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First published online April 20, 2007
Journal of Experimental Biology 210, 1559-1566 (2007)
Published by The Company of Biologists 2007
doi: 10.1242/jeb.002311
Review Article |
Modelling genotype–phenotype relationships and human disease with genetic interaction networks
EMBL/CRG Systems Biology Unit, Centre for Genomic Regulation (CRG), UPF, C/Dr Aiguader 88, Barcelona 08003, Spain
e-mail: ben.lehner{at}crg.es
Accepted 16 January 2007
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Summary |
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 TOP Summary IntroductionMapping genetic interaction...Mapping genetic interaction...Mechanistic interpretation of...Using genetic interactions to...Predicting genetic interactionsThe implications of genetic...References |
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Key words: genetic interactions, networks, systems biology
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Introduction |
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TOPSummaryIntroductionMapping genetic interaction...Mapping genetic interaction...Mechanistic interpretation of...Using genetic interactions to...Predicting genetic interactionsThe implications of genetic...References |
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).
For example even in the case of the simple `Mendelian' disease, cystic
fibrosis, it is not possible to predict the clinical phenotype of a patient
based solely on knowledge of the exact mutation in the `Cystic Fibrosis Gene',
CFTR. In fact at least seven different modifier genes have been described that
alter the clinical phenotype of this genetically simple disease
(Badano and Katsanis,
2002).
Most hereditary diseases are genetically much more complex than cystic
fibrosis; although an increasing number of genes have been identified as
mutated in common pathologies such as cardiovascular disease, cancer, diabetes
and neurodegenerative diseases, these mutations only account for a small
proportion of the total genetic predisposition to these conditions
(Badano and Katsanis, 2002).
One reason why causal mutations have proved so difficult to identify may be
the problem of synthetic interactions between genes: mutations that have
little effect on disease phenotypes alone can have strong synthetic effects
when combined (Hartman et al.,
2001). Indeed in most linkage or association studies there is
insufficient statistical power to identify these interactions between genes
(Badano and Katsanis, 2002).
Therefore the extent and importance of genetic interactions in human disease
remains largely unknown.
An alternative approach to understand how genes interact to produce
phenotypes is to identify genetic interactions between mutations in model
organisms (Hartman et al.,
2001). The idea of this approach is to take a simple phenotype
(typically the simplest, viability) and to identify comprehensively how
combinations of mutations in genes can affect this phenotype. Although there
are many important types of aggravating and alleviating genetic interactions
that can occur between genes (Drees et al.,
2005), to date most work has concentrated on synthetic lethal
interactions. A synthetic (or synergistic) lethal interaction is formally
defined when the survival resulting from combining mutations in two genes is
less than the product of the survival resulting from each mutation
individually (Drees et al.,
2005). Most commonly synthetic lethal interactions are identified
experimentally when the combination of mutations in two non-essential genes
produces a lethal phenotype (Fig.
1).
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Mapping genetic interaction networks in yeast |
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TOPSummaryIntroductionMapping genetic interaction...Mapping genetic interaction...Mechanistic interpretation of...Using genetic interactions to...Predicting genetic interactionsThe implications of genetic...References |
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;
Pan et al., 2006;
Schuldiner et al., 2005;
Tong et al., 2001;
Tong et al., 2004). Two
general strategies have been devised that make use of the library of yeast
gene deletion strains (Giaever et al.,
2002) to identify genetic interactions systematically in this
haploid organism. In the first approach, double mutants are constructed and
assayed for viability in parallel by mating a yeast strain carrying a query
mutation to the complete library of viable deletion strains in an arrayed
format [`synthetic genetic array' analysis, SGA
(Tong et al., 2001)]. In
contrast, in the second approach double mutants are constructed and assayed as
a single pool, and the relative growth rates of each strain are determined
using DNA microarrays that can detect the presence of each strain in the pool
[`synthetic lethal analysis by microarray', SLAM
(Ooi et al., 2003)]. I will
first outline these two methods, and then will discuss the genetic interaction
networks that have been produced using them.
