Gene function prediction from congruent synthetic lethal interactions in yeast
2005; Springer Nature; Volume: 1; Issue: 1 Linguagem: Inglês
10.1038/msb4100034
ISSN1744-4292
AutoresPing Ye, Brian D. Peyser, Xuewen Pan, Jef D. Boeke, Forrest Spencer, Joel S. Bader,
Tópico(s)Genomics and Chromatin Dynamics
ResumoArticle22 November 2005Open Access Gene function prediction from congruent synthetic lethal interactions in yeast Ping Ye Ping Ye Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, MD, USA The High-Throughput Biology Center, The Johns Hopkins University School of Medicine, Baltimore, MD, USA Search for more papers by this author Brian D Peyser Brian D Peyser McKusick-Nathans Institute of Genetic Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD, USA Department of Molecular Biology and Genetics, The Johns Hopkins University School of Medicine, Baltimore, MD, USA Search for more papers by this author Xuewen Pan Xuewen Pan The High-Throughput Biology Center, The Johns Hopkins University School of Medicine, Baltimore, MD, USA Department of Molecular Biology and Genetics, The Johns Hopkins University School of Medicine, Baltimore, MD, USA Search for more papers by this author Jef D Boeke Jef D Boeke The High-Throughput Biology Center, The Johns Hopkins University School of Medicine, Baltimore, MD, USA Department of Molecular Biology and Genetics, The Johns Hopkins University School of Medicine, Baltimore, MD, USA Search for more papers by this author Forrest A Spencer Corresponding Author Forrest A Spencer McKusick-Nathans Institute of Genetic Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD, USA Search for more papers by this author Joel S Bader Corresponding Author Joel S Bader Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, MD, USA The High-Throughput Biology Center, The Johns Hopkins University School of Medicine, Baltimore, MD, USA Search for more papers by this author Ping Ye Ping Ye Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, MD, USA The High-Throughput Biology Center, The Johns Hopkins University School of Medicine, Baltimore, MD, USA Search for more papers by this author Brian D Peyser Brian D Peyser McKusick-Nathans Institute of Genetic Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD, USA Department of Molecular Biology and Genetics, The Johns Hopkins University School of Medicine, Baltimore, MD, USA Search for more papers by this author Xuewen Pan Xuewen Pan The High-Throughput Biology Center, The Johns Hopkins University School of Medicine, Baltimore, MD, USA Department of Molecular Biology and Genetics, The Johns Hopkins University School of Medicine, Baltimore, MD, USA Search for more papers by this author Jef D Boeke Jef D Boeke The High-Throughput Biology Center, The Johns Hopkins University School of Medicine, Baltimore, MD, USA Department of Molecular Biology and Genetics, The Johns Hopkins University School of Medicine, Baltimore, MD, USA Search for more papers by this author Forrest A Spencer Corresponding Author Forrest A Spencer McKusick-Nathans Institute of Genetic Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD, USA Search for more papers by this author Joel S Bader Corresponding Author Joel S Bader Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, MD, USA The High-Throughput Biology Center, The Johns Hopkins University School of Medicine, Baltimore, MD, USA Search for more papers by this author Author Information Ping Ye1,2,‡, Brian D Peyser3,4,‡, Xuewen Pan2,4, Jef D Boeke2,4, Forrest A Spencer 3 and Joel S Bader 1,2 1Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, MD, USA 2The High-Throughput Biology Center, The Johns Hopkins University School of Medicine, Baltimore, MD, USA 3McKusick-Nathans Institute of Genetic Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD, USA 4Department of Molecular Biology and Genetics, The Johns Hopkins University School of Medicine, Baltimore, MD, USA ‡These authors contributed equally to this work. *Corresponding authors. McKusick-Nathans Institute of Genetic Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA. Tel.: +1 410 614 2536; Fax: +1 410 614 8600; E-mail: [email protected] of BioMedical Engineering, Johns Hopkins University, 210C Clark Hall, 3400 N Charles St, Baltimore, MD 21218, USA. Tel.: +1 410 516 7417; Fax: +1 410 516 5294; E-mail: [email protected] Molecular Systems Biology (2005)1:2005.0026https://doi.org/10.1038/msb4100034 PDFDownload PDF of article text and main figures. