Summary: Soybeans (Glycine max L. Merr.) are a major source of plant‐based protein for human nutrition and livestock feed. Enhancing the protein content of soybean seeds is vital for meeting growing dietary needs and promoting sustainable agricultural practices. In this study, we first performed QTL (Quantitative Trait Loci) mapping analysis and constructed a Soybean Seed Protein Content Co‐expression (Soy‐SPCC) network to identify key genes associated with soybean seed protein accumulation. Next, we investigated the role of High Seed Storage Protein1 (HSSP1) in regulating soybean seed protein content through a comprehensive analysis. Functional validation through overexpression and gene knockout experiments demonstrated that HSSP1, a key component of the Soy‐SPCC network, significantly influences seed storage protein levels. Particularly, HSSP1 enhances the expression of GmCG1 by binding directly to its cis‐acting element, leading to increased protein content in soybean seeds. Furthermore, we performed a molecular module stacking breeding analysis of 120 candidate genes identified from the Soy‐SPCC network, including HSSP1, to identify genetic variations associated with protein content. This study provides a novel perspective on soybean protein regulation. The identification of HSSP1 as a critical regulator offers valuable insights for developing high‐protein soybean varieties and advancing breeding strategies aimed at improving soybean seed quality.

Timely detection of pathogen spores is fundamental to ensuring early intervention and reducing the spread of corn diseases, like northern corn leaf blight, corn head smut, and corn rust. Traditional spore detection methods struggle to identify spore-level targets within complex backgrounds. To improve the recognition accuracy of various maize disease spores, this study introduced the YOLOv8s-SPM model by incorporating the space-to-depth and convolution (SPD-Conv) layers, the Partial Self-Attention (PSA) mechanism, and Minimum Point Distance Intersection over Union (MPDIoU) loss function. First, we combined SPD-Conv layers into the Backbone of the YOLOv8s to enhance recognition performance on small targets and low-resolution images. To improve computational efficiency, the PSA mechanism was incorporated within the Neck layer of the network. Finally, MPDIoU loss function was applied to refine the localization performance of bounding boxes. The results revealed that the YOLOv8s-SPM model achieved 98.9% accuracy on the mixed spore dataset. Relative to the baseline YOLOv8s, the YOLOv8s-SPM model yielded a 1.4% gain in accuracy. The improved model significantly improved spore detection accuracy and demonstrated superior performance in recognizing diverse spore types under complex background conditions. It met the demands for high-precision spore detection and filled a gap in intelligent spore recognition for maize, offering an effective starting point and practical path for future research in this field.

The Kunitz trypsin inhibitor (KTI) gene family encompasses a category of trypsin inhibitors, and the KTI proteins are important components of the 2S storage protein fraction in soybeans. In this study, fifty members of the GmKTI family were identified in the soybean genome, and their physicochemical properties, domain compositions, phylogenetic relationships, gene structures, and expression patterns were comprehensively analyzed to explore their impact on soybean seed protein content. The results revealed significant gene expansion within the GmKTI family in soybean. The gene structures and conserved motifs of GmKTI members exhibited both regularity and diversity, with distinct expression patterns across different soybean tissues. Haplotype analysis identified 7 GmKTI genes significantly associated with seed storage protein content, and the combination of superior haplotypes was found to enhance seed storage protein content. This is crucial for the improvement of soybean varieties and the enhancement of storage protein content. Additionally, the GmKTI family demonstrated evolutionary conservation, with its functions likely linked to light induction, biotic stress, and growth development. This study characterizes the structure, expression, genomic haplotypes, and molecular features of the soybean KTI domain for the first time, providing a foundation for functional analyses of the GmKTI domain in soybean and other plants.

Seed oil content is a key trait in soybean, yet its genetic basis remains largely unresolved. Here, we identify GmABHD6 , an α/β‐hydrolase domain‐containing gene, as the causal gene underlying a major quantitative trait locus (QTL) for seed oil content on chromosome 16. Through fine‐mapping in a chromosome segment substitution population derived from Glycine max (SN14) and wild Glycine soja (ZYD00006), we fine‐mapped the QTL to a 150‐kb interval. Functional analyses reveal that overexpression of GmABHD6 significantly reduces seed oil content, whereas gene editing to truncate GmABHD6 led to increased oil accumulation. These modifications also alter seed protein levels, seed size and 100‐seed weight, indicating pleiotropic effects on seed composition and yield traits. Integrated transcriptomic and metabolomic profiling of transgenic lines highlighted widespread changes in lipid metabolism and energy pathways associated with GmABHD6 activity. Further functional characterisation indicates that the ERF transcription factor GmERF physically binds to the GmABHD6 promoter region to regulate its expression. Population genetic analyses show strong signatures of selection at the GmABHD6 locus, suggesting that favourable alleles are enriched during soybean domestication and improvement. Our findings establish GmABHD6 as a domestication‐related gene that negatively regulates seed oil content and provide new insights into the genetic mechanisms driving soybean seed composition and evolution.