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1.  Powerful Partners: Arabidopsis and Chemical Genomics 
Chemical genomics (i.e. genomics scale chemical genetics) approaches capitalize on the ability of low molecular mass molecules to modify biological processes. Such molecules are used to modify the activity of a protein or a pathway in a manner that it is tunable and reversible. Bioactive chemicals resulting from forward or reverse chemical screens can be useful in understanding and dissecting complex biological processes due to the essentially limitless variation in structure and activities inherent in chemical space. A major advantage of this approach as a powerful addition to conventional plant genetics is the fact that chemical genomics can address loss-of-function lethality and redundancy. Furthermore, the ability of chemicals to be added at will and to act quickly can permit the study of processes that are highly dynamic such as endomembrane trafficking. An important aspect of utilizing small molecules effectively is to characterize bioactive chemicals in detail including an understanding of structure-activity relationships and the identification of active and inactive analogs. Bioactive chemicals can be useful as reagents to probe biological pathways directly. However, the identification of cognate targets and their pathways is also informative and can be achieved by screens for genetic resistance or hypersensitivity in Arabidopsis thaliana or other organisms from which the results can be translated to plants. In addition, there are approaches utilizing “tagged” chemical libraries that possess reactive moieties permitting the immobilization of active compounds. This opens the possibility for biochemical purification of putative cognate targets. We will review approaches to screen for bioactive chemicals that affect biological processes in Arabidopsis and provide several examples of the power and challenges inherent in this new approach in plant biology.
PMCID: PMC3243329  PMID: 22303245
2.  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.
PMCID: PMC3159983  PMID: 21694716
antifungal; combination; pathogen; resistance; synergism
3.  Cross-species chemogenomic profiling reveals evolutionarily conserved drug mode of action 
Chemogenomic screens were performed in both budding and fission yeasts, allowing for a cross-species comparison of drug–gene interaction networks.Drug–module interactions were more conserved than individual drug–gene interactions.Combination of data from both species can improve drug–module predictions and helps identify a compound's mode of action.
Understanding the molecular effects of chemical compounds in living cells is an important step toward rational therapeutics. Drug discovery aims to find compounds that will target a specific pathway or pathogen with minimal side effects. However, even when an effective drug is found, its mode of action (MoA) is typically not well understood. The lack of knowledge regarding a drug's MoA makes the drug discovery process slow and rational therapeutics incredibly difficult. More recently, different high-throughput methods have been developed that attempt to discern how a compound exerts its effects in cells. One of these methods relies on measuring the growth of cells carrying different mutations in the presence of the compounds of interest, commonly referred to as chemogenomics (Wuster and Babu, 2008). The differential growth of the different mutants provides clues as to what the compounds target in the cell (Figure 2). For example, if a drug inhibits a branch in a vital two-branch pathway, then mutations in the second branch might result in cell death if the mutants are grown in the presence of the drug (Figure 2C). As these compound–mutant functional interactions are expected to be relatively rare, one can assume that the growth rate of a mutant–drug combination should generally be equal to the product of the growth rate of the untreated mutant with the growth rate of the drug-treated wild type. This expectation is defined as the neutral model and deviations from this provide a quantitative score that allow us to make informed predictions regarding a drug's MoA (Figure 2B; Parsons et al, 2006).
The availability of these high-throughput approaches now allows us to perform cross-species studies of functional interactions between compounds and genes. In this study, we have performed a quantitative analysis of compound–gene interactions for two fungal species (budding yeast (S. cerevisiae) and fission yeast (S. pombe)) that diverged from each other approximately 500–700 million years ago. A collection of 2957 compounds from the National Cancer Institute (NCI) were screened in both species for inhibition of wild-type cell growth. A total of 132 were found to be bioactive in both fungi and 9, along with 12 additional well-characterized drugs, were selected for subsequent screening. Mutant libraries of 727 and 438 gene deletions were used for S. cerevisiae and S. pombe, respectively, and these were selected based on availability of genetic interaction data from previous studies (Collins et al, 2007; Roguev et al, 2008; Fiedler et al, 2009) and contain an overlap of 190 one-to-one orthologs that can be directly compared. Deviations from the neutral expectation were quantified as drug–gene interactions scores (D-scores) for the 21 compounds against the deletion libraries. Replicates of both screens showed very high correlations (S. cerevisiae r=0.72, S. pombe r=0.76) and reproduced well previously known compound–gene interactions (Supplementary information). We then compared the D-scores for the 190 one-to-one orthologs present in the data set of both species. Despite the high reproducibility, we observed a very poor conservation of these compound–gene interaction scores across these species (r=0.13, Figure 4A).
Previous work had shown that, across these same species, genetic interactions within protein complexes were much more conserved than average genetic interactions (Roguev et al, 2008). Similarly we observed a higher cross-species conservation of the compound–module (complex or pathway) interactions than the overall compound–gene interactions. Specifically, the data derived from fission yeast were a poor predictor of S. cerevisaie drug–gene interactions, but a good predictor of budding yeast compound–module connections (Figure 4B). Also, a combined score from both species improved the prediction of compound–module interactions, above the accuracy observed with the S. cerevisae information alone, but this improvement was not observed for the prediction of drug–gene interactions (Figure 4B). Data from both species were used to predict drug–module interactions, and one specific interaction (compound NSC-207895 interaction with DNA repair complexes) was experimentally verified by showing that the compound activates the DNA damage repair pathway in three species (S. cerevisiae, S. pombe and H. sapiens).
To understand why the combination of chemogenomic data from two species might improve drug–module interaction predictions, we also analyzed previously published cross-species genetic–interaction data. We observed a significant correlation between the conservation of drug–gene and gene–gene interactions among the one-to-one orthologs (r=0.28, P-value=0.0078). Additionally, the strongest interactions of benomyl (a microtubule inhibitor) were to complexes that also had strong and conserved genetic interactions with microtubules (Figure 4C). We hypothesize that a significant number of the compound–gene interactions obtained from chemogenomic studies are not direct interactions with the physical target of the compounds, but include many indirect interactions that genetically interact with the main target(s). This would explain why the compound interaction networks show similar evolutionary patterns as the genetic interactions networks.
In summary, these results shed some light on the interplay between the evolution of genetic networks and the evolution of drug response. Understanding how genetic variability across different species might result in different sensitivity to drugs should improve our capacity to design treatments. Concretely, we hope that this line of research might one day help us create drugs and drug combinations that specifically affect a pathogen or diseased tissue, but not the host.
We present a cross-species chemogenomic screening platform using libraries of haploid deletion mutants from two yeast species, Saccharomyces cerevisiae and Schizosaccharomyces pombe. We screened a set of compounds of known and unknown mode of action (MoA) and derived quantitative drug scores (or D-scores), identifying mutants that are either sensitive or resistant to particular compounds. We found that compound–functional module relationships are more conserved than individual compound–gene interactions between these two species. Furthermore, we observed that combining data from both species allows for more accurate prediction of MoA. Finally, using this platform, we identified a novel small molecule that acts as a DNA damaging agent and demonstrate that its MoA is conserved in human cells.
PMCID: PMC3018166  PMID: 21179023
chemogenomics; evolution; modularity
4.  Analysis of multiple compound–protein interactions reveals novel bioactive molecules 
The authors use machine learning of compound-protein interactions to explore drug polypharmacology and to efficiently identify bioactive ligands, including novel scaffold-hopping compounds for two pharmaceutically important protein families: G-protein coupled receptors and protein kinases.
We have demonstrated that machine learning of multiple compound–protein interactions is useful for efficient ligand screening and for assessing drug polypharmacology.This approach successfully identified novel scaffold-hopping compounds for two pharmaceutically important protein families: G-protein-coupled receptors and protein kinases.These bioactive compounds were not detected by existing computational ligand-screening methods in comparative studies.The results of this study indicate that data derived from chemical genomics can be highly useful for exploring chemical space, and this systems biology perspective could accelerate drug discovery processes.
The discovery of novel bioactive molecules advances our systems-level understanding of biological processes and is crucial for innovation in drug development. Perturbations of biological systems by chemical probes provide broader applications not only for analysis of complex systems but also for intentional manipulations of these systems. Nevertheless, the lack of well-characterized chemical modulators has limited their use. Recently, chemical genomics has emerged as a promising area of research applicable to the exploration of novel bioactive molecules, and researchers are currently striving toward the identification of all possible ligands for all target protein families (Wang et al, 2009). Chemical genomics studies have shown that patterns of compound–protein interactions (CPIs) are too diverse to be understood as simple one-to-one events. There is an urgent need to develop appropriate data mining methods for characterizing and visualizing the full complexity of interactions between chemical space and biological systems. However, no existing screening approach has so far succeeded in identifying novel bioactive compounds using multiple interactions among compounds and target proteins.
High-throughput screening (HTS) and computational screening have greatly aided in the identification of early lead compounds for drug discovery. However, the large number of assays required for HTS to identify drugs that target multiple proteins render this process very costly and time-consuming. Therefore, interest in using in silico strategies for screening has increased. The most common computational approaches, ligand-based virtual screening (LBVS) and structure-based virtual screening (SBVS; Oprea and Matter, 2004; Muegge and Oloff, 2006; McInnes, 2007; Figure 1A), have been used for practical drug development. LBVS aims to identify molecules that are very similar to known active molecules and generally has difficulty identifying compounds with novel structural scaffolds that differ from reference molecules. The other popular strategy, SBVS, is constrained by the number of three-dimensional crystallographic structures available. To circumvent these limitations, we have shown that a new computational screening strategy, chemical genomics-based virtual screening (CGBVS), has the potential to identify novel, scaffold-hopping compounds and assess their polypharmacology by using a machine-learning method to recognize conserved molecular patterns in comprehensive CPI data sets.
