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A Novel Gene Overexpression Plasmid Library and its application in
Mapping Genetic Networks by Systematic Dosage Suppression
by
Leslie Joyce Magtanong
A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy
Department of Molecular Genetics University of Toronto
© Copyright by Leslie Joyce Magtanong 2011
ii
A Novel Gene Overexpression Plasmid Library and its application in
Mapping Genetic Networks by Systematic Dosage Suppression
Leslie Joyce Magtanong
Doctor of Philosophy
Department of Molecular Genetics University of Toronto
2011
Abstract
Increasing gene dosage provides a powerful means of probing gene function, as it
tends to cause a gain-of-function effect due to increased gene activity. In the budding
yeast, Saccharomyces cerevisiae, systematic gene overexpression studies have shown
that in wild-type cells, overexpression of a small subset of genes results in an overt
phenotype. However, examining the effects of gene overexpression in sensitized cells
containing mutations in known genes is a powerful means for identifying functionally
relevant genetic interactions. When a query mutant phenotype is rescued by gene
overexpression, the genetic interaction is termed dosage suppression. I comprehensively
investigated dosage suppression genetic interactions in yeast using three approaches.
First, using one of two novel plasmid libraries cloned by two colleagues and myself, I
systematically performed dosage suppression screens and identified over 130 novel
dosage suppression genetic interactions for more than 25 essential yeast genes. The
plasmid libraries, called the molecular barcoded yeast ORF (MoBY-ORF) 1.0 and 2.0,
are designed to streamline dosage analysis by being compatible with high-throughput
genomics technologies that can monitor plasmid representation, including barcode
microarrays and next-generation sequencing methods. Second, I describe a detailed
analysis of the novel dosage suppression interactions, as well as of literature-curated
interactions, and show that the gene pairs exhibiting dosage suppression are often
functionally related and can overlap with physical as well as negative genetic
interactions. Third, I performed a systematic categorization of dosage suppression genetic
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interactions in yeast and show that the majority of the dosage suppression interactions
can be assigned to one of four general mechanistic classifications. With this
comprehensive analysis, I conclude that systematically identifying dosage suppression
genetic interactions will allow for their integration into other genetic and physical
interaction networks and should provide new insight into the global wiring diagram of the
cell.
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Acknowledgments
I have many people to thank for all of their support and encouragement
throughout the years. First and foremost, I would like to thank my parents, Vic and Anita,
my sisters, Lisa and Jill, and my husband, Scott Dixon, for their unconditional support
during my time in Toronto. I also want to thank the professors, postdocs, and students
who have provided valuable feedback and suggestions for my various experiments,
presentations, and manuscripts. In particular, I acknowledge and am grateful to my
supervisory committee, Drs. Brenda Andrews, Barbara Funnell, and Howard Lipshitz,
who have been incredibly supportive of my research and abilities as a doctoral student. I
thank the fellow graduate students who have contributed to my research. In particular, I
thank Cheuk Hei Ho, who spearheaded the development of the MoBY-ORF plasmid
libraries and gave me many helpful suggestions throughout my research; a postdoc, Sarah
Barker, and the various technicians and summer students, all of whom were integral to
the development of the MoBY-ORF plasmid libraries; Wei Jiao and Anastasia
Baryshnikova, who did invaluable computational work for this project; and Sondra Bahr,
a talented technician who made a significant contribution to the dosage suppression
studies. Finally, I would like to thank my supervisor, Dr. Charlie Boone, whose
intelligence and support for me will always be remembered.
This work would not have been possible without assistance provided by members
of the scientific community. In particular, I thank Andrew Smith, and Drs. Larry Heisler,
Marinella Gibella, and Corey Nislow, who provided access to and assistance with their
microarray facilities. I also thank the Natural Sciences and Engineering Research Council
(NSERC), the Canadian Institutes of Health Research (CIHR), and the University of
Toronto for financial support.
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Table of Contents Page
Abstract ii
Acknowledgments iv
List of Tables ix
List of Figures x
List of Appendices xi
List of Electronic Tables xii
Chapter One: Introduction 1
1.1 General Introduction 2
1.2 Genetic Interactions 2
1.2.1 Negative Genetic Interactions 4
1.2.1.1 Complex Haploinsufficiency 4
1.2.2 Positive Genetic Interactions 5
1.2.3 Synthetic Dosage Effects: Lethality and Suppression 7
1.3 Investigating Genetic Interactions in S. cerevisiae in a Systematic 7
Manner
1.3.1 Development of Genome-wide Strain Collections 8
1.3.1.1 Loss-of-Function Strains: The Deletion Strain Collection 8
1.3.1.1.1 Barcoded strains and barcode microarrays 8
1.3.1.2 Essential Gene Strain Collections 13
1.3.1.2.1 tetO promoter collection 13
1.3.1.2.2 URA3-marked temperature-sensitive allele collection 15
1.3.1.2.3 DAmP allele collection 15
1.3.2 Gene overexpression 16
1.3.2.1 The Yeast Two-Hybrid S. cerevisiae ORF Array 17
1.3.2.2 The PCUP1-GST Library 21
1.3.2.3 The PGAL1/10-GST Library 21
1.3.2.4 The Movable ORF Library 22
1.3.2.5 The FLEXGene ORF Collection 23
1.3.2.6 The Yeast Genome Tiling Collection 23
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1.3.2.7 Summary and Comparison of Various Existing Overexpression 24
Libraries
1.4 Systematic Identification of Genetic Interactions in Yeast 25
1.4.1 Synthetic Genetic Array (SGA) Analysis 25
1.4.1.1 Application of SGA to SDL analysis 27
1.4.1.2 Application of SGA to genetic mapping 27
1.4.1.3 Application of SGA to array-based high-content screening 27
1.4.2 Diploid-based Synthetic Lethal Analysis on Microarrays (dSLAM) 29
1.4.3 Genetic Interaction Mapping (GIM) 30
1.4.4 Summary of Genetic Interaction Mapping Strategies 30
1.5 Next-Generation Sequencing 31
1.6 Summary and Rationale 32
Chapter Two: The MoBY-ORF 1.0 Yeast Plasmid Library 34
2.1 Introduction 35
2.2 Results 35
2.2.1 Construction of a library of molecular barcoded yeast ORFs 35
2.2.2 Verification of constructed clones by sequencing 36
2.2.3 Assessment of clone function using temperature-sensitive mutants 39
2.2.4 Complementation cloning to identify drug-resistant mutants and 39
compound mode-of-action
2.3 Summary 39
2.4 Methods 40
2.4.1 Yeast Strains 40
2.4.2 Growth Media 40
2.4.3 Clone Construction and Analysis 40
2.4.4 Sequence Confirmation of the MoBY-ORF Collection Barcodes and 42
3’ ORF Junctions
2.4.5 Functional Complementation of Essential Genes 42
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Chapter Three: Mapping Genetic Networks by Systematic Dosage 43
Suppression
3.1 Introduction 44
3.2 Results 46
3.2.1 Construction of the MoBY-ORF 2.0 plasmid library 46
3.2.2. Dosage suppression analysis of temperature-sensitive conditional 46
mutants
Methods used to identify dosage suppressors 46
Description of results 53
3.2.3 An integrated dosage suppression genetic interaction network 57
Network overview 58
Identification of a genetic link between PKA signaling and the 58
kinetochore
3.2.4 Distribution of dosage suppressors across cellular processes 61
3.2.5. Overlap of dosage suppression interactions with protein-protein and 64
negative genetic network edges
3.2.6 Mechanistic categorization of dosage suppression interactions 64
Dosage suppression decision tree for categorizing dosage 64
suppression interactions
Description of categories 64
3.3 Discussion 73
3.4 Methods 76
3.4.1 Growth media 76
3.4.2 Clone construction and analysis 76
3.4.3 Plasmid pool preparation 77
3.4.4 Cloning of dosage suppressors with the 2µ MoBY-ORF library 77
3.4.5 Yeast barcode microarray hybridization and data analysis 80
3.4.6 Empirical determination of raw barcode microarray intensity cutoff 80
for identification of candidate dosage suppressors
3.4.7 Assessing fitness of barcoded yeast strains by Illumina/Solexa 81
sequencing
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3.4.8 Confirmation of candidate dosage suppressors and test for reciprocal 82
suppression
3.4.9 Overlap of dosage suppression genetic interactions with other types of 83
interactions
3.4.10 Analysis of functional relatedness 83
3.4.11 Identifying gene clusters in the integrated dosage suppression network 83
Chapter Four: Conclusions and Future Directions 84
4.1 General Overview 85
4.2 The MoBY-ORF gene overexpression libraries: present and future 85
applications
4.3 Dosage suppression genetic interaction networks: illuminating a new 88
facet of genetics
4.4 Understanding the mechanistic basis of dosage suppression 90
4.5 Concluding thoughts 92
Chapter Five: References 94
Chapter Six: Appendices 112
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List of Tables Page
1.1 Overview of existing essential gene strain collections for S. cerevisiae 14
1.2 Overview of gene overexpression plasmid libraries for S. cerevisiae 18
3.2.1 Overlap of dosage suppression interactions with other types of 65
interactions
3.2.2 Distribution of dosage suppression gene pairs annotated in the 66
Saccharomyces Genome Database
3.2.3 Gene pairs tested for reciprocal suppression 71
3.2.4 Yeast strains used in this study 78
x
List of Figures Page
1.1 The barcoded kanMX cassette of the S. cerevisiae deletion collection 9
1.2 Barcode microarray method in yeast 11
1.3 Plasmid libraries in S. cerevisiae 19
2.2.1 Plasmid map of p5472 37
2.2.2 Construction of the MoBY-ORF library by homologous 38
recombination in yeast
3.2.1 Schematic of a plasmid in the MoBY-ORF 2.0 plasmid library 47
3.2.2 Plasmid map of p5476 48
3.2.3 MAGIC with the MoBY-ORF 1.0 plasmid library 49
3.2.4 Using the MoBY-ORF 2.0 library to identify candidate dosage 51
suppressors by barcode microarray
3.2.5 Empirical determination of raw barcode microarray intensity cutoff 54
for identification of candidate dosage suppressors
3.2.6 Dosage suppression genetic interaction network for S. cerevisiae 59
3.2.7 Properties of the yeast dosage suppression network 62
3.2.8 Decision tree used to categorize dosage suppression interactions 67
3.2.9 Mechanisms of dosage suppression in yeast 69
xi
List of Appendices Page
6.1 All confirmed spot dilutions performed based on dosage suppression 113
screens reported in this study
6.2 Unique dosage suppression interactions identified in this study 120
xii
List of Electronic Tables (found on the DVD accompanying this thesis)
3.2.1 Dosage suppression interactions identified in this study (.xls file)
3.2.2 Dosage suppression interactions annotated in the Saccharomyces Genome
Database (.xls file)
1
Chapter One
Introduction to Genetic Interactions
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1.1 GENERAL INTRODUCTION
In the post-genome sequence era, a major goal in biology is to understand the function of
every gene encoded in an organism’s genome. One method to elucidate a gene’s function is to
generate a loss-of-function allele, either through targeted genomic replacement/deletion
(Shoemaker, Lashkari et al. 1996) or to generate the functional equivalent by depleting the target
gene messenger RNA (mRNA) using RNA interference (RNAi)(Fire et al., 1998), and see if this
results in a phenotype that differs from wild type. In the budding yeast Saccharomyces
cerevisiae, genome-wide systematic replacement of each predicted open reading frame (ORF)
with a G418 drug resistance cassette, kanMX, showed that ~19% of all genes are essential under
standard laboratory conditions (Giaever et al., 2002); a similar percentage (17.5%) of essential
genes was observed in a pilot systematic deletion study using the unrelated fission yeast
Schizosaccharomyces pombe genome (Decottignies, Sanchez-Perez et al. 2003). In the nematode
Caenorhabditis elegans, systematic RNAi experiments targeting ~86% (16,757/19,427) of all
coding genes demonstrated that reduced function of ~1,000 genes (6%) resulted in a phenotype
consistent with an essential function (e.g. embryonic lethal, larval lethal, larval arrest; (Kamath,
Fraser et al. 2003)). Compared to yeast, the lower percentage of essential genes observed in C.
elegans may be due to either a greater redundancy inherent in a multicellular organism or
technical limitations associated with the RNAi technique or phenotypic analysis. Genome-wide
RNAi libraries have also been developed for other sequenced multicellular organisms, including
Drosophila melanogaster (Boutros, Kiger et al. 2004; Dietzl, Chen et al. 2007), Arabidopsis
thaliana (Schwab, Ossowski et al. 2006), Danio rerio (Pickart, Klee et al. 2006), and cultured
mouse and human cells (Kittler, Putz et al. 2004; Paddison, Silva et al. 2004; Moffat, Grueneberg
et al. 2006). What is clear from all of these efforts is that reducing or eliminating the function of
most genes has no overt phenotypic effect; therefore, understanding gene function on a genomic
scale requires moving beyond loss of function paradigms. As discussed below in greater detail,
one approach which has been pursued with great vigor for almost a decade now is the analysis of
gene deletions or gene knockdowns in the context of other genetic alterations, to investigate
genetic interactions systematically.
1.2 GENETIC INTERACTIONS
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Epistasis is a term describing the interaction of genes that are not allelic to one another.
According to the Merriam-Webster dictionary, the word epistasis is derived from the Greek
words epi plus histanai, which mean “to cause to stand”. Two related but distinct definitions of
epistasis are both useful in explaining genetic interactions. William Bateson’s definition of
epistasis (Bateson 1909), which Patrick Phillips refers to as “compositional epistasis” (Phillips
2008), is not a quantitative measurement, but rather is directly related to the Greek derivation of
the word. When two single mutants (gene a and gene b) are combined, and mutant phenotype A
masks, or “stops”, mutant phenotype B, from manifesting, then gene a is defined as being
epistatic to gene b; the gene being masked is defined as hypostatic (from the Greek words hypo
plus histanai, which mean “to stand under”). R.A. Fisher’s definition of epistasis (Fisher 1918),
which Phillips refers to as “statistical epistasis” (Phillips 2008), is based on population genetics
studies, and involves the quantitative measurement of a genetic interaction. Each single mutant is
given a fitness measurement relative to wild type; therefore, if no genetic interaction occurs, then
the fitness of the double mutant is the expected additive effect of the two fitness values (Phillips
2008). Any deviation from this model, then, is considered a genetic interaction. Note that
Bateson’s strict definition of epistasis is actually one type of genetic interaction (masking or
suppression) and, thus, is encompassed by the broader Fisher definition.
A genetic interaction, therefore, occurs when an unexpected phenotype manifests from
the combination of at least two mutations. The unexpected, or mutant, phenotype implies that a
functional relationship of some kind exists between the gene products. The identification and
analysis of genetic interactions can therefore help unravel genetic and biochemical pathways and
networks and illuminate the underlying structure of biological systems (Tong, Evangelista et al.
2001; Tong, Lesage et al. 2004; Lehner, Crombie et al. 2006; Byrne, Weirauch et al. 2007;
Dixon, Fedyshyn et al. 2008; Roguev, Bandyopadhyay et al. 2008; Costanzo, Baryshnikova et al.
2010) To date, however, the majority of studies have relied upon loss-of-function mutants.
Genetic interactions involving gain-of-function mutations or gene overexpression are likely to be
equally informative, but this direction has been pursued much less vigorously.
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As discussed further in the following sections, the nature of the functional relationship
uncovered by a genetic interaction depends on what types of mutants are used and what type of
genetic interaction is observed. A genetic interaction can reflect a phenotype that is either worse
(negative) or better (positive) than expected. Several mathematical definitions of what the
expected value for a genetic interaction should be have been reported in the literature, including
the Product (or multiplicative), Additive, Log, and Min models (Mani, St Onge et al. 2008). In S.
cerevisiae, the Min model was initially used to define genetic interactions qualitatively (Tong,
Evangelista et al. 2001), but more recently, the Product model has gained favor as more
quantitative phenotypic measurements and data analysis models have been developed (Mani, St
Onge et al. 2008; Costanzo, Baryshnikova et al. 2010). Therefore, in what follows below, I will
introduce negative and positive genetic interactions in the context of the Product model.
Furthermore, unless otherwise noted, the phenotype under consideration is typically strain
fitness, as defined by colony size on solid agar or growth rate in liquid culture.
1.2.1 Negative Genetic Interactions
Negative genetic interactions, which are also referred to as enhancer or aggravating
interactions, occur when a double mutant is less fit than the product of its cognate single mutants.
The most extreme example of a negative genetic interaction is synthetic lethality, where the
combination of two single loss of function (LOF) mutants, which alone are viable, results in an
inviable double mutant phenotype (Dobzhansky 1946; Guarente 1993). When the query allele is
a null allele of a non-essential gene, a synthetic lethal interaction suggests a between-pathway
interaction, whereby the two pathways normally are able to “buffer”, or rescue, one another in
the event that one pathway is compromised (Tong, Evangelista et al. 2001; Tong, Lesage et al.
2004). When the query allele is a conditional allele of an essential gene, synthetic lethality can
also result from a within-pathway interaction (Tong, Evangelista et al. 2001; Mnaimneh,
Davierwala et al. 2004; Tong, Lesage et al. 2004).
1.2.1.1 Complex Haploinsufficiency
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Complex haploinsufficiency (CHI) is a diploid-specific negative genetic interaction. CHI
interactions are more commonly known as dominant enhancers or second-site non-
complementation in Drosophila (Reuter and Wolff 1981; Ashburner 1982; Cutforth and Rubin
1994) and unlinked or non-allelic non-complementation in worms and yeast (Bisson and Thorner
1982; Kusch and Edgar 1986; Stearns and Botstein 1988; Yook, Proulx et al. 2001). In a binary
CHI interaction, two recessive mutations in different genes fail to complement one another as
heterozygous diploid double mutants. CHI interactions have been shown to occur between genes
encoding proteins that physically interact within a protein complex (Bisson and Thorner 1982;
Rine and Herskowitz 1987; Stearns and Botstein 1988; Vinh, Welch et al. 1993; Baetz, Krogan
et al. 2004; Haarer, Viggiano et al. 2007). As an example, in a screen designed to identify new
alleles of the yeast β-tubulin gene, TUB2, the first conditional allele of the yeast α-tubulin gene,
TUB1, was isolated (Stearns and Botstein 1988); α- and β-tubulin heterodimerize to form
tubulin, which can then polymerize to form microtubules (Luduena, Shooter et al. 1977). Two
models of CHI have been described in the literature (Yook 2005). In both models, single
mutations of two genes encoding proteins that function in the same pathway do not cause a
mutant phenotype. In the “Poison” model, when the two mutations are present in the same cell,
the mutant proteins physically interact and act as poisons that interfere with the complex’s
normal function (Hays, Deuring et al. 1989). In the “Dosage” model, when the two mutations are
present in the same cell, the overall reduction in pathway activity leads to a mutant phenotype
(Kidd, Bland et al. 1999). A systematic genome-wide analysis of CHI interactions was reported
in yeast (Haarer, Viggiano et al. 2007). This screen searched for deletion alleles that resulted in a
general enhancement of the growth defect associated with a diploid strain deleted for one of two
copies of the ACT1 gene, which encodes yeast actin. The authors identified over 200 genes that
had a CHI interaction with ACT1. Approximately 15% of the genes were previously either
poorly characterized or had no known function. Interestingly, the authors showed that some
functional specialization related to actin appears to be associated with ribosome function because
several individual genes of ribosomal paralog pairs had CHI interactions with actin (Haarer,
Viggiano et al. 2007).
1.2.2 Positive Genetic Interactions
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A positive genetic interaction occurs when the combination of two mutations results in a
phenotype that is fitter than the product of its cognate single mutations. Also referred to as
alleviating interactions, positive genetic interactions can be divided into different categories
based on the resulting double mutant phenotype. Several classes of positive interactions have
been described (Drees, Thorsson et al. 2005; St Onge, Mani et al. 2007); however, since this is a
rapidly evolving field where the terminology remains unsettled, only definitions of symmetric
and asymmetric positive interactions are presented below.
In a symmetric positive interaction, the two single LOF mutant phenotypes and the
cognate double mutant phenotype are all indistinguishable. This type of interaction most often
occurs because the two genes encode proteins found in either the same complex or biological
pathway; therefore, a pathway or complex is compromised by removal of one component but is
not further compromised by removal of another component (Drees, Thorsson et al. 2005; Collins,
Miller et al. 2007; St Onge, Mani et al. 2007). As an example, St. Onge et al. (St Onge, Mani et
al. 2007) found that the fitness of the single mutant strains rad55Δ and rad57Δ, and of the double
mutant strain rad55Δ rad57Δ were indistinguishable from one another in quantitative growth
assays. The two proteins Rad55p and Rad57p form a heterodimer that is involved in Rad51p
nucleoprotein filament extension in yeast (Sung 1997), consistent with the fact that disruption of
either gene alone will destroy the activity of the entire complex and explaining why the single
and double mutants have the same fitness.
Two types of asymmetric positive interactions involving LOF mutations have been
described. Masking occurs when the fitness of the double mutant is equal to or greater than the
fitness of the least sick single mutant. Genetic suppression occurs when the fitness of the double
mutant is equal to or greater than the fitness of the sickest single mutant (Drees, Thorsson et al.
2005; Segre, Deluna et al. 2005; St Onge, Mani et al. 2007; Breslow, Cameron et al. 2008).
Several different mechanisms of genetic suppression have been identified in the literature
(Prelich 1999; Hodgkin 2005), including (but not limited to) altering the levels of the mutant
protein (McCusker, Yamagishi et al. 1991), altering the activity of the mutant protein (Sandrock,
O'Dell et al. 1997), and altering the activity of the biological pathway in which the mutant
protein operates (Stevenson, Rhodes et al. 1992).
