Identifying Novel Mechanisms of Genome Maintenance in Saccharomyces Cerevisiae Using the DNA Damage- Inducibility of Ribonucleotide Reductase

Identifying novel mechanisms of genome maintenance in Saccharomyces cerevisiae using the DNA damage- inducibility of ribonucleotide reductase

Krystal Annette Laframboise

A thesis submitted in conformity with the requirements for the degree of Master of Science Department of Biochemistry University of Toronto

Identifying novel mechanisms of genome maintenance in Saccharomyces cerevisiae using the DNA damage-inducibility of ribonucleotide reductase

Krystal Annette Laframboise

Master of Science

Department of Biochemistry University of Toronto

2016 Abstract

The identification of genome maintenance genes in Saccharomyces cerevisiae has been limited to loss-of-function screens that ignore the consequences of gene overexpression. Here, I assay expression of the DNA damage-inducible gene, RNR3, using reporter synthetic genetic array (R-

SGA) methodology to identify genes that result in genome instability when overexpressed. I find

41 of ~5100 genes screened result in increased RNR3 expression, including known DNA repair genes, transcriptional regulators of RNR3, and a subset of genes exhibiting elevated levels of spontaneous DNA damage as determined by the presence of Rad52 foci. 61% of genes identified had no reported connection to genome stability when compared to previous overexpression studies, leaving 25 novel putative genome maintenance genes for follow-up. Finally, I show that induction of RNR3 in known genome instability mutants is not always dependent on the upstream checkpoint kinase Dun1, suggesting a non-canonical Dun1-independent pathway for

RNR3 up-regulation following checkpoint activation.

Acknowledgments

The past two years of graduate school have taught me more about myself than any other academic experience I have had. For that, I must thank my supervisor, Dr. Grant Brown, who has afforded me the opportunity to work in his lab with such wonderful scientists and people.

To Jason: Thank you for laying the foundations of this project and sticking around long enough to help me get started. I would have been lost without your guidance and unpredictable bouts of questioning.

To Nikko, Tina, Raphael, Attila and Dave: Thank you for your scientific insight and mentorship when I started to panic over upcoming talks, committee meetings, and cockroaches.

To Minnie and Brandon: I have loved sharing this experience with you both; my graduate school life would have been completely different without you there. You have motivated me to work harder and find something I love as much as Brandon loves his bench/phone.

To Jiongwen: Thank you for knowing how to do everything and sharing your knowledge with us mere mortals.

Thank you to my supervisory committee for their excellent ideas and guidance as I questioned every inch of data I produced.

Finally, thank you to my family and closest friends who spent many phone calls listening to my project woes without knowing a single thing about molecular biology. Somehow they still managed to cheer me up every single time.

iii

Table of Contents

ACKNOWLEDGMENTS ...... III

TABLE OF CONTENTS ...... IV

LIST OF TABLES ...... VII

LIST OF FIGURES ...... VIII

LIST OF ABBREVIATIONS ...... IX

CHAPTER 1 ...... 1

1 INTRODUCTION ...... 1

1.1 GENOME INSTABILITY ...... 1

1.2 DNA DAMAGE AND REPLICATION CHECKPOINT ...... 1

1.3 RIBONUCLEOTIDE REDUCTASE (RNR) COMPLEX ...... 4 1.3.1 Cell cycle & DNA damage-induced regulation ...... 5 1.3.2 Evidence of Dun1-independent regulation of the RNR complex ...... 7

1.4 THESIS RATIONALE ...... 8 1.4.1 Genome-wide loss-of-function screens represent the primary approach used to identify novel genome maintenance genes ...... 8 1.4.2 Utility of gene overexpression in identifying novel players involved in genome maintenance ...... 10 1.4.3 Project Objectives ...... 11

CHAPTER 2 ...... 14

2 METHODS ...... 14

2.1 STRAINS AND MEDIA ...... 14

2.2 REPORTER SYNTHETIC GENETIC ARRAY (R-SGA) ANALYSIS ...... 15 2.2.1 Screening the FLEX overexpression collection ...... 15 2.2.1.1 Array Construction ...... 15 2.2.1.2 Fluorescence Scanning and Imaging ...... 15 2.2.1.3 Data Analysis ...... 15 2.2.2 Screening the yeast non-essential deletion collection ...... 16 2.2.2.1 Array Construction ...... 16 2.2.2.2 Fluorescence Scanning and Imaging ...... 16

2.2.2.3 Data Analysis ...... 16 2.3 SECONDARY SCREEN FOR RAD52 FOCI ...... 17 2.3.1 Array Construction ...... 17 2.3.2 Overexpression and Data Collection ...... 17 2.3.3 Quantification of Results ...... 17

2.4 GENE ONTOLOGY (GO) ENRICHMENT ...... 17

2.5 TARGET GENE IDENTIFICATION ...... 18

CHAPTER 3 ...... 19

3 RESULTS ...... 19

3.1 USING THE DNA DAMAGE-INDUCIBILITY OF RNR3 TO IDENTIFY NOVEL GENES CAUSING GENOME INSTABILITY

WHEN OVEREXPRESSED ...... 19 3.1.1 Optimization of R-SGA Protocol ...... 20 3.1.1.1 Incorporation of negative control strains grown on glucose is uninformative ...... 20 3.1.1.2 Increased dynamic range in Rnr3 abundance in haploid strains compared to isogenic diploids ...... 20 3.1.2 41 genes cause Rnr3 induction when overexpressed ...... 21 3.1.3 Identification of known genome maintenance genes and regulators of RNR3 ...... 24 3.1.4 GO enrichment analysis reveals processes important for genome maintenance ...... 24 3.1.5 Investigating mechanisms responsible for Rnr3 induction following gene overexpression .. 26 3.1.5.1 27% of increased RNR3 phenotypes may be explained by the balance hypothesis ...... 26 3.1.5.2 Majority of overexpressed genes exhibit gain-of-function phenotypes ...... 29 3.2 EVIDENCE OF SPONTANEOUS DNA DAMAGE IN OVEREXPRESSION STRAINS EXHIBITING RNR3 EXPRESSION ...... 30 3.2.1 Rad52 Foci Formation ...... 30

3.3 TOWARDS UNDERSTANDING THE DUN1-DEPENDENCY OF RNR3 INDUCTION IN GENOME INSTABILITY MUTANTS 33 3.3.1 Dun1-dependency of Rnr3 induction ...... 33 3.3.1.1 Dun1-dependent regulation of Rnr3 ...... 34 3.3.1.2 Dun1-independent regulation of Rnr3 ...... 34 3.3.1.3 Dun1-attenuation of Rnr3 ...... 37 3.3.2 Dun1-dependency in overexpression strains ...... 40

CHAPTER 4 ...... 45

4 DISCUSSION AND FUTURE DIRECTIONS ...... 45

4.1 IDENTIFICATION OF NOVEL GENES IMPLICATED IN GENOME INTEGRITY ...... 45

4.2 IDENTIFICATION OF NOVEL TRANSCRIPTIONAL REGULATORS OF RNR3 ...... 47

4.3 INVESTIGATING THE DUN1-INDEPENDENT INDUCTION OF RNR3 IN KNOWN GENOME INSTABILITY MUTANTS .... 48

4.3.1 Dun1-independent Checkpoint ...... 49 4.3.2 Alternative roles for genes in RNR3 transcription ...... 51

4.4 UNDERSTANDING THE DUN1-ATTENUATION OF RNR3 ...... 52 4.4.1 Checkpoint activation in strains lacking dun1∆ ...... 52 4.4.2 A role for Dun1 in RNR3 repression ...... 53 4.4.3 Functional role of Rnr3 due to decreased Rnr1 activity in dun1∆ strains ...... 53

4.5 SUMMARY AND CONCLUSIONS ...... 54

REFERENCES ...... 55

APPENDIX ...... 72

List of Tables

TABLE 2-1. YEAST STRAINS USED IN THIS STUDY...... 14

TABLE 3-1. OVEREXPRESSION STRAINS CAUSING RNR3 INDUCTION...... 23

TABLE 3-2. COMPLEXES WITH GENES WHOSE OVEREXPRESSION AND DELETION RESULTS IN GENOME INSTABILITY PHENOTYPES...... 28

TABLE 3-3. PERCENTAGE OF CELLS EXHIBITING RAD52 FOCI IN 41 OVEREXPRESSION STRAINS WITH ELEVATED LEVELS OF RNR3...... 32

TABLE 3-4. PERCENTAGE OF CELLS WITH MORE THAN TWO RAD52 FOCI...... 33

TABLE 3-5. RNR3 ABUNDANCE IN SINGLE MUTANTS (XXX∆) COMPARED TO DOUBLE MUTANTS LACKING CHECKPOINT KINASE DUN1 (XXX∆

DUN1∆)...... 35

TABLE 3-6. GENOME INSTABILITY PHENOTYPES PRESENT IN THE 40 DELETION MUTANTS CAUSING RNR3 INDUCTION...... 39

TABLE 3-7. DUN1-DEPENDENCY OF RNR3 INDUCTION IN 41 OVEREXPRESSION STRAINS IDENTIFIED BY PRIMARY SCREEN...... 42

TABLE 3-8. GENOME INSTABILITY (GIN) PHENOTYPES OBSERVED IN RESPONSE TO OVEREXPRESSION OF THE 40 STRAINS IDENTIFIED BY

PRIMARY SCREEN...... 43

TABLE 4-1. BIOLOGICAL PROCESSES REGULATED BY TRANSCRIPTION FACTORS IDENTIFIED IN OUR SCREEN AS DETERMINED BY GO

ENRICHMENT OF TARGETS...... 72

vii

List of Figures

FIGURE 1-1. CHECKPOINT SIGNALING CASCADE FOLLOWING DETECTION OF SSDNA OR DSBS...... 3

FIGURE 1-2. REGULATION OF THE RNR COMPLEX FOLLOWING CHECKPOINT ACTIVATION...... 6

FIGURE 1-3. PIPELINE OF R-SGA SCREEN USED TO IDENTIFY NOVEL GENES CAUSING GENOME INSTABILITY WHEN OVEREXPRESSED...... 13

FIGURE 3-1. OPTIMIZATION OF THE R-SGA EXPERIMENTAL PIPELINE...... 21

FIGURE 3-2. SCREENING THE OVEREXPRESSION FLEX COLLECTION FOR NOVEL GENES INVOLVED IN GENOME MAINTENANCE...... 22

FIGURE 3-3. TRANSCRIPTION FACTORS EXHIBITING ELEVATED RNR3 LEVELS REGULATE BIOLOGICAL PROCESSES IMPLICATED IN GENOME

MAINTENANCE...... 25

FIGURE 3-4. MOLECULAR MECHANISMS RESPONSIBLE FOR RNR3 INDUCTION FOLLOWING GENE OVEREXPRESSION...... 27

FIGURE 3-5. EVIDENCE OF SPONTANEOUS DNA DAMAGE IN OVEREXPRESSION STRAINS INDUCING RNR3...... 31

FIGURE 3-6. DETERMINING THE DUN1-DEPENDENCY OF RNR3 INDUCTION SEEN IN 40 DELETION MUTANTS...... 37

FIGURE 3-7. DETERMINING DUN1-DEPENDENCY OF THE RNR3 INDUCTION CAUSED BY THE 41 GENES IDENTIFIED IN THE OVEREXPRESSION

SCREEN...... 41

viii

List of Abbreviations

ADP adenosine diphosphate ALF a-Like Faker ATP adenosine triphosphate BiM Bi-Mater CDP cytosine diphosphate ChIP chromatin immunoprecipitation CIN chromosome instability CTF chromosome transmission fidelity dATP deoxyadenosine triphosphate dGTP deoxyguanosine triphosphate DMA non-essential deletion collection/deletion mutant array dNTP deoxyribonucleoside triphosphate DSBs double stranded breaks dTTP deoxythymidine triphosphate FLEX full-length expression ready collection GDP guanosine diphosphate GFP green fluorescent protein GIN genome instability GO gene ontology HU hydroxyurea MBF Mlu1 cell cycle box binding factor MMS methyl methanesulfonate NTP nucleoside triphosphate OD optical density PIKK phosphoinositol-3-kinase-related kinase qPCR quantitative polymerase chain reaction R-SGA reporter synthetic genetic array RFP red fluorescent protein RNR ribonucleotide reductase RPA replication protein A RT-PCR reverse transcription polymerase chain reaction SGA synthetic genetic array SGD Saccharomyces Genome Database ssDNA single stranded DNA UDP uridine diphosphate UV ultra violet

Chapter 1 1 INTRODUCTION 1.1 Genome Instability

Accurate transmission of genetic information from generation to generation is essential for maintaining cell function; however, the process is constantly threatened by the presence of DNA damage. Indeed, the combination of exogenous and endogenous sources of DNA damage leads to approximately 105 new lesions in the mammalian genome each day (Collins, 1999). Exogenous sources of DNA damage include a variety of environmental carcinogens and other genotoxic agents and often lead to physical lesions on the nitrogenous bases of the double helix. One of the primary sources of endogenous stress leading to DNA damage is replication stress. Replication stress is characterized a variety of events, such as the alteration of fork kinetics, or collision of forks with DNA lesions, or transcriptional/replication machinery of oncoming forks (Gaillard et al., 2015). Often replication stress and DNA damage lead to double-stranded breaks (DSBs) and exposed single-stranded DNA (ssDNA). When left unrepaired, this damage can lead to genome instability, some examples of which include a genome-wide increase in mutation rate and gross chromosomal rearrangements (Aguilera and García-Muse, 2013). Fortunately, eukaryotic cells have a coordinated DNA damage response and replication checkpoint to prevent such genome instability.

1.2 DNA Damage and Replication Checkpoint

The replication checkpoint, also referred to as the intra S-phase DNA damage checkpoint or response, is a highly conserved eukaryotic signaling cascade that functions to integrate cell cycle progression with DNA replication and repair. In Saccharomyces cerevisiae, it is activated upon detection of either ssDNA or DSBs. In essence, the checkpoint halts all processes involved in cell cycle progression until DNA lesions are successfully repaired to prevent passing harmful mutations on to subsequent generations.

DSBs and ssDNA are common lesions that result from a variety of sources of DNA damage and stress, making them both ideal signals for checkpoint activation. DSBs can form following exposure to gamma-radiation and other genotoxic agents; however, they also form in response to

1 2 endogenous events such as replication fork collapse (Symington and Gautier, 2011; Valko et al., 2006). It is essential for viability that these DSBs are repaired. While ssDNA is often an intermediate formed during repair processes such as nucleotide excision repair, mismatch repair, or homologous recombination, it can also be generated following replication fork stalling (Byun et al., 2005). Exposure to exogenous agents, such as the drugs methyl methanesulfonate (MMS) or hydroxyurea (HU), can cause replication forks to stall either through collision with alkylated bases or depletion of deoxynucleoside triphosphates (dNTPs). Forks can also be stalled by intrinsic sources of stress such as the absence of important replication machinery proteins or collisions with oncoming replication forks and transcriptional machinery (Zeman and Cimprich, 2014). Indeed, stalled forks generate tracts of ssDNA through decoupling of the helicase from the DNA polymerase during replication (Byun et al., 2005; Lopes et al., 2006; Nedelcheva et al., 2005; Sogo et al., 2002).

In budding yeast, the checkpoint is initiated when DSBs and ssDNA are recognized by the apical phosphoinositol-3-kinase-related (PIKK) kinases, Tel1 and Mec1, respectively (Nakada et al., 2003a, 2003b; Zou and Elledge, 2003). The PIKK kinases serve partially redundant functions; double mutants exhibit increased sensitivity to DNA damaging agents and increased mutation rate relative to either single mutant alone (Morrow et al., 1995; Myung and Kolodner, 2002). Still, they clearly have divergent roles. For example, Tel1 seems to be more important for telomere maintenance and plays a secondary role in checkpoint activation when compared to Mec1 (Ritchie et al., 1999). mec1∆ mutants are sensitive to DNA damaging agents and fail to activate the DNA damage checkpoint, proving that Tel1 alone is not sufficient for checkpoint initiation (Weinert et al., 1994). In terms of lesion recognition, neither kinase directly binds sites of damaged DNA, but recognizes these sites through interactions with DNA-binding proteins and protein complexes. When ssDNA is detected within the nucleus, it is rapidly coated by replication protein A (RPA), which facilitates recruitment of Mec1 through its binding partner, Ddc2 (Rouse and Jackson, 2002; Zou and Elledge, 2003). RPA also recruits the Rad24-Rfc2-5 alternative replication factor C (RFC) complex that functions to load the 9-1-1 complex (Rad17- Mec3-Ddc1) onto DNA (Majka and Burgers, 2003; Majka et al., 2006a). Much like the PCNA clamp, the 9-1-1 complex forms a trimer around DNA and stimulates the kinase activity of Mec1 (Majka et al., 2006b; Navadgi-Patil and Burgers, 2009). Indeed, this interaction is necessary for checkpoint interaction (Bonilla et al., 2008; Xu et al., 2008). Tel1 detects DSBs in a similar

3 manner through its interactions with the DNA end-binding complex, MRX (Mre11-Rad50-Xrs2) (Nakada et al., 2003a).

Figure 1-1. Checkpoint signaling cascade following detection of ssDNA or DSBs.

Mec1 is recruited to RPA-coated ssDNA through its interaction with Ddc2. Association with the 9-1-1 complex (Rad17, Mec3, Ddc1) stimulates the kinase activity of Mec1 allowing it to activate Rad53 in tandem with Rad9. Tel1 recognizes DSBs through the MRX complex and similarly activates Rad53. Rad53 carries out many functions in response to damage and replication stress, including the up-regulation of DNA repair genes, inhibition of late origin firing, and phosphorylation of the downstream Dun1. Dun1 further coordinates the damage response by increasing dNTP pools and DNA repair genes. Mec1 and Tel1 have also been shown to phosphorylate Chk1. Chk1 is less involved in S phase, and plays a larger role in regulation of the G2/M phase transition. Adapted from (Hustedt et al., 2013).

Once activated, Mec1 and Tel1 amplify checkpoint signaling through phosphorylation of the adaptor protein, Rad9, which in turn, acts as a scaffold to promote activation of the downstream kinases Rad53 and Chk1 (Gilbert et al., 2001; Nyberg et al., 2002; Schwartz et al., 2002; Sweeney et al., 2005; Toh and Lowndes, 2003; Zou et al., 2002). Chk1 has little involvement in the replication checkpoint, and predominantly functions in G2/M checkpoint arrest and the

4 prevention of sister chromatid separation (Nyberg et al., 2002). In contrast, Rad53 initiates many downstream checkpoint processes, including inhibition of late origin firing, replication fork stabilization, increased transcription of repair genes, and increased production of dNTPs necessary for repairing damaged segments of DNA (Huang et al., 1998; Lucca et al., 2004; Santocanale and Diffley, 1998; Zhao and Rothstein, 2002). The up-regulation of several repair genes is predominantly achieved through phosphorylation of the downstream kinase Dun1. Upon phosphorylation, Dun1 phosphorylates and inactivates Crt1, the transcriptional repressor of many DNA damage-inducible genes (Huang et al., 1998). This pathway regulates the genes encoding ribonucleotide reductase (RNR), a tetrameric complex involved in the synthesis of dNTPs. Interestingly, the increase in deoxyribonucleoside triphosphate (dNTP) pools facilitated by increased RNR activity appears to be the essential function of Mec1 and Rad53 (Zhao et al., 1998). A summary of the DNA damage and replication checkpoint is presented in Figure 1-1.

1.3 Ribonucleotide Reductase (RNR) Complex

RNR is the enzyme complex responsible for catalysis of the rate-limiting step in dNTP synthesis: the reduction of ribonucleoside diphosphates to deoxyribonucleoside diphosphates (Sanvisens et al., 2013). There are four genes that encode the RNR complex in S. cerevisiae: RNR1-4. RNR1 and -3 encode large subunits, while RNR2 and -4 encode the small subunits. The proteins synthesized from these genes form a tetramer with α2ββ’ architecture; two copies of Rnr1 bind to form a homodimeric large subunit, while Rnr2 and -4 come together as the heterodimeric small subunit (Chabes et al., 2000; Perlstein et al., 2005). Both genes encoding this latter subunit are essential for enzyme functionality. Rnr2 houses the diferric tyrosyl cofactor necessary for catalysis (Sanvisens et al., 2013). Rnr4 promotes the proper folding and assembly of the cofactor present in Rnr2 through an unknown mechanism (Chabes et al., 2000; Nguyen et al., 1999; Perlstein et al., 2005; Voegtli et al., 2001; Zhang et al., 2011).

In contrast, Rnr1 is the only large subunit essential for cell viability (Elledge and Davis, 1990). Rnr3 is undetectable in an unperturbed cell cycle, and therefore, is not a part of the canonical RNR complex. In terms of functionality, Rnr1 plays a major regulatory role in determining the overall activity and substrate specificity of the RNR complex by sensing the symmetry of dNTP pools through its two allosteric sites (Reichard, 2002; Sanvisens et al., 2013). The first allosteric site, termed the S site, determines the specificity of the enzyme by monitoring the concentration

5 of the dNTPs. High levels of adenosine triphosphate (ATP) and deoxyadenosine triphosphate (dATP) promote reduction of cytosine diphosphate (CDP) and uridine diphosphate (UDP). Deoxythymidine triphosphate (dTTP) increases the reduction of guanosine diphosphate (GDP), and deoxyguanosine triphosphate (dGTP) increases adenosine diphosphate (ADP) reduction (Sanvisens et al., 2013). The second allosteric site, the A site, controls the overall activity of the enzyme by monitoring the dATP/ATP ratio (Reichard, 2002). Both modes of feedback inhibition described are imperative for maintaining balanced dNTP pools, which is essential for the fidelity of DNA polymerases (Reichard, 1988). Interestingly, the feedback inhibition of Rnr1 in S. cerevisiae is fairly relaxed compared to the large subunit found in mammalian cells, and it is precisely this relaxed feedback inhibition that allows for the increase in dNTP pools following DNA damage checkpoint activation (Chabes et al., 2003). This increase in dNTP pools seems to be a double-edged sword in that it is necessary for survival, but also increases mutation rates within the cell, thereby promoting genome instability (Chabes et al., 2003; Davidson et al., 2012; Wheeler et al., 2005).

