Functional Genomic Characterization of Transcription Factors in Fission Yeast

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2016 Functional Genomic Characterization of Transcription Factors in Fission Yeast

Vachon, Lianne

Vachon, L. (2016). Functional Genomic Characterization of Transcription Factors in Fission Yeast (Unpublished doctoral thesis). University of Calgary, Calgary, AB. doi:10.11575/PRISM/26245 http://hdl.handle.net/11023/3218 doctoral thesis

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Functional Genomic Characterization of Transcription Factors in Fission Yeast

Lianne Vachon

A THESIS

SUBMITTED TO THE FACULTY OF GRADUATE STUDIES

IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE

DEGREE OF DOCTOR OF PHILOSOPHY

GRADUATE PROGRAM IN BIOLOGICAL SCIENCES

CALGARY, ALBERTA

AUGUST, 2016

Mapping an organism’s transcriptional regulatory network (TRN), which consists of all interactions between its transcription factors (TF) and target genes, is a necessary step in developing a complete understanding of how that organism normally develops, and how disease states can arise. However, the number of TFs, target genes, and potential regulatory interactions make this a difficult task, even in simple eukaryotes. In this study, the TRN of Schizosaccharomyces pombe was studied. To do so, we systematically deleted over 80% of fission yeast TFs, and characterized the effects of TFΔ on cell growth, length, and gene expression. Deletion of most TFs did not appear to impact the cell, suggesting that many may be inactive in rich medium. To circumvent this issue, we used two approaches. First, hypersensitivity of TFΔ strains to various drug compounds was determined to identify conditions that might induce TF activity. A four-way microarray expression profiling scheme was then used to identify the target genes and function of uncharacterized TF Toe1. This revealed that Toe1 regulated several genes implicated in the pyrimidine salvage pathway. Secondly, we systematically overexpressed S. pombe TF genes under control of the nmt1 promoter. Over 70% of the overexpressed TF genes resulted in altered cell length or fitness, indicating that their target genes might be inappropriately expressed. Expression microarrays and ChIP-chip were thus used to identify the putative target genes for three uncharacterized fungal TFs,

SPBC1773.16, SPBC16G5.17, and SPAC25B8.11, revealing potential roles for each of these TFs in regulating the utilization of alternative nitrogen sources. Finally, screens of our TFΔ array in flocculation inducing and rich mediums revealed six novel transcriptional activators (Foe1, Prr1, Prt1, SPBC530.08, Fep1, and Grt1) and two

ii transcriptional repressors (Scr1 and SPBC56F2.05) of flocculation. Microarray expression profiling was used to identify potential target genes for six of these TFs.

Additionally, ChIP-chip was used to identify direct Foe1 targets, revealing that this TF directly binds and regulates the expression of several genes encoding flocculins and cell wall remodeling/biosynthesis proteins. Collectively, these results should contribute to a better understanding of transcriptional regulation in S. pombe as a whole.

iii Acknowledgements

I would like to thank everyone who supported and encouraged me while I was pursuing my PhD. Without all of your help, this wouldn’t have been possible. In particular, I’d like to thank my PhD supervisor Dr. Gordon Chua, and my committee members Dr. Dave Hansen and Dr. Gregory Moorhead, for their insight and guidance along the way. I would also like to thank all of the members of the Chua lab, not only for helping train me when I came into the lab and discussing challenging problems, but also for making the lab a fun place to be. Finally, I would like to thank my family, especially my brother Eric, my father Mario, and my mom and stepdad Julie and Dwight, for your continuous support and encouragement throughout this process.

iv Table of Contents

Abstract ...... ii Acknowledgements ...... iv Table of Contents ...... v List of Tables ...... xi List of Figures and Illustrations ...... xii List of Symbols, Abbreviations and Nomenclature ...... xiv

CHAPTER ONE: INTRODUCTION ...... 1 1.1 Regulation of gene expression ...... 1 1.1.1 Regulated stages of gene expression ...... 1 1.2 Regulation of transcription ...... 3 1.2.1 The basal transcriptional machinery ...... 3 1.2.2 Sequence specific transcription factors ...... 4 1.2.2.1 DNA-binding domains and the classification of TFs ...... 4 1.2.2.2 TF regulatory domains and mechanisms of activation and repression ...... 6 1.2.3 Eukaryotic cis-regulatory elements ...... 9 1.2.3.1 Core promoter region ...... 9 1.2.3.2 Proximal promoter elements ...... 10 1.2.3.3 Other regulatory sequences in higher eukaryotes ...... 11 1.2.4 Cis-regulatory elements ...... 12 1.3 Transcriptional-regulatory networks ...... 12 1.3.1 Recurrent network motifs in transcriptional regulatory networks ...... 13 1.3.2 Semi independent modules ...... 15 1.3.3 Overall network topology ...... 16 1.3.4 Reconstruction of the S. pombe TRN ...... 16 1.4 The model organism Schizosaccharomyces pombe ...... 17 1.4.1 Sequencing of the S. pombe genome ...... 17 1.4.2 Advantages of S. pombe as a model organism ...... 18 1.5 Identification of TF target genes ...... 19 1.5.1 Systematic TF deletion and overexpression ...... 20 1.5.2 Identifying global changes in gene expression to elucidate TF target genes ..21 1.5.3 Identifying TF sequence specificities and mapping TF binding locations ...... 23 1.6 Flocculation ...... 24 1.6.1 Proteins mediating cell-cell adhesion ...... 25 1.6.2 Flocculation mechanisms ...... 26 1.6.3 Environmental conditions and signalling pathways that induce flocculation ...... 27 1.6.4 Transcriptional regulation of flocculation ...... 28 1.7 Objective of this study ...... 29 1.7.1 Specific aims ...... 29

CHAPTER TWO: MATERIALS AND METHODS ...... 31 2.1 Media ...... 31 2.2 Construction of transcription factor deletion library ...... 32

v 2.2.1 PCR amplification and stitching ...... 32 2.2.2 Lithium acetate transformation ...... 33 2.2.3 Yeast colony screens ...... 34 2.2.4 Determination of cell length and generation time ...... 34 2.2.5 Flocculation assays ...... 35 2.3 Drug sensitivity screens ...... 36 2.3.1 Determining minimum inhibitory concentrations ...... 36 2.3.2 TFΔ array design ...... 37 2.3.3 Drug screens ...... 37 2.3.4 Confirmation of drug sensitivity by serial dilution ...... 37 2.4 Transcription factor overexpression library screening ...... 38 2.4.1 Scoring of TFOE library for cell length and fitness defects ...... 38 2.4.2 Microscopy of TFOE strains with cell length and/or fitness defects ...... 39 2.5 Construction of pSLF272 HA-tagged nmt41 driven TFOE strains ...... 40 2.5.1 PCR amplification of TF genes ...... 40 2.5.2 Restriction digestion ...... 40 2.5.3 Ligation ...... 41 2.5.4 Bacterial transformation ...... 41 2.5.5 Bacterial colony screen ...... 41 2.5.6 Plasmid isolation ...... 42 2.5.7 Lithium acetate transformation ...... 42 2.5.8 PCR colony screen ...... 43 2.5.9 Western blotting ...... 43 2.6 Construction of nmt1 driven putative target strains ...... 45 2.7 Construction of nmt1 TFOE deletion strains ...... 45 2.8 Construction of double deletion strains ...... 45 2.9 Construction of integrated pREP1 nmt1-toe1+ ...... 46 2.10 Flow cytometry ...... 47 2.11 Microarray expression profiling ...... 47 2.11.1 Culturing for microarray expression profiling ...... 48 2.11.2 Total RNA extraction ...... 49 2.11.3 mRNA isolation ...... 49 2.11.4 Reverse transcription ...... 50 2.11.5 Cy3/5 coupling ...... 51 2.11.6 Sample hybridization to array ...... 52 2.11.7 Washing and scanning of array ...... 52 2.11.8 Normalization and data analysis ...... 53 2.12 ChIP-chip ...... 53 2.12.1 Culturing and cell lysate preparation ...... 54 2.12.2 Immunoprecipitation ...... 55 2.12.3 Protein removal and DNA recovery ...... 56 2.12.4 Blunting and ligation of linker DNA ...... 56 2.12.5 PCR labeling with aa-dUTP ...... 57 2.12.6 CyTM3 and CyTM5 dye coupling ...... 58 2.12.7 Array hybridization, washing, and scanning ...... 58 2.12.8 Array normalization and data analysis ...... 58

vi 2.13 Quantitative PCR ...... 59 2.13.1 Culturing and total RNA extractions ...... 59 2.13.2 Reverse transcription ...... 59 2.13.3 Quantitative PCR analysis ...... 59

CHAPTER THREE: CHARACTERIZATION OF S. POMBE TFΔ STRAINS AND IDENTIFICATION OF TF TARGET GENES BY FOUR-WAY MICROARRAY EXPRESSION PROFILING ...... 61 3.1 Systematic TF deletion ...... 61 3.1.1 Selection of putative TF genes ...... 61 3.1.2 Generation time of TFΔ strains ...... 62 3.1.3 Cell length defects of TFΔ strains ...... 63 3.2 Microarray expression profiling of TFΔ strains...... 65 3.3 Drug sensitivity of TFΔ strains ...... 67 3.3.1 MIC determination ...... 67 3.3.2 Drug sensitivity screens ...... 67 3.3.3 Confirmation of sensitivity by serial dilution ...... 69 3.3.4 Serial dilution sensitivity screens using tunicamycin and chlorpromazine hydrochloride ...... 71 3.4 Gene expression of wild-type S. pombe in response to drug treatment ...... 71 3.5 Proof of principle: Identification of Sre1 targets by expression microarray profiling of the sre1Δ strain ...... 73 3.5.1 Sensitivity of the sre1Δ strain to clotrimazole ...... 74 3.5.2 Four-way microarray expression analysis of the sre1Δ and wild-type strains in the absence and presence of clotrimazole ...... 74 3.5.3 Search for possible Sre1 binding motif ...... 78 3.6 Characterization of transcription factor Toe1 ...... 78 3.6.1 Four-way microarray expression analysis of the toe1Δ and wild-type strains in the absence and presence of chlorpromazine ...... 80 3.6.2 toe1+ overexpression causes cell elongation and cell cycle delay ...... 82 3.6.3 Microarray analysis of the toe1OE strain ...... 83 3.6.4 ChIP-chip analysis of the HA-tagged nmt1-toe1+ strain ...... 85 3.6.5 Search for possible Toe1 cis-regulatory sequences ...... 85 3.7 Phenotypic replication and genetic rescue of the nmt1-toe1+ phenotype ...... 86 3.8 Sensitivity of putative Toe1 target genes to chlorpromazine ...... 86 3.8.1 Growth inhibition of toe1Δ and target Δ strains on media containing uracil as the sole nitrogen source ...... 87

CHAPTER FOUR: IDENTIFICATION OF NOVEL TRANSCRIPTIONAL ACTIVATORS AND REPRESSORS OF FLOCCULATION IN S. POMBE ...... 89 4.1 Identifying positive transcriptional regulators of fission yeast flocculation ...... 89 4.1.1 TFΔ screen for reduced flocculation under inducing conditions ...... 89 4.1.2 TFOE screen for constitutive flocculation ...... 91 4.2 TF adhesion assays ...... 92 4.3 Microarray expression analysis of the wild-type strain in FIM ...... 93 4.4 Analysis of transcription factor Foe1 ...... 95 4.4.1 Microarray expression analysis of the foe1Δ strain in FIM ...... 96

vii 4.4.2 Phenotypic replication: abrogation of flocculation in FIM ...... 96 4.4.3 Microarray expression analysis of the foe1OE strain ...... 97 4.4.4 ChIP-chip analysis of HA-tagged nmt41-foe1+...... 98 4.4.5 Phenotypic suppression: abrogation of the foe1OE flocculation phenotype by deletion of putative target genes ...... 100 4.4.6 Flocculation mediated by foe1+ overexpression is not dependent on cbf12+ or mbx2+ ...... 102 4.5 Analysis of other putative transcriptional activators of flocculation ...... 102 4.5.1 Analysis of prt1+ deletion and overexpression strains...... 103 4.5.1.1 Microarray expression profiling of the prt1Δ strain in FIM ...... 103 4.5.1.2 Microarrays expression profiling of the prt1OE strain ...... 103 4.5.1.3 Search for potential Prt1 cis-regulatory sequences ...... 104 4.5.2 Analysis of SPBC530.08 deletion and overexpression ...... 104 4.5.2.1 Microarray expression profiling of the SPBC530.08Δ strain in FIM .104 4.5.2.2 Microarrays expression profiling of SPBC530.08OE strain ...... 107 4.5.3 Analysis of prr1+ deletion and overexpression ...... 108 4.5.3.1 Microarray expression profiling of the prr1Δ strain in FIM ...... 108 4.5.3.2 Microarray expression profiling of the prr1OE strain ...... 108 4.5.4 Phenotypic suppression: abrogation of the OE flocculation phenotype by deletion of putative target genes ...... 109 4.6 Epistatic relationships between TFs involved in flocculation ...... 111 4.7 Identifying negative transcriptional regulators of flocculation ...... 111 4.8 Analysis of transcription factor Scr1 ...... 113 4.8.1 Microarray expression profiling of the scr1Δ strain ...... 113 4.8.2 Search for putative Scr1 cis-regulatory sequences ...... 116 4.8.3 Phenotypic suppression: abrogation of scr1Δ flocculation by deletion of putative target genes ...... 116 4.9 Analysis of transcription factor SPBC56F2.05 ...... 117 4.9.1 Microarray expression profiling of the SPBC56F2.05cΔ strain ...... 117 4.9.2 Phenotypic suppression: abrogation of SPBC56F2.05cΔ flocculation by deletion of putative target genes ...... 119

CHAPTER FIVE: FUNCTIONAL GENETIC ANALYSIS OF THREE PREVIOUSLY UNCHARACTERIZED FUNGAL ZN2-CYS6 TRANSCRIPTION FACTORS ...... 121 5.1 Systematic overexpression of S. pombe TFs ...... 121 5.1.1 Cell length and fitness defects of nmt1-driven TFOE strains ...... 122 5.1.2 Cell cycle phenotypes of nmt1-driven TFOE strains ...... 124 5.2 Characterization of HA-tagged nmt41-driven TFOE strains ...... 124 5.2.1 Confirmation of nmt41-driven HA-fusion TF protein expression ...... 125 5.2.2 Comparison of untagged and HA-tagged TFOE strain phenotypes ...... 125 5.3 Identification of putative SPBC1773.16 target genes by phenotypic activation ...126 5.3.1 Expression microarray analysis of SPBC1773.16cOE strains ...... 127 5.3.2 ChIP-chip analysis of the HA-tagged nmt41-driven SPBC1773.16cOE strain ...... 129 5.3.3 Quantitative PCR validation of putative SPBC1773.16 target genes ...... 129

viii 5.3.4 DNA motif and functional enrichment analyses of putative SPBC1773.16 target genes ...... 130 5.3.5 Deletion and ectopic expression of putative target genes do not suppress or recapitulate, respectively, the curved, elongated phenotype of the SPBC1773.16cOE strain ...... 131 5.3.6 Growth of the SPBC1773.16cΔ strain is not inhibited on minimal medium containing arginine as the sole nitrogen source ...... 131 5.3.7 Overexpression of SPBC1773.16c and a subset of its putative target genes confers resistance to canavanine ...... 131 5.4 Identification of putative SPBC16G5.17 target genes by phenotypic activation ..132 5.4.1 Expression microarray analysis of the HA-tagged SPBC16G5.17OE strain 133 5.4.2 ChIP-chip analysis of the HA-tagged SPBC16G5.17OE strain ...... 134 5.4.3 DNA motif and functional enrichment analyses of putative SPAC16G5.17 target genes ...... 134 5.4.4 Growth of the SPBC16G5.17Δ strain is not inhibited on minimal media containing arginine or uracil as the sole nitrogen source ...... 135 5.5 Identification of SPAC25B8.11 putative target genes by phenotypic activation ..136 5.5.1 Microarrays expression analysis of the HA-tagged SPAC25B8.11OE strain ...... 136 5.5.2 ChIP-chip analysis of the HA-tagged SPAC25B8.11OE strain ...... 137 5.5.3 Further analysis of putative SPAC25B8.11 target genes ...... 138 5.5.4 Growth of the SPAC25B8.11Δ strain is not inhibited on minimal medium containing allantoin as the sole nitrogen source ...... 139

CHAPTER SIX: DISCUSSION ...... 140 6.1 Characterization of S. pombe TFΔ strains ...... 141 6.1.1 Deletion of most TF genes has minimal effect on cell phenotype under optimal conditions ...... 142 6.1.2 Most TFΔ strains with wild-type phenotypes also have minimal changes in gene expression ...... 144 6.1.3 Hypersensitive growth is not a good indication of TF gene expression in response to drug compounds ...... 145 6.1.4 Four-way microarray expression profiling can be an effective tool to identify TF target genes ...... 146 6.1.5 Toe1 regulates genes implicated in the pyrimidine salvage pathway ...... 147 6.2 Transcriptional regulation of flocculation in S. pombe ...... 148 6.2.1 Transfer of wild-type cells to FIM results in substantial changes in gene expression ...... 149 6.2.2 A significant number of TFs appear to impact S. pombe flocculation ...... 150 6.2.3 Transcriptional regulation of S. pombe flocculation by Foe1 ...... 151 6.2.4 Other novel transcriptional activators of flocculation ...... 154 6.2.4.1 Transcriptional regulation of flocculation by Prt1 ...... 155 6.2.4.2 Transcriptional regulation of flocculation by Prr1 ...... 157 6.2.4.3 Transcriptional regulation of flocculation by SPBC530.08 ...... 158 6.2.5 Scr1 and SPBC56F2.05 negatively regulation flocculation ...... 159 6.2.5.1 Transcriptional repression of flocculation by Scr1 ...... 160 6.2.5.2 Transcriptional repression of flocculation by SPBC56F2.05 ...... 161 ix 6.2.6 A comparison of the S. pombe and S. cerevisiae TRNs regulating flocculation ...... 162 6.3 Identification of S. pombe TF putative target genes by phenotypic activation ...... 163 6.3.1 Overexpression of many TF genes results in reduced fitness and abnormal cell phenotypes...... 164 6.3.2 SPBC1773.16 microarray analysis indicates potential involvement in arginine biosynthetic pathways ...... 165 6.3.3 SPBC16G5.17 may play a role in arginine metabolism and the proper segregation of sister chromatids during anaphase ...... 167 6.3.4 SPAC25B8.11 microarray expression analysis indicates potential involvement in metabolism of alternative nitrogen sources ...... 168 6.4 Significant findings and future directions ...... 169

REFERENCES ...... 175

APPENDIX A: ADDITIONAL TABLES ...... 185

APPENDIX B: COPYRIGHT PERMISSIONS ...... 237

x List of Tables

Table 3.1. MIC and drug concentration used for TFΔ strain sensitivity screens for 16 drug compounds in S. pombe...... 68

Table 3.2. Putative Toe1-OE target genes confirmed by qPCR...... 84

Table 4.1. Confirmation of microarray data of putative target genes induced in the foe1OE strain by qPCR...... 100

Table 4.2. Confirmation of microarray data of putative target genes induced in prr1OE, prt1OE, and SPBC530.08OE strains by qPCR...... 106

Table 4.3. Epistatic interactions between eight TFs that positively regulate flocculation in S. pombe...... 112

Table 4.4. Confirmation of microarray data of putative target genes induced in the of scr1Δ strain by qPCR...... 115

Table 4.5. Confirmation of microarray data of putative target genes induced in the SPBC56F2.05cΔ by qPCR...... 119

Table 5.1. Comparison of cell length and fitness scores for the HA-tagged nmt41- TFOE strains and the nmt1-driven TFOE strains...... 127

Table 5.2. Confirmation of microarray data of putative target genes induced in the SPBC1773.16cOE strain by qPCR...... 130

xi List of Figures and Illustrations

Figure 1.1. Structural organization of transcriptional regulatory networks...... 13

Figure 1.2. Overrepresented network motifs found in TRNs...... 14

Figure 1.3. Four-way microarray expression profiling experimental set-up...... 22

Figure 1.4. Partial model of the TRN of flocculation...... 29

Figure 3.1. Generation time of all 82 S. pombe TFΔ strains...... 63

Figure 3.2. Cell length of 82 S. pombe TFΔ strains...... 64

Figure 3.3. Clustergram of microarray expression profiles for 15 uncharacterized TFΔ strains grown in rich media...... 66

Figure 3.4. Heat map showing all 223 identified TFΔ drug interactions...... 70

Figure 3.5. Heat map showing all 78 TFΔ drug interactions identified by serial dilution...... 72

Figure 3.6. Response of sre1+ to clotrimazole treatment...... 75

Figure 3.7. Response of toe1+ to chlorpromazine treatment...... 79

Figure 3.8. toe1+ overexpression results in cell elongation and G1 delay...... 82

Figure 3.9. Identification of Toe1 putative target genes by phenotypic activation...... 84

Figure 3.10. Confirmation of putative Toe1 target genes...... 87

Figure 4.1. The deletion (A) and overexpression (B) of six TF genes (grt1+, SPBC530.08+, foe1+, prt1+, fep1+, and prr1+) results in abrogation and initiation of flocculation, respectively...... 90

Figure 4.2. TFΔ strains fail to adhere and invade solid media...... 93

Figure 4.4. Heat map of putative Foe1 target genes...... 99

Figure 4.5. Deletion of gsf2+(A) but not mbx2+ or cbf12+ (B) abrogates flocculation of the foe1OE strain...... 101

Figure 4.6. Microarray expression profiling of the prt1OE, prr1OE, and SPBC530.08 strains...... 106

xii Figure 4.7. Deletion of gsf2+, mbx2+, or cbf12+ abrogates the flocculation of the (A) SPBC530.08OE, (B) prt1OE, and (C) prr1OE strains...... 110

Figure 4.8. The deletion of 11 TF genes results in constitutive flocculation in minimal medium (EMM + ALU)...... 113

Figure 4.9. Microarray analysis of the scr1Δ strain...... 114

Figure 4.10. Abrogation of scr1Δ-mediated flocculation by deletion of mbx2+, cbf12+, and gsf2+...... 117

Figure 4.11. Microarray expression profiling of the SPBC56F2.05cΔ strain...... 118

Figure 4.12. Abrogation of SPBC56F2.05cΔ-mediated flocculation by deletion of mbx2+, cbf12+, gsf2+, and fta5+...... 120

Figure 5.1. Phenotypic characterization of the S. pombe transcription factor overexpression array...... 123

Figure 5.2. Confirmation of HA tagging of TFOE strains by western blotting...... 126

Figure 5.3. Identification of putative SPBC1773.16 target genes by phenotypic activation...... 128

Figure 5.4. Identification of putative SPBC16G5.17 target genes by phenotypic activation...... 133

Figure 5.5. Identification of putative SPAC25B8.11 target genes by phenotypic activation...... 137

xiii List of Symbols, Abbreviations and Nomenclature

Symbol Definition ALU Adenine, leucine, uracil BREd Downstream TFIIB recognition element BREu Upstream TFIIB recognition element C1-FFL Coherent type 1 feed forward loop ChIP Chromatin immunoprecipitation DNA Deoxyribonucleic acid DPE Downstream promoter element EMM Edinburgh minimal medium FFL Feed forward loop FIM Flocculation inducing medium GO Gene ontology GTF General transcription factor HA Human influenza hemagglutinin HAT Histone acetyltransferase HDAC Histone deacetylase I1-FFL Incoherent type 1 feed forward loop Inr Initiator KAN Geneticin LB Lysogeny broth LOWESS Locally weighted scatterplot smoothing MIC Minimum inhibitory concentration MIM Multiple-input motif mRNA Messenger ribonucleic acid MTE Motif ten element NAT Nourseothricin Nmt No message thiamine ORF Open reading frame PCR Polymerase chain reaction PIC Preinitiation complex RNA Ribonucleic acid RNAPII RNA polymerase II SAGA Spt-Ada-Gcn5 acetyltransferase SDL Synthetic dosage lethality SGA Synthetic genetic array SIM Single-input motif SWI/SNF SWItch/Sucrose non-fermentable TAF TBP associated factors TBP TATA-binding protein TF Transcription factor TFOE Transcription factor overexpression TFΔ Transcription factor deletion TRN Transcriptional regulatory network TSS Transcription start site

xiv WGD Whole genome duplication YES Yeast extract with supplements

Chapter One: Introduction

1.1 Regulation of gene expression

The regulation of gene expression is intricate and complex, consisting of a wide array of processes that control the variety and quantity of specific gene products produced by each cell. These mechanisms enable an organism with a single set of genes to generate multiple phenotypes, increasing its versatility and adaptability, and allowing it to perform a number of essential functions, including responding to favourable and harmful environmental conditions, maintaining homeostasis, and initiating cellular differentiation and morphogenesis (Gasch et al. 2000; Jongeneel et al. 2005; Rué &

Martinez Arias 2015).

It is therefore unsurprising that misregulation of gene expression results in disease. In fact, changes in gene expression have been linked to a vast array of illnesses, including numerous neurological diseases (such as Alzheimer’s disease, Parkinson’s disease, multiple sclerosis, and epilepsy), psychiatric disorders (such as depression, schizophrenia and bipolar disorder), cardiovascular diseases, inflammatory disorders, respiratory diseases, and various cancers (Baranzini 2004; Golub 1999; Heller et al. 1997;

Konradi 2005; Malerba & Pignatti 2005; Nanni et al. 2006). It is hoped that a better understanding of the changes in genes expression and involved regulatory processes will help elucidate novel therapeutic targets and treatment options.

1.1.1 Regulated stages of gene expression

Eukaryotic gene expression occurs in several steps, starting with the transcription of mRNA from DNA and ending with the translation of mRNAs to produce proteins.

While the mechanisms regulating these processes are varied and complex, they can be 1

categorized as occurring at one of three major levels: transcriptional, post-transcriptional, and translational. In addition, the stability and activity of translated proteins can be regulated by a variety of post-translational modifications.

The first, and potentially most impactful regulatory step, is transcriptional regulation, which involves the adjustment of mRNA synthesis by transcription factor binding. Studies suggest that protein levels (and thus phenotypes) are strongly correlated to transcriptional activity and the resultant mRNA levels, particularly during steady-state conditions (Liu et al. 2016). However, this correlation weakens during more dynamic phases, such as when rapid or short-term adaptation are necessary (Liu et al. 2016).

In these cases, a rapidly expanding array of processes, acting at the post- transcriptional, translational, and post-translational levels, appear to be more heavily utilized (Liu et al. 2016). Post-transcriptional regulation includes a diverse set of mechanisms that alter RNA stability and accessibility, including altering the timing of pre-mRNA transcript splicing and mRNA export, modifying and editing RNA (for example via pseudouridylation, methylation, or adenosine to inosine editing), and adjusting RNA stability and localization via non-coding RNA and RNA binding proteins

(Gurtan & Sharp 2013; Hao & Baltimore 2013; Hasan et al. 2014; Licht & Jantsch 2016;

Quenault et al. 2011; Wickramasinghe & Laskey 2015). While this list is by no means exhaustive, it hints at the immense diversity of post-transcriptional regulatory methods.

Similarly, the regulation of translation (both initiation and elongation), and post- translation, involves a plethora of processes, all of which alter protein abundance, stability, or activity, to modulate gene expression.

1.2 Regulation of transcription

While new research continues to show that post-transcriptional and translational regulatory processes have a more significant role in modulating gene expression than originally thought, the importance of transcriptional regulation has not diminished. In fact, an increasingly complex set of interactions between numerous cis-regulatory elements, proteins, and protein complexes, is now known to be involved.

1.2.1 The basal transcriptional machinery

To be transcribed, all protein coding genes require an assortment of protein complexes known collectively as the general/basal transcriptional machinery. This machinery, consisting of RNA polymerase II (RNAPII) and several general transcription factors (GTFs), is assembled at the core promoter. Typically, TATA-binding protein

(TBP) or one of several TBP associated factors (TAFs) (depending on which core promoter sequences are present) will bind specific DNA sequences within the core promoter. Several other general transcription factors and RNAPII are then recruited sequentially, until the preinitiation complex (PIC) is assembled, and transcription initiation can commence (Carlberg & Molnar 2014). Although PIC composition was originally thought to be universal, more recent studies indicate that its components vary with changes in core promoter sequence (Sikorski & Buratowski 2009). Despite these variations, different combinations of basal transcriptional machinery typically only produce a low, basal level of transcription (Sikorski & Buratowski 2009). Appreciable increases in transcription require additional regulatory elements and sequence specific transcription factors.

1.2.2 Sequence specific transcription factors

Sequence specific transcription factors (TFs) are DNA-binding proteins that recognize and bind specific regulatory sequences to control the rate of transcription of target genes. All TFs contain two major domains that facilitate this function: a DNA binding-domain and a regulatory domain (Latchman 1997).

1.2.2.1 DNA-binding domains and the classification of TFs

A TF’s DNA-binding domain is the part of the protein that directly contacts DNA and recognizes specific regulatory motifs (typically <20 bp long), restricting where it can bind and allowing a specific subset of target genes to be regulated (Latchman 1997). This binding event occurs because of chemical complementarity; that is, the surface of the

TF’s DNA-binding domain is structured so that multiple amino acids can form specific electrostatic and van der Waals interactions with the appropriate base pairs within the

DNA. Often, this involves the insertion of protruding α-helices or β-sheets into the

DNA’s major groove, although these interactions can also be stabilized by contacts with the minor groove or DNA backbone (Garvie & Wolberger 2001).

DNA-binding domains are used to sort TFs into superclasses, classes, and families, based on their general topology and mode of interaction with DNA, structural and sequence similarities, and sequence and functional similarities, respectively

(Wingender et al. 2013). In humans, approximately 10% of the genome is thought to encode an estimated 1700-1900 TFs, which can be subdivided into 111 families (Walhout

2006; Wingender et al. 2013). In contrast, only 2-3% of the S. pombe genome is thought

to encode approximately 99 TFs, with only 19 DNA-binding domains (Beskow & Wright

2006).

1.2.2.1.1 Fungal-specific Zn2-Cys6 TFs and other common S. pombe TF families

The most common class of TFs in S. pombe are the Zn2-Cys6 fungal type TFs.

Over one third of the TFs found in fission yeast belong to this class, the majority of which remain uncharacterized (Wood et al. 2012). First identified in S. cerevisiae with the discovery of GAL4, this fungal-specific TF class is now known to consist of an extensive assortment of TFs, regulating target genes involved in a variety of processes, including the metabolism of primary and secondary metabolites, asexual and sexual development, and multidrug resistance and the stress response (MacPherson et al. 2006;

Todd & Andrianopoulos 1997). In many cases, TFs of this class have multiple distinct functions, or functions that overlap with other Zn2-Cys6 TFs. In part, this may be because they have similar DNA-binding specificities, as most recognize two terminal trinucleotide repeats (typically CGG) with spacers of variable length. It could also result from the ability of these TFs to bind in multiple forms, including as monomers, and homo- and hetero-dimers (MacPherson et al. 2006; Todd & Andrianopoulos 1997).

Zn2-Cys6 TFs have a characteristic structure. At their N-terminus, they have a conserved zinc binuclear cluster (CX2CX6CX6CX2CX6C, X = any amino acid) that is involved in DNA binding. This motif forms two small α-helices, each consisting of three cysteine residues that coordinate two Zn2+ ions and recognize a conserved trinucleotide repeat. A non-conserved linker domain, located immediately 3’ to the zinc cluster, contributes to the TF’s DNA-binding specificity, by either directly contacting DNA or by

determining the length of the spacer found in the DNA-binding motif. Following this linker domain, most TFs contain a dimerization domain, consisting of heptad repeats, which is responsible for TF dimerization. At the C-terminus, there are two non-conserved domains, a regulatory domain and an acidic activation domain (Todd & Andrianopoulos

1997; MacPherson et al. 2006).

While Zn2-Cys6 DNA-binding domains are the most abundant in fission yeast, there are several other common types of DNA-binding domains, many of which are shared with more complex eukaryotes. For example, the second most abundant DNA- binding domain found in S. pombe TFs, the Cys2-His2 zinc finger, is also one of the most commonly found DNA-binding domain in eukaryotes in general. The DNA-binding domain of these TFs contains tandem repeats of consensus sequence

(F/Y)XCX2−5CX3(F/Y)X5ψX2HX3−5H (X = any amino acid, ψ = hydrophobic residue), each folded into a two-stranded antiparallel β sheet and an α helix coordinating a single

Zn2+. Typically, at least two to four of these “fingers,” separated by short linkers, are required for recognition of the appropriate cis-sequence. Unlike TFs containing a Zn2-

Cys6 DNA-binding domain, Cys2-His2 TFs usually bind as monomers to regulate a wide variety of target genes (Wolfe et al. 2000). Some other notable DNA-binding domains include HSF-type, LIS1-like, and MADS box domains. A complete list of S. pombe TFs and their corresponding DNA-binding domains can be found in Table A1.

1.2.2.2 TF regulatory domains and mechanisms of activation and repression

In addition to their DNA-binding domains, TFs also have a regulatory domain

(Latchman 1997). These domains do not interact with DNA, but rather with several

cofactors to activate or repress transcription by RNAPII (Latchman 1997). Unlike DNA- binding domains, these regulatory domains are typically unstructured when the TF is unbound and poorly conserved. Furthermore, the specific residues required and mode of cofactor recruitment still remain unclear (Hahn & Young 2011). What is clear however, is that most TFs can recruit a wide range of coactivators or corepressors to modulate transcription.

1.2.2.2.1 Mechanisms of transcription factor activation

Most commonly, TFs increase RNAPII directed transcription of target genes indirectly, by recruiting a number of coactivators that increase basal transcriptional machinery recruitment, PIC formation, and transcription initiation and elongation. One common method of transcriptional activation involves the recruitment of Mediator. This multifunctional complex not only appears to be required to stimulate basal transcription, but also interacts directly with GTFs and RNAPII, acting as an intermediary between transcriptional activators and the basal transcriptional machinery (Conaway & Conaway

2011; Hahn & Young 2011). For example, it can stimulate transcriptional initiation and elongation through its recruitment of GTFs TFIIE and TFIIH, and elongation factors P-

TEFb and BRD4, respectively (Fuda et al. 2009; Conaway & Conaway 2011). In addition to recruiting Mediator, TFs can recruit several GTFs, such as TBP, TFIID, and TFIIA, directly (Fuda et al. 2009). They can also recruit complexes with histone modification or chromatin remodelling activities, such as the NuA4 and SAGA complexes, or the

SWI/SNF and Ino80 complexes, respectively (Fuda et al. 2009; Hahn & Young 2011).

In most cases, it appears that TF-induced transcriptional activation involves the recruitment of multiple coactivators. For example, in S. cerevisiae, activation of the

PHO5 and PHO8 target genes by Pho4 involves the recruitment of all four of the aforementioned histone modification and chromatin remodelling complexes (Fuda et al.

2009). Another well studied TF, S. cerevisiae Gal4, interacts with both the SAGA complex and Mediator to induce GAL gene expression (Bryant & Ptashne 2003).

1.2.2.2.2 Mechanisms of transcription factor repression

Like transcriptional activators, transcriptional repressors can inhibit gene expression in a number of different ways, and individual repressors often appear to do so using multiple mechanisms (Gaston & Jayaraman 2003). In some cases they do so passively, competing with transcriptional activators for the same or overlapping binding sites, or by interacting with the transcriptional activator itself (Gaston & Jayaraman

2003). For example, the human TF CDP competes with transcriptional activators for binding to the gp91phox promoter (Luo & Skalnik 1996), while Foxp3 interacts with members of the NF-κB and NFAT transcription factor families (Bettelli et al. 2005), to inhibit target gene expression.

In contrast, TFs can also actively inhibit transcription by interacting directly with the general transcriptional machinery or by recruiting a variety of corepressors, including complexes with histone modifying or chromatin remodelling activity (Gaston &

Jayaraman 2003). For example, S. cerevisiae TF Mig1 represses target gene expression by recruitment of the Ssn6-Tup1 protein complex (Treitel & Carlson 1995), and Foxp3 represses transcription by recruiting a TIP60/HDAC7/HDAC9 complex with HAT and

HDAC activity (Li & Greene 2007). Additionally, the recruitment of some forms of cofactors that are typically considered to be activators, can also be used to repress transcription, such as the recruitment of a kinase-containing Mediator complex by a repressive form of the C/EBPβ TF (Conaway & Conaway 2011).

1.2.3 Eukaryotic cis-regulatory elements

While the general transcriptional machinery, sequence specific TFs, and cofactors are essential, the regulation of transcription also involves a number of different DNA elements. These include the core and proximal promoter regions, as well as the more distantly located enhancers, silencers, and insulators in higher eukaryotes (Maston 2006).

1.2.3.1 Core promoter region

The core promoter region, which encompasses approximately 50 bp upstream and downstream of the transcriptional start site (TSS), is the site of PIC assembly and transcriptional initiation (Juven-Gershon & Kadonaga 2010; Carlberg & Molnar 2014).

Original models based on prokaryotes and simple eukaryotes suggested that core promoters might share a simple structure consisting primarily of a TATA box. However, recent studies in higher eukaryotes have uncovered an unexpected level of complexity and diversity. Not only do many genes make use of alternative core promoters, in many cases the TATA box is accompanied or replaced by additional regulatory sequences, such as the upstream or downstream TFIIB recognition element (BREu and BREd), the initiator (Inr), the downstream core promoter elements (DPE), or the motif ten element

(MTE) (Juven-Gershon & Kadonaga 2010; Lenhard et al. 2012).

This diversity in core promoter sequences is able to impact basal transcription rates, as the presence of multiple different sequences increases TFIID recruitment and

PIC assembly (Juven-Gershon & Kadonaga 2010). Additionally, core promoters enriched for particular regulatory sequences have similar precision in TSS selection, and they often regulate genes with similar levels and patterns of expression (Lenhard et al. 2012).

Diversity in core promoter sequence can also affect sequence specific TF dependent regulation, as some activators and enhancers have a preference for certain core promoter elements over others (Juven-Gershon & Kadonaga 2010).

1.2.3.1.1 S. pombe core promoters

Core promoter architecture in the fission yeast S. pombe appears to be simpler though similar to metazoans in many ways. Fission yeast have fewer conserved regulatory sequences, with only the initiator element, TATA boxes, and a newly discovered motif [CC(T/A)(T/C)(T/C/A)(A/G)CCA(A/T/C)] enriched in core promoter regions (Li et al. 2015). However, like metazoans, these elements are often associated with genes initiated with similar levels of precision, and with similar patterns and levels of expression. Furthermore, like metazoans, S. pombe makes use of alternative promoters, often of different classes. In fact, 38% of S. pombe genes appear to have at least two associated core promoters (Li et al. 2015).

1.2.3.2 Proximal promoter elements

Proximal promoter elements consist of all regulatory sequences immediately upstream of the core promoter and TSS. Typically, this region has multiple binding sites for sequence specific TFs. Interestingly, these regions are usually found alongside CpG

islands, suggesting that one way in which bound TFs might enhance transcription is to prevent the methylation of these regions (Maston 2006). Most TF binding sites in yeast can be classified as proximal promoter elements; they are typically located 100-500 bp upstream of the protein coding region, with very few binding sites located <100 bp or

>500 bp away (Harbison et al. 2004). These regions are known as upstream activation or repression sequences (Hahn & Young 2011).

1.2.3.3 Other regulatory sequences in higher eukaryotes

In addition to core and proximal promoter regions, higher eukaryotic genomes contain more distally located enhancers, silencers, and insulators. Like these promoter elements, enhancers contain several short, regulatory sequences bound by transcriptional activators in order to upregulate target gene expression. However, they can be located both upstream and downstream of their target genes at great distances (Maston 2006). In contrast to enhancers, silencers contain regulatory sequences bound by transcriptional repressors, and are thus involved in repressing gene expression. They can be subdivided into two types based on their location relative to the core promoter; the first type must be located within 100 bp of their target genes to have the desired effect, while the second can be located much farther away (Maston 2006). Finally, insulators prevent non-target genes from being regulated incorrectly. They typically function in one of two ways, either by preventing off-target enhancer-promoter communication or by inhibiting the spread of repressive chromatin (Maston 2006).

1.2.4 Cis-regulatory elements

Within the aforementioned regulatory regions, TFs recognize and bind short, degenerate cis-regulatory elements (typically <20 bp) to alter gene transcription (Maston

2006). These elements can be organized in one of four major ways depending on the number of TFs regulating the associated target genes and the manner in which they do so.

In the simplest example, a single TF may be involved, either binding a single copy of a cis-regulatory sequence or several repeated copies of the same short sequence.

Alternatively, multiple TFs can regulate some genes, resulting in promoters containing multiple different cis-regulatory sequences. In some cases, these TFs may even physically interact, resulting in co-occurring cis-regulatory sequences (Harbison et al.

2004).

1.3 Transcriptional-regulatory networks

Once TFs and their target genes have been identified, a regulatory network consisting of all these interactions can be constructed. These transcriptional-regulatory networks (TRNs) consist of a series of nodes, representing TFs and their target genes, and edges, representing the regulatory interactions between them (Lee et al. 2002; Walhout

2006). These individual interactions are then organized into several higher levels of structure, starting with network motifs and semi-independent modules, before finally being assembled to form the network itself (Figure 1.1) (Babu et al. 2004). Despite the increased complexity and number of interactions to be mapped for the human TRN relative to those in simpler eukaryotes such as S. pombe and S. cerevisiae, these same basic organizational principles apply.

Figure 1.1. Structural organization of transcriptional regulatory networks. The TRN consists of nodes (TFs and their target genes) and edges (interactions between them). The basic structural unit consists of a single TF and one of its target gene. TFs and target genes are organized into various over-represented network motifs, which cluster into semi-independent modules that are finally assembled into the network as a whole. This figure was adapted from Babu et al. (2004).

1.3.1 Recurrent network motifs in transcriptional regulatory networks

While TFs could potentially interact with one another and their target genes in almost innumerable ways, this doesn’t tend to be the case. Instead, there are several patterns of interaction that are recurrent and overrepresented in all organisms; these patterns are known as network motifs. Each network motif has qualities that make its use advantageous in specific situations. Several of the most common network motifs are illustrated in Figure 1.2.

The simplest motif, a single-input motif (SIM), involves the regulation of a set of target genes by a single TF (Figure 1.2A). SIMs allow for the precise, coordinated, and sometimes stepwise, expression of a set of target genes that share a similar function

(Alon 2007; Lee et al. 2002; Macneil & Walhout 2011). In contrast, multiple-input motifs

(MIMs) involve several TFs activating the same set of target genes (Figure 1.2B). While this might reduce precision, it does increase the network’s robustness, as the removal of

A Single Input Motif (SIM) B Multiple-input Motif (SIM) C Feed Forward Loop – Coherent 1

TF TF TF TF TF TF Gene

TF Target Target Target Target Target Target Gene Gene Gene Gene Gene Gene

Target Gene

D Feed Forward Loop – Incoherent 1 E Autoregulation F Multi-component loop TF Positive Negative TF Gene TF TF TF TF Gene

TF TF TF TF TF Gene Gene Gene

Target Gene Figure 1.2. Overrepresented network motifs found in TRNs. (A) Single-input motifs involve a single TF regulating a set of target genes, while (B) multiple-input motifs involves several TFs regulating the same set of target genes. In feed-forward loops, a TF upregulates the expression of its target genes and a TF that also regulates their expression. This second TF may have (C) the same net effect on target gene expression as the TF inducing its expression, or (D) an opposing effect. (E) TFs can positively or negatively regulate their own expression directly by autoregulation. (F) Alternatively, they can do so indirectly, using a multi-component loop. This figure was adapted from Lee et al. (2002) and Alon (2007). one TF can often be offset by the activity of another (Lee et al. 2002; Alon 2007; Macneil

& Walhout 2011).

Feed-forward loops (FFLs) are also regularly utilized. Two of them, coherent type

1 (C1) and incoherent type 1 (I1), are particularly prevalent in yeast. In C1-FFLs, a TF upregulates the expression of its downstream target genes, as well as a TF target gene that has the same net effect on their expression (Figure 1.2C). This can be used to cause a

delay in turning expression of the target gene on or off, depending on if both, or one of the TFs, is required for expression, respectively (Alon 2007; Macneil & Walhout 2011).

In contrast, in I1-FFLs, the TF increases expression of an antagonistic TF target gene that opposes the upregulation of its target genes (Figure 1.2D) (Alon 2007; Macneil &

Walhout 2011).

In addition to altering the expression of other TFs and target genes many TFs also regulate their own expression. In some cases, this autoregulation might be negative, where the TF represses its own transcription. Alternatively, positive autoregulation can be used to upregulate its own transcription (Figure 1.2E). Additionally, this regulation can be indirect, such as in multi-component loops, where one TF affects the expression of another, which then affects the first TF’s expression (Figure 1.2F) (Alon 2007; Lee et al.

2002; Macneil & Walhout 2011).

1.3.2 Semi independent modules

At the next level of organization, nodes and motifs are clustered into modules.

These modules are semi-independent (discrete, but not entirely separable from the rest of the network), consist of genes with related functions, and come in varying sizes, with smaller modules frequently found enclosed within larger ones (Babu et al. 2004). This modular structure is beneficial, allowing genes with similar functions to be coordinately regulated in response to various stimuli and insulating most of the network from large changes in gene expression resulting from a single change within a module (Macneil &

Walhout 2011).

1.3.3 Overall network topology

Finally, all TFs and their target genes, organized within network motifs and modules, are combined to form the network as a whole. The overall topology of TRNs appears to be somewhat conserved amongst eukaryotes and can be described effectively using a few different parameters. First, the incoming connectivity (i.e. number of TFs regulating a gene) of most genes appears to be low and decreases exponentially. For example, 93% of the genes in S. cerevisiae are bound by 1-4 TFs (Babu et al. 2004;

Macneil & Walhout 2011). Similarly, the outgoing connectivity (i.e. number of genes regulated) of most TFs is low. In fact, most TFs are considered fine tuners, as they regulate the expression of very few genes. Very few TFs act as global regulators, forming network hubs and regulating a large number of genes (Babu et al. 2004; Macneil &

Walhout 2011).

1.3.4 Reconstruction of the S. pombe TRN

While the recreation of the human TRN is ultimately the goal for many, the sheer number of interactions and complexity of the network make its reconstruction a daunting task. Even in single-celled eukaryotes like S. pombe, construction of the network is far from simple. However, as many of the same organizational principles appear to apply to both simple and complex eukaryotes, mapping of the TRN in S. pombe is a useful intermediate step. Furthermore, there are other benefits to elucidating the TRN of S. pombe, especially due to its many potential uses in industry and research. For example, an understanding of the genes and TFs involved in multidrug resistance in S. pombe has allowed the construction of a S. pombe strain that is sensitive to a wide range of chemical

inhibitors. Thus, it is more useful in analyzing the mode of action of drug compounds

(Kawashima et al. 2012).

1.4 The model organism Schizosaccharomyces pombe

Schizosaccharomyces pombe is a rod-shaped, ascomycete yeast that divides by medial fission (Hoffman et al. 2015). Although originally isolated by Paul Lindner in

1893, S. pombe based research did not begin until the 1950s, when Urs Leupold and

Murdoch Mitchinson began their early experiments on S. pombe mating and the cell cycle, respectively (Hoffman et al. 2015). Over the next twenty years, most work in S. pombe was produced by these two labs or the students and post-doctoral fellows trained in them. In the late 1980s however, research truly began to take off, as the number of publications per year grew rapidly, culminating in the first international fission yeast meeting in 1999 (Hoffman et al. 2015).

1.4.1 Sequencing of the S. pombe genome

In 2002, S. pombe became the sixth eukaryotic genome to be fully sequenced and annotated, providing the fission yeast research community with an invaluable resource

(Wood et al. 2002). Sequencing revealed that there were approximately 5000 genes, divided amongst the three chromosome, 13.8 Mb genome, at a density of one gene per

2528 bp. In contrast to S. cerevisiae, there was no evidence of S. pombe having undergone a whole genome duplication (WGD), suggesting a lower level of gene redundancy. Furthermore, a much higher proportion of S. pombe genes (43%) were found to contain introns (43% vs 5%), and their centromeres were found to be far more diffuse and complex than their budding yeast counterparts (Yanagida 2002; Wood et al. 2002).

Analyses also indicated that while the two yeasts shared a considerable number of homologous genes, approximately 17% of the S. pombe genes were not found in S. cerevisiae. Furthermore, 3% of these genes were found in C. elegans, suggesting that some proteins or processes not found in S. cerevisiae could be studied in S. pombe (Wood et al. 2002). In fact, there are currently 338 genes conserved in metazoans and fission yeast that have been lost in S. cerevisiae (Hoffman et al. 2015).

Interestingly, while the two yeasts are estimated to have diverged from metazoans far before having diverged from one another (1000 Mya versus 330-420 Mya), the sequences of all three are equally similar (Forsburg 1999; Yanagida 2002). This level of conservation, despite large evolutionary distances, allows conserved proteins and potentially essential, core eukaryotic processes to be identified. In contrast, the diversity in proteins and mechanisms used by different species to solve similar problems can illuminate how and why alternative solutions to the same problems evolved (Forsburg

1999).

1.4.2 Advantages of S. pombe as a model organism

There are several advantages to using S. pombe as a model organism, both in general and in relation to S. cerevisiae. Some of these were highlighted in the previous section. For example, not only does S. pombe divide by medial fission, which more closely resemble metazoan cell division, but it also has several proteins functioning in eukaryotic processes (e.g. RNA interference and splicing; at centromeres) that are underdeveloped in budding yeast. Unlike S. cerevisiae, S. pombe is also stable as a haploid, meaning that the effects of recessive mutations can easily be studied.

Furthermore, it has not undergone WGD, and as a result, has both fewer genes and less functional redundancy than budding yeast. This is particularly relevant when studying

TRNs, as these networks are notoriously difficult to map, and fewer TFs and target genes

(~99 TFs and ~5000 genes), make it easier to do so.

Many of the qualities that make S. cerevisiae an ideal model organism, also apply to fission yeast. Both are small, have short generation times, and are amenable to molecular manipulation (Forsburg & Rhind 2006). A wide array of protocols and tools are available to work in both. S. pombe can be easily mated, synchronized, or visualized by microscopy. Genes can be knocked out or overexpressed with ease, using a variety of deletion cassettes or overexpression plasmids and well-developed transformation protocols. Furthermore, there is a commercially-available, barcoded deletion collection, consisting of ~3300 haploid strains, which allows high-throughput genetic and chemical screening to be done. Several commercially-available expression and ChIP microarrays

(and more recently, several next generation sequencing techniques) also allow global mRNA expression and protein binding to be monitored.

1.5 Identification of TF target genes

While the identification of TF target genes is undoubtedly easier in yeast than in more complex eukaryotes, it is still a significant challenge. To do so, a diverse set of techniques, including a number of high-throughput approaches, have been developed.

While initially implemented in simple model organisms, many of these approaches have since been successfully modified for use in more complex eukaryotes.

1.5.1 Systematic TF deletion and overexpression

With the complete sequencing and annotation of several eukaryotic genomes in the early 2000s, the use of reverse genetics to systematically identify gene function became possible. The first step in identifying TF targets and functions is thus often the construction of TF deletion or overexpression libraries.

In yeast, deletion libraries can be generated using a simple PCR-based stitching method followed by a lithium acetate transformation (Kaur et al. 1997). In most cases, this method allows the entire open reading frame (ORF) to be deleted, resulting in complete loss of function. These TFΔ strains can then be screened for phenotypic changes that might reveal TF function. Unfortunately, in many organisms, single deletions of the vast majority of TFs grow normally under optimal conditions (Giaever et al. 2002), suggesting either that many TFs are functionally redundant, or that they are inactive under the conditions tested. To account for this second possibility, TFΔ strains can be screened for hypersensitivity to various chemicals or environmental conditions

(Giaever et al. 2002), as hypersensitivity might indicate that the tested condition activates the TF in question.

Another way to address the problem of TF inactivity that does not require knowing conditions that induce TF activity, is artificial activation. This approach has been helpful in identifying the target genes and functions of many yeast TFs. For example, the target genes and roles of several Candida albicans Zn2-Cys6 TFs were identified using microarray expression analysis of hybrid proteins containing the TF’s

DNA-binding domain fused to a Gal4 activation domain (Schillig & Morschhäuser

2013). Similarly, in S. cerevisiae, microarray expression profiling of TF overexpression

(TFOE) strains causing growth defects helped identify several TF target genes and functions (Chua et al. 2006). However, while these techniques circumvent some of the issues of working with TFΔ strains, they have their own disadvantages. For example, overexpression of some TF genes does not result in an abnormal phenotype or target gene expression changes. Alternatively, TF overexpression may occasionally produce a dominant negative phenotype, which cannot always be easily differentiated from abnormalities associated with TF hyperactivity.

1.5.2 Identifying global changes in gene expression to elucidate TF target genes

Microarray expression profiling and its successor RNA sequencing (RNA-seq) allow researchers to simultaneously measure all changes in gene expression when cells are chemically or genetically perturbed. As TFs activate and/or repress their target genes’ expression, microarray analysis of the effects of gene deletion or overexpression can be very informative. As mentioned previously, microarray expression profiling of TFOE strains has been effective in identifying TF target genes and functions in budding yeast

(Chua et al. 2006). While microarray expression profiling of deletion strains is often less informative, it has still been used successfully to identify the target genes and functions of several TFs in S. pombe (Chua 2013).

In cases when neither deletion nor overexpression of a TF gene is informative, microarray expression profiling of the TFΔ strain in the presence of environmental perturbations that induce its activity can be illuminating. For example, the target genes and function of S. pombe TFs Atf1 and Zip1 were elucidated by microarray expression profiling with cadmium and osmotic stress, respectively (Harrison et al. 2005; Sansó et

al. 2008). In some cases, however, the combined stress of the environmental perturbation and the TFΔ strain’s inability to respond to this perturbation results in substantial changes in gene expression. Many of these differentially-regulated genes are stress response genes, or secondary effects, rather than direct target genes of the TF. To better identify true direct TF target genes, a four-way expression microarray strategy can be used.

Four-way microarray expression profiling makes use of three additional experiments. Differences in gene expression between (1) wild type and the TFΔ strain in the absence of the drug compound, (2) the TFΔ strain with and without the drug compound, (3) wild type with and without the drug, and (4) wild type and the TFΔ strain in the presence of the drug compound are measured. These four microarray experiments allow true TF target genes to be more easily distinguished from stress response genes and secondary effects, as they should be upregulated in treated wild type compared to untreated wild type, downregulated in the TFΔ strain relative to wild type when both are treated with the drug compound, and not differentially regulated when the untreated TFΔ strain is compared to the treated TFΔ strain or the untreated wild type (Figure 1.3).

WT vs TF∆ No drug WT +/- drug TFΔ +/- drug WT vs TF∆ +drug

Not active, no change in the presence of drug TF absent, no change with drug in the TFΔ

WT, rich media TFΔ, rich media WT, drug WT, no drug TFΔ, drug TFΔ, rich media WT, drug TFΔ, drug compound compound compound compound compound Figure 1.3. Four-way microarray expression profiling experimental set-up. Four microarray expression profiles are used to identify TF target genes. These target genes should be unchanged in the TFΔ strain versus the wild-type control in rich medium, as the TF is inactive under these conditions (Box 1). TF target genes will be upregulated in the presence of the drug compound, as the perturbation will induce TF activity and subsequently, target gene transcription (Box 2). Expression of the TF target genes will be unchanged in the TFΔ strain in the presence and absence of the drug compound (Box 3). Target genes expression will be reduced in the TFΔ strain relative to the wild-type control when both are treated with the drug compound (Box 4). 22

1.5.3 Identifying TF sequence specificities and mapping TF binding locations

To identify target genes that are directly regulated by a TF, it is important that microarray data be combined with information about the TF’s binding locations in the genome and sequence specificity. To do so, various experimental approaches have been used. MITOMI, SELEX, and most commonly, protein-binding microarrays can be used to identify sequences bound by TFs in vitro (Berger et al. 2006; Jolma et al. 2010; Maerkl

& Quake 2007). While these techniques can determine specific sequences bound by TFs, they do not indicate which genomic regions are bound in the cell; other factors, such as nucleosome occupancy, affect accessibility of these sequences. Furthermore, TF activity and binding can be affected by other trans factors within the cell, preventing sequence specificity from being identified in the absence of these conditions.

As a result, several in-vivo techniques have also been developed. The Calling- cards method, and its successor Calling cards-seq identify TF binding locations in the genome by fusing the TF of interest to the Sir4 protein. As Sir4 interacts with and recruits the Ty5 integrase protein, TF binding sites can be identified by sequencing Ty5 transposons integration sites (Wang et al. 2007). Alternatively, DNAseI footprinting, while not identifying the sequence specificities or locations bound by specific TFs, can be used to determine genomic regions bound by TFs in general (Hesselberth et al. 2009). By searching these regions for enriched DNA motifs and comparing them to known TF sequence specificities, the locations bound by specific TFs can be inferred (Hughes & de

Boer 2013).

Most frequently however, TF binding locations are mapped in the genome using

ChIP-based techniques. In ChIP-based techniques, the TF of interest is fused with an

epitope tag. Strains expressing the tagged TF are cultured and treated with formaldehyde to cause protein-DNA crosslinking. Subsequently, sonication, followed by a pull-down using anti-tag antibodies, reverse crosslinking, labelling, and hybridization to an array, allows the genomic locations bound by the TF of interest to be identified in vivo (Ren et al. 2000). First developed in the early 2000s, updates and modifications, first with the introduction of ChIP-seq, and then ChIP-exo, have improved the resolution of these techniques, so that TF binding events can be predicted almost to the exact base pair

(Hughes & de Boer 2013). However, while these techniques can accurately identify the genomic locations TFs bind, they do not determine the importance of the binding or reveal if the TF is active. In fact, the vast majority of target genes whose promoter regions are bound by a TF are not differentially regulated when that TF is deleted, indicating that the TF is inactive or redundant with other TFs (Hughes & de Boer 2013).

1.6 Flocculation

One TRN that is of particular interest in yeast is the network regulating a form of non-sexual cell-cell adhesion called flocculation. The flocculating ability of S. cerevisiae has long been used by the brewing industry to separate yeast cells from finished products easily and cost-effectively (Soares 2011; Vallejo et al. 2013). The ease with which these flocculating cells can be removed from media and their increased tolerance to adverse conditions also make them an ideal candidate for other uses, such as bioremediation of heavy metals, production of bioethanol and other biofuels, and production of a variety of other heterologous proteins (Machado et al. 2008; Zhao & Bai 2009; Ofuonye et al. 2013;

Vallejo et al. 2013).