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1.2.3 Synthetic Dosage Effects: Lethality and Suppression
Whereas a loss-of-function mutation in a gene may not result in a mutant phenotype,
overexpression (gain-of-function) of the same gene may provide insight into its function. In
yeast, 3-15% of genes are toxic when overexpressed in wild-type cells, with the percentage
dependent on the method of overexpression (see Section 2.2 for a detailed explanation of
overexpression in yeast) (Sopko, Huang et al. 2006; Jones, Stalker et al. 2008). Gene
overexpression can also be done in a (loss-of-function) mutant background in order to identify
genetic interactions and, by extension, elucidate gene function. In this case, suppression or
enhancement of the mutant phenotype is called dosage suppression or synthetic dosage lethality
(SDL), respectively. In a dosage suppression interaction, overexpression improves or rescues the
mutant phenotype. Conversely, in an SDL interaction, overexpression exacerbates a sick or
induces a lethal phenotype. Dosage suppression studies have been used classically to identify
new protein interaction partners of a particular query allele; for example, the G1 cyclins, CLN1
and CLN2, were initially discovered as dosage suppressors of cdc28-1, a temperature-sensitive
allele of the essential gene encoding the cyclin-dependent kinase Cdc28p (Reed, Hadwiger et al.
1989). Subsequent studies showed that the cyclins bind to and activate the enzymatic activity of
Cdc28p (Richardson, Wittenberg et al. 1989; Tyers and Futcher 1993), suggesting that
overexpression of the cyclins may in some way stabilize Cdc28-1p. SDL studies have been
useful in identifying function-specific relationships; for example, Kroll et al. (Kroll, Hyland et al.
1996) showed that overexpression of ORC6, which encodes a member of the origin recognition
complex, has synthetic dosage lethal interactions specifically with alleles of three other genes
involved in replication, but not with genes required for chromosome segregation. Conversely,
overexpression of the chromosome segregation gene CTF13 is specifically toxic in chromosome
segregation mutants but has no phenotype in replication mutants (Kroll, Hyland et al. 1996).
These small-scale studies demonstrate that overexpression is a powerful way to investigate gene
function and identify genetic interactions.
1.3 INVESTIGATING GENETIC INTERACTIONS IN S. CEREVISIAE IN A SYSTEMATIC
MANNER
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Classical genetic and biochemical analysis has focused on the function of one or a small
number of genes and/or proteins in any one study. In the last decade, there has been a decided
shift towards experimental paradigms that attempt to examine the role of most or all the
genes/proteins in parallel. These systematic approaches have largely been pioneered in the
budding yeast S. cerevisiae.
1.3.1 Development of Genome-wide Strain Collections
The study of genetic interactions in yeast has been greatly advanced by the availability of
several genome-wide collections of genetic “reagents”. Each of these collections has unique
characteristics that make it useful in both investigating specific aspects of gene function and
genetic interactions.
1.3.1.1 Loss-of-Function Strains: The Deletion Strain Collection
Each strain in the S. cerevisiae deletion collection is precisely deleted for one open
reading frame (ORF) from its ATG start codon to its stop codon; specifically, each ORF has
been replaced with a dominant drug-resistant cassette, kanMX, which confers resistance to the
antibiotic geniticin (G418). Furthermore, each cassette is barcoded with two unique 20
nucleotide tags flanked by common primer sequences (Figure 1.1). This allows for highly
parallel analyses of all deletion strains, as the relative abundance of each strain can be measured
from a barcode microarray readout (Shoemaker, Lashkari et al. 1996). Gene deletion strains for
non-essential genes are available as haploids or homozygous diploids, while for essential genes,
strains are available as heterozygous diploids (Giaever, Chu et al. 2002).
1.3.1.1.1 Barcoded strains and barcode microarrays
One innovation that makes the yeast deletion strain collection described above especially
useful in genomic studies is the unique set of barcodes carried by each deletion strain
(Shoemaker, Lashkari et al. 1996). Flanking the kanMX cassette used to replace each ORF are
9
Figure 1.1
10
two 20 nucleotide-long sequence tags, referred to as barcodes, which are unique to each ORF.
The “Uptag” and “Downtag” barcodes are found at the 5’ and 3’ ends respectively of the kanMX
cassette. These unique barcodes are flanked by common primer sequences, thereby allowing for
PCR amplification of all Uptags and Downtags present in a heterogeneous population of
barcoded strains. The relative abundance of each barcoded strain in the population is measured
by hybridizing the PCR products onto a barcode microarray and then detecting the fluorescence
intensity of each feature (Figure 1.2)(Shoemaker, Lashkari et al. 1996; Pierce, Davis et al. 2007).
Functional profiling of the yeast genome using the deletion strain collections in barcode
microarray experiments has provided valuable insight into drug target identification (Giaever,
Flaherty et al. 2004) and mechanisms of haploinsufficiency (Deutschbauer, Jaramillo et al.
2005). More recently, >1,100 barcode microarray experiments using both the heterozygous and
homozygous deletion strain collections were conducted for 408 unique chemical stress
conditions; remarkably, 97% of the deletion strains screened displayed a phenotype in at least
one of the conditions tested, helping to resolve the previous conundrum of why an organism
would maintain so many seemingly non-essential genes (Hillenmeyer, Fung et al. 2008).
Molecular barcodes are being applied in novel ways. For example, in theory, any strain
collection that is barcoded can be subjected to analysis by barcode microarray. To facilitate
barcoding of any S. cerevisiae collection, Yan et al. (Yan, Costanzo et al. 2008) developed a set
of “Barcoder” donor strains in which the HO locus of each strain has been replaced by a
uniquely barcoded kanMX cassette. An S. cerevisiae collection can be barcoded by using
synthetic genetic array (SGA) technology and selection methods (described below in Section
3.1). In proof-of-principle experiments, the above group barcoded 1,402 decreased abundance in
mRNA production, or DAmP, allele strains (described below in Section 2.1.5). Through barcode
microarray experiments, they showed that barcoding did not adversely affect fitness, as the
fitness of barcoded DAmP strains was virtually indistinguishable from that of the cognate non-
barcoded DAmP strains (Yan, Costanzo et al. 2008). This validates the barcodes as a useful way
to molecularly tag any strain of interest. In this thesis, we constructed a genome-wide set of
uniquely barcoded yeast overexpression plasmids for use in gene dosage studies, as described in
Chapters 2 and 3.
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Figure 1.2
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Figure 1.2 Barcode microarray method in yeast.
a) Each yeast strain is tagged with a unique molecular ‘barcode’ indicated by the different colors.
The barcode can either be integrated into the genome or contained on a plasmid; either way, the
downstream analysis is identical. The barcoded yeast strains are pooled for subsequent analyses.
b) The pool is grown competitively in the desired screening condition (e.g. mutant allele, drug
treatment).
c) Barcoded DNA is extracted.
d) Universal primers are used to PCR-amplify the Uptags and Downtags from the heterogeneous
cell population.
e) PCR products are hybridized to a barcode microarray. The intensity of the barcode on the
array is indicative of the relative abundance of the barcoded strain in the pool.
13
1.3.1.2 Essential Gene Strain Collections
Deletion strains of essential genes are by definition inviable and thus impossible to
propagate as either haploids or homozygous diploids. As a class, however, essential genes are of
great interest for a number of reasons. Most obviously, the fact that a gene is required for
viability immediately suggests that it is crucial for some vital and likely interesting aspect of cell
function. Using cell division as an example, the classical cdc screens in yeast identified 32
essential genes all required for some aspect of this process, including DNA replication, transition
between stages of the cell cycle, and cytokinesis (Hartwell, Culloti et al. 1970; Hartwell 1971;
Hartwell, Culotti et al. 1974).
Due to the biological importance of essential genes, much effort has been expended to
generate genetic reagent sets that allow the problem of lethality to be circumvented and thereby
facilitate genomic studies of these genes. In addition to the heterozygous diploid collection,
several groups have developed conditional or hypomorphic alleles of yeast essential genes,
which can be maintained in a haploid cell. This allows for a more direct comparison to the
haploid deletion collection of non-essential genes. It also facilitates looking at genetic
interactions involving essential genes, as haploid cells of opposite mating types carrying
selectable query alleles can be mated and selected for, in order to ultimately generate double
mutant haploids (see below, Sections 1.4.1 and 1.4.3). The development and characterization of
several essential gene strain collections is described below and summarized in Table 1.1.
1.3.1.2.1 tetO promoter collection
Mnaimneh et al. (Mnaimneh, Davierwala et al. 2004) produced 602 strains of essential
gene conditional alleles that are regulated by the small molecule doxycycline, a tetracycline
analogue. In each strain, a kanR-tetO7-TATA cassette was inserted into the promoter just
upstream of the ORF; additionally, each strain expresses tandem copies of the tet repressor,
which binds tetO7 and prevents transcription of the associated ORF. Therefore, in the absence of
doxycycline, this repressor is not active, and transcription of the cognate essential gene proceeds
normally. However, once doxycycline is added, the repressor binds tetO7 and represses
14
Table 1.1 Overview of existing essential gene strain collections for S. cerevisiae.
Collection Name
Number of essential ORFs
in collection
Nature of hypomorphic allele Reference
tetO promoter
602 Tetracycline- or doxycycline-repressible promoter directly upstream of ORF; partial repression of gene expression
(Mnaimneh, Davierwala et al. 2004)
URA3-ts 250 Temperature-sensitive mutation in ORF; partially functional protein
(Ben-Aroya, Coombes et al. 2008)
DAmP 842 Drug-resistant cassette inserted between ORF stop codon and 3’ UTR; destabilized RNA transcript
(Breslow, Cameron et al. 2008)
15
transcription of the essential gene. Using this system, the authors were able to predict and
subsequently verify the function of several previously uncharacterized essential genes
(Mnaimneh, Davierwala et al. 2004).
1.3.1.2.2 URA3-marked temperature-sensitive allele collection
Temperature-sensitive (ts) strains are a classical reagent for investigating essential gene
function in haploid cells. Ben-Aroya et al. (Ben-Aroya, Coombes et al. 2008) developed a
method called “diploid shuffling” to systematically generate ts alleles for ~250 essential genes.
First, PCR mutagenesis was used to make random point mutations in a particular essential gene.
Second, the mutagenized PCR product was cloned into a vector so that it ligates immediately
downstream of the 5’ half of kanMX (kan), and upstream of a URA3 marker, followed by the 3’
half of kanMX (MX). Third, the vector is linearized to free the kan-mutagenized allele-URA3-MX
fragment, and this fragment is transformed into the corresponding heterozygous diploid strain,
and URA3 transformants are selected. Fourth, the transformant is sporulated, and MATa URA3
haploids are selected. Fifth, haploids are replica plated at both the permissive and restrictive
temperatures, and any colonies that drop out specifically at the latter represent candidate ts
alleles. By screening this collection in several assays, it was possible to identify roles for ten
poorly characterized essential ORFs in RNA processing and six uncharacterized essential ORFs
in chromosome segregation (Ben-Aroya, Coombes et al. 2008).
1.3.1.2.3 DAmP allele collection
In the decreased abundance by mRNA perturbation (DAmP) approach to creating
hypomorphic alleles, a heterologous coding sequence, such as an antibiotic resistance marker, is
inserted between the stop codon of the ORF and the 3’ UTR. When a transcript is produced from
the DAmP ORF, it is unstable and results in a two- to ten-fold decrease in the amount of mRNA
that is produced (Schuldiner, Collins et al. 2005). Taking advantage of this technique, Breslow et
al. (Breslow, Cameron et al. 2008) created a collection of 842 DAmP allele strains for S.
cerevisiae essential genes. By screening a subset of the strains in a highly quantitative growth
assay, the authors showed that the DAmP alleles result in a range of measurable fitness defects
16
that are comparable to the range of growth defects they quantified for the haploid deletion
strains; however, it should be noted that many of the DAmP strains do not show growth defects.
They also demonstrated the usefulness of this collection by performing several chemical-genetic
screens, in which they correctly identified the known protein targets of several compounds,
including 5-fluorouracil, sulfanilamide, and clotrimazole, whose targets are CDC21,
FOL1/FOL3, and ERG11 respectively (Breslow, Cameron et al. 2008).
1.3.2 Gene overexpression
Gene overexpression is a complementary approach to loss-of-function studies and can
provide insight into the function of genes with no deletion phenotype. Furthermore, gene
overexpression is relevant to the molecular mechanisms of numerous diseases, such as cancer,
where amplification and overexpression of oncogenes encoding c-Myc and the Src family
kinases (SFKs) are implicated in disease initiation and progression (Little, Nau et al. 1983;
Meyer and Penn 2008; Kim, Song et al. 2009). A better understanding of how gene
overexpression perturbs genetic networks of simpler organisms could conceivably help shed
light on the general principles governing the effects of oncogene overexpression in human cancer
cells.
Several libraries are available to examine gene overexpression phenotypes in yeast. These
libraries can be distinguished on the basis of how gene overexpression is achieved. The two most
common methods of gene overexpression are the heterologous expression system and the high-
copy expression vector.
In a heterologous expression plasmid, an ORF is placed under the control of a strong,
inducible promoter; therefore, gene expression is regulated by repressing or activating the
promoter. One commonly used inducible promoter is GAL1/10 (Schneider and Guarente 1991).
This promoter contains upstream activating sequence (UAS) sites to which the transcription
factor Gal4p can bind. In the presence of glucose, Gal4p is inactive, and transcription from
GAL1/10 is repressed. When glucose is replaced by galactose in the growth medium, this
molecule binds to and activates Gal4p, which then activates transcription from GAL1/10. In the
17
presence of galactose, transcription of the associated GAL1/10 ORF can be induced 1000-fold (St
John and Davis 1979; St John and Davis 1981). This massive increase in gene product present
within the cell is one explanation for the observation that ~15% of yeast ORFs expressed from
GAL1/10 are toxic to yeast when overexpressed (Liu, Krizek et al. 1992; Akada, Yamamoto et
al. 1997; Stevenson, Kennedy et al. 2001; Sopko, Huang et al. 2006; Vavouri, Semple et al.
2009). Other examples of inducible promoters, from which various levels of transcription are
initiated, include the CUP1 promoter, induced by adding copper to the growth medium (Labbe
and Thiele 1999; Martzen, McCraith et al. 1999) the MET25 promoter, repressed by adding
excessive methionine to the growth medium (Thomas, Cherest et al. 1989), and the tetO-CYC1
TATA promoter, repressed by adding tetracycline or its analogue doxycycline to the growth
medium (Gari, Piedrafita et al. 1997; Boyer, Badis et al. 2004).
An alternative way to confer gene overexpression is to place the gene on a high-copy
plasmid, namely YEp (yeast episome) plasmid expression vectors that contain an origin of
replication from a naturally occurring yeast plasmid called the 2µ circle. The 2µ circle exists at
~60 copies per cell and maintains itself at such levels in part by its ability to drive site-specific
recombination that ultimately leads to copy number amplification (Murray 1987). The
development of YEp vectors containing various prototrophic markers has allowed for in-house
generation of random high-copy genomic libraries (Ma, Kunes et al. 1987). From genomic
libraries, gene expression is regulated by the endogenous promoter and terminator sequences, but
gene overexpression occurs because multiple copies of the expression plasmid are present within
the cell (provided the concentration of activating transcription factors is not limiting).
The development and characterization of several genome-wide overexpression libraries is
described below. The features of the libraries are summarized in Table 1.2 and depicted
schematically in Figure 1.3. The availability of these libraries in an arrayed format greatly
facilitates genome-wide investigations of genetic interactions in S. cerevisiae.
1.3.2.1 The Yeast Two-Hybrid S. cerevisiae ORF Array
18
19
Figure 1.3
20
Figure 1.3 Plasmid libraries in S. cerevisiae.
Schematic representations of the vector backbone and major features for each of the
plasmid libraries in yeast are shown. a) Yeast Two-Hybrid ORF Library. b) PCUP1-GST Library.
c) PGAL1/10-GST Library. d) Moveable ORF Library. e) FLEXGene ORF Collection. f) Yeast
Genome Tiling Collection. See Sections 1.3.2.1 to 1.3.2.6, respectively for detailed descriptions
of the plasmid libraries.
21
The ORF plasmid library developed by Uetz et al. (Uetz, Giot et al. 2000) was the first
genome-wide collection amenable to investigating full-length protein-protein interactions using
the yeast two-hybrid (Y2H) assay in an arrayed format. In this library, each strain carries a
plasmid that contains one ORF cloned in frame with a Gal4 transcription-activation domain. The
background yeast strain of this library has integrated yeast two-hybrid reporter genes (James,
Halladay et al. 1996). Using this arrayed library, the authors screened for protein-protein
interactions of 192 query ORFs and successfully identified 281 interacting gene pairs for 87
query ORFs (Uetz, Giot et al. 2000). Importantly, this landmark paper was the first description of
an arrayed, high-density, semi-automated screening procedure for S. cerevisiae, which showed
the potential to use an arrayed format as a basis for systematic genome-wide screening.
1.3.2.2 The PCUP1-GST Library
The PCUP1-GST library was the first systematic S. cerevisiae ORF plasmid library
designed for purification of biochemically active gene products. In total, 6,080 ORFs fused N-
terminally in frame with the coding sequence for the GST protein tag were individually cloned
downstream of the inducible CUP1 promoter (Martzen, McCraith et al. 1999). The GST tag
allows for easy purification of any ORF of interest. The authors used this library in biochemical
proof-of-principle experiments to rapidly identify three previously uncharacterized ORFs with
distinct biochemical activities: CPD1/YGR247W had substrate-specific cyclic phosphodiesterase
activity, POA1/YBR022W had substrate-specific phosphatase activity, and CTM1/YHR109W had
cytochrome c methyltransferase activity (Martzen et al., 1999).
1.3.2.3 The PGAL1/10-GST Library
In the PGAL1/10-GST library, each of the 5,800 yeast strains carries a plasmid that contains
a single yeast ORF under the regulation of the inducible GAL1/10 promoter; to facilitate
biochemical studies, each ORF is also N-terminally tagged with GST (Zhu, Bilgin et al. 2001;
Sopko, Huang et al. 2006).
22
This plasmid library was first used in proteome chip experiments. The authors
overexpressed all 5,800 ORFs in yeast and purified the protein products; these were spotted onto
a microscope slide to generate a yeast proteome microarray (Zhu, Bilgin et al. 2001). In proof-of-
principle experiments, the authors demonstrated that this proteome microarray can be used to
detect both protein-protein and protein-lipid interactions. To identify calmodulin-binding
proteins, the authors probed the array with biotinylated calmodulin in the presence of calcium,
and subsequently detected bound calmodulin with Cy3-labeled streptavidin. They identified both
known and novel calmodulin-interacting proteins (Zhu, Bilgin et al. 2001). To identify
phosphoinositide (PI)-binding proteins, the authors probed the array with six different types of
PI-containing liposomes that all also contained biotinylated phosphotidylcholine (PC); the
biotinylated lipid facilitated detection of protein-PI interactions by binding to Cy3-labeled
streptavidin. They identified 150 PI-binding proteins, including 52 that corresponded to
uncharacterized proteins. Of the 98 known proteins, 45 were either membrane-associated or
predicted to be membrane-associated, and another 8 were known to be involved in lipid
metabolism, suggesting a significant enrichment for bona fide lipid-regulated proteins (Zhu,
Bilgin et al. 2001).
Sopko et al. (Sopko, Huang et al. 2006) transformed this plasmid library into a yeast
strain that is compatible with synthetic genetic array (SGA) technology (described below in
Section 3.1)(Tong, Evangelista et al. 2001; Tong, Lesage et al. 2004). The authors performed a
comprehensive investigation to identify the set of genes in this library that is toxic when
overexpressed in yeast and identified 769 ORFs that cause lethality upon overexpression. They
also demonstrated how this overexpression system is useful in synthetic dosage lethality studies
(discussed below in Section 3.1.1).
1.3.2.4 The Movable ORF Library
In the movable ORF (MORF) plasmid library, a single yeast ORF was cloned into a 2µ
vector backbone with a galactose-inducible promoter and a C-terminal His6-HA tag (Gelperin,
White et al. 2005). This collection is “movable” because each ORF is flanked by directional att
recombination sequences, and therefore can be subcloned into any att-containing vector. The
23
vector-ORF junctions for every plasmid in this library were sequenced, with complete ORF
sequence verification for 55% of the collection. Protein expression levels were examined for
5,573/5,854 ORFs. The authors showed that 48 ORFs previously characterized as “dubious”,
meaning they have very limited experimental evidence for their existence and do not have
orthologs in other Saccharomyces species, were efficiently expressed using this overexpression
system. To demonstrate the utility of this library to identify non-C-terminal post-translational
modifications, the authors conducted a global analysis of glycosylation, as the preservation of the
native N terminus of the proteins allows native processing to occur; they confirmed 109 new
glycoproteins and identified another 345 candidate glycoproteins (Gelperin, White et al. 2005).
1.3.2.5 The FLEXGene ORF Collection
The Full Length EXpression-Ready (FLEXGene) plasmid collection comprises >5,000 S.
cerevisiae ORFs (~87% of the protein-coding genes), each cloned into a Gateway donor vector
(Hu, Rolfs et al. 2007). The ORFs in this collection contain their native stop codons, thereby
allowing for either N-terminally tagged or native proteins to be overexpressed. Each ORF in this
collection was PCR-amplified and fully sequenced from the start to the stop codons. To
demonstrate the usefulness of this sequence-verified collection, the authors selected a subset of
ORFs for protein overexpression, and applied a select set to protein binding microarrays in order
to examine DNA-binding specificities. In particular, they were able to correctly identify the
consensus DNA-binding sequence for the well-characterized transcription factor Rap1p (Hu,
Rolfs et al. 2007).
1.3.2.6 The Yeast Genome Tiling Collection
The Yeast Genome Tiling Collection (YGTC) is a collection of minimally overlapping,
ordered genomic fragments covering >97% of the S. cerevisiae genome (Jones, Stalker et al.
