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Showing 301 to 304 of 5186 entries
Published in 2022
Integrated Network Pharmacology and Comprehensive Bioinformatics Identifying the Mechanisms and Molecular Targets of Yizhiqingxin Formula for Treatment of Comorbidity With Alzheimer's Disease and Depression.
Abstract: Background: The Yizhiqinxin formula (YZQX) has been used to treat Alzheimer's disease (AD) or major depression disorder (MDD). However, its specific underlying mechanisms and therapeutic targets remain unclear. Methods: The ingredients and putative targets of YZQX were screened using the TCMSP and Drugbank databases. Next, the GEO database was used to retrieve relevant differentially expressed genes (DEGs) in AD or MDD and normal tissues. The PPI network was established, merged, and further screened to identify the main ingredients and core targets of YZQX against AD and MDD comorbidities. We performed enrichment analysis of core targets to identify biological processes and pathways. Finally, AutoDock software was used to validate the binding affinity between the crucial targets of direct action and their corresponding ingredients. Results: A total of 43 ingredients were identified from YZQX, of which 43 were screened to yield 504 targets. By establishing the PPI network, 92 targets were regarded as targets of YZQX against AD and MDD comorbidities in the core network. Promising targets (HSP90AA1, ESR1, AKT1, VCAM1, EGFR, CDK1, MAPK1, CDK2, MYC, HSPB1, and HSPA5) and signaling pathways (PI3K-Akt signaling pathway, ubiquitin-mediated proteolysis, MAPK signaling pathway, etc.) were filtered and refined to elucidate the underlying mechanism of YZQX against AD and MDD comorbidities. Molecular docking confirmed the ingredients of YZQX (quercetin and kaempferol) could bind well to multiple crucial targets. Conclusion: The ingredients of YZQX, such as quercetin and kaempferol, might treat AD and MDD comorbidities by acting on multiple targets and pathways.
Published in 2022
Network Pharmacology and Bioinformatics Methods Reveal the Mechanism of Berberine in the Treatment of Ischaemic Stroke.
Abstract: Aim: To elucidate the mechanism of action of berberine on ischaemic stroke based on network pharmacology, bioinformatics, and experimental verification. Methods: Berberine-related long noncoding RNAs (lncRNAs) were screened from public databases. Differentially expressed lncRNAs in ischaemic stroke were retrieved from the Gene Expression Omnibus (GEO) database. GSE102541 was comprehensively analysed using GEO2R. The correlation between lncRNAs and ischaemic stroke was evaluated by the mammalian noncoding RNA-disease repository (MNDR) database. The component-target-disease network and protein-protein interaction (PPI) network of berberine in the treatment of ischaemic stroke were constructed by using network pharmacology. We then performed gene ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) enrichment analyses. Finally, according to the molecular docking analysis and the binding probability between the lncRNA and key proteins, the effectiveness of the results was further verified by in vitro experiments. Results: After matching stroke-related lncRNAs with berberine-related lncRNAs, four genes were selected as potential targets of berberine in the treatment of ischaemic stroke. Subsequently, lncRNA H19 was identified as the potential crucial regulatory lncRNA of berberine. Here, 52 target proteins of berberine in the treatment of ischaemic stroke were identified through database mining. Through topological analysis, 20 key targets were identified which were enriched in inflammation, apoptosis, and immunity. Molecular docking results showed that MAPK8, JUN, and EGFR were central genes. Finally, in vitro experiments demonstrated that lncRNA H19, p-JNK1/JNK1, p-c-Jun/c-Jun, and EGFR expressions were significantly increased in hypoxia-treated SH-SY5Y cells and were restored by berberine treatment. Conclusion: The potential targets and biological effects of berberine in the treatment of ischaemic stroke were predicted in this study. The lncRNA H19/EGFR/JNK1/c-Jun signalling pathway may be a key mechanism of berberine-induced neuroprotection in ischaemic stroke.
Published in 2022
DTI-BERT: Identifying Drug-Target Interactions in Cellular Networking Based on BERT and Deep Learning Method.
