The genetic landscape of a cell

The BioPAX community standard for pathway data sharing

BioPAX (Biological Pathway Exchange) is a standard language to represent biological pathways at the molecular and cellular level. Its major use is to facilitate the exchange of pathway data (http://www.biopax.org). Pathway data captures our understanding of biological processes, but its rapid growth necessitates development of databases and computational tools to aid interpretation. However, the current fragmentation of pathway information across many databases with incompatible formats presents barriers to its effective use. BioPAX solves this problem by making pathway data substantially easier to collect, index, interpret and share. BioPAX can represent metabolic and signaling pathways, molecular and genetic interactions and gene regulation networks. BioPAX was created through a community process. Through BioPAX, millions of interactions organized into thousands of pathways across many organisms, from a growing number of sources, are available. Thus, large amounts of pathway data are available in a computable form to support visualization, analysis and biological discovery.

An integrative multi-network and multi-classifier approach to predict genetic interactions

Genetic interactions occur when a combination of mutations results in a surprising phenotype. These interactions capture functional redundancy, and thus are important for predicting function, dissecting protein complexes into functional pathways, and exploring the mechanistic underpinnings of common human diseases. Synthetic sickness and lethality are the most studied types of genetic interactions in yeast. However, even in yeast, only a small proportion of gene pairs have been tested for genetic interactions due to the large number of possible combinations of gene pairs. To expand the set of known synthetic lethal (SL) interactions, we have devised an integrative, multi-network approach for predicting these interactions that significantly improves upon the existing approaches. First, we defined a large number of features for characterizing the relationships between pairs of genes from various data sources. In particular, these features are independent of the known SL interactions, in contrast to some previous approaches. Using these features, we developed a non-parametric multi-classifier system for predicting SL interactions that enabled the simultaneous use of multiple classification procedures. Several comprehensive experiments demonstrated that the SL-independent features in conjunction with the advanced classification scheme led to an improved performance when compared to the current state of the art method. Using this approach, we derived the first yeast transcription factor genetic interaction network, part of which was well supported by literature. We also used this approach to predict SL interactions between all non-essential gene pairs in yeast (http://sage.fhcrc.org/downloads/downloads/predicted_yeast_genetic_interactions.zip). This integrative approach is expected to be more effective and robust in uncovering new genetic interactions from the tens of millions of unknown gene pairs in yeast and from the hundreds of millions of gene pairs in higher organisms like mouse and human, in which very few genetic interactions have been identified to date.

Dissecting BAR domain function in the yeast Amphiphysins Rvs161 and Rvs167 during endocytosis

BAR domains are protein modules that bind to membranes and promote membrane curvature. One type of BAR domain, the N-BAR domain, contains an additional N-terminal amphipathic helix, which contributes to membrane-binding and bending activities. The only known N-BAR-domain proteins in the budding yeast Saccharomyces cerevisiae, Rvs161 and Rvs167, are required for endocytosis. We have explored the mechanism of N-BAR-domain function in the endocytosis process using a combined biochemical and genetic approach. We show that the purified Rvs161-Rvs167 complex binds to liposomes in a curvature-independent manner and promotes tubule formation in vitro. Consistent with the known role of BAR domain polymerization in membrane bending, we found that Rvs167 BAR domains interact with each other at cortical actin patches in vivo. To characterize N-BAR-domain function in endocytosis, we constructed yeast strains harboring changes in conserved residues in the Rvs161 and Rvs167 N-BAR domains. In vivo analysis of the rvs endocytosis mutants suggests that Rvs proteins are initially recruited to sites of endocytosis through their membrane-binding ability. We show that inappropriate regulation of complex sphingolipid and phosphoinositide levels in the membrane can impinge on Rvs function, highlighting the relationship between membrane components and N-BAR-domain proteins in vivo.

Bose-Einstein distribution, condensation transition, and multiple stationary states in multiloci evolution of diploid populations

The mapping between genotype and phenotype is encoded in the complex web of epistatic interaction between genetic loci. In this rugged fitness landscape, recombination processes, which tend to increase variation in the population, compete with selection processes that tend to reduce genetic variation. Here, we show that the Bose-Einstein distribution describe the multiple stationary states of a diploid population under this multiloci evolutionary dynamics. Moreover, the evolutionary process might undergo an interesting condensation phase transition in the universality class of a Bose-Einstein condensation when a finite fraction of pairs of linked loci is fixed into given allelic states. Below this phase transition the genetic variation within a species is significantly reduced and only maintained by the remaining polymorphic loci.

The autophagy connection

For a process intimately connected to an immense range of physiological processes, the molecular understanding of macroautophagy remains far from complete. Recent large-scale studies, including those of Behrends et al. in Nature and Lipinski et al. in Developmental Cell, are now providing new insight into the machinery of autophagy regulation.

