Search Results (504)

Charcoal rot, caused by Macrophomina phaseolina, is an increasingly important constraint in soybean, particularly under hot and dry conditions. While heat and drought are known to favor disease development, short early-season cold spells—common in temperate regions—may predispose soybean to subsequent infection, yet this interaction remains poorly quantified. It was evaluated whether transient chilling increases charcoal rot severity and whether cultivar-specific differences modulate this predisposition. Nine commercial cultivars spanning MG 00, 0, and 0–I were grown in a controlled walk-in chamber under either optimal conditions (control) or a three-day cold spell initiated at the first fully expanded trifoliate (20–23 days after sowing, DAS). Standardized cut-stem inoculation was performed at 26 DAS, and stem lesion length was recorded every 3–4 days across five assessments at 3, 7, 10, 14, and 21 DPI. Two-way ANOVA (treatment, genotype, treatment × genotype) with Tukey’s HSD tested effects. Cold stress significantly increased lesion lengths at all assessments, with the strongest divergence at the earliest measurement. Genotype and treatment × genotype were also significant, revealing differential responses among cultivars; notably, one line (G9) showed consistently small treatment-induced increases. These results indicate that brief early-season cold exposure can predispose soybean to more severe charcoal rot, with the magnitude dependent on genotype and timing. Incorporating cold-stress predisposition into screening and breeding should enhance resilience under increasing climate variability. Full article

Soybean root rot, caused by diverse soil-borne pathogens, is a major constraint on production worldwide, with yield losses ranging from 10 to 60% under epidemic conditions. Symptomatic plants were collected from three locations in Harbin, Heilongjiang Province, China, and 23 fungal isolates were recovered using standard tissue isolation procedures. Integrated morphological characterization and rDNA-ITS sequencing identified these isolates as three Fusarium species: F. oxysporum (18 isolates, 78%), F. equiseti (3 isolates, 13%), and F. brachygibbosum (2 isolates, 9%). Pathogenicity assays following Koch’s postulates confirmed F. oxysporum as the predominant and most aggressive pathogen in this region. To identify resistance resources, 200 soybean germplasm accessions adapted to Northeast China were screened using an etiolated seedling hypocotyl inoculation method with Fusarium oxysporum isolate A3 (DSI = 68.5) as the test pathogen. Disease severity indices exhibited a continuous distribution (mean = 52.84, range = 0–100), suggesting quantitative inheritance. Accessions were classified as highly resistant (13, 6.5%), resistant (40, 20%), moderately susceptible (67, 33.5%), susceptible (43, 21.5%), or highly susceptible (37, 18.5%). To explore potential molecular mechanisms underlying resistance, RT-qPCR analysis was performed on two extreme genotypes—a highly resistant line (H9477F5, DSI = 15.3) and a highly susceptible line (HN91, DSI = 88.7) at 1, 3, and 5 days post-inoculation. The resistant line maintained consistently higher expression of positive regulators GmFER and GmSOD1, with GmFER reaching 15.89-fold induction at day 3. Conversely, expression of negative regulators GmJAZ1 and GmTAP1 remained lower in the resistant line, with susceptible plants showing 5.62-fold higher GmJAZ1 expression at day 3. These findings provide characterized pathogen isolates, resistant germplasm resources (53 accessions with HR or R classifications), and preliminary molecular insights that may inform breeding strategies for improving root rot resistance in Northeast China. Full article

