The genetic landscape of a cell

A large-scale complex haploinsufficiency-based genetic interaction screen in Candida albicans: analysis of the RAM network during morphogenesis

The morphogenetic transition between yeast and filamentous forms of the human fungal pathogen Candida albicans is regulated by a variety of signaling pathways. How these pathways interact to orchestrate morphogenesis, however, has not been as well characterized. To address this question and to identify genes that interact with the Regulation of Ace2 and Morphogenesis (RAM) pathway during filamentation, we report the first large-scale genetic interaction screen in C. albicans. Our strategy for this screen was based on the concept of complex haploinsufficiency (CHI). A heterozygous mutant of CBK1 (cbk1Δ/CBK1), a key RAM pathway protein kinase, was subjected to transposon-mediated, insertional mutagenesis. The resulting double heterozygous mutants (6,528 independent strains) were screened for decreased filamentation on Spider Medium (SM). From the 441 mutants showing altered filamentation, 139 transposon insertion sites were sequenced, yielding 41 unique CBK1-interacting genes. This gene set was enriched in transcriptional targets of Ace2 and, strikingly, the cAMP-dependent protein kinase A (PKA) pathway, suggesting an interaction between these two pathways. Further analysis indicates that the RAM and PKA pathways co-regulate a common set of genes during morphogenesis and that hyper-activation of the PKA pathway may compensate for loss of RAM pathway function. Our data also indicate that the PKA–regulated transcription factor Efg1 primarily localizes to yeast phase cells while the RAM–pathway regulated transcription factor Ace2 localizes to daughter nuclei of filamentous cells, suggesting that Efg1 and Ace2 regulate a common set of genes at separate stages of morphogenesis. Taken together, our observations indicate that CHI–based screening is a useful approach to genetic interaction analysis in C. albicans and support a model in which these two pathways regulate a common set of genes at different stages of filamentation.

Candida albicans is the most common cause of fungal infections in humans. As a diploid yeast without a classical sexual cycle, many genetic approaches developed for large-scale genetic interaction studies in the model yeast Saccharomyces cerevisiae cannot be applied to C. albicans. Genetic interaction studies have proven to be powerful genetic tools for the analysis of complex biological processes. Here, we demonstrate that libraries of C. albicans strains containing heterozygous mutations in two different genes can be generated and used to study genetic interactions in C. albicans on a large scale. Double heterozygous mutants that show more severe phenotypes than strains with single heterozygous mutations are indicative of genetic interactions through a phenomenon referred to as complex haploinsufficiency (CHI). We applied this approach to the study of the RAM (Regulation of Ace2 and Morphogenesis) signaling network during the morphogenetic transition of C. albicans from yeast to filamentous growth. Among the genes that interacted with CBK1, the key signaling kinase of the RAM pathway, were transcriptional targets of the RAM pathway and the protein kinase A pathway. Further analysis supports a model in which these two pathways co-regulate a common set of genes at different stages of filamentation.

Predicting mutation outcome from early stochastic variation in genetic interaction partners

Many mutations, including those that cause disease, only have a detrimental effect in a subset of individuals. The reasons for this are usually unknown, but may include additional genetic variation and environmental risk factors. However, phenotypic discordance remains even in the absence of genetic variation, for example between monozygotic twins, and incomplete penetrance of mutations is frequent in isogenic model organisms in homogeneous environments. Here we propose a model for incomplete penetrance based on genetic interaction networks. Using Caenorhabditis elegans as a model system, we identify two compensation mechanisms that vary among individuals and influence mutation outcome. First, feedback induction of an ancestral gene duplicate differs across individuals, with high expression masking the effects of a mutation. This supports the hypothesis that redundancy is maintained in genomes to buffer stochastic developmental failure. Second, during normal embryonic development we find that there is substantial variation in the induction of molecular chaperones such as Hsp90 (DAF-21). Chaperones act as promiscuous buffers of genetic variation, and embryos with stronger induction of Hsp90 are less likely to be affected by an inherited mutation. Simultaneously quantifying the variation in these two independent responses allows the phenotypic outcome of a mutation to be more accurately predicted in individuals. Our model and methodology provide a framework for dissecting the causes of incomplete penetrance. Further, the results establish that inter-individual variation in both specific and more general buffering systems combine to determine the outcome inherited mutations in each individual.

The unfolded protein response supports cellular robustness as a broad-spectrum compensatory pathway

Stress pathways monitor intracellular systems and deploy a range of regulatory mechanisms in response to stress. One of the best-characterized pathways, the unfolded protein response (UPR), is responsible for maintaining endoplasmic reticulum (ER) homeostasis. The highly conserved Ire1 branch regulates hundreds of gene targets by activating a UPR-specific transcription factor. To understand how the UPR manages ER stress, a unique genetic approach was applied to reveal how the system corrects disequilibria. The data show that the UPR can address a wide range of dysfunctions that are otherwise lethal if not for its intervention. Transcriptional profiling of stress-alleviated cells shows that the program can be modulated, not just in signal amplitude, but also through differential target gene expression depending on the stress. The breadth of the functions mitigated by the UPR further supports its role as a major mechanism maintaining systems robustness.

Distinct configurations of protein complexes and biochemical pathways revealed by epistatic interaction network motifs

Background

Gene and protein interactions are commonly represented as networks, with the genes or proteins comprising the nodes and the relationship between them as edges. Motifs, or small local configurations of edges and nodes that arise repeatedly, can be used to simplify the interpretation of networks.

Results

We examined triplet motifs in a network of quantitative epistatic genetic relationships, and found a non-random distribution of particular motif classes. Individual motif classes were found to be associated with different functional properties, suggestive of an underlying biological significance. These associations were apparent not only for motif classes, but for individual positions within the motifs. As expected, NNN (all negative) motifs were strongly associated with previously reported genetic (i.e. synthetic lethal) interactions, while PPP (all positive) motifs were associated with protein complexes. The two other motif classes (NNP: a positive interaction spanned by two negative interactions, and NPP: a negative spanned by two positives) showed very distinct functional associations, with physical interactions dominating for the former but alternative enrichments, typical of biochemical pathways, dominating for the latter.

Conclusion

We present a model showing how NNP motifs can be used to recognize supportive relationships between protein complexes, while NPP motifs often identify opposing or regulatory behaviour between a gene and an associated pathway. The ability to use motifs to point toward underlying biological organizational themes is likely to be increasingly important as more extensive epistasis mapping projects in higher organisms begin.

