Selection of GmSWEET39 for oil and protein improvement in soybean

Soybean hypocotyl elongation is regulated by a MYB33-SWEET11/21-GA2ox8c module involving long-distance sucrose transport

The length of hypocotyl affects the height of soybean and lodging resistance, thus determining the final grain yield. However, research on soybean hypocotyl length is scarce, and the regulatory mechanisms are not fully understood. Here, we identified a module controlling the transport of sucrose, where sucrose acts as a messenger moved from cotyledon to hypocotyl, regulating hypocotyl elongation. This module comprises four key genes, namely MYB33, SWEET11, SWEET21 and GA2ox8c in soybean. In cotyledon, MYB33 is responsive to sucrose and promotes the expression of SWEET11 and SWEET21, thereby facilitating sucrose transport from the cotyledon to the hypocotyl. Subsequently, sucrose transported from the cotyledon up-regulates the expression of GA2ox8c in the hypocotyl, which ultimately affects the length of the hypocotyl. During the domestication and improvement of soybean, an allele of MYB33 with enhanced abilities to promote SWEET11 and SWEET21 has gradually become enriched in landraces and cultivated varieties, SWEET11 and SWEET21 exhibit high conservation and have undergone a strong purified selection and GA2ox8c is under a strong artificial selection. Our findings identify a new molecular pathway in controlling soybean hypocotyl elongation and provide new insights into the molecular mechanism of sugar transport in soybean.

Deciphering PDH1's role in mung bean domestication: a genomic perspective on pod dehiscence

Mung bean (Vigna radiata) stands as a crucial legume crop in Asia, contributing to food security. However, our understanding of the underlying genetic foundation governing domesticated agronomic traits, especially those linked to pod architecture, remains largely unexplored. In this study, we delved into the genomic divergence between wild and domesticated mung bean varieties, leveraging germplasm obtained from diverse sources. Our findings unveiled pronounced variation in promoter regions (35%) between the two mung bean subpopulations, suggesting substantial changes in gene expression patterns during domestication. Leveraging transcriptome analysis using distinct reproductive stage pods and subpopulations, we identified candidate genes responsible for pod and seed architecture development, along with Genome-Wide Association Studies (GWAS) and Quantitative Trait Locus (QTL) analysis. Notably, our research conclusively confirmed PDH1 as a parallel domesticated gene governing pod dehiscence in legumes. This study imparts valuable insights into the genetic underpinnings of domesticated agronomic traits in mung bean, and simultaneously highlighting the parallel domestication of pivotal traits within the realm of legume crops.

MicroRNA164e suppresses NAC100 transcription factor-mediated synthesis of seed storage proteins in chickpea

Development of protein-enriched chickpea varieties necessitates an understanding of specific genes and key regulatory circuits that govern the synthesis of seed storage proteins (SSPs). Here, we demonstrated the novel involvement of Ca-miR164e-CaNAC100 in regulating SSP synthesis in chickpea. Ca-miRNA164e was significantly decreased during seed maturation, especially in high-protein accessions. The miRNA was found to directly target the transactivation conferring C-terminal region of a nuclear-localized transcription factor, CaNAC100 as revealed using RNA ligase-mediated-rapid amplification of cDNA ends and target mimic assays. The functional role of CaNAC100 was demonstrated through seed-specific overexpression (NACOE) resulting in significantly augmented seed protein content (SPC) consequential to increased SSP transcription. Further, NACOE lines displayed conspicuously enhanced seed weight but reduced numbers and yield. Conversely, a downregulation of CaNAC100 and SSP transcripts was evident in seed-specific overexpression lines of Ca-miR164e that culminated in significantly lowered SPC. CaNAC100 was additionally demonstrated to transactivate the SSP-encoding genes by directly binding to their promoters as demonstrated using electrophoretic mobility shift and dual-luciferase reporter assays. Taken together, our study for the first time established a distinct role of CaNAC100 in positively influencing SSP synthesis and its critical regulation by CamiR164e, thereby serving as an understanding that can be utilized for developing SPC-rich chickpea varieties.

Multi-Omics Analysis of a Chromosome Segment Substitution Line Reveals a New Regulation Network for Soybean Seed Storage Profile

Soybean, a major source of oil and protein, has seen an annual increase in consumption when used in soybean-derived products and the broadening of its cultivation range. The demand for soybean necessitates a better understanding of the regulatory networks driving storage protein accumulation and oil biosynthesis to broaden its positive impact on human health. In this study, we selected a chromosome segment substitution line (CSSL) with high protein and low oil contents to investigate the underlying effect of donor introgression on seed storage through multi-omics analysis. In total, 1479 differentially expressed genes (DEGs), 82 differentially expressed proteins (DEPs), and 34 differentially expressed metabolites (DEMs) were identified in the CSSL compared to the recurrent parent. Based on Gene Ontology (GO) term analysis and the Kyoto Encyclopedia of Genes and Genomes enrichment (KEGG), integrated analysis indicated that 31 DEGs, 24 DEPs, and 13 DEMs were related to seed storage functionality. Integrated analysis further showed a significant decrease in the contents of the seed storage lipids LysoPG 16:0 and LysoPC 18:4 as well as an increase in the contents of organic acids such as L-malic acid. Taken together, these results offer new insights into the molecular mechanisms of seed storage and provide guidance for the molecular breeding of new favorable soybean varieties.