Genome-wide association studies (GWAS) are essential for identifying genomic regions associated with agronomic traits, but Linear Mixed Model (LMM)-based GWAS face challenges in capturing complex gene interactions. This study explores the potential of machine learning (ML) methodologies to enhance marker identification and association modeling in plant breeding. Unlike LMM-based GWAS, ML approaches do not require prior assumptions about marker–phenotype relationships, enabling the detection of epistatic effects and non-linear interactions. The research sought to assess and contrast approaches utilizing ML (Decision Tree—DT; Bagging—BA; Random Forest—RF; Boosting—BO; and Multivariate Adaptive Regression Splines—MARS) and LMM-based GWAS. A simulated F₂ population comprising 1000 individuals was analyzed using 4010 SNP markers and ten traits modeled with epistatic interactions. The simulation included quantitative trait loci (QTL) counts varying between 8 and 240, with heritability levels set at 0.5 and 0.8. These characteristics simulate traits of candidate crops that represent a diverse range of agronomic species, including major cereal crops (e.g., maize and wheat) as well as leguminous crops (e.g., soybean), such as yield, with moderate heritability and a high number of QTLs, and plant height, with high heritability and an average number of QTLs, among others. To validate the simulation findings, the methodologies were further applied to a real Coffea arabica population (n = 195) to identify genomic regions associated with yield, a complex polygenic trait. Results demonstrated a fundamental trade-off between sensitivity and precision. Specifically, for the most complex trait evaluated (240 QTLs under epistatic control), Ensemble methods (Bagging and Random Forest) maintained a Detection Power (DP) exceeding 90%, significantly outperforming state-of-the-art GWAS methods (FarmCPU), which dropped to approximately 30%, and traditional Linear Mixed Models, which failed to detect signals (0%). However, this sensitivity resulted in lower precision for ensembles. In contrast, MARS (Degree 1) and BLINK achieved exceptional Specificity (>99%) and Precision (>90%), effectively minimizing false positives. The real data analysis corroborated these trends: while standard GWAS models failed to detect significant associations, the ML framework successfully prioritized consensus genomic regions harboring functional candidates, such as SWEET sugar transporters and NAC transcription factors. In conclusion, ML Ensembles are recommended for broad exploratory screening to recover missing heritability, while MARS and BLINK are the most effective methods for precise candidate gene validation. Full article

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. Full article

Annual wild soybean, the ancestor of cultivated soybean, underwent a significant increase in seed oil content during domestication. To elucidate the genetic basis of this change, a chromosome segment substitution line population (177 lines) constructed with cultivated soybean NN1138-2 as recipient and wild soybean N24852 as donor was used in this study. Phenotypic evaluation across three distinct environments led to the identification of two major QTL/segments, qOC14 on chromosome 14 and qOC20 on chromosome 20, which collectively explained 39.46% of the phenotypic variation, with individual contributions of 17.87% and 21.59%, respectively. Both wild alleles exhibited negative additive effects, with values of −0.35% and −0.42%, respectively, consistent with the inherently low oil content of wild soybeans. Leveraging transcriptome and genome data from the two parents, two candidate genes were predicted. Notably, Glyma.14G179800 is a novel candidate gene encoding a PHD-type zinc finger domain-containing protein, and the hap-A haplotype exhibits a positive effect on oil content. In contrast, Glyma.20G085100 is a reported POWR1 gene, known to regulate protein and oil content. Our findings not only validate the role of known gene but, more importantly, unveil a new candidate gene, offering valuable genetic resources and theoretical targets for molecular breeding of high-oil soybean. Full article

Calonectria ilicicola (anamorph: Cylindrocladium parasiticum) is a globally important soil-borne fungal pathogen, causing Cylindrocladium black rot (CBR) in peanuts (Arachis hypogaea) and red crown rot (RCR) in soybeans (Glycine max), two legume crops central to global food security. Under favorable conditions, these diseases can cause yield losses of 15–50%, with severe epidemics causing substantial economic damage. A defining feature of C. ilicicola is its production of melanized microsclerotia that persist in soil for up to seven years, complicating long-term disease management across major production regions worldwide. The recent spread of RCR into the U.S. Midwest highlights the adaptive potential of the pathogen and underscores the urgency of updated, integrated control strategies. This review synthesizes current knowledge on disease symptoms, pathogen biology, the life cycle, isolation techniques, and molecular diagnostics, with particular emphasis on recent genomic and multiomics advances. These approaches have identified key virulence-associated genes and core pathogenicity factors, providing new insights into host–pathogen interactions and enabling more targeted resistance breeding through marker-assisted selection and the use of wild germplasm. We critically evaluate integrated disease management strategies, including host resistance, chemical and biological control, cultural practices, and physical interventions, highlighting their complementarities and limitations. By integrating classical pathology with emerging molecular and ecological innovations, this review provides a comprehensive background for developing more effective and sustainable management approaches for CBR and RCR. Full article

