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A network of transcription factors (TFs) coordinates transcription with cell cycle events in eukaryotes. Most TFs in the network are phosphorylated by cyclin-dependent kinase (CDK), which limits their activities during the cell cycle. Here, we investigate the physiological consequences of disrupting CDK regulation of the paralogous repressors Yhp1 and Yox1 in yeast. Blocking Yhp1/Yox1 phosphorylation increases their levels and decreases expression of essential cell cycle regulatory genes which, unexpectedly, increases cellular fitness in optimal growth conditions. Using synthetic genetic interaction screens, we find that Yhp1/Yox1 mutations improve the fitness of mutants with mitotic defects, including condensin mutants. Blocking Yhp1/Yox1 phosphorylation simultaneously accelerates the G1/S transition and delays mitotic exit, without decreasing proliferation rate. This mitotic delay partially reverses the chromosome segregation defect of condensin mutants, potentially explaining their increased fitness when combined with Yhp1/Yox1 phosphomutants. These findings reveal how altering expression of cell cycle genes leads to a redistribution of cell cycle timing and confers a fitness advantage to cells.

Wheat stem rust disease caused by Puccinia graminis f. sp. tritici (Pgt) is a global threat to wheat production. Fast evolving populations of Pgt limit the efficacy of plant genetic resistance and constrain disease management strategies. Understanding molecular mechanisms that lead to rust infection and disease susceptibility could deliver novel strategies to deploy crop resistance through genetic loss of disease susceptibility. We used comparative transcriptome-based and orthology-guided approaches to characterize gene expression changes associated with Pgt infection in susceptible and resistant Triticum aestivum genotypes as well as the non-host Brachypodium distachyon. We targeted our analysis to genes with differential expression in T. aestivum and genes suppressed or not affected in B. distachyon and report several processes potentially linked to susceptibility to Pgt, such as cell death suppression and impairment of photosynthesis. We complemented our approach with a gene co-expression network analysis to identify wheat targets to deliver resistance to Pgt through removal or modification of putative susceptibility genes.

Gene expression differences among individuals are shaped by trans-acting expression quantitative trait loci (eQTLs). Most trans-eQTLs map to hotspot locations that influence many genes. The molecular mechanisms perturbed by hotspots are often assumed to involve “vertical” cascades of effects in pathways that can ultimately affect the expression of thousands of genes. Here, we report that trans-eQTLs can affect the expression of adjacent genes via “horizontal” mechanisms that extend along a chromosome. Genes affected by trans-eQTL hotspots in the yeast Saccharomyces cerevisiae were more likely to be located next to each other than expected by chance. These paired hotspot effects tended to occur at adjacent genes that also show coexpression in response to genetic and environmental perturbations, suggesting shared mechanisms. Physical proximity and shared chromatin state, in addition to regulation of adjacent genes by similar transcription factors, were independently associated with paired hotspot effects among adjacent genes. Paired effects of trans-eQTLs can occur at neighboring genes even when these genes do not share a common function. This phenomenon could result in unexpected connections between regulatory genetic variation and phenotypes.

Wheat stem rust disease caused byPuccinia graminisf. sp.tritici(Pgt) is a global threat to wheat production. Fast evolving populations ofPgtlimit the efficacy of plant genetic resistance and constrain disease management strategies. Understanding molecular mechanisms that lead to rust infection and disease susceptibility could deliver novel strategies to deploy crop resistance through genetic loss of disease susceptibility. We used comparative transcriptome-based and orthology-guided approaches to characterize gene expression changes associated withPgtinfection in susceptible and resistantTriticum aestivumgenotypes as well as the non-hostBrachypodium distachyon. We targeted our analysis to genes with differential expression inT. aestivumand genes suppressed or not affected inB. distachyonand report several processes potentially linked to susceptibility toPgt, such as cell death suppression and impairment of photosynthesis. We complemented our approach with a gene co-expression network analysis to identify wheat targets to deliver resistance toPgtthrough removal or modification of putative susceptibility genes.

Gene expression differences among individuals are shaped by trans-acting expression quantitative trait loci (eQTLs). Most trans-eQTLs map to hotspot locations that influence many genes. The molecular mechanisms perturbed by hotspots are often assumed to involve “vertical” cascades of effects in pathways that can ultimately affect the expression of thousands of genes. Here, we report that trans-eQTLs can affect the expression of adjacent genes via “horizontal” mechanisms that extend along a chromosome. Genes affected by trans-eQTL hotspots in the yeast Saccharomyces cerevisiae were more likely to be located next to each other than expected by chance. These paired hotspot effects tended to occur at adjacent genes that show coexpression in response to genetic and environmental perturbations. Physical proximity and shared chromatin state, in addition to regulation of adjacent genes by similar transcription factors, were independently associated with paired hotspot effects. The effects of trans-eQTLs can spread among neighboring genes even when these genes do not share a common function. This phenomenon could result in unexpected connections between regulatory genetic variation and phenotypes.

The evolutionarily conserved centromeric histone H3 variant (Cse4 in budding yeast, CENP-A in humans) is essential for faithful chromosome segregation. Mislocalization of CENP-A to non-centromeric chromatin contributes to chromosomal instability (CIN) in yeast, fly, and human cells and CENP-A is highly expressed and mislocalized in cancers. Defining mechanisms that prevent mislocalization of CENP-A is an area of active investigation. Ubiquitin-mediated proteolysis of overexpressed Cse4 (GALCSE4)byE3 ubiquitin ligases such as Psh1 prevents mislocalization of Cse4, and psh1D strains display synthetic dosage lethality (SDL) with GALCSE4. We previously performed a genome-wide screen and identified five alleles of CDC7 and DBF4 that encode the Dbf4-dependent kinase (DDK) complex, which regulates DNA replication initiation, among the top twelve hits that displayed SDL with GALCSE4. We determined that cdc7-7 strains exhibit defects in ubiquitin-mediated proteolysis of Cse4 and show mislocalization of Cse4. Mutation of MCM5 (mcm5-bob1) bypasses the requirement of Cdc7 for replication initiation and rescues replication defects in a cdc7-7 strain. We determined that mcm5-bob1 does not rescue the SDL and defects in proteolysis of GALCSE4 in a cdc7-7 strain, suggesting a DNA replication-independent role for Cdc7 in Cse4 proteolysis. The SDL phenotype, defects in ubiquitin-mediated proteolysis, and the mislocalization pattern of Cse4 in a cdc7-7 psh1D strain were similar to that of cdc7-7 and psh1D strains, suggesting that Cdc7 regulates Cse4 in a pathway that overlaps with Psh1. Our results define a DNA replication initiation-independent role of DDK as a regulator of Psh1-mediated proteolysis of Cse4 to prevent mislocalization of Cse4. Fil: Eisenstatt, Jessica R.. National Institutes of Health; Estados Unidos Fil: Boeckmann, Lars. National Institutes of Health; Estados Unidos Fil: Au, Wei Chun. National Institutes of Health; Estados Unidos Fil: Garcia, Valerie. National Institutes of Health; Estados Unidos Fil: Bursch, Levi. National Institutes of Health; Estados Unidos Fil: Ocampo, Josefina. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Ingeniería Genética y Biología Molecular "Dr. Héctor N. Torres"; Argentina. National Instituto of Child Health & Human Development; Estados Unidos Fil: Costanzo, Michael. National Institutes of Health; Estados Unidos. University of Toronto; Canadá Fil: Weinreich, Michael. Van Andel Research Institute; Estados Unidos Fil: Sclafani, Robert A.. University of Colorado; Estados Unidos Fil: Baryshnikova, Anastasia. University of Princeton; Estados Unidos Fil: Myers, Chad L.. University of Minnesota; Estados Unidos Fil: Boone, Charles. University of Toronto; Canadá. National Institutes of Health; Estados Unidos Fil: Clark, David J.. National Institutes of Health; Estados Unidos Fil: Baker, Richard. University of Massachusetts; Estados Unidos Fil: Basrai, Munira A.. National Institutes of Health; Estados Unidos

