Off-target effects of psychoactive drugs revealed by genome-wide assays in yeast

Predicting therapeutic and side effects from drug binding affinities to human proteome structures

Evaluation of the binding affinities of drugs to proteins is a crucial process for identifying drug pharmacological actions, but it requires three dimensional structures of proteins. Herein, we propose novel computational methods to predict the therapeutic indications and side effects of drug candidate compounds from the binding affinities to human protein structures on a proteome-wide scale. Large-scale docking simulations were performed for 7,582 drugs with 19,135 protein structures revealed by AlphaFold (including experimentally unresolved proteins), and machine learning models on the proteome-wide binding affinity score (PBAS) profiles were constructed. We demonstrated the usefulness of the method for predicting the therapeutic indications for 559 diseases and side effects for 285 toxicities. The method enabled to predict drug indications for which the related protein structures had not been experimentally determined and to successfully extract proteins eliciting the side effects. The proposed method will be useful in various applications in drug discovery.

Fast-evolving cofactors regulate the role of HEATR5 complexes in intra-Golgi trafficking

The highly conserved HEATR5 proteins are best known for their roles in membrane traffic mediated by the adaptor protein complex-1 (AP1). HEATR5 proteins rely on fast-evolving cofactors to bind to AP1. However, how HEATR5 proteins interact with these cofactors is unknown. Here, we report that the budding yeast HEATR5 protein, Laa1, functions in two biochemically distinct complexes. These complexes are defined by a pair of mutually exclusive Laa1-binding proteins, Laa2 and the previously uncharacterized Lft1/Yml037c. Despite limited sequence similarity, biochemical analysis and structure predictions indicate that Lft1 and Laa2 bind Laa1 via structurally similar mechanisms. Both Laa1 complexes function in intra-Golgi recycling. However, only the Laa2-Laa1 complex binds to AP1 and contributes to its localization. Finally, structure predictions indicate that human HEATR5 proteins bind to a pair of fast-evolving interacting partners via a mechanism similar to that observed in yeast. These results reveal mechanistic insight into how HEATR5 proteins bind their cofactors and indicate that Laa1 performs functions besides recruiting AP1.

Two functionally distinct HEATR5 protein complexes are defined by fast-evolving co-factors in yeast

The highly conserved HEATR5 proteins are best known for their roles in membrane traffic mediated by the adaptor protein complex-1 (AP1). HEATR5 proteins rely on fast-evolving co-factors to bind to AP1. However, how HEATR5 proteins interact with these co-factors is unknown. Here, we report that the budding yeast HEATR5 protein, Laa1, functions in two biochemically distinct complexes. These complexes are defined by a pair of mutually exclusive Laa1-binding proteins, Laa2 and the previously uncharacterized Lft1/Yml037c. Despite limited sequence similarity, biochemical analysis and structure predictions indicate that Lft1 and Laa2 bind Laa1 via structurally similar mechanisms. Both Laa1 complexes function in intra-Golgi recycling. However, only the Laa2-Laa1 complex binds to AP1 and contributes to its localization. Finally, structure predictions indicate that human HEATR5 proteins bind to a pair of fast-evolving interacting partners via a mechanism similar to that observed in yeast. These results reveal mechanistic insight into how HEATR5 proteins bind their co-factors and indicate that Laa1 performs functions besides recruiting AP1.

Global analysis of the yeast knockout phenome

Genome-wide phenotypic screens in the budding yeast Saccharomyces cerevisiae, enabled by its knockout collection, have produced the largest, richest, and most systematic phenotypic description of any organism. However, integrative analyses of this rich data source have been virtually impossible because of the lack of a central data repository and consistent metadata annotations. Here, we describe the aggregation, harmonization, and analysis of ~14,500 yeast knockout screens, which we call Yeast Phenome. Using this unique dataset, we characterized two unknown genes (YHR045W and YGL117W) and showed that tryptophan starvation is a by-product of many chemical treatments. Furthermore, we uncovered an exponential relationship between phenotypic similarity and intergenic distance, which suggests that gene positions in both yeast and human genomes are optimized for function.

Prediction and Identification of GPCRs Targeting for Drug Repurposing in Osteosarcoma

Background

Osteosarcoma (OS) is a malignant bone tumor common in children and adolescents. The 5-year survival rate is only 67-69% and there is an urgent need to explore novel drugs effective for the OS. G protein-coupled receptors (GPCRs) are the common drug targets and have been found to be associated with the OS, but have been seldom used in OS.

Methods

The GPCRs were obtained from GPCRdb, and the GPCRs expression profile of the OS was downloaded from the UCSC Xena platform including clinical data. 10-GPCRs model signatures related to OS risk were identified by risk model analysis with R software. The predictive ability and pathological association of the signatures in OS were explored by bio-informatics analysis. The therapeutic effect of the target was investigated, followed by the investigation of the targeting drug by the colony formation experiment were.

Results

We screened out 10 representative GPCRs from 50 GPCRs related to OS risk and established a 10-GPCRs prognostic model (with CCR4, HCRTR2, DRD2, HTR1A, GPR158, and GPR3 as protective factors, and HTR1E, OPN3, GRM4, and GPR144 as risk factors). We found that the low-risk group of the model was significantly associated with the higher survival probability, with the area under the curve (AUC) of the ROC greater than 0.9, conforming with the model. Moreover, both risk-score and metastasis were the independent risk factor of the OS, and the risk score was positively associated with the metastatic. Importantly, the CD8 T-cells were more aggregated in the low-risk group, in line with the predict survival rate of the model. Finally, we found that DRD2 was a novel target with approved drugs (cabergoline and bromocriptine), and preliminarily proved the therapeutic effects of the drugs on OS. These novel findings might facilitate the development of OS drugs.

Conclusion

This study offers a satisfactory 10-GPCRs model signature to predict the OS prognostic, and based on the model signature, candidate targets with approved drugs were provided.

Antimicrobial Properties of Antidepressants and Antipsychotics-Possibilities and Implications

The spreading of antibiotic resistance is responsible annually for over 700,000 deaths worldwide, and the prevision is that this number will increase exponentially. The identification of new antimicrobial treatments is a challenge that requires scientists all over the world to collaborate. Developing new drugs is an extremely long and costly process, but it could be paralleled by drug repositioning. The latter aims at identifying new clinical targets of an “old” drug that has already been tested, approved, and even marketed. This approach is very intriguing as it could reduce costs and speed up approval timelines, since data from preclinical studies and on pharmacokinetics, pharmacodynamics, and toxicity are already available. Antidepressants and antipsychotics have been described to inhibit planktonic and sessile growth of different yeasts and bacteria. The main findings in the field are discussed in this critical review, along with the description of the possible microbial targets of these molecules. Considering their antimicrobial activity, the manuscript highlights important implications that the administration of antidepressants and antipsychotics may have on the gut microbiome.

