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Identification of potential therapeutic targets and mechanisms of COVID-19 through network analysis and screening of chemicals and herbal ingredients.

ABSTRACT: After experiencing the COVID-19 pandemic, it is widely acknowledged that a rapid drug repurposing method is highly needed. A series of useful drug repurposing tools have been developed based on data-driven modeling and network pharmacology. Based on the disease module, we identified several hub proteins that play important roles in the onset and development of the COVID-19, which are potential targets for repositioning approved drugs. Moreover, different network distance metrics were applied to quantify the relationship between drug targets and COVID-19 disease targets in the protein-protein-interaction (PPI) network and predict COVID-19 therapeutic effects of bioactive herbal ingredients and chemicals. Furthermore, the tentative mechanisms of candidates were illustrated through molecular docking and gene enrichment analysis. We obtained 15 chemical and 15 herbal ingredient candidates and found that different drugs may play different roles in the process of virus invasion and the onset and development of the COVID-19 disease. Given pandemic outbreaks, our method has an undeniable immense advantage in the feasibility analysis of drug repurposing or drug screening, especially in the analysis of herbal ingredients.

SUBMITTER: Wang H

PROVIDER: S-EPMC8499921 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature

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Identification of potential therapeutic targets and mechanisms of COVID-19 through network analysis and screening of chemicals and herbal ingredients.

Wang Hong H Zhang Jingqing J Lu Zhigang Z Dai Weina W Ma Chuanjiang C Xiang Yun Y Zhang Yonghong Y

Briefings in bioinformatics 20220101 1

After experiencing the COVID-19 pandemic, it is widely acknowledged that a rapid drug repurposing method is highly needed. A series of useful drug repurposing tools have been developed based on data-driven modeling and network pharmacology. Based on the disease module, we identified several hub proteins that play important roles in the onset and development of the COVID-19, which are potential targets for repositioning approved drugs. Moreover, different network distance metrics were applied to ...[more]

PMID: 34505138

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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.

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.

Dataset Information

Identification of potential therapeutic targets and mechanisms of COVID-19 through network analysis and screening of chemicals and herbal ingredients.

Publications

Identification of potential therapeutic targets and mechanisms of COVID-19 through network analysis and screening of chemicals and herbal ingredients.

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