively low, 105 μg of lysate was used. When the lysate was treated with RNase prior to the methylation reaction, the observed radioactive band representing the bacterial contaminant at ~25 kDa remained, but the prominent radioactive band at 15 kDa visible in the absence of RNase and corresponding to the yeast substrate disappeared (Figure 4.3E). Similarly, when the reaction was treated with RNase after exhaustive methylation was allowed to occur, background, basal radioactivity was still detected but methylation of the yeast substrate at 15 kDa was not observed. 150 151 Figure 4.3. In vitro methylation activity of Ykl162cp. Exhaustive methylation of Δrmt1 (A) and Δykl162c (B) lysates by purified recombinant Ykl162cp. Reactions contained 10 μL of Ykl162cp, 50 μM [14C]-SAM, and 40 µg of yeast lysate (n=3). (C) 40 μg of yeast Δrmt1 lysate was incubated with 10 μL of Ykl162cp and 50 μM [14C]-SAM in MES-, HEPES-, Tricine-, and Tris-based methylation buffers ranging from pH 6 to pH 9.25 (n=2). (D) 7, 10, and 14 μL of Ykl162cp were incubated with 50, 75, or 100 μM [14C]-SAM and 35, 70, or 105 μg Δrmt1 lysate at pH 8.75 (n=2). (E) All lanes contain 50 μM [14C]-SAM and methylation buffer at pH 8.75. Reactions either contain 0 or 7 μL of Ykl162cp, 0 or 105 μg Δrmt1 lysate, or they contain no RNase, RNase treatment before incubation (●), or RNase treatment after incubation (■). All reactions were incubated for 16 hours at 30oC. Volumes of 15 μL of reactions were separated via SDS-PAGE on 10% polyacrylamide gels in duplicate. The dried gels were exposed to a blank phosphor screen for 5 days and the resulting phosphorimage was captured. The Coomassie stain is shown in purple and the phosphorimage is shown in greyscale (n=2). 152 Since our results showed very prominent Ynl092wp automethylation activity, the following work details Ynl092wp characterization efforts. Proposed plans for future characterization of Ykl162cp are discussed in Chapter 6. 4.4.2 Analysis of Ynl092wp automethylation via mass spectrometry To further investigate automethylated Ynl092wp residues, an exhaustively methylated sample of Ynl092wp was subjected to trypsin or Glu-C digestion and resulting peptides were analyzed via quadrupole time-of-flight mass spectrometry. Resulting peptide spectra were searched against E. coli database to identify contaminating bacterial methylated proteins and the S. cerevisiae database to identify regions of automethylation on Ynl092wp. No bacterial substrates were identified from the peptides. The peptides generated significantly matched Ynl092wp protein sequence, identified as CARME (p = 0.00001). From the trypsin digest, 24 unique peptides were identified with 36.75% sequence coverage (Figure 4.4A). From the Glu-C digest, 14 unique peptides were identified with 28.7% coverage (Figure 4.4B). A total, additive coverage of 55.5% was achieved (Appendix C.3). On peptides generated through both trypsin and Glu-C digestion, oxidation of both methionine and tryptophan residues was observed as a mass shift of +15.995. Additionally, because of blocking free sulfhydryl groups with iodoacetamide during sample preparation, cysteine residues were carboxamidomethylated, resulting in a mass shift of +57.021. No additional modifications were observed in the trypsin-digested sample, but a monomethylation corresponding to a mass shift of +14.016 was observed on histidine 314 within the peptide NDHGVETTYE that was generated via Glu-C digestion. Both methylated and unmethylated versions of this peptide were identified (Figure 4.4C and Figure 4.4D, respectively). In both the unmethylated and methylated spectra, the labelled peaks represent singly-charged peptide 153 fragments after the indicated amino acid in the peptide sequence (inset). Blue and red annotations indicate N- and C-terminal fragments, respectively; blue fragments harbour the positive charge on the C-terminal end of the fragment while red fragments harbour the positive charge on the N-terminus. Several fragments indicate a loss of water (-18). The a ions indicate that the peptide fragment backbone terminates without the C-terminal carboxylic acid group and the positive charge is localized to the amino group; the b ions indicate that the peptide fragment backbone terminates with a carbon monoxide group, and; the y ions indicate that the peptide fragment backbone is positively charged at the N-terminus [330]. The b3 peak represents the NDH fragment; in the methylated spectrum, b3 and all other labelled peaks are shifted +14 m/z, indicating a methylation event in the peptide that is present in the b3 ion and therefore must be on asparagine, aspartic acid, or histidine. Unfortunately, no b2 ions were formed in the fragmentation, so the fragmentation pattern alone cannot localize the methylation to one specific residue and instead we must consider the underlying chemistry. In the spectrum of the unmethylated peptide, the loss of water associated with the b3-18 ion is common and prevalent. A common source of dehydration is aspartic acid to succinimide conversion [331]. Interestingly, the b3-18 ion is not found at all in the modified peptide which suggests that methylation of either asparagine, aspartic acid, or histidine completely blocks succinimide formation. We speculate that delocalization of electron charge within the imidazole ring may allow for a side chain secondary amine attack on the adjacent aspartic acid side chain backbone to facilitate succinimide formation and subsequent dehydration, but when histidine is methylated, the tertiary amine prevents electron localization and thus prevents proton extraction from the aspartic acid backbone and inhibits succinimide formation. Therefore, we propose that the most likely methylation localization is to histidine, but additional experiments to confirm 154 histidine methylation should be performed. Identification of the unmodified and modified peptides is supported by significantly low p values, high scoring, and large differences between the top peptide hit and the second peptide hit, which signal the peptide identities with high confidence (Table 4.2). Aside from H314 monomethylation, no other methylations were identified. 155 Figure 4.4. Mass spectrometry data from Ynl092wp proteolysis. Peptides generated from (A) trypsin- and (B) Glu-C-digestion of methylated Ynl092wp. Green bars beneath the amino acid sequence indicate the peptides identified. Magenta marks denote a modification. The gold star in panel B represents the identified monomethylation of histidine 314. (C) The mass spectra of the unmodified peptide NDHGVETTYE and (D) the mass spectra of the modified peptide NDH[+14]GVETTYE. 156 Table 4.2. Proteomic data scoring. The associated p value, score, delta score, and delta modification (mod.) for the best-scored spectra for the unmodified and modified NDHGVETTYE peptide from Ynl092wp. The delta scores signify the difference in scoring between best hit and second-best hit. Peptide identification is more robust if the difference between the first and second hits are larger (ie. larger delta score). Scoring parameters were determined by Byonic™ peptide search engine. Peptide P value Score Delta Score Delta Mod. NDHGVETTYE 6.3 x 10-7 315.3 230.9 230.9 NDH[+14]GVETTYE 1.0 x 10-5 268.5 176.3 176.3 4.4.3 Inhibition of Ynl092wp automethylation by substrate amino acids and peptide To parse out active site characteristics of Ynl092wp, automethylation was monitored in the presence of carnosine and histidine, which Ynl092wp has been shown to methylate [320]. Automethylation was additionally monitored in the presence of arginine to observe if arginine can serve as an active site inhibitor. To determine an effective assay to monitor automethylation, a linear range assay was performed using 0.83 μM enzyme and 50 μM [14C]-SAM in a total reaction volume of 12 μL, incubated at 30oC for 0-5 hours and separated via 10% SDS-PAGE. Methylation was monitored via phosphorimaging. A parallel filter-binding assay was performed but radioactive signal above background was not detected (data not shown). From the gel-based radiometric assay, intensities of the radiometric bands were quantified using densitometry. The band intensities were normalized to the most intense Coomassie-stained band and plotted against the reaction time. The automethylation activity was linear from 0-3 hours where the detected radioactivity then began to plateau (Figure 4.5A). A linear regression was performed on the data from 0-3 hours and gives a strong correlation (R2 = 0.9812). For all subsequent inhibition experiments, reactions were performed at 30oC for 2 hours to stay within the linear range. To observe how histidine, carnosine, and arginine impacted Ynl092wp automethylation, 0.83 μM enzyme was incubated with 50 μM [14C]-SAM and 0-500 mM carnosine, histidine, and 157 arginine for 2 hours at 30oC. The concentration ranges were selected based on Ynl092wp KM for histidine-containing di- and tri-peptides of ~21.5 mM [320] and ensured that amino acid concentration was well in excess. Automethylation was measured using the radiometric gel-based assay, where the radioactive signal was normalized to the most intense Coomassie band. At all concentrations of carnosine, Ynl092wp band intensity in the Coomassie stain remained consistent. Radioactivity was detected at all concentrations of carnosine, but the radioactive signal began to attenuate at 100 mM and at the highest carnosine concentration, only ~25% was detected (Figure 4.5B). Normalized radioactive signal was calculated as a percent of the no inhibitor control and plotted against amino acid concentration to reveal an inhibition curve. Data were fit to Equation 4.1 to determine an IC50 of 77.58 ± 45.4 mM. Similarly for histidine, the Ynl092wp band intensity did not change across histidine concentrations. Radioactive signal was only detectable until 50 mM, but signal attenuation began at 10 mM (Figure 4.5C). Normalized, relative radioactive signal was plotted against histidine concentration and fit to Equation 4.1 to give an IC50 of 18.64 ± 3.1 mM. Interestingly, Ynl092wp precipitation occurs in a concentration-dependent manner in the presence of arginine, with noticeable decrease in enzyme at 50 mM arginine (Figure 4.5D). An IC50 was determined (41.71 ± 6.3 mM), but this observed inhibition effect is likely due to precipitation rather than inhibition of automethylation. 158 Figure 4.5. Inhibition of Ynl092wp automethylation by amino acids as measured by a gel-based radiometric assay. (A) The density of the radiograph (bottom grey image) was normalized to the most intense Coomassie stain (top, purple image). The normalized density was plotted against the time (hr) to reveal the linear range of automethylation to be within 3 hours. Concentrations of 0-500 mM carnosine (B), histidine (C), and arginine (D) were incubated with Ynl092wp and subjected to the gel-based radiometric assay. The density of the radiograph was normalized to the most intense Coomassie stain and subsequently converted to a relative percent of the no-inhibitor control. The relative density of the radioactivity was plotted against amino acid concentration and fit to the four-parameter logistic regression to determine an IC50 automethylation inhibition. In all experiments, 12 μL of the reaction in SDS-PAGE loading buffer was separated via SDS-PAGE on a 10% gel. Dried gels were exposed to a phosphor screen for 5 days and the resulting phosphorimage was detected and subsequently quantitated via densitometry (n = 2). We next sought to determine if Ynl092wp can methylate a 9mer N-terminal acetylated peptide representing the site of Ynl092wp automethylation (denoted as CARN) and captures residues 307-315. The peptide contains two histidine residues, His309 and His314. To observe methylation of the peptide, 0.83 μM Ynl092wp was incubated with 50 μM [14C]-SAM and 0.5-159 500 μM peptide and separated on a Tricine gel. The gels show a distinct band at ~48 kDa representing Ynl092wp, but also shows Coomassie-stained protein and radioactive signal in the stacking gel (Figure 4.6A). No protein nor phosphor signal is detected in the stacking gel in the absence of CARN peptide, suggesting that Coomassie staining and radioactive signal may be due to CARN peptide immobilized in stacking gel or a peptide-dependent aggregation of Ynl092wp. Automethylation of Ynl092wp in the presence of peptide was measured via densitometry relative to the Coomassie band in each lane (Figure 4.6B). No sizeable Ynl092wp automethylation inhibition was detected. Further, no peptide concentration-dependent increase in radioactive signal was detected in the stacking gel. Therefore, these results show qualitatively that Ynl092wp may weakly methylate CARN peptide or may interact with the CARN peptide to form a peptide-dependent aggregate. 160 Figure 4.6. Inhibition of Ynl092wp automethylation by CARN peptide. (A) The Coomassie staining (top panel) of Tricine gel and the corresponding radioactive phosphor screen (bottom panel). Reactions contained 0.83 μM Ynl092wp and increasing concentrations of CARN peptide (0–500 μM). Both Coomassie staining and phosphorimaging detect protein at ~48 kDa (corresponding to Ynl092wp) and in the stacking gel. Signal is only detected in the stacking gel when CARN peptide is present and may represent peptide or peptide-dependent enzyme aggregation. (B) Relative density of Ynl092wp automethylation relative to enzyme per lane shows no concentration-dependent inhibition of Ynl092wp activity by CARN peptide. Error bars represent the standard deviation of four replicates. We noticed that the automethylation sequence in Ynl092wp (LYHFENDHGVETTYE; methylated histidine in bold) is remarkably similar to that of actin found in yeast (TLRYPIEHGIVTNWD; NCBI: NP_116614.1) and mammals (TLKYPIEHGIVTNWD; NCBI: NP_001092.1) (these sequences differ only by a lysine/arginine conversion). Specifically, there is a high degree of conserved acidic and hydrophobic residues immediately around the histidine site 161 Figure 4.7A). Interestingly, mammalian actin is methylated at this corresponding histidine sequence, yet this methylation has not been identified in vivo in yeast [332]. This sequence similarity may indicate that actin is a possible substrate of Ynl092wp. Since the molecular weight of actin in yeast is ~42 kDa (NCBI: NP_116614.1), this would run congruently with Ynl092wp via SDS-PAGE in our in vitro lysate studies. Further, the intensity of the radioactive band that we attributed solely to Ynl092wp automethylation in Δynl092w in stationary phase appears to be 1.8- and 1.4-fold less than its log-phase counterpart in SC and YPD media respectively, as measured by densitometry (Figure 4.3B). In yeast, actin is downregulated in stationary phase so what we have interpreted as automethylation could be a combination of Ynl092wp automethylation and yeast actin [304]. To test this, Ynl092wp was incubated with 0-13 μM mammalian α-actin using [14C]-SAM as a methyl donor. Radioactive methyl transfer was monitored via phosphor imaging. Although actin and Ynl092wp co-migrate on an SDS-PAGE gel, we were able to separate two distinct bands corresponding to actin and Ynl092wp by Coomassie staining (Figure 4.7B). In the actin alone lane (which contained 1 μg actin) no radioactivity was detected, suggesting that actin and SAM do not spontaneously react to transfer a methyl group. We observed a small degree of Ynl092wp automethylation inhibition as actin concentration increased (Figure 4.7C). At the highest concentration of actin tested we observed only ~46% Ynl092wp automethylation activity. These data were unable to satisfactorily fit to Equation 4.1 to determine the IC50. Further, we saw a concentration-dependent increase in actin methylation, but we were unable to achieve substrate saturation and approach maximum reaction rate, so the KM could not be adequately determined (Figure 4.7D). We demonstrate that Ynl092wp methylates actin in vitro. 162 Figure 4.7 In vitro methylation of human actin by Ynl092wp. (A) Sequence alignments of the methylatable sequence in human actin, the homologous region in yeast actin, and the automethylation sequence we identified in Ynl092wp. Perfectly conserved residues (green), conserved basic charges (blue), conserved acidic charges (red) conserved non-polar side-chains (grey) and conserved aromatic features (yellow) are highlighted. Sequence alignment was performed using CLUSTAL [128]. (B) Coomassie stain (top panel) and resulting phosphor screen (bottom panel) of Ynl092wp and actin. Reactions containing 0.83 μM Ynl092wp, 50 μM [14C]-SAM, and 0–13 μM actin were incubated at 30oC for 2 hours. (C) Ynl092wp automethylation was determined by densitometry. Percent automethylation compared to the no actin control was plotted vs. actin concentration and fit to Equation 4.1, but the data did not fit to the model with high correlation coefficient (dotted line). Error bars represent the standard deviation (n=2). (D) Actin methylation was determined by densitometry. Relative actin methylation compared to the maximum methylation at 13 μM was plotted against actin concentration and fit to Equation 4.2, but the data did not fit to the model with a high correlation coefficient (dotted line). Error bars represent the standard deviation (n = 2). 4.4.4 Thermal stability of Ynl092wp in the presence of cofactor Since we saw a peptide-enzyme interaction in the Tricine gel, we next sought to determine how both cofactor and substrate peptide and amino acids impacted Ynl092wp thermal stability via 163 DSF. First, to determine ideal assay conditions, several concentrations of Ynl092wp (0.2-5 μM) were incubated with varying concentrations of SYPRO™ Orange dye (5x-10x) to determine which conditions produce viable melt curves. The resulting fluorescence data were normalized to the maximum and minimum curve fluorescence values to give a series of data that are a relative percent and were plotted against temperature and fit to the Boltzmann equation (Equation 4.4). All melt curves were monophasic, meaning that only one trough and one peak were observed. Ynl092wp concentrations of 0.83, 1, 2.5, and 5 μM gave viable melt curves that were greater than 3-fold above background (Figure 4.8A). There were no observed differences in melting temperature as the dye concentration changed, so 7.5x dye was selected for further use. Interestingly, as protein concentration increased, a significant decrease in derived Tm was observed (Figure 4.8B and Figure 4.8C and Appendix C.4). From 0.83 to 5 μM Ynl092wp, a Tm shift of -5.32 ± 0.11oC, indicating that the protein may aggregate at higher concentrations. To conserve protein and prevent confounding results due to possible aggregation, 0.83 μM enzyme was selected for further use. 164 Figure 4.8. Identification of differential scanning fluorimetry assay conditions. (A) Raw fluorescence readings of 0 (black), 0.83 (burgundy), 1 (purple), 2.5 (green) and 5 μM Ynl092wp (blue) incubated with 7.5x SYPRO™ Orange dye are shown as solid and dashed line duplicates. (B) Normalized relative fluorescence values (truncated at 3 data points beyond the minimum and maximum values) of 0.83 (burgundy), 1 (purple), 2.5 (green) and 5 μM Ynl092wp (blue) incubated with 7.5x SYPRO™ Orange dye are shown. Error bars represent the standard deviation of two replicates. (C) The melting temperatures derived from the melt curves using 7.5x SYPRO™ Orange dye and the indicated concentration of enzyme are plotted to reveal a concentration-dependent decrease. Error bars represent the standard deviation of two replicates. To analyze the effect of SAM and SAH cofactor on Ynl092wp thermal stability, DSF was performed using 0.83 μM Ynl092wp, 7.5x SYPRO™ Orange dye, and varying concentrations of 0-200 μM cofactor. The cofactor was prepared in 0.5 mM HCl to prevent hydrolysis. Control experiments were performed to assess interactions between cofactor and dye and to assess the impact of HCl on Ynl092wp thermal stability. The fluorescence was normalized to the relative maximum and minimum of the curve as 100 and 0%, respectively, and the data were fit to Equation 4.4 to derive the melting temperature. In the presence of additional HCl, the Tm of Ynl092wp minimally decreased in a concentration-dependent manner. Although the impact was statistically significant (p < 0.0001 at 56 μM), the magnitude was small, with a shift of -1.73 ± 0.15oC (Figure 4.9A, Figure 4.9B and Appendix C.5). At the highest concentration of HCl, an additional 1.12 x 10-9 moles of HCl were added, which does not impact the overall pH of the system. 165 At all concentrations of SAM and SAH cofactor when incubated with SYPRO™ Orange dye, graphs resembling melt curves were produced, but the raw fluorescent values of cofactor alone fell within background levels of SYPRO™ Orange alone, whereas with Ynl092wp and dye fluorescence was far outside background (Figure 4.9C). Figure 4.9. Control DSF experiments for cofactor. (A) Increasing HCl concentrations (0 μM, black circle; 12.5 μM, blue triangle; 25 μM, purple diamond; 28.5 μM, grey hexagon; 50 μM, pink star; and 56 μM, orange inverted triangle) were incubated with Ynl092wp and SYPRO™ Orange dye (n = 4). (B) The melting Tm for each HCl concentration with Ynl092wp. An ordinary one-way ANOVA for multiple comparisons was used to determine statistical significance between each HCl concentration and Ynl092wp alone (ns=not significant; * = p ≤ 0.05; ** =≤ 0.01; **** = p ≤ 0.0001). (C) Raw fluorescent values of fluorescent dye incubated with 0.5 μM SAM (green square), 200 μM SAM (blue triangle), 0.5 μM SAH (orange inverted triangle), and 200 μM SAH (purple diamond) do not differ from SYPRO™ Orange dye alone (black circle), but fluorescent values of 0.83 μM Ynl092wp with dye are significantly above background (n = 2). Since no notable interaction was observed, varying concentrations of each cofactor were incubated with 0.83 μM Ynl092wp and 7.5x SYPRO™ Orange dye. When comparing derived melting temperatures, the experiments were all performed on the same day in the same plate with the same reagents to account for small, variable changes in Tm values. The derived Tm for Ynl092wp without cofactor was approximately 55oC (Appendix C.6). The melt curves produced with enzyme alone and in conjunction with all cofactor concentrations were monophasic (Appendix C.7). With both SAM and SAH cofactors, a concentration-dependent decrease in Ynl092wp melting temperature is observed. With SAM, a significant negative shift is observed 166 starting at 1 μM SAM, with a shift of -0.65 ± 0.23oC and at the highest SAM concentration, a shift of -1.62 ± 0.11oC is observed (Figure 4.10A and Figure 4.10B). The resulting melting temperatures were plotted against ligand concentration and fit to the one site binding model (Equation 4.5) and a dissociation constant KD of 7.3 ± 2.1 μM was derived (Figure 4.10C). A similar trend is observed with SAH cofactor, with a significant negative shift observed at 1.5 μM SAH of -0.59 ± 0.59oC, but a sizeable thermal shift is observed at 200 μM SAH of -3.75 ± 0.42oC (Figure 4.10D and Figure 4.10E). The resulting data did not fit to the one site binding model, so no KD was derived (Figure 4.10F). Therefore, both SAM and SAH cofactor do not stabilize Ynl092wp suggesting that Ynl092wp is unable to bind to cofactor alone. 167 Figure 4.10. Thermal stability of Ynl092wp in the presence of SAM or SAH cofactor. (A) Truncated melt curves of Ynl092wp alone (black circle) or in the presence of 5 (green square), 10 (blue triangle), 50 (purple diamond), or 200 μM (grey hexagon) SAM. (B) Derived melting temperatures of Ynl092wp incubated with 0-200 μM SAM. An ordinary one-way ANOVA with multiple comparisons was used to determine significant differences compared to Ynl092wp alone. A p value of 0.05 was used as the cutoff for significance. (C) The melting temperatures were plotted against the SAM concentration and fit to the one-site model of binding to derive a dissociation constant. (D) Truncated melt curves of Ynl092wp alone (black circle) or in the presence of 5 (green square), 10 (blue triangle), 50 (purple diamond), or 200 μM (grey hexagon) SAH. (E) Derived melting temperatures of Ynl092wp incubated with 0-200 μM SAH. An ordinary one-way ANOVA with multiple comparisons was used to determine significant differences compared to Ynl092wp alone. A p value of 0.05 was used as the cutoff for significance. (F) The melting temperatures were plotted against the SAH concentration and fit to the one-site model of binding to derive a dissociation constant. The data did not fit to the model. In all cases, error bars show the standard deviation of four replicates (ns = not significant; * = p < 0.05; ** = p < 0.01; *** = p < 0.001; **** = p < 0.0001). 4.4.5 Thermal stability of Ynl092wp in the presence of amino acid and peptide substrates To observe the effect of carnosine, histidine, and the Ynl092wp-derived peptide (CARN) on Ynl092wp stability, DSF assays were performed using 0.83 μM protein, 7.5x SYPRO™ Orange, and varying concentrations of carnosine or histidine (0-500 mM) and CARN peptide (0-168 300 μM). Arginine was not used since it caused Ynl092wp to precipitate in the inhibition of automethylation experiments. The concentrations of histidine and carnosine were selected so that the highest concentration is greater than 10-fold the determined KM for carnosine [320]. To first observe if the ligands interact with the fluorescent dye in a notable way, ligand-only controls were performed, where increasing concentrations of the ligands were incubated with SYPRO™ Orange dye and subjected to heating. At all concentrations of carnosine, histidine, and CARN peptide, no viable melt curves were produced (Figure 4.11). Interestingly though, the raw fluorescence of histidine alone with SYPRO™ Orange dye are pooled into two distinct populations, with concentrations of 0-50 mM fluorescence reaching a maximum of approximately 60000 units, but with concentrations of 100-300 mM reaching maximum fluorescence of less than 20000 units (Figure 4.11). Although both carnosine and histidine do not interact with SYPRO™ Orange to create confounding melt curves, they do interact in a concentration-dependent manner to influence the amount of fluorescence. At increasing concentrations of carnosine, more raw fluorescence is observed, but with histidine, the opposite effect is seen, with highest concentrations of histidine quenching SYPRO™ Orange fluorescence. Conversely, CARN peptide does not interact with dye in a notable way. All raw fluorescent data fall within background levels well below the raw fluorescence produced by Ynl092wp. 169 Figure 4.11. Control DSF experiments for carnosine, histidine, and CARN peptide. Raw fluorescence units of SYPRO™ dye alone (black circle), 0.05 mM (green triangle) and 300 mM (blue triangle) carnosine; 0.05 mM (orange square) and 300 mM (teal square) histidine; 0.5 μM (purple diamond) and 500 μM (yellow diamond) CARN peptide; and 0.83 μM Ynl092wp alone incubated with 7.5x SYPRO™ Orange (n=2). With increasing concentrations of carnosine incubated with Ynl092wp, all melt curves were monophasic (Figure 4.12A). A small yet significant carnosine concentration-dependent increase in the melting temperature is observed, with the highest shift of 2.43 ± 0.38oC observed at 50 mM carnosine (Figure 4.12B). At higher carnosine concentrations, the Tm plateaus at approximately 55.4 ± 0.5oC, corresponding to a Tm shift of 1.0 ± 0.7oC. This indicates a weak stabilizing effect of carnosine ligand on Ynl092wp structure. No KD value was derived from these data because the data did not fit to the one-site binding model (Figure 4.12C). The same effect is not observed for histidine. All histidine melt curves were also monophasic (Figure 4.12D), but at low concentrations of histidine (0.05-5 mM) a weak yet significant positive thermal shift is observed up to 0.6 ± 0.2oC at 5 mM histidine (Figure 4.12E). However, from 10-50 mM histidine, a significant negative thermal shift is observed up to -8.9 ± 0.3oC at 50 mM. Interestingly, at 170 concentrations of 100 mM histidine and above, no melt curves for Ynl092wp were produced (Appendix C.9). Like carnosine, no KD value was determined (Figure 4.12F). Since high concentrations of histidine alone quenched SYPRO™ fluorescence, we suspected that the decrease in Tm observed above 50 mM histidine was not a result of histidine destabilization, but rather a result of confounding interactions between histidine and dye. To circumvent this, an in vitro thermal shift assay was performed by incubating Ynl092wp alone at 30-66oC or with a high concentration of histidine (300 mM) and the soluble fractions were separated via SDS-PAGE. In this case, the dye is not a part of the reaction incubation and therefore will not confound the results. The resulting gel was analyzed by densitometry to produce melt curves. A thermally stable protein, NUDT5, was also added to the reactions as a control. With Ynl092wp alone, we see a decrease in Ynl092wp soluble fraction starting at 42oC. In this assay, a Tm of 46.4 ± 0.4oC for Ynl092wp alone was determined (Figure 4.12G). In the presence of 300 mM histidine, we do not see attenuation of soluble Ynl092wp until >50oC and a Tm of 60 ± 6oC was observed, indicating a strong and significant positive thermal shift of 13 ± 6oC (p = 0.005) (Figure 4.12H). Lastly, with increasing concentrations of CARN peptide, all Ynl092wp melt curves were monophasic (Figure 4.12I). A small and significant concentration-dependent increase in Ynl092wp melting temperature is observed up to 0.6 ± 0.4oC at 500 μM peptide (Figure 4.12J). Although the data fit to the one-site binding model, no reliable KD value was determined since the goodness of fit was low (Figure 4.12K). A summary of all Tm data and untruncated melt curves for all conditions can be found in Appendix C.8 and Appendix C.9. Therefore, all tested amino acid and peptide substrates weakly yet significantly stabilize Ynl092wp, indicating that Ynl092wp can interact with target substrates without cofactor. 