摘要
蒙古黄芪(Astragalus mongholicus Bunge)为多年生草本植物,是中药黄芪的源植物之一,其种子在常规贮藏条件下活力丧失较快,不利于黄芪产业的发展.为探究蒙古黄芪种子老化过程中代谢物质的变化,揭示其种子老化机理,本研究采用人工老化方法得到四种不同活力水平的种子,再利用非靶向代谢组学技术检测不同活力水平蒙古黄芪种子的差异代谢物质.本研究共检测出代谢物质数量为10 346个,其中正离子5 946个,负离子4 400个,包括生物碱及其衍生物、苯甲酸酯类、木质素及其化合物、脂类及类脂分子、核苷及核苷酸类似物、有机酸及其衍生物、有机氮化合物、有机氧化合物、有机杂环化合物、有机硫化合物、种苯丙烷和聚酮化合物、未分类物质等.通过KEGG数据库分析,筛选出与蒙古黄芪种子老化密切相关的5个代谢物质(5,10-亚甲基四氢叶酸,二氢叶酸,水苏糖,棉子糖,鞘脂),涉及3个代谢通路(叶酸介导的一碳代谢,半乳糖代谢,鞘脂代谢),这些代谢物质和代谢通路可为种子老化机理研究提供参考.
Abstract
Astragalus mongholicus Bunge is a perennial herb,one of the source plants of traditional Chinese medicine Radix Astragali.Its seeds lose vigor quickly under conventional storage conditions,which is not benefi-cial to the development of Astragalus industry.In order to explore the changes of metabolites in the aging process of Astragalus mongolicus seeds and reveal the aging mechanism of seeds,this study used artificial aging method to obtain four kinds of seeds with different vigor levels,and then used non-targeted metabolomics technology to de-tect the different metabolites of Astragalus mongolicus seeds with different vigor levels.A total of 10 346 metabo-lites were detected,including 5 946 positive ions and 4 400 negative ions.Including alkaloids and their derivatives,benzoates,lignin and their compounds,lipids and lipid molecules,nucleosides and nucleotide analogues,organic acids and their derivatives,organic nitrogen compounds,organic oxygen compounds,organic heterocyclic com-pounds,organic sulfur compounds,phenylpropanes and polyketones compounds,unclassified substances,etc.Five metabolites(5,10-methylene-THF,dihydrofolic acid,stachyose,raffinose,GlcCer(d18:1/24:1(15Z)),which were closely related to the aging of Astragalus mongolicus seeds,were screened out by KEGG database analysis.Three metabolic pathways(Folate-mediated one-carbon metabolism,galactose metabolism and sphingolipid metabolism)were involved.These metabolites and metabolic pathways could provide reference for the study of seed aging mechanism.
基金项目
吉林省科技发展计划(20180201034YY)
中国农科院科技创新工程项目(CAAS-ASTIP-2016-ISAPS)
NETL
NSTL
万方数据