Synthetic Lethals Research Articles

Systematic discovery of mutation-specific synthetic lethals by mining pan-cancer human primary tumor data

Two genes are synthetically lethal (SL) when defects in both are lethal to a cell but a single defect is non-lethal. SL partners of cancer mutations are of great interest as pharmacological targets; however, identifying them by cell line-based methods is challenging. Here we develop MiSL (Mining Synthetic Lethals), an algorithm that mines pan-cancer human primary tumour data to identify mutation-specific SL partners for specific cancers. We apply MiSL to 12 different cancers and predict 145,891 SL partners for 3,120 mutations, including known mutation-specific SL partners. Comparisons with functional screens show that MiSL predictions are enriched for SLs in multiple cancers. We extensively validate a SL interaction identified by MiSL between the IDH1 mutation and ACACA in leukaemia using gene targeting and patient-derived xenografts. Furthermore, we apply MiSL to pinpoint genetic biomarkers for drug sensitivity. These results demonstrate that MiSL can accelerate precision oncology by identifying mutation-specific targets and biomarkers.

Abstract A27: Systematic discovery of mutation-specific synthetic lethals by mining pan-cancer primary tumor data

Abstract Synthetic lethality, in which a single gene defect leads to dependency on a second gene that is otherwise not essential, is an attractive paradigm to identify targeted therapies for somatic mutations. Current methods to detect synthetic lethal (SL) partners for somatic mutations use large-scale shRNA screens in cell lines, combine shRNA data with tumor genomic data or use human orthologs of yeast SL interactions. These approaches are limited as they rely on cell line or yeast data, which are not representative of primary tumors. We have developed MiSL, a novel computational algorithm that utilizes large pan-cancer patient datasets (mutation, copy number and gene expression) to identify SL partners for specific mutations in specific cancer types. The underlying assumption of our approach is that, across multiple cancers, SL partners of a mutation will be amplified more frequently or deleted less frequently, with concordant changes in expression, in primary tumor samples harboring the mutation. Application of MiSL produced candidate SL partners for 30-80% of recurrent mutations in 12 cancers. Importantly, MiSL identified candidate SL partners for mutations (mut) in genes such as IDH1 that are not well-represented in existing cell lines. This is a distinct advantage over recent computational methods that combine shRNA data along with genomic data to make their predictions. Since MiSL uses only genomic and gene expression data, it allows assessment of a wide range of primary human tumors and mutations found in large primary tumor data sets such as TCGA. We validated MiSL using existing data and large-scale shRNA experiments we performed in doxycycline-inducible expression systems. We found that IDH1mut MiSL candidates in acute myeloid leukemia (AML) were enriched (p=0.004) for essential genes specific to IDH1mut but not IDH1 wildtype cells determined by a DECIPHER shRNA screen covering 9,965 human genes performed in doxycycline-inducible IDH1 (R132) THP-1 cells. Importantly, 1 out of 5 MiSL candidates was a SL partner of IDH1mut in AML cells as per the shRNA screen, indicating MiSL's strong predictive power. Also, for multiple mutations in colorectal cancer, MiSL candidates were enriched (p<0.05) with genes that were selectively essential in the mutated colorectal cell-lines in Achilles data. Next, we used MiSL to identify novel and druggable SL partners in (i) AML and (ii) breast cancer. MiSL predicted a novel SL interaction in AML between IDH1mut and ACACA, the rate-limiting enzyme of fatty acid synthesis. Consistent with our prediction, pharmacologic or genetic blockade of ACACA prevented cell proliferation in the presence of IDH1mut, but not with IDH1 wildtype, in AML cell lines. Furthermore, when transduced with lentivirus encoding RFP-marked shRNA to ACACA, primary IDH1mut AML cells exhibited markedly reduced engraftment of RFP-positive human CD45+CD33+ leukemic cells compared to scrambled non-targeting shRNA (p<0.05) at 12 weeks post-engraftment, validating the SL interaction between mutant IDH1 and ACACA. This vulnerability indicates a novel role for IDH1mut in reprogramming lipid metabolism. MiSL also predicted that AKT1 is a SL partner of PIK3CAmut in breast cancer which we experimentally confirmed using 8 breast cancer lines. All four PIK3CAmut (but not wildtype) breast cancers were sensitive to AKT1 inhibition in viability and colony assays. In summary, MiSL is a general computational solution that finds novel SL interactions. Specifically, IDH1mut-ACACA is the first in vivo validated synthetic lethal in human tumor cells discovered purely by computational analysis of tumor genomic data. MiSL can greatly accelerate identification of pharmacologic targets associated with specific somatic mutations in specific tumor types for all kinds of mutations, thereby making it directly translatable to clinical applications. MiSL can also pinpoint predictive genetic biomarkers that can identify/extend indications for targeted therapies. Citation Format: Subarna Sinha, Daniel Thomas, Steven Chan, Yang Gao, Diede Brunen, Damoun Torabi, Andreas Reinisch, Rene Bernards, Ravindra Majeti, David L. Dill. Systematic discovery of mutation-specific synthetic lethals by mining pan-cancer primary tumor data. [abstract]. In: Proceedings of the AACR Precision Medicine Series: Targeting the Vulnerabilities of Cancer; May 16-19, 2016; Miami, FL. Philadelphia (PA): AACR; Clin Cancer Res 2017;23(1_Suppl):Abstract nr A27.

