A High-Throughput Screen for Novel Genes Involved in Maintaining Genome Stability and the DNA Damage Response Pathway in Saccharomyces Cerevisiae

A high-throughput screen for novel genes involved in maintaining genome stability and the DNA damage response pathway in Saccharomyces cerevisiae

Jason Alexander Hendry

A thesis submitted in conformity with the requirements for the degree of Master of Science

Department of Biochemistry University of Toronto

A high-throughput screen for novel genes involved in maintaining genome stability and the DNA damage response pathway in Saccharomyces cerevisiae

Jason Alexander Hendry Master of Science Department of Biochemistry University of Toronto 2015

Abstract

Oncogenesis is frequently accompanied by rampant genome instability, which fuels genetic heterogeneity and resistance to targeted cancer therapy. I have developed an approach that allows precise, quantitative measurement of genome instability in a high-throughput format in Saccharomyces cerevisiae. My approach takes advantage of the strongly DNA damage-inducible gene RNR3, in conjunction with the Reporter Synthetic Genetic Array methodology, to infer mutants exhibiting genome instability by assaying for increased Rnr3 abundance.

We screen for genome instability across ~4200 non-essential mutant yeast alleles in untreated conditions and in the presence of the DNA damaging agent

MMS. Our results provide broad insights into the cellular processes and pathways required for genome maintenance. Preliminary follow-up work suggests that several genes have bona fide roles in the maintenance of genome stability. In addition, I conducted both screens in their entirety on two, highly orthogonal fluorescence imaging platforms and compared their reproducibility and sensitivity.

Acknowledgements

Erin Styles and Patti Mero helped resolve more Opera issues than I care to remember. Without Lee Zamparo’s crash course on “The Cluster”, the Macbook Pro on which I currently type would still be engaged in image processing. Bryan San Luis kindly quantified zillions of images, containing often both yeast colonies and my fingerprints. Marinella Gebbia rescued my project from an obstacle course worth of robot malfunctions. Without these people, I doubt I’d have even a lonely datum to my name (thank you all).

I am thankful to my committee members, Brenda Andrews and Alex Palazzo, who were always critically attentive and insightful. Their direction and support helped move and expand the scope of the project.

To members of the Brown Lab (& Johnny Tkach): To many of you I am in a deep question-debt. I suppose now is as good a time as any to tell you it will probably never be repaid. Your forbearance was always appreciated and will be remembered for as long as my memory lasts. This work is as much your knowledge as it is my own.

To Grant W. Brown: For he who suffered the burden of my supervision, I teem with thanks. They say the best teachers teach by example, indeed, I attribute much of my growth as a scientist over these past years to your own critical and creative approach to science. I hope one day to have a lab like yours.

To Panayotis Ladas: I hear that Plato was Greek, and a very wise and influential teacher. I was never taught by Plato.

To my Grandfather: Some people like to say that men aren’t made of what they used to be. Though this statement is false (scientifically), I’ve come to understand the truth in it, knowing you. Strong beyond measure, your energy, keen enthusiasm and interest have always inspired me to work harder.

To my Mom and Dad: There is an aphorism relating learning to construction that I can’t quite remember. I do, however, remember clearly the innumerable hours you spent reading with me, correcting my math and helping refine (~rewrite) my English homework. Without your support, I would probably already have a real job, likely in construction. That instead I am still learning, it is to you that I owe my gratitude.

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Table of Contents Introduction ...... 1 1.1 Genome Instability ...... 1 1.3 The DNA Damage Response ...... 4 1.4 The Ribonucleotide diphosphate Reductase (RNR) Complex ...... 10 1.4.1 Cell-cycle and DNA damage regulation ...... 11 1.5 Thesis Objectives ...... 16 2 Materials and Methods ...... 22 2.0 Strains and Media ...... 22 2.1 EVOTEC Opera confocal microscope system (PerkinElmer) ...... 22 2.1.1 High-throughput confocal fluorescence imaging ...... 22 2.1.2 High-throughput confocal fluorescence image analysis ...... 23 2.2 Typhoon Trio Variable Mode Imager (GE Healthcare) ...... 24 2.2.1 Fluorescence scanning ...... 24 2.2.1 Fluorescence scanning analysis ...... 25 2.3 Gene Ontology Enrichment ...... 26 2.4 Precision Recall Analysis ...... 26 2.5 Immunoblotting ...... 26 2.6 Drug Sensitivity Assays ...... 27 3 Results ...... 28 3.1 Screening on the Typhoon (Fluorescence Scanning) ...... 28 3.1.1 Data Analysis ...... 28 3.1.2 Typhoon Results ...... 29 3.2 Screening on the Opera (high-throughput confocal microscopy) ...... 37 3.2.1 Analysis ...... 38 3.2.2 Opera Results ...... 43 3.3 Comparing the Opera and Typhoon ...... 46 3.4 Follow-up on the Typhoon ...... 49 3.4.1 Validating the increase in Rnr3 abundance by immunoblot ...... 50 3.4.2 Drug sensitivity of mutants with increased Rnr3 ...... 51 3.4.3 Rad53 phosphorylation in mutants with increased Rnr3 ...... 54 4 Discussion ...... 57 4.1 Using Rnr3 expression to create a comprehensive genome-wide perspective on genome instability ...... 57 4.2 Comparative analysis of orthogonal fluorescence imaging platforms ...... 58 5 Future Directions ...... 62 5.1 Distinguishing between transcriptional regulators of Rnr3 and mutants causing genome instability ...... 62 5.2 Expanding the effect of mutants to the entire RNR complex and other targets of the DDR ...... 63 5.3 Genome-wide characterization of the consequences of gene overexpression on genome stability ...... 65 5.4 Characterization of mutants with decreased Rnr3 abundance ...... 66 5.5 Examining the role of OLA1 in genome maintenance ...... 68 6 Summary ...... 72

List of Tables Table 1.1. Mutants with increased Rnr3 abundance (Z > 2) in untreated conditions on the Typhoon………………………………….73 Table 1.2. Mutants with decreased Rnr3 abundance (Z > 2) in untreated conditions on the Typhoon………………………………….75 Table 2.1. Mutants with increased Rnr3 abundance (Z > 2) in 0.03% MMS on the Typhoon………………………………………...………….76 Table 2.2. Mutants with decreased Rnr3 abundance (Z > 2) in 0.03% MMS on the Typhoon………………………………………...………….77 Table 3.1. Mutants with increased Rnr3 abundance (Z > 2) in untreated conditions on the Opera……..…………………...…………78 Table 3.2. Mutants with decreased Rnr3 abundance (Z > 2) in untreated conditions on the Opera……..…………………...…………79 Table 4.1. Mutants with increased Rnr3 abundance (Z > 2) in 0.03% MMS on the Opera…………………...…..…………………...…………80 Table 4.2. Mutants with decreased Rnr3 abundance (Z > 2) in 0.03% MMS on the Opera…………………….....…………………...…………81 Table 5.1. Gene Ontology Enrichment (GO) of Mutants with increased Rnr3 abundance (Z > 2) on the Typhoon……..……..……82 Table 5.2. Gene Ontology Enrichment (GO) of Mutants with decreased Rnr3 abundance (Z > 2) on the Typhoon……..……….…84 Table 6.1. Gene Ontology Enrichment (GO) of Mutants with increased Rnr3 abundance (Z > 2) on the Opera……..…………...…85

List of Figures Figure 1. Major cellular effects of the DNA damage response……………………5 Figure 2. The DNA damage reponse regulates ribonucleotide reductase through the kinase Dun1……………………………………………………………………..…13 Figure 3. A high-throughput screen for genome stability and DNA damage response (DDR) genes in S. cerevisiae. ……………………………………...... 19 Figure 4.1 Typhoon screening of Rnr3 abundance enriches for genes associated with transcriptional regulation and genome instability……………………………..30 Figure 4.2. Process enrichment for mutants with decreased Rnr3 abundance on the Typhoon……………………………………………………………………….……36 Figure 5.1. Two computational approaches for correcting time-of-imaging dependent fluctuations in GFP and tdTomato intensity…………………………...39 Figure 5.2. Correlation between replicate Opera screens in Untreated and 0.03% MMS, before and after normalization………………………………………..42 Figure 6. Opera screening of Rnr3 abundance enriches for genes associated with transcriptional regulation and genome instability in untreated conditions but not in MMS. …………………………………….………………………………………44 Figure 7. Comparing reproducibility of the Typhoon and Opera in UT and 0.03% MMS……………………………………….…………………………………………….47 Figure 8. Comparing biological process enrichment of the Typhoon and Opera ……………………………………………….…………………………………………..48 Figure 9. Confirming selected mutants with increased Rnr3 abundance on the Typhoon by immunoblot. ……………………………………………………………..51 Figure 10.1. Sensitivity of selected mutants causing increased Rnr3 on the Typhoon in untreated conditons to replication stress………………………………52 Figure 10.2. Sensitivity of selected mutants causing increased Rnr3 in 0.03% MMS on the Typhoon to replication stress………………………………………….53 Figure 11. Assessing levels of Rad53 phosphorylation in selected mutants with increased Rnr3 abundance on the Typhoon………………………………………..55 Figure 12. Summary of validation and characterization of selected mutants with increased Rnr3 on the Typhoon……………………………………………………...56

List of Abbreviations BP Biological Process (re. Gene Ontology) CC Cellular Component (re. Gene Ontology) DDC DNA Damage Checkpoint DDR DNA Damage Response DNA Deoxyribonucleic Acid DSB Double-strand break DSBR Double-stranded breaking repair GFP Green Fluorescent Protein GO Gene Ontology HU Hydroxyurea MF Molecular Function (re. Gene Ontology) MMS Methyl methanesulfonate NER Nucleotide excision repair PCNA Proliferating cell nuclear antigen PRR Post-replication Repair ROS Reactive Oxygen Species SDS sodium dodecyl sulfate UT Untreated UV Ultraviolet

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Introduction

1.1 Genome Instability

Maintaining the stability of the genome is a biological imperative. Almost all living organisms encode their genomes in deoxyribonucleic acid (DNA) polymers. Though DNA is stable relative to other known biopolymers, such as

RNA and protein, it can still suffer a myriad of chemical modifications that compromise the integrity of the information it encodes. Such modifications to

DNA are considered “DNA damage”, and their repair is necessary to prevent potentially deleterious changes in the sequence or structure of a genome.

Sources of DNA damage can be classified as either “endogenous” or

“exogenous” in reference to whether they originate from within or outside of the cell.

There are a variety of endogenous and exogenous sources of DNA damage. Firstly, the intrinsic instability of certain covalent bonds can lead to the spontaneous loss of chemical moieties or bases from DNA. These modifications include the loss of amino groups during base deamination, or the loss of entire bases during depurination and depyrimidation. By-products of normal cellular metabolism can also modify DNA. Most notably, reactive oxygen species (ROS), which are leaked from the mitochondria during oxidative phosphorylation, are capable of inducing base modification as well as the breakage of phosphodiester bonds. In aggregate, it is estimated that endogenous sources of DNA damage can result in as many as 105 DNA lesions per day (Hoeijmakers, 2009).

On top of endogenous DNA damage, there are also several exogenous, or environmental sources of DNA damage. These sources can be physical

(electromagnetic and cosmic radiation) or chemical (mutagens and carcinogens) in nature. The most ubiquitous source of exogenous DNA damage is UV- radiation emitted from the sun, which, when absorbed by DNA, causes dimerization of adjacent pyrimidine nucleotides through the formation of a cyclobutyl ring (Friedberg, Walker, & Siede, 1995). Ionizing radiation can also lead to DNA damage, through either the direct radiolysis of DNA or through the radiolysis of water, producing ROS (Friedberg et al., 1995; Valko, Rhodes,

Moncol, Izakovic, & Mazur, 2006). Chemical compounds represent another major source of exogenous damage. These compounds often take the form of mutagens or carcinogens and cause diverse modifications to DNA. Alkylating agents exploit nucleophilic nitrogen and oxygen atoms found in DNA bases by adding alkyl groups (Friedberg et al., 1995). Bi-functional alkylating agents can lead to DNA “cross-linking”: the generation of new covalent linkages between atoms within DNA. DNA cross-linking can be either intra- or inter-strand, leading to bulky lesions similar to UV-induced pyrimidine dimers, or in the latter case, completely preventing unwinding of DNA (Friedberg et al., 1995). Many of these agents, such the crosslinking agents mitomycin and cisplatin, find use in cancer chemotherapy, where they interfere with DNA synthesis, inducing replicative arrest in rapidly proliferating cancer cells.

DNA replication itself can be a major hazard to genome stability.

Collectively, genome instability associated with replication is often the result of

“replication stress”: the stalling or slowing of the replication machinery during

DNA synthesis (Zeman & Cimprich, 2014). A variety of events cause replication stress, including encounters between the replication fork and repetitive DNA, the transcriptional machinery or RNA/DNA hybrids, instances of insufficient cellular dNTPs, and, most commonly, DNA lesions (Zeman & Cimprich, 2014). Stalled replication forks can lead to extensive tracts of ssDNA, as the replicative helicase continues unwinding DNA in the absence of DNA synthesis, or even replication fork collapse, generating DSBs (Zeman & Cimprich, 2014).

DNA damage leads to genome instability only in the absence of its efficient repair. Depending on the source and form of the damage, genome instability can manifest itself as point mutations, small insertions or deletions, chromosomal translocation, loss or duplication and, in some cases, even in changes in ploidy (Friedberg et al., 1995; Lengauer, Kinzler, & Vogelstein, 1998;

Storchova & Pellman, 2004). Changes in the sequence or structure of the genome are almost always deleterious and so a broad set of proteins have evolved to prevent them. These proteins can be divided into two main functional arms that act to maintain genome stability. The first arm is composed of DNA repair proteins, which act to directly remove or reverse damage. The second arm is the DNA damage response, which coordinates cell cycle progression and DNA replication with DNA repair.

1.3 The DNA Damage Response

The DNA damage response (DDR) is a cell-signaling pathway that coordinates cell cycle progression, including DNA replication and mitosis, with

DNA repair. Its role in regulating the cell cycle has resulted in it also being termed the DNA damage checkpoint (DDC). Analogous to other cellular checkpoints (such as the G1/S checkpoint, which coordinates nutrient levels with growth), the DDC limits cell cycle progression until certain molecular conditions are satisfied. In this case, the DDC is initiated by stretches of single stranded

DNA (ssDNA) and/or double stranded breaks (DSBs) and limits DNA replication and progression through mitosis until they are resolved (Nakada, Matsumoto, &

Sugimoto, 2003; Rouse & Jackson, 2002; Zou & Elledge, 2003).

Both exposed stretches of ssDNA and DSBs are hallmarks of DNA damage. DSBs can be the direct result of gamma-irradiation or chemical agents and are lethal if not repaired (Friedberg et al., 1995; Valko et al., 2006).

Stretches of ssDNA are downstream of DNA damage in many DNA repair pathways, including nucleotide excision repair, homologous recombination and post-replication repair. Furthermore, ssDNA is generated as a product of replication fork stalling, which frequently occurs if the replication machinery encounters damaged DNA (Sogo, Lopes, & Foiani, 2002). In this case, ssDNA results from the helicase component of the replication machinery becoming decoupled from the synthesis activity of DNA polymerase (Byun, Pacek, Yee,

Walter, & Cimprich, 2005; Nedelcheva et al., 2005). The fact that such a

5 diversity of damage events all produce ssDNA make it and DSBs ideal initiating signals for the DDR.

A simplified outline of the structure of the DDR in S. cerevisiae is presented in Figure 1.

Figure 1. Major cellular effects of the DNA damage response. The DNA damage response is initiated by stretches of single-stranded DNA (ssDNA) or double-stranded breaks (DSBs) which activate the kinases Mec1 or Tel1, respectively. Both Mec1 and Tel1 are capable of activating the effector kinase Rad53, which amplifies the signal through trans-autophosphorylation. Additionally, Rad53 targets Pds1, Dbf4 and Dun1 for phosphorylation, mediating an inhibition of sister chromatid separation and origin firing, as well as regulating dexoyribonucleic acid levels via the ribonucleotide reductase complex (RNR).

The apical kinases in the DDR, activated by ssDNA or DSBs, are Mec1

(human ATR) and Tel1 (human ATM), respectively (reviewed in Abraham, 2001;

Bakkenist & Kastan, 2004; Lambert & Carr, 2005). These “sensor kinsases” are both large phosphatidylinositol 3’ kinase-like kinases (PIKKs) that phosphorylate serine and threonine residues at [S/T]Q motifs (Abraham, 2001). In yeast, Mec1 and Tel1 have partially redundant functions, however, Mec1 tends to play a greater role in S and G2 whereas Tel1 functions mainly in G1 and is considered secondary to Mec1 (Allen, Zhou, Siede, Friedberg, & Elledge, 1994; Sanchez et al., 1999). Despite being “sensor kinases”, neither Mec1 nor Tel1 interacts with

DNA directly. Instead, both recognize DNA-protein structures that are generated downstream of DNA lesions. Mec1 is recruited to ssDNA through its interaction with Ddc2 (human ATRIP) which in turn physically interacts with the yeast ssDNA binding protein Replication Protein A (RPA) (Rouse & Jackson, 2002; Zou &

Elledge, 2003). As a result, Mec1 is the primary sensor of stalled replication forks, at which large tracts of ssDNA can be generated (Sogo et al., 2002).

Recruitment of Mec1 to ssDNA is not sufficient for its full activation. Its kinase activity is also stimulated by the 9-1-1 (Mec3-Ddc1-Rad17) clamp which, similar to the sliding clamp PCNA, forms a trimer that is loaded around DNA (Majka,

Niedziela-Majka, & Burgers, 2006). The 9-1-1 clamp is loaded by the alternative clamp loader Rad24-Rfc2-5 specifically at 5’ dsDNA-ssDNA junctions coated with ssDNA, and stimulates Mec1 activity through an unstructured C-terminal domain on Ddc1 in order to activate its kinase activity (Majka et al., 2006; Navadgi-Patil &

Burgers, 2009). In contrast, Tel1 is activated by its recruitment to DSBs (Falck,

Coates, & Jackson, 2005; Nakada et al., 2003). Tel1 is recruited to DSBs by the

MRX (Mre11-Rad50-Xrs2) complex, a multi-subunit nuclease that can bind directly to DNA ends generated at DSBs (Falck et al., 2005; Nakada et al., 2003).

