Molecular mapping and genomics of soybean seed protein: a review and perspective for the future

Comparative transcriptomic analysis provides insights into the genetic networks regulating oil differential production in oil crops

BACKGROUND

Plants differ more than threefold in seed oil contents (SOCs). Soybean (Glycine max), cotton (Gossypium hirsutum), rapeseed (Brassica napus), and sesame (Sesamum indicum) are four important oil crops with markedly different SOCs and fatty acid compositions.

RESULTS

Compared to grain crops like maize and rice, expanded acyl-lipid metabolism genes and relatively higher expression levels of genes involved in seed oil synthesis (SOS) in the oil crops contributed to the oil accumulation in seeds. Here, we conducted comparative transcriptomics on oil crops with two different SOC materials. In common, DIHYDROLIPOAMIDE DEHYDROGENASE, STEAROYL-ACYL CARRIER PROTEIN DESATURASE, PHOSPHOLIPID:DIACYLGLYCEROL ACYLTRANSFERASE, and oil-body protein genes were both differentially expressed between the high- and low-oil materials of each crop. By comparing functional components of SOS networks, we found that the strong correlations between genes in "glycolysis/gluconeogenesis" and "fatty acid synthesis" were conserved in both grain and oil crops, with PYRUVATE KINASE being the common factor affecting starch and lipid accumulation. Network alignment also found a conserved clique among oil crops affecting seed oil accumulation, which has been validated in Arabidopsis. Differently, secondary and protein metabolism affected oil synthesis to different degrees in different crops, and high SOC was due to less competition of the same precursors. The comparison of Arabidopsis mutants and wild type showed that CINNAMYL ALCOHOL DEHYDROGENASE 9, the conserved regulator we identified, was a factor resulting in different relative contents of lignins to oil in seeds. The interconnection of lipids and proteins was common but in different ways among crops, which partly led to differential oil production.

CONCLUSIONS

This study goes beyond the observations made in studies of individual species to provide new insights into which genes and networks may be fundamental to seed oil accumulation from a multispecies perspective.

Genetically optimizing soybean nodulation improves yield and protein content

Symbiotic nitrogen fixation in legume nodules requires substantial energy investment from host plants, and soybean (Glycine max (L.) supernodulation mutants show stunting and yield penalties due to overconsumption of carbon sources. We obtained soybean mutants differing in their nodulation ability, among which rhizobially induced cle1a/2a (ric1a/2a) has a moderate increase in nodule number, balanced carbon allocation, and enhanced carbon and nitrogen acquisition. In multi-year and multi-site field trials in China, two ric1a/2a lines had improved grain yield, protein content and sustained oil content, demonstrating that gene editing towards optimal nodulation improves soybean yield and quality.

Effect of Growth Stage on Nutrition, Fermentation Quality, and Microbial Community of Semidry Silage from Forage Soybean

Soybean (Glycine max (Linn.) Merr.) is highly suitable as animal feed. The silage quality and microbial characteristics of soybean silage are still unclear. Forage soybean (HN389), at six different growth stages (R2-R7), were used as experimental materials to investigate the changes in fermentation, nutritional quality, and microbial characteristics of semidry silage after 0, 7, 14, 30, and 45 d. As the growth period extended, the content of crude protein (CP) and crude fat (EE) gradually increased, while the neutral detergent fiber (NDF) and the acid detergent fiber (ADF) content decreased. The pH value also decreased gradually with fermentation time, accompanied by increases in the proportion of ammonia-N and the content of lactic acid (LA) and acetic acid (AA). In addition, competitive inhibition was observed in the microbial fermentation. With the process of ensiling, Lactobacillus became the dominant bacterial species. The results indicate that the most active stage of fermentation during ensiling occurred within the first 7 days, the fermentation and nutritional quality of the soybean forage were improved, and the optimal mowing stage was the grain stage. Comparison of the microbial abundance showed that all microorganisms entered a stable stage at 30 days of silage. After storage, the dominant bacteria were Lactobacillus, Enterobacter, and Pantoea.

Construction of Genetic Map and QTL Mapping for Seed Size and Quality Traits in Soybean (Glycine max L.)

Soybean (Glycine max L.) is the main source of vegetable protein and edible oil for humans, with an average content of about 40% crude protein and 20% crude fat. Soybean yield and quality traits are mostly quantitative traits controlled by multiple genes. The quantitative trait loci (QTL) mapping for yield and quality traits, as well as for the identification of mining-related candidate genes, is of great significance for the molecular breeding and understanding the genetic mechanism. In this study, 186 individual plants of the F2 generation derived from crosses between Changjiangchun 2 and Yushuxian 2 were selected as the mapping population to construct a molecular genetic linkage map. A genetic map containing 445 SSR markers with an average distance of 5.3 cM and a total length of 2375.6 cM was obtained. Based on constructed genetic map, 11 traits including hundred-seed weight (HSW), seed length (SL), seed width (SW), seed length-to-width ratio (SLW), oil content (OIL), protein content (PRO), oleic acid (OA), linoleic acid (LA), linolenic acid (LNA), palmitic acid (PA), stearic acid (SA) of yield and quality were detected by the multiple- d size traits and 113 QTLs related to quality were detected by the multiple QTL model (MQM) mapping method across generations F2, F2:3, F2:4, and F2:5. A total of 71 QTLs related to seed size traits and 113 QTLs related to quality traits were obtained in four generations. With those QTLs, 19 clusters for seed size traits and 20 QTL clusters for quality traits were summarized. Two promising clusters, one related to seed size traits and the other to quality traits, have been identified. The cluster associated with seed size traits spans from position 27876712 to 29009783 on Chromosome 16, while the cluster linked to quality traits spans from position 12575403 to 13875138 on Chromosome 6. Within these intervals, a reference genome of William82 was used for gene searching. A total of 36 candidate genes that may be involved in the regulation of soybean seed size and quality were screened by gene functional annotation and GO enrichment analysis. The results will lay the theoretical and technical foundation for molecularly assisted breeding in soybean.

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.