Identification of epistatic effects using a protein-protein interaction database

Epistasis (i.e. gene-gene interaction) has long been recognized as an important mechanism underlying the complexity of the genetic architecture of human traits. Definitions of epistasis range from the purely molecular to the traditional statistical measures of interaction. The statistical detection of epistasis usually does not map onto or easily relate to the biological interactions between genetic variations through their combined influence on gene expression or through their interactions at the gene product (i.e. protein) or DNA level. Recently, greater high-dimensional data on protein-protein interaction (PPI) and gene expression profiles have been collected that enumerates sets of biological interactions. To better align statistical and molecular models of epistasis, we present an example of how to incorporate the PPI information into the statistical analysis of interactions between copy number variations (CNVs). Among the 23 640 pairs of known human PPIs and the 1141 common CNVs detected among HapMap samples, we identified 37 pairs of CNVs overlapping with both genes of a PPI pair. Two CNV pairs provided sufficient genotype variation to search for epistatic effects on gene expression. Using 47 294 probe-specific gene expression levels as the outcomes, five epistatic effects were identified with P-value less than 10(-6). We found a CNV-CNV interaction significantly associated with gene expression of TP53TG3 (P-value of 2 × 10(-20)). The proteins associated with the CNV pair also bind TP53 which regulates the transcription of TP53TG3. This study demonstrates that using PPI data can assist in targeting statistical hypothesis testing to biological plausible epistatic interaction that reflects molecular mechanisms.

The Saccharomyces cerevisiae anaphase-promoting complex interacts with multiple histone-modifying enzymes to regulate cell cycle progression

The anaphase-promoting complex (APC), a large evolutionarily conserved ubiquitin ligase complex, regulates cell cycle progression through mitosis and G(1). Here, we present data suggesting that APC-dependent cell cycle progression relies on a specific set of posttranslational histone-modifying enzymes. Multiple APC subunit mutants were impaired in total and modified histone H3 protein content. Acetylated H3K56 (H3K56(Ac)) levels were as reduced as those of total H3, indicating that loading histones with H3K56(Ac) is unaffected in APC mutants. However, under restrictive conditions, H3K9(Ac) and dimethylated H3K79 (H3K79(me2)) levels were more greatly reduced than those of total H3. In a screen for histone acetyltransferase (HAT) and histone deacetylase (HDAC) mutants that genetically interact with the apc5(CA) (chromatin assembly) mutant, we found that deletion of GCN5 or ELP3 severely hampered apc5(CA) temperature-sensitive (ts) growth. Further analyses showed that (i) the elp3Δ gcn5Δ double mutant ts defect was epistatic to that observed in apc5(CA) cells; (ii) gcn5Δ and elp3Δ mutants accumulate in mitosis; and (iii) turnover of the APC substrate Clb2 is not impaired in elp3Δ gcn5Δ cells. Increased expression of ELP3 and GCN5, as well as genes encoding the HAT Rtt109 and the chromatin assembly factors Msi1 and Asf1, suppressed apc5(CA) defects, while increased APC5 expression partially suppressed elp3Δ gcn5Δ growth defects. Finally, we demonstrate that Gcn5 is unstable during G(1) and following G(1) arrest and is stabilized in APC mutants. We present our working model in which Elp3/Gcn5 and the APC work together to facilitate passage through mitosis and G(1). To progress into S, we propose that at least Gcn5 must then be targeted for degradation in an APC-dependent fashion.

Structures of Get3, Get4, and Get5 provide new models for TA membrane protein targeting

The GET pathway, using several proteins (Gets 1-5 and probably Sgt2), posttranslationally conducts tail-anchored (TA) proteins to the endoplasmic reticulum (ER). At the ER, TA proteins are inserted into the lipid bilayer and then sorted and directed to their respective destinations in the secretory pathway. Until last year, there was no structural information on any of the GET components but now there are ten crystal structures of Get3 in a variety of nucleotide-bound states and conformations. The structures of Get4 and a portion of Get5 also emerged in 2010. This minireview provides a detailed comparison of the GET structures and discusses their mechanistic relevance to TA protein insertion. It also addresses the outstanding gaps in detailed molecular information on this system, including the structures of Get5, Sgt2, and the transmembrane complex comprising Get1 and Get2.

Chemical-genetic profile analysis of five inhibitory compounds in yeast

Background

Chemical-genetic profiling of inhibitory compounds can lead to identification of their modes of action. These profiles can help elucidate the complex interactions between small bioactive compounds and the cell machinery, and explain putative gene function(s).

Results

Colony size reduction was used to investigate the chemical-genetic profile of cycloheximide, 3-amino-1,2,4-triazole, paromomycin, streptomycin and neomycin in the yeast Saccharomyces cerevisiae. These compounds target the process of protein biosynthesis. More than 70,000 strains were analyzed from the array of gene deletion mutant yeast strains. As expected, the overall profiles of the tested compounds were similar, with deletions for genes involved in protein biosynthesis being the major category followed by metabolism. This implies that novel genes involved in protein biosynthesis could be identified from these profiles. Further investigations were carried out to assess the activity of three profiled genes in the process of protein biosynthesis using relative fitness of double mutants and other genetic assays.