Abstract: Drug-target interactions (DTIs) are regarded as an essential part of genomic drug discovery, and computational prediction of DTIs can accelerate to find the lead drug for the target, which can make up for the lack of time-consuming and expensive wet-lab techniques. Currently, many computational methods predict DTIs based on sequential composition or physicochemical properties of drug and target, but further efforts are needed to improve them. In this article, we proposed a new sequence-based method for accurately identifying DTIs. For target protein, we explore using pre-trained Bidirectional Encoder Representations from Transformers (BERT) to extract sequence features, which can provide unique and valuable pattern information. For drug molecules, Discrete Wavelet Transform (DWT) is employed to generate information from drug molecular fingerprints. Then we concatenate the feature vectors of the DTIs, and input them into a feature extraction module consisting of a batch-norm layer, rectified linear activation layer and linear layer, called BRL block and a Convolutional Neural Networks module to extract DTIs features further. Subsequently, a BRL block is used as the prediction engine. After optimizing the model based on contrastive loss and cross-entropy loss, it gave prediction accuracies of the target families of G Protein-coupled receptors, ion channels, enzymes, and nuclear receptors up to 90.1, 94.7, 94.9, and 89%, which indicated that the proposed method can outperform the existing predictors. To make it as convenient as possible for researchers, the web server for the new predictor is freely accessible at: https://bioinfo.jcu.edu.cn/dtibert or http://121.36.221.79/dtibert/. The proposed method may also be a potential option for other DITs.
Published in 2022
Evaluation of the Mechanism of Jiedu Huazhuo Quyu Formula in Treating Wilson's Disease-Associated Liver Fibrosis by Network Pharmacology Analysis and Molecular Dynamics Simulation.
Abstract: The Jiedu Huazhuo Quyu formula (JHQ) shows significant beneficial effects against liver fibrosis caused by Wilson's disease (WD). Hence, this study aimed to clarify the mechanisms of the JHQ treatment in WD-associated liver fibrosis. First, we collected 103 active compounds and 527 related targets of JHQ and 1187 targets related to WD-associated liver fibrosis from multiple databases. Next, 113 overlapping genes (OGEs) were obtained. Then, we built a protein-protein interaction (PPI) network with Cytoscape 3.7.2 software and performed the Gene Ontology (GO) term and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway enrichment analyses with GENE DENOVO online sites. Furthermore, module analysis was performed, and the core target genes in the JHQ treatment of WD-associated liver fibrosis were obtained. Pathway and functional enrichment analyses, molecular docking studies, molecular dynamic (MD) simulation, and Western blot (WB) were then performed. The results indicated that 8 key active compounds including quercetin, luteolin, and obacunone in JHQ might affect the 6 core proteins including CXCL8, MAPK1, and AKT1 and 107 related signaling pathways including EGFR tyrosine kinase inhibitor resistance, Kaposi sarcoma-associated herpesvirus infection, and human cytomegalovirus infection signaling pathways to exhibit curative effects on WD-associated liver fibrosis. Mechanistically, JHQ might inhibit liver inflammatory processes and vascular hyperplasia, regulate the cell cycle, and suppress both the activation and proliferation of hepatic stellate cells (HSCs). This study provides novel insights for researchers to systematically explore the mechanism of JHQ in treating WD-associated liver fibrosis.
Published in 2022
Molecular Targets and Mechanisms of Hedyotis diffusa-Scutellaria barbata Herb Pair for the Treatment of Colorectal Cancer Based on Network Pharmacology and Molecular Docking.
Abstract: Objective: Hedyotis diffusa-Scutellaria barbata herb pair (HS) has therapeutic effects on a variety of cancers, and this study aims to systematically explore the multiple mechanisms of HS in the treatment of colorectal cancer (CRC). Methods. The active ingredients of HS were obtained from TCMSP, and the potential targets related to these ingredients were screened from the STITCH, SuperPred, and Swiss TargetPrediction databases. Targets associated with CRC were retrieved by Drugbank, TTD, DisGeNET, and GeneCards. We used a Venn diagram to screen the intersection targets and used Cytoscape to construct the herb-ingredient-target-disease network, and the core targets were selected. The Go analysis and KEGG pathway annotation were performed by R language software. We used PyMol and Autodock Vina to achieve molecular docking of core ingredients and targets. Results: A total of 33 active ingredients were obtained from the HS, and 762 CRC-related targets were reserved from the four databases. We got 170 intersection targets to construct the network and found that the four ingredients with the most targets were quercetin, luteolin, baicalein, and dinatin, which were the core ingredients. The PPI analysis showed that the core targets were STAT3, TP53, MAPK3, AKT1, JUN, EGFR, MYC, VEGFA, EGF, and CTNNB1. Molecular docking results showed that these core ingredients had good binding potential with core targets, especially the docking of each component with MAPK obtained the lowest binding energy. HS acts simultaneously on various signaling pathways related to CRC, including the PI3K-Akt signaling pathway, proteoglycans in cancer, and the MAPK signaling pathway. Conclusions: This study systematically analyzed the active ingredients, core targets, and central mechanisms of HS in the treatment of CRC. It reveals the role of HS targeting PI3K-Akt signaling and MAPK signaling pathways in the treatment of CRC. We hope that our research could bring a new perspective to the therapy of CRC and find new anticancer drugs.
