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

A conserved coatomer-related complex containing Sec13 and Seh1 dynamically associates with the vacuole in Saccharomyces cerevisiae

The presence of multiple membrane-bound intracellular compartments is a major feature of eukaryotic cells. Many of the proteins required for formation and maintenance of these compartments share an evolutionary history. Here, we identify the SEA (Seh1-associated) protein complex in yeast that contains the nucleoporin Seh1 and Sec13, the latter subunit of both the nuclear pore complex and the COPII coating complex. The SEA complex also contains Npr2 and Npr3 proteins (upstream regulators of TORC1 kinase) and four previously uncharacterized proteins (Sea1-Sea4). Combined computational and biochemical approaches indicate that the SEA complex proteins possess structural characteristics similar to the membrane coating complexes COPI, COPII, the nuclear pore complex, and, in particular, the related Vps class C vesicle tethering complexes HOPS and CORVET. The SEA complex dynamically associates with the vacuole in vivo. Genetic assays indicate a role for the SEA complex in intracellular trafficking, amino acid biogenesis, and response to nitrogen starvation. These data demonstrate that the SEA complex is an additional member of a family of membrane coating and vesicle tethering assemblies, extending the repertoire of protocoatomer-related complexes.

The CRIT framework for identifying cross patterns in systems biology and application to chemogenomics

Biological data is often tabular but finding statistically valid connections between entities in a sequence of tables can be problematic - for example, connecting particular entities in a drug property table to gene properties in a second table, using a third table associating genes with drugs. Here we present an approach (CRIT) to find connections such as these and show how it can be applied in a variety of genomic contexts including chemogenomics data.

Cross-species chemogenomic profiling reveals evolutionarily conserved drug mode of action

Chemogenomic screens were performed in both budding and fission yeasts, allowing for a cross-species comparison of drug–gene interaction networks.

Drug–module interactions were more conserved than individual drug–gene interactions.

Combination of data from both species can improve drug–module predictions and helps identify a compound's mode of action.

Understanding the molecular effects of chemical compounds in living cells is an important step toward rational therapeutics. Drug discovery aims to find compounds that will target a specific pathway or pathogen with minimal side effects. However, even when an effective drug is found, its mode of action (MoA) is typically not well understood. The lack of knowledge regarding a drug's MoA makes the drug discovery process slow and rational therapeutics incredibly difficult. More recently, different high-throughput methods have been developed that attempt to discern how a compound exerts its effects in cells. One of these methods relies on measuring the growth of cells carrying different mutations in the presence of the compounds of interest, commonly referred to as chemogenomics (Wuster and Babu, 2008). The differential growth of the different mutants provides clues as to what the compounds target in the cell (Figure 2). For example, if a drug inhibits a branch in a vital two-branch pathway, then mutations in the second branch might result in cell death if the mutants are grown in the presence of the drug (Figure 2C). As these compound–mutant functional interactions are expected to be relatively rare, one can assume that the growth rate of a mutant–drug combination should generally be equal to the product of the growth rate of the untreated mutant with the growth rate of the drug-treated wild type. This expectation is defined as the neutral model and deviations from this provide a quantitative score that allow us to make informed predictions regarding a drug's MoA (Figure 2B;Parsons et al, 2006).

The availability of these high-throughput approaches now allows us to perform cross-species studies of functional interactions between compounds and genes. In this study, we have performed a quantitative analysis of compound–gene interactions for two fungal species (budding yeast (S. cerevisiae) and fission yeast (S. pombe)) that diverged from each other approximately 500–700 million years ago. A collection of 2957 compounds from the National Cancer Institute (NCI) were screened in both species for inhibition of wild-type cell growth. A total of 132 were found to be bioactive in both fungi and 9, along with 12 additional well-characterized drugs, were selected for subsequent screening. Mutant libraries of 727 and 438 gene deletions were used for S. cerevisiae and S. pombe, respectively, and these were selected based on availability of genetic interaction data from previous studies (Collins et al, 2007; Roguev et al, 2008; Fiedler et al, 2009) and contain an overlap of 190 one-to-one orthologs that can be directly compared. Deviations from the neutral expectation were quantified as drug–gene interactions scores (D-scores) for the 21 compounds against the deletion libraries. Replicates of both screens showed very high correlations (S. cerevisiae r=0.72, S. pombe r=0.76) and reproduced well previously known compound–gene interactions (Supplementary information). We then compared the D-scores for the 190 one-to-one orthologs present in the data set of both species. Despite the high reproducibility, we observed a very poor conservation of these compound–gene interaction scores across these species (r=0.13, Figure 4A).

Previous work had shown that, across these same species, genetic interactions within protein complexes were much more conserved than average genetic interactions (Roguev et al, 2008). Similarly we observed a higher cross-species conservation of the compound–module (complex or pathway) interactions than the overall compound–gene interactions. Specifically, the data derived from fission yeast were a poor predictor of S. cerevisaie drug–gene interactions, but a good predictor of budding yeast compound–module connections (Figure 4B). Also, a combined score from both species improved the prediction of compound–module interactions, above the accuracy observed with the S. cerevisae information alone, but this improvement was not observed for the prediction of drug–gene interactions (Figure 4B). Data from both species were used to predict drug–module interactions, and one specific interaction (compound NSC-207895 interaction with DNA repair complexes) was experimentally verified by showing that the compound activates the DNA damage repair pathway in three species (S. cerevisiae, S. pombe and H. sapiens).

To understand why the combination of chemogenomic data from two species might improve drug–module interaction predictions, we also analyzed previously published cross-species genetic–interaction data. We observed a significant correlation between the conservation of drug–gene and gene–gene interactions among the one-to-one orthologs (r=0.28, P-value=0.0078). Additionally, the strongest interactions of benomyl (a microtubule inhibitor) were to complexes that also had strong and conserved genetic interactions with microtubules (Figure 4C). We hypothesize that a significant number of the compound–gene interactions obtained from chemogenomic studies are not direct interactions with the physical target of the compounds, but include many indirect interactions that genetically interact with the main target(s). This would explain why the compound interaction networks show similar evolutionary patterns as the genetic interactions networks.

In summary, these results shed some light on the interplay between the evolution of genetic networks and the evolution of drug response. Understanding how genetic variability across different species might result in different sensitivity to drugs should improve our capacity to design treatments. Concretely, we hope that this line of research might one day help us create drugs and drug combinations that specifically affect a pathogen or diseased tissue, but not the host.

We present a cross-species chemogenomic screening platform using libraries of haploid deletion mutants from two yeast species, Saccharomyces cerevisiae and Schizosaccharomyces pombe. We screened a set of compounds of known and unknown mode of action (MoA) and derived quantitative drug scores (or D-scores), identifying mutants that are either sensitive or resistant to particular compounds. We found that compound–functional module relationships are more conserved than individual compound–gene interactions between these two species. Furthermore, we observed that combining data from both species allows for more accurate prediction of MoA. Finally, using this platform, we identified a novel small molecule that acts as a DNA damaging agent and demonstrate that its MoA is conserved in human cells.

