The chemical genomic portrait of yeast: uncovering a phenotype for all genes

Yeast biological networks unfold the interplay of antioxidants, genome and phenotype, and reveal a novel regulator of the oxidative stress response

BACKGROUND

Identifying causative biological networks associated with relevant phenotypes is essential in the field of systems biology. We used ferulic acid (FA) as a model antioxidant to characterize the global expression programs triggered by this small molecule and decipher the transcriptional network controlling the phenotypic adaptation of the yeast Saccharomyces cerevisiae.

METHODOLOGY/PRINCIPAL FINDINGS

By employing a strict cut off value during gene expression data analysis, 106 genes were found to be involved in the cell response to FA, independent of aerobic or anaerobic conditions. Network analysis of the system guided us to a key target node, the FMP43 protein, that when deleted resulted in marked acceleration of cellular growth (∼15% in both minimal and rich media). To extend our findings to human cells and identify proteins that could serve as drug targets, we replaced the yeast FMP43 protein with its human ortholog BRP44 in the genetic background of the yeast strain Δfmp43. The conservation of the two proteins was phenotypically evident, with BRP44 restoring the normal specific growth rate of the wild type. We also applied homology modeling to predict the 3D structure of the FMP43 and BRP44 proteins. The binding sites in the homology models of FMP43 and BRP44 were computationally predicted, and further docking studies were performed using FA as the ligand. The docking studies demonstrated the affinity of FA towards both FMP43 and BRP44.

CONCLUSIONS

This study proposes a hypothesis on the mechanisms yeast employs to respond to antioxidant molecules, while demonstrating how phenome and metabolome yeast data can serve as biomarkers for nutraceutical discovery and development. Additionally, we provide evidence for a putative therapeutic target, revealed by replacing the FMP43 protein with its human ortholog BRP44, a brain protein, and functionally characterizing the relevant mutant strain.

Genome-wide identification of Saccharomyces cerevisiae genes required for tolerance to acetic acid

Background

Acetic acid is a byproduct of Saccharomyces cerevisiae alcoholic fermentation. Together with high concentrations of ethanol and other toxic metabolites, acetic acid may contribute to fermentation arrest and reduced ethanol productivity. This weak acid is also a present in lignocellulosic hydrolysates, a highly interesting non-feedstock substrate in industrial biotechnology. Therefore, the better understanding of the molecular mechanisms underlying S. cerevisiae tolerance to acetic acid is essential for the rational selection of optimal fermentation conditions and the engineering of more robust industrial strains to be used in processes in which yeast is explored as cell factory.

Results

The yeast genes conferring protection against acetic acid were identified in this study at a genome-wide scale, based on the screening of the EUROSCARF haploid mutant collection for susceptibility phenotypes to this weak acid (concentrations in the range 70-110 mM, at pH 4.5). Approximately 650 determinants of tolerance to acetic acid were identified. Clustering of these acetic acid-resistance genes based on their biological function indicated an enrichment of genes involved in transcription, internal pH homeostasis, carbohydrate metabolism, cell wall assembly, biogenesis of mitochondria, ribosome and vacuole, and in the sensing, signalling and uptake of various nutrients in particular iron, potassium, glucose and amino acids. A correlation between increased resistance to acetic acid and the level of potassium in the growth medium was found. The activation of the Snf1p signalling pathway, involved in yeast response to glucose starvation, is demonstrated to occur in response to acetic acid stress but no evidence was obtained supporting the acetic acid-induced inhibition of glucose uptake.

Conclusions

Approximately 490 of the 650 determinants of tolerance to acetic acid identified in this work are implicated, for the first time, in tolerance to this weak acid. These are novel candidate genes for genetic engineering to obtain more robust yeast strains against acetic acid toxicity. Among these genes there are number of transcription factors that are documented regulators of a large percentage of the genes found to exert protection against acetic acid thus being considered interesting targets for subsequent genetic engineering. The increase of potassium concentration in the growth medium was found to improve the expression of maximal tolerance to acetic acid, consistent with the idea that the adequate manipulation of nutrient concentration of industrial growth medium can be an interesting strategy to surpass the deleterious effects of this weak acid in yeast cells.

Modularity and directionality in genetic interaction maps

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

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

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

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

Supplementary information:Supplementary data are available at Bioinformatics online.

Phenomic and transcriptomic analyses reveal that autophagy plays a major role in desiccation tolerance in Saccharomyces cerevisiae

Saccharomyces cerevisiae can survive extreme desiccation, but the molecular mechanisms are poorly understood. To define genes involved in desiccation tolerance, two complementary genome-wide approaches, phenomics and transcriptomics, have been used, together with a targeted analysis of gene deletion mutants tested individually for their ability to survive drying. Genome-wide phenotypic analyses carried out on a pooled library of single-gene deletion mutants subjected to three cycles of desiccation and re-growth to post-diauxic phase identified about 650 genes that contributed to strain survival in the drying process. Air-drying desiccation-tolerant post-diauxic phase cells significantly altered transcription in 12% of the yeast genome, activating expression of over 450 genes and down-regulating 330. Autophagy processes were significantly over-represented in both the phenomics study and the genes up-regulated on drying, indicating the importance of the clearance of protein aggregates/damaged organelles and the recycling of nutrients for the survival of desiccation in yeast. Functional carbon source sensing networks governed by the PKA, Tor and Snf1 protein kinase complexes were important for the survival of desiccation, as indicated by phenomics, transcriptomics, and individual analyses of mutant strains. Changes in nitrogen metabolism were evident during the drying process and parts of the environmental stress response were activated, repressing ribosome production and inducing genes for coping with oxidative and osmotic stress.

A detailed genome-wide reconstruction of mouse metabolism based on human Recon 1

BACKGROUND

Well-curated and validated network reconstructions are extremely valuable tools in systems biology. Detailed metabolic reconstructions of mammals have recently emerged, including human reconstructions. They raise the question if the various successful applications of microbial reconstructions can be replicated in complex organisms.

