Abstract: Background: Burn injury is a life-threatening disease that does not have ideal biomarkers. Therefore, this study first applied weighted gene co-expression network analysis (WGCNA) and differentially expressed gene (DEG) screening methods to identify pivotal genes and diagnostic biomarkers associated with the skin burn process. Methods: After obtaining transcriptomic datasets of burn patient skin and normal skin from Gene Expression Omnibus (GEO) and performing differential analysis and functional enrichment, WGCNA was used to identify hub gene modules associated with burn skin processes in the burn patient peripheral blood sample dataset and determine the correlation between modules and clinical features. Enrichment analysis was performed to identify the functions and pathways of key module genes. Differential analysis, WGCNA, protein-protein interaction analysis, and enrichment analysis were utilized to screen for hub genes. Hub genes were validated in two other GEO datasets, tested by immunohistochemistry for hub gene expression in burn patients, and receiver operating characteristic curve analysis was performed. Finally, we constructed the specific drug activity, transcription factors, and microRNA regulatory network of the five hub genes. Results: A total of 1,373 DEGs in GSE8056 were obtained, and the top 5 upregulated genes were S100A12, CXCL8, CXCL5, MMP3, and MMP1, whereas the top 5 downregulated genes were SCGB1D2, SCGB2A2, DCD, TSPAN8, and KRT25. DEGs were significantly enriched in the immunity, epidermal development, and skin development processes. In WGCNA, the yellow module was identified as the most closely associated module with tissue damage during the burn process, and the five hub genes (ANXA3, MCEMP1, MMP9, S100A12, and TCN1) were identified as the key genes for burn injury status, which consistently showed high expression in burn patient blood samples in the GSE37069 and GSE13902 datasets. Furthermore, we verified using immunohistochemistry that these five novel hub genes were also significantly elevated in burn patient skin. In addition, MCEMP1, MMP9, and S100A12 showed perfect diagnostic performance in the receiver operating characteristic analysis. Conclusion: In conclusion, we analyzed the changes in genetic processes in the skin during burns and used them to identify five potential novel diagnostic markers in blood samples from burn patients, which are important for burn patient diagnosis. In particular, MCEMP1, MMP9, and S100A12 are three key blood biomarkers that can be used to identify skin damage in burn patients.

Abstract: Gelsemium elegans (Gardner and Champ.) Benth. (Gelsemiaceae) (GEB) is a toxic plant indigenous to Southeast Asia especially China, and has long been used as Chinese folk medicine for the treatment of various types of pain, including neuropathic pain (NPP). Nevertheless, limited data are available on the understanding of the interactions between ingredients-targets-pathways. The present study integrated network pharmacology and experimental evidence to decipher molecular mechanisms of GEB against NPP. The candidate ingredients of GEB were collected from the published literature and online databases. Potentially active targets of GEB were predicted using the SwissTargetPrediction database. NPP-associated targets were retrieved from GeneCards, Therapeutic Target database, and DrugBank. Then the protein-protein interaction network was constructed. The DAVID database was applied to Gene Ontology and Kyoto Encyclopedia of Genes and Genome pathway enrichment analysis. Molecular docking was employed to validate the interaction between ingredients and targets. Subsequently, a 50 ns molecular dynamics simulation was performed to analyze the conformational stability of the protein-ligand complex. Furthermore, the potential anti-NPP mechanisms of GEB were evaluated in the rat chronic constriction injury model. A total of 47 alkaloids and 52 core targets were successfully identified for GEB in the treatment of NPP. Functional enrichment analysis showed that GEB was mainly involved in phosphorylation reactions and nitric oxide synthesis processes. It also participated in 73 pathways in the pathogenesis of NPP, including the neuroactive ligand-receptor interaction signaling pathway, calcium signaling pathway, and MAPK signaling pathway. Interestingly, 11-Hydroxyrankinidin well matched the active pockets of crucial targets, such as EGFR, JAK1, and AKT1. The 11-hydroxyrankinidin-EGFR complex was stable throughout the entire molecular dynamics simulation. Besides, the expression of EGFR and JAK1 could be regulated by koumine to achieve the anti-NPP action. These findings revealed the complex network relationship of GEB in the "multi-ingredient, multi-target, multi-pathway" mode, and explained the synergistic regulatory effect of each complex ingredient of GEB based on the holistic view of traditional Chinese medicine. The present study would provide a scientific approach and strategy for further studies of GEB in the treatment of NPP in the future.