Using the cotyledonary node method, four traits related to callus induction rate were identified in 185 soybean germplasm resources. Cultivation of callus tissue is crucial for soybean (Glycine max (L.) Merr.) genetic transformation and functional genomics studies. Identifying genes associated with the induction rate of soybean callus tissue is therefore essential for biotechnological breeding and for understanding the molecular genetic mechanisms of soybean regeneration. The efficiency of genetic transformation impacts the breeding rate of soybeans, with its success rate dependent on the soybean regeneration system. Subsequently, whole genome association analysis (GWAS) and multidimensional functional validation were conducted. GWAS identified 66 significantly associated SNP loci corresponding to the four traits. Expression analysis in extreme phenotypes highlighted four candidate genes: Glyma.12G164100 (GmARF1), Glyma.12G164700 (GmPPR), Glyma.02G006200 (GmERF1), and Glyma.19G128800 (GmAECC1), which positively regulate callus formation. Overexpression and gene-editing assays in hairy roots confirmed that these genes significantly enhanced callus formation rate and density, with GmARF1 exerting the most prominent effect. Hormone profiling revealed elevated levels of gibberellin (GA), auxin (IAA), cytokinin (CTK), and other phytohormones in transgenic lines, consistent with enhanced responsiveness to exogenous GA. Overall, the results suggest that these four candidate genes may promote soybean regeneration, with GmARF1 showing the most pronounced effect. These results provide valuable genetic resources for improving soybean regeneration efficiency and accelerating genetic transformation-based breeding. Full article

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. Full article

The cellulose synthase (CS) superfamily, comprising the cellulose synthase (CESA) and cellulose synthase-like (CSL) families, plays crucial roles in plant response to abiotic stresses, growth and development. However, there are few reports on the biological functions of CSs in soybean. In this study, 80 soybean CS members were identified and classified into seven subfamilies. Collinearity analyses revealed that the segmental duplication is likely the primary driver for the expansion of CS superfamily in soybean. The abundant stress-responsive and growth-related cis-acting elements in the promoter regions of soybean CS genes suggest their potential functions. Notably, GmCESA1 exhibited significantly higher expression levels in drought-tolerant soybean under drought stress. Soybean plants with lower GmCESA1 expression via virus-induced gene silencing (VIGS-GmCESA1) were less drought-tolerant than the control plants (VIGS-EV), showing reduced relative water content and dry weight than VIGS-EV under drought stress. Furthermore, VIGS-GmCESA1 soybean plants displayed reduced plant height under both well-watered and drought-stressed conditions. Our findings highlight that GmCESA1 has pleiotropic functions in regulating both drought tolerance and growth in soybean, contributing to our knowledge on CS and providing a valuable gene to breed drought-tolerant soybean in the future. Full article

The non-destructive estimation of grain yield could increase the efficiency of soybean breeding through early genotype testing, allowing for more precise selection of superior varieties. High-throughput phenotyping (HTPP) data can be combined with machine learning (ML) to develop accurate prediction models. In this study, an unmanned aerial vehicle (UAV) equipped with a multispectral camera was utilized to collect data on plant density (PD), plant height (PH), canopy cover (CC), biomass (BM), and various vegetation indices (VIs) from different stages of soybean development. These traits were used within random forest (RF) and partial least squares regression (PLSR) algorithms to develop models for soybean yield estimation. The initial RF model produced more accurate results, as it had a smaller error between actual and predicted yield compared with the PLSR model. To increase the efficiency of the RF model and optimize the data collection process, the number of predictors was gradually decreased by eliminating highly correlated VIs and selecting the most important variables. The final prediction was based only on several VIs calculated from a few mid-soybean stages. Although the reduction in the number of predictors increased the yield estimation error to some extent, the R² in the final model remained high (R² = 0.79). Therefore, the proposed ML model based on specific HTPP variables represents an optimal balance between efficiency and prediction accuracy for in-season soybean yield estimation. Full article