G-quadruplexes represent unique roadblocks to DNA replication, which tends to stall at these secondary structures. Although G-quadruplexes can be found throughout the genome, telomeres, due to their G-richness, are particularly predisposed to forming these structures and thus represent difficult-to-replicate regions. Here, we demonstrate that exonuclease 1 (EXO1) plays a key role in the resolution of, and replication through, telomeric G-quadruplexes. When replication forks encounter G-quadruplexes, EXO1 resects the nascent DNA proximal to these structures to facilitate fork progression and faithful replication. In the absence of EXO1, forks accumulate at stabilized G-quadruplexes and ultimately collapse. These collapsed forks are preferentially repaired via error-prone end joining as depletion of EXO1 diverts repair away from error-free homology-dependent repair. Such aberrant repair leads to increased genomic instability, which is exacerbated at chromosome termini in the form of dysfunction and telomere loss.

Centromeric localization of the evolutionarily conserved centromere-specific histone H3 variant CENP-A (Cse4 in yeast) is essential for faithful chromosome segregation. Overexpression and mislocalization of CENP-A lead to chromosome segregation defects in yeast, flies, and human cells. Overexpression of CENP-A has been observed in human cancers; however, the molecular mechanisms preventing CENP-A mislocalization are not fully understood. Here, we used a genome-wide synthetic genetic array (SGA) to identify gene deletions that exhibit synthetic dosage lethality (SDL) when Cse4 is overexpressed. Deletion for genes encoding the replication-independent histone chaperone HIR complex (HIR1, HIR2, HIR3, HPC2) and a Cse4-specific E3 ubiquitin ligase, PSH1, showed highest SDL. We defined a role for Hir2 in proteolysis of Cse4 that prevents mislocalization of Cse4 to noncentromeric regions for genome stability. Hir2 interacts with Cse4 in vivo, and hir2∆ strains exhibit defects in Cse4 proteolysis and stabilization of chromatin-bound Cse4. Mislocalization of Cse4 to noncentromeric regions with a preferential enrichment at promoter regions was observed in hir2∆ strains. We determined that Hir2 facilitates the interaction of Cse4 with Psh1, and that defects in Psh1-mediated proteolysis contribute to increased Cse4 stability and mislocalization of Cse4 in the hir2∆ strain. In summary, our genome-wide screen provides insights into pathways that regulate proteolysis of Cse4 and defines a novel role for the HIR complex in preventing mislocalization of Cse4 by facilitating proteolysis of Cse4, thereby promoting genome stability.

Plant immune responses activated through the perception of microbe-associated molecular patterns, leading to pattern-triggered immunity, are tightly regulated. This results in low immune responses in the absence of pathogens and a rapid return to the resting state following an activation event. Here, we show that two CALMODULIN-LIKE genes, CML46 and CML47, negatively regulate salicylic acid accumulation and immunity in Arabidopsis (Arabidopsis thaliana). The double mutant cml46 cml47 is highly resistant to the pathogen Pseudomonas syringae pv maculicola (Pma). The effects of cml46 cml47 on Pma growth are genetically additive to that of cbp60a, a known negative regulator in the CALMODULIN-BINDING PROTEIN60 (CBP60) family. Transcriptome profiling revealed the effects of cbp60a and cml46 cml47 on both common and separate sets of genes, with the majorities of these differentially expressed genes being Pma responsive. CBP60g, a positive regulator of immunity in the CBP60 family, was found to be transcriptionally regulated by CBP60a, CML46, and CML47 Analysis of the flg22-induced mRNA levels of CBP60g in cbp60a and cml46 cml47 revealed that cml46 cml47 plants have higher induced expression while cbp60a plants retain elevated levels longer than wild-type plants. Assays for the effect of flg22 treatment on Pma growth showed that the effect is stronger in cml46 cml47 plants and lasts longer in cbp60a plants. Thus, the expression pattern of CBP60g is reflected in flg22-induced resistance to Pma.

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.

The adaptation of CRISPR/SpCas9 technology to mammalian cell lines is transforming the study of human functional genomics. Pooled libraries of CRISPR guide RNAs (gRNAs) targeting human protein-coding genes and encoded in viral vectors have been used to systematically create gene knockouts in a variety of human cancer and immortalized cell lines, in an effort to identify whether these knockouts cause cellular fitness defects. Previous work has shown that CRISPR screens are more sensitive and specific than pooled-library shRNA screens in similar assays, but currently there exists significant variability across CRISPR library designs and experimental protocols. In this study, we reanalyze 17 genome-scale knockout screens in human cell lines from three research groups, using three different genome-scale gRNA libraries. Using the Bayesian Analysis of Gene Essentiality algorithm to identify essential genes, we refine and expand our previously defined set of human core essential genes from 360 to 684 genes. We use this expanded set of reference core essential genes, CEG2, plus empirical data from six CRISPR knockout screens to guide the design of a sequence-optimized gRNA library, the Toronto KnockOut version 3.0 (TKOv3) library. We then demonstrate the high effectiveness of the library relative to reference sets of essential and nonessential genes, as well as other screens using similar approaches. The optimized TKOv3 library, combined with the CEG2 reference set, provide an efficient, highly optimized platform for performing and assessing gene knockout screens in human cell lines.

