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Systematic Mapping of
Genetic Interaction Networks
Scott J. Dixon,1,2,∗ Michael Costanzo,1,∗
Anastasia Baryshnikova,1 Brenda Andrews,1
and Charles Boone1
1
Banting and Best Department of Medical Research, Terrence Donnelly Center for Cellular
and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 1A7, Canada;
email: [email protected]
2
Department of Biological Sciences, Columbia University, New York, New York 10027
Annu. Rev. Genet. 2009. 43:601–625
Key Words
First published online as a Review in Advance on
August 27, 2009
genetic interaction, network, synthetic lethal, Saccharomyces cerevisiae,
epistasis
The Annual Review of Genetics is online at
genet.annualreviews.org
This article’s doi:
10.1146/annurev.genet.39.073003.114751
c 2009 by Annual Reviews.
Copyright !
All rights reserved
0066-4197/09/1201-0601$20.00
∗
These authors contributed equally to this work.
Abstract
Genetic interactions influencing a phenotype of interest can be identified systematically using libraries of genetic tools that perturb biological
systems in a defined manner. Systematic screens conducted in the yeast
Saccharomyces cerevisiae have identified thousands of genetic interactions
and provided insight into the global structure of biological networks.
Techniques enabling systematic genetic interaction mapping have been
extended to other single-celled organisms, the bacteria Escherichia coli
and the yeast Schizosaccharomyces pombe, opening the way to comparative
investigations of interaction networks. Genetic interaction screens in
Caenorhabditis elegans, Drosophila melanogaster, and mammalian models
are helping to improve our understanding of metazoan-specific signaling pathways. Together, our emerging knowledge of the genetic wiring
diagrams of eukaryotic and prokaryotic cells is providing a new understanding of the relationship between genotype and phenotype.
601
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INTRODUCTION
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A genetic interaction refers to an unexpected
phenotype not easily explained by combining
the effects of individual genetic variants (7).
Genetic interactions are thought to underlie diverse biological phenomena such as the evolution of sex, speciation, and complex disease (1,
11, 67, 121). At the level of an individual organism, understanding how genes interact with
one another to produce a given phenotype is a
challenge of immense significance to basic biologists and clinicians alike. Despite recent advances (16, 17), mapping genetic interactions
within individuals from outbred populations
remains a difficult task. Researchers have therefore embraced inbred model systems, such as
yeast and worm, as well as isogenic populations
of cultured cells derived from fruit flies and
mammals, as platforms to map genetic interactions in a systematic, unbiased, and comprehensive fashion (13, 53). Compared with classic
forward genetic modifier screens, which typically focus on the identification of a small number of second-site mutations, systematic reverse
genetic approaches attempt to consider the
phenotypic effects of thousands of perturbations in parallel.
Systematic genetic interaction mapping
studies require two enablers: first, large collections of hundreds or thousands of defined
mutant alleles or other genetic reagents (e.g.,
libraries of double-stranded RNAs) that can be
used to test the phenotypic consequences of
combined perturbations; and second, a phenotype that can be scored easily and in parallel
for a large number of samples, such as growth
rate (a measure of biological fitness) (22, 129).
Computerized systems enabling accurate quantification of growth phenotypes are available for
yeast, bacteria, and mammalian cell culture systems (18, 26, 34, 54, 115, 128, 139). In a typical
systematic experiment, a panel of single mutant queries is screened against a set (or array)
of second site mutants or RNAis (Figure 1).
Depending on the experimental goals and the
libraries available, screens can either encompass
most genes in the genome (128) or focus on a
602
Dixon et al.
defined subset of genes (25, 138). Genetic interactions are identified by comparing the phenotype of the individual single mutants to that
of the combined mutant (Figure 1). By screening multiple queries against the same array, it
is possible to build up an extensive network
of genetic interactions. The genetic interaction
profiles associated with each query can be clustered together on the basis of similarity to identify genes that are functionally related, predict
biochemical pathways and protein complexes,
assign function to uncharacterized genes, and
study the large-scale structure of biological networks (65, 128, 129) (Figures 1 and 4). To
date, the systematic identification and analysis
of thousands of genetic interactions in diverse
organisms has provided substantial insight into
the genetic wiring diagram of the cell.
Chemical perturbagens (e.g., small
molecules, natural products) have been
successfully combined with genetic tools
to map chemical-genetic interactions on a
genome-wide scale (45, 46, 98, 99). These
interactions can be used to understand compound mechanisms of action, perform target
identification, and probe the robustness of
cellular networks, among other applications
(45, 46, 56, 58, 78, 98, 99). However, as this
is a vast field in its own right, in this review
we restrict ourselves to considering progress
specifically relevant to the systematic mapping
of genetic interactions.
Identifying Genetic Interactions
in a Systematic Manner
Genetic interactions are identified by detecting
double mutants whose phenotype deviates from
the expected value. Determining the expected
mutant phenotype remains, to a certain extent,
a matter of controversy (83). With respect to
fitness phenotypes, a widely adopted model assumes that the effects of mutations in independent genes combine in a multiplicative manner
(18, 38, 40, 65, 83, 114, 119). Thus, the expected
double mutant phenotype should be equivalent
to the product of the two individual mutations.
A genetic interaction is consequently measured
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Step 1:
Step 2:
Generate double mutant
Score phenotype and identify interactions
Saccharomyces cerevisiae
Mating
1
Report
X
3
Query strain
2
Deletion library
Caenorhabditis elegans
4
Measure colony size
1
2
Report
3
Query strain
Bacterial RNAi library
Mammalian cell culture
Query cell line
4
1
3
Viral shRNA library
98 pixels
99 pixels
95 pixels
17 pixels
Well 1:
Well 2:
Well 3:
Well 4:
30 worms
28 worms
29 worms
3 worms
Well 1:
Well 2:
Well 3:
Well 4:
998 a.u.
956 a.u.
972 a.u.
211 a.u.
Count viable worms
2
Report
Infection
4
Measure viability
using a dye
Step 3:
Build genetic interaction networks
Array 1
Array 2
Array 3
Array 4
Array 5
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Feeding
Colony 1:
Colony 2:
Colony 3:
Colony 4:
Query 1
Query 2
Query 3
Query 4
Query 5
Common biological
process
2
3
Explore function
of gene cluster
4
No interaction
Interaction
Figure 1
An outline of systematic screening strategies in three prominent model systems, Saccharomyces cerevisiae,
Caenorhabditis elegans, and mammalian cell culture. All systematic screens follow a similar pattern involving
the generation of double mutants (Step 1), the scoring of a double mutant phenotype, which must be
compared with the corresponding single mutant phenotypes (Step 2) and the construction and interpretation
of the resulting genetic interaction matrix (Step 3). In Step 1, the black line with a red x indicates a mutated
gene in a chromosome. In Step 2, a.u. stands for arbitrary units.