The role of Rnr3 in S. cerevisiae is not well understood. Due to its lack of feedback inhibition, and notable induction in response to checkpoint activation, it was hypothesized that an Rnr1- Rnr3 heterodimer might be advantageous following DNA damage by allowing for a greater increase in dNTP pools (Chabes et al., 2003; Domkin et al., 2002). The data did not support this hypothesis; the in vitro activity of Rnr3 is less than 1% of Rnr1, and there is no decrease in dNTP pools in rnr3∆ mutants following DNA damage (Chabes et al., 2003; Domkin et al., 2002).

1.3.1 Cell cycle & DNA damage-induced regulation

The RNR complex is induced during S-phase and in the presence of DNA damage to increase dNTP pools needed for replication and repair. As previously mentioned, the Mec1-Rad53-Dun1 signaling cascade mediates the induction of RNR during checkpoint activation and replication stress, as well as during S phase of the cell cycle (Figure 1-2A). This up-regulation occurs through several transcriptional and posttranslational mechanisms.

In terms of transcriptional regulation, the repressor Crt1 binds the “X-box” or damage response elements (DREs) found in the promoters of many DNA damage-inducible genes, including RNR2-4 (Huang et al., 1998). When bound, it recruits the co-repressors Ssn6-Tup1 together with

6 the ISW2 complex in order to reposition nucleosomes over the promoter to establish a “repressive chromatin state” (Li and Reese, 2001; Zhang and Reese, 2004a). Ssn6-Tup1 has also been proposed to be required for recruitment of histone deacetylases, which form a positive feedback loop for Tup1 recruitment (Watson et al., 2000; Zhang and Reese, 2004b). Rad53- dependent phosphorylation of the kinase Dun1 leads to phosphorylation of Crt1, subsequently causing its dissociation from DNA and the de-repression of the RNR genes (Huang et al., 1998) (Figure 1-2B).

Figure 1-2. Regulation of the RNR complex following checkpoint activation.

(A) Summary of all canonical pathways involved regulation of RNR. RNR2-4 are predominantly regulated through the Dun1-dependent phosphorylation of Crt1 and Dif1. While the transcriptional regulation of RNR1 occurs in a Mec1-Rad53-dependent, Dun1-independent manner, its activity is mediated by the Dun1-dependent phosphorylation of the inhibitor Sml1. Adapted from (Tsaponina et al., 2011). (B) Dun1-dependent regulation of the RNR complex. The transcriptional repressor Crt1 is phosphorylated by Dun1 causing induction of RNR2-4. Sml1 binds and inhibits the catalytic activity of Rnr1. It is phosphorylated and targeted for degradation by Dun1 following checkpoint activation. Dif1 imports Rnr2 and -4 into the nucleus to prevent the formation of an active complex. Dun1 phosphorylates Dif1, allowing for reconstitution of active RNR in the cytoplasm. Adapted from (Hendry, 2015).

Dun1 also regulates the localization and activity of the RNR complex through phosphorylation of its downstream targets, Dif1 and Sml1. While the large subunit of RNR, Rnr1, is localized in the cytoplasm, Dif1 actively imports the small subunits into the nucleus, where they are sequestered by Wtm1 (Lee and Elledge, 2006; Lee et al., 2008; Zhang et al., 2006). This spatial separation prevents premature activation of RNR to avoid unnecessary increases in dNTPs that raise mutation rates. Upon entry into S phase or activation of the checkpoint, Dun1 phosphorylates Dif1, thereby targeting it for degradation (Lee et al., 2008). In the absence of Dif1, Rnr2 and -4 can no longer be imported into the nucleus and bind the cytoplasmic Rnr1 homodimer, forming an active RNR complex (Yao et al., 2003) (Figure 1-2B). Sml1, on the other hand, binds Rnr1 preventing reduction of cysteine residues necessary for catalysis (Zhang et al., 2007; Zhao et al., 2000). Dun1 phosphorylates Sml1 during S phase or DNA damage, which, as in the case of Dif1, targets Sml1 for degradation (Zhao and Rothstein, 2002; Zhao et al., 2001). The end result is activation of Rnr1 (Figure 1-2B).

1.3.2 Evidence of Dun1-independent regulation of the RNR complex

Aside from the Dun1-mediated phosphorylation of Sml1 that activates Rnr1 activity, regulation of RNR1 is distinct from RNR2-4 in that it is primarily induced through Dun1-independent mechanisms. During DNA damage, Rnr1 is induced in a Mec1-Rad53-dependent, Dun1- independent manner that requires the transcription factor Ixr1 (Huang et al., 1998; Tsaponina et al., 2011). As one might expect, ixr1∆ mutants are synthetic lethal when combined with a dun1∆ mutation, and exhibit decreased dNTP pools (Costanzo et al., 2010; Pan et al., 2006; Tsaponina et al., 2011). The synthetic lethality of these double mutants is rescued by deleting SML1, suggesting that the absence of Rnr1 activity is responsible for inviability (Tsaponina et al., 2011). The MBF transcription complex (Mbp1/Swi6) is responsible for the transcriptional regulation of Rnr1 and a variety of other genes at the G1/S phase transition of an unperturbed cell cycle (Z. Hu et al., 2007; Iyer et al., 2001; Koch et al., 1993). The current model is that the co-repressor Nrm1 associates with MBF on the promoter of RNR1 and other genes regulated at G1/S boundary, establishing repression (de Bruin et al., 2006). Rad53-dependent phosphorylation of Nrm1 prevents its association with MBF and leads to RNR1 induction during S phase (Bastos de Oliveira et al., 2012; Travesa et al., 2012).

RNR1 is not the only RNR gene that exhibits Dun1-independent regulation. Induction of RNR2-4 following DNA damage is canonically described as dependent on the Dun1-mediated phosphorylation and inactivation of the transcriptional repressor, Crt1; however, the existence of a Dun1-independent replication checkpoint is supported by the literature. For example, Huang et al. have shown that Crt1 phosphorylation in MMS is slightly decreased in strains lacking DUN1, but is completely abolished in mec1∆ or rad53∆ mutants. Given that Rad53 acts downstream of Mec1 in the checkpoint, these data suggest that Rad53 may be able to phosphorylate targets of Dun1 in its absence (Huang et al., 1998). Moreover, other studies have shown that RNR2, -3, and -4 are detectable after treatment with MMS, even in the absence of Dun1 (Huang and Elledge, 1997; Jaehnig et al., 2013). Strains with an rnr4-99 mutation that delays S phase progression also exhibit Rnr3 levels that are not fully dependent on the presence of Dun1 (Huang and Elledge, 1997). Finally, our lab has identified pol30felg1∆ mutants with increased levels of genome instability, as well as elevated Rnr3 levels that are independent of Dun1 (Davidson et al., 2012). Clearly there are mechanisms in place to induce transcription of the RNR genes in the absence of Dun1; however, the exact mechanism and biological function of the Dun1-independent branch of the replication checkpoint remain poorly understood.

1.4 Thesis Rationale

1.4.1 Genome-wide loss-of-function screens represent the primary approach used to identify novel genome maintenance genes

While mechanisms of genome maintenance have been extensively studied in S. cerevisiae, almost all previous screens used to identify novel genes involved in these processes have employed loss-of-function alleles, such as those found in the conditional temperature-sensitive and non-essential deletion (DMA) collections (Ben-Aroya et al., 2008; Giaever et al., 2002; Li et al., 2011; Winzeler et al., 1999). Briefly, these collections represent an array of a particular number of strains (~4,800 for the non-essential deletion collection), each with a unique gene deletion or temperature-sensitive allele replacing a gene of interest. They allow researchers to conduct high-throughput screens for a variety of phenotypes in S. cerevisiae with ease.

Many different genome maintenance screens have been designed to detect mutants causing phenotypes associated with various types of DNA damage. Some of the earliest systematic screens involved testing mutant strains for sensitivity to a variety of genotoxic agents, such as

MMS, with the assumption that loss of genes necessary for resistance would cause increased sensitivity (Chang et al., 2002). Later screens measured an increase in mutation rate using a CAN1 forward mutation assay, wherein mutant strains causing spontaneous DNA damage are more likely to acquire resistance to the toxic, non-proteinogenic L-arginine analog, canavanine (Huang et al., 2003). Similarly, one early approach applied to determine the level of gross chromosomal rearrangements within a mutant strain monitored the loss of both the URA3 and CAN1 loci integrated on the long arm of chromosome V (Myung et al., 2001). Loss of both of these loci causes the strain to become resistant to canavanine and 5-FOA. Since the rate of independent mutation of both of these genes is 10-12 to 10-14 per generation, cells are assumed to gain resistance through chromosomal rearrangements resulting in a new stretch of DNA on chromosome V (Myung et al., 2001). Many other screens have been used to measure chromosome instability, including colony-sectoring assays (CTF) or mating-type based experiments, like the a-Like Faker (aLF) or Bi-Mater (BiM) assays. In the CTF assay, the loss of a small chromosome is monitored through colour changes, where red-pigment often implies chromosome loss. aLF and BiM assays specifically monitor the loss of the mating type locus MAT, either through chromosomal rearrangement, gene conversion, or loss of chromosome III (Stirling et al., 2011; Yuen et al., 2007). MATα mutants that are able to mate with MATα tester strains in the aLF screen are assumed to have lost the MATα locus through one of the aforementioned mechanisms. In the BiM assay, diploids capable of mating with either MATα or MATa tester strains are similarly inferred to have lost one of their MAT loci. Finally, many genome instability studies take advantage of fluorescence microscopy to visualize the formation of DNA damage-dependent foci, such as Rad52 or Ddc2 foci that co-localize with DSBs and ssDNA, respectively (Alvaro et al., 2007; Cheng et al., 2012).

The limitation of many of the aforementioned screens is that they focus on the detection of a phenotype associated with a specific type of DNA damage. Furthermore, few assays can claim they are sensitive to all genes involved in genome maintenance. In 2015, our lab attempted to overcome these limitations by using the DNA damage-inducibility of RNR3 as an indicator of genome instability in mutants found in the non-essential deletion and temperature sensitive yeast collections (Hendry et al., 2015). RNR3 expression was an ideal biological marker for genome instability for several reasons. Unlike the other RNR genes, which are also up-regulated during S phase, RNR3 induction occurs only in response to DNA damage and checkpoint activation

(Elledge et al., 1993). Moreover, RNR3 can be induced in response to exogenous or endogenous sources of DNA damage; treatment with MMS and HU, as well as the mutation of known repair genes all lead to constitutive RNR3 expression (Davidson et al., 2012; Huang et al., 1998; Li and Reese, 2001; Tang et al., 2009; Tsaponina et al., 2011). Indeed, Rnr3 abundance has been successfully used as a tool to identify genes necessary for genome maintenance (Zhou and Elledge, 1992).

To identify genome maintenance genes using RNR3 expression as an indicator, our lab made use of Reporter Synthetic Genetic Array (R-SGA) technology (Kainth et al., 2009). R-SGA takes advantage of automated S. cerevisiae genetics in tandem with fluorescent reporter systems to monitor the effect of a mutant gene on a gene of interest. In our assay, RNR3 was C-terminally tagged with GFP in a query strain containing the RFP, tdTomato, under the control of a constitutive promoter (Hendry et al., 2015). This query strain was crossed into the non-essential deletion and temperature-sensitive collections using SGA methodology (Tong and Boone, 2005). Strains were subjected to a fluorescence scanner, and the level of RNR3 expression in each mutant was quantified using a log2(Rnr3-GFP:tdTomato) ratio. Mutants causing an increase in RNR3 expression were inferred to be necessary for genome maintenance. Using this assay, we identified genes across all previous screened mentioned, and an additional 130 novel genome maintenance players (Hendry et al., 2015).

1.4.2 Utility of gene overexpression in identifying novel players involved in genome maintenance

While the aforementioned loss-of-function experiments have undeniably offered invaluable insight into the fundamental genes involved in the DNA damage response, they ignore the consequences of gene overexpression. These consequences are of particular interest as several disease states are associated with gene overexpression or gain-of-function mutations. Perhaps the earliest lines of evidence suggesting that an increase in gene copy number could lead to significant phenotypes arose from the correlation of aneuploidy with a variety of human diseases, such as Down’s syndrome (Lejeune et al., 1959). The exact molecular mechanisms behind these cases were particularly challenging to investigate as the duplication represents an increase of many individual genes on a single chromosome. Over time it has become apparent that the overexpression of single genes can also cause phenotypes associated with human disease. For example, the overexpression of HER2, MYC, REL, and AKT2 have been identified as the driving

11 force behind many human cancers (Prelich, 2012; Shastry, 1995). Still, relevance to disease is not the sole reason to study the effects of gene overexpression on genome maintenance. Due to the functional redundancy of the S. cerevisiae genome, many loss-of-function alleles cause no detectable phenotype; it is not until the redundant genes are also mutated that a phenotype is observed. Overexpression experiments may provide additional insight into the cellular roles of these elusive genes by activating pathways independent of conditions or genetic context (Chua et al., 2006).

One major disadvantage to studies involving gene overexpression is that the mechanism causing the observed phenotype may not be as readily understood when compared to gene deletion counterparts. Papp et al. hypothesized that overexpression results in mutant phenotypes by causing a stoichiometric imbalance in protein complexes that mimics the effects of a gene deletion (Papp et al., 2003). This hypothesis predicts that deletion and overexpression of the same gene should cause similar phenotypes; however, multiple studies comparing the effects of gene overexpression and deletion on growth rate, cell cycle progression, and chromosome loss fail to support this claim (Niu et al., 2008; Sopko et al., 2006; Yoshikawa et al., 2011; Zhu et al., 2015). Each of these studies has found that the majority of gene deletions cause a distinct phenotype from those caused by overexpression of the same gene. Overexpression has also been linked to inappropriate temporal gene expression leading to activation of a particular molecular pathway through protein hyperactivity or the counteraction of a pathway repressor (Prelich, 2012). Still others have hypothesized that phenotypes caused by gene overexpression may have no connection to the biological function of said gene because overexpression promotes binding promiscuity and non-specific protein-protein interactions (Vavouri et al., 2009). Other studies have shown that transcription factors do not lose specificity when overexpressed, proving that promiscuity is not an inevitable consequence of gross overexpression (Chua, 2009; Chua et al., 2006). Furthermore, it is worth stating that proteins that form promiscuous interactions upon overexpression may still be relevant towards understanding disease states where the gene of interest is overexpressed or hyperactive.

1.4.3 Project Objectives

While these studies clearly highlight the benefits of overexpression studies, there remains a lack of systematic investigation into the effects of gene overexpression in the context of genome

12 stability. Duffy et al. and Zhu et al. have investigated the effects of gene overexpression on chromosome instability, though this assay is limited in that it is specific to large defects in chromosomal maintenance, such as chromosome loss or chromosomal rearrangement (Duffy et al., 2016; Zhu et al., 2015). In the first section of this thesis, I address these limitations and identify novel players involved in genome maintenance previously ignored by loss-of-function and chromosome instability overexpression assays, by conducting a genome-wide R-SGA screen of the S. cerevisiae full-length expression ready (FLEX) collection for genes that cause an increase in RNR3 expression when overexpressed (Figure 1-3). The FLEX collection contains 90% of all yeast genes on a sequence-verified, low copy plasmid under the control of a GAL- inducible promoter (Douglas et al., 2012; Y. Hu et al., 2007). As mentioned, the RNR3 phenotype was chosen because it is not exclusive to any specific type of DNA damage.

To date, the role Dun1 plays in the regulation of Rnr3 has been exclusively studied in the context of DNA damage-inducing drugs or individual genome instability mutants scattered throughout the literature. In the second section of this thesis, I have taken a systematic approach towards understanding the role Dun1 plays in Rnr3 regulation. To do this, I have used the R-SGA approach to determine the Dun1-dependency of the Rnr3 induction observed in 40 unique gene deletions and 41 gene overexpression strains.

Figure 1-3. Pipeline of R-SGA screen used to identify novel genes causing genome instability when overexpressed.

Query strains containing Rnr3-GFP and an internal RFP control were mated to ~5100 unique strains in the FLEX overexpression collcction. Following selection of haploids using SGA (Tong and Boone, 2005), strains were grown on galactose for 40 hours to induce overexpression, and screened on a fluorescence scanner. Once GFP and RFP intensities were obtained, a log2(GFP:RFP) ratio was calculated to assess Rnr3 abundance.

Chapter 2 2 METHODS 2.1 Strains and Media

All strains used in this study were derived from BY4741 and are listed in Table 2-1 (Brachmann et al., 1998). Standard media and growth conditions were used unless otherwise stated (Sherman, 1991).

Table 2-1. Yeast strains used in this study. Strain Genotype Use Source MATa Rnr3-GFP::natMX HO::ACT1pr-tdTomato::hphMX FLEX This KLY8 leu2∆0 his3∆1 ura3∆0 met15∆0 Screen Study FLEX Screen; MATa dun1∆::kanMX Rnr3-GFP::natMX HO::RPL39pr- This KLY11 FLEX tdTomato::hphMX leu2∆0 his3∆1 ura3∆0 met15∆0 Study dun1∆ Screen FLEX MATa his3∆::kanMX, Rnr3-GFP::natMX, HO::RPL39pr- dun1∆ This KLY12 tdTomato::hphMX leu2∆0 his3∆1 ura3∆0 met15∆0 Screen Study (control) FLEX MATa Rad52-GFP::HIS3MX Ddc2-yemCherry::hphMX Rad52 This KLY14 leu2∆0 his3∆1 ura3∆0 met15∆ Foci Study Screen MATα dun1∆::natMX Rnr3-GFP::HIS3MX RPL39pr- DMA This KLY15 TdTomato::CaUra::can1∆::STE2pr_LEU2 leu2∆0 his3∆1 dun1∆ Study ura3∆0 met15∆0 lyp1∆ Screen DMA MATα his3∆::natMX Rnr3-GFP::HIS3MX RPL39pr- dun1∆ GW GBY692 TdTomato::CaUra::can1∆::STE2pr_LEU2 leu2∆0 his3∆1 Screen Brown ura3∆0 met15∆0 lyp1∆ (control)

14 15

2.2 Reporter Synthetic Genetic Array (R-SGA) Analysis

2.2.1 Screening the FLEX overexpression collection

2.2.1.1 Array Construction

Query strains (KLY8, KLY11, KLY12) were mated with the yeast overexpression FLEX collection using the standard SGA protocol (Douglas et al., 2012; Tong and Boone, 2005). Final haploid strains contained both fluorescent reporters and a unique overexpression FLEX plasmid. dun1∆ mutations were specific to those strains derived from KLY11. All crosses with the FLEX collection were done in duplicate.

2.2.1.2 Fluorescence Scanning and Imaging

Following the final SGA selection step, haploid strains from both biological replicates were pinned in duplicate onto final selection plates containing 2% galactose and incubated for 40 hours. Plates were then dried for 30 minutes in a laminar flow hood, and screened on the Typhoon Trio Variable Mode Imager (GE Healthcare) using the following settings: Tray, “User Select”; Pixel Size, 100 µm; Focal Plane, +3 mm; Press Sample, “No”; DIGE format, “No”. Channel 1 (Excitation Laser, 488 nm; Band-pass Emission Filter, 520/40 nm) and Channel 2 (Excitation Laser, 532 nm; Band-pass Emission Filter, 610/30 nm) were used to acquire the fluorescence intensity of Rnr3-GFP and RPL39pr-/ACT1pr-tdTomato, respectively. Finally, each plate was imaged using a Canon EOS Rebel XTi with an EF-S 60 mm 1:2.8 USM macro lens, and colony sizes were extracted using the Image Analysis tool on SGATools.

2.2.1.3 Data Analysis

Data analysis was performed essentially as described in Kainth et al., 2009 and Hendry et al., 2015. First, .GEL images acquired from the Typhoon were imported into GenePix Pro (Version 3.0) to extract the mean background-corrected GFP and tdTomato intensities for each colony. The R script “SGAFLEXanalysis_1.R” was used to combine colony sizes and intensities, and filter empty vector controls and low quality data, such as those colonies less than 600 pixels in area or whose fluorescence intensity was undetectable above background levels. To quantify the

Rnr3-GFP intensity, a log2(Rnr3-GFP:tdTomato) ratio was calculated to account for any confounding variables, such as colony size or changes in overall transcription/translation levels. LOESS normalization was applied to eliminate intensity-dependent biases in the data. Technical

16 and biological replicates were averaged using the R scripts “SGAFLEXanalysis_2techcombine.R” and “SGAFLEXanalysis_3biocombine.R” in tandem.

Finally, hits were identified as strains with an average log2(Rnr3-GFP:tdTomato) ratio more than two standard deviations away from the population mean using a Z-score.

The data analysis pipeline used to determine the Dun1-dependency of Rnr3 induction was essentially as described above, with some minor changes, and can be found in “dun1_FLEX_LOESSseparate.R”. This pipeline involved the incorporation of all empty vector negative controls and two separate LOESS normalization steps on the control screen and the dun1∆ screen.

2.2.2 Screening the yeast non-essential deletion collection

2.2.2.1 Array Construction

GBY692 and KLY15 were mated with the yeast non-essential deletion (DMA) collection using the standard SGA procedure (Tong and Boone, 2005). Final haploid strains contained both the fluorescent reporters, as well as a unique non-essential gene deletion. Strains created using KLY15 lacked DUN1. All crosses with the DMA were done in duplicate.

2.2.2.2 Fluorescence Scanning and Imaging

The data collection pipeline for the non-essential deletion screens was essentially the same as described in Section 2.2.1.2 with the following modifications: Haploid strains were pinned onto final selection plates containing 2% glucose and were incubated for 20 hours prior to Typhoon imaging.

2.2.2.3 Data Analysis

Data analysis essentially followed what is described in 2.2.1.3. R Scripts used were “DMAanalysis_dun1_1.R”, “DMAanalysis_dun1_2techcombine.R”, “DMAanalysis_dun1_3biocombine.R”, and “dun1_DMA_LOESSseparate.R”.

2.3 Secondary Screen for Rad52 Foci

2.3.1 Array Construction

The query strain KLY14 was introduced into a mini-array containing 44 unique FLEX strains using the standard SGA protocol (Giaver et al., 2002; Tong and Boone, 2006; Douglas et al., 2012).

