Identification of potential therapeutic targets and mechanisms of COVID-19 through network analysis and screening of chemicals and herbal ingredients.
Ontology highlight
ABSTRACT:
SUBMITTER: Wang H
PROVIDER: S-EPMC8499921 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:The information of protein targets and small molecule has been highly valued by biomedical and pharmaceutical research. Several protein target databases are available online for FDA-approved drugs as well as the promising precursors that have largely facilitated the mechanistic study and subsequent research for drug discovery. However, those related resources regarding to herbal active ingredients, although being unusually valued as a precious resource for new drug development, is rarely found. In this article, a comprehensive and fully curated database for Herb Ingredients' Targets (HIT, http://lifecenter.sgst.cn/hit/) has been constructed to complement above resources. Those herbal ingredients with protein target information were carefully curated. The molecular target information involves those proteins being directly/indirectly activated/inhibited, protein binders and enzymes whose substrates or products are those compounds. Those up/down regulated genes are also included under the treatment of individual ingredients. In addition, the experimental condition, observed bioactivity and various references are provided as well for user's reference. Derived from more than 3250 literatures, it currently contains 5208 entries about 1301 known protein targets (221 of them are described as direct targets) affected by 586 herbal compounds from more than 1300 reputable Chinese herbs, overlapping with 280 therapeutic targets from Therapeutic Targets Database (TTD), and 445 protein targets from DrugBank corresponding to 1488 drug agents. The database can be queried via keyword search or similarity search. Crosslinks have been made to TTD, DrugBank, KEGG, PDB, Uniprot, Pfam, NCBI, TCM-ID and other databases.
| S-EPMC3013727 | biostudies-literature
Project description:BackgroundThe identification of chemical-target interaction is key to pharmaceutical research and development, but the unclear materials basis and complex mechanisms of traditional medicine (TM) make it difficult, especially for low-content chemicals which are hard to test in experiments. In this research, we aim to apply the node2vec algorithm in the context of drug-herb interactions for expanding potential targets and taking advantage of molecular docking and experiments for verification.MethodsRegarding the widely reported risks between cardiovascular drugs and herbs, Salvia miltiorrhiza (Danshen, DS) and Ligusticum chuanxiong (Chuanxiong, CX), which are widely used in the treatment of cardiovascular disease (CVD), and approved drugs for CVD form the new dataset as an example. Three data groups DS-drug, CX-drug, and DS-CX-drug were applied to serve as the context of drug-herb interactions for link prediction. Three types of datasets were set under three groups, containing information from chemical-target connection (CTC), chemical-chemical connection (CCC) and protein-protein interaction (PPI) in increasing steps. Five algorithms, including node2vec, were applied as comparisons. Molecular docking and pharmacological experiments were used for verification.ResultsNode2vec represented the best performance with average AUROC and AP values of 0.91 on the datasets "CTC, CCC, PPI". Targets of 32 herbal chemicals were identified within 43 predicted edges of herbal chemicals and drug targets. Among them, 11 potential chemical-drug target interactions showed better binding affinity by molecular docking. Further pharmacological experiments indicated caffeic acid increased the thermal stability of the protein GGT1 and ligustilide and low-content chemical neocryptotanshinone induced mRNA change of FGF2 and MTNR1A, respectively.ConclusionsThe analytical framework and methods established in the study provide an important reference for researchers in discovering herb-drug interactions, alerting clinical risks, and understanding complex mechanisms of TM.
