Exploring Adomet-Dependent Methyltransferases in Yeast

EXPLORING ADOMET-DEPENDENT METHYLTRANSFERASES IN YEAST

Elena Lissina

A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy

Department of Molecular Genetics University of Toronto

Exploring AdoMet-Dependent Methyltransferases in Yeast

Elena Lissina

Doctor of Philosophy

Department of Molecular Genetics University of Toronto

2013

Abstract

This work presents the investigation of fungal AdoMet-dependent methyltransferases.

The first part of the dissertation focuses on two distinct methyltransferases with previously unknown functions in the budding yeast Saccharomyces cerevisiae and the human fungal pathogen Candida albicans. To characterize these enzymes I used a combinatorial approach that exploits contemporary high-throughput techniques available in yeast (chemical genetics, expression, lipid profiling and genetic interaction analysis) combined with rigorous biological follow-up. First, I showed that S. cerevisiae CRG1

(ScCRG1) is a small molecule methyltransferase that methylates cytotoxic drug cantharidin and is important for maintaining lipid homeostasis and actin cytoskeleton integrity in response to small-molecule cantharidin in the baker’s yeast. Similarly to

ScCRG1, orf19.633 in the human fungal pathogen C. albicans (CaCRG1) methylates cantharidin and is important for GlcCer biosynthesis. I also demonstrated that CaCrg1 is a ceramide- and PIP-binding methyltransferase involved in Candida’s morphogenesis, membrane trafficking and fungal virulence. Together, the analysis of two genes in yeast illuminated the important roles of the novel small molecule methyltransferases in

coupling drug response to lipid biosynthesis and fungal virulence. In the second part of my dissertation, I present the systematic characterization of the genetic architecture of the yeast methyltransferome by examining fitness of double-deletion methyltransferase mutants in standard and under environmental stress conditions. This analysis allowed me to describe specific properties of the methyltransferome network and to uncover functional relationships among methyltransferases inspiring multiple hypotheses and expanding the current knowledge of this family of enzymes.

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Acknowledgements

This dissertation is the result of six years commitment to science and would not have been possible without the inspiration and support of my supervisors, colleagues, friends, and my family. First and foremost, I am truly grateful to my supervisors Corey Nislow and Guri Giaever for giving me the opportunity to satisfy my thirst for knowledge and for providing an incredible work environment that allowed me to grow as a scientist. Thank you for your never-ending enthusiasm, keeping your office door always open and believing in me! I am also grateful to the members of my thesis advisory committee Brent Derry and Brenda Andrews for their expert guidance over the years.

I thank all members of HIPHOP lab (Andrew Smith, Kyle Tsui, Kahlin Cheung-Ong, Anthony Arnoldo, Elke Ericson, Zhun Yan, Ian Wallace, Kevin Song, Anna Lee, Nikko Torres, Ron Amar, Tanvi Shekhar, Simon Alfred, Anu Surendra, and Larry Heisler) for creating a perfect research environment to work in. Thanks to Malene Urbanus for her insightful discussions and help in the early years of my graduate career. Special thanks to Marinella Gebbia who gave me incredible technical support I needed to carry out my research and made my life so much easier and fun in many other aspects. I am also grateful to my friends-colleagues Anastasia Baryshnikova for her irreplaceable company throughout PhD years and to Marina Gorelik for being a reliable climbing partner.

Finally, I am forever indebted to my mom, brother and dad for their understanding and unconditional love. Above all, I thank the Universe for allowing me to participate in this incredible learning adventure – science.

Table of Contents

Acknowledgements...... iv Table of Contents...... v List of Figures...... viii List of Tables ...... ix List of Abbreviations ...... x List of Appendices ...... xv Chapter 1 Introduction...... 1 1 Introduction ...... 2 1.1 Introduction to AdoMet-dependent methyltransferases...... 2 1.1.1 Mechanism of the methylation reaction...... 3 1.1.2 Structural diversity of methyltransferases...... 4 1.1.3 Substrate promiscuity...... 5 1.1.4 Brief overview of biological functions ...... 6 1.2 Importance of AdoMet-dependent methyltransferases...... 7 1.2.1 Disease associations ...... 7 1.2.2 Drug interactions ...... 8 1.2.3 Methylation of exogenous small molecules...... 9 1.2.3.1 Arsenic methyltransferase (AS3MT)...... 9 1.2.3.2 Thiopurine S-methyltransferase (TPMT) ...... 10 1.2.3.3 Glycine N-methyltransferase (GNMT)...... 11 1.2.4 Methylation of endogenous small molecules...... 13 1.2.4.1 Catechol O-methyltransferase (COMT)...... 14 1.2.4.2 Yeast Trans-Aconitate Methyltransferase (TMT1) ...... 15 1.3 Non-canonical roles of methyltransferases...... 16 1.4 Methods to explore methyltransferases...... 18 1.4.1 In silico computational approaches...... 18 1.4.2 In vitro approaches...... 19 1.4.2.1 Methylation activity assays...... 20 1.4.2.2 Biochemical identification of methylated substrates ...... 20 1.4.2.3 Peptide and protein microarrays ...... 21 1.4.3 In vivo functional genomic methods ...... 23 1.4.4 Chemical genetics...... 25 1.5 Thesis rationale ...... 27 Chapter 2 Characterizing a putative Saccharomyces cerevisiae methyltransferase in response to chemical stress...... 29 2 Characterizing a putative Saccharomyces cerevisiae methyltransferase in response to chemical stress...... 30 2.1 Introduction ...... 30 2.2 Results...... 32 2.2.1 CRG1 is required for resistance to cantharidin ...... 32 2.2.2 A functional Crg1 methyltransferase domain is required for resistance to cantharidin ...... 34

2.2.3 Analysis of CRG1 transcript in response to cantharidin...... 36 2.2.4 Analysis of yeast transcriptome upon exposure to cantharidin...... 38 2.2.5 CRG1 is a sequence homologue of small molecule methyltransferase TMT1 .....42 2.2.6 Crg1 methylates cantharidin in vitro...... 42 2.2.7 Analysis of cantharidin-specific genetic interactors of CRG1 ...... 47 2.2.8 CRG1 is important for cantharidin-perturbed lipid homeostasis ...... 50 2.2.9 Orf19.633, a functional homolog of CRG1 in C. albicans, mediates lipid homeostasis in response to cantharidin...... 54 2.2.10 Crg1 is required for cytoskeleton organization upon exposure to cantharidin 56 2.2.11 CRG1 transcription is regulated via the Cell Wall Integrity (CWI) pathway.....59 2.3 Discussion ...... 61 2.4 Materials and methods...... 64 2.4.1 Strains, plasmids and growth conditions ...... 64 2.4.2 Site-directed mutagenesis of CRG1...... 66 2.4.3 RNA isolation, cDNA preparation and qRT-PCR analysis...... 68 2.4.4 Microarray analysis...... 68 2.4.5 Expression and purification of Crg1 fusion protein...... 69 2.4.6 Preparation and analysis of radiolabeled Crg1 reactions in vitro...... 70 2.4.7 Preparation of in vitro enzymatic reactions for analysis by mass spectrometry 71 2.4.8 Fitness profiling of double-deletion mutants with cantharidin...... 72 2.4.9 Lipidome analysis by liquid chromatography-tandem mass spectrometry...... 73 2.4.10 Actin staining...... 74 2.4.11 Construction of a Crg1-GFP fusion protein ...... 75 2.4.12 Synthetic Genetic Array (SGA)...... 75 2.4.13 Analysis of sterols intermediates by gas liquid chromatography - mass spectrometry ...... 75 2.4.14 Analysis of lipid droplets...... 76 Chapter 3 Exploring a functional homologue of CRG1 in the human fungal pathogen Candida albicans...... 77 3 Exploring a functional homologue of CRG1 in the human fungal pathogen Candida albicans ...... 78 3.1 Introduction ...... 78 3.2 Results...... 80 3.2.1 A functional AdoMet-dependent methyltransferase domain of CaCRG1 is important for cantharidin resistance...... 80 3.2.2 Cantharidin is methylated by CaCrg1 in vitro and in vivo...... 82 3.2.3 CaCRG1 is important for C. albicans morphogenesis in response to cantharidin 85 3.2.4 CaCrg1 maintains membrane trafficking during cantharidin exposure ...... 88 3.2.5 Affinity-purified CaCrg1 binds ceramides in vitro...... 90 3.2.6 CaCRG1 interacts genetically with genes of glucosylceramide biosynthesis pathway ...... 92 3.2.7 CaCrg1 is important for C. albicans virulence in a Galleria mellonella model of infection...... 95 3.3 Discussion ...... 97 3.4 Materials and Methods ...... 99

3.4.1 Strains, plasmids and growth conditions ...... 99 3.4.2 Microarray analysis...... 100 3.4.3 Cloning and purification of CaCrg1 fusion protein...... 101 3.4.4 Site-directed mutagenesis...... 102 3.4.5 Metabolomic profiling of C. albicans cellular extracts...... 102 3.4.6 In vitro methylation reactions ...... 103 3.4.7 Lipid-protein overlay assay...... 105 3.4.8 FM4-64 labeling for vacuolar membrane dynamics...... 105 3.4.9 Adhesion assay...... 106 3.4.10 qRT-PCR analysis...... 106 3.4.11 Construction of double-deletion mutants...... 106 3.4.12 Virulence assay...... 107 Chapter 4 Exploring genetic architecture of the yeast methyltransferome...108 4 Exploring genetic architecture of the yeast methyltransferome ...... 109 4.1 Introduction ...... 109 4.2 Results...... 111 4.2.1 Construction of double-deletion methyltransferase mutants...... 111 4.2.2 Evaluating genetic interaction scores and quality control...... 113 4.2.3 Assessing genetic interactions among methyltransferases...... 116 4.2.4 Genetic architecture of yeast methyltransferome ...... 119 4.2.5 Interpreting yeast methyltransferome genetic interaction data...... 122 4.2.6 Relating genetic interaction data with protein interactions ...... 125 4.2.7 Examining co-expression patterns among methyltransferases with similar genetic profiles ...... 127 4.2.8 Plasticity of yeast methyltransferome under stress conditions ...... 127 4.2.9 Examining effects of stress on similarity of genetic profiles...... 131 4.2.10 Characterizing the COMPASS complex in response to stress...... 134 4.2.11 Predicting functions for unknown methyltransferases...... 138 4.3 Discussion ...... 139 4.4 Materials and Methods ...... 144 4.4.1 Strains and growth conditions ...... 144 4.4.2 Construction of double-deletion mutants ...... 144 4.4.3 Data processing and scoring genetic interactions ...... 145 4.4.4 Data analysis ...... 145 Chapter 5 Summary and future directions ...... 147 5 Summary and future directions...... 147 5.1 Summary ...... 147 5.2 Future directions...... 150 5.2.1 Next steps to dissect Crg1 function...... 150 5.2.2 Next steps in the methyltransferome analysis...... 155 References...... 157 Appendices ...... 198

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List of Figures

Figure 2-1. Crg1 is required for resistance to cantharidin and its analogues...... 33 Figure 2-2. A functional Crg1 methyltransferase domain is required for resistance to cantharidin...... 35 Figure 2-3. Analysis of CRG1 transcript in response to cantharidin...... 37 Figure 2-4. Analysis of yeast transcriptome upon exposure to cantharidin...... 39 Figure 2-5. CRG1’s sequence homolog TMT1 methylates the substrates that share structural features with cantharidin...... 43 Figure 2-6. Crg1 methylates cantharidin in vitro...... 44 Figure 2-7. Uncovering cantharidin-specific genetic interactors of CRG1...... 48 Figure 2-8. Crg1 is important for cantharidin-perturbed lipid homeostasis...... 52 Figure 2-9. Orf19.633, a functional homolog of CRG1 in C. albicans, mediates lipid homeostasis in response to cantharidin...... 55 Figure 2-10. Crg1 is required for cytoskeleton upon exposure to cantharidin...... 57 Figure 2-11. CRG1 transcription is regulated via the Cell Wall Integrity pathway...... 60 Figure 3-1. A functional AdoMet-dependent methyltransferase domain of CaCrg1 is required for cantharidin resistance...... 81 Figure 3-2. CaCrg1 is small molecule AdoMet-dependent methyltransferase...... 83 Figure 3-3. CaCRG1 is important for cantharidin-perturbed morphogenesis in C. albicans...... 87 Figure 3-4. CaCrg1 maintains membrane trafficking during cantharidin exposure...... 89 Figure 3-5. Affinity-purified CaCrg1 binds ceramides in vitro...... 91 Figure 3-6. CaCrg1 is interacts with the genes of GlcCer pathway...... 94 Figure 3-7. CaCrg1 is important for C. albicans virulence in Galleria mellonella...... 96 Figure 4-1. Construction of double-deletion methyltransferase mutants...... 112 Figure 4-2. Evaluating genetic interaction scores and quality control...... 115 Figure 4-3. Assessing genetic interactions among methyltransferases...... 118 Figure 4-4. Genetic architecture of yeast methyltransferome...... 121 Figure 4-5. Interpreting yeast methyltransferome genetic interaction data...... 124 Figure 4-6. Relating genetic interaction data with protein interactions...... 126 Figure 4-7. Examining co-expression patterns among methyltransferases with similar genetic profiles...... 128 Figure 4-8. Plasticity of yeast methyltransferome under stress conditions...... 130 Figure 4-9. Examining effects of stress on similarity of genetic profiles...... 133 Figure 4-10. Characterizing the COMPASS complex in response to stress...... 137 Figure 5-1. Models depicting how Crg1- cantharidin interaction influences biological processes in baker’s yeast S. cerevisiae and the human fungal pathogen C. albicans...... 148 Figure 5-2. Characterizing the human methyltransferase METLL7A in response to cantharidin...... 154

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List of Tables

Table 2-1. Strains used in this study...... 65 Table 2-2. Plasmids used in this study...... 66 Table 2-3. Oligonucleotides used in this study...... 67 Table 3-1. Strains used in this study...... 100 Table 3-2. Plasmids used in this study...... 101 Table 3-3. Primers used in this study...... 103

List of Abbreviations

∆ deletion

°C degree Celcius 5’ or 3’ UTR five or three prime untranslated region 6-MP 6-mercaptopurine 6-TG 6-thioguanine AdoMet S-adenosyl methionine AFB1 aflatoxin B1 AS3MT arsenic (+3 oxidation state) methyltransferase ATP adenosine triphosphate AZA azathioprine BaP benzo(a)pyrene BME 2-mercaptoethanol BSA bovine serum albumin cAMP cyclic adenosine monophosphate CARM1 coactivator-associated arginine methyltransferase 1 cDNA complementary DNA ChIP chromatin immunoprecipitation COMPASS complex of proteins associated with a trithorax-related SET1-domain COMT catechol O-methyltransferase CPF cleavage and polyadenylation factor CRG1 cantharidin resistance gene CTD carboxy-terminal domain CWI cell wall integrity Da dalton DAG diacylglycerol DAmP decreased abundance by mRNA perturbation

DMSO dimethyl sulfoxide DNA deoxyribonucleic acid DNMT1 DNA methyltransferase dSLAM heterozygote diploid-based synthetic lethality analysis with microarrays DTT dithiothreitol E-MAP epistatic miniarray profiles ECL enhanced chemiluminescence EDTA ethylenediaminetetraacetic acid EGTA ethylene glycol tetraacetic acid ELISA enzyme-linked immunosorbent assay ESR environmental stress response EZH2 enhancer of zeste gene FITC fluorescein isothiocyanate G418 geneticin GA guanine-adenine GFP green fluorescent protein GlcCer glucosylceramide GNMT glycine N-methyltransferase GO gene ontology GPI glycophosphatidylinositol GSH glutathione H3K27 histone 3 lysine 27 H3K36 histone 3 lysine 36 H3K4 histone 3 lysine 4 H3K9 histone 3 lysine 9 HA hemagglutinin HEPES 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid

HIP haploInsufficiency profiling HIS histidine HMM hidden markov model HOP homozygous deletion profiling IC inhibitory concentration IPC inositolphosphoceramide KAN kanamycin MAPK mitogen-activated protein kinase MB-COMT membrane-bound catechol O-methyltransferase MDR multidrug resistance MIPC mannosylinositolphosphoceramide MMS methyl methanesulfonate MRM multiple reaction monitoring mRNA messenger ribonucleic acid MS mass spectrometry MSP multicopy suppression profiling NADP nicotinamide adenine dinucleotide phosphate NAT nourseothricin ORF open reading frame PA phosphatidic acid PAH polycyclic aromatic hydrocarbon PBS phosphate buffered saline PC phosphatidylcholine PCR polymerase chain reaction PE phosphatidylethanolamine PI phosphatidylinositol PI(3,5)P2 phosphatidylinositol 3,5-bisphosphate

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PI(3)P phosphatidylinositol 3-monophosphate PI(4,5)P2 phosphatidylinositol 4,5-bisphosphate PIP phosphoinositides PP1 protein phosphatase 1 PP2A protein phosphatase 2 PRMT protein arginine methyltransferases PS phosphatidylserine PSI-BLAST position-specific iterative basic local alignment search tool PVDF polyvinylidene fluoride qRT-PCR quantitative reverse transcriptase polymerase chain reaction R resistant RNA ribonucleic acid RPS-BLAST reversed position specific basic local alignment search tool rRNA ribosomal ribonucleic acid S-COMT soluble catechol O-methyltransferase SAH S-adenosylhomocysteine SC synthetic complete media SD synthetic defined media SDS sodium dodecyl sulfate SDS-PAGE sodium dodecyl sulfate polyacrilamide gel electrophoresis SET (Su(var)3-9, Enhancer-of-zeste, Trithorax) domain SGA synthetic genetic array SGD Saccharomyces genome database siRNA small interfering ribonucleic acid SNP single-nucleotide polymorphism TAP tandem affinity purification TBST tris-buffered saline and tween 20

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TCA tricarboxylic acid cycle TMT1 trans-aconitate methyltransferase TOR target of rapamycin TPMT thiopurine methyltransferase tRNA transfer ribonucleic acid UCLA University of California Los Angeles URA uracil US FDA US food and drug administration UV ultraviolet Wt wild type YMC yeast metabolic cycle YPD yeast peptone dextrose

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List of Appendices

Appendix 1: Data from Chapter 2 Table 1. Significantly enriched (P-value <1.0E-0.6, Bonferroni corrected) Gene Ontology Biological processes for significantly up- and down-regulated genes (log2 >1 and <-1). Table 2. Differentially expressed genes of methionine biosynthesis in crg1∆/∆ mutants under cantharidin stress.

Appendix 2: Data from Chapter 3 Table 1. Differentially expressed genes C. albicans wt cells treated with cantharidin (2 mM, 30 min). Table 2. Bioactive lipids present on the array.

Chapter 1 Introduction

1 Introduction 1.1 Introduction to AdoMet-dependent methyltransferases

To maintain intracellular homeostasis in standard physiological conditions and in response to external stimuli, such as hormones, environmental stresses, and pollutants, living organisms have evolved complex molecular networks. Methyltransferases comprise a large family of proteins which play a critical part in these dynamic cellular events by catalyzing the methylation of a wide variety of substrates (proteins, nucleic acids, lipids and small molecules) (Chiang et al, 1996; Loenen, 2006; Schubert et al, 2003). Biological methylation was first observed in 1887, when German physician Wilhelm His detected the presence of the methylated small molecule pyridine in the urine of a dog dosed with pyridine acetate (Acha et al, 2011). Later it was revealed that this reaction is catalyzed by S-adenosyl-methionine-dependent methyltransferase, and that requires an essential metabolic intermediate adenosyl methionine (AdoMet) (Cantoni, 1951). By the early 1960s, it was established that methylation also occurs on lysine residues of proteins (Ambler & Rees, 1959), including histones (Murray, 1964) , and nucleic acids (Gold et al, 1963). It took three decades to fully appreciate the biological importance of this modification, and currently, methylation is a widely recognized modification of biological molecules, that, along with phosphorylation, acetylation and ubiquitination, is found across all kingdoms of life (Paik et al, 2007).

The goal of my research is to investigate the roles of AdoMet-dependent methyltransferases in the cellular response to external perturbation (either chemical or environmental). I aim to 1) characterize two putative methyltransferases in the baker’s yeast Saccharomyces cerevisiae and in the human fungal pathogen Candida albicans, and 2) to examine the functional relationships among methyltransferases in S. cerevisiae in various environmental conditions. In this Chapter, I introduce AdoMet-dependent methyltransferases, describe their biochemical properties, catalytic promiscuity/substrate diversity, and their biological functions with reference to their association to disease states. Then I discuss the role of methyltransferases in drug interactions, focusing on small molecule methyltransferases. I conclude this chapter describing the available methods to investigate this enzyme family.

1.1.1 Mechanism of the methylation reaction

AdoMet-dependent methyltransferases are bisubstrate enzymes that transfer the methyl group from cofactor or methyl donor AdoMet via an SN2 displacement mechanism to nucleophilic atoms (carbon, oxygen, nitrogen, sulfur or halides) which are present on a wide range of substrates (Hegazi et al, 1976). AdoMet is synthesized from another coenzyme adenosine triphosphate (ATP) and methionine in two consecutive steps catalyzed by the highly conserved methionine adenosyltransferase (Mato et al, 1997; Schubert et al, 2003). Although AdoMet is primarily used in methylation reactions, it is also utilized for polyamine synthesis, radical-based catalysis, and as a source of methylene, amino, and ribosyl groups (Fontecave et al, 2004; Grillo & Colombatto, 2008). The by-product of methylation reactions S-adenosyl-homocysteine (SAH), a potent inhibitor of AdoMet-dependent methyltransferases in most cases, is normally broken down to homocysteine and adenosine by the action of SAH hydrolase (Williams & Schalinske, 2010). Elevated intracellular levels of homocysteine are associated with hyperhomocysteinemia, a condition that has been linked to premature onset of cardiovascular and neurological diseases (Williams & Schalinske, 2010). One of plausible explanations for this phenomenon is the inhibition of methyltransferases, because it was reported that high homocysteine levels correlate with a decrease in transmethylation rates (Perna et al, 1999).

A methyl group can be also transferred to electrophilic carbon atoms by an indirect “ping-pong” mechanism with an intermediate methylation step on a conserved cysteine residue. This mechanism was described for bacterial “radical AdoMet” domain- containing methyltransferases that target carbon C-2 and C-8 of adenosine residues of 23S ribosomal RNA in the large (50S) ribosomal subunit (Atta et al, 2010; Grove et al, 2011). Because modifications of these residues are implicated in the resistance to several classes of antibiotics, these methyltransferases may be exploited as targets for antibacterial drugs. Additionally, it has been reported that certain secondary metabolite methyltransferases (e.g. antibiotic pactamycin) may work via a similar mechanism (Kudo et al, 2007).

1.1.2 Structural diversity of methyltransferases

Whole genome sequencing has revealed that the family of AdoMet-dependent methyltransferases comprises 0.6-1.6% of all genes in the genomes of Escherichia coli, Saccharomyces cerevisiae, Homo sapiens, etc., predicting that over 200 methyltransferases exist in humans (Katz et al, 2003; Petrossian & Clarke, 2009b). The yeast methyltransferome consists of over 80 AdoMet-dependent methyltransferases, at least 30 of which have no known function (Petrossian & Clarke, 2009a; Wlodarski et al, 2011).

Structurally AdoMet-dependent methyltransferases are classified into five distinct families (Classes I-V). The majority of these enzymes has a structure referred to as the Class I fold (60% of all methyltransferases in human and 63% in yeast), including methyltransferases acting upon a wide variety of substrates, such as nucleic acids (e.g. DNA methyltransferase), proteins (e.g. arginine methyltransferase), and small molecules (e.g. catechol O-methyltransferase) (Kozbial & Mushegian, 2005; Martin & McMillan, 2002; Petrossian & Clarke, 2009a). Class I is characterized by a seven-strand twisted β- sheet (strand 7 is antiparallel to the other six strands) flanked by α helices forming α-β-α sandwich (Petrossian & Clarke, 2011; Schubert et al, 2003). Despite limited sequence similarity observed in their amino acid sequences (which can be as low as 10%), their tertiary structures share the common AdoMet-dependent methyltransferase fold which is structurally similar to NAD(P)-binding Rossmann fold (Schubert et al, 2003). The most prominent prototypical fold was first determined from the X-ray crystal structure of catechol-O-methyltransferase (COMT) (Vidgren et al, 1994). This structure, also known as the central topological switch-point, creates a deep cleft for the binding of the AdoMet that is found on the residues of Motif I and Post Motif I of the β sheet N-terminal region. The substrate-binding region is located within the last residues of β4 and β5 sheets in C- terminal (Motif II and III, respectively). Unlike nucleic acid methyltransferases, small molecule and protein methyltransferases do not have any distinctive secondary structural elements after strand 7. However, small molecule methyltransferases are distinguished by four to six cysteine residues in each monomer, that can form intra-molecular disulfide bonds which are involved in the inhibition of their methyltransferase activity (Fujioka et

al, 1988). For example, human arsenic (+3) methyltransferase (AS3MT) has 14 cysteine residues which are critical for its enzymatic activity and for the maintenance of the enzyme’s structure (Song et al, 2011a). In fact, GSH is important for the enzymatic reaction catalyzed by AS3MT and the presence of other reducing agents, such as DTT, and BME influences the methyltransferase activity in a positive manner (Zakharyan et al, 1995).

Classes II and III methyltransferases are the smallest families with drastically different structures and not surprisingly, distinct interactions with the AdoMet cofactor. These groups include the methyltransferases with the MetH activation domain and the precorrin-like methyltransferases. The SPOUT (Class IV) methyltransferase family comprises homodimers that catalyze the methylation of RNA substrates (tRNA and rRNA). These enzymes contain a distinctive α/β knot structure formed by the C-terminus which contains several catalytic residues (Tkaczuk et al, 2007). Similar to the SPOUT methyltransferases, SET domain-containing enzymes (Class V) have a knot-like structure, but this class is important for the methylation of protein targets, such as the flexible tails of histones, ribosomal proteins and Rubisco, an enzyme involved in carbon fixation (Couture et al, 2008; Dillon et al, 2005; Qian & Zhou, 2006; Schubert et al, 2003). The globular domain of histones is also methylated by the non-SET histone lysine methyltransferase Dot1 (Ng et al, 2002).

1.1.3 Substrate promiscuity

Although most methyltransferases are thought to be specific in their enzymatic activity, the structural flexibility observed for the methyltransferase family (Schubert et al, 2003) suggests they exhibit a ‘substrate promiscuity’, that is characterized by methyltransferases acting towards diverse substrates. For example, human G9a lysine methyltransferase was initially characterized as a histone H3 lysine 9 (H3K9) and lysine 27 (H3K27) methyltransferase, but has also been demonstrated to catalyze its own automethylation and to act on other non-histone targets (e.g. p53) (Rathert et al, 2008b; Sampath et al, 2007). The evolutionarily conserved histone lysine methyltransferase SET1, a core component of the COMPASS complex, methylates histone H3 lysine K4 (H3K4) and is active towards the kinetochore protein Dam1 (Zhang et al, 2005).

Additionally, multiple histone arginine methyltransferases also methylate biologically relevant non-histone proteins (Bedford & Richard, 2005). Such substrate promiscuity is more commonly reported for small molecule methyltransferases in bacteria and plants. For example, α-amino group N-methylation which is native to protein substrates also occurs on ribosomally synthesized natural products in bacteria (Zhang & van der Donk, 2012). The plant OMT2 methyltransferase accepts a wide range of structurally diverse small molecules (Nagel et al, 2008). Overall, unlike nucleic acid or protein methyltransferases, small molecule methyltransferases act towards a diverse set of substrate atoms (O-methyltransferases: COMT; N-methyltransferases: GNMT; S- methyltransferase: TPMT, halide methyltransferases: AS3MT; C-methyltransferases: CbiL) (Schubert et al, 2003). Although the majority of small molecule methyltransferases are selective towards specific types of acceptor atoms (Liscombe et al, 2012), methyltransferases that transfer a methyl moiety to more than one type of substrate atoms have been also reported (Schmidberger et al, 2010). Clearly, such diverse enzymatic reactivity for certain methyltransferases may be one of the biggest challenges in the characterization of these proteins.

1.1.4 Brief overview of biological functions

Because they act on a wide range of substrates, it is not surprising that methyltransferases play important roles in numerous biological processes, such as small molecule biosynthesis, detoxification of toxic molecules, cellular signaling pathways, transcriptional regulation, DNA repair, stabilization of RNA, translation, RNA processing and protein repair (Bedford & Richard, 2005). Interestingly, nine of the ten of essential yeast methyltransferases are required for growth in standard laboratory conditions and are particularly important for the methylation of RNA substrates, such as rRNA, tRNA and snoRNAs, indicating the importance of RNA methylation for normal cellular physiology and that defects in RNA methylation can not be compensated by other processes. Although the majority of methyltransferases are non-essential in rich media, they are likely essential under certain conditions (Hillenmeyer et al, 2008). For example, the small molecule/lipid methyltransferase phosphatidylethanolamine N-methyltransferase is directly involved in the biosynthesis of phosphatidylcholine, the major structural

component of membranes, and the activity of this enzyme is essential under nutritional deprivation (e.g. the choline-deficient diet) (Li & Vance, 2008). Overall, methyltransfer reactions are important at every level of cellular homeostasis including sensing external stimuli and adaptation to new conditions. For example, the observation that the methyltransferases are involved in bacterial chemotaxis (CheR methylates chemoreceptors) by influencing the flux of a phosphoryl group along the cell-signaling pathway provided the insight into the importance of the methylation reaction in responding to external perturbation (Springer et al, 1979). Furthermore, methylation is affected by stress. For example, heat shock and arsenite treatments affect methylation patterns of histones in fly Drosophila melanogaster, that results in gene silencing by altering chromatin structure (Desrosiers & Tanguay, 1988). Interestingly, certain evolutionarily conserved heat shock proteins (e.g. bacterial FtsJ) are also rRNA methyltransferases, revealing that RNA methylation is under heat shock control (Bugl et al, 2000; Caldas et al, 2000).

1.2 Importance of AdoMet-dependent methyltransferases

1.2.1 Disease associations

Consistent with the importance of the methylation reaction in essential biological processes, dysfunctions of human methyltransferases often lead to pathologies, such as cancer, inflammation, cardiovascular disease, spinal muscular atrophy, and neurodegenerative disorders (Bedford & Richard, 2005; Cheung et al, 2007; Copeland et al, 2009; Greer & Shi, 2012). It is estimated that about 30% of all human methyltransferases are associated with disease states (Petrossian & Clarke, 2011). Of particular interest, lysine and arginine protein methyltransferases, with their crucial roles in transcriptional control, are investigated extensively with regards to their roles in cancer (Cheung et al, 2007; Copeland et al, 2009). For example, overexpression of the SET- domain histone methyltransferase EZH2 (enhancer of zeste homologue 2) involved in transcriptional silencing by trimethylation of histone H3 lysine K27 (H3K27) was observed in prostate, breast, bladder, gastric cancers and melanoma (Varambally et al, 2008). Similarly the recurrent amplification of histone H3 lysine K9 (H3K9) methyltransferase SETDB1 accelerates melanoma formation (Ceol et al, 2011).

Additionally aberrant methylation resulting from overexpression of certain methyltransferases may lead to upregulation of oncogenes or to destabilization of tumor suppressors (Fernandez et al, 2012). For example, the tumor suppressor p53 is a substrate for various methyltransferases, such as G9a, GLP, SMYD2, SET8, and SET7/9 (Chuikov et al, 2004; Huang et al, 2006; Scoumanne & Chen, 2008). Protein arginine methyltransferase PRMT4 or CARM1 (coactivator associated arginine methyltransferase 1), identified by yeast-two-hybrid experiments as an interactor of the steroid receptor activator GRIP1, is directly involved in prostate and breast cancer proliferation by regulating gene transcription (Frietze et al, 2008; Majumder et al, 2006).

Methyltransferases play key roles in neuropathological conditions. For example, small molecule methyltransferases, hydroxyindole O-methyltransferase and phenylethanolamine N-methyltransferase, are involved in the terminal steps of the biosynthesis of melatonin and epinephrine, respectively, and are associated with diverse neurological disorders, such as bipolar disorder, depression, and attention deficiency (Etain et al, 2012).

1.2.2 Drug interactions

Due to their associations with disease states, there is a substantial interest in modulating aberrant methyltransferase functions with small molecule inhibitors, and they have been investigated as drug targets (Copeland et al, 2009). The approaches to inhibiting methyltransferases range from the indirect depletion of methyltransferases via blocking the enzyme SAH hydrolase (e.g. 3-deazaneplanocin) to the actual targeting of the methyltransferases with selective small molecules (e.g chaetocin). Currently, the broad spectrum DNA methyltransferase inhibitors azacitidine (Vidaza) and decitabine (Dacogen) are the only two US FDA-approved small molecules that target methyltransferases. Other DNA and histone methyltransferase inhibitors (e.g. ellagic acid) are in preclinical study (Boumber & Issa, 2011). Although crystallographic and mechanistic studies indicate that these enzymes are druggable (predicted to bind to a drug with high affinity), many important questions remain to be addressed. For example, it is not clear if the selective targeting can be achieved through binding to well-conserved AdoMet-binding pocket of an enzyme. Furthermore, the selective inhibition does not

guarantee potency of a compound in cellular assays (Allan et al, 2009), and off-target effects, along with perturbation of downstream events, must be considered. The evidence that a disease state is specifically due to aberrant methylation must be also carefully evaluated before implementing these therapeutics.

Methyltransferases have been also implicated in drug interactions either directly by participating in drug metabolism or indirectly by modulating the transcription of the genes encoding for proteins involved in cellular protective mechanisms. For example, upregulation of human DNMT1 (encoding a DNA methyltransferase) in tumor cells is important for resistance to exogenous cytotoxic agents (Mishra et al, 2008). Arginine methyltransferases PRMT7 confers resistance to the topoisomerase II inhibitor 9-OH- ellipticine (Gros et al, 2003). Small molecule methyltransferases directly interact with endogenous (e.g. neurotransmitters) as well as exogenous small molecules (e.g. drugs) via biotransformation reactions (Bentley & Chasteen, 2002; Thomas et al, 2004; Wuosmaa & Hager, 1990).

1.2.3 Methylation of exogenous small molecules

The clearing of exogenous chemicals (which typically evade recognition by the immune system) is accomplished by small molecule methyltransferases. Their importance is reflected by the fact that loss-of-function mutations in these detoxifying enzymes may cause toxicity. The critical roles of these enzymes in a survival of living organisms is also supported by the observations that they are evolutionarily conserved - the same toxic substrates are modified by similar eukaryotic and prokaryotic methyltransferases. Here, I will discuss three small molecule methyltransferases that, besides acting on diverse exogenous toxic substrates, are also relevant in human pathological conditions.

1.2.3.1 Arsenic methyltransferase (AS3MT)

Chronic exposure to arsenic, a naturally abundant contaminant in water, soil, and air, causes gastrointestinal distress and is associated with the development of cancer (skin, bladder and lung). Detoxification of this metalloid by methylation is a crucial step in cellular homeostasis (Fendorf et al, 2010; Oremland & Stolz, 2003; Stolz et al, 2006). It is, therefore, not surprising that arsenic methyltransferase AS3MT (CYT19) is widely

conserved among bacterial species (arsM) and many organisms ranging from simple nonchordates (e.g. sea squirts) to humans (Thomas et al, 2007; Thomas et al, 2010). Fungi, however, use alternative resistance mechanisms which includes efflux of arsenic via Acr3p and Fps1p transporters, that are also employed in higher eukaryotes to detoxify arsenic (Wysocki & Tamas, 2010).

Besides being infamous as a natural contaminant, arsenic was used as homicidal/suicidal poison, and medicinally by Greeks and Chinese over 2,400 years ago (Hughes et al, 2011). One of the most famous arsenic-based compounds, Salvarsan, or the “magic bullet” designed by the founder of chemotherapy Paul Ehrlich in 1910 was routinely used to treat syphilis (Kaufmann, 2008). Currently arsenic trioxide (As2O3) known under a trade name Trisenox is used for the treatment of certain leukemias (Zhu et al, 2002). Therefore, the capacity to methylate arsenic by AS3MT may be a critical factor in response to these compounds. Single nucleotide polymorphisms (SNPs) that are found in AS3MT may be indicators for predicting arsenic response phenotype (Agusa et al, 2011; Wood et al, 2006). For example, a study performed in cultured primary human hepatocytes demonstrated that single nucleotide polymorphism (SNP) M287T is associated with altered methylation of arsenic (Drobna et al, 2004).

1.2.3.2 Thiopurine S-methyltransferase (TPMT)

Thiopurine S-methyltransferase (TPMT) is a cytosolic drug-metabolizing enzyme that conjugates a methyl group to S-containing cytotoxic thiopurines (Krynetski & Evans, 2003; Wang & Weinshilboum, 2006). Thiopurines including 6-mercaptopurine (6-MP) and the prodrug azathiopurine (AZA), thioguanine (6-TG) are a class of drugs used for over 35 years as immunosuppressants for transplantations, for the treatment of pediatric acute lymphoblastic leukemia (ALL), and as steroid-sparing agents for autoimmune and chronic inflammatory diseases (Karran & Attard, 2008). By methylating 6- mercaptopurine and one of its metabolites, thioinosine monophosphate (an inhibitor of de novo purine synthesis) TPMT inactivates these cytotoxic compounds. Based on the fact that up to 28% of patients withdraw from AZA or 6-MP treatment due to adverse drug reactions ranging from mild nausea to life-threatening drug-induced toxicity (Budhiraja & Popovtzer, 2011), the level of TPMT’s activity is an important factor in individual

variations in response to thiopurines. The most common variant alleles that modulate enzyme activity or result in a loss-of-function phenotypes are TPMT*3A (G460A and A719G) and TPMT*3C (A719G) and the rare TPMT 3*B (G460A). Some of these variants are responsible for life-threatening adverse drug reactions in the presence of standard doses of thiopurines (Wang & Weinshilboum, 2006). For example, on a molecular level the presence of the TPMT*3A allele results in structural disruption, misfolding, and aggresome formation in the presence of a proteosome inhibitor (Wang et al, 2005). Indeed, a study with S. cerevisiae demonstrated the requirement of ubiquitin- dependent protein degradation, and vesicle trafficking, suggesting that autophagy is an important mechanism in TPMT degradation (Li et al, 2008). Although TPMT deficiency does not manifest unless individuals are challenged with thiopurines, genetic variants of TPMT have been associated with ototoxicity or cisplatin-induced hearing loss in children (Ross et al, 2009). Interestingly, another small molecule methyltransferase, catechol-O- methyltransferase, COMT is also essential for auditory function in mammals (Ahmed et al, 2008; Du et al, 2008). Also of note, human TPMT has been reported to methylate selenium compounds (Deininger et al, 1994), and catalyze the S-methylation of aromatic and heterocyclic sulfhydryl compounds, indicating that it is active against diverse organic S-containing small molecules.

Similar to arsenic methyltransferases, thiopurine methyltransferases are conserved among kingdoms. Resistance to the bactericidal agent oxide mineral tellurite in the pea blight pathogen Pseudomonas syringae and Escherichia coli is mediated by methyltransferases with high levels of sequence similarity to human TPMT (Cournoyer et al, 1998; Liu et al, 2000a). Similar to the human TPMT, the bacterial TPMT mediates a methylation reaction of selenite and methyl-selenocysteine to dimethylselenide and dimethyldiselenide (Ranjard et al, 2002). Despite their importance in response to clinically relevant compounds and exogenous chemicals, the endogenous substrates for these small molecule methyltransferases have not been determined yet (Peng et al, 2008).

1.2.3.3 Glycine N-methyltransferase (GNMT)

Unlike other small molecule methyltransferases, GNMT is an 130-kDa tetrameric protein with catalytic sites present on four identical subunits (Ogawa & Fujioka, 1982).

Interestingly, AdoMet does not bind to the consensus AdoMet-binding site on GNMT, and this enzyme is only weakly inhibited by the general methyltransferase inhibitor SAH (Takata et al, 2003). However, purified rat GNMT is inhibited with high specificity by folate, a source of methyl groups (Cook & Wagner, 1984; Wagner et al, 1985). Furthermore, this enzyme is important in the regulation of levels of SAH and AdoMet (Luka et al, 2009).

The active site of GNMT contains an unusually large number of tyrosine residues buried in the unique S-domain and covered by the N-terminal regions of neighboring subunits (Fu et al, 1996). Because GNMT’s structure resembles a molecular basket, it has been speculated that GNMT captures and detoxifies exogenous water-soluble molecules (Fu et al, 1996; Yen et al, 2009). Indeed, GNMT was demonstrated to act as a receptor for carcinogenic pollutants 4S polycyclic aromatic hydrocarbons (PAH) (Bhat & Bresnick, 1997; Bhat et al, 1997). Although hepatic GNMT is found primarily in the cytoplasm, PAH-bound GNMT was shown to translocate to nuclei and modulate the expression of cytochrome P450 CYP 1A1, involved in oxidation of organic substances and important for drug metabolism, thereby providing an alternative route of PAHs inactivation (Raha et al, 1995; Raha et al, 1994). Another study reported that only enzymatically inactive monomeric forms of GNMT entered rat liver nuclei where they interacted with chromatin (Krupenko & Wagner, 1997). Later, however, the reexamination of the GNMT-4S BAP interaction failed to demonstrate the formation of GNMT monomers and binding to 4S BAP (Ogawa et al, 1997). Despite this controversy, recent studies demonstrated that GNMT interacts with benzo[a]pyrene (BaP), protects cells from BaP-induced DNA- adduct formation (Chen et al, 2004), and alters the expression of genes involved in detoxification pathway following the exposure to BaP (Lee et al, 2006). Furthermore, GNMT interacts with another potent carcinogen aflatoxin B1 (AFB1), and was proposed to prevent AFB1-DNA adduct formation (Yen et al, 2009; Yeo & Wagner, 1994). Noteworthy, the synthesis of these mycotoxins and other diverse cellular metabolites also requires small molecule methyltransferases. For example, two O-methyltransferases OmtB and OmtA in the mold Aspergillus parasiticus are involved in the biosynthesis of aflatoxins (I and II) from the derivatives of sterigmatocystin supplemented to growth media (Yabe et al, 1989).