Conclusion

Chemical-genetic profiles provide insight into the molecular mechanism(s) of the examined compounds by elucidating their potential primary and secondary cellular target sites. Our follow-up investigations into the activity of three profiled genes in the process of protein biosynthesis provided further evidence concerning the usefulness of chemical-genetic analyses for annotating gene functions. We termed these genes TAE2, TAE3 and TAE4 for translation associated elements 2-4.

A ribosome-associating factor chaperones tail-anchored membrane proteins

Hundreds of proteins are inserted post-translationally into the endoplasmic reticulum (ER) membrane by a single carboxy-terminal transmembrane domain (TMD). During targeting through the cytosol, the hydrophobic TMD of these tail-anchored (TA) proteins requires constant chaperoning to prevent aggregation or inappropriate interactions. A central component of this targeting system is TRC40, a conserved cytosolic factor that recognizes the TMD of TA proteins and delivers them to the ER for insertion. The mechanism that permits TRC40 to find and capture its TA protein cargos effectively in a highly crowded cytosol is unknown. Here we identify a conserved three-protein complex composed of Bat3, TRC35 and Ubl4A that facilitates TA protein capture by TRC40. This Bat3 complex is recruited to ribosomes synthesizing membrane proteins, interacts with the TMDs of newly released TA proteins, and transfers them to TRC40 for targeting. Depletion of the Bat3 complex allows non-TRC40 factors to compete for TA proteins, explaining their mislocalization in the analogous yeast deletion strains. Thus, the Bat3 complex acts as a TMD-selective chaperone that effectively channels TA proteins to the TRC40 insertion pathway.

Robustness and evolvability in natural chemical resistance: identification of novel systems properties, biochemical mechanisms and regulatory interactions

A vast amount of data on the natural resistance of Saccharomyces cerevisiae to a diverse array of chemicals has been generated over the past decade (chemical genetics). We endeavored to use this data to better characterize the "systems" level properties of this phenomenon. By collating data from over 30 different genome-scale studies on growth of gene deletion mutants in presence of diverse chemicals, we assembled the largest currently available gene-chemical network. We also derived a second gene-gene network that links genes with significantly overlapping chemical-genetic profiles. We analyzed properties of these networks and investigated their significance by overlaying various sources of information, such as presence of TATA boxes in their promoters (which typically correlate with transcriptional noise), association with TFIID or SAGA, and propensity to function as phenotypic capacitors. We further combined these networks with ubiquitin and protein kinase-substrate networks to understand chemical tolerance in the context of major post-translational regulatory processes. Hubs in the gene-chemical network (multidrug resistance genes) are notably enriched for phenotypic capacitors (buffers against phenotypic variation), suggesting the generality of these players in buffering mechanistically unrelated deleterious forces impinging on the cell. More strikingly, analysis of the gene-gene network derived from the gene-chemical network uncovered another set of genes that appear to function in providing chemical tolerance in a cooperative manner. These appear to be enriched in lineage-specific and rapidly diverging members that also show a corresponding tendency for SAGA-dependent regulation, evolutionary divergence and noisy expression patterns. This set represents a previously underappreciated component of the chemical response that enables cells to explore alternative survival strategies. Thus, systems robustness and evolvability are simultaneously active as general forces in tolerating environmental variation. We also recover the actual genes involved in the above-discussed network properties and predict the biochemistry of their products. Certain key components of the ubiquitin system (e.g. Rcy1, Wss1 and Ubp16), peroxisome recycling (e.g. Irs4) and phosphorylation cascades (e.g. NPR1, MCK1 and HOG) are major participants and regulators of chemical resistance. We also show that a major sub-network boosting mitochondrial protein synthesis is important for exploration of alternative survival strategies under chemical stress. Further, we find evidence that cellular exploration of survival strategies under chemical stress and secondary metabolism draw from a common pool of biochemical players (e.g. acetyltransferases and a novel NTN hydrolase).

