scholarly journals Exploring the mechanism of Yixinyin for myocardial infarction by weighted co-expression network and molecular docking

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mengqi Huo ◽  
Lina Ma ◽  
Guoguo Liu

AbstractYixinyin, the traditional Chinese medicine, has the effects of replenishing righteous qi, and promoting blood circulation to eliminate blood stagnation. It is often used to treat patients with acute myocardial infarction (MI). The purpose of our study is to explore the key components and targets of Yixinyin in the treatment of MI. In this study, we analyzed gene expression data and clinical information from 248 samples of MI patients with the GSE34198, GSE29111 and GSE66360 data sets. By constructing a weighted gene co-expression network, gene modules related to myocardial infarction are obtained. These modules can be mapped in Yixinyin PPI network. By integrating differential genes of healthy/MI and unstable angina/MI, key targets of Yixinyin for the treatment of myocardial infarction were screened. We validated the key objectives with external data sets. GSEA analysis is used to identify the biological processes involved in key targets. Through molecular docking screening, active components that can combine with key targets in Yixinyin were obtained. In the treatment of myocardial infarction, we have obtained key targets of Yixinyin, which are ALDH2, C5AR1, FOS, IL1B, TLR2, TXNRD1. External data sets prove that they behave differently in the healthy and MI (P < 0.05). GSEA enrichment analysis revealed that they are mainly involved in pathways associated with myocardial infarction, such as viral myocarditis, VEGF signaling pathway and type I diabetes mellitus. The docking results showed that the components that can be combined with key targets in YixinYin are Supraene, Prostaglandin B1, isomucronulatol-7,2′-di-O-glucosiole, angusifolin B, Linolenic acid ethyl ester, and Mandenol. For that matter, they may be active ingredients of Yixinyin in treating MI. These findings provide a basis for the preliminary research of myocardial infarction therapy in traditional Chinese medicine and provide ideas for the design of related drugs.

2021 ◽  
Author(s):  
Daqiu Chen ◽  
Yanqing Wu ◽  
Yixing Chen ◽  
Qiaoxing Chen ◽  
Xianhua Ye ◽  
...  

Background: Suxiao Xintong dropping pills (SXXTDP), a traditional Chinese medicine, is widely applied for treating myocardial infarction (MI). However, its therapy mechanisms are still unclear. Therefore, this research is designed to explore the molecular mechanisms of SXXTDP in treating MI. Methods: The active ingredients of SXXTDP and their corresponding genes of the active ingredients were retrieved from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database. MI-related genes were identified via analyzing the expression profiling data (accession number: GSE97320). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed to study the shared genes of drug and disease. Through protein-protein interaction (PPI) network and the Cytoscape plugin cytoHubba, the hub genes were screened out. The compounds and hub targets binding were simulated through molecular docking method. Results: We obtained 21 active compounds and 253 corresponding target genes from TCMSP database. 1833 MI-related genes were identified according to P<0.05 and |log2FC| ≥ 0.5. 27 overlapping genes between drug and disease were acquired. GO analysis indicated that overlapping genes were mainly enriched in MAP kinase activity and antioxidant activity. KEGG analysis indicated that overlapping genes were mainly enriched in IL-17 signaling pathway and TNF signaling pathway. We obtained 10 hub genes via cytoHubba plugin. Six of the 10 hub genes, including PTGS2, MAPK14, MMP9, MAPK1, NFKBIA, and CASP8, were acted on molecular docking verification with their corresponding compounds of SXXTDP. Conclusion: SXXTDP may exert cardioprotection effect through regulating multiple targets and multiple pathways in MI.


1999 ◽  
Vol 1 (4) ◽  
pp. 313-323 ◽  
Author(s):  
Boris P. Kovatchev ◽  
Leon S. Farhy ◽  
Daniel J. Cox ◽  
Martin Straume ◽  
Vladimir I. Yankov ◽  
...  

A dynamical network model of insulin-glucose interactions in subjects with Type I Diabetes was developed and applied to data sets for 40 subjects. Each data set contained the amount of dextrose + insulin infused and blood glucose (BG) determinations, sampled every 5 minutes during a one-hour standardized euglycemic hyperinsulinemic clamp and a subsequent one-hour BG reduction to moderate hypoglycemic levels. The model approximated the temporal pattern of BG and on that basis predicted the counterregulatory response of each subject. The nonlinear fits explained more than 95% of the variance of subjects' BG fluctuations, with a median coefficient of determination 97.7%. For all subjects the model-predicted counterregulatory responses correlated with measured plasma epinephrine concentrations. The observed nadirs of BG during the tests correlated negatively with the model-predicted insulin utilization coefficient (r = -0.51,p< 0.001) and counterregulation rates (r= -0.63,p< 0.001). Subjects with a history of multiple severe hypoglycemic episodes demonstrated slower onset of counterregulation compared to subjects with no such history (p< 0.03).


BMJ Open ◽  
2019 ◽  
Vol 9 (12) ◽  
pp. e032256 ◽  
Author(s):  
Ruijin Qiu ◽  
Changming Zhong ◽  
Songjie Han ◽  
Tianmai He ◽  
Ya Huang ◽  
...  

IntroductionMyocardial infarction (MI) is the most dangerous complication in patients with coronary heart disease. In China, there is an increasing number of randomised controlled trials (RCTs) of traditional Chinese medicine (TCM) for treating MI. However, the inconsistency of outcome reporting means that a large number of clinical trials cannot be included in systematic reviews to provide the best evidence for clinical practice. The aim of this study is to develop a core outcome set (COS) for future TCM clinical trials of MI, which may improve the consistency of outcome reporting and facilitate the synthesis of data across studies in systematic reviews.Methods and analysisWe will conduct a systematic review of MI clinical trials with any intervention. Semistructured interviews will be conducted to obtain the perspectives of patients with MI. The outcomes from the systematic review and semistructured interviews will be grouped and used to develop a questionnaire. The questionnaire will be developed as a supplement for the TCM syndromes of MI and will be constructed from the results of a systematic review, existing medical records and a cross-sectional study. Then two rounds of the Delphi survey will be conducted with different stakeholders (TCM experts and Western medicine experts in cardiovascular disease, methodologists, magazine editors and patients) to determine the importance of the outcomes. Only the TCM experts will need to response to the questionnaire for core TCM syndromes. A face-to-face consensus meeting will be conducted to create a final COS and recommend measurement time for each outcome.Ethics and disseminationThis project has been approved by the Ethics Committee of Dongzhimen Hospital, Beijing University of Chinese Medicine. The final COS will be published and freely available.Trial registration numberThis study is registered with the Core Outcome Measures in Effectiveness Trials database as study 1243 (available at:http://www.comet-initiative.org/studies/details/1243).


2013 ◽  
Vol 756-759 ◽  
pp. 2868-2872
Author(s):  
Wei Ye Tao ◽  
Lai You Wang ◽  
Guo Hua Cheng ◽  
Jun Liu ◽  
Lang Ping Tang

Sini Decoction is a traditional Chinese medicine which has a curative effect. The mode of action between small molecules and the targets were presented visually, which provided an in-depth interpretation about the pharmacodynamic material basis. It is valuable for the research and development of new drugs. Experimental results show that we can reveal the treatment mechanism of Sini Decoction in molecular level by molecular docking.


2018 ◽  
Vol 17 (1) ◽  
pp. 41
Author(s):  
Ya-Ping Yan ◽  
Xiu-Qin Zhang ◽  
Xiu-Kui Wang ◽  
Lin-Xia Li ◽  
Jian-jun Ma

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