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Colorectal cancer (CRC) incidence changes with time and by variations in diet and lifestyle, as evidenced historically by migrant studies and recently by extensive epidemiologic evidence. The worldwide heterogeneity in CRC incidence is strongly suggestive of etiological involvement of environmental exposures, particularly lifestyle and diet. It is established that physical inactivity, obesity and some dietary factors (red/processed meats, alcohol) are positively associated with CRC, while healthy lifestyle habits show inverse associations. Mechanistic evidence shows that lifestyle and dietary components that contribute to energy excess are linked with increased CRC via metabolic dysfunction, inflammation, oxidative stress, bacterial dysbiosis and breakdown of gut barrier integrity while the reverse is apparent for components associated with decreased risk. This chapter will review the available evidence on lifestyle and dietary factors in CRC etiology and their underlying mechanisms in CRC development. This short review will also touch upon available information on potential gene-environment interactions, molecular sub-types of CRC and anatomical sub-sites within the colorectum. [ABSTRACT FROM AUTHOR]

Drug resistance is a rapidly emerging concern, thus prompting the development of novel therapeutics or combinatorial therapy. Currently, combinatorial therapy targets are based on knowledge of drug mode of action and/or resistance mechanisms, constraining the number of target proteins. Unbiased genome-wide screens could reveal novel genetic components within interaction networks as potential targets in combination therapies. Testing this, in the context of antimicrobial resistance, we implemented an unbiased genome-wide screen, performed in Saccharomyces cerevisiae expressing a Candida glabrata PDR1+ gain-of-function allele. Gain-of-function mutations in this gene are the principal mediators of fluconazole resistance in this human fungal pathogen. Eighteen synthetically lethal S. cerevisiae genetic mutants were identified in cells expressing C. glabrata PDR1+. One mutant, lacking the histone acetyltransferase Gcn5, was investigated further. Deletion or drug-mediated inhibition of Gcn5 caused a lethal phenotype in C. glabrata cells expressing PDR1+ alleles. Moreover, deletion or drug-mediated inactivation of Gcn5, inhibited the emergence of fluconazole-resistant C. glabrata isolates in evolution experiments. Thus, taken together, the data generated in this study provides proof of concept that synthetically lethal genetic screens can identify novel candidate proteins that when therapeutically targeted could allow effective treatment of drug-resistant infections. [ABSTRACT FROM AUTHOR]

LIS1 is the main causative gene for lissencephaly, while MeCP2 is the main causative gene for Rett syndrome, both of which are neurodevelopmental diseases. Here we report nuclear functions for LIS1 and identify previously unrecognized physical and genetic interactions between the products of these two genes in the cell nucleus, that has implications on MeCP2 organization, neuronal gene expression and mouse behavior. Reduced LIS1 levels affect the association of MeCP2 with chromatin. Transcriptome analysis of primary cortical neurons derived from wild type, Lis1 ±, MeCP2−/y , or double mutants mice revealed a large overlap in the differentially expressed (DE) genes between the various mutants. Overall, our findings provide insights on molecular mechanisms involved in the neurodevelopmental disorders lissencephaly and Rett syndrome caused by dysfunction of LIS1 and MeCP2, respectively. [ABSTRACT FROM AUTHOR]

The same mutation can have different effects in different individuals. One important reason for this is that the outcome of a mutation can depend on the genetic context in which it occurs. This dependency is known as epistasis. In recent years, there has been a concerted effort to quantify the extent of pairwise and higher-order genetic interactions between mutations through deep mutagenesis of proteins and RNAs. This research has revealed two major components of epistasis: nonspecific genetic interactions caused by nonlinearities in genotype-to-phenotype maps, and specific interactions between particular mutations. Here, we provide an overview of our current understanding of the mechanisms causing epistasis at the molecular level, the consequences of genetic interactions for evolution and genetic prediction, and the applications of epistasis for understanding biology and determining macromolecular structures. [ABSTRACT FROM AUTHOR]

Eukaryotic genomes contain thousands of genes organized into complex and interconnected genetic interaction networks. Most of our understanding of how genetic variation affects these networks comes from quantitative-trait loci mapping and from the systematic analysis of double-deletion (or knockdown) mutants, primarily in the yeast Saccharomyces cerevisiae. Evolve and re-sequence experiments are an alternative approach for identifying novel functional variants and genetic interactions, particularly between non-lossof-function mutations. These experiments leverage natural selection to obtain genotypes with functionally important variants and positive genetic interactions. However, no systematic methods for detecting genetic interactions in these data are yet available. Here, we introduce a computational method based on the idea that variants in genes that interact will co-occur in evolved genotypes more often than expected by chance. We apply this method to a previously published yeast experimental evolution dataset. We find that genetic targets of selection are distributed non-uniformly among evolved genotypes, indicating that genetic interactions had a significant effect on evolutionary trajectories. We identify individual gene pairs with a statistically significant genetic interaction score. The strongest interaction is between genes TRK1 and PHO84, genes that have not been reported to interact in previous systematic studies. Our work demonstrates that leveraging parallelism in experimental evolution is useful for identifying genetic interactions that have escaped detection by other methods. [ABSTRACT FROM AUTHOR]

