Comparative physiological and transcriptome analysis of leaf nitrogen fluxes in stay-green maize during the vegetative stage

Integrative meta-QTL and RNA-Seq analysis reveals valine-glutamine (VQ) motif-containing ZmVQ56 as a key regulator of water‑nitrogen interaction in maize (Zea mays L.)

Nitrogen and water interact synergistically to affect plant growth and crop productivity. Despite intensive research, the genetic and regulatory mechanisms of water‑nitrogen interactions remain unclear. In this study, we combined meta-QTL and RNA sequencing (RNA-Seq) to identify 18 candidate genes, with ZmVQ56 selected for functional analysis. The low nitrogen and drought-tolerant genotype 'TY6' and the sensitive genotype 'GEMS9' were used under four water‑nitrogen treatments: well-watered and normal nitrogen, water stress and normal nitrogen, well-watered and low nitrogen, and water stress and low nitrogen. We identified 3430 differentially expressed genes (DEGs) in roots and 7703 DEGs in leaves. Integrating meta-QTL, gene expression, and functional annotation identified ZmVQ56 as a candidate gene involved in water‑nitrogen interactions. Since the maize transgenic material has not yet been obtained, Arabidopsis thaliana was used for functional analysis. Functional analysis in Arabidopsis showed that overexpression of ZmVQ56 reduced primary root length, fresh weight, and shoot nitrate content under low nitrogen and drought conditions, while the atsib1 mutant (homologous of ZmVQ56) exhibited opposite results. These findings provide insights into the genetic basis of water‑nitrogen interactions and suggest a role for ZmVQ56 in regulating low nitrogen and drought tolerance, offering potential for molecular breeding in maize.

Combined BSA-Seq and RNA-Seq Reveal Genes Associated with the Visual Stay-Green of Maize (Zea mays L.)

Maize has become one of the most widely grown grains in the world, and the stay-green mutant allows these plants to maintain their green leaves and photosynthetic potential for longer following anthesis than in non-mutated plants. As a result, stay-green plants have a higher production rate than non-stay-green varieties due to their prolonged grain-filling period. In this study, the candidate genes related to the visual stay-green at the maturation stage of maize were investigated. The F2 population was derived from the T01 (stay-green) and the Xin3 (non-stay-green) cross. Two bulked segregant analysis pools were constructed. According to the method of combining ED (Euclidean distance), Ridit (relative to an identified distribution unit), SmoothG, and SNP algorithms, a region containing 778 genes on chromosome 9 was recognized as the candidate region associated with the visual stay-green in maize. A total of eight modules were identified using WGCNA (weighted correlation network analysis), of which green, brown, pink, and salmon modules were significantly correlated with visual stay-green. BSA, combined with the annotation function, discovered 7 potential candidate genes, while WGCNA discovered 11 stay-green potential candidate genes. The candidate range was further reduced due through association analysis of BSA-seq and RNA-seq. We identified Zm00001eb378880, Zm00001eb383680, and Zm00001eb384100 to be the most likely candidate genes. Our results provide valuable insights into this new germplasm resource with reference to increasing the yield for maize.