Systems-Level Chemical Biology to Accelerate Antibiotic Drug Discovery

Drug-resistant bacterial infections pose an imminent and growing threat to public health. The discovery and development of new antibiotics of novel chemical class and mode of action that are unsusceptible to existing resistance mechanisms is imperative for tackling this threat. Modern industrial drug discovery, however, has failed to provide new drugs of this description, as it is dependent largely on a reductionist genes-to-drugs research paradigm. We posit that the lack of success in new antibiotic drug discovery is due in part to a lack of understanding of the bacterial cell system as whole. A fundamental understanding of the architecture and function of bacterial systems has been elusive but is of critical importance to design strategies to tackle drug-resistant bacterial pathogens.Increasingly, systems-level approaches are rewriting our understanding of the cell, defining a dense network of redundant and interacting components that resist perturbations of all kinds, including by antibiotics. Understanding the network properties of bacterial cells requires integrative, systematic, and genome-scale approaches. These methods strive to understand how the phenotypic behavior of bacteria emerges from the many interactions of individual molecular components that constitute the system. With the ability to examine genomic, transcriptomic, proteomic, and metabolomic consequences of, for example, genetic or chemical perturbations, researchers are increasingly moving away from one-gene-at-a-time studies to consider the system-wide response of the cell. Such measurements are demonstrating promise as quantitative tools, powerful discovery engines, and robust hypothesis generators with great value to antibiotic drug discovery.In this Account, we describe our thinking and findings using systems-level studies aimed at understanding bacterial physiology broadly and in uncovering new antibacterial chemical matter of novel mechanism. We share our systems-level toolkit and detail recent technological developments that have enabled unprecedented acquisition of genome-wide interaction data. We focus on three types of interactions: gene-gene, chemical-gene, and chemical-chemical. We provide examples of their use in understanding cell networks and how these insights might be harnessed for new antibiotic discovery. By example, we show the application of these principles in mapping genetic networks that underpin phenotypes of interest, characterizing genes of unknown function, validating small-molecule screening platforms, uncovering novel chemical probes and antibacterial leads, and delineating the mode of action of antibacterial chemicals. We also discuss the importance of computation to these approaches and its probable dominance as a tool for systems approaches in the future. In all, we advocate for the use of systems-based approaches as discovery engines in antibacterial research, both as powerful tools and to stimulate innovation.

Complex interactions of lovastatin with 10 chemotherapeutic drugs: a rigorous evaluation of synergism and antagonism

Background

Evidence bearing on the role of statins in the prevention and treatment of cancer is confounded by the diversity of statins, chemotherapeutic agents and cancer types included in the numerous published studies; consequently, the adjunctive value of statins with chemotherapy remains uncertain.

Methods

We assayed lovastatin in combination with each of ten commonly prescribed chemotherapy drugs in highly reproducible in vitro assays, using a neutral cellular substrate, Saccharomyces cerevisiae. Cell density (OD₆₀₀) data were analyzed for synergism and antagonism using the Loewe additivity model implemented with the Combenefit software.

Results

Four of the ten chemotherapy drugs – tamoxifen, doxorubicin, methotrexate and rapamycin – exhibited net synergism with lovastatin. The remaining six agents (5-fluorouracil, gemcitabine, epothilone, cisplatin, cyclophosphamide and etoposide) compiled neutral or antagonistic scores. Distinctive patterns of synergism and antagonism, often coexisting within the same concentration space, were documented with the various combinations, including those with net synergism scores. Two drug pairs, lovastatin combined with tamoxifen or cisplatin, were also assayed in human cell lines as proof of principle.

Conclusions

The synergistic interactions of tamoxifen, doxorubicin, methotrexate and rapamycin with lovastatin – because they suggest the possibility of clinical utility - merit further exploration and validation in cell lines and animal models. No less importantly, strong antagonistic interactions between certain agents and lovastatin argue for a cautious, data-driven approach before adding a statin to any chemotherapeutic regimen. We also urge awareness of adventitious statin usage by patients entering cancer treatment protocols.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-07963-w.

Potentiated inhibition of Trichoderma virens and other environmental fungi by new biocide combinations

Abstract

Fungi cause diverse, serious socio-economic problems, including biodeterioration of valuable products and materials that spawns a biocides industry worth ~$11 billion globally. To help combat environmental fungi that commonly colonise material products, this study tested the hypothesis that combination of an approved fungicide with diverse agents approved by the FDA (Food and Drug Administration) could reveal potent combinatorial activities with promise for fungicidal applications. The strategy to use approved compounds lowers potential development risks for any effective combinations. A high-throughput assay of 1280 FDA-approved compounds was conducted to find those that potentiate the effect of iodopropynyl-butyl-carbamate (IPBC) on the growth of Trichoderma virens; IPBC is one of the two most widely used Biocidal Products Regulations–approved fungicides. From this library, 34 compounds in combination with IPBC strongly inhibited fungal growth. Low-cost compounds that gave the most effective growth inhibition were tested against other environmental fungi that are standard biomarkers for resistance of synthetic materials to fungal colonisation. Trifluoperazine (TFZ) in combination with IPBC enhanced growth inhibition of three of the five test fungi. The antifungal hexetidine (HEX) potentiated IPBC action against two of the test organisms. Testable hypotheses on the mechanisms of these combinatorial actions are discussed. Neither IPBC + TFZ nor IPBC + HEX exhibited a combinatorial effect against mammalian cells. These combinations retained strong fungal growth inhibition properties after incorporation to a polymer matrix (alginate) with potential for fungicide delivery. The study reveals the potential of such approved compounds for novel combinatorial applications in the control of fungal environmental opportunists.

Key points

• Search with an approved fungicide to find new fungicidal synergies in drug libraries.

• New combinations inhibit growth of key environmental fungi on different matrices.

• The approach enables a more rapid response to demand for new biocides.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00253-021-11211-3.

High-content phenotypic and pathway profiling to advance drug discovery in diseases of unmet need

Conventional thinking in modern drug discovery postulates that the design of highly selective molecules which act on a single disease-associated target will yield safer and more effective drugs. However, high clinical attrition rates and the lack of progress in developing new effective treatments for many important diseases of unmet therapeutic need challenge this hypothesis. This assumption also impinges upon the efficiency of target agnostic phenotypic drug discovery strategies, where early target deconvolution is seen as a critical step to progress phenotypic hits. In this review we provide an overview of how emerging phenotypic and pathway-profiling technologies integrate to deconvolute the mechanism-of-action of phenotypic hits. We propose that such in-depth mechanistic profiling may support more efficient phenotypic drug discovery strategies that are designed to more appropriately address complex heterogeneous diseases of unmet need.

