Elucidating the modes of action for bioactive compounds in a cell-specific manner by large-scale chemically-induced transcriptomics.
ABSTRACT: The identification of the modes of action of bioactive compounds is a major challenge in chemical systems biology of diseases. Genome-wide expression profiling of transcriptional responses to compound treatment for human cell lines is a promising unbiased approach for the mode-of-action analysis. Here we developed a novel approach to elucidate the modes of action of bioactive compounds in a cell-specific manner using large-scale chemically-induced transcriptome data acquired from the Library of Integrated Network-based Cellular Signatures (LINCS), and analyzed 16,268 compounds and 68 human cell lines. First, we performed pathway enrichment analyses of regulated genes to reveal active pathways among 163 biological pathways. Next, we explored potential target proteins (including primary targets and off-targets) with cell-specific transcriptional similarity using chemical-protein interactome. Finally, we predicted new therapeutic indications for 461 diseases based on the target proteins. We showed the usefulness of the proposed approach in terms of prediction coverage, interpretation, and large-scale applicability, and validated the new prediction results experimentally by an in vitro cellular assay. The approach has a high potential for advancing drug discovery and repositioning.
Project description:Traditional natural products discovery using a combination of live/dead screening followed by iterative bioassay-guided fractionation affords no information about compound structure or mode of action until late in the discovery process. This leads to high rates of rediscovery and low probabilities of finding compounds with unique biological and/or chemical properties. By integrating image-based phenotypic screening in HeLa cells with high-resolution untargeted metabolomics analysis, we have developed a new platform, termed Compound Activity Mapping, that is capable of directly predicting the identities and modes of action of bioactive constituents for any complex natural product extract library. This new tool can be used to rapidly identify novel bioactive constituents and provide predictions of compound modes of action directly from primary screening data. This approach inverts the natural products discovery process from the existing "grind and find" model to a targeted, hypothesis-driven discovery model where the chemical features and biological function of bioactive metabolites are known early in the screening workflow, and lead compounds can be rationally selected based on biological and/or chemical novelty. We demonstrate the utility of the Compound Activity Mapping platform by combining 10,977 mass spectral features and 58,032 biological measurements from a library of 234 natural products extracts and integrating these two datasets to identify 13 clusters of fractions containing 11 known compound families and four new compounds. Using Compound Activity Mapping we discovered the quinocinnolinomycins, a new family of natural products with a unique carbon skeleton that cause endoplasmic reticulum stress.
Project description:Despite increased understanding of the pathogenesis and targets for thyroid cancer and other cancers, developing a new anticancer chemical agent remains an expensive and long process. An alternative approach is the exploitation of clinically used and/or bioactive compounds.Our objective was to identify agents with an anticancer effect in thyroid cancer cell lines using quantitative high-throughput screening (qHTS).We used the newly assembled National Institutes of Health Chemical Genomic Center's pharmaceutical collection, which contains 2816 clinically approved drugs and bioactive compounds to perform qHTS.Multiple agents, across a variety of therapeutic categories and with different modes of action, were found to have an antiproliferative effect. We found the following therapeutic categories were the most enriched categories with antiproliferative activity: cardiotonic and antiobesity agents. Sixteen agents had an efficacy of greater than 60% and a 50% inhibitory concentration (IC50) in the nanomolar range. We validated the results of the qHTS using two agents (bortezomib and ouabain) in additional cell lines representing different histological subtypes of thyroid cancer and with different mutations (BRAF V600E, RET/PTC1, p53, PTEN). Both agents induced apoptosis, and ouabain also caused cell cycle arrest.To our knowledge, this is the first study to use qHTS of a large drug library to identify candidate drugs for anticancer therapy. Our results indicate such a screening approach can lead to the discovery of novel agents in different therapeutic categories and drugs with nonclassic chemotherapy mode of action. Our approach could lead to drug repurposing and accelerate clinical trials of compounds with well-established pharmacokinetics and toxicity profiles.
