胚胎发生
体细胞
生物
细胞生物学
胚胎发生
胚胎
转录组
转录因子
落叶松
基因表达
基因
植物
遗传学
作者
Xiaoyi Chen,Luyao Zhang,Chengbi Liu,Rui Wang,Jianfeng Dai,Lisheng Kong,Jinfeng Zhang,Jian Zhao
出处
期刊:Plant Physiology
[Oxford University Press]
日期:2025-06-30
卷期号:198 (3)
被引量:4
标识
DOI:10.1093/plphys/kiaf286
摘要
Somatic embryogenesis is a powerful system for studying embryo development and scaling up the production of elite genetic material. Somatic embryogenesis has been well established in Larix principis-rupprechtii, a Chinese larch species dominant in the world's largest man-made forest. However, genotype-dependent embryogenic variations hinder large-scale forestry, and the molecular mechanisms remain unclear. Here, we constructed stage-specific developmental transcriptomes of the somatic embryogenesis process using 2 lines with contrasting embryogenic capacities. Clustering and coexpression analyses identified LpWRKY65 as a central hub gene highly expressed in early somatic embryogenesis stages and with significantly higher expression in the high-embryogenic-capacity cell line (HEL) compared to the low-embryogenic-capacity cell line (LEL). Overexpressing LpWRKY65 significantly increased somatic embryo yield and quality. DNA affinity purification sequencing (DAP-seq) and RNA-seq were combined to identify a set of target genes downstream of and responsive to LpWRKY65, particularly including genes involved in reactive oxygen species (ROS) scavenging. We identified LpHmgB10 as a critical downstream regulator of LpWRKY65. LpWRKY65 directly binds to the W-box in the promoter of LpHmgB10, markedly enhancing its transcriptional activity. ROS profiling further demonstrated that overexpression of LpWRKY65 or LpHmgB10 enhances ROS scavenging and promotes a stable redox environment, which is crucial for improving embryogenic capacity. These findings suggest that LpWRKY65 regulates the cellular redox environment to promote embryogenic differentiation and somatic embryo development, advancing somatic embryogenesis research in conifers.
科研通智能强力驱动
Strongly Powered by AbleSci AI