In addition to its role in checkpoint activation by recruitment of Tel1, the MRX complex is also thought to function directly in homologous recombination by resection of blunt DNA ends.

The critical targets of Mec1 and Tel1 are the “effector kinases” Rad53

(human Chk2) and Chk1 (Allen et al., 1994; Sanchez et al., 1999; Sanchez et al.,

1996). Phosphorylation of both Rad53 and Chk1 is dependent on the “signaling mediator” Rad9 (human 53BP1) (Schwartz et al., 2002; Sweeney et al., 2005).

Rad9 is recruited to sites of damage and phosphorylated at [S/T]Q sites by Mec1 and Tel1 (Naiki, Wakayama, Nakada, Matsumoto, & Sugimoto, 2004).

Phosphorylated Rad9 then mediates Mec1 phosphorylation of Rad53 and Chk1 through interactions with these proteins (Sanchez et al., 1999; Schwartz et al.,

2002; Sweeney et al., 2005). While the sensor kinases form complexes that are directly associated with DNA, activated “effector kinases” are capable of diffusing throughout the nucleus and triggering molecular events spatially distant from the site(s) of damage.

Rad53 and Chk1 have a diverse set of phosphorylation targets (greater than 25 direct targets of Rad53 have been identified in phospho-proteomic screens) and as a result the DDR pathway diverges significantly downstream of

8 their activation (Chen, Albuquerque, Liang, Suhandynata, & Zhou, 2010; Smolka,

Albuquerque, Chen, & Zhou, 2007). The most well studied and arguably consequential effects of Rad53 activation include: (1) the inhibition of replication origin firing through the phosphorylation of Dbf4 and Sld3, (2) the inhibition of anaphase via stabilization of Pds1, and (3) the regulation of the ribonucleotide diphosphate reductase complex (RNR) through the phosphorylation of Dun1.

(1) Inhibition of replication origin firing. In S. cerevisiae, replication is initiated at specific sites throughout the genome known as replication origins. At the beginning of G1 phase, several replication-associated proteins are assembled at each origin, “licensing” them for replication initiation in S-phase (Kelly & Brown,

2000). Firing of these origins in S-phase requires the activity of the cyclin- dependent kinase (CDK) which phosphorylates initiation proteins Sld2 and Sld3, driving an essential interaction between them and Dpb11, and the Dbf4- dependent kinase (DDK), which facilitates origin firing by directly phosphorylating a subunit of the MCM replicative helicase (Sheu & Stillman, 2010). In response to DNA damage, Rad53 interacts with and phosphorylates Dbf4, inactivating

DDK and preventing helicase phosphorylation (Lopez-Mosqueda et al., 2010;

Zegerman & Diffley, 2010). In addition, Rad53 extensively phosphorylates the C- terminal domain of Sld3, inhibiting its interaction with Dpb11 (Lopez-Mosqueda et al., 2010; Zegerman & Diffley, 2010). As a result, origin firing is inhibited, providing time for DNA damage to be resolved before continued replication.

(2) Inhibition of anaphase. Sister chromatids are held in association during S- phase by the action of cohesin, a scaffolding protein complex that physically links the two chromosomes (Skibbens, 2009). During the anaphase portion of mitosis, a subunit of cohesin is cleaved allowing tension across the spindle pole body to drive the separation of sister chromatids. In S. cerevisiae, this event is mediated by the protease separin, named Esp1, which cleaves the cohesin subunit Scc1

(Uhlmann, Lottspeich, & Nasmyth, 1999). For the majority of the cell cycle, Esp1 is tightly bound by the anaphase inhibiting protein Pds1 (Cohen-Fix, Peters,

Kirschner, & Koshland, 1996). Upon entry into anaphase, the anaphase- promoting complex (APC), in conjunction with Cdc20, binds and ubiquitinates

Pds1 resulting in its degradation (Hilioti, Chung, Mochizuki, Hardy, & Cohen-Fix,

2001). In the presence of DNA damage, Rad53 and Chk1 inhibit Pds1 degradation through distinct mechanisms. Chk1 acts by directly phosphorylating

Pds1, whereas Rad53 inhibits the interaction between Cdc20 and Pds1, together preventing its ubiquitination by the APC (Agarwal, Tang, Yu, & Cohen-Fix, 2003;

Sanchez et al., 1999). As a result, cells experiencing DNA damage arrest at anaphase in what is termed the G2/M checkpoint, and avoid the segregation of damaged chromosomes.

(3) Regulation of ribonucleotide reductase. In addition to regulating cell cycle progression, the DNA damage response increases cellular dNTP levels. This increase in dNTP levels is achieved by increasing the activity of the ribonucleotide diphosphate reductase (RNR) complex and is critical for survival of

10 damage (Chabes et al., 2003). Regulation of RNR takes a variety of forms as is explained in the next section.

1.4 The Ribonucleotide diphosphate Reductase (RNR) Complex

Ribonucleotide diphosphate reductase (RNR) is a highly conserved enzyme complex that catalyzes the rate-limiting step of dNTP synthesis – the conversion of NDPs to dNDPs. In S. cerevisiae, four genes encode the RNR complex: RNR1, RNR2, RNR3 and RNR4. Two of these genes (RNR1 and

RNR3) encode large subunits (~100 kDa) and two encode small subunits (RNR2 and RNR4, ~45 kDa). This is distinct from most other eukaryotes – in which only one form of the large and small subunit exist – and is thought to be the result of a whole-genome duplication event in the evolutionary history of S. cerevisiae

(Dietrich et al., 2004). In support of this hypothesis, corresponding forms of the large and small subunits share striking sequence similarity. However, whereas both RNR2 and RNR4 have retained functionality, RNR1 is the primary functional form of the large subunit. Indeed, while RNR1 is essential, the deletion of RNR3 has no known phenotype – consistent with Rnr3 having approximately 1% of the in vitro dNDP synthesis activity of Rnr1 (Domkin, Thelander, & Chabes, 2002).

Despite its lesser activity, RNR3 can function in vivo, as RNR3 overexpression is capable of rescuing the lethality of rnr1Δ mutants (Elledge & Davis, 1990).

A single RNR complex consists of two Rnr1 molecules associated with

Rnr2 and Rnr4, assembled into an α2ββ’ architecture. Each Rnr1 molecule contains a catalytic site and two allosteric regulatory sites (Peter Reichard, 2002;

Sanvisens, de Llanos, & Puig, 2013). The first allosteric site is termed the activity site and regulates the overall rate of catalysis, ensuring an appropriate balance between dNTPs (necessary to synthesize DNA) and NTPs (necessary to synthesize mRNA). It is bound by ATP, which stimulates catalysis, and dATP, which inhibits catalysis (Peter Reichard, 2002; Sanvisens et al., 2013). The second allosteric site, termed the specificity site, modulates the specificity of

RNR for each type of nucleotide, allowing RNR to respond to individual dNTP concentrations (Peter Reichard, 2002). It is bound by dATP, dTTP and dGTP, which promote CDP, GDP and ADP reduction, respectively (Peter Reichard,

2002). Both allosteric sites are critical for normal nucleotide metabolism, since not only the overall concentration but also the relative proportion of each nucleotide is critical for DNA replication fidelity (Kunz et al., 1994; P. Reichard,

1988). Indeed, both an excessive or unbalanced pool of dNTPs results in pronounced increases in mutation rate (Kunz et al., 1994; P. Reichard, 1988;

Wheeler, Rajagopal, & Mathews, 2005).

The primary function of the small RNR subunits is in the co-ordination of a

3+ stable diferric tyrosyl radical co-factor (Fe 2 Y) that is necessary for catalysis.

Interestingly, in yeast, Rnr4 lacks several conserved amino acids rendering it unable to bind the diferric tyrosyl co-factor and instead supports the correct folding and binding of the co-factor by Rnr2 (Huang & Elledge, 1997; Wang et al.,

1997; Y. Zhang et al., 2011). Both small subunits are essential.

1.4.1 Cell-cycle and DNA damage regulation

The RNR complex is regulated during the cell-cycle and in response to

DNA damage. RNR activity is maximal during S-phase to accommodate the increased demand for dNTPs made by DNA replication (Elledge, Zhou, Allen, &

Navas, 1993). This increase in activity is at least partially a result of the increased mRNA levels of RNR complex during S-phase. The RNR genes, along with other genes essential for DNA synthesis, are induced at the G1/S transition by the MBF transcription factor (Dirick, Moll, Auer, & Nasmyth, 1992; Iyer et al.,

2001; Koch, Moll, Neuberg, Ahorn, & Nasmyth, 1993; Lowndes, Johnson,

Breeden, & Johnston, 1992). In particular, RNR1 and RNR2 exhibit a 10- and 2- fold increase in mRNA levels during S-phase relative to G1 levels, respectively

(Elledge et al., 1993).

RNR is also regulated in response to DNA damage. Many DNA repair pathways, such as nucleotide excision repair (NER), base excision repair (BER) and repair by homologous recombination (HR) involve a DNA synthesis step and thus consume dNTPs. Indeed, in S.cerevisiase, survival of DNA damage has been shown to be directly dependent on increased dNTP levels that are the result of RNR induction (Chabes et al., 2003).

The induction of RNR in response to DNA damage is facilitated by the protein kinase Dun1. In response to DNA damage, Dun1 is phosphorylated and activated by Mec1 and Rad53. Active Dun1 increases RNR activity by impinging upon three modes of RNR regulation: (1) regulation of RNR by an inhibitory

13 protein interaction, (2) regulation of RNR localization and (3) regulation of RNR transcription.

Figure 2. The DNA damage response regulates RNR through the kinase Dun1. (a) Crt1 binds to damage response elements (DREs) in the promoter regions of RNR2, RNR3 and RNR4 and promotes a repressive chromatin structure. Upon DDR activation, Dun1 hyperphosphorylates Crt1 causing its dissociation from DNA. (b) Sml1 binds to and inhibits Rnr1 activity. In response to DNA damage and during S-phase, it is phosphorylated and targeted for degradation by Dun1. (c) Similar to in (b), Dun1 phosphorylates and targets Dif1 for degradation upon damage. In the absence of damage, Dif1 sequesters Rnr2-Rnr4 in the nucleus limiting its association with Rnr1.

(1) Regulation of RNR activity by an inhibitory protein interaction. In unperturbed conditions, Rnr1 is bound by Sml1, a small protein (104 AA) that inhibits Rnr1 activity by preventing regenerative reduction of the catalytic site cysteines (Z. Zhang, Yang, Chen, Feser, & Huang, 2007; Zhao et al., 2000).

Sml1 is phosphorylated by active Dun1 driving its degradation and allowing for increased activity of Rnr1 (Zhao, Chabes, Domkin, Thelander, & Rothstein, 2001;

Zhao & Rothstein, 2002) (Fig. 2a). Impressively, deletion of SML1 can rescue the lethality of mec1∆ and rad53∆ mutants, suggesting that derepression of RNR is the essential activity of the DNA damage response (Zhao, Muller, & Rothstein,

1998).

(2) Regulation of RNR localization. The large subunit of the RNR complex,

Rnr1, is localized to the cytoplasm under unperturbed conditions, whereas the small subunits (Rnr2 and Rnr4) form a heterodimer that is localized to the nucleus. Localization of the Rnr2-Rnr4 heterodimer to the nucleus is dependent on Dif1, which directly binds and drives its nuclear import (Yang David Lee,

Wang, Stubbe, & Elledge, 2008). Once in the nucleus, the Rnr2-Rnr4 heterodimer associates with the nuclear anchor protein Wtm1, limiting its export

(Y. D. Lee & Elledge, 2006; Z. Zhang et al., 2006). During S-phase or in the presence of DNA damage, Rnr2-Rnr4 relocalizes from the nucleus to the cytoplasm and interact with Rnr1 to constitute an active RNR complex (Yao et al.,

2003) (Fig. 2b). This relocalization is driven by Dun1-dependent phosphorylation

15 of Dif1, which promotes its degradation and prevents the nuclear import of Rnr2-

Rnr4 (Yang David Lee et al., 2008).

(3) Transcriptional Regulation of RNR

All four genes of the RNR complex are induced in response to DNA damage, albeit to different degrees. Of all the subunits, RNR3 shows the most striking induction: being upregulated more than 100-fold at the mRNA level in response to DNA damage (Elledge & Davis, 1990). By comparison, RNR1 is only induced 5-fold, and RNR2 and RNR4 are induced 25- and 10-fold (Elledge &

Davis, 1990; Elledge et al., 1993).

Induction of RNR2, RNR3 and RNR4 is achieved through a mechanism of derepression involving the transcription factor Crt1/Rfx1 (Huang, Zhou, &

Elledge, 1998). Crt1 is a sequence specific DNA binding protein that recognizes a 13bp DNA sequence known as an x-box or damage responsive element (DRE).

At least two DREs are present in the promoters of RNR2, RNR3 and RNR4, as well as in Crt1’s own promoter (Huang et al., 1998). RNR3 alone possesses an additional DRE, perhaps partially accounting for its unique regulation. Upon binding to DNA, Crt1 recruits the general transcriptional co-repressor complex

Ssn6-Tup1 (Huang et al., 1998; Z. Zhang & Reese, 2004a). Ssn6-Tup1 is thought to promote a repressive chromatin structure both through repositioning of nucleosomes and through the recruitment of histone deacetylases (Z. Zhang &

Reese, 2004a). Though Ssn6-Tup1 recruitment is necessary for establishment of a repressed chromatin region, it is not sufficient. At least at the RNR3 loci,

16 repressive chromatin formation also requires the ATP-dependent chromatin remodeling activity of the ISW2 complex (Z. Zhang & Reese, 2004b).

In response to DNA damage, Dun1 hyper-phosphorylates Crt1 resulting in its dissociation from DNA and the activation of transcripton (Huang et al., 1998)

(Fig. 2c). Ssn6-Tup1 can also be recruited to the promoters of RNR2, RNR3 and

RNR4 through interaction with another sequence specific DNA binding protein,

Rox1: several Rox1 binding sites have been found in the promoters of these genes and deletion of ROX1 led to an increase in their abundance in a beta- galactosidase activity assay (Klinkenberg, Webb, & Zitomer, 2006).

Transcription of the RNR1 gene is also induced upon DNA damage, although in a manner independent of Crt1 (Huang et al., 1998; Klinkenberg et al.,

2006). Instead, upregulation of RNR1 in response to DNA damage depends on the HMG-box transcription factor Ixr1 (Tsaponina, Barsoum, Astrom, & Chabes,

2011). This induction is independent of Dun1 but still requires Mec1 and Rad53, though neither kinase has been found to directly phosphorylate Ixr1 (Tsaponina et al., 2011).

1.5 Thesis Objectives

A comprehensive description of genes involved in the maintenance of genome stability would be of considerable value, both expanding our understanding of the mechanisms by which genome instability arises and aiding in conceptualizing sources of genome instability in oncogenesis.

Several lines of evidence suggest that RNR3 expression serves as a sensitive and specific indicator of genome instability. Firstly, it is a well- characterized transcriptional target of the DNA damage response (Huang et al.,

1998). RNR3 upregulation in response to exogenous DNA damaging agents like

MMS has been demonstrated at both the mRNA and protein level and depends on known DDR kinases (Huang et al., 1998; Li & Reese, 2001; Tsaponina et al.,

2011). Secondly, mutation of several well-characterized DNA repair and replication genes leads to constitutive expression of RNR3, demonstrating that

RNR3 can be induced by both exogenous (i.e. environmental sources of damage) and endogenous (i.e. genetic perturbation) sources of genome instability (Davidson et al., 2012; Tang, Siu, Wong, & Jin, 2009). Finally, expression of RNR3 is negligible in the absence of perturbation, but it is precipitously (>100-fold at the mRNA level) induced in response to DNA damage

(Elledge et al., 1993). Such a large dynamic range suggests that RNR3 expression can be used to sharply delineate between genome stability and instability. Together, these points promote the view that RNR3 expression is a sensitive and specific hallmark of genome instability in S. cerevisiae.

With this in mind, we sought to adapt RNR3 expression into a reporter system that would facilitate a genome-wide perspective on genes involved in maintaining genome stability in S. cerevisiae. In such a system, individual loss- of-function mutants would be observed for increased Rnr3 abundance. We hypothesized that mutants causing increased RNR3 expression in the absence

18 of exogenous perturbation (untreated conditions) would unveil two classes of genes: (1) those involved in the maintenance of genome stability and (2) those directly regulating RNR3 transcription (Fig. 3). In addition, we hypothesized that assaying for increased Rnr3 abundance in the presence of an exogenous DNA damaging agent (such as MMS) would allow for identification genes functioning specifically in the repair of damage produced by that agent. Moreover, by inducing Rnr3 expression with exogenous damage, we hypothesized that we would also identify mutants that fail to induce Rnr3 (have decreased Rnr3 abundance), and that these genes might function in the DNA damage response pathway.

Use of Rnr3 abundance as a reporter for genome instability has the additional advantage that, due to the advent of several new functional genomic tools, protein abundance is readily amenable to high-throughput assay in S. cerevisiae. The most relevant and promising approach is the Reporter Synthetic

Genetic Array (R-SGA) technology, developed in Andrews Lab at University of

Toronto (Kainth et al., 2009). R-SGA allows high-throughput assay of the abundance of a protein of interest in the context of a yeast mutant collection, such as the yeast non-essential deletion mutant collection.

