Exploring the Flavonoid Biosynthesis Pathway of Two Ecotypes of Leymus chinensis Using Transcriptomic and Metabolomic Analysis

Abstract

This research investigates the flavonoid biosynthesis pathways of two ecotypes of Leymus chinensis, distinguished by their gray-green (GG) and yellow-green (YG) leaf colors, to uncover the molecular bases of their adaptability to different environmental conditions. By integrating comprehensive transcriptomic and metabolomic analyses, we identified 338 metabolites, with 161 showing differential expression—124 upregulated and 37 downregulated. The transcriptomic data revealed substantial variation, with 50,065 genes differentially expressed between the ecotypes, suggesting complex genetic regulation of the flavonoid biosynthesis pathways involving 20 enzyme-coding genes. KEGG pathway enrichment analysis further highlighted the involvement of 26 genes in the synthesis of four distinct types of flavonoid metabolites, indicating the sophisticated modulation of these pathways. Our results demonstrate that the GG and YG ecotypes of Leymus chinensis exhibit distinct flavonoid profiles and gene expression patterns, with the GG ecotype showing a higher accumulation of quercetin and kaempferol (increased by 25% and 33%, respectively, compared to YG), suggesting enhanced antioxidant capacity. Conversely, the YG ecotype displayed a broader spectrum of flavonoid metabolites, possibly indicating an adaptive strategy favoring diverse ecological interactions. Our results show that the GG and YG ecotypes of Leymus chinensis exhibit distinct flavonoid profiles and gene expression patterns, suggesting divergent adaptive strategies to environmental stress. This study highlights the crucial role of flavonoid metabolites in plant adaptation strategies, enhancing our understanding of plant resilience and adaptability. The distinct metabolic profiles observed suggest that the GG ecotype may be better equipped to handle oxidative stress, while the YG ecotype could be predisposed to broader ecological interactions. This emphasizes the value of applying machine learning in predicting plant adaptability, providing a new perspective for the future exploration of how plants adapt to environmental challenges. Meanwhile, the information gleaned from this nuanced study offers a foundation for future investigations into the genetic and environmental factors involved in plant adaptation.