The Role of Protein Interactions in Mediating Essentiality and Synthetic Lethality

Genes are characterized as essential if their knockout is associated with a lethal phenotype, and these “essential genes” play a central role in biological function. In addition, some genes are only essential when deleted in pairs, a phenomenon known as synthetic lethality. Here we consider genes displaying synthetic lethality as “essential pairs” of genes, and analyze the properties of yeast essential genes and synthetic lethal pairs together. As gene duplication initially produces an identical pair or sets of genes, it is often invoked as an explanation for synthetic lethality. However, we find that duplication explains only a minority of cases of synthetic lethality. Similarly, disruption of metabolic pathways leads to relatively few examples of synthetic lethality. By contrast, the vast majority of synthetic lethal gene pairs code for proteins with related functions that share interaction partners. We also find that essential genes and synthetic lethal pairs cluster in the protein-protein interaction network. These results suggest that synthetic lethality is strongly dependent on the formation of protein-protein interactions. Compensation by duplicates does not usually occur mainly because the genes involved are recent duplicates, but is more commonly due to functional similarity that permits preservation of essential protein complexes. This unified view, combining genes that are individually essential with those that form essential pairs, suggests that essentiality is a feature of physical interactions between proteins protein-protein interactions, rather than being inherent in gene and protein products themselves.     

Genetic interaction motif finding by expectation maximization – a novel statistical model for inferring gene modules from synthetic lethality

Background  

Synthetic lethality experiments identify pairs of genes with complementary function. More direct functional associations (for example greater probability of membership in a single protein complex) may be inferred between genes that share synthetic lethal interaction partners than genes that are directly synthetic lethal. Probabilistic algorithms that identify gene modules based on motif discovery are highly appropriate for the analysis of synthetic lethal genetic interaction data and have great potential in integrative analysis of heterogeneous datasets.     

Predicting genetic interactions with random walks on biological networks

Background  

Several studies have demonstrated that synthetic lethal genetic interactions between gene mutations provide an indication of functional redundancy between molecular complexes and pathways. These observations help explain the finding that organisms are able to tolerate single gene deletions for a large majority of genes. For example, system-wide gene knockout/knockdown studies in S. cerevisiae and C. elegans revealed non-viable phenotypes for a mere 18% and 10% of the genome, respectively. It has been postulated that the low percentage of essential genes reflects the extensive amount of genetic buffering that occurs within genomes. Consistent with this hypothesis, systematic double-knockout screens in S. cerevisiae and C. elegans show that, on average, 0.5% of tested gene pairs are synthetic sick or synthetic lethal. While knowledge of synthetic lethal interactions provides valuable insight into molecular functionality, testing all combinations of gene pairs represents a daunting task for molecular biologists, as the combinatorial nature of these relationships imposes a large experimental burden. Still, the task of mapping pairwise interactions between genes is essential to discovering functional relationships between molecular complexes and pathways, as they form the basis of genetic robustness. Towards the goal of alleviating the experimental workload, computational techniques that accurately predict genetic interactions can potentially aid in targeting the most likely candidate interactions. Building on previous studies that analyzed properties of network topology to predict genetic interactions, we apply random walks on biological networks to accurately predict pairwise genetic interactions. Furthermore, we incorporate all published non-interactions into our algorithm for measuring the topological relatedness between two genes. We apply our method to S. cerevisiae and C. elegans datasets and, using a decision tree classifier, integrate diverse biological networks and show that our method outperforms established methods.     

Mapping genetically compensatory pathways from synthetic lethal interactions in yeast

Background

Synthetic lethal genetic interaction analysis has been successfully applied to predicting the functions of genes and their pathway identities. In the context of synthetic lethal interaction data alone, the global similarity of synthetic lethal interaction patterns between two genes is used to predict gene function. With physical interaction data, such as protein-protein interactions, the enrichment of physical interactions within subsets of genes and the enrichment of synthetic lethal interactions between those subsets of genes are used as an indication of compensatory pathways.

Result

In this paper, we propose a method of mapping genetically compensatory pathways from synthetic lethal interactions. Our method is designed to discover pairs of gene-sets in which synthetic lethal interactions are depleted among the genes in an individual set and where such gene-set pairs are connected by many synthetic lethal interactions. By its nature, our method could select compensatory pathway pairs that buffer the deleterious effect of the failure of either one, without the need of physical interaction data. By focusing on compensatory pathway pairs where genes in each individual pathway have a highly homogenous cellular function, we show that many cellular functions have genetically compensatory properties.