Additionally, in many cases the proteins that regulate cell-cell adhesion also impact the yeast’s ability to grow invasively into solid media, form biofilms, and adhere to other surfaces (Dranginis et al. 2007; Matsuzawa, Yoritsune, et al. 2012). As these processes allow pathogenic yeasts such as C. albicans to persist on medical devices, to adhere to and invade host tissues, and to become resistant to a number of drugs, an understanding of the mechanisms and regulation of adhesion also has significant therapeutic applications (Mayer et al. 2013).

1.6.1 Proteins mediating cell-cell adhesion

In yeast, cell-to-cell and cell-to-surface adhesion appear to be mediated primarily by a group of cell-surface proteins called adhesins (can also be referred to as agglutinins or flocculins). While the structure of these proteins can vary, most of those originally characterized, including several FLO genes in S. cerevisiae and the ALS genes in C. albicans, share a modular structure consisting of three major domains (Dranginis et al.

2007; Vallejo et al. 2013) . The C-terminus, containing a glycosyl-phosphatidylinositol

(GPI) attachment site that is typically cleaved off, is required for covalent cross-linking to cell wall glucans (Dranginis et al. 2007). In contrast, the N-terminus consists of a ligand recognition domain that projects out from the cell surface and recognizes and binds specific carbohydrates or peptide sequences (Dranginis et al. 2007). Finally, a central domain, composed of serine/threonine rich repeats of variable length, both acts as a

“spacer” that holds the adhesion domain away from the cell wall and has its own adhesive properties. This domain is highly glycosylated and recombinant, likely being

responsible for the rapid expansion of this protein family (Dranginis et al. 2007; Vallejo et al. 2013).

While these adhesins are well-characterized in some yeasts, they have only begun to be characterized in S. pombe, and in many cases, appear not to contain all three characteristic flocculin domains (Linder & Gustafsson 2008). In fact, several pfl+ genes, while sharing sequence similarity with the ALS and FLO gene families, do not appear to have C-terminal GPI attachment sites. Instead, they contain DIPSY (Map4-like) or

GLEYA domains at their C-terminus, which appear to be for ligand binding and are necessary for cell-to-cell adhesion (Linder & Gustafsson 2008; Sharifmoghadam &

Valdivieso 2008). In contrast, some S. pombe adhesins, such as dominant flocculin Gsf2, do not share sequence similarity with the FLO or ALS families but do have the same three domain structure (Matsuzawa et al. 2011).

1.6.2 Flocculation mechanisms

Similar to adhesin structure, the flocculation mechanism has been studied most in

S. cerevisiae. Historically, two major mechanisms have been used to explain sugar sensitive, cell-to-cell adhesion. The generally accepted “lectin-recognition hypothesis,” suggests that lectins recognize and bind specific cell wall glycans. In this case, Ca2+ maintains the lectin’s rigid conformation, but does not directly mediate the interaction

(Miki et al. 1982). In contrast, the originally proposed “salt-bridging hypothesis,” suggested that Ca2+ directly mediated glycan-glycan interactions (Mill 1964). More recently, a study at the molecular level suggests that both glycan-lectin and Ca2+-

mediated glycan-glycan interactions contribute to the cell-to-cell adhesion observed for flocculating, FLO1-expressing cells (Goossens et al. 2015).

1.6.3 Environmental conditions and signalling pathways that induce flocculation

Although the exact environmental conditions that activate flocculation sometimes differ between S. cerevisiae strains, several environmental changes have been shown to affect flocculation (Sampermans et al. 2005). Changes in pH, ethanol levels, and Ca2+ availability all impact flocculation (Claro et al. 2007). Nutrient levels are also known to play a large role, as sterol, nitrogen, and glucose shortages all precede flocculation during brewing, and both carbon and nitrogen starvation rapidly induce flocculation in a lab setting (Sampermans et al. 2005). Interestingly, although flocculation is thought to potentially be an adaptation to stress, both osmotic shock and brief heat shock have been shown to delay, rather than induce, flocculation (Claro et al. 2007). In contrast, adhesion of pathogenic fungi to cell surfaces or host tissues often occurs in response to environmental changes that signal favourable conditions, such as elevated concentrations of indole acetic acid (Verstrepen & Klis 2006).

These changes in environmental conditions are then perceived by upstream sensors, resulting in the activation of several downstream signalling pathways that regulate TFs, ultimately resulting in flocculation. Although the exact sensors, signalling pathways, and mechanisms of TF activation are not all well characterized, several have been implicated. Those involved in regulating the flocculin gene FLO11 have been most studied, showing that at least four signalling pathways are involved in regulating its expression, including the MAPK-filamentous growth pathway, the Ras-cAMP/PKA

pathway, the main glucose repression pathway, and the target of rapamycin (TOR) pathway (Verstrepen & Klis 2006). While the signalling pathways regulating other S. cerevisiae FLO genes and S. pombe pfl+ gene have not been identified, it is likely that similar pathways are involved.

1.6.4 Transcriptional regulation of flocculation

In S. cerevisiae, the transcriptional regulation of flocculation is well understood.

There are two major transcriptional activators of flocculation, Flo8 and Mss11. These two

TFs activate flocculation, primarily by upregulation of FLO1 expression. In contrast, the transcriptional repressor Sfl1 inhibits flocculation by repressing FLO1 expression (Soares

2011).

In S. pombe, the transcriptional network of flocculation is also reasonably well understood, in large part due to recent work done by our lab and others. The primary transcriptional activator of flocculation Mbx2 directly regulates transcription of several putative adhesins, including the dominant flocculin gsf2+ (Figure 1.4) (Matsuzawa et al.

2011; Matsuzawa, Yoritsune, et al. 2012; Kwon et al. 2012). In contrast, transcriptional repressor Rfl1/Gsf1 inhibits flocculation by negatively regulating both mbx2+ and flocculin gene expression (Figure 1.4) (Kwon et al. 2012; Matsuzawa et al. 2013). In addition to these primary regulators, six other TFs also appear to have a minor role in regulating flocculation, including three (Cbf12, Adn2, and Adn3) that activate flocculation and three (Cbf11, Sre2, and Yox1) that repress it. The majority of these TFs also appear to do so by regulating expression of several flocculin genes, including gsf2+.

In contrast, Adn2 and Adn3 induce flocculation by regulating cell wall remodelling genes, not flocculins (Figure 1.4) (Kwon et al. 2012).

Figure 1.4. Partial model of the TRN of flocculation. Flocculation is regulated by a number of different TFs. Adn2 and Adn3 increase the expression of genes encoding cell wall remodeling proteins, which influence cell to surface adhesion, invasion, and flocculation. Rfl1 and Mbx2 are the major transcriptional repressor and activator of flocculation, respectively. Cbf11 and Cbf12 also repress and activate transcription. All four of these TFs regulate the expression of genes encoding cell surface glycoproteins, including the dominant flocculin gene, gsf2+. Modified from (Kwon et al. 2012).

1.7 Objective of this study

The primary objective of this study was to expand our understanding of the TRN in S. pombe by determining the function and target genes of several previously uncharacterized TFs.

1.7.1 Specific aims

Specific Aim 1: Systematically delete all non-essential S. pombe TF genes and screen the resulting deletion library for abnormal growth, cell length, and drug hypersensitivity. 29

Characterize drug hypersensitive TFΔ strains by four-way microarray expression analysis to identify target genes.

Specific Aim 2: Screen TFΔ strains for flocculation in rich medium and an absence of flocculation in flocculation-inducing medium (FIM) to identify novel repressors and activators of flocculation, respectively. Characterize TFs by microarray expression analysis and/or ChIP-chip to identify target genes.

Specific Aim 3: Screen a TFOE library for reduced fitness and altered cell length, and identify cell cycle phenotypes caused by TF overexpression. Analyze TFOE strains with abnormal phenotypes by microarray expression profiling and ChIP-chip to identify direct

TF target genes.

Chapter Two: Materials and Methods

2.1 Media

Chemicals used to make media and buffers were purchased from Sigma-Aldrich or Fisher unless stated otherwise.

Yeast extract with supplements (YES) was prepared by adding 5 g/l yeast extract,

30 g/l glucose, and 225 mg/l each of adenine, leucine, and uracil to distilled water and autoclaving. Antibiotic selection was achieved by adding either 100 mg/l G418 disulfate salt (kan) or 100 mg/l nourseothricin sulfate (nat) after autoclaving.

Edinburgh minimal medium (EMM) was prepared by adding 3 g/l potassium hydrogen phthalate, 2.2 g/l Na2HPO4, 5 g/l NH4Cl, and 20 g/l glucose to distilled water and autoclaving. 225 mg/l of adenine, leucine, and uracil were added as necessary. Post- autoclave, filter-sterilized 50X salt stock solution (52.5 g/l MgCl2·6H2O, 0.735 g/l

CaCl2·2H2O, 50 g/l KCl, 2 g/l Na2SO4), 1000X vitamin stock solution (1 g/l pantothenic acid, 10 g/l nicotinic acid, 10 g/l inositol, 10 mg/l biotin), and 10000X mineral stock solution (5 g/l boric acid, 4 g/l MnSO4, 4 g/l ZnSO4·7H2O, 2 g/l FeCl2·6H2O, 0.4 g/l molybdic acid, 1 g/l KI, 0.4 g/l CuSO4·5H2O, 10 g/l citric acid) were added at 1X concentration. To repress pREP1/41/81 and pSLF272 plasmid expression 5 mg/l thiamine was added after autoclaving. Low glucose EMM was prepared similarly, except 5 g/l glucose was added instead of 20g/l.

Sporulation agar with supplements (SPAS) was prepared by adding 10 g/l glucose, 1 g/l KH2PO4, and 45 mg/l each of adenine, histidine, leucine, uracil, and lysine hydrochloride to distilled water and autoclaving. 1000X vitamin stock was added at 1X concentration post-autoclave.

Flocculation-inducing medium (FIM) was prepared by adding 10 g/l yeast extract and 30 g/l glycerol to distilled water and autoclaving. 40 ml/l of 100% ethanol was added post-autoclave. 225 mg/l of adenine, leucine, and uracil were added when necessary.

Lysogeny broth (LB) was prepared by adding 25 g/l LB powder and autoclaving.

Antibiotic selection was achieved by adding 100 mg/l ampicillin (AMP) post-autoclave.

All solid media was prepared by adding 20 g/l agar to the aforementioned recipes.

2.2 Construction of transcription factor deletion library

A list of 101 genes encoding putative regulatory TFs was compiled, using GeneDB

(Hertz-Fowler et al. 2004) and Beskow and Wright (2006) to identify proteins containing known sequence-specific DNA binding domains. Eighty-eight of these putative TF genes were knocked out and replaced with a kanamycin resistance cassette (KANMX6). Of the remainder, eight are essential genes, and five were not successfully deleted. Knockouts were generated using a PCR-based stitching method followed by a lithium acetate transformation. Note that all deletion strains, and any other strains discussed in these studies, can be found in Table A2.

2.2.1 PCR amplification and stitching

The KANMX6 cassette and ~500 bp homologous regions upstream and downstream of the ORF to be deleted were amplified via PCR (one 2 min step at 98˚C,

30-35 cycles of 30 sec at 98˚C, 30 sec at 58˚C, and 1 min at 72˚C, and one final 5 min step at 72˚C) and gel purified using a QIAquick gel extraction kit and protocol (Qiagen).

The 3’ end of the upstream fragment and the 5’ end of the downstream fragment contained 20-25 bp homology to the 5’ and 3’ regions of the KANMX6 cassette

respectively, enabling the fragments and cassette to be stitched together in a second PCR reaction. Equimolar amounts (~50 ng) of each PCR fragment and the KANMX6 cassette were combined with 0.2 nmol dNTPs, 1X Phusion Buffer (New England Biolabs), nuclease free H2O (Promega), and 0.4 U of Phusion HF DNA Polymerase (New England

Biolabs) and stitched using the following PCR program: one 30 sec step at 98˚C, five cycles of 15 sec at 98˚C, 60 sec at 60˚C, and 90 sec at 72˚C, and one final 5 min step at

72˚C. Finally, this stitched product was combined with 6 nmol dNTPs, 1X Phusion

Buffer, 0.6 U of Phusion HF DNA polymerase, nuclease free H2O, and 20 pmol of the outermost forward and reverse primers, amplified via PCR (one 30 sec step at 98˚C, 30-

35 cycles of 15 sec at 98˚C, 30 sec at 58˚C, and 2 min at 72˚C, and one final 5 min step at

72˚C), and gel purified. Details regarding the primers used in the above PCR reactions, and in any reactions that follow, can be found in Table A3.

2.2.2 Lithium acetate transformation

A lithium acetate transformation, modified from Okazaki et al. (1990), was used to transform purified stitched products into the wild-type strain. Freshly struck out wild type was inoculated in EMM low glucose (0.5%) + ALU liquid medium and grown overnight to a max OD600 of 0.5. Cells were collected by centrifugation (3 min, 3000 rpm) and washed twice in 20 ml 0.1 M lithium acetate before being resuspended at a concentration of 1x108 cells/ml. 100 μl aliquots of cell suspension were transferred into

1.5 ml microcentrifuge tubes and incubated at 30˚C for 1 hr. Following this incubation,

PCR stitched products and 50 μg of denatured salmon sperm (boiled 10 min, and cooled on ice for 3 min) were added to the cell suspensions, which were incubated at 30˚C for another hour. 290 μl of 50% polyethylene glycol (PEG) was then mixed with the cells

and left at 30˚C for a third hour. Following this incubation, cells were heat shocked at

42˚C for 15 min and spun at 3000 rpm for 3 min. The cell pellet was resuspended in 1 ml of YES before being transferred to glass culture tubes containing 4 ml YES. These cultures were incubated for 16-18 hr at 30˚C in a shaking incubator. The following morning, two 200 μl aliquots of overnight cultures were spread onto two separate YES + kan plates. These plates were left to grow at 30˚C for ~5 days.

2.2.3 Yeast colony screens

Individual colonies were screened to confirm that the TF ORF had been replaced by the KANMX6 cassette. A dab of each colony was mixed with 25 mM NaOH and boiled for 12 min. 1 μl of boiled cells was added to 1X GoTaq® reaction mixture

(Promega), nuclease free H2O (Promega), and 10 pmol of a reverse primer located in the

KANMX6 cassette and a forward primer located ~100 bp upstream of the stitched product’s 5’ region. Reactions were amplified via PCR (one 2 min step at 95˚C, 35 cycles of 30 sec at 95˚C, 30 sec at 52˚C, and 1 min at 72˚C, and 1 final 5 min extension at

72˚C). Reactions were electrophoresed on a 0.8% agarose gel to check for 1000 bp PCR products confirming successful transformation. Stocks of confirmed colonies were made by adding cells from YES + kan plates into YES + 30% glycerol. They were then stored at -80˚C.

2.2.4 Determination of cell length and generation time

Each TFΔ strain (and a wild-type control) was inoculated in YES and left shaking at 30˚C O/N to grow to an OD600 between 0.1 and 0.5. The next morning, each strain was sub-cultured to a starting OD600 of 0.1 and the OD600 was measured at 1.5 hr intervals for

6 hr. Generation time was determined using an online calculator (Roth 2006), which took into account all five time points and their corresponding OD600. A t-test (two sample, unequal variance, two tailed) was then used to determine if there was a statistically significant difference between the average generation time for each TFΔ strain (from three independent replicates) when compared to the wild-type control.

TFΔ strain cell length was measured concurrently with generation time. At the 1.5 hr time point, 1 ml of culture was transferred to a microcentrifuge tube, and spun for 3 min at 3000 rpm. Cells were resuspended in 20 μl YES and immediately examined using a Zeiss Axiovert 40 inverted microscope (Carl Zeiss Microscopy GmbH) with a mounted digital camera. Pictures of at least 25 septated cells (per culture) were taken and the length of each cell was measured using ImageJ (Schneider et al. 2012). The average for each TFΔ strain (3 replicates, each with n=25) was calculated and compared to the wild- type control using a t-test (two-tailed, two sample, unequal variance) to determine if there was a statistically significant difference between the two.

2.2.5 Flocculation assays

Initial flocculation screens were performed by an undergraduate student. Briefly, all

88 TFΔ strains and a wild-type control were inoculated in glass culture tubes containing 2 ml FIM and grown at 30˚C in a shaking incubator for 72 hr. At 36, 48, and 72 hr, cells were resuspended, and 250 μl cell suspension was transferred to a 24 well plate. This plate was then agitated in the 30˚C incubator for 30 min to enable floc formation. Any

TFΔ strain that failed to flocculate was recorded. Two independent replicates were performed for each TFΔ strain in the flocculation assay.

Each TFΔ strain that failed to flocculate was then inoculated in 50 ml of FIM at a concentration of 106 cells/ml, incubated in a shaker at 30˚C for 72 hr, and examined at 36,

48, and 72 hr. 10 ml of each cell suspension was transferred to a 100 mm x 15 mm petri dish, photographed with a SGA Imagine System (SVP Robotics), and compared to the wild-type control.

2.3 Drug sensitivity screens

The sensitivity of each TFΔ strain to an assortment of drug compounds was tested. First, each drug’s minimum inhibitory concentration (MIC) was determined. Two

TFΔ arrays containing all 88 putative TFΔ strains and the wild-type control were made and bolt replicated onto YES plates containing the drug compound of interest. Each strain was later scored for sensitivity to the drug relative to wild type. Drug sensitivities of TFΔ strains were confirmed by serial dilution.

2.3.1 Determining minimum inhibitory concentrations

To gauge the drug concentrations required for these screens, the MIC of each drug compound was determined. Wild type was inoculated in YES liquid medium and shaken at 30˚C O/N to grow to an OD600 between 0.3 and 0.5. Seven glass culture tubes, each containing 5 ml YES and a range of drug concentrations (0 μg/ml to X μg/ml), were then inoculated with 30 μl of the O/N culture and incubated in the 30˚C shaking incubator

O/N. 24 hr later the drug concentrations for the last tube with growth and the first tube without growth were recorded. If the range between the two was still large, this procedure was repeated using the two recorded concentrations to generate a new, narrower range to be tested.

2.3.2 TFΔ array design

Multiple copies of two different TFΔ arrays were made in 96 well plates. All TFΔ and wild-type strains were plated on YES O/N. The next day, each strain was added to a well containing 200 μl YES + 30 % glycerol in a 96 well microtiter plate. The position of each strain was random and determined using an online list randomizer. In each TFΔ array, there were multiple wells containing the wild-type control strain, and at least one empty spot. The position of each strain differed between the two versions of the TFΔ array. These plates were stored at -80˚C.

2.3.3 Drug screens

The day before each screen, drug containing plates were poured and the TFΔ arrays were replicated onto YES plates. The next day, the freshly replicated arrays were transferred to 96 well microtiter plates containing 200 μl YES per well using a bolt replicator. These plates were secured in the 30˚C shaking incubator and left for 2-3 hr.

Post-incubation, cell suspensions were transferred to three plates with increasing drug concentrations and to a YES control plate. Four or five days later, the sensitivity of each

TFΔ strain relative to wild type was recorded as 0 (none), 1 (mild), 2 (moderate), or 3

(severe).

2.3.4 Confirmation of drug sensitivity by serial dilution

A list of potential TFΔ drug sensitivities was compiled from the original screens.

Each TFΔ strain was inoculated in YES medium, and grown O/N to an OD600 of 0.3 - 0.5 at 30˚C in the shaking incubator. The following morning an approximately equal number of cells for each TFΔ strain and wild type was added to the first column of a 96 well

microtiter plate and topped up with liquid YES medium to 200 μl. Five successive 10- fold dilutions were done, using a multichannel pipette to transfer 20 μl from each well of one column to the next. These dilutions were then immediately transferred to drug plates

(and a control plate) using a bolt replicator. Plates were left to grow in the 30˚C incubator for 5-7 days and TFΔ strain drug sensitivity was recorded.

2.4 Transcription factor overexpression library screening

As previously stated, a list of 101 genes encoding putative regulatory TFs was compiled. All 101 putative TF genes were previously cloned downstream of the nmt1 promoter in the pREP1 vector and transformed into a leucine auxotrophic strain (leu1-32) by Amy Laderoute and Gina Kwon. These TF overexpression (TFOE) strains were screened for cell length and fitness defects under inducing (- thiamine) conditions.

Subsequently, strains with cell length or fitness defects were fixed at 24 and 48 hr and examined to identify defects in septation, nuclear morphology, or chromosome segregation.

2.4.1 Scoring of TFOE library for cell length and fitness defects

99 of the 101 TFOE strains were screened for cell length and fitness defects under inducing conditions. TFOE strains and an empty vector control strain were plated on

EMM medium containing thiamine (non-inducing conditions) and left to grow at 30˚C

O/N. To induce nmt1 driven TF overexpression, the next day, each strain was struck onto

EMM medium lacking thiamine and left at 30˚C for 24 hr. The next day, strains were plated one final time on EMM medium lacking thiamine. 24 hr later, each TFOE strain was examined using the Axio Scope A1 dissection microscope (Carl Zeiss Microscopy

GmbH) and scored for cell length and fitness defects. Each TFOE strain was given a cell length score of -1 to 3 depending on whether it was shorter (-1), longer (1 = 1.5×, 2 = 2×,

3 = 3×), or the same size (0) as the empty vector control. Each strain was also given a fitness score of 0-3 depending on whether its colonies had the same (0) or fewer cells (1=

~30-100 cells, 2= ~10-30 cells, 3=<10 cells) than the empty vector control. Average scores for each TFOE strain were determined from two replicates.

2.4.2 Microscopy of TFOE strains with cell length and/or fitness defects

All TFOE strains with cell length and/or fitness defects were further examined to identify defects in septation, nuclear morphology, or chromosome segregation. Each

TFOE strain was plated on EMM + AU + thiamine medium and incubated at 30˚C O/N.

These strains were then inoculated in EMM medium without thiamine, and grown O/N to a max OD600 of 0.5. At 24 hr, 1 ml of each culture was transferred to fresh EMM + AU medium and left for another 24 hr in the 30˚C shaking incubator to allow maximal expression of the TF gene. 1 ml from the original culture was also transferred to an

Eppendorf tube and spun at 3000 rpm for 3 min. Each cell pellet was resuspended in 800

μl of ice cold methanol and rotated for 30 min on a LabquakeTM Shaker Rotisserie

(Thermo Scientific). These samples were centrifuged for 3 min at 3000 rpm, and the cell pellets were washed twice and eventually resuspended in 50 mM sodium citrate. All samples were stored at 4˚C. This same procedure was used to fix cells incubated for 48 hrs in – thiamine medium.

To visualize the nucleus and cells wall/division septum, cells were stained with 1

μg/ml DAPI (4’6-diamidino-2-phenylindole) and 50 μg/ml calcufluor white, respectively,

and examined using a Plan Apo 63× oil immersion objective lens (Leica) mounted on an epifluorescence microscope (Leica DMR). Images were captured using a cooled CCD camera (Retica 1350 EX, QImaging, Burnaby, BC, Canada).

2.5 Construction of pSLF272 HA-tagged nmt41 driven TFOE strains

Several poorly characterized TFs with interesting phenotypes when overexpressed under control of the strong nmt1 promoter, were cloned into the pSLF272 HA-tagging vector, and transformed into wild type. The resulting strain overexpressed an HA-tagged

TF ORF under control of the medium strength nmt41 promoter and allowed for analyses by expression microarray and ChIP-chip simultaneously.

2.5.1 PCR amplification of TF genes

Each TF ORF was amplified and purified from wild-type genomic DNA as previously described in Section 2.2.1. The forward and reverse primers both had 5’ overhangs containing restriction sites for different enzymes, to allow unidirectional insertion of the TF gene into the pSLF272 vector. The reverse primer ended before (and therefore removed) the stop codon, allowing the HA tag to be properly expressed.

2.5.2 Restriction digestion

The plasmid and DNA inserts were digested using the appropriate restriction enzymes (New England Biolabs) (Table A3). DNA inserts were simultaneously digested by two restriction enzymes, except when the enzymes required different buffers. These inserts, and the pSLF272 plasmid, were prepared using two sequential digests. 50 μl reactions, containing all 30 μl of the purified DNA insert, 80 U of each restriction enzyme, 1X Buffer 3.1/CutSmart Buffer (New England Biolabs), and nuclease free H2O

(Promega), were set up and incubated at 37˚C for 16-20 hr. Plasmid digests were set up similarly, except 10 μg of pSLF272 vector and 80 U of a single restriction enzyme were used. Following the first digestion, plasmids were run slowly (70 V) on a 0.8% agarose gel and purified using QIAquick gel extraction kit and protocol (Qiagen). The second plasmid digestion was then set up as previously described (except all 30 μl product were used). Digested DNA inserts and double digested plasmids were purified using the

QIAquick PCR purification protocol (Qiagen).

2.5.3 Ligation

Ligation of the digested insert and corresponding plasmid proceeded for 1 hr at

RT, then O/N at 4˚C. ~100 ng of insert was added to 20 ng plasmid, 1X T4 DNA ligase buffer (Promega), 1 U T4 DNA ligase (Promega), and Hyclone® water up to 20 μl.

2.5.4 Bacterial transformation

A bacterial transformation was performed using competent TOP10 Escherichia coli cells. Cells were thawed on ice for 2 min before 10 μl ligation mix was added.

Following another 30 min on ice, the cells were heat shocked at 42˚C for exactly 45 sec and immediately placed on ice for 2 min. The cell suspension was mixed with 450 μl pre- warmed LB medium and allowed to recover at 37˚C for 5 min. The samples were then centrifuged at 4000 rpm for 3 min and the cell pellets were resuspended in 150 μl of LB medium and spread on LB + amp plates. These plates were incubated at 37˚C for ~16 hr.

2.5.5 Bacterial colony screen

PCR colony screening was performed to confirm the presence of properly ligated

TF-containing pSLF272 plasmid in selected bacterial colonies. Single colonies were

picked from the transformant plates and plated on fresh LB + amp medium. These cells were also added to PCR tubes containing 1X GoTaq® reaction mixture (Promega), 0.05 mM forward and reverse confirmation primers, and H2O up to 20 μl. The forward and reverse primers, located in the promoter and terminator of the pSLF272 plasmid respectively, yield a product ~200 bp larger than the gene of interest. All products were amplified using the PCR program described in Section 2.2.3. 5 μl of each reaction was run on a 0.8% agarose gel to confirm successful ligation and transformation.

2.5.6 Plasmid isolation

Two to four PCR confirmed colonies were inoculated in 5 ml of LB + amp medium and left shaking at 37˚C O/N. 1 ml of each culture was then added to 50% glycerol and stored at -80˚C. Plasmid DNA was isolated from the remaining 4 ml using the QIAprep Spin Miniprep kit and protocol (Qiagen) and quantified using a

NanoDropTM. 800 ng was then combined with 3.2 pmol of a sequencing primer and

Hyclone® H2O in 12 μl and sent to the University of Calgary Core DNA Services for sequencing.

2.5.7 Lithium acetate transformation

Transformation of sequence-confirmed plasmids into ura- cells (ura4-D18) was performed using the protocol described in Section 2.2.2 with minor modifications.

Following the 15 min heat shock and subsequent 3 min centrifugation, cells were resuspended in 1 ml of ½ YES medium and transferred to falcon tubes containing 4 ml ½

YES medium. These samples were shaken at 30˚C for 1 hr, before once again being centrifuged for 3 min at 3000 rpm. The media was removed and cells were resuspended

in 500 μl EMM medium. This suspension was plated onto two EMM + AL + thiamine plates and incubated at 30˚C for 4-6 days.

2.5.8 PCR colony screen

Successful transformants were confirmed and stored as described in Section 2.2.3, with a few slight modifications. Individual colonies were selected and re-plated on EMM

+ AL + thiamine plates. The confirmation primers used for bacterial colony screens were used for PCR set-up. Finally, glycerol stocks of confirmed colonies were made by adding freshly grown cells into 1 ml EMM + AL + thiamine + 50% glycerol. They were then stored at -80˚C.

2.5.9 Western blotting

To ensure that each HA-tagged TF was expressed under control of the nmt41 promoter all PCR and sequence-confirmed strains were tested by western blotting using a previously described procedure (Moreno et al. 1991). Each strain was inoculated in 50 ml

EMM + AL medium and grown at 30˚C for 20-22 hr to an OD600 of ~0.3. All 50 ml of culture was transferred to a Falcon tube and cells were collected by centrifuging for 3 min at 3000 rpm. The cells were then washed twice in ice-cold 1X PBS (0.137 M NaCl,

2.7 mM KCl, 10 mM Na2HPO4, 18 mM KH2PO4, pH 7.4) and once in lysis buffer [50 mM NaCl, 50 mM HEPES-KOH pH 7.5, 0.1% w/v SDS, 1% w/v Triton X-100, 1 mM

EDTA, 0.1% w/v sodium deoxycholate, and 1 tablet protease inhibitor cocktail EDTA free (Roche)]. These cells were resuspended in 800 μl lysis buffer and transferred to a chilled 2 ml Sarstedt tube containing ~800 μl of 0.5 mm diameter glass beads (BioSpec).

To break open cells, samples were subjected to four 2 min rounds of bead beating at full power using a Mini Beadbeater 16 (Biospec). Between rounds, samples were placed on ice for 2 min to prevent overheating and protein denaturation. After bead beating, 2 μl of each sample was examined under the light microscope (Zeiss) to ensure that >95% of cells were lysed. To separate the remaining cell suspension from the glass beads, a hole was poked in the bottom of the Sarstedt tube and it was placed on top of a sonication tube in a falcon tube. This assembly was centrifuged for 3 min at 2000 rpm and 4˚C. The whole cell suspension was then transferred to a microcentrifuge tube and centrifuged for 10 min at 13000 rpm and 4˚C. The pellet was discarded and the cell lysate was transferred to new microcentrifuge tube. A rough estimate of protein concentration was determined using a NanoDropTM.

Cell lysate (~30 μg) was loaded and resolved on an SDS-PAGE (8% resolving gel, 4% stacking gel). Samples were transferred to a nitrocellulose membrane (Biorad)

O/N at 4˚C and 30 V using a protein transfer apparatus (Biorad). The following morning, a 1 hr blocking step was performed in 5% skim milk in 1X TBST (1X TBS, 0.5% Tween

20) at RT. The membrane was then rinsed 3X in 1X TBST and rotated for 1 hour in 20 ml of 5% skim milk containing 0.4 μg of primary F-7 anti-HA antibody (Santa Cruz).

Following this incubation, the membrane was rinsed twice in 1X TBST, rotated in fresh

5% skim milk for 20 min, and rinsed twice more and rotated in fresh 1X TBST for 15 min. The nitrocellulose membrane was then incubated for a final hour in 20 ml of 5% skim milk containing 2μl of the secondary antibody (goat anti-mouse IgG conjugated to horseradish peroxidase) (Biorad). Following this incubation, the membrane was rinsed twice with 1X TBST and rotated 3X 5 min in fresh 1X TBST. Amersham ECL western

blotting detection reagents (GE Healthcare) and the manufacturer’s protocol were used for detection. Exposure of the gel was carried out using Amersham HyperfilmTM (GE

Healthcare) in a HyperfilmTM cassette followed by 2 min development and 1 min fixation in a dark room.

2.6 Construction of nmt1 driven putative target strains

Putative TF target gene ORFs were cloned in the pREP1 plasmid expressed under control of the nmt1 promoter to see if their overexpression replicated the TFOE phenotype. The procedure outlined in sections 2.5.1 to 2.5.8 was used, except a leu- strain and EMM medium lacking leucine was used as pREP1 has a LEU2 selectable marker

(not ura4).

2.7 Construction of nmt1 TFOE deletion strains

The pREP1 plasmids for several TFs were transformed into putative target deletions to see if their deletion suppressed the TFOE phenotype. Suppression of the

TFOE phenotype by the deletion background could confirm the identity of the target gene. The transformation protocol outlined in section 2.2.2 and the modifications listed in section 2.5.7 were applied. Again, EMM medium was supplemented with leucine and not uracil.

2.8 Construction of double deletion strains

Double deletions were made by mating two haploid deletion strains of opposite mating types, one ORF replaced with the KANMX6 cassette, and the other replaced with the NATMX6 cassette. Strains were mixed in 10 μl H2O on an SPAS plate and incubated at 25˚C for 2-3 days. These patches were then transferred to Eppendorf tubes containing

1 ml 0.5% glusulase and incubated at 30˚C O/N. The following morning, samples were centrifuged at 3000 rpm for 3 min and the pellets were washed twice and resuspended in

Hyclone® H2O. 100 μl of 100 and 1000 fold dilutions were spread on YES + kan + nat plates to select for the double mutant strains and left to grow at 30˚C for 5-6 days.

Individual colonies were then confirmed using the PCR screening method outlined in section 2.2.3.

2.9 Construction of integrated pREP1 nmt1-toe1+

Because pREP1 plasmid copy number is variable, an integrated version of the nmt1-toe1+ strain was made. As the integrated strain contained only a single copy of toe1+ under control of the nmt1 promoter, its phenotype and genetic content could be more easily analyzed by microscopy and flow cytometry respectively. To make the integrated strain, h- nmt1-toe1+ leu1-32 was mated with h+ leu1-32 and treated O/N with glusulase as described in section 2.8. However, instead of being plated on selective medium, the pellet was resuspended in 1 ml YES, transferred to 100 ml YES, and incubated at 30˚C in the shaker. Three days later, 1 ml culture was transferred to 100 ml of fresh YES and re-incubated in the shaker. This process was repeated 5 – 6 times under non-selective conditions to remove the prep1-toe1+ plasmid from the cell. Following the final incubation, 100 μl of 10, 100, and 1000 fold dilutions of culture were spread on thiamine containing EMM plates. These plates were incubated for 4 – 5 days at 30˚C to select for possible integration of the pREP1-toe1+ plasmid.

Individual colonies were selected from these plates and PCR colony screens (as described in section 2.2.3) with four pairs of primers were used to check for plasmid

integration at the most likely integration sites (single and double crossover events at the nmt1 promoter or terminator, and a single crossover event in the toe1+ gene). Glycerol stocks of confirmed colonies were made and stored at -80˚C.

2.10 Flow cytometry

An integrated nmt1-toe1+ strain was analyzed using flow cytometry. Cells were inoculated in two 100 ml cultures of EMM medium, one with and the other without thiamine, and incubated at 30˚C for 24 hr. 107 cells were fixed using 1 ml 95% ethanol and resuspended in 50 mM sodium citrate before undergoing two successive treatments with 250 μg RNAse A (Roche Applied Science) and 2 mg/ml Proteinase K (Promega), for 2 hr at 50˚C and 1 hr at 37˚C, respectively. Following the second incubation, cells were washed and resuspended in 50 mM sodium citrate containing propidium iodide (8 mg/ ml) and then briefly sonicated. Flow cytometry was performed using a FACSCalibur

Flow Cytometer and FACS- Diva 6.0 software (BD Biosciences).

2.11 Microarray expression profiling

Several types of microarray expression profiling experiments were performed to elucidate potential TF targets. Primarily, TFΔ and TFOE strains were characterized by competitively hybridizing cDNA from each of these strains and cDNA isolated from their wild-type counterparts (972 h- and EVC, respectively) to a custom designed 8X15

Agilent expression microarray slide. Additionally, some four-way microarray expression experiments were performed, requiring that TFΔ and the wild-type strains be analyzed in the presence of specific drug compounds. Finally, microarray expression profiling of

several TFΔ strains with defects in flocculation and the corresponding wild type, was performed in FIM.

2.11.1 Culturing for microarray expression profiling

TFOE strains and their empty vector controls were inoculated in 100 ml EMM medium without thiamine and without either uracil or leucine (ORFs cloned into the pSLF272 and pREP1 plasmids, respectively). These cultures were shaken at 30˚C for 18-

24 hr, until both the experimental and control strain were at an OD600 between 0.2 and 0.3 and within 0.02 of one another. Cultures were then split into two 50 ml Falcon tubes and centrifuged at RT for 3 min at 3000 rpm. The supernatant was removed and the cell pellets were immediately flash frozen in liquid nitrogen and stored at -80˚C. Note that when ChIP-chip was being performed concurrently, the initial culture volume was 300 ml

(not 100 ml).

TFΔ strains and wild type were inoculated in 100 ml YES medium, grown at 30˚C

O/N in the shaking incubator to an OD600 between 0.2 and 0.3, and harvested as described above. Four TFΔ strains and wild type were instead characterized in FIM to identify putative target genes functioning in flocculation. These strains were grown O/N as previously described. At an OD600 of ~0.2, the cells were collected by centrifugation (RT,

3 min, 3000 rpm) and reintroduced into 100 ml FIM for 30 min. These cultures were then harvested as previously described. Finally, two TFΔ strains and wild type were characterized in the presence of specific drugs, including clotrimazole (1 μg/ml) and chlorpromazine hydrochloride (300 μg/ml). Cultures were grown O/N to an OD600 of ~0.2

and then the drug compound was added for 1 – 1.5 hr. Harvesting occurred as outlined above.

2.11.2 Total RNA extraction

Total RNA was isolated using a hot phenol extraction and isopropanol precipitation. Briefly, 4 ml of 65˚C acid phenol and approximately 400 μl of acid-washed glass beads (Sigma-Aldrich) were added to cell pellets resuspended in 4 ml of 65˚C AE +

SDS buffer (50 mM NaOAc, 10 mM EDTA, 1% SDS, pH 5.2). These samples were treated to four cycles of 4 min 65˚C incubations, with 1 min RT vortexing in between incubations. After a 10 min incubation on ice, samples were centrifuged at 3000 rpm and

4˚C for 5 min and the upper aqueous layer was removed and treated to two more rounds of RNA extraction using phenol:chloroform:isoamyl alcohol (25:24:1) and chloroform:isoamyl alcohol (24:1). The upper aqueous layer was then transferred to an empty falcon tube and a 1/10 volume of 3M NaOAc (pH 5.2) and an equal volume of

100% isopropanol were added. Tubes were inverted 40X and incubated at -20˚C O/N.

The following morning, samples were centrifuged at 4000 rpm and 4˚C for 30 min. The pellet was then washed in 1 ml ice cold 70% ethanol, air dried for 10-15 min, and resuspended in 1 ml DEPC water. Total RNA was quantified using the NanoDropTM.

2.11.3 mRNA isolation

Polyadenylated mRNA was isolated using oligo(dT) cellulose beads (Sigma-

Aldrich). Briefly, ~40 mg of beads were added to each Poly-Prep® Chromatography

Column (Biorad) and washed with DEPC H2O and 1X column loading buffer (20 mM

Tris pH 7.6, 0.5 M NaCl, 1 mM EDTA, 0.1% SLS) before being placed in 6 ml RNase

free polypropylene culture tubes (Sinport). The total RNA samples (5 min at 65˚C, 3 min on ice) were then combined with 1 ml ice cold 2X column loading buffer (40 mM Tris pH 7.6, 1 M NaCl, 2 mM EDTA, 0.2% SLS) and loaded onto the columns three times.

The columns were then washed twice with 1X column loading buffer and once with middle wash buffer (20 mM Tris pH 7.6, 150 mM NaCl, 1 mM EDTA, 0.1% SLS) before being placed in new 6 mL culture tubes. RNA was eluted by adding 3X 330 μl 65˚C elution buffer (10 mM Tris pH 7.6, 0.1 mM EDTA). The eluates were then heated and cooled while the columns were washed. Finally, the eluates were passed through the columns a second time using the procedure described above. Poly-A RNA was eluted into Eppendorf tubes with two 250 μl aliquots of elution buffer and combined with 50 μl of 3M NaOAc, 6 μl linear acrylamide, and 1.1 ml 95% EtOH. The samples were then vortexed and incubated at -20˚C O/N. The next day, samples were centrifuged for 30 min at 13000 rpm and 4˚C. The RNA pellets were then dried for 10 – 15 min and resuspended in 20 μl DEPC H2O.

2.11.4 Reverse transcription

For each dye-swap experiment, 4 μg of mRNA was resuspended in 19 μl DEPC

H2O and combined with 4 μl of 70 μM Oligo(dT)23 primer (Sigma Aldrich). These samples were incubated for 6 min at 65˚C and 6 min at 42˚C to denature and allow primer annealing, respectively. A reverse transcription reaction mix [1.7X first strand buffer (Invitrogen), 17.4 mM DTT (Invitrogen), 0.87 mM dNTPs (Promega), 0.87 mM 5-

(3-Aminoallyl)-2’ deoxyuridine 5’ triphosphate (aa-dUTP, Sigma), 200 U SuperScript®

II Reverse Transcriptase (Invitrogen)] was prepared. While still at 42˚C, 18 μl of this mix

was added to each sample and the reaction was allowed to proceed for 1 hr at 42˚C. The reaction was stopped by adding 20 μl of a 1 N NaOH/0.5 M EDTA mix and incubating at

65˚C for 20 min to hydrolyze RNA and then neutralized with 20 μl of 1M Tris pH 7.6.

The reaction volume was topped up to 100 μl with H2O and samples were purified using a Qiagen PCR purification kit and modified manufacturer’s protocol. First, samples were loaded 3X onto the columns. Then, columns were washed 3X with 80% ethanol. The cDNA was eluted with two 40 μl aliquots of pre-warmed 65˚C H2O and quantified using a NanoDropTM. Samples were split in two, dried down at 50˚C using a Savant

SpeedVac® concentrator (Thermo Fisher), and stored at -20˚C.

2.11.5 Cy3/5 coupling

Each desiccated sample aliquot was resuspended in 3.5 μl of water. The CyTM3 and CyTM5 dyes were prepared by resuspending each dye pack in 15 μl anhydrous DMSO and 30 μl 2X bicarbonate buffer [1 carbonate-bicarbonate pellet (Sigma-Aldrich), 0.15%

HCl]. 3.5 μl of dye was then added to each sample. Experimental samples were labelled with CyTM5 and CyTM3, and their corresponding controls were labelled with CyTM3 and

CyTM5, respectively. Samples were vortexed for 30 sec, pulse centrifuged, and incubated in the dark twice for 30 min. 3.5 μl 4M hydroxylamine was then added to quench each reaction and samples were incubated in the dark for another 15 min. Following this

TM incubation, 70 μl H2O and 500 μl Buffer PB (Qiagen) were added to the Cy 3 labelled samples. These samples were added to their corresponding CyTM5 labelled sample and purified using a Qiagen PCR purification kit and protocol with several modifications.

Samples were loaded 3X onto columns and washed 3X with Buffer PE (Qiagen) (1X 700

μl, 2X 500 μl). DNA was eluted with two 30 μl aliquots of Buffer EB (Qiagen) and quantified using a NanoDropTM. These samples were then dried down at 50˚C using a

Savant SpeedVac® concentrator (Thermo Fisher).

2.11.6 Sample hybridization to array

The hybridization chamber assembly (Agilent Technologies), 8X15K array

(Agilent Technologies), and 8X15 gasket slide (Agilent technologies) were preheated to

65˚C. Each sample was resuspended in 20 μl Hyclone® H2O and added to 25 μl 2X GEx

Hi RPM Hyb Buffer (Agilent Technologies) and 5 μl resuspended 1X Blocking Agent

(Agilent Technologies). Samples were incubated at 95˚C for 3 min and 37˚C for 30 min, and then 45 μl of each was loaded onto a sub-array in a gasket slide placed in the base of the hybridization chamber. The 8X15 array slide was placed on top, array side down, and the remainder of the hybridization chamber was assembled. The entire assembly was then rotated in the hybridization oven (Agilent technologies) at 65˚C and 20 rpm for 24 hr.

2.11.7 Washing and scanning of array

The hybridization chamber assembly was removed from the hybridization oven, and submerged into 50 ml of RT wash buffer 1 [6X SSPE (3 M NaCl, 200 mM Na2HPO4, pH 7.4), 0.005% N-lauroylsarcosine sodium salt]. The array was separated carefully from the gasket slide, placed in a glass slide rack, and submerged for 5 min in ~1 L RT wash buffer 1 and for 2 min in 1 L 42˚C wash buffer 2 (0.6X SSPE) with the stir setting on medium. The array was then loaded into a GenePix® 4200A laser scanner (Axon

Instruments) and scanned at the scanner’s highest resolution (5 μM) using the 532 nm

and 625 nm lasers for CyTM3 and CyTM5, respectively. Array images and .gpr files were generated using the Axon GenePix® Pro 6.0 software and exported to R for data analysis.

2.11.8 Normalization and data analysis

Array data was normalized in R using the Limma (Linear Models for Microarray data) library. A LOWESS (locally weighted scatterplot smoothing) normalization, based on the median spot intensity and ratio of the two dye channels, was used to generate the average log2 ratios, and their corresponding t-test p-values (Smyth & Speed 2003; Smyth

2004). As most genes had multiple probes, only the log2 ratio of the probe with the most significant p-value was used for further analysis.

Princeton GO Term finder was used to search for functional enrichment in gene ontology (GO) terms within the differentially-regulated genes. The promoter regions

(1000 bp upstream of the translational start sites) of these genes were also searched for putative DNA-binding motifs using MEME and RankMotif++ (Bailey et al. 2009; Chen et al. 2007). Motifs generated using RankMotif++ were visualized with enoLOGOS

(Workman et al. 2005).

2.12 ChIP-chip

To better identify direct TF targets, ChIP-chip was performed on several HA- tagged nmt41-TFOE strains whose gene expression profiles had also been analyzed by microarray expression profiling. Each sample was hybridized to one of four sub-arrays on a 4X44K S. pombe ChIP array (Agilent Technologies), containing 44000 60-mer tiled oligonucleotide probes providing ~85% coverage of the non-repetitive sequences in the S. pombe genome.

2.12.1 Culturing and cell lysate preparation

ChIP-chip culturing was typically performed concurrently with microarray culturing using the remaining 200 ml of culture. At 18-24 hr, four 45 ml aliquots of culture were distributed to 50 ml falcon tubes. Formaldehyde was added to a final concentration of 1% and the tubes were rotated at 20 rpm for 30 min on an orbitron rotator 1 (Boekell) at RT to allow cross-linking. This reaction was quenched by adding

0.125 M glycine and rotating for 5 min. Cells were then collected by centrifugation (4˚C,

2000 rpm, 5 min) and washed three times, twice in 25 ml ice-cold 1X PBS and once in 2 ml of ice-cold lysis buffer. Following the washes, the cell pellets were resuspended in

1600 μl ice-cold lysis buffer and distributed equally between two chilled Sarstedt tubes containing ~800 μl 0.5 mm diameter glass beads (BioSpec) each.

These cells were then lysed and the cell suspension was separated from the glass beads as described in section 2.5.9. This suspension was then transferred to two (not one) chilled Eppendorf tubes and spun for 15 min at 4˚C and 15000 rpm to remove any soluble and unbound proteins. Each pellet was resuspended in 800 μl ice-cold lysis buffer and the pellets for each sample were transferred to a single chilled sonication tube. To shear

DNA each sample was sonicated 4 X 30 sec at 30% amplitude using a Sonic

Dismembrator (Fisher). Between rounds, samples were cooled in an ice-water bath for 2 min. Post-sonication, each sample was split into two Eppendorf tubes and spun at 7000 rpm and 4˚C for 2 min. The majority of the supernatant was transferred to two new

Eppendorf tubes and stored at -80˚C. To confirm that sonicated DNA was ~500-1000 bp in size, 100 μl of each supernatant was removed and incubated at 65˚C O/N to allow reverse-crosslinking. The following morning, DNA was extracted using

phenol:chloroform, precipitated using ethanol, and quantified by NanoDropTM. 5 μg of each sample was then electrophoresed on a 1% agarose gel at 70V.

2.12.2 Immunoprecipitation

Two immunoprecipitation (IP) reactions using 100 μl Dynabeads® Sheep anti- mouse IgG (Invitrogen) were done for each TF. Well-suspended Dynabeads® were aliquoted into Eppendorf tubes and then supernatant was removed by placing on the

DynaMagTM-2 (Invitrogen). The beads were washed twice and resuspended in 800 μl and

400 μl of ice-cold PBS-BSA (100 mg BSA in 20 ml 1X PBS), respectively. 5 μg of anti-

HA antibody (Santa Cruz) was added and the beads were allowed to rotate for 2 hr at 4˚C on a Labquake Shaker Rotisserie (Thermo Scientific). Following this incubation, the beads were washed twice in 1 ml ice-cold deoxycholate buffer (100 mM Tris-HCl pH 8,

1 mM EDTA, 0.5% w/v sodium deoxycholate, 0.5% NP-40, 0.25 M LiCl) and twice in 1 ml ice-cold lysis buffer before being resuspended in 200 μl ice-cold 1X PBS-BSA. 400 μl of crude lysate was added to each aliquot of Dynabeads®, and allowed to rotate for 2 hr at 4˚C. The supernatant was then removed and the beads were washed four more times: twice for 5 min at 4˚C using 1 ml ice-cold lysis buffer and 1 ml ice cold lysis buffer with

400 mM NaCl, and twice for 5 min at RT using 1 ml deoxycholate buffer and 1 ml TE buffer (10 mM Tris-HCl pH 8, 1 mM EDTA pH 8). Finally, ChIP material was eluted from the beads using two 6 min 65˚C incubations in 200 μl TES (TE with 1% w/v SDS).

Both eluates from each IP reaction were combined in a single Eppendorf tube and these samples, along with a crude lysate control (200 μl crude lysate + 300 μl TES), were incubated at 65˚C O/N to reverse cross-linking.

2.12.3 Protein removal and DNA recovery

Before starting, 20 μl aliquots of each sample were removed to check for the HA- tagged protein of interest using western blotting (described in section 2.5.9). Then, 100 μl of TE was added to each IP sample and 200 μg Proteinase K (Promega) and 20 μg of glycogen were added to both IP and crude lysate samples. These samples were then incubated at 56˚C for 2 hr. Post-incubation, sequential phenol:chloroform:isoamyl alcohol and chloroform extractions and O/N ethanol precipitation at -20˚C were used to isolate DNA. The following morning, each sample was centrifuged for 40 min at 4˚C and

13000 rpm and the DNA pellet was washed in 70% ice-cold ethanol. The DNA pellet was then dried for 10 min at RT, resuspended in 42 μl TE with 0.1 μg RNAseA (DNAse free,

Roche), and incubated for 30 min at 37˚C. DNA was then quantified and stored at -20˚C.

2.12.4 Blunting and ligation of linker DNA

A ligation-mediated PCR step was performed to increase DNA concentration prior to labelling and hybridization. First, each 40 μl DNA sample (IP and non-IP) was combined with 70 μl blunting mix [1.6X NEB Buffer 2 (New England Biolabs), 0.07 mg

BSA, 0.014 mM dNTPs, 0.014 U T4 DNA polymerase (Invitrogen)] and incubated for 20 min at 12˚C. These samples were then placed on ice, and 2.9 M NaOAc and 0.83 mg glycogen were added. All reactions for the same strain were then combined and transferred to an Eppendorf tube, and DNA was isolated using two extractions

(phenol/chloroform extraction and phenol:chloroform:isoamyl alcohol) followed by an ethanol precipitation. Samples were centrifuged for 30 min at 13000 rpm and 4˚C and the

DNA pellet was washed in ice-cold 70% ethanol. The pellet was then dried for 10 min at

RT, resuspended in 25 μl ice cold water, and placed on ice for 30 min. Following this incubation, the samples were vortexed briefly, centrifuged, and placed back on ice. The whole IP sample and 5 μl of the corresponding non-IP sample (with 20 μl H2O) were then transferred to PCR tubes, combined with 25 μl ligation mix [1X ligase buffer

(Invitrogen), 6.7 μl annealed linkers (250 μl 1 M Tris pH 7.9, 375 μl Oligo 1, 375 μl

Oligo 2, heated at 95˚C for 5 min, cooled to 25˚C, incubation 4˚C O/N, stored at -20˚C),

2000 U T4 DNA ligase (New England Biolabs)], and incubated in the thermocycler at

16˚C O/N. The following morning, an ethanol precipitation was performed to isolate

DNA.

2.12.5 PCR labeling with aa-dUTP

Each ligation reaction was combined with 25 μl ice-cold H2O and left for 15 min on ice. The samples were then resuspended and left on ice for another 15 min before 15

μl labeling mix [2.7X ThermoPol Buffer (NEB), 2 μl aa-dUTP DNTP mix (5 mM of dATP, dCTP, dGTP, 3 mM dTTP, 2 mM aa-dUTP – NEB), 3.33 mM Oligo 1] was added. The samples were then placed in the thermocycler to undergo PCR (one 4 min step at 55˚C, one 5 min step at 72˚C, one 2 min step at 95˚C, 31 cycles of 30 sec at 95˚C,

30 sec at 55˚C, and 1 min at 72˚C, and one final 4 min at 72˚C). During the first 55˚C step, the PCR program was paused and 10 μl enzyme mix (1 X ThermoPol buffer, 50 U

Taq polymerase, 0.0025 U PFU turbo) was added to each sample. Following PCR program completion, the DNA was purified using the Qiagen PCR purification kit and protocol (Qiagen) with a few modifications. Samples were loaded three times onto the columns (not once), and phosphate wash buffer (5 mM KPO4 pH 8, 80% EtOH) (not PE

buffer) was used to wash the columns twice (not once). Samples were also eluted twice

(not once) using 30 μl phosphate elution buffer (4 mM KPO4 pH 8.5) (not EB buffer).

DNA was quantified by a NanodropTM. All 60 μl of the IP samples and an equal amount of input DNA were then dried down using a Savant SPD111V SpeedVac® at 50˚C.

Samples were stored at -20˚C.

2.12.6 CyTM3 and CyTM5 dye coupling

Dye coupling was performed as previously described in section 2.10.5. When a single replicate was being done for each sample, CyTM5 dye was used to label experimental samples and CyTM3 dye was used to label control (input) samples.

2.12.7 Array hybridization, washing, and scanning

The ChIP samples were hybridized as described in section 2.5.11, with a few small changes. As 4X44K and not 8X15 arrays and gasket slides were used, pellets were resuspended in 44 μl Hyclone H2O and combined with 55 μl 2X GEx Hi RPM Buffer

(Agilent Technologies) and 11 μl Agilent blocking agent. 100 μl of each sample was then loaded onto each sub-array. Washing and scanning of the array were also performed as previously described in section 2.5.12.

2.12.8 Array normalization and data analysis

The ChIP-chip data was normalized in R using scaling in the Limma library. The dye ratios of all 44000 probes were averaged to zero, allowing positive peaks to be identified using an excel macro package, ChiPOTle (Buck et al. 2005). Potential genes were assigned to a peak if their start codon was within a 3 kb region upstream or 2 kb region downstream of a peak.

2.13 Quantitative PCR

Quantitative PCR (qPCR) was used to confirm the expression levels of putative TF targets identified by microarray expression profiling and ChIP-chip. The act1+ or adh1+ gene was used as a reference to determine the relative expression levels of query genes in the experimental and control strains.

2.13.1 Culturing and total RNA extractions

Culturing and total RNA extractions were performed as previously described in sections 2.10.1 and 2.10.2, except 50 ml cultures were used.

2.13.2 Reverse transcription

cDNA was reverse transcribed directly from total RNA. A 12 μl reaction containing 1-5 μg of RNA, 0.5 μg anchored Oligo(dT)23 primers (Sigma-Aldrich), and

0.83 mM of each dNTP (Promega) was heated at 65˚C for 5 min and then chilled on ice.

These reactions were combined with 11.1 mM DTT (Invitrogen) and 1X first strand buffer (Invitrogen) and incubated at 42˚C for 2 min. While at 42˚C, 200 U SuperScriptTM

II Reverse Transcriptase (Invitrogen) and 1 μl Hyclone® H2O were added. These 20 μl reactions were incubated at 42˚C for 50 min and at 70˚C for 15 min.

2.13.3 Quantitative PCR analysis

qPCR reactions, containing 5-50 ng cDNA, 2.4 μl of a 0.5 μM amplicon primer mix and 10μl SYBR® green master mix (Invitrogen), were set up in MicroAmp® Fast

Optical 48-Well Reaction Plates (Applied Biosystems). Each reaction was set up in triplicate. The qPCR reactions were performed in a StepOne Real-Time PCR system

(Applied Biosystems) using the following program: one 10 min step at 95˚C, 40 cycles of

15 sec at 95˚C and 1 min at 58˚C, followed by a melting curve program (58˚C - 95˚C at a heating rate of 0.3˚C/sec). Fold changes were determined by the ΔΔCt method per manufacturer recommendation.

Chapter Three: Characterization of S. pombe TFΔ strains and identification of TF target genes by four-way microarray expression profiling

Parts of this chapter have been published in Vachon et al. (2013) Functional characterization of fission yeast transcription factors by overexpression analysis. Genetics

194: 873-884.

3.1 Systematic TF deletion

Previously, Giaever et. al. (2002) showed that deletion of the vast majority (~85%) of non-essential genes (including TF genes) in S. cerevisiae, did not negatively impact yeast growth in rich medium. To see if this was also true in S. pombe, 82 TF genes were knocked out, and the resultant TFΔ strains’ cell lengths and doubling times were measured and compared to wild type. While there is a commercially available haploid deletion collection containing knockout strains for ~80% of the TF genes in S. pombe, we decided to construct our own TFΔ strains because sequencing using primers upstream and downstream of the deleted genes revealed that some of these deletions still had significant portions of their ORFs still present. Furthermore, the collection also appears to contain numerous background mutations, as one study identifying deletions causing aberrant phosphatase activity determined that for over 25% of the strains identified, the aberrant phenotype was not linked to G418 resistance and was therefore not a result of the deleted gene (Henry et al. 2011).

3.1.1 Selection of putative TF genes

A list of 101 putative regulatory TFs was compiled, using (Beskow & Wright

2006) and GeneDB (Hertz-Fowler et al. 2004) to identify proteins containing known sequence-specific DNA-binding domains. In the past five years, ten of these genes have 61

been re-classified as members of the general transcriptional machinery or implicated in processes such as chromatin remodeling, tRNA metabolism, and rRNA processing

(Wood et al. 2012). Data was originally gathered for these genes but is not included in any of the tables or figures that follow. Furthermore, seven new putative regulatory TF genes have since been identified and annotated in PomBase (Wood et al. 2012) but have not been knocked out or characterized in any way. A list of all past (10) and current (99) genes annotated as TFs and their DNA-binding domains can be found in Table A1.

