Discovering modes of action for therapeutic compounds using a genome-wide screen of yeast heterozygotes

The yeast sphingolipid signaling landscape

Sphingolipids are recognized as signaling mediators in a growing number of pathways, and represent potential targets to address many diseases. The study of sphingolipid signaling in yeast has created a number of breakthroughs in the field, and has the potential to lead future advances. The aim of this article is to provide an inclusive view of two major frontiers in yeast sphingolipid signaling. In the first section, several key studies in the field of sphingolipidomics are consolidated to create a yeast sphingolipidome that ranks nearly all known sphingolipid species by their level in a resting yeast cell. The second section presents an overview of most known phenotypes identified for sphingolipid gene mutants, presented with the intention of illuminating not yet discovered connections outside and inside of the field.

Multidimensional profiling in the investigation of small-molecule-induced cell death

Numerous morphological variations of cell death have been described. These processes depend on a complex and overlapping cellular signaling network, making molecular definition of the pathways challenging. This review describes one solution to this problem for small-molecule-induced death, the creation of high-dimensionality profiles for compounds that can be used to define and compare pathways. Such profiles have been assembled from gene expression measurements, protein quantification, chemical-genetic interactions, chemical combination interactions, cancer cell line sensitivity profiling, quantitative imaging, and modulatory profiling. We discuss the advantages and limitations of these techniques in the study of cell death.

Chemical genetic and chemogenomic analysis in yeast

Chemogenomics is the systematic genome-wide study of the cellular response to small molecule agents. Modern high-throughput genetic techniques allow massively parallel examination of the genetic effects of such biologically active small molecules (BASM). Here we present methodology for the identification and characterization of potentially bioactive compounds using the budding yeast Saccharomyces cerevisiae as a model organism. First, we present a method for screening libraries of compounds for growth inhibition in solid or liquid phase, followed by techniques for potency determination using a half-log dose response. Then the Deletion Mutant Array (DMA), a genome-wide library of single gene deletion strains, is used to probe the chemical genetic interactions of individual BASMs on genetic networks-a process that can be achieved with a solid phase pinning assay or a pooled liquid assay utilizing barcode microarray techniques. Finally, we offer some considerations for optimizing these protocols.

DNA-damaging agents in cancer chemotherapy: serendipity and chemical biology

DNA-damaging agents have a long history of use in cancer chemotherapy. The full extent of their cellular mechanisms, which is essential to balance efficacy and toxicity, is often unclear. In addition, the use of many anticancer drugs is limited by dose-limiting toxicities as well as the development of drug resistance. Novel anticancer compounds are continually being developed in the hopes of addressing these limitations; however, it is essential to be able to evaluate these compounds for their mechanisms of action. This review covers the current DNA-damaging agents used in the clinic, discusses their limitations, and describes the use of chemical genomics to uncover new information about the DNA damage response network and to evaluate novel DNA-damaging compounds.

New insights into the RNA-based mechanism of action of the anticancer drug 5'-fluorouracil in eukaryotic cells

5-Fluorouracil (5FU) is a chemotherapeutic drug widely used in treating a range of advanced, solid tumours and, in particular, colorectal cancer. Here, we used high-density tiling DNA microarray technology to obtain the specific transcriptome-wide response induced by 5FU in the eukaryotic model Schizosaccharomyces pombe. This approach combined with real-time quantitative PCR analysis allowed us to detect splicing defects of a significant number of intron-containing mRNA, in addition to identify some rRNA and tRNA processing defects after 5FU treatment. Interestingly, our studies also revealed that 5FU specifically induced the expression of certain genes implicated in the processing of mRNA, tRNA and rRNA precursors, and in the post-transcriptional modification of uracil residues in RNA. The transcription of several tRNA genes was also significantly induced after drug exposure. These transcriptional changes might represent a cellular response mechanism to counteract 5FU damage since deletion strains for some of these up-regulated genes were hypersensitive to 5FU. Moreover, most of these RNA processing genes have human orthologs that participate in conserved pathways, suggesting that they could be novel targets to improve the efficacy of 5FU-based treatments.

ATP-competitive mTOR kinase inhibitors delay plant growth by triggering early differentiation of meristematic cells but no developmental patterning change

The TOR (target of rapamycin) protein, a large phosphatidylinositol 3-kinase-like protein kinase (PIKK) that is conserved in eukaryotes and is a central regulator of growth and metabolism. The analysis of function of TOR in plant growth and development has been limited by the fact that plants are very poorly sensitive to rapamycin. As the kinase domain of TOR is highly conserved, this study analysed the dose-dependent effect of three sets of first- and second-generation ATP-competitive inhibitors (called asTORis for active-site TOR inhibitors) recently developed for the human TOR kinase on Arabidopsis thaliana growth. All six asTORis inhibited plant root growth in a dose-dependent manner, with 50% growth inhibitory doses (GI50) of <10 μM and <1 μM for the first- and second-generation inhibitors, respectively, similarly to the values in mammalian cells. A genetic approach further demonstrated that only asTORis inhibited root growth in an AtTOR gene-dosage-dependent manner. AsTORis decreased the length of: (i) the meristematic zone (MZ); (ii) the division zone in the MZ; (iii) epidermal cells in the elongation zone; and (iv) root hair cells. Whereas meristematic cells committed to early differentiation, the pattern of cell differentiation was not affected per se. AsTORis-induced root hair growth phenotype was shown to be specific by using other growth inhibitors blocking the cell cycle or translation. AsTORis dose-dependent inhibition of growth and root hairs was also observed in diverse groups of flowering plants, indicating that asTORis can be used to study the TOR pathway in other angiosperms, including crop plants.