UNC-6/Netrin is a conserved axon guidance cue that directs growth cone migrations in the dorsal-ventral axis of C. elegans and in the vertebrate spinal cord. UNC-6/Netrin is expressed in ventral cells, and growth cones migrate ventrally toward or dorsally away from UNC-6/Netrin. Recent studies of growth cone behavior during outgrowth in vivo in C. elegans have led to a polarity/protrusion model in directed growth cone migration away from UNC-6/Netrin. In this model, UNC-6/Netrin first polarizes the growth cone via the UNC-5 receptor, leading to dorsally biased protrusion and F-actin accumulation. UNC-6/Netrin then regulates protrusion based on this polarity. The receptor UNC-40/DCC drives protrusion dorsally, away from the UNC-6/Netrin source, and the UNC-5 receptor inhibits protrusion ventrally, near the UNC-6/Netrin source, resulting in dorsal migration. UNC-5 inhibits protrusion in part by excluding microtubules from the growth cone, which are pro-protrusive. Here we report that the RHO-1/RhoA GTPase and its activator GEF RHGF-1 inhibit growth cone protrusion and MT accumulation in growth cones, similar to UNC-5. However, growth cone polarity of protrusion and F-actin were unaffected by RHO-1 and RHGF-1. Thus, RHO-1 signaling acts specifically as a negative regulator of protrusion and MT accumulation, and not polarity. Genetic interactions are consistent with RHO-1 and RHGF-1 acting with UNC-5, as well as with a parallel pathway, to regulate protrusion. The cytoskeletal interacting molecule UNC-33/CRMP was required for RHO-1 activity to inhibit MT accumulation, suggesting that UNC-33/CRMP might act downstream of RHO-1. In sum, these studies describe a new role of RHO-1 and RHGF-1 in regulation of growth cone protrusion by UNC-6/Netrin. [ABSTRACT FROM AUTHOR]

In light of increased co-prescription of multiple drugs, the ability to discern and predict drug-drug interactions (DDI) has become crucial to guarantee the safety of patients undergoing treatment with multiple drugs. However, information on DDI profiles is incomplete and the experimental determination of DDIs is labor-intensive and time-consuming. Although previous studies have explored various feature spaces for in silico screening of interacting drug pairs, their use of conventional cross-validation prevents them from achieving generalizable performance on drug pairs where neither drug is seen during training. Here we demonstrate for the first time targets of adversely interacting drug pairs are significantly more likely to have synergistic genetic interactions than non-interacting drug pairs. Leveraging genetic interaction features and a novel training scheme, we construct a gradient boosting-based classifier that achieves robust DDI prediction even for drugs whose interaction profiles are completely unseen during training. We demonstrate that in addition to classification power—including the prediction of 432 novel DDIs—our genetic interaction approach offers interpretability by providing plausible mechanistic insights into the mode of action of DDIs. [ABSTRACT FROM AUTHOR]

Genetic interactions, a phenomenon whereby combinations of mutations lead to unexpected effects, reflect how cellular processes are wired and play an important role in complex genetic diseases. Understanding the molecular basis of genetic interactions is crucial for deciphering pathway organization as well as understanding the relationship between genetic variation and disease. Several hypothetical molecular mechanisms have been linked to different genetic interaction types. However, differences in genetic interaction patterns and their underlying mechanisms have not yet been compared systematically between different functional gene classes. Here, differences in the occurrence and types of genetic interactions are compared for two classes, gene-specific transcription factors (GSTFs) and signaling genes (kinases and phosphatases). Genome-wide gene expression data for 63 single and double deletion mutants in baker’s yeast reveals that the two most common genetic interaction patterns are buffering and inversion. Buffering is typically associated with redundancy and is well understood. In inversion, genes show opposite behavior in the double mutant compared to the corresponding single mutants. The underlying mechanism is poorly understood. Although both classes show buffering and inversion patterns, the prevalence of inversion is much stronger in GSTFs. To decipher potential mechanisms, a Petri Net modeling approach was employed, where genes are represented as nodes and relationships between genes as edges. This allowed over 9 million possible three and four node models to be exhaustively enumerated. The models show that a quantitative difference in interaction strength is a strict requirement for obtaining inversion. In addition, this difference is frequently accompanied with a second gene that shows buffering. Taken together, these results provide a mechanistic explanation for inversion. Furthermore, the ability of transcription factors to differentially regulate expression of their targets provides a likely explanation why inversion is more prevalent for GSTFs compared to kinases and phosphatases. [ABSTRACT FROM AUTHOR]

Abstract Ischemic Stroke (IS) is a severe and complex disorder of high morbidity and mortality rates associated with clinical, environmental, and genetic predisposing factors. Despite previous studies have associated genetic variants to stroke, inconsistent results from different populations pointed to the genetic heterogeneity for IS. Therefore, we may hypothesize that an interaction effect among genetic variants could contribute to IS occurrence rather than genetic variants independently. In this context, we investigated the association and interaction between genetic variants and large-artery atherosclerosis IS (LAAS-IS) and cardioembolic IS (CE-IS). We genotyped 435 patients (195 LAAS-IS; 240 CE-IS) and 535 controls from a population of Joinville, Santa Catarina, Brazil. Association and interaction analysis were performed by chi-square test and Multifactor-dimensionality Reduction test. We found an association between rs2383207*A allele, nearby CDKN2B-AS1 , and LAAS-IS [OR 2.35 (95% CI = 1.79–3.08); p = 4.66 × 10−10]. We found an interaction among rs2910829, rs966221 and rs152312, with an accuracy of 0.62 (p = 4.3 × 10−5) demonstrating the interaction effect among variants from different genes can contribute to CE-IS risk. Further prediction analysis confirmed that clinical information, such as hypertension and dyslipidemia, presented high accuracy to predict LAAS-IS (86.47%) and CE-IS (90.47%); however, the inclusion of genetic variant information did not increase the accuracy. Highlights • The rs2383207 shows significant association with LAAS ischemic stroke risk in Brazil • There is an interaction effect between genetic variants on cardioembolic stroke risk • Genetic variants show lower accuracy on stroke prediction than classical risk factors [ABSTRACT FROM AUTHOR]

Head and neck squamous cell carcinoma (HNSCC) is highly variable by tumor site, histologic type, molecular characteristics, and clinical outcome. During recent years, emerging targeted therapies have been focused on driver genes. HNSCC involves several genetic alterations, such as co-occurrence, multiple feedback loops, and cross-talk communications. These different kinds of genetic alterations interact with each other and mediate targeted therapy response. In the current review, it is emphasized that future treatment strategy in HNSCC will not solely be based on "synthetic lethality" approaches directed against overactivated genes. More importantly, biologic, genetic, and epigenetic alterations of HNSCC will be taken into consideration to guide the therapy. The emerging genetic alterations in HNSCC and its effect on targeted therapy response are discussed in detail. Hopefully, novel combination regimens for the treatment of HNSCC can be developed. [ABSTRACT FROM AUTHOR]