3.1.2 Generation time of TFΔ strains

The average generation time (time required for cell number to double during the exponential growth phase) of each TFΔ strain was determined from three independent replicates and compared to the wild-type control using a t-test (two sample, unequal variance). The generation time for most of the TFΔ strains (74/82 or 90%) was not statistically different from the wild-type control (Figure 3.1). A small number of TFΔ strains (8/82 or 9.8%) did have a statistically significant (P < 0.05) difference in generation time compared to wild type (Figure 3.1). However, only one of these TFΔ strains, the CCAAT-binding factor php2Δ strain, grew more than 1.5X slower than the wild-type control (Figure 3.1). The remaining slow-growing TFΔ strains included another

CCAAT-binding factor complex component (php3Δ), TFs involved in meiotic cell cycle regulation (atf1Δ, atf31Δ, and res2Δ) and the cadmium stress response (zip1Δ), and two uncharacterized TFs (SPCC320.03Δ and SPCC417.09cΔ) (Figure 3.1).

TFΔ strain Average (min) P-value WT 116.4 +/- 5.0 php2Δ 214.8 +/- 13 0.00 res2Δ 130.8 +/- 2.1 0.00 SPCC417.09cΔ 133.2 +/- 1.8 0.02 php3Δ 126.6 +/- 9.0 0.02 atf1Δ 123 +/- 3.5 0.03 SPCC320.03Δ 155.4 +/- 7.4 0.03 zip1Δ 129 +/- 3.4 0.04

atf31Δ 142.2 +/- 7.4 0.04

Figure 3.1. Generation time of all 82 S. pombe TFΔ strains. TFΔ strains were grown in rich medium (YES) O/N and subcultured to an OD600 of 0.1. OD600 was measured at 1.5 hr intervals for 6 hr, and values from all five time points were used to determine generation time using an online calculator (Roth 2006). (A) The average generation time for each TFΔ strain and wild type, and the corresponding standard deviation were calculated using 3 replicates. (B) A t-test (two sample, unequal variance) was used to identify TFΔ strains with statistically significant difference in generation time compared to wild-type.

3.1.3 Cell length defects of TFΔ strains

The average cell length (length of septated cells) of each TFΔ strain was determined from three independent replicates (n = 25 for each) and compared to the wild- type control using a t-test (two sample, unequal variance). 3.7% of TFΔ strains (3/82),

including ace2Δ, fkh2Δ, and sep1Δ, were branched, multiseptate cells and thus could not be properly measured. The majority of the remainder (53/82 or 64.6%) were not significantly different in length from the wild-type control (Figure 3.2).

Cell Length Cell Length Strain (μm) P-Value Strain (μm) P-Value WT 13.46 +/-0.42 SPCC320.03Δ 12.73 +/- 0.44 0.032 php5Δ 20.40 +/- 1.26 0.006 SPAC3C7.04Δ 12.65 +/- 0.08 0.003 pcr1Δ 20.29 +/- 0.83 0.001 SPAC2H10.01Δ 12.64 +/- 0.25 0.026 cuf1Δ 20.27 +/- 1.65 0.019 fhl1Δ 12.44 +/- 0.24 0.023 atf1Δ 19.65 +/- 0.89 0.002 zip1Δ 12.39 +/- 0.33 0.028 rst2Δ 19.62 +/- 0.66 0.002 cbf11Δ 12.37 +/- 0.51 0.045 phx1Δ 19.49 +/- 0.21 0.002 SPCC777.02Δ 12.36 +/- 0.41 0.046 moc3Δ 18.94 +/- 0.05 0.002 grt1Δ 12.36 +/- 0.11 0.019 mca1Δ 18.63 +/- 0.61 0.015 SPAC3H8.08cΔ 12.34 +/- 0.42 0.048 res1Δ 18.26 +/- 1.40 0.018 SPAC1327.01cΔ 12.32 +/- 0.28 0.044 sfp1Δ 15.14 +/- 0.28 0.004 gaf1Δ 12.32 +/- 0.09 0.019 yox1Δ 14.71 +/- 0.37 0.002 atf31Δ 12.13 +/- 0.23 0.005 rsv1Δ 13.03 +/- 0.21 0.018 loz1Δ 12.03 +/- 0.46 0.031 prz1Δ 12.74 +/- 0.29 0.041 ste11Δ 11.76 +/- 0.34 0.010 Figure 3.2. Cell length of 82 S. pombe TFΔ strains. Wild type and TFΔ strains were grown in rich medium (YES) O/N and subcultured to an OD600 of 0.1. Cells were taken at the 1.5 hr time point, and examined immediately using a light microscope. (A) The average cell length for each TFΔ strain and wild type, and the corresponding standard deviation were calculated using 3 replicates. (B) A t-test (two sample, unequal variance) was used to identify TFΔ strains with statistically significant difference in generation time compared to wild type. 64

However, a substantial number of TFΔ strains (26/82 or 31.7%) did have a statistically significant difference in cell length (P < 0.05) from wild type (Figure 3.2). Fifteen of these TFΔ strains were shorter than wild type and 11 were longer. While 26 of these TFΔ strains were statistically different from the control, only 12 were more than 10% shorter or longer than wild type. Of these twelve, deletion of sfp1+, pcr1+, rst2+, phx1+, moc3+, atf1+, php5+, res1+, mca1+, and cuf1+ produced elongated cells, while deletion of loz1+ and ste11+ resulted in shortened cells (Figure 3.2).

3.2 Microarray expression profiling of TFΔ strains

In S. cerevisiae, microarray expression profiling of most TFΔ strains in rich media has shown that very few genes are differentially expressed compared to wild type (Chua et al. 2006). The TFΔ strains for 15 poorly-characterized TFs were analyzed by microarray expression profiling, to determine if this was also the case in S. pombe.

Microarray expression profiling showed that ten TFΔ strains had ten or fewer genes with at least two-fold differential expression relative to wild type in rich medium. The expression profiles of three of the remaining TFΔ strains, SPAC2H10.01Δ,

SPAC3F10.12Δ and SPBC16G5.17Δ showed that the TF gene itself was not differentially regulated in the deletion versus wild type, and thus not expressed at significant levels in the wild-type strain. Overall, there were few changes in gene expression upon deletion of each of these TF genes under rich media conditions (Figure 3.3). Furthermore, there were no regulatory motifs or functional enrichment found within the data sets for any of the

TFΔ strains.

Figure 3.3. Clustergram of microarray expression profiles for 15 uncharacterized TFΔ strains grown in rich media. The heatmap shows changes in mRNA expression for all ~5000 S. pombe genes. Genes and TFΔ strains were clustered using a complete linkage method in Cluster 3.0 (de Hoon et al. 2004), and visualized using Java Treeview (Saldanha 2004). The colour bar shows the log-fold change in expression between the TFΔ and the wild-type control strains.

3.3 Drug sensitivity of TFΔ strains

The sensitivity of all 82 TFΔ strains to 16 relatively inexpensive drug compounds, with known antifungal activity and diverse functions was determined in an effort to elucidate TF genes that are required to respond to particular environmental perturbations.

Identifying sensitivities might allow for better elucidation of target genes by microarray expression profiling TFΔ strains in the presence of the drug compound. First, two TFΔ arrays were constructed and the MIC for each drug compound was determined. These

TFΔ arrays were plated onto media containing the drug compound of interest and scored for sensitivity. Some TFΔ strains that appeared to be sensitive to each drug compound were confirmed by serial dilution. Additionally, the sensitivities of all 82 TFΔ strains to tunicamycin and chlorpromazine hydrochloride were determined using serial dilutions.

3.3.1 MIC determination

To determine the concentration of drug compound required to screen TFΔ arrays on solid media, the concentration that began to inhibit wild-type growth needed to be identified. The MIC, which is the lowest drug concentration that inhibits visible growth of an O/N culture, was used to approximate this value. MIC assays were performed for

12 of the 16 drug compounds. The MICs for these 12 drug compounds, and the eventual concentrations used for the screens, can be found in Table 3.1. The required concentrations for the remaining four drug compounds were found in the literature.

3.3.2 Drug sensitivity screens

The sensitivities of all 82 TFΔ strains to the 16 listed drug compounds were determined. Two separate arrays with the TFΔ strains in different, randomly assigned

Table 3.1. MIC and drug concentration used for TFΔ strain sensitivity screens for 16 drug compounds in S. pombe. MIC was determined by inoculating wild type in log-phase growth into YES medium containing a range of drug concentrations, and identifying the concentration at which O/N growth was inhibited. The drug concentration required for plates was then determined using the MIC as a starting point, followed by testing a range of concentrations. An * indicates that EMM, and not YES medium, was used. Drug MIC [Plate] Description Actidione 4-8 μg/ml 16 – 24 μg/ml inhibits translation elongation Disrupts membrane by Amphoterecin B 0.2 – 0.4 μg/ml 2.5 – 3.1 μg/ml ergosterol binding Benomyl 8 μg/ml 8 – 12 μg/ml Binds to microtubules CaCl2 n/a 0.1 – 0.2 M Possible inhibitor of Tor1 Caffeine n/a 10 – 14 mM kinase among other effects Antipsychotic; inserts into Chlorpromazine HCl 60 – 100 μg/ml 90 – 130 μg/ml membrane and binds anionic lipids Alters fungal cell wall Clotrimazole 0.3 – 0.4 μg/ml 0.02 – 0.05 μg/ml permeability and inhibits ergosterol synthesis Desipramine HCl 180 – 220 μg/ml 200 – 240 μg/ml Tricyclic antidepressant Dyclonine HCl 130 – 150 μg/ml 180 - 220 μg/ml Local anasthetic Edelfosine 1-2 μg/ml 2 – 4 μg/ml Targets cellular membranes Haloperidol 30 – 40 μg/ml 80 – 120 μg/ml Antipsychotic Inhibits ribonucleoside Hydroxyurea n/a 4 – 8 mM diphosphate reductase and DNA synthesis Sulfometuron- Inhibitor of amino acid 6 - 10 μg/ml 10 – 15 μg/ml methyl* biosynthesis Breast cancer drug treatment; Tamoxifen 12 – 16 μg/ml 90 – 110 μg/ml calmodulin disruption Prevention of N-linked Tunicamycin 0.15 – 0.2 μg/ml 0.1 – 0.14 μg/ml glycosylation and UPR induction Possible effect on membrane Valproic Acid n/a 10 – 14 mM trafficking and cell-wall integrity positions were designed, in an attempt to minimize positional effects within the array on observed sensitivity score. Additionally, between three and eight independent replicates of the screen were performed for each drug compound, to try and better identify the sensitive TFΔ strains. Despite these efforts, there was often a lack of consistency in the strains found to be sensitive between independent replicates for the same drug.

To best identify TFΔ strains that were truly sensitive to each drug compound, only strains that were identified in more than half of the replicates were deemed sensitive and included in the heat map. This final score for each drug compound and TFΔ strain pair was determined by taking the most frequently occurring score. Using this criteria, 80%

(65/82) of the TFΔ strains were found to be sensitive to at least one drug compound, with seven being sensitive to seven or more drug compounds, and 16 being sensitive to a single drug compound (Figure 3.4). 56% (124/223) of these sensitivities were mild (score of 1), while 39% (87/223) were considered moderate (score of 2), and only 5% (12/223) were considered severe (score of 3) (Figure 3.4). In PomBase, 29 TFΔ strain sensitivities to the drug compounds tested have been annotated (Wood et al. 2012). 55% (16/29) of these interactions were also identified in these screens.

3.3.3 Confirmation of sensitivity by serial dilution

A list of TFΔ strains with moderate or severe sensitivities to each drug was compiled. Serial dilutions were set up to confirm sensitivity of between one and eight

TFΔ strains to 12 of the 16 drug compounds. 89% (32/36) of the sensitivities tested were confirmed (data not shown). However, only ~1/4 (25/99) of the moderate TFΔ strain sensitivities were checked by serial dilution. In part, this was a result of potential TFΔ strain sensitivities being excluded (31/99) because they were sensitive to seven or more drugs. The remainder were not tested as a decision was made to test all ~80 TFΔ strains for sensitivity to two specific drug compounds by serial dilution, rather than continuing larger screens, which were yielding inconsistent results between replicates.

9 1 2 3 4 5 6 7 8 10 11 12 13 14 15 16 cha4Δ Drugs SPAC19B12.07Δ 1 – Amphoterecin atf31Δ rep2Δ 2 - Chlorpromazine hydrochloride ams2Δ 3 - CaCl2 tos4Δ 4 - Benomyl map1Δ 5 - Caffeine cbf11Δ 6 - Tamoxifen gaf1Δ atf21Δ 7 - Clotrimazole klf1Δ 8 - Haloperidol sfp1Δ 9 - Desipramine hydrochloride php3Δ 10 - Tunicamycin mug151Δ 11 - Dyclonine hydrochloride mat3-mcΔ SPAC3C7.04Δ 12 - Edelfosine moc3Δ 13 - Hydroxyurea phx1Δ 14 - Actidione SPBC1773.16cΔ 15 - Valproic Acid prt1Δ 16 - SMM SPAC25B8.11Δ SPBC56F2.05cΔ mca1Δ Sensitivity Score SPCC757.04Δ toe1Δ 3 toe3Δ 2 toe2Δ 1 SPAC1327.01cΔ 0 fkh2Δ thi5Δ res2Δ Figure 3.4. Heat map showing all SPCC1393.08Δ php5Δ 223 identified TFΔ drug gsf1Δ fep1Δ interactions. SPAC3H8.08cΔ TFΔ arrays were transferred from scr1Δ pap1Δ fresh YES plates, to 96 well plates prz1Δ containing YES and shaken at 30˚C res1Δ fhl1Δ for 2-3 hr. Cells were then transferred deb1Δ to drug plates (+ a control), incubated SPBC16G5.17Δ mbx1Δ at 30˚C, and scored five days later. A loz1Δ rep1Δ score of 0 indicates that the TFΔ SPBC1773.12Δ strain grew as well as wild type, SPCC417.09cΔ esc1Δ while scores of 1 – 3 indicate pho7Δ increasingly poor growth compared pcr1Δ rst2Δ to wild-type and sensitivity to the atf1Δ cuf1Δ drug. The drugs tested, and a colour php2Δ bar for sensitivity scores, can be thi1Δ sre1Δ found to the right of the heat map. grt1Δ Interactions were clustered using a prr1Δ yox1Δ complete linkage method in Cluster ste11Δ 3.0 (de Hoon et al. 2004) , and SPAC2H10.01Δ SPBC19G7.04Δ visualized using Java Treeview toe4Δ sep1Δ (Saldanha 2004). WT

3.3.4 Serial dilution sensitivity screens using tunicamycin and chlorpromazine hydrochloride

Because there was a lack of consistency between replicates, the sensitivities of all

TFΔ strains to two drug compounds, chlorpromazine hydrochloride and tunicamycin, were determined by serial dilution (two independent replicates). Tunicamycin and chlorpromazine were chosen because of the significant effect they had on gene expression, and their initiation of ER stress (and the UPR) and membrane stress, respectively. Using this method, a comparable number of TFΔ strains were found to be sensitive to tunicamycin (36 vs 35), while many more TFΔ strains were found to be sensitive to chlorpromazine (42 vs 9). Seven TFΔ strains were found to have severe (1;

Score = 3) or moderate (6; Score = 2) sensitivities to chlorpromazine, while 14 TFΔ strains were found to have severe (3; Score = 3) or moderate (11; Score = 2) sensitivities to tunicamycin (Figure 3.5). The sensitivity scores determined for each replicate screen overlapped >90%. 66% (6/9) and 57% (20/35) of the TFΔ strains found to be sensitive to chlorpromazine and tunicamycin in the original screens, respectively, were also found to be sensitive to the two drugs using serial dilutions.

3.4 Gene expression of wild-type S. pombe in response to drug treatment

Previously, Giaever et al. (2002) determined that in S. cerevisiae there is poor correlation (0.34 – 7%) between the gene deletions that cause hypersensitivity to a particular stressor and the increases in mRNA expression in response to that same stressor. We wanted to see if this was also the case in fission yeast, and thus microarray expression profiling was performed on wild-type yeast treated with a handful of different drug compounds (haloperidol, clotrimazole, chlorpromazine hydrochloride, or

Figure 3.5. Heat map showing all 78 TFΔ drug interactions identified by serial dilution. Each TFΔ strain and wild type was inoculated in YES medium and grown O/N to an OD600 between 0.3 and 0.5 in a shaking incubator. An approximately equal number of cells from each strain was added to the first column of each row in a 96 well plate, and ten-fold dilutions were carried out five times. Cells were then immediately transferred to drug plates (+ a control), incubated at 30˚C, and scored five to seven days later. A score of 0 indicates that the TFΔ strain grew as well as wild type, while scores of 1 – 3 indicate increasingly poor growth and sensitivity to the drug. The two drugs tested, and a colour bar for sensitivity scores, can be found to the right of the heat map. Interactions were clustered using a complete linkage method in Cluster 3.0 (de Hoon et al. 2004), and visualized using Java Treeview (Saldanha 2004).

tunicamycin). This poor correlation was also observed in S. pombe, as microarray expression profiling of wild-type yeast treated with these drug compounds showed that only 12.5% (4/32) of the TF genes identified as being very sensitive to one of these four drug compounds were also upregulated in wild type with their respective treatments.

Furthermore, microarray expression profiling showed that over 80% of the TF genes upregulated by drug treatment were not sensitive to the corresponding drug compound.

However, while unusual, cases where hypersensitivity and increased mRNA expression correlate can be used to help characterize TF function using a four-way microarray experiment.

3.5 Proof of principle: Identification of Sre1 targets by expression microarray profiling of the sre1Δ strain

Four-way microarray expression profiling makes use of four microarray experiments. Differences in gene expression between (1) wild type and the TFΔ strain in the absence of the drug compound, (2) the TFΔ strain with and without the drug compound, (3) wild type with and without the drug, and (4) wild type and the TFΔ strain in the presence of the drug compound are measured. Using these four experiments, direct

TF targets can be more easily distinguished from stress response genes and secondary effects, as they should be upregulated in treated wild type compared to untreated wild type, downregulated in the TFΔ strain relative to wild type when both are treated with the drug compound, and not differentially regulated when the untreated TFΔ strain is compared to the treated TFΔ strain or the untreated wild type (Figure 1.3).

As this approach had not previously been used to characterize TFs, we wanted to see if the proposed experimental design would be successful in identifying known targets

of a characterized TF. One potential candidate to be explored was the sterol regulatory element binding protein, Sre1, which was found to be sensitive to the antifungal agent clotrimazole. Sre1 is a known activator of sterol biosynthetic genes in yeast, with several known target genes, and a reasonably well conserved consensus binding sequence, making it a good choice for a proof of principle experiment (Hughes et al. 2005; Todd et al. 2006). It is also membrane bound and necessarily cleaved for activation, making it a poor candidate to be characterized by simple overexpression of the sre1+ gene (Hughes et al. 2005).

3.5.1 Sensitivity of the sre1Δ strain to clotrimazole

In our initial screens of the TFΔ array to several drug compounds, eight strains, including the sre1Δ strain, were identified as sensitive to the antifungal clotrimazole.

Serial dilutions confirmed that deletion of sre1+ caused sensitivity to clotrimazole, suggesting that sre1+ expression might be required to adapt to clotrimazole and that Sre1 target genes might be induced in response to this treatment (Figure 3.6A). Furthermore, clotrimazole is an antifungal agent known to target ergosterol synthesis and Sre1 is a known regulator of sterol genes, making it likely that sre1+ expression is required to respond to the drug treatment (Sud & Feingold 1981).

3.5.2 Four-way microarray expression analysis of the sre1Δ and wild-type strains in the absence and presence of clotrimazole

To identify potential Sre1 target genes, a four-way microarray experiment, consisting of four different but related comparisons of gene expression, was performed.

First, gene expression in the untreated wild-type and sre1Δ strains was compared. In total, 126 genes were differentially regulated at least two-fold, with 86 and 40 being 74

Figure 3.6. Response of sre1+ to clotrimazole treatment. (A) Loss of sre1+ results in sensitivity to clotrimazole. Exponentially growing sre1Δ and wild-type cells were spot diluted on YES plates with 0.035μg/ml clotrimazole (bottom panel) and without (top panel) and incubated for 3-5 days. (B) Four-way microarray profiling of sre1Δ and wild-type strains in the presence (1 μg/ml; 1 hr) and absence of clotrimazole. Putative Sre1 target genes were not differentially regulated in the sre1Δ strain. In contrast, these targets were upregulated in wild-type treated with clotrimazole, but not in the sre1Δ strain treated with clotrimazole. Consequently, the expression of Sre1 target genes was significantly decreased in the sre1Δ strain compared to the wild-type strain when both were treated with clotrimazole. The top and middle box include published Sre1 target genes (Todd et al. 2006) and Tf-2 retrotransposon genes (Sehgal et al. 2007), respectively. The color bar indicates the relative log-fold change in expression. All 279 genes differentially regulated in any one of the treatments were clustered using Cluster 3.0 (de Hoon et al. 2004), and the subset of genes that clustered with sre1+ and followed the expected expression pattern, was visualized with Java Treeview (Saldanha 2004). (C) and (D) Putative DNA motifs identified by MEME and RankMotif++, respectively, resembling the known sterol regulatory element bound by Sre1 (Todd et al. 2006).

upregulated and downregulated, respectively. However, less than one third (37/126) of these genes had a statistically significant p-value (P = 1.0e-3), and only two previously characterized Sre1 dependent target genes (hem13+ and SPBC23G7.10c) were differentially regulated (Todd et al. 2006). Furthermore, neither the upregulated nor downregulated genes were enriched for any particular functions or processes, and no statistically significant cis-regulatory sequences were identified by MEME or

RankMotif++ amongst the upregulated or downregulated genes (Bailey et al. 2009; Chen et al. 2007).

As microarray expression profiling of the sre1Δ strain under rich media conditions was not successful in identifying Sre1 target genes, three additional microarray experiments were performed, comparing gene expression in the wild-type strain with and without clotrimazole treatment, in the sre1Δ strain with and without clotrimazole treatment, and in the wild-type and sre1Δ strains with clotrimazole treatment. Treatment of the wild-type strain with clotrimazole resulted in relatively few changes in gene expression, with only 26 and 23 genes being upregulated and downregulated at least two- fold. Despite this weak response, the expression of sre1+ increased, as well as five of its targets (hem13+, sur2+, scs7+, erg31+, and erg25+). Additionally, within the upregulated genes, there was a statistical enrichment for a number of biological processes, including mannosyl-inositol phosphorylceramide metabolism (P = 1.5e-4), and fatty acid biosynthesis (P = 1.37e-3).

Changes in gene expression in the sre1Δ strain with and without clotrimazole were also minimal. Only 28 genes were differentially regulated at least two-fold, consisting of

16 upregulated genes and 12 downregulated genes, respectively. In contrast, there were

135 genes downregulated and 45 genes upregulated in the sre1Δ relative to wild-type when both were treated with clotrimazole. Fourteen target genes previously identified as being dependent on sre1+ activity for expression under anaerobic conditions were downregulated in the sre1Δ strain (Todd et al. 2006). Furthermore, the Tf2-type retrotransposon genes, which have been shown to be induced by Sre1 in response to low oxygen, were also downregulated (Sehgal et al. 2007).

To differentiate genes that are the most likely direct Sre1 target genes from others that are differentially regulated due to stress or secondary effects, all four experiments were combined, filtered, and clustered. Only genes with at least a two-fold difference in gene expression in at least one of the four experiments were selected, leaving 279 of

~5000 genes. These were then clustered using a complete linkage method, and the 29 genes that followed the expected pattern of expression were examined further (Figure

3.6B) (Table A4) (de Hoon et al. 2004).

These putative target genes included 10 previously identified potential Sre1 targets and the Tf2-retrotransposon genes (Figure 3.6B). Additionally, six genes not previously associated with Sre1 regulation were downregulated. This included genes encoding the mannosyltransferase Imt2, a PSP1 family protein, and four uncharacterized S. pombe specific proteins (Figure 3.6B). A search using the Princeton GO Term Finder confirmed enrichment for Tf2 retrotransposon genes, as well as for genes involved in several biological processes, including lipid (P = 1.7e-3) and ergosterol biosynthesis (P = 8.4e-3).

3.5.3 Search for possible Sre1 binding motif

MEME was then used to search for potential Sre1 regulatory motifs within the promoter regions (1000 bp upstream of the start codon) of the target genes identified in the four-way microarray experiment. Several statistically significant potential motifs were identified, one of which closely resembled a sterol regulatory element sequence previously identified as being bound by Sre1 (P = 1.2e-31) (Figure 3.6C) (Todd et al.

2006). Furthermore, a very similar motif was identified by RankMotif++ within the promoter regions of genes downregulated in the sre1Δ strain relative to wild-type when both were treated with clotrimazole (Figure 3.6D).

Finally, the promoter regions for the putative Sre1 target genes identified in the four-way were manually examined for instances of the identified cis-regulatory sequences. Of these genes, only two (imt2+ and SPBPB7E3.02) did not contain at least one cis-sequence that closely resembled the Sre1-binding sequence in their promoter regions (1000 bp upstream of the start codon). However, as the S. pombe SRE sequences have previously been shown to be degenerate and not to conform to a strict consensus sequence it is still possible that they do contain a cis-regulatory element bound by Sre1

(Todd et al. 2006).

3.6 Characterization of transcription factor Toe1

Another candidate that was explored by four-way microarray expression profiling

+ was the uncharacterized Zn2-Cys6 fungal-type TF encoded by SPAC1399.05c/toe1 . The toe1Δ strain was found to be sensitive to the antipsychotic chlorpromazine hydrochloride,

suggesting that toe1+ expression might be required to respond to the drug, and therefore

Toe1 target genes might be induced by this treatment (Figure 3.7A).

Figure 3.7. Response of toe1+ to chlorpromazine treatment. (A) Loss of toe1+ results in sensitivity to chlorpromazine. Exponentially growing toe1Δ and wild-type cells were spot diluted on YES plates with 100 μg/ml chlorpromazine (bottom panel) and without (top panel), and were incubated for 3-5 days. (B) Four-way microarray profiling of toe1Δ and wild-type strains in the presence and absence of chlorpromazine. Toe1 target genes were upregulated in wild-type treated with clotrimazole, but not in the sre1Δ strain treated with clotrimazole. Consequently, the expression of putative Toe1 target genes was significantly decreased in the toe1Δ strain compared to the wild-type strain when both were treated with chlorpromazine. Underlined genes were also upregulated in the toe1OE strain. The color bar indicates the relative log-fold change in expression. All 1594 genes differentially regulated in any one of the treatments were clustered using Cluster 3.0 (de Hoon et al. 2004), and the subset of genes that clustered with toe1+ and followed the expected expression pattern, was visualized with Java Treeview (Saldanha 2004).

Additionally, toe1+ overexpression caused a cell elongation phenotype, suggesting that its target genes might be inappropriately activated and may be involved in the cell cycle.

Thus, to identify potential direct Toe1 target genes, microarray profiling and ChIP-chip were performed on an HA-tagged nmt41-toe1+ overexpression strain.

3.6.1 Four-way microarray expression analysis of the toe1Δ and wild-type strains in the absence and presence of chlorpromazine

To identify potential Toe1 target genes a four-way microarray experiment consisting of four different but related comparisons of gene expression was performed.

Exposure of the wild type and toe1Δ strain individually to chlorpromazine resulted in widespread changes in gene expression compared to the untreated strains. 498 genes were upregulated and 337 genes were downregulated at least two-fold in wild type treated with chlorpromazine. Among the upregulated genes there were several TF genes, including toe1+. Similarly, the toe1Δ strain showed significant changes in gene expression when treated with chlorpromazine, including 626 and 694 upregulated and downregulated genes, respectively.

In contrast, microarray profiling of the toe1Δ strain showed relatively few differentially regulated genes. 49 genes were upregulated and 71 genes were downregulated at least two-fold. The downregulated genes were enriched for proteins involved in the pyrimidine salvage pathway (P = 1.5e-3). However, motif searches within the promoter regions of these putative target genes did not identify any likely Toe1 consensus binding sequences. Microarray expression profiling comparing the wild-type and toe1Δ strains treated with chlorpromazine revealed that a slightly larger number of genes were differentially regulated at least two-fold, with 92 and 73 upregulated and

downregulated, respectively. Strikingly, the four most downregulated genes consisted of the uracil-regulatable genes urg1+, urg2+, and urg3+ (Watt et al. 2008) and an uncharacterized gene (SPAC1399.04c) predicted to encode a uracil phosphoribosyltransferase. These genes are adjacent to one another and proximal to the toe1+ gene. The downregulated genes were also enriched for proteins involved in pyrimidine-containing compound salvage (P = 1e-4) and metabolism (P = 9.4e-4).

In total, 1594 genes were differentially regulated in at least one of the four microarray experiments. To better identify which of these genes might be direct Toe1 targets these genes were clustered using a complete linkage method. This clustergram was then searched for genes that were upregulated in wild type treated with chlorpromazine compared to untreated wild type, downregulated in the toe1Δ strain relative to wild type when treated with chlorpromazine, and not differentially regulated in the toe1Δ strain treated with chlorpromazine compared to untreated toe1Δ strain.

Nineteen genes with this expression pattern clustered together using this method (Figure

3.7B) (Table A5).

These putative target genes included three uracil-regulatable genes (urg1+, urg2+, and urg3+), as well as several other genes involved specifically in pyrimidine salvage

(ura6+, urk1+, SPBC1683.06c, and SPAC1399.04c) or more generally in pyrimidine

(ura6+ and cdd1+) or nucleotide (prs5+) metabolism (Figure 3.7B). A search using the

Princeton GO Term Finder confirmed that these putative target genes were enriched for proteins involved in several biological processes, including pyrimidine-containing compound salvage (P = 8.0e-9), pyrimidine-containing compound metabolism

(P = 2.4e-9), and nucleotide biosynthesis (P = 5.6e-5).

3.6.2 toe1+ overexpression causes cell elongation and cell cycle delay

In addition to the four-way microarray expression profiling of the toe1Δ strain an analysis of the effects of toe1+ overexpression was also carried out. First, the effects of toe1+ overexpression on cell length and fitness were determined. An empty vector control and the nmt1-driven toe1OE strains were grown for 24 hr in EMM medium lacking thiamine to induce TF expression. Interestingly, the toe1OE strain was elongated (Figure

3.8A) and had a fitness defect. This cell elongation phenotype suggested that toe1+ overexpression might cause a defect in the cell cycle. Examination of the septation index between toe1OE and wild-type strains revealed no significant difference. However, FACs analysis revealed that overexpression of toe1+ appeared to cause an accumulation of cells in G1, indicating a delay in this cell cycle phase (Figure 3.8B).

Figure 3.8. toe1+ overexpression results in cell elongation and G1 delay. (A) Overexpression of toe1+ by the nmt1 promoter produces elongated cells. The toe1OE and empty vector strains were grown for 24 hr in EMM lacking thiamine medium at 30˚C. Cells were fixed with methanol and stained with DAPI and calcofluor white to visualize nuclei and cell-wall material, respectively (top panels). Cells are shown with Normarski in the bottom panels. (D) Ectopic expression of toe1+ causes a G1 delay. Flow cytometric analysis of a chromosomal-integrated nmt1-toe1-HA strain under inducing (thiamine absent) and non-inducing (thiamine present) conditions. The histograms depict an increase in the percentage of cells in G1 and a reduction of cells in G2 in the toe1OE strain under inducing conditions compared to non-inducing conditions.

3.6.3 Microarray analysis of the toe1OE strain

Previously, Chua et al. (2006) showed that transcriptome profiling of S. cerevisiae

TFOE strains that exhibit reduced fitness can be used to identify their target genes and binding specificity. To identify potential Toe1 target genes, microarray expression profiling of an nmt41-driven toe1-HA strain was performed. The moderate-strength nmt41 promoter was chosen over the strong nmt1 promoter to reduce secondary transcriptional effects in the microarray experiments. Transcriptome profiling of the nmt41-driven toe1-HA strain revealed that 97 genes were induced at least two-fold. This included 12 of the 19 putative genes identified in the four-way microarray experiment

(Figure 3.7B). Additionally, gene ontology analysis of the top 50 most induced genes with the Princeton GO Term Finder showed functional enrichment for pyrimidine- containing compound salvage (P = 3.57e-6) and biosynthesis (P = 1.3e-4). The top 50 most induced genes can be found in Table A6.

Similar to the toe1Δ strain, the four most highly-induced genes (ranging from

35.5- to 113.8-fold relative to the empty vector control) consisted of the uracil- regulatable genes urg1+, urg2+, and urg3+ (Watt et al. 2008) and predicted uracil phosphoribosyltransferase SPAC1399.04c (Figure 3.9A). In addition, several putative target genes functioning in the pyrimidine-salvage pathway, including SPBC1683.06c

(uridine ribohydrolase), SPCC162.11c (uridine kinase), and ura6+ (uridylate kinase) were upregulated (10.7-, 2.8-, and 2.5-fold, respectively) in the toe1OE strain (Figure 3.9A).

The upregulation of these seven pyrimidine salvage genes was confirmed by qPCR, which showed similar log2 fold change values for all seven genes (Table 3.2).

toe1OE toe1OE ChIP toe1+ transcription factor urg2+ uracil phosphoribosyltransferase urg1+ GTP cyclohydrolase II SPAC1399.04c uracil phosphoribosyltransferase urg3+ DUF1688 family protein SPBC1683.06c uridine ribohydrolase SPCC162.11c uridine kinase SPCC1795.05c uridylate kinase

log2 ratio change -7 0 7

Enrichment log2 ratio 0 4 Figure 3.9. Identification of Toe1 putative target genes by phenotypic activation. (A) Putative target genes of Toe1 involved in pyrimidine salvage are induced in the nmt41-toe1OE-HA strain, and bound by Toe1 at their promoters. The heat map shows the relative expression of seven putative target genes in the nmt41-driven toe1-HA strain compared to an empty vector control (left column) by transcriptome profiling with dye reversal. The right column shows promoter occupancy of the putative target genes by Toe1 with ChIP-chip analysis of an nmt41-driven toe1-HA strain. The color bars indicate the relative expression and ChIP enrichment ratios between experimental and control strains. (B) A putative DNA motif resembling the binding specificity of Zn (2)-Cys (6) transcription factors was retrieved by promoter analysis of the Toe1 putative target genes found in the heat map. The promoter regions (1000 bp upstream of the start codon) of the Toe1 putative target genes were analyzed by MEME (Bailey et al. 2009).

Table 3.2. Putative Toe1-OE target genes confirmed by qPCR. Table comparing the log2FC values for seven pyrimidine salvage genes identified by microarray expression profiling, to the log2FC values determined by qPCR. A SYBR green master mix (Life Technologies, Carlsbad, CA) and a StepOne Real-Time PCR system were used to perform qPCR. The act1+ gene was used as a reference gene. Three replicates were set up for each combination of query gene and strain. Comparisons were made using the ΔΔCt method using the manufacturer’s recommendation (Life Technologies).

Target Gene qPCR (Log2FC) TFOE Microarray Log2FC SPAC1002.17c 7.7 6.8 SPAC1002.19 8.4 6.2 SPAC1399.04c 6.0 6.0 SPBC1683.06 4.6 3.4 SPCC162.11c 3.7 1.5 SPCC1795.05c 1.1 1.3 SPAC1002.18 6.0 5.2

3.6.4 ChIP-chip analysis of the HA-tagged nmt1-toe1+ strain

To confirm direct binding of Toe1 to putative target gene promoter regions ChIP- chip analysis of the nmt41-driven toe1-HA strain was performed, showing Toe1 association with 15 promoters (Table A7). Of the seven pyrimidine-salvage pathway genes upregulated by toe1+ overexpression five were detected with ChIP-chip, indicating that these genes are likely direct target genes of Toe1 (Figure 3.9A). Because urg2+ and urg3+ are adjacent divergent genes, Toe1 binding in the intergenic region may result in the regulation of both these genes. None of the remaining genes identified by the four- way microarray experiment and upregulated by toe1+ overexpression were bound by

Toe1.

3.6.5 Search for possible Toe1 cis-regulatory sequences

To identify possible Toe1 cis-regulatory sequences, the promoter regions (1000 bp upstream of the start codon) of the pyrimidine-salvage pathway genes bound by Toe1 were subjected to MEME analysis to elucidate its binding specificity (Bailey et al. 2009).

The highest-scoring DNA motif for Toe1 contained inverted terminal CCG/GGC trinucleotides flanking a predominantly degenerate region of 11 nucleotides (P = 5.5e-14;

Figure 3.9B). This DNA motif most resembled the known binding specificity

(CGGN11CCG) of the Zn2-Cys6 transcription factors Gal4p (S. cerevisiae) and Lac9p

(Kluyveromyces lactis) (Carey et al. 1989; Halvorsen et al. 1991; Todd &

Andrianopoulos 1997).

3.7 Phenotypic replication and genetic rescue of the nmt1-toe1+ phenotype

To determine which target genes might be causing the elongation phenotype caused by toe1+ overexpression, we overexpressed each of the pyrimidine salvage genes individually, under control of the nmt1 promoter, and examined the strains for cell elongation. As we are assuming that elongation of the toe1OE strain is a result of inappropriate activation of its target genes, it is possible that overexpression of some of these targets individually might also cause cell elongation. However, none of these overexpression strains resulted in cell elongation (data not shown). It is also possible that deletion of certain Toe1 target genes might abrogate the toe1OE elongation phenotype.

To determine if this was the case, we made single deletions of each of the pyrimidine salvage genes, and then overexpressed toe1+. Interestingly, deletion of urg2+ or

SPAC1399.04c suppressed the cell elongation phenotype caused by ectopic toe1+ expression (Figure 3.10A).

3.8 Sensitivity of putative Toe1 target genes to chlorpromazine

As the toe1Δ strain was sensitive to chlorpromazine, we wanted to determine if deletion of any of its putative targets would also confer sensitivity to chlorpromazine.

Single deletions for each of the pyrimidine salvage genes were made and plated on chlorpromazine-containing media. However, none of these single deletions were sensitive, possibly because many of the enzymes in the pyrimidine-salvage pathway are encoded by multiple genes with overlapping function. We also investigated whether overexpression of these putative target genes could confer resistance to chlorpromazine.

Similarly, chlorpromazine resistance was not conferred by overexpression of any of these

Figure 3.10. Confirmation of putative Toe1 target genes. (A) The cell elongation phenotype of the toe1OE strain is suppressed by the single deletion of the putative target genes urg1+ and SPAC1399.04c. An nmt1-driven toe1+ was ectopically expressed in each of the two corresponding deletion backgrounds. These strains were prepared and stained as described above. The presence of the pREP1-toe1+ vector in these strains was confirmed by growth on selective medium as well as by PCR. (B) Loss of toe1+ and its putative target gene SPAC1399.04c prevents growth in medium containing uracil as the sole nitrogen source (EMM-N+U). Strains were spot-diluted on EMM and EMM lacking ammonium chloride with uracil (200 mg/liter) and incubated for 4 days at 30˚C. putative target genes.

3.8.1 Growth inhibition of toe1Δ and target Δ strains on media containing uracil as the sole nitrogen source

Previously, we noted that the four genes most upregulated and downregulated by overexpression and deletion of toe1+, respectively, were the uracil-regulatable genes urg1+, urg2+, and urg3+ (Watt et al. 2008) and an uncharacterized gene SPAC1399.04c, predicted to encode a uracil phosphoribosyltransferase. These four putative target genes contained protein sequence homology to the URC genes of Saccharomyces kluyveri, which function in the pyrimidine- salvage pathway through degradation of uracil

(Andersen et al. 2008). Loss-of-function alleles of the URC genes results in growth

inhibition on medium containing uracil as the sole nitrogen source (Andersen et al. 2008).

Interestingly, one of the URC genes encodes a Zn2-Cys6 TF, suggesting that Toe1 could be a putative regulator of the homologous genes in S. pombe. To determine if this was the case, we tested to see if the toe1Δ or Toe1 target deletion strains were sensitive to medium containing uracil as the sole nitrogen source. Indeed, loss of toe1+ and

SPAC1399.04c prevented growth under this condition (Figure 3.10B).

Chapter Four: Identification of novel transcriptional activators and repressors of flocculation in S. pombe

4.1 Identifying positive transcriptional regulators of fission yeast flocculation

While considerable progress has been made recently in understanding the transcriptional regulation of flocculation in S. pombe, our elucidation of the TRN controlling this behavior remains incomplete. Previously, several TFs that positively regulate flocculation (Mbx2, Cbf12, Adn2, and Adn3) were identified by screening an overexpression array of TFs for constitutive flocculation (Kwon et al. 2012). However, the discovery of novel TFs functioning in this process has been hindered by the lack of knowledge regarding the conditions that induce flocculation in S. pombe. Recently, we identified a medium (1% yeast extract, 3% glycerol, and 4% ethanol; referred to as flocculation-inducing medium or FIM) that caused heterothallic wild-type cells to flocculate (Kwon et al. 2012). Subsequently, we decided to screen our TFΔ array for defects in flocculation in FIM to identify new positive regulators of S. pombe flocculation.

4.1.1 TFΔ screen for reduced flocculation under inducing conditions

As mentioned above, our lab previously identified a FIM that triggered flocculation in heterothallic wild-type cells (Kwon et al. 2012). To identify novel activators of flocculation, our TFΔ array was screened in FIM for reduced flocculation compared to wild type. After a 72 hr incubation in FIM, the wild-type control strain formed substantially sized flocs (Figure 4.1A). While the majority of the TFΔ array did flocculate comparably to wild type, the deletion strains for six different TF genes not previously implicated in flocculation exhibited a reduced flocculent phenotype in FIM.

Figure 4.1. The deletion (A) and overexpression (B) of six TF genes (grt1+, SPBC530.08+, foe1+, prt1+, fep1+, and prr1+) results in abrogation and initiation of flocculation, respectively. TFΔ strains and TFOE strains (and their respective wild type and empty vector controls) were inoculated at 106 cells/ml in 50 ml of FIM and EMM + AU – thiamine liquid medium respectively, and incubated at 30˚C for 1-5 days. 10 ml of cell suspension was then transferred to a 100 mm X 15 mm petri dish, rotated for 30 minutes, and photographed. The small squares in the bottom right hand corner of each image show a higher magnification to better view the flocs for weakly-flocculent strains.

As can be seen in Figure 4.1A, deletion of grt1+, prt1+, and fep1+significantly reduced the number and size of flocs, while deletion of SPCC320.03/foe1+ (flocculation overexpression 1+), prr1+, and SPBC530.08 abolished floc formation entirely in

FIM. This reduction or absence of flocculation was also independently observed for the corresponding TFΔ strain in a triple auxotrophic background.

While SPBC530.08, foe1+, and grt1+ encode three completely uncharacterized

Zn2-Cys6 TFs, the remaining three TFs identified have annotated target genes or assigned biological functions. The Zn2-Cys6 TF Prt1 functions in multidrug resistance by inducing expression of mfs1+, mfs3+, and bfr1+, which encode efflux transporters (Kawashima et al. 2012). Prr1 is an HSF-type transcriptional activator that cooperates with TFs Atf1 and

Pap1 to regulate its target genes (Calvo et al. 2012; Greenall et al. 2002; Quinn et al.

2011). It is implicated in the oxidative stress response and sexual development

(Nakamichi et al. 2003). In contrast, Fep1 is an iron-sensing zinc finger GATA-type transcriptional repressor that downregulates expression of several genes encoding iron transporters in response to the presence of elevated iron (Pelletier et al. 2002; Pelletier et al. 2003). However, while each of these three characterized TFs has annotated target genes and assigned biological functions it is unclear why disruption of these TF genes reduces or abolishes flocculation.

4.1.2 TFOE screen for constitutive flocculation

Since these six TFs were necessary for normal floc formation in FIM, we were interested in seeing if their overexpression could trigger flocculation. Overexpression strains for these six TFs and an empty vector control were incubated for 2-5 days in

EMM minus thiamine medium to induce the nmt1 promoter. While the empty vector control did not flocculate, overexpression of all six TFs singly induced flocculation to varying degrees. foe1+ and grt1+ overexpression caused floc formation within 24 hours after transfer to EMM minus thiamine medium. While the grt1OE strain initially had fewer, smaller flocs, it flocculated more strongly than the foe1OE strain at 48 hours

(Figure 4.1B). The SPBC530.08OE strain, which did not flocculate at 24 hours, also appeared to flocculate more strongly than the foe1OE strain at 48 hours (Figure 4.1B).

Overexpression of the three remaining TF genes, prt1+, prr1+, and fep1+ only resulted in weak flocculation, even after a five-day incubation in EMM minus thiamine medium

(Figure 4.1B).

4.2 TF adhesion assays

Previously, our lab and others have observed that TFs involved in flocculation, such as gsf1+ and mbx2+, also often impact cell to surface adhesion and invasive growth

(Matsuzawa, Yoritsune, et al. 2012; Kwon et al. 2012). Consequently, deletion and overexpression strains for the six TFs identified as potential activators of flocculation in our initial screen in FIM were tested for changes in their ability to adhere or invade agar plates. Each deletion strain and a wild-type control was patched on YES + 4 mM FeCl2 plates and allowed to grow for five days. They were then washed gently (to test for surface adhesion) and rigorously (to test for invasive growth). While the wild-type strain adhered to the surface and grew invasively, all six TFΔ strains adhered poorly and did not appear to invade the solid media at all (Figure 4.2). In contrast, while overexpression

Figure 4.2. TFΔ strains fail to adhere and invade solid media. Six TFΔ strains (grt1Δ, SPBC530.08Δ, foe1Δ, prt1Δ, fep1Δ, and prr1Δ) and a wild-type control were patched onto YES + 4 mM FeCl2 plates and incubated at 30˚C for five days. Post-incubation, the cells were photographed prior to being washed (NW), after being gently washed (GW) to assess surface adhesion, and after being roughly washed (RW) to assess invasive growth. strains for these six TFs did adhere and invade the media, these phenotypes were not enhanced compared to an empty vector control.

4.3 Microarray expression analysis of the wild-type strain in FIM

As deletion of foe1+, prr1+, SPBC530.08, grt1+, prt1+, and fep1+ reduced/abrogated flocculation in FIM we were interested in seeing if these TFs were transcriptionally upregulated in a wild-type strain grown in FIM. Therefore, microarray expression profiling was used to compare the wild-type strain grown in FIM for 30 min and 2 hr to the same strain grown in rich medium. While expression of grt1+ was not induced in FIM at the two time points, the remaining five TF genes were upregulated at

0.5 hr or 2 hr (or both) (Figure 4.3). The most significant induction in expression occurred at 30 min for foe1+ and prr1+ (19.7 and 4.3-fold, respectively). prt1+ and foe1+ were upregulated at both incubation times in FIM. In contrast, prr1+ and fep1+ expression

only increased at 30 min in FIM, while SPBC530.08 expression only increased at 2 hr in

FIM (Figure 4.3). This upregulated expression in FIM coupled with the reduced flocculation of the corresponding TFΔ strains in FIM and constitutive flocculation of the

TFOE strains prompted us to further characterize the role of four of these TFs in regulating flocculation.

+ Figure 4.3. Heat map showing log2 fold change in expression of six TF genes (foe1 , prr1+, SPBC530.08+, grt1+, prt1+, and fep1+) in the wild-type strain grown in flocculation inducing medium for 30 min and 2 hr. The log2 fold change listed for each TF gene is the average from multiple probes on the microarray. The color bar beside the heat map represents the log2 fold change in gene expression. The expression data was derived from a dye-swap microarray experiment, hybridizing cDNA from wild type grown in FIM (for 30 min or 2 hr) versus cDNA from wild type grown in YES medium. The wild-type strain was inoculated in two 200 ml cultures of YES liquid medium and grown in the shaking incubator at 30˚C O/N to an OD600 of ~0.2-0.3. Half of each culture was centrifuged to collect the cells, which were then re-suspended in 100 ml of FIM. All four cultures were then shaken at 30˚C for 30 min or 2 hr.

In addition to these six TFs, there were other substantial changes in gene expression during incubation in FIM. After 30 min and 2 hr in FIM, 346 and 380 genes were upregulated at least four-fold and 179 and 380 genes were downregulated at least four-fold, respectively. 193 and 80 of the genes upregulated and downregulated at 2 hr were also upregulated and downregulated at30 min. The downregulated genes were enriched for genes involved in ribosome biogenesis at 30 min and at two hr (P = 2.5e-16

and 1.2e-46, respectively) and cytoplasmic translation (P = 1.3e-40) at 2 hr. In contrast, the genes upregulated at 30 min were enriched for carbohydrate metabolism (P = 2.9e-5) and transport (P = 3.5e-5), signal transduction (1.5e-8), cell to cell communication

(P = 2.7e-10), and response to heat (P = 2.3e-4). At two hours, the upregulated genes were also enriched for genes involved in carbohydrate transport (P = 9.54e-4) and cell to cell communication (P = 4.1e-5), as well as responding to nutrient levels (P = 1.10e-3). At both time points, the genes encoding glucose transporters induced by low extracellular glucose

(e.g. ght3+, ght5+, ght4+, ght6+) and genes encoding proteins that metabolize non- preferred carbon sources (e.g. agl1+, inv1+, gld1+, gut1+, etc.) were amongst the most significantly upregulated. Surprisingly, none of the pfl+ flocculin genes were upregulated at 30 min and only two pfl+ genes, pfl2+ and pfl5+, were upregulated at two hr.

Additionally, mbx2+ was not upregulated at either time point. In contrast, cbf12+ and gsf1+ were both upregulated at 30 min and two hr. The top 50 genes upregulated and downregulated at each time point can be found in Tables A8 and A9.

4.4 Analysis of transcription factor Foe1

As foe1+ was highly upregulated in the wild-type strain grown in FIM, and its deletion and overexpression abrogated and activated flocculation, respectively, we decided to further characterize its role in regulating flocculation. Microarray expression profiling was performed in conjunction with ChIP-chip to try and identify direct Foe1 target genes. Several putative target genes were then confirmed by qPCR. Additionally, foe1+ was overexpressed in deletion strains of three putative target genes to see if flocculation would be abrogated.

4.4.1 Microarray expression analysis of the foe1Δ strain in FIM

Previously, our lab (among others) has shown that microarray expression profiling comparing a wild-type strain and a TFΔ strain under inducing conditions can be a useful tool in identifying TF target genes. As foe1+ expression was highly upregulated in wild type grown in FIM (at 30 min), we decided to utilize this strategy to try and identify putative Foe1 target genes under these conditions. We would expect target genes activated by Foe1 to be downregulated in the foe1Δ strain grown in FIM and upregulated in wild type in FIM. The microarray expression profile of the foe1Δ strain in FIM

(compared to a wild-type control also grown in FIM) showed that 47 genes were downregulated at least two-fold, but that only two of these genes were downregulated four-fold in the foe1Δ strain (Table A10). Furthermore, the majority of these genes

(33/47) were not upregulated in the wild-type strain grown in FIM, and only three genes

(pfl3+, bgs4+, and pmk1+) involved in flocculation or cell wall biogenesis were downregulated in the foe1Δ strain. Additionally, gene ontology analysis with Princeton

GO Term Finder was unable to find enrichment for any particular GO terms among these downregulated genes, and no statistically significant regulatory motifs were found in their promoter regions (1000 bp upstream of the start codon) (Bailey et al. 2009; Chen et al.

2007).

4.4.2 Phenotypic replication: abrogation of flocculation in FIM

Since deletion of foe1+ abrogates flocculation in FIM, we wanted to see if any of the putative target genes that were downregulated in the foe1Δ strain in FIM would also prevent or reduce flocculation when deleted. Deletion mutants for the three putative

target genes involved in flocculation or cell wall biogenesis and 12 of 14 genes that were downregulated and upregulated in the foe1Δ and wild-type strains in FIM, respectively, were tested for flocculation in FIM (Table A11). After three days, all of these deletion mutants flocculated comparably to wild type.

4.4.3 Microarray expression analysis of the foe1OE strain

Since overexpression of foe1+ resulted in flocculation, we wanted to identify genes upregulated in the foe1OE strain that might account for its flocculent phenotype.

Microarray expression profiling of two foe1OE strains under control of a strong (nmt1) and medium (nmt41) strength promoter was performed. 154 and 42 genes were upregulated and downregulated by nmt1-driven foe1+ overexpression (+/- 3.5-fold cut- off, P = 1.0e-3). Predictably, overexpression of foe1+ under control of the nmt41 promoter resulted in fewer differentially-regulated genes when using the same rigid cut-offs. Only

28 and 16 genes were upregulated and downregulated, respectively. Of these, 16/28 and

9/16 were also upregulated and downregulated, respectively, in the nmt1-driven foe1OE strain.

To increase the likelihood of identifying direct Foe1 target genes, genes upregulated in all the foe1OE microarray experiments were selected, instead of selecting differentially-regulated genes from a single experiment. Using this criteria there were 101 upregulated genes and 27 downregulated genes in response to foe1+ overexpression

(Table A12). The downregulated genes were enriched for proteins involved in conjugation (P = 1.6e-3) or localized to the plasma membrane (P = 7.2e-6). In contrast, the upregulated genes were enriched for proteins with oxidoreductase activity (P = 1.5e-6),

functioning in disaccharide catabolism (P = 8.0e-3), or localized to the cell wall (P = 4.4e-

4). The latter is interesting since cell wall proteins have been implicated in flocculation

(Kwon et al. 2012). These genes encoded several predicted cell surface glycoproteins

(Pfl3, Pfl5, Pfl7, and Pfl9), the cell wall protein Pwp1, and two proteins involved in cell wall biogenesis and organization (Bgl2 and Gas2). In addition, the galactose-specific flocculin Gsf2, the transcriptional repressor of flocculation Gsf1, and three other proteins involved in cell wall organization and biogenesis (SPAC4H3.03, Cfh2, and Meu7) were also upregulated. As single overexpression of pfl3+, pfl5+, pfl7+, pfl9+, gas2+, gsf2+, and

SPAC4H3.03c is sufficient to trigger flocculation, the upregulation of some of these genes could explain the flocculent phenotype of the foe1OE strain (Kwon et al. 2012). A heat map including these genes can be seen in Figure 4.4. The upregulation of eight of these genes was also confirmed by qPCR, which all showed similar log2 fold change values as the microarray data (Table 4.1).

4.4.4 ChIP-chip analysis of HA-tagged nmt41-foe1+

To better identify putative direct Foe1 target genes, an HA-tagged nmt41-foe1+ strain was analyzed using ChIP-chip. 229 promoter regions were enriched for Foe1 occupancy (enrichment ratio > two-fold). 108 of these promoter regions were associated with genes that were upregulated in at least one of the microarray expression profiles of the foe1OE strains, while 35 were associated with the 101 genes upregulated in all of the microarray experiments where foe1+ was overexpressed. Interestingly, Foe1 appears to directly bind the promoter regions of several genes that have been implicated or could be involved in flocculation, including two flocculin genes (gsf2+ and pfl5+), a transcriptional

OE Δ FIM in OE ChIP

Figure 4.4. Heat map of putative Foe1 target genes. Heat map showing several putative target genes upregulated in the foe1OE strain (Column 1), and the corresponding lack of differential expression in the foe1Δ strain grown in FIM (Column 2). The ChIP-chip data is found in the third column. Grey boxes indicate that Foe1 did not bind the target gene’s promoter. Several putative target genes encoding TFs involved in flocculation (box 1), cell surface glycoproteins (box 2), and proteins involved in cell wall organization and biogenesis (box 3), were upregulated in + response to foe1 overexpression. Relative log2 values are shown for microarray expression profiling and ChIP-chip performed (with dye swap) on the nmt41-driven foe1- HA strain. The color bars show the relative log2 fold change in gene expression and ChIP enrichment ratios between the experimental and control strains. repressor of flocculation (gsf1+), and three cell wall biosynthesis genes (bgl2+, gas2+, and cfh2+) (Figure 4.4). Additionally, although mbx2+ and cbf12+ were only upregulated in one of the expression microarrays where foe1+ was overexpressed, they were both bound by Foe1 in the ChIP experiment (Figure 4.4).

Table 4.1. Confirmation of microarray data of putative target genes induced in the foe1OE strain by qPCR. Table comparing the log2FC values of several putative Foe1 target genes encoding glycoproteins, cell wall remodeling proteins, and TFs regulating flocculation from microarray expression profiling to the average log2FC values determined by qPCR. A SYBR green master mix (Life Technologies, Carlsbad, CA) and a StepOne Real-Time PCR system were used to perform qPCR. The act1+ gene was used as a reference gene. Three replicates were set up for each combination of query gene and strain. Comparisons were made using the ΔΔCt method using the manufacturer’s recommendation (Life Technologies).

Gene qPCR Log2FC Microarray Log2FC + gas2 1.32 2.16 + gsf1 0.85 0.98 + gsf2 1.81 3.61 + pfl3 1.26 2.84 + pfl5 1.54 1.35 + pfl7 4.33 2.24 + pfl9 7.00 1.16

SPAC4H3.03c 2.32 3.01 A gene ontology search of the Foe1-bound and upregulated genes using the

Princeton GO Term Finder indicated that genes involved in galactose-specific flocculation (P = 7.7e-3) and carbohydrate metabolism (P = 6.6e-3) were enriched.

However, no statistically significant regulatory motifs could be found within the promoter regions (1000 bp upstream of the start codon) of the 35 Foe1 bound and upregulated genes using MEME (Bailey et al. 2009). A search using RankMotif++ also failed to identify any statistically significant motifs (Chen et al. 2007).

4.4.5 Phenotypic suppression: abrogation of the foe1OE flocculation phenotype by deletion of putative target genes

Since overexpression of foe1+ results in flocculation, we wanted to see if deletion of its putative target genes could suppress this phenotype. Since the foe1Δ strain fails to

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flocculate in FIM, we first hypothesized that deletion strains of putative target genes would exhibit the same phenotype. Deletion strains for 29 of the 35 putative target genes bound and upregulated in foe1OE and five additional unbound putative target genes implicated in flocculation or cell wall biogenesis and remodelling were tested for flocculation in FIM (Table A11). Thirty-three of these 34 deletion strains flocculated comparably to wild type in FIM. As observed previously, only the gsf2Δ strain failed to flocculate (Kwon et al. 2012).