| S-EPMC10233865 | biostudies-literature
Project description:Literature-described targets of herbal ingredients have been explored to facilitate the mechanistic study of herbs, as well as the new drug discovery. Though several databases provided similar information, the majority of them are limited to literatures before 2010 and need to be updated urgently. HIT 2.0 was here constructed as the latest curated dataset focusing on Herbal Ingredients' Targets covering PubMed literatures 2000-2020. Currently, HIT 2.0 hosts 10 031 compound-target activity pairs with quality indicators between 2208 targets and 1237 ingredients from more than 1250 reputable herbs. The molecular targets cover those genes/proteins being directly/indirectly activated/inhibited, protein binders, and enzymes substrates or products. Also included are those genes regulated under the treatment of individual ingredient. Crosslinks were made to databases of TTD, DrugBank, KEGG, PDB, UniProt, Pfam, NCBI, TCM-ID and others. More importantly, HIT enables automatic Target-mining and My-target curation from daily released PubMed literatures. Thus, users can retrieve and download the latest abstracts containing potential targets for interested compounds, even for those not yet covered in HIT. Further, users can log into 'My-target' system, to curate personal target-profiling on line based on retrieved abstracts. HIT can be accessible at http://hit2.badd-cao.net.
| S-EPMC8728248 | biostudies-literature
Project description:BackgroundThe pathogenesis of recurrent pelvic organ prolapse (POP) is currently unclear. Therefore, developing targeted preventive measures is difficult. This study identified potential key pathways, crucial genes, comorbidities, and therapeutic targets associated with the occurrence and development of recurrent POP.MethodsThe original microarray data GSE28660, GSE53868, and GSE12852 were downloaded from the GEO database. Identification and validation of differentially expressed genes (DEGs) and hub genes associated with recurrent POP were performed using R software and cytoHubba of Cytoscape. Protein-protein interaction (PPI) networks were constructed using the STRING tool and visualized using Cytoscape. Gene ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) enrichment analyses were effectively performed using DAVID platforms. In addition, the NetworkAnalyst platform was used to explore and visualize the miRNA-hub gene network, TF-hub gene network, hub gene-disease network, and hub gene-drug/chemical network.ResultsA total of 110 DEGs and 6 hub genes (ADIPOQ, IL6, PPARG, CEBPA, LPL, and LIPE) were identified in this study. These genes were primarily enriched in the PPAR, AMPK, and adipocytokine, non-alcoholic fatty liver disease, and signaling pathways related to glycerol metabolism. Moreover, 96 miRNAs and 97 TFs were identified to as being associated with recurrent POP. These genes were closely linked to adipocyte metabolism and distribution, energy metabolism, and the longevity regulatory pathway. In addition, 192 diseases or chronic complications were potentially related to the recurrence of POP, including diabetes, hypertension, obesity, inflammatory diseases, and chronic obstructive pulmonary disease. Furthermore, 954 drugs or compounds were shown to have therapeutic potential for recurrent POP, and the most critical target drugs were dexamethasone, bisphenol A, efavirenz, 1-methyl-3-isobutylxanthine, and estradiol.ConclusionsThe results of this study revealed that ADIPOQ, IL6, PPARG, CEBPA, LPL, and LIPE as potential hub genes associated with recurrent POP, and these hub genes may aid in the understanding of the mechanism underlying POP recurrence and the development of potential molecular drugs.
| S-EPMC10481308 | biostudies-literature
Project description:BackgroundOsteoporosis is characterized by low bone mass, deterioration of bone tissue structure, and susceptibility to fracture. New and more suitable therapeutic targets need to be discovered.MethodsWe collected osteoporosis-related datasets (GSE56815, GSE99624, and GSE63446). The methylation markers were obtained by differential analysis. Degree, DMNC, MCC, and MNC plug-ins were used to screen the important methylation markers in PPI network, then enrichment analysis was performed. ROC curve was used to evaluate the diagnostic effect of osteoporosis. In addition, we evaluated the difference in immune cell infiltration between osteoporotic patients and control by ssGSEA. Finally, differential miRNAs in osteoporosis were used to predict the regulators of key methylation markers.ResultsA total of 2351 differentially expressed genes and 5246 differentially methylated positions were obtained between osteoporotic patients and controls. We identified 19 methylation markers by PPI network. They were mainly involved in biological functions and signaling pathways such as apoptosis and immune inflammation. HIST1H3G, MAP3K5, NOP2, OXA1L, and ZFPM2 with higher AUC values were considered key methylation markers. There were significant differences in immune cell infiltration between osteoporotic patients and controls, especially dendritic cells and natural killer cells. The correlation between MAP3K5 and immune cells was high, and its differential expression was also validated by other two datasets. In addition, NOP2 was predicted to be regulated by differentially expressed hsa-miR-3130-5p.ConclusionOur efforts aim to provide new methylation markers as therapeutic targets for osteoporosis to better treat osteoporosis in the future.