171 172 Figure 4.12. Thermal stability of Ynl092wp in the presence of carnosine, histidine, or CARN peptide. (A) Truncated melt curves of Ynl092wp alone (black circle) or in the presence of 5 (green square), 25 (blue triangle), 100 (purple diamond), or 300 mM (grey hexagon) carnosine. (B) Derived melting temperatures of Ynl092wp incubated with 0-300 mM carnosine. An ordinary one-way ANOVA with multiple comparisons was used to determine significant differences compared to Ynl092wp alone. A p value of 0.05 was used as the cutoff for significance. (C) The melting temperatures were plotted against the carnosine concentration and fit to the one-site model of binding to derive a dissociation constant. The data did not fit to the model. (D) Truncated melt curves of Ynl092wp alone (black circle) or in the presence of 0.05 (green square), 0.5 (blue triangle), 1 (purple diamond), 5 (grey hexagon), 10 (red inverted triangle), 25 (orange plus), or 50 mM (teal X) histidine. (E) Derived melting temperatures of Ynl092wp incubated with 050 mM histidine. An ordinary one-way ANOVA with multiple comparisons was used to determine significant differences compared to Ynl092wp alone. A p value of 0.05 was used as the cutoff for significance. (F) The melting temperatures were plotted against the histidine concentration and fit to the one-site model. The data did not fit to the model. (G) Ynl092wp and NUDT5 and (H) Ynl092wp, NUDT5, and 300 mM histidine were incubated in methylation buffer from 30-66oC and separated via SDS-PAGE. Protein was stained using SYPRO™ Orange and imaged at 302 nm. The density of Ynl092wp bands (~48 kDa) were normalized to the NUDT5 band (~35 kDa) in each lane and subsequently normalized to 30oC as 100%. (I) Truncated melt curves of Ynl092wp alone (black circle) or in the presence of 5 (green square), 25 (blue triangle), 100 (purple diamond), or 300 μM (grey hexagon) CARN peptide. (J) Derived melting temperatures of Ynl092wp incubated with 0-300 μM CARN peptide. (K) The melting temperatures were plotted against the CARN peptide concentration and fit to the one-site model. The data did not fit to the model. An ordinary one-way ANOVA with multiple comparisons was used to determine significant differences of treatment compared to Ynl092wp alone. A p value of 0.05 was used as the cutoff for significance. Error bars show the standard deviation of four replicates (for all DSF experiments) and two replicates (for in vitro TSA experiments) (ns = not significant; * = p < 0.05; ** = p < 0.01; *** = p < 0.001; **** = p < 0.0001) . 173 4.4.6 Thermal stability of Ynl092wp in the presence of cofactor and substrate combinations After observing the singular effects of either cofactor, amino acid, or peptide substrate on Ynl092wp thermal stability, we next wanted to investigate how combined cofactor and substrate interact with Ynl092wp. First, increasing concentrations of carnosine, histidine, and CARN peptide were incubated with 200 μM excess SAH. As above, all experiments within a set of conditions were performed at the same time to minimize error due to variable changes in Tm. In all cases, in the presence of Ynl092wp and 200 μM SAH alone, the approximate derived Tm was 52oC (Appendix C.10), approximately 3oC less than Ynl092wp alone (Appendix C.6), again demonstrating that cofactor modestly destabilizes Ynl092wp. With increasing carnosine concentrations in the presence of 200 μM SAH, all melt curves were monophasic and an increase in Ynl092wp Tm is observed (Figure 4.14A). A small yet significant positive shift is observed from 0.05-300 mM carnosine (1.6 ± 0.2oC at 300 mM) (Figure 4.14B and Appendix C.10). However, with increasing histidine concentrations, there is no significant change in Tm as histidine increases up to 25 mM, although a steady positive trend in increasing Tms is observed (Figure 4.14C, Figure 4.14D, and Appendix C.10). Like with histidine alone, at 50 mM histidine added to 200 μM SAH and Ynl092wp, we see a significant destabilization of Ynl092wp with a thermal shift of -7.2 ± 0.8oC observed (Appendix C.10), but unsurprisingly, no melt curves are produced at histidine concentrations above 50 mM (data not shown). Last, with increasing CARN peptide in the presence of excess SAH, we see no change in Ynl092wp Tm at any concentration tested (Figure 4.14E and Figure 4.14F). Both increasing carnosine or increasing histidine in the presence of SAH cofactor display the same stabilization patterns of increasing carnosine or histidine alone (Figure 4.12), suggesting that the presence of bound SAH does not impact carnosine or histidine 174 interaction with Ynl092wp. However, with increasing CARN peptide alone, we see a significant stabilization of Ynl092wp (Figure 4.12). This stabilization is not captured in the presence of excess SAH, suggesting that bound SAH may decrease peptide binding to Ynl092wp. 175 Figure 4.13. Thermal stability of Ynl092wp in the presence of increasing carnosine, histidine, or CARN peptide pre-incubated with excess SAH. (A) Truncated melt curves of Ynl092wp with 200 μM SAH (black circle) or in the combined presence of 5 (green square), 25 (blue triangle), 100 (purple diamond), or 300 mM (grey hexagon) carnosine. (B) Derived melting temperatures of Ynl092wp and excess SAH incubated with 0-300 mM carnosine. An ordinary one-way ANOVA with multiple comparisons was used to determine significant differences compared to Ynl092wp alone. A p value of 0.05 was used as the cutoff for significance. (C) Truncated melt curves of Ynl092wp with 200 μM (black circle) or in the combined presence of 0.05 (green square), 0.5 (blue triangle), 1 (purple diamond), 5 (grey hexagon), 10 (red inverted triangle), 25 (orange plus), or 50 mM (teal X) histidine. (D) Derived melting temperatures of Ynl092wp and excess SAH incubated with 0-50 mM histidine. An ordinary one-way ANOVA with multiple comparisons was used to determine significant differences compared to Ynl092wp alone. A p value of 0.05 was used as the cutoff for significance. (E) Truncated melt curves of Ynl092wp with 200 μM SAH (black circle) or in the combined presence of 5 (green square), 25 (blue triangle), 100 (purple diamond), or 300 μM (grey hexagon) CARN peptide. (F) Derived melting temperatures of Ynl092wp and excess SAH incubated with 0-300 μM CARN peptide. An ordinary one-way ANOVA with multiple comparisons was used to determine significant differences of treatment compared to Ynl092wp with excess cofactor. A p value of 0.05 was used as the cutoff for significance. Error bars show the standard deviation of four replicates (ns = not significant; * = p < 0.05; ** = p < 0.01; *** = p < 0.001; **** = p < 0.0001). 176 We next observed the effects of increasing SAH in the presence of constant excess substrate. Since histidine negatively interferes with SYPRO™ Orange, histidine was omitted from this experiment. With increasing SAH concentrations in the presence of 300 mM carnosine, all melt curves were monophasic and a significant decrease in Ynl092wp Tm is observed (-3.04 ± 0.30oC at 200 μM) (Figure 4.14A, Figure 4.14B and Appendix C.11). With increasing SAH in the presence of 300 μM CARN peptide, unlike with increasing SAH alone, we see a remarkable and significant positive thermal shift of 3.6 ± 0.2oC at 200 μM (Figure 4.14C, Figure 4.14D, and Appendix C.11). Increasing SAH concentration in the presence of high carnosine concentration also resembles the effect of increasing SAH alone, suggesting that carnosine does not impact SAH binding. However, the significant stabilization of SAH synergistically with high concentrations of CARN peptide suggests that CARN peptide binding significantly improves SAH binding and thus, Ynl092wp stabilization. The untruncated melt curves of all combinatorial cofactor and substrate concentrations are shown in Appendix C.12. 177 Figure 4.14. Thermal stability of Ynl092wp in the presence of increasing SAH pre-incubated with excess carnosine or CARN peptide. (A) Truncated melt curves of Ynl092wp with 300 mM carnosine (black circle) or in the combined presence of 5 (green square), 10 (blue triangle), 50 (purple diamond), or 200 μM (grey hexagon) SAH. (B) Derived melting temperatures of Ynl092wp and excess carnosine incubated with 0-200 μM SAH. An ordinary one-way ANOVA with multiple comparisons was used to determine significant differences compared to Ynl092wp alone. A p value of 0.05 was used as the cutoff for significance. (C) Truncated melt curves of Ynl092wp with 300 μM CARN peptide (black circle) or in the combined presence of 5 (green square), 10 (blue triangle), 50 (purple diamond), or 200 μM (grey hexagon) SAH. (D) Derived melting temperatures of Ynl092wp and excess CARN peptide incubated with 0-200 μM SAH. An ordinary one-way ANOVA with multiple comparisons was used to determine significant differences compared to Ynl092wp alone. A p value of 0.05 was used as the cutoff for significance. In all cases, error bars show the standard deviation of four replicates (ns = not significant; * = p < 0.05; ** = p < 0.01; *** = p < 0.001; **** = p < 0.0001). 178 4.5 Discussion 4.5.1 Ykl162cp is a suspected putative RNA methyltransferase Since we observed that treatment of enzyme-lysate exhaustive methylation reaction before and after incubation absolved all detected methylation of a yeast substrate, we suspect that Ykl162cp is methylating an RNA substrate (Figure 4.3E). This putative RNA-methylation activity aligns with the predicted substrate of Ykl162cp (Table 4.1) which reports RNA due to its relatively high pI (7.98) making it most likely to methylate a negatively charged substrate [204]. We also show that Ykl162cp activity in vitro is positively correlated with increasing pH (Figure 4.3C). GFP-Ykl162cp has been shown to localize to the mitochondria, like its human homolog NDUFAF7 [317,333], which in yeast has a weakly basic pH of 7.5 and in mammalian cells has a reported basic pH of 7.5-8 [334,335]. Therefore, the observation that Ykl162cp has higher enzyme activity at basic pH is consistent with its subcellular localization. It is not surprising that Ykl162cp displays enzyme activity that is different than its mammalian homolog. Its homologous relationship to mammalian NDUFAF7 and the related Dictyostelium MidA is tenuous at best. As noted previously, a 27% sequence identity is seen between yeast Ykl162cp and human NDUFAF7 (Figure 4.1) yet in a current BLAST search for Ykl162cp homologs from all organisms, neither NDUFAF7 nor MidA are identified as hits, whereas they previously were identified [316]. Further, the regions of local homology between mammalian and yeast NDUFAF7 occur in the SAM-binding region, which is a semi-conserved Rossmann fold that is found in a wide range of methyltransferases. Based on little sequence homology, the Ykl162cp active site cannot be inferred from what is known about MidA active site. The active site residues for substrate binding in MidA, as determined through in silico substrate docking, are not conserved in Ykl162cp based on sequence alignment (Figure 4.1A) 179 [336]. If Ykl162cp is an RNA methyltransferase, then we would suspect that there may be considerable differences in active site architecture since both NDUFAF7 and MidA are confirmed arginine methyltransferases [97,336]. Interestingly though, the only known substrate of NDUFAF7 and MidA is not found in yeast. In mammals and Dictyostelium, the substrate NADH:Ubiquinone Oxidoreductase Core Subunit S2 (NDUFS2) is an integral subunit of mitochondrial complex I and its arginine methylation is crucial for complex I assembly [96]. In mammals, complex 1 is made of a large multi-subunit complex which serves as a proton pump, coupled with electron transfer. In yeast, this process differs in that complex I does not exist and instead, two proteins named NADH dehydrogenase, external 1 and 2 (Nde1p and Nde2p) work to generate protons as a result of electron transfer via ubiquinone, but do not serve as a proton pump [337]. These corresponding proteins in yeast have different functions and no homology to humans, so the corresponding target substrate of Ykl162cp has not been hypothesized. Therefore, since neither the NDUFAF7 substrate nor the methyl mark are evolutionarily conserved, it may stand to reason that Ykl162cp methylation activity may also be divergent. Attempts have been made to study the activity and function of Ykl162cp but have yet been unfruitful. Specifically, no in vitro activity nor any knockout phenotype has been detected [316]. We postulate that a lack of detected in vitro activity may be due to studies being performed at suboptimal pH. Further, we had difficulty producing a highly purified Ykl162cp enzyme which may in part be due to non-optimized buffer conditions (for example, maintaining the enzyme in a more basic storage buffer may be more advantageous) or non-optimal tagging conditions. Therefore, Ykl162cp was not included in any DSF analyses due to confounding crude purification. Future work will therefore focus on optimizing Ykl162cp expression and purification and 180 identifying in vitro activity towards yeast mitochondria extracts and yeast RNA (discussed in detail in Chapter 6). 4.5.2 Ynl092wp characterization 4.5.2.1 Ynl092wp substrate characterization In this research, we confirm for the first time that a site of Ynl092wp automethylation is histidine. Since other work has demonstrated that Ynl092wp has weak carnosine- and histidine-methylation activity, the observation of Ynl092wp automethylation was merely presumed to be histidine [204,320]. The exact type of histidine modification has not been clarified here (ie. 1- or 3-methylhistidine), but it is presumed to be 3-methylhistidine to resemble the structure of anserine. We also saw that free histidine is a much more potent inhibitor of automethylation than carnosine, suggesting that histidine rather than carnosine is the real methylation target in yeast, which together support that Ynl092wp is a protein methyltransferase, not a carnosine methyltransferase. Although we only identified one site of automethylation, we suspect that additional automethylation sites exist due to the strong radioactive signal we observed. Between the trypsin- and Glu-C-digest, we obtained ~55% coverage but only 3 of the 7 histidine residues in Ynl092wp were captured in the proteolytic digests. One specific histidine residue not captured, His309, is a part of the SAM-binding site and mutational studies of this residue resulted in decreased enzyme activity [338], but its proximity to the cofactor suggests it could easily be methylated by the same subunit (Figure 4.15A). The orientation of the His314 sidechain protruding away from the enzyme suggests that Ynl092wp automethylation is achieved by another enzyme unit. Therefore, more complete coverage of the enzyme is required to observe the methylation status of all histidine residues (detailed in Chapter 6). 181 Although we found evidence that Ynl092wp methylates histidine on proteins, we failed to find a native yeast substrate in either Δrmt1 or Δynl092w cells. This may in part be due to 3-methylhistidine being a relatively rare eukaryotic post-translational modification that has only yet been found on a yeast ribosomal protein Rpl3p and mammalian actin and myosin [79,332]. However, a recent analysis of proteomic data from common human cell lines and biopsied tissue revealed that histidine methylation is more widespread than originally thought and they identified several new proteins that are methylated on histidine [339]. In yeast, histidine methyltransferase activity towards Rpl3p has been attributed to histidine protein methyltransferase 1 (Hpm1p) but no additional Hpm1p substrate has been identified [79]. Further, it is notable that the methylatable sequence of yeast Rpl3p (KKLPRKTHRGLRKVA; methylated histidine in bold) bears no resemblance to the methylated sequence of mammalian actin nor the corresponding sequence of yeast actin (Figure 4.7A). Our observation that Ynl092wp can methylate mammalian actin in vitro may suggest that yeast actin may also harbour 3-methylhistidine in a Ynl092wp-dependent manner. If actin is a substrate of Ynl092wp in vivo, it may have been missed in previous investigations of actin methylation status due to perhaps being present in low amounts in cells. In mammals, the true function of 3-methylhistidine has been unclear, with mutagenesis studies showing that histidine methylation on actin is not required for any of its major functional roles, including cytoskeleton incorporation, nor polymer formation, nor association with DNase I [340]. It has been instead demonstrated that histidine methylation impacts ATP hydrolysis and inorganic phosphate release. Histidine methylation stabilizes the positive charge on histidine which in turn coordinates a series of electrostatic interactions that assist in ATP binding [341]. It remains unclear as to why this modification may not exist in yeast. Continued efforts to find a bona fide Ynl092wp 182 substrate will include further study of Ynl092wp activity towards actin as well as probing for other natural substrates (detailed in Chapter 6). 4.5.2.2 Ynl092wp substrate binding Using DSF, we demonstrated distinct effects of cofactor, amino acid substrate, and peptide substrate on Ynl092wp thermal stability. Based on our previous work applying DSF to determine bisubstrate binding to enzymes, we postulate that Ynl092wp may bind its substrates using a sequential ordered method in which substrate binding precedes cofactor binding [342]. Since carnosine, histidine, and CARN peptide alone stabilize Ynl092wp (Figure 4.12) and cofactor alone destabilizes Ynl092wp (Figure 4.10), this suggests that for methylation to occur, cofactor binding preceding substrate binding is not required. Previous work measuring SAH and Ynl092wp interactions using isothermal titration calorimetry (ITC) did produce a weak binding isotherm with stoichiometry N=0.341 [338]. The authors conclude that this may indicate that Ynl092wp catalyzes methyl transfer using only one active site, but their crystal structure produced a dimer with SAH bound to both active sites, suggesting that this theory is not likely. A more likely explanation is that cofactor binds weakly to the apo-enzyme, resulting in poor quality ITC data and inaccurate stoichiometry determination. A sequential ordered method of bisubstrate binding is further supported by the results of our combined cofactor and peptide substrate results. When 200 μM SAH and Ynl092wp were pre-incubated and subsequently titrated with increasing concentrations of CARN peptide, we saw no enhanced stabilization of the ternary complex, suggesting that cofactor binding did not improve peptide binding. However, using the inverse conditions where 300 μM CARN peptide was pre-incubated with Ynl092wp and increasing SAH was subsequently titrated, we saw a concentration-183 dependent increase in Ynl092wp Tm suggesting that in the presence of bound peptide, binding of cofactor works to stabilize Ynl092wp. This supports the notion that the ternary structure is stabilized if peptide substrate binding precedes cofactor binding. Examination of the SAH-binding site and proposed possible substrate binding sites (as determined by Liu et al., 2017 through in silico docking of carnosine to Ynl092wp) reveals a spacious opening in the enzyme that may allow for ample room for cofactor binding in the presence of bound substrate (Figure 4.15B and Figure 4.15C) [338]. Figure 4.15. Ynl092wp structure, sequence, and substrate binding mode. (A) Structure of Ynl092wp monomer (PDB: 5X62) bound to SAH cofactor (orange) [338]. The automethylation site His314 is shown in green and active site His309 is shown in yellow. (B) Crystalized SAH (pink) and possible substrate binding sites of each monomer (as proposed by Liu et al., 2017) are shown in cyan, red, and yellow spheres (PDB: 5X62) [338]. (C) Protein surface area of the dimer is shown in grey. A large cavity is formed at the dimer interface that exposes SAH and substrate. 184 Despite finding a methyl-accepting sequence and observing distinct enzyme-peptide interactions through gel electrophoresis (Figure 4.6) and through DSF (Figure 4.12), we believe that we did not identify an ideal peptide substrate for Ynl092wp. We observed minimal methylation of the peptide via phosphor imaging and only observed weak stabilization of the enzyme through DSF. Further, we were unable to calculate KD for histidine, carnosine, nor CARN peptide, suggesting that they are poor substrates. We were able to calculate a KD of 7.3 ± 2.1 μM for SAM, suggesting that cofactor can bind to the apo-protein in a destabilizing way or it preferentially binds to the unfolded protein. We were unable to determine a KD for SAH since the data did not fit to the one-site binding model; however, ITC studies also reported a poorer KD value for SAH than for SAM, so our data are consistent with this finding [338]. However, Liu et al. reported a KD for SAM that is an order of magnitude smaller than our finding, suggesting that Ynl092wp may have a higher affinity for SAM than we report. As stated above though, this was calculated using possibly weak ITC data [338]. Since we were unable to calculate KD values for the poor substrates, we can look to the Ynl092wp active site architecture for explanations. The large proposed active and substrate binding site of Ynl092wp may suggest that residues distal to the target histidine may be important for substrate binding. In the Ynl092wp crystal structure, the proposed binding site is located near the dimer interface which forms a large cavity in the center of the enzyme (Figure 4.15C) [338]. This is supported too by our observation that full length Ynl092wp is a much better substrate than its derived peptide. This effect has also been observed in PRMT1 where the best histone H4 peptide substrates are longer and improve enzyme activity due to allosteric interactions at distal residues [140]. Therefore, the poor methylation of CARN peptide may not indicate that the target substrate 185 sequence is inappropriate but rather that there is a minimal peptide length required for effective substrate binding and methyl transfer. 4.6 Conclusions In the search for a likely sDMA-forming enzyme, we instead identified a likely RNA methyltransferase. Here we present that its methyltransferase activity is only detectable under basic pH conditions that differs from our standard methylation buffer. This supports previous findings that Ykl162cp localizes to the weakly basic mitochondrial matrix, suggesting that the natural substrate of Ykl162cp is mitochondrial RNA [333]. Although deeper investigation into the RNA methyltransferase activity of Ykl162cp is required, this work is significant in that it is the first to identify any in vitro Ykl162cp activity. These findings can inform future work to identify a substrate and verify its activity by directing research efforts towards RNA rather than arginine, an unexpected deviation from its mammalian and slime mold homologs. We also demonstrate that Ynl092wp is a protein histidine methyltransferase. Although its previously observed automethylation was presumed to be histidine [204,320,338], we are the first to definitively show histidine monomethylation via mass spectrometry. Further, we demonstrate that Ynl092wp methylates mammalian actin in vitro, whose yeast homolog is notably hypomethylated. Using DSF, a methodology that we have previously adapted to determine bisubstrate binding to PRMT1 [342], we propose that Ynl092wp may bind its substrates using a sequential ordered mechanism where substrate binding precedes cofactor binding. Although this mechanism of action will require confirmation through robust enzyme kinetic analyses, our assay serves as a likely prediction in the absence of a bona fide in vitro substrate that is readily 186 methylated. Elucidation of the Ynl092wp automethylated sequence may serve as a valuable springboard for identification of natural yeast substrates, including the probable substrate actin. Therefore, although our efforts to identify a novel PRMT in yeast were unsuccessful, we do present new evidence to aid in the characterization of two putative yeast methyltransferases, Ykl162cp and Ynl092wp. This preliminary work will support and inform future efforts to characterize their methyltransferase activities and explore the broader biological significance of methylation in yeast. 187 Chapter 5: Exploration of the fates of methylarginines in the yeast stress response 5.1 Chapter Synopsis Protein arginine N-methyltransferases (PRMTs) have emerged as important actors in the eukaryotic stress response with implications in human disease, aging, and cell signaling. An important role of intracellular free methylarginines to cellular stress is their interaction with nitric oxide synthase (NOS). The arginine-dependent production of nitric oxide (NO), which is strongly inhibited by methylarginines, serves as a protective mark against oxidative stress in eukaryotic cells. Excessive methylarginine-mediated inhibition of NOS can contribute to cell toxicity or death due to an accumulation of reactive oxygen species. NO signaling is highly conserved between higher and lower eukaryotes, although a canonical NOS homolog has yet to be identified in yeast. Since stress signaling pathways are well conserved among eukaryotes, yeast is an ideal model organism to study the implications of PRMTs and methylarginines in stress. We sought to explore the roles and fates of methylarginines in Saccharomyces cerevisiae. We starved methyltransferase-, autophagy-, and permease-related yeast knockouts by growing them in water and monitored methylarginine production. We found that under starvation, methylarginines are readily expelled from yeast cells. We found that autophagy-deficient cells are unable to efflux methylarginines, which suggests that methylarginine-containing proteins are degraded via autophagy. For the first time, we demonstrate that yeast do not take up methylarginines, and we show that methylarginines inhibit yeast NO production. This study reveals that yeast circumvent a potential methylarginine toxicity by expelling them after autophagic degradation of arginine-modified proteins. 