Identification and functional analysis of long non-coding RNA (lncRNA) and metabolites response to mowing in hulless barley (Hordeum vulgare L. var. nudum hook. f.)

Author:

Bai Yixiong,He Jiaqi,Yao Youhua,An Likun,Cui Yongmei,Li Xin,Yao Xiaohua,Xiao Shanshan,Wu Kunlun

Abstract

Abstract Background Hulless barley (Hordeum vulgare L. var. nudum Hook. f.) is a significant cereal crop and a substantial source of forage for livestock. Long non-coding RNAs (lncRNAs) and metabolites play crucial roles in the nutrient accumulation and regeneration of hulless barley plants following mowing. The study aimed to identify differentially expressed lncRNAs and metabolites in hulless barley plants by analyzing transcriptomic and metabolomic datasets at 2 h, 24 h, and 72 h following mowing. Results The study revealed that 190, 90, and 438 lncRNA genes were differentially expressed at the 2 h, 24 h, and 72 h time points compared to the non-mowing control. We identified 14 lncRNA genes—11 downregulated and 3 upregulated—showing consistently significant differential expression across all time points after mowing. These differentially expressed lncRNAs target genes involved in critical processes such as cytokinin signaling, cell wall degradation, storage protein accumulation, and biomass increase. In addition, we identified ten differentially expressed metabolites targeting diverse metabolic pathways, including plant hormones, alkaloids, and flavonoids, before and after mowing at various time points. Endogenous hormone analysis revealed that cytokinin most likely played a crucial role in the regeneration of hulless barley after mowing. Conclusions This study created a comprehensive dataset of lncRNAs, metabolites, and hormones in hulless barley after mowing, revealing valuable insights into the functional characteristics of lncRNAs, metabolites, and hormones in regulating plant regeneration. The results indicated that cytokinin plays a significant role in facilitating the regeneration process of hulless barley after mowing. This comprehensive dataset is an invaluable resource for better understanding the complex mechanisms that underlie plant regeneration, with significant implications for crop improvement.

Publisher

Springer Science and Business Media LLC

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