Zongbiao Duan
Qing Li
Hong Wang
Xuemei He
Min Zhang- 1Hainan Yazhou Bay Seed Laboratory, Sanya, China
- 2State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China
- 3State Key Laboratory of Rice Biology and Breeding, China National Rice Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou, China
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.
1 Introduction
Oil and protein are essential nutrients for humans and livestock, with almost 70% of cooking oil and half of feed protein coming from plants. Soybean (Glycine max) provides nearly 60% of global oilseed production and accounts for more than 25% of the protein consumption for food and animal feed worldwide, making it a leading commercial crop for vegetable oil and protein production (Wang et al., 2020b). The cultivated soybean was domesticated from wild soybean (Glycine soja) in central China about 5000 years ago and then spread around the world (Carter et al., 2004; Wilson, 2008). As a dominant oilseed and fodder crop, modern cultivated soybean seeds contain approximately 17% oil, 35% protein (including essential and non-essential amino acids), 31% carbohydrates (including soluble and insoluble carbohydrates), 13% moisture, and 4% ash (Liu, 1997) (Figure 1). The oil content of soybean seeds ranges from 8.3 to 27.9%, and protein concentration varies from 34.1 to 56.8% depending on the soybean varieties and cultivation conditions (Wilson, 2004). Soybean oil is generated and stored mainly as fatty acids (FAs), triacylglycerols (TAGs), and tocopherols (Liu et al., 2022). There are five central FAs presented in soybean seeds, including stearic acid (C18:0), oleic acid (C18:1), linoleic acid (C18:2), linolenic acid (C18:3), and palmitic acid (C16:0), whose composition directly determined the soybean oil quality. Soybean seed protein consists mainly of storage proteins such as glycinin (11S globulin) and conglycinin (7S globulin) (Liu et al., 2022).
Figure 1 Composition of stored mature soybean seeds. The percentage value indicates the relative weight of the corresponding component in a seed (Liu, 1997).
Recent advances have shown that global crop yields need to be doubled by 2050 to keep up with the growing population and consumption (Godfray et al., 2010; Tilman et al., 2011), which means a 2.4% increase in crop production per year. However, soybean production seriously lags behind the projected demand, growing by an average of only 1.3% per year (Ray et al., 2013). Compared with staple crops, including rice, wheat, and maize, soybean yield is about one-third to one-half as much. Therefore, improving soybean yield is an essential and urgent task for soybean breeding. Increasing seed size is one of the crucial ways to boost soybean yield. Soybean seed size can be described using length (diameter parallel to the hilum), width (diameter from the hilum to the abaxial surface of seed), and thickness (diameter vertical to the hilum), and the composition and content of seed storage reserves directly determine it. Cultivated soybeans generally produce larger seeds with a higher oil level (Wang et al., 2020b). Wild soybeans have smaller seeds with lower oil content than cultivated soybeans. However, the seed protein content is not increased in the large-seed soybean cultivars (Wang et al., 2020b). Therefore, soybean improvement involves parallel increases in seed size, oil accumulation, and a possibly accompanying change in protein level.
For decades, increasing seed size, oil accumulation, and protein content have been the essential objectives of soybean breeding programs. The publication of the soybean reference genome (Williams 82) in 2010 has extensively promoted the development of soybean functional genomics (Zhang et al., 2022). Here, we review the advances in soybean functional genomics on seed size, oil accumulation, and protein content. In addition, we also discuss the challenges and prospects for developing high-yield soybean cultivars with high oil or protein content. As the biochemical synthesis of oils in the seed has been widely studied and well-reviewed (Bates et al., 2013; Xu and Shanklin, 2016; Song et al., 2017; Liu et al., 2022; Yang et al., 2022a), we will not repeat these comments here.