SGA
In the SGA approach (Tong et al.,
2001), a haploid yeast strain carrying a `query' mutation in a
gene of interest is mated to a library of yeast deletion strains in an arrayed
format using replicating tools and robotics. The diploid yeast are then
sporulated, and double mutant haploid progeny are selected using a cleverly
designed reporter construct (the `SGA reporter',
can1
::MFA1pr-HIS3, that is present in the
MATalpha query strain, but in the absence of histidine only allows
growth of haploids of mating type MATa, i.e. only the double mutant
progeny). Synthetic sick or nonviable double mutants are identified by weakly
growing or absent double mutant colonies, and their identity is determined by
their position on the array. Potential synthetic lethal or sick interactions
are then individually confirmed by tetrad or random spore analysis
(Tong et al., 2001).
SLAM
In the SLAM approach, a query mutation is introduced into the pool of
haploid deletion strains by direct integrative transformation
(Ooi et al., 2003). The double
mutants are then grown competitively in a single vessel, and non-growing or
slow growing double mutant strains are identified using microarrays. This
approach is possible because of the two `barcode' DNA sequences that uniquely
identify each deletion strain. These barcodes allow the deletion strains that
are present in a pool to be individually identified by hybridisation of
genomic DNA to a microarray containing sequences complementary to each of the
barcodes. In contrast to the qualitative SGA approach, in the SLAM procedure
the definition of a synthetic interaction depends upon a quantitative cut-off
in hybridisation intensity to identify slow growing or absent strains. A
modification of the SLAM procedure uses heterozygous diploid deletion strains
as a starting point [`diploid-based SLAM' or dSLAM
(Pan et al., 2004)].
Maintaining the deletion strains as heterozygous diploids protects them from
selection for compensatory or reversion mutations that overcome fitness
defects, so reducing the false negative rate of the approach. As a result, the
dSLAM approach probably has a lower false negative rate than SGA
(Tong et al., 2004). One
disadvantage of the dSLAM approach is that some barcode tags have low
hybridisation-signal intensities (for example because of mutations in the tag
sequences) with the result that there is little reliable information for some
genes (Eason et al., 2004).
However this limitation has been addressed by redesigning the microarrays used
to detect the barcodes (Pierce et al.,
2006; Yuan et al.,
2005).
Both the SGA and dSLAM approaches have been used to construct extensive
genetic interaction networks in yeast. Using the SGA approach, Tong et al.
screened 132 query strains (carrying mutations in genes with diverse functions
in cell polarity, cell wall biosynthesis, chromosome segregation and DNA
synthesis and repair) against the complete library of 
4700 viable haploid
deletion strains, and identified a total of 2012 synthetic lethal and 2113
synthetic sick interactions involving 1000 genes
(Tong et al., 2004). Both
deletions of non-essential genes and point mutations in essential genes were
used as query genes, and synthetic lethal interactions were detected for 80%
of query strains, with a mean of 34 interactions per query gene (and a range
of 1–146 interactions per gene). Using the dSLAM approach Pan et al.
screened 74 query strains known to function in DNA replication and repair
against the same deletion library and identified a total of 4956 synthetic
fitness or lethality defects involving 875 genes
(Pan et al., 2006). Over 91%
of these interactions were entirely novel
(Pan et al., 2006).
Mapping genetic interactions for essential genes
Both the Tong et al. and Pan et al. studies screened query strains against
the 4700 viable yeast deletion strains, so interactions with the
1000 essential genes in the yeast genome could not be detected. Two
approaches have been developed to identify genetic interactions with essential
genes (Davierwala et al., 2005;
Schuldiner et al., 2005). In
the first approach, Davierwala et al. constructed a library of yeast strains
that carry promoter-replacement alleles. These alleles allow the expression of
each gene to be switched off by the addition of the small molecule doxycycline
to the media (the `tet-off' system). Addition of intermediate levels of
doxycycline can therefore be used to reduce the expression of each essential
gene, so producing hypomorphic (reduction in function) alleles of each gene.
The authors created a library consisting of promoter replacement alleles for
575 essential genes (representing about half of the total number of essential
genes) and screened it against 30 query strains that were either conditional
alleles of essential genes or deletions of non-essential genes, identifying a
total of 567 interactions. Interestingly the mean number of interactions
detected for each essential gene was about sixfold more than for non-essential
genes (Davierwala et al.,
2005).