RESULTS

We mapped the published, detailed reconstruction of human metabolism (Recon 1) to other mammals. By searching for genes homologous to Recon 1 genes within mammalian genomes, we were able to create draft metabolic reconstructions of five mammals, including the mouse. Each draft reconstruction was created in compartmentalized and non-compartmentalized version via two different approaches. Using gap-filling algorithms, we were able to produce all cellular components with three out of four versions of the mouse metabolic reconstruction. We finalized a functional model by iterative testing until it passed a predefined set of 260 validation tests. The reconstruction is the largest, most comprehensive mouse reconstruction to-date, accounting for 1,415 genes coding for 2,212 gene-associated reactions and 1,514 non-gene-associated reactions.We tested the mouse model for phenotype prediction capabilities. The majority of predicted essential genes were also essential in vivo. However, our non-tissue specific model was unable to predict gene essentiality for many of the metabolic genes shown to be essential in vivo. Our knockout simulation of the lipoprotein lipase gene correlated well with experimental results, suggesting that softer phenotypes can also be simulated.

CONCLUSIONS

We have created a high-quality mouse genome-scale metabolic reconstruction, iMM1415 (Mus Musculus, 1415 genes). We demonstrate that the mouse model can be used to perform phenotype simulations, similar to models of microbe metabolism. Since the mouse is an important experimental organism, this model should become an essential tool for studying metabolic phenotypes in mice, including outcomes from drug screening.

Onge RP, Deutschbauer A, Nislow C. Gene annotation and drug target discovery in Candida albicans with a tagged transposon mutant collection

Candida albicans is the most common human fungal pathogen, causing infections that can be lethal in immunocompromised patients. Although Saccharomyces cerevisiae has been used as a model for C. albicans, it lacks C. albicans' diverse morphogenic forms and is primarily non-pathogenic. Comprehensive genetic analyses that have been instrumental for determining gene function in S. cerevisiae are hampered in C. albicans, due in part to limited resources to systematically assay phenotypes of loss-of-function alleles. Here, we constructed and screened a library of 3633 tagged heterozygous transposon disruption mutants, using them in a competitive growth assay to examine nutrient- and drug-dependent haploinsufficiency. We identified 269 genes that were haploinsufficient in four growth conditions, the majority of which were condition-specific. These screens identified two new genes necessary for filamentous growth as well as ten genes that function in essential processes. We also screened 57 chemically diverse compounds that more potently inhibited growth of C. albicans versus S. cerevisiae. For four of these compounds, we examined the genetic basis of this differential inhibition. Notably, Sec7p was identified as the target of brefeldin A in C. albicans screens, while S. cerevisiae screens with this compound failed to identify this target. We also uncovered a new C. albicans-specific target, Tfp1p, for the synthetic compound 0136-0228. These results highlight the value of haploinsufficiency screens directly in this pathogen for gene annotation and drug target identification.

Candida albicans is a normal inhabitant in our bodies, yet it can become pathogenic and cause infections that range from the superficial in healthy individuals to deadly in the immunocompromised. Comprehensive genetic analysis of C. albicans to identify mechanisms of virulence and new treatment strategies has been hampered by limited, publically accessible genomic resources. By combining the principles of Saccharomyces cerevisiae strain tagging with transposon mutagenesis to generate individually tagged mutants, we created the first entirely public resource that allows simultaneous measurement of strain fitness of ∼60% of the genome in a wide range of experimental treatments. By identifying genes that confer a fitness or growth defect when reduced in copy number, we uncovered genes whose protein products represent potential antifungal targets. Moreover, screening this strain collection with chemical compounds allowed us to identify anticandidal chemicals while concurrently gaining insight into their cellular mechanism of action. This resource, combined with straightforward screening methodology, provides powerful tools to generate hypotheses for functional annotation of the genome, and our results highlight the value of direct versus model-based pathogen studies.

Recruitment of genes and enzymes conferring resistance to the nonnatural toxin bromoacetate

Microbial niches contain toxic chemicals capable of forcing organisms into periods of intense natural selection to afford survival. Elucidating the mechanisms by which microbes evade environmental threats has direct relevance for understanding and combating the rise of antibiotic resistance. In this study we used a toxic small-molecule, bromoacetate, to model the selective pressures imposed by antibiotics and anthropogenic toxins. We report the results of genetic selection experiments that identify nine genes from Escherichia coli whose overexpression affords survival in the presence of a normally lethal concentration of bromoacetate. Eight of these genes encode putative transporters or transmembrane proteins, while one encodes the essential peptidoglycan biosynthetic enzyme, UDP-N-acetylglucosamine enolpyruvoyl transferase (MurA). Biochemical studies demonstrate that the primary physiological target of bromoacetate is MurA, which becomes irreversibly inactivated via alkylation of a critical active-site cysteine. We also screened a comprehensive library of E. coli single-gene deletion mutants and identified 63 strains displaying increased susceptibility to bromoacetate. One hypersensitive bacterium lacks yliJ, a gene encoding a predicted glutathione transferase. Herein, YliJ is shown to catalyze the glutathione-dependent dehalogenation of bromoacetate with a k(cat)/K(m) value of 5.4 × 10(3) M(-1) s(-1). YliJ displays exceptional substrate specificity and produces a rate enhancement exceeding 5 orders of magnitude, remarkable characteristics for reactivity with a nonnatural molecule. This study illustrates the wealth of intrinsic survival mechanisms that can be exploited by bacteria when they are challenged with toxins.

