Download Global synthetic-lethality analysis and yeast functional profiling

Review
TRENDS in Genetics Vol.22 No.1 January 2006
Global synthetic-lethality analysis
and yeast functional profiling
Siew Loon Ooi, Xuewen Pan, Brian D. Peyser, Ping Ye, Pamela B. Meluh, Daniel
S. Yuan, Rafael A. Irizarry, Joel S. Bader, Forrest A. Spencer and Jef D. Boeke
High Throughput Biology Center, Institute of Genetic Medicine, Department of Biostatistics, Johns Hopkins University School of
Medicine, 339 Broadway Research Building, 733 North Broadway, Baltimore, MD 21205, USA
The Saccharomyces genome-deletion project created
O5900 ‘molecularly barcoded’ yeast knockout mutants
(YKO mutants). The YKO mutant collections have
facilitated large-scale analyses of a multitude of mutant
phenotypes. For example, both synthetic genetic array
(SGA) and synthetic-lethality analysis by microarray
(SLAM) methods have been used for synthetic-lethality
screens. Global analysis of synthetic lethality promises
to identify cellular pathways that ‘buffer’ each other
biologically. The combination of global synthetic-lethality analysis, together with global protein–protein
interaction analyses, mRNA expression profiling and
functional profiling will, in principle, enable construction
of a cellular ‘wiring diagram’ that will help frame a
deeper understanding of human biology and disease.
regions and !7% of its w6000 genes contain introns [5].
These features make gene prediction in the yeast genome
less complex than in other eukaryotes and greatly simplify
functional analyses. The yeast open reading frames
(ORFs) were annotated based on the positions of predicted
start (ATG) and stop codons. Approximately 6200 ORFs of
O100 codons in length were initially identified. Currently,
the functions of w30% of all ORFs remain unidentified,
and these are annotated as unknown biological process
within the Gene Ontology (http://www.yeastgenome.org).
Furthermore, for many of the remaining ORFs that have
gene names, the functions of their gene products have
been deduced from obscure phenotypes of the corresponding mutants, and the functions of their encoded proteins
are still unclear. Finally, new functions are routinely
discovered for ‘well-known’ genes.
Introduction
Synthetic lethality describes the genetic interaction in
which the combination of two separately non-lethal
mutations results in lethality [1]. It has been useful in
deciphering the function of yeast genes [2] – global
synthetic-lethality analysis promises to comprehensively
identify the cellular pathways that ‘buffer’ each other
biologically [3]. Global synthetic-lethality analysis
between null alleles provides a means to generate a
genetic interaction map, in which the interactions suggest
the genes that are required for redundant biological
processes or function in parallel pathways, without any
requirement that proteins must directly interact. By
contrast, a physical interaction map identifies interactions
based on protein–protein interactions, without knowledge
of protein function, and tends to delineate linear
pathways. By an (oversimplified) analogy to electrical
engineering, the protein interactions identify ‘series
circuits’ and the genetic interactions (if based on null
alleles) identify ‘parallel circuits’ (Figure 1).
The Saccharomyces genome-deletion project
The Saccharomyces genome-deletion project, a worldwide
collaborative effort, systematically created deletion and
substitution mutants for almost all annotated yeast ORFs,
each of which was replaced by a kanMX4 cassette that
confers resistance to the drug G418 [6]. These ‘yeast
knockout’ (YKO) mutants were created by chromosomal
integration of PCR-generated disruption cassettes via
homologous recombination (Figure 2a). The YKOs exist in
four formats [6]: (i) MATa and MATa heterozygous diploid;
(ii) MATa haploid; (iii) MATa haploid; and (iv) MATa and
MATa homozygous diploid (the last three sets contain only
nonessential gene YKOs). These YKO mutants are a
valuable resource for genome-wide functional analyses.
They have been extensively studied in a systematic
manner to identify genes that function in a variety of
biological processes including meiosis, mitochondrial
respiration, determination of budding pattern and cell
sizes, and cellular response to various stresses and drugs.
A visionary component of the YKO collection was
invented in the laboratory of Ron Davis, and entailed
the systematic incorporation of unambiguous DNA
sequence identifiers called ‘TAGs’ in each mutant [6,7].