Introgression of a Danbaekkong high-protein allele across different genetic backgrounds in soybean

Soybean meal is a major component of livestock feed due to its high content and quality of protein. Understanding the genetic control of protein is essential to develop new cultivars with improved meal protein. Previously, a genomic region on chromosome 20 significantly associated with elevated protein content was identified in the cultivar Danbaekkong. The present research aimed to introgress the Danbaekkong high-protein allele into elite lines with different genetic backgrounds by developing and deploying robust DNA markers. A multiparent population consisting of 10 F5-derived populations with a total of 1,115 recombinant inbred lines (RILs) was developed using "Benning HP" as the donor parent of the Danbaekkong high-protein allele. A new functional marker targeting the 321-bp insertion in the gene Glyma.20g085100 was developed and used to track the Danbaekkong high-protein allele across the different populations and enable assessment of its effect and stability. Across all populations, the high-protein allele consistently increased the content, with an increase of 3.3% in seed protein. A total of 103 RILs were selected from the multiparent population for yield testing in five environments to assess the impact of the high-protein allele on yield and to enable the selection of new breeding lines with high protein and high yield. The results indicated that the high-protein allele impacts yield negatively in general; however, it is possible to select high-yielding lines with high protein content. An analysis of inheritance of the Chr 20 high-protein allele in Danbaekkong indicated that it originated from a Glycine soja line (PI 163453) and is the same as other G. soja lines studied. A survey of the distribution of the allele across 79 G. soja accessions and 35 Glycine max ancestors of North American soybean cultivars showed that the high-protein allele is present in all G. soja lines evaluated but not in any of the 35 North American soybean ancestors. These results demonstrate that G. soja accessions are a valuable source of favorable alleles for improvement of protein composition.

Predictor bias in genomic and phenomic selection

KEY MESSAGE

NIRS of wheat grains as phenomic predictors for grain yield show inflated prediction ability and are biased toward grain protein content. Estimating the breeding value of individuals using genome-wide marker data (genomic prediction) is currently one of the most important drivers of breeding progress in major crops. Recently, phenomic technologies, including remote sensing and aerial hyperspectral imaging of plant canopies, have made it feasible to predict the breeding value of individuals in the absence of genetic marker data. This is commonly referred to as phenomic prediction. Hyperspectral measurements in the form of near-infrared spectroscopy have been used since the 1980 s to predict compositional parameters of harvest products. Moreover, in recent studies NIRS from grains was used to predict grain yield. The same studies showed that phenomic prediction can outperform genomic prediction for grain yield. The genome is static and not environment dependent, thereby limiting genomic prediction ability. Gene expression is tissue specific and differs under environmental influences, leading to a tissue- and environment-specific phenome, potentially explaining the higher predictive ability of phenomic prediction. Here, we compare genomic prediction and phenomic prediction from hyperspectral measurements of wheat grains for the prediction of a variety of traits including grain yield. We show that phenomic predictions outperform genomic prediction for some traits. However, phenomic predictions are biased toward the information present in the predictor. Future studies on this topic should investigate whether population parameters are retained in phenomic prediction as they are in genomic prediction. Furthermore, we find that unbiased phenomic prediction abilities are considerably lower than previously reported and recommend a method to circumvent this issue.

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.

Emerging roles of long non-coding RNAs in regulating agriculturally important seed traits

Seeds have enormous economic importance as source of calories, nutrition, edible oil, and biofuels. Therefore, seed traits like seed size and shape, weight, micronutrient content, oil content, quality, post-harvest sprouting, etc., are some of the main targets in crop improvement. Designing the strategies for their improvement benefits heavily from understanding the regulatory aspects of seed development. Recent studies indicate that long non-coding RNAs (lncRNAs) are one of the important regulators of seed development. They played a significant role in crop domestication by influencing seed traits. LncRNAs are conventionally defined as non-coding RNAs greater than 200 bp in length but lacking protein coding potential. Here we highlight the emerging pieces of evidence of lncRNA-mediated regulation of seed development through diverse mechanisms, for instance, by acting as target mimics or precursors of regulatory small RNAs or through chromatin remodeling and post-transcriptional repression. We also enumerate the insights from high-throughput transcriptomic studies from developing seeds of cereal, oilseed, biofuel, and pulse crops. We highlight the lncRNA candidates and lncRNA-mediated regulatory networks regulating seed development and related agronomic traits. Further, we discuss the potential of lncRNAs for improvement of agriculturally important seed traits through marker-assisted breeding and/or transgenic approaches.

Exploring the Proteomic Profile of Soybean Bran: Unlocking the Potential for Improving Protein Quality and Quantity

Soybean is a rich source of vegetal protein for both animal and human consumption. Despite the high levels of protein in soybean seeds, industrial processing to obtain soybean bran significantly decreases the final protein content of the byproducts. To overcome this problem, cultivars with higher protein contents must be developed. However, selecting the target proteins is difficult because of the lack of information on the proteome profile of soybean bran. Therefore, this study obtained the comparative proteomic profiles of both natural coatless seeds and defatted bran from an elite tropical-soybean cultivar. Thus, their extracts were characterized using LC-MS/MS and a total of 550 proteins were identified. Among these, 526 proteins were detected in coatless seeds and 319 proteins in defatted bran. Moreover, a total of 139 proteins were identified as presenting different levels of content in coatless seeds and defatted bran. Among them, only 46 were retained after the seed processing. These proteins were clustered in several important metabolic pathways, such as amino-acid biosynthesis, sugar biosynthesis, and antioxidant activity, meaning that they could act as targets for bioactive products or genome editing to improve protein quality and quantity in soybean grains. These findings can enhance our understanding regarding protein robustness for both soybean crops and the commercial bran improvement because target proteins must remain intact after processing and must be bioactive when overexpressed. Overall, the soybean bran proteomic profile was explored for the first time, providing a valuable catalogue of target proteins that can tolerate the industrial process.

An Untargeted Metabolomics Approach to Study the Variation between Wild and Cultivated Soybeans

The differential metabolite profiles of four wild and ten cultivated soybeans genotypes were explored using an untargeted metabolomics approach. Ground soybean seed samples were extracted with methanol and water, and metabolic features were obtained using ultra-high-performance liquid chromatography coupled with high-resolution mass spectrometry (UHPLC-HRMS) in both positive and negative ion modes. The UHPLC-HRMS analysis of the two different extracts resulted in the putative identification of 98 metabolites belonging to several classes of phytochemicals, including isoflavones, organic acids, lipids, sugars, amino acids, saponins, and other compounds. The metabolic profile was significantly impacted by the polarity of the extraction solvent. Multivariate analysis showed a clear difference between wild and cultivated soybean cultivars. Unsupervised and supervised learning algorithms were applied to mine the generated data and to pinpoint metabolites differentiating wild and cultivated soybeans. The key identified metabolites differentiating wild and cultivated soybeans were isoflavonoids, free amino acids, and fatty acids. Catechin analogs, cynaroside, hydroxylated unsaturated fatty acid derivatives, amino acid, and uridine diphosphate-N-acetylglucosamine were upregulated in the methanol extract of wild soybeans. In contrast, isoflavonoids and other minor compounds were downregulated in the same soybean extract. This metabolic information will benefit breeders and biotechnology professionals to develop value-added soybeans with improved quality traits.