To determine if deletion of gsf2+ would prevent flocculation of the foe1OE strain, the nmt1-foe1+ plasmid was transformed into the gsf2Δ strain and the resulting strain was examined for flocculation in EMM without thiamine medium. The flocculation mediated by foe1+ overexpression was abrogated in the gsf2Δ strain after three days while the foe1OE strain flocculated at 24 hr (Figure 4.5A). Altogether, these results indicate that gsf2+ is a target gene of Foe1.

Figure 4.5. Deletion of gsf2+(A) but not mbx2+ or cbf12+ (B) abrogates flocculation of the foe1OE strain. TFOE strains were inoculated at 106 cells/ml in 50 ml of EMM + AU – thiamine liquid medium, and incubated at 30˚C for 1-3 days. 10 ml of cell suspension was then transferred to a 100 mm X 15 mm petri dish, rotated for 30 min, and photographed.

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4.4.6 Flocculation mediated by foe1+ overexpression is not dependent on cbf12+ or mbx2+

The TFs Mbx2 and Cbf12 induce flocculation by activating transcription of the dominant flocculin gene gsf2+ (Kwon et al. 2012). As the promoters of the cbf12+ and mbx2+ genes were bound by Foe1 in the ChIP-chip experiment, and mbx2+ and cbf12+ expression was upregulated by foe1+ overexpression, we were interested in seeing if flocculation induced by foe1+ overexpression was also dependent on the expression of either cbf12+ or mbx2+. The nmt1-foe1+ plasmid was thus transformed into the mbx2Δ and cbf12Δ strains. The resulting two strains were set up in EMM without thiamine medium, but unlike deletion of gsf2+, neither deletion of mbx2+, nor deletion of cbf12+, abrogated foe1OE flocculation (Figure 4.5B). These results suggest that although mbx2+ and cbf12+ are putative target genes of Foe1, the flocculent phenotype of the foe1OE strain is primarily due to activation of gsf2+ by Foe1.

4.5 Analysis of other putative transcriptional activators of flocculation

While the remaining TF genes, prr1+, prt1+, and SPBC530.08 were not as highly upregulated as foe1+ when the wild-type strain was grown in FIM, deletion and overexpression of each of these TF genes still abrogated and activated flocculation respectively. Consequently, we decided to characterize each of these TFs further.

Microarray expression profiling of the TFOE strains and the TFΔ strains grown in FIM was performed to identify potential target genes and changes in gene expression that might explain their aberrant flocculent phenotypes. Additionally, genes downregulated in the TFΔ strain or upregulated in the TFOE strain were examined for a corresponding

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increase in expression in the wild-type strain grown in FIM to identify putative target genes of these TFs.

4.5.1 Analysis of prt1+ deletion and overexpression strains

4.5.1.1 Microarray expression profiling of the prt1Δ strain in FIM

The microarray expression profile of the prt1Δ strain revealed that 27 genes were downregulated at least two-fold compared to wild type, when both strains were grown in

FIM (Table A13). Of these genes, 14 were also upregulated when wild type was grown in

FIM. This included three previously identified Prt1 target genes encoding the transmembrane efflux transporters Bfr1, Mfs1, and Mfs3 (Kawashima et al. 2012), as well as one other transmembrane transporter (SPBC83.13) and several integral membrane proteins (Pmp3, Tco1, SPAC977.02, SPBPB2B2.17, SPBC1348.03, and SPAC750.04).

However, neither gene list was enriched for any particular GO categories when analyzed with the Princeton GO-Term Finder, and none of the genes previously implicated in flocculation were differentially regulated in the prt1Δ strain grown in FIM.

4.5.1.2 Microarrays expression profiling of the prt1OE strain

In contrast to the prt1Δ strain, overexpression of prt1+ resulted in a greater change in gene expression, with 269 genes upregulated at least four-fold. There were several upregulated genes that might help explain the prt1OE flocculent phenotype. These included genes encoding three cell surface glycoproteins (Pfl3, Pfl9, and SPAPB2C8.01) and proteins involved in cell wall organization and biogenesis (SPCC970.02, Meu7,

Omh4, and Mkh1). Additionally, genes encoding two transcriptional activators of flocculation (Mbx2 and Cbf12) were also upregulated, albeit to a lesser extent (2.6 and

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3.4-fold, respectively). A heat map showing the relative expression levels of the TF and pfl+ genes can be seen in Figure 4.6. The upregulation of two of the flocculin genes and both TFs was also confirmed by qPCR (Table 4.2). Overexpression of each of these genes is sufficient to trigger flocculation (Kwon et al. 2012). The top 50 genes upregulated in the prt1OE strain are found in Table A14.

4.5.1.3 Search for potential Prt1 cis-regulatory sequences

To identify possible Prt1 binding sites, MEME was used to search the promoter regions (1000 bp upstream of the start codon) of the top 50 upregulated genes and 14 downregulated genes identified by microarray expression profiling of the prt1OE and prt1Δ strains, respectively (Bailey et al. 2009). None of these upregulated or downregulated genes contained a PDRE element that has previously been shown to be bound by the Prt1-related S. cerevisiae and C. glabrata TFs, Pdr1 and Pdr3 (Mamnun et al. 2002; Vermitsky et al. 2006). Furthermore, while MEME did detect a cis-sequence containing CCG/GGC trinucleotide repeats characteristic of Zn2-Cys6 TFs within the top

50 upregulated genes, none of the known Prt1 target genes or putative Prt1 target genes involved in flocculation had this motif within their promoter region (Todd &

Andrianopoulos 1997).

4.5.2 Analysis of SPBC530.08 deletion and overexpression

4.5.2.1 Microarray expression profiling of the SPBC530.08Δ strain in FIM

Microarray expression profiling of the SPBC530.08Δ strain compared to wild type grown in FIM showed that 67 genes were downregulated at least two-fold (Table A15).

However, only ten of these genes were also upregulated in the wild-type strain in FIM.

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OE Δ FIM A

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Figure 4.6. Microarray expression profiling of the prt1OE, prr1OE, and SPBC530.08 strains. Heat map showing several putative target genes upregulated in the TFOE strain (Column 1) and the corresponding lack of differential expression in the TFΔ grown in FIM (Column 2). Several putative target genes encoding TFs (box 1), cell surface glycoproteins (box 2), and proteins involved in cell wall biosynthesis (box 3) were upregulated by overexpression of (A) prt1+, (B) SPBC530.08, and (C) prr1+. Additionally, five known Prr1 targets (box 4) were upregulated in the prr1OE strain. Microarray expression profiling was performed with dye reversal. The color bars indicate the relative expression in log2 scale between the TFOE or TFΔ strains and the empty vector control or wild-type strain, respectively. Genes that are underlined were confirmed by qPCR.

Table 4.2. Confirmation of microarray data of putative target genes induced in prr1OE, prt1OE, and SPBC530.08OE strains by qPCR. Table comparing the log2FC values of several target genes encoding glycoproteins, cell wall remodeling proteins, and TFs regulating flocculation from microarray expression profiling to the average log2FC values determined by qPCR. A SYBR green master mix (Life Technologies, Carlsbad, CA) and a StepOne Real-Time PCR system were used to perform qPCR. The act1+ gene was used as a reference gene. Three replicates were set up for each combination of query gene and strain. Comparisons were made using the ΔΔCt method using the manufacturer’s recommendation (Life Technologies).

Gene Strain qPCR Log2FC Microarray Log2FC adn3+ prr1OE 0.93 1.34 agn2+ prr1OE 2.3 3.42 cbf12+ prr1OE 1.49 1.32 gas2+ prr1OE 1.77 3.1 gsf1+ prr1OE 2.11 1.96 gsf2+ prr1OE 1.43 1.81 mbx2+ prr1OE 1.14 1.48 pfl4+ prr1OE 1.07 2.34 pfl7+ prr1OE 5.41 2.22 pfl9+ prr1OE 6.76 2.31 SPAC4H3.03c prr1OE 4.26 4.51 cbf12+ prt1OE 2.2 1.77 mbx2+ prt1OE 1.54 1.38 pfl3+ prt1OE 1.93 2.62 pfl9+ prt1OE 7.96 4.06 cbf12+ SPBC530.08OE 1.59 1.06 gsf2+ SPBC530.08OE 2.35 2.3 pfl3+ SPBC530.08OE 2.48 2.65

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None of the genes previously implicated in flocculation were differentially regulated, and the Princeton GO Term Finder was unable to identify enrichment for any GO categories among either gene list. Additionally, no regulatory motifs were identified among the promoters of the downregulated genes using either RankMotif++ or MEME (Bailey et al.

2009; Chen et al. 2007).

4.5.2.2 Microarrays expression profiling of SPBC530.08OE strain

In contrast, there were 126 genes upregulated at least four-fold when SPBC530.08 was overexpressed. Approximately 45% or 56/126 of these genes were also upregulated at least two-fold in wild type grown in FIM. These 126 upregulated genes were enriched for several GO processes, including protein folding (P = 1.6e-4) and glycerol metabolism

(P = 3.5e-3), while those also upregulated in wild type were enriched for carbohydrate (P

= 8.2e-5) and glycerol (P = 8.4e-5) metabolism. Furthermore, several genes that might help explain the SPBC530.08OE flocculent phenotype were also upregulated, including the genes encoding cell surface glycoproteins Gsf2 and Pfl3 and three proteins involved in cell wall organization and biogenesis (Meu7, Bgl2, and Pck1) (Figure 4.6B). While mbx2+ expression was not increased, cbf12+ was increased 2.1-fold in the

SPBC530.08OE strain. The upregulation of cbf12+ and the two flocculins, gsf2+ and pfl3+ was also confirmed by qPCR (Table 4.2). Unfortunately, no statistically significant regulatory motifs could be identified within the promoters of the upregulated genes using either RankMotif++ or MEME (Bailey et al. 2009; Chen et al. 2007). The top 50 upregulated genes in the SPBC530.08OE strain can be found in Table A16.

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4.5.3 Analysis of prr1+ deletion and overexpression

4.5.3.1 Microarray expression profiling of the prr1Δ strain in FIM

Deletion of prr1+ resulted in substantial changes in gene expression, with 376 genes downregulated at least two-fold and 44 downregulated at least four-fold. However, only 71/376 genes were upregulated at least two-fold in the wild-type strain grown in

FIM (Table A17). Furthermore, while gene ontology analysis with Princeton GO Term

Finder identified functional enrichment for ribosome biogenesis (P = 5.0e-5) within the downregulated genes, there were no enriched GO categories within the 71 genes that were also upregulated in wild type. Additionally, only two known Prr1 target genes

(ctt1+ and srx1+) and four genes involved in flocculation or localized to the cell wall/surface (pfl3+, adg1+, SPAC2E1P3.05c, and SPAC750.07c) were downregulated in the prr1Δ strain. Of these, only ctt1+ and srx1+ were also upregulated in the wild-type strain in FIM.

4.5.3.2 Microarray expression profiling of the prr1OE strain

Overexpression of prr1+ also resulted in a substantial change in gene expression

(406 genes were upregulated at least four-fold). These genes were enriched for several

GO terms associated with carbohydrate metabolism, including cellular carbohydrate metabolism (P = 3.0e-6). Five known Prr1 targets were among the upregulated genes, including ctt1+, gpx1+, gst3+, srx1+, and trr1+ (Figure 4.6C). Several cell surface glycoprotein genes (gsf2+, pfl4+, pfl7+, pfl9+, and SPAPB18E9.04c), three genes involved in cell wall remodelling (gas2+, agn2+, and SPAC4H3.03c), and fourteen other genes involved in cell wall organization and biogenesis were also upregulated (Figure 4.6C).

Furthermore, the expression of three TFs that regulate flocculation (gsf1+, mbx2+, and 108

cbf12+) and one TF gene that regulates cell wall remodelling (adn3+) were also increased in the prr1OE strain (Figure 4.6C). Quantitative qPCR confirmed upregulation of all TFs and cell wall remodelling genes, as well as four of the five flocculin genes (Figure 4.6C).

All of these qPCR-confirmed putative target genes have been shown to trigger flocculation when overexpressed (Kwon et al. 2012). The top 50 upregulated genes in the prr1OE strain can be found in Table A18.

4.5.4 Phenotypic suppression: abrogation of the OE flocculation phenotype by deletion of putative target genes

As deletion of prt1+, prr1+, and SPBC530.08 abrogated flocculation in FIM, we wanted to see if deletion of any of their putative target genes could also prevent flocculation in FIM. Eleven of the 20 genes downregulated in the prt1Δ strain and upregulated in wild type grown in FIM, six of the ten genes downregulated in the

SPBC530.08Δ strain and upregulated in wild type grown in FIM, and 57 of the 71 genes downregulated in the prr1Δ strain and upregulated in wild-type grown in FIM were tested for reduced or absent flocculation in FIM (Table A11). Unfortunately, all of these deletion strains of putative target genes flocculated normally in FIM. Several genes implicated in flocculation or in cell wall organization and remodelling that were upregulated in each TFOE strain were also tested for abrogation of flocculation in FIM.

In total, 21 additional deletions of putative target genes were tested (eight each for Prr1 and Prt1, and five for SPBC530.08) (Table A11). Unfortunately, all 21 of these deletion strains also flocculated normally.

In all flocculent strains tested so far, flocculation has been found to be dependent upon gsf2+, which encodes the dominant flocculin. To determine if this was also the case

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for our three novel activators of flocculation, each TF gene was overexpressed in a gsf2Δ strain. We observed that the flocculation triggered by overexpression of these three TFs was also abrogated by deletion of gsf2+ (Figure 4.7). Additionally, as overexpression of all three TF genes resulted in upregulation of either mbx2+ and/or cbf12+, we also determined if deletion of cbf12+ or mbx2+ could abrogate flocculation caused by their overexpression. In all three cases, deletion of cbf12+ and mbx2+ abrogated flocculation indicating that these two TF genes could be direct target genes of Prr1, Prt1 or

SPBC530.08 (Figure 4.7).

Figure 4.7. Deletion of gsf2+, mbx2+, or cbf12+ abrogates the flocculation of the (A) SPBC530.08OE, (B) prt1OE, and (C) prr1OE strains. TFOE strains were inoculated at 106 cells/ml in 50 ml of EMM + AU – thiamine liquid medium, and incubated at 30˚C for 2-6 days. 10 ml of cell suspension was then transferred to a 100 mm X 15 mm petri dish, left to rotate for 30 minutes, and photographed.

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4.6 Epistatic relationships between TFs involved in flocculation

A total of eight different transcriptional activators of flocculation have been identified previously and from this study. We were next interested in determining which

TFs might be epistatic to one another in order to determine their genetic relationship within the flocculation TRN of S. pombe. To do so, each of these eight TFs was overexpressed in the other seven TFΔ strains to determine if constitutive flocculation would be abolished. The flocculent phenotypes of the mbx2OE, cbf12OE, and foe1OE strains does not appear to be dependent on expression of any of the other TF genes, as only deletion of dominant flocculin gsf2+ abrogated their flocculation (Table 4.3). In contrast, flocculation of the prr1OE, prt1OE, and SPBC530.08OE strains was abrogated by deletion of gsf2+, mbx2+, and cbf12+ (Table 4.3). This suggests that Prr1, Prt1, and

SPBC530.08 potentially act upstream and regulate mbx2+ and cbf12+. Lastly, flocculation of the adn2OE and adn3OE strains was abrogated by deletion of gsf2+ and cbf12+. As cbf12+ is downregulated by overexpression of adn2+ and adn3+, it is unclear why this is the case (Kwon et al. 2012).

4.7 Identifying negative transcriptional regulators of flocculation

While several transcriptional activators of flocculation were identified in our initial screens (Section 4.1), we were also interested in identifying new TFs that negatively regulate flocculation. Previously, Kwon et al. (2012) identified four TFΔ strains (gsf1Δ, yox1Δ, sre2Δ, and cbf11Δ) that flocculated constitutively. However, the majority of the

TFΔ collection was not systematically screened. Consequently, we decided to screen the entire TFΔ collection for constitutive flocculation in liquid EMM medium. At 48 hr, the

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Table 4.3. Epistatic interactions between eight TFs that positively regulate flocculation in S. pombe. Each of the eight TFs was overexpressed in the other seven TFΔ strains to determine if constitutive flocculation would be abolished. TFOE/Δ strains and their EVCs were inoculated at 106 cells/ml in 50 ml of EMM + AU – thiamine liquid medium, and incubated at 30˚C for 2-6 days. 10 ml of cell suspension was then transferred to a 100 mm X 15 mm petri dish, left to rotate for 30 minutes, and photographed. +++ and + indicate strong and weak flocculation, respectively. – indicates that the strains do not flocculate.

TF mbx2OE cbf12OE adn2OE adn3OE foe1OE prr1OE prt1OE SPBC530.08OE gsf2∆ ------mbx2∆ n/a + + + + - - - cbf12∆ +++ n/a - - + - - - adn2∆ +++ + n/a + + + + + adn3∆ +++ + + n/a + + + + foe1∆ +++ + + + n/a + + + prr1∆ +++ + + + + n/a + + prt1∆ +++ + + + + + n/a + spbc530.08∆ +++ + + + + + + n/a wild-type strain and the majority of the TFΔ strains did not flocculate. However,11 TFΔ strains did form flocs, including four (scr1Δ, SPBC56F2.05cΔ, SPAC3C7.04Δ, and

SPCC1393.08Δ) not previously implicated in flocculation (Figure 4.8). The remaining seven included deletions for several TF genes that have documented roles in flocculation

(gsf1Δ, prz1Δ, yox1Δ, and cbf11Δ), two TF genes known to regulate cell wall organization and septation (sep1Δ and ace2Δ), and a TF gene (rep2Δ) that regulates target genes involved in the G1/S transition in mitosis, including yox1+ (Wood et al. 2012). Of the four novel flocculent strains, deletion of scr1+ generated the strongest flocculation, while the remaining three flocculated quite weakly. In fact, when all four were screened for flocculation in YES liquid medium only the scr1Δ and SPBC56F2.05cΔ strains flocculated.

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Figure 4.8. The deletion of 11 TF genes results in constitutive flocculation in minimal medium (EMM + ALU). TFΔ strains and the wild-type control were inoculated at 106 cells/ml in 50 ml of EMM + ALU medium, and incubated at 30˚C for 24-48 hours. 10 ml of cell suspension was then transferred to a 100 mm X 15 mm petri dish, left to rotate for 30 minutes, and photographed.

4.8 Analysis of transcription factor Scr1

Since deletion of scr1+ resulted in constitutive flocculation in rich medium, we decided to perform microarray expression profiling to identify any changes in gene expression that might explain this phenotype. Additionally, several target genes potentially responsible for the scr1Δ flocculent phenotype were knocked out to determine if their deletion abolished flocculation.

4.8.1 Microarray expression profiling of the scr1Δ strain

Microarray expression profiling of the scr1Δ strain compared to wild-type yielded

127 differentially-regulated genes (+/- 2-fold, P < 1.0e-3). The majority of these genes

(99/127) were upregulated, which was expected, as Scr1 is known to repress the expression of several genes in response to high glucose (Tanaka et al. 1998; Matsuzawa et al. 2010; Saitoh et al. 2015). Three genes previously reported as being derepressed by disruption of scr1+ (inv1+, gld1+, and ght5+) were among the most highly upregulated

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genes (Figure 4.9A) (Tanaka et al. 1998; Matsuzawa et al. 2010; Saitoh et al. 2015).

Figure 4.9. Microarray analysis of the scr1Δ strain. (A) Heat map showing several putative Scr1 target genes upregulated in the scr1Δ strain. Several putative target genes encoding TFs (box 1) and glycoproteins (box 2) involved in flocculation were upregulated. Additionally, three known Scr1 target genes were upregulated (box 3), as well as several other genes encoding proteins involved in the metabolism of galactose (box 4), and the transport of hexoses (box 5). Microarray expression profiling was performed with dye reversal on the scr1Δ strain. The color bar indicates the relative expression in log2 scale between the scr1Δ and wild-type strains. Genes that are underlined were confirmed by qPCR. (B) DNA motif closely matching the binding specificity of the S. cerevisiae Scr1 homologs, Mig1-3 was retrieved using RankMotif++(Chen et al. 2007). The orange bars indicate the bases that match the Mig1-3 motifs. The DNA motif was produced using enoLOGOS (Workman et al. 2005). 114

In addition, several other genes encoding hexose transmembrane transporters

(ght1+, ght3+, ght4+, and ght6+) and four genes involved in galactose metabolism

(gal10+, gal1+, gal7+, and SPBP2B2.11) were upregulated (Figure 4.9A). Most importantly, the expression of several putative target genes that might explain the scr1Δ flocculent phenotype was increased, including the galactose-specific flocculin gene gsf2+, cell surface glycoprotein genes fta5+ and pfl9+, and three TF genes that activate flocculation, mbx2+, cbf12+, and foe1+ (Figure 4.9A). The upregulation of five of these putative target genes was also confirmed by qPCR (Table 4.4). The Top 50 upregulated genes in the scr1Δ strain can be found in (Table A19).

Table 4.4. Confirmation of microarray data of putative target genes induced in the of scr1Δ strain by qPCR. Table comparing the log2FC values of putative Scr1 target genes encoding three glycoproteins and two TFs involved in flocculation from microarray expression profiling to the average log2FC values determined by qPCR. A SYBR green master mix (Life Technologies, Carlsbad, CA) and a StepOne Real-Time PCR system were used to perform qPCR. The act1+ gene was used as a reference gene. Three replicates were set up for each combination of query gene and strain. Comparisons were made using the ΔΔCt method using the manufacturer’s recommendation (Life Technologies).

Gene qPCR Log2FC Microarray Log2FC

+ cbf12 0.9 1.9 mbx2+ 0.8 1.5

fta5+ 2.5 2.6 gsf2+ 3.1 2.7 pfl9+ 1.9 2.7

While a GO ontology search of the upregulated genes using the Princeton GO

Term Finder did not find statistical enrichment for proteins involved in flocculation or proteins localized to the cell wall, it did find enrichment for several GO terms associated with sexual reproduction (P = 6.2e-6) and carbohydrate metabolism (P = 1.6e-3).

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Additionally, there was an enrichment for proteins localized to the cellular periphery (P =

4.1e-3).

4.8.2 Search for putative Scr1 cis-regulatory sequences

RankMotif++ was used to search for cis-regulatory sequences that were overrepresented within the upregulated genes. Interestingly, a motif was identified that closely resembled the high-confidence binding motif of the S. cerevisiae Scr1 homologs

Mig1, Mig2, and Mig3 (Figure 4.9B) (Hughes & de Boer 2013). This short cis-regulatory sequence was also found in an approximately 250 bp UAS of fbp1+ previously identified as being bound by Scr1 (Hirota et al. 2006). However, this cis-regulatory sequence was not found in any of the pfl+ promoters (1000 bp upstream of the start codon) or in the promoter regions of either mbx2+ or cbf12+.

4.8.3 Phenotypic suppression: abrogation of scr1Δ flocculation by deletion of putative target genes

Since deletion of scr1+ causes flocculation, we wanted to see if knocking out the putative target genes that were upregulated in the scr1Δ strain and that are known to be involved in flocculation would abolish this phenotype. Consequently, each of the six upregulated genes involved in flocculation (gsf2+, fta5+ and pfl9+, mbx2+, cbf12+, and foe1+) were deleted in the scr1Δ strain and the resulting strains were tested in YES medium. As expected, after 48 hr, the scr1Δ strain forms substantially sized flocs (Figure

4.10). The deletion of pfl9+ and foe1+ did not appear to alter flocculation significantly in this genetic background (Figure 4.10). In contrast, deletion of fta5+ significantly reduced the number of flocs formed in the scr1Δ strain, while deletion of gsf2+, mbx2+, and cbf12+ abolished scr1Δ-mediated flocculation entirely (Figure 4.10). 116

Figure 4.10. Abrogation of scr1Δ-mediated flocculation by deletion of mbx2+, cbf12+, and gsf2+. Putative target genes encoding TFs (mbx2+, cbf12+, and foe1+) and cell-surface glycoproteins (gsf2+, fta5+, and pfl9+) known to be involved in flocculation were deleted in the scr1Δ strain to produce double knockouts. The double knockouts were grown for 48 hrs in YES medium and then the degree of flocculation was examined. Deletion of mbx2+, cbf12+, or gsf2+ abrogated flocculation in the scr1Δ strain entirely, while deletion of fta5+ reduced it. Deletion of foe1+ or pfl9+ did not appear to have an effect on the flocculation of the scr1Δ strain.

4.9 Analysis of transcription factor SPBC56F2.05

Since deletion of SPBC56F2.05c resulted in constitutive flocculation in rich medium, we decided to perform expression microarrays to identify any changes in gene expression that might explain this phenotype. Additionally, several putative target genes were knocked out in the SPBC56F2.05cΔ strain to determine if their deletion abolished flocculation.

4.9.1 Microarray expression profiling of the SPBC56F2.05cΔ strain

Microarray expression profiling of the SPBC56F2.05cΔ strain compared to wild type yielded 39 differentially regulated genes (at least two-fold), the majority of which

(26/39) were upregulated (Table A20). These upregulated genes included several putative

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target genes that might explain the strain’s flocculent phenotype, including the galactose- specific flocculin gene gsf2+, cell surface glycoprotein genes fta5+, pfl3+, and pfl9+, and two TF genes that activate flocculation, mbx2+ and cbf12+ (Figure 4.11). It also included

SPBC2G2.17c, a gene predicted to encode the beta-glucosidase Psu2, which is localized to the cell wall (Figure 4.11). The upregulation of five of these seven putative target genes was also confirmed via qPCR (Table 4.5). A GO ontology search using the

Princeton GO Term Finder confirmed that there was an enrichment for proteins involved in cell adhesion among the upregulated genes (P = 5.0e-4). However, no statistically significant cis-regulatory motifs could be identified within the promoters of the upregulated or downregulated genes using either RankMotif++ or MEME (Chen et al.

2007; Bailey et al. 2009).

Figure 4.11. Microarray expression profiling of the SPBC56F2.05cΔ strain. Heat map showing several putative SPBC56F2.05 target genes upregulated in the SPBC56F2.05cΔ strain. Putative target genes encoding two TFs known to regulate flocculation, four cell-surface glycoproteins, and cell-wall localized Psu2 were upregulated. Microarray expression profiling was performed with dye swap of the SPBC56F2.05cΔ strain. The color bar indicates the relative expression in log2 scale between the SPBC56F2.05cΔ and wild-type strain. Genes that are underlined were confirmed by qPCR.

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Table 4.5. Confirmation of microarray data of putative target genes induced in the SPBC56F2.05cΔ by qPCR. Table comparing the log2FC values of putative SPBC56F2.05 target genes encoding four glycoproteins and two TFs involved in flocculation from microarray expression profiling to the average log2FC values determined by qPCR. A SYBR green master mix (Life Technologies, Carlsbad, CA) and a StepOne Real-Time PCR system were used to perform qPCR. The act1+ gene was used as a reference gene. Three replicates were set up for each combination of query gene and strain. Comparisons were made using the ΔΔCt method using the manufacturer’s recommendation (Life Technologies).

Gene qPCR Log2FC Microarray Log2FC cbf12+ 1.32 1.7 mbx2+ 0.7 1.6 fta5+ 2.83 2.26 gsf2+ 3.34 2.37 pfl3+ 1.31 1.01 pfl9+ 1 1.45

4.9.2 Phenotypic suppression: abrogation of SPBC56F2.05cΔ flocculation by deletion of putative target genes

Since deletion of SPBC56F2.05c causes flocculation, we wanted to see if knocking out the putative target genes that were upregulated in the SPBC56F2.05cΔ strain and known to be involved in flocculation would abolish this phenotype. Consequently, five of the six upregulated genes involved in flocculation (gsf2+, fta5+, pfl9+, mbx2+ and cbf12+) were deleted in the SPBC56F2.05cΔ strain and the resulting strains were tested for constitutive flocculation in rich medium. As expected, after 48 hr, the SPBC56F2.05cΔ strain flocculated in rich medium (Figure 4.12). Deletion of fta5+, gsf2+, mbx2+, and cbf12+, but not pfl9+, abolished flocculation in the SPBC56F2.05cΔ strain (Figure 4.12).

Altogether, these results indicate that the flocculent phenotype of the SPBC56F2.05cΔ strain is due to the upregulation of fta5+, gsf2+, mbx2+, or cbf12+ expression, and that

SPBC56F2.05 may directly repress the transcription of these putative target genes.

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Figure 4.12. Abrogation of SPBC56F2.05cΔ-mediated flocculation by deletion of mbx2+, cbf12+, gsf2+, and fta5+. Putative target genes encoding TFs (mbx2+ and cbf12+) and glycoproteins (gsf2+, fta5+, and pfl9+) known to be involved in flocculation were deleted in the SPBC56F2.05cΔ strain to produce double knockouts. The double knockouts were grown for 48 hr in YES medium and then the degree of flocculation was examined. Deletion of mbx2+, cbf12+, gsf2+, or fta5+ abrogated the flocculation of the SPBC56F2.05cΔ strain entirely, while deletion of pfl9+ did not appear to have an effect.

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Chapter Five: Functional genetic analysis of three previously uncharacterized fungal Zn2-Cys6 transcription factors

Parts of this chapter have been published in Vachon et al. (2013) Functional characterization of fission yeast transcription factors by overexpression analysis. Genetics

194: 873-884.

5.1 Systematic overexpression of S. pombe TFs

Previously, Chua et al. (2006) showed that systematic overexpression of TF genes combined with microarray expression profiling in S. cerevisiae is an effective approach to elucidate potential target genes and biological functions. In contrast, microarray expression profiling of TFΔ strains in rich media was not very effective in identifying target genes and assigning biological functions. The primary reason for this is that most budding yeast TFΔ strains do not have a visible phenotype in rich media and, therefore,

TF activity is likely not required under these conditions. Similarly, we found that the vast majority of S. pombe TFΔ strains appeared to grow normally on rich media and had very few differentially-regulated genes in their microarray expression profiles. Because identifying the appropriate conditions to activate TF expression can be difficult, a similar strategy of systematic overexpression and microarray expression profiling was employed to functionally characterize S. pombe TFs and identify their target genes.

Two members of our lab, Amy Laderoute and Gina Kwon, systematically overexpressed 91 of the 99 putative TF ORFs in S. pombe. Each ORF was cloned downstream of the nmt1 promoter in the pREP1 plasmid containing the selectable LEU2 marker and then transformed into a strain auxotrophic for leucine. Subsequently, 89 of these TFOE strains were screened for cell length and fitness defects after incubation in

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minus thiamine medium for 24 and 48 hr to induce the nmt1 promoter, and also fixed to identify defects in septation, nuclear morphology, or chromosome segregation.

5.1.1 Cell length and fitness defects of nmt1-driven TFOE strains

Eighty-nine of the 91 TFOE strains were screened for cell length and fitness defects under inducing (minus thiamine) conditions. The average cell length and fitness scores were determined from two independent replicates, and then compared to another graduate student’s scores for confirmation. While 24% (21/89) of the TFOE strains did not have a discernible cell length or fitness defect relative to the empty vector control, the remaining 76% (68/89) of the TFOE strains did.

TF overexpression caused fitness defects in 73% (65/89) of the strains (Figure

5.1). The majority of these reductions in fitness were quite severe, with 63% (41/65) receiving a score of 2 (30) or 3 (11), indicating decreases in colony size from >100 cells in the empty vector control to 10-30 cells and < 10 cells, respectively. Eighty percent

(52/65) of the TFOE strains with reduced fitness also had altered cell length.

Overall, slightly fewer TFOE strains (61%; 54/89) had cell length defects compared to fitness defects (Figure 5.1). More than two thirds (69%; 37/54) of these

TFOE strains were significantly longer than the empty vector control (score of 2 or 3), while 28% (15/54) were slightly longer (score of 1). Only two TFOE strains (ace2OE and loz1OE) were shorter (score of -1) than the empty vector control. Additionally, defects in cell length were almost always accompanied by reduced fitness. Only three TFOE strains

(yox1OE, SPBC17D1.01OE, and SPCC757.04OE) displayed a cell length defect with no effects on fitness.

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Figure 5.1. Phenotypic characterization of the S. pombe transcription factor overexpression array. Graph showing the phenotypes associated with ectopic expression of TFs in S. pombe. Strains containing an nmt1-driven TF gene were scored for fitness (y-axis) and cell elongation (x-axis) on EMM minus thiamine plates after 48 hr. To observe cell cycle phenotypes, TFOE strains were grown in EMM minus thiamine medium for 24-48 hr and stained with DAPI and calcufluor white to visualize nuclei and cell wall material, respectively. TFs that did not result in a phenotype when ectopically expressed were not included. Fitness defects were scored as follows: (1) slight (30-100 cells per colony), (2) moderate (10-30 cells per colony), (3) severe (10 cells per colony). Cell elongation was scored as follows: (1) mild (1.5X longer than the empty vector control), (2) moderate (2X longer than the empty vector control), (3) severe (3X longer than the empty vector control), and short (shorter than the empty vector control). The relative fitness and cell length of the empty vector control were scored as 0. Cell cycle phenotypes were classified as (red) aberrant septal deposition and/or multiseptation, (green) abnormal nuclear morphology, (blue) chromosome missegregation, and (purple) both chromosome missegregation and aberrant septal deposition and/or multiseptation. Only TFOE strains with >10% cells exhibiting the indicated cell cycle phenotype were included. TFOE strains with no cell cycle phenotypes were shown in gray/black. Modified from (Vachon et al. 2013).

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5.1.2 Cell cycle phenotypes of nmt1-driven TFOE strains

The overexpression of several TFs resulted in cell elongation and shortening relative to the empty vector control, suggesting that the cell cycle might be affected in these strains. Each TFOE strain was fixed at 24 and 48 hr after induction of the nmt1 promoter and stained with DAPI and calcofluor to examine nuclei and septa, respectively.

As might have been expected, all 24 TFOE strains without cell length or fitness defects did not show cell cycle phenotypes. In contrast, 40% (27/68) of the TFOE strains with cell length or fitness defects also had a cell cycle phenotype (Figure 5.1). Most commonly (20/27), strains had aberrant septal deposition or multiple septa.

Overexpression of six TFs resulted in missegregation of the nuclei, but two of these also had abnormal septal formations. Only three TFOE strains had abnormal nuclear morphology. Interestingly, 96% (26/27) of the TFOE strains with cell cycle phenotypes displayed both reduced fitness and altered cell length.

5.2 Characterization of HA-tagged nmt41-driven TFOE strains

Three uncharacterized TFs with cell length or fitness defects when overexpressed under control of the nmt1 promoter were chosen for further study. These TFs contained a fungal-specific Zn2-Cys6 DNA-binding domain and included SPBC1773.16,

SPBC16G5.17 and SPAC25B8.11. HA-tagged versions of each TF were constructed so that ChIP-chip could be performed concurrently with expression microarrays to identify their target genes. Additionally, each TF ORF was placed under control of the medium- strength nmt41 promoter instead of the strong nmt1 promoter, in the hope that stress- induced changes in gene expression would be reduced, making the identification of direct

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TF target genes easier. Western blotting was carried out to ensure that the HA-tagged TF protein was being properly expressed. Each HA-tagged strain was also compared to the untagged nmt1-TFOE strain to ensure that all cell cycle, cell length, and fitness phenotypes were consistent between strains.

5.2.1 Confirmation of nmt41-driven HA-fusion TF protein expression

To confirm that the HA-tagged TF proteins were properly expressed, each HA- tagged TFOE strain was cultured in EMM medium with and without thiamine, and western blotting was performed. No protein products were identified in the cell lysates of strains grown in the presence of thiamine (the nmt41 promoter turned off) or in the empty vector control. However, in the absence of thiamine (nmt41 promoter turned on), several protein products were present. For each TFOE strain, there was a protein product that was approximately the predicted molecular weight of the TF (SPBC1773.16 = 68.84 kDa,

SPBC16G5.17 = 65.11 kDa, and SPAC25B8.11 = 74.72 kDa) (Figure 5.2). In addition, there were several smaller than expected protein products, particularly with overexpression of SPAC25B8.11, likely corresponding to partially degraded proteins

(Figure 5.2).

5.2.2 Comparison of untagged and HA-tagged TFOE strain phenotypes

Once we had confirmed that the HA-tagged protein was properly expressed, we wanted to determine if the HA tag interfered with TF function. To do so, the nmt41- driven HA-tagged TFOE strains and their corresponding nmt1-driven untagged TFOE strains were grown on EMM minus thiamine medium, and their cell length and fitness phenotypes were compared. In each case, the HA-tagged nmt41-TFOE strains exhibited a

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Figure 5.2. Confirmation of HA tagging of TFOE strains by western blotting. HA tagged nmt41-driven TFOE strains were grown to an OD600 of ~0.2-0.3 in EMM + AL – thiamine medium at 30˚C for 20-24 hr to ensure maximal expression of the HA- tagged protein from the nmt41 promoter. Cell lysate was isolated for SDS-PAGE and western blotting with anti-HA antibodies as outlined in Section 2.5.9. Molecular weight standards are indicated on the left of the blot. less severe fitness and/or cell length defect compared to the untagged nmt1-TFOE strain, likely because of their weaker promoter and lesser degree of TF overexpression (Table

5.1). However, the overall trend in fitness and cell length phenotypes matched between the TFOE strains with different nmt promoters (Table 5.1).

5.3 Identification of putative SPBC1773.16 target genes by phenotypic activation

One TFOE strain that was of interest was the SPBC1773.16cOE strain.

SPBC1773.16c overexpression resulted in a decrease in fitness and rare curved, elongated cells (Figure 5.3A). As these phenotypes could be the result of an inappropriate increase in expression of putative target genes due to aberrant TF activity, microarray expression profiling and ChIP-chip were performed on the SPBC1773.16cOE strain.

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Table 5.1. Comparison of cell length and fitness scores for the HA-tagged nmt41- TFOE strains and the nmt1-driven TFOE strains. Table comparing the cell length and fitness phenotypes of strains containing HA-tagged TF genes under control of the nmt41 promoter and untagged TF genes under control of the nmt1 promoter. TFOE strains were scored for fitness and cell elongation on EMM minus thiamine plates after 48 hr. Fitness defects were scored as follows: (1) slight (30- 100 cells per colony), (2) moderate (10-30 cells per colony), and (3) severe (10 cells per colony). Cell elongation was scored as follows: (1) mild (1.5X longer than the empty vector control), (2) moderate (2X longer than the empty vector control), (3) severe (3X longer than the empty vector control), and short (shorter than control). The relative fitness and cell length of the empty vector control were scored as 0.

TF being Cell Length Score Fitness Score overexpressed nmt41 (HA- nmt1 nmt41 (HA- nmt1 tagged) (untagged) tagged) (untagged) SPBC1773.16c 1 2 1 1 SPAC16G5.17 0 1 2 3 SPAC25B8.11 1 2 1 2

5.3.1 Expression microarray analysis of SPBC1773.16cOE strains

To determine the effect of SPBC1773.16c overexpression on global mRNA expression, the nmt41-driven HA-tagged SPBC1773.16c strain was grown in medium lacking thiamine for 20-24 hr to induce expression of the TF gene. The culture was then divided in two for transcriptome and ChIP-chip analyses. Transcriptome profiling of the

SPBC1773.16cOE strain revealed that 103 and 129 genes were upregulated and downregulated at least two-fold, respectively. To increase the likelihood of identifying direct SPBC1773.16 target genes, microarray expression profiling was performed on two additional SPBC1773.16cOE strains (nmt41- and nmt1-driven untagged SPBC1773.16c), and only genes differentially regulated in all three OE strains were selected for further analysis. Only seven genes were found to be upregulated at least two-fold in all three

SPBC1773.16cOE strains relative to the empty vector control (Figure 5.3B). These included the following genes and their corresponding gene products: SPBC1773.13

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Figure 5.3. Identification of putative SPBC1773.16 target genes by phenotypic activation. (A) Overexpression of SPBC1773.16c by the nmt1 promoter produces elongated, curved cells. (B) Heat map showing several putative target genes upregulated in the SPBC1773.16cOE strain (Column 2) and the corresponding ChIP-chip values (Column 3). Grey boxes indicate that the TF did not bind that gene’s promoter. The first column shows the differential expression in log2 scale for these putative target genes in the SPBC1773.16cΔ strain compared to wild type. Underlined genes were confirmed by qPCR. Values are shown for microarray expression profiling and ChIP-chip performed with dye swap on the nmt41-driven SPBC1773.16-HA strain. The color bars show the relative log2 fold change in expression, and ChIP enrichment ratios between the TFOE strain and empty vector control/input DNA. The log2FC and ChIP-chip enrichment values are also found in Table A21. (C) A putative DNA motif retrieved by promoter analysis of the putative SPBC1773.16 target genes found in the heat map. The promoter regions (1000 bp upstream of the start codon) of these putative target genes were analyzed by MEME (Bailey et al. 2009). (D) Overexpression of SPBC1773.16c and three of its putative target genes results in resistance to canavanine. Exponentially growing OE strains and the empty vector control were spot diluted on EMM plates with 180 μg/ml canavanine (right panel) and without (left panel), and were incubated at 30˚C for 3-5 days.

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(aromatic aminotransferase); dal52+ (dipeptide membrane transporter); arg7+

(arginosuccinate lyase); car1+ (arginase); SPBPB2B2.01 (amino acid transporter);

+ meu26 (conserved fungal protein); and SPBC1773.12 (Zn2-Cys6 TF). Interestingly, five of these seven upregulated genes (SPBC1773.12, SPBC1773.13, arg7+, dal52+, and

+ car1 were clustered around the SPBC1773.16c gene, as is also seen for the Zn2-Cys6 family TF Toe1. In contrast, no genes were found to be downregulated at least two-fold in the microarray expression profiles of all three SPBC1773.16cOE strains relative to the empty vector control.

5.3.2 ChIP-chip analysis of the HA-tagged nmt41-driven SPBC1773.16cOE strain

To determine which differentially-regulated genes might be directly bound by

SPBC1773.16, we ran ChIP-chip analysis on the HA-tagged SPBC1773.16cOE strain. A total of 93 promoter regions were enriched (at least two-fold enrichment) in the immunoprecipitated sample compared to input DNA. Approximately one third (30/93) of these promoters were associated with a gene that was differentially regulated in at least one of the SPBC1773.16cOE strains. Furthermore, SPBC1773.16 bound to its own promoter and the promoter regions of five of the seven putative target genes that were upregulated in all three SPBC1773.16cOE strains (Figure 5.3B). The former indicates that SPBC1773.16 may autoregulate its own transcription.

5.3.3 Quantitative PCR validation of putative SPBC1773.16 target genes

To confirm that the putative SPBC1773.16 target genes identified by microarray expression profiling and ChIP-chip were upregulated in response to TF overexpression, qPCR was performed. SPBC1773.16c and its five putative target genes were detected to

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be upregulated by qPCR, and the log2 fold changes measured by qPCR were comparable to the microarray expression data for all six genes (Table 5.2).

Table 5.2. Confirmation of microarray data of putative target genes induced in the SPBC1773.16cOE strain by qPCR. Table comparing the log2FC values of six putative SPBC1773.16 target genes from microarray expression profiling, to the average log2FC values determined by qPCR. A SYBR green master mix (Life Technologies, Carlsbad, CA) and a StepOne Real-Time PCR system were used to perform qPCR. The adh1+ gene was used as a reference gene. Three replicates were set up for each combination of query gene and strain. Comparisons were made using the ΔΔCt method using the manufacturer’s recommendation (Life Technologies). Gene qPCR LogFC Microarray LogFC SPBC1773.13 7.36 7.436209358 SPBC1773.16c 6.89 8.067407483 dal52+ 6.1 5.556412272 arg7+ 5.85 5.41417893 car1+ 4.78 4.42537648 SPBC1773.12 2.59 1.852141043

5.3.4 DNA motif and functional enrichment analyses of putative SPBC1773.16 target genes

To gain insight into the possible biological function and binding specificity of

SPBC1773.16, the six putative target genes identified by microarray expression profiling and ChIP-chip were analyzed further. SPBC1773.16c was included as a putative target gene since the gene product was associated with its own promoter by ChIP-chip. Gene ontology analysis with the Princeton GO Term Finder showed that there was functional enrichment for the urea cycle (P = 5.2e-3) and ornithine metabolism (P = 7.9e-3) among the six bound and upregulated genes. The promoter regions (1000 bp upstream of the start codon) of the six genes upregulated and bound by SPBC1773.16 were also searched using MEME (Bailey et al. 2009). The highest scoring DNA motif for SPBC1773.16 resembled the binding motifs of several S. cerevisiae Zn(2)-Cys(6) TF family members,

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but most closely matched the sequence TCCG(A/T/C)GGA that is recognized by Pdr8

(Figure 5.3C) (P = 7.9e-4) (MacPherson et al. 2006).

5.3.5 Deletion and ectopic expression of putative target genes do not suppress or recapitulate, respectively, the curved, elongated phenotype of the SPBC1773.16cOE strain

As overexpression of SPBC1773.16c results in elongated, curved cells, we wanted to see if overexpression or deletion of any of its putative target genes could recapitulate or suppress this phenotype, respectively. We found that the single deletion of

SPBC1773.13, dal52+, arg7+, car1+, and SPBC1773.12 were unable to suppress the

SPBC1773.16cOE phenotype. In addition, overexpression of these genes singly failed to recapitulate the SPBC1773.16cOE phenotype.

5.3.6 Growth of the SPBC1773.16cΔ strain is not inhibited on minimal medium containing arginine as the sole nitrogen source

The expression microarray and ChIP-chip data for the SPBC1773.16cOE strain suggested that this TF might activate expression of target genes involved in arginine metabolism (eg. arg7+ and car1+). To further investigate this possibility, we decided to see if the SPBC1773.16cΔ strain was sensitive to minimal medium containing arginine as the sole nitrogen source. However, we found that deletion of SPBC1773.16c did not appear to reduce growth compared to the wild-type control in this medium.

5.3.7 Overexpression of SPBC1773.16c and a subset of its putative target genes confers resistance to canavanine

Canavanine is a structural analog of arginine that can inhibit yeast growth when it is erroneously incorporated into newly-synthesized proteins (Rosenthal 1977). Since the

SPBC1773.16cOE data indicated that it might have a role in arginine metabolism,

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biosynthesis, and uptake, we suspected that the deletion or overexpression strain of this

TF might exhibit an altered response to canavanine relative to wild type. While deletion of SPBC1773.16c did not alter sensitivity to canavanine, its overexpression appeared to have increased resistance to canavanine compared to the empty vector control (Figure

5.3D).

As the SPBC1773.16cOE strain appeared have increased resistance to canavanine, we decided to check if overexpression of any of its putative target genes would also confer resistance. Overexpression of SPBC1773.13, arg7+, or car1+ increased resistance to canavanine relative to the empty vector control (Figure 5.3D). However, the single deletion of these putative target genes did not increase sensitivity to canavanine compared to wild type or abrogate the increased canavanine resistance caused by

SPBC1773.16c overexpression. Lastly, to check that it was not overexpression of a protein in general that caused the increased canavanine resistance observed, three unrelated TFOE strains were tested for increased canavanine resistance. None of these

TFOE strains exhibited increased resistance to canavanine, confirming that the increased resistance observed was specific to SPBC1773.16 and a subset of its target genes.

5.4 Identification of putative SPBC16G5.17 target genes by phenotypic activation

Another TF that was of interest was SPBC16G5.17. SPBC16G5.17 overexpression resulted in a decrease in fitness and slight cell elongation. Furthermore, although the penetrance was low, it also resulted in occasional chromosome missegregation and cut cells (Figure 5.4A). As these phenotypes could be the result of an inappropriate increase in expression of SPBC16G5.17 target genes due to aberrant TF activity, microarray

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Figure 5.4. Identification of putative SPBC16G5.17 target genes by phenotypic activation. (A) Overexpression of SPBC16G5.17 by the nmt1 promoter results in low penetrance of cut and nuclear missegregation phenotypes. (B) Heat map showing the log2 ratios for several putative target genes upregulated in the SPBC16G5.17OE strain (Column 2), and their corresponding ChIP values (Column 3). Grey boxes indicate that the TF did not bind that gene’s promoter. The first column shows the differential expression in log2 scale for these putative target genes in the SPBC16G5.17Δ strain compared to wild type. Values are shown for microarray expression profiling and ChIP-chip performed with dye swap on the nmt41-driven HA-tagged SPBC16G5.17OE strain. The color bars show the relative log2 fold change in expression and ChIP enrichment ratios between the TFOE strain and EVC/input DNA. Underlined genes have a role in arginine metabolism. Bolded genes indicate that the putative target gene is also regulated by Toe3. Grey text indicates that the putative target gene is also regulated by Toe1. Blue text indicates that the gene is a putative target gene of SPBC1773.16. expression profiling and ChIP-chip were performed on the SPBC16G5.17OE strain.

5.4.1 Expression microarray analysis of the HA-tagged SPBC16G5.17OE strain

The SPBC16G5.17OE strain and its corresponding empty vector control were grown for 20 hours in medium lacking thiamine to ensure maximal expression of

SPBC16G5.17 from the nmt41 promoter. Expression analysis revealed that 136 genes

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were differentially regulated at least two-fold in all replicates. The majority of these genes were upregulated, with only ten genes being downregulated.

5.4.2 ChIP-chip analysis of the HA-tagged SPBC16G5.17OE strain

To determine which changes in gene expression might be the direct result of

SPBC16G5.17 binding ChIP-chip was performed, comparing DNA immunoprecipitated with HA- tagged SPBC16G5.17 to input DNA. A total of 299 promoter regions were enriched (at least four-fold enrichment) in the immunoprecipitated DNA. Approximately

38% (113/299) of these promoters were associated with a gene that was differentially regulated greater than two-fold in at least one of the experiments. Additionally, while none of the downregulated genes appeared to be bound by the TF, 67 of the 136 genes upregulated by SPBC16G5.17 overexpression did appear to be bound. These 136 genes, their log2FC in the SPBC16G5.17OE strain and the corresponding ChIP-chip enrichment values can be found in Table A22.

5.4.3 DNA motif and functional enrichment analyses of putative SPAC16G5.17 target

genes

To determine the biological function and binding specificity of SPBC16G5.17, the

67 putative target genes identified by expression profiling and ChIP-chip were analyzed further. Unfortunately, gene ontology analysis using the Princeton GO Term Finder and a

DNA motif search using RankMotif++ and MEME were unsuccessful in identifying functional enrichment within these genes, or significantly overrepresented cis-regulatory sequences within their promoter regions (1000 bp upstream of the start codon), respectively (Bailey et al. 2009; Chen et al. 2007).

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A manual search revealed that six putative target genes involved in the arginine biosynthetic pathway were upregulated (Figure 5.4B). These included genes encoding an arginosuccinate lyase (Arg7), arginases (Aru1, Car1, and Car3), and agmatinases

(SPAC11D3.09 and SPAC8E4.03). It also appeared that several putative target genes of

SPBC16G5.17 has overlapped with those of other Zn2Cys6 TFs, including Toe1, Toe3, and SPBC1773.16. Four Toe1 target genes, including three uracil-regulatable genes

(urg1+, urg2+, and urg3+) and the uracil phosphoribosyltransferase gene SPAC1399.04c, appear to be upregulated and bound by SPBC16G5.17 (Figure 5.4B). Additionally, five of the seven positively-regulated putative target genes of SPBC1773.16 also appeared to be upregulated in the SPBC16G5.17OE strain (Figure 5.4B).

Finally, and perhaps most interestingly, the putative SPBC16G5.17 target genes included six of ten putative target genes of Toe3 (Vachon et al. 2013). Previously, another graduate student Justin Wood determined that toe3+ overexpression resulted in a nuclear missegregation phenotype similar to the one observed when SPBC16G5.17 is overexpressed. Although he was not able to identify any gene deletions that could abrogate nuclear missegregation in the toe3OE strain, he was able to show that overexpression of two genes could recapitulate this phenotype. One of these genes,

SPAC11D3.06, which encodes a MatE family transporter, is also upregulated in the

SPAC16G5.17OE strain.

5.4.4 Growth of the SPBC16G5.17Δ strain is not inhibited on minimal media containing arginine or uracil as the sole nitrogen source

As the expression microarray and ChIP data for the SPBC16G5.17OE strain suggested that SPBC16G5.17 might directly activate expression of several target genes

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involved in arginine metabolism (arg7+, car1+, car2+, SPBC8E4.03, SPAC11D3.09, and aru1+), we decided to see if the SPBC16G5.17Δ strain was sensitive to growth on minimal medium containing arginine as the sole nitrogen source. However, deletion of

SPBC16G5.17 did not appear to reduce growth compared to the wild-type control on this medium.

We also decided to test the SPBC16G5.17Δ strain for sensitivity to minimal medium containing uracil as the sole nitrogen source. Previously, we showed that loss of toe1+ and its target gene, SPAC1399.04c, prevented growth under these conditions. As

SPAC1399.04c appeared also to possibly be regulated by SPBC16G5.17, we thought that deletion of SPBC16G5.17 might also result in sensitivity to uracil as the sole nitrogen source. However, growth of the SPBC16G5.17Δ strain also did not appear to be affected compared to the wild-type control under these conditions.

5.5 Identification of SPAC25B8.11 putative target genes by phenotypic activation

Another TF that was of interest was SPAC25B8.11. SPAC25B8.11 overexpression resulted in a moderate decrease in fitness and increase in cell length (Figure 5.5A). As these phenotypes could be the result of an inappropriate increase in expression of the putative target genes due to aberrant TF activity, microarray expression profiling and

ChIP-chip were performed on the SPAC25B8.11OE strain.

5.5.1 Microarrays expression analysis of the HA-tagged SPAC25B8.11OE strain

The SPAC25B8.11OE strain and its corresponding empty vector control were grown for 20 hours in medium lacking thiamine to ensure maximal expression of

SPAC25B8.11 from the nmt41 promoter. Overexpression of SPAC25B8.11 resulted in

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Figure 5.5. Identification of putative SPAC25B8.11 target genes by phenotypic activation. (A) Overexpression of SPAC25B8.11 by the nmt1 promoter produces slightly elongated cells. (B) Heat map showing the log2 ratios for several putative target genes upregulated in the SPAC25B8.11OE strain (Column 2), and their corresponding ChIP values (Column 3). Grey boxes indicate that the TF did not bind that gene’s promoter. The first column shows the differential expression in log2 scale for these putative target genes in the SPAC25B8.11Δ strain versus wild type. Values are shown for microarray expression profiling and ChIP-chip performed with dye swap on the nmt41-driven HA-tagged SPAC25B8.11 strain. The color bars show the relative log2 fold change in expression and ChIP enrichment ratios between the TFOE strain and EVC/input DNA. relatively few changes in gene expression, with only 20 genes showing at least a two-fold increase in expression relative to the empty vector control (Table A23).

5.5.2 ChIP-chip analysis of the HA-tagged SPAC25B8.11OE strain

To determine which changes in gene expression might be the direct result of

SPAC25B8.11 binding, ChIP-chip was run comparing DNA immunoprecipitated with

HA-tagged SPAC25B8.11 to input DNA. In total, 187 promoter regions were enriched

(at least two-fold enrichment ratio) in the immunoprecipitated DNA relative to input

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DNA. The promoter regions of 65% (12/20) of the genes that were upregulated by

SPAC25B8.11 overexpression were also bound in the ChIP-chip experiment (Figure

5.5B). SPAC25B8.11 also appeared to bind to its own promoter suggesting the possibility of transcriptional autoregulation.

5.5.3 Further analysis of putative SPAC25B8.11 target genes

To gain insight into the possible biological function and binding specificity of

SPAC25B8.11, 13 putative target genes that were upregulated in the overexpression strain, and/or bound in the ChIP, were analyzed further. SPAC25B8.11 was also included as a putative target gene as binding was detected on its own promoter. Gene ontology analysis using Princeton GO Term Finder was performed on the putative target genes, and enrichment for protein folding (P = 2.5e-4) and gamma-aminobutyric acid (GABA) metabolism (P = 4.0e-3) was found. Although SPAC25B8.11 is completely uncharacterized in S. pombe, its orthologous gene in S. cerevisiae encodes Dal81, a Zn2-

Cys6 TF that cooperates with multiple other TFs (i.e. Uga3, Dal82, and Stp1/Stp2) to activate transcription of genes involved in nitrogen source utilization (Cherry et al. 2012).

Eleven of the 20 genes upregulated in the SPAC25B8.11OE strain had S. cerevisiae orthologs (Wood et al. 2012). Interestingly, three of these putative target genes (uga1+,

SPAC1002.12c, and SPCC584.13) corresponded to the S. cerevisiae orthologs UGA1,

UGA2, and UGA4, respectively, which require Uga3 and Dal81 to be expressed in response to GABA (Cardillo et al. 2012).

RankMotif++ and MEME were also used to search for possible SPAC25B8.11 cis- regulatory sequences within the promoter regions (1000 bp upstream of the start codon)

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of the 13 putative target genes (Chen et al. 2007; Bailey et al. 2009). Unfortunately, no statistically significant overrepresented cis-regulatory sequences were detected in the promoters of the putative target genes.

5.5.4 Growth of the SPAC25B8.11Δ strain is not inhibited on minimal medium containing allantoin as the sole nitrogen source

Deletion of S. cerevisiae DAL81 has previously been found to grow poorly on medium containing allantoin as the sole nitrogen source (Turoscy & Cooper 1982). Since

SPAC25B8.11 appears to be the S. pombe ortholog of DAL81, we tested the

SPAC25B8.11Δ strain and a wild-type control for growth sensitivity on minimal medium containing allantoin as the sole nitrogen source. However, unlike deletion of DAL81 in S. cerevisiae, the SPAC25B8.11Δ strain did not appear to affect yeast growth on this medium.

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Chapter Six: Discussion

Parts of this chapter have been published in Vachon et al. (2013) Functional characterization of fission yeast transcription factors by overexpression analysis. Genetics

194: 873-884.

A transcriptional-regulatory network (TRN) consists of interactions between TFs and their target genes in an organism. An understanding of these networks is essential to fully comprehend the existence of organisms, because they give rise to the gene expression programs underlying all cellular functions. As TRNs are responsible for proper transcriptional regulation of all genes in the genome, any disruptions can have dire consequences to the organism. Therefore, although mapping out the interactions in a

TRN is an arduous task, it is a necessary step in understanding how an organism normally develops, and how disease states can arise.