Phenotypic complexity is caused by the contributions of environmental factors and multiple genetic loci, interacting or acting independently. Studies of yeast and Arabidopsis often find that the majority of natural variation across phenotypes is attributable to independent additive quantitative trait loci (QTL). Detected loci in these organisms explain most of the estimated heritable variation. By contrast, many heritable components underlying phenotypic variation in metazoan models remain undetected. Before the relative impacts of additive and interactive variance components on metazoan phenotypic variation can be dissected, high replication and precise phenotypic measurements are required to obtain sufficient statistical power to detect loci contributing to this missing heritability. Here, we used a panel of 296 recombinant inbred advanced intercross lines of Caenorhabditis elegans and a highthroughput fitness assay to detect loci underlying responses to 16 different toxins, including heavy metals, chemotherapeutic drugs, pesticides, and neuropharmaceuticals. Using linkage mapping, we identified 82 QTL that underlie variation in responses to these toxins, and predicted the relative contributions of additive loci and genetic interactions across various growth parameters. Additionally, we identified three genomic regions that impact responses to multiple classes of toxins. These QTL hotspots could represent common factors impacting toxin responses. We went further to generate near-isogenic lines and chromosome substitution strains, and then experimentally validated these QTL hotspots, implicating additive and interactive loci that underlie toxin-response variation. [ABSTRACT FROM AUTHOR]

We found that for most gene pairs the effect was neutral (statistically insignificant), but in 2.6 million cases a second gene deletion increased the fitness of the original deletion mutant and in 3.3 million cases the second deletion decreased the fitness of the original mutant. We noticed that the genetic interaction networks containing fitness data of single and double mutants show whether in the background of a given deletion mutant a deletion of another gene is beneficial or deleterious, i.e., a fitness landscape. Those experiments were performed using yeast deletion mutants in BY4741 background grown in YPD medium and the starting genome sequence was determined following gene deletion to take into account possible off-target mutations caused by genetic manipulation. This network represents the fitness of a double mutant with two genes mutated or deleted in comparison with the fitnesses of the two single mutants which, in turn, are compared with the fitness of the wild-type ancestor. [Extracted from the article]

Aim. Evaluate antibacterial activity of an experimental mixture of phages, belonging to several well-studied species. Materials and methods. The study was carried out using a group of 55 clinical Pseudomonas aeruginosa strains of various origins, 4 mono-species mixtures of 32 virulent bacteriophages (species phiKZ-, phiKMV-, phiPBl-, РаРЗ-like phages) and 2 novel phages, phiMK (species PaK-P2) and phiPerm5. Activity of preparations from mono-species mixtures of bacteriophages of various species were compared with activity of 3 commercial mixtures. Standard methods of study of bacteriophages were used: determination of lytic activity by seeding onto bacterial lawns of P. aeruginosa, restriction analysis of phage DNA for confirmation of their belonging to certain species. Results. Cumulative activity of 6 mono-species mixtures of virulent phages was shown to be similar to lytic activity of commercial therapeutic mixtures used against P. aeruginosa infections. 54 of 55 strains of clinical isolates of P. aeruginosa showed sensitivity to experimental mixtures composed of mono-species mixtures of bacteriophages. 53 strains were lysed by commercial preparations. Wherein the possibility of accidental inclusion of moderate bacteriophages in the experimental mixture is excluded. Conclusion. A possibility of creation of highly active therapeutic antibacterial preparations against P. aeruginosa using mono-species mixtures of 6 species of lytic bacteriophages is shown. Use of such a mixture in therapy of lung infections reduces the risk of emergence of bacterial strains with increased virulence and pathogenicity during prolonged administration.

Abstract Cells must quickly respond and efficiently adapt to environmental changes. The yeast Saccharomyces cerevisiae has multiple pathways that respond to specific environmental insults, as well as a generic stress response program. The later is regulated by two transcription factors, Msn2 and Msn4, that integrate information from upstream pathways to produce fast, tunable, and robust response to different environmental changes. To understand this integration, we employed a systematic approach to genetically dissect the contribution of various cellular pathways to Msn2/4 regulation under a range of stress and growth conditions. We established a high‐throughput liquid handling and automated flow cytometry system and measured GFP levels in 68 single‐knockout and 1,566 double‐knockout strains that carry an HSP12‐GFP allele as a reporter for Msn2/4 activity. Based on the expression of this Msn2/4 reporter in five different conditions, we identified numerous genetic and epistatic interactions between different components in the network upstream to Msn2/4. Our analysis gains new insights into the functional specialization of the RAS paralogs in the repression of stress response and identifies a three‐way crosstalk between the Mediator complex, the HOG MAPK pathway, and the cAMP/PKA pathway.

Abstract Genetic interaction screens have been applied with great success in several organisms to study gene function and the genetic architecture of the cell. However, most studies have been performed under optimal growth conditions even though many functional interactions are known to occur under specific cellular conditions. In this study, we have performed a large‐scale genetic interaction analysis in Saccharomyces cerevisiae involving approximately 49 × 1,200 double mutants in the presence of five different stress conditions, including osmotic, oxidative and cell wall‐altering stresses. This resulted in the generation of a differential E‐MAP (or dE‐MAP) comprising over 250,000 measurements of conditional interactions. We found an extensive number of conditional genetic interactions that recapitulate known stress‐specific functional associations. Furthermore, we have also uncovered previously unrecognized roles involving the phosphatase regulator Bud14, the histone methylation complex COMPASS and membrane trafficking complexes in modulating the cell wall integrity pathway. Finally, the osmotic stress differential genetic interactions showed enrichment for genes coding for proteins with conditional changes in phosphorylation but not for genes with conditional changes in gene expression. This suggests that conditional genetic interactions are a powerful tool to dissect the functional importance of the different response mechanisms of the cell.

