bioRxivPhysiologyPreprint
Transcriptional and Metabolic Mechanism of Carbon Dots Enhancing Rice Growth and Resistance by Promoting Root
Yadong LiiD, Ronghua Xu, Jingyi Qi, Shang Lei, Qianying Han, Congli Ma, Yunlong Ru, Hongjie WangiD
Abstract
Increasing climate change and pollutant discharge induce constant challenges to crops, while crops are vulnerable to environmental and pollutant stresses. In this study, a carbon dots (CDs) was developed that significantly increased rice seedling growth, and successfully reduced the inhibition of heavy metal cadmium (Cd), salt (NaCl), and herbicide 2,4-D stresses on rice seedling growth by pre-spraying. The root of rice seedlings responded specifically to CDs exposure, with significant improvements in root biomass, architecture, cell wall thickness, mechanical strength, and metabolic vitality. Metabolomics and transcriptomics were combined to reveal the regulatory mechanism of CDs in rice seedlings. Transcriptome analysis indicated that CDs upregulated genes related to cytokinin, jasmonic acid, salicylic acid, MAPK signaling pathway, calcium homeostasis, and peroxidase, and downregulated those related to auxin, abscisic acid, and ethylene. Metabolomic analysis suggested CDs improved the metabolites related to antioxidant (betalain, ascorbate, aldarate, and glutathione), formation of cell wall, plasma membrane, xylem, and root cortex (phenylpropanoids biosynthesis, stilbenoid, diarylheptanoid and gingerol biosynthesis, and sphingolipid), and energy metabolism (nicotinate, nicotinamide, glyoxylate, dicarboxylate, and nitrate cycle) in rice seedlings. Therefore, pre-spraying CDs reprogrammed stress signaling pathways and enhanced adaptive responses in rice seedlings, ultimately increasing growth potential and stress resistance. This study presents a promising nano-bio-stimulant of CDs for crop resilience in the context of increasing climate change and contributes to sustainable agriculture.
- organism: plant (other)
- assay: bulk RNA-seq
- research focus: cell type annotation
- approach: statistical model
- Citations
- 8
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