Introduction: Rhizosphere microorganisms can effectively promote the stress resistance of plants, and some beneficial rhizosphere microorganisms can significantly promote the growth of crops under salt stress, which has the potential to develop special microbial fertilizers for increasing the yield of saline-alkali land and provides a low-cost and environmentally friendly new strategy for improving the crop yield of saline-alkali cultivated land by using agricultural microbial technology.Methods: In May 2022, a field study in a completely randomized block design was conducted at the Heilongjiang Academy of Agricultural Sciences to explore the correlation between plant rhizosphere microorganisms and soybean growth in saline-alkali soil. Two soybean cultivars (Hening 531, a salt-tolerant variety, and 20_1846, a salt-sensitive variety) were planted at two experimental sites [Daqing (normal condition) and Harbin (saline-alkali conditions)], aiming to investigate the performance of soybean in saline-alkali environments.Results: Soybeans grown in saline-alkali soil showed substantial reductions in key traits: plant height (25%), pod number (26.6%), seed yield (33%), and 100 seed weight (13%). This underscores the unsuitability of this soil type for soybean cultivation. Additionally, microbial analysis revealed 43 depleted and 56 enriched operational taxonomic units (OTUs) in the saline-alkali soil compared to normal soil. Furthermore, an analysis of ion-associated microbes identified 85 mOTUs with significant correlations with various ions. A co-occurrence network analysis revealed strong relationships between specific mOTUs and ions, such as Proteobacteria with multiple ions. In addition, the study investigated the differences in rhizosphere species between salt-tolerant and salt-sensitive soybean varieties under saline-alkali soil conditions. Redundancy analysis (RDA) indicated that mOTUs in saline-alkali soil were associated with pH and ions, while mOTUs in normal soil were correlated with Ca²⁺ and K⁺. Comparative analyses identified significant differences in mOTUs between salt-tolerant and salt-sensitive varieties under both saline-alkali and normal soil conditions. Planctomycetes, Proteobacteria, and Actinobacteria were dominant in the bacterial community of saline-alkali soil, with significant enrichment compared to normal soil. The study explored the functioning of the soybean rhizosphere key microbiome by comparing metagenomic data to four databases related to the carbon, nitrogen, phosphorus, and sulfur cycles. A total of 141 KOs (KEGG orthologues) were identified, with 66 KOs related to the carbon cycle, 16 KOs related to the nitrogen cycle, 48 KOs associated with the phosphorus cycle, and 11 KOs linked to the sulfur cycle. Significant correlations were found between specific mOTUs, functional genes, and phenotypic traits, including per mu yield (PMY), grain weight, and effective pod number per plant.Conclusion: Overall, this study provides comprehensive insights into the structure, function, and salt-related species of soil microorganisms in saline-alkali soil and their associations with salt tolerance and soybean phenotype. The identification of key microbial species and functional categories offers valuable information for understanding the mechanisms underlying plant-microbe interactions in challenging soil conditions.

Background: Microbial communities associated with roots play a crucial role in the growth and health of plants and are constantly influenced by plant development and alterations in the soil environment. Despite extensive rhizosphere microbiome research, studies examining multi-kingdom microbial variation across large-scale agricultural gradients remain limited. Results: This study investigates the rhizosphere microbial communities associated with soybean across 13 diverse geographical locations in China. Using high-throughput shotgun metagenomic sequencing on the BGISEQ T7 platform with 10 GB per sample, we identified a total of 43,337 microbial species encompassing bacteria, archaea, fungi, and viruses. Our analysis revealed significant site-specific variations in microbial diversity and community composition, underscoring the influence of local environmental factors on microbial ecology. Principal coordinate analysis (PCoA) indicated distinct clustering patterns of microbial communities, reflecting the unique environmental conditions and agricultural practices of each location. Network analysis identified 556 hub microbial taxa significantly correlated with soybean yield traits, with bacteria showing the strongest associations. These key microorganisms were found to be involved in critical nutrient cycling pathways, particularly in carbon oxidation, nitrogen fixation, phosphorus solubilization, and sulfur metabolism. Our findings demonstrate the pivotal roles of specific microbial taxa in enhancing nutrient cycling, promoting plant health, and improving soybean yield, with significant positive correlations (r = 0.5, p = 0.039) between microbial diversity and seed yield. Conclusion: This study provides a comprehensive understanding of the diversity and functional potential of rhizosphere microbiota in enhancing soybean productivity. The findings underscore the importance of integrating microbial community dynamics into crop management strategies to optimize nutrient cycling, plant health, and yield. While this study identifies key microbial taxa with potential functional roles, future research should focus on isolating and validating these microorganisms for their bioremediation and biofertilization activities under field conditions. This will provide actionable insights for developing microbial-based agricultural interventions to improve crop resilience and sustainability.