Advances in fungal chemical genomics for the discovery of new antifungal agents

Invasive fungal infections have escalated from a rare curiosity to a major cause of human mortality around the globe. This is in part due to a scarcity in the number of antifungal drugs available to combat mycotic disease, making the discovery of novel bioactive compounds and determining their mode of action of utmost importance. The development and application of chemical genomic assays using the model yeast Saccharomyces cerevisiae has provided powerful methods to identify the mechanism of action of diverse molecules in a living cell. Furthermore, complementary assays are continually being developed in fungal pathogens, most notably Candida albicans and Cryptococcus neoformans, to elucidate compound mechanism of action directly in the pathogen of interest. Collectively, the suite of chemical genetic assays that have been developed in multiple fungal species enables the identification of candidate drug target genes, as well as genes involved in buffering drug target pathways, and genes involved in general cellular responses to small molecules. In this review, we examine current yeast chemical genomic assays and highlight how such resources provide powerful tools that can be utilized to bolster the antifungal pipeline.

Chemical genetics in drug discovery

Chemical-genetic approaches are based on measuring the cellular outcome of combining genetic and chemical perturbations in large-numbers in tandem. In these approaches the contribution of every gene to the fitness of an organism is measured upon exposure to different chemicals. Current technological advances enable the application of chemical genetics to almost any organism and at an unprecedented throughput. Here we review the underlying concepts behind chemical genetics, present its different vignettes and illustrate how such approaches can propel drug discovery.

Possible biomarkers modulating haloperidol efficacy and/or tolerability

Haloperidol (HP) is widely used in the treatment of several forms of psychosis. Despite of its efficacy, HP use is a cause of concern for the elevated risk of adverse drug reactions. adverse drug reactions risk and HP efficacy greatly vary across subjects, indicating the involvement of several factors in HP mechanism of action. The use of biomarkers that could monitor or even predict HP treatment impact would be of extreme importance. We reviewed the elements that could potentially be used as peripheral biomarkers of HP effectiveness. Although a validated biomarker still does not exist, we underlined the several potential findings (e.g., about cytokines, HP metabolites and genotypic biomarkers) which could pave the way for future research on HP biomarkers.

Utilizing yeast chemogenomic profiles for the prediction of pharmacogenomic associations in humans

Understanding the genetic basis underlying individual responses to drug treatment is a fundamental task with implications to drug development and administration. Pharmacogenomics is the study of the genes that affect drug response. The study of pharmacogenomic associations between a drug and a gene that influences the interindividual drug response, which is only beginning, holds much promise and potential. Although relatively few pharmacogenomic associations between drugs and specific genes were mapped in humans, large systematic screens have been carried out in the yeast Saccharomyces cerevisiae, motivating the constructing of a projection method. We devised a novel approach for the prediction of pharmacogenomic associations in humans using genome-scale chemogenomic data from yeast. We validated our method using both cross-validation and comparison to known drug-gene associations extracted from multiple data sources, attaining high AUC scores. We show that our method outperforms a previous technique, as well as a similar method based on known human associations. Last, we analyze the predictions and demonstrate their biological relevance to understanding drug response.

Prediction of Synergism from Chemical-Genetic Interactions by Machine Learning

The structure of genetic interaction networks predicts that, analogous to synthetic lethal interactions between non-essential genes, combinations of compounds with latent activities may exhibit potent synergism. To test this hypothesis, we generated a chemical-genetic matrix of 195 diverse yeast deletion strains treated with 4,915 compounds. This approach uncovered 1,221 genotype-specific inhibitors, which we termed cryptagens. Synergism between 8,128 structurally disparate cryptagen pairs was assessed experimentally and used to benchmark predictive algorithms. A model based on the chemical-genetic matrix and the genetic interaction network failed to accurately predict synergism. However, a combined random forest and Naive Bayesian learner that associated chemical structural features with genotype-specific growth inhibition had strong predictive power. This approach identified previously unknown compound combinations that exhibited species-selective toxicity toward human fungal pathogens. This work demonstrates that machine learning methods trained on unbiased chemical-genetic interaction data may be widely applicable for the discovery of synergistic combinations in different species.

Functional genomics to uncover drug mechanism of action

The upswing in US Food and Drug Administration and European Medicines Agency drug approvals in 2014 may have marked an end to the dry spell that has troubled the pharmaceutical industry over the past decade. Regardless, the attrition rate of drugs in late clinical phases remains high, and a lack of target validation has been highlighted as an explanation. This has led to a resurgence in appreciation of phenotypic drug screens, as these may be more likely to yield compounds with relevant modes of action. However, cell-based screening approaches do not directly reveal cellular targets, and hence target deconvolution and a detailed understanding of drug action are needed for efficient lead optimization and biomarker development. Here, recently developed functional genomics technologies that address this need are reviewed. The approaches pioneered in model organisms, particularly in yeast, and more recently adapted to mammalian systems are discussed. Finally, areas of particular interest and directions for future tool development are highlighted.

Early-life serotonin dysregulation affects the migration and positioning of cortical interneuron subtypes

Early-life deficiency of the serotonin transporter (SERT) gives rise to a wide range of psychiatric-relevant phenotypes; however, the molecular and cellular targets of serotonin dyregulation during neural circuit formation remain to be identified. Interestingly, migrating cortical interneurons (INs) derived from the caudal ganglionic eminence (CGE) have been shown to be more responsive to serotonin-mediated signalling compared with INs derived from the medial ganglionic eminence (MGE). Here we investigated the impact of early-life SERT deficiency on the migration and positioning of CGE-derived cortical INs in SERT-ko mice and in mice exposed to the SERT inhibitor fluoxetine during the late embryonic period. Using confocal time-lapse imaging and microarray-based expression analysis we found that genetic and pharmacological SERT deficiency significantly increased the migratory speed of CGE-derived INs and affected transcriptional programmes regulating neuronal migration. Postnatal studies revealed that SERT deficiency altered the cortical laminar distribution of subtypes of CGE-derived INs but not MGE-derived INs. More specifically, we found that the distribution of vasointestinal peptide (VIP)-expressing INs in layer 2/3 was abnormal in both genetic and pharmacological SERT-deficiency models. Collectively, these data indicate that early-life SERT deficiency has an impact on the migration and molecular programmes of CGE-derived INs, thus leading to specific alterations in the positioning of VIP-expressing INs. These data add to the growing evidence that early-life serotonin dysregulation affects cortical microcircuit formation and contributes to the emergence of psychiatric-relevant phenotypes.