Providing access to quantitative genomic data is key to ensure large-scale data validation and promote new discoveries. TheCellMap.org serves as a central repository for storing and analyzing quantitative genetic interaction data produced by genome-scale Synthetic Genetic Array (SGA) experiments with the budding yeast Saccharomyces cerevisiae. In particular, TheCellMap.org allows users to easily access, visualize, explore, and functionally annotate genetic interactions, or to extract and reorganize subnetworks, using data-driven network layouts in an intuitive and interactive manner.

Restricting the localization of the histone H3 variant CENP-A (Cse4 in yeast, CID in flies) to centromeres is essential for faithful chromosome segregation. Mislocalization of CENP-A leads to chromosomal instability (CIN) in yeast, fly and human cells. Overexpression and mislocalization of CENP-A has been observed in many cancers and this correlates with increased invasiveness and poor prognosis. Yet genes that regulate CENP-A levels and localization under physiological conditions have not been defined. In this study we used a genome-wide genetic screen to identify essential genes required for Cse4 homeostasis to prevent its mislocalization for chromosomal stability. We show that two Skp, Cullin, F-box (SCF) ubiquitin ligases with the evolutionarily conserved F-box proteins Met30 and Cdc4 interact and cooperatively regulate proteolysis of endogenous Cse4 and prevent its mislocalization for faithful chromosome segregation under physiological conditions. The interaction of Met30 with Cdc4 is independent of the D domain, which is essential for their homodimerization and ubiquitination of other substrates. The requirement for both Cdc4 and Met30 for ubiquitination is specifc for Cse4; and a common substrate for Cdc4 and Met30 has not previously been described. Met30 is necessary for the interaction between Cdc4 and Cse4, and defects in this interaction lead to stabilization and mislocalization of Cse4, which in turn contributes to CIN. We provide the first direct link between Cse4 mislocalization to defects in kinetochore structure and show that SCF-mediated proteolysis of Cse4 is a major mechanism that prevents stable maintenance of Cse4 at non-centromeric regions, thus ensuring faithful chromosome segregation. In summary, we have identified essential pathways that regulate cellular levels of endogenous Cse4 and shown that proteolysis of Cse4 by SCF-Met30/Cdc4 prevents mislocalization and CIN in unperturbed cells., Author summary Genetic material on each chromosome must be faithfully transmitted to the daughter cell during cell division and chromosomal instability (CIN) results in aneuploidy, a hallmark of cancers. The kinetochore (centromeric DNA and associated proteins) regulates faithful chromosome segregation. Restricting the localization of CENP-A (Cse4 in yeast) to kinetochores is essential for chromosomal stability. Mislocalization of CENP-A contributes to CIN in yeast, fly and human cells and is observed in cancers where it correlates with increased invasiveness and poor prognosis. Hence, identification of pathways that regulate CENP-A levels will help us understand the correlation between CENP-A mislocalization and aneuploidy in cancers. We used a genetic screen to identify essential genes for Cse4 homeostasis and identified a major ubiquitin-dependent pathway where both nuclear F-box proteins, Met30 and Cdc4 of the SCF complex, cooperatively regulate proteolysis of Cse4 to prevent its mislocalization and CIN under physiological conditions. Our studies define a role for SCF-mediated proteolysis of Cse4 as a critical mechanism to ensure faithful chromosome segregation. These studies are significant because mutations in human homologs of Met30 (β-TrCP) and Cdc4 (Fbxw7) have been implicated in cancers, and future studies will determine if SCF-mediated proteolysis of CENP-A prevents its mislocalization for chromosomal stability in human cells.

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., Author summary Understanding how chemical compounds affect biological systems is of paramount importance as pharmaceutical companies strive to develop life-saving medicines, governments seek to regulate the safety of consumer products and agrichemicals, and basic scientists continue to study the fundamental inner workings of biological organisms. One powerful approach to characterize the effects of chemical compounds in living cells is chemical-genetic interaction screening. Using this approach, a collection of cells–each with a different defined genetic perturbation–is tested for sensitivity or resistance to the presence of a compound, resulting in a quantitative profile describing the functional effects of that compound on the cells. The work presented here describes our efforts to integrate compounds’ chemical-genetic interaction profiles with reference genetic interaction profiles containing information on gene function to predict the cellular processes perturbed by the compounds. We focused on specifically developing a method that could scale to perform these functional predictions for large collections of thousands of screened compounds and robustly control the false discovery rate. With chemical-genetic and genetic interaction screens now underway in multiple species including human cells, the method described here can be generally applied to enable the characterization of compounds’ effects across the tree of life.

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.

Genetic interactions have been reported to underlie phenotypes in a variety of systems, but the extent to which they contribute to complex disease in humans remains unclear. In principle, genome-wide association studies (GWAS) provide a platform for detecting genetic interactions, but existing methods for identifying them from GWAS data tend to focus on testing individual locus pairs, which undermines statistical power. Importantly, the global genetic networks mapped for a model eukaryotic organism revealed that genetic interactions often connect genes between compensatory functional modules in a highly coherent manner. Taking advantage of this expected structure, we developed a computational approach called BridGE that identifies pathways connected by genetic interactions from GWAS data. Applying BridGE broadly, we discovered significant interactions in Parkinson’s disease, schizophrenia, hypertension, prostate cancer, breast cancer, and type 2 diabetes. Our novel approach provides a general framework for mapping complex genetic networks underlying human disease from genome-wide genotype data.

INTRODUCTION Genetic interactions occur when mutations in different genes combine to result in a phenotype that is different from expectation based on those of the individual mutations. Negative genetic interactions occur when a combination of mutations leads to a fitness defect that is more exacerbated than expected. For example, synthetic lethality occurs when two mutations, neither of which is lethal on its own, generate an inviable double mutant. Alternatively, positive genetic interactions occur when genetic perturbations combine to generate a double mutant with a greater fitness than expected. Global digenic interaction studies have been useful for understanding the functional wiring diagram of the cell and may also provide insight into the genotype-to-phenotype relationship, which is important for tracking the missing heritability of human health and disease. Here we describe a network of higher-order trigenic interactions and explore its implications. RATIONALE Variation in phenotypic outcomes in different individuals is caused by genetic determinants that act as modifiers. Modifier loci are prevalent in human populations, but knowledge regarding how variants interact to modulate phenotype in different individuals is lacking. Similarly, in yeast, traits including conditional essentiality—in which certain genes are essential in one genetic background but nonessential in another—often result from an interplay of multiple modifier loci. Because complex modifiers may underlie the genetic basis of physiological states found in natural populations, it is critical to understand the landscape of higher-order genetic interactions. RESULTS To survey trigenic interactions, we designed query strains that sampled key features of the global digenic interaction network: (i) digenic interaction strength, (ii) average number of digenic interactions, and (iii) digenic interaction profile similarity. In total, we tested ~400,000 double and ~200,000 triple mutants for fitness defects and identified ~9500 digenic and ~3200 trigenic negative interactions. Although trigenic interactions tend to be weaker than digenic interactions, they were both enriched for functional relationships. About one-third of trigenic interactions identified “novel” connections that were not observed in our digenic control network, whereas the remaining approximately two-thirds of trigenic interactions “modified” a digenic interaction, suggesting that the global digenic interaction network is important for understanding the trigenic interaction network. Despite their functional enrichment, trigenic interactions also bridged distant bioprocesses. We estimate that the global trigenic interaction network is ~100 times as large as the global digenic network, highlighting the potential for complex genetic interactions to affect the biology of inheritance. CONCLUSION The extensive network of trigenic interactions and their ability to generate functionally diverse phenotypes suggest that higher-order genetic interactions may play a key role in the genotype-to-phenotype relationship, genome size, and speciation.