Abstract: Background: Due to the embryotoxicity found in animal studies and scarce clinical data in pregnant women, it is still controversial whether entecavir (ETV) and adefovir dipivoxil (ADV) are safe during human pregnancy. This is of paramount importance when counseling pregnant women with hepatitis B virus (HBV) on risks and benefits to their offspring. Objective: To quantify the association between administration of ETV and ADV in pregnant women and occurrence of adverse events (AEs) during pregnancy (AEDP). Methods: Pregnancy reports from the FDA Adverse Event Reporting System (FAERS) were used to perform a retrospective analysis of AEDP associated with ETV or ADV. Disproportionality analysis estimating the reporting odds ratio (ROR) was conducted to identify the risk signals. A signal was defined as ROR value >2, and lower limit of 95% confidence interval (CI)> 1. Results: A total of 1,286,367 reports involving AEDP were submitted to FAERS by healthcare professionals. Of these, there were 547 cases reporting ETV and 242 cases reporting ADV as primary suspected drugs. We found a moderate or strong signal for increased risk of spontaneous abortion when comparing ETV with tenofovir disoproxil fumarate (TDF) and telbivudine (LdT), with RORs equal to 1.58 (95% CI, 1.09-2.30) and 2.13 (95% CI, 1.04-4.36), respectively. However, when the included reports were limited to indication containing HBV infection, no signals for increased AEDP were detected. Futhermore, a strong signal for increased risk of spontaneous abortion was identified in patients with HBV infection when comparing ETV or ADV with lamivudine (LAM), with RORs of 3.55 (95% CI, 1.54-8.18) and 2.85 (95% CI, 1.15-7.08), respectively. Conclusion: We found a strong signal for increased risk of spontaneous abortion in patients with HBV infection taking ETV or ADV, in comparison with those prescribed with LAM. Moreover, no obvious signal association of human teratogenicity with exposure to ETV or ADV was identified in fetuses during pregnancy. Nevertheless, owing to the limitations of a spontaneous reporting database, which inevitably contains potential biases, there is a pressing need for well-designed comparative safety studies to validate these results in clinical practice.

Abstract: Thanks to the availability of multiomics data of individual cancer patients, precision medicine or personalized medicine is becoming a promising treatment for individual cancer patients. However, the association patterns, that is, the mechanism of response (MoR) between large-scale multiomics features and drug response are complex and heterogeneous and remain unclear. Although there are existing computational models for predicting drug response using the high-dimensional multiomics features, it remains challenging to uncover the complex molecular mechanism of drug responses. To reduce the number of predictors/features and make the model more interpretable, in this study, 46 signaling pathways were used to build a deep learning model constrained by signaling pathways, consDeepSignaling, for anti-drug response prediction. Multiomics data, like gene expression and copy number variation, of individual genes can be integrated naturally in this model. The signaling pathway-constrained deep learning model was evaluated using the multiomics data of approximately 1000 cancer cell lines in the Broad Institute Cancer Cell Line Encyclopedia (CCLE) database and the corresponding drug-cancer cell line response data set in the Genomics of Drug Sensitivity in Cancer (GDSC) database. The evaluation results showed that the proposed model outperformed the existing deep neural network models. Also, the model interpretation analysis indicated the distinctive patterns of importance of signaling pathways in anticancer drug response prediction.

