Traits In Soybean Research Articles

Automated Phenotypic Analysis of Mature Soybean Using Multi-View Stereo 3D Reconstruction and Point Cloud Segmentation

Phenotypic analysis of mature soybeans is a critical aspect of soybean breeding. However, manually obtaining phenotypic parameters not only is time-consuming and labor intensive but also lacks objectivity. Therefore, there is an urgent need for a rapid, accurate, and efficient method to collect the phenotypic parameters of soybeans. This study develops a novel pipeline for acquiring the phenotypic traits of mature soybeans based on three-dimensional (3D) point clouds. First, soybean point clouds are obtained using a multi-view stereo 3D reconstruction method, followed by preprocessing to construct a dataset. Second, a deep learning-based network, PVSegNet (Point Voxel Segmentation Network), is proposed specifically for segmenting soybean pods and stems. This network enhances feature extraction capabilities through the integration of point cloud and voxel convolution, as well as an orientation-encoding (OE) module. Finally, phenotypic parameters such as stem diameter, pod length, and pod width are extracted and validated against manual measurements. Experimental results demonstrate that the average Intersection over Union (IoU) for semantic segmentation is 92.10%, with a precision of 96.38%, recall of 95.41%, and F1-score of 95.87%. For instance segmentation, the network achieves an average precision (AP@50) of 83.47% and an average recall (AR@50) of 87.07%. These results indicate the feasibility of the network for the instance segmentation of pods and stems. In the extraction of plant parameters, the predicted values of pod width, pod length, and stem diameter obtained through the phenotypic extraction method exhibit coefficients of determination (R2) of 0.9489, 0.9182, and 0.9209, respectively, with manual measurements. This demonstrates that our method can significantly improve efficiency and accuracy, contributing to the application of automated 3D point cloud analysis technology in soybean breeding.

Identification of superior haplotypes and candidate gene for seed size-related traits in soybean (Glycine max L.)

Seed size is an economically important trait that directly determines the seed yield in soybean. In the current investigation, we used an integrated strategy of linkage mapping, association mapping, haplotype analysis and candidate gene analysis to determine the genetic makeup of four seed size-related traits viz., 100-seed weight (HSW), seed area (SA), seed length (SL), and seed width (SW) in soybean. Linkage mapping identified a total of 23 quantitative trait loci (QTL) associated with four seed size-related traits in the F2 population; among them, 17 were detected as novel QTLs, whereas the remaining six viz., qHSW3-1, qHSW4-1, qHSW18-1, qHSW19-1, qSL4-1 and qSW6-1 have been previously identified. Six out of 23 QTLs were major possessing phenotypic variation explained (PVE) ≥ 10%. Besides, the four QTL Clusters/QTL Hotspots harboring multiple QTLs for different seed size-related traits were identified on Chr.04, Chr.16, Chr.19 and Chr.20. Genome-wide association study (GWAS) identified a total of 62 SNPs significantly associated with the four seed size-related traits. Interestingly, the QTL viz., qHSW18-1 was identified by both linkage mapping and GWAS, and was regarded as the most stable loci regulating HSW in soybean. In-silico, sequencing and qRT-PCR analysis identified the Glyma.18G242400 as the most potential candidate gene underlying the qHSW18-1 for regulating HSW. Moreover, three haplotype blocks viz., Hap2, Hap6A and Hap6B were identified for the SW trait, and one haplotype was identified within the Glyma.18G242400 for the HSW. These four haplotypes harbor three to seven haplotype alleles across the association mapping panel of 350 soybean accessions, regulating the seed size from lowest to highest through intermediate phenotypes. Hence, the outcome of the current investigation can be utilized as a potential genetic and genomic resource for breeding the improved seed size in soybean.

Effect of different Bradyrhizobium japonicum inoculants on physiological and agronomic traits of soybean (Glycine max (L.) Merr.) associated with different expression of nodulation genes