Chemical-genetic interactions–observed when the treatment of mutant cells with chemical compounds reveals unexpected phenotypes–contain rich functional information linking compounds to their cellular modes of action. To systematically identify these interactions, an array of mutants is challenged with a compound and monitored for fitness defects, generating a chemical-genetic interaction profile that provides a quantitative, unbiased description of the cellular function(s) perturbed by the compound. Genetic interactions, obtained from genome-wide double-mutant screens, provide a key for interpreting the functional information contained in chemical-genetic interaction profiles. Despite the utility of this approach, integrative analyses of genetic and chemical-genetic interaction networks have not been systematically evaluated. We developed a method, called CG-TARGET (Chemical Genetic Translation via A Reference Genetic nETwork), that integrates large-scale chemical-genetic interaction screening data with a genetic interaction network to predict the biological processes perturbed by compounds. In a recent publication, we applied CG-TARGET to a screen of nearly 14,000 chemical compounds in Saccharomyces cerevisiae, integrating this dataset with the global S. cerevisiae genetic interaction network to prioritize over 1500 compounds with high-confidence biological process predictions for further study. We present here a formal description and rigorous benchmarking of the CG-TARGET method, showing that, compared to alternative enrichment-based approaches, it achieves similar or better accuracy while substantially improving the ability to control the false discovery rate of biological process predictions. Additional investigation of the compatibility of chemical-genetic and genetic interaction profiles revealed that one-third of observed chemical-genetic interactions contributed to the highest-confidence biological process predictions and that negative chemical-genetic interactions overwhelmingly formed the basis of these predictions. We also present experimental validations of CG-TARGET-predicted tubulin polymerization and cell cycle progression inhibitors. Our approach successfully demonstrates the use of genetic interaction networks in the high-throughput functional annotation of compounds to biological processes. [ABSTRACT FROM AUTHOR]

Although transcriptomes have recently been used as phenotypes with which to perform epistasis analyses, they are not yet used to study intragenic function/structure relationships. We developed a theoretical framework to study allelic series using transcriptomic phenotypes. As a proof-of-concept, we apply our methods to an allelic series of dpy-22, a highly pleiotropic Caenorhabditis elegans gene orthologous to the human gene MED12, which encodes a subunit of the Mediator complex. Our methods identify functional units within dpy-22 that modulate Mediator activity upon various genetic programs, including the Wnt and Ras modules. [ABSTRACT FROM AUTHOR]

Abstract: Genetic interaction between domesticated escapees and wild conspecifics represents a persistent challenge to an environmentally sustainable Atlantic salmon aquaculture industry. We used a recently developed eco‐genetic model (IBSEM) to investigate potential changes in a wild salmon population subject to spawning intrusion from domesticated escapees. At low intrusion levels (5%–10% escapees), phenotypic and demographic characteristics of the recipient wild population only displayed weak changes over 50 years and only at high intrusion levels (30%–50% escapees) were clear changes visible in this period. Our modeling also revealed that genetic changes in phenotypic and demographic characteristics were greater in situations where strayers originating from a neighboring wild population were domestication‐admixed and changed in parallel with the focal wild population, as opposed to nonadmixed. While recovery in the phenotypic and demographic characteristics was observed in many instances after domesticated salmon intrusion was halted, in the most extreme intrusion scenario, the population went extinct. Based upon results from these simulations, together with existing knowledge, we suggest that a combination of reduced spawning success of domesticated escapees, natural selection purging maladapted phenotypes/genotypes from the wild population, and phenotypic plasticity, buffer the rate and magnitude of change in phenotypic and demographic characteristics of wild populations subject to spawning intrusion of domesticated escapees. The results of our simulations also suggest that under specific conditions, natural straying among wild populations may buffer genetic changes in phenotypic and demographic characteristics resulting from introgression of domesticated escapees and that variation in straying in time and space may contribute to observed differences in domestication‐driven introgression among native populations. [ABSTRACT FROM AUTHOR]

Genetic variation in mitochondrial DNA (mtDNA) provides adaptive potential although the underlying genetic architecture of fitness components within mtDNAs is not known. To dissect functional variation within mtDNAs, we first identified naturally occurring mtDNAs that conferred high or low fitness in Saccharomyces cerevisiae by comparing growth in strains containing identical nuclear genotypes but different mtDNAs. During respiratory growth under temperature and oxidative stress conditions, mitotype effects were largely independent of nuclear genotypes even in the presence of mito--nuclear interactions. Recombinant mtDNAs were generated to determine fitness components within high- and low-fitness mtDNAs. Based on phenotypic distributions of isogenic strains containing recombinant mtDNAs, we found that multiple loci contributed to mitotype fitness differences. These mitochondrial loci interacted in epistatic, nonadditive ways in certain environmental conditions. Mito--mito epistasis (i.e., nonadditive interactions between mitochondrial loci) influenced fitness in progeny from four different crosses, suggesting that mito--mito epistasis is a widespread phenomenon in yeast and other systems with recombining mtDNAs. Furthermore, we found that interruption of coadapted mito--mito interactions produced recombinant mtDNAs with lower fitness. Our results demonstrate that mito--mito epistasis results in functional variation through mitochondrial recombination in fungi, providing modes for adaptive evolution and the generation of mito--mito incompatibilities. [ABSTRACT FROM AUTHOR]

The broadly conserved signaling nucleotide cyclic di-adenosine monophosphate (c-di-AMP) is essential for viability in most bacteria where it has been studied. However, characterization of the cellular functions and metabolism of c-di-AMP has largely been confined to the class Bacilli, limiting our functional understanding of the molecule among diverse phyla. We identified the cyclase responsible for c-di-AMP synthesis and characterized the molecule’s role in survival of darkness in the model photosynthetic cyanobacterium Synechococcus elongatus PCC 7942. In addition to the use of traditional genetic, biochemical, and proteomic approaches, we developed a high-throughput genetic interaction screen (IRB-Seq) to determine pathways where the signaling nucleotide is active. We found that in S. elongatus c-di-AMP is produced by an enzyme of the diadenylate cyclase family, CdaA, which was previously unexplored experimentally. A cdaA-null mutant experiences increased oxidative stress and death during the nighttime portion of day-night cycles, in which potassium transport is implicated. These findings suggest that c-di-AMP is biologically active in cyanobacteria and has non-canonical roles in the phylum including oxidative stress management and day-night survival. The pipeline and analysis tools for IRB-Seq developed for this study constitute a quantitative high-throughput approach for studying genetic interactions. [ABSTRACT FROM AUTHOR]