Targets of selection in a disease resistance network in wild tomatoes

Studies combining comparative genomics and information on biochemical pathways have revealed that protein evolution can be affected by the amount of pleiotropy associated with a particular gene. The amount of pleiotropy, in turn, can be a function of the position at which a gene operates in a pathway and the pathway structure. Genes that serve as convergence points and have several partners (so-called hubs) often show the greatest constraint and hence the slowest rate of protein evolution. In this article, we have studied five genes (Pto, Fen, Rin4, Prf and Pfi) in a defence signalling network in a wild tomato species, Solanum peruvianum. These proteins operate together and contribute to bacterial resistance in tomato. We predicted that Prf (and possibly Pfi), which serves as a convergence point for upstream signals, should show greater evolutionary constraint. However, we found instead that two of the genes which potentially interact with pathogen ligands, Rin4 and Fen, have evolved under strong evolutionary constraint, whereas Prf and Pfi, which probably function further downstream in the network, show evidence of balancing selection. This counterintuitive observation may be probable in pathogen defence networks, because pathogens may target positions throughout resistance networks to manipulate or nullify host resistance, thereby leaving a molecular signature of host-parasite co-evolution throughout a single network.

A quantitative analysis of monochromaticity in genetic interaction networks

BACKGROUND

A genetic interaction refers to the deviation of phenotypes from the expected when perturbing two genes simultaneously. Studying genetic interactions help clarify relationships between genes, such as compensation and masking, and identify gene groups of functional modules. Recently, several genome-scale experiments for measuring quantitative (positive and negative) genetic interactions have been conducted. The results revealed that genes in the same module usually interact with each other in a consistent way (pure positive or negative); this phenomenon was designated as monochromaticity. Monochromaticity might be the underlying principle that can be utilized to unveil the modularity of cellular networks. However, no appropriate quantitative measurement for this phenomenon has been proposed.

RESULTS

In this study, we propose the monochromatic index (MCI), which is able to quantitatively evaluate the monochromaticity of potential functional modules of genes, and the MCI was used to study genetic landscapes in different cellular subsystems. We demonstrated that MCI not only amend the deficiencies of MP-score but also properly incorporate the background effect. The results showed that not only within-complex but also between-complex connections present significant monochromatic tendency. Furthermore, we also found that significantly higher proportion of protein complexes are connected by negative genetic interactions in metabolic network, while transcription and translation system adopts relatively even number of positive and negative genetic interactions to link protein complexes.

CONCLUSION

In summary, we demonstrate that MCI improves deficiencies suffered by MP-score, and can be used to evaluate monochromaticity in a quantitative manner. In addition, it also helps to unveil features of genetic landscapes in different cellular subsystems. Moreover, MCI can be easily applied to data produced by different types of genetic interaction methodologies such as Synthetic Genetic Array (SGA), and epistatic miniarray profile (E-MAP).

Studying synthetic lethal interactions in the zebrafish system: insight into disease genes and mechanisms

The post-genomic era is marked by a pressing need to functionally characterize genes through understanding gene-gene interactions, as well as interactions between biological pathways. Exploiting a phenomenon known as synthetic lethality, in which simultaneous loss of two interacting genes leads to loss of viability, aids in the investigation of these interactions. Although synthetic lethal screening is a powerful technique that has been used with great success in many model organisms, including Saccharomyces cerevisiae, Drosophila melanogaster and Caenorhabditis elegans, this approach has not yet been applied in the zebrafish, Danio rerio. Recently, the zebrafish has emerged as a valuable system to model many human disease conditions; thus, the ability to conduct synthetic lethal screening using zebrafish should help to uncover many unknown disease-gene interactions. In this article, we discuss the concept of synthetic lethality and provide examples of its use in other model systems. We further discuss experimental approaches by which the concept of synthetic lethality can be applied to the zebrafish to understand the functions of specific genes.

Comparative interactomics with Funcoup 2.0

FunCoup (http://FunCoup.sbc.su.se) is a database that maintains and visualizes global gene/protein networks of functional coupling that have been constructed by Bayesian integration of diverse high-throughput data. FunCoup achieves high coverage by orthology-based integration of data sources from different model organisms and from different platforms. We here present release 2.0 in which the data sources have been updated and the methodology has been refined. It contains a new data type Genetic Interaction, and three new species: chicken, dog and zebra fish. As FunCoup extensively transfers functional coupling information between species, the new input datasets have considerably improved both coverage and quality of the networks. The number of high-confidence network links has increased dramatically. For instance, the human network has more than eight times as many links above confidence 0.5 as the previous release. FunCoup provides facilities for analysing the conservation of subnetworks in multiple species. We here explain how to do comparative interactomics on the FunCoup website.

Strategies to discover regulatory circuits of the mammalian immune system

Recent advances in technologies for genome- and proteome-scale measurements and perturbations promise to accelerate discovery in every aspect of biology and medicine. Although such rapid technological progress provides a tremendous opportunity, it also demands that we learn how to use these tools effectively. One application with great potential to enhance our understanding of biological systems is the unbiased reconstruction of genetic and molecular networks. Cells of the immune system provide a particularly useful model for developing and applying such approaches. Here, we review approaches for the reconstruction of signalling and transcriptional networks, with a focus on applications in the mammalian innate immune system.

Genetic interaction maps in Escherichia coli reveal functional crosstalk among cell envelope biogenesis pathways

As the interface between a microbe and its environment, the bacterial cell envelope has broad biological and clinical significance. While numerous biosynthesis genes and pathways have been identified and studied in isolation, how these intersect functionally to ensure envelope integrity during adaptive responses to environmental challenge remains unclear. To this end, we performed high-density synthetic genetic screens to generate quantitative functional association maps encompassing virtually the entire cell envelope biosynthetic machinery of Escherichia coli under both auxotrophic (rich medium) and prototrophic (minimal medium) culture conditions. The differential patterns of genetic interactions detected among >235,000 digenic mutant combinations tested reveal unexpected condition-specific functional crosstalk and genetic backup mechanisms that ensure stress-resistant envelope assembly and maintenance. These networks also provide insights into the global systems connectivity and dynamic functional reorganization of a universal bacterial structure that is both broadly conserved among eubacteria (including pathogens) and an important target.

