Combinatorial RNA interference in Caenorhabditis elegans reveals that redundancy between gene duplicates can be maintained for more than 80 million years of evolution

Background  

Systematic analyses of loss-of-function phenotypes have been carried out for most genes in Saccharomyces cerevisiae, Caenorhabditis elegans, and Drosophila melanogaster. Although such studies vastly expand our knowledge of single gene function, they do not address redundancy in genetic networks. Developing tools for the systematic mapping of genetic interactions is thus a key step in exploring the relationship between genotype and phenotype.     

Commensurate distances and similar motifs in genetic congruence and protein interaction networks in yeast

Background  

In a genetic interaction, the phenotype of a double mutant differs from the combined phenotypes of the underlying single mutants. When the single mutants have no growth defect, but the double mutant is lethal or exhibits slow growth, the interaction is termed synthetic lethality or synthetic fitness. These genetic interactions reveal gene redundancy and compensating pathways. Recently available large-scale data sets of genetic interactions and protein interactions in Saccharomyces cerevisiae provide a unique opportunity to elucidate the topological structure of biological pathways and how genes function in these pathways.     

Genome-wide mapping of unexplored essential regions in the Saccharomyces cerevisiae genome: evidence for hidden synthetic lethal combinations in a genetic interaction network

Despite systematic approaches to mapping networks of genetic interactions in Saccharomyces cerevisiae, exploration of genetic interactions on a genome-wide scale has been limited. The S. cerevisiae haploid genome has 110 regions that are longer than 10 kb but harbor only non-essential genes. Here, we attempted to delete these regions by PCR-mediated chromosomal deletion technology (PCD), which enables chromosomal segments to be deleted by a one-step transformation. Thirty-three of the 110 regions could be deleted, but the remaining 77 regions could not. To determine whether the 77 undeletable regions are essential, we successfully converted 67 of them to mini-chromosomes marked with URA3 using PCR-mediated chromosome splitting technology and conducted a mitotic loss assay of the mini-chromosomes. Fifty-six of the 67 regions were found to be essential for cell growth, and 49 of these carried co-lethal gene pair(s) that were not previously been detected by synthetic genetic array analysis. This result implies that regions harboring only non-essential genes contain unidentified synthetic lethal combinations at an unexpectedly high frequency, revealing a novel landscape of genetic interactions in the S. cerevisiae genome. Furthermore, this study indicates that segmental deletion might be exploited for not only revealing genome function but also breeding stress-tolerant strains.     

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.     

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.     

Identification of synthetic lethality based on a functional network by using machine learning algorithms

Synthetic lethality is the synthesis of mutations leading to cell death. Tumor-specific synthetic lethality has been targeted in research to improve cancer therapy. With the advances of techniques in molecular biology, such as RNAi and CRISPR/Cas9 gene editing, efforts have been made to systematically identify synthetic lethal interactions, especially for frequently mutated genes in cancers. However, elucidating the mechanism of synthetic lethality remains a challenge because of the complexity of its influencing conditions. In this study, we proposed a new computational method to identify critical functional features that can accurately predict synthetic lethal interactions. This method incorporates several machine learning algorithms and encodes protein-coding genes by an enrichment system derived from gene ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways to represent their functional features. We built a random forest-based prediction engine by using 2120 selected features and obtained a Matthews correlation coefficient of 0.532. We examined the top 15 features and found that most of them have potential roles in synthetic lethality according to previous studies. These results demonstrate the ability of our proposed method to predict synthetic lethal interactions and provide a basis for further characterization of these particular genetic combinations.     

Mining protein networks for synthetic genetic interactions

Background  

The local connectivity and global position of a protein in a protein interaction network are known to correlate with some of its functional properties, including its essentiality or dispensability. It is therefore of interest to extend this observation and examine whether network properties of two proteins considered simultaneously can determine their joint dispensability, i.e., their propensity for synthetic sick/lethal interaction. Accordingly, we examine the predictive power of protein interaction networks for synthetic genetic interaction in Saccharomyces cerevisiae, an organism in which high confidence protein interaction networks are available and synthetic sick/lethal gene pairs have been extensively identified.     

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.     

