Transcriptome-Based Analysis of the Co-Expression Network of Genes Related to Nitrogen Absorption in Rice Roots Under Nitrogen Fertilizer and Density

Nitrogen (N) management and planting density critically influence rice (Oryza sativa L.) N use efficiency (NUE) and yield stability, though excessive inputs risk ecological and productivity constraints. This study investigated molecular adaptations in japonica rice Hongyang 5 under three N density regimens: high N/low density (HNLD), medium N/medium density (MNMD), and low N/high density (LNHD). Our previous studies found that the N absorption efficiency, antioxidant enzyme activity, and energy metabolism-related phenotypes of rice roots showed significant differences under different treatments. In this study, we found that root morphology, such as root length, root surface area, root volume, and average root diameter, also showed significant differences among different treatments. Based on this, we further integrated transcriptome and co-expression network analysis, revealing 40,218 expressed genes with differential expression patterns across treatments. Weighted gene co-expression network analysis (WGCNA) identified 13 modules, with the Turquoise and Blue modules notably demonstrating strong associations with N assimilation, antioxidant activity, and ATP metabolism. Ten hub genes emerged through intramodular connectivity analysis, including LOC_Os02g53130 (N metabolism), LOC_Os06g48240 (peroxidase activity), and LOC_Os01g48420 (energy transduction), with RT-qPCR validation confirming transcriptome-derived expression profiles. Functional characterization revealed synergistic coordination between Turquoise module N metabolic pathways and Blue module redox homeostasis, suggesting an integrated regulatory mechanism for root adaptation to N density interactions. These findings establish a gene-network framework that reveals the molecular regulatory network of crop responses to N nutrition and planting density and provides important theoretical support for N fertilizer management, population quality optimization, and variety breeding in precision agriculture.