The second strategy that has been used to identify interactions for
essential genes is to generate hypomorphic alleles by replacing the
3'UTR of each gene with an antibiotic resistance cassette [`decreased
abundance by mRNA perturbation', DAmP
(Schuldiner et al., 2005)]. In
this approach, the antibiotic resistance cassette serves to destabilise the
expression of an mRNA, so reducing the expression of each essential gene. The
DAmP approach was used to identify genetic interactions between genes that
function in the early secretory pathway
(Schuldiner et al., 2005) (see
below).
In theory, all of these methods for identifying interactions between pairs
of genes could also be used to identify higher order interactions between more
than two genes. For example, Tong et al. also used SGA to screen two different
double mutant strains for interactions with a third gene to identify trigenic
interactions (Tong et al.,
2004). The authors identified a total of 171 and 156 interactions
in these screens, although only 4 and 29 of the interactions were attributable
to a triple mutant effect (the rest were also seen in one of the three
possible double mutant combinations alone). However, because there are
2000-fold more possible gene triplets than gene pairs in the S.
cerevisiae genome, the total number of trigenic synthetic lethal
interactions may still be greater than the number of digenic interactions.
Quantitative genetic interaction screens
The SGA and dSLAM approaches have been used to identify synthetic lethal
and sick phenotypes on a genomic-scale. However there are many other classes
of interactions that can occur between genes, and approaches have also been
devised to begin to identify these interactions systematically
(Collins et al., 2006;
Drees et al., 2005;
Hartman and Tippery, 2004;
Schuldiner et al., 2005). For
example, Schuldiner et al. used digital imaging to quantify the growth of
yeast colonies such that they could measure both aggravating and alleviating
interactions between 424 yeast genes that function in the early secretory
pathway (Schuldiner et al.,
2005). Rather than categorizing the observed interactions into
different types of genetic interaction, they used a continuous score to
describe the strength of an interaction and to cluster genes according to
their interaction profiles. The authors demonstrated that using quantitative
measurements of interaction strength helped to identify modules of genes that
share precise molecular functions
(Schuldiner et al., 2005).
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Mapping genetic interaction networks in C. elegans |
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TOPSummaryIntroductionMapping genetic interaction...Mapping genetic interaction...Mechanistic interpretation of...Using genetic interactions to...Predicting genetic interactionsThe implications of genetic...References |
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Systematically identifying genetic interactions by crossing mutant strains
is not logistically practical in multicellular organisms – comprehensive
collections of deletion strains are not available, and the diploidy of these
organisms requires cumbersome multigenerational mating and selection screens
to be used. An alternative approach for identifying genetic interactions in
metazoans is to use RNA interference (RNAi) to inhibit gene expression
(Baugh et al., 2005;
Holway et al., 2005;
Lehner et al., 2006a;
Lehner et al., 2006b;
Lehner et al., 2006c;
Suzuki and Han, 2006;
Tischler et al., 2006;
van Haaften et al., 2004).
Here either a genetic mutant is combined with RNAi against a second gene
(Fig. 1B), or RNAi can be used
to inhibit the expression of two genes simultaneously
(Fig. 1C)
(Tischler et al., 2006). One
advantage of using RNAi compared to deletion strains is that, because RNAi
normally produces a `knock-down' rather than a `knock-out', it is also
possible to identify interactions for essential genes.
C. elegans is a unique model animal in which genetic interactions
can be identified in vivo in the context of a developing organism;
the expression of any gene can be systemically inhibited using long dsRNAs
delivered by bacterial feeding (Timmons
and Fire, 1998). Although it should be possible to identify
genetic interactions between genes using RNAi in cell culture for mammalian or
fly cells, C. elegans is currently the only model organism in which
this approach can be used in vivo on a comprehensive scale. In C.
elegans, RNAi screens can be performed in liquid culture in 96-well
plates (Lehner et al., 2006c;
van Haaften et al., 2004)
using the bacterial feeding library
(Kamath et al., 2003). This
allows RNAi screens to be performed at sufficient throughput to be able to
test tens of thousands of gene pairs for their ability to interact genetically
in vivo.
