FAM201A, a Long Noncoding RNA Potentially Associated With Atrial Fibrillation Identified by ceRNA Network Analyses and WGCNA

Background: Atrial fibrillation (AF) is the most common cardiac arrhythmia that contributes to various complications. However, little is known about lncRNAs associated with AF susceptibility. In the present study, we aim to identify lncRNAs involved in pathogenesis of AF based on competing endogenous RNA (ceRNA) network analyses and weighted gene co-expression network analysis (WGCNA).Methods: Two lncRNA and mRNA microarray datasets GSE41177 and GSE79768 were retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed lncRNAs (DElncRNAs), mRNAs (DEmRNAs) between AF patients and patients with sinus rhythm (SR) were identified from dataset GSE41177. Then, those DElncRNAs associated target miRNAs were predicted. The ceRNA network was constructed based on DElncRNAs, predicted miRNAs and DEmRNAs. To validate the role of AF-related lncRNAs, all lncRNAs form dataset GSE79768 were selected to perform WGCNA. LncRNA modules relevant to AF were identified. Crucial lncRNAs in the module that was most relevant to AF were screened according to the criteria of | Gene significance (GS)| > 0.6 and |Module membership (MM)| > 0.5. Results: A total of 18 DElncRNAs and 350 DEmRNAs were identified between AF patients and SR patients. The final ceRNA network contained 5 lncRNAs, 10 miRNAs, and 21 mRNAs. According to the ceRNA theory, combined with the comparative toxicogenomics database (CTD) database, the ceRNA axis FAM201A-miR-33a-3p-RAC3 was considered associated with AF susceptibility. By WGCNA, the blue module was detected most highly relevant with AF. The lncRNA FAM201A was proved in the blue module and highly related to AF. Conclusions: These results demonstrated that FAM201A might have great potential for susceptibility of AF based on ceRNA network analyses and WGCNA. FAM201A may function, at least partly, as ceRNA to regulate RAC3 in AF susceptibility.

Mechanism of Action of Dengzhan Shengmai in Regulating Stroke from an Inflammatory Perspective: A Preliminary Analysis of Network Pharmacology

Stroke is a complicated disease with an increasing incidence and a very high mortality rate. A classical Chinese herbal medicine, Dengzhan Shengmai (DZSM), has shown to have therapeutic effects on stroke; however, its chemical basis and molecular mechanism are still unclear. In this study, a systems biology approach was applicable to elucidate the underlying mechanism of action of DZSM on stroke. All the compounds were obtained from databases, and pendant-related targets were obtained from various data platforms, including the TCM Systematic Pharmacology (TCMSP) database, TCM Integrated Database (TCMIP), High Throughput Experimental Reference Database (HERB), Comparative Toxicogenomics Database (CTD), SwissTargetPredicition, and SymMap, The Human Gene Database (GENECARD) and Comparative Toxicogenomics Database (CTD) were used for stroke disease target data, followed by network pharmacology analysis to predict the potential effect of DZSM on stroke. Animal experiments were intended to validate the underlying mechanisms. A total of 846 chemical components were compiled for the targets of DZSM drug, and quercetin, kaempferol, and Wuweizisu C are the highest chemical components compiled from DZSM. Overlapping with 375 disease-specific targets and 149 core targets, the core targets include TNF, IL-6, ALB, and AKT1, which are shown to regulate the disease process from an anti-inflammatory perspective. 198 enrichment messages were obtained by KEGG enrichment analysis, and we believe that the role of the AGE-RAGE signaling pathway in diabetic complications, TNF signaling pathway, and IL-17 signaling pathway is more important. Based on rat experiments, we also demonstrated that DZSM could effectively modulate the inflammation level of brain infarct tissues and effectively alleviate behavioral characteristics. Grouped together, our study suggests that the combination of network pharmacology prediction and experimental validation can provide a useful tool to describe the molecular mechanisms of DZSM in Chinese medicine (TCM).

Pharmacotherapy for SARS-CoV-2 and Seizures for drug repurposing presumed on Mechanistic Targets

Background: The currently circulating novel SARS-CoV-2 coronavirus disease (COVID-19) has brought the whole world to a standstill. Recent studies have deciphered the viral genome structure, epidemiology and are in the process of unveiling multiple mechanisms of pathogenesis. Apart from atypical pneumonia and lung disease manifestations, this disease has also been found to be associated with neurological symptoms, which include dizziness, headache, stroke, or seizures, among others. However, a possible direct or indirect association between SARS-CoV-2 and seizures is still not clear. In any manner, it may be of interest to analyze the drugs being used for viral infection in the background of epilepsy or vice versa. Objective: To identify the most credible drug candidate for COVID-19 in persons with epilepsy or COVID-19 patients experiencing seizures. Methods: A literature search for original and review articles was performed, and further, the Comparative Toxicogenomics Database was used to unearth the most credible drug candidate. Results: Our search based on common mechanistic targets affecting SARS-CoV-2 and seizures revealed ivermectin, dexamethasone, anakinra, and tocilizumab for protection against both COVID-19 and seizures. Amongst the antiseizure medications, we found valproic acid as the most probable pharmacotherapy for COVID-19 patients experiencing seizures. Conclusion: These findings would hopefully provide the basis for initiating further studies on the pathogenesis and drug targeting strategies for this emerging infection accompanied with seizures or in people with epilepsy.