| S-EPMC7969088 | biostudies-literature
Project description:Accumulating evidence indicates a central role for epigenetic modifications in the progression of stroke pathology. These epigenetic mechanisms are involved in complex and dynamic processes that modulate post-stroke gene expression, cellular injury response, motor function, and cognitive ability. Despite decades of research, stroke continues to be classified as a leading cause of death and disability worldwide with limited clinical interventions. Thus, technological advances in the field of epigenetics may provide innovative targets to develop new stroke therapies. This review presents the evidence on the impact of epigenomic readers, writers, and erasers in both ischemic and hemorrhagic stroke pathophysiology. We specifically explore the role of DNA methylation, DNA hydroxymethylation, histone modifications, and epigenomic regulation by long non-coding RNAs in modulating gene expression and functional outcome after stroke. Furthermore, we highlight promising pharmacological approaches and biomarkers in relation to epigenetics for translational therapeutic applications.
| S-EPMC9580166 | biostudies-literature
Project description:Immunotherapies like the adoptive transfer of gene-engineered T cells and immune checkpoint inhibitors are novel therapeutic modalities for advanced cancers. However, some patients are refractory or resistant to these therapies, and the mechanisms underlying tumor immune resistance have not been fully elucidated. Immunosuppressive cells such as myeloid-derived suppressive cells, tumor-associated macrophages, tumor-associated neutrophils, regulatory T cells (Tregs), and tumor-associated dendritic cells are critical factors correlated with immune resistance. In addition, cytokines and factors secreted by tumor cells or these immunosuppressive cells also mediate the tumor progression and immune escape of cancers. Thus, targeting these immunosuppressive cells and the related signals is the promising therapy to improve the efficacy of immunotherapies and reverse the immune resistance. However, even with certain success in preclinical studies or in some specific types of cancer, large perspectives are unknown for these immunosuppressive cells, and the related therapies have undesirable outcomes for clinical patients. In this review, we comprehensively summarized the phenotype, function, and potential therapeutic targets of these immunosuppressive cells in the tumor microenvironment.
| S-EPMC9118588 | biostudies-literature
Project description:BackgroundLiver fibrosis is a common result of the repair process of various chronic liver diseases. This study is a network pharmacology study on the potential therapeutic targets and biological mechanisms of Centella asiatica for liver fibrosis.MethodsThe chemical components and potential targets of Centella asiatica were screened through TCMSP, PubChem database, and Swiss Target Prediction database. The DisGeNET and GeneCards databases were used to obtain targets of HF. Venn diagrams were used to find key targets, and draw protein interaction maps. Cytoscape software was used to construct an interaction network map of drug-component-target-disease-pathway. The mechanisms of action were predicted through enrichment analysis and KEGG analysis.ResultsIn total, 6 main components, 297 drug targets, 337 HF targets, and 48 drug-disease targets were obtained in Centella asiatica. The key targets involved IL6, TNF, VEGFA, TP53, IL1β, MMP9, CXCL8, EGFR, JUN, SRC, MMP2, and TGF-β, among others. A total of 1293 entries were obtained by Gene Ontology (GO) enrichment analysis, which mainly involved the regulation of reactive oxygen species metabolic process, the regulation of smooth muscle cells, and the regulation of DNA-binding transcription factor activity. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment mainly screened 191 pathways, including the MAPK signaling pathway, the relaxin signaling pathway, and the Toll-like receptor signaling pathway, among others.ConclusionsCentella asiatica may have a therapeutic effect on HF through multiple targets and pathways. Its mechanism is mainly related to the MAPK signaling pathway and the relaxin signaling pathway.