Network-based function prediction and interactomics: the case for metabolic enzymes

As sequencing technologies increase in power, determining the functions of unknown proteins encoded by the DNA sequences so produced becomes a major challenge. Functional annotation is commonly done on the basis of amino-acid sequence similarity alone. Long after sequence similarity becomes undetectable by pair-wise comparison, profile-based identification of homologs can often succeed due to the conservation of position-specific patterns, important for a protein's three dimensional folding and function. Nevertheless, prediction of protein function from homology-driven approaches is not without problems. Homologous proteins might evolve different functions and the power of homology detection has already started to reach its maximum. Computational methods for inferring protein function, which exploit the context of a protein in cellular networks, have come to be built on top of homology-based approaches. These network-based functional inference techniques provide both a first hand hint into a proteins' functional role and offer complementary insights to traditional methods for understanding the function of uncharacterized proteins. Most recent network-based approaches aim to integrate diverse kinds of functional interactions to boost both coverage and confidence level. These techniques not only promise to solve the moonlighting aspect of proteins by annotating proteins with multiple functions, but also increase our understanding on the interplay between different functional classes in a cell. In this article we review the state of the art in network-based function prediction and describe some of the underlying difficulties and successes. Given the volume of high-throughput data that is being reported the time is ripe to employ these network-based approaches, which can be used to unravel the functions of the uncharacterized proteins accumulating in the genomic databases.

Application of genetic/genomic approaches to allergic disorders

Completion of the human genome project and rapid progress in genetics and bioinformatics have enabled the development of large public databases, which include genetic and genomic data linked to clinical health data. With the massive amount of information available, clinicians and researchers have the unique opportunity to complement and integrate their daily practice with the existing resources to clarify the underlying cause of complex phenotypes, such as allergic diseases. The genome itself is now often used as a starting point for many studies, and multiple innovative approaches have emerged applying genetic/genomic strategies to key questions in the field of allergy and immunology. There have been several successes that have uncovered new insights into the biologic underpinnings of allergic disorders. Herein we will provide an in-depth review of genomic approaches to identifying genes and biologic networks involved in allergic diseases. We will discuss genetic and phenotypic variation, statistical approaches for gene discovery, public databases, functional genomics, clinical implications, and the challenges that remain.

Genome-wide scoring of positive and negative epistasis through decomposition of quantitative genetic interaction fitness matrices

Recent technological developments in genetic screening approaches have offered the means to start exploring quantitative genotype-phenotype relationships on a large-scale. What remains unclear is the extent to which the quantitative genetic interaction datasets can distinguish the broad spectrum of interaction classes, as compared to existing information on mutation pairs associated with both positive and negative interactions, and whether the scoring of varying degrees of such epistatic effects could be improved by computational means. To address these questions, we introduce here a computational approach for improving the quantitative discrimination power encoded in the genetic interaction screening data. Our matrix approximation model decomposes the original double-mutant fitness matrix into separate components, representing variability across the array and query mutants, which can be utilized for estimating and correcting the single-mutant fitness effects, respectively. When applied to three large-scale quantitative interaction datasets in yeast, we could improve the accuracy of scoring various interaction classes beyond that obtained with the original fitness data, especially in synthetic genetic array (SGA) and in genetic interaction mapping (GIM) datasets. In addition to the known pairs of interactions used in the evaluation of the computational approach, a number of novel interaction pairs were also predicted, along with underlying biological mechanisms, which remained undetected by the original datasets. It was shown that the optimal choice of the scoring function depends heavily on the screening approach and on the interaction class under analysis. Moreover, a simple preprocessing of the fitness matrix could further enhance the discrimination power of the epistatic miniarray profiling (E-MAP) dataset. These systematic evaluation results provide in-depth information on the optimal analysis of the future, large-scale screening experiments. In general, the modeling framework, enabling accurate identification and classification of genetic interactions, provides a solid basis for completing and mining the genetic interaction networks in yeast and other organisms.

Molecular-level tradeoffs and metabolic adaptation to simultaneous stressors

Life is a dynamic process driven by the complex interplay between physical constraints and selection pressures, ranging from nutrient limitation to inhibitory substances to predators. These stressors are not mutually exclusive; microbes have faced concurrent challenges for eons. Genome-enabled systems biology approaches are adapting economic and ecological concepts like tradeoff curves and strategic resource allocation theory to analyze metabolic adaptations to simultaneous stressors. These methodologies can accurately describe and predict metabolic adaptations to concurrent stresses by considering the tradeoff between investment of limiting resources into enzymatic machinery and the resulting cellular function. The approaches represent promising links between computational biology and well-established economic and ecological methodologies for analyzing the interplay between physical constraints and microbial fitness.

A small molecule inhibitor of endoplasmic reticulum oxidation 1 (ERO1) with selectively reversible thiol reactivity

Endoplasmic reticulum oxidation 1 (ERO1) is a conserved eukaryotic flavin adenine nucleotide-containing enzyme that promotes disulfide bond formation by accepting electrons from reduced protein disulfide isomerase (PDI) and passing them on to molecular oxygen. Although disulfide bond formation is an essential process, recent experiments suggest a surprisingly broad tolerance to genetic manipulations that attenuate the rate of disulfide bond formation and that a hyperoxidizing ER may place stressed cells at a disadvantage. In this study, we report on the development of a high throughput in vitro assay for mammalian ERO1alpha activity and its application to identify small molecule inhibitors. The inhibitor EN460 (IC(50), 1.9 mum) interacts selectively with the reduced, active form of ERO1alpha and prevents its reoxidation. Despite rapid and promiscuous reactivity with thiolates, EN460 exhibits selectivity for ERO1. This selectivity is explained by the rapid reversibility of the reaction of EN460 with unstructured thiols, in contrast to the formation of a stable bond with ERO1alpha followed by displacement of bound flavin adenine dinucleotide from the active site of the enzyme. Modest concentrations of EN460 and a functionally related inhibitor, QM295, promote signaling in the unfolded protein response and precondition cells against severe ER stress. Together, these observations point to the feasibility of targeting the enzymatic activity of ERO1alpha with small molecule inhibitors.