Multicopy suppression screen in a Saccharomyces cerevisiae strain lacking the Rab GTPase-activating protein Msb3p

The yeast proteins, Msb3p and Msb4p, are two Ypt/Rab-specific GTPase-activating proteins sharing redundant functions in exocytosis, organization of the actin cytoskeleton, and budding site selection. To see if Msb3p might play an additional, specific role, we first tested the sensitivities of msb3 and msb4 mutant strains to different drugs and then screened a genomic library for multicopy suppressors of msb3 sensitivity to CdCl(2) or to the calcium channel blocker diltiazem hydrochloride. Three genes (ADH1, RNT1, and SUI1) were found to suppress the CdCl(2) sensitivity of the msb3 strain and three others (YAP6, ZEO1, and SLM1) its diltiazem-HCl sensitivity. The results suggest a possible involvement of Msb3p in calcineurin-mediated signalling.

A high-throughput screen for chemical inhibitors of exocytic transport in yeast

Most of the components of the membrane and protein traffic machinery were discovered by perturbing their functions, either with bioactive compounds or by mutations. However, the mechanisms responsible for exocytic transport vesicle formation at the Golgi and endosomes are still largely unknown. Both the exocytic traffic routes and the signaling pathways that regulate these routes are highly complex and robust, so that defects can be overcome by alternate pathways or mechanisms. A classical yeast genetic screen designed to account for the robustness of the exocytic pathway identified a novel conserved gene, AVL9, which functions in late exocytic transport. We now describe a chemical-genetic version of the mutant screen, in which we performed a high-throughput phenotypic screen of a large compound library and identified novel small-molecule secretory inhibitors. To maximize the number and diversity of our hits, the screen was performed in a pdr5Delta snq2Delta mutant background, which lacks two transporters responsible for pleiotropic drug resistance. However, we found that deletion of both transporters reduced the fitness of our screen strain, whereas the pdr5Delta mutation had a relatively small effect on growth and was also the more important transporter mutation for conferring sensitivity to our hits. In this and similar chemical-genetic yeast screens, using just a single pump mutation might be sufficient for increasing hit diversity while minimizing the physiological effects of transporter mutations.

A general lack of compensation for gene dosage in yeast

Gene copy number variation has been discovered in humans, between related species, and in different cancer tissues, but it is unclear how much of this genomic-level variation leads to changes in the level of protein abundance. To address this, we eliminated one of the two genomic copies of 730 different genes in Saccharomyces cerevisiae and asked how often a 50% reduction in gene dosage leads to a 50% reduction in protein level. For at least 80% of genes tested, and under several environmental conditions, it does: protein levels in the heterozygous strain are close to 50% of wild type. For <5% of the genes tested, the protein levels in the heterozygote are maintained at nearly wild-type levels. These experiments show that protein levels are not, in general, directly monitored and adjusted to a desired level. Combined with fitness data, this implies that proteins are expressed at levels higher than necessary for survival.

Automated identification of pathways from quantitative genetic interaction data

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

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

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

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

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

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

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

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

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

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

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

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

Identification of response-modulated genetic interactions by sensitivity-based epistatic analysis

Background

High-throughput genomics has enabled the global mapping of genetic interactions based on the phenotypic impact of combinatorial genetic perturbations. An important next step is to understand how these networks are dynamically remodelled in response to environmental stimuli. Here, we report on the development and testing of a method to identify such interactions. The method was developed from first principles by treating the impact on cellular growth of environmental perturbations equivalently to that of gene deletions. This allowed us to establish a novel neutrality function marking the absence of epistasis in terms of sensitivity phenotypes rather than fitness. We tested the method by identifying fitness- and sensitivity-based interactions involved in the response to drug-induced DNA-damage of budding yeast Saccharomyces cerevisiae using two mutant libraries - one containing transcription factor deletions, and the other containing deletions of DNA repair genes.

Results

Within the library of transcription factor deletion mutants, we observe significant differences in the sets of genetic interactions identified by the fitness- and sensitivity-based approaches. Notably, among the most likely interactions, only ~50% were identified by both methods. While interactions identified solely by the sensitivity-based approach are modulated in response to drug-induced DNA damage, those identified solely by the fitness-based method remained invariant to the treatment. Comparison of the identified interactions to transcriptional profiles and protein-DNA interaction data indicate that the sensitivity-based method improves the identification of interactions involved in the DNA damage response. Additionally, for the library containing DNA repair mutants, we observe that the sensitivity-based method improves the grouping of functionally related genes, as well as the identification of protein complexes, involved in DNA repair.

Conclusion

Our results show that the identification of response-modulated genetic interactions can be improved by incorporating the effect of a changing environment directly into the neutrality function marking the absence of epistasis. We expect that this extension of conventional epistatic analysis will facilitate the development of dynamic models of gene networks from quantitative measurements of genetic interactions. While the method was developed for growth phenotype, it should apply equally well for other phenotypes, including the expression of fluorescent reporters.

Evolution under fluctuating environments explains observed robustness in metabolic networks

A high level of robustness against gene deletion is observed in many organisms. However, it is still not clear which biochemical features underline this robustness and how these are acquired during evolution. One hypothesis, specific to metabolic networks, is that robustness emerges as a byproduct of selection for biomass production in different environments. To test this hypothesis we performed evolutionary simulations of metabolic networks under stable and fluctuating environments. We find that networks evolved under the latter scenario can better tolerate single gene deletion in specific environments. Such robustness is underlined by an increased number of independent fluxes and multifunctional enzymes in the evolved networks. Observed robustness in networks evolved under fluctuating environments was “apparent,” in the sense that it decreased significantly as we tested effects of gene deletions under all environments experienced during evolution. Furthermore, when we continued evolution of these networks under a stable environment, we found that any robustness they had acquired was completely lost. These findings provide evidence that evolution under fluctuating environments can account for the observed robustness in metabolic networks. Further, they suggest that organisms living under stable environments should display lower robustness in their metabolic networks, and that robustness should decrease upon switching to more stable environments.