Application of SVR-Mediated GWAS for Identification of Durable Genetic Regions Associated with Soybean Seed Quality Traits

Soybean (Glycine max L.) is an important food-grade strategic crop worldwide because of its high seed protein and oil contents. Due to the negative correlation between seed protein and oil percentage, there is a dire need to detect reliable quantitative trait loci (QTL) underlying these traits in order to be used in marker-assisted selection (MAS) programs. Genome-wide association study (GWAS) is one of the most common genetic approaches that is regularly used for detecting QTL associated with quantitative traits. However, the current approaches are mainly focused on estimating the main effects of QTL, and, therefore, a substantial statistical improvement in GWAS is required to detect associated QTL considering their interactions with other QTL as well. This study aimed to compare the support vector regression (SVR) algorithm as a common machine learning method to fixed and random model circulating probability unification (FarmCPU), a common conventional GWAS method in detecting relevant QTL associated with soybean seed quality traits such as protein, oil, and 100-seed weight using 227 soybean genotypes. The results showed a significant negative correlation between soybean seed protein and oil concentrations, with heritability values of 0.69 and 0.67, respectively. In addition, SVR-mediated GWAS was able to identify more relevant QTL underlying the target traits than the FarmCPU method. Our findings demonstrate the potential use of machine learning algorithms in GWAS to detect durable QTL associated with soybean seed quality traits suitable for genomic-based breeding approaches. This study provides new insights into improving the accuracy and efficiency of GWAS and highlights the significance of using advanced computational methods in crop breeding research.

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.

Dysfunction of GmVPS8a causes compact plant architecture in soybean

Vacuolar Protein Sorting 8 (Vps8) protein is a specific subunit of the class C core vacuole/endosome tethering (CORVET) complex that plays a key role in endosomal trafficking in yeast (Saccharomyces cerevisiae). However, its functions remain largely unclear in plant vegetative growth. Here, we identified a soybean (Glycine max) T4219 mutant characterized with compact plant architecture. Map-based cloning targeted to a candidate gene GmVPS8a (Glyma.07g049700) and further found that two nucleotides deletion in the first exon of GmVPS8a causes a premature termination of the encoded protein in the T4219 mutant. Its functions were validated by CRISPR/Cas9-engineered mutation in the GmVPS8a gene that recapitulated the T4219 mutant phenotypes. Furthermore, NbVPS8a-silenced tobacco (Nicotiana benthamiana) plants exhibited similar phenotypes to the T4219 mutant, suggesting its conserved roles in plant growth. The GmVPS8a is widely expressed in multiple organs and its protein interacts with GmAra6a and GmRab5a. Combined analysis of transcriptomic and proteomic data revealed that dysfunction of GmVPS8a mainly affects pathways on auxin signal transduction, sugar transport and metabolism, and lipid metabolism. Collectively, our work reveals the function of GmVPS8a in plant architecture, which may extend a new way for genetic improvement of ideal plant-architecture breeding in soybean and other crops.

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.

Improved Field-Based Soybean Seed Counting and Localization with Feature Level Considered

Developing automated soybean seed counting tools will help automate yield prediction before harvesting and improving selection efficiency in breeding programs. An integrated approach for counting and localization is ideal for subsequent analysis. The traditional method of object counting is labor-intensive and error-prone and has low localization accuracy. To quantify soybean seed directly rather than sequentially, we propose a P2PNet-Soy method. Several strategies were considered to adjust the architecture and subsequent postprocessing to maximize model performance in seed counting and localization. First, unsupervised clustering was applied to merge closely located overcounts. Second, low-level features were included with high-level features to provide more information. Third, atrous convolution with different kernel sizes was applied to low- and high-level features to extract scale-invariant features to factor in soybean size variation. Fourth, channel and spatial attention effectively separated the foreground and background for easier soybean seed counting and localization. At last, the input image was added to these extracted features to improve model performance. Using 24 soybean accessions as experimental materials, we trained the model on field images of individual soybean plants obtained from one side and tested them on images obtained from the opposite side, with all the above strategies. The superiority of the proposed P2PNet-Soy in soybean seed counting and localization over the original P2PNet was confirmed by a reduction in the value of the mean absolute error, from 105.55 to 12.94. Furthermore, the trained model worked effectively on images obtained directly from the field without background interference.

Expression of malic enzyme reveals subcellular carbon partitioning for storage reserve production in soybeans

Central metabolism produces amino and fatty acids for protein and lipids that establish seed value. Biosynthesis of storage reserves occurs in multiple organelles that exchange central intermediates including two essential metabolites, malate, and pyruvate that are linked by malic enzyme. Malic enzyme can be active in multiple subcellular compartments, partitioning carbon and reducing equivalents for anabolic and catabolic requirements. Prior studies based on isotopic labeling and steady-state metabolic flux analyses indicated malic enzyme provides carbon for fatty acid biosynthesis in plants, though genetic evidence confirming this role is lacking. We hypothesized that increasing malic enzyme flux would alter carbon partitioning and result in increased lipid levels in soybeans. Homozygous transgenic soybean plants expressing Arabidopsis malic enzyme alleles, targeting the translational products to plastid or outside the plastid during seed development, were verified by transcript and enzyme activity analyses, organelle proteomics, and transient expression assays. Protein, oil, central metabolites, cofactors, and acyl-acyl carrier protein (ACPs) levels were quantified overdevelopment. Amino and fatty acid levels were altered resulting in an increase in lipids by 0.5-2% of seed biomass (i.e. 2-9% change in oil). Subcellular targeting of a single gene product in central metabolism impacts carbon and reducing equivalent partitioning for seed storage reserves 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.

Identification and Mapping of QTLs for Sulfur-Containing Amino Acids in Soybean (Glycine max L.)

Soybean is a major source of high-quality protein for humans and animals. The content of sulfur-containing amino acids (SAA) in soybean is insufficient, which has become the main factor limiting soybean nutrition. In this study, we used the high-density genetic maps derived from Guizao 1 and Brazil 13 to evaluate the quantitative trait loci of cysteine (Cys), methionine (Met), SAA, glycinin (7S), β-conglycinin (11S), ratio of glycinin to β-conglycinin (RGC), and protein content (PC). In genetic map linkage analysis, the major and stable 44 QTLs were detected, which shared nine bin intervals. Among them, the bin interval (bin157-bin160) on chromosome 5 was detected in multiple environments as a stable QTL, which was linked to 11S, 7S, RGC, and SSA. Based on the analysis of bioinformatics and RNA-sequencing data, 16 differentially expressed genes (DEGs) within these QTLs were selected as candidate genes. These results will help to elucidate the genetic mechanism of soybean SAA-related traits and provide the basis for the gene mining of sulfur-containing amino acids.