In order to probe the effect of every non-essential gene deletion on RNR3 expression, the Reporter Synthetic Genetic Array (R-SGA) high-throughput screening methodology was employed (Kainth et al., 2009). R-SGA utilizes the

S. cerevisiae non-essential deletion collection, in combination with automated

Figure 3. A high-throughput screen for genome stability and DNA damage response (DDR) genes in S. cerevisiae. (a) Flow-diagram of screening approach and expected classes of hits. ~4700 non-essential deletion mutants harboring RNR3-GFP and RPL39pr-TdTomato were either left untreated or treated with 0.03% MMS before being subjected to fluorescence imaging on both the Opera and Typhoon. Rnr3 abundance is computed as the log2(Rnr3-GFP/tdTomato) ratio and mutants with significantly increased or decreased Rnr3 are identified. (b) Mutants were imaged on two orthogonal platforms, the Opera and Typhoon. On the Typhoon, a scanning fluoroimager, the GFP and tdTomato intensities are quantified per-colony directly from an agar plate. On the Opera, a high-throughput confocal fluorescence microscope, colonies are transferred to liquid media in a 384-well slide before imaging.

20 yeast genetics and a fluorescent-protein reporter system, to assay the effect of single gene knockouts on the expression of a gene of interest in a high- throughput format (Kainth et al., 2009). Briefly, RNR3 was C-terminally tagged with GFP in a strain containing tdTomato expressed from a constitutive promoter.

This strain was then mated with the entire non-essential deletion collection using the SGA methodology (Tong & Boone, 2006). The result was a collection of strains containing Rnr3-GFP, constitutive RFP expression, and a non-essential gene deletion (xxxΔ). These strains were subjected to fluorescence microscopy to determine their (GFP:RFP) ratio, which reflects the level of RNR3 expression and can be used to identify gene deletions that result in changes in Rnr3 abundance.

A variety of platforms have been developed that support high-throughput fluorescence intensity measurement. Here I focus on two such platforms that can both be used to quantify reporter expression in R-SGA, the Opera High

Content Screening System (PerkinElmer) and the Typhoon Trio Variable Mode

Imager (GE Healthcare). These platforms are of particular note due to their highly orthogonal natures: while the Opera is a high-throughput fluorescence confocal microscope that acquires single-cell information; the Typhoon is a scanning fluorescence imager, acquiring information on the whole-colony level.

Despite the diversity of available platforms, little has been done to compare the performance of such platforms with respect to novel functional genomic approaches like R-SGA. To this end, I performed the R-SGA Rnr3 screen in

21 duplicate on both platforms and compared the reproducibility, sensitivity and specificity of their results.

Objectives in Summary:

(1) Utilize Rnr3’s unique expression profile to generate a global, functional

genomic description of genes whose mutation leads to genome instability

in untreated conditions and in the context of replication stress (MMS).

(2) Identify novel genes involved in the maintenance of genome stability and

DNA damage response pathway.

(3) Compare the overall performance of two distinct fluorescence-imaging

platforms: the PerkinElmer Opera, a high-throughput fluorescence

confocal microscope, and the Typhoon, a fluorescence scanner.

2 Materials and Methods

2.0 Strains and Media

GBY691 (MATα RNR3::GFP[HIS3MX] leu2∆0 his3∆1 met15∆0 lyp1∆ RLP39pr-

TdTomato::CaURA3::can1∆::STE2pr-LEU2) was crossed to the yeast non- essential deletion collection using standard SGA procedures (Giaever et al.,

2002; Tong & Boone, 2006). Unless otherwise indicated, all experiments used standard media and growth conditions (Sherman, 2002).

2.1 EVOTEC Opera confocal microscope system (PerkinElmer)

2.1.1 High-throughput confocal fluorescence imaging

The collection of mutant strains generated by crossing GBY691 to the yeast non-essential deletion collection by SGA was grown overnight to saturation

(~24hr) in 96-well format in liquid YNB media and then subcultured, also in 96- well format, in low-fluorescence YNB at 30°C and grown to mid-log phase (~ 0.3-

0.8 OD ml-1). Cells from four 96-well plates were transferred to a 384-well slide using the Liquidator 96-channel pipette to a final density of 0.045 OD ml-1 and allowed to settle for ~30mins before imaging. Six images, each from a different location, were taken in both the green (405/488/640 primary dichroic, 540/75 emission band-pass filter, 800ms exposure) and red channels (405/561/640 primary dichroic, 600/40 band-pass filter, 800ms exposure) of each well of a 384- well slide. Imaging of a single 384-well slide, corresponding to the acquisition of

2304 images, took approximately 51 minutes. After imaging, each slide was

23 removed from the microscope and treated with 0.03% methyl methanesulfonate

(MMS, Sigma) for 2hr at 30°C before being re-imaged.

2.1.2 High-throughput confocal fluorescence image analysis

To quantify Rnr3 abundance, .flex images acquired from the Opera were converted to .TIFF files using ImageJ (http://imagej.nih.gov/ij/). .TIFF files were uploaded unto the Banting/Best Computer Cluster, which hosts 54 distributed computer nodes and over 500 CPUs. I used the open source software

CellProfiler (Carpenter et al., 2006) to generate a pipeline

“MyCellProfilerPipeline.cp” for image analysis.

The result of the CellProfiler pipeline was to convert each .TIFF file into a

.csv containing 42 single-cell parameters for each cell contained within the image. I combined the 2304 .csv files corresponding to each 384-well slide using the R script “csvRbind.R” to generate a .csv file for every 384-well slide. These files contained information on every cell imaged on the slide, and were typically between one-hundred to three-hundred thousand rows. I then used the script

“perGene_Summary.R” to convert the single-cell data to summary data (mean, median, standard deviation, cell count, etc.) for each well of the 384-well slide

(corresponding to a single deletion mutant). I then implemented LOESS and

Nearest-WT normalizations using “perGene_LOESS.R” or

“perGene_WTnorm.R”. Finally, 384-well slides corresponding to each replicate screen were combined and then averaged using “perGene_Combine.R”.

2.1.2.1 LOESS Normalization

In the LOESS normalization method, a scatterplot is generated of each

384-well slide, where the x-axis is arranged by the order in which each well is imaged, and the y-axis is the log2(Rnr3-GFP/tdTomato) ratio of each well.

Locally weighted robust scatterplot smoothing is performed on this plot, using a span value of 0.5, in order to correct for trends with respect to imaging order on each slide (Cleveland, 1979).

2.1.2.2 Nearest-WT Normalization

The Nearest-WT method takes advantage of the 76 wild-type strains (see genotype table) present around the border of each 384-well slide. Among the

WT strains, I defined those with a Z-score < |2| (relative to only WT strains), a cell count > 20 cells and a mean cell size > 500 pixels as “high-confidence” WT.

I normalized each well of a 384-well slide to its nearest high-confidence WT strain, which is determined in relation to the order of well-imaging. This method works on the reasoning that high-confidence WT mutants should capture trends in intensity with respect to the order of imaging; high-confidence WT mutants are expected, in the absence of such trends, to have all the same value. I named this method the “Nearest-WT” method.

2.2 Typhoon Trio Variable Mode Imager (GE Healthcare)

2.2.1 Fluorescence scanning

The collection of mutant strains generated by crossing GBY691 to the yeast non-essential deletion collection by SGA was pinned onto fresh solid media, either SD/MSG –His –Leu –Ura –Arg –Lys + G418 (untreated) or YPD +

0.03% MMS. Plates were grown for 19hrs at 30°C and then allowed to dry at room temperature in a laminar flow hood for ~30mins prior to imaging. Eight plates were arranged at a time on the Typhoon and images were acquired with the following settings: Tray, “User Select”; Pixel size, 100 microns; Focal plane,

+3mm; Press sample, no; DIGE, no; with channel 1 set to acquire GFP fluorescence (488nm laser, 520/40 band-pass emission filter) and channel 2 set to acquire tdTomato fluorescence (532nm laser, 610/30 band-pass emission filter). After fluorescence imaging, plates were individually photographed with a

Canon powershot G2 4.0 megapixel digital camera using Remote Capture software.

2.2.1 Fluorescence scanning analysis

Analysis followed essentially what is described in (Kainth et al., 2009).

Briefly, background subtracted GFP and tdTomato intensities were computed for each colony from .GEL images using GenePix Pro version 3.0 software. Colony size information was calculated from individual photographs using Qt

ColonyImager software version 1.0.1 (Boone and Andrews Lab, unpublished software). Colony size information was integrated with files containing GFP and tdTomato fluorescence intensities using the R scripts

“perColony_1aggregateSize.R” and “perColony_2aggregateIntensity.R”.

Colonies with an area of less than 500 pixels were filtered

(“perColony_3filterSize.R”) and LOESS was used to normalize GFP and tdTomato intensities (“perColony_4plateLOESS.R”). Finally, replicates were

26 combined and averaged using “perColony_5replicateCombine.R”. Z-scores were calculated by subtracting each strains log2(Rnr3-GFP/tdTomato) value from the mean value for all strains and dividing by the standard deviation.

2.3 Gene Ontology Enrichment

All Gene Ontology enrichment analysis was done using the R script

“GO_v3.0.R”. The Gene Ontology Annotations Database was downloaded on

December 19th, 2013. “GO_v3.0.R” uses the hypergeometric test to compute p- values for all GO terms within the biological process (BP), molecular function

(MF) and cellular component (CC) ontologies. P-values were calculated relative to all non-essenitial deletion mutants screened. BP ontology terms containing less than 10 genes were omitted from analysis. P-values were adjusted for multiple hypothesis testing using the Benjamini-Hochberg method with a cut-off of FDR < 0.05 used to define significantly enriched terms (Benjamini & Hochberg,

1995).

2.4 Precision Recall Analysis

Precision and recall were calculated using the script

“ComparativeAnalysis-PrecisionRecall.R”.

2.5 Immunoblotting

Strains were grown to mid-log phase (~0.3 – 0.8 OD ml-1) in YPD at 30°C.

Cells were collected and fixed with 10% trichloroacetic acid (Sigma). Cells were lysed by bead-beating and SDS samples were prepared as described (Pellicioli et al., 1999). Proteins were resolved by SDS-polyacrylamide gel electrophoresis

27 with stacking gels containing 4% acrylamide and resolving gels containing 10% acrylamide for anti-GFP and 8.5% acrylamide for anti-Rad53 immunoblots. Gels were transferred to nitrocellulose and blocked in tris buffered saline containing

0.05% Tween-20 supplemented with 5% skim milk powder (Sigma). For Rnr3-

GFP detection, immunoblots were incubated with a 1:5000 dilution of anti-GFP antibody (Roche) overnight at 4°C followed by a 1:5000 dilution of anti-mouse

HRP secondary antibody. For Rad53 detection, immunoblots were incubated with a 1:2000 dilution of anti-Rad53 antibody (abcam, ab104232) overnight at

4°C followed by anti-rabbit HRP (Pierce Chemical). For tubulin detection, immunoblots were incubated with a 1:1000 dilution of anti-Tub1 (Abcam; ab6160) at 4°C overnight followed by a 1:5000 dilution of anti-rat HRP secondary antibody. All antibodies were diluted in blocking solution.

2.6 Drug Sensitivity Assays

Strains of interest were picked from the deletion mutant array and grown in liquid YPD to saturation overnight at 30°C (~16hrs). Strains were diluted to 0.2

OD ml-1 and three addition 10-fold serial dilutions were made. Serial dilutions were spotted onto YPD, or YPD supplemented with MMS or HU using a manual pin tool and grown for 2-3 days before imaging.

3 Results

3.1 Screening on the Typhoon (Fluorescence Scanning)

Rnr3 abundance is sharply and specifically upregulated in response to genome instability. I sought to identify, on a genome-wide scale, genes whose deletion leads to increased Rnr3 abundance, and therefore implicate them in the maintenance of genome stability. Moreover, I wanted to use Rnr3 to gain insight into the repair of specific types of DNA lesions, in particular those produced by the DNA alkylating agent MMS. To this end, I assayed the entire yeast non- essential deletion collection, comprised of ~4700 unique strains, for increased

Rnr3 abundance on the Typhoon using the Reporter Synthetic Genetic Array (R-

SGA) methodology. The collection was imaged in duplicate in both untreated conditions and after being pinned onto plates containing 0.03% MMS.

3.1.1 Data Analysis

Single-colony GFP and tdTomato intensities were extracted using the microarray analysis software GenePix. Those colonies too small to obtain reliable fluorescence intensities were filtered from the data set and LOESS normalization was implemented as described (Kainth et al., 2009). Rnr3 abundance was calculated as the log2(Rnr3-GFP/tdTomato) ratio for each colony and averaged for the two replicates to generate a final value for each strain.

Rnr3 abundances were converted to Z-scores, and mutants with a Z-score greater than two or less than negative two were defined as having significantly increased or decreased Rnr3, respectively.

3.1.2 Typhoon Results

I identified 136 genes that when deleted cause increased Rnr3 in untreated conditions (136/4700 ~2.3% of mutants screened) and 48 that cause decreased Rnr3 (Fig. 4.1a, Table 1.1 & 1.2). Importantly, RFX1/CRT1, the primary transcriptional repressor of Rnr3, exhibited the greatest increase in Rnr3 abundance when deleted (Fig. 4.1a).

In addition to RFX1, several other known repressors of RNR3 transcription caused increased Rnr3 abundance when deleted, including ITC1 and ISW2, which form an ATP-dependent chromatin remodeling complex that repositions nucleosomes over RNR3’s promoter; ROX1, which, similar to RFX1, binds to

RNR3’s promoter and recruits Ssn6-Tup1; and HDA1 and HDA3, two members of a histone deacetylase complex recruited by Ssn6-Tup1 (Klinkenberg et al.,

2006; Tomar, Psathas, Zhang, Zhang, & Reese, 2009; Z. Zhang & Reese,

2004a).

I used Gene Ontology (GO) to characterize the set of mutants with increased Rnr3. I uncovered 10 biological process (BP), 5 molecular function

(MF) and 4 cellular component (CC) GO terms with a significant enrichment (p <

0.05) among the mutants with increased Rnr3 (Table 5.1). The greatest enriched

CC term was “nucleus” (GO:0005634) with 69/136 (51%) mutants with increased

Rnr3 being annotated as residing in the nucleus (compared to only 27% of mutants screened). Other significant CC terms included “nuclear replication fork”

(GO:0043596) and “nuclear chromosome”, as well as “Cul8-RING ubiquitin ligase

Figure 4.1 Typhoon screening of Rnr3 abundance enriches for genes associated with transcriptional regulation and genome instability. (a) Distribution of Rnr3 abundance across the entire non-essential deletion collection in untreated conditions. Rnr3 abundance is measured as log2(Rnr3-GFP/tdTomato) ratio and expressed as a Z-score. Mutants with a Z-score greater than 2 or less than -2 are defined as having increased or decreased Rnr3, respectively. Top hit, RFX1, is indicated. (b) Magnification of mutants with increased Rnr3 (RFX1 not included for clarity). Mutants associated with the Gene Ontology (GO) term “Transcriptional Regulation” (GO:0006355) are coloured in black. Known regulators of Rnr3 transcription are indicated. (c) Same as in (b), but focusing on mutants associated with maintaining genome stability (DNA repair, GO:0006281; Response to DNA damage, GO:0006974; DNA replication, GO:0006260). (d), (e) & (f) Same as (b), (c) & (d) but for Rnr3 abundance measured in 0.03% methyl methanesulfonate (MMS). (g) Overlap between untreated (UT) and MMS screens for mutants with increased and decreased Rnr3. (h) All significantly enriched GO biological processes for the UT and MMS screen are shown. Vertical dashed line indicates the significant p-value threshold of p < 0.05.

31 complex” (GO:0035361), of which all 4 members (MMS1, MMS22, RTT101 and

RTT107) were identified. All five significantly enriched MF terms pertained to

DNA associated activities with the most significantly enriched MF term being

“DNA binding” (GO:0003677). Molecular functions pertinent to repair and replication, such as “helicase activity” (GO:0004386) and “four-way DNA junction binding” (GO:0000400) were also identified.

An increase in the abundance of Rnr3 is expected to result from either disruption of its transcriptional repression or increased genome instability (Fig. 3).

Indeed, all 10 of the GO BP terms enriched among the mutants with increased

Rnr3 abundance were related to either transcriptional regulation or the maintenance of genome stability (Fig. 4.1h).

In order to more comprehensively assess the extent to which genes functioning in the maintenance of genome stability were present among my hits, I aggregated several genome stability-related GO BP terms to consider as a whole. These included DNA repair (GO:0006281), DNA replication

(GO:0006260), and Response to DNA Damage Stimulus (GO:0006974). This resulted in a set of 135 unique non-essential ORFs associated with genome stability, 26 of which had increased Rnr3 abundance in untreated conditions (Fig.

4c, 4h). Among the genome instability genes with increased Rnr3, processes associated with double-stranded break repair (DSBR) seemed to be most prevalent: “DSBR by single-stranded annealing” (GO:00450002) was significantly enriched, and a variety of genes with critical roles in the recognition

32 of resected DNA double stranded breaks, including RAD51, RAD52, RAD54,

RAD55, as well as genes capable of directly identifying broken DNA ends such as YKU80 and MRE11, were all present (Fig. 4.1c). Replication associated genes included factors important for preventing as well as stabilizing stalled replication forks, including RRM3, POL32 and MRC1.

In addition to genome instability associated processes, three transcriptional terms were enriched, which encompassed genes known to regulate Rnr3 (as mentioned) as well as many others (Fig. 4.1b), possibly representing new regulators of Rnr3 transcription. In total 49/136 genes were represented across all the significantly enriched biological process terms, approximately one-third of all mutants with increased Rnr3. The remaining two- thirds of mutants included 27 unknown ORFs and represent putative novel genes that impinge upon Rnr3 regulation or the maintenance of genome stability.