Published in 2022
Exploration of the Mechanism of Salvianolic Acid for Injection Against Ischemic Stroke: A Research Based on Computational Prediction and Experimental Validation.
Abstract: Ischemic stroke (IS) is an acute neurological injury that occurs when a vessel supplying blood to the brain is obstructed, which is a leading cause of death and disability. Salvia miltiorrhiza has been used in the treatment of cardiovascular and cerebrovascular diseases for over thousands of years due to its effect activating blood circulation and dissipating blood stasis. However, the herbal preparation is chemically complex and the diversity of potential targets makes difficult to determine its mechanism of action. To gain insight into its mechanism of action, we analyzed "Salvianolic acid for injection" (SAFI), a traditional Chinese herbal medicine with anti-IS effects, using computational systems pharmacology. The potential targets of SAFI, obtained from literature mining and database searches, were compared with IS-associated genes, giving 38 common genes that were related with pathways involved in inflammatory response. This suggests that SAFI might function as an anti-inflammatory agent. Two genes associated with inflammation (PTGS1 and PTGS2), which were inhibited by SAFI, were preliminarily validated in vitro. The results showed that SAFI inhibited PTGS1 and PTGS2 activity in a dose-dependent manner and inhibited the production of prostaglandin E2 induced by lipopolysaccharide in RAW264.7 macrophages and BV-2 microglia. This approach reveals the possible pharmacological mechanism of SAFI acting on IS, and also provides a feasible way to elucidate the mechanism of traditional Chinese medicine (TCM).
Published in 2022
Identification of an Autophagy-Related Signature Based on Whole Bone Marrow Sequencing for the Prognosis and Immune Microenvironment Characterization of Multiple Myeloma.
Abstract: Myeloma (MM) is a malignant plasma cell disorder, which is incurable owing to its drug resistance. Autophagy performs an integral function in homeostasis, survival, and drug resistance in multiple myeloma (MM). Therefore, the purpose of the present research was to identify potential autophagy-related genes (ARGs) in patients with MM. We downloaded the transcriptomic data (GSE136400) of patients with MM, as well as the corresponding clinical data from the Gene Expression Omnibus (GEO); the patients were classified at random into two groups in a ratio of 6: 4, with 212 samples in the training dataset and 142 samples in the test dataset. Both multivariate and univariate Cox regression analyses were performed to identify autophagy-related genes. The univariate Cox regression analysis demonstrated that 26 ARGs had a significant correlation with overall survival (OS). We constructed an autophagy-related risk prognostic model based on six ARGs: EIF2AK2 (ENSG00000055332), KIF5B (ENSG00000170759), MYC (ENSG00000136997), NRG2 (ENSG00000158458), PINK1 (ENSG00000158828), and VEGFA (ENSG00000112715) using LASSO-Cox regression analysis to predict risk outcomes, which revealed substantially shortened OS duration in the high-risk cohort in contrast with that in the low-risk cohort. Therefore, the ARG-based model significantly predicted the MM patients' prognoses and was verified in an internal test set. Differentially expressed genes were found to be predominantly enriched in pathways associated with inflammation and immune regulation. Immune infiltration of tumor cells resulted in the formation of a strong immunosuppressive microenvironment in high-risk patients. The potential therapeutic targets of ARGs were subsequently analyzed via protein-drug network analysis. Therefore, a prognostic model for MM was established via a comprehensive analysis of ARGs, through using the clinical models; we have further revealed the molecular landscape features of multiple myeloma.
Published in 2022
Pharmacological Mechanism of Shen Huang Chong Ji for Treating Alzheimer's Disease Based on Network Pharmacology and Experimental Validation.
