生物
转录组
谷胱甘肽
植物螯合素
石斛
基因
抗氧化剂
植物
生物化学
基因表达
酶
作者
Yuanyuan Liu,Erya Xu,Yijun Fan,Linlong Xu,Jie Ma,Xuebing Li,Hui Wang,Siyu He,Ting Li,Qin Yao-guo,Jingtao Xiao,Aoxue Luo
出处
期刊:Plant Science
[Elsevier]
日期:2024-01-15
卷期号:340: 111988-111988
标识
DOI:10.1016/j.plantsci.2024.111988
摘要
In this study, we investigated the tolerance and accumulation capacity of Dendrobium denneanum Kerr (D.denneanum) by analyzing the growth and physiological changes of D.denneanum under different levels of Zn treatments, and further transcriptome sequencing of D.denneanum leaves to screen and analyze the differentially expressed genes. The results showed that Zn400 treatment (400 mg·kg-1) promoted the growth of D.denneanum while both Zn800 (800 mg·kg-1) and Zn1600 treatment (1600 mg·kg-1) caused stress to D.denneanum. Under Zn800 treatment (800 mg·kg-1), the resistance contribution of physiological indexes was the most obvious: antioxidant system, photosynthetic pigment, osmoregulation, phytochelatins, and ASA-GSH cycle (Ascorbic acid-Glutathione cycle). D.denneanum leaves stored the most Zn, followed by stems and roots. The BCF(Bioconcentration Factor) of the D.denneanum for Zn were all more than 1.0 under different Zn treatments, with the largest BCF (1.73) for Zn400. The transcriptome revealed that there were 1500 differentially expressed genes between Zn800 treatment and group CK, of which 842 genes were up-regulated and 658 genes were down-regulated. The genes such as C4H, PAL, JAZ, MYC2, PP2A, GS, and GST were significantly induced under the Zn treatments. The differentially expressed genes were associated with phenylpropane biosynthesis, phytohormone signaling, and glutathione metabolism. There were three main pathways of response to Zn stress in Dendrobium: antioxidant action, compartmentalization, and cellular chelation. This study provides new insights into the response mechanisms of D.denneanum to Zn stress and helps to evaluate the phytoremediation potential of D.denneanum in Zn-contaminated soils.
科研通智能强力驱动
Strongly Powered by AbleSci AI