Conservation and rewiring of functional modules revealed by an epistasis map in fission yeast

Towards better accuracy for missing value estimation of epistatic miniarray profiling data by a novel ensemble approach

Epistatic miniarray profiling (E-MAP) is a powerful tool for analyzing gene functions and their biological relevance. However, E-MAP data suffers from large proportion of missing values, which often results in misleading and biased analysis results. It is urgent to develop effective missing value estimation methods for E-MAP. Although several independent algorithms can be applied to achieve this goal, their performance varies significantly on different datasets, indicating different algorithms having their own advantages and disadvantages. In this paper, we propose a novel ensemble approach EMDI based on the high-level diversity to impute missing values that consists of two global and four local base estimators. Experimental results on five E-MAP datasets show that EMDI outperforms all single base algorithms, demonstrating an appropriate combination providing complementarity among different methods. Comparison results between several fusion strategies also demonstrate that the proposed high-level diversity scheme is superior to others. EMDI is freely available at www.csbio.sjtu.edu.cn/bioinf/EMDI/.

The FUN30 chromatin remodeler, Fft3, protects centromeric and subtelomeric domains from euchromatin formation

The chromosomes of eukaryotes are organized into structurally and functionally discrete domains. This implies the presence of insulator elements that separate adjacent domains, allowing them to maintain different chromatin structures. We show that the Fun30 chromatin remodeler, Fft3, is essential for maintaining a proper chromatin structure at centromeres and subtelomeres. Fft3 is localized to insulator elements and inhibits euchromatin assembly in silent chromatin domains. In its absence, euchromatic histone modifications and histone variants invade centromeres and subtelomeres, causing a mis-regulation of gene expression and severe chromosome segregation defects. Our data strongly suggest that Fft3 controls the identity of chromatin domains by protecting these regions from euchromatin assembly.

Elimination of a specific histone H3K14 acetyltransferase complex bypasses the RNAi pathway to regulate pericentric heterochromatin functions

In Schizosaccharomyces pombe, the RNAi pathway is required for the formation of pericentric heterochromatin, proper chromosome segregation, and repression of pericentric meiotic recombination. Here we demonstrate that, when the activity of the histone H3 Lys 14 (H3K14) acetyltransferase Mst2 is eliminated, the RNAi machinery is no longer required for pericentric heterochromatin functions. We further reveal that reducing RNA polymerase II recruitment to pericentric regions is essential for maintaining heterochromatin in the absence of RNAi.

The balance of weak and strong interactions in genetic networks

Genetic interactions are being quantitatively characterized in a comprehensive way in several model organisms. These data are then globally represented in terms of genetic networks. How are interaction strengths distributed in these networks? And what type of functional organization of the underlying genomic systems is revealed by such distribution patterns? Here, I found that weak interactions are important for the structure of genetic buffering between signaling pathways in Caenorhabditis elegans, and that the strength of the association between two genes correlates with the number of common interactors they exhibit. I also determined that this network includes genetic cascades balancing weak and strong links, and that its hubs act as particularly strong genetic modifiers; both patterns also identified in Saccharomyces cerevisae networks. In yeast, I further showed a relation, although weak, between interaction strengths and some phenotypic/evolutionary features of the corresponding target genes. Overall, this work demonstrates a non-random organization of interaction strengths in genetic networks, a feature common to other complex networks, and that could reflect in this context how genetic variation is eventually influencing the phenotype.

Novel therapies in childhood heart failure: today and tomorrow

Heart failure is an important cause of morbidity and mortality in individuals of all ages. The many-faceted nature of the clinical heart failure syndrome has historically frustrated attempts to develop an overarching explanative theory. However, much useful information has been gained by basic and clinical investigation, even though a comprehensive understanding of heart failure has been elusive. Heart failure is a growing problem, in both adult and pediatric populations, for which standard medical therapy, as of 2010, can have positive effects, but these are usually limited and progressively diminish with time in most patients. If we want curative or near-curative therapy that will return patients to a normal state of health at a feasible cost, much better diagnostic and therapeutic technologies need to be developed. This review addresses the vexing group of heart failure etiologies that include cardiomyopathies and other ventricular dysfunctions of various types, for which current therapy is only modestly effective. Although there are many unique aspects to heart failure in patients with pediatric and congenital heart disease, many of the innovative approaches that are being developed for the care of adults with heart failure will be applicable to heart failure in childhood.

Probabilistic functional gene societies

A cellular system may be viewed as a social network of genes. Genes work together to conduct physiological processes in the cells. Thus if we have a view of the functional association among genes, we may also be able to unravel the association between genotypes and phenotypes; the emergent properties of interactive activities of genes. We could have various points of view for a gene network. Perhaps the most common standpoints are protein-protein interaction networks (PPIN) and transcriptional regulatory networks (TRN). Here I introduce another type of view for the gene network; the probabilistic functional gene network (PFGN). A 'functional view' of association between genes enables us to have a holistic model of the gene society. A 'probabilistic view' makes the model of gene associations derived from noisy high-throughput data more robust. In addition, the dynamics of gene association may be presented in a single static network model by the probabilistic view. By combining the two modeling views, the probabilistic functional gene networks have been constructed for various organisms and proved to be highly useful in generating novel biological hypotheses not only for simple unicellular microbes, but also for highly complex multicellular animals and plants.

Epistatic relationships reveal the functional organization of yeast transcription factors

The regulation of gene expression is, in large part, mediated by interplay between the general transcription factors (GTFs) that function to bring about the expression of many genes and site-specific DNA-binding transcription factors (STFs). Here, quantitative genetic profiling using the epistatic miniarray profile (E-MAP) approach allowed us to measure 48 391 pairwise genetic interactions, both negative (aggravating) and positive (alleviating), between and among genes encoding STFs and GTFs in Saccharomyces cerevisiae. This allowed us to both reconstruct regulatory models for specific subsets of transcription factors and identify global epistatic patterns. Overall, there was a much stronger preference for negative relative to positive genetic interactions among STFs than there was among GTFs. Negative genetic interactions, which often identify factors working in non-essential, redundant pathways, were also enriched for pairs of STFs that co-regulate similar sets of genes. Microarray analysis demonstrated that pairs of STFs that display negative genetic interactions regulate gene expression in an independent rather than coordinated manner. Collectively, these data suggest that parallel/compensating relationships between regulators, rather than linear pathways, often characterize transcriptional circuits.

Modular analysis of the probabilistic genetic interaction network

Motivation: Epistatic Miniarray Profiles (EMAP) has enabled the mapping of large-scale genetic interaction networks; however, the quantitative information gained from EMAP cannot be fully exploited since the data are usually interpreted as a discrete network based on an arbitrary hard threshold. To address such limitations, we adopted a mixture modeling procedure to construct a probabilistic genetic interaction network and then implemented a Bayesian approach to identify densely interacting modules in the probabilistic network.

