Mining of high throughput screening database reveals AP-1 and autophagy pathways as potential targets for COVID-19 therapeutics.
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ABSTRACT:
SUBMITTER: Zhu H
PROVIDER: S-EPMC7990955 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Identifying genes with prognostic importance could improve cancer treatment. An increasing number of reports suggest the existence of successful strategies based on seemingly "untargetable" transcription factors. In addition to embryogenesis, AP-2 transcription factors are known to play crucial roles in cancer development. Members of this family can be used as prognostic factors in oncological patients, and AP-2α/γ transcription factors were previously investigated in our pan-cancer comparative study using their target genes. The present study investigates tumors that were previously found similar with an emphasis on the possible role of AP-2 factors in specific cancer types. The RData workspace was loaded back to R environment and 3D trajectories were built via Monocle3. The genes that met the requirement of specificity were listed using top_markers(), separately for mutual and unique targets. Furthermore, the candidate genes had to meet the following requirements: correlation with AP-2 factor (through Correlation AnalyzeR) and validated prognostic importance (using GEPIA2 and subsequently KM-plotter or LOGpc). Eventually, the ROC analysis was applied to confirm their predictive value; co-dependence of expression was visualized via BoxPlotR. Some similar tumors were differentiated by AP-2α/γ targets with prognostic value. Requirements were met by only fifteen genes (EMX2, COL7A1, GRIA1, KRT1, KRT14, SLC12A5, SEZ6L, PTPRN, SCG5, DPP6, NTSR1, ARX, COL4A3, PPEF1 and TMEM59L); of these, the last four were excluded based on ROC curves. All the above genes were confronted with the literature, with an emphasis on the possible role played by AP-2 factors in specific cancers. Following ROC analysis, the genes were verified using immunohistochemistry data and progression-related signatures. Staining differences were observed, as well as co-dependence on the expression of e.g. CTNNB1, ERBB2, KRAS, SMAD4, EGFR or MKI67. In conclusion, prognostic value of targets suggested AP-2α/γ as candidates for novel cancer treatment. It was also revealed that AP-2 targets are related to tumor progression and that some mutual target genes could be inversely regulated.
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Project description:High-throughput screening (HTS) experiments provide a valuable resource that reports biological activity of numerous chemical compounds relative to their molecular targets. Building computational models that accurately predict such activity status (active vs. inactive) in specific assays is a challenging task given the large volume of data and frequently small proportion of active compounds relative to the inactive ones. We developed a method, DRAMOTE, to predict activity status of chemical compounds in HTP activity assays. For a class of HTP assays, our method achieves considerably better results than the current state-of-the-art-solutions. We achieved this by modification of a minority oversampling technique. To demonstrate that DRAMOTE is performing better than the other methods, we performed a comprehensive comparison analysis with several other methods and evaluated them on data from 11 PubChem assays through 1,350 experiments that involved approximately 500,000 interactions between chemicals and their target proteins. As an example of potential use, we applied DRAMOTE to develop robust models for predicting FDA approved drugs that have high probability to interact with the thyroid stimulating hormone receptor (TSHR) in humans. Our findings are further partially and indirectly supported by 3D docking results and literature information. The results based on approximately 500,000 interactions suggest that DRAMOTE has performed the best and that it can be used for developing robust virtual screening models. The datasets and implementation of all solutions are available as a MATLAB toolbox online at www.cbrc.kaust.edu.sa/dramote and can be found on Figshare.
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Project description:We are now seeing the benefit of investments made over the last decade in high-throughput screening (HTS) that is resulting in large structure activity datasets entering public and open databases such as ChEMBL and PubChem. The growth of academic HTS screening centers and the increasing move to academia for early stage drug discovery suggests a great need for the informatics tools and methods to mine such data and learn from it. Collaborative Drug Discovery, Inc. (CDD) has developed a number of tools for storing, mining, securely and selectively sharing, as well as learning from such HTS data. We present a new web based data mining and visualization module directly within the CDD Vault platform for high-throughput drug discovery data that makes use of a novel technology stack following modern reactive design principles. We also describe CDD Models within the CDD Vault platform that enables researchers to share models, share predictions from models, and create models from distributed, heterogeneous data. Our system is built on top of the Collaborative Drug Discovery Vault Activity and Registration data repository ecosystem which allows users to manipulate and visualize thousands of molecules in real time. This can be performed in any browser on any platform. In this chapter we present examples of its use with public datasets in CDD Vault. Such approaches can complement other cheminformatics tools, whether open source or commercial, in providing approaches for data mining and modeling of HTS data.
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Project description:Aging is the dominant risk factor for most chronic diseases. Development of antiaging interventions offers the promise of preventing many such illnesses simultaneously. Cellular stress resistance is an evolutionarily conserved feature of longevity. Here, we identify compounds that induced resistance to the superoxide generator paraquat (PQ), the heavy metal cadmium (Cd), and the DNA alkylator methyl methanesulfonate (MMS). Some rescue compounds conferred resistance to a single stressor, while others provoked multiplex resistance. Induction of stress resistance in fibroblasts was predictive of longevity extension in a published large-scale longevity screen in Caenorhabditis elegans, although not in testing performed in worms and flies with a more restricted set of compounds. Transcriptomic analysis and genetic studies implicated Nrf2/SKN-1 signaling in stress resistance provided by two protective compounds, cardamonin and AEG 3482. Small molecules identified in this work may represent attractive tools to elucidate mechanisms of stress resistance in mammalian cells.