Genetic analysis and complementation by germ-line transformation of lethal mutations in the unc-22 IV region of Caenorhabditis elegans

Summary The subject of this study is the organization of essential genes in the 2 map-unit unc-22 IV region of the Caenorhabditis elegans genome. With the goal of achieving mutational saturation of essential genes in this region, 6491 chromosomes mutagenized with ethyl methanesulfonate (EMS) were screened for the presence of lethal mutations in the unc-22 region. The genetic analysis of 21 lethal mutations in the unc-22 region resulted in the identification of 6 new essential genes, making a total of 36 characterized to date. A minimum of 49 essential genes are estimated to lie in this region. A set of seven formaldehyde-induced deficiencies of unc-22 and surrounding loci were isolated to facilitate the positioning of essential genes on the genetic and physical maps. In order to study essential genes at the molecular level, our approach was to rescue lethal mutations by the injection of genomic DNA in the form of cosmid clones into the germ-line of balanced heterozygotes carrying a lethal mutation. The cosmid clones containing let-56 and let-653 were identified by this method.     

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.     

酿酒酵母中基于CRISPR/Cas9工具的基因编辑与代谢调控研究进展

CRISPR/Cas9系统已广泛用于各种生物体的基因编辑和代谢工程。本文综述了CRISPR/Cas9在酿酒酵母中的基本原理和实际应用。首先总结了CRISPR/Cas9技术的发展历史、酿酒酵母基因组中基因缺失和多DNA片段插入的成功案例。这一先进的系统减少了劳动力,增强了对分子遗传学的理解,加速了微生物工程的发展。其次总结了基于CRISPR/Cas9的系统在生产高附加值化学品和提高酿酒酵母耐应激性方面的研究进展。该综述对酿酒酵母的遗传和合成生物学研究具有重要的参考价值。     

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.

     

Molecular cloning and analysis of CDC28 and cyclin homologues from the human fungal pathogen Candida albicans

In the budding yeast Saccharomyces cerevisiae, progress of the cell cycle beyond the major control point in G1 phase, termed START, requires activation of the evolutionarily conserved Cdc28 protein kinase by direct association with GI cyclins. We have used a conditional lethal mutation in CDC28 of S. cerevisiae to clone a functional homologue from the human fungal pathogen Candida albicans. The protein sequence, deduced from the nucleotide sequence, is 79% identical to that of S. cerevisiae Cdc28 and as such is the most closely related protein yet identified. We have also isolated from C. albicans two genes encoding putative G1 cyclins, by their ability to rescue a conditional GI cyclin defect in S. cerevisiae; one of these genes encodes a protein of 697 amino acids and is identical to the product of the previously described CCN1 gene. The second gene codes for a protein of 465 residues, which has significant homology to S. cerevisiae Cln3. These data suggest that the events and regulatory mechanisms operating at START are highly conserved between these two organisms.     

Synthetic Cytotoxicity: Digenic Interactions with TEL1/ATM Mutations Reveal Sensitivity to Low Doses of Camptothecin

Many tumors contain mutations that confer defects in the DNA-damage response and genome stability. DNA-damaging agents are powerful therapeutic tools that can differentially kill cells with an impaired DNA-damage response. The response to DNA damage is complex and composed of a network of coordinated pathways, often with a degree of redundancy. Tumor-specific somatic mutations in DNA-damage response genes could be exploited by inhibiting the function of a second gene product to increase the sensitivity of tumor cells to a sublethal concentration of a DNA-damaging therapeutic agent, resulting in a class of conditional synthetic lethality we call synthetic cytotoxicity. We used the Saccharomyces cerevisiae nonessential gene-deletion collection to screen for synthetic cytotoxic interactions with camptothecin, a topoisomerase I inhibitor, and a null mutation in TEL1, the S. cerevisiae ortholog of the mammalian tumor-suppressor gene, ATM. We found and validated 14 synthetic cytotoxic interactions that define at least five epistasis groups. One class of synthetic cytotoxic interaction was due to telomere defects. We also found that at least one synthetic cytotoxic interaction was conserved in Caenorhabditis elegans. We have demonstrated that synthetic cytotoxicity could be a useful strategy for expanding the sensitivity of certain tumors to DNA-damaging therapeutics.     

Size variation of rDNA clusters in the yeasts Saccharomyces cerevisiea and Schizosaccharomyces pombe

Summary The higher-order organization of rRNA genes was investigated in the yeasts Saccharomyces cerevisiae and Schizosaccharomyces pombe. We used pulsed-field gel electrophoresis (PFGE) in combination with frequent cutter endonucleases having no recognition sites within rDNA repeating units to characterize tandem arrays of ribosomal genes in these two species. Large variations in rDNA cluster length were detected in various S. cerevisiae and S. pombe strains commonly used as PFGE molecular weight markers. This wide range of variability implies that the sizes currently assessed for chromosomes bearing rRNA genes in these organisms are unreliable since they may vary within strains by several hundreds of kilobase pairs, depending on the size of the tandem arrays of rRNA genes. Consequently, there is now a lack of reliable PFGE size standards between 1.6 Mb and 4.5 Mb, even when established yeast strains with calibrated chromosomes are used.