Physical Forces Modulate Oxidative Status and Stress Defense Meditated Metabolic Adaptation of Yeast Colonies: Spaceflight and Microgravity Simulations.

Baker's yeast (Saccharomyces cerevisiae) has broad genetic homology to human cells. Although typically grown as 1-2mm diameter colonies under certain conditions yeast can form very large (10 + mm in diameter) or 'giant' colonies on agar. Giant yeast colonies have been used to study diverse biomedical processes such as cell survival, aging, and the response to cancer pharmacogenomics. Such colonies evolve dynamically into complex stratified structures that respond differentially to environmental cues. Ammonia production, gravity driven ammonia convection, and shear defense responses are key differentiation signals for cell death and reactive oxygen system pathways in these colonies. The response to these signals can be modulated by experimental interventions such as agar composition, gene deletion and application of pharmaceuticals. In this study we used physical factors including colony rotation and microgravity to modify ammonia convection and shear stress as environmental cues and observed differences in the responses of both ammonia dependent and stress response dependent pathways We found that the effects of random positioning are distinct from rotation. Furthermore, both true and simulated microgravity exacerbated both cellular redox responses and apoptosis. These changes were largely shear-response dependent but each model had a unique response signature as measured by shear stress genes and the promoter set which regulates them These physical techniques permitted a graded manipulation of both convection and ammonia signaling and are primed to substantially contribute to our understanding of the mechanisms of drug action, cell aging, and colony differentiation.

Using C. elegans Forward and Reverse Genetics to Identify New Compounds with Anthelmintic Activity.

BACKGROUND: The lack of new anthelmintic agents is of growing concern because it affects human health and our food supply, as both livestock and plants are affected. Two principal factors contribute to this problem. First, nematode resistance to anthelmintic drugs is increasing worldwide and second, many effective nematicides pose environmental hazards. In this paper we address this problem by deploying a high throughput screening platform for anthelmintic drug discovery using the nematode Caenorhabditis elegans as a surrogate for infectious nematodes. This method offers the possibility of identifying new anthelmintics in a cost-effective and timely manner. METHODS/PRINCIPAL FINDINGS: Using our high throughput screening platform we have identified 14 new potential anthelmintics by screening more than 26,000 compounds from the Chembridge and Maybridge chemical libraries. Using phylogenetic profiling we identified a subset of the 14 compounds as potential anthelmintics based on the relative sensitivity of C. elegans when compared to yeast and mammalian cells in culture. We showed that a subset of these compounds might employ mechanisms distinct from currently used anthelmintics by testing diverse drug resistant strains of C. elegans. One of these newly identified compounds targets mitochondrial complex II, and we used structural analysis of the target to suggest how differential binding of this compound may account for its different effects in nematodes versus mammalian cells. CONCLUSIONS/SIGNIFICANCE: The challenge of anthelmintic drug discovery is exacerbated by several factors; including, 1) the biochemical similarity between host and parasite genomes, 2) the geographic location of parasitic nematodes and 3) the rapid development of resistance. Accordingly, an approach that can screen large compound collections rapidly is required. C. elegans as a surrogate parasite offers the ability to screen compounds rapidly and, equally importantly, with specificity, thus reducing the potential toxicity of these compounds to the host and the environment. We believe this approach will help to replenish the pipeline of potential nematicides.

Identification of Chemical-Genetic Interactions via Parallel Analysis of Barcoded Yeast Strains.

The Yeast Knockout Collection is a complete set of gene deletion strains for the budding yeast, Saccharomyces cerevisiae In each strain, one of approximately 6000 open-reading frames is replaced with a dominant selectable marker flanked by two DNA barcodes. These barcodes, which are unique to each gene, allow the growth of thousands of strains to be individually measured from a single pooled culture. The collection, and other resources that followed, has ushered in a new era in chemical biology, enabling unbiased and systematic identification of chemical-genetic interactions (CGIs) with remarkable ease. CGIs link bioactive compounds to biological processes, and hence can reveal the mechanism of action of growth-inhibitory compounds in vivo, including those of antifungal, antibiotic, and anticancer drugs. The chemogenomic profiling method described here measures the sensitivity induced in yeast heterozygous and homozygous deletion strains in the presence of a chemical inhibitor of growth (termed haploinsufficiency profiling and homozygous profiling, respectively, or HIPHOP). The protocol is both scalable and amenable to automation. After competitive growth of yeast knockout collection cultures, with and without chemical inhibitors, CGIs can be identified and quantified using either array- or sequencing-based approaches as described here.

Systematic Mapping of Chemical-Genetic Interactions in Saccharomyces cerevisiae.