188 5.2 Introduction The baker’s yeast Saccharomyces cerevisiae has become an invaluable model for exploring the biological pathways of eukaryotic stress and aging [343,344]. Yeast provide a suitable tool to monitor the impact of a variety of stressors on processes like gene expression, protein activity, and cell viability given their short generation time coupled with facile manipulation of their genome and growth conditions. Complementary to this, the complete yeast knockout (YKO) collection has been created with single haploid or diploid knockouts of every possible viable null mutant, totaling over 21000 knockout strains for all ~6000 yeast genes [323]. This library provides an easy method for studying the impact of a gene and its gene product on yeast cellular processes. Use of this collection has contributed substantially to observe cause and effect of individual genes and has allowed for probing of protein involvement in signaling pathways [323]. Budding yeast is an ideal model organism to study human pathways not solely limited to its ease of growth and manipulation, but also due to considerable overlap between stress responses in higher and lower eukaryotes. In both mammals and S. cerevisiae, survival in response to stress requires rapid expression of genes to promote cell longevity. This is achieved by activating stress-related transcription factors in response to environmental stressors. This is mediated through two primary nutrient-sensing mitogen activated protein kinase (MAPK) pathways, which cause a downstream kinase cascade resulting in activation of key transcription factors [205]. One pathway is the cyclic adenosine monophosphate-protein kinase A (cAMP-PKA) pathway which is primarily inactivated in response to heat shock and glucose depletion [345]. Under non-stress conditions, PKA works to phosphorylate a pair of partially redundant transcription factors important for stress: multicopy suppressor of SNF1 mutation 2 and 4 (Msn2/4p). In certain stress conditions, inactivation of PKA contributes to dephosphorylation of Msn2/4, promoting its translocation from 189 the cytosol to the nucleus to activate its target genes that have a stress responsive element (STRE) in their promoter regions [206,207,210]. The cAMP-PKA pathway is also sensitive to oxidative stress, again leading to the activation of Msn2/4p [346]. These transcription factors are also activated by the other MAPK major nitrogen-sensing target of rapamycin (TOR) kinase pathway in eukaryotes. In response to nitrogen or carbon depletion, TOR kinase activity is inhibited. Under these conditions, Msn2/4p is also able to translocate to the nucleus, whereas Msn2/4p remains cytosolic in conditions where TOR is active [208]. Transcription factors Msn2/4p are therefore involved in mediating gene expression in response to a wide range of stressors, including oxidative stress, non-fermentable carbon source, carbon depletion, and nitrogen depletion and regulate the transcription of hundreds of genes [347]. An additional transcription factor Yap1p is also shown to translocate to the nucleus in response to oxidative stress and carbon stress independent of cAMP-PKA and TOR pathways to transcribe its target genes [348,349]. Another notable similarity between mammals and yeast cells is the induction of autophagy in response to nutrient starvation stress. Autophagy is an important cellular process that is induced to break down and recycle bulk or targeted cellular components in response to limited nutrient availability. This process is highly conserved among eukaryotes and provides a way for cells to extend lifespan in response to stress. The organelle responsible for autophagic degradation of cellular material in mammals is the lysosome, but the vacuole serves this role in yeast [350]. Autophagy is regulated by the TOR pathway and is inhibited when TOR signaling is active. Upon inhibition of TOR signaling in stress, downstream autophagy-related proteins (Atg proteins) are rapidly dephosphorylated and subsequently activated [350]. TOR inactivation also leads to nuclear translocation of Msn2/4p which promotes transcription of some Atg genes [351]. At the start of autophagy, TOR inactivation leads to rapid hypophosphorylation of Atg13p which then forms a 190 complex with Atg1p kinase and Atg17p scaffolding protein to form the phagophore assembly site (PAS) to begin encapsulating the targeted material for autophagy [352,353]. Phagophore elongation continues with several rounds of sequential recruitments of scaffolding proteins, eventually terminating in the recruitment of the ubiquitin-like Atg8p which is conjugated to phosphatidylethanolamine (PE) [350]. Although the specific function of Atg8p is not well elucidated, Atg8p activity is required to guide the phagophore to close, thus forming the autophagosome loaded with material to be degraded; Atg8p is highly essential and autophagy cannot proceed without it [354,355]. During this process, some Atg8p remains bound inside and outside of the phagosome membrane. The autophagosome vesicle is transported to the vacuole where it fuses with the vacuolar membrane where vacuolar hydrolases degrade the cargo into nutrient building blocks (e.g., amino acids). Recycled materials can be expelled from cells or can be transported back into the cytosol via vacuolar transporters as needed [356]. In mammals, protein arginine N-methyltransferases are emerging as important players in the stress response; however, a complete understanding of the biological implications of arginine methylation has yet to be achieved. In mammals, this family of 9 canonical enzymes (PRMT1-9) transfer one or two methyl groups to the guanidino group on arginine residues within protein substrates. Depending on the type of enzyme, mammalian PRMTs may transfer one methyl group to a terminal nitrogen to form NG-monomethylarginine (MMA), two methyl groups to the same terminal nitrogen to form NG’-NG’-asymmetric dimethylarginine (aDMA) or two methyl groups to two different terminal nitrogen atoms to form NG-NG’-symmetric dimethylarginine (sDMA). All known PRMTs use the universal methyl-donor cofactor S-adenosyl-L-methionine (SAM) as the methyl substrate and release the spent cofactor S-adenosyl-L-homocysteine (SAH) after catalysis has occurred [3]. The methylated substrates of PRMTs have roles in several crucial biological 191 processes that may contribute to their role in the stress response, including DNA damage repair [357], transcriptional regulation (histone H4) [40], and RNA processing [157]. Recent studies have identified that the formation of stress granules may be dependent on the formation of MMA and aDMA on upstream factors [358,359]. Additionally, sDMA has been identified as a component of stress granules [164]. Yeast is also an ideal model for studying the roles of PRMTs in eukaryotic stress because there is a degree of similarity between the human and yeast methyltransferasomes. Yeast arginine/HnRNP methyltransferase 1 (Rmt1p/Hmt1p) is the functional homolog of human PRMT1 and they form both MMA and aDMA [181]. Yeast histone synthetic lethal 7 (Hsl7p) is the yeast homolog for human PRMT5 and they form both MMA and sDMA [183]. In yeast, Rmt1p and Hsl7p are the only known aDMA- and sDMA-producing enzymes, respectively, but cells with these gene deletions are still capable of producing aDMA and sDMA, suggesting that there are additional yet uncharacterized yeast arginine methyltransferases [185]. Two additional yeast arginine methyltransferases exist without functional human homologs: arginine methyltransferase 2 (Rmt2p), which is uniquely capable of forming monomethylation on the internal nitrogen of the arginine guanidino group (δMMA) [360], and SPOUT-methyltransferase Sfm1 whose catalytic SAM-binding domain is different from all other known PRMT enzymes [188]. The downstream effect of methylarginines formed under stressful growth conditions in yeast have not yet been explored, although it has been established that the production of nitric oxide (NO) by mammalian nitric oxide synthase (NOS) enzymes, which convert L-arginine to L-citrulline and NO in an NADPH-dependent reaction, is inhibited in the presence of methylarginines, namely aDMA and MMA [361]. Nitric oxide (NO) is a crucial cell signaling molecule in eukaryotes and is produced in response to stressors that can generate stress tolerance, leading to increased cell viability [220,362]. However, in some instances, NO can direct cells to 192 stress-induced apoptosis, so crucial NO regulation is important for cell function [363]. In mammals, nitric oxide synthase expression is regulated by the NF-κB transcription factor in response to oxidative stress [364]. In yeast, the direct activator of yeast NOS activity is unknown, but transcription factors Yap1p or Msn2/4 may be implicated since they are both activated in response to oxidative stress [346,349]. In mammals, NO is produced by nitric oxide synthase (NOS) that converts L-arginine to L-citrulline and NO in an NADPH-dependent reaction [178]. The activity of this enzyme is readily inhibited by free methylarginines, namely aDMA and MMA [178]. To combat prevent methylarginine inhibition in higher eukaryotes, dimethylarginase (DDAH) metabolizes dimethylarginine to dimethylamine and citrulline, which do not inhibit nitric oxide synthase activity [365,366]. Although NO is produced in a similar manner in yeast, yeast do not have a gene that encodes for a canonical NOS homolog and accordingly, the enzyme responsible for NO production in yeast has not been identified. The mitochondrial oxidoreductase flavoprotein Tah18p, on the other hand, has been identified as an important player in the production of NO in yeast. Tah18p supports NOS activity by transferring a pair of electrons from NADPH in a reaction that is coupled to the reduction of the unknown NOS enzyme, resulting in the conversion of L-arginine to L-citrulline and NO [367]. The TAH18 gene is an essential gene in yeast, where its deletion is lethal, underscoring the importance of NO production in yeast. Therefore, there are both notable differences and similarities between yeast and mammalian NO production pathways. Although both eukaryotic pathways are arginine-dependent and they both are readily inhibited by a synthetic arginine analog inhibitor NG-nitro-L-arginine methyl ester (L-NAME) [363], it is unknown if yeast NOS is also inhibited by methylarginines like that in mammals. Unlike mammals, yeast do not have any DDAH enzymes to metabolize 193 methylarginines as an additional mechanism for preventing methylarginines from interfering with nitric oxide production [220]. It is unknown how and if yeast process free methylarginines. The purpose of this research is to investigate the fate of methylarginines in the stress response by using yeast as a model organism. Our lab previously noted an increase in sDMA on proteins in S. cerevisiae under stress conditions, such as stationary phase and heat shock, even in Δhsl7 yeast lacking the only known sDMA-producing enzyme [185]. We are interested in investigating the consequence of changes to methylarginines in the yeast stress response. Here, we demonstrate that loss of several individual genes coding for known and putative yeast methyltransferases exhibits variable impacts on methylarginine production under stress conditions. Further, we show that methylarginines are expelled from yeast during nutrient deprivation and that yeast are unable to take up methylarginines. We provide evidence that autophagy is the primary source for methylarginine accumulation and subsequent efflux. Last, for the first time we identify that methylarginines are unwanted byproducts in yeast cells able to inhibit NO production, thus providing a biological rationale for observed methylarginine efflux as a necessary survival mechanism. 5.3 Materials and Methods 5.3.1 Yeast strains and media The Saccharomyces cerevisiae haploid strain BY4741 (MATa his3Δ1 leu2Δ0 met15Δ0 ura3Δ0) was used as the wild-type (WT) and as the background for most single gene knockout (KO) strains. In few instances where the KO was not available in the haploid BY4741 strain, we used the diploid strain BY4743 (BY4743 MATa/α his3Δ1/his3Δ1 leu2Δ0 /leu2Δ0 lys2Δ0/LYS2 MET15/met15Δ0 ura3Δ0 /ura3Δ0) as the WT and background strain for the homozygous and 194 heterozygous diploid KOs. The haploid and diploid KO strains used in this study are listed in Table 5.1. The putative methyltransferase strains were selected as possible candidates for novel arginine methyltransferases as described in Chapter 4 Table 4.1. Knockouts were confirmed by PCR using the primers listed in Appendix D.1 [323]. Table 5.1. List of haploid, homozygous diploid, and heterozygous diploid KOs used in this study. All haploid strains were created in the BY4741 background and all diploid strains were created in the BY4743 background as part of the YKO collection [323]. Strains were either donated by Corey Nislow or were purchased from Horizon Discovery (formerly Dharmacon). Strain Genotype Source Known and Putative Methyltransferase KOs Δsfm1 MATa his3Δ1 leu2Δ0 met15Δ0 ura3Δ0 sfm1Δ::KanMX Gift from Corey Nislow Δrmt1 MATa his3Δ1 leu2Δ0 met15Δ0 ura3Δ0 rmt1Δ::KanMX Gift from Corey Nislow Δrmt2 MATa his3Δ1 leu2Δ0 met15Δ0 ura3Δ0 rmt2Δ::KanMX Gift from Corey Nislow Δybr225w MATa his3Δ1 leu2Δ0 met15Δ0 ura3Δ0 ybr225wΔ::KanMX Gift from Corey Nislow Δoms1 MATa his3Δ1 leu2Δ0 met15Δ0 ura3Δ0 oms1Δ::KanMX Gift from Corey Nislow Δygr283c MATa his3Δ1 leu2Δ0 met15Δ0 ura3Δ0 ygr283cΔ::KanMX Gift from Corey Nislow Δset4 MATa his3Δ1 leu2Δ0 met15Δ0 ura3Δ0 set4Δ::KanMX Gift from Corey Nislow Δykl162c MATa his3Δ1 leu2Δ0 met15Δ0 ura3Δ0 ykl162cΔ::KanMX Gift from Corey Nislow Δset3 MATa his3Δ1 leu2Δ0 met15Δ0 ura3Δ0 set3Δ::KanMX Gift from Corey Nislow Δymr209c MATa his3Δ1 leu2Δ0 met15Δ0 ura3Δ0 ymr209cΔ::KanMX Gift from Corey Nislow Δymr310c MATa his3Δ1 leu2Δ0 met15Δ0 ura3Δ0 ymr310cΔ::KanMX Gift from Corey Nislow Δynl092w MATa his3Δ1 leu2Δ0 met15Δ0 ura3Δ0 ynl092wΔ::KanMX Gift from Corey Nislow Δset6 MATa his3Δ1 leu2Δ0 met15Δ0 ura3Δ0 set6Δ::KanMX Gift from Corey Nislow Δhsl7 MATa/α his3Δ1/his3Δ1 leu2Δ0 /leu2Δ0 lys2Δ0/LYS2 MET15/met15Δ0 ura3Δ0 /ura3Δ0 hsl7Δ::KanMX /hsl7Δ::KanMX Gift from Corey Nislow Δkar4 MATa/α his3Δ1/his3Δ1 leu2Δ0 /leu2Δ0 lys2Δ0/LYS2 MET15/met15Δ0 ura3Δ0 /ura3Δ0 kar4Δ::KanMX /kar4Δ::KanMX Gift from Corey Nislow Δmtf1 MATa/α his3Δ1/his3Δ1 leu2Δ0 /leu2Δ0 lys2Δ0/LYS2 MET15/met15Δ0 ura3Δ0 /ura3Δ0 mtf1Δ::KanMX /mtf1Δ::KanMX Gift from Corey Nislow Autophagy-Related Kos 195 Strain Genotype Source Δatg8 MATa his3Δ1 leu2Δ0 met15Δ0 ura3Δ0 atg8Δ::KanMX Gift from Corey Nislow Δpep4 MATa his3Δ1 leu2Δ0 met15Δ0 ura3Δ0 pep4Δ::KanMX Gift from Corey Nislow Δatg22 MATa his3Δ1 leu2Δ0 met15Δ0 ura3Δ0 atg22Δ::KanMX Gift from Corey Nislow Permease-Deficient KOs Δcan1 MATa his3Δ1 leu2Δ0 met15Δ0 ura3Δ0 can1Δ::KanMX Purchased from Horizon Discovery (YSC6273-201930232) Δalp1 MATa his3Δ1 leu2Δ0 met15Δ0 ura3Δ0 alp1Δ::KanMX Purchased from Horizon Discovery (YSC6273-201918600) Δavt4 MATa his3Δ1 leu2Δ0 met15Δ0 ura3Δ0 avt4Δ::KanMX Purchased from Horizon Discovery (YSC6273-201929987) Miscellaneous KOs Δump1 MATa his3Δ1 leu2Δ0 met15Δ0 ura3Δ0 ump1Δ::KanMX Purchased from Horizon Discovery (YSC6273-201932030) Δtah18/TAH18 MATa/α his3Δ1/his3Δ1 leu2Δ0 /leu2Δ0 lys2Δ0/LYS2 MET15/met15Δ0 ura3Δ0 /ura3Δ0 tah18Δ::KanMX /TAH18 Purchased from Horizon Discovery (YSC6274-201928019) Most of the WT and KO strains lacking the genes of interest were generously provided by Dr. Corey Nislow from the Faculty of Pharmaceutical Sciences at the University of British Columbia. Gene deletion was accomplished using a PCR-based method which replaced the gene of interest with the KanMX gene conferring geneticin (G418) resistance, as described previously [323]. The Δrsm22, Δavt4, Δalp1, Δcan1, Δump1, and Δtah18/TAH18 strains were purchased from Horizon Discovery (previously Dharmacon). For routine growth, yeast were grown in nutrient rich YPD media (1% yeast extract, 2% peptone, 2% dextrose). For controlled amino acid compositions, yeast were grown in synthetic complete (SC) media which included 0.67% yeast nitrogen base (without amino acids) (BD Difco) and 2% dextrose supplemented with adenine, uracil, L-tryptophan, L-histidine, L-arginine, L-methionine, L-tyrosine, L-leucine, L-isoleucine, L-lysine, L-phenylalanine, L-glutamic acid, L-aspartic acid, L-valine, L-threonine, and L-serine in final concentrations as described previously 196 and adjusted to pH 5.6 [322]. For selective dropout media, SC media was prepared lacking the specific constituent. For growth on solid media, media was supplemented with 2% agar. 5.3.2 Heat-shock and stationary phase treatment to observe methylarginines on proteins For stationary phase growth, WT and putative methyltransferase KO strains were grown in 25 mL SC media at 30oC for 8 days and 10 OD units were harvested in triplicate. 10 OD units were also harvested at mid-log phase (OD600 = 0.5) to compare the change in methylarginine production. Cells were lysed and quantified by mass spectrometry (as described in sections 5.3.6 and 5.3.7, respectively). Methylarginine concentrations were normalized to OD600. Results from heat shock experiments are presented as fold-change. To determine if the change in methylarginines was significant for each strain, a student’s t-test was performed. For heat shock, WT and putative methyltransferase KO strains were subjected to heat shock treatment. Yeast were grown in 25 mL YPD at 25oC to mid-log phase (OD600 = 0.5) and 10 OD units were harvested in triplicate. An equal volume of 49oC YPD media was added to the remaining culture to reach 37oC. Cultures were grown at 37oC for 3 hours, after which 10 OD units were harvested in triplicate. Cells were lysed and quantified by mass spectrometry (as described in sections 5.3.6 and 5.3.7, respectively). Methylarginine concentrations were normalized to OD600. Results from heat shock experiments are presented as fold-change. To determine if the change in methylarginines was significant for each strain, a student’s t-test was performed. 197 5.3.3 Starvation in water A single yeast colony was inoculated into 5 mL of SC media in 13-mL culture tubes (Sarstedt) and grown overnight at 30oC and 200 RPM to reach an OD600 > 1. A 100-mL culture was inoculated from the overnight culture to a starting OD600 of 0.01 and grown for 3 days at 30oC and 200 RPM to reach an OD600 > 2. At this point, the OD600 of the culture was determined and 5 mL of cells and spent media were harvested in triplicate for downstream analysis. Harvested cells were washed twice with water. The remaining cells were harvested by centrifugation at 3000 x g for 5 min and washed with sterile, deionized water twice. The cells were then resuspended in an equal volume of 100 mL sterile water, separated into five replicates, and grown at 30oC and 200 RPM for 11 days. At this point, the OD600 of the culture was determined and 5 mL of cells and spent water for each replicate were harvested for downstream analysis. 5.3.4 Rapamycin treatment A single colony of yeast was grown in 5 mL SC media in 13 mL culture tubes at 30oC until an OD600 of 0.5-1 was reached (n = 3). At time 0, 1 OD unit was harvested from each replicate. Rapamycin was added to each culture at a final concentration of 1 μg/mL from a 1000 μg/mL stock solution prepared in ethanol, and the cultures were grown at 30oC. 1 OD unit was harvested from each replicate at 1-h and 4-h time points. Harvested cells were washed twice with sterile water before lysis. 5.3.5 Yeast viability assay Cells were harvested and diluted in phosphate-buffered saline (PBS; 137 mM NaCl, 2.7 mM KCl, 10 mM Na2HPO4, 1.8 mM KH2PO4, pH 7.4). Trypan blue (0.4%) was added to a final 198 concentration of 0.1% v/v. For each replicate, the number of unstained live cells and the number of stained dead cells were counted using the center square of a hemocytometer. The number of live and dead cells for each replicate was averaged. The percent live and dead were estimated based on total cells counted (approximately 50 cells per sample). 5.3.6 Preparation of samples for quantification of methylarginines by mass spectrometry To prepare growth media and water for extracellular amino acid analysis, the OD600 of the yeast culture was recorded and 5 mL of the spent media was harvested for analysis. As a control, equivalent volumes of media and water were also analyzed for methylarginine content. For analysis of methylarginines in yeast cells, 10 ODs of yeast were harvested by centrifugation at 1000 x g and washed twice with sterile deionized water and frozen at -80oC to aid with lysis. Harvested yeast cells were lysed in CelLytic™ Y lysis reagent (Sigma-Aldrich) supplemented with 10 mM PMSF in 200 μL. Glass beads (0.5 mm) were added to the cell pellet and lysis buffer to a volume approximately 50% of total lysis volume in 2 mL screw capped, skirted tubes and lysed by homogenization at seven 1-min intervals in a SpeedMill Plus (Analytik Jena) at 4oC. Lysis was interrupted with 1-min cold incubations to prevent sample overheating. The cell lysate and beads were centrifuged at 12000 x g for 10 min to pellet cellular debris. The supernatant was extracted, and the beads were washed with 200 μL of lysis buffer, followed by pelleting of the debris at 12000 x g for 10 min and the final supernatant saved. The pooled supernatants from the yeast lysis includes intracellular components. To precipitate proteins, the yeast lysate was added to 6x50 mm Pyrex glass tubes and trichloroacetic acid was added to a final concentration of 14% and incubated at room temperature for 20 min. The precipitate containing yeast protein was pelleted by centrifugation at 12000 x g at 4oC for 30 min and the supernatant was discarded. The pellet was 199 washed with 100 μL -20oC acetone and allowed to air dry. Dried precipitated proteins were acid hydrolyzed to their amino acid components under vacuum using a Pico•Tag work station (Waters) by heating samples to 110oC for 24 h in the presence of 6.0 N HCl. Dried amino acids were resuspended in 300 μL of 10 mM NaH2PO4 (pH 5.0) for purification of basic amino acids (described below). For both lysed cells and spent media, the samples needed to be further purified to remove additional cellular components. The entirety of the resuspended precipitated amino acids from cell lysis or the entirety of the spent media sample was applied to an Oasis MCX solid phase extraction cartridge (Waters) and purified according to the manufacturer’s instructions and what our lab has previously described [185]. After purification, the samples were eluted in 1 mL of 50/40/10 methanol/water/ammonium solution and dried using a Speed-Vac concentrator (Thermo Scientific) on high heat until dried. Dried samples were resuspended in 100 μL of mobile phase A (0.1% formic and 0.05% TFA in water) and analyzed by mass spectrometry. 5.3.7 Quantification of methylarginines by mass spectrometry Methylarginines were quantified via UHPLC-MS/MS according to a protocol previously optimized by our lab [185,324]. A volume of 5 μL from each sample reconstituted in mobile phase A (described above) was applied to an Acquity UPLC BEH C18 column (Waters) from a 96-well plate and chromatographically separated using an Agilent 1290 Infinity UHPLC system. A flow rate of 0.15 mL/min at 45°C was used ramping from 100% mobile phase A to 100% mobile phase B (0.1% formic acid, 0.05% trifluoroacetic acid and 30% methanol in water) over 1.1 min during a 5-min run time. The UHPLC was linked to an AB Sciex QTrap 5500 triple quadrupole mass spectrometer for subsequent tandem MS analysis via multiple reaction monitoring (MRM). 200 Samples were ionized by Turbo Spray in positive ionization mode. In the first quadrupole mass analyzer, precursor ions were selected for dimethylarginine (203.1 m/z) and monomethylarginine (189 m/z). For fragmentation of the product ions, the collision energy for dimethylarginine and monomethylarginine was 30 eV and 25 eV, respectively, while the declustering and cell exit potential for all methylarginine species was 40 V and 16 V, respectively. In the second mass analyzer, product ions were selected for aDMA (46 m/z), sDMA (172.1 m/z), and MMA (74 m/z). Methylarginines were quantified by preparing a standard curve containing 20-8000 nM aDMA and MMA or 1-5000 nM sDMA. In some experiments, histidine, lysine, and arginine were also quantified using 156.9/111.0 m/z, 147.1/84 m/z, and 175.2/70.0 m/z transitions, respectively, and a 5000-400000 nM standard curve [185,368]. A weighting factor of 1/x2 was applied to each curve [369]. Resulting media data were normalized to OD600 and per day of growth. Resulting protein data were normalized to the amount of histidine in the lysate. In some cases, the differences in methylarginine species between treatment are reported as fold change. A student’s t-test was used to test for significant difference between SC media and water for each strain and an ordinary one-way ANOVA test for multiple comparisons was used to compare the fold-change difference in methylarginine species for each strain compared to wild-type. To compare the amount of extracellular methylarginine species in SC or water in a specific strain to wild-type an ordinary one-way ANOVA test for multiple comparisons was used using Prism 9.0.2 (GraphPad). 5.3.8 Transformation of plasmids into yeast for protein expression The plasmid encoding GFP-Atg8p was a gift from Daniel Klionsky (Addgene plasmid # 49425). This construct was prepared with an N-terminal GFP tag under control of the endogenous promoter and containing the URA3 gene as a selectable marker [370]. The GFP-X0 plasmid 201 encoding GFP alone was a gift from Michael Downey (Addgene plasmid # 115565). This construct was prepared under control of the ADH1 promoter and containing the URA3 gene as a selectable marker. Each plasmid was transformed into the BY4741 and Δykl162c strains. Transformation was performed according to the protocol developed by Gietz and Schiestl [371]. Briefly, a single colony of yeast was inoculated into YPAD medium (standard YPD + adenine hemisulfate at 80 mg/L) and incubated at 30oC until an OD600 of 2 was reached. Approximately 1 x 108 harvested cells were used per transformation and resuspended with 0.1 μg plasmid DNA, 33% (w/v) PEG 3350, 0.1 M lithium acetate, and 0.3 mg/mL salmon sperm DNA in a total volume of 360 μL. Transformations were incubated at 42oC in a water bath for 40 min. The resulting transformation was plated onto selective SC-ura dropout plates at 30oC for 2-3 days. 5.3.9 Monitoring autophagy in yeast via Western blot BY4741 and Δykl162c cells containing GFP-Atg8p were starved in water or treated with rapamycin (as described above). For analysis of protein content via Western blot, 2 OD units of yeast cells were harvested and lysed (as described above). The concentration of total protein in lysates was determined using the Pierce™ Rapid Gold BCA assay kit. For Western blot, 40 μg of each sample was separated via SDS-PAGE. Transfer of protein to PVDF membrane was performed using a wet blotting system (Bio-Rad) in Towbin transfer buffer (25 mM Tris, 192 mM glycine, 20% v/v methanol, pH 8.3) onto methanol-activated PVDF membrane for 1 h at 100 V at 4oC with constant stirring. The membrane was washed in TBS-T (20 mM Tris, 0.15 M NaCl, 0.05% Tween-20, pH 7.3) and subsequently stained in Ponceau S (Sigma-Aldrich); an image of the Ponceau stain was captured as a loading normalization control. The membrane was blocked in blocking buffer (5% skim milk powder in TBS-T) for 1 h at room temperature and the primary 202 antibody anti-GFP (JL-8 monoclonal antibody, from mouse) was added overnight at 4oC using a 1:3000 dilution in blocking buffer. The secondary antibody (anti-mouse IgG, conjugated to HRP, GE Lifesciences NA931) was diluted 1:3000 in blocking buffer and added for 1 h at room temperature. Bands were visualized using Immobilon™ Crescendo chemiluminescent HRP substrate. Blots were visualized through exposure at 425 nm for 3 min on an AlphaImager 3400 (Alpha Innotech). Resulting bands were quantified via densitometry using ImageJ (National Institute of Health). Bands were normalized to total protein loaded, according to Ponceau stain. 5.3.10 Monitoring subcellular localization of proteins via microscopy BY4741 or Δykl162c cells containing the GFP or GFP-Atg8p plasmid were grown into log phase, starved in water, or treated with rapamycin (as described above). Cell cultures (5 mL) were harvested at 1000 x g and resuspended in equal volume of PBS. Cells were treated with 4 μM FM™ 4-64 dye ((N-(3-Triethylammoniumpropyl)-4-(6-(4-(Diethylamino) Phenyl) Hexatrienyl) Pyridinium Dibromide)) for 90 min at 30oC with shaking. Cells were then washed three times with PBS to remove free FM 4-64 and grown for an additional 90 min at 30oC to chase FM 4-64 into the vacuolar membrane. To stain the nuclei, DAPI was added to a final concentration of 6.25 μg/mL for 30 min at 30oC with shaking. Cells were finally washed with PBS to remove traces of dye and resuspended in 1 mL PBS. 2 μL of the concentrated yeast cells were added to a microscope slide. A glass coverslip (0.16-0.19 mm thick, 22 x 22 mm) coated with 5 μL of 0.5 mg/mL concanavalin A was placed on top of the cells and secured with clear nail polish. Cells were imaged using an Axio Observer inverted microscope (Zeiss) and processed using Zen (Blue Edition, Zeiss) and ImageJ Image Processing Software. Cells were imaged at 63x magnification plus a 10x eyepiece under oil immersion sing the built-in GFP, DAPI, FM 4-64, and white light filters using 203 the AxioCam 702 to detect each signal and phase-contrast. Cells lacking free GFP or GFP-Atg8p were used as a negative control. 5.3.11 Radioactive monitoring of methylarginine influx To test if yeast cells can take up free methylarginines, a radioactive assay was created to monitor uptake of methionine L-[methyl-3H] (PerkinElmer NET061X005MC), arginine-L-[2,3,4-3H]-monohydrochloride (PerkinElmer NET1123250UC), and 14C-methylarginine. Since it is established that yeast can take up methionine [372], methionine was used as a control in assay development. To develop the assay, BY4741 cells were grown to OD600 ~1 in 5 mL SC media cultures at 30oC. [3H]-methionine was added to final concentrations of 0.01, 0.1, and 1 μM, spiked with 0.02 mg/mL unlabeled methionine to drive the concentration gradient, and were grown for 90 min at 30oC to allow for methionine incorporation followed by washing cells twice with PBS. For each concentration of methionine treatment, 400 μL of cells were added to glass microfiber filter discs (Fisher Scientific 09-873A) that were loaded into ~21 mm columns in triplicate to immobilize yeast. Filter discs were washed with PBS until wash was no longer radioactive (>100 DPM). Paper discs were suspended in 2.5 mL scintillation fluid (Scintiverse BD Cocktail) and radioactivity was counted on a Tri-Carb 3110TR (PerkinElmer) liquid scintillation counter for 1 min. Since radiolabeled methylarginines are not commercially available, a mixed population of [14C]-aDMA and [14C]-MMA was synthesized by exhaustively methylating 5 mg/mL GST-GAR with 10 μM PRMT1 (expressed and purified as described in Chapters 2 and 3) and 100 μM S-adenosyl-L-[methyl-14C]-methionine ([14C]-SAM; PerkinElmer NEC363050UC) at 37oC overnight. Resulting methylated product was precipitated, acid hydrolyzed, and basic amino acids 204 were purified as described in Section 5.3.6. The concentration of radioactivity was determined by spotting 2 μL of sample onto filter paper and quantified on a Tri-Carb 3110TR as described above. To monitor influx of methylarginines, BY4741, Δcan1, Δalp1, and Δavt4 cells were grown into log phase (OD600 ~ 1) or starved in water (as described above) in 5 mL SC media or water. Cells were harvested by centrifugation at 1000 x g, washed twice with PBS, and resuspended in PBS to a final OD600 of 1. Cultures were treated with 12580 of 3H-arginine or 14C-methylarginines along with 0.02 mg/mL cold L-arginine or 0.02 mg/mL cold aDMA to drive the concentration gradient for 90 min at 30oC followed by two PBS washes to remove excess radioactivity. Cells (400 μL corresponding to ~1.2 x 107 cells) were added to glass filter discs in columns, washed with 40 mL PBS, and radioactivity corresponding to incorporated amino acids immobilized on glass filter discs was determined using a scintillation counter, as described above. As a background control, 400 μL of the 3H-arginine and 14C-methylarginine in PBS were added directly to the filter discs. Background radioactivity after PBS washing was subtracted from each treatment (n = 3). 