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Article

Seed Protein Genetics Linked with Nitrogen and Phosphorus Translocation Efficiency in Soybean

by
Qingsong Zhao
1,2,†,
Niannian Ma
1,†,
Ruirui Li
1,
Yongjia Zhong
1,
Xinxin Li
1 and
Hong Liao
1,*
1
Root Biology Center, College of Resources and Environment, Fujian Agriculture and Forestry University, Fuzhou 350002, China
2
Hebei Laboratory of Crop Genetics and Breeding, National Soybean Improvement Center Shijiazhuang Sub-Center, Huang-Huai-Hai Key Laboratory of Biology and Genetic Improvement of Soybean, Ministry of Agriculture and Rural Affairs, Institute of Cereal and Oil Crops, Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang 050035, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Agronomy 2023, 13(2), 598; https://doi.org/10.3390/agronomy13020598
Submission received: 30 December 2022 / Revised: 12 February 2023 / Accepted: 15 February 2023 / Published: 19 February 2023
(This article belongs to the Special Issue Genetic Improvement of Soybean Nutrient Use Efficiency)
Browse Figures
Versions Notes

Abstract

:
Soybean (Glycine max (L.) Merr.) is an important nutritional crop with high seed protein content. Production of high protein concentrations relies on sufficient nutrient supplies, especially of nitrogen (N) and phosphorus (P). Although the genetic basis for seed quality traits has been well studied, little information exists on any genetic connections between seed quality and nutrient supplies in soybean. Here, a recombinant inbred line (RIL) population of 179 progeny was generated using HC6 and JD17 as parents contrasting in seed quality and N and P translocation efficiencies. Seed protein and N and P translocation efficiencies were higher in HC6 than in JD17. Meanwhile, positive correlations were observed between seed protein content and translocation efficiency of N and P in RILs, implying that high N and P translocation efficiencies might facilitate seed protein accumulation. A genetic map was constructed using 5250 SNP markers covering a genetic distance of 3154.83 cM. A total of 6 loci for quality and 13 loci for N and P translocation efficiency were detected. Among them, two fragments on chromosome 6 and chromosome 20 contained multiple significant markers for both quality and N and P translocation efficiencies, with the respective observed LOD values ranging from 2.98 to 5.61, and 3.01 to 11.91, while the respective PVE values ranged from 8.2% to 13.9%, and 8.3% to 28.0%. Interestingly, one significant locus on chromosome 20 appears to be the product of a transposable element (TE) InDel in Glyma.20G085100, with progeny lacking the TE also exhibiting higher N and P translocation efficiencies, along with higher seed protein contents. Taken together, these results provide genetic evidence that increasing N and P translocation efficiencies may lead to increasing protein contents in soybean seeds. Furthermore, a TE InDel may be used as a genetic marker for breeding elite soybean cultivars with high protein content and N and P translocation efficiencies.

1. Introduction

Soybean (Glycine max (L.) Merr.) is one of the most economically important crops globally, owing largely to high seed protein and oil contents with 40% protein and 20% oil in seeds on a dry basis [1]. Over recent decades, as much as 56% of dietary protein consumed in human food and animal feed has been produced by soybean seeds [2,3], and global soybean production has reached 367.8 million metric tons. On the other hand, world protein meal consumption has reached 243.6 million metric tons and accounts for 70% [4,5]. Therefore, further clarification of seed protein genetics and methods for releasing elite soybean cultivars rich in seed protein are critical.
Genetic and molecular processes connected with soybean seed protein content have been well reported. In previous experiments, protein was found to be controlled more by genetics than by environment, with heritability above 0.8. Meanwhile, seed protein content negatively correlated with oil content, which also had a heritability of over 0.8 [4]. With these high heritabilities for complex protein and oil synthesis processes, quantitative trait locus (QTL) and genome-wide association study (GWAS) approaches may then be effective means for detecting candidate loci for seed protein content to propagate in breeding programs. As of September 2022, 241 loci associated with seed protein content have been detected in biparental populations and listed on the Soybase website [6]. To verify genetic loci associations with seed protein content, a meta-analysis was conducted, and 55 meta-QTLs for protein and oil contents were putatively identified [7]. An additional set of loci were associated with protein accumulation using GWAS methods [8,9,10,11]. Among significant chromosomal purlieus, LG15, LG19, and LG 20 are important due to the presence of major or multiple loci within their bounds. Recently, individual genes with specific functions in seed trait development underlying protein or oil content have been cloned and analyzed, such as GmSWEET39 on chromosome 15 [12] and POWR1 on chromosome 20 [13]. However, neither protein nor oil can accumulate without nutrients, especially nitrogen (N) and phosphorus (P) [14]. To date, genetic associations between seed quality and nutrient supply have yet to be evaluated.
Nitrogen is generally considered to be the most limiting factor for plant growth and development in various ecosystems [15]. As a component of proteins, enzymes, nucleic acids, and plant growth regulators, this element is involved in many physiological and biochemical processes in plants [16,17]. Phosphorus, another crucial plant macronutrient, participates in plant cells as a structural constituent of essential biomolecules involved in energy metabolism [18] and in the formation of key macromolecules, such as nucleic acids and phospholipids [19]. The essential role of P in many aspects of cellular metabolism is also evident from the large amounts of P that are stored in seeds to enable embryo development, germination, and seedling growth [20].
Soybean seeds are rich in protein and oils, and, therefore, require more N and P than other crops. Most plant seeds contain lower concentrations of N and P than soybean seeds. For example, cotton seeds may hold average N concentrations of around 38 mg·g−1 [21], and rice seed P concentrations can average around 3.0 mg·g−1 [22]. Meanwhile, soybean seeds can contain 61 mg·g−1N [23] and 4.8 mg·g−1P [24]. Without sufficient N and P supplies, soybean plants produce fewer seeds, but also may decrease protein and oil contents in the seeds that are produced. It is very important to understand the relationship between soybean protein content and nitrogen and phosphorus translocation efficiency as defined by the ability to move nutrients from leaves to seeds to cultivate high-protein soybean cultivars.
As the most direct and largest source of nutrients, leaves provide most of the N and P to seeds [25]. Therefore, nutrient translocation efficiency is crucial for allocating sufficient resources to seed formation [26,27]. Researchers have found that most N in wheat grains (~80%) comes from leaves [28]. Senescent leaves are primary sources of N and P resorption, and the proportion of P re-absorbed is higher than that of N [29]. Nitrogen stored in soybean leaves begins to flow out from the R6 stage, and is predominantly transferred to seeds for use in synthesis of storage proteins [30]. Remobilization from senescing leaves is a key factor for efficient utilization of N [31]. Similarly, when P is scarce, plants remobilize P from old leaves to maintain plant growth and metabolism [32,33]. Both QTLs and molecular factors of nutrient translocation efficiency have been reported. Genetic loci of mineral element accumulation in soybean seeds have also been reported [34,35], including three QTLS for P content on chromosomes 7, 12, and 17 that were detected using 916 SSR markers and 92 F2:4 lines [24]. An advantageous gene for P absorption and utilization efficiency identified on chromosome 11 and named PE1 also appears to improve P allocation to pods [27].
Although the genetic components of seed production, protein and oil quality, and N and P translocation efficiency have been studied separately, any existing relationships between them have rarely been reported and remain largely mysterious. In-depth observations are required to further decipher genetic and molecular connection basis between seed protein and translocation efficiencies of N and P. In this study, we aimed to analyze the genetic basis of three sets of traits in soybean using a RIL population bred from parents with significant differences in seed protein content and N and P translocation efficiency. The results provide the foundation for soybean marker-assisted breeding, and provide a theoretical reference for improvement in soybean seed quality.