In the presence of 0.03% MMS, I identified 147 mutants with increased

Rnr3 and 24 mutants with significantly decreased Rnr3 (Fig. 4.1d, Table 3.1 &

3.2). Importantly, although those mutants with both increased and decreased

Rnr3 overlap significantly with those from the untreated screen, a larger proportion of genes were only identified in the presence of MMS (86/177 ~ 58% for increased Rnr3; 18/24 ~78% for decreased Rnr3) (Fig. 4.1g).

More cellular component (CC), molecular function (MF) and biological process (BP) terms were enriched in the MMS screen (Table 5.1). In particular, the BP ontology highlighted the differences between hits in the two screens (Fig.

4.1e, f, h). Firstly, fewer genes had functions in transcription and accordingly terms associated with transcriptional regulation were not enriched. Additionally, of the known transcriptional regulators of Rnr3, only RFX1 remained. This is likely a consequence of Rnr3 already being partly de-repressed in MMS, causing the removal of any one transcriptional regulator to have little effect on Rnr3 expression.

In contrast to transcription, mutants with increased Rnr3 in MMS enriched for more terms associated with genome instability. Indeed, of the 9 additional BP terms enriched in MMS, only one: “non-functional rRNA decay” (GO:0070651), lacked a clear connection to genome instability (Fig. 4.1h). Moreover, in instances where both the UT and MMS screen enriched for the same GO BP term associated with genome instability, the MMS screen invariably had a greater enrichment. Together, these data suggest that additional genes are required to maintain genome instability in the presence of MMS.

As hypothesized, genes and processes identified specifically in the MMS screen were associated with known mechanisms of MMS repair. As an example,

MAG1, which specifically recognizes and removes adenine bases methylated by

MMS in the first step of base excision repair (BER), had one of the highest Rnr3 abundances. In addition, the BP terms “post-replication repair” (GO:0006301) and “recombinational repair” (GO:0000725), which together with BER comprise the three primary mechanisms of MMS repair, were both significantly enriched specifically in the MMS screen. These enrichments were driven by genes like

RAD5, UBC13 and MMS2, which form an E3 ubiquitin ligase complex that poly- ubiquitylates PCNA in an early step of error-free post-replication repair, and

PSY3 and SHU1, which function in recombinational repair of stalled replication forks (Ball, Zhang, Cobb, Boone, & Xiao, 2009; Gangavarapu et al., 2006). Other interesting mutants found uniquely in MMS were MPH1, which is thought to drive regression of stalled replication forks, and MMS4 and MUS81, which play an important role in processing of stalled replication forks for homologous recombination (Choi, Szakal, Chen, Branzei, & Zhao, 2010; Osman & Whitby,

2007). Another prominent BP enriched only in the MMS screen was “mitotic sister chromatid cohesion” (GO:0007064): 11/16 genes ascribed to this term were identified, whereas only 4 were identified in UT (<1 gene is expected by chance).

To complement the GO analysis, I assessed whether the untreated and

MMS screens overlapped with genes known to be MMS sensitive (Chang,

Bellaoui, Boone, & Brown, 2002). I found that both screens were highly significantly enriched for MMS sensitive mutants (UT screen, p < 10-14 ; MMS screen, p < 10-37). However, the MMS screen had almost twice as many mutants sensitive to MMS (39 compared with 20 mutants sensitive), consistent with the idea that increased Rnr3 abundance in MMS specifically enriches for genes that function in MMS repair.

I found that 24 mutants had decreased Rnr3 abundance in MMS (Table

3.2). I hypothesized that these genes may function in the DNA damage response

– i.e. are involved in propagating the damage signal to the RNR3 promoter.

Indeed, GO analysis revealed 5 genes with critical roles in the DNA damage response and associated with the GO BP term “DNA Damage Checkpoint”

(GO:0000077) (Fig. 4.2b, Table 5.2). These mutants spanned the entirety of the

DDR, including apical members such as Rad24 and two members of the 9-1-1 complex (Rad17 and Ddc1), as well as the mediator Rad9. Furthermore, this result was specific to MMS – the 48 genes with decreased Rnr3 in UT conditions had enrichment that was limited to processes associated with basal translation

(Fig. 4.2c, Table 5.2).

Figure 4.2. Process enrichment for mutants with decreased Rnr3 abundance on the Typhoon. (a) The 48 mutants with decreased Rnr3 abundance in untreated conditions are shown in red. Mutants annotated with the GO term “Translation” (GO:0006412) are coloured black and labeled. Z-score cut-off for mutants with significantly decreased Rnr3 abundance is indicated by a horizontal dashed line. (b) Same as (a) but for mutants with decreased Rnr3 abundance in MMS. Mutants with decreased Rnr3 are shown in blue, mutants associated with the GO term “DNA Damage Checkpoint” (GO:0000077) are coloured black and labeled. (c) All significantly enriched GO biological process terms (p < 0.05) among mutants with decreased Rnr3 abundance for both the UT and MMS screens presented as a horizontal adjacent bar plot. The untreated screen is significantly enriched for two terms associated with translation. The MMS screen is enriched for three terms associated with DNA repair and the DNA damage checkpoint.

3.2 Screening on the Opera (high-throughput confocal microscopy)

Every experimental system suffers from unique technical artifacts that can compromise data quality if not appropriately addressed. The Typhoon, for example, can suffer from low reproducibility due to variation in agar height and moisture content of individual plates. Though avoidable, controlling for these effects can be difficult in practice. Furthermore, in the context of genomic screens like R-SGA, the degree to which these effects and others alter the final data set is unknown.

I wanted to assess the extent to which the use of alternate imaging platforms influenced data quality, and also provide insight into the reproducibility of different platforms in the context of fluorescence intensity measurements.

Ideally, I would illuminate sources of technical variation on different platforms, increase confidence in my final data set and inform similar studies in the future with regards to optimal platform choice. Therefore, I complemented my

Typhoon-based Rnr3 screen with a second identical screen conducted on a highly orthogonal imaging platform: the PerkinElmer Opera.

The PerkinElmer Opera is a high-throughput confocal fluorescence microscope, capable of automated imaging of 96- and 384-well slides. I used it to screen the entire non-essential deletion collection for Rnr3 abundance in a

384-well format, either before or after a 2hr treatment with 0.03% MMS. Six images were acquired per well, and each screen was completed in duplicate, resulting in an average of 1021 single-cells quantified per mutant in the untreated

38 screen and 1891 single-cells per mutant in the MMS screen. The final data set comprised 129,024 images within which a total of 14,032,396 single-cells were captured. Each image contained a green channel to monitor Rnr3 abundance and a red channel to measure tdTomato expression.

3.2.1 Analysis

I developed an automated image analysis pipeline using the open source software CellProfiler that accurately segmented cells and acquired fluorescence intensity information from both channels (Carpenter et al., 2006). The pipeline incorporated features such as a background illumination correction as well as intensity and shape filters designed to remove dead or incorrectly segmented cells. 384-well slides on the Opera were imaged in a vertical zig-zag pattern, one well at a time, over the course of roughly one hour (Fig. 5.1a). As a result, measurements from well A1 are acquired roughly one hour prior to measurements from well P1. I investigated whether this within-slide time differential had an effect on Rnr3-GFP and tdTomato intensities. I addressed this by plotting the GFP and tdTomato intensities for each well of a given slide arranged along the x-axis by the order in which they were imaged (“image order”). Such a plot can reveal unanticipated effects in intensity as deviations from a linear cluster of points with a slope of zero. In this way, I noted that several of the 384-well slides exhibited clear intensity effects with respect to image order.

Figure 5.1. Two computational approaches for correcting within plate fluctuations in GFP and tdTomato intensity. (a) Schematic of Opera imaging path. 384-well slides are imaged individually in a vertical zig-zag pattern producing 6 images per well (2304 total images) over the course of ~ 60 minutes. (b) Mean integrated GFP and tdTomato intensities for each well (y-axis) ordered on the x-axis from the first to the last well imaged. Plate #1 of the Deletion mutant array (DMA), imaged in untreated conditions, shows a clear spike in GFP and tdTomato intensity visible starting around the 100th well imaged. (c) Same as in (b) but showing DMA Plate #2 imaged in MMS; the GFP intensity increases throughout imaging as cells are exposed to MMS for longer. (d) Correction of DMA Plate #1 as shown in (b). Two approaches, LOESS and Nearest-WT both remove fluctuations in intensity throughout individual plate imaging. Two scatterplots on the left show pre-normalized data; a red solid line indicates the LOESS fit (top-left), red “x”’s indicate high-confidence wild-type strains (bottom-left). Post-normalization is shown in the right two scatterplots. (e) Same as in (d), but for DMA Plate #2 imaged in 0.03% MMS (c).

Two types of intensity effects iwere readily apparent:

Effect (1): In the MMS screen, a gradual increase in the GFP intensity was observed throughout imaging (Fig. 5.1b).

Effect (2): In both screens, “spikes” in both, or either, of the GFP and tdTomato intensities were observed (Fig. 5.1c). Such spikes were clearly visible in 16/28 of the UT 384-well slides and 6/28 of the MMS 384-well slides.

The first effect was an anticipated consequence of the difference in exposure mutants receive to MMS during the one hour imaging time: those exposed to MMS for a greater duration have a higher average Rnr3 abundance.

The second effect was unexpected. Spikes in intensity during imaging could be of several origins. One possibility is that the spikes are a result of a cluster of mutants that effect Rnr3 or tdTomato abundance in a similar fashion (i.e. the spikes are a product of the biology of the mutants). However, in no instance were the spikes found to be reproducible, arguing against a biological explanation for their presence. A technical explanation is therefore more likely, such as inconsistencies in shutter speed or fluctuations in laser intensity.

Effects in individual laser channels result in changes in the log2(Rnr3-

GFP/tdTomato) ratio, which is used as a measure of Rnr3 abundance. Ignoring these effects would lead to a lower quality dataset, as the first effect would cause mutants imaged near the end of the 384-well slide to have higher Rnr3 abundances, and thus be spuriously enriched for hits, whereas the second effect

41 would both lower reproducibility and give aberrant Rnr3 abundance values for mutants located within spikes.

I developed two computational methods, both individually capable of addressing effect (1) and effect (2) (Fig 5.1d, e). The first method implements locally weighted scatterplot smoothing (LOESS) to correct for trends apparent with respect to the imaging order on a slide. In the second method, I took advantage of the 76 wild-type (WT) strains present around the border of each

384-well slide to define a set of “high-confidence” WT strains, which I use to capture and normalize imaging order trends. I named this method the “Nearest-

WT” method.

The correlation between replicate screens in UT conditions was low (R =

0.203) and in MMS was modest (R = 0.544) before normalization by either the

LOESS or the Nearest-WT method. In UT conditions, both LOESS and Nearest-

WT resulted in a greater than 2-fold increase in Pearson’s R, increasing it to

0.505 and 0.46, respectively (Fig 5.2). Both methods also increased the correlation between replicates in MMS, albeit to a much lesser degree: to 0.596 for LOESS and 0.55 for Nearest-WT. The differential performance of the normalizations in UT and MMS is expected, as MMS is dominated by trend (2), which is reproducible, whereas UT is dominated by trend (1), which is not. It should also be noted that in addition to correcting for trends with respect to image order, both methods also have the advantage of setting the median log2(Rnr3-

GFP/tdTomato) ratio very close to zero, allowing direct comparison between

42 plates. However, this feature is not sufficient to account for the improved reproducibility after application of the normalizations, as simply setting the median log2(Rnr3-GFP/tdTomato) ratio of each plate to zero results in only a mild improvement in correlation.

Figure 5.2. Correlation between replicate Opera screens in Untreated and 0.03% MMS, before and after normalization. Two approaches (LOESS, Nearest-WT) for normalizing “time- of-imaging” dependent fluctuations in were applied to unnormalized log2(Rnr3-GFP/tdTomato) ratios in both replicates Opera screens. Both methods improve correlation between replicates.

Since the LOESS normalization performed slightly better than Nearest-WT normalization in both UT and MMS screens, all subsequent analysis was performed using the LOESS-normalized log2(Rnr3-GFP/tdTomato) ratios as a measure of Rnr3 abundance.

3.2.2 Opera Results

I identified 66 mutants with significantly increased Rnr3 and 63 mutants with significantly decreased Rnr3 (Fig. 6a, Table 4.1 & 4.2). Similar to the results on the Typhoon, RFX1 had by far the highest expression of Rnr3 when deleted, with a Z-score of over thirty (Fig. 6a). Moreover, ITC1 and ISW2, ROX1, HDA1 and HDA3 were again all identified, confirming that the R-SGA approach can accurately recover known transcriptional regulators of RNR3 (Fig. 6b).

Using Gene Ontology (GO), I discovered 21 biological process (BP), 7 molecular function (MF) and 6 cellular component (CC) terms were significantly enriched among the mutants with increased Rnr3. The highest enriched CC term was “nucleus” (GO:0005634), with 42/66 (63%) of genes being annotated with a nuclear localization. In addition, several protein complexes were also enriched, including the “Cul8-RING ubiquitin ligase complex” (GO:0035361), which functions in replication repair, and the “CAF-1 complex” (GO:0033186), which functions to remove and reassemble histones during DNA repair and replication

(Myung, Pennaneach, Kats, & Kolodner, 2003). MF terms enriched were largely related to the manipulation of DNA and chromatin: “DNA binding” (GO:0003677),

“chromatin binding” (GO:0003682), “helicase activity” (GO:0004386) and “histone deacetylase activity” (GO:0004407) all had significant p-values.

Figure 6. Opera screening of Rnr3 abundance enriches for genes associated with transcriptional regulation and genome instability in untreated conditions but not in MMS. (a) Distribution of Rnr3 abundance across the entire non-essential deletion collection in untreated conditions. Rnr3 abundance is measured as log2(Rnr3-GFP/tdTomato) ratio and expressed as a Z-score. Mutants with a Z-score greater than 2 or less than -2 are defined as having increased or decreased Rnr3, respectively. (b) Magnification of mutants with increased Rnr3. Mutants associated with the Gene Ontology (GO) term “Transcriptional Regulation” (GO:0006355) are coloured in black. Known regulators of Rnr3 transcription are indicated. (c) Same as in (b), but focusing on mutants associated with maintaining genome stability (DNA repair, GO:0006281; Response to DNA damage, GO:0006974; DNA replication, GO:0006260). (d), (e) & (f) Same as (b), (c) & (d) but for Rnr3 abundance measured in 0.03% methyl methanesulfonate (MMS). (g) Overlap between untreated (UT) and MMS screens for mutants with increased and decreased Rnr3. (h) All significantly enriched GO biological processes for the UT and MMS screen are shown. Vertical dashed line indicates the significant p-value threshold of p < 0.05.

The twenty-one enriched biological processes largely covered terms associated with transcription and the maintenance of genome stability. Major genome instability associated terms included “DNA repair” (GO:0006281), “DNA replication” (GO:0006260) and response to “DNA damage stimulus”

(GO:0006974); 14/66 genes were associated with at least one of these three terms (Fig. 6b, h). The most significantly enriched term associated with genome instability was “negative regulation of transposition, RNA-mediated”

(GO:0010526): 5/7 genes within this term were identified, including ELG1,

MMS1, RTT101, RTT107, and RRT109. Deletion of these genes is known to cause increased Ty1 retrotransposon mobility that can lead to chromosomal rearrangements (Scholes, Banerjee, Bowen, & Curcio, 2001). In addition, several genes associated with efficient and robust replication, such as MRC1,

POL32 and RRM3, were identified.

A large fraction of the genes with increased Rnr3 abundance were associated with gene expression (19/66). Eight of twenty-one BP terms involved transcription or chromatin modification, and the second and third most significantly enriched terms were “regulation of transcription, DNA-dependent”

(GO:0006355) and “chromatin modification” (GO:0016568), respectively. These data suggests that perhaps a larger set of genes are involved in regulating Rnr3 transcription than is currently appreciated.

The screen was also conducted after a two-hour treatment in 0.03% MMS.

In the context of MMS, 83 mutants were identified with increased Rnr3 and 115 mutants were identified with decreased Rnr3 (Fig. 6d, Table 5.1 & 5.2).

Both mutants with increased and decreased Rnr3 in MMS significantly overlap with those from the untreated screen, however, in both cases, the vast majority (~85%) of MMS hits were new (Fig. 6g). Surprisingly, GO term analysis revealed no significantly enriched BP, MF, or CC terms among the mutants with increased Rnr3 in MMS. RFX1 was still identified as having increased Rnr3 abundance, although none of the other known regulators of RNR3 transcription were hits. Within the terms DNA repair, DNA replication and DNA damage stimulus, only five genes were found to have increased Rnr3. None of the genes were known to have specific roles in MMS repair.

3.3 Comparing the Opera and Typhoon

An important point of consideration when selecting an imaging platform for any high-throughput application is its reproducibility. In order to compare the reproducibility of the Opera and Typhoon, both the UT and MMS screens were completed in duplicate on both platforms. Using Pearson’s R as a metric to measure the reproducibility of replicate experiments, I found the Typhoon to be more reproducible than the Opera (Fig. 7). For the untreated screen, the correlation between replicate experiments on the Typhoon was 0.735, whereas the correlation on the Opera was 0.505. The MMS screen produced similar results, with a correlation of 0.726 on the Typhoon and 0.596 on the Opera.

Figure 7. Comparing reproducibility of the Typhoon and Opera in UT and 0.03% MMS. Scatterplots show the log2(Rnr3-GFP/tdTomato) ratios scored in replicate 1 (x-axis) and replicate 2 (y-axis) for all non-essential deletion mutants screened. Mutants falling on the diagonal dashed line (slope = 1, intersect = 0) gave identical values in both replicates, those below the line scored higher in replicate 1 and those above scored higher in replicate 2. The Pearson’s Correlation Co- efficient (R) is given for each scatterplot. Bottom two plots: Opera platform; Top two plots: Typhoon platform; Left two plots: Untreated (UT) conditions; Right two plots: 0.03% methyl methanesulfonate (MMS). The typhoon has a higher correlation in both MMS and UT conditions.