The CGBVS strategy used in this study was made up of five steps: CPI data collection, descriptor calculation, representation of interaction vectors, predictive model construction using training data sets, and predictions from test data (Figure 1A). Importantly, step 1, the construction of a data set of chemical structures and protein sequences for known CPIs, did not require the three-dimensional protein structures needed for SBVS. In step 2, compound structures and protein sequences were converted into numerical descriptors. These descriptors were used to construct chemical or biological spaces in which decreasing distance between vectors corresponded to increasing similarity of compound structures or protein sequences. In step 3, we represented multiple CPI patterns by concatenating these chemical and protein descriptors. Using these interaction vectors, we could quantify the similarity of molecular interactions for compound–protein pairs, despite the fact that the ligand and protein similarity maps differed substantially. In step 4, concatenated vectors for CPI pairs (positive samples) and non-interacting pairs (negative samples) were input into an established machine-learning method. In the final step, the classifier constructed using training sets was applied to test data.
To evaluate the predictive value of CGBVS, we first compared its performance with that of LBVS by fivefold cross-validation. CGBVS performed with considerably higher accuracy (91.9%) than did LBVS (84.4%; Figure 1B). We next compared CGBVS and SBVS in a retrospective virtual screening based on the human β2-adrenergic receptor (ADRB2). Figure 1C shows that CGBVS provided higher hit rates than did SBVS. These results suggest that CGBVS is more successful than conventional approaches for prediction of CPIs.
We then evaluated the ability of the CGBVS method to predict the polypharmacology of ADRB2 by attempting to identify novel ADRB2 ligands from a group of G-protein-coupled receptor (GPCR) ligands. We ranked the prediction scores for the interactions of 826 reported GPCR ligands with ADRB2 and then analyzed the 50 highest-ranked compounds in greater detail. Of 21 commercially available compounds, 11 showed ADRB2-binding activity and were not previously reported to be ADRB2 ligands. These compounds included ligands not only for aminergic receptors but also for neuropeptide Y-type 1 receptors (NPY1R), which have low protein homology to ADRB2. Most ligands we identified were not detected by LBVS and SBVS, which suggests that only CGBVS could identify this unexpected cross-reaction for a ligand developed as a target to a peptidergic receptor.
The true value of CGBVS in drug discovery must be tested by assessing whether this method can identify scaffold-hopping lead compounds from a set of compounds that is structurally more diverse. To assess this ability, we analyzed 11 500 commercially available compounds to predict compounds likely to bind to two GPCRs and two protein kinases. Functional assays revealed that nine ADRB2 ligands, three NPY1R ligands, five epidermal growth factor receptor (EGFR) inhibitors, and two cyclin-dependent kinase 2 (CDK2) inhibitors were concentrated in the top-ranked compounds (hit rate=30, 15, 25, and 10%, respectively). We also evaluated the extent of scaffold hopping achieved in the identification of these novel ligands. One ADRB2 ligand, two NPY1R ligands, and one CDK2 inhibitor exhibited scaffold hopping (Figure 4), indicating that CGBVS can use this characteristic to rationally predict novel lead compounds, a crucial and very difficult step in drug discovery. This feature of CGBVS is critically different from existing predictive methods, such as LBVS, which depend on similarities between test and reference ligands, and focus on a single protein or highly homologous proteins. In particular, CGBVS is useful for targets with undefined ligands because this method can use CPIs with target proteins that exhibit lower levels of homology.
In summary, we have demonstrated that data mining of multiple CPIs is of great practical value for exploration of chemical space. As a predictive model, CGBVS could provide an important step in the discovery of such multi-target drugs by identifying the group of proteins targeted by a particular ligand, leading to innovation in pharmaceutical research.
The discovery of novel bioactive molecules advances our systems-level understanding of biological processes and is crucial for innovation in drug development. For this purpose, the emerging field of chemical genomics is currently focused on accumulating large assay data sets describing compound–protein interactions (CPIs). Although new target proteins for known drugs have recently been identified through mining of CPI databases, using these resources to identify novel ligands remains unexplored. Herein, we demonstrate that machine learning of multiple CPIs can not only assess drug polypharmacology but can also efficiently identify novel bioactive scaffold-hopping compounds. Through a machine-learning technique that uses multiple CPIs, we have successfully identified novel lead compounds for two pharmaceutically important protein families, G-protein-coupled receptors and protein kinases. These novel compounds were not identified by existing computational ligand-screening methods in comparative studies. The results of this study indicate that data derived from chemical genomics can be highly useful for exploring chemical space, and this systems biology perspective could accelerate drug discovery processes.
PMCID: PMC3094066  PMID: 21364574
chemical genomics; data mining; drug discovery; ligand screening; systems chemical biology
5.  Subcellular distribution of the V-ATPase complex in plant cells, and in vivo localisation of the 100 kDa subunit VHA-a within the complex 
BMC Cell Biology  2004;5:29.
Vacuolar H+-ATPases are large protein complexes of more than 700 kDa that acidify endomembrane compartments and are part of the secretory system of eukaryotic cells. They are built from 14 different (VHA)-subunits. The paper addresses the question of sub-cellular localisation and subunit composition of plant V-ATPase in vivo and in vitro mainly by using colocalization and fluorescence resonance energy transfer techniques (FRET). Focus is placed on the examination and function of the 95 kDa membrane spanning subunit VHA-a. Showing similarities to the already described Vph1 and Stv1 vacuolar ATPase subunits from yeast, VHA-a revealed a bipartite structure with (i) a less conserved cytoplasmically orientated N-terminus and (ii) a membrane-spanning C-terminus with a higher extent of conservation including all amino acids shown to be essential for proton translocation in the yeast. On the basis of sequence data VHA-a appears to be an essential structural and functional element of V-ATPase, although previously a sole function in assembly has been proposed.
To elucidate the presence and function of VHA-a in the plant complex, three approaches were undertaken: (i) co-immunoprecipitation with antibodies directed to epitopes in the N- and C-terminal part of VHA-a, respectively, (ii) immunocytochemistry approach including co-localisation studies with known plant endomembrane markers, and (iii) in vivo-FRET between subunits fused to variants of green fluorescence protein (CFP, YFP) in transfected cells.
All three sets of results show that V-ATPase contains VHA-a protein that interacts in a specific manner with other subunits. The genomes of plants encode three genes of the 95 kDa subunit (VHA-a) of the vacuolar type H+-ATPase. Immuno-localisation of VHA-a shows that the recognized subunit is exclusively located on the endoplasmic reticulum. This result is in agreement with the hypothesis that the different isoforms of VHA-a may localize on distinct endomembrane compartments, as it was shown for its yeast counterpart Vph1.
PMCID: PMC516168  PMID: 15310389
6.  Acidic Di-leucine Motif Essential for AP-3–dependent Sorting and Restriction of the Functional Specificity of the Vam3p Vacuolar t-SNARE  
The Journal of Cell Biology  1998;142(4):913-922.
The transport of newly synthesized proteins through the vacuolar protein sorting pathway in the budding yeast Saccharomyces cerevisiae requires two distinct target SNAP receptor (t-SNARE) proteins, Pep12p and Vam3p. Pep12p is localized to the pre-vacuolar endosome and its activity is required for transport of proteins from the Golgi to the vacuole through a well defined route, the carboxypeptidase Y (CPY) pathway. Vam3p is localized to the vacuole where it mediates delivery of cargoes from both the CPY and the recently described alkaline phosphatase (ALP) pathways. Surprisingly, despite their organelle-specific functions in sorting of vacuolar proteins, overexpression of VAM3 can suppress the protein sorting defects of pep12Δ cells. Based on this observation, we developed a genetic screen to identify domains in Vam3p (e.g., localization and/or specific protein–protein interaction domains) that allow it to efficiently substitute for Pep12p. Using this screen, we identified mutations in a 7–amino acid sequence in Vam3p that lead to missorting of Vam3p from the ALP pathway into the CPY pathway where it can substitute for Pep12p at the pre-vacuolar endosome. This region contains an acidic di-leucine sequence that is closely related to sorting signals required for AP-3 adaptor–dependent transport in both yeast and mammalian systems. Furthermore, disruption of AP-3 function also results in the ability of wild-type Vam3p to compensate for pep12 mutants, suggesting that AP-3 mediates the sorting of Vam3p via the di-leucine signal. Together, these data provide the first identification of an adaptor protein–specific sorting signal in a t-SNARE protein, and suggest that AP-3–dependent sorting of Vam3p acts to restrict its interaction with compartment-specific accessory proteins, thereby regulating its function. Regulated transport of cargoes such as Vam3p through the AP-3–dependent pathway may play an important role in maintaining the unique composition, function, and morphology of the vacuole.
PMCID: PMC2132875  PMID: 9722605
vacuole; SNARE; di-leucine; Saccharomyces cerevisiae; AP-3
7.  Off-Target Effects of Psychoactive Drugs Revealed by Genome-Wide Assays in Yeast 
PLoS Genetics  2008;4(8):e1000151.
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.