Proper assembly of the cell envelope is essential for bacterial growth, environmental adaptation, and drug resistance. Yet, while the biological roles of the many genes and pathways involved in biosynthesis of the cell envelope have been studied extensively in isolation, how the myriad components intersect functionally to maintain envelope integrity under different growth conditions has not been explored systematically. Genome-scale genetic interaction screens have increasingly been performed to great impact in yeast; no analogous comprehensive studies have yet been reported for bacteria despite their prominence in human health and disease. We addressed this by using a synthetic genetic array technology to generate quantitative maps of genetic interactions encompassing virtually all the components of the cell envelope biosynthetic machinery of the classic model bacterium E. coli in two common laboratory growth conditions (rich and minimal medium). From the resulting networks of high-confidence genetic interactions, we identify condition-specific functional dependencies underlying envelope assembly and global remodeling of genetic backup mechanisms that ensure envelope integrity under environmental challenge.

Spindle assembly requires complete disassembly of spindle remnants from the previous cell cycle

Incomplete spindle disassembly causes lethality in budding yeast. We propose that spindle disassembly is required to reinitiate the spindle cycle during the subsequent mitosis by regenerating the nuclear pool of assembly-competent tubulin.

Incomplete mitotic spindle disassembly causes lethality in budding yeast. To determine why spindle disassembly is required for cell viability, we used live-cell microscopy to analyze a double mutant strain containing a conditional mutant and a deletion mutant compromised for the kinesin-8 and anaphase-promoting complex-driven spindle-disassembly pathways (td-kip3 and doc1Δ, respectively). Under nonpermissive conditions, spindles in td-kip3 doc1Δ cells could break apart but could not disassemble completely. These cells could exit mitosis and undergo cell division. However, the daughter cells could not assemble functional, bipolar spindles in the ensuing mitosis. During the formation of these dysfunctional spindles, centrosome duplication and separation, as well as recruitment of key midzone-stabilizing proteins all appeared normal, but microtubule polymerization was nevertheless impaired and these spindles often collapsed. Introduction of free tubulin through episomal expression of α- and β-tubulin or introduction of a brief pulse of the microtubule-depolymerizing drug nocodazole allowed spindle assembly in these td-kip3 doc1Δ mutants. Therefore we propose that spindle disassembly is essential for regeneration of the intracellular pool of assembly-competent tubulin required for efficient spindle assembly during subsequent mitoses of daughter cells.

Tail-anchored membrane protein insertion into the endoplasmic reticulum

Membrane proteins are inserted into the endoplasmic reticulum (ER) by two highly conserved parallel pathways. The well-studied co-translational pathway uses signal recognition particle (SRP) and its receptor for targeting and the SEC61 translocon for membrane integration. A recently discovered post-translational pathway uses an entirely different set of factors involving transmembrane domain (TMD)-selective cytosolic chaperones and an accompanying receptor at the ER. Elucidation of the structural and mechanistic basis of this post-translational membrane protein insertion pathway highlights general principles shared between the two pathways and key distinctions unique to each.

Construction of regulatory networks using expression time-series data of a genotyped population

The inference of regulatory and biochemical networks from large-scale genomics data is a basic problem in molecular biology. The goal is to generate testable hypotheses of gene-to-gene influences and subsequently to design bench experiments to confirm these network predictions. Coexpression of genes in large-scale gene-expression data implies coregulation and potential gene-gene interactions, but provide little information about the direction of influences. Here, we use both time-series data and genetics data to infer directionality of edges in regulatory networks: time-series data contain information about the chronological order of regulatory events and genetics data allow us to map DNA variations to variations at the RNA level. We generate microarray data measuring time-dependent gene-expression levels in 95 genotyped yeast segregants subjected to a drug perturbation. We develop a Bayesian model averaging regression algorithm that incorporates external information from diverse data types to infer regulatory networks from the time-series and genetics data. Our algorithm is capable of generating feedback loops. We show that our inferred network recovers existing and novel regulatory relationships. Following network construction, we generate independent microarray data on selected deletion mutants to prospectively test network predictions. We demonstrate the potential of our network to discover de novo transcription-factor binding sites. Applying our construction method to previously published data demonstrates that our method is competitive with leading network construction algorithms in the literature.

Predicting phenotypic variation in yeast from individual genome sequences

A central challenge in genetics is to predict phenotypic variation from individual genome sequences. Here we construct and evaluate phenotypic predictions for 19 strains of Saccharomyces cerevisiae. We use conservation-based methods to predict the impact of protein-coding variation within genes on protein function. We then rank strains using a prediction score that measures the total sum of function-altering changes in different sets of genes reported to influence over 100 phenotypes in genome-wide loss-of-function screens. We evaluate our predictions by comparing them with the observed growth rate and efficiency of 15 strains tested across 20 conditions in quantitative experiments. The median predictive performance, as measured by ROC AUC, was 0.76, and predictions were more accurate when the genes reported to influence a trait were highly connected in a functional gene network.

Direct regulation of nucleosome density by the conserved AAA-ATPase Yta7

Yta7 is a highly conserved bromodomain-containing protein with AAA-ATPase homology originally implicated in heterochromatin boundary function in Saccharomyces cerevisiae. Although increased activity of the human ortholog has been implicated in malignant breast tumors, Yta7's precise mode of action is unknown. Transcriptional analysis in yeast cells revealed a role for Yta7 and its ATPase function in gene induction, including galactose- and sporulation-induced transcription. This requirement was direct and activating, because Yta7 associated with the GAL gene cluster only upon transcriptional induction. Suggestive of a role in transcriptional elongation, Yta7 localized to the ORFs of highly transcribed genes. Intriguingly, the yta7Δ mutant's transcriptional defects were partially suppressed by decreased dosage of histones H3 and H4. Consistent with this suppression, cells lacking Yta7 exhibited both increased levels of chromatin-incorporated histone H3 and decreased nucleosome spacing. Importantly, this modulation of H3 levels occurred independently of changes in H3 transcript level. Because Yta7 binds histone H3 in vitro, these results suggested a direct role for Yta7 in H3 eviction or degradation. Further, local loss of Yta7 activity at a long inducible gene resulted in accumulation of H3 at the 3' end upon transcriptional activation, implying Yta7 may regulate H3 cotranscriptionally.

clusterMaker: a multi-algorithm clustering plugin for Cytoscape

Background

In the post-genomic era, the rapid increase in high-throughput data calls for computational tools capable of integrating data of diverse types and facilitating recognition of biologically meaningful patterns within them. For example, protein-protein interaction data sets have been clustered to identify stable complexes, but scientists lack easily accessible tools to facilitate combined analyses of multiple data sets from different types of experiments. Here we present clusterMaker, a Cytoscape plugin that implements several clustering algorithms and provides network, dendrogram, and heat map views of the results. The Cytoscape network is linked to all of the other views, so that a selection in one is immediately reflected in the others. clusterMaker is the first Cytoscape plugin to implement such a wide variety of clustering algorithms and visualizations, including the only implementations of hierarchical clustering, dendrogram plus heat map visualization (tree view), k-means, k-medoid, SCPS, AutoSOME, and native (Java) MCL.