One of the most surprising recent biological findings is the high level of tolerance organisms show towards loss of single genes. This observation suggests that there are certain features of biological systems that give them a high tolerance (i.e. robustness) towards gene loss. We still lack an exact understanding of what these features might be and how they could have been acquired during evolution. Here, we offer a possible answer for these questions in the context of metabolic networks. Using mathematical models capturing the structure and dynamics of metabolic networks, we simulate their evolution under stable and fluctuating environments (i.e., available metabolites). We find that the latter scenario leads to evolution of metabolic networks that display high robustness against gene loss. This robustness of in silico evolved networks is underlined by an increased number of multifunctional enzymes and independent paths leading from initial metabolites to biomass. These findings provide evidence that fluctuating environments can be a major evolutionary force leading to the emergence of robustness as a side effect. A direct prediction resulting from this study is that organisms living in stable and fluctuating environments should display differing levels of robustness against gene loss.

Global genotype-phenotype correlations in Pseudomonas aeruginosa

Once the genome sequence of an organism is obtained, attention turns from identifying genes to understanding their function, their organization and control of metabolic pathways and networks that determine its physiology. Recent technical advances in acquiring genome-wide data have led to substantial progress in identifying gene functions. However, we still do not know the function of a large number of genes and, even when a gene product has been assigned to a functional class, we cannot normally predict its contribution to the phenotypic behaviour of the cell or organism--the phenome. In this study, we assessed bacterial growth parameters of 4030 non-redundant PA14 transposon mutants in the pathogenic bacterium Pseudomonas aeruginosa. The genome-wide simultaneous analysis of 119 distinct growth-related phenotypes uncovered a comprehensive phenome and provided evidence that most genotypes are not phenotypically isolated but rather define specific complex phenotypic clusters of genotypes. Since phenotypic overlap was demonstrated to reflect the relatedness of genotypes on a global scale, knowledge of an organism's phenome might significantly contribute to the advancement of functional genomics.

Combined zebrafish-yeast chemical-genetic screens reveal gene-copper-nutrition interactions that modulate melanocyte pigmentation

Hypopigmentation is a feature of copper deficiency in humans, as caused by mutation of the copper (Cu(2+)) transporter ATP7A in Menkes disease, or an inability to absorb copper after gastric surgery. However, many causes of copper deficiency are unknown, and genetic polymorphisms might underlie sensitivity to suboptimal environmental copper conditions. Here, we combined phenotypic screens in zebrafish for compounds that affect copper metabolism with yeast chemical-genetic profiles to identify pathways that are sensitive to copper depletion. Yeast chemical-genetic interactions revealed that defects in intracellular trafficking pathways cause sensitivity to low-copper conditions; partial knockdown of the analogous Ap3s1 and Ap1s1 trafficking components in zebrafish sensitized developing melanocytes to hypopigmentation in low-copper environmental conditions. Because trafficking pathways are essential for copper loading into cuproproteins, our results suggest that hypomorphic alleles of trafficking components might underlie sensitivity to reduced-copper nutrient conditions. In addition, we used zebrafish-yeast screening to identify a novel target pathway in copper metabolism for the small-molecule MEK kinase inhibitor U0126. The zebrafish-yeast screening method combines the power of zebrafish as a disease model with facile genome-scale identification of chemical-genetic interactions in yeast to enable the discovery and dissection of complex multigenic interactions in disease-gene networks.

Comparative analysis of transcriptome and fitness profiles reveals general and condition-specific cellular functions involved in adaptation to environmental change in Saccharomyces cerevisiae

The transcriptional responses of yeast cells to a wide variety of stress conditions have been studied extensively. In addition, deletion mutant collections have been widely used to measure the combined effect of gene loss and stress on growth (fitness). Here we present a comparative analysis of 1,095 publicly available transcription and genome-wide fitness profiles in yeast, from different laboratories and experimental platforms. We analyzed these data, using T-profiler, to describe the correlation in behavior of a priori defined functional groups. Two-mode clustering analysis of the fitness T-profiles revealed that functional groups involved in regulating ribosome biogenesis and translation offer general stress resistance. These groups are closely related to growth rate and nutrient availability. General stress sensitivity was found in deletion mutant groups functioning in intracellular vesicular transport, actin cytoskeleton organization, and cell polarity, indicating that they play an key role in maintaining yeast adaptability. Analysis of the phenotypic and transcriptional variability of our a priori defined functional groups showed that the quantitative effect on fitness of both resistant and sensitive groups is highly condition-dependent. Finally, we discuss the implications of our results for combinatorial drug design.

Functional genomics of drug-induced ion homeostasis identifies a novel regulatory crosstalk of iron and zinc regulons in yeast

Pyrrolidine dithiocarbamate (PDTC), a known inhibitor of NFκB activation, has antioxidative as well as antiviral activities. PDTC is effective against several virus families, indicating that its antiviral mechanism targets host rather than viral functions. To investigate its mode of action, we used baker's yeast as a simple eukaryotic model system and two types of genome-wide analysis. First, expression profiling using whole-genome DNA microarrays identifies more than 200 genes differentially regulated upon PDTC exposure. Interestingly, the Aft1-dependent iron regulon is a main target of PDTC, indicating a lack of iron availability. Moreover, the PDTC-caused zinc influx triggers a strong regulatory effect on zinc transporters due to the cytoplasmic zinc excess. Second, phenotypic screening the EUROSCARF collection for PDTC hypersensitivity identifies numerous mutants implicated in vacuolar maintenance, acidification as well as in transport, mitochondrial organization, and translation. Notably, the screening data indicate significant overlaps of PDTC-sensitive genes and those mediating zinc tolerance. Hence, we show that PDTC induces cytoplasmic zinc excess, eliciting vacuolar detoxification, which in turn, disturbs iron homeostasis and activates the iron-dependent regulator Aft1. Our work reveals a complex crosstalk in yeast ion homeostasis and the underlying regulatory networks.

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.

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).

The function of communities in protein interaction networks at multiple scales

BACKGROUND

If biology is modular then clusters, or communities, of proteins derived using only protein interaction network structure should define protein modules with similar biological roles. We investigate the link between biological modules and network communities in yeast and its relationship to the scale at which we probe the network.