2 Genetic mapping associated with seed size, oil and protein contents
Seed size, oil and protein contents are complex traits controlled by genetic and environmental factors during seed development and maturation. Given their importance in soybean breeding, researchers have performed extensive linkage analysis to identify quantitative trait loci (QTL) associated with these three seed traits using various bi-parental derived populations, such as F2 population, recombinant inbred lines (RILs), chromosome segment substitution lines (CSSLs), and near-isogenic lines (NILs) (Han et al., 2012; Eskandari et al., 2013a; Eskandari et al., 2013b; Qi et al., 2014; Warrington et al., 2015; Wang et al., 2015a; Yang et al., 2019; Cui et al., 2020; Kumawat and Xu, 2021; Kumar et al., 2022; Luo et al., 2022; Yang et al., 2022b). So far, hundreds of QTLs related to seed size (including seed weight), oil accumulation, and protein content have been documented in the SoyBase Genome Database (http://www.soybase.org). For instance, there are 396 QTLs for seed size and weight (Figure 2; Supplementary Table 1), 333 QTLs for seed oil content (Figure 2; Supplementary Table 2), and 234 QTLs for seed protein content (Figure 2; Supplementary Table 3). Among these QTLs, some of the seed size, oil accumulation, and protein content-related QTLs shared overlapping regions, suggesting the presence of pleiotropic regulatory genes in these QTLs. However, due to the low-resolution and low-density molecular markers and limited population size, most QTLs were mapped in a large chromosome region, making these QTLs less effective in pinpointing the specific gene for crop improvement. At present, only a few genes involved in seed size, oil accumulation, and protein content have been isolated from QTL mappings, such as GmPP2C-1 (Lu et al., 2017), GmB1 (Zhang et al., 2018a), and Glyma.20G85100 (also known as GmSWEET39) (Zhang et al., 2020; Fliege et al., 2022). In addition, two genes related to seed size/weight were identified through mutant-dependent map-based cloning or comparative genome hybridization (CGH) analysis, including GmSSS1 (Zhu et al., 2022) and GmKIX8-1 (Nguyen et al., 2021).
Figure 2 QTLs related to seed size (weight), oil accumulation, and protein content in soybean. These QTLs are derived from the SoyBase database (https://soybase.org/).
With the development of omics, genome-wide association study (GWAS) has become a powerful gene or QTL mapping approach for analyzing complicated agronomic traits in crops. Compared with conventional QTL mapping or linkage analysis, GWAS offers significant advantages: 1) GWAS does not need to build a mapping population. 2) GWAS population includes more natural variation than the bi-parental population. 3) GWAS can achieve higher mapping resolution due to high-density molecular markers and diverse historical recombination events (Wang et al., 2020a; Li et al., 2022b). Over the past decade, dozens of GWAS have been performed to identify QTLs or quantitative trait nucleotides (QTNs) involving seed size, lipid accumulation, and protein level in soybean (Hwang et al., 2014; Zhou et al., 2015; Zhang et al., 2016a; Yan et al., 2017; Zhang et al., 2018b; Lee et al., 2019; Zhao et al., 2019; He et al., 2021; Zhang et al., 2021; Hong et al., 2022). Based on this approach, GmOLEO1 (Zhang et al., 2019b), GmPDAT (Liu et al., 2020a), GmSWEET10a (also known as GmSWEET39) (Miao et al., 2020; Wang et al., 2020b), and GmST05 (Duan et al., 2022) have been identified and confirmed to relate to these seed traits, suggesting this way is more effective. Although GWAS has advantages in genetic mapping, the population structure and individual relationships are likely to produce false positive results in association analysis. Therefore, it is better to integrate linkage mapping and GWAS analysis for dissecting complex traits. Mixed analysis methods have successfully employed and mapped QTLs or QTNs associated with these seed traits in soybean (Cao et al., 2017; Zhang et al., 2019c), and further cloned GmSWEET39 (Zhang et al., 2020), GmGA3ox1 (Hu et al., 2022), GmST1 (Li et al., 2022a), and POWR1 (Goettel et al., 2022).