Using high-throughput RNAi screens in C. elegans, we recently
constructed the first systematic genetic interaction network for any animal
(Lehner et al., 2006b). We
focussed on genes that function in signalling pathways, and tested >65 000
pairs of genes for their ability to interact in vivo using both
genetic mutant query strains and combinatorial RNAi. In total we identified
351 pairs of genes that when inactivated in combination produced a synthetic
nonviable phenotype.
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Mechanistic interpretation of genetic interaction networks |
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TOPSummaryIntroductionMapping genetic interaction...Mapping genetic interaction...Mechanistic interpretation of...Using genetic interactions to...Predicting genetic interactionsThe implications of genetic...References |
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). In
their original paper, Tong et al. noted that although 27% of the genetic
interactions that they identified link genes with similar Gene Ontology (GO)
annotations, only 1% of synthetic lethal interactions occur between genes
whose products reside in the same protein complex
(Tong et al., 2004). Rather,
they demonstrated that genes encoding products that function in the same
protein complex or pathway often have similar profiles of genetic interactions
(i.e. genes from a single pathway tend to interact with the same genes, rather
than with each other). Indeed the more genetic interactions two genes share,
the more likely those two gene products are to interact physically (see below)
(Tong et al., 2004).
By combining genetic interaction data with comprehensive
protein–protein, protein–DNA and metabolic network data, Kelley
and Ideker systematically compared the ability of `within-pathway' models
(also called `intra-pathway' or `series' models,
Fig. 2A) or `between-pathway'
(`inter-pathway' or `parallel') models
(Fig. 2B) to explain
systematically compiled genetic interaction data
(Kelley and Ideker, 2005).
Using a probabilistic model, they found that between-pathway models could
explain three-and-a-half times as many interactions as within-pathway models.
They were, however, unable to provide a mechanistic interpretation for
60% of the observed genetic interactions in yeast. Indeed the extensive
genetic interactions identified from both the SGA and dSLAM studies suggest
that many complex compensatory relationships can occur between seemingly
unrelated cellular pathways. For example Pan et al. observed extensive
functional compensation between loss of DNA damage response pathway genes and
genes involved in mRNA transcription, mRNA processing and Golgi integrity
(Pan et al., 2006). Therefore
the mechanistic interpretation of genetic interactions remains an important
area for future work.
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In contrast to the situation in yeast, in the C. elegans genetic
interaction network (Lehner et al.,
2006b), within-pathway interactions appear to account for more
interactions than between-pathway interactions: focussing on known components
of signalling pathways, twice as many interactions are seen between components
of the same pathway than between components of separate pathways (B.L.,
unpublished observation). The explanation for this probably lies in the
difference in experimental approaches used: in yeast the interactions analysed
are primarily between null alleles, whereas in C. elegans most
interactions are between an hypomorphic allele and RNAi knockdown of a second
gene. This makes intuitive sense: whereas the phenotypic consequences of a
null mutation in a gene in a linear pathway cannot be further enhanced by a
second mutation in that pathway, two partial loss-of-function mutations in a
single pathway can be combined to inhibit that pathway completely
(Fig. 2). Therefore with null
alleles, many interactions probably represent interactions between the
complete inactivation of two non-essential pathways that can functionally
compensate for each other, whereas with hypomorphic alleles many of the
interactions may represent interactions between the partial inactivation of
two genes that act in the same essential pathway.
A further simple class of synthetic lethal interaction is seen between two
duplicated genes that encode homologous proteins. Although many duplicated
genes clearly do encode redundant functions that are maintained over
considerable evolutionary periods
(Tischler et al., 2006),
careful analysis of single gene mutant phenotypes
(Wagner, 2005) and the SGA
synthetic lethal data (Tong et al.,
2004) suggests that gene duplications explain only a very small
minority of synthetic lethal interactions [<2% of the interactions
identified by SGA encode homologous proteins
(Tong et al., 2004)].