The aim of this work was to check the effects of five Bradyrhizobium japonicum inoculants from different producers on growth, photochemical efficiency, nitrate reductase activity, amount of hydrogen peroxide, activity of catalase, non-specific peroxidase, and superoxide dismutase, seed yield, and nodulation of soybean cv. ‘Malaga’. We also evaluated expression of such nodulation genes as NIC1, NOD21, and NORKb. The following inoculants were used: Nitragina IUNG, Nitragina Biofood, Nitroflora Mycoflor, Rhizobium Bio-Gen, and HiStick®Soy. The study was carried out on plants grown in pots in an open foil tunnel or glasshouse conditions. Experimental seeds were inoculated before sowing, while the control ones were not. Physiological and biochemical analyses involved the leaves, and gene expression was investigated in the roots. The bacterial inoculants significantly affected individual parameters of chlorophyll a fluorescence. The highest photochemical efficiency was induced by Nitroflora Mycoflor, which also activated nitrate reductase to the greatest extent. Nitragina IUNG most strongly stimulated accumulation of hydrogen peroxide, facilitated formation of the greatest number of nodules, and improved seed yield per plant in comparison with the control and other inoculants. Of the studied nodulation genes, NIC1 showed the strongest expression following HiStick®Soy treatment. Nitragina Biofood and Nitragina IUNG most significantly reduced the expression of all studied genes. Expression of NOD21 and NORKb exposed to other inoculants was the same as in the control plants. High content of hydrogen peroxide can be considered a marker of the inoculant ability to increase nodulation and yield of soybean. Commercial preparations containing the same amount of B. japonicum but different carriers of bacteria show different ability to form root nodules, and their differential impact on yield is related to the intensity of nodulation and expression of the Nod genes.

Transcriptional regulatory network reveals key transcription factors for regulating agronomic traits in soybean

BackgroundTranscription factors (TFs) bind regulatory genomic regions to orchestrate spatio-temporal expression of target genes. Global dissection of the cistrome is critical for elucidating transcriptional networks underlying complex agronomic traits in crops.ResultsHere, we generate a comprehensive genome-wide binding map for 148 TFs using DNA affinity purification sequencing in soybean. We find TF binding sites (TFBSs) exhibit elevated chromatin accessibility and contain more rare alleles than other genomic regions. Intriguingly, the methylation variations at TFBSs partially contribute to expression bias among whole genome duplication paralogs. Furthermore, we construct a soybean gene regulatory network (SoyGRN) by integrating TF-target interactions with diverse datasets encompassing gene expression, TFBS motifs, chromatin accessibility, and evolutionarily conserved regulation. SoyGRN comprises 2.44 million genome-wide interactions among 3188 TFs and 51,665 target genes. We successfully identify key TFs governing seed coat color and oil content and prioritize candidate genes within quantitative trait loci associated with various agronomic traits using SoyGRN. To accelerate utilization of SoyGRN, we develop an interactive webserver (www.soytfbase.cn) for soybean community to explore functional TFs involved in trait regulation.ConclusionsOverall, our study unravels intricate landscape of TF-target interactions in soybean and provides a valuable resource for dissecting key regulators for control of agronomic traits to accelerate soybean improvement.

Using red–green–blue vegetation indices to evaluate complex agronomical traits in soybean breeding

AbstractHigh‐throughput phenotyping is an emerging tool that allows access to identify simple and complex traits, accelerating genetic discoveries and selection. Vegetation indices strongly correlate with several economic crop traits, allowing plant breeders to detect variation in breeding populations. Thus, this study used red–green–blue (RGB) vegetation indices to evaluate the influence of the stink bug complex (Euschistus heros, Piezodorus guildinii, Nezara viridula, Dichelops melacanthus, and Edessa meditabunda) on the agronomical traits of soybean (Glycine max) lineages. For instance, two experiments were conducted to assess soybean resistance to the stink bug complex, (1) with and (2) without pesticide control. An unmanned aerial vehicle coupled with an RGB camera acquired aerial photography over the field during the R5 stage. Four vegetation indices and canopy were estimated from the orthomosaic, and the genotypes were evaluated based on agronomical traits. Linear mixed models were used to estimate the variance and significance test of each trait using the likelihood ratio test, and the principal component analysis was performed to verify the multivariate pattern among genotypes. The results showed significant genotypic effects for most traits with high broad‐sense heritability for agronomical traits and moderate for vegetation indices. Significant correlations using best linear unbiased predictions were observed among the agronomical traits with the vegetation indices and canopy coverage, which can be used as a tool for the indirect selection of soybean lineages in the breeding pipeline.

Characterization of high protein soybean using mass spectrometry based proteomic and metabolomic analyses.