3 Regulatory genes of seed size
The seeds of higher plants consist of the embryo, endosperm, and seed coat, among which the embryo and endosperm are generated from the fertilized egg cell and central cell, respectively. In contrast, the seed coat is developed from the sporophytic integument. Therefore, seed size is determined by the integrated signals of maternal and zygotic tissues that influence the coordinated growth of the embryo, endosperm, and seed coat (Li et al., 2019). Several signaling pathways that maternal control seed size have been identified in Arabidopsis and rice, such as G-protein signaling, ubiquitin-proteasome signaling, mitogen-activated protein kinase (MAPK) signaling, phytohormone signaling, and some transcriptional regulators. Meanwhile, the HAIKU (IKU) pathway and some phytohormones partially regulate the zygotic tissues’ growth (Li et al., 2019). However, compared with Arabidopsis and rice, the molecular networks regulating seed size in soybean are still lagging behind.
As critical regulatory components of gene expression, several transcriptional factors (TFs) involved in seed size have been identified in soybean (Figure 3; Table 1). BIG SEEDS1 (BS1) belongs to a group II member of the TIFY TF family. It plays a vital role in controlling the size of seeds, pods, and leaves via a regulatory module that targets cell proliferation in the model legume of Medicago truncatula (Ge et al., 2016). Down-regulation of BS1 orthologous genes (GmBS1 and GmBS2) in soybean resulted in increased seed size and amino acid content. SLB1 encodes an F-box protein that forms part of the SKP1/Cullin/F-box E3 ubiquitin ligase complex. Biochemical and genetic analyses showed that SLB1 interacts with BS1 to control lateral branching and organ growth by regulating BS1 protein stability in Medicago truncatula. In addition, overexpression of SLB1 resulted in increased leaf and seed size in both Medicago truncatula and soybean, suggesting the functional conservation of SLB1 (Yin et al., 2020). Plant WRKY TFs are involved in many biological processes, such as embryogenesis and seed development (Luo et al., 2005). The WRKY15a was differentially expressed during pod development between cultivated and wild soybeans. Four haplotypes (H1-H4) were present in WRKY15a, which varied in the CT-core microsatellite locus at the 5’-untranslated region (5’-UTR) of WRKY15a. The H1 haplotype with six CT-repeats was the only allele in cultivated soybeans, whereas the H3 haplotype with five CT-repeats was the primary allele in wild soybeans. The seed weight with haplotype H1 was heavier than that of wild soybeans harboring haplotypes H2, H3, and H4, and the seed weight was positively correlated with WRKY15a expression, indicating a positive effect of WRKY15a on seed size (Gu et al., 2017). Dt2, encoding a MADS-box TF, plays an essential role in controlling multiple agronomic traits, such as flowering time, stem growth habit, and plant height (Ping et al., 2014; Zhang et al., 2019a). A recent report has shown that Dt2 also determines shoot branching and seed size (Liang et al., 2022). Dt2 knockout lines performed multiple yield-related trait changes, such as the increased seed length and width, heavier seed weight, and higher grain weight per plant, thereby resulting in obviously improved yield per plot. In contrast, the Dt2 overexpression lines exhibited decreased seed length and width.
Figure 3 Genetic regulatory network of seed size (weight), oil accumulation, and protein content in soybean. The genes or proteins involving seed size (weight) and oil content are shown in red and blue fonts, respectively. The pleiotropic regulators for seed size (weight), oil accumulation, or protein content are indicated in green fonts. The regulatory genes, whose function has been validated only in Arabidopsis but not soybean, are shown in purple fonts.
Table 1 Representative genes related to seed size, oil accumulation, and protein content in soybean.