A tug-of-war between the host and the pathogen generates strategic hotspots for the development of novel therapeutic interventions against infectious diseases

Microbial pathogens are known to express an array of specific signaling molecules referred as Pathogen Associated Molecular Patterns (PAMPs), which are recognized by Pattern Recognition Receptors (PRRs), present on the surface of the host cells. Interactions between PAMPs and PRRs on the surface of the host cells lead to signaling events which could culminate into either successful infection or clearance of the pathogens. Here, we summarize how these events may generate novel host based as well as pathogen based molecular targets for designing effective therapeutic strategies against infections.

Genetic basis of haloperidol resistance in Saccharomyces cerevisiae is complex and dose dependent

The genetic basis of most heritable traits is complex. Inhibitory compounds and their effects in model organisms have been used in many studies to gain insights into the genetic architecture underlying quantitative traits. However, the differential effect of compound concentration has not been studied in detail. In this study, we used a large segregant panel from a cross between two genetically divergent yeast strains, BY4724 (a laboratory strain) and RM11_1a (a vineyard strain), to study the genetic basis of variation in response to different doses of a drug. Linkage analysis revealed that the genetic architecture of resistance to the small-molecule therapeutic drug haloperidol is highly dose-dependent. Some of the loci identified had effects only at low doses of haloperidol, while other loci had effects primarily at higher concentrations of the drug. We show that a major QTL affecting resistance across all concentrations of haloperidol is caused by polymorphisms in SWH1, a homologue of human oxysterol binding protein. We identify a complex set of interactions among the alleles of the genes SWH1, MKT1, and IRA2 that are most pronounced at a haloperidol dose of 200 µM and are only observed when the remainder of the genome is of the RM background. Our results provide further insight into the genetic basis of drug resistance.

Variation in response to a drug can be determined by many factors. In the model organism baker's yeast, many studies of chemical resistance traits have uncovered a complex genetic basis of such resistance. However, an in-depth study of how drug dose alters the effects of underlying genetic factors is lacking. Here, we employed linkage analysis to map the specific genetic loci underlying response to haloperidol, a small molecule therapeutic drug, using a large panel of segregants from a cross between two genetically divergent yeast strains BY (a laboratory strain) and RM (a vineyard strain). We found that loci associated with haloperidol resistance are dose-dependent. We also showed that variants in the oxysterol-binding-protein-like domain of the gene SWH1 underlie the major locus detected at all doses of haloperidol. Genetic interactions among genes SWH1, MKT1, and IRA2 in the RM background contribute to the differential response at high concentrations of haloperidol.

Antifungal drug discovery: the process and outcomes

New data suggest that the global incidence of several types of fungal diseases have traditionally been under-documented. Of these, mortality caused by invasive fungal infections remains disturbingly high, equal to or exceeding deaths caused by drug-resistant tuberculosis and malaria. It is clear that basic research on new antifungal drugs, vaccines and diagnostic tools is needed. In this review, we focus upon antifungal drug discovery including in vitro assays, compound libraries and approaches to target identification. Genome mining has made it possible to identify fungal-specific targets; however, new compounds to these targets are apparently not in the antimicrobial pipeline. We suggest that 'repurposing' compounds (off patent) might be a more immediate starting point. Furthermore, we examine the dogma on antifungal discovery and suggest that a major thrust in technologies such as structural biology, homology modeling and virtual imaging is needed to drive discovery.

Antifungal application of nonantifungal drugs

Candida species are the cause of 60% of all mycoses in immunosuppressed individuals, leading to ∼150,000 deaths annually due to systemic infections, whereas the current antifungal therapies either have toxic side effects or are insufficiently efficient. We performed a screening of two compound libraries, the Enzo and the Institute for Molecular Medicine Finland (FIMM) oncology collection library, for anti-Candida activity based on the European Committee on Antimicrobial Susceptibility Testing (EUCAST) guidelines. From a total of 844 drugs, 26 agents showed activity against Candida albicans. Of those, 12 were standard antifungal drugs (SADs) and 7 were off-target drugs previously reported to be active against Candida spp. The remaining 7 off-target drugs, amonafide, tosedostat, megestrol acetate, melengestrol acetate, stanozolol, trifluperidol, and haloperidol, were identified with this screen. The anti-Candida activities of the new agents were investigated by three individual assays using optical density, ATP levels, and microscopy. The antifungal activities of these drugs were comparable to those of the SADs found in the screen. The aminopeptidase inhibitor tosedostat, which is currently in a clinical trial phase for anticancer therapy, displayed a broad antifungal activity against different Candida spp., including Candida glabrata. Thus, this screen reveals agents that were previously unknown to be anti-Candida agents, which allows for the design of novel therapies against invasive candidiasis.

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.

New Perspectives on How to Discover Drugs from Herbal Medicines: CAM's Outstanding Contribution to Modern Therapeutics

With tens of thousands of plant species on earth, we are endowed with an enormous wealth of medicinal remedies from Mother Nature. Natural products and their derivatives represent more than 50% of all the drugs in modern therapeutics. Because of the low success rate and huge capital investment need, the research and development of conventional drugs are very costly and difficult. Over the past few decades, researchers have focused on drug discovery from herbal medicines or botanical sources, an important group of complementary and alternative medicine (CAM) therapy. With a long history of herbal usage for the clinical management of a variety of diseases in indigenous cultures, the success rate of developing a new drug from herbal medicinal preparations should, in theory, be higher than that from chemical synthesis. While the endeavor for drug discovery from herbal medicines is "experience driven," the search for a therapeutically useful synthetic drug, like "looking for a needle in a haystack," is a daunting task. In this paper, we first illustrated various approaches of drug discovery from herbal medicines. Typical examples of successful drug discovery from botanical sources were given. In addition, problems in drug discovery from herbal medicines were described and possible solutions were proposed. The prospect of drug discovery from herbal medicines in the postgenomic era was made with the provision of future directions in this area of drug development.

A genome-wide RNAi screen in Caenorhabditis elegans identifies the nicotinic acetylcholine receptor subunit ACR-7 as an antipsychotic drug target

We report a genome-wide RNA interference (RNAi) screen for Suppressors of Clozapine-induced Larval Arrest (scla genes) in Caenorhabditis elegans, the first genetic suppressor screen for antipsychotic drug (APD) targets in an animal. The screen identifies 40 suppressors, including the α-like nicotinic acetylcholine receptor (nAChR) homolog acr-7. We validate the requirement for acr-7 by showing that acr-7 knockout suppresses clozapine-induced larval arrest and that expression of a full-length translational GFP fusion construct rescues this phenotype. nAChR agonists phenocopy the developmental effects of clozapine, while nAChR antagonists partially block these effects. ACR-7 is strongly expressed in the pharynx, and clozapine inhibits pharyngeal pumping. acr-7 knockout and nAChR antagonists suppress clozapine-induced inhibition of pharyngeal pumping. These findings suggest that clozapine activates ACR-7 channels in pharyngeal muscle, leading to tetanus of pharyngeal muscle with consequent larval arrest. No APDs are known to activate nAChRs, but a number of studies indicate that α7-nAChR agonists may prove effective for the treatment of psychosis. α-like nAChR signaling is a mechanism through which clozapine may produce its therapeutic and/or toxic effects in humans, a hypothesis that could be tested following identification of the mammalian ortholog of C. elegans acr-7.