Conclusion

We conclude that synthetic lethal interaction data are a powerful source to map genetically compensatory pathways, especially in systems lacking physical interaction information, and that the cellular function network contains abundant compensatory properties.     

Genetic synthetic lethality screen at the single gene level in cultured human cells

Recently, we demonstrated the feasibility of a chemical synthetic lethality screen in cultured human cells. We now demonstrate the principles for a genetic synthetic lethality screen. The technology employs both an immortalized human cell line deficient in the gene of interest, which is complemented by an episomal survival plasmid expressing the wild-type cDNA for the gene of interest, and the use of a novel GFP-based double-label fluorescence system. Dominant negative genetic suppressor elements (GSEs) are selected from an episomal library expressing short truncated sense and antisense cDNAs for a gene likely to be synthetic lethal with the gene of interest. Expression of these GSEs prevents spontaneous loss of the GFP-marked episomal survival plasmid, thus allowing FACS enrichment for cells retaining the survival plasmid (and the GSEs). The dominant negative nature of the GSEs was validated by the decreased resident enzymatic activity present in cells harboring the GSEs. Also, cells mutated in the gene of interest exhibit reduced survival upon GSE expression. The identification of synthetic lethal genes described here can shed light on functional genetic interactions between genes involved in normal cell metabolism and in disease.     

Connectivity Homology Enables Inter-Species Network Models of Synthetic Lethality

Synthetic lethality is a genetic interaction wherein two otherwise nonessential genes cause cellular inviability when knocked out simultaneously. Drugs can mimic genetic knock-out effects; therefore, our understanding of promiscuous drugs, polypharmacology-related adverse drug reactions, and multi-drug therapies, especially cancer combination therapy, may be informed by a deeper understanding of synthetic lethality. However, the colossal experimental burden in humans necessitates in silico methods to guide the identification of synthetic lethal pairs. Here, we present SINaTRA (Species-INdependent TRAnslation), a network-based methodology that discovers genome-wide synthetic lethality in translation between species. SINaTRA uses connectivity homology, defined as biological connectivity patterns that persist across species, to identify synthetic lethal pairs. Importantly, our approach does not rely on genetic homology or structural and functional similarity, and it significantly outperforms models utilizing these data. We validate SINaTRA by predicting synthetic lethality in S. pombe using S. cerevisiae data, then identify over one million putative human synthetic lethal pairs to guide experimental approaches. We highlight the translational applications of our algorithm for drug discovery by identifying clusters of genes significantly enriched for single- and multi-drug cancer therapies.     

Studying synthetic lethal interactions in the zebrafish system: insight into disease genes and mechanisms

The post-genomic era is marked by a pressing need to functionally characterize genes through understanding gene-gene interactions, as well as interactions between biological pathways. Exploiting a phenomenon known as synthetic lethality, in which simultaneous loss of two interacting genes leads to loss of viability, aids in the investigation of these interactions. Although synthetic lethal screening is a powerful technique that has been used with great success in many model organisms, including Saccharomyces cerevisiae, Drosophila melanogaster and Caenorhabditis elegans, this approach has not yet been applied in the zebrafish, Danio rerio. Recently, the zebrafish has emerged as a valuable system to model many human disease conditions; thus, the ability to conduct synthetic lethal screening using zebrafish should help to uncover many unknown disease-gene interactions. In this article, we discuss the concept of synthetic lethality and provide examples of its use in other model systems. We further discuss experimental approaches by which the concept of synthetic lethality can be applied to the zebrafish to understand the functions of specific genes.     

Synthetic lethal interactions for the development of cancer therapeutics: biological and methodological advancements

Synthetic lethal interaction is defined as a combination of two mutations that is lethal when present in the same cell; each individual mutation is non-lethal. Synthetic lethal interactions attract attention in cancer research fields since the discovery of synthetic lethal genes with either oncogenes or tumor suppressor genes (TSGs) provides novel cancer therapeutic targets. Due to the selective lethal effect on cancer cells harboring specific genetic alterations, it is expected that targeting synthetic lethal genes would provide wider therapeutic windows compared with cytotoxic chemotherapeutics. Here, we review the current status of the application of synthetic lethal screening in cancer research fields from biological and methodological viewpoints. Very recent studies seeking to identify synthetic lethal genes with K-RAS and p53, which are known to be the most frequently occurring oncogenes and TSGs, respectively, are introduced. Among the accumulating amount of research on synthetic lethal interactions, the synthetic lethality between BRCA1/2 and PARP1 inhibition has been clinically proven. Thus, both preclinical and clinical data showing a preferential anti-tumor effect on BRCA1/2 deficient tumors by a PARP1 inhibitor are the best examples of the synthetic lethal approach of cancer therapeutics. Finally, methodological progress regarding synthetic lethal screening, including barcode shRNA screening and in vivo synthetic lethal screening, is described. Given the fact that an increasing number of synthetic lethal genes for major cancerous genes have been validated in preclinical studies, this intriguing approach awaits clinical verification of preferential benefits for cancer patients with specific genetic alterations as a clear predictive factor for tumor response.     