Results: Mixture modeling has been demonstrated as an effective soft-threshold technique of EMAP measures. The Bayesian approach was applied to an EMAP dataset studying the early secretory pathway in Saccharomyces cerevisiae. Twenty-seven modules were identified, and 14 of those were enriched by gold standard functional gene sets. We also conducted a detailed comparison with state-of-the-art algorithms, hierarchical cluster and Markov clustering. The experimental results show that the Bayesian approach outperforms others in efficiently recovering biologically significant modules.

Contact:dengmh@pku.edu.cn; fangtingli@pku.edu.cn; zhuyp@hupo.org.cn

Supplementary Information:Supplementary data are available at Bioinformatics online.

Measuring the evolutionary rewiring of biological networks

We have accumulated a large amount of biological network data and expect even more to come. Soon, we anticipate being able to compare many different biological networks as we commonly do for molecular sequences. It has long been believed that many of these networks change, or "rewire", at different rates. It is therefore important to develop a framework to quantify the differences between networks in a unified fashion. We developed such a formalism based on analogy to simple models of sequence evolution, and used it to conduct a systematic study of network rewiring on all the currently available biological networks. We found that, similar to sequences, biological networks show a decreased rate of change at large time divergences, because of saturation in potential substitutions. However, different types of biological networks consistently rewire at different rates. Using comparative genomics and proteomics data, we found a consistent ordering of the rewiring rates: transcription regulatory, phosphorylation regulatory, genetic interaction, miRNA regulatory, protein interaction, and metabolic pathway network, from fast to slow. This ordering was found in all comparisons we did of matched networks between organisms. To gain further intuition on network rewiring, we compared our observed rewirings with those obtained from simulation. We also investigated how readily our formalism could be mapped to other network contexts; in particular, we showed how it could be applied to analyze changes in a range of "commonplace" networks such as family trees, co-authorships and linux-kernel function dependencies.

Array-based synthetic genetic screens to map bacterial pathways and functional networks in Escherichia coli

Cellular processes are carried out through a series of molecular interactions. Various experimental approaches can be used to investigate these functional relationships on a large-scale. Recently, the power of investigating biological systems from the perspective of genetic (gene-gene or epistatic) interactions has been evidenced by the ability to elucidate novel functional relationships. Examples of functionally related genes include genes that buffer each other's function or impinge on the same biological process. Genetic interactions have traditionally been investigated in bacteria by combining pairs of mutations (e.g., gene deletions) and assessing deviation of the phenotype of each double mutant from an expected neutral (or no interaction) phenotype. Fitness is a particularly convenient phenotype to measure: when the double mutant grows faster or slower than expected, the two mutated genes are said to show alleviating or aggravating interactions, respectively. The most commonly used neutral model assumes that the fitness of the double mutant is equal to the product of individual single mutant fitness. A striking genetic interaction is exemplified by the loss of two nonessential genes that buffer each other in performing an essential biological function: deleting only one of these genes produces no detectable fitness defect; however, loss of both genes simultaneously results in systems failure, leading to synthetic sickness or lethality. Systematic large-scale genetic interaction screens have been used to generate functional maps for model eukaryotic organisms, such as yeast, to describe the functional organization of gene products into pathways and protein complexes within a cell. They also reveal the modular arrangement and cross talk of pathways and complexes within broader functional neighborhoods (Dixon et al., Annu Rev Genet 43:601-625, 2009). Here, we present a high-throughput quantitative Escherichia coli Synthetic Genetic Array (eSGA) screening procedure, which we developed to systematically infer genetic interactions by scoring growth defects among large numbers of double mutants in a classic Gram-negative bacterium. The eSGA method exploits the rapid colony growth, ease of genetic manipulation, and natural efficient genetic exchange via conjugation of laboratory E. coli strains. Replica pinning is used to grow and mate arrayed sets of single gene mutant strains and to select double mutants en masse. Strain fitness, which is used as the eSGA readout, is quantified by the digital imaging of the plates and subsequent measuring and comparing single and double mutant colony sizes. While eSGA can be used to screen select mutants to probe the functions of individual genes, using eSGA more broadly to collect genetic interaction data for many combinations of genes can help reconstruct a functional interaction network to reveal novel links and components of biological pathways as well as unexpected connections between pathways. A variety of bacterial systems can be investigated, wherein the genes impinge on a essential biological process (e.g., cell wall assembly, ribosome biogenesis, chromosome replication) that are of interest from the perspective of drug development (Babu et al., Mol Biosyst 12:1439-1455, 2009). We also show how genetic interactions generated by high-throughput eSGA screens can be validated by manual small-scale genetic crosses and by genetic complementation and gene rescue experiments.

Array-based synthetic genetic screens to map bacterial pathways and functional networks in Escherichia coli

Cellular processes are carried out through a series of molecular interactions. Various experimental approaches can be used to investigate these functional relationships on a large-scale. Recently, the power of investigating biological systems from the perspective of genetic (gene-gene, or epistatic) interactions has been evidenced by the ability to elucidate novel functional relationships. Examples of functionally related genes include genes that buffer each other's function or impinge on the same biological process. Genetic interactions have traditionally been investigated in bacteria by combining pairs of mutations (for example, gene deletions) and assessing deviation of the phenotype of each double mutant from an expected neutral (or no interaction) phenotype. Fitness is a particularly convenient phenotype to measure: when the double mutant grows faster or slower than expected, the two mutated genes are said to show alleviating or aggravating interactions, respectively. The most commonly used neutral model assumes that the fitness of the double mutant is equal to the product of individual single mutant fitness. A striking genetic interaction is exemplified by the loss of two nonessential genes that buffer each other in performing an essential biological function: deleting only one of these genes produces no detectable fitness defect; however, loss of both genes simultaneously results in systems failure, leading to synthetic sickness or lethality. Systematic large-scale genetic interaction screens have been used to generate functional maps for model eukaryotic organisms, such as yeast, to describe the functional organization of gene products into pathways and protein complexes within a cell. They also reveal the modular arrangement and cross-talk of pathways and complexes within broader functional neighborhoods (Dixon et al. Annu Rev Genet 43:601-625, 2009). Here, we present a high-throughput quantitative Escherichia coli synthetic genetic array (eSGA) screening procedure, which we developed to systematically infer genetic interactions by scoring growth defects among large numbers of double mutants in a classic gram-negative bacterium. The eSGA method exploits the rapid colony growth, ease of genetic manipulation, and natural efficient genetic exchange via conjugation of laboratory E. coli strains. Replica pinning is used to grow and mate arrayed sets of single-gene mutant strains as well as to select double mutants en mass. Strain fitness, which is used as the eSGA readout, is quantified by the digital imaging of the plates and subsequent measuring and comparing single and double mutant colony sizes. While eSGA can be used to screen select mutants to probe the functions of individual genes; using eSGA more broadly to collect genetic interaction data for many combinations of genes can help reconstruct a functional interaction network to reveal novel links and components of biological pathways as well as unexpected connections between pathways. A variety of bacterial systems can be investigated, wherein the genes impinge on a essential biological process (e.g., cell wall assembly, ribosome biogenesis, chromosome replication) that are of interest from the perspective of drug development (Babu et al. Mol Biosyst 12:1439-1455, 2009). We also show how genetic interactions generated by high-throughput eSGA screens can be validated by manual small-scale genetic crosses and by genetic complementation and gene rescue experiments.