Systematic exploration of essential yeast gene function with temperature-sensitive mutants

Conditional temperature-sensitive (ts) mutations are valuable reagents for studying essential genes in the yeast Saccharomyces cerevisiae. We constructed 787 ts strains, covering 497 (∼45%) of the 1,101 essential yeast genes, with ∼30% of the genes represented by multiple alleles. All of the alleles are integrated into their native genomic locus in the S288C common reference strain and are linked to a kanMX selectable marker, allowing further genetic manipulation by synthetic genetic array (SGA)-based, high-throughput methods. We show two such manipulations: barcoding of 440 strains, which enables chemical-genetic suppression analysis, and the construction of arrays of strains carrying different fluorescent markers of subcellular structure, which enables quantitative analysis of phenotypes using high-content screening. Quantitative analysis of a GFP-tubulin marker identified roles for cohesin and condensin genes in spindle disassembly. This mutant collection should facilitate a wide range of systematic studies aimed at understanding the functions of essential genes.

Yet1p-Yet3p interacts with Scs2p-Opi1p to regulate ER localization of the Opi1p repressor

A major phospholipid regulatory circuit in yeast is controlled by Scs2p, an ER membrane protein that binds the transcriptional repressor protein Opi1p. Here we show that the Yet1p–Yet3p complex acts in derepression of INO1 through physical association with Scs2p–Opi1p.

Lipid sensing mechanisms at the endoplasmic reticulum (ER) coordinate an array of biosynthetic pathways. A major phospholipid regulatory circuit in yeast is controlled by Scs2p, an ER membrane protein that binds the transcriptional repressor protein Opi1p. Cells grown in the absence of inositol sequester Scs2p–Opi1p at the ER and derepress target genes including INO1. We recently reported that Yet1p and Yet3p, the yeast homologues of BAP29 and BAP31, are required for normal growth in the absence of inositol. Here we show that the Yet1p–Yet3p complex acts in derepression of INO1 through physical association with Scs2p–Opi1p. Yet complex binding to Scs2p–Opi1p was enhanced by inositol starvation, although the interaction between Scs2p and Opi1p was not influenced by YET1 or YET3 deletion. Interestingly, live-cell imaging analysis indicated that Opi1p does not efficiently relocalize to the ER during inositol starvation in yet3Δ cells. Together our data demonstrate that a physical association between the Yet complex and Scs2p–Opi1p is required for proper localization of the Opi1p repressor to ER membranes and subsequent INO1 derepression.

Ten questions about systems biology

In this paper we raise 'ten questions' broadly related to 'omics', the term systems biology, and why the new biology has failed to deliver major therapeutic advances for many common diseases, especially diabetes and cardiovascular disease. We argue that a fundamentally narrow and reductionist perspective about the contribution of genes and genetic variants to disease is a key reason 'omics' has failed to deliver the anticipated breakthroughs. We then point out the critical utility of key concepts from physiology like homeostasis, regulated systems and redundancy as major intellectual tools to understand how whole animals adapt to the real world. We argue that a lack of fluency in these concepts is a major stumbling block for what has been narrowly defined as 'systems biology' by some of its leading advocates. We also point out that it is a failure of regulation at multiple levels that causes many common diseases. Finally, we attempt to integrate our critique of reductionism into a broader social framework about so-called translational research in specific and the root causes of common diseases in general. Throughout we offer ideas and suggestions that might be incorporated into the current biomedical environment to advance the understanding of disease through the perspective of physiology in conjunction with epidemiology as opposed to bottom-up reductionism alone.

High-throughput phenotyping using parallel sequencing of RNA interference targets in the African trypanosome

African trypanosomes are major pathogens of humans and livestock and represent a model for studies of unusual protozoal biology. We describe a high-throughput phenotyping approach termed RNA interference (RNAi) target sequencing, or RIT-seq that, using Illumina sequencing, maps fitness-costs associated with RNAi. We scored the abundance of >90,000 integrated RNAi targets recovered from trypanosome libraries before and after induction of RNAi. Data are presented for 7435 protein coding sequences, >99% of a non-redundant set in the Trypanosoma brucei genome. Analysis of bloodstream and insect life-cycle stages and differentiated libraries revealed genome-scale knockdown profiles of growth and development, linking thousands of previously uncharacterized and "hypothetical" genes to essential functions. Genes underlying prominent features of trypanosome biology are highlighted, including the constitutive emphasis on post-transcriptional gene expression control, the importance of flagellar motility and glycolysis in the bloodstream, and of carboxylic acid metabolism and phosphorylation during differentiation from the bloodstream to the insect stage. The current data set also provides much needed genetic validation to identify new drug targets. RIT-seq represents a versatile new tool for genome-scale functional analyses and for the exploitation of genome sequence data.

Saccharomyces cerevisiae genome-wide mutant screen for sensitivity to 2,4-diacetylphloroglucinol, an antibiotic produced by Pseudomonas fluorescens

2,4-Diacetylphloroglucinol (2,4-DAPG), an antibiotic produced by Pseudomonas fluorescens, has broad-spectrum antibiotic activity, inhibiting organisms ranging from viruses, bacteria, and fungi to higher plants and mammalian cells. The biosynthesis and regulation of 2,4-DAPG in P. fluorescens are well described, but the mode of action against target organisms is poorly understood. As a first step to elucidate the mechanism, we screened a deletion library of Saccharomyces cerevisiae in broth and agar medium supplemented with 2,4-DAPG. We identified 231 mutants that showed increased sensitivity to 2,4-DAPG under both conditions, including 22 multidrug resistance-related mutants. Three major physiological functions correlated with an increase in sensitivity to 2,4-DAPG: membrane function, reactive oxygen regulation, and cell homeostasis. Physiological studies with wild-type yeast validated the results of the mutant screens. The chemical-genetic fitness profile of 2,4-DAPG resembled those of menthol, sodium azide, and hydrogen peroxide determined in previous high-throughput screening studies. Collectively, these findings indicate that 2,4-DAPG acts on multiple basic cellular processes.