Using Gene Essentiality and Synthetic Lethality Information to Correct Yeast and CHO Cell Genome-Scale Models.

Essentiality (ES) and Synthetic Lethality (SL) information identify combination of genes whose deletion inhibits cell growth. This information is important for both identifying drug targets for tumor and pathogenic bacteria suppression and for flagging and avoiding gene deletions that are non-viable in biotechnology. In this study, we performed a comprehensive ES and SL analysis of two important eukaryotic models (S. cerevisiae and CHO cells) using a bilevel optimization approach introduced earlier. Information gleaned from this study is used to propose specific model changes to remedy inconsistent with data model predictions. Even for the highly curated Yeast 7.11 model we identified 50 changes (metabolic and GPR) leading to the correct prediction of an additional 28% of essential genes and 36% of synthetic lethals along with a 53% reduction in the erroneous identification of essential genes. Due to the paucity of mutant growth phenotype data only 12 changes were made for the CHO 1.2 model leading to an additional correctly predicted 11 essential and eight non-essential genes. Overall, we find that CHO 1.2 was 76% less accurate than the Yeast 7.11 metabolic model in predicting essential genes. Based on this analysis, 14 (single and double deletion) maximally informative experiments are suggested to improve the CHO cell model by using information from a mouse metabolic model. This analysis demonstrates the importance of single and multiple knockout phenotypes in assessing and improving model reconstructions. The advent of techniques such as CRISPR opens the door for the global assessment of eukaryotic models.

Fast-SL: an efficient algorithm to identify synthetic lethal sets in metabolic networks.

Synthetic lethal sets are sets of reactions/genes where only the simultaneous removal of all reactions/genes in the set abolishes growth of an organism. Previous approaches to identify synthetic lethal genes in genome-scale metabolic networks have built on the framework of flux balance analysis (FBA), extending it either to exhaustively analyze all possible combinations of genes or formulate the problem as a bi-level mixed integer linear programming (MILP) problem. We here propose an algorithm, Fast-SL, which surmounts the computational complexity of previous approaches by iteratively reducing the search space for synthetic lethals, resulting in a substantial reduction in running time, even for higher order synthetic lethals. We performed synthetic reaction and gene lethality analysis, using Fast-SL, for genome-scale metabolic networks of Escherichia coli, Salmonella enterica Typhimurium and Mycobacterium tuberculosis. Fast-SL also rigorously identifies synthetic lethal gene deletions, uncovering synthetic lethal triplets that were not reported previously. We confirm that the triple lethal gene sets obtained for the three organisms have a precise match with the results obtained through exhaustive enumeration of lethals performed on a computer cluster. We also parallelized our algorithm, enabling the identification of synthetic lethal gene quadruplets for all three organisms in under 6 h. Overall, Fast-SL enables an efficient enumeration of higher order synthetic lethals in metabolic networks, which may help uncover previously unknown genetic interactions and combinatorial drug targets. The MATLAB implementation of the algorithm, compatible with COBRA toolbox v2.0, is available at https://github.com/RamanLab/FastSL CONTACT: kraman@iitm.ac.in Supplementary data are available at Bioinformatics online.

