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Development of simultaneous interaction prediction approach (SiPA) for the expansion of interaction network of traditional Chinese medicine.

ABSTRACT: Background:Due to the lack of enough interaction data among compositions, targets and diseases, it is difficult to construct a complete network of Traditional Chinese Medicine (TCM) that comprehensively reflects active compositions and their synergistic network in terms of specific diseases. Therefore, mapping of the full spectrum of interaction between compounds and their targets is of central importance when we use network pharmacology approach to explore the therapeutic potential of the TCM. Methods:To address this challenge, we developed a large-scale simultaneous interaction prediction approach (SiPA) integrated one interaction network based simple inference model (SIM), focusing on 'logical relevance' between compounds, proteins or diseases, and another compound-target correlation space based interaction prediction model (CTCS-IPM) that was built on the basis of the canonical correlation analysis (CCA) to estimate the position of compounds (or targets) in compound-protein correlated space. Then SiPA was applied to discover reliable multiple interactions for interaction network expansion of a TCM, compound Salvia miltiorrhiza. By means of network analysis, potential active compounds and their related network synergy underlying cardiovascular diseases were evaluated between expanded and original interaction networks. Part of new interactions were validated with existing experimental evidence and molecular docking. Results:As evaluated with known test dataset, the established combination approach was proved to make highly accurate prediction, showing a well prediction performance for the SIM and a high recall rate of 85.2% for the CTCS-IPM. Then 710 pairs of new compound-target interactions, 24 pairs of new compound-cardiovascular disease interactions and 294 pairs of new cardiovascular disease-protein interactions were predicted for compound Salvia miltiorrhiza. Results of network analysis suggested the network expansion could dramatically improve the completeness and effectiveness of the network. Validation results of literature and molecular docking manifested that inferred interactions had good reliability. Conclusions:We provided a practical and efficient way for large-scale inference of multiple interactions of TCM ingredients, which was not limited by the lack of negative samples, sample size and target 3D structures. SiPA could help researchers more accurately prioritize the effective compounds and more completely explore network synergy of TCM for treating specific diseases, indicating a potential way for effectively identifying candidate compound (or target) in drug discovery.

SUBMITTER: Rui M

PROVIDER: S-EPMC7448979 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Development of simultaneous interaction prediction approach (SiPA) for the expansion of interaction network of traditional Chinese medicine.

Rui Mengjie M Pang Hui H Ji Wei W Wang Siqi S Yu Xuefei X Wang Lilong L Feng Chunlai C

Chinese medicine 20200826

<h4>Background</h4>Due to the lack of enough interaction data among compositions, targets and diseases, it is difficult to construct a complete network of Traditional Chinese Medicine (TCM) that comprehensively reflects active compositions and their synergistic network in terms of specific diseases. Therefore, mapping of the full spectrum of interaction between compounds and their targets is of central importance when we use network pharmacology approach to explore the therapeutic potential of t ...[more]