Soybean is an important food crop that contains high amounts of isoflavones. However, due to the expression of multiple genes, different soybean seeds have different isoflavone compositions. The underlying mechanisms for this complexity remain unknown. In this study, we identified potential differentially expressed genes (DEGs) in two wild soybean cultivars, ZYD7068 (high isoflavone) and ZYD7194 (low isoflavone), at different seed developmental stages using RNA-seq technology and compared their differences in isoflavone content. A total of 1067 and 6479 differentially metabolized genes were identified at R6 and R8 stages, respectively. Subsequent analysis of the KEGG pathway revealed that three of these differential metabolized genes were involved in the Isoflavonoid biosynthesis and Flavone and flavonol biosynthesis at the R6 stage. A total of 80 TF genes encoding differential expression of MYB, bZIP, and WRKY were identified in A1 vs. B1 and A3 vs. B3. Eight differentially expressed genes were identified in duplicates at both stages, and three genes showed the same expression trend at both stages. To confirm the results of RNA-seq, qRT-PCR was performed to analyze the expression of the six identified differentially expressed genes (DEGs). The results of qRT-PCR were consistent with the results of RNA-seq. We found that four genes (Glyma.13G173300, Glyma.13G173600, Glyma.14G103100, and Glyma.17G158900) may be involved in the positive regulation of isoflavone synthesis, while two genes (Glyma.04G036700 and Glyma.19G030500) may be involved in the negative regulation of isoflavone synthesis. These findings suggest that the observed difference in isoflavone levels between the two cultivars may be attributable to the differential expression of these six genes at later stages of seed development.

Objective: Seed weight is a key factor in soybean yield and value, but its genetic basis and environmental stability are not fully understood. Despite many QTL studies, there’s a lack of integration between bi-parental linkage mapping and diverse germplasm association analysis. We hypothesized that combining high-resolution QTL mapping in recombinant inbred lines with GWAS in natural populations could identify both population-specific and broadly segregating seed weight loci, aiding in candidate gene discovery for breeding. Methods: We integrated biparental QTL mapping with genome-wide association studies (GWAS) to comprehensively dissect the genetics of hundred-seed weight (HSW). A recombinant inbred line population of 325 F ₂ : ₅ lines from Qihuang 34 × Dongsheng 16 was phenotyped across three environments and genotyped using SLAF-seq, generating a high-density genetic map with 6,297 SNP markers spanning 2,945.26 cM (0.47 cM resolution). Simultaneously, 348 diverse soybean accessions underwent whole-genome resequencing (10× coverage), yielding 1,882,531 SNPs for association analysis across two years. Results: QTL mapping identified 11 significant loci explaining 2.47-8.59% of phenotypic variance, with broad-sense heritability of 0.78. The major-effect QTL qHSW-19-4 (44.84-44.85 Mb, LOD = 9.72) demonstrated unprecedented 11.4 kb mapping precision. GWAS independently detected six genome-wide significant associations (P < 1 × 10 ^–8 ), including a stable chromosome 19 peak at 45.28 Mb (P = 2.06 × 10 ^– ²³) explaining 15.3-18.7% of variance. Remarkably, this GWAS signal co-localized within 580 kb of qHSW-19-4 , providing robust cross-population validation of chromosome 19 as a major seed weight regulatory region. Functional analysis of 44 candidate genes, validated by quantitative RT-PCR across seed developmental stages, identified four high-priority candidates: Glyma.19G195400 (cell wall invertase, 2.7-fold upregulation in large-seeded parent, r = 0.68 with HSW), Glyma.19G194300 (PEBP/Dt1 family protein), Glyma.19G193400 (bZIP transcription factor), and Glyma.06G095100 (Myb DNA-binding domain). Novelty and conclusions: This first integrated QTL-GWAS analysis for soybean seed weight reveals both major-effect loci and polygenic architecture, providing validated molecular markers and candidate genes for breeding programs targeting yield improvement.

Soymilk gel quality hinges on soybean protein composition and structure, yet direct comparisons linking protein traits to gel properties are limited. This study compared seven Northeast Chinese soybean varieties to identify which protein characteristics best predict tofu gel quality. Protein analysis included composition (11S/7S ratio), structure, and functional properties. Gel quality was measured through yield, water retention, texture, rheology, and microstructure imaging. Results showed substantial variation among varieties: 11S/7S ratios ranged from 1.14 to 4.10, solubility from 57.50% to 69.74%, gel yield from 193.25% to 236.12%, water-holding capacity from 42.09% to 60.23%, and gel firmness from 1520 to 1889 gf. The 11S/7S ratio emerged as the strongest quality predictor, correlating with gel firmness (R = 0.92) and elasticity (R = 0.98), while solubility correlated with yield (R = 0.79) and water retention (R = 0.83). Microscopy revealed that variety HD-1, with the highest 11S/7S ratio (4.10) and solubility (69.74%), formed dense networks with small pores (20–50 μm), whereas variety HK-60 (ratio 1.14) produced coarse structures with large pores (100–200 μm). HD-1 showed the best overall performance. Varieties with 11S/7S ratios above 3.5 and solubility above 68% consistently produced high-quality gels, while ratios below 2.5 indicated poor gel formation regardless of total protein content. These findings demonstrate that protein composition matters more than protein quantity for tofu quality. The approach enables rapid variety screening and provides practical guidelines for tofu manufacturers and soybean breeders.