Each YKO mutation contains two 20-nucleotide (nt) TAGs
(also called ‘molecular barcodes’) linked to and uniquely
assigned to that genetic locus (Figure 2b). The two genespecific sequences, individually referred to as UPTAG and
DNTAG, are flanked by universal priming sites. They
produced w12 000 TAGs, which were designed to be as
The Saccharomyces cerevisiae genome
As the genomes of many organisms are sequenced, a major
challenge is to extract biologically meaningful information. S. cerevisiae was the first eukaryotic genome to
be sequenced [4]. In comparison with other eukaryotes,
the simple compact genome of yeast has short non-coding
Corresponding author: Boeke, J.D. ([email protected]).
Available online 23 November 2005
www.sciencedirect.com 0168-9525/$ - see front matter Q 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.tig.2005.11.003
Review
(a)
A bifurcated
cellular pathway
TRENDS in Genetics Vol.22 No.1 January 2006
(b)
Protein interaction map
A
B
1
Input
3
C
C
1
A B
3
2
(c)
D
2
Genetic interaction map
4
Essential
function
A
1
B
2
C
3
D
4
TRENDS in Genetics
Figure 1. Genetic versus physical interaction maps. (a) A bifurcated cellular pathway
will have distinct networks on genetic- and physical interaction maps. Proteins A, B,
C and D (blue) and proteins 1, 2, 3, and 4 (green) are members of two functionally
redundant pathways required to perform an essential function. Proteins A, B and C
interact with each other physically, so do proteins 1, 2 and 3. (b) A protein
interaction map, or physical interaction map, identifies interactors based on
protein–protein interactions, whereas genetic interaction map (c) identifies
‘interactors’ based on functions without the requirement that the proteins must
interact. The combination of these two complementary approaches can be used to
deduce a cellular pathway and, in principle, enable the construction of a ‘wiring
diagram’ of the yeast cell.
different from each other as possible, but retained
relatively similar hybridization properties [6,7]. These
features made functional profiling possible (i.e. microarray-based phenotypic analysis of YKO mutants that
were manipulated as a pool; Table 1 supplementary
material online) [6–10]. Functional profiling enables the
quantitative analysis of strain fitness under a given
condition and the simultaneous analysis of hundreds-tothousands of strains. By assaying a precisely defined
mixture of yeast YKO mutants for the presence of
corresponding TAGs before and after applying the
selection, genes required to survive the imposed selection
can be identified (Figure 2c). The resultant hybridization
patterns can be used to determine the presence, absence,
under- or over-representation of a particular mutant in
the population (Figure 2c). Mutants that are underrepresented after treatment are identified because they
have a greater TAG signal intensity in the control than in
the experiment [7,8,10–15] or by the identification of
mutants predicted to belong to two different populations
using a mixture model: one population is ‘present’ (single
mutant pool) and one is ‘absent’ (double mutant pool) [16].
Functional profiling of populations using TAG microarrays greatly expedites genetic screens and makes them
intrinsically quantitative.
Synthetic lethality
The systematic construction of YKOs revealed that w18%
of yeast genes (1105 of w6000) are essential for growth on
a rich glucose-containing medium [9]. A small proportion
of the ‘nonessential’ genes probably become essential
when yeast cells are grown under different conditions.
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57
However, this does not imply that only w18% of yeast
genes function in essential processes. Instead, this
number reflects extensive genetic redundancy or homeostatic buffering within essential processes. The interesting
question raised is what portion of the remaining w4800
genes have redundant functions in essential processes.
One way to reveal the functions of the remaining
nonessential genes and to identify essential processes is to
perform a systematic genome-wide synthetic-lethality
analysis. Synthetic lethality describes any combination
of two separately non-lethal mutations that leads to
inviability [1], whereas synthetic fitness indicates a
combination of two separate non-lethal mutations that
confers a growth defect more severe than that of either
single mutation. The interpretation is that synthetic
fitness reflects an important genetic interaction, whereas
synthetic lethality reflects an essential interaction.
Synthetic-lethality analysis has been used as a tool to
identify the function(s) of the gene of interest and/or the
pathway in which the gene of interest is involved. Proof-ofprinciple studies using traditional synthetic-lethality
methods have investigated a wide variety of biological
pathways and discovered a wealth of interactions [2].
Pairs of genes with redundant functions are most often
represented by diverged paralogs or by distinctly different
proteins functioning in compensatory pathways. Analysis
of yeast genome structure revealed evidence of a prior
whole-genome duplication event followed by large-scale
loss or divergence of duplicate regions [17]. As many as
905 genes appear to be present in duplicate, often
divergent, copies encoding w16% of the proteome.
Comparative genome-structure analysis among closely
related yeasts strongly supports the model for an
ancestral whole-genome duplication [18]. However, phenotypic analysis of individual yeast mutants suggests that
a major feature of genetic robustness is contributed by the
overlapping functions of nonhomologous genes [19,20].