Author Summary
Neuropsychiatric disorders such as depression and psychosis affect one-quarter of all individuals during their lifetime, and despite efforts to improve the selectivity of psychoactive drugs, all are associated with side effects. Drug efficacy and tolerance are known to be linked to an individual's genetic profile, but little is known about the nature of this correlation due, in part, to the current emphasis on screening compounds against targets in vitro. Here we present a comprehensive, genome-wide effort to understand drug effects on the cellular level using an unbiased genome-wide assay to determine the importance of every yeast gene for tolerance to 81 psychoactive drugs. We found that these medications perturbed many evolutionarily conserved genes and cellular pathways, such as those required for vesicle transport, establishment of cell polarity, and chromosome biology. The 500,000 drug–gene measurements obtained in this study increase our understanding of the mechanism of action of psychoactive drugs. Specifically, this study provides a framework to assess the next generation of psychoactive agents and to guide personalized medicine approaches that associate genotype and phenotype.
PMCID: PMC2483942  PMID: 18688276
8.  Inference of Protein Complex Activities from Chemical-Genetic Profile and Its Applications: Predicting Drug-Target Pathways 
PLoS Computational Biology  2008;4(8):e1000162.
The chemical-genetic profile can be defined as quantitative values of deletion strains' growth defects under exposure to chemicals. In yeast, the compendium of chemical-genetic profiles of genomewide deletion strains under many different chemicals has been used for identifying direct target proteins and a common mode-of-action of those chemicals. In the previous study, valuable biological information such as protein–protein and genetic interactions has not been fully utilized. In our study, we integrated this compendium and biological interactions into the comprehensive collection of ∼490 protein complexes of yeast for model-based prediction of a drug's target proteins and similar drugs. We assumed that those protein complexes (PCs) were functional units for yeast cell growth and regarded them as hidden factors and developed the PC-based Bayesian factor model that relates the chemical-genetic profile at the level of organism phenotypes to the hidden activities of PCs at the molecular level. The inferred PC activities provided the predictive power of a common mode-of-action of drugs as well as grouping of PCs with similar functions. In addition, our PC-based model allowed us to develop a new effective method to predict a drug's target pathway, by which we were able to highlight the target-protein, TOR1, of rapamycin. Our study is the first approach to model phenotypes of systematic deletion strains in terms of protein complexes. We believe that our PC-based approach can provide an appropriate framework for combining and modeling several types of chemical-genetic profiles including interspecies. Such efforts will contribute to predicting more precisely relevant pathways including target proteins that interact directly with bioactive compounds.
Author Summary
Finding the specific targets of chemicals and deciphering how drugs work in our body is important for the effective development of new drugs. Growth profiles of yeast genomewide deletion strains under many different chemicals have been used for identifying target proteins and a common mode-of-action of drugs. In this study, we integrated those growth profiles with biological information such as protein–protein interactions and genetic interactions to develop a new method to infer the mode-of-action of drugs. We assume that the protein complexes (PCs) are functional units for cell growth regulation, analogous to the transcriptional factors (TFs) for gene regulation. We also assume that the relative cell growth of a specific deletion mutant in the presence of a specific drug is determined by the interactions between the PCs and the deleted gene of the mutant. We then developed a computational model with which we were able to infer the hidden activities of PCs on the cell growth and showed that yeast growth phenotypes could be effectively modeled by PCs in a biologically meaningful way by demonstrating that the inferred activities of PCs contributed to predicting groups of similar drugs as well as proteins and pathways targeted by drugs.
PMCID: PMC2515108  PMID: 18769708
9.  The SLP1 gene of Saccharomyces cerevisiae is essential for vacuolar morphogenesis and function. 
Molecular and Cellular Biology  1990;10(5):2214-2223.
The SLP1 gene, which is involved in the expression of vacuolar functions in the yeast Saccharomyces cerevisiae (K. Kitamoto, K. Yoshizawa, Y. Ohsumi, and Y. Anraku, J. Bacteriol. 170:2687-2691, 1988), has been cloned from a yeast genomic library by complementation of the slp1-1 mutation. The isolated plasmid has a 7.8-kilobase BamHI-BamHI fragment that is sufficient to complement several characteristic phenotypes of the slp1-1 mutation. The fragment was integrated at the chromosomal SLP1 locus, indicating that it contains an authentic SLP1 gene. By DNA sequencing of the SLP1 gene, an open reading frame of 2,073 base pairs coding for a polypeptide of 691 amino acid residues (Mr, 79,270) was found. Gene disruption of the chromosomal SLP1 did not cause a lethal event. Vacuolar proteins in the delta slp1 mutant are not processed to vacuolar forms but remain in Golgi-modified forms. Carboxypeptidase Y in the delta slp1 mutant is localized mainly to the outsides of the cells. delta slp1 mutant cells have no prominent vacuolar structures but contain numerous vesicles in the cytoplasm, as seen by electron microscopy. Genetic and molecular biological analyses revealed that SLP1 is identical to VPS33, which is required for vacuolar protein sorting as reported by Robinson et al. (J. S. Robinson, D. J. Klionsky, L. M. Banta, and S. D. Emr, Mol. Cell. Biol. 8:4936-4948, 1988). These results indicate that the SLP1 (VPS33) gene is involved in the sorting of vacuolar proteins from the Golgi apparatus and their targeting to the vacuole and that it is required for the morphogenesis of vacuoles and subsequent expression of vacuolar functions.
PMCID: PMC360569  PMID: 2183024
10.  A novel Sec18p/NSF-dependent complex required for Golgi-to-endosome transport in yeast. 
Molecular Biology of the Cell  1997;8(6):1089-1104.
The vacuolar protein-sorting (VPS) pathway of Saccharomyces cerevisiae mediates localization of proteins from the trans-Golgi to the vacuole via a prevacuolar endosome compartment. Mutations in class D vacuolar protein-sorting (vps) genes affect vesicle-mediated Golgi-to-endosome transport and result in secretion of vacuolar proteins. Temperature-sensitive-for-function (tsf) and dominant negative mutations in PEP12, encoding a putative SNARE vesicle receptor on the endosome, and tsf mutations in VAC1, a gene implicated in vacuole inheritance and vacuolar protein sorting, were constructed and used to demonstrate that Pep12p and Vac1p are components of the VPS pathway. The sequence of Vac1p contains two putative zinc-binding RING motifs, a zinc finger motif, and a coiled-coil motif. Site-directed mutations in the carboxyl-terminal RING motif strongly affected vacuolar protein sorting. Vac1p was found to be tightly associated with membranes as a monomer and in a large SDS-resistant complex. By using Pep12p affinity chromatography, we found that Vac1p, Vps45p (SEC1 family member), and Sec18p (yeast N-ethyl maleimide-sensitive factor, NSF) bind Pep12p. Consistent with a functional role for this complex in vacuolar protein sorting, double pep12tsfvac1tsf and pep12tsf vps45tsf mutants exhibited synthetic Vps- phenotypes, the tsf phenotype of the vac1tsf mutant was rescued by overexpression of VPS45 or PEP12, overexpression of a dominant pep12 allele in a sec18-1 strain resulted in a severe synthetic growth defect that was rescued by deletion of PEP12 or VAC1, and subcellular fractionation of vac1 delta cells revealed a striking change in the fractionation of Pep12p and Vps21p, a rab family GTPase required for vacuolar protein sorting. The functions of Pep12p, Vps45p, and Vps21p indicate that key aspects of Golgi-to-endosome trafficking are similar to other vesicle-mediated transport steps, although the role of Vac1p suggests that there are also novel components of the VPS pathway.
PMCID: PMC305716  PMID: 9201718
11.  Genome-Wide Screen in Saccharomyces cerevisiae Identifies Vacuolar Protein Sorting, Autophagy, Biosynthetic, and tRNA Methylation Genes Involved in Life Span Regulation 
PLoS Genetics  2010;6(7):e1001024.
The study of the chronological life span of Saccharomyces cerevisiae, which measures the survival of populations of non-dividing yeast, has resulted in the identification of homologous genes and pathways that promote aging in organisms ranging from yeast to mammals. Using a competitive genome-wide approach, we performed a screen of a complete set of approximately 4,800 viable deletion mutants to identify genes that either increase or decrease chronological life span. Half of the putative short-/long-lived mutants retested from the primary screen were confirmed, demonstrating the utility of our approach. Deletion of genes involved in vacuolar protein sorting, autophagy, and mitochondrial function shortened life span, confirming that respiration and degradation processes are essential for long-term survival. Among the genes whose deletion significantly extended life span are ACB1, CKA2, and TRM9, implicated in fatty acid transport and biosynthesis, cell signaling, and tRNA methylation, respectively. Deletion of these genes conferred heat-shock resistance, supporting the link between life span extension and cellular protection observed in several model organisms. The high degree of conservation of these novel yeast longevity determinants in other species raises the possibility that their role in senescence might be conserved.
Author Summary
Model organisms have been instrumental in uncovering genes that function to control life span and to identify the molecular pathways whose role in aging is conserved between the evolutionarily distant unicellular yeast and mice. Because yeast are particularly amenable to genetics and genomics studies, they have been used widely as model system for aging research. Here we have exploited a powerful genomic tool, the yeast deletion collection, to screen a pool of non-essential deletion mutants (∼4,800) to identify novel genes involved in the regulation of yeast chronological life span. Our results show that normal life span depends on functional mitochondria and on the cell's ability to degrade cellular components and proteins by autophagy. Our data indicate that a cell signaling protein, CK2, and diverse cellular processes such as fatty acid metabolism, amino acid biosynthesis, and tRNA modification modulate yeast chronological aging. The high level of conservation of the novel life span regulatory genes uncovered in this study suggests that their role in longevity regulation might be conserved in higher eukaryotes.