Chemical-genetic interactions (CGIs) describe a phenomenon where the effects of a chemical compound (i.e., a small molecule) on cell growth are dependent on a particular gene. CGIs can reveal important functional information about genes and can also be powerful indicators of a compound's mechanism of action. Mapping CGIs can lead to the discovery of new chemical probes, which, in contrast to genetic perturbations, operate at the level of the gene product (or pathway) and can be fast-acting, tunable, and reversible. The simple culture conditions required for yeast and its rapid growth, as well as the availability of a complete set of barcoded gene deletion strains, facilitate systematic mapping of CGIs in this organism. This process involves two basic steps: first, screening chemical libraries to identify bioactive compounds affecting growth and, second, measuring the effects of these compounds on genome-wide collections of mutant strains. Here, we introduce protocols for both steps that have great potential for the discovery and development of new small-molecule tools and medicines.

Identification of a New Class of Antifungals Targeting the Synthesis of Fungal Sphingolipids.

UNLABELLED: Recent estimates suggest that >300 million people are afflicted by serious fungal infections worldwide. Current antifungal drugs are static and toxic and/or have a narrow spectrum of activity. Thus, there is an urgent need for the development of new antifungal drugs. The fungal sphingolipid glucosylceramide (GlcCer) is critical in promoting virulence of a variety of human-pathogenic fungi. In this study, we screened a synthetic drug library for compounds that target the synthesis of fungal, but not mammalian, GlcCer and found two compounds [N'-(3-bromo-4-hydroxybenzylidene)-2-methylbenzohydrazide (BHBM) and its derivative, 3-bromo-N'-(3-bromo-4-hydroxybenzylidene) benzohydrazide (D0)] that were highly effective in vitro and in vivo against several pathogenic fungi. BHBM and D0 were well tolerated in animals and are highly synergistic or additive to current antifungals. BHBM and D0 significantly affected fungal cell morphology and resulted in the accumulation of intracellular vesicles. Deep-sequencing analysis of drug-resistant mutants revealed that four protein products, encoded by genes APL5, COS111, MKK1, and STE2, which are involved in vesicular transport and cell cycle progression, are targeted by BHBM. IMPORTANCE: Fungal infections are a significant cause of morbidity and mortality worldwide. Current antifungal drugs suffer from various drawbacks, including toxicity, drug resistance, and narrow spectrum of activity. In this study, we have demonstrated that pharmaceutical inhibition of fungal glucosylceramide presents a new opportunity to treat cryptococcosis and various other fungal infections. In addition to being effective against pathogenic fungi, the compounds discovered in this study were well tolerated by animals and additive to current antifungals. These findings suggest that these drugs might pave the way for the development of a new class of antifungals.

Mapping the cellular response to small molecules using chemogenomic fitness signatures.

Genome-wide characterization of the in vivo cellular response to perturbation is fundamental to understanding how cells survive stress. Identifying the proteins and pathways perturbed by small molecules affects biology and medicine by revealing the mechanisms of drug action. We used a yeast chemogenomics platform that quantifies the requirement for each gene for resistance to a compound in vivo to profile 3250 small molecules in a systematic and unbiased manner. We identified 317 compounds that specifically perturb the function of 121 genes and characterized the mechanism of specific compounds. Global analysis revealed that the cellular response to small molecules is limited and described by a network of 45 major chemogenomic signatures. Our results provide a resource for the discovery of functional interactions among genes, chemicals, and biological processes.

A high-throughput yeast assay identifies synergistic drug combinations.

Drug combinations are commonly used in the treatment of a range of diseases such as cancer, AIDS, and bacterial infections. Such combinations are less likely to be thwarted by resistance, and they have the desirable potential to be synergistic. Synergistic combinations can have decreased toxicity if lower doses of the constituent agents can be used. Conversely, antagonistic combinations can lead to lower efficacy of a treatment. Unfortunately, practical limitations, including the large number of possible combinations to be tested and the importance of optimizing concentrations and order of addition, discourage systematic studies of compound combinations. To address these limitations, we present a platform to screen drug combinations at multiple concentrations with varying orders of addition in Saccharomyces cerevisiae, at high throughput. In a proof of principle, we screened all possible pairwise combinations of 11 DNA damaging agents and found that of the 66 combinations tested, six were synergistic and three were antagonistic. The strength of two-thirds of these combinations was dependent on the order in which the drugs were added to the cells. We further tested the synergistic and antagonistic combinations in two cancer cell lines and found the combination of mitomycin C and irinotecan to be synergistic in both cell lines. This pilot study demonstrates the utility of using yeast for screening large matrices of drug combinations, and it provides a means to prioritize drug combination tests in human cells. Finally, we underscore the importance of testing the order of addition for assessing drug combinations.