Synthetic lethals in HIV: ways to avoid drug resistance : Running title: Preventing HIV resistance.

BackgroundRNA viruses rapidly accumulate genetic variation, which can give rise to synthetic lethal (SL) and deleterious (SD) mutations. Synthetic lethal mutations (non-lethal when alone but lethal when combined in one genome) have been studied to develop cancer therapies. This principle can also be used against fast-evolving RNA-viruses. Indeed, targeting protein sites involved in SD + SL interactions with a drug would render any mutation of such sites, lethal.ResultsHere, we set up a strategy to detect intragenic pairs of SL and SD at the surface of the protein to predict less escapable drug target sites. For this, we detected SD + SL, studying HIV protease (PR) and reverse transcriptase (RT) sequence alignments from two groups of VIH+ individuals: treated with drugs (T) or not (NT). Using a series of statistical approaches, we were able to propose bona fide SD + SL couples. When focusing on spatially close co-variant SD + SL couples at the surface of the protein, we found 5 SD + SL groups (2 in the protease and 3 in the reverse transcriptase), which could be good candidates to form pockets to accommodate potential drugs.ConclusionsThus, designing drugs targeting these specific SD + SL groups would not allow the virus to mutate any residue involved in such groups without losing an essential function. Moreover, we also show that the selection pressure induced by the treatment leads to the appearance of new mutations, which change the mutational landscape of the protein. This drives the existence of differential SD + SL couples between the drug-treated and non-treated groups. Thus, new anti-viral drugs should be designed differently to target such groups.ReviewersThis article was reviewed by Neil Greenspan Csaba Pal and István Simon.Electronic supplementary materialThe online version of this article (doi:10.1186/s13062-015-0044-y) contains supplementary material, which is available to authorized users.

Between compensatory mutations and synthetic lethals: genetic mutations, a new challenge for tomorrow's medicine

Between compensatory mutations and synthetic lethals: genetic mutations, a new challenge for tomorrow's medicine

DAISY: Picking Synthetic Lethals from Cancer Genomes

A better understanding of genetic interactions in cancer might help identify new therapeutic approaches that exploit the concept of synthetic lethality. Ruppin and colleagues have developed a new computational method, DAISY, that predicts such interactions and potentially facilitates the delineation and validation of comprehensive genetic interaction networks.

Enumeration of Smallest Intervention Strategies in Genome-Scale Metabolic Networks