2008). To generate this library, S. cerevisiae genomic DNA was partially digested and then
ligated into a 2µ vector, and the resulting ligation products were transformed into bacteria. Over
13,000 transformants were picked for sequencing of the vector-insert junctions. The resultant
minimal tiling collection is a set of ~1,600 plasmids equally covering all 16 yeast chromosomes,
24
including centromeres, non-protein coding genes, and dubious ORFs. The authors used this
collection to rapidly identify small genomic regions that, when overexpressed, cause specific
transcription defects, and ultimately identified four novel transcriptional regulators (Jones,
Stalker et al. 2008).
1.3.2.7 Summary and Comparison of Various Existing Overexpression Libraries
By comparing the individual features of the overexpression libraries described in
Sections 1.3.2.1 to 1.3.2.6, it is apparent that each library has its own unique strengths and
weaknesses.
With the exception of the YGTC, all of the systematic plasmid libraries described above
rely on an inducer for overexpression. Several studies have shown that constitutive gene
overexpression results in toxic effects, with ~15% of GAL-GST ORFs causing some type of
overexpression phenotype in wild type haploid cells (Sopko, Huang et al. 2006). The YGTC is
2µ-based, so gene overexpression is due to high copy number. Jones et al. (Jones, Stalker et al.
2008) observed that less than 3% of the tiling collection plasmids caused toxicity when
transformed into wild type haploid cells. This observation suggests that the cell can tolerate
significant dosage increases of most ORFs when ORF expression is controlled by endogenous
regulatory sequences.
As implied by the library names, every ORF in the CUP1-GST, the MORF, and the
PGAL1/10-GST overexpression libraries is tagged either N- or C-terminally with a non-native
protein sequence. The tag is extremely useful in biochemical studies, as it facilitates ORF protein
purification, but it might interfere with protein-protein interactions, protein turnover or protein
stability. As well, some post-translational modifications required for proper protein function (e.g.
N-terminal myristylation) or organelle import (e.g. mitochondrial import) are found at the
termini of proteins, so the presence of the tag could also interfere with these modifications. The
resulting protein, therefore, may not be properly localized or functional.
25
In all of the heterologous expression libraries, each plasmid contains a single ORF. In the
YGTC, however, the average insert size in the YGTC is 8.7 kb (Jones, Stalker et al. 2008). Since
the yeast genome generally contains one protein-coding gene every 2 kb (Goffeau, Barrell et al.
1996), most plasmids in this library contain >1 ORF. When this library is used in a genetic
screen, deconvolution of the ORF ultimately responsible for the observed phenotype one is
investigating is likely required.
In principle, the ideal plasmid-based gene overexpression library would have the
following features: 1) endogenous regulatory sequences controlling ORF expression; 2) no
epitope tag; 3) a single ORF per plasmid; and, 4) unique barcodes to facilitate parallel analysis of
strain pools. A plasmid library that combines the “best” features of these libraries would
complement, and in some cases possibly supplant, the resources that are currently available.
Indeed, the construction, validation and application of such a library is the goal of the present
thesis.
1.4 SYSTEMATIC IDENTIFICATION OF GENETIC INTERACTIONS IN YEAST
The development of the S. cerevisiae genome deletion collection (Giaever, Chu et al.
2002) was a seminal event in the history of functional genomics, as it allowed, for the first time,
the systematic analysis of gene function across the majority of genes in an organism in parallel.
However, as noted above (Section 1.1), most single gene deletion mutants have no obvious
phenotype under standard growth conditions and, alone, this library is therefore unable to
provide insight into the function of many genes. As discussed above, small-scale
experimentation suggests that the interactions between genes contain rich functional information.
Thus, one way to exploit the single gene deletion collection is to combine individual deletions
together in higher order combinations. Several methods have been developed to accomplish this,
as described in the following sections.
1.4.1 Synthetic Genetic Array (SGA) Analysis
26
The haploid deletion collection led to the development of synthetic genetic array (SGA)
technology (Tong, Evangelista et al. 2001), which has two defining features. The first is that this
technology employs robotic pinning of the collection onto any type of solid (agar) media. The
second is that the query strain used has a mating type-specific reporter gene that allows for
efficient and selection of haploid cells of only one mating type, thereby bypassing the
requirement to dissect tetrads. In SGA, the deletion strains are placed in an ordered array format.
This input array is mated to a query strain that is of opposite mating type and marked with
nourseothricin (NAT)(Goldstein and McCusker 1999). Diploids are selected on media containing
G418 and NAT. The diploids are then pinned onto sporulation media so that they can undergo
meiosis to produce recombinant haploid cells. MATa haploids are selected on media lacking
histidine; the media also contains two drugs, canavanine and S-2-aminoethyl-L-cysteine (S-
AEC), specifically meant to kill any residual diploid cells. Subsequent pinning onto haploid
selective media containing G418 and NAT ultimately produces the systematic double mutant
array. Fitness is determined by quantifying double mutant colony size, which is compared to the
cognate single mutant fitnesses. Using this technology, thousands of negative, i.e. synthetic
lethal (SL)/synthetic sick (SS), interactions have been identified (Tong, Evangelista et al. 2001;
Tong, Lesage et al. 2004; Costanzo, Baryshnikova et al. 2010). Extrapolation of this network
suggests the existence of approximately 100,000 SL/SS interactions between non-essential genes
in S. cerevisiae (under the standard conditions used). This technology has also been used to
identify positive, i.e. alleviating, interactions for subsets of functionally related genes
(Schuldiner, Collins et al. 2005; Collins, Miller et al. 2007; Laribee, Shibata et al. 2007; Nagai,
Dubrana et al. 2008; Wilmes, Bergkessel et al. 2008).
SGA analysis was also used to identify genetic interactions involving essential genes by
employing various tetO7 alleles as array strains. This work found both previously identified and
novel genetic interactions for a temperature-sensitive allele of CDC40 (Mnaimneh, Davierwala
et al. 2004). In a subsequent study, Davierwala et al. (Davierwala, Haynes et al. 2005) screened
nine ts alleles against an array of 147 tetO7 strains. The genetic network produced from these
screens showed that essential genes have approximately five-fold more SS/SL interactions than
non-essential genes. Together with the results from Tong et al. (Tong, Lesage et al. 2004), the
27
prediction is that ~200,000 SS/SL interactions between all genes (essential and non-essential) in
the S. cerevisiae genome.
1.4.1.1 Application of SGA to SDL analysis
As demonstrated in Sopko et al. (Sopko, Huang et al. 2006), the array-based method of
identifying genetic interactions is not limited to the deletion collection nor limited to identifying
synthetic lethal interactions. By mating the PGAL1/10-GST arrayed yeast strain library to a query
strain deleted for the cyclin-dependent kinase PHO85 and employing a slightly modified SGA
selection method, Sopko et al. (Sopko, Huang et al. 2006) overexpressed the PGAL1/10-GST ORFs
and looked for synthetic dosage lethality (SDL) interactions to identify genetic interactors of
PHO85. Importantly, the SDL genetic interactions identified in this screen identified proteins
that were subsequently shown to be novel bona fide Pho85p phosphorylation targets (Sopko,
Huang et al. 2006; Sopko, Huang et al. 2007). These results illustrate the usefulness of
approaches that combine loss- and gain-of-function alleles as a means to explore genetic
interaction networks.
1.4.1.2 Application of SGA to genetic mapping
The SGA methodology has also been used to map second-site mutations present in
particular query strains, a technique referred to as Synthetic Genetic Array Mapping
(SGAM)(Costanzo and Boone 2009). Since the position of every deletion in the deletion array is
known, one can look for a second linkage group that is not linked to the query allele. SGAM has
been used to identify recessive suppressors of genes required for polarized morphogenesis
(Jorgensen, Nelson et al. 2002) and genome integrity (Chang, Bellaoui et al. 2005). More
recently, SGAM was used to map dominant suppressors of the yeast ortholog of the Shwachman-
Bodian-Diamond Syndrome protein, the human disease gene defective in Shwachman-Diamond
Syndrome (SDS); this analysis provided the first molecular mechanistic explanation underlying
SDS (Menne, Goyenechea et al. 2007).
1.4.1.3 Application of SGA to array-based high-content screening
28
In all applications described above, the fitness of the single and double mutants, as
inferred from colony size, has been used as the phenotype from which genetic interactions are
quantified. An alternative to measuring colony size as an indicator of fitness and thus a potential
genetic interaction is to quantify the readout from a reporter gene, such as GFP, in a double
mutant colony and then compare it to the readout from the cognate single mutants. Any
deviations from the expected fluorescence may be due to genetic interactions that regulate a
specific biological process. When a selectable fluorescent reporter gene is in the background of
an SGA query strain, SGA technology can be employed to generate an array of double mutant
strains carrying the reporter. Subsequent quantification of fluorescence can be done by either
high-throughput flow cytometry, which can detect overall intensity, or microscopy, which can
detect subcellular distribution in addition to intensity.
To comprehensively identify genes required in protein folding in the endoplasmic
reticulum (ER), Jonikas et al. (Jonikas, Collins et al. 2009) used high-throughput flow cytometry
to quantify single-cell overall fluorescence of a reporter gene that was activated as part of the
cell’s unfolded protein response (UPR). They first used SGA technology to introduce the
fluorescent reporter into the deletion collection and measured single mutant fluorescence
intensities. Based on these results, they selected 340 genes, constructed pairwise double mutants
expressing the reporter gene, and quantified the fluorescence to identify genetic interactions that
contribute to regulation of the UPR. Since the reporter gene had a basal level of expression in
wild-type cells, the authors were able to identify both positive and negative genetic interactions,
which had lower and higher fluorescence intensities, respectively, when compared to expected
values. They identified genetic interactions between a set of six poorly characterized ORFs and,
in subsequent biochemical experiments, showed that these genes encode members of a novel
transmembrane protein complex that may play a role in folding ER membrane proteins (Jonikas,
Collins et al. 2009).
In another example, Vizeacoumar et al. (Vizeacoumar, van Dyk et al. 2010) developed a
microscopy-based approach called high-content screening (HCS)-SGA, which uses high-content
microscope imaging to examine subcellular phenotypes associated with various single and
29
double mutants. As a proof-of-principle, the authors used the tubulin reporter gene, GFP-TUB1,
to investigate the spindle disassembly pathway. First, they used SGA technology to introduce
GFP-TUB1 into the haploid deletion array; second, they crossed this array to two query strains,
bni1Δ and bim1Δ, to generate double mutants carrying the fluorescent reporter. The authors used
microscopy to image wild-type, single mutant and double mutant strains. They employed
machine learning to detect and define the wild type fluorescence pattern, and then to detect and
rank mutant strains based on their deviation from wild type. From their extensive analysis, the
authors identified 122 genes that had not been previously implicated in spindle disassembly;
furthermore, they were able to elaborate on the spindle disassembly pathway, and identified a
novel role for sumoylation at the kinetochore as a mode of regulating this pathway.
1.4.2 Diploid-based Synthetic Lethal Analysis on Microarrays (dSLAM)
Diploid-based synthetic lethal analysis on microarrays (dSLAM; (Pan, Yuan et al. 2004))
takes a different approach to double mutant construction and analysis. In dSLAM, the diploid
heterozygous, kanMX-marked and barcoded deletion collection is pooled and transformed en
masse with a linearized, URA3-marked disruption cassette targeting the query gene of interest.
After transformants are selected, diploids are sporulated, and double mutant haploids are
subsequently isolated on selective media. The double mutants are pooled and their barcodes
amplified using universal primers, one of which is conjugated to the fluorescent dye Cy3.
Concurrently, a single mutant pool is also generated using the same selection steps (with the
exception that uracil is added to the medium since no URA3 cassette is transformed into this
pool) and serves as a control sample for comparison. For the control sample, one of the universal
primers used for PCR amplification of the barcodes is conjugate to Cy5. The Cy3- and Cy5-
labelled PCR products are competitively hybridized to a barcode microarray, and a
control/experiment (C/E) ratio is calculated for each barcode. High C/E ratios are indicative of
synthetic sick or synthetic lethal interactions, meaning the barcode is under-represented in the
experimental (double mutant) pool as compared to the control (single mutant) pool. Conversely,
low C/E ratios are indicative of potential synthetic suppression interactions. Using this method,
Pan et al. (Pan, Yuan et al. 2004) identified SS/SL interactions for CIN8, a kinesin motor protein
involved in mitotic spindle assembly and chromosome segregation, and CHI interactions for
30
TUB1. Recently, Kim et al. (Kim, Zhao et al. 2009) used dSLAM to investigate the genetic
requirements underlying the unfolded protein response, identifying vesicular trafficking as an
important compensatory mechanism for defects in ER protein maturation.
1.4.3 Genetic Interaction Mapping (GIM)
A third synthetic genetic interaction methodology is the Genetic Interaction Mapping
(GIM) approach (Decourty, Saveanu et al. 2008). The GIM method combines features of SGA
technology and dSLAM in order to identify genetic interactions in yeast (Decourty, Saveanu et
al. 2008). In GIM, the query strain carries a hygromycin-resistant plasmid, while the query gene
ORF is replaced by a MATα2-NATR cassette, which is expressed only in MATα cells. An SGA-
like approach is used to mate the query strain to the deletion array, and then to pin the mating
products onto diploid selection media containing hygromycin and G418. The diploids are then
pooled, and sporulation is performed en masse in liquid. Double mutant haploid cells are selected
for in rich media liquid containing NAT and G418. Genomic DNA is extracted from the pool,
and the barcodes are PCR-amplified and hybridized to a barcode microarray. To normalize the
microarray results, a reference screen, in which the MATα2-NATR cassette replaces a neutral
ORF, is done in parallel. Using the GIM method, the authors identified novel genetic
interactions, both positive and negative, that participate in mRNA decapping (Decourty, Saveanu
et al. 2008). They extended their method to 41 query strains, all involved in RNA metabolism,
and show that their results have statistically significant overlap with previously published high-
throughput genetic interaction studies (Decourty, Saveanu et al. 2008).
1.4.4 Summary of Genetic Interaction Mapping Strategies
With the exception of synthetic dosage lethality studies (Sopko, Huang et al. 2006), all of
the genome-wide genetic interaction mapping studies in yeast have used loss-of-function mutants
for identifying negative and positive interactions. With the development of several different
genome-wide overexpression libraries, it is now feasible to systematically examine synthetic
dosage lethality and suppression interactions in different genetic backgrounds.
31
Recent proof-of-principle experiments in other microorganisms have shown it is possible
to detect genetic interactions using next-generation sequencing methods ((van Opijnen, Bodi et
al. 2009), see below). A broader application of these novel methods has the potential to make the
detection of genetic interactions, especially when using barcoded strains and plasmids, much
more streamlined and quantitative, and these methods will therefore be introduced in greater
detail in the next section.
1.5 NEXT-GENERATION SEQUENCING
For 30 years, Sanger-based methods were the primary means to generate DNA sequence
data (Hunkapiller, Kaiser et al. 1991; Shendure and Ji 2008). The Sanger method is accurate,
allows long read lengths (500-1000 bp / template) and the cost per base of sequence has come
down exponentially since the initial development of this technique. Moreover, advances in
sample handling and automation, such as micro-capillary-based automated sequencers, enabled
Sanger sequencing to be applied to large genomes, including that of humans, but at tremendous
cost (Lander, Linton et al. 2001; Venter, Adams et al. 2001). More recently, a number of next-
generation (also known as ‘second-generation’ or ‘deep’) sequencing technologies have been
commercialized, and are known by names such as 454, Solexa, Helicos and SOLiD sequencing
(Shendure and Ji 2008). Each of these methods relies on a unique set of chemistry and imaging
tools. Common to all approaches is the ability to generate sequence data at vastly lower cost per
base and in a much shorter timeframe compared to traditional Sanger sequencing, albeit with the
caveats of generally shorter read lengths per template (30-400 bp, depending on the system),
higher error rates and more complex downstream bioinformatic analysis.
While next-generation sequencing methods have not yet been described to identify
genetic interactions in yeast, they have been successfully used in another unicellular organism to
identify novel genetic interactions. van Opijnen et al. (van Opijnen, Bodi et al. 2009) used the
Solexa/Illumina platform to establish genetic interaction profiles of five query genes in the gram-
positive bacterium Streptococcus pneumoniae as follows. First, the authors generated five query
strains by replacing each of the five query genes with a drug-resistant marker. Second, they
established transposon insertion libraries for each query strain, obtaining between 10,000-25,000
32
transposon mutants in each library. Third, they pooled the transposon mutants for each strain.
Fourth, a sample of the pool was grown in liquid from lag phase to late exponential phase, at
which time the DNA was harvested; to serve as a control, while DNA was also harvested from a
pre-liquid growth pool sample. Fifth, DNA samples were prepared for sequencing on a
Solexa/Illumina flow cell lane and sequenced using the Illumina Genome Analyzer. By
introducing a unique DNA sequence “tag” for each of the five samples during preparation of the
samples for sequencing, the authors were able to sequence DNA samples from all 5 query strains
on the same lane (van Opijnen, Bodi et al. 2009). The sequence reads from each query strain
were then separated based on the different DNA sequence tags. Based on the abundance of reads
for a given transposon insertion site in the different strains, it was possible to quantify which
strains ‘dropped out’ and which strains were ‘over-represented’ in a given background, similar to
what is done using barcode microarrays. Using this next-generation sequencing approach, the
authors identified 97 high-confidence genetic interactions for S. pneumoniae involved in
transcriptional regulation and carbohydrate transport (van Opijnen, Bodi et al. 2009). These
results show how next-generation sequencing can be applied in principle to the detection of
genetic interactions. In particular, they highlight how a single run in one lane of the
Solexa/Illumina platform can, in principle, detect genetic interactions for multiple strains in
parallel. A similar application of next generation sequencing to the quantification of genetic
interactions in S. cerevisiae has been suggested (Smith, Heisler et al. 2009), but yet to be
reported.
1.6 SUMMARY AND RATIONALE
Here I have introduced the various means which have been used to identify genetic
interactions and how this knowledge has been useful in discovering novel roles for individual
genes and in understanding the overall functional organization of the cell. Most genetic
interaction studies to date have used of loss-of-function alleles, in part because the methods to
generate such reagents are simple and well established. It is clear, however, from both small- and
large-scale studies that the analysis of gain-of-function mutations can provide information that is
either complementary to that obtained using loss of function alleles, or in fact inaccessible using
loss-of-function alleles only. Indeed, the analysis of essential gene function is by definition
33
especially difficult when using loss-of-function methods alone. An additional consideration is
that the time and cost involved in generating the existing loss-of-function interaction maps has
proven to be substantial; new methods such as next-generation sequencing may be able to help
bring down these resource costs and increase the amount of data that can be generated in a given
amount of time.
In this thesis, I address these issues. First, I describe the cloning of two novel gene
overexpression libraries that are designed to streamline gene dosage studies. Second, I describe
the application of one of the libraries in gene dosage studies, specifically in dosage suppression
screens that use barcode microarrays and next-generation sequencing as readouts for identifying
candidate dosage suppressors. Third, I show that dosage suppression interactions represent a new
functional edge in the yeast interaction landscape that can be used to both investigate gene
function and identify novel functional relationships in the cell.
34
Chapter Two
The MoBY-ORF 1.0 Yeast Plasmid Library
The work reported in this chapter was a collaboration between me, Leslie J.
Magtanong, Cheuk Hei Ho, and Sarah Barker. Cheuk Hei Ho and Bilal Sheikh, a former
computer support person in the Boone lab, designed the primers used in PCR
amplification of each ORF along with native upstream and downstream sequence. Cheuk
Hei Ho and I did the PCR amplification reactions. Cheuk Hei Ho, Sarah Barker, and I
did the yeast and bacterial transformations, plasmid extractions, restriction digests, and
agarose gel analyses to evaluate the restriction digest results. Sarah Barker did the
sequencing and functional tests of MoBY-ORF 1.0 clones. Cheuk Hei Ho performed all of
the chemical genetics experiments using MoBY-ORF 1.0. A complete description of this
work was published in Nature Biotechnology (2010), 27:369-77; doi:10.1038/nbt.1534
(URL: http://www.nature.com/nbt/journal/v27/n4/full/nbt.1534.html).
35
2.1 INTRODUCTION
As described in the general Introduction (Chapter 1), one goal of this project is to identify
dosage suppression genetic interactions for essential genes using a new overexpression library in
combination with barcode microarrays and next-generation sequencing. In this Chapter, I
describe the cloning and construction of the first of two new plasmid overexpression libraries for
yeast that we developed, called the molecular barcoded yeast ORF (MoBY-ORF) libraries. We
designed the MoBY-ORF vector backbone to be compatible with an in vivo bacterial cloning
method called mating-assisted genetic integrated cloning (MAGIC)(Li and Elledge 2005), which
uses homologous recombination to transfer DNA sequences from one vector backbone to
another. In the next chapter (Chapter 3), I discuss how we generated the second MoBY-ORF
library by using MAGIC to transfer the barcoded ORFs from the low-copy vector to a high-copy
vector, and then pursued dosage suppression genetic interaction studies of S. cerevisiae essential
genes using the high-copy overexpression MoBY-ORF library.
The MoBY-ORF 1.0 plasmid library is a low-copy overexpression library due to the
centromere (CEN) sequence found on each plasmid, which constrains it to 1-3 copies per cell
(Tschumper and Carbon 1983). Each plasmid in the library contains a single ORF that is flanked
by ~900 bp and ∼250 bp of native upstream and downstream genomic sequence respectively, and
is therefore largely under the control, to an extent, of its endogenous regulatory elements.
Additionally, each plasmid in the MoBY-ORF library carries unique barcodes which act as
molecular tags (Shoemaker, Lashkari et al. 1996); these features make this collection first yeast
ORF library amenable to barcode microarray analysis (Pierce, Fung et al. 2006). While this
plasmid library has many genetic and chemical-genetic applications, we first used the library to
identify the mode-of-action of several bioactive compounds. In the process, we discovered a new
class of sterol-binding chemicals.