RNAi and CRISPR-Cas silencing E3-RING ubiquitin ligase AIP2 enhances soybean seed protein content

The majority of plant protein in the world's food supply is derived from soybean (Glycine max). Soybean is a key protein source for global animal feed and is incorporated into plant-based foods for people, including meat alternatives. Soybean protein content is genetically variable and is usually inversely related to seed oil content. ABI3-interacting protein 2 (AIP2) is an E3-RING ubiquitin ligase that targets the seed-specific transcription factor ABI3. Silencing both soybean AIP2 genes (AIP2a and AIP2b) by RNAi enhanced seed protein content by up to seven percentage points, with no significant decrease in seed oil content. The protein content enhancement did not alter the composition of the seed storage proteins. Inactivation of either AIP2a or AIP2b by a CRISPR-Cas9-mediated mutation increased seed protein content, and this effect was greater when both genes were inactivated. Transactivation assays in transfected soybean hypocotyl protoplasts indicated that ABI3 changes the expression of glycinin, conglycinin, 2S albumin, and oleosin genes, indicating that AIP2 depletion increased seed protein content by regulating activity of the ABI3 transcription factor protein. These results provide an example of a gene-editing prototype directed to improve global food security and protein availability in soybean that may also be applicable to other protein-source crops.

Identification of loci governing soybean seed protein content via genome-wide association study and selective signature analyses

Soybean [Glycine max (L.) Merr.] is an excellent source of protein. Understanding the genetic basis of protein content (PC) will accelerate breeding efforts to increase soybean quality. In the present study, a genome-wide association study (GWAS) was applied to detect quantitative trait loci (QTL) for PC in soybean using 264 re-sequenced soybean accessions and a high-quality single nucleotide polymorphism (SNP) map. Eleven QTL were identified as associated with PC. The QTL qPC-14 was detected by GWAS in both environments and was shown to have undergone strong selection during soybean improvement. Fifteen candidate genes were identified in qPC-14, and three candidate genes showed differential expression between a high-PC and a low-PC variety during the seed development stage. The QTL identified here will be of significant use in molecular breeding efforts, and the candidate genes will play essential roles in exploring the mechanisms of protein biosynthesis.

Valorization of Local Legumes and Nuts as Key Components of the Mediterranean Diet

Legumes and nuts are components of high importance in the diet of many countries, mainly those in the Mediterranean region. They are also very versatile and culturally diverse foods found all over the world, acting as a basic protein source in certain countries. Their genetic diversity is needed to sustain the food supply and security for humans and livestock, especially because of the current loss of habitats, species, and genetic diversity worldwide, but also because of the ever present need to feed the increasing human population. Even though both legumes and nuts are considered as high-protein food and environmentally friendly crops, developed countries have lower consumption rates when compared to Asia or Africa. With a view to increasing the consumption of legumes and nuts, the objective of this review is to present the advantages on the use of autochthonous varieties from different countries around the world, thus providing a boost to the local market in the area. The consumption of these varieties could be helped by their use in ready-to-eat foods (RTE), which are now on the rise thanks to today's fast-paced lifestyles and the search for more nutritious and sustainable foods. The versatility of legumes and nuts covers a wide range of possibilities through their use in plant-based dairy analogues, providing alternative-protein and maximal amounts of nutrients and bioactive compounds, potential plant-based flours for bakery and pasta, and added-value traditional RTE meals. For this reason, information about legume and nut nutrition could possibly increase its acceptance with consumers.

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.

Irradiation with carbon ion beams affects soybean nutritional quality in early generations

As people's demand for healthy diet increases, improving soybean seed nutritional quality is becoming as important as yield. Carbon ion beam radiation (CIBR) is an effective method to create soybean mutants, and thus breeding cultivars with better seed nutritional quality. In this study, the high-yield soybean line 'Dongsheng 28' was used, and three CIBR doses (100, 120, and 140 Gy) were used to explore the characteristics of quality separation and variation in the offspring of early mutant populations. Eleven quality traits, including protein, oil, sucrose, soluble sugar, iron (Fe), manganese (Mn), zinc (Zn), cupper (Cu), daidzin, glycitin, and genistin concentrations were analyzed in the M₂ and M₃ generations. The results revealed that the range of protein and oil concentration of all three CIBR doses changed by 38.5-42.9% and 18.8-23.8% in the M₂ and M₃ generations, respectively, while soluble sugar and sucrose concentrations changed by 48.1-123.4 and 22.7-74.7 mg/g, with significant effects by 140 Gy across the two generations. Therefore, around the optimum range, a higher CIBR dose is better for high protein, oil, and sugar varieties selection. In general, irradiation raised isoflavone concentrations, but 140 Gy had an inhibitory effect on isoflavone concentrations in the M₃ generation. Although a variety could not be released in the M₂ or M₃ generation, the results of this study have important guiding significance for the targeted cultivation of specific nutritional quality materials. For instance, a lower irradiation dose is preferable when breeding targets are higher isoflavones and Mn concentrations. It is essential to increase the irradiation dose if the breeding targets contain high levels of protein, oil, sucrose, soluble sugars, Fe, Zn, and Cu.

Using transcriptomic and metabolomic data to investigate the molecular mechanisms that determine protein and oil contents during seed development in soybean

Soybean [Glycine max (L.) Merri.] is one of the most valuable global crops. And vegetable soybean, as a special type of soybean, provides rich nutrition in people's life. In order to investigate the gene expression networks and molecular regulatory mechanisms that regulate soybean seed oil and protein contents during seed development, we performed transcriptomic and metabolomic analyses of soybean seeds during development in two soybean varieties that differ in protein and oil contents. We identified a total of 41,036 genes and 392 metabolites, of which 12,712 DEGs and 315 DAMs were identified. Analysis of KEGG enrichment demonstrated that DEGs were primarily enriched in phenylpropanoid biosynthesis, glycerolipid metabolism, carbon metabolism, plant hormone signal transduction, linoleic acid metabolism, and the biosynthesis of amino acids and secondary metabolites. K-means analysis divided the DEGs into 12 distinct clusters. We identified candidate gene sets that regulate the biosynthesis of protein and oil in soybean seeds, and present potential regulatory patterns that high seed-protein varieties may be more sensitive to desiccation, show earlier photomorphogenesis and delayed leaf senescence, and thus accumulate higher protein contents than high-oil varieties.