Identification of double mutant pairs that exhibit synthetic lethality also indicates that genetic redundancy is
often contributed by pathways containing nonparalogous
genes [13–15,21,22]. To fully understand genetic redundancy, the relationships among genes will need to be
revealed, regardless of whether they were derived by
recent duplication.
Global synthetic-lethality analysis using SGA
Using the YKO mutant collection, Tong et al. developed a
systematic approach for generating ordered arrays of
double mutants termed synthetic genetic array (SGA)
analysis [21] (Figure 3, Box 1 and Table 1 in the
supplementary material online). Each potential ‘target’
gene is represented by its corresponding MATa haploid
YKO mutant on the array. A specific MATa ‘query
mutation’ haploid is combined with each MATa haploid
YKO strain by mating, producing an array of doubly
heterozygous diploid YKOs. The array is then sporulated,
and the set of desired haploid double mutants is
subsequently selected, exploiting a cleverly designed
‘SGA reporter’ gene that enables selection of only MATa
haploid progeny cells. The SGA reporter (can1D::MFA1prHIS3) is introduced by the MATa haploid query strain and
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Review
(a)
TRENDS in Genetics Vol.22 No.1 January 2006
PCR-generated targeting construct
yfg1 ::kanMX4
UPTAG
DNTAG
YFG1
Chromosomal integration by homologous recombination
(b)
UPTAG
yfg1 ::kanMX4
DNTAG
G418-resistant ‘molecular barcoded’ YKO strain for YFG1
(c)
Apply selection
Genomic DNA preparation
PCR with universal primers
Cy5- and Cy3-labeled PCR products
Oligonucleotide array hybridization
Defective mutant
TRENDS in Genetics
Figure 2. Features of yeast knockout (YKO) strains. (a) Each YKO strain was created by chromosomal integration of a PCR-generated disruption cassette by homologous
recombination. The PCR-generated disruption cassette contains kanMX4 marker, which confers G418 resistance. Each YKO strain is disrupted with the same kanMX4 marker.
(b) In addition, each disruption cassette contains two 20-mer molecular barcodes (UPTAG and DNTAG) unique to each YKO strain. The UPTAG and DNTAG are flanked by
universal priming sites (blue). The universal priming sites are 17–19-bp in length. The kanMX4 marker is 1468-bp in length, and is not drawn to scale with respect to the TAGs
and universal priming sites. The genomic sequence surrounding the deleted ORF is shown in yellow. (c) Molecular bar-coding facilitates genetic screens by microarray
analysis. The UPTAG and DNTAG features enable numerous YKO mutants to be pooled and analyzed in parallel. Pool of mutants can be subjected to selection. Genomic DNA
can then be isolated from YKO pools before and after selection, and can be used as a PCR template to amplify the DNTAGs or UPTAGs of the strains present. Genomic DNA
from the unselected and selected pool can be amplified using Cy5- (in red) and Cy3- (in green) labeled universal primers, respectively. These fluorescently-labeled probes are
then hybridized to an oligonucleotide array. A defective mutant (shown in red) that did not survive the imposed genetic condition would be under-represented in the pool
subjected to genetic selection and would have a reduced signal in the Cy3 channel.
consists of the HIS3 gene driven by the MFA1 promoter,
and is active only in MATa cells, inserted into the CAN1
locus, thus conferring recessive resistance to canavanine.
Plating spores on canavanine-containing medium that
lacks histidine enables selection of a pure culture of MATa
haploid progeny and prevents growth of unwanted diploid
parental cells, unmated parental haploids and other cell
types [21].
In a large-scale SGA synthetic-lethality analysis study
that focused on query genes involved in actin-based cell
polarity, cell-wall biosynthesis, microtubule-based
chromosome segregation and DNA synthesis and repair,
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only w20% of YKOs failed to show any synthetic lethal
interactions above the background level [22]. For 132
queries yielding at least one confirmed hit, the number of
interactions established per query gene ranged from 1 to
146, with an average of 34 interactions per gene. In total,
w4000 interactions were found among w1000 genes [22].