Results

Results are presented in the form of three scenarios of use: analysis of protein expression data using a recently published mouse interactome and a mouse microarray data set of nearly one hundred diverse cell/tissue types; the identification of protein complexes in the yeast Saccharomyces cerevisiae; and the cluster analysis of the vicinal oxygen chelate (VOC) enzyme superfamily. For scenario one, we explore functionally enriched mouse interactomes specific to particular cellular phenotypes and apply fuzzy clustering. For scenario two, we explore the prefoldin complex in detail using both physical and genetic interaction clusters. For scenario three, we explore the possible annotation of a protein as a methylmalonyl-CoA epimerase within the VOC superfamily. Cytoscape session files for all three scenarios are provided in the Additional Files section.

Conclusions

The Cytoscape plugin clusterMaker provides a number of clustering algorithms and visualizations that can be used independently or in combination for analysis and visualization of biological data sets, and for confirming or generating hypotheses about biological function. Several of these visualizations and algorithms are only available to Cytoscape users through the clusterMaker plugin. clusterMaker is available via the Cytoscape plugin manager.

Systematic exploration of synergistic drug pairs

Drug synergy allows a therapeutic effect to be achieved with lower doses of component drugs. Drug synergy can result when drugs target the products of genes that act in parallel pathways ('specific synergy'). Such cases of drug synergy should tend to correspond to synergistic genetic interaction between the corresponding target genes. Alternatively, 'promiscuous synergy' can arise when one drug non-specifically increases the effects of many other drugs, for example, by increased bioavailability. To assess the relative abundance of these drug synergy types, we examined 200 pairs of antifungal drugs in S. cerevisiae. We found 38 antifungal synergies, 37 of which were novel. While 14 cases of drug synergy corresponded to genetic interaction, 92% of the synergies we discovered involved only six frequently synergistic drugs. Although promiscuity of four drugs can be explained under the bioavailability model, the promiscuity of Tacrolimus and Pentamidine was completely unexpected. While many drug synergies correspond to genetic interactions, the majority of drug synergies appear to result from non-specific promiscuous synergy.

YMDB: the Yeast Metabolome Database

The Yeast Metabolome Database (YMDB, http://www.ymdb.ca) is a richly annotated ‘metabolomic’ database containing detailed information about the metabolome of Saccharomyces cerevisiae. Modeled closely after the Human Metabolome Database, the YMDB contains >2000 metabolites with links to 995 different genes/proteins, including enzymes and transporters. The information in YMDB has been gathered from hundreds of books, journal articles and electronic databases. In addition to its comprehensive literature-derived data, the YMDB also contains an extensive collection of experimental intracellular and extracellular metabolite concentration data compiled from detailed Mass Spectrometry (MS) and Nuclear Magnetic Resonance (NMR) metabolomic analyses performed in our lab. This is further supplemented with thousands of NMR and MS spectra collected on pure, reference yeast metabolites. Each metabolite entry in the YMDB contains an average of 80 separate data fields including comprehensive compound description, names and synonyms, structural information, physico-chemical data, reference NMR and MS spectra, intracellular/extracellular concentrations, growth conditions and substrates, pathway information, enzyme data, gene/protein sequence data, as well as numerous hyperlinks to images, references and other public databases. Extensive searching, relational querying and data browsing tools are also provided that support text, chemical structure, spectral, molecular weight and gene/protein sequence queries. Because of S. cervesiae's importance as a model organism for biologists and as a biofactory for industry, we believe this kind of database could have considerable appeal not only to metabolomics researchers, but also to yeast biologists, systems biologists, the industrial fermentation industry, as well as the beer, wine and spirit industry.

Networks in Caenorhabditis elegans

The network paradigm has become a pervasive theme in biology over the last decade, as increasingly large functional genomic datasets are being collected to interrogate regulatory influences, physical interactions, and genetic dependencies between genes, transcripts, and proteins. These 'molecular interaction' networks can be analyzed collectively and individually to define their global architecture and local patterns of connectivity. These structural features ultimately underlie functional properties such as robustness, modularity, component circuitry (e.g. feedback loops), dynamics, and responses to perturbations. This review focuses on recent progress in elucidating molecular interaction networks using different kinds of functional assays in the classical genetic model for animal development, the roundworm Caenorhabditis elegans, with representative examples to illustrate current directions in different areas of network biology.

Genome-wide association mapping of agronomic traits in sugar beet

Recent results indicate that association mapping in populations from applied plant breeding is a powerful tool to detect QTL which are of direct relevance for breeding. The focus of this study was to unravel the genetic architecture of six agronomic traits in sugar beet. To this end, we employed an association mapping approach, based on a very large population of 924 elite sugar beet lines from applied plant breeding, fingerprinted with 677 single nucleotide polymorphism (SNP) markers covering the entire genome. We show that in this population linkage disequilibrium decays within a short genetic distance and is sufficient for the detection of QTL with a large effect size. To increase the QTL detection power and the mapping resolution a much higher number of SNPs is required. We found that for QTL detection, the mixed model including only the kinship matrix performed best, even in the presence of a considerable population structure. In genome-wide scans, main effect QTL and epistatic QTL were detected for all six traits. Our full two-dimensional epistasis scan revealed that for complex traits there appear to be epistatic master regulators, loci which are involved in a large number of epistatic interactions throughout the genome.

Ubiquitin ligase Ufd2 is required for efficient degradation of Mps1 kinase

Ufd2 is a U-box-containing ubiquitylation enzyme that promotes ubiquitin chain assembly on substrates. The physiological function of Ufd2 remains poorly understood. Here, we show that ubiquitylation and degradation of the cell cycle kinase Mps1, a known target of the anaphase-promoting complex E3, require Ufd2 enzyme. Yeast cells lacking UFD2 exhibit altered chromosome stability and several spindle-related phenotypes, expanding the biological function of Ufd2. We demonstrate that Ufd2-mediated Mps1 degradation is conserved in humans. Our results underscore the significance of Ufd2 in proteolysis and further suggest that Ufd2-like enzymes regulate far more substrates than previously envisioned.

Yeast: an experimental organism for 21st Century biology

In this essay, we revisit the status of yeast as a model system for biology. We first summarize important contributions of yeast to eukaryotic biology that we anticipated in 1988 in our first article on the subject. We then describe transformative developments that we did not anticipate, most of which followed the publication of the complete genomic sequence of Saccharomyces cerevisiae in 1996. In the intervening 23 years it appears to us that yeast has graduated from a position as the premier model for eukaryotic cell biology to become the pioneer organism that has facilitated the establishment of the entirely new fields of study called "functional genomics" and "systems biology." These new fields look beyond the functions of individual genes and proteins, focusing on how these interact and work together to determine the properties of living cells and organisms.