Identification of Quantitative Trait Loci Associated with Seed Protein Concentration in a Pea Recombinant Inbred Line Population

This research aimed to identify quantitative trait loci (QTLs) associated with seed protein concentration in a recombinant inbred line (RIL) population of pea and aimed to validate the identified QTLs using chromosome segment-introgressed lines developed by recurrent backcrossing. PR-25, an RIL population consisting of 108 F7 bulked lines derived from a cross between CDC Amarillo (yellow cotyledon) and CDC Limerick (green cotyledon), was used in this research. The RIL population was genotyped using an Axiom 90K SNP array. A total of 10,553 polymorphic markers were used for linkage map construction, after filtering for segregation distortion and missing values. The linkage map represents 901 unique loci on 11 linkage groups which covered a map distance of 855.3 Centimorgans. Protein concentration was assessed using near-infrared (NIR) spectroscopy of seeds harvested from field trials in seven station-years in Saskatchewan, Canada, during the 2019-2021 field seasons. Three QTLs located on chromosomes 2, 3 and 5 were identified to be associated with seed protein concentration. These QTLs explained 22%, 11% and 17% of the variation for protein concentration, respectively. The identified QTLs were validated by introgression lines, developed by marker-assisted selection of backcross lines for introgression of corresponding chromosome segments (~1/4 chromosome) harboring the QTL regions. Introgression line PR-28-7, not carrying any protein-related QTLs identified in this study, was 4.7% lower in protein concentration than CDC Amarillo, the lower protein parent of PR-25 which carried one identified protein-related QTL. The SNP markers located at the peak of the three identified QTLs will be converted into breeder-friendly KASP assays, which will be used for the selection of high-protein lines from segregating populations.

Transgressive Potential Prediction and Optimal Cross Design of Seed Protein Content in the Northeast China Soybean Population Based on Full Exploration of the QTL-Allele System

Northeast China is a major soybean production region in China. A representative sample of the Northeast China soybean germplasm population (NECSGP) composed of 361 accessions was evaluated for their seed protein content (SPC) in Tieling, Northeast China. This SPC varied greatly, with a mean SPC of 40.77%, ranging from 36.60 to 46.07%, but it was lower than that of the Chinese soybean landrace population (43.10%, ranging from 37.51 to 50.46%). The SPC increased slightly from 40.32-40.97% in the old maturity groups (MG, MGIII + II + I) to 40.93-41.58% in the new MGs (MG0 + 00 + 000). The restricted two-stage multi-locus genome-wide association study (RTM-GWAS) with 15,501 SNP linkage-disequilibrium block (SNPLDB) markers identified 73 SPC quantitative trait loci (QTLs) with 273 alleles, explaining 71.70% of the phenotypic variation, wherein 28 QTLs were new ones. The evolutionary changes of QTL-allele structures from old MGs to new MGs were analyzed, and 97.79% of the alleles in new MGs were inherited from the old MGs and 2.21% were new. The small amount of new positive allele emergence and possible recombination between alleles might explain the slight SPC increase in the new MGs. The prediction of recombination potentials in the SPC of all the possible crosses indicated that the mean of SPC overall crosses was 43.29% (+2.52%) and the maximum was 50.00% (+9.23%) in the SPC, and the maximum transgressive potential was 3.93%, suggesting that SPC breeding potentials do exist in the NECSGP. A total of 120 candidate genes were annotated and functionally classified into 13 categories, indicating that SPC is a complex trait conferred by a gene network.

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.

Protein Sources Alternative to Meat: State of the Art and Involvement of Fermentation

Meat represents an important protein source, even in developing countries, but its production is scarcely sustainable, and its excessive consumption poses health issues. An increasing number of Western consumers would replace, at least partially, meat with alternative protein sources. This review aims at: (i) depicting nutritional, functional, sensory traits, and critical issues of single-cell proteins (SCP), filamentous fungi, microalgae, vegetables (alone or mixed with milk), and insects and (ii) displaying how fermentation could improve their quality, to facilitate their use as food items/ingredients/supplements. Production of SCP (yeasts, filamentous fungi, microalgae) does not need arable land and potable water and can run continuously, also using wastes and byproducts. Some filamentous fungi are also consumed as edible mushrooms, and others are involved in the fermentation of traditional vegetable-based foods. Cereals, pseudocereals, and legumes may be combined to offer an almost complete amino acid profile. Fermentation of such vegetables, even in combination with milk-based products (e.g., tarhana), could increase nutrient concentrations, including essential amino acids, and improve sensory traits. Different insects could be used, as such or, to increase their acceptability, as ingredient of foods (e.g., pasta). However, insects as a protein source face with safety concerns, cultural constraints, and a lack of international regulatory framework.

The Role of Phytohormones in Plant Response to Flooding

Climatic variations influence the morphological, physiological, biological, and biochemical states of plants. Plant responses to abiotic stress include biochemical adjustments, regulation of proteins, molecular mechanisms, and alteration of post-translational modifications, as well as signal transduction. Among the various abiotic stresses, flooding stress adversely affects the growth of plants, including various economically important crops. Biochemical and biological techniques, including proteomic techniques, provide a thorough understanding of the molecular mechanisms during flooding conditions. In particular, plants can cope with flooding conditions by embracing an orchestrated set of morphological adaptations and physiological adjustments that are regulated by an elaborate hormonal signaling network. With the help of these findings, the main objective is to identify plant responses to flooding and utilize that information for the development of flood-tolerant plants. This review provides an insight into the role of phytohormones in plant response mechanisms to flooding stress, as well as different mitigation strategies that can be successfully administered to improve plant growth during stress exposure. Ultimately, this review will expedite marker-assisted genetic enhancement studies in crops for developing high-yield lines or varieties with flood tolerance.

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.

Selenium Biofortification of Soybean Sprouts: Effects of Selenium Enrichment on Proteins, Protein Structure, and Functional Properties

Selenium (Se) biofortification during germination is an efficient method for producing Se-enriched soybean sprouts; however, few studies have investigated Se distribution in different germinated soybean proteins and its effects on protein fractions. Herein, we examined Se distribution and speciation in the dominant proteins 7S and 11S of raw soybean (RS), germinated soybean (GS), and germinated soybean with Se biofortification (GS-Se). The effects of germination and Se treatment on protein structure, functional properties, and antioxidant capacity were also determined. The Se concentration in GS-Se was 79.8-fold higher than that in GS. Selenomethionine and methylselenocysteine were the dominant Se species in GS-Se, accounting for 41.5–80.5 and 19.5–21.2% of the total Se with different concentrations of Se treatment, respectively. Se treatment had no significant effects on amino acids but decreased methionine in 11S. In addition, the α-helix contents decreased as the Se concentration increased; the other structures showed no significant changes. The Se treatment also had no significant effects on the water and oil-holding capacities in protein but increased the foaming capacity and emulsion activity index (EAI) of 7S, but only the EAI of 11S. The Se treatment also significantly increased the antioxidant capacity in 7S but not in 11S. This study indicates that the dominant proteins 7S and 11S have different Se enrichment abilities, and the protein structures, functional properties, and antioxidant capacity of GS can be altered by Se biofortification.