A network-based approach to integrate nutrient microenvironment in the prediction of synthetic lethality in cancer metabolism

Synthetic Lethality (SL) is currently defined as a type of genetic interaction in which the loss of function of either of two genes individually has limited effect in cell viability but inactivation of both genes simultaneously leads to cell death. Given the profound genomic aberrations acquired by tumor cells, which can be systematically identified with -omics data, SL is a promising concept in cancer research. In particular, SL has received much attention in the area of cancer metabolism, due to the fact that relevant functional alterations concentrate on key metabolic pathways that promote cellular proliferation. With the extensive prior knowledge about human metabolic networks, a number of computational methods have been developed to predict SL in cancer metabolism, including the genetic Minimal Cut Sets (gMCSs) approach. A major challenge in the application of SL approaches to cancer metabolism is to systematically integrate tumor microenvironment, given that genetic interactions and nutritional availability are interconnected to support proliferation. Here, we propose a more general definition of SL for cancer metabolism that combines genetic and environmental interactions, namely loss of gene functions and absence of nutrients in the environment. We extend our gMCSs approach to determine this new family of metabolic synthetic lethal interactions. A computational and experimental proof-of-concept is presented for predicting the lethality of dihydrofolate reductase (DHFR) inhibition in different environments. Finally, our approach is applied to identify extracellular nutrient dependences of tumor cells, elucidating cholesterol and myo-inositol depletion as potential vulnerabilities in different malignancies.     

Schizosaccharomyces pombe rsm1 genetically interacts with spmex67, which is involved in mRNA export

We have previously isolated three synthetic lethal mutants from Schizosaccharomyces pombe in order to identify mutations in the genes that are functionally linked to spmex67 with respect to mRNA export. A novel rsm1 gene was isolated by complementation of the growth defect in one of the synthetic lethal mutants, SLMexl. The rsm1 gene contains no introns and encodes a 296 amino-acid-long protein with the RING finger domain, a C3HC4 in the N-terminal half. The deltarsm1 null mutant is viable, but it showed a slight poly(A)+ RNA accumulation in the nucleus. Also, the combination of deltarsm1 and deltaspmex67 mutations confers synthetic lethality that is accompanied by the severe poly(A)+ RNA export defect. These results suggest that rsm1 is involved in mRNA export from the nucleus.     

Synthetic lethality: emerging targets and opportunities in melanoma

Great progress has been made in the treatment of melanoma through use of targeted therapies and immunotherapy. One approach that has not been fully explored is synthetic lethality, which exploits somatically acquired changes, usually driver mutations, to specifically kill tumour cells. We outline the various approaches that may be applied to identify synthetic lethal interactions and define how these interactions may drive drug discovery efforts.     

Functional maps of protein complexes from quantitative genetic interaction data

Recently, a number of advanced screening technologies have allowed for the comprehensive quantification of aggravating and alleviating genetic interactions among gene pairs. In parallel, TAP-MS studies (tandem affinity purification followed by mass spectroscopy) have been successful at identifying physical protein interactions that can indicate proteins participating in the same molecular complex. Here, we propose a method for the joint learning of protein complexes and their functional relationships by integration of quantitative genetic interactions and TAP-MS data. Using 3 independent benchmark datasets, we demonstrate that this method is >50% more accurate at identifying functionally related protein pairs than previous approaches. Application to genes involved in yeast chromosome organization identifies a functional map of 91 multimeric complexes, a number of which are novel or have been substantially expanded by addition of new subunits. Interestingly, we find that complexes that are enriched for aggravating genetic interactions (i.e., synthetic lethality) are more likely to contain essential genes, linking each of these interactions to an underlying mechanism. These results demonstrate the importance of both large-scale genetic and physical interaction data in mapping pathway architecture and function.     

Predicting synthetic lethal genetic interactions in <Emphasis Type="Italic">Saccharomyces cerevisiae</Emphasis> using short polypeptide clusters

Background

Protein synthetic lethal genetic interactions are useful to define functional relationships between proteins and pathways. However, the molecular mechanism of synthetic lethal genetic interactions remains unclear.