SummaryModular structures in biological networks are ubiquitous and well-described, yet this organization does not capture the complexity of genes individually influencing many modules. Pleiotropy, the phenomenon of a single genetic locus with multiple phenotypic effects, has previously been measured according to many definitions, which typically count phenotypes associated with genes. We take the perspective that, because genes work in complex and interconnected modules, pleiotropy can be treated as a network-derived characteristic. Here, we use the complete network of yeast genetic interactions (GI) to measure pleiotropy of nearly 2700 essential and nonessential genes. Our method uses frequent item set mining to discover GI modules, annotates them with high-level processes, and uses entropy to measure the functional spread of each gene’s set of containing modules. We classify genes whose modules indicate broad functional influence as having high pleiotropy, while genes with focused functional influence have low pleiotropy. These pleiotropy classes differed in a number of ways: high-pleiotropy genes have comparatively higher expression variance, higher protein abundance, more domains, and higher copy number, while low pleiotropy genes are more likely to be in protein complexes and have many curated phenotypes. We discuss the implications of these results regarding the nature and evolution of pleiotropy.

Previous genome-level genetic interaction screens with the single essential actin gene of yeast identified 238 nonessential genes that upon deletion result in deleterious, digenic complex haploinsufficiences with an actin null allele. Deletion alleles of these 238 genes were tested for complex heterozygous interactions with 32 actin alanine scan alleles, which target clusters of residues on the surface of actin. A total of 891 deleterious digenic combinations were identified with 203 of the 238 genes. Two-dimensional hierarchical cluster analysis of the interactions identified nine distinct groups, and the alleles within clusters tended to affect localized regions on the surface of actin. The mutants in one cluster all affect electrostatic interactions between stacked subunits in the long pitch helix of the actin filament. A second cluster that contains the most highly interactive alleles may disrupt the tropomyosin/myosin system, as one of the mutants in that cluster cannot support Type V myosin-dependent movement of secretory vesicles in haploids and causes processivity defects in heterozygous diploids. These examples suggest the clusters represent mutations with shared protein−protein interaction defects. These results show that complex heterozygous interaction screens have benefit for detecting actin-related genes and suggest that having actin filaments of mixed composition, containing both mutant and wild-type subunits, presents unique challenges to the cell.

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., Author summary Susceptibility to breast cancer is partially encoded in our genomes, but despite the development of new genomic technologies over the past decade, we are still not able to accurately predict disease susceptibility from genome sequences. One reason for this gap is that we lack methods for finding combinations of genome variants that lead to disease. Extensive studies in model organisms have experimentally constructed millions of double mutants to study genetic interactions and have defined the basic principles by which genes combine to cause phenotypes in an organism. One powerful outcome of these studies in model systems is that genetic interactions frequently form highly organized patterns that can be used as a basis for improved detection of them in humans. We developed a novel computational approach based on this principle for identifying pathway-level interactions that contribute to breast cancer disease risk. Applying this method to six different groups of breast cancer patients, we identified a core set of pathways, including glutathione conjugation, vitamin D receptor, purine metabolism, mitotic prometaphase, and steroid hormone biosynthesis. These pathways are well-supported across multiple cohorts and may contribute to breast cancer susceptibility.

Biological networks can be used to functionally annotate genes on the basis of interaction-profile similarities. Metrics known as association indices can be used to quantify interaction-profile similarity. We provide an overview of commonly used association indices, including the Jaccard index and the Pearson correlation coefficient, and compare their performance in different types of analyses of biological networks. We introduce the Guide for Association Index for Networks (GAIN), a web tool for calculating and comparing interaction-profile similarities and defining modules of genes with similar profiles.

Synthetic genetic array (SGA) analysis automates yeast genetics, enabling high-throughput construction of ordered arrays of double mutants. Quantitative colony sizes derived from SGA analysis can be used to measure cellular fitness and score for genetic interactions, such as synthetic lethality. Here we show that SGA colony sizes also can be used to obtain global maps of meiotic recombination because recombination frequency affects double-mutant formation for gene pairs located on the same chromosome and therefore influences the size of the resultant double-mutant colony. We obtained quantitative colony size data for ~1.2 million double mutants located on the same chromosome and constructed a genome-scale genetic linkage map at ~5 kb resolution. We found that our linkage map is reproducible and consistent with previous global studies of meiotic recombination. In particular, we confirmed that the total number of crossovers per chromosome tends to follow a simple linear model that depends on chromosome size. In addition, we observed a previously unappreciated relationship between the size of linkage regions surrounding each centromere and chromosome size, suggesting that crossovers tend to occur farther away from the centromere on larger chromosomes. The pericentric regions of larger chromosomes also appeared to load larger clusters of meiotic cohesin Rec8, and acquire fewer Spo11-catalyzed DNA double-strand breaks. Given that crossovers too near or too far from centromeres are detrimental to homolog disjunction and increase the incidence of aneuploidy, our data suggest that chromosome size may have a direct role in regulating the fidelity of chromosome segregation during meiosis.

Understanding the relationship between the genotypes and phenotypes of individuals is key for identifying genetic variants responsible for disease and developing successful therapeutic strategies. Mapping the phenotypic effects of individual genetic variants and their combinations in human populations presents numerous practical and statistical challenges. However, model organisms, such as the budding yeast Saccharomyces cerevisiae, provide an incredible set of molecular tools and advanced technologies that should be able to efficiently perform this task. In particular, large-scale genetic interaction screens in yeast and other model systems have revealed common properties of genetic interaction networks, many of which appear to be maintained over extensive evolutionary distances. Indeed, despite relatively low conservation of individual genes and their pairwise interactions, the overall topology of genetic interaction networks and the connections between broad biological processes may be similar in most organisms. Taking advantage of these general principles should provide a fundamental basis for mapping and predicting genetic interaction networks in humans.

