Systematic Exploration of Essential Yeast Gene Functions

with Temperature-sensitive Mutants

By

Zhijian Li

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

Department of Molecular Genetics

University of Toronto

© Copyright by Zhijian Li, 2011

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Systematic Exploration of Essential Yeast Gene Functions

with Temperature-sensitive Mutants Doctor of Philosophy

Zhijian Li

Department of Molecular Genetics University of Toronto

2011

ABSTRACT

The budding yeast Saccharomyces cerevisiae is the most well characterized

model organism for systematic analysis of fundamental eukaryotic processes.

Approximately 19% of S. cerevisiae genes are considered essential. Essential genes tend

to be more highly conserved from yeast to humans when compared to nonessential genes.

The set of essential yeast genes spans diverse biological processes and while the primary

role of most essential yeast genes has been characterized, the full breadth of function

associated with essential genes has not been examined, due, at least in part, to the lack of

adequate genetic reagents for their conditional and systematic perturbations.

To systematically study yeast essential gene functions using synthetic genetic

array analysis and to complement the current yeast deletion collection, I constructed a

collection of temperature-sensitive yeast mutants consisting of 795 ts strains, covering

501 (~45%) of the 1,101 essential yeast genes, with ~30% of the genes represented by

multiple alleles. This is the largest collection of isogenic ts yeast mutants constructed to

date.

I confirmed the correct integration of over 99% of the ts alleles using PCR-based

strategy and the identity of the ts allele by complementation of the ts phenotype with its

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cognate plasmid. The ts mutant collection was characterized by high-resolution profiling

of the temperature sensitivity of each ts strain, distribution analysis of gene ontology

molecular function and biological process, and comparison of ts allele strains to the

strains carrying Tet-repressible alleles of essential genes. The results demonstrated that

the ts collection is a powerful reagent for the systematic study of yeast essential gene

functions and provides a valuable resource to complement the current yeast deletion

collection.

I validated and demonstrated the usefulness of the ts collection in a number of

different ways. First, I carried out detailed temperature profiling of each mutant strain

using liquid growth assays and found that ts mutants that define particular biological

pathways often show highly similar profiles. Second, I showed that the ts mutant array

can be used to screen compounds for suppression of growth defects and thus is useful for

exploration of chemical-genetic interactions. Third, I demonstrated that the ts collection

represents a key reagent set for genetic interaction analysis because essential genes tend

to be highly connected hubs on the global genetic network. Fourth, I further validated the

ts array as a key resource for quantitative phenotypic analysis by using a high-content

screening protocol to score six different fluorescent markers, diagnostic for different

subcellular compartments or structures, in hundreds of different mutants. Quantification

of the marker behaviour at the single-cell level enabled integration of this data set to

generate a morphological profile for each ts mutant to reveal both known and previously

unappreciated functions for essential genes, including roles for cohesion and condensin

genes in spindle disassembly.

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ACKNOWLEDGMENTS

This thesis could not be finished without the help, the support and the

encouragement of many people. First of all, I greatly appreciate the excellent supervision

from my supervisor Dr. Charles Boone during my Ph.D. training. Charlie’s vision and

thinking about science, with the opportunities and resources available to me as a member

of his team, created an ideal environment for my graduate work.

I thank my committee members Dr. Brenda Andrews and Dr. Andrew Spence for

their suggestions of this project. In particular I appreciate Dr. Brenda Andrews for her

support and supervision on my training program. She is a great mentor and a model for

Ph.D. students.

I also thank Dr. Franco Vizeacoumar who helped in setting up the high content

screening and subsequent follow-up experiments in the last few years of my project. I am

grateful to Dr. Helena Friesen and Dr. Michael Costanzo for their wonderful ideas and

help in writing this thesis.

I would like to dedicate this thesis to my wife (Hong Lu), my son (Jerry), my

daughter (Terry), and my mother-in-law (Dr. Xiangqiong Ren). It is their great love,

selfless support and constant encouragement that helped me finish my Ph.D. program

training.

Many thanks to everybody in Dr. Charles Boone’s lab for forming an efficient and

friendly working environment that helped me learn and grow all these years.

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TABLE OF CONTENTS

ABSTRACT ii

ACKNOWLEDGEMENTS iv

TABLE OF CONTENTS v

LIST OF FIGURES ix

LIST OF TABLES xi

LIST OF ABBREVIATIONS xii

CHAPTER ONE: General Introduction

1.1 Budding yeast as a model organism 1

1.2 Essential genes and conditional mutants 2

1.2.1 Essential genes

1.2.2 Conditional mutants

1.3 Yeast deletion collection and barcode microarray assay 8

1.4 Genetic interactions and synthetic genetic array (SGA) analysis 9

1.4.1 Genetic interactions

1.4.2 Synthetic genetic array (SGA) analysis

1.5 High content screening 19

1.6 Mitotic spindle and spindle assembly checkpoint pathways 21

1.6.1 Mitotic spindle

1.6.2 Spindle assembly checkpoint pathways

1.6.3 Chromosome passenger complex (CPC)

1.6.4 The kinetochore CTF19 complex

1.7 Sister chromatid cohesion 27

1.7.1 Sister chromatid cohesion is mediated by the cohesin complex

1.7.2 Structure and roles of cohesin

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1.8 Condensin 29

1.8.1 Components and structure of condensin

1.8.2 Functions of condensin

1.9 Summary of thesis 31

CHAPTER TWO: Construction, Characterization and Evaluation of

Temperature-sensitive Mutant Library

2.1 Abstract 35

2.2 Introduction 35

2.3 Results 37

2.3.1 Construction of temperature sensitive strains

2.3.2 Confirmation of the constructed ts strains

2.3.3 High-resolution profiling of the temperature sensitivity of ts alleles

2.3.4 GO molecular function and biological process distribution of ts mutant

library

2.3.5 Essential genes are hubs of a genetic interaction network

2.3.6 Comparison of ts alleles to Tet alleles

2.3.7 Evaluating the usefulness of the ts mutant collection

2.4 Discussion 55

2.5 Materials and Methods 56

2.5.1 Yeast strains and medium

2.5.2 Checking ts phenotypes for the collected yeast strains

2.5.3 Designing primers for constructing new ts alleles

2.5.4 Synthetic genetic array (SGA) analysis

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2.5.5 Confirmation of temperature-sensitive strains by PCR

2.5.6 Functional complementation by plasmids

2.5.7 Random spore analysis

CHAPTER THREE: Application of the Temperature-sensitive Mutant Library

3.1 Abstract 63

3.2 Introduction 63

3.3 Results 66

3.3.1 Identification of pathway components using a barcoded ts library

3.3.2 Genetic interactions for essential genes

3.3.2.1 Properties of essential genetic interactions

3.3.2.2 Functional impact of essential genetic interactions

3.3.3 Functional prediction of the essential genes from high-content screening

3.3.4 A role for cohesin/condensin in spindle disassembly

3.3.5 Physical interactions of the cohesin, the condensin and the passenger

complexes

3.3.6 Condensin, cohesin and CPC complexes co-localize to the spindle

3.4 Discussion 103

3.5 Materials and Methods 105

3.5.1 Bar-coding temperature-sensitive strains

3.5.2 Microarray analysis

3.5.3 Analysis of growth rates in liquid medium

3.5.4 Spot Assay

3.5.5 Degree distribution analysis

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3.5.6 Protein-protein interaction network overlap

3.5.7 Function prediction from comparison of genetic interaction data

3.5.8 Automated image acquisition and analysis

3.5.9 Confocal microscopy and image quantitation

3.5.10 Analysis of biologically relevant mutants using HCS dataset

3.5.11 Affinity purification and mass spectrometry

CHAPTER FOUR: Summary and Future Directions

4.1 Summary of thesis 113

4.2 Future Directions 114

4.2.1 Exploring functions for uncharacterized yeast genes using ts mutant

collection

4.2.2 Discovery of new protein interaction partners for yeast essential genes using

systematic genetics

4.2.3 Discovery of drug targets for essential genes using the barcoded ts mutant

collection

4.2.4 Phenotypic analysis of the ts mutant collection using multiple fluorescent

reporter genes

4.2.5 Spatio-temporal regulation of spindle disassembly

References 121

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LIST OF FIGURES

Chapter 1

Figure 1-1 Three Different Types of Genetic Interactions 11

Figure 1-2 Synthetic Genetic Array (SGA) Analysis 15

Figure 1-3 Predicting Gene Function from Genetic Interaction Patterns 18

Figure 1-4 Organization of the Mitotic Spindle 22

Chapter 2

Figure 2-1 Construction of Temperature Sensitive Strains 38

Figure 2-2 PCR Confirmation of ts Strains 40

Figure 2-3 Functional Complementation by Plasmid 41

Figure 2-4 Profiling the Temperature Sensitivity of ts Strains 44

Figure 2-5 GO Molecular Function and Biological Process Distribution for Yeast

Genome, Yeast Essential Genes and Genes in ts Collection 47

Figure 2-6 Comparison of ts Alleles and Tet Alleles 50

Figure 2-7 rfc5-1::natMX Crossed into the ts Array 54

Figure 2-8 ts Phenotype Confirmation on YEPD Plates 57

Figure 2-9 Random Spore Analysis (bni1Δ::natMX cdc42-1::kanMX) 61

Chapter 3

Figure 3-1 Isoprenoid Pathway in Saccharomyces cerevisiae 67

Figure 3-2 Zaragozic Acid Rescues bet2-1 and cdc43-2 ts Phenotype 69

Figure 3-3 Properties of Essential Genetic Interactions 71

Figure 3-4 Function Prediction Performance of Different Query Genes 73

Figure 3-5 Network Connecting Cohesin, Condensin, and Other Genes Involved in

Spindle Dynamics 75

Figure 3-6 Fluorescent Reporters Introduced into the ts Collection 77

Figure 3-7 Temperature Shift Causes Increased Phenotype 79

Figure 3-8 Cohesin and Condensin Mutants Exhibit Abnormal Spindle

Morphology 82

Figure 3-9 Localization of Ipl1p-GFP in Cohesin/Condensin Mutants 84

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Figure 3-10 Localization of Sli15p-GFP/Bir1p-GFP in Cohesin/Condensin

Mutants 86

Figure 3-11 Cohesin Barrel Structure was Lost in the mcm21∆ Kinetochore

Mutant 90

Figure 3-12 Localization of Ase1p-GFP/Cdc14p-GFP in Cohesin/Condensin

Mutants 93

Figure 3-13 Localization of Sli15p-GFP and Ipl1p-GFP in smc3-1 mad1∆

and smc2-8 mad1Δ Double Mutants 95

Figure 3-14 Localization of Sli15p-6A-GFP in Cohesin Mutant 97

Figure 3-15 Physical Interaction Map for Cohesin, Condensin and CPC

Complexes 99

Figure 3-16 Cohesin, Condensin and CPC Localization in WT Cells 101

Figure 3-17 Cohesin, Condensin and CPC Localization in ipl1-2 Mutant 102

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LIST OF TABLES

Table 1. Primers used for constructing the ts strains

Table 2. List of temperature-sensitive alleles of essential genes

Table 3. More genetic interactions for essential genes

Table 4. Primers used for PCR confirmation

Table 5. List of cell morphology and reporter-specific parameters measured in HCS

experiments

Table 6. List of mutants that exhibited cellular reporter defects identified by HCS

Table 7. Filtered mass spectrometry data

Table 8. Markers used in HCS

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LIST OF ABBREVIATIONS

Individual amino acids are abbreviated in the text as three letters (e.g. alanine is

abbreviated Ala). Lengths and volumes are annotated using the prefix symbols approved

by the International Systems of Units (e.g. micron is abbreviated μm, microliter is

abbreviated μl).

Δ deletion (of a gene)

°C degree Celsius

AP/MS affinity purification/mass spectrometry

ATP adenosine triphosphate

bp base pair(s)

canavanine L-canavanine sulfate salt

CAP chromosome associated protein

C. elegans Caenorhabditis elegans

CEN centromere

clonNAT nourseothricin

CPC chromosomal passenger complex

cs cold-sensitive

DAmP decreased abundance by mRNA perturbation

D. melanogaster Drosophila melanogaster

DMSO dimethyl sulfoxide

DNA deoxyribonucleic acid

FEAR Cdc fourteen early anaphase release

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FPP farnesyl diphosphate

G418 geneticin

GFP green fluorescent protein

GGPP geranylgeranyl diphosphate

GO gene ontology

GST glutathione-S-transferase

HCS high content screening

HMG-CoA 3-hydroxy-3-methylglutaryl-coenzyme A

HMGR HMG-CoA reductase

kanMX kanamycin resistant cassette

kb kilobase pair(s)

MATa yeast mating type a

MATα yeast mating type alpha

MEN mitotic exit network

MoBY-ORF molecular barcoded yeast ORF

mRNA messenger RNA

MSG monosodium glutamic acid

MT microtubule

natMX nourseothricin resistant cassette

OD optical density

ORF open reading frame

PCR polymerase chain reaction

RFC replication factor C

xiv

RFP red fluorescent protein

RNA ribonucleic acid

RNAi RNA (mediated) interference

S. cerevisiae Saccharomyces cerevisiae

S. pombe Schizosaccharomyces pombe

SAC spindle assembly checkpoint

SC synthetic complete media

SCC sister chromatid cohesion

SD minimal synthetic defined/synthetic minimal glucose

SGA synthetic genetic array

SGD Saccharomyces Genome Database

SL synthetic lethal

SMC structural maintenance of chromosomes

SMF single mutant fitness

SS synthetic sick

TEF translation elongation factor

Tet tetracycline-regulatable promoter-replacement

ts temperature-sensitive

WT wild-type

Y2H yeast two hybrid

YEPD yeast extract peptone dextrose (rich medium)

YKO yeast knockout

ZA zaragozic acid A

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CHAPTER ONE

General Introduction

1.1 Budding yeast as a model organism

The budding yeast Saccharomyces cerevisiae is one of the best characterized

model organisms for systematic analysis of fundamental eukaryotic processes. S.

cerevisiae was the first eukaryotic genome to be sequenced, largely because it has a

relatively small genome of 12,052 kb (Goffeau et al., 1996). Yeast has short non-coding

regions and less than 7% of its ~6,000 genes contain introns (Grate and Ares, 2002).

These factors have facilitated the identification of genes in yeast and enabled the rapid

construction of genomic resources. The life cycle of budding yeast is well suited to

classical genetic studies. Importantly, S. cerevisiae can be propagated in either a haploid

or diploid form, which lends itself well to genetic manipulation and the analysis of

recessive and dominant mutations. In addition, yeast is highly amenable to plasmid

transformation and has very efficient homologous recombination, which allows the

efficient integration of genetically engineered DNA sequences into the genome.

The extensive conservation of many cellular processes between yeast and higher

organisms, especially with regard to basic cellular metabolism and cell division, makes

yeast an excellent model eukaryote (Botstein et al., 1997). The sequencing of yeast

(Goffeau et al., 1996) and human genomes (Lander et al., 2001; Venter et al., 2001) has

revealed thousands of yeast proteins that share amino-acid sequence similarity with at

least one human protein (Hughes, 2002). Several hundreds of these conserved proteins

are implicated in human disease (Botstein et al., 1997). Furthermore, 30-40% of human

disease-related genes have counterparts in the yeast genome (Foury and Kucej, 2002).

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Many fundamental questions in molecular biology have been answered in yeast, and

yeast continues to play a central role in eukaryotic cell biology.

In this introductory chapter, I provide an overview of tools and approaches that

have been used for systematic analysis of yeast genes with a focus on the technologies I

have adapted to understand the genetics of essential genes. In the following sections I

describe: (1) essential genes and conditional mutants; (2) the yeast deletion collection and

the application of pooled screens using microarrays; (3) the development of synthetic

genetic array (SGA) analysis and its power in identifying genetic interactions; (4) the

coupling of SGA with high content screening to understand cell biological phenotypes.

1.2 Essential genes and conditional mutants

1.2.1 Essential genes

Essential genes and nonessential genes are genetic descriptions referring to the

functional consequence of deleting a gene with respect to its effect on an organism’s

viability (Jordan et al., 2002). A gene is considered to be essential if it is required for

survival (Gerdes et al., 2006). In contrast, a nonessential gene is dispensable for viability,

and its inactivation yields viable individuals.

Several interesting observations have been made regarding the discernible

characteristics of essential genes in model organisms. Essential genes are more

evolutionarily conserved than nonessential genes, which are more functionally

dispensable and/or redundant (Jordan et al., 2002; Yang et al., 2003). Essential genes are

likely to be hubs in protein-protein interaction networks (Fraser et al., 2003; Goh et al.,

2007; He and Zhang, 2006; Hirsh and Fraser, 2001; Jeong et al., 2001; Wall et al., 2005;

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Yu et al., 2004) and in genetic interaction networks (Davierwala et al., 2005; Tong et al.,

2004). Also essential genes are more likely to be abundantly and ubiquitously expressed

in cells and tissues (Butte et al., 2001; Warrington et al., 2000) and have smaller-sized

introns (Bortoluzzi et al., 2003; Lopez-Bigas and Ouzounis, 2004; Rocha, 2006).

Essential genes in yeast tend to be more highly conserved in humans: 38% of essential

yeast proteins have clear counterparts in human, versus 20% for nonessential genes

(Hughes, 2002).

The yeast deletion collection, created by an international consortium of

laboratories, consists of a comprehensive set of over 20,000 S. cerevisiae gene deletion

strains. The collection contains heterozygous diploid strains corresponding to deletions in

each of the ~6,000 yeast genes, including the ~1,000 essential genes, and a homozygous

diploid, a MATa, and a MATα strain for each of the ~5,000 nonessential genes. Indeed,

the S. cerevisiae gene deletion project revealed that ~19% of yeast genes are essential

genes (required for viability in standard laboratory growth medium). Recently the

analysis of a genome-wide gene deletion set for the fission yeast Schizosaccharomyces

pombe revealed that ~26% of fission yeast genes (1,260/4,836) were essential and ~74%

(3,576/4,836) were nonessential for viability of haploid cells (Kim et al., 2010).

The percentage of essential genes in prokaryotic and eukaryotic genomes varies.

Genome-wide screens for gene essentiality found that the percentage of essential genes in

different bacteria ranged from 6% to 40% (Gerdes et al., 2006; Lamichhane et al., 2003).

Mycoplasma genitalium has the smallest genome of any organism that can be grown in

pure culture and has a minimal metabolism and little genomic redundancy and over 70%

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of protein-coding genes are essential genes as identified by global transposon

mutagenesis (Glass et al., 2006; Hutchison et al., 1999).

About 7% (1,170/16,757) of Caenorhabditis elegans genes were essential as

defined by embryonic or larval lethality or sterility (with or without associated post-

embryonic defects) using RNA interference (RNAi) to inhibit gene function. In this study

~86% of the 19,427 predicted genes of C. elegans were tested (Kamath et al., 2003).

Based on about 5,000 knockout mutants obtained from the 20,000 genes of C. elegans,

~20% of genes are essential (personal communication from Dr. Donald G. Moerman).

Furthermore, ~51% of C. elegans orthologues of S. cerevisiae essential genes (Mewes et

al., 2000) have a nonviable RNAi phenotype (Kamath et al., 2003). Genes involved in

basic metabolism and maintenance of the cell are significantly more likely to have a

nonviable RNAi phenotype and genes involved in more complex processes that are

expanded in metazoa, such as signal transduction and transcriptional regulation, are

enriched in the viable post-embryonic phenotype class, consisting of defects in post-

embryonic development (for example, in movement or body shape) without associated

lethality or slowed growth (Kamath et al., 2003). Yeast and worm genes essential for

viability have a similar distribution within the different functional classes (Fraser et al.,

2000), suggesting that similar types of genes are required for viability of yeast and animal

cells.

Analysis of the published data for 27 different chromosomal regions that were

subjected to extensive mutagenesis revealed that approximately 30% (3,600/12,000) of

genes are essential for viability in Drosophila (Miklos and Rubin, 1996). Large

insertional mutagenesis screens in zebrafish, using mouse retroviral vectors as the

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mutagen, revealed that around 20% of the 2,400 genes are required for the development

of a zebrafish embryo (Golling et al., 2002). More than 70% of embryonic-essential fish

genes have clear orthologues in yeast (Amsterdam et al., 2004). The apparent high

conservation of essential gene function suggests that studying yeast essential genes can

help us to better interpret essential gene function in animals.

1.2.2 Conditional mutants

Genetic analysis of essential proteins in yeast has traditionally relied on

conditional mutants. Conditional alleles enable a functional version of the gene under a

permissive condition and a compromised version under the non-permissive condition.

Conditional alleles in yeast include temperature-sensitive (ts) (Hartwell, 1967), cold-

sensitive (cs) (Moir et al., 1982), temperature-inducible degron (td) (Dohmen et al., 1994;

Kanemaki et al., 2003), tetracycline-regulatable promoter-replacement (Tet) (Mnaimneh

et al., 2004), and temperature-sensitive inteins (Tan et al., 2009).

One systematic approach for constructing ts alleles involves the fusion of the

coding sequence of a given gene to a heat-inducible-degron domain, which modulates the

stability of the protein (Dohmen et al., 1994; Kanemaki et al., 2003). The heat-activated

degron is long-lived at 23°C but becomes short-lived at 37°C and linking this portable ts-

degron to a protein of interest results in destruction of the protein at 37°C (Dohmen and

Varshavsky, 2005). The degron system appears highly variable in its effectiveness.

Nearly 40% of fusion proteins did not result in inviability when 104 essential genes were

fused to the N-degron domain (Kanemaki et al., 2003). Another limitation of the ts-

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degron technique is that it cannot be applied to proteins whose function is incompatible

with N-terminal insertions (Dohmen and Varshavsky, 2005).

Another systematic approach for creating mutant alleles of essential genes

involves replacement of the native promoter with a tetracycline-regulatable promoter. A

set of tetracycline-regulatable promoter-replacement (Tet) alleles was constructed by

replacing the native promoter (~100 bp upstream of the start codon) with a kanMX-TetO7-

CYC1TATA cassette (Mnaimneh et al., 2004). The parent yeast strain was also engineered

to express tetracycline-controlled transactivator (tTA), which is composed of the activator

domain of the herpes simplex virus VP16 fused to the tetracycline-inducible repressor

from the Tn10-encoded tetracycline-resistance operon. The tTA protein binds the TetO7-

CYC1TATA promoter and activates gene expression but can be repressed by addition of

doxycycline to the growth medium (Gari et al., 1997). In other words, tetO-driven

expression occurs in the absence of the effector (tetracycline or other molecules of the

same antibiotic family) while addition of antibiotic prevents the tTA protein from binding

to tetO promoter sequence and switches off gene expression. Much like the degron

system, Tet alleles are useful for regulating expression of a subset of the essential genes,

presumably due to contextual effects of the TetO7-CYC1TATA. Specifically, ~600 Tet

alleles were created and 74% showed a small colony phenotype in the presence of

doxycycline (promoter in the “off” state). Of these, ~20% were also growth-impaired in

the absence of doxycycline (promoter in the “on” state) (Mnaimneh et al., 2004).

Hypomorphic alleles of essential genes can often be created by inserting an

antibiotic-resistance marker into the 3’UTR to reduce mRNA abundance thus reducing

gene expression (Schuldiner et al., 2005). The so-called “decreased abundance by mRNA

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perturbation” or DAmP alleles are not conditional alleles because the reduced gene

expression is constitutive. The DAmP method appears to work very well for ~30% of

essential genes but variably for most essential genes. DAmP alleles may lower the

abundance of an essential gene product, but if the reduced protein level does not lead to a

fitness defect, then the DAmP allele will tend not to display genetic interactions, and thus

may be less informative for functional characterization as discussed in Chapter 3 of this

thesis.

Recently a temperature-sensitive intein approach for generating conditional alleles

was reported (Tan et al., 2009; Zeidler et al., 2004). An intein is an intervening protein

sequence that is embedded within a precursor protein sequence and spliced out during

protein maturation (Perler et al., 1994; Perler et al., 1997). More than 200 inteins have

been identified from unicellular organisms, including bacteria, archaea, and eukaryotes

(http://www.neb.com/neb/inteins.html). Splicing of the inteins (protein splicing) is

essential for the function of the host proteins and protein-splicing failure leads to a loss of

function of the host proteins. A temperature-sensitive intein (ts intein) is an intein that

self-splices only at the permissive temperature to generate a wild-type host protein (Tan

et al., 2009; Zeidler et al., 2004). The ts intein approach has been used in bacteria (Liang

et al., 2007), yeast (Tan et al., 2009; Zeidler et al., 2004), and Drosophila (Zeidler et al.,

2004). A collection containing 41 ts inteins, which function at different permissive

temperatures ranging from 18°C to 30°C, was recently created (Tan et al., 2009) and can

be selected for generating ts mutants in a variety of organisms. The advantage of the ts

intein strategy is that it can be used for any gene and it is readily available.

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The most common conditional allele in yeast is the temperature-sensitive allele. A

temperature-sensitive (ts) mutant retains the function of a gene at a low (permissive)

temperature but not at high (restrictive) temperature. Temperature-sensitive mutants can

be used effectively to gain information about essential gene function and have been

generated for many different functional classes of genes (Edgar and Lielausis, 1964).

Collections of temperature-sensitive mutants have been established in various model

microbes, including S. cerevisiae, Salmonella typhimurium, and Escherichia coli (Ben-

Aroya et al., 2008; Harris et al., 1992; Kaback et al., 1984; Schmid et al., 1989;

Sevastopoulos et al., 1977). A major goal of my thesis work was to construct a collection

of strains carrying ts alleles for yeast essential genes, which is compatible with large-

scale genetic analyses.