Some genes that encode various enzymes have also been shown to affect soybean seed size (Figure 3; Table 1). A phosphatase 2C-1 (GmPP2C-1) gene from wild soybean helps to increase seed weight or size by improving integument cell size and activating a subset of seed trait-related genes (Lu et al., 2017). In addition, GmPP2C-1 facilitates the accumulation of dephosphorylated GmBZR1 protein, which act as the key transcription factor in BR signaling. Furthermore, overexpression of GmBZR1 can improve seed size or weight in transgenic Arabidopsis. Cell wall invertase (CWI) plays a vital role in sugar signaling and metabolism, affecting the source–sink interaction and seed development (Tang et al., 2017). GmCIF1 encodes a cell wall invertase inhibitor, and suppression of GmCIF1 gene expression exhibited increased CWI activities and larger seed size while with more accumulations of protein, hexoses, and starch in soybean seeds. GmSSS1 encodes a putative O-GlcNAc transferase in soybean. Knockout GmSSS1 resulted in tiny seeds, whereas overexpressing GmSSS1 produced large seeds (Zhu et al., 2022). Modulating GmSSS1 could positively affect cell division and expansion in transgenic plants. GmGA3ox1, a gibberellin (GA) 3β-hydroxylase in soybean, is the critical enzyme in the GA biosynthesis pathway. Knockout of GmGA3ox1 resulted in reduced GA biosynthesis while enhanced photosynthesis (Hu et al., 2022). GmGA3ox1 knockout plants displayed decreased seed weight and length, but improved seed production by increasing branch, pod, and seed numbers. In contrast, overexpression of GmGA3ox1 increased seed weight and length in transgenic soybeans. Similarly, overexpression of GA20OX, encoding a gibberellin 20 oxidase in a rate-limiting step of GA biosynthesis, enhanced the seed size/weight of transgenic Arabidopsis plants (Lu et al., 2016).
Besides the above genes, some homologous soybean genes known to regulate seed size in Arabidopsis have also been shown to control soybean seed size (Figure 3; Table 1). For example, several P450/CYP78A family members are suggested for controlling seed size in Arabidopsis (Wang et al., 2008; Fang et al., 2012). The P450/CYP78A orthologs in soybean, such as GmCYP78A10, GmCYP78A57, GmCYP78A70, and GmCYP78A72, exhibited conserved function to improve seed size or weight (Wang et al., 2015b; Zhao et al., 2016; Du et al., 2017), but the underlying mechanism how they function remains largely elusive. A PPD/KIX/TPL repressor complex consisting of PPD2, KIX8/9, and TPL proteins was shown to affect organ size by modulating meristem proliferation in Arabidopsis (Baekelandt et al., 2018). GmKIX8-1, a soybean AtKIX8 ortholog, is also involved in controlling cell proliferation and organ size. Due to increased CYCLIN D3;1-10 expression and cell proliferation, the GmKIX8-1 loss-of-function mutants displayed an apparent increase in the size of leaves and seeds (Nguyen et al., 2021). Very recently, in both Arabidopsis and soybean, a crucial regulatory cascade involving CO (the central regulator of the photoperiodic pathway) and AP2 (specification of floral meristem identity) was demonstrated to mediate the photoperiod-regulated seed size in a maternal-dependent manner (Yu et al., 2023). GmCOL2b (a soybean CO homolog) positively promoted seed size under short days by directly inhibiting the expression of GmAP2-1 and GmAP2-2.
4 Regulatory genes of seed oil
Seed storage reserves, including oil, protein, and starch, are filled during seed development and maturation. Understanding the storage substance loading into the seeds thus is crucial to improving crop yield and nutritional quality. In the past decades, extensive efforts have been made toward the dissection of molecular pathways for accumulating seed storage reserves, particularly in Arabidopsis. TFs, such as LEC1, LEC2, ABI3, FUS3, and WRI1, and other activators or repressors for storage reserves accumulation during seed development, have been identified in plants (Yang et al., 2022a). However, more details and mechanisms have yet to be clarified, especially for essential crops such as soybean (Figure 3; Table 1).
LEC1 is an atypical TF subunit (NF-YB) that interacts with NF-YA and NF-YC subunits to form an NF-Y TF complex. It is central to controlling seed development, such as embryo morphogenesis, endosperm development, and storage reserve accumulation (Jo et al., 2019). In Arabidopsis, the lec1 null mutants displayed striking defects in embryos and severely restricted protein and lipid accumulation in seeds (Meinke et al., 1994; West et al., 1994). Furthermore, over-expression of LEC1 induced the activation of genes related to the accumulation of storage proteins and lipids, resulting in increased contents of lipids and FAs in the transgenic Arabidopsis (Kagaya et al., 2005). In soybean, GmLEC1 (GmLEC1a or GmLEC1b) transcriptionally regulates the genes involved in distinct cellular processes during seed development and activates seed FAs biosynthesis (Pelletier et al., 2017; Zhang et al., 2017). Further research revealed that GmLEC1 acts in combination with TFs such as GmAREB3, GmbZIP67, and GmABI3 to regulate soybean seed development (Jo et al., 2020).