One ultimate goal of metabolic network modeling is the rational redesign of biochemical networks to optimize the production of certain compounds by cellular systems. Although several constraint-based optimization techniques have been developed for this purpose, methods for systematic enumeration of intervention strategies in genome-scale metabolic networks are still lacking. In principle, Minimal Cut Sets (MCSs; inclusion-minimal combinations of reaction or gene deletions that lead to the fulfilment of a given intervention goal) provide an exhaustive enumeration approach. However, their disadvantage is the combinatorial explosion in larger networks and the requirement to compute first the elementary modes (EMs) which itself is impractical in genome-scale networks.We present MCSEnumerator, a new method for effective enumeration of the smallest MCSs (with fewest interventions) in genome-scale metabolic network models. For this we combine two approaches, namely (i) the mapping of MCSs to EMs in a dual network, and (ii) a modified algorithm by which shortest EMs can be effectively determined in large networks. In this way, we can identify the smallest MCSs by calculating the shortest EMs in the dual network. Realistic application examples demonstrate that our algorithm is able to list thousands of the most efficient intervention strategies in genome-scale networks for various intervention problems. For instance, for the first time we could enumerate all synthetic lethals in E.coli with combinations of up to 5 reactions. We also applied the new algorithm exemplarily to compute strain designs for growth-coupled synthesis of different products (ethanol, fumarate, serine) by E.coli. We found numerous new engineering strategies partially requiring less knockouts and guaranteeing higher product yields (even without the assumption of optimal growth) than reported previously. The strength of the presented approach is that smallest intervention strategies can be quickly calculated and screened with neither network size nor the number of required interventions posing major challenges.

Generalized adjacency and the conservation of gene clusters in genetic networks defined by synthetic lethals

BackgroundGiven genetic networks derived from two genomes, it may be difficult to decide if their local structures are similar enough in both genomes to infer some ancestral configuration or some conserved functional relationships. Current methods all depend on searching for identical substructures.MethodsWe explore a generalized vertex proximity criterion, and present analytic and probability results for the comparison of random lattice networks.ResultsWe apply this criterion to the comparison of the genetic networks of two evolutionarily divergent yeasts, Saccharomyces cerevisiae and Schizosaccharomyces pombe, derived using the Synthetic Genetic Array screen. We show that the overlapping parts of the networks of the two yeasts share a common structure beyond the shared edges. This may be due to their conservation of redundant pathways containing many synthetic lethal pairs of genes.ConclusionsDetecting the shared generalized adjacency clusters in the genetic networks of the two yeasts show that this analytical construct can be a useful tool in probing conserved network structure across divergent genomes.

The Population Genetics of X–Autosome Synthetic Lethals and Steriles

Epistatic interactions are widespread, and many of these interactions involve combinations of alleles at different loci that are deleterious when present in the same individual. The average genetic environment of sex-linked genes differs from that of autosomal genes, suggesting that the population genetics of interacting X-linked and autosomal alleles may be complex. Using both analytical theory and computer simulations, we analyzed the evolutionary trajectories and mutation-selection balance conditions for X-autosome synthetic lethals and steriles. Allele frequencies follow a set of fundamental trajectories, and incompatible alleles are able to segregate at much higher frequencies than single-locus expectations. Equilibria exist, and they can involve fixation of either autosomal or X-linked alleles. The exact equilibrium depends on whether synthetic alleles are dominant or recessive and whether fitness effects are seen in males, females, or both sexes. When single-locus fitness effects and synthetic incompatibilities are both present, population dynamics depend on the dominance of alleles and historical contingency (i.e., whether X-linked or autosomal mutations occur first). Recessive synthetic lethality can result in high-frequency X-linked alleles, and dominant synthetic lethality can result in high-frequency autosomal alleles. Many X-autosome incompatibilities in natural populations may be cryptic, appearing to be single-locus effects because one locus is fixed. We also discuss the implications of these findings with respect to standing genetic variation and the origins of Haldane's rule.

Improving the iMM904 S. cerevisiae metabolic model using essentiality and synthetic lethality data.