| S-EPMC8263891 | biostudies-literature
Project description:ObjectivesThis study aims to analyze gene expression in lung tissue and lung fibroblasts of patients with systemic sclerosis-associated interstitial lung disease (SSc-ILD) to identify potential biomarkers and therapeutic targets and to examine its possible role in the pathogenesis of SSc-ILD.Patients and methodsWe obtained datasets from Gene Expression Omnibus (GEO) database, and used Robust Rank Aggregation to calculate the co-expressed differentially-expressed-genes (DEGs) in three chips, then analyzed the function, signaling pathways and the protein-protein interaction network of the DEGs. Finally, we verified the DEGs related to SSc-ILD by three databases of Comparative Toxicogenomics Database (CTD), GENE, and DisGeNET, respectively.ResultsThere were 16 co-expressed DEGs related to SSc-ILD in three GEO series, of which six genes were upregulated, and 10 genes were downregulated. The CTD included 29,936 genes related to SSc, and the GENE and DisGeNET databases had 429 genes related to SSc.ConclusionThe results of gene differential expression analysis suggest that interleukin-6, chemokine ligand 2, intercellular adhesion molecule 1, tumor necrosis factor alpha-induced protein 3, pentraxin 3, and cartilage oligomeric matrix protein may be implicated in the pathogenesis of SSc-ILD and are expected to be potential biomarkers and therapeutic targets for SSc-ILD.
| S-EPMC8957772 | biostudies-literature
Project description:Objective Mingmu Dihuang Pill (MMDHP) is a traditional Chinese formula that has shown remarkable improvements of dry eyes, tearing, and blurry vision; however, the mechanisms underlying MMDHP treatment for diabetic retinopathy have not been fully understood. This study is aimed at identifying the molecular targets and active ingredients of MMDHP for the treatment of diabetic retinopathy based on network pharmacology. Methods All active ingredients of MMDHP were retrieved from TCMSP and BATMAN-TCM databases, and the targets of active ingredients of MMDHP were predicted on the SwissTargetPrediction website. Diabetic retinopathy-related target sets were retrieved from GeneCards and OMIM databases, and the intersecting targets between targets of active ingredients of MMDHP and potential therapeutic targets of diabetic retinopathy were collected to generate the traditional Chinese medicine-ingredient-target-diabetic retinopathy network and to create the protein-protein interaction network. In addition, GO terms and KEGG pathway enrichment analyses were performed to identify the potential pathways, and molecular docking was employed to verify the binding of active ingredients of MMDHP to key targets of diabetic retinopathy. Results Network pharmacology predicted 183 active ingredients and 904 targets from MMDHP, and 203 targets were intersected with the therapeutic targets of diabetic retinopathy. The top 10 hub targets included PIK3RA, TP53, SRC, JUN, HRAS, AKT1, VEGFA, EGFR, ESR1, and PI3KCA. GO terms and KEGG pathway enrichment analyses identified AGE-RAGE, PI3K-AKT, and Rap1 signaling pathways as major pathways involved in MMDHP treatment for diabetic retinopathy. Molecular docking confirmed a good binding affinity of active ingredients of MMDHP, including luteolin, acacetin, naringenin, and alisol B, with AKT1, SRC, and VEGFA as the three key targets of diabetic retinopathy. Conclusion MMDHP may be effective for the treatment of diabetic retinopathy through active ingredients luteolin, acacetin, naringenin, and alisol B via AKT1, SRC, and VEGFA in AGE-RAGE, PI3K-AKT, and Rap1 signaling pathways.
| S-EPMC9715340 | biostudies-literature