We have created several high protein (HPBL) and low protein breeding lines (LPBL) by crossing between NC-Raleigh and PI407228. The NC-Raleigh is a low protein cultivar widely grown in North Carolina with excellent yield potential and a high protein wild soybean (Glycine soja) with low yield. We analyzed the F6 generation of this cross for protein and oil compositions using near-infrared spectroscopy (NIR). Two soybean lines HPBL and LPBL were selected for biochemical analysis using mass spectrometry-based proteomic and metabolomic techniques. We identified 4,538 proteins and 267 metabolites in these lines. The HPBL showed the enrichment of proteins associated with protein synthesis, processing, and storage in addition to higher content of primary and secondary metabolites. The mapping of metabolites on the global metabolic pathways revealed the increased protein content in the HPBL that may have resulted from increased arginine content routed from citrulline-argininosuccinic acid. This information will be useful for breeders and biotechnologists to produce a value-added soybean trait.

Unravelling Yield and Yield-Related Traits in Soybean Using GGE Biplot and Path Analysis

Soybean (Glycine max) is a vital crop for food, animal feed, and industrial products. However, its yield performance is significantly affected by genotype-by-environment interaction (GEI), which complicates the selection of high-yielding, stable varieties. This study aimed to evaluate the yield performance and stability of 12 elite soybean varieties across five major production areas in Uganda using GGE biplot and path analysis. The varieties were planted in a randomized complete block design with three replications over two consecutive seasons. Results revealed significant differences in grain yield among the varieties, locations, and their interactions (p < 0.001). The highest-yielding varieties were Maksoy 5N (979 kg ha−1), Maksoy 4N (978 kg ha−1), Maksoy 3N (930 kg ha−1), and Signal (930 kg ha−1). GGE biplot analysis grouped the locations into two mega-environments, with the Maksoy varieties exhibiting greater yield stability compared to Seed Co. varieties. Path analysis showed that traits such as the number of lower internodes, central internode length, and filled pods had the highest positive direct effects on grain yield. This study provides insights into soybean breeding in tropical environments, highlighting traits that can be targeted to improve yield and stability. The findings offer a framework for breeding programs in Uganda and similar agro-ecological regions, promoting more resilient and productive soybean varieties. This study also illustrated the potential advantages of employing more complex mathematical techniques like path analysis to uncover yield and yield-related traits in soybean breeding programs.

Genome-Wide Association Study of Seed Quality and Yield Traits in a Soybean Collection from Southeast Kazakhstan

Soybean (Glycine max (L.) Merr.) is a vital agricultural crop and a key source of protein and oil for food and feed production. The search for new genetic factors affecting the main agronomic traits of soybean is a significant step for efficient breeding strategies. This study aimed to identify marker–trait associations (MTAs) for seed protein and oil content and yield by conducting a genome-wide association study (GWAS). The collection of 252 soybean accessions of five different origins was analyzed over a period of five years. The GWAS was conducted using 44,385 SNP markers extracted from whole-genome resequencing data using Illumina HiSeq X Ten. The multiple-locus mixed linear model (MLMM) facilitated the identification of 38 stable MTAs: nine for protein content, nine for oil content, seven for the number of fertile nodes, six for the number of seeds per plant, four for thousand seeds weight, and three for yield per plant. Fifteen of these MTAs are presumed to be novel, with one linked to seed protein content, three linked to seed oil content, and the remaining MTAs linked to yield-related traits. These findings offer valuable insights for soybean breeding programs aimed at developing new, competitive cultivars with improved seed quality and yield characteristics.

Line x Tester Analysis of Combing Ability for Seed Yield and Its Contributing Traits in Soybean [Glycine max (L.) Merrill

Hybridization in Line x Tester mating design was conducted with seven lines and three diverse testers in soybean to understand the nature of gene action, combining ability of the parents (gca) and to assess the potential for the exploitation of heterosis (sca) in hybrids. Data on eleven quantitative characters viz., days to 50% flowering, plant height (cm), number of branches per plant, number of pods per plant, number of seeds per pod, pod length (cm), 100-seed weight (g), seed yield per plant (g), biological yield per plant (g) and harvest index (%) were collected on 21 hybrids and their 10 parents. The ratio of GCA variance to SCA variance is less than unity indicated that non additive genetic effects were predominant for control of most of the seed yield and its attributing characters. Among the lines, AUKS 238, RKS 18, RVS 2011-10 were found high gca effects which are use as general combiner parents for enhancing yield potential by assembling the favourable genes and crosses with sca effects such as AUKS 224 x JS 20-98 and RVS 2011-10 x NRC 150 were found to be superior for seed yield and yield components. These crosses may be utilized for heterosis breeding as well as for isolating the superior recombinants in advance generation for seed yield.