High-throughput phenotyping using VIS/NIR spectroscopy in the classification of soybean genotypes for grain yield and industrial traits

Employing visible and near infrared sensors in high-throughput phenotyping provides insight into the relationship between the spectral characteristics of the leaf and the content of grain properties, helping soybean breeders to direct their program towards improving grain traits according to researchers' interests. Our research hypothesis is that the leaf reflectance of soybean genotypes can be directly related to industrial grain traits such as protein and fiber contents. Thus, the objectives of the study were: (i) to classify soybean genotypes according to the grain yield and industrial traits; (ii) to identify the algorithm(s) with the highest accuracy for classifying genotypes using leaf reflectance as model input; (iii) to identify the best input data for the algorithms to improve their performance. A field experiment was carried out in randomized block design with three replications and 32 soybean genotypes. At 60 days after emergence, spectral analysis was carried out on three leaf samples from each plot. A hyperspectral sensor was used to capture reflectance between the wavelengths from 450 to 824 nm. Representative spectral bands were selected and grouped into means. After harvest, grain yield was assessed and laboratory analyses of industrial traits were carried out. Spectral, industrial traits and yield data were subjected to statistical analysis. Data were analyzed by the following machine learning algorithms: J48 (J48) and REPTree (DT) decision trees, Random Forest (RF), Artificial Neural Networks (ANN), Support Vector Machine (SVM), and conventional Logistic Regression (LR) analysis. The clusters formed were used as the output of the models, while two groups of input data were used for the input of the models: the spectral variables (WL) noise-free obtained by the sensor (450-828 nm) and the spectral means of the selected bands (SB) (450.0-720.6 nm). Soybean genotypes were grouped according to their grain yield and industrial traits, in which the SVM and J48 algorithms performed better at classifying them. Using the spectral bands selected in the study improved the classification accuracy of the algorithms.

Genome-Wide Association Studies Prioritize Genes Controlling Seed Size and Reproductive Period Length in Soybean

Hundred-seed weight (HSW) and reproductive period length (RPL) are two major agronomic traits critical for soybean production and adaptation. However, both traits are quantitatively controlled by multiple genes that have yet to be comprehensively elucidated due to the lack of major genes; thereby, the genetic basis is largely unknown. In the present study, we conducted comprehensive genome-wide association analyses (GWAS) of HSW and RPL with multiple sets of accessions that were phenotyped across different environments. The large-scale analysis led to the identification of sixty-one and seventy-four significant QTLs for HSW and RPL, respectively. An ortholog-based search analysis prioritized the most promising candidate genes for the QTLs, including nine genes (TTG2, BZR1, BRI1, ANT, KLU, EOD1/BB, GPA1, ABA2, and ABI5) for HSW QTLs and nine genes (such as AGL8, AGL9, TOC1, and COL4) and six known soybean flowering time genes (E2, E3, E4, Tof11, Tof12, and FT2b) for RPL QTLs. We also demonstrated that some QTLs were targeted during domestication to drive the artificial selection of both traits towards human-favored traits. Local adaptation likely contributes to the increased genomic diversity of the QTLs underlying RPL. The results provide additional insight into the genetic basis of HSW and RPL and prioritize a valuable resource of candidate genes that merits further investigation to reveal the complex molecular mechanism and facilitate soybean improvement.

Exploring SoySNP50K and USDA Germplasm Collection Data to Find New QTLs Associated with Protein and Oil Content in Brazilian Genotypes

Genetic diversity within a germplasm collection plays a vital role in the success of breeding programs. However, comprehending this diversity and identifying accessions with desirable traits pose significant challenges. This study utilized publicly available data to investigate SNP markers associated with protein and oil content in Brazilian soybeans. Through this research, twenty-two new QTLs (Quantitative Trait Loci) were identified, and we highlighted the substantial influence of Roanoke, Lee and Bragg ancestor on the genetic makeup of Brazilian soybean varieties. Our findings demonstrate that certain markers are being lost in modern cultivars, while others maintain or even increase their frequency. These observations indicate genomic regions that have undergone selection during soybean introduction in Brazil and could be valuable in breeding programs aimed at enhancing protein or oil content.

Unveiling the structure-emulsifying function relationship of truncated recombinant forms of the SA01-OmpA protein opens up a new vista in bioemulsifiers

The emulsifying ability of SA01-OmpA (outer membrane protein A from Acinetobacter sp. SA01) was found to be constrained by challenges like low production efficiency and high costs associated with protein recovery from E. coli inclusion bodies, as described in our previous study. The present study sought to benefit from the advantages of the targeted truncating of SA01-OmpA protein, taking into account the reduced propensity of protein expression as inclusion bodies and cytotoxicity. Here, the structure and activity relationship of two truncated recombinant forms of SA01-OmpA protein was unraveled through a hybrid approach based on experimental data and computational methodologies, representing an innovative bioemulsifier with advantageous emulsifying activity. The recombinant truncated SA01-OmpA variants were cloned and heterologously expressed in E. coli host cells and subsequently purified. The results showed increased emulsifying activity of N-terminally truncated SA01-OmpA (NT-OmpA) compared to full-length SA01-OmpA. Molecular dynamics (MD) simulations analysis demonstrated a direct correlation between the C-terminally truncated SA01-OmpA (CT-OmpA) and its expression as inclusion bodies. Analysis of the structure-activity relationship of truncated variants of SA01-OmpA revealed that, compared to the full-length protein, deletion of the β-barrel portion from the N-terminal of SA01-OmpA increased the emulsifying activity of NT-OmpA while lowering its expression as inclusion bodies. Contrary to the full-length protein, the N-terminally truncated SA01-OmpA was not as cytotoxic, according to the MTT assay, FCM analysis, and AO/EB staining. The findings of this extensive study advance our knowledge of SA01-OmpA at the molecular level as well as the design and development of efficient bioemulsifiers.IMPORTANCEPrevious research (Shahryari et al. 2021, mSystems 6: e01175-20) introduced and characterized the SA01-OmpA protein as a multifaceted protein with a variety of functions, including maintaining cellular homeostasis under oxidative stress conditions, biofilm formation, outer membrane vesicles (OMV) biogenesis, and beneficial emulsifying capacity. By truncating the SA01-OmpA protein, the current study presents a unique method for developing protein-type bioemulsifiers. The findings indicate that the N-terminally truncated SA01-OmpA (NT-OmpA) has the potential to fully replace full-length SA01-OmpA as a novel bioemulsifier with significant emulsifying activity. This study opens up a new frontier in bioemulsifiers, shedding light on a possible relationship between the structure and activity of SA01-OmpA truncated forms.