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Negative genetic interactions
Fitness
Wild-type
1
0.7
Single mutant A
b
0.5
Expected
0.7 x 0.5 = 0.35
No
interaction
Negative
interactions
Synthetic sick
0.2
Synthetic lethal
0
c
Positive genetic interactions (symmetric)
Positive genetic interactions (asymmetric)
Fitness
Fitness
Wild-type
1
Wild-type
1
Single mutant A
0.5
Single mutant A
Single mutant B
0.5
Single mutant B
Expected
0.5 x 0.5 = 0.25
No
interaction
Double mutant AB
Double mutant AB
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Double mutant AB
Single mutant B
Symmetric positive
0.5
Positive
interaction
a
A
B
B
C
Protein
complex
C
Fitness = 1 Fitness = 0.5
b
0.5
Expected
0.7 x 0.5 = 0.35
Masking
0.5
Positive
interactions
Suppression
0.7
a
A
Protein
complex
No
interaction
0.7
C
Protein
complex
b
C
Protein
complex
Fitness = 0.5 Fitness = 0.5
Figure 2
A graphical representation of how genetic interactions are inferred from a measurable phenotype, in this case growth. (a) Negative
genetic interactions. Wild-type fitness is defined as 1.0. The fitness of two single mutants is 0.7 (single mutant A) and 0.5 (single mutant
B), respectively. The expected fitness of the AB double mutant based on a multiplicative model would therefore be 0.35. Negative
deviations from the value expected from the multiplicative model are scored as either synthetic sick or synthetic lethal. (b) Symmetric
positive interactions. In this case, each single mutant (A and B) exhibits a twofold fitness defect (0.5) relative to wild type (1.0). The
fitness of the resultant AB double mutant is greater than expected (0.25) and identical to the fitness of the two single mutants (0.5).
Symmetric interactions of this kind are enriched among members of the same nonessential protein complex. (c) Asymmetric positive
interactions. In this scenario, single mutants and double mutants differ in fitness. Positive deviations from expectation along with single
mutant fitness comparisons allow classification of asymmetric positive interactions into masking or suppression subcategories.
as the extent to which a double mutant deviates
from the multiplicative expectation (Figure 2).
Using this formulation, two broad classes of
genetic interactions can be recognized: nega604
Dixon et al.
tive (double mutants whose fitness is worse than
expected) and positive (double mutants whose
fitness is better than expected). Whether a multiplicative model can be used to detect genetic
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interactions when using phenotypes other than
those that are strictly fitness-related remains
largely unexplored, although a recent study suggests it may be possible (65).
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Negative Interactions
Negative interactions (also called aggravating
or synergistic interactions) describe double mutants exhibiting a more severe phenotype than
expected, such as synthetic sickness or synthetic
lethality (35, 49) (Figure 2a). A common interpretation of negative phenotypes, such as synthetic lethality, is that they reflect the function
of two genes operating in parallel biological
pathways, so that removal of either gene alone
is compatible with normal viability, whereas removal of both impairs viability (49) (Figure 3a).
For example, many genes operating in the DNA
damage response network demonstrate synthetic lethality with one another (39, 50, 96),
possibly reflecting the evolutionary importance
of having compensatory systems to maintain
the integrity of the heredity material. Alternatively, two genes functioning in the same
essential pathway or complex may share a synthetic lethal interaction if each mutation contributes to decreased flux through the pathway
(13) (Figure 3a).
Positive Interactions
Positive interactions describe double mutants
exhibiting a less severe phenotype than expected from the multiplicative model. Positive
interactions have also been referred to as alleviating or epistatic interactions. Because the term
epistasis has numerous definitions and meanings (reviewed in Reference 100), we will simply refer to genetic interactions that have an
unexpectedly healthy fitness as positive interactions. Positive interactions are typically subtle and, so far, have only been detected through
careful quantification of relative mutant growth
rates in single-celled organisms (18, 119).
Nevertheless, if measured accurately, positive interactions can be subclassified into categories associated with different biological
interpretations (Figure 2b,c). For example,
members of the same nonessential protein complex commonly share a specific type of positive
interaction, whereby the phenotype associated
with two single mutants and the resultant double mutant are quantitatively indistinguishable
(symmetric) (Figure 2b). This result is explained by the fact that once the function of
a complex is disrupted by the removal of one
component, the phenotype cannot be made
worse by the removal of additional components
(25, 36, 119) (Figure 2b).
Other positive interaction subclasses consist of asymmetric interactions in which the
strength of the phenotypic effect varies between
single and double mutants. For example, double
mutants are said to exhibit masking interactions
when growth is better than the expected double
mutant fitness and resembles the fitness of the
sickest single mutant (i.e., a mutant phenotype
is masked by a second, more severe mutation;
Figure 2c). Conversely, a double mutant with
increased fitness relative to the sickest single
mutant exhibits genetic suppression (Figure 2c)
(18, 36, 114, 119). It is important to note that
the spectrum of positive interactions subclasses
extends beyond the three general categories described above (36).
Positive interactions are interesting because
it is proposed that they can provide insight into
biochemical relationships between gene products and help define the architecture of biological pathways (3, 18, 107, 114, 119). Most of the
existing systematic studies examining positive
interactions have used loss-of-function (LOF)
alleles (112, 119). It is possible that in some
cases LOF mutations alone will be insufficient
to fully order individual pathways, such as when
the loss of an individual gene does not result
in an observable fitness defect. One solution to
this problem may be to exploit gain-of-function
(GOF) mutant libraries. Classic studies of
genetic suppression involving GOF mutations
indicate that these types of interactions can
occur between genes functioning in the same or
compensatory biochemical complexes or pathways (2, 61, 101, 103, 109, 124) (Figure 3b,c).
Thus, future studies using GOF libraries, such
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Negative interactions
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Between pathway genetic interactions (nonessential pathways)
Within pathway genetic interactions (essential pathways)
A
X
A
X
A
x*
A
x*
A
a*
A
a*
B
Y
b*
Y
B
Y
b*
Y
B
B
b*
b*
C
Z
C
Z
C
Z
C
Z
C
C
C
C
Essential
function
Essential
function
Essential
function
Essential
function
Essential
function
Essential
function
Essential
function
Essential
function
Wild-type
Viable
Viable
Lethal
Wild-type
Viable
Viable
Lethal
b
Positive interactions/genetic suppression
Loss-of-function suppression
Gain-of-function suppression
A
a*
a*
A
A
A
B
B
b*
B
b*
b*
C
C
C
C
C
C*
Function
Toxic
Function
Function
Function
Function
Wild-type
Lethal
Viable
Wild-type
Lethal
Viable
c
Gene dosage
Dosage lethality
A
a*
B
B
Dosage suppression
a*
A
B B
B
B
a*
B
Complex haploinsufficiency
A
B
A*
B
B B
B
A
B
A
B*
a*
Function
Function
Function
Function
Function
Function
A
B
Wild-type
Viable
Lethal
Wild-type
Lethal
Viable
A*
B*
606
Dixon et al.
Function
Viable
Function
Viable
Function
Lethal
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as gene overexpression libraries (44, 62), should
provide a complementary means to define
protein complex membership and refine our
understanding of biochemical pathways (36).
How positive interaction mapping might be
applied to growth phenotypes in multicellular
organisms or in cell culture settings remains
unexplored.