BackgroundSaccharomyces cerevisiae is the first eukaryotic organism for which a multi-compartment genome-scale metabolic model was constructed. Since then a sequence of improved metabolic reconstructions for yeast has been introduced. These metabolic models have been extensively used to elucidate the organizational principles of yeast metabolism and drive yeast strain engineering strategies for targeted overproductions. They have also served as a starting point and a benchmark for the reconstruction of genome-scale metabolic models for other eukaryotic organisms. In spite of the successive improvements in the details of the described metabolic processes, even the recent yeast model (i.e., iMM904) remains significantly less predictive than the latest E. coli model (i.e., iAF1260). This is manifested by its significantly lower specificity in predicting the outcome of grow/no grow experiments in comparison to the E. coli model.ResultsIn this paper we make use of the automated GrowMatch procedure for restoring consistency with single gene deletion experiments in yeast and extend the procedure to make use of synthetic lethality data using the genome-scale model iMM904 as a basis. We identified and vetted using literature sources 120 distinct model modifications including various regulatory constraints for minimal and YP media. The incorporation of the suggested modifications led to a substantial increase in the fraction of correctly predicted lethal knockouts (i.e., specificity) from 38.84% (87 out of 224) to 53.57% (120 out of 224) for the minimal medium and from 24.73% (45 out of 182) to 40.11% (73 out of 182) for the YP medium. Synthetic lethality predictions improved from 12.03% (16 out of 133) to 23.31% (31 out of 133) for the minimal medium and from 6.96% (8 out of 115) to 13.04% (15 out of 115) for the YP medium.ConclusionsOverall, this study provides a roadmap for the computationally driven correction of multi-compartment genome-scale metabolic models and demonstrates the value of synthetic lethals as curation agents.

Genome‐scale gene/reaction essentiality and synthetic lethality analysis

Synthetic lethals are to pairs of non-essential genes whose simultaneous deletion prohibits growth. One can extend the concept of synthetic lethality by considering gene groups of increasing size where only the simultaneous elimination of all genes is lethal, whereas individual gene deletions are not. We developed optimization-based procedures for the exhaustive and targeted enumeration of multi-gene (and by extension multi-reaction) lethals for genome-scale metabolic models. Specifically, these approaches are applied to iAF1260, the latest model of Escherichia coli, leading to the complete identification of all double and triple gene and reaction synthetic lethals as well as the targeted identification of quadruples and some higher-order ones. Graph representations of these synthetic lethals reveal a variety of motifs ranging from hub-like to highly connected subgraphs providing a birds-eye view of the avenues available for redirecting metabolism and uncovering complex patterns of gene utilization and interdependence. The procedure also enables the use of falsely predicted synthetic lethals for metabolic model curation. By analyzing the functional classifications of the genes involved in synthetic lethals, we reveal surprising connections within and across clusters of orthologous group functional classifications.

Genetic Evidence for the Importance of Protein Acetylation and Protein Deacetylation in the Halophilic Archaeon Haloferax volcanii

Protein acetylation and deacetylation reactions are involved in many regulatory processes in eukaryotes. Recently, it was found that similar processes occur in bacteria and archaea. Sequence analysis of the genome of the haloarchaeon Haloferax volcanii led to the identification of three putative protein acetyltransferases belonging to the Gcn5 family, Pat1, Pat2, and Elp3, and two deacetylases, Sir2 and HdaI. Intriguingly, the gene that encodes HdaI shares an operon with an archaeal histone homolog. We performed gene knockouts to determine whether the genes encoding these putative acetyltransferases and deacetylases are essential. A sir2 deletion mutant was able to grow normally, whereas an hdaI deletion mutant was nonviable. The latter is consistent with the finding that trichostatin A, a specific inhibitor of HdaI, inhibits cell growth in a concentration-dependent manner. We also showed that each of the acetyltransferases by itself is dispensable for growth but that deletion of both pat2 and elp3 could not be achieved. The corresponding genes are therefore "synthetic lethals," and the protein acetyltransferases probably have a common and essential substrate.