SoyMD: a platform combining multi-omics data with various tools for soybean research and breeding

Advanced multi-omics technologies offer much information that can uncover the regulatory mechanisms from genotype to phenotype. In soybean, numerous multi-omics databases have been published. Although they cover multiple omics, there are still limitations when it comes to the types and scales of omics datasets and analysis methods utilized. This study aims to address these limitations by collecting and integrating a comprehensive set of multi-omics datasets. This includes 38 genomes, transcriptomes from 435 tissue samples, 125 phenotypes from 6686 accessions, epigenome data involving histone modification, transcription factor binding, chromosomal accessibility and chromosomal interaction, as well as genetic variation data from 24 501 soybean accessions. Then, common analysis pipelines and statistical methods were applied to mine information from these multi-omics datasets, resulting in the successful establishment of a user-friendly multi-omics database called SoyMD (https://yanglab.hzau.edu.cn/SoyMD/#/). SoyMD provides researchers with efficient query options and analysis tools, allowing them to swiftly access relevant omics information and conduct comprehensive multi-omics data analyses. Another notable feature of SoyMD is its capability to facilitate the analysis of candidate genes, as demonstrated in the case study on seed oil content. This highlights the immense potential of SoyMD in soybean genetic breeding and functional genomics research.

Mapping yield and yield-related traits using diverse common bean germplasm

Common bean (bean) is one of the most important legume crops, and mapping genes for yield and yield-related traits is essential for its improvement. However, yield is a complex trait that is typically controlled by many loci in crop genomes. The objective of this research was to identify regions in the bean genome associated with yield and a number of yield-related traits using a collection of 121 diverse bean genotypes with different yields. The beans were evaluated in replicated trials at two locations, over two years. Significant variation among genotypes was identified for all traits analyzed in the four environments. The collection was genotyped with the BARCBean6K_3 chip (5,398 SNPs), two yield/antiyield gene-based markers, and seven markers previously associated with resistance to common bacterial blight (CBB), including a Niemann-Pick polymorphism (NPP) gene-based marker. Over 90% of the single-nucleotide polymorphisms (SNPs) were polymorphic and separated the panel into two main groups of small-seeded and large-seeded beans, reflecting their Mesoamerican and Andean origins. Thirty-nine significant marker-trait associations (MTAs) were identified between 31 SNPs and 15 analyzed traits on all 11 bean chromosomes. Some of these MTAs confirmed genome regions previously associated with the yield and yield-related traits in bean, but a number of associations were not reported previously, especially those with derived traits. Over 600 candidate genes with different functional annotations were identified for the analyzed traits in the 200-Kb region centered on significant SNPs. Fourteen SNPs were identified within the gene model sequences, and five additional SNPs significantly associated with five different traits were located at less than 0.6 Kb from the candidate genes. The work confirmed associations between two yield/antiyield gene-based markers (AYD1m and AYD2m) on chromosome Pv09 with yield and identified their association with a number of yield-related traits, including seed weight. The results also confirmed the usefulness of the NPP marker in screening for CBB resistance. Since disease resistance and yield measurements are environmentally dependent and labor-intensive, the three gene-based markers (CBB- and two yield-related) and quantitative trait loci (QTL) that were validated in this work may be useful tools for simplifying and accelerating the selection of high-yielding and CBB-resistant bean cultivars.

Soybean genetics, genomics, and breeding for improving nutritional value and reducing antinutritional traits in food and feed

Soybean [Glycine max (L.) Merr.] is a globally important crop due to its valuable seed composition, versatile feed, food, and industrial end-uses, and consistent genetic gain. Successful genetic gain in soybean has led to widespread adaptation and increased value for producers, processors, and consumers. Specific focus on the nutritional quality of soybean seed composition for food and feed has further elucidated genetic knowledge and bolstered breeding progress. Seed components are historical and current targets for soybean breeders seeking to improve nutritional quality of soybean. This article reviews genetic and genomic foundations for improvement of nutritionally important traits, such as protein and amino acids, oil and fatty acids, carbohydrates, and specific food-grade considerations; discusses the application of advanced breeding technology such as CRISPR/Cas9 in creating seed composition variations; and provides future directions and breeding recommendations regarding soybean seed composition traits.

k-mer-based GWAS enhances the discovery of causal variants and candidate genes in soybean

Genome-wide association studies (GWAS) are powerful statistical methods that detect associations between genotype and phenotype at genome scale. Despite their power, GWAS frequently fail to pinpoint the causal variant or the gene controlling a given trait in crop species. Assessing genetic variants other than single-nucleotide polymorphisms (SNPs) could alleviate this problem. In this study, we tested the potential of structural variant (SV)- and k-mer-based GWAS in soybean by applying these methods as well as conventional SNP/indel-based GWAS to 13 traits. We assessed the performance of each GWAS approach based on loci for which the causal genes or variants were known from previous genetic studies. We found that k-mer-based GWAS was the most versatile approach and the best at pinpointing causal variants or candidate genes. Moreover, k-mer-based analyses identified promising candidate genes for loci related to pod color, pubescence form, and resistance to Phytophthora sojae. In our dataset, SV-based GWAS did not add value compared to k-mer-based GWAS and may not be worth the time and computational resources invested. Despite promising results, significant challenges remain regarding the downstream analysis of k-mer-based GWAS. Notably, better methods are needed to associate significant k-mers with sequence variation. Our results suggest that coupling k-mer- and SNP/indel-based GWAS is a powerful approach for discovering candidate genes in crop species.

A loss-of-function mutant allele of a glycosyl hydrolase gene has been co-opted for seed weight control during soybean domestication

The resultant DNA from loss-of-function mutation can be recruited in biological evolution and development. Here, we present such a rare and potential case of "to gain by loss" as a neomorphic mutation during soybean domestication for increasing seed weight. Using a population derived from a chromosome segment substitution line of Glycine max (SN14) and Glycine soja (ZYD06), a quantitative trait locus (QTL) of 100-seed weight (qHSW) was mapped on chromosome 11, corresponding to a truncated β-1, 3-glucosidase (βGlu) gene. The novel gene hsw results from a 14-bp deletion, causing a frameshift mutation and a premature stop codon in the βGlu. In contrast to HSW, the hsw completely lost βGlu activity and function but acquired a novel function to promote cell expansion, thus increasing seed weight. Overexpressing hsw instead of HSW produced large soybean seeds, and surprisingly, truncating hsw via gene editing further increased the seed size. We further found that the core 21-aa peptide of hsw and its variants acted as a promoter of seed size. Transcriptomic variation in these transgenic soybean lines substantiated the integration hsw into cell and seed size control. Moreover, the hsw allele underwent selection and expansion during soybean domestication and improvement. Our work cloned a likely domesticated QTL controlling soybean seed weight, revealed a novel genetic variation and mechanism in soybean domestication, and provided new insight into crop domestication and breeding, and plant evolution.