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SYSTEMATIC MAPPING OF
GENETIC NETWORKS WITH
SACCHAROMYCES CEREVISIAE
Systematic Yeast Mutant Collection
The vast majority of large-scale genetic interaction screens completed to date have used the
budding yeast Saccharomyces cerevisiae. The crucial enabling tool for these studies is the library
of S. cerevisiae strains, in which each known
or suspected open reading frame is deleted
and replaced with the dominant drug-resistance
marker, kanMX (45, 137). This deletion collection contains ∼1000 essential genes that are
maintained as heterozygous diploids and ∼4800
strains that tolerate gene deletion and are viable
as haploids or homozygous diploids under regular growth conditions. Additional libraries have
subsequently been developed in which each of
the ∼1000 essential genes are altered in such a
way as to produce either conditional alleles (9,
27) or hypomorphic alleles that are compatible
with viability (18), allowing this important set
of genes to be screened for genetic interactions
as well (9, 27, 128).
Mapping Negative (Synthetic Lethal)
Genetic Networks
One use of the haploid deletion collection is
to map negative (synthetic lethal) genetic interactions between pairs of viable haploid deletion strains. Systematic mapping of synthetic
lethal genetic interactions was first facilitated by
development of an automated approach called
synthetic genetic array (SGA) analysis (128,
129). SGA methodology enables large-scale
mating and meiotic recombination via a series
of replica pinning procedures of high-density
arrays of yeast colonies on a solid agar surface, ultimately resulting in the isolation of haploid double mutants. Application of SGA led
to construction of the first large-scale genetic
interaction map of a cell (128). This network
was based on 132 genome-wide SGA screens
(i.e., 132 queries × ∼4800 array strains) and
consisted of ∼4000 genetic interactions among
∼1000 genes (128). Within this network, novel
buffering interactions were identified between
functionally diverse pathways. For example,
genes in the sister chromatid cohesion complex were synthetic lethal with genes in the
MAD/BUB spindle checkpoint pathway, the
RAD51 DNA repair pathway, the RAD9 DNA
damage checkpoint, and the MRC1 DNA replication checkpoint (128). This network also successfully predicted a novel role for CSM3 in the
Mrc1-Tof1 checkpoint and for the uncharacterized gene YMR299c in the dynein-dynactin
spindle orientation pathway (128). More recently, SGA mapping of synthetic lethal interactions helped reveal an unexpected role for the
←−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−
Figure 3
A representation of the molecular mechanisms underlying different classes of genetic interactions. (a) Negative interactions can arise
from the disruption of parallel pathways converging on a common process (between pathway genetic interactions) or by decreasing the
flux through the same essential pathway (within pathway genetic interactions). (b) Positive interactions/genetic suppression. Mutation
of a negative regulator (a∗ ) leads to hyperactivation of the pathway and accumulation of a toxic gene product (C). Subsequent
loss-of-function mutation of a downstream pathway component (b∗ ) reduces flux through the pathway, thereby suppressing the toxic
effects caused by mutant a∗ . Gain-of-function suppression may arise when a downstream or terminal pathway component acquires a
mutation (C∗ ) such that it is no longer dependent on upstream activation events. (c) Gene dosage. Increasing the dosage of a gene (B)
can be lethal (dosage lethality) in the presence of a mutation in another gene (a∗ ) when A negatively regulates the activity of B. The
lethal effects of a mutated essential gene (A) can be suppressed by overexpression of a downstream pathway component (dosage
suppression). Heterozygous mutation of two independent loci can result in complex haploinsufficient phenotypes.
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gene URM1, which encodes an evolutionarily
conserved ubiquitin-like protein, in tRNA processing (74). The SGA platform has also been
used to investigate essential genes. An extensive
set of promoter shut-off strains, in which each
essential gene is placed under the control of a
repressible promoter (TET-alleles), has been
constructed and combined with SGA to produce a genetic interaction network comprising
567 essential gene interactions (27, 88).
Each deletion mutant in the collection
is marked by unique DNA sequences—
molecular barcodes—that flank the kanMX
gene-replacement cassette (45, 46, 102). The
dSLAM (diploid synthetic lethality analysis
with microarrays) approach exploits this feature of the deletion collection to map synthetic
lethal interactions by measuring the relative
abundance of double mutants in a mixed population (96, 97). Briefly, a marked query mutation is introduced into a pooled set of heterozygote deletion strains by mass transformation.
Similar to the SGA-based method, the heterozygote mutants used in dSLAM contain an
SGA reporter that allows large-scale selection
of haploid double mutants. Following meiosis,
sporulation, and haploid double mutant selection, barcode microarray hybridization intensities are used to measure the relative abundance
of each barcode-tagged double mutant present
in a pooled population, thereby identifying potential synthetic lethal or sick genetic interactions. dSLAM has been used to define networks
involved in DNA integrity and histone modification (77, 96).
More recently, another system called genetic
interaction mapping (GIM) was developed that
combines properties of both SGA and dSLAM
methodologies. Similar to SGA, double mutants are generated by mating and sporulation
using haploid-specific reporters. However, in a
manner reminiscent of dSLAM, all GIM steps
are performed in a pooled format and relative
fitness is assessed by comparing microarray hybridization intensities between double mutants
and a reference population (29). A pilot experiment consisting of 41 genome-wide GIM
screens led to the identification of novel genetic
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Dixon et al.
interactions relevant to the process of mRNA
decapping (29).
Size and Global Connectivity of the
Saccharomyces cerevisiae Synthetic
Lethal Genetic Interaction Network
True genome-wide analyses of genetic interactions, in which all possible combinations of alleles are screened against one another, do not yet
exist for any organism. The closest approximation is in S. cerevisiae, in which large numbers of
query genes have been screened against the set
of ∼4800 viable haploid strains. Thus, the majority of our insights into the global properties
of genetic interaction networks comes from this
one organism. Initial analysis of the largest genetic interaction network available to date suggested that the complete network may contain
on the order of ∼100,000 interactions (128).
However, it appears that the inclusion of all essential genes will have a significant impact on
network size and complexity. For example, although the essential gene network described by
Davierwala and colleagues (27) shares a similar
topology to the nonessential network (128), it
is significantly more dense than the nonessential network. In fact, essential genes exhibit, on
average, five times more genetic interactions
than nonessential genes (27). Thus, the estimated size of the complete genetic network for
yeast is likely to double when essential gene
interactions are considered (27), and essential
genes are likely to act as hubs in genetic interaction networks (104). Importantly, network
size estimates are likely to change with continued genome-wide investigations and development of more sensitive methods for detecting
genetic interactions.
Although incomplete, existing yeast networks have provided significant insight into the
general principles of network connectivity. For
example, genes with related biological functions are connected by synthetic genetic interactions more often than expected by chance
(53, 55, 128) (Figure 4a,b). Furthermore, synthetic lethal interactions among nonessential
genes generally do not overlap with physical
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interactions between the corresponding gene
products; that is, synthetic lethal (negative) genetic interactions are more frequent between
genes lying in different pathways, whereas
physical interactions are more frequent among
gene products functioning within the same
pathway (4, 25, 68, 128, 140) (Figure 4c).
However, when a pathway or complex contains
at least one essential gene, it is often observed
to be enriched for so-called within-pathway
synthetic lethal interactions, which means that
a subset of the negative genetic interactions
for essential genes overlap with protein-protein
interactions (6, 13) (Figure 4d ).