Curcumin inhibits growth of Saccharomyces cerevisiae through iron chelation.

Curcumin, a polyphenol derived from turmeric, is an ancient therapeutic used in India for centuries to treat a wide array of ailments. Interest in curcumin has increased recently, with ongoing clinical trials exploring curcumin as an anticancer therapy and as a protectant against neurodegenerative diseases. In vitro, curcumin chelates metal ions. However, although diverse physiological effects have been documented for this compound, curcumin's mechanism of action on mammalian cells remains unclear. This study uses yeast as a model eukaryotic system to dissect the biological activity of curcumin. We found that yeast mutants lacking genes required for iron and copper homeostasis are hypersensitive to curcumin and that iron supplementation rescues this sensitivity. Curcumin penetrates yeast cells, concentrates in the endoplasmic reticulum (ER) membranes, and reduces the intracellular iron pool. Curcumin-treated, iron-starved cultures are enriched in unbudded cells, suggesting that the G(1) phase of the cell cycle is lengthened. A delay in cell cycle progression could, in part, explain the antitumorigenic properties associated with curcumin. We also demonstrate that curcumin causes a growth lag in cultured human cells that is remediated by the addition of exogenous iron. These findings suggest that curcumin-induced iron starvation is conserved from yeast to humans and underlies curcumin's medicinal properties.

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

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

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.

A predictive model for drug bioaccumulation and bioactivity in Caenorhabditis elegans.

The resistance of Caenorhabditis elegans to pharmacological perturbation limits its use as a screening tool for novel small bioactive molecules. One strategy to improve the hit rate of small-molecule screens is to preselect molecules that have an increased likelihood of reaching their target in the worm. To learn which structures evade the worm's defenses, we performed the first survey of the accumulation and metabolism of over 1,000 commercially available drug-like small molecules in the worm. We discovered that fewer than 10% of these molecules accumulate to concentrations greater than 50% of that present in the worm's environment. Using our dataset, we developed a structure-based accumulation model that identifies compounds with an increased likelihood of bioavailability and bioactivity, and we describe structural features that facilitate small-molecule accumulation in the worm. Preselecting molecules that are more likely to reach a target by first applying our model to the tens of millions of commercially available compounds will undoubtedly increase the success of future small-molecule screens with C. elegans.

Quantitative phenotyping via deep barcode sequencing.

Next-generation DNA sequencing technologies have revolutionized diverse genomics applications, including de novo genome sequencing, SNP detection, chromatin immunoprecipitation, and transcriptome analysis. Here we apply deep sequencing to genome-scale fitness profiling to evaluate yeast strain collections in parallel. This method, Barcode analysis by Sequencing, or "Bar-seq," outperforms the current benchmark barcode microarray assay in terms of both dynamic range and throughput. When applied to a complex chemogenomic assay, Bar-seq quantitatively identifies drug targets, with performance superior to the benchmark microarray assay. We also show that Bar-seq is well-suited for a multiplex format. We completely re-sequenced and re-annotated the yeast deletion collection using deep sequencing, found that approximately 20% of the barcodes and common priming sequences varied from expectation, and used this revised list of barcode sequences to improve data quality. Together, this new assay and analysis routine provide a deep-sequencing-based toolkit for identifying gene-environment interactions on a genome-wide scale.

Novel insights into iron metabolism by integrating deletome and transcriptome analysis in an iron deficiency model of the yeast Saccharomyces cerevisiae.

BACKGROUND: Iron-deficiency anemia is the most prevalent form of anemia world-wide. The yeast Saccharomyces cerevisiae has been used as a model of cellular iron deficiency, in part because many of its cellular pathways are conserved. To better understand how cells respond to changes in iron availability, we profiled the yeast genome with a parallel analysis of homozygous deletion mutants to identify essential components and cellular processes required for optimal growth under iron-limited conditions. To complement this analysis, we compared those genes identified as important for fitness to those that were differentially-expressed in the same conditions. The resulting analysis provides a global perspective on the cellular processes involved in iron metabolism. RESULTS: Using functional profiling, we identified several genes known to be involved in high affinity iron uptake, in addition to novel genes that may play a role in iron metabolism. Our results provide support for the primary involvement in iron homeostasis of vacuolar and endosomal compartments, as well as vesicular transport to and from these compartments. We also observed an unexpected importance of the peroxisome for growth in iron-limited media. Although these components were essential for growth in low-iron conditions, most of them were not differentially-expressed. Genes with altered expression in iron deficiency were mainly associated with iron uptake and transport mechanisms, with little overlap with those that were functionally required. To better understand this relationship, we used expression-profiling of selected mutants that exhibited slow growth in iron-deficient conditions, and as a result, obtained additional insight into the roles of CTI6, DAP1, MRS4 and YHR045W in iron metabolism. CONCLUSION: Comparison between functional and gene expression data in iron deficiency highlighted the complementary utility of these two approaches to identify important functional components. This should be taken into consideration when designing and analyzing data from these type of studies. We used this and other published data to develop a molecular interaction network of iron metabolism in yeast.