Molecular characterization of propolis-induced cell death in Saccharomyces cerevisiae

Propolis, a natural product of plant resins, is used by the bees to seal holes in their honeycombs and protect the hive entrance. However, propolis has also been used in folk medicine for centuries. Here, we apply the power of Saccharomyces cerevisiae as a model organism for studies of genetics, cell biology, and genomics to determine how propolis affects fungi at the cellular level. Propolis is able to induce an apoptosis cell death response. However, increased exposure to propolis provides a corresponding increase in the necrosis response. We showed that cytochrome c but not endonuclease G (Nuc1p) is involved in propolis-mediated cell death in S. cerevisiae. We also observed that the metacaspase YCA1 gene is important for propolis-mediated cell death. To elucidate the gene functions that may be required for propolis sensitivity in eukaryotes, the full collection of about 4,800 haploid S. cerevisiae deletion strains was screened for propolis sensitivity. We were able to identify 138 deletion strains that have different degrees of propolis sensitivity compared to the corresponding wild-type strains. Systems biology revealed enrichment for genes involved in the mitochondrial electron transport chain, vacuolar acidification, negative regulation of transcription from RNA polymerase II promoter, regulation of macroautophagy associated with protein targeting to vacuoles, and cellular response to starvation. Validation studies indicated that propolis sensitivity is dependent on the mitochondrial function and that vacuolar acidification and autophagy are important for yeast cell death caused by propolis.

Neo-Darwinism, the modern synthesis and selfish genes: are they of use in physiology?

This article argues that the gene-centric interpretations of evolution, and more particularly the selfish gene expression of those interpretations, form barriers to the integration of physiological science with evolutionary theory. A gene-centred approach analyses the relationships between genotypes and phenotypes in terms of differences (change the genotype and observe changes in phenotype). We now know that, most frequently, this does not correctly reveal the relationships because of extensive buffering by robust networks of interactions. By contrast, understanding biological function through physiological analysis requires an integrative approach in which the activity of the proteins and RNAs formed from each DNA template is analysed in networks of interactions. These networks also include components that are not specified by nuclear DNA. Inheritance is not through DNA sequences alone. The selfish gene idea is not useful in the physiological sciences, since selfishness cannot be defined as an intrinsic property of nucleotide sequences independently of gene frequency, i.e. the 'success' in the gene pool that is supposed to be attributable to the 'selfish' property. It is not a physiologically testable hypothesis.

Genetic interactions reveal the evolutionary trajectories of duplicate genes

Duplicate genes show significantly fewer interactions than singleton genes, and functionally similar duplicates can exhibit dissimilar profiles because common interactions are ‘hidden' due to buffering.

Genetic interaction profiles provide insights into evolutionary mechanisms of duplicate retention by distinguishing duplicates under dosage selection from those retained because of some divergence in function.

The genetic interactions of duplicate genes evolve in an extremely asymmetric way and the directionality of this asymmetry correlates well with other evolutionary properties of duplicate genes.

Genetic interaction profiles can be used to elucidate the divergent function of specific duplicate pairs.

Gene duplication and divergence serves as a primary source for new genes and new functions, and as such has broad implications on the evolutionary process. Duplicate genes within S. cerevisiae have been shown to retain a high degree of similarity with regard to many of their functional properties (Papp et al, 2004; Guan et al, 2007; Wapinski et al, 2007; Musso et al, 2008), and perturbation of duplicate genes has been shown to result in smaller fitness defects than singleton genes (Gu et al, 2003; DeLuna et al, 2008; Dean et al, 2008; Musso et al, 2008). Individual genetic interactions between pairs of genes and profiles of such interactions across the entire genome provide a new context in which to examine the properties of duplicate compensation.

In this study we use the most recent and comprehensive set of genetic interactions in yeast produced to date (Costanzo et al, 2010) to address questions of duplicate retention and redundancy. We show that the ability for duplicate genes to buffer the deletion of a partner has three main consequences. First it agrees with previous work demonstrating that a high proportion of duplicate pairs are synthetic lethal, a classic indication of the ability to buffer one another functionally (DeLuna et al, 2008; Dean et al, 2008; Musso et al, 2008). Second, it reduces the number of genetic interactions observed between duplicate genes and the rest of the genome by masking interactions relating to common function from experimental detection. Third, this buffering of common interactions serves to reduce profile similarity in spite of common function (Figure 1). The compensatory ability of functionally similar duplicates buffers genetic interactions related to their common function (reducing the number of genetic interactions overall), while allowing the measurement of interactions related to any divergent function. Thus, even functionally similar duplicates may have dissimilar genetic interaction profiles. As previously surmised (Ihmels et al, 2007), duplicate genes under selection for dosage amplification have differing profile characteristics. We show that dosage-mediated duplicates have much higher genetic interaction profile similarity than do other duplicate pairs. Furthermore, we show in a comparison with local neighbors on a protein–protein interaction (PPI) network, that although dosage-mediated duplicates more often have higher similarity to each other than they do to their neighbors, the reverse is true for duplicates in general. That is, slightly divergent duplicate genes more often exhibit a higher similarity with a common neighbor on the PPI network than they do with each other, and that observation is consistent with the idea that common interactions are buffered while interactions corresponding to divergent functions are observed.

We then asked whether duplicates' genetic interactions that are not buffered appear in a symmetric or an asymmetric fashion. Previous work has established asymmetric patterns with regard to PPI degree (Wagner, 2002; He and Zhang, 2005), sequence divergence (Conant and Wagner, 2003; Zhang et al, 2003; Kellis et al, 2004; Scannell and Wolfe, 2008) and expression patterns (Gu et al, 2002b; Tirosh and Barkai, 2007). Although genetic interactions are further removed from mechanism than protein–protein interactions, for example, they do offer a more direct measurement of functional consequence and, thus, may give a better indication of the functional differences between a duplicate pair. We found that duplicates exhibit a strikingly asymmetric pattern of genetic interactions, with the ratio of interactions between sisters commonly exceeding 7:1 (Figure 4A). The observations differ significantly from random simulations in which genetic interactions were redistributed between sisters with equal probability (Figure 4A). Moreover, the directionality of this interaction asymmetry agrees with other physiological properties of duplicate pairs. For example, the sister with more genetic interactions also tends to have more protein–protein interactions and also tends to evolve at a slower rate (Figure 4B).

Genetic interaction degree and profiles can be used to understand the functional divergence of particular duplicates pairs. As a case example, we consider the whole-genome-duplication pair CIK1–VIK1. Each of these genes encode proteins that form distinct heterodimeric complexes with the microtubule motor protein Kar3 (Manning et al, 1999). Although each of these proteins depend on a direct physical interaction with Kar3, Cik1 has a much higher profile similarity to Kar3 than does Vik1 (r=0.5 and r=0.3, respectively). Consistent with its higher similarity, Δcik1 and Δkar3 exhibit several similar phenotypes, including abnormally short spindles, chromosome loss and delayed cell cycle progression (Page et al, 1994; Manning et al, 1999). In contrast, a Δvik1 mutant strain exhibits no overt phenotype (Manning et al, 1999).