RNAi-based functional pharmacogenomics

Experimental alteration of gene expression is a powerful technique for functional characterization of disease genes. RNA interference (RNAi) is a naturally occurring mechanism of gene regulation, which is triggered by the introduction of double-stranded RNA into a cell. This phenomenon can be synthetically exploited to down-regulate expression of specific genes by transfecting mammalian cells with synthetic short interfering RNAs (siRNAs). These siRNAs can be designed to silence the expression of specific genes bearing a particular target sequence in high-throughput (HT) siRNA experimental systems and may potentially be presented as a therapeutic strategy for inhibiting transcriptional regulation of genes. This can constitute a strategy that can inhibit targets that are not tractable by small molecules such as chemical compounds. Large-scale experiments using low-dose drug exposure combined with siRNA also represent a promising discovery strategy for the purpose of identifying synergistic targets that facilitate synthetic lethal combination phenotypes. In light of such advantageous applications, siRNA technology has become an ideal research tool for studying gene function. In this chapter, we focus on the application of RNAi, with particular focus on HT siRNA phenotype profiling, to support cellular pharmacogenomics.

Reverse genetics in eukaryotes

Reverse genetics consists in the modification of the activity of a target gene to analyse the phenotypic consequences. Four main approaches are used towards this goal and will be explained in this review. Two of them are centred on genome alterations. Mutations produced by random chemical or insertional mutagenesis can be screened to recover only mutants in a specific gene of interest. Alternatively, these alterations may be specifically targeted on a gene of interest by HR (homologous recombination). The other two approaches are centred on mRNA. RNA interference is a powerful method to reduce the level of gene products, while MO (morpholino) antisense oligonucleotides alter mRNA metabolism or translation. Some model species, such as Drosophila, are amenable to most of these approaches, whereas other model species are restricted to one of them. For example, in mice and yeasts, gene targeting by HR is prevalent, whereas in Xenopus and zebrafish MO oligonucleotides are mainly used. Genome-wide collections of mutants or inactivated models obtained in several species by these approaches have been made and will help decipher gene functions in the post-genomic era.

Phenotypic landscape of a bacterial cell

The explosion of sequence information in bacteria makes developing high-throughput, cost-effective approaches to matching genes with phenotypes imperative. Using E. coli as proof of principle, we show that combining large-scale chemical genomics with quantitative fitness measurements provides a high-quality data set rich in discovery. Probing growth profiles of a mutant library in hundreds of conditions in parallel yielded > 10,000 phenotypes that allowed us to study gene essentiality, discover leads for gene function and drug action, and understand higher-order organization of the bacterial chromosome. We highlight new information derived from the study, including insights into a gene involved in multiple antibiotic resistance and the synergy between a broadly used combinatory antibiotic therapy, trimethoprim and sulfonamides. This data set, publicly available at http://ecoliwiki.net/tools/chemgen/, is a valuable resource for both the microbiological and bioinformatic communities, as it provides high-confidence associations between hundreds of annotated and uncharacterized genes as well as inferences about the mode of action of several poorly understood drugs.

Systematic screen of Schizosaccharomyces pombe deletion collection uncovers parallel evolution of the phosphate signal transduction pathway in yeasts

The phosphate signal transduction (PHO) pathway, which regulates genes in response to phosphate starvation, is well defined in Saccharomyces cerevisiae. We asked whether the PHO pathway was the same in the distantly related fission yeast Schizosaccharomyces pombe. We screened a deletion collection for mutants aberrant in phosphatase activity, which is primarily a consequence of pho1(+) transcription. We identified a novel zinc finger-containing protein (encoded by spbc27b12.11c(+)), which we have named pho7(+), that is essential for pho1(+) transcriptional induction during phosphate starvation. Few of the S. cerevisiae genes involved in the PHO pathway appear to be involved in the regulation of the phosphate starvation response in S. pombe. Only the most upstream genes in the PHO pathway in S. cerevisiae (ADO1, DDP1, and PPN1) share a similar role in both yeasts. Because ADO1 and DDP1 regulate ATP and IP(7) levels, we hypothesize that the ancestor of these yeasts must have sensed similar metabolites in response to phosphate starvation but have evolved distinct mechanisms in parallel to sense these metabolites and induce phosphate starvation genes.

CENP-B preserves genome integrity at replication forks paused by retrotransposon LTR

Centromere-binding protein B (CENP-B) is a widely conserved DNA binding factor associated with heterochromatin and centromeric satellite repeats1. In fission yeast, CENP-B homologs have been shown to silence Long Terminal Repeat (LTR) retrotransposons by recruiting histone deacetylases2. However, CENP-B factors also have unexplained roles in DNA replication3, 4. Here, we show that a molecular function of CENP-B is to promote replication fork progression through the LTR. Mutants have increased genomic instability caused by replication fork blockage that depends on the DNA binding factor Switch Activating Protein 1 (Sap1), which is directly recruited by the LTR. The loss of Sap1-dependent barrier activity allows the unhindered progression of the replication fork, but results in rearrangements deleterious to the retrotransposon. We conclude that retrotransposons influence replication polarity through recruitment of Sap1 and transposition near replication fork blocks, while CENP-B counteracts this activity and promotes fork stability. Our results may account for the role of LTR in fragile sites, and for the association of CENP-B with pericentromeric heterochromatin and tandem satellite repeats.

Rewiring of genetic networks in response to DNA damage

Although cellular behaviors are dynamic, the networks that govern these behaviors have been mapped primarily as static snapshots. Using an approach called differential epistasis mapping, we have discovered widespread changes in genetic interaction among yeast kinases, phosphatases, and transcription factors as the cell responds to DNA damage. Differential interactions uncover many gene functions that go undetected in static conditions. They are very effective at identifying DNA repair pathways, highlighting new damage-dependent roles for the Slt2 kinase, Pph3 phosphatase, and histone variant Htz1. The data also reveal that protein complexes are generally stable in response to perturbation, but the functional relations between these complexes are substantially reorganized. Differential networks chart a new type of genetic landscape that is invaluable for mapping cellular responses to stimuli.