Mapping Biochemical Pathways
and Complexes from Genetic
Interaction Networks
The synthetic lethal or negative genetic interaction profile for a particular query gene provides
a rich phenotypic signature reflecting the function of the query gene because it is made up of
all the other genes encoding components of the
various pathways that buffer the query. Clustering of negative genetic interaction profiles can
be used to infer the composition of biochemical
complexes based on shared patterns of interactions between components (128, 140). The
clustering of negative genetic interaction profiles can be enriched with the inclusion of positive genetic interactions (Figure 4c,d). Positive genetic interactions between members of
the same biochemical complex can be identified using an SGA-based approach in combination with a compatible quantitative scoring system (113) or liquid growth profiling techniques
(119). These methods have been used to examine the structure of the early secretory pathway,
chromatin modifying complexes, the homologous recombination pathway and the 26S proteasome (18, 25, 26, 112, 119).
A recent study provides a good illustration
of how new techniques and mutant collections
are being used to define positive genetic interaction with unprecedented detail in S. cerevisiae. Breslow et al. (18) generated a library of
835 hypomorphic strains for essential genes by
inserting a kanMX tag directly upstream of the
3& UTR. This modification destabilizes the target mRNA, resulting in a significant decrease
in mRNA expression while still being compatible with viability. This group also developed a novel assay to quantify yeast growth
in which a hypomorphic strain incorporating a
GFP marker is grown together with a wild-type
strain containing an integrated RFP cassette.
The ratio of GFP to RFP, measured by flow
cytometry of ∼30,000 cells at multiple time
points, is used to quantify the relative growth
rate of each strain. Using this sensitive growth
assay, capable of detecting single and double
mutants whose growth rate differed by as little as 1% of wild type, it was possible to resolve a broad spectrum of positive interactions,
such as those between the four components of
the conserved oligomeric Golgi (COG) complex, a structure that had been recalcitrant to
previous genetic interaction mapping efforts.
These methods were also used to examine genes
involved in chromatin remodeling and proteasome function, revealing novel genetic interactions for several essential genes. A similar approach was employed to assess the impact of
gene duplication on metabolic networks (30).
Notably, despite the sensitive detection of subtle growth defects, the method described by
Breslow et al. may be difficult to scale to a true
genome-wide scale, and therefore is complementary to array-based SGA platforms.
Dosage Lethality and Dosage
Suppression Genetic Networks
The use of LOF mutations to delineate pathway order is possible (119), but only when
the single and double mutants exhibit significant fitness defects. For a large fraction of the
genome, single mutant fitness defects are negligible; specific environmental (e.g., stress) conditions may be required to produce a growth
defect and therefore enable pathway ordering
when using LOF alleles alone (3). Historically,
dominant GOF mutations have provided an incredibly powerful means for determining gene
position within a biological pathway (see above
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a
Cell polarity
Cell structure
Cell wall
maintenance
Mitosis
DNA synthesis
Chromosome
structure
DNA repair
Unknown
Others
Figure 4
Properties of genetic interactions. (a) Example of a yeast synthetic lethal network. The synthetic lethal network is a sparse network,
indicating that genetic interactions are rare. The frequency of true synthetic lethal interactions is less than 1%. A detailed description of
how this initial network was generated can be found elsewhere (129). (b) Functional neighborhood corresponding to indicated region
(dashed gray circle) in a. Despite being rare, synthetic lethal interactions (blue lines) occur frequently among genes that are functionally
related, such as those involved in DNA replication and repair shown here. The frequency of synthetic lethal interaction between
functionally related genes ranges from 18% to 25%. (c) Orthogonal relationships. Negative interactions tend to occur between
nonessential complexes and pathways. Positive interactions overlap significantly with physical interactions and tend to connect members
of the same pathway or complex. Grouping genes according to patterns of genetic interactions revealed a functional relationship
between the elongator complex and the urmylation pathway. (d ) Increasingly complex patterns of negative and positive interactions will
likely be revealed as we continue to identify genetic interactions and move toward completion of a genetic interaction map. For
example, in some cases, negative (synthetic lethal) interactions will exist between components that also share physical interactions.
and Reference 122) (Figure 3c). Analysis of
dominant GOF mutants and gene overexpression phenotypes provides a unique insight into
gene function because it can lead to hypermorphic effects, often due to misregulation (117).
To systematically explore GOF phenotypes for
all yeast genes, a genome-wide overexpression
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Dixon et al.
library, where each gene is expressed at high
levels from the inducible galactose (GAL1/10)
promoter, has been combined with the SGA
platform to generate combinations of overexpressed genes in specific gene deletion mutant backgrounds (117). This system was first
used to identify synthetic dosage lethal (SDL)
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b
ESC2
RTT107
POL32
WSS1
TOP1
RAD27
RRM3
ASF1
RNR1
SLX1
MGS1
SGS1
PUB1
SLX4
RPL24A
SAE2
SOD1
HPR5
SIS2
SWE1
CSM3
c
ELP4
ELP1
ELP2
UBA4
KTI12
Elongator
complex
ELP5
ELP3
ELP6
ELP5
ELP2
URM1
NCS6
No interaction
ELP1
Positive genetic
interaction
Negative genetic
interaction
KTI12
UBA4
ELP6
Urmylation URM1
pathway
NCS2
NCS2
ELP3
ELP4
Elongator
complex
NCS6
NCS2
UBA4
URM1
ELP1
KTI12
ELP5
ELP6
ELP3
ELP2
ELP4
NCS6
Protein-protein interaction
Negative genetic interaction
Positive genetic interaction
Urmylation
pathway
d
E
A
Protein
complex 1
D
C
F
H
B
Protein
complex 1
G
X
M
N
O
I
J
L
K
Protein-protein interaction
Positive genetic interaction
Negative genetic interaction
Protein
complex 2
Protein
Protein
complex 3 complex 4
Gene A
Gene B
Gene C
Gene D
Gene E
Gene F
Gene X
Gene G
Gene H
Gene I
Gene J
Gene K
Gene L
Gene M
Gene N
Gene O
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YBR094W
RAD50
MUS81
MMS4
Protein
complex 2
Protein
complex 3
Protein
complex 4
Gene A
Gene B
Gene C
Gene D
Gene E
Gene F
Gene X
Gene G
Gene H
Gene I
Gene J
Gene K
Gene L
Gene M
Gene N
Gene O
No interaction
Positive genetic
interaction
Negative genetic
interaction
Uncharacterized
gene
Characterized gene
Uncharacterized gene
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interactions involving the cyclin-dependent kinase Pho85 (117, 118). SDL analysis exploits
the idea that increasing levels of a protein often cause no overt fitness defect in a wild-type
cell but may be deleterious in a mutant strain
with reduced activity of an interacting protein
(71, 86, 87). SDL analysis is particularly effective when a gene product that is normally
subject to negative regulation is overexpressed
in a deletion mutant defective for the negative
regulator, rendering the overexpressed protein
hyperactive (Figure 3c). Identification of bona
fide Pho85 substrates subject to negative regulation by this kinase indicated that large-scale
application of the SDL approach should provide a strategy for identifying molecular targets
of specific signaling pathways. The combination of synthetic lethal and SDL screens has
also proven fruitful in the systematic dissection
of chromosome segregation (86).
