Chemical Genomics Research Articles

An AlphaScreen™-Based High-Throughput Screen to Identify Inhibitors of Hsp90-Cochaperone Interaction

Hsp90 has emerged as an important anticancer drug target because of its essential role in promoting the folding and maturation of many oncogenic proteins. The authors describe the development of the first high-throughput screen, based on AlphaScreen technology, to identify a novel type of Hsp90 inhibitors that interrupt its interaction with the cochaperone HOP. The assay used the 20-mer C-terminal peptide of Hsp90 and the TPR2A domain of HOP. Assay specificity was demonstrated by measuring different interactions using synthetic peptides, with measured IC50s in good agreement with reported values. The assay was stable over 12 h and tolerated DMSO up to 5%. The authors first validated the assay by screening against 20,000 compounds in a 384-well format. After further optimization into a 1536-well format, it was screened against an NIH Chemical Genomics Center library of 76,134 compounds, with a signal-to-background ratio of 78 and Z' factor of 0.77. The present assay can be used for discovery of novel small-molecule Hsp90 inhibitors that can be used as chemical probes to investigate the role of cochaperones in Hsp90 function. Such molecules have the potential to be developed into novel anticancer drugs, for use alone or in combination with other Hsp90 inhibitors.

Gene expression-based chemical genomics identifies VPA to revert the oncogenic effect of GATA1s in Down syndrome leukemia

Gene expression-based chemical genomics identifies VPA to revert the oncogenic effect of GATA1s in Down syndrome leukemia

Identification of compounds that potentiate CREB signaling as possible enhancers of long-term memory

Many studies have implicated the cAMP Response Element Binding (CREB) protein signaling pathway in long-term memory. To identify small molecule enhancers of CREB activation of gene expression, we screened approximately 73,000 compounds, each at 7-15 concentrations in a quantitative high-throughput screening (qHTS) format, for activity in cells by assaying CREB mediated beta-lactamase reporter gene expression. We identified 1,800 compounds that potentiated CREB mediated gene expression, with potencies as low as 16 nM, comprising 96 structural series. Mechanisms of action were systematically determined, and compounds that affect phosphodiesterase 4, protein kinase A, and cAMP production were identified, as well as compounds that affect CREB signaling via apparently unidentified mechanisms. qHTS followed by interrogation of pathway targets is an efficient paradigm for lead generation for chemical genomics and drug development.

Combining chemical genomics screens in yeast to reveal spectrum of effects of chemical inhibition of sphingolipid biosynthesis.

BackgroundSingle genome-wide screens for the effect of altered gene dosage on drug sensitivity in the model organism Saccharomyces cerevisiae provide only a partial picture of the mechanism of action of a drug.ResultsUsing the example of the tumor cell invasion inhibitor dihydromotuporamine C, we show that a more complete picture of drug action can be obtained by combining different chemical genomics approaches – analysis of the sensitivity of ρ0 cells lacking mitochondrial DNA, drug-induced haploinsufficiency, suppression of drug sensitivity by gene overexpression and chemical-genetic synthetic lethality screening using strains deleted of nonessential genes. Killing of yeast by this chemical requires a functional mitochondrial electron-transport chain and cytochrome c heme lyase function. However, we find that it does not require genes associated with programmed cell death in yeast. The chemical also inhibits endocytosis and intracellular vesicle trafficking and interferes with vacuolar acidification in yeast and in human cancer cells. These effects can all be ascribed to inhibition of sphingolipid biosynthesis by dihydromotuporamine C.ConclusionDespite their similar conceptual basis, namely altering drug sensitivity by modifying gene dosage, each of the screening approaches provided a distinct set of information that, when integrated, revealed a more complete picture of the mechanism of action of a drug on cells.

Chemical genomics of cancer chemopreventive dithiolethiones.