Target identification of small molecules based on chemical biology approaches

Recently, a phenotypic approach-screens that assess the effects of compounds on cells, tissues, or whole organisms-has been reconsidered and reintroduced as a complementary strategy of a target-based approach for drug discovery. Although the finding of novel bioactive compounds from large chemical libraries has become routine, the identification of their molecular targets is still a time-consuming and difficult process, making this step rate-limiting in drug development. In the last decade, we and other researchers have amassed a large amount of phenotypic data through progress in omics research and advances in instrumentation. Accordingly, the profiling methodologies using these datasets expertly have emerged to identify and validate specific molecular targets of drug candidates, attaining some progress in current drug discovery (e.g., eribulin). In the case of a compound that shows an unprecedented phenotype likely by inhibiting a first-in-class target, however, such phenotypic profiling is invalid. Under the circumstances, a photo-crosslinking affinity approach should be beneficial. In this review, we describe and summarize recent progress in both affinity-based (direct) and phenotypic profiling (indirect) approaches for chemical biology target identification.

Metabolic suppression identifies new antibacterial inhibitors under nutrient limitation

Characterizing new drugs and chemical probes of biological systems is hindered by difficulties in identifying the mechanism of action (MOA) of biologically active molecules. Here we present a metabolite suppression approach to explore the MOA of antibacterial compounds under nutrient restriction. We assembled an array of metabolites that can be screened for suppressors of inhibitory molecules. Further, we identified inhibitors of Escherichia coli growth under nutrient limitation and charted their interactions with our metabolite array. This strategy led to the discovery and characterization of three new antibacterial compounds, MAC168425, MAC173979 and MAC13772. We showed that MAC168425 interferes with glycine metabolism, MAC173979 is a time-dependent inhibitor of p-aminobenzoic acid biosynthesis and MAC13772 inhibits biotin biosynthesis. We conclude that metabolite suppression profiling is an effective approach to focus MOA studies on compounds impairing metabolic capabilities. Such bioactives can serve as chemical probes of bacterial physiology and as leads for antibacterial drug development.

A Ham1p-dependent mechanism and modulation of the pyrimidine biosynthetic pathway can both confer resistance to 5-fluorouracil in yeast

5-Fluorouracil (5-FU) is an anticancer drug and pyrimidine analogue. A problem in 5-FU therapy is acquired resistance to the drug. To find out more about the mechanisms of resistance, we screened a plasmid library in yeast for genes that confer 5-FU resistance when overexpressed. We cloned five genes: CPA1, CPA2, HMS1, HAM1 and YJL055W. CPA1 and CPA2 encode a carbamoyl phosphate synthase involved in arginine biosynthesis and HMS1 a helix-loop-helix transcription factor. Our results suggest that CPA1, CPA2, and HMS1 confer 5-FU resistance by stimulating pyrimidine biosynthesis. Thus, they are unable to confer 5-FU resistance in a ura2 mutant, and inhibit the uptake and incorporation into RNA of both uracil and 5-FU. In contrast, HAM1 and YJL055W confer 5-FU resistance in a ura2 mutant, and selectively inhibit incorporation into RNA of 5-FU but not uracil. HAM1 is the strongest resistance gene, but it partially depends on YJL055W for its function. This suggests that HAM1 and YJL055W function together in mediating resistance to 5-FU. Ham1p encodes an inosine triphosphate pyrophosphatase that has been implicated in resistance to purine analogues. Our results suggest that Ham1p could have a broader specificity that includes 5-FUTP and other pyrimidine analogoue triphosphates.

Predicting complex phenotype-genotype interactions to enable yeast engineering: Saccharomyces cerevisiae as a model organism and a cell factory

There is an increasing use of systems biology approaches in both "red" and "white" biotechnology in order to enable medical, medicinal, and industrial applications. The intricate links between genotype and phenotype may be explained through the use of the tools developed in systems biology, synthetic biology, and evolutionary engineering. Biomedical and biotechnological research are among the fields that could benefit most from the elucidation of this complex relationship. Researchers have studied fitness extensively to explain the phenotypic impacts of genetic variations. This elaborate network of dependencies and relationships so revealed are further complicated by the influence of environmental effects that present major challenges to our achieving an understanding of the cellular mechanisms leading to healthy or diseased phenotypes or optimized production yields. An improved comprehension of complex genotype-phenotype interactions and their accurate prediction should enable us to more effectively engineer yeast as a cell factory and to use it as a living model of human or pathogen cells in intelligent screens for new drugs. This review presents different methods and approaches undertaken toward improving our understanding and prediction of the growth phenotype of the yeast Saccharomyces cerevisiae as both a model and a production organism.

Miniature short hairpin RNA screens to characterize antiproliferative drugs

The application of new proteomics and genomics technologies support a view in which few drugs act solely by inhibiting a single cellular target. Indeed, drug activity is modulated by complex, often incompletely understood cellular mechanisms. Therefore, efforts to decipher mode of action through genetic perturbation such as RNAi typically yields "hits" that fall into several categories. Of particular interest to the present study, we aimed to characterize secondary activities of drugs on cells. Inhibiting a known target can result in clinically relevant synthetic phenotypes. In one scenario, drug perturbation could, for example, improperly activate a protein that normally inhibits a particular kinase. In other cases, additional, lower affinity targets can be inhibited as in the example of inhibition of c-Kit observed in Bcr-Abl-positive cells treated with Gleevec. Drug transport and metabolism also play an important role in the way any chemicals act within the cells. Finally, RNAi per se can also affect cell fitness by more general off-target effects, e.g., via the modulation of apoptosis or DNA damage repair. Regardless of the root cause of these unwanted effects, understanding the scope of a drug's activity and polypharmacology is essential for better understanding its mechanism(s) of action, and such information can guide development of improved therapies. We describe a rapid, cost-effective approach to characterize primary and secondary effects of small-molecules by using small-scale libraries of virally integrated short hairpin RNAs. We demonstrate this principle using a "minipool" composed of shRNAs that target the genes encoding the reported protein targets of approved drugs. Among the 28 known reported drug-target pairs, we successfully identify 40% of the targets described in the literature and uncover several unanticipated drug-target interactions based on drug-induced synthetic lethality. We provide a detailed protocol for performing such screens and for analyzing the data. This cost-effective approach to mammalian knockdown screens, combined with the increasing maturation of RNAi technology will expand the accessibility of similar approaches in academic settings.