2.3.2 Overexpression and Data Collection

Haploid strains were grown in 96-well round bottom culture plates overnight in SD-ura. The following morning, strains were diluted in low fluorescence YNB media (LFgalactose-ura) and transferred to a 384-well slide using a 96-channel Liquidator to a final density of 0.004 OD ml-. This final plate was incubated for an additional 5 hours to allow for induction of genes under expression of the GAL1/10 promoter. All haploids were imaged using a high-throughput confocal microscope with a 60X objective (EvoTec Opera, ThermoFisher) Single plane images were taken at four distinct positions within each well using both the green (405/488/561/640 primary dichroic, 520/35 emission band-pass filter, 800 ms exposure) and red (405/488/561/640, 600/40 emission band-pass filter, 800 ms exposure) channels.

2.3.3 Quantification of Results

Images were quantified by counting the proportion of cells in each image with at least one focus. Data from all four images were concatenated to give a final proportion. To score strains, I quantified 4 separate empty vector controls and calculated the mean and standard deviation. Overexpression strains at least 1.5 standard deviations greater than the control mean were scored as having increased foci formation. I also counted the number of foci per cell and compared this value for each strain to the empty vector control. Strains with a score at least 2 standard deviations above the empty vector control mean were considered to have an elevated number of foci/cell.

2.4 Gene Ontology (GO) Enrichment

GO enrichment analysis was completed using the GO Term Finder (Version 0.83) on the Saccharomyces Genome Database (SGD) on June 6th-8th, 2016 (Cherry et al., 2012). GO Term Finder uses a hypergeometric distribution with a Bonferroni correction to determine GO terms

18 overrepresented in a list of genes compared to the gene universe provided. Enrichments presented in this study were determined using a universe of 1) all genes represented in the FLEX collection, excluding strains filtered from our R-SGA dataset during analysis or 2) the default universe in SGD for transcription factor target enrichments. A P-value of < 0.01 was used to define statistical support for enriched terms. As a final step, terms with a background frequency of > 5% were removed from the final list to eliminate broad, non-descriptive terms. For lists with > 20 terms, REVIGO was used to reduce redundancy using the following settings: List size, “small (0.5)”; Numbers provided, “p-values”; Database, “Saccharomyces cerevisiae”; Semantic Similarity Measure, “SimRel” (Supek et al., 2011).

2.5 Target Gene Identification

Transcription factor targets were identified using Yeastract, a curated repository of transcription factors and corresponding targets in S. cerevisiae (Teixeira et al., 2014). Only those genes with both indirect expression data (lacZ, GPF reporter assays, microarray analysis, Reverse Transcriptase Polymerase Chain Reaction, etc.) and direct binding data (Chromatin Immunoprecipitation (ChIP), ChIP-on-chip, Electrophoretic Mobility Shift Assays, etc.) to support their regulation by the transcription factors of interest were considered true targets.

Chapter 3 3 RESULTS 3.1 Using the DNA damage-inducibility of RNR3 to identify novel genes causing genome instability when overexpressed

Following DNA damage, the replication checkpoint in S. cerevisiae initiates a variety of changes within the cell aimed to bolster chances of survival, including up-regulation of RNR, the tetrameric enzyme responsible for the rate-limiting step of dNTP synthesis. During damage, this enzyme is necessary for facilitating the repair of extended tracts of damaged DNA by increasing dNTP pools. While most RNR subunits are also up-regulated during S phase to promote DNA replication, induction of one of the large subunits of RNR, Rnr3, is specific to the DNA damage response and, as such, has been proposed to be a general biological marker for checkpoint activation and genome instability (Davidson et al., 2012; Elledge et al., 1993; Hendry et al., 2015; Tang et al., 2009; Zhou and Elledge, 1992). Here, I employ Reporter Synthetic Genetic Array (R-SGA) methodology to monitor the Rnr3 abundance following overexpression of each gene in the S. cerevisiae genome to identify genes whose proper regulation is necessary for genome maintenance.

RNR3 was C-terminally tagged with GFP in a query strain containing the RFP, tdTomato, under the control of a constitutive promoter. This strain was mated to the galactose-inducible FLEX overexpression collection in duplicate. SGA methodology was employed to ensure final selection of haploid strains containing both reporters and a plasmid capable of overexpressing a unique gene. Plates were scanned on the Typhoon to determine the Rnr3-GFP and tdTomato intensities for each strain. The effect of overexpression on Rnr3 levels was monitored as a log2(Rnr3- GFP:tdTomato) ratio averaged across biological and technical replicates, where strains with gene overexpression causing increased DNA damage or genome instability were defined as having an increased Rnr3-GFP:tdTomato ratio. Hits were identified as anything more than two standard deviations away from the population mean using a Z-score.

3.1.1 Optimization of R-SGA Protocol

To optimize the experimental and data analysis pipelines used to quantify Rnr3 abundance following galactose-inducible overexpression, several experimental conditions were tested, such as the inclusion of negative control plates with strains grown on glucose, and the use of diploids over haploids.

3.1.1.1 Incorporation of negative control strains grown on glucose is uninformative

Early on in the optimization stage of my project, I incorporated isogenic, glucose-grown negative controls using a log2(GAL/GLU) ratio to quantify the Rnr3 induction, where the numerator and denominator represent Rnr3-GFP:tdTomato ratios measured on galactose and glucose media, respectively. I found that the correlation between the replicate averaged log2(GAL/GLU) and galactose-only log2(Rnr3-GFP/tdTomato)gal ratios was high, with a Pearson correlation coefficient of 0.915 (Figure 3-1A). Since they provided little additional information to the dataset and may have contributed to added noise, glucose plates were removed from the experimental pipeline. All future screens were performed on galactose media only.

3.1.1.2 Increased dynamic range in Rnr3 abundance in haploid strains compared to isogenic diploids

Yeast genetics has traditionally focused on experimentation using haploid strains due to the ease with which one can study recessive, loss-of-function mutations because each gene is expressed from a single allele. In contrast, dominant alleles can be easily studied in both haploids and diploids, as the allele of interest will exert its effects regardless of the presence of a second wild- type allele. This is certainly the case for gene overexpression, making it possible to study the effects of gene overexpression in diploid strains. In terms of SGA, screening in diploid shortens the standard protocol by approximately 2 weeks, resulting in increased throughput (Tong and Boone, 2005). Diploids may also represent a more accurate model than haploids for higher eukaryotes, such as humans, that exist exclusively as diploids. Despite this, diploid strains may be more resistant to DNA damage and may show more moderate phenotypes because they carry multiple copies of each gene.

To test whether my screen could be performed more efficiently in diploids without a loss in dynamic range, I compared Rnr3 abundance following overexpression between otherwise isogenic diploids and haploids. The range of log2(Rnr3-GFP/tdTomato)gal ratios in the haploid screen was larger than that of the diploid screen, with up to an 8-fold induction of Rnr3 over the internal control (Figure 3-1B). Since the haploid screen provided an increased dynamic range for Rnr3 induction over diploids, all future screens were performed using haploid strains.

Figure 3-1. Optimization of the R-SGA experimental pipeline.

(A) Analysis pipeline used to identify novel genes involved in genome maintenance was performed using two different ratios: 1) galactose only – log2(Rnr3-GFP/tdTomato)gal, and 2) inclusion of glucose-grown negative controls - log2(GAL/GLU). Correlation between these two ratios was high. (B) Comparison of Rnr3 induction between haploid and diploid strains. Both haploids and diploids were plotted along the x-axis in decreasing order with respect to the Z- score. Diploid curve was shifted to the right to improve visualization. Key genes are labeled to highlight difference in Rnr3 induction between haploid and diploid counterparts.

3.1.2 41 genes cause Rnr3 induction when overexpressed

In an attempt to reduce control promoter-dependent effects on the final list generated, I conducted two independent R-SGA screens, each with a different constitutive promoter driving the expression of tdTomato (ACT1pr and RPL39pr). ACT1 encodes actin, while RPL39 is the ribosomal 60S subunit L39. Both screens were in strong agreement with one another, with a Pearson correlation coefficient of 0.780 (Figure 3-2A). Following this, I identified 41 genes (~0.8% of the FLEX collection) whose overexpression caused an increase in Rnr3 levels independent of the control promoter used (Z-score > 2; Figure 3-2A; Table 3-1). This is a relatively small number when compared to the genome instability caused by gene deletions. For

22 example, our lab recently reported that roughly 3% of gene deletions result in an increased Rnr3 phenotype, suggesting that, in general, gene deletions are more deleterious than gene overexpression (Hendry et al., 2015).

Figure 3-2. Screening the overexpression FLEX collection for novel genes involved in genome maintenance.

(A) Distribution of Rnr3-GFP abundance across ~5,100 overexpression strains. Rnr3-GFP intensity was quantified using a log2(Rnr3-GFP/tdTomato) ratio and converted to a Z-score. R- SGA screen was repeated with two different control promoters (ACT1pr and RPL39pr) driving tdTomato expression. Both datasets correlated well, with 41 genes causing a significant induction of Rnr3 (Z-score ≥ 2) regardless of the control promoter used. (B) 76% of all identified genes had no previous connection with genome maintenance when compared to overexpression chromosome transmission fidelity screens. (C) GO terms significantly enriched in the list of 41 genes causing Rnr3 induction (BP – biological process; F – molecular function; C – cellular compartment).

Table 3-1. Overexpression strains causing Rnr3 induction.

Normalized Colony log (Rnr3- Number of Gene 2 log (Rnr3- Z-score Size (px) GFP/tdTomato) 2 Replicates GFP/tdTomato) YPR015C 1295.75 2.937 2.9566 18.7608 4 CIN5 1270 2.0706 2.2823 14.4842 4 WTM2 1423.5 1.9468 2.2192 14.0836 4 SWI5 1340.25 2.1381 2.0881 13.2524 4 SUT1 899.5 1.7693 1.8924 12.0108 4 BCK2 1007.25 1.4276 1.7596 11.1687 3 CDC13 1014.5 1.3154 1.636 10.3845 4 KNS1 1398.5 1.3643 1.4871 9.44 4 HAL9 1230.5 0.877 1.0643 6.7581 4 URA1 1597.75 0.7922 1.0618 6.7428 4 SNT1 1273 0.723 1.0401 6.605 4 PHD1 1025.25 0.6991 1.0052 6.3836 4 SSN6 1455.5 0.6663 0.9899 6.2863 4 RAD53 1183.5 0.6112 0.8866 5.631 4 YHR177W 1107 0.5403 0.8236 5.2317 4 SCP160 1402.75 0.5793 0.7943 5.0457 4 VHR1 1069.5 0.431 0.7434 4.723 4 PIF1 1330.5 0.3887 0.7082 4.4994 4 SRO9 1498.75 0.3759 0.6952 4.4172 4 MGS1 1740.75 0.3858 0.6881 4.3719 4 RAD26 1502.75 0.4047 0.687 4.365 16 MGA1 966.75 0.3161 0.6485 4.1207 3 CYS4 1481.5 0.3655 0.6396 4.0645 4 HMS1 819.25 0.3419 0.5742 3.6499 4 MSN5 1243.75 0.2728 0.5549 3.5276 16 SLF1 1492.25 0.2619 0.5392 3.4279 4 RIB1 1434.75 0.2173 0.5377 3.4182 4 FUN12 1395.75 0.2166 0.5224 3.3215 4 TOP1 1010.5 0.1518 0.4771 3.0336 4 CDC4 1390.75 0.1307 0.453 2.8808 4 NFI1 974 0.1476 0.4479 2.8486 1 INO80 1124 0.1323 0.4151 2.6404 4 RIM11 1225.75 0.0806 0.404 2.5705 4 SPT16 1384.75 0.1604 0.3818 2.4292 4 MBP1 1350 0.0873 0.3665 2.3323 4 UPF3 1497 0.0402 0.3623 2.3059 4 YKL023W 1550.75 0.1012 0.353 2.2466 4 CLB5 1341.25 0.02 0.3416 2.1745 4 ECM32 1557.25 0.0528 0.3371 2.1457 4 RSC2 1494.5 0.1092 0.3354 2.1348 4

HNM1 1582 0.0191 0.3159 2.0115 4

3.1.3 Identification of known genome maintenance genes and regulators of RNR3

Among the genes that caused increased Rnr3 when overexpressed were several known DNA repair and genome maintenance genes (RAD53, RAD26, CDC13, TOP1). Interestingly, I also identified genes previously implicated in the transcriptional regulation of RNR3, including WTM2, SSN6, and SUT1. Wtm2, a transcriptional modulator, binds to the promoter of RNR3 and causes increased RNR3 expression when overexpressed (Tringe et al., 2006). Moreover, wtm2∆ strains are deficient in Rnr3 induction following treatment with HU (Tringe et al., 2006). Ssn6 functions in complex with Tup1 to repress RNR3 by repositioning nucleosomes over its promoter region (Li and Reese, 2001). Sut1, a Zn(II)2Cys6 family transcription factor known to interact with Ssn6-Tup1 to relieve repression of hypoxic genes, has previously been shown to cause increased RNR3 expression when overexpressed using microarray analysis (Chua et al., 2006; Regnacq et al., 2001). Together, these results confirm the ability of my screen to uncover genes of biological relevance, including known genome maintenance genes and transcriptional regulators of RNR3.

3.1.4 GO enrichment analysis reveals processes important for genome maintenance

Using Gene Ontology to further characterize the list of genes causing Rnr3 induction, I detected significant enrichment for 3 biological processes (BP), 3 molecular functions (F), and 1 cellular compartment (C) (P value < 0.01)(Figure 3-2C). My dataset was enriched for the biological processes “DNA replication” (GO:0006260) and “G1/S transition of mitotic cell cycle” (GO:0000082), suggesting, perhaps unsurprisingly, that proper regulation of these two processes is important for genome maintenance.

A subset of the uncovered genes was associated with transcriptional regulation. In fact, all three molecular functions enriched in the dataset were related to sequence-specific DNA binding and transcription factor activity (GO: 0043565; GO: 0001071; GO: 0003100). The biological process “positive regulation of transcription, DNA-templated” (GO: 0045893) was also identified. Since several genes associated with these terms are known transcriptional regulators of the RNR genes

(ie. WTM2, MBP1), those genes remaining may represent novel, direct regulators of RNR3 transcription (Figure 3-3A). It is also foreseeable that these transcription factors act on RNR3 indirectly by instead targeting genes whose proper regulation is essential for genome integrity (Figure 3-3A). Indeed, GO enrichment analysis on the targets of these transcription factors reveals they regulate many biological processes intimately linked with DNA metabolism and genome stability, including, but not limited to, “mitotic recombination” (GO: 0006312), “DNA replication” (GO: 0006260), “response to oxidative stress” (GO: 0006979), and “cellular response to DNA damage stimulus” (GO: 0006974) (Figure 3-3B; Appendix Table 4-1).

Finally, of the 41 genes causing an increase in Rnr3 abundance when overexpressed, 5 (12.2%) were unexpectedly associated with the cellular compartment “polysome” (GO: 0005844) compared to 0.6% of total genes screened. This suggests that improper regulation of translation may also contribute to genome instability.

Figure 3-3. Transcription factors exhibiting elevated Rnr3 levels regulate biological processes implicated in genome maintenance.

(A) Model presenting two mechanisms through which transcription factors identified in our screen act to promote induction of RNR3: (1) Direct - transcription factors play a role in the transcriptional regulation of RNR3, (2) Indirect – transcription factors regulate genome instability (GIN) genes. Improper regulation of these genes causes genome instability that activates the checkpoint. (B) Four transcription factors shown to cause induction of RNR3 when overexpressed have targets that are significantly enriched for a variety of biological processes that could be important for genome maintenance.

3.1.5 Investigating mechanisms responsible for Rnr3 induction following gene overexpression

Phenotypes caused by overexpression can be more challenging to interpret than phenotypes resulting from gene deletion, which are attributed to loss of a particular function. Instead, a wide variety of basic molecular mechanisms may explain overexpression phenotypes, including the disruption and loss-of-function of a stoichiometric complex, protein hyperactivity, activation of an unrelated pathway, competition between two shared complexes, etc. (Prelich, 2012) (Figure 3- 4A). Comparing the overexpression and loss-of-function phenotypes for a gene of interest is one way to determine which of these mechanisms is likely responsible for the overexpression phenotype. Therefore, to further elucidate the broad mechanisms causing Rnr3 induction following overexpression of the 41 genes identified by my screen, I inspected the phenotypes in corresponding loss-of-function mutants.

3.1.5.1 27% of increased RNR3 phenotypes may be explained by the balance hypothesis

The balance hypothesis posits that one explanation for overexpression phenotypes is that gene overexpression causes a stoichiometric imbalance in a particular protein complex that effectively disrupts complex function (Papp et al., 2003). Despite this, multiple studies have shown that this hypothesis is not the primary mechanism through which gene overexpression causes phenotypes (Niu et al., 2008; Sopko et al., 2006; Yoshikawa et al., 2011; Zhu et al., 2015). I sought to identify whether the balance hypothesis could explain the increased Rnr3 phenotype in my study. As a first step, I found the list of 41 genes causing Rnr3 induction was significantly enriched for genes found in protein complexes as defined by Benschop et al., (2010) (p-value = 0.029). This result suggests the balance hypothesis could represent a significant mechanism behind Rnr3 induction following gene overexpression, though it does not provide evidence as to which genes, if any, actually fall into this category.

Phenotypes explained by a stoichiometric imbalance in a protein complex should be similar when the gene of interest is both overexpressed and deleted. I therefore compared my data to a variety of high-throughput studies that identified loss-of-function mutations causing genome instability as assessed by Rnr3 induction, chromosome loss, DNA damage response foci

Figure 3-4. Molecular mechanisms responsible for RNR3 induction following gene overexpression.

(A) Gene overexpression can cause genome instability through a variety of mechanisms. This model presents two examples: (1) Stoichiometric imbalance in protein complex that results in loss-of-function, and (2) Hyperactivity. (B) Comparison of Rnr3 induction following gene overexpression (x-axis) to the Rnr3 induction following deletion of that same gene (y-axis).

Dashed lines represent a Z-score of 1.5; orange data points represent genes where overexpression results in Rnr3 induction and deletion leads to a deficiency in Rnr3 expression. (C) Instability phenotypes observed in deletion mutants corresponding to the 41 genes whose overexpression causes Rnr3 induction. Bolded genes are found in a protein complex. (D) Proportion of genes where protein loss-of-function or hyperactivity can explain Rnr3 induction observed in overexpression strain.

formation, and increased mutation rate (Alvaro et al., 2007; Cheng et al., 2012; Choy et al., 2013; Hendry et al., 2015; Huang et al., 2003; Stirling et al., 2011) (Figure 3-4C). Of the 29 genes identified in our screen that were also available in the yeast non-essential deletion collection, none caused an increase in Rnr3 abundance when deleted. Still, 11 (27%) genes had at least one phenotype characteristic of spontaneous DNA damage when deleted. Of these, 8 were found to participate in a protein complex (Hypergeometric test p-value = 7.85 x 10-4). As such, the Rnr3 induction caused by the overexpression of these genes may be explained by the balance hypothesis as loss-of-function. It follows, then, that the corresponding protein complexes are likely necessary for genome maintenance. In support of this hypothesis, nearly all have been previously implicated in DNA metabolism (replication, repair, etc.) and transcription, including the replication fork protection complex, the RSC complex, and the FACT complex (Table 3-2). Complex components identified in my screen are bolded and underlined.

Table 3-2. Complexes with genes whose overexpression and deletion results in genome instability phenotypes. Gene Onto- Complex Name Source Complex Components logy ID PSF1 | TOP1 | PSF2 | TOF1 | CSM3 | POB3 | MCM10 | CTF4 | HTA1 | HTB1 | MCM2 | MCM4 | Replication Fork GO:0031298 Gene Ontology SPT16 | MCM6 | HTA2 | HTB2 | Protection Complex MCM3 | MCM7 | CDC45 | SLD5 | HTT1 | HHF1 | MCM5 | MRC1 | PSF3 ELP3 | ELA1 | CTK3 | POB3 | MFT1 | RTF1 | LEO1 | SPT16 | Transcription HPR1 | THO1 | SPT5 | SPT4 | Elongation GO:0008023 Gene Ontology CDC73 | THP2 | ELC1 | CTK2 | Factor Complex PAF1 | ELF1 | TUP1 | CTK1 | CTR9 | CCR4 Benschopp et al., FACT Complex GO:0035101 SPT16 | POB3 2010

MBF Transcription Benschopp et al., GO:0030907 MBP1 | SWI6 Complex 2010 CDC4 | HRT1 | CDC53 | SKP2 | UFO1 | MFB1 | DIA2 | GRR1 | SCF Ubiquitin RAD7 | MET30 | YDR306C | GO:0019005 Gene Ontology Ligase Complex YDR131C | MDM30 | YLR352W | HRT3 | AMN1 | SKP1 | UCC1 | DAS1 | SAF1 Nuclear SCF Ubiquitin Ligase GO:0043224 Gene Ontology CDC4 | HRT1 | MET30 Complex RTT102 | RSC4 | NPL6 | ARP9 | RSC9 | RSC1 | ARP7 | STH1 | RSC2 | SFH1 | RSC6 | RSC58 | Benschopp et al., RSC Complex GO:0016586 HTL1 | RSC3 | RSC30 | RSC8 | 2010 PDR1 | SNF12 | SNF6 | SWI1 | SWP82 | HTA1 | SNF11 | NFI1 | SNF2 | SNF5 | SWI3 Ssn6-Tup1 Benschopp et al., GO:0017053 SSN6 | TUP1 Corepressor Complex 2010 Benschopp et al., Asf1-Rad53 Complex N/A RAD53 | ASF1 2010

It should be noted that the Rnr3 induction following RAD53 overexpression might be one exception to the balance hypothesis, despite the fact that Rad53 is known to form a complex with Asf1. Rad53 undergoes trans-autophosphorylation upon clustering of multiple units of Rad53 with Rad9 (Gilbert et al., 2001; Wybenga-Groot et al., 2014). Furthermore, introducing high levels of S. cerevisiae-dervived Rad53 into E. coli is enough to cause extensive autophosphorylation in the absence of other yeast kinases (Gilbert et al., 2001). The possibility therefore remains that overexpression of Rad53 in yeast is also enough to cause activation of the checkpoint, even in the absence of DNA damage. This spurious checkpoint activation could account for the Rnr3 induction in these overexpression strains.