PMCID: PMC2904796  PMID: 20657825
12.  Role of three rab5-like GTPases, Ypt51p, Ypt52p, and Ypt53p, in the endocytic and vacuolar protein sorting pathways of yeast 
The Journal of Cell Biology  1994;125(2):283-298.
The small GTPase rab5 has been shown to represent a key regulator in the endocytic pathway of mammalian cells. Using a PCR approach to identify rab5 homologs in Saccharomyces cerevisiae, two genes encoding proteins with 54 and 52% identity to rab5, YPT51 and YPT53 have been identified. Sequencing of the yeast chromosome XI has revealed a third rab5-like gene, YPT52, whose protein product exhibits a similar identity to rab5 and the other two YPT gene products. In addition to the high degree of identity/homology shared between rab5 and Ypt51p, Ypt52p, and Ypt53p, evidence for functional homology between the mammalian and yeast proteins is provided by phenotypic characterization of single, double, and triple deletion mutants. Endocytic delivery to the vacuole of two markers, lucifer yellow CH (LY) and alpha-factor, was inhibited in delta ypt51 mutants and aggravated in the double ypt51ypt52 and triple ypt51ypt52ypt53 mutants, suggesting a requirement for these small GTPases in endocytic membrane traffic. In addition to these defects, the here described ypt mutants displayed a number of other phenotypes reminiscent of some vacuolar protein sorting (vps) mutants, including a differential delay in growth and vacuolar protein maturation, partial missorting of a soluble vacuolar hydrolase, and alterations in vacuole acidification and morphology. In fact, vps21 represents a mutant allele of YPT51 (Emr, S., personal communication). Altogether, these data suggest that Ypt51p, Ypt52p, and Ypt53p are required for transport in the endocytic pathway and for correct sorting of vacuolar hydrolases suggesting a possible intersection of the endocytic with the vacuolar sorting pathway.
PMCID: PMC2120022  PMID: 8163546
13.  A Novel RING Finger Protein Complex Essential for a Late Step in Protein Transport to the Yeast Vacuole 
Molecular Biology of the Cell  1997;8(11):2307-2327.
Protein transport to the lysosome-like vacuole in yeast is mediated by multiple pathways, including the biosynthetic routes for vacuolar hydrolases, the endocytic pathway, and autophagy. Among the more than 40 genes required for vacuolar protein sorting (VPS) in Saccharomyces cerevisiae, mutations in the four class C VPS genes result in the most severe vacuolar protein sorting and morphology defects. Herein, we provide complementary genetic and biochemical evidence that the class C VPS gene products (Vps18p, Vps11p, Vps16p, and Vps33p) physically and functionally interact to mediate a late step in protein transport to the vacuole. Chemical cross-linking experiments demonstrated that Vps11p and Vps18p, which both contain RING finger zinc-binding domains, are components of a hetero-oligomeric protein complex that includes Vps16p and the Sec1p homologue Vps33p. The class C Vps protein complex colocalized with vacuolar membranes and a distinct dense membrane fraction. Analysis of cells harboring a temperature-conditional vps18 allele (vps18tsf) indicated that Vps18p function is required for the biosynthetic, endocytic, and autophagic protein transport pathways to the vacuole. In addition, vps18tsf cells accumulated multivesicular bodies, autophagosomes, and other membrane compartments that appear to represent blocked transport intermediates. Overproduction of either Vps16p or the vacuolar syntaxin homologue Vam3p suppressed defects associated with vps18tsf mutant cells, indicating that the class C Vps proteins and Vam3p may functionally interact. Thus we propose that the class C Vps proteins are components of a hetero-oligomeric protein complex that mediates the delivery of multiple transport intermediates to the vacuole.
PMCID: PMC25710  PMID: 9362071
14.  Genomic analysis of severe hypersensitivity to hygromycin B reveals linkage to vacuolar defects and new vacuolar gene functions in Saccharomyces cerevisiae 
Current genetics  2009;56(2):121-137.
The vacuole of Saccharomyces cerevisiae has been a seminal model for studies of lysosomal trafficking, biogenesis, and function. Several yeast mutants defective in such vacuolar events have been unable to grow at low levels of hygromycin B, an aminoglycoside antibiotic. We hypothesized that such severe hypersensitivity to hygromycin B (hhy) is linked to vacuolar defects and performed a genomic screen for the phenotype using a haploid deletion strain library of non-essential genes. Fourteen HHY genes were initially identified and were subjected to bioinformatics analyses. The uncovered hhy mutants were experimentally characterized with respect to vesicular trafficking, vacuole morphology, and growth under various stress and drug conditions. The combination of bioinformatics analyses and phenotypic characterizations implicate defects in vesicular trafficking, vacuole fusion/fission, or vacuole function in all hhy mutants. The collection was enriched for sensitivity to monensin, indicative of vacuolar trafficking defects. Additionally, all hhy mutants showed severe sensitivities to rapamycin and caffeine, suggestive of TOR kinase pathway defects. Our experimental results also establish a new role in vacuolar and vesicular functions for two genes: PAF1, encoding a RNAP II-associated protein required for expression of cell cycle-regulated genes, and TPD3, encoding the regulatory subunit of protein phosphatase 2A. Thus, our results support linkage between severe hypersensitivity to hygromycin B and vacuolar defects.
PMCID: PMC2886664  PMID: 20043226
Yeast vacuole; Lysosome; Hygromycin B; Vesicular trafficking
15.  Ubiquitin initiates sorting of Golgi and plasma membrane proteins into the vacuolar degradation pathway 
BMC Plant Biology  2012;12:164.
In yeast and mammals, many plasma membrane (PM) proteins destined for degradation are tagged with ubiquitin. These ubiquitinated proteins are internalized into clathrin-coated vesicles and are transported to early endosomal compartments. There, ubiquitinated proteins are sorted by the endosomal sorting complex required for transport (ESCRT) machinery into the intraluminal vesicles of multivesicular endosomes. Degradation of these proteins occurs after endosomes fuse with lysosomes/lytic vacuoles to release their content into the lumen. In plants, some PM proteins, which cycle between the PM and endosomal compartments, have been found to be ubiquitinated, but it is unclear whether ubiquitin is sufficient to mediate internalization and thus acts as a primary sorting signal for the endocytic pathway. To test whether plants use ubiquitin as a signal for the degradation of membrane proteins, we have translationally fused ubiquitin to different fluorescent reporters for the plasma membrane and analyzed their transport.
Ubiquitin-tagged PM reporters localized to endosomes and to the lumen of the lytic vacuole in tobacco mesophyll protoplasts and in tobacco epidermal cells. The internalization of these reporters was significantly reduced if clathrin-mediated endocytosis was inhibited by the coexpression of a mutant of the clathrin heavy chain, the clathrin hub. Surprisingly, a ubiquitin-tagged reporter for the Golgi was also transported into the lumen of the vacuole. Vacuolar delivery of the reporters was abolished upon inhibition of the ESCRT machinery, indicating that the vacuolar delivery of these reporters occurs via the endocytic transport route.
Ubiquitin acts as a sorting signal at different compartments in the endomembrane system to target membrane proteins into the vacuolar degradation pathway: If displayed at the PM, ubiquitin triggers internalization of PM reporters into the endocytic transport route, but it also mediates vacuolar delivery if displayed at the Golgi. In both cases, ubiquitin-tagged proteins travel via early endosomes and multivesicular bodies to the lytic vacuole. This suggests that vacuolar degradation of ubiquitinated proteins is not restricted to PM proteins but might also facilitate the turnover of membrane proteins in the early secretory pathway.
PMCID: PMC3534617  PMID: 22970698
16.  Telomere Length as a Quantitative Trait: Genome-Wide Survey and Genetic Mapping of Telomere Length-Control Genes in Yeast 
PLoS Genetics  2006;2(3):e35.
Telomere length-variation in deletion strains of Saccharomyces cerevisiae was used to identify genes and pathways that regulate telomere length. We found 72 genes that when deleted confer short telomeres, and 80 genes that confer long telomeres relative to those of wild-type yeast. Among identified genes, 88 have not been previously implicated in telomere length control. Genes that regulate telomere length span a variety of functions that can be broadly separated into telomerase-dependent and telomerase-independent pathways. We also found 39 genes that have an important role in telomere maintenance or cell proliferation in the absence of telomerase, including genes that participate in deoxyribonucleotide biosynthesis, sister chromatid cohesion, and vacuolar protein sorting. Given the large number of loci identified, we investigated telomere lengths in 13 wild yeast strains and found substantial natural variation in telomere length among the isolates. Furthermore, we crossed a wild isolate to a laboratory strain and analyzed telomere length in 122 progeny. Genome-wide linkage analysis among these segregants revealed two loci that account for 30%–35% of telomere length-variation between the strains. These findings support a general model of telomere length-variation in outbred populations that results from polymorphisms at a large number of loci. Furthermore, our results laid the foundation for studying genetic determinants of telomere length-variation and their roles in human disease.