5.3.12 Measuring nitric oxide synthase activity in yeast lysates To monitor nitric oxide synthase activity in yeast lysates, BY4741, Δcan1, BY4743, and Δtah18/TAH18 cells were starved in 25 mL water as described in section 5.3.3. For each treatment, 1.9 x 106 cells were harvested by centrifugation at 1000 x g, washed twice with PBS, and lysed in total 400 μL lysis buffer as described in section 5.3.6. The concentration of each lysate was determined using Pierce™ Rapid Gold BCA assay kit. To monitor nitric oxide synthase activity, lysates were diluted to a final concentration of 1 μg/μL and treatments were added. Each reaction contained NADPH to a final concentration of 1 mM to serve as a nitric oxide synthase substrate. To observe a concentration-dependent effect of arginine on nitric oxide synthase activity in yeast 205 lysates, L-arginine was added to final concentrations of 1, 10, 25, and 50 mM. To observe the effects of a known inhibitor, 50 and 100 mM of L-canavanine treatments were used. To observe the effects of methylarginines on nitric oxide synthase activity, 50 and 100 mM of aDMA treatments were used. Each treatment was also supplemented with 1 mM L-arginine to drive endogenous nitric oxide synthase activity. Each treatment in lysis buffer alone was performed as a background control. Nitric oxide synthase activity was monitored with the addition of 4-amino-5-methylamino-2',7'-difluorofluorescein diacetate (DAF-FM DA) to 1 μM which binds to nitric oxide to form a fluorescent benzotriazole compound [373]. Samples were prepared in triplicate and 100 μg of each reaction was added to black 96-well plates. Samples were incubated at 30oC for 3 hours and fluorescence excitation and emission were performed at 495 and 515 nm, respectively. The matched treatment in lysis buffer was subtracted as background. An ordinary one-way ANOVA with multiple comparisons was performed to assess statistical significance using Prism 9.0.2 (GraphPad). 5.4 Results and Discussion 5.4.1 Quantitating methylarginines on proteins in yeast under stress and aging conditions Since our lab previously observed an increase in sDMA production on proteins and a decrease in aDMA and MMA on proteins in yeast under stationary phase and heat shock conditions [185], we repeated these assays using putative methyltransferase KO strains (as described in Chapter 4). We hoped to observe how each of the known and putative methyltransferases impacted methylarginine production under stressful growth conditions. Cells were harvested at mid-log phase and again at 8 days to quantify the fold change in methylarginine production from log to stationary phase growth. Cells were also harvested mid-log phase and after 3 hours of heat shock 206 to quantify the fold change in methylarginine production under heat stress compared to log phase growth. Quantification of methylarginines was determined by mass spectrometry. In stationary phase growth, we consistently observed a significant decrease in aDMA and MMA on proteins compared to log phase in all strains tested (Figure 5.1A). Two strains, Δynl092w and Δset6, had an increase in sDMA production in stationary phase, whereas all other strains had a decrease in sDMA production in stationary phase compared to log phase. However, all changes in sDMA were statistically insignificant. We did not observe trends as expected, even in the WT strains (there was no significant difference in BY4741 and BY4743 strains so only BY4741 is shown). These values had high error bars because sDMA was produced in such small amounts that it fell below the limit of detection of the assay (1 nM sDMA) in most samples, which was 1 nM. Both aDMA and MMA concentrations fell within confident quantification limits (5 nM MMA and aDMA) in all cases. Heat shock treatment elicited considerably less dramatic changes in methylarginines compared to what was observed in stationary phase. In heat shock, all strains showed a decrease in aDMA but in several strains, this observed decrease was statistically insignificant. For MMA, there was no significant change in its production in any strain. (Figure 5.1B). Nearly all strains show an increase in sDMA in heat shock as expected, but only WT, Δhsl7, and Δkar4 show a statistically significant increase in sDMA in heat shock. Interestingly, Δrmt2, Δmtf1, and Δset6 had a significant decrease in sDMA production in heat shock. It is important to note though that like we observed in stationary phase growth, sDMA was produced in incredibly small amounts, so quantification again fell below the limit of detection of the assay for most samples. Therefore, although these results were able to recapitulate trends that were previously observed in stationary phase and heat shock for aDMA and MMA, we failed to observe the sizeable increase in sDMA under either of these conditions. 207 Figure 5.1. Change in methylarginines on proteins in WT and putative KO strains during treatments (aDMA, black; MMA, dark grey stripes; sDMA, light grey dots). (A) Yeast strains were grown for 8 days in SC media into stationary phase. Ten OD units were harvested at mid-log phase and after 8 days of growth. Methylarginines were quantified by mass spectrometry and normalized to highest cellular OD. (B) Yeast strains were grown at 25oC in YPD and then switched to 37oC YPD for 3 hours. 10 OD units were harvested prior to heat shock and again after 3 hours of heat shock. Results are presented as log2 fold change on a log2 axis. Values below 0 indicate that the amount of methylarginine species decreased during treatment compared to log phase growth. Values above 0 indicate that the amount of methylarginine species increased during treatment compared to log phase growth. Error bars represent the standard deviation (n = 3). ns = not significant; * = p ≤ 0.05; ** = p ≤ 0.01; *** = p ≤ 0.001; **** = p ≤ 0.0001 208 Based on previous studies from our lab, we were expecting to see a sizeable increase in sDMA production on yeast proteins in response to stationary phase or heat shock, matched with a decrease in aDMA and MMA production. We found a notable decrease in aDMA and MMA as expected in stationary phase growth. However, the observed changes for each putative methyltransferase knockout strains did not differ from the changes observed in WT strains. Therefore, no putative methyltransferase knockout appears to sizeably impact aDMA or MMA production under stationary phase stress. Unfortunately, in all of the tested stress and aging conditions here, we are unable to draw confident conclusions about intracellular sDMA production in WT or KO cells due to the very low sDMA concentrations produced. These findings emphasize how difficult it has been to identify sDMA in yeast cells, which has only been detected by two groups [185,186,295,324]. We were surprised that we were unable to recapitulate the increase in sDMA on proteins under stress conditions that our lab previously identified [185]. We compared the genotype of the parent strain used in this study (MATa his3Δ1 leu2Δ0 met15Δ0 ura3Δ0) to the parent strain used in our previous study (MATα, leuΔ2-3, 112, trp1Δ0, can1Δ0, hisΔ4-149, uraΔ3-52) [374] and identified a notable difference. In our previous study, the yeast strains lacked the primary arginine permease Can1p. Can1p is known to both influx and efflux arginine [375]. The previously observed sDMA increase could have been a result of high intracellular concentrations of arginine. Since arginine is a substrate for yeast NOS activity, these cells may have also had a concomitantly high intracellular NO concentration. In other eukaryotes, a positive correlation between NO levels and sDMA production by PRMT5 has been observed [169]. Therefore, our previously observed sDMA increase may have been a result of high arginine accumulation resulting in high NO production. These tests were not performed, so we cannot confirm this hypothesis. Further, 209 although the permeability of Can1p to methylarginines has not been explored, we question if Can1p is also selective for methylarginines. If so, the accumulation of sDMA in cells could also be a result of sustained accumulation of intracellular sDMA due to impeded ability to efflux methylarginines from the cells. Comparing our data to our previous results, we predict that yeast strains used in our current study can efflux methylarginines, whereas the strains used in our previous had impaired methylarginine efflux. 5.4.2 Methylarginines are expelled from yeast during stress Since we did not detect any notable changes in methylarginines on proteins in yeast under any stress condition tested, we questioned if methylarginines were instead being expelled into the media. As a preliminary test, we grew BY4741 in SC media and looked for methylarginines in spent SC media after 15 days of growth and detected saw low levels of aDMA and MMA in SC media alone (less than 50 and 20 nM, respectively). Further, we observed an increase in MMA, aDMA, and sDMA in the media (data not shown), suggesting that we may observe changes in the levels of expelled methylarginines found in spent media more readily than changes to methylarginines on proteins. We sought to measure the accumulation of effluxed methylarginines during nutrient starvation of yeast grown in water after 11 days. Further, since we did detect some levels of methylarginines in SC media, we chose to shift the cells into incubation in water to eliminate confounding background media methylarginine concentrations. This shift into water also adds an additional nutritional starvation to the yeast to theoretically increase stress, thereby maximizing the impact of cellular stress to elicit changes in methylarginine levels. In all strains, we found methylarginines in the media at the beginning and end of our water incubation assay, as shown in Figure 5.2 A. A significant change in extracellular methylarginines 210 during this time interval was seen for most strains except for: Δset6 and Δybr225w for aDMA; Δybr225w and Δrmt2 for MMA, and; Δykl162c, Δymr209c, Δmtf1, Δygr283c, Δset3, Δynl092w and BY4741 for sDMA, where the observed change was not statistically significant. In many strains, we saw an increase in extracellular methylarginines after incubation in water. In strains Δygr283c, Δkar4, Δset6, Δrmt1, Δoms1, Δset3, Δset4, Δymr209c, and Δynl092w, efflux of individual methylarginine species are correlated. For aDMA, nearly all strains differed significantly from WT in terms of fold-change difference except Δmr209c and Δset6; for MMA, Δykl162c, Δymr209c, Δygr283c, Δset6, Δybr225w, and Δhsl7 fold change was not significantly different than WT; but for sDMA, Δkar4, Δrmt2, Δrmt1, Δoms1, Δset4, Δymr310c, and Δynl092w fold change was significantly different than the change in WT (Figure 5.2B and Appendix D.2). Growth curves of each yeast strain into stationary phase in SC media were created to observe if stationary phase growth significantly impacted any KO strains. All strains showed stable growth curves with no notable OD600 changes into stationary phase except for Δybr225w (Appendix D.3), suggesting that extracellular arginine levels are likely not solely due to cell death. The levels of extracellular methylarginines in SC media and in water for each strain was also compared to WT (Appendix D.4). Therefore, many putative methyltransferase genes impact methylarginine efflux in yeast and may impact arginine methylation overall. This suggests that the biological significance of methylarginines in yeast is wide-reaching and likely influenced by many factors. 211 Figure 5.2 Change in methylarginine species from SC media into water for putative methyltransferase KO strains. (A) The WT and putative methyltransferase KO strains were grown in synthetic complete media, transferred to water, and incubated for 11 days. Cells were harvested after three days of growth in SC media (SC, black, n=3) and after 11 days of incubation in water (stripes, n=5) and aDMA (top panel), MMA (middle panel) and sDMA (bottom panel) were quantified using mass spectrometry. Concentrations are normalized to number of cells and per day of growth. Asterisks indicate when the concentration of methylarginine species is significantly different. (B) The log2 fold change of extracellular methylarginine species aDMA (black), MMA (diagonal stripes), and sDMA (grey dots) (log2 scale) in WT, as well as known PRMT and putative 212 methyltransferase KO strains over the course of the 11-day water incubation assay. Asterisks indicate when the fold-change of methylarginine species is significantly different than the haploid or diploid WT By4741 or BY4743 strain, respectively. ns = not significant; * = p < 0.05; ** = p < 0.01; *** = p < 0.001; **** = p < 0.0001. Three KO strains resulting in a substantial decrease in methylarginine efflux stood out from this study. Loss of Ynl092wp results in a substantial decrease in aDMA and MMA efflux compared to WT. We now know that Ynl092wp is a protein histidine N-methyltransferase (Chapter 4) and the notable impact of Ynl092wp loss may suggest the presence of histidine and arginine methylation crosstalk; for example, both histidine and arginine methylation have been identified on actin [339,341], and it is possible that histidine methylation may positively impact arginine methylation. Loss of either Set4p or Ymr310cp resulted in a substantial decrease in the efflux of all methylarginine species. The human homologs of Set4p are the known lysine methyltransferases SET-domain containing protein SETD5 and lysine methyltransferase KMT2E that both methylate histone H3, for which there has been well-documented methylarginine-methyllysine crosstalk [376–378]. The human homolog of Ymr310cp is mammalian RNA-methyltransferase SPOUT1 with roles in gene expression [379]; it is possible that PRMT expression could in part be regulated by Ymr310cp. Therefore, these results lead us to speculate that methylarginines in yeast are involved in substantial PTM crosstalk. Additionally, two KO strains resulting in a substantial increase in methylarginine efflux stood out from this study. Loss of karyogamy protein Kar4p resulted in the most sizeable increase in sDMA efflux and an appreciable increase in aDMA and MMA efflux. Its human homolog is N6-adenosine-methyltransferase non-catalytic subunit METTL14 [297], which contains the PRMT consensus sequence RGG. PRMT1-mediated methylation of METTL14 is important for its function [380]. Further, a screen for gene expression in response to PRMT5 inhibition uniquely 213 found that only METTL14 expression is insensitive to PRMT5 inhibition [381]. Therefore, although there is evidence that the human homolog of Kar4p is impacted by arginine methylation, there is no evidence thus far to suggest that it influences PRMT activity. Our observation that Kar4p loss results in notable sDMA production may be related to their noted interactions in human cells. Loss of Ykl162cp resulted in an inordinately high increase in aDMA and MMA production in water compared to loss of any other protein. Interestingly, knockout of a Ykl162cp homolog from Dictyostelium resulted in increased levels of autophagy [26]. We questioned if the Δykl162c strain therefore had high levels of autophagy in our assay, resulting in a large efflux of methylarginines. We surmise that during water incubation, an increase in methylarginine efflux is a response to starvation conditions. In cells, autophagy is initiated when nitrogen is removed from the extracellular environment [225], so we suspect that yeast cells are undergoing autophagy in our assay. Therefore, we hypothesized that autophagy may be a possible mechanism by which yeast break down proteins, leading to methylarginine efflux. To observe the effect of autophagy on methylarginine efflux, we used three autophagy-related KO strains (Δatg22, Δatg8, and Δpep4) and monitored extracellular methylarginine levels at the beginning and end of our water starvation assay. In Δatg22 cells, cells are inhibited in their ability to transport amino acids out of the vacuole after proteolysis [382]. However, the role of Atg22p is at least partially redundant with other vacuolar amino acid transporter proteins Avt3/4p [356]; in particular, Avt4p is the major transporter responsible for efflux of basic amino acids from the vacuole [383]. Therefore, proteins targeted for autophagy can be brought into the vacuole for proteolysis in Δatg22 cells, but we anticipate that the efflux of their amino acids will be impaired. In Δatg8 cells, the phagophore cannot form so no cellular material can be brought into the vacuole [384]. Last, in Δpep4 cells, 214 there is no activation of major vacuolar proteases, so although the phagophore will form and material will be brought into the vacuole, proteolysis will not occur [385]. We observe a significant decrease in extracellular methylarginines in water incubation in Δatg8 and Δpep4 cells (Figure 5.3A). Interestingly, we observed an increase in extracellular methylarginines in Δatg22 cells in water incubation. The observed change in aDMA was significantly different than the change observed in WT cells for all autophagy-related strains and was significant for MMA and sDMA change in Δatg8 and Δpep4 cells (Figure 5.3B and Appendix D.5). To verify that cell viability is not the sole explanation for seeing a change in extracellular methylarginine in these conditions, cell viability of these strains both at the beginning and end of the water incubation assay was determined using a trypan blue assay to monitor dead and living cells. The viability of the Δatg8 and Δpep4 strains does not change significantly from WT (Figure 5.3C). In water incubation, the Δatg22 cells have noticeably decreased viability which has been observed previously [382], but the change in viability is not significantly different from WT or Δpep4 cells, so cell death alone cannot account for the sizeable increase in methylarginines we see being expelled in water starvation (Figure 5.3C). The levels of extracellular methylarginines in both SC media and in water is significantly different from WT for most strains (Appendix D.6). As noted above, Avt4p is still active in Δatg22 cells and, as the main basic amino acid vacuolar transporter, is likely responsible for the observed methylarginine efflux. Further, an accumulation of neutral amino acids was previously observed in Δatg22 cells [382]. This observation paired with our finding suggests that Atg22p is selective for neutral amino acids over basic amino acids. The increase in methylarginine efflux in Δatg22 cells begs the question if the partially redundant vacuolar transporters Avt3/4p are overexpressed in response to the ATG22 null mutant. No Δatg22 studies have examined relative expression of the vacuolar transporters in response to a null mutant. 215 Since we do not see an increase in methylarginine efflux in the Δatg8 and Δpep4 strains which are deficient in autophagy, we deduce that the proteasome is not processing methylarginine degradation in the absence of autophagy. If the proteasome was functioning to degrade methylarginine-containing proteins in these mutants, we would still expect to see a considerable efflux of methylarginines. We also tested methylarginine efflux in a null mutant lacking the proteasome maturation factor Ump1p, which has no proteasome activity, and found that methylarginine efflux still increased under stress conditions (data not shown) [386]. Therefore, it does not appear that the proteasome is important for methylarginine-containing protein degradation. We hypothesize that under our tested growth conditions, yeast degrade arginine-methylated proteins preferentially via autophagy. 216 Figure 5.3. Methylarginine efflux changes during incubation SC media and water in autophagy-related gene deletion yeast strains. (A) WT BY4741, Δatg8, Δpep4, and Δatg22 strains were grown in synthetic complete media, transferred to water, and incubated in water for 11 days. Cell media was collected after three days of growth in SC media (black, n=3) and after water starvation (stripes, n=5) and aDMA (top panel), MMA (middle panel) and sDMA (bottom panel) were quantified using mass spectrometry. Concentrations are normalized to number of cells and per day of growth. Asterisks indicate when the concentration of methylarginine species in water is significantly different from the concentration in SC media. (B) Changes in aDMA (black), MMA (diagonal stripes) and sDMA (grey dots) (log scale) are presented in log2 fold change on a log2 axis. Asterisks indicate when the fold-change is significantly different from the fold-change observed in the WT BY4741 strain. Error bars represent the standard deviation from the mean. (C) Viability was determined for these yeast strains through water incubation. Cells were subjected to water incubation and to growth in SC media and viability was estimated by counting dead and live cells, as measured by trypan blue (n = 4). A significant decrease in viability at day 11 was observed for each yeast strain as compared to day 0. The change in viability was compared to Δatg22 using an ordinary one-way ANOVA for multiple comparisons. ns = not significant; * = p < 0.05; ** = p < 0.01; *** = p < 0.001; **** = p < 0.0001. 217 5.4.3 Quantification of autophagy in water incubation assay Since we observed efflux of methylarginines from cells under water incubation conditions and this efflux is dependent on autophagy and not the proteasome, we hypothesize that methylarginines may be targeted for degradation via autophagy. To verify if this reasoning is probable, we sought to quantify the amount of autophagy present during our water incubation assay compared to the quantification of autophagy present during rapamycin treatment, which is a potent inducer of autophagy [305]. To monitor autophagy, we used GFP-Atg8p transformed into WT and Δykl162c cells as a marker for autophagy. We included Δykl162c cells in this investigation since we saw an inordinately high efflux of methylarginines in this strain and suspect it may be cause by a higher level of autophagy, like is seen in its Dictyostelium homolog null mutant [387]. During the induction of autophagy, some Atg8p is brought into the vacuole after the phagophore fuses, resulting in a population of Atg8p remaining inside the vacuole to be proteolytically cleaved [388]. Since GFP is a very stable tag that is degraded by proteases much more slowly, GFP liberation is routinely used as a marker of autophagy by comparing the relative amounts of free GFP tag and GFP-Atg8p by Western Blot [389]. In autophagy conditions, there will be more free GFP relative to GFP-Atg8p. To monitor these relative concentrations, a Western blot using anti-GFP was performed. The resulting bands were normalized to total amount of protein loaded and the relative GFP and GFP-Atg8p ratios were calculated. As an additional method to visualize autophagy in this assay, the subcellular localization of both GFP-Atg8p and free GFP was observed via microscopy. Microscope images were taken using the DAPI filter to identify DAPI-labeled nuclei, the FM 4-64 filter to identify the plasma and vacuolar membranes, the GFP filter to identify GFP-tagged proteins, and with phase contrast to identify outlines of the cell and vacuole. 218 In the rapamycin treatment as a positive control of autophagy induction, we see a time-dependent increase in free GFP at ~27 kDa relative to GFP-Atg8p at ~40 kDa corresponding to increased levels of autophagy in both WT and Δykl162c cells (Figure 5.4A). In both WT and Δykl162c cells, we do see some free GFP prior to rapamycin treatment (0 h), but there is significantly more relative free GFP in the Δykl162c strain, suggesting that Δykl162c cells have a higher level of basal autophagy. Similarly, after 4 hours of rapamycin treatment, there is significantly more GFP cleavage in the Δykl162c cells, but there is no difference in autophagy levels between the two strains after 1 hour of rapamycin treatment (Figure 5.4A). From the onset of the shift into water incubation (0 d), we see the same degree of autophagic GFP-Atg8p cleavage occurring at each time point in both yeast strains (Figure 5.4B). By quantifying the ratio of GFP/GFP-Atg8p at each time point under each condition for each strain, we observe that the amount of autophagy happening in our water incubation conditions is comparable to the amount of autophagy happening after ~1 hour of rapamycin treatment. Further, in both WT and Δykl162c cells, free GFP is distributed diffusely throughout the cytosol during all growth conditions and is not localized to one component of the cell, indicating that GFP does not confound the subcellular localization of these proteins. Further, in log phase, like free GFP, GFP-Atg8p was diffused throughout the cytosol and notably absent from the vacuole in both WT and Δykl162c cells. In water incubation, GFP-Atg8p was localized within the vacuole with rapamycin treatment and water incubation, further confirming autophagy by microscopy. Similar results were obtained for Δykl162c yeast (Figure 5.4C and D). Therefore, autophagy is further confirmed by microscopy. 219 Figure 5.4. Quantification of autophagy by monitoring GFP-Atg8p. Autophagic cleavage of GFP-Atg8p over course of (A) rapamycin treatment and (B) water incubation in BY4741 (WT) and Δykl162c cells. Cells expressing GFP-Atg8p were treated with rapamycin and harvested at 0, 1, and 4 h or were incubated in water and harvested at 0, 3, 6, 9, and 11 d. An equal amount of cell lysate was separated by SDS-PAGE on a 10% gel and transferred for Western blot analysis with anti-GFP antibody (representative blots in top panels). Western blots were quantitated using ImageJ to determine the relative ratios of GFP-Atg8p to free GFP (bottom panels). An independent student’s t-test was used to test for statistical significance between BY4742 and Δykl162c at each time point for each treatment. The difference in quantitated product is considered significant if p ≤ 0.05. Error bars represent standard deviation (n = 3). ns = not significant; * = p ≤ 0.05; ** = p ≤ 0.01. GFP alone and GFP-Atg8p were transformed into (C) BY4741 and (D) Δykl162c cells. Their subcellular localization was monitored in log phase (LP), under rapamycin treatment, and in our water incubation assay. Green indicates GFP, blue is DAPI staining of nucleic acids, red is FM 4-64 membrane dye overlapped with phase contrast. 220 The observation of increased autophagy in Δykl162c cells in log phase prior to rapamycin treatment confirms our prediction that Δykl162c cells have higher levels of autophagy. Although this has been observed for its Dictyostelium homolog [387], this has not been observed before in yeast. A genome wide screen for knockouts who are sensitive to starvation identified the Δykl162c mutant as less resistant to starvation stress [390]. Autophagy is an important cellular response that is activated in response to starvation stress with the intention of making cells more resistant to starvation stress, so their findings contradict what we report here [225]. Since we observe no significant difference in autophagy between WT and Δykl162c cells during our water incubation assay, the immense efflux of methylarginines in Δykl162c cells cannot be attributed to increased autophagy alone. Instead, we surmise that Ykl162cp must also influence methylarginine production and the reported efflux of methylarginines may be due to a combinatorial effect of increased autophagy and Ykl162cp-mediated effect on methylarginine production. In our water incubation assay, it is important to note that cells were grown to OD600 > 2 before shifting cells into water for extended incubation. Day 0 marks the point just before the shift into water. Notably, in both WT and Δykl162c cells, we detected autophagy at time 0. Therefore, it appears that by the time we began our water incubation assay, yeast were already undergoing some level of autophagy that persisted throughout the duration of the assay. Therefore, any increase in methylarginine efflux that we observed is likely not due to one standalone autophagy event, but rather due to an accumulation of methylarginines in the media due to sustained autophagy. It is also important to note that stable internalization of the GFP-Atg8p plasmid 221 depends on the URA3 selective marker. In yeast grown in water, there is a possibility of both WT and Δykl162c yeast losing the plasmid due to no selective pressure. 