Throughout the animal kingdom, the Notch signalling pathway allows cells to acquire diversified cell fates. Notch signals are translated into activation of Notch target genes by CSL transcription factors. In the absence of Notch signals, CSL together with co-repressors functions as a transcriptional repressor. In Drosophila, repression of Notch target genes involves the CSL homologue Suppressor of Hairless (Su(H)) and the Notch (N) antagonist Hairless (H) that together form a repressor complex. Guided by crystal structure, three mutations Su(H)LL, Su(H)LLF and Su(H)LLL were generated that specifically affect interactions with the repressor H, and were introduced into the endogenous Su(H) locus by gene engineering. In contrast to the wild type isoform, these Su(H) mutants are incapable of repressor complex formation. Accordingly, Notch signalling activity is dramatically elevated in the homozygotes, resembling complete absence of H activity. It was noted, however, that heterozygotes do not display a dominant H loss of function phenotype. In this work we addressed genetic interactions the three H-binding deficient Su(H) mutants display in combination with H and N null alleles. We included a null mutant of Delta (Dl), encoding the ligand of the Notch receptor, as well as of Su(H) itself in our genetic analyses. H, N or Dl mutations cause dominant wing phenotypes that are sensitive to gene dose of the others. Moreover, H heterozygotes lack bristle organs and develop bristle sockets instead of shafts. The latter phenotype is suppressed by Su(H) null alleles but not by H-binding deficient Su(H) alleles which we attribute to the socket cell specific activity of Su(H). Modification of the dominant wing phenotypes of either H, N or Dl, however, suggested some lack of repressor activity in the Su(H) null allele and likewise in the H-binding deficient Su(H) alleles. Overall, Su(H) mutants are recessive perhaps reflecting self-adjusting availability of Su(H) protein. [ABSTRACT FROM AUTHOR]

Chromatin remodeling complexes are essential for gene expression programs that coordinate cell function with metabolic status. However, how these remodelers are integrated in metabolic stability pathways is not well known. Here, we report an expansive genetic screen with chromatin remodelers and metabolic regulators in Saccharomyces cerevisiae. We found that, unlike the SWR1 remodeler, the INO80 chromatin remodeling complex is composed of multiple distinct functional subunit modules. We identified a strikingly divergent genetic signature for the Ies6 subunit module that links the INO80 complex to metabolic homeostasis. In particular, mitochondrial maintenance is disrupted in ies6 mutants. INO80 is also needed to communicate TORC1-mediated signaling to chromatin, as ino80 mutants exhibit defective transcriptional profiles and altered histone acetylation of TORC1-responsive genes. Furthermore, comparative analysis reveals subunits of INO80 and mTORC1 have high co-occurrence of alterations in human cancers. Collectively, these results demonstrate that the INO80 complex is a central component of metabolic homeostasis that influences histone acetylation and may contribute to disease when disrupted. [ABSTRACT FROM AUTHOR]

Computational analysis of biomolecular interaction networks is now gaining a lot of importance to understand the functions of novel genes/proteins. Gene interaction (GI) network analysis and protein-protein interaction (PPI) network analysis play a major role in predicting the functionality of interacting genes or proteins and gives an insight into the functional relationships and evolutionary conservation of interactions among the genes. An interaction network is a graphical representation of gene/protein interactome, where each gene/protein is a node, and interaction between gene/protein is an edge. In this review, we discuss the popular open source databases that serve as data repositories to search and collect protein/gene interaction data, and also tools available for the generation of interaction network, visualization and network analysis. Also, various network analysis approaches like topological approach and clustering approach to study the network properties and functional enrichment server which illustrates the functions and pathway of the genes and proteins has been discussed. Hence the distinctive attribute mentioned in this review is not only to provide an overview of tools and web servers for gene and protein-protein interaction (PPI) network analysis but also to extract useful and meaningful information from the interaction networks. [ABSTRACT FROM AUTHOR]

Abstract: Cancer genomes often harbor hundreds of molecular aberrations. Such genetic variants can be drivers or passengers of tumorigenesis and create vulnerabilities for potential therapeutic exploitation. To identify genotype‐dependent vulnerabilities, forward genetic screens in different genetic backgrounds have been conducted. We devised MINGLE, a computational framework to integrate CRISPR/Cas9 screens originating from different libraries building on approaches pioneered for genetic network discovery in model organisms. We applied this method to integrate and analyze data from 85 CRISPR/Cas9 screens in human cancer cells combining functional data with information on genetic variants to explore more than 2.1 million gene‐background relationships. In addition to known dependencies, we identified new genotype‐specific vulnerabilities of cancer cells. Experimental validation of predicted vulnerabilities identified GANAB and PRKCSH as new positive regulators of Wnt/β‐catenin signaling. By clustering genes with similar genetic interaction profiles, we drew the largest genetic network in cancer cells to date. Our scalable approach highlights how diverse genetic screens can be integrated to systematically build informative maps of genetic interactions in cancer, which can grow dynamically as more data are included. [ABSTRACT FROM AUTHOR]

