穗
狐尾
狗尾草
生物
基因
基因表达
食品科学
植物
生物化学
作者
Siyu Hou,Xiaxia Man,Boying Lian,Guifang Ma,Zhaoxia Sun,Lida Han,Lufei Yan,Hao Gao,Wei Du,Xinfang Wang,Yijuan Zhang,Hongying Li,Yuanhuai Han
摘要
Abstract BACKGROUND Foxtail millet grain has higher folate content than other cereal crops. However, the folate metabolite content and the expression patterns of folate metabolite‐related genes are unknown. RESULTS Liquid chromatography–mass spectrometry was used to investigate 12 folate metabolites in a foxtail millet panicle. The content of total folate and derivatives gradually decreased during panicle development. Polyglutamate 5‐formyl‐tetrahydrofolate was the major form. Twenty‐eight genes involved in the folate metabolic pathway were identified through bioinformatic analysis. These genes in Setaria italica , S. viridis and Zea mays showed genomic collinearity. Phylogenetic analysis revealed that the folate‐related genes were closely related among the C4 plants compared to C3 plants. The gene expressions were then studied at three panicle development stages. The gene expression patterns were classified into two groups, namely SiADCL1 and SiGGH as two key enzymes, which are responsible for folate synthesis and degradation; their expression levels were highest at the early panicle development stage, up to 179.11‐ and 163.88‐fold, respectively. Their expression levels had a similar downward trend during panicle development and were significantly positively correlated with the concentration of total folate and folate derivatives. However, SiSHMT3 expression levels were significantly negatively correlated with total folate concentration. CONCLUSION Besides being the major determinants of folate and folate derivatives accumulation, SiADCL1 and SiGGH expression levels are key limiting factors in the foxtail millet panicle. Therefore, SiADCL1 and SiGGH expression levels can be targeted in genetic modification studies to improve folate content in foxtail millet seeds in the future. © 2021 Society of Chemical Industry
科研通智能强力驱动
Strongly Powered by AbleSci AI