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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. www.sciencedirect.com 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 58 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, www.sciencedirect.com 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 www.sciencedirect.com 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 60 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 www.sciencedirect.com 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 Review 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 www.sciencedirect.com 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. 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