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Using genetic interactions to understand gene function |
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TOPSummaryIntroductionMapping genetic interaction...Mapping genetic interaction...Mechanistic interpretation of...Using genetic interactions to...Predicting genetic interactionsThe implications of genetic...References |
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;
Ye et al., 2005). Therefore
the number of shared genetic interaction partners for two genes can be used to
rank the probability of the two gene products physically interacting and
sharing a biological function. The similarity of the interaction profiles of
two genes can be mathematically defined by their `congruence'
(Ye et al., 2005). Congruence
can be defined as the negative log of the hypergeometric P-value of
the number of shared genetic interaction neighbours of two genes
(Ye et al., 2005), which
accounts for both the number of interactions each gene makes and the total
size of the interaction network. Using yeast genetic interaction data,
congruence performs better as a predictor of physical interactions and shared
function than either direct genetic interactions or counting the number of
interaction partners shared by two genes
(Ye et al., 2005). Pan et al.
were able to use congruence to define 16 functionally homogenous modules of
DNA damage response genes (Pan et al.,
2006) and were able to use the genetic interaction profiles of
novel genes to predict their functions in the DNA damage response
(Pan et al., 2006).
In the C. elegans genetic interaction network, because more
interactions occur within a pathway than between pathways, knowledge of the
direct interaction partners of a gene can be used to predict its function. For
example, we systematically tested whether all of the genes that were found to
interact with two or more known components of the EGF/Ras/MAPK pathway acted
as general modulators of that pathway. For 9/16 of such cases tested, we found
that the genes could indeed modulate EGF signalling in a precise developmental
setting, suggesting that they do indeed act as general modulators of this
pathway (Lehner et al.,
2006b).
One way in which the accuracy of gene function prediction from genetic
interaction networks can probably be improved is to identify both aggravating
and alleviating interactions, and to quantify the strength of interactions.
Indeed Schuldiner et al. found that including quantitative interaction data
helped to define modules of genes that share precise molecular functions in
the early secretory pathway (Schuldiner et
al., 2005).
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Predicting genetic interactions |
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TOPSummaryIntroductionMapping genetic interaction...Mapping genetic interaction...Mechanistic interpretation of...Using genetic interactions to...Predicting genetic interactionsThe implications of genetic...References |
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), the
inefficiency and expense of RNAi in mammalian cells
(Pei and Tuschl, 2006), and
the prevalence of off-target effects in both systems
(Kulkarni et al., 2006;
Lehner et al., 2004;
Ma et al., 2006), has to date
limited the application of this approach. Moreover it is not entirely clear
how synthetic lethal interactions identified in single cells will relate to
interactions in whole organisms.
An alternative to identifying genetic interactions using large-scale
experimental approaches is to use computational methods to predict genetic
interactions between genes. This is analogous to protein interaction networks,
where methods that computationally predict protein interactions are now
developed to the point that they are at least as accurate as most
high-throughput experimental protocols or interactions derived from the
literature (Jansen et al.,
2003; Lee et al.,
2004; Troyanskaya et al.,
2003). Here I discuss three approaches that have been used for
predicting genetic interactions: (i) using existing genetic interactions and
the local network structure to predict new interactions, (ii) using the
integration of other genomic datasets to predict genetic interactions, and
(iii) using interactions from one species to predict interactions in a second
species.
Predicting genetic interactions using network structure
One property of the yeast genetic interaction network is that two genes
that share a genetic interaction with a common partner are likely to interact
with each other (Tong et al.,
2004). Tong et al. first exploited this `small world' feature of
genetic interaction networks to predict further interactions and found that in
20% of cases the neighbours of a query gene could also interact with each
other (compared to <1% of random gene pairs).
Predicting genetic interactions using other genomic datasets
Genes that share known functions are likely to have similar genetic
interaction profiles. Genetic interactions can therefore be predicted using
additional genomic datasets that link genes according to their functions. For
example, genetic interactions for genes encoding proteins that physically
interact have been successfully predicted
(Kelley and Ideker, 2005;
Ye et al., 2005). However a
more powerful approach is to combine multiple different datasets that connect
functionally related genes, and to use these to predict genetic interactions.
Here I discuss the two approaches that have been applied to date: decision
trees, and Bayesian integration.