LEC1 interacts physically with LEC2, a B3 DNA binding domain TF, which has a crucial regulatory role in seed development and in controlling seed protein and oil levels in Arabidopsis (Santos-Mendoza et al., 2008; Angeles-Núñez and Tiessen, 2011; Kim et al., 2015; Jo et al., 2019). The loss-of-function lec2 mutant seeds showed a 30% and 15% decline in oil and protein, respectively, but accumulated more starch and sucrose than wild-type seeds (Angeles-Núñez and Tiessen, 2011). In contrast, in both transgenic Arabidopsis and tobacco plants, AtLEC2 inducible expression increased storage oil accumulation, such as TAGs and FAs (Mendoza et al., 2005; Andrianov et al., 2010; Kim et al., 2015). In soybean, GmLEC2 regulates a subset of genes involving the metabolism of seed storage reserves (Manan et al., 2017). Compared with the control seeds, the TAGs and long-chain FAs contents of GmLEC2a over-expression transgenic Arabidopsis seeds increased by 34% and 4%, respectively.
In the transcriptional network of seed oil accumulation in Arabidopsis, LEC1 and LEC2 synergistically promote WRI1 expression, an AP2 TF gene responsible for the transcriptional regulation of oil biosynthesis, and this regulatory mechanism is conserved in other plant species, for instance, soybean and maize (Baud et al., 2007; Mu et al., 2008; Shen et al., 2010; Manan et al., 2017; Pelletier et al., 2017; Yang et al., 2022a). Its two soybean orthologs, GmWRI1a and GmWRI1b, play a central role in seed oil accumulation. Over-expression of GmWRI1a or GmWRI1b significantly increased total oil and FAs contents and changed FAs composition in the seed, whereas GmWRI1 knockdown hairy roots interfered with lipid biosynthesis (Chen et al., 2018; Chen et al., 2020; Guo et al., 2020; Wang et al., 2022).
GmZF392, a seed-specific tandem CCCH zinc finger (TZF) protein, promotes seed oil accumulation by targeting a bipartite cis-element with TA- and TG-rich sequences in the promoter regions, thereby activating downstream gene expression involving in the lipid biosynthesis (Lu et al., 2021). GmZF392 interacts physically with GmZF351, another activator of lipid accumulation, to additive/synergistic increase the expression of downstream lipid biosynthesis genes (Li et al., 2017; Lu et al., 2021). And both GmZF392 and GmZF351 are positively regulated by GmNFYA, a TF correlated with oil content (Lu et al., 2016; Lu et al., 2021). In addition, GmZF392 and GmZF351 are also direct targets of GmLEC1 (Pelletier et al., 2017). More importantly, GmZF392 and GmZF351 were selected by domestication from wild soybeans to cultivated soybeans.
In addition to the above TFs forming the regulatory module, some functional genes were also involved in regulating seed oil content in soybean (Figure 3; Table 1). Overexpression of a bZIP TF gene (GmbZIP123) enhances lipid accumulation in transgenic Arabidopsis seeds through modulating sugar transport (Song et al., 2013). GmB1, encoding a transporter-like transmembrane protein for the biosynthesis of the bloom in pod endocarp, not only controls seed coat bloom in wild soybeans but also affects oil content in cultivated soybeans (Zhang et al., 2018a). GmOLEO1, a strong artificial-selected oleosin protein-encoding gene, conduces to the improvement in seed oil content during soybean domestication by affecting TAGs metabolism (Zhang et al., 2019b).