Mapping the TRN of even a simple eukaryote is challenging. Not only are there an overwhelming number of TFs, genes, and potential regulatory interactions, but identifying the biological role and target genes of even a single TF can be difficult. Many

TFs are only activated by specific environmental conditions, while others can regulate different sets of target genes depending on the external stimuli. Some TFs have multiple and overlapping target genes and functions, thereby complicating the mapping of such complex networks. Furthermore, TFs often bind chromosomal regions without altering transcription, and changes in gene expression caused by their disruption or activation are often the result of secondary effects. These problems have inspired the development of a variety of in vivo, in vitro, and in silico genome-wide approaches. However, while

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significant progress has been made, the TRNs of most organisms remain largely incomplete. For example, even one of the most well-studied and mapped networks, the

TRN of S. cerevisiae, is still incomplete, with the function of almost half its TFs remaining obscure (Hughes & de Boer 2013).

The TRN of the fission yeast S. pombe consists of approximately 100 TFs regulating around 5000 genes (Wood et al. 2012). Mapping the network of this organism is appealing, not only because of its small genome, but also because of the multitude of experimental approaches that have been developed to do so. However, despite these advantages, the biological roles and target genes of many S. pombe TFs are unknown. In fact, almost one third of its TFs have no known biological role, and many of the remaining two thirds have very few identified target genes (Wood et al. 2012). Thus, mapping of the S. pombe TRN remains largely incomplete. The goal of my study was to expand the current mapping of the S. pombe TRN. To achieve this, TFΔ and TFOE strains for over 80% of fission yeast TFs were constructed and functionally characterized.

Several TFOE and TFΔ strains were subjected to microarray expression profiling and

ChIP-chip to identify the potential target genes and biological roles of these TFs.

6.1 Characterization of S. pombe TFΔ strains

Here, a set of TFΔ strains was constructed, examined for abnormal growth or cell morphology, and screened for hypersensitivity to a variety of drug compounds.

Microarray expression profiling of wild type exposed to four drug compounds was also performed to determine if increased expression of a TF gene in response to a drug compound correlated well with hypersensitivity of the corresponding TFΔ strain to that

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same compound. A proof of principle four-way microarray expression profiling experiment was performed to validate its potential use identifying TF target genes and function. Finally, several target genes of the uncharacterized Zn2-Cys6 TF

SPAC1399.05c/toe1+ were identified using this approach in conjunction with analysis of the toe1OE strain.

6.1.1 Deletion of most TF genes has minimal effect on cell phenotype under optimal conditions

Examination of the 82 TFΔ strains by light microscopy showed that deletion of most S. pombe TF genes did not result in changes in cell length, with less than 15% being more than 10% shorter or longer than the wild-type control in rich media (Figure 3.2).

Deletion of many cell cycle genes results in a shortened or elongated phenotype, indicating accelerated or delayed progression, respectively, through the cell cycle (Hayles et al. 2013). The altered length of these TFΔ strains could indicate that the deleted TF regulates target genes required for normal cell cycle progression. In fact, three of these

TFs (Res1, Atf1, and Pcr1) have annotated roles in cell cycle progression (Wood et al.

2012). Furthermore, the TFs for half of these deletion strains (Pcr1, Phx1, Mca1, Atf1,

Php5, and Ste11) are periodically expressed during the cell cycle (Bushel et al. 2009) suggesting that they may be required to regulate expression of the approximately 8-15%

S. pombe genes that are also periodically expressed (Oliva et al. 2005; Peng et al. 2005;

Rustici et al. 2004). These TFs could also regulate expression of genes that result in cell elongation and shortening (513 and 25, respectively) when deleted (Hayles et al. 2013).

In addition to measuring each TFΔ strain’s cell length, the doubling time for each strain was also calculated. Deletion of few TF genes appeared to impact the cell’s growth

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rate under standard laboratory conditions, as less than 10% of the 82 TFΔ strains tested had an increased doubling time compared to wild type (Figure 3.1). An increased doubling time could indicate that these TFs regulate target genes and processes required for optimal growth in rich media. Minor increases in doubling time, such as those observed for deletion of SPCC417.09c or zip1+, might indicate that the target genes and processes being regulated do not have major roles in promoting cell growth or that deletion of the TF does not significantly impact expression of its target genes. In contrast, a substantial increase in doubling time, such as the 85% increase caused by deletion of php2+, might suggest that a core cellular process, such as macromolecule metabolism or cellular biosynthesis is being affected. For example, in S. cerevisiae, significant growth defects result from deletion of genes involved in mitochondrial function and respiration under optimal growth conditions (Giaever et al. 2002). Interestingly, Php2 is a direct regulator of the cyc1+ gene, encoding for cytochrome c, which is required for optimal growth and normal cellular respiration in S. pombe, potentially explaining the significant increase in doubling time caused by php2+ deletion (Kim et al. 2010; Takuma et al.

2016).

While a subset of the 82 TFΔ strains had reduced growth or altered cell length, the majority did not. The absence of abnormal phenotypes for most of these strains was not unexpected. The deletion of most non-essential genes in budding and fission yeasts does not impact cell growth or morphology in rich media (Giaever et al. 2002; Kim et al. 2010;

Hayles et al. 2013). For example, a screen of S. cerevisiae homozygous diploid deletions in rich medium showed that only 15% of the viable strains grew more slowly than wild type. Furthermore, only 15% of these deletions had abnormal cellular morphology, with

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~2.7% resulting in elongated cells compared to wild type (Giaever et al. 2002). A similar screen of 3576 non-essential haploid deletions in S. pombe also determined that ~15% of the deletion strains had an abnormal cellular morphology, with 5% (172/357) of these deletions resulting in cell elongation (Hayles et al. 2013).

The high percentage of TFΔ strains with a wild-type phenotype likely results from one of two factors. Either TFs are inactive and unnecessary for growth in rich medium, or

TFs are functionally redundant with one another. While both factors likely play a role, functional redundancy is not likely to be the primary reason for the high degree of TF gene dispensability, as synthetic genetic array (SGA) analysis examining over a quarter of all TF double mutants estimates that only 3-6% negatively interact (Ryan et al. 2012;

Chua 2013). Instead, it is more likely that many TFs are not required, and thus not active, in rich medium, and that conditions that induce TF activity need to be discovered.

6.1.2 Most TFΔ strains with wild-type phenotypes also have minimal changes in gene expression

Microarray expression profiling comparing gene expression of 15 S. pombe TFΔ strains to a wild-type control grown in rich medium showed that deletion of most TF genes did not result in significant changes in gene expression compared to wild type

(Figure 3.3). This result was not unexpected, as similar analysis of budding yeast TFΔ strains grown in rich medium by microarray expression profiling has shown that very few genes are differentially expressed in TFΔ strains compared to wild type (Chua et al.

2006). This lack of change in gene expression suggests that these TFs are inactive in rich media, or that other redundant TFs can compensate for their absence by regulating their target genes. Alternatively, deletion of these TFs may only result in minor changes in

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gene expression that cannot be readily identified by microarray expression profiling. In either case, microarray expression profiling of TFΔ strains compared to wild type in rich medium does not appear to be a high-yield approach to identifying TF target genes and function.

6.1.3 Hypersensitive growth is not a good indication of TF gene expression in response to drug compounds

Approximately 80% of the TFΔ strains were found to be sensitive to at least one drug (Figure 3.4). However, a comparison of the TF genes that display hypersensitive growth when deleted and transcriptional induction in response to various drug compounds, showed that the overlap between TF gene expression and essentiality in response to drug compounds was poor. Only 12.5% of the TF genes that were hypersensitive to the four tested drug compounds when deleted were also upregulated in microarray expression profiling of drug-treated wild type cells. In addition, over 80% of

TF genes upregulated in the microarray experiments of drug-treated wild type cells were not hypersensitive to the same drug compound when deleted. This poor correlation, while somewhat perplexing, was not entirely surprising, as it has also been observed in S. cerevisiae (Giaever et al. 2002). There are several possibilities for why this occurs. Some

TF genes may be required, but not differentially expressed, because they are regulated post-transcriptionally (Giaever et al. 2002). Alternatively, deletion of some TF genes may also result in minor stress that can be tolerated in rich media, but is exacerbated by the added stress put on the cell by exposure to the drug compound. On the other hand, genes may be upregulated but dispensable, because only a small subset of the genes that are

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differentially regulated are required to respond to the drug compound (Giaever et al.

2002).

6.1.4 Four-way microarray expression profiling can be an effective tool to identify TF target genes

The SREBP homolog Sre1 is a known activator of sterol biosynthetic genes in yeast, with several known target genes, and a reasonably well conserved consensus binding sequence (Hughes et al. 2005; Todd et al. 2006). Deletion of sre1+ results in sensitivity to the antifungal clotrimazole which inhibits ergosterol biosynthesis. This suggests that Sre1 activity is required when wild-type cells are exposed to this drug compound. A complication in microarray expression experiments involving drug treatments is the occurrence of large secondary transcriptional responses that can obscure the identification of target genes. To overcome this obstacle, a four-way microarray expression profiling scheme was used that involved four experiments: (1) untreated wild type versus untreated sre1Δ cells; (2) untreated wild type versus treated wild type; (3) untreated sre1Δ versus treated sre1Δ cells; and (4) treated wild type versus treated sre1Δ cells. Over 200 genes were differentially regulated greater than two-fold in at least one of the four experiments. However, only 29 genes showed the expected profile of a target gene in all four experiments after clustering. These were slight reduction/no differential expression in experiments #1 and #3 because sre1+ is either not active or not present, an increase in expression in clotrimazole-treated wild type relative to untreated wild type from experiment #2 and a decrease in expression in clotrimazole-treated sre1Δ cells relative to treated wild type from experiment #4. Twenty-three of these were previously identified Sre1 target genes (Sehgal et al. 2007; Todd et al. 2006), and four of the

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remaining six genes were previously unknown, but likely Sre1 targets, due to the presence of a Sre1-binding motif within their promoter regions. These results confirmed that the four-way microarray expression profiling scheme is a valid approach to identify

TF target genes. It can be used to help distinguish direct TF target genes from stress response genes and secondary transcriptional effects in the characterization of TFΔ strains in the presence of drug compounds. Furthermore, this approach could be very useful to characterize TFs that are inactive in rich media, but not easily activated by simply overexpression.

6.1.5 Toe1 regulates genes implicated in the pyrimidine salvage pathway

Using data from the four-way microarray expression profiling experiments and the genome-wide expression and binding profiles for the toe1OE strain, we discovered that the previously-uncharacterized TF Toe1 activates genes implicated in the pyrimidine- salvage pathway. The putative target genes urg1+, urg3+, and urg2+/SPAC1399.04c appear to be homologous to URC1, URC4, and URC6, respectively, in S. kluyveri, while toe1+ is probably the homolog of URC2 (Andersen et al. 2008). The URC genes function in the catabolism of uracil in S. kluyveri (Andersen et al. 2008). Similar to the URC genes, deletion of toe1+ and SPAC1399.04c prevented growth on medium containing uracil as the sole nitrogen source (Figure 3.10B). Moreover, several other genes involved in the pyrimidine-salvage pathway, such as SPBC1683.06c and SPCC162.11c, which encode a uridine ribohydrolase and uridine kinase, respectively, were induced by toe1+ overexpression (Figure 3.9A).

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We also detected chlorpromazine sensitivity in the toe1Δ strain, suggesting that

Toe1 activity and activation of its target genes may be required for the proper cellular response to this drug (Figure 3.7A). Chlorpromazine has been reported to possibly inhibit uridine kinase activity in murine sarcoma cells (Tseng et al. 1986). If this is also the case in S. pombe, then inhibition of uridine kinase by chlorpromazine treatment could compromise overall pyrimidine-salvage capacity, thereby triggering a compensatory response by activating other genes of similar function. Indeed, the uracil catabolic genes were induced in chlorpromazine-treated wild type but not in the chlorpromazine-treated toe1Δ strain (Figure 3.7B). Furthermore, we discovered that toe1+ overexpression causes a G1 delay (Figure 3.8B). It may be that induction of pyrimidine-salvage genes could represent a signal for insufficient levels of nucleotides, thus preventing cells from undergoing a round of DNA replication.

6.2 Transcriptional regulation of flocculation in S. pombe

In addition to screening our TFΔ array for changes in cell length, growth, and hypersensitivity to drug compounds, we wanted to determine which TFΔ strains had altered flocculent phenotypes. An understanding of yeast flocculation is of particular interest because of its potential applications to industry and healthcare. However, while some of the major transcriptional regulators of flocculation in S. pombe have been identified over the last five years, an understanding of the TRN controlling this behaviour remains incomplete. To identify novel activators and repressors of flocculation, the TFΔ array was screened in FIM and YES medium to identify strains with reduced and constitutive flocculent phenotypes, respectively. Microarray expression profiling of TFΔ

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and TFOE strains identified in these screens was carried out to identify TF target genes involved in flocculation. Additionally, microarray expression profiling was used to identify changes in wild-type gene expression triggered by transfer to FIM.

6.2.1 Transfer of wild-type cells to FIM results in substantial changes in gene expression

Microarray expression profiling comparing global gene expression of the wild- type strain grown in FIM compared to YES medium, showed that transfer to FIM resulted in a number of upregulated genes. Somewhat surprisingly, mbx2+ and most of the pfl+ genes were not upregulated, suggesting that induction of flocculation in FIM may not be the result of increased expression of flocculin genes. However, as we only captured the initial changes in gene expression at 30 minutes and two hours, it is possible that expression of these genes could increase at other time points after shift to FIM.

In S. cerevisiae, numerous environmental factors affect flocculation (Verstrepen

& Klis 2006; Soares 2011). Furthermore, several signalling pathways are involved in regulating FLO11-mediated adhesion, many of which are also likely to be involved in regulating flocculation (Verstrepen & Klis 2006). It is likely that multiple signalling pathways are also involved in regulation of S. pombe flocculation, and therefore, it was unsurprising that transfer to FIM resulted in upregulation of genes encoding the components of various signalling pathways, including the cAMP, MAPK, and TOR signalling pathways. These signalling cascades can activate a variety of TFs, some of which might directly regulate flocculin or cell wall biosynthesis genes, or other transcriptional activators of these genes.

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There were also numerous genes downregulated by transfer to FIM. Interestingly, these downregulated genes were highly enriched for genes involved in ribosome biogenesis. The single deletion of several ribosome genes, resulting in a reduction in ribosome levels, has been shown to trigger flocculation in S. pombe (Li et al. 2013).

Therefore, it is possible that the downregulation of ribosomal genes and ribosome levels also contributes to the induction of wild-type flocculation observed in FIM.

6.2.2 A significant number of TFs appear to impact S. pombe flocculation

Given the extensive changes in gene expression upon transfer of wild type to FIM and multiple signaling pathways that likely impinge on the regulation of flocculation, it is perhaps not surprising that a substantial proportion of S. pombe TFs appear to have a role in this process. Screening the TFΔ array in FIM and YES medium revealed that deletion of ~20% of S. pombe TF genes resulted in an aberrant flocculent phenotype, with approximately 10% each activating and repressing flocculation. Deletion of some of these

TF genes likely results in a flocculent phenotype indirectly. For example, while loss of yox1+ results in a mild flocculent phenotype, the pfl+ flocculin genes are not upregulated in the yox1Δ strain, and Yox1 does not appear to bind to their promoter regions

(Aligianni et al. 2009; Kwon et al. 2012). On the other hand, at least five of these TFs

(Mbx2, Cbf12, Gsf1, Prz1, and Foe1) appear to bind and regulate expression of some pfl+ genes directly (Kwon et al. 2012; Chatfield-Reed et al. 2016). As flocculation is a complex process that can be triggered by many different stimuli, specific environmental changes and signalling events might activate particular subsets of TFs, and/or pfl+ genes to generate the favorable cellular surfaces required for each environment. Not only would

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this help explain why disruption of so many yeast TFs impacts flocculation, but also why

S. pombe has multiple pfl+ genes, despite gsf2+ encoding the dominant flocculin (Kwon et al. 2012). In some other yeasts at least, this appears to be the case. For example, in C. glabrata, expression of the EPA2 adhesin encoding gene is induced by the TFs Yap1 and

Skn7 specifically in response to oxidative stress (Juarez-Cepeda et al. 2015). On the other hand, expression of EPA3 and EPA22 is induced by osmotic stress and glucose starvation, in part by the TF Msn2 (Roetzer et al. 2008). Interestingly, in S. cerevisiae, different FLO genes have also recently been shown to promote specific interactions to particular yeast species (Rossouw et al. 2015). This suggests that the complex transcriptional network and multiple pfl+ genes found in S. pombe may also have developed as a means to regulate flocculation in response to different external conditions and perhaps adhesion to other yeast species.

6.2.3 Transcriptional regulation of S. pombe flocculation by Foe1

The previously-uncharacterized Zn2-Cys6 TF Foe1 appears to have a role in the transcriptional regulation of flocculation. Overexpression of foe1+ triggered flocculation, while its deletion abolished flocculation under inducing conditions (Figure 4.1).

Microarray expression profiling also revealed that foe1+ expression was significantly increased (19.7-fold) in the wild-type strain grown in FIM, suggesting that Foe1 activity is induced by conditions that trigger flocculation (Figure 4.3).

Microarray expression profiling and ChIP-chip of the foe1OE strains revealed that five genes encoding flocculins were upregulated at least two-fold by foe1+ overexpression, and that two of these genes, gsf2+ and pfl5+, were bound by Foe1, and

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thus are likely its direct targets (Figure 4.4). Overexpression of each of these flocculin genes has previously been shown to be sufficient to trigger flocculation, making it likely that Foe1-mediated upregulation of these genes contributes to the flocculent phenotype of the foe1OE strain (Kwon et al. 2012). Consistent with this proposal, flocculation of the foe1OE strain was abrogated by deletion of gsf2+ (Figure 4.5). However, none of these flocculin genes were upregulated in the wild-type strain grown in FIM or downregulated in the foe1Δ strain, suggesting that upregulation of these genes may not be the primary mechanism by which flocculation is triggered in FIM. Alternatively, we may not have captured the gene expression changes at an appropriate time, because while foe1+ levels are increased at 30 min in FIM, expression of its target genes may not be increased at this time specifically. Given the low number of genes with a statistically significant change in expression (P < 1.0e-3) in the foe1Δ strain, the latter seems likely.

Several other putative Foe1 target genes encoding cell wall organization and biogenesis proteins were not only bound by Foe1 and upregulated at least two-fold in the foe1OE strain (Figure 4.4), but also upregulated from 5.5- to 64.4-fold in the wild-type strain grown in FIM. Two of these genes, encoding a glycosyltransferase (gas2+) and a glycoside hydrolase (SPAC4H3.03c), have previously been shown to trigger flocculation when overexpressed (Kwon et al. 2012). While three other putative Foe1 target genes, encoding two glycoside hydrolases (bgl2+ and agl1+) and a chitin synthase regulatory factor (cfh2+), have not been overexpressed to determine if they trigger flocculation, it is certainly a possibility. The mechanism by which these cell wall biosynthesis/remodelling proteins might induce flocculation in S. pombe is unclear. However, studies in S. cerevisiae have shown that flocculins are synthesized and inserted into the cell wall prior

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to flocculation onset (Stratford & Carter 1993), leading us to speculate that they may trigger flocculation by altering the accessibility of flocculins already localized to the cell wall through restructuring of the β-glucan layer during cell wall remodelling (Kwon et al.

2012). This hypothesis is supported by work done in S. cerevisiae, which has shown that disruption of PKC1 (which negatively regulates genes encoding cell wall remodelling proteins, such as BGL2), results in alteration of the cell wall structure and flocculation

(Shimizu et al. 1994; Zhang et al. 1999). Interestingly, in S. pombe, overexpression of adn2+ and adn3+, and deletion of dual specificity protein kinase lkh1+ triggers flocculation (Kim et al. 2001; Kwon et al. 2012). While deletion of gsf2+ abrogates adn2OE, adn3OE, and lkh1Δ flocculation (Matsuzawa et al. 2011; Matsuzawa,

Yoritsune, et al. 2012; Kwon et al. 2012), cell wall remodelling, and not flocculin genes or proteins, are upregulated (Cho et al. 2010; Kwon et al. 2012), suggesting that flocculation of these strains is caused by altered cell wall structure. Overall, these studies, coupled with our expression and binding data, suggest that Foe1 could also trigger flocculation by regulating genes encoding cell wall biosynthesis/remodelling proteins.

Our expression and binding data also suggest that Foe1 might regulate its own expression and the expression of three other TF genes (Figure 4.4). First, Foe1 appears to bind its own promoter, suggesting that like Mbx2 and Cbf12, it undergoes transcriptional autoregulation (Kwon et al. 2012). Foe1 also appears to bind and regulate expression of three other TF genes, two of which encode TFs that trigger flocculation (cbf12+ and mbx2+), and one that encodes a TF that represses flocculation (gsf1+). This suggests that

Foe1 may regulate the expression of gsf2+ and other pfl+ genes via Coherent type 1 feedforward loops and Incoherent type 1 feedforward loops. Interestingly, the gene

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expression and binding data for Cbf12 and Gsf1 indicate that these TFs may also regulate foe1+ expression, suggesting that foe1+ forms multi-component loops with Cbf12 and

Gsf1. Importantly, the foe1OE flocculent phenotype is not abrogated by deletion of either mbx2+ or cbf12+, and therefore it is very unlikely that Foe1 regulates its target pfl+ genes simply by regulating expression of these two TF genes (Figure 4.5).

6.2.4 Other novel transcriptional activators of flocculation

We also identified three other TFs (Prt1, Prr1, and SPBC530.08) that activate flocculation. Like foe1+, prr1+, prt1+, and SPBC530.08 were all found to be necessary to induce flocculation in FIM and sufficient to trigger flocculation when overexpressed

(Figure 4.1). Microarray expression profiling also indicated that like the foe1OE strain, flocculation of all three TFOE strains could potentially be caused by increased expression of genes encoding flocculins (e.g. pfl2-9+ and gsf2+) and/or proteins involved in cell wall remodelling and biogenesis (e.g. gas2+, bgl2+, agn2+, etc.) (Figure 4.6). Unlike foe1+ however, the flocculent phenotype of all three TFOE strains was abrogated by deletion of cbf12+ and mbx2+ in addition to deletion of gsf2+, indicating that they are less likely to directly regulate pfl+ gene expression, and more likely to act upstream of and regulate expression of other TFs (e.g. Mbx2 and Cbf12) that directly regulate flocculin gene expression (Figure 4.8). It is also possible that they form heterodimers with Mbx2 and/or

Cbf12 and help recruit them to pfl+ gene promoters under inducing conditions, such as the Prr1-mediated recruitment of Pap1 to some of its target genes in the oxidative stress response (Calvo et al. 2012). Lastly, it is possible that they do not regulate pfl+ or mbx2+/cbf12+ gene expression directly, but instead regulate the expression of target genes

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involved in other processes that impact flocculation. Unfortunately, in the absence of binding data, we can only speculate, and therefore to better understand the role these TFs have in flocculation, it will be important to HA-tag each TF, and perform ChIP-chip to determine the genomic regions they bind directly.

Interestingly, while the prt1OE, prr1OE, and SPBC530.08OE strains all required mbx2+ and cbf12+ expression to flocculate, none of the three required the expression of either of the other two TF genes to flocculate, suggesting that they are all able to activate flocculation independently of one another (Table 4.3). As has been mentioned previously, flocculation is a complex process that can be triggered by many different stimuli, and likely involves multiple signalling events. It is therefore tempting to speculate that each of these TFs is regulated by different environmental changes or signalling pathways, and that collectively they work together to ensure that the correct pfl+ genes are expressed to generate the appropriate cellular surfaces required for each environment. While additional experiments would be necessary to confirm whether or not this is the case, and to determine how this occurs, the microarray expression profiles of the TFOE and TFΔ strains, and the known TF functions, can be used to speculate about what might be occurring if this is the case.

6.2.4.1 Transcriptional regulation of flocculation by Prt1

As previously mentioned, Pdr1-related transcription factor Prt1 could directly regulate the expression of the pfl+ genes, or of TFs that regulate pfl+ gene expression. For example, in C. glabrata, Pdr1, a TF with sequence similarity and a similar function to

Prt1, appears to regulate expression of the adhesion gene FLO1 (Vermitsky et al. 2006).

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This suggests that there is a precedent for a TF like Prt1 to regulate flocculin gene expression directly.

However, Prt1 is also known to activate the drug-induced expression of drug efflux pumps. Deletion of prt1+ results in enhanced sensitivity to cytotoxic drugs, and decreased expression of the genes encoding transporters Bfr1, Mfs1, and Mfs3

(Kawashima et al. 2012). Similarly, these genes (bfr1+, mfs1+, and mfs3+) were found to be downregulated two to seven-fold in our microarray expression profile of the prt1Δ strain in FIM. Moreover, these three genes were all upregulated at least four-fold in wild type grown in FIM, and bfr1+ and mfs1+ were upregulated more than two-fold in the prt1OE strain. Several other genes encoding S. pombe specific 5Tm protein family members, potentially encoding other transporters, were also downregulated in the prt1Δ strain, and upregulated in the wild type grown in FIM and the prt1OE strain. It is therefore possible that Prt1 regulates the expression of other target genes that generate the flocculent and non-flocculent phenotypes caused by prt1+ overexpression and deletion, respectively.

To our knowledge, no one has previously shown a link between multidrug resistance and flocculation. However, while multidrug resistant transporters were initially thought to directly regulate drug efflux, many are now believed to have physiological roles not related to chemoprotection (dos Santos et al. 2014). For example, S. cerevisiae

MFS family transporters Dtr1, Qdr1, and Qdr3 have been shown to be required for transport of bisformyl dityrosine, an essential component of the spore wall (Lin et al.

2013). In C. albicans, MFS transporters have been shown to have a role in biofilm development, as disruption of the QDR genes results in changes in the architecture and

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thickness of the biofilms formed (Shah et al. 2014). These examples suggest that it is possible that some S. pombe transporters might also have functions unrelated to chemoprotection that could impact the cell surface, and thus affect flocculation.

However, single deletions of mfs1+ and bfr1+ did not result in reduced flocculation in

FIM. This does not necessary mean that these transporter genes do not have a role in flocculation as there are multiple members in the gene family with substantial sequence similarity, and likely redundancy. Therefore, even if these genes do have a role in transporting components affecting the cell wall, it may be necessary to construct multiple deletion strains to see any effect on flocculation in FIM.

6.2.4.2 Transcriptional regulation of flocculation by Prr1

Prr1 is an HSF-type TF that is implicated in multiple processes, including the oxidative stress response and sexual development. It is one of two effectors of the S. pombe phosphorelay transduction system, and appears to participate in a complex network regulating the response to oxidative stress in cooperation with Atf1 and Pap1.

Like Prt1, it is possible that Prr1 directly regulates the expression of genes encoding flocculin proteins or the TFs that regulate them (Figure 4.6C). While the S. cerevisiae

Prr1 homolog Skn7 has not been found to regulate any of the FLO genes, C. glabrata

Skn7 has been shown to induce expression of adhesin EPA2 in response to oxidative stress, suggesting that it is possible that Prr1 does the same for some of the pfl+ genes in

S. pombe (Juarez-Cepeda et al. 2015).

It is also possible that Prr1 induces flocculation not by increasing the expression of flocculin genes, but by altering the expression of genes encoding proteins involved in

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cell wall biosynthesis. In fact, in S. cerevisiae, Skn7 is known to regulate several genes involved in cell wall biosynthesis, including OCH1, which encodes an α-1,6- mannosyltransferase that initiates elongation of N-linked oligosaccharides of glycoproteins (Cui et al. 2002). Additionally, overexpression of Skn7 has been shown to suppress the cell wall defect caused by reduced β -1, 6 - glucan synthesis in the KRE6Δ strain (Brown et al. 1993), and deletion of SKN7 prevents biofilm formation (Sarode et al.

2014). These observations in other yeasts, coupled with the number of genes involved in cell wall biosynthesis and remodelling that are upregulated by prr1+ overexpression, suggest that Prr1 could induce flocculation by regulating cell wall biosynthesis and remodelling genes rather than regulating flocculin genes directly.

6.2.4.3 Transcriptional regulation of flocculation by SPBC530.08

Similar to Prt1 and Prr1, the uncharacterized Zn2Cys6 TF SPBC530.08 is an activator of flocculation that could function by directly regulating expression of pfl+ genes or TF genes that regulate pfl+ gene expression. Like these two TFs however, it is also possible that SPBC530.08 does not regulate any of these genes directly. Unlike Prt1 and Prr1, the function and target genes of SPBC530.08 have not been characterized at all, and it does not have a known ortholog in S. cerevisiae or any other commonly-studied yeasts that can be used to help deduce its potential function. Microarray expression profiling of the SPBC530.08OE strain showed that genes upregulated by the TF’s overexpression were enriched for protein folding, including the genes: ero11+, wos2+, hsp16+, psi1+, mdj1+, hsp104+, pss1+, aha1+, SPCC338.06+, SPBC4F6.17+, ssa1+, wis2+, and sti1+. Interestingly, ten of these genes were also upregulated at least two-fold

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in the wild-type strain grown in FIM compared to YES medium, and three encoding heat shock proteins (Hsp16, Ssa1, and Hsp104) were amongst the top 20 upregulated genes, indicating that they are likely important to respond to the change in medium, and could be regulated by SPBC530.08.

As their name suggests, heat shock protein (Hsp) gene expression is induced by increases in temperature. However, in addition to their role in heat tolerance, many of these proteins are expressed in response to a variety of other stressors. They often function as molecular chaperones, stabilizing newly-folded proteins, and helping to refold old proteins damaged by stress (Parsell & Lindquist 1993). In many yeasts, including C. albicans and S. cerevisiae, some Hsp proteins are found not only in the cytoplasm, but in the cell wall and at the cell surface (López-Ribot & Chaffin 1996; Chaffin et al. 1998).

Interestingly, in C. albicans, Hsp70 proteins Ssa1 and Ssa2 mediate adhesion and invasion of epithelial and endothelial cells (Sun et al. 2010). In S. pombe, these proteins could do the same, or alternatively, Hsp and other chaperones might be required to stabilize flocculin proteins localized to the cell wall, allowing them to achieve the correct conformation to induce flocculation.

6.2.5 Scr1 and SPBC56F2.05 negatively regulation flocculation

In addition to identifying several TFs that activate flocculation, we also discovered two TFs, Scr1 and SPBC56F2.05, with novel roles in the repression of flocculation.

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6.2.5.1 Transcriptional repression of flocculation by Scr1

Loss of scr1+ triggers mild flocculation under non-inducing conditions (Figure

4.8). In the presence of high glucose, the C2H2 Zn-Finger TF Scr1 represses transcription of its target genes, including gld1+, ght5+, and inv1+, likely in part through its interaction with its corepressors Tup11 and Tup12 (Tanaka et al. 1998; Hirota et al. 2006;

Matsuzawa et al. 2010; Saitoh et al. 2015). In low glucose, hyperphosphorylated Scr1 is transported to the cytoplasm, resulting in the derepression of its target genes (Matsuzawa,

Fujita, et al. 2012; Saitoh et al. 2015). As expected, microarray expression profiling of the scr1Δ strain indicated that Scr1’s known target genes, inv1+, ght5+, and gld1+ were upregulated 52 to 461-fold (Figure 4.9A). Deletion of scr1+ also resulted in 2.9 to 45.9- fold upregulation of genes encoding glucose transporters upregulated in low glucose

(Ght1, 3, 4, and 6) and proteins involved in galactose metabolism (Gal1, Gal7, Gal10, and SPBPB2B2.11) (Saitoh et al. 2015).

Interestingly, genes encoding flocculins (Gsf2, Fta5, and Pfl9) and transcriptional activators of flocculation (Mbx2, Cbf12, and Foe1), were also upregulated in the scr1Δ

(Figure 4.11A). Deletion of gsf2+, mbx2+, and cbf12+, abrogated flocculation of the scr1Δ strain, indicating that its flocculation is gsf2+, mbx2+, and cbf12+ dependent (Figure

4.10). In the absence of Scr1 binding data, we cannot determine if any of these genes are direct Scr1 targets. However, while a putative Scr1 consensus sequence resembling the high-confidence binding motif of S. cerevisiae Scr1 homologs Mig1/2/3 was found in genes upregulated in the scr1Δ strain, it was not found in any of the pfl+ promoters (1000 bp upstream of the start codon) or in the promoter regions of either mbx2+ or cbf12+, suggesting that Scr1 might not directly repress their expression. Alternatively, the Scr1

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consensus sequence could be located more than 1000 bp upstream of the start codons of these genes, or Scr1 might recognize a degenerate cis-regulatory sequence found within their promoters.

While it is unclear exactly why deletion of scr1+ triggers flocculation, both indirect and direct mechanisms are possible. However, in S. cerevisiae, the TFs Mig1 and

Nrg1 repress FLO11 expression in high glucose. In low glucose, phosphorylation by Snf1 causes their export from the nucleus, derepressing FLO11 expression and triggering adhesion and invasive growth (Kuchin et al. 2002; Verstrepen & Klis 2006). Therefore, it is certainly possible that Scr1 functions via a similar mechanism.

6.2.5.2 Transcriptional repression of flocculation by SPBC56F2.05

Loss of SPBC56F2.05 also triggers mild flocculation under non-inducing conditions, and results in the upregulation of genes encoding flocculins (Gsf2, Fta5, Pfl3, and Pfl9) and transcriptional activators of flocculation (Mbx2 and Cbf12) (Figure 4.11).

This flocculation appears to be dependent on the expression of many of these genes, as single deletion of gsf2+, fta5+, mbx2+, and cbf12+ abrogated the SPBC56F2.05cΔ strain flocculent phenotype (Figure 4.12). Like Scr1, it is unclear why deletion of

SPBC56F2.05c triggers flocculation, or if the pfl+ and TF genes are direct SPBC56F2.05 targets. However, in addition to upregulation of flocculin genes, a few genes expressed in low glucose (ght5+ and ght6+) or involved in metabolizing non-preferred carbon sources

(dak2+ and inv1+), are upregulated, suggesting that SPBC56F2.05 could also function in the glucose repression pathway. Interestingly, researchers found that while deletion of tup11+ and tup12+ causes derepression of dak2+ and dak1+, respectively, deletion of

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scr1+ did not, suggesting that another transcriptional repressor might do so (Matsuzawa et al. 2010). As dak2+ is upregulated in the SPBC56F2.05cΔ strain, it is possible that

SPBC56F2.05 is this repressor.

6.2.6 A comparison of the S. pombe and S. cerevisiae TRNs regulating flocculation

A previous comparison of the TRN in S. pombe and S. cerevisiae by Kwon et al.

(2012) showed that the TFs regulating flocculation in these two yeasts have diverged significantly. In S. pombe, the MADS-box TF Mbx2 and the CSL TF Cbf12 activate flocculation, while the CSL TF Cbf11 and the Zn2-Cys6 TF Gsf1 repressed this process.

In contrast, the S. cerevisiae TFs Mss11 and Flo8, which have undefined DNA-binding domains, activate flocculation, and the HSF-type TF Sfl1 represses flocculation (Soares

2011). Additionally, while at least three S. pombe transcriptional activators (Foe1, Mbx2, and Cbf12) appear to utilize autoregulation, the same cannot be said for the S. cerevisiae transcriptional activators Flo8 or Mss11 (Kwon et al. 2012). Despite this divergence however, many of the target genes of many TFs appear to be conserved. For example, like Gsf1 in S. pombe, S. cerevisiae Sfl1 represses expression of the flocculin gene FLO1, and like Mbx2, Mss11 and Flo8 activate multiple flocculin and cell wall remodelling genes (Bester et al. 2006; Kobayashi et al. 1999; Shen et al. 2006). Additionally, Flo8 and

Mbx2 are both able to induce expression of the dominant flocculin genes gsf2+ and FLO1 when expressed in S. pombe and S. cerevisiae, respectively, suggesting that their regulatory sequences may be conserved (Matsuzawa et al. 2011; Matsuzawa et al. 2012).

In addition to the divergence in the type of TF regulating flocculation, fission yeast also appears to have many more TFs involved in regulating flocculation than budding

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yeast. Only Sfl1, Flo8, and Mss11 have been implicated in flocculation in S. cerevisiae.

This is in stark contrast to S. pombe, as we have not only shown that five S. pombe TFs directly regulate expression of various pfl+ genes, but also that the deletion of ~20% of

TF genes triggers or abrogates flocculation. It is unclear why there is such a large discrepancy. To our knowledge however, despite the number of high-throughput screens haploid S. cerevisiae deletion collections have been subjected to, a large scale analysis of genes involved in flocculation has not been performed in budding yeasts. Therefore, there are perhaps other budding yeast TFs involved in flocculation that have simply not been identified yet.

6.3 Identification of S. pombe TF putative target genes by phenotypic activation

As analysis of the TFΔ array revealed that deletion of the majority of S. pombe TFs did not impact yeast growth or gene expression in rich medium, and as identifying conditions necessary to activate TF expression can be difficult, we tried to identify TF target genes and function by phenotypic activation. Previously, Chua et al. (2006) showed that expression microarray analysis of TFOE strains with fitness defects could be used to identify TF function and target genes. The reduced fitness manifested in these strains could be the result of activation of the TF by overexpression and inappropriate expression of target genes (hence the term phenotypic activation). Thus, a set of S. pombe

TFOE strains was examined for reduced fitness, abnormal cell length, and defects in septation, nuclear morphology, and chromosome segregation. Subsequently, microarray expression profiling and ChIP-chip of three TFOE strains with abnormal phenotypes was performed to identify potential direct TF target genes.

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6.3.1 Overexpression of many TF genes results in reduced fitness and abnormal cell phenotypes

Systematic overexpression analysis revealed that overexpression of 73% of S. pombe TFs resulted in reduced fitness (Figure 5.1), approximately twice the frequency observed in S. cerevisiae (Sopko et al. 2006). This difference could be attributed to variations in scoring for reduced fitness and promoter strength. Interestingly, 80% of S. pombe TFOE strains that showed reduced fitness also exhibited cell elongation, suggesting a potential role in the cell cycle. Examination of these TFOE strains by microscopy also showed that 40% of the TFOE strains with cell length or fitness defects also had specific cell cycle phenotypes, further supporting their potential role in the cell cycle (Figure 5.1).

Approximately 8–15% of S. pombe genes exhibit moderate-to strong periodic expression during the cell cycle, and a considerable number of TFs are likely required for their transcriptional control (Rustici et al. 2004; Oliva et al. 2005; Peng et al. 2005).

Moreover, approximately one-third of S. pombe TFs display strong periodic expression during the cell cycle (Bushel et al. 2009). Furthermore, in S. cerevisiae, genes causing reduced fitness when ectopically expressed were functionally enriched for TF and cell cycle regulator genes, which could be similar in S. pombe (Gelperin et al. 2005; Sopko et al. 2006; Yoshikawa et al. 2011).

Another possible explanation for TF overexpression toxicity is the occurrence of transcriptional squelching (Gill & Ptashne 1988). Ectopic expression of a strong transcriptional activator has been shown to sequester general TFs (GTFs) of RNA polymerase II (Liu & Berk 1995; Tavernarakis & Thireos 1995; McEwan & Gustafsson

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1997). The inhibition of cell growth usually associated with squelching is likely caused by the transcriptional repression of essential genes or a lethal combination of nonessential genes. These genes could potentially encode ribosomal proteins and cell cycle activators, which are found to be predominantly repressed in a hypomorphic allele encoding the

RNA polymerase II component Rpb11p (Mnaimneh et al. 2004). Although we cannot rule out squelching, none of the microarray expression profiles for the TFOE strains characterized were enriched for ribosomal and cell cycle genes.

6.3.2 SPBC1773.16 microarray analysis indicates potential involvement in arginine biosynthetic pathways

Microarray expression and ChIP-chip profiling of the SPBC1773.16cOE strain revealed that seven genes were increased at least two-fold, and that the promoter regions of five of these genes were bound by the TF (Figure 5.2B). Two of these five genes, arg7+ and car1+, have a conspicuous role in arginine metabolism, encoding arginosuccinate lyase Arg7 and arginase Car1, which catalyze the synthesis of arginine from arginosuccinate and ornithine from arginine, respectively. The three remaining genes encoding two amino acid transporters (Dal52 and SPBPB2B2.01) and an aromatic aminotransferase (SPBC1773.13), are involved in amino acid transport and metabolism, and therefore, could conceivably also contribute to arginine levels (Wood et al. 2012).

Since SPBC1773.16 appears to have a function in arginine metabolism, we speculated that the SPBC1773.16cΔ strain might have an altered response to the arginine analog canavanine, or might be sensitive to medium containing arginine as the sole nitrogen source. However, the SPBC1773.16cΔ strain did not exhibit an altered response to either of these media. In all likelihood, this is a result of functional redundancy, either

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amongst its putative target genes (e.g. car1+ and aru1+), or amongst itself and other TFs that also appear to regulate its putative target genes (e.g. SPBC16G5.17 and Toe3). To determine if this is the case, double and triple deletion strains for these three TF genes could be made and tested on plates containing canavanine, or arginine as the sole nitrogen source.

Interestingly, although deletion of SPBC1773.16c did not alter sensitivity to canavanine, its overexpression and the overexpression of three of its putative target genes did confer canavanine resistance (Figure 5.4D). While it is not clear exactly why overexpression of each of these genes results in canavanine resistance, it is possibly a consequence of increased arginine levels and/or canavanine breakdown. car1+ encodes an arginase that catalyzes the breakdown of arginine into ornithine and urea (Van Huffel et al. 1994). While we do not know if S. pombe arginases can break down canavanine, some plant arginases are able to do so, suggesting that it is a possibility (Korpela et al.

1982). In contrast, overexpression of Arg7 could increase arginine levels, as it encodes an arginosuccinate lyase that catalyzes arginine synthesis from arginosuccinate (Loppes et al. 1991). Higher intracellular arginine levels could signal to the cell that it does not need to scavenge external arginine, resulting in reduced arginine and canavanine uptake, and therefore, the observed canavanine resistance phenotype. In fact, deletion of arginine transporters such as Can1 has previously been shown to result in canavanine resistance

(Fantes & Creanor 1984). The potential effect of SPBC1773.13 overexpression is much less clear. However, it is predicted to encode an aromatic aminotransferase, which could also increase aromatic amino acid catabolism, contributing to increased arginine levels.

Overall, this increased resistance to canavanine, combined with the microarray

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expression and ChIP-chip profiling of the SPBC1773.16cOE strain, suggest that uncharacterized Zn2Cys6 TF SPBC1773.16 has a role in regulating arginine levels.

6.3.3 SPBC16G5.17 may play a role in arginine metabolism and the proper segregation of sister chromatids during anaphase

SPBC16G5.17 overexpression results in a reduction in fitness, coupled with rare nuclear missegregation and cut (cells untimely torn) cells, suggesting that SPBC16G5.17 might play a role in the separation of sister chromatids during mitosis and coupling of mitosis to cytokinesis (Figure 5.4A). However, genes differentially regulated by

SPBC16G5.17 overexpression were not enriched for nuclear segregation. Interestingly, however, many putative SPBC16G5.17 target genes overlap with target genes of other

Zn2-Cys6 TFs. Most notably, six of ten putative Toe3 target genes also appear to be upregulated and bound by SPBC16G5.17, including SPAC11D3.06, a gene encoding a

MatE transporter that causes nuclear missegregation when overexpressed (Figure 5.4B).

Deletion of SPAC11D3.06 was not able to suppress the nuclear missegregation phenotype of the toe3OE strain, likely in part because overexpression of another one of its target genes, dad5+, also resulted in nuclear missegregation (Vachon et al. 2013). However, as nuclear segregation was observed in a much smaller percentage of cells overexpressing

SPAC16G5.17 than in the toe3OE strain, and as dad5+ is not upregulated by

SPAC16G5.17 overexpression, it is possible that its single deletion might abrogate the nuclear missegregation phenotype observed in the SPAC16G5.17OE strain.

In addition to having putative target genes that overlap with Toe3, SPAC16G5.17 also appears to regulate Toe1 target genes involved in pyrimidine salvage. This included

SPAC1399.04c, a gene predicted to encode a uracil phosphoribosyltransferase, and

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previously shown, like toe1+, to be required for growth on medium containing uracil as a sole nitrogen source. However, deletion of SPBC16G5.17 did not result in sensitivity to this medium, possibly because it is dispensable due to functional redundancy with Toe1.

Finally, SPBC16G5.17 also appeared to regulate several genes involved in arginine metabolism, including SPAC11D3.06, car1+, car2+, SPAC8E4.03, and arg7+ (Figure

5.4B). However, the SPBC16G5.17Δ strain was still able to grow on medium containing arginine as the sole nitrogen source. Again, this could be the result of functional redundancy with Toe3 or SPBC1773.16, which also appear to regulate subsets of genes involved in arginine metabolism. As previously mentioned, to check if this is in fact the case, we could make double deletion strains, and recheck them for sensitivity to medium containing arginine as the sole nitrogen source.

6.3.4 SPAC25B8.11 microarray expression analysis indicates potential involvement in metabolism of alternative nitrogen sources

Microarray expression profiling and ChIP-chip analysis of the SPAC25B8.11OE strain, revealed that SPAC25B8.11 may have a similar function to its S. cerevisiae homolog Dal81. Dal81 has shown to be a transcriptional activator of genes involved in the catolism of nitrogen sources including urea, allantoin, and γ-amino butyrate (GABA)

(Coornaert et al. 1991). Overexpression of SPAC25B8.11 resulted in a two-fold increase in expression of 20 genes, enriched for genes encoding proteins involved in GABA metabolism. In fact, three of these genes (uga1+, SPAC1002.12c, and SPCC584.13) are homologous to UGA1, UGA2, and UGA4, which encode GABA transaminase, succinate semialdehyde dehydrogenase, and GABA permease, respectively, and all of which are required for GABA utilization in budding yeast. It therefore seems likely, that like Dal81,

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SPAC25B8.11 regulates genes involved in GABA utilization. Unlike Dal81 however,

SPAC25B8.11 does not appear to be necessary for allantoin utilization. None of the S. pombe homologs for S. cerevisiae genes involved in allantoin utilization are upregulated by SPAC25B8.11 overexpression. Furthermore, the SPAC25B8.11 strain grows normally on plates containing allantoin as the sole nitrogen source.

To better understand the function of SPAC25B8.11, further experiments will be required. In S. cerevisiae, the DAL81Δ strain grows poorly on medium containing GABA as a sole nitrogen source (Vissers et al. 1990). Furthermore, UGA1, UGA2, and UGA4 expression in response to GABA is abrogated in the DAL81Δ strain (Cardillo et al. 2012).

To confirm that SPAC25B8.11 is required for GABA utilization, the SPAC25B8.11Δ strain should be tested for reduced growth on media containing GABA as a sole nitrogen source. Additionally, qPCR could be used to determine if expression of SPAC25B8.11, uga1+, SPAC1002.12c, and SPCC584.13 is induced in response to GABA, and if deletion of SPAC25B8.11 abolishes this induction.

6.4 Significant findings and future directions

In this work, we expanded the current map of the S. pombe TRN by identifying target genes and functions for partially or wholly uncharacterized TFs. To do so, TFΔ and

TFOE arrays were constructed and characterized, and microarray expression profiling and/or ChIP-chip was performed to identify potential direct target genes for ten TFs.

While only a small subset of the constructed TFΔ and TFOE strains were analyzed by microarray and ChIP analysis, these arrays and the information collected about them will be a valuable resource for the S. pombe community. The methods used in these studies

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can be applied to other uncharacterized TFs to identify their target genes and roles for these TFs. Additionally, these collections can be used to perform additional drug screens, synthetic genetic array (SGA) analysis, and synthetic dosage lethality (SDL) analysis in the future.

Analysis of the TFΔ array indicated that most TF genes are dispensable for growth in rich medium, and that their deletion does not typically result in a significant change in gene expression under these conditions. This confirmed that like S. cerevisiae, many fission yeast TFs are either redundant with one another, or inactive in rich medium

(Hughes & de Boer 2013). To try and identify conditions that might induce TF activity, the TFΔ array was screened for hypersensitivity to various drug compounds. However, as is the case in S. cerevisiae, hypersensitivity of a gene deletion does not usually correlate with increased expression of that gene under the same condition (Giaever et al. 2002). In the future, it may therefore be prudent to utilize alternative strategies to induce TF activity. For example, specific genetic backgrounds can be identified that induce TF activity, and be used to elucidate TF target genes (Chatfield-Reed et al. 2016).

Analysis of the sre1Δ and wild-type strains by four-way microarray expression profiling identified several previously characterized Sre1 target genes, as well as four new putative target genes. More importantly, this proof of principle experiment showed that four-way microarrays are a valid approach for identifying TF target genes, particularly for TFs that are inactive in rich media, but not easily activated by simple TF overexpression. Unfortunately, because hypersensitivity of TFΔ strains to drug compounds is not a good indicator of a corresponding increase in expression of the gene, few TFΔ strains have been identified that are good candidates for four-way microarray

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expression profiling. It may be worthwhile instead to begin by microarray expression profiling wild type with and without drug compounds of interest to identify TFs that are upregulated by drug exposure, and thus potential candidates for analysis by four-way microarray expression profiling.

Together with analysis of the toe1OE strain, four-way microarray expression profiling was also used to help identify the biological role and target genes of uncharacterized TF Toe1. Using these methods, five direct Toe1 target genes involved in pyrimidine salvage were identified. A potential Toe1 binding sequence was identified within the promoter regions of these target genes. However, recognition of this sequence by Toe1 was never confirmed, and therefore electrophoretic mobility shift assays

(EMSA) or promoter deletion analysis could be used to do so (Geertz & Maerkl 2010). toe1+ overexpression also resulted in G1 arrest that could be suppressed by deletion of two of its target genes (urg2+ or SPAC1399.04). This suggests that regulation of pyrimidine levels is important for regulating cell cycle progression in S. pombe.

Interestingly, deregulation of pyrimidine metabolism promotes progression of some cancers (Edwards et al. 2016) and expression levels of the human homologs of urg2+,

SPAC1399.04c, and SPCC1795.05c determine the effectiveness of nucleoside analogs, suggesting that an understanding of the regulation of pyrimidine metabolism in S. pombe might prove useful (Cao et al. 2002; Edwards et al. 2016; Topalis et al. 2016; Valencia et al. 2014).

In this study we also showed that transfer of wild type to FIM resulted in substantial changes in gene expression. Additionally, six novel activators (Foe1, Prt1,

Prr1, SPBC530.08, Fep1, and Grt1) and two novel repressors (Scr1 and SPBC56F2.05)

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of flocculation were identified. Microarray expression profiling and ChIP-chip of an HA- tagged nmt41-driven foe1OE strain identified several potential direct Foe1 target genes.

These included several genes encoding flocculins and proteins involved in cell wall biosynthesis that likely contribute to flocculation of the foe1OE strain. Flocculation of the foe1OE strain was only abrogated by deletion of gsf2+, and not by deletion of either mbx2+ or cbf12+, providing additional support that Foe1 directly regulates flocculin genes. However, no potential Foe1 consensus binding sequences were identified within the promoter regions of these putative target genes, and therefore promoter deletion analysis and EMSA may also be useful to determine Foe1 binding specificity.

In addition to analysis of Foe1, microarray expression profiling was used to identify genes upregulated in the prt1OE, prr1OE, SPBC530.08OE, scr1Δ, and

SPBC56F2.05cΔ strains that might explain their abnormal flocculent phenotypes. In contrast to Foe1, flocculation of all five of these strains was dependent on mbx2+ and cbf12+ expression, suggesting that they likely do not regulate expression of pfl+ genes directly, but instead, may act upstream of and regulate mbx2+ or cbf12+. However, in the absence of binding data for each TF, it is impossible to differentiate direct TF target genes from other secondary effects. Therefore, in the future, it will be important to construct HA-tagged versions of each of these TFs so that ChIP-chip can be performed to identify direct TF targets. This should help elucidate the specific biological functions of each of these TFs, and what particular role they each have in flocculation.

However, even once these additional experiments are performed the upstream regulators and signalling pathways involved in triggering flocculation will likely still not be clear. Therefore, to develop a better understanding of the conditions and signalling

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pathways that initiate S. pombe flocculation, the complete haploid deletion collection, starting with genes encoding proteins significantly upregulated by transfer to FIM, could also be screened for reduced flocculation in FIM.

Despite these many unanswered questions however, with these studies our lab has now determined that disruption of ~20% of S. pombe TF genes results in aberrant flocculent phenotypes. Since flocculating yeasts are ideal candidates for use in many industrial processes, an improved understanding of the TRN regulating S. pombe flocculation may be useful. Furthermore, since disruption of the six TF activators identified also impacted surface adhesion and invasive growth, these studies could potentially have medical implications. Pathogenic yeasts can adhere to and invade human tissues, and thus a better understanding of the TRN regulating these behaviors could be useful in identifying new potential treatment targets.

Lastly, an S. pombe TFOE library was analyzed, showing that overexpression of almost 75% of the TF genes resulted in reduced fitness, and suggesting that like in S. cerevisiae, simple overexpression of TFs may result in induction of their target genes’ expression (Chua et al. 2006). Furthermore, ~80% of these TFOE strains had altered cell length, and ~40% had specific cell cycle defects, suggesting that many S. pombe TFs are likely involved in transcriptional control of genes involved in progression of the cell cycle.

Three of the TFOE strains (SPAC25B8.11OE, SPBC1773.16OE, and

SPBC16G5.17OE) with reduced fitness and cell length or cell cycle phenotypes, were analyzed by microarray expression analysis and ChIP-chip to identify potential direct target genes. Interestingly, all three of these Zn2-Cys6 TFs appear to regulate genes

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required for the utilization of various non-preferred nitrogen sources, including GABA, arginine, and allantoin. Many of them also appear to have partially overlapping target genes. In particular, the putative target genes of SPAC16G5.17 overlap with target genes for Toe1, Toe3, and SPBC1773.16, suggesting that it may regulate the levels of multiple nitrogen sources. Genome wide studies in other yeasts have revealed that many Zn2-Cys6

TFs have overlapping functions (MacPherson et al. 2006). Given our recent observations, this also appears to be true in S. pombe. In many cases, double-knockouts of these TFs with overlapping functions results in non-viability in some backgrounds (MacPherson et al. 2006). It may therefore be interesting not just to make and test double-knockouts for

SPBC16G5.17 and toe1+/toe3+/SPBC1773.16c, but also to couple SGA screens with various drug compounds and environmental conditions, to see if other TFs with overlapping, condition-specific functions can be identified.

Together, these studies have identified potential TF target genes and biological roles for ten uncharacterized TFs. Collectively, they will help map the TRN in S. pombe, and contribute to a better understanding of transcriptional regulation in S. pombe as a whole. In addition, as previously mentioned, some of these TFs and their target genes are conserved in other yeasts and in higher eukaryotes. Therefore, not only should these studies help map out the TRN in S. pombe, they may also shed light on the regulation of some of these processes in other yeasts or in higher eukaryotes.

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References

Aligianni, S. et al., 2009. The fission yeast homeodomain protein Yox1p binds to MBF and confines MBF-dependent cell-cycle transcription to G1-S via negative feedback. PLoS Genetics, 5(8). Alon, U., 2007. Network motifs: theory and experimental approaches. Nat Rev Genet, 8(6), pp.450–461. Andersen, G. et al., 2008. A Second Pathway to Degrade Pyrimidine Nucleic Acid Precursors in Eukaryotes. Journal of Molecular Biology, 380(4), pp.656–666. Babu, M.M. et al., 2004. Structure and evolution of transcriptional regulatory networks. Current Opinion in Structural Biology, 14(3), pp.283–291. Bailey, T.L. et al., 2009. MEME Suite: Tools for motif discovery and searching. Nucleic Acids Research, 37, pp.1–7. Baranzini, S.E., 2004. Gene expression profiling in neurological disorders: toward a systems-level understanding of the brain. Neuromolecular medicine, 6(1), pp.31–51. Berger, M.F. et al., 2006. Compact, universal DNA microarrays to comprehensively determine transcription-factor binding site specificities. Nature biotechnology, 24(11), pp.1429–1435. Beskow, A. & Wright, A.P., 2006. Comparative analysis of regulatory transcription factors in Schizosaccharomyces pombe and budding yeasts. Yeast, 23, pp.929–935. Bester, M.C., Pretorius, I.S. & Bauer, F.F., 2006. The regulation of Saccharomyces cerevisiae FLO gene expression and Ca2+-dependent flocculation by Flo8p and Mss11p. Current Genetics, 49(6), pp.375–383. Bettelli, E., Dastrange, M. & Oukka, M., 2005. Foxp3 interacts with nuclear factor of activated T cells and NF-kappa B to repress cytokine gene expression and effector functions of T helper cells. Proceedings of the National Academy of Sciences of the United States of America, 102(14), pp.5138–43. Brown, J.L., North, S. & Bussey, H., 1993. SKN7, a yeast multicopy suppressor of a mutation affecting cell wall β- glucan assembly, encodes a product with domains homologous to prokaryotic two-component regulators and to heat shock transcription factors. Journal of Bacteriology, 175(21), pp.6908–6915. Bryant, G.O. & Ptashne, M., 2003. Independent recruitment in vivo by Gal4 of two complexes required for transcription. Molecular Cell, 11(5), pp.1301–1309. Buck, M.J., Nobel, A.B. & Lieb, J.D., 2005. ChIPOTle : a user-friendly tool for the analysis of ChIP-chip data. Genome biology, 6(11). Bushel, P.R. et al., 2009. Dissecting the fission yeast regulatory network reveals phase- specific control elements of its cell cycle. BMC systems biology, 3(93). Calvo, I. a. et al., 2012. The transcription factors Pap1 and Prr1 collaborate to activate antioxidant, but not drug tolerance, genes in response to H 2O 2. Nucleic Acids Research, 40(11), pp.4816–4824. Cao, D. et al., 2002. Uridine phosphorylase (-/-) murine embryonic stem cells clarify the key role of this enzyme in the regulation of the pyrimidine salvage pathway and in the activation of fluoropyrimidines. Cancer Research, 62(8), pp.2313–2317. Cardillo, S.B. et al., 2012. Interplay between the transcription factors acting on the GATA- and GABA-responsive elements of Saccharomyces cerevisiae UGA

175

promoters. Microbiology, 158(4), pp.925–935. Carey, M. et al., 1989. An amino-terminal fragment of GAL4 binds DNA as a dimer. Journal of Molecular Biology, 209(3), pp.423–432. Carlberg, C. & Molnar, F., 2014. Mechanisms of gene regulation, Springer Berlin Heidelberg. Chaffin, W.L. et al., 1998. Cell wall and secreted proteins of Candida albicans: identification, function, and expression. Microbiology and molecular biology reviews : MMBR, 62(1), pp.130–180. Chatfield-Reed, K. et al., 2016. Conserved and Diverged Functions of the Calcineurin- Activated Prz1 Transcription Factor in Fission Yeast. Genetics, 202(April), pp.1365–1375. Chen, X., Hughes, T.R. & Morris, Q., 2007. RankMotif++: A motif-search algorithm that accounts for relative ranks of K-mers in binding transcription factors. Bioinformatics, 23(13), pp.72–79. Cherry, J.M. et al., 2012. Saccharomyces Genome Database: The genomics resource of budding yeast. Nucleic Acids Research, 40(D1), pp.700–705. Cho, S.-J. et al., 2010. Possible Roles of LAMMER Kinase Lkh1 in Fission Yeast by Comparative Proteome Analysis. Mycobiology, 38(2), pp.108–12. Chua, G. et al., 2006. Identifying transcription factor functions and targets by phenotypic activation. Proceedings of the National Academy of Sciences of the United States of America, 103(32), pp.12045–50. Chua, G., 2013. Systematic genetic analysis of transcription factors to map the fission yeast transcription-regulatory network. Biochemical Society transactions, 41(6), pp.1696–700. Claro, F.B., Rijsbrack, K. & Soares, E. V., 2007. Flocculation onset in Saccharomyces cerevisiae: Effect of ethanol, heat and osmotic stress. Journal of Applied Microbiology, 102(3), pp.693–700. Conaway, R.C. & Conaway, J.W., 2011. Function and regulation of the Mediator complex. Current Opinion in Genetics and Development, 21(2), pp.225–230. Available at: http://dx.doi.org/10.1016/j.gde.2011.01.013. Coornaert, D., Vissers, S. & Andrc, B., 1991. The pleiotropic UGA35(DURL) regulatory gene of Saccharomyces cerevisiae: cloning, sequence and identity with the DAL81 gene. Gene, 97, pp.163–171. Cui, Z., Horecka, J. & Jigami, Y., 2002. Cdc4 is involved in the transcriptional control of OCH1, a gene encoding alpha-1,6-mannosyltransferase in Saccharomyces cerevisiae. Yeast (Chichester, England), 19(1), pp.69–77. Dranginis, A.M. et al., 2007. A biochemical guide to yeast adhesins: glycoproteins for social and antisocial occasions. Microbiology and molecular biology reviews : MMBR, 71(2), pp.282–94. Edwards, L., Gupta, R. & Filipp, F.V., 2016. Hypermutation of DPYD Deregulates Pyrimidine Metabolism and Promotes Malignant Progression. Molecular cancer research : MCR, 14(2), pp.196–206. Fantes, P. & Creanor, J., 1984. Canavanine resistance and the mechanism of arginine uptake in the fission yeast Schizosaccharomyces pombe. Journal of general microbiology, 130(12), pp.3265–73.