1.3 Yeast deletion collection and barcode microarray assay

The yeast genome-deletion project, a worldwide collaborative effort,

systematically created deletion/substitution mutants for almost all of the annotated yeast

ORFs, each of which was replaced by a kanamycin resistance cassette (kanMX4) that

confers resistance to the drug G418 (Winzeler et al., 1999). These yeast knockout (YKO)

mutants were created by chromosomal integration of a PCR-generated disruption cassette

via homologous recombination (http://www-

sequence.stanford.edu/group/yeast_deletion_project/PCR_strategy.html). There are four

different YKO collections in the commonly used S288c laboratory strain: (1) MATa

haploid (referred to as BY4741); (2) MATα haploid (referred to as BY4742); (3) MATa/α

homozygous diploid (referred to as BY4743); (4) MATa/α heterozygous diploid

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(Winzeler et al., 1999). The first three YKO collections contain only strains carrying

disruption alleles of nonessential genes and the heterozygous diploid YKO collection

contains disruptions in both essential and nonessential ORFs. The YKO mutant

collections have opened the door to systematic, genome-wide functional analyses.

Each deletion mutant is marked by two unique 20 mer nucleotide sequences

flanked by sequences common to all mutants. These ‘molecular barcodes’ enable the

identification of each strain within a mixed population of mutants through PCR-based

amplification of the barcodes using the flanking common sequences followed by

microarray-based quantitative detection (Giaever et al., 2002). This highly parallel

technique is particularly useful for fitness testing of large numbers of mutants in small

volumes of media. In this strategy, strains are pooled and grown in parallel in liquid

culture under selective conditions (for example, in the presence of a drug). Genomic

DNA is isolated from the mixed culture and a pool of barcode PCR amplimers is

prepared and hybridized to high-density DNA microarrays containing oligonucleotides

corresponding to the barcodes. The relative abundance of each strain in the pool is then

assessed by measuring the abundance of the relevant gene signal from the microarray

readout compared to a control. Using this approach, the entire yeast deletion set can be

pooled, grown competitively and assessed quantitatively in only a few milliliters of

growth medium (Giaever et al., 2002). The approach has recently been adapted for use

with a sequencing-based readout (Bar-seq) (Smith et al., 2009; Smith et al., 2010).

1.4 Genetic interactions and synthetic genetic array (SGA) analysis

1.4.1 Genetic interactions

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The yeast haploid deletion mutant collection sparked the development of a high-

throughput genome-wide method to identify and study genetic interactions between

nonessential genes in S. cerevisiae (Tong et al., 2001; Tong et al., 2004). Genetic

interactions have long been studied in model organisms as a means of identifying

functional relationships among genes or their corresponding gene products (Boone et al.,

2007; Hartman et al., 2001). Genetic interactions can be classified into three different

groups: negative interactions, positive interactions and gene dosage interactions (Figure

1-1) (Dixon et al., 2009).

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Adapted from Dixon et al. (2009)

Figure 1-1 Three Different Types of Genetic Interactions

12

A. Negative interactions. Negative interactions can occur between two non-essential

genes that belong to parallel pathways (between-pathway genetic interactions, left panel)

or between two hypomorphic alleles of essential genes encoding components within the

same pathway (within-pathway genetic interactions, right panel). Uppercase and

lowercase represent wild type and mutant (either deletion or hypomorphic allele),

respectively. Dashed arrows show decreased activity of the mutant. Black and blue letters

indicate wild type and red letters indicate mutants.

B. Positive interactions. Inactivation of a negative regulator (a) may result in

overexpression of downstream gene (B) and lead to buildup of a toxic gene product C. A

loss-of-function mutation in a downstream component (b) could reduce flux through the

same pathway and suppress the toxic effects caused by mutation of the upstream

component (a). Gain-of-function suppression (shown as an allele of c*) may occur when

a downstream pathway component is mutated (c*) such that it now has upregulated or

novel activity and therefore suppresses the lethal/sick phenotype of an upstream mutation

(b).

C. Gene dosage. Accumulation of gene product B can cause lethality/sickness (dosage

lethality) when an upstream negative regulator A is mutated (left panel). Overexpression

of a downstream component B can suppress the lethality/sickness caused by mutation of

an upstream component (a) (dosage suppression, right panel).

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Negative interactions (also called aggravating or synergistic interactions) occur

when double mutants exhibit a more severe phenotype than expected based on the fitness

of each single mutant alone. This is referred to as synthetic lethality or sickness.

Synthetic lethality or sickness in yeast results when two mutations in different genes are

each viable as single mutations but lead to lethality or sickness when combined in the

same haploid genome (Hartman et al., 2001; Hartwell, 2004). Synthetic lethality or

sickness is of particular interest because it can identify genes whose products buffer one

another and are involved in the same essential biological process (Hartman et al., 2001;

Hartwell, 2004). Thus synthetic lethality or sickness indicates a functional relationship

between genes.

Positive interactions describe a situation in which double mutants exhibit a less

severe phenotype than would be expected from a multiplicative model, in which the joint

effect of two mutants is the product of their effects if they were acting alone. Positive

interactions have also been referred to as alleviating or epistatic interactions (Dixon et al.,

2009; Schuldiner et al., 2005).

Gene dosage interactions include dosage lethality and dosage suppression.

Synthetic dosage lethality is detected when overexpression of a gene is lethal only if

another, normally nonlethal, mutation is present (Boone et al., 2007; Kroll et al., 1996;

Liu et al., 2009; Measday and Hieter, 2002). Dosage suppression can be achieved if a

mutation in one gene rescues the lethality caused by mutation of another gene. For

example, Bts1p (GGPP synthase) is a upstream of Bet2p in yeast isoprenoid pathway

(Figure 3-1). Overexpression of BTS1can suppress the lethality of the bet2-1 ts mutant at

37oC (Jiang et al., 1995).

14

Synthetic lethal interactions are interpreted in several ways: between-pathway

models, within-pathway models and indirect effects (Dixon et al., 2009). Between-

pathway models posit that the genetic interactions bridge genes operating in two

pathways with redundant or complementary functions, and deletion of either gene is

expected to abrogate the function of one but not both pathways. Within-pathway models

predict that the genetic interaction occurs between protein subunits within a single

pathway. A single gene is dispensable for the function of the overall pathway, but the

additive effect of several gene deletions is lethal. The indirect effort models can occur

because a deletion/mutation phenotype represents not just the absence or function

reduction of a particular gene, but also the response of the cell to the absence or function

reduction of that gene, which may include upregulating or downregulating diverse

pathways. For non-essential genes, genetic interactions between deletion mutants usually

occur between pathways that are redundant for an essential biological process. In

contrast, genetic interactions between essential genes can occur between two

hypomorphic alleles within the same biochemical pathway (within-pathway model).

1.4.2 Synthetic genetic array (SGA) analysis

Synthetic genetic array (SGA) analysis is an approach that automates the isolation

of yeast double mutants, enabling large-scale mapping of genetic interactions (Tong et

al., 2001). In the SGA analysis system, a series of robotic pinning steps is used to cross a

MATα query mutation strain into an ordered MATa mutant (deletion or conditional allele)

array. Through mating, sporulation, haploid selection, and double mutant selection,

inviable or slow growing double mutants are identified as synthetic lethal or sick (Figure

15

1-2) (Tong et al., 2001). The key to this system was the development of SGA reporters

that allow the germination of only MATa meiotic progeny from a population containing

both MATa and MATα spores.

Figure 1-2 Synthetic Genetic Array (SGA) Analysis

In SGA analysis, a MATα natMX-marked query strain is crossed to the MATa kanMX-

marked mutant array. After mating, sporulation, haploid selection and double mutant

selection, inviable/slow growing double mutants are identified as synthetic lethal/sick.

Synthetic lethality/sickness may indicate a functional relationship between genes.

16

To first look at genetic interactions on a global scale, our lab performed 132 SGA

screens, focused on certain well characterized genes - termed query genes involved in

actin-based cell polarity, cell wall biosynthesis, microtubule-based chromosome

segregation, and DNA synthesis and repair (Tong et al., 2004). The resulting confirmed

data set allowed construction of a genetic interaction network containing ~1,000 genes

and ~4,000 interactions. Genes with related biological functions are connected by

synthetic genetic interactions more often than expected by chance (Tong et al., 2004),

revealing that synthetic genetic interactions tend to occur among functionally related

genes. The number of interactions per query gene ranged from 1 to 146, with an average

of 34 interactions per screen. Genetic interactions are generally non-overlapping with

protein-protein interactions but two-dimensional hierarchical clustering of the

interactions reveals that genes within the same pathway or complex often show similar

patterns of genetic interactions. Clustering of previously uncharacterized genes with well-

studied pathways enables the prediction of precise molecular roles. Presumably, a

complete map of synthetic genetic interactions for yeast will identify sets of protein

complexes and pathways that buffer each other. Based on the network connectivity,

genetic clusters can be used to predict functions for uncharacterized genes. For example,

PAR32, ECM30, and UBP15 had similar genetic interaction profiles to those of members

of Gap1-sorting module and deletions of all three genes resulted in Gap1-sorting and

transport defects (Costanzo et al., 2010).

As noted above, genes that show similar genetic interaction profiles often encode

proteins within the same pathway or complex. For example, the uncharacterized gene

YMR299C showed a genetic interaction profile that is highly similar with the genes

17

encoding the dynein-dynactin spindle orientation pathway (ARP1, JUM1, NUM1, DYN1,

PAC1, NIP100, PAC11, DYN2) (Tong et al., 2004). Consistent with a role in the spindle

orientation pathway, a ymr299cΔ mutant exhibits abnormal cytoplasmic microtubules and

defects in mitotic spindle positioning similar to dyn1Δ and arp1Δ (Figure 1-3). Clustering

genetic interactions of uncharacterized genes with genetic interactions from components

of defined functions and/or pathways should enable the prediction of specific biological

functions for uncharacterized genes. Thus SGA stands to be a very powerful approach to

uncover gene functions for uncharacterized ORFs and to discover novel functions for

previously characterized genes.

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Adapted from Tong et al., 2004

Figure 1-3 Predicting Gene Function from Genetic Interaction Patterns

Two-dimensional hierarchical clustering of synthetic genetic interactions determined by

SGA analysis suggests that the novel ORF YMR299C may be a component of the dynein-

dynactin complex. Rows, 76 query genes; columns, 108 array genes. Synthetic genetic

interactions are represented as red squares.

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Large-scale mapping of synthetic lethal interactions has shown that ~20% of the

query genes have no detectable genetic interactions with the nonessential gene deletion

array under the conditions tested (growth on standard media). Only nonessential genes

are represented in the yeast deletion collection because essential genes cannot be deleted

in a yeast haploid strain. As such, ~1,100 essential genes are missed in the yeast deletion

array, accounting for 18.7% of the yeast genome. It is possible that many of the query

genes that fail to show genetic interactions with the nonessential array would do so with

an essential strain array. This possibility was a major motivation for my thesis project

which aimed to construct an SGA-compatible array of ts mutants in essential genes. In

fact, I performed an SGA screen with a temperature-sensitive allele of an essential gene

RFC5 (rfc5-1), which is involved in sister chromatid cohesion, DNA replication, and

DNA damage response (Tong and Boone, 2006) and generated a profile of functionally

informative genetic interactions (discussed more in Chapter Two).

1.5 High content screening

High content screening (HCS) is an automated imaging technology that facilitates

the capture and analysis of thousands of cellular images following different

genetic/chemical/environmental perturbations. HCS screening was first used in the fields

of toxicology and drug discovery. In recent years, image-based assays for early-stage

drug discovery have demanded an increase in the throughput and several developments

have enabled high-throughput image acquisition and analysis. These technological

advances have led to significant applications in both academic research and drug

discovery. For example, HCS has been effectively used to identify targets of small

20

molecules using assays in mammalian cells (Eggert et al., 2004; Loo et al., 2007; Paran et

al., 2007; Perlman et al., 2004; Rickardson et al., 2007). More recently, HCS has been

coupled with RNAi-based gene knockdown to characterize gene function in culture cells

(Bakal et al., 2007; Boutros et al., 2004; Moffat et al., 2006; Mukherji et al., 2006;

Narayanaswamy et al., 2006; Pelkmans et al., 2005; Tanaka et al., 2005). Coupling

genetic perturbation with HCS will enhance our understanding of functional connections

between genes.

Our group developed an experimental system that combines SGA-based high-

throughput strain manipulation and HCS to enable the visualization and quantitative

measurement of specific morphological features for numerous single cells within a field

(Vizeacoumar et al., 2009; Vizeacoumar et al., 2010). We took advantage of the deletion

collection for yeast non-essential genes and SGA technology to introduce fluorescent

reporters into every mutant in a collection, and samples were imaged in a systematic

format. A major bottleneck in our approach is the automated image analysis. Several

software solutions such as the open-source software “CellProfiler” are now available for

complicated image segmentation. Image analysis converts the images to numbers that can

then be subjected to several statistical tests to define mutants that deviate from the wild-

type. Indeed machine-learning approaches are increasingly being used to establish a

morphometric signature for a known set of mutants and identify novel components (Chen

et al., 2007). For example, Ohya et al. have employed support vector machines (SVMs),

which are capable of transforming the originally measured data to a high-dimensional

space through kernel mapping, to predict gene functions based on morphological profiles,

the rationale being genes within different functional groups are more separable in the

21

induced feature space (Ohya et al., 2005). Similarly, Bakal et al. trained a neural network

where a non-linear mapping of the original data set was done by minimizing a certain

objective function to identify local signaling networks that regulate cell protrusion,

adhesion and tension (Bakal et al., 2007).

1.6 Mitotic spindle and spindle assembly checkpoint pathways

1.6.1 Mitotic spindle

The combination of SGA analysis with HCS (SGA-HCS) provides a powerful

method for identifying and deciphering specific cellular functions, and this approach has

been applied previously by our group to study the morphogenesis of the mitotic spindle

(Vizeacoumar et al., 2010). I have extended this approach to identify mutants within the

ts collection that affect specific pathways by crossing different subcellular reporters and

querying for abnormal cellular phenotypes. I crossed several subcellular reporters to the

essential gene collection including a GFP-TUB1 reporter to monitor spindle dynamics in

the ts array. Since our assay of spindle phenotypes led to the identification of a novel role

for cohesin and condensin in spindle disassembly, I outline some of the background

required for spindle dynamics below.

Completion of the cell cycle requires the temporal and spatial coordination of

chromosome segregation with mitotic spindle disassembly and cytokinesis. Chromosome

segregation depends on a microtubule-based machine, the mitotic spindle, which is

composed of three sets of microtubules (MT). Kinetochore MTs connect kinetochores on

the sister chromatids to the spindle pole bodies, while astral (cytoplasmic) MTs are

involved in interactions between the spindle pole body and the cell cortex to help position

22

the nucleus in the bud neck. Interpolar MTs link the two spindle pole bodies by

interdigitating with each other to form an antiparallel MT array which is known as the

spindle midzone (Figure 1-4) (Khmelinskii and Schiebel, 2008).

Figure 1-4 Organization of the Mitotic Spindle

The spindle pole bodies (red), chromosomes (blue) and microtubules (MTs, black lines)

are shown. (1) kinetochore MTs; (2) astral (cytoplasmic) MTs; (3) interpolar MTs.

Overlapping interpolar MTs form the spindle midzone (4) with the onset of anaphase.

23

The spindle midzone is required to maintain the integrity of the anaphase spindle

in organisms from yeast to humans. The kinetics of pole separation and chromosome

segregation needs a stable spindle midzone. For example, interfering with the spindle

midzone, either genetically or by laser ablation, accelerates the rate of spindle elongation

in C. elegans embryos (Grill et al., 2001; Verbrugghe and White, 2004).

In budding yeast, Ase1p is a member of a conserved family of midzone-specific

proteins with MT bundling activity. The midzone-specific proteins are required for

proper midzone formation, and for promoting spindle extension and stability in anaphase,

and later they play a role in cytokinesis (Balasubramanian et al., 2004; Juang et al., 1997;

Mollinari et al., 2002; Norden et al., 2006; Schuyler et al., 2003).

The formation of the spindle midzone is regulated by the conserved protein

phosphatase Cdc14p. Cdc14p is essential for mitotic exit and meiotic progression (Schild

and Byers, 1980; Stegmeier and Amon, 2004; Taylor et al., 1997). Cdc14p

dephosphorylates key mitotic targets leading to inactivation of mitotic cyclins (Visintin et

al., 1998), proper spindle disassembly (Khmelinskii et al., 2007; Khmelinskii and

Schiebel, 2008), and completion of cytokinesis (Hall et al., 2008). The activity of Cdc14p

is regulated by two signaling pathways, the Cdc fourteen early anaphase release (FEAR)

network (Pereira et al., 2002; Pereira and Schiebel, 2003; Stegmeier et al., 2002; Visintin

et al., 2003; Yoshida et al., 2002) and the mitotic exit network (MEN) (Shou et al., 1999;

Visintin et al., 1998; Visintin et al., 1999; Visintin et al., 2003).

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1.6.2 Spindle assembly checkpoint pathways

After duplication of its chromosomes, the cell must segregate its chromosomes

equally. Two important processes, sister chromatid cohesion and kinetochore spindle

attachment, ensure the proper segregation of sister chromatids. Two kinetochores of each

sister chromatid pair capture microtubules emanating from opposite spindle pole bodies

to produce the metaphase plate at which all the chromosomes are bilaterally attached and

aligned at the equator of the spindle (Bloom, 2005; Lew and Burke, 2003; Skibbens et al.,

1993). Tension is produced when sister kinetochores are properly attached to opposite

spindle pole bodies, so that the pole-ward force exerted on the chromosomes by the

microtubules is counteracted by cohesion between sister chromatids.

The state of kinetochore spindle attachment is monitored by the spindle assembly

checkpoint (SAC) pathway. Unattached kinetochores emit negative signals that activate

the spindle assembly checkpoint pathway and prevent the onset of anaphase. In addition

to detecting unattached kinetochores, the checkpoint pathway also senses the tension

resulting from successful bilateral attachment of microtubules to the kinetochores

(Nasmyth, 2005; Tanaka, 2005).

Once sister chromatids are separated, mitotic spindle disassembly is triggered by a

complex of proteins referred to as the chromosomal passenger complex (CPC) and the

kinetochore CTF19 complex (Buvelot et al., 2003; Vizeacoumar et al., 2010). Below I

highlight the significance of these complexes and their roles in spindle disassembly.

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1.6.3 Chromosome passenger complex (CPC)

The chromosomal passenger complex (CPC) is an important regulator of

chromosome segregation during mitosis and is conserved from yeast to humans. The CPC

regulates chromosome condensation, chromosome biorientation, signaling to the spindle

checkpoint machinery, spindle assembly, and cytokinesis (Ruchaud et al., 2007a;

Ruchaud et al., 2007b). Yeast CPC members include Ipl1p, Sli15p, Bir1p and Nbl1p

(Chan and Botstein, 1993; Kang et al., 2001; Kim et al., 1999; Nakajima et al., 2009;

Yoon and Carbon, 1999).

The functions of CPC are associated with dynamic changes in localization

throughout the cell cycle. In the budding yeast S. cerevisiae, the CPC (e.g., Ipl1p) is

diffusely associated with kinetochores in G1, S phase, and metaphase cells, localizes

along the whole spindle. It becomes concentrated at the spindle midzone in anaphase

cells, and focuses at the spindle pole bodies in telophase cells (Buvelot et al., 2003). This

localization pattern is similar to that seen in mammalian cells (Ruchaud et al., 2007b).

Ipl1p is an Aurora kinase subunit of the conserved CPC that is involved in

regulating kinetochore-microtubule attachments and maintaining condensed

chromosomes during anaphase and early telophase (Cheeseman et al., 2002a; Kang et al.,

2001; Nakajima et al., 2009; Vas et al., 2007). Sli15p is a subunit of the chromosomal

passenger complex and involved in regulating kinetochore-microtubule attachments,

activation of the spindle assembly checkpoint, and mitotic spindle disassembly (Biggins

and Murray, 2001; Buvelot et al., 2003; Kim et al., 1999; Nakajima et al., 2009; Tanaka

et al., 2002). Bir1p regulates chromosome segregation, and is required for chromosome

bi-orientation and for spindle assembly checkpoint activation (Li et al., 2000; Makrantoni

26

and Stark, 2009; Nakajima et al., 2009; Yoon and Carbon, 1999). Recently identified

NBL1 is an essential gene and nbl1 mutations cause chromosome missegregation and

lagging chromosomes. Nbl1p colocalizes and copurifies with the CPC, and is essential for

CPC localization, stability, integrity, and function (Nakajima et al., 2009).

1.6.4 The kinetochore CTF19 complex

Ctf19p (chromosome transmission fidelity protein 19) was identified as part of the

kinetochore complex that functions as a link between the kinetochore and the mitotic

spindle (Hyland et al., 1999). The central kinetochore complex is composed of at least 13

different proteins and the kinetochore CTF19 complex consists of Ctf19p, Mcm21p, and

Okp1p (Hyland et al., 1999; Measday et al., 2002; Measday and Hieter, 2004; Ortiz et al.,

1999; Pot et al., 2003). The CTF19 complex mediates centromere attachment to the

mitotic spindle by forming interactions between the microtubule-associated outer

kinetochore proteins and the centromere-associated inner kinetochore proteins

(Cheeseman et al., 2002b). CTF19 regulates kinetochore architecture (Cheeseman et al.,

2002b; McAinsh et al., 2003; Measday and Hieter, 2004; Westermann et al., 2007) and

ensures loading of cohesin at centromeres prior to passage of the replication fork (Fernius

and Marston, 2009). Defective pericentromeric cohesion was found in CTF19 complex

mutants (Fernius and Marston, 2009). Recently, our group discovered a new role for

CTF19 complex components in mitotic spindle disassembly (Vizeacoumar et al., 2010).

27

1.7 Sister chromatid cohesion

1.7.1 Sister chromatid cohesion is mediated by the cohesin complex

Sister chromatid cohesion is the physical link that associates two sister chromatids

and is mediated by an essential core complex called cohesin (Guacci et al., 1997;

Michaelis et al., 1997; Toth et al., 1999). Chromatid cohesion is a highly conserved

process and I below outline some of the components identified in other eukaryotes.

Unless otherwise stated, however, I focus on key components of the cohesion complex

identified in S. cerevisiae.

The core cohesin complex consists of Scc1p, Scc3p, Smc1p, and Smc3p. These

core components are found in C. elegans (Mito et al., 2003), D. melanogaster (Warren et

al., 2000), and mammals (Ball Jr and Yokomori, 2001). Immunoprecipitation of any one

cohesin subunit coprecipitates the other three subunits in stoichiometric quantities. All

four subunits are nuclear proteins and associate with chromatin in an interdependent

manner. Colocalization of the cohesin complex and chromatin can be detected in samples

from S, G2, and metaphase cells, but not in early G1 cells (Michaelis et al., 1997). In S.

cerevisiae, sister chromatid separation is induced by the cleavage of cohesins by the

protease Esp1p (Guacci et al., 1997; Uhlmann, 2004). In C. elegans, RNA interference

(RNAi) experiments revealed that depletion of any conserved cohesin homolog caused a

defect in mitotic chromosome segregation but not in chromosome condensation and

cytokinesis (Mito et al., 2003). In human cells, separase is recruited to mitotic

chromosomes and cohesin cleavage by separase requires DNA in a sequence-nonspecific

manner (Sun et al., 2009).

28

In addition to the core cohesin complex, the following components are also

required for proper chromatid cohesion: (1) Pds5p which binds loosely to the cohesin

complex; (2) a separate complex, containing Scc2p and Scc4p, which is essential for

association of cohesin with chromosomes; (3) Eco1p, an acetyl transferase, which is

required for establishing cohesion during S phase but not for cohesion maintenance

(Nasmyth and Haering, 2009). Together these components function with the cohesin

complex to ensure accurate chromosome segregation.

1.7.2 Structure and roles of cohesin

The Smc1p and Smc3p subunits of the cohesin complex form a heterodimer,

which sits at the heart of the cohesin complex. Each subunit consists of an intramolecular

antiparallel coiled coil domain and forms a rod-shaped protein with a globular “hinge”

domain at one end and an ATP nucleotide-binding domain at the other end (Haering et

al., 2002; Hirano and Hirano, 2002; Melby et al., 1998).

In every cell cycle, each chromosome is replicated in S phase to form two

identical sister chromatids that are held together by sister chromatid cohesion (SCC),

which is mediated by the cohesin complex. The cohesin complex is a major component of

interphase and mitotic chromosomes, and this complex associates with chromosomes

before their replication and is converted into a cohesive state as replication forks pass.

Sister chromatids are held together by SCC continuously from the time of their formation

until their separation during mitosis when cohesion is removed to allow chromosome

segregation (Nasmyth and Haering, 2009).

29

In addition to its role in mediating sister chromatid cohesion, cohesin is also

important for the repair of DNA double-strand breaks in mitotic (Cortes-Ledesma and

Aguilera, 2006; Sjogren and Nasmyth, 2001) and meiotic cells (Ellermeier and Smith,

2005; Klein et al., 1999; van Heemst et al., 1999). Two recent studies reported the

detection of cohesin subunits at spindle pole bodies (Wong and Blobel, 2008) or

centrosomes (Kong et al., 2009). Cohesin depletion causes spindle pole defects, but it is

difficult to exclude the possibility that these defects are an indirect consequence of prior

mitotic defects caused by a lack of sister chromatid cohesion.

1.8 Condensin

Condensin is important during mitosis for the compaction and resolution of

chromosomes and plays a key role in the condensation of chromosomes. Analysis of the

in vivo localization of an Smc4p-GFP fusion protein revealed that condensin binds to in

the repetitive regions of yeast chromosomes during the G2/M phase of the cell cycle

(Freeman et al., 2000). Through interactions with RNA polymerase III transcription

factor TFIIIC, the yeast condensin complex also binds to tRNA genes (D'Ambrosio et al.,

2008; Haeusler et al., 2008). Condensin-binding sites on chromosomes were analyzed by

chromatin immunoprecipitation and hybridization to whole-genome microarrays and

validated by quantitative PCR. The results showed that the condensin-occupied sites span

the length of every chromosome in yeast and also occur in specialized chromatin regions

(near centromeres and telomeres) and in heterochromatic regions (Wang et al., 2005).