Results

In this study we used the clusters of short polypeptide sequences, which are typically shorter than the classically defined protein domains, to characterize the functionalities of proteins. We developed a framework to identify significant short polypeptide clusters from yeast protein sequences, and then used these short polypeptide clusters as features to predict yeast synthetic lethal genetic interactions. The short polypeptide clusters based approach provides much higher coverage for predicting yeast synthetic lethal genetic interactions. Evaluation using experimental data sets showed that the short polypeptide clusters based approach is superior to the previous protein domain based one.

Conclusion

We were able to achieve higher performance in yeast synthetic lethal genetic interactions prediction using short polypeptide clusters as features. Our study suggests that the short polypeptide cluster may help better understand the functionalities of proteins.

     

Establishing Genetic Interactions by a Synthetic Dosage Lethality Phenotype

We have devised a genetic screen, termed synthetic dosage lethality, in which a cloned ``reference' gene is inducibly overexpressed in a set of mutant strains carrying potential ``target' mutations. To test the specificity of the method, two reference genes, CTF13, encoding a centromere binding protein, and ORC6, encoding a subunit of the origin of replication binding complex, were overexpressed in a large collection of mutants defective in either chromosome segregation or replication. CTF13 overexpression caused synthetic dosage lethality in combination with ctf14-42 (cbf2, ndc10), ctf17-61 (chl4), ctf19-58 and ctf19-26. ORC6 overexpression caused synthetic dosage lethality in combination with cdc2-1, cdc6-1, cdc14-1, cdc16-1 and cdc46-1. These relationships reflect specific interactions, as overexpression of CTF13 caused lethality in kinetochore mutants and overexpression of ORC6 caused lethality in replication mutants. In contrast, only one case of dosage suppression was observed. We suggest that synthetic dosage lethality identifies a broad spectrum of interacting mutations and is of general utility in detecting specific genetic interactions using a cloned wild-type gene as a starting point. Furthermore, synthetic dosage lethality is easily adapted to the study of cloned genes in other organisms.     

Chromosome instability and tumor lethality suppression in carcinogenesis

The maintenance and survival of each organism depends on its genome integrity. Alterations of essential genes, or aberrant chromosome number and structure lead to cell death. Paradoxically, cancer cells, especially in solid tumors, contain somatic gene mutations and are chromosome instability (CIN), suggesting a mechanism that cancer cells have acquired to suppress the lethal mutations and/or CIN. Herein we will discuss a tumor lethality suppression concept based on the studies of yeast genetic interactions and transgenic mice. During the early stages of the multistep process of tumorigenesis, incipient cancer cells probably have adopted genetic and epigenetic alterations to tolerate the lethal mutations of other genes that ensue, and to a larger extent CIN. In turn, CIN mediated massive gain and loss of genes provides a wider buffer for further genetic reshuffling, resulting in cancer cell heterogeneity, drug resistance and evasion of oncogene addiction, thus CIN may be both the effector and inducer of tumorigenesis. Accordingly, interfering with tumor lethality suppression could lead to cancer cell death or growth defects. Further validation of the tumor lethality suppression concept would help to elucidate the role of CIN in tumorigenesis, the relationship between CIN and somatic gene mutations, and would impact the design of anticancer drug development.     

Cross-Species Functional Genomic Analysis Identifies Resistance Genes of the Histone Deacetylase Inhibitor Valproic Acid

The mechanisms of successful epigenetic reprogramming in cancer are not well characterized as they involve coordinated removal of repressive marks and deposition of activating marks by a large number of histone and DNA modification enzymes. Here, we have used a cross-species functional genomic approach to identify conserved genetic interactions to improve therapeutic effect of the histone deacetylase inhibitor (HDACi) valproic acid, which increases survival in more than 20% of patients with advanced acute myeloid leukemia (AML). Using a bidirectional synthetic lethality screen revealing genes that increased or decreased VPA sensitivity in C. elegans, we identified novel conserved sensitizers and synthetic lethal interactors of VPA. One sensitizer identified as a conserved determinant of therapeutic success of HDACi was UTX (KDM6A), which demonstrates a functional relationship between protein acetylation and lysine-specific methylation. The synthetic lethal screen identified resistance programs that compensated for the HDACi-induced global hyper-acetylation, and confirmed MAPKAPK2, HSP90AA1, HSP90AB1 and ACTB as conserved hubs in a resistance program for HDACi that are drugable in human AML cell lines. Hence, these resistance hubs represent promising novel targets for refinement of combinatorial epigenetic anti-cancer therapy.     