Quantitative analysis of fitness and genetic interactions in yeast on a genome scale

Global quantitative analysis of genetic interactions is a powerful approach for deciphering the roles of genes and mapping functional relationships among pathways. Using colony size as a proxy for fitness, we developed a method for measuring fitness-based genetic interactions from high-density arrays of yeast double mutants generated by synthetic genetic array (SGA) analysis. We identified several experimental sources of systematic variation and developed normalization strategies to obtain accurate single- and double-mutant fitness measurements, which rival the accuracy of other high-resolution studies. We applied the SGA score to examine the relationship between physical and genetic interaction networks, and we found that positive genetic interactions connect across functionally distinct protein complexes revealing a network of genetic suppression among loss-of-function alleles.

Charting the genetic interaction map of a cell

Genome sequencing projects have revealed a massive catalog of genes and astounding genetic diversity in a variety of organisms. We are now faced with the formidable challenge of assigning functions to thousands of genes, and how to use this information to understand how genes interact and coordinate cell function. Studies indicate that the majority of eukaryotic genes are dispensable, highlighting the extensive buffering of genomes against genetic and environmental perturbations. Such robustness poses a significant challenge to those seeking to understand the wiring diagram of the cell. Genome-scale screens for genetic interactions are an effective means to chart the network that underlies this functional redundancy. A complete atlas of genetic interactions offers the potential to assign functions to most genes identified by whole genome sequencing projects and to delineate a functional wiring diagram of the cell. Perhaps more importantly, mapping genetic networks on a large-scale will shed light on the general principles and rules governing genetic networks and provide valuable information regarding the important but elusive relationship between genotype and phenotype.

Systems biology approaches to dissect mammalian innate immunity

Advances in experimental tools have allowed for the systematic identification of components and biological processes as well as quantification of their activities over time. Together with computational analysis, these measurement and perturbation technologies have given rise to the field of systems biology, which seeks to discover, analyze and model the interactions of physical components in a biological system. Although in its infancy, recent application of this approach has resulted in novel insights into the machinery that regulates and modifies innate immune cell functions. Here, we summarize contributions that have been made through the unbiased interrogation of the mammalian innate immune system, emphasizing the importance of integrating orthogonal datasets into models. To enable application of approaches more broadly, however, a concerted effort across the immunology community to develop reagent and tool platforms will be required.

Synthetic lethality: general principles, utility and detection using genetic screens in human cells

Synthetic lethality occurs when the simultaneous perturbation of two genes results in cellular or organismal death. Synthetic lethality also occurs between genes and small molecules, and can be used to elucidate the mechanism of action of drugs. This area has recently attracted attention because of the prospect of a new generation of anti-cancer drugs. Based on studies ranging from yeast to human cells, this review provides an overview of the general principles that underlie synthetic lethality and relates them to its utility for identifying gene function, drug action and cancer therapy. It also identifies the latest strategies for the large-scale mapping of synthetic lethalities in human cells which bring us closer to the generation of comprehensive human genetic interaction maps.

Genome-wide fitness and genetic interactions determined by Tn-seq, a high-throughput massively parallel sequencing method for microorganisms

The lagging annotation of bacterial genomes and the inherent genetic complexity of many phenotypes is hindering the discovery of new drug targets and the development of new antimicrobials and vaccines. Here we present the method Tn-seq, with which it has become possible to quantitatively determine fitness for most genes in a microorganism and to screen for quantitative genetic interactions on a genome-wide scale and in a high-throughput fashion. Tn-seq can thus direct studies in the annotation of genes and untangle complex phenotypes. The method is based on the construction of a saturated Mariner transposon insertion library. After library selection, changes in frequency of each insertion mutant are determined by sequencing of the flanking regions en masse. These changes are used to calculate each mutant's fitness. The method has been developed for the Gram-positive bacterium Streptococcus pneumoniae, a causative agent of pneumonia and meningitis; however, due to the wide activity of the Mariner transposon, Tn-seq can be applied to many different microbial species.

A decade of systems biology

Systems biology provides a framework for assembling models of biological systems from systematic measurements. Since the field was first introduced a decade ago, considerable progress has been made in technologies for global cell measurement and in computational analyses of these data to map and model cell function. It has also greatly expanded into the translational sciences, with approaches pioneered in yeast now being applied to elucidate human development and disease. Here, we review the state of the field with a focus on four emerging applications of systems biology that are likely to be of particular importance during the decade to follow: (a) pathway-based biomarkers, (b) global genetic interaction maps, (c) systems approaches to identify disease genes, and (d) stem cell systems biology. We also cover recent advances in software tools that allow biologists to explore system-wide models and to formulate new hypotheses. The applications and methods covered in this review provide a set of prime exemplars useful to cell and developmental biologists wishing to apply systems approaches to areas of interest.

Modularity and directionality in genetic interaction maps

Motivation: Genetic interactions between genes reflect functional relationships caused by a wide range of molecular mechanisms. Large-scale genetic interaction assays lead to a wealth of information about the functional relations between genes. However, the vast number of observed interactions, along with experimental noise, makes the interpretation of such assays a major challenge.

Results: Here, we introduce a computational approach to organize genetic interactions and show that the bulk of observed interactions can be organized in a hierarchy of modules. Revealing this organization enables insights into the function of cellular machineries and highlights global properties of interaction maps. To gain further insight into the nature of these interactions, we integrated data from genetic screens under a wide range of conditions to reveal that more than a third of observed aggravating (i.e. synthetic sick/lethal) interactions are unidirectional, where one gene can buffer the effects of perturbing another gene but not vice versa. Furthermore, most modules of genes that have multiple aggravating interactions were found to be involved in such unidirectional interactions. We demonstrate that the identification of external stimuli that mimic the effect of specific gene knockouts provides insights into the role of individual modules in maintaining cellular integrity.

Availability: We designed a freely accessible web tool that includes all our findings, and is specifically intended to allow effective browsing of our results (http://compbio.cs.huji.ac.il/GIAnalysis).

Contact:maya.schuldiner@weizmann.ac.il; hanahm@ekmd.huji.ac.il; nir@cs.huji.ac.il

Supplementary information:Supplementary data are available at Bioinformatics online.

Activity of a C-terminal plant homeodomain (PHD) of Msc1 is essential for function

Msc1, a member of the Jarid1 family of putative histone demethylases, is required for chromosome stability in fission yeast. Msc1 associates with the Swr1 complex that facilitates deposition of histone H2A.Z into chromatin. To assess the function of Msc1 in the Swr1 complex, domains of Msc1 necessary for interaction with Swr1 were identified. The C-terminal plant homeodomain (PHD) 2 and PHD3 of Msc1 are sufficient to confer association with Swr1 and allow Msc1 to function in the context of kinetochore mutants. On the other hand, a mutant with a single amino acid substitution in PHD2 within the full-length Msc1 protein retains the ability to bind to Swr1 but eliminates the function of Msc1 in combination with kinetochore mutants. Thus, Swr1 association is critical but not sufficient for Msc1 function. An activity of Msc1 that depends on the cysteine residue within PHD2 of Msc1 is likewise critical for function. On the basis of our observation that the PHDs of Msc1 act as E3 ubiquitin ligases and that mutations of cysteine residues within those domains abolish ligase activity, we speculate that the ability of Msc1 to facilitate ubiquitin transfer is critical for the function it mediates through its association with Swr1.