3.1.5.2 Majority of overexpressed genes exhibit gain-of-function phenotypes

The majority (73%) of genes whose overexpression caused induction of Rnr3 lacked a null or temperature-sensitive mutant phenotype associated with genome instability, suggesting the primary mechanism by which overexpression leads to genome instability is through a gain-of-

30 function or a novel effect (Figure 3-4D). Sopko et al. reported similar findings regarding overexpression toxicity (Sopko et al., 2006).

Indeed, another popular explanation for the mechanism behind overexpression phenotypes is that overexpression simply causes hyperactivity of the protein of interest (Figure 3-4A). In the case of hyperactivity, deletion and overexpression of the gene of interest should result in opposite phenotypes (Prelich, 2012). I compared my data to Rnr3 abundance in gene deletion mutants to detect whether deletion of any of the 41 genes identified by my screen also showed a decreased level of Rnr3 when deleted. Since Rnr3 is virtually undetectable in an untreated cell, comparisons were made to the Rnr3 levels in deletion mutants treated with 0.03% MMS (Hendry et al., 2015). Two genes in my list, BCK2 and MSN5, also show decreased Rnr3 abundance in MMS when deleted (Figure 3-4B). Furthermore, the corresponding Rnr3 increase in strains overexpressing these genes may be due to the hyperactivity of their protein products. Whether their protein products are necessary for the induction of RNR3, or they facilitate genome instability is yet to be determined.

3.2 Evidence of spontaneous DNA damage in overexpression strains exhibiting RNR3 expression

Genes that cause Rnr3 induction when overexpressed may be 1) genes whose improper regulation causes genome instability, or 2) transcriptional regulators of RNR3. To distinguish between these categories and further characterize the genes identified in the primary R-SGA screen, I sought to investigate which of these genes caused additional phenotypes suggesting the presence of spontaneous DNA damage and genome instability.

3.2.1 Rad52 Foci Formation

Protein re-localization is an important facet of the DNA damage response. One illustrative example of this is the re-localization of the homologous recombination protein, Rad52, to distinct sub-nuclear foci following genotoxic stress (Lisby et al., 2001). In support of the hypothesis that these foci play a functional role in repair, they have been shown to co-localize with doubled stranded breaks (Lisby et al., 2003). Indeed, the presence of Rad52 foci has become a common marker used to detect spontaneous DNA damage.

Figure 3-5. Evidence of spontaneous DNA damage in overexpression strains inducing RNR3.

(A) Strains containing endogenous Rad52 C-terminally tagged with GFP and a unique FLEX overexpression plasmid were grown in 2% galactose for 5 hours to induce overexpression of the gene of interest. Following induction, strains were imaged on a high-throughput confocal microscope to quantify the percentage of cells with foci. (B) Four genes whose overexpression resulted in elevated levels of Rad52 foci. (C) Percentage of cells with Rad52-foci following overexpression of the 41 genes identified in the primary screen. Orange dots represent the 4 distinct empty vector control replicates; SRS2 and UPL2 are positive controls. (−) represents a cut-off of 1.5 standard deviations above empty vector control mean.

To identify genes whose overexpression causes spontaneous DNA damage, I quantified the presence of Rad52-GFP foci following a 5-hour overexpression period in galactose (Figure 3- 5A). It should be noted that this assay, as performed in this study, is limited in its dynamic range. On average, 11% of the cells in the empty vector control had at least one Rad52-GFP focus (Figure 3-5C). The positive controls used, ULP2 and SRS2, only scored 2- and 3-fold higher than the empty vector control, respectively (Duffy et al., 2016) (Figure 3-5C). In an attempt to improve the range of this assay, strains could be grown in raffinose prior to galactose-induction, or grown in galactose longer than 5 hours. Despite these shortcomings, I found that 4/41 genes (CDC13, RAD53, MSN5, and CLB5) screened caused an increase in Rad52-GFP foci above background levels when overexpressed (Figure 3-5B; Figure 3-5C; Table 3-3).

Table 3-3. Percentage of cells exhibiting Rad52 foci in 41 overexpression strains with elevated levels of Rnr3. Percentage of cells w/ Rad52 focus ORF Gene Replicate 1 Replicate 2 Replicate 3 Replicate 4 Empty Vector - 10.65 7.73 14.29 11.11 YJL092W SRS2 32.15* YIL031W ULP2 20.50* YAL035W FUN12 11.48 YBL033C RIB1 14.23 YBR112C SSN6 8.94 YCR033W SNT1 9.62 YDL220C CDC13 24.73* YDR146C SWI5 12.26 YDR335W MSN5 15.47* YDR515W SLF1 7.55 YER167W BCK2 11.56 YER176W ECM32 8.72 YFL009W CDC4 9.69 YGL077C HNM1 11.5 YGL150C INO80 10.82 YGL162W SUT1 10.14 YGL207W SPT16 10.16 YGR072W UPF3 9.4 YGR155W CYS4 10.4 YIL056W VHR1 12.18 YJL080C SCP160 11.04 YKL023W YKL023W 12.25 YKL043W PHD1 9.52 YKL216W URA1 11.07 YLL019C KNS1 12.84 YLR357W RSC2 14.29 YML061C PIF1 5.13 YMR139W RIM11 11.38 YNL218W MGS1 11.4 YOL006C TOP1 11.68 YOR028C CIN5 9.71 YOR032C HMS1 11.11 YOR229W WTM2 9.62 YPL153C RAD53 16.24* YPR015C YPR015C 6.58 YPR120C CLB5 22.27*

* Percentage of cells exhibiting Rad52 foci is greater than 1.5 standard deviations above the mean of the negative control.

Rad52 typically forms a single focus in the nucleus of a cell upon genotoxic stress; however, increasing the level of genotoxin treatment can induce the formation of multiple foci per cell (Lisby et al., 2003). As such, I sought to identify whether any strains exhibited a multi-foci phenotype that might suggest a severe increase in DNA damage. Of the 4 strains with elevated Rad52-GFP foci, only cells overexpressing CDC13 exhibited a higher proportion of cells with 2 or more foci than the empty vector negative control (Table 3-4).

Table 3-4. Percentage of cells with more than two Rad52 foci.

Percentage of cells w/ ≥ 2 Rad52 foci ORF Gene Replicate 1 Replicate 2 Replicate 3 Replicate 4 Empty Vector - 1.03 1.55 2.6 3.33 YJL092W SRS2 5.6* - YIL031W ULP2 1.8 - YDL220C CDC13 6.91* - YDR335W MSN5 1.66 - YPL153C RAD53 1.41 - YPR120C CLB5 2.34 -

* Percentage of cells exhibiting more than two Rad52 foci is greater than 2 standard deviations above the mean of the negative control. 3.3 Towards understanding the Dun1-dependency of Rnr3 induction in genome instability mutants

As mentioned, the canonical pathway of Rnr3 induction following checkpoint activation involves the phosphorylation and inactivation of its transcriptional repressor Crt1 by Dun1; however, there is data in the literature that supports the existence of a Dun1-independent replication checkpoint. For example, in our lab we’ve shown that pol30felg1∆ mutants exhibit increased levels of genome instability, as well as elevated Rnr3 levels that are comparable in a pol30felg1∆dun1∆ triple mutant (Davidson et al., 2012). Moreover, other studies have shown that RNR2, -3, and -4 are detectable after treatment with MMS, even in the absence of Dun1. Despite this evidence, the biological function and mechanism of action for the Dun1-independent branch of the replication checkpoint are not well understood.

3.3.1 Dun1-dependency of Rnr3 induction

To investigate the Dun1-dependency of Rnr3 induction in well-characterized genome maintenance genes, I conducted an R-SGA screen of the yeast non-essential deletion collection

34 using query strains with and without Dun1 in quadruplicate (Figure 3-6A). The control R-SGA was essentially a biological replicate of the untreated screen performed in Hendry et al., (2015). Fortunately, the control screen correlated well with this dataset (Pearson correlation coefficient of 0.599; Figure 3-6B). 40 mutants showing increased levels of Rnr3 in both Hendry et al. and our control screen were considered for their Dun1-dependency. It should be noted that crt1∆ was not included because the pixels for this colony were consistently saturated, and therefore unquantifiable, in the Typhoon images. Following the calculation of log2(Rnr3-GFP/tdTomato) ratios in all 4 replicates, a Welch-corrected T-test was used to determine whether there was a significant difference between the Rnr3 levels in the 40 single mutants (xxx∆) compared to their double mutant counterparts (xxx∆ dun1∆). Using this analysis, I uncovered 3 categories of Rnr3 induction in the mutants tested: Dun1-dependent, Dun1-independent, and Dun1-attenuated (Figure 3-6A; Table 3-5).

3.3.1.1 Dun1-dependent regulation of Rnr3

13 of the 40 single mutants under investigation caused a Dun1-dependent induction of Rnr3 (Figure 3-6C; Table 3-5). With the exception of met6∆, all of these mutants have been previously implicated in genome maintenance (Table 3-5; Table 3-6). Since Dun1 is a checkpoint kinase involved in the up-regulation of the RNR genes in response to DNA damage, elevated Rnr3 levels in these mutants are likely the direct result of genome instability causing downstream checkpoint activation. Moreover, MET6 may represent a novel gene involved in genome maintenance.

3.3.1.2 Dun1-independent regulation of Rnr3

The largest category I identified included 16 mutants whose induction of Rnr3 was independent of Dun1 (Figure 3-6C; Table 3-5). Notably, this category included strains lacking known transcriptional repressors of Rnr3, such as ISW2, ITC1 and HDA1 (Table 3-6). It is expected that the absence of these repressors would cause a Dun1-independent increase in Rnr3 levels.

Surprisingly, 12 of the 32 known genome maintenance mutants resulting in Rnr3 induction did so through a mechanism independent of Dun1 (Table 3-5; Table 3-6). The fact that these genes are known to cause genome instability phenotypes supports the existence of a Dun1-independent DNA damage checkpoint. Why some genome maintenance mutants require Dun1 for full

35 checkpoint activation, including Rnr3 induction, while others do not remains to be seen. I hypothesized that mutants causing different types of damage might cause Rnr3 induction through different checkpoint pathways; however, the genome instability mutants belonging to the Dun1- independent category did not share any characteristic genome instability phenotype distinct from those mutants whose Rnr3 induction was dependent on Dun1 (Table 3-6).

Table 3-5. Rnr3 abundance in single mutants (xxx∆) compared to double mutants lacking checkpoint kinase Dun1 (xxx∆ dun1∆). Control dun1∆ Dun1 Allele P-value GIN log2(Rnr3- Standard log2(Rnr3- Standard Status GFP/tdTomato) Dev. GFP/tdTomato) Dev. his3∆ -0.1054 0.0899 -0.0384 0.1256 1.46E-98 Atten. vac17∆ 0.706 0.0632 0.844 0.2279 0.3175 Indep. mrc1∆ 0.5046 0.0646 0.3371 0.066 0.011 Dep. Y ycl060c∆ 0.5071 0.0618 0.9224 0.129 0.0035 Atten. slx5∆ 1.0107 0.1461 0.665 0.1031 0.0102 Dep. Y met6∆ 0.5413 0.0547 0.3612 0.0783 0.0114 Dep. crd1∆ 0.8721 0.0565 0.4922 0.1595 0.0127 Dep. Y rad51∆ 1.0705 0.1037 0.9262 0.0678 0.0655 Indep. Y ent1∆ 1.238 0.1222 0.5635 0.1145 0.0002 Dep. Y (ydl162∆) rad55∆ 0.9131 0.1515 0.6176 0.0739 0.0216 Dep. Y pat1∆ 0.5463 0.0322 0.6483 0.0528 0.0218 Atten. Y cem1∆ 0.394 0.1249 0.3014 0.0719 0.2572 Indep. Y ice2∆ 0.5831 0.0898 0.5191 0.0888 0.5486 Indep. Y pol32∆ 0.3896 0.027 1.2716 0.062 0.0003 Atten. Y itc1∆ 1.3878 0.0243 1.314 0.1012 0.2419 Indep. rad27∆ 1.8064 0.1414 0.6799 0.2591 0.0009 Dep. Y rtt109∆ 0.5848 0.1081 0.786 0.0737 0.0257 Atten. Y aim22∆ 0.5944 0.0396 0.4865 0.085 0.0791 Indep. Y rtt101∆ 0.5531 0.0624 0.5402 0.1231 0.8598 Indep. Y rad54∆ 0.8108 0.0905 0.7391 0.1402 0.4282 Indep. Y rtt107∆ 0.4231 0.0619 0.6847 0.0349 0.0009 Atten. Y rrm3∆ 1.3768 0.1875 0.4367 0.0848 0.0006 Dep. Y lsm1∆ 0.6211 0.0417 0.739 0.0605 0.0217 Atten. Y asf1∆ 0.8168 0.1741 0.8007 0.1265 0.8868 Indep. Y top3∆ 0.5484 0.0427 0.7733 0.0374 0.0002 Atten. Y (ylr235c∆) leo1∆ 0.5836 0.0292 0.647 0.0504 0.0837 Indep. Y sgs1∆ 0.374 0.0775 0.6432 0.0731 0.0023 Atten. Y rad5∆ 0.6719 0.1311 0.6742 0.1578 0.9832 Indep. Y spt5∆ 0.3831 0.0636 0.2597 0.0665 0.0365 Dep. Y (yml010c-b∆) hfa1∆ 0.4431 0.0268 0.2535 0.0318 0.0001 Dep. Y

36 mre11∆ 0.8085 0.1726 0.6761 0.0328 0.2228 Indep. Y rad52∆ 1.0138 0.1431 0.5339 0.081 0.0025 Dep. Y ber1∆ 0.5384 0.0212 0.51 0.0509 0.361 Indep. Y sic1∆ 0.8691 0.1169 0.5117 0.2591 0.0632 Dep. Y mms22∆ 0.9527 0.0339 0.9907 0.1329 0.6146 Indep. Y hda1∆ 0.9372 0.0653 0.8917 0.085 0.4302 Indep. mms1∆ 0.5153 0.0665 0.7037 0.0974 0.0223 Atten. Y hda3∆ 0.5027 0.0438 0.7594 0.035 0.0001 Atten. rox1∆ 0.5717 0.0275 0.8115 0.1271 0.0297 Atten. isw2∆ 1.2972 0.0894 1.2986 0.0526 0.9806 Indep. slx8∆ 1.4771 0.0995 0.7301 0.1328 0.0286 Dep. Y

I next sought to determine whether the Dun1-dependency was related to the level of Rnr3 expressed in the original single mutant (xxx∆). Perhaps the R-SGA assay is not sensitive enough to detect a decrease in what is an already low level of Rnr3, which results in all single mutants exhibiting low levels of Rnr3 being categorized as “Dun1-independent”. This explanation is unlikely since I have shown that known genome instability mutants, such as spt5∆ and hfa1∆, causing a low level of Rnr3 induction are categorized as Dun1-dependent (Table 3-5). Finally, using a Welch-corrected T-test, I found there was not a significant difference between the mean log2(Rnr3-GFP/tdTomato) ratio for “Dun1-dependent” and “–independent” mutants (p-value = 0.2523), suggesting the initial Rnr3 induction carries no predictive power in determining the Dun1-dependency of a particular mutant (Figure 3-6D). Furthermore, the majority of Dun1- independent mutants have an intermediate level of Rnr3 induction, suggesting the Dun1- independent induction of Rnr3 is not caused by massive activation of the checkpoint allowing bypass of Dun1.

Finally, of the 9 mutants tested that were associated with the biological processes GO term “regulation of transcription, DNA-templated”, 5 were categorized as causing Dun1-independent induction of Rnr3. 3 of these genes have been previously implicated in transcriptional regulation of RNR3 (ie. itc1∆, isw2∆, and hda1∆). Though the remaining mutants, leo1∆ and asf1∆, have been associated with genome instability phenotypes, it is possible that the Dun1-independent up- regulation of Rnr3 is evidence that they are also involved in the direct transcriptional regulation of RNR3 independent of checkpoint activation.

Figure 3-6. Determining the Dun1-dependency of Rnr3 induction seen in 40 deletion mutants.

(A) The non-essential deletion collection was screened with a query strain containing a dun1∆ mutation, along with a control query strain. Rnr3 abundance was compared for each strain in the dun1∆ condition vs. the control condition using a Welch-corrected T-test. Strains with similar Rnr3 levels in both conditions were considered “Dun1-independent”. Those with significantly less Rnr3 in the dun1∆ condition were annotated as “Dun1-dependent”. Double mutants with elevated levels of Rnr3 compared to single mutant counterpart were named “Dun1-attenuated”. (B) Correlation of control screen with untreated screen from Hendry et al., 2015. (C) Rnr3 abundance in representative strains from each category. Error bars represent standard deviation. (D) Rnr3 abundance in single mutant control does not determine Dun1-dependency.

3.3.1.3 Dun1-attenuation of Rnr3

I uncovered a third subset of 11 mutants that caused a significantly higher level of Rnr3 induction in the absence of DUN1 than in the presence of DUN1. I therefore categorized these mutants as having Dun1-attenuated Rnr3 induction (Figure 3-6A; Figure 3-6C; Table 3-5). Of

38 the genes belonging to this category, the pol32∆dun1∆ double mutant had the largest increase in Rnr3 compared to the single mutant counterpart (pol32∆) (Figure 3-6C). POL32 is the third subunit of polymerase delta, and is therefore involved in DNA replication (Gerik et al., 1998). pol32∆ mutants are known to cause replication stress, are defective in DNA repair, and are sensitive to genotoxic stress induced by MMS, HU, and UV irradiation (Burgers and Gerik, 1998; Gerik et al., 1998). It is therefore consistent that pol32∆ mutants show a slight elevation in Rnr3 levels, which is likely indicative of checkpoint activation in these mutants. Interestingly, strains lacking POL32 also show an antimutator phenotype, exhibiting a decrease in mutation rate compared to wild-type strains following exposure to MMS and UV irradiation (Gerik et al., 1998; Huang et al., 2002). These data have implicated POL32 in error-prone damage bypass pathways. Why deletion of DUN1 causes further induction of Rnr3 in the absence of error-prone repair remains to be seen.

The identification of a subset of genes in which DUN1 attenuates RNR3 induction was particularly surprising because, up until now, DUN1 has solely been implicated in the de- repression of RNR3. In contrast, my data may point towards a role for Dun1 in RNR3 repression. A more thorough discussion of the potential mechanisms behind Dun1-attenuation of Rnr3 induction can be found in Section 4.4.

Table 3-6. Genome instability phenotypes present in the 40 deletion mutants causing Rnr3 induction. GENOME INSTABILITY PHENOTYPES Sensitivity Foci CIN

Loss

Faker

Mater (Stirling, 2011) (Stirling, Mater like - Allele - HU MMS 2007) (Alvaro, Rad52 2012) (Cheng, Ddc2 Chromosome (Strome,2008; Stirling, 2011; 2015) Zhu, 2013; Choy, Bi A (Stirling,2011; Cheng, 2012) Heterozygosity of Loss (Andersen,2008) Chromosomal Gross Rearrangements (Kanellis,2007) 2003) (Huang, Rate Mutation Regulation* Transcriptional rtt107∆ Y Y Y Y Y

rtt109∆ Y Y Y Y Y Y Y

mrc1∆ (ycl060c∆) pat1∆ Y

pol32∆ Y Y Y Y Y

lsm1∆ Y Y Y Y

top3∆ Y Y Y Y Y (ylr235c∆) sgs1∆ Y Y Y Y Y Y

mms1∆ Y Y Y Y Y Y Y

hda3∆ Y

rox1∆ Y

mrc1∆ Y Y Y Y Y

slx5∆ Y Y Y Y Y

met6∆

crd1∆

ent1∆ Y (ydl162c∆) rad55∆ Y Y Y Y Y

rad27∆ Y Y Y Y Y Y Y Y

rrm3∆ Y Y Y Y

spt5∆ Y Y yml010c-b∆ hfa1∆ Y

rad52∆ Y Y Y Y Y Y

sic1∆ Y Y Y Y Y

slx8∆ Y Y Y Y Y Y Y

vac17∆

rad51∆ Y Y Y Y Y Y

cem1∆ Y Y

ice2∆ Y

itc1∆ Y Y

aim22∆

rtt101∆ Y Y Y Y

rad54∆ Y Y Y Y Y Y Y

asf1∆ Y Y Y Y Y Y

leo1∆ Y Y Y

rad5∆ Y Y Y Y Y Y

mre11∆ Y Y Y Y Y Y

ber1∆ Y

mms22∆ Y Y Y Y Y

hda1∆ Y Y

isw2∆ Y Y

* As determined by association w/ the GO term: “Regulation of Transcription, DNA-templated”

**grey = “Dun1-attenuated”; orange = “Dun1-dependent”; blue = “Dun1-independent”

3.3.2 Dun1-dependency in overexpression strains

To assess the Dun1-dependency of the overexpression-induced Rnr3 phenotype, the dun1∆ screen was repeated in the context of the FLEX collection. I observed the same 3 categories of Rnr3 induction as were uncovered in the deletion screen: Dun1-independent, Dun1-dependent, and Dun1-attenuated (Figure 3-7A; Table 3-7).

As expected, all 3 of the known RNR transcriptional regulators tested (SSN6, WTM2, MBP1) showed Rnr3 induction that was independent of Dun1. Since the results of the dun1∆ deletion screen show that the Dun1-independent induction of Rnr3 can also occur in genome instability mutants, one cannot conclude that all genes belonging to the Dun1-independent category are transcriptional regulators. In support of this, I have shown that overexpression of RAD53 and MSN5 cause 1) Dun1-independent Rnr3 induction and 2) an increase in Rad52 foci indicative of spontaneous damage. Of the 20 genes in this category, an additional 3 genes cause genome instability phenotypes when overexpressed (RSC2, SUT1, and PIF1) (Chang et al., 2009; Chua et al., 2006; Duffy et al., 2016) (Table 3-8). The remaining 12 genes may be novel transcriptional regulators of Rnr3 or genes where improper regulation leads to genome instability.

Figure 3-7. Determining Dun1-dependency of the Rnr3 induction caused by the 41 genes identified in the overexpression screen.

(A) The FLEX collection was screened with a query strain containing a dun1∆ mutation, along with a control query strain. Rnr3 abundance was compared for each strain in the dun1∆ condition vs. the control condition. Strains with similar Rnr3 levels in both conditions were considered “Dun1-independent”. Those with significantly less Rnr3 in the dun1∆ condition were annotated as “Dun1-dependent”. “Dun1-attenuated” strains showed Rnr3 induction that was further elevated in the absence of Dun1. (B) Rnr3 abundance in representative strains from each category.