2.2 RESULTS
2.2.1 Construction of a library of molecular barcoded yeast ORFs
36
The MoBY-ORF 1.0 library consists of plasmids that each carry a pair of oligonucleotide
barcodes and a single yeast ORF that is flanked by its native upstream and downstream genomic
sequences. The plasmid vector p5472 carries a URA3 selectable marker and a yeast centromere,
which maintains one to three copies of the plasmid per cell (Figure 2.2.1). The vector was
designed to be compatible with an in vivo bacterial cloning method, mating-assisted genetically
integrated cloning (MAGIC)(Li and Elledge 2005), which facilitates the rapid construction of
recombinant DNA molecules, enabling the barcoded clones to be transferred efficiently to other
vector backbones, such as a high-copy vector. The barcode cassettes were obtained from the
yeast deletion mutant collection (Giaever, Chu et al. 2002) and comprise two unique 20-
nucleotide DNA sequences (labeled the UPTAG and DNTAG) flanking a dominant selectable
marker (kanMX) that confers resistance to the drug G418/kanamycin. The barcodes can be
amplified with universal primers, enabling cells carrying a specific ORF to be quantitatively
detected with a microarray having probes that hybridize to the barcode sequences (Pierce, Fung
et al. 2006). Each plasmid was constructed in a three-step process. First, each yeast ORF was
PCR-amplified from an average of 900 bp upstream of the start codon to an average of 250 bp
downstream of the stop codon using a DNA template isolated from the sequenced S288C strain.
In addition, the kanMX barcode sequences that uniquely identify the ORF were PCR-amplified
from the appropriate strain in the yeast deletion collection. Second, the plasmid was assembled
by homologous recombination by transforming yeast with the ORF, the barcode PCR products
and linearized p5472 (Figure 2.2.2). Third, recombinant plasmids were recovered and used to
transform bacteria to facilitate plasmid DNA isolation and subsequent diagnostic restriction
digests to confirm the sizes of both fragments.
2.2.2 Verification of constructed clones by sequencing
Each clone in the MoBY-ORF library was sequenced to confirm the 3’ portion of the
gene and the barcodes. We identified 4,396 ORFs (88.7%) with two unique barcodes, but 560
with only one barcode (344 with only an UPTAG and 216 with a DNTAG), as the other barcode
was either not unique within the collection (i.e. multiple clones contained the same barcode
sequence), or it had no corresponding sequence on the Affymetrix TAG4 microarray (Pierce,
Fung et al. 2006). In summary, the MoBY-ORF 1.0 library contains 4,956 uniquely barcoded
37
Figure 2.2.1
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38
Figure 2.2.2
39
ORFs, representing ~90% of all non-dubious ORFs annotated in the Saccharomyces Genome
Database (SGD). This collection is available from Open Biosystems.
2.2.3 Assessment of clone function using temperature-sensitive mutants
To assess the functionality of our clones, we first introduced 254 different MoBY-ORF
plasmids into a synthetic genetic array (SGA)(Tong, Evangelista et al. 2001) query strain. We
then used the SGA method to cross the plasmids into a set of corresponding temperature-
sensitive mutants covering alleles of the same 254 essential genes, and tested the transformants
for functional complementation at the restrictive temperatures. In total, 17 clones failed to rescue
the temperature sensitivity of the corresponding mutant strain, suggesting that ~93% of the
clones in the library should be functional.
2.2.4 Complementation cloning to identify drug-resistant mutants and compound mode-of-action
Cheuk Hei Ho, a graduate student in the Boone lab, developed and used a strategy to
efficiently clone drug-resistant genes by complementation with the MoBY-ORF library. In
proof-of-concept experiments, he successfully identified the mutated loci in a first mutant
conferring drug resistance to cycloheximide and a second mutant conferring drug resistance to
rapamycin: CYH2 and FPR1, respectively. Cheuk Hei went on to use this strategy to eventually
identify the ergosterol biosynthesis pathway as the target of two natural compounds,
stichloroside and theopalauamide, that are structurally different but have virtually identical
chemical-genetic profiles, which catalog the set of deletion mutants that are hypersensitive to a
chemical compound (Parsons, Lopez et al. 2006). By identifying the target pathway of these two
compounds, stichloroside was characterized as a member of the α-tomatine class of sterol-
binding compounds, while theopalauamide and another compound, theonellamide A, belong to a
novel class of sterol-binding compounds.
2.3 SUMMARY
40
In this Chapter, I described the cloning and construction of the MoBY-ORF library, the
first yeast plasmid library amenable to analysis by barcode microarray. The MoBY-ORF 1.0
collection contains barcoded plasmids for ~90% of all non-dubious ORFs annotated in the
Saccharomyces Genome Database (SGD, www.yeastgenome.org). While the MoBY-ORF 1.0
library has many applications, we used the library in chemical-genetic experiments to identify
the mode-of-action of several bioactive compounds. Through this process, we discovered a novel
class of steroid-binding chemicals.
2.4 METHODS
2.4.1 Yeast Strains
Y1239 (BY4741) MATa ura3Δ0 leu2Δ0 his3Δ met15Δ; Y1241 (BY4743) MATa/α
his3Δ1/his3Δ1 leu2Δ0/leu2Δ0 ura3Δ0/ura3Δ0 met15Δ0/+ lys2Δ0/+; Y7092 MATα can1Δ::STE2
pr-Sp_his5 lyp1Δ; his3Δ1 leu2Δ0 ura3Δ0 met15Δ0
2.4.2 Growth Media
Yeast strains were grown in YPD (1 % yeast extract, 2 % peptone, 2 % glucose) with
G418 (200 µg/ml) to select for the plasmid. SD-URA+G418 (0.17 % yeast nitrogen base without
amino acid or ammonium sulfate, 0.1% L-glutamic acid sodium salt hydrate, 0.2% amino acid
supplement minus URA, 2% glucose) was used in homologous recombination cloning for library
construction. Bacteria were grown 2YT (1 % yeast extract, 1.6 % tryptone, 0.5 % sodium
chloride) with tetracycline at 5 µg/ml, kanamycin at 50 µg/ml, and chloramphenicol at 12.5
µg/ml.
2.4.3 Clone Construction and Analysis
Each ORF in the library was amplified using ORF-specific primers (Operon)(50 mM)
with 2.1 U Roche HF Taq (Roche) or 3 U TaKaRa ExTaq (Takara Bio Inc.) polymerase, 0.2 mM
dNTPs, and 4 ng S. cerevisiae genomic DNA (extracted from strain Y1239) in 50 µl reactions.
41
The following cycling conditions were used: 94°C for 2 min, 39 cycles of 94°C for 15 sec, 55°C
for 30 sec, and 68°C for 4 min, followed by 68°C for 10 min. A sample of each ORF PCR
product was visualized for the correct size on a 1% agarose gel, and upon confirmation, the
remaining PCR product was column-purified (Invitrogen PureLink PCR Purification Kit).
Barcoded kanMX cassettes were PCR-amplified using one of three templates. We first amplified
the kanMX cassettes from whole cell lysate from the deletion strains (template 1). If we were
unable to amplify from whole cell lysate, we prepared genomic DNA from the deletion strain
(template 2). If we were still unable to amplify from either of the two templates, we synthesized
oligonucleotide primers that contained the barcodes and PCR-amplified a kanMX cassette from a
kanMX cassette-carrying plasmid (template 3). Template 1 was made from 5µl of S. cerevisiae
heterozygous deletion strain cell lysate prepared by zymolyase digestion (~2x107 cells in 30 µl
zymolyase at 5 mg/ml). Template 2 was 4 ng of S. cerevisiae heterozygous deletion strain
genomic DNA prepared by standard phenol-chloroform extraction. The kanMX cassette using
these two templates was PCR-amplified in two overlapping fragments. Template 3 was 0.2 ng of
plasmid P1970, which carries the kanMX4 cassette. The kanMX cassette for template 3 was PCR-
amplified as a single fragment. Each PCR reaction contained 1 U Roche HF Taq (Roche) or 3 U
TaKaRa ExTaq (Takara Bio Inc.) polymerase, and 0.2 mM dNTPs. The following cycling
conditions were used for all three templates: 94°C for 2 min, 39 cycles of 94°C for 15 sec, 50°C
for 1 min, and 72°C for 2 min, followed by 72°C for 7 min. A wild-type diploid strain (Y1241)
was co-transformed using standard procedures with the ORF, the kanMX PCR products and
XhoI-linearized p5472 (Figure 2.2.2). Yeast transformants were selected for positively on
SDMSG-URA+G418. Recombinant plasmids were extracted from individual yeast transformants
by zymolyase digestion followed by a miniprep plasmid preparation (Macherey Nagel).
Miniprep DNA was transformed into competent P5505 [Dlac-169 rpoS(Am) robA1 creC510
hsdR514 DuidA(MluI):pir-116 endA(BT333) recA1 F’(lac+ pro+ DoriT:tet)](provided by
Gwenael Baedis), followed by selection on 2YT containing tetracycline at 5 µg/ml, kanamycin at
50 µg/ml, and chloramphenicol at 12.5 µg/ml. Miniprep DNA was prepared from a single
bacterial transformant, doubly digested using BamHI and EcoRI (Fermentas), and run out on a
0.8% agarose gel to confirm vector and ORF fragment sizes . Two individuals performed gel
analysis independently, and the results were compared to determine clone validity. If the primary
BamHI/EcoRI digest was ambiguous, a secondary digest using NotI/HindIII was performed.
42
ORF fragment sizes for both double digests, along with complete ORF sequence and barcodes,
can be obtained from the MoBY-ORF database: http://moby.ccbr.utoronto.ca.
2.4.4 Sequence Confirmation of the MoBY-ORF Collection Barcodes and 3’ ORF Junctions
For each clone, two sequencing reactions were performed, the first covering the UPTAG
and 3’ ORF junction and the second covering the DNTAG (with sequencing primers 5’-
TATACATGGGGATGTATGGGC-3’ and 5’-GGGCAACAACAGATGGCTG-3’ respectively).
The sequencing reads were analyzed using computational scripts developed to identify the
barcode position, based on the adjacent universal primer sequences (5’-
GACCTGCAGCGTACG-3’ for the UPTAG and 5’CGGTGTCGGTCTCGTAG-3’ for the
DNTAG), and the identified barcodes were extracted. Since the reverse complement of the
UPTAGs were sequenced (to accommodate reading the 3’ ORF junction within the same read),
the UPTAGs were transposed into the common 5’-3’ top-strand notation. In cases where
barcodes failed to be extracted or there was an ambiguous nucleotide call within the barcode, a
manual review of the sequence was performed and barcodes recorded. The nucleotide BLAST
program was used to map the 3’ ORF junction sequence to the full genome sequence from the
Saccharomyces Genome Database (SGD, www.yeastgenome.org), and the position results were
compared to the expected ORF annotation for confirmation of the clone.
2.4.5 Functional Complementation of Essential Genes
350 temperature sensitive strains (strain background Y7092) marked with natMX4 were
selected for our complementation studies. Clones for the ORF of each ts allele were transformed
into a complementary mating strain (Y1239). The temperature sensitive strains were mated with
yeast carrying the cognate MoBY-ORF plasmid, and the diploids were taken through SGA
(Tong, Evangelista et al. 2001) to obtain the double positive progeny: NATR representing the ts
allele and G418R for the MoBY-ORF plasmid. These progeny were grown at both 26oC and 35oC
and scored for rescue of the temperature sensitive phenotype. For strains in which the initial
temperature sensitive nature was subtle, the NATR G418R cells were individually assessed at
higher temperatures.
43
Chapter Three
Mapping Genetic Networks by Systematic Dosage Suppression
Cheuk Hei Ho, Sarah Barker, and I, Leslie J. Magtanong were involved in MoBY-
ORF 2.0 construction. Cheuk Hei Ho, Sondra Bahr, Elena Kuzmin and I carried out
barcode microarray experiments to identify candidate dosage suppressors. Cheuk Hei
Ho, Sondra Bahr, Elena Kuzmin, Kerry Andrusiak, and Anna Kobylianski did
transformations to confirm suppressors. Andrew Smith carried out all the sample
preparation and data analysis of the Bar-seq experiments. Wei Jiao and Anastasia
Baryshnikova did the computational analysis of features of dosage suppression gene
pairs, which included the overlap with other types of interactions, shared gold standard
GO terms, heat maps displaying frequencies within and between biological processes,
and identification of clusters in the integrated dosage suppression genetic interaction
network. I developed the decision tree to categorize dosage suppression interactions.
Cheuk Hei Ho and I were involved in re-categorizing unknown dosage suppression
interactions. Cheuk Hei Ho performed the reciprocal suppression tests. All electronic
tables can be found on the DVD accompanying this thesis document.
44
3.1 INTRODUCTION
Increasing gene dosage provides a powerful means of probing gene function as it tends to
cause an increase in gene activity referred to as a gain-of-function effect (Sopko, Huang et al.
2006; Vavouri, Semple et al. 2009). Gene overexpression is relevant to the molecular
mechanisms of diseases, such as cancer, where gene amplification and gain-of-function
mutations are prominently implicated in disease initiation and progression (Santarius, Shipley et
al. 2010). In yeast, systematic analysis of gene overexpression has revealed that only a subset of
genes cause an overt phenotype when overexpressed in wild-type cells (Moriya, Shimizu-
Yoshida et al. 2006; Sopko, Huang et al. 2006; Jones, Stalker et al. 2008; Kaizu, Moriya et al.
2010). However, examining gene overexpression in sensitized cells containing mutations in
genes of known function is an effective way to probe gene activity because it can identify
functionally relevant genetic interactions (Rine 1991; Prelich 1999; Sopko, Huang et al. 2006;
Boone, Bussey et al. 2007; Dixon, Costanzo et al. 2009).
Genetic interaction networks map functional connections occurring both within and
between cellular pathways. An extensive genetic interaction network based upon loss-of-function
double mutant analysis has recently been described for budding yeast (Costanzo, Baryshnikova
et al. 2010). In addition, a large-scale physical interaction network for yeast has been assembled
from multiple data sources (Gavin, Bosche et al. 2002; Krogan, Cagney et al. 2006; Tarassov,
Messier et al. 2008; Yu, Braun et al. 2008). Direct interactions between genes or gene products
are referred to as ‘edges’ in biological networks. The edges in genetic interaction networks
largely complement those found in protein-protein interaction networks, as only a small fraction
of gene pairs that show a genetic interaction also physically interact (Costanzo, Baryshnikova et
al. 2010). This implies that an integrated network is more informative than either type of network
alone. Despite the mapping of these genome-scale networks, our knowledge of the cell remains
incomplete and novel methodologies that map new types of genetic interactions should improve
our global understanding of the functional wiring diagram of the cell and provide further insight
into the roles of specific genes.
45
Suppression or enhancement of a query mutant phenotype by gene overexpression is
called dosage suppression (DS) or synthetic dosage lethality (SDL) respectively. Identification of
SDL interactions in yeast has largely relied on plasmid libraries containing genes whose
overexpression can be induced from the heterologous GAL1 promoter (Schneider and Guarente
1991). Using this approach, SDL interactions have been identified for genes encoding
kinetochore components and members of the origin recognition complex (Kroll, Hyland et al.
1996; Measday, Hailey et al. 2002; Measday, Baetz et al. 2005). More recently, an arrayed
galactose-inducible overexpression library was developed and used in SDL experiments to
identify novel kinase-substrate relationships (Sopko, Huang et al. 2006; Sopko, Huang et al.
2007).
Classical gene dosage suppression studies in yeast have been productively performed
using random genomic high-copy libraries (Ma, Kunes et al. 1987); however, plasmids in these
libraries often carry large genomic fragments and the suppressing gene must be identified
through an additional round of experiments. The recent availability of an ordered tiling library
(Jones, Stalker et al. 2008) has simplified the identification of genomic fragments with
suppressing activity but may not precisely identify the key gene of interest. To facilitate the
process of gene identification and to enable facile and systematic gene dosage analysis, we
generated an overexpression plasmid library, MoBY-ORF 2.0, that is compatible with high-
throughput genomics technologies, such as barcode microarrays and next-generation sequencing
methods (Pierce, Davis et al. 2007; Smith, Heisler et al. 2009), which can monitor plasmid
representation in pools of transformed strains. As proof-of-principle, we use the MoBY-ORF 2.0
library for dosage suppression screens of an extensive collection of temperature-sensitive
conditional alleles of essential genes. Analysis of data from these screens demonstrates the utility
of systematic studies of dosage suppression analysis in yeast to provide a new class of functional
edge in the global yeast interaction network.
46
3.2 RESULTS
3.2.1 Construction of the MoBY-ORF 2.0 plasmid library
We developed a high-copy (2µ-based) plasmid library in which each plasmid contains a
DNA insert composed of a 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 (Figure 3.2.1). The DNA insert was derived from the low-copy (CEN-based) molecular
barcoded yeast ORF (MoBY-ORF) 1.0 library (Ho, Magtanong et al. 2009) and transferred to the
high copy vector (Figure 3.2.2) by mating-assisted genetically integrated cloning (MAGIC) (Li
and Elledge 2005) (Figure 3.2.3). The final MoBY-ORF 2.0 plasmid library contains 4547
clones (representing 4499 ORFs), of which 91% have 2 usable barcodes, 5% have a unique uptag
only, and 4% have a unique downtag only; therefore, all plasmids in the library are represented
by at least one barcode.
3.2.2. Dosage suppression analysis of temperature-sensitive conditional mutants
The MoBY-ORF 2.0 plasmid library provides a reagent set tailored for gene dosage
analysis because the barcodes enable highly parallel assessment of individual plasmid abundance
within a mixed population. While there are numerous applications of the MoBY-ORF 2.0
plasmid library, we developed a multi-step protocol to explore its use in identifying dosage
suppressors of conditional temperature-sensitive (ts) alleles of essential genes (Figure 3.2.4).
First, a ts strain was transformed with the MoBY-ORF 2.0 plasmid library, aiming for >10-fold
representative coverage of each plasmid. Accordingly, in ~50,000 transformants, each plasmid
should be represented ~11 times. Second, the transformants were pooled, and 50,000 cells were
plated on selective media and incubated at both permissive and semi-permissive temperatures.
Third, colonies appearing after 3 days were pooled. Fourth, the barcodes from the dosage
suppressor (semi-permissive temperature) pool were PCR-amplified using biotinylated universal
primers; concurrently, the barcodes from the control (permissive temperature) pool were PCR-
amplified using non-biotinylated universal primers. Fifth, the PCR products from the dosage
suppressor and starting pools were competitively hybridized to a barcode microarray. The
47
Figure 3.2.1
48
Figure 3.2.2
49
Figure 3.2.3
50
Figure 3.2.3 MAGIC with the MoBY-ORF 1.0 plasmid library.
a. The donor bacterial strain carrying the CEN-based MoBY-ORF plasmid is mated to a recipient
bacterial strain carrying a 2µ-based vector.
b. Expression of a restriction enzyme, I-SceI, releases the barcoded ORF and linearizes the
recipient vector. Expression of a recombinase in the recipient strain induces homologous
recombination between the barcoded ORF and linearized vector using the MAGIC sequences
(filled yellow circles).
c. The recipient strain is grown on media containing ampicillin, kanamycin, and DL-
chlorophenylalanine that selects both against any non-recombined vector and for the
recombinant plasmid.
51
Figure 3.2.4
52
Figure 3.2.4 Using the MoBY-ORF 2.0 plasmid library to identify candidate dosage suppressors
by barcode microarray.
A ts mutant strain is transformed with the MoBY-ORF 2.0 plasmid library. In this case,
the ts mutation is in a gene designated as C. Transformants (represented in different colors) are
pooled and plated at permissive and semi-permissive temperatures. Barcoded plasmids are
extracted from colonies present at the semi-permissive temperature after 3 days, and barcodes are
amplified using biotinylated universal primers. Concurrently, barcoded plasmids are extracted
from the control pool grown at the permissive temperature, and barcodes are amplified using
non-biotinylated universal primers. The biotinylated and non-biotinylated PCR products are
mixed and competitively hybridized to a barcode microarray. Only the microarray-bound
biotinylated PCR products (indicated with blue circles) will bind to the streptavidin-conjugated
fluorescent dye used to detect hybridization to the microarray. Barcodes with intensities 5-fold
and higher above background represent candidate dosage suppressors of the ts allele. In this case,
barcodes from plasmid C are detected. This is expected, as plasmid C carries the wild-type gene
of the ts allele c. Barcodes from plasmid A are also detected; therefore, gene A represents a
candidate dosage suppressor.
53
competition between biotinylated and non-biotinylated PCR products allowed for candidate
dosage suppressors to be identified by their barcodes as having a significantly higher signal
intensity, which was defined empirically as any signal 5-fold or higher above background.
(Figure 3.2.5; see Methods 3.4.6). To confirm dosage suppression interactions, candidate dosage
suppressor plasmids were individually transformed into the cognate ts strain, and spot dilutions
were performed at the semi-permissive temperature.
When selecting the 40 different query genes for dosage suppression screens, we focused
on genes with a variety of different functional roles and relatively few previously published
dosage suppression interactions (Appendix 6.1). In total, we performed confirmation
transformations and spot dilution assays to validate 214 different suppressing plasmids
(Appendix 6.1). The wild-type complementing ORF was recovered for all but 3 query strains.
We expected a few cases in which we would not observe complementation because the 2µ
MoBY-ORF library does not contain wild-type plasmids for ~20% of yeast ORFs, including
those of the remaining 3 strains, and most (~93%) but not all of the PCR-amplified genes are
functional (Ho, Magtanong et al. 2009). Of the remaining 168 extragenic dosage suppressors,
three plasmid clones carried genes immediately next to the wild-type complementing ORF
(Appendix 6.1). Since each MoBY-ORF 2.0 plasmid carries native upstream and downstream
ORF sequence, two ORFs on a single plasmid can occur if the intergenic region is relatively
small; in these three cases, the plasmids each contained the respective wild-type query gene. For
several query genes, we screened multiple alleles and recovered the same suppressor gene.
