Abstract: Treatment of the cytokine release syndrome (CRS) has become an important part of rescuing hospitalized COVID-19 patients. Here, we systematically explored the transcriptional regulators of inflammatory cytokines involved in the COVID-19 CRS to identify candidate transcription factors (TFs) for therapeutic targeting using approved drugs. We integrated a resource of TF-cytokine gene interactions with single-cell RNA-seq expression data from bronchoalveolar lavage fluid cells of COVID-19 patients. We found 581 significantly correlated interactions, between 95 TFs and 16 cytokines upregulated in the COVID-19 patients, that may contribute to pathogenesis of the disease. Among these, we identified 19 TFs that are targets of FDA approved drugs. We investigated the potential therapeutic effect of 10 drugs and 25 drug combinations on inflammatory cytokine production in peripheral blood mononuclear cells, which revealed two drugs that inhibited cytokine production and numerous combinations that show synergistic efficacy in downregulating cytokine production. Further studies of these candidate repurposable drugs could lead to a therapeutic regimen to treat the CRS in COVID-19 patients.

Abstract: Covid-19 urges a deeper understanding of the underlying molecular mechanisms involved in illness progression to provide a prompt therapeutical response with an adequate use of available drugs, including drug repurposing. Recently, it was suggested that a dysregulated bradykinin signaling can trigger the cytokine storm observed in patients with severe Covid-19. In the scope of a drug repurposing campaign undertaken to identify bradykinin antagonists, raloxifene was identified as prospective compound in a virtual screening process. The pharmacodynamics profile of raloxifene towards bradykinin receptors is reported in the present work, showing a weak selective partial agonist profile at the B2 receptor. In view of this new profile, its possible use as a therapeutical agent for the treatment of severe Covid-19 is discussed.

Abstract: Microorganisms produce secondary metabolites with a remarkable range of bioactive properties. The constantly increasing amount of published genomic data provides the opportunity for efficient identification of biosynthetic gene clusters by genome mining. On the other hand, for many natural products with resolved structures, the encoding biosynthetic gene clusters have not been identified yet. Of those secondary metabolites, the scaffolds of nonribosomal peptides and polyketides (type I modular) can be predicted due to their building block-like assembly. SeMPI v2 provides a comprehensive prediction pipeline, which includes the screening of the scaffold in publicly available natural compound databases. The screening algorithm was designed to detect homologous structures even for partial, incomplete clusters. The pipeline allows linking of gene clusters to known natural products and therefore also provides a metric to estimate the novelty of the cluster if a matching scaffold cannot be found. Whereas currently available tools attempt to provide comprehensive information about a wide range of gene clusters, SeMPI v2 aims to focus on precise predictions. Therefore, the cluster detection algorithm, including building block generation and domain substrate prediction, was thoroughly refined and benchmarked, to provide high-quality scaffold predictions. In a benchmark based on 559 gene clusters, SeMPI v2 achieved comparable or better results than antiSMASH v5. Additionally, the SeMPI v2 web server provides features that can help to further investigate a submitted gene cluster, such as the incorporation of a genome browser, and the possibility to modify a predicted scaffold in a workbench before the database screening.

Abstract: Macrophages are critical effector cells of the immune system, and understanding genes involved in their viability and function is essential for gaining insights into immune system dysregulation during disease. We use a high-throughput, pooled-based CRISPR-Cas screening approach to identify essential genes required for macrophage viability. In addition, we target 3' UTRs to gain insights into previously unidentified cis-regulatory regions that control these essential genes. Next, using our recently generated nuclear factor kappaB (NF-kappaB) reporter line, we perform a fluorescence-activated cell sorting (FACS)-based high-throughput genetic screen and discover a number of previously unidentified positive and negative regulators of the NF-kappaB pathway. We unravel complexities of the TNF signaling cascade, showing that it can function in an autocrine manner in macrophages to negatively regulate the pathway. Utilizing a single complex library design, we are capable of interrogating various aspects of macrophage biology, thus generating a resource for future studies.