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info We predicted gene function using synthetic lethal genetic interactions between null alleles in Saccharomyces cerevisiae. Phenotypic and protein interaction data indicate that synthetic lethal gene pairs function in parallel or compensating pathways. Congruent gene pairs, defined as sharing synthetic lethal partners, are in single pathway branches. We predicted benomyl sensitivity and nuclear migration defects using congruence; these phenotypes were uncorrelated with direct synthetic lethality. We also predicted YLL049W as a new member of the dynein–dynactin pathway and provided new supporting experimental evidence. We performed synthetic lethal screens of the parallel mitotic exit network (MEN) and Cdc14 early anaphase release pathways required for late cell cycle. Synthetic lethal interactions bridged genes in these pathways, and high congruence linked genes within each pathway. Synthetic lethal interactions between MEN and all components of the Sin3/Rpd3 histone deacetylase revealed a novel function for Sin3/Rpd3 in promoting mitotic exit in parallel to MEN. These in silico methods can predict phenotypes and gene functions and are applicable to genomic synthetic lethality screens in yeast and analogous RNA interference screens in metazoans. Synopsis With the completion of the genome sequence for human and model organisms, the next phase is to understand how genes and gene products function together in pathways. Assays for physical interactions between proteins reveal how protein subunits assemble into larger machines and how protein–protein interactions provide the mechanism for regulation. Distinct from assays for physical interactions are assays for genetic interactions. Two genes have a genetic interaction if a double mutant (including null alleles, other mutant alleles, and dosage-dependent effects) has a phenotype distinct from the phenotype of the individual single mutants. Unlike a physical interaction, however, a genetic interaction does not provide direct evidence for the pathway wiring underlying the observation. This manuscript describes a method for reverse-engineering a pathway wiring diagram underneath genetic interaction data and applies the method to high-throughput screens in yeast, which has ∼6000 total genes and ∼5000 non-essential genes. While each of the 5000 non-essential gene deletions yields a viable phenotype, pairwise deletions of non-essential genes may be lethal. A lethal pairwise mutation is termed a synthetic lethal genetic interaction and indicates how gene functions buffer or compensate each other. The metaphor we employ for an essential biological process is an electric circuit where nodes represent genes or gene products and wires represent physical interactions between biomolecules (Figure 1). Deleting a gene corresponds to cutting the wires it connects. Robustness arises from multiple pathway branches connected in parallel. If one branch is cut, current still flows, but if both are cut the process fails and the cell dies. In this picture, synthetic lethal interactions should be observed between pathway branches, but not within branches. Synthetic lethal interactions between pathways are orthogonal to physical interactions within pathways. Two genes that share synthetic lethal interaction partners are therefore likely to function within the same pathway branch. The genes that share synthetic lethal partners, termed congruent genes, should have greater functional similarity than genes with direct synthetic lethal interactions. The congruence score provides a numerical ranking of the degree of partner sharing. It is defined as the −log10 of the P-value for the number of shared genetic interaction partners of two genes. We find that genes with significant congruence score have more similar database annotations than genes with direct synthetic lethal interactions. Products of congruent genes are also more likely to have direct physical interactions or to share protein complex membership than products of synthetic lethal genes. We conducted unbiased, genome-scale tests of the concept of congruence by identifying landmark genes whose mutants have a distinct phenotype, ranking the rest of the genome by congruence to the landmarks, and scoring the phenotypes of the mutants in rank order (Figure 3). Genes congruent to known members of the dynein–dynactin spindle orientation pathway exhibit a nuclear migration defect rate that increases with increasing congruence score (Figure 3A). One of these genes is YLL049w, an uncharacterized ORF. Pathway membership for YLL049w is further defined by the observation that the temperature dependence of its defect rate matches JNM1, a component of dynactin, rather than KIP2, a kinesin-like motor protein involved in delivering dynein to the cell cortex. We have also independently validated a physical interaction between Yll049w and Jnm1p. Taken together, these data indicate a role