Screening genome-wide sets of mutants for fitness defects provides a simple but powerful approach for exploring gene function, mapping genetic networks and probing mechanisms of drug action. For yeast and other microorganisms with global mutant collections, genetic or chemical-genetic interactions can be effectively quantified by growing an ordered array of strains on agar plates as individual colonies, and then scoring the colony size changes in response to a genetic or environmental perturbation. To do so, requires efficient tools for the extraction and analysis of quantitative data. Here, we describe SGAtools (http://sgatools.ccbr.utoronto.ca), a web-based analysis system for designer genetic screens. SGAtools outlines a series of guided steps that allow the user to quantify colony sizes from images of agar plates, correct for systematic biases in the observations and calculate a fitness score relative to a control experiment. The data can also be visualized online to explore the colony sizes on individual plates, view the distribution of resulting scores, highlight genes with the strongest signal and perform Gene Ontology enrichment analysis.

Repetitive elements comprise a significant portion of most eukaryotic genomes. Minisatellites, a type of repetitive element composed of repeat units 15−100 bp in length, are stable in actively dividing cells but change in composition during meiosis and in stationary-phase cells. Alterations within minisatellite tracts have been correlated with the onset of a variety of diseases, including diabetes mellitus, myoclonus epilepsy, and several types of cancer. However, little is known about the factors preventing minisatellite alterations. Previously, our laboratory developed a color segregation assay in which a minisatellite was inserted into the ADE2 gene in the yeast Saccharomyces cerevisiae to monitor alteration events. We demonstrated that minisatellite alterations that occur in stationary-phase cells give rise to a specific colony morphology phenotype known as blebbing. Here, we performed a modified version of the synthetic genetic array analysis to screen for mutants that produce a blebbing phenotype. Screens were conducted using two distinctly different minisatellite tracts: the ade2-min3 construct consisting of three identical 20-bp repeats, and the ade2-h7.5 construct, consisting of seven-and-a-half 28-bp variable repeats. Mutations in 102 and 157 genes affect the stability of the ade2-min3 and ade2-h7.5 alleles, respectively. Only seven hits overlapped both screens, indicating that different factors regulate repeat stability depending upon minisatellite size and composition. Importantly, we demonstrate that mismatch repair influences the stability of the ade2-h7.5 allele, indicating that this type of DNA repair stabilizes complex minisatellites in stationary phase cells. Our work provides insight into the factors regulating minisatellite stability.

Trimethylation of histone H3 Lys-27 (H3K27me3) plays a critical role in regulating gene expression during plant and animal development. We characterized the genome-wide distribution of H3K27me3 in five developmentally distinct tissues in maize (Zea mays) plants of two genetic backgrounds, B73 and Mo17. There were more substantial differences in the genome-wide profile of H3K27me3 between different tissues than between the two genotypes. The tissue-specific patterns of H3K27me3 were often associated with differences in gene expression among the tissues and most of the imprinted genes that are expressed solely from the paternal allele in endosperm are targets of H3K27me3. A comparison of the H3K27me3 targets in rice (Oryza sativa), maize, and Arabidopsis thaliana provided evidence for conservation of the H3K27me3 targets among plant species. However, there was limited evidence for conserved targeting of H3K27me3 in the two maize subgenomes derived from whole-genome duplication, suggesting the potential for subfunctionalization of chromatin regulation of paralogs. Genomic profiling of H3K27me3 in loss-of-function mutant lines for Maize Enhancer of zeste-like2 (Mez2) and Mez3, two of the three putative H3K27me3 methyltransferases present in the maize genome, suggested partial redundancy of this gene family for maintaining H3K27me3 patterns. Only a portion of the targets of H3K27me3 required Mez2 and/or Mez3, and there was limited evidence for functional consequences of H3K27me3 at these targets.

A global genetic suppression network The genetic background of an organism can influence the overall effects of new genetic variants. Some mutations can amplify a deleterious phenotype, whereas others can suppress it. Starting with a literature survey and expanding into a genomewide assay, van Leeuwen et al. generated a large-scale suppression network in yeast. The data set reveals a set of general properties that can be used to predict suppression interactions. Furthermore, the study provides a template for extending suppression studies to other genes or to more complex organisms. Science , this issue p. 599

Background Gene duplication is prevalent in many species and can result in coding and regulatory divergence. Gene duplications can be classified as whole genome duplication (WGD), tandem and inserted (non-syntenic). In maize, WGD resulted in the subgenomes maize1 and maize2, of which maize1 is considered the dominant subgenome. However, the landscape of co-expression network divergence of duplicate genes in maize is still largely uncharacterized. Results To address the consequence of gene duplication on co-expression network divergence, we developed a gene co-expression network from RNA-seq data derived from 64 different tissues/stages of the maize reference inbred-B73. WGD, tandem and inserted gene duplications exhibited distinct regulatory divergence. Inserted duplicate genes were more likely to be singletons in the co-expression networks, while WGD duplicate genes were likely to be co-expressed with other genes. Tandem duplicate genes were enriched in the co-expression pattern where co-expressed genes were nearly identical for the duplicates in the network. Older gene duplications exhibit more extensive co-expression variation than younger duplications. Overall, non-syntenic genes primarily from inserted duplications show more co-expression divergence. Also, such enlarged co-expression divergence is significantly related to duplication age. Moreover, subgenome dominance was not observed in the co-expression networks – maize1 and maize2 exhibit similar levels of intra subgenome correlations. Intriguingly, the level of inter subgenome co-expression was similar to the level of intra subgenome correlations, and genes from specific subgenomes were not likely to be the enriched in co-expression network modules and the hub genes were not predominantly from any specific subgenomes in maize. Conclusions Our work provides a comprehensive analysis of maize co-expression network divergence for three different types of gene duplications and identifies potential relationships between duplication types, duplication ages and co-expression consequences. Electronic supplementary material The online version of this article (doi:10.1186/s12864-016-3194-0) contains supplementary material, which is available to authorized users.

Transcription factors (TFs) play a central role in controlling spatiotemporal gene expression and the response to environmental cues. A comprehensive understanding of gene regulation requires integrating physical protein–DNA interactions (PDIs) with TF regulatory activity, expression patterns, and phenotypic data. Although great progress has been made in mapping PDIs using chromatin immunoprecipitation, these studies have only characterized ~10% of TFs in any metazoan species. The nematode C. elegans has been widely used to study gene regulation due to its compact genome with short regulatory sequences. Here, we delineated the largest gene‐centered metazoan PDI network to date by examining interactions between 90% of C. elegans TFs and 15% of gene promoters. We used this network as a backbone to predict TF binding sites for 77 TFs, two‐thirds of which are novel, as well as integrate gene expression, protein–protein interaction, and phenotypic data to predict regulatory and biological functions for multiple genes and TFs.