Synthetic growth phenotypes of Escherichia coli lacking ppGpp and transketolase A (tktA) are due to ppGpp‐mediated transcriptional regulation of tktB

Many physiological adjustments to nutrient changes involve ppGpp. Recent attempts to deduce ppGpp regulatory effects using proteomics or gene profiling can rigorously identify proteins or transcripts, but the functional significance is often unclear. Using a random screen for synthetic lethals we found a ppGpp-dependent functional pathway that operates through transketolase B (TktB), and which is 'buffered' in wildtype strain by the presence of an isozyme, transketolase A (TktA). Transketolase activity is required in cells to make erythrose-4-phosphate, a precursor of aromatic amino acids and vitamins. By studying tktB-dependent nutritional requirements as well as measuring activities using PtalA-tktB'-lacZ transcriptional reporter fusion, we show positive transcriptional regulation of the talA-tktB operon by ppGpp. Our results show the existence of RpoS-dependent and RpoS-independent modes of positive regulation by ppGpp. Both routes of activation are magnified by elevating ppGpp levels with a spoT mutation (spoT-R39A) defective in hydrolase but not synthetase activity or with the stringent suppressor mutations rpoB-A532Delta or rpoB-T563P in the absence of ppGpp.

Protein Network Comparative Genomics

With the appearance of large networks of protein‐protein and protein‐DNA interactions as a new type of biological measurement, methods are needed for constructing cellular pathway models using interaction data as the central framework. The key idea is that, by comparing the molecular interaction network with other biological data sets, it will be possible to organize the network into modules representing the repertoire of distinct functional processes in the cell. Three distinct types of network comparisons will be discussed, including those to identify: (1) Protein interaction networks that are conserved across species (2) Networks in control of gene expression changes (3) Networks correlating with systematic phenotypes and synthetic lethals Using these computational modeling and query tools, we are constructing network models to explain the physiological response of yeast to DNA damaging agents.

The Clathrin Adaptor Gga2p Is a Phosphatidylinositol 4-phosphate Effector at the Golgi Exit

Phosphatidylinositol 4-phosphate (PI(4)P) is a key regulator of membrane transport required for the formation of transport carriers from the trans-Golgi network (TGN). The molecular mechanisms of PI(4)P signaling in this process are still poorly understood. In a search for PI(4)P effector molecules, we performed a screen for synthetic lethals in a background of reduced PI(4)P and found the gene GGA2. Our analysis uncovered a PI(4)P-dependent recruitment of the clathrin adaptor Gga2p to the TGN during Golgi-to-endosome trafficking. Gga2p recruitment to liposomes is stimulated both by PI(4)P and the small GTPase Arf1p in its active conformation, implicating these two molecules in the recruitment of Gga2p to the TGN, which ultimately controls the formation of clathrin-coated vesicles. PI(4)P binding occurs through a phosphoinositide-binding signature within the N-terminal VHS domain of Gga2p resembling a motif found in other clathrin interacting proteins. These data provide an explanation for the TGN-specific membrane recruitment of Gga2p.

Annotating novel genes by integrating synthetic lethals and genomic information

BackgroundLarge scale screening for synthetic lethality serves as a common tool in yeast genetics to systematically search for genes that play a role in specific biological processes. Often the amounts of data resulting from a single large scale screen far exceed the capacities of experimental characterization of every identified target. Thus, there is need for computational tools that select promising candidate genes in order to reduce the number of follow-up experiments to a manageable size.ResultsWe analyze synthetic lethality data for arp1 and jnm1, two spindle migration genes, in order to identify novel members in this process. To this end, we use an unsupervised statistical method that integrates additional information from biological data sources, such as gene expression, phenotypic profiling, RNA degradation and sequence similarity. Different from existing methods that require large amounts of synthetic lethal data, our method merely relies on synthetic lethality information from two single screens. Using a Multivariate Gaussian Mixture Model, we determine the best subset of features that assign the target genes to two groups. The approach identifies a small group of genes as candidates involved in spindle migration. Experimental testing confirms the majority of our candidates and we present she1 (YBL031W) as a novel gene involved in spindle migration. We applied the statistical methodology also to TOR2 signaling as another example.ConclusionWe demonstrate the general use of Multivariate Gaussian Mixture Modeling for selecting candidate genes for experimental characterization from synthetic lethality data sets. For the given example, integration of different data sources contributes to the identification of genetic interaction partners of arp1 and jnm1 that play a role in the same biological process.