30

1.8.1 Components and structure of condensin

The budding yeast has a single condensin complex that consists of Ycg1p, Brn1p,

Smc2p, Smc4p, and Ycs4p, and five genes encoding condensin components are essential

(Freeman et al., 2000; Ouspenski et al., 2000; Strunnikov et al., 1995). All five condensin

subunits are present in an equimolar ratio in the condensin complex (Freeman et al.,

2000). Two SMC subunits (Smc2p and Smc4p) form a stable heterodimer, the “Smc2/4

complex”, and self-association of Smc2/4 heterodimers interacting reversibly appears to

form heterotetramers (Stray and Lindsley, 2003).

1.8.2 Functions of condensin

Condensins associate with condensing chromosomes during mitosis and are

involved in mitotic chromosome dynamics. Condensins are required for proper

chromosome condensation and segregation in budding yeast (Bhalla et al., 2002; Freeman

et al., 2000; Lavoie et al., 2002; Lavoie et al., 2000; Ouspenski et al., 2000; Strunnikov et

al., 1995) and fission yeast (Saka et al., 1994; Sutani et al., 1999). Consistent with

conservation of condensin function in eukaryotes, severe defects in chromosome

segregation are seen when individual condensin subunits are inactivated in C. elegans

(Hagstrom et al., 2002) and Drosophila (Steffensen et al., 2001).

Consistent with a broad role in chromosome regulating chromosome architecture,

condensin also appears to play a role in regulating repression of gene expression at the

silent mating type loci in yeast (Bhalla et al., 2002). Mutation of YCS4 alters the response

of MATa haploid cells so that they continue to grow in the presence of α-factor, and the

defect in α-factor-induced arrest is overcome when the silent mating locus HML is

31

deleted (Bhalla et al., 2002). This result is consistent with inappropriate expression of the

HML locus in MATa haploids.

1.9 Summary of thesis

In this thesis, I described the construction of a novel collection of temperature-

sensitive yeast mutants consisting of 795 ts strains, covering 501 (~45%) of the 1,101

essential yeast genes, with ~30% of the genes represented by multiple alleles. I have

validated and demonstrated the use of the ts collection in a number of different ways.

First, I collaborated with colleagues at the University of Gothenburg to carry out detailed

temperature profiling of each mutant in liquid growth assays and found that mutants in

particular pathways often showed highly similar profiles. Second, collaborating with

members of Chris Bulawa’s lab in FoldRx Pharmaceuticals Inc., I showed that the ts

array is useful for exploration of chemical-genetic interactions since it enables screens for

compounds that suppress defects in vital pathways. Third, I demonstrated that the ts

collection represents a key reagent set for genetic interaction analysis because essential

genes tend to be highly connected hubs on the global genetic network in collaboration

with the Boone and Andrews labs’ members. Fourth, I collaborated with members of the

Boone, Andrews, Bloom and Zhang labs to further validate the ts array as a key resource

for quantitative phenotypic analysis by using a high-content screening protocol to score

six different fluorescent markers, diagnostic for different subcellular compartments or

structures, in hundreds of different mutants. Quantification of the marker behaviour at the

single-cell level enabled integration of this data set to generate a morphological profile

32

for each ts mutant to reveal both known and previously unappreciated functions for

essential genes, including roles for cohesin and condensin genes in spindle disassembly.

33

CHAPTER TWO

Construction, Characterization and Evaluation of Temperature-sensitive Mutant Library

Work in Chapter 2 was published in the following papers:

Li, Z., Vizeacoumar, F.V., Bahr, S. et al. (2011). Systematic exploration of essential yeast gene function with temperature-sensitive mutants. Nat Biotechnol 29, 361-367. Magtanong, L., Ho, C.H., Barker, S.l., Jiao, W., Baryshnikova, A., Bahr, S., Smith, A., Heisler, L., Kuzmin, E., Andrusiak, K., Kobylianski, A., Li, Z. et al. (2011). Dosage suppression genetic interaction networks map a functional wiring diagram of the cell. Nat Biotechnol (accepted). Stirling, P.C., Bloom, M.S., Solanki-Patil, T., Smith, S., Sipahimalani, P. Li, Z., Kofoed, M., Ben-Aroya, S., Myung, K., Hieter, P. (2011). The complete spectrum of yeast chromosome instability genes identifies candidate CIN cancer genes and functional roles for ASTRA complex components. PLoS Genetics (accepted). Yen, W. L., Shintani, T., Nair, U., Cao, Y., Richardson, B. C., Li, Z., Hughson, F. M., Baba, M., and Klionsky, D. J. (2010). The conserved oligomeric Golgi complex is involved in double-membrane vesicle formation during autophagy. J Cell Biol 188, 101-114. Vizeacoumar, F. J., van Dyk, N., F, S. V., Cheung, V., Li, J., Sydorskyy, Y., Case, N., Li, Z., Datti, A., Nislow, C., et al. (2010). Integrating high-throughput genetic interaction mapping and high-content screening to explore yeast spindle morphogenesis. J Cell Biol 188, 69-81. Costanzo, M., Baryshnikova, A., Bellay, J., Kim, Y., Spear, E. D., Sevier, C. S., Ding, H., Koh, J. L., Toufighi, K., Mostafavi, S., Prinz, J., St Onge, R. P., VanderSluis, B., Makhnevych, T., Vizeacoumar, F. J., Alizadeh, S., Bahr, S., Brost, R. L., Chen, Y., Cokol, M., Deshpande, R., Li, Z., et al. (2010). The genetic landscape of a cell. Science 327, 425-431. Chang, M., Luke, B., Kraft, C., Li, Z., Peter, M., Lingner, J., and Rothstein, R. (2009). Telomerase is essential to alleviate pif1-induced replication stress at telomeres. Genetics 183, 779-791. Makhnevych, T., Sydorskyy, Y., Xin, X., Srikumar, T., Vizeacoumar, F. J., Jeram, S. M., Li, Z., Bahr, S., Andrews, B. J., Boone, C., and Raught, B. (2009). Global map of SUMO function revealed by protein-protein interaction and genetic networks. Mol Cell 33, 124-135.

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Luke, B., Panza, A., Redon, S., Iglesias, N., Li, Z., and Lingner, J. (2008). The Rat1p 5' to 3' exonuclease degrades telomeric repeat-containing RNA and promotes telomere elongation in Saccharomyces cerevisiae. Mol Cell 32, 465-477. Monastyrska, I., He, C., Geng, J., Hoppe, A. D., Li, Z., and Klionsky, D. J. (2008). Arp2 links autophagic machinery with the actin cytoskeleton. Mol Biol Cell 19, 1962-1975. Davierwala, A. P., Haynes, J., Li, Z., et al. (2005). The synthetic genetic interaction spectrum of essential genes. Nat Genet 37, 1147-1152. Parsons, A. B., Brost, R. L., Ding, H., Li, Z., Zhang, C., Sheikh, B., Brown, G. W., Kane, P. M., Hughes, T. R., and Boone, C. (2004). Integration of chemical-genetic and genetic interaction data links bioactive compounds to cellular target pathways. Nat Biotechnol 22, 62-69. Wong, S. L., Zhang, L. V., Tong, A. H., Li, Z., et al. (2004). Combining biological networks to predict genetic interactions. Proc Natl Acad Sci U S A 101, 15682-15687. Mnaimneh, S., Davierwala, A. P., Haynes, J., Moffat, J., Peng, W. T., Zhang, W., Yang, X., Pootoolal, J., Chua, G., Lopez, A., Trochesset, M., Morse, D., Krogan, N. J., Hiley, S. L., Li, Z., et al. (2004). Exploration of essential gene functions via titratable promoter alleles. Cell 118, 31-44. Tong, A. H., Lesage, G., Bader, G. D., Ding, H., Xu, H., Xin, X., Young, J., Berriz, G. F., Brost, R. L., Chang, M., Chen, Y., Cheng, X., Chua, G., Friesen, H., Goldberg, D. S., Haynes, J., Humphries, C., He, G., Hussein, S., Ke, L., Krogan, N., Li, Z., et al. (2004). Global mapping of the yeast genetic interaction network. Science 303, 808-813. Contributions: I co-coordinated and led the project, and performed the majority of (1) searching and collecting ts alleles from more than 300 labs; (2) designing primers; (3) construction and confirmation of ts strains; (4) SGA analysis and data analysis. S. Bahr, H. Lu, Y.Q Chen, and R.L. Brost assisted with ts strain collection, construction , and confirmation, and SGA analysis. H. Ding assisted with the SGA data analysis. A. P. Davierwala assisted with screening the Tet alleles. J. Warringer (Department of Cell and Molecular biology, Medicinaregatan 9c, Göteborg University, 41390 Göteborg, Sweden) performed the high-resolution profiling of the temperature sensitivity of ts alleles.

35

2.1 Abstract

Essential genes are required for the viability of yeast haploid cells. To

systematically study yeast essential gene functions using synthetic genetic array (SGA)

analysis (Tong et al., 2001), I constructed a temperature-sensitive (ts) mutant array which

contains 795 ts alleles representing ~45% of yeast essential genes. The integration of the

ts allele was confirmed by PCR analysis and the identity of the ts allele was confirmed by

complementation of the ts phenotype with the cognate plasmid. The ts mutant collection

was characterized by high-resolution profiling of the temperature sensitivity of each ts

strain, distribution analysis of gene ontology (GO) molecular function and biological

process, and comparison of ts alleles to Tet alleles of essential genes. The usefulness of

the ts allele collection was also evaluated by SGA analysis. These results demonstrated

that the ts collection is a powerful reagent for systematic study of yeast essential gene

functions and provides a valuable resource to complement the current yeast deletion

collection.

2.2 Introduction

Gene disruption studies demonstrated that 1,105 (18.7%) of the ~5,800 protein-

coding yeast genes (Cliften et al., 2003; Kellis et al., 2003) are essential for haploid

viability when tested for spore germination at 30oC in rich medium with glucose as the

carbon source (Giaever et al., 2002). Transcription, splicing, ribosome biosynthesis,

translation, cell wall and membrane biogenesis, DNA replication, nuclear transport, and

basic cytoskeletal functions are all required for cell proliferation, and so genes involved

in these processes tend to be essential (Mnaimneh et al., 2004). Essential genes also tend

36

to be highly conserved in humans: ~38% of essential yeast proteins have counterparts in

humans, versus ~20% for nonessential genes (Hughes, 2002). Nevertheless, the precise

molecular and genetic functions of many essential yeast proteins have not been studied in

detail, at least in part because essential genes cannot be easily studied using deletion

mutants.

Because essential genes cannot be deleted in haploid strains, one way to

systematically study yeast essential genes is to use a conditional system to modulate

essential gene function. As described in Chapter 1, systems for modulating essential gene

function include temperature-sensitive (ts) (Hartwell, 1967), cold-sensitive (cs) (Moir et

al., 1982), temperature-inducible degron (td) (Kanemaki et al., 2003), tetracycline-

regulatable promoter-replacement (Tet) (Mnaimneh et al., 2004), temperature-sensitive

inteins (Tan et al., 2009), or decreased abundance by mRNA perturbation (DAmP) alleles

(Schuldiner et al., 2005). The type of genetic perturbation most commonly used for

studying essential genes, ts alleles, provides a simple and fine-tuned control of gene

function, enabling permissive, semi-permissive, and restrictive conditions to be easily

established. For example, at the permissive temperature, the phenotype of a ts mutant

resembles that of the wild-type strain, whereas at the restrictive temperature, the activity

of the essential gene is substantially reduced or abolished, resulting in a slow-growth or

lethal phenotype.

I constructed a collection of 795 ts alleles for 501 yeast essential genes, which is

useful to the yeast research community as a resource that complements the yeast non-

essential deletion array.

37

2.3 Results

2.3.1 Construction of temperature sensitive strains

Over 1,300 yeast strains and DNA constructs carrying ts alleles were collected from

more than 300 laboratories. Once the ts phenotypes were confirmed, I designed primers for

moving the ts alleles into the S288c reference background (BY4741, MATa his3Δ1 leu2Δ0

ura3Δ0 met15Δ0) (Brachmann et al., 1998). Each ts allele was integrated into its native

locus such that it was linked to a kanamycin-resistant cassette, kanMX, which was targeted

to the 3’UTR region of the gene and placed 0-300 bp downstream of the stop codon (Table

1). I designed the integration such that the intervening sequences were not altered to avoid

perturbing the expression of neighboring genes (Figure 2-1). In total, I constructed a set of

795 ts strains representing more than 500 essential genes (Table 2), accounting for ~45% of

the yeast essential genes, which covers a representative proportion of the GO molecular

functions and biological processes for yeast essential genes. Over ~30% of the essential

genes in the ts mutant collection have multiple alleles (Table 2).

38

Figure 2-1 Construction of Temperature Sensitive Strains

A. Strategy for constructing strains harboring ts alleles of essential genes is depicted (not

drawn to scale). A ts allele including 200-350 bp downstream of its stop codon was

amplified by PCR using primers F1 and R2 (Table 1). The kanMX cassette was amplified

by PCR using primers F3 and R4 (Table 1). The F1 primer sequence is unique to a region

50-400 bp upstream of the ts allele ATG while the R2 primer sequence is complementary

to a region 200-350 bp downstream of the stop codon and includes an additional 32 to 36

bp sequence complementary to the 5’ end of the TEF promoter. The F3 primer sequence

is unique to the TEF promoter and primer R4 sequence is complementary to the TEF

terminator of the kanMX cassette and a 45 bp sequence immediately downstream of the ts

allele stop codon. The F3 primer sequence is AGATCTGTTTAGCTTGCCTCGTCC and

sequences for F1, R2, and R4 primers are listed in Table 1.

B-C. The wild-type ORF was replaced by ts allele::kanMX through homologous

recombination.

39

2.3.2 Confirmation of the constructed ts strains

To confirm proper integration of the ts allele and kanMX cassette, I carried out two

PCR reactions. One PCR reaction was used to test whether the kanMX cassette was linked

to the expected target gene. A second PCR reaction was used to check whether the ts

allele::kanMX was integrated at the expected genomic location (Figure 2-2). Correct

integration was confirmed for ~99% of ts alleles (787/795) using this PCR-based strategy

(Table 2).

To confirm that the ts phenotype was due to the expected allele, I transformed 794

of 795 ts strains with plasmids carrying a corresponding wild-type gene and then tested the

resultant transformants for growth at both the permissive and restrictive temperatures

(Figure 2-3). The plasmids were derived from either the low-copy molecular-barcoded yeast

ORF (MoBY-ORF) library (Ho et al., 2009) or the high-copy yeast genomic tiling

collection (Jones et al., 2008) (neither library contained a plasmid encoding CDC39). The

high quality of the ts mutant library was confirmed because the ts phenotype of over 99% of

(787/794) ts strains was rescued in a plasmid-dependent manner (Table 2).

40

Figure 2-2 PCR Confirmation of ts Strains

Two PCR reactions were used to confirm proper integration of the ts allele and kanMX

cassettes. Primers A and B were used to check the linkage of kanMX cassette to the target

gene. Primers C and D were used to verify the integration of the ts allele::kanMX at the

target gene locus. The primer sequences for confirmation PCR are listed in Table 4.

41

Figure 2-3 Functional Complementation by Plasmid

The ts mutant strains were transformed with plasmids carrying a corresponding wild-type

gene (i.e., pACT1 or pRSC8), and with a vector control. After growth for 3-5 days at

22°C, transformants were replica-plated (A) and/or streaked-out (B) and incubated at

22oC and at the restrictive temperature for 1-3 days.

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2.3.3 High-resolution profiling of the temperature sensitivity of ts alleles

Next, I collaborated with Jonas Warringer and Anders Blomberg to apply high

resolution growth profiling to characterize the temperature sensitivity of each mutant in the

ts collection. Lag time, growth rate and growth efficiency (Warringer and Blomberg, 2003)

were quantified across a range of temperatures to generate a growth profile for each ts

mutant (Figure 2-4A). The reference strain BY4741 reached its maximal growth rate

between 32°C and 36°C, whereas growth efficiency and growth lag more or less

continuously decreased over the temperature range (Figure 2-4B). Individual ts strain

growth profiles were normalized against a reference wild-type profile to provide a measure

of relative growth defects at each temperature (Warringer and Blomberg, 2003). As

expected, the frequency distribution of relative growth rates reflected the tendency towards

exaggerated growth defects at higher temperatures (Figure 2-4C). In addition, after

normalizing for defects at 22°C, the fraction of ts strains with significant temperature-

dependent growth defects expanded with increasing temperature, from 24°C to 34°C

(Figure 2-4D). Hierarchical clustering of liquid growth profiles revealed that ts mutants in

functionally related genes showed similar growth behavior in response to temperature

suggesting that temperature-dependent growth profiles may represent distinct phenotypic

fingerprints reflecting an underlying physiological defect (Figure 2-4E). For example,

subsets of ts mutants in components of DNA repair, cell division cycle, spindle pole body,

RNA splicing, RNA polymerase II-dependent transcription, and ER to Golgi transport

cluster together in specific cohorts (Figure 2-4E). These findings suggest that defects in

different essential cellular functions may translate into diagnostic growth curves. For

example, our data suggests that Cdc48p, which is a component of the Cdc48p-Npl4p-Ufd1p

43

AAA ATPase complex and participates in retrotranslocation of ubiquitinated proteins from

the ER into the cytosol for degradation by the proteasome (Metzger et al., 2008;

Nakatsukasa et al., 2008), may have roles in DNA repair (Figure 2-4F). Also, proteins

involved in nuclear mRNA splicing (Msl5p and Yhc1p) may function in ER to Golgi

transport (Figure 2-4F).

44

E

45

Figure 2-4 Profiling the Temperature Sensitivity of ts Strains

A. General interpretation of high-resolution liquid growth profiles of strains harboring ts

alleles. Growth rate was determined by measuring the slope of the exponential phase of

the growth curve and converted into population doubling time. Lag (hrs) is given by the

intercept of the initial density and the slope, and growth efficiency (OD units) is

calculated as the total change in density for cells having reached stationary phase.

B. Liquid growth variables, doubling time (rate), lag and efficiency of mitotic growth, for

the reference WT strain (n=72) measured at various temperatures. Growth rate (red) is

given as population doubling time (h), lag (blue) is given as time to initiate growth (h),

and efficiency (black) is given as the total change in population density (OD units).

C. Frequency distribution of the relative growth rate defects (log2[WT/ts-strain]) for ts

alleles at 22oC (blue), 30oC (black) and 40oC (red). Dotted line=0 (WT growth rate).

D. Fraction of ts strains that exhibit significant temperature sensitivity (p<0.001)

compared to WT at the indicated temperatures. Growth rate (red), lag (blue), and

efficiency (black) are shown.

E-F. Uncentered hierarchical clustering of ts allele temperature sensitivity profiles.

Profile similarity was measured using a Pearson similarity metric and average linkage

46

mapping. Functional clusters are indicated and alleles in each cluster are shown in colors.

The ts-strain sensitivity ratio is expressed as log2(WT/ts strain) at temperature X –

log2(WT/ts strain) at 22oC.

2.3.4 GO molecular function and biological process distribution of ts mutant library

Using SGD Gene Ontology Slim Mapper (http://db.yeastgenome.org/cgi-

bin/GO/goTermMapper), the GO molecular function distribution and biological process

distribution of the entire yeast genome, all yeast essential genes and essential genes on the ts

allele array were analyzed. In each molecular function (Figure 2-5A) or biological process

(Figure 2-5B) group, the percentage of the essential genes on the ts mutant array is very

similar to that of the essential genes in yeast, indicating that the ts mutant collection can

represent the spectrum of the essential gene functions and biological processes in the yeast

genome.

47

Figure 2-5 GO Molecular Function and Biological Process Distribution for Yeast

Genome, Yeast Essential Genes and Genes in ts Collection

Genes for the whole yeast genome (5,795, black), essential genes in yeast genome (1,062,

blue) and essential genes in the ts mutant collection (501, red) were entered into SGD

Gene Ontology Slim Mapper (http://db.yeastgenome.org/cgi-bin/GO/goTermMapper). The percentage

was calculated by gene number in a specific molecular function group (A) or biological

process group (B) out of three categories (yeast genome, essential genes in yeast genome,

or essential genes in the ts mutant collection).

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2.3.5 Essential genes are hubs of a genetic interaction network

To illustrate the utility of the ts mutants for exploring essential gene functions, I

conducted SGA analysis by crossing the nonessential gene deletion mutant

(bni1Δ::natMX) and the essential gene ts mutant (rfc5-1::natMX) into three different

collections: (1) an early version of the ts allele array containing 191 ts alleles (133

essential genes); (2) the Tet-array containing tetracycline-regulatable promoter-

replacement alleles for 602 essential genes (Mnaimneh et al., 2004); and (3) the deletion

array containing 4,700 nonessential genes (Tong et al., 2001). In these experiments the

genetic interaction (GI) percentage for the ts collection and Tet collection (3.99% -

12.78%, GIs/gene) was much higher than the GI percentage for the deletion collection

(0.45% - 1.38%, GIs/gene) (Table 3), suggesting that the ts array and the Tet array may

provide an opportunity to detect more genetic interactions. These results indicate that the

frequency of genetic interactions for essential genes may be higher than the frequency of

genetic interactions for nonessential genes, consistent with previous work (Davierwala et

al., 2005). This could suggest that essential genes are hubs of the genetic interaction

network (Davierwala et al., 2005; Tong et al., 2004), which is similar to what was seen in

the physical interaction network: protein-protein interactions were higher amongst

essential proteins (44%) than nonessential proteins (17%) (Gavin et al., 2002).

49

2.3.6 Comparison of ts alleles to Tet alleles

To compare two conditional alleles (ts allele versus Tet allele), I crossed sec1-1,

sec15-1, arp2-14, bni1Δ, and rfc5-1 strains against a combined array containing only

alleles of essential genes that were represented both in the ts mutant collection and Tet

allele collection (Mnaimneh et al., 2004) using the SGA method. The genetic interaction

profiles for SEC1, SEC15, and RFC5 between the two different conditional alleles were

similar (Figure 2-6). Specifically, 4/5, 8/9, and 7/11 genetic interactions were observed

for both the ts allele and Tet allele when SEC1, SEC15, and RFC5 respectively were used

as a query. These results indicated that the ts allele is comparable to the Tet allele in these

cases; however, I observed different genetic interaction profiles for ARP2 (only one of 9

interactions common to both alleles) and BNI1 (2 of 7 in both alleles), suggesting that

these different conditional alleles can generate complementary data for essential genes

(Figure 2-6).

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Figure 2-6 Comparison of ts Alleles and Tet Alleles

Two-dimensional clustering of the synthetic genetic interactions determined by SGA

analysis when crossing sec1-1, sec15-1, arp2-14, bni1Δ, and rfc5-1 into a combined array

containing both ts alleles and Tet alleles of a subset of essential genes. Rows, query

genes; columns, conditional alleles. Blue, genetic interactions observed in both ts allele

and Tet allele; red, genetic interaction observed in ts allele only; green, genetic

interaction observed in Tet allele only.

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2.3.7 Evaluating the usefulness of the ts mutant collection

To further evaluate the usefulness of the ts allele collection, I crossed a deletion

allele of a nonessential gene (bni1Δ::natMX) against the ts mutant array. Bni1p is

required for a proper bipolar budding pattern (Evangelista et al., 1997; Evangelista et al.,

2002; Pruyne et al., 2002) and is involved in the regulation of actomyosin ring dynamics

(Vallen et al., 2000), concentrating polarized growth to the bud tip during apical growth

(Sheu et al., 2000), septation (Sheu et al., 2000), and localization of Bud8p to the bud tip

(Ni and Snyder, 2001).

Septins are proteins associated with the neck filaments. The septin ring is seen at

the bud neck using electron microscopy (Field and Kellogg, 1999). There are seven septin

genes (CDC3, CDC10, CDC11, CDC12, SHS1, SPR3, and SPR28) in S. cerevisiae.

CDC3, CDC10, CDC11, and CDC12 are essential genes. Previous studies showed that

bni1Δ is synthetic lethal with septin alleles, shs1Δ (Tong et al., 2001) and cdc12-ts

(Akada et al., 1997; Longtine et al., 1996).

The NH2-terminal portion of Bni1p interacts with Cdc42p, whereas the COOH-

terminal portion interacts with Pfy1p, Bud6p, and Act1p (Evangelista et al., 1997). Bni1p

occurs as part of a complex that directs the assembly of actin filaments in response to

Cdc42p signaling during polarized morphogenesis. It was previously shown that bni1Δ is

synthetic lethal with act1-120 (Evangelista et al., 1997) and cdc42-118 (Kozminski et al.,

2003).

When I crossed the bni1Δ::natMX query strain into an early version of the ts

allele array containing 191 ts alleles (133 essential genes), I identified synthetic genetic

interactions with ACT1, CDC3, CDC10, CDC11, CDC12, and CDC42. These results

52

were consistent with the known functions of BNI1, as all six genes are involved in

budding, bud growth, establishment of cell polarity and cytokinesis.

I also crossed one essential gene as a ts allele (rfc5-1::natMX) against the ts array.

Rfc5p is a subunit of the Replication Factor C (RFC) complex. There are four different

RFC complexes: (1) Ctf18p, Ctf8p, Dcc1p, and Rfc2p-Rfc5p form a RFC complex

involved in sister chromatid cohesion, (2) Rfc1p-Rfc5p complex is involved in DNA

replication, (3) Rad24p, Rfc2p-Rfc5p complex is involved in the DNA damage response,

and (4) Elg1p, Rfc2p-Rfc5p complex is involved in DNA replication and DNA damage

response (Bellaoui et al., 2003; Jones and Sgouros, 2001; Kenna and Skibbens, 2003).