I wanted to assess which of the two imaging platforms more readily recovered genes functioning in the maintenance of genome stability or transcription. I addressed this question using precision-recall analysis: a method that allows for visual comparison of the extent to which an ordered list of genes

(i.e., ranked by Rnr3 abundance) has clustered a set of interesting genes (i.e., genome instability or transcription genes) to the top of the list. I found that for

48 both the UT and MMS screens, the Opera tended to have a higher precision for

Figure 8. Comparing biological process enrichment of the Typhoon and Opera. (a) Precision-recall analysis of Typhoon and Opera datasets in UT and MMS screens. Top panel: True-positives are genes with GO annotations of DNA repair (GO:0006281), Response to DNA damage stimulus (GO:0006974) or DNA replication GO:0006260). Bottom panel: True-positives are genes with the GO annotation Regulation of transcription, DNA-templated (GO:0006355). (b) Hit overlap for mutants with increased Rnr3. (c) Comparing the number of GO terms enriched on the Opera and Typhoon for mutants with increased Rnr3. Number of GO terms with a p < 0.05 within the biological process (BP), molecular function (MF) and cellular component (CC) ontologies are shown. GO terms enriched for both the Opera and Typhoon are highlighted in blue, whereas GO terms unique to either platform are shown in red and grey respectively. TP: True-positives; FP: False-positives.

49 genes associated with transcriptional regulation than the Typhoon. Overall, however, the total number of transcription associated genes with high Rnr3 abundance tended to be relatively low. In contrast, the Typhoon tended to have a higher precision than the Opera for mutants associated with genome instability, to a recall value as large as 0.4 (Fig 8a.). Increased Rnr3 abundance on the

Opera in MMS had little correspondence with increased precision, for either genome instability or transcriptional genes, a result consistent with earlier GO analysis. Also consistent with previous GO analysis, the Typhoon MMS screen had a much greater precision for genome instability genes than the UT screen.

Differences in the total number of GO terms enriched within each ontology

(CC, MF and BP) were also observed. In the untreated screen, the Opera had more terms enriched than the Typhoon for all 3 ontologies, whereas, in MMS only the Typhoon enriched for GO terms (Fig. 8c).

3.4 Follow-up on the Typhoon

The results of my comparative analysis drove me to focus my follow up efforts on genes identified on the Typhoon platform, as it had a higher reproducibility and greater degree of specificity for genes associated with genome stability.

I selected a set of 24 mutants with increased Rnr3 abundance to validate and further characterize. Of these mutants, 12 were selected from the untreated screen and 12 were selected from the MMS screen. Mutants were selected on the basis of three criteria: (1) novelty with respect to a role in genome stability or

Rnr3 regulation, (2) rank-order and (3) by cross-referencing with other relevant high-throughput data sets (DNA-damaging agent sensitivities, chromosome instability phenotypes, localization/abundance changes in response to DNA damage, etc.). The final set of 24 mutants contained 8 readily identifiable human homologs (“top-hit” BLASTp e-values < 10-10) and covered a diversity of biological processes not commonly associated with the maintenance of genome stability.

3.4.1 Validating the increase in Rnr3 abundance by immunoblot

I attempted to validate the set of 24 mutants for an increase in Rnr3 abundance using immunoblotting, a “gold-standard” approach for assessing changes in protein abundance. Mutants were grown to mid-log in rich media and probed for Rnr3-GFP abundance using an anti-GFP antibody. I used rad54∆ as a positive control, as it is a well-known DNA repair gene and scored highly in both screens. I was able to validate five of the mutants selected from the untreated screen and seven mutants selected from the MMS screen by immunoblot (Fig. 9; validated mutants indicated in bold). Importantly, the R-SGA and immunoblot assays are highly divergent in terms of growth conditions (e.g. growth on solid-agar, vs. growth in liquid media; colony vs. mid-log; chronic vs. acute MMS treatment) and so mutants validated in this way are of high confidence. Furthermore, the mutants subjected to immunoblot analysis were selected for novelty and, in general, rank lower than known genome instability mutants, suggesting they will be harder to validate. However, on account of their

51 novelty and the stringency of validation, the mutants validated in this fashion represent promising candidates for further study.

Figure 9. Confirming selected mutants with increased Rnr3 abundance on the Typhoon by immunoblot. (a) Mutants with increased Rnr3 abundance in untreated conditions on the Typhoon were grown to mid logarithmic phase in rich media and probed for Rnr3-GFP abundance by western blot. rad54∆ serves as a positive control and a wild-type (WT) border strain from the deletion mutant array is used as a negative control. Tubulin was blotted as a loading control. % WT was calculated by densitometry and is indicated below. (b) Same as (a) but for mutants with increased Rnr3 abundance in the MMS typhoon screen. Mutants were grown to mid logarithmic phase in rich media and then treated with 0.01% MMS for two hours before protein extracts were collected for western blot.

3.4.2 Drug sensitivity of mutants with increased Rnr3

When mutated, genes that function in the maintenance of genome stability often confer growth defects in the context of DNA damage. A classic example is the RAD genes, which play central roles in a variety of DNA repair mechanisms and were identified by screening for mutants that were sensitive to UV- and gamma-radiation (Cox & Parry, 1968).

I tested the same set of 24 mutants that were subjected to immunoblot analysis for sensitivity to two canonical DNA damaging agents: MMS, and hydroxyurea (HU). HU causes replication fork stalling by decreasing dNTP pools. Of the 24 mutants, one was sensitive to only MMS, three were sensitive to only HU, and six were sensitive to both HU and MMS (Fig. 10.1, Fig. 10.2). Six

Figure 10.1. Sensitivity of selected mutants causing increased Rnr3 on the Typhoon in untreated conditions to replication stress. 12 non-essential mutants with increased Rnr3 abundance on the Typhoon in untreated conditions were assessed for sensitivity to replication stress. Cells were spotted at an equal density and subject to ten-fold serial dilutions from left to right. Mutants indicated in bold exhibit sensitivity to either HU, MMS or both. HU: hydroxyurea.

Figure 10.2. Sensitivity of selected mutants causing increased Rnr3 in 0.03% MMS on the Typhoon to replication stress. 12 non-essential mutants with increased Rnr3 abundance on the Typhoon in untreated conditions were assessed for sensitivity to replication stress. Cells were spotted at an equal density and subject to ten-fold serial dilutions from left to right. Mutants indicated in bold exhibit sensitivity to either HU, MMS or both. HU: hydroxyurea.

54 of these mutants were from the set of 11 mutants that had a validated increased in Rnr3 abundance by immunoblot (Fig. 12). Surprisingly, perhaps, an equal number of mutants from both the untreated and MMS screens were sensitive to

MMS, indicating that within the selected subset of mutants, increased Rnr3 abundance in MMS does not strongly bias for MMS sensitivity.

3.4.3 Rad53 phosphorylation in mutants with increased Rnr3

Rad53 is a central effector kinase in the DNA damage response that undergoes hyperphosphorylation when activated (Sanchez et al., 1996).

Hyperphosphorylation of Rad53 is easily assayed by mobility shift on an anti-

Rad53 immunoblot. However, since Rad53 is the target of a specific phosphatase complex (Pph3-Psy2), its phosphorylation in the absence of exogenous damage is rare, even in the context of known genome stability mutants (O'Neill et al., 2007). Consequently, Rad53 phosphorylation is a stringent indication of DNA damage response activation.

I tested the levels of Rad53 phosphorylation in my set of 24 mutants with increased Rnr3. All mutants were tested for Rad53 phosphorylation levels both before and after a 2hr MMS treatment, as a means to increase the sensitivity of the assay. Only one mutant, ola1∆, had a clear increase in Rad53 phosphorylation after treatment with MMS (Fig. 11).

Figure 11. Assessing levels of Rad53 phosphorylation in selected mutants with increased Rnr3 abundance on the Typhoon. Non-essential deletion mutants with increased Rnr3 abundance in untreated conditions (a) and 0.03% MMS (b) were assayed for increased Rad53 phosphorylation by immunoblot. Mutants were assayed for Rad53 in both untreated conditions and in the presence of 0.01% MMS in order to induce Rad53 phosphorylation. Phosphorylated Rad53 is identified as a slower migrating band in protein extracts probed with anti-Rad53 antibody. rad54∆ is a positive control and his3∆ is a negative control. ola1∆ (indicated in bold) exhibits increased Rad53 phosphorylation.

Figure 12. Summary of validation and characterization of selected mutants with increased Rnr3 on the Typhoon. Selected mutants with increased Rnr3 abundance were validated by western blot and assayed for MMS and HU sensitivity. Mutants from the MMS screen are indicated in red and mutants from the untreated screen are black.

4 Discussion

Genome instability is a major enabler of cancer as it underlies the increased rate of mutation necessary to transition from a normal cell to one with oncogenic capabilities (Hanahan & Weinberg, 2000). Here, I have taken advantage of the cells own ability to sense DNA damage by using Rnr3 expression to detect and quantify genome instability. I have combined this approach with a novel functional genomics methodology, R-SGA, in order to assay genome instability in high-throughput and gain a genome-wide perspective on genome instability in S. cereviase.

4.1 Using Rnr3 expression to create a comprehensive genome-wide perspective on genome instability

I anticipated that mutants with increased Rnr3 abundance in the absence of exogenous damage would likely fall into two classes: (1) those directly regulating RNR3 transcription and (2) those involved in maintaining genome stability. Gene Ontology enrichment analysis strongly supported this idea. With fluorescence scanning, 26 of 136 mutants with increased Rnr3 abundance could be associated with GO terms related to genome instability (DNA repair, DNA replication and Response to DNA damage stimulus). Similarly, the 14 of 66 mutants identified by fluorescence confocal microscopy are associated with genome stability. It was also clear that both platforms were capable of recovering known regulators of Rnr3 transcription, such as RFX1, ITC1 and

ISW2. However, the number of mutants with transcription-related functions and

58 increased Rnr3 abundance extended well beyond what is currently known.

These mutants may represent novel regulators of Rnr3. Alternatively, they could be necessary for the expression of other DNA repair or DNA replication genes critical for the maintenance of genome stability.

I also conducted the R-SGA screen for Rnr3 abundance in the context of an exogenous source of DNA damage, methyl methanesulfonate (MMS). With fluorescence scanning, I was able to identify genes involved in repair processes specific to MMS, such as important players in base excision repair, like MAG1, and in postreplication repair, like the Rad5-Ubc13-Mms2 ubiquitin ligase complex. This demonstrates that the Rnr3 R-SGA screening approach is capable of highlighting agent-specific requirements for genome stability.

Moreover, it suggests that novel genes identified specifically in the MMS screen may function specifically in MMS repair or tolerance.

Though a significant fraction of hits play known roles in genome maintenance, a greater fraction still remains to be characterized. Focusing on fluorescence scanning data, even when all 10 of the biological processes exhibiting significant enrichment in untreated conditions are considered, they only encompass 49 of the 136 (36%) mutants with increased Rnr3. The considerable number of mutants lacking a connection to genome maintenance suggests that plenty remains to be discovered regarding the mechanisms that lead to genome instability.

4.2 Comparative analysis of orthogonal fluorescence imaging platforms

Phenomics studies, involving high-throughput acquisition of fluorescence information, are becoming an increasingly important means of providing new systems-level biological insights. As such, comparing performance of various approaches for fluorescence imaging is of high relevance to the high-throughput community. Two notable and highly orthogonal fluorescence-imaging platforms are the Opera High Content Screening System (PerkinElmer), a high-throughput confocal fluorescence microscope, and the Typhoon Trio Variable Mode Imager

(GE Healthcare), a fluorescence scanner.

In comparison to single-cells imaged in liquid media during logarithmic growth, the imaging of whole-colonies, near saturation on an agar-plate, might seem susceptible to more technical variation. For example, variation in agar- height and also plate moisture can both strongly influence fluorescence measurements and can be difficult to control across a large screen. In addition, the highly heterogenous nature of yeast colonies may contribute noise to a biological measurement. However, whatever the contribution of these effects, the Typhoon performed superior to the Opera, both in terms of reproducibility and enrichment for expected biological processes. Considering reproducibility, replicates on the Typhoon had correlations of 0.735 and 0.726, whereas those on the Opera had values of 0.505 and 0.596. Biologically, the Typhoon had a greater enrichment for genes associated with genome instability: the GO terms

DNA repair and DNA replication both had lower p-values on the Typhoon.

Transcriptionally, both platforms successfully identified a large set of known

60 transcriptional regulators of Rnr3 and, though the Opera identified more transcriptionally associated genes overall, it is currently unclear whether these represent true regulators of Rnr3. The most pronounced difference between the two platforms was evident when the Rnr3 screen was conducted in MMS. While the Typhoon strongly enriched for MMS repair associated process, the Opera failed to enrich for any GO terms.

The Opera fluorescence intensities required two additional normalization steps in order to be informative. Firstly, each image produced by the Opera was subjected to a background illumination correction to mitigate uneven brightness across the field. This was done for both the GFP and tdTomato channels using

CellProfiler. Secondly, I developed two novel normalization methods to correct for fluctuations in intensity that occur during imaging of individual plates. These normalizations, LOESS and Nearest-WT, produced a marked increase in correlation for both the untreated and MMS screens. These normalizations are both relatively easy to implement and are amenable to applications outside of R-

SGA and even confocal fluorescence microscopy. For example, flow-cytometry is frequently used as an alternative method to acquire fluorescence intensity information at a single-cell level in high-throughput. Analogous to the Opera, many flow cytometers perform automated imaging of 96- and 384-well slides, increasing throughput but also incurring a within-plate differences in analysis time. Often, researchers attempt to obviate such differences using experimental approaches, such as fixation. However, not only does this introduced additional

61 manipulation steps and prevent the advantage of live-cell imaging, it also often requires the usage of special fluorescent proteins that are resistant to fixatives.

As each plate is fixed independently, it can also contribute another source of technical variability. The LOESS and Nearest-WT computational methods used here could provide an alternative approach that retains live-cell analysis, and avoids additional manipulation and the use of special fluorescent proteins.

The ability of the Opera to acquire single-cell data comes with both greater computational and experimental costs; the Opera is lower-throughput and more labor intensive. Considering only imaging time, the Opera takes approximately

1hr per 384 mutant strains imaged, whereas the Typhoon can acquire information from 12,1288 mutant strains (8 plates each of 1536 colonies) in 30 minutes (32-fold quicker acquisition). Furthermore, the Opera requires two additional sub-culturing steps that result in two days of added growth time before imaging relative to the Typhoon.

Overall, the Typhoon is technically and practically preferable to the Opera in combination with fluorescence intensity measurements in S. cerevisiae.

However, it should be noted that the Opera maintains the sizeable advantage of being able to acquire single-cell fluorescence images. For applications where protein localization as well as abundance is of interest, the Opera is the only suitable option.

5 Future Directions

5.1 Distinguishing between transcriptional regulators of Rnr3 and mutants causing genome instability

As described, mutants exhibiting increased Rnr3 abundance in untreated conditions may function either as direct transcriptional regulators of Rnr3 or be involved in the maintenance of genome stability. These two possibilities can be distinguished by assaying hits for Rnr3 abundance in the context of a mutation in the DDR pathway that prevents RNR3 derepression in response to DNA damage. One such mutation is deletion of DUN1. Dun1 is a serine-threonine kinase activated downstream of Rad53 and whose primary targets, Sml1, Dif1 and the transcriptional repressor Crt1, all repress dNTP production (Huang et al.,

1998; Yang David Lee et al., 2008; Z. Zhang et al., 2007; Zhao et al., 2001).

Importantly, hyper-phosphorylation of Crt1 by Dun1 results in its dissociation from

DNA and derepression of RNR3 transcription (Huang et al., 1998). A dun1∆ strain therefore lacks the ability to induce RNR3 transcription in the presence of genome instability. This effect results in different predictions for Rnr3 abundance in mutants causing upregulation of Rnr3 by increasing genome instability vs. those causing upregulation by transcriptional regulation when assayed in the context of a dun1∆. mutation Specifically, those mutants acting via genome instability will no longer exhibit increased Rnr3 in a dun1∆ strain. In contrast, mutants causing increased Rnr3 via transcriptional effects will still exhibit increased Rnr3 in a dun1∆ background.

In order to introduce dun1∆ into hits with increased Rnr3 from the untreated screen, traditional SGA methods can be used. A mini-array of ‘hits’ will first be generated, with WT control strains distributed throughout. SGA will be performed to introduce a dun1∆ deletion into each mutant, such that on the final array each colony contains Rnr3-GFP, RPL39pr-tdTomato, xxx∆ and dun1∆.

Rnr3 abundance will then be assayed on the Typhoon and compared to Rnr3 abundance in xxx∆ strains alone. Mutants with a low [Rnr3-GFPdun1∆ xxx∆:Rnr3-

GFPxxx∆] ratio will be defined as putative genome instability mutants, whereas those with a [Rnr3-GFPdun1∆ xxx∆:Rnr3-GFPxxx∆] ~ 1 will be considered putative transcriptional regulators of Rnr3. Importantly, the mini-array will contain several known genome maintenance genes and genes that regulate Rnr3 transcription, which will serve as positive controls in the assay.

5.2 Expanding the effect of mutants to the entire RNR complex and other targets of the DDR

Treatment of S. cerevisiae with exogenous DNA damage, such as MMS or

HU, can result in abundance changes in hundreds of proteins (Tkach et al.,

2012). However, only a handful of these abundance changes are well- characterized effects of DNA damage response activation, such as regulation of the RNR complex, degradation of Sml1, and upregulation of UBI4 (Huang et al.,

1998; Tsaponina et al., 2011; Zhao et al., 2001; Zhao & Rothstein, 2002).

Interestingly, even within this small set well-studied abundance changes, distinct modes of regulation exist. For example, within the RNR complex, RNR2, RNR3

64 and RNR4 are all transcriptional regulated by RFX1 whereas transcriptional regulation of RNR1 is dependent on the HMG transcription factor IXR1 and the transcription of UBI4 depends on stress-response transcription factors MSN2 and

MSN4 (Huang et al., 1998; Simon, Treger, & McEntee, 1999; Tsaponina et al.,

2011). The DDR can also regulate protein abundance post-transcriptionally.