Lethality and synthetic lethality in the genome-wide metabolic network of Escherichia coli

Recent genomic analyses on the cellular metabolic network show that reaction flux across enzymes are diverse and exhibit power-law behavior in its distribution. While intuition might suggest that the reactions with larger fluxes are more likely to be lethal under the blockade of its catalysing gene products or gene knockouts, we find, by in silico flux analysis, that the lethality rarely has correlations with the flux level owing to the widespread backup pathways innate in the genome-wide metabolism of Escherichia coli. Lethal reactions, of which the deletion generates cascading failure of following reactions up to the biomass reaction, are identified in terms of the Boolean network scheme as well as the flux balance analysis. The avalanche size of a reaction, defined as the number of subsequently blocked reactions after its removal, turns out to be a useful measure of lethality. As a means to elucidate phenotypic robustness to a single deletion, we investigate synthetic lethality in reaction level, where simultaneous deletion of a pair of nonlethal reactions leads to the failure of the biomass reaction. Synthetic lethals identified via flux balance and Boolean scheme are consistently shown to act in parallel pathways, working in such a way that the backup machinery is compromised.

The Aspergillus nidulans sldIRAD50 gene interacts with bimEAPC1, a homologue of an anaphase‐promoting complex subunit

The Mre11-Rad50-Nbs1 protein complex has emerged as a central component in the human cellular DNA damage response, and recent observations suggest that these proteins are at least partially responsible for the linking of DNA damage detection to DNA repair and cell cycle checkpoint functions. We have identified Aspergillus nidulans sldI1444D mutant in a screen for dynein synthetic lethals. The sldI(RAD50) gene was cloned by complementation of the sporulation deficiency phenotype of this mutant. A transversion G-->C at the position 2509 (Ala-692-Pro amino acid change) in the sldI1444D mutant causes sensitivity to several DNA-damaging agents. The mutation sldI1 occurs at the CXXC hinge domain of Rad50. We have deleted part of the coiled-coil and few amino acids of the Rad50-Mre11 interaction region and assessed several phenotypic traits in this deletion strain. Besides sensitivity to a number of DNA-damaging agents, this deletion strain is also impaired in the DNA replication checkpoint response, and in ascospore viability. There is no delay of the S-phase when germlings of both sldI (RAD50) and mreA(MRE11) inactivation strains were exposed to the DNA damage caused by bleomycin. Transformation experiments and Southern blot analysis indicate homologous recombination is dependent on scaA(NBS1) function in the Mre11 complex. There are epistatic and synergistic interactions between sldI( RAD50) and bimE(APC1) at S-phase checkpoints and response to hydroxyurea and UV light. Our results suggest a possible novel feature of the Mre11 complex in A. nidulans, i.e. a relationship with bimE (APC1).

The contribution of epistasis to the architecture of fitness in an RNA virus.

The tendency for genetic architectures to exhibit epistasis among mutations plays a central role in the modern synthesis of evolutionary biology and in theoretical descriptions of many evolutionary processes. Nevertheless, few studies unquestionably show whether, and how, mutations typically interact. Beneficial mutations are especially difficult to identify because of their scarcity. Consequently, epistasis among pairs of this important class of mutations has, to our knowledge, never before been explored. Interactions among genome components should be of special relevance in compacted genomes such as those of RNA viruses. To tackle these issues, we first generated 47 genotypes of vesicular stomatitis virus carrying pairs of nucleotide substitution mutations whose separated and combined deleterious effects on fitness were determined. Several pairs exhibited significant interactions for fitness, including antagonistic and synergistic epistasis. Synthetic lethals represented 50% of the latter. In a second set of experiments, 15 genotypes carrying pairs of beneficial mutations were also created. In this case, all significant interactions were antagonistic. Our results show that the architecture of the fitness depends on complex interactions among genome components.