Rfc5p is a component of all four RFC complexes. The cohesin complex links sister

chromatids together at metaphase during mitosis. Sister chromatid cohesion is established

during DNA replication and dissolved during the metaphase-to-anaphase transition

(Campbell and Cohen-Fix, 2002). The core cohesin complex consists of Smc1p, Smc3p,

Scc1p, and Scc3p (Nasmyth, 1999; Nasmyth, 2001). Loading complex requires Scc2p

and Scc4p, which load the cohesin complex onto chromosomes (Ciosk et al., 2000).

When I crossed the rfc5-1 ts mutant into the ts allele array, I identified genetic

interactions with SMC1, SMC3, SCC1, and SCC2, all of which are involved in sister

chromatid cohesion (Figure 2-7).

Thirty-three genes, including 18 essential genes, are involved in mitotic sister

chromatid segregation (http://www.geneontology.org). Five of 18 essential genes (CDC16,

CDC20, CDC23, CDC27, and SMC1) are represented on an early version of the ts array

containing 191 ts alleles (133 essential genes). I identified four (CDC16, CDC23,

CDC27, and SMC1) of five in the RFC5 screen (Figure 2-7).

53

Both Rfc5p and Pri2p are the members of the Pol12p associated complex (Gavin

et al., 2002) and are involved in DNA replication. RFC5 also showed synthetic

interactions with several genes involved in DNA replication when I crossed rfc5-1 to the

ts allele array (Figure 2-7).

Thus, genetic interactions amongst functionally related genes for both BNI1 and

RFC5 were identified from SGA analysis with the ts allele array, illustrating the potential

of this collection as a valuable resource for exploring essential gene function.

54

Figure 2-7 rfc5-1::natMX Crossed into the ts Array

The ts query strain rfc5-1::natMX was crossed into the ts mutant array, which contained

191 ts alleles (133 essential genes), by SGA analysis. The double mutant selection plates

were incubated at 22oC, 26oC, and 30oC. The synthetic genetic interactions shown here

were confirmed by random spore analysis and/or tetrad analysis. Genes are represented as

nodes, and interactions are represented as edges that connect the nodes. Functionally

related genes are grouped by ovals.

55

2.4 Discussion

Approximately 19% (1,105) of all S. cerevisiae genes are considered essential in

the S288c reference genetic background because the haploid spores carrying a deletion

allele of these genes fail to germinate and form a colony under standard laboratory

conditions (Giaever et al., 2002). The essential gene set is largely the same within other S.

cerevisiae genetic backgrounds; however, it may vary in gene content by as much as

~5%, possibly due to complex genetic interactions associated with strain-specific genetic

variation (Dowell et al., 2010). Essential gene function appears to be highly conserved

because 83% of the S. cerevisiae orthologs that are conserved in S. pombe retain their

essential gene functions (Kim et al., 2010).

Our ts mutant collection contains ts alleles of 501 (~45%) different S. cerevisiae

essential genes. Because each gene is linked to a kanMX marker the alleles can be readily

used in SGA-based automated genetic analysis, which enables their barcoding for

competitive growth assays in pools (Yan et al., 2008), genetic network mapping

(Costanzo et al., 2010), high-content screening (Vizeacoumar et al., 2010), and a host of

other applications. Over ~30% of the essential genes in the ts mutant collection have

multiple alleles (Table 2), which can be useful for dissecting the different roles of

multifunctional proteins, such as actin. The ultimate aim is to expand the collection to

include a range of ts alleles that cover all domains of essential genes. Indeed, previous

efforts to dissect allele-specific interactions involving essential genes have provided

significant insight into protein structure and function (Amberg et al., 1995; Dreze et al.,

2009).

56

High resolution growth profiling of the temperature sensitivity of mutants in the

strain collection revealed that ts mutations in functionally related genes showed similar

growth behavior in response to temperature (Figure 2-4E-F). Thus, temperature-

dependent growth profiles may represent distinct phenotypic fingerprints that reveal

physiological defects associated with different essential cellular functions.

2.5 Materials and Methods

2.5.1 Yeast strains and medium

Strains used in this study were all S. cerevisiae. BY4741 (MATa, his3Δ1 leu2Δ0

met15Δ0 ura3Δ0) was used as a wild-type control and for constructing ts strains.

Standard yeast media and growth conditions were used as previously described

(Sherman, 1991; Sherman, 2002). Nonessential haploid deletion strains were made by the

Saccharomyces Gene Deletion Project (Winzeler et al., 1999) and were obtained from

Open Biosystems and EUROpean Saccharomyces cerevisiae ARchive for Functional

analysis (EUROSCARF).

2.5.2 Checking ts phenotypes for the collected yeast strains

I refreshed the yeast strains on YEPD (or SC) plates for 2-4 days at 23oC. Single

colonies of the yeast strains received from other labs and the control wild-type strain

(BY4741) were re-streaked on YEPD (or SC) plates and incubated at different

temperatures (23oC, 26oC, 30oC, 35oC, 37oC, and 38.5oC) for 3-5 days (Figure 2-8). The

strains which grew well at 23oC and died or were sick at higher temperatures were used

for constructing new ts strains in the deletion background BY4741.

57

Figure 2-8 ts Phenotype Confirmation on YEPD Plates

The ts strain candidates and the wild-type strains were streaked out on YEPD plates and

incubated at different temperatures for 3-5 days. In these experiments the restrictive

temperature for sec18-1 and sec12-1 was 30oC, and for sec7-1 was 37oC. This ypt1-1

strain was still able to grow at 38.5oC and rejected as not a ts strain.

58

2.5.3 Designing primers for constructing new ts alleles

I designed primers for constructing new ts alleles in the deletion background

strain once the ts phenotypes of the received yeast strains were confirmed. The F1 primer

and the R2 primer were used to amplify the ts allele. The F3 primer and R4 primer were

used to amplify the kanMX cassette (Figure 2-1). The F3 primer sequence is

AGATCTGTTTAGCTTGCCTCGTCC and sequences for F1, R2, and R4 primers are

listed in Table 1.

2.5.4 Synthetic genetic array (SGA) analysis

Synthetic genetic array analysis (SGA) was carried out as described previously

(Tong and Boone, 2006; Tong et al., 2001; Tong et al., 2004). First, a MATα SGA query

strain (xxx∆::natMX or ts allele::natMX) was crossed into an ordered array of MATa

xxx∆::kanMX deletion strains or MATa ts mutants (ts allele::kanMX); second, the

resultant diploids were selected on YEPD+G418+clonNAT plates; third, sporulation was

induced by replicating diploids onto sporulation plates; fourth, MATa meiotic progenies

were germinated on SD-His-Arg-Lys medium supplemented with L-canavanine and

thialysine; fifth, MATa xxx∆::kanMX meiotic progenies were selected on SD/MSG-His-

Arg-Lys plates supplemented with L-canavanine, thialysine, G418; sixth, MATa double

mutants were selected on SD/MSG-His-Arg-Lys supplemented with L-canavanine,

thialysine, G418 and clonNAT. The double mutant selection plates were put at 22oC,

26oC, and/or 30oC for ts mutant strains. Compared to colony size at 22oC the double

mutants at 22oC and/or 30oC were scored as synthetic sick (SS), synthetic lethal (SL) or

59

no interaction (No). The putative synthetic genetic interactions were confirmed by

random spore analysis and/or tetrad analysis.

2.5.5 Confirmation of temperature-sensitive strains by PCR

Two PCR reactions were carried out to confirm the correct integration of the ts

allele and kanMX cassette for the candidate ts strains. One PCR reaction was used to test

whether the kanMX cassette was linked to the target gene. Another PCR reaction was

used to verify that the ts allele and kanMX cassette were integrated at the target gene

locus (Figure 2-2). The primer sequences for confirmation PCR are listed in Table 4.

2.5.6 Functional complementation by plasmids

Each ts strain was transformed with the cognate CEN plasmid from the MoBY-

ORF library (Ho et al., 2009) and/or a high-copy plasmid from the yeast genomic tiling

collection (Jones et al., 2008) (Open Biosystem, Cat# YSC4613, AL, USA) carrying the

wild-type gene, or with a vector control. After growth for 3-5 days at 22°C, transformants

were replica-plated and incubated at 22oC and at the restrictive temperature for 1-2 days.

2.5.7 Random spore analysis

A small amount of spores was resuspended in 1 mL of sterile water, mixed well,

and plated out onto different media.

(1) 20 µL on SD-His-Arg-Lys + canavanine/thialysine.

(2) 40 µL on SD/MSG-His/Arg/Lys + canavanine/thialysine/G418.

(3) 40 µL on SD/MSG-His/Arg/Lys + canavanine/thialysine/clonNAT.

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(4) 80 µL on SD/MSG-His/Arg/Lys + canavanine/thialysine/G418/clonNAT.

The plates were incubated at 26°C and/or 30°C for 2-4 days (Figure 2-9). Colony growth

under the four conditions was compared and double mutants were scored as synthetic sick

(SS), synthetic lethal (SL) or no interaction (No).

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Figure 2-9 Random Spore Analysis (bni1Δ::natMX cdc42-1::kanMX)

Resuspended spores were plated out on different media (see Material and Methods). The

plates were incubated at 26oC or 30oC for 2-4 days. Most genetic interactions can be seen

at 26oC. I repeated the experiment at higher temperatures if no genetic interaction was

observed. For example, genetic interaction between cdc42-1::kanMX and bni1Δ::natMX

was not clearly apparent at 26oC (left panel) but the double mutant was synthetic lethal at

30oC (right panel).

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CHAPTER THREE

Application of the Temperature-sensitive Mutant Library

Work in Chapter 3 was published in the following papers:

Li, Z., Vizeacoumar, F.V., Bahr, S. et al. (2011). Systematic exploration of essential yeast gene function with temperature-sensitive mutants. Nat Biotechnol 29, 361-367. Vizeacoumar, F. J., van Dyk, N., F, S. V., Cheung, V., Li, J., Sydorskyy, Y., Case, N., Li, Z., Datti, A., Nislow, C., et al. (2010). Integrating high-throughput genetic interaction mapping and high-content screening to explore yeast spindle morphogenesis. J Cell Biol 188, 69-81. Costanzo, M., Baryshnikova, A., Bellay, J., Kim, Y., Spear, E. D., Sevier, C. S., Ding, H., Koh, J. L., Toufighi, K., Mostafavi, S., Prinz, J., St Onge, R. P., VanderSluis, B., Makhnevych, T., Vizeacoumar, F. J., Alizadeh, S., Bahr, S., Brost, R. L., Chen, Y., Cokol, M., Deshpande, R., Li, Z., et al. (2010). The genetic landscape of a cell. Science 327, 425-431. Davierwala, A. P., Haynes, J., Li, Z., et al. (2005). The synthetic genetic interaction spectrum of essential genes. Nat Genet 37, 1147-1152. Mnaimneh, S., Davierwala, A. P., Haynes, J., Moffat, J., Peng, W. T., Zhang, W., Yang, X., Pootoolal, J., Chua, G., Lopez, A., Trochesset, M., Morse, D., Krogan, N. J., Hiley, S. L., Li, Z., et al. (2004). Exploration of essential gene functions via titratable promoter alleles. Cell 118, 31-44. Tong, A. H., Lesage, G., Bader, G. D., Ding, H., Xu, H., Xin, X., Young, J., Berriz, G. F., Brost, R. L., Chang, M., Chen, Y., Cheng, X., Chua, G., Friesen, H., Goldberg, D. S., Haynes, J., Humphries, C., He, G., Hussein, S., Ke, L., Krogan, N., Li, Z., et al. (2004). Global mapping of the yeast genetic interaction network. Science 303, 808-813. Contributions: I co-coordinated and led the project, and performed following experiments: (1) chemical-genetic analysis; (2) construction of reporter strains for HCS screening and run HCS screening by SGA method; (3) follow-up experiments for cohesin and condensin mutants with the following members. F. J. Vizeacoumar acquired the HCS screening images, and involved in follow-up experiments. J. Li, F. S. Vizeacoumar, R. Min, and K. Jin did HCS data analysis. M. Costanzo, A. Baryshnikova, B. VanderSluis, J. Bellay and C. L. Myers generated and analyzed the SGA data (Figure 3-3, Figure 3-4, and Figure 3-5). F. J. Vizeacoumar, A. Stephens, J. Haase and K. Bloom performed cohesin, condensin and CPC localization (Figure 3-13, Figure 3-14). Z. Lin and A. C. Gingras performed the mass spectrometry analysis on physical interaction for cohesin, condensin and CPC complexes (Figure 3-12).

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3.1 Abstract

Temperature-sensitive alleles represent an important reagent set for genetic

interaction analysis because essential genes tend to be highly connected hubs on the

global genetic network and are more evolutionarily conserved than nonessential genes.

By evaluating their power to predict known annotations to the Gene Ontology, I found

that the ts alleles of essential genes have greater predictive power than either deletions of

nonessential genes or DAmP alleles of essential genes. Also, the ts collection provides a

powerful resource for exploring chemical-genetic interactions because it enables screens

for compounds that suppress defects in essential gene function. I validated the ts

collection as an important new resource for quantitative phenotypic analysis using a high-

content screening protocol. I crossed six different fluorescent markers, diagnostic for

different subcellular compartments or structures, into hundreds of mutants and quantified

the marker behavior at the single-cell level. Integrating this cell biological data generated

a morphological profile for each ts mutant, revealing previously unappreciated functions

for essential genes, including roles for cohesin and condensin genes in spindle

disassembly.

3.2 Introduction

As discussed in Chapter 1, the budding yeast S. cerevisiae is the most well

characterized model organism for systematic analysis of fundamental eukaryotic

processes. Approximately 19% of S. cerevisiae genes are considered essential, because

haploid spores carrying a deletion allele of these genes fail to germinate and form

colonies under standard laboratory conditions (Giaever et al., 2002). The majority of S.

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cerevisiae essential gene orthologs (~83%) are also essential for viability in the distantly

related yeast, Schizosaccharomyces pombe, suggesting that there has been strong

selective pressure to retain essential gene activity across large evolutionary distances

(Kim et al., 2010). I wanted to evaluate the potential use of the ts mutant collection

described in Chapter 2 in several large scale applications such as SGA, HCS and

chemical-genetic analysis.

Our lab recently applied SGA analysis to map an extensive network of more than

~170,000 genetic interactions, including some interactions involving temperature-

sensitive alleles of essential genes (Costanzo et al., 2010). This SGA genetic interaction

dataset is composed of 1712 queries crossed to 3885 deletion strains. Of 1712 queries,

1378 are deletion mutants of nonessential genes and 334 are essential gene alleles (214

temperature-sensitive and 120 DAmP alleles) (Costanzo et al., 2010). This analysis

provided a quantitative estimate of fitness for both the single and the double mutants and

identified double mutants whose fitness defect is worse or better than expected for the

combined effect of the single mutants to score either negative or positive genetic

interactions, respectively.

Another sensitive approach to define genetic interactions is high content screening

(HCS). In contrast to the terminal fitness assayed in SGA, this system integrates

automated instrumentation, application software, and informatics/bioinformatics (Taylor,

2007) to evaluate intermediary phenotypes by large-scale cell biological investigations

(Abraham et al., 2004). The HCS approach involves quantitative single cell image

analysis of fluorescent markers for specific pathways and structures in hundreds of

different mutants. Recently our group has developed an experimental system that

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combines SGA-based high-throughput strain manipulation and HCS to enable the

visualization and quantitative measurement of specific morphological features for

numerous single cells within a field (Vizeacoumar et al., 2009; Vizeacoumar et al., 2010).

The combination of SGA analysis with HCS (SGA-HCS) provides a powerful

method for identifying and deciphering specific cellular functions and this approach has

been applied previously to study the morphogenesis of the mitotic spindle. Specifically,

integrating these systems has doubled the number of genetic interactions known for two

genes BNI1 and BIM1 by SGA alone (Vizeacoumar et al., 2010). Now, I have exploited

this phenomics SGA-HCS platform to explore a broad range of cellular pathways and

developed a morphological profile for the ts mutant collection. In combination with

manual scoring of images, automated image analysis has helped us to identify several

mutants suggesting not only novel functions for several well-characterized genes but also

revealing new roles for uncharacterized genes in well-established pathways. This has

helped us to uncover novel functions for members of two essential protein complexes,

namely cohesins and condensins, in mitotic spindle disassembly. Accordingly, the

essential gene conditional array is useful to the yeast research community as a resource

that complements the yeast nonessential deletion array.

Below I highlight the three major large-scale applications I have used in

characterizing the temperature-sensitive mutant array to explore the pleiotropic roles of

highly conserved essential pathways in yeast. In particular, the integration of data from

SGA and HCS points to a novel role for two essential protein complexes, cohesins and

condensins, in mitotic spindle disassembly.

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3.3 Results

3.3.1 Identification of pathway components using a barcoded ts library

The yeast deletion mutant collection has been particularly useful for chemical-

genetic analysis in which large sets of mutants are pooled and scored for sensitivity to a

specific compound in competitive-growth assays (Giaever et al., 2004). Each of the deletion

mutant strains carries two oligonucleotide molecular barcodes flanked by common primer

sites, which enable a quantitative estimate of strain abundance using a barcode microarray

or high-throughput sequencing readout (Pierce et al., 2007; Shoemaker et al., 1996; Smith et

al., 2009). I reasoned that introducing similar barcodes into the ts mutant collection would

provide a new reagent set for systematic chemical-genetic analysis, which can be examined

for exaggerated growth defects at a semi-permissive temperature as well as for suppression

of growth defects at non-permissive temperature.

To enable a barcode-based analysis of the ts collection, I took advantage of the

barcoder approach to introduce molecular barcodes into a subset of ~400 ts strains such that

the HO allele is replaced by a drug–resistance marker flanked by two unique

oligonucleotide in these strains (Yan et al., 2008). As a proof-of-principle for suppression

analysis, I examined suppression by zaragozic acid A (Squalestatin), an inhibitor of

mammalian and fungal squalene synthase, both in vivo and in vitro (Baxter et al., 1992;

Bergstrom et al., 1993). Squalene synthase (farnesyl diphosphate farnesyltransferase 1,

FDFT1, encoded by the ERG9 gene in yeast) is an enzyme in the sterol synthesis pathway

(Do et al., 2009; Menys and Durrington, 2003), catalyzing the reductive condensation of

farnesyl diphosphate (FPP) to form squalene, the first specific intermediate in the sterol

biosynthetic pathway (Figure 3-1).

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Figure 3-1 Isoprenoid Pathway in Saccharomyces cerevisiae

The solid lines indicate single synthetic steps and the dashed lines indicate multiple

synthetic steps in the ergosterol and protein prenylation pathways (Dimster-Denk et al.,

1999; Kuranda et al., 2009; Song et al., 2003). HMG-CoA: 3-hydroxy-3-methylglutaryl-

coenzyme A; HMGR: HMG-CoA reductase; FPP: farnesyl diphosphate; GGPP:

geranylgeranyl diphosphate.

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Squalene synthase, which is present in yeast, bacteria, plants, and humans (Okada et

al., 2000; Robinson et al., 1993), is responsible for directing the flow of the metabolite FPP

to either the sterol or the non-sterol branch of the pathway. Squalene synthase has received

attention as a potential target for lipid lowering agents in humans (Charlton-Menys and

Durrington, 2008). In contrast to the statins, which inhibit HMG-CoA reductase (HMGR),

inhibitors of squalene synthase do not inhibit the biosynthesis of isoprenoids, including FPP

and geranylgeranyl diphosphate (GGPP), two substrates for protein prenylation (Dimster-

Denk et al., 1999; Kuranda et al., 2009). Efforts to identify novel inhibitors of squalene

synthase have been limited by difficulties in configuring high-throughput assays for lipid-

metabolizing, integral membrane proteins. The pooled subset of ts strains was grown at

36.5oC in the presence or absence of zaragozic acid A and then their corresponding

molecular barcodes were quantified by PCR and hybridized to a microarray (GeneChip

Genflex Tag 16K Array v2, Affymetrix). Two strains, bet2-1 and cdc43-2, were over-

represented in the pooled population exposed to inhibitor indicating that zaragozic acid A

suppressed temperature-sensitive growth defects associated with these mutant alleles

(Figure 3-2A). Suppression of bet2-1 and cdc43-2 growth defects was also confirmed by

liquid growth (Figure 3-2B) and spot dilution assays on solid media (Figure 3-2C).

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Figure 3-2 Zaragozic Acid Rescues bet2-1 and cdc43-2 ts Phenotype

A. A pool of 440 barcoded ts mutants was grown in rich (YEPD) medium in the presence

of 4 μM zaragozic acid A (ZA) or DMSO at 36.5°C. Genomic DNA was prepared from

cells after 5 generations of growth. Molecular barcodes were amplified by PCR and

hybridized to a microarray (GeneChip Genflex Tag 16K Array v2, Affymetrix). The x-

axis represents the 440 ts strains ordered alphabetically by ORF name. The y-axis

represents the log2 ratio of barcode hybridization intensity between the ZA treatment and

the solvent (DMSO) treatment. Mutants with highest log2 ratios at the restrictive

temperature were identified as suppressors.

B. The diluted cells were grown in YEPD with or without ZA in a 96-well plate at

36.5°C. The fitness was defined as the ratio of the doubling times of yeast exposed to the

solvent DMSO or to ZA treatment. The error bars represent the standard deviation for

four independent experiments.

C. Spot Assay. Ten-fold serially diluted cells of wild-type and ts stains were spotted on

YEPD plates containing DMSO or 4 μM ZA. The plates were incubated for 2 days at

23°C or 36.5°C.

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3.3.2 Genetic interactions for essential genes

3.3.2.1 Properties of essential genetic interactions

As observed in other studies (Davierwala et al., 2005; He and Zhang, 2006) and in

my primary experimental results (Table 3), ts alleles tend to have more genetic

interactions than deletion mutants, indicating that essential genes are hubs in the genetic

interaction network (Figure 3-3A).

In contrast to genetic interactions observed among nonessential mutants, the set of

essential genetic interactions show a much higher overlap between negative genetic

interactions and protein-protein interactions (~0.8% for negative as compared to ~0.3%

for positive) (Figure 3-3B). The higher overlap between protein-protein and negative

genetic interactions likely reflects so-called “within pathway” genetic interactions which

are predicted to be enriched for essential genes (Boone et al., 2007; Kelley and Ideker,

2005).

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Figure 3-3 Properties of Essential Genetic Interactions

A. The distribution of genetic interaction network degree for essential ts mutant alleles

and nonessential deletion mutants (see Materials and Methods for details).

B. The percentage of negative (red) and positive (blue) genetic interactions that overlap

physically interacting protein pairs derived from affinity purification/mass spectrometry,

yeast two hybrid and protein-fragment complementation assay studies (see Materials and

Methods for details). Background (dashed line) represents the random expectation for a

protein-protein interaction. Error bars represent 95% confidence intervals.

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3.3.2.2 Functional impact of essential genetic interactions

Given that essential genes tend to be highly connected in the genetic interaction

network (Figure 3-3A), the utility of essential versus nonessential genetic interaction

screens was compared for characterizing the functions of other genes. Genes that share

similar genetic interaction partners are typically members of the same pathway, protein

complex, or biological process (Costanzo et al., 2010; Tong et al., 2004). Using this

property, a function prediction approach was developed by simply assessing the interaction

profile similarity of a gene of interest to genes annotated to known biological process. Each

gene’s known annotations were withheld and then genes were ranked for their suspected

involvement in each biological process. The performance of this approach in predicting the

held-out functions of genes was evaluated by using different sets of SGA screens as a basis

for the interactions profiles (ts alleles or DAmP alleles).

In particular, the information provided by screens of essential ts alleles was directly

compared to screens of deletion alleles and DAmP alleles and their capacity to predict

known annotations to the Gene Ontology was evaluated. Although the most accurate gene

function predictions were obtained from the combination of essential and non-essential

screens, for a comparable number of screens (92), ts alleles of essential genes provided

greater predictive power than either deletions of nonessential genes or DAmP alleles of

essential genes (Figure 3-4). For example, at a predictive performance of 30% precision and

25% recall (P30R25), interaction profiles based on ts allele screens were able to cover 4-

fold more GO biological process terms compared to predictions based on either deletion

mutants or DAmP alleles (Figure 3-4). In general DAmP alleles may lower the abundance

of an essential gene product (Schuldiner et al., 2005), but if the reduced protein level does

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not lead to a fitness defect, then the DAmP allele will tend not to display genetic

interactions, and thus may be less informative for functional characterization.

Figure 3-4 Function Prediction Performance of Different Query Genes

Comparative functional evaluation of genetic interactions involving essential ts alleles (red),

essential DAmP alleles (green), nonessential deletion alleles (blue), or essential and

nonessential genes combined (black). True positive examples were defined as interacting

gene pairs annotated to the same non-redundant GO term as described elsewhere (Myers et

al., 2006). All other gene pairs, excluding uncharacterized genes, were considered negative

examples. The number of true positives (TP), positive examples that were correctly

predicted, and false positives (FP), negative examples that were incorrectly predicted were

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measured for a range of similarity cutoffs. The precision (TP/(TP+FP)) at 25% recall

(TP/(TP+FN)) is plotted against the number of different GO terms for which the function

prediction performance met the corresponding threshold. The set of GO terms was restricted

to the non-redundant set defined in (Myers et al., 2006). For example, the DAmP query

collection (92 queries) can only achieve 30% precision at 25% recall on 3 different terms,

while an equal number of randomly selected ts alleles can achieve the same level of

precision on 13 different terms. Precision scores shown represent the median over 20

iterations, each with 92 randomly chosen queries for both ts alleles and deletions.