5.4.4 Transport of methylarginine species across yeast cell membranes To explore how methylarginine species were being expelled, we looked at permease-deficient KO strains including two arginine permease KOs (Δcan1 and Δalp1) and a large amino acid vacuolar transporter (Δavt4) [391–393]. We compared extracellular methylarginine levels at the beginning and end of our water incubation assay by determining the fold change. We expected to see much less methylarginine efflux in these KO strains compared to WT, but surprisingly we observed a higher basal level of methylarginine expulsion pre-water incubation in the permease KOs (Figure 5.5A). Of the three knockout strains, Δcan1 cells had the least amount of extracellular methylarginines at the end of water incubation but had the highest amount of extracellular methylarginines at the beginning of the assay. In looking at the change in methylarginines efflux, we observed a large increase in aDMA and MMA efflux in water incubation in the Δalp1 strain and a significant increase of MMA efflux in the Δavt4 strain (Figure 5.5A). In Δcan1 cells, we observed a significant decrease in efflux of both aDMA and MMA. In all mutant strains, we observed a significant decrease of extracellular sDMA efflux in water incubation. Further, the change in methylarginine efflux for each strain differs significantly from the change observed in WT cells for all methylarginine species except for MMA fold-change in Δavt4 cells compared to WT (Figure 5.5B and Appendix D.7). The levels of extracellular methylarginines in both SC media and in water are significantly different from WT for each strain (Appendix D.8). Therefore, methylarginines are still expelled from permease-deficient yeast in water incubation, although to a notably lesser extent in Δcan1 cells. 222 We questioned if cell viability of these permease-deficient strains impacted methylarginine efflux. We performed viability assays by counting dead and live cells using trypan blue at the beginning and end of our water incubation assay. No significant changes in viability compared to WT were observed except for Δcan1 (Figure 5.5B), suggesting that cell death is not solely responsible for extracellular methylarginines. However, Δcan1 cells did have lower viability throughout the assay, which may account for the initial larger release of methylarginines pre-water incubation. Therefore, methylarginines are still expelled in permease-deficient yeast in water incubation, although to a notably lesser extent in Δcan1 cells in water. 223 Figure 5.5. Methylarginine efflux changes during incubation in SC media and water in permease-deficient yeast strains. (A) WT BY4741, Δcan1, Δalp1, and Δavt4 strains were grown for three days in SC media, transferred to water, and incubated for 11 days. Cells were harvested in SC media (black, n=3) and after water starvation (stripes, n=5), and aDMA (top panel), MMA (middle panel) and sDMA (bottom panel) were quantified using mass spectrometry. Concentrations are normalized to number of cells and per day of growth. (B) Changes in aDMA (black), MMA (diagonal stripes) and sDMA (grey dots) (log scale) are presented in log2 fold change on a log2 axis. Asterisks indicate when the fold-change difference is significantly different from the fold-change observed in the WT strain. Error bars represent the standard deviation from the mean. (C) Viability of permease-deficient yeast strains through water incubation growth. Cells were subjected to water incubation growth in SC media and viability was estimated by counting dead and live cells, as measured by trypan blue (n = 4). The change in viability was compared to BY4741 using an ordinary one-way ANOVA test for multiple comparisons test. ns = not significant; * = p < 0.05; ** = p < 0.01; *** = p < 0.001; **** = p < 0.0001. Knockout of Avt4p does not inhibit efflux of methylarginines from the vacuole, which supports its reported redundancy with Avt3p and Atg22p [356]. Therefore, these multiple redundant proteins maintain methylarginine flux from the vacuole. We were surprised though to 224 see a significant increase in methylarginine efflux in water incubation in Δalp1 cells compared to WT cells. In the Δalp1 strain, Can1p should still be expressed, so observing some methylarginine efflux is unsurprising. However, if Can1p was expressed at the same level it is expressed in WT cells, we would not expect to see a significantly greater efflux of methylarginines compared to WT. Since we do observe this, we propose that Δalp1 cells make use of a compensatory mechanism, just like in Δavt4 cells, to continue efflux of methylarginines. One theory is that Can1p may be overexpressed in Δalp1 cells to accommodate the loss of an amino acid transporter. Since Can1p is selective for arginine (and, we propose, methylarginines) while Alp1p is a general amino acid transporter, then the significant observed increase may be due to more selective or efficient efflux of methylarginines as a result Can1p being the dominant permease [392]. Another possible theory is that the general amino acid permease Gap1p may be overcompensating for the loss of Alp1p. However, Gap1p expression is highly sensitive to nitrogen, as it has been shown to be upregulated in response to low extracellular nitrogen-containing nutrients [394,395] and also downregulated in response to full nitrogen starvation (i.e., autophagy) [396]. Further, its activity has only been shown to take up amino acids from the environment and it is not believed to be able to efflux amino acids [395]. Of these two theories, we posit that Can1p overexpression in Δalp1 cells may contribute to the increased methylarginine efflux in water incubation. We next sought to determine if methylarginine influx into yeast is possible. We proposed to monitor methylarginine influx using radiolabeled methylarginines; however, these compounds are not commercially available, so we synthesized a mixed population of [14C]-MMA and [14C]-aDMA by exhaustively methylating a GST-tagged glycine-and-arginine rich substrate (GST-GAR) in vitro using recombinant human PRMT1 (expression and purification described in 225 Chapters 2 and 3) and [14C]-SAM, and subsequently hydrolyzing proteins from the reaction to amino acid components. We designed an assay to immobilize yeast cells onto filter discs and measure the incorporation of amino acids into cells by measuring the retained radioactivity on the discs using a scintillation counter. To validate this assay, we used [3H]-methionine because it is well established that methionine is taken into yeast cells [372]. WT cells were incubated with increasing concentrations of [3H]-methionine spiked with 0.02 mg/mL unlabeled methionine to drive the concentration gradient for 90 min at 30oC in PBS. Washed cells were immobilized on glass filter discs inside ~21-mm columns and washed with PBS. Radioactivity was monitored in the wash to determine adequate wash volume. Media with [3H]-methionine treatment was added to a filter disc and washed to measure how much free radioactivity remained bound to the disc as a background control. We observed a concentration-dependent increase in radioactivity on the filter discs indicating an increase in methionine incorporation into yeast cells (Figure 5.6A). After 30 mL of PBS wash, the radioactivity in the wash buffer decreased to under 200 DPM, so 40 mL of wash was used in subsequent experiments (Figure 5.6B). Next, we monitored the influx of both [3H]-arginine and [14C]-methylarginines into WT cells and into transporter-deficient strains Δcan1, Δalp1, and Δavt4. In both arginine methylarginine influx experiments, 12580 Bq/mL of [3H]-arginine or [14C]-methylarginine and 0.02 mg/mL unlabeled arginine was added to the media to drive the concentration gradient. Cultures were grown for 90 min at 30oC in PBS and 400 μL of washed cells were added to the filter discs. Results show incorporation of radiolabeled species as a percentage of total DPM in 400 μL media that was added to glass filter discs and subsequently washed. We found that in both log phase and water incubation, all yeast strains do take up arginine as expected, but 226 unsurprisingly, the Δcan1 strain is deficient in arginine uptake under both growth conditions (Figure 5.6C). In log phase, the arginine influx into Δcan1 and Δalp1 strains significantly differs from WT. Further, Δalp1 imports significantly more arginine under log phase compared to water incubation, whereas WT, Δcan1, and Δavt4 arginine influx does not significantly differ in either growth condition (Appendix D.9). The observation that Δalp1 cells import significantly more arginine than other strains may further strengthen our hypothesis that Can1p, the main arginine permease known to influx arginine, may be overexpressed in the Δalp1 strain to compensate for its loss. Further, the lack of change in Δavt4 arginine influx further demonstrates its redundancy with Avt3p and Atg22p, like we observed in methylarginine efflux studies (Figure 5.5). Drastically less methylarginine was incorporated into yeast cells than arginine by 1-2 orders of magnitude (Figure 5.6C). In all cases, background subtracted carbon-14 DPMs fell within 3 standard deviations of the background control, suggesting that the amount of methylarginine incorporation was below the limit of detection of the assay (data not shown). Consequently, we saw no significant difference in methylarginine incorporation among any yeast strains tested (Appendix D.9), but we did observe markedly less methylarginine incorporation in all strains in water incubation compared to log phase. We also tried spiking the media with 0.02 mg/mL unlabeled aDMA to drive the concentration gradient instead of arginine, but we similarly saw no methylarginine incorporation (data not shown). Regardless, these low radioactivity levels of methylarginine suggests that in yeast, arginine flux is bi-directional, but methylarginine flux is not. Although there is evidence to support that Can1p can both influx and efflux amino acids [375], to our knowledge, this has not been explored for Alp1p. Our findings here that Δalp1 cells have increased arginine flux and increased methylarginine efflux do not allow us to conclude that Alp1p has bidirectional flow since we anticipate a compensation mechanism is in place. Since we 227 consistently saw very little methylarginine incorporation into cells in WT or KO strains, we conclude that neither Can1p, Alp1p, nor any other membrane permease is selective for methylarginine uptake. Therefore, if methylarginine transport in yeast is unidirectional and only allows for export, this finding may suggest that methylarginines are intentionally expelled due to their possible cell toxicity. Methylarginine uptake in yeast has not previously been explored. Although many studies report that extracellular methylarginines can inhibit arginine uptake, there is very little evidence to suggest that higher eukaryotic cells broadly take up methylarginines [397,398]. The exception to this is the observation that bovine endothelial cells readily take up aDMA and MMA [399,400]. However, endothelial cells are markedly different from many other eukaryotic cell types in that they are a broad and heterogeneous cell type with diverse structures, functions, and gene expressions [401]. Further, an important role of endothelial cells is to serve as a protective barrier that strictly controls nutrient transfer, so it is feasible that endothelial cells may have increased permeability or selectivity for methylarginines compared to other cell types. 228 Figure 5.6. Monitoring amino acid influx into yeast cells. (A) WT BY4741 cells were grown to log phase (OD600 = 0.7) in SC media. As a positive control to optimize the assay, cells were switched into PBS and spiked with 0.02 mg/mL unlabeled methionine and 0.01, 0.1, or 1 μM [3H]-methionine. Cells were grown for 1.5 h at 30oC and then 400 μL cells were immobilized on a glass filter disc. After sufficient washing, the DPM of each sample was determined using a scintillation counter (n = 2). (B) To determine ideal wash conditions to remove background radioactivity, the DPM of the wash was periodically determined between 4 and 30 mL (n = 2). (C) WT BY4741, Δcan1, Δalp1, and Δavt4 were grown into log phase in SC media (top panel) and water incubation (bottom panel). Cells were switched to PBS and spiked with 0.02 mg/mL unlabeled arginine and either 12580 Bq of [3H]-arginine or [14C]-methylarginine. After incubation at 30oC for 1.5 h, cells were washed twice with PBS and 400 μL were spotted onto glass filter paper and washed with 40 mL PBS. To normalize to percent incorporated, the DPM on the filter paper was converted to a fraction of the total DPM in 400 μL treated PBS (n = 3). Error bars show the standard deviation. For each of the methyltransferase-, autophagy-, and permease-related knockout strains used in this study, we plotted the observed log fold change of basic amino acid efflux versus the 229 log fold change of total methylarginine efflux (Figure 5.7). Among all strains, we found that methylarginine efflux correlates most strongly with arginine efflux, to a lesser degree with lysine efflux, and much less with histidine efflux. We also found that aDMA and MMA efflux is highly correlated with arginine efflux while sDMA efflux is correlated less so (data not shown). We conclude then that both methylarginines and arginines are expelled from yeast cells using the same mechanisms and transporters. Figure 5.7. Correlation of change in basic amino acid efflux to change in methylarginine efflux. Log2 fold change of arginine (blue circles, dotted trend line), lysine (orange squares, solid trend line), and histidine (grey triangle, mixed dotted and dashed trend line) versus log fold change in total methylarginines for all yeast strains used in this study. A linear regression and corresponding correlation coefficient (R2) is shown. 5.4.5 Inhibition of yeast nitric oxide synthase by methylarginines in yeast lysates Since we observed consistent efflux of methylarginines in water incubation, we questioned if methylarginine efflux is important for yeast survival. Since NO is a crucial element for yeast survival in water incubation [402] and methylarginines in mammals are potent inhibitors of NOS activity [366], we hypothesized that methylarginines are toxic to yeast through inhibition of 230 noncanonical NOS. To test this hypothesis, we wanted to observe the production of NO in the presence of methylarginine species. Since we saw that yeast have a severely diminished capacity to take up extracellular methylarginines, we were unable to measure intracellular NO production in the presence of high concentration of extracellular methylarginine. Instead, we monitored NO production in yeast lysates that were grown into water incubation. To drive the production of NO, NADPH was added to a final concentration of 1 mM to each lysate. Nitric oxide was measured using 1 μM DAF-FM DA, which reacts with NO to form a fluorescent benzotriazole structure [373]. For all treatments, we included a lysis buffer control with matched treatment and subtracted this fluorescence from the samples as a background control. All treatments had background fluorescence that was comparable to the no treatment control (Appendix D.10). Since NO production in yeast is L-arginine-dependent [403], we first monitored fluorescence of yeast lysates in the presence of increasing arginine. We saw a concentration-dependent increase in fluorescence as arginine concentrations increased to 50 mM, showing that this in vitro NOS activity assay is responsive (Figure 5.8A). We next wanted to observe the impact of aDMA on nitric oxide production in yeast lysates. To further drive the production of NO, we included 1 mM arginine in all inhibitor treatments. We compared 50 and 100 mM aDMA to the impact of no treatment and 1 mM arginine. As a positive control, we also used 50 and 100 mM L-canavanine (Can), which has also been shown to inhibit mammalian NOS [404]. In both WT, Δcan1, and Δtah18/TAH18 lysates, we consistently see significant decreases in fluorescence at all concentrations of Can and aDMA compared to the 1 mM arginine positive control (Figure 5.8B-E). No treatment and 1 mM arginine treatments reveal significantly less NO production in Δcan1 lysates compared to WT (p < 0.0001 for both comparisons). Since we observed a significant decrease in methylarginine efflux fold-change in 231 Δcan1 cells incubated in water compared to WT, we suggest the possibility that starved Δcan1 cells may have a decreased ability to efflux methylarginines compared to WT, resulting in less basal NOS activity due to inhibition by methylarginines. Comparing Δtah18/TAH18 and BY4743 no treatment and 1 mM arginine treatments, we see significantly less NO production in the no treatment control (p = 0.004). Although less fluorescence is detected in Δtah18/TAH18 lysates than WT, the difference is not statistically significant. Since this diploid strain is missing one allele that is required for NO production, we hypothesized that Δtah18/TAH18 lysates would also have lower basal NO production compared to the parent strain BY4743 (diploid WT). Therefore, we conclude that L-canavanine and aDMA inhibit NOS activity in yeast lysates. We were unable to design an experiment to directly test the impact of high intracellular or extracellular methylarginine concentrations on yeast viability and instead relied on NOS inhibition as a proxy for yeast toxicity. However, since yeast NOS is dependent on Tah18p [405,406] and the homozygous diploid deletion of TAH18 is nonviable [297], we conclude that the significant inhibition of NOS activity in yeast lysates in the presence of L-canavanine and aDMA is detrimental to cell survival. Considering the similarities between yeast and mammalian nitric oxide signaling, as well as the requirement of arginine as a substrate for yeast NOS and its demonstrated inhibition by L-NAME [407], perhaps this finding is not surprising; however, this study is the first to directly demonstrate methylarginine-dependent inhibition of yeast NOS. We were unable to test the impact of all methylarginines on NO production in yeast lysates because MMA gave very high background fluorescence in lysis buffer with DAF-FM DA and sDMA (as a p-hydroxyazobenzene-p′-sulfonate salt) gave high background fluorescence in the absence of DAF-FM DA (data not shown). While MMA is also known to inhibit NOS activity in mammals, MMA is present in lower concentrations compared to aDMA in mammalian and yeast 232 cells, so its impact on NO production may not be as biologically relevant [185]. In mammals, sDMA has not been shown to inhibit NOS and it is also present in very low amounts in yeast, as evidenced by our detection of sDMA on yeast proteins. Interestingly, L-NAME, the most notable inhibitor of mammalian NOS, also gave high background fluorescence in lysis buffer with DAF-FM DA, so we were unable to use it as a positive control and instead needed to use L-canavanine. These findings demonstrate that DAF-FM DA is not an ideal reagent for use in in vitro studies due to nonspecific interactions caused only by certain compounds in the presence of lysis buffer. Specifically, DAF-FM DA has been shown to interact with superoxide and peroxyl radicals which may have spontaneously been introduced in the presence of certain compounds and lysis buffer [408]. Therefore, careful monitoring of each compound in lysis buffer is an important control in this assay. 233 Figure 5.8. Nitric oxide synthase activity in yeast lysates. (A) Effect of 0-50 mM arginine on nitric oxide production in BY4741 lysates. Nitric oxide was measured in yeast lysates with no treatment, 1 mM arginine (Arg), 50 and 100 mM canavanine (Can), and 50 and 100 mM aDMA. (B) WT haploid BY4741 (grey solid), (C) haploid Δcan1 (yellow dotted), (D) WT diploid BY4743 (blue checkered), and (E) heterozygous diploid Δtah18/TAH18 (orange diagonal stripes). All reactions contained 1 μg/μL yeast lysates, 1 mM NADPH as NOS substrate (including the no treatment experiments), 1 μM DAF-FM DA, and the indicated concentration of inhibitor. Fluorescence of DAF-FM DA was measured (495ex/515em nm) to monitor nitric oxide. Error bars show the standard deviation of three replicates (ns = not significant; * = p < 0.05; ** = p < 0.01; *** = p < 0.001; **** = p < 0.0001). 234 5.5 Conclusions This study is the first investigation of free methylarginine amino acid fate in yeast. Our broad KO studies revealed that the loss of several genes results in significant changes to methylarginine production or efflux. This indicates that there are many determinants of methylarginine production in yeast. Our autophagy exploration studies reveal that cells that are deficient in autophagy are unable to efflux free methylarginines, suggesting that autophagy may be the primary degradation pathway for methylarginine-containing proteins. We show for the first time that methylarginines are unable to be taken up by cells and that aDMA inhibits yeast NOS. NO has been shown to confer stress tolerance in yeast as well as promote apoptosis in damaged cells to maintain a healthy population of aging yeast [220]. In order to remove methylarginines that may negatively impact NO signaling, yeast have developed a severely diminished capacity to take up methylarginines from media. Yeast, therefore, expel methylarginines as their only detoxification method. Although this study is the first to describe the fate and consequences of methylarginines in yeast, our prediction that methylarginine-containing proteins are degraded via the vacuole is not unique. A study in mammalian cells monitoring free intracellular methylarginines found that inhibition of the proteasome pathway led to a decrease in free aDMA and sDMA production while inhibition of autophagy resulted in a decrease in free aDMA production [409]. Further, studies in mice experiencing cancer-mediated muscle atrophy revealed a significant decrease in protein synthesis coupled with a sizeable increase in methylarginine production. This study also identified enrichment of autophagy-related transcripts, meaning that activation of autophagy could be a possible link to methylarginine accumulation [410]. Therefore, this work supports literature findings and provides additional evidence that autophagy may increase cellular methylarginine 235 accumulation. However, these findings also add to the complexity of the importance of regulating arginine methylation in yeast. It was recently demonstrated that Rmt1p-mediated methylation of an RNA-binding protein Psp2p was necessary for the proper induction of autophagy during nutrient starvation. Methylated Psp2p binds to and stabilizes ATG1 and ATG13 transcripts to facilitate their translation into necessary autophagy components [411]. This study demonstrates for the first time the negative consequences of methylarginine accumulation in yeast, but taken together with existing literature, demonstrate that the precise monitoring and balance of intracellular methylarginines in yeast is required for cells to respond to stress, thus demonstrating the complex regulatory roles and impacts of PRMTs. 236 Chapter 6: Conclusions and Future Directions 6.1 Major Thesis Findings, Overall Significance, and Impact The purpose of this research was to contribute to our understanding of the mechanisms by which protein arginine N-methyltransferases (PRMTs) methylate their substrates and to elucidate the biological significance of PRMTs and methylarginine-containing proteins in Saccharomyces cerevisiae. Both Chapters 2 and 3 describe the bisubstrate binding and methylation processivity mechanism of PRMT1 using multiple methods. Further, Chapter 2 offers a detailed literature review identifying research and analysis methodology that has historically contributed to inconclusive and inconsistent conclusions about PRMT kinetics. Chapter 4 describes the methylation activity of a putative methyltransferase Ynl092wp and identifies an in vitro substrate for the first time. Further, we provide additional evidence for the methylation activity of another putative methyltransferase, Ykl162cp, whose activity has otherwise been elusive. Last, Chapter 5 explores the fate of methylarginines in the yeast stress response and how yeast cells can mediate this modified amino acid’s toxic effects. 6.1.1 The kinetic mechanism of human PRMT1 While we corroborate previous findings that PRMT1 processivity depends on substrate identity [141], we demonstrate for the first time that PRMT1 processivity is multifactorial and also depends on both cofactor and enzyme concentrations, impacting enzyme dimerization and oligomerization. Our kinetic data also reveal that PRMT1 uses a sequential ordered substrate binding mechanism in which cofactor binding must precede substrate binding for catalysis; however, we also report that PRMT1 may bind to peptide substrates in the absence of cofactor to form a dead-end complex that precludes cofactor-binding. We further confirm the PRMT1 237 substrate-binding mechanism using a biophysical method, differential scanning fluorimetry (DSF). We find evidence to support a sequential ordered mechanism in that only cofactor and cofactor analogues stabilize PRMT1 and that active-site-binding ligands alone, such as peptides, do not stabilize the enzyme. Last, we demonstrate the additive stabilization effect of both cofactor and active site ligand on PRMT1, which indicates how conducive the stable tertiary complex is to methyl transfer. This published work significantly contributes to the PRMT enzyme kinetics field, which has been divisive. Many groups have explored PRMT steady-state enzyme kinetics, but conclusions are largely split between two theories of bisubstrate binding: random substrate binding or sequential binding where cofactor binds first [101,105,245,246,249,108,130,137,153,239,242–244]. Rather than simply offering our own interpretation of steady-state kinetic data, we present all raw data and all nonlinear regression analyses. This is the first kinetic study in the PRMT field to publish this level of transparency and we invite readers to draw their own conclusions from our data as well. We also describe the importance of this transparency by detailing the pitfalls of interpreting standard reciprocal plots, like the Lineweaver-Burke, upon which several PRMT kinetic research studies relied to draw their conclusions. Therefore, we are confident that our conclusions are objective. An additional result of our kinetic study was the finding that enzyme and cofactor concentrations not only impact PRMT oligomerization but also, surprisingly, impact product formation and drive processivity, a consequence that had never been considered in the PRMT research community. This means that to better understand how PRMT functions in vivo, in vitro processivity experiments must be performed using biologically relevant conditions. To verify our findings, we performed a literature search of all PRMT processivity studies and was able to predict their processivity conclusions with 100% accuracy by only studying their enzyme and 238 cofactor concentrations. This demonstrates a key methodology consideration that has been overlooked in previous studies and should be more carefully controlled in future work. Therefore, while our kinetic study offers important primary research, it also serves as a comprehensive literature review identifying limitations of past kinetic and processivity research. These data and comprehensive analyses should influence future in vitro methodologies, which are important considerations in PRMT inhibitor design and development since PRMTs have emerged as ideal candidates for drug targets for cancer therapy [49]. As a result of difficult kinetic data, we stress the need for additional nonenzymatic methods to explore enzyme substrate binding. The gold-standard biophysical method is isothermal titration calorimetry (ITC), which is robust and replicable; it also requires expensive equipment and very large sample volumes of highly concentrated protein and ligand [412]. As an alternative, I present an experimental workflow that uses DSF as a suitable biophysical method to investigate order of substrate binding as a complement to kinetic data, thus contributing to more robust and thorough mechanistic investigations of enzymes. This method requires substantially less sample than ITC and makes use of common real-time PCR equipment [267]. This published work clearly outlines detailed protocol, important controls, and experimental considerations to successfully use DSF to investigate bisubstrate binding to enzymes. Although this technology is theoretically applicable to other enzyme systems, a notable limitation of this method is the need to first do a preliminary investigation into how the enzyme of interest and any ligands of interest will interact with the fluorescent dye. Strong interactions that produce melt curves will confound results and interfere with conclusions. We also noticed that the use of active cofactor and reaction conditions where catalysis can occur will lead to poor quality data. Therefore, DSF may not be compatible with all ligands nor all enzyme systems. 