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

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

The Fitness Consequences of Aneuploidy Are Driven by Condition-Dependent Gene Effects

Aneuploidy is a hallmark of tumor cells, and yet the precise relationship between aneuploidy and a cell’s proliferative ability, or cellular fitness, has remained elusive. In this study, we have combined a detailed analysis of aneuploid clones isolated from laboratory-evolved populations of Saccharomyces cerevisiae with a systematic, genome-wide screen for the fitness effects of telomeric amplifications to address the relationship between aneuploidy and cellular fitness. We found that aneuploid clones rise to high population frequencies in nutrient-limited evolution experiments and show increased fitness relative to wild type. Direct competition experiments confirmed that three out of four aneuploid events isolated from evolved populations were themselves sufficient to improve fitness. To expand the scope beyond this small number of exemplars, we created a genome-wide collection of >1,800 diploid yeast strains, each containing a different telomeric amplicon (Tamp), ranging in size from 0.4 to 1,000 kb. Using pooled competition experiments in nutrient-limited chemostats followed by high-throughput sequencing of strain-identifying barcodes, we determined the fitness effects of these >1,800 Tamps under three different conditions. Our data revealed that the fitness landscape explored by telomeric amplifications is much broader than that explored by single-gene amplifications. As also observed in the evolved clones, we found the fitness effects of most Tamps to be condition specific, with a minority showing common effects in all three conditions. By integrating our data with previous work that examined the fitness effects of single-gene amplifications genome-wide, we found that a small number of genes within each Tamp are centrally responsible for each Tamp’s fitness effects. Our genome-wide Tamp screen confirmed that telomeric amplifications identified in laboratory-evolved populations generally increased fitness. Our results show that Tamps are mutations that produce large, typically condition-dependent changes in fitness that are important drivers of increased fitness in asexually evolving populations.     

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

Modern medicine faces the challenge of developing safer and more effective therapies to treat human diseases. Many drugs currently in use were discovered without knowledge of their underlying molecular mechanisms. Understanding their biological targets and modes of action will be essential to design improved second-generation compounds. Here, we describe the use of a genome-wide pool of tagged heterozygotes to assess the cellular effects of 78 compounds in Saccharomyces cerevisiae. Specifically, lanosterol synthase in the sterol biosynthetic pathway was identified as a target of the antianginal drug molsidomine, which may explain its cholesterol-lowering effects. Further, the rRNA processing exosome was identified as a potential target of the cell growth inhibitor 5-fluorouracil. This genome-wide screen validated previously characterized targets or helped identify potentially new modes of action for over half of the compounds tested, providing proof of this principle for analyzing the modes of action of clinically relevant compounds.     

A Multidimensional Strategy to Detect Polypharmacological Targets in the Absence of Structural and Sequence Homology

Conventional drug design embraces the “one gene, one drug, one disease” philosophy. Polypharmacology, which focuses on multi-target drugs, has emerged as a new paradigm in drug discovery. The rational design of drugs that act via polypharmacological mechanisms can produce compounds that exhibit increased therapeutic potency and against which resistance is less likely to develop. Additionally, identifying multiple protein targets is also critical for side-effect prediction. One third of potential therapeutic compounds fail in clinical trials or are later removed from the market due to unacceptable side effects often caused by off-target binding. In the current work, we introduce a multidimensional strategy for the identification of secondary targets of known small-molecule inhibitors in the absence of global structural and sequence homology with the primary target protein. To demonstrate the utility of the strategy, we identify several targets of 4,5-dihydroxy-3-(1-naphthyldiazenyl)-2,7-naphthalenedisulfonic acid, a known micromolar inhibitor of Trypanosoma brucei RNA editing ligase 1. As it is capable of identifying potential secondary targets, the strategy described here may play a useful role in future efforts to reduce drug side effects and/or to increase polypharmacology.     