Automated identification of pathways from quantitative genetic interaction data

We present a novel Bayesian learning method that reconstructs large detailed gene networks from quantitative genetic interaction (GI) data.

The method uses global reasoning to handle missing and ambiguous measurements, and provide confidence estimates for each prediction.

Applied to a recent data set over genes relevant to protein folding, the learned networks reflect known biological pathways, including details such as pathway ordering and directionality of relationships.

The reconstructed networks also suggest novel relationships, including the placement of SGT2 in the tail-anchored biogenesis pathway, a finding that we experimentally validated.

Recent developments have enabled large-scale quantitative measurement of genetic interactions (GIs) that report on the extent to which the activity of one gene is dependent on a second. It has long been recognized (Avery and Wasserman, 1992; Hartman et al, 2001; Segre et al, 2004; Tong et al, 2004; Drees et al, 2005; Schuldiner et al, 2005; St Onge et al, 2007; Costanzo et al, 2010) that functional dependencies revealed by GI data can provide rich information regarding underlying biological pathways. Further, the precise phenotypic measurements provided by quantitative GI data can provide evidence for even more detailed aspects of pathway structure, such as differentiating between full and partial dependence between two genes (Drees et al, 2005; Schuldiner et al, 2005; St Onge et al, 2007; Jonikas et al, 2009) (Figure 1A). As GI data sets become available for a range of quantitative phenotypes and organisms, such patterns will allow researchers to elucidate pathways important to a diverse set of biological processes.

We present a new method that exploits the high-quality, quantitative nature of recent GI assays to automatically reconstruct detailed multi-gene pathway structures, including the organization of a large set of genes into coherent pathways, the connectivity and ordering within each pathway, and the directionality of each relationship. We introduce activity pathway networks (APNs), which represent functional dependencies among a set of genes in the form of a network. We present an automatic method to efficiently reconstruct APNs over large sets of genes based on quantitative GI measurements. This method handles uncertainty in the data arising from noise, missing measurements, and data points with ambiguous interpretations, by performing global reasoning that combines evidence from multiple data points. In addition, because some structure choices remain uncertain even when jointly considering all measurements, our method maintains multiple likely networks, and allows computation of confidence estimates over each structure choice.

We applied our APN reconstruction method to the recent high-quality GI data set of Jonikas et al (2009), which examined the functional interaction between genes that contribute to protein folding in the ER. Specifically, Jonikas et al used the cell's endogenous sensor (the unfolded protein response), to first identify several hundred yeast genes with functions in endoplasmic reticulum folding and then systematically characterized their functional interdependencies by measuring unfolded protein response levels in double mutants. Our analysis produced an ensemble of 500 likelihood-weighted APNs over 178 genes (Figure 2).

We performed an aggregate evaluation of our results by comparing to known biological relationships between gene pairs, including participation in pathways according to the Kyoto Encyclopedia of Genes and Genomes (KEGG), correlation of chemical genomic profiles in a recent high-throughput assay (Hillenmeyer et al, 2008) and similarity of Gene Ontology (GO) annotations. In each evaluation performed, our reconstructed APNs were significantly more consistent with the known relationships than either the raw GI values or the Pearson correlation between profiles of GI values.

Importantly, our approach provides not only an improved means for defining pairs or groups of related genes, but also enables the identification of detailed multi-gene network structures. In many cases, our method successfully reconstructed known cellular pathways, including the ER-associated degradation (ERAD) pathway, and the biosynthesis of N-linked glycans, ranking them among the highest confidence structures. In-depth examination of the learned network structures indicates agreement with many known details of these pathways. In addition, quantitative analysis indicates that our learned APNs are indicative of ordering within KEGG-annotated biological pathways.

Our results also suggest several novel relationships, including placement of uncharacterized genes into pathways, and novel relationships between characterized genes. These include the dependence of the J domain chaperone JEM1 on the PDI homolog MPD1, dependence of the Ubiquitin-recycling enzyme DOA4 on N-linked glycosylation, and the dependence of the E3 Ubiquitin ligase DOA10 on the signal peptidase complex subunit SPC2. Our APNs also place the poorly characterized TPR-containing protein SGT2 upstream of the tail-anchored protein biogenesis machinery components GET3, GET4, and MDY2 (also known as GET5), suggesting that SGT2 has a function in the insertion of tail-anchored proteins into membranes. Consistent with this prediction, our experimental analysis shows that sgt2Δ cells show a defect in localization of the tail-anchored protein GFP-Sed5 from punctuate Golgi structures to a more diffuse pattern, as seen in other genes involved in this pathway.

Our results show that multi-gene, detailed pathway networks can be reconstructed from quantitative GI data, providing a concrete computational manifestation to intuitions that have traditionally accompanied the manual interpretation of such data. Ongoing technological developments in both genetics and imaging are enabling the measurement of GI data at a genome-wide scale, using high-accuracy quantitative phenotypes that relate to a range of particular biological functions. Methods based on RNAi will soon allow collection of similar data for human cell lines and other mammalian systems (Moffat et al, 2006). Thus, computational methods for analyzing GI data could have an important function in mapping pathways involved in complex biological systems including human cells.

High-throughput quantitative genetic interaction (GI) measurements provide detailed information regarding the structure of the underlying biological pathways by reporting on functional dependencies between genes. However, the analytical tools for fully exploiting such information lag behind the ability to collect these data. We present a novel Bayesian learning method that uses quantitative phenotypes of double knockout organisms to automatically reconstruct detailed pathway structures. We applied our method to a recent data set that measures GIs for endoplasmic reticulum (ER) genes, using the unfolded protein response as a quantitative phenotype. The results provided reconstructions of known functional pathways including N-linked glycosylation and ER-associated protein degradation. It also contained novel relationships, such as the placement of SGT2 in the tail-anchored biogenesis pathway, a finding that we experimentally validated. Our approach should be readily applicable to the next generation of quantitative GI data sets, as assays become available for additional phenotypes and eventually higher-level organisms.