Clozapine is the most effective medication for treatment-refractory schizophrenia but produces toxic side effects such as agranulocytosis, metabolic syndrome, and developmental defects after exposure early in life. However, clozapine's molecular mechanisms of action remain poorly understood. In past studies, we showed that pharmacogenomic experiments in C. elegans identify novel signaling pathways through which clozapine exerts its biological effects. Here, we report the first genetic suppressor screen for antipsychotic (APD) drug targets in an animal and identify 40 suppressors of clozapine-induced larval arrest, including the α-like nicotinic acetylcholine receptor (nAChR) acr-7. We validate our RNAi result by showing that an acr-7 knockout suppresses clozapine-induced larval arrest and inhibition of pharyngeal pumping. Expression of a full-length translational acr-7::GFP (Green Fluorescent Protein) construct in the acr-7 mutant rescues suppression of these phenotypes. Clozapine-induced phenotypes are phenocopied by nAChR agonists and blocked by nAChR antagonists. The results suggest that clozapine induces these phenotypes through activation of the ACR-7 receptor. Recent studies have underscored the potential importance of nAChRs in the pathophysiology of schizophrenia. A clearer understanding of APD mechanisms would facilitate the design of improved drugs and may inform our understanding, not only of drug mechanisms, but also of disease pathogenesis.

A repurposing approach identifies off-patent drugs with fungicidal cryptococcal activity, a common structural chemotype, and pharmacological properties relevant to the treatment of cryptococcosis

New, more accessible therapies for cryptococcosis represent an unmet clinical need of global importance. We took a repurposing approach to identify previously developed drugs with fungicidal activity toward Cryptococcus neoformans, using a high-throughput screening assay designed to detect drugs that directly kill fungi. From a set of 1,120 off-patent medications and bioactive molecules, we identified 31 drugs/molecules with fungicidal activity, including 15 drugs for which direct antifungal activity had not previously been reported. A significant portion of the drugs are orally bioavailable and cross the blood-brain barrier, features key to the development of a widely applicable anticryptococcal agent. Structural analysis of this set revealed a common chemotype consisting of a hydrophobic moiety linked to a basic amine, features that are common to drugs that cross the blood-brain barrier and access the phagolysosome, two important niches of C. neoformans. Consistent with their fungicidal activity, the set contains eight drugs that are either additive or synergistic in combination with fluconazole. Importantly, we identified two drugs, amiodarone and thioridazine, with activity against intraphagocytic C. neoformans. Finally, the set of drugs is also enriched for molecules that inhibit calmodulin, and we have confirmed that seven drugs directly bind C. neoformans calmodulin, providing a molecular target that may contribute to the mechanism of antifungal activity. Taken together, these studies provide a foundation for the optimization of the antifungal properties of a set of pharmacologically attractive scaffolds for the development of novel anticryptococcal therapies.

NetwoRx: connecting drugs to networks and phenotypes in Saccharomyces cerevisiae

Drug modes of action are complex and still poorly understood. The set of known drug targets is widely acknowledged to be biased and incomplete, and so gives only limited insight into the system-wide effects of drugs. But a high-throughput assay unique to yeast-barcode-based chemogenomic screens-can measure the individual drug response of every yeast deletion mutant in parallel. NetwoRx (http://ophid.utoronto.ca/networx) is the first resource to store data from these extremely valuable yeast chemogenomics experiments. In total, NetwoRx stores data on 5924 genes and 466 drugs. In addition, we applied data-mining approaches to identify yeast pathways, functions and phenotypes that are targeted by particular drugs, compute measures of drug-drug similarity and construct drug-phenotype networks. These data are all available to search or download through NetwoRx; users can search by drug name, gene name or gene set identifier. We also set up automated analysis routines in NetwoRx; users can query new gene sets against the entire collection of drug profiles and retrieve the drugs that target them. We demonstrate with use case examples how NetwoRx can be applied to target specific phenotypes, repurpose drugs using mode of action analysis, investigate bipartite networks and predict new drugs that affect yeast aging.

Screen of FDA-approved drug library reveals compounds that protect hair cells from aminoglycosides and cisplatin

Loss of mechanosensory hair cells in the inner ear accounts for many hearing loss and balance disorders. Several beneficial pharmaceutical drugs cause hair cell death as a side effect. These include aminoglycoside antibiotics, such as neomycin, kanamycin and gentamicin, and several cancer chemotherapy drugs, such as cisplatin. Discovering new compounds that protect mammalian hair cells from toxic insults is experimentally difficult because of the inaccessibility of the inner ear. We used the zebrafish lateral line sensory system as an in vivo screening platform to survey a library of FDA-approved pharmaceuticals for compounds that protect hair cells from neomycin, gentamicin, kanamycin and cisplatin. Ten compounds were identified that provide protection from at least two of the four toxins. The resulting compounds fall into several drug classes, including serotonin and dopamine-modulating drugs, adrenergic receptor ligands, and estrogen receptor modulators. The protective compounds show different effects against the different toxins, supporting the idea that each toxin causes hair cell death by distinct, but partially overlapping, mechanisms. Furthermore, some compounds from the same drug classes had different protective properties, suggesting that they might not prevent hair cell death by their known target mechanisms. Some protective compounds blocked gentamicin uptake into hair cells, suggesting that they may block mechanotransduction or other routes of entry. The protective compounds identified in our screen will provide a starting point for studies in mammals as well as further research discovering the cellular signaling pathways that trigger hair cell death.

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.

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.

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).

Target identification by chromatographic co-elution: monitoring of drug-protein interactions without immobilization or chemical derivatization

Bioactive molecules typically mediate their biological effects through direct physical association with one or more cellular proteins. The detection of drug-target interactions is therefore essential for the characterization of compound mechanism of action and off-target effects, but generic label-free approaches for detecting binding events in biological mixtures have remained elusive. Here, we report a method termed target identification by chromatographic co-elution (TICC) for routinely monitoring the interaction of drugs with cellular proteins under nearly physiological conditions in vitro based on simple liquid chromatographic separations of cell-free lysates. Correlative proteomic analysis of drug-bound protein fractions by shotgun sequencing is then performed to identify candidate target(s). The method is highly reproducible, does not require immobilization or derivatization of drug or protein, and is applicable to diverse natural products and synthetic compounds. The capability of TICC to detect known drug-protein target physical interactions (K(d) range: micromolar to nanomolar) is demonstrated both qualitatively and quantitatively. We subsequently used TICC to uncover the sterol biosynthetic enzyme Erg6p as a novel putative anti-fungal target. Furthermore, TICC identified Asc1 and Dak1, a core 40 S ribosomal protein that represses gene expression, and dihydroxyacetone kinase involved in stress adaptation, respectively, as novel yeast targets of a dopamine receptor agonist.