In this study, both nonessential YKO mutants and
conditional alleles of essential genes were used as query
genes. It might be important to interpret the results of
these two types of results in distinct ways. Although the
arguments that genetic interactions imply parallelism
and redundancy certainly apply to interactions between
Review
TRENDS in Genetics Vol.22 No.1 January 2006
Synthetic genetic array
(a)
Synthetic lethality analyzed by microarray
MATa haploid YKO pool
MATα query mutant
+
MATα yfg1∆::NATMX4
can1∆::pMFA1::HIS3
NatR, His–, CanR
(b)
59
13-kb genomic
URA3 fragment
yfg1 ::URA3
Control targeting construct
Query gene
targeting construct
Integrative transformation into
YKO pools
MAT a orf∆::KANMX4
KanR, Can sensitive, His–
Mating
Select for Ura+ transformants
Genomic DNA preparation
Select for heterozygous diploid
KanR: mutation of interest
NatR: query mutation
His–
Can sensitive
Sporulation
Select for double mutants:
KanR: mutation of interest
NatR: query mutation
His+: MAT a haploid cells from meiosis
CanR: to select against diploid cells
PCR
Cy5-labeled PCR products
Cy3-labeled PCR products
Oligonucleotide array hybridization
Synthetic lethal with yfg1∆
TRENDS in Genetics
Figure 3. Synthetic genetic array and synthetic lethality analyzed by microarray. (a) SGA. The query mutation is introduced into MATa haploid YKO strains individually by
mating it to a MATa query mutant, containing the can1D::MFA1pr-HIS3 reporter, to generate doubly heterozygous diploid YKOs. These are then sporulated, and the desired
haploid double mutants are obtained by selecting for expression of the MFA1pr-HIS3 reporter, which is active only in MATa haploid progeny cells. Residual diploids and
unmated haploid parent strains are His–. (b) SLAM. Parallel analysis of YKO strains for synthetic lethality with yfg1D. MATa haploid YKO pool was transformed with a 13-kb
genomic URA3 fragment and a PCR-generated query construct to disrupt YFG1 in parallel, and plated onto SC-Ura plates. Genomic DNA was isolated from pooled Ura(
transformants for each condition and used as PCR template to amplify the DNTAGs or UPTAGs in the strains present. Genomic DNA from the control transformation was
amplified using Cy5- labeled universal primer (red), whereas DNA from the experimental transformation was amplified using Cy3-labeled universal primers (green). These
fluorescently labeled probes were then co-hybridized to an oligonucleotide array bearing each tag in triplicate. A strain that is synthetic lethal (yeast strain shown in brown)
with yfg1D would be under-represented in the experimental condition transformed pool, would have a reduced signal in the Cy3 channel and would appear as a red spot.
Abbreviations: CanR, resistance to canavanine; HisK, inability to grow on minimal media lacking histidine; HisC, can grow on minimal media lacking histidine; KanR,
resistance to G418, conferred by the gene product of kanMX4; NatR, resistance to nourseothricin, conferred by the gene product of natMX4.
null alleles, when uncharacterized mutant alleles such as
TsK mutants or promoter-shutoff constructs are used as
queries, results must be interpreted more cautiously. For
promoter-shutoff constructs, it is easy to imagine synthetic lethality arising from intrapathway or ‘series’
interactions rather than, or in addition to, ‘parallel’
interactions (Figure 1). Thus, in building and parsing
large-scale genetic interaction data sets, segregation of
these different data types is crucial.
In contrast to the numerous genetic interactions, yeast
proteins have an average of only eight physical interactions per protein as defined by two-hybrid tests and/or
copurification, providing independent evidence that genetic interactions reveal functional relationships that
transcend direct physical interactions [22]. Synthetic
genetic interactions between genes encoding paralogous
proteins accounted for only 2% of all observed interactions
[22]. A network of genetically connected gene functions
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was generated and showed that many types of cellular
pathways apparently buffer each other (e.g. microtubulebased functions buffer both actin-based and DNA synthesis or repair functions).
Analyses of this SGA genetic-interaction data set
revealed that genes within the same pathway or complex
tend to share similar synthetic lethality profiles [22,23].
Synthetic lethal-genetic interactions were significantly
more abundant between pairs of genes with the same
mutant phenotypes, between pairs of genes encoding
proteins with the same subcellular localization and
between genes encoding proteins within the same
complex. In addition, Tong et al. [22] found that the
genetic network is an example of a small world network,
such that the immediate neighbors of a gene, its
interaction partners, tend to interact with each other.
These features of a genetic network can be exploited to
predict genetic interactions and biological functions of
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Review
TRENDS in Genetics Vol.22 No.1 January 2006
Box 1. Drug target identification using global gene-compound synthetic-lethality (GCSL) screens
A global GCSL screen, which studies the sensitivity of genome-wide
haploid or homozygous diploid YKOs to a chemical compound, when
combined with the synthetic-lethality screens, discussed in the main text,
can be used to identify drug targets [34] (supplementary material online).
