小青龙汤治疗咳喘的有效成分预测

目的：通过一种基于通路模式的方法，对传统中药名方小青龙汤治疗咳喘的有效成分进行预测。方法：首先搜索TCM中医药资料库与CTD数据库来整理出小青龙汤已知的各种化合物成分与相关基因，再通过DAVID生物分析系统与KEGG数据库挖掘出与咳喘相关的通路模式。然后利用该通路模式对候选基因打分，筛选出相关性高的基因，并据此推测出治疗咳喘的有效成分。结果：根据上述的计算方式，我们将相关基因根据得分进行排序，得到相关性最好的三种基因分别为MAPK1、MAPK3与RELA。最终预测出的有效成分中效能最高的三种分别是Paeonol（丹皮酚）、Glycyrol（甘草醇）、Geraniin（老鹳草素）。结论：此种基于通路模式的方法可以较好的预测出小青龙汤治疗咳喘的有效成分，加以推广更有利于中药基础药理研究的标准化。

Bioinformatics Prediction of Active Components in Xiao-Qing-Long Decoction against Symptoms of Cough and Dyspnea

This study was aimed to identify active components from traditional Chinese medicine (TCM) formula Xiao- Qing- Long (XQL) decoction against cough and dyspnea through a pathway pattern- based method. All compounds contained in candidate herbs and associated genes of XQL decoction were collected from TCM Database and CTD database. And then, the pathway pattern of cough and dyspnea was revealed using the DAVID Functional Annotation Bioinformatics Microarray Analysis and KEGG database. The extracted pathway pattern was used to calculate gene scores, which was employed to reflect the importance of genes to the disease. The effectiveness of compounds was calculated through matching associated genes of compounds to the disease associated gene target list. The results showed that based on analysis mentioned above, the top three ranked genes closely related to the disease were MAPK1, MAPK3 and RELA. The compounds with top three efficacy indices were paeonol, glycyrol and geraniin. It was concluded that the pathway pattern-based method can better predict active components from XQL decoction, which may be effective as a small molecule drug against the symptom of cough and dyspnea. The data mining method would be beneficial for standardization and modernization of TCM.