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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
ii
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
iii
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.
iv
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.
v
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
vi
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
vii
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
viii
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
ix
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
x
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
xi
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
xii
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
xiii
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
1
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).
2
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;
3
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%
4
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
5
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-
6
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
7
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.
8
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
9
(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
10
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).
11
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).
13
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.
18
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.
19
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.
25
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.
34
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.
42
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).
48
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).
50
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.
51
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
65
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
73
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).
78
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.
83
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).
84
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.
86
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.
89
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
118
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
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