2. Materials and Methods

2.1. Plant Materials and Growth Conditions of Field Trials

In this study, the variety HC6, with relatively high protein content and high N and P translocation efficiencies, was crossed with JD17, of relatively low protein content and low N and P translocation efficiencies, to develop a population comprising 176 F7-derived recombinant inbred lines (RILs) using the single seed descent (SSD) method [27,36,37]. The RIL population was used to construct a genetic linkage map, and detect QTLs for N and P translocation efficiencies and quality traits. The RILs and parents were planted in a randomized completed block design (RCBD), with three replications in 2020 at Yangzhong Experimental Farm (118.20° E, 26.17° N) of Fujian Agriculture and Forestry University, Fuzhou City, China. The RILs and the two parental lines were sown in single row plots 1.2 m long, with 10 plants retained in each row spaced 0.5 m apart. Irrigation and pest control in the field followed local practices. Before sowing, the physical and chemical properties of the surface 20 cm of soils were measured as follows: pH, 4.64 ± 0.05; organic matter (%), 2.63 ± 0.03; available N, P (Bray I) and K (mg/kg), 123.21 ± 6.11, 179.30 ± 15.13, and 159.21 ± 12.95, respectively.

2.2. Plant Sampling and Trait Evaluation

Three representative soybean plants of each plot were harvested at the R6 stage. For each plot, the leaves and seeds were sampled separately and dried at 60 °C, then measured and weighed per plant. After ash digestion of leaves and seeds, the content of N and P was assessed using a continuous flow analyzer as previously reported [38]. Ten N- and P-related traits were measured, including N concentration of leaves (NcL), N concentration of seeds (NcS), P concentration of leaves (PcL), P concentration of seeds (PcS), total N content of leaves (TNL), total P content of leaves (TPL), N translocation efficiency (NTE), P translocation efficiency (PTE), difference in N concentration between seeds and leaves (NcD), and difference in P concentration between seeds and leaves (PcD). The translocation efficiencies NTE and PTE were calculated as follows:
  N / PTE = TN / PS TN / PS   +   TN / PL
where TN/PS and TN/PL were the total nutrient (either N or P) content in seeds and leaves per plant (mg·plant−1), respectively. The total N (P) content means N or P of leaves (seeds) per plant, while concentrations means N or P content in unit biomass of leaves or seeds. Ten g of dry seeds from each plot was sampled, and protein and oil content was determined using near-infrared spectrometry (NIS) using a prior method [39]. Flower color at full flowering stage was used to verify the reliability of the genetic map for the investigated RIL population.

2.3. Genotyping by High-Throughput Sequencing

Genomic DNA was extracted from the two parents using the commercial extraction kit DNeasy Plant Mini Kit (QIAGEN, Venlo, Germany). GBS libraries were constructed and sequenced on an Illumina sequencing platform. In short, genomic DNA was fragmented to 100–300 bp sizes and sequenced on a Hiseq X10 PE150 sequencing system after terminal repair, splicing, isolation and purification, amplification, and enrichment. Following extraction, GBS libraries were digested by the EcoRI and NIaIII enzymes according to Elshire’s protocol [40]. Genomic DNA was then extracted, and 100 ng of DNA was used for each plant in digestion reactions with EcoRI and NIaIII (New England Biolabs, Ipswich, MA, USA) in 96-well plates. Digested products were mixed with A1 and A2 adaptors for addition to the two DNA ends. After mixing the 96-well library, 400–600 bp DNA fragments were screened out and DNA was purified using a PCR kit (NEB, Beijing, China). Finally, pooled library concentrations were adjusted to 10 nmol prior to sequencing with PE125 on a HiSeq4000 (Illumina, San Diego, CA, USA). Sequencing of parental and progeny GBS libraries was performed by Genedenovo Biotechnology Co., Ltd. (Guangzhou, China) using a HiSeq X10 NGS platform (Illumina, San Diego, CA, USA).