The SUN protein Mps3 is required for spindle pole body insertion into the nuclear membrane and nuclear envelope homeostasis

The budding yeast spindle pole body (SPB) is anchored in the nuclear envelope so that it can simultaneously nucleate both nuclear and cytoplasmic microtubules. During SPB duplication, the newly formed SPB is inserted into the nuclear membrane. The mechanism of SPB insertion is poorly understood but likely involves the action of integral membrane proteins to mediate changes in the nuclear envelope itself, such as fusion of the inner and outer nuclear membranes. Analysis of the functional domains of the budding yeast SUN protein and SPB component Mps3 revealed that most regions are not essential for growth or SPB duplication under wild-type conditions. However, a novel dominant allele in the P-loop region, MPS3-G186K, displays defects in multiple steps in SPB duplication, including SPB insertion, indicating a previously unknown role for Mps3 in this step of SPB assembly. Characterization of the MPS3-G186K mutant by electron microscopy revealed severe over-proliferation of the inner nuclear membrane, which could be rescued by altering the characteristics of the nuclear envelope using both chemical and genetic methods. Lipid profiling revealed that cells lacking MPS3 contain abnormal amounts of certain types of polar and neutral lipids, and deletion or mutation of MPS3 can suppress growth defects associated with inhibition of sterol biosynthesis, suggesting that Mps3 directly affects lipid homeostasis. Therefore, we propose that Mps3 facilitates insertion of SPBs in the nuclear membrane by modulating nuclear envelope composition.

Enhanced yeast one-hybrid assays for high-throughput gene-centered regulatory network mapping

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

An in silico approach combined with in vivo experiments enables the identification of a new protein whose overexpression can compensate for specific respiratory defects in Saccharomyces cerevisiae

BACKGROUND

The mitochondrial inner membrane contains five large complexes that are essential for oxidative phosphorylation. Although the structure and the catalytic mechanisms of the respiratory complexes have been progressively established, their biogenesis is far from being fully understood. Very few complex III assembly factors have been identified so far. It is probable that more factors are needed for the assembly of a functional complex, but that the genetic approaches used to date have not been able to identify them. We have developed a systems biology approach to identify new factors controlling complex III biogenesis.

RESULTS

We collected all the physical protein-protein interactions (PPI) involving the core subunits, the supernumerary subunits and the assembly factors of complex III and used Cytoscape 2.6.3 and its plugins to construct a network. It was then divided into overlapping and highly interconnected sub-graphs with clusterONE. One sub-graph contained the core and the supernumerary subunits of complex III, it also contained some subunits of complex IV and proteins participating in the assembly of complex IV. This sub-graph was then split with another algorithm into two sub-graphs. The subtraction of these two sub-graphs from the previous sub-graph allowed us to identify a protein of unknown function Usb1p/Ylr132p that interacts with the complex III subunits Qcr2p and Cor1p. We then used genetic and cell biology approaches to investigate the function of Usb1p. Preliminary results indicated that Usb1p is an essential protein with a dual localization in the nucleus and in the mitochondria, and that the over-expression of this protein can compensate for defects in the biogenesis of the respiratory complexes.

CONCLUSIONS

Our systems biology approach has highlighted the multiple associations between subunits and assembly factors of complexes III and IV during their biogenesis. In addition, this approach has allowed the identification of a new factor, Usb1p, involved in the biogenesis of respiratory complexes, which could not have been found using classical genetic screens looking for respiratory deficient mutants. Thus, this systems biology approach appears to be a fruitful new way to study the biogenesis of mitochondrial multi-subunit complexes.

Removing bias against membrane proteins in interaction networks

Background

Cellular interaction networks can be used to analyze the effects on cell signaling and other functional consequences of perturbations to cellular physiology. Thus, several methods have been used to reconstitute interaction networks from multiple published datasets. However, the structure and performance of these networks depends on both the quality and the unbiased nature of the original data. Due to the inherent bias against membrane proteins in protein-protein interaction (PPI) data, interaction networks can be compromised particularly if they are to be used in conjunction with drug screening efforts, since most drug-targets are membrane proteins.

Results

To overcome the experimental bias against PPIs involving membrane-associated proteins we used a probabilistic approach based on a hypergeometric distribution followed by logistic regression to simultaneously optimize the weights of different sources of interaction data. The resulting less biased genome-scale network constructed for the budding yeast Saccharomyces cerevisiae revealed that the starvation pathway is a distinct subnetwork of autophagy and retrieved a more integrated network of unfolded protein response genes. We also observed that the centrality-lethality rule depends on the content of membrane proteins in networks.

Conclusions

We show here that the bias against membrane proteins can and should be corrected in order to have a better representation of the interactions and topological properties of protein interaction networks.

Determinants of endocytic membrane geometry, stability, and scission

During endocytic vesicle formation, distinct subdomains along the membrane invagination are specified by different proteins, which bend the membrane and drive scission. Bin-Amphiphysin-Rvs (BAR) and Fer-CIP4 homology-BAR (F-BAR) proteins can induce membrane curvature and have been suggested to facilitate membrane invagination and scission. Two F-BAR proteins, Syp1 and Bzz1, are found at budding yeast endocytic sites. Syp1 arrives early but departs from the endocytic site before formation of deep membrane invaginations and scission. Using genetic, spatiotemporal, and ultrastructural analyses, we demonstrate that Bzz1, the heterodimeric BAR domain protein Rvs161/167, actin polymerization, and the lipid phosphatase Sjl2 cooperate, each through a distinct mechanism, to induce membrane scission in yeast. Additionally, actin assembly and Rvs161/167 cooperate to drive formation of deep invaginations. Finally, we find that Bzz1, acting at the invagination base, stabilizes endocytic sites and functions with Rvs161/167, localized along the tubule, to achieve proper endocytic membrane geometry necessary for efficient scission. Together, our results reveal that dynamic interplay between a lipid phosphatase, actin assembly, and membrane-sculpting proteins leads to proper membrane shaping, tubule stabilization, and scission.