for YLL049w in a dynactin-related activity within the dynein–dynactin spindle orientation pathway. In a second test, we identified genes congruent to CIN1, a microtubule biogenesis gene, whose deletion mutant confers sensitivity to the anti-microtubule drug benomyl. Genes congruent to CIN1 are enriched for benomyl sensitivity (Figure 3B). Furthermore, the quantitative LD50 benomyl concentration is correlated with the congruence score to seven benomyl-sensitive landmarks (Figure 3C). Finally, a predictor based on the number of direct synthetic lethal interactions with benomyl-sensitivity landmarks, rather than on the congruence score, fails to predict benomyl sensitivity (Figure 3D). This result is consistent with a metric that successfully identifies within-pathway gene pairs, which are expected to exhibit more phenotypic similarity than between-pathway gene pairs. Beyond providing novel evidence for the function of Yll049wp, this work is significant in providing a framework for interpreting the results of genetic interaction screens. Networks are becoming increasingly popular models for visualizing and analyzing biological information. Genetic interaction screens of knockout alleles yield pairwise relationships between genes. While it is tempting to use genetic interactions as evidence for network edges, we show that a more powerful interpretation is to infer edges that are orthogonal to the direct genetic interactions. While the methods have been developed and applied to yeast knockout screens, they should be applicable to reduction-of-function screens using RNA interference in higher eukaryotes and metazoans. Introduction The robustness of a biological network to defects can be probed by synthetic lethality, which reveals that a cell survives individual gene deletions, but cannot survive deletion of specific gene pairs. Synthetic lethal interactions have been rationalized with two hypotheses: (i) two genes in a single linear pathway can show synthetic lethality; (ii) synthetic lethal genes act in parallel or compensating pathways (Tucker and Fields, 2003). These two hypotheses predict distinctly different patterns of synthetic lethality: enrichment of interactions within single pathways versus depletion of interactions within pathways and enrichment between pathways. These two hypotheses also make different predictions for the non-lethal phenotypes of the underlying single gene deletions: a shared phenotype for genes in a single pathway, or possibly differing phenotypes for genes in parallel pathways. Hypothesis (i) is possible only when alleles are hypomorphic but not complete loss-of-function mutants: each mutation reduces flux partially, but the combined reduction from two mutations leads to lethality. Hypothesis (i) does not apply to synthetic lethality between null alleles, with complete loss of function. Hypothesis (ii) is expected in this case, with each null mutation knocking out one of the two parallel pathways that sustain normal growth. In this view, an essential protein complex that retains function when single non-essential subunits are deleted (but not multiple subunits simultaneously) is formally represented by multiple pathways, one for each functional stoichiometry, connected in parallel. Data sets to test these rationales are arising from high-throughput synthetic lethality screens accomplished in Saccharomyces cerevisiae using synthetic genetic array (SGA) and synthetic lethality analysis on microarrays (SLAM). These screens test a deletion of interest (query gene) against all possible viable yeast single-deletion strains (target genes) (Tong et al, 2001; Ooi et al, 2003; Pan et al, 2004). As human disease susceptibility may encompass gene mutations in multiple pathways, synthetic lethality is relevant to human disease processes (Tucker and Fields, 2003). We focus on the subset of genetic interactions restricted to synthetic lethal interactions and synthetic fitness (slow growth) defects between null alleles. These interactions are easier to interpret than more general genetic interactions (enhancer, suppressor screens) or other types of mutant alleles (e.g., hypomorphs of essential genes). Null mutants constructed by the International Yeast Gene Deletion Consortium represent the vast majority currently under study by the yeast community (Giaever et al, 2002). For brevity, we use the term synthetic lethal to include both the lethal and reduced fitness phenotypes. Synthetic lethal interactions have been used to predict that interaction partners share function in the same pathway (Tong et al, 2001, 2004; Wong et al, 2004). Here, we emphasize the alternative hypothesis suggested above, that synthetic lethal interactions bridge