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.

Comparative interaction networks: bridging genotype to phenotype

Over the past decade, biomedical research has witnessed an exponential increase in the throughput of the characterization of biological systems. Here we review the recent progress in large-scale methods to determine protein-protein, genetic and chemical-genetic interaction networks. We discuss some of the limitations and advantages of the different methods and give examples of how these networks are being used to study the evolutionary process. Comparative studies have revealed that different types of protein-protein interactions diverge at different rates with high conservation of co-complex membership but rapid divergence of more promiscuous interactions like those that mediate post-translational modifications. These evolutionary trends have consistent genetic consequences with highly conserved epistatic interactions within complex subunits but faster divergence of epistatic interactions across complexes or pathways. Finally, we discuss how these evolutionary observations are being used to interpret cross-species chemical-genetic studies and how they might shape therapeutic strategies. Together, these interaction networks offer us an unprecedented level of detail into how genotypes are translated to phenotypes, and we envision that they will be increasingly useful in the interpretation of genetic and phenotypic variation occurring within populations as well as the rational design of combinatorial therapeutics.

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.

Functional expression of parasite drug targets and their human orthologs in yeast

Background

The exacting nutritional requirements and complicated life cycles of parasites mean that they are not always amenable to high-throughput drug screening using automated procedures. Therefore, we have engineered the yeast Saccharomyces cerevisiae to act as a surrogate for expressing anti-parasitic targets from a range of biomedically important pathogens, to facilitate the rapid identification of new therapeutic agents.

Methodology/Principal Findings

Using pyrimethamine/dihydrofolate reductase (DHFR) as a model parasite drug/drug target system, we explore the potential of engineered yeast strains (expressing DHFR enzymes from Plasmodium falciparum, P. vivax, Homo sapiens, Schistosoma mansoni, Leishmania major, Trypanosoma brucei and T. cruzi) to exhibit appropriate differential sensitivity to pyrimethamine. Here, we demonstrate that yeast strains (lacking the major drug efflux pump, Pdr5p) expressing yeast (^ScDFR1), human (^HsDHFR), Schistosoma (^SmDHFR), and Trypanosoma (^TbDHFR and ^TcDHFR) DHFRs are insensitive to pyrimethamine treatment, whereas yeast strains producing Plasmodium (^PfDHFR and ^PvDHFR) DHFRs are hypersensitive. Reassuringly, yeast strains expressing field-verified, drug-resistant mutants of P. falciparum DHFR (^Pfdhfr^51I,59R,108N) are completely insensitive to pyrimethamine, further validating our approach to drug screening. We further show the versatility of the approach by replacing yeast essential genes with other potential drug targets, namely phosphoglycerate kinases (PGKs) and N-myristoyl transferases (NMTs).

Conclusions/Significance

We have generated a number of yeast strains that can be successfully harnessed for the rapid and selective identification of urgently needed anti-parasitic agents.

Parasites kill millions of people every year and leave countless others with chronic debilitating disease. These diseases, which include malaria and sleeping sickness, mainly affect people in developing countries. For this reason, few drugs have been developed to treat them. To make matters worse, many parasites are developing resistance to the drugs that are available. Thus, there is an urgent need to develop new drugs, but this is hampered by the fact that most parasites are difficult or impossible to grow in the laboratory. To address this, we have engineered baker's yeast to be dependent on the function of enzymes from either parasites or humans. In all, our engineered yeast constructs encompass six parasites (causing malaria, schistosomiasis, leishmaniasis, sleeping sickness, and Chagas disease) and three different enzymes that are known or potential drug targets. Further, we have increased yeast's sensitivity to drugs by deleting the gene for its major drug efflux pump. Because yeast is robust and easy to grow in the laboratory, we can use a robot to screen for drugs that will kill yeast dependent on a parasite enzyme, but not touch yeast dependent on the equivalent human enzyme.

Is prolonged stem cell mobilization detrimental for hematopoiesis?

Multiple hematological side effects have been reported to result from treatment with psychoactive phenothiazines. These reported toxicities include leucopenia, granulocytopenia, thrombocytopenia, agranulocytosis, and bone marrow aplasia. The physiological mechanism causing these potentially life-threatening blood dyscrasias is unknown. Recently, we discovered that phenothiazines exhibit antagonistic properties towards the VLA-4 integrin, an adhesion molecule that is responsible for homing and retention of hematological stem/progenitor cells (HSPCs) in the bone marrow. After administration of thioridazine we detected rapid mobilization of HSPCs into the peripheral blood. We propose that in patients receiving phenothiazines over a prolonged time period, continuous mobilization of stem cells out of the stem cell niche, results in a disorder of hematopoiesis. Furthermore, we also postulate that such cytopenias are caused by a loss of the niche environment, which is known to be essential for stem cell maintenance.

A systems biology strategy for predicting similarities and differences of drug effects: evidence for drug-specific modulation of inflammation in atherosclerosis

Background

Successful drug development has been hampered by a limited understanding of how to translate laboratory-based biological discoveries into safe and effective medicines. We have developed a generic method for predicting the effects of drugs on biological processes. Information derived from the chemical structure and experimental omics data from short-term efficacy studies are combined to predict the possible protein targets and cellular pathways affected by drugs.

Results

Validation of the method with anti-atherosclerotic compounds (fenofibrate, rosuvastatin, LXR activator T0901317) demonstrated a great conformity between the computationally predicted effects and the wet-lab biochemical effects. Comparative genome-wide pathway mapping revealed that the biological drug effects were realized largely via different pathways and mechanisms. In line with the predictions, the drugs showed differential effects on inflammatory pathways (downstream of PDGF, VEGF, IFNγ, TGFβ, IL1β, TNFα, LPS), transcriptional regulators (NFκB, C/EBP, STAT3, AP-1) and enzymes (PKCδ, AKT, PLA2), and they quenched different aspects of the inflammatory signaling cascade. Fenofibrate, the compound predicted to be most efficacious in inhibiting early processes of atherosclerosis, had the strongest effect on early lesion development.

Conclusion

Our approach provides mechanistic rationales for the differential and common effects of drugs and may help to better understand the origins of drug actions and the design of combination therapies.