RESULTS

Our results demonstrate that the functional homogeneity of communities depends on the scale selected, and that almost all proteins lie in a functionally homogeneous community at some scale. We judge functional homogeneity using a novel test and three independent characterizations of protein function, and find a high degree of overlap between these measures. We show that a high mean clustering coefficient of a community can be used to identify those that are functionally homogeneous. By tracing the community membership of a protein through multiple scales we demonstrate how our approach could be useful to biologists focusing on a particular protein.

CONCLUSIONS

We show that there is no one scale of interest in the community structure of the yeast protein interaction network, but we can identify the range of resolution parameters that yield the most functionally coherent communities, and predict which communities are most likely to be functionally homogeneous.

Genome-wide screen in Saccharomyces cerevisiae identifies vacuolar protein sorting, autophagy, biosynthetic, and tRNA methylation genes involved in life span regulation

The study of the chronological life span of Saccharomyces cerevisiae, which measures the survival of populations of non-dividing yeast, has resulted in the identification of homologous genes and pathways that promote aging in organisms ranging from yeast to mammals. Using a competitive genome-wide approach, we performed a screen of a complete set of approximately 4,800 viable deletion mutants to identify genes that either increase or decrease chronological life span. Half of the putative short-/long-lived mutants retested from the primary screen were confirmed, demonstrating the utility of our approach. Deletion of genes involved in vacuolar protein sorting, autophagy, and mitochondrial function shortened life span, confirming that respiration and degradation processes are essential for long-term survival. Among the genes whose deletion significantly extended life span are ACB1, CKA2, and TRM9, implicated in fatty acid transport and biosynthesis, cell signaling, and tRNA methylation, respectively. Deletion of these genes conferred heat-shock resistance, supporting the link between life span extension and cellular protection observed in several model organisms. The high degree of conservation of these novel yeast longevity determinants in other species raises the possibility that their role in senescence might be conserved.

Insights into the mechanisms of toxicity and tolerance to the agricultural fungicide mancozeb in yeast, as suggested by a chemogenomic approach

Abstract Saccharomyces cerevisiae was used to uncover the mechanisms underlying tolerance and toxicity of the agricultural fungicide mancozeb, linked to cancer and Parkinson's disease development. Chemogenomics screening of a yeast deletion mutant collection revealed 286 genes that provide protection against mancozeb toxicity. The most significant Gene Ontology (GO) terms enriched in this dataset are associated to transcriptional machinery, vacuolar organization and biogenesis, intracellular trafficking, and cellular pH regulation. Clustering based on physical and genetic interactions further highlighted the role of oxidative stress response, protein degradation and carbohydrate/energy metabolism in mancozeb stress tolerance. Mancozeb was found to act in yeast as a thiol-reactive compound, but not as a free radical or reative oxygen species (ROS) inducer, leading to massive oxidation of protein cysteins, consistent with the requirement of genes involved in glutathione biosynthesis and reduction and in protein degradation to provide mancozeb resistance. The identification of Botrytis cinerea homologues of yeast mancozeb tolerance determinants is expected to guide studies on mancozeb mechanisms of action and tolerance in phytopathogenic fungi. The generated networks of protein-protein associations of yeast mancozeb tolerance determinants and their human orthologues share a high degree of similarity. This toxicogenomics analysis may, thus, increase the understanding of mancozeb toxicity and adaptation mechanisms in humans.

Identification of genes required for maximal tolerance to high-glucose concentrations, as those present in industrial alcoholic fermentation media, through a chemogenomics approach

Chemogenomics, the study of genomic responses to chemical compounds, has the potential to elucidate the basis of cellular resistance to those chemicals. This knowledge can be applied to improve the performance of strains of industrial interest. In this study, a collection of approximately 5,000 haploid single deletion mutants of Saccharomyces cerevisiae in which each nonessential yeast gene was individually deleted, was screened for strains with increased susceptibility toward stress induced by high-glucose concentration (30% w/v), one of the main stresses occurring during industrial alcoholic fermentation processes aiming the production of alcoholic beverages or bio-ethanol. Forty-four determinants of resistance to high-glucose stress were identified. The most significant Gene Ontology (GO) terms enriched in this dataset are vacuolar organization, late endosome to vacuole transport, and regulation of transcription. Clustering the identified resistance determinants by their known physical and genetic interactions further highlighted the importance of nutrient metabolism control in this context. A concentration of 30% (w/v) of glucose was found to perturb vacuolar function, by reducing cell ability to maintain the physiological acidification of the vacuolar lumen. This stress also affects the active rate of proton efflux through the plasma membrane. Based on results of published studies, the present work revealed shared determinants of yeast resistance to high-glucose and ethanol stresses, including genes involved in vacuolar function, cell wall biogenesis (ANP1), and in the transcriptional control of nutrient metabolism (GCN4 and GCR1), with possible impact on the design of more robust strains to be used in industrial alcoholic fermentation processes.

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.

S. pombe genome deletion project: an update

The fission yeast Schizosaccharomyces pombe is a model organism used widely to study various aspects of eukaryotic biology. A collection of heterozygous diploid strains containing individual deletions in nearly all S. pombe genes has been created using a PCR based strategy. However, deletion of some genes has not been possible using this methodology. Here we use an efficient knockout strategy based on plasmids that contain large regions homologous to the target gene to delete an additional 29 genes. The collection of deletion mutants now covers 99% of the fission yeast open reading frames.

Global fitness profiling of fission yeast deletion strains by barcode sequencing

A genome-wide deletion library is a powerful tool for probing gene functions and one has recently become available for the fission yeast Schizosaccharomyces pombe. Here we use deep sequencing to accurately characterize the barcode sequences in the deletion library, thus enabling the quantitative measurement of the fitness of fission yeast deletion strains by barcode sequencing.