Considering the important role of GNMT in the detoxification of these environmental carcinogens, long-term exposure to these chemicals and the presence of genetic modifications of GNMT can be associated with the susceptibility to certain cancers. Indeed, a guanine-adenine (GA) dinucleotide short tandem repeat polymorphism (STRP1) within GNMT has been shown to influence the levels of urinary metabolites 1- OHP and 8-OHdG in coke-oven workers exposed to environmental PAH (Chen et al, 2011), and is associated with their susceptibility to hepatocellular carcinoma and prostate cancer (Huang et al, 2007; Tseng et al, 2003). Consistent with this connection, GNMT expression is reduced or completely absent in hepatocellular carcinomas and prostate tumor tissues suggesting that GNMT is a tumor suppressor gene (Chen et al, 2000; Tseng et al, 2003; Wang et al, 2011). The deletion of GNMT in a mouse model leads to tumor formation (Martinez-Chantar et al, 2008). Furthermore, it was observed that the absence of GNMT results in a dramatic increase in the AdoMet/SAH ratio, global DNA and histone hypermethylation in hepatic cells (Lu & Mato, 2012). Mechanistically, GNMT was proposed to be involved in prostate cancer via regulation of apoptosis and interaction with mTOR signaling pathway (Song et al, 2011b; Yen et al, 2012).

Unlike AS3MT and TPMT, the endogenous substrates of GNMT are characterized. GNMT catalyzes the methylation of the smallest amino acid glycine to form sarcosine or N-methylglycine (Blumenstein & Williams, 1963; Heady & Kerr, 1973). Sarcosine has been associated with schizophrenia and depression (Lane et al, 2006). In contradiction to the previous observations that GNMT activity has tumor-suppressing properties, the product of GNMT, sarcosine was proposed to be a major marker for metastatic prostate cancer (Sreekumar et al, 2009). Of note, in plants GNMT plays a role in the formation of glycinebetaine, an important osmolyte produced in response to abiotic stresses (Waditee et al, 2003).

1.2.4 Methylation of endogenous small molecules

Small molecule methyltransferases are also important for modifications of the molecules that, after having served their functions, may be intrinsically toxic to cells. Next, I describe two methyltransferases that are involved in the inactivation of endogenous metabolites in human and yeast.

1.2.4.1 Catechol O-methyltransferase (COMT)

The smallest of all methyltransferases Mg2+-dependent catechol O-methyltransferase, is present ubiquitously in all tissues and exists as an abundant cytosolic (S-COMT) and a membrane-bound (MB-COMT) form (Myohanen & Mannisto, 2010). S-COMT is important in the metabolism of exogenous toxic catechols and hydroxylated metabolites, whereas MB-COMT is required for the O-methylation of endogenous dopaminergic and noradrenergic neurotransmitters (Axelrod & Tomchick, 1958; Huh & Friedhoff, 1979; Mannisto & Kaakkola, 1999; Vidgren et al, 1994). Because COMT regulates dopamine levels, particularly in prefrontal cortex, it is a key factor in prefrontal cortex-dependent cognitive functions, including pain perception, addiction and affective mood. Therefore, COMT is an important therapeutic target in various neurobiological disorders (e.g. schizophrenia, Parkinson’s disease, pre-eclampsia, hearing loss) (Du et al, 2008; Kanasaki et al, 2008; Marsala et al, 2012). The variations in COMT activity are,] in part genetic, characterized by the presence of functional nonsynonymous SNPs (val108met and val158met) (Tunbridge, 2010) and/or synonymous SNPs (his62his and leu136leu) that modulate the enzyme’s expression by influencing mRNA secondary structure that is important for RNA thermodynamic stability (Nackley et al, 2006).

Besides metabolizing endogenous catechols COMT has an important role in the methylation of exogenous catechol-containing xenobiotics, such as flavonoids (quercetin and fisetin), tea polyphenols (Zhu et al, 2000) and carcinogenic PAHs. Interestingly, the rates of the methylation of quercetin and fisetin by porcine and hamster cytosolic COMT were substantially higher than those for endogenous catechols (Zhu et al, 1994). The biotransformation of these molecules by COMT is important because these molecules besides having anticancer properties are also mutagenic in vitro (Rietjens et al, 2005; Zhu et al, 1994). Similarly COMT from Mycobacterium vanbaalenii inactivates environmentally hazardous PAH catechols by O-methylation (Kim et al, 2004). Human recombinant S-COMT also catalyzes the detoxification of structurally diverse PAH o- quinones (Zhang et al, 2011), and the application of a COMT inhibitor enhances ROS formation in mammalian cells in the presence of PAH o-quinones (Park et al, 2008), indicating the importance of the methylation of these carcinogens by COMT. COMT also

methylates tyrphostin, a synthetic small molecule inhibitor of protein-tyrosine kinases (Lipson & Clarke, 2007). COMT’s expression is also dynamically regulated in response to various drugs, environmental and nutritional factors, including astrocytic toxin fluorocitrate, amphetamine, L-dopa, clozapine, haloperidol and folate deficiency (Mattay et al, 2003; Tunbridge, 2010). However, it is not clear if the observed changes in COMT levels have a neuroprotective role in response to these chemicals.

1.2.4.2 Yeast Trans-Aconitate Methyltransferase (TMT1)

In yeast the small molecule O-methyltransferase, TMT1 (YER175C) encodes a trans- aconitate methyltransferase that catalyzes methylation reactions of a TCA cycle intermediate. In E. coli, this methyltransferase is expressed early in stationary phase characterized by a lack of nutrients and cell cycle arrest. Tmt1 catalyzes the methyl esterification reaction of trans-aconitate which is spontaneously formed from the cis- aconitate intermediate of cistrate isomerization (Cai & Clarke, 1999). Trans-aconitate is a potent inhibitor of aconitase, a TCA enzyme involved in conversion of citrate to isocitrate (Cai & Clarke, 1999; Lauble et al, 1994). Although mutants do not show any fitness defects relative to wild type in the standard conditions (Cai & Clarke, 1999), the methylation of trans-aconitate has been proposed to prevent the interference of this toxic intermediate with normal metabolic pathway under stress conditions (e.g. oxidative stress). For example, trans-aconitate accumulates during oxidative stress in Streptomyces coelicolor, attenuating the binding of the trans-aconitate methyltransferase regulator to DNA. This followed by the increased expression of trans-aconitate methyltransferase and subsequent alleviation of the consequences of aconitase inactivation (Huang & Grove, 2013). In yeast, trans-aconitate methyltransferase catalyzes the methylation of trans- aconitate on a carboxyl group that is different from the corresponding methyltransferases in other organisms (Cai et al, 2001b), and has a higher affinity towards 3- isopropylmalate, an intermediate of a biosynthetic branch of the leucine pathway, suggesting that yeast Tmt1 is involved in two distinct biological processes (Katz et al, 2004). Indeed, yeast Tmt1 is substantially overexpressed during amino acid starvation (particularly leucine) and the methylated 3-isopropylmalate is secreted to the media

where it signals baker’s yeast to switch to invasive growth in response to the starvation (Dumlao et al, 2008).

Taken together, these findings demonstrate that “dispensable” methyltransferases that lack obvious phenotypes in standard conditions are “essential” when a cell is exposed to chemical perturbation or environmental stresses. In other words, these observations highlight the importance of small molecule methyltransferases in cellular survival during chemical perturbations by detoxification of environmental carcinogens and clearing endogenous molecules that are toxic to a normal cellular physiology. Additionally, the relevance of small molecule methyltransferases in pathophysiological conditions, such as cancer or neurobiological diseases justifies further investigation of these enzymes.

1.3 Non-canonical roles of methyltransferases

Large-scale genetic interaction studies reveal that there is tremendous cross-talk between biological processes (Costanzo et al, 2010) suggesting the possibility of functional interplay between the distinct methyltransferases. Furthermore, considering the biological complexity and the interdependence of cellular pathways, methyltransferases likely perform multiple functions. This premise is supported by the findings that at least half of yeast methyltransferases have additional RNA-binding domains and several possess lipid- binding motifs (Gallego et al, 2010; Wlodarski et al, 2011). Furthermore, additional methyltransferases without any known lipid or RNA-binding motifs bind to these molecules, indicating that a large diversity of functions for these enzymes remains to be discovered (Tsvetanova et al, 2010). Indeed, the coupling of different biological processes has been reported for several methyltransferases. For example, CARM1 is a protein arginine methyltransferase that methylates histone H3 and histone acetyltransferases p160 resulting in co-activation of nuclear receptor-directed transcription and mRNA splicing (Cheng et al, 2007). Recently, it was demonstrated that CARM1 methylates a single arginine (R1810) on the carboxy-terminal domain (CTD) of RNA polymerase II (mRNA, snRNA and microRNA) which facilitates the expression of small nuclear and nucleolar RNAs (Sims et al, 2011). In the filamentous fungus Neurospora crassa, DNA methylation is directed by a histone H3 methyltransferase (Tamaru & Selker, 2001). Similarly, the symmetric dimethylation of histone H4R3 by the

protein arginine methyltransferase PRMT5 in human beta-globin locus was found to serve as a direct target for binding of the DNA methyltransferase DNMT3A, providing a direct order of gene silencing events (Zhao et al, 2009a). Methylation of microRNAs and siRNAs by the conserved RNA methyltransferase Hen1 is crucial step in RNA biogenesis (Horwich et al, 2007; Yu et al, 2005). Since histone methyltransferases are targeted to heterochromatin by siRNAs, it links methylation of RNAs to epigenetic phenomena (Wassenegger, 2005).

The histone lysine H3K36 methyltransferase Set2 interacts with the activator protein Ino2 that binds to inositol/choline responsive elements (ICRE) required for expression of phospholipid biosynthesis genes (Dettmann et al, 2010). Because the Ino2 protein region contains multiple lysine residues and interacts with the SET domain of Set2, it is possible that the histone methyltransferase Set2 methylates Ino2 activator. Ino2/Ino4 heterodimers regulate the transcription of the genes encoding phospholipid methyltransferases in the CDP-DAG pathway involved in the generation of phosphatidylcholine (Carman & Han, 2011). In addition to being an abundant structural component of membranes, phosphatidylcholine activates nuclear phosphatidylcholine-dependent phospholipase C, a major source of diacylglycerol (DAG) and phosphatidate (PA), which are implicated in the transduction of intranuclear signals and the transcriptional regulation of phospholipid biosynthesis genes. Furthermore, diverse phospholipids, including phosphatidylcholine, are incorporated into nuclear chromatin, influencing chromatin structure and interfering with nucleosome function (Albi et al, 1994; Albi & Viola Magni, 2004). Because the content of phospholipids changes during development (Albi et al, 1991), this observation also opens the possibility that phospholipid methyltransferases may regulate transcription in a global manner.

Fungal lipid methyltransferases are important for the response to antifungal drugs and for the virulence of fungal pathogens. For example, fungal-specific sterol methyltransferases are critical for sterol biosynthesis pathways that are a major target for antifungal drugs and mutations in these genes confer drug resistance (Anderson et al, 2003; Nes et al, 2009). Glucosylceramide, another lipid abundantly present in cellular membranes, is also a source of bioactive ceramides. Fungal glucosylceramides C9-methyltransferases have

been recently demonstrated to play a role in fungal pathogenesis (Noble et al, 2010; Ramamoorthy et al, 2009), but these methyltransferases are not important in resistance to antifungal defensins and membrane disrupting agents (Oura & Kajiwara, 2010).

Taken together, these observations demonstrate that the functions of methyltransferases are broad, and that the consequences of abnormal methylation reactions may manifest in different phenotypes. Applying current advances in molecular biology, genomics and computational biology will have a profound effect on our understanding of the complexity of methyltransferase function.

1.4 Methods to explore methyltransferases

The importance of methyltransferases in many biological processes and their strong relevance in pathological states and in response to chemical and environmental perturbations justifies the intense interest in the investigation of these enzymes. Many techniques have been developed to interrogate this family of enzymes. Here, I review representative approaches that fall in three broad categories: 1) in silico, 2) in vitro and 3) in vivo.

1.4.1 In silico computational approaches

Discovery of novel methyltransferases via bioinformatic methods relies on the knowledge of previously described methyltransferases. In this way, known methyltransferase sequences are used as probes against protein databases with programs, such as PSI- BLAST, RPS-BLAST, a method that detects distant homology, Meta-BASIC, etc. Early application of these tools enabled the discovery of the conserved protein-arginine methyltransferase Rmt1 or Hmt1 (Gary et al, 1996). However, low levels of sequence similarity and variations in spacing among the motifs complicate the identification of novel methyltransferases. Furthermore, the substantial sequence similarity does not always reveal the identity of substrates for the putative methyltransferase. For example, a N-methyltransferase that catalyzes the methylation of an anticancer alkaloid is highly similar to the γ-tocopherol C-methyltransferase of vitamin E biosynthesis, but these enzymes methylate two structurally different substrates (Liscombe et al, 2010). In a recent chemogenetic analysis of human protein methyltransferases, METTL11A was

identified and experimentally confirmed as a histone methyltransferase (Richon et al, 2011; Webb et al, 2010), despite its having been predicted to act on RNA substrates. Similarly, the Dnmt2-like methyltransferase (which is highly similar to authentic DNA cytosine methyltransferases based on its sequence and structure homology) exhibits a catalytic activity towards tRNA and not DNA molecules (Goll et al, 2006).

Initially, a motif-based search that utilizes position-based amino acid frequency profiles known as Motif Alignment and Search Tool was successfully applied to Class I methyltransferases (Kagan & Clarke, 1994; Katz et al, 2003). However, the alignment of sequences through additional structural information (either predicted or solved) appeared to be more effective in the identification of putative methyltransferases (Ansari et al, 2008; Petrossian & Clarke, 2009b; Petrossian & Clarke, 2011). For example, the Multiple Motif Scanning (MMS) program recognizes all four motifs and the spacing between them by utilizing advanced Hidden Markov model (HMM) profile algorithm (HHpred) that aligns methyltransferases based on both primary and predicted secondary structures (Petrossian & Clarke, 2009b). This is followed by ranking yeast proteins based on their similarity to the reference methyltransferase motif profiles, and subsequent grouping of methyltransferases based on their substrate specificity. The successful application of this approach underlines the importance of the structural information for these enzymes, advancing our understanding of catalytic mechanisms and substrate specificity.

In another recent study, the authors integrated sequence, structural, biochemical and genetic information to compile a comprehensive classification of substrate specificity for yeast methyltransferome. The authors applied the distant homology sequence detection tool (Meta-BASIC) and fold recognition (3D-Jury) to identify methyltransferases in the yeast genome, and these data, along with data on isoelectric points, cellular localization and gene expression within the yeast metabolic cycle was used to predict substrate specificity (Wlodarski et al, 2011).

1.4.2 In vitro approaches

Despite the predictive power and the rich information generated from computational methods, direct biochemical approaches are an invaluable tool to identify and

characterize methyltransferases. These include assays of methylation activity and analysis of methylated substrates.

1.4.2.1 Methylation activity assays

Methyltransferases can be covalently linked to [Me-3H]-AdoMet with UV light treatment and the binding of this probe can be used as a proxy for identification of novel methyltransferases (Subbaramaiah & Simms, 1992). However, this approach also identifies non-methyltransferase enzymes that catalyze reactions with the cofactor AdoMet (e.g. spermidine synthase), thus, assays assessing actual enzymatic reactions are more definitive.

The slow enzymatic turnover of methyltransferases allows methylation activity to be determined by detecting the accumulation of the methyl donor product SAH during a reaction. SAH amounts can be analyzed directly by mass spectrometry (Lakowski & Frankel, 2010) or by anti-SAH antibody-based competitive assay, where SAH competes for binding to anti-SAH antibody with SAH-BSA conjugate in ELISA or SAH- fluorescein conjugate in fluorescence polarization assays (Capdevila et al, 2007; Graves et al, 2008). Enzyme-coupled colorimetric assays are an indirect method that is suitable for measuring the kinetics of methyltransferases (Ibanez et al, 2010; Luo, 2012). The method relies on quantification of SAH’s derivatives (e.g. adenosine and homocysteine) produced by the coupling enzymes (e.g. SAH hydrolase) and measures changes in emission following their conjugation to a sensitive fluorophore. Caveats with these assays that result in inaccurate measurements include the intrinsic inhibition of methyltransferases by SAH accumulation and a spontaneous breakdown of AdoMet to SAH.

1.4.2.2 Biochemical identification of methylated substrates

Alternatively, AdoMet analogs in which the methyl groups are replaced by extended carbon chains (or S-substituted SAH analogs), such as ketone or other groups amenable to direct bio-orthogonal labeling can serve as probes to characterize enzymatic activity and to identify substrates. These methyl group substitutes are readily transferred

enzymatically to substrate molecules allowing functional characterization of acceptor molecules (Dalhoff et al, 2006; Lee et al, 2010a).

Methylation reactions can be also determined by assessing substrates that are methylated with radioactively labeled methyl group derived from either [Me-3H]- or [Me-14C]- AdoMet. The unreacted AdoMet is separated from the methylated substrates by filtration through charged nylon or phosphocellulose filter paper, and the radioactivity is quantified by autoradiography or liquid scintillation counting (Suh-Lailam & Hevel, 2010). Alternatively, the methylated substrates (mono-/di-/trimethylation) can be detected with antibodies that recognize specific methylated epitopes (Barski et al, 2007). Although this method is not quantitative, it is widely employed in Western blot, ChIP, ChIP-on-chip and ChIP-seq analyses.

Mass spectrometry approaches typically rely on the detection of corresponding mass shifts (e.g. +14 Da), and are best suited for small peptides or digested protein samples of high quality. The shotgun MS and tandem MS (MS/MS) of digested samples are also used to map the methylation sites of truncated or site-directed mutated proteins (Carr et al, 2011). Furthermore, MS analysis can be used to distinguish the type of methylation on amino acids (monomethylation, asymmetric dimethylation and symmetric dimethylation) by assessing acid-hydrolyzed radioactively labeled substrate samples (Zou & Wang, 2005). This method can be used for systematic analysis of in vivo dynamics of multiple histone methylations (Zee et al, 2010) and also identifies other post-translational modifications on a single target. The merit for the majority of these approaches is that they can be adapted to a high-throughput format that is suitable for large-scale screening of methyltransferase inhibitors or identification of substrates.

1.4.2.3 Peptide and protein microarrays

Peptide/protein microarrays are a promising technology for substrate identification. Synthetic peptides that are prepared through solid-phase peptide synthesis and are arrayed in well-defined structures on cellulose membranes have been used to study the sequence specificity of protein methyltransferases (Rathert et al, 2008a; Wooderchak et al, 2008). For example, a library of peptides was generated by systematically replacing each amino

acid in the sequences of known substrates (a focused peptide library) (Rathert et al, 2008c). For the assay, peptides immobilized onto functionalized cellulose membranes are incubated with a recombinant methyltransferase and radiolabeled AdoMet, followed by autoradiography to detect hot spots or incorporations. The randomized peptide library comprised 1.4x106 unique peptide sequences functionalized with a Cl-acetamidine moiety that covalently interacts with protein methyltransferases. For protein arginine methyltransferase PRMT1, this approach enabled the identification of multiple distinct hits as potential targets (Bicker et al, 2011). Furthermore, the analysis of peptide methylation allows systematic study of the effects of residues adjacent to the target residue and post-translational modifications on methylation efficiency (Chin et al, 2005). Despite the power of this technique, the relevance of the full-length protein or protein complexes as targets and their post-translational modifications should be considered (An et al, 2004; Daujat et al, 2002; Li et al, 2009). High-density functionalized glass arrays, such as the commercially available ProtoArray glass slide coated with 9,500 proteins, have been successful in the identification of protein methyltransferase substrates (Levy et al, 2011). Nonetheless, all array-based approaches carry the caveats of any in vitro assay. For example, in vitro assays fail to reflect cellular context and altered activity may be detected resulting from solid-phase immobilization. Methylation may depend on specific environment and/or the association of methyltransferase with other proteins, therefore, analysis of methyltransferases in their native contexts is desirable. This can be achieved by incubating recombinant methyltransferases with whole cell extracts and radiolabeled AdoMet (Frankel et al, 2002; Lee et al, 2010b).

Generally these approaches rely on the knowledge of a substrate or at least the class of molecule. Unlike other protein-modifying enzymes AdoMet-dependent methyltransferases act on a wide range of substrates, which limits the predictive power of candidate approaches. Another relevant challenge in the characterization of methyltransferases biochemically is that that critical interactions with components involved directly in the pathways are missed in biochemically defined assays. Therefore, the functional relevance for the majority of methyltransferases remains unknown.

1.4.3 In vivo functional genomic methods

The strategies described above have been used successfully to predict (bioinformatics) and to detect (biochemistry) methylation activity, yet these techniques have a limited ability to characterize functional relationships among methyltransferases and the consequences of methylation events within a cell. This is important, because understanding functional relationships among genetic factors may allow us to gain an insight in human complex diseases. However, the difficulty arises from the observed abundance of genetic variants and the complexity of their interactions in the human genome. The unicellular model organism baker’s yeast S. cerevisiae is well-suited for the study of gene function due to its fast growth, its amenability to straightforward genetics, high degree of conservation of core biological pathways with higher eukaryotes and the presence of inbred population (Botstein et al, 1997; Botstein & Fink, 2011; Estruch, 2000). For example, it is estimated that a large fraction of methyltransferases with similar substrates are shared between human and yeast (Petrossian & Clarke, 2011). Furthermore, because baker’s yeast is related to human fungal pathogen Candida albicans, the knowledge derived from studying this model organism serves as a strong foundation to the characterization of pathogenic genes (Dujon et al, 2004).

The availability of large-scale mutant strain collections (e.g. deletion-, overexpression- mutant collections) and powerful high-throughput technologies make the budding yeast an invaluable model to investigate this class of enzymes on proteome- and genome-wide levels (Giaever et al, 2002; Mnaimneh et al, 2004; Sopko et al, 2006). Genome-wide analyses are unbiased, and typically do not rely on prior knowledge of gene function, but rather suggest functional connections between interactors (either direct or indirect), revealing the architecture of cellular pathways and uncovering the nature of biological robustness (Boone et al, 2007). The combination of these genomic tools enabled systematic exploration of gene function in a genome-wide manner (Costanzo et al, 2010; Tong et al, 2004) and can be applied to large protein classes, such as kinases, phosphatases and acetylases (Breitkreutz et al, 2010; Ficarro et al, 2002; Kaluarachchi Duffy et al, 2012; Kurdistani & Grunstein, 2003; Mok et al, 2010; Sharifpoor et al, 2012; Sopko et al, 2006). Such systematic genetic screens in yeast enhance our understanding

of the global relationships among genes and provide a powerful counterpart to computational and biochemical efforts to studying cell biology.

One of the approaches to study gene function and/or functional relationships among genes is to analyze the interactions among genes. Genetic interactions are characterized by unexpected or surprising phenotypes of double-deletion mutants when compared to predicted combined effects (either the sum or the product) of the corresponding single mutant phenotypes (Mani et al, 2008). Genetic interactions range from synthetic sickness/lethality (negative or aggravating) to synthetic suppression (positive or alleviating). Negative genetic interactions occur when the phenotypic effect of the absence of one gene is enhanced by the deletion of another gene, and typically represent the genes operating in separate but buffering or parallel pathways that impinge on the same essential biological function. In contrast, positive genetic interactions occur when the phenotypic effect of the absence of one gene is concealed by the deletion of another, and are usually associated with members of the same pathway (e.g. a negative regulator and its downstream effectors) or within the same non-essential protein complex.

The Synthetic Genetic Array (SGA) technology (Baryshnikova et al, 2010a; Tong et al, 2004) and diploid-based synthetic lethality analysis on microarrays (dSLAM) (Pan et al, 2006) have been used to detect genetic relationships in yeast on a genome-wide level using double-deletion mutant fitness as a primary phenotype. For example, in the most recent SGA effort over 5.4 million gene pairs representing ~30% of yeast genetic interaction network were described (Costanzo et al, 2010). This genetic landscape revealed clusters of genes with similar genetic profiles that were mapped to various biological processes highlighting the inter-connectivity of various functions and enabling characterization of unknown genes.

An extension of the SGA strategy, the Epistatic MiniArray Profile (E-MAP) approach relies on measuring a range of epistatic relationships within a defined subset of genes (Schuldiner et al, 2005), and it has been successfully applied to interrogate genetic interactions among genes implicated in chromosome biology, mitochondria,

phosphorylation-related processes, secretion and RNA processing (Aguilar et al, 2010; Collins et al, 2007; Fiedler et al, 2009; Hoppins et al, 2011; Wilmes et al, 2008).

The genomic approaches uncover genetic relationships among genes, and while they do not reveal methylation substrates directly, the application of “guilt-by-association” logic can reveal novel functions for unknown methyltransferases and identify methyltransferases acting on the same substrate. Importantly, despite the fact that there is a substantial rewiring of genetic interactions observed among species, many functional modules are conserved as well as the properties of the networks, suggesting that the lessons we learn in a single-cell fungus can be translated to metazoans (Dixon et al, 2008; Roguev et al, 2008).

1.4.4 Chemical genetics

Despite significant advances in technology and informatics analyses, investigation of gene functions remains challenging, due to the wide-spread redundancy among components of cellular pathways which manifests as gene dispensability in standard growth conditions. Such functional robustness in the face of genetic and environmental perturbations is a widely observed phenomenon in biological systems. This robustness ensures cell survival, yet represents a significant challenge for experimentalists aiming to understand gene function. In fact, most yeast genes (~80%) are not essential for growth in standard laboratory conditions, and only a small fraction of double-deletion mutants in nonessential genes demonstrate fitness defects (Costanzo et al, 2010; Giaever et al, 2002; Tong et al, 2004; Wagner, 2000).

These observations suggest that additional perturbations, either genetic (e.g. higher order mutant combinations, conditional alleles) or environmental, are needed to reveal the functions of methyltransferases to capture their dynamic genetic interactions. Indeed, a large-scale perturbation study showed that the sensitization of mutants with chemical and/or environmental stresses reveals that for 97% of the genes in the yeast genome a fitness phenotype that would not otherwise have been detected (Hillenmeyer et al, 2008). Chemogenomic approaches have proved effective tools for the unbiased characterization of gene function and biological processes that are otherwise overlooked in laboratory

conditions. Chemical genomics combines the advantages of small molecules (as rapid, reversible inhibitors of function) to modify biological processes by affecting the activity of a protein or a pathway, thereby overcoming the limitations of simple genetic analysis in the face of redundancy. Studies from our lab and others have demonstrated that chemogenomic profiling of the Saccharomyces cerevisiae yeast deletion collection (Giaever, 2003; Giaever et al, 2002) is a powerful approach for the identification and characterization of genes required for growth in the presence of bioactive compounds (Butcher et al, 2006; Giaever et al, 2002; Giaever et al, 2004; Hillenmeyer et al, 2008; Hoon et al, 2008; Hughes et al, 2000; Kung et al, 2005; Lee et al, 2005; Lum et al, 2004; Parsons et al, 2006). Well-established chemogenomic assays in yeast, such as drug- induced Haploinsufficiency Profiling (HIP), Homozygous Profiling (HOP) and Multicopy Suppression Profiling (MSP) are designed to identify small molecule-gene interactions. For example, the HIP assay has been used to detect compounds that target essential genes, and HOP and MSP are suitable for identification of genetic modifiers of drug resistance (Giaever et al, 2004; Lee et al, 2005; Lum et al, 2004; Parsons et al, 2006).

Because fitnesses of mutants lacking a particular gene correlate with a number of genetic interactions for this gene (Costanzo et al, 2010; Tong et al, 2004), it is expected that the frequency of a gene’s interactions will increase in conditions in which the gene is required. Indeed, a two-fold increase in number of condition-dependent genetic interactions among genes of DNA repair and recombination pathways was revealed in a focused study using the model DNA-damaging agent methyl methanesulfonate (MMS) (St Onge et al, 2007). Similarly, sensitizing kinase mutants by salt stress resulted in a larger spectrum of genetic interactions (Synthetic Dosage Lethality) than under normal growth conditions (Sharifpoor et al, 2012). In a recent study, the differential epistatic mapping (i.e. conditional SGA) was applied to interrogate a substantially larger set of double-deletion mutants under DNA damage, (Bandyopadhyay et al, 2010). This study confirmed that genetic networks are altered dramatically in response to DNA damage. Also it showed that while protein complexes are generally stable, the genetic interactions between protein complexes are rewired in response to this chemical stress.

1.5 Thesis rationale

To advance our understanding of cellular biology at the level of interacting systems, the characterization of enzymes involved in biomolecule modifications, such as methyltransferases, kinases, phosphatases, acetyltransferases, etc. is essential. Methyltransferases play fundamental roles by modifying all classes of biomolecules, but a comprehensive characterization of the most methyltransferases remains challenging, because most are dispensable in standard growth conditions, and because they have a wide diversity of substrates targeted at different atoms. Low levels of sequence similarity and identity among methyltransferases further confound characterization of this family of enzymes. Finally, their inherent substrate promiscuity and a lack of universal substrates suitable to test their enzymatic activity provide a challenge for candidate-based approaches.

While previous studies have focused on characterizing particular methyltransferases, there are no published large-scale efforts that have investigated functional interactions among methyltransferases in yeast (or those of any other organism) in a systematic manner. Furthermore, there are no comprehensive studies that attempt to understand how methyltransferases mediate the cellular response to a chemical or environmental stress. In my research, I focused my efforts on yeast AdoMet-dependent methyltransferases using chemical biology/genomics approach in combination with molecular assays to dissect the functional roles of methyltransferases in a cell.

In the first part of my thesis, I present the characterization of two putative methyltransferases of previously unknown functions in the baker’s yeast S. cerevisiae and in the human fungal pathogen C. albicans. The exposure to a chemical stress allowed me: 1) to characterize these two putative AdoMet-dependent methyltransferases in the presence of a toxic small molecule; 2) to uncover their biological functions under stress; and 3) to characterize the mode of action of this small molecule.

In the second part of my thesis, I present an extension of my investigation in which I consider all non-essential yeast methyltransferases and examine their functional relationships by interrogating their pairwise genetic interactions in both standard and in

stressed conditions. This investigation revealed novel properties of the yeast methyltransferome and illustrated how the genetic network of yeast methyltransferases is remodeled in response to environmental perturbations.

Chapter 2 Characterizing a putative Saccharomyces cerevisiae methyltransferase in response to chemical stress

This chapter has been published as:

‘A systems biology approach reveals the role of a novel methyltransferase in response to chemical stress and lipid homeostasis’ in PLoS Genetics.

Elena Lissina1,2, Brian Young3, Malene L. Urbanus2,4, Xue Li Guan5,6, Jonathan Lowenson3, Shawn Hoon7, Anastasia Baryshnikova1,2, Isabelle Riezman6, Magali Michaut2, Gary Bader1,2, Howard Riezman6, Leah E. Cowen1, Markus R. Wenk5, Steven G. Clarke3, Guri Giaever1,2,8, and Corey Nislow1,2,4

All experiments were performed by Elena Lissina except Figures 2-5, Figure 2-6B-H (Brian Young3), Figure 2-8C, E, G (Xue Li Guan5,6), Figure 2-9B (Isabelle Riezman6).

1Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada 2Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada 3Department of Chemistry and Biochemistry and the Molecular Biology Institute, University of California, Los Angeles, CA, USA 4Banting and Best Department of Medical Research, University of Toronto, Toronto, ON, Canada 5Department of Biological Sciences, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 6Department of Biochemistry, University of Geneva, Geneva, Switzerland 7Molecular Engineering Lab, Agency for Science, Technology and Research, Singapore 8Department of Pharmacy and Pharmaceutical Sciences, University of Toronto, Toronto, ON, Canada

2 Characterizing a putative Saccharomyces cerevisiae methyltransferase in response to chemical stress 2.1 Introduction

“Every poorly annotated gene is a challenge to our science.” - David Botstein

A combination of chemogenomic assays in S. cerevisiae enabled identification of a novel gene, YHR209W, that was subsequently named CRG1 (Cantharidin Resistance Gene 1), due to its requirement for growth in the presence of cantharidin (Hoon et al, 2008). Specifically, both CRG1 heterozygous and homozygous deletion strains exhibited sensitivity to the drug, and the overexpression of CRG1 conferred resistance to the drug. Nonetheless, Crg1 has been uncharacterized, except for annotation derived from large- scale analyses (Costanzo et al, 2010; Hillenmeyer et al, 2008; Tsvetanova et al, 2010).

Based on its primary sequence, Crg1 is predicted to encode a Class I AdoMet-dependent methyltransferase (Niewmierzycka & Clarke, 1999). Crg1 shares close sequence similarity with trans-aconitate methyltransferase Tmt1 (BLAST-P expect value 2x10-31 and 3x10-34 for the full length proteins and the methyltransferase domains, respectively). Tmt1 is known to modify and detoxify small molecules by methylation (Chapter 1.2.3.2) (Cai et al, 2001a; Dumlao et al, 2008; Katz et al, 2004). The Clarke group demonstrated previously that Crg1 does not likely possess Tmt1 methyltransferase activity towards trans-aconitate, 3-isopropylmalate, and isopropylmaleate, indicating that these closely related proteins have divergent substrates (neo-function) (Cai et al, 2001a; Dumlao et al, 2008). Bioinformatics analysis has shown, however, that Crg1 clusters with a family of eight methyltransferases based on their methyl-accepting substrate specificity, including Tmt1, the lipid methyltransferases (Coq3, Coq5, and Erg6), and a tRNA methyltransferase, Trm9 (Petrossian & Clarke, 2009b). All of these proteins methylate carboxylic acids present in small molecules to form methyl esters, suggesting that Crg1 might have a similar biochemical activity and catalyze the formation of a methyl ester.

CRG1 is a gene dose-dependent interactor of cantharidin. Cantharidin, a natural product produced by Chinese blister beetles of the Meloidae family of Coleoptera, has been used in Traditional Chinese Medicine for the treatment of a variety of cancers (Wang, 1989). Cantharidin has potent anticancer activity characterized by cell cycle arrest in G2/M phase, apoptosis, and DNA damage, presumably as a result of the generation of reactive oxygen species (Efferth et al, 2005; Huh et al, 2004; Laidley et al, 1997; Li et al, 2010a; Moed et al, 2001; Sakoff et al, 2002; Wang, 1989), yet its use is limited due to renal and mucous membrane toxicity. Although the activity of cantharidin is usually attributed to its high affinity towards Type 1 and 2A serine/threonine protein phosphatases (Honkanen, 1993; Li & Casida, 1992), several studies suggested that cantharidin has additional cellular targets. Specifically, cantharidin has been reported to stimulate xanthine oxidase activity and to inhibit N-acyltransferase and cAMP phosphodiesterase in liver cells, suggesting its complex mode of action (Tsauer et al, 1997; Wang et al, 2000; Wu et al, 2001). Using the data derived from HIP and HOP genome-wide assays (Chapter 1.4.4) (Hillenmeyer et al, 2008), we discovered that a surprisingly large number of methyltransferase deletion mutants are sensitive to cantharidin, suggesting that, as a class, these enzymes may interact directly or indirectly with cantharidin and participate in the response to cantharidin stress. Notably, among these methyltransferases only the overexpression of CRG1 is able to confer resistance to cantharidin, suggesting that Crg1 may be the only methyltransferase that is required for efficient cantharidin detoxification.

In this chapter, I present a study where I employed chemical genomics tools combined with conventional biological techniques to further explore the function of Crg1 and the mechanism of cantharidin cytotoxicity. I report here that Crg1 methylates cantharidin in vitro, establishing this putative gene as a novel small molecule methyltransferase. To reveal biological processes maintained by Crg1 upon exposure to cantharidin, I present the chemogenomic identification of cantharidin-specific CRG1 genetic interactors, and the analysis of the lipid profile of mutants grown in the presence of cantharidin. These results demonstrate that cantharidin resistance involves Crg1-dependent maintenance of lipid homeostasis.

2.2 Results

2.2.1 CRG1 is required for resistance to cantharidin

To confirm that CRG1 levels are important in response to cantharidin (Hoon et al, 2008), I measured the growth of three strains 1) wild-type diploid strain BY4743, 2) a crg1∆/∆ homozygous deletion strain, and 3) a crg1∆/∆ homozygous deletion strain overexpressing CRG1 (2µ plasmid) as a function of cantharidin concentration. I observed that the gene dosage of the putative AdoMet-dependent methyltransferase CRG1 correlated with the sensitivity/resistance of these strains to cantharidin (Figure 2-1A). In agreement with this gene-dose dependent effect, crg1Δ/CRG1 heterozygous mutants grew worse than the wild-type strain, but better than a crg1∆/∆ homozygous mutant in the presence of cantharidin (500 µM) (Figure 2-1B). I also found that cantharidin is more potent against cells grown in synthetically defined medium (SD) than in YPD medium (5 µM and 250

µM, IC20 for wild-type in SD and YPD, respectively; Figure 2-1C). The observed differential drug sensitivity in defined media and rich YPD media is a common phenomenon in our drug screens (unpublished data). I also tested structural analogues of cantharidin, including cantharidic acid and norcantharidin, and found that these compounds produced a similar gene-dose dependent response in crg1 mutants (Figure 2- 1D).

Figure 2-1. Crg1 is required for resistance to cantharidin and its analogues.

(A) Dose-response curves for wt, crg1Δ/Δ and CRG1-overexpressing crg1Δ/Δ mutants grown in the presence of various concentrations of cantharidin in YPD. Dose-response curves were obtained by plotting OD600 at saturation point vs. tested drug concentrations. Values are means of three independent replicates, and error bars represent standard deviation. (B) CRG1 gene dose is important for cantharidin tolerance. Wt, crg1Δ/CRG1 heterozygous, crg1Δ/Δ homozygous and CRG1-overexpressing crg1Δ/Δ mutants were assessed in the presence of cantharidin in YPD. Growth curves were obtained by plotting OD600 vs. time at the tested concentrations of cantharidin. At least three independent replicates were analyzed and the representative growth curves are shown. (C) Cantharidin is more potent in SD media than in YPD (~167 fold). Wt, crg1Δ/Δ and CRG1-overexpressing crg1Δ/Δ mutants were assessed in the presence of cantharidin in SD medium. (D) CRG1 is important for resistance to cantharidin analogues, cantharidic acid and norcantharidin. Growth for wt, crg1Δ/Δ mutant and crg1Δ/Δ cells overexpressing CRG1 were assessed in the presence of cantharidin analogues in YPD. Values are means of three independent replicates, and error bars represent standard deviation.

2.2.2 A functional Crg1 methyltransferase domain is required for resistance to cantharidin

Because CRG1 is annotated (based on its amino acid sequence) as a putative AdoMet- dependent methyltransferase (Niewmierzycka & Clarke, 1999), I next tested whether its methyltransferase domain is required for cantharidin tolerance by mutating amino acids (D44A, D67A, E105A-D108A) within the conserved motifs (Figure 2-2A). These amino acids have previously been shown to be critical for activity of other methyltransferases (Liu et al, 2000b). Overexpression of these crg1 site-specific mutants in a crg1∆/∆ strain failed to confer cantharidin resistance, while, in contrast, the mutation of a non-conserved residue (G96A) in the methyltransferase domain allowed resistance equivalent to wild- type CRG1 (Figure 2-2A). The observed decrease in resistance to cantharidin was not due to reduced expression of the mutated Crg1 proteins (Figure 2-2B), suggesting that the methyltransferase domain of Crg1 is both functional and important for cellular survival in the presence of the drug, and not the result of a simple sequestering of cantharidin.

Figure 2-2. A functional Crg1 methyltransferase domain is required for resistance to cantharidin.

(A) Site-specific mutations in conserved residues of methyltransferase domain reduce cantharidin tolerance. Mutated CRG1 was cloned under GAL1 promoter and transformed into crg1∆/∆ cells. The transformants were grown in SD-ura with raffinose (2%) to mid-exponential phase, induced with galactose (2%) and treated with cantharidin (6 µM). (B) The levels of Crg1 proteins were not altered by mutations in its methyltransferase domain. Cells were collected after induction with galactose (2%) for 3 hours. The cell lysates were analyzed by western blotting with anti-TAP antibody. Tubulin was used as an internal loading control.

2.2.3 Analysis of CRG1 transcript in response to cantharidin

Because our data suggested that yeast responds to cantharidin in a CRG1 dose-dependent manner, I next tested whether the transcription of CRG1 is induced in the presence of the drug. qRT-PCR analysis showed that the relative abundance of CRG1 transcripts increased ~50-fold in the wild-type strain after 60 min of the drug treatment (250 µM) compared to the DMSO control (P-value <0.02; Figure 2-3A). Importantly, a gene-dose dependent effect in the response to cantharidin was also observed for CRG1 transcript levels in crg1Δ/CRG1 heterozygous and CRG1-overexpressing mutants (Figure 2-3B). In agreement with the qRT-PCR data, I also observed induction of Crg1 at the protein level. I GFP-tagged Crg1, and detected that Crg1 accumulated to high levels (restricted to the cytoplasm) following 1 hour of cantharidin treatment (Figure 2-3C).

Given the known high affinity of cantharidin for Type 2A protein phosphatases (PP2A) and to a lesser degree for Type 1 (PP1) (Honkanen, 1993; Li & Casida, 1992), I tested if CRG1 induction was mediated by chemical inhibition of protein phosphatase function. I phenocopied cantharidin treatment using a panel of protein phosphatase homozygous deletion strains. Consistent with the results of chemical inhibition of protein phosphatases with cantharidin, I found that the homozygous deletion strains sit4∆/∆ (PP2A), ptc1∆/∆ (PP2C) and the heterozygous deletion strain glc7∆/GLC7 (PP1) also resulted in transcriptional upregulation of CRG1 in the absence of cantharidin (Figure 2-3D). It is important to note that perturbation of these protein phosphatases accounted for only ~20% of the transcript induction observed followed by cantharidin treatment. Furthermore, the treatment with calyculin A, a structurally distinct PP2 and PP1 inhibitor (Ishihara et al, 1989), known to interact with the yeast PP1 GLC7 (Hoon et al, 2008), resulted in an increase of CRG1 transcript level to a similar degree as in glc7∆/GLC7 mutant (~2.5 fold; Figure 2-3E). This observation opens up the possibility that cantharidin acts independently of this PPase. This hypothesis is also supported by our observation that overexpression of GLC7 confers resistance to calyculin A, but not to cantharidin (Hoon et al, 2008). These results also suggest that these protein phosphatases are likely to be negative regulators of the cellular pathway regulating CRG1 induction.