QTLs and Candidate Genes for Seed Protein Content in Two Recombinant Inbred Line Populations of Soybean

This study aimed to discover the quantitative trait loci (QTL) associated with a high seed protein content in soybean and unravel the potential candidate genes. We developed two recombinant inbred line populations: YS and SI, by crossing Saedanbaek (high protein) with YS2035-B-91-1-B-1 (low protein) and Saedanbaek with Ilmi (low protein), respectively, and evaluated the protein content for three consecutive years. Using single-nucleotide polymorphism (SNP)-marker-based linkage maps, four QTLs were located on chromosomes 15, 18, and 20 with high logarithm of odds values (5.9-55.0), contributing 5.5-66.0% phenotypic variance. In all three experimental years, qPSD20-1 and qPSD20-2 were stable and identified in overlapping positions in the YS and SI populations, respectively. Additionally, novel QTLs were identified on chromosomes 15 and 18. Considering the allelic sequence variation between parental lines, 28 annotated genes related to soybean seed protein-including starch, lipid, and fatty acid biosynthesis-related genes-were identified within the QTL regions. These genes could potentially affect protein accumulation during seed development, as well as sucrose and oil metabolism. Overall, this study offers insights into the genetic mechanisms underlying a high soybean protein content. The identified potential candidate genes can aid marker-assisted selection for developing soybean lines with an increased protein content.

Natural variation of GmFATA1B regulates seed oil content and composition in soybean

Soybean (Glycine max) produces seeds that are rich in unsaturated fatty acids and is an important oilseed crop worldwide. Seed oil content and composition largely determine the economic value of soybean. Due to natural genetic variation, seed oil content varies substantially across soybean cultivars. Although much progress has been made in elucidating the genetic trajectory underlying fatty acid metabolism and oil biosynthesis in plants, the causal genes for many quantitative trait loci (QTLs) regulating seed oil content in soybean remain to be revealed. In this study, we identified GmFATA1B as the gene underlying a QTL that regulates seed oil content and composition, as well as seed size in soybean. Nine extra amino acids in the conserved region of GmFATA1B impair its function as a fatty acyl-acyl carrier protein thioesterase, thereby affecting seed oil content and composition. Heterogeneously overexpressing the functional GmFATA1B allele in Arabidopsis thaliana increased both the total oil content and the oleic acid and linoleic acid contents of seeds. Our findings uncover a previously unknown locus underlying variation in seed oil content in soybean and lay the foundation for improving seed oil content and composition in soybean.

Identification of quantitative trait loci controlling soybean seed protein and oil content

Soybean is a major source of seed protein and oil globally with an average composition of 40% protein and 20% oil in the seed. The goal of this study was to identify quantitative trait loci (QTL) conferring seed protein and oil content utilizing a population constructed by crossing an above average protein content line, PI 399084 to another line that had a low protein content value, PI 507429, both from the USDA soybean germplasm collection. The recombinant inbred line (RIL) population, PI 507429 x PI 399084, was evaluated in two replications over four years (2018-2021); the seeds were analyzed for seed protein and oil content using near-infrared reflectance spectroscopy. The recombinant inbred lines and the two parents were re-sequenced using genotyping by sequencing. A total of 12,761 molecular markers, which came from genotyping by sequencing, the SoySNP6k BeadChip and selected simple sequence repeat (SSR) markers from known protein QTL chromosomal regions were used for mapping. One QTL was identified on chromosome 2 explaining up to 56.8% of the variation for seed protein content and up to 43% for seed oil content. Another QTL identified on chromosome 15 explained up to 27.2% of the variation for seed protein and up to 41% of the variation for seed oil content. The protein and oil QTLs of this study and their associated molecular markers will be useful in breeding to improve nutritional quality in soybean.

Identification of an important QTL for seed oil content in soybean

Seed oil content is one of the most important quantitative traits in soybean (Glycine max) breeding. Here, we constructed a high-density single nucleotide polymorphism linkage map using two genetically similar parents, Heinong 84 and Kenfeng 17, that differ dramatically in their seed oil contents, and performed quantitative trait loci (QTL) mapping of seed oil content in a recombinant inbred line (RIL) population derived from their cross. We detected five QTL related to seed oil content distributed on five chromosomes. The QTL for seed oil content explained over 10% of the phenotypic variation over two years. This QTL was mapped to an interval containing 20 candidate genes, including a previously reported gene, soybean RING Finger 1a (RNF1a) encoding an E3 ubiquitin ligase. Notably, two short sequences were inserted in the GmRNF1a coding region of KF 17 compared to that of HN 84, resulting in a longer protein variant in KF 17. Our results thus provide information for uncovering the genetic mechanisms determining seed oil content in soybean, as well as identifying an additional QTL and highlighting GmRNF1a as candidate gene for modulating seed oil content in soybean.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-023-01384-2.

Genetic mapping and functional genomics of soybean seed protein

Soybean is an utterly important crop for high-quality meal protein and vegetative oil. Soybean seed protein content has become a key factor in nutrients for livestock feed as well as human dietary consumption. Genetic improvement of soybean seed protein is highly desired to meet the demands of rapidly growing world population. Molecular mapping and genomic analysis in soybean have identified many quantitative trait loci (QTL) underlying seed protein content control. Exploring the mechanisms of seed storage protein regulation will be helpful to achieve the improvement of protein content. However, the practice of breeding higher protein soybean is challenging because soybean seed protein is negatively correlated with seed oil content and yield. To overcome the limitation of such inverse relationship, deeper insights into the property and genetic control of seed protein are required. Recent advances of soybean genomics have strongly enhanced the understandings for molecular mechanisms of soybean with better seed quality. Here, we review the research progress in the genetic characteristics of soybean storage protein, and up-to-date advances of molecular mappings and genomics of soybean protein. The key factors underlying the mechanisms of the negative correlation between protein and oil in soybean seeds are elaborated. We also briefly discuss the future prospects of breaking the bottleneck of the negative correlation to develop high protein soybean without penalty of oil and yield.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-023-01373-5.