Such is the case with SML1, which is regulated by DDR induced degradation.

The diverse mechanisms by which RNR, UBI4 and SML1 are regulated in response to DNA damage can be leveraged to gain information about mutants causing increased Rnr3 using additional R-SGA assays. A miniarray, similar to that described in section 5.1, can be generated of mutants with increased Rnr3 abundance. Then, analogous to the primary screen, the miniarray can be mated by R-SGA with RNR1-GFP, RNR2-GFP, RNR4-GFP, UBI4-GFP and SML1-GFP and assayed on the Typhoon. In this way, one could determine the generality of each mutant’s effect in high-throughput and gain information that allows inferences regarding its mechanism of action. For example, a mutant functioning transcriptionally, and in pathway with Rfx1, would be expected to alter the abundance of Rnr2 and Rnr4 but not Rnr1, Ubi4 and Sml1. Mutants with a more general role in transcription would affect the entire RNR complex as well as UBI4.

Finally, mutants causing increased genome instability, i.e. by activating the DDR, should alter the abundance of the entire set of genes, since they are all regulated downstream of DDR activation. Such a secondary screen would extend our

65 understanding of hits beyond simply Rnr3, and has advantages in that it is high- throughput and using a platform for which we already have expertise.

5.3 Genome-wide characterization of the consequences of gene overexpression on genome stability

Large-scale genome instability screens to date have exclusively made use of hypomorphic mutant collections, such as the non-essential deletion collection, ts collection, and DamP collection; with the consequences of gene hyperactivity being largely unconsidered. However, the recent development of several plasmid based overexpression collections allows for investigation of the consequences of gene overexpression on genome stability (Gelperin et al., 2005;

Hu et al., 2007; Sopko et al., 2006). The study of gene overexpression in relation to genome instability is particularly pertinent, as genome instability and gene overexpression are very frequent occurrences in oncogensis.

The FLEXGene overexpression collection contains nearly all ORFs available in a sequence-verified, CEN plasmid backbone, under the control of the strongly inducible GAL1/10 promoter (Hu et al., 2007). Though it has recently been used to assay the effects of gene overexpression on growth, it has not been applied to the question of genome instability. The ~6000 ORFs within the collection are available in agar-plate colony arrays as is ideal for use in R-SGA.

To understand the effects of gene overexpression on genome instability, we will assay Rnr3 expression in the context of the FLEXGene collection using the R-SGA approach. In this set up, each colony would contain Rnr3-GFP,

RPL39pr-tdTomato and a gene under the control of the GAL1/10 plasmid.

Overexpression would be induced by replica pinning onto media containing galactose. Since ~15% of genes in the overexpression collection cause a detectable decrease in growth rate, some strains may produce colonies too small for reliable fluorescence scanning (Sopko et al., 2006). However, given the high- throughput nature of the assay, expression time and galactose concentration could be varied to optimize Rnr3-GFP signal and colony-size. “Hits” would be identified by comparison with a distribution of strains containing an empty-vector after multiple experimental replicates.

5.4 Characterization of mutants with decreased Rnr3 abundance

To date, characterization of potentially novel genes involved in genome maintenance or Rnr3 regulation has focused on mutants with increased Rnr3 abundance in untreated or MMS conditions. However, mutants with decreased

Rnr3 abundance in MMS are also of considerable interest. The deletion of these genes results in a failure to induce Rnr3 in the presence of MMS damage, which suggests that they function in the propagation of the DNA damage response signal to the RNR3 promoter. Supporting this interpretation, of the 24 mutants with decreased Rnr3 abundance in MMS on the Typhoon, five are well-known members of the DNA damage response pathway (GO:0000077, p < 2.3×10-5), suggesting that remaining 19 mutants may contain novel DNA damage response pathway genes. Several simple experimental steps can be taken to validate and characterize the 19 novel mutants with decreased Rnr3 abundance in MMS.

Firstly, as with those mutants with increased Rnr3 in MMS, validation can take the form of immunoblot in the context of MMS. The extent to which Rnr3 is induced by MMS in each deletion will be compared to wild-type and mutants with decreased Rnr3 induction will be subjected to further experiments.

Mutants with decreased Rnr3 abundance in MMS may function in the

DDR, but it is also possible that they are directly involved in the transcriptional activation of Rnr3. To distinguish between these two possibilities, Rnr3 abundance in these mutants can be assayed in the context of an rfx1∆ mutant background. The terminal effect of the DDR with respect to RNR3 induction is the hyperphosphorylation and deactivation of Rfx1. Therefore, an rfx1∆ mutant exhibits constitutive Rnr3 expression, in the absence of DDR activity. As such, mutants that compromise DDR signaling should no longer exhibit a decrease in

Rnr3 abundance in the context of an rfx1∆ . On the contrary, those mutants necessary for Rnr3 transcriptional activation should still exhibit decreased Rnr3 in an rfx1∆ mutant background.

Mutants that are classified as putative DDR genes in the rfx1∆ assay can be further characterized in terms of their position within the pathway. This can be achieved by assaying the phosphorylation status of various DDR members spanning the length of the pathway. Phosphorylation of histone H2A, Rad53, and Rfx1, are good first candidates since they are all readily probed for phosphorylation status by immunoblotting. Additionally, mutants could be

68 subjected to flow-cytometry determine whether the DDR-mediated cell cycle arrest in response to MMS is intact.

5.5 Examining the role of OLA1 in genome maintenance

I selected 24 mutants (12 from the untreated screen, 12 from the MMS screen) with increased Rnr3 abundance on the typhoon for validation and characterization. These mutants were selected on the basis of novelty with respect to a function in the maintenance of genome instability. All 24 mutants were tested in three assays: (1) western blot validation of Rnr3 abundance; (2) sensitivity to the DNA damaging agents, HU and MMS; and (3) increased phosphorylation of the central DDR kinase Rad53. Half of the mutants exhibited increased Rnr3 abundance by western blot, corroborating the original observation of increased Rnr3 abundance by R-SGA. Of the 12 mutants with increased Rnr3 abundance in the western blot assay, 7 had clear sensitivity to either HU or MMS, and 4 showed sensitivity to both drugs. All four of these mutants – eaf6∆, gas1∆, ssk1∆ and ola1∆ – represent promising candidates for in-depth follow up analysis. However, ola1∆ is of particular interest, as it was the only mutant (of all 24 mutants tested) with increased Rad53 phosphorylation.

OLA1 (Obg Like ATPase 1) is a relatively small (396AA, 44kDa), highly conserved G protein that was first named and biochemically characterized in

2007 (Koller-Eichhorn et al., 2007). Interestingly, despite being a G protein

(containing a guanine nucleotide binding domain or G domain), OLA1 preferentially binds and hydrolyzes ATP in vitro (Koller-Eichhorn et al., 2007).

Although it is highly conserved (from yeast to human, 54% identity across 98% of the coding sequence, e-value < 10-137), little is known about the cellular role of

OLA1. Recently, however, evidence has emerged suggesting that human OLA1

(hOLA1) may function to maintain genome stability in the context of breast cancer (Matsuzawa et al., 2014). In a study published January of 2014, Ayako

Matsuzawa et al. demonstrated that hOLA1 interacts directly with the amino terminus of breast cancer associated 1 (BRCA1), and also with BRCA1 associated RING domain 1 (BARD1), which is a well characterized BRCA1 interacting protein (Matsuzawa et al., 2014). BRCA1 has roles in DNA damage checkpoint activation and DNA repair and is one of the most frequently mutated genes in breast and ovarian cancer (mutations in BRCA1 account for 5 to 10% of all breast cancers and 15% of all ovarian cancers) (Campeau, Foulkes, &

Tischkowitz, 2008; Pal et al., 2005). The BRCA1/BARD1 heterodimer functions as an E3 ubiquitin ligase at centrosomes, ubiquitylating several centrosome components and thereby regulating mitotic spindle formation (review in Pal et al.,

2005). The authors found that hOLA1 localizes to centrosomes with

BRCA1/BARD1, and knock down of hOLA1 in breast cancer cell lines results in centrosome amplification that is epistatic with knock down of BRCA1 (Matsuzawa et al., 2014). Finally, they note that an E168Q hOLA1 mutant that is found in breast cancer fails to interact with BRCA1 (Matsuzawa et al., 2014).

In yeast, OLA1 is largely uncharacterized. However, two distinct high- throughput studies have found that OLA1 protein abundance increases in

70 response to DNA damage, approximately two-fold after treatment with MMS and six-fold after treatment with hydrogen peroxide (which causes ROS) (Godon et al., 1998; Tkach et al., 2012). These studies, viewed in combination with ola1∆ exhibiting increased Rnr3 abundance, Rad53 hyperphosphorylation, MMS and

HU sensitivity (all from this study), hint that OLA1 may play a role in maintaining genome stability in S. cerevisiae.

I plan to take a two-pronged approach to further pursue the function of

OLA1 in relation to genome stability. Though Ayako Matsuzawa et al. present strong evidence that hOLA1 functions with BRCA1 in centrosome maintenance, they do not address whether hOLA1 functions alongside BRCA1 in any of its other genome maintenance roles. BRCA1 is surprisingly diverse in this regard: it is essential for homologous-recombination, and has several mutually exclusive interacting partners at DNA lesions (such as CtIP, Abraxas, and BRIP1), associating it with different repair and checkpoint complexes (reviewed in Pal et al., 2005). My first effort, therefore, will be to assess whether knock-down of hOLA1 by siRNA in mammalian cells leads to signs of genome instability characteristic of BRCA1’s non-centrosomal functions. Assaying γH2Ax phosphorylation by immunofluorescence is a standard approach, as H2A is locally phosphorylated by ATR (Mec1 homologue) around DNA double-stranded breaks. It is worth noting that this aim has the potential of illuminating the increased Rad53 phosphorylation observed in ola1∆ in yeast, since Rad53 phosphorylation occurs downstream of Mec1. My other effort will be focused on

71 determining whether the centrosome defects detected in mammalian cells are present in yeast. Initial experiments will involve testing for co-localization with spindle-pole proteins in yeast, such as Spc42. Finally, it is worth noting that while hOLA1 is conserved to S. cerevisiae, BRCA1 and BARD1 are not. It would be interesting to systematically query Ola1 interacting partners in S. cerevisiae, to determine whether any individual or combination of them play roles functionally similar to that of BRCA1/BARD1.

However, preliminary efforts on OLA1 suggest that ola1∆ alone is insufficient to cause DNA damage sensitivity. Eight independent ola1∆ clones

(four MATa and four MATα), generated by PCR transformation of a G418 resistance cassette into the OLA1 locus, were tested for DNA damage sensitivity and all exhibited wild-type viability. In addition, a separate ola1∆ acquired from the Boone Lab, containing a nourseothricin resistant cassette at the OLA1 locus, also exhibited wild-type growth in the presence of DNA damage. DNA damage sensitivity was, however, observed in the ola1∆ mutant present on the deletion mutant array that was used for Rnr3 R-SGA screening, suggesting that strain may contain a secondary mutation(s). I propose two possible models for the

DNA damage sensitivity in this strain: (1) the secondary mutation(s) is alone sufficient to cause DNA damage sensitivity or (2) DNA damage sensitivity is the result of a genetic interaction between OLA1 and the secondary mutation(s). In order to distinguish between these two possibilities, I will attempt to rescue DNA damage sensitivity in the original ola1∆ by OLA1 overexpression. If rescue is

72 successful, it indicates that the secondary mutation alone is not sufficient for DNA damage sensitivity, i.e. that DNA damage sensitivity is the result of a genetic interaction with OLA1.

6 Summary

The continued development of high-throughput technologies is facilitating a systems level understanding of cellular biology, integrating the functions of individual genes into larger pathways and networks with coordinated cellular roles. Here, I have applied the high-throughput technology R-SGA to gain a genome-wide perspective on the diversity of pathways and processes critical to the maintenance of genome stability. Many genes have been newly implicated in genome maintenance and several show promise as having a bona fide role in genome stability. Future work will be directed at providing a detailed understanding of the contribution of individual genes of interest to the DNA repair circuitry and genome maintenance. In addition, by taking advantage of the flexibility of the R-SGA approach, a global perspective on the role of gene overexpression in genome stability will be obtained.

Tables

Table 1.1. Mutants with increased Rnr3 abundance (Z > 2) in untreated conditions on the Typhoon

log2 (Rnr3- log2 (Rnr3- Rank Gene Z-score GFP/tdTomato) Rank Gene Z-score GFP/tdTomato) 1 RFX1 15.52 2.94 60 NIT2 3.20 0.61 2 EAF6 9.84 1.86 61 HDA1 3.18 0.60 3 NPR2 8.69 1.65 62 LSM6 3.18 0.60 4 RAD27 8.13 1.54 63 MMM1 3.14 0.59 5 ISW2 7.92 1.50 64 YGR151C 3.13 0.59 6 ISU1 7.74 1.47 65 IML2 3.03 0.57 7 ITC1 7.06 1.34 66 LEO1 3.02 0.57 8 RPS18B 6.50 1.23 67 KIN1 3.00 0.57 9 YKL123W 6.40 1.21 68 SIC1 2.99 0.57 10 PPM2 6.20 1.18 69 RPS9B 2.99 0.57 11 NCE4 6.18 1.17 70 YNL195C 2.99 0.57 12 POL32 6.05 1.15 71 GSH1 2.86 0.54 13 HEX3 6.01 1.14 72 DLT1 2.85 0.54 14 APA2 5.90 1.12 73 HMS1 2.83 0.54 15 SLX8 5.50 1.04 74 SNO2 2.82 0.53 16 MRC1 5.36 1.02 75 RTT101 2.81 0.53 17 RRM3 5.18 0.98 76 PAT1 2.80 0.53 18 RAD51 5.07 0.96 77 YOR062C 2.79 0.53 19 PNT1 5.02 0.95 78 SRB2 2.78 0.53 20 DIA2 4.99 0.94 79 RTT107 2.70 0.51 21 ICE2 4.97 0.94 80 MUB1 2.69 0.51 22 IRA2 4.95 0.94 81 HMO1 2.67 0.50 23 RAD55 4.92 0.93 82 HDA3 2.66 0.50 24 ASF1 4.78 0.91 83 RAD5 2.64 0.50 25 RAD54 4.70 0.89 84 SSF2 2.62 0.50 26 ZIP2 4.64 0.88 85 PRC1 2.62 0.50 27 YCL060C 4.59 0.87 86 PDR1 2.62 0.49 28 YMR009W 4.50 0.85 87 ETR1 2.61 0.49 29 YKE2 4.47 0.85 88 SRO9 2.60 0.49 30 YDL162C 4.44 0.84 89 WSS1 2.59 0.49 31 ARR4 4.35 0.82 90 APQ12 2.57 0.49 32 ROX1 4.34 0.82 91 MMS1 2.57 0.48 33 FMP37 4.21 0.80 92 ARP8 2.50 0.47 34 YJL103C 4.18 0.79 93 YHR003C 2.50 0.47 35 RTT109 4.13 0.78 94 YPL108W 2.48 0.47 36 RAD52 3.97 0.75 95 ZWF1 2.44 0.46 37 SPO21 3.89 0.74 96 RTF1 2.41 0.45 38 MMS22 3.88 0.73 97 YNL100W 2.40 0.45 39 LSM1 3.77 0.71 98 YJL135W 2.38 0.45 40 SGS1 3.65 0.69 99 YLR365W 2.38 0.45 41 VAC17 3.58 0.68 100 YML048W-A 2.36 0.45 42 YBR261C 3.53 0.67 101 SAM2 2.34 0.44 43 MDM35 3.51 0.66 102 GAL7 2.34 0.44 44 YKU80 3.51 0.66 103 UBP13 2.34 0.44 45 CRD1 3.50 0.66 104 AST2 2.34 0.44 46 YHL005C 3.46 0.65 105 YBR028C 2.32 0.44 47 WSC3 3.46 0.65 106 PAD1 2.30 0.43 48 YGR263C 3.43 0.65 107 CEM1 2.30 0.43 49 YIL067C 3.42 0.65 108 YDR474C 2.26 0.43 50 CAF20 3.40 0.64 109 NTC20 2.23 0.42 51 SAC3 3.39 0.64 110 YJR061W 2.21 0.42 52 FCY2 3.33 0.63 111 LYS14 2.20 0.42 53 ZTA1 3.32 0.63 112 MED1 2.19 0.41 54 MRE11 3.26 0.62 113 TOS4 2.19 0.41 55 RCY1 3.23 0.61 114 MSH5 2.19 0.41 56 YLR235C 3.23 0.61 115 SSE1 2.19 0.41 57 MET6 3.22 0.61 116 SKI8 2.17 0.41 58 LIP2 3.21 0.61 117 FYV4 2.17 0.41 59 IPK1 3.21 0.61 118 YGL214W 2.16 0.41

Table 1.1. continued. Mutants with increased Rnr3 abundance (Z > 2) in untreated conditions on the Typhoon

log2 (Rnr3- Rank Gene Z-score GFP/tdTomato) 119 YER084W 2.14 0.40 120 CTF18 2.13 0.40 121 TOF1 2.12 0.40 122 SWD1 2.11 0.40 123 GCV2 2.09 0.40 124 BUD27 2.09 0.39 125 YDJ1 2.09 0.39 126 RAD30 2.09 0.39 127 YGR051C 2.07 0.39 128 HTD2 2.06 0.39 129 YBR063C 2.05 0.39 130 BEM2 2.05 0.39 131 ADE1 2.03 0.38 132 YER030W 2.03 0.38 133 SKN7 2.03 0.38 134 SPT21 2.02 0.38 135 SMK1 2.01 0.38 136 DBR1 2.00 0.38