Decision trees provide a method for classifying data into two or more
classes (here `interacting' and `non-interacting') using multiple different
evidence types. At each step in the tree a list of genes is divided into those
that do or do not possess a particular characteristic. At the top of the tree
the gene list is first split using the characteristic that is most informative
for predicting the property of interest (here the ability to predict a genetic
interaction). Additional characteristics are then used to make additional
subdivisions of the gene list until no additional characteristic is
informative and a branch is terminated. An advantage of decision trees over
`black box' methods such as neural networks and support vector machines is
that they explicitly reveal the characteristics used to classify the data.
They also do not assume independence between predictive evidence types, which
allows multiple related datasets to be used even if they contain correlations
with each other. Wong et al. used decision trees to integrate protein
localisation, mRNA expression, physical interaction, known function and
network topology data in order to predict synthetic lethal or sick
interactions between yeast genes (Wong et
al., 2004). Using cross-validation tests, they found that decision
trees could reliably predict genetic interactions between yeast genes. They
also tested the predictions for eight new SGA screens not seen in training;
49/318 predictions were verified, compared to 2/318 expected by chance
(Wong et al., 2004). The top
predictor in the decision tree was the previously noted network property that
genes that share genetic interaction partners are also likely to interact
genetically. `Between-pathway' models were also found to be useful predictors
of interactions. However, individually omitting other kinds of functional data
alone had little effect on the quality of predictions
(Wong et al., 2004).
An alternative method for integrating genomic datasets to predict genetic
interactions is Bayesian integration. In this approach predictions made using
different data types are weighted according to the ability of each data type
to predict known genetic interactions, as opposed to genes that are known not
to interact (Jansen et al.,
2003; Lee et al.,
2004; Troyanskaya et al.,
2003). Zhong and Sternberg recently used this approach to predict
genetic interactions for 10% of C. elegans genes, using
information on anatomical expression patterns, phenotypes, functional
annotations, microarray coexpression and protein interactions to predict
genetic interactions (Zhong and Sternberg,
2006). The authors used their network to identify twelve subtle
modifiers of mutations in the let-60 Ras gene and two novel
suppressors of mutations in the itr-1 gene. Using a modified Bayesian
integration, we have extended this approach to construct a network of >100
000 interactions that covers >60% of C. elegans genes, and have
used this network to identify new suppressors of mutations in the
Retinoblastoma pathway and cross-talk between the Dystrophin complex and the
EGF/Ras/MAPK pathway (I. Lee, B.L., C. Crombie, A. Fraser and E. M. Marcotte,
unpublished data).
Predicting genetic interactions using orthology relationships
A final approach for predicting genetic interactions is to use genetic
interactions identified in one species to predict genetic interactions in a
second species. The evolutionary conservation of protein–protein
interactions between species (Matthews et
al., 2001), means that human protein–protein interactions
can be successfully predicted by using data from model organisms
(Lehner and Fraser, 2004). To
test whether genetic interactions can also be successfully transferred between
species, we have tested whether the orthologs of genes that are synthetically
lethal in S. cerevisiae are also synthetically lethal in C.
elegans. In total we have tested >1000 predicted interactions, but
have found that <1% are conserved (J. Tischler, B.L. and A. Fraser,
unpublished data). This is in contrast to mutations in single genes; >60%
of the orthologs of genes that are essential in S. cerevisiae are
also essential in C. elegans
(Kamath et al., 2003).
Therefore at least for synthetic lethal interactions, interactions are
probably not directly conserved between unicellular and multicellular
organisms. This may reflect the presence of additional compensatory pathways
in higher eukaryotes, or alternatively suggests that there is little
evolutionary selection for `higher order' interactions between mutations in
pairs of genes. This does not mean, however, that synthetic lethal
interactions between yeast genes are uninformative for predicting human gene
function – clearly the synthetic lethal profiles of yeast genes are
highly informative for predicting the molecular functions of orthologous human
genes (Pan et al., 2006), and
this information could be used to predict genetic interactions in humans. In
future work it will be important to test whether genetic interactions can be
successfully transferred between more related species (for example between
C. elegans and humans). Anecdotal examples from the literature [for
example the conservation of genetic interactions between components of the
EGF/Ras/MAPK pathway between worms and flies
(Sundaram, 2005)], suggest
that this should be more successful. In addition it is possible to envisage
more sophisticated approaches, whereby interactions between pathways, rather
than genes, are predicted.