239 6.1.2 Activity of yeast putative methyltransferases We also observed in vitro methylation activity for Ykl162cp, although we were not successful in identifying the type of methylation formed nor the type of substrate that was methylated. We also show that Ykl162cp activity increases in a basic pH environment. Further, RNase treatment of yeast lysates abolished Ykl162cp-mediated methylation of substrates, suggesting that Ykl162cp is likely an RNA methyltransferase. Although Ykl162cp has been studied in the past, no activity has been identified [316]. Therefore, finding trace Ykl162cp activity in this study is an important finding. Since no activity or substrate has previously been detected, we approached this investigation by first looking for activity under unusual pH conditions and by using a high amount of yeast lysate. This research is also significant since it suggests that RNA is the likely substrate, which, as for Ykl162cp, would deviate from its mammalian and Dictyostelium homologs, protein arginine mitochondrial methyltransferase NDUFAF7 and MidA, respectively [97,318]. However, Ykl162cp similarity to these homologs has been tenuous at best and these preliminary findings may suggest that any sequence similarity between these species may be less significant and less evolutionarily conserved than originally believed [316]. Further, these results can inform future studies to characterize Ykl162cp by refocusing substrate identification efforts to RNA rather than protein. Although we were unable to identify a bona fide yeast substrate for Ynl092wp, we demonstrate for the first time its automethylation site, identify a peptide sequence that it can monomethylate, and demonstrate that it can methylate mammalian actin in vitro. These data demonstrate that Ynl092wp is a protein methyltransferase rather than a small molecule methyltransferase like its human homolog [320]. Further, using our developed DSF workflow to 240 investigate enzyme bisubstrate binding, we provide evidence that supports that Ynl092wp may bind its substrates in a sequential binding order in which target substrate binding precedes cofactor binding. We observed that cofactor destabilized the enzyme while substrates stabilized the enzyme, yet SAH-dependent thermal stabilization of enzyme depended on the presence of excess substrate peptide. Characterization of Ynl092wp as a protein histidine N-methyltransferase is significant. First, although Ynl092wp automethylation had been previously reported [204] and it was shown to weakly methylate histidine containing di- and tri-peptides [320], histidine as its site of automethylation was never determined, so this work confirms previous hypotheses. Ynl092wp should hereafter be classified as a protein methyltransferase rather than a small molecule carnosine methyltransferase. This distinction is important because yeast do not produce carnosine, so its putative identification as a carnosine methyltransferase is illogical [297,321]. Further, classification as a protein methyltransferase deviates from the classification of its human homolog, carnosine N-methyltransferase 1 (CARME). Despite high sequence identity, it appears that these enzymes distinctly functionally differ, providing evidence of another yeast-mammal homolog pair without conserved evolutionary function. However, this finding could lead to the investigation of human CARME as a possible protein methyltransferase. Of these finding, perhaps most notable is the discovery that Ynl092wp methylates mammalian actin in vitro. Actin has a very conserved methylhistidine among most eukaryotes and has only notably not been found in yeast species and it has been generally accepted that yeast uniquely did not produce methylhistidine on actin [332]. This has called into question the role of methylhistidine on actin and how the highly conserved yeast actin does not require the ubiquitous post-translational modification that is otherwise present in virtually every other mechanism [341]. This finding, along with recent advancements in detecting methylhistidine reveals that histidine 241 methylation is a more widespread than originally anticipated [339]. Since the Ynl092wp automethylation site and the actin methylation site are highly similar, it suggests that Ynl092wp may have a preferred methylatable sequence. 6.1.3 Biological significance of methylarginines in yeast stress response We used a novel approach to explore the production of free methylarginines in yeast in response to single gene deletions of putative methyltransferases, autophagy-related genes, and amino acid permease genes. We found that single deletion of many putative methyltransferase genes significantly impacted methylarginine production in yeast, demonstrating that regulation of PRMTs is widely impacted. Further, we demonstrated that autophagy-deficient strains were unable to efflux methylarginines, which suggests that autophagy or vacuolar processes are the primary driver of free methylarginine generation. Further, yeast are unable to take up methylarginines from their environment under log or water incubation growth. Last, we demonstrated that yeast nitric oxide synthase (NOS) activity is inhibited by methylarginines in vitro. These findings together indicate that under stress conditions, yeast efflux methylarginines and do not take them up from their environment as a way to regulate NO production, which must be under strict control in stress conditions since it is crucial for cell survival [220,362]. To do this, methylarginine-containing proteins are proposed to be degraded through the vacuole via autophagy for production of free methylarginines and eventual expulsion. We had originally intended to look for methylarginine production on proteins in yeast in response to putative methyltransferase knockouts to identify yet unknown arginine methyltransferases. Unfortunately, symmetric dimethylarginine (sDMA) is produced in very low amounts in yeast cells, so we were unable to accurately quantify the amount of sDMA in cells and 242 looked in the media instead. We were surprised to find high concentrations of sDMA and other methylarginine species in the media which gave way to a novel and exciting research question about why methylarginines were readily detected in the extracellular environment rather than in cells. This research further demonstrates the conservation between mammalian and yeast stress systems, showing that, like in mammals, yeast NOS is inhibited by methylarginine. Last, we link methylarginine production to autophagy, a relationship that has not yet been explored at length, although two other groups have reported similar observations that a pool of free methylarginines are produced via autophagy [409,410]. Since autophagy is an important response to stress in eukaryotic cells and the roles of PRMTs in stress are still not well-elucidated, this work provides an important new research direction that may reveal insights into human health. 6.2 Future Directions 6.2.1 The application of DSF to other PRMTs and other enzyme families Since PRMT enzymes have a high degree of structural similarity, it is anticipated that all members would operate using the same kinetic mechanism of bisubstrate binding. The most prevalent method for determining bisubstrate binding mode is through kinetic analyses, but we stress that non-enzymatic analyses such as DSF should be used to complement kinetic findings and more robustly determine bisubstrate binding. Although biophysical methods reveal a lack of substrate binding to CARM1 [105], PRMT6 [101], and now to PRMT1 in the absence of cofactor, the kinetic mechanism of substrate binding to CARM1 and PRMT6 in particular still remains conflicted. Future studies can make use of DSF to provide additional evidence to reveal the interactions of PRMTs and their substrates. 243 Since the application of DSF has only been explored at length within PRMT1, it is also important to consider its application outside of the PRMT enzyme family. We also demonstrate the efficacy of DSF to monitor substrate interactions in the histidine methyltransferase Ynl092wp (as described in Chapter 4), but since no other robust bisubstrate binding studies have been performed for Ynl092wp, our findings cannot be confirmed. The application of DSF will be useful to explore enzyme-substrate binding in situations where activity assays are not amenable. For example, PRMT2 has the weakest in vitro methylation activity out of the PRMT family and kinetic characterization is difficult [53]. Further, its use in exploring order of substrate binding should also be explored outside the realm of SAM-dependent methyltransferases to confirm validation of this application and to determine its usefulness more broadly to other enzyme systems. 6.2.2 Characterization of Ykl162cp activity and identification of a native yeast substrate Future efforts must first aim to improve Ykl162cp purification from bacterial contaminant proteins to ensure that methylation activity is not coming from a non-Ykl162cp source, which is a current limitation of this study. Expression and purification of protein methyltransferases is ideally performed in bacterial hosts since there is very little protein methylation activity in bacteria [77]. However, since Ykl162cp may be an RNA methyltransferase, activity which bacteria also have, expression and purification in yeast may result in better expression of its endogenous protein [74]. Further, these preliminary data can inform more directed searches for Ykl162cp substrates. Since we suspect Ykl162cp may be an RNA methyltransferase, purified Ykl162cp can be incubated with yeast RNA extract to theoretically achieve higher methylation signal and a higher concentration of possible substrate. Further, since Ykl162cp localizes to the mitochondria [333], in vitro methylation studies using purified mitochrondial yeast extract may also allow for a more 244 narrowed substrate identification approach [413]. Although the mitochrondrial transcriptome has been well established, it has not been comprehensively searched for post-translational modifications, so we cannot predict what might exist as mitochondrial substrates for Ykl162cp [414]. Last, since we observe an increase in methylarginine efflux in Δykl162c cells (Chapter 5), once Ykl162cp activity has been confidently established, activity of a common catalytically inactive mutant should be performed to probe for methylation activity-dependent phenotypes. 6.2.3 Characterization of Ynl092wp activity and identification of a native yeast substrate Future work to characterize Ynl092wp activity should include exploring yeast actin as a substrate, identifying native yeast substrates, and characterizing its mechanism of action. Although we postulate that Ynl092wp may bind its substrates using a sequential mechanism, since there are no other mechanism studies that have been performed, we cannot confirm this prediction. Further, our selected 9mer peptide representing its automethylation sequence is a relatively poor substrate, as measured by very minimal methylation. Therefore, DSF data and other future substrate-binding studies will likely be stronger with a peptide substrate that is methylated more in vitro. The lack of methylation of the 9mer peptide suggests that a minimal peptide substrate length is required for stable binding or substrate positioning that is conducive to methylation, like has been previously observed for PRMT1 [140]. Several peptides corresponding to this sequence could be synthesized to probe for this minimal length. Identification of a highly methylated substrate will also allow for investigation into the substrate-binding site of Ynl092wp. It is predicated to be located at the dimer interface, although the exact residues involved are still unknown since Ynl092wp has not been successfully crystalized bound to a peptide substrate nor a small molecule substrate [338]. Another important research direction is to further explore methylation of yeast actin. Since actin and 245 Ynl092wp migrate very closely on a gel, a method to explore actin methylation in lysates or in vivo would be to generate an enzymatically active yet automethylation-deficient enzyme. Our lab hopes to generate an H314Q or H314R mutation to eliminate automethylation and incubate this with mammalian actin and yeast lysates to test for actin methylation without the overwhelming automethylation. If this mutant still has automethylation activity, then this will also create an opportunity for additional proteomic approaches using additional proteases to more comprehensively understand its automethylation activity. Last, since we identified a highly similar methylatable sequence in Ynl092wp and actin, we question if native yeast substrates can be identified through this consensus sequence. A preliminary BLASTp search identifies GTPase-activating protein 1 (Gyp1p) and ISWI one complex protein 4 (Ioc4p) aligning with the methylated sequence in Ynl092wp (LYHFENDHGVETTYE), as well as ectonucleotide pyrophosphatase/phosphodiesterase 2 (Npp2p) and centractin (Arp1p) aligning with yeast sequence (TLRYPIEHGIVTNWD) that contains the invariant histidine residue; therefore, these hits may be possible Ynl092wp substrates. 6.2.4 The roles and implications of methylarginines in yeast By growing strains lacking putative sDMA-producing enzymes under conditions that enriched for sDMA, we anticipated that we would be able to identify a candidate sDMA-producing enzyme if a knockout strain failed to produce sDMA. Since sDMA is produced in such low amounts compounded with our observed methylarginine efflux, we were unable to accurately quantify sDMA on proteins. To better quantitate sDMA on proteins in response to stress and in response to knockout of putative methyltransferase genes, future work can attempt to re-examine sDMA production on proteins on a much larger scale. By lysing ten-fold more cells, sDMA levels 246 should consistently fall within confidently quantifiable levels. Further, exploring the effect of gene deletions in a background strain that would inhibit methylarginine efflux would allow for retention of sDMA in cells for quantification. We showed that cells lacking the vacuolar protease Pep4p are incapable of methylarginine efflux. Therefore, to observe the impact of gene deletions on sDMA accumulation on proteins, gene deletions should be generated in a Δpep4 strain. This work may allow for identification of a likely sDMA-producing enzyme in yeast that has not yet been identified. In the absence of identifying a novel methyltransferase, this approach could also be used to complement our extracellular methylarginine quantification. In our attempt to explore how single knockouts of arginine permeases impacted methylarginine efflux, we noticed that Δalp1 cells efflux methylarginines at an enhanced rate. We question if Can1p expression changes in response to Alp1p deficiency. Future studies should explore the compensatory relationship between Can1p and Alp1p expression in Δalp1 and Δcan1 cells. We were unable to explore permease expression levels in this study because antibodies for these permeases are not commercially available. Similarly, Avt4p and Atg22p are partially redundant along with Avt3p (not used in this study) [356]. In both knockout strains, we saw no decrease in methylarginine efflux and questioned if each permease was compensating for the loss of the other. Future work probing for their expression levels may reveal how these transporters cooperate and provide a clear explanation for our observations [297,356]. Last, since we saw changes in methylarginine efflux between log phase and water incubation, probing for expression levels of all permeases and vacuolar transporters at each growth condition may also reveal more information about what is driving the methylarginine flux. Both Atg8p and Pep4p are responsible for all types of autophagy, including bulk macroautophagy and for targeted autophagy of specific organelles [354,355,415]. To narrow down 247 the autophagic source of free methylarginine production, these studies can be repeated using different null mutants for specific microautophagic processes. For example nucleus-vacuole junction protein 1 Nvj1p and vacuole-related protein Vac8p are specifically needed for micronucleophagy [416], ubiquitin protease Ubp3p and ubiquitin protease cofactor Bre5p are required for ribophagy [417], and both protein phosphatase Ptc6p and autophagy-related protein Atg33 are specifically required for mitophagy [418]. Single knockouts may then reveal the subcellular localization of methylated proteins associated with vacuolar-targeted degradation. Last, a limitation of this study lies within the use of the yeast knockout (YKO) collection. It has been reported that several deletion strains have been shown to develop aneuploidy of chromosomal segments that may serve as a compensatory mechanism for the yeast to overcome loss of gene function [419,420]. Aneuploidy can impact the observed phenotypes by altering yeast growth or by expressing proteins with analogous function. Therefore, an important follow up to this study would be to investigate our key findings in a different parent strain, such as in W303, which is ~85% similar to S288c strains [421]. Interestingly, the chromosome where W303 and S288C strains are most divergent is chromosome XI bearing the chromosome identifier of YKL [421]. Therefore, our phenotypic observations of Δykl162c should particularly be explored in another yeast strains. It will also be important to investigate how Δcan1 and Δalp1 cells efflux and influx methylarginines to confirm how yeast mediate arginine flux and to confirm that Δatg8 and Δpep4 cells are deficient in methylarginine efflux to confirm autophagy as the driving process for free methylarginine generation. 248 6.3 Conclusion The identification of non-canonical PRMTs in both mammals and budding yeast together with the recent finding of PRMT involvement in the stress response demonstrates that there is much left to learn about the impact of methylarginines. Methylarginines in yeast have almost exclusively been explored under nutrient-rich conditions, which has led to a misunderstanding of the full importance and function of methylarginines. Although we were unable to identify new PRMT enzymes, we have contributed to a more complete knowledge of the yeast methyltransferasome by identifying methyltransferase activity for Ynl092wp and for Ykl162cp. Our conclusions about the fates of methylarginines in yeast opens a yet unexplored research direction of how PRMT products can be toxic to yeast cells, underscoring the importance of proper PRMT function and regulation. Our demonstration of a new application of DSF for exploring how PRMTs bind to their substrates and our commentary on PRMT in vitro experimental considerations are valuable contributions to the design and testing of PRMT inhibitors in vitro to modulate PRMT regulation. 249 References [1] S. Gayatri, M.W. Cowles, V. Vemulapalli, D. Cheng, Z.-W. Sun, M.T. 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Timmermann, The Saccharomyces cerevisiae W303-K6001 cross-platform genome sequence: Insights into ancestry and physiology of a laboratory mutt, Open Biol. 2 (2012) 120093. 303 Appendices Supplemental data for Chapter 2 A.1 Characterization of peptides in this study Peptide substrates synthesized to contain either an unmodified arginine residue, or an N-hydroxyl-, N-monomethyl-, or N,N-dimethyl-substituted arginine residue (bolded) are listed. Peptide masses were analyzed via mass spectroscopy using positive electrospray ionization. Peptide Sequence Expected mass (Da)[a] Observed mass (Da) Charge (z) H4 Ac-SGRGKGGKGLGKGGAKR 1611.93 1614.40 +3 H4-CH3 Ac-SGRmeGKGGKGLGKGGAKR 1625.94 1630.80 +3 H4-OH Ac-SGROHGKGGKGLGKGGAKR 1627.92 1629.90 +3 eIF4A1 Ac-YIHRIGRGGR 1225.68 1229.40 +3 eIF4A1-CH3 Ac-YIHRIGRmeGGR 1239.69 1245.30 +3 eIF4A1-OH Ac-YIHRIGROHGGR 1241.67 1243.50 +3 KRK Ac-KGGFGGRGGFGGKW 1408.70 1412.40 +3 KRK-CH3 Ac-KGGFGGRmeGGFGGKW 1422.72 1425.90 +3 KRK-OH Ac-KGGFGGROHGGFGGKW 1424.69 1426.20 +3 RKK Ac-GGRGGFGGKGGFGGKW 1522.74 1525.80 +3 RKK-CH3 Ac-GGRmeGGFGGKGGFGGKW 1536.76 1539.60 +3 RKK-OH Ac-GGROHGGFGGKGGFGGKW 1538.74 1540.20 +3 RKK-(CH3)2 Ac-GGRme2aGGFGGKGGFGGKW 1551.78 1554.00 +2 R1 Ac-WGGYSRGGYGGW 1343.57 1346.00 +2 [a]Calculated in the absence of protonation 304 A.2 Purified recombinant PRMT1 proteins Eluted fractions of His6-PRMT1, mCit-PRMT1, and mCer-PRMT1 from size-exclusion column were visualized on a 10% SDS-PAGE and gel stained with 0.025% Coomassie Blue. A gel is shown with a 20-250 kDa ladder (lane 1), 0.07 μg mCit-PRMT1 (lane 2), 0.10 μg mCer-PRMT1 (lane 3), 0.14 μg mCit-PRMT1 (lane 4), 0.19 μg mCer-PRMT1 (lane 5), 0.22 μg mCit-PRMT1 (lane 6), 0.29 μg mCer-PRMT1 (lane 7), 0.37 μg His6-PRMT1 (lane 8), 0.50 μg His6-PRMT1 (lane 9), and 0.65 μg His6-PRMT1 (lane 10). Lanes from the same gel were reordered and spliced together, as indicated by black lines. The indicated masses in each lane are of total protein in the fraction. Purified fractions appear to be greater than 90% pure. 305 A.3 Linear range of PRMT1 with unmodified peptides Linear range of PRMT1 with unmodified peptides. PRMT1 (200 nM) and 14C-SAM (50 μM) were incubated with 100 μM H4 (closed circle), eIF4A1 (grey diamond), KRK (grey square), or RKK (open triangle) at 37oC for 20 – 180 min. Methylation reactions were spotted (12 μL) onto P81 Whatman filter paper, washed, and dried, and resulting radioactivity was measured using a scintillation counter. Resulting data were plotted and analyzed via linear regression. Peptide methylation: H4 is linear within 120 minutes (solid line; R2 = 0.9974), eIF4A1 is linear within 120 minutes (dashed line; R2 = 0.9893), KRK is linear within 150 minutes (dotted and dashed line; R2 = 0.9994), and RKK is linear within 150 minutes (dotted line; R2 = 0.9714). Error bars represent standard deviation (n = 2). 306 A.4 PRMT1 methylation of R1 peptide PRMT1 methylation of R1 peptide. A radioactive gel-based assay was used to quantitate methyl transfer from 14C-SAM to the R1 peptide substrate. (A) Reactions containing 200 nM PRMT1, 50 μM 14C-SAM, and 0-100 μM R1 peptide in a final reaction volume of 100 µL were incubated for 1 h at 37oC. Reactions were concentrated to 15 μL and separated on a 16.5% tricine gel in duplicate. Peptides were fixed in the gels using 5% glutaraldehyde. Each lane was excised and dissolved in 4 mL 30% hydrogen peroxide at 70oC for 4 h and disintegrations per minute (DPM) measured in a scintillation counter. (B) Resulting data was fit to the Michaelis-Menten equation (Equation 2.1) and apparent enzyme kinetic parameters were determined. Error bars represent standard error (n = 2). 307 A.5 PRMT1 concentration effect on H4-OH peptide methylation PRMT1 concentration effect on H4-OH peptide methylation. PRMT1 concentrations at 200, 400, or 800 nM were incubated with 0-100 μM H4-OH and 50 μM 14C-SAM at 37oC for 1 h. Methylation reactions were spotted onto P81 Whatman filter paper, washed, dried and resulting radioactivity was measured using a scintillation counter. Data was free fitted to the Michaelis-Menten equation (Equation 2.1 or Equation 2.2). Error bars represent standard error (n = 2). Apparent enzyme kinetic parameters for different PRMT1 concentrations methylating H4-OH peptide substrate are listed. PRMT1 concentration KM peptide (μM) kcat (s-1) (x10-4) kcat/KM (M-1 s-1) Ki peptide (μM) 200 nM 2.03 ± 0.49 7.20 ± 0.73 355 ± 93 47.6 ± 12 400 nM 1.39 ± 0.14 4.10 ± 0.083 295 ± 30 – 800 nM 1.94 ± 0.13 4.23 ± 0.067 218 ± 15 – 308 A.6 Nonlinear regression analyses Each dataset was fit to equations (Equation 2.3), (Equation 2.4), (Equation 2.5), and (Equation 2.6) that represent competitive, uncompetitive, noncompetitive, and mixed inhibition, respectively. The coefficient of determination, standard error of estimate, and sum of squares are presented as estimates of accuracy of fit to the data. Calculated Vmax (nM/min), KM (M), and Ki (Kia and Kib; (M)) values are reported with standard error and p value. Vary SAM, Vary SAH, low RKK (data points from Figure 2.7A) [Inhibitor] (μM) Fit to Model Results Model R2 Standard Error of Estimate Sum of Squares Coefficient Standard Error p value 0 Michaelis-Menten 0.978 0.29 0.66 Vmax = 5.83 0.28 <0.0001 KM = 5.29 0.81 0.0002 0.5 Competitive 0.985 0.40 1.5 Ki = 1.59 x 102 6.5 x 1011 1.0 Uncompetitive 0.951 0.39 1.4 Ki = 13.3 19 0.49 Noncompetitive 0.949 0.40 1.4 Ki = 35.6 1.0 x 102 0.74 Mixed DID NOT CONVERGE 5 Competitive 0.933 0.43 1.6 Ki = 5.08 1.4 0.0059 Uncompetitive 0.893 0.54 2.6 Ki = 17.3 6.3 0.022 Noncompetitive 0.919 0.47 2.0 Ki = 20.9 6.2 0.0082 Mixed 0.938 0.43 1.5 Kia = 6.63 3.1 0.063 Kib = 58.3 70 0.43 25 Competitive 0.966 0.27 0.64 Ki = 8.09 1.0 <0.0001 Uncompetitive 0.747 0.73 4.7 Ki = 32.2 10 0.011 Noncompetitive 0.859 0.54 2.7 Ki = 40.6 9.2 0.0017 Mixed 0.968 0.27 0.60 Kia = 9.12 1.9 0.0012 Kib = 325 4.3 x 102 0.48 Model(s) Fit COMPETITIVE 309 Vary SAM, Vary SAH, high RKK (data points from Figure 2.7B) [Inhibitor] (μM) Fit to Model Results Model R2 Standard Error of Estimate Sum of Squares Coefficient Standard Error p value 0 Michaelis-Menten 0.976 0.25 0.48 Vmax = 4.63 0.19 <0.0001 KM = 2.99 0.46 0.0002 0.5 Competitive 0.967 0.29 0.75 Ki = 3.32 3.0 0.29 Uncompetitive 0.963 0.3044 0.8337 Ki = 18.8 30 0.55 Noncompetitive 0.964 0.30 0.81 Ki = 17.5 21 0.43 Mixed DID NOT CONVERGE 5 Competitive 0.983 0.20 0.37 Ki = 5.56 0.95 0.0002 Uncompetitive 0.908 0.47 2.0 Ki = 33.4 18 0.092 Noncompetitive 0.931 0.40 1.5 Ki = 33.3 13 0.030 Mixed DID NOT CONVERGE 10 Competitive 0.951 0.35 1.1 Ki = 5.36 1.2 0.0013 Uncompetitive 0.755 0.79 5.6 Ki = 38.1 23 0.13 Noncompetitive 0.807 0.70 4.4 Ki = 38.6 18 0.061 Mixed DID NOT CONVERGE 25 Competitive 0.987 0.14 0.18 Ki = 5.10 0.38 <0.0001 Uncompetitive 0.623 0.78 5.4 Ki = 29.0 10 0.021 Noncompetitive 0.767 0.61 3.3 Ki = 34.6 9.5 0.0053 Mixed DID NOT CONVERGE Model(s) Fit COMPETITIVE 310 Vary RKK, Vary SAH, low SAM (data points from Figure 2.7C) [Inhibitor] (μM) Fit to Model Results Model R2 Standard Error of Estimate Sum of Squares Coefficient Standard Error p value 0 Michaelis-Menten 0.941 0.50 2.0 Vmax = 8.18 0.44 <0.0001 KM = 12.4 2.3 0.0006 2.5 Competitive 0.455 1.0 9.4 Ki = 4.21 2.2 0.094 Uncompetitive 0.859 0.52 2.4 Ki = 7.67 1.5 0.0006 Noncompetitive 0.792 0.63 3.6 Ki = 10.6 2.6 0.0025 Mixed 0.859 0.55 2.4 Kia = 1.15 x 103 1.0 x 1013 1.0 Kib = 7.67 2.1 0.0073 5 Competitive 0.470 0.92 7.5 Ki = 3.30 0.97 0.0081 Uncompetitive 0.933 0.33 0.95 Ki = 9.13 0.85 <0.0001 Noncompetitive 0.918 0.36 1.2 Ki = 12.2 1.2 <0.0001 Mixed 0.940 0.33 0.86 Kia = 36.3 41 0.41 Kib = 9.92 1.4 <0.0001 10 Competitive 0.819 0.58 3.0 Ki = 4.28 0.69 0.0002 Uncompetitive 0.846 0.53 2.5 Ki = 14.4 2.0 <0.0001 Noncompetitive 0.943 0.32 0.93 Ki = 18.9 1.6 <0.0001 Mixed 0.959 0.29 0.67 Kia = 10.9 2.7 0.0037 Kib = 24.4 4.4 0.0005 25 Competitive 0.237 0.83 6.2 Ki = 4.41 0.93 0.0010 Uncompetitive 0.775 0.45 1.8 Ki = 16.7 1.8 <0.0001 Noncompetitive 0.925 0.26 0.61 Ki = 22.7 1.4 <0.0001 Mixed 0.925 0.28 0.61 Kia = 23.6 7.5 0.014 Kib = 22.4 2.6 <0.0001 Model(s) Fit NONCOMPETITIVE/MIXED 311 Vary SAM, Vary aDMA-RKK, low RKK (data points from Figure 2.8A) [Inhibitor] (μM) Fit to Model Results Model R2 Standard Error of Estimate Sum of Squares Coefficient Standard Error p value 0 Michaelis-Menten 0.977 0.34 0.92 Vmax = 6.55 0.26 <0.0001 KM = 2.91 0.44 0.0002 100 Competitive 0.946 0.41 1.5 Ki = 218 82 0.025 Uncompetitive 0.990 0.18 0.28 Ki = 553 67 <0.0001 Noncompetitive 0.990 0.18 0.28 Ki = 684 83 <0.0001 Mixed 0.991 0.18 0.25 Kia = 1.38 x 103 1.5 x 103 0.39 Kib = 606 1.0 x 102 0.0003 300 Competitive 0.878 0.60 3.2 Ki = 449 1.9 x 102 0.042 Uncompetitive 0.968 0.30 0.83 Ki = 1.15 x 103 1.9 x 102 0.0002 Noncompetitive 0.967 0.30 0.85 Ki = 1.42 x 103 2.3 x 102 0.0002 Mixed 0.970 0.31 0.79 Kia = 3.41 x 103 6.0 x 103 0.58 Kib = 1.24 x 103 2.8 x 102 0.0022 600 Competitive -0.529 1.3 14 Ki = 76.6 35 0.057 Uncompetitive 0.846 0.40 1.4 Ki = 449 56 <0.0001 Noncompetitive 0.820 0.43 1.7 Ki = 573 76 <0.0001 Mixed 0.865 0.40 1.2 Kia = 1.75 x 103 2.1 x 102 0.43 Kib = 484 76 0.0002 Model(s) Fit NONCOMPETITIVE/MIXED/UNCOMPETITIVE 312 Vary SAM, Vary aDMA-RKK, high RKK (data points from Figure 2.7B) [Inhibitor] (μM) Fit to Model Results Model R2 Standard Error of Estimate Sum of Squares Coefficient Standard Error p value 0 Michaelis-Menten 0.984 0.12 0.11 Vmax = 3.24 0.12 <0.0001 KM = 7.26 0.87 <0.0001 450 Competitive 0.637 0.30 0.83 Ki = 132 28 0.0011 Uncompetitive 0.869 0.18 0.30 Ki = 428 51 <0.0001 Noncompetitive 0.934 0.13 0.15 Ki = 581 49 <0.0001 Mixed 0.934 0.14 0.15 Kia = 576 2.5 x 102 0.047 Kib = 583 1.0 x 102 0.0004 600 Competitive 0.820 0.23 0.48 Ki = 186 30 0.0002 Uncompetitive 0.779 0.26 0.59 Ki = 610 1.0 x 102 0.0002 Noncompetitive 0.882 0.19 0.31 Ki = 823 1.0 x 102 <0.0001 Mixed 0.909 0.17 0.24 Kia = 406 1.4 x 102 0.021 Kib = 1.24 x 103 4.1x102 0.017 800 Competitive 0.837 0.20 0.37 Ki = 186 26 <0.0001 Uncompetitive 0.813 0.22 0.42 Ki = 632 89 <0.0001 Noncompetitive 0.941 0.12 0.13 Ki = 864 67 <0.0001 Mixed 0.963 0.10 0.085 Kia = 476 1.0 x 102 0.0019 Kib = 1.18 x 103 2.0 x 102 0.0004 Model(s) Fit NONCOMPETITIVE/MIXED 313 Vary RKK, Vary aDMA-RKK, low SAM (data points from Figure 2.7C) [Inhibitor] (μM) Fit to Model Results Model R2 Standard Error of Estimate Sum of Squares Coefficient Standard Error p value 0 Michaelis-Menten 0.902 0.13 0.14 Vmax = 1.99 0.16 <0.0001 KM = 20.3 4.4 0.0018 600 Competitive 0.931 0.094 0.079 Ki = 417 51 <0.0001 Uncompetitive 0.727 0.19 0.31 Ki = 985 2.4 x 102 0.0025 Noncompetitive 0.857 0.13 0.16 Ki = 1.38 x 103 2.3 x 102 0.0002 Mixed 0.935 0.10 0.074 Kia = 483 1.2 x 102 0.0034 Kib = 6.98 x 103 9.3 x 102 0.47 800 Competitive 0.813 0.12 0.13 Ki = 355 50 <0.0001 Uncompetitive 0.695 0.16 0.22 Ki = 810 1.4 x 102 0.0003 Noncompetitive 0.870 0.10 0.093 Ki = 1.19 x 103 1.3 x 102 <0.0001 Mixed 0.907 0.091 0.066 Kia = 647 1.8 x 102 0.0077 Kib = 1.91 x 103 6.7 x 102 0.022 1000 Competitive 0.666 0.15 0.21 Ki = 360 62 0.0003 Uncompetitive 0.644 0.16 0.22 Ki = 826 1.4 x 102 0.0002 Noncompetitive 0.825 0.11 0.11 Ki = 1.23 x 103 1.4 x 102 <0.0001 Mixed 0.844 0.11 0.10 Kia = 808 3.1 x 102 0.030 Kib = 1.61 x 103 5.5 x 102 0.019 Model(s) Fit NONCOMPETITIVE/MIXED 314 Vary RKK, Vary aDMA-RKK, high SAM (data points from Figure 2.7D) [Inhibitor] (μM) Fit to Model Results Model R2 Standard Error of Estimate Sum of Squares Coefficient Standard Error p value 0 Michaelis-Menten 0.953 0.27 0.58 Vmax = 4.70 0.26 <0.0001 KM = 15.2 2.7 0.0005 300 Competitive 0.878 0.33 0.98 Ki = 332 78 0.0021 Uncompetitive 0.908 0.29 0.74 Ki = 868 1.7 x 102 0.0006 Noncompetitive 0.933 0.24 0.54 Ki = 1.14 x 103 1.8 x 102 0.0002 Mixed 0.935 0.26 0.52 Kia = 836 5.0 x 102 0.13 Kib = 1.32 x 103 5.0 x 102 0.029 450 Competitive 0.802 0.42 1.6 Ki = 353 89 0.0034 Uncompetitive 0.833 0.38 1.3 Ki = 958 2.1 x 102 0.0014 Noncompetitive 0.876 0.33 0.98 Ki = 1.27 x 103 2.3 x 102 0.0004 Mixed 0.880 0.35 0.95 Kia = 866 5.7 x 102 0.17 Kib = 1.52 x 103 6.9 x 102 0.057 600 Competitive 0.434 0.59 3.1 Ki = 254 74 0.0076 Uncompetitive 0.618 0.48 2.1 Ki = 741 1.7 x 102 0.0017 Noncompetitive 0.644 0.47 2.0 Ki = 1.02 x 103 2.2 x 102 0.0012 Mixed 0.645 0.49 2.0 Kia = 1.32 x 103 1.7 x 103 0.46 Kib = 943 3.8 x 102 0.0393 1000 Competitive 0.502 0.46 1.9 Ki = 368 81 0.0014 Uncompetitive 0.908 0.20 0.35 Ki = 1.07 x 10 96 <0.0001 Noncompetitive 0.958 0.13 0.16 Ki = 1.48 x 103 88 <0.0001 Mixed 0.963 0.13 0.14 Kia = 1.99 x 10 6.8 x 102 0.019 Kib = 1.35 x 103 1.4 x 102 <0.0001 Model(s) Fit NONCOMPETITIVE/MIXED 315 A.7 Estimated axes intercepts for substrate inhibition Estimated axes intercepts for substrate inhibition. A Lineweaver-Burk plot was constructed by directly plotting the reciprocal rate (min/nmol) versus reciprocal SAM concentration (M-1). The apparent x- and y-intercepts were extrapolated from the plot. H4-OH Concentration (μM) -1/KM (μM-1) x-intercept 1/Vmax (min/nmol) y-intercept R2 2 -0.4167 0.1817 0.9941 5 -0.3651 0.1707 0.9985 10 -0.3322 0.1524 0.9942 25 -0.3724 0.2253 0.9976 50 -0.3577 0.2710 0.9282 100 -0.2397 0.2901 0.9937 316 Supplemental data for Chapter 3 B.1 Purity and linear range of P81 filter-binding assay for PRMT1 Purity and linear range of P81 filter-binding assay for PRMT1. (A) A fraction of purified PRMT1 was separated on a 10% SDS-PAGE gel. Approximately 6.5 μg of protein was loaded. PRMT1 is estimated to be approximately 90% pure via densitometry. (B) Methylation reactions were performed with 200 nM PRMT1, 10 µM GST-GAR substrate, and 50 µM [14C]-SAM from 30-360 min in 27 µL reaction volume for each time point. To quench the reaction, 12 µL of each reaction was spotted onto P81 Whatman paper in duplicate, which was then washed five times with sodium carbonate-bicarbonate buffer (pH 9.0) with shaking. Dried filter papers were submerged in 2.5 mL Scintiverse E scintillation cocktail (Fisher Scientific SX16). Disintegrations per minute (DPM) of the samples were quantified using a Tri-Carb 3110TR (PerkinElmer) liquid scintillation counter for 1 min. Error bars represent the standard deviation; some bars are occluded by the symbols. 