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

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

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 universal TagModule collection for parallel genetic analysis of microorganisms

Systems-level analyses of non-model microorganisms are limited by the existence of numerous uncharacterized genes and a corresponding over-reliance on automated computational annotations. One solution to this challenge is to disrupt gene function using DNA tag technology, which has been highly successful in parallelizing reverse genetics in Saccharomyces cerevisiae and has led to discoveries in gene function, genetic interactions and drug mechanism of action. To extend the yeast DNA tag methodology to a wide variety of microorganisms and applications, we have created a universal, sequence-verified TagModule collection. A hallmark of the 4280 TagModules is that they are cloned into a Gateway entry vector, thus facilitating rapid transfer to any compatible genetic system. Here, we describe the application of the TagModules to rapidly generate tagged mutants by transposon mutagenesis in the metal-reducing bacterium Shewanella oneidensis MR-1 and the pathogenic yeast Candida albicans. Our results demonstrate the optimal hybridization properties of the TagModule collection, the flexibility in applying the strategy to diverse microorganisms and the biological insights that can be gained from fitness profiling tagged mutant collections. The publicly available TagModule collection is a platform-independent resource for the functional genomics of a wide range of microbial systems in the post-genome era.

Most, but not all, yeast strains in the deletion library contain the [PIN(+)] prion

The yeast deletion library is a collection of over 5100 single gene deletions that has been widely used by the yeast community. The presence of a non-Mendelian element, such as a prion, within this library could affect the outcome of many large-scale genomic studies. We previously showed that the deletion library parent strain contained the [PIN(+)] prion. [PIN(+)] is the misfolded infectious prion form of the Rnq1 protein that displays distinct fluorescent foci in the presence of RNQ1-GFP and exists in different physical conformations, called variants. Here, we show that over 97% of the library deletion strains are [PIN(+)]. Of the 141 remaining strains that have completely (58) or partially (83) lost [PIN(+)], 139 deletions were able to efficiently maintain three different [PIN(+)] variants despite extensive growth and storage at 4 degrees C. One strain, cue2Delta, displayed an alteration in the RNQ1-GFP fluorescent shape, but the Rnq1p prion aggregate shows no biochemical differences from the wild-type. Only strains containing a deletion of either HSP104 or RNQ1 are unable to maintain [PIN(+)], indicating that 5153 non-essential genes are not required for [PIN(+)] propagation.

Highly-multiplexed barcode sequencing: an efficient method for parallel analysis of pooled samples

Next-generation sequencing has proven an extremely effective technology for molecular counting applications where the number of sequence reads provides a digital readout for RNA-seq, ChIP-seq, Tn-seq and other applications. The extremely large number of sequence reads that can be obtained per run permits the analysis of increasingly complex samples. For lower complexity samples, however, a point of diminishing returns is reached when the number of counts per sequence results in oversampling with no increase in data quality. A solution to making next-generation sequencing as efficient and affordable as possible involves assaying multiple samples in a single run. Here, we report the successful 96-plexing of complex pools of DNA barcoded yeast mutants and show that such 'Bar-seq' assessment of these samples is comparable with data provided by barcode microarrays, the current benchmark for this application. The cost reduction and increased throughput permitted by highly multiplexed sequencing will greatly expand the scope of chemogenomics assays and, equally importantly, the approach is suitable for other sequence counting applications that could benefit from massive parallelization.

Improved network performance via antagonism: From synthetic rescues to multi-drug combinations

Recent research shows that a faulty or sub-optimally operating metabolic network can often be rescued by the targeted removal of enzyme-coding genes – the exact opposite of what traditional gene therapy would suggest. Predictions go as far as to assert that certain gene knockouts can restore the growth of otherwise nonviable gene-deficient cells. Many questions follow from this discovery: What are the underlying mechanisms? How generalizable is this effect? What are the potential applications? Here, I approach these questions from the perspective of compensatory perturbations on networks. Relations are drawn between such synthetic rescues and naturally occurring cascades of reaction inactivation, as well as their analogs in physical and other biological networks. I specially discuss how rescue interactions can lead to the rational design of antagonistic drug combinations that select against resistance and how they can illuminate medical research on cancer, antibiotics, and metabolic diseases.

The next frontier of systems biology: higher-order and interspecies interactions

Systems biology is set to go beyond single species to the study of interspecies interactions.

Systems approaches are not so different in essence from classical genetic and biochemical approaches, and in the future may become adopted so widely that the term 'systems biology' itself will become obsolete.

Modelling neurodegeneration in Saccharomyces cerevisiae: why cook with baker's yeast?

In ageing populations, neurodegenerative diseases increase in prevalence, exacting an enormous toll on individuals and their communities. Multiple complementary experimental approaches are needed to elucidate the mechanisms underlying these complex diseases and to develop novel therapeutics. Here, we describe why the budding yeast Saccharomyces cerevisiae has a unique role in the neurodegeneration armamentarium. As the best-understood and most readily analysed eukaryotic organism, S. cerevisiae is delivering mechanistic insights into cell-autonomous mechanisms of neurodegeneration at an interactome-wide scale.

Yet1p and Yet3p, the yeast homologs of BAP29 and BAP31, interact with the endoplasmic reticulum translocation apparatus and are required for inositol prototrophy

The mammalian B-cell receptor-associated proteins of 29 and 31 kDa (BAP29 and BAP31) are conserved integral membrane proteins that have reported roles in endoplasmic reticulum (ER) quality control, ER export of secretory cargo, and programmed cell death. In this study we investigated the yeast homologs of BAP29 and BAP31, known as Yet1p and Yet3p, to gain insight on cellular function. We found that Yet1p forms a complex with Yet3p (Yet complex) and that complex assembly was important for subunit stability and proper ER localization. The Yet complex was not efficiently packaged into ER-derived COPII vesicles and therefore does not appear to act as an ER export receptor. Instead, a fraction of the Yet complex was detected in association with the ER translocation apparatus (Sec complex). Specific mutations in the Sec complex or Yet complex influenced these interactions. Moreover, associations between the Yet complex and Sec complex were increased by ER stress and diminished when protein translocation substrates were depleted. Surprisingly, yet1Delta and yet3Delta mutant strains displayed inositol starvation-related growth defects. In accord with the biochemical data, these growth defects were exacerbated by a combination of certain mutations in the Sec complex with yet1Delta or yet3Delta mutations. We propose a model for the Yet-Sec complex interaction that places Yet1p and Yet3p at the translocation pore to manage biogenesis of specific transmembrane secretory proteins.