GTP-independent tRNA delivery to the ribosomal P-site by a novel eukaryotic translation factor

During translation, aminoacyl-tRNAs are delivered to the ribosome by specialized GTPases called translation factors. Here, we report the tRNA binding to the P-site of 40 S ribosomes by a novel GTP-independent factor eIF2D isolated from mammalian cells. The binding of tRNA(i)(Met) occurs after the AUG codon finds its position in the P-site of 40 S ribosomes, the situation that takes place during initiation complex formation on the hepatitis C virus internal ribosome entry site or on some other specific RNAs (leaderless mRNA and A-rich mRNAs with relaxed scanning dependence). Its activity in tRNA binding with 40 S subunits does not require the presence of the aminoacyl moiety. Moreover, the factor possesses the unique ability to deliver non-Met (elongator) tRNAs into the P-site of the 40 S subunit. The corresponding gene is found in all eukaryotes and includes an SUI1 domain present also in translation initiation factor eIF1. The versatility of translation initiation strategies in eukaryotes is discussed.

Structural characterization of the Get4/Get5 complex and its interaction with Get3

The recently elucidated Get proteins are responsible for the targeted delivery of the majority of tail-anchored (TA) proteins to the endoplasmic reticulum. Get4 and Get5 have been identified in the early steps of the pathway mediating TA substrate delivery to the cytoplasmic targeting factor Get3. Here we report a crystal structure of Get4 and an N-terminal fragment of Get5 from Saccharomyces cerevisae. We show Get4 and Get5 (Get4/5) form an intimate complex that exists as a dimer (two copies of Get4/5) mediated by the C-terminus of Get5. We further demonstrate that Get3 specifically binds to a conserved surface on Get4 in a nucleotide dependent manner. This work provides further evidence for a model in which Get4/5 operates upstream of Get3 and mediates the specific delivery of a TA substrate.

Characterization of yeast extracellular vesicles: evidence for the participation of different pathways of cellular traffic in vesicle biogenesis

BACKGROUND

Extracellular vesicles in yeast cells are involved in the molecular traffic across the cell wall. In yeast pathogens, these vesicles have been implicated in the transport of proteins, lipids, polysaccharide and pigments to the extracellular space. Cellular pathways required for the biogenesis of yeast extracellular vesicles are largely unknown.

METHODOLOGY/PRINCIPAL FINDINGS

We characterized extracellular vesicle production in wild type (WT) and mutant strains of the model yeast Saccharomyces cerevisiae using transmission electron microscopy in combination with light scattering analysis, lipid extraction and proteomics. WT cells and mutants with defective expression of Sec4p, a secretory vesicle-associated Rab GTPase essential for Golgi-derived exocytosis, or Snf7p, which is involved in multivesicular body (MVB) formation, were analyzed in parallel. Bilayered vesicles with diameters at the 100-300 nm range were found in extracellular fractions from yeast cultures. Proteomic analysis of vesicular fractions from the cells aforementioned and additional mutants with defects in conventional secretion pathways (sec1-1, fusion of Golgi-derived exocytic vesicles with the plasma membrane; bos1-1, vesicle targeting to the Golgi complex) or MVB functionality (vps23, late endosomal trafficking) revealed a complex and interrelated protein collection. Semi-quantitative analysis of protein abundance revealed that mutations in both MVB- and Golgi-derived pathways affected the composition of yeast extracellular vesicles, but none abrogated vesicle production. Lipid analysis revealed that mutants with defects in Golgi-related components of the secretory pathway had slower vesicle release kinetics, as inferred from intracellular accumulation of sterols and reduced detection of these lipids in vesicle fractions in comparison with WT cells.

CONCLUSIONS/SIGNIFICANCE

Our results suggest that both conventional and unconventional pathways of secretion are required for biogenesis of extracellular vesicles, which demonstrate the complexity of this process in the biology of yeast cells.