Although overexpression libraries that place
genes under conditional expression are particularly useful for SDL analysis, high-copy libraries that enable the overexpression of genes
under the control of their own promoters are
also useful for dosage suppression studies. A
collection consisting of more than 7000 highcopy plasmids tiling the entire yeast genome
with ∼fivefold depth has been generated (62).
In this collection, library genes are expressed in
an untagged form from their endogenous promoter, potentially bypassing toxic effects that
could be due to high-level constitutive expression from a promoter such as the inducible
GAL1/10 promoter. As a proof-of-concept experiment, this library was used to identify genes
resulting in transcriptional defects when overexpressed (62).
The latest development is a library in which
every ORF in the genome is cloned into a lowcopy plasmid and tagged with a unique molecular barcode, the MoBY-ORF library (57). The
MoBY-ORF library can therefore be used in
a pooled format to identify genes that can restore the sensitivity of drug resistant mutants
(57) and, potentially, suppress the lethal phenotype of essential genes (e.g., dosage suppression) (Figure 3c). The identification of
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different classes of genetic interactions through
dosage studies will enrich our understanding
of gene networks, especially for essential genes
and genes lacking a phenotype when deleted.
Complex Haploinsufficiency
Reductions in gene copy number (dosage) can
be exploited to identify genetic interactions.
In diploid yeast, haploinsufficiency arises when
a heterozygous loss-of-function mutation results in a dominant phenotype (132). Approximately 3% of yeast genes are haploinsufficient
and require two functional gene copies for normal growth in rich medium (31). When two
heterozygous mutations, lacking phenotype,
combine to form a hemizygote double mutant exhibiting a synergistic phenotype, such as
synthetic lethality, this is referred to as complex haploinsufficiency (CHI) (51) (Figure 3c).
CHI interactions are related to the genetic concept of unlinked noncomplementation (UNC),
in which mutations in two different genes fail
to complement one another (43, 120, 133). In
a recent genome-wide screen, a collection of
strains harbouring a hemizygous null allele in
the ACT1 actin gene and a hemizygous deletion of a nonessential gene, covering the complete set of nonessential genes, was generated to
look for CHI interactions with ACT1 (51). This
screen isolated 208 CHI interactions including,
as anticipated, many genes required for remodeling of the actin cytoskeleton, as well as a number of uncharacterized genes (51). Thus, CHI
studies provide a unique window into genetic
interactions that cannot be obtained with other
approaches and may be of special relevance to
human diseases characterized by complex interactions between multiple disease alleles present
in a heterozygous state (51).
SYSTEMATIC GENETIC
INTERACTION MAPPING
IN OTHER SINGLE-CELLED
ORGANISMS
Large-scale genetic interaction mapping techniques have recently been developed for
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additional eukaryotic and prokaryotic organisms including the fission yeast Schizosaccharomyces pombe (34, 108) and the bacterium
Escherichia coli (21, 130). These techniques are
directly analogous to those employed in S. cerevisiae in that they exploit genome-wide deletion collections and special mating procedures
to generate comprehensive sets of double mutants. These approaches have all the advantages
associated with S. cerevisiae mapping, including full genome coverage with precise, targeted
deletions and the ability to rapidly scale double
mutant construction and phenotypic analysis to
a genome-wide scale.
The first applications of these methods to
large-scale mapping in S. pombe were recently
reported (34, 107). Both studies found significant differences in the wiring of genetic interaction networks between S. pombe and S. cerevisiae as well as a common set of several hundred
conserved interactions. As the S. pombe genome
contains some unique gene sets, like those involved in RNA interference (RNAi), mapping
interactions involving these genes is providing novel insights that are not obtainable using
S. cerevisiae alone (107).
One of the first systematic genetic interaction mapping projects was undertaken in the
bacterium E. coli to test the contribution of genetic interactions to the evolution of sex (38).
This study examined 27 double mutants and
their related single mutants and, using a multiplicative model for genetic interaction, detected 7 combinations that had a negative effect
on fitness (e.g., synthetic sickness) and 7 that
had a positive effect (38). More recently, new
techniques termed GIANT-coli and eSGA have
been described that should enable true genomewide mapping of genetic interactions in this
organism using arrayed SGA-like approaches
(21, 130). These methods should accelerate the
functional annotation of E. coli and related bacterial genomes. Moreover, comparison of genetic interaction networks generated in a variety of prokaryotic and eukaryotic species should
provide important insight into genetic network
evolution and functional specialization associated with speciation.
SYSTEMATIC GENETIC
INTERACTION MAPPING
IN METAZOANS
The tools available to map genetic interactions in S. cerevisiae and other single-celled
organisms are precise and offer the potential
for true genome-wide coverage. Many interactions identified in these systems are likely to be
relevant to all eukaryotes (34, 107). However,
many genes of interest, especially human disease genes, are found only in metazoans. Therefore, several groups have sought to investigate
genetic interactions in a systematic, large-scale
manner using the nematode worm Caenorhabditis elegans, the fruit fly Drosophila melanogaster,
and cultured mammalian cells. These studies are made possible by the development of
organism-specific, genome-wide RNA interference (RNAi) libraries, which can be used to reduce the abundance of individual transcripts,
thereby mimicking the effect of gene deletions
to some extent, and which are compatible with
high-throughput experimental approaches (14,
32, 66, 69, 89, 95, 142).
Systematic Analyses of Genetic
Interactions in Caenorhabditis elegans
and Drosophila melanogaster
In C. elegans, it is possible to induce an RNAi
effect by soaking worms in a double-stranded
RNA (dsRNA)-containing solution or feeding
worms upon a bacterial lawn expressing the
dsRNA of interest (81, 126). Both RNAi-bysoaking and RNAi-by-feeding have been used
in combination with loss-of-function gene mutations in a query strain of interest to construct genetic interaction networks containing
up to several hundred interactions. These studies have focused mostly on metazoan-specific
genes, such as the egl-15/fibroblast growth factor receptor (FGFR), let-23/epidermal growth
factor receptor (EGFR) and daf-2/insulin receptor (IR), as well as conserved pathways involved in lin-35/retinoblastoma (Rb) function,
chromatin remodeling, and mitotic spindle assembly (8, 22, 24, 73, 125). For example, one
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study screened 37 query genes against ∼1750
individual RNAis (∼65,000 total pairs) in a
96-well format (73). This screen identified a
total of 350 synthetic lethal interactions, including novel interactions between the EGF
receptor tyrosine kinase signaling pathway and
the RSC and SWI/SNF chromatin remodeling
complexes (73). Although these studies have uncovered many novel, metazoan-specific genetic
interactions that would be undetectable (indeed, untestable) in yeast, these studies have not
approached a true genome-wide scale. Moreover, the hypomorphic nature of the starting
mutant strains and the uncertain nature of effects produced by RNAi (see below) complicate
the assignment of synthetic lethal gene pairs to
the same or different (e.g., parallel) pathways.
Nevertheless, the resulting networks appear to
resemble those mapped in yeast, showing a similar network topology (22, 73, 127).
It is more difficult to introduce dsRNA
species into Drosophila than into C. elegans.