The Ca2+/calmodulin-dependent protein phosphatase calcineurin orchestrates sexual reproduction, stress responses, and virulence via branched downstream pathways in the opportunistic human fungal pathogen Cryptococcus neoformans. The calcineurinbinding protein Cbp1, the calcineurin temperature suppressor Cts1, the calcineurin-responsive zinc finger transcription factor Crz1, and the calcineurin targets Pbp1, Tif3, and Puf4, all function downstream of calcineurin to orchestrate distinct cellular processes. To elucidate how the calcineurin pathway regulatory network governs unisexual reproduction, stress responses, and virulence, we have analyzed the self-filamentous C. deneoformans strain, XL280a, and generated double mutants of these calcineurin downstream genes. We demonstrated that calcineurin governs unisexual reproduction at different sexual developmental stages, in which the initiation of the yeast--hyphal morphological transition is independent of Crz1, whereas the sporulation process is dependent on Crz1. Calcineurin-dependent unisexual reproduction is independent of the pheromone response pathway. Crz1 synergistically interacts with different calcineurin downstream targets in responding to ER, high-calcium, and cell wall stresses. We observed a widespread synergy suggesting that these proteins function in complex branched pathways downstream of calcineurin with some functional redundancy, which may allow efficient signaling network rewiring within the pathway for prompt adaptation to changing environments. Finally, we showed that deletion of PBP1 or TIF3 in the cna1Δ mutant background conferred a modest level of growth tolerance at 37Δ, but that the cna1D pbp1D and cna1D tif3D double mutants were both avirulent, suggesting that calcineurin may control virulence via mechanisms beyond thermotolerance. [ABSTRACT FROM AUTHOR]

AKAP200 is a Drosophila melanogaster member of the “A Kinase Associated Protein” family of scaffolding proteins, known for their role in the spatial and temporal regulation of Protein Kinase A (PKA) in multiple signaling contexts. Here, we demonstrate an unexpected function of AKAP200 in promoting Notch protein stability. In Drosophila, AKAP200 loss-of-function (LOF) mutants show phenotypes that resemble Notch LOF defects, including eye patterning and sensory organ specification defects. Through genetic interactions, we demonstrate that AKAP200 interacts positively with Notch in both the eye and the thorax. We further show that AKAP200 is part of a physical complex with Notch. Biochemical studies reveal that AKAP200 stabilizes endogenous Notch protein, and that it limits ubiquitination of Notch. Specifically, our genetic and biochemical evidence indicates that AKAP200 protects Notch from the E3-ubiquitin ligase Cbl, which targets Notch to the lysosomal pathway. Indeed, we demonstrate that the effect of AKAP200 on Notch levels depends on the lysosome. Interestingly, this function of AKAP200 is fully independent of its role in PKA signaling and independent of its ability to bind PKA. Taken together, our data indicate that AKAP200 is a novel tissue specific posttranslational regulator of Notch, maintaining high Notch protein levels and thus promoting Notch signaling. [ABSTRACT FROM AUTHOR]

Genome-wide association studies are moving to genome-wide interaction studies, as the genetic background of many diseases appears to be more complex than previously supposed. Thus, many statistical approaches have been proposed to detect gene-gene (GxG) interactions, among them numerous information theory-based methods, inspired by the concept of entropy. These are suggested as particularly powerful and, because of their nonlinearity, as better able to capture nonlinear relationships between genetic variants and/or variables. However, the introduced entropy-based estimators differ to a surprising extent in their construction and even with respect to the basic definition of interactions. Also, not every entropybased measure for interaction is accompanied by a proper statistical test. To shed light on this, a systematic review of the literature is presented answering the following questions: (1) How are GxG interactions defined within the framework of information theory? (2) Which entropy-based test statistics are available? (3) Which underlying distribution do the test statistics follow? (4) What are the given strengths and limitations of these test statistics?. [ABSTRACT FROM AUTHOR]

The complex of central problems in data analysis consists of three components: (1) detecting the dependence of variables using quantitative measures, (2) defining the significance of these dependence measures, and (3) inferring the functional relationships among dependent variables. We have argued previously that an information theory approach allows separation of the detection problem from the inference of functional form problem. We approach here the third component of inferring functional forms based on information encoded in the functions. We present here a direct method for classifying the functional forms of discrete functions of three variables represented in data sets. Discrete variables are frequently encountered in data analysis, both as the result of inherently categorical variables and from the binning of continuous numerical variables into discrete alphabets of values. The fundamental question of how much information is contained in a given function is answered for these discrete functions, and their surprisingly complex relationships are illustrated. The all-important effect of noise on the inference of function classes is found to be highly heterogeneous and reveals some unexpected patterns. We apply this classification approach to an important area of biological data analysis-that of inference of genetic interactions. Genetic analysis provides a rich source of real and complex biological data analysis problems, and our general methods provide an analytical basis and tools for characterizing genetic problems and for analyzing genetic data. We illustrate the functional description and the classes of a number of common genetic interaction modes and also show how different modes vary widely in their sensitivity to noise. [ABSTRACT FROM AUTHOR]

Neural tube defects (NTDs) are among the commonest and most severe forms of developmental defect, characterized by disruption of the early embryonic events of central nervous system formation. NTDs have long been known to exhibit a strong genetic dependence, yet the identity of the genetic determinants remains largely undiscovered. Initiation of neural tube closure is disrupted in mice homozygous for mutations in planar cell polarity (PCP) pathway genes, providing a strong link between NTDs and PCP signaling. Recently, missense gene variants have been identified in PCP genes in humans with NTDs, although the range of phenotypes is greater than in the mouse mutants. In addition, the sequence variants detected in affected humans are heterozygous, and can often be detected in unaffected individuals. It has been suggested that interactions between multiple heterozygous gene mutations cause the NTDs in humans. To determine the phenotypes produced in double heterozygotes, we bred mice with all three pairwise combinations of Vangl2Lp, ScribCrc and Celsr1Crsh mutations, the most intensively studied PCP mutants. The majority of double-mutant embryos had open NTDs, with the range of phenotypes including anencephaly and spina bifida, therefore reflecting the defects observed in humans. Strikingly, even on a uniform genetic background, variability in the penetrance and severity of the mutant phenotypes was observed between the different double-heterozygote combinations. Phenotypically, Celsr1Crsh;Vangl2Lp;ScribCrc triply heterozygous mutants were no more severe than doubly heterozygous or singly homozygous mutants. We propose that some of the variation between double-mutant phenotypes could be attributed to the nature of the protein disruption in each allele: whereas ScribCrc is a null mutant and produces no Scrib protein, Celsr1Crsh and Vangl2Lp homozygotes both express mutant proteins, consistent with dominant effects. The variable outcomes of these genetic interactions are of direct relevance to human patients and emphasize the importance of performing comprehensive genetic screens in humans.