2.4. SNP Identification and Bin Map Construction

Using the Burrows–Wheeler Aligner (BWA), the parameter “MEM 4-K 32-M” was set to compare the 400–600 bp double-terminal sequence with the Glycine Max Wm82.A2.v1 reference genome in the Genome Analysis Toolkit (GATK) (-Window, 4–filter “QD < 2.0 ||FS > 60.0||MQ < 40.0, “-g_filter” GQ < 20 “) for SNP calling. Genotypes of all samples were determined using GATK’s Unified Genotyper [41]. The physical position of each SNP was further determined by using the software tool ANNOVAR [42]. Polymorphic markers consistent with the JD17 genotype or HC6 genotype were recorded as “a” or “b”, respectively, and ambiguous segregation patterns were recorded as missing data “−”.
Offspring genotypes were screened for differences from parental genotypes to ensure that 1 kb sequences holding polymorphism markers were otherwise similar to at least 95% of individual plant genotypes using Chi-square (χ2) testing (p ≤ 0.05). Highest average depth of sequencing was used to select sequences for further analysis in cases of multiple overlapping sections. A genetic linkage map was constructed using Join Map 4.1 for the derived markers. Colocalization analysis was performed using the Auto QTLs method in MapChart 2.32, with colocalized markers being those around graph maxima, and within the inner and outer intervals, defined as positions where the graph falls below the maximum minus the “inner threshold” and the “outer threshold”, respectively [43].

2.5. QTL Detection and Statistical Analysis

MapQTL 6.0 was used to map markers for nutrient traits. MapQTL6.0 uses Interval Mapping (IM) and the multiple-QTL model (MQM) [44]. The position of each QTL was first checked by the interval mapping (IM) option, and then the markers with peak logarithm of odds (LOD) values were selected as cofactors using subsequent multiple QTL mapping (MQM) QTL detection. A LOD score threshold of 2.5 was set for declaring the presence of putative QTLs in given genomic regions. MapChart 2.32 software was used to draw the final QTL Map. Trait data were subjected to variance and QTL analysis. Analysis of variance (ANOVA) and Pearson correlation analysis were implemented using R software [45]. Genetic correlation was calculated using the softwares Plink and CGTA. Broad sense heritability (h2b) was estimated for each trait in QTL ICIMapping V4.1 software using the following formula: h2b = VG/(VG + VE), where VG is the genetic variance, and VE is the environmental variance [37].

3. Results

3.1. Comparison of Seed Protein and Oil Contents and Nutrient Translocation Efficiency Traits between the Parental Genotypes

In order to explore genetic loci regulating nitrogen (N) and phosphorus (P) translocation efficiency, along with seed protein and oil contents, the contrasting genotypes JD17 and HC6 were selected as parents to construct recombinant inbred lines (RILs). The results showed that seed protein content was higher and seed oil content lower in parent HC6 than in parent JD17 (p < 0.05) (Figure 1A). Interestingly, N and P translocation efficiencies all displayed similar trends as seed contents of protein (Figure 1A, B), indicating that higher N and P translocation efficiencies from leaves to seeds might lead to higher seed protein content. In correlation analysis using the population of 179 RILs, significant positive correlations were found between seed protein content and both N and P translocation efficiencies (Figure S1), with r values of 0.34 (***) and 0.29 (***) observed for N and P translocation efficiency, respectively. At the same time, there was a negative correlation between oil and N and P translocation efficiencies, with r values of −0.16 (*) and −0.08 (NS), respectively. For genetic correlation analysis, rG between protein and N and P translocation efficiency was 0.77 (***) and 0.76 (***), respectively. rG between oil and N and P translocation efficiency was −0.40 (*) and −0.30, respectively (Table S1). Taken together, the N and P positive correlations with protein content and negative correlations with oil content indicate that seed protein content is intimately related to N and P translocation efficiencies. Therefore, RILs derived from these two soybean accessions were used to further study the genetic loci that control soybean N and P translocation efficiencies and seed protein content.

3.2. Phenotypic and Genetic Variation among RILs

To further study the genetic basis of N and P translocation efficiency traits, a total of 10 N and P traits were evaluated along with seed protein and oil contents using a population of 179 RILs derived from soybean parents HC6 and JD17 (Table 1). Considerable genetic variation was detected among the parents and RILs in all measured traits. The average CVs of leaf traits (NcL, PcL, TNL, and TPL) were greater than for seed traits (Pro, Oil, NcS, PcS). Extensive transgressive heritability among the 179 RILs was observed for each observed trait. All average values of traits fell between parent values, while maximum and minimum values lay beyond parental bounds, suggesting that both parents might contribute to phenotypic variation (Figure 2). According to histograms of traits, all traits followed continuous distribution (Figure 2). According to kurtosis and skew values observed across the population, the peak of TNL and TPL fell in the left portion of the distribution, while the peak of NTE and PTE fell in the right portion of the distribution. Results above indicate that these traits are inherited as multiple loci in a quantitative fashion (Table 1, Figure 2). The ANOVA test showed that lines were the main reason for variation (Table S2). In addition, the broad sense heritability of the observed traits ranged from 0.73 to 0.91 (Table 1), suggesting that genetic variation accounted for most of the observed phenotypic variation, and location effects were relatively minor.

3.3. Construction of Genetic Linkage Map

A total of 5250 SNPs with polymorphism between parents were used to construct a RIL population genetic map (Table 2) covering a total genetic distance of 3154.833 cM at an average coverage distance of 0.642 cM. Chromosome 5 had the largest average coverage distance of 1.139 cM, which held 132 molecular markers over a genetic distance of 150.356 cM. The average coverage distance of chromosome 18 was the smallest of those observed at 0.29 cM, containing 500 molecular markers over a genetic distance of 144.744 cM (Table 2). The above results indicate that the genetic map constructed in this study has a small average coverage distance in a high resolution, and uniformly distributed context.
The genetic distances and reference physical distances were compared to assess the quality of our linkage map, with the result that genetic distance and physical distance exhibited good collinearity in both heterochromatic and euchromatinic regions of chromosomes (Figure S2). Overall, physical locations were better than genetic distance at finding candidate genes near QTLs. To determine whether the genetic maps constructed in this study were capable of QTL mapping, the flower color quality trait was also mapped in the RIL population (Figure 3). Here, the primary QTL for flower color was identified with an LOD value of 13.25 and a PVE value of 30% on chromosome 13 near the physical location of 16,983,990 bp, where the W gene responsible for flower color has previously been reported [46]. Taken together, these results indicate that the genetic linkage map constructed here would be useful for further QTL mapping of seed quality and nutrient translocation efficiency traits.