For 8 query genes, the wild-type complementing ORF was the only clone recovered. For
the remaining 32 query genes, we identified at least one dosage suppressor, with a total of 150
extragenic dosage suppressors. The number of dosage suppressors varied widely from one to 24
(Appendix 6.2; Electronic Table 3.2.1), but on average, we recovered ~5 dosage suppressors per
query gene. For RFA3, which encodes a subunit of the Replication Protein A (RPA), a highly
conserved single-stranded DNA binding protein involved in DNA replication, repair, and
recombination (Brill and Stillman 1991), we only identified one dosage suppressor, RFA2,
whose product forms a complex with Rfa3p (Gavin, Bosche et al. 2002; Dickson, Krasikova et
al. 2009). For CDC48, which encodes the yeast ortholog of the mammalian ATPase p97 (Ye,
54
Figure 3.2.5
55
Figure 3.2.5 Continued
56
Figure 3.2.5 Empirical determination of raw barcode microarray intensity cutoff for
identification of candidate dosage suppressors.
Three different ts strains were used in the determination: cdc48-9 (a.), stu1-5 (b.), and
nse3-ts5 (c.). Bar graphs were generated to display the raw barcode microarray intensities. Only
the barcodes with raw intensities >200 average fluorescence units (a.f.u.) are shown. Dotted lines
are drawn at raw microarray intensities of 2000, 1000, and 500.
57
Meyer et al. 2003), we identified 24 dosage suppressors. These dosage suppressors were derived
from numerous different pathways, and included UBX4, which encodes a UBX (ubiquitin
regulatory X) domain-containing protein that interacts with Cdc48p (Decottignies, Evain et al.
2004), and RPL17A, which encodes a protein subunit of the large ribosome (Mager, Planta et al.
1997). In total, we found 137 novel dosage suppression interactions for 29 query genes. We
identified novel interactions for 26 query genes that had three or fewer literature-curated dosage
suppressors, including 13 query genes that had no previously known dosage suppressors
(Appendix 6.2; Electronic Table 3.2.1). A significant portion of the novel dosage suppressors we
identified are functionally related to their respective query genes, as 40% (p-value < 2.6 x 10-10)
of the gene pairs have shared gold standard Gene Ontology (GO) terms (Myers, Barrett et al.
2006) (Appendix Table 6.2.2; Electronic Table 3.2.1), indicating that the dosage suppressors
identified in our screens represent functionally relevant interactions.
As a complement to the barcode microarray analysis, we also used a next-generation
sequencing method called Bar-seq (Smith, Heisler et al. 2009; Smith, Heisler et al. 2010) to
measure the abundance of each barcode sequence present in the dosage suppressor pools. We
employed a modified version of Bar-seq (Smith, Heisler et al. 2009), multiplexing 25
independent experiments at once, such that 75 independent dosage suppressor pools of varying
complexity of barcode representation were analyzed in three lanes. Candidate dosage suppressors
were identified as those whose barcode sequences exceeded 5% of the total sequencing
reads/experiment and having greater than 500 raw sequencing counts for each dosage suppressor
pool. Using spot dilutions, we attempted to confirm dosage suppressors that represented
anywhere from 5% to >99% of the sequencing reads within a unique dosage suppressor pool.
Approximately 37% of the dosage suppressors we identified were confirmed by both microarray
and sequencing methods (Appendix 6.2; Electronic Table 3.2.1). Despite identifying fewer
interactions, the Bar-seq method was more precise than the microarray-based approach.
Specifically, using spot dilutions, 64% of the interactions identified by Bar-seq were confirmed
as dosage suppressors compared to a 20% confirmation rate for interactions identified by
barcode microarray.
3.2.3 An integrated dosage suppression genetic interaction network
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Our screens for dosage suppressors of essential gene phenotypes with the MoBY-ORF
2.0 library identified mostly novel interactions, emphasizing the need for systematic analysis to
generate a comprehensive view of the dosage suppression genetic network. Nonetheless, other
dosage suppression interactions have been reported previously and thus to generate a global view
of the current dosage suppression network, we combined our results with a set of 1503 dosage
suppression genetic interactions that were annotated in the Saccharomyces Genome Database
(Electronic Table 3.2.2) to map a network containing 1077 genes (including 437 essential query
genes) and 1640 interactions (Figure 3.2.6a). As mentioned above, most query genes have only a
few dosage suppressors, while a small set of genes has a large number of dosage suppression
interactions. The network was visualized in Cytoscape using a forced-directed layout (Shannon,
Markiel et al. 2003), such that genes that share common dosage suppression interactions formed
distinct clusters. Markov clustering (MCL) analysis (van Dongen 2002) identified nine clusters,
each containing 30 or more genes, that correspond to specific bioprocesses. Similar to the
synthetic genetic network (Costanzo, Baryshnikova et al. 2010), the relative distance between
these clusters appears to reflect shared functionality (Figure 3.2.6a). For example, the functional
relationships between vesicle-mediated transport, exocytosis, and cell polarity and
morphogenesis are illustrated by the relatively close proximity of their corresponding gene
clusters to one another in the network, along with a significant number of dosage suppression
interactions that occur between genes functioning in these different bioprocesses. This suggests
that dosage suppression interactions, like other forms of genetic interactions (Dixon, Costanzo et
al. 2009; Costanzo, Baryshnikova et al. 2010), can be used to independently cluster genes on the
basis of functional interrelatedness.
A detailed look at specific interactions can provide new mechanistic insight into
particular pathways and complexes. For example, we screened two components of the essential
MIND (Mtw1p including Nnf1p-Nsl1p-Dsn1p) kinetochore complex (Figure 3.2b), which
participates in bridging centromeric heterochromatin and kinetochore microtubule-associated
proteins (MAPs) and motors (De Wulf, McAinsh et al. 2003; Pagliuca, Draviam et al. 2009). No
dosage suppressors have been mapped previously for NSL1 and DSN1, but here we identified a
network of 31 interactions and 28 genes impinging on these two query genes (Appendix 6.2;
59
Figure 3.2.6
60
Figure 3.2.6 Dosage suppression genetic interaction network for S. cerevisiae.
a. Integrated network diagram of dosage suppression genetic interactions annotated in the
literature and identified in this study. Genes are represented as nodes and interactions are
represented as edges. Colored nodes indicate sets of genes enriched for GO biological processes
summarized by the indicated terms. The nodes were distributed using a force-directed layout,
such that genes (nodes) that share common dosage suppression interactions form distinct
clusters.
b. Dosage suppression provides new biological insight into functional relationships. Novel
dosage suppressors were identified for two components of the MIND kinetochore complex,
NSL1 and DSN1. Green: kinetochore function; blue: PKA signaling; red: ribosome biogenesis.
61
Electronic Table 3.2.1). Among the dosage suppressors identified for both NSL1 and DSN1 were
the S-phase transcription factor HCM1 and a ribosomal biogenesis gene, FCF1. In a previous
study, HCM1 was predicted to directly upregulate transcription of both genes (Pramila, Wu et al.
2006), and nsl1(ts) hcm1Δ and dsn1(ts) hcm1Δ double mutants each display a negative genetic
interaction (Costanzo, Baryshnikova et al. 2010); therefore, our results are consistent with both
the in silico predictions and the double mutant phenotypes. We also identified SPC105 as a
DSN1 dosage suppressor. Spc105p forms a complex with Kre28p, and this complex also acts as
an essential kinetochore linker complex.
Two redundant genes that downregulate PKA signaling at various steps in the pathway,
GPB1 and GPB2, were also identified as dosage suppressors of DSN1 (Figure 3.2b). A third
gene, GIS2, is not well characterized but is thought to act as a negative regulator, similar to
PDE2, in PKA signaling (Balciunas and Ronne 1999). While a genetic link between the PKA
pathway and the Dam1p-Duo1p (or DASH) kinetochore- and microtubule-associated complex
has been previously observed (Li, Li et al. 2005), our results are the first to identify a possible
functional relationship between downregulation of PKA signaling and the MIND complex. Thus,
our results support the hypothesis that attenuating PKA signaling contributes to proper
kinetochore function.
3.2.4 Distribution of dosage suppressors across cellular processes
We examined the occurrence of dosage suppression genetic interactions within and
across different cellular processes. The heat map identified functions enriched (yellow) or
depleted (blue) for dosage suppression interactions relative to the expected frequency of a
random gene set (Figure 3.2.7a). Consistent with connectivity of other biological networks
(Gavin, Bosche et al. 2002; Krogan, Cagney et al. 2006; Tarassov, Messier et al. 2008; Yu,
Braun et al. 2008; Costanzo, Baryshnikova et al. 2010), we found that genes involved in the
same biological process were highly enriched for dosage suppression interactions. Importantly,
we also observed a significant number of dosage suppression interactions between distinct yet
related bioprocesses (Figure 3.2.7a). For example, the growth defect of mutants compromised for
cell polarity and morphogenesis pathways are suppressed by overexpression of genes involved in
62
Figure 3.2.7
63
Figure 3.2.7 Properties of the yeast dosage suppression network.
a. Frequency of dosage suppression genetic interactions within and across biological processes
for the integrated dosage suppression network. The frequency of gene pairs exhibiting dosage
suppression interactions was measured for 19 broadly defined functional gene sets (Costanzo,
Baryshnikova et al. 2010); blue: below the frequency of random pairs; black: statistically
indistinguishable from a random set of gene pairs; yellow: above the frequency of random pairs.
Dosage suppressor gene function is on the x-axis, and query ORF gene function is on the y-axis.
The diagonal represents within-process interactions. The red line in the color scale indicates the
frequency of interactions expected by chance (0.0005).
b. Scaled square Venn diagram showing the fraction of dosage suppression gene pairs that also
exhibit negative genetic and protein-protein interactions. Only gene pairs known to be tested for
both genetic and physical interactions were considered. Light blue: gene pairs showing dosage
suppression interactions only; red: gene pairs showing dosage suppression and negative genetic
interactions; dark blue: gene pairs showing dosage suppression and physical interactions; yellow:
gene pairs showing dosage suppression, negative genetic and physical interactions.
64
several different functional categories, including those that act at various steps in intracellular
vesicle-mediated transport.
3.2.5 Overlap of dosage suppression interactions with protein-protein and negative genetic
network edges
We explored the overlap of dosage suppression genetic interactions with three other
interaction networks: the physical (protein-protein) (Gavin, Bosche et al. 2002; Krogan, Cagney
et al. 2006; Tarassov, Messier et al. 2008; Yu, Braun et al. 2008), negative genetic, and positive
genetic networks (Costanzo, Baryshnikova et al. 2010). We found that both our experimentally-
derived and literature-curated dosage suppression networks were enriched significantly for both
physical and negative genetic interactions but not positive genetic interactions (Table 3.2.1).
Despite this overlap with physical and negative genetic interactions, most dosage suppression
interactions (68%) in the integrated network did not overlap with any previously mapped
network edge (Figure 3.2.7b; Appendix 6.2; Electronic Tables 3.2.1 and 3.2.2). Importantly,
these unique dosage suppression interactions were enriched for co-annotated gene pairs (55%; p-
value << 10-16). Thus, dosage suppression identifies a new type of interaction capable of
covering novel and functionally relevant network space, one that has not been interrogated
previously by the currently established interaction mapping approaches.
3.2.6 Mechanistic categorization of dosage suppression interactions
General mechanistic categories of second-site genetic suppression have been described
previously (Prelich 1999; Hodgkin 2005). We have extended this analysis to dosage suppression
genetic interactions by developing a decision tree to systematically categorize dosage
suppression genetic interactions in yeast (Figure 3.2.8). with the remaining interactions (13%)
falling into an unknown category (Figure 3.2.8; Table 3.2.2; Appendix 6.2; Electronic Tables
3.2.1 and 3.2.2)
First, a gene pair was determined to be functionally related if it was co-annotated to the
same GO term within a gold standard set of terms (Myers, Barrett et al. 2006). Based on this
65
Table 3.2.1 Overlap of dosage suppression interactions with other types of interactions.
Dosage suppression (SGD)
Dosage suppression (this study)
Type of interaction
# tested pairs
# overlap. pairs p-value a # tested
pairs # overlap.
pairs p-value a
Physical 1503 b 525 << 10-16 150 27 << 10-16 Negative genetic 254 c 59 << 10-16 57 c 8 1.03x10-5
Positive genetic 254 c 4 0.52 57 c 0 N/A
a p-values based on hypergeometric test. b Bait-hit gene pairs annotated in the Saccharomyces Genome Database as “Dosage Rescue” in which the bait is an essential gene. c Subset of gene pairs from b that have been screened for genetic interactions (Costanzo, Baryshnikova et al. 2010).
66
Table 3.2.2 Distribution of dosage suppression gene pairs annotated in the Saccharomyces
Genome Database.
Category Number of Dosage Suppression Gene Pairs
Functionally Related (No Protein-Protein Interaction) 732 a Functionally Related (With Protein-Protein Interaction) 515 a, b Chaperone 13 b
RNA Processing/Protein Synthesis 69 Unknown 174 TOTAL 1503 a Gene pairs were determined to be functionally related if they shared a GO term found in the gold standard set of terms (Myers, Barrett et al. 2006) as annotated in the Saccharomyces Genome Database. b Protein-protein interactions as annotated in the Saccharomyces Genome Database.
67
Figure 3.2.8
68
standard and consistent with our previous results (Figures 3.2.6 and 3.2.7), we found that ~80%
of all gene pairs with reported dosage suppression interactions were co-annotated to the same
GO term (Table 3.2.2; Appendices 6.1 and 6.2; Electronic Tables 3.2.1 and 3.2.2), confirming
that dosage suppression interactions can identify genes that participate in the same general
biological pathway or process (Figure 3.2.9a and 3.2.9b).
Second, functionally related gene pairs that exhibit a physical interaction between their
gene pairs were grouped into the “Complex Component” category, because the physical
interaction between the gene products affects the activity of the complex (Figure 3.5b). For
example, the G1 cyclins, CLN1 and CLN2, were initially discovered as dosage suppressors of
cdc28-1, a ts allele of the essential gene encoding the cyclin-dependent kinase Cdc28p (Reed,
Hadwiger et al. 1989). Subsequent studies showed that the cyclins bind to and activate Cdc28p
(Richardson, Wittenberg et al. 1989; Tyers and Futcher 1993). A direct physical interaction
between a mutant query gene product and its dosage suppressor may also reflect the ability of the
dosage suppressor to stabilize a protein complex containing the query mutant protein. Based on
this hypothesis, reciprocal dosage suppression (Gene A suppresses query mutant b and Gene B
suppresses query mutant a) may be expected between two essential gene products belonging to
the same protein complex. We identified 28 dosage suppression interactions in our screens in
which both the query mutant and the dosage suppressor were essential (Table 3.2.3; Appendix
6.2.1). Eight of the 27 gene pairs exhibited reciprocal dosage suppression (Table 3.2.3), such that
growth defects associated with mutations in either gene can be suppressed by overexpressing its
partner. Interestingly, all eight gene pairs also shared a physical interaction among their gene
products, which is highly unlikely to occur by chance (Table 3.2.3; p-value << 10-16). Similarly,
75% of reciprocal dosage suppression interactions reported in the literature also share a physical
interaction (Appendix 6.2). Thus, the strong overlap between reciprocal dosage suppression and
physical interactions provides evidence to support a mechanism whereby phenotypic suppression
is mediated by increased protein complex stability.
In the absence of any physical interactions, however, functionally related genes can still
exhibit dosage suppression interactions (Figure 3.2.9a). Mutations in SEC3, which encodes an
essential member of the exocyst complex (Finger and Novick 1997) that transports secretory
69
Figure 3.2.9
70
Figure 3.2.9 Mechanisms of dosage suppression in yeast.
a. Functional Relationship: A dosage suppressor can function either upstream or downstream of
its respective mutant query allele in the same biological process. In the example shown, at the
semi-permissive temperature, the function of the mutant allele b gene product is impaired and
unable to transmit information to a downstream effector. A dosage suppressor, encoded by gene
A, can act upstream of the mutant allele to activate the pathway.
b. Complex Component: A dosage suppressor can be a gene that encodes an interacting protein
of the mutant gene product which is required for its normal function. At the semi-permissive
temperature, the mutant protein b predominantly occurs in an unfolded state, likely because the
mutation renders the gene product unstable, and is therefore unable to interact with its normal
physical partner(s). Overexpression of a dosage suppressor, protein A, increases the levels of
properly folded mutant protein so that the physical complex can execute its essential function.
c. Chaperone: A dosage suppressor can affect the amount of the mutant gene product. In the
example shown, the dosage suppressor protein does not normally interact with the mutant gene
product. At the semi-permissive temperature, the mutant protein b is unfolded, but
overexpression of a dosage suppressor, such as a chaperone (protein A), can re-fold and stabilize
the mutant protein, enabling it to carry out its essential function.
d. RNA Processing/Ribosome: A dosage suppressor can be a gene that acts during transcription
or translation. In the example show, the dosage suppressor protein A is normally involved in
some aspect of transcription. At the semi-permissive temperature, transcription of mutant allele b
leads to a poor quality mRNA product that may be translated but more likely will be degraded.
By increasing some aspect of transcription, however, it might be possible to improve the quality
of the mRNA product, which can then be translated instead of degraded, leading to enough
functional mutant protein for the cell to be viable.
71
Table 3.2.3 Gene pairs tested for reciprocal suppression.
ts allele Dosage Suppressor
Gene Name Reciprocal Suppression
Observed? Known Protein-Protein
Interaction? ame1-4 OKP1 No Yes
apc11-13 APC2 Yes Yes ccl1-ts4 KIN28 Yes b Yes cdc11-1 YEF3 No No
cdc11-4 a CDC3 No Yes cdc11-5 a CDC3 No Yes cdc11-5 CDC10 No Yes cdc14-1 CDC33 No No cdc24-H CDC24 Yes c Yes cdc48-3 NOP1 No No dsn1-7 SPC105 No Yes ipl1-1 SDS22 No No
nse3-ts4 NSE1 No Yes nsl1-5 DSN1 No Yes orc2-3 CDC48 No No
orc2-3 a ORC3 Yes Yes orc3-70 a ORC2 Yes Yes pol12-ts HYP2 No No scc4-4 SCC2 No Yes sec14-3 YPT1 No No sec17-1 SEC18 No Yes
sec26-11D26 BET1 No No sec26-11D26 OLE1 No No sec26-11D26 SEC11 No No sec26-11D26 SEC22 Yes Yes
stu1-5 NOP1 No No taf12-9 a TAF4 Yes Yes
taf12-W486stop a TAF4 Yes Yes taf8-ts7 TAF10 Yes Yes taf9-ts2 TAF4 Yes Yes
a CDC11-CDC3, ORC2-ORC3 and TAF12-TAF4 gene pairs are only counted once; however, we screened two different alleles of CDC11 and TAF12 and recovered CDC3 and TAF4 respectively with both alleles. b reported by (Valay, Simon et al. 1993) c reported by (Ziman and Johnson 1994; Richman, Sawyer et al. 1999; Barale, McCusker et al. 2006).
72
vesicles from the trans-Golgi network to the plasma membrane, are suppressed by
overexpression of either of two functionally redundant plasma membrane t-SNAREs, SSO1 and
SSO2, involved in secretory vesicle fusion to the plasma membrane (Aalto, Ronne et al. 1993).
Dosage suppression interactions have also been identified for gene pairs that represent gene
duplications (Tanaka, Nakafuku et al. 1990; Roberg, Crotwell et al. 1999; Angus-Hill, Schlichter
et al. 2001; Norgaard, Westphal et al. 2001) and gene pairs that have the same molecular
function (Matsui and Toh-e 1992; Arevalo-Rodriguez, Cardenas et al. 2000; Han, Audhya et al.
2002; Chloupkova, LeBard et al. 2003); in both cases, the biochemical activity of the dosage
suppressor can functionally substitute for the mutant gene product (Rine 1991).
If a dosage suppression gene pair is not co-annotated to the same gold standard GO term,
then a dosage suppressor may be characterized as a chaperone suppressor or an RNA
processing/ribosome suppressor, or an unknown suppressor. Chaperones, such as heat shock
proteins (HSPs) or RNA stability factors, can act as dosage suppressors by stabilizing the levels
of a query gene product (Figure 3.2.9c). Indeed, increased dosage of HSPs has been shown to
suppress diverse sets of genes which do not share any obvious functional relationship
(Shirayama, Kawakami et al. 1993; Shea, Toyn et al. 1994; Kosodo, Imai et al. 2001; Orlowski,
Machula et al. 2007). Genes encoding ribosomal subunits and RNA processing factors have also
been identified as dosage suppressors for a variety of query genes. While the molecular
mechanism of dosage suppression in these cases is not well understood, it is possible that the
suppressing genes may lead to increased transcription or translation of the ts query gene product
(Figure 3.2.9d). The ts mutation may specifically lead to transcriptional repression of RNA
processing and/or ribosomal subunit genes. For example, transcriptional profiling of myo1Δ
cells, which are deficient for the single yeast type II myosin heavy chain (Watts, Shiels et al.
1987), showed down-regulated expression of many genes, including several ribosomal subunit
genes (Rodriguez-Quinones, Irizarry et al. 2008), and overexpression of some of these genes can
rescue the associated mutant phenotypes (Diaz-Blanco and Rodriguez-Medina 2007; Rodriguez-
Quinones, Irizarry et al. 2008).
Gene pairs that were not co-annotated to the same gold standard GO term, or considered
a chaperone suppressor or an RNA processing/ribosome suppressor, were classified as having an
73
unknown dosage suppression mechanism. Interestingly, within the “unknown” category, we
identified a small number of dosage suppression gene pairs whose protein products have a
known physical interaction but the two genes do not share any gold standard GO terms in
common (Appendix 6.2; Electronic Table 3.2.2). For every interaction initially placed in the
unknown category, including gene pairs both with and without known physical interactions
between their gene products, we went back to the primary literature in an effort to find other
functional information that might support re-classification of a particular interaction into a
mechanistic category. By doing so, we were able to re-classify 13% of the unknown interactions
into one of the other mechanisms of dosage suppression. In total we found that a relatively small
fraction (7%) of dosage suppression interactions fall into the chaperone and RNA
processing/ribosome category. Using our dosage suppression decision tree, we classified the
remaining ~87% of the dosage suppression interactions within the integrated network into one of
four general mechanistic categories.