Abstract Pathways are a universal paradigm for functionally describing cellular processes. Even though advances in high‐throughput data generation have transformed biology, the core of our biological understanding, and hence data interpretation, is still predicated on human‐defined pathways. Here, we introduce an unbiased, pathway structure for genome‐scale metabolic networks defined based on principles of parsimony that do not mimic canonical human‐defined textbook pathways. Instead, these minimal pathways better describe multiple independent pathway‐associated biomolecular interaction datasets suggesting a functional organization for metabolism based on parsimonious use of cellular components. We use the inherent predictive capability of these pathways to experimentally discover novel transcriptional regulatory interactions in Escherichia coli metabolism for three transcription factors, effectively doubling the known regulatory roles for Nac and MntR. This study suggests an underlying and fundamental principle in the evolutionary selection of pathway structures; namely, that pathways may be minimal, independent, and segregated.

Breast cancer is the second largest cause of cancer death among U.S. women and the leading cause of cancer death among women worldwide. Genome-wide association studies (GWAS) have identified several genetic variants associated with susceptibility to breast cancer, but these still explain less than half of the estimated genetic contribution to the disease. Combinations of variants (i.e. genetic interactions) may play an important role in breast cancer susceptibility. However, due to a lack of statistical power, the current tests for genetic interactions from GWAS data mainly leverage prior knowledge to focus on small sets of genes or SNPs that are known to have an association with breast cancer. Thus, many genetic interactions, particularly among novel variants, remain understudied. Reverse-genetic interaction screens in model organisms have shown that genetic interactions frequently cluster into highly structured motifs, where members of the same pathway share similar patterns of genetic interactions. Based on this key observation, we recently developed a method called BridGE to search for such structured motifs in genetic networks derived from GWAS studies and identify pathway-level genetic interactions in human populations. We applied BridGE to six independent breast cancer cohorts and identified significant pathway-level interactions in five cohorts. Joint analysis across all five cohorts revealed a high confidence consensus set of genetic interactions with support in multiple cohorts. The discovered interactions implicated the glutathione conjugation, vitamin D receptor, purine metabolism, mitotic prometaphase, and steroid hormone biosynthesis pathways as major modifiers of breast cancer risk. Notably, while many of the pathways identified by BridGE show clear relevance to breast cancer, variants in these pathways had not been previously discovered by traditional single variant association tests, or single pathway enrichment analysis that does not consider SNP-SNP interactions. [ABSTRACT FROM AUTHOR]

The DNA repair enzyme polynucleotide kinase/phosphatase (PNKP) protects genome integrity by restoring ligatable 5’-phosphate and 3’-hydroxyl termini at single-strand breaks (SSBs). In humans, PNKP mutations underlie the neurological disease known as MCSZ, but these individuals are not predisposed for cancer, implying effective alternative repair pathways in dividing cells. Homology-directed repair (HDR) of collapsed replication forks was proposed to repair SSBs in PNKP-deficient cells, but the critical HDR protein Rad51 is not required in PNKP-null (pnk1Δ) cells of Schizosaccharomyces pombe. Here, we report that pnk1Δ cells have enhanced requirements for Rad3 (ATR/Mec1) and Chk1 checkpoint kinases, and the multi-BRCT domain protein Brc1 that binds phospho-histone H2A (γH2A) at damaged replication forks. The viability of pnk1Δ cells depends on Mre11 and Ctp1 (CtIP/Sae2) double-strand break (DSB) resection proteins, Rad52 DNA strand annealing protein, Mus81-Eme1 Holliday junction resolvase, and Rqh1 (BLM/WRN/Sgs1) DNA helicase. Coupled with increased sister chromatid recombination and Rad52 repair foci in pnk1Δ cells, these findings indicate that lingering SSBs in pnk1Δ cells trigger Rad51-independent homology-directed repair of collapsed replication forks. From these data, we propose models for HDR-mediated tolerance of persistent SSBs with 3’ phosphate in pnk1Δ cells. [ABSTRACT FROM AUTHOR]

Summary A comprehensive view of molecular chaperone function in the cell was obtained through a systematic global integrative network approach based on physical (protein-protein) and genetic (gene-gene or epistatic) interaction mapping. This allowed us to decipher interactions involving all core chaperones (67) and cochaperones (15) of Saccharomyces cerevisiae . Our analysis revealed the presence of a large chaperone functional supercomplex, which we named the naturally joined (NAJ) chaperone complex, encompassing Hsp40, Hsp70, Hsp90, AAA+, CCT, and small Hsps. We further found that many chaperones interact with proteins that form foci or condensates under stress conditions. Using an in vitro reconstitution approach, we demonstrate condensate formation for the highly conserved AAA+ ATPases Rvb1 and Rvb2, which are part of the R2TP complex that interacts with Hsp90. This expanded view of the chaperone network in the cell clearly demonstrates the distinction between chaperones having broad versus narrow substrate specificities in protein homeostasis. [ABSTRACT FROM AUTHOR]

Understanding genome and gene function in a whole organism requires us to fully comprehend the life cycle and the physiology of the organism in question. Caenorhabditis elegans XX animals are hermaphrodites that exhaust their sperm after 3 d of egg-laying. Even though C. elegans can live for many days after cessation of egg-laying, the molecular physiology of this state has not been as intensely studied as other parts of the life cycle, despite documented changes in behavior and metabolism. To study the effects of sperm depletion and aging of C. elegans during the first 6 d of adulthood, we measured the transcriptomes of first-day adult hermaphrodites and sixth-day sperm-depleted adults, and, at the same time points, mutant fog-2(lf) worms that have a feminized germline phenotype. We found that we could separate the effects of biological aging from sperm depletion. For a large subset of genes, young adult fog-2(lf) animals had the same gene expression changes as sperm-depleted sixth-day wild-type hermaphrodites, and these genes did not change expression when fog-2(lf) females reached the sixth day of adulthood. Taken together, this indicates that changing sperm status causes a change in the internal state of the worm, which we call the female-like state. Our data provide a high-quality picture of the changes that happen in global gene expression throughout the period of early aging in the worm. [ABSTRACT FROM AUTHOR]