Genome-Wide Association study for root system architecture traits in field soybean [Glycine max (L.) Merr.]

Roots play a crucial role in plant development, serving to absorb water and nutrients from the soil while also providing structural stability. However, the impacts of global warming can impede root growth by altering soil conditions that hinder overall plant growth. To address this challenge, there is a need to screen and identify plant genotypes with superior Root System Architecture traits (RSA), that can be used for future breeding efforts in enhancing their resilience to these environmental changes. In this project, 500 mid to late-maturity soybean accessions were grown on blue blotting papers hydroponically with six replicates and assessed seven RSA traits. Genome-Wide Association Studies (GWAS) were carried out with root phenotypic data and SNP data from the SoySNP50K iSelect SNP BeadChip, using both the TASSEL 5.0 and FarmCPU techniques. A total of 26 significant SNP-trait correlations were discovered, with 11 SNPs on chromosome 13. After SNP selection, we identified 14 candidate genes within 100-kb regions flanking the SNPs, which are related to root architecture. Notably, Glyma.17G258700, which exhibited substantial differential expression in root tips and its Arabidopsis homolog, AT4G24190 (GRP94) is involved in the regulation of meristem size and organization. Other candidate genes includes Glyma.03G023000 and Glyma.13G273500 that are also play a key role in lateral root initiation and root meristem growth, respectively. These findings significantly contribute to the discovery of key genes associated with root system architecture, facilitating the breeding of resilient cultivars adaptable to changing climates.

Nitrogen Application and Seed Inoculation with Bradyrhizobium japonicum Bacteria Improved Yield and Quality Traits of Soybean [Glycine max (L.) Merrill] Grown as Second Crop

Nitrogen Application and Seed Inoculation with Bradyrhizobium japonicum Bacteria Improved Yield and Quality Traits of Soybean [Glycine max (L.) Merrill] Grown as Second Crop

Root traits of soybeans exposed to polyethylene films, polypropylene fragments, and biosolids

Biosolid use imports microplastics into the rhizosphere where they may interfere with root-soil-microbial interactions and cause morphological adaptations in crop root systems. Few studies have examined the response of crop roots to microplastics at documented soil concentrations, and many studies collect root traits using destructive techniques. Hence, there is little information on when and how microplastics effect the physical structure of root systems. Using the rhizobox method, soybeans (Glycine max) were grown in soil amended with biosolid microplastic mimics (polyethylene film or polypropylene fragments at 2,000 and 15,000 particles/kg dry soil) or biosolids and imaged weekly until maturity (11 weeks) using a custom scanner system. Plant biomass increased in the polyethylene treatments and decreased in the high concentration polypropylene treatment. Relative to the Control, polyethylene treatments had larger root length, reduced root diameters, reached maturity faster, had deeper root systems, and had a greater number of lateral roots. In contrast, polypropylene treatments had a mixed response, with high concentrations eliciting a lower root length, fewer laterals, and a more vertical root orientation. Segmented linear regression revealed that root growth in the Control and Biosolid treatments continued through the course of the experiment, while the microplastic treatments reached maturity up to two weeks earlier. Imagery revealed that microplastics elicited deeper rooting depth within the first week and differences in all root traits were evident by the development of the first trifoliate leaflets. Microplastic effects on root traits at early life stages suggest soil physiological drivers, while increased branching frequency and lower lateral elongation are suggestive of changes in soil nitrogen availability. The minimal difference in root traits in the biosolid treatment may be attributable to differences in microplastic properties or counteractive effects by other biosolid constituents.

Integrating targeted genetic markers to genotyping-by-sequencing for an ultimate genotyping tool.