Identification of QTLs and Putative Candidate Genes for Plant Architecture of Lotus Revealed by Regional Association Mapping

The lotus (Nelumbo Adans.) is one of the most economically relevant ornamental aquatic plants. Plant architecture (PA) is an important trait for lotus classification, cultivation, breeding, and applications. However, the underlying genetic and molecular basis controlling PA remains poorly understood. In this study, an association study for PA-related traits was performed with 93 genome-wide microsatellite markers (simple sequence repeat, SSR) and 51 insertion-deletion (InDel) markers derived from the candidate regions using a panel of 293 lotus accessions. Phenotypic data analysis of the five PA-related traits revealed a wide normal distribution and high heritability from 2013 to 2016, which indicated that lotus PA-related traits are highly polygenic traits. The population structure (Q-matrix) and the relative kinships (K-matrix) of the association panels were analyzed using 93 SSR markers. The mixed linear model (MLM) taking Q-matrix and K-matrix into account was used to estimate the association between markers and the traits. A total of 26 markers and 65 marker-trait associations were identified by considering associations with p < 0.001 and Q < 0.05. Based on the significant markers, two QTLs on Chromosome 1 were identified, and two candidate genes were preliminarily determined. The results of our study provided useful information for the lotus breeding aiming at different PA phenotypes using a molecular-assisted selection (MAS) method and also laid the foundation for the illustration of the molecular mechanism underlying the major QTL and key markers associated with lotus PA.

Genetic regulatory networks of soybean seed size, oil and protein contents

As a leading oilseed crop that supplies plant oil and protein for daily human life, increasing yield and improving nutritional quality (high oil or protein) are the top two fundamental goals of soybean breeding. Seed size is one of the most critical factors determining soybean yield. Seed size, oil and protein contents are complex quantitative traits governed by genetic and environmental factors during seed development. The composition and quantity of seed storage reserves directly affect seed size. In general, oil and protein make up almost 60% of the total storage of soybean seed. Therefore, soybean's seed size, oil, or protein content are highly correlated agronomical traits. Increasing seed size helps increase soybean yield and probably improves seed quality. Similarly, rising oil and protein contents improves the soybean's nutritional quality and will likely increase soybean yield. Due to the importance of these three seed traits in soybean breeding, extensive studies have been conducted on their underlying quantitative trait locus (QTLs) or genes and the dissection of their molecular regulatory pathways. This review summarized the progress in functional genome controlling soybean seed size, oil and protein contents in recent decades, and presented the challenges and prospects for developing high-yield soybean cultivars with high oil or protein content. In the end, we hope this review will be helpful to the improvement of soybean yield and quality in the future breeding process.

Genetic architecture of protein and oil content in soybean seed and meal

Soybean is grown primarily for the protein and oil extracted from its seed and its value is influenced by these components. The objective of this study was to map marker-trait associations (MTAs) for the concentration of seed protein, oil, and meal protein using the soybean nested association mapping (SoyNAM) population. The composition traits were evaluated on seed harvested from over 5000 inbred lines of the SoyNAM population grown in 10 field locations across 3 years. Estimated heritabilities were at least 0.85 for all three traits. The genotyping of lines with single nucleotide polymorphism markers resulted in the identification of 107 MTAs for the three traits. When MTAs for the three traits that mapped within 5 cM intervals were binned together, the MTAs were mapped to 64 intervals on 19 of the 20 soybean chromosomes. The majority of the MTA effects were small and of the 107 MTAs, 37 were for protein content, 39 for meal protein, and 31 for oil content. For cases where a protein and oil MTAs mapped to the same interval, most (94%) significant effects were opposite for the two traits, consistent with the negative correlation between these traits. A coexpression analysis identified candidate genes linked to MTAs and 18 candidate genes were identified. The large number of small effect MTAs for the composition traits suggest that genomic prediction would be more effective in improving these traits than marker-assisted selection.

Novel Seed Size: A Novel Seed-Developing Gene in Glycine max

Soybean-seed development is controlled in multiple ways, as in many known regulating genes. Here, we identify a novel gene, Novel Seed Size (NSS), involved in seed development, by analyzing a T-DNA mutant (S006). The S006 mutant is a random mutant of the GmFTL4pro:GUS transgenic line, with phenotypes with small and brown seed coats. An analysis of the metabolomics and transcriptome combined with RT-qPCR in the S006 seeds revealed that the brown coat may result from the increased expression of chalcone synthase 7/8 genes, while the down-regulated expression of NSS leads to small seed size. The seed phenotypes and a microscopic observation of the seed-coat integument cells in a CRISPR/Cas9-edited mutant nss1 confirmed that the NSS gene conferred small phenotypes of the S006 seeds. As mentioned in an annotation on the Phytozome website, NSS encodes a potential DNA helicase RuvA subunit, and no such genes were previously reported to be involved in seed development. Therefore, we identify a novel gene in a new pathway controlling seed development in soybeans.

MOTHER-OF-FT-AND-TFL1 regulates the seed oil and protein content in soybean

Soybean is a major crop that produces valuable seed oil and protein for global consumption. Seed oil and protein are regulated by complex quantitative trait loci (QTLs) and have undergone intensive selections during the domestication of soybean. It is essential to identify the major genetic components and understand their mechanism behind seed oil and protein in soybean. We report that MOTHER-OF-FT-AND-TFL1 (GmMFT) is the gene of a classical QTL that has been reported to regulate seed oil and protein content in many studies. Mutation of MFT decreased seeds oil content and weight in both Arabidopsis and soybean, whereas increased expression of GmMFT enhanced seeds oil content and weight. Haplotype analysis showed that GmMFT has undergone selection, which resulted in the extended haplotype homozygosity in the cultivated soybean and the enriching of the oil-favorable allele in modern soybean cultivars. This work unraveled the GmMFT-mediated mechanism regulating seed oil and protein content and seed weight, and revealed a previously unknown function of MFT that provides new insights into targeted soybean improvement and breeding.