Identification of drug targets by chemogenomic and metabolomic profiling in yeast

OBJECTIVE

To advance our understanding of disease biology, the characterization of the molecular target for clinically proven or new drugs is very important. Because of its simplicity and the availability of strains with individual deletions in all of its genes, chemogenomic profiling in yeast has been used to identify drug targets. As measurement of drug-induced changes in cellular metabolites can yield considerable information about the effects of a drug, we investigated whether combining chemogenomic and metabolomic profiling in yeast could improve the characterization of drug targets.

BASIC METHODS

We used chemogenomic and metabolomic profiling in yeast to characterize the target for five drugs acting on two biologically important pathways. A novel computational method that uses a curated metabolic network was also developed, and it was used to identify the genes that are likely to be responsible for the metabolomic differences found.

RESULTS AND CONCLUSION

The combination of metabolomic and chemogenomic profiling, along with data analyses carried out using a novel computational method, could robustly identify the enzymes targeted by five drugs. Moreover, this novel computational method has the potential to identify genes that are causative of metabolomic differences or drug targets.

Construction of the first compendium of chemical-genetic profiles in the fission yeast Schizosaccharomyces pombe and comparative compendium approach

Genome-wide chemical genetic profiles in Saccharomyces cerevisiae since the budding yeast deletion library construction have been successfully used to reveal unknown mode-of-actions of drugs. Here, we introduce comparative approach to infer drug target proteins more accurately using two compendiums of chemical-genetic profiles from the budding yeast S. cerevisiae and the fission yeast Schizosaccharomyces pombe. For the first time, we established DNA-chip based growth defect measurement of genome-wide deletion strains of S. pombe, and then applied 47 drugs to the pooled heterozygous deletion strains to generate chemical-genetic profiles in S. pombe. In our approach, putative drug targets were inferred from strains hypersensitive to given drugs by analyzing S. pombe and S. cerevisiae compendiums. Notably, many evidences in the literature revealed that the inferred target genes of fungicide and bactericide identified by such comparative approach are in fact the direct targets. Furthermore, by filtering out the genes with no essentiality, the multi-drug sensitivity genes, and the genes with less eukaryotic conservation, we created a set of drug target gene candidates that are expected to be directly affected by a given drug in human cells. Our study demonstrated that it is highly beneficial to construct the multiple compendiums of chemical genetic profiles using many different species. The fission yeast chemical-genetic compendium is available at http://pombe.kaist.ac.kr/compendium.

Cell wall perturbation sensitizes fungi to the antimalarial drug chloroquine

Chloroquine (CQ) has been a mainstay of antimalarial drug treatment for several decades. Additional therapeutic actions of CQ have been described, including some reports of fungal inhibition. Here we investigated the action of CQ in fungi, including the yeast model Saccharomyces cerevisiae. A genomewide yeast deletion strain collection was screened against CQ, revealing that bck1Δ and slt2Δ mutants of the cell wall integrity pathway are CQ hypersensitive. This phenotype was rescued with sorbitol, consistent with cell wall involvement. The cell wall-targeting agent caffeine caused hypersensitivity to CQ, as did cell wall perturbation by sonication. The phenotypes were not caused by CQ-induced changes to cell wall components. Instead, CQ accumulated to higher levels in cells with perturbed cell walls: CQ uptake was 2- to 3-fold greater in bck1Δ and slt2Δ mutants than in wild-type yeast. CQ toxicity was synergistic with that of the major cell wall-targeting antifungal drug, caspofungin. The MIC of caspofungin against the yeast pathogen Candida albicans was decreased 2-fold by 250 μM CQ and up to 8-fold at higher CQ concentrations. Similar effects were seen in Candida glabrata and Aspergillus fumigatus. The results show that the cell wall is critical for CQ resistance in fungi and suggest that combination treatments with cell wall-targeting drugs could have potential for antifungal treatment.

Systems-level antimicrobial drug and drug synergy discovery

Here, we review the 'target-centric' genomic strategy to antimicrobial discovery and share our perspective on identification, validation and prioritization of potential antimicrobial drug targets in the context of emerging chemical biology, genomics and phenotypic screening strategies. We propose that coupling the dual processes of antimicrobial small-molecule screening and target identification in a whole-cell context is essential to empirically annotate 'druggable' targets and advance early stage antimicrobial discovery. We also advocate a systems-level approach to annotating synthetic-lethal genetic interactions comprehensively within yeast and bacteria models. The resulting genetic interaction networks provide a landscape to rationally predict and exploit drug synergy between cognate inhibitors. We posit that synergistic combination agents provide an important and largely unexploited strategy to 'repurpose' existing chemical space and simultaneously address issues of potency, spectrum, toxicity and drug resistance in early stages of antimicrobial drug discovery.

Target identification for small bioactive molecules: finding the needle in the haystack

Identification and confirmation of bioactive small-molecule targets is a crucial, often decisive step both in academic and pharmaceutical research. Through the development and availability of several new experimental techniques, target identification is, in principle, feasible, and the number of successful examples steadily grows. However, a generic methodology that can successfully be applied in the majority of the cases has not yet been established. Herein we summarize current methods for target identification of small molecules, primarily for a chemistry audience but also the biological community, for example, the chemist or biologist attempting to identify the target of a given bioactive compound. We describe the most frequently employed experimental approaches for target identification and provide several representative examples illustrating the state-of-the-art. Among the techniques currently available, protein affinity isolation using suitable small-molecule probes (pulldown) and subsequent mass spectrometric analysis of the isolated proteins appears to be most powerful and most frequently applied. To provide guidance for rapid entry into the field and based on our own experience we propose a typical workflow for target identification, which centers on the application of chemical proteomics as the key step to generate hypotheses for potential target proteins.

FitSearch: a robust way to interpret a yeast fitness profile in terms of drug's mode-of-action

Background

Yeast deletion-mutant collections have been successfully used to infer the mode-of-action of drugs especially by profiling chemical-genetic and genetic-genetic interactions on a genome-wide scale. Although tens of thousands of those profiles are publicly available, a lack of an accurate method for mining such data has been a major bottleneck for more widespread use of these useful resources.