Genotype imputation for soybean nested association mapping population to improve precision of QTL detection

KEY MESSAGE

Software for high imputation accuracy in soybean was identified. Imputed dataset could significantly reduce the interval of genomic regions controlling traits, thus greatly improve the efficiency of candidate gene identification. Genotype imputation is a strategy to increase marker density of existing datasets without additional genotyping. We compared imputation performance of software BEAGLE 5.0, IMPUTE 5 and AlphaPlantImpute and tested software parameters that may help to improve imputation accuracy in soybean populations. Several factors including marker density, extent of linkage disequilibrium (LD), minor allele frequency (MAF), etc., were examined for their effects on imputation accuracy across different software. Our results showed that AlphaPlantImpute had a higher imputation accuracy than BEAGLE 5.0 or IMPUTE 5 tested in each soybean family, especially if the study progeny were genotyped with an extremely low number of markers. LD extent, MAF and reference panel size were positively correlated with imputation accuracy, a minimum number of 50 markers per chromosome and MAF of SNPs > 0.2 in soybean line were required to avoid a significant loss of imputation accuracy. Using the software, we imputed 5176 soybean lines in the soybean nested mapping population (NAM) with high-density markers of the 40 parents. The dataset containing 423,419 markers for 5176 lines and 40 parents was deposited at the Soybase. The imputed NAM dataset was further examined for the improvement of mapping quantitative trait loci (QTL) controlling soybean seed protein content. Most of the QTL identified were at identical or at similar position based on initial and imputed datasets; however, QTL intervals were greatly narrowed. The resulting genotypic dataset of NAM population will facilitate QTL mapping of traits and downstream applications. The information will also help to improve genotyping imputation accuracy in self-pollinated crops.

Genome-Wide Association Study and Genomic Selection for Proteinogenic Methionine in Soybean Seeds

Soybean [Glycine max (L.) Merr.] seeds have an amino acid profile that provides excellent viability as a food and feed protein source. However, low concentrations of an essential amino acid, methionine, limit the nutritional utility of soybean protein. The objectives of this study were to identify genomic associations and evaluate the potential for genomic selection (GS) for methionine content in soybean seeds. We performed a genome-wide association study (GWAS) that utilized 311 soybean accessions from maturity groups IV and V grown in three locations in 2018 and 2019. A total of 35,570 single nucleotide polymorphisms (SNPs) were used to identify genomic associations with proteinogenic methionine content that was quantified by high-performance liquid chromatography (HPLC). Across four environments, 23 novel SNPs were identified as being associated with methionine content. The strongest associations were found on chromosomes 3 (ss715586112, ss715586120, ss715586126, ss715586203, and ss715586204), 8 (ss715599541 and ss715599547) and 16 (ss715625009). Several gene models were recognized within proximity to these SNPs, such as a leucine-rich repeat protein kinase and a serine/threonine protein kinase. Identification of these linked SNPs should help soybean breeders to improve protein quality in soybean seeds. GS was evaluated using k-fold cross validation within each environment with two SNP sets, the complete 35,570 set and a subset of 248 SNPs determined to be associated with methionine through GWAS. Average prediction accuracy (r ²) was highest using the SNP subset ranging from 0.45 to 0.62, which was a significant improvement from the complete set accuracy that ranged from 0.03 to 0.27. This indicated that GS utilizing a significant subset of SNPs may be a viable tool for soybean breeders seeking to improve methionine content.

Plant Molecular Farming, a Tool for Functional Food Production

The demand of functional food is increasing for improving human health. Plant molecular farming (PMF) employs plants as bioreactors for the production of pharmaceuticals. Now PMF has been used to produce antibodies, vaccines, and medicinal proteins, but it has not been well-studied for production of nutraceuticals and functional food. In this perspective, we extend the concept of PMF, present an updated overview of PMF for functional food development, including the progress, problem, and strategy, and then speculate how to use the PMF strategy to produce functional foods, especially with four major staple food crops (rice, wheat, maize, and soybean). Finally, we discuss the opportunities and challenges of PMF on functional food production in the future.

Raffinose Family Oligosaccharides: Friend or Foe for Human and Plant Health?

Raffinose family oligosaccharides (RFOs) are widespread across the plant kingdom, and their concentrations are related to the environment, genotype, and harvest time. RFOs are known to carry out many functions in plants and humans. In this paper, we provide a comprehensive review of RFOs, including their beneficial and anti-nutritional properties. RFOs are considered anti-nutritional factors since they cause flatulence in humans and animals. Flatulence is the single most important factor that deters consumption and utilization of legumes in human and animal diets. In plants, RFOs have been reported to impart tolerance to heat, drought, cold, salinity, and disease resistance besides regulating seed germination, vigor, and longevity. In humans, RFOs have beneficial effects in the large intestine and have shown prebiotic potential by promoting the growth of beneficial bacteria reducing pathogens and putrefactive bacteria present in the colon. In addition to their prebiotic potential, RFOs have many other biological functions in humans and animals, such as anti-allergic, anti-obesity, anti-diabetic, prevention of non-alcoholic fatty liver disease, and cryoprotection. The wide-ranging applications of RFOs make them useful in food, feed, cosmetics, health, pharmaceuticals, and plant stress tolerance; therefore, we review the composition and diversity of RFOs, describe the metabolism and genetics of RFOs, evaluate their role in plant and human health, with a primary focus in grain legumes.

Distribution and speciation of selenium in soybean proteins and its effect on protein structure and functionality

Although the Se concentration and recovery efficiency of soybean seeds treated with selenate were ∼ 1.8 times those of the selenite treatment, the Se was mainly in the organic form of selenomethionine (>90% of total Se) irrespective of the Se source. The Se concentrations of soybean protein isolate (SPI) and glycinin (11S) were 29.1%-38.6% higher than those of soybean protein concentrate (SPC) and β-conglycinin (7S) in Se-enriched soybeans, with selenomethionine accounting for > 80% of the Se in all proteins. The content of sulfur-containing methionine in SPI and 11S markedly decreased in Se-enriched soybeans compared with the control. No significant effect of Se was observed on protein content, subunit composition, secondary structure, micromorphology, or functionality. Foliar spray of selenate provides an economical and efficient way to produce Se-enriched soybeans without affecting protein structure and functionality, where SPI and 11S display a high ability to enrich Se (mainly selenomethionine).

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.