Sustainable improvement of crop performance requires integrative approaches that link genomic variation to phenotypic expression through intermediate molecular pathways. Here, we present Reciprocal Best Linear Unbiased Prediction (Reciprocal BLUP), a predictability-guided multi-omics framework that quantifies the cross-layer relationships among the genome, metabolome, and microbiome to enhance phenotype prediction. Using a panel of 198 soybean accessions grown under well-watered and drought conditions, we first evaluated four direction-specific prediction models (genome → microbiome, genome → metabolome, metabolome → microbiome, and microbiome → metabolome) to estimate the predictability of individual omics features. We evaluated whether subsets of features with high cross-omics predictability improved phenotype prediction. These cross-layer models identify features that play physiologically meaningful roles within multi-omics systems, enabling the prioritization of variables that capture coherent biological signals enriched with phenotype-relevant information. Consequently, metabolome features were highly predictable from microbiome data, whereas microbiome predictability from metabolomic data was weaker and more environmentally dependent, revealing an asymmetric relationship between these layers. In the subsequent phenotype prediction analysis, the model incorporating predictability-based feature selection substantially outperformed models using randomly selected features and achieved prediction accuracies comparable to those of the full-feature model. Under drought conditions, the phenotype prediction models based on metabolomic or microbiomic kernels (MetBLUP or MicroBLUP) outperformed the genomic baseline (GBLUP) for several biomass-related traits, indicating that the environment-responsive omics layers captured phenotypic variations that were not explained by additive genetic effects. Our results highlight the hierarchical interactions among genomic, metabolic, and microbial systems, with the metabolome functioning as an integrative mediator linking the genotype, environment, and microbiome composition. The Reciprocal BLUP framework provides a biologically interpretable and practical approach for integrating multi-omics data, improving phenotype prediction, and guiding omics-based feature selection in plant breeding. Full article

Salinity stress is a major abiotic constraint limiting soybean (Glycine max L.) productivity in saline–alkali soils; however, the physiological and biochemical mechanisms underlying genotypic tolerance remain poorly understood. This study aimed to identify key traits that underpin salinity tolerance and can inform breeding and agronomic strategies to enhance soybean performance under saline conditions. Two contrasting soybean genotypes, YAKARTA and POCA, were exposed to 25, 50, 75, and 100 mM NaCl from the first to the fourth trifoliate stage (V1–V4) under controlled conditions for 30 days. YAKARTA maintained higher relative water content (75.51% vs. 66.97%), stomatal conductance (342 vs. 286 mmol H₂O m⁻² s⁻¹), proline (6.15 vs. 4.36 µmol g⁻¹ fresh weight), K⁺/Na⁺ ratio (61.8 vs. 32.2), and H₂O₂ (833.8 vs. 720.2 µmol g⁻¹ fresh weight) compared with POCA, whereas POCA exhibited elevated solute leakage (87.1% vs. 79.21%), malondialdehyde (122 vs. 112 µg g⁻¹), and ascorbic acid (334 vs. 293 µg g⁻¹), indicating greater sensitivity. At 100 mM NaCl, relative water content, stomatal conductance, K⁺/Na⁺ ratio, and H₂O₂ declined by 44.5%, 81.9%, 99.8%, and 49.5%, respectively, while proline, solute leakage, malondialdehyde, and ascorbic acid increased by 56-, 1.27-, 11.6-, and 1.68-fold, respectively. The contrasting physiological and biochemical responses between these genotypes highlight key traits, such as relative water content, stomatal conductance, proline accumulation, malondialdehyde content, and the K⁺/Na⁺ ratio, as promising potential markers associated with salinity tolerance in soybean. These findings provide a foundational understanding that can guide future research to validate these markers across a wider genetic pool and under field conditions. Full article