Methylated H3K4, a transcription-associated histone modification, is involved in the DNA damage response pathway

Eukaryotic genomes are associated with a number of proteins such as histones that constitute chromatin. Post-translational histone modifications are associated with regulatory aspects executed by chromatin and all transactions on genomic DNA are dependent on them. Thus, it will be relevant to understand how histone modifications affect genome functions. Here we show that the mono ubiquitylation of histone H2B and the tri-methylation of histone H3 on lysine 4 (H3K4me3), both known for their involvement in transcription, are also important for a proper response of budding yeast cells to DNA damaging agents and the passage through S-phase. Cells that cannot methylate H3K4 display a defect in double-strand break (DSB) repair by non-homologous end joining. Furthermore, if such cells incur DNA damage or encounter a stress during replication, they very rapidly lose viability, underscoring the functional importance of the modification. Remarkably, the Set1p methyltransferase as well as the H3K4me3 mark become detectable on a newly created DSB. This recruitment of Set1p to the DSB is dependent on the presence of the RSC complex, arguing for a contribution in the ensuing DNA damage repair process. Taken together, our results demonstrate that Set1p and its substrate H3K4me3, which has been reported to be important for the transcription of active genes, also plays an important role in genome stability of yeast cells. Given the high degree of conservation for the methyltransferase and the histone mark in a broad variety of organisms, these results could have similar implications for genome stability mechanisms in vertebrate and mammalian cells.

Quantitative genetic interactions reveal biological modularity

Traditionally, research has been reductionist, characterizing the individual components of biological systems. But new technologies have increased the size and scope of biological data, and systems approaches have broadened the view of how these components are interconnected. Here, we discuss how quantitative mapping of genetic interactions enhances our view of biological systems, allowing their deeper interrogation across different biological scales.

Predicting genetic modifier loci using functional gene networks

Most phenotypes are genetically complex, with contributions from mutations in many different genes. Mutations in more than one gene can combine synergistically to cause phenotypic change, and systematic studies in model organisms show that these genetic interactions are pervasive. However, in human association studies such nonadditive genetic interactions are very difficult to identify because of a lack of statistical power--simply put, the number of potential interactions is too vast. One approach to resolve this is to predict candidate modifier interactions between loci, and then to specifically test these for associations with the phenotype. Here, we describe a general method for predicting genetic interactions based on the use of integrated functional gene networks. We show that in both Saccharomyces cerevisiae and Caenorhabditis elegans a single high-coverage, high-quality functional network can successfully predict genetic modifiers for the majority of genes. For C. elegans we also describe the construction of a new, improved, and expanded functional network, WormNet 2. Using this network we demonstrate how it is possible to rapidly expand the number of modifier loci known for a gene, predicting and validating new genetic interactions for each of three signal transduction genes. We propose that this approach, termed network-guided modifier screening, provides a general strategy for predicting genetic interactions. This work thus suggests that a high-quality integrated human gene network will provide a powerful resource for modifier locus discovery in many different diseases.

Analysis of a genome-wide set of gene deletions in the fission yeast Schizosaccharomyces pombe

We report the construction and analysis of 4,836 heterozygous diploid deletion mutants covering 98.4% of the fission yeast genome providing a tool for studying eukaryotic biology. Comprehensive gene dispensability comparisons with budding yeast--the only other eukaryote for which a comprehensive knockout library exists--revealed that 83% of single-copy orthologs in the two yeasts had conserved dispensability. Gene dispensability differed for certain pathways between the two yeasts, including mitochondrial translation and cell cycle checkpoint control. We show that fission yeast has more essential genes than budding yeast and that essential genes are more likely than nonessential genes to be present in a single copy, to be broadly conserved and to contain introns. Growth fitness analyses determined sets of haploinsufficient and haploproficient genes for fission yeast, and comparisons with budding yeast identified specific ribosomal proteins and RNA polymerase subunits, which may act more generally to regulate eukaryotic cell growth.

Recursive expectation-maximization clustering: a method for identifying buffering mechanisms composed of phenomic modules

Interactions between genetic and/or environmental factors are ubiquitous, affecting the phenotypes of organisms in complex ways. Knowledge about such interactions is becoming rate-limiting for our understanding of human disease and other biological phenomena. Phenomics refers to the integrative analysis of how all genes contribute to phenotype variation, entailing genome and organism level information. A systems biology view of gene interactions is critical for phenomics. Unfortunately the problem is intractable in humans; however, it can be addressed in simpler genetic model systems. Our research group has focused on the concept of genetic buffering of phenotypic variation, in studies employing the single-cell eukaryotic organism, S. cerevisiae. We have developed a methodology, quantitative high throughput cellular phenotyping (Q-HTCP), for high-resolution measurements of gene-gene and gene-environment interactions on a genome-wide scale. Q-HTCP is being applied to the complete set of S. cerevisiae gene deletion strains, a unique resource for systematically mapping gene interactions. Genetic buffering is the idea that comprehensive and quantitative knowledge about how genes interact with respect to phenotypes will lead to an appreciation of how genes and pathways are functionally connected at a systems level to maintain homeostasis. However, extracting biologically useful information from Q-HTCP data is challenging, due to the multidimensional and nonlinear nature of gene interactions, together with a relative lack of prior biological information. Here we describe a new approach for mining quantitative genetic interaction data called recursive expectation-maximization clustering (REMc). We developed REMc to help discover phenomic modules, defined as sets of genes with similar patterns of interaction across a series of genetic or environmental perturbations. Such modules are reflective of buffering mechanisms, i.e., genes that play a related role in the maintenance of physiological homeostasis. To develop the method, 297 gene deletion strains were selected based on gene-drug interactions with hydroxyurea, an inhibitor of ribonucleotide reductase enzyme activity, which is critical for DNA synthesis. To partition the gene functions, these 297 deletion strains were challenged with growth inhibitory drugs known to target different genes and cellular pathways. Q-HTCP-derived growth curves were used to quantify all gene interactions, and the data were used to test the performance of REMc. Fundamental advantages of REMc include objective assessment of total number of clusters and assignment to each cluster a log-likelihood value, which can be considered an indicator of statistical quality of clusters. To assess the biological quality of clusters, we developed a method called gene ontology information divergence z-score (GOid_z). GOid_z summarizes total enrichment of GO attributes within individual clusters. Using these and other criteria, we compared the performance of REMc to hierarchical and K-means clustering. The main conclusion is that REMc provides distinct efficiencies for mining Q-HTCP data. It facilitates identification of phenomic modules, which contribute to buffering mechanisms that underlie cellular homeostasis and the regulation of phenotypic expression.

A genome-wide map of human genetic interactions inferred from radiation hybrid genotypes

Using radiation hybrid genotyping data, 99% of all possible gene pairs across the mammalian genome were tested for interactions based on co-retention frequencies higher (attraction) or lower (repulsion) than chance. Gene interaction networks constructed from six independent data sets overlapped strongly. Combining the data sets resulted in a network of more than seven million interactions, almost all attractive. This network overlapped with protein-protein interaction networks on multiple measures and also confirmed the relationship between essentiality and centrality. In contrast to other biological networks, the radiation hybrid network did not show a scale-free distribution of connectivity but was Gaussian-like, suggesting a closer approach to saturation. The radiation hybrid (RH) network constitutes a platform for understanding the systems biology of the mammalian cell.