3.3 DISCUSSION
We report a simple and efficient method to clone dosage suppressors in yeast. Central to
this method was the development of the high-copy MoBY-ORF 2.0 library which was derived
from the low-copy MoBY-ORF 1.0 library using the MAGIC bacterial mating and
recombination system for transfer of the ORF and its unique molecular barcodes (Li and Elledge
2005; Ho, Magtanong et al. 2009). Because each gene in the MoBY-ORF 2.0 library is under the
control of its own regulatory sequences, it should be particularly useful for systematic dosage
suppression analysis of genetic or chemical perturbations (Hoon, Smith et al. 2008) that
compromise cellular fitness. Preserving the gene under its own regulation may reduce the
potential for synthetic dosage lethality, which has been the focus of most of the previous studies
of gene dosage (Liu, Krizek et al. 1992; Akada, Yamamoto et al. 1997; Stevenson, Kennedy et
al. 2001; Boyer, Badis et al. 2004; Sopko, Huang et al. 2006) and can confound dosage
suppression screens.
We validated the use of the MoBY-ORF 2.0 plasmid library in dosage suppression
experiments using a barcode microarray and a deep sequencing readout (Bar-seq). The two
74
methods identified a largely overlapping set of candidate genes; however, each technique also
identified several unique hits. This is not unexpected, due to differences in the dynamic range
and sensitivity of the two techniques. The barcode microarray provided a larger list of candidate
and greater number of total confirmed dosage suppressors than did Bar-seq; however, Bar-seq
had a significantly higher precision (confirmation) rate. Barcodes that are present in a dosage
suppressor pool but have mutations (either point or deletion/insertion) may fail to hybridize to a
barcode microarray and therefore would lead to a false negative. However, barcodes carrying
mutations could be identified by Bar-seq, as the analysis can be customized to allow for
nucleotide mismatches in a particular barcode. Nevertheless, if a barcode is present at a relatively
low amount in a dosage suppressor pool, it may not be above a given cutoff used in Bar-seq;
however, it may still be identified on a barcode microarray due to the signal detection method,
which allows for amplification of weak but true barcode PCR product hybridization. The
difference in confirmation rates we observed may in part be attributed to how we harvested the
candidate suppressor colonies, which involved washing the plates completely, and, therefore,
may include some general background colonies.
Gene pairs exhibiting dosage suppression are highly enriched for physical interactions
between the encoded proteins (Table 3.2.1). This observation supports the hypothesis that
increased expression of wild-type genes can rescue mutations in a target gene because their
functional association results directly from some sort of physical interaction between the
respective encoded proteins (Reed, Hadwiger et al. 1989). Thus, direct interaction between a
mutant query gene product and its dosage suppressor may serve to stabilize a complex containing
the query mutant protein.
Dosage suppression genetic interactions were also enriched for negative genetic
interactions (Table 3.2.1). A negative genetic interaction is defined as a double mutant fitness
defect that is significantly stronger than expected, given the two single mutant fitness defects
(Dixon, Costanzo et al. 2009). In a dosage suppression genetic interaction, the fitness of one
single mutant is improved by overexpressing the wild-type copy of a second gene. Thus, the
interacting gene can be viewed as behaving in opposite ways; it has decreased activity in tests for
negative genetic interactions, and increased activity in an overexpression suppression test, which
75
is logically consistent because the query mutant phenotype is enhanced and suppressed,
respectively.
In contrast, we do not see a significant enrichment of positive genetic interactions for
dosage suppression gene pairs (Table 3.2.1). Positive genetic interactions occur when a double
mutant shows a fitness defect that is less severe than expected based on the fitness of the
corresponding single mutants (Mani, St Onge et al. 2008; Dixon, Costanzo et al. 2009). This
means that positive genetic interactions would only be logically consistent if the loss-of-function
allele and the overexpressed gene showed the same suppression phenotype. In rare cases,
overexpressed genes can lead to a dominant-negative phenotype; however, large-scale analysis
has shown that most dosage phenotypes can be attributed to a gain-of-function role associated
with the overexpressed gene (Sopko, Huang et al. 2006), which supports the lack of overlap
between these types of interactions.
Mechanistic classification of the dosage suppression interactions revealed that the vast
majority of dosage suppression interactions (80%) occur between functionally related genes, of
which ~60% are between gene pairs whose products have no known physical interactions. Thus,
dosage suppression represents a functionally relevant yet unique type of genetic interaction.
Conditional alleles are being developed for the majority of essential genes in yeast (Ben-
Aroya, Coombes et al. 2008) (Z. Li and C. Boone, unpublished data, and P. Heiter, personal
communication), and thus the potential exists to map a dosage suppression genetic interaction
network for the entire spectrum of essential genes. This mapping effort 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, Baryshnikova et al. 2010).
With on the order of ~5 dosage suppression interactions per query gene, the dosage suppression
network offers the potential of a wealth of new functional information and connections. While
we demonstrate the utility of this type of genetic interaction in yeast, an analogous mapping of
genetic interactions should be possible in mammalian cells and metazoan model systems. We
conclude that a global dosage suppression map adds a highly prevalent and new type of
76
functional ‘edge’ that can be integrated into the construction of a complete cellular landscape
comprising all types of genetic and physical interactions.
3.4 METHODS
3.4.1 Growth Media
Yeast strains were grown in SD-LEU (0.67% yeast nitrogen base, 0.2% amino acid
supplement minus LEU, 2% glucose) or SC (0.67% yeast nitrogen base, 0.2% amino acid
supplement, 2% glucose) medium. Bacteria were grown in 2X YT (1% yeast extract, 1.6%
tryptone, 0.5% sodium chloride) or in YE (0.5% yeast extract, 1% NaCl).
3.4.2 Clone construction and analysis
MoBY-ORF, v1.0 bacterial strains (Ho, Magtanong et al. 2009) were inoculated from
frozen stocks in 96-well plates into a shallow 96-well plate in which each well had 100 µl 2X YT
containing tetracycline at 5 µg/ml, kanamycin at 50 µg/ml, and chloramphenicol at 12.5 µg/ml.
Cultures were grown for ~16 hours at 37oC. P5530 (genotype: lacIQrrnB3 ΔlacZ4787 hsdR514
Δ(araBAD)567 Δ(rhaBAD)568 galU95 ΔendA9:FRT ΔrecA635:FRT umuC:ParaBAD-I-SceI-
FRT), the MAGIC recipient strain carrying plasmid p5476, was inoculated into 5 ml of
YE+Gluc, 0.2% glucose, spectinomycin at 10 µg/ml, and carbenicillin at 200 µg/ml. Cultures
were grown for ~22 hours at 30oC.
The following day, the OD600 values of the recipient strain and of 3 bacterial (donor)
strains from the MoBY-ORF 96-well plate were taken; the average of the 3 wells was used as the
average OD600 for the entire plate. Cultures were diluted to OD600 ~0.10 and mixed together for
mating in a 1:1 ratio in a total volume of 100 µl in a fresh 96-well plate. Cells were shaken at
30oC for 2 hours, at which time L-arabinose was added to a final concentration of 0.2% to each
well. Cells were incubated without shaking at 37oC for 2 hours, and then transferred to a shaking
incubator at 37oC for 2 hours. 2 µl of the 100 µl mating reaction were plated onto YE+Glyc
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containing 0.2% glycerol, 0.2% DL-chlorophenylalanine, carbenicillin at 200 µg/ml, and
kanamycin at 50 µg/ml, and incubated at 41oC overnight.
Mating products were streaked out for individual colonies onto 2X YT containing 0.2%
glucose, carbenicillin at 200 µg/ml, and kanamycin at 50 µg/ml and incubated at 37oC overnight.
Miniprep DNA was prepared from a single bacterial colony, doubly digested using XhoI and
EcoRI (Fermentas), and resolved on a 0.8% agarose gel to confirm vector and insert fragment
sizes. Two individuals performed gel analysis independently, and the results were compared to
determine clone validity. If the primary XhoI/EcoRI digest was ambiguous, a secondary digest
using BamHI/HindIII was performed. ORF fragment sizes for both double digests, along with
complete ORF sequence and barcodes, can be obtained from the MoBY-ORF database:
(http://moby.ccbr.utoronto.ca).
3.4.3 Plasmid pool preparation
Individual E. coli transformants containing a barcoded high-copy plasmid were grown in
100 µl of 2X YT containing glucose (0.2%), carbenicillin (200 µg/ml) and kanamycin (50 µg/ml)
at 37°C for 15 hours in a shallow 96-well plate. 55 µl of each culture was mixed to form the E.
coli MoBY-ORF version 2.0 pool. Plasmid DNA was prepared from the E. coli pool.
3.4.4 Cloning of dosage suppressors with the 2µ MoBY-ORF library
Each temperature-sensitive query strain (Table 3.2.4) was transformed with MoBY-ORF
v2.0; ≥ 50,000 transformants were pooled and was frozen in 15% glycerol. For identification of
suppressors, a sample of the transformant pool was thawed, and 50,000 cells were plated onto
SD-LEU. The incubation temperatures for each strain were dependent on the observed restrictive
temperature for the untransformed temperature-sensitive mutant (Z. Li, unpublished
observations). For a given strain at a particular temperature, colonies that appeared after 3 days
were pooled (to form the “dosage suppressor pool”) and stored at -80oC in 15% glycerol. To
isolate suppressing plasmids, a sample of the dosage suppressor pool was thawed, and plasmids
78
Table 3.2.4 Yeast strains used in this study. Yeast Strain Genotype
Y5041 MATa cdc42-1::KanR ura3Δ0 leu2Δ0 his3Δ met15Δ Y5300 MATa cdc24-H::KanR ura3Δ0 leu2Δ0 his3Δ met15Δ Y5361 MATa sec17-1::KanR ura3Δ0 leu2Δ0 his3Δ met15Δ Y5583 MATa sec14-3::KanR ura3Δ0 leu2Δ0 his3Δ met15Δ Y5587 MATa sec18-1::KanR ura3Δ0 leu2Δ0 his3Δ met15Δ Y5588 MATa sec18-1::KanR ura3Δ0 leu2Δ0 his3Δ met15Δ Y6270 MATa sec19-1::KanR ura3Δ0 leu2Δ0 his3Δ met15Δ Y6271 MATa scc2-4::KanR ura3Δ0 leu2Δ0 his3Δ met15Δ Y6323 MATa smc3-42::KanR ura3Δ0 leu2Δ0 his3Δ met15Δ Y6415 MATa ccl1-ts4::KanR ura3Δ0 leu2Δ0 his3Δ met15Δ Y6417 MATa cdc10-1::KanR ura3Δ0 leu2Δ0 his3Δ met15Δ Y6426 MATa cdc11-2::KanR ura3Δ0 leu2Δ0 his3Δ met15Δ Y6432 MATa cdc11-5::KanR ura3Δ0 leu2Δ0 his3Δ met15Δ Y6434 MATa cdc23-1::KanR ura3Δ0 leu2Δ0 his3Δ met15Δ Y6437 MATa cdc23-4::KanR ura3Δ0 leu2Δ0 his3Δ met15Δ Y6462 MATa kin28-ts::KanR ura3Δ0 leu2Δ0 his3Δ met15Δ Y6494 MATa sec22-3::KanR ura3Δ0 leu2Δ0 his3Δ met15Δ Y6525 MATa cdc11-4::KanR ura3Δ0 leu2Δ0 his3Δ met15Δ Y6538 MATa nop1-3::KanR ura3Δ0 leu2Δ0 his3Δ met15Δ Y7236 MATa cdc3-3::KanR ura3Δ0 leu2Δ0 his3Δ met15Δ Y7354 MATa cdc14-1::KanR ura3Δ0 leu2Δ0 his3Δ met15Δ Y7425 MATa ipl1-1::KanR ura3Δ0 leu2Δ0 his3Δ met15Δ Y7488 MATa cdc28-1::KanR ura3Δ0 leu2Δ0 his3Δ met15Δ Y7731 MATa cdc48-9::KanR ura3Δ0 leu2Δ0 his3Δ met15Δ Y7827 MATa cdc48-3::KanR ura3Δ0 leu2Δ0 his3Δ met15Δ Y7829 MATa cdc48-2::KanR ura3Δ0 leu2Δ0 his3Δ met15Δ Y7912 MATa okp1-5::KanR ura3Δ0 leu2Δ0 his3Δ met15Δ Y8076 MATa apc2-8::KanR ura3Δ0 leu2Δ0 his3Δ met15Δ Y8183 MATa stu1-5::KanR ura3Δ0 leu2Δ0 his3Δ met15Δ Y8198 MATa cep3-1::KanR ura3Δ0 leu2Δ0 his3Δ met15Δ Y8224 MATa orc2-1::KanR ura3Δ0 leu2Δ0 his3Δ met15Δ Y8270 MATa cdc33-E72G::KanR ura3Δ0 leu2Δ0 his3Δ met15Δ Y8309 MATa cdc36-16::KanR ura3Δ0 leu2Δ0 his3Δ met15Δ Y8324 MATa sec11-2::KanR ura3Δ0 leu2Δ0 his3Δ met15Δ Y8437 MATa orc2-3::KanR ura3Δ0 leu2Δ0 his3Δ met15Δ Y8846 MATa orc3-70::KanR ura3Δ0 leu2Δ0 his3Δ met15Δ Y8950 MATa pol12-ts::KanR ura3Δ0 leu2Δ0 his3Δ met15Δ Y9020 MATa cdc11-1::KanR ura3Δ0 leu2Δ0 his3Δ met15Δ
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Table 3.2.4 Continued Yeast Strain Genotype
Y9313 MATa dsn1-7::KanR ura3Δ0 leu2Δ0 his3Δ met15Δ Y9315 MATa dsn1-7::KanR ura3Δ0 leu2Δ0 his3Δ met15Δ Y9320 MATa dsn1-8::KanR ura3Δ0 leu2Δ0 his3Δ met15Δ Y9423 MATa spc105-15::KanR ura3Δ0 leu2Δ0 his3Δ met15Δ Y9452 MATa prp6-1::KanR ura3Δ0 leu2Δ0 his3Δ met15Δ Y9495 MATa arc35-5::KanR ura3Δ0 leu2Δ0 his3Δ met15Δ Y9587 MATa prp4-1::KanR ura3Δ0 leu2Δ0 his3Δ met15Δ Y9588 MATa prp4-1::KanR ura3Δ0 leu2Δ0 his3Δ met15Δ Y9606 MATa ole1-m2::KanR ura3Δ0 leu2Δ0 his3Δ met15Δ Y9831 MATa taf12-9::KanR ura3Δ0 leu2Δ0 his3Δ met15Δ Y9834 MATa taf12-W486stop::KanR ura3Δ0 leu2Δ0 his3Δ met15Δ Y9854 MATa smc2-8::KanR ura3Δ0 leu2Δ0 his3Δ met15Δ Y9918 MATa rpn11-14::KanR ura3Δ0 leu2Δ0 his3Δ met15Δ Y9987 MATa stu2-11::KanR ura3Δ0 leu2Δ0 his3Δ met15Δ Y10087 MATa sds22-5::KanR ura3Δ0 leu2Δ0 his3Δ met15Δ Y10121 MATa yef3-F650S::KanR ura3Δ0 leu2Δ0 his3Δ met15Δ Y10137 MATa ypt1-3::KanR ura3Δ0 leu2Δ0 his3Δ met15Δ Y10166 MATa tel2-7::KanR ura3Δ0 leu2Δ0 his3Δ met15Δ Y10247 MATa arp4-G161D::KanR ura3Δ0 leu2Δ0 his3Δ met15Δ Y10306 MATa taf4-18::KanR ura3Δ0 leu2Δ0 his3Δ met15Δ Y10626 MATa rfa3-313::KanR ura3Δ0 leu2Δ0 his3Δ met15Δ Y10803 MATa taf8-ts7::KanR ura3Δ0 leu2Δ0 his3Δ met15Δ Y11140 MATa ame1-4::KanR ura3Δ0 leu2Δ0 his3Δ met15Δ Y11188 MATa sec26-11D26::KanR ura3Δ0 leu2Δ0 his3Δ met15Δ Y11213 MATa taf9-ts2::KanR ura3Δ0 leu2Δ0 his3Δ met15Δ Y11232 MATa hyp2-1::KanR ura3Δ0 leu2Δ0 his3Δ met15Δ Y11237 MATa med4-6::KanR ura3Δ0 leu2Δ0 his3Δ met15Δ Y11357 MATa tcp1-1::KanR ura3Δ0 leu2Δ0 his3Δ met15Δ Y11359 MATa apc11-13::KanR ura3Δ0 leu2Δ0 his3Δ met15Δ Y11386 MATa bet1-1::KanR ura3Δ0 leu2Δ0 his3Δ met15Δ Y11413 MATa taf10-ts34::KanR ura3Δ0 leu2Δ0 his3Δ met15Δ Y11540 MATa nsl1-5::KanR ura3Δ0 leu2Δ0 his3Δ met15Δ Y11745 MATa scc4-4::KanR ura3Δ0 leu2Δ0 his3Δ met15Δ Y11860 MATa nse3-ts4::KanR ura3Δ0 leu2Δ0 his3Δ met15Δ Y11912 MATa nse5-ts2::KanR ura3Δ0 leu2Δ0 his3Δ met15Δ Y12371 MATa nse1-16::KanR ura3Δ0 leu2Δ0 his3Δ met15Δ
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were isolated using a modified miniprep protocol of the Qiagen miniprep kit as previously
described (Butcher and Schreiber 2006).
3.4.5 Yeast barcode microarray hybridization and data analysis
PCR amplification of the barcodes and TAG4 microarray hybridization were performed
as previously described (Pierce, Fung et al. 2006). For each array, a competitive hybridization
was performed. Biotinylated universal TAG4 primers were used to PCR-amplify the barcodes
from the dosage suppressor pool, while non-biotinylated universal TAG4 primers were used to
PCR-amplify the barcodes from the original transformant pool. Each hybridization mix
contained 9:1 (v/v) non-biotinylated:biotinylated PCR product. A signal 5-fold or higher above
background, determined empirically, was used as the cutoff for identifying candidate dosage
suppressors.
3.4.6 Empirical determination of raw barcode microarray intensity cutoff for identification of
candidate dosage suppressors.
50,000 cells, representing a pooled sample of the transformants of each strain were plated
on selected medium and incubated at the semi-permissive temperature. Colonies appearing after
3 days at the semi-permissive temperature were individually picked and pooled. We anticipated
that the plasmid pool contained in this mixture would identify a distinct set of barcodes that
display raw intensity signals at a level above the standard barcode microarray background cutoff
(200 average fluorescence units (a.f.u.)) and thereby identify a cutoff for candidate dosage
suppressors. Barcoded plasmids were extracted from the pooled colonies, the barcodes were
PCR-amplified, and a competitive microarray hybridization was performed as described.
a. For cdc48-9, 24 barcodes were above the standard background.
Using a cutoff of 2000, 14/24 barcodes (58%) of the barcodes in the sample are retrieved. Using
a cutoff of 1000, 19/24 barcodes (80%) of the barcodes in the sample are retrieved. Using a
cutoff of 500, 21/24 barcodes (88%) of the barcodes in the sample are retrieved.
b. For nse3-ts5, 6 barcodes were above the standard background.
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Using a cutoff of 2000, 3/6 barcodes (50%) of the barcodes in the sample are retrieved. Using a
cutoff of 1000, 4/6 barcodes (66%) of the barcodes in the sample are retrieved. Using a cutoff of
500, 4/6 barcodes (66%) of the barcodes in the sample are retrieved
c. For stu2-11, 10 barcodes were above the standard background.
Using a cutoff of 2000, 4/10 barcodes (40%) of the barcodes in the sample are retrieved. Using a
cutoff of 1000, 5/10 barcodes (50%) of the barcodes in the sample are retrieved. Using a cutoff
of 500, 5/10 barcodes (50%) of the barcodes in the sample are retrieved.
Because for two of the three strains, there was no difference in the number of barcodes
retrieved between the 500 and 1000 raw intensity cutoffs, and for one strain, there is a marginal
(<10%) difference, we decided to use the more stringent cutoff of 1000 raw a.f.u. for
identification of candidate dosage suppressors.
3.4.7 Assessing fitness of barcoded yeast strains by Illumina/Solexa sequencing
Each 20mer uptag and barcode was amplified with composite primers comprising the
sequences of the common barcode primers and the sequences required for attachment to the
Illumina/Solexa slide.
For the Uptags the following primers were used:
5’-
AATGATACGGCGACCACCGACACTCTTTCCCTACACGACGCTCTTCCGATCTNN
NNNGTCGACCTGCAGCGTACG -3’ (Forward) and
5’- CAAGCAGAAGACGGCATACGAGCTCTTCCGATCTGATGTCCACGAGGTCTCT -
3’ (Reverse).
For the Downtags the following primers were used:
5’-
AATGATACGGCGACCACCGACACTCTTTCCCTACACGACGCTCTTCCGATCTNN
NNNCGGTGTCGGTCTCGTAG -3’ (Forward) and
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5’-
CAAGCAGAAGACGGCATACGAGCTCTTCCGATCTGAAAACGAGCTCGAATTCAT
CG -3’ (Reverse).