New selection methods, using trait-specific markers (marker-assisted selection (MAS)) and/or genome-wide markers (genomic selection (GS)), are becoming increasingly widespread in breeding programs. This new era requires innovative and cost-efficient solutions for genotyping. Reduction in sequencing cost has enhanced the use of high-throughput low-cost genotyping methods such as genotyping-by-sequencing (GBS) for genome-wide single-nucleotide polymorphism (SNP) profiling in large breeding populations. However, the major weakness of GBS methodologies is their inability to genotype targeted markers. Conversely, targeted methods, such as amplicon sequencing (AmpSeq), often face cost constraints, hindering genome-wide genotyping across a large cohort. Although GBS and AmpSeq data can be generated from the same sample, an efficient method to achieve this is lacking. In this study, we present the Genome-wide & Targeted Amplicon (GTA) genotyping platform, an innovative way to integrate multiplex targeted amplicons into the GBS library preparation to provide an all-in-one cost-effective genotyping solution to breeders and research communities. Custom primers were designed to target 23 and 36 high-value markers associated with key agronomical traits in soybean and barley, respectively. The resulting multiplex amplicons were compatible with the GBS library preparation enabling both GBS and targeted genotyping data to be produced efficiently and cost-effectively. To facilitate data analysis, we have introduced Fast-GBS.v3, a user-friendly bioinformatic pipeline that generates comprehensive outputs from data obtained following sequencing of GTA libraries. This high-throughput low-cost approach will greatly facilitate the application of DNA markers as it provides required markers for both MAS and GS in a single assay.

Identification of quantitative trait loci for lodging and related agronomic traits in soybean (Glycine max [L.] Merr.)

BackgroundLodging, a crucial agronomic trait linked to soybean yield, poses a significant challenge in soybean production. Nevertheless, there has been less research on soybean lodging compared to other important agronomic traits, hindering progress in breeding high-yield soybeans. Our goals were to investigate lodging, pinpoint quantitative trait loci (QTL) linked to lodging, and forecast potential candidate genes linked to this trait. To achieve this, we employed a recombinant inbred line (RIL) population derived from a cross between Guizao 1 and B13 (GB) across various environments.ResultsThe lodging score of the RIL population was found to be significantly positively correlated with flowering time, maturity time, plant height, number of main stem nodes, stem diameter, and internode length, with correlation coefficients ranging from 0.457 to 0.783. A total of 84 QTLs associated with soybean lodging and related traits were identified using the GB population. The contribution of phenotypic variance ranged from 1.26 to 66.87%, with LOD scores ranging from 2.52 to 69.22. Additionally, within these QTLs, a stable major QTL associated with lodging was newly discovered in the GB population. Out of the ten major QTLs associated with other related traits, nine of them were situated within the qLD-4-1 interval of the major lodging score locus, displaying phenotypic variations ranging from 12.10 to 66.87%. Specific alterations in gene expression were revealed through the analysis of resequencing data from the two parental lines, potentially indicating their significant roles in lodging. Subsequently, it was determined through qRT-PCR that four genes are likely to be the major genes controlling soybean lodging.ConclusionsThis study’s findings offer valuable insights into the genetic underpinnings of soybean lodging resistance traits. By comprehending the potential genetic factors associated with lodging, this research lays the groundwork for breeding high-yield soybeans with improved lodging resistance.

Effects of warming and parasitism on root traits and the root economics space

Abstract Plants infected by parasitic species might be further stressed by climate warming as both factors can influence the host plant's nutrient acquisition and growth. The root economics space, defined by root functional traits, reflects a plant's nutrient acquisition strategy. However, the combined effects of warming and parasitism on root functional traits and their positions within the root economics space have not been extensively studies. We grew soybean plants in pot cultures outdoor in the absence or presence of parasitism by Cuscuta gronovii and with or without simulated climate warming using infrared heater lamps. We measured various root functional traits of soybean, including root morphological traits, symbiosis with arbuscular mycorrhizal fungi (AMF) and rhizobia and enzyme activity. Correlations among these traits were analysed, and principal component analysis was used to determine the position of the plants in the root economics space. Parasitism significantly reduced AMF colonization rate, nodule biomass, root tissue density (RTD) and phosphatase activity in the rhizosheath, while increasing root nitrogen concentration (RN). Nodule biomass was positively correlated with AMF colonization, indicating an ‘outsourcing’ strategy for nutrient foraging. Parasitized soybeans occupied a trait space with higher RN and lower RTD, indicative of a ‘fast’ nutrient acquisition strategy. Warming did not significantly affect root functional traits or the plants' positions in the root economics space, regardless of parasitism presence. This study is the first to demonstrate that rhizobial colonization, similar to AMF colonization, is part of the ‘outsourcing’ strategy and that parasitism shifts host plants from a ‘slow’ to a ‘fast’ nutrient uptake strategy. These findings provide new insights into plant below‐ground strategies under warming and parasitism. Read the free Plain Language Summary for this article on the Journal blog.