Integrating omics approaches to discover and prioritize candidate genes involved in oil biosynthesis in soybean

Soybean is a major source of edible protein and oil. Oil content is a quantitative trait that is significantly determined by genetic and environmental factors. Over the past 30 years, a large volume of soybean genetic, genomic, and transcriptomic data have been accumulated. Nevertheless, integrative analyses of such data remain scarce, in spite of their importance for crop improvement. We hypothesized that the co-occurrence of genomic regions for oil-related traits in different studies may reveal more stable regions encompassing important genetic determinants of oil content and quality in soybean. We integrated publicly available data, obtained with distinct techniques, to discover and prioritize candidate genes involved in oil biosynthesis and regulation in soybean. We detected key fatty acid biosynthesis genes (e.g., BCCP2 and ACCase, FADs, KAS family proteins) and several transcription factors, which are likely regulators of oil biosynthesis. In addition, we identified new candidates for seed oil accumulation and quality, such as Glyma.03G213300 and Glyma.19G160700, which encode a translocator protein homolog and a histone acetyltransferase, respectively. Further, oil and protein genomic hotspots are strongly associated with breeding and not with domestication, suggesting that soybean domestication prioritized other traits. The genes identified here are promising targets for breeding programs and for the development of soybean lines with increased oil content and quality.

Protoplast Isolation, Transfection, and Gene Editing for Soybean (Glycine max )

Protoplast is a versatile system for conducting cell-based assays, analyzing diverse signaling pathways, studying functions of cellular machineries, and functional genomics screening. Protoplast engineering has become an important tool for basic plant molecular biology research and developing genome-edited crops. This system allows the direct delivery of DNA, RNA, or proteins into plant cells and provides a high-throughput system to validate gene-editing reagents. It also facilitates the delivery of homology-directed repair templates (donor molecules) into plant cells, enabling precise DNA edits in the genome. There is a great deal of interest in the plant community to develop these precise edits, as they may expand the potential for developing value-added traits which may be difficult to achieve by other gene-editing applications and/or traditional breeding alone. This chapter provides improved working protocols for isolating and transforming protoplast from immature soybean seeds with 44% of transfection efficiency validated by the green fluorescent protein reporter. We also describe a method for gene editing in soybean protoplasts using single guide RNA molecules.

Protein Biofortification in Lentils (Lens culinaris Medik.) Toward Human Health

Lentil (Lens culinaris Medik.) is a nutritionally dense crop with significant quantities of protein, low-digestible carbohydrates, minerals, and vitamins. The amino acid composition of lentil protein can impact human health by maintaining amino acid balance for physiological functions and preventing protein-energy malnutrition and non-communicable diseases (NCDs). Thus, enhancing lentil protein quality through genetic biofortification, i.e., conventional plant breeding and molecular technologies, is vital for the nutritional improvement of lentil crops across the globe. This review highlights variation in protein concentration and quality across Lens species, genetic mechanisms controlling amino acid synthesis in plants, functions of amino acids, and the effect of antinutrients on the absorption of amino acids into the human body. Successful breeding strategies in lentils and other pulses are reviewed to demonstrate robust breeding approaches for protein biofortification. Future lentil breeding approaches will include rapid germplasm selection, phenotypic evaluation, genome-wide association studies, genetic engineering, and genome editing to select sequences that improve protein concentration and quality.

Identification of Candidate Genes and Genomic Selection for Seed Protein in Soybean Breeding Pipeline

Soybean is a primary meal protein for human consumption, poultry, and livestock feed. In this study, quantitative trait locus (QTL) controlling protein content was explored via genome-wide association studies (GWAS) and linkage mapping approaches based on 284 soybean accessions and 180 recombinant inbred lines (RILs), respectively, which were evaluated for protein content for 4 years. A total of 22 single nucleotide polymorphisms (SNPs) associated with protein content were detected using mixed linear model (MLM) and general linear model (GLM) methods in Tassel and 5 QTLs using Bayesian interval mapping (IM), single-trait multiple interval mapping (SMIM), single-trait composite interval mapping maximum likelihood estimation (SMLE), and single marker regression (SMR) models in Q-Gene and IciMapping. Major QTLs were detected on chromosomes 6 and 20 in both populations. The new QTL genomic region on chromosome 6 (Chr6_18844283-19315351) included 7 candidate genes and the Hap.X ^AA at the Chr6_19172961 position was associated with high protein content. Genomic selection (GS) of protein content was performed using Bayesian Lasso (BL) and ridge regression best linear unbiased prediction (rrBULP) based on all the SNPs and the SNPs significantly associated with protein content resulted from GWAS. The results showed that BL and rrBLUP performed similarly; GS accuracy was dependent on the SNP set and training population size. GS efficiency was higher for the SNPs derived from GWAS than random SNPs and reached a plateau when the number of markers was >2,000. The SNP markers identified in this study and other information were essential in establishing an efficient marker-assisted selection (MAS) and GS pipelines for improving soybean protein content.

Training set design in genomic prediction with multiple biparental families

Genomic selection is a powerful tool to reduce the cycle length and enhance the genetic gain of complex traits in plant breeding. However, questions remain about the optimum design and composition of the training set. In this study, we used 944 soybean [Glycine max (L.) Merr.] recombinant inbred lines from eight families derived through a partial-diallel mating design among five parental lines. The cross-validated prediction accuracies for the six traits seed yield, 1,000-seed weight, protein yield, plant height, protein content, and oil content were high, ranging from 0.79 to 0.87. We investigated among-family predictions, making use of the special mating design with different degrees of relatedness among families. Generally, the prediction accuracy decreased from full-sibs to half-sib families to unrelated families. However, half-sib and unrelated families also showed substantial variation in their prediction accuracy for a given family, which appeared to be caused at least in part by the shared segregation of quantitative trait loci in both the training and prediction sets. Combining several half-sib families in composite training sets generally led to an increase in the prediction accuracy compared with the best family alone. The prediction accuracy increased with the size of the training set, but for comparable prediction accuracy, substantially more half-sibs were required than full-sibs. Collectively, our results highlight the potential of genomic selection for soybean breeding and, in a broader context, illustrate the importance of the targeted design of the training set.