The budding yeast protein Sum1 functions independently of its binding partners Hst1 and Sir2 histone deacetylases to regulate microtubule assembly

The budding yeast protein Sum1 is a transcription factor that associates with the histone deacetylase Hst1p or, in its absence, with Sir2p to form repressed chromatin. In this study, SUM1 has been identified as an allele-specific dosage suppressor of mutations in the major alpha-tubulin-coding gene TUB1. When cloned in a 2mu vector, SUM1 suppressed the cold-sensitive and benomyl-hypersensitive phenotypes associated with the tub1-1 mutation. The suppression was Hst1p- and Sir2p-independent, suggesting that it was not mediated by deacetylation events associated with Sum1p when it functions along with its known partner histone deacetylases. This protein was confined to the nucleus, but did not colocalize with the microtubules nor did it bind to alpha- or beta-tubulin. Cells deleted of SUM1 showed hypersensitivity to benomyl and cold-sensitive growth, phenotypes exhibited by mutants defective in microtubule function and cytoskeletal defects. These observations suggest that Sum1p is a novel regulator of microtubule function. We propose that as a dosage suppressor, Sum1p promotes the formation of microtubules by increasing the availability of the alphabeta-heterodimer containing the mutant alpha-tubulin subunit.

A systems-biology approach to modular genetic complexity

Multiple high-throughput genetic interaction studies have provided substantial evidence of modularity in genetic interaction networks. However, the correspondence between these network modules and specific pathways of information flow is often ambiguous. Genetic interaction and molecular interaction analyses have not generated large-scale maps comprising multiple clearly delineated linear pathways. We seek to clarify the situation by discerning the difference between genetic modules and classical pathways. We review a method to optimize the discovery of biologically meaningful genetic modules based on a previously described context-dependent information measure to obtain maximally informative networks. We compare the results of this method with the established measures of network clustering and find that it balances global and local clustering information in networks. We further discuss the consequences for genetic interaction networks and propose a framework for the analysis of genetic modularity.

Introduction to focus issue: genetic interactions

The perturbation of a gene in an organism's genome often causes changes in the organism's observable properties or phenotypes. It is not obvious a priori whether the simultaneous perturbation of two genes produces a phenotypic change that is easily predictable from the changes caused by individual perturbations. In fact, this is often not the case: the nonlinearity and interdependence between genetic variants in determining phenotypes, also known as epistasis, is a prevalent phenomenon in biological systems. This focus issue presents recent developments in the study of epistasis and genetic interactions, emphasizing the broad implications of this phenomenon in evolutionary biology, functional genomics, and human diseases.

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.

Bat3 promotes the membrane integration of tail-anchored proteins

The membrane integration of tail-anchored proteins at the endoplasmic reticulum (ER) is post-translational, with different tail-anchored proteins exploiting distinct cytosolic factors. For example, mammalian TRC40 has a well-defined role during delivery of tail-anchored proteins to the ER. Although its Saccharomyces cerevisiae equivalent, Get3, is known to function in concert with at least four other components, Get1, Get2, Get4 and Get5 (Mdy2), the role of additional mammalian proteins during tail-anchored protein biogenesis is unclear. To this end, we analysed the cytosolic binding partners of Sec61beta, a well-defined substrate of TRC40, and identified Bat3 as a previously unknown interacting partner. Depletion of Bat3 inhibits the membrane integration of Sec61beta, but not of a second, TRC40-independent, tail-anchored protein, cytochrome b5. Thus, Bat3 influences the in vitro membrane integration of tail-anchored proteins using the TRC40 pathway. When expressed in Saccharomyces cerevisiae lacking a functional GET pathway for tail-anchored protein biogenesis, Bat3 associates with the resulting cytosolic pool of non-targeted chains and diverts it to the nucleus. This Bat3-mediated mislocalisation is not dependent upon Sgt2, a recently identified component of the yeast GET pathway, and we propose that Bat3 either modulates the TRC40 pathway in higher eukaryotes or provides an alternative fate for newly synthesised tail-anchored proteins.

A survey of yeast genomic assays for drug and target discovery

Over the past decade, the development and application of chemical genomic assays using the model organism Saccharomyces cerevisiae has provided powerful methods to identify the mechanism of action of known drugs and novel small molecules in vivo. These assays identify drug target candidates, genes involved in buffering drug target pathways and also help to define the general cellular response to small molecules. In this review, we examine current yeast chemical genomic assays and summarize the potential applications of each approach.

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.

Searching for signaling balance through the identification of genetic interactors of the Rab guanine-nucleotide dissociation inhibitor gdi-1

Background

The symptoms of numerous diseases result from genetic mutations that disrupt the homeostasis maintained by the appropriate integration of signaling gene activities. The relationships between signaling genes suggest avenues through which homeostasis can be restored and disease symptoms subsequently reduced. Specifically, disease symptoms caused by loss-of-function mutations in a particular gene may be reduced by concomitant perturbations in genes with antagonistic activities.