As I have shown in the dun1∆ deletion screen, genes inducing Rnr3 in a manner dependent on Dun1 are very likely to be causing genome instability. Indeed, 5 of the 10 strains exhibiting Dun1-dependent induction of Rnr3 have evidence of genome instability in the literature: MGS1, PHD1, CDC13, HMS1, and YPR015C (Duffy et al., 2016; Hishida et al., 2001; Niu et al., 2008) (Table 3-8). The remaining 5 genes likely represent novel genes implicated in genome maintenance.

Finally, in agreement with the dun1∆ deletion screen, I found an additional 9 genes whose overexpression causes elevated levels of Rnr3 in the absence of DUN1. Moreover, introducing a dun1∆ mutation into a strain containing an empty FLEX vector results in a small, but significant, increase in Rnr3 levels (Figure 3-7B; n = 616; p-value = 4.5137 x 10-93). There are multiple hypotheses that could explain this Dun1-attenuated nature of Rnr3 induction, which will be explained in further detail in Section 4.4 of the following chapter.

Table 3-7. Dun1-dependency of Rnr3 induction in 41 overexpression strains identified by primary screen. Control dun1∆ Dun1 Gene P-value log2(Rnr3- Standard log2(Rnr3- Standard Status GFP/tdTomato) Dev. GFP/tdTomato) Dev. Empty Vector -0.2609 0.0919 -0.2002 0.1108 4.51E-93 Atten. ECM32 0.3668 0.0265 0.2189 0.0433 0.0022 Dep. CDC13 1.6698 0.1773 1.3306 0.0542 0.0265 Dep. PIF1 0.7439 0.0884 0.7142 0.2362 0.8258 Indep. SNT1 1.0727 0.2406 1.3357 0.1351 0.1184 Indep. PHD1 1.0384 0.0887 0.7584 0.1567 0.0285 Dep. HNM1 0.3462 0.0986 0.1007 0.1136 0.0177 Dep. YPR015C 2.9342 0.5111 1.6468 0.1713 0.0109 Dep. KNS1 1.4875 0.1354 1.3339 0.2211 0.2897 Indep. CIN5 2.2938 0.3247 2.4185 0.0735 0.5035 Indep. MGS1 0.7132 0.0711 0.376 0.0798 0.0008 Dep. UPF3 0.3962 0.0613 0.3846 0.1052 0.8577 Indep. SUT1 1.8925 0.1131 2.0465 0.27 0.3519 Indep. RSC2 0.348 0.0419 0.3415 0.0895 0.9021 Indep. RAD53 0.9147 0.0859 0.8925 0.0629 0.6928 Indep. CYS4 0.6656 0.0447 0.542 0.0834 0.0516 Indep. SLF1 0.5642 0.0539 0.4272 0.0157 0.0113 Dep. FUN12 0.5615 0.0352 0.4376 0.0528 0.0104 Dep. RIB1 0.5764 0.0515 0.5185 0.0641 0.2112 Indep. URA1 1.0865 0.0375 1.1553 0.1277 0.367 Indep. CLB5 0.3748 0.0409 0.6167 0.0739 0.0028 Atten. BCK2 1.7825 0.0657 2.5523 0.0414 0.0002 Atten. HMS1 0.601 0.074 0.4088 0.055 0.007 Dep. SSN6 1.0221 0.1165 1.1538 0.0376 0.1051 Indep. YHR177W 0.8511 0.0965 1.2219 0.0423 0.0019 Atten. RAD26 0.7145 0.0607 0.7361 0.0829 0.5628 Indep. SCP160 0.8124 0.0647 0.8331 0.0215 0.5803 Indep. MSN5 0.5836 0.8281 0.5704 0.0339 0.6928 Indep. SRO9 0.7287 0.0436 0.6269 0.0569 0.0317 Dep. MGA1 0.6829 0.0437 0.6627 0.0559 0.649 Indep. VHR1 0.779 0.1786 1.1323 0.0921 0.0203 Atten. TOP1 0.5108 0.0407 0.685 0.0755 0.0115 Atten. HAL9 1.071 0.1138 0.9578 0.1876 0.3501 Indep. WTM2 2.2428 0.0659 2.4863 0.1881 0.0756 Indep. SPT16 0.4005 0.0318 0.4579 0.0635 0.1747 Indep. MBP1 0.3921 0.0537 0.4488 0.0702 0.2499 Indep. CDC4 0.4837 0.0611 0.6212 0.0251 0.0142 Atten. YKL023W 0.3775 0.0486 0.5472 0.0706 0.0095 Atten. INO80 0.4422 0.1089 0.6329 0.0543 0.0307 Atten.

RIM11 0.4419 0.0429 0.5554 0.0404 0.0085 Atten. SWI5 2.0642 0.2901 2.6018 0.0567 0.0317 Atten.

Table 3-8. Genome instability (GIN) phenotypes observed in response to overexpression of the 40 strains identified by primary screen. Overexpression-induced GIN Phenotype? Known Gene Dun1 Status Duffy et al., Transcriptional Literature Novel (2016) Regulator * SWI5 Atten. Y Y

BCK2 Atten. Y

YHR177W Atten. Y

VHR1 Atten. Y Y

TOP1 Atten. Y Nitiss et al., (2001)

CDC4 Atten. Y

INO80 Atten. Y Y

RIM11 Atten. Y

YKL023W Atten. Y

CLB5 Atten. Y Sarafan-Vasseur et al., (2002)

YPR015C Dep. Niu et al., (2008)

CDC13 Dep. Y

PHD1 Dep. Y Y

SRO9 Dep. Y

MGS1 Dep. Hishida et al., (2001)

HMS1 Dep. Y

SLF1 Dep. Y

FUN12 Dep. Y

ECM32 Dep. Y

HNM1 Dep. Y

CIN5 Indep. Y Y

WTM2 Indep. Y

SUT1 Indep. Chua et al., (2006) Y

KNS1 Indep. Y

HAL9 Indep. Y Y

URA1 Indep. Y

SNT1 Indep. Y Y

SSN6 Indep. Y Y

RAD53 Indep. Y

SCP160 Indep. Y

PIF1 Indep. Chang et al., (2009)

RAD26 Indep. Y

MGA1 Indep. Y Y

CYS4 Indep. Y

MSN5 Indep. Y

RIB1 Indep. Y

SPT16 Indep. Y Y

MBP1 Indep. Y Y

UPF3 Indep. Y

RSC2 Indep. Y Y

*As determined by association w/ the GO term: “Regulation of Transcription, DNA-templated”

Chapter 4 4 DISCUSSION AND FUTURE DIRECTIONS 4.1 Identification of novel genes implicated in genome integrity

Here I have employed the DNA damage-inducibility of RNR3 to optimize and conduct an R- SGA screen for novel genes implicated in maintaining genome stability. As a result, I have identified 41 genes that cause genome instability when overexpressed. Unsurprisingly, it appears that the proper regulation of many cellular processes is necessary for genome maintenance, including regulation of transcription/translation, the cell cycle, and DNA replication. Interestingly, I have also found that not all gene perturbations (deletion or overexpression) causing genome instability require the DNA damage checkpoint kinase, Dun1, for Rnr3 induction. In fact, nearly half of all genetic perturbations causing an increase in Rnr3 levels do so independent of DUN1. Even more surprising is the fact that there exist mutants whose Rnr3 induction is reduced in the presence of Dun1, a kinase typically involved in de-repression of RNR3. These results highlight the unchartered complexity of the DNA damage checkpoint that will be further discussed in this chapter of my thesis.

When I began work on this project, the effect of gene overexpression on genome maintenance had not been investigated using a high-throughput, systematic approach. In the last year, two groups have published screens characterizing the effects of gene overexpression on chromosome transmission fidelity. In 2015, Zhu et al. undertook a single-cell flow cytometry approach to quantify defects in the transmission of a mini-chromosome following the moderate overexpression of genes from their native promoters using MoBY-ORF plasmids (Ho et al., 2009; Zhu et al., 2015). Using this assay, 25 dosage-dependent chromosome instability genes (CIN) were uncovered. Shortly after, Duffy et al. employed the FLEX collection for two colony- based assays, a chromosome transmission fidelity colony sectoring (CTF) screen and an a-Like Faker screen (aLF) (Duffy et al., 2016). Similar to the assay described in Zhu et al., the CTF assay measured the loss of a mini-chromosome, while the aLF screen assayed loss of the MATα locus on chromosome III (Duffy et al., 2016). These two assays identified nearly 10 times as many genes as the flow cytometry approach, with a total of 245 dosage-dependent chromosome instability genes uncovered. Still, less than half these genes (108) gave rise to phenotypes

46 detected by both the CTF and aLF assays. What is more surprising is that when comparing the two independent studies, only 4 genes are shared. A study conducted by Ouspenski et al. using a cDNA library to identify overexpressed genes causing a colony sectoring phenotype also showed poor overlap with both studies (Ouspenski et al., 1999). Only 5 of these genes were found in the Duffy et al. dataset; none were found in the single-cell flow cytometry screen. These findings are particularly surprising since all three assays were designed to determine chromosome instability phenotypes. Duffy et al. state that the 245 genes identified by their primary screen were retested for both CTF and aLF phenotypes using three independent transformants to ensure reproducibility. Furthermore, any differences in detection between studies may have arisen because of several experimental distinctions, including alternative overexpression methods used (promoter: native vs. GAL1/10) and the fact that Duffy et al. employ two independent assays.

Despite the fact that the Rnr3 screen presented in this thesis no longer represents the first comprehensive study to investigate genome instability in the context of gene overexpression, it exemplifies a divergent approach. For example, the previous studies specifically look at one phenotype resulting from overexpression: chromosome instability. These studies are limited in that genes whose overexpression does not cause chromosome instability will not be detected. The use of Rnr3 as a biological marker for genome instability allows detection of any gene that causes a variety of genome instability phenotypes, so long as the checkpoint is activated. Moreover, the degree of overlap between the three chromosome instability datasets and my own is small, suggesting 76% of the genes identified by the Rnr3 assay are novel (Figure 3-2B). Novelty is reduced to 61% when comparing my list to small-scale, often single gene, overexpression studies in the literature (Chang et al., 2009; Hishida et al., 2001; Nitiss et al., 2001; Niu et al., 2008; Sarafan-Vasseur et al., 2002) (Table 3-8).

As mentioned, genes whose overexpression causes Rnr3 induction represent either 1) genes whose proper regulation is important for genome maintenance, or 2) transcriptional activators of RNR3. Accumulating evidence of genome instability will allow classification of genes into these two categories. Furthermore, I intend to screen the 25 putative genome instability strains for the presence of additional phenotypes that suggest spontaneous DNA damage, such as the formation of DNA damage-dependent foci and checkpoint activation. Rad52 and Ddc2 are two proteins that form foci upon genotoxic stress. Rad52 is involved in double stranded break repair via homologous recombination, and forms foci that co-localize with double-stranded DNA breaks

(Lisby et al., 2003, 2001). To date, I have identified 4 genes whose overexpression causes an increase in double stranded breaks, as determined by the formation of these foci. Ddc2, on the other hand, facilitates the recruitment of Mec1 to sites of replication protein A (RPA)-coated single stranded DNA in order to initiate checkpoint activation (Rouse and Jackson, 2002; Zou and Elledge, 2003). To uncover mutants with elevated levels of single stranded DNA and early signs of checkpoint activation, I will use the FLEX overexpression plasmids to overexpress the genes of interest in a strain where Ddc2 is C-terminally tagged with GFP. The percentage of cells with Ddc2 foci will be quantified as described for the Rad52 foci assay. Finally, I will gather additional evidence of checkpoint activation by investigating Rad53 phosphorylation status through the use of western blots. This experiment is especially useful because not all genome instability mutants activate the canonical Mec1-Rad53-Dun1 signaling cascade (Davidson et al., 2012; This study).

4.2 Identification of novel transcriptional regulators of RNR3

Overexpression strains lacking additional evidence of genome instability beyond Rnr3 induction (ie. Rad52 foci, Ddc2 foci, Rad53 activation) represent putative transcriptional regulators of RNR3. To investigate this hypothesis and identify novel transcriptional activators of RNR3 within my list, I intend to adopt a chromatin immunoprecipitation (ChIP) qPCR approach to detect which gene products are physically associated with the RNR3 promoter. Strains will be tested following growth in glucose and galactose to determine if binding occurs when there is an endogenous level of protein, or if it is specific to gene overexpression. While binding upon overexpression could be non-specific, it is worth noting that previous studies have suggested that overexpression of transcription factors does not increase promiscuity (Chua, 2009; Chua et al., 2006). To determine whether binding to the promoter is specific to a distinct type of damage, I will also perform the ChIP qPCR experiment in the presence of drugs that induce a variety of types of DNA damage and replication stress, such as HU and MMS. Moreover, if deletion of these genes result in a defect in Rnr3 induction following drug treatment, it is likely they are necessary for transcriptional activation. Together, these experiments will contribute towards understanding all pathways responsible for regulating RNR expression, which is particularly helpful in light of the Dun1-independent pathways of Rnr3 induction discussed in Section 4.3 and 4.4.

4.3 Investigating the Dun1-independent induction of RNR3 in known genome instability mutants

The canonical pathway of Rnr3 induction is described as being dependent on the activation of the DNA damage checkpoint through the sequential phosphorylation of three main checkpoint kinases: Mec1, Rad53, and Dun1. Evidence of a non-canonical, Dun1-independent checkpoint prompted me to investigate the role Dun1 plays in Rnr3 regulation. To date, the role Dun1 plays in these pathways has been exclusively studied in the context of DNA damage-inducing drugs or individual genome instability mutants, in large part because Rnr3 is undetectable in strains free from damage. Moreover, the evidence for Dun1-independent induction of Rnr3 is, at best, anecdotal and condition-specific, coming from a multitude of independent papers. Here, I have taken a more comprehensive approach towards understanding the role Dun1 plays in Rnr3 regulation. Our laboratory has now identified all gene perturbations, deletion and overexpression, resulting in an increased Rnr3 phenotype (Hendry et al., 2015; This study). Using R-SGA, I have quantified the effect a dun1∆ mutation has on the Rnr3 levels found in these 40 deletion mutants and 41 overexpression strains.

My expectation was that genes known to function in the transcriptional regulation of Rnr3, such as ITC1, ISW2, WTM2, and SSN6, would do so independent of the checkpoint, and therefore, independent of Dun1. Indeed, I found that nearly all of the known repressors and activators of Rnr3 in both my non-essential deletion and FLEX overexpression datasets behaved in this manner, with the exception of rox1∆ and hda3∆ mutants which showed Dun1-attenuated induction of Rnr3. I discuss Dun1-attenuation in more detail in Section 4.4; however, it is not immediately clear why these genes are unlike the rest of the transcriptional activators and repressors of Rnr3.

It seemed like a logical progression to predict that most, if not all, genome instability mutants would cause induction of Rnr3 through the canonical damage checkpoint. Surprisingly, the data from the dun1∆ screens did not support this hypothesis. 37.5% of the known genome instability mutants tested in the non-essential deletion screen caused a similar induction of Rnr3 in both wild type and dun1∆ strain backgrounds. In agreement with this data, several genes known to cause genome instability phenotypes when overexpressed, such as MSN5, RAD53, RSC2, SUT1, and PIF1, also show Dun1-independent induction of Rnr3.

There are several possible explanations for the observation that many known genome instability mutants cause up-regulation of Rnr3 through a mechanism independent of Dun1. It is tempting to interpret these data as further support of the existence of a Dun1-independent DNA damage checkpoint; however, one cannot yet eliminate the possibility that these genes function in RNR3 transcription.

4.3.1 Dun1-independent Checkpoint

One interpretation is that these data support the existence of a Dun1-independent DNA damage checkpoint. The most straightforward explanation is that Rad53, the kinase directly upstream of Dun1 in the checkpoint, may be able to phosphorylate targets of Dun1 in its absence. There exists evidence in the literature to support this hypothesis. Following treatment with MMS, Rnr3 transcript is significantly elevated (Huang and Elledge, 1997; Jaehnig et al., 2013). While Rnr3 transcript abundance is significantly decreased in a dun1∆ mutant, low levels are still detectable (Huang and Elledge, 1997; Jaehnig et al., 2013). Furthermore, these levels are completely abolished in rad53∆ mutants, suggesting Rad53 targets substrates of Dun1 in response to damage (Huang and Elledge, 1997; Jaehnig et al., 2013). Indeed, Crt1, the main transcriptional repressor of RNR3 whose Dun1-mediated phosphorylation facilitates induction, also has multiple Rad53 consensus sites (S/T-bulky residue). Huang et al. have shown that phosphorylation of Crt1 in response to MMS is decreased in a dun1∆ mutant, but phosphorylation is, again, completely abolished in strains lacking the upstream kinases Rad53 or Mec1 (Huang et al., 1998). Together, these data suggest that inherent redundancy of the checkpoint kinases makes it possible to bypass Dun1 when necessary. To determine whether Rad53 is necessary for Rnr3 induction, I propose repeating the R-SGA screen with a query strain lacking RAD53. Since rad53∆ mutants are inviable, the query strain will also lack SML1.

There are questions left unanswered by the aforementioned hypothesis. If Rad53 is able to bypass Dun1 during checkpoint activation in 12 of the genome instability mutants tested, it remains unclear why there is another subset of 12 mutants that require Dun1 for RNR3 induction. One hypothesis is that only those mutants causing hyper-activation of the checkpoint, and therefore of Rad53, are able to bypass Dun1. If this were the case, one might expect genome instability mutants causing Dun1-independent activation of Rnr3 to have higher levels of Rnr3 than those mutants causing Dun1-dependent induction; however, this is not what I observe. Since

Rnr3 levels are not a direct readout for Rad53 activity, I propose using an in situ kinase assay to directly test the level of Rad53 activity in genome instability mutants categorized as having either Dun1-dependent or –independent Rnr3 induction. This experiment will determine whether the level of Rad53 activity is predictive of the Dun1-dependency of downstream Rnr3 induction.

The hypothesis that Rad53 can target downstream Dun1 substrates fails to explain the Dun1- independent increase in Rnr3 levels in the pol30felg1∆ mutant discussed earlier, as there is no evidence of Rad53 phosphorylation/activation in these strains (Davidson et al., 2012). It is therefore possible that the Dun1-independence of Rnr3 induction observed in known genome maintenance mutants may be due to an entirely non-canonical branch of the checkpoint response. SWI4 and SWI6 have previously been implicated in the regulation of the RNR genes (Davidson et al., 2012; Ho et al., 1997). Strains lacking SWI4 and SWI6 are deficient in HU-induced transcription of both RNR2 and RNR3 (Ho et al., 1997). Additionally, Ccr4-Not, the mRNA deadenylase complex in yeast, has been implicated in the Dun1-independent regulation of RNR genes in response to replication stress (Mulder et al., 2005; Woolstencroft et al., 2006). Deletion of CCR4 has been shown to increase the polyA tail, and subsequently, the translational capacity of the CRT1 transcript (Woolstencroft et al., 2006). This upregulation of Crt1 was proposed to cause increased repression of RNR2-4 (Woolstencroft et al., 2006). Interestingly, our lab has shown that the Rad53-Dun1-independent elevation of Rnr3 in pol30felg1∆ mutants was dependent on both SWI4 and CCR4 (Davidson et al., 2012). To test if either of these pathways plays a role in the Dun1-independent induction of Rnr3 seen in 12 of the genome instability mutants tested in this study, I will individually delete SWI4, SWI6, and CCR4 and monitor the effect on RNR3 transcription and protein abundance using quantitative reverse transcription PCR (RT-qPCR) and western blotting techniques, respectively.

Perhaps neither of the aforementioned pathways provide sufficient explanation for the Dun1- independent increase in Rnr3 levels observed in some well-established genome maintenance mutants. As mentioned in Section 4.2, it is possible that my screen has uncovered novel transcriptional regulators of RNR3. If any of these genes are shown to bind the promoter of RNR3 using the ChIP experiment proposed, it will be beneficial to test whether the Rnr3 induction observed is abolished when these genes are deleted.

If none of these experiments provide meaningful insight into the Dun1-independent pathways that regulate RNR3, I propose conducting an R-SGA screen of the yeast non-essential deletion collection to uncover additional genes required for induction of Rnr3. More specifically, the screen will involve introducing a mutation known to induce Rnr3 levels independent of Dun1 (ie. asf1∆, mms22∆, xxx∆, etc.,) into the non-essential deletion collection using SGA methodology and then screening plates on the Typhoon to measure Rnr3-GFP fluorescence intensity. Double mutants exhibiting significantly lower levels of Rnr3 than the original single mutant (xxx∆) will be considered necessary for Rnr3 up-regulation. The expectation is that genes uncovered by these screens will provide insight into the specific pathways involved in Dun1-independent Rnr3 regulation.

4.3.2 Alternative roles for genes in RNR3 transcription

Albeit less probable, an alternative hypothesis to explain the Dun1-independent induction of Rnr3 is that a subset of these genes are involved in the direct transcriptional regulation of RNR3. Perhaps these mutants cause induction of Rnr3 that subsequently leads to genome instability. Indeed, asf1∆ and leo1∆ are two genome instability mutants that cause Dun1-independent induction of RNR3 and are associated with the biological processes GO term, “regulation of transcription, DNA-templated”. This hypothesis predicts that both of these genes act as transcriptional repressors of RNR3; however, Asf1, a conserved H3/H4 chaperone, has been proposed to stimulate transcription through nucleosome disassembly at promoter regions (Adkins et al., 2007, 2004; Rufiange et al., 2007; Schwabish and Struhl, 2006; Williams et al., 2008). Moreover, strains lacking ASF1 are deficient in Rnr3 up-regulation following treatment with HU, which is consistent with a role for Asf1 in activation, not repression (Minard et al., 2011).