Flexibility and binding affinity in protein-ligand, protein-protein and multi-component protein interactions: limitations of current computational approaches

The recognition process between a protein and a partner represents a significant theoretical challenge. In silico structure-based drug design carried out with nothing more than the three-dimensional structure of the protein has led to the introduction of many compounds into clinical trials and numerous drug approvals. Central to guiding the discovery process is to recognize active among non-active compounds. While large-scale computer simulations of compounds taken from a library (virtual screening) or designed de novo are highly desirable in the post-genomic area, many technical problems remain to be adequately addressed. This article presents an overview and discusses the limits of current computational methods for predicting the correct binding pose and accurate binding affinity. It also presents the performances of the most popular algorithms for exploring binary and multi-body protein interactions.

A mitochondrial-focused genetic interaction map reveals a scaffold-like complex required for inner membrane organization in mitochondria

Statement MITO-MAP, a high-density genetic interaction map in budding yeast, identifies a mitochondrial inner membrane–associated complex that promotes normal mitochondrial membrane organization and morphology.

To broadly explore mitochondrial structure and function as well as the communication of mitochondria with other cellular pathways, we constructed a quantitative, high-density genetic interaction map (the MITO-MAP) in Saccharomyces cerevisiae. The MITO-MAP provides a comprehensive view of mitochondrial function including insights into the activity of uncharacterized mitochondrial proteins and the functional connection between mitochondria and the ER. The MITO-MAP also reveals a large inner membrane–associated complex, which we term MitOS for mitochondrial organizing structure, comprised of Fcj1/Mitofilin, a conserved inner membrane protein, and five additional components. MitOS physically and functionally interacts with both outer and inner membrane components and localizes to extended structures that wrap around the inner membrane. We show that MitOS acts in concert with ATP synthase dimers to organize the inner membrane and promote normal mitochondrial morphology. We propose that MitOS acts as a conserved mitochondrial skeletal structure that differentiates regions of the inner membrane to establish the normal internal architecture of mitochondria.

The causes of epistasis

Since Bateson's discovery that genes can suppress the phenotypic effects of other genes, gene interactions-called epistasis-have been the topic of a vast research effort. Systems and developmental biologists study epistasis to understand the genotype-phenotype map, whereas evolutionary biologists recognize the fundamental importance of epistasis for evolution. Depending on its form, epistasis may lead to divergence and speciation, provide evolutionary benefits to sex and affect the robustness and evolvability of organisms. That epistasis can itself be shaped by evolution has only recently been realized. Here, we review the empirical pattern of epistasis, and some of the factors that may affect the form and extent of epistasis. Based on their divergent consequences, we distinguish between interactions with or without mean effect, and those affecting the magnitude of fitness effects or their sign. Empirical work has begun to quantify epistasis in multiple dimensions in the context of metabolic and fitness landscape models. We discuss possible proximate causes (such as protein function and metabolic networks) and ultimate factors (including mutation, recombination, and the importance of natural selection and genetic drift). We conclude that, in general, pleiotropy is an important prerequisite for epistasis, and that epistasis may evolve as an adaptive or intrinsic consequence of changes in genetic robustness and evolvability.

Regulation of septin dynamics by the Saccharomyces cerevisiae lysine acetyltransferase NuA4

In the budding yeast Saccharomyces cerevisiae, the lysine acetyltransferase NuA4 has been linked to a host of cellular processes through the acetylation of histone and non-histone targets. To discover proteins regulated by NuA4-dependent acetylation, we performed genome-wide synthetic dosage lethal screens to identify genes whose overexpression is toxic to non-essential NuA4 deletion mutants. The resulting genetic network identified a novel link between NuA4 and septin proteins, a group of highly conserved GTP-binding proteins that function in cytokinesis. We show that acetyltransferase-deficient NuA4 mutants have defects in septin collar formation resulting in the development of elongated buds through the Swe1-dependent morphogenesis checkpoint. We have discovered multiple sites of acetylation on four of the five yeast mitotic septins, Cdc3, Cdc10, Cdc12 and Shs1, and determined that NuA4 can acetylate three of the four in vitro. In vivo we find that acetylation levels of both Shs1 and Cdc10 are reduced in a catalytically inactive esa1 mutant. Finally, we determine that cells expressing a Shs1 protein with decreased acetylation in vivo have defects in septin localization that are similar to those observed in NuA4 mutants. These findings provide the first evidence that yeast septin proteins are acetylated and that NuA4 impacts septin dynamics.

Benchmarking of the 2010 BioCreative Challenge III text-mining competition by the BioGRID and MINT interaction databases

Background

The vast amount of data published in the primary biomedical literature represents a challenge for the automated extraction and codification of individual data elements. Biological databases that rely solely on manual extraction by expert curators are unable to comprehensively annotate the information dispersed across the entire biomedical literature. The development of efficient tools based on natural language processing (NLP) systems is essential for the selection of relevant publications, identification of data attributes and partially automated annotation. One of the tasks of the Biocreative 2010 Challenge III was devoted to the evaluation of NLP systems developed to identify articles for curation and extraction of protein-protein interaction (PPI) data.

Results

The Biocreative 2010 competition addressed three tasks: gene normalization, article classification and interaction method identification. The BioGRID and MINT protein interaction databases both participated in the generation of the test publication set for gene normalization, annotated the development and test sets for article classification, and curated the test set for interaction method classification. These test datasets served as a gold standard for the evaluation of data extraction algorithms.

Conclusion

The development of efficient tools for extraction of PPI data is a necessary step to achieve full curation of the biomedical literature. NLP systems can in the first instance facilitate expert curation by refining the list of candidate publications that contain PPI data; more ambitiously, NLP approaches may be able to directly extract relevant information from full-text articles for rapid inspection by expert curators. Close collaboration between biological databases and NLP systems developers will continue to facilitate the long-term objectives of both disciplines.

Modeling Huntington disease in yeast: perspectives and future directions

Yeast have been extensively used to model aspects of protein folding diseases, yielding novel mechanistic insights and identifying promising candidate therapeutic targets. In particular, the neurodegenerative disorder Huntington disease (HD), which is caused by the abnormal expansion of a polyglutamine tract in the huntingtin (htt) protein, has been widely studied in yeast. This work has led to the identification of several promising therapeutic targets and compounds that have been validated in mammalian cells, Drosophila and rodent models of HD. Here we discuss the development of yeast models of mutant htt toxicity and misfolding, as well as the mechanistic insights gleaned from this simple model. The role of yeast prions in the toxicity/misfolding of mutant htt is also highlighted. Furthermore, we provide an overview of the application of HD yeast models in both genetic and chemical screens, and the fruitful results obtained from these approaches. Finally, we discuss the future of yeast in neurodegenerative research, in the context of HD and other diseases.