Abstract: The outbreak of COVID-19, the disease caused by SARS-CoV-2, continues to affect millions of people around the world. The absence of a globally distributed effective treatment makes the exploration of new mechanisms of action a key step to address this situation. Stabilization of non-native Protein-Protein Interactions (PPIs) of the nucleocapsid protein of MERS-CoV has been reported as a valid strategy to inhibit viral replication. In this study, the applicability of this unexplored mechanism of action against SARS-CoV-2 is analyzed. During our research, we were able to find three inducible interfaces of SARS-CoV-2 N protein NTD, compare them to the previously reported MERS-CoV stabilized dimers, and identify those residues that are responsible for their formation. A drug discovery protocol implemented consisting of docking, molecular dynamics and MM-GBSA enabled us to find several compounds that might be able to exploit this mechanism of action. In addition, a common catechin skeleton was found among many of these molecules, which might be useful for further drug design. We consider that our findings could motivate future research in the fields of drug discovery and design towards the exploitation of this previously unexplored mechanism of action against COVID-19. Communicated by Ramaswamy H. Sarma.

Abstract: Drug repurposing/repositioning, which aims to find novel indications for existing drugs, contributes to reducing the time and cost for drug development. For the recent decade, gene expression profiles of drug stimulating samples have been successfully used in drug repurposing. However, most of the existing methods neglect the gene modules and the interactions among the modules, although the cross-talks among pathways are common in drug response. It is essential to develop a method that utilizes the cross-talks information to predict the reliable candidate associations. In this study, we developed MNBDR (Module Network Based Drug Repositioning), a novel method that based on module network to screen drugs. It integrated protein-protein interactions and gene expression profile of human, to predict drug candidates for diseases. Specifically, the MNBDR mined dense modules through protein-protein interaction (PPI) network and constructed a module network to reveal cross-talks among modules. Then, together with the module network, based on existing gene expression data set of drug stimulation samples and disease samples, we used random walk algorithms to capture essential modules in disease development and proposed a new indicator to screen potential drugs for a given disease. Results showed MNBDR could provide better performance than popular methods. Moreover, functional analysis of the essential modules in the network indicated our method could reveal biological mechanism in drug response.

Abstract: The global health emergency caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to alarming numbers of fatalities across the world. So far the researchers worldwide have not been able to discover a breakthrough in the form of a potent drug or an effective vaccine. Therefore, it is imperative to discover drugs to curb the ongoing menace. In silico approaches using FDA approved drugs can expedite the drug discovery process by providing leads that can be pursued. In this report, two drug targets, namely the spike protein and main protease, belonging to structural and non-structural class of proteins respectively, were utilized to carry out drug repurposing based screening. The exposed nature of the spike protein on the viral surface along with its instrumental role in host infection and the involvement of main protease in processing of polyproteins along with no human homologue make these proteins attractive drug targets. Interestingly, the screening identified a common high efficiency binding molecule named rutin. Further, molecular dynamics simulations in explicit solvent affirmed the stable and sturdy binding of rutin with these proteins. The decreased Rg value (4 nm for spike-rutin and 2.23 nm for main protease-rutin) and stagnant SASA analysis (485 nm/S(2)/N in spike-rutin and 152 nm/S2/N in main protease-rutin) for protein surface and its orientation in the exposed and buried regions suggests a strong binding interaction of the drug. Further, cluster analysis and secondary structure analysis of complex trajectories validated the conformational changes due to binding of rutin.

Abstract: The human serine protease serine 2 TMPRSS2 is involved in the priming of proteins of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and represents a possible target for COVID-19 therapy. The TMPRSS2 gene may be co-expressed with SARS-CoV-2 cell receptor genes angiotensin-converting enzyme 2 (ACE2) and Basigin (BSG), but only TMPRSS2 demonstrates tissue-specific expression in alveolar cells according to single-cell RNA sequencing data. Our analysis of the structural variability of the TMPRSS2 gene based on genome-wide data from 76 human populations demonstrates that a functionally significant missense mutation in exon 6/7 in the TMPRSS2 gene is found in many human populations at relatively high frequencies, with region-specific distribution patterns. The frequency of the missense mutation encoded by rs12329760, which has previously been found to be associated with prostate cancer, ranged between 10% and 63% and was significantly higher in populations of Asian origin compared with European populations. In addition to single-nucleotide polymorphisms, two copy number variants were detected in the TMPRSS2 gene. A number of microRNAs have been predicted to regulate TMPRSS2 and BSG expression levels, but none of them is enriched in lung or respiratory tract cells. Several well-studied drugs can downregulate the expression of TMPRSS2 in human cells, including acetaminophen (paracetamol) and curcumin. Thus, the interactions of TMPRSS2 with SARS-CoV-2, together with its structural variability, gene-gene interactions, expression regulation profiles, and pharmacogenomic properties, characterize this gene as a potential target for COVID-19 therapy.