However, this has not prevented the recent execution of a large-scale genetic interaction study
in which a Drosophila cell culture approach was
used to identify genetic interactions involving
the Drosophila Jun N-terminal kinase (dJNK)
pathway (5). In this work, cultured BG-2 cells
expressing a dJUN fluorescence resonance energy transfer (FRET) reporter construct that
monitored protein phosphorylation were transfected individually with one of 1536 dsRNAs
targeting kinases, phosphatases, and their corresponding regulatory subunits, and the effect
of these treatments on dJUN phosphorylation
was monitored. Subsequently, a panel of 12 cell
lines with dsRNAs targeting 12 different known
components of the dJUN pathway were generated and used as starting lines to identify sensitizers from among the 1536-gene screening set.
This analysis of 17,724 combinations resulted
in the identification of 79 putative regulators
of the dJUN pathway (5). Further work is required to validate the functional relevance of
these novel interactions. However, this screen
is especially notable for its use of protein phosphorylation as a phenotypic readout of genetic
interaction and suggests that similar reporters
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could be used to probe the function of other
signal transduction pathways.
Systematic Analyses of Genetic
Interactions in Mammalian Systems
Currently, the only practical way to examine
genetic interactions in a large-scale manner in
mammalian systems is to take a cell culture
approach that renders these systems accessible to genetic manipulation by RNAi. For example, tumor cells harboring oncogenic alleles of the NRAS and HRAS genes were used
as queries in a screen of 5760 short hairpin
RNAs targeting ∼1000 human genes, including
571 kinases. This screen identified a synthetic
lethal interaction between oncogenic Ras and
the gene CSNK1E, encoding a casein kinase 1
epsilon kinase (138). It was subsequently shown
that chemical inhibition of casein kinase 1 epsilon recapitulated the synthetic lethal effect
observed with shRNA-mediated knockdown,
validating this gene as a potential anticancer
drug target. In another screen, it was found
that shRNAs targeting the kinase genes CDK6,
MET, and MAP2K1 were synthetic lethal in
the background of renal cell carcinoma cells
with a null mutation in the von Hippel-Lindau
(VHL) tumor suppressor gene (12). These studies demonstrate the power of RNAi screening to
identify novel synthetic lethal interactions that
may be useful to kill tumor cells with defined
mutations in a highly specific manner.
Studies using larger RNAi libraries, and involving comparative analyses of different cell
lines, have the potential to reveal genetic interactions specific to particular starting mutations.
Recently, several groups identified common
and cancer cell–type-specific essential genes in
a panel of human cancer cell lines (79, 111, 116).
The cell-type specific interactions are presumably due to specific synthetic lethal interactions
between the RNAi species and the genetic background of the tumor cell. However, the relevant
endogenous second site mutation(s) remain to
be determined. The fact that most of these studies are conducted in a tissue culture setting, ex
vivo, and, moreover, involve the use of cultured
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cancer cells rather than wild-type cells must be
borne in mind. The genetic interactions identified may not be relevant to the situation faced
by diseased or normal cells in vivo. The recent
development of in vivo RNAi screening techniques in a mouse liver cancer model (141) represents an important step toward addressing at
least part of this concern, albeit at the cost of
much higher experimental complexity.
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Caveats Associated with
Gene Inactivation Approaches
Gene deletion collections, such as the
S. cerevisiae deletion collection, suffer from
several known design, technical, and acquired
limitations associated with the construction of
the individual deletions and the propagation of
these strains over time, including the preservation of duplicated wild-type copies of individual
deleted genes elsewhere in the genome, and
the corruption of individual barcode sequences
(110). The RNAi approaches used in C. elegans,
Drosophila, and human cell culture studies
also suffer from several known limitations.
First, RNAi approaches are associated with
significant rates of false negatives (127). This
makes it difficult to draw strong conclusions
from negative data (e.g., when no interaction is
observed). Second, the ability of small interfering RNA (siRNA) species to bind and silence
the expression of sequences with imperfect base
pair complementarity can result in off-target,
false-positive effects (37). For example, in
a cell-based RNAi screen conducted using
Drosophila S2 cells, many of the best candidate
RNAis were found to produce their phenotypes
mostly through off-target effects on an unrelated pathway (80). These observations suggest
that some siRNAs modulate the expression of
multiple genes simultaneously. Third, certain
processes, such as choroidal neovascularization,
appear to be sensitive to dsRNAs in a sequenceindependent fashion (70). Fourth, introduction
of double-stranded RNA into certain mammalian cell types can induce an interferon
response (19, 105), potentially interfering with
the observation of appropriate phenotypes.
More generally, the effects of exogenous
double-stranded RNA on normal cellular
processes, such as microRNA processing, need
further clarification. These considerations suggest that caution is needed when interpreting
the results of genetic interaction derived from
either gene deletion or RNAi data, especially in
the absence of rigorous follow-up studies (37).
NEW FRONTIERS IN
SYSTEMATIC GENETIC
INTERACTION ANALYSIS
Expanding the Spectrum
of Mappable Phenotypes
As noted above, most large-scale genetic interaction mapping studies conducted to date
have used growth rate as a phenotypic end
point. Thus, the resulting genetic interaction
networks do not report on processes that have
no effect on cell viability or growth. To increase our ability to detect genetic interactions, researchers are beginning to explore the
use of additional phenotypic outputs. For example, Drees and colleagues identified genetic
interactions between genes involved in a distinct S. cerevisiae growth phenotype: filamentous growth (36). The regulatory mechanisms
responsible for the switch between normal and
filamentous growth are well characterized and
involve an extensive signaling network including mitogen-activated protein kinase (MAPK)
and Ras/cAMP pathways. To further characterize this regulatory network, a filamentous agarinvasion assay was used to measure phenotypes
associated with a number of mutant allele combinations. The investigators were able to distinguish nine general types of genetic interactions
and enabled the construction of a directional
interaction network, demonstrating a complex
relationship between different signaling pathways and regulatory components that impinge
on filamentous growth (36). Flux balance analysis (52, 114) and microarray-based gene expression (131) have also been used as phenotypic
readouts for identifying and measuring genetic
interactions. These methods may enable the
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detection of genetic interactions that would not
be apparent from growth rate data alone; however, this remains to be formally demonstrated.
Advances in assay development continue to
increase the spectrum of phenotypic traits available for systemic- and genome-scale mapping
of genetic networks. For example, by combining cytological reporters with high-content
screening methodologies it is possible to classify yeast morphological and protein localization phenotypes in a large-scale manner (10,
59, 92–94), phenotypes that could be used as
the basis of a genetic screen. For example, in
one study, morphological data pertaining to the
cell wall, actin cytoskeleton, and nuclear DNA
were systematically collected and analyzed for
the entire set of S. cerevisiae nonessential gene
deletion mutants (94). Similar approaches have
been extended to mammalian cells to characterize genes involved in regulation of various
cellular processes, including cell morphology
and cell cycle progression (89, 91). These advances are being driven by developments in
high-throughput microscopy and image analysis that enable large numbers of cell images to be
captured and important features extracted in an
automated manner (20, 64). Combining highcontent screening and high-throughput genetic
analysis should help expand the spectrum of
phenotypes that can be mapped quantitatively
in genetic networks.
