类黄酮生物合成
类黄酮
生物
转录组
生物化学
代谢组学
代谢途径
转录因子
WRKY蛋白质结构域
次生代谢
木犀草素
生物合成
基因
基因表达
抗氧化剂
生物信息学
作者
Yihan Yue,Sijia Ma,J. Niu,Yan Ren,Xiaochun Zhao,Qiaofang Shi,Yi‐He Yu
摘要
ABSTRACT As plants with medicinal and edible properties, the response mechanism to low temperature (LT) stress of plants in the genus Physalis remains unclear. To explore the molecular mechanisms in Physalis grisea under LT, transcriptomic, metabolomic, and physiological analyses were carried out. LT induced the accumulation of malondialdehyde and proline, and enhanced antioxidant enzyme activity, as evidenced by deeper NBT and DAB staining. Differentially upregulated genes were enriched in pathways including secondary metabolism and transcription factor regulation. The black module enriched in flavonoid biosynthesis and phenylalanine metabolism was further screened out through weighted gene co‐expression network analysis (WGCNA). The co‐expression network revealed the relationships of key structural genes related to flavonoid synthesis and transcription factors (TFs). To elucidate the association between treatment duration and flavonoid metabolism, total flavonoid content was measured and found to exhibit a significant positive correlation with treatment time. Based on the 45 differentially accumulated flavonoid metabolites (DAFMs) identified using High‐Performance Liquid Chromatography, four kinds of shared DAFMs (luteolin, quercetin, apigenin, and dihydrokaempferol) exhibited continuous increases throughout the treatment period. Based on the metabolic pathway map and correlation network analysis of flavonoid structural genes and DAFMs, 12 structural genes were found to be involved in regulating the biosynthesis of these DAFMs. Reverse transcription quantitative PCR verified the expression patterns of structural genes and potential upstream TFs, which highlight the critical regulatory role of flavonoids in Physalis grisea LT adaptation. This study established a fundamental framework for understanding the mechanism of LT response and flavonoid biosynthesis in Physalis .
科研通智能强力驱动
Strongly Powered by AbleSci AI