Figure 2-3. Analysis of CRG1 transcript in response to cantharidin.

(A) Cantharidin induces CRG1 transcription. Wt cells grown to mid-exponential phase were incubated with or without cantharidin (250 µM). For each time point, total RNA was extracted, cDNA synthesized and the relative abundance of CRG1 transcript was analyzed by qRT-PCR. Data are means of at least three independent experimental replicates, and error bars are standard deviation. (B) Cantharidin induces CRG1 transcription in a gene-dose dependent manner. Wt, crg1∆/CRG1 heterozygous, crg1∆/∆ homozygous deletion mutants and CRG1-overexpressing crg1∆/∆ mutant grown to mid-exponential phase were incubated with or without cantharidin (250 µM) for 1 hour. Total RNA was extracted, cDNA synthesized and the relative abundance of CRG1 transcript was analyzed by qRT-PCR. Data are means of at least three independent experimental replicates, and error bars are standard deviation. (C) GFP-tagged Crg1 is localized to the cytosol after 1hr treatment with cantharidin (low fluorescence medium, 4 µM). Bar, 2.5 µm. (D) Genetic reduction in protein phosphatases results in the induction of CRG1 transcript levels. Wt, glc7∆/GLC7 heterozygous, sit4∆/∆, ptc1∆/∆ mutants were grown to mid-exponential phase. (E) Chemical treatment with protein phosphatase inhibitor calyculin A results in the transcriptional induction of CRG1. Wt cells grown to mid-exponential phase in YPD were treated with calyculin A (2 µM) for 30, 60 and 120 min. (F) CRG1 is a stress-responsive methyltransferase. Expression profile of CRG1 was compared with other genes during 174 diverse environmental stresses (Gasch, 2002). Expression profiles of CRG1, ACT1 and SSE2 are shown.

And would be interesting to investigate if other methyltransferases are also upregulated by the treatment upon exposure to PP inhibitors as a defense response.

I also observed that cantharidin-induced transcription of CRG1 follows a temporal pattern characteristic of diverse environmental stress responses, following a peak at 60 min of treatment the transcript levels began to decrease at 120 min (~40 fold, P-value <0.01; Figure 2-3A). Indeed, a comprehensive genome-wide analysis of diverse environmental stresses from publicly available expression data (Gasch, 2002; Gasch et al, 2000) revealed that the transcription profile of CRG1 in diverse stress conditions correlates highly (r =0.8) with a well-characterized stress-responsive gene, the heat shock protein SSE2 (Figure 2-3F), suggesting that CRG1 is also transcriptionally activated by other stress conditions in addition to cantharidin.

2.2.4 Analysis of yeast transcriptome upon exposure to cantharidin

To identify other potential cellular factors important for Crg1-mediated cantharidin resistance, I profiled the complete yeast transcriptome using whole-genome tiling microarrays. Transcriptional changes in wild type, crg1∆/∆ deletion and CRG1- overexpressing crg1∆/∆ strains were analyzed after 1 hour of exposure to the drug. To ensure that the transcriptome datasets for the different strains are comparable, the IC20 for wild type (250 µM) was applied to all strains. Even at this high dose, the hypersensitive crg1∆/∆ strain is viable after 1 hour of exposure (Figure 2-4A). When applied for an extended period, this dose is, in fact, inhibitory for growth of crg1∆/∆ strains (Figure 2-

1A). Furthermore, the treatment of crg1∆/∆ strain with a lower dose (30 µM, the IC20 for this mutant) resulted in a quantitative difference in the transcriptome profile rather than in any qualitative differences, suggesting that the transcriptional changes are consistent across a range of concentrations. In particular, this observation was relevant to downregulated genes (Figure 2-4B and 2.4C). It is also worth noting that the expression of most genes was not affected by crg1 deletion. To uncover cantharidin-specific genes in my transcriptome analysis, I eliminated Environmental Stress Response (ESR) genes that are activated by a large number of stresses, such as genes required for vacuole biogenesis, response to stress, ribosome biogenesis, and RNA processing (Gasch et al, 2000). I also eliminated those genes that did not demonstrate at least two-fold difference in the

Figure 2-4. Analysis of yeast transcriptome upon exposure to cantharidin.

(A) Viability of crg1 mutants treated with cantharidin for 1 hour. Wt, crg1∆/CRG1 heterozygous, crg1∆/∆ homozygous deletion mutants and CRG1-overexpressing crg1∆/∆ mutants grown to mid-exponential phase were incubated with or without cantharidin (30 µM and 250 µM, the IC20 for crg1∆/∆ and the IC20 for wt, respectively) for 1 hour. Cells were normalized to an equivalent OD600, 10-fold diluted, spotted onto YPD solid medium and incubated at 30°C. (B) The comparison of GO term Biological processes for the genes that are significantly upregulated and downregulated (log2 ratio >1 and <-1) in a crg1∆/∆ mutant treated with cantharidin (30 µM and 250 µM) for 1 hour. (C) Heat map of the transcriptional profiles of wt, crg1∆/∆ and CRG1- overexpressing crg1∆/∆ mutants in response to cantharidin. (D) Hierarchical clustering was used to group all significantly expressed genes (two fold) in at least one of the strains in the presence of cantharidin (250 µM). Clusters of genes exhibiting highly similar profiles across the strains are boxed, and the overrepresented GO Biological process in the gene clusters are indicated on the right. (E) A plot comparing the transcriptome profiles of crg1∆/∆ and CRG1-overexpressing crg1∆/∆ mutants. Exponentially grown cells were treated with cantharidin (250 µM) for 1 hour or

DMSO, total RNA extracted and synthesized cDNA was hybridized to Affymetrix Tiling arrays. (F) The methionine biosynthesis is linked to AdoMet cycling in methylation reactions coordinated by methyltransferases. The genes that are transcriptionally different between cantharidin-resistant and sensitive mutant are shown in red. STR3 (P-value <0.0057), MET17 (P- value <0.0036), MET6 (P-value <0.012), SAM1 (P-value <0.02), SAH1 (P-value <0.03), MMP1 (P-value <0.04), SAM2 (P-value <0.056). (G) Cantharidin induces STR3 transcript levels in CRG1-overexpressing crg1∆/∆ mutant. Wt, crg1∆/CRG1 heterozygous, crg1∆/∆ homozygous deletion mutants and CRG1-overexpressing crg1∆/∆ mutants grown to mid-exponential phase were incubated with or without cantharidin (250 µM) for 1 hour. (H) Treatment of cells with general methyltransferase inhibitor SAH increases sensitivity to cantharidin. Wt cells were grown in SC media with or without cantharidin (4 µM) and SAH (50 µM).

presence of cantharidin or if their differential expression failed to show statistical significance. To detect genes and biological processes that are differentially expressed among the strains and treatments, the enrichments of genes for Gene Ontology (GO) term Biological process in the transcriptomes of wild type, crg1∆/∆ and CRG1-overexpressing crg1∆/∆ strains in the presence and absence of cantharidin were compared (Appendix 1, Table 1). I also clustered genes according to their expression pattern (Figure 2-4D). The clustering and GO term comparative analysis revealed that significantly downregulated genes (log2 (drug/DMSO) <-1, P-value <0.05) in crg1∆/∆ mutant and the wild type were enriched for the genes of amino acid biosynthetic process (multiple-testing corrected P- value <1.0x10-7 and P-value <7.0x10-7, respectively), while the transcriptional profile of cantharidin-resistant CRG1-overexpressing crg1∆/∆ strain did not demonstrate a similar enrichment (Figure 2-4D). Of particular interest, most of the genes that comprise methionine biosynthetic process (MET6, MET17, MMP1, STR3, ADE3, SAM1, SAM2, SAH1, MET22, MET31) were differentially expressed between cantharidin-resistant CRG1-overexpressing crg1∆/∆, wild type and cantharidin-sensitive crg1∆/∆ strain in the presence of cantharidin (P-value <0.05; Appendix 1, Table 2; Figure 2-4E and Figure 2-4F). One noteworthy example is STR3, a cystathionine beta-lyase, the gene that demonstrated the most differential expression in the strains. STR3 was significantly induced by cantharidin in CRG1-overexpressing crg1∆/∆ strain (log2 (drug/DMSO) =4.7, P-value <0.022) and mildly downregulated in the wild type (log2 = -0.6) and crg1∆/∆ (log2 = -0.55). The observed differential expression of STR3 was further confirmed by qRT-PCR (Figure 2-4G). Str3 is of interest because it functions in methionine biosynthesis by converting cystathionine into homocysteine, a precursor for methionine, which is a substrate for the generation of AdoMet. AdoMet is required as a methyl donor for methylation reactions (Figure 2-4F). Because the high levels of STR3 transcripts correlate with cantharidin resistance, one should predict that the deletion of STR3 should affect response to cantharidin.

To further explore the role of the Crg1 AdoMet-dependent methyltransferase, I treated wild-type cells with a combination of cantharidin and S-adenosyl homocysteine (SAH), a non-specific methyltransferase inhibitor. I found that wild-type strains were more sensitive to the cantharidin/SAH combination compared to either single agent (Figure 2-

4H). These observations confirm the requirement of AdoMet-dependent methyltransferase activity in response to cantharidin, and suggest that Crg1 is a functional methyltransferase that catalyzes a AdoMet-dependent methylation reaction important for cantharidin resistance.

2.2.5 CRG1 is a sequence homologue of small molecule methyltransferase TMT1

Given that CRG1 provides cantharidin resistance in a gene dose-dependent manner and because of its close sequence similarity to TMT1, a small molecule methyltransferase that catalyzes the formation of methyl esters (Figure 2-5A), we hypothesized that Crg1 might methylate cantharidin because this drug bears some structural similarity to the substrates of Tmt1 (Figure 2-5B).

2.2.6 Crg1 methylates cantharidin in vitro

To test this possibility, I purified recombinant Crg1 from S. cerevisiae (Figure 2-6A), and Brian Young (UCLA) performed in vitro biochemical assays with the purified Crg1, cantharidin, and S-adenosyl-[methyl-14C] methionine (Figure 2-6A). These in vitro reactions were separated via reverse phase liquid chromatography and the radioactivity of the collected fractions was quantified with a scintillation counter. We detected a unique peak of radioactivity eluting in the 18-20 min fraction (Figure 2-6B). The appearance of this peak was both cantharidin and Crg1-dependent, suggesting that it could correspond to methylated cantharidin.

To confirm that this novel activity was catalyzed by Crg1 rather than by a co-purifying protein, I mutated Crg1 at critical residues within the methyltransferase domain and cloned the mutated genes under control of GAL1 promoter with a C-terminal TAP tag. Next I purified TAP-tagged Crg1 (Figure 2-6C) and we repeated the methylation reactions with these forms of Crg1. As described earlier, the D44A and E105A-D108A mutations abolished resistance to cantharidin (Figure 2-2), so we assessed whether these mutated proteins were able to methylate the drug molecule. Brian Young (UCLA) prepared in vitro reactions containing varying concentrations of cantharidin and quantified the amount of acid-labile volatile radioactivity because methyl esters are

Figure 2-5. CRG1’s sequence homolog TMT1 methylates the substrates that share structural features with cantharidin.

(A) Tmt1 modifies small molecule intermediates of TCA cycle (trans-aconitate) and leucine biosynthesis (3-isopropylmalate and isopropylmaleate) to form methyl esters. (B) Hypothetical methylation of cantharidin by Crg1.

Figure 2-6. Crg1 methylates cantharidin in vitro.

(A) Silver stained 12% SDS-PAGE of purified His-tagged Crg1. Wild-type cells (Y258) carrying empty BG1805 and BG1805-GAL1-CRG1 were grown in SD-ura and 2% raffinose to mid- exponential phase. The expression of CRG1 was induced with galactose (2%) for 5 hours. His- tagged Crg1 was purified with Ni-sepharose resin. The diagram (right panel) shows the transfer of [methyl-14C] from S-adenosyl-[methyl-14C] methionine to a potential substrate by purified Crg1. (B) In vitro enzymatic reaction mixtures containing cantharidin, S-adenosyl-[methyl-14C] methionine, and Crg1 were separated by reverse phase chromatography. Radioactivity in the fractions was quantified with a scintillation counter, and a cantharidin and Crg1-dependent peak with a retention time of 18-20 minutes was identified (asterisk). (C) Silver stained 12% SDS- PAGE of purified TAP-tagged wild-type and mutated Crg1. (D) In vitro analysis of the reactions containing cantharidin and mutated forms of Crg1 by measurement of the amount of acid-labile volatile radioactivity. (E) Single ion chromatogram of the major species identified in the spectra with unreacted cantharidin. (F) Additional in vitro reactions with unlabeled AdoMet were prepared in a similar manner and analyzed by liquid chromatography-mass spectrometry with positive ionization. The mass spectrum of the peak from the full reaction with an elution time of 18.6-18.8 minutes is shown. (G) Single ion chromatograms of the major species identified in the

spectra corresponding to methyl-cantharidin (H) The mass spectrum of the peak with an elution time of 19.2 minutes from the complete reaction with cantharidin, radioactively labeled AdoMet and Crg1.

known to readily hydrolyze in both strongly acidic and basic conditions to yield methanol (Cai et al, 2001a; Kleene et al, 1977). Unlike the reactions performed with wild-type Crg1, addition of cantharidin to the reactions with mutant forms of the enzyme showed no increase in acid-labile radioactivity (Figure 2-6D), strongly suggesting that a functional methyltransferase domain in Crg1 is required for cantharidin methylation.

To definitively determine whether cantharidin is a substrate of Crg1, we prepared and analyzed unlabeled reactions containing purified Crg1, cantharidin, and AdoMet by liquid chromatography-mass spectrometry. We first looked at the extracted ion + chromatogram expected for unreacted cantharidin (C10H13O4 ; m/z=197.0814 ± 100 ppm) and found a large peak in cantharidin-containing reactions with an elution time of 18.6- 18.8 min (Figure 2-6E). The combined spectra of this peak in the complete reaction mixture contained several species: m/z=197.0942 and 215.1065, corresponding to the m/z + for cantharidin and hydrated cantharidin (C10H15O5 ; m/z=215.0919), respectively (Figure 2-6F). Next, we analyzed the full reaction to determine whether Crg1 methylates cantharidin. Specifically, we analyzed the extracted ion chromatogram expected for + methyl cantharidin (C11H15O4 ; m/z=211.0970 ± 100 ppm). We identified a peak eluting after cantharidin at 19.2 min corresponding to the mass of methyl cantharidin in the complete reaction mixture (Figure 2-6G). Importantly, this species was absent in each of our control reactions lacking cantharidin, AdoMet, or Crg1. This is strong evidence that cantharidin is indeed methylated by Crg1. Finally we analyzed the combined spectra of the 19.2-min peak (Figure 2-6H). In addition to the m/z=211.1105 species, we observed m/z=229.1215 and m/z=197.0930 species, corresponding to hydrated methyl cantharidin + (C11H17O5 ; m/z=229.1076) and cantharidin, respectively.

Based on its close sequence similarity to Tmt1, Crg1 likely catalyzes the formation of a cantharidin methyl ester. In solution, this putative methyl ester is likely in equilibrium with its ring-closed methyl anhydride form. If the equilibrium favors the ester form, some fraction of the cantharidin methyl ester could undergo ring-closing elimination reactions during ionization to yield methyl cantharidin and cantharidin products. Likewise, if the equilibrium favors the methyl anhydride, it may possibly undergo in- source fragmentation to give cantharidin, and like cantharidin, it may simply form a water

adduct during ionization. Although our mass spectrometry data strongly support our hypothesis that Crg1 methylates cantharidin in vitro, additional analysis is needed to determine the structure of the methylated drug molecule. It is possible that the observed methyl cantharidin and hydrated methyl cantharidin species are ionization products of another methylated cantharidin derivative that is the actual product of the Crg1-catalyzed reaction.

2.2.7 Analysis of cantharidin-specific genetic interactors of CRG1

To identify cellular processes required for cantharidin resistance and to define the spectrum of genes that compensate for the absence of CRG1 in the presence of cantharidin, I used a chemogenomic approach to analyze genetic interactions between CRG1 and each of the ~4800 non-essential yeast genes both in the presence and absence of the drug. A genetic interaction between two genes occurs when the phenotype of the double-deletion mutant shows significant deviation in fitness compared with the expected effect of combining two single mutants (e.g. sickness or synthetic lethality) (Costanzo et al, 2010; Tong et al, 2001). When synthetic lethality is observed, it suggests that the genes may have overlapping functions. By analogy, identification of drug-gene interactions will similarly uncover genes that act in parallel with a gene of interest and these interactions can illuminate a compound’s effect on a cell. To perform this experiment, I generated double-deletion mutants (with a crg1∆ strain as the query) using the SGA technology (Tong et al, 2001), pooled all viable double-deletion mutants and analyzed their growth in a competitive fitness assay in the presence and absence of cantharidin (Figure 2-7A) (Pierce et al, 2007). Six highly reproducible and independent crg1∆xxx∆ pools (r =0.72, Figure 2-7B) were further averaged yielding 70 double- deletion mutants (Appendix 1, Table 3) that showed significant growth defects (log2

(drug/DMSO) <-1, P-value <0.05) in the presence of an IC20 dose of cantharidin (30 µM) in YPD when grown in a pool. Noteworthy, these genes were not sensitive as single deletion mutants (P-value <0.025; Figure 2-7C and 2.7D), and, thus, the effect was specific to the double mutant combination. To obtain a general overview of “aggravating” (negative) interactors of CRG1 in the presence of cantharidin, I categorized this set of genes according to their GO term Biological process (Figure 2-7C). This

Figure 2-7. Uncovering cantharidin-specific genetic interactors of CRG1.

(A) Experimental scheme for analysis of cantharidin-specific genetic interactors of CRG1. Double-deletion mutants crg1ΔxxxΔ generated through SGA were pooled together and treated with cantharidin (30 µM) for 20 generations in YPD. Genomic DNA was isolated, unique strain- representative barcodes were PCR amplified, and the PCR products were hybridized to TAG4 arrays for the quantitative analysis of fitness of the mutants. (B) Correlation of two independent replicates for crg1∆xxx∆ pools. The strains with significant log2 (drug/DMSO) (P-value <0.05) are included in the analysis. (C) Scatter plot representing cantharidin-gene interactions obtained from the comparative analysis of ura3ΔxxxΔ (control single deletion pool) and crg1ΔxxxΔ pools. CRG1-dependent interactors are highlighted in the red square. The hits are obtained from the averaged datasets (n=6 for crg1ΔxxxΔ pools and n=4 for ura3ΔxxxΔ). The significant log2 (drug/DMSO) negative genetic interactors were categorized according to their biological processes (P-value <0.002 before multiple testing correction). (D) Representative growth curves for the top hits (sensitive and resistant) that genetically interact with CRG1 in the presence of cantharidin. Cells were grown in YPD media with and without cantharidin. MET22 and DBF2 deletion strains were treated with cantharidin (6 µM and 25 µM, respectively) to test their sensitivity and resistance, respectively. (E) The deletion of DBF2 suppresses CRG1 sensitivity to cantharidin. Cells were normalized to an equivalent OD600, 10-fold diluted, spotted onto synthetic

complete medium containing cantharidin (10 µM) and incubated at 30°C. (F) CRG1-dependent changes of DBF2 transcript levels in the presence of cantharidin. Wt, crg1∆/CRG1 heterozygous, crg1∆/∆ homozygous deletion mutants and CRG1-overexpressing crg1∆/∆ mutants grown to mid-exponential phase were incubated with or without cantharidin (250 µM) for 1 hour. Total RNA was extracted, cDNA synthesized and the relative abundance of DBF2 transcript was analyzed by qRT-PCR. Data are means of at least three independent experimental replicates, and error bars are standard deviation.

dataset comprised diverse biological processes, including vesicle-mediated transport (P- value <0.008), chromosome organization (P-value <0.001), response to chemical stimulus (P-value <0.019), lipid metabolic process (P-value <2.0x10-5), response to stress (P-value <0.018), and protein modification process (P-value <0.003).

I also identified the serine/threonine kinase DBF2 as a strong suppressor of CRG1- dependent cantharidin toxicity (log2 (drug/DMSO) =1.12, P-value <4.0x10-5; Figure 2.7C). This interaction was confirmed by evaluating the fitness of individual strains in liquid and on solid SD medium in the presence of 25 µM and 10 µM cantharidin, respectively (lethal doses for crg1∆ strain in these media conditions; Figure 2-7D and 2.7E). Furthermore, the alleviating interaction between DBF2 and CRG1 was not observed at 37ºC, indicating cantharidin-specific nature of this interaction (Figure 2-7E). In addition to the well-characterized roles of Dbf2 in the mitotic exit network (Hergovich et al, 2006), this newly uncovered interaction suggests that this kinase may have an opposing function to the protein phosphatases (PP2A and PP1), the primary targets of cantharidin (Honkanen, 1993; Li & Casida, 1992). As independent evidence for an interaction with cantharidin, I found that DBF2 transcript levels were significantly decreased in a crg1∆/∆ mutant in the presence of cantharidin (250 µM) (~2-fold, P-value <0.013) compared to DMSO control, and that change was not detected in other strains (Figure 2-7F). These observations implicate phosphorylation/dephosphorylation events in response to cantharidin stress.

2.2.8 CRG1 is important for cantharidin-perturbed lipid homeostasis

To identify genes specifically required for growth in the presence of cantharidin, I removed the genes that behave as multidrug resistance (MDR) genes from my chemogenomic dataset described above. MDR genes are defined here as the genes that are required for growth in the presence of multiple stress conditions (at least 20% of tested conditions for homozygous deletion strains) (Hillenmeyer et al, 2008). This filtering removed apparent enrichment of genes involved in vesicle-mediated transport genes (P-value =0.216), response to stress (P-value =0.024), chromosome organization (P-value =0.075) and protein modification process (P-value =0.021). Following this, I

found that cantharidin-specific CRG1 interactors are significantly enriched for genes required for lipid metabolic process (multiple testing corrected P-value <0.0003) (Figure 2-8A). In particular, lipid methyltransferases (CHO2, ERG6), glycosylphosphatidylinositol (GPI) lipid biosynthesis genes (ARV1, GUP1, PER1) and lipid-related genes (SAC1, MOT3, DEP1, RVS167, YTA7) are essential in crg1∆ deletion strains in the presence but not absence of cantharidin treatment (Figure 2-8B). Furthermore, I demonstrated that an increase in cantharidin concentration (10 µM) did not result in cantharidin sensitivity for these genes compared to wild type, confirming the dependence of the detected interactions on the presence of CRG1.

To explore further the role of CRG1 in lipid metabolism and related processes, we compared the lipid content (or “lipidome”) of wild type, crg1∆/∆ homozygous deletion and CRG1-overexpressing crg1∆/∆ strains in the presence and absence of cantharidin (250 µM) using electrospray ionization tandem mass spectrometry (ESI-MS/MS) analysis (performed by Xue Li Guan from University of Singapore) (Guan et al, 2009; Guan & Wenk, 2006). I observed significant changes in the abundance of most glycerophospholipids and sphingolipids in both the wild type and crg1∆/∆ strains after growth in cantharidin-containing medium (P-value <0.05, Kruskal-Wallis test). The strains with a CRG1-overexpressing construct did not exhibit significant cantharidin- induced lipid alterations (P-value >0.08, Kruskal-Wallis test; Figure 2-8C, Appendix 1, Table 4). Specifically, in both the wild type and crg1∆/∆ strains, cantharidin measurably increased the levels of short chain phosphatidylcholine (PC), phosphatidylethanolamine (PE), and phosphatidylinositol (PI) species, while the levels of long-chain PCs and PIs were reduced (Figure 2-8C). In the crg1∆/∆ strain I also noted a substantial decrease in the levels of mixed size phosphatidylserine (PS) species after cantharidin stress, while the wild type and crg1∆/∆ strain had increased levels of saturated short chain (C16 and C18) PI species compared to mono-unsaturated short chain PIs in cantharidin (Appendix 1, Table 4). Such abundance changes with respect to acyl chain length and saturation were not observed in the CRG1-overexpressing mutant, suggesting that extra copies of CRG1 complemented the cantharidin-induced defects.

Figure 2-8. Crg1 is important for cantharidin-perturbed lipid homeostasis.

(A) GO term enrichment analysis of cantharidin-specific genetic interactors of CRG1 before and after MDR gene filtering. Only the terms with significant P-values (<0.05) are shown. Bonferroni correction was applied to the MDR-filtered terms. (B) CRG1 is synthetically lethal with lipid- related genes in the presence of cantharidin. Cells were normalized to an equivalent OD600, 10- fold diluted, spotted onto synthetic complete defined medium containing cantharidin and incubated at 30°C. (C) Comparative phospholipid profiles of wild type and crg1 mutants in response to cantharidin. Cells grown to mid-exponential phase in YPD were treated with cantharidin (250 µM) for 2 hours. Lipid standards were added to the cells, and extracted lipids were measured using ESI-MS. The quantities of lipid species are expressed as ion intensities relative to the levels in DMSO, and converted to a log2 scale. Data are the average of three samples. Statistical significance in the abundance of lipid species in the presence of cantharidin between wild type and mutants was determined using Kruskal Wallis test, *P-value <0.05. (D) The simplified diagram demonstrating how phospholipid biosynthesis linked to sphingolipid

biosynthesis. PIs species contributes to biosynthesis of complex sphingolipids, GPI anchors, and PIPs. (E) Comparison of sphingolipid profiles of wt, crg1Δ/Δ and CRG1-overexpressing crg1Δ/Δ mutants in the presence of cantharidin (250 µM). The suffixes –B, -C, and –D on IPC and MIPC denote hydroxylation states, having two, three, or four hydroxyl groups, respectively. Statistical significance in the abundance of lipid species in the presence of cantharidin between wild type and mutants was determined using Kruskal Wallis test, *P-value <0.05. (F) The representative images of cells stained with Nile Red for lipid droplets. Cells were grown at 30 ºC for 42 hours to reach stationary phase, and inoculated into fresh medium with cantharidin (250 µM) for 2 hours. Cells were fixed and stained with Nile Red. Bar, 2.5 µm. (G) Sterol species are not affected by the deletion of CRG1 and cantharidin treatment.

It has been previously reported that phospholipid and sphingolipid biosynthetic pathways are interconnected (Figure 2-8D) (Ejsing et al, 2009; Guan et al, 2009; Sims et al, 2004). One way in which this interconnection is seen is when a single gene deletion or chemical perturbation results in the so-called “ripple effect” (Ejsing et al, 2009) characterized by lipidome-wide perturbations in a cell. I also noted evidence of this effect: the amounts of the most abundant sphingolipid inositolphosphoceramide (IPC) and mannosylinositolphosphoceramide (MIPC) were also affected by cantharidin in a crg1∆/∆ mutant (Figure 2-8E). To investigate if other lipid intermediates are affected by the drug in a similar manner in crg1 mutants, we analyzed both sterol content (performed by Isabelle Riezman from University of Geneva) and the formation of lipid droplets, which serve as storage pools of triacylglycerols and steryl esters (Czabany et al, 2008). I found no obvious changes in these lipid species in the presence of drug (Figure 2-8F and 2.8G). Taken together, these results demonstrated that cantharidin's effect is specific towards phospholipids and sphingolipids in crg1 mutants.

2.2.9 Orf19.633, a functional homolog of CRG1 in C. albicans, mediates lipid homeostasis in response to cantharidin

To test if cantharidin-induced alterations in yeast lipidomes are evolutionally conserved, we examined the lipidome of the human fungal pathogen Candida albicans in response to cantharidin. A C. albicans homozygous crg1 deletion (orf19.633∆/∆) displayed similar growth defects to those observed in S. cerevisiae when challenged with cantharidin

(Figure 2-9A). Lipidomic analysis demonstrated that cantharidin treatment (2 mM, IC20 for C. albicans wild type) resulted in significant changes in most phospholipid species in both wild type and orf19.633∆/∆ homozygous mutant (P-value <0.05). Furthermore, although to a more modest degree than seen in S. cerevisiae, I found that C. albicans CRG1 may account for some difference between wild type and a mutant strain (P-value <0.05; Figure 2-9B). In addition, Shawn Hoon (Stanford) and I have shown previously that overexpressing C. albicans orf19.633 restored cantharidin resistance in S. cerevisiae crg1∆/∆ mutant (Hoon et al, 2008), further suggesting that the lipid homeostasis functions of this C. albicans putative AdoMet-dependent methyltransferase are conserved.

Figure 2-9. Orf19.633, a functional homolog of CRG1 in C. albicans, mediates lipid homeostasis in response to cantharidin.

(A) A putative methyltransferase orf19.633 is required for cantharidin resistance. Fitness of wt (SN87) and orf19.633Δ/Δ mutant were measured in liquid YPD medium in the presence of cantharidin (2 mM, the IC20 for wt). Dose-response growth curves were obtained by plotting OD600 at saturation point in liquid versus tested drug concentrations. (B) Comparative lipidomics of cantharidin-treated C. albicans wild type and orf19.633Δ/Δ mutant. The cells were treated with 2 mM cantharidin (2 hours) and further prepared as described in Figure 2-8. Only lipid species with significant changes in their abundance between wt and orf19.633Δ/Δ mutant are shown (P- value <0.05, Student’s t-test).

2.2.10 Crg1 is required for cytoskeleton organization upon exposure to cantharidin One of the phospholipids that manifested substantial changes in our lipidome analysis was phosphatidylinositol (PI) (Figure 2-10A). PI is an essential phospholipid with multiple roles in the biosynthesis and metabolism of phosphoinositides (PIP), inositol polyphosphates (IPs), complex sphingolipids and glycerophosphoinositols (GPIs) (Figure 2-8D) (Gardocki et al, 2005). It has been previously reported that phosphorylated derivatives of PI species (mainly PI(4,5)P) are well-conserved second messengers involved in the regulation of the actin cytoskeleton in Pkc1-dependent manner (Figure 2- 10B) (Gardocki et al, 2005; Yin & Janmey, 2003). Therefore, to examine one of the possible physiological consequences of altered levels of PI, I tested if cantharidin affects the actin cytoskeleton. Microscopy of FITC-phalloidin stained cells revealed that crg1∆/∆ strain treated with 250 µM cantharidin for 1 hour lacked actin patches and displayed highly disorganized actin cables compared to wild type. Overexpression of CRG1 in crg1∆/∆ strain restored the number of actin patches close to that seen in the wild-type strain without cantharidin (Figure 2-10C). These results demonstrate that Crg1 is critical for both actin patch and actin cytoskeleton integrity during cantharidin stress. The observed role of Crg1 in cytoskeleton organization might be indirect, and rather due to toxicity of cantharidin, since similar effect is detected in wild-type strains upon the drug treatment. Nonetheless, in my genome-wide screen for genetic interactions (without cantharidin) I found that positive genetic interactions (alleviating) of CRG1 were significantly enriched for the genes involved in the actin cytoskeleton, bud emergence, and cell polarity (P-value <1.0x10-5; Figure 2-10D). In particular, the deletion of RVS167, a well-characterized actin patch and lipid-interacting protein, manifested fitness defects that are suppressed by the deletion of CRG1 (Figure 2-10E) (Janmey & Lindberg, 2004; Ren et al, 2006). Although these findings further support the role of Crg1 in actin-related biological process, additional experimental evidence is required to confirm a direct role of Crg1 in these processes and not simply by inactivating cantharidin.

Figure 2-10. Crg1 is required for cytoskeleton upon exposure to cantharidin.

(A) PI species are significantly affected by cantharidin in Crg1-dependent manner. Cells were prepared as described in Figure 2-8. Only PI species with significant changes in their abundance between wt and crg1 Δ/Δ mutant are shown (P-value <0.05, Student’s t-test). (B) Diagram showing how PIP species are involved in Pkc1-dependent changes in actin cytoskeleton. (C) Crg1 is important for actin patch integrity during cantharidin stress. Wt, crg1∆/∆ mutant and CRG1- overexpressing crg1∆/∆ cells were grown to mid-exponential phase at 30°C in YPD in the presence of cantharidin (250 µM) or DMSO for 1 hour. Cells fixed with formaldehyde were stained for actin with rhodamine-phalloidin and visualized by fluorescence microscopy. Bar, 5 µm. The number of actin patches per cell in each sample was quantified. Values are means of three independent replicates (n= 270-1000), error bars are standard deviation; * P-value <0.025, ** P-value <0.0002 (Student’s t-test). (D) Genetic interactors of CRG1 identified through SGA analysis. The double and single mutant fitness (based on colony sizes) from two independent SGA screens were used to quantify the strength of genetic interaction between CRG1 and other gene. Quantification was performed using quantitative SGA scoring algorithm (Baryshnikova et al, 2010b). The significant genetic interactors (P-value <0.05) common between two SGA

screens were considered as hits. The mutants with SGA score >0.08 are significant positive or alleviating interactors, the ones with <0.08 are significant negative or aggravating interactors. (E) Deletion of CRG1 suppresses fitness defect of rvs167 mutant. Double and single mutants were grown in synthetic complete (SC) medium to the saturation. ura3∆ his3∆ is the wild-type strain.

2.2.11 CRG1 transcription is regulated via the Cell Wall Integrity (CWI) pathway

Finally, to determine how Crg1 is regulated at the transcriptional level in response to cantharidin, I explored which pathways, if any, are required for cantharidin resistance. Based on our observation that the homozygous deletion strains slt2∆/∆ and bck1∆/∆ (both CWI kinases) are hypersensitive to cantharidin (Hillenmeyer et al, 2008; Hoon et al, 2008), combined with the fact that the promoter region of CRG1 contains a binding site for Rlm1 (a transcriptional regulator of CWI pathway) (Levin, 2005), I asked if CRG1 expression is activated by cantharidin via the CWI pathway. I found that deletion of these genes blunted the increase of CRG1 transcript in response to cantharidin (250 µM) compared to the wild type (Figure 2-11A), indicating that CWI pathway components are required for CRG1 expression in the presence of cantharidin. The CRG1 promoter also contains a binding site for Yap1, a transcription factor required for cadmium tolerance and the oxidative stress response. In contrast to Rlm1 and Slt2, the relative amount of CRG1 transcript in the yap1∆/∆ mutant was unchanged in the presence of cantharidin (Figure 2-11B). While these data suggest that Crg1 may be regulated via the CWI pathway and is transcriptionally responsive to numerous cell wall stressing agents (Figure 2-11C), I did not detect any drastic fitness defects when crg1 mutants were grown in the presence of cell wall perturbing agents (Figure 2-11C). However, overexpression of CRG1 in the crg1∆/∆ mutant did confer resistance to lithium chloride and fenpropimorph, both of which are known perturbants of cellular membranes and lipid processes (Figure 2-11D) (Ding & Greenberg, 2003; Liepkalns et al, 1993; Marcireau et al, 1990; Sengupta et al, 1981; Strunecka et al, 1985). Together these results further supports that Crg1 is involved in specific lipid-related processes (e.g. membrane biogenesis).

Figure 2-11. CRG1 transcription is regulated via the Cell Wall Integrity pathway.

(A) Cantharidin induces CRG1 transcription via the Cell Wall Integrity (CWI) pathway. Cells were grown to mid-exponential phase and treated with cantharidin (250 µM) in YPD for indicated time. Total RNA was extracted, cDNA was prepared and analyzed by qRT-PCR. CRG1 transcript levels are normalized to ACT1. The simplified diagram of CWI pathway is shown. Slt2 is a kinase, Rlm1 is a transcriptional activator governed by CWI pathway. (B) Yap1 is not required to activate CRG1 transcription during cantharidin stress. (C) CRG1 is transcriptionally activated by cell wall perturbing agents; however, it is not required for fitness in their presence. Cells normalized to equal OD600 were 10-fold diluted, spotted onto solid YPD medium containing various cell wall and membrane perturbing agents, and incubated at 30°C for 2–3 days. CFW – calcofluor white (D) CRG1 confers resistance to fenpropimorph. Mid-exponentially grown cells were normalized to equal OD600 were 10-fold diluted, spotted onto solid YPD medium containing fenpropimorph (250 µM), and incubated at 30°C for 2–3 days.

2.3 Discussion

In this study I demonstrated that chemical genomic approaches in yeast, when combined with rigorous biological follow-up, effectively characterizes a novel gene that, despite being subject to numerous large-scale phenotypic studies, had little functional annotation. Our previous work demonstrated that Crg1, a putative AdoMet-dependent methyltransferase, was a novel mediator of resistance to the protein phosphatase inhibitor, cantharidin (Hoon et al, 2008). In this Chapter, I showed that Crg1 methylates cantharidin in vitro, and that CRG1 gene dose plays an essential role in the cellular response to cantharidin-perturbed cellular processes, such as lipid homeostasis and actin cytoskeleton.

The initial observation that cantharidin cytotoxicity is suppressed by overexpression of CRG1 (Chapter 2.2.1) suggested a specific, although not necessarily direct, cantharidin- Crg1 interaction in vivo (Hoon et al, 2008). Indeed, we demonstrated that Crg1 is able to interact with cantharidin in vitro, resulting in the formation of the methylated cantharidin species (Chapter 2.2.6). Methylation of biological molecules removes negative charges altering hydrophobicity of the molecules that influences biological processes. Given the clear phenotype of Crg1-deficient cells and the results from our in vitro biochemical characterization of Crg1, I hypothesized that methylation of cantharidin alters its physical properties such that it is no longer harmful to yeast cells. In a manner similar to other methyltransferases that are known to detoxify small molecules (Gros et al, 2003; Jancova et al, 2010; Mishra et al, 2008; Weinshilboum, 1988), chemical modifications of cantharidin provides some insight regarding how its methylation may modify its activity. For example, endothall, that is an unmethylated and ring-opened form of cantharidin, has been assayed for protein phosphatase inhibition (Sakoff et al, 2002) and the methyl, ethyl, and propyl esters of endothall are still potent inhibitors of PP1 and PP2A. Several lactol derivatives of norcantharidin (the anhydride form of endothall) formed by reducing one of the carbonyl groups to a hydroxyl group have been synthesized and characterized. Modification of the free hydroxyl to form methyl, ethyl, and propyl ethers sharply reduced the ability of the drug derivatives to inhibit protein phosphatases. While the unmodified lactol form inhibited PP2A with an IC50 of 5 µM, the IC50 for the methyl

ether lactol form was >1000 µM. Collectively, these observations suggest that methylation of closed-ring forms, not open-ring forms, reduces cellular toxicity. Cantharidin is more sterically hindered than norcantharidin, and as such, it would be expected that its equilibrium would favor the closed-ring anhydride form more than that of norcantharidin. Accordingly, there is an intriguing possibility that the methyl- cantharidin product of the reaction catalyzed by Crg1 resembles the closed-ring lactol ether compounds that are less potent inhibitors of both growth and protein phosphatase activity. Further study to elucidate the structure of this product will enhance our understanding of how the methylation of cantharidin by Crg1 facilitates its detoxification.

In addition to characterizing the physical interaction of Crg1 with cantharidin, I investigated cellular pathways of Crg1-mediated cantharidin resistance using cells sensitized by deleting CRG1. This analysis revealed that genes involved in lipid-related processes are required for survival under cantharidin-induced stress in the absence of CRG1 (CHO2, OPI3, ERG6, SAC1, ARV1, GUP1, PER1, MOT3, DEP1) (Chapter 2.2.8). Because CRG1 is both not essential and shows very few genetic interactions under standard laboratory conditions (Chapter 2.2.10), the identification of these genes required condition-specific assays. My chemogenomic data are supported by lipidome-wide analysis, which demonstrated that cantharidin-induced alterations in glycerophospholipids and sphingolipids occur in a CRG1 gene dose-dependent manner (Chapter 2.2.8). Specifically, I observed the accumulation of short chain phospholipids in the crg1∆/∆ mutant, suggesting that the drug affects fatty acid elongation in a Crg1- dependent fashion. Consistent with this result, I also observed that overexpression of CRG1 confers resistance to lipid-stressing agents, such as lithium salt and the ergosterol inhibitor fenpropimorph. Resistance to fenpropimorph is also acquired by mutations in the fatty acid elongase FEN1 (ELO2), which is known to be involved in sphingolipid biosynthesis (Lorenz & Parks, 1991). Thus, it will be informative to test next if CRG1 and FEN1 have overlapping functions in lipid biosynthesis.

Another possible explanation for the observation that cantharidin-perturbed lipidome can be maintained by increasing the gene dose of CRG1 is potentially found in the transcriptional changes that occur in these strains. I found that the genes involved in

methionine biosynthesis are differentially expressed in CRG1-overexpressing strains in the presence of the drug compared to wild type and the crg1∆/∆ mutant (Chapter 2.2.4). This is of particular interest because changes in methionine metabolism can regulate methylation reactions by altering levels of a donor of methylation reactions AdoMet (Banerjee & Zou, 2005; Grillo & Colombatto, 2008). For example, Tehlivets et al. showed that defects in the enzymes of methionine cycling result in an imbalance of phospholipid and triacylglycerol synthesis (Malanovic et al, 2008; Tehlivets et al, 2004). The mechanisms underlying these relationships are not clear, but it is possible that cells sense that the level of AdoMet is depleted via Crg1 activity, which results in transcriptional changes in methionine biosynthesis genes, in particular, the cystathionine beta-lyase Str3. These findings suggest that Crg1-dependent buffering of cantharidin- treated lipidome in part occurs through changes in the methionine cycle.

To define the ‘core’-buffering network to CRG1 in the presence of cantharidin, I compared the transcriptome and cantharidin-SGA profiles. Although I did not find any obvious overlap in GO term biological processes between these datasets, in my cantharidin-SGA one of the most sensitive mutants was MET22 (Figure 4C), a gene with a role in sulfur assimilation and methionine biosynthesis. This gene was also differentially expressed in CRG1-overexpressing mutant vs. wild-type strain (P-value <0.02; Appendix 1, Table 2).