176

Forsburg, S.L., 1999. The best yeast? Trends in Genetics, 15(9), pp.340–344. Forsburg, S.L. & Rhind, N., 2006. Basic methods for fission yeast. Yeast, 23(3), pp.173– 183. Fuda, N.J., Ardehali, M.B. & Lis, J.T., 2009. Defining mechanisms that regulate RNA polymerase II transcription in vivo. Nature, 461(7261), pp.186–192. Garvie, C.W. & Wolberger, C., 2001. Recognition of specific DNA sequences. Molecular Cell, 8(5), pp.937–946. Gasch, a P. et al., 2000. Genomic expression programs in the response of yeast cells to environmental changes. Molecular biology of the cell, 11(12), pp.4241–4257. Gaston, K. & Jayaraman, P.S., 2003. Transcriptional repression in eukaryotes: Repressors and repression mechanisms. Cellular and Molecular Life Sciences, 60(4), pp.721– 741. Geertz, M. & Maerkl, S.J., 2010. Experimental strategies for studying transcription factor-DNA binding specificities. Briefings in Functional Genomics, 9(5-6), pp.362– 373. Gelperin, D.M. et al., 2005. Biochemical and genetic analysis of the yeast proteome with a movable ORF collection Biochemical and genetic analysis of the yeast proteome with a movable ORF collection. Genes and Development, 19, pp.2816–2826. Giaever, G. et al., 2002. Functional profiling of the Saccharomyces cerevisiae genome. Nature, 418(6896), pp.387–391. Gill, G. & Ptashne, M., 1988. Negative effect of the transcriptional activator GAL4. Nature, 334(6184), pp.721–724. Golub, T.R., 1999. Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring. Science, 286(5439), pp.531–537. Goossens, K.V.Y. et al., 2015. Molecular mechanism of flocculation self-recognition in yeast and its role in mating and survival. mBio, 6(2), pp.1–16. Greenall, A. et al., 2002. Role of Fission Yeast Tup1-like Repressors and Prr1 Transcription Factor in Response to Salt Stress. Molecular biology of the cell, 13(9), pp.2977–2989. Gurtan, A.M. & Sharp, P.A., 2013. The role of miRNAs in regulating gene expression networks. Journal of Molecular Biology, 425(19), pp.3582–3600. Hahn, S. & Young, E.T., 2011. Transcriptional regulation in saccharomyces cerevisiae: Transcription factor regulation and function, mechanisms of initiation, and roles of activators and coactivators. Genetics, 189(3), pp.705–736. Halvorsen, Y.D., Nandabalan, K. & Dickson, R.C., 1991. Identification of base and backbone contacts used for DNA sequence recognition and high-affinity binding by LAC9, a transcription activator containing a C6 zinc finger. Molecular and cellular biology, 11(4), pp.1777–84. Hao, S. & Baltimore, D., 2013. RNA splicing regulates the temporal order of TNF- induced gene expression. Proceedings of the National Academy of Sciences, 110(29), pp.11934–11939. Harbison, C.T. et al., 2004. Transcriptional regulatory code of a eukaryotic genome. Nature, 431, pp.99–104. Harrison, C. et al., 2005. SCF(Pof1)-ubiquitin and its target Zip1 transcription factor mediate cadmium response in fission yeast. The EMBO journal, 24(3), pp.599–610.

177

Hasan, A. et al., 2014. Systematic Analysis of the Role of RNA-Binding Proteins in the Regulation of RNA Stability. PLoS Genetics, 10(11). Hayles, J. et al., 2013. A genome-wide resource of cell cycle and cell shape genes of fission yeast. Open biology, 3(5), p.130053. Heller, R. a et al., 1997. Discovery and analysis of inflammatory disease-related genes using cDNA microarrays. Proceedings of the National Academy of Sciences of the United States of America, 94(6), pp.2150–2155. Henry, T.C. et al., 2011. Systematic screen of schizosaccharomyces pombe deletion collection uncovers parallel evolution of the phosphate signal transduction pathway in yeasts. Eukaryotic Cell, 10(2), pp.198–206. Hertz-Fowler, C. et al., 2004. GeneDB: a resource for prokaryotic and eukaryotic organisms. Nucleic acids research, 32, pp.D339–D343. Hesselberth, J.R. et al., 2009. Global mapping of protein-DNA interactions in vivo by digital genomic footprinting. Nature methods, 6(4), pp.283–9. Hirota, K., Hoffman, C.S. & Ohta, K., 2006. Reciprocal nuclear shuttling of two antagonizing Zn finger proteins modulates tup family corepressor function to repress chromatin remodeling. Eukaryotic Cell, 5(12), pp.1980–1989. Hoffman, C.S., Wood, V. & Fantes, P.A., 2015. An ancient yeast for young geneticists: A primer on the Schizosaccharomyces pombe model system. Genetics, 201(2), pp.403–423. de Hoon, M.J.L. et al., 2004. Open source clustering software. Bioinformatics, 20(9), pp.1453–1454. Van Huffel, C., Dubois, E. & Messenguy, F., 1994. Cloning and sequencing of Schizosaccharomyces pombe car1 gene encoding arginase. Expression of the arginine anabolic and catabolic genes in response to arginine and related metabolites. Yeast, 10(7), pp.923–933. Hughes, A.L., Todd, B.L. & Espenshade, P.J., 2005. SREBP pathway responds to sterols and functions as an oxygen sensor in fission yeast. Cell, 120(6), pp.831–842. Hughes, T.R. & de Boer, C.G., 2013. Mapping yeast transcriptional networks. Genetics, 195(1), pp.9–36. Jolma, A. et al., 2010. Multiplexed massively parallel SELEX for characterization of human transcription factor binding specificities. Genome Research, 20, pp.861–873. Jongeneel, C.V. et al., 2005. An atlas of human gene expression from massively parallel signature sequencing ( MPSS ). Genome Research, 15, pp.1007–1014. Juarez-Cepeda, J. et al., 2015. The EPA2 adhesin encoding gene is responsive to oxidative stress in the opportunistic fungal pathogen Candida glabrata. Current Genetics, 61(4), pp.529–544. Juven-Gershon, T. & Kadonaga, J.T., 2010. Regulation of gene expression via the core promoter and the basal transcriptional machinery. Developmental Biology, 339(2), pp.225–229. Kaur, R., Ingavale, S.S. & Bachhawat, A.K., 1997. PCR-mediated direct gene disruption in Schizosaccharomyces pombe. Nucleic Acids Research, 25(5), pp.1080–1081. Kawashima, S.A. et al., 2012. Analyzing fission yeast multidrug resistance mechanisms to develop a genetically tractable model system for chemical biology. Chemistry and Biology, 19(7), pp.893–901.

178

Kim, D.U. et al., 2010. Analysis of a genome-wide set of gene deletions in the fission yeast Schizosaccharomyces pombe. Nat Biotechnol, 28(6), pp.617–623. Kim, K.H. et al., 2001. Negative regulation of filamentous growth and flocculation by Lkh1, a fission yeast LAMMER kinase homolog. Biochemical and biophysical research communications, 289(5), pp.1237–1242. Kobayashi, O., Yoshimoto, H. & Sone, H., 1999. Analysis of the genes activated by the FLO8 gene in Saccharomyces cerevisiae. Current Genetics, 36(5), pp.256–261. Konradi, C., 2005. Gene expression microarray studies in polygenic psychiatric disorders: Applications and data analysis. Brain Research Reviews, 50(1), pp.142– 155. Korpela, T.K., Lorenz, H. & Laakso, S., 1982. A specific arginase-based assay for l- canavanine in leguminous plants. Journal of Biochemical and Biophysical Methods, 7(1), pp.67–70. Kuchin, S., Vyas, V.K. & Carlson, M., 2002. Snf1 protein kinase and the repressors Nrg1 and Nrg2 regulate FLO11, haploid invasive growth, and diploid pseudohyphal differentiation. Molecular and Cellular Biology, 22(12), pp.3994–4000. Kwon, E.J.G. et al., 2012. Deciphering the Transcriptional-Regulatory Network of Flocculation in Schizosaccharomyces pombe. PLoS Genetics, 8(12). Latchman, D.S., 1997. Transcription factors: An overview. International Journal of Biochemistry and Cell Biology, 29(12), pp.1305–1312. Lee, T.I. et al., 2002. Transcriptional regulatory networks in Saccharomyces cerevisiae. Science, 298(5594), pp.799–804. Lenhard, B., Sandelin, A. & Carninci, P., 2012. Metazoan promoters: emerging characteristics and insights into transcriptional regulation. Nat Rev Genet, 13(4), pp.233–245. Li, B. & Greene, M.I., 2007. FOXP3 actively represses transcription by recruiting the HAT/HDAC complex. Cell Cycle, 6(12), pp.1432–1436. Li, H. et al., 2015. Genome-wide analysis of core promoter structures in Schizosaccharomyces pombe with DeepCAGE. RNA Biology, 12(May), pp.525– 537. Li, R. et al., 2013. Reduction of ribosome level triggers flocculation of fission yeast cells. Eukaryotic Cell, 12(3), pp.450–459. Licht, K. & Jantsch, M.F., 2016. Rapid and dynamic transcriptome regulation by RNA editing and RNA modifications. The Journal of Cell Biology, 213(1), p.jcb.201511041. Lin, C.P.C. et al., 2013. A Highly Redundant Gene Network Controls Assembly of the Outer Spore Wall in S. cerevisiae. PLoS Genetics, 9(8). Linder, T. & Gustafsson, C.M., 2008. Molecular phylogenetics of ascomycotal adhesins- A novel family of putative cell-surface adhesive proteins in fission yeasts. Fungal Genetics and Biology, 45(4), pp.485–497. Liu, X. & Berk, A.J., 1995. Reversal of in vitro p53 squelching by both TFIIB and TFIID. Molecular and cellular biology, 15(11), pp.6474–8. Liu, Y., Beyer, A. & Aebersold, R., 2016. On the Dependency of Cellular Protein Levels on mRNA Abundance. Cell, 165, pp.535–550. López-Ribot, J.L. & Chaffin, W.L., 1996. Members of the Hsp70 family of proteins in

179

the cell wall of Saccharomyces cerevisiae. Journal of bacteriology, 178(15), pp.4724–6. Loppes, R. et al., 1991. Sequence analysis of the ARG7 gene of Schizosaccharomyces pombe coding for argininosuccinate lyase. Expression of the gene in Saccharomyces cerevisiae. Curr Genet, 19(4), pp.255–260. Luo, W. & Skalnik, D.G., 1996. CCAAT displacement protein competes with multiple transcriptional activators for binding to four sites in the proximal gp91(phox) promoter. Journal of Biological Chemistry, 271(30), pp.18203–18210. Machado, M.D. et al., 2008. Removal of heavy metals using a brewer’s yeast strain of Saccharomyces cerevisiae: The flocculation as a separation process. Bioresource Technology, 99(7), pp.2107–2115. Macneil, L.T. & Walhout, A.J.M., 2011. Gene regulatory networks and the role of robustness and stochasticity in the control of gene expression. , pp.645–657. MacPherson, S., Larochelle, M. & Turcotte, B., 2006. A fungal family of transcriptional regulators: the zinc cluster proteins. Microbiology and molecular biology reviews : MMBR, 70(3), pp.583–604. Maerkl, S.J. & Quake, S.R., 2007. Systems approach to measuring the binding energy landscapes of transcription factors. Science, 315, pp.233–237. Malerba, G. & Pignatti, P.F., 2005. A review of asthma genetics: gene expression studies and recent candidates. Journal of applied genetics, 46(1), pp.93–104. Mamnun, Y.M. et al., 2002. The yeast zinc finger regulators Pdr1p and Pdr3p control pleiotropic drug resistance (PDR) as homo- and heterodimers in vivo. Molecular Microbiology, 46(5), pp.1429–1440. Maston, G.A. et al., 2006. Transcriptional Regulatory Elements in the Human Genome. Annual Review of Genomics and Human Genetics, 7(1), pp.29–59. Matsuzawa, T. et al., 2011. Identification of a galactose-specific flocculin essential for non-sexual flocculation and filamentous growth in Schizosaccharomyces pombe. Molecular Microbiology, 82(6), pp.1531–1544. Matsuzawa, T., Fujita, Y., et al., 2012. Snf1-like protein kinase ssp2 regulates glucose derepression in schizosaccharomyces pombe. Eukaryotic Cell, 11(2), pp.159–167. Matsuzawa, T. et al., 2010. The gld1+ gene encoding glycerol dehydrogenase is required for glycerol metabolism in Schizosaccharomyces pombe. Applied Microbiology and Biotechnology, 87(2), pp.715–727. Matsuzawa, T. et al., 2013. The zinc finger protein Gsf1 regulates Gsf2-dependent flocculation in fission yeast. FEMS Yeast Research, 13(3), pp.259–266. Matsuzawa, T., Yoritsune, K.I. & Takegawa, K., 2012. Mads box transcription factor Mbx2/pvg4 regulates invasive growth and flocculation by inducing Gsf2 + expression in fission yeast. Eukaryotic Cell, 11(2), pp.151–158. Mayer, F.L., Wilson, D. & Hube, B., 2013. Candida albicans pathogenicity mechanisms. Virulence, 4(2), pp.119–28. McEwan, I.J. & Gustafsson, J., 1997. Interaction of the human androgen receptor transactivation function with the general transcription factor TFIIF. Proceedings of the National Academy of Sciences of the United States of America, 94(16), pp.8485– 90. Miki, B.L.A. et al., 1982. Possible mechanism for flocculation interactions governed by

180

gene FLO1 in Saccharomyces cerevisiae. Journal of Bacteriology, 150(2), pp.878– 889. Mill, P.J., 1964. the Nature of the Interactions Between Flocculent Cells in the Flocculation of Saccharomyces Cerevisiae. Journal of general microbiology, 35(1964), pp.61–68. Mnaimneh, S. et al., 2004. Exploration of essential gene functions via titratable promoter alleles. Cell, 118(1), pp.31–44. Moreno, S., Klar, A. & Nurse, P., 1991. Guide to Yeast Genetics and Molecular Biology. Methods in Enzymology, 194, pp.795–823. Nakamichi, N. et al., 2003. Characterization of the Prr1 response regulator with special reference to sexual development in Schizosaccharomyces pombe. Bioscience, biotechnology, and biochemistry, 67(3), pp.547–555. Nanni, L. et al., 2006. Differential gene expression profiling in genetic and multifactorial cardiovascular diseases. Journal of Molecular and Cellular Cardiology, 41(6), pp.934–948. Ofuonye, E., Kutin, K. & Stuart, D.T., 2013. Engineering Saccharomyces cerevisiae fermentative pathways for the production of isobutanol. Biofuels, 4(2), pp.185–201. Okazaki, K. et al., 1990. High-frequency transformation method and library transducing vectors for cloning mammalian cDNAs by trans-complementation of Schizosaccharomyces pombe. Nucleic acids research, 18(22), pp.6485–6489. Oliva, A. et al., 2005. The cell cycle-regulated genes of Schizosaccharomyces pombe. PLoS Biology, 3(7), pp.1239–1260. Parsell, D. & Lindquist, S., 1993. The Function of Heat-Shock Proteins in Stress Tolerance: Degradation and Reactivation of Damaged Proteins. Annual Review of Genetics, 27(1), pp.437–496. Pelletier, B. et al., 2003. Fep1 represses expression of the fission yeast Schizosaccharomyces pombe siderophore-iron transport system. Nucleic Acids Research, 31(15), pp.4332–4344. Pelletier, B. et al., 2002. Fep1, an iron sensor regulating iron transporter gene expression in Schizosaccharomyces pombe. Journal of Biological Chemistry, 277(25), pp.22950–22958. Peng, X. et al., 2005. Identification of cell cycle-regulated genes in fission yeast. Molecular biology of the cell, 16, pp.1026–1042. Quenault, T., Lithgow, T. & Traven, A., 2011. PUF proteins: Repression, activation and mRNA localization. Trends in Cell Biology, 21(2), pp.104–112. Quinn, J. et al., 2011. Two-component mediated peroxide sensing and signal transduction in fission yeast. Antioxidants & redox signaling, 15(1), pp.153–165. Ren, B. et al., 2000. Genome-wide location and function of DNA binding proteins. Science, 290(5500), pp.2306–2309. Roetzer, A. et al., 2008. Candida glabrata environmental stress response involves Saccharomyces cerevisiae Msn2/4 orthologous transcription factors. Molecular Microbiology, 69(3), pp.603–620. Rosenthal, G., 1977. The Biological Effects and Mode of Action of L-Canavanine , a Structural Analogue of L- Arginine. The Quarterly Review of Biology, 52(2), pp.155–178.

181

Rossouw, D. et al., 2015. Co-flocculation of yeast species, a new mechanism to govern population dynamics in microbial ecosystems. PLoS ONE, 10(8), pp.1–17. Rué, P. & Martinez Arias, A., 2015. Cell dynamics and gene expression control in tissue homeostasis and development [review]. Molecular systems biology, 11 VN - r(1), p.792. Rustici, G. et al., 2004. Periodic gene expression program of the fission yeast cell cycle. Nature genetics, 36(8), pp.809–17. Ryan, C.J. et al., 2012. Hierarchical Modularity and the Evolution of Genetic Interactomes across Species. Molecular Cell, 46(5), pp.691–704. Saitoh, S. et al., 2015. Mechanisms of expression and translocation of major fission yeast glucose transporters regulated by CaMKK/phosphatases, nuclear shuttling, and TOR. Molecular biology of the cell, 26(2), pp.373–86. Saldanha, A.J., 2004. Java Treeview - Extensible visualization of microarray data. Bioinformatics, 20(17), pp.3246–3248. Sampermans, S., Mortier, J. & Soares, E. V., 2005. Flocculation onset in Saccharomyces cerevisiae: The role of nutrients. Journal of Applied Microbiology, 98(2), pp.525– 531. Sansó, M. et al., 2008. Transcription factors Pcr1 and Atf1 have distinct roles in stress- and Sty1-dependent gene regulation. Eukaryotic Cell, 7(5), pp.826–835. dos Santos, S.C. et al., 2014. MFS transporters required for multidrug/multixenobiotic (MD/MX) resistance in the model yeast: Understanding their physiological function through post-genomic approaches. Frontiers in Physiology, 5 MAY(May), pp.1–15. Sarode, N. et al., 2014. The Wsc1p cell wall signaling protein controls biofilm (Mat) formation independently of Flo11p in Saccharomyces cerevisiae. G3 (Bethesda, Md.), 4(2), pp.199–207. Schillig, R. & Morschhäuser, J., 2013. Analysis of a fungus-specific transcription factor family, the Candida albicans zinc cluster proteins, by artificial activation. Molecular microbiology, 89(5), pp.1003–1017. Schneider, C.A., Rasband, W.S. & Eliceri, K.W., 2012. NIH Image to ImageJ: 25 years of image analysis. Nature Methods, 9(7), pp.671–675. Sehgal, A., Lee, C.Y.S. & Espenshade, P.J., 2007. SREBP controls oxygen-dependent mobilization of retrotransposons in fission yeast. PLoS Genetics, 3(8), pp.1389– 1396. Shah, A.H. et al., 2014. Novel role of a family of major facilitator transporters in biofilm development and virulence of Candida albicans. Biochemical Journal, 460(2), pp.223–235. Sharifmoghadam, M.R. & Valdivieso, M.H., 2008. The Schizosaccharomyces pombe Map4 adhesin is a glycoprotein that can be extracted from the cell wall with alkali but not with beta-glucanases and requires the C-terminal DIPSY domain for function. Molecular Microbiology, 69(6), pp.1476–1490. Shen, H. et al., 2006. The mechanism by which overexpression of Gts1p induces flocculation in a FLO8-inactive strain of the yeast Saccharomyces cerevisiae. FEMS Yeast Research, 6(6), pp.914–923. Shimizu, J., Yoda, K. & Yamasaki, M., 1994. The hypo-osmolarity-sensitive phenotype of the Saccharomyces cerevisiae hpo2 mutant is due to a mutation in PKC1, which

182

regulates expression of β-glucanase. Molecular & general genetics : MGG, 242, pp.641–648. Sikorski, T.W. & Buratowski, S., 2009. The basal initiation machinery: beyond the general transcription factors. Current Opinion in Cell Biology, 21(3), pp.344–351. Smyth, G.K., 2004. Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments. Statistical Applications in Genetics and Molecular Biology, 3(1), pp.1–26. Smyth, G.K. & Speed, T., 2003. Normalization of cDNA microarray data. Methods, 31(4), pp.265–273. Soares, E. V., 2011. Flocculation in Saccharomyces cerevisiae: A review. Journal of Applied Microbiology, 110(1), pp.1–18. Sopko, R. et al., 2006. Mapping pathways and phenotypes by systematic gene overexpression. Molecular Cell, 21(3), pp.319–330. Stratford, M. & Carter, a T., 1993. Yeast flocculation: lectin synthesis and activation. Yeast (Chichester, England), 9(4), pp.371–8. Sud, I.J. & Feingold, D.S., 1981. Mechanisms of action of the antimycotic imidazoles. The Journal of investigative dermatology, 76(6), pp.438–441. Sun, J.N. et al., 2010. Host cell invasion and virulence mediated by Candida albicans Ssa1. PLoS Pathogens, 6(11). Takuma, K. et al., 2016. The Fission Yeast php2 Mutant Displays a Lengthened Chronological Lifespan. , 8451(July). Tanaka, N. et al., 1998. Isolation and characterization of an invertase and its repressor genes from Schizosaccharomyces pombe. Biochemical and biophysical research communications, 245(1), pp.246–253. Tavernarakis, N. & Thireos, G., 1995. Transcriptional interference caused by GCN4 overexpression reveals multiple interactions mediating transcriptional activation. Molecular and General Genetics, 247(5), pp.571–578. Todd, B.L. et al., 2006. Sterol Regulatory Element Binding Protein Is a Principal Regulator of Anaerobic Gene Expression in Fission Yeast. , 26(7), pp.2817–2831. Todd, R.B. & Andrianopoulos, a, 1997. Evolution of a fungal regulatory gene family: the Zn(II)2Cys6 binuclear cluster DNA binding motif. Fungal genetics and biology : FG & B, 21(3), pp.388–405. Topalis, D. et al., 2016. Resistance to the nucleotide analogue cidofovir in HPV(+) cells: a multifactorial process involving UMP/CMP kinase 1. Oncotarget, 7(9). Treitel, M.A. & Carlson, M., 1995. Repression by SSN6-TUP1 is directed by MIG1, a repressor/activator protein. Proceedings of the National Academy of Sciences of the United States of America, 92(8), pp.3132–6. Tseng, A., Brooks, M. & Cadman, E., 1986. Modulation of fluoropyrimidine metabolism by chlorpromazine. Biochemical and Biophysical Research Communications, 138(2), pp.1009–1014. Turoscy, V. & Cooper, T.G., 1982. Pleiotropic control of five eucaryotic genes by multiple regulatory elements. Journal of Bacteriology, 151(3), pp.1237–1246. Vachon, L. et al., 2013. Functional characterization of fission yeast transcription factors by overexpression analysis. Genetics, 194(4), pp.873–884.

183

Valencia, A. et al., 2014. Expression of nucleoside-metabolizing enzymes in myelodysplastic syndromes and modulation of response to azacitidine. Leukemia, 28(3), pp.621–8. Vallejo, J.A. et al., 2013. Cell aggregations in yeasts and their applications. Applied Microbiology and Biotechnology, 97(6), pp.2305–2318. Vermitsky, J.-P. et al., 2006. Pdr1 regulates multidrug resistance in Candida glabrata: gene disruption and genome-wide expression studies. Molecular microbiology, 61(3), pp.704–722. Verstrepen, K.J. & Klis, F.M., 2006. Flocculation, adhesion and biofilm formation in yeasts. Molecular Microbiology, 60(1), pp.5–15. Vissers, S. et al., 1990. Induction of the 4-aminobutyrate and urea-catabolic pathways in Saccharomyces cerevisiae. Specific and common transcriptional regulators. European journal of biochemistry / FEBS, 187(3), pp.611–6. Walhout, A.J.M., 2006. Unraveling transcription regulatory networks by protein – DNA and protein – protein interaction mapping. Genome Research, pp.1–10. Wang, H., Johnston, M. & Mitra, R.D., 2007. Calling cards for DNA-binding proteins Calling cards for DNA-binding proteins. Genome Research, (314), pp.1202–1209. Watt, S. et al., 2008. urg1: A uracil-regulatable promoter system for fission yeast with short induction and repression times. PLoS ONE, 3(1), pp.1–8. Wickramasinghe, V.O. & Laskey, R.A., 2015. Control of mammalian gene expression by selective mRNA export. Nature reviews. Molecular cell biology, 16(7), pp.431–442. Wingender, E., Schoeps, T. & Dönitz, J., 2013. TFClass: An expandable hierarchical classification of human transcription factors. Nucleic Acids Research, 41(D1), pp.165–170. Wolfe, S.A., Nekludova, L. & Pabo, C.O., 2000. DNA Recognition by Cys2His2 Zinc Finger Proteins. Annual reviews of biophysical and biomolecular structures, 29, pp.183–212. Wood, V. et al., 2012. PomBase: A comprehensive online resource for fission yeast. Nucleic Acids Research, 40(D1), pp.695–699. Wood, V. et al., 2002. The genome sequence of Schizosaccharomyces pombe. Nature, 415(6874), pp.871–880. Workman, C.T. et al., 2005. enoLOGOS: A versatile web tool for energy normalized sequence logos. Nucleic Acids Research, 33(SUPPL. 2), pp.389–392. Yanagida, M., 2002. The model unicellular eukaryote, Schizosaccharomyces pombe. Genome biology, 3(3), p.COMMENT2003. Yoshikawa, K. et al., 2011. Comprehensive phenotypic analysis of single-gene deletion and overexpression strains of Saccharomyces cerevisiae. Yeast, 28, pp.349–361. Zhang, N. et al., 1999. Down-regulation of the expression of PKC1 and SRB1/PSA1/VIG9, two genes involved in cell wall integrity in Saccharomyces cerevisiae, causes flocculation. Microbiology, 145(2), pp.309–316. Zhao, X.Q. & Bai, F.W., 2009. Yeast flocculation: New story in fuel ethanol production. Biotechnology Advances, 27(6), pp.849–856.

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APPENDIX A: ADDITIONAL TABLES

Table A1. Putative S. pombe transcription factors

TFΔ Gene Name Deletion DNA Binding Domain Description

ace2 YES C2H2 Zn finger transcription factor adn2 NO LIS1-like transcription factor adn3 NO LIS1-like transcription factor ams2 YES Zinc finger, GATA type transcription factor atf1 YES Basic leucine zipper transcription factor atf21 YES Basic leucine zipper transcription factor atf31 YES Basic leucine zipper transcription factor cbf11 YES CBF1/LAG-1 family transcription factor cbf12 YES CBF1/LAG-1 family transcription factor cdc10 ESSENTIAL APSES transcription factor cha4 YES Zn(2)-C6 fungal type transcription factor cuf1 YES Copperfist transcription factor cuf2 YES Copperfist transcription factor

deb1 YES C2H2 Zn finger transcription factor esc1 YES Basic helix-loop-helix transcription factor fep1 YES Zinc finger, GATA type transcription factor fhl1 YES Fork head transcription factor fkh2 YES Fork head transcription factor gaf1 YES Zinc finger, GATA type transcription factor grt1 YES Zn(2)-C6 fungal type transcription factor gsf1 YES Zn(2)-C6 fungal type transcription factor hsf1 ESSENTIAL HSF-type transcription factor

hsr1 NO C2H2 Zn finger transcription factor

klf1 YES C2H2 Zn finger transcription factor

loz1 YES C2H2 Zn finger transcription factor map1 YES MADS box transcription factor mat1-mc* NO HMG box transcription factor mat3-mc YES HMG box transcription factor matPc N/A HMG box transcription factor mbx1 YES MADS box transcription factor mbx2 YES MADS box transcription factor mca1 YES Zn(2)-C6 fungal type transcription factor mei4 YES Fork head transcription factor moc3 YES Zn(2)-C6 fungal type transcription factor mug151 YES SAP30 like transcription factor

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pap1 YES Basic leucine zipper transcription factor pcr1 YES Basic leucine zipper transcription factor pho7 YES Zn(2)-C6 fungal type transcription factor php2 YES NF-Y transcription factor php3 YES NF-Y transcription factor php4* NO NF-Y transcription factor php5 YES NF-Y transcription factor phx1 YES Homeobox transcription factor prr1 YES HSF-type transcription factor prt1 YES Zn(2)-C6 fungal type transcription factor

prz1 YES C2H2 Zn finger transcription factor rap1* NO BRCT transcription factor

rep1 YES C2H2 Zn finger transcription factor

rep2 YES C2H2 Zn finger transcription factor res1 YES APSES transcription factor res2 YES APSES transcription factor

rst2 YES C2H2 Zn finger transcription factor

rsv1 YES C2H2 Zn finger transcription factor

rsv2 YES C2H2 Zn finger transcription factor sak1 ESSENTIAL RFX-type winged-helix transcription factor

scr1 YES C2H2 Zn finger transcription factor sep1 YES Fork head transcription factor

sfp1 YES C2H2 Zn finger transcription factor SPAC105.03c* NO Zn(2)-C6 fungal type transcription factor SPAC11D3.17* NO Zn(2)-C6 fungal type transcription factor SPAC1327.01c YES Zn(2)-C6 fungal type transcription factor

SPAC19B12.07c YES C2H2 Zn finger transcription factor SPAC1F7.11c YES Zn(2)-C6 fungal type transcription factor SPAC25B8.11 YES Zn(2)-C6 fungal type transcription factor SPAC2H10.01 YES Zn(2)-C6 fungal type transcription factor SPAC3C7.04 YES Zn(2)-C6 fungal type transcription factor SPAC3F10.12c YES Basic helix-loop-helix transcription factor SPAC3H8.08c YES Zn(2)-C6 fungal type transcription factor SPBC1348.12* NO Zn(2)-C6 fungal type transcription factor SPBC16G5.16 YES Zn(2)-C6 fungal type transcription factor SPBC16G5.17 YES Zn(2)-C6 fungal type transcription factor SPBC1773.12 YES Zn(2)-C6 fungal type transcription factor SPBC1773.16c YES Zn(2)-C6 fungal type transcription factor

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SPBC17D1.01 ESSENTIAL Coiled-coil transcription factor SPBC19G7.04 YES HMG box transcription factor SPBC29A10.12 YES Coiled-coil transcription factor SPBC530.08 YES Zn(2)-C6 fungal type transcription factor SPBC530.11c* NO Zn(2)-C6 fungal type transcription factor SPBC56F2.05c YES Zn(2)-C6 fungal type transcription factor SPCC1393.08 YES Zinc finger, GATA type transcription factor SPCC320.03 YES Zn(2)-C6 fungal type transcription factor SPCC417.09c YES Zn(2)-C6 fungal type transcription factor SPCC757.04 YES Zn(2)-C6 fungal type transcription factor SPCC777.02 YES Zn(2)-C6 fungal type transcription factor SPCC965.10 YES Zn(2)-C6 fungal type transcription factor sre1 YES Basic helix-loop-helix transcription factor sre2 YES Basic helix-loop-helix transcription factor ste11 YES HMG box transcription factor thi1 YES Zn(2)-C6 fungal type transcription factor thi5 YES Zn(2)-C6 fungal type transcription factor toe1 YES Zn(2)-C6 fungal type transcription factor toe2 YES Zn(2)-C6 fungal type transcription factor toe3 YES Zn(2)-C6 fungal type transcription factor toe4 YES Zn(2)-C6 fungal type transcription factor tos4 YES Forkhead transcription factor yox1 YES Homeobox transcription factor

zas1 ESSENTIAL C2H2 Zn finger transcription factor zip1 YES Basic leucine zipper transcription factor bdp1 ESSENTIAL Homeobox TFIIIB complex subunit bqt4 YES APSES Nuclear membrane component eta2 YES SANT/myb RNA pol I termination factor

iec1 NO C2H2 Zn finger Ino80 chromatin remodelling complex ncb2 ESSENTIAL CBF/NF-Y Transcriptional corepressor nht1 YES HMG box Ino80 chromatin remodelling complex pcc1 YES CTAG family EKC/KEOPS tRNA metabolism complex

pir2 ESSENTIAL C2H2 Zn finger Implicated in RNAi

sfc2 YES C2H2 Zn finger TFIIIA complex subunit SPBC1652.01 YES SIN3 binding RRPE binding protein

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Table A2: Schizosaccharomyces pombe strains used in this study. Strain Genotype 972h- 972 h- JK25 leu1-32 h- GCY3312 pREP1 ade6-M216 leu1-32 ura4D18 h+ GCY1188 pREP1-ace2+ leu1-32 h- GCY3377 pREP1-adn2+ ade6-M216 leu1-32 ura4D18 h+ GCY3352 pREP1-adn3+ ade6-M216 leu1-32 ura4D18 h+ GCY1160 pREP1-ams2+ leu1-32 h- GCY3248 pREP1-arg7+ leu1-32 h- GCY1179 pREP1-atf1+ leu1-32 h- GCY1175 pREP1-atf21+ leu1-32 h- GCY1240 pREP1-atf31+ leu1-32 h- GCY3244 pREP1-car1+ leu1-32 h- GCY1210 pREP1-cbf11+ leu1-32 h- GCY3354 pREP1-cbf12+ ade6-M216 leu1-32 ura4D18 h+ GCY1207 pREP1-cbf12+ leu1-32 h- GCY1218 pREP1-cdc10+ leu1-32 h- GCY1203 pREP1-cha4+ leu1-32 h- GCY1199 pREP1-cuf1+ leu1-32 h- GCY1159 pREP1-cuf2+ leu1-32 h- GCY1155 pREP1-deb1+ leu1-32 h- GCY1174 pREP1-esc1+ leu1-32 h- GCY1150 pREP1-fep1+ leu1-32 h- GCY1136 pREP1-fhl1+ leu1-32 h- GCY1185 pREP1-fkh2+ leu1-32 h- GCY3320 pREP1-foe1+ ade6-M216 leu1-32 ura4D18 h+ GCY1194 pREP1-gaf1+ leu1-32 h- GCY1215 pREP1-grt1+ leu1-32 h- GCY1143 pREP1-gsf1+ leu1-32 h- GCY1275 pREP1-hsf1+ leu1-32 h- GCY1196 pREP1-hsr1+ leu1-32 h- GCY1204 pREP1-klf1+ leu1-32 h- GCY1141 pREP1-loz1+ leu1-32 h- GCY1157 pREP1-map1+ leu1-32 h- GCY1219 pREP1-mat3-mc+ leu1-32 h- GCY1216 pREP1-mat-pc+ leu1-32 h- GCY1208 pREP1-mbx1+ leu1-32 h- GCY3353 pREP1-mbx2+ ade6-M216 leu1-32 ura4D18 h+ GCY1201 pREP1-mbx2+ leu1-32 h- GCY1178 pREP1-mca1+ leu1-32 h- GCY1135 pREP1-mei4+ leu1-32 h- GCY1137 pREP1-moc3+ leu1-32 h- GCY1221 pREP1-mug151+ leu1-32 h- GCY1146 pREP1-pap1+ leu1-32 h- GCY1162 pREP1-pcr1+ leu1-32 h- GCY1209 pREP1-pho7+ leu1-32 h- GCY1173 pREP1-php2+ leu1-32 h- GCY1154 pREP1-php3+ leu1-32 h- GCY1140 pREP1-php5+ leu1-32 h- GCY1206 pREP1-phx1+ leu1-32 h- GCY3425 pREP1-prr1+ ade6-M216 leu1-32 ura4D18 h+ GCY1177 pREP1-prr1+ leu1-32 h- GCY1151 pREP1-prt1+ leu1-32 h-

188

GCY1148 pREP1-prz1+ leu1-32 h- GCY1163 pREP1-rep1+ leu1-32 h- GCY1244 pREP1-rep2+ leu1-32 h- GCY1220 pREP1-res1+ leu1-32 h- GCY1153 pREP1-res2+ leu1-32 h- GCY1166 pREP1-rst2+ leu1-32 h- GCY1142 pREP1-rsv1+ leu1-32 h- GCY1212 pREP1-rsv2+ leu1-32 h- GCY1063 pREP1-sak1+ leu1-32 h- GCY1138 pREP1-scr1+ leu1-32 h- GCY1200 pREP1-sep1+ leu1-32 h- GCY1134 pREP1-sfp1+ leu1-32 h- GCY2460 pREP1-SPAC1002.17 leu1-32 GCY2462 pREP1-SPAC1002.19 leu1-32 h- GCY1189 pREP1-SPAC1327.01 leu1-32 h- GCY2458 pREP1-SPAC1399.04c leu1-32 GCY1197 pREP1-SPAC16G5.16 leu1-32 h- GCY1205 pREP1-SPAC16G5.17 leu1-32 h- GCY1191 pREP1-SPAC19B12.07 leu1-32 h- GCY1195 pREP1-SPAC1F7.11 leu1-32 h- GCY1187 pREP1-SPAC25B8.11 leu1-32 h- GCY1149 pREP1-SPAC2H10.01 leu1-32 h- GCY1273 pREP1-SPAC3C7.04 leu1-32 h- GCY1158 pREP1-SPAC3F10.12 leu1-32 h- GCY1181 pREP1-SPAC3H8.08 leu1-32 h- GCY1271 pREP1-SPBC1393.08 leu1-32 h- GCY1145 pREP1-SPBC1773.12 leu1-32 h- GCY3246 pREP1-SPBC1773.13 leu1-32 GCY1184 pREP1-SPBC1773.16 leu1-32 h- GCY1190 pREP1-SPBC1773.16 leu1-32 h- GCY1192 pREP1-SPBC1773.16 leu1-32 h- GCY1165 pREP1-SPBC17D1.01 leu1-32 h- GCY3325 pREP1-SPBC530.05 ade6-M216 leu1-32 ura4D18 h+ GCY3309 pREP1-SPBC530.08 ade6-M216 leu1-32 ura4D18 h+ GCY1211 pREP1-SPBC530.08 leu1-32 h- GCY1176 pREP1-SPBC56F2.05 leu1-32 h- GCY2454 pREP1-SPCC1795.05c leu1-32 h- GCY1183 pREP1-SPCC320.03 leu1-32 h- GCY1198 pREP1-SPCC417.09 leu1-32 h- GCY1167 pREP1-SPCC757.04 leu1-32 h- GCY1171 pREP1-SPCC777.02 leu1-32 h- GCY1139 pREP1-SPCC965.10 leu1-32 h- GCY1222 pREP1-sre1+ leu1-32 h- GCY1168 pREP1-sre2+ leu1-32 h- GCY1147 pREP1-ste11+ leu1-32 h- GCY1267 pREP1-thi1+ leu1-32 h- GCY1202 pREP1-thi5+ leu1-32 h- GCY1144 pREP1-toe1+ leu1-32 h- GCY1193 pREP1-toe2+ leu1-32 h- GCY1156 pREP1-toe3+ leu1-32 h- GCY1152 pREP1-toe4+ leu1-32 h- GCY1248 pREP1-tos4+ leu1-32 h- GCY369 pREP1-yox1+ leu1-32 h- GCY1214 pREP1-zas1+ leu1-32 h-

189

GCY1169 pREP1-zip1+ leu1-32 h- GCY3089 pSLF272-foe1-HA ura4D18 h- GCY2905 pSLF272-SPAC25B8.11-HA ura4D18 h- GCY2864 pSLF272-SPBC16G5.17-HA ura4D18 h- GCY2855 pSLF272-SPBC1773.16-HA ura4D18 h- GCY799 pSLF272-toe1+-HA ura4D18 h- GCY2739 SPAC1002.17c::KanMX4 pREP1-toe1+ leu1-32 hx GCY2735 SPAC1399.04c::KanMX4 pREP1-toe1+ leu1-32 hx GCY2733 SPCC1795.05::KanMX4 pREP1-toe1+ ade6-M216 leu1-32 ura4D18 h+ GCY990 Δace2::KanMX6 h- GCY3389 Δadn2::KANMX4 ade6-M216 leu1-32 ura4D18 h+ GCY3436 Δadn2::KANMX4 pREP1 ade6-M216 leu1-32 ura4D18 h+ GCY3433 Δadn2::KANMX4 pREP1-adn3+ ade6-M216 leu1-32 ura4D18 h+ GCY3434 Δadn2::KANMX4 pREP1-cbf12+ ade6-M216 leu1-32 ura4D18 h+ GCY3437 Δadn2::KANMX4 pREP1-foe1+ ade6-M216 leu1-32 ura4D18 h+ GCY3435 Δadn2::KANMX4 pREP1-mbx2+ ade6-M216 leu1-32 ura4D18 h+ GCY3430 Δadn2::KANMX4 pREP1-prr1+ ade6-M216 leu1-32 ura4D18 h+ GCY3431 Δadn2::KANMX4 pREP1-prt1+ ade6-M216 leu1-32 ura4D18 h+ GCY3432 Δadn2::KANMX4 pREP1-SPBC530.08 ade6-M216 leu1-32 ura4D18 h+ GCY3390 Δadn3::KANMX4 ade6-M216 leu1-32 ura4D18 h+ GCY3393 Δadn3::KANMX4 pREP1 ade6-M216 leu1-32 ura4D18 h+ GCY3391 Δadn3::KANMX4 pREP1-adn2+ ade6-M216 leu1-32 ura4D18 h+ GCY3398 Δadn3::KANMX4 pREP1-cbf12+ ade6-M216 leu1-32 ura4D18 h+ GCY3392 Δadn3::KANMX4 pREP1-foe1+ ade6-M216 leu1-32 ura4D18 h+ GCY3397 Δadn3::KANMX4 pREP1-mbx2+ ade6-M216 leu1-32 ura4D18 h+ GCY3418 Δadn3::KANMX4 pREP1-prr1+ ade6-M216 leu1-32 ura4D18 h+ GCY3394 Δadn3::KANMX4 pREP1-SPBC530.05 ade6-M216 leu1-32 ura4D18 h+ GCY3403 Δadn3::KANMX4 pREP1-SPBC530.08 ade6-M216 leu1-32 ura4D18 h+ GCY827 Δams2::KanMX6 h- GCY3272 Δarg7::KANMX6 pSLF272 ura4D18 h- GCY3273 Δarg7::KANMX6 pSLF272-SPBC1773.16c ura4D18 h- GCY1012 Δatf1::KanMX6 h- GCY965 Δatf21::KanMX6 h- GCY825 Δatf31::KanMX6 h- GCY3274 Δcar1::KANMX6 pSLF272 ura4D18 h- GCY3275 Δcar1::KANMX6 pSLF272-SPBC1773.16c ura4D18 h- GCY953 Δcbf11:KanMX6 h- GCY3314 Δcbf12::KANMX4 pREP1 ade6-M216 leu1-32 ura4D18 h+ GCY3358 Δcbf12::KANMX4 pREP1-adn2+ ade6-M216 leu1-32 ura4D18 h+ GCY3386 Δcbf12::KANMX4 pREP1-adn3+ ade6-M216 leu1-32 ura4D18 h+ GCY3383 Δcbf12::KANMX4 pREP1-mbx2+ ade6-M216 leu1-32 ura4D18 h+ GCY3419 Δcbf12::KANMX4 pREP1-prr1+ ade6-M216 leu1-32 ura4D18 h+ GCY3384 Δcbf12::KANMX4 pREP1-SPBC530.05 ade6-M216 leu1-32 ura4D18 h+ GCY3322 Δcbf12::KANMX4 pREP1-SPBC530.08 ade6-M216 leu1-32 ura4D18 h+ GCY1038 Δcbf12::KanMX6 h- GCY3278 Δcbf12:KANMX4 pREP1-foe1+ ade6-M216 leu1-32 ura4D18 h+ GCY929 Δcha4::KanMX6 h- GCY964 Δcuf1::KanMX6 h- GCY967 Δcuf2::KanMX6 h- GCY823 Δdeb1::KanMX6 h- GCY994 Δesc1::KanMX6 h- GCY996 Δfep1::KanMX6 h- GCY969 Δfhl1::KanMX6 h- GCY520 Δfkh2::KanMX6 h-

190

GCY3380 Δfoe1::KANMX4 pREP1-adn3+ ade6-M216 leu1-32 ura4D18 h+ GCY3367 Δfoe1::KANMX4 pREP1-cbf12+ ade6-M216 leu1-32 ura4D18 h+ GCY3460 Δfoe1::KANMX4 pREP1-mbx2+ ade6-M216 leu1-32 ura4D18 h+ GCY3424 Δfoe1::KANMX4 pREP1-prr1+ ade6-M216 leu1-32 ura4D18 h+ GCY3375 Δfoe1::KANMX4 pREP1-SPBC530.05 ade6-M216 leu1-32 ura4D18 h+ GCY3376 Δfoe1::KANMX4 pREP1-SPBC530.08 ade6-M216 leu1-32 ura4D18 h+ GCY3284 Δfoe1::KANMX6 pREP1 ade6-M216 leu1-32 ura4D18 h+ GCY3360 Δfoe1::KANMX6 pREP1-adn2+ ade6-M216 leu1-32 ura4D18 h+ GCY3282 Δfoe1::KANMX6 pREP1-mbx2+ ade6-M216 leu1-32 ura4D18 h+ GCY949 Δgaf1::KanMX6 h- GCY712 Δgrt1::KanMX6 h- GCY828 Δgsf1::KanMX6 h- GCY3311 Δgsf2::KANMX4 pREP1 ade6-M216 leu1-32 ura4D18 h+ GCY3362 Δgsf2::KANMX4 pREP1-adn2+ ade6-M216 leu1-32 ura4D18 h+ GCY3374 Δgsf2::KANMX4 pREP1-adn3+ ade6-M216 leu1-32 ura4D18 h+ GCY3361 Δgsf2::KANMX4 pREP1-cbf12+ ade6-M216 leu1-32 ura4D18 h+ GCY3321 Δgsf2::KANMX4 pREP1-foe1+ ade6-M216 leu1-32 ura4D18 h+ GCY3379 Δgsf2::KANMX4 pREP1-mbx2+ ade6-M216 leu1-32 ura4D18 h+ GCY3426 Δgsf2::KANMX4 pREP1-prr1+ ade6-M216 leu1-32 ura4D18 h+ GCY3385 Δgsf2::KANMX4 pREP1-SPBC530.05 ade6-M216 leu1-32 ura4D18 h+ GCY3387 Δgsf2::KANMX4 pREP1-SPBC530.08 ade6-M216 leu1-32 ura4D18 h+ GCY1033 Δklf1::KanMX6 h- GCY986 Δloz1::KanMX6 h- GCY988 Δmap1::KanMX6 h- GCY771 Δmat3-mc::KanMX6 h- GCY951 Δmbx1::KanMX6 h- GCY3286 Δmbx2::KANMX4 pREP1 ade6-M216 leu1-32 ura4D18 h+ GCY3445 Δmbx2::KANMX4 pREP1-adn3+ ade6-M216 leu1-32 ura4D18 h+ GCY3415 Δmbx2::KANMX4 pREP1-cbf12+ ade6-M216 leu1-32 ura4D18 h+ GCY3416 Δmbx2::KANMX4 pREP1-foe1+ ade6-M216 leu1-32 ura4D18 h+ GCY3446 Δmbx2::KANMX4 pREP1-prr1+ ade6-M216 leu1-32 ura4D18 h+ GCY3414 Δmbx2::KANMX4 pREP1-SPBC530.05 ade6-M216 leu1-32 ura4D18 h+ GCY3447 Δmbx2::KANMX4 pREP1-SPBC530.08 ade6-M216 leu1-32 ura4D18 h+ GCY971 Δmbx2::KanMX6 h- GCY1015 Δmca1::KanMX6 h- GCY973 Δmei4::KanMX6 h- GCY998 Δmoc3::KanMX6 h- GCY774 Δmug151::KanMX6 h- GCY976 Δpap1::KanMX6 h- GCY975 Δpcr1::KanMX6 h- GCY813 Δpho7::KanMX6 h- GCY717 Δphp2::KanMX6 h- GCY962 Δphp3::KanMX6 h- GCY984 Δphp5::KanMX6 h- GCY1022 Δphx1::KanMX6 h- GCY3319 Δprr1::KanMX6 ade6-M216 leu1-32 ura4D18 h- GCY715 Δprr1::KanMX6 h- GCY3396 Δprr1::KANMX6 pREP1 ade6-M216 leu1-32 ura4D18 h+ GCY3356 Δprr1::KANMX6 pREP1-adn2+ ade6-M216 leu1-32 ura4D18 h+ GCY3521 Δprr1::KANMX6 pREP1-adn3+ ade6-M216 leu1-32 ura4D18 h+ GCY3363 Δprr1::KANMX6 pREP1-cbf12+ ade6-M216 leu1-32 ura4D18 h+ GCY3370 Δprr1::KANMX6 pREP1-foe1+ ade6-M216 leu1-32 ura4D18 h+ GCY3371 Δprr1::KANMX6 pREP1-mbx2+ ade6-M216 leu1-32 ura4D18 h+ GCY3372 Δprr1::KANMX6 pREP1-SPBC530.05 ade6-M216 leu1-32 ura4D18 h+

191

GCY3357 Δprr1::KANMX6 pREP1-SPBC530.08 ade6-M216 leu1-32 ura4D18 h+ GCY3417 Δprt1::KanMX6 ade6-M216 leu1-32 ura4D18 h- GCY1043 Δprt1::KanMX6 h- GCY3455 Δprt1::KANMX6 pREP1 ade6-M216 leu1-32 ura4D18 h+ GCY3459 Δprt1::KANMX6 pREP1-adn2+ ade6-M216 leu1-32 ura4D18 h+ GCY3456 Δprt1::KANMX6 pREP1-adn3+ ade6-M216 leu1-32 ura4D18 h+ GCY3458 Δprt1::KANMX6 pREP1-cbf12+ ade6-M216 leu1-32 ura4D18 h+ GCY3452 Δprt1::KANMX6 pREP1-foe1+ ade6-M216 leu1-32 ura4D18 h+ GCY3457 Δprt1::KANMX6 pREP1-mbx2+ ade6-M216 leu1-32 ura4D18 h+ GCY3453 Δprt1::KANMX6 pREP1-prr1+ ade6-M216 leu1-32 ura4D18 h+ GCY3454 Δprt1::KANMX6 pREP1-SPBC530.08 ade6-M216 leu1-32 ura4D18 h+ GCY978 Δprz1::KanMX6 h- GCY980 Δrep1::KanMX6 h- GCY982 Δrep2::KanMX6 h- GCY526 Δres1::KanMX6 h- GCY1026 Δres2::KanMX6 h- GCY765 Δrst2::KanMX6 h- GCY1018 Δrsv1::KanMX6 h- GCY955 Δrsv2::KanMX6 h- GCY805 Δscr1::KanMX6 h- GCY3511 Δscr1::NATMX4 Δcbf12::KANMX6 GCY3513 Δscr1::NATMX4 Δfoe1::KANMX6 GCY3523 Δscr1::NATMX4 Δfta5::KANMX6 GCY3527 Δscr1::NATMX4 Δgsf2::KANMX6 GCY3509 Δscr1::NATMX4 Δmbx2::KANMX6 GCY3525 Δscr1::NATMX4 Δpfl9::KANMX6 GCY530 Δsep1::KanMX6 h- GCY989 Δsfp1::KanMX6 h- V3-P20-42 ΔSPAC1002.17::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P20-43 ΔSPAC1002.18::KANMX4 ade6-M216 leu1-32 ura4D18 h+ GCY2759 ΔSPAC1002.18::KanMX4 pREP1-toe1+ ade6-M216 leu1-32 ura4D18 h+ V3-P16-30 ΔSPAC1002.19::KANMX4 ade6-M216 leu1-32 ura4D18 h+ GCY2755 ΔSPAC1002.19::KanMX4 pREP1-toe1+ ade6-M216 leu1-32 ura4D18 h+ GCY1006 ΔSPAC1327.01::KanMX6 h- V3-P24-30 ΔSPAC1399.04c::KANMX4 ade6-M216 leu1-32 ura4D18 h+ GCY935 ΔSPAC19B12.07::KanMX6 h- GCY1010 ΔSPAC1F7.11::KanMX6 h- GCY1041 ΔSPAC25B8.11::KanMX6 h- GCY1024 ΔSPAC2H10.01::KanMX6 h- GCY807 ΔSPAC3C7.04::KanMX6 h- GCY763 ΔSPAC3F10.12::KanMX6 h- GCY809 ΔSPAC3H8.08::KanMX6 h- GCY819 ΔSPBC16G5.16::KanMX6 h- GCY937 ΔSPBC16G5.17::KanMX6 h- GCY999 ΔSPBC1773.12::KanMX6 h- GCY3305 ΔSPBC1773.13::KanMX6 ade6-M216 leu1-32 ura4D18 h- GCY3317 ΔSPBC1773.13::KANMX6 pSLF272 ura4D18 h- GCY3448 ΔSPBC1773.13::KANMX6 pSLF272-SPBC1773.16c ura4D18 h- GCY3306 ΔSPBC1773.15::KanMX6 ade6-M216 leu1-32 ura4D18 h- GCY3318 ΔSPBC1773.15::KANMX6 pSLF272 ura4D18 h- GCY3316 ΔSPBC1773.15::KANMX6 pSLF272-SPBC1773.16c ura4D18 h- GCY933 ΔSPBC1773.16::KanMX6 h- GCY1035 ΔSPBC19G7.04::KanMX6 h- GCY767 ΔSPBC29A10.12::KanMX6 h-

192

GCY3315 ΔSPBC530.08::KANMX4 pREP1 ade6-M216 leu1-32 ura4D18 h+ GCY3381 ΔSPBC530.08::KANMX4 pREP1-adn2+ ade6-M216 leu1-32 ura4D18 h+ GCY3382 ΔSPBC530.08::KANMX4 pREP1-adn3+ ade6-M216 leu1-32 ura4D18 h+ GCY3307 ΔSPBC530.08::KANMX4 pREP1-cbf12+ ade6-M216 leu1-32 ura4D18 h+ GCY3369 ΔSPBC530.08::KANMX4 pREP1-foe1+ ade6-M216 leu1-32 ura4D18 h+ GCY3359 ΔSPBC530.08::KANMX4 pREP1-mbx2+ ade6-M216 leu1-32 ura4D18 h+ GCY3420 ΔSPBC530.08::KANMX4 pREP1-prr1+ ade6-M216 leu1-32 ura4D18 h+ GCY3368 ΔSPBC530.08::KANMX4 pREP1-SPBC530.05 ade6-M216 leu1-32 ura4D18 h+ GCY992 ΔSPBC530.08::KanMX6 h- GCY1000 ΔSPBC56F2.05::KanMX6 h- GCY3505 ΔSPBC56F2.05c::NATMX4 Δcbf12::KANMX6 GCY3515 ΔSPBC56F2.05c::NATMX4 Δfta5::KANMX6 GCY3518 ΔSPBC56F2.05c::NATMX4 Δgsf2::KANMX6 GCY3507 ΔSPBC56F2.05c::NATMX4 Δmbx2::KANMX6 GCY3516 ΔSPBC56F2.05c::NATMX4 Δpfl9::KANMX6 GCY824 ΔSPCC1393.08::KanMX6 h- GCY930 ΔSPCC320.03::KanMX6 h- GCY939 ΔSPCC417.09::KanMX6 h- GCY1030 ΔSPCC757.04::KanMX6 h- GCY1004 ΔSPCC777.02::KanMX6 h- GCY1002 ΔSPCC965.10::KanMX6 h- GCY710 Δsre1::KanMX6 h- GCY959 Δsre2::KanMX6 h- GCY709 Δste11::KanMX6 h- GCY708 Δthi1::KanMX6 h- GCY1016 Δthi5::KanMX6 h- GCY929 Δtoe1::KanMX6 h- GCY1008 Δtoe2::KanMX6 h- GCY769 Δtoe3::KanMX6 h- GCY932 Δtoe4::KanMX6 h- GCY946 Δtos4::KanMX6 h- GCY944 Δyox1::KanMX6 h- GCY788 Δzip1::KanMX6 h-