As mentioned, strains lacking LEO1 show Dun1-independent induction of Rnr3 along with exhibiting phenotypes often associated with genome instability mutants. In support of the hypothesis that the gene plays a role in the transcriptional regulation of RNR3, Leo1 is known to function in the Paf1 complex. Most commonly, the Paf1 complex associates with RNA polymerase II to initiate transcription; however, it also has known roles in transcriptional repression, which would support our hypothesis (Mueller and Jaehning, 2002; Pruneski et al., 2011). Still, it is not immediately clear what involvement the Paf1 complex has in the regulation of RNR3. Ccr4 has been shown to associate with the Paf1 complex (Chang et al., 1999). Since

Ccr4-Not is involved in the regulation of RNR genes (described in Section 4.3.1), these data may provide evidence linking the Paf1 complex to the transcriptional regulation of RNR3. Strains lacking PAF1 are sensitive to HU and have lower levels of RNR1 transcript than wild type, suggesting PAF1 is necessary for the transcriptional activation of RNR1 following replication stress (Betz et al., 2002). Both overexpression of RNR1 and deletion of LEO1 suppress this sensitivity, which may support a role for LEO1 in the repression of RNR1 that could foreseeably extend to RNR3, since leo1∆ mutants show increased levels of Rnr3 (Betz et al., 2002; Mueller and Jaehning, 2002). Why loss of different members of the Paf1 complex have different effects on transcription remains to be seen. One theory is that loss of PAF1 renders the complex partially defective leading to decreased transcriptional activation of its target genes, whereas the loss of both PAF1 and LEO1 completely inactivates the complex, allowing for compensation by other initiation complexes, such as the Srb-mediated form of Pol II (Mueller and Jaehning, 2002). Assuming RNR3 is a target of the Paf1 complex, this theory is disfavoured by our data since loss of LEO1 alone is enough to induce Rnr3 levels. It remains possible that LEO1 plays a repressive role in the transcriptional regulation of the RNR genes when it is present in the Paf1 complex.

4.4 Understanding the Dun1-attenuation of Rnr3

Unexpectedly, I identified a group of 11 mutants that exhibited an increased level of Rnr3 following the deletion of Dun1. This phenotype was also observed in 9 overexpression strains inducing Rnr3. These results were particularly surprising since Dun1 functions to induce expression of RNR2-4 through phosphorylation, and subsequent inactivation, of the transcriptional repressor, Crt1. There are several possible explanations for the observed phenotype, including Dun1-independent checkpoint activation or Dun1-mediated repression of RNR3.

4.4.1 Checkpoint activation in strains lacking dun1∆

Due to its role in the DNA damage response, it is unsurprising that deletion of DUN1 results in genome instability, including chromosome loss and decreased telomere length (Gupta et al., 2013; Strome et al., 2008; Zhu et al., 2015). It is possible that this increase in genome instability activates a Dun1-independent DNA damage response, resulting in an increase in Rnr3 abundance. Indeed, in untreated cells, dun1∆ mutants show statistically supported elevation of

Rnr3 when compared to wild type strains, even though this increase is minimal. It is also worth noting that the initial level of Rnr3 induction in the single mutants belonging to the “Dun1- attenuated” category was lower than those mutants in the “Dun1-dependent” or “–independent” categories (p-value < 0.003). Perhaps the level of Rnr3 present in these mutants is minimal enough that the Rnr3 induction resulting from the subsequent deletion of DUN1 is synergistic, resulting in what we call Dun1-attenuation induction of Rnr3. In genome instability mutants causing a large increase in Rnr3, such as rrm3∆ or rad27∆, the small increase in Rnr3 caused by dun1∆-induced genome instability is likely not enough to compensate for the loss of Rnr3 due to the inactivation of the canonical DNA damage response.

4.4.2 A role for Dun1 in RNR3 repression

An alternative hypothesis to explain the fact that the initial level of Rnr3 induction in the “Dun1- attenuated” single mutants was lower than those mutants in the “Dun1-dependent” or “– independent” categories is that there is a threshold or type of DNA damage under which Rnr3 induction may be harmful to the cell. Under these conditions, Dun1 may play a repressive role in RNR regulation. In support of this hypothesis, our lab has previously suggested a model in which checkpoint-dependent induction of RNR genes increases dNTP pools, which in turn increases the spontaneous mutation rate (Davidson et al., 2012). It is not hard to rationalize, then, that DNA damage-dependent induction of the RNR genes would only be beneficial when the activating damage is worse than the consequential mutation rate.

To begin testing this hypothesis, I propose treating wild type (BY4741) and dun1∆ strains with various concentrations of DNA damage-inducing drugs, like MMS, and quantifying the subsequent level of RNR3 transcript level through RT-qPCR. If there exists a threshold of damage under which Dun1 functions to maintain RNR3 repression, one would expect the dun1∆ strain to exhibit higher levels of Rnr3 than the wild type strain at MMS concentrations below this threshold.

4.4.3 Functional role of Rnr3 due to decreased Rnr1 activity in dun1∆ strains

S. cerevisiae has two large RNR subunits: RNR1 and RNR3. Despite this, studies have shown that only the essential large subunit subunit, Rnr1, is found in the active complex. Indeed, RNR1 is up-regulated throughout the cell cycle to accommodate DNA replication, while RNR3 levels are

54 essentially undetectable (Elledge and Davis, 1990). Strains lacking DUN1 are unable to fully activate Rnr1 due to the increased stability of its inhibitor Sml1 (Chabes et al., 1999; Zhang et al., 2007; Zhao and Rothstein, 2002; Zhao et al., 2000, 1998). Since overexpression of RNR3 has previously been shown to rescue the lethality of rnr1∆ mutants despite having low specific in vitro activity, I am interested in investigating whether the increase in Rnr3 observed in dun1∆ mutants plays a functional role in compensating for the decreased activity in Rnr1 (Domkin et al., 2002; Elledge and Davis, 1990). To test this hypothesis, I will mutate the active site of Rnr3 to determine if 1) inactivating Rnr3 further decreases the viability of dun1∆ mutants, and 2) if there is a decrease in dNTP pools using the protocol described in Kumar et al., (2010). While it has already been shown that Rnr3 plays a negligible role in dNTP synthesis in conditions of DNA damage, it will be interesting to see if this changes specifically in a dun1∆ strain.

4.5 Summary and Conclusions

Here, I have identified 41 genes that cause induction of Rnr3 when overexpressed. 25 of these genes (61%) represent novel discoveries, with no previously established connection to genome maintenance in the context of overexpression, making them ideal targets for follow-up.

Due to the nature of this screen, genes identified were hypothesized to be either 1) transcriptional regulators of RNR3 or 2) genes causing genome instability that activates the canonical DNA damage checkpoint through the Mec1-Rad53-Dun1 signaling cascade. Interestingly, I find that not all mutants exhibiting genome instability phenotypes cause Rnr3 induction that is dependent on this cascade; the increase in Rnr3 observed in nearly half of the deletion mutants and overexpression strains tested is independent of Dun1. This data supports the existence of a Dun1- independent checkpoint; however, why some mutants require Dun1 for activation while others do not is an interesting question that requires further experimentation.

References

Adkins, M.W., Howar, S.R., Tyler, J.K., 2004. Chromatin disassembly mediated by the histone chaperone Asf1 is essential for transcriptional activation of the yeast PHO5 and PHO8 genes. Mol. Cell 14, 657–666. doi:10.1016/j.molcel.2004.05.016

Adkins, M.W., Williams, S.K., Linger, J., Tyler, J.K., 2007. Chromatin disassembly from the PHO5 promoter is essential for the recruitment of the general transcription machinery and coactivators. Mol. Cell. Biol. 27, 6372–6382. doi:10.1128/MCB.00981-07

Aguilera, A., García-Muse, T., 2013. Causes of genome instability. Annu. Rev. Genet. 47, 1–32. doi:10.1146/annurev-genet-111212-133232

Alvaro, D., Lisby, M., Rothstein, R., 2007. Genome-wide analysis of Rad52 foci reveals diverse mechanisms impacting recombination. PLoS Genet. 3, 2439–2449. doi:10.1371/journal.pgen.0030228

Bastos de Oliveira, F.M., Harris, M.R., Brazauskas, P., de Bruin, R.A., Smolka, M.B., 2012. Linking DNA replication checkpoint to MBF cell-cycle transcription reveals a distinct class of G1/S genes. EMBO J. 31, 1798–1810. doi:10.1038/emboj.2012.27

Ben-Aroya, S., Coombes, C., Kwok, T., O’Donnell, K.A., Boeke, J.D., Hieter, P., 2008. Toward a Comprehensive Temperature-Sensitive Mutant Repository of the Essential Genes of Saccharomyces cerevisiae. Mol. Cell 30, 248–258. doi:10.1016/j.molcel.2008.02.021

Benschop, J.J., Brabers, N., van Leenen, D., Bakker, L. V, van Deutekom, H.W.M., van Berkum, N.L., Apweiler, E., Lijnzaad, P., Holstege, F.C.P., Kemmeren, P., 2010. A consensus of core protein complex compositions for Saccharomyces cerevisiae. Mol. Cell 38, 916–928. doi:10.1016/j.molcel.2010.06.002

Betz, J.L., Chang, M., Washburn, T.M., Porter, S.E., Mueller, C.L., Jaehning, J.A., 2002. Phenotypic analysis of Paf1/RNA polymerase II complex mutations reveals connections to cell cycle regulation, protein synthesis, and lipid and nucleic acid metabolism. Mol. Genet. Genomics 268, 272–285. doi:10.1007/s00438-002-0752-8

55 56

Bonilla, C.Y., Melo, J.A., Toczyski, D.P., 2008. Colocalization of Sensors Is Sufficient to Activate the DNA Damage Checkpoint in the Absence of Damage. Mol. Cell 30, 267–276. doi:10.1016/j.molcel.2008.03.023

Brachmann, C.B., Davies, A., Cost, G.J., Caputo, E., Li, J., Hieter, P., Boeke, J.D., 1998. Designer deletion strains derived from Saccharomyces cerevisiae S288C: A useful set of strains and plasmids for PCR-mediated gene disruption and other applications. Yeast 14, 115–132.

Burgers, P.M., Gerik, K.J., 1998. Structure and processivitiy of two forms of Saccharomyces cerevisiae DNA polymerase delta. J. Biol. Chem. 273, 19756–19762.

Byun, T.S., Pacek, M., Yee, M.C., Walter, J.C., Cimprich, K.A., 2005. Functional uncoupling of MCM helicase and DNA polymerase activities activates the ATR-dependent checkpoint. Genes Dev. 19, 1040–1052. doi:10.1101/gad.1301205

Chabes, A., Domkin, V., Larsson, G., Liu, A., Graslund, A., Wijmenga, S., Thelander, L., 2000. Yeast ribonucleotide reductase has a heterodimeric iron-radical-containing subunit. Proc. Natl. Acad. Sci. U. S. A. 97, 2474–9. doi:10.1073/pnas.97.6.2474

Chabes, A., Domkin, V., Thelander, L., 1999. Yeast Sml1, a protein inhibitor of ribonucleotide reductase. J. Biol. Chem. 274, 36679–36683. doi:10.1074/jbc.274.51.36679

Chabes, A., Georgieva, B., Domkin, V., Zhao, X., Rothstein, R., Thelander, L., 2003. Survival of DNA damage in yeast directly depends on increased dNTP levels allowed by relaxed feedback inhibition of ribonucleotide reductase. Cell 112, 391–401.

Chang, M., Bellaoui, M., Boone, C., Brown, G.W., 2002. A genome-wide screen for methyl methanesulfonate-sensitive mutants reveals genes required for S phase progression in the presence of DNA damage. Proc. Natl. Acad. Sci. U. S. A. 99, 16934–16939.

Chang, M., French-Cornay, D., Fan, H.Y., Klein, H., Denis, C.L., Jaehning, J.A., 1999. A complex containing RNA polymerase II, Paf1p, Cdc73p, Hpr1p, and Ccr4p plays a role in protein kinase C signaling. Mol Cell Biol 19, 1056–1067.

Chang, M., Luke, B., Kraft, C., Li, Z., Peter, M., Lingner, J., Rothstein, R., 2009. Telomerase is

essential to alleviate pif1-induced replication stress at telomeres. Genetics 183, 779–91. doi:10.1534/genetics.109.107631

Cheng, E., Vaisica, J.A., Ou, J., Baryshnikova, A., Lu, Y., Roth, F.P., Brown, G.W., 2012. Genome rearrangements caused by depletion of essential DNA replication proteins in Saccharomyces cerevisiae. Genetics 192, 147–160. doi:10.1534/genetics.112.141051

Cherry, J.M., Hong, E.L., Amundsen, C., Balakrishnan, R., Binkley, G., Chan, E.T., Christie, K.R., Costanzo, M.C., Dwight, S.S., Engel, S.R., Fisk, D.G., Hirschman, J.E., Hitz, B.C., Karra, K., Krieger, C.J., Miyasato, S.R., Nash, R.S., Park, J., Skrzypek, M.S., Simison, M., Weng, S., Wong, E.D., 2012. Saccharomyces Genome Database: The genomics resource of budding yeast. Nucleic Acids Res. 40, 700–705. doi:10.1093/nar/gkr1029

Choy, J.S., O’Toole, E., Schuster, B.M., Crisp, M.J., Karpova, T.S., McNally, J.G., Winey, M., Gardner, M.K., Basrai, M.A., 2013. Genome-wide haploinsufficiency screen reveals a novel role for γ-TuSC in spindle organization and genome stability. Mol. Biol. Cell 24, 2753–63. doi:10.1091/mbc.E12-12-0902

Chua, G., 2009. Identification of Transcription Factor Targets by Phenotypic Activation and Microarray Expression Profiling in Yeast, in: Stagljar, I. (Ed.), Yeast Functional Genomics and Proteomics. Humana Press, pp. 19–35. doi:10.1007/978-1-59745-540-4_2

Chua, G., Morris, Q.D., Sopko, R., Robinson, M.D., Ryan, O., Chan, E.T., Frey, B.J., Andrews, B.J., Boone, C., Hughes, T.R., 2006. Identifying transcription factor functions and targets by phenotypic activation. Proc. Natl. Acad. Sci. U. S. A. 103, 12045–50. doi:10.1073/pnas.0605140103

Collins, A.R., 1999. Oxidative DNA damage, antioxidants, and cancer. BioEssays 21, 238–246.

Costanzo, M., Baryshnikova, A., Bellay, J., Kim, Y., Spear, E.D., Sevier, C.S., Ding, H., Koh, J.L.Y., Toufighi, K., Mostafavi, S., Prinz, J., St. Onge, R.P., VanderSluis, B., Makhnevych, T., Vizeacoumar, F.J., Alizadeh, S., Bahr, S., Brost, R.L., Chen, Y., Cokol, M., Deshpande, R., Li, Z., Lin, Z.-Y., Liang, W., Marback, M., Paw, J., San Luis, B.-J., Shuteriqi, E., Tong, A.H.Y., van Dyk, N., Wallace, I.M., Whitney, J.A., Weirauch, M.T., Zhong, G., Zhu, H., Houry, W.A., Brudno, M., Ragibizadeh, S., Papp, B., Pal, C., Roth, F.P., Giaever, G.,

Nislow, C., Troyanskaya, O.G., Bussey, H., Bader, G.D., Gingras, A.-C., Morris, Q.D., Kim, P.M., Kaiser, C.A., Myers, C.L., Andrews, B.J., Boone, C., 2010. The Genetic Landscape of a Cell. Science 327, 425–431. doi:10.1126/science.1180823

Davidson, M.B., Katou, Y., Keszthelyi, A., Sing, T.L., Xia, T., Ou, J., Vaisica, J.A., Thevakumaran, N., Marjavaara, L., Myers, C.L., Chabes, A., Shirahige, K., Brown, G.W., 2012. Endogenous DNA replication stress results in expansion of dNTP pools and a mutator phenotype. EMBO J. 31, 895–907. doi:10.1038/emboj.2011.485 de Bruin, R.A.M., Kalashnikova, T.I., Chahwan, C., McDonald, W.H., Wohlschlegel, J., Yates, J., Russell, P., Wittenberg, C., 2006. Constraining G1-Specific Transcription to Late G1 Phase: The MBF-Associated Corepressor Nrm1 Acts via Negative Feedback. Mol. Cell 23, 483–496. doi:10.1016/j.molcel.2006.06.025

Domkin, V., Thelander, L., Chabes, A., 2002. Yeast DNA damage-inducible Rnr3 has a very low catalytic activity strongly stimulated after the formation of a cross-talking Rnr1/Rnr3 complex. J. Biol. Chem. 277, 18574–18578. doi:10.1074/jbc.M201553200

Douglas, A.C., Smith, A.M., Sharifpoor, S., Yan, Z., Durbic, T., Heisler, L.E., Lee, A.Y., Ryan, O., Gottert, H., Surendra, A., van Dyk, D., Giaever, G., Boone, C., Nislow, C., Andrews, B.J., 2012. Functional Analysis With a Barcoder Yeast Gene Overexpression System. G3 2, 1279–1289. doi:10.1534/g3.112.003400

Duffy, S., Fam, H.K., Wang, Y., Styles, E.B., Kim, J.-H., Ang, J.S., Singh, T., Larionov, V., Shah, S., Andrews, B., Boerkoel, C., Hieter, P., 2016. Systematic over-expression screens for chromosome instability identify conserved dosage chromosome instability genes in yeast and human tumors. bioRxiv. doi:dx.doi.org/10.1101/038489

Elledge, S., Davis, R., 1990. Two genes differentially regulated in the cell cycle and by DNA- damaging agents encode alternate regulatory subunits of ribonucleotide reductase. Genes Dev. 4, 740–751. doi:10.1101/gad.4.5.740

Elledge, S.J., Zhou, Z., Allen, J.B., Navas, T.A., 1993. DNA Damage and Cell Cycle Regulation of Ribonucleotide Reductase. BioEssays 15, 333–339.

Gaillard, H., García-Muse, T., Aguilera, A., 2015. Replication stress and cancer. Nat. Rev. Cancer 15, 276–289. doi:10.1038/nrc3916

Gerik, K.J., Li, X., Pautz, A., Burgers, P.M., 1998. Characterization of the Two Small Subunits of Saccharomyces cerevisiae DNA Polymerase delta. J. Biol. Chem. 273, 19747–19755.

Giaever, G., Chu, A.M., Ni, L., Connelly, C., Riles, L., Véronneau, S., Dow, S., Lucau-Danila, A., Anderson, K., André, B., Arkin, A.P., Astromoff, A., El-Bakkoury, M., Bangham, R., Benito, R., Brachat, S., Campanaro, S., Curtiss, M., Davis, K., Deutschbauer, A., Entian, K.-D., Flaherty, P., Foury, F., Garfinkel, D.J., Gerstein, M., Gotte, D., Güldener, U., Hegemann, J.H., Hempel, S., Herman, Z., Jaramillo, D.F., Kelly, D.E., Kelly, S.L., Kötter, P., LaBonte, D., Lamb, D.C., Lan, N., Liang, H., Liao, H., Liu, L., Luo, C., Lussier, M., Mao, R., Menard, P., Ooi, S.L., Revuelta, J.L., Roberts, C.J., Rose, M., Ross-Macdonald, P., Scherens, B., Schimmack, G., Shafer, B., Shoemaker, D.D., Sookhai-Mahadeo, S., Storms, R.K., Strathern, J.N., Valle, G., Voet, M., Volckaert, G., Wang, C., Ward, T.R., Wilhelmy, J., Winzeler, E.A., Yang, Y., Yen, G., Youngman, E., Yu, K., Bussey, H., Boeke, J.D., Snyder, M., Philippsen, P., Davis, R.W., Johnston, M., 2002. Functional profiling of the Saccharomyces cerevisiae genome. Nature 418, 387–391. doi:10.1038/nature00935

Gilbert, C.S., Green, C.M., Lowndes, N.F., 2001. Budding yeast Rad9 is an ATP-dependent Rad53 activating machine. Mol. Cell 8, 129–136.

Gupta, A., Sharma, S., Reichenbach, P., Marjavaara, L., Nilsson, A.K., Lingner, J., Chabes, A., Rothstein, R., Chang, M., 2013. Telomere length homeostasis responds to changes in intracellular dNTP pools. Genetics 193, 1095–1105. doi:10.1534/genetics.112.149120

Hendry, J., Guihong, T., Ou, J., Boone, C., Brown, G., 2015. Leveraging DNA Damage Response Signaling to Identify Yeast Genes Controlling Genome Stability. G3 5, 997–1006. doi:10.1534/g3.115.016576

Hendry, J.A., 2015. A high-throughput screen for novel genes involved in maintaining genome stability and the DNA damage response in Saccharomyces cerevisiae. University of Toronto.

Hishida, T., Iwasaki, H., Ohno, T., Morishita, T., Shinagawa, H., 2001. A yeast gene , MGS1 , encoding a DNA-dependent AAA(+) ATPase is required to maintain genome stability. Proc. Natl. Acad. Sci. U. S. A. 98, 8283–8289.

Ho, C.H., Magtanong, L., Barker, S.L., Gresham, D., Nishimura, S., Natarajan, P., Koh, J.L.Y., Porter, J., Gray, C.A., Andersen, R.J., Giaever, G., Nislow, C., Andrews, B., Botstein, D., Graham, T.R., Yoshida, M., Boone, C., 2009. A molecular barcoded yeast ORF library enables mode-of-action analysis of bioactive compounds. Nat. Biotechnol. 27, 369–377. doi:10.1038/nbt.1534

Ho, Y., Mason, S., Kobayashi, R., Hoekstra, M., Andrews, B., 1997. Role of the casein kinase I isoform, Hrr25, and the cell cycle-regulatory transcription factor, SBF, in the transcriptional response to DNA damage in Saccharomyces cerevisiae. Proc. Natl. Acad. Sci. U. S. A. 94, 581–6. doi:10.1073/pnas.94.2.581

Hu, Y., Rolfs, A., Bhullar, B., Murthy, T.V.S., Zhu, C., Berger, M.F., Camargo, A. a., Kelley, F., McCarron, S., Jepson, D., Richardson, A., Raphael, J., Moreira, D., Taycher, E., Zuo, D., Mohr, S., Kane, M.F., Williamson, J., Simpson, A., Bulyk, M.L., Harlow, E., Marsischky, G., Kolodner, R.D., LaBaer, J., 2007. Approaching a complete repository of sequence- verified protein-encoding clones for Saccharomyces cerevisiae. Genome Res. 17, 536–543. doi:10.1101/gr.6037607

Hu, Z., Killion, P.J., Iyer, V.R., 2007. Genetic reconstruction of a functional transcriptional regulatory network. Nat. Genet. 39, 683–687. doi:10.1038/ng2012

Huang, M., Elledge, S.J., 1997. Identification of RNR4, encoding a second essential small subunit of ribonucleotide reductase in Saccharomyces cerevisiae. Mol. Cell. Biol. 17, 6105– 13.