Biofilm formation by Candida albicans is a key aspect of its pathobiology and is regulated by an integrated network of transcription factors (Bcr1, Brg1, Efg1, Ndt80, Rob1, and Tec1). To understand the details of how the transcription factors function together to regulate biofilm formation, we used a systematic genetic interaction approach based on generating all possible double heterozygous mutants of the network genes and quantitatively analyzing the genetic interactions between them. Overall, the network is highly susceptible to genetic perturbation with the six network heterozygous mutants all showing alterations in biofilm formation (haploinsufficiency). In addition, many double heterozygous mutants are as severely affected as homozygous deletions. As a result, the network shows properties of a highly interdependent ‘small-world’ network that is highly efficient but not robust. In addition, these genetic interaction data indicate that TEC1 represents a network component whose expression is highly sensitive to small perturbations in the function of other networks TFs. We have also found that expression of ROB1 is dependent on both auto-regulation and cooperative interactions with other network TFs. Finally, the heterozygous NDT80 deletion mutant is hyperfilamentous under both biofilm and hyphae-inducing conditions in a Tec1-dependent manner. Taken together, genetic interaction analysis of this network has provided new insights into the functions of individual TFs as well as into the role of the overall network topology in its function. [ABSTRACT FROM AUTHOR]

Recent analysis of genome sequences has identified individuals that are healthy despite carrying severe disease-associated mutations. A possible explanation is that these individuals carry a second genomic perturbation that can compensate for the detrimental effects of the disease allele, a phenomenon referred to as suppression. In model organisms, suppression interactions are generally divided into two classes: genomic suppressors which are secondary mutations in the genome that bypass a mutant phenotype, and dosage suppression interactions in which overexpression of a suppressor gene rescues a mutant phenotype. Here, we describe the general properties of genomic and dosage suppression, with an emphasis on the budding yeast. We propose that suppression interactions between genetic variants are likely relevant for determining the penetrance of human traits. Consequently, an understanding of suppression mechanisms may guide the discovery of protective variants in healthy individuals that carry disease alleles, which could direct the rational design of new therapeutics. [ABSTRACT FROM AUTHOR]

For over a decade functional gene-to-gene interaction (epistasis) has been suspected to be a determinant in the "missing heritability" of complex traits. However, searching for epistasis on the genome-wide scale has been challenging due to the prohibitively large number of tests which result in a serious loss of statistical power as well as computational challenges. In this article, we propose a two-stage method applicable to existing case-control data sets, which aims to lessen both of these problems by pre-assessing whether a candidate pair of genetic loci is involved in epistasis before it is actually tested for interaction with respect to a complex phenotype. The pre-assessment is based on a two-locus genotype independence test performed in the sample of cases. Only the pairs of loci that exhibit non-equilibrium frequencies are analyzed via a logistic regression score test, thereby reducing the multiple testing burden. Since only the computationally simple independence tests are performed for all pairs of loci while the more demanding score tests are restricted to the most promising pairs, genome-wide association study (GWAS) for epistasis becomes feasible. By design our method provides strong control of the type I error. Its favourable power properties especially under the practically relevant misspecification of the interaction model are illustrated. Ready-to-use software is available. Using the method we analyzed Parkinson's disease in four cohorts and identified possible interactions within several SNP pairs in multiple cohorts. [ABSTRACT FROM AUTHOR]

Chemical genomic screens have recently emerged as a systematic approach to drug discovery on a genome-wide scale. Drug target identification and elucidation of the mechanism of action (MoA) of hits from these noisy high-throughput screens remain difficult. Here, we present GIT (Genetic Interaction Network-Assisted Target Identification), a network analysis method for drug target identification in haploinsufficiency profiling (HIP) and homozygous profiling (HOP) screens. With the drug-induced phenotypic fitness defect of the deletion of a gene, GIT also incorporates the fitness defects of the gene’s neighbors in the genetic interaction network. On three genome-scale yeast chemical genomic screens, GIT substantially outperforms previous scoring methods on target identification on HIP and HOP assays, respectively. Finally, we showed that by combining HIP and HOP assays, GIT further boosts target identification and reveals potential drug’s mechanism of action. [ABSTRACT FROM AUTHOR]

Background: Genetic interactions are keys to understand complex traits and evolution. Epistasis analysis is an effective method to map genetic interactions. Large-scale quantitative epistasis analysis has been well established for single cells. However, there is a substantial lack of such studies in multicellular organisms and their complex phenotypes such as development. Here we present a method to extend quantitative epistasis analysis to developmental traits. Methods: In the nematode Caenorhabditis elegans, we applied RNA interference on mutants to inactivate two genes, used an imaging system to quantitatively measure phenotypes, and developed a set of statistical methods to extract genetic interactions from phenotypic measurement. Results: Using two different C. elegans developmental phenotypes, body length and sex ratio, as examples, we showed that this method could accommodate various metazoan phenotypes with performances comparable to those methods in single cell growth studies. Comparing with qualitative observations, this method of quantitative epistasis enabled detection of new interactions involving subtle phenotypes. For example, several sex-ratio genes were found to interact with brc-1 and brd-1, the orthologs of the human breast cancer genes BRCA1 and BARD1, respectively. We confirmed the brc-1 interactions with the following genes in DNA damage response: C34F6.1, him-3 (ortholog of HORMAD1, HORMAD2), sdc-1, and set-2 (ortholog of SETD1A, SETD1B, KMT2C, KMT2D), validating the effectiveness of our method in detecting genetic interactions. Conclusions: We developed a reliable, high-throughput method for quantitative epistasis analysis of developmental phenotypes. [ABSTRACT FROM AUTHOR]