3H-1,2-dithiole-3-thione (D3T) and its analogues 4-methyl-5-pyrazinyl-3H-1,2-dithiole-3-thione (OLT) and 5-tert-butyl-3H-1,2-dithiole-3-thione (TBD) are chemopreventive agents that block or diminish early stages of carcinogenesis by inducing activities of detoxication enzymes. While OLT has been used in clinical trials, TBD has been shown to be more efficacious and possibly less toxic than OLT in animals. Here, we utilize a robust and high-resolution chemical genomics procedure to examine the pharmacological structure–activity relationships of these compounds in livers of male rats by microarray analyses. We identified 226 differentially expressed genes that were common to all treatments. Functional analysis identified the relation of these genes to glutathione metabolism and the nuclear factor, erythroid derived 2-related factor 2 pathway (Nrf2) that is known to regulate many of the protective actions of dithiolethiones. OLT and TBD were shown to have similar efficacies and both were weaker than D3T. In addition, we identified 40 genes whose responses were common to OLT and TBD, yet distinct from D3T. As inhibition of cytochrome P450 (CYP) has been associated with the effects of OLT on CYP expression, we determined the half maximal inhibitory concentration (IC50) values for inhibition of CYP1A2. The rank order of inhibitor potency was OLT ≫ TBD ≫ D3T, with IC50 values estimated as 0.2, 12.8 and >100 μM, respectively. Functional analysis revealed that OLT and TBD, in addition to their effects on CYP, modulate liver lipid metabolism, especially fatty acids. Together, these findings provide new insight into the actions of clinically relevant and lead dithiolethione analogues.

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.

Chemical genomics: a key to the epigenome - An interview with Minoru Yoshida

The identification and characterization of the first potent histone deacetylase inhibitors constituted a major step, which not only gave a key to researchers worldwide to tackle the unknown universe of cell signalling by protein lysine acetylation, but also opened the way for the development of large series of molecules with important therapeutic properties. Minoru Yoshida, who played a critical role in characterizing the fist potent and specific histone deacetylase inhibitor, trichostatin A, reveals here how and why his work led to this discovery. He also comments on his other outstanding contributions and gives his opinion on the impact of chemical genomics as a key to open the mysteries of the epigenome.

Chemical genomics and molecular medicine

Chemical genomics and molecular medicine

Chemical genomics and molecular medicine

Chemical genomics and molecular medicine

A Chemical Genomics Study Identifies Snf1 as a Repressor of GCN4 Translation

The Saccharomyces cerevisiae Snf1 kinase plays a critical role in recalibrating cellular metabolism in response to glucose depletion. Hundreds of genes show changes in expression levels when the SNF1 gene is deleted. However, cells can adapt to the absence of a specific gene when grown in long term culture. Here we apply a chemical genetic method to rapidly and selectively inactivate a modified Snf1 kinase using a pyrazolopyrimidine inhibitor. By allowing cells to adjust to a change in carbon source prior to inhibition of the Snf1 kinase activity, we identified a set of genes whose expression increased when Snf1 was inhibited. Prominent in this set are genes that are activated by Gcn4, a transcriptional activator of amino acid biosynthetic genes. Deletion of Snf1 increased Gcn4 protein levels without affecting its mRNA levels. The increased Gcn4 protein levels required the Gcn2 kinase and Gcn20, regulators of GCN4 translation. These data indicate that Snf1 functions upstream of Gcn20 to regulate control of GCN4 translation in S. cerevisiae.

Genes Dysregulated in a Murine Model of Leukemogenesis Comprise a Signature for Identification of Therapeutics in Humans.

The identification of genes and pathways that are critical for the development and maintenance of leukemia is important for designing new anti-leukemia therapeutics. However, selection of aberrantly regulated genes and pathways and the translation of these features into targeted anti-cancer drugs represent a significant challenge. Studies have characterized the various oncogenic lesions associated with leukemia, and have shown that specific combinations of mutations are sufficient to induce malignancy in model systems. Thus, we sought to identify the gene signature arising from the introduction of cooperating oncogenic mutations into non-malignant cells. We hypothesized that compounds capable of maximally antagonizing the aberrant expression of this subset of genes would represent potential anti-leukemia therapeutics. To test this hypothesis, we turned to the use of murine genetics and chemical genomics. To this end, we adapted mouse systems created by retroviral transduction of primary marrow cells with the BCR-ABL translocation alone or in combination with the NUP98-HOXA9 translocation. This approach creates authentic models of chronic and blast crisis myeloid leukemia (CML and bcCML, respectively), which has been shown to closely resemble human disease. Next, we employed flow cytometry to separate the transduced marrow into purified primitive lin- populations of either normal, BCR-ABL+, or BCR-ABL+/NUP98-HOXA9+ cells. Importantly, lin- cells were used to focus specifically on those genes most relevant to leukemic stem and progenitor populations. Microarray gene expression profiling was performed on these purified populations in six independent experiments using the Affymetrix Mouse 430 2.0 platform. Gene expression signatures for each transduced population relative to normal lin- cells were obtained. Next, in silico screening for agents that antagonize these signatures was performed comparing the resulting signatures to a local microarray compendium that included the CMap dataset. Gene expression signatures arising from the introduction of BCR-ABL alone identified traditional chemotherapeutics including cytarabine, etoposide, idarubicin – agents considered less effective against leukemic stem cell populations – as well as agents acting along the PI3 kinase pathway. Surprisingly, drugs shown to antagonize the gene signature derived by the introduction the two oncogenic mutations (BCR-ABL and NUP98-HOXA9) included 15-delta-prostaglandin-J2, celastrol, MG- 132, and parthenolide. Each of these drugs eradicate malignant stem and progenitor cells, while sparing normal cells. Thus, the murine-derived signature successfully identified drugs highly relevant to human disease. Our studies therefore demonstrate the utility of combining gene expression-based drug screening and mouse cancer genetics to reveal novel anti-leukemia compounds in humans. DCH and JMA contributed equally.