Understandings and future challenges in soybean functional genomics and molecular breeding

Soybean (Glycine max) is a major source of plant protein and oil. Soybean breeding has benefited from advances in functional genomics. In particular, the release of soybean reference genomes has advanced our understanding of soybean adaptation to soil nutrient deficiencies, the molecular mechanism of symbiotic nitrogen (N) fixation, biotic and abiotic stress tolerance, and the roles of flowering time in regional adaptation, plant architecture, and seed yield and quality. Nevertheless, many challenges remain for soybean functional genomics and molecular breeding, mainly related to improving grain yield through high-density planting, maize-soybean intercropping, taking advantage of wild resources, utilization of heterosis, genomic prediction and selection breeding, and precise breeding through genome editing. This review summarizes the current progress in soybean functional genomics and directs future challenges for molecular breeding of soybean.

Bioengineering of Soybean Oil and Its Impact on Agronomic Traits

Soybean is a major oil crop and is also a dominant source of nutritional protein. The 20% seed oil content (SOC) of soybean is much lower than that in most oil crops and the fatty acid composition of its native oil cannot meet the specifications for some applications in the food and industrial sectors. Considerable effort has been expended on soybean bioengineering to tailor fatty acid profiles and improve SOC. Although significant advancements have been made, such as the creation of high-oleic acid soybean oil and high-SOC soybean, those genetic modifications have some negative impacts on soybean production, for instance, impaired germination or low protein content. In this review, we focus on recent advances in the bioengineering of soybean oil and its effects on agronomic traits.

Pinpointing Genomic Regions and Candidate Genes Associated with Seed Oil and Protein Content in Soybean through an Integrative Transcriptomic and QTL Meta-Analysis

Soybean with enriched nutrients has emerged as a prominent source of edible oil and protein. In the present study, a meta-analysis was performed by integrating quantitative trait loci (QTLs) information, region-specific association and transcriptomic analysis. Analysis of about a thousand QTLs previously identified in soybean helped to pinpoint 14 meta-QTLs for oil and 16 meta-QTLs for protein content. Similarly, region-specific association analysis using whole genome re-sequenced data was performed for the most promising meta-QTL on chromosomes 6 and 20. Only 94 out of 468 genes related to fatty acid and protein metabolic pathways identified within the meta-QTL region were found to be expressed in seeds. Allele mining and haplotyping of these selected genes were performed using whole genome resequencing data. Interestingly, a significant haplotypic association of some genes with oil and protein content was observed, for instance, in the case of FAD2-1B gene, an average seed oil content of 20.22% for haplotype 1 compared to 15.52% for haplotype 5 was observed. In addition, the mutation S86F in the FAD2-1B gene produces a destabilizing effect of (ΔΔG Stability) -0.31 kcal/mol. Transcriptomic analysis revealed the tissue-specific expression of candidate genes. Based on their higher expression in seed developmental stages, genes such as sugar transporter, fatty acid desaturase (FAD), lipid transporter, major facilitator protein and amino acid transporter can be targeted for functional validation. The approach and information generated in the present study will be helpful in the map-based cloning of regulatory genes, as well as for marker-assisted breeding in soybean.

A Multi-Year, Multi-Cultivar Approach to Differential Expression Analysis of High- and Low-Protein Soybean (Glycine max)

Soybean (Glycine max (L.) Merr.) is among the most valuable crops based on its nutritious seed protein and oil. Protein quality, evaluated as the ratio of glycinin (11S) to β-conglycinin (7S), can play a role in food and feed quality. To help uncover the underlying differences between high and low protein soybean varieties, we performed differential expression analysis on high and low total protein soybean varieties and high and low 11S soybean varieties grown in four locations across Eastern and Western Canada over three years (2018-2020). Simultaneously, ten individual differential expression datasets for high vs. low total protein soybeans and ten individual differential expression datasets for high vs. low 11S soybeans were assessed, for a total of 20 datasets. The top 15 most upregulated and the 15 most downregulated genes were extracted from each differential expression dataset and cross-examination was conducted to create shortlists of the most consistently differentially expressed genes. Shortlisted genes were assessed for gene ontology to gain a global appreciation of the commonly differentially expressed genes. Genes with roles in the lipid metabolic pathway and carbohydrate metabolic pathway were differentially expressed in high total protein and high 11S soybeans in comparison to their low total protein and low 11S counterparts. Expression differences were consistent between East and West locations with the exception of one, Glyma.03G054100. These data are important for uncovering the genes and biological pathways responsible for the difference in seed protein between high and low total protein or 11S cultivars.

Identification of QTN-by-environment interactions and their candidate genes for soybean seed oil-related traits using 3VmrMLM

INTRODUCTION

Although seed oil content and its fatty acid compositions in soybean were affected by environment, QTN-by-environment (QEIs) and gene-by-environment interactions (GEIs) were rarely reported in genome-wide association studies.

METHODS

The 3VmrMLM method was used to associate the trait phenotypes, measured in five to seven environments, of 286 soybean accessions with 106,013 SNPs for detecting QTNs and QEIs.

RESULTS

Seven oil metabolism genes (GmSACPD-A, GmSACPD-B, GmbZIP123, GmSWEET39, GmFATB1A, GmDGAT2D, and GmDGAT1B) around 598 QTNs and one oil metabolism gene GmFATB2B around 54 QEIs were verified in previous studies; 76 candidate genes and 66 candidate GEIs were predicted to be associated with these traits, in which 5 genes around QEIs were verified in other species to participate in oil metabolism, and had differential expression across environments. These genes were found to be related to soybean seed oil content in haplotype analysis. In addition, most candidate GEIs were co-expressed with drought response genes in co-expression network, and three KEGG pathways which respond to drought were enriched under drought stress rather than control condition; six candidate genes were hub genes in the co-expression networks under drought stress.

DISCUSSION

The above results indicated that GEIs, together with drought response genes in co-expression network, may respond to drought, and play important roles in regulating seed oil-related traits together with oil metabolism genes. These results provide important information for genetic basis, molecular mechanisms, and soybean breeding for seed oil-related traits.