Cross-species discovery of syncretic drug combinations that potentiate the antifungal fluconazole

The authors screen for compounds that show synergistic antifungal activity when combined with the widely-used fungistatic drug fluconazole. Chemogenomic profiling explains the mode of action of synergistic drugs and allows the prediction of additional drug synergies.

The authors screen for compounds that show synergistic antifungal activity when combined with the widely-used fungistatic drug fluconazole. Chemogenomic profiling explains the mode of action of synergistic drugs and allows the prediction of additional drug synergies.

Chemical screens with a library enriched for known drugs identified a diverse set of 148 compounds that potentiated the action of the antifungal drug fluconazole against the fungal pathogens Cryptococcus neoformans, Cryptococcus gattii and Candida albicans, and the model yeast Saccharomyces cerevisiae, often in a species-specific manner.

Chemogenomic profiles of six confirmed hits in S. cerevisiae revealed different modes of action and enabled the prediction of additional synergistic combinations; three-way synergistic interactions exhibited even stronger synergies at low doses of fluconazole.

The synergistic combination of fluconazole and the antidepressant sertraline was active against fluconazole-resistant clinical fungal isolates and in an in vivo model of Cryptococcal infection.

Rising fungal infection rates, especially among immune-suppressed individuals, represent a serious clinical challenge (Gullo, 2009). Cancer, organ transplant and HIV patients, for example, often succumb to opportunistic fungal pathogens. The limited repertoire of approved antifungal agents and emerging drug resistance in the clinic further complicate the effective treatment of systemic fungal infections. At the molecular level, the paucity of fungal-specific essential targets arises from the conserved nature of cellular functions from yeast to humans, as well as from the fact that many essential yeast genes can confer viability at a fraction of wild-type dosage (Yan et al, 2009). Although only ∼1100 of the ∼6000 genes in yeast are essential, almost all genes become essential in specific genetic backgrounds in which another non-essential gene has been deleted or otherwise attenuated, an effect termed synthetic lethality (Tong et al, 2001). Genome-scale surveys suggest that over 200 000 binary synthetic lethal gene combinations dominate the yeast genetic landscape (Costanzo et al, 2010). The genetic buffering phenomenon is also manifest as a plethora of differential chemical–genetic interactions in the presence of sublethal doses of bioactive compounds (Hillenmeyer et al, 2008). These observations frame the difficulty of interdicting network functions in eukaryotic pathogens with single agent therapeutics. At the same time, however, this genetic network organization suggests that judicious combinations of small molecule inhibitors of both essential and non-essential targets may elicit additive or synergistic effects on cell growth (Sharom et al, 2004; Lehar et al, 2008). Unbiased screens for drugs that synergistically enhance a specific bioactive effect, but which are not themselves individually active—termed a syncretic combination—are one means to substantially elaborate chemical space (Keith et al, 2005). Indeed, compounds that enhance the activity of known agents in model yeast and cancer cell line systems have been identified both by focused small molecule library screens and by computational methods (Borisy et al, 2003; Lehar et al, 2007; Nelander et al, 2008; Jansen et al, 2009; Zinner et al, 2009).

To extend the stratagem of chemical synthetic lethality to clinically relevant fungal pathogens, we screened a bioactive library of known drugs for synergistic enhancers of the widely used fungistatic drug fluconazole against the clinically relevant pathogens C. albicans, C. neoformans and C. gattii, as well as the genetically tractable budding yeast S. cerevisiae. Fluconazole is an azole drug that inhibits lanosterol 14α-demethylase, the gene product of ERG11, an essential cytochrome P450 enzyme in the ergosterol biosynthetic pathway (Groll et al, 1998). We identified 148 drugs that potentiate the antifungal action of fluconazole against the four species. These syncretic compounds had not been previously recognized in the clinic as antifungal agents, and many acted in a species-specific manner, often in a potent fungicidal manner.

To understand the mechanisms of synergism, we interrogated six syncretic drugs—trifluoperazine, tamoxifen, clomiphene, sertraline, suloctidil and L-cycloserine—in genome-wide chemogenomic profiles of the S. cerevisiae deletion strain collection (Giaever et al, 1999). These profiles revealed that membrane, vesicle trafficking and lipid biosynthesis pathways are targeted by five of the synergizers, whereas the sphingolipid biosynthesis pathway is targeted by L-cycloserine. Cell biological assays confirmed the predicted membrane disruption effects of the former group of compounds, which may perturb ergosterol metabolism, impair fluconazole export by drug efflux pumps and/or affect active import of fluconazole (Kuo et al, 2010; Mansfield et al, 2010). Based on the integration of chemical–genetic and genetic interaction space, a signature set of deletion strains that are sensitive to the membrane active synergizers correctly predicted additional drug synergies with fluconazole. Similarly, the L-cycloserine chemogenomic profile correctly predicted a synergistic interaction between fluconazole and myriocin, another inhibitor of sphingolipid biosynthesis. The structure of genetic networks suggests that it should be possible to devise higher order drug combinations with even greater selectivity and potency (Sharom et al, 2004). In an initial test of this concept, we found that the combination of a non-synergistic pair drawn from the membrane active and sphingolipid target classes exhibited potent three-way synergism with a low dose of fluconazole. Finally, the combination of sertraline and fluconazole was active in a G. mellonella model of Cryptococcal infection, and was also efficacious against fluconazole-resistant clinical isolates of C. albicans and C. glabrata.

Collectively, these results demonstrate that the combinatorial redeployment of known drugs defines a powerful antifungal strategy and establish a number of potential lead combinations for future clinical assessment.

Resistance to widely used fungistatic drugs, particularly to the ergosterol biosynthesis inhibitor fluconazole, threatens millions of immunocompromised patients susceptible to invasive fungal infections. The dense network structure of synthetic lethal genetic interactions in yeast suggests that combinatorial network inhibition may afford increased drug efficacy and specificity. We carried out systematic screens with a bioactive library enriched for off-patent drugs to identify compounds that potentiate fluconazole action in pathogenic Candida and Cryptococcus strains and the model yeast Saccharomyces. Many compounds exhibited species- or genus-specific synergism, and often improved fluconazole from fungistatic to fungicidal activity. Mode of action studies revealed two classes of synergistic compound, which either perturbed membrane permeability or inhibited sphingolipid biosynthesis. Synergistic drug interactions were rationalized by global genetic interaction networks and, notably, higher order drug combinations further potentiated the activity of fluconazole. Synergistic combinations were active against fluconazole-resistant clinical isolates and an in vivo model of Cryptococcus infection. The systematic repurposing of approved drugs against a spectrum of pathogens thus identifies network vulnerabilities that may be exploited to increase the activity and repertoire of antifungal agents.