The Research on Huanglian Jiedu Decoction against Atopic Dermatitis

Objective. Study on the pharmacodynamic basis and mechanism of Huanglian Jiedu Decoction against atopic dermatitis (AD). Methods. Based on network pharmacology, the targets of Huanglian Jiedu Decoction and AD were screened by Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), SwissTargetPrediction databases, and the database of Online Mendelian Inheritance in Man (OMIM), Therapeutic Targets Database (TTD) and the Comparative Toxicogenomics Database (CTD); then, “chemical composition-target-related pathway-disease target” network graph of Huanglian Jiedu Decoction against AD was constructed by using STRING and Cytoscape software. In combination with in vitro experiments, the levels of IL-4, IL-6, and IL-10 in T cells were determined by ELISA; the pharmacodynamic basis and mechanism of Huanglian Jiedu Decoction against AD were preliminarily explored. Results. 81 active ingredients in Huanglian Jiedu Decoction were screened by network pharmacology, 31 of which were related to atopic dermatitis, corresponding to 12 target proteins. A total of 14 pathways were obtained by KEGG pathway analysis, and 8 were associated with atopic dermatitis. Compared with the control group, 20 and 40 µg/ml of Huanglian Jiedu Decoction could significantly reduce the contents of IL-4, IL-6, and IL-10 in T lymphocytes of mice with atopic dermatitis ( p < 0.01 ). Conclusion. Huanglian Jiedu Decoction can act against AD by multicomponent, multitarget, and multichannel mode of action.

APPLYING „IN SILICO“ TOXICOGENOMIC DATA MINING TO PREDICT MOLECULAR MECHANISMS AND PATHWAYS AGAINST CARCINOMA: IMMUNOMODULATOR SULFORAPHANE AS A CASE STUDY

The aim of this study was to predict the molecular mechanisms and pathways of immunomodulator sulforaphane (SFN) against carcinoma using in silico toxicogenomic data mining. Three key tools applied in our analysis were Comparative Toxicogenomics Database (CTD; http://CTD.mdibl. org), ToppGene Suite portal (https://toppgene.cchmc.org) and Reactome Knowledgebase (https://reactome.org). Sulforaphane interacted with a total of 1896, among which NFE2L2, NQO1, HMOX1, GCLC, TXNRD1, IL1B, IFNG, AGT, KEAP1, and CASP3 had the highest number of interactions. In the CTD, there were direct evidences that SFN interacts with a total of 169 genes to express a therapeutic effect against different types of cancer such as: hepatocellular carcinoma (113), colorectal neoplasms (67), uterine cervical neoplasms (10), and adenomatous polyposis coli (4). This set of genes was further uploaded into the Gene Mania software, ToppGene Suite portal, and Reactome Knowledgebase, which confirmed that molecular functions, biological processes and pathways of SFN-affected genes were mostly related to oxidoreductase activity, regulation of immune system, and apoptosis. In conclusion, we may suggest that SFN interacts with host immunity to enhance the eradication of tumor cells mainly by inducing immune-response and stimulating apoptotic process of tumor cells. Moreover, its antioxidative activity could contribute to better anti-cancerogenic effects.

Identification of Biomarkers and Study of Mechanisms Related to Metastatic of Osteosarcoma Based on Integrated Bioinformatics Analyses

BackgroundOsteosarcoma (OS) is a serious threat to public health. Because of high morbidity and fairly complicated pathogenesis. The study aim to identify candidate biomarkers and research the molecular mechanisms correlated of patients with metastatic OS. MethodsThe GSE21257 was downloaded from Gene Expression Omnibus(GEO) database, and the differentially expressed RNAs (DERs) were identified and functional enriched analysis by statistical soft-ware in R. Subsequently, the co-expression modules and its clinical characteristics of OS were identified by weighted gene co-expression network analysis (WGCNA) Following, the KEGG pathways directly related to metastatic OS was to researched by the Comparative Toxicogenomics Database 2019 update (CTD). Finally, the “survival” package in R was used to survival analysis and the DERs were verified using another independent profiling GSE14827. ResultsA total of 1,464 DERs were classified including 702 up-regulated and 762 down-regulated. In addition, a total of 1248 DERs were obtained by WGCNA analysis, the blue modules is the highest negative correlation (P=0) and the turquoise modules is highest positive correlation (P=3E-196) among all correlations with OS metastatic. The lncRNA-mRNA co-expression network including 4 lncRNAs and 507 mRNAs, and the cytokine-cytokine receptor interaction and JAK-STAT signaling pathway were found significantly correlation with metastatic. Finally, the increased expression levels of IFNGR1, lower DLEU1 and DLEU2 related to better prognosis. Which were significantly consistent in the another independent profiling GSE14827. ConclusionsA bioinformatics analysis related to the IFNGR1, DLEU1 and DLEU2 may as candidate biomarkers for metastatic OS.