Predictions were made using a variant of K-nearest neighbors (see Materials and Methods

for details).

The profile of interactions across a set of query mutants is a powerful approach for

characterizing function because genes from the same pathway or protein complex often

exhibit similar patterns of genetic interaction (Costanzo et al., 2010; Tong et al., 2004). For

example, genes encoding components of the cohesin complex share similar patterns of

genetic interactions with those of the CTF19 kinetochore complex, suggesting they have

overlapping functions (Figure 3-5A). Moreover, cohesin genes show negative genetic

interactions with CTF19 complex genes (Figure 3-5B). Because our recent study defined a

role for the CTF19 complex components in the mitotic spindle disassembly pathway

(Vizeacoumar et al., 2010), the similarities in their genetic interaction profiles suggest a

similar role for cohesin proteins in this essential process.

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Figure 3-5 Network Connecting Cohesin, Condensin, and Other Genes Involved in

Spindle Dynamics

A. A network derived from correlation profiles between cohesin, condensin, and other genes

involved in spindle dynamics. Nodes indicate genes and edges highlight the similarity in the

pattern of genetic interactions shared between pairs of genes. Numbers represent Pearson

correlation coefficients.

B. A genetic interaction network amongst cohesin, condensin, and other genes involved in

spindle dynamics. Nodes represent genes and edges highlight genetic interactions. Negative

(red) and positive (green) quantitative genetic interactions were measured as described

previously (Costanzo et al., 2010). The edge opacity is proportional to the score strength.

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3.3.3 Functional prediction of the essential genes from high-content screening

Combining SGA-based high-throughput strain manipulation with high-content

screening enables functional discovery through the visualization and quantitative

measurement of specific morphological features at a single cell level (Vizeacoumar et al.,

2010). To exploit this general platform in the context of the ts collection, I constructed a

panel of SGA query strains expressing fluorescent markers, representing six fundamental

subcellular compartments or structures, including the nucleus, DNA damage foci,

mitochondria, mitotic spindle, actin patches, and the plasma membrane (Figure 3-6). I

introduced these reporters into an array of 769 ts strains and then imaged both the ts

mutant strains and the wild-type control strains at 26°C and 32°C. I chose 32°C as a

restrictive temperature because ~80% of the mutant alleles showed a growth defect at this

temperature (Figure 2-4D). The customized journaling of MetaXpress software

(Vizeacoumar et al., 2010) automates image analysis and measures several morphological

features, generating a quantitative and unique cell morphology profile for each ts mutant

strain. The cell morphology profile comprises features such as cell shape, budding index,

organelle density, as well as 85 reporter-specific parameters (Table 5). Importantly, all

computationally derived phenotypes were confirmed by visual inspection of the

corresponding images (Table 6) generating a high confidence and systematic profile for

each ts strain.

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Figure 3-6 Fluorescent Reporters Introduced into the ts Collection

The left panels show the localization of six different GFP-tagged marker proteins in wild-

type cells as well as computationally processed images. Representative mutants with

defects in localization and/or abundance of a particular marker are shown on the right.

Markers included a plasma membrane marker (Psr1p-GFP), a reporter of DNA damage

(Ddc2p-GFP), a nuclear marker (Mad1p-NLS-RFP), a mitotic spindle reporter (GFP-

Tub1), a mitochondrial marker (OM45p-GFP), and an actin reporter (Sac6p-GFP).

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In principle, a cell morphology profile should provide a rich phenotypic signature

revealing functional relationships between genes. For example, mutants exhibiting

increased DNA damage foci, as measured by Ddc2p-GFP localization, were enriched for

DNA replication (P = 2.1 x 10-10), pre-replicative complex assembly (P = 1.4 x 10-5),

and/or DNA synthesis during DNA repair (P = 2.0 x 10-5). The number of gene-specific

profiles indicative of morphological defects increased at elevated temperature further

validated our approach (Figure 3-7).

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Figure 3-7 Temperature Shift Causes Increased Phenotype

Increased temperature exacerbates ts allele phenotypes. The percentage of mutant alleles

exhibiting a phenotype that deviated significantly from wild-type at both permissive

(26oC, blue) and restrictive temperature (32oC, red) was plotted (P<0.05, Wilcoxon Rank

Sum Test after Bonferroni correction) following normalization.

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Comparison of cell morphology profiles uncovered gene-specific phenotypes

suggesting unanticipated functions for well-characterized essential genes. For example,

components of cohesin, condensin and the chromosomal passenger complex (CPC),

shared similar morphological profiles (Figure 3-6). According to the genetic interaction

profiles described above (Figure 3-5A), ts alleles of cohesin and condensin components

shared similar interactions to the CTF19 components. Our lab has previously shown that

the components of the CTF19 complex along with the CPC can regulate spindle

disassembly and that these mutants exhibit hyper-elongated spindles as well

(Vizeacoumar et al., 2010). The products of IPL1, SLI15 and BIR1 interact to form the

Chromosomal Passenger Complex (CPC) that re-localizes dynamically in dividing cells

to perform key mitotic roles (Buvelot et al., 2003; Pereira and Schiebel, 2003; Ruchaud et

al., 2007a; Sullivan et al., 2001; Zeng et al., 1999). Specifically, I found that cohesin

mutants, including smc3-1, mcd1-73, irr1-1, and smc1-2 (Figure 3-6), as well as

condensin mutants, ycs4-1, smc2-8 and smc4-1 (Table 6) displayed a hyper-elongated fish

hook spindle phenotype as visualized with a tubulin marker, GFP-Tub1p, suggesting that

similar to the CPC both cohesin complex and condensin complex may be involved in

spindle disassembly.

3.3.4 A role for cohesin/condensin in spindle disassembly

Cohesins bind the sister chromatids together during metaphase and are cleaved in

a separase-dependent fashion during anaphase (Huang et al., 2005; Nasmyth and Haering,

2005). When cells are synchronized in G1 phase with α factor and then released into the

cell cycle at a restrictive temperature, ts cohesin mutants activate the mitotic spindle

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assembly checkpoint and arrest as large-budded cells with short or partially elongated

spindles (Guacci et al., 1997). However, in our screen, when asynchronous cohesin ts

mutants were shifted to restrictive temperature, I found two populations of cells: (1) large

budded cells with short or partially elongated spindles and (2) a subpopulation of large

budded cells with hyper-elongated fish hook spindles, which presumably represent cells

exhibiting a post-checkpoint spindle disassembly defect (Figure 3-8).

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Figure 3-8 Cohesin and Condensin Mutants Exhibit Abnormal Spindle Morphology

A sub-population of cohesin and condensin mutants exhibit abnormal spindle

morphology. Spindle length and morphology were quantified in large-budded wild-type

cells and strains harboring a cohesin (smc3-1) or condensin (smc2-1) mutant allele grown

asynchronously at permissive (26ºC) and restrictive temperatures (32ºC). The fraction of

cells exhibiting either short (left panel) or fish hook-shaped spindles (right panel) were

quantified in three independent populations and error bars represent standard deviation

across three independent experiments.

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An extended fish hook spindle may result from changes in microtubule (Straight

et al., 1998) or CPC complex dynamics (Buvelot et al., 2003; Vizeacoumar et al., 2010).

To examine this more carefully, I monitored spindle association and midzone enrichment

of CPC components (Ipl1p-GFP, Sli15p-GFP, and Bir1p-GFP) in a cohesin, smc3-1, and

a condensin mutant, smc2-8. In wild-type cells, Ipl1p-GFP normally associates with short

spindles ranging from ~1.5 to 2 microns in length and this localization persists until the

spindle disassembles at which point Ipl1p localization is more prominent at the spindle

midzone (Figure 3-9). Interestingly, in the cohesin mutants, Ipl1p-GFP, Sli15p-GFP and

Bir1p-GFP associated with the kinetochore but failed to associate with the spindle until

the spindle reached a length greater than 6 microns. In contrast, Ipl1p-GFP, Sli15p-GFP

and Bir1p-GFP failed to localize to either the kinetochore or the spindle in condensin

mutants (Figure 3-9 and Figure 3-10).

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85

Figure 3-9 Localization of Ipl1p-GFP in Cohesin/Condensin Mutants

Ipl1p-GFP localization to the mitotic spindle was monitored in wild type as well as in the

smc3-1, and smc2-8 mutants at the permissive (26oC) and restrictive temperatures (32oC).

Cells were grown to mid log phase at the permissive temperature and shifted to the

restrictive temperature for 60 minutes before imaging. Spindle morphology was

monitored using an RFP-Tub1p marker. DIC images, fluorescent micrographs and

merged images of representative single cells are shown.

Spindle length was also measured in these cells using a RFP-Tub1p marker. Wild-

type and mutant cells were divided into three groups based on spindle length (2-4 μm, 4-6

μm, >6 μm). The percentage of cells exhibiting mis-localized Ipl1p-GFP was measured

for each spindle length category. In each spindle length category 60-100 cells were

counted at 26oC and 32oC. Error bars represent the standard deviation across three

independent experiments.

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87

88

Figure 3-10 Localization of Sli15p-GFP/Bir1p-GFP in Cohesin/Condensin Mutants

Localization of Sli15p-GFP (A and B) and Bir1p-GFP (C and D) to the mitotic

spindle was monitored in wild-type as well as in the smc3-1 and smc2-8 mutants at the

permissive (26oC) and restrictive temperatures (32oC). Cells were grown to mid log phase

at the permissive temperature and shifted to the restrictive temperature for 60 minutes

before imaging. Spindle morphology was monitored using an RFP-Tub1p marker. DIC

images, fluorescent micrographs and merged images of representative single cells are

shown.

Spindle length was also measured in these cells using a RFP-Tub1p marker. Wild-

type and mutant cells were divided into three groups based on spindle length (2-4 μm, 4-6

μm, >6 μm). The percentage of cells exhibiting mis-localized Sli15p-GFP (B) and Bir1p-

GFP (D) was measured for each spindle length category. In each spindle length category

60-100 cells were counted at 26oC and 32oC. Error bars represent the standard deviation

across three independent experiments.

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Vizeacoumar et al. showed that in the kinetochore mutant (mcm21∆), a member

of the CTF19 complex, CPC components were mislocalized (Vizeacoumar et al., 2010).

Since I observed that CPC components were mislocalized in the cohesin mutants, I

hypothesized that CPC mislocalization in mcm21∆ is due to disruption of cohesin

structure. Consistent with the genetic interactions between genes encoding cohesin and

CTF19 kinetochore complex components (Figure 3-5A), I found that the normal

distribution of cohesin around the spindle was lost in a mcm21Δ kinetochore mutant

(Figure 3-11), suggesting that cohesin is required to stabilize the CPC on the spindle

(Vizeacoumar et al., 2010; Yeh et al., 2008).

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Figure 3-11 Cohesin Barrel Structure was Lost in the mcm21∆ Kinetochore Mutant

The top two panels show the distribution of Smc3p-GFP in wild-type cells. The cohesin

barrel structure (shown in yellow arrows) was defined by the two oblongate lobes of

fluorescence with an intevening dim region. A representative image of an mcm21∆ cell is

shown in the bottom panel.

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While the experiments suggest that cohesin and condensin may regulate the CPC

components, one might speculate that the hyper-extended spindle might not represent a

true anaphase-specific event and that it could be a continuation of prolonged metaphase

and that these cells might still linger with elongated spindles. To test if the elongated

spindles in smc3-1 and smc2-8 mutants are actually in true anaphase, I monitored the

localization of the midzone bundling protein Ase1p and mitotic exit trigger Cdc14p.

Ase1p, which is required for spindle elongation and stabilization, is a member of a

conserved family of midzone-specific microtubule-associated proteins (Schuyler et al.,

2003). Mutation of ASE1 leads to delayed spindle disassembly (Juang et al., 1997) and

loss of Ase1p results in premature spindle disassembly in mid-anaphase (Schuyler et al.,

2003). Importantly, Ase1p enriches to the spindle midzone only in anaphase (Khmelinskii

et al., 2007).

CDC14 encodes a protein phosphatase which is essential for mitotic exit and

meiotic progression (Schild and Byers, 1980; Stegmeier and Amon, 2004; Taylor et al.,

1997). Cdc14p dephosphorylates key mitotic targets leading to the coordinated

inactivation of mitotic cyclins (Visintin et al., 1998), proper spindle disassembly

(Khmelinskii et al., 2007; Khmelinskii and Schiebel, 2008), and completion of

cytokinesis (Hall et al., 2008). Cdc14p gets released from the nucleolus only after

anaphase onset, particularly to trigger mitotic exit. Mutants that have not initiated

anaphase would still retain Cdc14p inside the nucleolus.

Therefore I monitored the localization of Ase1p-GFP and Cdc14p-GFP in

cohesin/condensin mutants. The spindle bundling protein Ase1p was strictly co-localized

to the spindle midzone in the smc3-1 and smc2-8 mutants (Figure 3-12A) and the release

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of the mitotic trigger Cdc14p from the nucleolus was unaffected in the smc3-1 and smc2-

8 mutants (Figure 3-12B). These observations are important because they suggests that

cohesin and condensin mutants have initiated mitotic exit (Fridman et al., 2009;

Khmelinskii et al., 2007; Pereira and Schiebel, 2003; Schuyler et al., 2003) and further

suggests the fish hook spindle observed in these mutants is an anaphase-specific

phenotype.

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Figure 3-12 Localization of Ase1p-GFP/Cdc14p-GFP in Cohesin/Condensin

Mutants

Ase1p-GFP/Cdc14p-GFP localization to the mitotic spindle was monitored in wild-type

as well as in the smc3-1 and smc2-8 mutants at the permissive (26oC) and restrictive

temperatures (32oC). Cells were grown to mid log phase at the permissive temperature

and shifted to the restrictive temperature for 60 minutes before imaging. Spindle

morphology was monitored using an RFP-Tub1p marker. DIC images, fluorescent

micrographs and merged images of representative single cells are shown.

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An alternative explanation is that activation of the spindle assembly checkpoint

might prevent the localization of CPC to the spindle midzone. Another test of whether the

fish hook spindle and its associated mislocalization of the CPC components in the

cohesin and condensin mutants is linked to the spindle assembly checkpoint is to

determine whether these phenotypes are observed in the mutants of checkpoint proteins.

MAD1 encodes a component of the spindle assembly checkpoint (Hardwick and Murray,

1995; Li and Murray, 1991). The spindle assembly checkpoint delays the onset of

anaphase in cells with defects in mitotic spindle assembly or in the attachment of

chromosomes to the spindle microtubules (Hardwick, 1998). To see whether the CPC

mislocalization in cohesin/condensin mutants is due to the spindle checkpoint activation

or not, I checked Sli15p localization in mad1∆ smc3-1 and Ipl1p-GFP localization in

mad1∆ smc2-8 (Figure 3-13). The results showed that the association of components of

the CPC (Sli15p-GFP and Ipl1p-GFP) with elongated spindles (>6 microns) was

observed in double mutants defective in a cohesin or condensin component and lacking a

functional spindle assembly checkpoint (mad1∆ smc3-1 and mad1∆ smc2-8) (Figure 3-

13), indicating that CPC mislocalization observed in cohesin/condensin mutants is not

due to spindle assembly checkpoint activation (Pinsky and Biggins, 2005; Stern and

Murray, 2001).

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Figure 3-13 Localization of Sli15p-GFP and Ipl1p-GFP in smc3-1 mad1∆ and smc2-8

mad1Δ Double Mutants

Exponential phase cells were grown at the permissive temperature (26oC) and shifted to

the restrictive temperature (32oC) for 60 minutes before imaging. Spindle morphology

was monitored using an RFP-Tub1 marker. DIC images, fluorescent micrographs and

merged images of representative single cells are shown.

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Although dephosphorylation of Sli15p by Cdc14p could trigger spindle

association of the CPC (Pereira and Schiebel, 2003), our results so far have suggested

that stabilization of the CPC to the spindle is regulated by cohesin and condensin. To

confirm this, I monitored the localization of a phosphorylation-deficient CPC mutant

(Sli15p-6A-GFP) which binds to the spindle throughout mitosis (Pereira and Schiebel,

2003) in the smc3-1 mutant background. Compared to the wild-type cells, where Sli15p-

6A-GFP strictly binds the spindles, in the smc3-1 mutant, the association with spindles

was reduced, at least until the spindle reached over ~4 to 6 microns, and the enrichment

to the spindle midzone was delayed resulting in elongated fish hook spindles (Figure 3-

14). Because Sli15p dephosphorylation prevents mitotic checkpoint reengagement during

anaphase (Mirchenko and Uhlmann, 2010), this finding is consistent with our checkpoint

mutant observations.

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Figure 3-14 Localization of Sli15p-6A-GFP in Cohesin Mutant

Cells were grown to mid log phase at the permissive temperature (26oC) and shifted to

the restrictive temperature (32oC) for 60 minutes before imaging. Spindle morphology

was monitored using an RFP-Tub1 marker. DIC images, fluorescent micrographs and

merged images of representative single cells are shown.

Taken together, these results suggest that the observed defect in spindle midzone

accumulation of the CPC complex and the fish hook spindle phenotype associated with

cohesin and condensin complexes is not dependent on activation of the Cdc14p pathway

but rather is an anaphase-specific event.

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3.3.5 Physical interactions of the cohesin, the condensin and the passenger complexes

I next asked if the stabilization of CPC on the spindle by the cohesin and

condensin components is by direct interaction of these components or bridged by an

indirect interaction. Cohesin is concentrated at the pericentromeric region of the yeast

chromosomes that overlaps with the spindle midzone (Vizeacoumar et al., 2010; Yeh et

al., 2008) while condensin associates with the kinetochore, chromosomal arms and

ribosomal DNA (rDNA) (Bachellier-Bassi et al., 2008; Freeman et al., 2000; Lavoie et

al., 2004). To test for physical interactions between CPC components, cohesin, and

condensin, Ipl1p-GFP, Sli15p-GFP, Bir1p-GFP, Smc3p-GFP and Smc4p-GFP were

affinity purified from yeast extracts and subjected to LC-MS/MS (see Materials and

Methods). These experiments recapitulated all previously reported interactions (Figure 3-

15 and Table 7), and uncovered reproducible interactions between the CPC component,

Bir1p-GFP, and members of the condensin complex, supporting a direct role for

condensin components in stabilizing the CPC (Figure 3-15).

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Figure 3-15 Physical Interaction Map for Cohesin, Condensin and CPC Complexes

Physical interaction network illustrating protein-protein interactions involving cohesin,

condensin and CPC complexes. Nodes represent individual proteins and edges represent

physical interactions. Proteins found in the same complex are grouped by ovals.

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3.3.6 Condensin, cohesin and CPC complexes co-localize to the spindle

Physical, genetic and cell biological analyses of cohesin and condensin

components suggest that these complexes may regulate the localization of the CPC. To

understand the spatial association of these components, collaborating with Andrew

Stephens and Julian Haase in Kerry Bloom Lab and Franco Vizeacoumar, I monitored the

localization pattern of the CPC, cohesin and condensin complexes in wild-type cells. As

reported previously (Yeh et al., 2008), cohesin forms a cylindrical barrel that surrounds

Ipl1p (Figure 3-16). During metaphase, Ipl1p-GFP localized to the spindle axis and was

surrounded by the cohesin barrel, as identified with Smc3p-GFP (Figure 3-16B). As the

cells enter anaphase, Ipl1p-GFP was largely restricted within the cohesin-enriched region

(Figure 3-16B), suggesting that cohesin restricts the movement of the CPC along the

spindle. The condensin component, Smc4p-GFP, displayed a localization pattern similar

to that of Ipl1p-GFP along the spindle axis, distinct from the spindle pole body marker,

Spc29p-RFP (Figure 3-16A). Importantly, while both cohesin and condensin were

necessary for the spindle localization of Ipl1p, the localization of cohesin and condensin

proteins was unaffected in an ipl1-2 mutant (Figure 3-17), suggesting that cohesin and

condensin complexes regulate the localization of the CPC and not vice versa. Thus, the

co-localization of the CPC and condensin, within a cohesin barrel, is consistent with the

physical interactions observed between the CPC and condensin and between condensin

and cohesin.

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Figure 3-16 Cohesin, Condensin and CPC Localization in WT Cells

A. Ipl1p-GFP, Smc3p-GFP and Smc4p-GFP localization was assessed with respect to the

spindle pole body marker, Spc29p-RFP in a wild-type strain. Fluorescent micrograph

images illustrate representative single cells. Blue arrow indicates the localization of

condensin (Smc4p-GFP) between the spindle pole bodies (Spc29p-RFP) and white arrow

indicates the localization of condensin (Smc4p-GFP) to rDNA.

B. Localization of Ipl1p-GFP and cohesin (Smc3p-RFP) in wild-type cells. Cells were

grown to mid log phase before imaging. Fluorescent micrographs and merged images of

representative single cells are shown. The top frames represent a budded cell with the

transverse section of the mitotic spindle surrounded by the cohesin barrel.

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Figure 3-17 Cohesin, Condensin and CPC Localization in ipl1-2 Mutant

Cells were grown to mid-log phase at the permissive temperature (26oC) and shifted to

the restrictive temperature (32oC) for 60 minutes before imaging. Fluorescent

micrographs and merged images of representative single cells are shown. In the lower

panel, blue arrows indicate the localization of condensin (Smc4p-GFP) between the

spindle pole bodies (Spc29p-RFP) and white arrows indicate the localization of

condensin (Smc4p-GFP) to rDNA.

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3.4 Discussion

In this chapter I described the validation and use of the ts collection in a number of

different ways. First, I showed that the ts array is useful for exploration of chemical-genetic

interactions because it enables screens for compounds that suppress defects in vital

pathways. Second, in collaboration with Michael Costanzo, Anastasia Baryshnikova and

Chad Myers, I demonstrated that the ts collection represents a key reagent set for genetic

interaction analysis because essential genes tend to be highly connected hubs on the global

genetic network. Third, in collaboration with Franco Vizeacoumar, I further validated the ts

array as a key resource for quantitative phenotypic analysis by using a high-content

screening protocol to score six different fluorescent markers, diagnostic for different

subcellular compartments or structures, in hundreds of different mutants. Quantification of

the marker behaviour at the single-cell level enabled integration of this data set to generate a

morphological profile for each ts mutant to reveal both known and previously unappreciated

functions for essential genes, including roles for cohesion and condensin genes in spindle

disassembly.

BET2 encodes the catalytic subunit of the type II geranylgeranyl transferase and

CDC43 encodes the beta subunit of geranylgeranyltransferase type I involved in prenylation

(Figure 3-1). Previous studies show that when squalene synthase is inhibited by zaragozic

acid A, squalene and post-squalene metabolites are decreased (Bergstrom et al., 1993),

whereas FPP (Baxter et al., 1992) and farnesoic acid-derived metabolites (Bergstrom et al.,

1993) are increased. Thus, increased levels of cellular FPP and GGPP, may rescue the bet2-

1 and cdc43-2 ts phenotype at restrictive temperature. Further support for this hypothesis

comes from previous studies showing that the lethality of the bet2-1 mutant at 37oC was

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suppressed by the overexpression of BTS1 (Jiang et al., 1995), which encodes

geranylgeranyldiphosphate (GGPP) synthase (Figure 3-1).

Our analysis shows that a screen for rescue of the ts growth of a bet2-1 mutant

can identify inhibitors of squalene synthase and may lead to a cell-based screen for

squalene synthase inhibitors. I expect that any cellular mechanism to raise GGPP levels

would be captured by a bet2-1 rescue screen, potentially providing novel compounds

and/or targets for developing lipid lowering agents.

Combining SGA-based high-throughput strain manipulation with high-content

screening enables functional discovery through the visualization and quantitative

measurement of specific morphological features for numerous single cells within a field

(Vizeacoumar et al., 2010). High content microscopy-based screening of the ts array has

revealed new functions for multiple genes (Figure 3-6). For example, cellular profiling of

our ts collection revealed a connection between the cohesin and condensin complexes and

spindle disassembly. Spindle disassembly is a spatio-temporally regulated event such that

disassembly of the mitotic spindle occurs only after separation of sister chromatids from

the spindle midzone region. The findings are consistent with a model whereby the CPC

decorates the spindle in part through interactions with condensin and cohesin within the

vicinity of the centromere (Yeh et al., 2008). Loss of cohesin/condensin function results

in the mislocalization of the CPC. I speculate that the CPC accesses the spindle midzone

only after the cohesin is cleaved, such that cohesin cleavage and clearing of chromosome

midzone regions are prerequisites for the mitotic spindle disassembly.

Our collection of ts alleles builds upon a non-overlapping set of ts strains

constructed recently (Ben-Aroya et al., 2008). The combined collections cover the

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majority of essential genes in the yeast cell and enable numerous systematic approaches

for querying the pleiotropic roles of essential genes. For example, I have highlighted the

use of the collection in chemical-genetic screens for small molecule suppressors of the ts

phenotype, a general screen that may be of particular interest for yeast orthologs of

human disease genes because in theory such small molecule suppressors may represent

therapeutic leads.

Another powerful approach involving the ts collection will involve the mapping

of genetic interactions by systematic dosage suppression analysis using high-copy

libraries, such as a high-copy version of the molecular barcoded yeast ORF (MoBY-

ORF) library (Ho et al., 2009; Magtanong et al., submitted) or the systematic library for

comprehensive overexpression analysis (Jones et al., 2008). Dosage suppression network

interactions are highly abundant and functionally relevant and, while some of these

interactions overlap with protein-protein negative genetic interactions, most of these

suppression edges are unique and offer the potential for expanding our global view of the

functional wiring diagram of the cell (Magtanong et al., submitted).