Evaluating the impact of phyto-hormones on the morpho-biochemical traits of soybean through seed treatment and foliar application

Global climate variations, mainly extreme temperatures reduce crop production and contributed to global food insecurity. Plant growth regulators (PGRs) improve physiological efficiency, photosynthetic capacity, and assimilate partitioning in field crops. However, their influence on soybean (Glycine max L.) yield and seed quality is under-researched. This study investigates the synergistic effects of auxin (2,4-D) and gibberellin (GA3) at 60 and 90 mg L−1 concentrations. This study investigates the impact of phytohormones, specifically gibberellic acid (GA3) and 2,4-dichlorophenoxyacetic acid (2,4-D), on the morphological and biochemical traits of soybean (Glycine max L.) through seed treatment and foliar application. These Growth regulators were applied through seed treatment at the time of sowing and foliar application at the time of flowering initiation stage. The experiment was designed using a RCBD arrangement and each block replicated thrice time. Seed treatment with 2,4-D at 90 mg/L showed that maximum plant height (116.80 cm), first node height (8.04 cm), No. of branches per plant (18.67), root length (18.12 cm) stem diameter (0.05 cm), no. of pods per plant (32.60), seed yield (1532.30 kg ha−1), biological yield (4109.30 ha−1), and harvest index (39.36 %). The GA3 (seed treatment) at conc. of 90 and 60 mg L−1 resulted in a significantly increased no. of nodule fresh weight, nodules per plant, and dry weight respectively. But the no. of seeds per pod did not showed significant results throughout the study. However, the foliar applied of 2,4-D at 90 mg L−1 resulted in significantly increased leaf area index (LAI) compared to the seed treatment. PGRs had a significant influence on quality traits. Thus, the application notably improved the potential of soybean and successful oilseed crop. Therefore, it is showed that using PGRs are extremely helpful for attaining higher growth, yield production, and improved quality of soybean.

Use of Phenomics in the Selection of UAV-Based Vegetation Indices and Prediction of Agronomic Traits in Soybean Subjected to Flooding

Flooding is a frequent environmental stress that reduces soybean growth and grain yield in many producing areas in the world, such as the United States, Southeast Asia, and Southern Brazil. In these regions, soybean is frequently cultivated in lowland areas in crop rotation with rice, which provides numerous technical, economic, and environmental benefits. In this context, the identification of the most important spectral variables for the selection of more flooding-tolerant soybean genotypes is a primary demand within plant phenomics, with faster and more reliable results enabled using multispectral sensors mounted on unmanned aerial vehicles (UAVs). Accordingly, this research aimed to identify the optimal UAV-based multispectral vegetation indices for characterizing the response of soybean genotypes subjected to flooding and to test the best linear model fit in predicting tolerance scores, relative maturity group, biomass, and grain yield based on phenomics analysis. Forty-eight soybean cultivars were sown in two environments (flooded and non-flooded). Ground evaluations and UAV-image acquisition were conducted at 13, 38, and 69 days after flooding and at grain harvest, corresponding to the phenological stages V8, R1, R3, and R8, respectively. Data were subjected to variance component analysis and genetic parameters were estimated, with stepwise regression applied for each agronomic variable of interest. Our results showed that vegetation indices behave differently in their suitability for more tolerant genotype selection. Using this approach, phenomics analysis efficiently identified indices with high heritability, accuracy, and genetic variation (>80%), as observed for MSAVI, NDVI, OSAVI, SAVI, VEG, MGRVI, EVI2, NDRE, GRVI, BNDVI, and RGB index. Additionally, variables predicted based on estimated genetic data via phenomics had determination coefficients above 0.90, enabling the reduction in the number of important variables within the linear model.