Project description:Summary:Chemical-genomic approaches that map interactions between small molecules and genetic perturbations offer a promising strategy for functional annotation of uncharacterized bioactive compounds. We recently developed a new high-throughput platform for mapping chemical-genetic (CG) interactions in yeast that can be scaled to screen large compound collections, and we applied this system to generate CG interaction profiles for more than 13 000 compounds. When integrated with the existing global yeast genetic interaction network, CG interaction profiles can enable mode-of-action prediction for previously uncharacterized compounds as well as discover unexpected secondary effects for known drugs. To facilitate future analysis of these valuable data, we developed a public database and web interface named MOSAIC. The website provides a convenient interface for querying compounds, bioprocesses (Gene Ontology terms) and genes for CG information including direct CG interactions, bioprocesses and gene-level target predictions. MOSAIC also provides access to chemical structure information of screened molecules, chemical-genomic profiles and the ability to search for compounds sharing structural and functional similarity. This resource will be of interest to chemical biologists for discovering new small molecule probes with specific modes-of-action as well as computational biologists interested in analysing CG interaction networks. Availability and implementation:MOSAIC is available at http://mosaic.cs.umn.edu. Contact:email@example.com, firstname.lastname@example.org, email@example.com or firstname.lastname@example.org. Supplementary information:Supplementary data are available at Bioinformatics online.
Project description:The Library of Integrated Network-based Cellular Signatures (LINCS) project is a large-scale coordinated effort to build a comprehensive systems biology reference resource. The goals of the program include the generation of a very large multidimensional data matrix and informatics and computational tools to integrate, analyze, and make the data readily accessible. LINCS data include genome-wide transcriptional signatures, biochemical protein binding profiles, cellular phenotypic response profiles and various other datasets for a wide range of cell model systems and molecular and genetic perturbations. Here we present a partial survey of this data facilitated by data standards and in particular a robust compound standardization workflow; we integrated several types of LINCS signatures and analyzed the results with a focus on mechanism of action (MoA) and chemical compounds. We illustrate how kinase targets can be related to disease models and relevant drugs. We identified some fundamental trends that appear to link Kinome binding profiles and transcriptional signatures to chemical information and biochemical binding profiles to transcriptional responses independent of chemical similarity. To fill gaps in the datasets we developed and applied predictive models. The results can be interpreted at the systems level as demonstrated based on a large number of signaling pathways. We can identify clear global relationships, suggesting robustness of cellular responses to chemical perturbation. Overall, the results suggest that chemical similarity is a useful measure at the systems level, which would support phenotypic drug optimization efforts. With this study we demonstrate the potential of such integrated analysis approaches and suggest prioritizing further experiments to fill the gaps in the current data.
Project description:The global prevalence of antibacterial resistance requires new antibacterial drugs with novel chemical scaffolds and modes of action. It is also vital to design compounds with optimal physicochemical properties to permeate the bacterial cell envelope. We described an approach of combining and integrating whole cell screening and metabolomics into early antibacterial drug discovery using a library of small polar compounds. Whole cell screening of a diverse library of small polar compounds against Staphylococcus aureus gave compound 2. Hit expansion was carried out to determine structure-activity relationships. A selection of compounds from this series, together with other screened active compounds, was subjected to an initial metabolomics study to provide a metabolic fingerprint of the mode of action. It was found that compound 2 and its analogues have a different mode of action from some of the known antibacterial compounds tested. This early study highlighted the potential of whole cell screening and metabolomics in early antibacterial drug discovery. Future works will require improving potency and performing orthogonal studies to confirm the modes of action.
Project description:Image-based screening has become a mature field over the past decade, largely due to the detailed information that can be obtained about compound mode of action by considering the phenotypic effects of test compounds on cellular morphology. However, very few examples exist of extensions of this approach to bacterial targets. We now report the first high-throughput, high-content platform for the prediction of antibiotic modes of action using image-based screening. This approach employs a unique feature segmentation and extraction protocol to quantify key size and shape metrics of bacterial cells over a range of compound concentrations, and matches the trajectories of these metrics to those of training set compounds of known molecular target to predict the test compound's mode of action. This approach has been used to successfully predict the modes of action of a panel of known antibiotics, and has been extended to the evaluation of natural products libraries for the de novo prediction of compound function directly from primary screening data.