My chemical genomics results were corroborated by traditional SGA analysis. This analysis demonstrated that CRG1 has an alleviating (or suppressing) genetic interaction with RVS167 (Chapter 2.2.10). It is established that a similar phenotype is observed when RVS167 is deleted in the combination with genes involved in sphingolipid biosynthesis (e.g. SUR1, SUR2, FEN1, ELO3 and IPT1), implicating sphingolipid biosynthesis in the regulation of the actin cytoskeleton (Balguerie et al, 2002; Desfarges et al, 1993; Germann et al, 2005; Janmey & Lindberg, 2004; Yin & Janmey, 2003). Similarly to S. cerevisiae and C. albicans, studies in the ciliate Tetrahymena showed that cantharidin treatment also influences PI metabolism (structural precursors of sphingolipids), and the actin cytoskeleton (Kovacs & Pinter, 2001), demonstrating the conservation of cantharidin-lipid-actin interactions in other living organisms.

Understanding the transcriptional regulation of CRG1 during cantharidin stress adds many layers to the picture of the complex physiological roles of this methyltransferase. CRG1 transcription is activated by cantharidin via the conserved MAPK family components of the CWI signaling pathway (Chapter 2.2.11) (Gustin et al, 1998; Levin, 2005). Hoon et al. previously demonstrated that deletion of slt2 and bck1 results in cantharidin sensitivity, suggesting that this pathway is critical for cantharidin resistance (Hoon et al, 2008). In mammalian cells, several studies have reported that the MAP kinases ERK and JNK are also activated by cantharidin (Huh et al, 2004; Li et al, 2010a), likely as a consequence of the inhibition of protein phosphatases. Moreover, other studies reported that the intact CWI cascade is essential for maintaining lipid homeostasis (Nunez et al, 2008). It remains to be determined what specific steps are involved in the activation of CRG1 by cantharidin. One possible scenario is that the CWI pathway is activated by the accumulation of aberrant lipid species in a manner analogous to previous reports that suggest that long chain bases induce the Pkc1-MAPK CWI pathway in yeast (Zhang et al, 2004; Zhang et al, 2001).

2.4 Materials and methods

2.4.1 Strains, plasmids and growth conditions

Yeast strains and plasmids used in this study are described in Table 2-1 and Table 2-2. Unless otherwise stated, wild-type (wt) strain is BY4743; crg1∆/∆ was derived from BY4743. Yeast cells were grown in YPD (2% yeast extract, 1% peptone, 2% glucose) or in synthetically defined medium, SD (0.67% yeast nitrogen base, 2% glucose, and amino acids). Cantharidin, norcantharidin, cantharidic acid, and fenpropimorph were purchased from Sigma Aldrich (Toronto, Canada). Lithium chloride was purchased from Teknova (Hollister, CA, USA). Cantharidin, cantharidic acid, norcantharidin, and fenpropimorph were dissolved in DMSO and stored at -20°C. The IC20 of cantharidin in YPD for wild-

Table 2-1. Strains used in this study.

Strain name Genotype and source BY4743 MATa/α his3Δ1/his3Δ1 leu2Δ0/leu2Δ0 LYS2/lys2Δ0 met15Δ0/MET15 ura3Δ0/ura3Δ0 (Brachmann et al, 1998) crg1∆/∆ Isogenic to BY4743, except for crg1::KanMX crg1::KanMX (Giaever et al, 2002) bck1∆/∆ Isogenic to BY4743, except for bck1::KanMX bck1::KanMX (Giaever et al, 2002) slt2∆/∆ Isogenic to BY4743, except for slt2::KanMX slt2::KanMX (Giaever et al, 2002) rlm1∆/∆ Isogenic to BY4743, except for rlm1::KanMX rlm1::KanMX (Giaever et al, 2002) yap1∆/∆ Isogenic to BY4743, except for yap1::KanMX yap1::KanMX (Giaever et al, 2002) glc7∆/GLC7 Isogenic to BY4743, except for GLC7/glc7::KanMX (Giaever et al, 2002) BY4741 MATa his3Δ1 leu2Δ0 met15Δ0 ura3Δ0; EUROSCARF collection CRG1-GFP Isogenic to BY4741, except CRG1-GFP::NatMX; this study

SGA query strain Matα crg1::NatMX can1Δ::STE2pr-Sp_his5 lyp1Δ ura3Δ0 leu2Δ0 his3Δ1 met15Δ0 (Costanzo et al, 2010) Y258 Mata, pep4-3, his4-580, ura3-52, leu2-3, 112; Open Biosystems

SN87 C. albicans Derivative of SC5314 leu2Δ/leu2Δ his1Δ/his1Δ URA3/ura3::imm434 IRO1/iro1::imm434 (Noble & Johnson, 2005) CaLC941 Isogenic to SN87, except C. albicans orf19.633::CdHIS1/orf19.633::CmLEU2; Leah Cowen

Collection of deletion mutants Array ORFΔ MATa geneX::KanMX4 LYS2 his3Δ1 leu2Δ0 met15Δ0 ura3Δ0

Table 2-2. Plasmids used in this study.

Plasmid Description and Source YEp351 2 µm, LEU2 (the Boone lab)

YEp351-CRG1 CRG1 in YEp352 (the Boone lab)

BG1805 2 µm, URA3, GAL1prom, triple affinity tag (His6-HA epitope-3C protease site-ZZproteinA) at C-terminal; this study BG1805-CRG1 CRG1 in BG1805; Open Biosystems

P426-GAL1 2 µm, URA3, GAL1prom, TAP tag at C-terminal (the Brown lab) P426-GAL1-CRG1 2 µm, URA3, GAL1prom, TAP tag fused to CRG1 at C- terminal; this study E105A_D108A 2 µm, URA3, GAL1prom, TAP tag fused to crg1- E105A,D108A at C-terminal; this study D67A 2 µm, URA3, GAL1prom, TAP tag fused to crg1-D67A at C- terminal; this study D44A 2 µm, URA3, GAL1prom, TAP tag fused to crg1-D44A at C- terminal; this study C119Y 2 µm, URA3, GAL1prom, TAP tag fused to crg1-C119Y at C- terminal; this study G96A 2 µm, URA3, GAL1prom, TAP tag fused to crg1-G96A at C- terminal; this study

type is 250 µM, in SD it is 5 µM, both determined in liquid culture as described (Ericson et al, 2010).

2.4.2 Site-directed mutagenesis of CRG1

CRG1 was amplified from wild-type strain using primers (Table 2.3) with sequences complimentary to the vector p426-GAL1-TAP at the 5’ end. The amplified CRG1 and HindIII linearized vector were directly co-transformed into a crg1∆/∆ mutant and transformant colonies were selected in synthetic defined media lacking uracil (SD-ura). CRG1 was cloned downstream of a GAL1 inducible promoter and in frame with the TAP coding sequence. Transformants were screened by PCR and for cantharidin resistance.

CRG1 missense mutants were prepared using the QuickChange Lightning Site-directed mutagenesis kit

Table 2-3. Oligonucleotides used in this study. Primer name Purpose Oligonucleotide sequence (5’-3’) frwCRG1 p426-GAL1-CRG1-TAP CTGCAGGAATTCGATATCATGCCTA AAACTAGTTATTTA revCRG1 p426-GAL1-CRG1-TAP CTTTTCCATATCGATAAGTTCTCTTT TCCTACATAAGTA frwE105A_D108A CRG1 mutagenesis GCATTCGACCAGCAAGTGTAGCTAT GGTTATTTCAGC revE105A_D108A CRG1 mutagenesis GCTGAAATAACCATATCTACACTTG CTGGTCGAATGC frwD67A CRG1 mutagenesis GGAAGTGATTGGGATTGCTCCTTCT TCTGCTATG revD67A CRG1 mutagenesis CATAGCAGAAGAAGGAGCAATCCC AATCACTTCC’ frwD44A CRG1 mutagenesis CGCAAAAGTTTGGTTGCTATTGGAT GTGGCACA revD44A CRG1 mutagenesis TGTGCCACATCCAATAGCAACCAAA CTTTTGCG frwG96A CRG1 mutagenesis ATTAATGCGCCTGCTGAAGATTTAT CC revG96A CRG1 mutagenesis GGATAAATCTTCAGCAGGCGCATTA AT frwACT1_qPCR qRT-PCR TGTGATGTCGATGTCCGTAAG revACT1_qPCR qRT-PCR CGGTGATTTCCTTTTGCATT frwCRG1_qPCR qRT-PCR GAAAGGCTGTTTCAGCAGGT revCRG1_qPCR qRT-PCR ATTCAAGGCTTCGGGAAAGT frwCRG1-GFP Crg1-GFP tag TTGAATGTACCTTTAAAAATAGAGT GGTCAACGTTTTATTACTTATGTAG GAAAAGAGAAGGTGAAGCTCAAAA ACTTAAT revCRG1-GFP Crg1-GFP tag TTGACGGTTTTAAATTACTCCCAACT GCGCAACAACACCTTATTTTCTTTTT GCCATATTGCTGACGGTATCGATAA GCCT

(Stratagene - Agilent Technologies Company, La Jolla, CA, USA). Clones were sequenced to verify the mutations. To express Crg1, transformants were grown to mid- exponential phase in SD-ura and raffinose (2%), then induced by the addition of galactose to a final concentration of 2%. Treatment with cantharidin (30 µM) was used to test sensitivity of mutants. Cells were harvested after 3 hours of induction, and Crg1 expression was verified by Western blots of 12% SDS-PAGE gels using anti-TAP antibodies (OpenBiosystems – Thermo Fisher Scientific, Huntsville, AL, USA).

2.4.3 RNA isolation, cDNA preparation and qRT-PCR analysis

Cells grown to mid-exponential phase in YPD medium were incubated with or without cantharidin (250 µM) for various amounts of time, harvested by centrifugation, frozen in liquid N2 and stored at -80ºC. RNA was extracted with hot acidic phenol (Collart & Oliviero, 2001) and treated with the Turbo DNA-free kit (Ambion – Applied Biosystems, Austin, TX, USA). RNA purity was tested using a spectrophotometer and integrity was evaluated by denaturing gel electrophoresis. First-strand cDNA was synthesized from 1

µg of DNase-treated RNA with 0.5 µg of oligo(dT12-18) primers (Invitrogen, Burlington, ON, Canada) using 200 units of Superscript II Reverse transcriptase (Invitrogen, Burlington, ON, Canada). Real-time PCR analysis was conducted with Power SYBR Green PCR master mix (Applied Biosystems, Foster City, CA, USA) and gene-specific primers (Table 2.3) at a final concentration of 250 nM. qRT-PCR was carried out on a 7900HT Fast system (Applied Biosystems) using Sequence Detection System software version 2.3. Fold change in CRG1 transcript level normalized to ACT1 was calculated using the 2-∆∆Ct method. At least three independent replicates of each reaction were performed. Student’s t-test was applied for statistical analysis (paired for drug vs. DMSO treatments, and unpaired for mutants vs. wild type).

2.4.4 Microarray analysis

Cells grown to mid-exponential phase in YPD medium were incubated with or without cantharidin (250 µM) for 1 hour, and then harvested by centrifugation. Isolation of RNA and hybridization to the tiling arrays was performed as described (Juneau et al, 2007), except that actinomycin D was added in a final concentration of 6 µg/mL during cDNA synthesis to prevent antisense artifacts (Perocchi et al, 2007). Two independent replicates were used for the analysis. Hybridization to Affymetrix Tiling Arrays using the GeneChip Fluidics Station 450 (Affymetrix) was followed by the extraction of intensity values for the probes using the GeneChip Operating Software (Affymetrix). Acquisition and quantification of array images were performed using the Affymetrix tiling analysis software (http://www.affymetrix.com/support/developer/downloads/TilingArrayTools/ index.affx). The resulting .BAR files containing probe position and intensities were

further analyzed by aligning the probes that match the position of the S. cerevisiae Genome Database list of defined ORFs (http://downloads.yeastgenome.org/ chromosomal_feature/saccharomyces_cerevisiae.gff). The log2 of signal intensity of each ORF was defined as the average across the probes associated with the ORF. Quantile normalized datasets were clustered with a correlation similarity metric and the average linkage method using Cluster 3.0 software (http://bonsai.hgc.jp/~mdehoon/software/ cluster/software.htm). The cluster was visualized using TreeView software (http://jtreeview.sourceforge.net/). The significance for differential expression was set as log2 (drug/DMSO) >1 and <-1, P-value <0.05 as determined by Student’s t test. Significantly up- and downregulated transcripts were further tested for Gene Ontology (GO) biological process enrichment using FunSpec (http://funspec.med.utoronto.ca/) with P-value cutoff of 0.01 and multiple testing correction (Bonferroni) (Appendix 1, Table 1). The probability was calculated using a test employing hypergeometric distribution (see below). To detect cantharidin-specific genes the genes involved in ESR (Gasch et al, 2000) were eliminated from the gene-set. Microarray data (Dataset S6) can be found at: http://chemogenomics.med.utoronto.ca/supplemental/methyltransferase/datasets.php.

2.4.5 Expression and purification of Crg1 fusion protein

Purification of 6xHis-Crg1 was performed as previously described (Gelperin et al, 2005). Wild-type yeast strain Y258 carrying a vector pBG1805-GAL1-CRG1 with a triple epitope protease site protein A affinity tag at C-terminal (His6-HA -3C -ZZ ) was grown in 660 mL of synthetically defined medium (SD-ura and 2% raffinose) to mid-exponential phase at 30°C. To induce expression of CRG1 340 mL of 3x YP (yeast extract and peptone) and 6% galactose was added to a final concentration of 2%. Cells were harvested by centrifugation at 3,000 rpm for 5 min. All steps following harvest were performed at 4°C. Cells were washed with PBS buffer, resuspended in 7 mL of resuspension buffer (20 mM HEPES pH 7.5, 1 M NaCl, 5% glycerol), and lysed using acid washed Zirconia beads in the presence of protease inhibitors (1 mM Pefablock, 2.5 µg/mL pepstatin A, 2.5 µg/mL leupeptin, 1 mM PMSF). Cell lysates were centrifuged at 20,000 rpm for 45 min, and diluted two fold with binding buffer (20 mM HEPES pH 7.5, 40 mM imidazole, 5%

glycerol). 300 µL of Ni Sepharose 6 Fast Flow beads (50% slurry in 20% ethanol) was added to the sample and rotated for 1.5 hours, followed by three washes with 40 mL of wash buffer (20 mM HEPES pH 7.5, 40 mM imidazole, 5% glycerol, 0.5 M NaCl). To elute Crg1 the Ni beads were resuspended in 1 mL of elution buffer (20 mM HEPES pH 7.5, 250 mM imidazole pH 7.7, 5% glycerol, 0.5 M NaCl), and Crg1p was released by rotating the mixture for 15 min at 4°C. The protein was further concentrated with Amicon Ultra tubes (10K) (Millipore, Etobicoke, ON, Canada) to 100 µL.

Purification of TAP-tagged wild-type and mutant forms of Crg1 was performed in BY4743 carrying p426-GAL1-CRG1-TAP as described in Rigaut et al. (Rigaut et al, 1999). Cell growth, induction of Crg1 with galactose (2%), and preparation of cell lysates were performed as described for the Crg1-6xHis fusion. 300 µL IgG-agarose was added to the extract and incubated for 2 hours followed by a triple wash with 25 mL of Low Salt and High Salt Wash Buffer (50 mM HEPES pH 7.5, 10% glycerol, 150 mM/750 mM NaCl, 0.1% Tween20). The final wash was performed with 15 mL TEV Cleavage Buffer (10 mM, Tris pH 8.0, 150 mM NaCl, 0.05% Tween20, 10% glycerol, 0.5 mM EDTA, 1 mM DTT). The extract was incubated overnight with 100 U TEV protease (Invitrogen, #12575-015). 1.2 mL CaM Binding Buffer (10 mM Tris pH 8.0, 150 mM NaCl, 0.05% Tween20, 10% glycerol, 1 mM MgOAc, 1 mM imidazole pH 8.0, 2 mM CaCl2, 1 mM DTT) and 2.4 µL 1 M CaCl2 were added to the protein eluates. The eluates were then incubated with 400 µL (50% slurry) Calmodulin Sepharose in 5 mL CaM Binding Buffer for 2 hours, followed by a wash with 25 mL CaM Binding Buffer and elution with 5x 200 µL Elution Buffer (10 mM Tris pH 8.0, 150 mM NaCl, 0.05% Tween20, 10% glycerol, 1 mM MgOAc, 1 mM imidazole pH 8.0, 2 mM EGTA, 1 mM DTT). Protein eluates were stored at -20°C in the presence of 50% glycerol. Recovery of Crg1 was determined using Bradford reagent (BioRad Laboratories, Mississauga, ON, Canada) and its integrity and purity was assessed with 12% silver-stained SDS-PAGE gel.

2.4.6 Preparation and analysis of radiolabeled Crg1 reactions in vitro

In vitro enzymatic reactions were prepared with 0.09 µg 6×His-tagged Crg1, 0.2 mM cantharidin dissolved in DMSO (Sigma Aldrich, St. Louis, MO, USA), and 20 µM S-

adenosyl-[methyl-14C]methionine (55.8 mCi/mmol) (GE Healthcare, Piscataway, NJ, USA) in 0.1 M sodium phosphate, pH 7.4 with a final volume of 50 µL and 2% DMSO. The complete reactions and relevant controls were incubated at 30°C for 2 hours. Following incubation, the enzymatic reactions were separated by reverse-phase high- performance liquid chromatography (Series II 1090 Liquid Chromatograph, Hewlett Packard, Palo Alto, CA, USA). The chromatography gradient was adapted from (Bennett et al, 2009), except that the flow rate was 1 mL/min. Mobile phase A contained 0.1% trifluoroacetic acid in water and mobile phase B was 0.1% trifluoroacetic acid in acetonitrile. A BetaBasic-18 column (250 mm × 4.6 mm; 5-µm particle size) (Thermo, Waltham, MA) was used. 40 µL was injected, 2-minute fractions were collected, and 350 µl of each fraction was mixed with 5 mL of Safety-Solve (Research Products International, Mt. Prospect, IL, USA) before quantification of radioactivity with a LS6500 liquid scintillation counter (Beckman Coulter, La Brea, CA, USA). Each fraction was counted three times for 3 minutes.

Other in vitro reactions were prepared in an identical manner with 0.09 µg of either mutant or wild-type TAP-tagged Crg1 and varying concentrations of cantharidin (USB, Cleveland, OH, USA). After incubation at 30°C for 2 hours, 40 µL 2 N HCl was added to each 50 µL reaction. Immediately, 80 µL of this mixture was transferred to a 1.9-cm × 9-cm folded piece of filter paper in the neck of a scintillation vial containing 5 mL of Safety-Solve and the vials were capped. After 4 hour incubation at room temperature, the pieces of filter paper were removed from the neck of each vial and the acid-labile volatile radioactivity was quantified with a liquid scintillation counter as described above (Murray & Clarke, 1986).

2.4.7 Preparation of in vitro enzymatic reactions for analysis by mass spectrometry

In vitro enzymatic reactions using unlabeled AdoMet (Sigma Aldrich) were prepared in a similar manner with 0.09 µg of 6xHis-tagged Crg1, 200 µM cantharidin (Sigma Aldrich), and a AdoMet concentration of 1.6 mM. These reactions were quenched with addition of 200 µL acetonitrile, and 12.5 µL of 15% ammonium bicarbonate was added to reduce product degradation. After concentration with a vacuum centrifuge and resuspension in

50 µL of water, these reaction mixtures were analyzed by liquid chromatography-tandem mass spectrometry (1100 Series Liquid Chromatograph, Agilent, Santa Clara, CA; QSTAR Elite Mass Spectrometer, Applied Biosystems, Foster City, CA) in positive ionization mode with a Turbo Spray source. 8 µL of sample was injected onto a reverse- phase column (Luna C18 (2), 150 mm × 1 mm, 5-µm particle size, Phenomenex, Aschaffenburg, Germany) with a flow rate of 50 µL/min and the following gradient: t=0- 1 min, 2% mobile phase B; t=10 min, 35% B; t=14-16 min, 90% B; t=16.5-35 min, 2% B. Mobile phase A was 0.1% formic acid in 98% water and 2% acetonitrile, while mobile phase B was 0.1% formic acid in 98% acetonitrile and 2% water. Spectra were collected with the instrument information-dependent acquisition mode (full scan: m/z=70-600, 1.000071 s; 3 product experiments: m/z=65-500, <2 s per experiment). The following mass spectrometer parameters were used: declustering potential, 85 V; focusing potential, 300 V; declustering potential II, 15 V; ionspray voltage, 5500 V; ion source gas, 45 units; ion source gas II, 5 units; curtain gas, 45 units; collision gas, 7 units.

2.4.8 Fitness profiling of double-deletion mutants with cantharidin

A pool of haploid double-deletion mutants (crg1∆xxx∆) was prepared by generating viable mutants using SGA technology with crg1∆ as a query strain (Tong et al, 2001) (Text S1). ~4800 viable double-deletion mutant colonies were collected, normalized to 50 OD’s/mL and stored at -80ºC in media containing 7% DMSO. Two independent pools were generated for the analysis. Each was tested in triplicate. The pooling of the strains was possible due to the presence of strain-specific sequence tags flanking each gene deletion region (Giaever et al, 2002). The double-deletion pool was treated with 30 µM of cantharidin, a dose which inhibits growth of the crg1 double-deletion pool by ~20%. Fitness analysis using a tag-specific algorithm that takes into account the intensities of each tag in cantharidin-treated cells compared to non-treated cells was performed as described (Pierce et al, 2007). Hybridization to Affymetrix Gene Chips using GeneChip Fluidics Station 450 (Affymetrix) was followed by the extraction of intensity values for the probes using the GeneChip Operating Software (Affymetrix). The data was quantile normalized, outliers (one standard deviation off) were omitted, and fitness defect scores as the log2 ratio between the mean signal intensities of the control (DMSO) and the drug

were calculated for each deletion strain in the pool as previously described (Pierce et al, 2007). As a control, the relative fitness of the double-gene deletion mutants exhibiting high sensitivity to cantharidin (log2 (drug/DMSO) <-1; P-value <0.05) was compared to the relative fitness of the corresponding single gene deletion mutants. For a given double- deletion mutant, the resulting interaction was evaluated using comparison of the observed double mutant growth rate to the expected assuming that no interaction exists. Log2 ratio <-1 represents those strains with a measurable growth defect (or lethality) and log2 ratio >1 demonstrates resistance to the drug. Cantharidin-specific CRG1 negative genetic interactors were further verified as individual clones in liquid growth assays and/or by spot dilutions. To evaluate the chemogenomic dataset for statistically significant enrichment for general biological processes (Gene Ontology Slim mapper), we used a standard hypergeometric test, that asses the probability that the intersection of given list with any given functional category occurs by chance. Obtained P-values were corrected for multiple testing correction (Bonferroni) by multiplying P-values with the number of genes in the test. The probability was calculated as follows: the P-value of observing x genes, belonging to the same functional category, is: " M%" N ( M% max(M .n ) $ '$ ' # i &# n ( i & P = ) " N% i=x $ ' # n & where M is the total number of genes involved in a functional category, n is the total number of genes in the! cluster, and N is the total number of yeast ORFs. Chemical SGA data (Dataset S1 and S2) can be found at: http://chemogenomics.med.utoronto.ca/supplemental/methyltransferase/datasets.php.

2.4.9 Lipidome analysis by liquid chromatography-tandem mass spectrometry

Cells were grown to mid-exponential phase and treated with cantharidin (250 µM) for 2 hours. Lipids were extracted from 25 OD600-equivalent of cells and analyzed as described (Guan & Wenk, 2006). All lipid standards were obtained from Avanti Polar Lipids (Alabaster, AL, USA), with the exception of dioctanoyl glycerophosphoethanolamine,

which was obtained from Echelon Biosciences (Salt Lake City, UT, USA). Quantification of individual molecular species was carried out using multiple reaction monitoring (MRM) with an Applied Biosystems 4000 Q-Trap mass spectrometer (Applied Biosystems, Foster City, CA, USA). 25 µl of samples were subjected to analysis as described previously (Guan et al, 2010; Guan & Wenk, 2006). Lipid levels in each sample were normalized to internal standards. For each lipid species, the mean normalized signal from the wild type and mutant strains grown in the presence or absence of cantharidin was calculated. Three independent experiments were used for analysis. Lipid levels were calculated relative to relevant internal standards. The quantities of lipids are expressed as ion intensities relative to the levels without cantharidin, and then converted to a log2 (drug/DMSO) scale. The difference in levels of individual lipid species in DMSO vs. cantharidin was determined with Kruskal-Wallis test (Appendix 1, Table 4). Similarly, the difference between wild type and mutants was assessed statistically using the Kruskal-Wallis test, with a P-value cutoff of 0.05. Lipidome data (Dataset S3 and S4) can be found at: http://chemogenomics.med.utoronto.ca/supplemental/methyltransferase/datasets.php.

2.4.10 Actin staining

Cells were grown to mid-exponential phase in YPD media, and treated with and without cantharidin (250 µM) for 1 hour. Cells were then fixed by addition of methanol-free formaldehyde (Polysciences, Warrington, PA) to 4% for 1 hour, centrifuged at 3,000 rpm 5 min and washed with PBS buffer three times. Cells were permeabilized with 0.2% Triton X-100 in PBS at 25°C for 15 min, washed with PBS three times and a normalized number of cells were stained with Alexa Fluor 488 phalloidin (Invitrogen, Burlington, ON) in the dark at 25°C for 1 hour. Cells were observed with 100x objective, and fluorescence images were acquired using AxioVision software on an Axiovert 200M fluorescence microscope (Carl Zeiss) using a 1.5 s exposure for all images. The average number of actin patches per cell was determined by dividing the total number of actin patches per total number of cells (N=3, n≥300 cells).

2.4.11 Construction of a Crg1-GFP fusion protein

To construct a chromosomally GFP-tagged Crg1 protein, a pair of oligonucleotides (Table 2.3) with sequence homology to the desired chromosomal insertion site at the 5’end of each primer and homology to a vector containing the GFP tag at the 3’end was used to amplify the GFP tag and NATMX resistance marker from a plasmid template (Vizeacoumar et al, 2006), and the resulting PCR products were transformed directly into wild-type BY4741 using the high-efficiency lithium acetate transformation protocol (Gietz & Woods, 2002). Transformants were selected on medium containing nourseothricin and assessed by genomic DNA PCR with primers specific for GFP and CRG1. For fluorescence microscopy, cells were used without fixation. Cells were grown to mid-exponential phase in low-fluorescence synthetically complete medium (MP Biomedicals, LLC, Solon, Ohio, USA) and incubated with or without cantharidin (4 µM). Cells were visualized with a 100x objective on an Axiovert 200M fluorescence microscope (Carl Zeiss). Images were acquired with a Zeiss HRM digital camera using AxioVision software.

2.4.12 Synthetic Genetic Array (SGA)

Two independent SGA screens of crg1∆ as the query strain against the non-essential gene deletion array (4293 strains) were performed as previously described (Tong & Boone, 2006). Computer-based quantification analysis of digital images was used to identify double mutant strains (crg1∆xxx∆) exhibiting growth differences relative to a control set of double deletion mutants (ura3∆xxx∆) (Baryshnikova et al, 2010b). The enrichment of GO Biological process terms in the CRG1 gene interaction set in normal growth conditions was calculated using FunSpec (Robinson et al, 2002).

2.4.13 Analysis of sterols intermediates by gas liquid chromatography - mass spectrometry

Analysis of sterols was performed as previously described in Guan et al. (Guan et al, 2010) without modifications.

2.4.14 Analysis of lipid droplets

It was performed as described previously (Connerth et al, 2009). Stationary phase cells (42 hours) were treated with cantharidin (250 µM) for 2 hours. Cells were fixed with formaldehyde, washed and stained with the Nile Red solution (final concentration 0.4 µg/mL) for 10 min. Fluorescence excited at 488 nm and detected in the range from 550 – 575 nm.

Chapter 3 Exploring a functional homologue of CRG1 in the human fungal pathogen Candida albicans

This chapter has been submitted to ACS Chemical Biology as:

‘A Ceramide-Binding Methyltransferase is Important for Virulence in Candida albicans’ Elena Lissina1,2, David Weiss3, Brian Young3, Kahlin Cheung-Ong1,2, Steven G. Clarke3, Guri Giaever1,2,4, and Corey Nislow1,2,5,

All experiments were performed by Elena Lissina except Figures 3-2B-E (David Weiss3 and Brian Young3), Figure 3-5E (David Weiss3), and Figure 3-7B (with Corey Nislow 1,2,5).

3 Exploring a functional homologue of CRG1 in the human fungal pathogen Candida albicans 3.1 Introduction

“If we hope to have a bigger harvest, we will need more seeds, not fewer.” - David Botstein

The fungus Candida albicans is a normally harmless commensal present in gastrointestinal tracts of the majority of humans, where it exists as a part of healthy microbiome (Peleg et al, 2010). However, this fungus can cause life-threatening infections in immunocompromised individuals (Calderone & Fonzi, 2001; Odds, 1987). Despite being a significant health concern, our current understanding of Candida’s pathogenecity mechanisms is incomplete. According to the Candida Genome database (www.candidagenome.org) over 70% of C. albicans genes are annotated as uncharacterized, and much of the current characterization relies on sequence similarity to genes in the model yeast S. cerevisiae.

Along with other protein families, AdoMet-dependent methyltransferases are poorly studied in C. albicans. Small molecule methyltransferases in C. albicans are of particular interest, because similarly to S. cerevisiae they should be involved in biotransformation of exogenous (e.g. antifungal compounds) as well as endogenous small molecules (ergosterol, toxic metabolites) maintaining cellular homeostasis by clearing toxic chemicals, generating cellular intermediates and regulating intra- and interspecies interactions. Mutations in small molecule methyltransferases can lead to the intracellular accumulation of toxic substrates resulting in cellular dysfunction or altered drug response. For example, point mutations or gene amplification of delta(24)-sterol C- methyltransferase ERG6 (ergosterol biosynthesis) lead to resistance to antifungal agents (Jensen-Pergakes et al, 1998). Considering the importance of small molecule methyltransferases in response to toxic small molecules in pathogenic C. albicans, the characterization of these enzymes will enhance our understanding of the drug response in this human pathogen and may provide a starting point for the development of novel antifungal drugs.

Despite their potential to illuminate basic and applied aspects of Candida growth, small molecule methyltransferases have been refractory to interrogation. In most cases mutants lacking a small molecule methyltransferase do not have an obvious phenotype in standard laboratory conditions, and biochemical strategies designed for protein methyltransferases (Luo, 2012; Wlodarski et al, 2011) are not effective for small molecule methyltransferases because these tests rely on prior knowledge of substrates (Chapter 1.4.2). Furthermore, as I mentioned in Chapter 1 computational analysis can predict functions for small molecule methyltransferases (Petrossian & Clarke, 2009a; Petrossian & Clarke, 2009b; Wlodarski et al, 2011), yet experimental approaches are required to determine the cellular ligands of small molecule methyltransferases.

In Chapter 2, I presented the chemical genetics approach in S. cerevisiae that was used to characterize an AdoMet-dependent methyltransferase CRG1, a gene dose-dependent interactor of cantharidin (Hoon et al, 2008; Lissina et al, 2011). Cantharidin is a secondary metabolite, produced by blister beetles of the Meloidae family (Chapter 2.1). It also functions as a precopulatory agent and as a protection for beetle eggs (presumably from predators and microbial infection) (Eisner et al, 1996a; Eisner et al, 1996b). Although the primary targets of cantharidin are type I and type II protein phosphatases (Honkanen, 1993; Laidley et al, 1997; Li & Casida, 1992), I showed that in baker’s yeast Crg1 is required for methylation of cantharidin in vitro, and CRG1 gene dose is important to maintain lipidome homeostasis in response to the drug (Lissina et al, 2011). We also presented evidence that in the human fungal pathogen C. albicans putative methyltransferase orf19.633 (Hoon et al, 2008; Lissina et al, 2011) is a gene-dose modulator of cantharidin response, and similarly to the baker’s yeast the deletion of orf19.633 leads to changes in cantharidin-perturbed lipidome (Chapter 2.2.9). Interestingly, at the sequence level ScCRG1 and orf19.633 (referred to as CaCRG1) have a limited sequence similarity even within their putative methyltransferase domains (19.2% identity and 38.5% similarity), indicating that the function of CaCRG1 in its response to the toxin could not be inferred solely from its sequence. BLASTp analysis reveals that CaCRG1 shares sequence similarity with other genes with unknown functions from diverse human fungal pathogens: Candida dubliniensis (CD36_30360, 77.9% identity, 85.2% similarity), Candida tropicalis (CTRG_00537, 65.4% identity,

80.3% similarity), Candida parapsilosis (CPAR2_204610, 57.9% identity, 74.3% similarity), Candida orthopsilosis (CORT_0D04720, 57.8% identity, 74% similarity). Given the steady increase in non-albicans infections these observations suggest that the study of CaCRG1 can provide an insight into these pathogens (Pfaller & Diekema, 2007). Phylogenetic analysis suggested that CaCrg1 is a putative glucosylceramide (GlcCer) methyltransferase (Ternes et al, 2006). Interestingly, methylation of GlcCer is absent in S. cerevisiae and Schizosaccharomyces pombe, yet this modification has been reported to play a role in virulence of C. albicans, Fusarium graminearum, and Cryptococcus neoformans (Heung et al, 2006; Noble et al, 2010; Oura & Kajiwara, 2010; Ramamoorthy et al, 2009; Singh et al, 2012).

In the present Chapter, I present efforts to characterize the putative methyltransferase CaCRG1 in C. albicans using cantharidin as a probe. Here, I demonstrate that CaCrg1 is a lipid-binding small molecule methyltransferase essential for cellular defense against chemical stress and for maintenance of virulence-related processes in the response to cantharidin. I also demonstrate that CaCRG1 is important for virulence of C. albicans in the waxworm G. mellonella, an established infection model.

3.2 Results

3.2.1 A functional AdoMet-dependent methyltransferase domain of CaCRG1 is important for cantharidin resistance

Orf19.633 (hereafter CaCrg1) is annotated as a putative AdoMet-dependent methyltransferase with diagnostic AdoMet-binding motifs (Petrossian & Clarke, 2009b). To test if orf19.633 is a functional methyltransferase enzyme, a codon-optimized CaCRG1 sequence was synthesized (Bio Basic Inc, Markham, ON, Canada), and I expressed this construct from a plasmid in S. cerevisiae (Figure 3-1A) for complementation testing. I further used the synthesized gene as a template to produce mutant alleles (point mutants D48A and E153A-R156G, and a deletion mutant in the methyltransferase motif III), using S. cerevisiae crg1∆/∆ mutants as the expression host. Wild-type CaCRG1 completely rescued crg1∆/∆ sensitivity to cantharidin in S. cerevisiae, whereas the

Figure 3-1. A functional AdoMet-dependent methyltransferase domain of CaCrg1 is required for cantharidin resistance.

(A) Diagram of cloning and subcloning steps of the synthesized CaCRG1 onto BG1805 vector. (B) Methyltransferase domain with the point mutations and the deletion of Motif III (top). Assessment of fitness of S. cerevisiae wt and crg1∆/∆ cells overexpressing wt and mutated CaCRG1 in the presence of cantharidin (bottom). S. cerevisiae crg1Δ/Δ cells overexpressing empty vector BG1805, wild-type and mutated CaCRG1 alleles (D48A, E153A/R156G, and Motif III∆) were normalized to an equal OD600,10-fold diluted, spotted onto solid YP+2% galactose, 1% raffinose medium containing cantharidin (80 µM) and incubated at 30°C for 2 days. (C) Analysis of the levels of wt and mutated CaCrg1 proteins by immunoblotting. Cells were grown overnight in SD-ura, diluted to OD600 of 0.2 and grown to mid-exponential stage (OD600 of 0.8). The expression was induced in YP and galactose (2%) for 3 hours. The cell lysates were analyzed by anti-HA antibody.

mutant alleles of CaCRG1 (D48A and Motif III∆) failed to confer cantharidin resistance in a S. cerevisiae crg1∆/∆ strain (Figure 3-1B). The failure to complement was not due to reduced expression of the mutated CaCrg1proteins (Figure 3-1C), indicating that the methyltransferase domain of CaCrg1 is necessary for cellular survival in the presence of cantharidin.

3.2.2 Cantharidin is methylated by CaCrg1 in vitro and in vivo

Because CaCrg1 is important for cantharidin resistance, we tested if CaCrg1 catalyzes a methylation reaction on cantharidin similar to that of ScCrg1 (Chapter 2) (Lissina et al, 2011). I purified the Candida enzyme expressed in baker’s yeast (Figure 3-2A), and David Weiss (UCLA) demonstrated the formation of volatile radioactive methyl ester (as methanol) from an acid-hydrolyzed reaction mixture of the purified CaCrg1, cantharidin and a methyl donor, S-adenosyl-[methyl-14C]-L-methionine (Figure 3-2B). This activity was dependent on the presence of both the protein and cantharidin, and the reactions containing DTT, EGTA, imidazole, or sodium chloride were found to have no effect on enzyme activity (Figure 3-2C). These results suggest that CaCrg1 is a functional methyltransferase that catalyzes the methylation of cantharidin in vitro.

To determine if CaCrg1 is required for in vivo methylation of cantharidin, Brian Young (UCLA) investigated the metabolism of cantharidin in wt and cacrg1∆/∆ homozygous deletion mutant. Mid-exponentially grown cells were treated with either cantharidin (100 µM) or DMSO for 90 min. Intracellular metabolites were rapidly extracted and analyzed by liquid chromatography-tandem mass spectrometry. In the m/z=197 single-ion chromatogram, we observed the peak corresponding to cantharidin (m/z=197) in wild- type and cacrg1∆/∆ cells grown in the presence of the drug (Figure 3-2D, left panel). These chromatographic peaks eluting at 19.4 min with m/z ratios matching cantharidin were absent in control cultures that were treated only with DMSO. Next, we examined the m/z=211 single-ion chromatogram, which corresponds to the mass range of methyl cantharidin (m/z=211) (Figure 3-2D, right panel). We observed a large peak eluting at 19.9 min in the wild-type strain treated with cantharidin in the mass range matching

Figure 3-2. CaCrg1 is small molecule AdoMet-dependent methyltransferase.

(A) Coomassie-stained 12% SDS-PAGE of the purified His-tagged CaCrg1. Cells carrying an empty BG1805 and BG1805-GAL1-CaCrg1 were grown in SD-ura and 2% raffinose to mid- exponential phase. The expression of CaCrg1 was induced with galactose (2%) overnight. His- tagged CaCrg1 was purified with Ni-Sepharose resin. lane 1, molecular weight standards; lane 2, soluble cell extract; lane 3, insoluble fraction; lane 4, Ni2+ Sepharose beads after 1st wash; lane 5, unbound to beads cell extract; lane 6, first wash; lane 7, beads after three washes; lane 8, non- concentrated elute; lane 9, flow-through; lane 10, concentrated and desalted elute. The expression of CaCrg1 was assessed with Western blot using a mouse monoclonal anti-HA antibody. (B) CaCrg1 shows robust methyltransferase activity with cantharidin as the substrate in vitro. The reactions containing varying amounts of CaCrg1 enzyme and cantharidin were tested for production of acid-labile methylated ester. The error bars represent the standard deviation of two separate experiments each performed in duplicate. (C) Possible effectors of CaCrg1 enzyme activity show little effect on methylation of cantharidin. Addition of 1 mM DTT, 2 mM EGTA, 0.5 mM imidazole, or 0.2 mM sodium chloride to reactions with 0.0225 µg of CaCrg1 enzyme do not significantly alter enzyme activity as measured by acid-labile methylation assays. Error bars represent standard deviation from duplicate samples in one single experiment. (D) CaCrg1 is required for formation of methyl cantharidin in vivo. Wt and cacrg1∆/∆ cells were cultured in the

presence and absence of cantharidin before extraction of intracellular metabolites and analysis by liquid chromatography-tandem mass spectrometry. Single-ion chromatograms of various cellular extracts are shown for the mass ranges corresponding to cantharidin (m/z=197±100 ppm) (left panel) and methyl cantharidin (m/z=211±100 ppm) (right panel). Arrows mark the elution patterns for cantharidin and methyl cantharidin. In the m/z=197 single-ion chromatogram, there is a methyl cantharidin peak because methyl cantharidin undergoes in-source fragmentation to form cantharidin. (E) Averaged spectra of the cantharidin (left panel) and methyl cantharidin (right panel). Chromatographic peaks from the cantharidin-treated wild-type cells are shown and ions of interest are indicated.

methylated cantharidin. In contrast, no peak in this mass range was observed in cantharidin-treated cacrg1∆/∆ cells or in cells treated with DMSO alone. We examined the spectra of the 19.4-min cantharidin peak in the wild-type, drug-treated strain and found ions corresponding to cantharidin (m/z=197) and cantharidin water adduct or hydrated cantharidin derivative (m/z=215) (Figure 3-2E, left panel). When we analyzed the spectra of the CaCrg1-dependent 19.9-min methyl cantharidin peak in the wild-type drug-treated culture, we saw ions corresponding to methyl cantharidin (m/z=211), hydrated methyl cantharidin (m/z=229), as well as unmodified cantharidin (m/z=197), a possible product of in-source fragmentation (Figure 3-2E, right panel). Our findings indicate that cantharidin is methylated in vivo in C. albicans, and that CaCrg1 is a novel small molecule AdoMet-dependent methyltransferase responsible for this activity.

3.2.3 CaCRG1 is important for C. albicans morphogenesis in response to cantharidin

Cantharidin is a cytotoxic metabolite produced by blister beetles to attract mating partners and deter predators and microbes from eating or infecting their eggs (Eisner et al, 1996a). The potency of cantharidin has been demonstrated in multiple studies investigating drug cytotoxicity towards tumor cells (Efferth et al, 2005; Moed et al, 2001; Wang et al, 2000; Wang, 1989). However, the knowledge of the antifungal activity of cantharidin is very limited (Hoon et al, 2008; Korting et al, 1986; Lissina et al, 2011). To define biological processes affected by cantharidin in the fungal pathogen C. albicans, I profiled the Candida transcriptome using custom Affymetrix gene expression arrays

(stCANDIDA 1a). Exponentially grown cells were treated with cantharidin at its IC50 (2 mM in YPD) or with DMSO for 30 min. Analysis of the transcriptome revealed 235 differentially expressed genes (log2 (cantharidin/DMSO)<|2|, P-value <0.05; Figure 3- 3A and Appendix 2, Table 1). The majority (91%) of genes exhibiting changes in the transcript abundance were downregulated, and these were significantly enriched in the following Gene Ontology (GO) term processes: “cell adhesion” (P-value <8.65x10-4), “positive regulation of response to stimulus” (P-value <2.37x10-2), and “regulation of filamentous growth” (P-value <3.6x10-2) (Figure 3-3A). CaCRG1 was among the upregulated genes (log2 >2.3, P-value <9.0x10-4), and qRT-PCR analysis confirmed that

the relative abundance of CaCRG1 transcript increases in the response to cantharidin in a time-dependent manner (Figure 3-3B). These results suggest that treatment with cantharidin elicits a transcriptional response characterized by the genes with role in adhesion and filamentation, and that CaCRG1 is upregulated by cantharidin treatment.