Chemical-genetic approaches offer the potential for unbiased functional annotation of chemical libraries. Mutations can alter the response of cells to a compound, revealing chemical-genetic interactions that can elucidate a compound’s mode of action. We developed a highly parallel and unbiased yeast chemical-genetic screening system involving three key components. First, in a drug-sensitive genetic background, we constructed an optimized, diagnostic mutant collection that is predictive for all major yeast biological processes. Second, we implemented a multiplexed (768-plex) barcode sequencing protocol, enabling assembly of thousands of chemical-genetic profiles. Finally, based on comparison of the chemical-genetic profiles with a compendium of genome-wide genetic interaction profiles, we predicted compound functionality. Applying this high-throughput approach, we screened 7 different compound libraries and annotated their functional diversity. We further validated biological process predictions, prioritized a diverse set of compounds, and identified compounds that appear to have dual modes of action., Graphical abstract

A significant challenge of functional genomics is to develop methods for genome-scale acquisition and analysis of cell biological data. Here, we present an integrated method that combines genome-wide genetic perturbation of Saccharomyces cerevisiae with high-content screening to facilitate the genetic description of sub-cellular structures and compartment morphology. As proof of principle, we used a Rad52-GFP marker to examine DNA damage foci in ∼20 million single cells from ∼5,000 different mutant backgrounds in the context of selected genetic or chemical perturbations. Phenotypes were classified using a machine learning-based automated image analysis pipeline. 345 mutants were identified that had elevated numbers of DNA damage foci, almost half of which were identified only in sensitized backgrounds. Subsequent analysis of Vid22, a protein implicated in the DNA damage response, revealed that it acts together with the Sgs1 helicase at sites of DNA damage and preferentially binds G-quadruplex regions of the genome. This approach is extensible to numerous other cell biological markers and experimental systems.

Nonsyndromic cleft lip with or without cleft palate (NSCL/P) is one of the most common congenital birth defects. NSCL/P is a complex multifactorial disease caused by interactions between multiple environmental and genetic factors. However, the causal single nucleotide polymorphism (SNP) signature profile underlying the risk of familial NSCL/P still remains unknown. We previously reported a 5.7-Mb genomic region on chromosome 18q21.1 locus that potentially contributes to autosomal dominant, low-penetrance inheritance of NSCL/P. In the current study, we performed exome sequencing on 12 familial genomes (six affected individuals, two obligate carriers, and four seemingly unaffected individuals) of a six-generation family to identify candidate SNPs associated with NSCL/P risk. Subsequently, targeted bidirectional DNA re-sequencing of polymerase chain reaction (PCR)-amplified high-risk regions of MYO5B gene and sequenom iPLEX genotpying of 29 candidate SNPs were performed on a larger set of 33 members of this NSCL/P family (10 affected + 4 obligate carriers + 19 unaffected relatives) to find SNPs significantly associated with NSCL/P trait. SNP vs. NSCL/P association analysis showed the MYO5B SNP rs183559995 GA genotype had an odds ratio of 18.09 (95% Confidence Interval = 1.86–176.34; gender-adjusted P = 0.0019) compared to the reference GG genotype. Additionally, the following SNPs were also found significantly associated with NSCL/P risk: rs1450425 (LOXHD1), rs6507992 (SKA1), rs78950893 (SMAD7), rs8097060, rs17713847 (SCARNA17), rs6507872 (CTIF), rs8091995 (CTIF), and rs17715416 (MYO5B). We could thus identify mutations in several genes as key candidate SNPs associated with the risk of NSCL/P in this large multi-generation family.

Plant immunity protects plants from numerous potentially pathogenic microbes. The biological network that controls plant inducible immunity must function effectively even when network components are targeted and disabled by pathogen effectors. Network buffering could confer this resilience by allowing different parts of the network to compensate for loss of one another's functions. Networks rich in buffering rely on interactions within the network, but these mechanisms are difficult to study by simple genetic means. Through a network reconstitution strategy, in which we disassemble and stepwise reassemble the plant immune network that mediates Pattern-Triggered-Immunity, we have resolved systems-level regulatory mechanisms underlying the Arabidopsis transcriptome response to the immune stimulant flagellin-22 (flg22). These mechanisms show widespread evidence of interactions among major sub-networks-we call these sectors-in the flg22-responsive transcriptome. Many of these interactions result in network buffering. Resolved regulatory mechanisms show unexpected patterns for how the jasmonate (JA), ethylene (ET), phytoalexin-deficient 4 (PAD4), and salicylate (SA) signaling sectors control the transcriptional response to flg22. We demonstrate that many of the regulatory mechanisms we resolved are not detectable by the traditional genetic approach of single-gene null-mutant analysis. Similar to potential pathogenic perturbations, null-mutant effects on immune signaling can be buffered by the network.

Through domestication, humans have substantially altered the morphology of Zea mays ssp. parviglumis (teosinte) into the currently recognizable maize. This system serves as a model for studying adaptation, genome evolution, and the genetics and evolution of complex traits. To examine how domestication has reshaped the transcriptome of maize seedlings, we used expression profiling of 18,242 genes for 38 diverse maize genotypes and 24 teosinte genotypes. We detected evidence for more than 600 genes having significantly different expression levels in maize compared with teosinte. Moreover, more than 1,100 genes showed significantly altered coexpression profiles, reflective of substantial rewiring of the transcriptome since domestication. The genes with altered expression show a significant enrichment for genes previously identified through population genetic analyses as likely targets of selection during maize domestication and improvement; 46 genes previously identified as putative targets of selection also exhibit altered expression levels and coexpression relationships. We also identified 45 genes with altered, primarily higher, expression in inbred relative to outcrossed teosinte. These genes are enriched for functions related to biotic stress and may reflect responses to the effects of inbreeding. This study not only documents alterations in the maize transcriptome following domestication, identifying several genes that may have contributed to the evolution of maize, but highlights the complementary information that can be gained by combining gene expression with population genetic analyses.

A combinatorial genetic perturbation strategy was applied to interrogate the yeast kinome on a genome-wide scale. We assessed the global effects of gene overexpression or gene deletion to map an integrated genetic interaction network of synthetic dosage lethal (SDL) and loss-of-function genetic interactions (GIs) for 92 kinases, producing a meta-network of 8700 GIs enriched for pathways known to be regulated by cognate kinases. Kinases most sensitive to dosage perturbations had constitutive cell cycle or cell polarity functions under standard growth conditions. Condition-specific screens confirmed that the spectrum of kinase dosage interactions can be expanded substantially in activating conditions. An integrated network composed of systematic SDL, negative and positive loss-of-function GIs, and literature-curated kinase–substrate interactions revealed kinase-dependent regulatory motifs predictive of novel gene-specific phenotypes. Our study provides a valuable resource to unravel novel functional relationships and pathways regulated by kinases and outlines a general strategy for deciphering mutant phenotypes from large-scale GI networks.