For clarity, we refer synthetic interaction between a pair of mutations as
gene–gene synthetic lethality (GGSL). The global GGSL profile of a gene
can serve as a ‘fingerprint’. A drug treatment mimics mutations in its
target gene and thus the GCSL profile of a drug is probably similar to the
GGSL profile of its target. By comparing the GCSL profile of a drug of
unknown mechanism with a comprehensive compendium of GGSL
profiles, one can, in theory, identify the pathways or sometimes even the
direct target [34]. In a proof-of-principle study, Parsons et al. [34] obtained
global GCSL profiles for 12 drugs with known target proteins or
processes in yeast. These included fluconazole, an antifungal drug that
inhibits ERG11, and FK506 and cyclosporin A, two immunosuppressant
drugs that inhibit calcineurin [34]. There was significant (but incomplete)
overlap between the GCSL profile of fluconazole and the GGSL profile of
ERG11. The same was true for the GCSL profiles of FK506 and cyclosporin
A versus the GGSL profile of cnb1D (CNB1 encodes the regulatory
subunit of calcineurin, the target of these drugs). In addition, this study
also found a large group of genes that were apparently required for multidrug resistance, including genes involved in vacuolar acidification.
An advantage of GCSL is that it can be used to identify nonessential
target genes (e.g. the subunits of calcineurin) if GGSL profiles exist for
many genes. Although the pathway inhibited can be deduced, the
specific ‘target’ gene inhibited by the drug must still be identified.
Drug target identification using heterozygous diploid YKO
mutants
Essential genes cannot be assayed by screens based on haploid
mutants as discussed. However, heterozygous YKOs, in which gene
a gene. For example, the genetic interaction patterns
enabled the prediction and confirmation of the role of
CSM3 in DNA replication checkpoint and the role of
YMR299C in dynein–dynactin functions [22].
SLAM-synthetic lethality analyzed by microarray
As an alternative to SGA, a new technique termed
synthetic-lethality analysis by microarray (SLAM) was
developed. Here, the query mutation is introduced into a
haploid YKO pool through direct integrative transformation (Figure 3 and Table 1 in the supplementary material
online) [13], and production of double mutants in haploid
pools is monitored by microarray as described previously.
SLAM detects synthetic lethality efficiently and the
quantitative aspect of a microarray-based approach allows
one to rank the order of candidate genetic interactions.
SLAM was first performed for SGS1 and SRS2, two 3 0 /5 0
DNA helicase genes, to identify their unique and common
genetic interactors [13]. Most recently, a robust heterozygous diploid-based synthetic-lethality analysis by
microarray (dSLAM) technique was developed [14]. A
modified SGA reporter (can1D::LEU2-MFA1pr-HIS3) was
first incorporated into the set of heterozygous diploid
YKOs, in which the mutant phenotypes are normally
absent. The query mutation was then introduced into a
pool of these haploid-convertible heterozygotes by high
efficiency PCR-mediated integrative transformation.
These YKOs are sporulated and isogenic haploid MATa
single or double mutant populations are selected on
appropriate haploid-selection media and immediately
studied for synthetic lethality by microarray analysis of
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dosage is reduced by half, rather than eliminated, can be tested for
conditional haploinsufficiency. Heterozygote YKO mutant pools have
been used to identify drug targets and to characterize drug
mechanisms [11,35–37]. Reducing the relative gene dosage from
two copies to one sensitizes heterozygous diploid yeast cells to any
drug that acts on the gene product that is then identified as a
candidate drug target. By using a TAG microarray-based approach to
profile such heterozygous deletion mutations for drug supersensitivity, Lum et al. [11] found that the anti-neoplastic agent
5-fluorouracil might target the rRNA processing exosome. In
addition, they found that the anti-anginal drug molsidomine targets
lanosterol synthase of the sterol biosynthetic pathway, explaining
the cholesterol-lowering effects of this compound. With a colonybased assay, Baetz et al. [35] found that dihydromotuporamine C, a
compound in preclinical development that inhibits angiogenesis and
metastasis, might target sphingolipid metabolism.
