Abstract: BACKGROUND: The advent of high-throughput transcriptomic screening technologies has resulted in a wealth of publicly available gene expression data associated with chemical treatments. From a regulatory perspective, data sets that cover a large chemical space and contain reference chemicals offer utility for the prediction of molecular initiating events associated with chemical exposure. Here, we integrate data from a large compendium of transcriptomic responses to chemical exposure with a comprehensive database of chemical-protein associations to train binary classifiers that predict mechanism(s) of action from transcriptomic responses. First, we linked reference chemicals present in the LINCS L1000 gene expression data collection to chemical identifiers in RefChemDB, a database of chemical-protein interactions. Next, we trained binary classifiers on MCF7 human breast cancer cell line derived gene expression profiles and chemical-protein labels using six classification algorithms to identify optimal analysis parameters. To validate classifier accuracy, we used holdout data sets, training-excluded reference chemicals, and empirical significance testing of null models derived from permuted chemical-protein associations. To identify classifiers that have variable predicting performance across training data derived from different cellular contexts, we trained a separate set of binary classifiers on the PC3 human prostate cancer cell line. RESULTS: We trained classifiers using expression data associated with chemical treatments linked to 51 molecular initiating events. This analysis identified and validated 9 high-performing classifiers with empirical p-values lower than 0.05 and internal accuracies ranging from 0.73 to 0.94 and holdout accuracies of 0.68 to 0.92. High-ranking predictions for training-excluded reference chemicals demonstrating that predictive accuracy extends beyond the set of chemicals used in classifier training. To explore differences in classifier performance as a function of training data cellular context, MCF7-trained classifier accuracies were compared to classifiers trained on the PC3 gene expression data for the same molecular initiating events. CONCLUSIONS: This methodology can offer insight in prioritizing candidate perturbagens of interest for targeted screens. This approach can also help guide the selection of relevant cellular contexts for screening classes of candidate perturbagens using cell line specific model performance.

Abstract: Nicotine induces long-term changes in the neural activity of the mesocorticolimbic reward pathway structures. The mechanisms involved in this process have not been fully characterized. The hypothesis discussed here proposed that epigenetic regulation participates in the installation of persistent adaptations and long-lasting synaptic plasticity generated by nicotine action on the mesolimbic dopamine neurons of zebrafish. The epigenetic mechanisms induced by nicotine entail histone and DNA chemical modifications, which have been described to lead to changes in gene expression. Among the enzymes that catalyze epigenetic chemical modifications, histone deacetylases (HDACs) remove acetyl groups from histones, thereby facilitating DNA relaxation and making DNA more accessible to gene transcription. DNA methylation, which is dependent on DNA methyltransferase (DNMTs) activity, inhibits gene expression by recruiting several methyl binding proteins that prevent RNA polymerase binding to DNA. In zebrafish, phenylbutyrate (PhB), an HDAC inhibitor, abolishes nicotine rewarding properties together with a series of typical reward-associated behaviors. Furthermore, PhB and nicotine alter long- and short-term object recognition memory in zebrafish, respectively. Regarding DNA methylation effects, a methyl group donor L-methionine (L-met) was found to dramatically reduce nicotine-induced conditioned place preference (CPP) in zebrafish. Simultaneous treatment with DNMT inhibitor 5-aza-2'-deoxycytidine (AZA) was found to reverse the L-met effect on nicotine-induced CPP as well as nicotine reward-specific effects on genetic expression in zebrafish. Therefore, pharmacological interventions that modulate epigenetic regulation of gene expression should be considered as a potential therapeutic method to treat nicotine addiction.

Abstract: Adverse drug-drug interaction (DDI) is a major concern to polypharmacy due to its unexpected adverse side effects and must be identified at an early stage of drug discovery and development. Many computational methods have been proposed for this purpose, but most require specific types of information, or they have less concern in interpretation on underlying genes. We propose a deep learning-based framework for DDI prediction with drug-induced gene expression signatures so that the model can provide the expression level of interpretability for DDIs. The model engineers dynamic drug features using a gating mechanism that mimics the co-administration effects by imposing attention to genes. Also, each side-effect is projected into a latent space through translating embedding. As a result, the model achieved an AUC of 0.889 and an AUPR of 0.915 in unseen interaction prediction, which is competitively very accurate and outperforms other state-of-the-art methods. Furthermore, it can predict potential DDIs with new compounds not used in training. In conclusion, using drug-induced gene expression signatures followed by gating and translating embedding can increase DDI prediction accuracy while providing model interpretability. The source code is available on GitHub ( https://github.com/GIST-CSBL/DeSIDE-DDI ).