Table 1.2. Mutants with decreased Rnr3 abundance (Z < -2) in untreated conditions on the Typhoon

log2 (Rnr3- Rank Gene Z-score GFP/tdTomato) -1 MET13 -3.58 -0.68 -2 YNR018W -3.44 -0.65 -3 PIN4 -3.40 -0.65 -4 YPL080C -3.33 -0.63 -5 YIL110W -3.21 -0.61 -6 HCR1 -2.93 -0.56 -7 OMS1 -2.92 -0.56 -8 NEW1 -2.87 -0.55 -9 CYC2 -2.74 -0.52 -10 PTH1 -2.70 -0.52 -11 YOL008W -2.68 -0.51 -12 LPE10 -2.57 -0.49 -13 YLR184W -2.54 -0.48 -14 ATP18 -2.53 -0.48 -15 PUF6 -2.50 -0.48 -16 LIA1 -2.49 -0.47 -17 RPL19A -2.47 -0.47 -18 MRPL1 -2.45 -0.47 -19 YER119C-A -2.43 -0.46 -20 RPL35A -2.43 -0.46 -21 CYT2 -2.40 -0.46 -22 RPL9B -2.36 -0.45 -23 CBP4 -2.36 -0.45 -24 SLM3 -2.35 -0.45 -25 YBR266C -2.33 -0.44 -26 RPL16B -2.30 -0.44 -27 YKL137W -2.25 -0.43 -28 RPS8A -2.25 -0.43 -29 SAP155 -2.20 -0.42 -30 RPL21B -2.18 -0.42 -31 JID1 -2.18 -0.42 -32 YDR128W -2.17 -0.41 -33 PAN2 -2.15 -0.41 -34 MDM38 -2.14 -0.41 -35 RPL6B -2.13 -0.41 -36 SPT8 -2.13 -0.41 -37 AGP1 -2.10 -0.40 -38 YPR044C -2.10 -0.40 -39 SPE3 -2.07 -0.40 -40 SHM2 -2.06 -0.39 -41 TOM71 -2.05 -0.39 -42 PRY2 -2.04 -0.39 -43 YMR193C-A -2.03 -0.39 -44 LEA1 -2.03 -0.39 -45 PSD1 -2.02 -0.39 -46 RPL37A -2.01 -0.38 -47 YER077C -2.00 -0.38 -48 RPL8A -2.00 -0.38

Table 2.1. Mutants with increased Rnr3 abundance (Z > 2) in 0.03% MMS on the Typhoon

log2 (Rnr3- log2 (Rnr3- Rank Gene Z-score GFP/tdTomato) Rank Gene Z-score GFP/tdTomato) 1 NCE4 10.75 3.36 62 YBL009W 3.66 1.14 2 EAF6 7.66 2.39 63 RAD59 3.66 1.14 3 MAG1 7.47 2.33 64 YML131W 3.63 1.13 4 YKL123W 6.99 2.18 65 GLO4 3.60 1.13 5 APA2 6.98 2.18 66 PAT1 3.60 1.12 6 RAD5 6.83 2.13 67 YHL005C 3.55 1.11 7 RAD52 6.80 2.12 68 YER030W 3.55 1.11 8 RAD51 6.78 2.12 69 LIP2 3.49 1.09 9 YPS1 6.41 2.00 70 VPS41 3.49 1.09 10 RAD27 6.37 1.99 71 SSF2 3.46 1.08 11 APE2 6.13 1.91 72 YGR149W 3.45 1.08 12 REX2 6.11 1.91 73 TPS2 3.44 1.08 13 SGS1 6.07 1.90 74 MMS22 3.42 1.07 14 YKU80 5.77 1.80 75 MUB1 3.42 1.07 15 ISU1 5.62 1.75 76 YBR028C 3.41 1.07 16 YGR263C 5.41 1.69 77 DCC1 3.38 1.06 17 RAD55 5.38 1.68 78 SRB2 3.33 1.04 18 SHU1 5.19 1.62 79 YOR062C 3.32 1.04 19 PSR1 5.12 1.60 80 YLR042C 3.31 1.03 20 MRE11 5.10 1.59 81 NPR2 3.13 0.98 21 RTT107 4.95 1.55 82 GSH1 3.13 0.98 22 CTF4 4.89 1.53 83 RRM3 3.11 0.97 23 ASF1 4.89 1.53 84 SAC3 3.04 0.95 24 RAD61 4.88 1.52 85 YNL191W 3.04 0.95 25 ZTA1 4.84 1.51 86 PPH3 3.03 0.95 26 DLT1 4.82 1.51 87 GAS1 3.01 0.94 27 RTT109 4.77 1.49 88 CKB2 3.01 0.94 28 MMS2 4.73 1.48 89 MET18 3.00 0.94 29 YML048W-A 4.69 1.46 90 SSK1 2.94 0.92 30 RTT101 4.67 1.46 91 TOF1 2.93 0.92 31 YGR151C 4.66 1.45 92 NTC20 2.88 0.90 32 MED1 4.57 1.43 93 PSY2 2.87 0.90 33 YLR281C 4.56 1.42 94 KEM1 2.86 0.89 34 SPO21 4.55 1.42 95 SLX4 2.85 0.89 35 YDR336W 4.53 1.42 96 RBG1 2.79 0.87 36 UBC13 4.53 1.41 97 NPT1 2.78 0.87 37 MYO5 4.49 1.40 98 FCY2 2.78 0.87 38 TOP3 4.47 1.40 99 LSM7 2.75 0.86 39 RAD54 4.35 1.36 100 ADE17 2.74 0.86 40 CTF18 4.34 1.36 101 ZIP1 2.68 0.84 41 YNR040W 4.33 1.35 102 DOG2 2.67 0.83 42 DIA2 4.32 1.35 103 YDL162C 2.67 0.83 43 YPL095C 4.27 1.33 104 YGR272C 2.66 0.83 44 YMR206W 4.19 1.31 105 CTF8 2.64 0.82 45 YBR025C 4.19 1.31 106 YBR100W 2.63 0.82 46 SAE2 4.12 1.28 107 SLX8 2.60 0.81 47 CSE2 4.11 1.28 108 PAU4 2.60 0.81 48 PSY3 4.05 1.27 109 ELG1 2.58 0.81 49 RFX1 4.00 1.25 110 PNT1 2.57 0.80 50 MMS1 3.99 1.25 111 YBR099C 2.55 0.80 51 MPH1 3.91 1.22 112 SIP3 2.54 0.79 52 POL32 3.90 1.22 113 DST1 2.53 0.79 53 CHL1 3.88 1.21 114 YDR474C 2.52 0.79 54 YDL034W 3.87 1.21 115 YDR089W 2.49 0.78 55 YLR365W 3.87 1.21 116 VPS24 2.47 0.77 56 PRP12 3.80 1.19 117 DEG1 2.44 0.76 57 KIN1 3.79 1.18 118 MUS81 2.44 0.76 58 YPL197C 3.77 1.18 119 ADK2 2.42 0.76 59 YLR235C 3.75 1.17 120 ARP6 2.41 0.75 60 SHM2 3.72 1.16 121 HMS1 2.40 0.75 61 APS2 3.67 1.14 122 MMS4 2.40 0.75

Table 2.1. continued. Mutants with increased Rnr3 abundance (Z > 2) in 0.03% MMS on the Typhoon

log2 (Rnr3- Rank Gene Z-score GFP/tdTomato) 123 HEX3 2.39 0.75 124 YBR094W 2.39 0.75 125 ASG7 2.36 0.74 126 YOR082C 2.36 0.74 127 YOL053C-A 2.35 0.73 128 SNF4 2.34 0.73 129 PFK2 2.31 0.72 130 SOH1 2.29 0.71 131 MKT1 2.28 0.71 132 SMP1 2.27 0.71 133 YBR063C 2.26 0.71 134 BUD27 2.25 0.70 135 TIF1 2.25 0.70 136 PER1 2.22 0.69 137 THR1 2.22 0.69 138 PRC1 2.21 0.69 139 EST1 2.18 0.68 140 RPN4 2.17 0.68 141 BUD31 2.16 0.68 142 ESC2 2.12 0.66 143 WHI5 2.07 0.65 144 IPK1 2.06 0.64 145 DBR1 2.04 0.64 146 KRE27 2.04 0.64 147 YLR412W 2.04 0.64

Table 2.2. Mutants with decreased Rnr3 abundance (Z < -2) in 0.03% MMS on the Typhoon

log2 (Rnr3- Rank Gene Z-score GFP/tdTomato) -1 YKL137W -4.46 -1.39 -2 RAD24 -3.47 -1.08 -3 SPT8 -3.04 -0.95 -4 YER119C-A -2.77 -0.87 -5 RAD9 -2.75 -0.86 -6 DDC1 -2.68 -0.84 -7 SWI4 -2.63 -0.82 -8 DOT1 -2.50 -0.78 -9 SNF6 -2.41 -0.75 -10 CSG2 -2.39 -0.74 -11 RPL40A -2.35 -0.73 -12 RPS8A -2.31 -0.72 -13 RAD17 -2.31 -0.72 -14 RPS21B -2.30 -0.72 -15 SOY1 -2.28 -0.71 -16 YJL043W -2.24 -0.70 -17 VPS1 -2.23 -0.69 -18 MMM1 -2.22 -0.69 -19 PTK2 -2.15 -0.67 -20 BRE5 -2.14 -0.67 -21 REI1 -2.11 -0.66 -22 BRE1 -2.10 -0.65 -23 RPL19A -2.07 -0.65

Table 3.1. Mutants with increased Rnr3 abundance (Z > 2) in untreated conditions on the Opera

log2 (Rnr3- log2 (Rnr3- Rank Gene Z-score GFP/tdTomato) Rank Gene Z-score GFP/tdTomato) 1 RFX1 30.26 4.00 62 CTF18 2.10 0.26 2 ROX1 6.86 0.90 63 YOR318C 2.05 0.26 3 ISW2 6.26 0.82 64 GTS1 2.04 0.26 4 NUP60 6.17 0.80 65 YJL016W 2.03 0.25 5 ASF1 5.91 0.77 66 YOR105W 2.01 0.25 6 RAD55 5.81 0.76 7 ITC1 5.67 0.74 8 EST3 5.29 0.69 9 RRM3 5.18 0.67 10 ELG1 5.15 0.67 11 RTT101 4.78 0.62 12 RTT107 4.60 0.60 13 RLF2 4.42 0.57 14 RTT109 4.37 0.57 15 HEX3 4.36 0.56 16 MMS1 4.25 0.55 17 BRE1 4.13 0.53 18 MRC1 4.10 0.53 19 PHO2 3.98 0.51 20 PNT1 3.94 0.51 21 HDA3 3.91 0.50 22 LGE1 3.88 0.50 23 YCL060C 3.77 0.49 24 SPT21 3.74 0.48 25 HDA1 3.58 0.46 26 RAD5 3.44 0.44 27 VPS17 3.40 0.44 28 CAC2 3.34 0.43 29 RPL34A 3.34 0.43 30 YDL118W 3.30 0.42 31 ARR4 3.24 0.42 32 HAL9 3.18 0.41 33 RAD54 3.07 0.39 34 HHT2 3.05 0.39 35 SLX8 3.04 0.39 36 CHD1 2.98 0.38 37 POL32 2.98 0.38 38 YKL171W 2.92 0.37 39 YMR153C-A 2.86 0.37 40 MUP1 2.83 0.36 41 YPL199C 2.72 0.35 42 CTF4 2.70 0.34 43 SAP30 2.69 0.34 44 CTI6 2.68 0.34 45 RTT106 2.57 0.33 46 CLB5 2.54 0.32 47 URA7 2.44 0.31 48 PEX32 2.43 0.31 49 YMR073C 2.33 0.29 50 RAD52 2.32 0.29 51 SET2 2.31 0.29 52 YJL017W 2.28 0.29 53 PRE9 2.25 0.28 54 MUB1 2.24 0.28 55 YML053C 2.24 0.28 56 SGS1 2.17 0.27 57 YLR437C 2.14 0.27 58 PDR1 2.13 0.27 59 YNL235C 2.12 0.27 60 YBR032W 2.11 0.27 61 SIP3 2.11 0.27

Table 3.2. Mutants with decreased Rnr3 abundance (Z < -2) in untreated conditions on the Opera

log2 (Rnr3- log2 (Rnr3- Rank Gene Z-score GFP/tdTomato) Rank Gene Z-score GFP/tdTomato) -1 YER119C-A -5.90 -0.80 -59 RPL31B -2.09 -0.29 -2 CBC2 -5.79 -0.78 -60 BOI2 -2.07 -0.29 -3 RPL6B -4.56 -0.62 -61 RPL2A -2.03 -0.29 -4 RPL13A -4.20 -0.57 -62 YLR152C -2.03 -0.28 -5 RPL9B -4.13 -0.56 -63 YJL070C -2.02 -0.28 -6 JJJ1 -4.11 -0.56 -7 RPP1B -4.09 -0.56 -8 PIH1 -4.07 -0.56 -9 RPL19A -4.00 -0.55 -10 MRT4 -4.00 -0.55 -11 RPL8A -3.80 -0.52 -12 YLR184W -3.71 -0.51 -13 YDL062W -3.68 -0.50 -14 PSP2 -3.52 -0.48 -15 RPL36A -3.40 -0.47 -16 RPL21B -3.40 -0.47 -17 RPL43B -3.35 -0.46 -18 YMR193C-A -3.30 -0.45 -19 RPL16A -3.28 -0.45 -20 RPL23A -3.21 -0.44 -21 RPL17B -3.21 -0.44 -22 YMR269W -3.18 -0.44 -23 ORM1 -3.10 -0.43 -24 YIL110W -3.09 -0.42 -25 RPS8A -2.99 -0.41 -26 LEA1 -2.95 -0.41 -27 RPL7B -2.89 -0.40 -28 YOR309C -2.83 -0.39 -29 DBP3 -2.79 -0.39 -30 YNL226W -2.77 -0.38 -31 LRP1 -2.77 -0.38 -32 RPL33B -2.75 -0.38 -33 NOP12 -2.75 -0.38 -34 DAK2 -2.69 -0.37 -35 RPL11B -2.68 -0.37 -36 YEL067C -2.61 -0.36 -37 RPL8B -2.57 -0.36 -38 BIM1 -2.57 -0.36 -39 YBR266C -2.56 -0.36 -40 YPL080C -2.56 -0.35 -41 FAR7 -2.52 -0.35 -42 YSC83 -2.46 -0.34 -43 PHO8 -2.45 -0.34 -44 RPL27B -2.43 -0.34 -45 YIL025C -2.33 -0.32 -46 VPS51 -2.29 -0.32 -47 MUD2 -2.28 -0.32 -48 CGR1 -2.25 -0.31 -49 YKR033C -2.24 -0.31 -50 RPL34B -2.24 -0.31 -51 FET5 -2.24 -0.31 -52 NAM8 -2.22 -0.31 -53 SRL3 -2.19 -0.31 -54 TOP1 -2.16 -0.30 -55 RPL35B -2.15 -0.30 -56 YER034W -2.14 -0.30 -57 YOR291W -2.13 -0.30 -58 LIA1 -2.10 -0.29

Table 4.1. Mutants with increased Rnr3 abundance (Z > 2) in 0.03% MMS on the Opera

log2 (Rnr3- log2 (Rnr3- Rank Gene Z-score GFP/tdTomato) Rank Gene Z-score GFP/tdTomato) 1 SWD1 8.07 2.36 62 FKH2 2.42 0.75 2 REX2 7.66 2.24 63 HDA3 2.41 0.75 3 CAF20 7.33 2.15 64 DCN1 2.40 0.75 4 APA2 7.25 2.13 65 CSE2 2.40 0.75 5 RFX1 7.01 2.06 66 RLF2 2.39 0.75 6 YGR263C 6.73 1.98 67 YOR062C 2.38 0.74 7 YDR474C 6.19 1.83 68 MVP1 2.38 0.74 8 GAL7 6.10 1.80 69 PEX14 2.38 0.74 9 CWP1 6.08 1.79 70 DLT1 2.26 0.71 10 YJL017W 5.83 1.72 71 SLM4 2.19 0.69 11 YLR365W 5.66 1.67 72 NTC20 2.16 0.68 12 YKL171W 5.46 1.62 73 HOG1 2.15 0.68 13 YER030W 5.30 1.57 74 YKU80 2.14 0.67 14 ERP6 5.00 1.49 75 YBR134W 2.12 0.67 15 PDR1 4.96 1.48 76 CLN2 2.12 0.67 16 NCE4 4.86 1.45 77 LAS21 2.11 0.67 17 EAF6 4.68 1.40 78 ERG3 2.10 0.66 18 ROX1 4.38 1.31 79 SIP3 2.10 0.66 19 YBL009W 4.34 1.30 80 PET494 2.06 0.65 20 YCR022C 4.26 1.28 81 PDX1 2.06 0.65 21 MOT3 4.22 1.26 82 YJL211C 2.03 0.64 22 UTR2 4.09 1.23 83 YGL250W 2.02 0.64 23 EST1 4.06 1.22 24 YKL123W 3.90 1.17 25 SPO21 3.89 1.17 26 YPR064W 3.88 1.17 27 YEL033W 3.83 1.15 28 YJL103C 3.70 1.12 29 SKN7 3.61 1.09 30 MYO5 3.60 1.09 31 PNT1 3.58 1.08 32 PAD1 3.55 1.08 33 YPL197C 3.49 1.06 34 TMT1 3.49 1.06 35 YAP1 3.49 1.06 36 YML100W-A 3.44 1.05 37 YGL176C 3.39 1.03 38 AVT3 3.37 1.02 39 ECM31 3.11 0.95 40 APP1 3.10 0.95 41 TOS5 3.05 0.93 42 CHD1 3.05 0.93 43 YPR078C 3.03 0.93 44 APE2 3.02 0.92 45 ZTA1 2.96 0.91 46 HOC1 2.90 0.89 47 ELF1 2.89 0.89 48 YBR280C 2.88 0.88 49 GAD1 2.84 0.87 50 RPL41B 2.83 0.87 51 MSH5 2.77 0.85 52 YBR028C 2.76 0.85 53 PEX1 2.68 0.83 54 HSL1 2.65 0.82 55 PEX4 2.65 0.82 56 IMP2 2.52 0.78 57 PEX2 2.50 0.78 58 ZDS1 2.48 0.77 59 YEL006W 2.47 0.77 60 YMR031C 2.46 0.77 61 YCR079W 2.42 0.75