Methodology/Principal Findings

Here we use network-neighborhood analyses to predict genetic interactions in Caenorhabditis elegans towards mapping antagonisms and synergisms between genes in an animal model. Most of the predicted interactions are novel, and the experimental validation establishes that our approach provides a gain in accuracy compared to previous efforts. In particular, we identified genetic interactors of gdi-1, the orthologue of GDI1, a gene associated with mental retardation in human. Interestingly, some gdi-1 interactors have human orthologues with known neurological functions, and upon validation of the interactions in mammalian systems, these orthologues would be potential therapeutic targets for GDI1-associated neurological disorders. We also observed the conservation of a gdi-1 interaction between different cellular systems in C. elegans, suggesting the involvement of GDI1 in human muscle degeneration.

Conclusions/Significance

We developed a novel predictor of genetic interactions that may have the ability to significantly streamline the identification of therapeutic targets for monogenic disorders involving genes conserved between human and C. elegans.

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.

Functional genomics analysis of the Saccharomyces cerevisiae iron responsive transcription factor Aft1 reveals iron-independent functions

The Saccharomyces cerevisiae transcription factor Aft1 is activated in iron-deficient cells to induce the expression of iron regulon genes, which coordinate the increase of iron uptake and remodel cellular metabolism to survive low-iron conditions. In addition, Aft1 has been implicated in numerous cellular processes including cell-cycle progression and chromosome stability; however, it is unclear if all cellular effects of Aft1 are mediated through iron homeostasis. To further investigate the cellular processes affected by Aft1, we identified >70 deletion mutants that are sensitive to perturbations in AFT1 levels using genome-wide synthetic lethal and synthetic dosage lethal screens. Our genetic network reveals that Aft1 affects a diverse range of cellular processes, including the RIM101 pH pathway, cell-wall stability, DNA damage, protein transport, chromosome stability, and mitochondrial function. Surprisingly, only a subset of mutants identified are sensitive to extracellular iron fluctuations or display genetic interactions with mutants of iron regulon genes AFT2 or FET3. We demonstrate that Aft1 works in parallel with the RIM101 pH pathway and the role of Aft1 in DNA damage repair is mediated by iron. In contrast, through both directed studies and microarray transcriptional profiling, we show that the role of Aft1 in chromosome maintenance and benomyl resistance is independent of its iron regulatory role, potentially through a nontranscriptional mechanism.

Genotype to phenotype: a complex problem

We generated a high-resolution whole-genome sequence and individually deleted 5100 genes in Sigma1278b, a Saccharomyces cerevisiae strain closely related to reference strain S288c. Similar to the variation between human individuals, Sigma1278b and S288c average 3.2 single-nucleotide polymorphisms per kilobase. A genome-wide comparison of deletion mutant phenotypes identified a subset of genes that were conditionally essential by strain, including 44 essential genes unique to Sigma1278b and 13 unique to S288c. Genetic analysis indicates the conditional phenotype was most often governed by complex genetic interactions, depending on multiple background-specific modifiers. Our comprehensive analysis suggests that the presence of a complex set of modifiers will often underlie the phenotypic differences between individuals.

Origin and fate of pseudogenes in Hemiascomycetes: a comparative analysis

Background

Pseudogenes are ubiquitous genetic elements that derive from functional genes after mutational inactivation. Characterization of pseudogenes is important to understand genome dynamics and evolution, and its significance increases when several genomes of related organisms can be compared. Among yeasts, only the genome of the S. cerevisiae reference strain has been analyzed so far for pseudogenes.

Results

We present here the first comparative analysis of pseudogenes within the fully sequenced and annotated genomes of eight yeast species, spanning the entire phylogenetic range of Hemiascomycetes. A total of 871 pseudogenes were found, out of which mutational degradation patterns and consequences on the genetic repertoire of each species could be identified. We found that most pseudogenes in yeasts originate from mutational degradation of gene copies formed after species-specific duplications but duplications of pseudogenes themselves are also encountered. In all yeasts, except in Y. lipolytica, pseudogenes tend to cluster in subtelomeric regions where they can outnumber the number of functional genes from 3 to 16 times. Pseudogenes are generally not conserved between the yeast species studied (except in two cases), consistent with their large evolutionary distances, but tend to be conserved among S. cerevisiae strains. Reiterated pseudogenization of some genes is often observed in different lineages and may affect functions essential in S. cerevisiae, which are, therefore, lost in other species. Although a variety of functions are affected by pseudogenization, there is a bias towards functions involved in the adaptation of the yeasts to their environment, and towards genes of unknown functions.

Conclusions

Our work illustrates for the first time the formation of pseudogenes in different branches of hemiascomycetous yeasts, showing their limited conservation and how they testify for the adaptation of the yeasts functional repertoires.

Challenges in experimental data integration within genome-scale metabolic models

A report of the meeting "Challenges in experimental data integration within genome-scale metabolic models", Institut Henri Poincaré, Paris, October 10-11 2009, organized by the CNRS-MPG joint program in Systems Biology.

