Comparative transcriptomic and hormonal analyses reveal potential regulation networks of adventitious root formation in Metasequoia glyptostroboides Hu et Cheng.
IF 3.5 2区 生物学Q2 BIOTECHNOLOGY & APPLIED MICROBIOLOGY
Yuping Xiong, Xiaohong Chen, Junyu Liu, Yuan Li, Zhan Bian, Xinhua Zhang, Songjun Zeng, Jaime A Teixeira da Silva, Guohua Ma
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引用次数: 0
Abstract
Background: The extract from Metasequoia glyptostroboides Hu et Cheng, a rare and endangered species native to China, exhibits numerous biological and pharmacological activities. The species is recalcitrant to rooting during micropropagation, a challenge that has yet to be resolved. In this study, transcriptomic and hormonal analyses were conducted to appreciate the molecular mechanism of adventitious root (AR) formation in optimized rooting conditions.
Results: The use of 2/5-strength Woody Plant Medium (WPM) significantly promoted AR formation of M. glyptostroboides shoots while the content of endogenous auxin, cytokinins and gibberellins (GAs) varied at different stages of AR formation. Transcriptomic analysis showed the significant up- or down-regulation of differentially expressed genes (DEGs) associated with plant hormone signal transduction and the phenylpropanoid biosynthesis pathway in response to 2/5-strength WPM. DEGs related to the biosynthesis of indole-3-acetic acid, cytokinins and GAs were identified. Transcript factors involved in 13 families were also revealed. A weighted gene co-expression network analysis indicated a strong correlation between hormones and genes involved in plant hormone signal transduction and the phenylpropanoid biosynthetic pathway.
Conclusions: These results indicate that the AR-promoting potential of 2/5-strength WPM in M. glyptostroboides was due to complex interactions between hormones and the expression of genes related to plant hormone signal transduction and the phenylpropanoid biosynthetic pathway.
期刊介绍:
BMC Genomics is an open access, peer-reviewed journal that considers articles on all aspects of genome-scale analysis, functional genomics, and proteomics.
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