3.4. QTL Identification for N and P Translocation Efficiency and Seed Quality Traits

Using the suitable high-resolution genetic map described above, QTLs were identified for translocation efficiencies of N and P, along with seed protein and oil contents (Table 3). In the end, a total of 41 loci were detected for 12 traits, including two protein QTLs, three oil QTLs, two NTE QTLs, three PTE QTLs, five NcD QTLs, two PcD QTLs, and twenty-two other nutrient-related QTLs. The LOD values ranged from 2.5 to 11.91, and the PVE values ranged from 6.4 to 28.0. A marker for difference in P between seeds and leaves, qPcD20, had the highest LOD and PVE values of all markers for all measured traits, and the additive effect (Add) was −0.37.
Two QTLs were detected for seed protein content (Table 3). One was qPro6, with an LOD of 5.61, an Add of 0.89, and a PVE of 13.9, and was located on chromosome 6 in the interval 06_17296433–06_20707785. The other was qPro20, with an LOD of 8.87, an Add of −1.08, and a PVE of 21.0, and was located on chromosome 20 near 20_31536419. The Add value of qPro20 was less than 0, indicating that the favorable gene comes from HC6 while the favorable gene of qPro6 comes from JD17, according to the negative Add value. There were four QTLs detected for seed oil content with LOD values ranging from 4.03 to 5.49, and PVE values of 10.1 to 13.5 (Table 3). Interestingly, two of the four detected seed oil markers were found near the two protein loci, while the other two seed oil QTLs were found on chromosome 5 and chromosome 9. All favorable seed oil genes originated with JD17, except for qOil6 on chromosome 6, with the favorable gene from HC6.
For N translocation efficiency, two significant QTLs loci were detected (Table 3). One on chromosome 6, qNTE6, was detected with an LOD of 2.99, an Add of 1.34, and a PVE of 8.4. The other, qNTE10, was found on chromosome 10 and had an LOD of 5.92, an Add of −1.99, and a PVE of 15.8. Here, again, JD17 provided the favorable gene for locus qNTE6 on chromosome 6, and HC6 provided the favorable gene for locus qNTE10. For P translocation efficiency, the three significant QTLs, qPTE6, qPTE10, and qPTE20, were detected on chromosomes 6, 10, and 20, respectively (Table 3). The LOD values for PTE loci ranged from 2.98 to 4.48, and the PVE values ranged from 8.2 to 12.0. According to their Add values, PTE favorable genes were contributed by JD17 for qPTE10 and qPTE20, and by HC6 for qPTE6.
Five QTLs were detected for the differences in N concentration between seed and leaves (Table 3). Single representatives were detected on chromosomes 6, 18, and 20 and were named according to the conventions used above along with two other QTLs identified on chromosome 10 that were labelled qNcD10.1 and qNcD10.2. The NcD LOD values ranged from 2.68 to 8.03, while the PVE values ranged from 7.0 to 16.4. All of the NcD favorable genes came from HC6, except for qNcD6, which was another protein- and N-related locus on chromosome 6 with the favorable allele contributed by JD17. qNcD10.2 had the greatest LOD and PVE values. For difference in P concentration between seed and leaves, three significant QTLs were detected that were located on chromosomes 2, 12, and 20. The LOD values ranged from 2.50 to 11.91, and the PVE values ranged from 7.0 to 28.0. All favorable PcD genes came from HC6. For the other nutrient traits, four NcS QTLs, two PcS QTLs, four NcL QTLs, two PcL QTLs, three TNL QTLs and six TPL QTLs were detected and placed on chromosomes 6, 10, 11, 12, 16, 18, and 19. The LOD values ranged from 2.51 to 9.9, and the PVE values ranged from 6.4 to 23.6. Among them, qTNL6 had the greatest LOD and PVE values.
Overall, a number of major QTLs associated with protein, oil, and nutrient translocation efficiency were detected. Chromosomes 6, 10, and 20 were represented across multiple traits, and there appear to be interesting regions of colocalization. The set of loci detected herein provides numerous targets for potential future study, particularly in regions of colocalization.

3.5. Loci of Colocalized Seed Quality and Nutrient Translocation Efficiency Traits in Soybean

In this study, two major QTL regions for seed protein were identified and named qPro6 and qPro20. In addition, QTLs for N and P translocation efficiency were identified near qPro6 and qPro20 (Figure 4). qPro6 is located between 17,296,433 bp and 20,707,785 bp, in a genetic interval of 6.202 cm where qNTE6 and qPTE6 were also identified (Figure 4, Table 3). Positive Add values mean that favorable alleles were contributed by JD17, and there appeared to be some positive synergistic interactions between allele effects on traits, as suggested by the observed positive correlation between NTE, PTE, and protein content (Figure 1C). Furthermore, the seed P and N concentration loci qPcS6 and qNcS6, and the leaf P concentration locus qPcL6 were all also detected within the recurring region of significance on chromosome 6. Here, the LOD value was higher for qPcS6 than for qNcS6 (Table 3), which suggests that P concentration effects might have been more impactful on protein production than N concentration effects for this locus under the given experimental conditions. In addition, correlation analysis also revealed clear positive correlations of protein with PcL, PcS, and NcS (Figure S1). Similarly, the locus qOil6 was also located in the region on chromosome 6 that appears to hold loci of interest for many of the traits studied herein. Chromosome 20 appears to harbor an important region, with detailed analysis in this report showing that qPro20 and qOil20 localize near 31,540,803 bp on chromosome 20, which is close to Glyma.20G085100 (31,774,769–31,779,804 bp), a newly reported potential region of candidate genes behind cqSeed protein-003 QTL effects [47]. This is the same region in which qPTE20, qNcS20, qPcS20, qNcD20, and qPcD20 were also detected in this study, with qPcD20 returning the highest LOD value of 11.91 and a PVE value that was 28% greater than the PVE for qNcD20. Taken together, these results imply that concentration differences for P were more impactful on seed quality than those of N, and Glyma.20G085100 is a strong candidate gene for imparting these effects in regulating nutrient translocation efficiency in soybean.