317 B.2 Raw fluorescence of SYPRO™ orange dye is plotted against reaction temperature Raw fluorescence of SYPRO™ orange dye is plotted against reaction temperature. Varying concentrations of dye (panels (A) through (E)) were incubated with varying concentrations of PRMT1: 0 (black), 1.0 (red), 2.0 (orange), 5.0 (yellow), 7.5 (green), 10 (teal), 15 (blue) and 20 μM (pink). Duplicate reactions of each condition were heated from 25˚C to 99˚C at a rate of 1˚C/min and are represented by solid and dashed lines. 318 B.3 Normalized fluorescence of SYPRO™ orange dye is plotted against reaction temperature. Normalized fluorescence of SYPRO™ orange dye is plotted against reaction temperature. Varying concentrations of dye (panels (A) through (E)) were incubated with varying concentrations of PRMT1: 0 (black circle), 1.0 (red square), 2.0 (orange triangle), 5.0 (yellow triangle), 7.5 (green diamond), 10 (teal hexagon), 15 (blue open circle) and 20 μM (magenta open square). Duplicate reactions of each condition were heated from 25˚C to 99˚C at a rate of 1˚C/min and are represented by solid and dashed lines. 319 B.4 Derived melting temperature of PRMT1 is dependent on protein concentration. Derived melting temperature of PRMT1 is dependent on protein concentration. Protein concentrations from 5.0-20 μM were plotted against the derived Tm for each reaction (n = 2). As protein concentration increases, the derived Tm also increases for SYPRO™ dye concentrations of 5x and 10x. Error bars represent the standard deviation. Statistical significance was determined using an ordinary one-way ANOVA for multiple comparisons (* = p ≤ 0.05). 320 B.5 Summary of dye and protein concentration array data The change in raw fluorescence (individual replicate values listed) between maxima and minima of each differential scanning fluorimetry reaction was used to calculate Tm of each reaction derived from the Boltzmann Equation 3.2 where R2 is the estimate of fit. N/A indicates that a Tm could not be derived. The standard deviation of the determined Tm values is reported, where applicable. Dye (x) Protein (μM) Δ Raw Fluorescence (absolute units) Tm (˚C) Standard Deviation (˚C) R2 0.5 0 2989 4357 46.47 N/A N/A 0.932 0.972 1 10746 6008 46.68 43.03 2.58 0.993 0.999 2 8266 4272 46.12 44.34 1.26 0.992 0.997 5 12876 11177 47.60 45.39 1.56 0.996 0.989 7.5 9796 9855 46.17 45.92 0.18 0.956 0.984 10 10339 7750 47.02 45.74 0.91 0.982 0.932 15 5136 3011 46.63 45.00 1.15 0.930 0.189 20 2790 2431 50.34 24.63 18.18 0.955 0.999 1 0 1043 1358 N/A 60.13 N/A 0.000 0.725 1 7288 5924 46.49 45.70 0.56 0.944 0.982 2 5872 3776 44.79 42.30 1.76 0.969 0.991 5 12490 6276 57.44 45.25 8.62 0.986 0.935 7.5 18180 8814 51.03 47.38 2.58 0.987 0.935 10 12070 12047 47.78 45.84 1.37 0.974 0.963 15 2335 2534 49.12 49.69 0.40 0.944 0.960 20 113784 25680 N/A 29.61 N/A 0.915 0.978 2 0 1461 11307 59.28 55.73 2.51 0.941 0.988 1 1462 549 44.44 44.96 0.37 0.988 0.640 2 1239 1190 43.10 43.23 0.09 0.960 0.985 5 7127 7744 48.71 48.44 0.19 0.986 0.984 7.5 13396 11656 51.15 49.79 0.96 0.982 0.985 321 Dye (x) Protein (μM) Δ Raw Fluorescence (absolute units) Tm (˚C) Standard Deviation (˚C) R2 10 10606 9818 51.83 52.99 0.82 0.988 0.992 15 16869 11785 52.15 53.00 0.60 0.995 0.998 20 24950 13558 53.20 53.46 0.18 0.998 0.997 5 0 1430 10914 68.62 26.88 29.51 0.996 0993 1 425 8776 77.24 68.83 5.95 0.954 0.952 2 10902 16899 4.766 69.38 45.69 0.991 0.991 5 4981 6077 42.96 45.42 1.74 0.986 0.994 7.5 14102 14853 46.59 49.09 1.77 0.998 0.999 10 15513 13310 50.63 56.32 4.02 0.997 0.997 15 40340 29795 54.92 57.71 1.97 0.997 0.996 20 119416 39701 53.76 55.59 1.29 0.998 0.999 7.5 0 5309 8139 71.85 49.23 15.99 0.999 0.926 1 937 158 44.58 N/A N/A 0.983 0.734 2 1936 19749 47.16 N/A N/A 0.963 0.943 5 3789 4755 49.79 52.02 1.58 0.989 0.990 7.5 4372 6547 52.41 51.35 0.75 0.995 0.990 10 9471 10446 49.37 51.22 1.31 0.996 0.996 15 36971 48116 56.99 55.94 0.74 0.998 0.998 20 40782 34972 56.76 58.03 0.90 0.998 0.998 10 0 14035 14945 76.79 73.58 2.27 0.995 0.997 1 6855 8116 76.63 78.52 1.34 0.997 0.999 2 2521 1604 48.69 47.54 0.81 0.989 0.993 5 5143 5057 50.77 51.23 0.33 0.986 0.995 7.5 5085 1073 53.17 63.11 7.03 0.986 0.947 10 4527 7529 51.60 53.99 1.69 0.997 0.992 322 Dye (x) Protein (μM) Δ Raw Fluorescence (absolute units) Tm (˚C) Standard Deviation (˚C) R2 15 30158 40933 59.47 57.06 1.70 0.996 0.994 20 37610 35182 58.90 57.70 0.85 0.997 0.995 323 B.6 Summary of PRMT1 with HCl and cofactor negative control data The calculated Tm values ± standard error for PRMT1 in the presence of HCl (n = 2). An ordinary one-way ANOVA for multiple comparisons was used to determine statistical significance of each analyte concentration to the 0 μM control. The change in Tm is considered significant if p ≤ 0.05. (ns = not significant; * p ≤ 0.05; ** p ≤ 0.01; *** p ≤ 0.001; **** p ≤ 0.0001.) Concentration (μM) Tm (˚C) Adjusted p value Significance HCl with PRMT1 and SYPRO™ Orange dye 0 59.75 ± 0.22 - - 8.5 58.58 ± 0.06 0.0335 * 21 59.41 ± 0.76 0.5593 ns B.7 Summary of PRMT1 and cofactor data The calculated Tm values ± standard error for PRMT1 in the presence of increasing SAH (n = 4) and SAM concentrations (n = 3). An ordinary one-way ANOVA for multiple comparisons was used to determine statistical significance of each cofactor concentration to the 0 μM control. The change in Tm is considered significant if p ≤ 0.05. (ns = not significant; * p ≤ 0.05; ** p ≤ 0.01; *** p ≤ 0.001; **** p ≤ 0.0001.) Concentration (μM) Tm (˚C) Adjusted p value Significance SAH with PRMT1 0 56.26 ± 0.39 - - 0.5 56.40 ± 0.31 0.9994 ns 1 56.01 ± 0.27 0.9204 ns 1.5 56.78 ± 0.33 0.9849 ns 2.5 57.22 ± 0.34 0.6217 ns 5 57.69 ± 0.34 0.1187 ns 10 58.11 ± 0.26 0.0179 * 15 58.96 ± 0.26 0.0001 *** 25 59.98 ± 0.31 <0.0001 **** 50 61.06 ± 0.34 <0.0001 **** 100 62.22 ± 0.27 <0.0001 **** 200 62.50 ± 0.14 <0.0001 **** SAM with PRMT1 0 54.08 ± 0.44 - - 0.5 51.67 ± 0.3301 0.1472 ns 1 53.69 ± 0.583 0.9993 ns 1.5 53.07 ± 0.431 0.8849 ns 2.5 51.99 ± 0.340 0.2297 ns 5 51.47 ± 0.413 0.0393 * 10 54.56 ± 0.454 0.9992 ns 15 54.52 ± 0.428 0.9993 ns 25 54.56 ± 0.427 0.9968 ns 50 56.37 ± 0.742 0.0757 ns 100 56.49 ± 0.611 0.0992 ns 200 59.17 ± 0.570 0.0002 *** 324 B.8 Untruncated melt curves for cofactor with PRMT1 Untruncated melt curves for cofactor with PRMT1. Relative fluorescence of SYPRO™ Orange dye is plotted against reaction temperature. (A) Varying concentrations of SAH (n = 4) and (B) varying concentrations of SAM (n = 3) were incubated with 15 μM PRMT1 and 7.5x SYPRO™ Orange dye. Melt curves for 0 μM (red circle), 5.0 μM (green square), 10 μM (blue triangle), 50 μM (purple diamond), and 200 μM (grey hexagon) cofactor are shown. All melt curves are monophasic. B.9 Summary of sinefungin and PRMT1 data The calculated Tm values ± standard error for sinefungin with PRMT1 (n = 3). An ordinary one-way ANOVA for multiple comparisons was used to determine statistical significance of each inhibitor concentration to the 0 μM control. The change in Tm is considered significant if p ≤ 0.05. (ns = not significant; * p ≤ 0.05; ** p ≤ 0.01; *** p ≤ 0.001; **** p ≤ 0.0001.) Concentration (μM) Tm (˚C) Adjusted p value Significance Sinefungin with PRMT1 0 49.74 ± 0.1230 - - 0.5 49.80 ± 0.1674 >0.9999 ns 1 49.39 ± 0.1541 0.9996 ns 1.5 49.81 ± 0.1612 >0.9999 ns 2.5 51.65 ± 0.1682 0.6103 ns 5 51.41 ± 0.1503 0.7570 ns 10 50.47 ± 0.1235 0.9990 ns 15 51.32 ± 0.0944 0.8322 ns 25 52.27 ± 0.1146 0.3023 ns 50 52.12 ± 0.0751 0.2364 ns 100 54.68 ± 0.1063 0.0038 ** 200 54.08 ± 0.0511 0.0133 * 400 54.89 ± 0.2004 0.0042 ** 325 B.10 Untruncated melt curves for sinefungin with PRMT1 Untruncated melt curves for sinefungin with PRMT1. Relative fluorescence of SYPRO™ Orange dye is plotted against reaction temperature. Varying concentrations of sinefungin were incubated with 5.0 μM PRMT1 and 7.5x SYPRO™ Orange dye. Melt curves for 0 μM (red circle), 5.0 μM (green square), 10 μM (blue triangle), 50 μM (purple diamond), 200 μM (grey hexagon), and 400 μM (black cross) sinefungin are shown (n = 3). All melt curves are monophasic. 326 B.11 Summary of RKK and H4 peptides with PRMT1 data The calculated Tm values ± standard error for PRMT1 in the presence of increasing RKK (n = 4) and H4 concentrations (n = 3). An ordinary one-way ANOVA for multiple comparisons was used to determine statistical significance of each cofactor concentration to the 0 μM control. The change in Tm is considered significant if p ≤ 0.05. (ns = not significant: * p ≤ 0.05; ** p ≤ 0.01; *** p ≤ 0.001; **** p ≤ 0.0001.) Concentration (μM) Tm (˚C) Adjusted p value Significance RKK with PRMT1 0 53.81 ± 0.1809 - - 0.5 54.09 ± 0.1834 0.9969 ns 1 53.00 ± 0.1963 0.5957 ns 2.5 53.52 ± 0.2300 0.9993 ns 5 53.75 ± 0.3639 0.9997 ns 10 54.24 ± 0.1744 0.9646 ns 15 54.42 ± 0.1915 0.8565 ns 25 53.96 ± 0.2464 0.9995 ns 50 55.28 ± 0.2300 0.0660 ns 100 48.31 ± 0.0969 <0.0001 **** 150 48.90 ± 0.0835 <0.0001 **** 300 48.45 ± 0.0825 <0.0001 **** H4 with PRMT1 0 56.81 ± 0.3768 - - 0.5 56.84 ± 0.4155 >0.9999 ns 1 56.21 ± 0.2409 0.6072 ns 1.5 55.73 ± 0.3269 0.1112 ns 2.5 55.91 ± 0.4347 0.1795 ns 5 55.30 ± 0.2277 0.0085 ** 10 54.09 ± 0.2739 <0.0001 **** 15 51.95 ± 0.2304 <0.0001 **** 25 49.01 ± 0.3265 <0.0001 **** 50 49.15 ± 0.2776 <0.0001 **** 100 47.96 ± 0.3675 <0.0001 **** 300 47.18 ± 0.5451 <0.0001 **** 327 B.12 Untruncated melt curves for RKK and H4 peptides with PRMT1 Untruncated melt curves for RKK and H4 peptides with PRMT1. Relative fluorescence of SYPRO™ orange dye is plotted against reaction temperature. (A) Varying concentrations of RKK (n = 3) and (B) varying concentrations of H4 (n = 3) were incubated with 15 μM PRMT1 and 7.5x SYPRO™ Orange dye. Melt curves for 0 μM (red circle), 5.0 μM (green square), 10 μM (blue triangle), 50 μM (purple diamond), 100 μM (grey hexagon), and 300 μM (black triangle) peptides are shown. All melt curves are monophasic, with the exception of 100 and 300 μM RKK and 300 μM H4, which are biphasic. 328 B.13 Summary of varying RKK with saturating SAH and varying SAH with saturating RKK with PRMT1 The method used to determine the Tm values (either by fitting to the Boltzmann Sigmoidal Equation 3.2 or using the first derivative data) is listed. The calculated Tm values ± standard error for PRMT1 in the presence of increasing RKK with constant 200 μM SAH (n = 3) and increasing SAH with constant 100 μM RKK (n = 4). An ordinary one-way ANOVA for multiple comparisons was used to determine statistical significance of each cofactor concentration to the 0-μM control. Additionally, a Student’s t-test was performed to test for significance between the derived Tm data from both Boltzmann and first derivative methods. The change in Tm is considered significant if p ≤ 0.05. (ns = not significant * p ≤ 0.05; ** p ≤ 0.01; *** p ≤ 0.001; **** p ≤ 0.0001.) Concentration (μM) Tm (˚C) Adjusted p value Significance Tm (˚C) Adjusted p value Significance Adjusted p value Significance RKK with PRMT1 and constant SAH Boltzmann First derivative Comparing Boltzmann and first derivative data 0 61.52 ± 0.3166 - - 62.53 ± 0.2301 - - 0.0650 ns 0.5 62.22 ± 0.6449 0.5378 ns 63.08 ± 0.2040 0.7809 ns 0.3840 ns 1 61.61 ± 0.3793 0.9997 ns 62.74 ± 0.1696 0.9993 ns 0.0630 ns 2.5 62.77 ± 0.7720 0.0662 ns 63.13 ± 0.3230 0.7021 ns 0.8713 ns 5 63.34 ± 0.9029 0.0176 * 63.19 ± 0.1194 0.6142 ns 0.9548 ns 10 61.63 ± 0.3757 0.9997 ns 62.72 ± 0.0770 0.9994 ns 0.0622 ns 25 61.88 ± 0.3669 0.9962 ns 62.98 ± 0.0591 0.9078 ns 0.0603 ns 50 63.38 ± 0.6179 0.0158 * 63.42 ± 0.0013 0.2959 ns 0.9545 ns 100 59.90 ± 0.6283 0.0556 ns 62.27 ± 0.3288 0.9965 ns 0.0434 * 150 59.18 ± 0.3743 0.0029 ** 61.38 ± 0.2310 0.1021 ns 0.0107 * 300 55.21 ± 0.1391 <0.0001 **** 54.08 ± 1.6705 <0.0001 **** 0.7696 ns 329 Concentration (μM) Tm (˚C) Adjusted p value Significance Tm (˚C) Adjusted p value Significance Adjusted p value Significance SAH with PRMT1 and constant RKK Boltzmann First derivative Comparing Boltzmann and first derivative data 0 52.32 ± 0.1286 - - 53.14 ± 0.8718 - - 0.51552 ns 0.5 50.95 ± 0.0992 0.0042 ** 51.29 ± 0.7472 0.0706 ns 0.7720 ns 1 52.42 ± 0.1080 0.9996 ns 51.52 ± 1.5519 0.1492 ns 0.7513 ns 2.5 52.20 ± 0.2068 0.9999 ns 53.24 ± 2.2344 0.9998 ns 0.7720 ns 5 53.11 ± 0.1236 0.1937 ns 54.43 ± 0.3837 0.3462 ns 0.0053 ** 10 55.28 ± 0.1334 <0.0001 **** 55.86 ± 0.5238 0.0025 ** 0.4190 ns 15 56.34 ± 0.1857 <0.0001 **** 57.41 ± 1.1492 <0.0001 **** 0.5152 ns 25 57.04 ± 0.2131 <0.0001 **** 59.18 ± 0.2908 <0.0001 **** 0.0002 *** 50 57.85 ± 0.2877 <0.0001 **** 59.74 ± 0.0859 <0.0001 **** 0.0001 *** 100 57.03 ± 0.3185 <0.0001 **** 60.78 ± 0.5461 <0.0001 **** 0.0002 *** 200 63.53 ± 0.641 <0.0001 **** 62.78 ± 0.0902 <0.0001 **** 0.3856 ns 300 62.87 ± 0.5316 <0.0001 **** 63.03 ± 0.2228 <0.0001 **** 0.7720 ns B.14 Untruncated melt curves for RKK and SAH with PRMT1 Untruncated melt curves for RKK and SAH with PRMT1. Relative fluorescence of SYPRO™ orange dye is plotted against reaction temperature. (A) Varying concentrations of RKK were incubated with 15 μM PRMT1 in the presence of 200 μM SAH (n = 3). (B) Varying concentrations of SAH were incubated with 15 μM PRMT1 in the presence of 100 μM RKK (n = 4). Melt curves for 0 μM (red circle), 5.0 μM (green square), 10 μM (blue triangle), 50 μM (purple diamond), 100 μM (grey hexagon), and 300 μM (black triangle) for RKK and 200 μM (black triangle) for SAH are shown. All melt curves are monophasic, with the exception of 0 μM SAH with 100 μM RKK pre-incubation, which appears biphasic. 331 B.15 Untruncated melt curves for H4 and SAH with PRMT1 Untruncated melt curves for H4 and SAH with PRMT1. Relative fluorescence of SYPRO™ orange dye is plotted against reaction temperature. (A) In the presence of 200 μM SAH, varying concentrations of H4 were incubated with PRMT1 (n = 3): 0 μM (red circle), 5.0 μM (green square), 10 μM (blue triangle), 50 μM (purple diamond), 100 μM (grey hexagon), and 300 μM (black triangle) (B) In the presence of 100 µM H4, varying concentrations of SAH were incubated with PRMT1 (n = 3): 0 μM (red circle), 5.0 μM (green square), 10 μM (blue triangle), 50 μM (purple diamond), 100 μM (grey hexagon), and 200 μM (black triangle). Biphasic melt curves are produced when titrating SAH in the presence of constant H4, except at 2.5 and 5.0 μM SAH. Biphasic melt curves are produced when titrating H4 in the presence of constant SAH at concentrations of 50-300 μM. 332 B.16 Summary of varying H4 with saturating SAH and varying SAH with saturating H4 with PRMT1 data The calculated Tm values ± standard error for PRMT1 in the presence of increasing H4 with constant 200 μM SAH (n = 3) and increasing SAH with constant 100 μM H4 (n = 3). An ordinary one-way ANOVA for multiple comparisons was used to determine statistical significance of each cofactor concentration to the 0 μM control. The change in Tm is considered significant if p ≤ 0.05. (ns = not significant; * p ≤ 0.05; ** p ≤ 0.01; *** p ≤ 0.001; **** p ≤ 0.0001.) Concentration (μM) Tm (˚C) Adjusted p value Significance H4 with PRMT1 and constant SAH 0 60.94 ± 0.3189 - - 0.5 61.59 ± 0.0700 0.0564 ns 1 60.95 ± 0.5341 >0.9999 ns 2.5 61.68 ± 0.2130 0.0233 * 5 61.97 ± 0.1883 0.0010 ** 10 62.10 ± 0.1286 0.0003 *** 15 62.26 ± 0.2033 <0.0001 **** 25 62.09 ± 0.1176 0.0003 *** 50 42.47 ± 0.5439 62.23 ± 0.1270 <0.0001 <0.0001 **** **** 100 41.27 ± 0.1049 62.88 ± 0.1910 <0.0001 <0.0001 **** **** 200 39.24 ± 0.1357 64.48 ± 0.1246 <0.0001 <0.0001 **** **** 300 39.19 ± 0.1308 64.26 ± 0.0525 <0.0001 <0.0001 **** **** SAH with PRMT1 and constant H4 0 43.88 ± 0.7111 61.03 ± 0.1065 - - - - 0.5 44.84 ± 0.5469 57.60 ± 1.1219 0.2867 <0.0001 ns **** 1 46.54 ± 0.3060 60.27 ± 0.3936 0.0001 0.5793 *** ns 2.5 48.22 ± 0.0410 59.10 ± 0.9128 <0.0001 0.0057 **** ** 5 49.08 ± 0.1286 59.88 ± 1.2227 <0.0001 0.1694 **** ns 10 39.77 ± 0.1.0505 61.98 ± 0.2475 <0.0001 0.3365 **** ns 15 39.16 ± 0.5873 62.39 ± 0.2170 <0.0001 0.0731 **** ns 25 38.91 ± 0.5426 62.83 ± 0.1500 <0.0001 0.0106 **** * 50 38.57 ± 0.8292 63.33 ± 0.1813 <0.0001 0.0010 **** *** 100 39.93 ± 0.4832 63.36 ± 0.2516 <0.0001 0.0008 **** *** 200 38.47 ± 0.1541 64.60 ± 0.0729 <0.0001 <0.0001 **** **** 333 B.17 Interactions of MS023 within the PRMT1 active site Interactions of MS023 within the PRMT1 active site. The inhibitor MS023 was docked to the PRMT1 active site within the homology model. Interactions between the inhibitor and active site residues are highlighted. Interactions were captured using Discovery Studio 4.5. 334 B.18 Visualization of the pan type-I PRMT1 inhibitor, MS023 (pink) with PRMT1 and SAH (blue) Visualization of the pan type-I PRMT1 inhibitor, MS023 (pink) with PRMT1 and SAH (blue). (A) MS023 binds within the active site in close proximity to SAH. MS023 protrudes deep into the arginine-binding pocket while SAH is buried within the protein. (B) Detailed interactions of MS023 with SAH and active site residues. Dashed lines indicate hydrogen bonds (green and orange) and pi-bonds (orange and pink). Visualization of MS023 within the active site was achieved using Discovery Studio 4.5. 335 B.19 Summary of DMSO with PRMT1 and with SAH data The calculated Tm values ± standard error for PRMT1 with SAH and DMSO (n = 3). A Student’s t-test was used to test for significance between PRMT1 alone and with 1% DMSO. An ordinary one-way ANOVA for multiple comparisons was used to determine statistical significance of SAH to PRMT1 alone and SAH with DMSO to PRMT1 alone. The change in Tm is considered significant if p ≤ 0.05. (ns = not significant * p ≤ 0.05; ** p ≤ 0.01; *** p ≤ 0.001; **** p ≤ 0.0001.) Condition Tm (˚C) Adjusted p value Significance PRMT1 and DMSO 0% DMSO 51.00 ± 0.0965 - - 1% DMSO 48.50 ± 0.1135 0.0007 *** PRMT1 with SAH and DMSO PRMT1 alone 51.18 ± 0.1518 - - 200 μM SAH 60.45 ± 0.1277 - - 200 μM SAH + 1% DMSO 59.45 ± 0.1249 0.0004 *** 336 B.20 Untruncated melt curves for DMSO with PRMT1 Untruncated melt curves for DMSO with PRMT1. Relative fluorescence of SYPRO™ orange dye is plotted against reaction temperature. (A) PRMT1 alone (red circle) and incubated with 1% DMSO (green square) (n = 3). (B) PRMT1 alone (red circle), incubated with 200 μM SAH (green square) and incubated with 200 μM and 1% DMSO (blue triangle) (n = 3). All melt curves are monophasic. B.21 Untruncated melt curves for MS023 with PRMT1 Untruncated melt curves for MS023 with PRMT1. Relative fluorescence of SYPRO™ orange dye is plotted against reaction temperature. Varying concentrations of MS023 (n = 3) were incubated with (A) PRMT1 alone or (B) with PRMT1 that was pre-incubated with 200 μM SAH. Melt curves for 0 nM (red circle), 1.0 nM (green square), 5.0 nM (blue triangle), 50 nM (purple diamond), 1000 nM (grey hexagon), and 10000 nM (black triangle) MS023 are shown. All melt curves are monophasic. 337 B.22 Summary of MS023 with PRMT1 alone and with SAH data The calculated Tm values ± standard error for PRMT1 with MS023 and in combination with excess SAH (n = 3). An ordinary one-way ANOVA for multiple comparisons was used to determine statistical significance of MS023 to PRMT1 alone and with MS023 in combination with SAH to PRMT1 with SAH reference. The change in Tm is considered significant if p ≤ 0.05. (ns = not significant * p ≤ 0.05; ** p ≤ 0.01; *** p ≤ 0.001; **** p ≤ 0.0001.) Concentration (nM) Tm (˚C) Adjusted p value Significance MS023 with PRMT1 0 51.00 ± 0.0965 - - 0.1 49.71 ± 0.1201 0.0044 ** 1 50.25 ± 0.0967 0.1535 ns 2 49.63 ± 0.1075 0.0020 ** 5 49.84 ± 0.1109 0.0110 * 10 49.42 ± 0.1257 0.0005 *** 50 49.84 ± 0.1126 0.0100 * 250 49.33 ± 0.1018 0.0002 *** 1000 48.56 ± 0.1006 <0.0001 **** 5000 48.85 ± 0.0983 <0.0001 **** 10000 48.42 ± 0.0894 <0.0001 **** MS023 with PRMT1 and SAH 0 60.69 ± 0.1300 - - 0.1 60.10 ± 0.1256 0.8320 ns 1 60.32 ± 0.1300 0.9872 ns 2 61.19 ± 0.2520 0.8981 ns 5 60.67 ± 0.1337 >0.9999 ns 10 60.63 ± 0.1309 0.9999 ns 50 61.54 ± 0.2287 0.3716 ns 250 61.46 ± 0.0934 0.5781 ns 1000 64.49 ± 0.0604 <0.0001 **** 5000 67.35 ± 0.0475 <0.0001 **** 10000 68.19 ± 0.0575 <0.0001 **** B.23 Comparing the interactions of crystalized SAH with docked SAM and sinefungin within the cofactor-binding site of PRMT1 homology model Comparing the interactions of crystalized SAH with docked SAM and sinefungin within the cofactor-binding site of PRMT1 homology model. Interactions between the molecules and the binding site residues are highlighted. Hydrogen bonds are shown in dark green, hydrophobic interactions are shown in light green. Attractive charges (orange) are high-strength hydrogen bonds. Pi-carbon, pi-alkyl, and pi-sulfur bonds represent aromatic interactions of the ligand with amino acid atoms. Interactions were captured using Discovery Studio 4.5. 339 B.24 The interactions of SAH, SAM, and sinefungin within the cofactor-binding site of PRMT1 The interactions of (A) SAH (blue), (B) SAM (red,), and (C) sinefungin (green) within the cofactor-binding site of PRMT1. Hydrogen bonds are shown in green and orange. Aromatic interactions are shown in pink and orange. (D) SAH, SAM, and sinefungin were overlaid in the cofactor-binding site of PRMT1. Specifically, the adenine moiety in each compound forms hydrogen and pi bonds with C101, V128, E129, K32, and M155. The ribose component forms hydrogen bonds with H45 and E100 in all three ligands, with an additional strong interaction with G80 in sinefungin. The carboxy-amine terminus interacts with R54 and G78 and additionally with H45 in SAH and sinefungin. The conserved E144 and E153 of the double-E loop form hydrogen bonds with the amine group that is analogous to the methyl group of SAM. Interactions were captured using Discovery Studio 4.5.340 Supplemental data for Chapter 4 C.1 Purification of yeast His6-Ynl092wp and His6-Ykl162cp Purification of (A) His6-Ynl092wp (0.5 ug) and (B) His6-Ykl162cp (10 ug). Expression and purification of Ynl092wp was highly successful. By densitometry analysis, the purified Ynl092p is 85% pure. Expression and purification of Ykl162cp requires additional optimization. The crudely purified sample that was used for this study is only estimated to be 10.5% pure by densitometry. 341 C.2 PCR deletion primer pairs Each deletion strain was confirmed using the indicated primer pairs. All primers, melting temperatures (Tm) and expected PCR amplicon size was accessed from the Saccharomyces Genome Deletion Project webpage [323]. Gene Forward Primer (5’ – 3’) Reverse Primer (5’ – 3’) PCR Product Size Deletion Strain WT Strain YBR034C/HMT1 GGGACTGTTAATGAAAAATTCAATG Tm = 51.3oC ATATTGAAAAAGTTTGCCGACATAG Tm = 52.3oC 2185 1648 YKL162C TCTTCGACCTAGAAAAAGAACTTGA Tm = 53.9oC TTTAAATATGGGGTTACTATTCGCA Tm = 52.5oC 2192 1817 YNL092W GTAGAAAGTACTAGGGAGACTGGGG Tm = 57.5oC ATTGCTTATTGTTGAAAGTAATGGC Tm = 52.7oC 2265 1884 Genotypes of strains used in this study: Δrmt1 (MATa his3Δ1 leu2Δ0 met15Δ0 ura3Δ0 rmt1Δ::KanMX) Δynl092w (MATa his3Δ1 leu2Δ0 met15Δ0 ura3Δ0 ynl092wΔ::KanMX) Δykl162c (MATa his3Δ1 leu2Δ0 met15Δ0 ura3Δ0 ykl162cΔ::KanMX) 342 C.3 Unique peptides generated by trypsin and Glu-C digests The unique generated peptides and their observed m/z after trypsin and Glu-C digest of Ynl092wp. The terminal amino acids demarcated by periods represent the upstream and downstream amino acids of the peptide. Further, the numbers in square brackets correspond to a modification on the amino acid immediately upstream. [+15.995] corresponds to oxidation, [+57.021] corresponds to carboxamidomethylation, and [+14.016] corresponds to methylation. Protease Peptide Observed m/z Trypsin R.VIISFLK.Y 410.271 K.YEEYALK.E 458.228 K.EIYNLR.V 404.222 K.KWASISDR.Q 481.758 K.WASISDR.Q 417.710 R.QKDMVPNYTK.Y 612.310 K.DMVPNYTK.Y 484.232 K.DM[+15.995]VPNYTK.Y 492.230 K.YLANLK.A 361.216 K.TC[+57.021]SLLTQVYR.E 620.822 R.EWSAEAISER.N 589.279 R.EW[+15.995]SAEAISER.N 597.277 K.ADILIPGC[+57.021]GTGR.L 615.321 R.LLVDLSR.M 408.254 K.KIEDQLSPIK.V 585.841 K.IEDQLSPIK.V 521.794 K.VPDIEAWSSNK.G 623.311 K.VPDIEAW[+15.995]SSNK.G 631.309 K.GMGSM[+15.995]SIC[+57.021]AGSFVDC[+57.021]YGR.N 985.898 K.GM[+15.995]GSM[+15.995]SIC[+57.021]AGSFVDC[+57.021]YGR.N 993.897 K.ISSHYTFSR.R 549.274 R.MQLSR.A 317.672 R.RESGILC[+57.021]GYGR.Y 634.317 R.ESGILC[+57.021]GYGR.Y 556.265 Glu-C E.NEFDNQRE.N 526.227 E.YALKE.I 312.175 E.NGKFFRSVAE.Y 577.804 D.MSKTC[+57.021]SLLTQVYRE.W 858.429 D.M[+15.995]SKTC[+57.021]SLLTQVYRE.W 866.426 D.LSRMGYNC[+57.021]E.G 565.244 E.DQLSPIKVPD.I 556.306 E.DQLSPIKVPDIE.A 677.368 343 Protease Peptide Observed m/z E.NDHGVETTYE.V 582.747 E.NDH[+14.016]GVETTYE.V 589.754 E.VNPYSGFQDKIND.Y 748.859 D.YTPLMGLE.L 923.454 D.IISIATNHLDFE.L 686.861 E.SGILC[+57.021]GYGRYAGPE.S 750.354 C.4 Summary of increasing Ynl092wp with 7.5x SYPRO™ Orange DSF data The calculated Tm values ± standard error for Ynl092wp with dye alone (n = 2). A Dunnett’s multiple comparison test for significance was used for multiple comparisons of each enzyme concentration to the 0.83 μM baseline. The change in Tm is considered significant if p ≤ 0.05. (ns = not significant; * p ≤ 0.05; ** p ≤ 0.01; *** p ≤ 0.001; **** p ≤ 0.0001.) Concentration (μM) Tm (oC) Adjusted p value Significance Increasing Ynl092wp with 7.5x SYPRO™ Orange 0.83 53.65 ± 0.1072 - - 1 52.69 ± 0.08125 0.0034 ** 2.5 49.77 ± 0.04800 <0.0001 **** 5 48.33 ± 0.04288 <0.0001 **** 344 C.5 Summary of Ynl092wp with HCl and cofactor negative control DSF data The calculated Tm values ± standard error for Ynl092wp in the presence of HCl (n = 2). A Dunnett’s multiple comparison test for significance was used for multiple comparisons of each analyte concentration to the 0-μM control. The change in Tm is considered significant if p ≤ 0.05. (ns = not significant; * p ≤ 0.05; ** p ≤ 0.01; *** p ≤ 0.001; **** p ≤ 0.0001.) Concentration (μM) Tm (oC) Adjusted p value Significance Ynl092wp with HCl 0 54.07 ± 0.0789 - - 12.5 53.6 ± 0.1572 0.0746 ns 25 53.44 ± 0.1589 0.0105 * 28.5 52.44 ± 0.2800 <0.0001 **** 50 53.41 ± 0.09452 0.0073 ** 56 52.34 ± 0.1217 <0.0001 **** 345 C.6 Summary of Ynl092wp with SAM and SAH cofactor DSF data The calculated Tm values ± standard error for Ynl092wp in the presence of increasing SAM and SAH concentrations (n = 4). A Dunnett’s multiple comparison test for significance was used for multiple comparisons of each analyte concentration to the 0-μM control. The change in Tm is considered significant if p ≤ 0.05. (ns = not significant; * p ≤ 0.05; ** p ≤ 0.01; *** p ≤ 0.001; **** p ≤ 0.0001.) Concentration (μM) Tm (oC) Adjusted p value Significance SAM with Ynl092wp 0 54.07 ± 0.0789 - - 0.5 54.23 ± 0.2865 0.7498 ns 1 53.42 ± 0.2226 <0.0001 **** 1.5 53.55 ± 0.07274 0.0016 ** 2.5 53.53 ± 0.1014 0.0011 ** 5 53.59 ± 0.1344 0.0041 ** 10 52.87 ± 0.2387 <0.0001 **** 15 53.00 ± 0.1966 <0.0001 **** 25 52.52 ± 0.1299 <0.0001 **** 50 52.59 ± 0.2619 <0.0001 **** 100 52.14 ± 0.08888 <0.0001 **** 200 52.45 ± 0.08287 <0.0001 **** SAH with Ynl092wp 0 55.52 ± 0.4050 - - 0.5 55.56 ± 0.1916 0.9998 ns 1 54.95 ± 0.1337 0.0576 ns 1.5 54.93 ± 0.4302 0.0444 * 2.5 54.69 ± 0.2351 0.002 ** 5 54.13 ± 0.3629 <0.0001 **** 10 53.07 ± 0.4887 <0.0001 **** 15 52.36 ± 0.2936 <0.0001 **** 25 51.88 ± 0.1512 <0.0001 **** 50 52.05 ± 0.1080 <0.0001 **** 100 51.5 ± 0.06449 <0.0001 **** 200 51.77 ± 0.09106 <0.0001 **** 346 C.7 Untruncated melt curves for SAM and SAH cofactor with Ynl092wp Untruncated melt curves for SAM and SAH cofactor with Ynl092wp. Relative fluorescence of SYPRO™ Orange dye is plotted against reaction temperature. (A) Varying concentrations of SAM (n = 4) and (B) varying concentrations of SAH (n = 4) were incubated with 0.83 μM Ynl092wp and 7.5x SYPRO™ Orange dye. Melt curves for 0 μM (black circle), 5.0 μM (green square), 10 μM (blue triangle), 50 μM (purple diamond), and 200 μM (grey hexagon) cofactor are shown. All melt curves are monophasic. 