High spatial resolution datasets analyses identify genes associated with multi-layered secondary cell wall thickening in Pinus bungeana

生物 木质部 转录组 增稠 WRKY蛋白质结构域 次生细胞壁 管胞 转录因子 维管形成层 MYB公司 基因 形成层 次生生长 细胞生物学 植物 计算生物学 基因表达 遗传学 化学 高分子科学
作者
Yu Guo,Lichao Jiao,Jie Wang,Lingyu Ma,Yang Lu,Yonggang Zhang,Juan Guo,Yafang Yin
出处
期刊:Annals of Botany [Oxford University Press]
卷期号:133 (7): 953-968
标识
DOI:10.1093/aob/mcae023
摘要

Abstract Background and Aims Secondary cell wall (SCW) thickening is a major cellular developmental stage determining wood structure and properties. Although the molecular regulation of cell wall deposition during tracheary element differentiation has been well established in primary growth systems, less is known about the gene regulatory processes involved in the multi-layered SCW thickening of mature trees. Methods Using third-generation [long-read single-molecule real-time (SMRT)] and second-generation [short-read sequencing by synthesis (SBS)] sequencing methods, we established a Pinus bungeana transcriptome resource with comprehensive functional and structural annotation for the first time. Using these approaches, we generated high spatial resolution datasets for the vascular cambium, xylem expansion regions, early SCW thickening, late SCW thickening and mature xylem tissues of 71-year-old Pinus bungeana trees. Key Results A total of 79 390 non-redundant transcripts, 31 808 long non-coding RNAs and 5147 transcription factors were annotated and quantified in different xylem tissues at all growth and differentiation stages. Furthermore, using this high spatial resolution dataset, we established a comprehensive transcriptomic profile and found that members of the NAC, WRKY, SUS, CESA and LAC gene families are major players in early SCW formation in tracheids, whereas members of the MYB and LBD transcription factor families are highly expressed during late SCW thickening. Conclusions Our results provide new molecular insights into the regulation of multi-layered SCW thickening in conifers. The high spatial resolution datasets provided can serve as important gene resources for improving softwoods.
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