Integrative structure modeling of macromolecular assemblies from proteomics data

Proteomics techniques have been used to generate comprehensive lists of protein interactions in a number of species. However, relatively little is known about how these interactions result in functional multiprotein complexes. This gap can be bridged by combining data from proteomics experiments with data from established structure determination techniques. Correspondingly, integrative computational methods are being developed to provide descriptions of protein complexes at varying levels of accuracy and resolution, ranging from complex compositions to detailed atomic structures.

Networks inferred from biochemical data reveal profound differences in toll-like receptor and inflammatory signaling between normal and transformed hepatocytes

Systematic study of cell signaling networks increasingly involves high throughput proteomics, transcriptional profiling, and automated literature mining with the aim of assembling large scale interaction networks. In contrast, functional analysis of cell signaling usually focuses on a much smaller sets of proteins and eschews computation but focuses directly on cellular responses to environment and perturbation. We sought to combine these two traditions by collecting cell response measures on a reasonably large scale and then attempting to infer differences in network topology between two cell types. Human hepatocytes and hepatocellular carcinoma cell lines were exposed to inducers of inflammation, innate immunity, and proliferation in the presence and absence of small molecule drugs, and multiplex biochemical measurement was then performed on intra- and extracellular signaling molecules. We uncovered major differences between primary and transformed hepatocytes with respect to the engagement of toll-like receptor and NF-κB-dependent secretion of chemokines and cytokines that prime and attract immune cells. Overall, our results serve as a proof of principle for an approach to network analysis that is systematic, comparative, and biochemically focused. More specifically, our data support the hypothesis that hepatocellular carcinoma cells down-regulate normal inflammatory and immune responses to avoid immune editing.

Missing value imputation for epistatic MAPs

Background

Epistatic miniarray profiling (E-MAPs) is a high-throughput approach capable of quantifying aggravating or alleviating genetic interactions between gene pairs. The datasets resulting from E-MAP experiments typically take the form of a symmetric pairwise matrix of interaction scores. These datasets have a significant number of missing values - up to 35% - that can reduce the effectiveness of some data analysis techniques and prevent the use of others. An effective method for imputing interactions would therefore increase the types of possible analysis, as well as increase the potential to identify novel functional interactions between gene pairs. Several methods have been developed to handle missing values in microarray data, but it is unclear how applicable these methods are to E-MAP data because of their pairwise nature and the significantly larger number of missing values. Here we evaluate four alternative imputation strategies, three local (Nearest neighbor-based) and one global (PCA-based), that have been modified to work with symmetric pairwise data.

Results

We identify different categories for the missing data based on their underlying cause, and show that values from the largest category can be imputed effectively. We compare local and global imputation approaches across a variety of distinct E-MAP datasets, showing that both are competitive and preferable to filling in with zeros. In addition we show that these methods are effective in an E-MAP from a different species, suggesting that pairwise imputation techniques will be increasingly useful as analogous epistasis mapping techniques are developed in different species. We show that strongly alleviating interactions are significantly more difficult to predict than strongly aggravating interactions. Finally we show that imputed interactions, generated using nearest neighbor methods, are enriched for annotations in the same manner as measured interactions. Therefore our method potentially expands the number of mapped epistatic interactions. In addition we make implementations of our algorithms available for use by other researchers.

Conclusions

We address the problem of missing value imputation for E-MAPs, and suggest the use of symmetric nearest neighbor based approaches as they offer consistently accurate imputations across multiple datasets in a tractable manner.

An atlas of combinatorial transcriptional regulation in mouse and man

Combinatorial interactions among transcription factors are critical to directing tissue-specific gene expression. To build a global atlas of these combinations, we have screened for physical interactions among the majority of human and mouse DNA-binding transcription factors (TFs). The complete networks contain 762 human and 877 mouse interactions. Analysis of the networks reveals that highly connected TFs are broadly expressed across tissues, and that roughly half of the measured interactions are conserved between mouse and human. The data highlight the importance of TF combinations for determining cell fate, and they lead to the identification of a SMAD3/FLI1 complex expressed during development of immunity. The availability of large TF combinatorial networks in both human and mouse will provide many opportunities to study gene regulation, tissue differentiation, and mammalian evolution.

RNAi-dependent formation of heterochromatin and its diverse functions

Expression profiling of eukaryotic genomes has revealed widespread transcription outside the confines of protein-coding genes, leading to production of antisense and non-coding RNAs (ncRNAs). Studies in Schizosaccharomyces pombe and multicellular organisms suggest that transcription and ncRNAs provide a framework for the assembly of heterochromatin, which has been linked to various chromosomal processes. In addition to gene regulation, heterochromatin is crucial for centromere function, cell fate determination as well as transcriptional and posttranscriptional silencing of repetitive DNA elements. Recently, heterochromatin factors have been shown to suppress antisense RNAs at euchromatic loci. These findings define conserved pathways that probably have major impact on the epigenetic regulation of eukaryotic genomes.

On the classification of epistatic interactions

Modern genomewide association studies are characterized by the problem of "missing heritability." Epistasis, or genetic interaction, has been suggested as a possible explanation for the relatively small contribution of single significant associations to the fraction of variance explained. Of particular concern to investigators of genetic interactions is how to best represent and define epistasis. Previous studies have found that the use of different quantitative definitions for genetic interaction can lead to different conclusions when constructing genetic interaction networks and when addressing evolutionary questions. We suggest that instead, multiple representations of epistasis, or epistatic "subtypes," may be valid within a given system. Selecting among these epistatic subtypes may provide additional insight into the biological and functional relationships among pairs of genes. In this study, we propose maximum-likelihood and model selection methods in a hypothesis-testing framework to choose epistatic subtypes that best represent functional relationships for pairs of genes on the basis of fitness data from both single and double mutants in haploid systems. We gauge the performance of our method with extensive simulations under various interaction scenarios. Our approach performs reasonably well in detecting the most likely epistatic subtype for pairs of genes, as well as in reducing bias when estimating the epistatic parameter (epsilon). We apply our approach to two available data sets from yeast (Saccharomyces cerevisiae) and demonstrate through overlap of our identified epistatic pairs with experimentally verified interactions and functional links that our results are likely of biological significance in understanding interaction mechanisms. We anticipate that our method will improve detection of epistatic interactions and will help to unravel the mysteries of complex biological systems.