Abstract: The traditional Chinese medicine (TCM) formula, Sheng Huang Chong Ji (SHCJ) is largely applied for treating Alzheimer's disease (AD), but not much is known regarding its active compounds, molecular targets, and mechanism of action. The current study aimed to predict the potential molecular mechanism of SHCJ against AD based on network pharmacology combined with in vitro validation. Using public databases, SHCJ's active compounds, their potential targets, and AD-related genes were screened, while Cytoscape Version 3.7.2 was used to build protein-protein interaction (PPI) and compound-disease-target (C-D-T) networks. Analysis of enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and Gene Ontology (GO) terms was then carried out in R 4.0.2, including associated packages. Subsequently, molecular docking analysis was performed with AutoDock Vina 1.1.2, with intro experiments involving SH-SY5Y cells used to further investigate the mechanism of SHCJ against AD. Finally, a total of 56 active compounds of SHCJ and 192 SHCJ-AD-related targets were identified. Quercetin was identified as the top potential candidate agent. HSP90AA1, AKT1, and MAPK1 represent potential therapeutic targets. The PI3K-Akt signaling pathway potentially represents a core one mediating the effects of SHCJ against AD. Additionally, molecular docking analysis indicated that quercetin could combine well with AKT1 and multiple apoptosis-related target genes. During cell experiments, a significant increase in cell viability along with a decrease in Abeta 25-35-induced apoptosis was observed after treatment with SHCJ. Furthermore, SHCJ significantly increased the phosphorylation of PI3K and Akt while reversing Abeta 25-35-induced apoptosis-related protein expression downregulation.
Published in 2022
Deciphering the Effects and Mechanisms of Yi-Fei-San-Jie-pill on Non-Small Cell Lung Cancer With Integrating Network Target Analysis and Experimental Validation.
Abstract: Non-small cell lung cancer (NSCLC), which accounts for 85% of lung cancer cases, calls for better therapy. Yi-Fei-San-Jie-pill (YFSJ), a well-applicated traditional Chinese medicine formula, was reported to be effective in the treatment of NSCLC. However, its anti-tumor mechanism still needs to be fully elucidated. Herein, a reliable preclinical orthotopic but not subcutaneous model of NSCLC in mice was established to evaluate the anti-cancer properties and further validate the mechanisms of YFSJ. A bioinformatic analysis was executed to identify the potential targets and key pathways of YFSJ on NSCLC. In detail, the anti-tumor effect of YFSJ and the autophagy inhibitor 3-MA was evaluated according to the tumor fluorescence value and comparison of different groups' survival times. As a result, YFSJ markedly decreased tumor size and prolonged survival time in contrast with those in the orthotopic model group (p < 0.05), and it also significantly regulated the protein expression levels of apoptosis- and autophagy-related proteins. In conclusion, this study provides convincing evidence that YFSJ could inhibit the growth of tumors and prolong the survival time of tumor-bearing mice based on the NSCLC orthotopic model, and its anti-tumor effect was closely associated with the promotion of apoptosis and interference of autophagy coupled with regulation of immune infiltration.
Published in 2022
Integrated bioinformatics and statistical approaches to explore molecular biomarkers for breast cancer diagnosis, prognosis and therapies.
Abstract: Integrated bioinformatics and statistical approaches are now playing the vital role in identifying potential molecular biomarkers more accurately in presence of huge number of alternatives for disease diagnosis, prognosis and therapies by reducing time and cost compared to the wet-lab based experimental procedures. Breast cancer (BC) is one of the leading causes of cancer related deaths for women worldwide. Several dry-lab and wet-lab based studies have identified different sets of molecular biomarkers for BC. But they did not compare their results to each other so much either computationally or experimentally. In this study, an attempt was made to propose a set of molecular biomarkers that might be more effective for BC diagnosis, prognosis and therapies, by using the integrated bioinformatics and statistical approaches. At first, we identified 190 differentially expressed genes (DEGs) between BC and control samples by using the statistical LIMMA approach. Then we identified 13 DEGs (AKR1C1, IRF9, OAS1, OAS3, SLCO2A1, NT5E, NQO1, ANGPT1, FN1, ATF6B, HPGD, BCL11A, and TP53INP1) as the key genes (KGs) by protein-protein interaction (PPI) network analysis. Then we investigated the pathogenetic processes of DEGs highlighting KGs by GO terms and KEGG pathway enrichment analysis. Moreover, we disclosed the transcriptional and post-transcriptional regulatory factors of KGs by their interaction network analysis with the transcription factors (TFs) and micro-RNAs. Both supervised and unsupervised learning's including multivariate survival analysis results confirmed the strong prognostic power of the proposed KGs. Finally, we suggested KGs-guided computationally more effective seven candidate drugs (NVP-BHG712, Nilotinib, GSK2126458, YM201636, TG-02, CX-5461, AP-24534) compared to other published drugs by cross-validation with the state-of-the-art alternatives top-ranked independent receptor proteins. Thus, our findings might be played a vital role in breast cancer diagnosis, prognosis and therapies.
Showing 301 to 304 of 5186 entries