Systematic discovery of nonobvious human disease models through orthologous phenotypes

Biologists have long used model organisms to study human diseases, particularly when the model bears a close resemblance to the disease. We present a method that quantitatively and systematically identifies nonobvious equivalences between mutant phenotypes in different species, based on overlapping sets of orthologous genes from human, mouse, yeast, worm, and plant (212,542 gene-phenotype associations). These orthologous phenotypes, or phenologs, predict unique genes associated with diseases. Our method suggests a yeast model for angiogenesis defects, a worm model for breast cancer, mouse models of autism, and a plant model for the neural crest defects associated with Waardenburg syndrome, among others. Using these models, we show that SOX13 regulates angiogenesis, and that SEC23IP is a likely Waardenburg gene. Phenologs reveal functionally coherent, evolutionarily conserved gene networks-many predating the plant-animal divergence-capable of identifying candidate disease genes.

Homoserine toxicity in Saccharomyces cerevisiae and Candida albicans homoserine kinase (thr1Delta) mutants

In addition to threonine auxotrophy, mutation of the Saccharomyces cerevisiae threonine biosynthetic genes THR1 (encoding homoserine kinase) and THR4 (encoding threonine synthase) results in a plethora of other phenotypes. We investigated the basis for these other phenotypes and found that they are dependent on the toxic biosynthetic intermediate homoserine. Moreover, homoserine is also toxic for Candida albicans thr1Delta mutants. Since increasing levels of threonine, but not other amino acids, overcome the homoserine toxicity of thr1Delta mutants, homoserine may act as a toxic threonine analog. Homoserine-mediated lethality of thr1Delta mutants is blocked by cycloheximide, consistent with a role for protein synthesis in this lethality. We identified various proteasome and ubiquitin pathway components that either when mutated or present in high copy numbers suppressed the thr1Delta mutant homoserine toxicity. Since the doa4Delta and proteasome mutants identified have reduced ubiquitin- and/or proteasome-mediated proteolysis, the degradation of a particular protein or subset of proteins likely contributes to homoserine toxicity.

Biophysics and systems biology

Biophysics at the systems level, as distinct from molecular biophysics, acquired its most famous paradigm in the work of Hodgkin and Huxley, who integrated their equations for the nerve impulse in 1952. Their approach has since been extended to other organs of the body, notably including the heart. The modern field of computational biology has expanded rapidly during the first decade of the twenty-first century and, through its contribution to what is now called systems biology, it is set to revise many of the fundamental principles of biology, including the relations between genotypes and phenotypes. Evolutionary theory, in particular, will require re-assessment. To succeed in this, computational and systems biology will need to develop the theoretical framework required to deal with multilevel interactions. While computational power is necessary, and is forthcoming, it is not sufficient. We will also require mathematical insight, perhaps of a nature we have not yet identified. This article is therefore also a challenge to mathematicians to develop such insights.

Systematic analysis of genome-wide fitness data in yeast reveals novel gene function and drug action

The relationship between co-fitness and co-inhibition of genes in chemicogenomic yeast screens provides insights into gene function and drug target prediction.

We systematically analyzed the relationships between gene fitness profiles (co-fitness) and drug inhibition profiles (co-inhibition) from several hundred chemogenomic screens in yeast. Co-fitness predicted gene functions distinct from those derived from other assays and identified conditionally dependent protein complexes. Co-inhibitory compounds were weakly correlated by structure and therapeutic class. We developed an algorithm predicting protein targets of chemical compounds and verified its accuracy with experimental testing. Fitness data provide a novel, systems-level perspective on the cell.

Synthetic genetic array (SGA) analysis in Saccharomyces cerevisiae and Schizosaccharomyces pombe

A genetic interaction occurs when the combination of two mutations leads to an unexpected phenotype. Screens for synthetic genetic interactions have been used extensively to identify genes whose products are functionally related. In particular, synthetic lethal genetic interactions often identify genes that buffer one another or impinge on the same essential pathway. For the yeast Saccharomyces cerevisiae, we developed a method termed synthetic genetic array (SGA) analysis, which offers an efficient approach for the systematic construction of double mutants and enables a global analysis of synthetic genetic interactions. In a typical SGA screen, a query mutation is crossed to an ordered array of ~5000 viable gene deletion mutants (representing ~80% of all yeast genes) such that meiotic progeny harboring both mutations can be scored for fitness defects. This approach can be extended to all ~6000 genes through the use of yeast arrays containing mutants carrying conditional or hypomorphic alleles of essential genes. Estimating the fitness for the two single mutants and their corresponding double mutant enables a quantitative measurement of genetic interactions, distinguishing negative (synthetic lethal) and positive (within pathway and suppression) interactions. The profile of genetic interactions represents a rich phenotypic signature for each gene and clustering genetic interaction profiles group genes into functionally relevant pathways and complexes. This array-based approach automates yeast genetic analysis in general and can be easily adapted for a number of different genetic screens or combined with high-content screening systems to quantify the activity of specific reporters in genome-wide sets of single or more complex multiple mutant backgrounds. Comparison of genetic and chemical-genetic interaction profiles offers the potential to link bioactive compounds to their targets. Finally, we also developed an SGA system for the fission yeast Schizosaccharomyces pombe, providing another model system for comparative analysis of genetic networks and testing the conservation of genetic networks over millions of years of evolution.