The 5’ portion (in Bold) is the sequences incorporated into the F and R primer,
respectively which are required for Illumina/Solexa cluster formation. The variable sequence
(italics) represents the 10mer indexing tag used for multiplexing. The 3’ portion (underlined)
represents the common primer flanking the barcode and is required to amplify the MoBY-ORF
barcodes. PCR amplification was conducted in 100µL volumes, using Invitrogen Platinum PCR
Supermix (Cat. No. 11306-016) with the following conditions: 95°C/3 min; 25 cycles of 94°C/30
sec, 55°C/30 sec, 68°C/30 sec; followed by 68°C/10 min. PCR product was then purified with
Qiagen MinEluteTM 96 UF PCR Purification Kit (Cat. No. 28051). Following PCR purification,
DNA was quantified with the Invitrogen Quant-iTTM dsDNA BR Assay Kit (Cat No. Q32853)
and then adjusted to a concentration of 10µg/mL. Equal volumes of normalized DNAs were then
pooled. Theses pool DNA samples (25-plex) consist of 130bp PCR products that was gel
purified from 12% polyacrylamide TBE gels using the crush and soak method(Sambrook,
Russell et al. 2001) followed by ethanol precipitation. Samples were used directly for cluster
formation. Each lane was sequenced once, using the standard single-read sequencing primer. The
sequence was as follows: multiplexing tag – common primer (U1 or D1) – MoBY-ORF barcode.
The first 5 bases represent the multiplexing tag allowed post-sequencing assignment of each
amplicon to a particular experiment. The identity of each clusters multiplexing tag was
determined allowing 0 mismatches. The last 20 bases were used to identify which ORFs were
potential dosage suppressors. Clusters were binned according to their multiplexing tag, than
tallied using the MoBY-ORF barcodes. These tallies were transformed into a percentage of total
counts for that experimental bin. Within each 25-plex, we removed the ORF barcodes that
corresponded to wild-type alleles of other temperature sensitive mutants that were screened. A
potential dosage suppressor was defined by greater than 5% of the Bar-seq counts for a particular
experimental bin.
3.4.8 Confirmation of candidate dosage suppressors and test for reciprocal suppression
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Individual 2µ MoBY-ORF plasmids were transformed into the corresponding
temperature-sensitive strain using standard methods. Spot dilutions were performed on SD-LEU
media using standard methods and incubated at the same temperature at which the dosage
suppressor was initially identified (for candidate dosage suppressors) or the corresponding semi-
permissive temperature of the temperature-sensitive strain (for reciprocal suppression tests).
3.4.9 Overlap of dosage suppression genetic interactions with other types of interactions
The literature-curated dosage suppression dataset and protein-protein interaction dataset
were downloaded from the Saccharomyces Genome Database (SGD, www.yeastgenome.org) on
May 20th, 2010. Dosage suppression data were filtered to include only gene pairs containing an
essential gene as the query ORF. The list of essential genes was downloaded from
Saccharomyces Genome Deletion Project (www-
sequence.stanford.edu/group/yeast_deletion_project) on March 20th, 2010.
3.4.10 Analysis of functional relatedness
Two genes sharing a dosage suppression interaction were considered to be functionally
related if they are co-annotated to the same Gene Ontology (GO) term. Only GO terms from a
published gold standard were considered (Myers, Barrett et al. 2006). Gene Ontology
annotations were downloaded from the Saccharomyces Genome Database (SGD,
www.yeastgenome.org) on May 11th, 2010.
3.4.11 Identifying gene clusters in the integrated dosage suppression network
The integrated network was clustered using the Markov Clustering algorithm (van
Dongen 2002). Nine clusters containing more than 30 genes were tested for functional
enrichment using the BiNGO plugin for Cytoscape (Maere, Heymans et al. 2005). The Gene
Ontology Biological Process term showing the highest enrichment in a particular cluster was
used to label the cluster on the network.
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Chapter Four
Conclusions and Future Directions
85
4.1 General Overview
The goal of this thesis was two-fold: first, to construct the first barcoded library of gene
expression and overexpression plasmids covering every gene in the Saccharomyces cerevisiae
genome; and, second, to use this library to identify genes that, when overexpressed from a multi-
copy plasmid, suppress the lethality of temperature-sensitive alleles of essential genes. This type
of genetic interaction is called dosage suppression. Gene overexpression generally leads to a
gain-of-function effect (Sopko, Huang et al. 2006; Vavouri, Semple et al. 2009); therefore,
dosage suppression genetic interactions can provide information that is difficult or indeed
impossible to obtain using loss-of-function alleles alone. One important reason for pursuing
dosage suppression interaction studies is that gain-of-function-based genetic interactions are
likely to provide a rich source of functional information (Dixon, Costanzo et al. 2009) because
these interactions are largely unique and rich in functionally coherent links. Moreover, the global
structure of dosage suppression genetic interactions, which should reveal a functional wiring
diagram of the cell highlighting functional relationships between different processes, remains
largely unexplored. A second important reason is that the mechanistic basis of individual dosage
suppression interactions is still poorly understood; therefore, a systematic survey of dosage
suppression interactions may help reveal different general mechanisms of dosage suppression. In
this thesis, for the first time, I was able to explore the relationship between functional edges in
the dosage suppression network and those in the existing S. cerevisiae genetic and physical
interaction networks. As an extension of this analysis, I also mined the S. cerevisiae literature to
identify four general mechanisms of dosage suppression. Together, this thesis described the first
comprehensive analysis of dosage suppression in any organism.
4.2 The MoBY-ORF gene overexpression libraries: present and future applications
Although it is possible to envision engineering the genome to contain multiple, integrated
copies of a wild-type gene, the simplest experimental approach to studying dosage suppression is
to use plasmid-based gene overexpression. One necessary reagent for investigating dosage
suppression genetic interactions is a gene overexpression library. Over the years, several such
libraries have been developed for yeast. However, as I previously described (see Introduction,
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Section 1.3.2.7), each of these libraries has certain features that are not optimal for studying
dosage suppression in a systematic and genome-wide manner. We reasoned that the ideal
plasmid-based gene overexpression library would have the following features: 1) endogenous
regulatory sequences controlling ORF expression; 2) no epitope tag; 3) a single ORF per
plasmid; and, 4) unique barcodes to facilitate parallel analysis of strain pools. In this thesis, I
described my role in the cloning and application of two related gene overexpression libraries in
which each plasmid has all four of these features. The MoBY-ORF 1.0 and 2.0 plasmid libraries
are respectively low-copy (CEN-based) and high-copy (2µ-based) gene overexpression libraries
that can be used in a variety of dosage analysis studies. In this thesis, I described the first
systematic approach specifically developed to studying dosage suppression in yeast. The MoBY-
ORF 2.0 plasmid library is central to this method because it streamlines dosage analysis by being
compatible with high-throughput genomics technologies that can monitor plasmid representation,
including barcode microarrays and next-generation sequencing methods (Pierce, Davis et al.
2007; Smith, Heisler et al. 2009).
The MoBY-ORF 2.0 plasmid library is a versatile reagent that can be applied in
systematic gene dosage studies to identify novel enhancers (dosage lethality) or suppressors
(dosage suppression) of a phenotype of interest. The library can be used to identify genetic
interactions occurring in either a conditional (e.g. temperature-sensitive) or constitutive (e.g.
deletion) mutant strain. One limitation of the library is that, alone, it may not be able to identify
dosage lethal interactions in certain genetic contexts. For example, if a dosage lethal interaction
occurs in a deletion or some other type of constitutive mutant, then the transformed mutant will
not be identified because the cell will be dead or arrested. Such an interaction could be detected,
however, if the query mutant strain is first transformed with the wild-type complementing ORF
carried on a plasmid containing a counterselectable marker, such as URA3, before mass
transformation with the MoBY-ORF 2.0 plasmid library. After the mass transformation, replica
plating the transformants onto media containing a drug used for counterselection, which in the
case of URA3 is 5-fluoroorotic acid (5’-FOA), will allow for growth of cells that have lost the
URA3-containing plasmid. However, if a dosage lethal interaction does occur, then the colony
will not appear on the 5’-FOA-containing media, and this could be detected using a barcode
readout. This strategy has successfully been used in an analogous manner in small-scale studies
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to identify functionally relevant bypass suppressors of essential genes (Li, Routt et al. 2000;
Marcoux, Cloutier et al. 2000; Kurischko, Weiss et al. 2005).
In the short term, an obvious future goal is to elaborate upon the dosage suppression
network that we have begun to construct (see Chapter 3, Results, and below). For the majority of
essential genes in yeast, ts alleles in a defined genetic background have been constructed (Ben-
Aroya, Coombes et al. 2008)(Z. Li and C. Boone, unpublished data). Therefore, it should now be
feasible to apply the systematic screening method I developed to construct an extensive network
of dosage suppression interactions. It should also be possible to systematically investigate dosage
suppression of single deletion mutants of non-essential genes that have quantifiable fitness
defects (Costanzo, Baryshnikova et al. 2010); upon transformation with the MoBY-ORF 2.0
plasmid library, a strain carrying such an allele may have improved fitness that is a result of a
dosage suppression genetic interaction. Interestingly, one mechanism of dosage suppression of a
deletion allele is by hyperactivation of a redundant, parallel pathway. By virtue of its definition,
essential pathways are not suppressed by overexpression of a parallel pathway. The use of the
MoBY-ORF library may therefore allow for existence of such parallel, compensatory pathways
to be investigated.
Another envisioned application of the existing MoBY-ORF 2.0 plasmid library is in
higher-order genetics studies. Triple loss-of-function mutant analysis has been examined for
specific genes on a small-scale (Lam, Krogh et al. 2008; Nugent, Johnsson et al. 2010) and a
handful of genome-wide synthetic lethal screens have been mapped using a double mutant query
strain (Tong, Lesage et al. 2004), but no large-scale systematic analysis has been reported. Such
mutant analysis can highlight both overlapping and unique roles for individual genes. For
example, dosage suppression genetic interactions between a query gene and two paralogous
genes have been reported (Drebot, Johnston et al. 1993; Baudin-Baillieu, Tollervey et al. 1997;
Helliwell, Schmidt et al. 1998; Kota, Melin-Larsson et al. 2007; Demmel, Beck et al. 2008). If
no double mutant phenotype is observed between the query gene and a loss-of-function allele of
only one of the two paralogs, this observation suggests the other paralog may be compensating
for the loss-of-function. To determine if this compensation may be occurring, one could create a
triple mutant comprising the mutant query gene and loss-of-function alleles of the two paralogs.
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If a triple genetic interaction is observed, as determined by significant deviation from the
predicted product model phenotype, then another functional edge representing an interaction
between three different genes can be added to the overall interaction network of the cell. In
another example, one could look at dosage suppression of a double deletion mutant. Double
deletion mutants that exhibit a negative genetic interaction usually identify functional
relationships occurring between redundant pathways that effectively buffer one another in the
event that the activity of one pathway is compromised (Costanzo, Baryshnikova et al. 2010). By
identifying dosage suppression interactions of synthetic lethal double mutants, perhaps with a ts
allele of one gene, it might be possible to identify pathway-specific activities. As well, the novel
activities of other previously known genes and/or pathways contributing to the double mutant
genetic interaction might also be illuminated.
Analogous to the systematic analysis reported for genes that, under standard laboratory
conditions, cause lethality upon overexpression in yeast (Sopko, Huang et al. 2006), the MoBY-
ORF 2.0 plasmid library could be used to identify genes that either increase or decrease tolerance
of certain environmental conditions, such as osmotic stress or pH change. For example,
knowledge of overexpressed genes that increase tolerance to salt stress and ethanol has industrial
applications in crop production and fermentation processes respectively (Hong, Lee et al. ; Hou,
Cao et al. 2009; Hong, Lee et al. 2010; Hong, Lee et al. 2010; Sun, Guo et al. 2010). By
definition, an inducible gene overexpression system requires an external molecule to achieve
increased gene dosage. When looking at phenotypes under a certain environmental condition,
using an inducer may effectively alter that condition in an unknown way and confound the
experimental results. By contrast, no external requirements for gene overexpression are
necessary with MoBY-ORF plasmids, making it more likely that appropriate context-dependent
dosage effects will be identified.
4.3 Dosage suppression genetic interaction networks: illuminating a new facet of genetics
In comparison to the extensive genetic and physical interaction networks that have been
determined for yeast and other organisms (Tong, Evangelista et al. 2001; Gavin, Bosche et al.
2002; Krogan, Cagney et al. 2006; Tarassov, Messier et al. 2008; Yu, Braun et al. 2008;
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Costanzo, Baryshnikova et al. 2010), the existing dosage suppression genetic interaction network
for yeast is quite small and for other organisms is largely non-existent. The limiting factor was
that, before this work, no approach existed that enabled dosage suppression genetic interactions
to be studied in a systematic manner. What distinguishes our protocol from previous dosage
suppression studies is the method used in identifying candidate dosage suppressors from a
dosage suppression screen, which is more rapid and efficient than traditional dosage suppression
screens at identifying candidate dosage suppressors. Furthermore, unlike the initial results from
screens using certain types of gene overexpression libraries (specifically, those in which a given
plasmid contains more than one ORF), no ambiguity exists as to which ORF is the candidate
dosage suppressor. In our proof-of-principle set of dosage suppression screens, we used barcode
microarrays to identify candidate dosage suppressors for a selected set of query ts strains. We
confirmed 214 dosage suppression interactions for ~50 ts strains queried. After accounting for
multiple alleles screened and strains for which only the wild-type clone was recovered, we
ultimately identified 150 dosage suppression interactions for 32 query genes. Our dosage
suppression genetic interaction network is the first of its kind for any eukaryote.
Our dosage suppression network allowed us to explore the relationship between the
dosage suppression genetic interactions and other known genetic and physical interactions. Using
our experimental network, we examined the overlap of negative genetic, positive genetic and
protein-protein interactions with dosage suppression interactions. We found that gene pairs
exhibiting dosage suppression are enriched for negative genetic interactions, as well as for
physical interactions between the encoded proteins. This was expected, as (1) essential genes
encoding components of protein complexes are known to have numerous negative genetic
interactions with other components of the same complex (Davierwala, Haynes et al. 2005) ; and
(2) previous small-scale studies have suggested that physical stabilization of protein structure is a
plausible mechanism of dosage suppression (Reed, Hadwiger et al. 1989). On the other hand, no
significant overlap with positive genetic interactions is observed. This is likely due to the fact
that we only analyzed dosage suppression interactions in which the queries were essential genes,
as previous studies have reported that positive genetic interactions are not generally observed for
genes encoding proteins found in the same complex when at least one member of the complex is
essential (Bandyopadhyay, Kelley et al. 2008) (Baryshnikova et al., in press). Interestingly, many
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gene pairs that exhibit dosage suppression interactions have not been identified as having a
functional interaction in either of these more expansive genetic or physical interaction networks.
Therefore, dosage suppression genetic interactions represent a unique type of functional edge
that can be used in developing a complete network of all interactions occurring within the cell.
Looking ahead, we can envision a complete map of dosage suppression interactions for
all essential and non-essential genes in S. cerevisiae. As noted above, this information is likely to
provide a unique insight into the structure of the S. cerevisiae genetic network that will be of
exceptional value on its own. More speculative questions include, for example, the conservation
of this network in other species. It should be possible to develop similar tools for other single-
celled organisms, and it will be interesting to determine whether these interactions are conserved
in other species such as S. pombe. The impacts of gene dosage on multi-cellular animals, where
cellular-level redundancy can compensate for the loss of individual cells, will be interesting to
explore, as will issues related to cell non-autonomous processes such as inter-cellular
communication and tissue function. Indeed, a better global understanding of increased gene
dosage could have implications for diseases characterized by gene copy gains, such as Down’s
syndrome or various types of cancer (Santarius, Shipley et al. 2010). A complete S. cerevisiae
gene dosage interaction map will be an important starting point for these studies.
4.4 Understanding the mechanistic basis of dosage suppression
What are the underlying mechanisms of dosage suppression? To empirically determine
how dosage suppression occurs, we mined the S. cerevisiae literature for dosage suppression
interactions involving essential gene queries to determine what the biological relationship, if any,
was between a dosage suppressor gene and its respective query gene. We found that, analogous
to mechanisms described for second-site suppression, dosage suppression interactions involving
essential gene queries fall into a small number of functional categories. As expected, many
(>80%) dosage suppressors are genes that have some sort of functional relationship with the
query gene. This functional relationship may be some sort of physical interaction that is required
for activation of a protein complex or subcellular localization to a particular site of action.
However, no physical interaction is required, as dosage suppression interactions have been
91
reported for gene pairs that act at distinct steps within a more general biological process. For
those dosage suppression gene pairs that do not have an annotated functional relationship, the
types of dosage suppressors typically fall into one of two categories: 1) an RNA
processing/ribosomal gene; or 2) a gene with chaperone activity which stabilizes the RNA or
protein product. As more and more functional relationships between genes and their products are
identified, and more types of query alleles, such as deletion and environmental condition-
dependent alleles, it is very possible that other mechanisms of dosage suppression will be
determined.
Transcription factors that are functionally unrelated to their respective query genes have
been identified as dosage suppressors. Upon transcription factor overexpression, the assumption
is that the expression of its targets, which are either normally not expressed under the tested
conditions or expressed and functioning at basal levels, is increased, leading to a physiological
change that results in viability of a cell that would normally be arrested (or dead) in the semi-
permissive condition. Performing gene expression analysis, using either microarrays or deep
sequencing methods, on a mutant strain that is overexpressing a transcription factor dosage
suppressor may provide insight into the biological changes going on in such a cell as well as
possibly highlight previously unknown genetic relationships.
In our experimental network, we identified several ORFs that suppress seemingly
functionally unrelated query genes. It is possible that the query and dosage suppressor genes are
indeed functionally related, but an unexplored aspect of dosage suppression is allele-specific,
gene non-specific suppression. Such suppression is known for second-site suppression; for
example, amber mutations are suppressed by the corresponding tRNA mutations (Murgola
1985). No equivalent mechanism has been described for dosage suppression. Identification of
such suppressors requires knowing the nature of the query mutation, be it a point mutation, a
base pair insertion or deletion, or some other type of genomic alteration. Knowledge of such
dosage suppressors may allow for refinement of the dosage suppression genetic interaction
network, as well as, perhaps, the assignment of new functional information to genes that behave
as dosage suppressors exclusively by this mechanism.
92
Previous reports have shown that heat shock induces a transient, albeit significant,
repression of protein biosynthesis genes (Warner 1999; Gasch, Spellman et al. 2000). Therefore,
protein biosynthesis genes are expected to represent a general class of dosage suppressors in
screens that rely on heat shock to induce a conditional phenotype. In our screens, we used strains
harboring temperature-sensitive query alleles that cause mutant phenotypes when incubated at a
higher (semi-permissive) temperature. Not surprisingly, approximately one-third of the novel
interactions we identified involved a dosage suppressor that encoded a gene that is (or is
predicted to be) involved protein biosynthesis. The common assumption is that dosage
suppression by overexpression of a protein biosynthesis gene is likely non-specific; therefore, we
would expect that a given protein biosynthesis gene would suppress a variety of temperature-
sensitive alleles of unrelated genes. In fact, we did observe a few such interactions: specifically,
two dosage suppressors each interacted with three different query genes. These dosage
suppressors could be “frequent flyers”, or non-specific dosage suppressors. However, (1) no
definition currently exists that would allow for the confident identification of a “frequent flyer”
(i.e. 50%, 75%, 95% of all screens – the criteria are unclear), (2) in most interactions, dosage
suppression of a given query gene was only observed with one or two protein biosynthesis genes
and, (3) it is likely that some dosage suppression interactions involving protein biosynthesis
genes have functional relevance, as non-canonical (e.g. extraribosomal) functions for ribosomal
genes has been reported (Warner and McIntosh 2009), and functional specialization of ribosomal
paralogs has been observed (Haarer, Viggiano et al. 2007; Komili, Farny et al. 2007). Expanding
dosage suppression screens to the rest of the genome will allow for the more accurate
identification and analysis of non-specific dosage suppressors. As well, detailed functional
studies may also reveal additional novel roles for protein biosynthesis genes in other processes.
4.5 Concluding thoughts
Dosage suppression is an underappreciated type of genetic interaction. The MoBY-ORF
2.0 plasmid library enables the systematic and streamlined study of gene dosage effects,
including dosage suppression, in unprecedented detail and resolution. Future developments
should enable the complete mapping of dosage suppression interactions in S. cerevisiae as well
as other organisms. Investigating the genetic networks underlying improved fitness upon gene
93
overexpression may shed light on mechanisms of certain human diseases. Finally, the integration
of dosage suppression genetic interactions into other types of functional networks will improve
our overall understanding of the functional wiring diagram of the cell and contribute to
discovering the function of every gene encoded in an organism’s genome.
94
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Chapter Six
Appendices
113
Appendix 6.1 All confirmed spot dilutions performed based on dosage suppression screens
reported in this study.
ts allele ORF Gene Detected in both?
Detected in microarray
only?
Detected in Bar-
seq only?
ame1-4 YBR211C AME1 x ame1-4 YGR179C OKP1 x
apc11-13 YDL008W APC11 x apc11-13 YLR079W SIC1 x apc11-13 YLR127C APC2 x apc11-13 YGL050W TYW3 x arc35-5 YNR035C ARC35 x arc35-5 YBR234C ARC40 x arc35-5 YPR104C FHL1 x
arp4-G161D YJL081C ARP4 x ccl1-ts4 YPR025C CCL1 x ccl1-ts4 YDL108W KIN28 x cdc11-1 YJR076C CDC11 x cdc11-1 YLR249W YEF3 x cdc11-1 YER007C-A TMA20 x cdc11-1 YLL039C UBI4 x cdc11-1 YHR115C DMA1 x cdc11-2 YJR076C CDC11 x cdc11-4 YJR076C CDC11 x cdc11-4 YLL026W HSP104 x cdc11-4 YLR314C CDC3 x cdc11-4 YCR065W HCM1 x cdc11-5 YJR076C CDC11 x cdc11-5 YCR002C CDC10 x cdc11-5 YLR314C CDC3 x cdc14-1 YFR028C CDC14 x cdc14-1 YPL184C MRN1 x cdc14-1 YGL147C RPL9A x cdc14-1 YER126C NSA2 x cdc14-1 YOL139C CDC33 x cdc14-1 YDL051W LHP1 x cdc14-1 YPR104C FHL1 x cdc14-1 YMR073C IRC21 x cdc14-1 YHL034C SBP1 x cdc14-1 YLR030W YLR030W x
114
Appendix 6.1 Continued
ts allele ORF Gene Detected in both?