High-content, image-based screening for drug targets in yeast

Background

Drug discovery and development are predicated on elucidation of the potential mechanisms of action and cellular targets of candidate chemical compounds. Recent advances in high-content imaging techniques allow simultaneous analysis of a range of cellular events. In this study, we propose a novel strategy to identify drug targets by combining genetic screening and high-content imaging in yeast.

Methodology

In this approach, we infer the cellular functions affected by candidate drugs by comparing morphologic changes induced by the compounds with the phenotypes of yeast mutants.

Conclusions

Using this method and four well-characterized reagents, we successfully identified previously known target genes of the compounds as well as other genes involved with functionally related cellular pathways. This is the first demonstration of a genetic high-content assay that can be used to identify drug targets based on morphologic phenotypes of a reference mutant panel.

Elongator, a conserved multitasking complex?

Post-transcriptional modifications on transfer RNA (tRNA) molecules occur frequently but their implication on the translational regulation is only recently becoming fully appreciated. Several tRNA molecules in the eukaryotic cytoplasm carry a methoxycarbonylmethyl (mcm) or carbamoylmethyl (ncm) group on their wobble uridine to ensure the efficient and reliable decoding of A- or G-ending codons. Evidence suggests that the six subunits of the conserved Elongator complex are all required for an early step in the synthesis of the mcm and ncm groups in Saccharomyces cerevisiae as well as in Caenorhabditis elegans. In this issue of Molecular Microbiology, Mehlgarten et al. convincingly show that the tRNA-modifying role of Elongator is also conserved in the plant Arabidopsis thaliana. Moreover, combinations of subunits of the Arabidopsis Elongator complex can structurally and functionally complement deletion mutants in yeast and substitute for the tRNA modification activity. The data suggest that Elongator might be a unique multitasking complex with at least two conserved roles in all eukaryotes, i.e. transcriptional activation via histone acetylation in the nucleus and translational control through tRNA modification in the cytoplasm.

Microtubule-stabilizing drugs from marine sponges: focus on peloruside A and zampanolide

Marine sponges are an excellent source of bioactive secondary metabolites with potential therapeutic value in the treatment of diseases. One group of compounds of particular interest is the microtubule-stabilizing agents, the most well-known compound of this group being paclitaxel (Taxol), an anti-cancer compound isolated from the bark and leaves of the Pacific yew tree. This review focuses on two of the more recent additions to this important class of drugs, peloruside A and zampanolide, both isolated from marine sponges. Peloruside A was isolated from Mycale hentscheli collected in New Zealand coastal waters, and it already shows promising anti-cancer activity. Two other potent bioactive compounds with different modes of action but isolated from the same sponge, mycalamide A and pateamine, will also be discussed. The fourth compound, zampanolide, most recently isolated from the Tongan sponge Cacospongia mycofijiensis, has only recently been added to the microtubule-stabilizing group of compounds, and further work is in progress to determine its activity profile relative to peloruside A and other drugs of this class.

The structure of Get4 reveals an alpha-solenoid fold adapted for multiple interactions in tail-anchored protein biogenesis

Tail-anchored proteins play important roles in protein translocation, membrane fusion and apoptosis. They are targeted to the endoplasmic reticulum membrane via the guided-entry of tail-anchored proteins (Get) pathway. We present the 2A crystal structure of Get4 which participates in early steps of the Get pathway. The structure shows an alpha-solenoid fold with particular deviations from the regular pairwise arrangement of alpha-helices. A conserved hydrophobic groove accommodates the flexible C-terminal region in trans. The structural organization of the Get4 helical hairpin motifs provides a scaffold for protein-protein interactions in the Get pathway.

The exosome and heterochromatin : multilevel regulation of gene silencing

Heterochromatic silencing is important for repressing gene expression, protecting cells against viral invasion, maintaining DNA integrity and for proper chromosome segregation. Recently, it has become apparent that expression of eukaryotic genomesis far more complex than had previously been anticipated. Strikingly, it has emerged that most of the genome is transcribed including intergenic regions and heterochromatin, calling for us to re-address the question of how gene silencing is regulated and re-evaluate the concept ofheterochromatic regions of the genome being transcriptionally inactive. Although heterochromatic silencing can be regulated at the transcriptional level, RNA degrading activities supplied either by the exosome complex or RNAi also significantly contribute to this process. The exosome also regulates noncoding RNAs (ncRNAs) involved in the establishment of heterochromatin, further underscoring its role as the major cellular machinery involved in RNA processing and turn-over. This multilevel control of the transcriptome may be utilized to ensure greater accuracy of gene expression and allow distinction between functional transcripts and background noise. In this chapter, we will discuss the regulation of gene silencing across species, with special emphasis on the exosome's contribution to the process. We will also discuss the links between transcriptional and posttranscriptional mechanisms for gene silencing and their impact on the regulation of eukaryotic transcriptomes.