Heinong 84 is one of the major soybean varieties growing in Northeast China, and is resistant to the infection of all strains of soybean mosaic virus (SMV) in the region including the most prevalent strain, N3. However, the resistance gene(s) in Heinong 84 and the resistant mechanism are still elusive. In this study, genetic and next-generation sequencing (NGS)-based bulk segregation analysis (BSA) were performed to map the resistance gene using a segregation population from the cross of Heinong 84 and a susceptible cultivar to strain N3, Zhonghuang 13. Results show that the resistance of Heinong 84 is controlled by a dominant gene on chromosome 13. Further analyses suggest that the resistance gene in Heinong 84 is probably an allele of Rsv1. Finally, two pairs of single-nucleotide-polymorphism (SNP)-based primers that are tightly cosegregated with the resistance gene were designed for rapidly identifying resistant progenies in breeding via the cleaved amplified polymorphic sequence (CAPS) assay.

Soybeans are essential crops that supply protein and oil. The composition and contents of soybean fatty acids are relevant to human health and have a significant relationship with soybean oil processing and applications. Identifying quantitative trait locus (QTL) genes related to palmitic acid could facilitate the development of a range of nutritive soybean cultivars using molecular marker-assisted selection. In this study, we used a cultivar with higher palmitic acid content, ‘Dongnong42’, and a lower palmitic acid content cultivar, ‘Hobbit’, to establish F_2:6 recombinant inbred lines. A high-density genetic map containing 9980 SLAF markers was constructed and distributed across 20 soybean chromosomes. The genetic map contained a total genetic distance of 2602.58 cM and an average genetic distance of 0.39 cM between adjacent markers. Two QTLs related to palmitic acid content were mapped using inclusive composite interval mapping, explaining 4.2–10.1% of the phenotypic variance in three different years and environments, including the QTL included in seed palmitic 7-3, which was validated by developing SSR markers. Based on the SNP/Indel and significant differential expression analyses of Dongnong42 and Hobbit, two genes, Glyma.15g119700 and Glyma.15g119800, were selected as candidate genes. The high-density genetic map, QTLs, and molecular markers will be helpful for the map-based cloning of palmitic acid content genes. These could be used to accelerate breeding for high nutritive value cultivars via molecular marker-assisted breeding.

Drought is the main abiotic stress limiting soybean production worldwide and seriously limits the reproductive growth of soybean. To elucidate the molecular mechanisms of drought stress response in soybean seed developmental stages, transcriptome analysis was performed on five seed developmental stages under drought stress and control conditions. Thousands of genes were found to change in expression level under drought stress, GO and KEGG enrichment analysis showed that differentially expressed genes (DEGs) were involved in drought resistance at every stage or tissue, but there were very few DEGs at the primary stage of seed development, which indicated that the seeds at the primary developmental stage were influenced little by drought stress. There was no shared DEG among all five stages or tissues, this indicated that they have different mechanisms to resist drought stress. Furthermore, we investigated DEGs associated with drought response at different stages to identify candidate regulators of drought stress response during soybean seed development. These genes are associated with metabolic and regulatory networks, among others. Taken together, the results will expand our understanding of the adaptation of soybean to drought stress and provide new insights into the molecular mechanisms of soybean drought tolerance development.

Low phosphorus (P) stress impacts nitrogen (N) metabolism in soybeans. This study investigated the effects of exogenous cytokinin (Trans-Zeatin) on soybean N metabolism under low P stress by treating seeds with Trans-Zeatin and analyzing N accumulation, ¹⁵N abundance, nodule N fixation accumulation, nodule N fixation rate, nodule nitrogenase activity, soluble protein content, and free amino acid profiles. The results showed that exogenous cytokinin enhanced N accumulation in aboveground tissues, roots, and nodules, as well as nodule N fixation accumulation and fixation rate (from day 35 onward) under low P stress. Additionally, it promoted both acetylene reduction activity (ARA) and specific nitrogenase activity (SNA) in soybean nodules. By increasing the absorption of fertilizer-derived N, exogenous cytokinin alleviated the inhibitory effects of low P stress on the early growth and development of soybeans. Notably, under low P conditions, exogenous cytokinin significantly elevated the soluble protein content in nodules. However, the underlying mechanisms governing changes in free amino acid profiles require further investigation. This study provides a theoretical foundation for developing strategies to regulate soybean N metabolism under low P stress.

Soybean (Glycine max L.) is an essential food and economic crop in China, yet its growth and yield are severely constrained by saline–alkali stress. A saline–alkali soil exacerbates root absorption barriers, leading to 30–50% yield losses. Understanding the mechanisms underlying alkali tolerance is therefore crucial for developing stress-resilient soybean varieties and improving the productivity of saline–alkali land. In our previous study, we evaluated 99 soybean germplasms from Northeast China and obtained the alkali-tolerant varieties HN48 and HN69, along with the alkali-sensitive varieties HNWD4 and HN83. In this study, fifteen-day-old soybean seedlings were subjected to (30 mM NaHCO₃) alkali stress for 72 h, and whole plants were sampled to assess their morphology and physiology, while leaf tissues were harvested for biochemical analysis. For transcriptomic analysis, soybean seedlings were exposed to alkali stress (50 mM NaHCO₃, pH 9.0) for 6 h, and leaf and root tissues were harvested for RNA sequencing. The results showed that alkali-tolerant varieties mitigated these effects by suppressing excessive ROS generation by 55–63%, decreasing malondialdehyde (MDA) accumulation by 37–39%, and increasing photosynthetic efficiency by 18.3%, as well as accumulating more osmoprotectants and activating antioxidant enzymes such as superoxide dismutase (SOD) and catalase (CAT) under alkaline stress. Transcriptome analysis showed that the alkali-tolerant variety HN69 exhibited cultivar-specific enrichment of metabolism cytochrome P450, estrogen signaling, and GnRH signaling pathways under alkali stress. These results collectively indicate that alkali-tolerant soybean varieties adapt to alkali stress through coordinated multi-pathway responses, with differential pathway enrichment potentially underlying the variation in alkali tolerance between cultivars. Overall, this study elucidates the physiological and molecular mechanisms of alkali tolerance in soybean, providing a theoretical foundation for breeding stress-tolerant germplasms.

Soybean (Glycine max (L.) Merr.), as a crucial source of oil and protein globally, is widely cultivated in many countries. Low-temperature stress has become one of the major environmental factors affecting soybean production, especially in colder regions, making the improvement of cold tolerance traits in soybean a key breeding objective. This study integrates Genome-Wide Association Studies (GWAS) and Marker-Assisted Selection (MAS) to enhance the predictive performance of soybean cold tolerance traits. First, three GWAS methods—Fast3VmrMLM, fastGWA, and FarmCPU—were used to analyze soybean cold tolerance traits, and significant SNP markers were identified. Principal Component Analysis (PCA) was employed to reveal genetic differences among various soybean germplasm. Then, based on the identified SNP markers, multiple Genomic Selection (GS) models, such as GBLUP, BayesA, BayesB, BayesC, BL, and BRR, were used for prediction to evaluate the contribution of genetic effects to phenotypic variation. The results showed that the markers selected through GWAS significantly improved the prediction accuracy of genomic selection, especially with the Fast3VmrMLM and FarmCPU methods in larger datasets. Finally, Gene Ontology (GO) analysis was performed to further identify candidate genes associated with cold tolerance traits and their biological functions, providing theoretical support for molecular breeding of cold-tolerant soybean varieties.