Effects of titanium oxide nanoparticles and 24-epibrassinosteroid to mitigate the toxicity of cadmium (Cd) and improve physio-morphological traits of soybean (Glycine max L.) cultivated under Cd-contaminated soil

Cadmium (Cd) toxicity is a serious environmental threat to living organisms. Nanoparticles (NPs) and plant growth regulators are able to mitigate Cd toxicity and restore crop growth in heavy metals-contaminated soils. However, the synergistic potential of combining 24-epibrassinosteroid (24-epiBRs) and titanium oxide nanoparticles (TiO2-NPs) to alleviate Cd toxicity and restore soybean (Glycine max L.) production remains unexplored. Thus, a pot-based experimental trial was conducted to assess the effects of applying TiO2-NPs (15 mg L−1) and 24-epiBRs (10−7 M), individually and in combination, on soybean growth in soil cultivated with 30 ppm of Cd. The study revealed that Cd toxicity significantly inhibited soybean root length (11.0 %), root dry biomass (63.5 %), root fresh biomass (84.9 %), shoot length (11.7 %), shoot dry biomass (49.0 %), and shoot fresh biomass (27.3 %), compared to the control. Additionally, the toxicity of Cd enhanced the oxidative stress and lowered the photosynthetic efficiency, gas exchange characteristics, and antioxidant defense system of soybeans. Interestingly, the combined application of TiO2-NPs and 24-epiBRs ameliorated the Cd toxic effects and improved the agronomic traits, photosynthesis efficiency, and antioxidant activity in soybeans by lowering oxidative stress. Specifically, the dual application of 24-epiBRs and TiO2-NPs effectively lowered the Cd levels in roots, shoots, and leaves of soybean plants by 62.5, 162.7, and 87.1 %, respectively, relative to the control soybean plants grown under Cd stress. Overall, the combined treatment of TiO2-NPs and 24-epiBRs synergistically reduced Cd uptake and restored soybean physiology in Cd-contaminated soils. Moving forward, further research should include field trials to assess the effectiveness and economic viability of this novel method.

Revolutionizing soybean genomics: How CRISPR and advanced sequencing are unlocking new potential.

Soybean Glycine max L., paleopolyploid genome, poses challenges to its genetic improvement. However, the development of reference genome assemblies and genome sequencing has completely changed the field of soybean genomics, allowing for more accurate and successful breeding techniques as well as research. During the single-cell revolution, one of the most advanced sequencing tools for examining the transcriptome landscape is single-cell RNA sequencing (scRNA-seq). Comprehensive resources for genetic improvement of soybeans may be found in the SoyBase and other genomics databases. CRISPR-Cas9 genome editing technology provides promising prospects for precise genetic modifications in soybean. This method has enhanced several soybean traits, including as yield, nutritional value, and resistance to both biotic and abiotic stresses. With base editing techniques that allow for precise DNA modifications, the use of CRISPR-Cas9 is further increased. With the availability of the reference genome for soybeans and the following assembly of wild and cultivated soybeans, significant chromosomal rearrangements and gene duplication events have been identified, offering new perspectives on the complex genomic structure of soybeans. Furthermore, major single nucleotide polymorphisms (SNPs) linked to stachyose and sucrose content have been found through genome-wide association studies (GWAS), providing important tools for enhancing soybean carbohydrate profiles. In order to open up new avenues for soybean genetic improvement, future research approaches include investigating transcriptional divergence processes, enhancing genetic resources, and incorporating CRISPR-Cas9 technologies.

Utilization of natural hybridization and intra‐cultivar variations for improving soybean yield, seed weight, and agronomic traits

AbstractSoybean (Glycine max) is a highly self‐pollinated species, but cross‐pollination occasionally occurs and variations within cultivars can be observed under certain conditions. To explore the potential uses of natural hybridization and intra‐cultivar/advanced line variations, 78 of breeding lines derived from the segregants of natural hybridization and the intra‐cultivar/line variations and their 17 source cultivars/lines were evaluated over four crop seasons for yield, seed weight, and other agronomic traits. All the lines were also genotyped using BARCSoySNP6K assays to compare the genetic similarities between the new lines and the source genotypes. Analysis of variance results indicated that genotypic differences, year effects, and genotype × year interactions were significant for all the traits. The broad‐sense heritability of the traits was estimated to be 67.22%–98.80%, suggesting that the traits were mainly affected by genetic factors. Compared with the source materials, yield of 34 breeding lines exceeded by >5%, and 17 of them had yield increases of 11.85%–41.59%. Seed weight increased significantly in 24 lines, and 11 lines showed improvements in both seed weight and yield, although there was a negative correlation between these two traits. In addition, 36 and 29 lines showed a shortened period of flowering and maturity, respectively. Plant height of 20 lines decreased by >8.5 cm. Genotypic matching rate between the new lines and the source materials varied from 48.86% to 99.90%. These results demonstrated that both segregations resulting from natural crossing and intra‐cultivar/line variations could be used to improve important traits in soybean.