Quantitative genetic interaction mapping using the E-MAP approach

Genetic interactions represent the degree to which the presence of one mutation modulates the phenotype of a second mutation. In recent years, approaches for measuring genetic interactions systematically and quantitatively have proven to be effective tools for unbiased characterization of gene function and have provided valuable data for analyses of evolution. Here, we present protocols for systematic measurement of genetic interactions with respect to organismal growth rate for two yeast species.

Inference of RhoGAP/GTPase regulation using single-cell morphological data from a combinatorial RNAi screen

Biological networks are highly complex systems, consisting largely of enzymes that act as molecular switches to activate/inhibit downstream targets via post-translational modification. Computational techniques have been developed to perform signaling network inference using some high-throughput data sources, such as those generated from transcriptional and proteomic studies, but comparable methods have not been developed to use high-content morphological data, which are emerging principally from large-scale RNAi screens, to these ends. Here, we describe a systematic computational framework based on a classification model for identifying genetic interactions using high-dimensional single-cell morphological data from genetic screens, apply it to RhoGAP/GTPase regulation in Drosophila, and evaluate its efficacy. Augmented by knowledge of the basic structure of RhoGAP/GTPase signaling, namely, that GAPs act directly upstream of GTPases, we apply our framework for identifying genetic interactions to predict signaling relationships between these proteins. We find that our method makes mediocre predictions using only RhoGAP single-knockdown morphological data, yet achieves vastly improved accuracy by including original data from a double-knockdown RhoGAP genetic screen, which likely reflects the redundant network structure of RhoGAP/GTPase signaling. We consider other possible methods for inference and show that our primary model outperforms the alternatives. This work demonstrates the fundamental fact that high-throughput morphological data can be used in a systematic, successful fashion to identify genetic interactions and, using additional elementary knowledge of network structure, to infer signaling relations.

Synthetic biology: tools to design, build, and optimize cellular processes

The general central dogma frames the emergent properties of life, which make biology both necessary and difficult to engineer. In a process engineering paradigm, each biological process stream and process unit is heavily influenced by regulatory interactions and interactions with the surrounding environment. Synthetic biology is developing the tools and methods that will increase control over these interactions, eventually resulting in an integrative synthetic biology that will allow ground-up cellular optimization. In this review, we attempt to contextualize the areas of synthetic biology into three tiers: (1) the process units and associated streams of the central dogma, (2) the intrinsic regulatory mechanisms, and (3) the extrinsic physical and chemical environment. Efforts at each of these three tiers attempt to control cellular systems and take advantage of emerging tools and approaches. Ultimately, it will be possible to integrate these approaches and realize the vision of integrative synthetic biology when cells are completely rewired for biotechnological goals. This review will highlight progress towards this goal as well as areas requiring further research.

Prevalent positive epistasis in Escherichia coli and Saccharomyces cerevisiae metabolic networks

Epistasis refers to the interaction between genes. Although high-throughput epistasis data from model organisms are being generated and used to construct genetic networks1-3, to what extent genetic epistasis reflects biologically meaningful interactions remains unclear4-6. We address this question by in silico mapping of positive and negative epistatic interactions amongst biochemical reactions within the metabolic networks of E. coli and S. cerevisiae using flux balance analysis. We found that negative epistasis occurs mainly between nonessential reactions with overlapping functions, whereas positive epistasis usually involves essential reactions, is highly abundant, and surprisingly, often occurs between reactions without overlapping functions. We offered mechanistic explanations of these findings and experimentally validated them for 61 S. cerevisiae gene pairs.

Simplistic pathways or complex networks?

Signaling events are frequently described in textbooks as linear cascades. However, in reality, input cues are processed by dynamic and context-specific networks, which are assembled from numerous signaling molecules. Diseases, such as cancer, are typically associated with multiple genomic alterations that likely change the structure and dynamics of cellular signaling networks. To assess the impact of such genomic alterations on the structure of signaling networks and on the ability of cells to accurately translate environmental cues into phenotypic changes, we argue studies must be conducted on a network level. Advances in technologies and computational approaches for data integration have permitted network studies of signaling events in both cancer and normal cells. Here we will review recent advances and how they have impacted our view on signaling networks with a specific angle on signal processing in cancer.

Histone variant H2A.Z regulates centromere silencing and chromosome segregation in fission yeast

The incorporation of histone variant H2A.Z into nucleosomes plays essential roles in regulating chromatin structure and gene expression. A multisubunit complex containing chromatin remodeling protein Swr1 is responsible for the deposition of H2A.Z in budding yeast and mammals. Here, we show that the JmjC domain protein Msc1 is a novel component of the fission yeast Swr1 complex and is required for Swr1-mediated incorporation of H2A.Z into nucleosomes at gene promoters. Loss of Msc1, Swr1, or H2A.Z results in loss of silencing at centromeres and defective chromosome segregation, although centromeric levels of CENP-A, a centromere-specific histone H3 variant that is required for setting up the chromatin structure at centromeres, remain unchanged. Intriguingly, H2A.Z is required for the expression of another centromere protein, CENP-C, and overexpression of CENP-C rescues centromere silencing defects associated with H2A.Z loss. These results demonstrate the importance of H2A.Z and CENP-C in maintaining a silenced chromatin state at centromeres.

RNA-directed DNA methylation and plant development require an IWR1-type transcription factor

RNA-directed DNA methylation (RdDM) in plants requires two RNA polymerase (Pol) II-related RNA polymerases, namely Pol IV and Pol V. A genetic screen designed to reveal factors that are important for RdDM in a developmental context in Arabidopsis identified DEFECTIVE IN MERISTEM SILENCING 4 (DMS4). Unlike other mutants defective in RdDM, dms4 mutants have a pleiotropic developmental phenotype. The DMS4 protein is similar to yeast IWR1 (interacts with RNA polymerase II), a conserved putative transcription factor that interacts with Pol II subunits. The DMS4 complementary DNA partly complements the K1 killer toxin hypersensitivity of a yeast iwr1 mutant, suggesting some functional conservation. In the transgenic system studied, mutations in DMS4 directly or indirectly affect Pol IV-dependent secondary short interfering RNAs, Pol V-mediated RdDM, Pol V-dependent synthesis of intergenic non-coding RNA and expression of many Pol II-driven genes. These data suggest that DMS4 might be a regulatory factor for several RNA polymerases, thus explaining its diverse roles in the plant.

Towards accurate imputation of quantitative genetic interactions

A new method for calculating quantitative genetic interactions allows for the inference of 190,000 new genetic interactions in Saccharomyces cerevisae.

Recent technological breakthroughs have enabled high-throughput quantitative measurements of hundreds of thousands of genetic interactions among hundreds of genes in Saccharomyces cerevisiae. However, these assays often fail to measure the genetic interactions among up to 40% of the studied gene pairs. Here we present a novel method, which combines genetic interaction data together with diverse genomic data, to quantitatively impute these missing interactions. We also present data on almost 190,000 novel interactions.