Because cantharidin treatment downregulates genes with a role in adhesion and filamentation, two phenotypes that are directly related to C. albicans virulence (Zakikhany et al, 2008), and CaCrg1 is essential for cantharidin tolerance, I assessed these phenotypes in cacrg1∆/∆ mutant in the presence of cantharidin. I found that cacrg1∆/∆ failed to adhere to plastic in the presence of non-inhibitory doses of cantharidin (10µM) (P-value <8.0x10-10; Figure 3-3C), and demonstrated a reduced adherence to plastic surface in the absence of the drug (P-value <1.0x10-3; Figure 3-3C and Figure 3-3D). Because adhesion of yeast cells to a host surface usually precedes germination, a trait important for host tissue invasion, I tested if CaCRG1 has a role in the germination. The mutant failed to germinate when exposed to a non-growth inhibitory dose of cantharidin (10 µM), although cacrg1∆/∆ underwent hyphal elongation in a similar manner to wild type (Figure 3-3E). These findings clearly indicate that CaCrg1 is required for the virulence-related processes upon exposure to cantharidin in C. albicans; thus, it negatively regulates cytotoxicity of cantharidin. Furthermore, CaCrg1 also contributes to adherence in the absence of cantharidin.

Figure 3-3. CaCRG1 is important for cantharidin-perturbed morphogenesis in C. albicans.

(A) Transcriptional profile of wild type C. albicans grown in the presence of cantharidin. Exponentially grown cells were treated with cantharidin (2 mM for 30 min), total RNA was extracted and synthesized cDNA was hybridized to Affymetrix microarrays. Significantly (P value<0.05) downregulated genes (log2 (drug/DMSO) >|2|) were analyzed for GO Biological Term Enrichment. (B) CaCRG1 is a cantharidin-responsive gene. Wt cells grown to mid- exponential phase in YPD were incubated with or without cantharidin. For each time point, total RNA was extracted, cDNA synthesized, and the relative abundance of CaCRG1 transcript was analyzed by qRT-PCR. Data are means of at least three independent experimental replicates, and error bars are standard deviation. (C) cacrg1∆/∆ has reduced adherence to plastic surface in the presence and absence of cantharidin. Overnight cells diluted to OD600 0.5 and incubated in SC media at 37 °C for 2 hrs with 10 µM cantharidin or DMSO. The adherence of cells to plastic surface was assessed by staining with 0.1% crystal violet. *p-value <0.05, ** <0.01. (D) CaCRG1 is required for adhesion of cells to plastic surface at log phase at 37ºC. Wt and cacrg1Δ/Δ mutants were grown overnight in liquid YPD, diluted to OD600 of 0.2 and grown to mid-exponential stage (OD600 of 0.8) in SC media. Cells were diluted to 0.5 OD600 in SC media and incubated at 30ºC or 37ºC for 2hrs. (E). cacrg1∆/∆ fails to form hyphae in the presence of cantharidin. Overnight cells were diluted and incubated in SC media at 37 °C for 2 hrs with or without cantharidin (10 uM).

3.2.4 CaCrg1 maintains membrane trafficking during cantharidin exposure

To understand how cantharidin-perturbed morphogenesis is manifested on a cellular level I examined C. albicans wt and cacrg1∆/∆ cells microscopically. In my previous study (Chapter 2) in baker’s yeast I reported that genes involved in vesicle-mediated transport are required to compensate for the absence of ScCRG1 upon cantharidin exposure (Lissina et al, 2011). SGD database also reports that ScCRG1 mutant has abnormal vacuolar morphology in a large-scale survey. Using the lipophilic styryl dye FM 4-64 to follow the dynamics of membrane internalization and transport via endosomal intermediates to the vacuole (Vida & Emr, 1995), I found that in C. albicans cantharidin perturbs membrane trafficking in a cacrg1∆/∆ mutant. I stained wild-type C. albicans and cacrg1∆/∆ cells, treated with or without cantharidin (250 µM) in YPD, and tracked internalization of the dye over time (15 and 60 min) (Figure 3-4A and Figure 3-4B). I did not detect drastic difference in vacuolar morphology in cacrg1∆/∆ mutant under standard conditions. After 15 min of cantharidin treatment (250 µM), both wild type and the mutant demonstrated brightly stained plasma membrane and vacuolar membranes (Figure 3-4A). Within 60 min of the drug exposure, the wild-type cells had exclusively vacuolar membrane staining (Figure 3-4B). In contrast, the membrane staining and structures of vacuoles in cacrg1∆/∆ differed drastically from the ones in wt within 1 hr: the plasma membrane staining remained as small puncta, and enlarged vacuoles were observed (Figure 3-4B). These observations suggest that cantharidin interferes with the transport of FM4-64 to the vacuole, and, furthermore, that recycling of vacuolar membranes is impaired in cantharidin-treated cacrg1∆/∆ mutants. My findings indicate that CaCrg1 is required to resist cantharidin-perturbed membrane recycling. Interestingly, I found that CaCrg1 preferentially binds to membrane phosphoinositides phosphatidylinositol phosphate PI(3)P and phosphatidylinositol bisphosphate PI(3,5)P2 in vitro (Figure 3-4C and Figure 3-4D), the established biomarkers of early and late endosomes (DiNitto et al, 2003; Downes et al, 2005; Gillooly et al, 2003; Kutateladze, 2010; Michell et al, 2006) (Figure 3-4E).

Figure 3-4. CaCrg1 maintains membrane trafficking during cantharidin exposure.

(A) Visualization of endosome dynamics in wt and cacrg1∆/∆ after 15 min in the presence and absence of cantharidin. Wild type and cacrg1∆/∆ mutant were grown to mid-exponential phase at 30°C in YPD, incubated with FM4-64 (40 µM). Endosome dynamics was assessed in the presence and absence of cantharidin (250 µM) or DMSO at 15 min. Cells were visualized with a 60x objective on an Axiovert 200M fluorescence microscope (Zeiss). Images were acquired with a Zeiss HRM digital camera using AxioVision software. (B) Wild type and cacrg1∆/∆ cells grown to mid-exponential phase at 30°C in YPD were incubated with FM4-64 (40 µM). Endosome dynamics was assessed in the presence and absence of cantharidin (250 µM) or DMSO at 60 min. (C) Lipid-protein overlay assay of CaCrg1. 6xHis tagged CaCrg1 was expressed in baker’s yeast followed by affinity purification. CaCrg1 (38 pmol; 2ug/ml) was incubated with lipid-spotted membrane overnight at 4ºC. After rigorous washing, the binding of CaCrg1 to lipids (100 pmol per spot) was detected using anti-HA antibody. Lipids: lysophosphatidic acid (LPA), lysophosphocholine (LPC), phosphatidylinositol (PtdIns), PtdIns phosphate (PI(n)P), phosphatidylethanolamine (PE), phosphatidylcholine (PC), sphingosine-1-phosphate (S1P), phosphatidic acid (PA), phosphatidylserine (PS). (D) Validation of the lipid-overlay experiment. PI(3)P and PI(3,5)P2 species were individually spotted onto a hydrophobic membrane. Wild-type CaCrg1 were incubated overnight at 4°C. The detection of the binding was performed with anti- HA antibody. (E) Simplified diagram of endocytic pathway. CaCrg1 interacts with phosphoinositides PI(3)P and PI(3,5)P2 known to be associated with the membranes of early and later endosomes.

Binding to the phosphoinositide species specific for early and late endosomes and the requirement for membrane trafficking upon cantharidin exposure suggest a potential mechanism for how CaCrg1 maintains membrane trafficking in response to cantharidin (Michell et al, 2006; Simonsen et al, 1998). For example, CaCrg1 is recruited to endosome compartments upon cantharidin treatment. Based on these data CaCrg1 is a lipid-binding small molecule methyltransferase that is required for the maintenance of cantharidin-perturbed membrane trafficking, adherence and filamentation.

3.2.5 Affinity-purified CaCrg1 binds ceramides in vitro

Because it is unlikely that human fungal pathogen C. albicans will ever come in a close contact with blister beetles, a source of cantharidin in nature, I asked why the response to cantharidin is conserved in distantly related fungi? One of the explanations is that CaCrg1 may be required for detoxification of endogenous toxic metabolites. For example, ScCRG1, a small molecule methyltransferase interacting with cantharidin in S. cerevisiae (Chapter 2) (Lissina et al, 2011), shares sequence similarity to ScTMT1, a small molecule methyltransferase involved in methylation of trans-aconitate, a toxic TCA cycle intermediate, and isopropylmalate, an yeast metabolite involved in invasive growth of S. cerevisiae (Chapter 1.2.3.2) (Cai et al, 2001a; Dumlao et al, 2008). In its methyltransferase domain CaCRG1 demonstrates limited similarity to small molecule methyltransferases, such as orf19.300 (27.2% identity, 54.3% similarity), orf19.752 (28% identity, 46.6% similarity), CaCOQ3 (27.3% identity, 51.2% similarity), CaERG6 (28% identity, 44.1% similarity) (Figure 3A). Having established that CaCrg1 is small molecule methyltransferase, its binding to membrane lipids and its partial sequence similarity and identity to lipid-related methyltransferases (Figure 3-5A), I tested whether CaCrg1 interacts with bioactive lipids. To address this, Patrick Yau (University Health Network Microarray Center) prepared a microarray comprising of 195 bioactive lipids spotted on a nitrocellulose-coated slide (see Appendix 2, Table 2). I assessed binding of CaCrg1 by a lipid-overlay assay. I found that purified CaCrg1 binds specifically to C8- ceramide (N-octanoylsphingosine) and its analogue C8-ceramine (N-octylsphingosine) (Figure 3-5B). I also found that CaCrg1 binds to C2-ceramide with higher affinity than to C8-ceramide,

Figure 3-5. Affinity-purified CaCrg1 binds ceramides in vitro.

(A) Alignment of protein sequences of CaCRG1 methyltransferase domain and its closest sequence homologues. The protein sequences are aligned using the MUSCLE software with EMBL-EBI Alignment program. Conserved motifs in the methyltransferase domain are underlined. The structures for the known and putative substrates are shown. (B) CaCrg1 binds to C8-ceramide and C8-ceramine molecules in vitro. Library of bioactive lipids was spotted onto a glass slide covered with nitrocellulose membrane. Affinity purified CaCrg1 was incubated with the membrane overnight at 4 ºC. After rigorous washing steps, the bound CaCrg1 was detected using anti-HA antibody. (C) Lipid-CaCrg1 overlay assay. Biologically active ceramide analogs (C2- and C8-ceramides were spotted on the nitrocellulose membrane. Affinity purified CaCrg1 (19 pmol; 1 µg/ml) was incubated with lipids overnight at 4ºC, vigorously washed and its binding was assessed with anti-HA antibody. Quantification of relative binding of CaCrg1 to ceramides was performed with ImageJ software. (D) C16-ceramide - CaCrg1 overlay assay. Biologically active C16-ceramide was spotted onto nitrocellulose membrane. Affinity purified CaCrg1 (19 pmol; 1 ug/ml) was incubated with C16-ceramide overnight at 4ºC and its binding was assessed with anti-HA antibody. Quantification of relative binding of CaCrg1 to ceramide was performed with ImageJ software. (E) The addition of ceramides decreases acid-labile methylation of cantharidin by CaCrg1 in vitro.

and did not bind to C16-ceramide (N-palmitoylsphingosine) (Figure 3-5C and Figure 3- 5D). The CaCrg1-ceramide interaction is of a particular interest, because ceramide is a bioactive molecule involved in stress response, cell growth, senescence, apoptosis, and autophagy (Hannun & Luberto, 2000). Furthermore, consistent with the previous phylogenetic analysis proposing that CaCRG1 is a putative glucosylceramide methyltransferase (Ternes et al, 2006), my results provide the evidence that ceramide may act as a substrate or a regulator of CaCrg1. Although we did not detect the formation of methyl ceramides when purified CaCrg1 was incubated with C2- or C8-ceramides, we did observe that co-incubation with C2- and C8-ceramides significantly decreased acid labile methylation of cantharidin by CaCrg1 (Figure 3-5E). Because ceramides interfere with the methylation of cantharidin in vitro, it suggests that ceramides may be negative regulators of CaCrg1 activity. Furthermore, I cannot rule out the possibility that ceramides are methylated by CaCrg1.

3.2.6 CaCRG1 interacts genetically with genes of glucosylceramide biosynthesis pathway

Ceramides serve as both bioactive molecules and structural elements that are required for the biosynthesis of glucosylcermides (GlcCer) (Cowart & Obeid, 2007; Hannun & Obeid, 2008; Hannun & Obeid, 2011; Obeid et al, 2002). Characterized C. albicans genes involved in GlcCer biosynthesis include: GlcCer synthase HSX11 (orf19.4592), sphingolipid delta-8 desaturase SLD1 (orf19.260), and a putative sphingolipid transfer protein HET1 (orf19.6327). The pathway also encompasses small molecule methyltransferases which have been demonstrated to modify GlcCer: sphingolipid C9- methyltransferase MTS1 (orf19.4831) and the predicted sphingolipid methyltransferase orf19.752 (Figure 3-6A) (Noble et al, 2010; Oura & Kajiwara, 2010; Ramamoorthy et al, 2009; Ternes et al, 2006). Additionally, methylation of the sphingoid long-chain base is absent in two non-pathogenic fungi Schizosaccharomyces pombe and S. cerevisiae.

To test if CaCRG1 is functionally related to GlcCer biosynthesis, I assessed genetic interaction(s) between CaCRG1 and GlcCer genes by constructing isogenic double- deletion mutants with a cacrg1∆/∆ strain. Considering that the occurrence of a genetic interaction between two genes is an extremely rare event (due to prevalent gene

redundancy), genetic analysis performed by assessing fitness of constructed double- deletion mutations is a powerful method to investigate gene function, as was successfully demonstrated in baker’s yeast (Tong et al, 2004). Unlike baker’s yeast, C. albicans double-deletion mutants are not readily constructed; their construction requires successive deletions of both copies of each gene using marker recycling (Figure 3-6B) (Reuss et al, 2004).

I constructed the double mutants and their fitnesses were analyzed in the presence of elevated temperatures and cantharidin. Both cacrg1∆/∆sld1∆/∆ and cacrg1∆/∆mts1∆/∆ showed drastically altered fitness when grown in liquid medium at 39°C compared to the corresponding single mutants and wt (Figure 3-6C). Specifically, I found that the double homozygous deletion strain cacrg1∆/∆sld1∆/∆ is synthetically sick or has a negative genetic interaction at 39°C in liquid media and at 43°C on solid SD media. cacrg1∆/∆mts1∆/∆ mutant showed an alleviating (positive) interaction at 39°C and in the presence of cantharidin (Figure 3-6C and Figure 3-6D). Mutants singly deleted for either gene were not sensitive, thus the effect is specific to the double mutant combination. Additionally, I observed synthetic lethality in cacrg1∆/∆sld1∆/∆ mutant grown at 37°C in the presence of non-inhibitory concentration of cantharidin (2.5 µM), and an enhanced fitness of cacrg1∆/∆mts1∆/∆ mutant at 37°C and cantharidin (25 µM) compared to the single mutants. Phenotypically, cacrg1∆/∆sld1∆/∆ had significantly reduced adherence to plastic (Figure 3-6E), and has a drastically different colony appearance compared to wt and the corresponding single deletion mutants (Figure 3-6F). These results suggest that CaCRG1 is required to buffer the absence of sphingolipid delta-8 desaturase SLD1. Furthermore, the positive interaction observed between CaCRG1 and MTS1 indicates the suppressive relationship between these two genes. Combined, these data show that CaCrg1 may participate along with these other gene products in GlcCer biosynthesis. Therefore, my in vivo study further confirms the connection between CaCrg1 and ceramide interaction observed in vitro.

Figure 3-6. CaCrg1 is interacts with the genes of GlcCer pathway.

(A) A simplified pathway for the biosynthesis of GlcCer in C. albicans. The genes encoding GlcCer synthase (Hsx11), sphingolipid delta-8 desaturase (Sld1) and sphingolipid C9- methyltransferase (Mts1) are shown. Phylogenetic profiling predicts the presence of additional C9-methyltransferases; therefore, the pathway is unlikely linear. (B) Scheme demonstrating the construction of double mutants in C. albicans. (C) Fitness of double-deletion mutants in liquid SC media at 39ºC. Overnight cells diluted to OD600 0.06 were grown in SC media at 39ºC for 2-3 days. Relative to no drug OD600 at saturation was used for the comparison of fitness of the mutants. (D) CaCRG1 interacts with GlcCer-related genes in a condition-dependent manner. Overnight cultures were 10-fold diluted, spotted onto SC defined medium with or without cantharidin and incubated at various temperatures (37 ºC and 43 ºC). The unexpected phenotypes for double-deletion mutants (relative to wt and crg1∆/∆ mutant) are highlighted: “+” denotes positive genetic interactions, “-“ denotes negative genetic interaction. (E) Adherence of cacrg1∆/∆sld1∆/∆ to abiotic surface. Overnight cells were diluted to OD600 0.5 and incubated in liquid SC media at 37 °C for 2 hrs. (F) Colony morphology of double-deletion mutants. Cells were streaked onto solid YPD media and incubated at 37 °C for 5 days.

3.2.7 CaCrg1 is important for C. albicans virulence in a Galleria mellonella model of infection

GlcCer biosynthesis has been implicated in the virulence of pathogenic fungi (Heung et al, 2006; Noble et al, 2010). In C. albicans, GlcCer is a virulence effector molecule known to exert its activity independent of morphogenetic switching, because mutants have normal morphology and normal proliferation in vitro (Noble et al, 2010). Yet, these genes are required for virulence in a murine model. Furthermore, methylation of the sphingoid long-chain base is a distinguishing feature of certain pathogenic fungi (Warnecke & Heinz, 2003), which is absent in the non-pathogenic fungi Schizosaccharomyces pombe and S. cerevisiae. For example, orf19.4831/MTS1, (predicted by phylogenetic analysis to encode C9-sphingolipid methyltransferase in C. albicans) has been shown experimentally to methylate GlcCer, and to be important for hyphal elongation and virulence in a mouse model (Noble et al, 2010; Oura & Kajiwara, 2010; Ternes et al, 2006). Therefore, to test the role of CaCrg1 in the pathogenecity of C. albicans, I examined the effect of deletion of CaCRG1 on pathogenecity of C. albicans using the greater wax moth G. mellonella, an established invertebrate model of infection (Figure 3-7A) (Brennan et al, 2002; Fallon et al, 2012; Fuchs et al, 2010). At least 16 larvae were used for each treatment and controls using a single blind design. Each larva was injected with 5x105 stationary phase cells, incubated at 37°C and assessed for viability every 24 hrs. I found that mts∆/∆ has decreased virulence in infected waxmoth larvae (Figure 3-7B), in accordance with the previous infection experiments performed in mice (Noble et al, 2010). A survival analysis of the infected larvae revealed that the deletion of CaCRG1 also significantly attenuated the virulence of C. albicans compared to the wild-type injected larvae (P- value <0.0001, log-rank test; Figure 3-7B). I also found that cacrg1∆/∆mts1∆/∆ has increased virulence relative to the single mutants (Figure 3-7B) suggesting that the condition-dependent positive genetic interactions we observed between CaCRG1 and MTS1 in vitro can be recapitulated in the infection model. Based on these data I conclude that CaCrg1 is required for virulence in a model of C. albicans infection.

Figure 3-7. CaCrg1 is important for C. albicans virulence in Galleria mellonella.

(A) Alive (left) and dead (right) Galleria mellonella larvae following infection with C. albicans. Image adopted from www.emergenbio.com. (B) Deletion of CaCRG1 results in increased survival (relative to wild-type strain) of G. mellonella larvae injected with C. albicans. Survival (%) is represented by Kaplan-Meier survival plot. Larvae were inoculated with 5x105 wild type or mutant C. albicans cells and incubated at 37 °C.

3.3 Discussion

Here I presented the characterization of a putative methyltransferase encoded by orf19.633 (CaCRG1) in response to cantharidin and revealed its endogenous functions in the human fungal pathogen C. albicans. My results uncovered that CaCrg1 is a functional small molecule methyltransferase that interacts with toxic molecule cantharidin, short- chain ceramides and early endosome phosphoinositides, and is required for virulence of C. albicans.

Despite a low sequence similarity between ScCRG1 and CaCRG1 (Chapter 3.1), including in their methyltransferase domains, CaCrg1 also methylates cantharidin in vitro and is important for methylation of the drug in vivo. Because cantharidin is a potent protein phosphatase inhibitor (Honkanen, 1993; Li & Casida, 1992), it interferes with multiple cellular processes via perturbations of phosphorylation events (Chapter 2). Consistent with this, I also found that exposure to cantharidin in C. albicans resulted in transcriptional changes of multiple genes (Chapter 3.2.3). For example, the downregulated genes were enriched for virulence-related processes, such as adhesion and filamentation, that are proposed to be promising targets for antifungal therapy (Jiang et al, 2002). Because CaCrg1 is required to maintain C. albicans morphogenesis in response to cantharidin, and is important in membrane trafficking, a process also linked to C. albicans virulence (Cornet et al, 2005; Douglas et al, 2009; Heung et al, 2006), I propose that CaCrg1 is important in the cellular processes involved in fungal virulence perturbed by cantharidin treatment. In other words, CaCrg1 negatively regulates cytotoxicity of cantharidin that is likely manifested via interference with phosphatases or other targets of cantharidin.

Because it is unlikely that human pathogen C. albicans will ever come into contact with blister beetles, a source of cantharidin, in nature, I asked why the response to cantharidin is conserved in distantly related fungi? One of the explanations is that CaCrg1 may be required for detoxification of endogenous toxic metabolites. For example, cantharidin methyltransferase ScCRG1 (Chapter 2) (Lissina et al, 2011) shares sequence similarity to ScTMT1, a small molecule methyltransferase involved in the methylation of trans- aconitate, a toxic TCA cycle intermediate, and isopropylmalate, an yeast metabolite

involved in invasive growth of S. cerevisiae (Chapter 1.2.4.2) (Cai et al, 2001a; Dumlao et al, 2008). My findings that CaCrg1 binds cytotoxic ceramides in vitro (Chapter 3.2.5) support the hypothesis that CaCrg1 may interact with endogenous bioactive small molecules. Ceramides are second messengers involved in diverse biological processes, such as stress response, cell growth, multidrug resistance, senescence, apoptosis, and autophagy (Hannun & Luberto, 2000). Interestingly, similarly to cantharidin, ceramides have been reported to interact with protein phosphatases, but opposite to cantharidin, this interaction results in the activation of phosphatases (Chalfant et al, 2004; Ruvolo et al, 2002). Taken together, CaCrg1 is a small molecule methyltransferase that interacts with two structurally different protein phosphatase ligands. It was logical to hypothesize that ceramides may act as substrates or regulators of CaCrg1. Although we failed to demonstrate methylation of ceramides by CaCrg1 in our acid labile methylation assay (Chapter 3.2.5), one cannot rule out possibility that ceramides are still modified by CaCrg1 in a way that it does not result in the formation of methyl esters. In fact, we showed that ceramides interfere with methylation of cantharidin by CaCrg1, proposing that ceramides may compete with cantharidin for the active site on CaCrg1.

My analysis of the genetic interactors of CaCRG1 revealed that CaCRG1 has a functional relationship with the components of GlcCer biosynthesis. Glucosylceramides (GlcCer) are complex sphingolipids generated by an addition of a sugar group to otherwise cytotoxic ceramides (Hannun & Obeid, 2008). Therefore, my in vivo study confirms the connection between CaCrg1 and ceramide observed in vitro. In C. albicans, GlcCer is a virulence effector molecule known to exert its activity independent of morphogenetic switching, as mutants have normal morphology and normal proliferation in vitro (Noble et al, 2010). Yet, the genes of GlcCer biosynthesis pathway are required for virulence in a murine model. Furthermore, methylation of the sphingoid long-chain base is a distinguishing feature of certain pathogenic fungi (Warnecke & Heinz, 2003), which is absent in the non-pathogenic fungi S. pombe and S. cerevisiae. In F. gramenearum sphingolipid C9-methyltransferase genes (FgMT1 and FgMT2) are essential for its survival and virulence (Ramamoorthy et al, 2009). In C. albicans MTS1 (orf19.4831) is important for virulence of the pathogen in a murine model, and mutants manifest a delay in hyphal elongation (Noble et al, 2010; Oura & Kajiwara, 2010). Similarly, I found that

the deletion of CaCRG1 attenuates virulence of C. albicans in an infection model using waxworms G. mellonella, but cacrg1∆/∆ demonstrates normal morphology in vitro. Having observed the interactions between CaCrg1 and ceramides in vitro and the in vivo genetic relationship between the components of GlcCer pathway it is plausible to assume that CaCrg1-dependent virulence of C. albicans involves these lipid bioactive molecules. Furthermore, because my findings demonstrate the role of CaCrg1 in host-pathogen interactions, it opens an attractive possibility to explore this interface as a point of intervention with antifungals.

Another interesting finding revealed in my study is that despite the fact that CaCrg1 lacks any detectable lipid-binding motifs (DiNitto et al, 2003; Fernandez-Murray & McMaster, 2006), it has an affinity to biomarkers of early and late endosomes or phosphoinositides PI(3)P and PI(3,5)P2. The endosome system is a point of sorting cargo either for degradation or recycling back to plasma membrane (Downes et al, 2005; Gillooly et al, 2003; Michell et al, 2006). It is well established that GlcCers are abundantly present in within cellular membranes and are constantly recycled via membrane trafficking (Breslow & Weissman, 2010; van Meer et al, 2008). As a confirmation for the CaCrg1- endosome connection, I found that cantharidin treatment perturbs membrane recycling in cacrg1∆/∆ mutant leading to the formation of enlarged vacuoles. In a similar manner, ceramides have been reported to modulate membrane trafficking. For example, the treatment with cytotoxic short-chain ceramides and GlcCer inhibitor PDMP decreases fluid-phase and receptor-mediated endocytosis, and leads to formation of enlarged late endosomes (Chen et al, 1995; Hannun & Obeid, 2011; Li et al, 1999; Tserng & Griffin, 2004). Thus, it is possible that the binding of CaCrg1 to endosome markers facilitates membrane trafficking upon cantharidin exposure.

3.4 Materials and Methods

3.4.1 Strains, plasmids and growth conditions

Yeast strains and plasmids used in this study are described in Table 3-1 and Table 3-2, respectively. Cantharidin from Sigma Aldrich (Toronto, ON, Canada) was dissolved in DMSO and stored at -20°C. Ceramides (N-acetylsphingosine, N-octanoylsphingosine, N-

palmitoylsphingosine) were from Avanti Polar Lipids, Inc. (Alabaster, Alabama, USA), dissolved in DMSO or ethanol.

3.4.2 Microarray analysis

Cells grown to mid-exponential phase in YPD medium were incubated with or without cantharidin (2 mM) for 30 min and harvested by centrifugation. Isolation of RNA and hybridization to the microarrays was performed as described (Lissina et al, 2011). Three independent replicates were used for the analyses. Hybridization to Affymetrix custom expression array (stCANDIDA 1a) (Affymetrix) was followed by the extraction of intensity values for the probes using the GeneChip Operating Software (Affymetrix). The resulting files containing probe position and intensities were further analyzed by aligning the probes that match the position of the Candida Genome Database list of defined ORFs. Quantile normalized datasets were further analyzed. The significance for a differential expression was set as log2 (drug/DMSO) >|2|, P-value <0.05 as determined by Student’s t test. Significantly up- and downregulated transcripts were further tested for Gene Ontology (GO) Biological process term enrichment using AmiGo (http://amigo.geneontology.org) with P-value cutoff of 0.05 and multiple testing corrections (Bonferroni).

Table 3-1. Strains used in this study.

Strain name Genotype and source SN87 SN87, derivative of SC5314 leu2Δ/leu2Δ his1Δ/his1Δ URA3/ura3::imm434 IRO1/iro1::imm434 (Noble & Johnson, 2005) CaEL1 Isogenic to SN87, except his1::CdHIS1/his1∆ leu2::CmLEU2/leu2∆; this study cacrg11∆/∆ Isogenic to SN87, except orf19.633::CdHIS1/orf19.633::CmLEU2 (Lissina et al, 2011) sld1∆/∆ Isogenic to CaEL1, except sld1∆/sld1::CaNAT; this study mts1∆/∆ Isogenic to CaEL1, except mts1∆/mts1::CaNAT; this study

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orf19.752∆/∆ Isogenic to CaEL1, except orf19.752∆/orf19.752::CaNAT; this study hsx11∆/∆ Isogenic to CaEL1, except hsx11∆/hsx11::CaNAT; this study sld1∆/∆ cacrg1∆/∆ Isogenic to CaLC941, except sld1∆/sld1::CaNAT; this study mts1∆/∆ cacrg1∆/∆ Isogenic to CaLC941, except mts1∆/mts1::CaNAT; this study orf19.752∆/∆ cacrg1∆/∆ Isogenic to CaLC941, except orf19.752∆/orf19.752::CaNAT; this study hsx11∆/∆ cacrg1∆/∆ Isogenic to CaLC941, except hsx11∆/hsx11::CaNAT; this study BY4743 MATa/α his3Δ1/his3Δ1 leu2Δ0/leu2Δ0 LYS2/lys2Δ0 met15Δ0/MET15 ura3Δ0/ura3Δ0 (Brachmann et al, 1998) crg1∆/∆ Isogenic to BY4743, except for crg1::KanMX crg1::KanMX (Giaever et al, 2002)

Table 3-2. Plasmids used in this study.

Plasmid name Description and source pJK863 (pLC49) FLP-CaNAT, ampR (the Cowen lab) pUC57 With optimized sequence orf19.633, AmpR; this study BG1805 2 µ, URA3, GAL1prom, triple affinity tag (His6-HA epitope-3Cprotease site-ZZproteinA) at C-terminal (Lissina et al, 2011) CaCRG1 As BG1805, with orf19.633; this study D48A As BG1805, orf19.633–D48A; this study E153A_R156G As BG1805 orf19.633–E153A/R156G; this study Motif II/III ∆ As BG1805 orf19.633–Motif II/III∆; this study

3.4.3 Cloning and purification of CaCrg1 fusion protein

The sequence of CaCRG1 was optimized for purification in S. cerevisiae and synthesized with sequences for restriction enzyme digestion sites BsrGI in the universal vector pUC57 . The synthesized CaCRG1 was cut out with BsrGI, SAP-treated and co- transformed with BsrGI-linearized BG1805 vector into a S. cerevisiae crg1∆/∆ mutant. CaCRG1 was cloned downstream of a GAL1 inducible promoter and in frame with a epitope protease site protein A triple affinity tag at its C-terminus (His6-HA -3C -ZZ ). Transformants

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selected in SD media lacking uracil (SD-ura) were screened by PCR and for cantharidin resistance. Clones were sequence-verified. To express CaCrg1, cells were grown to mid- exponential phase in SD-ura containing 2% raffinose, then induced by the addition of galactose to a final concentration of 2%. Cells were harvested after overnight induction, and CaCrg1 expression was verified by Western blots of 12% SDS-PAGE gels using anti-HA antibodies. Induction and purification of CaCrg1 was performed as described in Lissina et al (Lissina et al, 2011).

3.4.4 Site-directed mutagenesis

CaCRG1 missense and deletion mutants were prepared using the Phusion Site-directed mutagenesis kit (Finnzymes – Thermo Fisher Scientific, Espoo, Finland) with the primers listed in Table 3.3. Clones were sequence-verified. To express mutated CaCrg1, transformants were grown to mid-exponential phase in SD-ura and 2% raffinose, and induced by the addition of galactose to a final concentration of 2%. 30 µM of cantharidin was used to test sensitivity of mutants. Cells were harvested after 3 hours of induction, and CaCrg1 expression was verified by Western blots of 12% SDS-PAGE gels using anti-HA antibodies.

3.4.5 Metabolomic profiling of C. albicans cellular extracts

C. albicans wt and cacrg∆/∆ mutants were cultured in the presence and absence of cantharidin, and cellular extracts were prepared for metabolomic analysis by mass spectrometry based on methods described previously (Lu et al, 2010). Briefly, cells were cultured in SC medium overnight at 30ºC. Mid-exponential cells were treated with cantharidin (100 µM) or DMSO alone (1%). After 90 min of growth at 30 ºC, cells were rapidly isolated onto 45-mm diameter Millipore nylon filter membranes (0.45-µm pore size) via vacuum filtration. The filter was then transferred to a petri dish containing 800

µl 80:20 acetonitrile:H2O and the dish was incubated at 4ºC for 15 min before the extract was transferred to a tube. The filters were washed again with 200 µl of extraction buffer and this was added to the extract. The extract was centrifuged at 20,800 rcf for 5 min and the supernatant was isolated. The pellet was re-extracted with 200 µl of extraction buffer and incubated at 4 ºC for 15 min. After centrifugation at 20,800 rcf for 5 min, the

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supernatants from both extraction steps were pooled, neutralized with 120 µl 15% ammonium bicarbonate, dried by vacuum centrifugation, and frozen. The samples were resuspended in 100 µl of H2O before analysis by liquid chromatography-tandem mass spectrometry using methods that we described previously (Lissina et al, 2011).

3.4.6 In vitro methylation reactions

Reaction mixtures containing 0.2 mM cantharidin (prepared as a stock solution of 10 mM cantharidin in DMSO) and 20 µM S-adenosyl-[methyl-14C]-L-methionine (48.8 mCi/mmol; PerkinElmer Inc., Boston, MA) in a buffer of 0.1 M sodium phosphate, pH 7.4, were mixed with either 0.015 µg, 0.03 µg, or 0.06 µg of recombinant C. albicans CaCrg1 protein in a final volume of 50 µl. Control reactions were performed in the

Table 3-3. Primers used in this study.

Primer name Purpose Oligonucleotide sequence (5’-3’) CaCRG1 (mid)_F qRT-PCR ATCACCGGTGGAGAAATACG CaCRG1 (mid)_R qRT-PCR TGATTCATATCCTGCTTCTAATGC ACT1 (3’)_F qRT-PCR AGGTTTGGAAGCTGCTGGTA ACT1 (3’)_R qRT-PCR AGCAATACCTGGGAACATGG D48A_F CaCRG1 TTCAAGATTTTAGCTGTTGGGTGC mutagenesis GGTCCTGG D48A_R CaCRG1 GTTTGGTTTTATCAGTGGAATGAC mutagenesis GTACTTAGC E153A/R156G_F CaCRG1 GAAGCCTTGAAGGCATTGAAAGG mutagenesis AGTTACGAAACCAG E153A/R156G_R CaCRG1 GATTGGATTCTGTAAATGAATGAT mutagenesis CACCTGG

Motif III_F CaCRG1 ATCTGTATTAGAGATGCAGATTTG mutagenesis GAATCTAGTATAG

Motif III_R CaCRG1 CAGTTCATAGATAGAACCAATTTG mutagenesis AAACGAAATATTAGTC MTS1_F Double mutant GTTTTCGTCTTTTGTCGAGTTTAAC construction ATTTCAATTGAATATCAATTTTTGT AACAATGCGTATGTTGTGTGGAAT TGTGAG MTS1_R Double mutant TATTCAGATCAGAATAAATAAAAA construction TCTATACAAATACACCATAAAAGC TCAACCAGTTTAGGCGATTAAGTT GGGTAACG

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SLD1_F Double mutant TTATTTATTTATTGTTATTTTTTTTT construction TTTCACCTTCGTTAACAAACCTTTA TGAGATAAACCATGCGTATGTTGT GTGGAATTGTGAG SLD1_R Double mutant GTCTCTATATATATATCTATACCCT construction CATATAAATTTGCATAAAAGTTAA GAAAACAGGATACTGTCTAGGCG ATTAAGTTGGGTAACG HSX11_F Double mutant TACATTTGATCTTGTTTCTTTTATC construction AACACTGTGAAATATCGTAATCTT TTGTTCTTCTTTCTAATGCGTATGT TGTGTGGAATTGTGAG HSX11_R Double mutant TTACATTTAACTTTTACTTATCTAT construction ATATACATGTCCTGTCTATCTATCT ATAACAATTGGCGTATCAGGCGAT TAAGTTGGGTAACG orf19.752_F Double mutant TTCTTTGTATTATTGGTTAGATTTC construction CATTCCATATACACACAAGATGCG TATGTTGTGTGGAATTGTGAG orf19.752_R Double mutant TTGAAGTGAGTTGATGTAGTAAAT construction TATGTATGAATGTATATAAACCCT CAGGCGATTAAGTTGGGTAACG confirm_Flip_NAT_F Confirmation CAACCACAAATGACCAGCAC confirm_Flip_NAT_R Confirmation GTGATTTGGCTGGTTTCGTT confirm_MTS1_F Confirmation CACATTTGCCCATCACTCTG confirm_MTS1_R Confirmation CGAAGTCATGTTCGTGCATC confirm orf19.752_F Confirmation CAAGCCGAGTCGGAAAACTA confirm orf19.752_R Confirmation TGTGTTCGACACATCCTGGT confirm_HSX11_F Confirmation TCGAACTGTCATCCTGTCCA confirm_HSX11_R Confirmation TACAACCAAGCTGCGAAAAA confirm_SLD1_F Confirmation CTTTTGGCAGGATTCTTGGA confirm_SLD1_R Confirmation CCAAAAGAACCAGAGCTTGC confirm_SLD1_1100bp_F Confirmation TGGACGTTGATTGTCCTGAA confirm_HSX11_430bp_F Confirmation AATCGATTGGAGGGGAAGAC confirm_MTS1_990bp_F Confirmation AGAGACGCTTTGGAAGACGA confirm_orf19.752_995bp_F Confirmation CGACCTAATTGTTGCCAAGG

GAL1_CaCRG1_F CaCRG1 TTAACGTCAAGGAGAAGGAATTAT cloning CAAGTTTGTACAATGAGCGGTGCT AACAACAACCATCAAGTG Cterm_CaCRG1_R CaCRG1 ATGGTGATGATGATGTCTAGACAC cloning ATCAACCACTTTTGTACACACTTG ACGTGTAGTTGTTGGTTTTTGGTA

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absence of protein (no enzyme) or in the absence of cantharidin (DMSO solvent alone) with 0.09 µg of the CaCrg1 protein. Samples were incubated for 120 min at 30 ˚C and the reaction quenched by the addition of 40 µl of 2 M HCl. Methylation of cantharidin was determined by acid-labile volatility as described in Lissina et al. 2011(Lissina et al, 2011). A portion of the quenched reaction mixture (80 µl) was spotted on a filter paper which was then placed in the neck of a scintillation vial containing 5 ml of Safety-Solve cocktail (Research Products International) and incubated for 4 hrs at room temperature. Radioactivity released as 14C-methanol was measured by counting the vial after removal of the paper.

3.4.7 Lipid-protein overlay assay

The Screen-Well Bioactive lipid library containing 195 bioactive lipids (Appendix 2, Table 2) were obtained from Enzo Life Sciences, Inc via Cedarlane Laboratories (Burlington, ON, Canada). Lipids were spotted onto FAST glass slides covered with nitrocellulose polymer (Whatman Ltd, GE Healthcare) and the binding between CaCrg1 and lipids was analyzed by standard lipid-overlay assay. Briefly, lipids dissolved in chloroform/methanol/water (1:2:0.8) were spotted on PVDF membrane. Dried membranes were blocked for 1 hr in 3% fatty acid-free BSA in TBST (50 mM Tris/HCl pH 7.5, 150 mM NaCl, 0.1% Tween20). The arrays were incubated with affinity purified HA-tagged CaCrg1 (2 µg/ml) overnight at 4ºC with gentle stirring. The membrane was rigorously washed six times for 30 min in TBST, incubated with mouse anti-HA monoclonal antibody for 1 hr, washed again as before, incubated with anti-mouse- horseradish peroxidase conjugate. Finally, the membrane was washed 12 times for 1 hr in TBST, and the membrane-bound HA-fusion CaCrg1 was detected by ECL.

3.4.8 FM4-64 labeling for vacuolar membrane dynamics

C. albicans wt and cacrg1∆/∆ cells were grown overnight in SC. Cells grown to mid- exponential phase in YPD a 30ºC were concentrated to OD600 of 20, and stained with lipophilic dye FM4-64 (40 µM) for 45 min at 25ºC. Cells were washed twice and resuspended in 200 µL YPD. Cells were treated with 250 µM cantharidin and incubated at 30ºC for 1 hr with shaking. Cells were observed after 15 min and 1 hr of the treatment

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with cantharidin with 63x objective, and fluorescence images (Cy3 filter) were acquired using AxioVision software on an Axiovert 200M fluorescence microscope (Zeiss).

3.4.9 Adhesion assay

C. albicans wt and cacrg11∆/∆ cells grown in YPD at 30°C overnight were washed with phosphate buffered saline pH 7.4 (PBS) two times. Cells were inoculated into SC media to final OD600 of 0.5. After 2 hr incubation at 37°C, non-adherent cells were removed by three washes with PBS pH 7.4. Adherent cells stained with 0.1% crystal violet for 5 min, then washed with PBS three times, 0.25% SDS one time, PBS two times. To resolubilize crystal violet, 150 µl isopropanol-0.04N HCl and 50 µl of 0.25% SDS were added to each well. The absorbance of each well was measured using a microplate reader at A590.

3.4.10 qRT-PCR analysis

Cells grown to mid-exponential phase in YPD medium were incubated with or without cantharidin for varying amounts of time, harvested by centrifugation, and frozen in liquid

N2 and stored at -80 ºC. RNA extraction and QRT-PCR analysis was performed as described in Lissina et al (Lissina et al, 2011).