3.5 Materials and Methods

3.5.1 Bar-coding temperature-sensitive strains

The ts alleles were introduced into the MATα query strain background by

switching selectable marker and mating type (Tong and Boone, 2006). The kanMX-

marked ts strains were transformed with a natMX fragment, plated on YEPD, incubated at

22oC for 3-4 days, and replica-plated to YEPD+clonNAT at 22oC for 3-5 days to select

for natR colonies. Transformants were replica-plated to YEPD+G418 and

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YEPD+clonNAT at 22oC to select strains resistant to clonNAT and sensitive to G418.

Three natR-kanS colonies were picked to test for ts phenotype at 22oC and 39oC. The

resultant natR-kanS ts strains were mated to the SGA starting strain. The diploids were

sporulated at 22oC for one week. Strains were streaked for singles on haploid selection

media and grown at 22oC for 2-5 days. The haploid strains were replica-plated on final

selection plates and grown for 2-3 days. Colonies from final selection plates were

streaked on a fresh final selection plate for singles. The MATα ts query strains were

verified and confirmed by fluorescence activated cell sorting (FACS), mating type test

and colony PCR.

The resultant natMX-marked ts query strains were arrayed in 96-format on agar

plates and mated to an array of barcoder strains, each carrying a unique kanMX-marked

barcode cassette (Yan et al., 2008). Haploid bar-coded ts strains were selected using SGA

methodology (Tong and Boone, 2006). The resultant individual bar-coded ts strains were

streaked out to obtain single colonies on SGA final selection plate. A single colony from

each strain was re-arrayed in 96-format.

3.5.2 Microarray analysis

The homozygous deletion pool containing ~4700 strains (Lee et al., 2005) and the

ts pool consisting of 440 ts strains plus 162 control strains were prepared, frozen and

stored as previously described (Pierce et al., 2007). Both pools were thawed and diluted

in 700 μl of YEPD to OD600 of 0.06. The homozygous deletion pool and the ts pool were

separately grown in YEPD with zaragozic acid A trisodium salt (ZA, Sigma-Aldrich) or

with the solvent DMSO (at 2% final concentration) at 36.5°C. Cells were collected after

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five generations using a Robot Liquid Handling System (The MultiPROBE II PLUS,

PerkinElmer). Both pools were mixed together for genomic DNA preparation, PCR

amplification of molecular barcodes, and microarray hybridization as previously

described (Pierce et al., 2007; Yan et al., 2008). Data from both pools were then plotted

independently. Microarray experiments were repeated three times.

3.5.3 Analysis of growth rates in liquid medium

Wild-type (BY4741) and ts strains were grown in YEPD for 24 hours at 23°C.

The cultures were diluted to an OD600 of 0.0625 and grown in 100 μl of YEPD with

either the solvent DMSO or 4 μM of ZA in a 96-well plate. The 96-well plate was

constantly shaken in a microplate reader (Tecan, GENios) at 36.5°C for 20 hours and the

OD600 was read every 15 minutes. The doubling time of strains was calculated according

to the previously described method (St Onge et al., 2007). The fitness was defined as the

ratio of the doubling time of strains grown in the presence of solvent alone (DMSO)

relative to ZA treatment. The fitness assays were repeated four times.

3.5.4 Spot Assay

Wild-type and ts strains were grown overnight in YEPD. Cultures were serially

diluted 10 fold in YEPD. An aliquot (2.5 μl) from each of the 10X serial dilutions of cells

was spotted on YEPD plates containing either the solvent DMSO or 4 μM ZA. The plates

were incubated for 2 days at 23ºC or 36.5ºC and photographed.

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3.5.5 Degree Distribution Analysis

The total number of interactions, including both positive and negative, was

measured for each query strain using the intermediate confidence cutoff on the genetic

interaction network as described in Costanzo et al., 2010 (|ε| > 0.08, p < 0.05). The degree

distribution was then plotted separately for deletion queries and ts queries.

3.5.6 Protein-protein interaction network overlap

To analyze the overlap between genetic interactions and the physical interaction

network, a combined protein interaction network was constructed by taking the union of

four recent high-throughput studies (Gavin et al., 2006; Krogan et al., 2006; Tarassov et

al., 2008; Yu et al., 2008), which include interactions derived from affinity

purification/mass spectrometry (Kaiser et al., 2008), yeast two hybrid (Suter et al., 2008)

and protein-fragment complementation assay (Morell et al., 2009) studies. Positive and

negative genetic interactions were defined using an intermediate confidence threshold

(|ε| > 0.08, p < 0.05) as described in Costanzo et al., 2010.

3.5.7 Function prediction from comparison of genetic interaction data

Deletion array genes were classified into functional categories using a variation of

k-nearest neighbors with leave one out cross-validation. Functional categories for

classification were taken from a subset of the Gene Ontology annotations downloaded on

April 29, 2010 (Ashburner et al., 2000). Classification was restricted to the non-

redundant set of GO terms described in (Myers et al., 2006), and was performed

independently on each GO term. Terms with fewer than 10 participating genes were

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removed leaving 132 GO terms for prediction. The full set of 3,885 SGA array genes was

reduced to 2,709 after removing genes with no annotations in the participating terms as

these would provide no information for classification and would be impossible to

correctly classify.

Each gene then received a score for every GO term in the following way. The K

(=5) largest similarity scores between the gene in question and members of the term in

question were summed. Similarity scores were calculated as inner products between array

gene profiles, using the selected subset of query genes. Genes were then ranked within

each term according to this summed similarity with known term participants. This

process was then repeated using different subsets of SGA queries to calculate similarities

between array genes.

No more than one allele of each gene was used in either the DAmP or ts analysis,

and the allele with the largest single mutant fitness defect was selected as representative.

Additionally, only query strains that exhibited a single mutant fitness defect (fitness <

0.98) were allowed to participate in prediction. This restriction was imposed to remove

both ts alleles that behaved like wild-type under the tested conditions as well as

nonessential deletions with no functional impact. Since there were fewer DAmP alleles

than alleles in the other classes of SGA queries, their performance was compared against

an equal number (92) of randomly selected ts alleles and deletion mutants with average

results being bootstrapped over 20 iterations.

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3.5.8 Automated image acquisition and analysis

MATα query strains carrying different cellular markers were mated to the ts allele

array, which contained 769 ts alleles representing 485 genes. MATa haploid ts strains

expressing GFP and/or RFP fusion proteins were isolated using SGA technology

(Costanzo and Boone, 2009; Tong and Boone, 2006), transferred into liquid selection

media and cultured for 1-2 days. Automated imaging and image analysis were performed

as described previously (Baryshnikova et al., 2010; Vizeacoumar et al., 2009;

Vizeacoumar et al., 2010). To optimize cell density for automated image analysis, a

liquid handling robot (Biomek FX Lab automation work station, Beckman Coulter) was

used to dispense sample volumes based on the optical density of each strain. Image

acquisitions were achieved using an ImageXpress 5000A fluorescence microscopy

system (Molecular Devices) with 96-well format glass-bottomed plates (MMI Greiner M

plates) at 26oC and 32oC. Cytomat automated incubators (Thermo Fisher Scientific Inc.)

were linked to the ImageXpress system to increase throughput.

3.5.9 Confocal microscopy and image quantitation

Images were captured using the Quorum WaveFX Spinning Disc Confocal

System (Quorum Technologies, Guelph, ON). The Z-axis images were converted into a

single composite image by using the brightest pixel over all images in the Z-stack for

every position in image. This maximum pixel projection technique produced a 2-D

representation of the GFP fusion proteins within the cell from the 3-D data set.

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3.5.10 Analysis of biologically relevant mutants using the HCS dataset

To ensure that the identified HCS phenotypes are due to genetic mutations,

instead of uneven distribution of cell shape, size or cell-cycle stages between mutant and

wild-type cell population, the relevant measurements of cellular organelles were scaled

into relative morphometric features in comparison with the global cell geometry. For

example, for the spindle marker, fiber length was normalized by cell length; for the

mitochondria marker, mitochondria count was then scaled into mitochondria density by

normalizing cell area. After the normalization procedure, for each of the morphometric

features, the values between the mutant cell population and the wild-type cell population

were compared. The statistical comparison was made based on Wilcoxon Rank Sum test

(P < 0.05 after Bonferroni correction).

3.5.11 Affinity purification and mass spectrometry

Cells expressing GFP- or TAP-tagged proteins (1 L) were collected at mid-

logarithmic phase (A600 = ~0.6), and lysed using glass bead beating as described

(Gingras et al., 2005). TAP purification was performed as previously described

(Breitkreutz et al., 2010). For GFP AP-MS, lysates (approximately 150-200 mg protein)

were incubated for 2 hours at 4°C with 25 μl GFP-Trap magnetic particles (Chromotek).

Beads were then subjected to one rapid wash in 1 ml lysis buffer and one wash in 20 mM

Tris pH 8 containing 2 mM CaCl2. On-bead trypsin digestion was performed, followed by

LC-MS/MS on a Thermo Finnigan LTQ using the protocols and parameters described

previously (Breitkreutz et al., 2010). Sequence database searching using Mascot 2.2.1 and

the S. cerevisiae complement of RefSeq release 21 (both forward and reverse entries) was

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performed, and hits with scores above 35 corresponded to a protein false discovery rate of

<4%, determined using a target-decoy strategy (Perkins et al., 1999). A stringent set of

filters were applied to remove likely false positives: 1) all proteins detected in any of

three parallel purifications from untagged yeast were removed from the dataset; 2)

proteins detected with a frequency of ≥15% across 800 purifications were removed; 3)

only those proteins detected with at least 2 unique peptides in two biological replicates

for any given bait were included in the analysis (their interactions with any of the

members of the network are shown).

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CHAPTER FOUR

Summary and Future Directions

4.1 Summary of thesis

Synthetic genetic array (SGA) analysis automates the systematic construction of

double mutants in yeast, enabling a comprehensive and high-throughput analysis of

genetic interactions (Tong et al., 2001). Because essential genes cannot be deleted in

yeast haploid strains, one of the ways to apply SGA analysis to essential genes is to

construct yeast strains harboring conditional alleles in essential genes. The goals of my

thesis work were to: (1) construct a collection of yeast strains carrying temperature-

sensitive (ts) conditional alleles of essential genes in a background compatible with

automated genetics; (2) confirm and characterize the ts mutant collection; (3) validate and

demonstrate the biological utilities of the ts mutant collection.

My newly constructed collection of ts mutants consists of 795 ts strains, covering

501 (~45%) of the 1,101 essential yeast genes, with ~30% of the genes represented by

multiple alleles. This is the largest collection of ts yeast mutants constructed to date. The

integrity of more than 99% of the ts alleles was confirmed using a PCR-based strategy

and ~ 99% of the ts mutants were rescued by introduction of the wild-type version of the

relevant gene.

I validated and demonstrated the usefulness of the ts collection in a number of

different ways. First, in collaboration with colleagues at the University of Gothenburg,

the ts mutant collection was characterized by high-resolution profiling of the temperature

sensitivity of each ts strain in liquid growth assays. This analysis revealed that genes

involved in similar biological pathways showed highly similar growth profiles. Second, I

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used the ts array to explore genetic interactions involving genes whose interactions had

been previously explored using either the yeast deletion mutant (bni1Δ::natMX) or

another type of conditional allele (rfc5-1::natMX). This analysis confirmed that the ts

array should be a valuable additional tool available to yeast geneticists for exploring gene

function. Third, I collaborated with members of Chris Bulawa’s lab in FoldRx

Pharmaceuticals Inc. to show that the ts mutant array is useful for exploration of

chemical-genetic interactions because it enables the screening of compounds that may

suppress defects in vital pathways. Fourth, I collaborated with members of the Boone,

Andrews and Zhang labs to use the ts array in quantitative phenotypic analysis to assess

defects in the subcellular localization of six fluorescent markers, diagnostic for different

subcellular compartments or structures. Quantification of the marker behaviour at the

single-cell level enabled integration of this data set to generate a morphological profile

for each ts mutant. This analysis revealed previously unappreciated functions for essential

genes, including those involved in chromosome cohesion and condensation.

4.2 Future Directions

4.2.1 Exploring functions for uncharacterized yeast genes using the ts mutant

collection

Essential genes function as hubs in the genetic interaction network (Chapter

3)(Davierwala et al., 2005; He and Zhang, 2006). This property means that exploration of

genetic interactions involving essential genes ought to be particularly useful for

dissection of the function of the many remaining uncharacterized yeast genes (Pena-

Castillo and Hughes, 2007). The ts mutant collection provides an important complement

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to the deletion collection (and other resources) that should enable the comprehensive

assessment of gene function in a model eukaryotic cell. In addition, I found that the

phenotypic analysis of strains carrying ts alleles in essential genes provides greater

predictive power about gene function than comparable analysis of either strains carrying

deletions in nonessential genes or DAmP alleles of essential genes (Chapter 3).

For example, a subset of deletion mutants showed few or no genetic interactions

when crossed with the nonessential deletion array (Tong et al., 2004). It may be fruitful to

assess genetic interactions involving these mutants using the ts mutant collection that I

constructed. In addition, the general properties of genetic interactions involving essential

genes have not been thoroughly explored, since no appropriate strain collection had been

constructed. It is now possible to systematically explore genetic interactions involving

essential genes by crossing a ts mutant in a particular essential gene to the entire ts

collection

4.2.2 Discovery of new protein interaction partners for yeast essential genes using

systematic genetics

Dosage suppression studies have been productively used as a genetic means to

identify protein-protein interactions. For example, genes encoding the positive regulatory

subunits, Cln1p and Cln2p, were discovered as dosage suppressors of cdc28-1, a

temperature-sensitive allele of the essential gene encoding the cyclin-dependent kinase

Cdc28p (Reed et al., 1989). One application of the ts array that I constructed is systematic

dosage suppression analysis. To enable this application, our lab developed a high-copy

(2μ-based) plasmid library in which each plasmid contains a DNA insert composed of a

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single yeast ORF with its native upstream and downstream genomic sequences, along

with a kanMX marker flanked by two unique 20-nucleotide molecular barcode tags

(Magtanong et al., submitted). As proof-of principle, Leslie Magtanong, a former

graduate student in our lab, transformed 41 different ts mutants from my ts array with the

high-copy plasmid library and quantified the barcodes associated with colonies that were

able to grow at the semi-restrictive temperature. Leslie’s analysis identified 150 dosage

suppression interactions for 32 essential genes including 13 query genes that had no

previously known dosage suppressors (Magtanong et al., submitted). Mapping a dosage

suppression genetic interaction network for the entire spectrum of essential genes could

extend to the majority of nonessential genes on the dosage suppression network by

creating ts alleles of each gene within the context of a synthetic lethal background

(Costanzo et al., 2010).

In addition to identifying new protein interaction partners by dosage suppression

studies, another genetic way to find interacting components in a protein complex is by

using nonallelic noncomplementation (Stearns and Botstein, 1988). Nonallelic

noncomplementation occurs when two recessive mutations in two different genes do not

complement one another even though a wild-type copy of each gene is present (Yook et

al., 2001). This is also known as unlinked noncomplementation (Stearns and Botstein,

1988), second-site noncomplementation (Halsell and Kiehart, 1998), or extragenic

noncomplementation (Vinh et al., 1993). Conditional mutants in TUB2 (tub2-501) and

TUB3 (tub3-1), two yeast tubulin genes, were isolated by unlinked noncomplementation

of a tub1-1 strain (Stearns and Botstein, 1988). Thus, my large ts mutant collection can be

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used to identify functionally related genes by nonallelic noncomplementation, which will

expand the global functional wiring diagram of the cell.

4.2.3 Discovery of drug targets for essential genes using the barcoded ts mutant

collection

Conditional mutations in essential genes ought to be equally useful compared to

deletion collection since yeast strains carrying deletion mutations in nonessential genes

have been useful tools in drug discovery (Hillenmeyer et al., 2008). For example, I found

that treatment of bet2-1 and cdc43-2 mutants with zaragozic acid could rescue the ts

phenotype of these strains (Chapter 3). Identification of ts mutants that are suppressed by

zaragozic acid A could provide a cell-based screen for inhibitors of squalene synthase, a

potential target for lipid lowering agents (Charlton-Menys and Durrington, 2008).

Screens for compounds that suppress defects in other ts mutants could lead to the

discovery of new treatment strategies for human diseases. For example, we could screen

human disease-associated genes with homologues to essential yeast genes such as SPT14,

ERG11, and HEM2 (Foury, 1997).

4.2.4 Phenotypic analysis of the ts mutant collection using multiple fluorescent

reporter genes

To explore genetic interactions in more detail, our group developed methods to

systematically assess cell biological phenotypes in arrays of yeast mutants (Vizeacoumar

et al., 2010). I collaborated with Franco Vizeacoumar and Jingjing Li to explore the

changes in the subcellular localization of a number of fluorescent reporter proteins in my

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ts mutant collection. We discovered a functional relationship between cohesin and

condensin components, the CPC and the CTF19 kinetochore complex (Chapter 3). Our

large scale phenotypic analysis identified an elongated spindle phenotype in the cohesion

and condensin mutants suggesting a novel role for these components in spindle

disassembly. By extending this approach to the entire ts mutant array, one can examine

multiple cell biological compartments, and this should provide mechanistic insight to

virtually any cellular function. In addition, unexpected pleiotropic functions of multiple

genes can be uncovered. For example, I would explore the potential role of Cdc48p in

DNA damage repair. A functional connection between Cdc48p, which is encoded by an

essential gene, and DNA damage and repair pathways was suggested by large-scale

assessment of synthetic lethal interactions (Costanzo et al., 2010). We used high content

screening experiments to assay the formation of DNA damage foci in the ts mutant

collection, and discovered an increased fraction of cells with focus formation in cdc48

mutants. Furthermore, high-resolution profiling of the temperature sensitivity of ts alleles

showed that cdc48-2 had similar growth behavior to mutants with ts mutations in DNA

repair genes (Figure 2-4E). Cdc48p is an ER resident protein with a major role in protein

folding. However, our assay suggests a novel function for this protein in DNA damage

and repair pathway. Components required for DNA damage pathway may be recruited

from the cytosol to the nucleus and such components might go through a quality control

for proper folding in the ER in a Cdc48p dependent fashion. In addition to confirm the

increased focus formation in cdc48 mutants using different reporters (e.g., Rad52p-GFP),

we also can detect the sensitivity of cdc48 mutants to DNA damage agents (e.g., MMS,

HU, UV). To further understand the role of Cdc48p, it will be best to map its complete

119

physical and genetic interaction profile and test if any of these components share the

same phenotype as that of Cdc48p.

Our lab is currently exploring a wide range of cell biological reporters for

different cellular structures and components such as spindle pole, kinetochore, septin,

peroxisomes etc. (Table 8). While experiments can be done in the deletion array, a major

advantage of the ts mutant collection is the smaller size of the array as opposed to the

deletion array. From our genetic interaction studies, it seems clear that the ts array has at

least six times higher predictive power in gene function. Utilization of such an array for

cell biological experiments, would thus be a powerful way to define gene function.

4.2.5 Spatio-temporal regulation of spindle disassembly

From our detailed analysis we have shown how cohesin and condensin complexes

regulate spindle disassembly through the chromosomal passenger complex. However, it

is still unclear how the chromosomal passenger proteins regulate spindle disassembly and

the mechanism of this signaling remains unknown. Previous work suggests that Ipl1p

might play a potential role in this process (Vizeacoumar et al., 2010). Ipl1p localizes to

the trailing end of the disassembling spindle, starting from a midzone localization

(Buvelot et al., 2003). Ipl1p was initially identified for its role during metaphase where it

regulates the biorientation of the spindle (Biggins and Murray, 2001; Biggins et al.,

1999). The simplest explanation would be that Ipl1p can signal the detachment of the

incorrectly attached kinetochore from the microtubule and ensure that it is attached from

two opposing microtubules from the opposite poles. This “scissoring” activity of Ipl1p

might very well be utilized as a cellular mechanism to disassemble the spindle.

120

Consistent with this idea, increased kinase activity of Ipl1p has been detected in late

anaphase (Buvelot et al., 2003; Vizeacoumar et al., 2010). The kinase activity of Ipl1p

may activate microtubule depolymerizers to efficiently break the spindle. Another

potential substrate for Ipl1p to initiate spindle disassembly might be Kar3p, which also

localizes to the spindle midzone and has microtubule depolymerizing activity. It would be

interesting to assess whether Kar3p is a substrate of Ipl1p and identify its associated

components to understand how Ipl1p can signal spindle disassembly.

121

References Abraham, V. C., Taylor, D. L., and Haskins, J. R. (2004). High content screening applied to large-scale cell biology. Trends Biotechnol 22, 15-22.

Akada, R., Yamamoto, J., and Yamashita, I. (1997). Screening and identification of yeast sequences that cause growth inhibition when overexpressed. Mol Gen Genet 254, 267-274.

Amberg, D. C., Basart, E., and Botstein, D. (1995). Defining protein interactions with yeast actin in vivo. Nat Struct Biol 2, 28-35.

Amsterdam, A., Nissen, R. M., Sun, Z., Swindell, E. C., Farrington, S., and Hopkins, N. (2004). Identification of 315 genes essential for early zebrafish development. Proc Natl Acad Sci U S A 101, 12792-12797.

Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000). Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 25, 25-29.

Bachellier-Bassi, S., Gadal, O., Bourout, G., and Nehrbass, U. (2008). Cell cycle-dependent kinetochore localization of condensin complex in Saccharomyces cerevisiae. J Struct Biol 162, 248-259.

Bakal, C., Aach, J., Church, G., and Perrimon, N. (2007). Quantitative morphological signatures define local signaling networks regulating cell morphology. Science 316, 1753-1756.

Balasubramanian, M. K., Bi, E., and Glotzer, M. (2004). Comparative analysis of cytokinesis in budding yeast, fission yeast and animal cells. Curr Biol 14, R806-818.

Ball Jr, A. R., and Yokomori, K. (2001). The structural maintenance of chromosomes (SMC) family of proteins in mammals. Chromosome Res 9, 85-96.

Baryshnikova, A., Costanzo, M., Dixon, S., Vizeacoumar, F. J., Myers, C. L., Andrews, B. J., and Boone, C. (2010). Synthetic Genetic Array (SGA) Analysis in Saccharomyces cerevisiae and Schizosaccharomyces pombe. Methods in Enzymology 470, 145-179.

122

Baxter, A., Fitzgerald, B. J., Hutson, J. L., McCarthy, A. D., Motteram, J. M., Ross, B. C., Sapra, M., Snowden, M. A., Watson, N. S., Williams, R. J., and et al. (1992). Squalestatin 1, a potent inhibitor of squalene synthase, which lowers serum cholesterol in vivo. J Biol Chem 267, 11705-11708.

Bellaoui, M., Chang, M., Ou, J., Xu, H., Boone, C., and Brown, G. W. (2003). Elg1 forms an alternative RFC complex important for DNA replication and genome integrity. Embo J 22, 4304-4313.

Ben-Aroya, S., Coombes, C., Kwok, T., O'Donnell, K. A., Boeke, J. D., and Hieter, P. (2008). Toward a comprehensive temperature-sensitive mutant repository of the essential genes of Saccharomyces cerevisiae. Mol Cell 30, 248-258.

Bergstrom, J. D., Kurtz, M. M., Rew, D. J., Amend, A. M., Karkas, J. D., Bostedor, R. G., Bansal, V. S., Dufresne, C., VanMiddlesworth, F. L., Hensens, O. D., and et al. (1993). Zaragozic acids: a family of fungal metabolites that are picomolar competitive inhibitors of squalene synthase. Proc Natl Acad Sci U S A 90, 80-84.

Bhalla, N., Biggins, S., and Murray, A. W. (2002). Mutation of YCS4, a budding yeast condensin subunit, affects mitotic and nonmitotic chromosome behavior. Mol Biol Cell 13, 632-645.

Biggins, S., and Murray, A. W. (2001). The budding yeast protein kinase Ipl1/Aurora allows the absence of tension to activate the spindle checkpoint. Genes Dev 15, 3118-3129.

Biggins, S., Severin, F. F., Bhalla, N., Sassoon, I., Hyman, A. A., and Murray, A. W. (1999). The conserved protein kinase Ipl1 regulates microtubule binding to kinetochores in budding yeast. Genes Dev 13, 532-544.

Bloom, K. (2005). Chromosome segregation: seeing is believing. Curr Biol 15, R500-503.

Boone, C., Bussey, H., and Andrews, B. J. (2007). Exploring genetic interactions and networks with yeast. Nat Rev Genet 8, 437-449.

Bortoluzzi, S., Romualdi, C., Bisognin, A., and Danieli, G. A. (2003). Disease genes and intracellular protein networks. Physiol Genomics 15, 223-227.

123

Botstein, D., Chervitz, S. A., and Cherry, J. M. (1997). Yeast as a model organism. Science 277, 1259-1260.

Boutros, M., Kiger, A. A., Armknecht, S., Kerr, K., Hild, M., Koch, B., Haas, S. A., Paro, R., and Perrimon, N. (2004). Genome-wide RNAi analysis of growth and viability in Drosophila cells. Science 303, 832-835.

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

Breitkreutz, A., Choi, H., Sharom, J. R., Boucher, L., Neduva, V., Larsen, B., Lin, Z. Y., Breitkreutz, B. J., Stark, C., Liu, G., et al. (2010). A global protein kinase and phosphatase interaction network in yeast. Science 328, 1043-1046.

Butte, A. J., Dzau, V. J., and Glueck, S. B. (2001). Further defining housekeeping, or "maintenance," genes Focus on "A compendium of gene expression in normal human tissues". Physiol Genomics 7, 95-96.

Buvelot, S., Tatsutani, S. Y., Vermaak, D., and Biggins, S. (2003). The budding yeast Ipl1/Aurora protein kinase regulates mitotic spindle disassembly. J Cell Biol 160, 329-339.