Abstract: Objective: This study aimed to explore the mechanism of Modified Danggui Sini Decoction in the treatment of knee osteoarthritis via a combination of network pharmacology and molecular docking. Methods: The main chemical components and corresponding targets of Modified Danggui Sini Decoction were searched and screened in TCMSP database. The disease targets of knee osteoarthritis were summarized in GeneCards, OMIM, PharmGkb, TTD, and DrugBank databases. The visual interactive network of "drugs-active components-disease targets" was drawn by Cytoscape 3.8.1 software. The protein-protein interaction network was constructed by STRING database. Then, GO function and KEGG pathway enrichment were analyzed by Bioconductor/R, and the pathway of the highest degree of correlation with knee osteoarthritis was selected for specific analysis. Finally, molecular docking was used to screen and verify core genes by AutoDockTools software. Results: Seventy-one main components of Modified Danggui Sini Decoction and 116 potential therapeutic targets of knee osteoarthritis were selected. The KEGG pathway and the GO function enrichment analysis showed that the targets of Modified Danggui Sini Decoction in the treatment of knee osteoarthritis were mainly concentrated on PI3K-Akt signaling pathway, TNF signaling pathway, IL-17 signaling pathway, apoptosis signaling pathway, Toll-like receptor signaling pathway, Th17 cell differentiation signaling pathway, HIF-1 signaling pathway, and NF-kappaB signaling pathway. It mainly involved inflammatory reaction, regulation of apoptotic signaling pathway, cellular response to regulation of inflammatory response, cellular response to oxidative stress, and other biological processes. The molecular docking results showed that ESR1-wogonin, MAPK1-quercetin, RELA-wogonin, RELA-baicalein, TP53-baicalein, TP53-quercetin, and RELA-quercetin have strong docking activities. Conclusion: Modified Danggui Sini Decoction has the hierarchical network characteristics of "multicomponent, multitarget, multifunction, and multipathway" in the treatment of knee osteoarthritis. It mainly regulates the proliferation and apoptosis of chondrocytes by regulating the PI3K-Akt signaling pathway and establishes cross-talk with many downstream inflammatory-related pathways to reduce the overall inflammatory response. Meanwhile, HIF-1 expression was used to ensure the normal function and metabolism of knee joint under hypoxia condition, and the above processes play a key role in the treatment of knee osteoarthritis.

Abstract: Background: Calculus Bovis is a valuable Chinese medicine, which is widely used in the clinical treatment of ischemic stroke. The present study is aimed at investigating its target and the mechanism involved in ischemic stroke treatment by network pharmacology. Methods: Effective compounds of Calculus Bovis were collected using methods of network pharmacology and using the Bioinformatics Analysis Tool for Molecular Mechanism of Traditional Chinese Medicine (BATMAN-TCM) and the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). Potential compound targets were searched in the TCMSP and SwissTargetPrediction databases. Ischemic stroke-related disease targets were searched in the Drugbank, DisGeNet, OMIM, and TTD databases. These two types of targets were uploaded to the STRING database, and a network of their interaction (PPI) was built with its characteristics calculated, aiming to reveal a number of key targets. Hub genes were selected using a plug-in of the Cytoscape software, and Gene Ontology (GO) biological processes and pathway enrichment analyses of Kyoto Encyclopedia of Genes and Genomes (KEGG) were conducted using the clusterProfiler package of R language. Results: Among 12 compounds, deoxycorticosterone, methyl cholate, and biliverdin were potentially effective components. A total of 344 Calculus Bovis compound targets and 590 ischemic stroke targets were found with 92 overlapping targets, including hub genes such as TP53, AKT, PIK2CA, MAPK3, MMP9, and MMP2. Biological functions of Calculus Bovis are associated with protein hydrolyzation, phosphorylation of serine/threonine residues of protein substrates, peptide bond hydrolyzation of peptides and proteins, hydrolyzation of intracellular second messengers, antioxidation and reduction, RNA transcription, and other biological processes. Conclusion: Calculus Bovis may play a role in ischemic stroke by activating PI3K-AKT and MAPK signaling pathways, which are involved in regulating inflammatory response, cell apoptosis, and proliferation.

Abstract: Proposing a theory about the pathophysiology of cytokine storm in COVID19, we were to find the potential drugs to treat this disease and to find any effect of these drugs on the virus infectivity through an in silico study. COVID-19-induced ARDS is linked to a cytokine storm phenomenon not explainable solely by the virus infectivity. Knowing that ACE2, the hydrolyzing enzyme of AngII and SARS-CoV2 receptor, downregulates when the virus enters the host cells, we hypothesize that hyperacute AngII upregulation is the eliciting factor of this ARDS. We were to validate this theory through reviewing previous studies to figure out the role of overzealous activation of AT1R in ARDS. According to this theory losartan may attenuate ARDS in this disease. Imatinib, has previously been elucidated to be promising in modulating lung inflammatory reactions and virus infectivity in SARS and MERS. We did an in silico study to uncover any probable other unconsidered inhibitory effects of losartan and imatinib against SARS-CoV2 pathogenesis. Reviewing the literature, we could find that over-activation of AT1R could explain precisely the mechanism of cytokine storm in COVID19. Our in silico study revealed that losartan and imatinib could probably: (1) decline SARS-CoV2 affinity to ACE2. (2) inhibit the main protease and furin, (3) disturb papain-like protease and p38MAPK functions. Our reviewing on renin-angiotensin system showed that overzealous activation of AT1R by hyper-acute excess of AngII due to acute downregulation of ACE2 by SARS-CoV2 explains precisely the mechanism of cytokine storm in COVID-19. Besides, based on our in silico study we concluded that losartan and imatinib are promising in COVID19.