Table 4.2. Mutants with decreased Rnr3 abundance (Z < -2) in 0.03% MMS on the Opera

log2 (Rnr3- log2 (Rnr3- Rank Gene Z-score GFP/tdTomato) Rank Gene Z-score GFP/tdTomato) -1 CBC2 -6.93 -1.90 -59 VAM3 -2.60 -0.67 -2 CEM1 -5.24 -1.42 -60 ELP2 -2.59 -0.67 -3 RPS1B -5.03 -1.36 -61 BAS1 -2.58 -0.67 -4 ETR1 -4.87 -1.32 -62 YLR255C -2.56 -0.66 -5 SWI4 -4.79 -1.30 -63 NCS6 -2.55 -0.66 -6 OPI3 -4.67 -1.26 -64 TSR2 -2.55 -0.66 -7 RPL6B -4.59 -1.24 -65 THI6 -2.55 -0.66 -8 YDJ1 -4.46 -1.20 -66 YDR049W -2.53 -0.65 -9 LIP2 -4.21 -1.13 -67 ARC18 -2.52 -0.65 -10 YER119C-A -4.14 -1.11 -68 RIM13 -2.50 -0.64 -11 ATS1 -4.07 -1.09 -69 TPS1 -2.49 -0.64 -12 YDR266C -3.80 -1.01 -70 PRE9 -2.48 -0.64 -13 LAT1 -3.79 -1.01 -71 MLP1 -2.47 -0.63 -14 IKI3 -3.64 -0.97 -72 DEG1 -2.46 -0.63 -15 LEA1 -3.58 -0.95 -73 YPL062W -2.45 -0.63 -16 UBC4 -3.57 -0.95 -74 CSG2 -2.44 -0.63 -17 YLR184W -3.56 -0.94 -75 YGR139W -2.43 -0.62 -18 LRP1 -3.50 -0.93 -76 YLR346C -2.43 -0.62 -19 BUL1 -3.48 -0.92 -77 YHR162W -2.42 -0.62 -20 RPS11B -3.46 -0.92 -78 GMH1 -2.42 -0.62 -21 BUB3 -3.46 -0.92 -79 YIL110W -2.40 -0.61 -22 SGF73 -3.42 -0.91 -80 SET3 -2.39 -0.61 -23 SUR4 -3.42 -0.90 -81 SRN2 -2.36 -0.61 -24 INO4 -3.39 -0.90 -82 YNL120C -2.36 -0.60 -25 SPT8 -3.32 -0.88 -83 RPS23A -2.31 -0.59 -26 ALD6 -3.29 -0.87 -84 YTA7 -2.29 -0.58 -27 PIH1 -3.26 -0.86 -85 YML036W -2.28 -0.58 -28 VIP1 -3.25 -0.86 -86 ORM1 -2.25 -0.57 -29 ELP4 -3.17 -0.83 -87 RPS11A -2.25 -0.57 -30 CPR7 -3.16 -0.83 -88 RAD24 -2.25 -0.57 -31 RPS6A -3.13 -0.82 -89 NHP10 -2.24 -0.57 -32 RPP1B -3.09 -0.81 -90 HBS1 -2.24 -0.57 -33 NCS2 -3.07 -0.81 -91 TSA1 -2.23 -0.57 -34 RPS16B -3.06 -0.80 -92 YMD8 -2.23 -0.57 -35 GCR2 -2.98 -0.78 -93 SDS24 -2.23 -0.57 -36 YPL102C -2.95 -0.77 -94 RPS30A -2.22 -0.56 -37 RPS21B -2.95 -0.77 -95 RPS14A -2.22 -0.56 -38 SAC3 -2.93 -0.77 -96 LSM1 -2.21 -0.56 -39 GCN20 -2.92 -0.76 -97 PAT1 -2.21 -0.56 -40 RPS29B -2.92 -0.76 -98 RPL21B -2.20 -0.56 -41 SKI3 -2.92 -0.76 -99 MIR1 -2.19 -0.56 -42 RPS28B -2.92 -0.76 -100 YJR111C -2.17 -0.55 -43 HFA1 -2.91 -0.76 -101 YML013C-A -2.17 -0.55 -44 ELP6 -2.86 -0.75 -102 YLR021W -2.16 -0.55 -45 BMH1 -2.85 -0.74 -103 HHF1 -2.16 -0.55 -46 JJJ1 -2.81 -0.73 -104 RPL11B -2.15 -0.54 -47 RPL8A -2.79 -0.73 -105 CHS3 -2.14 -0.54 -48 RPL19A -2.77 -0.72 -106 BRE5 -2.13 -0.54 -49 ARC1 -2.76 -0.72 -107 SKI2 -2.13 -0.54 -50 VPS8 -2.74 -0.71 -108 GDH1 -2.11 -0.53 -51 UBA4 -2.74 -0.71 -109 UBP2 -2.10 -0.53 -52 CTF4 -2.73 -0.71 -110 SAP190 -2.10 -0.53 -53 RPL13A -2.70 -0.70 -111 RPL19B -2.08 -0.52 -54 SLH1 -2.70 -0.70 -112 IES5 -2.07 -0.52 -55 IPK1 -2.68 -0.70 -113 NOP12 -2.04 -0.51 -56 RPN10 -2.65 -0.69 -114 RDH54 -2.03 -0.51 -57 MDR1 -2.64 -0.68 -115 TEF2 -2.03 -0.51 -58 RPS18B -2.61 -0.67

Table 5.1 Gene Ontology (GO) Enrichment of Mutants with increased Rnr3 abundance (Z > 2) on the Typhoon Untreated (UT) Methyl methanesulfonate (MMS)

Ontology Name Term -log10(p-value) Count/Size Expected Genes -log10(p-value) Count/Size Expected Genes GO:0005634 nucleus 5.9 69/1131 37.3 ... 6 73/1125 40.3 ... GO:0035361 Cul8-RING ubiquitin ligase complex 3.7 4/4 0.1 MMS1 | MMS22 | RTT101 | RTT107 3.7 4/4 0.1 MMS1 | MMS22 | RTT101 | RTT107 GO:0043596 nuclear replication fork 3.1 4/5 0.2 CTF18 | DIA2 | MRC1 | TOF1 3.2 4/5 0.2 CTF18 | CTF4 | DIA2 | TOF1 GO:0000228 nuclear chromosome 2.4 5/13 0.4 MRC1 | RAD51 | RAD52 | SKI8 | TOF1 1.4 4/13 0.5 CTF4 | RAD51 | RAD52 | TOF1 GO:0031422 RecQ helicase-Topo III complex 0.9 2/3 0.1 RMI1 | SGS1 2.5 3/3 0.1 RMI1 | SGS1 | TOP3 GO:0016592 mediator complex 0.5 2/5 0.2 MED1 | SRB2 3.2 4/5 0.2 CSE2 | MED1 | SOH1 | SRB2 GO:0070847 core mediator complex 0.5 2/5 0.2 MED1 | SRB2 3.2 4/5 0.2 CSE2 | MED1 | SOH1 | SRB2

CellularComponenet (CC) GO:0031390 Ctf18 RFC-like complex 0 1/3 0.1 CTF18 2.5 3/3 0.1 CTF18 | CTF8 | DCC1 GO:0003677 DNA binding 2.7 24/287 9.5 ... 2.3 24/288 10.3 ... MSH5 | RAD5 | RAD51 | RAD54 | GO:0008094 DNA-dependent ATPase activity 1.8 5/17 0.6 RAD55 1 4/17 0.6 RAD5 | RAD51 | RAD54 | RAD55 ISW2 | RAD5 | RAD54 | RRM3 | SGS1 | CHL1 | MPH1 | RAD5 | RAD54 | GO:0004386 helicase activity 1.4 6/32 1.1 YKU80 2.1 7/32 1.1 RRM3 | SGS1 | YKU80 GO:0000400 four-way junction DNA binding 1.3 3/6 0.2 HMO1 | RAD5 | RMI1 0.3 2/6 0.2 RAD5 | RMI1 ATP-dependent DNA helicase GO:0004003 activity 1.3 3/6 0.2 RRM3 | SGS1 | YKU80 2.7 4/6 0.2 CHL1 | RRM3 | SGS1 | YKU80 GO:0005524 ATP binding 1 22/346 11.4 ... 1.3 24/343 12.3 ... RNA polymerase II transcription

MolecularFunciton (MF) GO:0001104 cofactor activity 0.5 2/5 0.2 MED1 | SRB2 3.2 4/5 0.2 CSE2 | MED1 | SOH1 | SRB2 MKT1 | MMS4 | MRE11 | MUS81 | GO:0004518 nuclease activity 0 3/51 1.7 MRE11 | RAD27 | YDR034C-D 1.6 8/52 1.9 RAD27 | REX2 | SAE2 | XRN1 GO:0016787 hydrolase activity 0 17/413 13.6 ... 2.2 30/412 14.8 ... GO:0006974 response to DNA damage stimulus 8.3 23/128 4.2 ... 19 35/128 4.6 ... GO:0006281 DNA repair 7.5 21/116 3.8 ... 16.5 31/116 4.2 ... CTF18 | CTF4 | CTF8 | DCC1 | CTF18 | MRC1 | POL32 | RAD27 | POL32 | RAD27 | RRM3 | SGS1 | GO:0006260 DNA replication 5.2 8/17 0.6 RAD30 | RRM3 | SGS1 | TOF1 8 10/17 0.6 SLX4 | TOF1 regulation of transcription, DNA- GO:0006355 dependent 2.1 24/315 10.4 ... 0.6 20/316 11.3 ... ELG1 | EST1 | SLX5 | SLX8 | GO:0000723 telomere maintenance 1.8 5/17 0.6 MRC1 | SLX5 | SLX8 | SWD1 | YKU80 1.8 5/17 0.6 YKU80 GO:0006351 transcription, DNA-dependent 1.7 22/299 9.9 ... 0.5 19/300 10.8 ... ASF1 | ISW2 | ITC1 | MRC1 | SPT21 | GO:0006348 chromatin silencing at telomere 1.4 6/32 1.1 SWD1 0 2/32 1.1 ASF1 | NPT1

BiologicalProcess (BP) GO:0007064 mitotic sister chromatid cohesion 1.1 4/16 0.5 CTF18 | MRC1 | RMI1 | TOF1 10 11/16 0.6 ... double-strand break repair via CTF18 | ELG1 | ESC2 | PPH3 | GO:0000724 homologous recombination 0.8 3/10 0.3 CTF18 | SGS1 | YKU80 4.3 6/10 0.4 SGS1 | YKU80 HED1 | MMS4 | MUS81 | RAD51 | RAD51 | RAD52 | SGS1 | YKU80 | RAD52 | RAD59 | SGS1 | SHU1 | GO:0006310 DNA recombination 0.5 5/41 1.4 YDR034C-D 3.8 10/41 1.5 SLX4 | YKU80 GO terms with significant enrichment in either the MMS or UT screen are shown. Green background indicates significant enrichment of GO term in given screening condition. p-value is adjusted for multiple hypothesis testing using the false-discovery rate method (FDR < 0.05). Count/Size: Count = number of genes belonging to given GO term with increased Rnr3 expression; Size = total number of genes screened belonging to given GO term Expected: = number of genes from belonging to given GO category expected by random chance to have increased Rnr3

Table 5.2 Gene Ontology (GO) Enrichment of Mutants with decreased Rnr3 abundance (Z < -2) on the Typhoon Untreated (UT) Methyl methanesulfonate (MMS)

Table 6.1 Gene Ontology (GO) Enrichment of Mutants with increased Rnr3 abundance (Z < -2) on the Opera Untreated (UT) Methyl methanesulfonate (MMS)

Ontology Name Term -log10(p-value) Count/Size Expected Genes -log10(p-value) Count/Size Expected Genes

GO:0005634 nucleus 6.9 42/1038 17.7 ... 0 29/1050 22.3 ... Cul87RING;ubiquitin;ligase; GO:0035361 complex 2.7 3/4 0.1 MMS1;|;RTT101;|;RTT107 0 0/3 0.1 GO:0043596 nuclear;replication;fork 2.7 3/4 0.1 CTF18;|;CTF4;|;MRC1 0 0/5 0.1 GO:0070823 HDA1;complex 1.7 2/2 0 HDA1;|;HDA3 0 1/2 0 HDA3 GO:0033186 CAF71;complex 1.4 2/3 0.1 CAC2;|;RLF2 0 1/3 0.1 RLF2 Cellular;Component;(CC) GO:0000228 nuclear;chromosome 1.3 3/12 0.2 CTF4;|;MRC1;|;RAD52 0 0/12 0.3 GO:0003677 DNA;binding 3 16/269 4.6 ... 0 13/272 5.8 ... CHD1;|;ISW2;|;RAD5;|;RAD54;|; GO:0004386 helicase;activity 2.9 6/31 0.5 RRM3;|;SGS1 0 2/31 0.7 CHD1;|;YKU80 CTF4;|;ELG1;|;HDA1;|;HDA3;|; GO:0003682 chromatin;binding 2.4 5/24 0.4 ISW2 0 1/25 0.5 HDA3 GO:0042393 histone;binding 2.2 4/14 0.2 ASF1;|;CAC2;|;RLF2;|;RTT106 0 2/15 0.3 CHZ1;|;RLF2 DNA7dependent;ATPase; GO:0008094 activity 2 4/16 0.3 CHD1;|;RAD5;|;RAD54;|;RAD55 0 2/16 0.3 CHD1;|;MSH5 Molecular;Function;(MF) GO:0004407 histone;deacetylase;activity 1.4 3/11 0.2 HDA1;|;HDA3;|;SAP30 0 1/11 0.2 HDA3 GO:0015616 DNA;translocase;activity 1.4 2/3 0.1 ISW2;|;RAD54 0 0/3 0.1

Table 6.1 Continued Gene Ontology (GO) Enrichment of Mutants with increased Rnr3 abundance (Z < -2) on the Opera Untreated (UT) Methyl methanesulfonate (MMS)

Ontology Name Term -log10(p-value) Count/Size Expected Genes -log10(p-value) Count/Size Expected Genes ASF12|2BRE12|2CHD12|2CTI62|2 HDA12|2HDA32|2ISW22|2ITC12|2 GO:0016568 chromatin2modification 5 10/60 1 LGE12|2SAP30 0 3/62 1.3 CHD12|2EAF62|2HDA3 regulation2of2transcription,2 GO:0006355 DNAPdependent 4.7 19/282 4.8 ... 0.2 15/288 6.1 ... CTF182|2CTF42|2MRC12|2POL322|2 GO:0006260 DNA2replication 4.6 6/16 0.3 RRM32|2SGS1 0 0/16 0.3 GO:0006281 DNA2repair 4.5 12/107 1.8 ... 0 3/107 2.3 EAF62|2MSH52|2YKU80 transcription,2DNAP GO:0006351 dependent 4.4 18/265 4.5 ... 0 12/270 5.7 ... chromatin2silencing2at2 ASF12|2BRE12|2ISW22|2ITC12|2 GO:0006348 telomere 2.9 6/31 0.5 MRC12|2SPT21 0 1/32 0.7 SWD1 doublePstrand2break2repair2 via2homologous2 GO:0000724 recombination 2.7 4/10 0.2 BRE12|2CTF182|2ELG12|2SGS1 0 1/10 0.2 YKU80 MMS12|2RAD52|2RAD522|2 response2to2DNA2damage2 RAD542|2RAD552|2RRM32|2 ALK22|2EAF62|2MSH52|2RMI12|2 GO:0006974 stimulus 2.6 10/117 2 RTT1092|2SGS12|2SLX52|2SLX8 0 5/119 2.5 YKU80 Biological2Process2(BP) mitotic2sister2chromatid2 GO:0007064 cohesion 2.2 4/14 0.2 CTF182|2CTF42|2ELG12|2MRC1 0 1/15 0.3 RMI1 positive2regulation2of2 transcription2from2RNA2 GTS12|2HAL92|2HDA12|2PDR12|2 CSE22|2HOG12|2MOT32|2PDR12 GO:0045944 polymerase2II2promoter 2.2 8/82 1.4 PHO22|2RFX12|2SAP302|2SIP3 0 6/85 1.8 |2RFX12|2SIP3 GO:0000723 telomere2maintenance 2 4/16 0.3 ELG12|2MRC12|2SLX52|2SLX8 0 3/17 0.4 EST12|2SWD12|2YKU80 chromatin2silencing2at2silent2 GO:0030466 matingPtype2cassette 1.9 4/17 0.3 ASF12|2MRC12|2NUP602|2SPT21 0 0/17 0.4 negative2regulation2of2 transcription2from2RNA2 HDA12|2HDA32|2RFX12|2ROX12|2 CSE22|2HDA32|2MOT32|2RFX12 GO:0000122 polymerase2II2promoter 1.7 5/36 0.6 RTT106 0.2 5/38 0.8 |2ROX1 GO:0006338 chromatin2remodeling 1.4 4/25 0.4 ISW22|2ITC12|2PHO22|2RAD54 0 2/27 0.6 CHZ12|2FKH2 GO terms with significant enrichment in either the MMS or UT screen are shown. Green background indicates significant enrichment of GO term in given screening condition. p-value is adjusted for multiple hypothesis testing using the false-discovery rate method (FDR < 0.05). Count/Size: Count = number of genes belonging to given GO term with increased Rnr3 expression; Size = total number of genes screened belonging to given GO term Expected: = number of genes from belonging to given GO category expected by random chance to have increased Rnr3

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