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

To better understand off-target effects of widely prescribed psychoactive drugs, we performed a comprehensive series of chemogenomic screens using the budding yeast Saccharomyces cerevisiae as a model system. Because the known human targets of these drugs do not exist in yeast, we could employ the yeast gene deletion collections and parallel fitness profiling to explore potential off-target effects in a genome-wide manner. Among 214 tested, documented psychoactive drugs, we identified 81 compounds that inhibited wild-type yeast growth and were thus selected for genome-wide fitness profiling. Many of these drugs had a propensity to affect multiple cellular functions. The sensitivity profiles of half of the analyzed drugs were enriched for core cellular processes such as secretion, protein folding, RNA processing, and chromatin structure. Interestingly, fluoxetine (Prozac) interfered with establishment of cell polarity, cyproheptadine (Periactin) targeted essential genes with chromatin-remodeling roles, while paroxetine (Paxil) interfered with essential RNA metabolism genes, suggesting potential secondary drug targets. We also found that the more recently developed atypical antipsychotic clozapine (Clozaril) had no fewer off-target effects in yeast than the typical antipsychotics haloperidol (Haldol) and pimozide (Orap). Our results suggest that model organism pharmacogenetic studies provide a rational foundation for understanding the off-target effects of clinically important psychoactive agents and suggest a rational means both for devising compound derivatives with fewer side effects and for tailoring drug treatment to individual patient genotypes.

Identification of small molecule inhibitors of Pseudomonas aeruginosa exoenzyme S using a yeast phenotypic screen.

Pseudomonas aeruginosa is an opportunistic human pathogen that is a key factor in the mortality of cystic fibrosis patients, and infection represents an increased threat for human health worldwide. Because resistance of Pseudomonas aeruginosa to antibiotics is increasing, new inhibitors of pharmacologically validated targets of this bacterium are needed. Here we demonstrate that a cell-based yeast phenotypic assay, combined with a large-scale inhibitor screen, identified small molecule inhibitors that can suppress the toxicity caused by heterologous expression of selected Pseudomonas aeruginosa ORFs. We identified the first small molecule inhibitor of Exoenzyme S (ExoS), a toxin involved in Type III secretion. We show that this inhibitor, exosin, modulates ExoS ADP-ribosyltransferase activity in vitro, suggesting the inhibition is direct. Moreover, exosin and two of its analogues display a significant protective effect against Pseudomonas infection in vivo. Furthermore, because the assay was performed in yeast, we were able to demonstrate that several yeast homologues of the known human ExoS targets are likely ADP-ribosylated by the toxin. For example, using an in vitro enzymatic assay, we demonstrate that yeast Ras2p is directly modified by ExoS. Lastly, by surveying a collection of yeast deletion mutants, we identified Bmh1p, a yeast homologue of the human FAS, as an ExoS cofactor, revealing that portions of the bacterial toxin mode of action are conserved from yeast to human. Taken together, our integrated cell-based, chemical-genetic approach demonstrates that such screens can augment traditional drug screening approaches and facilitate the discovery of new compounds against a broad range of human pathogens.

Accelerating the discovery of biologically active small molecules using a high-throughput yeast halo assay.

The budding yeast Saccharomyces cerevisiae, a powerful model system for the study of basic eukaryotic cell biology, has been used increasingly as a screening tool for the identification of bioactive small molecules. We have developed a novel yeast toxicity screen that is easily automated and compatible with high-throughput screening robotics. The new screen is quantitative and allows inhibitory potencies to be determined, since the diffusion of the sample provides a concentration gradient and a corresponding toxicity halo. The efficacy of this new screen was illustrated by testing materials including 3104 compounds from the NCI libraries, 167 marine sponge crude extracts, and 149 crude marine-derived fungal extracts. There were 46 active compounds among the NCI set. One very active extract was selected for bioactivity-guided fractionation, resulting in the identification of crambescidin 800 as a potent antifungal agent.

A chemical genomics approach to understanding drug action.

The complete collection of yeast deletion strains represents a unique, living biological computer for understanding gene function. The molecular 'barcodes' present in each of the deletion strains allow a quantitative ranking of the importance of any gene under any experimental condition of choice. In this article, some of the recent results generated from experiments that exploit the yeast deletion collection to understand mechanisms of drug action are discussed.