5 Regulatory genes of seed protein
Compared with seed size and oil content, only a few genes controlling seed protein or amino acid content have been functionally identified (Figure 3; Table 1) (Krishnan and Jez, 2018). The small GTPase GmRab5a and its guanine exchange factors GmVPS9s are shown to function in the storage protein post-Golgi trafficking in soybean (Wei et al., 2020). Transient over-expression of the dominant negative variant of GmRab5a, or RNAi of either GmRab5a or GmVPS9s, obviously reduced the transport of the cargo marker, which used to reflect storage protein trafficking to protein storage vacuoles in soybean cotyledon cells. In addition, several genes, including POWR1, GmSWEET10a, GmSWEET10b, and GmST05, pleiotropically regulate seed protein, oil content, and seed size (Wang et al., 2020b; Duan et al., 2022; Goettel et al., 2022), which are detailed discussion in the next section.
6 Pleiotropic regulatory genes of seed size, oil and protein contents
Seed size, oil accumulation, and protein content in soybean are highly correlated agronomical traits. However, the selection and underlying molecular basis of these seed-correlated traits during soybean domestication are poorly understood, which is one of the obstacles to soybean yield and quality improvement. So far, several pleiotropic regulatory genes controlling seed size, oil accumulation, and protein content have been cloned and functionally identified in soybean (Figure 3; Table 1).
For instance, the ectopic expression of GmDof4, GmDof11, GmMYB73, and GmDREBL enhanced both seed size/weight and oil accumulation in transgenic Arabidopsis seeds (Wang et al., 2007; Liu et al., 2014; Zhang et al., 2016b). GmPDAT, a phospholipid diacylglycerol acyltransferase encoding gene, was expressed higher in large-seed and high-oil soybean accessions than in small-seed and low-oil accessions. Over-expression of GmPDAT improved seed size and oil level, whereas GmPDAT RNAi plants had reduced seed size and oil accumulation (Liu et al., 2020a). GmST1 encodes a UDP-D-glucuronate 4-epimerase that positively regulates seed size and oil content by modulating pectin biosynthesis and glycolysis pathways, and underwent selection during soybean domestication (Li et al., 2022a).
The sugar transporter SWEET family members play critical roles in seed development (Chen et al., 2015; Wang et al., 2019). A pair of SWEET paralogs in soybean, GmSWEET10a and GmSWEET10b, underwent the stepwise selection that synchronously changed seed size, oil accumulation, and protein level during soybean domestication, by regulating sugar sorting from seed coat to embryo (Zhang et al., 2020; Wang et al., 2020b). Compared with wild-type plants, GmSWEET10a or GmSWEET10b over-expression soybeans displayed significantly increased seed size and higher oil accumulation but decreased protein level, while their knockout plants had reduced seed size and oil content but increased protein level (Wang et al., 2020b). Very recently, a phosphatidylethanolamine-binding protein (PEBP) family member, GmST05 (also known as GmMFT), has been shown to positively regulate seed size and altered oil and protein levels, likely by affecting GmSWEET10a transcription (Li et al., 2014; Duan et al., 2022). In addition, a CCT-domain gene, POWR1, is domesticated and pleiotropically regulates seed quality and yield in soybean, possibly by regulating lipid metabolism and nutrient transport (Goettel et al., 2022). A transposable element (TE) insertion in the CCT-domain of POWR1 resulted in increased seed weight and oil content but decreased protein content. In contrast, over-expression of POWR1 exhibited improved protein content and declined seed weight and oil accumulation in transgenic plants.
7 Challenges and perspectives
Seed size, oil and protein contents are complex quantitative traits governed by multiple genes. Although linkage mapping and GWAS analysis have identified numerous QTLs controlling seed size, oil accumulation, and protein content in soybean, only a few genes have been isolated and functionally validated. One fundamental reason for this phenomenon is that these researchers usually use only one or two approaches, making it hard to pinpoint the target underlying these seed traits. The other key obstacle is the lack of a fast and efficient soybean genetic transformation system for different soybean genotypes, such as Agrobacterium-mediated cotyledonary node soybean transformation, which has been widely used in recently years. The slow and inefficient genetic transformation system makes it more challenging to identify and verify the function of soybean genes (Zhang et al., 2022). That’s why, in some studies, especially those prior to 2015, functional validation was done in Arabidopsis instead of soybean.