Biochemical, cell biological, and genetic assays to analyze amyloid and prion aggregation in yeast

Protein aggregates are associated with a variety of debilitating human diseases, but they can have functional roles as well. Both pathological and nonpathological protein aggregates display tremendous diversity, with substantial differences in aggregate size, morphology, and structure. Among the different aggregation types, amyloids are particularly remarkable, because of their high degree of order and their ability to form self-perpetuating conformational states. Amyloids form the structural basis for a group of proteins called prions, which have the ability to generate new phenotypes by a simple switch in protein conformation that does not involve changes in the sequence of the DNA. Although protein aggregates are notoriously difficult to study, recent technological developments and, in particular, the use of yeast prions as model systems, have been very instrumental in understanding fundamental aspects of aggregation. Here, we provide a range of biochemical, cell biological and yeast genetic methods that are currently used in our laboratory to study protein aggregation and the formation of amyloids and prions.

Chemotherapeutic drugs inhibit ribosome biogenesis at various levels

Drugs for cancer therapy belong to different categories of chemical substances. The cellular targets for the therapeutic efficacy are often not unambiguously identified. Here, we describe the process of ribosome biogenesis as a target of a large variety of chemotherapeutic drugs. We determined the inhibitory concentration of 36 chemotherapeutic drugs for transcription and processing of ribosomal RNA by in vivo labeling experiments. Inhibitory drug concentrations were correlated to the loss of nucleolar integrity. The synergism of drugs inhibiting ribosomal RNA synthesis at different levels was studied. Drugs inhibited ribosomal RNA synthesis either at the level of (i) rRNA transcription (e.g. oxaliplatin, doxorubicin, mitoxantrone, methotrexate), (ii) early rRNA processing (e.g. camptothecin, flavopiridol, roscovitine), or (iii) late rRNA processing (e.g. 5-fluorouracil, MG-132, homoharringtonine). Blockage of rRNA transcription or early rRNA processing steps caused nucleolar disintegration, whereas blockage of late rRNA processing steps left the nucleolus intact. Flavopiridol and 5-fluorouracil showed a strong synergism for inhibition of rRNA processing. We conclude that inhibition of ribosome biogenesis by chemotherapeutic drugs potentially may contribute to the efficacy of therapeutic regimens.

Experimental genomics of fitness in yeast

The set of single-gene deletions in yeast can be used to evaluate the effect of mutation on fitness over the whole genome. The measurement of growth in pure culture or relative growth in mixtures has confirmed that most deletions have little effect in laboratory culture. Moreover, there is a sharp distinction between lethality and a very mild impairment of growth, with very few intermediate cases. Different components of fitness, such as growth rate and yield, are positively correlated. Growth is also positively correlated across environments, although new conditions of growth usually identify a few conditionally impaired strains. Double mutants on average show alleviating epistasis, although a few per cent of combinations are synthetic lethal. The properties of the yeast deletion set provide us with the first genome-wide account of fitness, although transferring these conclusions to the field is a task for the future.

Genes confer similar robustness to environmental, stochastic, and genetic perturbations in yeast

Gene inactivation often has little or no apparent consequence for the phenotype of an organism. This property-enetic (or mutational) robustness-is pervasive, and has important implications for disease and evolution, but is not well understood. Dating back to at least Waddington, it has been suggested that mutational robustness may be related to the requirement to withstand environmental or stochastic perturbations. Here I show that global quantitative data from yeast are largely consistent with this idea. Considering the effects of mutations in all nonessential genes shows that genes that confer robustness to environmental or stochastic change also buffer the effects of genetic change, and with similar efficacy. This means that selection during evolution for environmental or stochastic robustness (also referred to as canalization) may frequently have the side effect of increasing genetic robustness. A dynamic environment may therefore promote the evolution of phenotypic complexity. It also means that "hub" genes in genetic interaction (synthetic lethal) networks are generally genes that confer environmental resilience and phenotypic stability.

Crystal structure of Get4-Get5 complex and its interactions with Sgt2, Get3, and Ydj1

Get3, Get4, and Get5 in Saccharomyces cerevisiae participate in the insertion of tail-anchored proteins into the endoplasmic reticulum membrane. We elucidated the interaction between Get4 and Get5 and investigated their interaction with Get3 and a tetratricopeptide repeat-containing protein, Sgt2. Based on co-immunoprecipitation and crystallographic studies, Get4 and Get5 formed a tight complex, suggesting that they constitute subunits of a larger complex. In contrast, although Get3 interacted physically with the Get4-Get5 complex, low amounts of Get3 co-precipitated with Get5, implying a transient interaction between Get3 and Get4-Get5. Sgt2 also interacted with Get5, although the amount of Sgt2 that co-precipitated with Get5 varied. Moreover, GET3, GET4, and GET5 interacted genetically with molecular chaperone YDJ1, suggesting that chaperones might also be involved in the insertion of tail-anchored proteins.

An integrated view of the correlations between genomic and phenomic variables

Genome sequencing opened the flood gate of "-omics" studies, among which the research about correlations between genomic and phenomic variables is an important part. With the development of functional genomics and systems biology, genome-wide investigation of the correlations between many genomic and phenomic variables became possible. In this review, five genomic variables, such as evolution rate (or "age" of the gene), the length of intron and ORF (protein length) in one gene, the biases of amino acid composition and codon usage, along with the phenomic variables related to expression patterns (level and breadth) are focused on. In most cases, genes with higher mRNA/protein expression level tend to evolve slowly, have less intronic DNA, code for smaller proteins, and have higher biases of amino acid composition and codon usage. In addition, broadly expressed proteins evolve more slowly and are shorter than tissue-specific proteins. Studies in this field are helpful for deeper understanding the signatures of selection mediated by the features of gene expression and are of great significance to enrich the evolution theory.