GCSL, GGSL profile matching and the drug-induced haploinsufficiency approaches are likely to have different strengths and
will complement each other in drug-target identification. The first
method has the advantage of studying broader gene-compound
interactions because the mutations are not ‘covered’ by the
corresponding wild-type genes, and the phenotype of each mutation
is readily detectable. It also has the potential to be used to identify
target pathways or processes for any drug that acts on one single
yeast gene product if a compendium of GGSL profiles is available as
the key. These include those drugs that target both essential and
nonessential gene products as well as non-protein molecules. Druginduced haplo-insufficiency approaches have the potential to identify
directly some drug targets that could be on a list of target genes. It
can also be used to directly study the function of essential genes in
response to a drug.
the TAGs representing each YKO [14]. Because the
heterozygous YKOs have excellent genetic quality and
are more suitable for molecular manipulation as a
population than the haploid YKOs, dSLAM has proven
to be more robust than SGA and haploid SLAM in
identifying genome-wide synthetic interactions. Using
cin8D, bim1D, and sgs1D as query mutations, dSLAM
achieved lower false negative rates, better data reproducibility and potentially lower false positive rates than SGA
and haploid SLAM. dSLAM has been modified to study a
range of other genetic interactions such as chemicalgenetic interactions, genetic suppression by a second
mutation, dosage-dependent lethality and suppression,
synthetic haploid insufficiency and synthetic interactions
between a specific allele and genome-wide YKOs [14].
The issue of false positive and false negatives is
important in global synthetic-lethality screening but
devilishly difficult to pin down with precision. With both
SGA and SLAM, these rates will vary with the operator,
the query gene and the specific instance of the strain
collection used, in addition to many other factors. For
recent comparisons of hit lists identified for a single query
gene see publications by Pan et al. [14] and Loeillet et al.
[24]. Although neither technique is perfect, overall there is
excellent concordance in the list of target genes obtained
after verification by random spore or tetrad analyses.
Both haploid and diploid SLAM methods employ the
speed and comprehensiveness of microarray analysis and
could be useful tools to unravel the 5000!5000 synthetic
interaction matrix defined by the set of nonessential yeast
genes. dSLAM might also be easier to scale up because it
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TRENDS in Genetics Vol.22 No.1 January 2006
works well owing to the use of the existing full-length
deletions as query constructs, whereas high efficiency
haploid SLAM requires that the query construct deletes
!500 bp of the original sequence [13,14]. dSLAM might be
the most practical method to generate a complete yeast
genetic interaction map.
A technical weakness of SLAM is that some TAGs have
low hybridization-signal intensity because of mutations in
the actual TAGs or flanking sequences in the YKO strains
[25]. This problem could be partially solved by re-design of
TAG arrays based on the TAG sequences in the heterozygote YKO strains.
The future of global synthetic-lethality analysis
In principle, an extremely valuable byproduct of global
synthetic-lethality screens is the creation of heterozygous
diploid double mutant pools for every gene in the yeast
genome. Such double mutant collections could serve as a
starting point for additional functional profiling. For
example, growth of double mutant pools under multiple
culture conditions might uncover additional genetic
interactions not revealed using standard glucose-rich
medium growth conditions. In this scenario, the 5000!
5000 synthetic-lethality matrix identified using a set of
standard growth conditions might be viewed as the ‘base
layer’ of an ever-growing three-dimensional matrix in
61
which the first two dimensions are the query and target
genes and the third dimension represents the various
assay conditions.
The heterozygous, diploid double-mutant pools could
also be used to expand from pairwise to triple- and higher
level tuple interactions that lead to reduced fitness,
synthetic lethality or even suppression of a known
pairwise synthetic lethal interaction. A triple mutant
synthetic-lethality screen, in which lethality resulted from
the combination of three but not any pair of mutations,
was performed using BNI1 and BIM1 as dual query
mutations [22]. These two genes have no synthetic genetic
interactions with each other, but they share some common
genetic interactors. Only four out of 171 genetic interactions identified in this screen were attributable to a
triple mutant effect. This experiment suggests that,
although trigenic interactions exist, their frequency
might be less than that of digenic interactions on a
probability per combination basis. The total number of
possible triple mutant combinations is w1600 times more
than the number of possible double mutant combinations.