Improving Quantitative Traits in Self-Pollinated Crops Using Simulation-Based Selection With Minimal Crossing

Genomic selection and marker-assisted recurrent selection have been applied to improve quantitative traits in many cross-pollinated crops. However, such selection is not feasible in self-pollinated crops owing to laborious crossing procedures. In this study, we developed a simulation-based selection strategy that makes use of a trait prediction model based on genomic information to predict the phenotype of the progeny for all possible crossing combinations. These predictions are then used to select the best cross combinations for the selection of the given trait. In our simulated experiment, using a biparental initial population with a heritability set to 0.3, 0.6, or 1.0 and the number of quantitative trait loci set to 30 or 100, the genetic gain of the proposed strategy was higher or equal to that of conventional recurrent selection method in the early selection cycles, although the number of cross combinations of the proposed strategy was considerably reduced in each cycle. Moreover, this strategy was demonstrated to increase or decrease seed protein content in soybean recombinant inbred lines using SNP markers. Information on 29 genomic regions associated with seed protein content was used to construct the prediction model and conduct simulation. After two selection cycles, the selected progeny had significantly higher or lower seed protein contents than those from the initial population. These results suggest that our strategy is effective in obtaining superior progeny over a short period with minimal crossing and has the potential to efficiently improve the target quantitative traits in self-pollinated crops.

Genetic mapping high protein content QTL from soybean 'Nanxiadou 25' and candidate gene analysis

BACKGROUND

Soybean is a globally important legume crop that provides a primary source of high-quality vegetable protein and oil. Seed protein content (SPC) is a valuable quality trait controlled by multiple genes in soybean.

RESULTS

In this study, we performed quantitative trait loci (QTL) mapping, QTL-seq, and RNA sequencing (RNA-seq) to reveal the genes controlling protein content in the soybean by using the high protein content variety Nanxiadou 25. A total of 50 QTL for SPC distributed on 14 chromosomes except chromosomes 4, 12, 14, 17, 18, and 19 were identified by QTL mapping using 178 recombinant inbred lines (RILs). Among these QTL, the major QTL qSPC_20-1 and qSPC_20-2 on chromosome 20 were repeatedly detected across six tested environments, corresponding to the location of the major QTL detected using whole-genome sequencing-based QTL-seq. 329 candidate DEGs were obtained within the QTL region of qSPC_20-1 and qSPC_20-2 via gene expression profile analysis. Nine of which were associated with SPC, potentially representing candidate genes. Clone sequencing results showed that different single nucleotide polymorphisms (SNPs) and indels between high and low protein genotypes in Glyma.20G088000 and Glyma.16G066600 may be the cause of changes in this trait.

CONCLUSIONS

These results provide the basis for research on candidate genes and marker-assisted selection (MAS) in soybean breeding for seed protein content.

Mining QTLs for elevated protein and other major seed composition traits from diverse soybean germplasm

Soybean is the world's largest source of protein for animal feed and the second largest source of vegetable oil. Improving the seed protein of soybean without negatively affecting yield and oil content is an important goal for soybean breeders. A population consisting of 132 recombinant inbred lines (RILs) was developed by crossing an elite breeding line, G00-3213 with a plant introduction, PI 594458A, with elevated protein content. In 2016 and 2017, each of the RILs was grown as a single row in Watkinsville, GA, while in 2018, the population was grown at two locations. The seed composition of RILs was analyzed with near-infrared (NIR) spectroscopy. The RIL population was genotyped using the SoySNP6k BeadChip for quantitative trait locus (QTL) mapping. Significant genotype × environment interaction was observed. QTL analyses in and across four environments identified 16, 10, 10, 16, and 5 QTLs for protein, oil, sucrose, and normalized cysteine and methionine contents, respectively. QTLs for protein content identified on chromosomes (Chrs) 3, 6, 13, and 20 were detected in multiple environments. Eight genomic regions on Chrs 3, 6, 8, 10, 13, 17, and 20 were detected that influenced two to four traits, indicating that pleiotropic or linkage effects of these loci may influence multiple seed composition traits. The results of this research provide additional genomic resources for genetic improvement of seed composition and help breeders to better understand the environmental impacts on these QTLs.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-021-01242-z.

Development of soybean experimental lines with enhanced protein and sulfur amino acid content

Soybean is the preferred protein source for both poultry and swine feed. However, this preferred status is being challenged due to competition from alternative feed ingredients. To overcome this, it becomes necessary for breeders to develop soybean cultivars that contain higher protein and better nutritional composition. In this study, we have developed experimental soybean lines that not only contain significantly higher amounts of protein but also improved sulfur amino acid content. This objective was achieved by crossing a O-acetylserine sulfhydrylase (OASS) overexpressing transgenic soybean line with elevated levels of sulfur amino acid content (CS) with a high protein Korean soybean cultivar (Lee 5). Introgression of high protein and overexpression of OASS was monitored in the experimental lines at each successive generation (F2-F6) by measuring protein content and OASS activity. The average protein content of transgenic CS and Lee 5 seeds were 34.8 % and 44.7 %, while in the experimental soybean lines the protein content ranged from 41.3 %-47.7 %, respectively. HPLC and inductively coupled plasma-mass spectrometry analyses revealed that all the experimental lines developed in this study contained significantly higher amounts of sulfur containing amino acids and elemental sulfur in the seeds. The sulfur amino acid (cysteine + methionine) content of the experimental lines ranged from 1.1 % to 1.26 % while the parents Lee 5 and CS had 0.79 % and 1.1 %, respectively. SDS-PAGE and western blot analysis demonstrated that the accumulation of Bowman-Birk protease inhibitor and lunasin, two sulfur amino acid rich peptides, were elevated in experimental soybean lines. High-resolution 2D-gel electrophoresis and Delta2D gel analysis validated that an overall increase in the different subunits of 7S β-conglycinin and 11S glycinin were mainly responsible for the observed increase in the total amount of protein in experimental lines.

Temporal changes in metabolism late in seed development affect biomass composition

The negative association between protein and oil production in soybean (Glycine max) seed is well-documented. However, this inverse relationship is based primarily on the composition of mature seed, which reflects the cumulative result of events over the course of soybean seed development and therefore does not convey information specific to metabolic fluctuations during developmental growth regimes. In this study, we assessed maternal nutrient supply via measurement of seed coat exudates and metabolite levels within the cotyledon throughout development to identify trends in the accumulation of central carbon and nitrogen metabolic intermediates. Active metabolic activity during late seed development was probed through transient labeling with 13C substrates. The results indicated: (1) a drop in lipid contents during seed maturation with a concomitant increase in carbohydrates, (2) a transition from seed filling to maturation phases characterized by quantitatively balanced changes in carbon use and CO2 release, (3) changes in measured carbon and nitrogen resources supplied maternally throughout development, (4) 13C metabolite production through gluconeogenic steps for sustained carbohydrate accumulation as the maternal nutrient supply diminishes, and (5) oligosaccharide biosynthesis within the seed coat during the maturation phase. These results highlight temporal engineering targets for altering final biomass composition to increase the value of soybeans and a path to breaking the inverse correlation between seed protein and oil content.