The characterization of functional redundancy and divergence between duplicate genes is an important step in understanding the evolution of genetic systems. Large-scale genetic network analysis in Saccharomyces cerevisiae provides a powerful perspective for addressing these questions through quantitative measurements of genetic interactions between pairs of duplicated genes, and more generally, through the study of genome-wide genetic interaction profiles associated with duplicated genes. We show that duplicate genes exhibit fewer genetic interactions than other genes because they tend to buffer one another functionally, whereas observed interactions are non-overlapping and reflect their divergent roles. We also show that duplicate gene pairs are highly imbalanced in their number of genetic interactions with other genes, a pattern that appears to result from asymmetric evolution, such that one duplicate evolves or degrades faster than the other and often becomes functionally or conditionally specialized. The differences in genetic interactions are predictive of differences in several other evolutionary and physiological properties of duplicate pairs.

Protein complexes are central in the yeast genetic landscape

If perturbing two genes together has a stronger or weaker effect than expected, they are said to genetically interact. Genetic interactions are important because they help map gene function, and functionally related genes have similar genetic interaction patterns. Mapping quantitative (positive and negative) genetic interactions on a global scale has recently become possible. This data clearly shows groups of genes connected by predominantly positive or negative interactions, termed monochromatic groups. These groups often correspond to functional modules, like biological processes or complexes, or connections between modules. However it is not yet known how these patterns globally relate to known functional modules. Here we systematically study the monochromatic nature of known biological processes using the largest quantitative genetic interaction data set available, which includes fitness measurements for ∼5.4 million gene pairs in the yeast Saccharomyces cerevisiae. We find that only 10% of biological processes, as defined by Gene Ontology annotations, and less than 1% of inter-process connections are monochromatic. Further, we show that protein complexes are responsible for a surprisingly large fraction of these patterns. This suggests that complexes play a central role in shaping the monochromatic landscape of biological processes. Altogether this work shows that both positive and negative monochromatic patterns are found in known biological processes and in their connections and that protein complexes play an important role in these patterns. The monochromatic processes, complexes and connections we find chart a hierarchical and modular map of sensitive and redundant biological systems in the yeast cell that will be useful for gene function prediction and comparison across phenotypes and organisms. Furthermore the analysis methods we develop are applicable to other species for which genetic interactions will progressively become more available.

Genetic interactions indicate functional dependencies between genes and are a powerful tool to predict gene function. Functionally related genes tend to have similar profiles of genetic interactions. Recently, global scale mapping of quantitative (positive and negative) genetic interactions has been performed. This data clearly shows groups of genes connected by predominantly positive or negative interactions, termed monochromatic groups. These groups often correspond to functional modules, such as biological processes or protein complexes, or connections between modules, but it is not yet known how these patterns globally relate to known functional modules. Here we systematically evaluate the monochromatic nature of known biological processes and their connections in the yeast Saccharomyces cerevisiae. We find that 10% of biological processes and less than 1% of inter-process connections are monochromatic. Further, we show that protein complexes are responsible for a surprisingly large fraction of these monochromatic groups. The monochromatic processes, complexes and connections we find chart a hierarchical and modular map of sensitive and redundant biological systems in the yeast cell that will be useful for gene function prediction and comparison across phenotypes and organisms.

Bringing order to protein disorder through comparative genomics and genetic interactions

BACKGROUND

Intrinsically disordered regions are widespread, especially in proteomes of higher eukaryotes. Recently, protein disorder has been associated with a wide variety of cellular processes and has been implicated in several human diseases. Despite its apparent functional importance, the sheer range of different roles played by protein disorder often makes its exact contribution difficult to interpret.

RESULTS

We attempt to better understand the different roles of disorder using a novel analysis that leverages both comparative genomics and genetic interactions. Strikingly, we find that disorder can be partitioned into three biologically distinct phenomena: regions where disorder is conserved but with quickly evolving amino acid sequences (flexible disorder); regions of conserved disorder with also highly conserved amino acid sequences (constrained disorder); and, lastly, non-conserved disorder. Flexible disorder bears many of the characteristics commonly attributed to disorder and is associated with signaling pathways and multi-functionality. Conversely, constrained disorder has markedly different functional attributes and is involved in RNA binding and protein chaperones. Finally, non-conserved disorder lacks clear functional hallmarks based on our analysis.

CONCLUSIONS

Our new perspective on protein disorder clarifies a variety of previous results by putting them into a systematic framework. Moreover, the clear and distinct functional association of flexible and constrained disorder will allow for new approaches and more specific algorithms for disorder detection in a functional context. Finally, in flexible disordered regions, we demonstrate clear evolutionary selection of protein disorder with little selection on primary structure, which has important implications for sequence-based studies of protein structure and evolution.

Bringing order to protein disorder through comparative genomics and genetic interactions

BACKGROUND

Intrinsically disordered regions are widespread, especially in proteomes of higher eukaryotes. Recently, protein disorder has been associated with a wide variety of cellular processes and has been implicated in several human diseases. Despite its apparent functional importance, the sheer range of different roles played by protein disorder often makes its exact contribution difficult to interpret.

RESULTS

We attempt to better understand the different roles of disorder using a novel analysis that leverages both comparative genomics and genetic interactions. Strikingly, we find that disorder can be partitioned into three biologically distinct phenomena: regions where disorder is conserved but with quickly evolving amino acid sequences (flexible disorder); regions of conserved disorder with also highly conserved amino acid sequences (constrained disorder); and, lastly, non-conserved disorder. Flexible disorder bears many of the characteristics commonly attributed to disorder and is associated with signaling pathways and multi-functionality. Conversely, constrained disorder has markedly different functional attributes and is involved in RNA binding and protein chaperones. Finally, non-conserved disorder lacks clear functional hallmarks based on our analysis.

CONCLUSIONS

Our new perspective on protein disorder clarifies a variety of previous results by putting them into a systematic framework. Moreover, the clear and distinct functional association of flexible and constrained disorder will allow for new approaches and more specific algorithms for disorder detection in a functional context. Finally, in flexible disordered regions, we demonstrate clear evolutionary selection of protein disorder with little selection on primary structure, which has important implications for sequence-based studies of protein structure and evolution.

From sequence to function: Insights from natural variation in budding yeasts

Background

Natural variation offers a powerful approach for assigning function to DNA sequence—a pressing challenge in the age of high throughput sequencing technologies.

Scope of Review

Here we review comparative genomic approaches that are bridging the sequence–function and genotype–phenotype gaps. Reverse genomic approaches aim to analyse sequence to assign function, whereas forward genomic approaches start from a phenotype and aim to identify the underlying genotype responsible.

Major Conclusions

Comparative genomic approaches, pioneered in budding yeasts, have resulted in dramatic improvements in our understanding of the function of both genes and regulatory sequences. Analogous studies in other systems, including humans, demonstrate the ubiquity of comparative genomic approaches. Recently, forward genomic approaches, exploiting natural variation within yeast populations, have started to offer powerful insights into how genotype influences phenotype and even the ability to predict phenotypes.