parallel pathways, which are in a sense orthogonal to direct synthetic lethal interactions (Figure 1A). This concept is formalized computationally as follows. Pathway membership is inferred using the hypergeometric P-value for a shared pattern of interaction partners, which we abbreviate as the congruence score (Figure 1B). We present evidence that functional associations inferred from the congruence score are stronger than associations between the synthetic lethal interaction partners themselves. Two types of functional associations are explored: biochemical participation in protein complexes, through joint analysis of synthetic lethal interactions (Tong et al, 2004) with protein complex data (Gavin et al, 2002; Ho et al, 2002; Mewes et al, 2004) (see Supplementary information, Supplementary Figures S1 and S2); and phenotypes of the underlying single gene deletion mutants, including nuclear migration and drug sensitivity. The nuclear migration assay and the physical interaction detected between Jnm1p and Yll049wp confirm our prediction that the previously uncharacterized yeast gene YLL049W is a new member of the dynein–dynactin pathway. Figure 1.Congruent synthetic lethal (SL) interactions are consistent with functional pathway membership. (A) A simplified synthetic lethality pathway model. Black arrows indicate the schematic flow of a process, with essential genes (red circles) connected by non-essential genes (black circles) organized into two parallel pathway branches (black dashed lines). If at least one of the pathway branches is required for viability, SL interactions (red lines) will be observed between the pathway branches but not within a pathway branch. In this picture, deleting any component of a pathway branch destroys its activity. (B) Directly observed SL genetic interactions bridge pathway branches. The table indicates that SL interactions will be observed between components of the two pathway branches, whereas no interactions will be observed within a branch. (C) Functional associations inferred from the congruence score (blue lines) join the components of a pathway branch. The table indicates raw number of SL interaction partners shared by a pair of genes and its conversion to the congruence score, calculated as the −log10P-value for partner sharing. The congruence connections are orthogonal to the direct SL interactions and align with pathway membership. Download figure Download PowerPoint Results Congruent genes function in the same pathway As has been noted previously, only ∼1% of synthetic lethal interactions occur between genes whose products reside in a single protein complex (Tong et al, 2001). While, as pointed out by the authors of that paper, this is a greater fraction than would be expected by chance, it is clear that the vast majority of synthetic lethal interactions are not explained by common protein complex membership and we would argue that this 1% represents the exception and not the rule. The parallel pathway model suggests that genes sharing synthetic lethal interaction partners may function in a single pathway, and their gene products should have an increased probability to reside in a single protein complex. The raw number of shared genetic interaction partners has been used previously to rank the probability of a physical interaction between the corresponding gene products (Tong et al, 2004). Here, we instead use the hypergeometric P-value for the number of shared neighbors, which accounts for the number of interaction partners of each gene (Figure 1B). To convert this value to a convenient scale, we define the congruence score as the negative log10 of the P-value; related measures have been used to analyze protein interaction networks (Goldberg and Roth, 2003; Schlitt et al, 2003) and multiple characters from single RNA interference (RNAi) screens (Gunsalus et al, 2004). The congruence score has the benefit of providing a natural significance threshold incorporating the size of the network. The performance of a predictive method can be visualized by plotting the number of true positives versus the number of false positives as a function of the number of predictions made, known as a receiver operating characteristic (ROC) curve. Based on the area under the ROC curve, the performance of congruence score method is superior to counting the number of shared partners in predicting protein complex membership in the stringent regime (Supplementary Figure S3 and Supplementary Table S1). We separated the synthetic lethal interaction data into ‘query’ and ‘target’ sets, based on whether each gene node represents a non-essential query gene (126 are included in the published data) or a target gene (982 of which are synthetic