Modeling Huntington disease in yeast: perspectives and future directions

Yeast have been extensively used to model aspects of protein folding diseases, yielding novel mechanistic insights and identifying promising candidate therapeutic targets. In particular, the neurodegenerative disorder Huntington disease (HD), which is caused by the abnormal expansion of a polyglutamine tract in the huntingtin (htt) protein, has been widely studied in yeast. This work has led to the identification of several promising therapeutic targets and compounds that have been validated in mammalian cells, Drosophila and rodent models of HD. Here we discuss the development of yeast models of mutant htt toxicity and misfolding, as well as the mechanistic insights gleaned from this simple model. The role of yeast prions in the toxicity/misfolding of mutant htt is also highlighted. Furthermore, we provide an overview of the application of HD yeast models in both genetic and chemical screens, and the fruitful results obtained from these approaches. Finally, we discuss the future of yeast in neurodegenerative research, in the context of HD and other diseases.

A systems biology approach reveals the role of a novel methyltransferase in response to chemical stress and lipid homeostasis

Using small molecule probes to understand gene function is an attractive approach that allows functional characterization of genes that are dispensable in standard laboratory conditions and provides insight into the mode of action of these compounds. Using chemogenomic assays we previously identified yeast Crg1, an uncharacterized SAM-dependent methyltransferase, as a novel interactor of the protein phosphatase inhibitor cantharidin. In this study we used a combinatorial approach that exploits contemporary high-throughput techniques available in Saccharomyces cerevisiae combined with rigorous biological follow-up to characterize the interaction of Crg1 with cantharidin. Biochemical analysis of this enzyme followed by a systematic analysis of the interactome and lipidome of CRG1 mutants revealed that Crg1, a stress-responsive SAM-dependent methyltransferase, methylates cantharidin in vitro. Chemogenomic assays uncovered that lipid-related processes are essential for cantharidin resistance in cells sensitized by deletion of the CRG1 gene. Lipidome-wide analysis of mutants further showed that cantharidin induces alterations in glycerophospholipid and sphingolipid abundance in a Crg1-dependent manner. We propose that Crg1 is a small molecule methyltransferase important for maintaining lipid homeostasis in response to drug perturbation. This approach demonstrates the value of combining chemical genomics with other systems-based methods for characterizing proteins and elucidating previously unknown mechanisms of action of small molecule inhibitors.

A structural model of the Sgt2 protein and its interactions with chaperones and the Get4/Get5 complex

The insertion of tail-anchored transmembrane (TA) proteins into the appropriate membrane is a post-translational event that requires stabilization of the transmembrane domain and targeting to the proper destination. Sgt2 is a heat-shock protein cognate (HSC) co-chaperone that preferentially binds endoplasmic reticulum-destined TA proteins and directs them to the GET pathway via Get4 and Get5. Here, we present the crystal structure from a fungal Sgt2 homolog of the tetratrico-repeat (TPR) domain and part of the linker that connects to the C-terminal domain. The linker extends into the two-carboxylate clamp of the TPR domain from a symmetry-related molecule mimicking the binding to HSCs. Based on this structure, we provide biochemical evidence that the Sgt2 TPR domain has the ability to directly bind multiple HSC family members. The structure allows us to propose features involved in this lower specificity relative to other TPR containing co-chaperones. We further show that a dimer of Sgt2 binds a single Get5 and use small angle x-ray scattering to characterize the domain arrangement of Sgt2 in solution. These results allow us to present a structural model of the Sgt2-Get4/Get5-HSC complex.

Insights into the role of yeast eIF2A in IRES-mediated translation

Eukaryotic initiation factor 2A is a single polypeptide that acts to negatively regulate IRES-mediated translation during normal cellular conditions. We have found that eIF2A (encoded by YGR054w) abundance is reduced at both the mRNA and protein level during 6% ethanol stress (or 37°C heat shock) under conditions that mimic the diauxic shift in the yeast Saccharomyces cerevisiae. Furthermore, eIF2A protein is posttranslationally modified during ethanol stress. Unlike ethanol and heat shock stress, H₂O₂ and sorbitol treatment induce the loss of eIF2A mRNA, but not protein and without protein modification. To investigate the mechanism of eIF2A function we employed immunoprecipitation-mass spectrometry and identified an interaction between eIF2A and eEF1A. The interaction between eIF2A and eEF1A increases during ethanol stress, which correlates with an increase in IRES-mediated translation from the URE2 IRES element. These data suggest that eIF2A acts as a switch to regulate IRES-mediated translation, and eEF1A may be an important mediator of translational activation during ethanol stress.

The population genetics of X-autosome synthetic lethals and steriles

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

Annotation concept synthesis and enrichment analysis: a logic-based approach to the interpretation of high-throughput experiments

Motivation: Annotation Enrichment Analysis (AEA) is a widely used analytical approach to process data generated by high-throughput genomic and proteomic experiments such as gene expression microarrays. The analysis uncovers and summarizes discriminating background information (e.g. GO annotations) for sets of genes identified by experiments (e.g. a set of differentially expressed genes, a cluster). The discovered information is utilized by human experts to find biological interpretations of the experiments.

However, AEA isolates and tests for overrepresentation only individual annotation terms or groups of similar terms and is limited in its ability to uncover complex phenomena involving relationship between multiple annotation terms from various knowledge bases. Also, AEA assumes that annotations describe the whole object of interest, which makes it difficult to apply it to sets of compound objects (e.g. sets of protein–protein interactions) and to sets of objects having an internal structure (e.g. protein complexes).

Results: We propose a novel logic-based Annotation Concept Synthesis and Enrichment Analysis (ACSEA) approach. ACSEA fuses inductive logic reasoning with statistical inference to uncover more complex phenomena captured by the experiments. We evaluate our approach on large-scale datasets from several microarray experiments and on a clustered genome-wide genetic interaction network using different biological knowledge bases. The discovered interpretations have lower P-values than the interpretations found by AEA, are highly integrative in nature, and include analysis of quantitative and structured information present in the knowledge bases. The results suggest that ACSEA can boost effectiveness of the processing of high-throughput experiments.

Contact:mjiline@site.uottawa.ca

Inferring mechanisms of compensation from E-MAP and SGA data using local search algorithms for max cut

A new method based on a mathematically natural local search framework for max cut is developed to uncover functionally coherent module and BPM motifs in high-throughput genetic interaction data. Unlike previous methods, which also consider physical protein-protein interaction data, our method utilizes genetic interaction data only; this becomes increasingly important as high-throughput genetic interaction data is becoming available in settings where less is known about physical interaction data. We compare modules and BPMs obtained to previous methods and across different datasets. Despite needing no physical interaction information, the BPMs produced by our method are competitive with previous methods. Biological findings include a suggested global role for the prefoldin complex and a SWR subcomplex in pathway buffering in the budding yeast interactome.