General Significance

Comparative genomic experiments are defining the fundamental rules that govern complex traits in natural populations from yeast to humans.

This article is part of a Special Issue entitled Systems Biology of Microorganisms.

Dispensability of Escherichia coli's latent pathways

Gene-knockout experiments on single-cell organisms have established that expression of a substantial fraction of genes is not needed for optimal growth. This problem acquired a new dimension with the recent discovery that environmental and genetic perturbations of the bacterium Escherichia coli are followed by the temporary activation of a large number of latent metabolic pathways, which suggests the hypothesis that temporarily activated reactions impact growth and hence facilitate adaptation in the presence of perturbations. Here, we test this hypothesis computationally and find, surprisingly, that the availability of latent pathways consistently offers no growth advantage and tends, in fact, to inhibit growth after genetic perturbations. This is shown to be true even for latent pathways with a known function in alternate conditions, thus extending the significance of this adverse effect beyond apparently nonessential genes. These findings raise the possibility that latent pathway activation is in fact derivative of another, potentially suboptimal, adaptive response.

Genomic expression program involving the Haa1p-regulon in Saccharomyces cerevisiae response to acetic acid

The alterations occurring in yeast genomic expression during early response to acetic acid and the involvement of the transcription factor Haa1p in this transcriptional reprogramming are described in this study. Haa1p was found to regulate, directly or indirectly, the transcription of approximately 80% of the acetic acid-activated genes, suggesting that Haa1p is the main player in the control of yeast response to this weak acid. The genes identified in this work as being activated in response to acetic acid in a Haa1p-dependent manner include protein kinases, multidrug resistance transporters, proteins involved in lipid metabolism, in nucleic acid processing, and proteins of unknown function. Among these genes, the expression of SAP30 and HRK1 provided the strongest protective effect toward acetic acid. SAP30 encode a subunit of a histone deacetylase complex and HRK1 encode a protein kinase belonging to a family of protein kinases dedicated to the regulation of plasma membrane transporters activity. The deletion of the HRK1 gene was found to lead to the increase of the accumulation of labeled acetic acid into acid-stressed yeast cells, suggesting that the role of both HAA1 and HRK1 in providing protection against acetic acid is, at least partially, related with their involvement in the reduction of intracellular acetate concentration.

Adaptive response and tolerance to weak acids in Saccharomyces cerevisiae: a genome-wide view

Weak acids are widely used as food preservatives (e.g., acetic, propionic, benzoic, and sorbic acids), herbicides (e.g., 2,4-dichlorophenoxyacetic acid), and as antimalarial (e.g., artesunic and artemisinic acids), anticancer (e.g., artesunic acid), and immunosuppressive (e.g., mycophenolic acid) drugs, among other possible applications. The understanding of the mechanisms underlying the adaptive response and resistance to these weak acids is a prerequisite to develop more effective strategies to control spoilage yeasts, and the emergence of resistant weeds, drug resistant parasites or cancer cells. Furthermore, the identification of toxicity mechanisms and resistance determinants to weak acid-based pharmaceuticals increases current knowledge on their cytotoxic effects and may lead to the identification of new drug targets. This review integrates current knowledge on the mechanisms of toxicity and tolerance to weak acid stress obtained in the model eukaryote Saccharomyces cerevisiae using genome-wide approaches and more detailed gene-by-gene analysis. The major features of the yeast response to weak acids in general, and the more specific responses and resistance mechanisms towards a specific weak acid or a group of weak acids, depending on the chemical nature of the side chain R group (R-COOH), are highlighted. The involvement of several transcriptional regulatory networks in the genomic response to different weak acids is discussed, focusing on the regulatory pathways controlled by the transcription factors Msn2p/Msn4p, War1p, Haa1p, Rim101p, and Pdr1p/Pdr3p, which are known to orchestrate weak acid stress response in yeast. The extrapolation of the knowledge gathered in yeast to other eukaryotes is also attempted.

Mne1 is a novel component of the mitochondrial splicing apparatus responsible for processing of a COX1 group I intron in yeast

Saccharomyces cerevisiae cells lacking Mne1 are deficient in intron splicing in the gene encoding the Cox1 subunit of cytochrome oxidase but contain wild-type levels of the bc(1) complex. Thus, Mne1 has no role in splicing of COB introns or expression of the COB gene. Northern experiments suggest that splicing of the COX1 aI5β intron is dependent on Mne1 in addition to the previously known Mrs1, Mss116, Pet54, and Suv3 factors. Processing of the aI5β intron is similarly impaired in mne1Δ and mrs1Δ cells and overexpression of Mrs1 partially restores the respiratory function of mne1Δ cells. Mrs1 is known to function in the initial transesterification reaction of splicing. Mne1 is a mitochondrial matrix protein loosely associated with the inner membrane and is found in a high mass ribonucleoprotein complex specifically associated with the COX1 mRNA even within an intronless strain. Mne1 does not appear to have a secondary function in COX1 processing or translation, because disruption of MNE1 in cells containing intronless mtDNA does not lead to a respiratory growth defect. Thus, the primary defect in mne1Δ cells is splicing of the aI5β intron in COX1.

New regulators of a high affinity Ca2+ influx system revealed through a genome-wide screen in yeast

The bakers' yeast Saccharomyces cerevisiae utilizes a high affinity Ca(2+) influx system (HACS) to survive assaults by mating pheromones, tunicamycin, and azole-class antifungal agents. HACS consists of two known subunits, Cch1 and Mid1, that are homologous and analogous to the catalytic α-subunits and regulatory α2δ-subunits of mammalian voltage-gated calcium channels, respectively. To search for additional subunits and regulators of HACS, a collection of gene knock-out mutants was screened for abnormal uptake of Ca(2+) after exposure to mating pheromone or to tunicamycin. The screen revealed that Ecm7 is required for HACS function in most conditions. Cycloheximide chase experiments showed that Ecm7 was stabilized by Mid1, and Mid1 was stabilized by Cch1 in non-signaling conditions, suggesting they all interact. Ecm7 is a member of the PMP-22/EMP/MP20/Claudin superfamily of transmembrane proteins that includes γ-subunits of voltage-gated calcium channels. Eleven additional members of this superfamily were identified in yeast, but none was required for HACS activity in response to the stimuli. Remarkably, many dozens of genes involved in vesicle-mediated trafficking and protein secretion were required to prevent spontaneous activation of HACS. Taken together, the findings suggest that HACS and calcineurin monitor performance of the membrane trafficking system in yeasts and coordinate compensatory processes. Conservation of this quality control system in Candida glabrata suggests that many pathogenic species of fungi may utilize HACS and calcineurin to resist azoles and other compounds that target membrane biosynthesis.

Functional specialization of ribosomes?