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Project description:Autophagy is an intracellular recycling pathway with implications for intracellular homeostasis and cell survival. Its pharmacological modulation can aid chemotherapy by sensitizing cancer cells toward approved drugs and overcoming chemoresistance. Recent translational data on autophagy modulators show promising results in reducing tumor growth and metastasis, but also reveal a need for more specific compounds and novel lead structures. Here, we searched for such autophagy-modulating compounds in a flow cytometry-based high-throughput screening of an in-house natural compound library. We successfully identified novel inducers and inhibitors of the autophagic pathway. Among these, we identified arzanol as an autophagy-modulating drug that causes the accumulation of ATG16L1-positive structures, while it also induces the accumulation of lipidated LC3. Surprisingly, we observed a reduction of the size of autophagosomes compared to the bafilomycin control and a pronounced accumulation of p62/SQSTM1 in response to arzanol treatment in HeLa cells. We, therefore, speculate that arzanol acts both as an inducer of early autophagosome biogenesis and as an inhibitor of later autophagy events. We further show that arzanol is able to sensitize RT-112 bladder cancer cells towards cisplatin (CDDP). Its anticancer activity was confirmed in monotherapy against both CDDP-sensitive and -resistant bladder cancer cells. We classified arzanol as a novel mitotoxin that induces the fragmentation of mitochondria, and we identified a series of targets for arzanol that involve proteins of the class of mitochondria-associated quinone-binding oxidoreductases. Collectively, our results suggest arzanol as a valuable tool for autophagy research and as a lead compound for drug development in cancer therapy.
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Project description:Macroautophagy/autophagy, a eukaryotic homeostatic process that sequesters cytoplasmic constituents for lysosomal degradation, is orchestrated by a number of autophagy-related (ATG) proteins tightly controlled by post-translational modifications. However, the involvement of reversible ubiquitination in the regulation of autophagy remains largely unclear. Here, we performed a single-guide RNA-based screening assay to investigate the functions of deubiquitinating enzymes (DUBs) in regulating autophagy. We identified previously unrecognized roles of several DUBs in modulating autophagy at multiple levels by targeting various ATG proteins. Mechanistically, we demonstrated that STAMBP/AMSH (STAM-binding protein) promotes the stabilization of ULK1 by removing its lysine 48 (K48)-linked ubiquitination, whereas OTUD7B mediates the degradation of PIK3 C3 by enhancing its K48-linked ubiquitination, thus positively or negatively affects autophagy flux, respectively. Together, our study elaborated on the broad involvement of DUBs in regulating autophagy and uncovered the critical roles of the reversible ubiquitination in the modification of ATG proteins.Abbreviations: ATG: autophagy-related; Baf A1: bafilomycin A1; DUB: deubiquitinating enzyme; EBSS: Earle's balanced salt solution; KO: knockout; MAP1LC3/LC3: microtubule associated protein 1 light chain 3; OTUD7B: OTU domain-containing protein 7B; PIK3C3: phosphatidylinositol 3-kinase catalytic subunit type 3; sgRNA: single-guide RNA; SQSTM1/p62: sequestosome 1; STAMBP/AMSH: STAM-binding protein; ULK1: unc-51 like autophagy activating kinase 1; USP: ubiquitin specific peptidase.
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Project description:Recent cancer pharmacogenomic studies profiled large panels of cell lines against hundreds of approved drugs and experimental chemical compounds. The overarching goal of these screens is to measure sensitivity of cell lines to chemical perturbations, correlate these measures to genomic features, and thereby develop novel predictors of drug response. However, leveraging these valuable data is challenging due to the lack of standards for annotating cell lines and chemical compounds, and quantifying drug response. Moreover, it has been recently shown that the complexity and complementarity of the experimental protocols used in the field result in high levels of technical and biological variation in the in vitro pharmacological profiles. There is therefore a need for new tools to facilitate rigorous comparison and integrative analysis of large-scale drug screening datasets. To address this issue, we have developed PharmacoDB (pharmacodb.pmgenomics.ca), a database integrating the largest cancer pharmacogenomic studies published to date. Here, we describe how the curation of cell line and chemical compound identifiers maximizes the overlap between datasets and how users can leverage such data to compare and extract robust drug phenotypes. PharmacoDB provides a unique resource to mine a compendium of curated cancer pharmacogenomic datasets that are otherwise disparate and difficult to integrate.
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Project description:Genes expressed specifically in malignant tissue may have potential as therapeutic targets but have been difficult to locate for most cancers. The information hidden within certain public databases can reveal RNA transcripts specifically expressed in transformed tissue. To be useful, database information must be verified and a more complete pattern of tissue expression must be demonstrated. We tested database mining plus rapid screening by fluorescent-PCR expression comparison (F-PEC) as an approach to locate candidate brain tumor antigens. Cancer Genome Anatomy Project (CGAP) data was mined for genes highly expressed in glioblastoma multiforme. From 13 mined genes, seven showed potential as possible tumor markers or antigens as determined by further expression profiling. Now that large-scale expression information is readily available for many of the commonly occurring cancers, other candidate tumor markers or antigens could be located and evaluated with this approach.
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