Lysine is an essential amino acid that cannot be synthesized in humans. Rice is a global staple food for humans but has a rather low lysine content. Identification of the quantitative trait nucleotides (QTNs) and genes underlying lysine content is crucial to increase lysine accumulation. In this study, five grain and three leaf lysine content datasets and 4,630,367 single nucleotide polymorphisms (SNPs) of 387 rice accessions were used to perform a genome-wide association study (GWAS) by ten statistical models. A total of 248 and 71 common QTNs associated with grain/leaf lysine content were identified. The accuracy of genomic selection/prediction RR-BLUP models was up to 0.85, and the significant correlation between the number of favorable alleles per accession and lysine content was up to 0.71, which validated the reliability and additive effects of these QTNs. Several key genes were uncovered for fine-tuning lysine accumulation. Additionally, 20 and 30 QTN-by-environment interactions (QEIs) were detected in grains/leaves. The QEI-sf0111954416 candidate gene LOC_Os01g21380 putatively accounted for gene-by-environment interaction was identified in grains. These findings suggested the application of multi-model GWAS facilitates a better understanding of lysine accumulation in rice. The identified QTNs and genes hold the potential for lysine-rich rice with a normal phenotype.

Soil salinization threatens global agriculture, reducing crop productivity and food security. Developing strategies to improve salt tolerance is crucial for sustainable agriculture. This study examines the role of organic fertilizer in mitigating salt stress in rice (Oryza sativa L.) by integrating NDVI and metabolomics. Using salt-sensitive (19X) and salt-tolerant (HHZ) cultivars, we aimed to (1) evaluate changes in NDVI and metabolite content under salt stress, (2) assess the regulatory effects of organic fertilizer, and (3) identify key metabolites involved in stress response and fertilizer-induced regulation. Under salt stress, survival rate of the 19X plants dropped to 6%, while HHZ maintained 38%, with organic fertilizer increasing survival rate to 25% in 19X and 66% in HHZ. NDVI values declined sharply in 19X (from 0.56 to <0.25) but remained stable in HHZ (~0.56), showing a strong correlation with survival rate (R² = 0.87, p < 0.01). NDVI provided a dynamic, non-destructive assessment of rice health, offering a faster and more precise evaluation of salt tolerance than survival rate analysis. Metabolomic analysis identified 12 key salt-tolerant metabolites, including citric acid, which is well recognized for regulating salt tolerance. HTPA, pipecolic acid, maleamic acid, and myristoleic acid have previously been reported but require further study. Additionally, seven novel salt-tolerant metabolites—tridecylic acid, propentofylline, octadeca penten-3-one, 14,16-dihydroxy-benzoxacyclotetradecine-dione, cyclopentadecanolide, HpODE, and (±)8,9-DiHETE—were discovered, warranting further investigation. Organic fertilizer alleviated salt stress through distinct metabolic mechanisms in each cultivar. In 19X, it enhanced antioxidant defenses and energy metabolism, mitigating oxidative damage and improving fatty acid metabolism. In contrast, HHZ primarily benefitted from improved membrane stability and ion homeostasis, reducing lipid peroxidation and oxidative stress. These findings primarily support the identification and screening of salt-tolerant rice cultivars while also highlighting the need for cultivar-specific fertilization strategies to optimize stress resilience and crop performance. Based on the correlation analysis, 26 out of 53 differential metabolites were significantly correlated with NDVI, confirming a strong association between NDVI shifts and key metabolic changes in response to salt stress and organic fertilizer application. By integrating NDVI and metabolomics, this study provides a refined method for evaluating salt stress responses, capturing early NDVI changes and key salinity stress biomarkers. This approach may prove valuable for application in salt-tolerant variety screening, precision agriculture, and sustainable farming, contributing to scientific strategies for future crop improvement and agricultural resilience.

Green soybean (Glycine max (L.) Merrill) is a highly nutritious food that is a good source of protein and fiber. However, it is sensitive to low temperatures during the growing season, and enhancing cold tolerance has become a research hotspot for breeding improvement. Background/Objectives: The underlying molecular mechanisms of cold tolerance in green soybean are not well understood. Methods: Here, a comprehensive analysis of transcriptome and metabolome was performed on a cold-tolerant cultivar treated at 10 °C for 24 h. Results: Compared to control groups, we identified 17,011 differentially expressed genes (DEGs) and 129 differentially expressed metabolites (DEMs). The DEGs and DEMs were further subjected to KEGG functional analysis. Finally, 11 metabolites (such as sucrose, lactose, melibiose, and dehydroascorbate) and 17 genes (such as GOLS, GLA, UGDH, and ALDH) were selected as candidates associated with cold tolerance. Notably, the identified metabolites and genes were enriched in two common pathways: ‘galactose metabolism’ and ‘ascorbate and aldarate metabolism’. Conclusions: The findings suggest that green soybean modulates the galactose metabolism and ascorbate and aldarate metabolism pathways to cope with cold stress. This study contributes to a deeper understanding of the complex molecular mechanisms enabling green soybeans to better avoid low-temperature damage.

Green soybean (Glycine max), commonly known as vegetable soybean or edamame, is harvested at reproductive stages 6 to 7 of pod development. At this stage, the seeds are fully grown but still green and not yet mature. Green soybean is a nutritious food high in protein and micronutrients; however, low temperatures negatively impact its production and quality. The mechanisms underlying cold stress in green soybean remain unclear. This study aims to identify differentially expressed genes (DEGs) and key pathways associated with cold tolerance through a comprehensive transcriptomic analysis of cold stress responses in a cold-tolerant green soybean variety at three time points: 3 h, 12 h, and 24 h. We identified 3415 common DEGs across three time points, with significant enrichment in categories such as “rhythmic process”, “response to blue light”, “fatty acid metabolism”, and “fatty acid degradation”. Notably, expression patterns of these pathways were similar after 3 and 24 h of cold exposure. Weighted gene co-expression network analysis (WGCNA) revealed 20 distinct modules, with two principal modules—turquoise and blue—correlating with rhythmic processes and fatty acid pathways. Additionally, we analyzed the genetic regulatory networks within these modules and identified four candidate genes (Glyma.04G015200, Glyma.18G202800, Glyma.02G123700 and Glyma.13G266500) potentially linked to cold tolerance. This study enhances our understanding of the molecular mechanisms of cold stress in green soybean and highlights key cold-responsive genes for further research.