Association of an UCP4 (SLC25A27) haplotype with ultra-resistant schizophrenia

AIMS

Neuronal uncoupling proteins are involved in the regulation of reactive oxygen species production and intracellular calcium homeostasis, and thus, play a neuroprotective role. In order to explore the potential consequences of neuronal uncoupling proteins variants we examined their association in a sample of Caucasian patients suffering from schizophrenia and phenotyped them according to antipsychotic response.

MATERIALS & METHODS

Using a case-control design, we compared the frequencies of 15 genetic variants spanning UCP2, UCP4 and UCP5 in 106 French Caucasian patients suffering from schizophrenia and 127 healthy controls. In addition, patients with schizophrenia who responded to antipsychotic treatment were compared with patients with ultra-resistant schizophrenia (URS). This latter population presented no clinical, social and/or occupational remission despite at least two periods of treatment with conventional or atypical antipsychotic drugs and also with clozapine.

RESULTS

There were no differences in the distribution of the respective alleles between URS and responding patients. However, one haplotype spanning UCP4 was found to be significantly under-represented in URS patients. This relationship remained significant after multiple testing corrections.

CONCLUSION

Although our sample is of limited size and not representative of schizophrenia as a whole, the association found between the URS group and the UCP4 haplotype is noteworthy as it may influence treatment outcome in schizophrenia.

Design, synthesis, and characterization of a highly effective Hog1 inhibitor: a powerful tool for analyzing MAP kinase signaling in yeast

The Saccharomyces cerevisiae High-Osmolarity Glycerol (HOG) pathway is a conserved mitogen-activated protein kinase (MAPK) signal transduction system that often serves as a model to analyze systems level properties of MAPK signaling. Hog1, the MAPK of the HOG-pathway, can be activated by various environmental cues and it controls transcription, translation, transport, and cell cycle adaptations in response to stress conditions. A powerful means to study signaling in living cells is to use kinase inhibitors; however, no inhibitor targeting wild-type Hog1 exists to date. Herein, we describe the design, synthesis, and biological application of small molecule inhibitors that are cell-permeable, fast-acting, and highly efficient against wild-type Hog1. These compounds are potent inhibitors of Hog1 kinase activity both in vitro and in vivo. Next, we use these novel inhibitors to pinpoint the time of Hog1 action during recovery from G(1) checkpoint arrest, providing further evidence for a specific role of Hog1 in regulating cell cycle resumption during arsenite stress. Hence, we describe a novel tool for chemical genetic analysis of MAPK signaling and provide novel insights into Hog1 action.

The synthetic genetic interaction network reveals small molecules that target specific pathways in Sacchromyces cerevisiae

High-throughput elucidation of synthetic genetic interactions (SGIs) has contributed to a systems-level understanding of genetic robustness and fault-tolerance encoded in the genome. Pathway targets of various compounds have been predicted by comparing chemical-genetic synthetic interactions to a network of SGIs. We demonstrate that the SGI network can also be used in a powerful reverse pathway-to-drug approach for identifying compounds that target specific pathways of interest. Using the SGI network, the method identifies an indicator gene that may serve as a good candidate for screening a library of compounds. The indicator gene is selected so that compounds found to produce sensitivity in mutants deleted for the indicator gene are likely to abrogate the target pathway. We tested the utility of the SGI network for pathway-to-drug discovery using the DNA damage checkpoint as the target pathway. An analysis of the compendium of synthetic lethal interactions in yeast showed that superoxide dismutase 1 (SOD1) has significant SGI connectivity with a large subset of DNA damage checkpoint and repair (DDCR) genes in Saccharomyces cerevisiae, and minimal SGIs with non-DDCR genes. We screened a sod1Δ strain against three National Cancer Institute (NCI) compound libraries using a soft agar high-throughput halo assay. Fifteen compounds out of ∼3100 screened showed selective toxicity toward sod1Δ relative to the isogenic wild type (wt) strain. One of these, 1A08, caused a transient increase in growth in the presence of sublethal doses of DNA damaging agents, suggesting that 1A08 inhibits DDCR signaling in yeast. Genome-wide screening of 1A08 against the library of viable homozygous deletion mutants further supported DDCR as the relevant targeted pathway of 1A08. When assayed in human HCT-116 colorectal cancer cells, 1A08 caused DNA-damage resistant DNA synthesis and blocked the DNA-damage checkpoint selectively in S-phase.

Nonproteasomal targets of the proteasome inhibitors bortezomib and carfilzomib: a link to clinical adverse events

PURPOSE

Bortezomib (Velcade), a dipeptide boronate 20S proteasome inhibitor and an approved treatment option for multiple myeloma, is associated with a treatment-emergent, painful peripheral neuropathy (PN) in more than 30% of patients. Carfilzomib, a tetrapeptide epoxyketone proteasome inhibitor, currently in clinical investigation in myeloma, is associated with low rates of PN. We sought to determine whether PN represents a target-mediated adverse drug reaction (ADR).

EXPERIMENTAL DESIGN

Neurodegenerative effects of proteasome inhibitors were assessed in an in vitro model utilizing a differentiated neuronal cell line. Secondary targets of both inhibitors were identified by a multifaceted approach involving candidate screening, profiling with an activity-based probe, and database mining. Secondary target activity was measured in rats and patients receiving both inhibitors.

RESULTS

Despite equivalent levels of proteasome inhibition, only bortezomib reduced neurite length, suggesting a nonproteasomal mechanism. In cell lysates, bortezomib, but not carfilzomib, significantly inhibited the serine proteases cathepsin G (CatG), cathepsin A, chymase, dipeptidyl peptidase II, and HtrA2/Omi at potencies near or equivalent to that for the proteasome. Inhibition of CatG was detected in splenocytes of rats receiving bortezomib and in peripheral blood mononuclear cells derived from bortezomib-treated patients. Levels of HtrA2/Omi, which is known to be involved in neuronal survival, were upregulated in neuronal cells exposed to both proteasome inhibitors but was inhibited only by bortezomib exposure.

CONCLUSION

These data show that bortezomib-induced neurodegeneration in vitro occurs via a proteasome-independent mechanism and that bortezomib inhibits several nonproteasomal targets in vitro and in vivo, which may play a role in its clinical ADR profile.

Fangjiomics: in search of effective and safe combination therapies

Millennia-old Chinese medicine treats disease with many combination therapies involving ingredients used in clinic practice. Fangjiomics is the science of identifying and designing effective mixtures of bioactive agents and elucidating their modes of action beyond those of Chinese patent medicines. Omics profiling and quantitative optimal modeling have been used to associate the various responses with biological pathways related to disease phenotype. Fangjiomics seeks to study myriad compatible combinations that may act through multiple targets, modes of action, and biological pathways balancing on off-target and on-target effects. This approach may lead to the discovery of controllable array-designed therapies to combine less potent elements that are more effective collectively but have fewer adverse side effects than does any element singly.

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.