A New Structure-Based QSAR Method Affords both Descriptive and Predictive Models for Phosphodiesterase-4 Inhibitors

We describe the application of a new QSAR (quantitative structure-activity relationship) formalism to the analysis and modeling of PDE-4 inhibitors. This new method takes advantage of the X-ray structural information of the PDE-4 enzyme to characterize the small molecule inhibitors. It calculates molecular descriptors based on the matching of their pharmacophore feature pairs with those (the reference) of the target binding pocket. Since the reference is derived from the X-ray crystal structures of the target under study, these descriptors are target-specific and easy to interpret. We have analyzed 35 indole derivative-based PDE-4 inhibitors where Partial Least Square (PLS) analysis has been employed to obtain the predictive models. Compared to traditional QSAR methods such as CoMFA and CoMSIA, our models are more robust and predictive measured by statistics for both the training and test sets of molecules. Our method can also identify critical pharmacophore features that are responsible for the inhibitory potency of the small molecules. Thus, this structure-based QSAR method affords both descriptive and predictive models for phosphodiesterase-4 inhibitors. The success of this study has also laid a solid foundation for systematic QSAR modeling of the PDE family of enzymes, which will ultimately contribute to chemical genomics research and drug discovery targeting the PDE enzymes.

<i>In silico</i> Drug Discovery Based on the Integration of Bioinformatics and Chemoinformatics

With the near completion of the human genome sequencing, bioinformatics and chemoinformatics are expected as promising tools in genome-based drug discovery. The emerging field of chemical genomics is accumulating large-scale assay data on compound-protein interactions. We are now developing new mining methods for the chemical genomics data based on the integration of bioinformatics and chemoinformatics. Here we present a GPCR-ligand database (GLIDA) and a novel in silico screening method, which we have developed. GLIDA is a novel public GPCR-related chemical genomics database that is primarily focused on the correlation of information between GPCRs and their ligands. Our in silico screening method is based on statistical machine learning of the conserved patterns of molecular recognition extracted from comprehensive compound-protein interaction data. These are promising approaches to accelerating drug discovery processes.

Identification and Preliminary Characterization of a New Chemical Affecting Glucosyltransferase Activities Involved in Plant Cell Wall Biosynthesis

Chemical genetics as a part of chemical genomics is a powerful and fast developing approach to dissect biological processes that may be difficult to characterize using conventional genetics because of gene redundancy or lethality and, in the case of polysaccharide biosynthesis, plant flexibility. Polysaccharide synthetic enzymes are located in two main compartments-the Golgi apparatus and plasma membrane-and can be studied in vitro using membrane fractions. Here, we first developed a high-throughput assay that allowed the screening of a library of chemicals with a potential effect on glycosyltransferase activities. Out of the 4800 chemicals screened for their effect on Golgi glucosyltransferases, 66 compounds from the primary screen had an effect on carbohydrate biosynthesis. Ten of these compounds were confirmed to inhibit glucose incorporation after a second screen. One compound exhibiting a strong inhibition effect (ID 6240780 named chemical A) was selected and further studied. It reversibly inhibits the transfer of glucose from UDP-glucose by Golgi membranes, but activates the plasma membrane-bound callose synthase. The inhibition effect is dependent on the chemical structure of the compound, which does not affect endomembrane morphology of the plant cells, but causes changes in cell wall composition. Chemical A represents a novel drug with a great potential for the study of the mechanisms of Golgi and plasma membrane-bound glucosyltransferases.