Genetic maps versus physical interaction maps
Global synthetic-lethality analysis will generate a catalog
of genetic interactions and identify cellular pathways that
buffer each other in essential processes, providing
Box 2. Elucidating connections between genetic and physical interaction networks
Genes with common synthetic lethal partners have an increased
probability of a physical interaction as represented by highthroughput two-hybrid and protein complex data and well-annotated
protein complex catalogs [22,23]. Synthetic lethal interactions have
the potential to identify gene products that act in the same molecular
environment or gene products that act in physically distant environments, as long as cell viability depends on both functions being
present. Initial analysis of the first available data set of large-scale
synthetic lethal interactions demonstrated that physical interaction
probability increased with the raw number of shared genetic
interaction partners [22]. Converting the raw number of shared
partners to a P-value, calculated using a hypergeometric distribution
for the null hypothesis [38], provides better enrichment of physical
interactions [23]. For convenience, the negative log10 (P-value) is
termed the congruence score, and gene pairs with a statistically
significant congruence score (P-value !0.01 after correcting for
multiple testing) are termed congruent. Congruent gene pairs have
tighter functional associations than direct synthetic-lethal gene pairs,
as indicated by higher enrichment for co-residence in a single protein
complex, and greater similarity of biological process, molecular
function and cellular localization annotations. A genetic congruence
map can be constructed by connecting pairs of congruent genes, and
can serve as a visualization tool to define pathway components [23].
Thus, functional associations predicted by congruence should be an
important addition to probabilistic functional association networks
[39,40]. Prediction of pathway membership has also been performed
using hierarchical clustering [22]. The advantage of genetic congruence analysis is that it produces a P-value to quantify functional
association, whereas hierarchical clustering depends on visual
inspection of clusters and definition of cluster boundaries. Pathway
membership was also predicted using statistical models that combine
physical interactions and synthetic lethal interactions [28]. Congruence remains independent of physical interaction data, but combining
the genetic and physical data can provide value when mutant and
wild-type systems are appropriately handled.
In a more global comparison, individual networks were constructed
using physical interaction, genetic interaction and genetic congruence
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links. When distances between pairs of genes and or gene products,
defined as the number of links in the shortest path, were compared
across networks [43], genes closest to each other in the genetic
congruence network were also likely to be close in the physical
interaction network. Genes connected by a few genetic interaction
links, however, typically were not close in the physical interaction or
congruence networks.
Enrichment or depletion of network motifs, defined as isomorphic
patterns of interactions, can also reveal similarities and differences
between networks. The pattern of motif enrichment resembles a
fingerprint for network structure [41,42]. Notably, the physical and
congruence interaction networks have similar fingerprints, whereas
the direct genetic interaction network has a distinct fingerprint [43].
The differences can be explained in large part by defining motifs as
transitive (interactions A–B and B–C imply higher probability of A–C)
and intransitive (interactions A–B and B–C do not imply A–C). A
triangle is the simplest transitive motif, and a square or four-cycle of
four genes connected head-to-tail is a simple intransitive motif. The
physical and congruence networks are highly enriched for transitive
motifs, whereas the genetic interaction network is somewhat enriched
for transitive motifs and also enriched for intransitive motifs [43].
Detection of enrichment for the triangle motif in the genetic interaction
network agrees with a previous study showing that genetic-interaction
partners of a gene have an increased likelihood to interact with each
other [44]. These results, and others obtained by motif analysis for
merged physical and genetic networks [28,45] provide a consistent
picture of genetic interactions bridging protein complexes and
pathways with complementary function. These motifs have also
been useful for predicting synthetic lethal interactions [44].
Correct interpretation of the relationship between genetic and
physical interactions enables interesting biological predictions from
synthetic-lethality analysis. Genetic congruence can predict novel
gene functions and pathway membership [39,40], and provide
confirmatory evidence for the true positives in high-throughput
physical interaction data sets. The analysis methods developed for
yeast will be useful for analogous genetic interaction screens
conducted using RNAi in higher organisms [32].
62
Review
TRENDS in Genetics Vol.22 No.1 January 2006
significant amounts of new information for systems
biologist and geneticist to understand fundamental
cellular processes. Global synthetic-lethality analysis
provides a means of generating a genetic interaction
map. Genome-wide two-hybrid analysis and large-scale
protein co-pulldown analyses followed by mass spectrometry identification of interacting proteins have been
used to identify protein–protein interactions in yeast with
the goal of generating a physical interaction map [26,27].
Synthetic-lethality analysis using YKO mutants provides
different kind of information from the physical protein
interaction map [22,28]. It tends to identify proteins that
provide essential functions in redundant or
parallel pathways.
Although genetically interacting genes encode proteins
in the same complex more often than expected by chance
[22], most synthetic lethal interactions between knockout
mutations identify genes that function in parallel pathways without direct physical interaction, such as the DNA
damage and DNA- replication-checkpoint pathways [23].