High-throughput phenotypic profiling of gene-environment interactions by quantitative growth curve analysis in Saccharomyces cerevisiae

Cell-based assays are widely used in high-throughput screening to determine the effects of toxicants and drugs on their biological targets. To enable a functional genomics modeling of gene-environment interactions, quantitative assays are required both for gene expression and for the phenotypic responses to environmental challenge. To address this need, we describe an automated high-throughput methodology that provides phenotypic profiling of the cellular responses to environmental stress in Saccharomyces cerevisiae. Standardized assay conditions enable the use of a single metric value to quantify yeast microculture growth curves. This assay format allows precise control of both genetic and environmental determinants of the cellular responses to oxidative stress, a common mechanism of environmental insult. These yeast-cell-based assays are validated with hydrogen peroxide, a simple direct-acting oxidant. Phenotypic profiling of the oxidative stress response of a yap1 mutant strain demonstrates the mechanistic analysis of genetic susceptibility to oxidative stress. As a proof of concept for analysis of more complex gene-environment interactions, we describe a combinatorial assay design for phenotypic profiling of the cellular responses to tert-butyl hydroperoxide, a complex oxidant that is actively metabolized by its target cells. Thus, the yeast microculture assay format supports comprehensive applications in toxicogenomics.     

Validation of RNAi silencing specificity using synthetic genes: salicylic acid-binding protein 2 is required for innate immunity in plants

RNA interference (RNAi) is widely used to specifically silence the expression of any gene to study its function and to identify and validate therapeutic targets. Despite the popularity of this technology, recent studies have shown that RNAi may also silence non-targeted genes. Here we demonstrate the utility of a quick, efficient and robust approach to directly validate the specificity of RNAi as an alternative to indirect validation of RNAi through gene expression profiling. Our approach involves reversing (complementing) the RNAi-induced phenotype by introducing a synthetic version of the target gene that is designed to escape silencing. This synthetic gene complementation approach can also be used for mutational analysis of the target gene, or to provide a functional version of a defective protein after silencing the defective gene by RNAi. Using this approach we demonstrate that the loss of systemic acquired resistance, a form of innate immunity in plants, is indeed due to the silencing of salicylic acid-binding protein 2 rather than to off-target effects.     

Identification of evolutionarily conserved genetic regulators of cellular aging

To identify new genetic regulators of cellular aging and senescence, we performed genome-wide comparative RNA profiling with selected human cellular model systems, reflecting replicative senescence, stress-induced premature senescence, and distinct other forms of cellular aging. Gene expression profiles were measured, analyzed, and entered into a newly generated database referred to as the GiSAO database. Bioinformatic analysis revealed a set of new candidate genes, conserved across the majority of the cellular aging models, which were so far not associated with cellular aging, and highlighted several new pathways that potentially play a role in cellular aging. Several candidate genes obtained through this analysis have been confirmed by functional experiments, thereby validating the experimental approach. The effect of genetic deletion on chronological lifespan in yeast was assessed for 93 genes where (i) functional homologues were found in the yeast genome and (ii) the deletion strain was viable. We identified several genes whose deletion led to significant changes of chronological lifespan in yeast, featuring both lifespan shortening and lifespan extension. In conclusion, an unbiased screen across species uncovered several so far unrecognized molecular pathways for cellular aging that are conserved in evolution.     

Discovering Thiamine Transporters as Targets of Chloroquine Using a Novel Functional Genomics Strategy

Chloroquine (CQ) and other quinoline-containing antimalarials are important drugs with many therapeutic benefits as well as adverse effects. However, the molecular targets underlying most such effects are largely unknown. By taking a novel functional genomics strategy, which employs a unique combination of genome-wide drug-gene synthetic lethality (DGSL), gene-gene synthetic lethality (GGSL), and dosage suppression (DS) screens in the model organism Saccharomyces cerevisiae and is thus termed SL/DS for simplicity, we found that CQ inhibits the thiamine transporters Thi7, Nrt1, and Thi72 in yeast. We first discovered a thi3Δ mutant as hypersensitive to CQ using a genome-wide DGSL analysis. Using genome-wide GGSL and DS screens, we then found that a thi7Δ mutation confers severe growth defect in the thi3Δ mutant and that THI7 overexpression suppresses CQ-hypersensitivity of this mutant. We subsequently showed that CQ inhibits the functions of Thi7 and its homologues Nrt1 and Thi72. In particular, the transporter activity of wild-type Thi7 but not a CQ-resistant mutant (Thi7^T287N) was completely inhibited by the drug. Similar effects were also observed with other quinoline-containing antimalarials. In addition, CQ completely inhibited a human thiamine transporter (SLC19A3) expressed in yeast and significantly inhibited thiamine uptake in cultured human cell lines. Therefore, inhibition of thiamine uptake is a conserved mechanism of action of CQ. This study also demonstrated SL/DS as a uniquely effective methodology for discovering drug targets.     