Abstracts

The U.S. Environmental Protection Agency (EPA), National Toxicology Program (NTP), and National Institutes of Health (NIH) Chemical Genomics Center (NCGC) have complementary research programs designed to improve chemical toxicity evaluations by developing high throughput screening (HTS) methods that evaluate the impact of environmental chemicals on key toxicity pathways. These federal partners are coordinating an extension of the EPA’s ToxCast program, the NTP’s HTS initiative, and the NCGC’s Molecular Libraries Initiative into a collaborative research program focused on identifying toxicity pathways and developing in vitro assays to characterize the ability of chemicals to perturb those pathways. The goal is to develop new paradigm for high throughput toxicity testing that collects mechanistic and quantitative data from in vitro assays measuring chemical modulation of biological processes involved in the progression to toxicity. As toxicity pathways are identified, the in vitro assays can be optimized for comparison to in vivo animal studies, and for predicting effects in humans. Subsequent computational modeling of toxicity pathway responses and appropriate chemical dosimetry will need to be developed to make these predictions relevant for human health risk assessment. This work was reviewed by EPA and approved for publication but does not necessarily reflect official Agency policy. Index Terms: Toxicogenomics, High Throughput Screening/Testing, EPA ToxCast, Chemical Risk Assessment

A Robotic Platform for Quantitative High-Throughput Screening

High-throughput screening (HTS) is increasingly being adopted in academic institutions, where the decoupling of screening and drug development has led to unique challenges, as well as novel uses of instrumentation, assay formulations, and software tools. Advances in technology have made automated unattended screening in the 1,536-well plate format broadly accessible and have further facilitated the exploration of new technologies and approaches to screening. A case in point is our recently developed quantitative HTS (qHTS) paradigm, which tests each library compound at multiple concentrations to construct concentration-response curves (CRCs) generating a comprehensive data set for each assay. The practical implementation of qHTS for cell-based and biochemical assays across libraries of > 100,000 compounds (e.g., between 700,000 and 2,000,000 sample wells tested) requires maximal efficiency and miniaturization and the ability to easily accommodate many different assay formats and screening protocols. Here, we describe the design and utilization of a fully integrated and automated screening system for qHTS at the National Institutes of Health's Chemical Genomics Center. We report system productivity, reliability, and flexibility, as well as modifications made to increase throughput, add additional capabilities, and address limitations. The combination of this system and qHTS has led to the generation of over 6 million CRCs from > 120 assays in the last 3 years and is a technology that can be widely implemented to increase efficiency of screening and lead generation.

Machine Learning for In Silico Virtual Screening and Chemical Genomics: New Strategies

Support vector machines and kernel methods belong to the same class of machine learning algorithms that has recently become prominent in both computational biology and chemistry, although both fields have largely ignored each other. These methods are based on a sound mathematical and computationally efficient framework that implicitly embeds the data of interest, respectively proteins and small molecules, in high-dimensional feature spaces where various classification or regression tasks can be performed with linear algorithms. In this review, we present the main ideas underlying these approaches, survey how both the “biological” and the “chemical” spaces have been separately constructed using the same mathematical framework and tricks, and suggest different avenues to unify both spaces for the purpose of in silico chemogenomics.

Genomics: when the chemistry is good

Could the latest high-throughput technologies propel chemical genomics screens forward in academic settings? After 18 months of careful design and planning, scientists at the Broad Institute's chemical biology platform are about to flip the switches and find out.

Yeast microarray-based chemogenomics

Yeast microarray-based chemogenomics

ChemmineR: a compound mining framework for R

Motivation: Software applications for structural similarity searching and clustering of small molecules play an important role in drug discovery and chemical genomics. Here, we present the first open-source compound mining framework for the popularstatistical programming environment R. The integration with a powerful statistical environment maximizes the flexibility, expandability and programmability of the provided analysis functions.Results: We discuss the algorithms and compound mining utilities provided by the R package ChemmineR. It contains functions for structural similarity searching, clustering of compound libraries with a wide spectrum of classification algorithms and various utilities for managing complex compound data. It also offers a wide range of visualization functions for compound clusters and chemical structures. The package is well integrated with the online ChemMine environment and allows bidirectional communications between the two services.Availability: ChemmineR is freely available as an R package from the ChemMine project site: http://bioweb.ucr.edu/ChemMineV2/chemminerContact: thomas.girke@ucr.edu