The authors describe the enhanced yeast one-hybrid platform for large-scale screening of protein-DNA interactions and test its performance by mapping Caenorhabditis elegans gene regulatory networks. Also in this issue, Hens et al. describe an alternative platform for this purpose and apply it to screen for transcription factor–DNA interactions in Drosophila melanogaster. A major challenge in systems biology is to understand the gene regulatory networks that drive development, physiology and pathology. Interactions between transcription factors and regulatory genomic regions provide the first level of gene control. Gateway-compatible yeast one-hybrid (Y1H) assays present a convenient method to identify and characterize the repertoire of transcription factors that can bind a DNA sequence of interest. To delineate genome-scale regulatory networks, however, large sets of DNA fragments need to be processed at high throughput and high coverage. Here we present enhanced Y1H (eY1H) assays that use a robotic mating platform with a set of improved Y1H reagents and automated readout quantification. We demonstrate that eY1H assays provide excellent coverage and identify interacting transcription factors for multiple DNA fragments in a short time. eY1H assays will be an important tool for mapping gene regulatory networks in Caenorhabditis elegans and other model organisms as well as in humans.

Intense experimental and theoretical efforts have been made to globally map genetic interactions, yet we still do not understand how gene-gene interactions arise from the operation of biomolecular networks. To bridge the gap between empirical and computational studies, we: i) quantitatively measure genetic interactions between ~185,000 metabolic gene pairs in Saccharomyces cerevisiae, ii) superpose the data on a detailed systems biology model of metabolism, and iii) introduce a machine-learning method to reconcile empirical interaction data with model predictions. We systematically investigate the relative impacts of functional modularity and metabolic flux coupling on the distribution of negative and positive genetic interactions. We also provide a mechanistic explanation for the link between the degree of genetic interaction, pleiotropy, and gene dispensability. Last, we demonstrate the feasibility of automated metabolic model refinement by correcting misannotations in NAD biosynthesis and confirming them by in vivo experiments.

Ubiquitination of the replication clamp proliferating cell nuclear antigen (PCNA) at the conserved residue lysine (K)164 triggers postreplicative repair (PRR) to fill single-stranded gaps that result from stalled DNA polymerases. However, it has remained elusive as to whether cells engage PRR in response to replication defects that do not directly impair DNA synthesis. To experimentally address this question, we performed synthetic genetic array (SGA) analysis with a ubiquitination-deficient K164 to arginine (K164R) mutant of PCNA against a library of S. cerevisiae temperature-sensitive alleles. The SGA signature of the K164R allele showed a striking correlation with profiles of mutants deficient in various aspects of lagging strand replication, including rad27Δ and elg1Δ. Rad27 is the primary flap endonuclease that processes 5’ flaps generated during lagging strand replication, whereas Elg1 has been implicated in unloading PCNA from chromatin. We observed chronic ubiquitination of PCNA at K164 in both rad27Δ and elg1Δ mutants. Notably, only rad27Δ cells exhibited a decline in cell viability upon elimination of PRR pathways, whereas elg1Δ mutants were not affected. We further provide evidence that K164 ubiquitination suppresses replication stress resulting from defective flap processing during Okazaki fragment maturation. Accordingly, ablation of PCNA ubiquitination increased S phase checkpoint activation, indicated by hyperphosphorylation of the Rad53 kinase. Furthermore, we demonstrate that alternative flap processing by overexpression of catalytically active exonuclease 1 eliminates PCNA ubiquitination. This suggests a model in which unprocessed flaps may directly participate in PRR signaling. Our findings demonstrate that PCNA ubiquitination at K164 in response to replication stress is not limited to DNA synthesis defects but extends to DNA processing during lagging strand replication., Author Summary Genome duplication via the process of DNA replication is a prerequisite for cell division and underlies the propagation of all living organisms. This fundamentally important mechanism has been highly conserved throughout eukaryotic evolution, allowing us to use the relatively simple and genetically tractable Saccharomyces cerevisiae as a model to better understand DNA replication in human cells. Furthermore, there is strong evidence to suggest that defects in DNA replication are prominent contributors to mutation and genome instability, a hallmark of cancer. Not surprisingly, evolution has selected for mechanisms to mitigate the effects of defective replication and avoid the most harmful outcomes. Postreplicative repair (PRR) pathways are two such mechanisms with well described functions in promoting the completion of replication under adverse conditions. In this study, we utilized a non-biased genome wide genetic screen to systematically identify conditions under which PRR is required. Our findings indicate that in addition to previously described roles in rescuing DNA synthesis defects, PRR is also required in response to aberrant DNA processing. Specifically, we report a requirement for PRR in cells lacking RAD27, the yeast homolog of the tumor suppressor FEN1. These findings expand the known functions of PRR and reveal their importance in promoting the viability of cells lacking a known tumor suppressor.

Chemical genomics is an unbiased, whole-cell approach to characterizing novel compounds to determine mode of action and cellular target. Our version of this technique is built upon barcoded deletion mutants of Saccharomyces cerevisiae and has been adapted to a high-throughput methodology using next-generation sequencing. Here we describe the steps to generate a chemical genomic profile from a compound of interest, and how to use this information to predict molecular mechanism and targets of bioactive compounds.

Motivation: Chromosomal copy number changes (aneuploidies) are common in cell populations that undergo multiple cell divisions including yeast strains, cell lines and tumor cells. Identification of aneuploidies is critical in evolutionary studies, where changes in copy number serve an adaptive purpose, as well as in cancer studies, where amplifications and deletions of chromosomal regions have been identified as a major pathogenetic mechanism. Aneuploidies can be studied on whole-genome level using array CGH (a microarray-based method that measures the DNA content), but their presence also affects gene expression. In gene expression microarray analysis, identification of copy number changes is especially important in preventing aberrant biological conclusions based on spurious gene expression correlation or masked phenotypes that arise due to aneuploidies. Previously suggested approaches for aneuploidy detection from microarray data mostly focus on array CGH, address only whole-chromosome or whole-arm copy number changes, and rely on thresholds or other heuristics, making them unsuitable for fully automated general application to gene expression datasets. There is a need for a general and robust method for identification of aneuploidies of any size from both array CGH and gene expression microarray data. Results: We present ChARM (Chromosomal Aberration Region Miner), a robust and accurate expectation–maximization based method for identification of segmental aneuploidies (partial chromosome changes) from gene expression and array CGH microarray data. Systematic evaluation of the algorithm on synthetic and biological data shows that the method is robust to noise, aneuploidal segment size and P-value cutoff. Using our approach, we identify known chromosomal changes and predict novel potential segmental aneuploidies in commonly used yeast deletion strains and in breast cancer. ChARM can be routinely used to identify aneuploidies in array CGH datasets and to screen gene expression data for aneuploidies or array biases. Our methodology is sensitive enough to detect statistically significant and biologically relevant aneuploidies even when expression or DNA content changes are subtle as in mixed populations of cells. Availability: Code available by request from the authors and on Web supplement at http://function.cs.princeton.edu/ChARM/