Synthetic lethality of cohesins with PARPs and replication fork mediators

Synthetic lethality has been proposed as a way to leverage the genetic differences found in tumor cells to affect their selective killing. Cohesins, which tether sister chromatids together until anaphase onset, are mutated in a variety of tumor types. The elucidation of synthetic lethal interactions with cohesin mutants therefore identifies potential therapeutic targets. We used a cross-species approach to identify robust negative genetic interactions with cohesin mutants. Utilizing essential and non-essential mutant synthetic genetic arrays in Saccharomyces cerevisiae, we screened genome-wide for genetic interactions with hypomorphic mutations in cohesin genes. A somatic cell proliferation assay in Caenorhabditis elegans demonstrated that the majority of interactions were conserved. Analysis of the interactions found that cohesin mutants require the function of genes that mediate replication fork progression. Conservation of these interactions between replication fork mediators and cohesin in both yeast and C. elegans prompted us to test whether other replication fork mediators not found in the yeast were required for viability in cohesin mutants. PARP1 has roles in the DNA damage response but also in the restart of stalled replication forks. We found that a hypomorphic allele of the C. elegans SMC1 orthologue, him-1(e879), genetically interacted with mutations in the orthologues of PAR metabolism genes resulting in a reduced brood size and somatic cell defects. We then demonstrated that this interaction is conserved in human cells by showing that PARP inhibitors reduce the viability of cultured human cells depleted for cohesin components. This work demonstrates that large-scale genetic interaction screening in yeast can identify clinically relevant genetic interactions and suggests that PARP inhibitors, which are currently undergoing clinical trials as a treatment of homologous recombination-deficient cancers, may be effective in treating cancers that harbor cohesin mutations.     

Npp106p, a Schizosaccharomyces pombe nucleoporin similar to Saccharomyces cerevisiae Nic96p, functionally interacts with Rae1p in mRNA export.

To identify components of the mRNA export machinery in Schizosaccharomyces pombe, a screen was developed to identify mutations that were synthetically lethal with the conditional mRNA export allele rae1-167. Mutations defining three complementation groups were isolated, and here we report the characterization of npp106 (for nuclear pore protein of 106 kDa). This gene encodes a predicted protein that has significant similarity to the Nic96p nucleoporin of Saccharomyces cerevisiae. Consistent with Npp106p being a nucleoporin, a functional green fluorescent protein (GFP)-tagged Npp106p localized to the nuclear periphery. In contrast to NIC96, the npp106 gene is not essential. Moreover, a delta npp106 mutant did not show cytoplasmic mislocalization of a simian virus 40 nuclear localization signal-GFP-LacZ reporter protein, and a fraction of cells had accumulation of poly(A)+ RNA in the nucleus. A consequence of the synthetic lethality between rae1-167 and npp106-1 was the accumulation of poly(A)+ RNA in the nucleus when cells were grown under synthetic lethal conditions. In addition to npp106-1, which is a nonsense mutation that truncates the protein at amino acid 292, the delta npp106 mutation was synthetically lethal with rae1-167, suggesting that the synthetic lethality is a consequence of the loss of a function of npp106. We further demonstrate that a region between amino acids 74 and 348 of Npp106p is required for complementation of the synthetic lethality. These results uncover a potential direct or indirect involvement of Npp106p in mRNA export.     

Screening for synthetic lethal mutants in Escherichia coli and identification of EnvC (YibP) as a periplasmic septal ring factor with murein hydrolase activity

Bacterial cytokinesis is driven by the septal ring apparatus, the assembly of which in Escherichia coli is directed to mid-cell by the Min system. Despite suffering aberrant divisions at the poles, cells lacking the minCDE operon (Min(-)) have an almost normal growth rate. We developed a generally applicable screening method for synthetic lethality in E. coli, and used it to select for transposon mutations (slm) that are synthetically lethal (or sick) in combination with DeltaminCDE. One of the slm insertions mapped to envC (yibP), proposed to encode a lysostaphin-like, metallo-endopeptidase that is exported to the periplasm by the general secretory (Sec) pathway. Min(-) EnvC(-) cells showed a severe division defect, supporting a role for EnvC in septal ring function. Accordingly, we show that an EnvC-green fluorescent protein fusion, when directed to the periplasm via the twin-arginine export system, is both functional and part of the septal ring apparatus. Using an in-gel assay, we also present evidence that EnvC possesses murein hydrolytic activity. Our results suggest that EnvC plays a direct role in septal murein cleavage to allow outer membrane constriction and daughter cell separation. By uncovering genetic interactions, the synthetic lethal screen described here provides an attractive new tool for studying gene function in E. coli.