Formation of Hydrogen Sulfide from Cysteine in Saccharomyces cerevisiae BY4742: Genome Wide Screen Reveals a Central Role of the Vacuole

Discoveries on the toxic effects of cysteine accumulation and, particularly, recent findings on the many physiological roles of one of the products of cysteine catabolism, hydrogen sulfide (H₂S), are highlighting the importance of this amino acid and sulfur metabolism in a range of cellular activities. It is also highlighting how little we know about this critical part of cellular metabolism. In the work described here, a genome-wide screen using a deletion collection of Saccharomyces cerevisiae revealed a surprising set of genes associated with this process. In addition, the yeast vacuole, not previously associated with cysteine catabolism, emerged as an important compartment for cysteine degradation. Most prominent among the vacuole-related mutants were those involved in vacuole acidification; we identified each of the eight subunits of a vacuole acidification sub-complex (V₁ of the yeast V-ATPase) as essential for cysteine degradation. Other functions identified included translation, RNA processing, folate-derived one-carbon metabolism, and mitochondrial iron-sulfur homeostasis. This work identified for the first time cellular factors affecting the fundamental process of cysteine catabolism. Results obtained significantly contribute to the understanding of this process and may provide insight into the underlying cause of cysteine accumulation and H₂S generation in eukaryotes.     

Structure of Protein Interaction Networks and Their Implications on Drug Design

Protein-protein interaction networks (PINs) are rich sources of information that enable the network properties of biological systems to be understood. A study of the topological and statistical properties of budding yeast and human PINs revealed that they are scale-rich and configured as highly optimized tolerance (HOT) networks that are similar to the router-level topology of the Internet. This is different from claims that such networks are scale-free and configured through simple preferential-attachment processes. Further analysis revealed that there are extensive interconnections among middle-degree nodes that form the backbone of the networks. Degree distributions of essential genes, synthetic lethal genes, synthetic sick genes, and human drug-target genes indicate that there are advantageous drug targets among nodes with middle- to low-degree nodes. Such network properties provide the rationale for combinatorial drugs that target less prominent nodes to increase synergetic efficacy and create fewer side effects.     

VEGF inhibitor (Iressa) arrests histone deacetylase expression: single-cell cotransfection imaging cytometry for multi-target-multi-drug analysis

Multi-target-multi-drug approaches are needed to accelerate the process of drug discovery screening and to design efficient therapeutic strategies against diseases that involve alterations in multiple cellular targets. Herein we report single-cell cotransfection imaging cytometry to quantitatively screen drug-induced off-target effects. Vascular endothelial growth factor (VEGF) and histone deacetylase (HDAC) genes amplified from the genomic DNA were cloned in fluorescently tagged gene constructs (RFP-HDAC/YFP-VEGF). These gene constructs were cotransfected in HEK-293 cells to explore the possibility of off-target effects of 4-phenylbutyrate and Iressa on the expression of VEGF and HDAC through single-cell imaging cytometry. Iressa (10 μM) treatment at the time of cotransfection or 48 h after cotransfection of RFP-HDAC/YFP-VEGF plasmids in HEK-293 cells resulted in off-target effects on HDAC expression. These results suggest possible applications of Iressa in the treatment of diseases in which expression of both HDAC and VEGF should be inhibited. 4-Phenylbutyrate (2.0 mM) did not show any off-target effects on VEGF expression. The developed quantitative multicolor live single-cell cotransfection imaging can be employed to select better drug combinations for faster screening and greater accuracy in multi-target-multi-drug analysis by increasing the on-target/desired off-target effects and eliminating the undesirable off-target effects.     