Abstract: Understanding the mechanisms of tissue-specific transcriptional regulation is crucial as mis-regulation can cause a broad range of diseases. Here, we investigated transcription factors (TF) that are indispensable for the topological control of tissue specific and cell-type specific regulatory networks as a function of their binding to regulatory elements on promoters and enhancers of corresponding target genes. In particular, we found that promoter-binding TFs that were indispensable for regulatory network control regulate genes that are tissue-specifically expressed and overexpressed in corresponding cancer types. In turn, indispensable, enhancer-binding TFs were enriched with disease and signaling genes as they control an increasing number of cell-type specific regulatory networks. Their target genes were cell-type specific for blood and immune-related cell-types and over-expressed in blood-related cancers. Notably, target genes of indispensable enhancer-binding TFs in cell-type specific regulatory networks were enriched with cancer drug targets, while target genes of indispensable promoter-binding TFs were bona-fide targets of cancer drugs in corresponding tissues. Our results emphasize the significant role control analysis of regulatory networks plays in our understanding of transcriptional regulation, demonstrating potential therapeutic implications in tissue-specific drug discovery research.

Abstract: BACKGROUND: Novel Corona Virus Disease 2019 (COVID-19) is closely associated with cytokines storms. The Chinese medicinal herb Artemisia annua L. (A. annua) has been traditionally used to control many inflammatory diseases, such as malaria and rheumatoid arthritis. We performed network analysis and employed molecular docking and network analysis to elucidate active components or targets and the underlying mechanisms of A. annua for the treatment of COVID-19. METHODS: Active components of A. annua were identified through the TCMSP database according to their oral bioavailability (OB) and drug-likeness (DL). Moreover, target genes associated with COVID-19 were mined from GeneCards, OMIM, and TTD. A compound-target (C-T) network was constructed to predict the relationship of active components with the targets. A Compound-disease-target (C-D-T) network has been built to reveal the direct therapeutic target for COVID-19. Molecular docking, molecular dynamics simulation studies (MD), and MM-GBSA binding free energy calculations were used to the closest molecules and targets between A. annua and COVID-19. RESULTS: In our network, GO, and KEGG analysis indicated that A. annua acted in response to COVID-19 by regulating inflammatory response, proliferation, differentiation, and apoptosis. The molecular docking results manifested excellent results to verify the binding capacity between the hub components and hub targets in COVID-19. MD and MM-GBSA data showed quercetin to be the more effective candidate against the virus by target MAPK1, and kaempferol to be the other more effective candidate against the virus by target TP53. We identified A. annua's potentially active compounds and targets associated with them that act against COVID-19. CONCLUSIONS: These findings suggest that A. annua may prevent and inhibit the inflammatory processes related to COVID-19.

Abstract: Introduction: The AOP-Wiki is the main platform for the development and storage of adverse outcome pathways (AOPs). These AOPs describe mechanistic information about toxicodynamic processes and can be used to develop effective risk assessment strategies. However, it is challenging to automatically and systematically parse, filter, and use its contents. We explored solutions to better structure the AOP-Wiki content, and to link it with chemical and biological resources. Together, this allows more detailed exploration, which can be automated. Materials and Methods: We converted the complete AOP-Wiki content into resource description framework (RDF) triples. We used >20 ontologies for the semantic annotation of property-object relations, including the Chemical Information Ontology, Dublin Core, and the AOP Ontology. Results: The resulting RDF contains >122,000 triples describing 158 unique properties of >15,000 unique subjects. Furthermore, >3500 link-outs were added to 12 chemical databases, and >7500 link-outs to 4 gene and protein databases. The AOP-Wiki RDF has been made available at https://aopwiki.rdf.bigcat-bioinformatics.org. Discussion: SPARQL queries can be used to answer biological and toxicological questions, such as listing measurement methods for all Key Events leading to an Adverse Outcome of interest. The full power that the use of this new resource provides becomes apparent when combining the content with external databases using federated queries. Conclusion: Overall, the AOP-Wiki RDF allows new ways to explore the rapidly growing AOP knowledge and makes the integration of this database in automated workflows possible, making the AOP-Wiki more FAIR.

Abstract: Combinations of direct-acting antivirals are needed to minimize drug resistance mutations and stably suppress replication of RNA viruses. Currently, there are limited therapeutic options against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and testing of a number of drug regimens has led to conflicting results. Here, we show that cobicistat, which is an FDA-approved drug booster that blocks the activity of the drug-metabolizing proteins cytochrome P450-3As (CYP3As) and P-glycoprotein (P-gp), inhibits SARS-CoV-2 replication. Two independent cell-to-cell membrane fusion assays showed that the antiviral effect of cobicistat is exerted through inhibition of spike protein-mediated membrane fusion. In line with this, incubation with low-micromolar concentrations of cobicistat decreased viral replication in three different cell lines including cells of lung and gut origin. When cobicistat was used in combination with remdesivir, a synergistic effect on the inhibition of viral replication was observed in cell lines and in a primary human colon organoid. This was consistent with the effects of cobicistat on two of its known targets, CYP3A4 and P-gp, the silencing of which boosted the in vitro antiviral activity of remdesivir in a cobicistat-like manner. When administered in vivo to Syrian hamsters at a high dose, cobicistat decreased viral load and mitigated clinical progression. These data highlight cobicistat as a therapeutic candidate for treating SARS-CoV-2 infection and as a potential building block of combination therapies for COVID-19. IMPORTANCE The lack of effective antiviral treatments against SARS-CoV-2 is a significant limitation in the fight against the COVID-19 pandemic. Single-drug regimens have so far yielded limited results, indicating that combinations of antivirals might be required, as previously seen for other RNA viruses. Our work introduces the drug booster cobicistat, which is approved by the FDA and typically used to potentiate the effect of anti-HIV protease inhibitors, as a candidate inhibitor of SARS-CoV-2 replication. Beyond its direct activity as an antiviral, we show that cobicistat can enhance the effect of remdesivir, which was one of the first drugs proposed for treatment of SARS-CoV-2. Overall, the dual action of cobicistat as a direct antiviral and a drug booster can provide a new approach to design combination therapies and rescue the activity of compounds that are only partially effective in monotherapy.

