多组学鉴定小麦穗器官发育的关键遗传和代谢网络

Yangyang Liu, Lili Zhang, Anting Zhu, Liping Shen, Jiaqi Zhang, Jun Chen, Guowei Chang, Changbin Yin, Ziying Wang, Zhiwen Sun, Kuocheng Shen, Xiaowan Xu, Mengjing Sun, Mingming Xin, Jianhui Wu, Zefu Lu, Yiping Tong, Zhonghu He, Fei Lu, Yuanfeng Hao, Wei Chen, Zifeng Guo
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引用次数: 0

摘要

小麦(Triticum aestivum L.)穗发育受到遗传和代谢程序的严格调控,这些程序驱动着器官生长和形态变化。然而,代谢变化、基因表达模式和它们在穗发育过程中的调节作用之间的动态相互作用,仍然没有得到很好的描述。为了解决这一知识差距,我们在小麦穗器官发育的12个阶段进行了综合代谢组学和转录组学分析。我们的分析在233个穗、小穗和小花样本中检测到1105种代谢物,揭示了植物激素相关代谢物的不均匀分布。外源植物激素处理验证了植物激素在穗形发生中的调控作用。来自心皮器官的高分辨率时空数据使调控网络得以重建,确定了关键基因(包括12-氧-植物二烯酸还原酶3 (TaOPR3)、晶粒长度1 (GL1)和晶粒长度2 (GL2))是晶粒大小的关键决定因素。基因组分析揭示了基因单倍型的地理分化及其在育种过程中的选择性保留,优越的等位基因与籽粒增大有关。这一全面的数据集为了解小麦籽粒产量的分子基础提供了宝贵的资源,并为作物改良提供了潜在的目标。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-omics identifies key genetic and metabolic networks regulating spike organ development in wheat
Wheat (Triticum aestivum L.) spike development is tightly regulated by genetic and metabolic programs that drive organ growth and morphological changes. However, the dynamic interplay between metabolic shifts, gene expression patterns, and their regulatory roles during spike development, remains poorly characterized. To address this knowledge gap, we performed integrated metabolomic and transcriptomic profiling across 12 stages of wheat spike organ development. Our analysis detected 1,105 metabolites in 233 spike, spikelet, and floret samples, uncovering an uneven distribution of phytohormone-related metabolites. The exogenous phytohormone treatments validated the regulatory roles of phytohormones in spike morphogenesis. High-resolution spatiotemporal data from carpel organs enabled the reconstruction of a regulatory network, identifying key genes (including 12-oxo-phytodienoic acid reductase3 (TaOPR3), Grain Length1 (GL1), and Grain Length2 (GL2)) as critical determinants of grain size. Genomic analyses revealed geographical differentiation in gene haplotypes and their selective retention during breeding, with superior alleles associated with increased grain size. This comprehensive dataset provides a valuable resource for understanding the molecular basis of wheat grain yield and offers potential targets for crop improvement.
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