193

Table A3: Primers used in this study Log Entry Name SEQUENCE Purpose GCO946 sfp1KOUpFor GAAGTATCACCACTCTTTTCGATTG GGACGAGGCAAGCTAAACAGATCTCTCGACTTAAGGAAGGCAAGTT GCO947 sfp1KanMX6UpRev C GCO948 sfp1KanMX6DownFor GATACTAACGCCGCCATCCAGGTAGACATGTTGTGTGGAGTTTG Knockout GCO949 sfp1KODownRev CTGGTGACTTATATACGAGTGATCTGT SFP1 with GCO950 sfp1KOConfirmFor CGCTTGCAACATATATTGCGTC KANMX6 GCO896 mei4KOUpFor TTCCAACGTTGATGACACTCC GCO897 mei4KanMX6UpRev GGACGAGGCAAGCTAAACAGATCTCTCTGTTTTTGGATGACGGG GCO898 mei4KanMX6DownFor GATACTAACGCCGCCATCCACTAGATCAAAGGCCATGCATTC Knockout GCO899 mei4KODownRev AGCCCGATTAGCCAGTTGTA MEI4 with GCO900 mei4KOConfirmFor CCAACGAGCAACATCCATCT KANMX6 GCO881 fhl1KOUpFor CGGTACATTTCCTGATGGAAG GCO882 fhl1KanMX6UpRev GGACGAGGCAAGCTAAACAGATCTGCTTTCTGACGCTGTAACAACG GCO883 fhl1KanMX6DownFor GATACTAACGCCGCCATCCACACTTACCAGCATGCATAAGCTG Knockout GCO884 fhl1KODownRev GTGAGCGGTCTATGGAAAAGTG FHL1 with GCO885 fhl1KOConfirmFor GCAGTGAGGGCCACTATATGTA KANMX6 GCO901 moc3KOUpFor GGGCTTATCAAATTTGCCG GCO902 moc3KanMX6UpRev GGACGAGGCAAGCTAAACAGATCTAAAACGCTACGCTGCTTGAG Knockout GCO903 moc3KanMX6DownFor GATACTAACGCCGCCATCCACGCATAGTGTAAAAAGAGCCG MOC3 GCO904 moc3KODownRev TGATTCTGATGACGCCGATG with GCO905 moc3KOConfirmFor GCTTGGCTCGTGATTTTCTCA KANMX6 GCO318 SCR1UpFor CATTCCTCTCTTCGTTCGATC GCO319 SCR1UpRev GGACGAGGCAAGCTAAACAGATCTCAACGATGAGAGAGGATTGG GCO320 SCR1DownFor GATACTAACGCCGCCATCCACTCGACATTTGAGGTTCCATTG Knockout GCO321 SCR1DownRev GGCTGTTTTACTGACCCATAC SCR1 with GCO398 SCR1KOConfirmFoward CCTGTTGTATTCGGTCCGTT KANMX6 GCO1031 SPCC965.10KOUpFor CGGACTCATTTACTCCATCATCG GCO1032 SPCC965.10KanMX6UpRev GGACGAGGCAAGCTAAACAGATCTCCTACTAGCGCGTACAAAGTCA Knockout GCO1033 SPCC965.10KanMX6DownFor GATACTAACGCCGCCATCCAGTGCTGGATTCGATGATTTGC SPCC965.1 GCO1034 SPCC965.10KODownRev ACCACCTCATACCAGTCAATGC 0 with GCO1035 SPCC965.10KOConfirmFor CTATCCTTTCAAGCATTCGATGAG KANMX6 GCO921 php5KOUpFor CACTTCGACGAGTTAAGGTGCT Knockout GCO922 php5KanMX6UpRev GGACGAGGCAAGCTAAACAGATCTGGAAACGTAAAGGCCTTTGC PHP5 with GCO923 php5KanMX6DownFor GATACTAACGCCGCCATCCACCAACAGTGATGCATTTTTAGGTG KANMX6

194

GCO924 php5KODownRev GAAAGCAGCTGTAATTTCCTTGC GCO925 php5KOConfirmFor CTGCCAGCCACCAATAAATT GCO966 SPAC25B8.19cKOUpFor GTTCGTTGGAATTTGGTCACGT GCO967 SPAC25B8.19cKanMX6UpRev GGACGAGGCAAGCTAAACAGATCTGCAAAGAAGCGTCACTGGATAG Knockout GCO968 SPAC25B8.19cKanMX6DownFor GATACTAACGCCGCCATCCAACCACATAGCAGTTGGCTGC SPAC25B8 GCO969 SPAC25B8.19cKODownRev CCAACGACCGATTTTTACGTATC .19 with GCO970 SPAC25B8.19cKOConfirmFor GTTCCCTTTGTTGCTGCATCT KANMX6 GCO941 rsv1KOUpFor CTTGCTAGCAATTCGCTGCT GCO942 rsv1KanMX6UpRev GGACGAGGCAAGCTAAACAGATCTGTAGAGTGGAAAAGGCCGATTC GCO943 rsv1KanMX6DownFor GATACTAACGCCGCCATCCAGAGGTAACATTTCCGCAGCT Knockout GCO944 rsv1KODownRev GGATGGATGGTAATGAAAAGTCG RSV1 with GCO945 rsv1KOConfirmFor GTCGTTCTGCCTTTCATAGCC KANMX6 GCO986 SPBC15D4.02KOUpFor CTCCACTCCCAATTGATTGAAC GCO987 SPBC15D4.02KanMX6UpRev GGACGAGGCAAGCTAAACAGATCTGATGTAGGAGTAAAAGGCGGC GCO988 SPBC15D4.02KanMX6DownFor GATACTAACGCCGCCATCCAGCCGTGTACTTTGTTGCTTG Knockout GCO989 SPBC15D4.02KODownRev GAAAACACGTAAACTGCATGGG GSF1 with GCO990 SPBC15D4.02KOConfirmFor GACTCACTCGACCACTTTTCTG KANMX6 GCO514 SPAC1399.05NatKOUpFor TTTTTGCCAGGTGTTTTGAGGG GGACGAGGCAAGCTAAACAGATCTCACCTTGGAGCAACTATTAGCG GCO518 SPAC1399.05KanMX6KOUpRev T GCO519 SPAC1399.05KanMX6KODownFor GATACTAACGCCGCCATCCAGAAAATCGGCTGTAAAGTGTCTG Knockout GCO517 SPAC1399.05NatKODownRev ACGGGAACTGAGTAGTTGCAA TOE1 with GCO506 SPAC1399.05KOConfirmFor CTGATGTAAGTGGTGATCACCG KANMX6 GCO996 SPBC1773.12KOUpFor GTGAATGATTATGGCTCCTTCC GCO997 SPBC1773.12KanMX6UpRev GGACGAGGCAAGCTAAACAGATCTAAACTACTCGTACAGGGCACCT Knockout GCO998 SPBC1773.12KanMX6DownFor GATACTAACGCCGCCATCCAGATTTTCGCATGTACTCAGTAAGG SPBC1773. GCO999 SPBC1773.12KODownRev CTGACTACGGTCCCATTTGGA 12 with GCO1000 SPBC1773.12KOConfirmFor CTCAAAGTCGAAATCTCATTAGGC KANMX6 GCO614 PAP1KanMX6KOUpFor CTATGTTTCGGAATGGAGGC GCO615 PAP1KanMX6KOUPRev GGACGAGGCAAGCTAAACAGATCTGAAAAGAAGAAGGTGCAGCC GCO616 PAP1KanMX6KODownFor GATACTAACGCCGCCATCCACGCCTCGTGATATAATCAACGC Knockout GCO617 PAP1KanMX6KODownRev GTCTCAATGGATGGAAGCTG PAP1 with GCO618 PAP1KanMX6KOConfirmUpFor CAACGATAACAGCATGAATTCG KANMX6 GCO290 STE11UpFor TCACTCCTTCTTCCACTTGG Knockout GCO291 STE11UpRev GGACGAGGCAAGCTAAACAGATCTAGAAGCGAACAGCACAAACC STE11 GCO292 STE11DownFor GATACTAACGCCGCCATCCACGTCACAAAAGCATGCAGCT with GCO293 STE11DownRev ACCTTTCGCCATTGAAACCG KANMX6

195

GCO406 STE11KOConfirmForward GCCTTAGAGGCTCGTGATTTTG GCO554 PRZ1KanMX6KOUpFor AAAGCGTTTAGCTGACGCTG GCO555 PRZ1KanMX6KOUpRev GGACGAGGCAAGCTAAACAGATCTTAAACCAGCAACACCAAGGG GCO556 PRZ1KanMX6KODownFor GATACTAACGCCGCCATCCAGCACAGTTTTGCATTTAGGGTTT Knockout GCO557 PRZ1KanMX6KODownRev CTTGCGAAAACCAAAAGCGT PRZ1 with GCO558 PRZ1KanMX6KOConfirmFor GATCACGTCTTGTGCAATCCT KANMX6 GCO971 SPAC2H10.01KOUpFor CGCCGGTTTTGTTTCTTACG GGACGAGGCAAGCTAAACAGATCTGAACCAGCTCTTATATAACAGG GCO972 SPAC2H10.01KanMX6UpRev CA Knockout GCO973 SPAC2H10.01KanMX6DownFor GATACTAACGCCGCCATCCACATTTCTCTTCAAAATCCGGC SPAC2H10 GCO974 SPAC2H10.01KODownRev GTTGTCATTTGCGCTGAATGA .01 with GCO975 SPAC2H10.01KOConfirmFor CTTCTAGCCATCGAATTGGTACTG KANMX6 GCO876 fep1KOUpFor GGTGCCTTGGTACTTTAAGGCT GGACGAGGCAAGCTAAACAGATCTGTACTGAACGAAATGATCCAAG GCO877 fep1KanMX6UpRev C GATACTAACGCCGCCATCCACCCGTTTAGCTGTCTATTACTTACAAG GCO878 fep1KanMX6DownFor C Knockout GCO879 fep1KODownRev GATACTGCTTTTTACAAGCAACTCC FEP1 with GCO880 fep1KOConfirmFor GTTTTCTGCGAGTCGGTTTTC KANMX6 GCO1011 SPBC530.05KOUpFor TCTAATGCTACCCCTCAGGTATGT GCO1012 SPBC530.05KanMX6UpRev GGACGAGGCAAGCTAAACAGATCTCAAGTGCAACGACAGTCGAT Knockout GCO1013 SPBC530.05KanMX6DownFor GATACTAACGCCGCCATCCACTAGATGCGCATGATATGATAATCC SPBC530.0 GCO1014 SPBC530.05KODownRev CATTCAGAGTCAGAAGTGTTTGC 5 with GCO771 SPBC530.05ConfirmFor AGAAGTCGGACTTCGGACTTC KANMX6 GCO951 SPAC11D3.07cKOUpFor CAGGAGAACGAGTACATATCTGGTG GGACGAGGCAAGCTAAACAGATCTGCTGAATGCGTATCAATTGTAA GCO952 SPAC11D3.07cKanMX6UpRev GC GCO953 SPAC11D3.07cKanMX6DownFor GATACTAACGCCGCCATCCATCATTACCGGTTAACAATGAAGC Knockout GCO954 SPAC11D3.07cKODownRev TGTAGGTGTTGCAGTAGGAAGC TOE4 with GCO955 SPAC11D3.07cKOConfirmFor GTAATGGTCTTTTGCGTGGTG KANMX6 GCO936 res2KOUpFor GTGAAAACGGATTCCCAGGT GCO937 res2KanMX6UpRev GGACGAGGCAAGCTAAACAGATCTAGGGTGGGGCAATACAGAAA GCO938 res2KanMX6DownFor GATACTAACGCCGCCATCCACCATGACACTCACTGTCAAACTG Knockout GCO939 res2KODownRev AAAGGCTGCACTAACGTCCTC RES2 with GCO940 res2KOConfirmFor CCTGACGCAAGTACTGATGATG KANMX6 GCO916 php3KOUpFor CCCTTATACGCCACTTGAAAGT GCO917 php3KanMX6UpRev GGACGAGGCAAGCTAAACAGATCTCCCCGCTAAAATTGGTTTGAC

196

GCO918 php3KanMX6DownFor GATACTAACGCCGCCATCCAGATCGTCGATCGCCTTATTTG Knockout GCO919 php3KODownRev AAACAGATGGGAAACGAGGC PHP3 with GCO920 php3KOConfirmFor CTTTTTACTGGGAAACCTTGGC KANMX6 GCO549 RDP1KanMX6KOUpFor CATCTTCCACCCCGGATATAAAC GCO550 RDP1KanMX6KOUpRev GGACGAGGCAAGCTAAACAGATCTTCGACTTTGCTTTGCTCAGTC GCO551 RDP1KanMX6KODownFor GATACTAACGCCGCCATCCACCCTAATCACCATGTTCTCTTTACA Knockout GCO552 RDP1KanMX6KODownRev GCTGAGACGTGCATACAAACCA DEB1 with GCO553 RDP1KanMX6KOConfirmFor GCAAGTCAGGGCAGTTCTTC KANMX6 GCO564 SPAPB24D3.01KanMX6KOUpFor AAGTCCGAAAGTAAAGTCCGC GCO565 SPAPB24D3.01KanMX6KOUpRev GGACGAGGCAAGCTAAACAGATCTAATTCAGGCAAAATGAGCAGC SPAPB24D3.01KanMX6KODownF GCO566 or GATACTAACGCCGCCATCCATAGTTTAACTCGGTGTTGATTGC SPAPB24D3.01KanMX6KODownR GCO567 ev TTGGTTATGGTTCATGGCGC Knockout SPAPB24D3.01KanMX6KOConfir TOE3 with GCO568 mFor AGAGTTGCTTTCTGGTTTATGGAG KANMX6 GCO589 MAP1KanMX6KOUpFor TCTGCGAGAAATGCTATAGCC GGACGAGGCAAGCTAAACAGATCTCCTTATAACGAACTTGGAGTTC GCO590 MAP1KanMX6KOUpRev G GCO591 MAP1KanMX6KODownFor GATACTAACGCCGCCATCCAGATGCCATTCTGTATTGCCTATTT Knockout GCO592 MAP1KanMX6KODownRev TGGGACGTGGTTTAAGTCGTAC MAP1 with GCO593 MAP1KanMX6KOConfirmFor CTCACGAATTTTCCTCACCAAG KANMX6 GCO569 SPAC3F10.12KanMX6KOUpFor GCTTGTCAATGTTAGGGATCCG GCO570 SPAC3F10.12KanMX6KOUpRev GGACGAGGCAAGCTAAACAGATCTGCTGATGAATCCAGCCAATG GCO571 SPAC3F10.12KanMX6KODownFor GATACTAACGCCGCCATCCACCCTTGAGGTCCACGTTTATAG Knockout GCO572 SPAC3F10.12KanMX6KODownRev CCTGAAACAGTTATTTCCTGCG SPAC3F10 SPAC3F10.12KanMX6KOConfirmF .12 with GCO573 or AGCGTAGCCCTTCCTTAAGTG KANMX6 GCO871 cuf2KOUpFor GTCTTTAGGTTGCAAACTTTTAAGC GCO872 cuf2KanMX6UpRev GGACGAGGCAAGCTAAACAGATCTCCAGAAGAATTGCACAACGG GCO873 cuf2KanMX6DownFor GATACTAACGCCGCCATCCAGTTTTGGTCGTCTAAAAAGGTGC Knockout GCO874 cuf2KODownRev ATCGAAGTGAACTAAGCGCC CUF2 with GCO875 cuf2KOConfirmFor GGTCTGACAAGCCTACCTCATTT KANMX6 GCO539 AMS2KanMX6KOUpFor GAGCACAAGAAAAGTGATGCTC GCO540 AMS2KanMX6KOUpRev GGACGAGGCAAGCTAAACAGATCTGGTGGGTTAAATCTTTGGCTAG Knockout GCO541 AMS2KanMX6KODownFor GATACTAACGCCGCCATCCACAAGAAGTCATGTGTGATCAAAATTG AMS2 with GCO542 AMS2KanMX6KODownRev GCTTTGTCCATATCGGCTTTC KANMX6

197

GCO543 AMS2KanMX6KOConfirmFor GCGGCCGTATAAATCACCTC GCO911 pcr1KOUpFor GGTTTTCACAGTGGATTCGC GCO912 pcr1KanMX6UpRev GGACGAGGCAAGCTAAACAGATCTACAAAGGGGGAGATGGGAAAT GCO913 pcr1KanMX6DownFor GATACTAACGCCGCCATCCAATGTTCTCTCCTAGATGACAGTCG Knockout GCO914 pcr1KODownRev CATCATGATACACGGATTGTGG PCR1 with GCO915 pcr1KOConfirmFor CCGTCTATGGTCCATTGTAGC KANMX6 GCO926 rep1KOUpFor GAAAGGATACGTAGTAGTGCGTACG GGACGAGGCAAGCTAAACAGATCTGTTCAGAAACAAACGTAAGGCT GCO927 rep1KanMX6UpRev G GCO928 rep1KanMX6DownFor GATACTAACGCCGCCATCCAGAGGCGATCTTTGAAGTCTCTC Knockout GCO929 rep1KODownRev TGCTCGTTCATGTTCTGTAGGAG REP1 with GCO930 rep1KOConfirmFor CCCTTACCCACTTTTTGTCG KANMX6 GCO559 RST2KanMX6KOUpFor GTGTGGATGGCGTTTATCTTTTT GGACGAGGCAAGCTAAACAGATCTGAAAGTGATTGCAATCAGGATT GCO560 RST2KanMX6KOUpRev C GCO561 RST2KanMX6KODownFor GATACTAACGCCGCCATCCAGGGGTTCAATTGTTGATAGGAA Knockout GCO562 RST2KanMX6KODownRev AGTGCATGCAGAGTGAATGC RST2 with GCO563 RST2KanMX6KOConfirmFor CCTTTCCTTACATTTCCGATTCTC KANMX6 GCO1046 SPCC757.04KOUpFor GCCATGGTCTTTACCGTGTTT GGACGAGGCAAGCTAAACAGATCTCCGTAACCGACATTCCTAAACT GCO1047 SPCC757.04KanMX6UpRev TC Knockout GCO1048 SPCC757.04KanMX6DownFor GATACTAACGCCGCCATCCAGATGCATGCTGCTGCATTATT SPCC757.0 GCO1049 SPCC757.04KODownRev ATTCACCTCCTCAATACCTCGA 4 with GCO1050 SPCC757.04KOConfirmFor ACCTACTAAACAATCGTGGCGA KANMX6 GCO1151 sre2KOUpFor CTCCGTCATCATATAATTGCTGTC GGACGAGGCAAGCTAAACAGATCTCACTCAGTAGTAATGGAGAGAA GCO1152 sre2KanMX6UpRev TTCG GCO1153 sre2KanMX6DownFor GATACTAACGCCGCCATCCACGTGTTCTTTTGTGGATGTTTCC Knockout GCO1154 sre2KODownRev CCTCATTAGAGGGAGCTGGATA SRE2 with GCO1155 sre2KOConfirmFor GACTCATTACATATCGGAACACCTG KANMX6 GCO282 ZIP1UpFor TGCGAATTGTCTGACGCACT GCO283 ZIP1UpRev GGACGAGGCAAGCTAAACAGATCTCACACTCAACCGTTCGCAAT GCO284 ZIP1DownFor GATACTAACGCCGCCATCCATGCATCCAACTGCTACGTTC Knockout GCO285 ZIP1DownRev CTCAAGTAGCAGTACGCTGA ZIP1 with GCO408 ZIP1KOConfirmForward GAAGAATATGACTTTGTTCCGGAA KANMX6 GCO1051 SPCC777.02KOUpFor TTCCAGAGAGTGTGTAGGCATCA Knockout GCO1052 SPCC777.02KanMX6UpRev GGACGAGGCAAGCTAAACAGATCTAAGCTTGCCATGTGTGCTGA SPCC777.0

198

GATACTAACGCCGCCATCCATTACCTATCTATATGTGCATACGGTGT 2 with GCO1053 SPCC777.02KanMX6DownFor A KANMX6 GCO1054 SPCC777.02KODownRev TGAACTTGGAAAGTCGCTGGT GCO1055 SPCC777.02KOConfirmFor CAACAATGCCAGCACCTTCA GCO326 PHP2UpFor GGTGGTGGGAATAAGAGTTG GCO327 PHP2UpRev GGACGAGGCAAGCTAAACAGATCTGACTACCAATGGTAAGCAGC GCO328 PHP2DownFor GATACTAACGCCGCCATCCAGAAATCACTCAGTCATGGCC Knockout GCO329 PHP2DownRev CGAATGCCTCTCGAAATGGA PHP2 with GCO395 PHP2KOConfirmForward CAAAAGACGGTACTTCTGGGTG KANMX6 GCO342 ESC1UpFor CGACTCGAATTGGAATTCGT GCO343 ESC1UpRev GGACGAGGCAAGCTAAACAGATCTCCGACTAACAAACAACCAACG GCO344 ESC1DownFor GATACTAACGCCGCCATCCACTCTGGACTGATAGGCATTCA Knockout GCO345 ESC1DownRev GCCATTGATGAACGATGAACC ESC1 with GCO389 ESC1KOConfirmForward CGTCTTCAGTGCTCTTCTTGTTT KANMX6 GCO856 atf21KOUpFor TAAGCCTCTTTACTTGCCAGGG GCO857 atf21KanMX6UpRev GGACGAGGCAAGCTAAACAGATCTATGTTATTCAGGTGCTGGCG Knockout GCO858 atf21KanMX6DownFor GATACTAACGCCGCCATCCAGAGATGCTTAAAAGACGGCCA ATF21 GCO859 atf21KODownRev GATCTTTTGCTGCAAAACGC with GCO860 atf21KOConfirmFor CGACTGCAGCATCATATTTCTC KANMX6 GCO1016 SPBC56F2.05cKOUpFor CGATCGTCGTTGATTATACCATC GGACGAGGCAAGCTAAACAGATCTCACACTCTAAAAAAATTGGCAT GCO1017 SPBC56F2.05cKanMX6UpRev CC Knockout GCO1018 SPBC56F2.05cKanMX6DownFor GATACTAACGCCGCCATCCAGCAGCTTCAAATTGTTTACGACC SPBC56F2. GCO1019 SPBC56F2.05cKODownRev AAAGTTGGCGCGACTGTGTT 05 with GCO1020 SPBC56F2.05cKOConfirmFor ACTTTTCCCCCTTCGTGTTG KANMX6 GCO322 PRR1UpFor CAACGAGCATATTGCACAGTG GCO323 PRR1UpRev GGACGAGGCAAGCTAAACAGATCTGTTAGCTTACGCAACGTTGG GCO324 PRR1DownFor GATACTAACGCCGCCATCCAGGGGTTCAAGTTAAGGCTTTC Knockout GCO325 PRR1DownRev GGTGGCAAACAATTTGTCGAC PRR1 with GCO396 PRR1KOConfirmFoward GCAAGGTCAATTGTGTTGAATGC KANMX6 GCO981 SPAPB1A11.04cKOUpFor GTTTTGAACACAACTGAGGGC GGACGAGGCAAGCTAAACAGATCTGAAATCCTGCGTTGAGTAGACA GCO982 SPAPB1A11.04cKanMX6UpRev G Knockout GCO983 SPAPB1A11.04cKanMX6DownFor GATACTAACGCCGCCATCCACTACACCCCTGGAATTCCTTT MCA1 GCO984 SPAPB1A11.04cKODownRev GACAACAAGATGGAGGTGTTGC with GCO769 SPAPB1A11.04cConfirmFor GCAACCTATCTCTTTATACAGCCC KANMX6 GCO851 atf1KOUpFor CTCTACGCTTGACTGTACTCTCTCAC

199

GGACGAGGCAAGCTAAACAGATCTGGCTTTCCAAATTTGACAAGAC GCO852 atf1KanMX6UpRev AG GCO853 atf1KanMX6DownFor GATACTAACGCCGCCATCCACTTCCCTTTGACTTTCCCTCC Knockout GCO854 atf1KODownRev CATCCTCAATGTAGCGACAAAC ATF1 with GCO855 atf1KOConfirmFor GTGTTTTAGCAGGTGTATTTGCTG KANMX6 GCO1096 SPAC3H8.08cKOUpFor GCACTTTGTTAATATGGTTTTGCG GCO1097 SPAC3H8.08cKanMX6UpRev GGACGAGGCAAGCTAAACAGATCTAGACCGACCAGCGGTTGTTAT Knockout GCO1098 SPAC3H8.08cKanMX6DownFor GATACTAACGCCGCCATCCACGATTAATGTTCCAAAGTGGTTC SPAC3H8. GCO1099 SPAC3H8.08cKODownRev GAAGAACTCATTCTCTATGCAACAG 08 with GCO1100 SPAC3H8.08cKOConfirmFor GCAGAGCTCAAGCAACCTGAT KANMX6 GCO1021 SPCC320.03KOUpFor GCTCTTTGTTTCGGTTCTGC GGACGAGGCAAGCTAAACAGATCTGAACGAGGGAACGATATTTTAA GCO1022 SPCC320.03KanMX6UpRev AG Knockout GCO1023 SPCC320.03KanMX6DownFor GATACTAACGCCGCCATCCAAGCCTTTGTTACCGACGTATCG SPCC320.0 GCO1024 SPCC320.03KODownRev CAGCCATTTGGAACACCTCAA 3 with GCO1025 SPCC320.03KOConfirmFor ATAATTTGCATCTGCGCGTC KANMX6 GCO1006 SPBC19G7.04KOUpFor CAGGTCTTCTATCGGGTACTATGAG GCO1007 SPBC19G7.04KanMX6UpRev GGACGAGGCAAGCTAAACAGATCTCGTGACTGCTTTTTTGCTGAC Knockout GCO1008 SPBC19G7.04KanMX6DownFor GATACTAACGCCGCCATCCAGCAGACGGTGCACCAATAA SPBC19G7 GCO1009 SPBC19G7.04KODownRev CCTTATATCGACAAACTTCACTCG .04 with GCO770 SPBC19G7.04ConfirmFor CCTCAAGCATGGTAAGTACCGA KANMX6 GCO94 GCFKH2KOFORUP GGGTAATTACTAGGATATGCAC GCO95 GCFKH2KOREVDN CAGTGATTAGTAGCATACTAC GCO96 GCFKH2KOREVUP CTACAGCAATTGTGCTGTTG Knockout GCO97 GCFKH2KOFORDN GACCTATTCTTGTGCAAACG FKH2 with GCO358 FKH2ConfirmUpFor GCTATGCAATTCGAGCAAGG KANMX6 GCO961 SPAC25B8.11KOUpFor ATCGCAAAATTAGGGCTCGT GCO962 SPAC25B8.11KanMX6UpRev GGACGAGGCAAGCTAAACAGATCTGCCAAATGAAACCTTCGAGC Knockout GCO963 SPAC25B8.11KanMX6DownFor GATACTAACGCCGCCATCCAGTACATCTGAATTGGTGAAAATGC SPAC25B8 GCO964 SPAC25B8.11KODownRev CGTACGTCGTTGTTGAAGCAAC .11 with GCO965 SPAC25B8.11KOConfirmFor GCTTCGCTTATCATATGCAGCT KANMX6 GCO1136 ace2KOUpFor GGCATTTTTTCTCTTGCTAGCTC GGACGAGGCAAGCTAAACAGATCTGCCAGTCTAACCCAGTAGATGA GCO1137 ace2KanMX6UpRev TTG GCO1138 ace2KanMX6DownFor GATACTAACGCCGCCATCCAGCATCGCATTGCATTATAATGAG Knockout GCO1139 ace2KODownRev ATAATAAATGCTTGCTTGGCAGC ACE2 with GCO1140 ace2KOConfirmFor AAGGTGGAGGTACACCGGATT KANMX6

200

GCO1061 SPAC1327.01cKOUpFor TGCCTGTCGCTAAGAAGAATC GGACGAGGCAAGCTAAACAGATCTGTTGAGTATAGTGGCGTGCTAA GCO1062 SPAC1327.01cKanMX6UpRev GG Knockout GCO1063 SPAC1327.01cKanMX6DownFor GATACTAACGCCGCCATCCACTCGTTGACCATCGACATTAAGTAC SPAC1327. GCO1064 SPAC1327.01cKODownRev ACTTTATCGAGAGCAAGACACCAA 01 with GCO1065 SPAC1327.01cKOConfirmFor CATTCTAGCGAATGTACGGCAC KANMX6 GCO1111 SPBC1773.16cKOUpFor GGTATGGTACATGAATCGATCCTTC GCO1112 SPBC1773.16cKanMX6UpRev GGACGAGGCAAGCTAAACAGATCTGGGCTTTACGAATGGTCCA Knockout GCO1113 SPBC1773.16cKanMX6DownFor GATACTAACGCCGCCATCCAGCTTACTGTATCGTGTTCTTATCGC SPBC1773. GCO1114 SPBC1773.16cKODownRev TTCCCATGTTGTGGTCCACA 16 with GCO1115 SPBC1773.16cKOConfirmFor GATCTAAGGCTAAAATCATAAACTGC KANMX6 GCO1116 SPAC19B12.07cKOUpFor ACTACGATAAGTACTTACGACCTGCT GGACGAGGCAAGCTAAACAGATCTCAGTTTCAAGTCTAAGTAATGC GCO1117 SPAC19B12.07cKanMX6UpRev GGA Knockout GCO1118 SPAC19B12.07cKanMX6DownFor GATACTAACGCCGCCATCCAGGCATAGTTTCCTGAAGTCTCGTA SPAC19B1 GCO1119 SPAC19B12.07cKODownRev TAAGCGCTGGTTATACCAGCC 2.07 with GCO1120 SPAC19B12.07cKOConfirmFor ACTCACCGCAGTTCTACGTATCCT KANMX6 GCO820 SPBC29A10.12KOUpFor TGAAAGCAATTGTTCACTGTACG GCO821 SPBC29A10.12KanMX6UpRev GGACGAGGCAAGCTAAACAGATCTGGCGACGAAGGATGGATTAA Knockout GCO822 SPBC29A10.12KanMX6DownFor GATACTAACGCCGCCATCCACTCGACATTCAAAAATGAAATCTT SPAC29A1 GCO823 SPBC29A10.12KODownRev GCAATAGCAGAAGCAGTTGC 0.12 with GCO824 SPBC29A10.12KOConfirmFor GTTTGTACGTTATTTGTATCGAAGATG KANMX6 GCO956 SPAC139.03KOUpFor TAAGTGCATGCTCTCAACCGT GGACGAGGCAAGCTAAACAGATCTGGAGCGAAAAGAAGTTAATTCC GCO957 SPAC139.03KanMX6UpRev G GCO958 SPAC139.03KanMX6DownFor GATACTAACGCCGCCATCCACAATTACAGAACAACTACGTCCGC Knockout GCO959 SPAC139.03KODownRev TTCTGACACCGAAGATGCAAAC TOE2 with GCO960 SPAC139.03KOConfirmFor CGTTGGAAACGCATGGTAGA KANMX6 GCO1076 gaf1KOUpFor TTCCACCACACCTCTGGATT GCO1077 gaf1KanMX6UpRev GGACGAGGCAAGCTAAACAGATCTCAGCCAGCTTGAAATGTGGA GCO1078 gaf1KanMX6DownFor GATACTAACGCCGCCATCCAGTCGATTAATTGTATGAGCAAGAGG Knockout GCO1079 gaf1KODownRev AACGAACGCCAAGGATTGAG GAF1 with GCO1080 gaf1KOConfirmFor GATTTGAGGAAGCCATAGCG KANMX6 GCO1121 SPAC1F7.11cKOUpFor GTTGCTTGCTTGTCTCTTCTACTAGT Knockout GCO1122 SPAC1F7.11cKanMX6UpRev GGACGAGGCAAGCTAAACAGATCTCATCGAGGCATAGGAAATGC SPAC1F7. GCO1123 SPAC1F7.11cKanMX6DownFor GATACTAACGCCGCCATCCAAGAATTATCCTCATAAGCCGGCTA 11 with GCO1124 SPAC1F7.11cKODownRev TAGACGACACACAATGCATGATC KANMX6

201

GCO1125 SPAC1F7.11cKOConfirmFor CATTTTCCTCACATAGTCGATTGC GCO1106 SPBC16G5.16KOUpFor GTTCGTAGCGCACTATAATGTGTG GCO1107 SPBC16G5.16KanMX6UpRev GGACGAGGCAAGCTAAACAGATCTGCAAGTGAGCAAAAATGCAG Knockout GCO1108 SPBC16G5.16KanMX6DownFor GATACTAACGCCGCCATCCAAGGGTTCTAGCTCCAACTTTTAGG SPBC16G5 GCO1109 SPBC16G5.16KODownRev TTAGCCTAGATGCCAAAGGTACTC .16 with GCO1110 SPBC16G5.16KOConfirmFor CTAACTACGCGATTCGCGAA KANMX6 GCO1026 SPCC417.09cKOUpFor AACGCTTCCTAAGCAGTCGA GCO1027 SPCC417.09cKanMX6UpRev GGACGAGGCAAGCTAAACAGATCTGTGGTTTGCACGTTTGCGTA Knockout GCO1028 SPCC417.09cKanMX6DownFor GATACTAACGCCGCCATCCATCCCATGGTCACATTGCATC SPCC417.0 GCO1029 SPCC417.09cKODownRev TGATTGCAGACGCTATCCAG 9 with GCO1030 SPCC417.09cKOConfirmFor CATCCAAGATCGGATATCAGG KANMX6 GCO866 cuf1KOUpFor CCCAGTTTCCGACAACCAAA GCO867 cuf1KanMX6UpRev GGACGAGGCAAGCTAAACAGATCTCACGTTCGAAGATGAATGACC GCO868 cuf1KanMX6DownFor GATACTAACGCCGCCATCCAAAGTGTGTGGCACGCAAAAC Knockout GCO869 cuf1KODownRev GTTCATTGCAAGAACTTCACCAG CUF1 with GCO870 cuf1KOConfirmFor CAGAAAAAGCAGCTTCTTCGTAC KANMX6 GCO163 SEP1UpFor AATTGGAGCAATTCGGAGCA GCO164 SEP1UpRev GGACGAGGCAAGCTAAACAGATCTCCGTTAGATAATGGCAATGCC GCO165 SEP1DownFor GATACTAACGCCGCCATCCACTTTCAAACCCCCGATGTTAC Knockout GCO166 SEP1DownRev CTTATCCAATCCTTCATCTCGG SEP1 with GCO360 SEP1ConfirmUpFor GCTTTCACGAGGTGGAGATT KANMX6 GCO891 mbx2KOUpFor CACGCACTTATAAACGCACACA GCO892 mbx2KanMX6UpRev GGACGAGGCAAGCTAAACAGATCTCAAACGAGCAAACGAGCGA Knockout GCO893 mbx2KanMX6DownFor GATACTAACGCCGCCATCCAGGCATACATCTCGATCAAATCC MBX2 GCO894 mbx2KODownRev GAATAGACAAAATGCGTGCCTG with GCO895 mbx2KOConfirmFor CGATGCTGGCGTCGATACTT KANMX6 GCO1036 thi5KOUpFor CAACGCATTTTGGGAATTCG GCO1037 thi5KanMX6UpRev GGACGAGGCAAGCTAAACAGATCTCCCGAAGGTGGTAAGTAGCA GCO1038 thi5KanMX6DownFor GATACTAACGCCGCCATCCAGGACCGCTAGCTATATTGACTGTTC Knockout GCO1039 thi5KODownRev GAAGCGTGAGGATGCTTGAA THI5 with GCO1040 thi5KOConfirmFor GAAGGCTTAAATGGTGCAGTG KANMX6 GCO991 SPBC1683.13cKOUpFor CCTGTGGCTAATAGCACTTTGTC GCO992 SPBC1683.13cKanMX6UpRev GGACGAGGCAAGCTAAACAGATCTGCTAGTCCATCGAATTTCCAAA GCO993 SPBC1683.13cKanMX6DownFor GATACTAACGCCGCCATCCAGGAACTTCAGTGGAACACTCGA Knockout GCO994 SPBC1683.13cKODownRev CAAATCTGGAAACATCGTCCAG CHA4 with GCO764 SPBC1683.13ConfirmFor CAAGACGCCACATCGCTTTT KANMX6 GCO1131 klf1KOUpFor CACCGATCTGCATCAGAATC

202

GGACGAGGCAAGCTAAACAGATCTGAAGAAACGCAAGTAACCTCAC GCO1132 klf1KanMX6UpRev TTT GCO1133 klf1KanMX6DownFor GATACTAACGCCGCCATCCACCATAGTTTTAATTTACGCATTTTG Knockout GCO1134 klf1KODownRev GTTCCTGCGCTTGGGATTA KLF1 with GCO1135 klf1KOConfirmFor CGTATCACTAAATCACGAGATAAAGC KANMX6 GCO1126 SPBC16G5.17KOUpFor AGCCTACTGCTTCATGTCCAT GGACGAGGCAAGCTAAACAGATCTCCATCGAACTTTTAAGCTGGGA GCO1127 SPBC16G5.17KanMX6UpRev T Knockout GCO1128 SPBC16G5.17KanMX6DownFor GATACTAACGCCGCCATCCATAAAGGATCAATGCCTTGCG SPBC16G5 GCO1129 SPBC16G5.17KODownRev GTTCTATGAAATGGCAGTAGCAAC .17 with GCO1130 SPBC16G5.17KOConfirmFor CGATTGAATATCTAGATTATCTGTCTTCC KANMX6 GCO1071 cbf12KOUpFor CGGTCTTGCTAAGAGGCTTACCTA GCO1072 cbf12KanMX6UpRev GGACGAGGCAAGCTAAACAGATCTTCATGTACGCGCAATGTCTG Knockout GCO1073 cbf12KanMX6DownFor GATACTAACGCCGCCATCCAGCCATATCCTTTCGCACTACTG CBF12 GCO1074 cbf12KODownRev TAGAAATCCGACCTTCCAGAAG with GCO1075 cbf12KOConfirmFor CGGCAAAACTTCATCTGCTT KANMX6 GCO1056 phx1KOUpFor CGTTAACGAATATCTCAATTACATTCG GGACGAGGCAAGCTAAACAGATCTCCGTGGAATCTCTGAGTACTTG GCO1057 phx1KanMX6UpRev AG GCO1058 phx1KanMX6DownFor GATACTAACGCCGCCATCCAGCCTCGTTTGTATTTTTATGACCG Knockout GCO1059 phx1KODownRev ACTGTGATGCTGAAACGTTCC PHX1 with GCO1060 phx1KOConfirmFor GGCTCTTGCAAGGATCAAATT KANMX6 GCO1081 mbx1KOUpFor CGCAAACTTTACATTGCCG GGACGAGGCAAGCTAAACAGATCTCCGAGGAAATTCAGTGAAGTCT GCO1082 mbx1KanMX6UpRev AG Knockout GCO1083 mbx1KanMX6DownFor GATACTAACGCCGCCATCCAGCCTCCTTTTCTTCCCGATA MBX1 GCO1084 mbx1KODownRev GTTATGAACGATCAAGCCACTG with GCO1085 mbx1KOConfirmFor TTCCGTACTGCCTATTCAGTGTT KANMX6 GCO1086 SPBC27B12.11cKOUpFor GAGCGCTGTTTCGCTAAGAT GCO1087 SPBC27B12.11cKanMX6UpRev GGACGAGGCAAGCTAAACAGATCTAATGGCTCCTTCACTTTCGTC GCO1088 SPBC27B12.11cKanMX6DownFor GATACTAACGCCGCCATCCAGTCAGACGACAGATCATCTTATGATC Knockout GCO1089 SPBC27B12.11cKODownRev TGCAAGTGTAATGTCAAGCCAT PHO7 with GCO1090 SPBC27B12.11cKOConfirmFor GTTTGCAAGGCCTTGTGAA KANMX6 GCO1101 cbf11KOUpFor GCAATTAGTCGTTTGATGAAGC Knockout GCO1102 cbf11KanMX6UpRev GGACGAGGCAAGCTAAACAGATCTGACATTTGCAGTATGGCGCT CBF11 GCO1103 cbf11KanMX6DownFor GATACTAACGCCGCCATCCATGATGGAATTAGGTTGAAATCTCTC with GCO1104 cbf11KODownRev GGTGTTTGTGACATGACTGGTG KANMX6

203

GCO1105 cbf11KOConfirmFor GATGCTAAGTCATTATTGTAATTTCCC GCO1141 SPBC530.08KOUpFor TAACGGAACTAGAGTTGCTCACCT GGACGAGGCAAGCTAAACAGATCTTGTACGGTATAGAGAACGATGG GCO1142 SPBC530.08KanMX6UpRev C Knockout GCO1143 SPBC530.08KanMX6DownFor GATACTAACGCCGCCATCCACGTAATCAGGATTTACTTACGGC SPBC530.0 GCO1144 SPBC530.08KODownRev AGGGTAGTACTTAATGCACGCG 8 with GCO1145 SPBC530.08KOConfirmFor AGCCGAAGGAAAGCGAATCT KANMX6 GCO1146 rsv2KOUpFor GATCGGGAACATGAAGCACTT GCO1147 rsv2KanMX6UpRev GGACGAGGCAAGCTAAACAGATCTAGAGGCCGCCAATGAAGTA GCO1148 rsv2KanMX6DownFor GATACTAACGCCGCCATCCACACCTCTTACGCTTCTTCATTGA Knockout GCO1149 rsv2KODownRev TACAGACCTTGATCCACCGTCT RSV2 with GCO1150 rsv2KOConfirmFor TAACGGAAGACGAGATTACCGAG KANMX6 GCO338 GRT1UpFor CGCAAGTGAATTGGAGCAAG GCO339 GRT1UpRev GGACGAGGCAAGCTAAACAGATCTCGGTACTATTAACTTCGGCC GCO340 GRT1DownFor GATACTAACGCCGCCATCCACTGCCGCCAGACATTTTTCT Knockout GCO341 GRT1DownRev GCCCATTATTCGCGCTTTGA GRT1 with GCO390 GRT1KOConrifirmForward GTACACAAAAAGTACACGAAAAATCC KANMX6 GCO795 matmc_2KOUpFor AAGTGGGATGAGTGCTTGCT GGACGAGGCAAGCTAAACAGATCTGGTAGGTGTAGAGTGTGGAGGG GCO796 matmc_2KanMX6UpRev T Knockout GCO797 matmc_2KanMX6DownFor GATACTAACGCCGCCATCCAGCTTCTTAGAGTTACATTCACTGAAG MAT3-MC GCO798 matmc_2KODownRev CGTAGCGTAGCGAAGCAAAT with GCO799 matmc_2KOConfirmFor CGAACTTGACAATGAAACGTTCC KANMX6 GCO167 RES1UpFor AGGATCCGCATTTCCTGAAG GGACGAGGCAAGCTAAACAGATCTCCACAGCGCCAGCTATTAATAT GCO168 RES1UpRev C GCO169 RES1DownFor GATACTAACGCCGCCATCCACAGTCGTTGGCTATCTAATG Knockout GCO170 RES1DownRev TCATCAGAGTATATGGAGCG RES1 with GCO362 RES1ConfirmUpFor CCACTAACTCACCATAAGAGAGGG KANMX6 GCO805 MUG151KOUpFor ACAATGTCCCGTTTCCCAAC GGACGAGGCAAGCTAAACAGATCTCATCCAAAAGATGCAGACTTTT GCO806 MUG151KanMX6UpRev TA Knockout GCO807 Mug151KanMX6DownFor GATACTAACGCCGCCATCCAGTTTGTAAAAATAATTTCGTTACCG MUG151 GCO808 Mug151KODownRev TTTCGGCGCAGGTAAGTTCTAC with GCO809 Mug151KOConfirmFor GGCGAAATGATGTGTCATCAG KANMX6 GCO302 SRE1UpFor GCGAAGCACGCATTTTGTGA GCO303 SRE1UpRev GGACGAGGCAAGCTAAACAGATCTGCTATCCAAGGCGATAGATAGC

204

GCO304 SRE1DownFor GATACTAACGCCGCCATCCAACAGTTGTCGTCGGTAAGAGCA Knockout GCO305 SRE1DownRev GGTCCATTGTGAAAGCTTTGC SRE1 with GCO403 SRE1KOConfirmForward GCAATGAAGGTGCTGTAGATCG KANMX6 GCO861 atf31KOUpFor GGGGTGAATCTGAGGATTTAGC GGACGAGGCAAGCTAAACAGATCTGACTTCTTTTTCGCAAACGAGTA GCO862 atf31KanMX6UpRev G Knockout GCO863 atf31KanMX6DownFor GATACTAACGCCGCCATCCACAAGGAGCTTTTGCAAAGTGC ATF31 GCO864 atf31KODownRev CGGGTGAAGACATCAAGACACT with GCO865 atf31KOConfirmFor GATGCTGTTCGGTAAGTTCAATG KANMX6 GCO931 rep2KOUpFor CTCTCGGTTACTTAGCAGCCAT GCO932 rep2KanMX6UpRev GGACGAGGCAAGCTAAACAGATCTCTGATAAGGGCGATGAACACAG GCO933 rep2KanMX6DownFor GATACTAACGCCGCCATCCACTCAGTCGTTTTACTCTTGAGAGCT Knockout GCO934 rep2KODownRev TAGCATGCATTGCTTCCCTC REP2 with GCO935 rep2KOConfirmFor CGGTGCAAGGATATGGTAACAG KANMX6 GCO1066 tos4KOUpFor CTACCAAACAGTATACGCAGATATGC GCO1067 tos4KanMX6UpRev GGACGAGGCAAGCTAAACAGATCTGATCCGTGATCAAGGTGCCT GCO1068 tos4KanMX6DownFor GATACTAACGCCGCCATCCAGGGTTCAACAAAATACAATTGAACTC Knockout GCO1069 tos4KODownRev GAAATGAAGCTGAGATCTGGCC TOS4 with GCO1070 tos4KOConfirmFor TAAGTCAACGTACACTGGCTTCC KANMX6 GCO286 THI1UpFor CTCTCTGCTTACCGGTGTTA GCO287 THI1UpRev GGACGAGGCAAGCTAAACAGATCTCACTGTTAATCCTAACGCGC GCO288 THI1DownFor GATACTAACGCCGCCATCCAGCTTTGTTCGCGTGGTTAAG Knockout GCO289 THI1DownRev CAAAAGCCCCGGATAACAATG THI1 with GCO407 THI1KOConfirmForward AATATCAACGTGCTGTACGCC KANMX6 GCO604 SPCC1393.08KanMX6KOUpFor CGGATCTTTTGTTTCCACCC GCO605 SPCC1393.08KanMX6KOUpRev GGACGAGGCAAGCTAAACAGATCTGAAGCATAATGCACTTTGGGG GCO606 SPCC1393.08KanMX6KODownFor GATACTAACGCCGCCATCCATTACAGCTTGCAAGTTTCAATCG Knockout GCO607 SPCC1393.08KanMX6KODownRev CTCGTCTGAGCTTCTCAAAGCT SPCC1393. SPCC1393.08KanMX6KOConfirmF 08 with GCO608 or CAGATTTAATTTTTCCCAAGATGTG KANMX6 GCO976 SPAC3C7.04KOUpFor GACTAGGTAGGTCAACAGGAATGC GCO977 SPAC3C7.04KanMX6UpRev GGACGAGGCAAGCTAAACAGATCTTGGCAATGACTTGGCAGAAC Knockout GCO978 SPAC3C7.04KanMX6DownFor GATACTAACGCCGCCATCCACATCTGAGCATATCGAGTTTTTGG SPAC3C7. GCO979 SPAC3C7.04KODownRev ATGTCTATAACAAACATCATTGCCAC 04 with GCO980 SPAC3C7.04KOConfirmFor CGACAAAGGAAATCCTACGTG KANMX6 GCO1041 yox1KOUpFor GCCTTTCTAAGATGTGAGCCA GCO1042 yox1KanMX6UpRev GGACGAGGCAAGCTAAACAGATCTGCGTCGCGCTATATCATTTTTC

205

GCO1043 yox1KanMX6DownFor GATACTAACGCCGCCATCCAGAATTTGCTGGAATTTAACGAGC Knockout GCO1044 yox1KODownRev GCAGGGTCAATATCTCGCATAC YOX1 with GCO1045 yox1KOConfirmFor GAATCACTGTATCGATTTGCATG KANMX6 GCO738 SPAC1399.05For GATGACTCGAGATGGGCGAAGTGGAACGG HA-tagged GCO739 SPAC1399.05Rev TCATCGGATCCGCCACGTTCCACAGTAGCG Toe1 OE GCO1897 SPBC1773.16c HA FOR GACTAGTCGACATGAAAAGGATCAGAAATGCTTG HA-tagged SPBC1773. GCO1898 SPBC1773.16c HA REV GACTAGGATCCGCAATTGAAAAACTTTCGCTCAAGG 16 GCO773 ALSPBC16G5.17FOR GATTAGTCGAC ATGGTTGGGAAATCTAAAAATAGG HA-tagged SPBC16G5 GCO1900 SPBC16G5.17 HA REV GACTAGCGGCCGCGTAAGTTTGGTAATGGAAAATTAAAATTAAAAT .17 GCO2144 SPAC25B8.11 pSLF272 FOR GACTAAGATCTATGTCTAATTTGATTTTAACGCCTTC HA-tagged SPAC25B8 GCO2145 SPAC25B8.11 pSLF272 REV GACTAGCGGCCGCGATCGGACTTGGGAAACGTC .11 GCO1777 pREP1 SPBP26C9.02c FOR AGATACTCGAGATGTCTCCTCATAAAATACCCGAAGT Car1 GCO1778 pREP1 SPBP26C9.02c REV TATCTAGATCTCTACAATAACGTTTGACCAAGACAAGT pREP1 GCO1781 pREP1 SPBC1773.13 FOR AGATAGTCGACATGATCCGGAATAGTGAGGATTTT SPBC1773. GCO1782 pREP1 SPBC1773.13 REV TATCTGGATCCTCAAGGGCAGATTCGTACTCTG 13 pREP1 GCO1783 pREP1 SPBC1773.14 FOR AGATAGTCGACATGGCAGAAAAATCAAGCAAAAA Clone Arg7 GCO1784 pREP1 SPBC1773.14 REV TATCTGGATCCTTAAAGAATTGCTGATCGTATATGCTC into pREP1 GCO1872 pREP1 SPAC1002.19 FOR GACTAGTCGACATGCTTGCCACTGAACAAAGC SPAC1002. GCO1873 pREP1 SPAC1002.19 REV GACTAAGATCTTTACCAATGACGACCGTGAACA 19 pREP1 GCO1862 pREP1 SPCC1795.05c FOR GACTAGTCGACATGTATAACGTGATTTTTGTTTTAGGC SPCC1795. GCO1863 pREP1 SPCC1795.05c REV GACTAGGATCCCTACAGGTAAGGTTGAAGTGCCTT 05 pREP1 GCO2140 SPCC320.03 pSLF272 FOR GACTAAGATCTATGGATCTTAACACCTTGCCTTT HA-tagged GCO2141 SPCC320.03 pSLF272 REV GACTAGCGGCCGCGAATAAGAAAGCCAAGATATCCTAAATTACA Foe1 GCO1870 pREP1 SPAC1002.17 FOR GACTACTCGAGATGTCGACTACTACCACCGTTTCT SPAC1002. GCO1871 pREP1 SPAC1002.17 REV GACTAGGATCCTTAAGCTGTGGCACCATACAAA 18 pREP1 GCO1868 pREP1 SPAC1399.04c FOR GACTAGTCGACATGTCCATTCCTCTTGAACAGC SPAC1399. GCO1869 pREP1 SPAC1399.04c REV GACTAAGATCTCTAGCAAGTGCCAAAGTAGCG 04 pREP1 GCO2112 SPAC1399.05 Q-PCR FOR CACGGACGTACTAAACATCG qPCR GCO2113 SPAC1399.05 Q-PCR REV GGTCGATAAAGGGTTAGCATAG confirm GCO2128 SPAC1002.17c Q-PCR FOR CCTTCTCAAGTTCGCTCTG qPCR GCO2129 SPAC1002.17c Q-PCR REV CGAAGAACAGGCACAATAGA confirm GCO2130 SPAC1399.04c Q-PCR FOR TAAGAGCTGGAGAGAGTATGG qPCR GCO2131 SPAC1399.04c Q-PCR REV GCTTCAAACGTAGTTTCATCTC confirm GCO2146 SPAC1002.18 qPCR FOR CGAGGAACTCCTTACTGGATTG

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qPCR GCO2147 SPAC1002.18 qPCR REV CTCCACAATGGTTACGCTACT confirm GCO2148 SPAC1002.19 qPCR FOR GCTGTTGAGCCTGTATGGTAT qPCR GCO2149 SPAC1002.19 qPCR REV CGGGTGATAAGTTCGGGATAAG confirm GCO2156 SPCC1795.05c qPCR FOR CATGCCTGTCGTCGAATATCT qPCR GCO2157 SPCC1795.05c qPCR REV AGGTAAGGTTGAAGTGCCTTT confirm GCO2150 SPBC1683.06c qPCR FOR GGGCTCCAATTACAGCTACTC qPCR GCO2151 SPBC1683.06c qPCR REV ACAGCATGTCCTTCGTTCTC confirm GCO2152 SPCC162.11c qPCR FOR ACTATCGAGGAAGGGACATAGT qPCR GCO2153 SPCC162.11c qPCR REV CCACGAGGAACGATAAGATCAG confirm GCO2335 SPBC1773.12 qPCR for GGAGATTAACGGAGAGTTTGCT qPCR GCO2336 SPBC1773.12 qPCR rev TGTTGGAACCGACAGAAGATTTA confirm GCO2337 SPBC1773.13 qPCR for CTGATCCTTCTGAGGGTCTTT qPCR GCO2338 SPBC1773.13 qPCR rev CGCCGAATGTGTTCCTTTAT confirm GCO2339 SPBC1773.14 qPCR for TTTGGGCAGGCATGGTAA qPCR GCO2340 SPBC1773.14 qPCR rev GTAGAATACGCATCGGAGAGTG confirm GCO2341 SPBC1773.15 qPCR for ATGGGTATTCGCACCGATTTA qPCR GCO2342 SPBC1773.15 qPCR rev CAGAGTGGATAATGGTAACGAGAA confirm GCO2343 SPBC1773.16c qPCR for GTATATGGCGAAGAGTGTCGTT qPCR GCO2344 SPBC1773.16c qPCR rev TGAGCGGAGTGTAGAGTATAGG confirm GCO2347 SPBP26C9.02c qPCR for GCTTGTGTCTTGTGGATTGATG qPCR GCO2348 SPBP26C9.02c qPCR rev CAGGAAGAGGTTCGGCATATC confirm GCO2032 Gsf1 qPCR Forward Primer TAGTGGGTTGGTTAAACGCCAAGC qPCR GCO2033 Gsf1 qPCR Reverse Primer TCGAGATGCCGTAAATGTGCCAGA confirm GCO2034 cbf12 qPCR Forward Primer ACGGAAACCTCTGATTCCTGCTCA qPCR GCO2035 cbf12 qPCR Reverse Primer TGCTCTGCTTGGAAGTTGAGGCAT confirm GCO2036 Gsf2 qPCR Forward Primer TCTACCGAAGCACCTGAAAGCAGT qPCR GCO2037 Gsf2 qPCR Reverse Primer AGCTGAACCGTTCCATTGACGAAC confirm GCO2038 Agn2 qPCR Forward Primer ATGCGACAGCATCGTCAGACTCAA qPCR GCO2039 Agn2 qPCR Reverse Primer ACCTAGTACGGTGCCATTAACGCT confirm GCO2040 Gas2 qPCR Forward Primer ACCAGCACCATCGTTGATCCCTTA qPCR GCO2041 Gas2 qPCR Reverse Primer TAGCGTTGACGGCATAGACACGAA confirm SPAC4H3.03c qPCR Forward GCO2042 Primer TTTCCAGCCTTGGACTATGCTCGT qPCR GCO2043 SPAC4H3.03c qPCR Reverse Primer ATTTGGGATCACCTGAAGCGGGTA confirm GCO2044 Pfl9 qPCR Forward Primer ACAGCTTCTGGAACACAGTCAGGT qPCR GCO2045 Pfl9 qPCR Reverse Primer TAGTTCTCACGGGTGCGTTTGGAT confirm

207

GCO2046 Pfl4 qPCR Forward Primer AGTACGCCTGCGTCTACTGTTGTT qPCR GCO2047 Pfl4 qPCR Reverse Primer TAGGCTGGCTGGGTGAAAGAAGAA confirm GCO2048 Fta5 qPCR Forward Primer ACTGGCAGTTGCGTTCGTTACACA qPCR GCO2049 Fta5 qPCR Reverse Primer GAGGTGGTGTTCGTAGCACTTGAA confirm GCO2050 Pfl3 qPCR Forward Primer ACCGACTGCTGGTGTTGTTACTGA qPCR GCO2051 Pfl3 qPCR Reverse Primer AAGCTGTAGCCACGGTAGTAGCAT confirm GCO2052 Pfl7 qPCR Forward Primer TCACTTGAGACTCGTGGGTGATTC qPCR GCO2053 Pfl7 qPCR Reverse Primer TGCTTCGGTGTTTCCCTCAGTAGA confirm GCO2054 Pfl5 qPCR Forward Primer TCATGACACTTCGTCTGGTGCTGT qPCR GCO2055 Pfl5 qPCR Reverse Primer TGATGTCGTACCTTGAGCAGGGAT confirm GCO2060 Adn3 qPCR Forward Primer AGCTCTGGTAATCAGCCACCTCAA qPCR GCO2061 Adn3 qPCR Reverse Primer TGTCGTGCCATCATCAAACGCTTC confirm GCO2066 adh1 qRTPCR forward primer CTATGGCTATGCGTGTTGTTGCCA qPCR GCO2067 adh1 qRTPCR reverse primer AAGGAAGACCTCAGCACCAAAGGA confirm GCO2030 Mbx2 qPCR Forward Primer AAGGTCCTTATGCCTCCAGCAAGT qPCR GCO2031 Mbx2 qPCR Reverse Primer AACATCTCAGGCGTTAGCTGTGGA confirm GCO1876 act1ForqPCR TGAACCCCAAATCCAACCG qPCR GCO1877 act1RevqPCR CGACCAGAGGCATACAAAGA confirm