The design of effective antimicrobial therapies for serious eukaryotic pathogens requires a clear understanding of their highly variable genomes. To facilitate analysis of copy number variations, single nucleotide polymorphisms and loss of heterozygosity events in these pathogens, we developed a pipeline for analyzing diverse genome-scale datasets from microarray, deep sequencing, and restriction site associated DNA sequence experiments for clinical and laboratory strains of Candida albicans, the most prevalent human fungal pathogen. The Y[subscript MAP] pipeline (http://lovelace.cs.umn.edu/Ymap/) automatically illustrates genome-wide information in a single intuitive figure and is readily modified for the analysis of other pathogens with small genomes., Howard Hughes Medical Institute, Burroughs Wellcome Fund (Career Award at the Scientific Interface), National Institutes of Health (U.S.) (PIONEER Award), Alfred P. Sloan Foundation (Fellowship), National Institute of Allergy and Infectious Diseases (U.S.) (R01 AI-0624273)

Summary The fungal meningitis pathogen Cryptococcus neoformans is a central driver of mortality in HIV/AIDS. We report a genome-scale chemical genetic data map for this pathogen that quantifies the impact of 439 small-molecule challenges on 1,448 gene knockouts. We identified chemical phenotypes for 83% of mutants screened and at least one genetic response for each compound. C. neoformans chemical-genetic responses are largely distinct from orthologous published profiles of Saccharomyces cerevisiae , demonstrating the importance of pathogen-centered studies. We used the chemical-genetic matrix to predict novel pathogenicity genes, infer compound mode of action, and to develop an algorithm, O2M, that predicts antifungal synergies. These predictions were experimentally validated, thereby identifying virulence genes, a molecule that triggers G2/M arrest and inhibits the Cdc25 phosphatase, and many compounds that synergize with the antifungal drug fluconazole. Our work establishes a chemical-genetic foundation for approaching an infection responsible for greater than one-third of AIDS-related deaths. PaperClip

Tools that provide improved ability to relate genotype to phenotype have the potential to accelerate breeding for desired traits and to improve our understanding of the molecular variants that underlie phenotypes. The availability of large-scale gene expression profiles in maize provides an opportunity to advance our understanding of complex traits in this agronomically important species. We built co-expression networks based on genome-wide expression data from a variety of maize accessions as well as an atlas of different tissues and developmental stages. We demonstrate that these networks reveal clusters of genes that are enriched for known biological function and contain extensive structure which has yet to be characterized. Furthermore, we found that co-expression networks derived from developmental or tissue atlases as compared to expression variation across diverse accessions capture unique functions. To provide convenient access to these networks, we developed a public, web-based Co-expression Browser (COB), which enables interactive queries of the genome-wide networks. We illustrate the utility of this system through two specific use cases: one in which gene-centric queries are used to provide functional context for previously characterized metabolic pathways, and a second where lists of genes produced by mapping studies are further resolved and validated using co-expression networks.

Genetic interactions occur when mutant alleles of two or more genes collaborate to generate an unusual composite phenotype, one that would not be predicted based on the expected combined effects of the individual mutant alleles. Synthetic Genetic Array (SGA) methodology was developed to automate yeast genetic analysis and enable systematic genetic interaction studies. In its simplest form, SGA consists of a series of replica pinning steps, which enable the construction of haploid double mutants through mating and meiotic recombination. For example, a strain carrying a query mutation, such as a deletion allele of a nonessential gene or a conditional temperature sensitive allele of an essential gene, could be crossed to an input array of yeast mutants, such as the complete set of ~5,000 viable deletion mutants, to generate an output array of double mutants, that can be scored for genetic interactions based on estimates of cellular fitness derived from colony-size measurements. A simple quantitative measure of genetic interactions can be derived from colony size, which serves as a proxy for fitness. Furthermore, SGA can be applied in a variety of other contexts, such as Synthetic Dosage Lethality (SDL), in which a query mutation is crossed into an array of yeast strains, each of which overexpresses a different gene, thus making use of SGA to probe for gain-of-function phenotypes in specific genetic backgrounds. High-Content Screening (HCS) also integrates SGA to perform genome-wide screens for quantitative analysis of morphological phenotypes or pathway activity based upon fluorescent markers, extending genetic interaction analysis beyond fitness-based measurements. Genetic interaction studies offer insight into gene function, pathway structure, and buffering, and thus a complete genetic interaction network of yeast will generate a global functional wiring diagram for a eukaryotic cell.

Synthetic lethal interactions enable a novel approach for discovering specific genetic vulnerabilities in cancer cells that can be exploited for the development of therapeutics. Despite successes in model organisms such as yeast, discovering synthetic lethal interactions on a large scale in human cells remains a significant challenge. We describe a comparative genomic strategy for identifying cancer-relevant synthetic lethal interactions whereby candidate interactions are prioritized on the basis of genetic interaction data available in yeast, followed by targeted testing of candidate interactions in human cell lines. As a proof of principle, we describe two novel synthetic lethal interactions in human cells discovered by this approach, one between the tumor suppressor gene SMARCB1 and PSMA4, and another between alveolar soft-part sarcoma-associated ASPSCR1 and PSMC2. These results suggest therapeutic targets for cancers harboring mutations in SMARCB1 or ASPSCR1 and highlight the potential of a targeted, cross-species strategy for identifying synthetic lethal interactions relevant to human cancer. Cancer Res; 73(20); 6128–36. ©2013 AACR.

DNA methylation is a chromatin modification that is frequently associated with epigenetic regulation in plants and mammals. However, genetic changes such as transposon insertions can also lead to changes in DNA methylation. Genome-wide profiles of DNA methylation for 20 maize (Zea mays) inbred lines were used to discover differentially methylated regions (DMRs). The methylation level for each of these DMRs was also assayed in 31 additional maize or teosinte genotypes, resulting in the discovery of 1966 common DMRs and 1754 rare DMRs. Analysis of recombinant inbred lines provides evidence that the majority of DMRs are heritable. A local association scan found that nearly half of the DMRs with common variation are significantly associated with single nucleotide polymorphisms found within or near the DMR. Many of the DMRs that are significantly associated with local genetic variation are found near transposable elements that may contribute to the variation in DNA methylation. Analysis of gene expression in the same samples used for DNA methylation profiling identified over 300 genes with expression patterns that are significantly associated with DNA methylation variation. Collectively, our results suggest that DNA methylation variation is influenced by genetic and epigenetic changes that are often stably inherited and can influence the expression of nearby genes.