Project description:The comparative analysis of complex behavioral phenotypes is valuable as a reductionist tool for both drug discovery and defining chemical bioactivity. Flavonoids are a diverse class of chemicals that elicit robust neuroactive and hormonal actions, though bioactivity information is limited for many, particularly for neurobehavioral endpoints. Here, we used a zebrafish larval chemomotor response (LCR) bioassay to comparatively evaluate a suite of 24 flavonoids, and in addition a panel of 30 model neuroactive compounds representing diverse modes of action (e.g. caffeine, chlorpyrifos, methamphetamine, nicotine, picrotoxin). Naïve larval zebrafish were exposed to concentration ranges of each compound at 120?hour post-fertilization (hpf) and locomotor activity measured for 5?h. The model neuroactive compounds were largely behaviorally bioactive (20 of 30) with most effects phenotypic of their known modes of action. Flavonoids rapidly and broadly elicited hyperactive locomotor effects (22 of 24). Multidimensional analyses compared responses over time and identified three distinct bioactive groups of flavonoids based on efficacy and potency. Using GABAergics to modulate hyperactive responses, two flavonoids, (S)-equol and kaempferol were tested for GABAA receptor antagonism, as well as a known GABAA receptor antagonist, picrotoxin. Pharmacological intervention with positive allosteric modulators of the GABAA receptor, alfaxalone and chlormethiazole, ameliorated the hyperactive response to picrotoxin, but not for (S)-equol or kaempferol. Taken together, these studies demonstrate that flavonoids are differentially bioactive and that the chemobehavioral effects likely do not involve a GABAA receptor mediated mode of action. Overall, the integrative zebrafish platform provides a useful framework for comparatively evaluating high-content chemobehavioral data for sets of structurally- and mechanistically-related flavonoids and neuroactive compounds.
Project description:BACKGROUND: Chemical-genetic profiling of inhibitory compounds can lead to identification of their modes of action. These profiles can help elucidate the complex interactions between small bioactive compounds and the cell machinery, and explain putative gene function(s). RESULTS: Colony size reduction was used to investigate the chemical-genetic profile of cycloheximide, 3-amino-1,2,4-triazole, paromomycin, streptomycin and neomycin in the yeast Saccharomyces cerevisiae. These compounds target the process of protein biosynthesis. More than 70,000 strains were analyzed from the array of gene deletion mutant yeast strains. As expected, the overall profiles of the tested compounds were similar, with deletions for genes involved in protein biosynthesis being the major category followed by metabolism. This implies that novel genes involved in protein biosynthesis could be identified from these profiles. Further investigations were carried out to assess the activity of three profiled genes in the process of protein biosynthesis using relative fitness of double mutants and other genetic assays. CONCLUSION: Chemical-genetic profiles provide insight into the molecular mechanism(s) of the examined compounds by elucidating their potential primary and secondary cellular target sites. Our follow-up investigations into the activity of three profiled genes in the process of protein biosynthesis provided further evidence concerning the usefulness of chemical-genetic analyses for annotating gene functions. We termed these genes TAE2, TAE3 and TAE4 for translation associated elements 2-4.
Project description:Target identification remains a major challenge for modern drug discovery programs aimed at understanding the molecular mechanisms of drugs. Computational target prediction approaches like 2D chemical similarity searches have been widely used but are limited to structures sharing high chemical similarity. Here, we present a new computational approach called chemical similarity network analysis pull-down 3D (CSNAP3D) that combines 3D chemical similarity metrics and network algorithms for structure-based drug target profiling, ligand deorphanization, and automated identification of scaffold hopping compounds. In conjunction with 2D chemical similarity fingerprints, CSNAP3D achieved a >95% success rate in correctly predicting the drug targets of 206 known drugs. Significant improvement in target prediction was observed for HIV reverse transcriptase (HIVRT) compounds, which consist of diverse scaffold hopping compounds targeting the nucleotidyltransferase binding site. CSNAP3D was further applied to a set of antimitotic compounds identified in a cell-based chemical screen and identified novel small molecules that share a pharmacophore with Taxol and display a Taxol-like mechanism of action, which were validated experimentally using in vitro microtubule polymerization assays and cell-based assays.
Project description:<h4>Background</h4>Predicting the drug response of the cancer diseases through the cellular perturbation signatures under the action of specific compounds is very important in personalized medicine. In the process of testing drug responses to the cancer, traditional experimental methods have been greatly hampered by the cost and sample size. At present, the public availability of large amounts of gene expression data makes it a challenging task to use machine learning methods to predict the drug sensitivity.<h4>Results</h4>In this study, we introduced the WRFEN-XGBoost cell viability prediction algorithm based on LINCS-L1000 cell signatures. We integrated the LINCS-L1000, CTRP and Achilles datasets and adopted a weighted fusion algorithm based on random forest and elastic net for key gene selection. Then the FEBPSO algorithm was introduced into XGBoost learning algorithm to predict the cell viability induced by the drugs. The proposed method was compared with some new methods, and it was found that our model achieved good results with 0.83 Pearson correlation. At the same time, we completed the drug sensitivity validation on the NCI60 and CCLE datasets, which further demonstrated the effectiveness of our method.<h4>Conclusions</h4>The results showed that our method was conducive to the elucidation of disease mechanisms and the exploration of new therapies, which greatly promoted the progress of clinical medicine.