lethal partners of at least one query). We calculate congruence scores for each pair of target genes (Supplementary Figure S4). The fraction of target gene pairs in the same protein complex (Gavin et al, 2002; Ho et al, 2002) increases with congruence score, rising to 100% at the highest values (Figure 2A). Analysis using the MIPS database of curated complexes (Mewes et al, 2004) yields similar results (Supplementary Figure S5). Even for the smallest non-zero congruence scores, the observed fractions of pairs within the same complex are greater than expected by chance (P<0.005). Gene products of pairs with congruence score ⩾5 have a higher probability of protein complex co-residence than products of synthetic lethal interaction partners. Moreover, using synthetic lethal interactions to predict complex co-residence shows higher false positive rate ([false positives]/[false positives+true negatives]) and higher false discovery rate ([false positives]/[false positives+true positives]) than using congruence score (Supplementary Figure S3). Figure 2.Genetic congruence predicts physical colocalization and shared gene function. Cumulative bins were constructed for all target gene pairs using a threshold congruence score. (A) High congruence score predicts protein complex membership. The red dot at congruence score 5 indicates the threshold at which congruent gene products are more likely than synthetic lethal partners to reside in the same protein complex (P 0.05). (D) Synthetic lethal interactions have been used to calculate congruence scores (blue lines, threshold congruence score ⩾10) that connect genes in the same pathway branch. Congruence edges are generally orthogonal to the underlying synthetic lethal interactions and parallel to protein complex membership (green lines, membership in a single complex; black lines, overlap of congruence and protein complex edge). The shaded inset shows the synthetic lethal interactions (red lines) underlying the congruence edge between UBA4 and ELP6. Congruence networks at thresholds 8 and 15 are shown as Supplementary Figures S7 and S8. Download figure Download PowerPoint Functional associations, determined by extracting Gene Ontology (GO) (Ashburner et al, 2000) annotations and calculating correlations based on the depth of the deepest parent term (see Materials and methods), are greater for congruent genes than for synthetic lethal pairs. Biological Process and Cellular Component correlations increase with congruence score and are greater than the similarity between direct genetic interaction partners (Figure 2B). As is typically the case, the GO Molecular Function annotations have smaller correlation as they refer to molecular, rather than biological, roles. For congruence scores ⩾7, ⩾10, and ⩾6, respectively, the GO process, function, and component correlations for congruent gene pairs are significantly higher than the corresponding correlations for the raw synthetic lethal pairs (0.25, 0.05, and 0.31), respectively (P 0.05). A network generated by setting a threshold congruence value ⩾10 recapitulates known functional associations and suggests novel associations (Figure 2D). Sets of genes known to function within the same pathway tend to cluster together. As expected, the congruence links overlap known protein interactions, whereas synthetic lethal links do not. For example, a prefoldin complex gene cluster inferred from congruence links (PAC10, GIM3, GIM4, GIM5, and YKE2) corresponds to the PAC10 complex shown in Supplementary Figure S1B. In some cases where proteins encoded by genes with congruence links were not detected within the same protein complex by high-throughput studies (Gavin et al, 2002; Ho et al, 2002), other experiments have indicated physical interactions. SWR1, SWC1, VPS71, VPS72, SIF2, and ARP6 encode subunits of SWR1 chromatin remodeling complex catalyzing exchange of histone H2A with histone variant Htz1p (Mizuguchi et al, 2004). Genes in a highly connected congruence cluster may function in the same pathway through transient physical interactions, or they may participate in a pathway as separate physical entities. For example, Cin1p, Cin2p, and Pac2p are all tubulin folding factors that function in a pathway leading to microtubule stability (Hoyt et al, 1997). Physical interaction between Pac2p and Cin1p has been reported (Fleming et al, 2000). Cin8p is a kinesin motor protein involved in mitotic spindle assembly and chromosome segregation, and interacts with microtubules (Gheber et al, 1999). Possibilities include that Cin1p, Cin2p, Pac2p, and Cin8p interact transiently during mitosis, or that they influence the same molecular environment independently. For example, activities of