3.4.11 Construction of double-deletion mutants

The double knockout strains were generated using SAT technology (Reuss et al, 2004). SAT was PCR amplified from pJK863 (pLC49) using specific primers (Table 3.3), containing sequence homologous to SAT and a gene of interest. PCR-amplified product was transformed into wt and cacrg11∆/∆ mutants using standard transformation protocol. Nourseothricin (NAT)-resistant transformants were PCR tested (Table 3.3) for a proper integration of the construct. The SAP2 promoter was induced to drive expression of FLP recombinase to excise the NAT marker cassette for a subsequent reuse. The same procedure was repeated until all alleles were knocked out with SAT cassette. This strain was additionally tested for the absence of any wild-type alleles by PCR (Table 3.3).

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3.4.12 Virulence assay

The C. albicans virulence assay was performed on waxworm larvae of G. mellonella as described (Fuchs et al, 2010). Larvae were obtained from Port Credit Pet Center (Mississauga, ON, Canada). C. albicans cells grown overnight in YPD at 30 ºC, were washed with PBS, and 5x105 cells were injected into the larvae in 20 µL of PBS and incubated at 37 ºC. Dead larvae were scored daily. Kaplan-Meier plots were generated using GraphPad Prism software and significant difference in survival was analyzed by log-rank test.

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Chapter 4 Exploring genetic architecture of the yeast methyltransferome

All experiments including mutant construction, data processing and analysis were performed by Elena Lissina.

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4 Exploring genetic architecture of the yeast methyltransferome 4.1 Introduction

In this chapter, I present the analysis of genetic interactions among AdoMet-dependent methyltransferases in S. cerevisiae (referred to as yeast methyltransferome) under standard as well as environmental stress conditions. The main objectives of this study are to investigate the functional relationships among yeast methyltransferases in a comprehensive manner and to evaluate the robustness or buffering capacity of these enzymes under environmental stress conditions.

As I described in Chapter 1 and demonstrated in Chapter 2 and 3, AdoMet-dependent methyltransferases are important in multiple biological processes, such as small molecule biosynthesis, detoxification, protein-protein interaction, signal transduction, transcriptional regulation, DNA repair, translation, and ageing (Schubert et al, 2003). In baker’s yeast over 80 genes are annotated as enzymes containing a diagnostic methyltransferase domain (Katz et al, 2003; Petrossian & Clarke, 2009a; Petrossian & Clarke, 2009b; Wlodarski et al, 2011). Methyltransferases are so-called bisubstrate enzymes which catalyze the transfer of a methyl group from a cofactor AdoMet to a range of substrates (Martin & McMillan, 2002). Compared to other enzymes involved in post-translation modifications (e.g. kinases, phosphatases, acetylases, etc) methyltransferases are unique in the diversity of their substrates and ability to target multiple atoms (O, C, N, S and halides). Such substrate flexibility combined with the limited sequence identity (or similarity) in their methyltransferase domains (Martin & McMillan, 2002) are two challenges to studying these important enzymes.

Although the majority of yeast methyltransferases are characterized on a basic level, 35% have no known function (Petrossian & Clarke, 2009b; Wlodarski et al, 2011). Accordingly, many questions regarding this enzyme family remain to be answered. Importantly, clinical interest in understanding this family of enzymes derives from the numerous observations of their involvement in cancer, inflammation, neurodegenerative disease, fungal virulence and drug response (Chapter 1.2.1, Chapter 2 and 3) (Bedford &

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Richard, 2005; Cheung et al, 2007; Wang & Weinshilboum, 2006). Indeed, Petrossian reported that about 30% of known human methyltransferases are associated with disease states (Petrossian & Clarke, 2011), motivating our efforts to understand these molecular players, and uncover inhibitors or activators that can modulate their functions (Arrowsmith et al, 2012; Copeland et al, 2009; Greer & Shi, 2012).

In addition to focusing on individual methyltransferases, an overall understanding of methyltransferome (the compilation of all methyltransferases in a genome) is crucial because many methyltransferases participate in shared functions (Chapter 1.3). Based on the observed flexibility in methyltransferase domains, the idea of one methyltransferase- one substrate is an oversimplification and is not supported by experimental evidence (Chapter 1.1.4). For example, the human G9a protein lysine methyltransferase (involved in the modification of euchromatic histone H3K7) demonstrates additional methylation activity towards non-histone targets (e.g. p53) (Huang et al, 2010; Rathert et al, 2008b). Therefore, investigation of functional relationships between methyltransferases will likely reveal different levels of cross-talk between biological pathways and, may provide insights into novel functions for this family of enzymes.

Because biological systems are extremely robust (Kitano, 2004), organisms are characterized by high level of redundancy on a gene level and their ability to rewire their responses to external perturbations on genetic and protein levels (Gasch et al, 2000; Ideker & Krogan, 2012). Therefore, investigating how genetic architecture of methyltransferome changes during environmental conditions will expand our knowledge of nature of this class of enzymes (Chapter 1.4.4). Furthermore, it may help to uncover novel roles for yeast methyltransferases that are otherwise masked in standard conditions.

In the past decade, unbiased genome-wide studies in yeast (Costanzo et al, 2010; Tong et al, 2004) and the focused applications of well-established genomic tools (Fiedler et al, 2009; Kaluarachchi Duffy et al, 2012; Schuldiner et al, 2005; Sharifpoor et al, 2012; Wilmes et al, 2008) have proven quite valuable in the interrogation of enzyme families and functionally-linked genes in a systematic and quantitative manner. The unbiased genomic approach (e.g. SGA) offers several advantages over other methods

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(computational or biochemical) (Chapter 1.4): prior knowledge of a substrate is not required and it allows to characterize a gene function in a cellular context (in vivo) by investigating how cells respond to genetic and chemical perturbation.

In this chapter, I present my analysis of the genetic architecture of the AdoMet-dependent methyltransferome in baker’s yeast. To explore genetic relatedness of yeast methyltransferases in systematic and quantitative manner, I generated all possible double- mutant methyltransferase combinations using SGA technology with non-essential genes represented as deletion alleles and essential genes represented as partial-loss-of-function alleles. The set contains 7614 mutants represented by reciprocal as well as unique double- deletion mutants. These mutants were tested in standard (30ºC) and environmental stress conditions (16ºC, 37ºC, and LiCl). This allowed me to detect specific features observed for this class of enzymes in standard conditions and to uncover how yeast methyltransferome is modulated in response to external perturbations. This investigation provides an unbiased study of the functional relationships between yeast methyltransferases in both the reference condition as well as in the face of environmental stress.

4.2 Results

4.2.1 Construction of double-deletion methyltransferase mutants

To systematically assess genetic interactions between AdoMet-dependent methyltransferases in S. cerevisiae, I selected 78 known methyltransferases, 16 putative methyltransferases, and 4 Jmj domain-containing demethylases (GIS1, RPH1, JHD1, and JHD2). The set also included 8 essential methyltransferases (RNA) as hypomorphic alleles. In total, this screen interrogated 98 methyltransferases and demethylases acting towards diverse molecules: nucleic acids (tRNA, rRNA, mRNA, snRNA), proteins (histones, ribosomal proteins, transcription factors, etc.), small molecules (lipids, metabolites) and unknown molecules (Figure 4-1A). To construct all possible pairwise double-deletion combinations of methyltransferases I used the SGA technology

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Figure 4-1. Construction of double-deletion methyltransferase mutants.

(A) A composition of yeast methyltransferome. Methyltransferases are categorized according to their proven and predicted targeted substrate. (B) A workflow procedure for double mutant generation and data processing. (C) A representative image of the final plate with haploid double- deletion mutant colonies. Each mutant is present in four replicates. To obtain relative colony sizes images are converted to pixels using ColonyImager software. Each plate contains three query NatR-marked strains (gene∆::NatMX) crossed to an array of KanR-marked mutants (gene∆::KanMX).

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(Baryshnikova et al, 2010a; Tong et al, 2001). The strategy is summarized in a flowchart (Figure 4-1B). In total, the S. cerevisiae double-deletion methyltransferase set consisted of 33 plates (Figure 4-1C), and contained 7614 double-deletion mutants, where each mutant was presented by 4 replicates, and the reciprocal mutants (over 80%) were represented by 8 replicates (KanR-NatR and NatR-KanR pairs).

4.2.2 Evaluating genetic interaction scores and quality control

Recently, several large-scale studies have reported detailed analysis of genetic interactions in yeast (Baryshnikova et al, 2010b; Collins et al, 2010; Collins et al, 2006). I adopted these established procedures to determine the fitness of my 7614 methyltransferase mutants (represented by colony sizes) and quantified their digenic interactions. The generated scores offer an independent and quantitative assessment of genetic interactions among yeast methyltransferases.

The initial interactome set contained 7614 reciprocal and single genetic pairs that were generated by crossing 81 KanR-marked methyltransferases with 94 NatR-marked methyltransferases, and, as expected, it had a wide range of negative and positive genetic interaction scores (Figure 4-2A). The examination of the generated matrix revealed that majority of slow-growing strains had a range of genetic interactions characterized by a large number and a large magnitude of genetic interactions (e.g. RSM22) compared to control (HIS3) and other methyltransferases (e.g. OPI3) (Figure 4-2B). This observation is consistent with the previously described positive relationship between single mutant fitness defects and an increased number of genetic interactions (both negative and positive) (Costanzo et al, 2010). These slow-growing methyltransferases were all mitochondria-localized methyltransferases FMT1, RSM22, MTF1, COQ3, MRM1, COQ5, known to have respiratory deficiency phenotypes (Saveanu et al, 2001; Tauche et al, 2008). Although the media used in this study contained a fermentable carbon source, the fitness defects of mutants lacking these mitochondrial enzymes highlights their extensive buffering capacity and reflects their importance for functional mitochondria.

Because the mated strains in which the markers (NatMT and KanMX) are present in the exactly same chromosomal locations result in a lethal phenotype of a double mutant (i.e.

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the strains on the diagonal in the matrix) due to inability to grow in the presence of both selection drugs (G418 and nourseothricin), I used this phenotype to check for the accuracy of the strains (Figure 4-2C). Additionally, to decrease the rate of false positive scores for the genes located within 50 kb of each other, these gene pairs were also filtered out (Figure 4-2C) due to a possible linkage of these genes that may result in failure to segregate during meiosis. I also found that the genetic profiles of the reciprocal strains (NatR-KanR vs KanR-NatR) tend to correlate strongly to each other (Figure 4-2D). Based on these measures I was able to identify incorrect strains. After the removal of noisy and incorrect strains, the analysis was repeated again to generate novel scores.

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Figure 4-2. Evaluating genetic interaction scores and quality control.

(A) An orthogonal array of double mutants represented by 81 (KanR-marked) crossed to 94 (NatR- marked) methyltransferase mutants. Double-mutant colony sizes were scored, resulting in the generation of a range of negative or synthetic sick/lethal (blue) and positive scores or suppression (yellow). (B) Representative distributions of genetic interaction scores for double-deletion genetic interaction profiles of RMS22, OPI3, and HIS3. HIS3 is a control strain, RMS22 is a slow- growing strain. (C) Unaveraged genetic interaction scores for a given gene pair as a function of the chromosomal distance between these two genes. Kb – kilobases. Strains with linked genes (within 50 kb) were removed from the analysis. (D) A relationship between correlation of genetic profiles for reciprocal pairs (KanR-NatR vs NatR-KanR) and genetic interaction score for a given methyltransferase.

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4.2.3 Assessing genetic interactions among methyltransferases

The final matrix contained 66 KanR - to 78 NatR-marked methyltransferases resulting in 5148 genetic interactions, with each row and column representing a genetic interaction profile for the particular gene (Figure 4-3A).

To evaluate the reproducibility of the scores, I first compared the scores obtained in two independent screens and found them highly similar (r =0.83) (Figure 4-3B). The comparison of only significant scores (based on the established score threshold) (|score|>2.5) resulted in a higher correlation between the experiments (r =0.92, p-value <1x10-200). Because the majority of methyltransferases in the set was represented by two independently constructed double-deletion strains (NatR-KanR and KanR-NatR), the scores for these reciprocal gene pairs were also compared. I found the correlation of r =0.27, p- value <1x10-22 (Figure 4-3C), that was increased when the comparison was restricted to only significant values (r =0.68, p-value <1x10-11, |score| > 2.5) (Figure 4-3D). For the final calculations of the scores, I removed those with opposite signs for reciprocal gene pairs, and took an average of the scores with the same sign. The scores for the gene pairs present only in “one direction” in the screen (either NatR-KanR or KanR-NatR) were halved, so they can be compared to the average score of the reciprocal strains. The final list contained 2059 scores comprising distinct digenic interactions among yeast methyltransferases (Figure 4-3E).

To evaluate the biological relevance of the genetic interaction scores and to confirm that the established score threshold (|score| >2.5) (Collins et al, 2006) was appropriate for my dataset, I tested several mutants with scores <-2.5 (synthetic sick/lethal) and which were previously reported to interact in a large-scale study (Costanzo et al, 2010). The following double mutants were assessed: opi3∆cho2∆ (score =-30.5, SGA ε=-0.18 with p-value <0.05) and tgs1∆hmt1∆ (score =-3.9, SGA ε=-0.41 with p-value <0.05) (Figure 4-3E and 4.3F). Although a genetic interaction between SWD3 and CHO2 was not reported before, I found that these two genes interact (score =-2.44, ε=0.02), and confirmed the swd3∆cho2∆ mutant’s synthetic sick phenotype in a sensitive spot dilution assay (Figure 4-3F).

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Importantly, the scores I obtained for genetic interactions among methyltransferases were in an agreement with the SGA epsilon values, an independent measure of genetic interactions, derived from a genome-wide study (r =0.49, p-value <0.003) (Costanzo et al, 2010) (Figure 4-3G). The observed agreement between two independently derived measures for genetic interactions confirms the accuracy of the methytransferome scores and encouraged me to examine the data in more detail.

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Figure 4-3. Assessing genetic interactions among methyltransferases.

(A) An orthogonal array of the filtered unaveraged scores for double-deletion mutants represented by 66 (KanR-marked) crossed to 78 (NatR-marked) methyltransferase mutants. Methyltransferases are arranged according to their position on a chromosome. (B) A scatter plot of genetic interactions (unaveraged scores) derived from two independent screens. (C) A scatter plot of unaveraged genetic scores between independently generated reciprocal gene pairs (NatR-KanR and KanR-NatR). (D) A scatter plot of high-confidence unaveraged genetic scores (|score| >2.5) between independently generated reciprocal gene pairs (NatR-KanR and KanR-NatR). (E) A distribution of final genetic interaction scores among methyltransferases. High-confidence negative (red) and positive (green) genetic interaction scores are circled. (F) Spot dilution growth assays of the selected double methyltransferase mutants with strong negative scores. Cells grown overnight, ten-fold diluted and plated on SD solid media and incubated at 30°C for 2 days. (G) A scatter plot of the significant genetic interaction scores derived independently from the present study and a large-scale genome-wide SGA study (|ε| >0.08, p-value <0.05).

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4.2.4 Genetic architecture of yeast methyltransferome

The frequency of genetic interactions is estimated to be ~0.5-2% for randomly selected non-essential genes in baker’s yeast (Costanzo et al, 2010; Tong et al, 2004). Although the majority of gene interactions among methyltransferases in my dataset were neutral and centered around zero (Figure 4-4A), I found that the methyltransferome network was enriched with significant genetic interactions compared to random genes (229, |score| >2.5): 4.9% of negative (aggravating) genetic interactions and 6.9% of positive (alleviating) interactions (Figure 4-4A). This is consistent with other studies performed with functionally related genes (Fiedler et al, 2009; Schuldiner et al, 2005; St Onge et al, 2007). I also observed that there is a slight prevalence of positive interactions over negative ones in my dataset (6.9% vs 4.9%). The number of significant genetic interactions varied greatly among methyltransferases. For example, essential (DIM1) and slow growing (ERG6 and TGS1) exhibited the highest number of genetic interactions, and all three showing a bias toward negative interactions (Figure 4-4A). Interestingly, the methyltransferases with highest number of genetic interactions (“hubs”) were either small molecule- or RNA methyltransferases. Methyltransferases with unknown functions demonstrated the lowest number of genetic interactions. I also found a correlation between the number of positive and negative genetic interactions for methyltransferases (r = 0.36, p-value <0.0018), indicating that there is a tendency for certain methyltransferases to have similar number of positive and negative genetic interactions with other methyltransferase genes.

To begin to infer functional relationships among yeast methyltransferases, I arranged the genetic interaction network according to the methyltransferase’s substrate-accepting properties (histone, ribosomal protein, other protein, tRNA, rRNA, other nucleic acid, small molecules, and unknown) (Figure 4-4B). Positive genetic interactions were abundant within these substrate-based clusters as well as between the clusters. In contrast, the negative genetic interactions were less abundant, but were of higher magnitude than the positive interactions (|score| <-5). Furthermore, essential genes and genes required for normal growth (SWD2, COQ5, TGS1, ERG6) had a bias toward strong negative genetic interactions. With the exception of lipid-related methyltransferases, I also found

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that the strong negative interactions connected different substrate-acting methyltransferases. For example, the nucleolar snRNA/snoRNA methyltransferases TGS1 exhibited strong negative interactions with the essential protein methyltransferase (SWD2). Consistent with this observation, it was reported that Swd2 is a part of both the cleavage and polyadenylation factor (CPF) and COMPASS complexes, and is involved in snoRNA 3’ end formation (Cheng et al, 2004). In another example, the arginine methyltransferase HSL7 showed strong negative genetic interactions with multiple lysine histone H3 methyltransferases SDC1, SWD3, SWD1, and BRE2, the members of the COMPASS complex involved in transcriptional regulation (Figure 4-4B), indicating the importance of arginine methyltransferase in this process. These observations suggest that the yeast methyltransferome genetic network is highly interconnected and the functional relationships among individual methyltransferases can be used to uncover buffering capacities for methyltransferases.

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Figure 4-4. Genetic architecture of yeast methyltransferome.

(A) Quantitative overview of high-confidence positive and negative scores (|score| >2.5) across the methyltransferome. Genes in red are essential genes, *-marked are slow growers. (B) Genetic interaction score network of yeast methyltransferome at the standard growth condition (30°C). Nodes are methyltransferases, and edges represent genetic interactions between two nodes. The nodes are colored according to a substrate type. The width of the edges represents the strength of genetic interaction score, and the color represents the type (red is negative, green is positive).

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4.2.5 Interpreting yeast methyltransferome genetic interaction data

Genes encoding proteins that act in the same biological process tend to have similar genetic profiles (Costanzo et al, 2010; Tong et al, 2004). This functional relatedness becomes apparent when hierarchical clustering of genetic interaction scores is performed (Figure 4-5A). One of the most striking patterns of genetic interactions was observed for the components of the well-characterized COMPASS complex (Miller et al, 2001), many components of which exhibited strong positive interactions (Figure 4-5A). To use an additional robust predictor of shared function, I used the degree of similarity between genetic profiles (correlation coefficients values) and compared methyltransferases based on this measure. This allowed me further to visualize a modular structure of the methyltransferome (Figure 4-5B). Similarly to score-based clustering, correlation-based clustering revealed that the members of the COMPASS complex (SWD1, SWD3, SET1, SDC1, and BRE2) clustered together demonstrating that their patterns of genetic interactions are much more similar to each other than to the patterns of other methyltransferases (Figure 4-5C). For example, the genetic profile of swd1∆ mutant is highly correlated to that of swd3∆ (r =0.83).

I also observed that the methyltransferases with similar genetic profiles (r >0.5) were enriched for gene pairs that tend to act on similar substrate type (e.g. protein, nucleic acid, or small molecule) (32% of highly similar gene pairs (235) vs. 28% for all gene pairs (3160), p-value <0.016, hypergeometric test). For example, correlation-based clustering revealed that lipid (OPI3, CHO2, ERG6), small molecule (MET1 and MHT1; DPH5 and NNT1), protein (RKM3 and DOT1; COMPASS) and nucleic acid (RRP8 and TGS1) methyltransferases are clustered together (Figure 4-5D). These gene pairs were also significantly enriched for positive genetic interactions (score >2.5, p-value <1x10-13, hypergeometric test). Overall, I noted measurable correlation between correlation coefficient and genetic interaction scores between two genes (r = 0.2, p-value <1x10-34) (Figure 4-5E). This trend was also observed for methyltransferase pairs that fell below the significance threshold (|score| <2.5, r = 0.31, p-value <1x10-38).

On the other hand, two phospholipid methyltransferases OPI3 and CHO2, both involved in phosphatidylcholine biosynthesis have similar genetic profiles (r =0.68), but the

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double-deletion opi3∆cho2∆ results in a strong negative genetic interaction (score <-30). This pattern suggests that these two methyltransferases function in the same biological pathway, and that this pathway is essential. In another example, mutants with strong negative genetic interactions between OPI3 and the components of COMPASS demonstrated low correlations for their genetic profiles, indicating that these methyltransferases’ function are independent, but in buffering biological processes.

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Figure 4-5. Interpreting yeast methyltransferome genetic interaction data.

(A) Hierarchical cluster analysis of genetic interaction scores. Each row/column represents a genetic interaction pattern for a specific methyltransferase. Subset of positive score interactions among the components of COMPASS complex is shown. Negative and positive genetic interactions are indicated by blue or yellow colors, respectively. (B) Correlation-based hierarchical cluster analysis. Each row/column represents a genetic interaction profile pattern for a specific methyltransferase. Strong positive and negative correlations among methyltransferases are indicated by red or blue colors, respectively. (C) A scatter plot of the correlation coefficients of swd1∆ and swd3∆ with their genetic profiles. The top similar profiles that belong to the components of COMPASS complex are labeled. (D) Clusters of similar methyltransferase profiles (correlation coefficient >0.5) at standard growth conditions (30°C). Nodes are methyltransferases, and edges are correlation coefficient-based interaction between two nodes. The nodes are colored according to a substrate type. The width and color of the edges represents the magnitude of similarity. (E) A scatter plot of genetic interaction scores and correlation coefficients for methyltransferase gene pairs.

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4.2.6 Relating genetic interaction data with protein interactions

In addition to acting in the same biological pathway, genes with highly similar genetic interaction profiles and strong positive interactions are proposed to associate physically (Collins et al, 2007; Schuldiner et al, 2005). Consistent with this observation, I found that in my dataset genes of the COMPASS complex, GCD14 and GCD10, NOP2 and DIM1 follow this trend. Moreover, examination of the distribution of genetic interaction scores and correlation coefficients profiles revealed that gene pairs encoding physically interacting proteins have positive scores and similar genetic profiles more frequently compared to the gene pairs encoding proteins that do not interact (Figure 4-6A and 4.6B). I also found that the negative genetic interaction scores were enriched for the genes whose products are reported to physically associate (Figure 4-6B and 4.6C). In total, I found 33 out of 54 gene pairs (61%) encoding proteins known to associate physically exhibit genetic interactions in my dataset and 19 of them (35%) have significant genetic interactions. Because only about 7.8% of all gene pairs with significant genetic interaction scores encode for the proteins that associate physically, this suggests that genetic interactions (positive and negative) represent functional relationships between complexes and pathways. Gene pairs comprising essential genes did not have similar genetic profiles (negative correlation coefficients) and exhibited negative genetic scores relative to the gene pairs with only nonessential genes (Figure 4- 6D). For example, (and consistent with large-scale studies), two out of four gene pairs with significant negative genetic interaction had one essential gene (SWD2 or NOP2) vs. 0 out 14 for gene pairs with positive genetic interactions (Figure 4-6D) (Bandyopadhyay et al, 2008; Baryshnikova et al, 2010b). These observations indicate that complexes containing essential components are more vulnerable to additional genetic perturbation. Furthermore, despite the small scale of the study, the fact that the patterns previously reported in other genome-wide studies can be detected in the present analysis further validates the predictive utility of the methyltransferome dataset.

Finally, to evaluate the relationship between functional linkage and scores for gene pairs, I examined the distributions of scores for gene pairs that share specific gene ontology (GO) terms and those that do not. Both distributions were visually centered near zero.

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Figure 4-6. Relating genetic interaction data with protein interactions.

(A) Distribution of correlation coefficients for all gene pairs (black line) and the gene pairs with genes encoding physically associated partners (PI-pairs; red line). (B) Distribution of genetic interaction scores for all gene pairs (black line) and the gene pairs with genes encoding physically associated partners (PI-pairs; red line). (C) Comparison of genetic interaction scores and physical interactions for a corresponding methyltransferase gene pair (each dot). Physical interaction (PI) weights were obtained from GeneMANIA. (D) Analysis of a number of essential genes and genetic interaction scores or correlation coefficients (positive or negative). (E) Distribution of genetic scores for all gene pairs with specific functional links (GO-linked pairs) and the gene pairs that do not share function (non-GO-linked pairs).

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However, I observed a measurable difference (p-value <0.05) between two populations (Figure 4-6E), indicating that there is a difference in scores between functionally-linked methyltransferases and those without the functional link.

4.2.7 Examining co-expression patterns among methyltransferases with similar genetic profiles

When grown under nutrient-limited conditions yeast exhibits highly periodic cycles in the form of glycolytic and respiratory oscillations known as Yeast Metabolic Cycle (YMC) and which are characterized by robust changes in genome-wide transcription patterns (Richard, 2003; Tu et al, 2005). The majority of yeast methyltransferases (74 out of 80) are periodically expressed within YMC, and these co-expression profiles, along with other enzymatic properties, were previously used to infer substrate specificity for methyltransferases (Tu et al, 2005; Wlodarski et al, 2011). In my study, I found that methyltransferases that clustered together based on their genetic profiles have similar expression profiles within YMC (Figure 4-7A). For example, based on the similarity of genetic interaction profiles putative methyltransferase YBR141C clustered with characterized tRNA methyltransferases (TRM3, TRM8, TRM1, TRM4, TRM13), and it was also co-expressed with the same tRNA genes within metabolic cycle-dependent expression (Figure 4-7A and 4.7B). Although experimental confirmation is required, this observation suggests that the unknown YBR141C is functionally related to tRNA methyltransferases. Overall, I found positive correlation between gene pairs with similar genetic profiles and their co-expression degree in YMC (r =0.34, p-value <0.0007) (Figure 4-7C). These results further confirm the utility of combining genetic cluster profiles with other genome-wide established datasets to predict functions for uncharacterized genes.

4.2.8 Plasticity of yeast methyltransferome under stress conditions

Cells exposed to stress maintain their homeostasis, in part, by altering their patterns of transcription, translation and signaling (Gasch et al, 2000). It is known that the certain genetic interactions are condition-dependent and wiring of genetic networks can be

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Figure 4-7. Examining co-expression patterns among methyltransferases with similar genetic profiles.

(A) A network of positively correlated genetic interaction clusters (correlation coefficient >0.5) with the corresponding metabolic cycle-dependent co-expression profiles. YMC data was obtained from SCEPTRANS. (B) Expression profiles of unknown methyltransferase YBR141C and characterized tRNA methyltransferases. Expression units are arbitrary. The data are obtained from SCEPTRANS. (C) Correlation coefficient scores for methyltransferases as a function of co- expression degree in Yeast Metabolic Cycle.

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altered by specific stress (e.g. DNA-damaging agents) (Bandyopadhyay et al, 2010; Game & Mortimer, 1974; St Onge et al, 2007).

To evaluate the response of yeast methyltransferome to environmental stress conditions, I profiled the fitness of double methyltransferase mutants in the presence of several environmental stresses (16°C (3 days), 37°C (3 days), and 0.25 mM LiCl). Comparison of the genetic networks across the conditions revealed that all stress networks and reference had similar correlations (r = ~0.6) (Figure 4-8A). I also found that positive genetic interactions were more prevalent than negative ones at 16°C and 37°C, but not in cells exposed to LiCl (Figure 4-8A). Despite the observed differences, the actual numbers of significant positive and negative genetic interactions (|score| >2.5) in these stress networks were similar to the reference dataset (Figure 4-8B). Furthermore, I found that the stress-induced genetic networks had more unique genetic interactions (Figure 4- 8B). For example, 104 significant positive interactions (~75%) identified at 16°C were not detected in the reference sample. On average, 25% of significant genetic interactions (either positive or negative) were shared between any stress network and the reference, with the smallest overlap observed for LiCl positive interactions (18%). Therefore, despite the observed changes in the genetic interactions across stress condition and reference, the substantial conservation (25%) was consistently present among the networks.

To analyze stress-specific gene interactions among methyltransferases, I evaluated if the gene pairs were enriched for any specific substrate-type methyltransferases (Figure 4- 8C). I detected significant differences between stress-specific networks. For example, the 16°C- and LiCl-specific genetic interaction datasets were enriched for unknown methyltransferases (p-value <0.003 and p-value <0.05, respectively) and for small molecule methyltransferases (p-value <0.05) (Figure 4-8C). 37°C-specific gene pairs were enriched for tRNA and ribosomal protein methyltransferases (p-value <0.02 and p- value <0.011), and histone methyltransferases were substantially underrepresented (p- value <4.8x10-5) in the 37°C-specific dataset. Furthermore, rRNA methyltransferases were underrepresented in LiCl- and 37°C-specific datasets. This tailoring of genetic interactions for specific stress conditions indicates extensive functional rewiring and

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Figure 4-8. Plasticity of yeast methyltransferome under stress conditions.

(A) A scatter plot of genetic interaction for methyltransferase gene pairs at 30°C vs. a stress condition (16°C, 37°C or LiCl). Insert: percentage of significant positive and negative genetic interactions out of all detected genetic interactions for a specific condition. (B) Overlap of significant genetic interactions (|score| >2.5) between 30°C and a stress condition. (C) The enrichment analysis for substrate type among the gene pairs specific for a given condition. (D) The core of housekeeping methyltransferase gene pairs shared by all stress conditions. Nodes are methyltransferases; edges are genetic interactions between methyltransferases. Red denotes negative genetic interaction and green – positive genetic interaction.

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To define a core of stress-specific genetic interactions, that are consistent across all tested environmental stresses, I assembled the genetic network representing methyltransferase gene pairs (|score|>2.5) shared among stress conditions (Figure 4-8D). This core revealed 32 gene pairs shared among stresses, including small molecule (lipid, AdoMet biosynthesis), tRNA, histone methyltransferases and demethylases. The shared network was significantly enriched for small molecule methyltransferases (p-value <0.002, hypergeometric) when compared to the reference condition, indicating the importance of this understudied type of methyltransferases in response to stress. Methyltransferases of unknown functions (YBR225W and OMS1) were significantly underrepresented (p-value <0.007, hypergeometric) relative to the reference condition (Figure 4-8D). Interestingly, the ybr225w∆erg6∆ double mutant demonstrated strong positive genetic interactions in 37°C and 16°C (score =5 and 2.8, respectively), but had a strong negative interaction (score = -3.9) in the presence of salt LiCl. Moreover, I found thirteen such gene pairs exhibiting differential genetic interactions across stress conditions and the reference.

I also found that only four subunits of the COMPASS present in the core, and their genetic interactions are not conserved relative to the reference, suggesting that COMPASS methyltransferases rearrange their genetic interactions in response to the stress.

4.2.9 Examining effects of stress on similarity of genetic profiles

To examine if the extensive rearrangement of genetic interactions in response to stress affects the similarity degree among methyltransferases, I compared the distributions of correlation coefficients for the reference and the stress conditions. The distribution of similarity profiles at 16°C did not differ from the reference dataset (Figure 4-9A). However, in the 37°C and LiCl datasets there were more gene pairs with similar as well as different genetic profiles than at 30°C (Figure 4-9A and 4.9B). For example, there was a two-fold increase in the number of positively correlated (above r =0.5) gene pairs at 37°C compared to 30°C. This disruption in the genetic interaction profiles suggests that the exposure to stress leads to functional rearrangement among methyltransferases.

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I also found that the gene pairs with similar genetic profiles (r >0.5) at 37°C, 16°C and LiCl are not coexpressed within YMC, in contrast to what I observed for gene pairs with similar genetic profiles at 30°C (Figure 4-7A).

It has been long recognized that the genes are not randomly positioned along a chromosome and there is a direct relationship between adjacently positioned genes and their transcription (Arnone et al, 2012; Fraser & Bickmore, 2007). Furthermore, exposure to stress leads to conformation changes in chromosomes which in turn has a profound effect on gene localization (e.g. nuclear periphery), and ultimately on the co-regulated transcription of certain genes (Ben-Elazar et al, 2013; Brickner et al, 2007; Zhao et al, 2009b). In my study, I found that the frequency of genes positioned on the same chromosome with similar genetic profiles (r =0.5) increases when exposed to environmental stress (p-value <0.001) (Figure 4-9C). The trend was not affected by distance between two genes, as distantly located genes also demonstrated similar genetic profiles upon exposure to stress (Figure 4-9D). At 30°C the gene pairs (r =0.5) located on the same chromosome were significantly enriched with methyltransferases acting towards nucleic acids (p-value <0.01, hypergeometric test). At 37°C the gene pairs on the same chromosome were enriched for protein methyltransferases (p-value <0.03, hypergeometric test).

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Figure 4-9. Examining effects of stress on similarity of genetic profiles.

(A) Distribution of the correlation coefficients between methyltransferase at various conditions. Ksdensity function is used. (B) Similarity clusters for 30°C and 37°C. (C) Distribution of the correlation coefficients between methyltransferase genes at various conditions either on the same chromosome (dash line) or different chromosomes (solid line). (D) A relationship between correlation coefficients and chromosomal distance between interacting methyltransferases at 30°C and 37°C.

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4.2.10 Characterizing the COMPASS complex in response to stress

To investigate further the plasticity of the methyltransferome I focused on the well- studied COMPASS complex comprising the histone lysine (K4) methyltransferases SWD1, SWD2, SWD3, SPP1, BRE2, SHG1, SET1, SDC1. It was previously reported that biological modules of functionally associated molecular factors are typically conserved among distantly related species and across conditions, but that the genetic wiring between them differs and is reprogrammed in response to stress (Bandyopadhyay et al, 2010; Roguev et al, 2008). I found that, in my dataset, the core COMPASS complex members (SET1, SDC1, BRE2, SWD1, SWD2, and SWD3) exhibited strong positive genetic interactions and have highly similar genetic profiles in reference condition (30ºC) (Figure 4-10A and 4.10B). However, the genetic relationships within the complex were drastically affected by exposure to stress. In particular, I found that the magnitude of positive interactions among the components changed under stress conditions, and a substantial loss of positive interaction was observed (Figure 4-10A). Furthermore, I found that at 16ºC, the complex members also interacted with tRNA methyltransferase PPM2, and at 37ºC unknown methyltransferase YBR225W exhibited numerous genetic interactions with COMPASS (Figure 4-10B). At 30ºC the majority of COMPASS components had buffering (negative interaction) genetic interactions with the arginine methyltransferases HSL7 and phospholipid methyltransferase OPI3. Some of these interactions were not present under other stress conditions, suggesting that these enzymes are required to buffer the compromised activity of COMPASS in a specific environmental condition. Interestingly, a chemical that requires Opi3 presence in a cell and sensitizes opi3∆/∆ mutant (Figure 4-10C) had a visible dose-dependent effect on the methylation status of histones (Figure 4-10D). Importantly the reduced methylation of histones (H3K4 di) in the presence of the compound was restored by overexpressing OPI3 gene in wt cells (Figure 4-10E). Similarly, opi3∆/∆ mutant demonstrated drastically reduced levels of methylation (H3K4di) in the absence of the drug and dose- dependent reduction of H3K36 di-methylation in the presence of the drug. I also found that overexpressing OPI3 gene in the opi3∆/∆ mutant restores the methylation mark. Taken together, these observations suggest that OPI3 is important for tolerance to a

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chemical that influences the methylation levels of histones in a dose-dependent manner. Furthermore, OPI3 itself is important for histone methylation.

Additionally, I found that, although some of the complex components had similar genetic profiles among themselves under standard conditions (Figure 4-10F), upon stress exposure the degree of similarity was significantly reduced compared to the reference condition, and the members were not readily clustered together (Figure 4-10F and 4.10G). Specifically, the disintegration of the complex was clearly observed at 37ºC and LiCl condition. For example, at 37ºC and LiCl the genetic profiles of the majority of COMPASS components were similar to SET-domain containing SET2 (histone lysine K36 methyltransferase) and phospholipid methyltransferase CHO2. At 16ºC although the degree of similarity among the members was lower than at 30ºC, the genetic relationships within the complex were largely

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Figure 4-10. Characterizing the COMPASS complex in response to stress.

(A) Fitness of the genes of the COMPASS complex represented by the genetic interactions. Each row/column represents a genetic interaction pattern for a methyltransferase. Negative and positive genetic interactions are indicated by blue or yellow colors, respectively. (B) The genetic interaction – based relationships among the COMPASS methyltransferases under standard and environmental stress conditions. Nodes are methyltransferases, and edges are genetic interaction between two nodes. The width and color of the edges represents the magnitude and the type of genetic interaction, respectively. (C) OPI3 gene dose is important for drug tolerance. Fitness of the strains represented by growth in the absence and the presence of the drug (6772625). Wt, opi3Δ/Δ homozygous and OPI3-overexpressing opi3Δ/Δ mutants were assessed in the presence of chemical (6772625) in SC media. Growth curves were obtained by plotting OD600 vs. time at the tested concentrations of the compound. Three independent replicates were analyzed and the growth curves are shown. (D) OPI3 sensitizer effects histone methylation (H3K4). Wt and wt overexpressing OPI3 were collected after the treatment with the compound for 2 hours in SC media. The cell lysates were analyzed by western blotting with anti-H3K4di methyl antibody. Hexokinase (HK) was used for an internal loading control. (E) OPI3 gene dose is important for histone methylation (H3K4 and H3K36). Wt, opi3∆/∆ and opi3∆/∆ overexpressing OPI3 were collected after the treatment with the compound for 2 hours in SC media. The cell lysates were analyzed by western blotting with anti-H3K4di methyl and anti-H3K36di methyl antibody. Anti PGK was used for an internal loading control. (F) Correlation-based hierarchical cluster analysis of genetic profiles of the COMPASS genes at 30°C, 16°C, and LiCl. Each row/column represents a genetic interaction profile pattern for a specific methyltransferase. Positive and negative correlations among methyltransferases are indicated by red or blue colors, respectively. (G) Autocorrelation of the profiles of the COMPASS complex genes.

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conserved. These observations indicate that the physically associated members of COMPASS complex rearrange their genetic interactions within the complex and with other methyltransferases upon exposure to environmental stress.

4.2.11 Predicting functions for unknown methyltransferases

Confident that the methyltransferome genetic interaction maps robustly detected both known and novel interactions for characterized methyltransferases, I asked if the dataset could be used to predict functions for methyltransferases with no known function. I found that the profile of the recently characterized histone methyltransferase SET5 was highly similar to putative methyltransferase AML1 (YGR001C) at 30ºC, 37ºC and in LiCl (Figure 4-10C). SET5 methylates histone H4 lysines 5, 8, 12 (H4K5, H4K8, H4K12) (Green et al, 2012a; Green et al, 2012b). Consistent with it having a similar genetic profile to a histone methyltransferase, I found that the profile of AML1 clustered with other histone-modifying proteins JHD1, JHD2 and RPH1, each of which is involved in the removal of methyl group from histone H3 lysines 4 (H3K4) and lysine 36 (H3K36). Taken together, these observations suggest that S. cerevisiae AML1 is likely a histone- interacting methyltransferase. Aml1 is highly similar to putative human N-6 adenine- specific DNA methyltransferase N6AMT2 (E-value = 4.0E-34) (Petrossian & Clarke, 2011).

In another example, the unknown methyltransferase YGR283C showed a limited number of genetic interactions in standard conditions, restricting its characterization. I found that YGR283C positively interacts with the delta(24)-sterol C-methyltransferase ERG6 in two stress conditions (37ºC and LiCl). It has been previously reported, that either point mutations or gene amplification of ERG6 (involved in ergosterol biosynthesis) lead to resistance to antifungal agents (Anderson et al, 2003; Jensen-Pergakes et al, 1998). Similarly, in a genome-wide study the null mutant of YGR283C along with other ergosterol and lipid-related genes (13 out ~4700 genes) has been reported to be resistant to the ergosterol biosynthesis inhibitor flucanozole (Anderson et al, 2003). My observations of stress-specific interactions suggest that YGR283C participates in the same pathway as Erg6 either directly by contributing to sterol biosynthesis or indirectly by sustaining membrane fluidity during stress.

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4.3 Discussion

The analysis of genetic interactions in the yeast methyltransferome network demonstrated that this dataset is of a high quality and is comparable to other established large-scale datasets. For example, the observed enrichment in genetic interactions among methyltransferases is in the agreement with other studies reporting that functionally related genes are enriched for genetic interactions (Fiedler et al, 2009; St Onge et al, 2007). I also found in my dataset a slight bias toward positive genetic interactions (Chapter 4.2.4). The observed preference for positive genetic interactions in my analysis is in agreement with other genome-focused studies performed with functionally related genes that used a different measure of genetic interactions named S-score (Collins et al, 2007; Fiedler et al, 2009). However, this observation is in conflict with the large-scale genome data (Tong et al, 2004), and the datasets with defined subsets of genes, such as the genes involved in DNA-damage response and integrative data for kinases employing SDL and SGA strategies (Sharifpoor et al, 2012; St Onge et al, 2007). Furthermore, it seems not to be supported by the deterministic theory stating that aggravating epistasis should be prevalent in sexually reproducing organisms, because it results in elimination of deleterious mutations from the genomes (Kondrashov, 1988).

One of the possible explanations for the observed discrepancy is the method used for quantification of genetic interactions. For example, there is a two-fold decrease in number of positive interactions relative to negative ones (ε >0.08, p-value <0.05) when I looked at epsilon (ε) values for the same set of methyltransferases obtained from genome- wide SGA study (Costanzo et al, 2010; Koh et al, 2010). Alternatively, biological explanation for this phenomenon is that the presence of a large number of RNA (~30%) and ribosomal protein (~9%) methyltransferases in my dataset result in the observed bias toward positive genetic interactions. It was reported previously in large-scale genome- wide studies that the genes of RNA-related processes, such as translation and RNA processing, tend to have positive genetic interactions (Costanzo et al, 2010). Nonetheless, my scores were highly correlated to SGA epsilons when significant scores were assessed (Chapter 4.2.3), highlighting the importance of integration of various datasets in order to accurately evaluate functional relationships between the genes. The observation that

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methyltransferases with highly similar profiles tend to have similar expression profiles during YMC under standard, but not stress conditions can be used as a additional factor for functional prediction for unknown methyltransferases. Although experimental confirmations are invaluable, the integration of different genome-wide sets, such as co- affinity purification, gene expression profiling, mass spectrometry, genetic interactome will result in a more complete understanding of yeast methyltransferome in yeast.