Results

For general usage of those public resources, we designed FitRankDB as a general repository of fitness profiles, and developed a new search algorithm, FitSearch, for identifying the profiles that have a high similarity score with statistical significance for a given fitness profile. We demonstrated that our new repository and algorithm are highly beneficial to researchers who attempting to make hypotheses based on unknown modes-of-action of bioactive compounds, regardless of the types of experiments that have been performed using yeast deletion-mutant collection in various types of different measurement platforms, especially non-chip-based platforms.

Conclusions

We showed that our new database and algorithm are useful when attempting to construct a hypothesis regarding the unknown function of a bioactive compound through small-scale experiments with a yeast deletion collection in a platform independent manner. The FitRankDB and FitSearch enhance the ease of searching public yeast fitness profiles and obtaining insights into unknown mechanisms of action of drugs. FitSearch is freely available at http://fitsearch.kaist.ac.kr.

Integrated compound profiling screens identify the mitochondrial electron transport chain as the molecular target of the natural products manassantin, sesquicillin, and arctigenin

Phenotypic compound screens can be used to identify novel targets in signaling pathways and disease processes, but the usefulness of these screens depends on the ability to quickly determine the target and mechanism of action of the molecules identified as hits. One fast route to discovering the mechanism of action of a compound is to profile its properties and to match this profile with those of compounds of known mechanism of action. In this work, the Novartis collection of over 12,000 pure natural products was screened for effects on early zebrafish development. The largest phenotypic class of hits, which caused developmental arrest without necrosis, contained known electron transport chain inhibitors and many compounds of unknown mechanism of action. High-throughput transcriptional profiling revealed that these compounds are mechanistically related to one another. Metabolic and biochemical assays confirmed that all of the molecules that induced developmental arrest without necrosis inhibited the electron transport chain. These experiments demonstrate that the electron transport chain is the target of the natural products manassantin, sesquicillin, and arctigenin. The overlap between the zebrafish and transcriptional profiling screens was not perfect, indicating that multiple profiling screens are necessary to fully characterize molecules of unknown function. Together, zebrafish screening and transcriptional profiling represent sensitive and scalable approaches for identifying bioactive compounds and elucidating their mechanism of action.

A novel calcineurin-independent activity of cyclosporin A in Saccharomyces cerevisiae

Fungi rely on regulatory networks to coordinate sensing of environmental stress with initiation of responses crucial for survival. Antifungal drugs are a specific type of environmental stress with broad clinical relevance. Small molecules with antifungal activity are ubiquitous in the environment, and are produced by a myriad of microbes in competitive natural communities. The echinocandins are fungal fermentation products and the most recently developed class of antifungals, with those in clinical use being semisynthetic derivatives that target the fungal cell wall by inhibiting 1,3-β-D-glucan synthase. Recent studies implicate the protein phosphatase calcineurin as a key regulator of cellular stress responses required for fungal survival of echinocandin-induced cell wall stress. Pharmacological inhibition of calcineurin can be achieved using the natural product and immunosuppressive drug cyclosporin A, which inhibits calcineurin by binding to the immunophilin Cpr1. This drug-protein complex inhibits the interaction between the regulatory and catalytic subunits of calcineurin, an interaction necessary for calcineurin function. Here, we report on potent activity of cyclosporin A when combined with the echinocandin micafungin against the model yeast Saccharomyces cerevisiae that is independent of its known mechanism of action of calcineurin inhibition. This calcineurin-independent synergy does not involve any of the 12 immunophilins known in yeast, individually or in combination, and is not mediated by any of the multidrug transporters encoded or controlled by YOR1, SNQ2, PDR5, PDR10, PDR11, YCF1, PDR15, ADP1, VMR1, NFT1, BPT1, YBT1, YNR070w, YOL075c, AUS1, PDR12, PDR1 and/or PDR3. Genome-wide haploinsufficiency profiling (HIP) and homozygous deletion profiling (HOP) strongly implicate the cell wall biosynthesis and integrity pathways as being central to the calcineurin-independent activity of cyclosporin A. Thus, systems level chemical genomic approaches implicate key cellular pathways in a novel mechanism of antifungal drug synergy.

Large-scale reverse docking profiles and their applications

BACKGROUND

Reverse docking approaches have been explored in previous studies on drug discovery to overcome some problems in traditional virtual screening. However, current reverse docking approaches are problematic in that the target spaces of those studies were rather small, and their applications were limited to identifying new drug targets. In this study, we expanded the scope of target space to a set of all protein structures currently available and developed several new applications of reverse docking method.

RESULTS

We generated 2D Matrix of docking scores among all the possible protein structures in yeast and human and 35 famous drugs. By clustering the docking profile data and then comparing them with fingerprint-based clustering of drugs, we first showed that our data contained accurate information on their chemical properties. Next, we showed that our method could be used to predict the druggability of target proteins. We also showed that a combination of sequence similarity and docking profile similarity could predict the enzyme EC numbers more accurately than sequence similarity alone. In two case studies, 5-fluorouracil and cycloheximide, we showed that our method can successfully find identifying target proteins.

CONCLUSIONS

By using a large number of protein structures, we improved the sensitivity of reverse docking and showed that using as many protein structure as possible was important in finding real binding targets.

Chemical genomic screening of a Saccharomyces cerevisiae genomewide mutant collection reveals genes required for defense against four antimicrobial peptides derived from proteins found in human saliva

To compare the effects of four antimicrobial peptides (MUC7 12-mer, histatin 12-mer, cathelicidin KR20, and a peptide containing lactoferricin amino acids 1 to 11) on the yeast Saccharomyces cerevisiae, we employed a genomewide fitness screen of combined collections of mutants with homozygous deletions of nonessential genes and heterozygous deletions of essential genes. When an arbitrary fitness score cutoffs of 1 (indicating a fitness defect, or hypersensitivity) and -1 (indicating a fitness gain, or resistance) was used, 425 of the 5,902 mutants tested exhibited altered fitness when treated with at least one peptide. Functional analysis of the 425 strains revealed enrichment among the identified deletions in gene groups associated with the Gene Ontology (GO) terms "ribosomal subunit," "ribosome biogenesis," "protein glycosylation," "vacuolar transport," "Golgi vesicle transport," "negative regulation of transcription," and others. Fitness profiles of all four tested peptides were highly similar, particularly among mutant strains exhibiting the greatest fitness defects. The latter group included deletions in several genes involved in induction of the RIM101 signaling pathway, including several components of the ESCRT sorting machinery. The RIM101 signaling regulates response of yeasts to alkaline and neutral pH and high salts, and our data indicate that this pathway also plays a prominent role in regulating protective measures against all four tested peptides. In summary, the results of the chemical genomic screens of S. cerevisiae mutant collection suggest that the four antimicrobial peptides, despite their differences in structure and physical properties, share many interactions with S. cerevisiae cells and consequently a high degree of similarity between their modes of action.