Huang, M., Zhou, Z., Elledge, S.J., 1998. The DNA Replication and Damage Checkpoint Pathways Induce Transcription by Inhibition of the Crt1 Repressor. Cell 94, 595–605.

Huang, M.-E., Rio, A.-G., Nicolas, A., Kolodner, R.D., 2003. A genomewide screen in Saccharomyces cerevisiae for genes that suppress the accumulation of mutations. Proc. Natl. Acad. Sci. U. S. A. 100, 11529–34. doi:10.1073/pnas.2035018100

Huang, M.E., Rio, A.-G., Galibert, M.-D., Galibert, F., 2002. POL32, a subunit of the Saccharomyces cerevisiae DNA polymerase delta, defines a link between DNA replication and the mutagenic bypass repair pathway. Genetics 160, 1409–1422.

Hustedt, N., Gasser, S.M., Shimada, K., 2013. Replication checkpoint: Tuning and coordination of replication forks in S phase. Genes (Basel). 4, 388–434. doi:10.3390/genes4030388

Iyer, V.R., Horak, C.E., Scafe, C.S., Botstein, D., Snyder, M., Brown, P.O., 2001. Genomic binding sites of the yeast cell-cycle transcription factors SBF and MBF. Nature 409, 533– 538. doi:10.1038/35054095

Jaehnig, E.J., Kuo, D., Hombauer, H., Ideker, T.G., Kolodner, R.D., 2013. Checkpoint Kinases Regulate a Global Network of Transcription Factors in Response to DNA Damage. Cell Rep. 4, 174–188. doi:10.1016/j.celrep.2013.05.041

Kainth, P., Sassi, H.E., Peña-Castillo, L., Chua, G., Hughes, T.R., Andrews, B., 2009. Comprehensive genetic analysis of transcription factor pathways using a dual reporter gene system in budding yeast. Methods 48, 258–264. doi:10.1016/j.ymeth.2009.02.015

Koch, C., Moll, T., Neuberg, M., Ahorn, H., Nasmyth, K., 1993. A role for the transcription factors Mbp1 and Swi4 in progression from G1 to S phase. Science (80-. ). 261, 1551–1557.

Kumar, D., Viberg, J., Nilsson, A.K., Chabes, A., 2010. Highly mutagenic and severely imbalanced dNTP pools can escape detection by the S-phase checkpoint. Nucleic Acids Res. 38, 3975–3983. doi:10.1093/nar/gkq128

Lee, Y.D., Elledge, S.J., 2006. Control of ribonucleotide reductase localization through an anchoring mechanism involving Wtm1. Genes Dev. 20, 334–344. doi:10.1101/gad.1380506

Lee, Y.D., Wang, J., Stubbe, J., Elledge, S.J., 2008. Dif1 Is a DNA-Damage-Regulated Facilitator of Nuclear Import for Ribonucleotide Reductase. Mol. Cell 32, 70–80. doi:10.1016/j.molcel.2008.08.018

Lejeune, J., Turpin, R., Gautier, M., 1959. Mongolism; a chromosomal disease (trisomy). Bull. l Acad. Natl. Med. 143, 256–265.

Li, B., Reese, J.C., 2001. Ssn6-Tup1 Regulates RNR3 by Positioning Nucleosomes and Affecting the Chromatin Structure at the Upstream Repression Sequence. J. Biol. Chem. 276, 33788–33797. doi:10.1074/jbc.M104220200

Li, Z., Vizeacoumar, F.J., Bahr, S., Li, J., Warringer, J., Vizeacoumar, F.S., Min, R., Vandersluis, B., Bellay, J., Devit, M., Fleming, J.A., Stephens, A., Haase, J., Lin, Z.-Y., Baryshnikova, A., Lu, H., Yan, Z., Jin, K., Barker, S., Datti, A., Giaever, G., Nislow, C., Bulawa, C., Myers, C.L., Costanzo, M., Gingras, A.-C., Zhang, Z., Blomberg, A., Bloom, K., Andrews, B., Boone, C., 2011. Systematic exploration of essential yeast gene function with temperature-sensitive mutants. Nat. Biotechnol. 29, 361–367. doi:10.1038/nbt.1832

Lisby, M., Mortensen, U.H., Rothstein, R., 2003. Colocalization of multiple DNA double-strand breaks at a single Rad52 repair centre. Nat. Cell Biol. 5, 572–7. doi:10.1038/ncb997

Lisby, M., Rothstein, R., Mortensen, U.H., 2001. Rad52 forms DNA repair and recombination centers during S phase. Proc. Natl. Acad. Sci. U. S. A. 98, 8276–82. doi:10.1073/pnas.121006298

Lopes, M., Foiani, M., Sogo, J.M., 2006. Multiple mechanisms control chromosome integrity after replication fork uncoupling and restart at irreparable UV lesions. Mol. Cell 21, 15–27. doi:10.1016/j.molcel.2005.11.015

Lucca, C., Vanoli, F., Cotta-Ramusino, C., Pellicioli, A., Liberi, G., Haber, J., Foiani, M., 2004. Checkpoint-mediated control of replisome-fork association and signalling in response to replication pausing. Oncogene 23, 1206–1213. doi:10.1038/sj.onc.1207199

Majka, J., Binz, S.K., Wold, M.S., Burgers, P.M.J., 2006a. Replication protein a directs loading of the DNA damage checkpoint clamp to 5’-DNA junctions. J. Biol. Chem. 281, 27855– 27861. doi:10.1074/jbc.M605176200

Majka, J., Burgers, P.M.J., 2003. Yeast Rad17/Mec3/Ddc1: a sliding clamp for the DNA damage checkpoint. Proc. Natl. Acad. Sci. U. S. A. 100, 2249–54. doi:10.1073/pnas.0437148100

Majka, J., Niedziela-Majka, A., Burgers, P.J., 2006b. The Checkpoint Clamp Activates Mec1 Kinase during Initiation of the DNA Damage Checkpoint. Mol. Cell 24, 891–901.

doi:10.1016/j.molcel.2006.11.027

Minard, L. V., Williams, J.S., Walker, A.C., Schultz, M.C., 2011. Transcriptional regulation by Asf1: New mechanistic insights from studies of the dna damage response to replication stress. J. Biol. Chem. 286, 7082–7092. doi:10.1074/jbc.M110.193813

Morrow, D.M., Tagle, D.A., Shiloh, Y., Collins, F.S., Hieter, P., 1995. TEL1, an S. cerevisiae homolog of the human gene mutated in ataxia telangiectasia, is functionally related to the yeast checkpoint gene MEC1. Cell 82, 831–840.

Mueller, C.L., Jaehning, J.A., 2002. Ctr9, Rtf1, and Leo1 are components of the Paf1/RNA polymerase II complex. Mol. Cell. Biol. 22, 1971–80. doi:10.1128/MCB.22.7.1971– 1980.2002

Mulder, K.W., Winkler, G.S., Timmers, H.T.M., 2005. DNA damage and replication stress induced transcription of RNR genes is dependent on the Ccr4-Not complex. Nucleic Acids Res. 33, 6384–6392. doi:10.1093/nar/gki938

Myung, K., Datta, A., Kolodner, R.D., 2001. Suppression of spontaneous chromosomal rearrangements by S phase checkpoint functions in Saccharomyces cerevisiae. Cell 104, 397–408.

Myung, K., Kolodner, R.D., 2002. Suppression of genome instability by redundant S-phase checkpoint pathways in Saccharomyces cerevisiae. Proc. Natl. Acad. Sci. U. S. A. 99, 4500–7. doi:10.1073/pnas.062702199

Nakada, D., Matsumoto, K., Sugimoto, K., 2003a. ATM-related Tel1 associates with double- strand breaks through an Xrs2-dependent mechanism. Genes Dev. 17, 1957–1962. doi:10.1101/gad.1099003

Nakada, D., Shimomura, T., Matsumoto, K., Sugimoto, K., 2003b. The ATM-related Tel1 protein of Saccharomyces cerevisiae controls a checkpoint response following phleomycin treatment. Nucleic Acids Res. 31, 1715–1724. doi:10.1093/nar/gkg252

Navadgi-Patil, V.M., Burgers, P.M., 2009. The Unstructured C-Terminal Tail of the 9-1-1 Clamp Subunit Ddc1 Activates Mec1/ATR via Two Distinct Mechanisms. Mol. Cell 36, 743–753.

doi:10.1016/j.molcel.2009.10.014

Nedelcheva, M.N., Roguev, A., Dolapchiev, L.B., Shevchenko, A., Taskov, H.B., Shevchenko, A., Stewart, A.F., Stoynov, S.S., 2005. Uncoupling of unwinding from DNA synthesis implies regulation of MCM helicase by Tof1/Mrc1/Csm3 checkpoint complex. J. Mol. Biol. 347, 509–521. doi:10.1016/j.jmb.2005.01.041

Nguyen, H.H., Ge, J., Perlstein, D.L., Stubbe, J., 1999. Purification of ribonucleotide reductase subunits Y1, Y2, Y3, and Y4 from yeast: Y4 plays a key role in diiron cluster assembly. Proc. Natl. Acad. Sci. U. S. A. 96, 12339–44. doi:10.1073/pnas.96.22.12339

Nitiss, J.L., Nitiss, K.C., Rose, A., Waltman, J.L., 2001. Overexpression of Type I Topoisomerases Sensitizes Yeast Cells to DNA Damage. J. Biol. Chem. 276, 26708–26714. doi:10.1074/jbc.M102674200

Niu, W., Li, Z., Zhan, W., Iyer, V.R., Marcotte, E.M., 2008. Mechanisms of cell cycle control revealed by a systematic and quantitative overexpression screen in S. cerevisiae. PLoS Genet. 4. doi:10.1371/journal.pgen.1000120

Nyberg, K.A., Michelson, R.J., Putnam, C.W., Weinert, T.A., 2002. Toward maintaining the genome: DNA damage and replication checkpoints. Annu. Rev. Genet. 36, 617–656. doi:10.1146/annurev.genet.36.060402.113540

Ouspenski, I.I., Elledge, S.J., Brinkley, B.R., 1999. New yeast genes important for chromosome integrity and segregation identified by dosage effects on genome stability. Nucleic Acids Res. 27, 3001–3008. doi:10.1093/nar/27.15.3001

Pan, X., Ye, P., Yuan, D.S., Wang, X., Bader, J.S., Boeke, J.D., 2006. A DNA integrity network in the yeast Saccharomyces cerevisiae. Cell 124, 1069–1081. doi:10.1016/j.cell.2005.12.036

Papp, B., Pál, C., Hurst, L.D., 2003. Dosage sensitivity and the evolution of gene families in yeast. Nature 424, 194–197. doi:10.1038/nature01771

Perlstein, D.L., Ge, J., Ortigosa, A.D., Robblee, J.H., Zhang, Z., Huang, M., Stubbe, J., 2005. The active form of the Saccharomyces cerevisiae ribonucleotide reductase small subunit is a heterodimer in vitro and in vivo. Biochemistry 44, 15366–15377. doi:10.1021/bi051616+

Prelich, G., 2012. Gene overexpression: Uses, mechanisms, and interpretation. Genetics 190, 841–854. doi:10.1534/genetics.111.136911

Pruneski, J.A., Hainer, S.J., Petrov, K.O., Martens, J.A., 2011. The Paf1 Complex Represses SER3 Transcription in Saccharomyces cerevisiae by Facilitating Intergenic Transcription- Dependent Nucleosome Occupancy of the SER3 Promoter. Eukaryot. Cell 10, 1283–1294. doi:10.1128/EC.05141-11

Regnacq, M., Alimardani, P., El Moudni, B., Berges, T., 2001. Sut1p interaction with Cyc8p(Ssn6p) relieves hypoxic genes from Cyc8p-Tup1p repression in Saccharomyces cerevisiae. Mol. Microbiol. 40, 1085–1096. doi:10.1046/j.1365-2958.2001.02450.x

Reichard, P., 2002. Ribonucleotide Reductases: The Evolution of Allosteric Regulation. Arch. Biochem. Biophys. 397, 149–155. doi:10.1006/abbi.2001.2637

Reichard, P., 1988. Interactions Between Deoxyribonucleotide And DNA Synthesis. Annu. Rev. Biochem. 57, 349–374. doi:10.1146/annurev.biochem.57.1.349

Ritchie, K.B., Mallory, J.C., Petes, T.D., 1999. Interactions of TLC1 (which encodes the RNA subunit of telomerase), TEL1, and MEC1 in regulating telomere length in the yeast Saccharomyces cerevisiae. Mol. Cell. Biol. 19, 6065–6075. doi:10.1128/MCB.19.9.6065

Rouse, J., Jackson, S.P., 2002. Lcd1p recruits Mec1p to DNA lesions in vitro and in vivo. Mol. Cell 9, 857–869.

Rufiange, A., Jacques, P.E., Bhat, W., Robert, F., Nourani, A., 2007. Genome-Wide Replication- Independent Histone H3 Exchange Occurs Predominantly at Promoters and Implicates H3 K56 Acetylation and Asf1. Mol. Cell 27, 393–405. doi:10.1016/j.molcel.2007.07.011

Santocanale, C., Diffley, J.F., 1998. A Mec1- and Rad53-dependent checkpoint controls late- firing origins of DNA replication. Nature 395, 615–618. doi:10.1038/27001

Sanvisens, N., de Llanos, R., Puig, S., 2013. Function and regulation of yeast ribonucleotide reductase: cell cycle, genotoxic stress, and iron bioavailability. Biomed. J. 36, 51–8. doi:10.4103/2319-4170.110398

Sarafan-Vasseur, N., Lamy, A., Bourguignon, J., Le Pessot, F., Hieter, P., Sesboüé, R., Bastard, C., Frébourg, T., Flaman, J.-M., 2002. Overexpression of B-type cyclins alters chromosomal segregation. Oncogene 21, 2051–2057. doi:10.1038/sj.onc.1205257

Schwabish, M.A., Struhl, K., 2006. Asf1 Mediates Histone Eviction and Deposition during Elongation by RNA Polymerase II. Mol. Cell 22, 415–422. doi:10.1016/j.molcel.2006.03.014

Schwartz, M.F., Duong, J.K., Sun, Z., Morrow, J.S., Pradhan, D., Stern, D.F., 2002. Rad9 phosphorylation sites couple Rad53 to the Saccharomyces cerevisiae DNA damage checkpoint. Mol. Cell 9, 1055–1065.

Shastry, B.S., 1995. Overexpression of genes in health and sickness. A bird’s eye view. Comp. Biochem. Physiol. -- Part B Biochem. Biochem. 112, 1–13.

Sherman, F., 1991. Getting started with yeast. Methods Enzymol. 194, 3–21.

Sogo, J.M., Lopes, M., Foiani, M., 2002. Fork Reversal and ssDNA Accumulation at Stalled Replication Forks Owing to Checkpoint Defects. Science (80-. ). 297, 599–602. doi:10.1126/science.1074023

Sopko, R., Huang, D., Preston, N., Chua, G., Papp, B., Kafadar, K., Snyder, M., Oliver, S.G., Cyert, M., Hughes, T.R., Boone, C., Andrews, B., 2006. Mapping pathways and phenotypes by systematic gene overexpression. Mol. Cell 21, 319–330. doi:10.1016/j.molcel.2005.12.011

Stirling, P.C., Bloom, M.S., Solanki-Patil, T., Smith, S., Sipahimalani, P., Li, Z., Kofoed, M., Ben-Aroya, S., Myung, K., Hieter, P., 2011. The complete spectrum of yeast chromosome instability genes identifies candidate CIN cancer genes and functional roles for astra complex components. PLoS Genet. 7, e1002057. doi:10.1371/journal.pgen.1002057

Strome, E.D., Wu, X., Kimmel, M., Plon, S.E., 2008. Heterozygous screen in Saccharomyces cerevisiae identifies dosage-sensitive genes that affect chromosome stability. Genetics 178, 1193–1207. doi:10.1534/genetics.107.084103

Supek, F., Bosnjak, M., Skunca, N., Smuc, T., 2011. Revigo summarizes and visualizes long lists

of gene ontology terms. PLoS One 6. doi:10.1371/journal.pone.0021800

Sweeney, F.D., Yang, F., Chi, A., Shabanowitz, J., Hunt, D.F., Durocher, D., 2005. Saccharomyces cerevisiae Rad9 acts as a Mec1 adaptor to allow Rad53 activation. Curr. Biol. 15, 1364–1375. doi:10.1016/j.cub.2005.06.063

Symington, L.S., Gautier, J., 2011. Double-Strand Break End Resection and Repair Pathway Choice. Annu. Rev. Genet. 45, 247–271. doi:10.1146/annurev-genet-110410-132435

Tang, H.M.V., Siu, K.L., Wong, C.M., Jin, D.Y., 2009. Loss of yeast peroxiredoxin Tsa1p induces genome instability through activation of the DNA damage checkpoint and elevation of dNTP levels. PLoS Genet. 5, e1000697. doi:10.1371/journal.pgen.1000697

Teixeira, M.C., Monteiro, P.T., Guerreiro, J.F., Gonçalves, J.P., Mira, N.P., Dos Santos, S.C., Cabrito, T.R., Palma, M., Costa, C., Francisco, A.P., Madeira, S.C., Oliveira, A.L., Freitas, A.T., Sá-Correia, I., 2014. The YEASTRACT database: An upgraded information system for the analysis of gene and genomic transcription regulation in Saccharomyces cerevisiae. Nucleic Acids Res. 42, 161–166. doi:10.1093/nar/gkt1015

Toh, G.W.-L., Lowndes, N.F., 2003. Role of the Saccharomyces cerevisiae Rad9 protein in sensing and responding to DNA damage. Biochem. Soc. Trans. 31, 242–6. doi:10.1042/

Tong, A.H.Y., Boone, C., 2005. Synthetic Genetic Array Analysis in Saccharomyces cerevisiae, in: Yeast Protocols, Second Edition. Methods in Molecular Biology. The Humana Press Inc., Totowa, NJ, U.S.A, pp. 171–192.

Travesa, A., Kuo, D., de Bruin, R.A., Kalashnikova, T.I., Guaderrama, M., Thai, K., Aslanian, A., Smolka, M.B., Yates, J.R., Ideker, T., Wittenberg, C., 2012. DNA replication stress differentially regulates G1/S genes via Rad53-dependent inactivation of Nrm1. EMBO J. 31, 1811–1822. doi:10.1038/emboj.2012.28

Tringe, S.G., Willis, J., Liberatore, K.L., Ruby, S.W., 2006. The WTM genes in budding yeast amplify expression of the stress-inducible gene RNR3. Genetics 174, 1215–1228. doi:10.1534/genetics.106.062042

Tsaponina, O., Barsoum, E., Åström, S.U., Chabes, A., 2011. Ixr1 is required for the expression

of the ribonucleotide reductase Rnr1 and maintenance of dNTP pools. PLoS Genet. 7. doi:10.1371/journal.pgen.1002061

Valko, M., Rhodes, C.J., Moncol, J., Izakovic, M., Mazur, M., 2006. Free radicals, metals and antioxidants in oxidative stress-induced cancer. Chem. Biol. Interact. 160, 1–40. doi:10.1016/j.cbi.2005.12.009

Vavouri, T., Semple, J.I., Garcia-Verdugo, R., Lehner, B., 2009. Intrinsic Protein Disorder and Interaction Promiscuity Are Widely Associated with Dosage Sensitivity. Cell 138, 198–208. doi:10.1016/j.cell.2009.04.029

Voegtli, W.C., Ge, J., Perlstein, D.L., Stubbe, J., Rosenzweig, A.C., 2001. Structure of the yeast ribonucleotide reductase Y2Y4 heterodimer. Proc. Natl. Acad. Sci. U. S. A. 98, 10073–8. doi:10.1073/pnas.181336398

Watson, A.D., Edmondson, D.G., Bone, J.R., Mukai, Y., Yu, Y., Du, W., Stillman, D.J., Roth, S.Y., 2000. Ssn6-Tup1 interacts with class I histone deacetylases required for repression. Genes Dev. 14, 2737–2744. doi:10.1101/gad.829100

Weinert, T.A., Kiser, G.L., Hartwell, L.H., 1994. Mitotic checkpoint genes in budding yeast and the dependence of mitosis on DNA replication and repair. Genes Dev. 8, 652–665. doi:10.1101/gad.8.6.652

Wheeler, L.J., Rajagopal, I., Mathews, C.K., 2005. Stimulation of mutagenesis by proportional deoxyribonucleoside triphosphate accumulation in Escherichia coli. DNA Repair (Amst). 4, 1450–1456. doi:10.1016/j.dnarep.2005.09.003

Williams, S.K., Truong, D., Tyler, J.K., 2008. Acetylation in the globular core of histone H3 on lysine-56 promotes chromatin disassembly during transcriptional activation. Proc. Natl. Acad. Sci. U. S. A. 105, 9000–5. doi:10.1073/pnas.0800057105

Winzeler, E.A., Shoemaker, D.D., Astromoff, A., Liang, H., Anderson, K., Andre, B., Bangham, R., Benito, R., Boeke, J.D., Bussey, H., Chu, A.M., Connelly, C., Davis, K., Dietrich, F., Dow, S.W., El Bakkoury, M. El, Foury, F., Friend, S.H., Gentalen, E., Giaever, G., Hegemann, J.H., Jones, T., Laub, M., Liao, H., Liebundguth, N., Lockhart, D.J., Lucau-

Danila, A., Lussier, M., M’Rabet, N., Menard, P., Mittmann, M., Pai, C., Rebischung, C., Revuelta, J.L., Riles, L., Roberts, C.J., Ross-MacDonald, P., Scherens, B., Snyder, M., Sookhai-Mahadeo, S., Storms, R.K., Véronneau, S., Voet, M., Volckaert, G., Ward, T.R., Wysocki, R., Yen, G.S., Yu, K., Zimmermann, K., Philippsen, P., Johnston, M., Davis, R.W., 1999. Functional characterization of the S. cerevisiae genome by gene deletion and parallel analysis. Science 285, 901–906. doi:10.1126/science.285.5429.901

Woolstencroft, R.N., Beilharz, T.H., Cook, M.A., Preiss, T., Durocher, D., Tyers, M., 2006. Ccr4 contributes to tolerance of replication stress through control of CRT1 mRNA poly(A) tail length. J. Cell Sci. 119, 5178–5192. doi:10.1242/jcs.03221

Wybenga-Groot, L.E., Ho, C.S., Sweeney, F.D., Ceccarelli, D.F., McGlade, C.J., Durocher, D., Sicheri, F., 2014. Structural basis of Rad53 kinase activation by dimerization and activation segment exchange. Cell. Signal. 26, 1825–1836. doi:10.1016/j.cellsig.2014.05.004

Xu, X., Vaithiyalingam, S., Glick, G.G., Mordes, D.A., Chazin, W.J., Cortez, D., 2008. The basic cleft of RPA70N binds multiple checkpoint proteins, including RAD9, to regulate ATR signaling. Mol. Cell. Biol. 28, 7345–7353. doi:10.1128/MCB.01079-08

Yao, R., Zhang, Z., An, X., Bucci, B., Perlstein, D.L., Stubbe, J., Huang, M., 2003. Subcellular localization of yeast ribonucleotide reductase regulated by the DNA replication and damage checkpoint pathways. Proc. Natl. Acad. Sci. U. S. A. 100, 6628–6633.