Similarly to genome-wide analysis my dataset revealed that slow-growing and essential methyltransferase mutants have more genetic interactions than other genes (Chapter 4.2.4). Specifically, I found that mitochondrial methyltransferases exhibited a range of genetic interactions indicating their critical roles in buffering other methyltransferases. This may be the result of already compromised fitness characterized by respiratory deficiency for this genes and their reliance on a specific type of carbon source.

Despite higher frequency of genetic interactions in the methyltransferome than in randomly selected genes, my observations also suggest that the redundancy among methyltransferases is unlikely to be revealed at a digenic level. Interestingly, the stress conditions did not result in a dramatic increase in the number of genetic interactions (Chapter 4.2.8). However, I found that their magnitude and identity was drastically changed compared to the reference set. The lack of a dramatic increase in the number of genetic interactions can be due to the use of non-specific environmental stresses known to have global affects on cellular processes (e.g. transcription and translation) opposed to using more specific perturbations. Indeed, it was previously observed that salt stress and other environmental stress conditions increase genetic interactions among duplicate genes by 16% (Musso et al, 2008). In contrast, when mutants of functionally relevant genes are treated with a DNA-damaging agent it results in a dramatic shift (two-fold) in the number of genetic interactions compared to the reference (Bandyopadhyay et al, 2010; St Onge et al, 2007). Nonetheless, the observed change in the identity of genetic interactions in gene pairs indicates that the rewiring in the interacting methyltransferase pairs is important for buffering the pathways that are affected by the general stresses. Furthermore, in the certain cases I did find a condition-dependent increase in the number of genetic interactions. For example, eight out of thirteen unknown methyltransferases exhibited

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about a two-fold increase in the number of interactions in the presence of a stress opening the possibility to explore these condition-dependent interactions to uncover functions for these genes. At stress conditions, such as 37ºC and LiCl, the frequency of gene pairs with similar as well as different genetic profiles increased compared to the reference condition. I also observed the enrichment for certain substrate-acting methyltransferases at specific stresses. It can be explained that stress-dependent rewiring that occurs among methyltransferases is important for the stabilization of cellular homeostasis.

Despite the observed rewiring of the methyltransferome upon stress exposure, there were gene pairs with conserved interactions. The conserved genetic interactions that were shared across standard and stress conditions were consistently ~25% of all gene pairs in a specific condition. Interestingly, the overlap between the genetic interaction networks in two distantly related fungi S. cerevisiae and S. pombe is also about 29% (Dixon et al, 2008). The core set represented by gene pairs conserved across stress conditions was enriched with small molecule methyltransferases. For example, the hubs of this core dataset were lipid-related methyltransferases, such as cho2∆opi3∆, erg6∆opi3∆, and cho2∆erg6, underlying the importance of studying these types of methyltransferases. The “core” set also revealed the genetic interconnection of small molecule and histone methyltransferases indicating the importance of these two groups in buffering each other, in particularly, in stress conditions.

The plasticity of yeast methyltransferome can be exemplified by considering the examination of the COMPASS complex. The COMPASS complex (Complex Proteins Associated with Set1) involved in methylation of histone 3 lysine 4 (H3K4) and is a highly evolutionary conserved, trithorax-containing complex that consists of seven polypeptides with a highly conserved ~140 amino acid SET domain (Miller et al, 2001). At 30ºC, I found the core of COMPASS represented by the catalytic subunit SET1 and regulatory and structural components SDC1, BRE2, SWD1, and SWD3 have strong positive interactions among themselves and their genetic profiles clustered at 30ºC (Chapter 4.2.10). This minimal core is in perfect agreement with the recently characterized subunit composition required for histone H3K4 methylation (Takahashi et al, 2011). The essential subunit SWD2, and nonessential genes SPP1 and SHG1 exhibited

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a range of genetic interactions that is consistent with their diverse roles (Acquaviva et al, 2013; Dehe et al, 2006). Because the genetic profiles of the COMPASS subunits are highly correlated at 30ºC and demonstrate the loss of these patterns across stress conditions, it opens the possibility that the complex is dissociated at these conditions, at least on a functional level. It would be interesting to investigate next if the functional dissolution occurs at a physical level.

At 30ºC, the minimum core of COMPASS comprising SDC1, SWD1, SWD3, and BRE2 exhibited strong negative interactions with the protein methyltransferase HSL7, that were lost at 16ºC and in LiCl, suggesting condition-dependent nature of these interactions and a functional rearrangement. HSL7 is an arginine protein methyltransferase with close sequence similarity to the mammalian PRMT5 (protein arginine methyltransferase) (Pollack et al, 1999). Although the recombinant Hsl7 demonstrates an enzymatic activity towards histones H2A and H4 in vitro (Lee et al, 2000), in vivo methylation of the yeast histones was not detected suggesting that Hsl7 may be active against histone proteins in certain conditions or towards non-histone proteins (Miranda et al, 2006). Consistent with my observation, HSL7 has been reported to have synthetic lethal interaction specifically with SET1 and other chromatin-remodeling enzymes (Ruault & Pillus, 2006). However, the lethality was not due to aberrant methylation of histones, but rather due to constitutively activated morphogenesis checkpoint. Indeed, the observed strong negative correlation of the genetic profiles of HSL7 and the COMPASS members in my dataset leads me to speculate that Hsl7 may antagonize COMPASS activity.

Moreover, upon exposure to stress, the components of COMPASS complex were less correlated to each other, and, instead, appear to have profiles that are similar to other methyltransferases. For example, I consistently observed the genetic profiles of the COMPASS genes clustered with SET2. Closer inspection of these methyltransferases revealed that SET2 exhibits the strong positive interactions with the COMPASS genes across conditions, suggesting that these genes are functionally related. SET2 is involved in the methylation of histone H3 lysine 36 (H3K36) associated with transcriptional silencing and elongation (Schaft et al, 2003; Strahl et al, 2002). Similarly, COMPASS methyltransferases (H3K4) are required for transcriptional silencing at telomeric regions

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(Krogan et al, 2002; Nislow et al, 1997). Furthermore, both of these marks are enriched at coding regions of actively transcribed genes (Shilatifard, 2006) and both methyltransferases directly interact with the elongating forms of RNA Polymerase II (Hampsey & Reinberg, 2003), however, methylation is not required to facilitate the initial transcription and does not influence transcriptional elongation (Pavri et al, 2006). Nonetheless, my genetic analysis suggests that these methyltransferases may overlap functionally. Interestingly, my observation that SET2 and the COMPASS genes have strong positive interactions is in the conflict with previous genome-wide data reporting that SET2 is synthetic sick with COMPASS (Krogan et al, 2003), but in the agreement with another study where the mutants were individually confirmed (Verzijlbergen et al, 2009), indicating the importance of individual confirmations and smaller scale datasets.

The clustering of SET2 with the genetic profile of CHO2 in the presence of stress is also surprising. CHO2 encodes phosphatidylethanolamine methyltransferase that is involved in phospholipid biosynthesis (Summers et al, 1988). Set2 is important for ICRE- dependent gene expression of phospholipid genes, by interacting with directly Ino2, a transcriptional activator (Dettmann et al, 2010). It is noteworthy that in a large-scale SGA-based dataset CHO2 genetic profile correlates with both transcriptional activators INO2 and INO4 (r > 0.33). Furthermore, in the same dataset CHO2 has strong positive interactions with the majority of components of histone deacetylase promoter targeted corepressor Rpd3L (Large) complex, such as RPD3 (ε=+0.28), RXT2 (ε=+0.21), SDS3 (ε=+0.28), and PHO23 (ε=+0.13) (Costanzo et al, 2010). Set2, on the other hand, has been shown to direct deacetylation of coding regions by Rpd3S (Small) leading to transcriptional suppression (Carrozza et al, 2005). Interestingly, Cho2 has been reported to possess a coiled-coil coactivator (CoCoA) domain within its trans-membrane domain (Wlodarski et al, 2011). CoCoA is known to be a coactivator for nuclear receptors, receptor of the aryl hydrocarbon and involved in the transcriptional activation of target genes in Wnt/β-catenin pathway by direct binding to β-catenin (Kim et al, 2003; Kim & Stallcup, 2004; Yang et al, 2006a; Yang et al, 2006b). It would be interesting to examine next if Cho2 has any role in transcriptional regulation rather than simply playing a role in the enzymatic reaction.

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4.4 Materials and Methods

4.4.1 Strains and growth conditions

Single-deletion query strains (methyltransferase∆::NatR ) were kindly provided by the Boone lab (Baryshnikova et al, 2010a). Essential alleles used in this study were previously made in our lab using a DAmP (decreased abundance by mRNA perturbation) strategy (Yan et al, 2008). To construct double-deletion methyltransferases these mutants were mated with the yeast deletion collection (methyltransferase∆::KanR) (Giaever et al, 2002) using the Synthetic Genetic Array (SGA) protocol as described (Tong et al, 2001; Tong et al, 2004). The generated double mutants were maintained in YPD containing geneticin (G418) and nourseothricin (NAT) and stored at -80ºC. For growth tests, cells were grown in YPD containing G418 and NAT or SD to mid-exponential phase and diluted to OD600 0.2. Liquid growth assays were performed using TECAN GENios microplate reader 30ºC (Proctor et al, 2011).

4.4.2 Construction of double-deletion mutants

In brief, I designed an ordered (by systematic name) array containing 82 methyltransferase deletion mutants (Mat a haploids), where methyltransferase gene is replaced with a gene encoding the kanamycin resistance marker (KanR). The array was consolidated into a 1536 colony format by pining three sets of methyltransferase arrays onto one plate where each mutant is represented four times. To minimize the well-known technical variation of colony sizes due to plate location effects, two outermost rows and columns, as well as four columns between the array sets were filled with control HIS3 strain (YHR202W). Next, using SGA technology I crossed the arrayed methyltransferase mutants with deletion query mutants (Mat alpha haploids) where methyltransferase gene is replaced with the nourseothricin resistance marker gene (NatR). That was followed by a series of robotic pinnings (BM4-10, S&P Robotics Inc) of the arrays on various media used to induce meiosis and select for haploid mutants carrying both deletions. Haploid double-mutant colonies were photographed after a defined interval (2-3 days), and colony sizes were quantified using ColonyImager software (Tong et al, 2004).

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4.4.3 Data processing and scoring genetic interactions

Colony sizes were first normalized to correct for systematic artifacts. The normalization procedure was adopted from Collins et al. 2006 (Collins et al, 2006). Briefly, normalization steps included scaling each colony size to 1) the median of all double mutants carrying this NatR-marked mutation 2) the median of all double mutants in a particular set and 3) the median of all double mutants carrying this KanR-marked mutation within the same position on the plate.

The calculation of the final interaction score was based on the t-value equation that takes into account the difference in the medians of the normalized sizes of the double mutants and their expected sizes, dived by the sum of their standard deviations. The score was obtained using the following equation: (µdouble " µcontrol) Score = # S var & # S var & % ( + % ( $ ndouble ' $ ncontrol '

where: µ double = mean of normalized colony sizes for the double mutant of interest, ! µ control = median of normalized colony sizes for all double mutant of containing the Kan-marked mutant of interest (array); n double = the number of measurements of colony sizes for the double mutant of interest (n=4); n control = 4; var exp = the variance of normalized colony sizes the double mutant of interest; var control = median of the variances in normalized colony sizes observed for all double mutants containing the Kan- marked mutant of interest (array). (var exp* (n exp"1) + var control * (ncontrol "1) S var = n exp+ ncontrol " 2

4.4.4 Data analysis ! The raw and normalized data can be found in the Supplementary data files. The data manipulation and statistical analysis was performed with MATLAB. Hierarchical clustering analysis was performed with Cluster 3.0 (http://bonsai.ims.utokyo.ac.jp/-

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mdehoon/software/cluster/software.htm) using average linkage as a distance metric and visualized using Java Treeview (http://jtreeview.sourceforge.net/).

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Chapter 5 Summary and future directions

5 Summary and future directions 5.1 Summary

In the first part of my thesis (Chapter 2 and 3), I characterized two AdoMet-dependent methyltransferases in baker’s yeast S. cerevisiae (ScCRG1) and in the human fungal pathogen C. albicans (CaCRG1) using chemical genetics, expression and lipid profiling along with biochemical assays. In Chapter 2, I presented a combinatorial approach that exploits contemporary high-throughput techniques available in S. cerevisiae combined with rigorous biological follow-up to characterize the interaction of the methyltransferase Crg1 with the small molecule cantharidin. My initial biochemical analysis revealed that Crg1, a stress responsive AdoMet-dependent methyltransferase, methylates cantharidin in vitro. Chemogenomic assays uncovered that lipid-related processes are essential for cantharidin resistance in cells sensitized by deletion of the CRG1 gene. Lipidome-wide analysis of CRG1 mutants further showed that cantharidin induces alterations in the abundance of glycerophospholipids and sphingolipids in a Crg1-dependent manner. Furthermore, I presented evidence that CRG1 is regulated by the Cell Wall Integrity (CWI) pathway and is required for actin cytoskeleton organization when perturbed by cantharidin. In summary, Chapter 2 data reveals that Crg1 is a small molecule methyltransferase important for maintaining lipid homeostasis and actin cytoskeleton architecture in response to small-molecule perturbation in baker’s yeast (Figure 5-1A).

In Chapter 3, I focused on a functional homologue of ScCRG1 (orf19.633, renamed as CaCRG1) in the pathogenic fungus C. albicans. CaCRG1 bears little similarity to any known proteins, and was initially identified functionally based on its ability to complement a baker’s yeast crg1 mutant (Hoon et al, 2008). Similarly to ScCrg1, I demonstrated that CaCrg1 is a bona fide small molecule methyltransferase that is requird for methylation of cantharidin in vitro as well as in vivo. I further found that CaCrg1 is

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Figure 5-1. Models depicting how Crg1- cantharidin interaction influences biological processes in baker’s yeast S. cerevisiae and the human fungal pathogen C. albicans.

(A) Cantharidin treatment inhibits PP2A and PP1, resulting in the perturbation of both lipid homeostasis and actin cytoskeleton organization. This perturbation activates the CWI pathway, which in turn induces CRG1 transcription. The Crg1 protein directly methylates cantharidin, alleviating its cytotoxicity and restoring lipid homeostasis, normal actin patch morphology, and reversing other cantharidin-associated effects. (B) A proposed model for a functional role of CaCrg1 in C. albicans. Cantharidin inhibits protein phosphatases (PP1 and PP2A) that are involved in multiple biological processes. The drug exposure upregulates CaCRG1, which, in its turn, maintains membrane trafficking, adhesion and hyphal elongation, processes required for fungal virulence. Furthermore, interactions between CaCRG1 and short-chain ceramides and the components of GlcCer biosynthesis may contribute to virulence of C. albicans. The solid line indicates a direct/physical interaction, and the dashed line represents an indirect/genetic interaction.

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important for virulence-related processes, such as adhesion, hyphal elongation and membrane trafficking, in the response to cantharidin. Consistent with the observations that ScCrg1 maintains lipid homeostasis in response to the drug (Chapter 2), CaCrg1 is also involved in lipid processes. I demonstrated that the recombinant CaCrg1 produced in yeast binds short-chained ceramides (building blocks of glucosylceramides (GlcCer) and interacts genetically with genes of the GlcCer pathway that is implicated in fungal virulence. Furthermore, I found that CaCrg1 is required for membrane trafficking upon cantharidin exposure and binds specifically to early endosome markers PI(4,5)P and PI(3)P. Finally, I found that this lipid-binding methyltransferase is required for virulence in the waxmoth G. mellonella, model of infection (Figure 5-1B).

In summary, in this part of my thesis (Chapters 2 and 3) I demonstrated the value of combining classic molecular biology approaches and chemical genetics with other “omic”-based methods for de-orphaning genes and elucidating previously unknown mechanisms of therapeutics action for small molecules. Furthermore, I illustrated how the knowledge derived from a model yeast S. cerevisiae can be successfully translated to clinically relevant human fungal pathogen C. albicans.

In the second part of my thesis (Chapter 4), I extended my investigation to consider all non-essential AdoMet-dependent methyltransferases in S. cerevisiae and examined their functional relationships interrogating genetic interactions in both standard and in stressed conditions. My analysis of genetic architecture of yeast methyltransferome uncovered the following properties of the network: 1) the methyltransferome genetic interaction network is enriched for genetic interactions (both positive and negative) compared to unbiased gene sets; 2) methyltransferases with similar genetic profiles tend to act on similar substrates; 3) methyltransferases with similar genetic profiles tend to be coexpressed within the Yeast Metabolic Cycle; 4) the analysis of genetic interactions and protein-protein interactions in the methyltransferome dataset is in the agreement with other independent genome-wide data; 5) the presence of stress does not affect the absolute number of genetic interactions, but reveals novel genetic interactions among methyltransferases; 6) despite the dynamic condition-dependent rearrangements in the genetic interaction networks, there is a conserved core of genetic interactions; 7) the gene

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pairs shared across this stress core interactome are enriched for the genes encoding small molecule methyltransferases; 8) exposure to stress results in massive network rewiring; 9) the protein complex COMPASS by virtue of its ability to reconfigure its network or interactions is genetically plastic in response to environmental stress; 10) the dynamic methyltransferome networks can be used to infer the functions of unknown methyltransferases; 11) the analysis of the yeast methyltransferome resulted in generation of many testable hypothesis. In summary, the interrogation of methyltransferome network in the presence of environmental stresses contributes to understanding robustness of biological system and adds functional value to basic interactome mapping.

5.2 Future directions

5.2.1 Next steps to dissect Crg1 function

The research presented in Chapter 2 and 3 elucidates molecular functions of two fungal AdoMet-dependent methyltransferase in response to chemical stress. Prior to my work both of these proteins were annotated as “putative” methyltransferases. Although I clearly demonstrated that both ScCrg1 and CaCrg1 act on exogenous small molecule cantharidin (Chapter 2.2.5 and 3.2.2), we have yet to discover endogenous substrates which further contribute to our understanding of these small molecule methyltransferases and how cells cope with chemical stress. Endogenous substrates can be identified by profiling cellular metabolomes of wild type and CRG1 mutants with sensitive mass spectrometry methods (e.g. reverse-phase ion pairing chromatography coupled with high-resolution full-scan mass spectrometry) (Lu et al, 2010; Saghatelian & Cravatt, 2005). The presence of unknown metabolites or a lack of characterized molecules in the strains profiles may point to an endogenous substrate or a specific metabolic pathway. Considering the critical role of Crg1 under stress (Chapter 2.2.1, 2.2.9, 3.2.1, 3.2.6 and 3.2.7), the profiling should be also performed under various stress conditions. Alternatively, assessment of Crg1-small molecule interaction can be performed by affinity methods, such as pulling down epitope-tagged Crg1 from whole cell extract, and assessing associated metabolites by mass spectrometry (Li et al, 2010b). However, transient interactions may be difficult to observe, therefore, for this technique we should assume that interaction between Crg1 and small molecule is stable during the procedure. Furthermore, metabolites associated

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with other affinity-purified proteins can be detected, because this method does not distinguish between physical and indirect interactions. Another promising way to detect the actual methylated substrate is to incubate recombinant Crg1 (wt and catalytically inactive) with cell extracts (+/- protease treatment) and radiolabeled AdoMet (e.g. S- adenosyl-[methyl-14C] methionine), separate the mixture by liquid chromatography and analyze collected fractions by mass spectrometry. Additionally, incubation of whole cell extract with a recombinant protein and modified cofactor AdoMet (a functionalized group labeled with fluorophore is transferred to a substrate instead of methyl group) followed by HPLC analysis is an alternative method to detect endogenous substrates (Chapter 1.4.2) (Lee et al, 2010a).

Because I detected that in C. albicans CaCrg1 binds to lipid molecules (Chapter 3.2.4 and 3.2.5), a candidate-based approach can be applied. For example, one can test in vitro methylation activity of CaCrg1 incubated with a library of diverse lipid molecules in the presence of radioactively labeled AdoMet (Chapter 1.4.2). Because of the condition- dependent nature of genetic interactions between CaCRG1 and GlcCer pathway (Chapter 3.2.6), and the requirement of CaCrg1 for C. albicans virulence G. mellonella (Chapter 3.2.7), this suggests that the endogenous substrates of CaCrg1 are likely involved in pathogenesis and may be revealed under these conditions. Therefore, to confirm in vivo CaCrg1-lipid interactions the endogenous binding between Crg1 and lipids should be tested in vivo by co-immunoprecipitation of CaCrg1 and subsequent analysis of bound lipids with mass spectrometry (Chapter 1.4.3). Furthermore, it would be interesting to determine if any host-related lipid species interact or serve as substrates for CaCrg1.

I found that ScCRG1 is transcriptionally regulated by many stresses (Chapter 2.2.11) in a similar manner to the known heat shock protein SSE2 (Chapter 2.2.3), allowing me to classify ScCRG1 as a stress-responsive transcript. Therefore, the questions about transcriptional and/or post-translational regulation of CRG1 are also of a particular interest. In baker’s yeast CRG1 transcription is governed by the genes of the CWI pathway (Chapter 2.2.9). Because many components of CWI signaling are conserved between S. cerevisiae and C. albicans (Levin, 2005) it would be of interest to elucidate if the same pathway mediates CaCRG1 transcription in C. albicans. This can be done by

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asking if orthologues of transcription factors in C. albicans are required for cantharidin tolerance in C. albicans and if mutants in these proteins affect CaCRG1 transcription. Furthermore, assessing the binding partners of CaCrg1 can reveal additional information about its function. This can be done by co-immunoprecipitation analysis coupled to mass spectrometry. Alternatively, the identification of any post-translational modifications on Crg1 protein (by mass spectrometry) can point out interesting modifications in Crg1’s function which can be verified by site-directed mutagenesis at these sites followed by an assessment of the phenotypic consequences. Also these or other posttranslational modification marks can be used to identify upstream modulators of Crg1’s function by performing epistatic analysis (SGA or SDL screens) in the presence of cantharidin. For example, in my S. cerevisiae SGA screen (Chapter 2.2.7), I found that the double- deletion mutant dbf2∆ crg1∆ has a strong positive interaction in the presence of cantharidin. In other words, when these mutants are combined and treated with a dose of cantharidin that inhibits the crg1 single mutant, the double mutant is viable, indicating the epistatic relationship between two genes, with one gene in the pair possibly acting as a negative regulator of the other. Therefore, the SDL analysis may reveal additional regulators of CRG1 or its downstream targets.

To confirm the role of CaCrg1 in virulence of C. albicans the use of murine model will be desirable. Next, it would be also interesting to assess antifungal properties of cantharidin on its own and in a combination with known antifungals in C. albicans. For example, cantharidin has been shown to be synergistic with calyculin a, protein phosphatase inhibitor from marine sponge and fluconazole (Hoon et al, 2008). Furthermore, cantharidin enhances the efficacy of cisplatin and doxyrubicin in the treatment of hepatocarcinoma and sarcoma (To et al, 2005; Zhang et al, 2010). Alternatively, because cantharidin analogues (e.g. norcantharidin) are less toxic than cantharidin itself, these compounds can be also utilized for various treatments. For example, several groups are currently investigating cantharidin analogues as lead compounds in the design of non-toxic anticancer drugs (Shan et al, 2006; Tarleton et al, 2012; Yeh et al, 2010).

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Looking ahead, it will be interesting to explore if similar mechanisms of cellular detoxification are also mediated by methyltransferases in mammalian cells. The potential of this knowledge is enormous as it will further elucidate the molecular mechanisms of cytotoxicity of cantharidin, which as described in the introduction has been extensively used in Tradition Chinese Medicine for treatment of cancers (Chapter 2.1) (Karras et al, 1996; Sandroni, 2001).

BLASTp analysis of the substrate-binding motif sequence homologues of CaCRG1 in Homo sapiens revealed the following top hits: METTL7B (E value 1xe-6), KIAA1456 (2xe-5), COQ5 (1xe-4), METTLA (2xe-4) (Figure 5-2A). Noteworthy, both METTL7A and 7B are lipid-related methyltransferases associated with lipid droplets and are highly expressed in liver, a main site for cantharidin action (Turro et al, 2006). In my initial efforts to elucidate the function of these putative mammalian methyltransferase in response to chemical stress I found that one of the METTLA knockdown cell lines shows resistance to cantharidin (Figure 5-2B and 5-2C and 5-2D). Furthermore, I found that the treatment with cantharidin results in significant upregulation of METTL7A and METTL7B transcripts in a control cell line (shGFP) confirming its cantharidin- responsive nature (Figure 5-2E). Because the observed cantharidin phenotype is opposite to what I detected in yeast (deletion results in sensitivity), to begin to understand the mechanism whereby silencing of METTL7A results in resistance to cantharidin, I measured drug uptake and efflux by cells using rhodamine 6G, a fluorescent dye transported into and out of the cells by the means of MDR (multidrug resistance) proteins. I found that intracellular R6G fluorescence signal in shGFP cells was significantly higher than in METTL7A after 30 minutes (Figure 5-2F), suggesting that METTL7A has an increased efflux of the dye. Taken together, these results suggest that the observed resistance in METTL7A cells are likely due to altered efflux of cantharidin. Although the obtained results do not suggest a direct role of METTL7A in the cantharidin response, one of the possible explanations is that silencing of METTL7A interferes either directly

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Figure 5-2. Characterizing the human methyltransferase METLL7A in response to cantharidin.

(A) Alignment of protein sequences of CaCRG1 methyltransferase domain and its closest sequence homologues in Homo sapiens. The protein sequences are aligned using the MUSCLE software with EMBL-EBI Alignment program. Conserved motifs in the methyltransferase domain are underlined. The structures for the known and putative substrates are shown. (B) Viability of A549 expressing siRNA in the presence cantharidin. (C) Dose-response analysis of shMETTL7A and control A549 cells in response to a range of cantharidin concentrations. Cells were treated with the indicated concentrations of cantharidin for 2 days, and relative cell numbers were measured. Four independent experiments, error bars are standard deviation. (D) Crystal violet- stained culture dishes of colony formation analysis. shMETTL7A cells demonstrate cantharidin resistant phenotype relative to a control shGFP. Cells were seeded to low density and treated with cantharidin for 7 days. (E) qRT-PCR analysis of relative levels of METTL7A and METTL7B transcripts in A549 cells expressing control shGFP, shMETTL7A, or shMETTL7B (siRNAs) in the presence and absence of cantharidin. (F) Measurements of efflux of intracellular R6G in A549 cells expressing shMETTL7A or control shGFP. Fluorescence signal was detected in fluorescence microplate reader.

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or indirectly with membrane biogenesis resulting in the observed resistance phenotype. Besides exploring enzymatic activity of this human protein with the methods described before, it would be also informative to investigate if any variants in these genes correlate with the observed toxicity to cantharidin or other small molecules.

5.2.2 Next steps in the methyltransferome analysis

As I described previously the majority of single and double yeast deletion mutants do not demonstrate obvious fitness defects in standard laboratory conditions (Giaever et al, 2002; Tong et al, 2004). Although my methyltransferome network revealed higher frequency of genetic interactions than sets of randomly selected genes (Chapter 4.2.4) (Costanzo et al, 2010; Tong et al, 2004), analysis of higher order mutations, additional perturbations (e.g. chemical stress) and assessment of other phenotypes will be beneficial for uncovering a complete architecture of methyltransferome (or any other gene family). For example, sensitizing methyltransferase mutants with small molecules or an environmental perturbation should increase a frequency of their condition-dependent genetic interactions (Chapter 2.2.6) (Lissina et al, 2011; Sharifpoor et al, 2012). This can be readily done by searching for sensitizing conditions for methyltransferases using the freely available FitDB database (Hillenmeyer et al, 2008), and measuring fitness of double mutants in the presence of particular stress condition. The fitness can be analyzed either by measuring colony sizes on solid media or in a parallel liquid growth assays. The latter strategy involves pooling double-deletion mutants into a single culture (~ 84 methyltransferase pools) to reduce the usage of usually expensive small molecules and measuring relative abundance of individual mutants based on a presence mutant-specific barcode sequences with microarrays or by sequencing (Giaever et al, 2004; Pierce et al, 2007; Smith et al, 2009). Alternatively all 7614 double methyltransferase mutants can be pooled together and the abundance of each double mutant in a pool can be detected by stitching together unique barcodes (“barcode-fusion genetics”) that identifies two knockout loci followed by sequencing of these constructs (F. Roth, personal communication).

Evaluation of higher order mutations by generating all possible triple knockout mutants could help further elucidate robustness of biological system and identify redundant genes

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and may enable to elucidate complex genetic traits (Boone et al, 2007; Zuk et al, 2012). Although this task seems very difficult for systematic assessment of all possible triple combination mutants in yeast genomes, gene-prioritizing can simplify the task and shed light on the functional divergence among certain gene families (E. Kuzmin, the Boone lab, personal communication). For example, about ~ 600,000 triple deletion mutants (84 x 84 x 84) have to be generated to evaluate trigenic interactions among yeast methyltransferases. Analogous to colony-size measures, phenotype-based assays can be used to determine biological consequences of double deletions. For example, I can investigate my methyltransferome network microscopically. Cellular morphology, actin cytoskeleton organization, endocytosis dynamics may reveal phenotypic defects that are not readily observed in fitness-based analysis of genetic interactions (Burston et al, 2009; Sopko et al, 2006; Vizeacoumar et al, 2010). Additionally, profiling of metabolomes of double-deletion mutants by mass spectrometry analysis can be performed (Chapter 5.3.1) (Clasquin et al, 2011). As a starting point, this can be achieved by focusing on double mutants containing unknown methyltransferases, as majority of them do not possess fitness defects or any other obvious phenotypes.

Another important use for this methyltransferome collection is screening the mutants in the presence of known methyltransferase inhibitors (e.g. FDA- and Health Canada- approved) to determine their toxicities (Chapter 1.2.2). Because yeast does not have characterized DNA-methylating enzymes, evaluation of approved DNA- methyltransferase inhibitors can uncover possible mediators of “off-target” toxicity to these inhibitors.

Another powerful approach to investigating functional relationships among methyltransferases in my collection in a systematic manner is the Synthetic Dosage Lethality (SDL), that relies on over-expressing of a gene in a deletion background (Measday & Hieter, 2002; Sopko et al, 2006), and uncovers genes that participate in the same pathway or in opposing pathways. For example, I can use this strategy to reveal methyltransferase-demethylase pairs that work on same substrates and have opposing regulatory functions. For this I over-express a methyltransferase gene in my demethylase- containing double-deletion mutants.

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Appendices

Appendix 1: Data from Chapter 2. Table 1. Significantly enriched (P-value <1.0E-0.6, Bonferroni corrected) Gene Ontology Biological processes for significantly up- and down-regulated genes (log2 >1 and <-1).

wt 250 uM 1 hr GO process for 1344 UP genes p-value k f response to temperature stimulus [GO:0009266] <1e-14 22 23 vacuolar protein catabolic process [GO:0007039] <1e-14 86 129 autophagy [GO:0006914] 3.18E-13 70 151 biological_process [GO:0008150] 6.91E-13 346 1237 energy reserve metabolic process [GO:0006112] 7.14E-10 21 28 sporulation resulting in formation of a cellular spore [GO:0030435] 8.24E-09 71 185 cellular carbohydrate metabolic process [GO:0044262] 1.07E-07 33 67 carbohydrate metabolic process [GO:0005975] 1.75E-07 44 103

After ESR filter: GO process for 1139 UP genes p-value k f biological_process [GO:0008150] 3.95E-09 285 1237 maltose metabolic process [GO:0000023] 3.89E-06 10 13

GO process for 1519 DOWN genes p-value k f cellular metabolic compound salvage [GO:0043094] <1e-14 44 53 endonucleolytic cleavage in ITS1 to separate SSU-rRNA from 5.8S <1e-14 37 45 rRNA and LSU-rRNA from tricistronic rRNA transcript (SSU-rRNA) RNA modification [GO:0009451] <1e-14 51 67 ribosome biogenesis [GO:0042254] <1e-14 136 184 peptidyl-amino acid modification [GO:0018193] <1e-14 59 84 maturation of SSU-rRNA from tricistronic rRNA transcript (SSU-rRNA) <1e-14 51 75 rRNA processing [GO:0006364] 5.11E-14 153 292 regulation of translation [GO:0006417] 1.48E-13 83 172 endonucleolytic cleavage to generate mature 5'-end of SSU-rRNA from 5.47E-11 26 34 (SSU-rRNA) endonucleolytic cleavage in 5'-ETS of tricistronic rRNA transcript (SSU- 6.17E-10 24 32 rRNA) ribosomal large subunit assembly [GO:0000027] 1.39E-09 34 56 cellular amino acid and derivative metabolic process [GO:0006519] 5.00E-09 39 71 cellular amino acid biosynthetic process [GO:0008652] 1.50E-08 51 107 cellular aromatic compound metabolic process [GO:0006725] 1.75E-08 37 68 maturation of LSU-rRNA from tricistronic rRNA transcript (SSU-rRNA) 2.72E-08 16 19 ribosomal large subunit biogenesis [GO:0042273] 8.87E-08 38 74 nitrogen compound metabolic process [GO:0006807] 8.99E-08 45 94 ribosomal subunit export from nucleus [GO:0000054] 2.36E-07 22 34 ribosomal small subunit biogenesis [GO:0042274] 4.33E-07 31 58 ribosome assembly [GO:0042255] 1.30E-06 14 18 organic acid metabolic process [GO:0006082] 1.70E-06 38 81 maturation of 5.8S rRNA from tricistronic rRNA transcript (SSU-rRNA) 4.75E-06 12 15

After ESR filter: GO process for 1080 DOWN genes p-value k f

198

cellular amino acid and derivative metabolic process [GO:0006519] 9.87E-11 35 71 nitrogen compound metabolic process [GO:0006807] 3.12E-10 41 94 cellular aromatic compound metabolic process [GO:0006725] 5.76E-10 33 68 cellular metabolic compound salvage [GO:0043094] 5.59E-09 27 53 organic acid metabolic process [GO:0006082] 3.37E-08 34 81 cellular amino acid biosynthetic process [GO:0008652] 1.02E-07 40 107 peptidyl-amino acid modification [GO:0018193] 3.82E-06 31 84

crg1null 250 uM 1hr GO process for 1334 UP genes p-value k f response to temperature stimulus [GO:0009266] <1e-14 22 23 vacuolar protein catabolic process [GO:0007039] <1e-14 83 129 autophagy [GO:0006914] <1e-14 74 151 biological_process [GO:0008150] 1.86E-11 337 1237 energy reserve metabolic process [GO:0006112] 6.18E-10 21 28 sporulation resulting in formation of a cellular spore [GO:0030435] 5.92E-09 71 185 carbohydrate metabolic process [GO:0005975] 1.40E-07 44 103 cellular carbohydrate metabolic process [GO:0044262] 3.42E-07 32 67

After ESR filter: GO process for 1128 UP genes p-value k f biological_process [GO:0008150] 5.21E-08 277 1237 autophagy [GO:0006914] 2.34E-06 49 151 maltose metabolic process [GO:0000023] 3.55E-06 10 13 GO process for 1512 DOWN genes p-value k f cellular metabolic compound salvage [GO:0043094] <1e-14 44 53 endonucleolytic cleavage in ITS1 to separate SSU-rRNA from 5.8S <1e-14 37 45 rRNA and LSU-rRNA from tricistronic rRNA transcript (SSU-rRNA) RNA modification [GO:0009451] <1e-14 51 67 ribosome biogenesis [GO:0042254] <1e-14 134 184 peptidyl-amino acid modification [GO:0018193] <1e-14 58 84 maturation of SSU-rRNA from tricistronic rRNA transcript (SSU-rRNA) <1e-14 49 75 rRNA processing [GO:0006364] <1e-14 150 292 regulation of translation [GO:0006417] <1e-14 86 172 endonucleolytic cleavage to generate mature 5'-end of SSU-rRNA from 4.90E-11 26 34 (SSU-rRNA) cellular amino acid and derivative metabolic process [GO:0006519] 2.16E-10 41 71 endonucleolytic cleavage in 5'-ETS of tricistronic rRNA transcript (SSU- 5.58E-10 24 32 rRNA) cellular amino acid biosynthetic process [GO:0008652] 1.16E-09 53 107 nitrogen compound metabolic process [GO:0006807] 1.93E-09 48 94 cellular aromatic compound metabolic process [GO:0006725] 3.59E-09 38 68 ribosomal large subunit assembly [GO:0000027] 6.28E-09 33 56 maturation of LSU-rRNA from tricistronic rRNA transcript (SSU-rRNA) 2.53E-08 16 19 ribosomal large subunit biogenesis [GO:0042273] 7.79E-08 38 74 ribosomal small subunit biogenesis [GO:0042274] 3.88E-07 31 58 organic acid metabolic process [GO:0006082] 4.74E-07 39 81 ribosomal subunit export from nucleus [GO:0000054] 1.28E-06 21 34 maturation of 5.8S rRNA from tricistronic rRNA transcript (SSU-rRNA) 4.51E-06 12 15

After ESR filter: GO process for 1069 DOWN genes p-value k f cellular amino acid and derivative metabolic process [GO:0006519] 2.39E-12 37 71

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nitrogen compound metabolic process [GO:0006807] 2.51E-12 44 94 cellular aromatic compound metabolic process [GO:0006725] 8.37E-11 34 68 cellular metabolic compound salvage [GO:0043094] 4.44E-09 27 53 organic acid metabolic process [GO:0006082] 6.44E-09 35 81 cellular amino acid biosynthetic process [GO:0008652] 6.70E-09 42 107 peptidyl-amino acid modification [GO:0018193] 3.06E-06 31 84 OE CRG1 250uM 1hr GO process for 1270 UP genes p-value k f response to temperature stimulus [GO:0009266] <1e-14 22 23 vacuolar protein catabolic process [GO:0007039] <1e-14 90 129 biological_process [GO:0008150] <1e-14 340 1237 autophagy [GO:0006914] 5.52E-14 69 151 energy reserve metabolic process [GO:0006112] 2.36E-10 21 28 sporulation resulting in formation of a cellular spore [GO:0030435] 1.19E-08 68 185 carbohydrate metabolic process [GO:0005975] 3.09E-08 44 103 cellular carbohydrate metabolic process [GO:0044262] 1.04E-07 32 67 metabolic process [GO:0008152] 1.05E-06 110 377 After ESR filter: GO process for 1060 UP genes p-value k f biological_process [GO:0008150] 1.60E-11 279 1237

GO process for 1452 DOWN genes p-value k f endonucleolytic cleavage in ITS1 to separate SSU-rRNA from 5.8S <1e-14 37 45 rRNA and LSU-rRNA from tricistronic rRNA transcript (SSU-rRNA) RNA modification [GO:0009451] <1e-14 53 67 cellular metabolic compound salvage [GO:0043094] <1e-14 40 53 ribosome biogenesis [GO:0042254] <1e-14 137 184 peptidyl-amino acid modification [GO:0018193] <1e-14 55 84 maturation of SSU-rRNA from tricistronic rRNA transcript (SSU-rRNA) <1e-14 47 75 rRNA processing [GO:0006364] <1e-14 157 292 endonucleolytic cleavage to generate mature 5'-end of SSU-rRNA from 1.88E-11 26 34 (SSU-rRNA) endonucleolytic cleavage in 5'-ETS of tricistronic rRNA transcript (SSU- 2.32E-10 24 32 rRNA) regulation of translation [GO:0006417] 3.50E-10 74 172 ribosomal large subunit biogenesis [GO:0042273] 1.50E-09 40 74 ribosomal small subunit biogenesis [GO:0042274] 7.39E-09 33 58 ribosomal large subunit assembly [GO:0000027] 1.08E-08 32 56 maturation of LSU-rRNA from tricistronic rRNA transcript (SSU-rRNA) 1.38E-08 16 19 ribosome assembly [GO:0042255] 5.33E-08 15 18 ribosomal subunit export from nucleus [GO:0000054] 1.02E-07 22 34 maturation of 5.8S rRNA from tricistronic rRNA transcript (SSU-rRNA) 1.82E-07 13 15 tRNA metabolic process [GO:0006399] 3.33E-06 16 24

After ESR filter: GO process for 1018 DOWN genes p-value k f M phase [GO:0000279] 1.90E-07 29 71 cell cycle [GO:0007049] 5.64E-07 83 322 cellular metabolic compound salvage [GO:0043094] 9.85E-07 23 53 mitotic cell cycle [GO:0000278] 4.54E-06 30 85

200

Table 2. Differentially expressed genes of methionine biosynthesis in crg1∆/∆ mutants under cantharidin stress.