Forward chemical genetics in yeast for discovery of chemical probes targeting metabolism

The many virtues that made the yeast Saccharomyces cerevisiae a dominant model organism for genetics and molecular biology, are now establishing its role in chemical genetics. Its experimental tractability (i.e., rapid doubling time, simple culture conditions) and the availability of powerful tools for drug-target identification, make yeast an ideal organism for high-throughput phenotypic screening. It may be especially applicable for the discovery of chemical probes targeting highly conserved cellular processes, such as metabolism and bioenergetics, because these probes would likely inhibit the same processes in higher eukaryotes (including man). Importantly, changes in normal cellular metabolism are associated with a variety of diseased states (including neurological disorders and cancer), and exploiting these changes for therapeutic purposes has accordingly gained considerable attention. Here, we review progress and challenges associated with forward chemical genetic screening in yeast. We also discuss evidence supporting these screens as a useful strategy for discovery of new chemical probes and new druggable targets related to cellular metabolism.

Air proteins control differential TRAMP substrate specificity for nuclear RNA surveillance

RNA surveillance systems function at critical steps during the formation and function of RNA molecules in all organisms. The RNA exosome plays a central role in RNA surveillance by processing and degrading RNA molecules in the nucleus and cytoplasm of eukaryotic cells. The exosome functions as a complex of proteins composed of a nine-member core and two ribonucleases. The identity of the molecular determinants of exosome RNA substrate specificity remains an important unsolved aspect of RNA surveillance. In the nucleus of Saccharomyces cerevisiae, TRAMP complexes recognize and polyadenylate RNAs, which enhances RNA degradation by the exosome and may contribute to its specificity. TRAMPs contain either of two putative RNA-binding factors called Air proteins. Previous studies suggested that these proteins function interchangeably in targeting the poly(A)-polymerase activity of TRAMPs to RNAs. Experiments reported here show that the Air proteins govern separable functions. Phenotypic analysis and RNA deep-sequencing results from air mutants reveal specific requirements for each Air protein in the regulation of the levels of noncoding and coding RNAs. Loss of these regulatory functions results in specific metabolic and plasmid inheritance defects. These findings reveal differential functions for Air proteins in RNA metabolism and indicate that they control the substrate specificity of the RNA exosome.

Yeast and the AIDS virus: the odd couple

Despite being simple eukaryotic organisms, the yeasts Saccharomyces cerevisiae and Schizosaccharomyces pombe have been widely used as a model to study human pathologies and the replication of human, animal, and plant viruses, as well as the function of individual viral proteins. The complete genome of S. cerevisiae was the first of eukaryotic origin to be sequenced and contains about 6,000 genes. More than 75% of the genes have an assigned function, while more than 40% share conserved sequences with known or predicted human genes. This strong homology has allowed the function of human orthologs to be unveiled starting from the data obtained in yeast. RNA plant viruses were the first to be studied in yeast. In this paper, we focus on the use of the yeast model to study the function of the proteins of human immunodeficiency virus type 1 (HIV-1) and the search for its cellular partners. This human retrovirus is the cause of AIDS. The WHO estimates that there are 33.4 million people worldwide living with HIV/AIDS, with 2.7 million new HIV infections per year and 2.0 million annual deaths due to AIDS. Current therapy is able to control the disease but there is no permanent cure or a vaccine. By using yeast, it is possible to dissect the function of some HIV-1 proteins and discover new cellular factors common to this simple cell and humans that may become potential therapeutic targets, leading to a long-lasting treatment for AIDS.

Polymorphisms in the mitochondrial ribosome recycling factor EF-G2mt/MEF2 compromise cell respiratory function and increase atorvastatin toxicity

Mitochondrial translation, essential for synthesis of the electron transport chain complexes in the mitochondria, is governed by nuclear encoded genes. Polymorphisms within these genes are increasingly being implicated in disease and may also trigger adverse drug reactions. Statins, a class of HMG-CoA reductase inhibitors used to treat hypercholesterolemia, are among the most widely prescribed drugs in the world. However, a significant proportion of users suffer side effects of varying severity that commonly affect skeletal muscle. The mitochondria are one of the molecular targets of statins, and these drugs have been known to uncover otherwise silent mitochondrial mutations. Based on yeast genetic studies, we identify the mitochondrial translation factor MEF2 as a mediator of atorvastatin toxicity. The human ortholog of MEF2 is the Elongation Factor Gene (EF-G) 2, which has previously been shown to play a specific role in mitochondrial ribosome recycling. Using small interfering RNA (siRNA) silencing of expression in human cell lines, we demonstrate that the EF-G2mt gene is required for cell growth on galactose medium, signifying an essential role for this gene in aerobic respiration. Furthermore, EF-G2mt silenced cell lines have increased susceptibility to cell death in the presence of atorvastatin. Using yeast as a model, conserved amino acid variants, which arise from non-synonymous single nucleotide polymorphisms (SNPs) in the EF-G2mt gene, were generated in the yeast MEF2 gene. Although these mutations do not produce an obvious growth phenotype, three mutations reveal an atorvastatin-sensitive phenotype and further analysis uncovers a decreased respiratory capacity. These findings constitute the first reported phenotype associated with SNPs in the EF-G2mt gene and implicate the human EF-G2mt gene as a pharmacogenetic candidate gene for statin toxicity in humans.