Detected in microarray
only?
Detected in Bar-
seq only?
cdc14-1 YBR083W TEC1 x cdc14-1 YGL122C NAB2 x cdc14-1 YLR079W SIC1 x cdc14-1 YMR144W YMR144W x cdc23-1 YHR166C CDC23 x cdc23-1 YLR079W SIC1 x cdc23-4 YHR166C CDC23 x cdc23-4 YLR079W SIC1 x cdc24-H YAL041W CDC24 b cdc24-H YGR152C RSR1 x cdc24-H YLR229C CDC42 x cdc28-1 YBR160W CDC28 x cdc28-1 YMR199W CLN1 x cdc28-1 YPL256C CLN2 x cdc28-1 YER167W BCK2 x cdc36-16 YDL165W CDC36 x cdc36-16 YER068W MOT2 x cdc36-16 YDL166C FAP7 x cdc48-2 YDL126C CDC48 x cdc48-2 YER007C-A TMA20 x cdc48-2 YKL180W RPL17A x cdc48-2 YDR266C YDR266C x cdc48-2 YPR104C FHL1 x cdc48-2 YDL122W UBP1 x cdc48-2 YMR067C UBX4 x cdc48-2 YDL020C RPN4 x cdc48-2 YDR505C PSP1 x cdc48-2 YDR512C EMI1 x cdc48-2 YJL151C SNA3 x cdc48-2 YDR177W UBC1 x cdc48-3 YDL126C CDC48 x cdc48-3 YER007C-A TMA20 x cdc48-3 YOR310C NOP58 x cdc48-3 YDL014W NOP1 x cdc48-3 YJR022W LSM8 x cdc48-3 YER165W PAB1 x
115
Appendix 6.1 Continued
ts allele ORF Gene Detected in both?
Detected in microarray
only?
Detected in Bar-
seq only?
cdc48-3 YBR172C SMY2 x cdc48-3 YMR067C UBX4 x cdc48-3 YLR354C TAL1 x cdc48-3 YDL020C RPN4 x cdc48-3 YJL044C GYP6 x cdc48-3 YDR505C PSP1 x cdc48-3 YDL160C DHH1 x cdc48-3 YIL063C YRB2 x cdc48-9 YDL126C CDC48 x cdc48-9 YER007C-A TMA20 x cdc48-9 YBL072C RPS8A x cdc48-9 YBL092W RPL32 x cdc48-9 YDR266C YDR266C x cdc48-9 YPR104C FHL1 x cdc48-9 YDL122W UBP1 x cdc48-9 YMR067C UBX4 x cdc48-9 YDL020C RPN4 x cdc48-9 YDR505C PSP1 x cdc48-9 YMR184W ADD37 x cdc48-9 YDR014W RAD61 x cep3-1 YMR168C CEP3 x dsn1-7 YIR010W DSN1 x dsn1-7 YCR065W HCM1 x dsn1-7 YER088C DOT6 x dsn1-7 YBL054W TOD6 x dsn1-7 YER112W LSM4 x dsn1-7 YKL078W DHR2 x dsn1-7 YDR339C FCF1 x dsn1-7 YJR008W YJR008W x dsn1-7 YLR025W SNF7 x dsn1-7 YBR165W UBS1 x dsn1-7 YER026C CHO1 x dsn1-7 YOR215C AIM41 x dsn1-7 YNL255C GIS2 x dsn1-7 YJR044C VPS55 x dsn1-7 YNR048W YNR048W x
116
Appendix 6.1 Continued
ts allele ORF Gene Detected in both?
Detected in microarray
only?
Detected in Bar-
seq only?
dsn1-7 YDR177W UBC1 x dsn1-7 YAL056W GPB2 x dsn1-7 YDR266C YDR266C x dsn1-8 YIR010W DSN1 x dsn1-8 YBR172C SMY2 x dsn1-8 YER026C CHO1 x dsn1-8 YDL160C DHH1 x dsn1-8 YHR171W ATG7 x dsn1-8 YEL027W CUP5 x dsn1-8 YOR371C GPB1 x dsn1-8 YIL063C YRB2 x dsn1-8 YGL093W SPC105 x ipl1-1 YPL209C IPL1 x ipl1-1 YAL031C GIP4 x ipl1-1 YKL193C SDS22 x
med4-6 YOR174W MED4 x med4-6 YPR070W MED1 x nse3-ts4 YDR288W NSE3 x nse3-ts4 YLR007W NSE1 x nse5-ts2 YML023C NSE5 x nsl1-5 YPL233W NSL1 x nsl1-5 YIR010W DSN1 x nsl1-5 YCR065W HCM1 x nsl1-5 YDR339C FCF1 x nsl1-5 YMR309C NIP1 x nsl1-5 YDL122W UBP1 x nsl1-5 YBR212W NGR1 x nsl1-5 YPL171C OYE3 x orc2-3 YBR060C ORC2 x orc2-3 YLL004W ORC3 x orc2-3 YPL184C MRN1 x orc2-3 YBR082C UBC4 x orc2-3 YDL126C CDC48 x orc3-70 YLL004W ORC3 x orc3-70 YBR060C ORC2 x orc3-70 YPL241C CIN2 x
117
Appendix 6.1 Continued
ts allele ORF Gene Detected in both?
Detected in microarray
only?
Detected in Bar-
seq only?
orc3-70 YOR295W UAF30 x pol12-ts YBL035C POL12 x pol12-ts YEL034W HYP2 x pol12-ts YBL051C PIN4 x pol12-ts YDR505C PSP1 x pol12-ts YEL035C UTR5 x pol12-ts YNL245C CWC25 x pol12-ts YNL007C SIS1 x prp4-1 YPR178W PRP4 b prp4-1 YPR104C FHL1 x prp4-1 YIL063C YRB2 x prp6-1 YBR055C PRP6 x
rfa3-313 YJL173C RFA3 x rfa3-313 YNL312W RFA2 x rpn11-14 YFR004W RPN11 x
scc4-4 YER147C SCC4 x scc4-4 YDR180W SCC2 x sec14-3 YMR079W SEC14 x sec14-3 YLR380W CSR1 x sec14-3 YNL264C PDR17 x sec14-3 YHL033C RPL8A x sec14-3 YCR065W HCM1 x sec14-3 YPL184C MRN1 x sec14-3 YBL011W SCT1 x sec14-3 YOR113W AZF1 x sec14-3 YOR327C SNC2 x sec14-3 YGL012W ERG4 x sec14-3 YKL047W ANR2 x sec14-3 YJR075W HOC1 x sec14-3 YPL128C TBF1 x sec14-3 YGR284C ERV29 x sec14-3 YFL038C YPT1 x sec14-3 YML027W YOX1 x sec14-3 YML115C VAN1 x sec14-3 YKR067W GPT2 x sec14-3 YGL083W SCY1 x
118
Appendix 6.1 Continued
ts allele ORF Gene Detected in both?
Detected in microarray
only?
Detected in Bar-
seq only?
sec14-3 YKR001C VPS1 x sec17-1 YBL050W SEC17 x sec17-1 YBR080C SEC18 x sec17-1 YBL011W SCT1 x sec18-1 YBR080C SEC18 x
sec19-1 a YER136W SEC19 x sec19-1 a YPL106C SSE1 x
sec26-11D26 YDR238C SEC26 x sec26-11D26 YER122C GLO3 x sec26-11D26 YLR268W SEC22 x sec26-11D26 YIL004C BET1 x sec26-11D26 YDR266C YDR266C x sec26-11D26 YGL055W OLE1 x sec26-11D26 YOR071C NRT1 x sec26-11D26 YIR022W SEC11 x
smc2-8 a YFR031C SMC2 x smc2-8 a YDR116C MRPL1 x smc2-8 a YGR251W YGR251W x smc3-42 a YJL074C SMC3 x stu1-5 a YBL034C STU1 x stu1-5 a YLR354C TAL1 x stu1-5 a YDR505C PSP1 x stu1-5 a YDL182W LYS20 x stu1-5 a YDL014W NOP1 x stu2-11 YLR045C STU2 x taf12-9 YDR145W TAF12 x taf12-9 YMR005W TAF4 x
taf12-W486stop YDR145W TAF12 x taf12-W486stop YMR005W TAF4 x
taf8-ts7 YML114C TAF8 x taf8-ts7 YDR167W TAF10 x taf9-ts2 YMR236W TAF9 x taf9-ts2 YMR005W TAF4 x taf9-ts2 YMR237W BCH1 x taf9-ts2 YMR235C RNA1 x tcp1-1 YDR212W TCP1 b
119
Appendix 6.1 Continued
ts allele ORF Gene Detected in both?
Detected in microarray
only?
Detected in Bar-
seq only?
tcp1-1 YMR211W DML1 x tel2-7 YGR099W TEL2 x
a Sample not sent for Bar-seq analysis. b Wild-type CEN clone used in confirmation spot dilutions.
120
Appendix 6.2 Unique dosage suppression interactions identified in this study.
Query ORF Gene
Dosage Suppressor
ORF Gene Cat. a Known
PPI? b SGA Score c Shared
GO Term? d
YAL041W CDC24 YGR152C RSR1 6 Yes -0.2198 Yes YAL041W CDC24 YLR229C CDC42 6 Yes N/A Yes YBL034C STU1 YDL014W NOP1 4 No N/A No YBL034C STU1 YDL182W LYS20 5 No -0.08 < SGA score < 0.08 No YBL034C STU1 YDR505C PSP1 5 No -0.08 < SGA score < 0.08 No YBL034C STU1 YLR354C TAL1 5 No -0.08 < SGA score < 0.08 No YBL035C POL12 YBL051C PIN4 5 No N/A No YBL035C POL12 YDR505C PSP1 5 No -0.08 < SGA score < 0.08 No YBL035C POL12 YEL034W HYP2 1 No N/A Yes YBL035C POL12 YEL035C UTR5 5 No N/A No YBL035C POL12 YNL007C SIS1 1 No N/A Yes YBL035C POL12 YNL245C CWC25 1 No N/A Yes YBL050W SEC17 YBL011W SCT1 5 No N/A No YBL050W SEC17 YBR080C SEC18 2 Yes N/A Yes YBR060C ORC2 YBR082C UBC4 5 No N/A No YBR060C ORC2 YDL126C CDC48 5 No N/A No YBR060C ORC2 YLL004W ORC3 2 Yes N/A Yes YBR060C ORC2 YPL184C MRN1 4 No -0.08 < SGA score < 0.08 No YBR160W CDC28 YER167W BCK2 1 No -0.0859 Yes YBR160W CDC28 YMR199W CLN1 6 Yes -0.08 < SGA score < 0.08 Yes YBR160W CDC28 YPL256C CLN2 6 Yes -0.08 < SGA score < 0.08 Yes YBR211C AME1 YGR179C OKP1 6 Yes N/A Yes YDL008W APC11 YGL050W TYW3 4 No -0.08 < SGA score < 0.08 No YDL008W APC11 YLR079W SIC1 1 No -0.08 < SGA score < 0.08 Yes YDL008W APC11 YLR127C APC2 2 Yes N/A Yes YDL126C CDC48 YBL072C RPS8A 4 No -0.08 < SGA score < 0.08 No YDL126C CDC48 YBL092W RPL32 4 No N/A No YDL126C CDC48 YBR172C SMY2 1 No -0.08 < SGA score < 0.08 Yes YDL126C CDC48 YDL014W NOP1 4 No N/A No YDL126C CDC48 YDL020C RPN4 1 No -0.1871,-0.1950 Yes YDL126C CDC48 YDL122W UBP1 1 No N/A Yes YDL126C CDC48 YDL160C DHH1 4 No N/A No YDL126C CDC48 YDR014W RAD61 1 No -0.08 < SGA score < 0.08 Yes YDL126C CDC48 YDR177W UBC1 1 No N/A Yes YDL126C CDC48 YDR266C YDR266C 4 No -0.08 < SGA score < 0.08 No YDL126C CDC48 YDR505C PSP1 5 No -0.08 < SGA score < 0.08 No YDL126C CDC48 YDR512C EMI1 1 No -0.08 < SGA score < 0.08 Yes YDL126C CDC48 YER007C-A TMA20 4 No N/A No
121
Appendix 6.2 Continued
Query ORF Gene
Dosage Suppressor
ORF Gene Cat. a Known
PPI? b SGA Score c Shared
GO Term? d
YDL126C CDC48 YER165W PAB1 4 No N/A No YDL126C CDC48 YIL063C YRB2 1 No N/A Yes YDL126C CDC48 YJL044C GYP6 1 No -0.08 < SGA score < 0.08 Yes YDL126C CDC48 YJL151C SNA3 5 No -0.08 < SGA score < 0.08 No YDL126C CDC48 YJR022W LSM8 4 No N/A No YDL126C CDC48 YKL180W RPL17A 4 No N/A No YDL126C CDC48 YLR354C TAL1 5 No -0.08 < SGA score < 0.08 No YDL126C CDC48 YMR067C UBX4 2 Yes -0.2860,-0.5502 Yes YDL126C CDC48 YMR184W ADD37 1 No N/A Yes YDL126C CDC48 YOR310C NOP58 4 No N/A No YDL126C CDC48 YPR104C FHL1 5 No N/A No YDL165W CDC36 YER068W MOT2 6 Yes N/A Yes YDR145W TAF12 YMR005W TAF4 2 Yes N/A Yes YDR212W TCP1 YMR211W DML1 5 No N/A No YDR238C SEC26 YDR266C YDR266C 4 No N/A No YDR238C SEC26 YER122C GLO3 6 Yes N/A Yes YDR238C SEC26 YGL055W OLE1 5 No N/A No YDR238C SEC26 YIL004C BET1 1 No N/A Yes YDR238C SEC26 YIR022W SEC11 1 No N/A Yes YDR238C SEC26 YLR268W SEC22 2 Yes N/A Yes YDR238C SEC26 YOR071C NRT1 5 No N/A No YDR288W NSE3 YLR007W NSE1 2 Yes N/A Yes YER136W GDI1 YPL106C SSE1 3 No N/A No YER147C SCC4 YDR180W SCC2 2 Yes N/A Yes YFR028C CDC14 YBR083W TEC1 5 No -0.08 < SGA score < 0.08 No YFR028C CDC14 YDL051W LHP1 4 No -0.08 < SGA score < 0.08 No YFR028C CDC14 YER126C NSA2 4 No N/A No YFR028C CDC14 YGL122C NAB2 4 No N/A No YFR028C CDC14 YGL147C RPL9A 4 No -0.08 < SGA score < 0.08 No YFR028C CDC14 YHL034C SBP1 4 No -0.08 < SGA score < 0.08 No YFR028C CDC14 YLR030W YLR030W 5 No -0.08 < SGA score < 0.08 No YFR028C CDC14 YLR079W SIC1 6 Yes -0.3181 Yes YFR028C CDC14 YMR073C IRC21 5 No -0.08 < SGA score < 0.08 No YFR028C CDC14 YMR144W YMR144W 5 No -0.08 < SGA score < 0.08 No YFR028C CDC14 YOL139C CDC33 1 No N/A Yes YFR028C CDC14 YPL184C MRN1 4 No -0.08 < SGA score < 0.08 No YFR028C CDC14 YPR104C FHL1 5 No N/A No
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Appendix 6.2 Continued
Query ORF Gene
Dosage Suppressor
ORF Gene Cat. a Known
PPI? b SGA Score c Shared
GO Term? d
YFR031C SMC2 YDR116C MRPL1 5 No N/A No YFR031C SMC2 YGR251W YGR251W 4 No N/A No YHR166C CDC23 YLR079W SIC1 1 No -0.08 < SGA score < 0.08 Yes YIR010W DSN1 YAL056W GPB2 5 No -0.08 < SGA score < 0.08 No YIR010W DSN1 YBL054W TOD6 4 No -0.08 < SGA score < 0.08 No YIR010W DSN1 YBR165W UBS1 5 No -0.08 < SGA score < 0.08 No YIR010W DSN1 YBR172C SMY2 5 No -0.08 < SGA score < 0.08 No YIR010W DSN1 YCR065W HCM1 1 No -0.2502,-0.1432 Yes YIR010W DSN1 YDL160C DHH1 4 No N/A No YIR010W DSN1 YDR177W UBC1 5 No N/A No YIR010W DSN1 YDR266C YDR266C 4 No -0.08 < SGA score < 0.08 No YIR010W DSN1 YDR339C FCF1 4 No N/A No YIR010W DSN1 YEL027W CUP5 5 No N/A No YIR010W DSN1 YER026C CHO1 5 No N/A No YIR010W DSN1 YER088C DOT6 4 No -0.08 < SGA score < 0.08 No YIR010W DSN1 YER112W LSM4 4 No N/A No YIR010W DSN1 YGL093W SPC105 2 Yes N/A Yes YIR010W DSN1 YHR171W ATG7 5 No -0.08 < SGA score < 0.08 No YIR010W DSN1 YIL063C YRB2 5 No N/A No YIR010W DSN1 YJR008W YJR008W 5 No N/A No YIR010W DSN1 YJR044C VPS55 5 No -0.08 < SGA score < 0.08 No YIR010W DSN1 YKL078W DHR2 4 No N/A No YIR010W DSN1 YLR025W SNF7 5 No N/A No YIR010W DSN1 YNL255C GIS2 5 No N/A No YIR010W DSN1 YNR048W CRF1 5 No -0.08 < SGA score < 0.08 No YIR010W DSN1 YOR215C AIM41 5 No -0.08 < SGA score < 0.08 No YIR010W DSN1 YOR371C GPB1 5 No -0.08 < SGA score < 0.08 No YJL173C RFA3 YNL312W RFA2 2 Yes N/A Yes YJR076C CDC11 YCR002C CDC10 2 Yes N/A Yes YJR076C CDC11 YCR065W HCM1 1 No -0.3444,-0.2485 No YJR076C CDC11 YER007C-A TMA20 4 No N/A No YJR076C CDC11 YHR115C DMA1 1 No N/A Yes YJR076C CDC11 YLL026W HSP104 3 No -0.08 < SGA score < 0.08 No YJR076C CDC11 YLL039C UBI4 1 No -0.08 < SGA score < 0.08 Yes YJR076C CDC11 YLR249W YEF3 4 No N/A No YJR076C CDC11 YLR314C CDC3 2 Yes N/A Yes YLL004W ORC3 YBR060C ORC2 2 Yes N/A Yes YLL004W ORC3 YOR295W UAF30 1 No N/A Yes
123
Appendix 6.2 Continued
Query ORF Gene
Dosage Suppressor
ORF Gene Cat. a Known
PPI? b SGA Score c Shared
GO Term? d
YLL004W ORC3 YPL241C CIN2 5 No -0.08 < SGA score < 0.08 No YML114C TAF8 YDR167W TAF10 2 Yes N/A Yes YMR079W SEC14 YBL011W SCT1 1 No N/A Yes YMR079W SEC14 YCR065W HCM1 1 No N/A Yes YMR079W SEC14 YFL038C YPT1 1 No N/A Yes YMR079W SEC14 YGL012W ERG4 1 No N/A Yes YMR079W SEC14 YGL083W SCY1 5 No N/A No YMR079W SEC14 YGR284C ERV29 1 No N/A Yes YMR079W SEC14 YHL033C RPL8A 4 No N/A No YMR079W SEC14 YJR075W HOC1 5 No N/A No YMR079W SEC14 YKL047W ANR2 5 No N/A No YMR079W SEC14 YKR001C VPS1 1 No N/A Yes YMR079W SEC14 YKR067W GPT2 1 No N/A Yes YMR079W SEC14 YLR380W CSR1 6 No N/A Yes YMR079W SEC14 YML027W YOX1 1 No N/A Yes YMR079W SEC14 YML115C VAN1 1 No N/A Yes YMR079W SEC14 YNL264C PDR17 6 No N/A Yes YMR079W SEC14 YOR113W AZF1 1 No N/A Yes YMR079W SEC14 YOR327C SNC2 6 No N/A Yes YMR079W SEC14 YPL128C TBF1 1 No N/A Yes YMR079W SEC14 YPL184C MRN1 4 No N/A No YMR236W TAF9 YMR005W TAF4 2 Yes N/A Yes YNR035C ARC35 YBR234C ARC40 2 Yes N/A Yes YNR035C ARC35 YPR104C FHL1 5 No N/A No YOR174W MED4 YPR070W MED1 2 Yes N/A Yes YPL209C IPL1 YAL031C GIP4 6 No N/A Yes YPL209C IPL1 YKL193C SDS22 6 No N/A Yes YPL233W NSL1 YBR212W NGR1 4 No -0.08 < SGA score < 0.08 No YPL233W NSL1 YCR065W HCM1 1 No -0.2881 Yes YPL233W NSL1 YDL122W UBP1 5 No -0.08 < SGA score < 0.08 No YPL233W NSL1 YDR339C FCF1 4 No N/A No YPL233W NSL1 YIR010W DSN1 2 Yes N/A Yes YPL233W NSL1 YMR309C NIP1 4 No N/A No YPL233W NSL1 YPL171C OYE3 5 No N/A No YPR025C CCL1 YDL108W KIN28 2 Yes N/A Yes YPR178W PRP4 YIL063C YRB2 1 No N/A Yes YPR178W PRP4 YPR104C FHL1 1 No N/A Yes
124
a Dosage suppression categories (Cat.) are as follows and described in detail in Chapter 3, Section 3.2.6: 1) Functional Relationship; 2) Functional Relationship with Protein-Protein Interaction; 3) Chaperone; 4) RNA Processing/Protein Synthesis; 5) Unknown; 6) Previously Reported b Protein-protein interactions as annotated in the Saccharomyces Genome Database. c SGA score as reported in (Costanzo, Baryshnikova et al. 2010). d Gene pairs were determined if they share a GO term found in the gold standard set of terms (Myers, Barrett et al. 2006) as annotated in the Saccharomyces Genome Database.