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The implications of genetic interaction networks for human disease: hubs, buffers and new paradigms for human disease |
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TOPSummaryIntroductionMapping genetic interaction...Mapping genetic interaction...Mechanistic interpretation of...Using genetic interactions to...Predicting genetic interactionsThe implications of genetic...References |
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). This suggests that most hereditary diseases are also likely
to result primarily from interactions between mutations in multiple genes
rather than from mutations in single genes
(Badano and Katsanis, 2002).
Indeed the prevalence of synthetic interactions between genes may explain the
relative lack of success in identifying the causal mutations in most common
hereditary diseases – each disease may result from many different
combinations of mutations in many different genes, each of which has only a
minor or zero effect on the disease alone. This paints a rather pessimistic
picture for the practicalities of genetically dissecting and treating complex
genetic diseases.
However the structure of model organism genetic interaction networks also
has a positive implication for human disease genetics – the existence of
highly connected `hub' genes in the networks suggests that there will be
common modifiers of genetic mutations in humans. For example, in the C.
elegans genetic interaction network we identified a class of genes that
interact genetically with many diverse genes. Inactivation of each of these
`hub' genes can enhance the loss-of-function phenotype resulting from
mutations in many different genes with diverse molecular functions
(Fig. 3). Indeed, loss of a hub
gene can enhance many different phenotypes, depending upon the other gene that
is mutated in combination. In this way they can be thought of as `buffering'
an organism from the phenotypic consequences of mutations. Remarkably all of
the most connected hub genes that we identified function in
chromatin-modifying complexes (Lehner et
al., 2006b).
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There are probably many more hub genes in addition to these
chromatin-modifying genes. For example, in the SGA yeast genetic interaction
network, four of the top five most connected genes encode components of the
prefoldin chaperone complex (Tong et al.,
2004). Moreover the chaperone hsp90 can also be classed
as a hub gene, because of its ability to enhance the phenotypic consequences
of mutations in multiple genes when inactivated in flies, plants and yeast
(Queitsch et al., 2002;
Rutherford and Lindquist,
1998; Zhao et al.,
2005). Thus at least two classes of genes can function as genetic
hubs: chromatin-modifiers and chaperones. Interestingly hsp90 may
bridge these two functional classes; although it is well known as a chaperone,
it may also affect phenotypic variation via its effects on chromatin
structure (Sollars et al.,
2003).
Although there are likely many other hub genes that remain to be
identified, their implication for human disease is clear: there will probably
be human genes that act as modifier genes in many mechanistically unrelated
diseases. Indeed the concept of hub genes suggests a new paradigm for genetic
disease in humans (Lehner et al.,
2006b). In this paradigm there are two classes of human disease
gene: the first class consists of `specifier' genes that define the particular
disease, and the second class consists of `modifier' or `hub' genes that serve
to enhance the strength of the disease resulting from a mutation in a
specifier gene. There is good evidence to suggest that hub genes identified in
one organism also function as hubs in other species
(Lehner et al., 2006b;
Queitsch et al., 2002;
Zhao et al., 2005), and so the
particular genes identified as hubs in model organisms may also function as
hub genes in humans.
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Acknowledgments |
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Footnotes |
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References |
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TOPSummaryIntroductionMapping genetic interaction...Mapping genetic interaction...Mechanistic interpretation of...Using genetic interactions to...Predicting genetic interactionsThe implications of genetic...References |
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Badano, J. L. and Katsanis, N. (2002). Beyond Mendel: an evolving view of human genetic disease transmission. Nat. Rev. Genet. 3,779 -789.[Medline]
Baugh, L. R., Wen, J. C., Hill, A. A., Slonim, D. K., Brown, E. L. and Hunter, C. P. (2005). Synthetic lethal analysis of Caenorhabditis elegans posterior embryonic patterning genes identifies conserved genetic interactions. Genome Biol. 6,R45 .[CrossRef][Medline]
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