Yoshikawa, K., Tanaka, T., Ida, Y., Furusawa, C., Hirasawa, T., Shimizu, H., 2011. Comprehensive phenotypic analysis of single-gene deletion and overexpression strains of Saccharomyces cerevisiae. Yeast 28, 349–361.

Yuen, K.W.Y., Warren, C.D., Chen, O., Kwok, T., Hieter, P., Spencer, F.A., 2007. Systematic genome instability screens in yeast and their potential relevance to cancer. Proc. Natl. Acad. Sci. U. S. A. 104, 3925–3930.

Zeman, M.K., Cimprich, K.A., 2014. Causes and consequences of replication stress. Nat. Cell Biol. 16, 2–9. doi:10.1038/ncb2897

Zhang, Y., Liu, L., Wu, X., An, X., Stubbe, J., Huang, M., 2011. Investigation of in vivo diferric

tyrosyl radical formation in Saccharomyces cerevisiae Rnr2 protein: Requirement of Rnr4 and contribution of Grx3/4 and Dre2 proteins. J. Biol. Chem. 286, 41499–41509. doi:10.1074/jbc.M111.294074

Zhang, Z., An, X., Yang, K., Perlstein, D.L., Hicks, L., Kelleher, N., Stubbe, J., Huang, M., 2006. Nuclear localization of the Saccharomyces cerevisiae ribonucleotide reductase small subunit requires a karyopherin and a WD40 repeat protein. Proc. Natl. Acad. Sci. U. S. A. 103, 1422–7. doi:10.1073/pnas.0510516103

Zhang, Z., Reese, J.C., 2004a. Ssn6-Tup1 requires the ISW2 complex to position nucleosomes in Saccharomyces cerevisiae. EMBO J. 23, 2246–57. doi:10.1038/sj.emboj.7600227

Zhang, Z., Reese, J.C., 2004b. Redundant mechanisms are used by Ssn6-Tup1 in repressing chromosomal gene transcription in Saccharomyces cerevisiae. J. Biol. Chem. 279, 39240– 39250. doi:10.1074/jbc.M407159200

Zhang, Z., Yang, K., Chen, C.-C., Feser, J., Huang, M., 2007. Role of the C terminus of the ribonucleotide reductase large subunit in enzyme regeneration and its inhibition by Sml1. Proc. Natl. Acad. Sci. U. S. A. 104, 2217–22. doi:10.1073/pnas.0611095104

Zhao, X., Chabes, A., Domkin, V., Thelander, L., Rothstein, R., 2001. The ribonucleotide reductase inhibitor Sml1 is a new target of the Mec1/Rad53 kinase cascade during growth and in response to DNA damage. EMBO J. 20, 3544–3553. doi:10.1093/emboj/20.13.3544

Zhao, X., Georgieva, B., Chabes, A., Domkin, V., Ippel, J.H., Schleucher, J., Wijmenga, S., Thelander, L., Rothstein, R., 2000. Mutational and structural analyses of the ribonucleotide reductase inhibitor Sml1 define its Rnr1 interaction domain whose inactivation allows suppression of mec1 and rad53 lethality. Mol. Cell. Biol. 20, 9076–83. doi:10.1128/MCB.20.23.9076-9083.2000

Zhao, X., Muller, E.G., Rothstein, R., 1998. A Suppressor of Two Essential Checkpoint Genes Identifies a Novel Protein that Negatively Affects dNTP Pools. Mol. Cell 2, 329–340.

Zhao, X., Rothstein, R., 2002. The Dun1 checkpoint kinase phosphorylates and regulates the ribonucleotide reductase inhibitor Sml1. Proc. Natl. Acad. Sci. U. S. A. 99, 3746–3751.

Zhou, Z., Elledge, S.J., 1992. Isolation of crt Mutants Constitutive for Transcription of the DNA Damage Inducible Gene RNR3 in Saccharomyces cerevisiae. Genetics 131, 851–866.

Zhu, J., Heinecke, D., Mulla, W.A., Bradford, W.D., Rubinstein, B., Box, A., Haug, J.S., Li, R., 2015. Single-Cell Based Quantitative Assay of Chromosome Transmission Fidelity. G3 (Bethesda). 5, 1043–56. doi:10.1534/g3.115.017913

Zou, L., Cortez, D., Elledge, S.J., 2002. Regulation of ATR substrate selection by Rad17- dependent loading of Rad9 complexes onto chromatin. Genes Dev. 16, 198–208. doi:10.1101/gad.950302

Zou, L., Elledge, S.J., 2003. Sensing DNA damage through ATRIP recognition of RPA-ssDNA complexes. Science 300, 1542–1548. doi:10.1126/science.1083430

Table 4-1. Biological processes regulated by transcription factors identified in our screen as determined by GO enrichment of targets.

Transcription GO Cluster Genome Regulation Term P-value Targets Associated w/ Term Factor ID Freq. Freq. TIP1/YBR067C:SED1/YDR077W:BGL2/YGR282C: fungal-type cell wall SIM1/YIL123W:PIR3/YKL163W:PIR1/YKL164C: CIN5 Negative 31505 15.90% 2.10% 0.00021 organization MID2/YLR332W:WSC2/YNL283C:SRL1/YOR247W: KRE6/YPR159W

TIP1/YBR067C:SED1/YDR077W:BGL2/YGR282C:

ix external encapsulating SIM1/YIL123W:PIR3/YKL163W:PIR1/YKL164C: CIN5 Negative 45229 15.90% 2.10% 0.00022 structure organization MID2/YLR332W:WSC2/YNL283C:SRL1/YOR247W: 2

KRE6/YPR159W 7 end p ADH5/YBR145W:TDH3/YGR192C:ENO1/YGR254 p

A ribose phosphate W:GND1/YHR183W:ADE16/YLR028C:IMD3/YLR4 CIN5 Negative 19693 14.30% 1.70% 0.00032 metabolic process 32W:PFK2/YMR205C:PFK27/YOL136C:TKL1/YPR0 74C

ADH5/YBR145W:TDH3/YGR192C:ENO1/YGR254 nicotinamide nucleotide CIN5 Negative 46496 11.10% 1.00% 0.00069 W:PFK2/YMR205C:PFK27/YOL136C:TKL1/YPR074 metabolic process C:GND1/YHR183W

ADH5/YBR145W:TDH3/YGR192C:ENO1/YGR254 CIN5 Negative 46031 ADP metabolic process 7.90% 0.40% 0.00165 W:PFK27/YOL136C:PFK2/YMR205C

ADH5/YBR145W:GLY1/YEL046C:TDH3/YGR192C: ENO1/YGR254W:MAL11/YGR289C:SUC2/YIL162 single-organism CIN5 Negative 44712 20.60% 4.70% 0.00177 W:BAT2/YJR148W:ALT1/YLR089C:MID2/YLR332 catabolic process W:PFK2/YMR205C:LAP3/YNL239W:PFK27/YOL13 6C:KES1/YPL145C

73 C C C C C C C C I I I I I I I I N N N N N N N N 5 5 5 5 5 5 5 5

N N N P N N N P o o ega ega ega ega ega ega s s iti iti ti ti ti ti ti ti v v v v v v v v e e e e e e e e

1605 7252 4472 7155 130 609 913 697 2 4 4 4 0 0 2 9

ca py ce c p py ca ca s b ce ca nu c ox r m p e i h o r i r ng og s e o o r ll t r ll t r m r r c i a a pon bohyd bohyd t ono d i ce uv ce l a bo bo d l a w e pound e a e bo i g o s n s ti a n - a s li li s i s s e o l t v ll e n li

i og e e c c s r e d - g

t c i g

o c r r

m s e p p

s o a a i a on r p

ca r r t m g t t n d e

r o o r e e

t e a i o i t ce ce

l a e s pho a s n

ce m bo t

s i i a s s n

za s bo

s s i li

s s

ng ti ph

c li on

o e

11 15 11 11 5 7 7 9 ...... 10 90 00 90 10 90 10 20 % % % % % % % %

0 0 1 2 1 0 1 1 ...... 40 50 20 80 30 50 20 4 0 % % % % % % % %

0 0 0 0 0 0 . . . . . 0 . 4 0007 0055 0029 0024 0040 0021 . . 002 59 0 E 9 1 9 8 2 7 8 5 -

C : NQ W ADH W 5 W ADH K M S T W W A Z G W H 5 ADH ADH C ADH M C C I I W : : RE : P SP I M P : : : : : HDA : GND I DH : : D PF M M PF PF VH X R M PF PF 1 F 1 31 2 / A 1 6 1 A A 5 5 5 5 5 Y / 1 K K K / / / Y K K / 1 / / / / / R Y 2 YK Y / L L / YN Y Y Y Y Y BR 2 2 2 / YD 3 1 YG / I 27 27 1 YK L 11 11 YO P / / / / / L B B B B B / Y Y Y Y YH R R Y L 123 / / L 067 R R R R R / / R R M YO M M YO P 332 YG YG 159 L 026 I 2 L 145 145 145 145 145 R L 043 533 R 4 085 R R R 081 W 056 C 179 1 183 L L 205 205 2 R R W W C C : W W W W W 136 136 05 : C C S 289 289 P W : W : :

S W E C : DDR : W : : : : : W I C C C S NQ T T T T T R R : D :

C C ND : CH G : : : DH DH DH DH DH C C S

X X 3 PF PF PF 1

C C / : : M / 1 B YK S S YD 2 2 I 9 Y K K K / 3 3 3 3 3 UC UC P 1 YK / 1 / / / / / / / YO YN 1 YHR 27 27 27 1 / / YGR YGR YGR YGR YGR Y L YGR R / / YO Y 2 2 163 / / / L M 077 YO YO YO L / / L I Y Y 086 L L 052 283 205 R 192 192 192 192 192 I I W 101 043 120 W L L L L L 120 W 162 162 136 136 136 : C C : W P C C C C C B C C C : - : I CC W : : : : : S G R : : : : E E E E E W W VHR C C C G S ND R 1 L NO NO NO NO NO

CH :

S L P / : : T 2 YK PF PF H 1 1 / K E YG / 1 1 1 1 1 / 9 1 1 YK 1 YO K K L / / / / / / / L / / YGR YGR YGR YGR YG YHR Y Y 1 Y 2 2 164 R / / / J I R Y R M Y Y 282 L L R 066 247 P M M 056 101 R C 25 25 25 25 25 R 20 14 R R : C 07

C 4 4 4 4 4 W 5 W 2 2 : C 5

: 0 0 4 :

74 M M M M M M M C I B B B B B B B N P P P P P P P 5

P P P P P P P N o o o o o o o ega s s s s s s s iti iti iti iti iti iti iti ti v v v v v v v v e e e e e e e e

3459 2240 4484 626 625 626 631 8 9 1 2 9 2 3 0 2

t ce DN DNA ce o G ph ce DNA m DNA o f 1

ll ll ll a ox m / o u A s S c c e ti it l i

y y

- a t t d c m r r o d r c c r ep ep r a

an ti

e l l r a e r ti e e ec p c e n t li li

v s abo

e s p G s ce iti ca ca o e nd pon iti r

1 m o s ll on ti ti / o li ce t e S b r o o n n c s c e

n n s e y

s p

c s a

l ti oce e o

n s

35 15 15 25 16 35 25 7 ...... 90 40 40 60 70 90 80 60 % % % % % % % %

0 6 1 8 0 2 0 1 ...... 90 30 30 70 90 10 80 90 % % % % % % % %

0 0 0 0 0 . . . . . 1 1 4 0002 0014 0007 0002 0056 . . . 48 39 62 0 0 0 E E E 9 5 1 6 9 4 6 7 - - -

C : C DUN R - : W H 4 3 1 W R N S N S C C : R C C : R 6 HO C S C C A W C C / W W : : F F : : F : : F SP R R YN R L C C : : L L D C D C : : : A A A A VH DUN I M M / : R C I I X B B B D L D D R P P YD A 31 4 4 1 1 1 1 F L 1 6 B 6 6 L B B 1 1 1 C A / / C C / / / / C Y Y / R A / YA YA YA YA / / B / / / / 339 5 2 2 YD YG YG YG 45 45 45 YK 2 YN YN YD M L 1 E E / 1 / / 2 6 / Y Y Y 227 YO / / / / R R / / / 1 Y R R R R YN YG YD Y Y Y C L P P P R R R L R R / R 111 111 Y 007 I 007 007 0 R R R L 1 L L : 0 1 1 1 L 009 009 533 C L Y 07 P 01 8 R 120 R R L R L 175 175 09 09 09 056 081 : R L 6 C C 1 Y 312 1 103 103 103 C C C C C W C C C W W 267 F 01 C : : 09 R : : : : C C C W W : 1 H : W HC : M : M W : D : CDC CDC CDC : : F NQ C L L W - : C CC C C C C C T D 7

W I CD CD 1 : : B B : : : : : N XB I I / A G RAD : M - R C R R P Y P P NR 6 6 3 : M P1 7 2 B C CLB F D F F 1 1 / / / P 1 1 1 6 6 6 / YG YG YGR / P1 / / A A A 2 Y YDR 1 / / / Y / L C / / / Y Y M YCR YD YD Y Y Y YKR / / 2 2 2 1 283 Y 51 YGR 6 B P P / J J J / / / / Y 1 R R / 5 YN YN YN L P L L YO L L L Y / / L L / 296 109 109 I YNR R Y 003 014 014 Y 263 194 194 194 C L J 003 003 065 066 L 175 E P 101 043 L L L

R 194 R R W C C 312 312 312 C W W 120 C W W W W W W 120 095 C : : 009 W : : : C C C C M : : RAD : : : YR : W CLB CLB RAD RAD : RAD : : DDR W W W D D : : W DUN DUN M

G S C C W W : C C CH : : : C F CD P

D C C H

6 6 : : 1 X 51 D 5 5 S 1 C L L / / 27 27 27 S - 2 Y Y / / 1 1 9 / 1 W C 1 T I B B Y Y YD / / / / / / P1 YO / J J Y / / / 45 YD YD YHR YK 2 YD 5 5 YK YK YK P P I L L 4 / E / / R R / Y 194 194 Y Y L / / Y R Y L Y 120 120 L L L P L 003 L L L P P P 095 052 LR E 101 101 026 L 20 00 R R 11 1 11 W W L R 1 01 12 12 C C 0 W 5 3 3 3 3 1 : : 1 W C 1 C C

5 0 1 0 0 :

75 M M M M M M B B B B B B P P P P P P

P P P P P P o o o o o o s s s s s s iti iti iti iti iti iti v v v v v v e e e e e e

5127 3049 4471 3465 631 704 6 2 1 1 4 9

o c m b s b c nu h ce f o i h o r i i e ng it o o g r ll r m t c m e o s s a o l yn yn r l c pound e n m ti a e odup y ob i ti c - za o c t t o

o h h s l r a r e ec ti o n e e g s

ti ti m

o e l a

e o c c -

n n e x c m

i p p

on s

b r r m o o i t n

ce ce a

a i n ti s s i s s o ng

33 38 38 35 12 7 ...... 30 50 50 90 70 80 % % % % % %

11 11 7 0 9 0 ...... 60 10 80 50 10 80 % % % % % %

0 0 0 0 0 . . . . . 0 0010 0037 0050 0038 0012 . 00 4 2 8 5 2 6 2

C 1 R R : W 4 : C W R 175 017 03 W W R W R 4 3 1 W R C R R C C C W C C : AD F : F F F F : F D AD D : : : : C R C : : : : A A A A R A C R DUN A R : P R C W W H F P C : A N D N 1 1 1 1 1 1 R L 51 F F L A B 45 S 27 / / / / / : / C

R C R C F A A YA YA YA YA YA YA B C 2 7 2 / M A 1 6 1 1 / Y / / 2 / 1 / 2 6 D Y YK Y YH YN / / / / / / / / 2 1 Y Y Y Y E C R R R R YN R YN YG YD R L P / / YN R E J E Y 21 P 007 0 007 0 0 007 R R L R L L 0 0 L 07 R R 095 P 1 L 175 L R L 1 312 1 / 142 7 7 268 YO L 070 070 L 03 1 1 312 31 9 1 C C C C C C 267 01 3 312 4 09 W : : : : : : W C W C 2 H R W W A HC HC RAD W R W W C W W : C : : AD T L

R R : 074 W CDC : : W : : H : RAD CDC : : A G : RAD C : M M F CDC SP5 R S E N : S : A 2 14 W L 51 CLB 51 SP5 NR S 1 1 C R T / B 2 / / Y C 4 I 2 : / YCR M YCR / / / / 6 H 27 4 YBR 5 Y Y Y 5 51 8 YN 6 B / M / / Y / / 1 / / / S 5 Y Y E E Y E Y L Y YO Y / 1 / / P / T YK R R R YN Y E J 003 Y E P L / L J L 065 065 L 2 YNR 095 125 R 070 L 095 R R E P 312 R 015 / L 194 Y 194 L 125 120 R R 111 R 103 C 017 W W 113 P 120 095 009 W W C W : W C M L W 009 W W : : : C C C : W : A : A M 015

: R CDC RAD C C : : C CDC W : W : : E : D R T T V CDC CD F : C D : W

H CDC L P16 P16 : SP5 P : A C PS L S 1 C O S : B / 2 : W E B 1 6 27 I YD D T 21 1 2 71 / P / / S / / 6 45 YN / YD 2 Y / / I YD YD P Y 1 45 C Y / / Y 4 / / YK YGR / B YOR / J Y L / Y / E 8 Y J P L Y Y / L 2 L L 003 / P L Y R L M R P 194 YO / L E 31 196 L 00 L Y 00 00 12 L L 17 R L R 11 01 10 0 P R 0 2 W 3 0 4 4 1 1 L 5 W 5 7 1 R C 1 4 3 0 5 1 9 :

76 S S P M M M W W HD B B B I I P P P 5 5 1

P P P P P P o o o o o o s s s s s s iti iti iti iti iti iti v v v v v v e e e e e e

1943 5130 613 218 697 92 8 0 9 1 1 4

a nu c ce c DNA ce b c c m r e y o y i o o e ll ll ll t m t c op ok t m s u l a yn s d e pound l bo a e

ob i a l i da v ti a n p r t s h li i

e a c a r s m m e s r

c e s i c a i ti o

e s i s age o p

ti c pon n c -

m r c on t

o p on r pound

ce r a s

s o a n t t e s i ce f a s m

s t i t l e

n o a s u r i ti s

l ng

u o s n

20 13 50 23 38 56 ...... 50 60 00 10 50 40 % % % % % %

12 24 0 3 2 4 ...... 30 30 50 20 30 80 % % % % % %

0 0 0 . . . 0 0 3 0025 0001 0075 . . . 009 007 81 0 E 5 3 7 7 1 4 -

E : A C D R R R W : DUN R W R 03 W 5 C M R 175 017 03 C 120 017 C C W G H P PS P F F : F : M S C T D : : D C C C L A A R A S C E T S : W W C W S D N D C I : : S 26 29 24 1 N D 2 1 1 1 1 R R N 1 1 : 1 W 1 45 C / / / / / / : : D / R C F F / Y YN YN YA YA YA 7 C C B / B A / Y YD 6 YD Y A A P / I 1 6 / ER D D / / YD / / 4 L Y Y YD B / / Y YG B 2 2 Y Y / C C L L R R R R L Y L / / 2 E R L J 124 YN YN 327 192 E J 21 21 007 0 007 286 L / R R 1 R E 0 L 158 Y L L 0 R 194 01 103 2 03 R 131 1 / / 061 7 031 P YO YO 070 L L 63 C W W 9 C C C 111 C R C W W 312 312 4 : : : W : : C D 175 C : : : W H H C W HC N C D D E R R : W : : : S A C D : : T T I R

RNR G : 074 I 074 S R : R W W S E R N RAD : : T A A W S M F E S R T AD P 1 2 P E P16 : : 7 A 2 2 W 4 / 2 NR SP5 L NR / 1

L C C YN / YH 1 / / / / 2 YDR / 35 Y Y 1 YN YN 29 / I 27 : YCR : / Y H 4 27 / H / YN B B M M Y YD / B / L R / E / S Y S Y L L Y R / E YK L 1 1 / 078 T 143 R YK T YD E 003 003 263 E L R / / 067 327 F 065 L 2 YNR YNR 2 124 R R 312 R 070 / / 004 L W Y Y L 125 W 111 L 032 C C C 113 C W W 113 P P C 136 : : : : W : W : C M H L RAD W L CT 009 009 : A : : W C D C D A H 015 C 015 : C : I C C : D : W RAD - S S M DUN : R T : S S M A C D : C W W P E E CDC 1 1 P16 SP5 : 44 C 27 C L : D

B 1 1 4 2 / /

: : YN Y / / : E E B : / / 2 C YD Y D C YN / / YHR 51 Y LR 1 YK S S / / 6 / 45 Y YD L CR P Y / 45 C C / L P Y / YGR B B P Y L R E / 8 8 L 286 192 L Y / D R 2 5 003 065 / / R L Y E 067 143 158 YO YO 113 / / LR L 17 Y 00 R Y 12 L C W 10 10 0 P R P W W 5 C W C 4 : L 5 L 1 C 9 R R

: 1 1 9

: :