One application of these new technologies,
as discussed above in the case of the Drosophila
FRET reporter screen, is to introduce pathwayspecific reporters into single mutants and double mutant cells or organisms, enabling the
identification of genetic interactions that impact pathway activity. If the pathway-specific
reporter is a fluorescent marker, then its activity
can be quantified either by microscopic imaging (10, 33, 59, 92–94) or by cell sorting (18).
Another example is provided by Jonikas et al.
(65), who used SGA to cross a green fluorescent protein (GFP)-based reporter that monitors the endoplasmic reticulum unfolded protein response (UPR) into the complete set of
viable single deletion mutants and a select set
of double mutants. The median single-celled
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fluorescence for each strain was determined
by high-throughput flow cytrometry. A phenotypic interaction scoring method was developed
based on the UPR reporter level, which enabled
the detection of negative interactions, reflecting exaggerated UPR inductions, and positive
interactions, reflecting unexpectedly low inductions. As observed with quantitative genetic
interaction analysis based upon fitness measurements, clustering of double mutant genetic interactions derived from the UPR phenotypic
measurements sorted genes into pathways and
complexes. This report illustrates the power of
novel, focused phenotypic readouts combined
with genetic interaction analysis to explore the
function of complex biological processes.
Exploring the Overlap of Genetic
Interaction Networks Between Species
Evidence suggests that the molecular function
and essentiality of individual genes can be well
conserved between species, even those as divergent as yeast and humans (28, 41). An important
question is to what extent genetic interactions
between genes are conserved between species.
Preliminary evidence suggested that global
network properties, such as the degree of
interconnectedness and interaction topology,
are conserved from yeast to worm (22, 73).
Interestingly, however, analyses of individual
genetic interactions between orthologous
genes of yeast and worm genes provided weak
support for the notion that specific genetic
interactions are conserved between these two
species: two studies found that less than 5% of
synthetic lethal genetic interactions identified
by large-scale synthetic lethal mapping in
S. cerevisiae were conserved in C. elegans
(22). On the other hand, a smaller study that
quantified worm mitotic spindle morphology
as a phenotypic readout detected moderate
but significant (∼29%) conservation of genetic
interactions between orthologous genes in
S. cerevisiae and C. elegans (125). There are a
number of possible reasons for the different
levels of conservation estimated from the
high- versus low-throughput studies in
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C. elegans (discussed in Reference 34 and
below).
The development of systematic genetic interaction mapping technologies for S. pombe
that are analogous to those employed with
S. cerevisiae has enabled larger, more systematic
comparisons of genetic interactions to be made
between two divergent species under highly
similar experimental conditions. These studies
have found that that some (∼30%) synthetic
lethal genetic interactions are conserved between these two divergent species (34, 107).
This suggests that the majority (∼70%) of
genetic interactions may be species-specific,
a result not wholly unanticipated given that
S. pombe and S. cerevisiae are separated from each
other by roughly a billion years of evolution
and consequently have significant differences in
genome structure and physiology. Indeed, the
essential phenotype of S. cerevisae genes is only
conserved for some ∼65% of their corresponding S. pombe orthologs (28). Thus, a ∼30%
overlap of the synthetic lethal genetic networks
between these two yeasts indicates that there
is significant conservation of synthetic lethal
genetic interactions over hundreds of millions
of years of evolution. Differences in genetic
network buffering capacity, for example due
to gene duplications (30), in one species but
not the other could account for some of the
∼70% of genetic interactions that appear to be
species-specific. This hypothesis can be tested
using more complex genetic analysis, such as
triple mutant synthetic lethal screens, to uncover conserved edges on the network that are
not apparent when performing double mutant
analysis.
One possibility that accounts for the
higher degree of conservation observed in the
S. cerevisiae–S. pombe comparison versus the
S. cerevisiae–C. elegans comparison is that
certain genetic interactions detectable in singlecelled yeasts are likely to be conserved in
multicellular organisms but difficult to detect
because of cellular-level redundancy or altered functions. It may be possible to detect
these interactions using alternative phenotypic
readouts that assay anatomical, developmental,
or behavioral phenotypes. For now, the general level of conservation of genetic networks
between these two species remains an open
question.
An interesting application of comparative
genetic interactions data is to use synthetic
lethal pairs identified in lower organisms to
guide the selection of interactions that can be
exploited to kill cancer cells in mammals (34,
55). A recent study validates this approach (85).
In this work, the human genes RAD54B and
FEN1, both encoding DNA repair enzymes
commonly mutated in cancer, were selected for
study because the yeast orthologs were known
to exhibit synthetic lethality. shRNA-mediated
targeting of FEN1 was found to be synthetic
lethal in the background of RAD54B−/− but not
RAD54B+/+ mouse embryonic fibroblasts (85).
A caveat associated with this study is that mouse
embryonic fibroblasts are not true cancer cells.
Nevertheless, by focusing on synthetic lethal
pairs conserved in lower organisms, it may be
possible to minimize the number of combinations that have to be screened in mammalian
systems in future studies—an important consideration given the cost of mammalian cell
culture-based screens and the uncertainty associated with RNAi treatments.
Mapping Genetic Interactions
in Outbred Populations
As noted above, the overwhelming number of
genetic backgrounds makes it difficult to map
genetic interactions in outbred populations (1,
53, 75, 90, 123). However, there is much interest in finding ways to do so, as this knowledge
has enormous implications for our understanding of disease.
Genetic interaction mapping studies in outbred populations rely on the variability present
in the DNA sequence of different individuals within a population to serve as endogenous
perturbagens whose effects can be associated
with specific phenotypes. To generate an experimentally tractable degree of diversity, several groups have used the variability present in
small sets (∼100) of progeny derived from a
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single F2 cross between parents of different but
compatible genetic backgrounds as reagents to
map genetic interactions (15, 17, 42, 106). In
these studies, the expression levels of individual
genes or proteins are used as phenotypic readouts that report on the contribution of different
allele combinations to the observed expression
patterns (15, 17, 42). The complex nature of genetic regulation of transcription is hinted at by
studies examining gene expression in the segregants from a cross between a standard laboratory yeast strain (BY, a derivative of the S288C
genetic background) and a wild, vineyard isolate
(RM). This study found that 3% of all highly
heritable transcripts are likely to be regulated by
a single genomic locus, 16% of transcripts are
controlled by 2–3 loci, whereas 40% of transcripts display such complex linkage that no
loci, alone or together, reach statistical significance (15, 17). By analogy, it is possible that
genetic interactions, both positive and negative,
occur within individuals of a natural population
as a result of complex combination of loci.
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Mapping Genetic Interactions
in Artificial Networks
We are entering an age of synthetic biology, in
which genes, genomes, pathways, and organisms will be designed and built to order (47,
48, 72, 84, 134–136). Genetic interaction networks derived from existing species are helping to define gene sets required to perform specific functions. These data could presumably be
used to help design synthetic organisms with a
given desired function. Unanswered questions
include the best ways to improve upon the performance of existing organisms using novel, engineered components, how these components
will interact with existing genetic networks, and
how best to model these networks.