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Table A4. Four-way Sre1 microarray data from heat map. Gene WT/sre1Δ WT +/- clot sre1Δ +/- clot WT/sre1Δ + clot sre1 -6.75 1.14 -0.50 -6.87 hem13 -1.64 1.59 -0.13 -3.01 erg31 -0.97 1.16 0.35 -2.27 sur2 -0.36 1.59 0.35 -2.19 erg25 -0.74 1.11 0.28 -1.90 mmd1 -0.81 0.97 -0.10 -1.84 osm1 -0.69 0.84 0.23 -1.66 scs7 -0.30 1.23 0.42 -1.60 rcf2 -0.66 0.63 0.18 -1.15 erg6 -0.44 0.53 -0.16 -1.00 Tf2-5 -0.77 0.61 0.04 -1.49 Tf2-2 -0.77 0.63 0.13 -1.46 Tf2-1 -0.90 0.62 -0.15 -1.39 Tf2-9 -0.83 0.59 0.11 -1.37 Tf2-8 -0.76 0.55 -0.07 -1.34 Tf2-12 -0.84 0.56 -0.09 -1.33 Tf2-7 -0.77 0.51 -0.24 -1.32 Tf2-3 -0.81 0.57 -0.12 -1.25 TF2-6 -0.87 0.56 -0.19 -1.24 Tf2-4 -0.85 0.51 0.12 -1.20 Tf2-11 -0.88 0.53 0.06 -1.12 Tf2-13 -0.76 0.80 0.06 -1.09 Tf2-10 -0.96 0.57 0.24 -1.07 imt2 -1.22 1.23 -0.18 -2.33 SPAC13G6.13 -0.57 0.76 -0.20 -1.48 mug108 -1.05 1.20 0.42 -1.41 SPAC683.03 -0.26 0.77 0.09 -1.05 SPBPB7E8.02 -1.04 0.51 0.27 -0.91 SPBPB2B2.18 -1.29 1.21 0.49 -0.76

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Table A5. Four-way Toe1 microarray data from heat map. Gene WT/toe1Δ WT +/- chlor toe1Δ +/- chlor WT/toe1Δ + chlor toe1 -5.26 1.00 0.22 -5.62 urg2 -5.25 4.17 -1.13 -7.33 urg1 -6.26 5.46 -1.86 -6.13 urg3 -2.26 2.81 -0.94 -4.41 SPAC1399.04c -1.59 4.24 -0.88 -3.86 SPBC1683.06c -1.18 3.58 -1.28 -3.58 prs5 -1.03 0.46 -0.68 -1.84 cdd1 -0.24 0.88 -0.46 -1.80 urk1 -0.36 0.63 -0.32 -1.74 ura6 -0.64 0.57 0.25 -1.49 SPAC1039.02 -1.32 1.60 -0.52 -3.75 SPAC1039.01 -0.88 1.54 0.24 -2.55 SPAC521.03 -1.25 2.02 -1.07 -2.42 SPBPB2B2.06c -0.42 1.32 -0.46 -1.85 SPBC1348.10c -0.36 0.55 -0.62 -1.50 SPAC186.05c -0.21 0.80 -0.42 -1.21 trp1322 -0.40 0.61 -0.39 -1.04 SPCC63.13 -0.16 0.55 -0.46 -1.00 mba1 -0.16 0.65 -0.34 -1.00

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Table A6. Top 50 upregulated genes by toe1+ overexpression. Gene log2FC P-value urg2 6.83 3.1E-12 urg1 6.18 4.6E-11 SPAC1399.04c 5.95 2.08E-11 urg3 5.15 3E-10 toe1 5.01 3.06E-10 SPBC1683.06c 3.42 6.78E-09 SPAC1039.02 2.91 1.4E-07 ght1 2.88 6.35E-08 ght4 2.69 1.35E-07 SPAC521.03 2.65 1.87E-07 mug45 2.32 2.17E-06 atg1801 2.26 1.05E-06 ght5 2.24 1.09E-06 meu10 2.23 1.15E-06 ssm4 2.10 2.28E-06 SPAC1556.06.1 2.09 3.24E-06 map2 2.04 5.58E-06 mug8 1.97 5.11E-06 meu6 1.97 8.64E-06 SPAC3H1.06c 1.82 1.16E-05 mug128 1.81 5.49E-05 SPAC1556.06.2 1.71 2.05E-05 nep2 1.70 3.97E-05 SPAC9E9.01 1.66 3.21E-05 mal1 1.62 3.58E-05 SPCC576.06c 1.58 4.51E-05 meu32 1.58 4.88E-05 atp9 1.55 0.000087 mcp6 1.53 0.000077 mug9 1.51 8.47E-05 SPAC977.09c 1.51 0.000223 nap1 1.51 7.55E-05 mug4 1.50 7.51E-05 SPCC162.11c 1.50 0.000101 dic1 1.44 0.000139 SPAC186.06 1.44 0.000157 cwf4 1.42 0.000119 mei4 1.42 0.000177 rec11 1.38 0.000162 SPBC1683.05 1.37 0.000202 rec25 1.35 0.000192 tht2 1.34 0.000269 fft2 1.34 0.001706 SPCC1795.05c 1.31 0.000259 SPAC27E2.11c 1.30 0.000333 spo5 1.28 0.000322 hdd1 1.27 0.006783 SPAC1F8.07c 1.27 0.000591 SPCC1739.08c 1.24 0.000512 mug10 1.24 0.000463

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Table A7. ChIP-chip analysis of nmt41-driven Toe1-HA. Gene ChIP Ratio mba1 9.66 SPAC3A11.04 5.81 yih1 5.13 SPAC1399.04c 3.70 cwf4 3.25 urg1 3.10 nap1 2.86 SPCC1795.05c 2.59 yrs1 2.32 SPCC11E10.01 2.29 urg2 2.16 SPAPB18E9.04c 1.96 pho84 1.86 SPAC19B12.11c 1.36

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Table A8. Top 50 upregulated and downregulated genes in wild type grown in FIM for 30 minutes. Gene Log2FC P-value ght5 8.49 8.82E-07 SPCC794.04c 8.02 1.30E-06 SPBC660.05 7.38 6.70E-06 cta3 7.24 2.63E-06 gld1 7.11 8.80E-06 SPCC191.01 6.96 3.43E-06 SPBC725.03 6.95 3.45E-06 inv1 6.93 2.71E-07 pyp2 6.84 3.84E-06 mug108 6.84 1.72E-05 rsv2 6.64 4.71E-06 ght6 6.20 7.43E-06 agl1 6.02 9.10E-06 plb1 5.99 1.47E-07 hsp16 5.96 1.85E-07 ssa1 5.92 2.35E-06 SPCC417.15 5.82 1.93E-06 SPBC19C7.04c 5.42 6.35E-05 SPAC27D7.11c 5.22 3.99E-06 hsp104 5.11 7.51E-07 SPBP4H10.10 5.02 1.11E-05 SPBC21C3.19 5.01 5.57E-07 srk1 5.01 3.08E-05 pka1 4.91 4.56E-06 SPAC3G6.07 4.81 9.75E-06 gut2 4.78 3.49E-06 mug24 4.74 2.25E-05 mug143 4.74 1.28E-06 SPCC320.03 4.73 2.76E-06 ntp1 4.66 9.79E-07 ctt1 4.59 1.10E-06 ght3 4.43 2.11E-05 SPAC637.03 4.39 1.64E-06 srx1 4.38 3.90E-06 rds1 4.36 6.72E-06 SPAC23H3.15c 4.32 3.35E-06 SPBC36.02c 4.29 1.03E-05 ish1 4.26 8.90E-06 SPCC576.17c 4.15 0.000104 git5 4.14 3.63E-05 maf1 4.13 3.46E-06 bxi1 4.10 8.53E-06 SPBPB21E7.08 4.04 1.56E-05 SPAC22F8.05 4.02 1.30E-05 nep2 4.00 2.56E-05 SPBC16A3.02c 3.98 3.82E-06 SPCPB16A4.06c 3.94 2.82E-05 SPAC4H3.03c 3.91 1.58E-05 SPBPB7E8.02 3.90 3.32E-05 pcr1 3.89 0.000156 mmf2 -2.43 0.000567 rrs1 -2.44 0.000123

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trm9 -2.45 0.000114 dph4 -2.47 0.000108 rad17 -2.48 0.000343 med11 -2.48 0.000243 trm10 -2.48 0.000112 SPAC22E12.18 -2.50 9.98E-05 imp4 -2.51 9.66E-05 SPAC890.05 -2.52 9.58E-05 gir2 -2.55 0.000119 set13 -2.58 0.001809 SPCC126.11c -2.58 9.91E-05 rrb1 -2.60 0.000106 SPBC557.02c -2.60 0.001721 nop4 -2.64 7.50E-05 nce103 -2.65 7.28E-05 bfr2 -2.65 6.87E-05 cif1 -2.65 0.000116 set5 -2.65 6.66E-05 deg1 -2.68 0.000122 bud23 -2.69 6.63E-05 saf1 -2.72 5.59E-05 rrp8 -2.73 5.28E-05 eng1 -2.74 5.17E-05 efg1 -2.74 0.000109 SPAC664.13 -2.75 5.27E-05 mni1 -2.78 0.000112 rps1401 -2.79 0.000476 his5 -2.80 8.42E-05 yar1 -2.80 4.65E-05 SPBPB8B6.05c -2.83 5.01E-05 esf2 -2.83 0.001079 dpb4 -2.84 4.15E-05 SPAC25B8.15c -2.84 0.000153 SPAC11H11.03c -2.85 5.08E-05 SPCC23B6.02c -2.86 3.91E-05 SPBC16D10.01c -2.87 3.91E-05 kri1 -2.88 3.92E-05 mis15 -2.90 0.000938 SPAC2F3.07c -3.03 0.075133 chp2 -3.10 2.40E-05 tpt1 -3.11 3.54E-05 SPCC4G3.16 -3.12 0.0006 nop52 -3.17 2.11E-05 str3 -3.23 0.000119 SPACUNK4.09 -3.29 1.81E-05 SPAC8E11.10 -3.41 1.11E-05 SPBC215.06c -3.56 1.02E-05 SPAC683.02c -4.04 7.79E-06

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Table A9. Top 50 upregulated and downregulated genes in wild type grown in FIM for two hours. Gene LogFC P-value agl1 6.61 9.76E-10 SPCC338.18 5.91 8.35E-10 hsp16 5.65 1.83E-09 mug14 5.60 1.59E-09 SPAC3G6.07 5.39 9.31E-08 ght5 5.28 1.60E-06 inv1 5.16 4.55E-08 SPBC660.05 5.14 1.41E-21 mug24 4.98 1.81E-20 SPBC725.03 4.84 3.13E-08 SPCPB16A4.06c 4.82 1.83E-06 SPAC637.03 4.72 2.50E-08 mug143 4.65 3.09E-08 SPBC19C7.04c 4.61 1.42E-07 mug108 4.56 1.13E-07 SPAPJ691.02 4.55 1.08E-07 rga3 4.46 1.20E-07 SPAC186.04c 4.36 1.57E-07 SPAC3G9.11c 4.25 8.46E-07 SPAC977.17 4.24 5.41E-16 gld1 4.24 8.19E-06 SPCC191.10 4.18 1.45E-15 SPCC794.04c 4.18 3.68E-06 fbp1 4.15 1.82E-06 cta3 4.12 1.97E-07 wsc1 4.07 4.72E-12 gal10 4.01 1.11E-05 pcm2 4.00 8.40E-06 rsv2 3.99 4.75E-07 SPAC4F10.17 3.97 2.63E-06 meu10 3.93 1.04E-06 SPCC191.01 3.90 5.86E-14 nep2 3.89 7.61E-14 SPBC36.02c 3.83 1.03E-06 SPBC3H7.08c 3.83 8.43E-07 SPAC4H3.03c 3.82 6.51E-07 SPBC36.10 3.79 6.83E-07 SPBPB21E7.06 3.78 4.25E-13 mug146 3.75 8.82E-06 SPBP4H10.10 3.75 4.36E-06 bit2 3.74 2.04E-06 SPCC576.16c 3.74 1.60E-06 pof15 3.73 1.37E-06 SPAC23C11.06c 3.73 9.69E-07 SPAC2H10.01 3.69 2.45E-06 ntp1 3.65 1.53E-06 ste7 3.62 2.12E-12 SPCC1393.12 3.62 2.73E-06 gti1 3.62 1.56E-06 mel1 3.57 4.55E-12 rpl801 -4.97 3.56E-06 rpp101 -4.82 4.90E-08

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ssa2 -4.57 1.08E-07 rps1201 -4.47 1.35E-07 rps2302 -4.38 8.39E-08 SPBC19G7.04 -4.27 1.71E-07 gar2 -4.25 3.25E-07 ace2 -4.25 2.50E-05 dbp2 -4.20 7.98E-07 rps1401 -4.15 4.68E-07 gpm1 -4.12 6.08E-07 rps001 -4.06 5.69E-07 rps2601 -4.03 2.82E-07 rpl2701 -4.03 4.82E-07 rpl13 -4.03 2.73E-07 rps1001 -4.03 7.88E-07 rps802 -4.00 3.03E-07 rps1701 -3.99 3.31E-07 rpl2402 -3.98 3.31E-07 efg1 -3.96 3.93E-07 rpp103 -3.92 6.28E-07 rpp203 -3.91 4.64E-07 rpl2802 -3.90 4.56E-07 fkbp39 -3.90 8.10E-07 rpl1602 -3.90 5.78E-07 rpl4301 -3.87 5.92E-07 rps1102 -3.86 5.06E-07 SPAC8E11.10 -3.85 5.46E-07 rpl1802 -3.84 7.60E-07 rpl803 -3.81 7.38E-07 nop52 -3.80 6.76E-07 egd2 -3.77 7.34E-07 rpl302 -3.77 1.07E-06 rps101 -3.75 8.45E-07 SPAC1F8.07c -3.73 9.78E-07 rpl3702 -3.72 1.44E-06 rps2202 -3.69 2.73E-06 rpl2401 -3.66 1.42E-06 agn1 -3.65 1.12E-06 adg1 -3.65 2.00E-06 rpl401 -3.65 1.30E-06 rps002 -3.63 2.50E-06 rpl3002 -3.60 1.66E-06 rpl802 -3.60 1.40E-06 apt1 -3.58 1.53E-06 SPBPB10D8.02c -3.57 1.79E-06 rpl1102 -3.57 1.64E-06 rps7 -3.56 2.11E-06 rpl502 -3.56 2.11E-06 rpl1702 -3.53 2.06E-06

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Table A10. Genes downregulated in the foe1Δ strain compared to wild type grown in FIM. Gene Name Log2FC P-Value SPCC320.03 -3.89 2.19E-07 pfl3 -2.09 9.95E-05 SPAC27D7.09c -1.69 3.58E-04 SPAC186.03 -1.54 1.07E-02 SPBPB2B2.06c -1.43 1.84E-03 chk1 -1.41 1.68E-02 met17 -1.35 1.90E-03 SPBC660.06 -1.32 2.25E-02 SPAPB1A11.03 -1.27 2.73E-02 hxk2 -1.25 5.10E-03 SPCC965.12 -1.25 3.37E-03 bgs4 -1.24 3.35E-03 rsv2 -1.23 4.25E-03 ura1 -1.20 8.80E-03 grn1 -1.17 5.64E-03 nsa2 -1.17 5.08E-03 gar1 -1.17 5.06E-03 SPCC1322.10 -1.17 9.52E-03 rps102 -1.16 3.89E-02 SPBP4H10.10 -1.15 9.48E-03 hta1 -1.14 1.50E-02 prs1 -1.12 5.92E-03 dbp2 -1.10 9.37E-03 SPCC1827.03c -1.08 1.11E-02 nif1 -1.08 5.19E-02 vps1302 -1.08 7.86E-03 fio1 -1.07 8.89E-03 SPAC694.02 -1.07 8.59E-03 sec21 -1.06 9.36E-03 pabp -1.06 8.53E-03 scs2 -1.06 8.64E-03 rpl3402 -1.05 1.08E-02 SPCC584.01c -1.05 1.26E-02 ilv5 -1.05 4.25E-02 not2 -1.05 9.65E-03 sap1 -1.05 5.81E-02 pil1 -1.04 1.18E-02 atp4 -1.04 1.49E-02 SPBC56F2.05c -1.04 1.66E-02 srx1 -1.03 9.64E-03 cch1 -1.02 1.01E-02 gar2 -1.02 1.22E-02 SPAC1399.02 -1.02 6.42E-02 mpc2 -1.02 6.45E-02 urb1 -1.01 6.59E-02 SPAC26F1.07 -1.01 1.61E-02 pmk1 -1.00 1.75E-02 dbr1 -1.00 1.56E-02

217

Table A11. Bioneer strains tested for reduced flocculation in FIM. Strain Genotype V3-P30-43 Δrsv2::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P33-92 ΔSPAC27D7.09c::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P06-76 Δsrx1::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P29-06 ΔSPAC26F1.07::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P09-36 ΔSPBP4H10.10::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P24-22 ΔSPCC1322.10::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P20-36 Δhta1::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P34-04 ΔSPBC56F2.05::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P31-10 ΔSPBPB2B2.06C::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P11-46 Δnif1::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P26-64 Δpmk1::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P07-23 Δvps1302::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P24-33 Δerg31::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P26-13 Δhsp3105::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P32-62 Δbgl2::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P32-09 Δrds1::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P22-37 ΔSPAC3G9.11::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P32-20 Δspac869.03::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P06-42 ΔSPAC869.04::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P06-94 Δpfl5::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P13-25 Δcao2::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P07-04 Δarp1::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P23-41 ΔSPBC1683.06::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P07-71 ΔSPBC19C2.10::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P07-85 Δgpd1::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P34-86 Δhsp16::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P13-73 Δwis1::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P32-80 ΔSPBC8E4.04::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P14-06 Δmug146::KANMX4 ade6-M216 leu1-32 ura4D18 h+ GCY3222 Δgsf2::KANMX6 leu1-32 ura4D18 h+ V3-P20-30 Δgst1::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P24-54 ΔSPCC191.10::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P11-89 Δinv1::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P17-70 Δcfh2::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P14-35 ΔSPCC550.07::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P19-14 ΔSPCC576.17::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P32-92 ΔSPCC584.15::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P16-22 Δgdh1::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P16-28 Δzwf2::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P20-92 Δmeu7::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P24-07 Δmel1::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P14-70 Δpfl9::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P13-90 Δpfl3::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P20-10 Δpfl7::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P10-92 ΔSPAC4H3.03::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P32-23 ΔSPACUNK4.17::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P14-27 Δbfr1::ANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P09-08 Δpmp3::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P26-87 Δmfs1::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P04-08 Δtco1::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P13-76 Δprt1::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P26-77 ΔSPAC2E1P3.01::KANMX4 ade6-M216 leu1-32 ura4D18 h+

218

V3-P23-66 ΔSPAC186.06::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P25-11 ΔSPBC11C11.06::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P26-55 Δhsp9::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P27-84 Δmug116::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P16-41 ΔSPAC186.05::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P33-55 ΔSPAC869.06::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P15-35 ΔSPAPB24D3.07::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P02-22 Δght7::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P09-45 ΔSPBPB2B2.05::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P12-26 ΔSPAC139.05::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P26-75 ΔSPAC23H3.15::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P29-78 Δslt1::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P26-56 ΔSPAPJ695.01::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P31-35 ΔSPCC569.05::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P24-52 Δmug150::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P33-42 Δhsp3101::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P11-77 Δgut2::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P13-37 Δmas5::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P25-79 ΔSPCPB16A4.06::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P31-69 ΔSPAC4G8.03::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P16-94 ΔSPAPB1A10.05::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P05-84 ΔSPAC513.02::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P09-48 Δgal10::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P35-20 Δmug108::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P19-79 ΔSPBC1271.08::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P35-12 ΔSPAPB1A11.03::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P14-44 ΔSPCP1C11.02::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P11-95 Δght8::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P17-03 Δfbp1::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P07-24 Δhsp104::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P05-01 Δetr1::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P33-91 Δrps102::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P10-84 ΔSPAC32A11.02::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P17-80 Δosr1::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P35-07 ΔSPBC660.05::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P33-49 ΔSPAC23C11.06::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P05-09 ΔSPAC27E2.03::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P20-37 ΔSPCC70.10::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P31-95 Δetf202::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P16-27 Δctt1::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P16-26 Δmug24::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P29-88 Δpyp2::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P32-37 Δish1::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P29-95 Δura1::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P30-33 Δmus7::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P31-56 Δgti1::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P14-94 ΔSPAC2F3.05::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P17-16 Δomh4::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P12-07 ΔSPCC970.02::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P21-46 Δpck1::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P17-61 Δmeu10::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P14-83 ΔSPAC23H3.11::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P21-14 Δagn2::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P06-07 Δgmh1::KANMX4 ade6-M216 leu1-32 ura4D18 h+

219

V3-P17-24 Δomh1::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P10-72 Δbgs2::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P12-65 Δeng2::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P03-67 Δmok11::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P30-77 Δmok12::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P11-21 Δexg1::KANMX4 ade6-M216 leu1-32 ura4D18 h+ V3-P14-26 Δpfl4::KANMX4 ade6-M216 leu1-32 ura4D18 h+

220

Table A12. Genes upregulated by nmt1 and nmt41-driven foe1+ overexpression. Gene ChIP Ratio nmt1 log2FC P-value nmt41 log2FC P-value SPBP4G3.03 N/A 7.90 4.43E-16 4.30 1.30E-10 SPAC869.03c 2.86 6.30 1.94E-12 6.44 8.99E-05 SPAPB1A11.03 N/A 6.05 1.39E-19 5.44 5.07E-09 SPAC27D7.09c 3.84 5.69 9.73E-17 4.49 5.37E-05 inv1 3.35 5.04 9.19E-11 3.08 1.36E-10 SPAPB24D3.07c N/A 4.86 5.73E-18 4.74 1.51E-09 hsp16 3.38 4.55 3.53E-11 3.18 1.07E-09 SPBC1289.14 N/A 4.29 6.96E-15 5.72 4.47E-07 gsf2 2.98 4.11 1.61E-12 3.61 2.72E-09 SPCC191.10 3.35 3.79 1.09E-06 2.72 6.60E-05 adh8 N/A 3.77 1.59E-10 3.14 5.06E-08 dmc1 N/A 3.67 2.18E-05 2.61 2.90E-02 SPCC584.15c 2.17 3.52 7.99E-06 1.95 5.77E-08 SPAC869.04 2.86 3.51 4.23E-06 2.45 1.15E-04 mug146 2.44 3.51 3.36E-08 2.50 6.16E-08 SPAC27D7.11c N/A 3.46 7.16E-05 2.42 8.86E-08 SPCC320.03 3.91 3.38 3.95E-10 7.15 4.98E-07 meu7 N/A 3.37 1.53E-08 2.59 2.88E-07 SPCC965.14c N/A 3.24 1.36E-04 1.62 1.08E-07 SPCC594.03 4.66 3.23 5.82E-03 1.16 1.65E-04 bgl2 2.37 3.23 3.54E-05 1.78 1.37E-07 SPBC1683.06c 3.96 3.22 2.06E-02 0.95 9.07E-08 SPAC3G9.11c 3.05 3.13 1.27E-14 4.02 1.64E-07 mel1 N/A 2.91 1.40E-12 4.97 3.84E-05 SPBC215.11c N/A 2.91 1.16E-10 3.15 4.81E-07 yak3 N/A 2.88 3.19E-06 2.04 4.62E-07 rrg9 N/A 2.85 1.22E-02 1.03 4.14E-07 SPAPB1A11.02 N/A 2.64 1.08E-26 7.12 3.51E-04 SPCC1322.10 N/A 2.62 3.42E-04 1.52 1.20E-06 hsp3105 3.20 2.62 8.02E-06 1.94 1.15E-04 SPAC186.02c N/A 2.56 6.33E-09 4.13 5.31E-04 SPBC23G7.10c N/A 2.39 2.32E-06 2.07 5.97E-06 wis1 3.44 2.39 5.45E-03 1.15 1.03E-05 grt1 N/A 2.36 5.30E-05 2.47 5.57E-04 SPAC27D7.10c N/A 2.34 4.38E-10 2.96 1.53E-05 mug4 4.00 2.28 1.22E-05 1.90 7.42E-06 car1 N/A 2.27 3.85E-14 3.88 8.49E-06 arp1 2.21 2.20 5.78E-04 1.46 7.35E-04 cao2 2.69 2.19 2.80E-08 2.57 1.99E-05 gas2 N/A 2.14 2.10E-06 2.16 1.83E-05 SPAC869.01 N/A 2.06 1.83E-03 1.50 2.08E-03 SPAC8E11.08c 2.48 1.97 2.53E-02 0.92 6.40E-04 SPAPB24D3.08c N/A 1.96 1.01E-03 1.39 4.33E-05 byr2 N/A 1.95 3.01E-03 1.23 3.89E-05 SPBC1348.11 N/A 1.93 1.35E-08 3.70 2.29E-03 trm13 N/A 1.91 7.24E-05 1.72 9.72E-05 pfl9 N/A 1.91 1.54E-02 1.16 2.98E-03 SPBPB10D8.03 N/A 1.85 1.30E-09 4.03 8.23E-04 pdi4 N/A 1.79 1.28E-04 1.63 1.49E-04 SPAC7D4.12c N/A 1.78 6.14E-03 1.13 2.14E-04 gst1 3.35 1.77 7.57E-03 1.10 1.22E-04 SPCC191.01 3.35 1.75 6.05E-10 2.96 1.15E-04

221

erg31 2.21 1.72 1.08E-06 2.15 1.67E-04 meu25 N/A 1.68 1.43E-06 2.12 3.65E-04 SPBC8E4.04 2.98 1.65 1.87E-03 1.31 2.26E-04 SPAC186.04c N/A 1.65 2.34E-04 1.56 2.96E-03 SPCC550.07 2.00 1.64 1.89E-03 1.30 2.39E-04 SPAC5H10.04 N/A 1.61 5.51E-03 1.15 5.16E-04 SPAC3G6.07 N/A 1.57 1.03E-13 3.75 7.17E-03 gal7 N/A 1.55 1.40E-04 1.63 5.27E-04 SPAC2E1P3.01 N/A 1.48 3.11E-09 2.76 7.72E-04 SPAC26F1.07 N/A 1.47 8.74E-05 1.67 6.01E-04 SPCC4B3.13 N/A 1.47 1.31E-03 1.34 6.27E-04 pfl3 N/A 1.46 2.21E-09 2.84 1.64E-03 SPBC725.10 N/A 1.46 1.19E-08 2.60 7.31E-04 pcm2 N/A 1.44 6.06E-06 2.81 4.95E-03 mug182 N/A 1.41 2.08E-06 2.08 1.35E-03 zym1 N/A 1.39 1.86E-03 1.30 1.23E-03 pfl5 2.08 1.39 1.31E-03 1.35 1.01E-03 SPAC4H3.03c N/A 1.37 2.43E-10 3.01 2.85E-02 gpd1 3.59 1.36 3.02E-06 2.13 1.31E-03 SPAC750.01 N/A 1.35 1.61E-03 1.32 5.50E-03 SPBC19C2.10 2.68 1.32 2.14E-02 0.95 2.59E-03 SPBC12C2.04 N/A 1.32 1.82E-02 0.97 1.56E-03 zwf2 3.42 1.31 9.77E-04 1.38 3.73E-03 msy1 N/A 1.26 2.83E-06 2.06 2.45E-02 SPCC794.06 N/A 1.26 5.82E-04 1.45 2.49E-02 sxa2 N/A 1.25 6.52E-06 1.97 2.58E-03 SPBC18H10.09 N/A 1.25 1.53E-02 1.00 1.10E-02 aif1 N/A 1.25 6.19E-12 3.35 3.02E-03 psp3 N/A 1.24 2.93E-07 2.36 2.71E-03 mug180 N/A 1.22 2.25E-12 3.47 2.72E-03 ght7 N/A 1.22 1.82E-04 2.02 2.89E-02 SPBC16A3.02c N/A 1.21 2.33E-06 2.12 3.07E-03 pfl7 N/A 1.20 5.09E-07 2.24 3.81E-03 gsf1 3.72 1.16 1.73E-02 0.98 5.27E-03 tlh2 N/A 1.15 8.39E-08 2.41 4.45E-03 dal51 N/A 1.15 1.05E-03 1.38 6.62E-03 rds1 2.28 1.14 3.56E-18 4.76 5.30E-03 vip1 N/A 1.11 7.21E-07 2.25 6.02E-03 SPAC1039.03 N/A 1.07 2.47E-04 1.56 7.64E-03 agl1 2.47 1.06 6.26E-07 2.21 5.23E-02 dbr1 N/A 1.06 2.86E-03 1.25 2.16E-02 mdh1 N/A 1.05 1.91E-03 1.30 8.02E-03 SPAC139.05 N/A 1.05 7.43E-18 4.70 1.43E-02 gdh1 3.51 1.05 2.87E-03 1.25 8.02E-03 SPBPB8B6.02c N/A 1.01 6.65E-07 3.14 3.05E-02 cfh2 3.00 0.97 1.09E-02 1.06 1.32E-02 SPCC576.17c 3.00 0.94 2.81E-06 2.05 4.01E-02 SPAC186.03 N/A 0.92 1.34E-17 6.62 8.88E-02 cum1 N/A 0.92 4.22E-08 2.48 5.83E-02

222

Table A13. Genes downregulated in the prt1Δ strain compared to wild type in FIM. Gene log2FC P-value SPAC186.05c -4.07 1.42E-05 SPAC186.04c -3.67 1.31E-05 prt1 -3.30 1.02E-04 mfs1 -2.83 1.00E-04 SPBC26H8.11c -2.09 1.51E-03 tco1 -2.02 5.59E-04 SPAC750.01 -1.98 4.12E-04 SPAC513.07 -1.92 1.66E-03 SPAC186.03 -1.77 4.32E-03 SPBPB2B2.17c -1.76 8.80E-03 mfs3 -1.64 9.37E-04 SPBPB2B2.06c -1.44 1.90E-03 SPAC750.04c -1.38 2.17E-02 rpl3001 -1.38 2.18E-02 SPAC977.02 -1.27 2.89E-02 SPACUNK4.17 -1.20 6.46E-03 SPAC2E1P3.01 -1.16 1.23E-02 SPBC1348.03 -1.15 3.95E-02 aes1 -1.15 7.40E-03 SPBPB21E7.08 -1.13 4.03E-02 pmp3 -1.11 4.47E-02 shm1 -1.11 1.21E-01 SPBC83.13 -1.10 1.08E-02 rps401 -1.05 5.33E-02 qcr9 -1.04 5.49E-02 cue2 -1.03 3.94E-02 bfr1 -1.01 9.64E-03

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Table A14. Top 50 genes upregulated by prt1+ overexpression. Gene Log2FC P-value SPAPB1A11.03 7.17 3.76E-09 prt1 6.92 4.83E-07 SPAPB1A11.02 6.59 1.47E-08 SPBPB2B2.01 6.49 1.35E-08 mug121 6.21 3.05E-10 SPBP4G3.03 6.08 5.15E-08 cao2 6.01 4.98E-10 cox18 5.84 5.67E-10 inv1 5.58 2.80E-10 rec10 5.52 3.25E-08 glc8 5.37 1.01E-09 SPAC32A11.02c 5.30 9.32E-09 SPBC1348.03 5.19 3.53E-06 SPBC1105.13c 5.09 2.77E-08 ned1 5.08 1.59E-09 SPBPB2B2.17c 5.05 3.11E-06 srk1 5.03 8.39E-07 SPBPB21E7.08 4.96 2.33E-09 SPAC3H5.08c 4.95 2.97E-09 hsp16 4.93 2.95E-09 SPAC869.01 4.88 1.91E-06 gut2 4.83 6.83E-09 gpd3 4.82 3.50E-09 SPAC3G9.11c 4.72 6.66E-08 SPAPB24D3.07c 4.64 6.47E-09 tel1 4.62 7.93E-09 SPAC2E1P3.01 4.60 2.82E-08 pet802 4.59 1.37E-08 zwf2 4.58 1.61E-07 SPCC584.03c 4.56 2.15E-07 mrm2 4.52 3.70E-06 SPAC186.04c 4.42 6.28E-06 SPAC922.03 4.40 2.67E-08 SPAC27D7.09c 4.39 1.62E-08 mug174 4.37 7.60E-08 SPAC29A4.14c 4.35 2.98E-08 wtf11 4.31 1.38E-07 rib1 4.31 2.89E-08 SPAC11D3.11c 4.26 7.55E-08 nab3 4.25 1.56E-07 trm401 4.25 2.78E-08 sey1 4.25 2.77E-08 hif2 4.25 2.80E-08 SPAC11E3.14 4.17 3.70E-08 SPCC4G3.12c 4.17 3.98E-08 yak3 4.15 4.01E-08 hsp9 4.15 6.82E-08 pim1 4.15 4.51E-08 SPAC750.07c 4.14 2.81E-07 SPBC8E4.04 4.14 4.44E-08

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Table A15. Top 50 downregulated genes in the SPBC530.08Δ strain compared to wild type in FIM. Gene log2FC P-value SPBC530.08 -5.28 2.13E-05 SPAC186.04c -4.39 6.99E-05 SPAC186.05c -4.39 7.00E-05 nrd1 -3.48 4.16E-03 SPAC186.06 -2.78 7.32E-07 rps2501 -2.21 1.43E-05 mug10 -2.05 5.01E-03 hsp9 -1.97 6.10E-03 dli1 -1.93 6.73E-03 rps1701 -1.92 6.96E-03 SPBC11C11.06c -1.66 1.15E-03 grx1 -1.61 1.54E-02 SPBC800.14c -1.61 1.55E-02 pmt3 -1.57 1.04E-02 mcp7 -1.50 2.10E-03 atp10 -1.48 1.20E-02 rpl3001 -1.47 2.26E-02 SPAC1556.06.1 -1.40 2.73E-02 pre8 -1.39 2.86E-02 rpl902 -1.38 2.87E-02 SPCC126.11c -1.37 3.55E-03 wtf14 -1.36 1.88E-03 SPAC750.01 -1.35 2.26E-03 rpl801 -1.33 3.32E-02 rps5 -1.33 3.36E-02 fra2 -1.32 2.43E-03 mrt4 -1.29 3.43E-02 mcp6 -1.21 5.20E-03 mat3-Mc -1.21 6.68E-03 snf30 -1.20 1.12E-02 oma1 -1.20 7.05E-03 mrpl32 -1.19 1.85E-02 sdu1 -1.18 8.60E-03 rpl3202 -1.17 5.24E-02 fmd3 -1.17 5.32E-02 atg10 -1.16 8.32E-03 pth4 -1.15 5.62E-02 SPAP27G11.02 -1.15 1.32E-02 glo2 -1.14 1.04E-02 mrps16 -1.14 9.47E-03 SPCPB16A4.05c -1.14 4.45E-02 mrpl19 -1.13 9.75E-03 fta6 -1.12 1.30E-02 SPAC2G11.04 -1.12 1.84E-02 hrr1 -1.10 1.54E-02 ypt1 -1.10 1.39E-02 pof11 -1.10 6.51E-02 rpl3401 -1.09 3.08E-02 SPBPB21E7.02c -1.09 6.72E-02 SPAC17A2.11 -1.08 2.32E-02

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Table A16. Top 50 genes upregulated by SPBC530.08 overexpression. Name log2FC P-value SPBC530.08 7.53 6.75E-18 SPAC27D7.09c 7.03 3.52E-19 SPAPB1A11.02 6.08 8.58E-08 hsp16 5.69 1.75E-15 hsp104 5.61 6.38E-16 ssa1 5.55 1.62E-15 adh8 5.48 2.37E-15 meu7 5.33 4.70E-14 inv1 5.29 7.68E-15 dak2 5.19 4.08E-14 tms1 5.13 9.13E-15 SPBC4F6.17c 4.83 5.32E-14 gpd3 4.38 1.11E-08 SPAPB24D3.07c 4.31 1.86E-12 fmd3 4.27 1.94E-08 fes1 4.25 3.79E-11 SPBP4G3.03 4.14 8.72E-07 SPBC12C2.04 4.08 6.83E-12 SPCC338.18 3.94 1.03E-07 dak1 3.83 3.70E-11 SPAPB1A11.03 3.82 2.31E-05 psi1 3.80 1.67E-10 wos2 3.73 1.01E-10 SPCC1450.09c 3.70 1.55E-10 deb1 3.69 1.13E-10 SPAC27D7.11c 3.68 1.65E-10 jmj1 3.58 6.85E-07 SPAC27D7.10c 3.57 2.35E-10 nsk1 3.56 3.95E-06 SPAC3G6.07 3.52 3.46E-10 hsp3106 3.48 1.28E-09 SPAC4H3.08 3.45 5.57E-06 rds1 3.42 1.28E-09 SPCC777.04 3.38 9.09E-10 slt1 3.38 1.61E-09 SPAC2E1P3.01 3.35 2.81E-09 SPAC3G9.11c 3.34 1.55E-09 SPBC23G7.10c 3.28 1.91E-09 SPAPB24D3.08c 3.27 2.31E-09 wis2 3.27 3.77E-09 SPBC660.05 3.23 4.18E-06 gst2 3.17 5.12E-09 SPAC8E11.08c 3.12 7.65E-06 alr1 3.04 1.08E-08 yak3 3.03 1.65E-08 SPCC417.12 3.01 1.31E-08 SPCC191.01 3.01 1.41E-08 SPCC4G3.12c 2.99 1.88E-08 SPBC119.03 2.98 1.95E-08 SPCC191.10 2.95 2.63E-05

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Table A17. Genes downregulated in the prr1Δ strain compared to wild type in FIM and upregulated in wild type in FIM compared to wild type in YES medium. Name log2FC P-value prr1 -7.03 3.01E-10 SPCC576.17c -3.51 4.72E-08 mel1 -3.27 6.54E-06 SPAC869.06c -3.25 7.11E-06 SPAPB24D3.07c -2.86 6.38E-06 ght7 -2.76 1.13E-06 SPAC186.04c -2.74 5.65E-07 SPBPB2B2.05 -2.50 1.49E-06 SPAC139.05 -2.42 1.80E-04 SPAC23H3.15c -2.27 5.93E-06 slt1 -2.22 1.87E-05 SPAC27D7.09c -2.09 1.17E-05 SPAPJ695.01c -2.09 1.30E-05 SPBPB2B2.06c -2.05 8.18E-04 SPCC569.05c -2.01 7.77E-05 bip1 -1.94 2.02E-04 mug150 -1.94 3.09E-05 hsp3101 -1.92 8.50E-05 gut2 -1.91 9.45E-05 mas5 -1.82 6.08E-05 SPAC2E1P3.01 -1.80 7.34E-05 SPCPB16A4.06c -1.80 6.88E-05 SPAC4G8.03c -1.74 1.08E-04 pss1 -1.74 2.38E-04 SPAPB1A10.05 -1.73 1.44E-04 SPAC513.02 -1.73 1.18E-04 gal10 -1.72 1.05E-04 SPBPB21E7.09 -1.69 1.28E-04 SPBC1348.12 -1.67 1.43E-04 mug108 -1.67 2.28E-04 SPBC1271.08c -1.64 1.73E-04 SPACUNK4.17 -1.61 4.93E-04 SPBC4F6.17c -1.56 9.21E-04 pil1 -1.56 4.97E-03 SPCC191.10 -1.52 7.60E-03 SPAPB1A11.03 -1.44 8.50E-04 erg1 -1.43 2.39E-03 rsv2 -1.41 9.90E-04 SPCPB1C11.02 -1.39 8.07E-04 ght8 -1.37 1.18E-03 mfs1 -1.35 1.29E-03 fbp1 -1.32 1.74E-02 hsp104 -1.32 2.13E-03 etr1 -1.32 2.09E-03 rps102 -1.26 9.95E-03 tef3 -1.24 2.20E-03 SPAC32A11.02c -1.22 6.85E-03 lsd90 -1.21 3.81E-03 osr1 -1.20 3.73E-03 SPCC794.04c -1.19 8.29E-03 srx1 -1.17 3.73E-03

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vps1302 -1.16 4.69E-03 SPBC660.05 -1.16 4.33E-03 SPAC23C11.06c -1.16 9.89E-03 SPAC27E2.03c -1.15 1.50E-02 SPAC26F1.07 -1.12 7.31E-03 SPCC70.10 -1.11 1.25E-02 SPBPB2B2.14c -1.10 6.89E-03 eft202 -1.10 1.34E-02 ctt1 -1.08 6.25E-03 mug24 -1.08 7.30E-03 pyp2 -1.07 8.36E-03 pap1 -1.06 7.09E-03 ish1 -1.06 3.61E-02 SPCC1322.10 -1.06 7.88E-03 ura1 -1.05 1.21E-02 hsp90 -1.03 1.05E-02 mus7 -1.02 9.74E-03 gti1 -1.02 1.19E-02

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Table A18. Top 50 genes upregulated by prr1+ overexpression. Name log2FC P-value SPBC1289.14 7.70 7.51E-24 SPAC3G6.07 7.16 7.19E-21 SPAC4H3.08 6.97 7.31E-20 shu1 6.81 5.16E-19 SPAPB24D3.07c 6.15 7.87E-16 pfl4 5.98 4.70E-15 tms1 5.97 5.64E-15 SPAC27D7.09c 5.90 1.13E-14 mug108 5.74 5.41E-14 hsp3102 5.73 6.35E-14 SPCC417.09c 5.57 2.94E-13 uga1 5.56 3.44E-13 meu17 5.55 3.52E-13 SPAC4F10.17 5.50 5.67E-13 map4 5.48 7.15E-13 SPCC777.04 5.39 1.69E-12 atg13 5.38 1.89E-12 meu25 5.32 3.27E-12 SPBC3H7.08c 5.09 2.53E-11 SPAC22A12.17c 5.08 2.76E-11 SPBC1105.13c 5.07 3.21E-11 rds1 5.04 3.95E-11 SPAC27D7.10c 5.03 4.27E-11 meu7 4.98 7.05E-11 hsp16 4.93 1.05E-10 SPCC550.07 4.85 2.10E-10 ssa1 4.83 2.55E-10 opt3 4.82 2.71E-10 SPAC23A1.14c 4.81 2.81E-10 dic1 4.81 2.99E-10 fmd2 4.78 3.82E-10 ctt1 4.78 3.92E-10 SPBC12C2.04 4.75 4.98E-10 SPBC725.03 4.69 7.92E-10 SPAC2F3.05c 4.66 1.03E-09 SPAC16A10.01 4.65 1.12E-09 SPBC6B1.03c 4.60 1.72E-09 SPBPJ4664.02 4.59 1.84E-09 gst1 4.58 1.93E-09 fap2 4.58 1.95E-09 SPAC4H3.03c 4.56 2.29E-09 mug62 4.55 2.49E-09 dak2 4.53 2.93E-09 cdc16 4.49 3.86E-09 SPAC27F1.05c 4.47 4.57E-09 mug126 4.46 4.97E-09 SPAC11D3.09 4.40 7.87E-09 mug123 4.34 1.23E-08 sua5 4.34 1.24E-08 frp2 4.34 1.24E-08

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Table A19. Top 50 genes upregulated in the scr1Δ strain. Gene log2FC P-value inv1 8.85 6.55E-47 ght5 7.80 5.67E-43 mug14 7.79 1.14E-41 tlh2 7.48 4.20E-31 tlh1 7.09 1.29E-29 SPCC191.10 6.16 6.39E-26 gld1 5.68 1.86E-33 SPCC794.04c 5.59 6.11E-33 gal10 5.52 1.79E-32 ght1 4.92 1.74E-29 mei2 4.79 1.10E-28 agl1 4.73 2.17E-28 ght4 4.67 1.24E-27 ght3 4.60 1.25E-27 SPCC1739.08c 4.46 6.87E-26 isp3 4.40 2.04E-26 plb1 4.18 7.97E-25 SPAC212.06c 4.05 2.50E-16 gal1 4.00 4.97E-24 mfm1 3.59 5.79E-21 mfm2 3.57 6.14E-21 mfm3 3.48 1.08E-20 ght6 3.41 6.51E-13 SPBP4H10.10 3.39 5.04E-20 SPBC660.05 3.14 1.98E-18 gut2 2.99 2.51E-17 gal7 2.97 2.97E-17 rsv1 2.97 9.84E-12 pfl9 2.75 2.12E-15 gsf2 2.67 3.89E-15 mug108 2.66 4.45E-15 SPBPB7E8.02 2.61 1.04E-14 fta5 2.58 1.63E-14 mug146 2.55 2.90E-14 SPAC27D7.11c 2.55 2.74E-14 rgs1 2.49 7.46E-14 idn1 2.42 1.14E-12 SPAC513.04 2.42 2.19E-13 SPCC1235.01 2.35 6.79E-13 map1 2.32 1.13E-12 SPBC660.16 2.27 2.69E-12 hsp9 2.26 3.06E-12 isp5 2.24 4.84E-12 ssa1 2.14 2.01E-11 nep2 2.14 2.15E-11 SPCC2H8.02 2.12 2.19E-07 mat2-Pc 2.08 9.79E-11 SPCC191.01 2.08 5.95E-11 mam1 2.04 1.15E-10 mdh1 2.01 2.53E-10

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Table A20. Genes upregulated in the SPBC56F2.05Δ strain. Gene log2FC P-value inv1 2.85 3.18E-07 SPAC186.04c 2.68 1.10E-04 SPAC186.05c 2.65 2.15E-07 dak2 2.48 3.53E-05 gsf2 2.37 7.03E-07 fta5 2.26 1.27E-06 mug15 2.25 3.30E-04 SPAC1002.16c 2.18 2.94E-06 SPCC1529.01 2.02 4.52E-06 ght5 1.91 8.68E-06 SPBP4H10.10 1.74 2.45E-05 atf21 1.71 7.14E-04 cbf12 1.70 2.03E-05 mug14 1.68 8.72E-04 mbx2 1.65 3.12E-03 mug146 1.63 7.22E-04 ght6 1.54 1.04E-03 SPAC17G6.03 1.46 8.11E-05 mei2 1.46 2.62E-03 tms1 1.44 1.01E-04 rds1 1.41 1.21E-04 but1 1.19 7.67E-04 ppk33 1.19 1.90E-02 SPBC8E4.02c 1.16 4.58E-03 SPBC2G2.17c 1.06 9.76E-03 pfl3 1.01 2.11E-03

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Table A21. Genes upregulated by SPBC1773.16c overexpression. Gene ChIP Ratio log2FC P-value SPBC1773.16c 3.37 8.07 3.89E-27 SPBC1773.13 5.68 7.44 6.68E-26 dal52 4.73 5.56 1.43E-21 arg7 5.68 5.41 4.70E-23 car1 3.11 4.43 6.55E-24 SPBPB2B2.01 2.76 4.15 1.81E-22 meu26 1.67 2.90 9.03E-13 SPBC1773.12 1.85 9.51E-12

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Table A22. Genes upregulated by SPBC16G5.17 overexpression. Gene ChIP Ratio log2FC P-value SPAC977.15 3.16 7.73 1.68E-35 SPBP4G3.03 2.57 6.98 5.76E-05 SPAC977.04 6.00 1.26E-26 SPAC750.02c 5.86 9.17E-37 urg1 3.47 5.57 3.44E-36 SPBC1348.05 5.56 5.60E-36 SPBPB2B2.16c 5.38 2.36E-23 SPBC16G5.17 2.23 4.97 4.85E-21 urg2 2.74 4.42 2.22E-27 SPAC521.03 2.73 3.42 6.09E-20 SPBC24C6.09c 2.09 3.38 5.48E-20 inv1 2.00 3.25 5.05E-19 SPBC119.03 3.15 6.17E-18 car1 3.26 3.01 5.16E-17 SPCC584.16c 3.85 2.80 5.37E-15 SPCC4B3.06c 3.14 2.78 3.17E-15 SPBC1683.06c 3.85 2.68 1.44E-14 zwf2 3.01 2.62 4.73E-14 SPBC1773.13 3.25 2.59 7.30E-14 SPAC11D3.09 3.43 2.57 2.23E-13 SPBC1348.02 2.54 2.23E-13 gti1 2.41 2.47 6.27E-13 SPAC1F7.11c 2.36 5.41E-12 ftp105 2.35 5.17E-12 pmd1 2.62 2.30 1.58E-11 meu7 2.36 2.25 5.30E-11 coa4 2.32 2.21 6.75E-11 spn1 2.21 7.92E-11 SPAC1399.04c 2.31 2.19 9.74E-11 mal1 3.85 2.15 1.92E-10 hri1 2.09 5.67E-10 SPAPB24D3.07c 2.09 5.69E-10 bit61 2.65 2.08 7.70E-10 fhl1 2.62 2.04 1.70E-09 SPBPB2B2.19c 2.01 2.73E-09 myh1 3.21 1.99 7.65E-09 SPBC428.10 2.74 1.99 3.50E-09 SPCC1223.09 2.44 1.93 1.07E-08 rqc1 1.89 2.03E-08 jmj1 2.13 1.86 2.80E-08 dak2 3.20 1.84 3.99E-08 ski3 1.83 4.71E-08 SPAC3G9.11c 2.35 1.79 1.04E-07 tel1 1.76 1.61E-07 hsp3106 1.76 1.69E-07 tpt1 1.75 1.99E-07 SPBC1652.01 3.12 1.74 2.29E-07 SPCC4G3.12c 2.65 1.74 2.61E-07 alp16 2.23 1.74 2.46E-07 SPAC977.01 1.73 2.64E-07 SPAC1A6.03c 3.83 1.69 4.78E-07 SPAC11D3.08c 3.43 1.66 8.69E-07

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klf1 1.61 1.61E-06 srk1 3.43 1.60 2.13E-06 SPAC3H5.08c 3.38 1.60 2.13E-06 urg3 2.74 1.56 3.42E-06 sds3 1.56 3.42E-06 SPCC417.09c 3.17 1.55 4.24E-06 SPAC1002.12c 2.43 1.55 4.29E-06 erv41 2.80 1.55 4.34E-06 cyc1 3.45 1.55 4.42E-06 SPAC11D3.06 2.73 1.55 7.99E-06 adh1 2.11 1.54 5.08E-06 SPBC8E4.03 3.81 1.48 1.40E-05 loz1 1.46 1.80E-05 SPBC1271.07c 2.35 1.44 2.22E-05 dal52 1.40 3.91E-05 cip1 1.39 4.33E-05 car2 2.82 1.38 4.60E-05 its3 2.35 1.38 5.01E-05 hsp16 1.37 5.56E-05 dbp10 2.44 1.37 6.29E-05 SPAPB2B4.04c 2.51 1.35 7.09E-05 SPBP8B7.26 1.35 7.30E-05 mrp51 1.35 9.00E-05 SPAC637.09 1.34 8.46E-05 ppk8 2.74 1.31 1.31E-04 SPAC27D7.09c 2.18 1.29 1.72E-04 fap1 1.28 1.94E-04 aru1 1.26 2.43E-04 ned1 1.25 2.62E-04 SPCC550.08 1.24 3.56E-04 SPAPB24D3.08c 1.23 3.41E-04 knk1 1.23 3.73E-04 ksg1 2.40 1.22 3.70E-04 rnf10 2.27 1.22 4.07E-04 SPCC320.05 1.22 4.35E-04 bpl1 1.21 6.41E-04 SPAC2E1P3.05c 1.20 4.90E-04 psd2 1.20 5.16E-04 ubp16 2.49 1.19 5.83E-04 sib2 1.19 5.96E-04 per1 1.18 6.01E-04 SPBC1773.12 1.18 7.07E-04 SPAC6G9.14 3.02 1.18 6.74E-04 pgc1 1.17 6.76E-04 sif3 1.16 7.79E-04 iss10 1.16 7.76E-04 mok13 2.95 1.16 8.23E-04 SPBC20F10.03 1.16 7.95E-04 lsd1 2.66 1.15 9.16E-04 rsv2 2.06 1.14 1.01E-03 SPAC1952.09c 1.14 1.10E-03 sec31 2.05 1.12 1.29E-03 aes1 1.12 1.32E-03 sds23 2.46 1.11 1.41E-03

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SPCC2H8.02 1.11 3.61E-03 ssp1 3.16 1.10 1.72E-03 SPAC869.02c 1.10 1.71E-03 cph1 1.09 2.18E-03 SPBC685.03 1.09 2.10E-03 rds1 1.09 1.86E-03 taf2 2.01 1.08 2.21E-03 SPAC26H5.09c 2.07 1.07 2.13E-03 rng2 1.06 2.46E-03 alp1 1.06 2.39E-03 nrl1 1.06 2.91E-03 liz1 1.06 2.51E-03 dbl8 1.05 3.24E-03 nup61 2.55 1.05 3.34E-03 cld1 1.04 3.30E-03 msa1 3.14 1.04 3.20E-03 arg7 3.25 1.02 4.23E-03 cao1 2.73 1.02 4.09E-03 rna15 1.02 4.00E-03 pop2 1.01 4.11E-03

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Table A23. Genes upregulated by SPAC25B8.11 overexpression. Gene ChIP Ratio log2FC P-value SPAC27F1.05c 5.78 9.93E-21 SPCC550.08 1.41 4.14 1.23E-17 atd3 1.41 3.08 1.35E-14 SPCC550.07 1.41 2.19 8.88E-11 SPBC21C3.19 2.17 7.60E-11 SPAC25B8.11 1.69 2.01 3.16E-10 SPCC584.13 2.41 1.96 6.12E-10 SPAC2E12.03c 1.77 1.94 6.92E-10 SPAC11D3.03c 1.55 1.81 7.64E-08 SPBC4F6.17c 2.37 1.79 3.68E-09 ssa1 1.64 3.21E-08 pdc201 2.47 1.59 6.58E-09 hsp104 1.39 1.58 4.26E-08 SPCC191.06 2.21 1.58 2.04E-05 rds1 1.70 1.37 5.30E-07 SPAC2H10.01 1.26 2.19E-06 gsf2 1.19 6.27E-06 uga1 1.17 9.35E-06 SPAC1002.12c 1.13 1.22E-05 tlh1 1.87 1.05 1.96E-03 SPBC1861.05 1.05 3.59E-05

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License Number 3930921230904 License date Aug 11, 2016 Licensed content publisher Genetics Society of America Licensed content title Genetics Licensed content date Dec 31, 1969 Type of Use Thesis/Dissertation Requestor type Author of requested content Format Print, Electronic Portion chapter/article Title or numeric reference of Construction and phenotypic characterization of the portion(s) transcription factor overexpression array, Figure 1, Toe1 is a novel transcriptional reglator of the pyrimidine-salvage pathway, Figure 2, Figure 3, Discussion Title of the article or Functional characterization of fission yeast chapter the portion is from transcription factors by overexpression analysis Editor of portion(s) n/a

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Author of portion(s) Lianne Vachon, Justin Wood, Eun-Joo Gina Kwon, Amy Laderoute, Kate Chatfield-Reed, Jim Karagiannis, Gordon Chua Volume of serial or 194 monograph. Issue, if republishing an 4 article from a serial Page range of the portion

Publication date of portion August 2013 Rights for Main product Duration of use Life of current and all future editions Creation of copies for the no disabled With minor editing yes privileges For distribution to Canada In the following language(s) Original language of publication With incidental promotional no use The lifetime unit quantity of Up to 499 new product Made available in the education following markets Specified additional Minor editing previleges - few small adjustments to information text for incorporation into thesis The requesting Lianne Vachon/University of Calgary person/organization is: Order reference number

Author/Editor Lianne Vachon The standard identifier of vachon2016 New Work The proposed price n/a Title of New Work Functional genomic characterization of transcription factors in fission yeast Publisher of New Work University of Calgary Expected publication date Aug 2016 Estimated size (pages) 180 Total (may include CCC user 0.00 USD fee)

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Re: Permissions to use Genetics Paper On Aug 16, 2016 4:03 PM, "Lianne Vachon" wrote:

Hi coauthors,

I'm writing to get your permission to use parts of the paper "Functional characterization of fission yeast transcription factors by overexpression" in my PhD thesis. The paper, as part of the thesis, will be added to the repository at the University of Calgary as well as the Library and Archives of Canada.

A reply to this email indicating that you agree to the use of this material in my thesis is sufficient.

Thank you!

Sincerely,

Lianne Vachon Re: Permissions to use Genetics Paper

JW Justin Wood

Reply all| Tue 1:05 PM

Lianne Vachon

Greetings Lianne,

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I agree to the use of this material in your thesis.

Sincerely,

Justin Wood Re: Permissions to use Genetics Paper

GC Gordon Chua

Reply all| Tue 3:04 PM

Lianne Vachon Hi Lianne,

I agree to the use of the content of your Genetics paper for your thesis.

Sincerely,

Gordon

Gordon Chua, Ph.D. Associate professor, Department of Biological Sciences Biological Sciences Building, Room 560 University of Calgary 2500 University Drive, N.W. Calgary, Alberta Canada T2N 1N4 office: (403) 220-7769 lab: (403) 210-6283 FAX: (403) 210-8655 email: [email protected]

Re: Permissions to use Genetics Paper

GK Gina Kwon 244

Reply all| Tue 1:07 PM

Lianne Vachon; [email protected]; Gordon Chua ;

Kate Chatfield-Reed Hi Lianne,

I agree to the use of the paper in your thesis.

Gina

Re: Permissions to use Genetics Paper

KC Kate Chatfield-Reed

Reply all| Tue 1:32 PM

Lianne Vachon I agree to the use of the paper in your thesis.

Re: Permissions for Genetics paper use

AA Amy Austin

Reply all| Tue 2:12 PM

Lianne Vachon; [email protected] Permission granted. All the best, Lianne! Sincerely, Amy

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