Ribosomes are highly conserved macromolecular machines that are responsible for protein synthesis in all living organisms. Work published in the past year has shown that changes to the ribosome core can affect the mechanism of translation initiation that is favored in the cell, which potentially leads to specific changes in the relative efficiencies with which different proteins are made. Here, I examine recent data from expression and proteomic studies that suggest that cells make slightly different ribosomes under different growth conditions, and discuss genetic evidence that such differences are functional. In particular, I argue that eukaryotic cells probably produce ribosomes that lack one or more core ribosomal proteins (RPs) under some conditions, and that core RPs contribute differentially to translation of distinct subpopulations of mRNAs.

Protein homeostasis and the phenotypic manifestation of genetic diversity: principles and mechanisms

Changing a single nucleotide in a genome can have profound consequences under some conditions, but the same change can have no consequences under others. Indeed, organisms can be surprisingly robust to environmental and genetic perturbations. Yet, the mechanisms underlying such robustness are controversial. Moreover, how they might affect evolutionary change remains enigmatic. Here, we review the recently appreciated central role of protein homeostasis in buffering and potentiating genetic variation and discuss how these processes mediate the critical influence of the environment on the relationship between genotype and phenotype. Deciphering how robustness emerges from biological organization and the mechanisms by which it is overcome in changing environments will lead to a more complete understanding of both fundamental evolutionary processes and diverse human diseases.

Rapid analysis of pharmacology for infectious diseases

Pandemic, epidemic and endemic infectious diseases are united by a common problem: how do we rapidly and cost-effectively identify potential pharmacological interventions to treat infections? Given the large number of emerging and neglected infectious diseases and the fact that they disproportionately afflict the poorest members of the global society, new ways of thinking are required to developed high productivity discovery systems that can be applied to a larger number of pathogens. The growing availability of parasite genome data provides the basis for developing methods to prioritize, a priori, the potential drug target and pharmacological landscape of an infectious disease. Thus the overall objective of infectious disease informatics is to enable the rapid generation of plausible, novel medical hypotheses of testable pharmacological experiments, by uncovering undiscovered relationships in the wealth of biomedical literature and databases that were collected for other purposes. In particular our goal is to identify potential drug targets present in a pathogen genome and prioritize which pharmacological experiments are most likely to discover drug-like lead compounds rapidly against a pathogen (i.e. which specific compounds and drug targets should be screened, in which assays and where they can be sourced). An integral part of the challenge is the development and integration of methods to predict druggability, essentiality, synthetic lethality and polypharmacology in pathogen genomes, while simultaneously integrating the inevitable issues of chemical tractability and the potential for acquired drug resistance from the start.

Use of genome-scale metabolic models in evolutionary systems biology

One of the major aims of the nascent field of evolutionary systems biology is to test evolutionary hypotheses that are not only realistic from a population genetic point of view but also detailed in terms of molecular biology mechanisms. By providing a mapping between genotype and phenotype for hundreds of genes, genome-scale systems biology models of metabolic networks have already provided valuable insights into the evolution of metabolic gene contents and phenotypes of yeast and other microbial species. Here we review the recent use of these computational models to predict the fitness effect of mutations, genetic interactions, evolutionary outcomes, and to decipher the mechanisms of mutational robustness. While these studies have demonstrated that even simplified models of biochemical reaction networks can be highly informative for evolutionary analyses, they have also revealed the weakness of this modeling framework to quantitatively predict mutational effects, a challenge that needs to be addressed for future progress in evolutionary systems biology.

Contributions of Saccharomyces cerevisiae to understanding mammalian gene function and therapy

Due to its genetic tractability and ease of manipulation, the yeast Saccharomyces cerevisiae has been extensively used as a model organism to understand how eukaryotic cells grow, divide, and respond to environmental changes. In this chapter, we reasoned that functional annotation of novel genes revealed by sequencing should adopt an integrative approach including both bioinformatics and experimental analysis to reveal functional conservation and divergence of complexes and pathways. The techniques and resources generated for systems biology studies in yeast have found a wide range of applications. Here we focused on using these technologies in revealing functions of genes from mammals, in identifying targets of novel and known drugs and in screening drugs targeting specific proteins and/or protein-protein interactions.

Signature-tagged mutagenesis to characterize genes through competitive selection of bar-coded genome libraries

The availability of collections of genome-wide deletion mutants greatly accelerates systematic analyses of gene function. However, each of the thousands of genes that comprise a genome must be phenotyped individually unless they can be assayed in parallel and subsequently deconvolved. To this end, unique molecular identifiers have been developed for a variety of microbes. Specifically, the addition of DNA "tags," or "bar codes," to each mutant allows all mutants in a collection to be pooled and phenotyped in parallel, greatly increasing experimental throughput. In this chapter, we provide an overview of current methodologies used to create such tagged mutant collections and outline how they can be applied to understand gene function, gene-gene interactions, and drug-gene interactions. Finally, we present a methodology that uses universal TagModules, capable of bar coding a wide range of microorganisms, and demonstrate its reduction to practice by creating tagged mutant collections in the pathogenic yeast Candida albicans.

The automated cell: compound and environment screening system (ACCESS) for chemogenomic screening

The automated cell, compound and environment screening system (ACCESS) was developed as an automated platform for chemogenomic research. In the yeast Saccharomyces cerevisiae, a number of genomic screens rely on the modulation of gene dose to determine the mode of action of bioactive compounds or the effects of environmental/compound perturbations. These and other phenotypic experiments have been shown to benefit from high-resolution growth curves and a highly automated controlled environment system that enables a wide range of multi-well assays that can be run over many days without any manual intervention. Furthermore, precise control of drug dosing, timing of drug exposure, and precise timing of cell harvesting at specific generation times are important for optimal results. Some of these benefits include the ability to derive fine distinctions between growth rates of mutant strains (1) and the discovery of novel compounds and drug targets (2). The automation has also enabled large-scale screening projects with over 100,000 unique compounds screened to date including a thousand genome-wide screens (3). The ACCESS system also has a diverse set of software tools to enable users to set up, run, annotate, and evaluate complex screens with minimal training.

High-throughput analyses and curation of protein interactions in yeast

The yeast Saccharomyces cerevisiae is the model organism in which protein interactions have been most extensively analyzed. The vast majority of these interactions have been characterized by a variety of sophisticated high-throughput techniques probing different aspects of protein association. This chapter summarizes the major techniques, highlights their complementary nature, discusses the data they produce, and highlights some of the biases from which they suffer. A main focus is the key role played by computational methods for processing, analyzing, and validating the large body of noisy data produced by the experimental procedures. It also describes how computational methods are used to extend the coverage and reliability of protein interaction data by integrating information from heterogeneous sources and reviews the current status of literature-curated data on yeast protein interactions stored in specialized databases.