Telomere maintenance is of great importance to ensure genome stability in organisms with linear genomes. In humans, telomeres shorten as a function of age and serve as a marker of cell replication history. Understanding the genetic differences in telomere length-maintenance may help provide the insights into the basis for different rates of aging among individuals and differences in individuals' propensity for aging-associated diseases such as cancer. Studies in yeast and other model organisms have defined several pathways that ensure stability of chromosome ends. In order to capture full complement of genes that participate in telomere maintenance in yeast Saccharomyces cerevisiae, the authors undertook a comprehensive screen for genes that affect telomere length. Among 152 identified genes, the authors found 39 genes whose function is critical for telomere maintenance in the absence of telomerase. The authors extended their studies from laboratory yeast strains to outbred populations of yeast and discovered significant phenotypic variation in telomere length among the isolates. Telomere length-analysis of a cross between a wild yeast isolate and a laboratory strain support a general model of telomere length-variation in outbred populations that results from polymorphisms at a large number of loci. This finding provides a basis for genetic studies of telomere maintenance in human populations.
PMCID: PMC1401499  PMID: 16552446
17.  Characterization of VPS34, a gene required for vacuolar protein sorting and vacuole segregation in Saccharomyces cerevisiae. 
Molecular and Cellular Biology  1990;10(12):6742-6754.
VPS34 gene function is required for the efficient localization of a variety of vacuolar proteins. We have cloned and sequenced the wild-type VPS34 gene in order to gain a better understanding of the role of its protein product in this intracellular sorting pathway. Interestingly, disruption of the VPS34 locus resulted in a temperature-sensitive growth defect, indicating that the VPS34 gene is essential for vegetative growth only at elevated growth temperatures. As with the original vps34 alleles, vps34 null mutants exhibited severe vacuolar protein sorting defects and possessed a morphologically normal vacuolar structure. The VPS34 gene DNA sequence identifies an open reading frame that could encode a hydrophilic protein of 875 amino acids. The predicted protein sequence lacks any apparent signal sequence or membrane-spanning domains, suggesting that Vps34p does not enter the secretory pathway. Results from immunoprecipitation experiments with antiserum prepared against a TrpE-Vps34 fusion protein were consistent with this prediction: a rare, unglycosylated protein of approximately 95,000 Da was detected in extracts of wild-type Saccharomyces cerevisiae cells. Cell fractionation studies indicated that a significant portion of the Vps34p is found associated with a particulate fraction of yeast cells. This particulate Vps34p was readily solubilized by treatment with 2 M urea but not with Triton X-100, suggesting that the presence of Vps34p in this pelletable structure is mediated by protein-protein interactions. vp34 mutant cells also exhibited a defect in the normal partitioning of the vacuolar compartment between mother and daughter cells during cell division. In more than 80% of the delta vps34 dividing cells examined, no vacuolar structures were observed in the newly emerging bud, whereas in wild-type dividing cells, more than 95% of the buds had a detectable vacuolar compartment. Our results suggest that the Vps34p may act as a component of a relatively large intracellular structure that functions to facilitate specific steps of the vacuolar protein delivery and inheritance pathways.
PMCID: PMC362952  PMID: 2247081
18.  A Systems Biology Approach Reveals the Role of a Novel Methyltransferase in Response to Chemical Stress and Lipid Homeostasis 
PLoS Genetics  2011;7(10):e1002332.
Using small molecule probes to understand gene function is an attractive approach that allows functional characterization of genes that are dispensable in standard laboratory conditions and provides insight into the mode of action of these compounds. Using chemogenomic assays we previously identified yeast Crg1, an uncharacterized SAM-dependent methyltransferase, as a novel interactor of the protein phosphatase inhibitor cantharidin. In this study we used a combinatorial approach that exploits contemporary high-throughput techniques available in Saccharomyces cerevisiae combined with rigorous biological follow-up to characterize the interaction of Crg1 with cantharidin. Biochemical analysis of this enzyme followed by a systematic analysis of the interactome and lipidome of CRG1 mutants revealed that Crg1, a stress-responsive SAM-dependent methyltransferase, methylates cantharidin in vitro. Chemogenomic assays uncovered that lipid-related processes are essential for cantharidin resistance in cells sensitized by deletion of the CRG1 gene. Lipidome-wide analysis of mutants further showed that cantharidin induces alterations in glycerophospholipid and sphingolipid abundance in a Crg1-dependent manner. We propose that Crg1 is a small molecule methyltransferase important for maintaining lipid homeostasis in response to drug perturbation. This approach demonstrates the value of combining chemical genomics with other systems-based methods for characterizing proteins and elucidating previously unknown mechanisms of action of small molecule inhibitors.
Author Summary
Chemical genetics uses small molecules to perturb biological systems to study gene function. By analogy with genetic lesions, chemical probes act as fast-acting, reversible, and “tunable” conditional alleles. Furthermore, small molecules can target multiple protein targets and target pathways simultaneously to uncover phenotypes that may be masked by genes encoding partially redundant proteins. Finally, potent chemical probes can be useful starting points for the development of human therapeutics. Here, we used cantharidin, a natural toxin, to uncover otherwise “hidden” phenotypes for a methyltransferase that has resisted characterization. This enzyme, Crg1, has no phenotype in standard conditions but is indispensible for survival in the presence of cantharidin. Using this chemical genetic relationship, we characterized novel functions of Crg1, and by combining diverse genomic assays with small molecule perturbation we characterized the mechanism of cantharidin cytotoxicity. These observations are relevant beyond yeast Crg1 because cantharidin and its analogues have potent anticancer activity, yet its therapeutic use has been limited to topical applications because of its cytotoxicity. Considering that methyltransferases are an extremely abundant and diverse class of cellular proteins, chemical probes such as cantharidin are critical for understanding their cellular roles and defining potential points of therapeutic intervention.
PMCID: PMC3197675  PMID: 22028670
19.  Chemical combination effects predict connectivity in biological systems 
Chemical synergies can be novel probes of biological systems.Simulated response shapes depend on target connectivity in a pathway.Experiments with yeast and cancer cells confirm simulated effects.Profiles across many combinations yield target location information.
Living organisms are built of interacting components, whose function and dysfunction can be described through dynamic network models (Davidson et al, 2002). Systems Biology involves the iterative construction of such models (Ideker et al, 2001), and may eventually improve the understanding of diseases using in silico simulations. Such simulations may eventually permit drugs to be prioritized for clinical trials, reducing potential risks and increasing the likelihood of successful outcomes. Given the complexity of biological systems, constructing realistic models will require large and diverse sets of connectivity data.
Chemical combinations provide a new window into biological connectivity. Information gleaned from targeted combinations, such as paired mutations (Tong et al, 2004), has proven to be especially useful for revealing functional interactions between components. We have been screening chemical combinations for therapeutic synergies (Borisy et al, 2003; Zimmermann et al, 2007), collecting full-dose matrices where combinations are tested in all possible pairings of serially diluted single agent doses (Figure 1). Such screens yield a variety of response surfaces with distinct shapes for combinations that work through different known mechanisms, suggesting that combination effects may contain information on the nature of functional connections between drug targets.
Simulations of biological pathways predict synergistic responses to inhibitors that depend on target connectivity. We explored theoretical predictions by simulating a metabolic pathway with pairs of inhibitors aimed at different targets with varying doses. We found that the shape of each combination response depended on how the inhibitor pair's targets were connected in the pathway (Figure 2). The predicted response shapes were robust to plausible variations in the simulated pathway that did not affect the network topology (e.g., kinetic assumptions, parameter values, and nonlinear response functions), but were very sensitive to topological alterations in the modelled network (e.g., feedback regulation or changing the type of junction at a branch point). These findings suggest that connectivity of the inhibitor targets has a major influence on combination response morphology.
The predicted shapes were experimentally confirmed in yeast combination experiments. The proliferation experiment used drugs focused on the sterol biosynthesis pathway, which is mostly linear between the targets covered in this study, and is known to be regulated by negative feedback (Gardner et al, 2001). The combinations between sterol inhibitors confirmed expectations from our simulations, showing dose-additive responses for pairs targeting the same enzyme and strong synergies across enzymes of the shape predicted in our simulations for linear pathways under negative feedback. Combinations across pathways showed much more variable responses with a trend towards less synergy on average.
Further experimental support was obtained from human cells. A combination screen of 90 annotated drugs in a human tumour cell line (HCT116) proliferation assay produced strong synergies for combinations within pathways and more variable effects between targeted functions. Synergy profiles (sets of all synergy scores involving each drug) also showed a greater degree of similarity for pairs of drugs with related targets. Finally, the most extreme outliers were dominated by inhibitors of kinases that are especially critical for HCT116 proliferation (Awwad et al, 2003), with effects that are consistent across mechanistic replicates, showing that chemical combinations can highlight biologically relevant cellular processes.
This study demonstrates the potential of chemical combinations for exploring functional connectivity in biological systems. This information complements genetic studies by providing more details through variable dosing, by directly targeting single domains of multi-domain proteins, and by probing cell types that are not amenable to mutagenesis. Responses from large chemical combination screens can be used to identify molecular targets through chemical–genetic profiling (Macdonald et al, 2006), or to directly constrain network models by means of a prediction-validation procedure (Ideker et al, 2001). This initial exploration can be extended to cover a wider range of response shapes and network topologies, as well as to combinations of three or more chemical agents. Moreover, this approach may even be applicable to non-biological systems where responses to targeted perturbations can be measured.