Campbell, J. L., and Cohen-Fix, O. (2002). Chromosome cohesion: ring around the sisters? Trends Biochem Sci 27, 492-495.

Chan, C. S., and Botstein, D. (1993). Isolation and characterization of chromosome-gain and increase-in-ploidy mutants in yeast. Genetics 135, 677-691.

Charlton-Menys, V., and Durrington, P. N. (2008). Human cholesterol metabolism and therapeutic molecules. Exp Physiol 93, 27-42.

Cheeseman, I. M., Anderson, S., Jwa, M., Green, E. M., Kang, J., Yates, J. R., 3rd, Chan, C. S., Drubin, D. G., and Barnes, G. (2002a). Phospho-regulation of kinetochore-microtubule attachments by the Aurora kinase Ipl1p. Cell 111, 163-172.

124

Cheeseman, I. M., Drubin, D. G., and Barnes, G. (2002b). Simple centromere, complex kinetochore: linking spindle microtubules and centromeric DNA in budding yeast. J Cell Biol 157, 199-203.

Chen, S. C., Zhao, T., Gordon, G. J., and Murphy, R. F. (2007). Automated image analysis of protein localization in budding yeast. Bioinformatics 23, i66-71.

Ciosk, R., Shirayama, M., Shevchenko, A., Tanaka, T., Toth, A., and Nasmyth, K. (2000). Cohesin's binding to chromosomes depends on a separate complex consisting of Scc2 and Scc4 proteins. Mol Cell 5, 243-254.

Cliften, P., Sudarsanam, P., Desikan, A., Fulton, L., Fulton, B., Majors, J., Waterston, R., Cohen, B. A., and Johnston, M. (2003). Finding functional features in Saccharomyces genomes by phylogenetic footprinting. Science 301, 71-76.

Cortes-Ledesma, F., and Aguilera, A. (2006). Double-strand breaks arising by replication through a nick are repaired by cohesin-dependent sister-chromatid exchange. EMBO Rep 7, 919-926.

Costanzo, M., Baryshnikova, A., Bellay, J., Kim, Y., Spear, E. D., Sevier, C. S., Ding, H., Koh, J. L., Toufighi, K., Mostafavi, S., et al. (2010). The genetic landscape of a cell. Science 327, 425-431.

Costanzo, M., and Boone, C. (2009). SGAM: an array-based approach for high-resolution genetic mapping in Saccharomyces cerevisiae. Methods Mol Biol 548, 37-53.

D'Ambrosio, C., Schmidt, C. K., Katou, Y., Kelly, G., Itoh, T., Shirahige, K., and Uhlmann, F. (2008). Identification of cis-acting sites for condensin loading onto budding yeast chromosomes. Genes Dev 22, 2215-2227.

Davierwala, A. P., Haynes, J., Li, Z., Brost, R. L., Robinson, M. D., Yu, L., Mnaimneh, S., Ding, H., Zhu, H., Chen, Y., et al. (2005). The synthetic genetic interaction spectrum of essential genes. Nat Genet 37, 1147-1152.

Dimster-Denk, D., Rine, J., Phillips, J., Scherer, S., Cundiff, P., DeBord, K., Gilliland, D., Hickman, S., Jarvis, A., Tong, L., and Ashby, M. (1999). Comprehensive evaluation of isoprenoid biosynthesis regulation in Saccharomyces cerevisiae utilizing the Genome Reporter Matrix. J Lipid Res 40, 850-860.

125

Dixon, S. J., Costanzo, M., Baryshnikova, A., Andrews, B., and Boone, C. (2009). Systematic mapping of genetic interaction networks. Annu Rev Genet 43, 601-625.

Do, R., Kiss, R. S., Gaudet, D., and Engert, J. C. (2009). Squalene synthase: a critical enzyme in the cholesterol biosynthesis pathway. Clin Genet 75, 19-29.

Dohmen, R. J., and Varshavsky, A. (2005). Heat-inducible degron and the making of conditional mutants. Methods Enzymol 399, 799-822.

Dohmen, R. J., Wu, P., and Varshavsky, A. (1994). Heat-inducible degron: a method for constructing temperature-sensitive mutants. Science 263, 1273-1276.

Dowell, R. D., Ryan, O., Jansen, A., Cheung, D., Agarwala, S., Danford, T., Bernstein, D. A., Rolfe, P. A., Heisler, L. E., Chin, B., et al. (2010). Genotype to phenotype: a complex problem. Science 328, 469.

Dreze, M., Charloteaux, B., Milstein, S., Vidalain, P. O., Yildirim, M. A., Zhong, Q., Svrzikapa, N., Romero, V., Laloux, G., Brasseur, R., et al. (2009). 'Edgetic' perturbation of a C. elegans BCL2 ortholog. Nat Methods 6, 843-849.

Edgar, R. S., and Lielausis, I. (1964). Temperature-Sensitive Mutants of Bacteriophage T4d: Their Isolation and Genetic Characterization. Genetics 49, 649-662.

Eggert, U. S., Kiger, A. A., Richter, C., Perlman, Z. E., Perrimon, N., Mitchison, T. J., and Field, C. M. (2004). Parallel chemical genetic and genome-wide RNAi screens identify cytokinesis inhibitors and targets. PLoS Biol 2, e379.

Ellermeier, C., and Smith, G. R. (2005). Cohesins are required for meiotic DNA breakage and recombination in Schizosaccharomyces pombe. Proc Natl Acad Sci U S A 102, 10952-10957.

Evangelista, M., Blundell, K., Longtine, M. S., Chow, C. J., Adames, N., Pringle, J. R., Peter, M., and Boone, C. (1997). Bni1p, a yeast formin linking cdc42p and the actin cytoskeleton during polarized morphogenesis. Science 276, 118-122.

Evangelista, M., Pruyne, D., Amberg, D. C., Boone, C., and Bretscher, A. (2002). Formins direct Arp2/3-independent actin filament assembly to polarize cell growth in yeast. Nat Cell Biol 4, 260-269.

126

Fernius, J., and Marston, A. L. (2009). Establishment of cohesion at the pericentromere by the Ctf19 kinetochore subcomplex and the replication fork-associated factor, Csm3. PLoS Genet 5, e1000629.

Field, C. M., and Kellogg, D. (1999). Septins: cytoskeletal polymers or signalling GTPases? Trends Cell Biol 9, 387-394.

Foury, F. (1997). Human genetic diseases: a cross-talk between man and yeast. Gene 195, 1-10.

Foury, F., and Kucej, M. (2002). Yeast mitochondrial biogenesis: a model system for humans? Curr Opin Chem Biol 6, 106-111.

Fraser, A. G., Kamath, R. S., Zipperlen, P., Martinez-Campos, M., Sohrmann, M., and Ahringer, J. (2000). Functional genomic analysis of C. elegans chromosome I by systematic RNA interference. Nature 408, 325-330.

Fraser, H. B., Wall, D. P., and Hirsh, A. E. (2003). A simple dependence between protein evolution rate and the number of protein-protein interactions. BMC Evol Biol 3, 11.

Freeman, L., Aragon-Alcaide, L., and Strunnikov, A. (2000). The condensin complex governs chromosome condensation and mitotic transmission of rDNA. J Cell Biol 149, 811-824.

Fridman, V., Gerson-Gurwitz, A., Movshovich, N., Kupiec, M., and Gheber, L. (2009). Midzone organization restricts interpolar microtubule plus-end dynamics during spindle elongation. EMBO Rep 10, 387-393.

Gari, E., Piedrafita, L., Aldea, M., and Herrero, E. (1997). A set of vectors with a tetracycline-regulatable promoter system for modulated gene expression in Saccharomyces cerevisiae. Yeast 13, 837-848.

Gavin, A. C., Aloy, P., Grandi, P., Krause, R., Boesche, M., Marzioch, M., Rau, C., Jensen, L. J., Bastuck, S., Dumpelfeld, B., et al. (2006). Proteome survey reveals modularity of the yeast cell machinery. Nature 440, 631-636.

Gavin, A. C., Bosche, M., Krause, R., Grandi, P., Marzioch, M., Bauer, A., Schultz, J., Rick, J. M., Michon, A. M., Cruciat, C. M., et al. (2002). Functional organization of the yeast proteome by systematic analysis of protein complexes. Nature 415, 141-147.

127

Gerdes, S., Edwards, R., Kubal, M., Fonstein, M., Stevens, R., and Osterman, A. (2006). Essential genes on metabolic maps. Curr Opin Biotechnol 17, 448-456.

Giaever, G., Chu, A. M., Ni, L., Connelly, C., Riles, L., Veronneau, S., Dow, S., Lucau-Danila, A., Anderson, K., Andre, B., et al. (2002). Functional profiling of the Saccharomyces cerevisiae genome. Nature 418, 387-391.

Giaever, G., Flaherty, P., Kumm, J., Proctor, M., Nislow, C., Jaramillo, D. F., Chu, A. M., Jordan, M. I., Arkin, A. P., and Davis, R. W. (2004). Chemogenomic profiling: identifying the functional interactions of small molecules in yeast. Proc Natl Acad Sci U S A 101, 793-798.

Gingras, A. C., Caballero, M., Zarske, M., Sanchez, A., Hazbun, T. R., Fields, S., Sonenberg, N., Hafen, E., Raught, B., and Aebersold, R. (2005). A novel, evolutionarily conserved protein phosphatase complex involved in cisplatin sensitivity. Mol Cell Proteomics 4, 1725-1740.

Glass, J. I., Assad-Garcia, N., Alperovich, N., Yooseph, S., Lewis, M. R., Maruf, M., Hutchison, C. A., 3rd, Smith, H. O., and Venter, J. C. (2006). Essential genes of a minimal bacterium. Proc Natl Acad Sci U S A 103, 425-430.

Goffeau, A., Barrell, B. G., Bussey, H., Davis, R. W., Dujon, B., Feldmann, H., Galibert, F., Hoheisel, J. D., Jacq, C., Johnston, M., et al. (1996). Life with 6000 genes. Science 274, 546, 563-547.

Goh, K. I., Cusick, M. E., Valle, D., Childs, B., Vidal, M., and Barabasi, A. L. (2007). The human disease network. Proc Natl Acad Sci U S A 104, 8685-8690.

Golling, G., Amsterdam, A., Sun, Z., Antonelli, M., Maldonado, E., Chen, W., Burgess, S., Haldi, M., Artzt, K., Farrington, S., et al. (2002). Insertional mutagenesis in zebrafish rapidly identifies genes essential for early vertebrate development. Nat Genet 31, 135-140.

Grate, L., and Ares, M., Jr. (2002). Searching yeast intron data at Ares lab Web site. Methods Enzymol 350, 380-392.

Grill, S. W., Gonczy, P., Stelzer, E. H., and Hyman, A. A. (2001). Polarity controls forces governing asymmetric spindle positioning in the Caenorhabditis elegans embryo. Nature 409, 630-633.

128

Guacci, V., Koshland, D., and Strunnikov, A. (1997). A direct link between sister chromatid cohesion and chromosome condensation revealed through the analysis of MCD1 in S. cerevisiae. Cell 91, 47-57.

Haering, C. H., Lowe, J., Hochwagen, A., and Nasmyth, K. (2002). Molecular architecture of SMC proteins and the yeast cohesin complex. Mol Cell 9, 773-788.

Haeusler, R. A., Pratt-Hyatt, M., Good, P. D., Gipson, T. A., and Engelke, D. R. (2008). Clustering of yeast tRNA genes is mediated by specific association of condensin with tRNA gene transcription complexes. Genes Dev 22, 2204-2214.

Hagstrom, K. A., Holmes, V. F., Cozzarelli, N. R., and Meyer, B. J. (2002). C. elegans condensin promotes mitotic chromosome architecture, centromere organization, and sister chromatid segregation during mitosis and meiosis. Genes Dev 16, 729-742.

Hall, M. C., Jeong, D. E., Henderson, J. T., Choi, E., Bremmer, S. C., Iliuk, A. B., and Charbonneau, H. (2008). Cdc28 and Cdc14 control stability of the anaphase-promoting complex inhibitor Acm1. J Biol Chem 283, 10396-10407.

Halsell, S. R., and Kiehart, D. P. (1998). Second-site noncomplementation identifies genomic regions required for Drosophila nonmuscle myosin function during morphogenesis. Genetics 148, 1845-1863.

Hardwick, K. G. (1998). The spindle checkpoint. Trends Genet 14, 1-4.

Hardwick, K. G., and Murray, A. W. (1995). Mad1p, a phosphoprotein component of the spindle assembly checkpoint in budding yeast. J Cell Biol 131, 709-720.

Harris, S. D., Cheng, J., Pugh, T. A., and Pringle, J. R. (1992). Molecular analysis of Saccharomyces cerevisiae chromosome I. On the number of genes and the identification of essential genes using temperature-sensitive-lethal mutations. J Mol Biol 225, 53-65.

Hartman, J. L. t., Garvik, B., and Hartwell, L. (2001). Principles for the buffering of genetic variation. Science 291, 1001-1004.

Hartwell, L. (2004). Genetics. Robust interactions. Science 303, 774-775.

129

Hartwell, L. H. (1967). Macromolecule synthesis in temperature-sensitive mutants of yeast. J Bacteriol 93, 1662-1670.

He, X., and Zhang, J. (2006). Why do hubs tend to be essential in protein networks? PLoS Genet 2, e88.

Hillenmeyer, M. E., Fung, E., Wildenhain, J., Pierce, S. E., Hoon, S., Lee, W., Proctor, M., St Onge, R. P., Tyers, M., Koller, D., et al. (2008). The chemical genomic portrait of yeast: uncovering a phenotype for all genes. Science 320, 362-365.

Hirano, M., and Hirano, T. (2002). Hinge-mediated dimerization of SMC protein is essential for its dynamic interaction with DNA. Embo J 21, 5733-5744.

Hirsh, A. E., and Fraser, H. B. (2001). Protein dispensability and rate of evolution. Nature 411, 1046-1049.

Ho, C. H., Magtanong, L., Barker, S. L., Gresham, D., Nishimura, S., Natarajan, P., Koh, J. L., Porter, J., Gray, C. A., Andersen, R. J., et al. (2009). A molecular barcoded yeast ORF library enables mode-of-action analysis of bioactive compounds. Nat Biotechnol 27, 369-377.

Huang, C. E., Milutinovich, M., and Koshland, D. (2005). Rings, bracelet or snaps: fashionable alternatives for Smc complexes. Philos Trans R Soc Lond B Biol Sci 360, 537-542.

Hughes, T. R. (2002). Yeast and drug discovery. Funct Integr Genomics 2, 199-211.

Hutchison, C. A., Peterson, S. N., Gill, S. R., Cline, R. T., White, O., Fraser, C. M., Smith, H. O., and Venter, J. C. (1999). Global transposon mutagenesis and a minimal Mycoplasma genome. Science 286, 2165-2169.

Hyland, K. M., Kingsbury, J., Koshland, D., and Hieter, P. (1999). Ctf19p: A novel kinetochore protein in Saccharomyces cerevisiae and a potential link between the kinetochore and mitotic spindle. J Cell Biol 145, 15-28.

Jeong, H., Mason, S. P., Barabasi, A. L., and Oltvai, Z. N. (2001). Lethality and centrality in protein networks. Nature 411, 41-42.

130

Jiang, Y., Proteau, P., Poulter, D., and Ferro-Novick, S. (1995). BTS1 encodes a geranylgeranyl diphosphate synthase in Saccharomyces cerevisiae. J Biol Chem 270, 21793-21799.

Jones, G. M., Stalker, J., Humphray, S., West, A., Cox, T., Rogers, J., Dunham, I., and Prelich, G. (2008). A systematic library for comprehensive overexpression screens in Saccharomyces cerevisiae. Nat Methods 5, 239-241.

Jones, S., and Sgouros, J. (2001). The cohesin complex: sequence homologies, interaction networks and shared motifs. Genome Biol 2, RESEARCH0009.

Jordan, I. K., Rogozin, I. B., Wolf, Y. I., and Koonin, E. V. (2002). Essential genes are more evolutionarily conserved than are nonessential genes in bacteria. Genome Res 12, 962-968.

Juang, Y. L., Huang, J., Peters, J. M., McLaughlin, M. E., Tai, C. Y., and Pellman, D. (1997). APC-mediated proteolysis of Ase1 and the morphogenesis of the mitotic spindle. Science 275, 1311-1314.

Kaback, D. B., Oeller, P. W., Yde Steensma, H., Hirschman, J., Ruezinsky, D., Coleman, K. G., and Pringle, J. R. (1984). Temperature-sensitive lethal mutations on yeast chromosome I appear to define only a small number of genes. Genetics 108, 67-90.

Kaiser, P., Meierhofer, D., Wang, X., and Huang, L. (2008). Tandem affinity purification combined with mass spectrometry to identify components of protein complexes. Methods Mol Biol 439, 309-326.

Kamath, R. S., Fraser, A. G., Dong, Y., Poulin, G., Durbin, R., Gotta, M., Kanapin, A., Le Bot, N., Moreno, S., Sohrmann, M., et al. (2003). Systematic functional analysis of the Caenorhabditis elegans genome using RNAi. Nature 421, 231-237.

Kanemaki, M., Sanchez-Diaz, A., Gambus, A., and Labib, K. (2003). Functional proteomic identification of DNA replication proteins by induced proteolysis in vivo. Nature 423, 720-724.

Kang, J., Cheeseman, I. M., Kallstrom, G., Velmurugan, S., Barnes, G., and Chan, C. S. (2001). Functional cooperation of Dam1, Ipl1, and the inner centromere protein (INCENP)-related protein Sli15 during chromosome segregation. J Cell Biol 155, 763-774.

131

Kelley, R., and Ideker, T. (2005). Systematic interpretation of genetic interactions using protein networks. Nat Biotechnol 23, 561-566.

Kellis, M., Patterson, N., Endrizzi, M., Birren, B., and Lander, E. S. (2003). Sequencing and comparison of yeast species to identify genes and regulatory elements. Nature 423, 241-254.

Kenna, M. A., and Skibbens, R. V. (2003). Mechanical link between cohesion establishment and DNA replication: Ctf7p/Eco1p, a cohesion establishment factor, associates with three different replication factor C complexes. Mol Cell Biol 23, 2999-3007.

Khmelinskii, A., Lawrence, C., Roostalu, J., and Schiebel, E. (2007). Cdc14-regulated midzone assembly controls anaphase B. J Cell Biol 177, 981-993.

Khmelinskii, A., and Schiebel, E. (2008). Assembling the spindle midzone in the right place at the right time. Cell Cycle 7, 283-286.

Kim, D. U., Hayles, J., Kim, D., Wood, V., Park, H. O., Won, M., Yoo, H. S., Duhig, T., Nam, M., Palmer, G., et al. (2010). Analysis of a genome-wide set of gene deletions in the fission yeast Schizosaccharomyces pombe. Nat Biotechnol 28, 617-623.

Kim, J. H., Kang, J. S., and Chan, C. S. (1999). Sli15 associates with the ipl1 protein kinase to promote proper chromosome segregation in Saccharomyces cerevisiae. J Cell Biol 145, 1381-1394.

Klein, F., Mahr, P., Galova, M., Buonomo, S. B., Michaelis, C., Nairz, K., and Nasmyth, K. (1999). A central role for cohesins in sister chromatid cohesion, formation of axial elements, and recombination during yeast meiosis. Cell 98, 91-103.

Kong, X., Ball, A. R., Jr., Sonoda, E., Feng, J., Takeda, S., Fukagawa, T., Yen, T. J., and Yokomori, K. (2009). Cohesin associates with spindle poles in a mitosis-specific manner and functions in spindle assembly in vertebrate cells. Mol Biol Cell 20, 1289-1301.

Kozminski, K. G., Beven, L., Angerman, E., Tong, A. H., Boone, C., and Park, H. O. (2003). Interaction between a Ras and a Rho GTPase couples selection of a growth site to the development of cell polarity in yeast. Mol Biol Cell 14, 4958-4970.

132

Krogan, N. J., Cagney, G., Yu, H., Zhong, G., Guo, X., Ignatchenko, A., Li, J., Pu, S., Datta, N., Tikuisis, A. P., et al. (2006). Global landscape of protein complexes in the yeast Saccharomyces cerevisiae. Nature 440, 637-643.

Kroll, E. S., Hyland, K. M., Hieter, P., and Li, J. J. (1996). Establishing genetic interactions by a synthetic dosage lethality phenotype. Genetics 143, 95-102.

Kuranda, K., Francois, J., and Palamarczyk, G. (2009). The isoprenoid pathway and transcriptional response to its inhibitors in the yeast Saccharomyces cerevisiae. FEMS Yeast Res 10, 14-27.

Lamichhane, G., Zignol, M., Blades, N. J., Geiman, D. E., Dougherty, A., Grosset, J., Broman, K. W., and Bishai, W. R. (2003). A postgenomic method for predicting essential genes at subsaturation levels of mutagenesis: application to Mycobacterium tuberculosis. Proc Natl Acad Sci U S A 100, 7213-7218.

Lander, E. S., Linton, L. M., Birren, B., Nusbaum, C., Zody, M. C., Baldwin, J., Devon, K., Dewar, K., Doyle, M., FitzHugh, W., et al. (2001). Initial sequencing and analysis of the human genome. Nature 409, 860-921.

Lavoie, B. D., Hogan, E., and Koshland, D. (2002). In vivo dissection of the chromosome condensation machinery: reversibility of condensation distinguishes contributions of condensin and cohesin. J Cell Biol 156, 805-815.

Lavoie, B. D., Hogan, E., and Koshland, D. (2004). In vivo requirements for rDNA chromosome condensation reveal two cell-cycle-regulated pathways for mitotic chromosome folding. Genes Dev 18, 76-87.

Lavoie, B. D., Tuffo, K. M., Oh, S., Koshland, D., and Holm, C. (2000). Mitotic chromosome condensation requires Brn1p, the yeast homologue of Barren. Mol Biol Cell 11, 1293-1304.

Lee, W., St Onge, R. P., Proctor, M., Flaherty, P., Jordan, M. I., Arkin, A. P., Davis, R. W., Nislow, C., and Giaever, G. (2005). Genome-wide requirements for resistance to functionally distinct DNA-damaging agents. PLoS Genet 1, e24.

Lew, D. J., and Burke, D. J. (2003). The spindle assembly and spindle position checkpoints. Annu Rev Genet 37, 251-282.

133

Li, F., Flanary, P. L., Altieri, D. C., and Dohlman, H. G. (2000). Cell division regulation by BIR1, a member of the inhibitor of apoptosis family in yeast. J Biol Chem 275, 6707-6711.

Li, R., and Murray, A. W. (1991). Feedback control of mitosis in budding yeast. Cell 66, 519-531.

Liang, R., Liu, X., Liu, J., Ren, Q., Liang, P., Lin, Z., and Xie, X. (2007). A T7-expression system under temperature control could create temperature-sensitive phenotype of target gene in Escherichia coli. J Microbiol Methods 68, 497-506.

Liu, C., van Dyk, D., Li, Y., Andrews, B., and Rao, H. (2009). A genome-wide synthetic dosage lethality screen reveals multiple pathways that require the functioning of ubiquitin-binding proteins Rad23 and Dsk2. BMC Biol 7, 75.

Longtine, M. S., DeMarini, D. J., Valencik, M. L., Al-Awar, O. S., Fares, H., De Virgilio, C., and Pringle, J. R. (1996). The septins: roles in cytokinesis and other processes. Curr Opin Cell Biol 8, 106-119.

Loo, L. H., Wu, L. F., and Altschuler, S. J. (2007). Image-based multivariate profiling of drug responses from single cells. Nat Methods 4, 445-453.

Lopez-Bigas, N., and Ouzounis, C. A. (2004). Genome-wide identification of genes likely to be involved in human genetic disease. Nucleic Acids Res 32, 3108-3114.

Magtanong et al., L., Ho, C.H., Barker, S.l., Jiao, W., Baryshnikova, A., Bahr, S., Smith, A., Heisler, L., Kuzmin, E., Andrusiak, K., Kobylianski, A., Li, Z., (submitted). Mapping Genetic Networks by Systematic Dosage Suppression. Nat Biotechnol.

Makrantoni, V., and Stark, M. J. (2009). Efficient chromosome biorientation and the tension checkpoint in Saccharomyces cerevisiae both require Bir1. Mol Cell Biol 29, 4552-4562.

McAinsh, A. D., Tytell, J. D., and Sorger, P. K. (2003). Structure, function, and regulation of budding yeast kinetochores. Annu Rev Cell Dev Biol 19, 519-539.

Measday, V., Hailey, D. W., Pot, I., Givan, S. A., Hyland, K. M., Cagney, G., Fields, S., Davis, T. N., and Hieter, P. (2002). Ctf3p, the Mis6 budding yeast homolog, interacts with Mcm22p and Mcm16p at the yeast outer kinetochore. Genes Dev 16, 101-113.

134

Measday, V., and Hieter, P. (2002). Synthetic dosage lethality. Methods Enzymol 350, 316-326.

Measday, V., and Hieter, P. (2004). Kinetochore sub-structure comes to MIND. Nat Cell Biol 6, 94-95.

Melby, T. E., Ciampaglio, C. N., Briscoe, G., and Erickson, H. P. (1998). The symmetrical structure of structural maintenance of chromosomes (SMC) and MukB proteins: long, antiparallel coiled coils, folded at a flexible hinge. J Cell Biol 142, 1595-1604.

Menys, V. C., and Durrington, P. N. (2003). Squalene synthase inhibitors. Br J Pharmacol 139, 881-882.

Metzger, M. B., Maurer, M. J., Dancy, B. M., and Michaelis, S. (2008). Degradation of a cytosolic protein requires endoplasmic reticulum-associated degradation machinery. J Biol Chem 283, 32302-32316.