Cell wall integrity is linked to mitochondria and phospholipid homeostasis in Candida albicans through the activity of the post-transcriptional regulator Ccr4-Pop2

The cell wall is essential for viability of fungi and is an effective drug target in pathogens such as Candida albicans. The contribution of post-transcriptional gene regulators to cell wall integrity in C. albicans is unknown. We show that the C. albicans Ccr4-Pop2 mRNA deadenylase, a regulator of mRNA stability and translation, is required for cell wall integrity. The ccr4/pop2 mutants display reduced wall β-glucans and sensitivity to the echinocandin caspofungin. Moreover, the deadenylase mutants are compromised for filamentation and virulence. We demonstrate that defective cell walls in the ccr4/pop2 mutants are linked to dysfunctional mitochondria and phospholipid imbalance. To further understand mitochondrial function in cell wall integrity, we screened a Saccharomyces cerevisiae collection of mitochondrial mutants. We identify several mitochondrial proteins required for caspofungin tolerance and find a connection between mitochondrial phospholipid homeostasis and caspofungin sensitivity. We focus on the mitochondrial outer membrane SAM complex subunit Sam37, demonstrating that it is required for both trafficking of phospholipids between the ER and mitochondria and cell wall integrity. Moreover, in C. albicans also Sam37 is essential for caspofungin tolerance. Our study provides the basis for an integrative view of mitochondrial function in fungal cell wall biogenesis and resistance to echinocandin antifungal drugs.

A mammalian functional-genetic approach to characterizing cancer therapeutics

Identifying mechanisms of drug action remains a fundamental impediment to the development and effective use of chemotherapeutics. Here we describe an RNA interference (RNAi)-based strategy to characterize small-molecule function in mammalian cells. By examining the response of cells expressing short hairpin RNAs (shRNAs) to a diverse selection of chemotherapeutics, we could generate a functional shRNA signature that was able to accurately group drugs into established biochemical modes of action. This, in turn, provided a diversely sampled reference set for high-resolution prediction of mechanisms of action for poorly characterized small molecules. We could further reduce the predictive shRNA target set to as few as eight genes and, by using a newly derived probability-based nearest-neighbors approach, could extend the predictive power of this shRNA set to characterize additional drug categories. Thus, a focused shRNA phenotypic signature can provide a highly sensitive and tractable approach for characterizing new anticancer drugs.

Towards a systems approach in the genetic analysis of archaea: Accelerating mutant construction and phenotypic analysis in Haloferax volcanii

With the availability of a genome sequence and increasingly sophisticated genetic tools, Haloferax volcanii is becoming a model for both Archaea and halophiles. In order for H. volcanii to reach a status equivalent to Escherichia coli, Bacillus subtilis, or Saccharomyces cerevisiae, a gene knockout collection needs to be constructed in order to identify the archaeal essential gene set and enable systematic phenotype screens. A streamlined gene-deletion protocol adapted for potential automation was implemented and used to generate 22 H. volcanii deletion strains and identify several potentially essential genes. These gene deletion mutants, generated in this and previous studies, were then analyzed in a high-throughput fashion to measure growth rates in different media and temperature conditions. We conclude that these high-throughput methods are suitable for a rapid investigation of an H. volcanii mutant library and suggest that they should form the basis of a larger genome-wide experiment.

Phenotypic landscape of a bacterial cell

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

Phosphoproteomic analysis reveals interconnected system-wide responses to perturbations of kinases and phosphatases in yeast

The phosphorylation and dephosphorylation of proteins by kinases and phosphatases constitute an essential regulatory network in eukaryotic cells. This network supports the flow of information from sensors through signaling systems to effector molecules and ultimately drives the phenotype and function of cells, tissues, and organisms. Dysregulation of this process has severe consequences and is one of the main factors in the emergence and progression of diseases, including cancer. Thus, major efforts have been invested in developing specific inhibitors that modulate the activity of individual kinases or phosphatases; however, it has been difficult to assess how such pharmacological interventions would affect the cellular signaling network as a whole. Here, we used label-free, quantitative phosphoproteomics in a systematically perturbed model organism (Saccharomyces cerevisiae) to determine the relationships between 97 kinases, 27 phosphatases, and more than 1000 phosphoproteins. We identified 8814 regulated phosphorylation events, describing the first system-wide protein phosphorylation network in vivo. Our results show that, at steady state, inactivation of most kinases and phosphatases affected large parts of the phosphorylation-modulated signal transduction machinery-and not only the immediate downstream targets. The observed cellular growth phenotype was often well maintained despite the perturbations, arguing for considerable robustness in the system. Our results serve to constrain future models of cellular signaling and reinforce the idea that simple linear representations of signaling pathways might be insufficient for drug development and for describing organismal homeostasis.

A systems approach to bone pathophysiology

With evolving interest in multiscalar biological systems one could assume that reductionist approaches may not fully describe biological complexity. Instead, tools such as mathematical modeling, network analysis, and other multiplexed clinical- and research-oriented tests enable rapid analyses of high-throughput data parsed at the genomic, proteomic, metabolomic, and physiomic levels. A physiomic-level approach allows for recursive horizontal and vertical integration of subsystem coupling across and within spatiotemporal scales. Additionally, this methodology recognizes previously ignored subsystems and the strong, nonintuitively obvious and indirect connections among physiological events that potentially account for the uncertainties in medicine. In this review, we flip the reductionist research paradigm and review the concept of systems biology and its applications to bone pathophysiology. Specifically, a bone-centric physiome model is presented that incorporates systemic-level processes with their respective therapeutic implications.

MicroRNA functions in stress responses

MicroRNAs (miRNAs) are a class of ∼22 nucleotide short noncoding RNAs that play key roles in fundamental cellular processes, including how cells respond to changes in environment or, broadly defined, stresses. Responding to stresses, cells either choose to restore or reprogram their gene expression patterns. This decision is partly mediated by miRNA functions, in particular by modulating the amount of miRNAs, the amount of mRNA targets, or the activity/mode of action of miRNA-protein complexes. In turn, these changes determine the specificity, timing, and concentration of gene products expressed upon stresses. Dysregulation of these processes contributes to chronic diseases, including cancers.

Functional toxicogenomics: mechanism-centered toxicology

Traditional toxicity testing using animal models is slow, low capacity, expensive and assesses a limited number of endpoints. Such approaches are inadequate to deal with the increasingly large number of compounds found in the environment for which there are no toxicity data. Mechanism-centered high-throughput testing represents an alternative approach to meet this pressing need but is limited by our current understanding of toxicity pathways. Functional toxicogenomics, the global study of the biological function of genes on the modulation of the toxic effect of a compound, can play an important role in identifying the essential cellular components and pathways involved in toxicity response. The combination of the identification of fundamental toxicity pathways and mechanism-centered targeted assays represents an integrated approach to advance molecular toxicology to meet the challenges of toxicity testing in the 21^st century.