Efforts to construct therapeutically useful models of biological systems require large and diverse sets of data on functional connections between their components. Here we show that cellular responses to combinations of chemicals reveal how their biological targets are connected. Simulations of pathways with pairs of inhibitors at varying doses predict distinct response surface shapes that are reproduced in a yeast experiment, with further support from a larger screen using human tumour cells. The response morphology yields detailed connectivity constraints between nearby targets, and synergy profiles across many combinations show relatedness between targets in the whole network. Constraints from chemical combinations complement genetic studies, because they probe different cellular components and can be applied to disease models that are not amenable to mutagenesis. Chemical probes also offer increased flexibility, as they can be continuously dosed, temporally controlled, and readily combined. After extending this initial study to cover a wider range of combination effects and pathway topologies, chemical combinations may be used to refine network models or to identify novel targets. This response surface methodology may even apply to non-biological systems where responses to targeted perturbations can be measured.
PMCID: PMC1828746  PMID: 17332758
chemical genetics; combinations and synergy; metabolic and regulatory networks; simulation and data analysis
20.  A Phenomics Approach in Yeast Links Proton and Calcium Pump Function in the Golgi 
Molecular Biology of the Cell  2007;18(4):1480-1489.
The Golgi-localized Ca2+- and Mn2+-transporting ATPase Pmr1 is important for secretory pathway functions. Yeast mutants lacking Pmr1 show growth sensitivity to multiple drugs (amiodarone, wortmannin, sulfometuron methyl, and tunicamycin) and ions (Mn2+ and Ca2+). To find components that function within the same or parallel cellular pathways as Pmr1, we identified genes that shared multiple pmr1 phenotypes. These genes were enriched in functional categories of cellular transport and interaction with cellular environment, and predominantly localize to the endomembrane system. The vacuolar-type H+-transporting ATPase (V-ATPase), rather than other Ca2+ transporters, was found to most closely phenocopy pmr1Δ, including a shared sensitivity to Zn2+ and calcofluor white. However, we show that pmr1Δ mutants maintain normal vacuolar and prevacuolar pH and that the two transporters do not directly influence each other's activity. Together with a synthetic fitness defect of pmr1ΔvmaΔ double mutants, this suggests that Pmr1 and V-ATPase work in parallel toward a common function. Overlaying data sets of growth sensitivities with functional screens (carboxypeptidase secretion and Alcian Blue binding) revealed a common set of genes relating to Golgi function. We conclude that overlapping phenotypes with Pmr1 reveal Golgi-localized functions of the V-ATPase and emphasize the importance of calcium and proton transport in secretory/prevacuolar traffic.
PMCID: PMC1839000  PMID: 17314395
21.  Integrative genome-scale metabolic analysis of Vibrio vulnificus for drug targeting and discovery 
Chromosome 1 of Vibrio vulnificus tends to contain larger portion of essential or housekeeping genes on the basis of the genomic analysis and gene knockout experiments performed in this study, while its chromosome 2 seems to have originated and evolved from a plasmid.The genome-scale metabolic network model of V. vulnificus was reconstructed based on databases and literature, and was used to identify 193 essential metabolites.Five essential metabolites finally selected after the filtering process are 2-amino-4-hydroxy-6-hydroxymethyl-7,8-dihydropteridine (AHHMP), D-glutamate (DGLU), 2,3-dihydrodipicolinate (DHDP), 1-deoxy-D-xylulose 5-phosphate (DX5P), and 4-aminobenzoate (PABA), which were predicted to be essential in V. vulnificus, absent in human, and are consumed by multiple reactions.Chemical analogs of the five essential metabolites were screened and a hit compound showing the minimal inhibitory concentration (MIC) of 2 μg/ml and the minimal bactericidal concentration (MBC) of 4 μg/ml against V. vulnificus was identified.
Discovering new antimicrobial targets and consequently new antimicrobials is important as drug resistance of pathogenic microorganisms is becoming an increasingly serious problem in human healthcare management (Fischbach and Walsh, 2009). There clearly exists a gap between genomic studies and drug discovery as the accumulation of knowledge on pathogens at genome level has not successfully transformed into the development of effective drugs (Mills, 2006; Payne et al, 2007). In this study, we dissected the genome of a microbial pathogen in detail, and subsequently developed a systems biological strategy of employing genome-scale metabolic modeling and simulation together with metabolite essentiality analysis for effective drug targeting and discovery. This strategy was used for identifying new drug targets in an opportunistic pathogen Vibrio vulnificus CMCP6 as a model.
V. vulnificus is a Gram-negative halophilic bacterium that is found in estuarine waters, brackish ponds, or coastal areas, and its Biotype 1 is an opportunistic human pathogen that can attack immune-compromised patients, and causes primary septicemia, necrotized wound infections, and gastroenteritis. We previously found that many metabolic genes were specifically induced in vivo, suggesting that specific metabolic pathways are essential for in vivo survival and virulence of this pathogen (Kim et al, 2003; Lee et al, 2007). These results motivated us to carry out systems biological analysis of the genome and the metabolic network for new drug target discovery.
V. vulnificus CMCP6 has two chromosomes. We first re-sequenced genomic regions assembled in low quality and low depth, and subsequently re-annotated the whole genome of V. vulnificus. Horizontal gene transfer was suspected to be responsible for the diversification of each chromosome of V. vulnificus, and the presence of metabolic genes was more biased to chromosome 1 than chromosome 2. Further studies on V. vulnificus genome revealed that chromosome 2 is more prone to diversification for better adaptation to the environment than its chromosome 1, while chromosome 1 tends to expand their genetic repertoire while maintaining the core genes at a constant level.
Next, a genome-scale metabolic network VvuMBEL943 was reconstructed based on literature, databases and experiments for systematic studies on the metabolism of this pathogen and prediction of drug targets. The VvuMBEL943 model is composed of 943 reactions and 765 metabolites, and covers 673 genes. The model was validated by comparing its simulated cell growth phenotype obtained by constraints-based flux analysis with the V. vulnificus-specific experimental data previously reported in the literature. In this study, constraints-based flux analysis is an optimization-based simulation method that calculates intracellular fluxes under the specific genetic and environmental condition (Kim et al, 2008). As a result, 17 growth phenotypes were correctly predicted out of 18 cases, which demonstrate the validity of VvuMBEL943.
The main objective of constructing VvuMBEL943 in this study is to predict potential drug targets by system-wide analysis of the metabolic network for the effective treatment of V. vulnificus. To achieve this goal, a set of drug target candidates was predicted by taking a metabolite-centric approach. Metabolite essentiality analysis is a concept recently introduced for the study of cellular robustness to complement conventional reaction or gene-centric approach (Kim et al, 2007b). Metabolite essentiality analysis observes changes in flux distribution by removing each metabolite from the in silico metabolic network. Hence, metabolite essentiality predicts essential metabolites whose absence causes cell death. By selecting essential metabolites, it is possible to directly screen only their structural analogs, which substantially reduces the number of chemical compounds to screen from the chemical compound library. As a result of implementing this approach, 193 metabolites were initially identified to be essential to the cell. These essential metabolites were then further filtered based on the predetermined criteria, mainly organism specificity and multiple connectivity associated with each metabolite, in order to reduce the number of initial target candidates towards identifying the most effective ones.
Five essential metabolites finally selected are 2-amino-4-hydroxy-6-hydroxymethyl-7,8-dihydropteridine (AHHMP), D-glutamate (DGLU), 2,3-dihydrodipicolinate (DHDP), 1-deoxy-D-xylulose 5-phosphate (DX5P), and 4-aminobenzoate (PABA). Enzymes that consume these essential metabolites were experimentally verified to be essential, which indeed demonstrates the essentiality of these five metabolites. On the basis of the structural information of these five essential metabolites, whole-cell screening assay was performed using their analogs for possible antibacterial discovery. We screened 352 chemical analogs of the essential metabolites selected from the chemical compound library, and found a hit compound 24837, which shows the minimal inhibitory concentration (MIC) of 2 μg/ml and minimal bactericidal concentration (MBC) of 4 μg/ml, showing good antibacterial activity without further structural modification. Although this study demonstrates a proof-of-concept, the approaches and their rationale taken here should serve as a general strategy for discovering novel antibiotics and drugs based on systems-level analysis of metabolic networks.
Although the genomes of many microbial pathogens have been studied to help identify effective drug targets and novel drugs, such efforts have not yet reached full fruition. In this study, we report a systems biological approach that efficiently utilizes genomic information for drug targeting and discovery, and apply this approach to the opportunistic pathogen Vibrio vulnificus CMCP6. First, we partially re-sequenced and fully re-annotated the V. vulnificus CMCP6 genome, and accordingly reconstructed its genome-scale metabolic network, VvuMBEL943. The validated network model was employed to systematically predict drug targets using the concept of metabolite essentiality, along with additional filtering criteria. Target genes encoding enzymes that interact with the five essential metabolites finally selected were experimentally validated. These five essential metabolites are critical to the survival of the cell, and hence were used to guide the cost-effective selection of chemical analogs, which were then screened for antimicrobial activity in a whole-cell assay. This approach is expected to help fill the existing gap between genomics and drug discovery.
PMCID: PMC3049409  PMID: 21245845
drug discovery; drug targeting; genome analysis; metabolic network; Vibrio vulnificus
22.  Vam7p, a SNAP-25-Like Molecule, and Vam3p, a Syntaxin Homolog, Function Together in Yeast Vacuolar Protein Trafficking 
Molecular and Cellular Biology  1998;18(9):5308-5319.