Abstract: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for coronavirus disease 2019 (COVID-19). Since its emergence, the COVID-19 pandemic has not only distressed medical services but also caused economic upheavals, marking urgent the need for effective therapeutics. The experience of combating SARS-CoV and MERS-CoV has shown that inhibiting the 3-chymotrypsin-like protease (3CLpro) blocks the replication of the virus. Given the well-studied properties of FDA-approved drugs, identification of SARS-CoV-2 3CLpro inhibitors in an FDA-approved drug library would be of great therapeutic value. Here, we screened a library consisting of 774 FDA-approved drugs for potent SARS-CoV-2 3CLpro inhibitors, using an intramolecularly quenched fluorescence (IQF) peptide substrate. Ethacrynic acid, naproxen, allopurinol, butenafine hydrochloride, raloxifene hydrochloride, tranylcypromine hydrochloride, and saquinavir mesylate have been found to block the proteolytic activity of SARS-CoV-2 3CLpro. The inhibitory activity of these repurposing drugs against SARS-CoV-2 3CLpro highlights their therapeutic potential for treating COVID-19 and other Betacoronavirus infections.

Abstract: Molecular docking is commonly used for identification of drug candidates targeting a specified protein of known structure. With the increasing emphasis on drug repurposing over recent decades, molecular inverse docking has been widely applied to prediction of the potential protein targets of a specified molecule. In practice, inverse docking has many advantages, including early supervision of drugs' side effects and toxicity. MDock developed from our laboratory is a protein-ligand docking software based on a knowledge-based scoring function and has numerous applications to lead identification. In addition to its computational efficiency on ensemble docking for multiple protein conformations, MDock is well suited for inverse docking. In this chapter, we focus on introducing the protocol of inverse docking with MDock. For academic users, the MDock package is freely available at http://zoulab.dalton.missouri.edu/mdock.htm .

Abstract: A genome-wide association study (GWAS) has discovered that a polymorphism in the ZFP90 gene is associated with systemic lupus erythematosus (SLE). In this study, we explored the candidate function of a ZFP90 variant (rs1170426) in the context of SLE and detected the relationship between SLE susceptible genes and SLE drug target genes. First, we investigated the regulatory role of rs1170426 on ZFP90 expression by expression quantitative trait loci (eQTL) analysis in peripheral blood mononuclear cells (PBMCs), T, B, and monocytes cells and annotated the regulatory function of rs1170426 using bioinformatic databases. Second, we compared the case-control difference in ZFP90 expression levels. Third, we analyzed the association of genotype and ZFP90 expression levels with SLE clinical characters. Last, we showed the interaction of SLE susceptibility genes with SLE drug target genes. Subjects with the risk allele "C" of rs1170426 had lower expression levels of ZFP90 in PBMCs (P = 0.006) and CD8+ T cells (P = 0.003) from controls. SLE cases also had lower expression levels compared with controls (P = 2.78E-9). After correction for multiple testing, the ZFP90 expression levels were related to serositis (FDR p = 0.004), arthritis (FDR p = 0.020), hematological involvement (FDR p = 0.021), and increased C-reactive protein (CRP) (FDR p = 0.005) in cases. Furthermore, the SLE susceptible genes and the recognized SLE drug target genes were more likely to act upon each other compared with non-SLE genetic genes (OR = 2.701, P = 1.80E-5). These findings suggest that ZFP90 might play a role in the pathogenesis of SLE, and SLE genetics would contribute to therapeutic drug discovery.