Yeast as a tool to uncover the cellular targets of drugs

Knowledge of the spectrum of cellular proteins targeted by experimental therapeutic agents would greatly facilitate drug development. However, identifying the targets of drugs is a daunting challenge. The yeast Saccharomyces cerevisiae is a valuable model organism for human diseases and pathways because it is genetically tractable and shares many functional homolog with humans. In yeast, it is possible to increase or decrease the expression level of essentially every gene and measure changes in drug sensitivity to uncover potential targets. It is also possible to infer mechanism of action from comparing the changes in mRNA expression elicited by drug treatment with those induced by gene deletions or by other drugs. Proteins that bind drugs directly can be identified using yeast protein chips. This review of the use of yeast for discovering targets of drugs discusses the advantages and drawbacks of each approach and how combining methods may reveal targets more efficiently.     

Adverse Drug Reaction Prediction Using Scores Produced by Large-Scale Drug-Protein Target Docking on High-Performance Computing Machines

Late-stage or post-market identification of adverse drug reactions (ADRs) is a significant public health issue and a source of major economic liability for drug development. Thus, reliable in silico screening of drug candidates for possible ADRs would be advantageous. In this work, we introduce a computational approach that predicts ADRs by combining the results of molecular docking and leverages known ADR information from DrugBank and SIDER. We employed a recently parallelized version of AutoDock Vina (VinaLC) to dock 906 small molecule drugs to a virtual panel of 409 DrugBank protein targets. L1-regularized logistic regression models were trained on the resulting docking scores of a 560 compound subset from the initial 906 compounds to predict 85 side effects, grouped into 10 ADR phenotype groups. Only 21% (87 out of 409) of the drug-protein binding features involve known targets of the drug subset, providing a significant probe of off-target effects. As a control, associations of this drug subset with the 555 annotated targets of these compounds, as reported in DrugBank, were used as features to train a separate group of models. The Vina off-target models and the DrugBank on-target models yielded comparable median area-under-the-receiver-operating-characteristic-curves (AUCs) during 10-fold cross-validation (0.60–0.69 and 0.61–0.74, respectively). Evidence was found in the PubMed literature to support several putative ADR-protein associations identified by our analysis. Among them, several associations between neoplasm-related ADRs and known tumor suppressor and tumor invasiveness marker proteins were found. A dual role for interstitial collagenase in both neoplasms and aneurysm formation was also identified. These associations all involve off-target proteins and could not have been found using available drug/on-target interaction data. This study illustrates a path forward to comprehensive ADR virtual screening that can potentially scale with increasing number of CPUs to tens of thousands of protein targets and millions of potential drug candidates.     

Higher duplicability of less important genes in yeast genomes

Gene duplication plays an important role in evolution because it is the primary source of new genes. Many recent studies showed that gene duplicability varies considerably among genes. Several considerations led us to hypothesize that less important genes have higher rates of successful duplications, where gene importance is measured by the fitness reduction caused by the deletion of the gene. Here, we test this hypothesis by comparing the importance of two groups of singleton genes in the yeast Saccharomyces cerevisiae (Sce). Group S genes did not duplicate in four other yeast species examined, whereas group D experienced duplication in these species. Consistent with our hypothesis, we found group D genes to be less important than group S genes. Specifically, 17% of group D genes are essential in Sce, compared to 28% for group S. Furthermore, deleting a group D gene in Sce reduces the fitness by 24% on average, compared to 38% for group S. Our subsequent analysis showed that less important genes have more cis-regulatory motifs, which could lead to a higher chance of subfunctionalization of duplicate genes and result in an enhanced rate of gene retention. Less important genes may also have weaker dosage imbalance effects and cause fewer genetic perturbations when duplicated. Regardless of the cause, our observation indicates that the previous finding of a less severe fitness consequence of deleting a duplicate gene than deleting a singleton gene is at least in part due to the fact that duplicate genes are intrinsically less important than singleton genes and suggests that the contribution of duplicate genes to genetic robustness has been overestimated.