A genetic screen to isolate gene products required for vacuolar morphogenesis in the yeast Saccharomyces cerevisiae identified VAM7, a gene which encodes a protein containing a predicted coiled-coil domain homologous to the coiled-coil domain of the neuronal t-SNARE, SNAP-25 (Y. Wada and Y. Anraku, J. Biol. Chem. 267:18671–18675, 1992; T. Weimbs, S. H. Low, S. J. Chapin, K. E. Mostov, P. Bucher, and K. Hofmann, Proc. Natl. Acad. Sci. USA 94:3046–3051, 1997). Analysis of a temperature-sensitive-for-function (tsf) allele of VAM7 (vam7tsf) demonstrated that the VAM7 gene product directly functions in vacuolar protein transport. vam7tsf mutant cells incubated at the nonpermissive temperature displayed rapid defects in the delivery of multiple proteins that traffic to the vacuole via distinct biosynthetic pathways. Examination of vam7tsf cells at the nonpermissive temperature by electron microscopy revealed the accumulation of aberrant membranous compartments that may represent unfused transport intermediates. A fraction of Vam7p was localized to vacuolar membranes. Furthermore, VAM7 displayed genetic interactions with the vacuolar syntaxin homolog, VAM3. Consistent with the genetic results, Vam7p physically associated in a complex containing Vam3p, and this interaction was enhanced by inactivation of the yeast NSF (N-ethyl maleimide-sensitive factor) homolog, Sec18p. In addition to the coiled-coil domain, Vam7p also contains a putative NADPH oxidase p40phox (PX) domain. Changes in two conserved amino acids within this domain resulted in synthetic phenotypes when combined with the vam3tsf mutation, suggesting that the PX domain is required for Vam7p function. This study provides evidence for the functional and physical interaction between Vam7p and Vam3p at the vacuolar membrane, where they function as part of a t-SNARE complex required for the docking and/or fusion of multiple transport intermediates destined for the vacuole.
PMCID: PMC109116  PMID: 9710615
23.  A putative zinc finger protein, Saccharomyces cerevisiae Vps18p, affects late Golgi functions required for vacuolar protein sorting and efficient alpha-factor prohormone maturation. 
Molecular and Cellular Biology  1991;11(12):5813-5824.
Saccharomyces cerevisiae strains carrying vps18 mutations are defective in the sorting and transport of vacuolar enzymes. The precursor forms of these proteins are missorted and secreted from the mutant cells. Most vps18 mutants are temperature sensitive for growth and are defective in vacuole biogenesis; no structure resembling a normal vacuole is seen. A plasmid complementing the temperature-sensitive growth defect of strains carrying the vps18-4 allele was isolated from a centromere-based yeast genomic library. Integrative mapping experiments indicated that the 26-kb insert in this plasmid was derived from the VPS18 locus. A 4-kb minimal complementing fragment contains a single long open reading frame predicted to encode a 918-amino-acid hydrophilic protein. Comparison of the VPS18 sequence with the PEP3 sequence reported in the accompanying paper (R. A. Preston, H. F. Manolson, K. Becherer, E. Weidenhammer, D. Kirkpatrick, R. Wright, and E. W. Jones, Mol. Cell. Biol. 11:5801-5812, 1991) shows that the two genes are identical. Disruption of the VPS18/PEP3 gene (vps18 delta 1::TRP1) is not lethal but results in the same vacuolar protein sorting and growth defects exhibited by the original temperature-sensitive vps18 alleles. In addition, vps18 delta 1::TRP1 MAT alpha strains exhibit a defect in the Kex2p-dependent processing of the secreted pheromone alpha-factor. This finding suggests that vps18 mutations alter the function of a late Golgi compartment which contains Kex2p and in which vacuolar proteins are thought to be sorted from proteins destined for the cell surface. The Vps18p sequence contains a cysteine-rich, zinc finger-like motif at the COOH terminus. A mutant in which the first cysteine of this motif was changed to serine results in a temperature-conditional carboxypeptidase Y sorting defect shortly after a shift to nonpermissive conditions. We identified a similar cysteine-rich motif near the COOH terminus of another Vps protein, the Vps11/Pep5/End1 protein. Preston et al. (Mol. Cell. Biol. 11:5801-5812, 1991) present evidence that the Vps18/Pep3 protein colocalizes with the Vps11/Pep5 protein to the cytosolic face of the vacuolar membrane. Together with the similar phenotypes exhibited by both vps11 and vps18 mutants, this finding suggests that they may function at a common step during vacuolar protein sorting and that the integrity of their zinc finger motifs may be required for this function.
PMCID: PMC361725  PMID: 1840635
24.  Chemical combinations elucidate pathway interactions and regulation relevant to Hepatitis C replication 
SREBP-2, oxidosqualene cyclase (OSC) or lanosterol demethylase were identified as novel sterol pathway-associated targets that, when probed with chemical agents, can inhibit hepatitis C virus (HCV) replication.Using a combination chemical genetics approach, combinations of chemicals targeting sterol pathway enzymes downstream of and including OSC or protein geranylgeranyl transferase I (PGGT) produce robust and selective synergistic inhibition of HCV replication. Inhibition of enzymes upstream of OSC elicit proviral responses that are dominant to the effects of inhibiting all downstream targets.Inhibition of the sterol pathway without inhibition of regulatory feedback mechanisms ultimately results in an increase in HCV replication because of a compensatory upregulation of 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGCR) expression. Increases in HMGCR expression without inhibition of HMGCR enzymatic activity ultimately stimulate HCV replication through increasing the cellular pool of geranylgeranyl pyrophosphate (GGPP).Chemical inhibitors that ultimately prevent SREBP-2 activation, inhibit PGGT or encourage the production of polar sterols have great potential as HCV therapeutics if associated toxicities can be reduced.
Chemical inhibition of enzymes in either the cholesterol or the fatty acid biosynthetic pathways has been shown to impact viral replication, both positively and negatively (Su et al, 2002; Ye et al, 2003; Kapadia and Chisari, 2005; Sagan et al, 2006; Amemiya et al, 2008). FBL2 has been identified as a 50 kDa geranylgeranylated host protein that is necessary for localization of the hepatitis C virus (HCV) replication complex to the membranous web through its close association with the HCV protein NS5A and is critical for HCV replication (Wang et al, 2005). Inhibition of the protein geranylgeranyl transferase I (PGGT), an enzyme that transfers geranylgeranyl pyrophosphate (GGPP) to cellular proteins such as FBL2 for the purpose of membrane anchoring, negatively impacts HCV replication (Ye et al, 2003). Conversely, chemical agents that increase intracellular GGPP concentrations promote viral replication (Kapadia and Chisari, 2005). Statin compounds that inhibit 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGCR), the rate-limiting enzyme in the sterol pathway (Goldstein and Brown, 1990), have been suggested to inhibit HCV replication through ultimately reducing the cellular pool of GGPP (Ye et al, 2003; Kapadia and Chisari, 2005; Ikeda et al, 2006). However, inhibition of the sterol pathway with statin drugs has not yielded consistent results in patients. The use of statins for the treatment of HCV is likely to be complicated by the reported compensatory increase in HMGCR expression in vitro and in vivo (Stone et al, 1989; Cohen et al, 1993) in response to treatment. Enzymes in the sterol pathway are regulated on a transcriptional level by sterol regulatory element-binding proteins (SREBPs), specifically SREBP-2 (Hua et al, 1993; Brown and Goldstein, 1997). When cholesterol stores in cells are depleted, SREBP-2 activates transcription of genes in the sterol pathway such as HMGCR, HMG-CoA synthase, farnesyl pyrophosphate (FPP) synthase, squalene synthase (SQLS) and the LDL receptor (Smith et al, 1988, 1990; Sakai et al, 1996; Brown and Goldstein, 1999; Horton et al, 2002). The requirement of additional downstream sterol pathway metabolites for HCV replication has not been completely elucidated.
To further understand the impact of the sterol pathway and its regulation on HCV replication, we conducted a high-throughput combination chemical genetic screen using 16 chemical probes that are known to modulate the activity of target enzymes relating to the sterol biosynthesis pathway (Figure 1). Using this approach, we identified several novel antiviral targets including SREBP-2 as well as targets downstream of HMGCR in the sterol pathway such as oxidosqualene cyclase (OSC) and lanosterol demethylase. Many of our chemical probes, specifically SR-12813, farnesol and squalestatin, strongly promoted replicon replication. The actions of both farnesol and squalestatin ultimately result in an increase in the cellular pool of GGPP, which is known to increase HCV replication (Ye et al, 2003; Kapadia and Chisari, 2005; Wang et al, 2005).
Chemical combinations targeting enzymes upstream of squalene epoxidase (SQLE) at the top of the sterol pathway (Figure 4A) elicited Bateson-type epistatic responses (Boone et al, 2007), where the upstream agent's response predominates over the effects of inhibiting all downstream targets. This was especially notable for combinations including simvastatin and either U18666A or squalestatin, and for squalestatin in combination with Ro48-8071. Treatment with squalestatin prevents the SQLS substrate, farnesyl pyrophosphate (FPP) from being further metabolized by the sterol pathway. As FPP concentrations increase, the metabolite can be shunted away from the sterol pathway toward farnesylation and GGPP synthetic pathways, resulting in an increase in host protein geranylgeranylation, including FBL2, and consequently replicon replication. This increase in replicon replication explains the source of the observed epistasis over Ro48-8071 treatment.
Combinations between probes targeting enzymes downstream of and including OSC produced robust synergies with each other or with a PGGT inhibitor. Figure 4B highlights examples of antiviral synergy resulting from treatment of cells with an OSC inhibitor in combination with an inhibitor of either an enzyme upstream or downstream of OSC. A combination of terconazole and U18666A is synergistic without similar combination effects in the host proliferation screen. Likewise, clomiphene was also synergistic when added to replicon cells in combination with U18666A. One of the greatest synergies observed downstream in the sterol pathway is a combination of amorolfine and AY 9944, suggesting that there is value in developing combinations of drugs that target enzymes in the sterol pathway, which are downstream of HMGCR.