/ ∆ / ∆ ∆ ∆ /

ype OE t ∆ ∆ Gene val val val CRG1 - - - CRG1 p p p ild wt vs vs wt crg1 CRG1 crg1 crg1 wt vs vs wt W OE OE vs

ADE3 -2.12 0.00371 -2.67 0.01425 -1.30 0.04937 0.08383 0.08750 0.01932 ADI1 1.71 0.05860 1.96 0.00418 0.78 0.12739 0.33182 0.20783 0.09109 CBF1 -1.16 0.02333 -1.29 0.07556 -1.17 0.04464 0.63402 0.95953 0.34406 HOM2 -1.34 0.02371 -1.38 0.11400 -0.63 0.16632 0.92100 0.18886 0.06894 HOM6 -1.07 0.05254 -1.01 0.11683 -0.48 0.05929 0.84271 0.14080 0.16780 IRC7 -1.18 0.12197 -1.36 0.06593 -0.96 0.23492 0.70889 0.37666 0.58171 MET1 0.61 0.00446 0.55 0.08719 0.90 0.18041 0.53202 0.47276 0.48432 MET2 1.29 0.13936 1.51 0.23284 1.13 0.13548 0.84160 0.81782 0.46143 MET22 -3.06 0.00234 -2.96 0.03818 -2.46 0.00765 0.71421 0.01940 0.24658 MET28 -1.54 0.13015 -1.85 0.21579 -0.32 0.81331 0.52034 0.34751 0.16508 MET10 -0.20 0.08003 -0.24 0.48992 0.26 0.02199 0.90534 0.04708 0.26446 MET13 -0.50 0.24412 -0.87 0.07333 -0.62 0.28324 0.17669 0.43233 0.41915 MET14 -2.31 0.07480 -1.98 0.17852 -1.08 0.05553 0.46315 0.09149 0.30905 MET16 -0.34 0.11181 -0.38 0.42936 -0.11 0.86870 0.87820 0.70840 0.42010 MET17 -2.74 0.03685 -3.48 0.00331 -0.45 0.05007 0.14965 0.05387 0.00365 MET3 0.64 0.07178 0.39 0.25360 1.60 0.00478 0.23314 0.05576 0.09341 MET30 -0.39 0.13965 -0.55 0.07997 -0.55 0.06688 0.49770 0.46927 0.86283 MET4 0.22 0.31948 0.12 0.39530 -0.07 0.42728 0.20922 0.14491 0.11046 MET6 -4.79 0.00179 -5.32 0.00484 -1.11 0.06823 0.03264 0.01839 0.01201 MET8 1.79 0.02351 1.09 0.03628 0.31 0.46227 0.11389 0.08774 0.25838 MHT1 1.66 0.00329 1.17 0.05209 1.41 0.02613 0.11184 0.12439 0.09883 MIS1 -2.20 0.01620 -2.27 0.08307 -2.39 0.02837 0.87903 0.17107 0.82413 MRI1 -2.84 0.03071 -2.86 0.05060 -2.22 0.01407 0.96713 0.08961 0.25961 SAM4 -2.68 0.01230 -2.40 0.06662 -1.88 0.01538 0.40417 0.07675 0.32672 SAM1 -3.35 0.00980 -4.05 0.01070 -2.29 0.00384 0.10746 0.03932 0.01963 SAM2 -2.46 0.01342 -2.68 0.03631 -1.55 0.02148 0.27842 0.00017 0.05650 STR2 -1.62 0.08649 -1.39 0.04286 -1.01 0.17144 0.33089 0.43716 0.48756 STR3 -0.66 0.08706 -0.55 0.13372 4.67 0.02231 0.16014 0.00879 0.00567 UTR4 -0.67 0.09510 -0.66 0.23994 0.06 0.87356 0.95983 0.31718 0.41955 YLL058W 1.07 0.00201 0.91 0.04299 1.00 0.11566 0.21943 0.74284 0.60995 YML082W -2.51 0.16590 -2.12 0.00633 -2.27 0.07112 0.67874 0.84231 0.64210 MMP1 -3.33 0.00323 -3.42 0.09967 -0.56 0.17268 0.89161 0.03995 0.15235 MET31 -2.96 0.10033 -3.40 0.00076 -2.77 0.00479 0.51676 0.74316 0.01683 MET4 0.22 0.31948 0.12 0.39530 -0.07 0.42728 0.20922 0.14491 0.11046 SAH1 -3.48 0.00412 -3.80 0.01458 -2.40 0.00597 0.12651 0.00001 0.02931 CYS3 -0.61 0.28556 -1.43 0.07628 -0.61 0.00224 0.32501 0.99970 0.12924 CYS4 -0.78 0.04172 -0.90 0.06091 -0.39 0.03128 0.18360 0.05212 0.08344

Table 3. Cantharidin-specific genetic interactors of CRG1.

p-val, p-val, single double GENE DMSO single vs MDR Lipid metabolic process pool pool vs drug double VPS27 -0.03 -3.06 0.02315 0.00000 MDR MET22 0.10 -2.62 0.00111 0.00176 cantharidin-specific

201

PDR5 0.02 -2.40 0.00143 0.00000 cantharidin-specific ERV14 0.00 -2.38 0.00071 0.00001 MDR VPS41 0.45 -2.37 0.04910 0.00672 MDR SLT2 0.04 -2.15 0.00046 0.00000 MDR SDS3 0.03 -2.05 0.01230 0.00042 cantharidin-specific VPS24 -0.20 -1.85 0.00460 0.00066 MDR IRC21 0.24 -1.76 0.00115 0.00005 cantharidin-specific DEP1 0.24 -1.71 0.01280 0.00128 cantharidin-specific lipid metabolic process FPS1 0.45 -1.70 0.00968 0.00046 cantharidin-specific BRE1 -0.23 -1.68 0.01309 0.00004 MDR PTC1 0.14 -1.65 0.02046 0.01162 cantharidin-specific RRP6 -0.05 -1.62 0.00261 0.01803 cantharidin-specific YNL171C 0.24 -1.62 0.00547 0.00031 cantharidin-specific RRD1 0.08 -1.56 0.01896 0.00001 MDR PPM1 -0.02 -1.56 0.01719 0.00301 MDR RVS167 -0.53 -1.50 0.00614 0.02114 cantharidin-specific lipid metabolic process VPS51 0.02 -1.50 0.00560 0.00026 cantharidin-specific RPN4 0.37 -1.48 0.01439 0.00039 cantharidin-specific LGE1 -0.22 -1.47 0.00062 0.00143 MDR CKB2 -0.09 -1.46 0.00049 0.00104 cantharidin-specific SAC1 0.09 -1.45 0.04403 0.02480 cantharidin-specific lipid metabolic process OPI3 -0.05 -1.38 0.05022 0.02224 MDR lipid metabolic process MON1 0.07 -1.35 0.01296 0.00443 MDR VPS9 0.85 -1.34 0.01266 0.00907 MDR SWR1 0.04 -1.32 0.01032 0.00002 MDR RIC1 -0.05 -1.31 0.00391 0.00814 cantharidin-specific CHO2 0.12 -1.29 0.00432 0.00822 cantharidin-specific lipid metabolic process ARP6 0.07 -1.27 0.00164 0.00064 MDR XRS2 0.54 -1.23 0.00609 0.00803 cantharidin-specific PDC1 0.12 -1.22 0.00621 0.00477 cantharidin-specific UBA3 0.09 -1.20 0.00803 0.01934 cantharidin-specific HTZ1 -0.05 -1.20 0.01942 0.00013 cantharidin-specific GUP1 0.16 -1.19 0.00065 0.00000 cantharidin-specific lipid metabolic process RPA34 0.07 -1.18 0.00599 0.00029 cantharidin-specific SRB2 0.03 -1.17 0.01964 0.00001 cantharidin-specific MOT3 0.08 -1.15 0.00194 0.00130 cantharidin-specific lipid metabolic process VPS72 0.21 -1.13 0.00554 0.00084 MDR GSG1 0.23 -1.13 0.01872 0.00695 MDR YTA7 0.05 -1.11 0.00017 0.00050 MDR lipid metabolic process ILM1 0.17 -1.11 0.00367 0.01272 cantharidin-specific RTT103 -0.03 -1.10 0.01988 0.00002 MDR ARO2 0.20 -1.10 0.00781 0.00214 MDR SAP30 0.00 -1.08 0.00776 0.01423 MDR RPP1B 0.59 -1.03 0.02380 0.00781 cantharidin-specific RIM21 -0.13 -1.03 0.00055 0.00369 MDR PER1 0.87 -1.02 0.00967 0.00091 cantharidin-specific lipid metabolic process YPT7 0.12 -1.01 0.03020 0.00199 MDR MED1 -0.09 -1.00 0.01422 0.00175 cantharidin-specific SRN2 0.37 -0.98 0.00713 0.00059 MDR ARV1 0.49 -0.97 0.03874 0.00316 cantharidin-specific lipid metabolic process MRE11 0.02 -0.97 0.00361 0.00759 cantharidin-specific FUM1 0.12 -0.96 0.02785 0.00373 MDR VPS71 -0.30 -0.96 0.00225 0.01890 MDR VPS35 0.31 -0.94 0.03153 0.00017 MDR KEX2 0.37 -0.93 0.01209 0.00266 cantharidin-specific CBC2 0.07 -0.93 0.03215 0.00967 cantharidin-specific

202

LSM1 -0.18 -0.93 0.00272 0.01089 cantharidin-specific ERG6 0.22 -0.90 0.01145 0.00090 cantharidin-specific lipid metabolic process VPS4 0.01 -0.89 0.02567 0.00973 MDR TPS1 -0.07 -0.89 0.02057 0.00411 cantharidin-specific VPS75 0.06 -0.88 0.02707 0.00427 cantharidin-specific RPL8A 0.18 -0.88 0.00473 0.00035 cantharidin-specific PUS7 0.15 -0.86 0.00667 0.00008 cantharidin-specific COG8 -0.13 -0.86 0.00852 0.01044 MDR YDR431W 0.04 -0.85 0.00791 0.00941 cantharidin-specific RPL42A 0.26 1.06 0.00021 0.02421 cantharidin-specific DBF2 0.57 1.12 0.00041 0.01264 MDR RPL14A 0.11 1.28 0.03566 0.00042 cantharidin-specific

Table 4. Statistical analysis (Kruskal-Wallis test) of CRG1 mutants lipidomes exposed to cantharidin.

wt crg1∆/∆ OE CRG1 wt vs wt vs OE Lipids (drug/DMSO) (drug/DMSO) (drug/DMSO) crg1∆/∆ CRG1 16:1 PC 0.050 0.050 1.000 0.050 0.083 18:1 PC 0.050 0.050 0.121 0.050 0.083 26:1 PC 0.050 0.050 0.121 0.050 1.000 26:0 PC 0.050 0.050 0.121 0.275 0.083 28:1 PC 0.050 0.050 0.121 0.827 0.564 28:0 PC 0.050 0.050 0.121 0.827 0.083 30:2 PC 0.827 0.127 0.439 0.513 0.083 30:1 PC 0.050 0.050 0.121 0.513 0.083 32:2 PC 0.827 0.275 0.439 0.827 0.564 32:1 PC 0.050 0.050 1.000 0.275 0.083 34:2 PC 0.513 0.275 0.121 0.513 0.083 34:1 PC 0.050 0.050 0.121 0.050 0.083 36:2 PC 0.050 0.275 0.121 0.275 0.564 36:1 PC 0.275 0.050 0.121 0.050 0.083 16:1 PE 0.050 0.050 1.000 0.050 0.083 16:0 PE 0.050 0.050 0.439 0.050 0.083 18:1 PE 0.050 0.050 0.121 0.050 0.083 28:1 PE 0.275 0.513 1.000 0.513 1.000 30:1 PE 0.127 0.827 0.439 0.127 1.000 32:2 PE 0.050 0.050 1.000 0.050 0.083 32:1 PE 0.827 0.050 0.121 0.050 0.083 34:2 PE 0.827 0.050 1.000 0.050 0.083 34:1 PE 0.050 0.050 0.121 0.050 0.083 36:2 PE 0.050 0.275 0.121 0.050 0.083 36:1 PE 0.513 0.827 0.121 0.050 0.083 16:1 PS 0.050 0.127 0.121 0.827 0.083 16:0 PS 0.275 0.050 0.121 0.050 0.083 18:1 PS 0.827 0.050 0.121 0.050 0.083 30:1 PS 0.050 0.050 0.121 0.513 0.083 32:2 PS 0.050 0.050 0.439 0.050 0.083 32:1 PS 0.050 0.050 0.121 0.050 0.083 34:2 PS 0.275 0.050 0.121 0.050 0.083 34:1 PS 0.050 0.050 0.121 0.050 0.083 16:1 PI 0.050 0.050 0.439 0.050 0.083 16:0 PI 0.050 0.050 0.121 0.050 0.083

203

18:1 PI 0.050 0.050 0.121 0.050 0.248 18:0 PI 0.050 0.050 0.121 0.050 0.083 26:1 PI 0.275 0.275 0.121 0.275 0.083 26:0 PI 0.050 0.050 0.121 0.050 0.564 28:1 PI 0.127 0.050 1.000 0.127 0.083 28:0 PI 0.127 0.827 0.121 0.050 0.564 30:1 PI 0.827 0.127 1.000 0.127 0.564 30:0 PI 0.827 0.513 0.439 0.127 1.000 32:2 PI 0.050 0.050 0.121 0.275 0.083 32:1 PI 0.513 0.827 1.000 0.275 0.564 34:2 PI 0.050 0.050 0.121 0.827 0.083 34:1 PI 0.513 0.275 0.121 0.827 0.564 36:2 PI 0.050 0.050 0.121 0.050 0.083 36:1 PI 0.275 0.827 0.121 0.127 0.564 t18:0/18:0 IPC-B 0.275 0.050 0.121 0.050 0.083 t18:0/18:0 IPC-C 0.050 0.050 1.000 0.050 0.083 t18:0/20:0 IPC-C 0.513 0.050 1.000 0.050 0.248 t18:0/24:0 IPC-B 0.275 0.050 1.000 0.050 0.083 t18:0/24:0 IPC-C 0.127 0.050 0.121 0.050 0.564 t18:0/26:0 IPC-B 0.513 0.050 0.439 0.513 0.564 t18:0/26:0 IPC-C 0.827 0.050 0.439 0.513 0.564 t18:0/26:0 IPC-D 0.513 0.050 0.121 0.513 0.248 t18:0/24:0 MIPC-C 0.050 0.050 0.121 0.513 1.000 t18:0/26:0 MIPC-B 0.127 0.050 0.121 0.513 1.000 t18:0/26:0 MIPC-C 0.050 0.050 0.121 0.827 0.248

Appendix 2. Data from Chapter 3. Table 1. Differentially expressed genes C. albicans wt cells treated with cantharidin (2 mM, 30 min).

Mean Mean Log2 T-test, p- ORF GENE DMSO drug (drug/DMSO) val (2,2) orf19.6475 53.03 9379.51 7.5 0.00010 orf19.1048 IFD6 18.93 3283.63 7.4 0.00571 orf6.2005 2.54 316.44 7.0 0.00875 orf19.4173 ScDPH2 55.97 6731.72 6.9 0.00434

204

orf19.2713 ScMSH5 8.39 868.15 6.7 0.00355 orf19.5604 MDR1 16.94 1338.45 6.3 0.00347 orf19.629 IFD7 6.67 425.83 6.0 0.00150 orf19.3848 53.61 1814.50 5.1 0.00922 orf19.4309 GRP2 44.31 993.20 4.5 0.00012 orf19.7296 29.42 618.37 4.4 0.00319 orf6.4916 12.31 210.82 4.1 0.00016 orf19.2451 PGA45 32.88 533.30 4.0 0.00431 orf19.3735 18.27 228.68 3.6 0.00100 orf19.4477 CSH1 33.28 369.28 3.5 0.00157 orf19.2023 HGT7 133.77 1039.73 3.0 0.02302 orf19.7085 78.12 525.47 2.7 0.01160 orf19.633 CaCRG1 200.37 986.84 2.3 0.00090 orf19.251 ScHSP31 119.47 544.13 2.2 0.00274 orf19.5806 ALD5 609.95 2671.22 2.1 0.00336 orf19.4631 ERG251 327.36 1410.31 2.1 0.00686 orf19.1426 534.51 137.64 -2.0 1.9E-04 orf19.7492 SWC4 233.12 59.97 -2.0 2.6E-05 orf19.5599 MDL2 243.86 62.69 -2.0 9.1E-04 orf19.1607 ALR1 374.28 96.22 -2.0 4.8E-03 orf6.1737 773.62 198.45 -2.0 6.7E-03 orf19.1490 MSB2 310.23 79.38 -2.0 3.3E-03 orf19.6114 1517.96 387.56 -2.0 8.5E-03 orf19.1623 CAP1 247.04 62.60 -2.0 2.4E-03 orf19.866 RAD32 312.07 78.96 -2.0 5.5E-04 orf19.3752 RAD51 360.51 91.11 -2.0 1.4E-02 orf19.5676 210.81 53.23 -2.0 1.5E-03 orf19.5148 CYR1 284.23 71.69 -2.0 6.6E-03 orf19.6476 ScAVL9 245.29 61.80 -2.0 6.8E-04 orf19.789 PYC2 241.54 60.77 -2.0 2.5E-02 orf19.5908 TEC1 486.13 122.17 -2.0 4.5E-03 orf19.3858 224.49 56.14 -2.0 3.8E-06 orf19.2990 XOG1 231.64 57.89 -2.0 7.6E-03 orf6.2673 2955.41 735.23 -2.0 8.7E-05 orf19.4772 SSU81 402.77 100.17 -2.0 3.7E-04 orf19.7123 481.84 119.78 -2.0 5.3E-03 orf19.6745 TPI1 3343.17 830.10 -2.0 1.5E-03 orf19.3926 266.56 66.17 -2.0 2.4E-03 orf19.1806 214.78 53.30 -2.0 2.6E-03 orf19.1658 549.76 136.27 -2.0 2.2E-03 orf19.1135 CAS1 452.47 111.72 -2.0 8.5E-04 orf19.3630 RRP8 863.37 212.94 -2.0 6.6E-05 orf19.2051 ScRPN4 336.69 83.02 -2.0 5.9E-03 orf19.4281 310.53 76.38 -2.0 2.3E-04 orf19.3644 978.79 240.18 -2.0 8.1E-05 orf19.6121 MNL1 214.67 52.14 -2.0 4.5E-03 orf19.6493 559.48 135.80 -2.0 9.7E-04 orf19.4820 224.86 54.10 -2.1 4.9E-04 orf19.6573 BEM2 883.57 212.34 -2.1 1.7E-03 orf19.723 BCR1 358.56 85.78 -2.1 3.0E-03 orf19.5071 NRP1 450.09 106.93 -2.1 4.9E-05 orf19.6202 RBT4 394.16 93.41 -2.1 3.8E-03 orf19.4346 933.18 220.99 -2.1 4.8E-03 orf19.1685 253.81 59.98 -2.1 1.3E-02 orf19.7278 1644.19 387.85 -2.1 3.9E-02 orf19.5041 300.70 70.82 -2.1 2.7E-04

205

orf19.4728 ScHOS4 294.68 69.33 -2.1 9.5E-04 orf19.7079 208.27 48.97 -2.1 1.1E-03 orf6.326 498.90 117.17 -2.1 1.5E-04 orf19.5678 ScDPH2 296.46 69.58 -2.1 7.9E-04 orf19.1842 352.63 82.70 -2.1 1.2E-04 orf19.4775 CTA8 639.66 149.36 -2.1 9.8E-06 orf19.7150 NRG1 829.72 193.69 -2.1 3.1E-03 orf19.2460 231.70 53.87 -2.1 1.3E-02 orf19.6186 252.94 58.57 -2.1 3.7E-03 orf19.6713 424.30 98.23 -2.1 6.9E-05 orf19.1281 208.12 48.00 -2.1 1.3E-04 orf19.3252 219.57 50.21 -2.1 1.8E-05 orf19.4377 KRE1 702.67 160.37 -2.1 7.7E-04 orf19.182 1709.35 389.72 -2.1 5.8E-03 orf19.2331 ADA2 205.35 46.78 -2.1 3.3E-03 orf19.2877 PDC11 7456.90 1691.80 -2.1 9.4E-03 orf19.5903 RAX1 273.83 61.83 -2.1 2.9E-03 orf6.895 373.42 83.86 -2.2 6.1E-05 orf19.2812 1543.60 346.39 -2.2 7.2E-03 orf19.932 510.83 114.09 -2.2 3.9E-04 orf19.3678 281.27 62.73 -2.2 2.9E-03 orf19.1741 4474.98 996.82 -2.2 2.0E-03 orf19.5469 HPR5 447.01 99.42 -2.2 2.8E-02 orf19.3728 ScGIP4 246.09 54.60 -2.2 2.5E-03 trna6.2289.1.5prime 201.30 44.44 -2.2 7.9E-05 orf19.5483 224.08 49.33 -2.2 7.5E-05 orf6.579 684.20 150.59 -2.2 3.9E-04 orf19.4225 LEU3 263.29 57.84 -2.2 1.8E-04 orf19.2501 FLC1 350.16 76.46 -2.2 3.7E-02 orf19.1871 ScSWR1 432.63 94.24 -2.2 1.6E-06 orf6.3074 938.23 204.29 -2.2 2.8E-03 orf19.1277 486.81 105.82 -2.2 3.7E-03 orf6.2111 203.57 43.93 -2.2 2.3E-03 orf19.4555 ALS4; 133.41 28.70 -2.2 2.4E-03 orf19.3083 337.63 72.50 -2.2 6.7E-03 orf19.1103 324.62 69.61 -2.2 9.4E-04 orf19.1393 216.91 46.48 -2.2 2.3E-03 orf19.2296 503.18 107.47 -2.2 6.8E-03 orf19.7506 210.82 45.02 -2.2 2.9E-03 orf19.469 HST7 369.95 78.94 -2.2 9.9E-04 orf19.1259 224.63 47.75 -2.2 6.4E-05 orf19.4099 ECM17 358.01 75.87 -2.2 7.1E-06 orf19.6760 MDS3 279.29 59.16 -2.2 6.5E-04 orf19.4192 CDC14 232.73 48.75 -2.3 2.8E-02 orf19.3967 PFK1 1129.72 236.63 -2.3 3.2E-03 orf19.655 PHO84 814.54 170.44 -2.3 1.9E-02 orf19.2724 211.55 43.68 -2.3 7.5E-03 orf19.1617 203.45 41.89 -2.3 5.2E-03 orf19.5877 665.62 136.78 -2.3 3.4E-04 orf19.7450 210.61 43.21 -2.3 1.1E-04 orf19.7254 212.34 43.52 -2.3 1.9E-04 orf19.1133 MSB1 232.83 47.44 -2.3 4.8E-05 orf19.6414 234.29 47.71 -2.3 1.5E-05 orf19.5372 1994.33 406.04 -2.3 2.8E-02 orf19.4076 MET10 373.72 75.64 -2.3 3.9E-05 orf19.3728 344.00 68.76 -2.3 2.3E-04

206

orf19.4712 FGR6-3 243.90 48.06 -2.3 4.5E-03 orf19.3211 RCF3 440.08 86.65 -2.3 8.5E-03 orf19.5406 776.40 152.72 -2.3 8.9E-03 orf19.3487 206.57 40.33 -2.4 3.8E-02 orf19.4890 CLA4 609.07 118.73 -2.4 2.8E-05 orf19.3789 RPL24A 994.33 192.29 -2.4 2.3E-03 orf19.132 ScSIF2 282.25 53.96 -2.4 2.6E-04 orf19.6960 229.99 43.77 -2.4 2.6E-02 orf19.2374 1166.05 220.97 -2.4 3.0E-03 orf19.3447 646.09 122.31 -2.4 1.2E-02 orf19.7017 YOX1 206.09 38.90 -2.4 4.9E-04 orf19.3207 CCN1 248.32 46.72 -2.4 3.1E-04 orf19.3764 GSG1 240.36 45.11 -2.4 1.1E-02 orf19.3555 BUD14 211.38 39.59 -2.4 3.2E-03 orf19.580 1248.70 233.73 -2.4 1.5E-02 orf19.7272 418.26 78.22 -2.4 9.7E-04 orf19.914 272.21 50.89 -2.4 6.7E-04 orf19.2467 PRN1 260.22 48.48 -2.4 5.0E-04 orf19.173 446.49 82.60 -2.4 2.9E-02 orf19.7506 235.88 43.56 -2.4 1.6E-04 orf19.985 474.61 87.59 -2.4 1.6E-04 orf19.6276 436.39 80.40 -2.4 2.7E-03 orf19.1409 VAC7 233.22 42.58 -2.5 1.3E-04 orf19.3624 ScDSS1 210.32 38.25 -2.5 1.4E-03 orf6.2737 262.92 47.05 -2.5 1.3E-03 orf19.5292 AXL2 498.94 88.86 -2.5 9.9E-05 orf19.2641 ARP1 441.08 78.14 -2.5 5.2E-04 orf19.2608 ADH5 38.02 6.67 -2.5 5.0E-02 orf19.2680 ScDNF3 546.73 95.12 -2.5 4.4E-04 orf19.3622 ANP1 290.44 50.36 -2.5 6.0E-03 orf19.6686 ENP2 1002.10 173.09 -2.5 6.4E-06 orf19.3820 218.27 37.27 -2.6 6.1E-03 orf19.3089 484.42 82.28 -2.6 1.3E-03 orf19.5518 572.37 96.50 -2.6 8.2E-05 orf19.2372 928.93 156.31 -2.6 7.1E-03 orf19.3469 371.38 62.47 -2.6 6.1E-03 orf19.1119 MTR10 298.82 50.06 -2.6 4.9E-04 orf19.4365 830.24 139.05 -2.6 8.1E-05 orf6.279 1568.85 261.70 -2.6 2.1E-03 orf19.3144 270.44 44.67 -2.6 6.7E-04 orf19.5531 CDC37 406.64 66.93 -2.6 3.8E-03 orf19.4552 550.39 90.48 -2.6 5.0E-04 orf19.4284 BUR2 238.06 39.08 -2.6 1.6E-05 orf19.2827 310.53 50.90 -2.6 6.1E-03 orf19.2236 306.91 50.24 -2.6 2.9E-03 orf19.946 MET14 394.41 64.24 -2.6 4.7E-03 orf19.1351 205.74 33.43 -2.6 2.6E-04 orf19.7359 CRZ1 309.84 49.73 -2.6 3.1E-05 orf19.564 KAR3 269.41 43.03 -2.6 2.8E-03 orf19.4276 266.44 42.42 -2.7 3.3E-05 orf6.1533 CDC39 1021.42 162.41 -2.7 1.1E-03 orf19.3345 SIZ1 289.83 44.90 -2.7 6.2E-04 orf19.4365 663.84 102.14 -2.7 1.1E-03 orf19.1666 253.45 38.96 -2.7 8.5E-04 orf19.5280 MUP1 660.51 98.83 -2.7 7.2E-03 25S.6.3p N/A 2066.83 309.08 -2.7 4.6E-02

207

orf19.68 423.42 63.31 -2.7 2.9E-03 orf19.6514 CUP9 323.39 48.20 -2.7 2.5E-03 orf19.4722 309.70 44.97 -2.8 3.4E-03 orf19.5755 291.21 42.26 -2.8 4.4E-04 orf19.6537 231.79 32.73 -2.8 6.0E-03 orf19.4643 263.80 36.29 -2.9 5.6E-03 orf19.1383 201.85 27.60 -2.9 2.5E-03 orf19.2028 MXR1 1632.44 222.54 -2.9 2.9E-03 orf19.4952 647.02 87.72 -2.9 2.4E-03 orf19.6978 244.63 33.09 -2.9 3.9E-03 orf6.4835 439.29 58.84 -2.9 3.5E-03 orf19.414 274.37 36.51 -2.9 1.7E-04 orf19.660 278.97 36.90 -2.9 1.8E-04 orf19.3111 PRA1 449.24 58.86 -2.9 4.2E-05 orf19.422 SPT20 224.95 29.41 -2.9 1.9E-03 orf19.3148 602.75 77.88 -3.0 5.5E-04 orf19.5953 569.52 73.11 -3.0 7.2E-05 orf19.4553 335.99 43.10 -3.0 1.0E-04 orf19.1479 432.98 55.50 -3.0 2.0E-04 orf19.4912 234.89 30.08 -3.0 4.1E-03 orf19.522 1516.38 193.77 -3.0 1.8E-04 orf19.5528 MOB1 1124.01 143.39 -3.0 8.1E-04 orf19.6209 1201.19 153.15 -3.0 5.8E-03 orf19.3202 268.62 34.18 -3.0 4.5E-05 orf19.3840 442.03 55.67 -3.0 6.5E-06 orf6.1268 1505.45 188.98 -3.0 1.4E-03 orf19.4666 887.20 108.32 -3.0 1.0E-03 orf19.3142 287.08 34.62 -3.1 2.8E-03 orf19.267 943.48 113.24 -3.1 3.0E-03 orf19.2550 1797.45 214.88 -3.1 4.2E-03 orf19.5056 85.29 9.97 -3.1 2.3E-02 orf6.3626 1478.49 172.21 -3.1 1.2E-03 orf19.4347 280.17 32.17 -3.1 3.0E-04 orf19.1555 SAC3 1566.52 179.34 -3.1 3.2E-03 orf19.3603 984.41 111.29 -3.1 6.6E-04 orf19.4741 1243.92 139.75 -3.2 1.8E-03 orf19.3956 2761.71 304.61 -3.2 4.0E-04 orf19.5645 MET15 994.34 109.00 -3.2 4.9E-03 orf19.750 1506.08 159.66 -3.2 4.6E-03 orf19.1353 1922.26 203.26 -3.2 1.5E-03 orf19.6309 880.34 93.02 -3.2 4.0E-03 orf19.1960 CLN3 303.77 30.57 -3.3 6.1E-03 orf19.4750 309.87 29.88 -3.4 9.0E-05 orf19.156 FGR51 259.83 23.70 -3.5 7.7E-04 orf19.176 OPT4 1212.63 110.14 -3.5 4.6E-03 orf19.4699 1255.18 113.67 -3.5 1.2E-03 orf19.6981 237.14 21.04 -3.5 5.8E-05 orf19.5842 596.72 52.35 -3.5 2.8E-03 orf19.1960 CLN3 914.76 77.71 -3.6 6.4E-03 orf19.3563 245.46 20.69 -3.6 1.1E-03 orf19.4322 DAP2 278.76 22.18 -3.7 2.0E-03 orf19.2555 URA5 1160.78 90.13 -3.7 1.0E-03 orf19.1868 RNR22 123.73 9.60 -3.7 5.2E-03 orf19.177 267.06 20.33 -3.7 2.5E-05 orf19.843 1387.07 102.46 -3.8 1.4E-04 orf19.3695 858.38 60.32 -3.8 4.9E-03

208

orf6.2920 1559.11 104.55 -3.9 2.6E-03 orf19.5742 ALS9 1606.72 105.02 -3.9 2.0E-03 orf6.4638 219.69 11.73 -4.2 1.7E-06 orf19.1995 ScMNN2 266.12 13.48 -4.3 6.1E-05 orf6.2374 338.80 17.03 -4.3 7.0E-04 orf19.3869 1808.51 87.68 -4.4 3.0E-05 orf19.334 468.07 10.90 -5.4 1.5E-03

Table 2. Bioactive lipids present on the array.

Name Conc. MW 5(S)-HETE 0.1mM 320.2 (±)5-HETE 0.1mM 320.2 (±)5-HETE LACTONE 0.1mM 302.2 8(S)-HETE 0.1mM 320.2 9(S)-HETE 0.1mM 320.2 EMPTY 12(S)-HETE 0.1mM 320.2 12(R)-HETE 0.1mM 320.2 15(S)-HETE 0.1mM 320.2 15(S)-HEDE 0.1mM 324.3 (±)5-HETrE 0.1mM 322.3 TETRANOR-12(R)-HETE 0.1mM 266.2 15(S)-HETrE 0.1mM 322.3 (±)5-HEPE 0.1mM 318.2 15(S)-HEPE 0.1mM 318.2 5(S)-HPETE 0.1mM 336.2 12(S)-HPETE 0.1mM 336.2 15(S)-HPETE 0.1mM 336.2 15(S)-HPEDE 0.1mM 340.3 15(S)-HPEPE 0.1mM 334.2 (±)4-HYDROXYNON-2-ENAL 1mM 156.1 HEPOXILIN A3 0.1mM 336.2 HEPOXILIN B3 0.1mM 336.2 12(S),20-DIHETE 0.1mM 336.2 5(S),15(S)-DIHETE 0.1mM 336.2 8(S),15(S)-DIHETE 0.1mM 336.2 5(S),6(R)-DIHETE 0.1mM 336.2 5(S),12(R)-DIHETE all trans 0.1mM 336.2 8(R),15(S)-DIHETE all trans 0.1mM 336.2 5(S),12(S)-DIHETE all trans 0.1mM 336.2 8(S),15(S)-DIHETE all trans 0.1mM 336.2 5,6-EPOXYEICOSATRIENOIC ACID 0.1mM 320.2 8,9-EPOXYEICOSATRIENOIC ACID 0.1mM 320.2 11,12-EPOXYEICOSATRIENOIC ACID 0.1mM 320.2 14,15-EPOXYEICOSATRIENOIC ACID 0.1mM 320.2 5-KETOEICOSATETRAENOIC ACID 0.1mM 318.2 15-KETOEICOSATETRAENOIC ACID 0.1mM 318.2 13-KETOOCTADECADIENOIC ACID 0.1mM 294.2 LEUKOTRIENE B3 0.1mM 338.2 LEUKOTRIENE B4 0.1mM 336.2 20-HYDROXY-LEUKOTRIENE B4 0.1mM 352.2 20-CARBOXY-LEUKOTRIENE B4 0.1mM 366.2

209

LEUKOTRIENE C4 0.1mM 625.3 LEUKOTRIENE D4 0.1mM 496.3 LEUKOTRIENE E4 0.1mM 439.2 N-ACETYL-LEUKOTRIENE E4 0.1mM 481.2 LIPOXIN A4 0.1mM 352.2 EPOXY-OLEIC ACID 0.1mM 298.3 PROSTAGLANDIN A1 1mM 336.2 PROSTAGLANDIN A2 1mM 334.2 PROSTAGLANDIN B1 1mM 336.2 PROSTAGLANDIN B2 1mM 334.2 PROSTAGLANDIN D2 1mM 352.2 PROSTAGLANDIN E1 1mM 354.2 PROSTAGLANDIN E2 1mM 352.2 PROSTAGLANDIN F2a 1mM 354.2 PROSTAGLANDIN F1a 1mM 356.3 PROSTAGLANDIN I2 Na 1mM 352.2 15-KETO-PROSTAGLANDIN E2 1mM 350.2 15-KETO-PROSTAGLANDIN F2a 1mM 352.2 13,14-DIHYDRO-15-KETO-PGF2a 1mM 354.2 6-KETO-PROSTAGLANDIN F1a 1mM 370.2 16,16-DIMETHYL-PROSTAGLANDIN E2 1mM 380.3 U-46619 1mM 350.2 9b,11a PROSTAGLANDIN F2 1mM 354.2 9a,11b PROSTAGLANDIN F2 1mM 354.2 PROSTAGLANDIN J2 1mM 334.2 2,3-DINOR-6-KETO-PGF1a 0.1mM 342.2 CARBACYCLIN 1mM 350.2 (±)13-AZAPROSTANOIC ACID 1mM 311.3 19(R)-HYDROXY-PROSTAGLANDIN E2 1mM 368.2 19(R)-HYDROXY-PROSTAGLANDIN F2a 0.1mM 370.2 17-PHENYL-TRINOR-PGE2 1mM 386.2 D12-PROSTAGLANDIN J2 1mM 334.2 13,14-DIHYDRO-PGE1 1mM 356.3 8-EPI-PROSTAGLANDIN F2a 1mM 354.2 15d-PGJ2 1mM 316.2 MISOPROSTOL, FREE ACID 1mM 368.3 THROMBOXANE B2 1mM 370.2 11-DEHYDRO-THROMBOXANE B2 1mM 368.2 ANANDAMIDE (20:4, n-6) 1mM 347.3 PALMITYLETHANOLAMIDE 1mM 299.3 ANANDAMIDE (18:2,n-6) 1mM 323.3 ANANDAMIDE (20:3,n-6) 1mM 349.3 ANANDAMIDE (22:4,n-6) 1mM 375.3 MEAD ETHANOLAMIDE 1mM 349.3 (R)-METHANANDAMIDE 1mM 361.3 BML-190 1mM 426.1 N-Arachidonylglycine 1mM 361.3 EMPTY WIN 55,212-2 1mM 426.2 ARACHIDONAMIDE 1mM 303.3 LINOLEAMIDE 1mM 279.3 9,10-OCTADECENOAMIDE 1mM 281.3 ACETYL-FARNESYL-CYSTEINE 1mM 367.2 S-FARNESYL-L-CYSTEINE ME 1mM 339.2

210

AGGC 1mM 435.3 AGC 1mM 299.2 FARNESYLTHIOACETIC ACID 1mM 296.2 9(S)-HODE 0.1mM 296.2 (±)9-HODE 0.1mM 296.2 13(S)-HODE 0.1mM 296.2 (±)13-HODE 0.1mM 296.2 13(S)-HOTE 0.1mM 294.2 9(S)-HPODE 0.1mM 312.2 13(S)-HPODE 0.1mM 312.2 LEUKOTOXIN A (9,10-EODE) 0.1mM 296.2 LEUKOTOXIN B (12,13-EODE) 0.1mM 296.2 12(S)-HHT 0.1mM 280.2 25-HYDROXYVITAMIN D3 1mM 400.3 1,25-DIHYDROXYVITAMIN D3 1mM 416.3 24,25-DIHYDROXYVITAMIN D3 1mM 416.3 RETINOIC ACID, ALL TRANS 1mM 300.2 9-CIS RETINOIC ACID 1mM 300.2 13-CIS RETINOIC ACID 1mM 300.2 4-HYDROXYPHENYLRETINAMIDE 1mM 391.3 AM-580 1mM 351.2 TTNPB 1mM 348.2 METHOPRENE ACID 1mM 268.2 WY-14643 1mM 323.0 CIGLITAZONE 1mM 333.1 CLOFIBRATE 1mM 242.1 5,8,11-EICOSATRIYNOIC ACID 1mM 300.2 5,8,11,14-EICOSATETRAYNOIC ACID 1mM 296.2 1,2-DIDECANOYL-GLYCEROL (10:0) 1mM 400.3 1,2-DIOCTANOYL-SN-GLYCEROL 1mM 344.3 1,2-DIOLEOYL-GLYCEROL (18:1) 1mM 620.5 1-OLEOYL-2-ACETYL-GLYCEROL 1mM 398.3 1-STEAROYL-2-ARACHIDONOYL- GLYCEROL 1mM 644.5 RICINOLEIC ACID 1mM 298.3 1-HEXADECYL-2-ARACHIDONOYL- GLYCEROL 1mM 602.5 1-HEXADECYL-2-O-METHYL-GLYCEROL 1mM 330.3 1-HEXADECYL-2-O-ACETYL-GLYCEROL 1mM 358.3 2,3-DINOR-THROMBOXANE B2 0.1mM 342.2 14,15-DEHYDRO-LEUKOTRIENE B4 0.1mM 334.2 REV-5901 1mM 335.2 LY-171883 1mM 318.2 U-75302 0.1mM 361.3 SQ-29548 1mM 387.2 FLUPROSTENOL 1mM 458.2 CLOPROSTENOL Na 1mM 424.2 EICOSAPENTAENOIC ACID (20:5 n-3) 1mM 302.2 DOCOSAHEXAENOIC ACID (22:6 n-3) 1mM 328.2 ARACHIDONIC ACID (20:4 n-6) 1mM 304.2 MEAD ACID (20:3 n-9) 1mM 306.3 LINOLENIC ACID (18:3 n-3) 1mM 278.2 GAMMA-LINOLENIC ACID (18:3 n-6) 1mM 278.2 EICOSA-5,8-DIENOIC ACID (20:2 n-12) 1mM 308.3

211

EICOSADIENOIC ACID (20:2 n-6) 1mM 308.3 7,7-DIMETHYLEICOSADIENOIC ACID 1mM 336.3 EICOSATRIENOIC ACID (20:3 n-3) 1mM 306.3 DIHOMO-GAMMA-LINOLENIC ACID 1mM 306.3 DOCOSATRIENOIC ACID (22:3 n-3) 1mM 334.3 ADRENIC ACID (22:4 n-6) 1mM 332.3 DOCOSAPENTAENOIC ACID 1mM 330.3 LINOLEIC ACID 1mM 280.2 17-OCTADECYNOIC ACID 1mM 280.2 2-HYDROXYMYRISTIC ACID 1mM 244.2 2-FLUOROPALMITIC ACID 1mM 274.2 4-OXATETRADECANOIC ACID 1mM 230.2 12-METHOXYDODECANOIC ACID 1mM 230.2 SPHINGOSINE 1mM 299.3 C2 CERAMIDE 1mM 341.3 C2 DIHYDROCERAMIDE 1mM 343.3 N,N-DIMETHYLSPHINGOSINE 1mM 327.3 C8 CERAMIDE 1mM 425.4 C8 DIHYDROCERAMIDE 1mM 427.4 C16 CERAMIDE 1mM 537.5 DIHYDROSPHINGOSINE 1mM 301.3 SPHINGOMYELIN * 100nmol 730.6 SPHINGOSINE-1-PHOSPHATE * 100nmol 379.2 SPHINGOSYLPHOSPHORYL CHOLINE * 100nmol 464.3 DIHYDROSPHINGOSINE-1-PHOSPHATE * 100nmol 381.3 C8 CERAMINE 1mM 411.4 DL-DIHYDROSPHINGOSINE 1mM 301.3 DL-PDMP 1mM 390.3 DL-PPMP 1mM 474.4 MAPP, D-erythro 1mM 361.3 MAPP, L-erythro 1mM 361.3 PAF C16 1mM 523.4 LYSO-PAF C16 1mM 481.4 PAF C18 1mM 551.4 LYSO-PAF C18 * 100nmol 509.4 PAF C18:1 1mM 549.4 ENANTIO-PAF C16 1mM 523.4 ARACHIDONOYL-PAF 1mM 767.6 2-EPA-PAF 1mM 751.6 2-DHLA-PAF 1mM 769.6 DCHA-PAF 1mM 791.6 1-HEXADECYL-2-METHYLGLYCERO-3 PC 1mM 495.4 1-OCTADECYL-2-METHYLGLYCERO-3 PC 1mM 523.4 C-PAF 1mM 538.4 1-ACYL-PAF 1mM 537.3 LYSOPHOSPHATIDIC ACID 1mM 436.3 L-NASPA 1mM 423.2 PHOSPHATIDIC ACID, DIPALMITOYL 1mM 648.5 AM251 1mM 554.0 2-ARACHIDONOYLGLYCEROL 1mM 378.3 6-FORMYLINDOLO [3,2-B] CARBAZOLE 1mM 284.1 DIINDOLYLMETHANE 1mM 246.1 N-LINOLEOYLGLYCINE 1mM 337.3 PALMITOYL DOPAMINE 1mM 391.3

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OLEOYL DOPAMINE 1mM 417.3 ARACHIDONOYL DOPAMINE 1mM 439.3

213