3.6. Effects of Glyma.20G085100 on Nutrient Translocation Efficiency

The QTLs on chromosome 20 associated with seed protein and oils identified in this study co-localize with a newly reported major locus for seed protein and oil, with variation of the gene caused by a transposable element (TE) InDel in the candidate gene Glyma.20G085100 [13,47]. In order to evaluate whether the QTL identified in this study is also caused by the TE Indel, fragments near the TE insertion site within the gene in each of the two parents were re-sequenced after PCR using the reported primers [47]. Here, the PCR fragment length was about 900 bp for JD17 and about 600 bp for HC6 (Figure 5A). In addition, re-sequencing results showed that HC6 appeared to experience a 321 bp deletion in comparisons with JD17 and Williams82 (Figure 5B). These results demonstrate that the candidate gene of qPro20 and qOil20 is likely the same gene Glyma.20G085100 as previously reported.
To further investigate the effect of Glyma.20G085100 on N and P translocation efficiencies, the RIL population was grouped into two groups according to the TE genotype. The flank markers of the gene were 20_31540803 and 20_31966484 in the present RIL population. The fragment from 20_30257371 to 20_31540803 was then considered as the upstream fragment of the gene, while fragment from 20_31966484 to 20_33401768 was the downstream fragment of the gene. When the upstream and downstream fragments of tested lines both came from JD17, the line was defined as +TE genotype, while those lacking both JD17 fragments were defined as −TE genotypes. This led to the identification of 77 +TE lines and 78 −TE lines in the RIL population (Figure 5C).
NTE, PTE, NcD, and PcD served as indexes of N and P translocation efficiencies. The higher the NTE and PTE, the more N and P were allocated to seeds. The higher NcD and PcD were, the more N and P were translocated from leaves to seeds. According to the genotype categories described above, 77 +TE progeny and 78 −TE progeny were selected to study the effect of this TE on N and P translocation efficiency (Figure 6). The results showed that −TE lines had significantly higher NTE, PTE, NcD, and PcD values than the +TE lines. In comparison with the translocation abilities of −TE lines with +TE lines, we found that NcD and PcD were greater than NTE and PTE, which indicates that Glyma.20G085100 may be most impactful on NcD and PcD, especially PcD (Figure 6A–D). These results are consistent with the observation that qPcD20 had the largest LOD and PVE values of all QTLs detected in the study. Meanwhile, −TE lines had higher protein contents than +TE lines (Figure 6E). Taken together, the results above imply that higher N and P translocation efficiencies facilitate the accumulation of protein in seeds.

4. Discussion

Soybean is a main source of protein for humans due to its high seed protein content. To improve seed quality, both seed protein and oil contents have been studied in numerous populations, with the result that a few interesting candidate genes have been identified [13,48,49,50].
Acquisition and utilization of nutrients, especially N and P, is critical for soybean quality [14]. A number of studies on NUE have been reported in soybean [51,52] and rice [53,54,55], though potential genetic connections between efficiency of nutrient translocation and seed quality have received little attention in soybean. In this study, two parents contrasting in N and P translocation efficiencies, as well as seed quality traits, were used to construct a population of recombinant inbred lines (RILs) comprising 179 individuals (Figure 1). Then, a high-density genetic map was constructed with 5250 markers (Figure 3). QTLs related to N and P translocation efficiencies and quality were detected and analyzed for possible genetic relationships.
Through genetic analysis, it was found that the observed nutrient and quality traits were quantitative (Table 1), which is consistent with previous reports [24,56], suggesting they are controlled by multiple genes. The heritabilities of NTE and PTE observed herein were about 0.7, while those of seed protein and oil were about 0.90 and 0.88, respectively, all of which is consistent with previous reports [4,57,58,59]. The heritability of PcS in this study was higher than the heritability of PcS reported in a previous study, which may result from the testing of different populations or experimenting in different environments [24].
Based on a constructed high-resolution genetic map, the identified quality loci and some N translocation efficiency loci are consistent with those reported in previous studies. For example, qOil5 overlaps with GmST05 [60], while qOil9 lies near qOC9(21) [4], Seed oil 42–26 [61] and Seed oil 43–22 [4,59,61]. N and P translocation efficiency markers colocalize with qPro6 and qOil6 in a heterochromatic region of chromosome 6 near qQ6.1 and qQ6.2 [4]. Plus, N and P translocation efficiency markers colocalizing with qPro20 and qOil20 also overlap with the gene Glyma.20G085100 [13,47]. In addition, the qNcS18 locus was found near Satt 570, which is known to control seed N concentration at the R7 stage [56].
The production of seeds with high protein contents requires high levels of P and N [62,63,64]. This seems to be affected by N and P translocation efficiencies, as evident in the present results through the observed positive correlations of seed quality with N and P translocation efficiencies (Figure 1 and Figure S1), as well as the colocalization of numerous relevant loci in chromosomes 6 and 20 (Figure 4). The region on chromosome 6 holding multiple significant QTLs was mined for candidate genes related to N or P translocation efficiency. The interval from 06_18864382 to 06_19585884 contains Glyma.06G204300, encoding a TCP-type transcription factor with roles in plant development at the cellular, organ, and tissue levels [65], along with Glyma.06g196400, with functions in phospholipid transport according to the gene ontology annotation in SoyBase (http://soybase.org, accessed on 5 December 2022). In addition, the region from 06_17275501 to 06_17280964 is known to harbor Glyma.06G194500, which, according to the annotation on SoyBase (http://soybase.org, accessed on 5 December 2022), is a GDSL-like Lipase/Acyl hydrolase superfamily protein, a family with functions that are highly correlated with seed quality and size in other plants [66,67,68,69]. Finally, the qNTE06 and qPTE06 loci were found near Glyma.06G200200, a homologue to SWEET17 in Arabidopsis, which codes a facilitative transporter that mediates fructose transport across the tonoplast of Arabidopsis root and leaf cells [70].
Another region of interest was identified on chromosome 20, which appears to contain genes regulating soybean seed quality. This region is known to hold a CCT gene, Glyma.20G085100, that underlies a large-effect protein/oil QTL, along with a TE insertion in Glyma.20G085100 that is associated with seed oil and protein content, all of which have been suggested to describe a gene involved in nutrient transport, though direct genetic evidence remains lacking [13]. In our work, re-sequencing showed that HC6 had a 321 bp deletion compared with JD17 and Williams82 (Figure 5), and confirmed that the candidate gene for qPro20 and qOil20 was Glyma.20G085100, with −TE lines displaying significantly higher NTE, PTE, NcD, PcD and seed protein than +TE lines (Figure 6). Taken together, these colocalization results suggest that high nutrient translocation efficiency facilitates protein accumulation. In addition, the results herein also indicate that Glyma.20G085100 may have a bigger effect on NcD and PcD, especially PcD, than on NTE and PTE (Figure 6), which suggests a preference for maintaining high concentration differences over nutrient allocation, especially for P. Most directly, this study provides evidence that a TE increases N and P translocation efficiencies and leads to increasing protein content in soybean seeds.
In short, the present study identified and localized QTLs for soybean N and P translocation efficiencies, along with quality traits in a high-resolution genetic map. Loci for N and P translocation efficiencies and seed protein content were mapped to colocalized regions on chromosome 6 and chromosome 20. The gene Glyma.20G085100 could be the likely candidate gene for qPro20. Our study also provides direct proof that a TE in Glyma.20G085100 is behind increasing N and P translocation efficiencies that have led to increasing protein contents in soybean seeds. On the whole, this study provides a theoretical basis for breeding soybean lines with high seed protein concentrations and high N and P translocation efficiencies, with a TE InDel identified as a likely genetic marker.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agronomy13020598/s1, Figure S1: Correlation analysis of nutrient and quality traits in seeds. Figure S2: Plots of genetic distance vs physical distance for soybean chromosomes. Figure S3: BLAST of resequenced JD17 and HC6 sequences with William82. Table S1: Genetic correlation analysis of nutrient efficiency traits and quality traits in seeds. Table S2: ANOVA analysis for nutrient efficiency traits and quality traits in seeds.