347 C.8 Summary of Ynl092wp with carnosine, histidine, and CARN peptide DSF data The calculated Tm values ± standard error for Ynl092wp in the presence of increasing carnosine, histidine, and CARN concentrations (n = 4). A Dunnett’s multiple comparison test for significance was used for multiple comparisons of each analyte concentration to the 0-μM control. The change in Tm is considered significant if p ≤ 0.05. (ns = not significant; * p ≤ 0.05; ** p ≤ 0.01; *** p ≤ 0.001; **** p ≤ 0.0001.) Concentration (mM) Tm (oC) Adjusted p value Significance Carnosine with Ynl092wp 0 54.36 ± 0.3588 - - 0.05 55.20 ± 0.1042 <0.0001 **** 0.5 55.19 ± 0.1761 <0.0001 **** 1 54.51 ± 0.06377 0.9422 ns 5 55.39 ± 0.2063 <0.0001 **** 10 54.63 ± 0.1459 0.4395 ns 25 55.94 ± 0.1203 <0.0001 **** 50 56.79 ± 0.1269 <0.0001 **** 100 55.42 ± 0.2717 <0.0001 **** 150 55.31 ± 0.4234 <0.0001 **** 200 55.46 ± 0.1311 <0.0001 **** 300 55.23 ± 0.1650 <0.0001 **** Histidine with Ynl092wp 0 54.85 ± 0.07042 - - 0.05 55.42 ± 0.07411 0.0034 ** 0.5 55.27 ± 0.1338 0.0401 * 1 54.79 ± 0.2176 0.9954 ns 5 55.40 ± 0.1952 0.0044 ** 10 54.22 ± 0.1413 0.0011 ** 25 52.67 ± 0.3470 <0.0001 **** 50 45.91 ± 0.2644 <0.0001 **** 100 No melt curve - - 150 No melt curve - - 200 No melt curve - - 300 No melt curve - - CARN peptide with Ynl092wp 0 54.09 ± 0.3492 - - 0.0005 54.07 ± 0.2820 0.9999 ns 0.001 54.21 ± 0.2491 0.9806 ns 348 Concentration (mM) Tm (oC) Adjusted p value Significance 0.0025 54.36 ± 0.2323 0.4404 ns 0.005 54.29 ± 0.2460 0.7586 ns 0.010 54.48 ± 0.2047 0.1261 ns 0.025 54.43 ± 0.2411 0.2183 ns 0.050 54.56 ± 0.09899 0.0388 * 0.100 54.71 ± 0.1383 0.0035 ** 0.300 54.55 ± 0.04435 0.0484 * 0.500 54.66 ± 0.2217 0.0088 ** 349 C.9 Untruncated melt curves for amino acids and peptide substrates with Ynl092wp Untruncated melt curves for amino acids and peptide substrates with Ynl092wp. Relative fluorescence of SYPRO™ Orange dye is plotted against reaction temperature. (A) Varying concentrations of carnosine were incubated with 0.83 μM Ynl092wp and 7.5x SYPRO™ Orange dye. Untruncated melt curves of Ynl092wp alone (black circle) or in the presence of 5 (green square), 25 (blue triangle), 100 (purple diamond), or 300 mM (grey hexagon) carnosine. (B) Untruncated melt curves of Ynl092wp alone (black circle) or in the presence of 0.05 (green square), 0.5 (blue triangle), 1 (purple diamond), 5 (grey hexagon), 10 (red inverted triangle), 25 (orange plus), or 50 mM (teal X) histidine. (C) Untruncated melt curves of Ynl092wp alone (black circle) or in the presence of 5 (green square), 25 (blue triangle), 100 (purple diamond), or 300 μM (grey hexagon) CARN peptide. Error bars represent the standard deviation of four replicates. 350 C.10 Summary of Ynl092wp with constant SAH and increasing carnosine, histidine, and CARN peptide The calculated Tm values ± standard error for Ynl092wp in the presence of increasing carnosine, histidine, and CARN concentrations in the presence of 200 μM SAH (n = 4). A Dunnett’s multiple comparison test for significance was used for multiple comparisons of each analyte concentration to the Ynl092wp + 200 μM SAH control. The change in Tm is considered significant if p ≤ 0.05. (ns = not significant; * p ≤ 0.05; ** p ≤ 0.01; *** p ≤ 0.001; **** p ≤ 0.0001.) Concentration (mM) Tm (oC) Adjusted p value Significance Increasing Carnosine with 200 μM SAH and Ynl092wp 0 52.87 ± 0.1854 - - 0.05 54.76 ± 0.1556 <0.0001 **** 0.5 54.98 ± 0.3200 <0.0001 **** 1 53.30 ± 0.06583 <0.0001 **** 5 54.78 ± 0.2685 0.0508 ns 10 53.30 ± 0.04573 <0.0001 **** 25 54.15 ± 0.1500 0.0528 ns 50 55.22 ± 0.1607 <0.0001 **** 100 53.63 ± 0.2820 <0.0001 **** 150 53.97 ± 0.3731 0.0001 *** 200 53.90 ± 0.2072 <0.0001 **** 300 54.42 ± 0.1426 <0.0001 **** Increasing Histidine with 200 μM SAH and Ynl092wp 0 52.12 ± 0.1145 - - 0.05 52.77 ± 0.2754 0.0854 ns 0.5 52.82 ± 0.1459 0.0572 ns 1 52.24 ± 0.05598 0.9971 ns 5 52.57 ± 0.09764 0.3650 ns 10 52.78 ± 0.1907 0.0803 ns 25 52.46 ± 0.2691 0.6642 ns 50 44.94 ± 0.8306 <0.0001 **** 100 No melt curve - - 150 No melt curve - - 200 No melt curve - - 300 No melt curve - - Increasing CARN peptide with 200 μM SAH and Ynl092wp 351 Concentration (mM) Tm (oC) Adjusted p value Significance 0 51.62 ± 0.04717 - - 0.0005 51.66 ± 0.2282 0.9999 ns 0.001 51.83 ± 0.1109 0.9966 ns 0.0025 51.59 ± 0.1887 0.9999 ns 0.005 51.70 ± 0.09626 0.9997 ns 0.010 51.69 ± 0.09764 0.9997 ns 0.025 51.57 ± 0.1746 0.9998 ns 0.050 51.50 ± 0.06702 0.9995 ns 0.100 51.60 ± 0.07136 >0.9999 ns 0.300 51.55 ± 0.1040 0.9997 ns 0.500 51.69 ± 0.04646 0.9997 ns 352 C.11 Summary of Ynl092wp with constant carnosine or CARN peptide and increasing SAH The calculated Tm values ± standard error for Ynl092wp in the presence of increasing SAH with 300 mM carnosine or 300 μM CARN peptide (n = 4). A Dunnett’s multiple comparison test for significance was used for multiple comparisons of each analyte concentration to the Ynl092wp + high concentration of substrate control. The change in Tm is considered significant if p ≤ 0.05. (ns = not significant; * p ≤ 0.05; ** p ≤ 0.01; *** p ≤ 0.001; **** p ≤ 0.0001.) Concentration (uM) Tm (oC) Adjusted p value Significance Increasing SAH with 300 mM Carnosine and Ynl092wp 0 55.82 ± 0.1931 - - 0.5 55.89 ± 0.3375 0.9995 ns 1 55.92 ± 0.1843 0.9991 ns 1.5 55.73 ± 0.4189 0.9992 ns 2.5 55.59 ± 0.2616 0.8107 ns 5 55.63 ± 0.05477 0.9234 ns 10 55.41 ± 0.1507 0.1707 ns 15 55.19 ± 0.3182 0.0091 ** 25 55.09 ± 0.3946 0.0017 ** 50 55.17 ± 0.1638 0.0069 ** 100 53.35 ± 0.1459 <0.0001 **** 200 52.78 ± 0.2298 <0.0001 **** Increasing SAH with 300 μM CARN peptide and Ynl092wp 0 54.71 ± 0.1833 - - 0.5 56.53 ± 0.7885 0.0001 *** 1 54.91 ± 0.2079 0.9965 ns 1.5 54.51 ± 0.07550 0.9965 ns 2.5 55.06 ± 0.2950 0.9158 ns 5 55.15 ± 0.09292 0.7768 ns 10 54.99 ± 0.1021 0.9819 ns 15 55.47 ± 0.03606 0.1966 ns 25 56.06 ± 0.07000 0.0035 ** 50 56.58 ± 0.1102 <0.0001 **** 100 57.44 ± 0.09539 <0.0001 **** 200 58.30 ± 0.03606 <0.0001 **** 353 C.12 Untruncated melt curves for combinatorial amino acids and peptide substrates with cofactor and Ynl092wp Untruncated melt curves for combinatorial amino acids and peptide substrates with cofactor and Ynl092wp. Relative fluorescence of SYPRO™ Orange dye is plotted against reaction temperature. All reaction contained 0.83 μM Ynl092wp and 7.5x SYPRO™ and a combination of amino acid or peptide substrate and cofactor. (A) Varying concentrations of carnosine were incubated with Ynl092wp that was pre-incubated with 200 μM SAH. Melt curves for 0 (black circle), 5.0 (green square), 25 (blue triangle), 100 (purple diamond), and 300 mM (grey hexagon) carnosine are shown. (B) Varying concentrations of histidine were incubated with Ynl092wp that was pre-incubated with 200 μM SAH. Melt curves for 0 (black circle), 0.05 (green square), 0.5 (blue triangle), 1 (purple diamond), 5 (grey hexagon), 10 (red inverted triangle), 25 (orange plus), or 50 mM (teal X) histidine are shown. (C) Varying concentrations of CARN peptide were incubated with Ynl092wp that was pre-incubated 200 μM SAH. Melt curves for 0 (black circle), 5.0 (green square), 25 (blue triangle), 100 (purple diamond), and 300 μM (grey hexagon) CARN peptide are shown. (D) Varying concentrations of SAH were incubated with Ynl092wp that was pre-incubated 300 mM carnosine. Melt curves for 0 (black circle), 5.0 (green square), 10 (blue triangle), 50 (purple diamond), and 200 μM (grey hexagon) SAH are shown. (E) Varying concentrations of SAH were incubated with Ynl092wp that was pre-incubated 300 μM CARN peptide. Melt curves for 0 (black circle), 5.0 (green square), 10 (blue triangle), 50 (purple diamond), and 200 μM (grey hexagon) SAH are shown. All melt curves are monophasic. Error bars represent the standard deviation of 4 replicates. 354 Supplemental data for Chapter 5 D.1 PCR primer pairs Each deletion strain was confirmed using the indicated primer pairs. All primers, melting temperatures (Tm) and expected PCR amplicon size was accessed from the Saccharomyces Genome Deletion Project webpage [323]. Gene Forward Primer (A) (5’ – 3’) Reverse Primer (D) (5’ – 3’) PCR Product Size AD Deletion AD WT YOR021C/SFM1 AGAAAGACTGGTTGATCAGTAAACG TM = 55oC GTTTTTATTTTTCTTGCATGCACTT TM = 51.9oC 2097 1155 YBR034C/HMT1 GGGACTGTTAATGAAAAATTCAATG TM = 51.3oC ATATTGAAAAAGTTTGCCGACATAG TM = 52.3oC 2185 1648 YBR133C/HSL7 GACATATTTTTATAACCCGCCTTTT TM = 52.1oC AGTTATGGAAGCAAAATGTAAGTGC TM = 54.2oC 2317 3217 YDR465C/RMT2 ACATTAGCTGCGAGTAAGGAAGTTA TM = 55.9oC GAGATTTTGAATATTCTTGCGGTAA TM = 51.8oC 2183 1838 YBR225W TTGAAATTTGTTTCTCGTGACATTA TM = 51.7oC CAATTCCCAAGAGAAGCTTAACATA TM = 53.2oC 2206 3325 YCL055W/KAR4 GACATTAGGAGCCTTATCTCCTCTT TM = 55.6oC TATTTTCTTCCTCTTTGTTCTGCAC TM = 53.7oC 2330 1754 YDR316W/OMS1 CCCCATAATCAATCAGTATATTTGC TM = 52oC ATATAAAAGACAGACCACCCATGAA TM = 54oC 2135 1967 YGR283C CGTAATTGTTTTACACTGTCACTGC TM = 54.8oC TAGCTGCAGTAGCGAGTGTAGTAGA TM = 58.5oC 2226 1668 YJL105W/SET4 ATCAATAACCACCTCTTTTTCCTTC TM = 53.4oC CTGCAGCGAGGAGCCGTAAT TM = 59.9oC 2181 2280 YKL162C TCTTCGACCTAGAAAAAGAACTTGA TM = 53.9oC TTTAAATATGGGGTTACTATTCGCA TM = 52.5oC 2192 1817 YKR029C/SET3 TGGAAGTGATCGTTGTATGAAATTA TM = 52.5oC ATGAAGATGAAGAGAATGATATGGG TM = 52.5oC 2186 2858 YMR209C GTATCTATTAGCTCAGGGATGGTGA TM = 55.4oC CTTCAAAAAGAAATTGCAAGATGAT TM = 51.1oC 2277 2067 YMR228W/MTF1 GATTATTGCGACTAATTTGAATGGT TM = 52.1oC CCTTTTCTTAAAGTTTTAGTTCCGC TM = 52.9oC 1968 1410 YMR310C TGATAGTACGACAAAAGAACCATGA TM = 54oC ATGAAGAACTGGCTACATCGTATTC TM = 54.8oC 2196 1566 355 Gene Forward Primer (A) (5’ – 3’) Reverse Primer (D) (5’ – 3’) PCR Product Size AD Deletion AD WT YNL092W GTAGAAAGTACTAGGGAGACTGGGG TM = 57.5oC ATTGCTTATTGTTGAAAGTAATGGC TM = 52.7oC 2265 1884 YPL165C/SET6 TGCAGAATTGAAGTAAAGAGAACCT TM = 54.5oC GAAAGACAGGATTCTGAAGAAGTTG TM = 53.8oC 2099 1637 YBL078C/ATG8 CCAATTGGTGATGAAGAACAATCTA TM = 53.4oC TTGAATTTCTTCCTATTTCTGATGC TM = 51.9oC 2235 1005 YPL154C/PEP4 AGTAAAGAAGTTTGGGTAATTCGCT TM = 54.7oC AGTGTTCTATGTTTGCCTTGATTTC TM = 54oC 2198 1832 YBR173C/UMP1 CATGTGATGTGACTAGTGTTTGTGA TM = 55.2oC TGGGCTGAGAAGTTGAGTATATAGG TM = 55.6oC 2329 1192 YEL063C/CAN1 CTATCAATGAAAATTTCGAGGAAGA TM = 51.4oC GACGTGAAGATAACGAAAAATGAGT TM = 53.6oC 2210 2399 YNL270C/ALP1 GCACAGTATGTATGTTTTTGAGTCG TM = 54.4oC AAAAGGGATTCTAGTTTTCCACATC TM = 53.4oC 2153 2291 YNL101W/AVT4 TTATTGAAGGTTTTCAGCGATATTC TM = 51.8oC ATGTTTAAAAGTTCTCAGGTTGTCG TM = 53.8oC 2332 2890 Gene Forward Primer (A) Reverse Primer PCR Product Size AkanB Deletion AB WT YCL038C/ATG22 TCTAAGACCATGCTTTAGCTTCTGT TM = 55.9oC kanB: CTGCAGCGAGGAGCCGTAAT TM = 59.9oC 572 802 356 D.2 Summary of methylarginine fold change at beginning and end of water incubation assay for putative methyltransferase knockouts Fold change of each putative methyltransferase knockout expelled into the media at the end of the water incubation assay (n = 5) and in SC media prior to the shift to water (n = 3) normalized to per day. A student’s t-test was performed to test for significant difference in extracellular methylarginines at each time point. An ordinary one-way ANOVA test for multiple comparisons was performed to test for significant difference in fold-change of each KO compared to WT. The difference is considered significant if p ≤ 0.05. (ns = not significant; * p ≤ 0.05; ** p ≤ 0.01; *** p ≤ 0.001; **** p ≤ 0.0001.) Strain Fold Change Fold Change Significance Fold Change Compared to WT Significance P value Significance P value Significance aDMA Δykl162c 37.18 ± 16.2 0.0037 ** 0.0186 * Δymr209c 27.63 ± 21.3 <0.0001 **** 0.0999 ns Δmtf1 6.83 ± 2.1 0.0030 ** 0.0055 ** Δsfm1 7.69 ± 1.5 0.0013 ** 0.0018 ** Δkar4 3.41 ± 1.2 <0.0001 **** 0.0091 ** Δygr283c 3.40 ± 0.8 0.0027 ** 0.0164 * Δset6 1.14 ± 0.4 0.4135 ns 0.3651 ns Δybr225w 0.81 ± 0.3 0.3221 ns 0.0478 * BY4741 1.40 ± 0.2 0.0122 * - Δhsl7 0.29 ± 0.03 <0.0001 **** 0.0271 * Δrmt2 0.54 ± 0.1 0.0039 ** 0.0037 ** BY4743 0.21 ± 0.03 <0.0001 **** - Δrmt1 0.067 ± 0.03 <0.0001 **** 0.0003 *** Δoms1 0.083 ± 0.02 <0.0001 **** 0.0003 *** Δset3 0.018 ± 0.006 <0.0001 **** 0.0003 *** Δset4 0.055 ± 0.02 <0.0001 **** 0.0003 *** Δymr310c 0.011 ± 0.005 <0.0001 **** 0.0003 *** Δynl092w 0.019 ± 0.006 <0.0001 **** 0.0003 *** MMA Δykl162c 19.74 ± 10.5 0.0057 ** 0.0911 ns Δymr209c 6.76 ± 2.1 0.0003 *** 0.6814 ns Δmtf1 2.95 ± 0.9 0.0125 * 0.0151 * Δsfm1 15.82 ± 5.7 0.0002 *** 0.0471 * Δkar4 8.4 ± 1.8 <0.0001 **** 0.0020 ** Δygr283c 3.62 ± 1.2 0.0002 *** 0.1478 ns Δset6 3.22 ± 1.1 0.0017 ** 0.0948 ns Δybr225w 2.47 ± 1.5 0.1407 ns 0.0692 ns BY4741 6.02 ± 2.0 0.0007 *** - Δhsl7 1.32 ± 0.3 0.0872 ns 0.0520 ns Δrmt2 1.41 ± 0.8 0.1336 ns 0.0190 * BY4743 0.80 ± 0.07 0.0025 ** - Δrmt1 0.29 ± 0.1 0.0017 ** 0.0072 ** Δoms1 0.36 ± 0.08 <0.0001 **** 0.0075 ** Δset3 0.25 ± 0.07 0.0001 *** 0.0070 ** Δset4 0.14 ± 0.04 <0.0001 **** 0.0065 ** 357 Strain Fold Change Fold Change Significance Fold Change Compared to WT Significance P value Significance P value Significance Δymr310c 0.12 ± 0.02 <0.0001 **** 0.0064 ** Δynl092w 0.16 ± 0.03 <0.0001 **** 0.0066 ** sDMA Δykl162c 0.97 ± 0.8 0.9421 ns 0.9959 ns Δymr209c 0.96 ± 0.47 0.8639 ns 0.9584 ns Δmtf1 0.85 ± 0.3 0.3383 ns 0.2210 ns Δsfm1 0.51 ± 0.1 0.0041 ** 0.0696 ns Δkar4 2.91 ± 1.2 0.0056 ** 0.0300 * Δygr283c 1.36 ± 0.9 0.4316 ns 0.5344 ns Δset6 2.08 ± 1.4 0.0322 * 0.2652 ns Δybr225w 1.64 ± 0.4 0.0292 * 0.0761 ns BY4741 0.97 ± 0.3 0.8682 ns - - Δhsl7 0.64 ± 0.1 0.0066 ** 0.7597 ns Δrmt2 0.36 ± 0.1 0.0011 ** 0.0289 * BY4743 0.60 ± 0.2 0.0106 * - - Δrmt1 0.18 ± 0.09 0.0045 ** 0.0115 * Δoms1 0.57 ± 0.2 0.0458 * 0.1401 ns Δset3 0.40 ± 0.3 0.0970 ns 0.0806 ns Δset4 0.34 ± 0.2 0.0107 * 0.0311 * Δymr310c 0.33 ± 0.1 0.0077 ** 0.0275 * Δynl092w 0.52 ± 0.3 0.0904 ns 0.1359 ns 358 D.3 Growth curves for putative methyltransferase knockouts Growth curves for each known and putative methyltransferase KO and each haploid and diploid WT strain. Yeast were grown in SC media for 8 days. At each time point, the OD600 was determined. Error bars represent the standard deviation (n = 3). 359 D.4 Summary of methylarginines in SC media and water for methyltransferase knockouts 360 Comparing (A) aDMA, (B) MMA, and (C) sDMA in SC media (n = 3) and water (n = 5) from each methyltransferase knockout strain to wild-type normalized to per day. An ordinary one-way ANOVA test for multiple comparisons was performed to test for significant difference in extracellular methylarginines compared to WT at each time point. The difference is considered significant if p ≤ 0.05. (ns = not significant; * p ≤ 0.05; ** p ≤ 0.01; *** p ≤ 0.001; **** p ≤ 0.0001.) Comparing methylarginines in SC media (n = 3) and water (n = 5) from each methyltransferase knockout strain to wild-type normalized to per day. An ordinary one-way ANOVA test for multiple comparisons was performed to test for significant difference in extracellular methylarginines compared to WT at each time point. The difference is considered significant if p ≤ 0.05. (ns = not significant; * p ≤ 0.05; ** p ≤ 0.01; *** p ≤ 0.001; **** p ≤ 0.0001.) Strain Concentration (nM) SC Media Comparison to WT Significance SC Media Comparison to WT Significance SC Media Water P value Significance P value Significance aDMA Δykl162c 39.82 ± 11.7 1480.32 ± 475.0 >0.9999 ns <0.0001 **** Δymr209c 11.19 ± 8.4 309.33 ± 53.3 0.0246 * 0.0061 ** Δmtf1 50.16 ± 7.6 342.77 ± 92.0 <0.0001 **** 0.0024 ** Δsfm1 28.59 ± 1.2 220.03 ± 40.9 0.8598 ns 0.4990 ns Δkar4 70.04 ± 23.7 239.17 ± 15.2 <0.0001 **** 0.1330 ns Δygr283c 90.28 ± 11.5 307.40 ± 65.9 0.0004 *** 0.0133 * Δset6 45.51 ± 15.0 51.85 ± 5.8 >0.9999 ns >0.9999 ns Δybr225w 93.81 ± 17.1 76.08 ± 24.8 0.0001 *** >0.9999 ns BY4741 44.09 ± 2.2 61.76 ± 8.2 - - - - Δhsl7 179.41 ± 14.6 52.13 ± 3.5 <0.0001 **** >0.9999 ns Δrmt2 14.34 ± 2.8 7.73 ± 1.4 0.0589 ns 0.9998 ns BY4743 292.78 ± 17.5 60.74 ± 7.6 - - - - Δrmt1 60.34 ± 2.1 4.04 ± 1.6 0.8120 ns 0.9996 ns Δoms1 208.24 ± 4.3 17.33 ± 3.6 <0.0001 **** >0.9999 ns Δset3 195.02 ± 18.2 3.55 ± 1.2 <0.0001 **** 0.9996 ns 361 Strain Concentration (nM) SC Media Comparison to WT Significance SC Media Comparison to WT Significance SC Media Water P value Significance P value Significance Δset4 192.89 ± 3.2 10.71 ± 2.9 <0.0001 **** >0.9999 ns Δymr310c 139.95 ± 7.07 1.56 ± 0.7 <0.0001 **** 0.9993 ns Δynl092w 284.85 ± 8.2 5.32 ± 1.6 <0.0001 **** 0.9997 ns MMA Δykl162c 10.20 ± 4.1 201.28 ± 69.8 >0.9999 ns <0.0001 **** Δymr209c 8.32 ± 2.1 56.24 ± 10.6 >0.9999 ns 0.8384 ns Δmtf1 22.39 ± 2.2 66.12 ± 19.3 <0.0001 **** 0.7402 ns Δsfm1 2.22 ± 0.8 35.11 ± 4.09 0.9138 ns 0.1371 ns Δkar4 21.37 ± 3.4 178.85 ± 26.2 <0.0001 **** <0.0001 **** Δygr283c 41.07 ± 13.6 148.65 ± 14.8 0.0089 ** 0.0004 *** Δset6 24.37 ± 6.2 78.46 ± 16.3 0.9087 ns >0.9999 ns Δybr225w 37.59 ± 8.6 92.72 ± 54.2 0.0333 * 0.9994 ns BY4741 13.25 ± 3.2 79.76 ± 17.2 - - - - Δhsl7 48.56 ± 8.0 64.17 ± 11.5 <0.0001 **** 0.5406 ns Δrmt2 5.85 ± 3.1 8.25 ± 0.8 0.9980 ns <0.0001 **** BY4743 116.30 ± 7.3 93.20 ± 5.8 - - - - Δrmt1 65.78 ± 20.1 19.25 ± 3.2 <0.0001 **** 0.0013 ** Δoms1 86.00 ± 4.3 31.29 ± 6.8 <0.0001 **** 0.0202 * Δset3 101.95 ± 19.7 25.82 ± 5.7 <0.0001 **** 0.0061 ** Δset4 137.74 ± 10.9 18.84 ± 4.8 <0.0001 **** 0.0012 ** Δymr310c 121.35 ± 6.7 15.15 ± 2.5 <0.0001 **** 0.0005 *** Δynl092w 164.94 ± 3.0 25.86 ± 4.6 <0.0001 **** 0.0061 ** sDMA Δykl162c 1.83 ± 1.4 1.78 ± 0.3 >0.9999 ns >0.9999 ns Δymr209c 1.41 ± 0.5 1.35 ± 0.5 0.9999 ns 0.6221 ns Δmtf1 2.30 ± 0.6 1.95 ± 0.3 0.7363 ns >0.9999 ns Δsfm1 3.56 ± 0.2 1.81 ± 0.5 0.4980 ns >0.9999 ns Δkar4 1.55 ± 0.5 4.50 ± 1.1 0.1178 ns <0.0001 **** Δygr283c 2.25 ± 1.3 3.05 ± 1.2 >0.9999 ns 0.0202 * Δset6 1.16 ± 0.8 2.41 ± 0.5 0.9928 ns 0.9057 ns 362 Strain Concentration (nM) SC Media Comparison to WT Significance SC Media Comparison to WT Significance SC Media Water P value Significance P value Significance Δybr225w 1.50 ± 0.09 2.47 ± 0.6 >0.9999 ns 0.7944 ns BY4741 2.00 ± 0.4 1.94 ± 0.4 - - - - Δhsl7 3.01 ± 0.5 1.92 ± 0.3 0.9997 ns 0.9998 ns Δrmt2 1.50 ± 0.3 0.54 ± 0.1 >0.9999 ns 0.0004 *** BY4743 3.63 ± 0.9 2.17 ± 0.2 - - - - Δrmt1 4.12 ± 1.8 0.75 ± 0.2 0.1054 ns 0.0041 ** Δoms1 1.71 ± 0.7 0.98 ± 0.1 >0.9999 ns 0.0403 * Δset3 2.31 ± 1.6 0.93 ± 0.2 >0.9999 ns 0.0264 * Δset4 1.73 ± 0.7 0.59 ± 0.1 >0.9999 ns 0.0007 *** Δymr310c 2.00 ± 0.8 0.66 ± 0.1 >0.9999 ns 0.0015 ** Δynl092w 1.92 ± 1.0 0.99 ± 0.2 >0.9999 ns 0.0470 * 363 D.5 Summary of methylarginine fold change at end and beginning of water incubation assay for autophagy-related strains Fold change of each autophagy-related strain expelled into the media at the end of the water incubation assay (n = 5) and in SC media prior to the shift to water (n = 3) normalized to per day. A student’s t-test was performed to test for significant difference in extracellular methylarginines at each time point. An ordinary one-way ANOVA test for multiple comparisons was performed to test for significant difference in fold-change of each KO compared to WT. The difference is considered significant if p ≤ 0.05. (ns = not significant; * p ≤ 0.05; ** p ≤ 0.01; *** p ≤ 0.001; **** p ≤ 0.0001.) Strain Fold Change Fold Change Significance Fold Change Compared to WT Significance P value Significance P value Significance aDMA BY4741 0.71 ± 0.3 0.2214 ns - - Δatg8 0.015 ± 0.008 <0.0001 **** 0.0124 * Δpep4 0.0092 ± 0.01 <0.0001 **** 0.0163 * Δatg22 1.76 ± 0.5 0.0006 *** 0.0006 *** MMA BY4741 2.43 ± 1.1 0.0046 ** - - Δatg8 0.040 ± 0.008 <0.0001 **** <0.0001 **** Δpep4 0.57 ± 0.06 <0.0001 **** <0.0001 **** Δatg22 3.25 ± 0.5 <0.0001 **** 0.1093 ns sDMA BY4741 0.94 ± 0.6 0.8587 ns - - Δatg8 0.18 ± 0.03 <0.0001 **** 0.0489 * Δpep4 0.095 ± 0.1 0.0089 ** 0.0366 * Δatg22 1.18 ± 0.6 0.4425 ns 0.7351 ns 364 D.6 Summary of methylarginines at beginning and end of water incubation assay for autophagy-related strains Comparing methylarginines in SC media (n = 3) and water (n = 5) from each autophagy-related strain to wild-type normalized to per day. An ordinary one-way ANOVA test for multiple comparisons was performed to test for significant difference in extracellular methylarginines compared to WT at each time point. The difference is considered significant if p ≤ 0.05. (ns = not significant; * p ≤ 0.05; ** p ≤ 0.01; *** p ≤ 0.001; **** p ≤ 0.0001.) 365 Comparing methylarginines in SC media (n = 3) and water (n = 5) from each autophagy-related strain to wild-type normalized to per day. An ordinary one-way ANOVA test for multiple comparisons was performed to test for significant difference in extracellular methylarginines compared to WT at each time point. The difference is considered significant if p ≤ 0.05. (ns = not significant; * p ≤ 0.05; ** p ≤ 0.01; *** p ≤ 0.001; **** p ≤ 0.0001.) Strain Concentration (nM) SC Media Comparison to WT Significance SC Media Comparison to WT Significance SC Media Water P value Significance P value Significance aDMA BY4741 11.46 ± 4.8 8.17 ± 0.4 - - - - Δatg8 33.41 ±3.3 0.51 ± 0.3 0.0003 *** <0.0001 **** Δpep4 63.37 ± 9.7 0.58 ± 0.7 <0.0001 **** <0.0001 **** Δatg22 27.49 ± 7.9 48.26 ± 3.0 0.0049 ** <0.0001 **** MMA BY4741 8.14 ± 2.7 19.77 ± 5.7 - - - - Δatg8 26.02 ± 3.7 1.03 ± 0.2 <0.0001 **** <0.0001 **** Δpep4 31.10 ± 1.2 1.77 ± 1.9 <0.0001 **** <0.0001 **** Δatg22 14.44 ± 0.7 46.91 ± 6.3 0.0031 ** <0.0001 **** sDMA BY4741 1.19 ± 0.8 1.11 ± 0.05 - - - - Δatg8 5.66 ± 0.5 1.03 ± 0.2 <0.0001 **** 0.9720 ns Δpep4 1.61 ± 0.9 0.15 ± 0.2 0.9108 ns 0.0019 ** Δatg22 4.11 ± 1.9 4.83 ± 0.6 0.0036 ** <0.0001 **** 366 D.7 Summary of methylarginine fold change at end and beginning of water incubation assay for permease-deficient strains Fold change of each permease-related strain expelled into the media at the end of the water incubation assay (n = 5) and in SC media prior to the shift to water (n = 3) normalized to per day. A student’s t-test was performed to test for significant difference in extracellular methylarginines at each time point. An ordinary one-way ANOVA test for multiple comparisons was performed to test for significant difference in fold-change of each KO compared to WT. The difference is considered significant if p ≤ 0.05. (ns = not significant; * p ≤ 0.05; ** p ≤ 0.01; *** p ≤ 0.001; **** p ≤ 0.0001.) Strain Fold Change Fold Change Significance Fold Change Compared to WT Significance P value Significance P value Significance aDMA BY4741 0.50 ± 0.3 0.1356 ns - - Δcan1 0.18 ± 0.007 <0.0001 **** 0.0210 * Δalp1 3.59 ± 0.06 <0.0001 **** <0.0001 **** Δavt4 1.03 ± 0.06 0.3886 ns 0.0009 *** MMA BY4741 2.91 ± 1.2 0.0017 ** - - Δcan1 0.37 ± 0.01 <0.0001 **** 0.0001 *** Δalp1 6.23 ± 0.2 <0.0001 **** <0.0001 **** Δavt4 4.06 ± 0.5 <0.0001 **** 0.0515 ns sDMA BY4741 0.73 ± 0.4 0.3191 ns - - Δcan1 0.035 ± 0.006 <0.0001 **** 0.0005 *** Δalp1 0.25 ± 0.01 <0.0001 **** 0.0116 * Δavt4 0.14 ± 0.03 <0.0001 **** 0.0029 ** 367 D.8 Summary of methylarginines at the beginning and end of water incubation assay for permease-deficient strains Comparing methylarginines in SC media (n = 3) and water (n = 5) from each permease-related strain to wild-type normalized to per day. An ordinary one-way ANOVA test for multiple comparisons was performed to test for significant difference in extracellular methylarginines compared to WT at each time point. The difference is considered significant if p ≤ 0.05. (ns = not significant; * p ≤ 0.05; ** p ≤ 0.01; *** p ≤ 0.001; **** p ≤ 0.0001.) 368 Comparing methylarginines in SC media (n = 3) and water (n = 5) from each permease-related strain to wild-type normalized to per day. An ordinary one-way ANOVA test for multiple comparisons was performed to test for significant difference in extracellular methylarginines compared to WT at each time point. The difference is considered significant if p ≤ 0.05. (ns = not significant; * p ≤ 0.05; ** p ≤ 0.01; *** p ≤ 0.001; **** p ≤ 0.0001.) Strain Concentration (nM) SC Media Comparison to WT Significance SC Media Comparison to WT Significance SC Media Water P value Significance P value Significance aDMA BY4741 16.32 ± 9.4 8.18 ± 1.7 - - - - Δcan1 2243.50 ± 54.5 404.36 ± 11.6 <0.0001 **** <0.0001 **** Δalp1 218.23 ± 1.1 782.92 ± 12.8 <0.0001 **** <0.0001 **** Δavt4 314.15 ± 10.4 322.38 ± 16.5 <0.0001 **** <0.0001 **** MMA BY4741 7.62 ± 2.2 22.14 ± 6.2 - - - - Δcan1 970.19 ± 20.4 355.12 ± 11.1 <0.0001 **** <0.0001 **** Δalp1 111.89 ± 3.6 696.98 ± 6.1 <0.0001 **** <0.0001 **** Δavt4 160.20 ± 4.4 650.22 ± 75.3 <0.0001 **** <0.0001 **** sDMA BY4741 1.47 ± 0.6 1.08 ± 0.4 - - - - Δcan1 69.33 ± 4.6 2.46 ± 0.3 <0.0001 **** 0.0081 ** Δalp1 22.73 ± 0.4 5.64 ± 0.3 <0.0001 **** <0.0001 **** Δavt4 32.54 ± 1.2 4.69 ± 1.0 <0.0001 **** <0.0001 **** 369 D.9 Summary of [3H]-arginine and [14C]-methylarginine incorporation into yeast cells The proportion of [3H]-arginine and [14C]-methylarginine incorporated into WT (BY4741), Δcan1, Δalp1, and Δavt4 cells under log and water incubation growth ± standard deviation. A Dunnett’s multiple comparison test for significance was used for multiple comparisons of each strain compared to WT. The difference in arginine or methylarginine incorporation is considered significant if p ≤ 0.05. (ns = not significant; * p ≤ 0.05; ** p ≤ 0.01; *** p ≤ 0.001; **** p ≤ 0.0001.) Strain Incorporation (%) Adjusted p value Significance [3H]-arginine Log phase BY4741 22.06 ± 5.6 - - Δcan1 4.08 ± 1.2 0.0219 * Δalp1 63.69 ± 2.1 0.0002 *** Δavt4 28.14 ± 14.0 0.5992 ns Water Incubation BY4741 9.57 ± 2.2 - - Δcan1 4.61 ± 0.65 0.4498 ns Δalp1 7.45 ± 1.5 0.9179 ns Δavt4 28.49 ± 10.0 0.0085 ** [14C]-methylarginine Log Phase BY4741 1.93 ± 3.0 - - Δcan1 5.07 ± 0.55 0.1029 ns Δalp1 2.77 ± 1.1 0.8565 ns Δavt4 4.12 ± 0.41 0.2909 ns Water Incubation BY4741 0.2927 ± 0.30 - - Δcan1 0.2602 ± 0.48 0.9990 ns Δalp1 0.03253 ± 0.28 0.7229 ns Δavt4 0.5204 ± 0.37 0.7892 ns 370 The proportion of [3H]-arginine and [14C]-methylarginine incorporated into WT, Δcan1, Δalp1, and Δavt4 cells under log and water incubation growth ± standard deviation. A student’s t-test was performed to test for significant difference in arginine or methylarginine incorporation between log and water incubation for each yeast strain. The difference in arginine or methylarginine incorporation is considered significant if p ≤ 0.05. (ns = not significant; * p ≤ 0.05; ** p ≤ 0.01; *** p ≤ 0.001; **** p ≤ 0.0001.) Strain Log Phase Incorporation (%) Water Incubation Incorporation (%) Adjusted p value Significance [3H]-arginine BY4741 22.06 ± 5.6 9.57 ± 2.2 0.022668 ns Δcan1 4.08 ± 1.2 4.61 ± 0.65 0.544853 ns Δalp1 63.69 ± 2.1 7.45 ± 1.5 0.000069 **** Δavt4 28.14 ± 14.0 28.49 ± 10.0 0.979139 ns [14C]-methylarginine BY4741 1.93 ± 3.0 0.2927 ± 0.30 0.394605 ns Δcan1 5.07 ± 0.55 0.2602 ± 0.48 0.000335 *** Δalp1 2.77 ± 1.1 0.03253 ± 0.28 0.012148 * Δavt4 4.12 ± 0.41 0.5204 ± 0.37 0.000349 *** 371 D.10 Lysis buffer background controls Each treatment in lysis buffer alone was included as background control. In addition to the specified treatment, each reaction also contained 1 mM NADPH as NOS substrate (including the no treatment experiment) and 1 μM DAF-FM DA. Fluorescence of DAF-FM DA was measured (495ex/515em nm) to monitor nitric oxide. Error bars show the standard deviation of three replicates.
Thesis/Dissertation
eng
Pharmaceutical Sciences
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Frankel, Adam
Mechanistic investigations of protein arginine N-methyltransferases and elucidation of their implications in the yeast stress response
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