The mitochondria are responsible for producing the cell's energy. Energy production is the result of carefully orchestrated interactions between proteins encoded by the mitochondrial DNA and by nuclear DNA. Sequence variations in genes encoding these proteins have been shown to cause disease and adverse drug reactions in patients. The cholesterol-lowering drugs statins are one class of drugs that interfere with mitochondrial function. Statins are one of the most prescribed drugs in the western world, but many users suffer side effects, commonly muscle pain. In severe cases this can lead to muscle breakdown and liver failure. In this study, we discover that disruption of a mitochondrial translation gene, EF-G2mt, impedes respiration and increases cell death when exposed to statin. Using the simple unicellular organism yeast as a model, the activity of naturally occurring human EF-G2mt variants is tested. Three of these variants render yeast cells more sensitive to statin. Patients who possess these EF-G2mt variations may be more susceptible to statin side effects. Importantly, the test for statin sensitivity also led to the discovery of mutants that have a reduced energy production capacity. The decreased ability to produce energy is linked to a number of diseases, including myopathies and liver failure.

Multiplex assay for condition-dependent changes in protein-protein interactions

Changes in protein-protein interactions that occur in response to environmental cues are difficult to uncover and have been poorly characterized to date. Here we describe a yeast-based assay that allows many binary protein interactions to be assessed in parallel and under various conditions. This method combines molecular bar-coding and tag array technology with the murine dihydrofolate reductase-based protein-fragment complementation assay. A total of 238 protein-fragment complementation assay strains, each representing a unique binary protein complex, were tagged with molecular barcodes, pooled, and then interrogated against a panel of 80 diverse small molecules. Our method successfully identified specific disruption of the Hom3:Fpr1 interaction by the immunosuppressant FK506, illustrating the assay's capacity to identify chemical inhibitors of protein-protein interactions. Among the additional findings was specific cellular depletion of the Dst1:Rbp9 complex by the anthracycline drug doxorubicin, but not by the related drug idarubicin. The assay also revealed chemical-induced accumulation of several binary multidrug transporter complexes that largely paralleled increases in transcript levels. Further assessment of two such interactions (Tpo1:Pdr5 and Snq2:Pdr5) in the presence of 1,246 unique chemical compounds revealed a positive correlation between drug lipophilicity and the drug response in yeast.

Yeast toxicogenomics: genome-wide responses to chemical stresses with impact in environmental health, pharmacology, and biotechnology

The emerging transdisciplinary field of Toxicogenomics aims to study the cell response to a given toxicant at the genome, transcriptome, proteome, and metabolome levels. This approach is expected to provide earlier and more sensitive biomarkers of toxicological responses and help in the delineation of regulatory risk assessment. The use of model organisms to gather such genomic information, through the exploitation of Omics and Bioinformatics approaches and tools, together with more focused molecular and cellular biology studies are rapidly increasing our understanding and providing an integrative view on how cells interact with their environment. The use of the model eukaryote Saccharomyces cerevisiae in the field of Toxicogenomics is discussed in this review. Despite the limitations intrinsic to the use of such a simple single cell experimental model, S. cerevisiae appears to be very useful as a first screening tool, limiting the use of animal models. Moreover, it is also one of the most interesting systems to obtain a truly global understanding of the toxicological response and resistance mechanisms, being in the frontline of systems biology research and developments. The impact of the knowledge gathered in the yeast model, through the use of Toxicogenomics approaches, is highlighted here by its use in prediction of toxicological outcomes of exposure to pesticides and pharmaceutical drugs, but also by its impact in biotechnology, namely in the development of more robust crops and in the improvement of yeast strains as cell factories.

CMRF: analyzing differential gene regulation in two group perturbation experiments

BACKGROUND

Microarray experiments often measure expressions of genes taken from sample tissues in the presence of external perturbations such as medication, radiation, or disease. The external perturbation can change the expressions of some genes directly or indirectly through gene interaction network. In this paper, we focus on an important class of such microarray experiments that inherently have two groups of tissue samples. When such different groups exist, the changes in expressions for some of the genes after the perturbation can be different between the two groups. It is not only important to identify the genes that respond differently across the two groups, but also to mine the reason behind this differential response. In this paper, we aim to identify the cause of this differential behavior of genes, whether because of the perturbation or due to interactions with other genes.

RESULTS

We propose a new probabilistic Bayesian method CMRF based on Markov Random Field to identify such genes. CMRF leverages the information about gene interactions as the prior of the model. We compare the accuracy of CMRF with SSEM and Student's t test and our old method SMRF on semi-synthetic dataset generated from microarray data. CMRF obtains high accuracy and outperforms all the other three methods. We also conduct a statistical significance test using a parametric noise based experiment to evaluate the accuracy of our method. In this experiment, CMRF generates significant regions of confidence for various parameter settings.

CONCLUSIONS

In this paper, we solved the problem of finding primarily differentially regulated genes in the presence of external perturbations when the data is sampled from two groups. The probabilistic Bayesian method CMRF based on Markov Random Field incorporates dependency structure of the gene networks as the prior to the model. Experimental results on synthetic and real datasets demonstrated the superiority of CMRF compared to other simple techniques.

Regulation of cell wall biogenesis in Saccharomyces cerevisiae: the cell wall integrity signaling pathway

The yeast cell wall is a strong, but elastic, structure that is essential not only for the maintenance of cell shape and integrity, but also for progression through the cell cycle. During growth and morphogenesis, and in response to environmental challenges, the cell wall is remodeled in a highly regulated and polarized manner, a process that is principally under the control of the cell wall integrity (CWI) signaling pathway. This pathway transmits wall stress signals from the cell surface to the Rho1 GTPase, which mobilizes a physiologic response through a variety of effectors. Activation of CWI signaling regulates the production of various carbohydrate polymers of the cell wall, as well as their polarized delivery to the site of cell wall remodeling. This review article centers on CWI signaling in Saccharomyces cerevisiae through the cell cycle and in response to cell wall stress. The interface of this signaling pathway with other pathways that contribute to the maintenance of cell wall integrity is also discussed.