Interactions with the protein prenylation pathway also showed strong mechanistic patterns (Figure 4C). GGTI-286 is a peptidomimetic compound resembling the CAAX domain of a protein to be geranylgeranylated and is a competitive inhibitor of protein geranylgeranylation. Simvastatin impedes the antiviral effect of GGTI-286 at low concentrations but that antagonism is balanced by comparable synergy at higher concentrations. At the low simvastatin concentrations, a compensatory increase in HMGCR expression leads to increased cellular levels of GGPP, which are likely to result in an increase in PGGT enzymatic turnover and decreased GGTI-286 efficacy. The antiviral synergy observed at the higher inhibitor concentrations is likely nonspecific as synergy was also observed in a host viability assay. Further downstream, however, a competitive interaction was observed between GGTI-286 and squalestatin, where the opposing effect of one compound obscures the other compound's effect. This competitive relationship between GGTI and SQLE explains the epistatic response observed between those two agents. For inhibitors of targets downstream of OSC, such as amorolfine, there are strong antiviral synergies with GGTI-286. Notably, combinations with OSC inhibitors and GGTI-286 were selective, in that comparable synergy was not found in a host viability assay. This selectivity suggests that jointly targeting OSC and PGGT is a promising avenue for future HCV therapy development.
This study provides a comprehensive and unique perspective into the impact of sterol pathway regulation on HCV replication and provides compelling insight into the use of chemical combinations to maximize antiviral effects while minimizing proviral consequences. Our results suggest that HCV therapeutics developed against sterol pathway targets must consider the impact on underlying sterol pathway regulation. We found combinations of inhibitors of the lower part of the sterol pathway that are effective and synergistic with each other when tested in combination. Furthermore, the combination effects observed with simvastatin suggest that, though statins inhibit HMGCR activity, the resulting regulatory consequences of such inhibition ultimately lead to undesirable epistatic effects. Inhibitors that prevent SREBP-2 activation, inhibit PGGT or encourage the production of polar sterols have great potential as HCV therapeutics if associated toxicities can be reduced.
The search for effective Hepatitis C antiviral therapies has recently focused on host sterol metabolism and protein prenylation pathways that indirectly affect viral replication. However, inhibition of the sterol pathway with statin drugs has not yielded consistent results in patients. Here, we present a combination chemical genetic study to explore how the sterol and protein prenylation pathways work together to affect hepatitis C viral replication in a replicon assay. In addition to finding novel targets affecting viral replication, our data suggest that the viral replication is strongly affected by sterol pathway regulation. There is a marked transition from antagonistic to synergistic antiviral effects as the combination targets shift downstream along the sterol pathway. We also show how pathway regulation frustrates potential hepatitis C therapies based on the sterol pathway, and reveal novel synergies that selectively inhibit hepatitis C replication over host toxicity. In particular, combinations targeting the downstream sterol pathway enzymes produced robust and selective synergistic inhibition of hepatitis C replication. Our findings show how combination chemical genetics can reveal critical pathway connections relevant to viral replication, and can identify potential treatments with an increased therapeutic window.
PMCID: PMC2913396  PMID: 20531405
chemical genetics; combinations and synergy; hepatitis C; replicon; sterol biosynthesis
25.  A systematic screen for protein–lipid interactions in Saccharomyces cerevisiae 
Lipids are important cellular metabolites, with a wide range of structural and functional diversity. Many operate as signaling molecules. Lipids though have rarely been studied in large-scale interaction screen; they are poorly represented in current biological networks.Here, we describe the use of miniaturized lipid–arrays for the large-scale study of protein–lipid interactions. In yeast, we show general feasibility with a systematic screen implying 172 proteins. We report 530 protein–lipid associations, the majority is novel and several were validated using other techniques.The screen uncovers numerous insights into lipid function in yeast and equivalent systems in humans. It revealed (i) previously undetected cryptic lipid-binding domains, (ii) series of new cellular targets for sphingolipids and (iii) new ligands for some PH domains that can cooperatively bind additional lipids and work as coincidence sensor to integrate both phosphatidylinositol phosphates and sphingolipid signaling pathways.The significant number of biological insights uncovered shows that even major classes of metabolites have been insufficiently studied. This illustrates the general relevance of such systematic screens and calls for further system-wide analyses.
Deciphering the molecular mechanisms behind cellular processes requires the systematic charting of the multitude of interactions between all cellular components. While protein–protein and protein–DNA networks have been the subject of many systematic surveys, other critically important cellular components, such as lipids, have to date rarely been studied in large-scale interaction screens. Growing numbers of lipids are known to operate as signaling molecules. The importance of protein–lipid interactions is evident from the variety of protein domains that have evolved to bind particular lipids (Lemmon, 2008 #392) and from the large list of disorders, such as cancer and bipolar disorder, arising from altered protein–lipid interactions. The current understanding of protein–lipid recognition comes from the study of a limited number of lipids, principally PtdInsPs (Zhu et al, 2001 #16), and lipid-binding domains (LBDs) in isolation (Dowler et al, 2000 #81; Yu and Lemmon, 2001 #396; Yu et al, 2004 #31). For other signaling lipids, such as sphingolipids, intracellular targets and molecular mechanisms are only partially understood (Hannun and Obeid, 2008 #397). The importance of lipids in biological processes and their under-representation in current biological networks suggest the need for systematic, unbiased biochemical screens.
To systematically study protein–lipid interactions, we developed miniaturized arrays that contained sets of 56 lipids covering the main lipid classes in yeast. We used the arrays to determine the binding profiles of 172 soluble proteins. The selection included proteins that contained one or several predicted LBD that were lipid regulated or enzymes involved in lipid metabolism (Figure 1). We obtained 530 protein–lipid interactions (accuracy and coverage: 61 and 60%, respectively). More than half were supported by additional experimental evidences obtained from a large validation effort using a variety of biochemical and cell biology approaches, and the integration of a data set of genetic interactions (Figure 1). As a substantial fraction (45%) of the analyzed proteins were conserved in humans, the protein–lipid data set will have functional implications for higher eukaryotes and thus for human biology.
Overall, 68% of all interactions were novel or unexpected from either protein sequences or known LBDs specificities. We discovered cryptic LBDs that were previously undetected in Ecm25 (a RhoGAP) and Ira2 (a RasGAP). We also identified a set of proteins that bound sphingolipids, a class of bioactive lipids that play important signaling functions in yeast and higher eukaryotes. The exact mode of action for these lipids remains elusive and the data set points to series of new cellular targets. We identified 63 proteins, involved in endocytosis, cell polarity and lipid metabolism that interacted with sphingoid long-chain bases (LCBs), ceramides or phosphorylated LCBs (Figure 5).
Despite the importance of sphingolipids in signaling processes, only a few domains, such as START or Saposins, have been reported to specifically bind these lipids in higher eukaryotes, and none of them have been found in yeast. Interestingly, almost 60% of proteins binding to phosphorylated LCBs in our assay also contained a pleckstrin homology (PH) domain and bound PtdInsPs (Figure 5). This suggests some PH domains might have unanticipated ligands and also have a function in sphingolipid recognition. We showed, using a variety of biochemical and cell-based assays, that the PH domain of Slm1, a component of the TORC2 signaling pathway (Fadri et al, 2005 #429), can bind PtdIns(4,5)P2 and sphingolipid cooperatively. The structure of Slm1-PH, which we solved by X-ray crystallography at 2 Å resolution, suggests the presence of two positively charged binding pockets for anionic lipids. These results indicate that the PH domain of Slm1 might work as a coincidence sensor to integrate both PtdInsP and sphingolipid signaling pathways. This reinforces the emerging notion that cooperative mechanisms have important functions in PH domains functioning (Maffucci and Falasca, 2001 #528). These mechanisms initially described between PtdInsPs and proteins can now be extended to new lipid classes, illustrating the benefit of unbiased and systematic analyses.
This work shows the feasibility and benefits of large-scale analyses combining biochemical arrays and live-cell imaging for charting protein–lipid interactions. Accurate representations of biological processes require systematic charting of the physical and functional links between all cellular components. There is a clear need to expand molecular interaction space from proteome- to metabolome-wide efforts and of systematic classifications of bioactive molecules based on their binding profiles. The data provided here represents an excellent resource to enhance the understanding of lipids function in eukaryotic systems.
Protein–metabolite networks are central to biological systems, but are incompletely understood. Here, we report a screen to catalog protein–lipid interactions in yeast. We used arrays of 56 metabolites to measure lipid-binding fingerprints of 172 proteins, including 91 with predicted lipid-binding domains. We identified 530 protein–lipid associations, the majority of which are novel. To show the data set's biological value, we studied further several novel interactions with sphingolipids, a class of conserved bioactive lipids with an elusive mode of action. Integration of live-cell imaging suggests new cellular targets for these molecules, including several with pleckstrin homology (PH) domains. Validated interactions with Slm1, a regulator of actin polarization, show that PH domains can have unexpected lipid-binding specificities and can act as coincidence sensors for both phosphatidylinositol phosphates and phosphorylated sphingolipids.
PMCID: PMC3010107  PMID: 21119626
interactome; lipid–array; network; pleckstrin homology domains; sphingolipids

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