Author Contributions

Conceptualization, Q.Z. and H.L.; Data curation, N.M., R.L. and Y.Z.; Funding acquisition, H.L.; Investigation, N.M. and R.L.; Methodology, Q.Z., Y.Z., X.L. and H.L.; Resources, H.L.; Validation, N.M.; Visualization, Q.Z.; Writing—original draft, Q.Z. and N.M.; Writing—Review and editing, Y.Z., X.L. and H.L. All authors have read and agreed to the published version of the manuscript.

Funding

This work was support by the National Natural Science Foundation of China (31830083) and the National Key Research and Development Program of China (2021YFF1000500).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Acknowledgments

The authors would like to thank Thomas Walk of Golden Fidelity LLC for English assistance.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Comparison of seed protein and oil contents and nutrient translocation efficiency. (A) Protein and oil content of JD17 and HC6; (B) Nitrogen and phosphorus translocation efficiency of JD17 and HC6; (C) Correlation analysis between seed protein and oil contents and nitrogen translocation efficiency; (D) Correlation analysis between seed protein and oil contents and phosphorus translocation efficiency. NTE represents nitrogen translocation efficiency, and PTE represents phosphorus translocation efficiency. Asterisks denote significance of differences according to Student’s t tests (* p < 0.05).
Figure 1. Comparison of seed protein and oil contents and nutrient translocation efficiency. (A) Protein and oil content of JD17 and HC6; (B) Nitrogen and phosphorus translocation efficiency of JD17 and HC6; (C) Correlation analysis between seed protein and oil contents and nitrogen translocation efficiency; (D) Correlation analysis between seed protein and oil contents and phosphorus translocation efficiency. NTE represents nitrogen translocation efficiency, and PTE represents phosphorus translocation efficiency. Asterisks denote significance of differences according to Student’s t tests (* p < 0.05).
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Figure 2. Frequency distribution of quality and N and P translocation efficiency traits among RIL population containing 179 lines. The red and blue dashed lines represent the value of traits for JD17 and HC6, respectively. Pro, Oil: protein and oil content in seeds (%); NcS, PcS: nitrogen and phosphorus concentrations in seeds (mg/g); NcL, PcL: nitrogen and phosphorus concentrations in leaves (mg/g); TNL, TPL: nitrogen and phosphorus total content in leaves (mg); NcD, PcD: nitrogen and phosphorus concentration difference between seeds and leaves; NTE, PTE: nitrogen and phosphorus translocation efficiency.
Figure 2. Frequency distribution of quality and N and P translocation efficiency traits among RIL population containing 179 lines. The red and blue dashed lines represent the value of traits for JD17 and HC6, respectively. Pro, Oil: protein and oil content in seeds (%); NcS, PcS: nitrogen and phosphorus concentrations in seeds (mg/g); NcL, PcL: nitrogen and phosphorus concentrations in leaves (mg/g); TNL, TPL: nitrogen and phosphorus total content in leaves (mg); NcD, PcD: nitrogen and phosphorus concentration difference between seeds and leaves; NTE, PTE: nitrogen and phosphorus translocation efficiency.
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Figure 3. Genetic map of RIL population developed from a cross between HC6 × JD17. Genetic map was constructed in Join Map 4.1 and drawn with MapChart 2.3. The scale on the left represents the genetic distance between bin markers using centimorgans as the unit. Letter “w” represents the flower locus. Numbers at the top represent the chromosome number of soybean.
Figure 3. Genetic map of RIL population developed from a cross between HC6 × JD17. Genetic map was constructed in Join Map 4.1 and drawn with MapChart 2.3. The scale on the left represents the genetic distance between bin markers using centimorgans as the unit. Letter “w” represents the flower locus. Numbers at the top represent the chromosome number of soybean.
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Figure 4. Colocalization for nutrient translocation efficiency and quality traits in RIL population. Genetic maps constructed using MapChart 2.32. The LOD graphs and linkage groups are shown together with a constant line indicating the LOD threshold of 2.5, while 1- and 2-LOD intervals are also shown.
Figure 4. Colocalization for nutrient translocation efficiency and quality traits in RIL population. Genetic maps constructed using MapChart 2.32. The LOD graphs and linkage groups are shown together with a constant line indicating the LOD threshold of 2.5, while 1- and 2-LOD intervals are also shown.