One way to answer these questions is to construct artificial gene interaction networks, study
how these systems behave, and from them extract key engineering principles. Recently, a series of 598 plasmids encoding novel, chimeric
genes were generated that contained all possible combinations of the 5& regulatory region
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Dixon et al.
and coding sequences for 15 transcription and
7 alpha-factors for the bacteria E. coli (60).
These plasmids were then expressed in bacteria and the growth rate of the artificially
rewired networks was examined. Surprisingly,
the growth rate of ∼84% of the tested networks was within the 95% confidence interval
for a set of wild-type networks, suggesting that
genetic networks are capable of tolerating significant disruptions in genetic network wiring
(60). Moreover, it was possible to select for specific networks that exhibited gains in viability
under specific conditions over generations. For
example, conditions of heat stress consistently
selected for a network containing the specific
combination of the rpoS promoter and ompR
coding sequence, which together up-regulated
a suite of chaperone and shock genes (60).
Overall, these results are consistent with results previously reported in S. cerevisiae, where
overexpression of only 15% of all S. cerevisiae
genes was found to cause measurable growth
defects (117). Moreover, these results are
consistent with the observation that in human
cancers, multiple simultaneous genetic perturbations (63, 76, 82) are compatible with viability. It will be interesting to further explore
how genetic interactions are altered in systematically rewired networks in eukaryotes. The
recent development of a novel yeast synthetic
network reporter strain called in vivo reverseengineering and modeling assessment (IRMA)
should facilitate these studies (23).
CONCLUSIONS
It has been less than a decade since the publication of the first large-scale genetic interaction map for S. cerevisiae in 2001 (129). Since
then, new means to identify and quantify genetic interactions for multiple systems have
rapidly emerged. In some cases, we are now
able to predict the structure of biochemical
networks from genetic interaction data alone;
moreover, we are able to start drawing general conclusions about genetic network structure and the conservation of network wiring
between species. In the future, the combination
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of high-throughput genotyping and phenotypic
profiling techniques should provide even
higher resolution and functionally relevant
genetic interaction maps, bringing us closer to
the goal of a complete understanding of all genetic interactions relevant to cell function.
FUTURE ISSUES
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1. Most large-scale studies to date have examined interactions between two alleles. Many
genetic interactions of biological and medical relevance are likely to involve higher order
combinations of interactions (i.e., three or more). Advances in robotics and computational
tools should enable these complex studies to be undertaken more easily, thereby extending
our understanding of genetic interaction networks.
2. The effects of different environmental conditions and genetic backgrounds on genetic
interaction networks are poorly understood on a global scale. Studies of the same gene
pairs under different environmental conditions or in different backgrounds should help
reveal both highly stable and condition-dependent (plastic) elements of genetic networks.
3. Further application of large-scale genetic interaction mapping to species other than S.
cerevisiae is in its infancy. The pursuit of these methods will enable comparative genetic
interactomic studies to be carried out on a global scale and provide important insight
into the evolution of genetic networks over time and between species.
4. The deletion and overexpression alleles used in studies to date represent an extreme and
perhaps uncommon form of genetic variation. Studies of genetic interactions between
more common mutations, as between common single nucleotide polymorphisms, will
provide a more nuanced view of genetic interaction. Studies of genetic interactions in
outbred populations should facilitate this work.
DISCLOSURE STATEMENT
The authors are not aware of any affiliations, memberships, funding, or financial holdings that
might be perceived as affecting the objectivity of this review.
ACKNOWLEDGMENTS
The authors thank Chad Myers and Leslie Magtanong for critical comments. This work was supported by a postdoctoral fellowship from the Canadian Institutes of Health Research to S.J.D. and
grants from Genome Canada (2004-OGI-3-01) and the Canadian Institutes of Health Research
(GSP-41567) to B.A. and C.B.
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14 October 2009
Annual Review of
Genetics
19:16
Contents
Volume 43, 2009
Annu. Rev. Genet. 2009.43:601-625. Downloaded from www.annualreviews.org
by Columbia University on 01/18/13. For personal use only.
Genetic and Epigenetic Mechanisms Underlying Cell-Surface
Variability in Protozoa and Fungi
Kevin J. Verstrepen and Gerald R. Fink ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! 1
Regressive Evolution in Astyanax Cavefish
William R. Jeffery ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !25
Mimivirus and its Virophage
Jean-Michel Claverie and Chantal Abergel ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !49
Regulation Mechanisms and Signaling Pathways of Autophagy
Congcong He and Daniel J. Klionsky ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !67
The Role of Mitochondria in Apoptosis
Chunxin Wang and Richard J. Youle ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !95
Biomineralization in Humans: Making the Hard Choices in Life
Kenneth M. Weiss, Kazuhiko Kawasaki, and Anne V. Buchanan ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! 119
Active DNA Demethylation Mediated by DNA Glycosylases
Jian-Kang Zhu ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! 143
Gene Amplification and Adaptive Evolution in Bacteria
Dan I. Andersson and Diarmaid Hughes ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! 167
Bacterial Quorum-Sensing Network Architectures
Wai-Leung Ng and Bonnie L. Bassler ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! 197
How the Fanconi Anemia Pathway Guards the Genome
George-Lucian Moldovan and Alan D. D’Andrea ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! 223
Nucleomorph Genomes
Christa Moore and John M. Archibald ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! 251
Mechanism of Auxin-Regulated Gene Expression in Plants
Elisabeth J. Chapman and Mark Estelle ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! 265
Maize Centromeres: Structure, Function, Epigenetics
James A. Birchler and Fangpu Han ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! 287
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The Functional Annotation of Mammalian Genomes: The Challenge
of Phenotyping
Steve D.M. Brown, Wolfgang Wurst, Ralf Kühn, and John Hancock ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! 305
Thioredoxins and Glutaredoxins: Unifying Elements in Redox Biology
Yves Meyer, Bob B. Buchanan, Florence Vignols, and Jean-Philippe Reichheld ! ! ! ! ! ! ! ! ! ! 335
Annu. Rev. Genet. 2009.43:601-625. Downloaded from www.annualreviews.org
by Columbia University on 01/18/13. For personal use only.
Roles for BMP4 and CAM1 in Shaping the Jaw: Evo-Devo and Beyond
Kevin J. Parsons and R. Craig Albertson ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! 369
Regulation of Tissue Growth through Nutrient Sensing
Ville Hietakangas and Stephen M. Cohen ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! 389
Hearing Loss: Mechanisms Revealed by Genetics and Cell Biology
Amiel A. Dror and Karen B. Avraham ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! 411
The Kinetochore and the Centromere: A Working Long Distance
Relationship
Marcin R. Przewloka and David M. Glover ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! 439
Multiple Roles for Heterochromatin Protein 1 Genes in Drosophila
Danielle Vermaak and Harmit S. Malik ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! 467
Genetic Control of Programmed Cell Death During Animal
Development
Barbara Conradt ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! 493
Cohesin: Its Roles and Mechanisms
Kim Nasmyth and Christian H. Haering ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! 525
Histones: Annotating Chromatin
Eric I. Campos and Danny Reinberg ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! 559
Systematic Mapping of Genetic Interaction Networks
Scott J. Dixon, Michael Costanzo, Charles Boone, Brenda Andrews,
and Anastasia Baryshnikova ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! 601
Errata
An online log of corrections to Annual Review of Genetics articles may be found at http://
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