Identification of Auxin-Associated Genes in Wheat Through Comparative Transcriptome Analysis and Validation of the Candidate Receptor-like Kinase Gene TaPBL7-2B in Arabidopsis - Mengjie Zhang; Guangzhu Chen; Jie Cai; Yongjie Ji; Linrun Xiang; Xinhong Chen; Jun Wang (Plants)
Auxin (IAA), a key natural signaling molecule, plays a pivotal role in regulating plant growth, development, and stress responses. Understanding its signal transduction mechanisms is crucial for improving crop yields. In this study, we conducted a comparative transcriptome analysis of wheat leaf and root tissues treated with different concentrations of IAA (0, 1, and 50 μM). Functional enrichment analysis revealed that differentially expressed genes (DEGs) exhibited tissue-specific regulatory patterns in response to auxin. Weighted Gene Co-expression Network Analysis (WGCNA) identified receptor-like kinase genes within the MEgreen module as highly correlated with auxin response, suggesting their involvement in both root and leaf regulation. Among them, TaPBL7-2B, a receptor-like kinase gene significantly upregulated under 50 μM IAA treatment, was selected for functional validation. Ectopic overexpression of TaPBL7-2B in Arabidopsis thaliana (Col-0) enhanced auxin sensitivity and inhibited plant growth by suppressing root development and leaf expansion. In contrast, knockout of the Arabidopsis homolog AtPBL7 reduced auxin sensitivity and promoted both root and leaf growth. Transcriptome analysis of Col-0, the TaPBL7-2B overexpression line, and the pbl7 mutant indicated that TaPBL7-2B primarily functions through the MAPK signaling pathway and plant hormone signal transduction pathway. Furthermore, qRT-PCR analysis of wheat varieties with differing auxin sensitivities confirmed a positive correlation between TaPBL7-2B expression and auxin response. In conclusion, TaPBL7-2B acts as a negative regulator of plant growth, affecting root development and leaf expansion in both Arabidopsis and wheat. These findings enhance our understanding of auxin signaling and provide new insights for optimizing crop architecture and productivity.