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Figure 5. Validation of transposition in causative gene. PCR genotyping assay for TE insertion (A) was conducted in the parental lines of the studied RIL populations. PCR amplicons of about 900 bp and 600 bp represent the presence and absence of the TE insertion, respectively. Alignments of Glyma.20G085100 sequence fragments from Williams82, JD17, and HC6 (B) showing the 321-bp InDel (dashed line), with the whole alignment also provided in Figure S3. Genotypes of RILs (C) with the upstream (green highlighted) and downstream (orange highlighted) fragments of Glyma.20G085100 are shown. The SNP highlighted in red had the greatest LOD value for qPro20 and qOil20. Light green filled columns represents +TE lines and light red filled columns represents −TE lines.
Figure 5. Validation of transposition in causative gene. PCR genotyping assay for TE insertion (A) was conducted in the parental lines of the studied RIL populations. PCR amplicons of about 900 bp and 600 bp represent the presence and absence of the TE insertion, respectively. Alignments of Glyma.20G085100 sequence fragments from Williams82, JD17, and HC6 (B) showing the 321-bp InDel (dashed line), with the whole alignment also provided in Figure S3. Genotypes of RILs (C) with the upstream (green highlighted) and downstream (orange highlighted) fragments of Glyma.20G085100 are shown. The SNP highlighted in red had the greatest LOD value for qPro20 and qOil20. Light green filled columns represents +TE lines and light red filled columns represents −TE lines.
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Figure 6. Comparison of nitrogen and phosphorus translocation efficiencies and quality traits of RIL lines carrying +TE or −TE. Of 155 graphed genotypes, 77 were +TE and 78 were −TE. Asterisks denote significance of differences (threshold p = 0.05) according to Student’s t tests; ** p < 0.01 and *** p < 0.001. Trait values are shown with violin and box plots. The percentage values above asterisks denote increase or decrease in −TE lines compared with +TE lines. The square red dot represents the mean value. Black round dots denote outliers. (A, B) Villon plot of N and P translocation efficiency (N/PTE); (C, D) Villon plot of N and P concentration difference (N/PcD); (E, F) Villon plot of protein and oil content.
Figure 6. Comparison of nitrogen and phosphorus translocation efficiencies and quality traits of RIL lines carrying +TE or −TE. Of 155 graphed genotypes, 77 were +TE and 78 were −TE. Asterisks denote significance of differences (threshold p = 0.05) according to Student’s t tests; ** p < 0.01 and *** p < 0.001. Trait values are shown with violin and box plots. The percentage values above asterisks denote increase or decrease in −TE lines compared with +TE lines. The square red dot represents the mean value. Black round dots denote outliers. (A, B) Villon plot of N and P translocation efficiency (N/PTE); (C, D) Villon plot of N and P concentration difference (N/PcD); (E, F) Villon plot of protein and oil content.
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Table
Table 1. Phenotypic variation and genetic analysis of nutrient and quality traits in 179 soybean recombination inbred lines.
Table 1. Phenotypic variation and genetic analysis of nutrient and quality traits in 179 soybean recombination inbred lines.
TraitsParentsRILs
JD17HC6Min.Max.MeanSDCV (%)Kurt.Skew.h2b
Pro40.04 42.13 37.28 50.84 42.76 2.45 5.72 0.36 0.49 0.90
Oil22.18 19.71 16.28 24.28 20.39 1.38 6.76 −0.07 −0.18 0.88
NcS66.73 70.28 52.47 79.86 65.81 4.79 7.28 −0.09 0.12 0.89
PcS6.89 7.99 5.63 8.78 7.36 0.55 7.47 0.07 −0.03 0.91
NcL21.81 16.25 10.15 24.55 16.03 2.90 18.08 0.11 0.49 0.75
PcL2.65 2.25 1.39 4.41 2.40 0.53 22.26 1.15 0.76 0.75
TNL189.98 154.96 18.23 449.58 116.82 82.19 70.36 3.74 1.80 0.84
TPL23.12 21.51 2.46 52.19 16.57 9.78 59.01 1.35 1.20 0.82
NcD44.92 54.02 28.77 63.31 49.84 5.70 36.18 0.36 −0.11 0.87
PcD4.24 5.74 2.50 6.87 4.98 0.74 55.48 0.55 −0.20 0.85
NTE0.84 0.93 0.64 0.98 0.90 0.06 6.83 2.75 −1.49 0.74
PTE0.82 0.92 0.58 0.97 0.87 0.07 8.05 1.92 −1.19 0.73
RILs: recombinant inbred lines. NcL, PcL: nitrogen and phosphorus concentrations in leaves (mg/g); TNL, TPL: nitrogen and phosphorus total content in leaves (mg); NcS, PcS: nitrogen and phosphorus concentrations in seeds (mg/g); NTE, PTE: nitrogen and phosphorus translocation efficiency; NcD, PcD: nitrogen and phosphorus concentration difference between seeds and leaves. Pro, Oil: protein and oil content in seeds (%).
Table