Mewes, H. W., Frishman, D., Gruber, C., Geier, B., Haase, D., Kaps, A., Lemcke, K., Mannhaupt, G., Pfeiffer, F., Schuller, C., et al. (2000). MIPS: a database for genomes and protein sequences. Nucleic Acids Res 28, 37-40.

Michaelis, C., Ciosk, R., and Nasmyth, K. (1997). Cohesins: chromosomal proteins that prevent premature separation of sister chromatids. Cell 91, 35-45.

Miklos, G. L., and Rubin, G. M. (1996). The role of the genome project in determining gene function: insights from model organisms. Cell 86, 521-529.

Mirchenko, L., and Uhlmann, F. (2010). Sli15(INCENP) Dephosphorylation Prevents Mitotic Checkpoint Reengagement Due to Loss of Tension at Anaphase Onset. Curr Biol 20, 1396-1401.

Mito, Y., Sugimoto, A., and Yamamoto, M. (2003). Distinct developmental function of two Caenorhabditis elegans homologs of the cohesin subunit Scc1/Rad21. Mol Biol Cell 14, 2399-2409.

Mnaimneh, S., Davierwala, A. P., Haynes, J., Moffat, J., Peng, W. T., Zhang, W., Yang, X., Pootoolal, J., Chua, G., Lopez, A., et al. (2004). Exploration of essential gene functions via titratable promoter alleles. Cell 118, 31-44.

135

Moffat, J., Grueneberg, D. A., Yang, X., Kim, S. Y., Kloepfer, A. M., Hinkle, G., Piqani, B., Eisenhaure, T. M., Luo, B., Grenier, J. K., et al. (2006). A lentiviral RNAi library for human and mouse genes applied to an arrayed viral high-content screen. Cell 124, 1283-1298.

Moir, D., Stewart, S. E., Osmond, B. C., and Botstein, D. (1982). Cold-sensitive cell-division-cycle mutants of yeast: isolation, properties, and pseudoreversion studies. Genetics 100, 547-563.

Mollinari, C., Kleman, J. P., Jiang, W., Schoehn, G., Hunter, T., and Margolis, R. L. (2002). PRC1 is a microtubule binding and bundling protein essential to maintain the mitotic spindle midzone. J Cell Biol 157, 1175-1186.

Morell, M., Ventura, S., and Aviles, F. X. (2009). Protein complementation assays: approaches for the in vivo analysis of protein interactions. FEBS Lett 583, 1684-1691.

Mukherji, M., Bell, R., Supekova, L., Wang, Y., Orth, A. P., Batalov, S., Miraglia, L., Huesken, D., Lange, J., Martin, C., et al. (2006). Genome-wide functional analysis of human cell-cycle regulators. Proc Natl Acad Sci U S A 103, 14819-14824.

Myers, C. L., Barrett, D. R., Hibbs, M. A., Huttenhower, C., and Troyanskaya, O. G. (2006). Finding function: evaluation methods for functional genomic data. BMC Genomics 7, 187.

Nakajima, Y., Tyers, R. G., Wong, C. C., Yates, J. R., 3rd, Drubin, D. G., and Barnes, G. (2009). Nbl1p: a Borealin/Dasra/CSC-1-like protein essential for Aurora/Ipl1 complex function and integrity in Saccharomyces cerevisiae. Mol Biol Cell 20, 1772-1784.

Nakatsukasa, K., Huyer, G., Michaelis, S., and Brodsky, J. L. (2008). Dissecting the ER-associated degradation of a misfolded polytopic membrane protein. Cell 132, 101-112.

Narayanaswamy, R., Niu, W., Scouras, A. D., Hart, G. T., Davies, J., Ellington, A. D., Iyer, V. R., and Marcotte, E. M. (2006). Systematic profiling of cellular phenotypes with spotted cell microarrays reveals mating-pheromone response genes. Genome Biol 7, R6.

Nasmyth, K. (1999). Separating sister chromatids. Trends Biochem Sci 24, 98-104.

Nasmyth, K. (2001). Disseminating the genome: joining, resolving, and separating sister chromatids during mitosis and meiosis. Annu Rev Genet 35, 673-745.

136

Nasmyth, K. (2005). How do so few control so many? Cell 120, 739-746.

Nasmyth, K., and Haering, C. H. (2005). The structure and function of SMC and kleisin complexes. Annu Rev Biochem 74, 595-648.

Nasmyth, K., and Haering, C. H. (2009). Cohesin: its roles and mechanisms. Annu Rev Genet 43, 525-558.

Ni, L., and Snyder, M. (2001). A genomic study of the bipolar bud site selection pattern in Saccharomyces cerevisiae. Mol Biol Cell 12, 2147-2170.

Norden, C., Mendoza, M., Dobbelaere, J., Kotwaliwale, C. V., Biggins, S., and Barral, Y. (2006). The NoCut pathway links completion of cytokinesis to spindle midzone function to prevent chromosome breakage. Cell 125, 85-98.

Ohya, Y., Sese, J., Yukawa, M., Sano, F., Nakatani, Y., Saito, T. L., Saka, A., Fukuda, T., Ishihara, S., Oka, S., et al. (2005). High-dimensional and large-scale phenotyping of yeast mutants. Proc Natl Acad Sci U S A 102, 19015-19020.

Okada, S., Devarenne, T. P., and Chappell, J. (2000). Molecular characterization of squalene synthase from the green microalga Botryococcus braunii, race B. Arch Biochem Biophys 373, 307-317.

Ortiz, J., Stemmann, O., Rank, S., and Lechner, J. (1999). A putative protein complex consisting of Ctf19, Mcm21, and Okp1 represents a missing link in the budding yeast kinetochore. Genes Dev 13, 1140-1155.

Ouspenski, II, Cabello, O. A., and Brinkley, B. R. (2000). Chromosome condensation factor Brn1p is required for chromatid separation in mitosis. Mol Biol Cell 11, 1305-1313.

Paran, Y., Ilan, M., Kashman, Y., Goldstein, S., Liron, Y., Geiger, B., and Kam, Z. (2007). High-throughput screening of cellular features using high-resolution light-microscopy; application for profiling drug effects on cell adhesion. J Struct Biol 158, 233-243.

Pelkmans, L., Fava, E., Grabner, H., Hannus, M., Habermann, B., Krausz, E., and Zerial, M. (2005). Genome-wide analysis of human kinases in clathrin- and caveolae/raft-mediated endocytosis. Nature 436, 78-86.

137

Pena-Castillo, L., and Hughes, T. R. (2007). Why are there still over 1000 uncharacterized yeast genes? Genetics 176, 7-14.

Pereira, G., Manson, C., Grindlay, J., and Schiebel, E. (2002). Regulation of the Bfa1p-Bub2p complex at spindle pole bodies by the cell cycle phosphatase Cdc14p. J Cell Biol 157, 367-379.

Pereira, G., and Schiebel, E. (2003). Separase regulates INCENP-Aurora B anaphase spindle function through Cdc14. Science 302, 2120-2124.

Perkins, D. N., Pappin, D. J., Creasy, D. M., and Cottrell, J. S. (1999). Probability-based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis 20, 3551-3567.

Perler, F. B., Davis, E. O., Dean, G. E., Gimble, F. S., Jack, W. E., Neff, N., Noren, C. J., Thorner, J., and Belfort, M. (1994). Protein splicing elements: inteins and exteins--a definition of terms and recommended nomenclature. Nucleic Acids Res 22, 1125-1127.

Perler, F. B., Olsen, G. J., and Adam, E. (1997). Compilation and analysis of intein sequences. Nucleic Acids Res 25, 1087-1093.

Perlman, Z. E., Slack, M. D., Feng, Y., Mitchison, T. J., Wu, L. F., and Altschuler, S. J. (2004). Multidimensional drug profiling by automated microscopy. Science 306, 1194-1198.

Pierce, S. E., Davis, R. W., Nislow, C., and Giaever, G. (2007). Genome-wide analysis of barcoded Saccharomyces cerevisiae gene-deletion mutants in pooled cultures. Nat Protoc 2, 2958-2974.

Pinsky, B. A., and Biggins, S. (2005). The spindle checkpoint: tension versus attachment. Trends Cell Biol 15, 486-493.

Pot, I., Measday, V., Snydsman, B., Cagney, G., Fields, S., Davis, T. N., Muller, E. G., and Hieter, P. (2003). Chl4p and iml3p are two new members of the budding yeast outer kinetochore. Mol Biol Cell 14, 460-476.

Pruyne, D., Evangelista, M., Yang, C., Bi, E., Zigmond, S., Bretscher, A., and Boone, C. (2002). Role of formins in actin assembly: nucleation and barbed-end association. Science 297, 612-615.

138

Reed, S. I., Hadwiger, J. A., Richardson, H. E., and Wittenberg, C. (1989). Analysis of the Cdc28 protein kinase complex by dosage suppression. J Cell Sci Suppl 12, 29-37.

Rickardson, L., Wickstrom, M., Larsson, R., and Lovborg, H. (2007). Image-based screening for the identification of novel proteasome inhibitors. J Biomol Screen 12, 203-210.

Robinson, G. W., Tsay, Y. H., Kienzle, B. K., Smith-Monroy, C. A., and Bishop, R. W. (1993). Conservation between human and fungal squalene synthetases: similarities in structure, function, and regulation. Mol Cell Biol 13, 2706-2717.

Rocha, E. P. (2006). The quest for the universals of protein evolution. Trends Genet 22, 412-416.

Ruchaud, S., Carmena, M., and Earnshaw, W. C. (2007a). The chromosomal passenger complex: one for all and all for one. Cell 131, 230-231.

Ruchaud, S., Carmena, M., and Earnshaw, W. C. (2007b). Chromosomal passengers: conducting cell division. Nat Rev Mol Cell Biol 8, 798-812.

Saka, Y., Sutani, T., Yamashita, Y., Saitoh, S., Takeuchi, M., Nakaseko, Y., and Yanagida, M. (1994). Fission yeast cut3 and cut14, members of a ubiquitous protein family, are required for chromosome condensation and segregation in mitosis. Embo J 13, 4938-4952.

Schild, D., and Byers, B. (1980). Diploid spore formation and other meiotic effects of two cell-division-cycle mutations of Saccharomyces cerevisiae. Genetics 96, 859-876.

Schmid, M. B., Kapur, N., Isaacson, D. R., Lindroos, P., and Sharpe, C. (1989). Genetic analysis of temperature-sensitive lethal mutants of Salmonella typhimurium. Genetics 123, 625-633.

Schuldiner, M., Collins, S. R., Thompson, N. J., Denic, V., Bhamidipati, A., Punna, T., Ihmels, J., Andrews, B., Boone, C., Greenblatt, J. F., et al. (2005). Exploration of the function and organization of the yeast early secretory pathway through an epistatic miniarray profile. Cell 123, 507-519.

139

Schuyler, S. C., Liu, J. Y., and Pellman, D. (2003). The molecular function of Ase1p: evidence for a MAP-dependent midzone-specific spindle matrix. Microtubule-associated proteins. J Cell Biol 160, 517-528.

Sevastopoulos, C. G., Wehr, C. T., and Glaser, D. A. (1977). Large-scale automated isolation of Escherichia coli mutants with thermosensitive DNA replication. Proc Natl Acad Sci U S A 74, 3485-3489.

Sherman, F. (1991). Getting started with yeast. Methods Enzymol 194, 3-21.

Sherman, F. (2002). Getting started with yeast. Methods Enzymol 350, 3-41.

Sheu, Y. J., Barral, Y., and Snyder, M. (2000). Polarized growth controls cell shape and bipolar bud site selection in Saccharomyces cerevisiae. Mol Cell Biol 20, 5235-5247.

Shoemaker, D. D., Lashkari, D. A., Morris, D., Mittmann, M., and Davis, R. W. (1996). Quantitative phenotypic analysis of yeast deletion mutants using a highly parallel molecular bar-coding strategy. Nat Genet 14, 450-456.

Shou, W., Seol, J. H., Shevchenko, A., Baskerville, C., Moazed, D., Chen, Z. W., Jang, J., Charbonneau, H., and Deshaies, R. J. (1999). Exit from mitosis is triggered by Tem1-dependent release of the protein phosphatase Cdc14 from nucleolar RENT complex. Cell 97, 233-244.

Sjogren, C., and Nasmyth, K. (2001). Sister chromatid cohesion is required for postreplicative double-strand break repair in Saccharomyces cerevisiae. Curr Biol 11, 991-995.

Skibbens, R. V., Skeen, V. P., and Salmon, E. D. (1993). Directional instability of kinetochore motility during chromosome congression and segregation in mitotic newt lung cells: a push-pull mechanism. J Cell Biol 122, 859-875.

Smith, A. M., Heisler, L. E., Mellor, J., Kaper, F., Thompson, M. J., Chee, M., Roth, F. P., Giaever, G., and Nislow, C. (2009). Quantitative phenotyping via deep barcode sequencing. Genome Res 19, 1836-1842.

Smith, A. M., Heisler, L. E., St Onge, R. P., Farias-Hesson, E., Wallace, I. M., Bodeau, J., Harris, A. N., Perry, K. M., Giaever, G., Pourmand, N., and Nislow, C. (2010).

140

Highly-multiplexed barcode sequencing: an efficient method for parallel analysis of pooled samples. Nucleic Acids Res 38, e142.

St Onge, R. P., Mani, R., Oh, J., Proctor, M., Fung, E., Davis, R. W., Nislow, C., Roth, F. P., and Giaever, G. (2007). Systematic pathway analysis using high-resolution fitness profiling of combinatorial gene deletions. Nat Genet 39, 199-206.

Stearns, T., and Botstein, D. (1988). Unlinked noncomplementation: isolation of new conditional-lethal mutations in each of the tubulin genes of Saccharomyces cerevisiae. Genetics 119, 249-260.

Steffensen, S., Coelho, P. A., Cobbe, N., Vass, S., Costa, M., Hassan, B., Prokopenko, S. N., Bellen, H., Heck, M. M., and Sunkel, C. E. (2001). A role for Drosophila SMC4 in the resolution of sister chromatids in mitosis. Curr Biol 11, 295-307.

Stegmeier, F., and Amon, A. (2004). Closing mitosis: the functions of the Cdc14 phosphatase and its regulation. Annu Rev Genet 38, 203-232.

Stegmeier, F., Visintin, R., and Amon, A. (2002). Separase, polo kinase, the kinetochore protein Slk19, and Spo12 function in a network that controls Cdc14 localization during early anaphase. Cell 108, 207-220.

Stern, B. M., and Murray, A. W. (2001). Lack of tension at kinetochores activates the spindle checkpoint in budding yeast. Curr Biol 11, 1462-1467.

Straight, A. F., Sedat, J. W., and Murray, A. W. (1998). Time-lapse microscopy reveals unique roles for kinesins during anaphase in budding yeast. J Cell Biol 143, 687-694.

Stray, J. E., and Lindsley, J. E. (2003). Biochemical analysis of the yeast condensin Smc2/4 complex: an ATPase that promotes knotting of circular DNA. J Biol Chem 278, 26238-26248.

Strunnikov, A. V., Hogan, E., and Koshland, D. (1995). SMC2, a Saccharomyces cerevisiae gene essential for chromosome segregation and condensation, defines a subgroup within the SMC family. Genes Dev 9, 587-599.

Sullivan, M., Lehane, C., and Uhlmann, F. (2001). Orchestrating anaphase and mitotic exit: separase cleavage and localization of Slk19. Nat Cell Biol 3, 771-777.

141

Sun, Y., Kucej, M., Fan, H. Y., Yu, H., Sun, Q. Y., and Zou, H. (2009). Separase is recruited to mitotic chromosomes to dissolve sister chromatid cohesion in a DNA-dependent manner. Cell 137, 123-132.

Sutani, T., Yuasa, T., Tomonaga, T., Dohmae, N., Takio, K., and Yanagida, M. (1999). Fission yeast condensin complex: essential roles of non-SMC subunits for condensation and Cdc2 phosphorylation of Cut3/SMC4. Genes Dev 13, 2271-2283.

Suter, B., Kittanakom, S., and Stagljar, I. (2008). Two-hybrid technologies in proteomics research. Curr Opin Biotechnol 19, 316-323.

Tan, G., Chen, M., Foote, C., and Tan, C. (2009). Temperature-sensitive mutations made easy: generating conditional mutations by using temperature-sensitive inteins that function within different temperature ranges. Genetics 183, 13-22.

Tanaka, M., Bateman, R., Rauh, D., Vaisberg, E., Ramachandani, S., Zhang, C., Hansen, K. C., Burlingame, A. L., Trautman, J. K., Shokat, K. M., and Adams, C. L. (2005). An unbiased cell morphology-based screen for new, biologically active small molecules. PLoS Biol 3, e128.

Tanaka, T. U. (2005). Chromosome bi-orientation on the mitotic spindle. Philos Trans R Soc Lond B Biol Sci 360, 581-589.

Tanaka, T. U., Rachidi, N., Janke, C., Pereira, G., Galova, M., Schiebel, E., Stark, M. J., and Nasmyth, K. (2002). Evidence that the Ipl1-Sli15 (Aurora kinase-INCENP) complex promotes chromosome bi-orientation by altering kinetochore-spindle pole connections. Cell 108, 317-329.

Tarassov, K., Messier, V., Landry, C. R., Radinovic, S., Serna Molina, M. M., Shames, I., Malitskaya, Y., Vogel, J., Bussey, H., and Michnick, S. W. (2008). An in vivo map of the yeast protein interactome. Science 320, 1465-1470.

Taylor, D. L. (2007). Past, present, and future of high content screening and the field of cellomics. Methods Mol Biol 356, 3-18.

Taylor, G. S., Liu, Y., Baskerville, C., and Charbonneau, H. (1997). The activity of Cdc14p, an oligomeric dual specificity protein phosphatase from Saccharomyces cerevisiae, is required for cell cycle progression. J Biol Chem 272, 24054-24063.

142

Tong, A. H., and Boone, C. (2006). Synthetic genetic array analysis in Saccharomyces cerevisiae. Methods Mol Biol 313, 171-192.

Tong, A. H., Evangelista, M., Parsons, A. B., Xu, H., Bader, G. D., Page, N., Robinson, M., Raghibizadeh, S., Hogue, C. W., Bussey, H., et al. (2001). Systematic genetic analysis with ordered arrays of yeast deletion mutants. Science 294, 2364-2368.

Tong, A. H., Lesage, G., Bader, G. D., Ding, H., Xu, H., Xin, X., Young, J., Berriz, G. F., Brost, R. L., Chang, M., et al. (2004). Global mapping of the yeast genetic interaction network. Science 303, 808-813.

Toth, A., Ciosk, R., Uhlmann, F., Galova, M., Schleiffer, A., and Nasmyth, K. (1999). Yeast cohesin complex requires a conserved protein, Eco1p(Ctf7), to establish cohesion between sister chromatids during DNA replication. Genes Dev 13, 320-333.

Uhlmann, F. (2004). The mechanism of sister chromatid cohesion. Exp Cell Res 296, 80-85.

Vallen, E. A., Caviston, J., and Bi, E. (2000). Roles of Hof1p, Bni1p, Bnr1p, and myo1p in cytokinesis in Saccharomyces cerevisiae. Mol Biol Cell 11, 593-611.

van Heemst, D., James, F., Poggeler, S., Berteaux-Lecellier, V., and Zickler, D. (1999). Spo76p is a conserved chromosome morphogenesis protein that links the mitotic and meiotic programs. Cell 98, 261-271.

Vas, A. C., Andrews, C. A., Kirkland Matesky, K., and Clarke, D. J. (2007). In vivo analysis of chromosome condensation in Saccharomyces cerevisiae. Mol Biol Cell 18, 557-568.

Venter, J. C., Adams, M. D., Myers, E. W., Li, P. W., Mural, R. J., Sutton, G. G., Smith, H. O., Yandell, M., Evans, C. A., Holt, R. A., et al. (2001). The sequence of the human genome. Science 291, 1304-1351.

Verbrugghe, K. J., and White, J. G. (2004). SPD-1 is required for the formation of the spindle midzone but is not essential for the completion of cytokinesis in C. elegans embryos. Curr Biol 14, 1755-1760.

Vinh, D. B., Welch, M. D., Corsi, A. K., Wertman, K. F., and Drubin, D. G. (1993). Genetic evidence for functional interactions between actin noncomplementing (Anc) gene

143

products and actin cytoskeletal proteins in Saccharomyces cerevisiae. Genetics 135, 275-286.

Visintin, R., Craig, K., Hwang, E. S., Prinz, S., Tyers, M., and Amon, A. (1998). The phosphatase Cdc14 triggers mitotic exit by reversal of Cdk-dependent phosphorylation. Mol Cell 2, 709-718.

Visintin, R., Hwang, E. S., and Amon, A. (1999). Cfi1 prevents premature exit from mitosis by anchoring Cdc14 phosphatase in the nucleolus. Nature 398, 818-823.

Visintin, R., Stegmeier, F., and Amon, A. (2003). The role of the polo kinase Cdc5 in controlling Cdc14 localization. Mol Biol Cell 14, 4486-4498.

Vizeacoumar, F. J., Chong, Y., Boone, C., and Andrews, B. J. (2009). A picture is worth a thousand words: genomics to phenomics in the yeast Saccharomyces cerevisiae. FEBS Lett 583, 1656-1661.

Vizeacoumar, F. J., van Dyk, N., F, S. V., Cheung, V., Li, J., Sydorskyy, Y., Case, N., Li, Z., Datti, A., Nislow, C., et al. (2010). Integrating high-throughput genetic interaction mapping and high-content screening to explore yeast spindle morphogenesis. J Cell Biol 188, 69-81.

Wall, D. P., Hirsh, A. E., Fraser, H. B., Kumm, J., Giaever, G., Eisen, M. B., and Feldman, M. W. (2005). Functional genomic analysis of the rates of protein evolution. Proc Natl Acad Sci U S A 102, 5483-5488.

Wang, B. D., Eyre, D., Basrai, M., Lichten, M., and Strunnikov, A. (2005). Condensin binding at distinct and specific chromosomal sites in the Saccharomyces cerevisiae genome. Mol Cell Biol 25, 7216-7225.

Warren, W. D., Steffensen, S., Lin, E., Coelho, P., Loupart, M., Cobbe, N., Lee, J. Y., McKay, M. J., Orr-Weaver, T., Heck, M. M., and Sunkel, C. E. (2000). The Drosophila RAD21 cohesin persists at the centromere region in mitosis. Curr Biol 10, 1463-1466.

Warringer, J., and Blomberg, A. (2003). Automated screening in environmental arrays allows analysis of quantitative phenotypic profiles in Saccharomyces cerevisiae. Yeast 20, 53-67.

144

Warrington, J. A., Nair, A., Mahadevappa, M., and Tsyganskaya, M. (2000). Comparison of human adult and fetal expression and identification of 535 housekeeping/maintenance genes. Physiol Genomics 2, 143-147.

Westermann, S., Drubin, D. G., and Barnes, G. (2007). Structures and functions of yeast kinetochore complexes. Annu Rev Biochem 76, 563-591.

Winzeler, E. A., Shoemaker, D. D., Astromoff, A., Liang, H., Anderson, K., Andre, B., Bangham, R., Benito, R., Boeke, J. D., Bussey, H., et al. (1999). Functional characterization of the S. cerevisiae genome by gene deletion and parallel analysis. Science 285, 901-906.

Wong, R. W., and Blobel, G. (2008). Cohesin subunit SMC1 associates with mitotic microtubules at the spindle pole. Proc Natl Acad Sci U S A 105, 15441-15445.

Yan, Z., Costanzo, M., Heisler, L. E., Paw, J., Kaper, F., Andrews, B. J., Boone, C., Giaever, G., and Nislow, C. (2008). Yeast Barcoders: a chemogenomic application of a universal donor-strain collection carrying bar-code identifiers. Nat Methods 5, 719-725.

Yang, J., Gu, Z., and Li, W. H. (2003). Rate of protein evolution versus fitness effect of gene deletion. Mol Biol Evol 20, 772-774.

Yeh, E., Haase, J., Paliulis, L. V., Joglekar, A., Bond, L., Bouck, D., Salmon, E. D., and Bloom, K. S. (2008). Pericentric chromatin is organized into an intramolecular loop in mitosis. Curr Biol 18, 81-90.

Yook, K. J., Proulx, S. R., and Jorgensen, E. M. (2001). Rules of nonallelic noncomplementation at the synapse in Caenorhabditis elegans. Genetics 158, 209-220.

Yoon, H. J., and Carbon, J. (1999). Participation of Bir1p, a member of the inhibitor of apoptosis family, in yeast chromosome segregation events. Proc Natl Acad Sci U S A 96, 13208-13213.

Yoshida, S., Asakawa, K., and Toh-e, A. (2002). Mitotic exit network controls the localization of Cdc14 to the spindle pole body in Saccharomyces cerevisiae. Curr Biol 12, 944-950.

145

Yu, H., Braun, P., Yildirim, M. A., Lemmens, I., Venkatesan, K., Sahalie, J., Hirozane-Kishikawa, T., Gebreab, F., Li, N., Simonis, N., et al. (2008). High-quality binary protein interaction map of the yeast interactome network. Science 322, 104-110.

Yu, H., Greenbaum, D., Xin Lu, H., Zhu, X., and Gerstein, M. (2004). Genomic analysis of essentiality within protein networks. Trends Genet 20, 227-231.

Zeidler, M. P., Tan, C., Bellaiche, Y., Cherry, S., Hader, S., Gayko, U., and Perrimon, N. (2004). Temperature-sensitive control of protein activity by conditionally splicing inteins. Nat Biotechnol 22, 871-876.

Zeng, X., Kahana, J. A., Silver, P. A., Morphew, M. K., McIntosh, J. R., Fitch, I. T., Carbon, J., and Saunders, W. S. (1999). Slk19p is a centromere protein that functions to stabilize mitotic spindles. J Cell Biol 146, 415-425.