Recent advances in molecular breeding techniques have greatly accelerated the development of improved soybean varieties with enhanced agronomic and nutritional traits. This review summarizes current research on innovative molecular approaches, including marker-assisted selection (MAS), genomic selection (GS), CRISPR/Cas9-mediated gene editing, and RNA interference (RNAi) for soybean improvement. Marker-assisted selection using simple sequence repeats (SSRs) and single-nucleotide polymorphisms (SNPs) has facilitated the efficient identification and incorporation of desired traits such as disease resistance, abiotic stress tolerance, and improved seed quality. Genomic selection has improved prediction accuracy for complex quantitative traits such as yield by integrating genome-wide molecular markers with phenotypic data. CRISPR/Cas9 technology has enabled precise genetic modification, resulting in soybeans with improved oil composition, increased isoflavone content and resistance to biotic stresses. RNA interference has successfully modulated gene expression to optimize nutritional properties and stress responses. These molecular breeding approaches overcome the limitations of traditional methods by shortening the breeding cycle and allowing for simultaneous improvement of multiple traits. The integration of these complementary techniques offers promising avenues for developing climate-resilient, high-yielding soybean varieties with improved nutritional profiles to address global food security challenges. Full article

Stem strength is a key factor influencing lodging resistance in soybeans and other crops. To identify quantitative trait loci (QTLs) associated with stem strength in soybean, we assessed the peak forces required to break a 20 cm stem base segment for each individual within a collection of 2138 plants from eight F₂ and F₃ segregating populations in 2023 and 2024. These populations were derived from four crosses between soybean varieties with contrasting stem strength. Most populations exhibited an approximately normal distribution of stem strength. Using BSA-seq, we identified 17 QTLs associated with stem strength from four populations. Among these, one QTL overlapped with a previously reported locus, while the remaining 16 represented novel loci. Notably, nine loci overlapped with known lodging QTLs, suggesting a genetic relationship between stem strength and lodging. Three QTLs were repeatedly detected in multiple populations, indicating their stability. Further linkage mapping with molecular markers confirmed these three stable QTLs. Among them, qSS10 and qSS19-2 were identified as major QTLs, refined to 1.06 Mb and 1.54 Mb intervals, with phenotypic variation explained (PVE) 23.31–25.15% and 14.21–19.93%, respectively. Within these stable QTL regions, we identified 13 candidate genes and analyzed their sequence variation and expression profiles. Collectively, our findings provide a valuable foundation for future research on stem strength in soybeans and reveal novel genetic loci and candidate genes that may be utilized for the genetic improvement of soybean lodging resistance and yield stability. Full article

Annual wild soybean is characterized by a high protein content. To elucidate the genetic basis, this study utilized a chromosome segment substitution line population (177 lines) constructed with cultivated soybean NN1138-2 as the recipient and wild soybean N24852 as the donor. Phenotypic analyses across three environments revealed significant variation in protein content ranging from 42.86% to 49.08%, with a high heritability of 0.70, indicating strong genetic control. Through high-throughput sequencing, six wild segments associated with high protein content were detected on chromosomes 3, 6, 9, 15, and 20, with phenotypic variation explained (PVE) by individual segments ranged from 3.58% to 22.46%, with segments on chromosomes 9, 15, and 20 as large-effect segments with PVE > 10%. All wild segments exhibited positive additive effects (0.42–1.09%), consistent with the characteristic of a high protein content in wild soybean. Compared with previous studies, five segments overlapped with reported loci, while qPro6.1 on chromosome 6 was a novel discovery. Integration of genomic and transcriptomic data identified 10 genes involved in nucleic acid binding, transmembrane protein transport, and amino acid synthesis pathway, with homologs validated in soybean, rice, and rapeseed. This research deepens the understanding of wild soybean’s high protein and offers new gene resources for breeding high-protein cultivated soybean. Full article