Novel interactions between actin and the proteasome revealed by complex haploinsufficiency

Saccharomyces cerevisiae has been a powerful model for uncovering the landscape of binary gene interactions through whole-genome screening. Complex heterozygous interactions are potentially important to human genetic disease as loss-of-function alleles are common in human genomes. We have been using complex haploinsufficiency (CHI) screening with the actin gene to identify genes related to actin function and as a model to determine the prevalence of CHI interactions in eukaryotic genomes. Previous CHI screening between actin and null alleles for non-essential genes uncovered ∼240 deleterious CHI interactions. In this report, we have extended CHI screening to null alleles for essential genes by mating a query strain to sporulations of heterozygous knock-out strains. Using an act1Δ query, knock-outs of 60 essential genes were found to be CHI with actin. Enriched in this collection were functional categories found in the previous screen against non-essential genes, including genes involved in cytoskeleton function and chaperone complexes that fold actin and tubulin. Novel to this screen was the identification of genes for components of the TFIID transcription complex and for the proteasome. We investigated a potential role for the proteasome in regulating the actin cytoskeleton and found that the proteasome physically associates with actin filaments in vitro and that some conditional mutations in proteasome genes have gross defects in actin organization. Whole-genome screening with actin as a query has confirmed that CHI interactions are important phenotypic drivers. Furthermore, CHI screening is another genetic tool to uncover novel functional connections. Here we report a previously unappreciated role for the proteasome in affecting actin organization and function.

Chemical and Synthetic Genetic Array Analysis Identifies Genes that Suppress Xylose Utilization and Fermentation in Saccharomyces cerevisiae

Though highly efficient at fermenting hexose sugars, Saccharomyces cerevisiae has limited ability to ferment five-carbon sugars. As a significant portion of sugars found in cellulosic biomass is the five-carbon sugar xylose, S. cerevisiae must be engineered to metabolize pentose sugars, commonly by the addition of exogenous genes from xylose fermenting fungi. However, these recombinant strains grow poorly on xylose and require further improvement through rational engineering or evolutionary adaptation. To identify unknown genes that contribute to improved xylose fermentation in these recombinant S. cerevisiae, we performed genome-wide synthetic interaction screens to identify deletion mutants that impact xylose utilization of strains expressing the xylose isomerase gene XYLA from Piromyces sp. E2 alone or with an additional copy of the endogenous xylulokinase gene XKS1. We also screened the deletion mutant array to identify mutants whose growth is affected by xylose. Our genetic network reveals that more than 80 nonessential genes from a diverse range of cellular processes impact xylose utilization. Surprisingly, we identified four genes, ALP1, ISC1, RPL20B, and BUD21, that when individually deleted improved xylose utilization of both S. cerevisiae S288C and CEN.PK strains. We further characterized BUD21 deletion mutant cells in batch fermentations and found that they produce ethanol even the absence of exogenous XYLA. We have demonstrated that the ability of laboratory strains of S. cerevisiae to utilize xylose as a sole carbon source is suppressed, which implies that S. cerevisiae may not require the addition of exogenous genes for efficient xylose fermentation.

A genome-wide immunodetection screen in S. cerevisiae uncovers novel genes involved in lysosomal vacuole function and morphology

Vacuoles of yeast Saccharomyces cerevisiae are functionally analogous to mammalian lysosomes. Both are cellular organelles responsible for macromolecular degradation, ion/pH homeostasis, and stress survival. We hypothesized that undefined gene functions remain at post-endosomal stage of vacuolar events and performed a genome-wide screen directed at such functions at the late endosome and vacuole interface – ENV genes. The immunodetection screen was designed to identify mutants that internally accumulate precursor form of the vacuolar hydrolase carboxypeptidase Y (CPY). Here, we report the uncovering and initial characterizations of twelve ENV genes. The small size of the collection and the lack of genes previously identified with vacuolar events are suggestive of the intended exclusive functional interface of the screen. Most notably, the collection includes four novel genes ENV7, ENV9, ENV10, and ENV11, and three genes previously linked to mitochondrial processes – MAM3, PCP1, PPE1. In all env mutants, vesicular trafficking stages were undisturbed in live cells as assessed by invertase and active α-factor secretion, as well as by localization of the endocytic fluorescent marker FM4-64 to the vacuole. Several mutants exhibit defects in stress survival functions associated with vacuoles. Confocal fluorescence microscopy revealed the collection to be significantly enriched in vacuolar morphologies suggestive of fusion and fission defects. These include the unique phenotype of lumenal vesicles within vacuoles in the novel env9Δ mutant and severely fragmented vacuoles upon deletion of GET4, a gene recently implicated in tail anchored membrane protein insertion. Thus, our results establish new gene functions in vacuolar function and morphology, and suggest a link between vacuolar and mitochondrial events.

The mechanism of membrane-associated steps in tail-anchored protein insertion

Tail-anchored (TA) membrane proteins destined for the endoplasmic reticulum are chaperoned by cytosolic targeting factors that deliver them to a membrane receptor for insertion. Although a basic framework for TA protein recognition is now emerging, the decisive targeting and membrane insertion steps are not understood. Here we reconstitute the TA protein insertion cycle with purified components, present crystal structures of key complexes between these components and perform mutational analyses based on the structures. We show that a committed targeting complex, formed by a TA protein bound to the chaperone ATPase Get3, is initially recruited to the membrane through an interaction with Get2. Once the targeting complex has been recruited, Get1 interacts with Get3 to drive TA protein release in an ATPase-dependent reaction. After releasing its TA protein cargo, the now-vacant Get3 recycles back to the cytosol concomitant with ATP binding. This work provides a detailed structural and mechanistic framework for the minimal TA protein insertion cycle.

Genetic architecture of growth traits revealed by global epistatic interactions

Epistasis has long been recognized as fundamentally important in understanding the structure, function, and evolutionary dynamics of biological systems. Yet, little is known about how it is distributed underlying specific traits. Based on a global map of epistatic interactions in baker's yeast, Saccharomyces cerevisiae, we show that epistasis is prevalent (∼13% increase from random expectation) and displays modular architecture among genes that underlie the same growth traits. More interestingly, our results indicate that hub genes responsible for the same growth traits tend to link epistatically with each other more frequently than random expectation. Our results provide a genome-wide perspective on the genetic architecture of growth traits in a eukaryotic organism.