Compound prioritization methods increase rates of chemical probe discovery in model organisms

Preselection of compounds that are more likely to induce a phenotype can increase the efficiency and reduce the costs for model organism screening. To identify such molecules, we screened ~81,000 compounds in Saccharomyces cerevisiae and identified ~7500 that inhibit cell growth. Screening these growth-inhibitory molecules across a diverse panel of model organisms resulted in an increased phenotypic hit-rate. These data were used to build a model to predict compounds that inhibit yeast growth. Empirical and in silico application of the model enriched the discovery of bioactive compounds in diverse model organisms. To demonstrate the potential of these molecules as lead chemical probes, we used chemogenomic profiling in yeast and identified specific inhibitors of lanosterol synthase and of stearoyl-CoA 9-desaturase. As community resources, the ~7500 growth-inhibitory molecules have been made commercially available and the computational model and filter used are provided.

Evolutionary and Biochemical Aspects of Chemical Stress Resistance in Saccharomyces cerevisiae

Large-scale chemical genetics screens (chemogenomics) in yeast have been widely used to find drug targets, understand the mechanism-of-action of compounds, and unravel the biochemistry of drug resistance. Chemogenomics is based on the comparison of growth of gene deletants in the presence and absence of a chemical substance. Such studies showed that more than 90% of the yeast genes are required for growth in the presence of at least one chemical. Analysis of these data, using computational approaches, has revealed non-trivial features of the natural chemical tolerance systems. As a result two non-overlapping sets of genes are seen to respectively impart robustness and evolvability in the context of natural chemical resistance. The former is composed of multidrug-resistance genes, whereas the latter comprises genes sharing chemical genetic profiles with many others. Recent publications showing the potential applications chemogenomics in studying the pharmacological basis of various drugs are discussed, as well as the expansion of chemogenomics to other organisms. Finally, integration of chemogenomics with sensitive sequence analysis and ubiquitination/phosphorylation data led to the discovery of a new conserved domain and important post-translational modification pathways involved in stress resistance.

Yeast as a model eukaryote in toxinology: a functional genomics approach to studying the molecular basis of action of pharmacologically active molecules

Yeast Saccharomyces cerevisiae has proven to be a relevant and convenient model organism for the study of diverse biological phenomena, due to its straightforward genetics, cost-effectiveness and rapid growth, combined with the typical characteristics of a eukaryotic cell. More than 40% of yeast proteins share at least part of their primary amino acid sequence with the corresponding human protein, making yeast a valuable model in biomedical research. In the last decade, high-throughput and genome-wide experimental approaches developed in yeast have paved the way to functional genomics that aims at a global understanding of the relationship between genotype and phenotype. In this review we first present the yeast strain and plasmid collections for genome-wide experimental approaches to study complex interactions between genes, proteins and endo- or exogenous small molecules. We describe methods for protein-protein, protein-DNA, genetic and chemo-genetic interactions, as well as localization studies, focussing on their application in research on small pharmacologically active molecules. Next we review the use of yeast as a model organism in neurobiology, emphasizing work done towards elucidating the pathogenesis of neurodegenerative diseases and the mechanism of action of neurotoxic phospholipases A(2).

Structural determination of NSC 670224, synthesis of analogues and biological evaluation

Follow my lead! NSC 670224, previously shown to be toxic to Saccharomyces cerevisiae at low micromolar concentrations, potentially acts via a mechanism of action related to that of tamoxifen (NSC 180973), breast cancer drug. The structure of NSC 670224, previously thought to be a 2,4-dichloro arene, was established as the 3,4-dichloro arene, and a focused library of analogues were synthesized and biologically evaluated.

Mitochondrial electron transport is the cellular target of the oncology drug elesclomol

Elesclomol is a first-in-class investigational drug currently undergoing clinical evaluation as a novel cancer therapeutic. The potent antitumor activity of the compound results from the elevation of reactive oxygen species (ROS) and oxidative stress to levels incompatible with cellular survival. However, the molecular target(s) and mechanism by which elesclomol generates ROS and subsequent cell death were previously undefined. The cellular cytotoxicity of elesclomol in the yeast S. cerevisiae appears to occur by a mechanism similar, if not identical, to that in cancer cells. Accordingly, here we used a powerful and validated technology only available in yeast that provides critical insights into the mechanism of action, targets and processes that are disrupted by drug treatment. Using this approach we show that elesclomol does not work through a specific cellular protein target. Instead, it targets a biologically coherent set of processes occurring in the mitochondrion. Specifically, the results indicate that elesclomol, driven by its redox chemistry, interacts with the electron transport chain (ETC) to generate high levels of ROS within the organelle and consequently cell death. Additional experiments in melanoma cells involving drug treatments or cells lacking ETC function confirm that the drug works similarly in human cancer cells. This deeper understanding of elesclomol's mode of action has important implications for the therapeutic application of the drug, including providing a rationale for biomarker-based stratification of patients likely to respond in the clinical setting.

Barcode sequencing for understanding drug-gene interactions

With the advent of next-generation sequencing (NGS) technology, methods previously developed for microarrays have been adapted for use by NGS. Here we describe in detail a protocol for Barcode analysis by sequencing (Bar-seq) to assess pooled competitive growth of individually barcoded yeast deletion mutants. This protocol has been optimized on two sequencing platforms: Illumina's Genome Analyzer IIx/HiSeq2000 and Life Technologies SOLiD3/5500. In addition, we provide guidelines for assessment of human knockdown cells using short-hairpin RNAs (shRNA) and an Illumina sequencing readout.

Genetic interactions in translational research on cancer

Genetic interactions are functional crosstalk among different genetic loci that lead to phenotypic changes, such as health or viability alterations. A disease or lethal phenotype that results from the combined effects of gene mutations at different loci is termed a synthetic sickness or synthetic lethality, respectively. Studies of genetic interaction have provided insight on the relationships among biochemical processes or pathways. Cancer results from genetic interactions and is a major focus of current studies in genetic interactions. Various basic and translational cancer studies have explored the concept of genetic interactions, including studies of the mechanistic characterization of genes, drug discovery, biomarker identification and the rational design of combination therapies. This review discusses the implications of genetic interactions in the development of personalized cancer therapies, the identification of treatment-responsive genes, the delineation of mechanisms of chemoresistance and the rational design of combined therapeutic strategies to overcome drug resistance.