Table 2. Genetic and physical map with recombination rates of population containing 179 RILs derived from the cross HC6 × JD17.
Table 2. Genetic and physical map with recombination rates of population containing 179 RILs derived from the cross HC6 × JD17.
Number of MarkersStart (bp)Stop (bp)Genetic Distance (cM)Physical Size (bp)Average Coverage Distance (cM)Recombination Rate (cM/Mb)
Chr.01256289,31654,169,376139.68353,880,0600.5462.592
Chr.02302331,98046,721,845223.73346,389,8650.7414.823
Chr.03187162,17142,036,871135.45541,874,7000.7243.235
Chr.04262560,70651,913,854144.98951,353,1480.5532.823
Chr.0513274,22742,218,304150.35642,144,0771.1393.568
Chr.06264105,14051,169,402157.94051,064,2620.5983.093
Chr.07205176,83844,351,629174.22544,174,7910.8503.944
Chr.08229133,69947,621,113187.70147,487,4140.8203.953
Chr.0936924,93247,374,864157.68547,349,9320.4273.330
Chr.10294166,53751,028,020165.82350,861,4830.5643.260
Chr.1127828,68634,363,362168.11134,334,6760.6054.896
Chr.12152105,68039,977,366128.18439,871,6860.8433.215
Chr.13294935,10343,521,095179.91742,585,9920.6124.225
Chr.14281291,96747,851,291169.87847,559,3240.6053.572
Chr.152341,974,82850,562,456114.32648,587,6280.4892.353
Chr.1628012,48137,757,414150.73337,744,9330.5383.993
Chr.172481,847,75941,500,612161.74339,652,8530.6524.079
Chr.18500539157,905,267144.74457,899,8760.2892.500
Chr.1922681,69849,599,441156.19149,517,7430.6913.154
Chr.202572,789,67947,717,330143.41644,927,6510.5583.192
Min132539134,363,362114.3263,433,46760.2892.353
Max5002,789,67957,905,267223.7335,789,98761.1394.896
Mean262.5 157.74145,963,1040.6423.490
Total5250 3154.833919,262,094
Table
Table 3. Putative QTLs for quality and N and P translocation efficiency traits detected in a population derived from an HC6×JD17 cross containing 179 RILs.
Table 3. Putative QTLs for quality and N and P translocation efficiency traits detected in a population derived from an HC6×JD17 cross containing 179 RILs.
TraitQTLChrPosition (cM)Locus/IntervalLODAddPVE (%)
NcLqNcL1010137.07610_452431942.80.75 7.1
qNcL131388.63913_277378332.510.72 6.4
qNcL1616120.32916_326513042.680.74 6.8
qNcL1818102.61118_550341662.840.77 7.2
PcLqPcL66116.324–127.16106_18481680–06_450550225.290.18 13.2
qPcL181828.70118_39305252.980.14 7.6
TNLqTNL66118.45306_195858849.9−27.94 23.6
qTNL1010137.07610_452431947.6424.55 18.8
qTNL131325.3713_107850452.71−15.08 7.1
TPLqTPL66118.45306_195858846.85−3.42 16.8
qTPL10.1107.4510_42894193.23−2.40 8.3
qTPL10.21078.47910_189674682.98−2.32 7.7
qTPL10.310137.07610_452431945.693.09 14.2
qTPL1111125.65911_31189747–11_320375322.67−2.32 6.9
qTPL131371.91313_22901190–13_268652582.58−2.41 6.7
qTPL191940.95619_55012202.59−2.20 6.7
NcSqNcS66111.393–124.7806_16674718–06_442706224.961.64 12.2
qNcS1010138.23210_455502304.25−1.50 10.6
qNcS18189.89118_4729642.98−1.30 7.5
qNcS20208.31120_311603465.1−1.67 12.6
PcSqPcS66111.39306_166747186.480.22 15.9
qPcS202010.0220_315364199.46−0.26 22.4
NTEqNTE66117.27606_188643822.991.34 8.4
qNTE1010142.79710_45640909–10_466810505.92−1.99 15.8
PTEqPTE66116.8306_188545522.981.77 8.2
qPTE1010149.90410_475849604.48−2.15 12.0
qPTE20208.31120_311603463.01−1.84 8.3
NcDqNcD66111.39306_166747183.721.71 9.5
qNcD10.11097.54410_392225502.94−1.52 7.6
qNcD10.210138.23210_455502308.03−2.42 19.4
qNcD181812.04318_8835792.68−1.47 7.0
qNcD20209.64620_31274128–20_315364196.65−2.27 16.4
PcDqPcD22220.03402_450852082.64−0.18 7.0
qPcD12120.50412_2860082.5−0.18 6.7
qPcD202010.0220_3153641911.91−0.37 28.0
ProqPro66112.474–118.67606_17296433–06_207077855.610.89 13.9
qPro202010.0220_315364198.87−1.08 21.0
OilqOil55150.35605_420792834.550.47 11.3
qOil66118.229–124.49806_19369334–06_434397764.23−0.45 10.6
qOil9931.21709_1558594–09_28110504.030.52 10.1
qOil202010.24320_315408035.490.51 13.5
ADD values of >0 and <0 represent increasing effects of the QTLs derived from JD17 and HC6, respectively.
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MDPI and ACS Style

Zhao, Q.; Ma, N.; Li, R.; Zhong, Y.; Li, X.; Liao, H. Seed Protein Genetics Linked with Nitrogen and Phosphorus Translocation Efficiency in Soybean. Agronomy 2023, 13, 598. https://doi.org/10.3390/agronomy13020598

AMA Style

Zhao Q, Ma N, Li R, Zhong Y, Li X, Liao H. Seed Protein Genetics Linked with Nitrogen and Phosphorus Translocation Efficiency in Soybean. Agronomy. 2023; 13(2):598. https://doi.org/10.3390/agronomy13020598

Chicago/Turabian Style

Zhao, Qingsong, Niannian Ma, Ruirui Li, Yongjia Zhong, Xinxin Li, and Hong Liao. 2023. "Seed Protein Genetics Linked with Nitrogen and Phosphorus Translocation Efficiency in Soybean" Agronomy 13, no. 2: 598. https://doi.org/10.3390/agronomy13020598

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