Inferences about the distribution of dominance drawn from yeast gene knockout data

Data from several thousand knockout mutations in yeast (Saccharomyces cerevisiae) were used to estimate the distribution of dominance coefficients. We propose a new unbiased likelihood approach to measuring dominance coefficients. On average, deleterious mutations are partially recessive, with a mean dominance coefficient ~0.2. Alleles with large homozygous effects are more likely to be more recessive than are alleles of weaker effect. Our approach allows us to quantify, for the first time, the substantial variance and skew in the distribution of dominance coefficients. This heterogeneity is so great that many population genetic processes analyses based on the mean dominance coefficient alone will be in substantial error. These results are applied to the debate about various mechanisms for the evolution of dominance, and we conclude that they are most consistent with models that depend on indirect selection on homeostatic gene expression or on the ability to perform well under periods of high demand for a protein.

Molecular networks in drug discovery

Despite the dramatic increase of global spending on drug discovery and development, the approval rate for new drugs is declining, due chiefly to toxicity and undesirable side effects. Simultaneously, the growth of available biomedical data in the postgenomic era has provided fresh insight into the nature of redundant and compensatory drug-target pathways. This stagnation in drug approval can be overcome by the novel concept of polypharmacology, which is built on the fundamental concept that drugs modulate multiple targets. Polypharmacology can be studied with molecular networks that integrate multidisciplinary concepts including cheminformatics, bioinformatics, and systems biology. In silico techniques such as structure- and ligand-based approaches can be employed to study molecular networks and reduce costs by predicting adverse drug reactions and toxicity in the early stage of drug development. By amalgamating strides in this informatics-driven era, designing polypharmacological drugs with molecular network technology exemplifies the next generation of therapeutics with less of-target properties and toxicity. In this review, we will first describe the challenges in drug discovery, and showcase successes using multitarget drugs toward diseases such as cancer and mood disorders. We will then focus on recent development of in silico polypharmacology predictions. Finally, our technologies in molecular network analysis will be presented.

Measuring growth rate in high-throughput growth phenotyping

Growth rate is an important variable and parameter in biology with a central role in evolutionary, functional genomics, and systems biology studies. In this review the pros and cons of the different technologies presently available for high-throughput measurements of growth rate are discussed. Growth rate can be measured in liquid microcultivation of individual strains, in competition between strains, as growing colonies on agar, as division of individual cells, and estimated from molecular reporters. Irrespective of methodology, statistical issues such as spatial biases and batch effects are crucial to investigate and correct for to ensure low false discovery rates. The rather low correlations between studies indicate that cross-laboratory comparison and standardization are pressing issue to assure high-quality and comparable growth-rate data.

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

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

Predicting genome-wide redundancy using machine learning

BACKGROUND

Gene duplication can lead to genetic redundancy, which masks the function of mutated genes in genetic analyses. Methods to increase sensitivity in identifying genetic redundancy can improve the efficiency of reverse genetics and lend insights into the evolutionary outcomes of gene duplication. Machine learning techniques are well suited to classifying gene family members into redundant and non-redundant gene pairs in model species where sufficient genetic and genomic data is available, such as Arabidopsis thaliana, the test case used here.

RESULTS

Machine learning techniques that combine multiple attributes led to a dramatic improvement in predicting genetic redundancy over single trait classifiers alone, such as BLAST E-values or expression correlation. In withholding analysis, one of the methods used here, Support Vector Machines, was two-fold more precise than single attribute classifiers, reaching a level where the majority of redundant calls were correctly labeled. Using this higher confidence in identifying redundancy, machine learning predicts that about half of all genes in Arabidopsis showed the signature of predicted redundancy with at least one but typically less than three other family members. Interestingly, a large proportion of predicted redundant gene pairs were relatively old duplications (e.g., Ks > 1), suggesting that redundancy is stable over long evolutionary periods.

CONCLUSIONS

Machine learning predicts that most genes will have a functionally redundant paralog but will exhibit redundancy with relatively few genes within a family. The predictions and gene pair attributes for Arabidopsis provide a new resource for research in genetics and genome evolution. These techniques can now be applied to other organisms.

Differential and integral views of genetics in computational systems biology

This article uses an integrative systems biological view of the relationship between genotypes and phenotypes to clarify some conceptual problems in biological debates about causality. The differential (gene-centric) view is incomplete in a sense analogous to using differentiation without integration in mathematics. Differences in genotype are frequently not reflected in significant differences in phenotype as they are buffered by networks of molecular interactions capable of substituting an alternative pathway to achieve a given phenotype characteristic when one pathway is removed. Those networks integrate the influences of many genes on each phenotype so that the effect of a modification in DNA depends on the context in which it occurs. Mathematical modelling of these interactions can help to understand the mechanisms of buffering and the contextual-dependence of phenotypic outcome, and so to represent correctly and quantitatively the relations between genomes and phenotypes. By incorporating all the causal factors in generating a phenotype, this approach also highlights the role of non-DNA forms of inheritance, and of the interactions at multiple levels.

A genome-wide perspective on the response and tolerance to food-relevant stresses in Saccharomyces cerevisiae

The success of food and beverage production processes carried out by Saccharomyces cerevisiae and the thriving of food spoilage fungi are dependent on the ability of a cell to cope with the many environmental insults imposed during food production and preservation processes. Post-genomic approaches, especially transcriptomics, proteomics and chemogenomics, applied to S. cerevisiae made possible the unveiling of general and specific genome-wide adaptive response programs against stress induced by weak acids, ethanol, sulfite, heat and cold shock, osmotic pressure and nutrient limitation. These programs and the underlying signaling pathways are overviewed herein, highlighting the recent identification of genes and pathways found to be involved in stress response and tolerance. These are good candidate targets for genetic engineering aiming at the development of improved strains. The extension of the data gathered in S. cerevisiae to food spoilage fungi is considered. The relevance of the different genome-wide approaches in this context is also discussed.

Extensive in vivo metabolite-protein interactions revealed by large-scale systematic analyses

Natural small compounds comprise most cellular molecules and bind proteins as substrates, products, cofactors, and ligands. However, a large-scale investigation of in vivo protein-small metabolite interactions has not been performed. We developed a mass spectrometry assay for the large-scale identification of in vivo protein-hydrophobic small metabolite interactions in yeast and analyzed compounds that bind ergosterol biosynthetic proteins and protein kinases. Many of these proteins bind small metabolites; a few interactions were previously known, but the vast majority are new. Importantly, many key regulatory proteins such as protein kinases bind metabolites. Ergosterol was found to bind many proteins and may function as a general regulator. It is required for the activity of Ypk1, a mammalian AKT/SGK kinase homolog. Our study defines potential key regulatory steps in lipid biosynthetic pathways and suggests that small metabolites may play a more general role as regulators of protein activity and function than previously appreciated.

A decade of systems biology

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

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.