Chemical-genomic profiling: systematic analysis of the cellular targets of bioactive molecules

Chemical-genomic (CG) profiling of bioactive compounds is a powerful approach for drug target identification and mode of action studies. Within the last decade, research focused largely on the development and application of CG approaches in the model yeast Saccharomyces cerevisiae. The success of these methods has sparked interest in transitioning CG profiling to other biological systems to extend clinical and evolutionary relevance. Additionally, CG profiling has proven to enhance drug-synergy screens for developing combinatorial therapies. Herein, we briefly review CG profiling, focusing on emerging cross-species technologies and novel drug-synergy applications, as well as outlining needs within the field.

Identification and validation of protein targets of bioactive small molecules

Identification and validation of protein targets of bioactive small molecules is an important problem in chemical biology and drug discovery. Currently, no single method is satisfactory for this task. Here, we provide an overview of common methods for target identification and validation that historically were most successful. We have classified for the first time the existing methods into two distinct and complementary types, the 'top-down' and 'bottom-up' approaches. In a typical top-down approach, the cellular phenotype is used as a starting point and the molecular target is approached through systematic narrowing down of possibilities by taking advantage of the detailed existing knowledge of cellular pathways and processes. In contrast, the bottom-up approach entails the direct detection and identification of the molecular targets using affinity-based or genetic methods. A special emphasis is placed on target validation, including correlation analysis and genetic methods, as this area is often ignored despite its importance.

Bugs, drugs and chemical genomics

The serendipitous discovery of penicillin inspired intensive research into how small molecules affect basic cellular processes and their potential to treat disease. Biochemical and genetic approaches have been fundamental for clarifying small-molecule modes of action. Genomic technologies have permitted the use of chemical-genetic strategies that comprehensively study compound-target relationships in the context of a living cell, providing a systems biology view of both the cellular targets and the interdependent networks that respond to chemical stress. These studies highlight the fact that in vitro determinations of mechanism rarely translate into a complete understanding of drug behavior in the cell. Here, we review key discoveries that gave rise to the field of chemical genetics, with particular attention to chemical-genetic strategies developed for bakers' yeast, their extension to clinically relevant microbial pathogens, and the potential of these approaches to affect antimicrobial drug discovery.

Genetic and proteomic approaches to identify cancer drug targets

While target-based small-molecule discovery has taken centre-stage in the pharmaceutical industry, there are many cancer-promoting proteins not easily addressed with a traditional target-based screening approach. In order to address this problem, as well as to identify modulators of biological states in the absence of knowing the protein target of the state switch, alternative phenotypic screening approaches, such as gene expression-based and high-content imaging, have been developed. With this renewed interest in phenotypic screening, however, comes the challenge of identifying the binding protein target(s) of small-molecule hits. Emerging technologies have the potential to improve the process of target identification. In this review, we discuss the application of genomic (gene expression-based), genetic (short hairpin RNA and open reading frame screening), and proteomic approaches to protein target identification.

Competition experiments coupled with high-throughput analyses for functional genomics studies in yeast

Competition experiments are an effective way to provide a measurement of the fitness of yeast strains. The availability of the Saccharomyces cerevisiae yeast knock-out (YKO) deletion collection allows scientists to retrieve fitness data for the ~6,000 S. cerevisiae genes at the same time in a given environment. The molecular barcodes, characterizing each yeast mutant, serve as strain identifiers, which can be detected in a single microarray analysis. Competition experiments in continuous culture using chemically defined media allow a more specific discrimination of the strains based on their fitness profile. With this high-throughput approach, a series of genes that, when one allele is missing, result in either defective (haplo-insufficient) or favored (haplo-proficient) growth phenotype have been discovered, for each nutrient-limiting condition tested. While haplo-insufficient genes seemed to overlap largely across all the media used, the haplo-proficient ones seem to be more environment specific. For example, genes involved in the protein secretion pathway were highly haplo-insufficient in all the contexts, whereas most of the genes encoding for proteasome components showed a haplo-proficient phenotype specific to nitrogen-limiting conditions. In this chapter, the method used for implementation of competition experiments for high-throughput studies in yeast is presented.

A METHOD FOR THE DETECTION OF MEANINGFUL AND REPRODUCIBLE GROUP SIGNATURES FROM GENE EXPRESSION PROFILES

Gene expression microarrays are commonly used to detect the biological signature of a disease or to gain a better understanding of the underlying mechanism of how a group of drugs treat a specific disease. The outcome of such experiments, e.g. the signature, is a list of differentially expressed genes. Reproducibility across independent experiments remains a challenge. We are interested in creating a method that can detect the shared signature of a group of expression profiles, e.g. a group of samples from individuals with the same disease or a group of drugs that treat the same therapeutic indication. We have developed a novel Weighted Influence — Rank of Ranks (WIMRR) method, and we demonstrate its ability to produce both meaningful and reproducible group signatures.

Genome-wide assessment of the carriers involved in the cellular uptake of drugs: a model system in yeast

Background

The uptake of drugs into cells has traditionally been considered to be predominantly via passive diffusion through the bilayer portion of the cell membrane. The recent recognition that drug uptake is mostly carrier-mediated raises the question of which drugs use which carriers.

Results

To answer this, we have constructed a chemical genomics platform built upon the yeast gene deletion collection, using competition experiments in batch fermenters and robotic automation of cytotoxicity screens, including protection by 'natural' substrates. Using these, we tested 26 different drugs and identified the carriers required for 18 of the drugs to gain entry into yeast cells.

Conclusions

As well as providing a useful platform technology, these results further substantiate the notion that the cellular uptake of pharmaceutical drugs normally occurs via carrier-mediated transport and indicates that establishing the identity and tissue distribution of such carriers should be a major consideration in the design of safe and effective drugs.