Soybean Protein and Oil Variants Identified through a Forward Genetic Screen for Seed Composition

Mutagenesis remains an important tool in soybean biology. In classical plant mutation breeding, mutagenesis has been a trusted approach for decades, creating stable non-transgenic variation, and many mutations have been incorporated into germplasm for several crops, especially to introduce favorable seed composition traits. We performed a genetic screen for aberrant oil or protein composition of soybean seeds, and as a result isolated over 100 mutant lines for seed composition phenotypes, with particular interest in high protein or high oil phenotypes. These lines were followed for multiple seasons and generations to select the most stable traits for further characterization. Through backcrossing and outcrossing experiments, we determined that a subset of the lines showed recessive inheritance, while others showed a dominant inheritance pattern that suggests the involvement of multiple loci and genetic mechanisms. These lines can be used as a resource for future studies of the genetic control of seed protein and oil content in soybean.

Soybean genetic resources contributing to sustainable protein production

KEY MESSAGE

Genetic resources contributes to the sustainable protein production in soybean. Soybean is an important crop for food, oil, and forage and is the main source of edible vegetable oil and vegetable protein. It plays an important role in maintaining balanced dietary nutrients for human health. The soybean protein content is a quantitative trait mainly controlled by gene additive effects and is usually negatively correlated with agronomic traits such as the oil content and yield. The selection of soybean varieties with high protein content and high yield to secure sustainable protein production is one of the difficulties in soybean breeding. The abundant genetic variation of soybean germplasm resources is the basis for overcoming the obstacles in breeding for soybean varieties with high yield and high protein content. Soybean has been cultivated for more than 5000 years and has spread from China to other parts of the world. The rich genetic resources play an important role in promoting the sustainable production of soybean protein worldwide. In this paper, the origin and spread of soybean and the current status of soybean production are reviewed; the genetic characteristics of soybean protein and the distribution of resources are expounded based on phenotypes; the discovery of soybean seed protein-related genes as well as transcriptomic, metabolomic, and proteomic studies in soybean are elaborated; the creation and utilization of high-protein germplasm resources are introduced; and the prospect of high-protein soybean breeding is described.

GmSWEET29 and Paralog GmSWEET34 Are Differentially Expressed between Soybeans Grown in Eastern and Western Canada

Over the past two decades soybeans grown in western Canada have persistently had lower seed protein than those grown in eastern Canada. To understand the discrepancy in seed protein content between eastern- and western-grown soybeans, RNA-seq and differential expression analysis have been investigated. Ten soybean genotypes, ranging from low to high in seed protein content, were grown in four locations across eastern (Ottawa) and western (Morden, Brandon, and Saskatoon) Canada. Differential expression analysis revealed 34 differentially expressed genes encoding Glycine max Sugars Will Eventually be Exported Transporters (GmSWEETs), including paralogs GmSWEET29 and GmSWEET34 (AtSWEET2 homologs) that were consistently upregulated across all ten genotypes in each of the western locations over three years. GmSWEET29 and GmSWEET34 are likely candidates underlying the lower seed protein content of western soybeans. GmSWEET20 (AtSWEET12 homolog) was downregulated in the western locations and may also play a role in lower seed protein content. These findings are valuable for improving soybean agriculture in western growing regions, establishing more strategic and efficient agricultural practices.

Natural allelic variation of GmST05 controlling seed size and quality in soybean

Seed size is one of the most important agronomic traits determining the yield of crops. Cloning the key genes controlling seed size and pyramiding their elite alleles will facilitate yield improvement. To date, few genes controlling seed size have been identified in soybean, a major crop that provides half of the plant oil and one quarter of the plant protein globally. Here, through a genome-wide association study of over 1800 soybean accessions, we determined that natural allelic variation at GmST05 (Seed Thickness 05) predominantly controlled seed thickness and size in soybean germplasm. Further analyses suggested that the two major haplotypes of GmST05 differed significantly at the transcriptional level. Transgenic experiments demonstrated that GmST05 positively regulated seed size and influenced oil and protein contents, possibly by regulating the transcription of GmSWEET10a. Population genetic diversity analysis suggested that allelic variations of GmST05 were selected during geographical differentiation but have not been fixed. In summary, natural variation in GmST05 determines transcription levels and influences seed size and quality in soybean, making it an important gene resource for soybean molecular breeding.

Ensuring Global Food Security by Improving Protein Content in Major Grain Legumes Using Breeding and 'Omics' Tools

Grain legumes are a rich source of dietary protein for millions of people globally and thus a key driver for securing global food security. Legume plant-based 'dietary protein' biofortification is an economic strategy for alleviating the menace of rising malnutrition-related problems and hidden hunger. Malnutrition from protein deficiency is predominant in human populations with an insufficient daily intake of animal protein/dietary protein due to economic limitations, especially in developing countries. Therefore, enhancing grain legume protein content will help eradicate protein-related malnutrition problems in low-income and underprivileged countries. Here, we review the exploitable genetic variability for grain protein content in various major grain legumes for improving the protein content of high-yielding, low-protein genotypes. We highlight classical genetics-based inheritance of protein content in various legumes and discuss advances in molecular marker technology that have enabled us to underpin various quantitative trait loci controlling seed protein content (SPC) in biparental-based mapping populations and genome-wide association studies. We also review the progress of functional genomics in deciphering the underlying candidate gene(s) controlling SPC in various grain legumes and the role of proteomics and metabolomics in shedding light on the accumulation of various novel proteins and metabolites in high-protein legume genotypes. Lastly, we detail the scope of genomic selection, high-throughput phenotyping, emerging genome editing tools, and speed breeding protocols for enhancing SPC in grain legumes to achieve legume-based dietary protein security and thus reduce the global hunger risk.

POWR1 is a domestication gene pleiotropically regulating seed quality and yield in soybean

Seed protein, oil content and yield are highly correlated agronomically important traits that essentially account for the economic value of soybean. The underlying molecular mechanisms and selection of these correlated seed traits during soybean domestication are, however, less known. Here, we demonstrate that a CCT gene, POWR1, underlies a large-effect protein/oil QTL. A causative TE insertion truncates its CCT domain and substantially increases seed oil content, weight, and yield while decreasing protein content. POWR1 pleiotropically controls these traits likely through regulating seed nutrient transport and lipid metabolism genes. POWR1 is also a domestication gene. We hypothesize that the TE insertion allele is exclusively fixed in cultivated soybean due to selection for larger seeds during domestication, which significantly contributes to shaping soybean with increased yield/seed weight/oil but reduced protein content. This study provides insights into soybean domestication and is significant in improving seed quality and yield in soybean and other crop species.