Abstract: About 350 million people worldwide suffered from depression, but less than half of the patients received effective and regular treatments. Traditional Chinese Medicine (TCM) such as pinellia has been proven effective for antidepressant treatment with fewer side effects. However, the exact mechanisms remain unclear. Herein, we use the methods of network pharmacology and molecular docking to analyze the effective monomer components of pinellia and reveal the involved signaling pathways to produce antidepressant effects. TCMSP, BATMAN-TCM, and TCMID databases were utilized to analyze the bioactive ingredients and target genes derived from pinellia via the screening the molecular weight (MW), oral bioavailability (OB), blood-brain barrier (BBB) and drug similarity (DL). OMIM, TTD, DisGeNET, GeneCards and DrugBank databases were used to obtain key genes of depression. Then, the networks of protein-protein interaction (PPI) and "medicine-ingredients-targets-pathways" were built. The target signaling pathways were enriched by GO and KEGG by using R language. Furthermore, bioactive ingredients binding of the targets were verified by molecular docking. Nine active monomer ingredients and 96 pivotal gene targets were selected from pinellia. 10,124 disease genes and 87 drug-disease intersecting genes were verified. GO analysis proposed that the receptor activity of neurotransmitter, postsynaptic neurotransmitter, G protein-coupled neurotransmitter, and acetylcholine through the postsynaptic membrane could be modulated by pinellia. KEGG pathway analysis revealed that pinellia influenced depression-related neural tissue interaction, cholinergic synapse, serotonin activated synapse and calcium signaling pathway. Besides, the reliability and accuracy of results obtained from the indirect network pharmacology were validated by molecular docking. The bioactive components of pinellia made significant antidepressant effects by regulating the key target genes/proteins in the pathophysiology of depression.

Abstract: BACKGROUND: Saussurea involucrata (SAIN), also known as Snow lotus (SI), is mainly distributed in high-altitude areas such as Tibet and Xinjiang in China. To identify novel targets for the prevention or treatment of lung adenocarcinoma and lung squamous cell carcinoma (LUAD&LUSC), and to facilitate better alternative new drug discovery as well as clinical application services, the therapeutic effects of SAIN on LUAD&LUSC were evaluated by gene differential analysis of clinical samples, compound target molecular docking, and GROMACS molecular dynamics simulation. RESULTS: Through data screening, alignment, analysis, and validation it was confirmed that three of the major active ingredients in SAIN, namely quercetin (Q), luteolin (L), and kaempferol (K), mainly act on six protein targets, which mainly regulate signaling pathways in cancer, transcriptional misregulation in cancer, EGFR tyrosine kinase inhibitor resistance, adherens junction, IL-17 signaling pathway, melanoma, and non-small cell lung cancer. In addition, microRNAs in cancer exert preventive or therapeutic effects on LUAD&LUSC. Molecular dynamics (MD) simulations of Q, L, or K in complex with EGFR, MET, MMP1, or MMP3 revealed the presence of Q in a very stable tertiary structure in the human body. CONCLUSION: There are three active compounds of Q, L, and K in SAIN, which play a role in the treatment and prevention of non-small cell lung cancer (NSCLC) by directly or indirectly regulating the expression of genes such as MMP1, MMP3, and EGFR.

Abstract: The continuous emergence of severe acute respiratory coronavirus 2 (SARS-CoV-2) variants and the increasing number of breakthrough infection cases among vaccinated people support the urgent need for research and development of antiviral drugs. Viral entry is an intriguing target for antiviral drug development. We found that diltiazem, a blocker of the L-type calcium channel Cav1.2 pore-forming subunit (Cav1.2 alpha1c) and an FDA-approved drug, inhibits the binding and internalization of SARS-CoV-2, and decreases SARS-CoV-2 infection in cells and mouse lung. Cav1.2 alpha1c interacts with SARS-CoV-2 spike protein and ACE2, and affects the attachment and internalization of SARS-CoV-2. Our finding suggests that diltiazem has potential as a drug against SARS-CoV-2 infection and that Cav1.2 alpha1c is a promising target for antiviral drug development for COVID-19.