PMID: 32863859

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Project description:Osteoporosis is a widespread bone disease that affects million cases annually. The underlying mechanisms behind the progress of osteoporosis remain enigmatic, which limits detections of biomarkers and therapeutic targets. Hence, this study was aimed at exploring hub molecules to better understand the mechanism of osteoporosis development and discover the traditional Chinese medicine potential drugs for osteoporosis. miRNA and gene expression profiles were downloaded from Gene Expression Omnibus (GEO). Weighted correlation network analysis (WGCNA) was used to identify the key modules for osteoporosis. DIANA Tools was applied to perform pathway enrichment. A miRNA-gene interaction network was constructed, and hub miRNAs and genes were distinguished using Cytoscape software. Receiver operating characteristic (ROC) curves of hub miRNAs and genes were plotted, and correlations with hub genes and osteoporosis-associated factors were evaluated. Potential drugs for osteoporosis in Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) were screened, and molecular docking models between these drugs and target genes were showed by AutoDock tools. Two hub modules, 1 miRNA module and 1 gene module, were identified to be the most strongly correlated with osteoporosis by using WGCNA. Then, 3 KEGG pathways including focal adhesion, PI3K-Akt signaling pathway, and gap junction were shared pathways enriched with the miRNAs and genes screened out by WGCNA and differential expression analyses. Finally, after constructing a miRNA-gene interaction network, 6 hub miRNAs (hsa-miR-18b-3p, hsa-miR-361-3p, hsa-miR-484, hsa-miR-519e-5p, hsa-miR-940, and hsa-miR-1275) and 6 hub genes (THBS1, IFNAR2, ARHGAP5, TUBB2B, FLNC, and NTF3) were detected. ROC curves showed good performances of miRNAs and genes for osteoporosis. Correlations with hub genes and osteoporosis-associated factors suggested implicational roles of them for osteoporosis. Based on these hub genes, 3 natural compounds (kainic acid, uridine, and quercetin), which were the active ingredients of 192 herbs, were screened out, and a target-compound-herb network was extracted using TCMSP. Molecular docking models of kainic acid-NTF3, uridine-IFNAR2, and quercetin-THBS1 were exhibited with AutoDock tools. Our study sheds light on the pathogenesis of osteoporosis and provides promising therapeutic targets and traditional Chinese medicine drugs for osteoporosis.

Project description:Background:Traditional Chinese Patent Medicine (TCPM) is now being used more and more extensively for primary hypertension in China. However, the comparative efficacy and safety of it need more clarified evidence. Thus, we conducted a Bayesian network meta-analysis to compare TCPMs with other interventions. Methods:We searched China National Knowledge Infrastructure (CNKI), WanFang Data, PubMed, Embase, and Cochrane Library from inception to April 2019 for randomized controlled trials (RCTs) with diagnosis of primary hypertension that compared the efficacy of TCPMs with antihypertension drugs (ADs). Two researchers screened literature, extracted data, and evaluated risk of bias independently. The primary outcomes were systolic blood pressure (SBP) and diastolic blood pressure (DBP). The secondary outcomes were adverse effects (AEs), total cholesterol (TC), and triglyceride (TG). We used the Bayesian network meta-analysis to compare interventions and described the categorical variable and the continuous variable as odds ratio (OR) and mean difference (MD), respectively. Besides, we ranked all interventions via the Surface Under the Cumulative Ranking (SUCRA) values and conducted metaregression with nine covariates as additional analysis. Results:We included 192 studies with 23366 patients diagnosed as primary hypertension in total. For SBP reduction, eighteen interventions were significantly better than AD. Among them, Yinxingye (YXY) + AD (MD = -12, 95% CrI [-16, -8.5]) was superior to others in the rank plot with SUCRA 0.91. For DBP reduction, sixteen interventions were significantly better than AD. Among them, Qinggan Jiangya (QGJY) + AD (MD = -8.7, 95% CrI [-12, -5.5]) and Qiju Dihuang (QJDH) + AD (MD = -8.8, 95% CrI [-12, -5.2]) were superior to others in the rank plot with SUCRA 0.89. To summarize the SUCRA values, we found that QGJY + AD and YXY + AD had the most significant reductions for both SBP and DBP. YXY + AD was the best one for both TC (MD = -1.3, 95% CrI [-1.9, -0.64]) and TG (MD = -0.52, 95% CrI [-0.92, -0.11]) reductions. Considering adverse effects, we found two interventions had significant differences comparing with AD. Among them, YXY + AD was the best one with SUCRA of 0.01. Conclusion:In all TCPMs, QGJY + AD and YXY + AD may be the best options for hypertension. Meanwhile, YXY + AD can improve blood lipids in patients with hypertension. However, due to the vague reports of adverse effects and other limitations, more evidence, especially that provided by high-quality studies, is needed to prove the advantages of TCMPs.

Dataset Information

Development of simultaneous interaction prediction approach (SiPA) for the expansion of interaction network of traditional Chinese medicine.

Publications

Development of simultaneous interaction prediction approach (SiPA) for the expansion of interaction network of traditional Chinese medicine.

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