With the rapid progress of omics research and the reduction of testing cost, more and more soybean omics data were produced, such as the re-sequencing genome, transcriptome, metabolome, proteome, epigenome, pan-genome, and 3D genome (Ohyanagi et al., 2012; Lin et al., 2014; Shen et al., 2014; Zhou et al., 2015; Liu et al., 2016; Fang et al., 2017; Shen et al., 2018a; Shen et al., 2018b; Liu et al., 2020b; Silva et al., 2021; Ni et al., 2023). These released omics resources will extensively promote the research of soybean functional genomics. Currently, like GWAS, TWAS (transcriptome-wide association study), EWAS (epigenome-wide association study), and PWAS (proteome-wide association study), as well as multi-omics data association studies, such as eGWAS (gene expression-based genome-wide association study) and mGWAS (metabolome-based genome-wide association study) have been successfully developed and applied (Shen et al., 2022). Integration of multiple omics approaches will provide more clues and help narrow the target range underlying these seed traits. However, utilizing these vast omics data that exist in various forms is a considerable challenge. Thus, mathematical methods, like meta-analysis, are expected to address such trouble. Moreover, artificial intelligence (AI) technology or machine learning approach can make mining big data more efficient, for instance, omics data processing, protein structure construction, and pan-omics data integration (Baek et al., 2021; Jumper et al., 2021; Reel et al., 2021).
CRISPR/Cas-based genome editing technology that enables precise modification of genomes to obtain predictable and desired traits has been successfully applied to gene function research and crop germplasm creation. Compared with other crops, such as rice, the soybean genome-editing process is primarily in its infancy; however, successful stories have demonstrated the feasibility of gene editing in soybean (Cai et al., 2018; Bai et al., 2020; Wang et al., 2020b; Nguyen et al., 2021; Bai et al., 2022; Duan et al., 2022; Hu et al., 2022; Liang et al., 2022; Li et al., 2022a). In the future, the improved soybean transformation and more applications of single - or multi-gene ‘base editing’ will greatly facilitate functional research in soybean, ultimately allowing us to decode these complex seed traits and identify critical genes underlying seed size, oil and protein contents.
The ultimate goal of soybean breeding is to cultivate high-yield and high-quality soybean. So far, crop breeding has developed from artificial selection (stage 1.0) and hybrid breeding (stage 2.0) to molecular breeding (stage 3.0). However, to solve the crisis of food shortage caused by the growing population, intelligent breeding (stage 4.0) that can quickly aggregate excellent alleles through precise design is coming (Shen et al., 2022). In previous breeding stages, breeders usually have to stack desirable traits into a single line to create a super variety, which is a huge task. In breeding stage 4.0, optimal and precise design to rapidly pyramid multiple elite alleles with desirable seed traits will facilitate yield, oil, and protein content improvement in soybean.
Author contributions
QL and MZ designed and supervised the study. ZD, QL, and HW drafted the manuscript. XH participated in the production of the article pictures. ZD and QL responded to review comments. All authors contributed to the article and approved the submitted version.
Funding
This work was financially supported by the Hainan Yazhou Bay Seed Laboratory Project (B21HJ0002), the National Natural Science Foundation of China (32101755, 32272107), and the Zhejiang Provincial Natural Science Foundation (LY22C130005).
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2023.1160418/full#supplementary-material
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Keywords: soybean, seed size, oil, protein, QTL, functional genome
Citation: Duan Z, Li Q, Wang H, He X and Zhang M (2023) Genetic regulatory networks of soybean seed size, oil and protein contents. Front. Plant Sci. 14:1160418. doi: 10.3389/fpls.2023.1160418
Received: 07 February 2023; Accepted: 24 February 2023;
Published: 07 March 2023.
Edited by:
Xiaobo Wang, Anhui Agricultural University, ChinaReviewed by:
Dan Zhang, Henan Agricultural University, ChinaYingpeng Han, Northeast Agricultural University, China
Copyright © 2023 Duan, Li, Wang, He and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Qing Li, bGlxaW5nMTk4NjEwMkAxNjMuY29t; Min Zhang, emhhbmdtaW5AZ2VuZXRpY3MuYWMuY24=
†These authors have contributed equally to this work