Cin1p, Cin2p, and Pac2p might generate an optimal microtubule substrate for Cin8p. The largest connected component in Figure 2D includes known members of the dynein–dynactin spindle orientation pathway (ARP1, NUM1, DYN1, PAC11, PAC1, DYN2, JNM1, YMR299C, and NIP100) and corresponds to a group observed previously using clustering (Tong et al, 2004). The dynactin protein complex (Arp1p, Jnm1p, and Nip100p) defined by biochemical studies is required for proper spindle orientation and chromosome partitioning to daughter cells during anaphase (Kahana et al, 1998). Additional reported protein–protein interactions in this congruence cluster include Jnm1p–Yll049wp, Nip100p–Pac11p, Pac11p–Dyn2p, and Pac11p–Num1p (Uetz et al, 2000; Farkasovsky and Kuntzel, 2001; Ito et al, 2001). We predict YLL049W as a new component of the dynein–dynactin spindle orientation pathway, which is consistent with previous observation (Tong et al, 2004). We have experimentally validated the functional prediction of YLL049W by showing that its null mutant allele exhibits a nuclear migration defect similar to dynactin component JNM1. Furthermore, we have successfully detected a physical interaction between Jnm1p and Yll049wp using a directed two-hybrid test. Both experiments will be described in detail in the next section. The second uncharacterized open reading frame (ORF), YDR149C, is also congruent to dynein–dynactin components. Its ORF overlaps the beginning of its neighbor NUM1, and we suggest that the ydr149cΔ phenotype is in fact due to concomitant mutation of NUM1. Congruence scores predict pathway components and quantitative phenotypes Distinct lesions to a single pathway branch should result in similar systems-level perturbations. We reasoned that similarity of a numeric phenotype of a deletion mutant should be better predicted by congruence score than by a direct synthetic lethal interaction. We investigated the ability of the congruence score to predict the penetrance of nuclear migration defects in a population of mutant cells. Mutations in the dynein–dynactin spindle orientation pathway are known to increase the nuclear migration defect rate. We selected six genes in the pathway as landmarks (DYN1, ARP1, DYN2, JNM1, NUM1, and NIP100) and then measured the defect rate at 13°C for 59 mutants of genes with congruence score ⩾4 to at least one of the landmarks (Supplementary Figure S6 and Supplementary Table S2). To summarize the relationship between phenotype and congruence score, each mutant's migration defect (% abnormal) was plotted as a function of congruence scores to landmark genes (Figure 3A). The average congruence score is highly correlated with the defect rate (Spearman correlation coefficient=0.51, two-sided P=3.9 × 10−5). Additionally, at or above congruence score of 10, all mutants exhibit moderate to severe nuclear migration defects (14–80% abnormal cells). Figure 3.The congruence score but not the number of synthetic lethal interactions predicts numeric phenotypes for deletion mutants. (A) Null mutants of 59 genes with congruence score ⩾4 for six landmark genes (DYN1, ARP1, DYN2, JNM1, NUM1, and NIP100) known to be required for robust nuclear migration were measured for percent abnormal nuclear migration at 13°C. Each mutant's nuclear migration defect is plotted by congruence score to each landmark gene (congruence score range is labeled) and by average congruence score (dots). (B) Null mutants of 31 genes with congruence score ⩾4 for landmark gene CIN1 known to be required for benomyl resistance were tested for benomyl sensitivity at concentration 5 μg/ml. The fraction of benomyl-sensitive null mutants is plotted with each congruence score cutoff. (C, D) Null mutants of 451 candidate benomyl-resistant genes are ranked based on their average congruence score or number of synthetic lethal interactions with seven landmark genes (CIN1, YML094C-A, PAC10, PFD1, GIM3, TUB3, and GIM5) known to be required for benomyl resistance (Pan et al, 2004). The LD50 benomyl concentration is defined by the lowest benomyl concentration when the control/experimental hybridization signal concentration ⩾2. The red mark represents the median LD50 benomyl concentration. Download figure Download PowerPoint Among the mutants found to exhibit a nuclear migration defect was one representing the unstudied gene YLL049W (Supplementary Table S2). Further analysis of the yll049w mutant showed that the observed defects are temperature-dependent, similar to jnm1 mutants, whereas a mutant for the Kinesin-related KIP2 gene displayed temperature-independent defects (Supplementary Table S3). Notably, the JNM1–YLL049W congruence score (15.2) is higher
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