Abstract: Novel coronavirus disease (COVID-19) was identified from China in December 2019 and spread rapidly through human-to-human transmission, affecting so many people worldwide. Until now, there has been no specific treatment against the disease and repurposing of the drug. Our investigation aimed to screen potential inhibitors against coronavirus for the repurposing of drugs. Our study analyzed sequence comparison among SARS-CoV, SARS-CoV-2, and MERS-CoV to determine the identity matrix using discovery studio. SARS-CoV-2 M(pro) was targeted to generate an E-pharmacophore hypothesis to screen drugs from the DrugBank database having similar features. Promising drugs were used for docking-based virtual screening at several precisions. Best hits from virtual screening were subjected to MM/GBSA analysis to evaluate binding free energy, followed by the analysis of binding interactions. Furthermore, the molecular dynamics simulation approaches were carried out to assess the docked complex's conformational stability. A total of 33 drug classes were found from virtual screening based on their docking scores. Among them, seven potential drugs with several anticancer, antibiotic, and immunometabolic categories were screened and showed promising MM/GBSA scores. During interaction analysis, these drugs exhibited different types of hydrogen and hydrophobic interactions with amino acid residue. Besides, 17 experimental drugs selected from virtual screening might be crucial for drug discovery against COVID-19. The RMSD, RMSF, SASA, Rg, and MM/PBSA descriptors from molecular dynamics simulation confirmed the complex's firm nature. Seven promising drugs for repurposing against SARS-CoV-2 main protease (M(pro)), namely sapanisertib, ornidazole, napabucasin, lenalidomide, daniquidone, indoximod, and salicylamide, could be vital for the treatment of COVID-19. However, extensive in vivo and in vitro studies are required to evaluate the mentioned drug's activity.

Abstract: XiaoLuoWan (XLW) is a classical formula in traditional Chinese medicine (TCM) that has satisfactory therapeutic effects for uterine fibroids (UFs). However, its underlying mechanisms remain unclear. To elucidate the pharmacological actions of XLW in treating UFs, an ingredient-target-disease framework was proposed based on network pharmacology strategies. The active ingredients in XLW and their putative targets were obtained from the TCM systems pharmacology database and analysis platform (TCMSP) and Bioinformatics Analysis Tool for Molecular mechANism of Traditional Chinese Medicine (BATMAN-TCM) platforms. The known therapeutic targets of UFs were acquired from the DigSee and DrugBank databases. Then, the links between putative XLW targets and therapeutic UF targets were identified to establish interaction networks by Cytoscape. Finally, Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses of overlapping gene targets were performed in the STRING database and visualized in R software. In total, 9 active compounds were obtained from 74 ingredients, with 71 curative targets predicted in XLW. Moreover, 321 known therapeutic targets were closely related to UFs, with 29 targets overlapping with XLW and considered interacting genes. Pathway enrichment revealed that the calcium signaling pathway was significantly enriched and the mitogen-activated protein kinase (MAPK) signaling pathway, cAMP signaling pathway, cancer and vascular smooth muscle contraction pathways, cGMP-PKG signaling pathway, and AGE-RAGE signaling pathway were closely associated with XLW intervention for UFs. In conclusion, the network pharmacology detection identified 9 available chemicals as the active ingredients in XLW that may relieve UFs by regulating 29 target genes involved in the calcium signaling pathway, MAPK pathway and cAMP pathway. Network pharmacology analyses may provide more convincing evidence for the investigation of classical TCM prescriptions, such as XLW.