Synthetic lethal interactions that correlate with physical
interactions are truly the exception. Therefore, synthetic
lethal interactions are generally orthogonal to physical
interactions and, more importantly, to pathway membership (Figure 1). We therefore draw a distinction between
the genetic-interaction network (obtained by connecting
pairs of interacting YKOs) and a network based on the
similarity between lists of interaction partners for a given
pair of query genes or target genes, called a congruence
network (Box 2) [23].
Genetic map versus mRNA profiling map
Functional profiling data of the YKO mutants can be
compared with RNA expression-profiling data performed
under similar conditions. The hypothesis that genes in
which RNA expression is induced after a particular
treatment are important for optimal growth under the
same conditions was tested using treatments with DNAdamaging agents, such as ionizing radiation, UV radiation
and growth in the presence of 1 M NaCl or 1.5 M sorbitol
[9,29]. These studies concluded that surprisingly few of
the genes in which RNA expression increased significantly
ware actually required for optimal growth or recovery
under the same condition.
Potentially, RNA expression changes can be due to
indirect downstream events in cells unrelated to the
primary treatment (e.g. drug treatment). RNA expression
can change dramatically owing to minor perturbations
during culture conditions unrelated to the stimulus of
interest. Thus, RNA expression profiling identifies significant RNA expression changes, but the data are easily
‘contaminated’ by confounding changes resulting from
unknown perturbations. By contrast, functional profiling
directly assays the ability of the mutant to survive a
particular growth condition.
However, a simple explanation for this paradox could be
the existence of genetic buffering. Two redundant pathways are turned ‘on’ at the mRNA level following a
particular treatment. However, when flux through one of
these pathways is ablated, for example, by mutation of
a gene for a pathway component, the alternative pathway
www.sciencedirect.com
could still support growth under the same condition. In
this example, flux through both pathways would need to
be reduced to observe a growth requirement under the
particular treatment. The catalog of synthetic genetic
interactions, combined with mRNA profiling data will help
researchers prioritize experiments using double mutant
libraries. For example, assume that for gene X, mRNA
expression is induced under a particular growth condition,
but mutation of gene X has no effect on growth under the
same condition, its double mutant library could be assayed
for growth under the same condition to identify important
genetic interactors.
Global synthetic-lethality analysis as a tool to understand genetic buffering
Central to biology is the question of how genotypes define
phenotypes. However, to understand human biology and
disease, the question is usually one of how complex
genotypes confer complex phenotypes, because many
common diseases with a genetic component exhibit
properties of complex traits and might have polygenic
rather than mono- or even digenic determinants. Many
genetic variations are not manifested phenotypically as a
result of genetic buffering [3,20]. Genetic buffering can be
understood as a continuum over which the organism will
remain insensitive to variations in the activities of gene A,
probably because the function of gene B or the cellular
environment as interpreted by gene B can still support the
function of the organism. However when gene B is
missing, the organism will become sensitive to the activity
level of gene A. Global synthetic-lethality analysis can
help us understand genetic buffering, which could be
important to our understanding of genetic predisposition
to cancer and other diseases. Because many cellular
processes are highly conserved phylogenetically, a catalog
of genetic interactions identified in the yeast model system
holds promise for the effective identification of disease
susceptibility genes in humans.
A common theme across many multicellular organisms
is that the number of essential genes ranges from 10–30%.
For Caenorhabditis elegans, the number of essential genes
is predicted at 15–30% based on forward-genetic screens
[30]. Essential genes include classes of genes that cause
embryonic lethality, maternal-effect lethality, maternaleffect sterility and post-embryonic growth defects. Furthermore, a genome-wide RNA interference (RNAi) screen
identified phenotypes for w10% of the genes assayed [31],
and high-throughput synthetic genetic screens have been
performed in C. elegans [32]. In Drosophila melanogaster,
the number of essential genes has been postulated to be
3600 out of 12 000 predicted genes (30%) [33]. Global
synthetic-lethality analysis might soon reveal the nature
and extent of genetic buffering in metazoa.
Concluding remarks
The integration of the yeast synthetic-genetic interactions, physical interactions, mRNA expression profiling
and functional profiling data will enable the construction
of a ‘wiring diagram’ of the yeast cell, and serve as an
important framework for understanding human biology
and disease.
Review
TRENDS in Genetics Vol.22 No.1 January 2006
Acknowledgements
We thank our anonymous reviewers for their thoughtful comments that
greatly improved this article. We apologize to authors whose work could
not be cited owing to space limitations. Our work is supported by grants
from the NIH.
Supplementary data
Supplementary data associated with this article can be
found at doi:10.1016/j.tig.2005.11.003
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