Fine mapping and cloning of the major seed protein quantitative trait loci on soybean chromosome 20

Soybean is the most important source of protein meal worldwide and the quantitative trait loci (QTL) cqSeed protein‐003 on chromosome 20 exerts the greatest additive effect of any protein QTL mapped in the crop. Through genetic mapping and candidate gene downregulation, we identified that an insertion/deletion variant in Glyma.20G85100 is the likely gene that underlies this important QTL.

Development of a versatile resource for post-genomic research through consolidating and characterizing 1500 diverse wild and cultivated soybean genomes

BACKGROUND

With advances in next-generation sequencing technologies, an unprecedented amount of soybean accessions has been sequenced by many individual studies and made available as raw sequencing reads for post-genomic research.

RESULTS

To develop a consolidated and user-friendly genomic resource for post-genomic research, we consolidated the raw resequencing data of 1465 soybean genomes available in the public and 91 highly diverse wild soybean genomes newly sequenced. These altogether provided a collection of 1556 sequenced genomes of 1501 diverse accessions (1.5 K). The collection comprises of wild, landraces and elite cultivars of soybean that were grown in East Asia or major soybean cultivating areas around the world. Our extensive sequence analysis discovered 32 million single nucleotide polymorphisms (32mSNPs) and revealed a SNP density of 30 SNPs/kb and 12 non-synonymous SNPs/gene reflecting a high structural and functional genomic diversity of the new collection. Each SNP was annotated with 30 categories of structural and/or functional information. We further identified paired accessions between the 1.5 K and 20,087 (20 K) accessions in US collection as genomic "equivalent" accessions sharing the highest genomic identity for minimizing the barriers in soybean germplasm exchange between countries. We also exemplified the utility of 32mSNPs in enhancing post-genomics research through in-silico genotyping, high-resolution GWAS, discovering and/or characterizing genes and alleles/mutations, identifying germplasms containing beneficial alleles that are potentially experiencing artificial selection.

CONCLUSION

The comprehensive analysis of publicly available large-scale genome sequencing data of diverse cultivated accessions and the newly in-house sequenced wild accessions greatly increased the soybean genome-wide variation resolution. This could facilitate a variety of genetic and molecular-level analyses in soybean. The 32mSNPs and 1.5 K accessions with their comprehensive annotation have been made available at the SoyBase and Ag Data Commons. The dataset could further serve as a versatile and expandable core resource for exploring the exponentially increasing genome sequencing data for a variety of post-genomic research.

Progress in soybean functional genomics over the past decade

Soybean is one of the most important oilseed and fodder crops. Benefiting from the efforts of soybean breeders and the development of breeding technology, large number of germplasm has been generated over the last 100 years. Nevertheless, soybean breeding needs to be accelerated to meet the needs of a growing world population, to promote sustainable agriculture and to address future environmental changes. The acceleration is highly reliant on the discoveries in gene functional studies. The release of the reference soybean genome in 2010 has significantly facilitated the advance in soybean functional genomics. Here, we review the research progress in soybean omics (genomics, transcriptomics, epigenomics and proteomics), germplasm development (germplasm resources and databases), gene discovery (genes that are responsible for important soybean traits including yield, flowering and maturity, seed quality, stress resistance, nodulation and domestication) and transformation technology during the past decade. At the end, we also briefly discuss current challenges and future directions.

Global analysis of lysine acetylation in soybean leaves

Protein lysine acetylation (Kac) is an important post-translational modification in both animal and plant cells. Global Kac identification has been performed at the proteomic level in various species. However, the study of Kac in oil and resource plant species is relatively limited. Soybean is a globally important oil crop and resouce plant. In the present study, lysine acetylome analysis was performed in soybean leaves with proteomics techniques. Various bioinformatics analyses were performed to illustrate the structure and function of these Kac sites and proteins. Totally, 3148 acetylation sites in 1538 proteins were detected. Motif analysis of these Kac modified peptides extracted 17 conserved motifs. These Kac modified protein showed a wide subcellular location and functional distribution. Chloroplast is the primary subcellular location and cellular component where Kac proteins were localized. Function and pathways analyses indicated a plenty of biological processes and metabolism pathways potentially be influenced by Kac modification. Ribosome activity and protein biosynthesis, carbohydrate and energy metabolism, photosynthesis and fatty acid metabolism may be regulated by Kac modification in soybean leaves. Our study suggests Kac plays an important role in soybean physiology and biology, which is an available resource and reference of Kac function and structure characterization in oil crop and resource plant, as well as in plant kingdom.

Transcriptome profiling reveals the spatial-temporal dynamics of gene expression essential for soybean seed development

BACKGROUND

Seeds are the economic basis of oilseed crops, especially soybeans, the most widely cultivated oilseed crop worldwide. Seed development is accompanied by a multitude of diverse cellular processes, and revealing the underlying regulatory activities is critical for seed improvement.

RESULTS

In this study, we profiled the transcriptomes of developing seeds at 20, 25, 30, and 40 days after flowering (DAF), as these stages represent critical time points of seed development from early to full development. We identified a set of highly abundant genes and highlighted the importance of these genes in supporting nutrient accumulation and transcriptional regulation for seed development. We identified 8925 differentially expressed genes (DEGs) that exhibited temporal expression patterns over the course and expression specificities in distinct tissues, including seeds and nonseed tissues (roots, stems, and leaves). Genes specific to nonseed tissues might have tissue-associated roles, with relatively low transcript abundance in developing seeds, suggesting their spatially supportive roles in seed development. Coexpression network analysis identified several underexplored genes in soybeans that bridge tissue-specific gene modules.

CONCLUSIONS

Our study provides a global view of gene activities and biological processes critical for seed formation in soybeans and prioritizes a set of genes for further study. The results of this study help to elucidate the mechanism controlling seed development and storage reserves.